diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py index d980db4bd..bcbf05a93 100755 --- a/.ci/update_windows/update.py +++ b/.ci/update_windows/update.py @@ -71,7 +71,12 @@ except: print("checking out master branch") # noqa: T201 branch = repo.lookup_branch('master') if branch is None: - ref = repo.lookup_reference('refs/remotes/origin/master') + try: + ref = repo.lookup_reference('refs/remotes/origin/master') + except: + print("pulling.") # noqa: T201 + pull(repo) + ref = repo.lookup_reference('refs/remotes/origin/master') repo.checkout(ref) branch = repo.lookup_branch('master') if branch is None: diff --git a/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt b/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt new file mode 100755 index 000000000..96a500be2 --- /dev/null +++ b/.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt @@ -0,0 +1,27 @@ +As of the time of writing this you need this preview driver for best results: +https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.html + +HOW TO RUN: + +If you have a AMD gpu: + +run_amd_gpu.bat + +If you have memory issues you can try disabling the smart memory management by running comfyui with: + +run_amd_gpu_disable_smart_memory.bat + +IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints + +You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors + + +RECOMMENDED WAY TO UPDATE: +To update the ComfyUI code: update\update_comfyui.bat + + +TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI: +In the ComfyUI directory you will find a file: extra_model_paths.yaml.example +Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor. + + diff --git a/.ci/windows_base_files/run_nvidia_gpu.bat b/.ci/windows_amd_base_files/run_amd_gpu.bat similarity index 100% rename from .ci/windows_base_files/run_nvidia_gpu.bat rename to .ci/windows_amd_base_files/run_amd_gpu.bat diff --git a/.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat b/.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat new file mode 100755 index 000000000..cece0aeb2 --- /dev/null +++ b/.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat @@ -0,0 +1,2 @@ +.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory +pause diff --git a/.ci/windows_base_files/README_VERY_IMPORTANT.txt b/.ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt similarity index 82% rename from .ci/windows_base_files/README_VERY_IMPORTANT.txt rename to .ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt index d46acbcbf..8ab70c890 100755 --- a/.ci/windows_base_files/README_VERY_IMPORTANT.txt +++ b/.ci/windows_nvidia_base_files/README_VERY_IMPORTANT.txt @@ -4,6 +4,9 @@ if you have a NVIDIA gpu: run_nvidia_gpu.bat +if you want to enable the fast fp16 accumulation (faster for fp16 models with slightly less quality): + +run_nvidia_gpu_fast_fp16_accumulation.bat To run it in slow CPU mode: diff --git a/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat new file mode 100644 index 000000000..cfe4b9f0e --- /dev/null +++ b/.ci/windows_nvidia_base_files/advanced/run_nvidia_gpu_disable_api_nodes.bat @@ -0,0 +1,2 @@ +..\python_embeded\python.exe -s ..\ComfyUI\main.py --windows-standalone-build --disable-api-nodes +pause diff --git a/.ci/windows_base_files/run_cpu.bat b/.ci/windows_nvidia_base_files/run_cpu.bat similarity index 100% rename from .ci/windows_base_files/run_cpu.bat rename to .ci/windows_nvidia_base_files/run_cpu.bat diff --git a/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat b/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat new file mode 100755 index 000000000..274d7c948 --- /dev/null +++ b/.ci/windows_nvidia_base_files/run_nvidia_gpu.bat @@ -0,0 +1,2 @@ +.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build +pause diff --git a/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat b/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat new file mode 100644 index 000000000..38f06ecb2 --- /dev/null +++ b/.ci/windows_nvidia_base_files/run_nvidia_gpu_fast_fp16_accumulation.bat @@ -0,0 +1,2 @@ +.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast fp16_accumulation +pause diff --git a/.gitattributes b/.gitattributes index 4391de678..5b3c15bb4 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,3 @@ /web/assets/** linguist-generated /web/** linguist-vendored +comfy_api_nodes/apis/__init__.py linguist-generated diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml index 39d1992d7..3cf2717b7 100644 --- a/.github/ISSUE_TEMPLATE/bug-report.yml +++ b/.github/ISSUE_TEMPLATE/bug-report.yml @@ -15,6 +15,14 @@ body: steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: checkboxes + id: custom-nodes-test + attributes: + label: Custom Node Testing + description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. + options: + - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) + required: false - type: textarea attributes: label: Expected Behavior diff --git a/.github/ISSUE_TEMPLATE/user-support.yml b/.github/ISSUE_TEMPLATE/user-support.yml index df28804c6..281661f92 100644 --- a/.github/ISSUE_TEMPLATE/user-support.yml +++ b/.github/ISSUE_TEMPLATE/user-support.yml @@ -11,6 +11,14 @@ body: **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics. If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first. + - type: checkboxes + id: custom-nodes-test + attributes: + label: Custom Node Testing + description: Please confirm you have tried to reproduce the issue with all custom nodes disabled. + options: + - label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help) + required: false - type: textarea attributes: label: Your question diff --git a/.github/workflows/check-line-endings.yml b/.github/workflows/check-line-endings.yml new file mode 100644 index 000000000..eeb594d6c --- /dev/null +++ b/.github/workflows/check-line-endings.yml @@ -0,0 +1,40 @@ +name: Check for Windows Line Endings + +on: + pull_request: + branches: ['*'] # Trigger on all pull requests to any branch + +jobs: + check-line-endings: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Fetch all history to compare changes + + - name: Check for Windows line endings (CRLF) + run: | + # Get the list of changed files in the PR + CHANGED_FILES=$(git diff --name-only ${{ github.event.pull_request.base.sha }}..${{ github.event.pull_request.head.sha }}) + + # Flag to track if CRLF is found + CRLF_FOUND=false + + # Loop through each changed file + for FILE in $CHANGED_FILES; do + # Check if the file exists and is a text file + if [ -f "$FILE" ] && file "$FILE" | grep -q "text"; then + # Check for CRLF line endings + if grep -UP '\r$' "$FILE"; then + echo "Error: Windows line endings (CRLF) detected in $FILE" + CRLF_FOUND=true + fi + fi + done + + # Exit with error if CRLF was found + if [ "$CRLF_FOUND" = true ]; then + exit 1 + fi diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml new file mode 100644 index 000000000..5c1024599 --- /dev/null +++ b/.github/workflows/release-stable-all.yml @@ -0,0 +1,61 @@ +name: "Release Stable All Portable Versions" + +on: + workflow_dispatch: + inputs: + git_tag: + description: 'Git tag' + required: true + type: string + +jobs: + release_nvidia_default: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release NVIDIA Default (cu129)" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "cu129" + python_minor: "13" + python_patch: "6" + rel_name: "nvidia" + rel_extra_name: "" + test_release: true + secrets: inherit + + release_nvidia_cu128: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release NVIDIA cu128" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "cu128" + python_minor: "12" + python_patch: "10" + rel_name: "nvidia" + rel_extra_name: "_cu128" + test_release: true + secrets: inherit + + release_amd_rocm: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release AMD ROCm 6.4.4" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "rocm644" + python_minor: "12" + python_patch: "10" + rel_name: "amd" + rel_extra_name: "" + test_release: false + secrets: inherit diff --git a/.github/workflows/release-webhook.yml b/.github/workflows/release-webhook.yml new file mode 100644 index 000000000..6fceb7560 --- /dev/null +++ b/.github/workflows/release-webhook.yml @@ -0,0 +1,108 @@ +name: Release Webhook + +on: + release: + types: [published] + +jobs: + send-webhook: + runs-on: ubuntu-latest + steps: + - name: Send release webhook + env: + WEBHOOK_URL: ${{ secrets.RELEASE_GITHUB_WEBHOOK_URL }} + WEBHOOK_SECRET: ${{ secrets.RELEASE_GITHUB_WEBHOOK_SECRET }} + run: | + # Generate UUID for delivery ID + DELIVERY_ID=$(uuidgen) + HOOK_ID="release-webhook-$(date +%s)" + + # Create webhook payload matching GitHub release webhook format + PAYLOAD=$(cat <> ./python3${{ inputs.python_minor }}._pth curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py ./python.exe get-pip.py - ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/* - sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth - cd .. + ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/* + + grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt + ./python.exe -s -m pip install -r requirements_comfyui.txt + rm requirements_comfyui.txt + + sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth + + if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then + rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space + rm ./Lib/site-packages/torch/lib/libprotoc.lib + rm ./Lib/site-packages/torch/lib/libprotobuf.lib + fi + + cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable @@ -80,25 +142,29 @@ jobs: mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ - cp -r ComfyUI/.ci/windows_base_files/* ./ + cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./ cp ../update_comfyui_and_python_dependencies.bat ./update/ cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable - mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z + - shell: bash + if: ${{ inputs.test_release }} + run: | + cd .. cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release - uses: svenstaro/upload-release-action@v2 + uses: softprops/action-gh-release@v2 with: - repo_token: ${{ secrets.GITHUB_TOKEN }} - file: ComfyUI_windows_portable_nvidia.7z - tag: ${{ inputs.git_tag }} - overwrite: true - prerelease: true - make_latest: false + files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z + tag_name: ${{ inputs.git_tag }} + draft: true + overwrite_files: true diff --git a/.github/workflows/test-build.yml b/.github/workflows/test-build.yml index 444d6b254..419873ad8 100644 --- a/.github/workflows/test-build.yml +++ b/.github/workflows/test-build.yml @@ -18,7 +18,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.8", "3.9", "3.10", "3.11"] + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} @@ -28,4 +28,4 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -r requirements.txt \ No newline at end of file + pip install -r requirements.txt diff --git a/.github/workflows/test-execution.yml b/.github/workflows/test-execution.yml new file mode 100644 index 000000000..00ef07ebf --- /dev/null +++ b/.github/workflows/test-execution.yml @@ -0,0 +1,30 @@ +name: Execution Tests + +on: + push: + branches: [ main, master ] + pull_request: + branches: [ main, master ] + +jobs: + test: + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + runs-on: ${{ matrix.os }} + continue-on-error: true + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.12' + - name: Install requirements + run: | + python -m pip install --upgrade pip + pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu + pip install -r requirements.txt + pip install -r tests-unit/requirements.txt + - name: Run Execution Tests + run: | + python -m pytest tests/execution -v --skip-timing-checks diff --git a/.github/workflows/test-launch.yml b/.github/workflows/test-launch.yml index c56283c2d..1735fd83b 100644 --- a/.github/workflows/test-launch.yml +++ b/.github/workflows/test-launch.yml @@ -17,7 +17,7 @@ jobs: path: "ComfyUI" - uses: actions/setup-python@v4 with: - python-version: '3.9' + python-version: '3.10' - name: Install requirements run: | python -m pip install --upgrade pip diff --git a/.github/workflows/test-unit.yml b/.github/workflows/test-unit.yml index b3a4b4ea0..00caf5b8a 100644 --- a/.github/workflows/test-unit.yml +++ b/.github/workflows/test-unit.yml @@ -10,7 +10,7 @@ jobs: test: strategy: matrix: - os: [ubuntu-latest, windows-latest, macos-latest] + os: [ubuntu-latest, windows-2022, macos-latest] runs-on: ${{ matrix.os }} continue-on-error: true steps: @@ -18,7 +18,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.12' - name: Install requirements run: | python -m pip install --upgrade pip diff --git a/.github/workflows/update-api-stubs.yml b/.github/workflows/update-api-stubs.yml new file mode 100644 index 000000000..c99ec9fc1 --- /dev/null +++ b/.github/workflows/update-api-stubs.yml @@ -0,0 +1,56 @@ +name: Generate Pydantic Stubs from api.comfy.org + +on: + schedule: + - cron: '0 0 * * 1' + workflow_dispatch: + +jobs: + generate-models: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install 'datamodel-code-generator[http]' + npm install @redocly/cli + + - name: Download OpenAPI spec + run: | + curl -o openapi.yaml https://api.comfy.org/openapi + + - name: Filter OpenAPI spec with Redocly + run: | + npx @redocly/cli bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components + + - name: Generate API models + run: | + datamodel-codegen --use-subclass-enum --input filtered-openapi.yaml --output comfy_api_nodes/apis --output-model-type pydantic_v2.BaseModel + + - name: Check for changes + id: git-check + run: | + git diff --exit-code comfy_api_nodes/apis || echo "changes=true" >> $GITHUB_OUTPUT + + - name: Create Pull Request + if: steps.git-check.outputs.changes == 'true' + uses: peter-evans/create-pull-request@v5 + with: + commit-message: 'chore: update API models from OpenAPI spec' + title: 'Update API models from api.comfy.org' + body: | + This PR updates the API models based on the latest api.comfy.org OpenAPI specification. + + Generated automatically by the a Github workflow. + branch: update-api-stubs + delete-branch: true + base: master diff --git a/.github/workflows/update-frontend.yml b/.github/workflows/update-frontend.yml deleted file mode 100644 index 0c5774789..000000000 --- a/.github/workflows/update-frontend.yml +++ /dev/null @@ -1,58 +0,0 @@ -name: Update Frontend Release - -on: - workflow_dispatch: - inputs: - version: - description: "Frontend version to update to (e.g., 1.0.0)" - required: true - type: string - -jobs: - update-frontend: - runs-on: ubuntu-latest - permissions: - contents: write - pull-requests: write - - steps: - - name: Checkout ComfyUI - uses: actions/checkout@v4 - - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - name: Install requirements - run: | - python -m pip install --upgrade pip - pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu - pip install -r requirements.txt - pip install wait-for-it - # Frontend asset will be downloaded to ComfyUI/web_custom_versions/Comfy-Org_ComfyUI_frontend/{version} - - name: Start ComfyUI server - run: | - python main.py --cpu --front-end-version Comfy-Org/ComfyUI_frontend@${{ github.event.inputs.version }} 2>&1 | tee console_output.log & - wait-for-it --service 127.0.0.1:8188 -t 30 - - name: Configure Git - run: | - git config --global user.name "GitHub Action" - git config --global user.email "action@github.com" - # Replace existing frontend content with the new version and remove .js.map files - # See https://github.com/Comfy-Org/ComfyUI_frontend/issues/2145 for why we remove .js.map files - - name: Update frontend content - run: | - rm -rf web/ - cp -r web_custom_versions/Comfy-Org_ComfyUI_frontend/${{ github.event.inputs.version }} web/ - rm web/**/*.js.map - - name: Create Pull Request - uses: peter-evans/create-pull-request@v7 - with: - token: ${{ secrets.PR_BOT_PAT }} - commit-message: "Update frontend to v${{ github.event.inputs.version }}" - title: "Frontend Update: v${{ github.event.inputs.version }}" - body: | - Automated PR to update frontend content to version ${{ github.event.inputs.version }} - - This PR was created automatically by the frontend update workflow. - branch: release-${{ github.event.inputs.version }} - base: master - labels: Frontend,dependencies diff --git a/.github/workflows/update-version.yml b/.github/workflows/update-version.yml new file mode 100644 index 000000000..d9d488974 --- /dev/null +++ b/.github/workflows/update-version.yml @@ -0,0 +1,58 @@ +name: Update Version File + +on: + pull_request: + paths: + - "pyproject.toml" + branches: + - master + +jobs: + update-version: + runs-on: ubuntu-latest + # Don't run on fork PRs + if: github.event.pull_request.head.repo.full_name == github.repository + permissions: + pull-requests: write + contents: write + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: "3.11" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + + - name: Update comfyui_version.py + run: | + # Read version from pyproject.toml and update comfyui_version.py + python -c ' + import tomllib + + # Read version from pyproject.toml + with open("pyproject.toml", "rb") as f: + config = tomllib.load(f) + version = config["project"]["version"] + + # Write version to comfyui_version.py + with open("comfyui_version.py", "w") as f: + f.write("# This file is automatically generated by the build process when version is\n") + f.write("# updated in pyproject.toml.\n") + f.write(f"__version__ = \"{version}\"\n") + ' + + - name: Commit changes + run: | + git config --local user.name "github-actions" + git config --local user.email "github-actions@github.com" + git fetch origin ${{ github.head_ref }} + git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }} + git add comfyui_version.py + git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update comfyui_version.py to match pyproject.toml" + git push origin HEAD:${{ github.head_ref }} diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml index 85e6a52fd..f61ee21a2 100644 --- a/.github/workflows/windows_release_dependencies.yml +++ b/.github/workflows/windows_release_dependencies.yml @@ -17,19 +17,19 @@ on: description: 'cuda version' required: true type: string - default: "124" + default: "130" python_minor: description: 'python minor version' required: true type: string - default: "12" + default: "13" python_patch: description: 'python patch version' required: true type: string - default: "7" + default: "9" # push: # branches: # - master @@ -56,7 +56,8 @@ jobs: ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 pause" > update_comfyui_and_python_dependencies.bat - python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir + grep -v comfyui requirements.txt > requirements_nocomfyui.txt + python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir python -m pip install --no-cache-dir ./temp_wheel_dir/* echo installed basic ls -lah temp_wheel_dir diff --git a/.github/workflows/windows_release_dependencies_manual.yml b/.github/workflows/windows_release_dependencies_manual.yml new file mode 100644 index 000000000..0799feef1 --- /dev/null +++ b/.github/workflows/windows_release_dependencies_manual.yml @@ -0,0 +1,64 @@ +name: "Windows Release dependencies Manual" + +on: + workflow_dispatch: + inputs: + torch_dependencies: + description: 'torch dependencies' + required: false + type: string + default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128" + cache_tag: + description: 'Cached dependencies tag' + required: true + type: string + default: "cu128" + + python_minor: + description: 'python minor version' + required: true + type: string + default: "12" + + python_patch: + description: 'python patch version' + required: true + type: string + default: "10" + +jobs: + build_dependencies: + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }} + + - shell: bash + run: | + echo "@echo off + call update_comfyui.bat nopause + echo - + echo This will try to update pytorch and all python dependencies. + echo - + echo If you just want to update normally, close this and run update_comfyui.bat instead. + echo - + pause + ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2 + pause" > update_comfyui_and_python_dependencies.bat + + grep -v comfyui requirements.txt > requirements_nocomfyui.txt + python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir + python -m pip install --no-cache-dir ./temp_wheel_dir/* + echo installed basic + ls -lah temp_wheel_dir + mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps + tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps + + - uses: actions/cache/save@v4 + with: + path: | + ${{ inputs.cache_tag }}_python_deps.tar + update_comfyui_and_python_dependencies.bat + key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }} diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml index f90488705..ca1ef71ae 100644 --- a/.github/workflows/windows_release_nightly_pytorch.yml +++ b/.github/workflows/windows_release_nightly_pytorch.yml @@ -7,7 +7,7 @@ on: description: 'cuda version' required: true type: string - default: "126" + default: "129" python_minor: description: 'python minor version' @@ -19,7 +19,7 @@ on: description: 'python patch version' required: true type: string - default: "1" + default: "5" # push: # branches: # - master @@ -34,7 +34,7 @@ jobs: steps: - uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 30 persist-credentials: false - uses: actions/setup-python@v5 with: @@ -53,10 +53,12 @@ jobs: ls ../temp_wheel_dir ./python.exe -s -m pip install --pre ../temp_wheel_dir/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth + + rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable_nightly_pytorch mv python_embeded ComfyUI_windows_portable_nightly_pytorch @@ -66,7 +68,7 @@ jobs: mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ - cp -r ComfyUI/.ci/windows_base_files/* ./ + cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./ cp -r ComfyUI/.ci/windows_nightly_base_files/* ./ echo "call update_comfyui.bat nopause @@ -74,7 +76,7 @@ jobs: pause" > ./update/update_comfyui_and_python_dependencies.bat cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=512m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z cd ComfyUI_windows_portable_nightly_pytorch diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml index 11e724ba7..7955325fc 100644 --- a/.github/workflows/windows_release_package.yml +++ b/.github/workflows/windows_release_package.yml @@ -7,19 +7,19 @@ on: description: 'cuda version' required: true type: string - default: "124" + default: "129" python_minor: description: 'python minor version' required: true type: string - default: "12" + default: "13" python_patch: description: 'python patch version' required: true type: string - default: "7" + default: "6" # push: # branches: # - master @@ -50,7 +50,7 @@ jobs: - uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 150 persist-credentials: false - shell: bash run: | @@ -64,10 +64,14 @@ jobs: ./python.exe get-pip.py ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/* sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth + + rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space + rm ./Lib/site-packages/torch/lib/libprotoc.lib + rm ./Lib/site-packages/torch/lib/libprotobuf.lib cd .. git clone --depth 1 https://github.com/comfyanonymous/taesd - cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ + cp taesd/*.safetensors ./ComfyUI_copy/models/vae_approx/ mkdir ComfyUI_windows_portable mv python_embeded ComfyUI_windows_portable @@ -77,17 +81,19 @@ jobs: mkdir update cp -r ComfyUI/.ci/update_windows/* ./update/ - cp -r ComfyUI/.ci/windows_base_files/* ./ + cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./ cp ../update_comfyui_and_python_dependencies.bat ./update/ cd .. - "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable + "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z cd ComfyUI_windows_portable python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu + python_embeded/python.exe -s ./update/update.py ComfyUI/ + ls - name: Upload binaries to release diff --git a/.gitignore b/.gitignore index 61881b8a4..4e8cea71e 100644 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,6 @@ venv/ *.log web_custom_versions/ .DS_Store +openapi.yaml +filtered-openapi.yaml +uv.lock diff --git a/CODEOWNERS b/CODEOWNERS index 814d1ecdc..b7aca9b26 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1,23 +1,3 @@ # Admins * @comfyanonymous - -# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org. -# Inlined the team members for now. - -# Maintainers -*.md @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink -/tests/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink -/tests-unit/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink -/notebooks/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink -/script_examples/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink -/.github/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata @Kosinkadink - -# Python web server -/api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata -/app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata - -# Frontend assets -/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang - -# Extra nodes -/comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink +* @kosinkadink diff --git a/README.md b/README.md index 000d76801..434d4ff06 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,12 @@
# ComfyUI -**The most powerful and modular diffusion model GUI and backend.** +**The most powerful and modular visual AI engine and application.** [![Website][website-shield]][website-url] [![Dynamic JSON Badge][discord-shield]][discord-url] +[![Twitter][twitter-shield]][twitter-url] [![Matrix][matrix-shield]][matrix-url]
[![][github-release-shield]][github-release-link] @@ -20,6 +21,8 @@ [discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total [discord-url]: https://www.comfy.org/discord +[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI +[twitter-url]: https://x.com/ComfyUI [github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver [github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases @@ -31,15 +34,28 @@ ![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
-This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out: -### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/) +ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS. -### [Installing ComfyUI](#installing) +## Get Started + +#### [Desktop Application](https://www.comfy.org/download) +- The easiest way to get started. +- Available on Windows & macOS. + +#### [Windows Portable Package](#installing) +- Get the latest commits and completely portable. +- Available on Windows. + +#### [Manual Install](#manual-install-windows-linux) +Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend). + +## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/) +See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/). ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. - Image Models - - SD1.x, SD2.x, + - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)) - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/) - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) @@ -47,17 +63,33 @@ This ui will let you design and execute advanced stable diffusion pipelines usin - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/) - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/) - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/) + - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/) + - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/) + - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/) + - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/) +- Image Editing Models + - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/) + - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model) + - [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11) + - [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model) - Video Models - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/) - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/) - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/) -- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) + - [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/) + - [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/) +- Audio Models + - [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/) + - [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/) +- 3D Models + - [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2) - Asynchronous Queue system - Many optimizations: Only re-executes the parts of the workflow that changes between executions. -- Smart memory management: can automatically run models on GPUs with as low as 1GB vram. +- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading. - Works even if you don't have a GPU with: ```--cpu``` (slow) -- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models. +- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models. +- Safe loading of ckpt, pt, pth, etc.. files. - Embeddings/Textual inversion - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/) - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/) @@ -68,17 +100,32 @@ This ui will let you design and execute advanced stable diffusion pipelines usin - [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models. - [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/) - [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/) -- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/) - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/) - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/) - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/) - Latent previews with [TAESD](#how-to-show-high-quality-previews) -- Starts up very fast. -- Works fully offline: will never download anything. +- Works fully offline: core will never download anything unless you want to. +- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview). - [Config file](extra_model_paths.yaml.example) to set the search paths for models. Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/) +## Release Process + +ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories: + +1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)** + - Releases a new stable version (e.g., v0.7.0) + - Serves as the foundation for the desktop release + +2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)** + - Builds a new release using the latest stable core version + +3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** + - Weekly frontend updates are merged into the core repository + - Features are frozen for the upcoming core release + - Development continues for the next release cycle + ## Shortcuts | Keybind | Explanation | @@ -119,7 +166,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git # Installing -## Windows +## Windows Portable There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases). @@ -129,17 +176,32 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you If you have trouble extracting it, right click the file -> properties -> unblock +#### Alternative Downloads: + +[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) + +[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs). + #### How do I share models between another UI and ComfyUI? See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor. -## Jupyter Notebook -To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb) +## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started) + +You can install and start ComfyUI using comfy-cli: +```bash +pip install comfy-cli +comfy install +``` ## Manual Install (Windows, Linux) -Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended. +Python 3.14 will work if you comment out the `kornia` dependency in the requirements.txt file (breaks the canny node) but it is not recommended. + +Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12 + +### Instructions: Git clone this repo. @@ -148,48 +210,58 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints Put your VAE in: models/vae -### AMD GPUs (Linux only) +### AMD GPUs (Linux) + AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: -```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2``` +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4``` -This is the command to install the nightly with ROCm 6.2 which might have some performance improvements: +This is the command to install the nightly with ROCm 7.0 which might have some performance improvements: -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0``` + + +### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only. + +These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware. + +RDNA 3 (RX 7000 series): + +```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/``` + +RDNA 3.5 (Strix halo/Ryzen AI Max+ 365): + +```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/``` + +RDNA 4 (RX 9000 series): + +```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/``` ### Intel GPUs (Windows and Linux) -(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html) - -1. To install PyTorch nightly, use the following command: +(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html) + +1. To install PyTorch xpu, use the following command: + +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu``` + +This is the command to install the Pytorch xpu nightly which might have some performance improvements: ```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu``` -2. Launch ComfyUI by running `python main.py` - - (Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance. -1. For Intel® Arc™ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below: - -``` -conda install libuv -pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/ -``` - -For other supported Intel GPUs with IPEX, visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information. - -Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476). +1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information. ### NVIDIA Nvidia users should install stable pytorch using this command: -```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124``` +```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130``` -This is the command to install pytorch nightly instead which might have performance improvements: +This is the command to install pytorch nightly instead which might have performance improvements. -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130``` #### Troubleshooting @@ -220,10 +292,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux). -#### DirectML (AMD Cards on Windows) - -```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml``` - #### Ascend NPUs For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method: @@ -233,6 +301,20 @@ For models compatible with Ascend Extension for PyTorch (torch_npu). To get star 3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page. 4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier. +#### Cambricon MLUs + +For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method: + +1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html) +2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html) +3. Launch ComfyUI by running `python main.py` + +#### Iluvatar Corex + +For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method: + +1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536) +2. Launch ComfyUI by running `python main.py` # Running @@ -248,7 +330,7 @@ For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 pyt ### AMD ROCm Tips -You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command: +You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default. ```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention``` @@ -284,11 +366,13 @@ Generate a self-signed certificate (not appropriate for shared/production use) a Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`. -> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. +> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.

If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal. ## Support and dev channel +[Discord](https://comfy.org/discord): Try the #help or #feedback channels. + [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source). See also: [https://www.comfy.org/](https://www.comfy.org/) @@ -305,7 +389,7 @@ For any bugs, issues, or feature requests related to the frontend, please use th The new frontend is now the default for ComfyUI. However, please note: -1. The frontend in the main ComfyUI repository is updated weekly. +1. The frontend in the main ComfyUI repository is updated fortnightly. 2. Daily releases are available in the separate frontend repository. To use the most up-to-date frontend version: @@ -322,7 +406,7 @@ To use the most up-to-date frontend version: --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2 ``` -This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes. +This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes. ### Accessing the Legacy Frontend diff --git a/alembic.ini b/alembic.ini new file mode 100644 index 000000000..12f18712f --- /dev/null +++ b/alembic.ini @@ -0,0 +1,84 @@ +# A generic, single database configuration. + +[alembic] +# path to migration scripts +# Use forward slashes (/) also on windows to provide an os agnostic path +script_location = alembic_db + +# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s +# Uncomment the line below if you want the files to be prepended with date and time +# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file +# for all available tokens +# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s + +# sys.path path, will be prepended to sys.path if present. +# defaults to the current working directory. +prepend_sys_path = . + +# timezone to use when rendering the date within the migration file +# as well as the filename. +# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library. +# Any required deps can installed by adding `alembic[tz]` to the pip requirements +# string value is passed to ZoneInfo() +# leave blank for localtime +# timezone = + +# max length of characters to apply to the "slug" field +# truncate_slug_length = 40 + +# set to 'true' to run the environment during +# the 'revision' command, regardless of autogenerate +# revision_environment = false + +# set to 'true' to allow .pyc and .pyo files without +# a source .py file to be detected as revisions in the +# versions/ directory +# sourceless = false + +# version location specification; This defaults +# to alembic_db/versions. When using multiple version +# directories, initial revisions must be specified with --version-path. +# The path separator used here should be the separator specified by "version_path_separator" below. +# version_locations = %(here)s/bar:%(here)s/bat:alembic_db/versions + +# version path separator; As mentioned above, this is the character used to split +# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. +# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. +# Valid values for version_path_separator are: +# +# version_path_separator = : +# version_path_separator = ; +# version_path_separator = space +# version_path_separator = newline +# +# Use os.pathsep. Default configuration used for new projects. +version_path_separator = os + +# set to 'true' to search source files recursively +# in each "version_locations" directory +# new in Alembic version 1.10 +# recursive_version_locations = false + +# the output encoding used when revision files +# are written from script.py.mako +# output_encoding = utf-8 + +sqlalchemy.url = sqlite:///user/comfyui.db + + +[post_write_hooks] +# post_write_hooks defines scripts or Python functions that are run +# on newly generated revision scripts. See the documentation for further +# detail and examples + +# format using "black" - use the console_scripts runner, against the "black" entrypoint +# hooks = black +# black.type = console_scripts +# black.entrypoint = black +# black.options = -l 79 REVISION_SCRIPT_FILENAME + +# lint with attempts to fix using "ruff" - use the exec runner, execute a binary +# hooks = ruff +# ruff.type = exec +# ruff.executable = %(here)s/.venv/bin/ruff +# ruff.options = check --fix REVISION_SCRIPT_FILENAME diff --git a/alembic_db/README.md b/alembic_db/README.md new file mode 100644 index 000000000..3b808c7ca --- /dev/null +++ b/alembic_db/README.md @@ -0,0 +1,4 @@ +## Generate new revision + +1. Update models in `/app/database/models.py` +2. Run `alembic revision --autogenerate -m "{your message}"` diff --git a/alembic_db/env.py b/alembic_db/env.py new file mode 100644 index 000000000..4d7770679 --- /dev/null +++ b/alembic_db/env.py @@ -0,0 +1,64 @@ +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + + +from app.database.models import Base +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline() -> None: + """Run migrations in 'offline' mode. + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + Calls to context.execute() here emit the given string to the + script output. + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + """Run migrations in 'online' mode. + In this scenario we need to create an Engine + and associate a connection with the context. + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, target_metadata=target_metadata + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/alembic_db/script.py.mako b/alembic_db/script.py.mako new file mode 100644 index 000000000..480b130d6 --- /dev/null +++ b/alembic_db/script.py.mako @@ -0,0 +1,28 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision: str = ${repr(up_revision)} +down_revision: Union[str, None] = ${repr(down_revision)} +branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} +depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} + + +def upgrade() -> None: + """Upgrade schema.""" + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + """Downgrade schema.""" + ${downgrades if downgrades else "pass"} diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py index 8f74529ba..613b0f7c7 100644 --- a/api_server/routes/internal/internal_routes.py +++ b/api_server/routes/internal/internal_routes.py @@ -1,9 +1,9 @@ from aiohttp import web from typing import Optional -from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths -from api_server.services.file_service import FileService +from folder_paths import folder_names_and_paths, get_directory_by_type from api_server.services.terminal_service import TerminalService import app.logger +import os class InternalRoutes: ''' @@ -15,26 +15,10 @@ class InternalRoutes: def __init__(self, prompt_server): self.routes: web.RouteTableDef = web.RouteTableDef() self._app: Optional[web.Application] = None - self.file_service = FileService({ - "models": models_dir, - "user": user_directory, - "output": output_directory - }) self.prompt_server = prompt_server self.terminal_service = TerminalService(prompt_server) def setup_routes(self): - @self.routes.get('/files') - async def list_files(request): - directory_key = request.query.get('directory', '') - try: - file_list = self.file_service.list_files(directory_key) - return web.json_response({"files": file_list}) - except ValueError as e: - return web.json_response({"error": str(e)}, status=400) - except Exception as e: - return web.json_response({"error": str(e)}, status=500) - @self.routes.get('/logs') async def get_logs(request): return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()])) @@ -67,6 +51,20 @@ class InternalRoutes: response[key] = folder_names_and_paths[key][0] return web.json_response(response) + @self.routes.get('/files/{directory_type}') + async def get_files(request: web.Request) -> web.Response: + directory_type = request.match_info['directory_type'] + if directory_type not in ("output", "input", "temp"): + return web.json_response({"error": "Invalid directory type"}, status=400) + + directory = get_directory_by_type(directory_type) + sorted_files = sorted( + (entry for entry in os.scandir(directory) if entry.is_file()), + key=lambda entry: -entry.stat().st_mtime + ) + return web.json_response([entry.name for entry in sorted_files], status=200) + + def get_app(self): if self._app is None: self._app = web.Application() diff --git a/api_server/services/file_service.py b/api_server/services/file_service.py deleted file mode 100644 index 115edccd3..000000000 --- a/api_server/services/file_service.py +++ /dev/null @@ -1,13 +0,0 @@ -from typing import Dict, List, Optional -from api_server.utils.file_operations import FileSystemOperations, FileSystemItem - -class FileService: - def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None): - self.allowed_directories: Dict[str, str] = allowed_directories - self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations() - - def list_files(self, directory_key: str) -> List[FileSystemItem]: - if directory_key not in self.allowed_directories: - raise ValueError("Invalid directory key") - directory_path: str = self.allowed_directories[directory_key] - return self.file_system_ops.walk_directory(directory_path) diff --git a/app/app_settings.py b/app/app_settings.py index efe87adbd..c7ac73bf6 100644 --- a/app/app_settings.py +++ b/app/app_settings.py @@ -1,6 +1,7 @@ import os import json from aiohttp import web +import logging class AppSettings(): @@ -8,11 +9,21 @@ class AppSettings(): self.user_manager = user_manager def get_settings(self, request): - file = self.user_manager.get_request_user_filepath( - request, "comfy.settings.json") + try: + file = self.user_manager.get_request_user_filepath( + request, + "comfy.settings.json" + ) + except KeyError as e: + logging.error("User settings not found.") + raise web.HTTPUnauthorized() from e if os.path.isfile(file): - with open(file) as f: - return json.load(f) + try: + with open(file) as f: + return json.load(f) + except: + logging.error(f"The user settings file is corrupted: {file}") + return {} else: return {} diff --git a/app/custom_node_manager.py b/app/custom_node_manager.py index 7f9f645cd..281febca9 100644 --- a/app/custom_node_manager.py +++ b/app/custom_node_manager.py @@ -4,31 +4,142 @@ import os import folder_paths import glob from aiohttp import web +import json +import logging +from functools import lru_cache + +from utils.json_util import merge_json_recursive + + +# Extra locale files to load into main.json +EXTRA_LOCALE_FILES = [ + "nodeDefs.json", + "commands.json", + "settings.json", +] + + +def safe_load_json_file(file_path: str) -> dict: + if not os.path.exists(file_path): + return {} + + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.load(f) + except json.JSONDecodeError: + logging.error(f"Error loading {file_path}") + return {} + class CustomNodeManager: - """ - Placeholder to refactor the custom node management features from ComfyUI-Manager. - Currently it only contains the custom workflow templates feature. - """ + @lru_cache(maxsize=1) + def build_translations(self): + """Load all custom nodes translations during initialization. Translations are + expected to be loaded from `locales/` folder. + + The folder structure is expected to be the following: + - custom_nodes/ + - custom_node_1/ + - locales/ + - en/ + - main.json + - commands.json + - settings.json + + returned translations are expected to be in the following format: + { + "en": { + "nodeDefs": {...}, + "commands": {...}, + "settings": {...}, + ...{other main.json keys} + } + } + """ + + translations = {} + + for folder in folder_paths.get_folder_paths("custom_nodes"): + # Sort glob results for deterministic ordering + for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))): + locales_dir = os.path.join(custom_node_dir, "locales") + if not os.path.exists(locales_dir): + continue + + for lang_dir in glob.glob(os.path.join(locales_dir, "*/")): + lang_code = os.path.basename(os.path.dirname(lang_dir)) + + if lang_code not in translations: + translations[lang_code] = {} + + # Load main.json + main_file = os.path.join(lang_dir, "main.json") + node_translations = safe_load_json_file(main_file) + + # Load extra locale files + for extra_file in EXTRA_LOCALE_FILES: + extra_file_path = os.path.join(lang_dir, extra_file) + key = extra_file.split(".")[0] + json_data = safe_load_json_file(extra_file_path) + if json_data: + node_translations[key] = json_data + + if node_translations: + translations[lang_code] = merge_json_recursive( + translations[lang_code], node_translations + ) + + return translations + def add_routes(self, routes, webapp, loadedModules): + example_workflow_folder_names = ["example_workflows", "example", "examples", "workflow", "workflows"] + @routes.get("/workflow_templates") async def get_workflow_templates(request): """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted.""" - files = [ - file - for folder in folder_paths.get_folder_paths("custom_nodes") - for file in glob.glob(os.path.join(folder, '*/example_workflows/*.json')) - ] - workflow_templates_dict = {} # custom_nodes folder name -> example workflow names + + files = [] + + for folder in folder_paths.get_folder_paths("custom_nodes"): + for folder_name in example_workflow_folder_names: + pattern = os.path.join(folder, f"*/{folder_name}/*.json") + matched_files = glob.glob(pattern) + files.extend(matched_files) + + workflow_templates_dict = ( + {} + ) # custom_nodes folder name -> example workflow names for file in files: - custom_nodes_name = os.path.basename(os.path.dirname(os.path.dirname(file))) + custom_nodes_name = os.path.basename( + os.path.dirname(os.path.dirname(file)) + ) workflow_name = os.path.splitext(os.path.basename(file))[0] - workflow_templates_dict.setdefault(custom_nodes_name, []).append(workflow_name) + workflow_templates_dict.setdefault(custom_nodes_name, []).append( + workflow_name + ) return web.json_response(workflow_templates_dict) # Serve workflow templates from custom nodes. for module_name, module_dir in loadedModules: - workflows_dir = os.path.join(module_dir, 'example_workflows') - if os.path.exists(workflows_dir): - webapp.add_routes([web.static('/api/workflow_templates/' + module_name, workflows_dir)]) + for folder_name in example_workflow_folder_names: + workflows_dir = os.path.join(module_dir, folder_name) + + if os.path.exists(workflows_dir): + if folder_name != "example_workflows": + logging.debug( + "Found example workflow folder '%s' for custom node '%s', consider renaming it to 'example_workflows'", + folder_name, module_name) + + webapp.add_routes( + [ + web.static( + "/api/workflow_templates/" + module_name, workflows_dir + ) + ] + ) + + @routes.get("/i18n") + async def get_i18n(request): + """Returns translations from all custom nodes' locales folders.""" + return web.json_response(self.build_translations()) diff --git a/app/database/db.py b/app/database/db.py new file mode 100644 index 000000000..1de8b80ed --- /dev/null +++ b/app/database/db.py @@ -0,0 +1,112 @@ +import logging +import os +import shutil +from app.logger import log_startup_warning +from utils.install_util import get_missing_requirements_message +from comfy.cli_args import args + +_DB_AVAILABLE = False +Session = None + + +try: + from alembic import command + from alembic.config import Config + from alembic.runtime.migration import MigrationContext + from alembic.script import ScriptDirectory + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + _DB_AVAILABLE = True +except ImportError as e: + log_startup_warning( + f""" +------------------------------------------------------------------------ +Error importing dependencies: {e} +{get_missing_requirements_message()} +This error is happening because ComfyUI now uses a local sqlite database. +------------------------------------------------------------------------ +""".strip() + ) + + +def dependencies_available(): + """ + Temporary function to check if the dependencies are available + """ + return _DB_AVAILABLE + + +def can_create_session(): + """ + Temporary function to check if the database is available to create a session + During initial release there may be environmental issues (or missing dependencies) that prevent the database from being created + """ + return dependencies_available() and Session is not None + + +def get_alembic_config(): + root_path = os.path.join(os.path.dirname(__file__), "../..") + config_path = os.path.abspath(os.path.join(root_path, "alembic.ini")) + scripts_path = os.path.abspath(os.path.join(root_path, "alembic_db")) + + config = Config(config_path) + config.set_main_option("script_location", scripts_path) + config.set_main_option("sqlalchemy.url", args.database_url) + + return config + + +def get_db_path(): + url = args.database_url + if url.startswith("sqlite:///"): + return url.split("///")[1] + else: + raise ValueError(f"Unsupported database URL '{url}'.") + + +def init_db(): + db_url = args.database_url + logging.debug(f"Database URL: {db_url}") + db_path = get_db_path() + db_exists = os.path.exists(db_path) + + config = get_alembic_config() + + # Check if we need to upgrade + engine = create_engine(db_url) + conn = engine.connect() + + context = MigrationContext.configure(conn) + current_rev = context.get_current_revision() + + script = ScriptDirectory.from_config(config) + target_rev = script.get_current_head() + + if target_rev is None: + logging.warning("No target revision found.") + elif current_rev != target_rev: + # Backup the database pre upgrade + backup_path = db_path + ".bkp" + if db_exists: + shutil.copy(db_path, backup_path) + else: + backup_path = None + + try: + command.upgrade(config, target_rev) + logging.info(f"Database upgraded from {current_rev} to {target_rev}") + except Exception as e: + if backup_path: + # Restore the database from backup if upgrade fails + shutil.copy(backup_path, db_path) + os.remove(backup_path) + logging.exception("Error upgrading database: ") + raise e + + global Session + Session = sessionmaker(bind=engine) + + +def create_session(): + return Session() diff --git a/app/database/models.py b/app/database/models.py new file mode 100644 index 000000000..6facfb8f2 --- /dev/null +++ b/app/database/models.py @@ -0,0 +1,14 @@ +from sqlalchemy.orm import declarative_base + +Base = declarative_base() + + +def to_dict(obj): + fields = obj.__table__.columns.keys() + return { + field: (val.to_dict() if hasattr(val, "to_dict") else val) + for field in fields + if (val := getattr(obj, field)) + } + +# TODO: Define models here diff --git a/app/frontend_management.py b/app/frontend_management.py index 6f20e439c..cce0c117d 100644 --- a/app/frontend_management.py +++ b/app/frontend_management.py @@ -3,16 +3,92 @@ import argparse import logging import os import re +import sys import tempfile import zipfile +import importlib from dataclasses import dataclass from functools import cached_property from pathlib import Path from typing import TypedDict, Optional +from importlib.metadata import version import requests from typing_extensions import NotRequired + +from utils.install_util import get_missing_requirements_message, requirements_path + from comfy.cli_args import DEFAULT_VERSION_STRING +import app.logger + + +def frontend_install_warning_message(): + return f""" +{get_missing_requirements_message()} + +This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead. +""".strip() + +def parse_version(version: str) -> tuple[int, int, int]: + return tuple(map(int, version.split("."))) + +def is_valid_version(version: str) -> bool: + """Validate if a string is a valid semantic version (X.Y.Z format).""" + pattern = r"^(\d+)\.(\d+)\.(\d+)$" + return bool(re.match(pattern, version)) + +def get_installed_frontend_version(): + """Get the currently installed frontend package version.""" + frontend_version_str = version("comfyui-frontend-package") + return frontend_version_str + + +def get_required_frontend_version(): + """Get the required frontend version from requirements.txt.""" + try: + with open(requirements_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line.startswith("comfyui-frontend-package=="): + version_str = line.split("==")[-1] + if not is_valid_version(version_str): + logging.error(f"Invalid version format in requirements.txt: {version_str}") + return None + return version_str + logging.error("comfyui-frontend-package not found in requirements.txt") + return None + except FileNotFoundError: + logging.error("requirements.txt not found. Cannot determine required frontend version.") + return None + except Exception as e: + logging.error(f"Error reading requirements.txt: {e}") + return None + + +def check_frontend_version(): + """Check if the frontend version is up to date.""" + + try: + frontend_version_str = get_installed_frontend_version() + frontend_version = parse_version(frontend_version_str) + required_frontend_str = get_required_frontend_version() + required_frontend = parse_version(required_frontend_str) + if frontend_version < required_frontend: + app.logger.log_startup_warning( + f""" +________________________________________________________________________ +WARNING WARNING WARNING WARNING WARNING + +Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}. + +{frontend_install_warning_message()} +________________________________________________________________________ +""".strip() + ) + else: + logging.info("ComfyUI frontend version: {}".format(frontend_version_str)) + except Exception as e: + logging.error(f"Failed to check frontend version: {e}") REQUEST_TIMEOUT = 10 # seconds @@ -68,9 +144,22 @@ class FrontEndProvider: response.raise_for_status() # Raises an HTTPError if the response was an error return response.json() + @cached_property + def latest_prerelease(self) -> Release: + """Get the latest pre-release version - even if it's older than the latest release""" + release = [release for release in self.all_releases if release["prerelease"]] + + if not release: + raise ValueError("No pre-releases found") + + # GitHub returns releases in reverse chronological order, so first is latest + return release[0] + def get_release(self, version: str) -> Release: if version == "latest": return self.latest_release + elif version == "prerelease": + return self.latest_prerelease else: for release in self.all_releases: if release["tag_name"] in [version, f"v{version}"]: @@ -109,9 +198,98 @@ def download_release_asset_zip(release: Release, destination_path: str) -> None: class FrontendManager: - DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web") CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions") + @classmethod + def get_required_frontend_version(cls) -> str: + """Get the required frontend package version.""" + return get_required_frontend_version() + + @classmethod + def get_installed_templates_version(cls) -> str: + """Get the currently installed workflow templates package version.""" + try: + templates_version_str = version("comfyui-workflow-templates") + return templates_version_str + except Exception: + return None + + @classmethod + def get_required_templates_version(cls) -> str: + """Get the required workflow templates version from requirements.txt.""" + try: + with open(requirements_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line.startswith("comfyui-workflow-templates=="): + version_str = line.split("==")[-1] + if not is_valid_version(version_str): + logging.error(f"Invalid templates version format in requirements.txt: {version_str}") + return None + return version_str + logging.error("comfyui-workflow-templates not found in requirements.txt") + return None + except FileNotFoundError: + logging.error("requirements.txt not found. Cannot determine required templates version.") + return None + except Exception as e: + logging.error(f"Error reading requirements.txt: {e}") + return None + + @classmethod + def default_frontend_path(cls) -> str: + try: + import comfyui_frontend_package + + return str(importlib.resources.files(comfyui_frontend_package) / "static") + except ImportError: + logging.error( + f""" +********** ERROR *********** + +comfyui-frontend-package is not installed. + +{frontend_install_warning_message()} + +********** ERROR *********** +""".strip() + ) + sys.exit(-1) + + @classmethod + def templates_path(cls) -> str: + try: + import comfyui_workflow_templates + + return str( + importlib.resources.files(comfyui_workflow_templates) / "templates" + ) + except ImportError: + logging.error( + f""" +********** ERROR *********** + +comfyui-workflow-templates is not installed. + +{frontend_install_warning_message()} + +********** ERROR *********** +""".strip() + ) + + @classmethod + def embedded_docs_path(cls) -> str: + """Get the path to embedded documentation""" + try: + import comfyui_embedded_docs + + return str( + importlib.resources.files(comfyui_embedded_docs) / "docs" + ) + except ImportError: + logging.info("comfyui-embedded-docs package not found") + return None + @classmethod def parse_version_string(cls, value: str) -> tuple[str, str, str]: """ @@ -124,7 +302,7 @@ class FrontendManager: Raises: argparse.ArgumentTypeError: If the version string is invalid. """ - VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$" + VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+[-._a-zA-Z0-9]*|latest|prerelease)$" match_result = re.match(VERSION_PATTERN, value) if match_result is None: raise argparse.ArgumentTypeError(f"Invalid version string: {value}") @@ -132,7 +310,9 @@ class FrontendManager: return match_result.group(1), match_result.group(2), match_result.group(3) @classmethod - def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str: + def init_frontend_unsafe( + cls, version_string: str, provider: Optional[FrontEndProvider] = None + ) -> str: """ Initializes the frontend for the specified version. @@ -148,17 +328,26 @@ class FrontendManager: main error source might be request timeout or invalid URL. """ if version_string == DEFAULT_VERSION_STRING: - return cls.DEFAULT_FRONTEND_PATH + check_frontend_version() + return cls.default_frontend_path() repo_owner, repo_name, version = cls.parse_version_string(version_string) if version.startswith("v"): - expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v")) + expected_path = str( + Path(cls.CUSTOM_FRONTENDS_ROOT) + / f"{repo_owner}_{repo_name}" + / version.lstrip("v") + ) if os.path.exists(expected_path): - logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}") + logging.info( + f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}" + ) return expected_path - logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...") + logging.info( + f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..." + ) provider = provider or FrontEndProvider(repo_owner, repo_name) release = provider.get_release(version) @@ -201,4 +390,5 @@ class FrontendManager: except Exception as e: logging.error("Failed to initialize frontend: %s", e) logging.info("Falling back to the default frontend.") - return cls.DEFAULT_FRONTEND_PATH + check_frontend_version() + return cls.default_frontend_path() diff --git a/app/logger.py b/app/logger.py index 9e9f84ccf..3d26d98fe 100644 --- a/app/logger.py +++ b/app/logger.py @@ -82,3 +82,17 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool logger.addHandler(stdout_handler) logger.addHandler(stream_handler) + + +STARTUP_WARNINGS = [] + + +def log_startup_warning(msg): + logging.warning(msg) + STARTUP_WARNINGS.append(msg) + + +def print_startup_warnings(): + for s in STARTUP_WARNINGS: + logging.warning(s) + STARTUP_WARNINGS.clear() diff --git a/app/model_manager.py b/app/model_manager.py index 74d942fb8..ab36bca74 100644 --- a/app/model_manager.py +++ b/app/model_manager.py @@ -130,10 +130,21 @@ class ModelFileManager: for file_name in filenames: try: - relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) - result.append(relative_path) - except: - logging.warning(f"Warning: Unable to access {file_name}. Skipping this file.") + full_path = os.path.join(dirpath, file_name) + relative_path = os.path.relpath(full_path, directory) + + # Get file metadata + file_info = { + "name": relative_path, + "pathIndex": pathIndex, + "modified": os.path.getmtime(full_path), # Add modification time + "created": os.path.getctime(full_path), # Add creation time + "size": os.path.getsize(full_path) # Add file size + } + result.append(file_info) + + except Exception as e: + logging.warning(f"Warning: Unable to access {file_name}. Error: {e}. Skipping this file.") continue for d in subdirs: @@ -144,7 +155,7 @@ class ModelFileManager: logging.warning(f"Warning: Unable to access {path}. Skipping this path.") continue - return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter() + return result, dirs, time.perf_counter() def get_model_previews(self, filepath: str) -> list[str | BytesIO]: dirname = os.path.dirname(filepath) diff --git a/app/subgraph_manager.py b/app/subgraph_manager.py new file mode 100644 index 000000000..dbe404541 --- /dev/null +++ b/app/subgraph_manager.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +from typing import TypedDict +import os +import folder_paths +import glob +from aiohttp import web +import hashlib + + +class Source: + custom_node = "custom_node" + +class SubgraphEntry(TypedDict): + source: str + """ + Source of subgraph - custom_nodes vs templates. + """ + path: str + """ + Relative path of the subgraph file. + For custom nodes, will be the relative directory like /subgraphs/.json + """ + name: str + """ + Name of subgraph file. + """ + info: CustomNodeSubgraphEntryInfo + """ + Additional info about subgraph; in the case of custom_nodes, will contain nodepack name + """ + data: str + +class CustomNodeSubgraphEntryInfo(TypedDict): + node_pack: str + """Node pack name.""" + +class SubgraphManager: + def __init__(self): + self.cached_custom_node_subgraphs: dict[SubgraphEntry] | None = None + + async def load_entry_data(self, entry: SubgraphEntry): + with open(entry['path'], 'r') as f: + entry['data'] = f.read() + return entry + + async def sanitize_entry(self, entry: SubgraphEntry | None, remove_data=False) -> SubgraphEntry | None: + if entry is None: + return None + entry = entry.copy() + entry.pop('path', None) + if remove_data: + entry.pop('data', None) + return entry + + async def sanitize_entries(self, entries: dict[str, SubgraphEntry], remove_data=False) -> dict[str, SubgraphEntry]: + entries = entries.copy() + for key in list(entries.keys()): + entries[key] = await self.sanitize_entry(entries[key], remove_data) + return entries + + async def get_custom_node_subgraphs(self, loadedModules, force_reload=False): + # if not forced to reload and cached, return cache + if not force_reload and self.cached_custom_node_subgraphs is not None: + return self.cached_custom_node_subgraphs + # Load subgraphs from custom nodes + subfolder = "subgraphs" + subgraphs_dict: dict[SubgraphEntry] = {} + + for folder in folder_paths.get_folder_paths("custom_nodes"): + pattern = os.path.join(folder, f"*/{subfolder}/*.json") + matched_files = glob.glob(pattern) + for file in matched_files: + # replace backslashes with forward slashes + file = file.replace('\\', '/') + info: CustomNodeSubgraphEntryInfo = { + "node_pack": "custom_nodes." + file.split('/')[-3] + } + source = Source.custom_node + # hash source + path to make sure id will be as unique as possible, but + # reproducible across backend reloads + id = hashlib.sha256(f"{source}{file}".encode()).hexdigest() + entry: SubgraphEntry = { + "source": Source.custom_node, + "name": os.path.splitext(os.path.basename(file))[0], + "path": file, + "info": info, + } + subgraphs_dict[id] = entry + self.cached_custom_node_subgraphs = subgraphs_dict + return subgraphs_dict + + async def get_custom_node_subgraph(self, id: str, loadedModules): + subgraphs = await self.get_custom_node_subgraphs(loadedModules) + entry: SubgraphEntry = subgraphs.get(id, None) + if entry is not None and entry.get('data', None) is None: + await self.load_entry_data(entry) + return entry + + def add_routes(self, routes, loadedModules): + @routes.get("/global_subgraphs") + async def get_global_subgraphs(request): + subgraphs_dict = await self.get_custom_node_subgraphs(loadedModules) + # NOTE: we may want to include other sources of global subgraphs such as templates in the future; + # that's the reasoning for the current implementation + return web.json_response(await self.sanitize_entries(subgraphs_dict, remove_data=True)) + + @routes.get("/global_subgraphs/{id}") + async def get_global_subgraph(request): + id = request.match_info.get("id", None) + subgraph = await self.get_custom_node_subgraph(id, loadedModules) + return web.json_response(await self.sanitize_entry(subgraph)) diff --git a/app/user_manager.py b/app/user_manager.py index e7381e621..a2d376c0c 100644 --- a/app/user_manager.py +++ b/app/user_manager.py @@ -20,13 +20,15 @@ class FileInfo(TypedDict): path: str size: int modified: int + created: int def get_file_info(path: str, relative_to: str) -> FileInfo: return { "path": os.path.relpath(path, relative_to).replace(os.sep, '/'), "size": os.path.getsize(path), - "modified": os.path.getmtime(path) + "modified": os.path.getmtime(path), + "created": os.path.getctime(path) } @@ -197,6 +199,112 @@ class UserManager(): return web.json_response(results) + @routes.get("/v2/userdata") + async def list_userdata_v2(request): + """ + List files and directories in a user's data directory. + + This endpoint provides a structured listing of contents within a specified + subdirectory of the user's data storage. + + Query Parameters: + - path (optional): The relative path within the user's data directory + to list. Defaults to the root (''). + + Returns: + - 400: If the requested path is invalid, outside the user's data directory, or is not a directory. + - 404: If the requested path does not exist. + - 403: If the user is invalid. + - 500: If there is an error reading the directory contents. + - 200: JSON response containing a list of file and directory objects. + Each object includes: + - name: The name of the file or directory. + - type: 'file' or 'directory'. + - path: The relative path from the user's data root. + - size (for files): The size in bytes. + - modified (for files): The last modified timestamp (Unix epoch). + """ + requested_rel_path = request.rel_url.query.get('path', '') + + # URL-decode the path parameter + try: + requested_rel_path = parse.unquote(requested_rel_path) + except Exception as e: + logging.warning(f"Failed to decode path parameter: {requested_rel_path}, Error: {e}") + return web.Response(status=400, text="Invalid characters in path parameter") + + + # Check user validity and get the absolute path for the requested directory + try: + base_user_path = self.get_request_user_filepath(request, None, create_dir=False) + + if requested_rel_path: + target_abs_path = self.get_request_user_filepath(request, requested_rel_path, create_dir=False) + else: + target_abs_path = base_user_path + + except KeyError as e: + # Invalid user detected by get_request_user_id inside get_request_user_filepath + logging.warning(f"Access denied for user: {e}") + return web.Response(status=403, text="Invalid user specified in request") + + + if not target_abs_path: + # Path traversal or other issue detected by get_request_user_filepath + return web.Response(status=400, text="Invalid path requested") + + # Handle cases where the user directory or target path doesn't exist + if not os.path.exists(target_abs_path): + # Check if it's the base user directory that's missing (new user case) + if target_abs_path == base_user_path: + # It's okay if the base user directory doesn't exist yet, return empty list + return web.json_response([]) + else: + # A specific subdirectory was requested but doesn't exist + return web.Response(status=404, text="Requested path not found") + + if not os.path.isdir(target_abs_path): + return web.Response(status=400, text="Requested path is not a directory") + + results = [] + try: + for root, dirs, files in os.walk(target_abs_path, topdown=True): + # Process directories + for dir_name in dirs: + dir_path = os.path.join(root, dir_name) + rel_path = os.path.relpath(dir_path, base_user_path).replace(os.sep, '/') + results.append({ + "name": dir_name, + "path": rel_path, + "type": "directory" + }) + + # Process files + for file_name in files: + file_path = os.path.join(root, file_name) + rel_path = os.path.relpath(file_path, base_user_path).replace(os.sep, '/') + entry_info = { + "name": file_name, + "path": rel_path, + "type": "file" + } + try: + stats = os.stat(file_path) # Use os.stat for potentially better performance with os.walk + entry_info["size"] = stats.st_size + entry_info["modified"] = stats.st_mtime + except OSError as stat_error: + logging.warning(f"Could not stat file {file_path}: {stat_error}") + pass # Include file with available info + results.append(entry_info) + except OSError as e: + logging.error(f"Error listing directory {target_abs_path}: {e}") + return web.Response(status=500, text="Error reading directory contents") + + # Sort results alphabetically, directories first then files + results.sort(key=lambda x: (x['type'] != 'directory', x['name'].lower())) + + return web.json_response(results) + def get_user_data_path(request, check_exists = False, param = "file"): file = request.match_info.get(param, None) if not file: @@ -255,10 +363,17 @@ class UserManager(): if not overwrite and os.path.exists(path): return web.Response(status=409, text="File already exists") - body = await request.read() + try: + body = await request.read() - with open(path, "wb") as f: - f.write(body) + with open(path, "wb") as f: + f.write(body) + except OSError as e: + logging.warning(f"Error saving file '{path}': {e}") + return web.Response( + status=400, + reason="Invalid filename. Please avoid special characters like :\\/*?\"<>|" + ) user_path = self.get_request_user_filepath(request, None) if full_info: diff --git a/comfy/audio_encoders/audio_encoders.py b/comfy/audio_encoders/audio_encoders.py new file mode 100644 index 000000000..46ef21c95 --- /dev/null +++ b/comfy/audio_encoders/audio_encoders.py @@ -0,0 +1,91 @@ +from .wav2vec2 import Wav2Vec2Model +from .whisper import WhisperLargeV3 +import comfy.model_management +import comfy.ops +import comfy.utils +import logging +import torchaudio + + +class AudioEncoderModel(): + def __init__(self, config): + self.load_device = comfy.model_management.text_encoder_device() + offload_device = comfy.model_management.text_encoder_offload_device() + self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) + model_type = config.pop("model_type") + model_config = dict(config) + model_config.update({ + "dtype": self.dtype, + "device": offload_device, + "operations": comfy.ops.manual_cast + }) + + if model_type == "wav2vec2": + self.model = Wav2Vec2Model(**model_config) + elif model_type == "whisper3": + self.model = WhisperLargeV3(**model_config) + self.model.eval() + self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + self.model_sample_rate = 16000 + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=False) + + def get_sd(self): + return self.model.state_dict() + + def encode_audio(self, audio, sample_rate): + comfy.model_management.load_model_gpu(self.patcher) + audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate) + out, all_layers = self.model(audio.to(self.load_device)) + outputs = {} + outputs["encoded_audio"] = out + outputs["encoded_audio_all_layers"] = all_layers + outputs["audio_samples"] = audio.shape[2] + return outputs + + +def load_audio_encoder_from_sd(sd, prefix=""): + sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""}) + if "encoder.layer_norm.bias" in sd: #wav2vec2 + embed_dim = sd["encoder.layer_norm.bias"].shape[0] + if embed_dim == 1024:# large + config = { + "model_type": "wav2vec2", + "embed_dim": 1024, + "num_heads": 16, + "num_layers": 24, + "conv_norm": True, + "conv_bias": True, + "do_normalize": True, + "do_stable_layer_norm": True + } + elif embed_dim == 768: # base + config = { + "model_type": "wav2vec2", + "embed_dim": 768, + "num_heads": 12, + "num_layers": 12, + "conv_norm": False, + "conv_bias": False, + "do_normalize": False, # chinese-wav2vec2-base has this False + "do_stable_layer_norm": False + } + else: + raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim)) + elif "model.encoder.embed_positions.weight" in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""}) + config = { + "model_type": "whisper3", + } + else: + raise RuntimeError("ERROR: audio encoder not supported.") + + audio_encoder = AudioEncoderModel(config) + m, u = audio_encoder.load_sd(sd) + if len(m) > 0: + logging.warning("missing audio encoder: {}".format(m)) + if len(u) > 0: + logging.warning("unexpected audio encoder: {}".format(u)) + + return audio_encoder diff --git a/comfy/audio_encoders/wav2vec2.py b/comfy/audio_encoders/wav2vec2.py new file mode 100644 index 000000000..4e34a40a7 --- /dev/null +++ b/comfy/audio_encoders/wav2vec2.py @@ -0,0 +1,252 @@ +import torch +import torch.nn as nn +from comfy.ldm.modules.attention import optimized_attention_masked + + +class LayerNormConv(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): + super().__init__() + self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) + self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype) + + def forward(self, x): + x = self.conv(x) + return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1)) + +class LayerGroupNormConv(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): + super().__init__() + self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) + self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype) + + def forward(self, x): + x = self.conv(x) + return torch.nn.functional.gelu(self.layer_norm(x)) + +class ConvNoNorm(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): + super().__init__() + self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype) + + def forward(self, x): + x = self.conv(x) + return torch.nn.functional.gelu(x) + + +class ConvFeatureEncoder(nn.Module): + def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None): + super().__init__() + if conv_norm: + self.conv_layers = nn.ModuleList([ + LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ]) + else: + self.conv_layers = nn.ModuleList([ + LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations), + ]) + + def forward(self, x): + x = x.unsqueeze(1) + + for conv in self.conv_layers: + x = conv(x) + + return x.transpose(1, 2) + + +class FeatureProjection(nn.Module): + def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None): + super().__init__() + self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype) + self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype) + + def forward(self, x): + x = self.layer_norm(x) + x = self.projection(x) + return x + + +class PositionalConvEmbedding(nn.Module): + def __init__(self, embed_dim=768, kernel_size=128, groups=16): + super().__init__() + self.conv = nn.Conv1d( + embed_dim, + embed_dim, + kernel_size=kernel_size, + padding=kernel_size // 2, + groups=groups, + ) + self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2) + self.activation = nn.GELU() + + def forward(self, x): + x = x.transpose(1, 2) + x = self.conv(x)[:, :, :-1] + x = self.activation(x) + x = x.transpose(1, 2) + return x + + +class TransformerEncoder(nn.Module): + def __init__( + self, + embed_dim=768, + num_heads=12, + num_layers=12, + mlp_ratio=4.0, + do_stable_layer_norm=True, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim) + self.layers = nn.ModuleList([ + TransformerEncoderLayer( + embed_dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + do_stable_layer_norm=do_stable_layer_norm, + device=device, dtype=dtype, operations=operations + ) + for _ in range(num_layers) + ]) + + self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype) + self.do_stable_layer_norm = do_stable_layer_norm + + def forward(self, x, mask=None): + x = x + self.pos_conv_embed(x) + all_x = () + if not self.do_stable_layer_norm: + x = self.layer_norm(x) + for layer in self.layers: + all_x += (x,) + x = layer(x, mask) + if self.do_stable_layer_norm: + x = self.layer_norm(x) + all_x += (x,) + return x, all_x + + +class Attention(nn.Module): + def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + + self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) + self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) + self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) + self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype) + + def forward(self, x, mask=None): + assert (mask is None) # TODO? + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention_masked(q, k, v, self.num_heads) + return self.out_proj(out) + + +class FeedForward(nn.Module): + def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None): + super().__init__() + self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype) + self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype) + + def forward(self, x): + x = self.intermediate_dense(x) + x = torch.nn.functional.gelu(x) + x = self.output_dense(x) + return x + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + embed_dim=768, + num_heads=12, + mlp_ratio=4.0, + do_stable_layer_norm=True, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations) + + self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) + self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations) + self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype) + self.do_stable_layer_norm = do_stable_layer_norm + + def forward(self, x, mask=None): + residual = x + if self.do_stable_layer_norm: + x = self.layer_norm(x) + x = self.attention(x, mask=mask) + x = residual + x + if not self.do_stable_layer_norm: + x = self.layer_norm(x) + return self.final_layer_norm(x + self.feed_forward(x)) + else: + return x + self.feed_forward(self.final_layer_norm(x)) + + +class Wav2Vec2Model(nn.Module): + """Complete Wav2Vec 2.0 model.""" + + def __init__( + self, + embed_dim=1024, + final_dim=256, + num_heads=16, + num_layers=24, + conv_norm=True, + conv_bias=True, + do_normalize=True, + do_stable_layer_norm=True, + dtype=None, device=None, operations=None + ): + super().__init__() + + conv_dim = 512 + self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations) + self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations) + + self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype)) + self.do_normalize = do_normalize + + self.encoder = TransformerEncoder( + embed_dim=embed_dim, + num_heads=num_heads, + num_layers=num_layers, + do_stable_layer_norm=do_stable_layer_norm, + device=device, dtype=dtype, operations=operations + ) + + def forward(self, x, mask_time_indices=None, return_dict=False): + x = torch.mean(x, dim=1) + + if self.do_normalize: + x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7) + + features = self.feature_extractor(x) + features = self.feature_projection(features) + batch_size, seq_len, _ = features.shape + + x, all_x = self.encoder(features) + return x, all_x diff --git a/comfy/audio_encoders/whisper.py b/comfy/audio_encoders/whisper.py new file mode 100755 index 000000000..93d3782f1 --- /dev/null +++ b/comfy/audio_encoders/whisper.py @@ -0,0 +1,186 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchaudio +from typing import Optional +from comfy.ldm.modules.attention import optimized_attention_masked +import comfy.ops + +class WhisperFeatureExtractor(nn.Module): + def __init__(self, n_mels=128, device=None): + super().__init__() + self.sample_rate = 16000 + self.n_fft = 400 + self.hop_length = 160 + self.n_mels = n_mels + self.chunk_length = 30 + self.n_samples = 480000 + + self.mel_spectrogram = torchaudio.transforms.MelSpectrogram( + sample_rate=self.sample_rate, + n_fft=self.n_fft, + hop_length=self.hop_length, + n_mels=self.n_mels, + f_min=0, + f_max=8000, + norm="slaney", + mel_scale="slaney", + ).to(device) + + def __call__(self, audio): + audio = torch.mean(audio, dim=1) + batch_size = audio.shape[0] + processed_audio = [] + + for i in range(batch_size): + aud = audio[i] + if aud.shape[0] > self.n_samples: + aud = aud[:self.n_samples] + elif aud.shape[0] < self.n_samples: + aud = F.pad(aud, (0, self.n_samples - aud.shape[0])) + processed_audio.append(aud) + + audio = torch.stack(processed_audio) + + mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device) + + log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0) + log_mel_spec = (log_mel_spec + 4.0) / 4.0 + + return log_mel_spec + + +class MultiHeadAttention(nn.Module): + def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None): + super().__init__() + assert d_model % n_heads == 0 + + self.d_model = d_model + self.n_heads = n_heads + self.d_k = d_model // n_heads + + self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device) + self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) + self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + batch_size, seq_len, _ = query.shape + + q = self.q_proj(query) + k = self.k_proj(key) + v = self.v_proj(value) + + attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask) + attn_output = self.out_proj(attn_output) + + return attn_output + + +class EncoderLayer(nn.Module): + def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None): + super().__init__() + + self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations) + self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device) + + self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device) + self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device) + self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device) + + def forward( + self, + x: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + residual = x + x = self.self_attn_layer_norm(x) + x = self.self_attn(x, x, x, attention_mask) + x = residual + x + + residual = x + x = self.final_layer_norm(x) + x = self.fc1(x) + x = F.gelu(x) + x = self.fc2(x) + x = residual + x + + return x + + +class AudioEncoder(nn.Module): + def __init__( + self, + n_mels: int = 128, + n_ctx: int = 1500, + n_state: int = 1280, + n_head: int = 20, + n_layer: int = 32, + dtype=None, + device=None, + operations=None + ): + super().__init__() + + self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device) + self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device) + + self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device) + + self.layers = nn.ModuleList([ + EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations) + for _ in range(n_layer) + ]) + + self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + + x = x.transpose(1, 2) + + x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x) + + all_x = () + for layer in self.layers: + all_x += (x,) + x = layer(x) + + x = self.layer_norm(x) + all_x += (x,) + return x, all_x + + +class WhisperLargeV3(nn.Module): + def __init__( + self, + n_mels: int = 128, + n_audio_ctx: int = 1500, + n_audio_state: int = 1280, + n_audio_head: int = 20, + n_audio_layer: int = 32, + dtype=None, + device=None, + operations=None + ): + super().__init__() + + self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device) + + self.encoder = AudioEncoder( + n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer, + dtype=dtype, device=device, operations=operations + ) + + def forward(self, audio): + mel = self.feature_extractor(audio) + x, all_x = self.encoder(mel) + return x, all_x diff --git a/comfy/cli_args.py b/comfy/cli_args.py index 812798bf8..cc1f12482 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -1,7 +1,6 @@ import argparse import enum import os -from typing import Optional import comfy.options @@ -43,13 +42,15 @@ parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certific parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.") parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.") +parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.") parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.") -parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.") -parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).") -parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.") +parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.") +parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.") +parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.") parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.") parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.") -parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.") +parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use. All other devices will not be visible.") +parser.add_argument("--default-device", type=int, default=None, metavar="DEFAULT_DEVICE_ID", help="Set the id of the default device, all other devices will stay visible.") cm_group = parser.add_mutually_exclusive_group() cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).") cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.") @@ -66,6 +67,7 @@ fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diff fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16") fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.") fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.") +fpunet_group.add_argument("--fp8_e8m0fnu-unet", action="store_true", help="Store unet weights in fp8_e8m0fnu.") fpvae_group = parser.add_mutually_exclusive_group() fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.") @@ -79,6 +81,7 @@ fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Stor fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).") fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.") fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.") +fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.") parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.") @@ -86,6 +89,7 @@ parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE" parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") +parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") class LatentPreviewMethod(enum.Enum): NoPreviews = "none" @@ -100,12 +104,14 @@ parser.add_argument("--preview-size", type=int, default=512, help="Sets the maxi cache_group = parser.add_mutually_exclusive_group() cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.") cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.") +cache_group.add_argument("--cache-none", action="store_true", help="Reduced RAM/VRAM usage at the expense of executing every node for each run.") attn_group = parser.add_mutually_exclusive_group() attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization. Ignored when xformers is used.") attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.") attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.") attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.") +attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.") parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.") @@ -124,12 +130,25 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.") +parser.add_argument("--async-offload", action="store_true", help="Use async weight offloading.") + +parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.") parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.") parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.") parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.") -parser.add_argument("--fast", action="store_true", help="Enable some untested and potentially quality deteriorating optimizations.") + +class PerformanceFeature(enum.Enum): + Fp16Accumulation = "fp16_accumulation" + Fp8MatrixMultiplication = "fp8_matrix_mult" + CublasOps = "cublas_ops" + AutoTune = "autotune" + +parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: {}".format(" ".join(map(lambda c: c.value, PerformanceFeature)))) + +parser.add_argument("--mmap-torch-files", action="store_true", help="Use mmap when loading ckpt/pt files.") +parser.add_argument("--disable-mmap", action="store_true", help="Don't use mmap when loading safetensors.") parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.") parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.") @@ -137,6 +156,8 @@ parser.add_argument("--windows-standalone-build", action="store_true", help="Win parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.") parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.") +parser.add_argument("--whitelist-custom-nodes", type=str, nargs='+', default=[], help="Specify custom node folders to load even when --disable-all-custom-nodes is enabled.") +parser.add_argument("--disable-api-nodes", action="store_true", help="Disable loading all api nodes.") parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.") @@ -160,13 +181,14 @@ parser.add_argument( """, ) -def is_valid_directory(path: Optional[str]) -> Optional[str]: - """Validate if the given path is a directory.""" - if path is None: - return None - +def is_valid_directory(path: str) -> str: + """Validate if the given path is a directory, and check permissions.""" + if not os.path.exists(path): + raise argparse.ArgumentTypeError(f"The path '{path}' does not exist.") if not os.path.isdir(path): - raise argparse.ArgumentTypeError(f"{path} is not a valid directory.") + raise argparse.ArgumentTypeError(f"'{path}' is not a directory.") + if not os.access(path, os.R_OK): + raise argparse.ArgumentTypeError(f"You do not have read permissions for '{path}'.") return path parser.add_argument( @@ -176,7 +198,21 @@ parser.add_argument( help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.", ) -parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.") +parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.") + +parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.") + +parser.add_argument( + "--comfy-api-base", + type=str, + default="https://api.comfy.org", + help="Set the base URL for the ComfyUI API. (default: https://api.comfy.org)", +) + +database_default_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), "..", "user", "comfyui.db") +) +parser.add_argument("--database-url", type=str, default=f"sqlite:///{database_default_path}", help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.") if comfy.options.args_parsing: args = parser.parse_args() @@ -188,3 +224,17 @@ if args.windows_standalone_build: if args.disable_auto_launch: args.auto_launch = False + +if args.force_fp16: + args.fp16_unet = True + + +# '--fast' is not provided, use an empty set +if args.fast is None: + args.fast = set() +# '--fast' is provided with an empty list, enable all optimizations +elif args.fast == []: + args.fast = set(PerformanceFeature) +# '--fast' is provided with a list of performance features, use that list +else: + args.fast = set(args.fast) diff --git a/comfy/clip_model.py b/comfy/clip_model.py index 23ddea9c0..7c0cadab5 100644 --- a/comfy/clip_model.py +++ b/comfy/clip_model.py @@ -61,8 +61,12 @@ class CLIPEncoder(torch.nn.Module): def forward(self, x, mask=None, intermediate_output=None): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + all_intermediate = None if intermediate_output is not None: - if intermediate_output < 0: + if intermediate_output == "all": + all_intermediate = [] + intermediate_output = None + elif intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output intermediate = None @@ -70,6 +74,12 @@ class CLIPEncoder(torch.nn.Module): x = l(x, mask, optimized_attention) if i == intermediate_output: intermediate = x.clone() + if all_intermediate is not None: + all_intermediate.append(x.unsqueeze(1).clone()) + + if all_intermediate is not None: + intermediate = torch.cat(all_intermediate, dim=1) + return x, intermediate class CLIPEmbeddings(torch.nn.Module): @@ -97,14 +107,19 @@ class CLIPTextModel_(torch.nn.Module): self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) - def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32): - x = self.embeddings(input_tokens, dtype=dtype) + def forward(self, input_tokens=None, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32, embeds_info=[]): + if embeds is not None: + x = embeds + comfy.ops.cast_to(self.embeddings.position_embedding.weight, dtype=dtype, device=embeds.device) + else: + x = self.embeddings(input_tokens, dtype=dtype) + mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) + + causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1) - causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) if mask is not None: mask += causal_mask else: @@ -115,7 +130,10 @@ class CLIPTextModel_(torch.nn.Module): if i is not None and final_layer_norm_intermediate: i = self.final_layer_norm(i) - pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] + if num_tokens is not None: + pooled_output = x[list(range(x.shape[0])), list(map(lambda a: a - 1, num_tokens))] + else: + pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),] return x, i, pooled_output class CLIPTextModel(torch.nn.Module): @@ -203,6 +221,15 @@ class CLIPVision(torch.nn.Module): pooled_output = self.post_layernorm(x[:, 0, :]) return x, i, pooled_output +class LlavaProjector(torch.nn.Module): + def __init__(self, in_dim, out_dim, dtype, device, operations): + super().__init__() + self.linear_1 = operations.Linear(in_dim, out_dim, bias=True, device=device, dtype=dtype) + self.linear_2 = operations.Linear(out_dim, out_dim, bias=True, device=device, dtype=dtype) + + def forward(self, x): + return self.linear_2(torch.nn.functional.gelu(self.linear_1(x[:, 1:]))) + class CLIPVisionModelProjection(torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() @@ -212,7 +239,16 @@ class CLIPVisionModelProjection(torch.nn.Module): else: self.visual_projection = lambda a: a + if "llava3" == config_dict.get("projector_type", None): + self.multi_modal_projector = LlavaProjector(config_dict["hidden_size"], 4096, dtype, device, operations) + else: + self.multi_modal_projector = None + def forward(self, *args, **kwargs): x = self.vision_model(*args, **kwargs) out = self.visual_projection(x[2]) - return (x[0], x[1], out) + projected = None + if self.multi_modal_projector is not None: + projected = self.multi_modal_projector(x[1]) + + return (x[0], x[1], out, projected) diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py index c9c82e9ad..447b1ce4a 100644 --- a/comfy/clip_vision.py +++ b/comfy/clip_vision.py @@ -9,6 +9,7 @@ import comfy.model_patcher import comfy.model_management import comfy.utils import comfy.clip_model +import comfy.image_encoders.dino2 class Output: def __getitem__(self, key): @@ -17,6 +18,7 @@ class Output: setattr(self, key, item) def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True): + image = image[:, :, :, :3] if image.shape[3] > 3 else image mean = torch.tensor(mean, device=image.device, dtype=image.dtype) std = torch.tensor(std, device=image.device, dtype=image.dtype) image = image.movedim(-1, 1) @@ -34,6 +36,12 @@ def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], s image = torch.clip((255. * image), 0, 255).round() / 255.0 return (image - mean.view([3,1,1])) / std.view([3,1,1]) +IMAGE_ENCODERS = { + "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection, + "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection, + "dinov2": comfy.image_encoders.dino2.Dinov2Model, +} + class ClipVisionModel(): def __init__(self, json_config): with open(json_config) as f: @@ -42,10 +50,17 @@ class ClipVisionModel(): self.image_size = config.get("image_size", 224) self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073]) self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711]) + model_type = config.get("model_type", "clip_vision_model") + model_class = IMAGE_ENCODERS.get(model_type) + if model_type == "siglip_vision_model": + self.return_all_hidden_states = True + else: + self.return_all_hidden_states = False + self.load_device = comfy.model_management.text_encoder_device() offload_device = comfy.model_management.text_encoder_offload_device() self.dtype = comfy.model_management.text_encoder_dtype(self.load_device) - self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast) + self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast) self.model.eval() self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) @@ -59,12 +74,19 @@ class ClipVisionModel(): def encode_image(self, image, crop=True): comfy.model_management.load_model_gpu(self.patcher) pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float() - out = self.model(pixel_values=pixel_values, intermediate_output=-2) + out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2) outputs = Output() outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device()) outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device()) - outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) + if self.return_all_hidden_states: + all_hs = out[1].to(comfy.model_management.intermediate_device()) + outputs["penultimate_hidden_states"] = all_hs[:, -2] + outputs["all_hidden_states"] = all_hs + else: + outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device()) + + outputs["mm_projected"] = out[3] return outputs def convert_to_transformers(sd, prefix): @@ -101,12 +123,25 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False): elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: + embed_shape = sd["vision_model.embeddings.position_embedding.weight"].shape[0] if sd["vision_model.encoder.layers.0.layer_norm1.weight"].shape[0] == 1152: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") - elif sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577: - json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") + if embed_shape == 729: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_384.json") + elif embed_shape == 1024: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_siglip_512.json") + elif embed_shape == 577: + if "multi_modal_projector.linear_1.bias" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336_llava.json") + else: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json") else: json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") + + # Dinov2 + elif 'encoder.layer.39.layer_scale2.lambda1' in sd: + json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json") + elif 'encoder.layer.23.layer_scale2.lambda1' in sd: + json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_large.json") else: return None diff --git a/comfy/clip_vision_config_vitl_336_llava.json b/comfy/clip_vision_config_vitl_336_llava.json new file mode 100644 index 000000000..f23a50d8b --- /dev/null +++ b/comfy/clip_vision_config_vitl_336_llava.json @@ -0,0 +1,19 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 336, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-5, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "projector_type": "llava3", + "torch_dtype": "float32" +} diff --git a/comfy/clip_vision_siglip_512.json b/comfy/clip_vision_siglip_512.json new file mode 100644 index 000000000..7fb93ce15 --- /dev/null +++ b/comfy/clip_vision_siglip_512.json @@ -0,0 +1,13 @@ +{ + "num_channels": 3, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 512, + "intermediate_size": 4304, + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 16, + "image_mean": [0.5, 0.5, 0.5], + "image_std": [0.5, 0.5, 0.5] +} diff --git a/comfy/comfy_types/__init__.py b/comfy/comfy_types/__init__.py index 19ec33f98..7640fbe3f 100644 --- a/comfy/comfy_types/__init__.py +++ b/comfy/comfy_types/__init__.py @@ -1,6 +1,6 @@ import torch from typing import Callable, Protocol, TypedDict, Optional, List -from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin +from .node_typing import IO, InputTypeDict, ComfyNodeABC, CheckLazyMixin, FileLocator class UnetApplyFunction(Protocol): @@ -42,4 +42,5 @@ __all__ = [ InputTypeDict.__name__, ComfyNodeABC.__name__, CheckLazyMixin.__name__, + FileLocator.__name__, ] diff --git a/comfy/comfy_types/node_typing.py b/comfy/comfy_types/node_typing.py index 056b1aa65..071b98332 100644 --- a/comfy/comfy_types/node_typing.py +++ b/comfy/comfy_types/node_typing.py @@ -1,7 +1,8 @@ """Comfy-specific type hinting""" from __future__ import annotations -from typing import Literal, TypedDict +from typing import Literal, TypedDict, Optional +from typing_extensions import NotRequired from abc import ABC, abstractmethod from enum import Enum @@ -26,6 +27,7 @@ class IO(StrEnum): BOOLEAN = "BOOLEAN" INT = "INT" FLOAT = "FLOAT" + COMBO = "COMBO" CONDITIONING = "CONDITIONING" SAMPLER = "SAMPLER" SIGMAS = "SIGMAS" @@ -35,6 +37,8 @@ class IO(StrEnum): CONTROL_NET = "CONTROL_NET" VAE = "VAE" MODEL = "MODEL" + LORA_MODEL = "LORA_MODEL" + LOSS_MAP = "LOSS_MAP" CLIP_VISION = "CLIP_VISION" CLIP_VISION_OUTPUT = "CLIP_VISION_OUTPUT" STYLE_MODEL = "STYLE_MODEL" @@ -46,6 +50,7 @@ class IO(StrEnum): FACE_ANALYSIS = "FACE_ANALYSIS" BBOX = "BBOX" SEGS = "SEGS" + VIDEO = "VIDEO" ANY = "*" """Always matches any type, but at a price. @@ -67,90 +72,148 @@ class IO(StrEnum): return not (b.issubset(a) or a.issubset(b)) +class RemoteInputOptions(TypedDict): + route: str + """The route to the remote source.""" + refresh_button: bool + """Specifies whether to show a refresh button in the UI below the widget.""" + control_after_refresh: Literal["first", "last"] + """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" + timeout: int + """The maximum amount of time to wait for a response from the remote source in milliseconds.""" + max_retries: int + """The maximum number of retries before aborting the request.""" + refresh: int + """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" + + +class MultiSelectOptions(TypedDict): + placeholder: NotRequired[str] + """The placeholder text to display in the multi-select widget when no items are selected.""" + chip: NotRequired[bool] + """Specifies whether to use chips instead of comma separated values for the multi-select widget.""" + + class InputTypeOptions(TypedDict): """Provides type hinting for the return type of the INPUT_TYPES node function. Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`). - Comfy Docs: https://docs.comfy.org/essentials/custom_node_datatypes + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes """ - default: bool | str | float | int | list | tuple + default: NotRequired[bool | str | float | int | list | tuple] """The default value of the widget""" - defaultInput: bool - """Defaults to an input slot rather than a widget""" - forceInput: bool - """`defaultInput` and also don't allow converting to a widget""" - lazy: bool + defaultInput: NotRequired[bool] + """@deprecated in v1.16 frontend. v1.16 frontend allows input socket and widget to co-exist. + - defaultInput on required inputs should be dropped. + - defaultInput on optional inputs should be replaced with forceInput. + Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3364 + """ + forceInput: NotRequired[bool] + """Forces the input to be an input slot rather than a widget even a widget is available for the input type.""" + lazy: NotRequired[bool] """Declares that this input uses lazy evaluation""" - rawLink: bool + rawLink: NotRequired[bool] """When a link exists, rather than receiving the evaluated value, you will receive the link (i.e. `["nodeId", ]`). Designed for node expansion.""" - tooltip: str + tooltip: NotRequired[str] """Tooltip for the input (or widget), shown on pointer hover""" + socketless: NotRequired[bool] + """All inputs (including widgets) have an input socket to connect links. When ``true``, if there is a widget for this input, no socket will be created. + Available from frontend v1.17.5 + Ref: https://github.com/Comfy-Org/ComfyUI_frontend/pull/3548 + """ + widgetType: NotRequired[str] + """Specifies a type to be used for widget initialization if different from the input type. + Available from frontend v1.18.0 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/3550""" # class InputTypeNumber(InputTypeOptions): # default: float | int - min: float + min: NotRequired[float] """The minimum value of a number (``FLOAT`` | ``INT``)""" - max: float + max: NotRequired[float] """The maximum value of a number (``FLOAT`` | ``INT``)""" - step: float + step: NotRequired[float] """The amount to increment or decrement a widget by when stepping up/down (``FLOAT`` | ``INT``)""" - round: float + round: NotRequired[float] """Floats are rounded by this value (``FLOAT``)""" # class InputTypeBoolean(InputTypeOptions): # default: bool - label_on: str + label_on: NotRequired[str] """The label to use in the UI when the bool is True (``BOOLEAN``)""" - label_on: str + label_off: NotRequired[str] """The label to use in the UI when the bool is False (``BOOLEAN``)""" # class InputTypeString(InputTypeOptions): # default: str - multiline: bool + multiline: NotRequired[bool] """Use a multiline text box (``STRING``)""" - placeholder: str + placeholder: NotRequired[str] """Placeholder text to display in the UI when empty (``STRING``)""" # Deprecated: # defaultVal: str - dynamicPrompts: bool + dynamicPrompts: NotRequired[bool] """Causes the front-end to evaluate dynamic prompts (``STRING``)""" + # class InputTypeCombo(InputTypeOptions): + image_upload: NotRequired[bool] + """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`.""" + image_folder: NotRequired[Literal["input", "output", "temp"]] + """Specifies which folder to get preview images from if the input has the ``image_upload`` flag. + """ + remote: NotRequired[RemoteInputOptions] + """Specifies the configuration for a remote input. + Available after ComfyUI frontend v1.9.7 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/2422""" + control_after_generate: NotRequired[bool] + """Specifies whether a control widget should be added to the input, adding options to automatically change the value after each prompt is queued. Currently only used for INT and COMBO types.""" + options: NotRequired[list[str | int | float]] + """COMBO type only. Specifies the selectable options for the combo widget. + Prefer: + ["COMBO", {"options": ["Option 1", "Option 2", "Option 3"]}] + Over: + [["Option 1", "Option 2", "Option 3"]] + """ + multi_select: NotRequired[MultiSelectOptions] + """COMBO type only. Specifies the configuration for a multi-select widget. + Available after ComfyUI frontend v1.13.4 + https://github.com/Comfy-Org/ComfyUI_frontend/pull/2987""" class HiddenInputTypeDict(TypedDict): """Provides type hinting for the hidden entry of node INPUT_TYPES.""" - node_id: Literal["UNIQUE_ID"] + node_id: NotRequired[Literal["UNIQUE_ID"]] """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - unique_id: Literal["UNIQUE_ID"] + unique_id: NotRequired[Literal["UNIQUE_ID"]] """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" - prompt: Literal["PROMPT"] + prompt: NotRequired[Literal["PROMPT"]] """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" - extra_pnginfo: Literal["EXTRA_PNGINFO"] + extra_pnginfo: NotRequired[Literal["EXTRA_PNGINFO"]] """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" - dynprompt: Literal["DYNPROMPT"] + dynprompt: NotRequired[Literal["DYNPROMPT"]] """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" class InputTypeDict(TypedDict): """Provides type hinting for node INPUT_TYPES. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs """ - required: dict[str, tuple[IO, InputTypeOptions]] + required: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] """Describes all inputs that must be connected for the node to execute.""" - optional: dict[str, tuple[IO, InputTypeOptions]] + optional: NotRequired[dict[str, tuple[IO, InputTypeOptions]]] """Describes inputs which do not need to be connected.""" - hidden: HiddenInputTypeDict + hidden: NotRequired[HiddenInputTypeDict] """Offers advanced functionality and server-client communication. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs """ class ComfyNodeABC(ABC): """Abstract base class for Comfy nodes. Includes the names and expected types of attributes. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview """ DESCRIPTION: str @@ -167,12 +230,14 @@ class ComfyNodeABC(ABC): CATEGORY: str """The category of the node, as per the "Add Node" menu. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#category + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category """ EXPERIMENTAL: bool """Flags a node as experimental, informing users that it may change or not work as expected.""" DEPRECATED: bool """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" + API_NODE: Optional[bool] + """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" @classmethod @abstractmethod @@ -181,9 +246,9 @@ class ComfyNodeABC(ABC): * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute. * The ``optional`` key can be added to describe inputs which do not need to be connected. - * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs + * The ``hidden`` key offers some advanced functionality. More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#input-types + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types """ return {"required": {}} @@ -198,7 +263,7 @@ class ComfyNodeABC(ABC): By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#output-node + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node """ INPUT_IS_LIST: bool """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. @@ -209,9 +274,9 @@ class ComfyNodeABC(ABC): A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ - OUTPUT_IS_LIST: tuple[bool] + OUTPUT_IS_LIST: tuple[bool, ...] """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items. Connected nodes that do not implement `INPUT_IS_LIST` will be executed once for every item in the list. @@ -227,29 +292,29 @@ class ComfyNodeABC(ABC): the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`, specifying which outputs which should be so treated. - Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing """ - RETURN_TYPES: tuple[IO] + RETURN_TYPES: tuple[IO, ...] """A tuple representing the outputs of this node. Usage:: RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE") - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-types + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types """ - RETURN_NAMES: tuple[str] + RETURN_NAMES: tuple[str, ...] """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")`` - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-names + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names """ - OUTPUT_TOOLTIPS: tuple[str] + OUTPUT_TOOLTIPS: tuple[str, ...] """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`.""" FUNCTION: str """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"` - Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#function + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function """ @@ -267,8 +332,19 @@ class CheckLazyMixin: Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). - Comfy Docs: https://docs.comfy.org/essentials/custom_node_lazy_evaluation#defining-check-lazy-status + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status """ need = [name for name in kwargs if kwargs[name] is None] return need + + +class FileLocator(TypedDict): + """Provides type hinting for the file location""" + + filename: str + """The filename of the file.""" + subfolder: str + """The subfolder of the file.""" + type: Literal["input", "output", "temp"] + """The root folder of the file.""" diff --git a/comfy/conds.py b/comfy/conds.py index 660690af8..5af3e93ea 100644 --- a/comfy/conds.py +++ b/comfy/conds.py @@ -1,11 +1,9 @@ import torch import math import comfy.utils +import logging -def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9) - return abs(a*b) // math.gcd(a, b) - class CONDRegular: def __init__(self, cond): self.cond = cond @@ -13,12 +11,15 @@ class CONDRegular: def _copy_with(self, cond): return self.__class__(cond) - def process_cond(self, batch_size, device, **kwargs): - return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) + def process_cond(self, batch_size, **kwargs): + return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size)) def can_concat(self, other): if self.cond.shape != other.cond.shape: return False + if self.cond.device != other.cond.device: + logging.warning("WARNING: conds not on same device, skipping concat.") + return False return True def concat(self, others): @@ -27,15 +28,19 @@ class CONDRegular: conds.append(x.cond) return torch.cat(conds) + def size(self): + return list(self.cond.size()) + + class CONDNoiseShape(CONDRegular): - def process_cond(self, batch_size, device, area, **kwargs): + def process_cond(self, batch_size, area, **kwargs): data = self.cond if area is not None: dims = len(area) // 2 for i in range(dims): data = data.narrow(i + 2, area[i + dims], area[i]) - return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device)) + return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size)) class CONDCrossAttn(CONDRegular): @@ -46,10 +51,13 @@ class CONDCrossAttn(CONDRegular): if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen return False - mult_min = lcm(s1[1], s2[1]) + mult_min = math.lcm(s1[1], s2[1]) diff = mult_min // min(s1[1], s2[1]) if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much return False + if self.cond.device != other.cond.device: + logging.warning("WARNING: conds not on same device: skipping concat.") + return False return True def concat(self, others): @@ -57,7 +65,7 @@ class CONDCrossAttn(CONDRegular): crossattn_max_len = self.cond.shape[1] for x in others: c = x.cond - crossattn_max_len = lcm(crossattn_max_len, c.shape[1]) + crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) conds.append(c) out = [] @@ -67,11 +75,12 @@ class CONDCrossAttn(CONDRegular): out.append(c) return torch.cat(out) + class CONDConstant(CONDRegular): def __init__(self, cond): self.cond = cond - def process_cond(self, batch_size, device, **kwargs): + def process_cond(self, batch_size, **kwargs): return self._copy_with(self.cond) def can_concat(self, other): @@ -81,3 +90,48 @@ class CONDConstant(CONDRegular): def concat(self, others): return self.cond + + def size(self): + return [1] + + +class CONDList(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, **kwargs): + out = [] + for c in self.cond: + out.append(comfy.utils.repeat_to_batch_size(c, batch_size)) + + return self._copy_with(out) + + def can_concat(self, other): + if len(self.cond) != len(other.cond): + return False + for i in range(len(self.cond)): + if self.cond[i].shape != other.cond[i].shape: + return False + + return True + + def concat(self, others): + out = [] + for i in range(len(self.cond)): + o = [self.cond[i]] + for x in others: + o.append(x.cond[i]) + out.append(torch.cat(o)) + + return out + + def size(self): # hackish implementation to make the mem estimation work + o = 0 + c = 1 + for c in self.cond: + size = c.size() + o += math.prod(size) + if len(size) > 1: + c = size[1] + + return [1, c, o // c] diff --git a/comfy/context_windows.py b/comfy/context_windows.py new file mode 100644 index 000000000..041f380f9 --- /dev/null +++ b/comfy/context_windows.py @@ -0,0 +1,540 @@ +from __future__ import annotations +from typing import TYPE_CHECKING, Callable +import torch +import numpy as np +import collections +from dataclasses import dataclass +from abc import ABC, abstractmethod +import logging +import comfy.model_management +import comfy.patcher_extension +if TYPE_CHECKING: + from comfy.model_base import BaseModel + from comfy.model_patcher import ModelPatcher + from comfy.controlnet import ControlBase + + +class ContextWindowABC(ABC): + def __init__(self): + ... + + @abstractmethod + def get_tensor(self, full: torch.Tensor) -> torch.Tensor: + """ + Get torch.Tensor applicable to current window. + """ + raise NotImplementedError("Not implemented.") + + @abstractmethod + def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torch.Tensor: + """ + Apply torch.Tensor of window to the full tensor, in place. Returns reference to updated full tensor, not a copy. + """ + raise NotImplementedError("Not implemented.") + +class ContextHandlerABC(ABC): + def __init__(self): + ... + + @abstractmethod + def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: + raise NotImplementedError("Not implemented.") + + @abstractmethod + def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: ContextWindowABC, device=None) -> list: + raise NotImplementedError("Not implemented.") + + @abstractmethod + def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): + raise NotImplementedError("Not implemented.") + + + +class IndexListContextWindow(ContextWindowABC): + def __init__(self, index_list: list[int], dim: int=0): + self.index_list = index_list + self.context_length = len(index_list) + self.dim = dim + + def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor: + if dim is None: + dim = self.dim + if dim == 0 and full.shape[dim] == 1: + return full + idx = [slice(None)] * dim + [self.index_list] + return full[idx].to(device) + + def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor: + if dim is None: + dim = self.dim + idx = [slice(None)] * dim + [self.index_list] + full[idx] += to_add + return full + + +class IndexListCallbacks: + EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows" + COMBINE_CONTEXT_WINDOW_RESULTS = "combine_context_window_results" + EXECUTE_START = "execute_start" + EXECUTE_CLEANUP = "execute_cleanup" + + def init_callbacks(self): + return {} + + +@dataclass +class ContextSchedule: + name: str + func: Callable + +@dataclass +class ContextFuseMethod: + name: str + func: Callable + +ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window']) +class IndexListContextHandler(ContextHandlerABC): + def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop=False, dim=0): + self.context_schedule = context_schedule + self.fuse_method = fuse_method + self.context_length = context_length + self.context_overlap = context_overlap + self.context_stride = context_stride + self.closed_loop = closed_loop + self.dim = dim + self._step = 0 + + self.callbacks = {} + + def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool: + # for now, assume first dim is batch - should have stored on BaseModel in actual implementation + if x_in.size(self.dim) > self.context_length: + logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.") + return True + return False + + def prepare_control_objects(self, control: ControlBase, device=None) -> ControlBase: + if control.previous_controlnet is not None: + self.prepare_control_objects(control.previous_controlnet, device) + return control + + def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, window: IndexListContextWindow, device=None) -> list: + if cond_in is None: + return None + # reuse or resize cond items to match context requirements + resized_cond = [] + # cond object is a list containing a dict - outer list is irrelevant, so just loop through it + for actual_cond in cond_in: + resized_actual_cond = actual_cond.copy() + # now we are in the inner dict - "pooled_output" is a tensor, "control" is a ControlBase object, "model_conds" is dictionary + for key in actual_cond: + try: + cond_item = actual_cond[key] + if isinstance(cond_item, torch.Tensor): + # check that tensor is the expected length - x.size(0) + if self.dim < cond_item.ndim and cond_item.size(self.dim) == x_in.size(self.dim): + # if so, it's subsetting time - tell controls the expected indeces so they can handle them + actual_cond_item = window.get_tensor(cond_item) + resized_actual_cond[key] = actual_cond_item.to(device) + else: + resized_actual_cond[key] = cond_item.to(device) + # look for control + elif key == "control": + resized_actual_cond[key] = self.prepare_control_objects(cond_item, device) + elif isinstance(cond_item, dict): + new_cond_item = cond_item.copy() + # when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor) + for cond_key, cond_value in new_cond_item.items(): + if isinstance(cond_value, torch.Tensor): + if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim): + new_cond_item[cond_key] = window.get_tensor(cond_value, device) + # if has cond that is a Tensor, check if needs to be subset + elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor): + if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim): + new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device)) + elif cond_key == "num_video_frames": # for SVD + new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond) + new_cond_item[cond_key].cond = window.context_length + resized_actual_cond[key] = new_cond_item + else: + resized_actual_cond[key] = cond_item + finally: + del cond_item # just in case to prevent VRAM issues + resized_cond.append(resized_actual_cond) + return resized_cond + + def set_step(self, timestep: torch.Tensor, model_options: dict[str]): + mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001) + matches = torch.nonzero(mask) + if torch.numel(matches) == 0: + raise Exception("No sample_sigmas matched current timestep; something went wrong.") + self._step = int(matches[0].item()) + + def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]: + full_length = x_in.size(self.dim) # TODO: choose dim based on model + context_windows = self.context_schedule.func(full_length, self, model_options) + context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows] + return context_windows + + def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]): + self.set_step(timestep, model_options) + context_windows = self.get_context_windows(model, x_in, model_options) + enumerated_context_windows = list(enumerate(context_windows)) + + conds_final = [torch.zeros_like(x_in) for _ in conds] + if self.fuse_method.name == ContextFuseMethods.RELATIVE: + counts_final = [torch.ones(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] + else: + counts_final = [torch.zeros(get_shape_for_dim(x_in, self.dim), device=x_in.device) for _ in conds] + biases_final = [([0.0] * x_in.shape[self.dim]) for _ in conds] + + for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_START, self.callbacks): + callback(self, model, x_in, conds, timestep, model_options) + + for enum_window in enumerated_context_windows: + results = self.evaluate_context_windows(calc_cond_batch, model, x_in, conds, timestep, [enum_window], model_options) + for result in results: + self.combine_context_window_results(x_in, result.sub_conds_out, result.sub_conds, result.window, result.window_idx, len(enumerated_context_windows), timestep, + conds_final, counts_final, biases_final) + try: + # finalize conds + if self.fuse_method.name == ContextFuseMethods.RELATIVE: + # relative is already normalized, so return as is + del counts_final + return conds_final + else: + # normalize conds via division by context usage counts + for i in range(len(conds_final)): + conds_final[i] /= counts_final[i] + del counts_final + return conds_final + finally: + for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EXECUTE_CLEANUP, self.callbacks): + callback(self, model, x_in, conds, timestep, model_options) + + def evaluate_context_windows(self, calc_cond_batch: Callable, model: BaseModel, x_in: torch.Tensor, conds, timestep: torch.Tensor, enumerated_context_windows: list[tuple[int, IndexListContextWindow]], + model_options, device=None, first_device=None): + results: list[ContextResults] = [] + for window_idx, window in enumerated_context_windows: + # allow processing to end between context window executions for faster Cancel + comfy.model_management.throw_exception_if_processing_interrupted() + + for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.EVALUATE_CONTEXT_WINDOWS, self.callbacks): + callback(self, model, x_in, conds, timestep, model_options, window_idx, window, model_options, device, first_device) + + # update exposed params + model_options["transformer_options"]["context_window"] = window + # get subsections of x, timestep, conds + sub_x = window.get_tensor(x_in, device) + sub_timestep = window.get_tensor(timestep, device, dim=0) + sub_conds = [self.get_resized_cond(cond, x_in, window, device) for cond in conds] + + sub_conds_out = calc_cond_batch(model, sub_conds, sub_x, sub_timestep, model_options) + if device is not None: + for i in range(len(sub_conds_out)): + sub_conds_out[i] = sub_conds_out[i].to(x_in.device) + results.append(ContextResults(window_idx, sub_conds_out, sub_conds, window)) + return results + + + def combine_context_window_results(self, x_in: torch.Tensor, sub_conds_out, sub_conds, window: IndexListContextWindow, window_idx: int, total_windows: int, timestep: torch.Tensor, + conds_final: list[torch.Tensor], counts_final: list[torch.Tensor], biases_final: list[torch.Tensor]): + if self.fuse_method.name == ContextFuseMethods.RELATIVE: + for pos, idx in enumerate(window.index_list): + # bias is the influence of a specific index in relation to the whole context window + bias = 1 - abs(idx - (window.index_list[0] + window.index_list[-1]) / 2) / ((window.index_list[-1] - window.index_list[0] + 1e-2) / 2) + bias = max(1e-2, bias) + # take weighted average relative to total bias of current idx + for i in range(len(sub_conds_out)): + bias_total = biases_final[i][idx] + prev_weight = (bias_total / (bias_total + bias)) + new_weight = (bias / (bias_total + bias)) + # account for dims of tensors + idx_window = [slice(None)] * self.dim + [idx] + pos_window = [slice(None)] * self.dim + [pos] + # apply new values + conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight + biases_final[i][idx] = bias_total + bias + else: + # add conds and counts based on weights of fuse method + weights = get_context_weights(window.context_length, x_in.shape[self.dim], window.index_list, self, sigma=timestep) + weights_tensor = match_weights_to_dim(weights, x_in, self.dim, device=x_in.device) + for i in range(len(sub_conds_out)): + window.add_window(conds_final[i], sub_conds_out[i] * weights_tensor) + window.add_window(counts_final[i], weights_tensor) + + for callback in comfy.patcher_extension.get_all_callbacks(IndexListCallbacks.COMBINE_CONTEXT_WINDOW_RESULTS, self.callbacks): + callback(self, x_in, sub_conds_out, sub_conds, window, window_idx, total_windows, timestep, conds_final, counts_final, biases_final) + + +def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor, *args, **kwargs): + # limit noise_shape length to context_length for more accurate vram use estimation + model_options = kwargs.get("model_options", None) + if model_options is None: + raise Exception("model_options not found in prepare_sampling_wrapper; this should never happen, something went wrong.") + handler: IndexListContextHandler = model_options.get("context_handler", None) + if handler is not None: + noise_shape = list(noise_shape) + noise_shape[handler.dim] = min(noise_shape[handler.dim], handler.context_length) + return executor(model, noise_shape, *args, **kwargs) + + +def create_prepare_sampling_wrapper(model: ModelPatcher): + model.add_wrapper_with_key( + comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, + "ContextWindows_prepare_sampling", + _prepare_sampling_wrapper + ) + + +def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor: + total_dims = len(x_in.shape) + weights_tensor = torch.Tensor(weights).to(device=device) + for _ in range(dim): + weights_tensor = weights_tensor.unsqueeze(0) + for _ in range(total_dims - dim - 1): + weights_tensor = weights_tensor.unsqueeze(-1) + return weights_tensor + +def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]: + total_dims = len(x_in.shape) + shape = [] + for _ in range(dim): + shape.append(1) + shape.append(x_in.shape[dim]) + for _ in range(total_dims - dim - 1): + shape.append(1) + return shape + +class ContextSchedules: + UNIFORM_LOOPED = "looped_uniform" + UNIFORM_STANDARD = "standard_uniform" + STATIC_STANDARD = "standard_static" + BATCHED = "batched" + + +# from https://github.com/neggles/animatediff-cli/blob/main/src/animatediff/pipelines/context.py +def create_windows_uniform_looped(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): + windows = [] + if num_frames < handler.context_length: + windows.append(list(range(num_frames))) + return windows + + context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) + # obtain uniform windows as normal, looping and all + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(handler._step))) + for j in range( + int(ordered_halving(handler._step) * context_step) + pad, + num_frames + pad + (0 if handler.closed_loop else -handler.context_overlap), + (handler.context_length * context_step - handler.context_overlap), + ): + windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) + + return windows + +def create_windows_uniform_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): + # unlike looped, uniform_straight does NOT allow windows that loop back to the beginning; + # instead, they get shifted to the corresponding end of the frames. + # in the case that a window (shifted or not) is identical to the previous one, it gets skipped. + windows = [] + if num_frames <= handler.context_length: + windows.append(list(range(num_frames))) + return windows + + context_stride = min(handler.context_stride, int(np.ceil(np.log2(num_frames / handler.context_length))) + 1) + # first, obtain uniform windows as normal, looping and all + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(handler._step))) + for j in range( + int(ordered_halving(handler._step) * context_step) + pad, + num_frames + pad + (-handler.context_overlap), + (handler.context_length * context_step - handler.context_overlap), + ): + windows.append([e % num_frames for e in range(j, j + handler.context_length * context_step, context_step)]) + + # now that windows are created, shift any windows that loop, and delete duplicate windows + delete_idxs = [] + win_i = 0 + while win_i < len(windows): + # if window is rolls over itself, need to shift it + is_roll, roll_idx = does_window_roll_over(windows[win_i], num_frames) + if is_roll: + roll_val = windows[win_i][roll_idx] # roll_val might not be 0 for windows of higher strides + shift_window_to_end(windows[win_i], num_frames=num_frames) + # check if next window (cyclical) is missing roll_val + if roll_val not in windows[(win_i+1) % len(windows)]: + # need to insert new window here - just insert window starting at roll_val + windows.insert(win_i+1, list(range(roll_val, roll_val + handler.context_length))) + # delete window if it's not unique + for pre_i in range(0, win_i): + if windows[win_i] == windows[pre_i]: + delete_idxs.append(win_i) + break + win_i += 1 + + # reverse delete_idxs so that they will be deleted in an order that doesn't break idx correlation + delete_idxs.reverse() + for i in delete_idxs: + windows.pop(i) + + return windows + + +def create_windows_static_standard(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): + windows = [] + if num_frames <= handler.context_length: + windows.append(list(range(num_frames))) + return windows + # always return the same set of windows + delta = handler.context_length - handler.context_overlap + for start_idx in range(0, num_frames, delta): + # if past the end of frames, move start_idx back to allow same context_length + ending = start_idx + handler.context_length + if ending >= num_frames: + final_delta = ending - num_frames + final_start_idx = start_idx - final_delta + windows.append(list(range(final_start_idx, final_start_idx + handler.context_length))) + break + windows.append(list(range(start_idx, start_idx + handler.context_length))) + return windows + + +def create_windows_batched(num_frames: int, handler: IndexListContextHandler, model_options: dict[str]): + windows = [] + if num_frames <= handler.context_length: + windows.append(list(range(num_frames))) + return windows + # always return the same set of windows; + # no overlap, just cut up based on context_length; + # last window size will be different if num_frames % opts.context_length != 0 + for start_idx in range(0, num_frames, handler.context_length): + windows.append(list(range(start_idx, min(start_idx + handler.context_length, num_frames)))) + return windows + + +def create_windows_default(num_frames: int, handler: IndexListContextHandler): + return [list(range(num_frames))] + + +CONTEXT_MAPPING = { + ContextSchedules.UNIFORM_LOOPED: create_windows_uniform_looped, + ContextSchedules.UNIFORM_STANDARD: create_windows_uniform_standard, + ContextSchedules.STATIC_STANDARD: create_windows_static_standard, + ContextSchedules.BATCHED: create_windows_batched, +} + + +def get_matching_context_schedule(context_schedule: str) -> ContextSchedule: + func = CONTEXT_MAPPING.get(context_schedule, None) + if func is None: + raise ValueError(f"Unknown context_schedule '{context_schedule}'.") + return ContextSchedule(context_schedule, func) + + +def get_context_weights(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, sigma: torch.Tensor=None): + return handler.fuse_method.func(length, sigma=sigma, handler=handler, full_length=full_length, idxs=idxs) + + +def create_weights_flat(length: int, **kwargs) -> list[float]: + # weight is the same for all + return [1.0] * length + +def create_weights_pyramid(length: int, **kwargs) -> list[float]: + # weight is based on the distance away from the edge of the context window; + # based on weighted average concept in FreeNoise paper + if length % 2 == 0: + max_weight = length // 2 + weight_sequence = list(range(1, max_weight + 1, 1)) + list(range(max_weight, 0, -1)) + else: + max_weight = (length + 1) // 2 + weight_sequence = list(range(1, max_weight, 1)) + [max_weight] + list(range(max_weight - 1, 0, -1)) + return weight_sequence + +def create_weights_overlap_linear(length: int, full_length: int, idxs: list[int], handler: IndexListContextHandler, **kwargs): + # based on code in Kijai's WanVideoWrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/dbb2523b37e4ccdf45127e5ae33e31362f755c8e/nodes.py#L1302 + # only expected overlap is given different weights + weights_torch = torch.ones((length)) + # blend left-side on all except first window + if min(idxs) > 0: + ramp_up = torch.linspace(1e-37, 1, handler.context_overlap) + weights_torch[:handler.context_overlap] = ramp_up + # blend right-side on all except last window + if max(idxs) < full_length-1: + ramp_down = torch.linspace(1, 1e-37, handler.context_overlap) + weights_torch[-handler.context_overlap:] = ramp_down + return weights_torch + +class ContextFuseMethods: + FLAT = "flat" + PYRAMID = "pyramid" + RELATIVE = "relative" + OVERLAP_LINEAR = "overlap-linear" + + LIST = [PYRAMID, FLAT, OVERLAP_LINEAR] + LIST_STATIC = [PYRAMID, RELATIVE, FLAT, OVERLAP_LINEAR] + + +FUSE_MAPPING = { + ContextFuseMethods.FLAT: create_weights_flat, + ContextFuseMethods.PYRAMID: create_weights_pyramid, + ContextFuseMethods.RELATIVE: create_weights_pyramid, + ContextFuseMethods.OVERLAP_LINEAR: create_weights_overlap_linear, +} + +def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod: + func = FUSE_MAPPING.get(fuse_method, None) + if func is None: + raise ValueError(f"Unknown fuse_method '{fuse_method}'.") + return ContextFuseMethod(fuse_method, func) + +# Returns fraction that has denominator that is a power of 2 +def ordered_halving(val): + # get binary value, padded with 0s for 64 bits + bin_str = f"{val:064b}" + # flip binary value, padding included + bin_flip = bin_str[::-1] + # convert binary to int + as_int = int(bin_flip, 2) + # divide by 1 << 64, equivalent to 2**64, or 18446744073709551616, + # or b10000000000000000000000000000000000000000000000000000000000000000 (1 with 64 zero's) + return as_int / (1 << 64) + + +def get_missing_indexes(windows: list[list[int]], num_frames: int) -> list[int]: + all_indexes = list(range(num_frames)) + for w in windows: + for val in w: + try: + all_indexes.remove(val) + except ValueError: + pass + return all_indexes + + +def does_window_roll_over(window: list[int], num_frames: int) -> tuple[bool, int]: + prev_val = -1 + for i, val in enumerate(window): + val = val % num_frames + if val < prev_val: + return True, i + prev_val = val + return False, -1 + + +def shift_window_to_start(window: list[int], num_frames: int): + start_val = window[0] + for i in range(len(window)): + # 1) subtract each element by start_val to move vals relative to the start of all frames + # 2) add num_frames and take modulus to get adjusted vals + window[i] = ((window[i] - start_val) + num_frames) % num_frames + + +def shift_window_to_end(window: list[int], num_frames: int): + # 1) shift window to start + shift_window_to_start(window, num_frames) + end_val = window[-1] + end_delta = num_frames - end_val - 1 + for i in range(len(window)): + # 2) add end_delta to each val to slide windows to end + window[i] = window[i] + end_delta diff --git a/comfy/controlnet.py b/comfy/controlnet.py index ee29251b9..f08ff4b36 100644 --- a/comfy/controlnet.py +++ b/comfy/controlnet.py @@ -28,6 +28,7 @@ import comfy.model_detection import comfy.model_patcher import comfy.ops import comfy.latent_formats +import comfy.model_base import comfy.cldm.cldm import comfy.t2i_adapter.adapter @@ -35,6 +36,7 @@ import comfy.ldm.cascade.controlnet import comfy.cldm.mmdit import comfy.ldm.hydit.controlnet import comfy.ldm.flux.controlnet +import comfy.ldm.qwen_image.controlnet import comfy.cldm.dit_embedder from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -43,7 +45,6 @@ if TYPE_CHECKING: def broadcast_image_to(tensor, target_batch_size, batched_number): current_batch_size = tensor.shape[0] - #print(current_batch_size, target_batch_size) if current_batch_size == 1: return tensor @@ -236,11 +237,11 @@ class ControlNet(ControlBase): self.cond_hint = None compression_ratio = self.compression_ratio if self.vae is not None: - compression_ratio *= self.vae.downscale_ratio + compression_ratio *= self.vae.spacial_compression_encode() else: if self.latent_format is not None: raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.") - self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center") + self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[-1] * compression_ratio, x_noisy.shape[-2] * compression_ratio, self.upscale_algorithm, "center") self.cond_hint = self.preprocess_image(self.cond_hint) if self.vae is not None: loaded_models = comfy.model_management.loaded_models(only_currently_used=True) @@ -252,7 +253,10 @@ class ControlNet(ControlBase): to_concat = [] for c in self.extra_concat_orig: c = c.to(self.cond_hint.device) - c = comfy.utils.common_upscale(c, self.cond_hint.shape[3], self.cond_hint.shape[2], self.upscale_algorithm, "center") + c = comfy.utils.common_upscale(c, self.cond_hint.shape[-1], self.cond_hint.shape[-2], self.upscale_algorithm, "center") + if c.ndim < self.cond_hint.ndim: + c = c.unsqueeze(2) + c = comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[2], dim=2) to_concat.append(comfy.utils.repeat_to_batch_size(c, self.cond_hint.shape[0])) self.cond_hint = torch.cat([self.cond_hint] + to_concat, dim=1) @@ -265,12 +269,12 @@ class ControlNet(ControlBase): for c in self.extra_conds: temp = cond.get(c, None) if temp is not None: - extra[c] = temp.to(dtype) + extra[c] = comfy.model_base.convert_tensor(temp, dtype, x_noisy.device) timestep = self.model_sampling_current.timestep(t) x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) - control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra) + control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=comfy.model_management.cast_to_device(context, x_noisy.device, dtype), **extra) return self.control_merge(control, control_prev, output_dtype=None) def copy(self): @@ -390,8 +394,9 @@ class ControlLora(ControlNet): pass for k in self.control_weights: - if k not in {"lora_controlnet"}: - comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) + if (k not in {"lora_controlnet"}): + if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k): + comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device())) def copy(self): c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) @@ -418,10 +423,7 @@ def controlnet_config(sd, model_options={}): weight_dtype = comfy.utils.weight_dtype(sd) supported_inference_dtypes = list(model_config.supported_inference_dtypes) - if weight_dtype is not None: - supported_inference_dtypes.append(weight_dtype) - - unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes) + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) load_device = comfy.model_management.get_torch_device() manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) @@ -584,6 +586,22 @@ def load_controlnet_flux_instantx(sd, model_options={}): control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) return control +def load_controlnet_qwen_instantx(sd, model_options={}): + model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd, model_options=model_options) + control_latent_channels = sd.get("controlnet_x_embedder.weight").shape[1] + + extra_condition_channels = 0 + concat_mask = False + if control_latent_channels == 68: #inpaint controlnet + extra_condition_channels = control_latent_channels - 64 + concat_mask = True + control_model = comfy.ldm.qwen_image.controlnet.QwenImageControlNetModel(extra_condition_channels=extra_condition_channels, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config) + control_model = controlnet_load_state_dict(control_model, sd) + latent_format = comfy.latent_formats.Wan21() + extra_conds = [] + control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, concat_mask=concat_mask, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds) + return control + def convert_mistoline(sd): return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."}) @@ -657,8 +675,11 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}): return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format else: return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet + elif "transformer_blocks.0.img_mlp.net.0.proj.weight" in controlnet_data: + return load_controlnet_qwen_instantx(controlnet_data, model_options=model_options) elif "controlnet_x_embedder.weight" in controlnet_data: return load_controlnet_flux_instantx(controlnet_data, model_options=model_options) + elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True, model_options=model_options) @@ -689,10 +710,7 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}): if supported_inference_dtypes is None: supported_inference_dtypes = [comfy.model_management.unet_dtype()] - if weight_dtype is not None: - supported_inference_dtypes.append(weight_dtype) - - unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes) + unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype) load_device = comfy.model_management.get_torch_device() @@ -742,6 +760,7 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}): return control def load_controlnet(ckpt_path, model=None, model_options={}): + model_options = model_options.copy() if "global_average_pooling" not in model_options: filename = os.path.splitext(ckpt_path)[0] if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py index 26e8d96d5..fb9495348 100644 --- a/comfy/diffusers_convert.py +++ b/comfy/diffusers_convert.py @@ -4,105 +4,6 @@ import logging # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(4): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i < 3: - # no attention layers in down_blocks.3 - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(3): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i > 0: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) - -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2 * j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} - return new_state_dict - - # ================# # VAE Conversion # # ================# @@ -213,6 +114,7 @@ textenc_pattern = re.compile("|".join(protected.keys())) # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp code2idx = {"q": 0, "k": 1, "v": 2} + # This function exists because at the time of writing torch.cat can't do fp8 with cuda def cat_tensors(tensors): x = 0 @@ -229,6 +131,7 @@ def cat_tensors(tensors): return out + def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): new_state_dict = {} capture_qkv_weight = {} @@ -284,5 +187,3 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): def convert_text_enc_state_dict(text_enc_dict): return text_enc_dict - - diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py index 5b80a8aff..c57e081e4 100644 --- a/comfy/extra_samplers/uni_pc.py +++ b/comfy/extra_samplers/uni_pc.py @@ -661,7 +661,7 @@ class UniPC: if x_t is None: if use_predictor: - pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_p, D1s) else: pred_res = 0 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res @@ -669,7 +669,7 @@ class UniPC: if use_corrector: model_t = self.model_fn(x_t, t) if D1s is not None: - corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0])) # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) else: corr_res = 0 D1_t = (model_t - model_prev_0) diff --git a/comfy/gligen.py b/comfy/gligen.py index 161d8a5e5..1d7b6c2f4 100644 --- a/comfy/gligen.py +++ b/comfy/gligen.py @@ -1,55 +1,10 @@ import math import torch from torch import nn -from .ldm.modules.attention import CrossAttention -from inspect import isfunction +from .ldm.modules.attention import CrossAttention, FeedForward import comfy.ops ops = comfy.ops.manual_cast -def exists(val): - return val is not None - - -def uniq(arr): - return{el: True for el in arr}.keys() - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -# feedforward -class GEGLU(nn.Module): - def __init__(self, dim_in, dim_out): - super().__init__() - self.proj = ops.Linear(dim_in, dim_out * 2) - - def forward(self, x): - x, gate = self.proj(x).chunk(2, dim=-1) - return x * torch.nn.functional.gelu(gate) - - -class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): - super().__init__() - inner_dim = int(dim * mult) - dim_out = default(dim_out, dim) - project_in = nn.Sequential( - ops.Linear(dim, inner_dim), - nn.GELU() - ) if not glu else GEGLU(dim, inner_dim) - - self.net = nn.Sequential( - project_in, - nn.Dropout(dropout), - ops.Linear(inner_dim, dim_out) - ) - - def forward(self, x): - return self.net(x) - class GatedCrossAttentionDense(nn.Module): def __init__(self, query_dim, context_dim, n_heads, d_head): diff --git a/comfy/hooks.py b/comfy/hooks.py index 3cb0f3963..9d0731072 100644 --- a/comfy/hooks.py +++ b/comfy/hooks.py @@ -16,91 +16,132 @@ import comfy.model_management import comfy.patcher_extension from node_helpers import conditioning_set_values +# ####################################################################################################### +# Hooks explanation +# ------------------- +# The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to +# make explicit special cases like it does for ControlNet and GLIGEN. +# +# This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those +# that should run special code when a 'marked' cond is used in sampling. +# ####################################################################################################### + class EnumHookMode(enum.Enum): + ''' + Priority of hook memory optimization vs. speed, mostly related to WeightHooks. + + MinVram: No caching will occur for any operations related to hooks. + MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups. + ''' MinVram = "minvram" MaxSpeed = "maxspeed" class EnumHookType(enum.Enum): + ''' + Hook types, each of which has different expected behavior. + ''' Weight = "weight" - Patch = "patch" ObjectPatch = "object_patch" - AddModels = "add_models" - Callbacks = "callbacks" - Wrappers = "wrappers" - SetInjections = "add_injections" + AdditionalModels = "add_models" + TransformerOptions = "transformer_options" + Injections = "add_injections" class EnumWeightTarget(enum.Enum): Model = "model" Clip = "clip" +class EnumHookScope(enum.Enum): + ''' + Determines if hook should be limited in its influence over sampling. + + AllConditioning: hook will affect all conds used in sampling. + HookedOnly: hook will only affect the conds it was attached to. + ''' + AllConditioning = "all_conditioning" + HookedOnly = "hooked_only" + + class _HookRef: pass -# NOTE: this is an example of how the should_register function should look -def default_should_register(hook: 'Hook', model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]): + +def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + '''Example for how custom_should_register function can look like.''' return True +def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]: + '''Creates base dictionary for use with Hooks' target param.''' + d = {} + if target is not None: + d['target'] = target + d.update(kwargs) + return d + + class Hook: def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None, - hook_keyframe: 'HookKeyframeGroup'=None): + hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning): self.hook_type = hook_type + '''Enum identifying the general class of this hook.''' self.hook_ref = hook_ref if hook_ref else _HookRef() + '''Reference shared between hook clones that have the same value. Should NOT be modified.''' self.hook_id = hook_id + '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.''' self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup() + '''Keyframe storage that can be referenced to get strength for current sampling step.''' + self.hook_scope = hook_scope + '''Scope of where this hook should apply in terms of the conds used in sampling run.''' self.custom_should_register = default_should_register - self.auto_apply_to_nonpositive = False + '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register''' @property def strength(self): return self.hook_keyframe.strength - def initialize_timesteps(self, model: 'BaseModel'): + def initialize_timesteps(self, model: BaseModel): self.reset() self.hook_keyframe.initialize_timesteps(model) def reset(self): self.hook_keyframe.reset() - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: Hook = subtype() + def clone(self): + c: Hook = self.__class__() c.hook_type = self.hook_type c.hook_ref = self.hook_ref c.hook_id = self.hook_id c.hook_keyframe = self.hook_keyframe + c.hook_scope = self.hook_scope c.custom_should_register = self.custom_should_register - # TODO: make this do something - c.auto_apply_to_nonpositive = self.auto_apply_to_nonpositive return c - def should_register(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]): - return self.custom_should_register(self, model, model_options, target, registered) + def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + return self.custom_should_register(self, model, model_options, target_dict, registered) - def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]): + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): raise NotImplementedError("add_hook_patches should be defined for Hook subclasses") - def on_apply(self, model: 'ModelPatcher', transformer_options: dict[str]): - pass - - def on_unapply(self, model: 'ModelPatcher', transformer_options: dict[str]): - pass - - def __eq__(self, other: 'Hook'): + def __eq__(self, other: Hook): return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref def __hash__(self): return hash(self.hook_ref) class WeightHook(Hook): + ''' + Hook responsible for tracking weights to be applied to some model/clip. + + Note, value of hook_scope is ignored and is treated as HookedOnly. + ''' def __init__(self, strength_model=1.0, strength_clip=1.0): - super().__init__(hook_type=EnumHookType.Weight) + super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly) self.weights: dict = None self.weights_clip: dict = None self.need_weight_init = True self._strength_model = strength_model self._strength_clip = strength_clip + self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs @property def strength_model(self): @@ -110,36 +151,36 @@ class WeightHook(Hook): def strength_clip(self): return self._strength_clip * self.strength - def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]): - if not self.should_register(model, model_options, target, registered): + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): return False weights = None - if target == EnumWeightTarget.Model: - strength = self._strength_model - else: + + target = target_dict.get('target', None) + if target == EnumWeightTarget.Clip: strength = self._strength_clip + else: + strength = self._strength_model if self.need_weight_init: key_map = {} - if target == EnumWeightTarget.Model: - key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) - else: + if target == EnumWeightTarget.Clip: key_map = comfy.lora.model_lora_keys_clip(model.model, key_map) + else: + key_map = comfy.lora.model_lora_keys_unet(model.model, key_map) weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False) else: - if target == EnumWeightTarget.Model: - weights = self.weights - else: + if target == EnumWeightTarget.Clip: weights = self.weights_clip + else: + weights = self.weights model.add_hook_patches(hook=self, patches=weights, strength_patch=strength) - registered.append(self) + registered.add(self) return True # TODO: add logs about any keys that were not applied - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: WeightHook = super().clone(subtype) + def clone(self): + c: WeightHook = super().clone() c.weights = self.weights c.weights_clip = self.weights_clip c.need_weight_init = self.need_weight_init @@ -147,127 +188,158 @@ class WeightHook(Hook): c._strength_clip = self._strength_clip return c -class PatchHook(Hook): - def __init__(self): - super().__init__(hook_type=EnumHookType.Patch) - self.patches: dict = None - - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: PatchHook = super().clone(subtype) - c.patches = self.patches - return c - # TODO: add functionality - class ObjectPatchHook(Hook): - def __init__(self): + def __init__(self, object_patches: dict[str]=None, + hook_scope=EnumHookScope.AllConditioning): super().__init__(hook_type=EnumHookType.ObjectPatch) - self.object_patches: dict = None + self.object_patches = object_patches + self.hook_scope = hook_scope - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: ObjectPatchHook = super().clone(subtype) + def clone(self): + c: ObjectPatchHook = super().clone() c.object_patches = self.object_patches return c - # TODO: add functionality -class AddModelsHook(Hook): - def __init__(self, key: str=None, models: list['ModelPatcher']=None): - super().__init__(hook_type=EnumHookType.AddModels) - self.key = key + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + raise NotImplementedError("ObjectPatchHook is not supported yet in ComfyUI.") + +class AdditionalModelsHook(Hook): + ''' + Hook responsible for telling model management any additional models that should be loaded. + + Note, value of hook_scope is ignored and is treated as AllConditioning. + ''' + def __init__(self, models: list[ModelPatcher]=None, key: str=None): + super().__init__(hook_type=EnumHookType.AdditionalModels) self.models = models - self.append_when_same = True - - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: AddModelsHook = super().clone(subtype) - c.key = self.key - c.models = self.models.copy() if self.models else self.models - c.append_when_same = self.append_when_same - return c - # TODO: add functionality - -class CallbackHook(Hook): - def __init__(self, key: str=None, callback: Callable=None): - super().__init__(hook_type=EnumHookType.Callbacks) self.key = key - self.callback = callback - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: CallbackHook = super().clone(subtype) + def clone(self): + c: AdditionalModelsHook = super().clone() + c.models = self.models.copy() if self.models else self.models c.key = self.key - c.callback = self.callback - return c - # TODO: add functionality - -class WrapperHook(Hook): - def __init__(self, wrappers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None): - super().__init__(hook_type=EnumHookType.Wrappers) - self.wrappers_dict = wrappers_dict - - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: WrapperHook = super().clone(subtype) - c.wrappers_dict = self.wrappers_dict return c - def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]): - if not self.should_register(model, model_options, target, registered): + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): return False - add_model_options = {"transformer_options": self.wrappers_dict} - comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False) - registered.append(self) + registered.add(self) return True -class SetInjectionsHook(Hook): - def __init__(self, key: str=None, injections: list['PatcherInjection']=None): - super().__init__(hook_type=EnumHookType.SetInjections) +class TransformerOptionsHook(Hook): + ''' + Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options. + ''' + def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None, + hook_scope=EnumHookScope.AllConditioning): + super().__init__(hook_type=EnumHookType.TransformerOptions) + self.transformers_dict = transformers_dict + self.hook_scope = hook_scope + self._skip_adding = False + '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.''' + + def clone(self): + c: TransformerOptionsHook = super().clone() + c.transformers_dict = self.transformers_dict + c._skip_adding = self._skip_adding + return c + + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + if not self.should_register(model, model_options, target_dict, registered): + return False + # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks + self._skip_adding = False + if self.hook_scope == EnumHookScope.AllConditioning: + add_model_options = {"transformer_options": self.transformers_dict, + "to_load_options": self.transformers_dict} + # skip_adding if included in AllConditioning to avoid double loading + self._skip_adding = True + else: + add_model_options = {"to_load_options": self.transformers_dict} + registered.add(self) + comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False) + return True + + def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]): + if not self._skip_adding: + comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False) + +WrapperHook = TransformerOptionsHook +'''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.''' + +class InjectionsHook(Hook): + def __init__(self, key: str=None, injections: list[PatcherInjection]=None, + hook_scope=EnumHookScope.AllConditioning): + super().__init__(hook_type=EnumHookType.Injections) self.key = key self.injections = injections + self.hook_scope = hook_scope - def clone(self, subtype: Callable=None): - if subtype is None: - subtype = type(self) - c: SetInjectionsHook = super().clone(subtype) + def clone(self): + c: InjectionsHook = super().clone() c.key = self.key c.injections = self.injections.copy() if self.injections else self.injections return c - def add_hook_injections(self, model: 'ModelPatcher'): - # TODO: add functionality - pass + def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup): + raise NotImplementedError("InjectionsHook is not supported yet in ComfyUI.") class HookGroup: + ''' + Stores groups of hooks, and allows them to be queried by type. + + To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly; + always use the provided functions on HookGroup. + ''' def __init__(self): self.hooks: list[Hook] = [] + self._hook_dict: dict[EnumHookType, list[Hook]] = {} + + def __len__(self): + return len(self.hooks) def add(self, hook: Hook): if hook not in self.hooks: self.hooks.append(hook) + self._hook_dict.setdefault(hook.hook_type, []).append(hook) + + def remove(self, hook: Hook): + if hook in self.hooks: + self.hooks.remove(hook) + self._hook_dict[hook.hook_type].remove(hook) + + def get_type(self, hook_type: EnumHookType): + return self._hook_dict.get(hook_type, []) def contains(self, hook: Hook): return hook in self.hooks + def is_subset_of(self, other: HookGroup): + self_hooks = set(self.hooks) + other_hooks = set(other.hooks) + return self_hooks.issubset(other_hooks) + + def new_with_common_hooks(self, other: HookGroup): + c = HookGroup() + for hook in self.hooks: + if other.contains(hook): + c.add(hook.clone()) + return c + def clone(self): c = HookGroup() for hook in self.hooks: c.add(hook.clone()) return c - def clone_and_combine(self, other: 'HookGroup'): + def clone_and_combine(self, other: HookGroup): c = self.clone() if other is not None: for hook in other.hooks: c.add(hook.clone()) return c - def set_keyframes_on_hooks(self, hook_kf: 'HookKeyframeGroup'): + def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup): if hook_kf is None: hook_kf = HookKeyframeGroup() else: @@ -275,36 +347,29 @@ class HookGroup: for hook in self.hooks: hook.hook_keyframe = hook_kf - def get_dict_repr(self): - d: dict[EnumHookType, dict[Hook, None]] = {} - for hook in self.hooks: - with_type = d.setdefault(hook.hook_type, {}) - with_type[hook] = None - return d - def get_hooks_for_clip_schedule(self): scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {} - for hook in self.hooks: - # only care about WeightHooks, for now - if hook.hook_type == EnumHookType.Weight: - hook_schedule = [] - # if no hook keyframes, assign default value - if len(hook.hook_keyframe.keyframes) == 0: - hook_schedule.append(((0.0, 1.0), None)) - scheduled_hooks[hook] = hook_schedule - continue - # find ranges of values - prev_keyframe = hook.hook_keyframe.keyframes[0] - for keyframe in hook.hook_keyframe.keyframes: - if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength): - hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe)) - prev_keyframe = keyframe - elif keyframe.start_percent == prev_keyframe.start_percent: - prev_keyframe = keyframe - # create final range, assuming last start_percent was not 1.0 - if not math.isclose(prev_keyframe.start_percent, 1.0): - hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe)) + # only care about WeightHooks, for now + for hook in self.get_type(EnumHookType.Weight): + hook: WeightHook + hook_schedule = [] + # if no hook keyframes, assign default value + if len(hook.hook_keyframe.keyframes) == 0: + hook_schedule.append(((0.0, 1.0), None)) scheduled_hooks[hook] = hook_schedule + continue + # find ranges of values + prev_keyframe = hook.hook_keyframe.keyframes[0] + for keyframe in hook.hook_keyframe.keyframes: + if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength): + hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe)) + prev_keyframe = keyframe + elif keyframe.start_percent == prev_keyframe.start_percent: + prev_keyframe = keyframe + # create final range, assuming last start_percent was not 1.0 + if not math.isclose(prev_keyframe.start_percent, 1.0): + hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe)) + scheduled_hooks[hook] = hook_schedule # hooks should not have their schedules in a list of tuples all_ranges: list[tuple[float, float]] = [] for range_kfs in scheduled_hooks.values(): @@ -336,7 +401,7 @@ class HookGroup: hook.reset() @staticmethod - def combine_all_hooks(hooks_list: list['HookGroup'], require_count=0) -> 'HookGroup': + def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup: actual: list[HookGroup] = [] for group in hooks_list: if group is not None: @@ -433,7 +498,7 @@ class HookKeyframeGroup: c._set_first_as_current() return c - def initialize_timesteps(self, model: 'BaseModel'): + def initialize_timesteps(self, model: BaseModel): for keyframe in self.keyframes: keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent) @@ -522,6 +587,17 @@ def get_sorted_list_via_attr(objects: list, attr: str) -> list: sorted_list.extend(object_list) return sorted_list +def create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup, transformer_options: dict[str]=None): + # if no hooks or is not a ModelPatcher for sampling, return empty dict + if hooks is None or model.is_clip: + return {} + if transformer_options is None: + transformer_options = {} + for hook in hooks.get_type(EnumHookType.TransformerOptions): + hook: TransformerOptionsHook + hook.on_apply_hooks(model, transformer_options) + return transformer_options + def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): hook_group = HookGroup() hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip) @@ -548,7 +624,7 @@ def create_hook_model_as_lora(weights_model, weights_clip, strength_model: float hook.need_weight_init = False return hook_group -def get_patch_weights_from_model(model: 'ModelPatcher', discard_model_sampling=True): +def get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True): if model is None: return None patches_model: dict[str, torch.Tensor] = model.model.state_dict() @@ -560,7 +636,7 @@ def get_patch_weights_from_model(model: 'ModelPatcher', discard_model_sampling=T return patches_model # NOTE: this function shows how to register weight hooks directly on the ModelPatchers -def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: dict[str, torch.Tensor], +def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float): key_map = {} if model is not None: @@ -612,24 +688,26 @@ def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, H else: c_dict[hooks_key] = cache[hooks_tuple] -def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True): +def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True, + cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): c = [] - hooks_combine_cache: dict[tuple[HookGroup, HookGroup], HookGroup] = {} + if cache is None: + cache = {} for t in conditioning: n = [t[0], t[1].copy()] for k in values: if append_hooks and k == 'hooks': - _combine_hooks_from_values(n[1], values, hooks_combine_cache) + _combine_hooks_from_values(n[1], values, cache) else: n[1][k] = values[k] c.append(n) return c -def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True): +def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None): if hooks is None: return cond - return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks) + return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache) def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]): if timestep_range is None: @@ -664,9 +742,10 @@ def combine_with_new_conds(conds: list, new_conds: list): def set_conds_props(conds: list, strength: float, set_cond_area: str, mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): final_conds = [] + cache = {} for c in conds: # first, apply lora_hook to conditioning, if provided - c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks) + c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache) # next, apply mask to conditioning c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area) # apply timesteps, if present @@ -678,9 +757,10 @@ def set_conds_props(conds: list, strength: float, set_cond_area: str, def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str="default", mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): combined_conds = [] + cache = {} for c, masked_c in zip(conds, new_conds): # first, apply lora_hook to new conditioning, if provided - masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks) + masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache) # next, apply mask to new conditioning, if provided masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength) # apply timesteps, if present @@ -692,9 +772,10 @@ def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1. def set_default_conds_and_combine(conds: list, new_conds: list, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True): combined_conds = [] + cache = {} for c, new_c in zip(conds, new_conds): # first, apply lora_hook to new conditioning, if provided - new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks) + new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache) # next, add default_cond key to cond so that during sampling, it can be identified new_c = conditioning_set_values(new_c, {'default': True}) # apply timesteps, if present diff --git a/comfy/image_encoders/dino2.py b/comfy/image_encoders/dino2.py new file mode 100644 index 000000000..9b6dace9d --- /dev/null +++ b/comfy/image_encoders/dino2.py @@ -0,0 +1,160 @@ +import torch +from comfy.text_encoders.bert import BertAttention +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention_for_device + + +class Dino2AttentionOutput(torch.nn.Module): + def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations): + super().__init__() + self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device) + + def forward(self, x): + return self.dense(x) + + +class Dino2AttentionBlock(torch.nn.Module): + def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations): + super().__init__() + self.attention = BertAttention(embed_dim, heads, dtype, device, operations) + self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations) + + def forward(self, x, mask, optimized_attention): + return self.output(self.attention(x, mask, optimized_attention)) + + +class LayerScale(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + + def forward(self, x): + return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype) + +class Dinov2MLP(torch.nn.Module): + def __init__(self, hidden_size: int, dtype, device, operations): + super().__init__() + + mlp_ratio = 4 + hidden_features = int(hidden_size * mlp_ratio) + self.fc1 = operations.Linear(hidden_size, hidden_features, bias = True, device=device, dtype=dtype) + self.fc2 = operations.Linear(hidden_features, hidden_size, bias = True, device=device, dtype=dtype) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.fc1(hidden_state) + hidden_state = torch.nn.functional.gelu(hidden_state) + hidden_state = self.fc2(hidden_state) + return hidden_state + +class SwiGLUFFN(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + in_features = out_features = dim + hidden_features = int(dim * 4) + hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8 + + self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype) + self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype) + + def forward(self, x): + x = self.weights_in(x) + x1, x2 = x.chunk(2, dim=-1) + x = torch.nn.functional.silu(x1) * x2 + return self.weights_out(x) + + +class Dino2Block(torch.nn.Module): + def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn): + super().__init__() + self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations) + self.layer_scale1 = LayerScale(dim, dtype, device, operations) + self.layer_scale2 = LayerScale(dim, dtype, device, operations) + if use_swiglu_ffn: + self.mlp = SwiGLUFFN(dim, dtype, device, operations) + else: + self.mlp = Dinov2MLP(dim, dtype, device, operations) + self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, x, optimized_attention): + x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention)) + x = x + self.layer_scale2(self.mlp(self.norm2(x))) + return x + + +class Dino2Encoder(torch.nn.Module): + def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn): + super().__init__() + self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) + for _ in range(num_layers)]) + + def forward(self, x, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, False, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layer) + intermediate_output + + intermediate = None + for i, layer in enumerate(self.layer): + x = layer(x, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + + +class Dino2PatchEmbeddings(torch.nn.Module): + def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None): + super().__init__() + self.projection = operations.Conv2d( + in_channels=num_channels, + out_channels=dim, + kernel_size=patch_size, + stride=patch_size, + bias=True, + dtype=dtype, + device=device + ) + + def forward(self, pixel_values): + return self.projection(pixel_values).flatten(2).transpose(1, 2) + + +class Dino2Embeddings(torch.nn.Module): + def __init__(self, dim, dtype, device, operations): + super().__init__() + patch_size = 14 + image_size = 518 + + self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations) + self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device)) + self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device)) + self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device)) + + def forward(self, pixel_values): + x = self.patch_embeddings(pixel_values) + # TODO: mask_token? + x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1) + x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype) + return x + + +class Dinov2Model(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + layer_norm_eps = config_dict["layer_norm_eps"] + use_swiglu_ffn = config_dict["use_swiglu_ffn"] + + self.embeddings = Dino2Embeddings(dim, dtype, device, operations) + self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations, use_swiglu_ffn = use_swiglu_ffn) + self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x, i = self.encoder(x, intermediate_output=intermediate_output) + x = self.layernorm(x) + pooled_output = x[:, 0, :] + return x, i, pooled_output, None diff --git a/comfy/image_encoders/dino2_giant.json b/comfy/image_encoders/dino2_giant.json new file mode 100644 index 000000000..f6076a4dc --- /dev/null +++ b/comfy/image_encoders/dino2_giant.json @@ -0,0 +1,21 @@ +{ + "attention_probs_dropout_prob": 0.0, + "drop_path_rate": 0.0, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 1536, + "image_size": 518, + "initializer_range": 0.02, + "layer_norm_eps": 1e-06, + "layerscale_value": 1.0, + "mlp_ratio": 4, + "model_type": "dinov2", + "num_attention_heads": 24, + "num_channels": 3, + "num_hidden_layers": 40, + "patch_size": 14, + "qkv_bias": true, + "use_swiglu_ffn": true, + "image_mean": [0.485, 0.456, 0.406], + "image_std": [0.229, 0.224, 0.225] +} diff --git a/comfy/image_encoders/dino2_large.json b/comfy/image_encoders/dino2_large.json new file mode 100644 index 000000000..43fbb58ff --- /dev/null +++ b/comfy/image_encoders/dino2_large.json @@ -0,0 +1,22 @@ +{ + "hidden_size": 1024, + "use_mask_token": true, + "patch_size": 14, + "image_size": 518, + "num_channels": 3, + "num_attention_heads": 16, + "initializer_range": 0.02, + "attention_probs_dropout_prob": 0.0, + "hidden_dropout_prob": 0.0, + "hidden_act": "gelu", + "mlp_ratio": 4, + "model_type": "dinov2", + "num_hidden_layers": 24, + "layer_norm_eps": 1e-6, + "qkv_bias": true, + "use_swiglu_ffn": false, + "layerscale_value": 1.0, + "drop_path_rate": 0.0, + "image_mean": [0.485, 0.456, 0.406], + "image_std": [0.229, 0.224, 0.225] +} diff --git a/comfy/k_diffusion/sa_solver.py b/comfy/k_diffusion/sa_solver.py new file mode 100644 index 000000000..0c6821b60 --- /dev/null +++ b/comfy/k_diffusion/sa_solver.py @@ -0,0 +1,121 @@ +# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019) +# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf +# Codebase ref: https://github.com/scxue/SA-Solver + +import math +from typing import Union, Callable +import torch + + +def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor: + """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts. + + Integral of exp((1 + tau^2) * x) * x^p dx + = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx, + with base case p=0 where integral equals product_terms[0]. + + where + product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2). + + Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1). + Return coefficients used by the SA-Solver in data prediction mode. + + Args: + s: Start time s. + t: End time t. + solver_order: Current order of the solver. + tau_t: Stochastic strength parameter in the SDE. + + Returns: + Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,). + """ + tau_mul = 1 + tau_t ** 2 + h = t - s + p = torch.arange(solver_order, dtype=s.dtype, device=s.device) + + # product_terms after factoring out exp((1 + tau^2) * t) + # Includes (1 + tau^2) factor from outside the integral + product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp()) + + # Lower triangular recursive coefficient matrix + # Accumulates recursive coefficients based on p / (1 + tau^2) + recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0) + log_factorial = (p + 1).lgamma() + recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0) + if tau_t > 0: + recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul)) + signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0) + recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril() + + return recursive_coeff_mat @ product_terms_factored + + +def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor: + """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details).""" + tau_mul = 1 + tau_t ** 2 + h = lambda_t - lambda_s + alpha_t = sigma_next * lambda_t.exp() + if is_corrector_step: + # Simplified 1-step (order-2) corrector + b_1 = alpha_t * (0.5 * tau_mul * h) + b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1 + else: + # Simplified 2-step predictor + b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s) + b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2 + return torch.stack([b_2, b_1]) + + +def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor: + """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18). + + The solver order corresponds to the number of input lambdas (half-logSNR points). + + Args: + sigma_next: Sigma at end time t. + curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,). + lambda_s: Lambda at start time s. + lambda_t: Lambda at end time t. + tau_t: Stochastic strength parameter in the SDE. + simple_order_2: Whether to enable the simple order-2 scheme. + is_corrector_step: Flag for corrector step in simple order-2 mode. + + Returns: + b_i coefficients for the SA-Solver, shape (N,), where N is the solver order. + """ + num_timesteps = curr_lambdas.shape[0] + + if simple_order_2 and num_timesteps == 2: + return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step) + + # Compute coefficients by solving a linear system from Lagrange basis interpolation + exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t) + vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T + lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs) + + # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t) + # = sigma_t * exp(lambda_t) = alpha_t + # exp((1 + tau^2) * lambda_t) is extracted from the integral + alpha_t = sigma_next * lambda_t.exp() + return alpha_t * lagrange_integrals + + +def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]: + """Return a function that controls the stochasticity of SA-Solver. + + When eta = 0, SA-Solver runs as ODE. The official approach uses + time t to determine the SDE interval, while here we use sigma instead. + + See: + https://github.com/scxue/SA-Solver/blob/main/README.md + """ + + def tau_func(sigma: Union[torch.Tensor, float]) -> float: + if eta <= 0: + return 0.0 # ODE + + if isinstance(sigma, torch.Tensor): + sigma = sigma.item() + return eta if start_sigma >= sigma >= end_sigma else 0.0 + + return tau_func diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py index d37d7dd95..0e2cda291 100644 --- a/comfy/k_diffusion/sampling.py +++ b/comfy/k_diffusion/sampling.py @@ -1,4 +1,5 @@ import math +from functools import partial from scipy import integrate import torch @@ -8,6 +9,7 @@ from tqdm.auto import trange, tqdm from . import utils from . import deis +from . import sa_solver import comfy.model_patcher import comfy.model_sampling @@ -40,7 +42,7 @@ def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): """Constructs a continuous VP noise schedule.""" t = torch.linspace(1, eps_s, n, device=device) - sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t)) return append_zero(sigmas) @@ -84,24 +86,24 @@ class BatchedBrownianTree: """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" def __init__(self, x, t0, t1, seed=None, **kwargs): - self.cpu_tree = True - if "cpu" in kwargs: - self.cpu_tree = kwargs.pop("cpu") + self.cpu_tree = kwargs.pop("cpu", True) t0, t1, self.sign = self.sort(t0, t1) - w0 = kwargs.get('w0', torch.zeros_like(x)) + w0 = kwargs.pop('w0', None) + if w0 is None: + w0 = torch.zeros_like(x) + self.batched = False if seed is None: - seed = torch.randint(0, 2 ** 63 - 1, []).item() - self.batched = True - try: - assert len(seed) == x.shape[0] + seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),) + elif isinstance(seed, (tuple, list)): + if len(seed) != x.shape[0]: + raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.") + self.batched = True w0 = w0[0] - except TypeError: - seed = [seed] - self.batched = False - if self.cpu_tree: - self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] else: - self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + seed = (seed,) + if self.cpu_tree: + t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu() + self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed) @staticmethod def sort(a, b): @@ -109,11 +111,10 @@ class BatchedBrownianTree: def __call__(self, t0, t1): t0, t1, sign = self.sort(t0, t1) + device, dtype = t0.device, t0.dtype if self.cpu_tree: - w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) - else: - w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) - + t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float() + w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign) return w if self.batched else w[0] @@ -142,6 +143,43 @@ class BrownianTreeNoiseSampler: return self.tree(t0, t1) / (t1 - t0).abs().sqrt() +def sigma_to_half_log_snr(sigma, model_sampling): + """Convert sigma to half-logSNR log(alpha_t / sigma_t).""" + if isinstance(model_sampling, comfy.model_sampling.CONST): + # log((1 - t) / t) = log((1 - sigma) / sigma) + return sigma.logit().neg() + return sigma.log().neg() + + +def half_log_snr_to_sigma(half_log_snr, model_sampling): + """Convert half-logSNR log(alpha_t / sigma_t) to sigma.""" + if isinstance(model_sampling, comfy.model_sampling.CONST): + # 1 / (1 + exp(half_log_snr)) + return half_log_snr.neg().sigmoid() + return half_log_snr.neg().exp() + + +def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4): + """Adjust the first sigma to avoid invalid logSNR.""" + if len(sigmas) <= 1: + return sigmas + if isinstance(model_sampling, comfy.model_sampling.CONST): + if sigmas[0] >= 1: + sigmas = sigmas.clone() + sigmas[0] = model_sampling.percent_to_sigma(percent_offset) + return sigmas + + +def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor: + """Compute the result of h*phi_1(h) in exponential integrator methods.""" + return torch.expm1(h) + + +def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor: + """Compute the result of h*phi_2(h) in exponential integrator methods.""" + return (torch.expm1(h) - h) / h + + @torch.no_grad() def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" @@ -384,9 +422,13 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o ds.pop(0) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - cur_order = min(i + 1, order) - coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] - x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) return x @@ -475,7 +517,7 @@ class DPMSolver(nn.Module): return x_3, eps_cache def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): - noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler if not t_end > t_start and eta: raise ValueError('eta must be 0 for reverse sampling') @@ -514,7 +556,7 @@ class DPMSolver(nn.Module): return x def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): - noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler if order not in {2, 3}: raise ValueError('order should be 2 or 3') forward = t_end > t_start @@ -682,49 +724,61 @@ def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=Non # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0) return x + @torch.no_grad() def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): """DPM-Solver++ (stochastic).""" if len(sigmas) <= 1: return x + extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() seed = extra_args.get("seed", None) noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) - sigma_fn = lambda t: t.neg().exp() - t_fn = lambda sigma: sigma.log().neg() + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) if sigmas[i + 1] == 0: - # Euler method - d = to_d(x, sigmas[i], denoised) - dt = sigmas[i + 1] - sigmas[i] - x = x + d * dt + # Denoising step + x = denoised else: # DPM-Solver++ - t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) - h = t_next - t - s = t + h * r + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + lambda_s_1 = lambda_s + r * h fac = 1 / (2 * r) + sigma_s_1 = sigma_fn(lambda_s_1) + + alpha_s = sigmas[i] * lambda_s.exp() + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + # Step 1 - sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) - s_ = t_fn(sd) - x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised - x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su - denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta) + lambda_s_1_ = sd.log().neg() + h_ = lambda_s_1_ - lambda_s + x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised + if eta > 0 and s_noise > 0: + x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) # Step 2 - sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) - t_next_ = t_fn(sd) + sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta) + lambda_t_ = sd.log().neg() + h_ = lambda_t_ - lambda_s denoised_d = (1 - fac) * denoised + fac * denoised_2 - x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d - x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d + if eta > 0 and s_noise > 0: + x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su return x @@ -753,6 +807,7 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No old_denoised = denoised return x + @torch.no_grad() def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): """DPM-Solver++(2M) SDE.""" @@ -762,15 +817,18 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl if solver_type not in {'heun', 'midpoint'}: raise ValueError('solver_type must be \'heun\' or \'midpoint\'') + extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + old_denoised = None - h_last = None - h = None + h, h_last = None, None for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) @@ -781,26 +839,34 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl x = denoised else: # DPM-Solver++(2M) SDE - t, s = -sigmas[i].log(), -sigmas[i + 1].log() - h = s - t - eta_h = eta * h + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) - x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised + alpha_t = sigmas[i + 1] * lambda_t.exp() + + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised if old_denoised is not None: r = h_last / h if solver_type == 'heun': - x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) + x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised) elif solver_type == 'midpoint': - x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) + x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised) - if eta: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + if eta > 0 and s_noise > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise old_denoised = denoised h_last = h return x + +@torch.no_grad() +def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): + return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + + @torch.no_grad() def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """DPM-Solver++(3M) SDE.""" @@ -808,12 +874,16 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl if len(sigmas) <= 1: return x + extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler - extra_args = {} if extra_args is None else extra_args s_in = x.new_ones([x.shape[0]]) + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + denoised_1, denoised_2 = None, None h, h_1, h_2 = None, None, None @@ -825,13 +895,16 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl # Denoising step x = denoised else: - t, s = -sigmas[i].log(), -sigmas[i + 1].log() - h = s - t + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s h_eta = h * (eta + 1) - x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised + alpha_t = sigmas[i + 1] * lambda_t.exp() + + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised if h_2 is not None: + # DPM-Solver++(3M) SDE r0 = h_1 / h r1 = h_2 / h d1_0 = (denoised - denoised_1) / r0 @@ -840,43 +913,57 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl d2 = (d1_0 - d1_1) / (r0 + r1) phi_2 = h_eta.neg().expm1() / h_eta + 1 phi_3 = phi_2 / h_eta - 0.5 - x = x + phi_2 * d1 - phi_3 * d2 + x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2 elif h_1 is not None: + # DPM-Solver++(2M) SDE r = h_1 / h d = (denoised - denoised_1) / r phi_2 = h_eta.neg().expm1() / h_eta + 1 - x = x + phi_2 * d + x = x + (alpha_t * phi_2) * d - if eta: + if eta > 0 and s_noise > 0: x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise denoised_1, denoised_2 = denoised, denoised_1 h_1, h_2 = h, h_1 return x + @torch.no_grad() def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): if len(sigmas) <= 1: return x - + extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) + +@torch.no_grad() +def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'): + if len(sigmas) <= 1: + return x + extra_args = {} if extra_args is None else extra_args + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + + @torch.no_grad() def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): if len(sigmas) <= 1: return x - + extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + @torch.no_grad() def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): if len(sigmas) <= 1: return x - + extra_args = {} if extra_args is None else extra_args sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) @@ -894,7 +981,8 @@ def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): extra_args = {} if extra_args is None else extra_args - noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): @@ -914,7 +1002,8 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, @torch.no_grad() def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): extra_args = {} if extra_args is None else extra_args - noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) @@ -1007,7 +1096,9 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) - if order == 1: # First Euler step. + if t_next == 0: # Denoising step + x_next = denoised + elif order == 1: # First Euler step. x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: # Use one history point. x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2 @@ -1025,6 +1116,7 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, return x_next + #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py #under Apache 2 license def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4): @@ -1048,7 +1140,9 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non d_cur = (x_cur - denoised) / t_cur order = min(max_order, i+1) - if order == 1: # First Euler step. + if t_next == 0: # Denoising step + x_next = denoised + elif order == 1: # First Euler step. x_next = x_cur + (t_next - t_cur) * d_cur elif order == 2: # Use one history point. h_n = (t_next - t_cur) @@ -1088,6 +1182,7 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non return x_next + #From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py #under Apache 2 license @torch.no_grad() @@ -1138,39 +1233,22 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, return x_next -@torch.no_grad() -def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): - extra_args = {} if extra_args is None else extra_args - - temp = [0] - def post_cfg_function(args): - temp[0] = args["uncond_denoised"] - return args["denoised"] - - model_options = extra_args.get("model_options", {}).copy() - extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) - - s_in = x.new_ones([x.shape[0]]) - for i in trange(len(sigmas) - 1, disable=disable): - sigma_hat = sigmas[i] - denoised = model(x, sigma_hat * s_in, **extra_args) - d = to_d(x, sigma_hat, temp[0]) - if callback is not None: - callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) - # Euler method - x = denoised + d * sigmas[i + 1] - return x @torch.no_grad() def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): - """Ancestral sampling with Euler method steps.""" + """Ancestral sampling with Euler method steps (CFG++).""" extra_args = {} if extra_args is None else extra_args seed = extra_args.get("seed", None) noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler - temp = [0] + model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + + uncond_denoised = None + def post_cfg_function(args): - temp[0] = args["uncond_denoised"] + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] return args["denoised"] model_options = extra_args.get("model_options", {}).copy() @@ -1179,15 +1257,33 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No s_in = x.new_ones([x.shape[0]]) for i in trange(len(sigmas) - 1, disable=disable): denoised = model(x, sigmas[i] * s_in, **extra_args) - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) if callback is not None: callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) - d = to_d(x, sigmas[i], temp[0]) - # Euler method - x = denoised + d * sigma_down - if sigmas[i + 1] > 0: - x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp() + alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp() + d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise + + # DDIM stochastic sampling + sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta) + sigma_down = alpha_t * sigma_down + + # Euler method + x = alpha_t * denoised + sigma_down * d + if eta > 0 and s_noise > 0: + x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up return x + + +@torch.no_grad() +def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): + """Euler method steps (CFG++).""" + return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None) + + @torch.no_grad() def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" @@ -1263,3 +1359,428 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis x = denoised + denoised_mix + torch.exp(-h) * x old_uncond_denoised = uncond_denoised return x + +@torch.no_grad() +def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False): + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + phi1_fn = lambda t: torch.expm1(t) / t + phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t + + old_sigma_down = None + old_denoised = None + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + if cfg_pp: + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + if sigma_down == 0 or old_denoised is None: + # Euler method + if cfg_pp: + d = to_d(x, sigmas[i], uncond_denoised) + x = denoised + d * sigma_down + else: + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # Second order multistep method in https://arxiv.org/pdf/2308.02157 + t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1]) + h = t_next - t + c2 = (t_prev - t_old) / h + + phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h) + b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0) + b2 = torch.nan_to_num(phi2_val / c2, nan=0.0) + + if cfg_pp: + x = x + (denoised - uncond_denoised) + x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised) + else: + x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised) + + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + + if cfg_pp: + old_denoised = uncond_denoised + else: + old_denoised = denoised + old_sigma_down = sigma_down + return x + +@torch.no_grad() +def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False) + +@torch.no_grad() +def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True) + +@torch.no_grad() +def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False) + +@torch.no_grad() +def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True) + + +@torch.no_grad() +def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False): + """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + old_d = None + + uncond_denoised = None + def post_cfg_function(args): + nonlocal uncond_denoised + uncond_denoised = args["uncond_denoised"] + return args["denoised"] + + if cfg_pp: + model_options = extra_args.get("model_options", {}).copy() + extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if cfg_pp: + d = to_d(x, sigmas[i], uncond_denoised) + else: + d = to_d(x, sigmas[i], denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + dt = sigmas[i + 1] - sigmas[i] + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # Euler method + if cfg_pp: + x = denoised + d * sigmas[i + 1] + else: + x = x + d * dt + + if i >= 1: + # Gradient estimation + d_bar = (ge_gamma - 1) * (d - old_d) + x = x + d_bar * dt + old_d = d + return x + + +@torch.no_grad() +def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.): + return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True) + + +@torch.no_grad() +def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3): + """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169. + Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py. + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + def default_er_sde_noise_scaler(x): + return x * ((x ** 0.3).exp() + 10.0) + + noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler + num_integration_points = 200.0 + point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device) + + model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling) + er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t + + old_denoised = None + old_denoised_d = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + stage_used = min(max_stage, i + 1) + if sigmas[i + 1] == 0: + x = denoised + else: + er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1] + alpha_s = sigmas[i] / er_lambda_s + alpha_t = sigmas[i + 1] / er_lambda_t + r_alpha = alpha_t / alpha_s + r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s) + + # Stage 1 Euler + x = r_alpha * r * x + alpha_t * (1 - r) * denoised + + if stage_used >= 2: + dt = er_lambda_t - er_lambda_s + lambda_step_size = -dt / num_integration_points + lambda_pos = er_lambda_t + point_indice * lambda_step_size + scaled_pos = noise_scaler(lambda_pos) + + # Stage 2 + s = torch.sum(1 / scaled_pos) * lambda_step_size + denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1]) + x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d + + if stage_used >= 3: + # Stage 3 + s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size + denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2) + x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u + old_denoised_d = denoised_d + + if s_noise > 0: + x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0) + old_denoised = denoised + return x + + +@torch.no_grad() +def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5): + """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2. + arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + inject_noise = eta > 0 and s_noise > 0 + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + fac = 1 / (2 * r) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + if sigmas[i + 1] == 0: + x = denoised + continue + + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + lambda_s_1 = torch.lerp(lambda_s, lambda_t, r) + sigma_s_1 = sigma_fn(lambda_s_1) + + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + + # Step 1 + x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised + if inject_noise: + sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) + x_2 = x_2 + sde_noise * sigma_s_1 * s_noise + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) + + # Step 2 + denoised_d = torch.lerp(denoised, denoised_2, fac) + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d + if inject_noise: + segment_factor = (r - 1) * h * eta + sde_noise = sde_noise * segment_factor.exp() + sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1]) + x = x + sde_noise * sigmas[i + 1] * s_noise + return x + + +@torch.no_grad() +def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3): + """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3. + arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023) + """ + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + inject_noise = eta > 0 and s_noise > 0 + + model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling') + sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling) + lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling) + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + if sigmas[i + 1] == 0: + x = denoised + continue + + lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1]) + h = lambda_t - lambda_s + h_eta = h * (eta + 1) + lambda_s_1 = torch.lerp(lambda_s, lambda_t, r_1) + lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2) + sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2) + + alpha_s_1 = sigma_s_1 * lambda_s_1.exp() + alpha_s_2 = sigma_s_2 * lambda_s_2.exp() + alpha_t = sigmas[i + 1] * lambda_t.exp() + + # Step 1 + x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised + if inject_noise: + sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1) + x_2 = x_2 + sde_noise * sigma_s_1 * s_noise + denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args) + + # Step 2 + a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta) + a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2 + x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2) + if inject_noise: + segment_factor = (r_1 - r_2) * h * eta + sde_noise = sde_noise * segment_factor.exp() + sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2) + x_3 = x_3 + sde_noise * sigma_s_2 * s_noise + denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args) + + # Step 3 + b3 = ei_h_phi_2(-h_eta) / r_2 + b1 = ei_h_phi_1(-h_eta) - b3 + x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3) + if inject_noise: + segment_factor = (r_2 - 1) * h * eta + sde_noise = sde_noise * segment_factor.exp() + sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1]) + x = x + sde_noise * sigmas[i + 1] * s_noise + return x + + +@torch.no_grad() +def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False): + """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023).""" + if len(sigmas) <= 1: + return x + extra_args = {} if extra_args is None else extra_args + seed = extra_args.get("seed", None) + noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling") + sigmas = offset_first_sigma_for_snr(sigmas, model_sampling) + lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling) + + if tau_func is None: + # Use default interval for stochastic sampling + start_sigma = model_sampling.percent_to_sigma(0.2) + end_sigma = model_sampling.percent_to_sigma(0.8) + tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0) + + max_used_order = max(predictor_order, corrector_order) + x_pred = x # x: current state, x_pred: predicted next state + + h = 0.0 + tau_t = 0.0 + noise = 0.0 + pred_list = [] + + # Lower order near the end to improve stability + lower_order_to_end = sigmas[-1].item() == 0 + + for i in trange(len(sigmas) - 1, disable=disable): + # Evaluation + denoised = model(x_pred, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised}) + pred_list.append(denoised) + pred_list = pred_list[-max_used_order:] + + predictor_order_used = min(predictor_order, len(pred_list)) + if i == 0 or (sigmas[i + 1] == 0 and not use_pece): + corrector_order_used = 0 + else: + corrector_order_used = min(corrector_order, len(pred_list)) + + if lower_order_to_end: + predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i) + corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i) + + # Corrector + if corrector_order_used == 0: + # Update by the predicted state + x = x_pred + else: + curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1] + b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( + sigmas[i], + curr_lambdas, + lambdas[i - 1], + lambdas[i], + tau_t, + simple_order_2, + is_corrector_step=True, + ) + pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...) + corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) + x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res + + if tau_t > 0 and s_noise > 0: + # The noise from the previous predictor step + x = x + noise + + if use_pece: + # Evaluate the corrected state + denoised = model(x, sigmas[i] * s_in, **extra_args) + pred_list[-1] = denoised + + # Predictor + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + tau_t = tau_func(sigmas[i + 1]) + curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1] + b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs( + sigmas[i + 1], + curr_lambdas, + lambdas[i], + lambdas[i + 1], + tau_t, + simple_order_2, + is_corrector_step=False, + ) + pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...) + pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...) + h = lambdas[i + 1] - lambdas[i] + x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res + + if tau_t > 0 and s_noise > 0: + noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise + x_pred = x_pred + noise + return x + + +@torch.no_grad() +def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False): + """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023).""" + return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index a50a70aec..77e642a94 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -382,3 +382,267 @@ class HunyuanVideo(LatentFormat): ] latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761] + +class Cosmos1CV8x8x8(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + + latent_rgb_factors = [ + [ 0.1817, 0.2284, 0.2423], + [-0.0586, -0.0862, -0.3108], + [-0.4703, -0.4255, -0.3995], + [ 0.0803, 0.1963, 0.1001], + [-0.0820, -0.1050, 0.0400], + [ 0.2511, 0.3098, 0.2787], + [-0.1830, -0.2117, -0.0040], + [-0.0621, -0.2187, -0.0939], + [ 0.3619, 0.1082, 0.1455], + [ 0.3164, 0.3922, 0.2575], + [ 0.1152, 0.0231, -0.0462], + [-0.1434, -0.3609, -0.3665], + [ 0.0635, 0.1471, 0.1680], + [-0.3635, -0.1963, -0.3248], + [-0.1865, 0.0365, 0.2346], + [ 0.0447, 0.0994, 0.0881] + ] + + latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976] + +class Wan21(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + + latent_rgb_factors = [ + [-0.1299, -0.1692, 0.2932], + [ 0.0671, 0.0406, 0.0442], + [ 0.3568, 0.2548, 0.1747], + [ 0.0372, 0.2344, 0.1420], + [ 0.0313, 0.0189, -0.0328], + [ 0.0296, -0.0956, -0.0665], + [-0.3477, -0.4059, -0.2925], + [ 0.0166, 0.1902, 0.1975], + [-0.0412, 0.0267, -0.1364], + [-0.1293, 0.0740, 0.1636], + [ 0.0680, 0.3019, 0.1128], + [ 0.0032, 0.0581, 0.0639], + [-0.1251, 0.0927, 0.1699], + [ 0.0060, -0.0633, 0.0005], + [ 0.3477, 0.2275, 0.2950], + [ 0.1984, 0.0913, 0.1861] + ] + + latent_rgb_factors_bias = [-0.1835, -0.0868, -0.3360] + + def __init__(self): + self.scale_factor = 1.0 + self.latents_mean = torch.tensor([ + -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, + 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921 + ]).view(1, self.latent_channels, 1, 1, 1) + self.latents_std = torch.tensor([ + 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, + 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160 + ]).view(1, self.latent_channels, 1, 1, 1) + + + self.taesd_decoder_name = None #TODO + + def process_in(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return (latent - latents_mean) * self.scale_factor / latents_std + + def process_out(self, latent): + latents_mean = self.latents_mean.to(latent.device, latent.dtype) + latents_std = self.latents_std.to(latent.device, latent.dtype) + return latent * latents_std / self.scale_factor + latents_mean + +class Wan22(Wan21): + latent_channels = 48 + latent_dimensions = 3 + + latent_rgb_factors = [ + [ 0.0119, 0.0103, 0.0046], + [-0.1062, -0.0504, 0.0165], + [ 0.0140, 0.0409, 0.0491], + [-0.0813, -0.0677, 0.0607], + [ 0.0656, 0.0851, 0.0808], + [ 0.0264, 0.0463, 0.0912], + [ 0.0295, 0.0326, 0.0590], + [-0.0244, -0.0270, 0.0025], + [ 0.0443, -0.0102, 0.0288], + [-0.0465, -0.0090, -0.0205], + [ 0.0359, 0.0236, 0.0082], + [-0.0776, 0.0854, 0.1048], + [ 0.0564, 0.0264, 0.0561], + [ 0.0006, 0.0594, 0.0418], + [-0.0319, -0.0542, -0.0637], + [-0.0268, 0.0024, 0.0260], + [ 0.0539, 0.0265, 0.0358], + [-0.0359, -0.0312, -0.0287], + [-0.0285, -0.1032, -0.1237], + [ 0.1041, 0.0537, 0.0622], + [-0.0086, -0.0374, -0.0051], + [ 0.0390, 0.0670, 0.2863], + [ 0.0069, 0.0144, 0.0082], + [ 0.0006, -0.0167, 0.0079], + [ 0.0313, -0.0574, -0.0232], + [-0.1454, -0.0902, -0.0481], + [ 0.0714, 0.0827, 0.0447], + [-0.0304, -0.0574, -0.0196], + [ 0.0401, 0.0384, 0.0204], + [-0.0758, -0.0297, -0.0014], + [ 0.0568, 0.1307, 0.1372], + [-0.0055, -0.0310, -0.0380], + [ 0.0239, -0.0305, 0.0325], + [-0.0663, -0.0673, -0.0140], + [-0.0416, -0.0047, -0.0023], + [ 0.0166, 0.0112, -0.0093], + [-0.0211, 0.0011, 0.0331], + [ 0.1833, 0.1466, 0.2250], + [-0.0368, 0.0370, 0.0295], + [-0.3441, -0.3543, -0.2008], + [-0.0479, -0.0489, -0.0420], + [-0.0660, -0.0153, 0.0800], + [-0.0101, 0.0068, 0.0156], + [-0.0690, -0.0452, -0.0927], + [-0.0145, 0.0041, 0.0015], + [ 0.0421, 0.0451, 0.0373], + [ 0.0504, -0.0483, -0.0356], + [-0.0837, 0.0168, 0.0055] + ] + + latent_rgb_factors_bias = [0.0317, -0.0878, -0.1388] + + def __init__(self): + self.scale_factor = 1.0 + self.latents_mean = torch.tensor([ + -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, + -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825, + -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, + -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230, + -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748, + 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667, + ]).view(1, self.latent_channels, 1, 1, 1) + self.latents_std = torch.tensor([ + 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013, + 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, + 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, + 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, + 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, + 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744 + ]).view(1, self.latent_channels, 1, 1, 1) + +class HunyuanImage21(LatentFormat): + latent_channels = 64 + latent_dimensions = 2 + scale_factor = 0.75289 + + latent_rgb_factors = [ + [-0.0154, -0.0397, -0.0521], + [ 0.0005, 0.0093, 0.0006], + [-0.0805, -0.0773, -0.0586], + [-0.0494, -0.0487, -0.0498], + [-0.0212, -0.0076, -0.0261], + [-0.0179, -0.0417, -0.0505], + [ 0.0158, 0.0310, 0.0239], + [ 0.0409, 0.0516, 0.0201], + [ 0.0350, 0.0553, 0.0036], + [-0.0447, -0.0327, -0.0479], + [-0.0038, -0.0221, -0.0365], + [-0.0423, -0.0718, -0.0654], + [ 0.0039, 0.0368, 0.0104], + [ 0.0655, 0.0217, 0.0122], + [ 0.0490, 0.1638, 0.2053], + [ 0.0932, 0.0829, 0.0650], + [-0.0186, -0.0209, -0.0135], + [-0.0080, -0.0076, -0.0148], + [-0.0284, -0.0201, 0.0011], + [-0.0642, -0.0294, -0.0777], + [-0.0035, 0.0076, -0.0140], + [ 0.0519, 0.0731, 0.0887], + [-0.0102, 0.0095, 0.0704], + [ 0.0068, 0.0218, -0.0023], + [-0.0726, -0.0486, -0.0519], + [ 0.0260, 0.0295, 0.0263], + [ 0.0250, 0.0333, 0.0341], + [ 0.0168, -0.0120, -0.0174], + [ 0.0226, 0.1037, 0.0114], + [ 0.2577, 0.1906, 0.1604], + [-0.0646, -0.0137, -0.0018], + [-0.0112, 0.0309, 0.0358], + [-0.0347, 0.0146, -0.0481], + [ 0.0234, 0.0179, 0.0201], + [ 0.0157, 0.0313, 0.0225], + [ 0.0423, 0.0675, 0.0524], + [-0.0031, 0.0027, -0.0255], + [ 0.0447, 0.0555, 0.0330], + [-0.0152, 0.0103, 0.0299], + [-0.0755, -0.0489, -0.0635], + [ 0.0853, 0.0788, 0.1017], + [-0.0272, -0.0294, -0.0471], + [ 0.0440, 0.0400, -0.0137], + [ 0.0335, 0.0317, -0.0036], + [-0.0344, -0.0621, -0.0984], + [-0.0127, -0.0630, -0.0620], + [-0.0648, 0.0360, 0.0924], + [-0.0781, -0.0801, -0.0409], + [ 0.0363, 0.0613, 0.0499], + [ 0.0238, 0.0034, 0.0041], + [-0.0135, 0.0258, 0.0310], + [ 0.0614, 0.1086, 0.0589], + [ 0.0428, 0.0350, 0.0205], + [ 0.0153, 0.0173, -0.0018], + [-0.0288, -0.0455, -0.0091], + [ 0.0344, 0.0109, -0.0157], + [-0.0205, -0.0247, -0.0187], + [ 0.0487, 0.0126, 0.0064], + [-0.0220, -0.0013, 0.0074], + [-0.0203, -0.0094, -0.0048], + [-0.0719, 0.0429, -0.0442], + [ 0.1042, 0.0497, 0.0356], + [-0.0659, -0.0578, -0.0280], + [-0.0060, -0.0322, -0.0234]] + + latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206] + +class HunyuanImage21Refiner(LatentFormat): + latent_channels = 64 + latent_dimensions = 3 + scale_factor = 1.03682 + +class Hunyuan3Dv2(LatentFormat): + latent_channels = 64 + latent_dimensions = 1 + scale_factor = 0.9990943042622529 + +class Hunyuan3Dv2_1(LatentFormat): + scale_factor = 1.0039506158752403 + latent_channels = 64 + latent_dimensions = 1 + +class Hunyuan3Dv2mini(LatentFormat): + latent_channels = 64 + latent_dimensions = 1 + scale_factor = 1.0188137142395404 + +class ACEAudio(LatentFormat): + latent_channels = 8 + latent_dimensions = 2 + +class ChromaRadiance(LatentFormat): + latent_channels = 3 + + def __init__(self): + self.latent_rgb_factors = [ + # R G B + [ 1.0, 0.0, 0.0 ], + [ 0.0, 1.0, 0.0 ], + [ 0.0, 0.0, 1.0 ] + ] + + def process_in(self, latent): + return latent + + def process_out(self, latent): + return latent diff --git a/comfy/ldm/ace/attention.py b/comfy/ldm/ace/attention.py new file mode 100644 index 000000000..670eb9783 --- /dev/null +++ b/comfy/ldm/ace/attention.py @@ -0,0 +1,768 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/attention.py +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Tuple, Union, Optional + +import torch +import torch.nn.functional as F +from torch import nn + +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention + +class Attention(nn.Module): + def __init__( + self, + query_dim: int, + cross_attention_dim: Optional[int] = None, + heads: int = 8, + kv_heads: Optional[int] = None, + dim_head: int = 64, + dropout: float = 0.0, + bias: bool = False, + qk_norm: Optional[str] = None, + added_kv_proj_dim: Optional[int] = None, + added_proj_bias: Optional[bool] = True, + out_bias: bool = True, + scale_qk: bool = True, + only_cross_attention: bool = False, + eps: float = 1e-5, + rescale_output_factor: float = 1.0, + residual_connection: bool = False, + processor=None, + out_dim: int = None, + out_context_dim: int = None, + context_pre_only=None, + pre_only=False, + elementwise_affine: bool = True, + is_causal: bool = False, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads + self.query_dim = query_dim + self.use_bias = bias + self.is_cross_attention = cross_attention_dim is not None + self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim + self.rescale_output_factor = rescale_output_factor + self.residual_connection = residual_connection + self.dropout = dropout + self.fused_projections = False + self.out_dim = out_dim if out_dim is not None else query_dim + self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim + self.context_pre_only = context_pre_only + self.pre_only = pre_only + self.is_causal = is_causal + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + # for slice_size > 0 the attention score computation + # is split across the batch axis to save memory + # You can set slice_size with `set_attention_slice` + self.sliceable_head_dim = heads + + self.added_kv_proj_dim = added_kv_proj_dim + self.only_cross_attention = only_cross_attention + + if self.added_kv_proj_dim is None and self.only_cross_attention: + raise ValueError( + "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`." + ) + + self.group_norm = None + self.spatial_norm = None + + self.norm_q = None + self.norm_k = None + + self.norm_cross = None + self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) + + if not self.only_cross_attention: + # only relevant for the `AddedKVProcessor` classes + self.to_k = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + else: + self.to_k = None + self.to_v = None + + self.added_proj_bias = added_proj_bias + if self.added_kv_proj_dim is not None: + self.add_k_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + self.add_v_proj = operations.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias, dtype=dtype, device=device) + if self.context_pre_only is not None: + self.add_q_proj = operations.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias, dtype=dtype, device=device) + else: + self.add_q_proj = None + self.add_k_proj = None + self.add_v_proj = None + + if not self.pre_only: + self.to_out = nn.ModuleList([]) + self.to_out.append(operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device)) + self.to_out.append(nn.Dropout(dropout)) + else: + self.to_out = None + + if self.context_pre_only is not None and not self.context_pre_only: + self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) + else: + self.to_add_out = None + + self.norm_added_q = None + self.norm_added_k = None + self.processor = processor + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + transformer_options={}, + **cross_attention_kwargs, + ) -> torch.Tensor: + return self.processor( + self, + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + transformer_options=transformer_options, + **cross_attention_kwargs, + ) + + +class CustomLiteLAProcessor2_0: + """Attention processor used typically in processing the SD3-like self-attention projections. add rms norm for query and key and apply RoPE""" + + def __init__(self): + self.kernel_func = nn.ReLU(inplace=False) + self.eps = 1e-15 + self.pad_val = 1.0 + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + *args, + **kwargs, + ) -> torch.FloatTensor: + hidden_states_len = hidden_states.shape[1] + + input_ndim = hidden_states.ndim + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + if encoder_hidden_states is not None: + context_input_ndim = encoder_hidden_states.ndim + if context_input_ndim == 4: + batch_size, channel, height, width = encoder_hidden_states.shape + encoder_hidden_states = encoder_hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size = hidden_states.shape[0] + + # `sample` projections. + dtype = hidden_states.dtype + query = attn.to_q(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) + + # `context` projections. + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + if encoder_hidden_states is not None and has_encoder_hidden_state_proj: + encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states) + encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states) + encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states) + + # attention + if not attn.is_cross_attention: + query = torch.cat([query, encoder_hidden_states_query_proj], dim=1) + key = torch.cat([key, encoder_hidden_states_key_proj], dim=1) + value = torch.cat([value, encoder_hidden_states_value_proj], dim=1) + else: + query = hidden_states + key = encoder_hidden_states + value = encoder_hidden_states + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + key = key.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1).transpose(-1, -2) + value = value.transpose(-1, -2).reshape(batch_size, attn.heads, head_dim, -1) + + # RoPE需要 [B, H, S, D] 输入 + # 此时 query是 [B, H, D, S], 需要转成 [B, H, S, D] 才能应用RoPE + query = query.permute(0, 1, 3, 2) # [B, H, S, D] (从 [B, H, D, S]) + + # Apply query and key normalization if needed + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + # 此时 query是 [B, H, S, D],需要还原成 [B, H, D, S] + query = query.permute(0, 1, 3, 2) # [B, H, D, S] + + if attention_mask is not None: + # attention_mask: [B, S] -> [B, 1, S, 1] + attention_mask = attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S, 1] + query = query * attention_mask.permute(0, 1, 3, 2) # [B, H, S, D] * [B, 1, S, 1] + if not attn.is_cross_attention: + key = key * attention_mask # key: [B, h, S, D] 与 mask [B, 1, S, 1] 相乘 + value = value * attention_mask.permute(0, 1, 3, 2) # 如果 value 是 [B, h, D, S],那么需调整mask以匹配S维度 + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + encoder_attention_mask = encoder_attention_mask[:, None, :, None].to(key.dtype) # [B, 1, S_enc, 1] + # 此时 key: [B, h, S_enc, D], value: [B, h, D, S_enc] + key = key * encoder_attention_mask # [B, h, S_enc, D] * [B, 1, S_enc, 1] + value = value * encoder_attention_mask.permute(0, 1, 3, 2) # [B, h, D, S_enc] * [B, 1, 1, S_enc] + + query = self.kernel_func(query) + key = self.kernel_func(key) + + query, key, value = query.float(), key.float(), value.float() + + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=self.pad_val) + + vk = torch.matmul(value, key) + + hidden_states = torch.matmul(vk, query) + + if hidden_states.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.float() + + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + + hidden_states = hidden_states.view(batch_size, attn.heads * head_dim, -1).permute(0, 2, 1) + + hidden_states = hidden_states.to(dtype) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.to(dtype) + + # Split the attention outputs. + if encoder_hidden_states is not None and not attn.is_cross_attention and has_encoder_hidden_state_proj: + hidden_states, encoder_hidden_states = ( + hidden_states[:, : hidden_states_len], + hidden_states[:, hidden_states_len:], + ) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + if encoder_hidden_states is not None and not attn.context_pre_only and not attn.is_cross_attention and hasattr(attn, "to_add_out"): + encoder_hidden_states = attn.to_add_out(encoder_hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + if encoder_hidden_states is not None and context_input_ndim == 4: + encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if torch.get_autocast_gpu_dtype() == torch.float16: + hidden_states = hidden_states.clip(-65504, 65504) + if encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504) + + return hidden_states, encoder_hidden_states + + +class CustomerAttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). + """ + + def apply_rotary_emb( + self, + x: torch.Tensor, + freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings + to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are + reshaped as complex numbers, and the frequency tensor is reshaped for broadcasting compatibility. The resulting + tensors contain rotary embeddings and are returned as real tensors. + + Args: + x (`torch.Tensor`): + Query or key tensor to apply rotary embeddings. [B, H, S, D] xk (torch.Tensor): Key tensor to apply + freqs_cis (`Tuple[torch.Tensor]`): Precomputed frequency tensor for complex exponentials. ([S, D], [S, D],) + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. + """ + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) + + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + return out + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + transformer_options={}, + *args, + **kwargs, + ) -> torch.Tensor: + + residual = hidden_states + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + has_encoder_hidden_state_proj = hasattr(attn, "add_q_proj") and hasattr(attn, "add_k_proj") and hasattr(attn, "add_v_proj") + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if rotary_freqs_cis is not None: + query = self.apply_rotary_emb(query, rotary_freqs_cis) + if not attn.is_cross_attention: + key = self.apply_rotary_emb(key, rotary_freqs_cis) + elif rotary_freqs_cis_cross is not None and has_encoder_hidden_state_proj: + key = self.apply_rotary_emb(key, rotary_freqs_cis_cross) + + if attn.is_cross_attention and encoder_attention_mask is not None and has_encoder_hidden_state_proj: + # attention_mask: N x S1 + # encoder_attention_mask: N x S2 + # cross attention 整合attention_mask和encoder_attention_mask + combined_mask = attention_mask[:, :, None] * encoder_attention_mask[:, None, :] + attention_mask = torch.where(combined_mask == 1, 0.0, -torch.inf) + attention_mask = attention_mask[:, None, :, :].expand(-1, attn.heads, -1, -1).to(query.dtype) + + elif not attn.is_cross_attention and attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + hidden_states = optimized_attention( + query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options, + ).to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + +def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore + """Repeat `val` for `repeat_time` times and return the list or val if list/tuple.""" + if isinstance(x, (list, tuple)): + return list(x) + return [x for _ in range(repeat_time)] + + +def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore + """Return tuple with min_len by repeating element at idx_repeat.""" + # convert to list first + x = val2list(x) + + # repeat elements if necessary + if len(x) > 0: + x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))] + + return tuple(x) + + +def t2i_modulate(x, shift, scale): + return x * (1 + scale) + shift + + +def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]: + if isinstance(kernel_size, tuple): + return tuple([get_same_padding(ks) for ks in kernel_size]) + else: + assert kernel_size % 2 > 0, f"kernel size {kernel_size} should be odd number" + return kernel_size // 2 + +class ConvLayer(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + padding: Union[int, None] = None, + use_bias=False, + norm=None, + act=None, + dtype=None, device=None, operations=None + ): + super().__init__() + if padding is None: + padding = get_same_padding(kernel_size) + padding *= dilation + + self.in_dim = in_dim + self.out_dim = out_dim + self.kernel_size = kernel_size + self.stride = stride + self.dilation = dilation + self.groups = groups + self.padding = padding + self.use_bias = use_bias + + self.conv = operations.Conv1d( + in_dim, + out_dim, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=use_bias, + device=device, + dtype=dtype + ) + if norm is not None: + self.norm = operations.RMSNorm(out_dim, elementwise_affine=False, dtype=dtype, device=device) + else: + self.norm = None + if act is not None: + self.act = nn.SiLU(inplace=True) + else: + self.act = None + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.conv(x) + if self.norm: + x = self.norm(x) + if self.act: + x = self.act(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_features: int, + hidden_features: int, + out_feature=None, + kernel_size=3, + stride=1, + padding: Union[int, None] = None, + use_bias=False, + norm=(None, None, None), + act=("silu", "silu", None), + dilation=1, + dtype=None, device=None, operations=None + ): + out_feature = out_feature or in_features + super().__init__() + use_bias = val2tuple(use_bias, 3) + norm = val2tuple(norm, 3) + act = val2tuple(act, 3) + + self.glu_act = nn.SiLU(inplace=False) + self.inverted_conv = ConvLayer( + in_features, + hidden_features * 2, + 1, + use_bias=use_bias[0], + norm=norm[0], + act=act[0], + dtype=dtype, + device=device, + operations=operations, + ) + self.depth_conv = ConvLayer( + hidden_features * 2, + hidden_features * 2, + kernel_size, + stride=stride, + groups=hidden_features * 2, + padding=padding, + use_bias=use_bias[1], + norm=norm[1], + act=None, + dilation=dilation, + dtype=dtype, + device=device, + operations=operations, + ) + self.point_conv = ConvLayer( + hidden_features, + out_feature, + 1, + use_bias=use_bias[2], + norm=norm[2], + act=act[2], + dtype=dtype, + device=device, + operations=operations, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.transpose(1, 2) + x = self.inverted_conv(x) + x = self.depth_conv(x) + + x, gate = torch.chunk(x, 2, dim=1) + gate = self.glu_act(gate) + x = x * gate + + x = self.point_conv(x) + x = x.transpose(1, 2) + + return x + + +class LinearTransformerBlock(nn.Module): + """ + A Sana block with global shared adaptive layer norm (adaLN-single) conditioning. + """ + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + use_adaln_single=True, + cross_attention_dim=None, + added_kv_proj_dim=None, + context_pre_only=False, + mlp_ratio=4.0, + add_cross_attention=False, + add_cross_attention_dim=None, + qk_norm=None, + dtype=None, device=None, operations=None + ): + super().__init__() + + self.norm1 = operations.RMSNorm(dim, elementwise_affine=False, eps=1e-6) + self.attn = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + added_kv_proj_dim=added_kv_proj_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + bias=True, + qk_norm=qk_norm, + processor=CustomLiteLAProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.add_cross_attention = add_cross_attention + self.context_pre_only = context_pre_only + + if add_cross_attention and add_cross_attention_dim is not None: + self.cross_attn = Attention( + query_dim=dim, + cross_attention_dim=add_cross_attention_dim, + added_kv_proj_dim=add_cross_attention_dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + context_pre_only=context_pre_only, + bias=True, + qk_norm=qk_norm, + processor=CustomerAttnProcessor2_0(), + dtype=dtype, + device=device, + operations=operations, + ) + + self.norm2 = operations.RMSNorm(dim, 1e-06, elementwise_affine=False) + + self.ff = GLUMBConv( + in_features=dim, + hidden_features=int(dim * mlp_ratio), + use_bias=(True, True, False), + norm=(None, None, None), + act=("silu", "silu", None), + dtype=dtype, + device=device, + operations=operations, + ) + self.use_adaln_single = use_adaln_single + if use_adaln_single: + self.scale_shift_table = nn.Parameter(torch.empty(6, dim, dtype=dtype, device=device)) + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: torch.FloatTensor = None, + attention_mask: torch.FloatTensor = None, + encoder_attention_mask: torch.FloatTensor = None, + rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None, + temb: torch.FloatTensor = None, + transformer_options={}, + ): + + N = hidden_states.shape[0] + + # step 1: AdaLN single + if self.use_adaln_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + comfy.model_management.cast_to(self.scale_shift_table[None], dtype=temb.dtype, device=temb.device) + temb.reshape(N, 6, -1) + ).chunk(6, dim=1) + + norm_hidden_states = self.norm1(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + + # step 2: attention + if not self.add_cross_attention: + attn_output, encoder_hidden_states = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + transformer_options=transformer_options, + ) + else: + attn_output, _ = self.attn( + hidden_states=norm_hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=None, + transformer_options=transformer_options, + ) + + if self.use_adaln_single: + attn_output = gate_msa * attn_output + hidden_states = attn_output + hidden_states + + if self.add_cross_attention: + attn_output = self.cross_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=rotary_freqs_cis_cross, + transformer_options=transformer_options, + ) + hidden_states = attn_output + hidden_states + + # step 3: add norm + norm_hidden_states = self.norm2(hidden_states) + if self.use_adaln_single: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + # step 4: feed forward + ff_output = self.ff(norm_hidden_states) + if self.use_adaln_single: + ff_output = gate_mlp * ff_output + + hidden_states = hidden_states + ff_output + + return hidden_states diff --git a/comfy/ldm/ace/lyric_encoder.py b/comfy/ldm/ace/lyric_encoder.py new file mode 100644 index 000000000..ff4359b26 --- /dev/null +++ b/comfy/ldm/ace/lyric_encoder.py @@ -0,0 +1,1067 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py +from typing import Optional, Tuple, Union +import math +import torch +from torch import nn + +import comfy.model_management + +class ConvolutionModule(nn.Module): + """ConvolutionModule in Conformer model.""" + + def __init__(self, + channels: int, + kernel_size: int = 15, + activation: nn.Module = nn.ReLU(), + norm: str = "batch_norm", + causal: bool = False, + bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an ConvolutionModule object. + Args: + channels (int): The number of channels of conv layers. + kernel_size (int): Kernel size of conv layers. + causal (int): Whether use causal convolution or not + """ + super().__init__() + + self.pointwise_conv1 = operations.Conv1d( + channels, + 2 * channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + # self.lorder is used to distinguish if it's a causal convolution, + # if self.lorder > 0: it's a causal convolution, the input will be + # padded with self.lorder frames on the left in forward. + # else: it's a symmetrical convolution + if causal: + padding = 0 + self.lorder = kernel_size - 1 + else: + # kernel_size should be an odd number for none causal convolution + assert (kernel_size - 1) % 2 == 0 + padding = (kernel_size - 1) // 2 + self.lorder = 0 + self.depthwise_conv = operations.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=padding, + groups=channels, + bias=bias, + dtype=dtype, device=device + ) + + assert norm in ['batch_norm', 'layer_norm'] + if norm == "batch_norm": + self.use_layer_norm = False + self.norm = nn.BatchNorm1d(channels) + else: + self.use_layer_norm = True + self.norm = operations.LayerNorm(channels, dtype=dtype, device=device) + + self.pointwise_conv2 = operations.Conv1d( + channels, + channels, + kernel_size=1, + stride=1, + padding=0, + bias=bias, + dtype=dtype, device=device + ) + self.activation = activation + + def forward( + self, + x: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + cache: torch.Tensor = torch.zeros((0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute convolution module. + Args: + x (torch.Tensor): Input tensor (#batch, time, channels). + mask_pad (torch.Tensor): used for batch padding (#batch, 1, time), + (0, 0, 0) means fake mask. + cache (torch.Tensor): left context cache, it is only + used in causal convolution (#batch, channels, cache_t), + (0, 0, 0) meas fake cache. + Returns: + torch.Tensor: Output tensor (#batch, time, channels). + """ + # exchange the temporal dimension and the feature dimension + x = x.transpose(1, 2) # (#batch, channels, time) + + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + if self.lorder > 0: + if cache.size(2) == 0: # cache_t == 0 + x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0) + else: + assert cache.size(0) == x.size(0) # equal batch + assert cache.size(1) == x.size(1) # equal channel + x = torch.cat((cache, x), dim=2) + assert (x.size(2) > self.lorder) + new_cache = x[:, :, -self.lorder:] + else: + # It's better we just return None if no cache is required, + # However, for JIT export, here we just fake one tensor instead of + # None. + new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + + # GLU mechanism + x = self.pointwise_conv1(x) # (batch, 2*channel, dim) + x = nn.functional.glu(x, dim=1) # (batch, channel, dim) + + # 1D Depthwise Conv + x = self.depthwise_conv(x) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.activation(self.norm(x)) + if self.use_layer_norm: + x = x.transpose(1, 2) + x = self.pointwise_conv2(x) + # mask batch padding + if mask_pad.size(2) > 0: # time > 0 + x.masked_fill_(~mask_pad, 0.0) + + return x.transpose(1, 2), new_cache + +class PositionwiseFeedForward(torch.nn.Module): + """Positionwise feed forward layer. + + FeedForward are appied on each position of the sequence. + The output dim is same with the input dim. + + Args: + idim (int): Input dimenstion. + hidden_units (int): The number of hidden units. + dropout_rate (float): Dropout rate. + activation (torch.nn.Module): Activation function + """ + + def __init__( + self, + idim: int, + hidden_units: int, + dropout_rate: float, + activation: torch.nn.Module = torch.nn.ReLU(), + dtype=None, device=None, operations=None + ): + """Construct a PositionwiseFeedForward object.""" + super(PositionwiseFeedForward, self).__init__() + self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device) + self.activation = activation + self.dropout = torch.nn.Dropout(dropout_rate) + self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device) + + def forward(self, xs: torch.Tensor) -> torch.Tensor: + """Forward function. + + Args: + xs: input tensor (B, L, D) + Returns: + output tensor, (B, L, D) + """ + return self.w_2(self.dropout(self.activation(self.w_1(xs)))) + +class Swish(torch.nn.Module): + """Construct an Swish object.""" + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Return Swish activation function.""" + return x * torch.sigmoid(x) + +class MultiHeadedAttention(nn.Module): + """Multi-Head Attention layer. + + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an MultiHeadedAttention object.""" + super().__init__() + assert n_feat % n_head == 0 + # We assume d_v always equals d_k + self.d_k = n_feat // n_head + self.h = n_head + self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device) + self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device) + self.dropout = nn.Dropout(p=dropout_rate) + + def forward_qkv( + self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Transform query, key and value. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + + Returns: + torch.Tensor: Transformed query tensor, size + (#batch, n_head, time1, d_k). + torch.Tensor: Transformed key tensor, size + (#batch, n_head, time2, d_k). + torch.Tensor: Transformed value tensor, size + (#batch, n_head, time2, d_k). + + """ + n_batch = query.size(0) + q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) + k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) + v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) + q = q.transpose(1, 2) # (batch, head, time1, d_k) + k = k.transpose(1, 2) # (batch, head, time2, d_k) + v = v.transpose(1, 2) # (batch, head, time2, d_k) + return q, k, v + + def forward_attention( + self, + value: torch.Tensor, + scores: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool) + ) -> torch.Tensor: + """Compute attention context vector. + + Args: + value (torch.Tensor): Transformed value, size + (#batch, n_head, time2, d_k). + scores (torch.Tensor): Attention score, size + (#batch, n_head, time1, time2). + mask (torch.Tensor): Mask, size (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + + Returns: + torch.Tensor: Transformed value (#batch, time1, d_model) + weighted by the attention score (#batch, time1, time2). + + """ + n_batch = value.size(0) + + if mask is not None and mask.size(2) > 0: # time2 > 0 + mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) + # For last chunk, time2 might be larger than scores.size(-1) + mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2) + scores = scores.masked_fill(mask, -float('inf')) + attn = torch.softmax(scores, dim=-1).masked_fill( + mask, 0.0) # (batch, head, time1, time2) + + else: + attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) + + p_attn = self.dropout(attn) + x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) + x = (x.transpose(1, 2).contiguous().view(n_batch, -1, + self.h * self.d_k) + ) # (batch, time1, d_model) + + return self.linear_out(x) # (batch, time1, d_model) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute scaled dot product attention. + + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2). + 1.When applying cross attention between decoder and encoder, + the batch padding mask for input is in (#batch, 1, T) shape. + 2.When applying self attention of encoder, + the mask is in (#batch, T, T) shape. + 3.When applying self attention of decoder, + the mask is in (#batch, L, L) shape. + 4.If the different position in decoder see different block + of the encoder, such as Mocha, the passed in mask could be + in (#batch, L, T) shape. But there is no such case in current + CosyVoice. + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + + """ + q, k, v = self.forward_qkv(query, key, value) + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + new_cache = torch.cat((k, v), dim=-1) + + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, mask), new_cache + + +class RelPositionMultiHeadedAttention(MultiHeadedAttention): + """Multi-Head Attention layer with relative position encoding. + Paper: https://arxiv.org/abs/1901.02860 + Args: + n_head (int): The number of heads. + n_feat (int): The number of features. + dropout_rate (float): Dropout rate. + """ + + def __init__(self, + n_head: int, + n_feat: int, + dropout_rate: float, + key_bias: bool = True, + dtype=None, device=None, operations=None): + """Construct an RelPositionMultiHeadedAttention object.""" + super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations) + # linear transformation for positional encoding + self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device)) + # torch.nn.init.xavier_uniform_(self.pos_bias_u) + # torch.nn.init.xavier_uniform_(self.pos_bias_v) + + def rel_shift(self, x: torch.Tensor) -> torch.Tensor: + """Compute relative positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). + time1 means the length of query vector. + + Returns: + torch.Tensor: Output tensor. + + """ + zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1), + device=x.device, + dtype=x.dtype) + x_padded = torch.cat([zero_pad, x], dim=-1) + + x_padded = x_padded.view(x.size()[0], + x.size()[1], + x.size(3) + 1, x.size(2)) + x = x_padded[:, :, 1:].view_as(x)[ + :, :, :, : x.size(-1) // 2 + 1 + ] # only keep the positions from 0 to time2 + return x + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + pos_emb: torch.Tensor = torch.empty(0), + cache: torch.Tensor = torch.zeros((0, 0, 0, 0)) + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute 'Scaled Dot Product Attention' with rel. positional encoding. + Args: + query (torch.Tensor): Query tensor (#batch, time1, size). + key (torch.Tensor): Key tensor (#batch, time2, size). + value (torch.Tensor): Value tensor (#batch, time2, size). + mask (torch.Tensor): Mask tensor (#batch, 1, time2) or + (#batch, time1, time2), (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): Positional embedding tensor + (#batch, time2, size). + cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2), + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + Returns: + torch.Tensor: Output tensor (#batch, time1, d_model). + torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2) + where `cache_t == chunk_size * num_decoding_left_chunks` + and `head * d_k == size` + """ + q, k, v = self.forward_qkv(query, key, value) + q = q.transpose(1, 2) # (batch, time1, head, d_k) + + if cache.size(0) > 0: + key_cache, value_cache = torch.split(cache, + cache.size(-1) // 2, + dim=-1) + k = torch.cat([key_cache, k], dim=2) + v = torch.cat([value_cache, v], dim=2) + # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's + # non-trivial to calculate `next_cache_start` here. + new_cache = torch.cat((k, v), dim=-1) + + n_batch_pos = pos_emb.size(0) + p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) + p = p.transpose(1, 2) # (batch, head, time1, d_k) + + # (batch, head, time1, d_k) + q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2) + # (batch, head, time1, d_k) + q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch, head, time1, time2) + matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) + + # compute matrix b and matrix d + # (batch, head, time1, time2) + matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) + # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used + if matrix_ac.shape != matrix_bd.shape: + matrix_bd = self.rel_shift(matrix_bd) + + scores = (matrix_ac + matrix_bd) / math.sqrt( + self.d_k) # (batch, head, time1, time2) + + return self.forward_attention(v, scores, mask), new_cache + + + +def subsequent_mask( + size: int, + device: torch.device = torch.device("cpu"), +) -> torch.Tensor: + """Create mask for subsequent steps (size, size). + + This mask is used only in decoder which works in an auto-regressive mode. + This means the current step could only do attention with its left steps. + + In encoder, fully attention is used when streaming is not necessary and + the sequence is not long. In this case, no attention mask is needed. + + When streaming is need, chunk-based attention is used in encoder. See + subsequent_chunk_mask for the chunk-based attention mask. + + Args: + size (int): size of mask + str device (str): "cpu" or "cuda" or torch.Tensor.device + dtype (torch.device): result dtype + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_mask(3) + [[1, 0, 0], + [1, 1, 0], + [1, 1, 1]] + """ + arange = torch.arange(size, device=device) + mask = arange.expand(size, size) + arange = arange.unsqueeze(-1) + mask = mask <= arange + return mask + + +def subsequent_chunk_mask( + size: int, + chunk_size: int, + num_left_chunks: int = -1, + device: torch.device = torch.device("cpu"), + ) -> torch.Tensor: + """Create mask for subsequent steps (size, size) with chunk size, + this is for streaming encoder + + Args: + size (int): size of mask + chunk_size (int): size of chunk + num_left_chunks (int): number of left chunks + <0: use full chunk + >=0: use num_left_chunks + device (torch.device): "cpu" or "cuda" or torch.Tensor.device + + Returns: + torch.Tensor: mask + + Examples: + >>> subsequent_chunk_mask(4, 2) + [[1, 1, 0, 0], + [1, 1, 0, 0], + [1, 1, 1, 1], + [1, 1, 1, 1]] + """ + ret = torch.zeros(size, size, device=device, dtype=torch.bool) + for i in range(size): + if num_left_chunks < 0: + start = 0 + else: + start = max((i // chunk_size - num_left_chunks) * chunk_size, 0) + ending = min((i // chunk_size + 1) * chunk_size, size) + ret[i, start:ending] = True + return ret + +def add_optional_chunk_mask(xs: torch.Tensor, + masks: torch.Tensor, + use_dynamic_chunk: bool, + use_dynamic_left_chunk: bool, + decoding_chunk_size: int, + static_chunk_size: int, + num_decoding_left_chunks: int, + enable_full_context: bool = True): + """ Apply optional mask for encoder. + + Args: + xs (torch.Tensor): padded input, (B, L, D), L for max length + mask (torch.Tensor): mask for xs, (B, 1, L) + use_dynamic_chunk (bool): whether to use dynamic chunk or not + use_dynamic_left_chunk (bool): whether to use dynamic left chunk for + training. + decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + static_chunk_size (int): chunk size for static chunk training/decoding + if it's greater than 0, if use_dynamic_chunk is true, + this parameter will be ignored + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + enable_full_context (bool): + True: chunk size is either [1, 25] or full context(max_len) + False: chunk size ~ U[1, 25] + + Returns: + torch.Tensor: chunk mask of the input xs. + """ + # Whether to use chunk mask or not + if use_dynamic_chunk: + max_len = xs.size(1) + if decoding_chunk_size < 0: + chunk_size = max_len + num_left_chunks = -1 + elif decoding_chunk_size > 0: + chunk_size = decoding_chunk_size + num_left_chunks = num_decoding_left_chunks + else: + # chunk size is either [1, 25] or full context(max_len). + # Since we use 4 times subsampling and allow up to 1s(100 frames) + # delay, the maximum frame is 100 / 4 = 25. + chunk_size = torch.randint(1, max_len, (1, )).item() + num_left_chunks = -1 + if chunk_size > max_len // 2 and enable_full_context: + chunk_size = max_len + else: + chunk_size = chunk_size % 25 + 1 + if use_dynamic_left_chunk: + max_left_chunks = (max_len - 1) // chunk_size + num_left_chunks = torch.randint(0, max_left_chunks, + (1, )).item() + chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + elif static_chunk_size > 0: + num_left_chunks = num_decoding_left_chunks + chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size, + num_left_chunks, + xs.device) # (L, L) + chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L) + chunk_masks = masks & chunk_masks # (B, L, L) + else: + chunk_masks = masks + return chunk_masks + + +class ConformerEncoderLayer(nn.Module): + """Encoder layer module. + Args: + size (int): Input dimension. + self_attn (torch.nn.Module): Self-attention module instance. + `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` + instance can be used as the argument. + feed_forward (torch.nn.Module): Feed-forward module instance. + `PositionwiseFeedForward` instance can be used as the argument. + feed_forward_macaron (torch.nn.Module): Additional feed-forward module + instance. + `PositionwiseFeedForward` instance can be used as the argument. + conv_module (torch.nn.Module): Convolution module instance. + `ConvlutionModule` instance can be used as the argument. + dropout_rate (float): Dropout rate. + normalize_before (bool): + True: use layer_norm before each sub-block. + False: use layer_norm after each sub-block. + """ + + def __init__( + self, + size: int, + self_attn: torch.nn.Module, + feed_forward: Optional[nn.Module] = None, + feed_forward_macaron: Optional[nn.Module] = None, + conv_module: Optional[nn.Module] = None, + dropout_rate: float = 0.1, + normalize_before: bool = True, + dtype=None, device=None, operations=None + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = self_attn + self.feed_forward = feed_forward + self.feed_forward_macaron = feed_forward_macaron + self.conv_module = conv_module + self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module + self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module + if feed_forward_macaron is not None: + self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + if self.conv_module is not None: + self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module + self.norm_final = operations.LayerNorm( + size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block + self.dropout = nn.Dropout(dropout_rate) + self.size = size + self.normalize_before = normalize_before + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool), + att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)), + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Compute encoded features. + + Args: + x (torch.Tensor): (#batch, time, size) + mask (torch.Tensor): Mask tensor for the input (#batch, time,time), + (0, 0, 0) means fake mask. + pos_emb (torch.Tensor): positional encoding, must not be None + for ConformerEncoderLayer. + mask_pad (torch.Tensor): batch padding mask used for conv module. + (#batch, 1,time), (0, 0, 0) means fake mask. + att_cache (torch.Tensor): Cache tensor of the KEY & VALUE + (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. + cnn_cache (torch.Tensor): Convolution cache in conformer layer + (#batch=1, size, cache_t2) + Returns: + torch.Tensor: Output tensor (#batch, time, size). + torch.Tensor: Mask tensor (#batch, time, time). + torch.Tensor: att_cache tensor, + (#batch=1, head, cache_t1 + time, d_k * 2). + torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2). + """ + + # whether to use macaron style + if self.feed_forward_macaron is not None: + residual = x + if self.normalize_before: + x = self.norm_ff_macaron(x) + x = residual + self.ff_scale * self.dropout( + self.feed_forward_macaron(x)) + if not self.normalize_before: + x = self.norm_ff_macaron(x) + + # multi-headed self-attention module + residual = x + if self.normalize_before: + x = self.norm_mha(x) + x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, + att_cache) + x = residual + self.dropout(x_att) + if not self.normalize_before: + x = self.norm_mha(x) + + # convolution module + # Fake new cnn cache here, and then change it in conv_module + new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device) + if self.conv_module is not None: + residual = x + if self.normalize_before: + x = self.norm_conv(x) + x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache) + x = residual + self.dropout(x) + + if not self.normalize_before: + x = self.norm_conv(x) + + # feed forward module + residual = x + if self.normalize_before: + x = self.norm_ff(x) + + x = residual + self.ff_scale * self.dropout(self.feed_forward(x)) + if not self.normalize_before: + x = self.norm_ff(x) + + if self.conv_module is not None: + x = self.norm_final(x) + + return x, mask, new_att_cache, new_cnn_cache + + + +class EspnetRelPositionalEncoding(torch.nn.Module): + """Relative positional encoding module (new implementation). + + Details can be found in https://github.com/espnet/espnet/pull/2816. + + See : Appendix B in https://arxiv.org/abs/1901.02860 + + Args: + d_model (int): Embedding dimension. + dropout_rate (float): Dropout rate. + max_len (int): Maximum input length. + + """ + + def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000): + """Construct an PositionalEncoding object.""" + super(EspnetRelPositionalEncoding, self).__init__() + self.d_model = d_model + self.xscale = math.sqrt(self.d_model) + self.dropout = torch.nn.Dropout(p=dropout_rate) + self.pe = None + self.extend_pe(torch.tensor(0.0).expand(1, max_len)) + + def extend_pe(self, x: torch.Tensor): + """Reset the positional encodings.""" + if self.pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if self.pe.size(1) >= x.size(1) * 2 - 1: + if self.pe.dtype != x.dtype or self.pe.device != x.device: + self.pe = self.pe.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vecotr and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i Tuple[torch.Tensor, torch.Tensor]: + """Add positional encoding. + + Args: + x (torch.Tensor): Input tensor (batch, time, `*`). + + Returns: + torch.Tensor: Encoded tensor (batch, time, `*`). + + """ + self.extend_pe(x) + x = x * self.xscale + pos_emb = self.position_encoding(size=x.size(1), offset=offset) + return self.dropout(x), self.dropout(pos_emb) + + def position_encoding(self, + offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + """ For getting encoding in a streaming fashion + + Attention!!!!! + we apply dropout only once at the whole utterance level in a none + streaming way, but will call this function several times with + increasing input size in a streaming scenario, so the dropout will + be applied several times. + + Args: + offset (int or torch.tensor): start offset + size (int): required size of position encoding + + Returns: + torch.Tensor: Corresponding encoding + """ + pos_emb = self.pe[ + :, + self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size, + ] + return pos_emb + + + +class LinearEmbed(torch.nn.Module): + """Linear transform the input without subsampling + + Args: + idim (int): Input dimension. + odim (int): Output dimension. + dropout_rate (float): Dropout rate. + + """ + + def __init__(self, idim: int, odim: int, dropout_rate: float, + pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None): + """Construct an linear object.""" + super().__init__() + self.out = torch.nn.Sequential( + operations.Linear(idim, odim, dtype=dtype, device=device), + operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device), + torch.nn.Dropout(dropout_rate), + ) + self.pos_enc = pos_enc_class #rel_pos_espnet + + def position_encoding(self, offset: Union[int, torch.Tensor], + size: int) -> torch.Tensor: + return self.pos_enc.position_encoding(offset, size) + + def forward( + self, + x: torch.Tensor, + offset: Union[int, torch.Tensor] = 0 + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Input x. + + Args: + x (torch.Tensor): Input tensor (#batch, time, idim). + x_mask (torch.Tensor): Input mask (#batch, 1, time). + + Returns: + torch.Tensor: linear input tensor (#batch, time', odim), + where time' = time . + torch.Tensor: linear input mask (#batch, 1, time'), + where time' = time . + + """ + x = self.out(x) + x, pos_emb = self.pos_enc(x, offset) + return x, pos_emb + + +ATTENTION_CLASSES = { + "selfattn": MultiHeadedAttention, + "rel_selfattn": RelPositionMultiHeadedAttention, +} + +ACTIVATION_CLASSES = { + "hardtanh": torch.nn.Hardtanh, + "tanh": torch.nn.Tanh, + "relu": torch.nn.ReLU, + "selu": torch.nn.SELU, + "swish": getattr(torch.nn, "SiLU", Swish), + "gelu": torch.nn.GELU, +} + + +def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor: + """Make mask tensor containing indices of padded part. + + See description of make_non_pad_mask. + + Args: + lengths (torch.Tensor): Batch of lengths (B,). + Returns: + torch.Tensor: Mask tensor containing indices of padded part. + + Examples: + >>> lengths = [5, 3, 2] + >>> make_pad_mask(lengths) + masks = [[0, 0, 0, 0 ,0], + [0, 0, 0, 1, 1], + [0, 0, 1, 1, 1]] + """ + batch_size = lengths.size(0) + max_len = max_len if max_len > 0 else lengths.max().item() + seq_range = torch.arange(0, + max_len, + dtype=torch.int64, + device=lengths.device) + seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) + seq_length_expand = lengths.unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + return mask + +#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml +class ConformerEncoder(torch.nn.Module): + """Conformer encoder module.""" + + def __init__( + self, + input_size: int, + output_size: int = 1024, + attention_heads: int = 16, + linear_units: int = 4096, + num_blocks: int = 6, + dropout_rate: float = 0.1, + positional_dropout_rate: float = 0.1, + attention_dropout_rate: float = 0.0, + input_layer: str = 'linear', + pos_enc_layer_type: str = 'rel_pos_espnet', + normalize_before: bool = True, + static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask + use_dynamic_chunk: bool = False, + use_dynamic_left_chunk: bool = False, + positionwise_conv_kernel_size: int = 1, + macaron_style: bool =False, + selfattention_layer_type: str = "rel_selfattn", + activation_type: str = "swish", + use_cnn_module: bool = False, + cnn_module_kernel: int = 15, + causal: bool = False, + cnn_module_norm: str = "batch_norm", + key_bias: bool = True, + dtype=None, device=None, operations=None + ): + """Construct ConformerEncoder + + Args: + input_size to use_dynamic_chunk, see in BaseEncoder + positionwise_conv_kernel_size (int): Kernel size of positionwise + conv1d layer. + macaron_style (bool): Whether to use macaron style for + positionwise layer. + selfattention_layer_type (str): Encoder attention layer type, + the parameter has no effect now, it's just for configure + compatibility. #'rel_selfattn' + activation_type (str): Encoder activation function type. + use_cnn_module (bool): Whether to use convolution module. + cnn_module_kernel (int): Kernel size of convolution module. + causal (bool): whether to use causal convolution or not. + key_bias: whether use bias in attention.linear_k, False for whisper models. + """ + super().__init__() + self.output_size = output_size + self.embed = LinearEmbed(input_size, output_size, dropout_rate, + EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations) + self.normalize_before = normalize_before + self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device) + self.use_dynamic_chunk = use_dynamic_chunk + + self.static_chunk_size = static_chunk_size + self.use_dynamic_chunk = use_dynamic_chunk + self.use_dynamic_left_chunk = use_dynamic_left_chunk + activation = ACTIVATION_CLASSES[activation_type]() + + # self-attention module definition + encoder_selfattn_layer_args = ( + attention_heads, + output_size, + attention_dropout_rate, + key_bias, + ) + # feed-forward module definition + positionwise_layer_args = ( + output_size, + linear_units, + dropout_rate, + activation, + ) + # convolution module definition + convolution_layer_args = (output_size, cnn_module_kernel, activation, + cnn_module_norm, causal) + + self.encoders = torch.nn.ModuleList([ + ConformerEncoderLayer( + output_size, + RelPositionMultiHeadedAttention( + *encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations), + PositionwiseFeedForward( + *positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None, + ConvolutionModule( + *convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None, + dropout_rate, + normalize_before, dtype=dtype, device=device, operations=operations + ) for _ in range(num_blocks) + ]) + + def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: + for layer in self.encoders: + xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) + return xs + + def forward( + self, + xs: torch.Tensor, + pad_mask: torch.Tensor, + decoding_chunk_size: int = 0, + num_decoding_left_chunks: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Embed positions in tensor. + + Args: + xs: padded input tensor (B, T, D) + xs_lens: input length (B) + decoding_chunk_size: decoding chunk size for dynamic chunk + 0: default for training, use random dynamic chunk. + <0: for decoding, use full chunk. + >0: for decoding, use fixed chunk size as set. + num_decoding_left_chunks: number of left chunks, this is for decoding, + the chunk size is decoding_chunk_size. + >=0: use num_decoding_left_chunks + <0: use all left chunks + Returns: + encoder output tensor xs, and subsampled masks + xs: padded output tensor (B, T' ~= T/subsample_rate, D) + masks: torch.Tensor batch padding mask after subsample + (B, 1, T' ~= T/subsample_rate) + NOTE(xcsong): + We pass the `__call__` method of the modules instead of `forward` to the + checkpointing API because `__call__` attaches all the hooks of the module. + https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2 + """ + masks = None + if pad_mask is not None: + masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T) + xs, pos_emb = self.embed(xs) + mask_pad = masks # (B, 1, T/subsample_rate) + chunk_masks = add_optional_chunk_mask(xs, masks, + self.use_dynamic_chunk, + self.use_dynamic_left_chunk, + decoding_chunk_size, + self.static_chunk_size, + num_decoding_left_chunks) + + xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad) + if self.normalize_before: + xs = self.after_norm(xs) + # Here we assume the mask is not changed in encoder layers, so just + # return the masks before encoder layers, and the masks will be used + # for cross attention with decoder later + return xs, masks + diff --git a/comfy/ldm/ace/model.py b/comfy/ldm/ace/model.py new file mode 100644 index 000000000..399329853 --- /dev/null +++ b/comfy/ldm/ace/model.py @@ -0,0 +1,411 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/ace_step_transformer.py + +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Optional, List, Union + +import torch +from torch import nn + +import comfy.model_management +import comfy.patcher_extension + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +from .attention import LinearTransformerBlock, t2i_modulate +from .lyric_encoder import ConformerEncoder as LyricEncoder + + +def cross_norm(hidden_states, controlnet_input): + # input N x T x c + mean_hidden_states, std_hidden_states = hidden_states.mean(dim=(1,2), keepdim=True), hidden_states.std(dim=(1,2), keepdim=True) + mean_controlnet_input, std_controlnet_input = controlnet_input.mean(dim=(1,2), keepdim=True), controlnet_input.std(dim=(1,2), keepdim=True) + controlnet_input = (controlnet_input - mean_controlnet_input) * (std_hidden_states / (std_controlnet_input + 1e-12)) + mean_hidden_states + return controlnet_input + + +# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2 +class Qwen2RotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, dtype=None, device=None): + super().__init__() + + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32 + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + + freqs = torch.outer(t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:seq_len].to(dtype=x.dtype), + self.sin_cached[:seq_len].to(dtype=x.dtype), + ) + + +class T2IFinalLayer(nn.Module): + """ + The final layer of Sana. + """ + + def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True, dtype=dtype, device=device) + self.scale_shift_table = nn.Parameter(torch.empty(2, hidden_size, dtype=dtype, device=device)) + self.out_channels = out_channels + self.patch_size = patch_size + + def unpatchfy( + self, + hidden_states: torch.Tensor, + width: int, + ): + # 4 unpatchify + new_height, new_width = 1, hidden_states.size(1) + hidden_states = hidden_states.reshape( + shape=(hidden_states.shape[0], new_height, new_width, self.patch_size[0], self.patch_size[1], self.out_channels) + ).contiguous() + hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) + output = hidden_states.reshape( + shape=(hidden_states.shape[0], self.out_channels, new_height * self.patch_size[0], new_width * self.patch_size[1]) + ).contiguous() + if width > new_width: + output = torch.nn.functional.pad(output, (0, width - new_width, 0, 0), 'constant', 0) + elif width < new_width: + output = output[:, :, :, :width] + return output + + def forward(self, x, t, output_length): + shift, scale = (comfy.model_management.cast_to(self.scale_shift_table[None], device=t.device, dtype=t.dtype) + t[:, None]).chunk(2, dim=1) + x = t2i_modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + # unpatchify + output = self.unpatchfy(x, output_length) + return output + + +class PatchEmbed(nn.Module): + """2D Image to Patch Embedding""" + + def __init__( + self, + height=16, + width=4096, + patch_size=(16, 1), + in_channels=8, + embed_dim=1152, + bias=True, + dtype=None, device=None, operations=None + ): + super().__init__() + patch_size_h, patch_size_w = patch_size + self.early_conv_layers = nn.Sequential( + operations.Conv2d(in_channels, in_channels*256, kernel_size=patch_size, stride=patch_size, padding=0, bias=bias, dtype=dtype, device=device), + operations.GroupNorm(num_groups=32, num_channels=in_channels*256, eps=1e-6, affine=True, dtype=dtype, device=device), + operations.Conv2d(in_channels*256, embed_dim, kernel_size=1, stride=1, padding=0, bias=bias, dtype=dtype, device=device) + ) + self.patch_size = patch_size + self.height, self.width = height // patch_size_h, width // patch_size_w + self.base_size = self.width + + def forward(self, latent): + # early convolutions, N x C x H x W -> N x 256 * sqrt(patch_size) x H/patch_size x W/patch_size + latent = self.early_conv_layers(latent) + latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC + return latent + + +class ACEStepTransformer2DModel(nn.Module): + # _supports_gradient_checkpointing = True + + def __init__( + self, + in_channels: Optional[int] = 8, + num_layers: int = 28, + inner_dim: int = 1536, + attention_head_dim: int = 64, + num_attention_heads: int = 24, + mlp_ratio: float = 4.0, + out_channels: int = 8, + max_position: int = 32768, + rope_theta: float = 1000000.0, + speaker_embedding_dim: int = 512, + text_embedding_dim: int = 768, + ssl_encoder_depths: List[int] = [9, 9], + ssl_names: List[str] = ["mert", "m-hubert"], + ssl_latent_dims: List[int] = [1024, 768], + lyric_encoder_vocab_size: int = 6681, + lyric_hidden_size: int = 1024, + patch_size: List[int] = [16, 1], + max_height: int = 16, + max_width: int = 4096, + audio_model=None, + dtype=None, device=None, operations=None + + ): + super().__init__() + + self.dtype = dtype + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + self.inner_dim = inner_dim + self.out_channels = out_channels + self.max_position = max_position + self.patch_size = patch_size + + self.rope_theta = rope_theta + + self.rotary_emb = Qwen2RotaryEmbedding( + dim=self.attention_head_dim, + max_position_embeddings=self.max_position, + base=self.rope_theta, + dtype=dtype, + device=device, + ) + + # 2. Define input layers + self.in_channels = in_channels + + self.num_layers = num_layers + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + LinearTransformerBlock( + dim=self.inner_dim, + num_attention_heads=self.num_attention_heads, + attention_head_dim=attention_head_dim, + mlp_ratio=mlp_ratio, + add_cross_attention=True, + add_cross_attention_dim=self.inner_dim, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(self.num_layers) + ] + ) + + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=self.inner_dim, dtype=dtype, device=device, operations=operations) + self.t_block = nn.Sequential(nn.SiLU(), operations.Linear(self.inner_dim, 6 * self.inner_dim, bias=True, dtype=dtype, device=device)) + + # speaker + self.speaker_embedder = operations.Linear(speaker_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # genre + self.genre_embedder = operations.Linear(text_embedding_dim, self.inner_dim, dtype=dtype, device=device) + + # lyric + self.lyric_embs = operations.Embedding(lyric_encoder_vocab_size, lyric_hidden_size, dtype=dtype, device=device) + self.lyric_encoder = LyricEncoder(input_size=lyric_hidden_size, static_chunk_size=0, dtype=dtype, device=device, operations=operations) + self.lyric_proj = operations.Linear(lyric_hidden_size, self.inner_dim, dtype=dtype, device=device) + + projector_dim = 2 * self.inner_dim + + self.projectors = nn.ModuleList([ + nn.Sequential( + operations.Linear(self.inner_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, projector_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(projector_dim, ssl_dim, dtype=dtype, device=device), + ) for ssl_dim in ssl_latent_dims + ]) + + self.proj_in = PatchEmbed( + height=max_height, + width=max_width, + patch_size=patch_size, + embed_dim=self.inner_dim, + bias=True, + dtype=dtype, + device=device, + operations=operations, + ) + + self.final_layer = T2IFinalLayer(self.inner_dim, patch_size=patch_size, out_channels=out_channels, dtype=dtype, device=device, operations=operations) + + def forward_lyric_encoder( + self, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + out_dtype=None, + ): + # N x T x D + lyric_embs = self.lyric_embs(lyric_token_idx, out_dtype=out_dtype) + prompt_prenet_out, _mask = self.lyric_encoder(lyric_embs, lyric_mask, decoding_chunk_size=1, num_decoding_left_chunks=-1) + prompt_prenet_out = self.lyric_proj(prompt_prenet_out) + return prompt_prenet_out + + def encode( + self, + encoder_text_hidden_states: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + lyrics_strength=1.0, + ): + + bs = encoder_text_hidden_states.shape[0] + device = encoder_text_hidden_states.device + + # speaker embedding + encoder_spk_hidden_states = self.speaker_embedder(speaker_embeds).unsqueeze(1) + + # genre embedding + encoder_text_hidden_states = self.genre_embedder(encoder_text_hidden_states) + + # lyric + encoder_lyric_hidden_states = self.forward_lyric_encoder( + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + out_dtype=encoder_text_hidden_states.dtype, + ) + + encoder_lyric_hidden_states *= lyrics_strength + + encoder_hidden_states = torch.cat([encoder_spk_hidden_states, encoder_text_hidden_states, encoder_lyric_hidden_states], dim=1) + + encoder_hidden_mask = None + if text_attention_mask is not None: + speaker_mask = torch.ones(bs, 1, device=device) + encoder_hidden_mask = torch.cat([speaker_mask, text_attention_mask, lyric_mask], dim=1) + + return encoder_hidden_states, encoder_hidden_mask + + def decode( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + encoder_hidden_states: torch.Tensor, + encoder_hidden_mask: torch.Tensor, + timestep: Optional[torch.Tensor], + output_length: int = 0, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + transformer_options={}, + ): + embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype)) + temb = self.t_block(embedded_timestep) + + hidden_states = self.proj_in(hidden_states) + + # controlnet logic + if block_controlnet_hidden_states is not None: + control_condi = cross_norm(hidden_states, block_controlnet_hidden_states) + hidden_states = hidden_states + control_condi * controlnet_scale + + # inner_hidden_states = [] + + rotary_freqs_cis = self.rotary_emb(hidden_states, seq_len=hidden_states.shape[1]) + encoder_rotary_freqs_cis = self.rotary_emb(encoder_hidden_states, seq_len=encoder_hidden_states.shape[1]) + + for index_block, block in enumerate(self.transformer_blocks): + hidden_states = block( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_hidden_mask, + rotary_freqs_cis=rotary_freqs_cis, + rotary_freqs_cis_cross=encoder_rotary_freqs_cis, + temb=temb, + transformer_options=transformer_options, + ) + + output = self.final_layer(hidden_states, embedded_timestep, output_length) + return output + + def forward(self, + x, + timestep, + attention_mask=None, + context: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + lyrics_strength=1.0, + **kwargs + ): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) + ).execute(x, timestep, attention_mask, context, text_attention_mask, speaker_embeds, lyric_token_idx, lyric_mask, block_controlnet_hidden_states, + controlnet_scale, lyrics_strength, **kwargs) + + def _forward( + self, + x, + timestep, + attention_mask=None, + context: Optional[torch.Tensor] = None, + text_attention_mask: Optional[torch.LongTensor] = None, + speaker_embeds: Optional[torch.FloatTensor] = None, + lyric_token_idx: Optional[torch.LongTensor] = None, + lyric_mask: Optional[torch.LongTensor] = None, + block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None, + controlnet_scale: Union[float, torch.Tensor] = 1.0, + lyrics_strength=1.0, + **kwargs + ): + hidden_states = x + encoder_text_hidden_states = context + encoder_hidden_states, encoder_hidden_mask = self.encode( + encoder_text_hidden_states=encoder_text_hidden_states, + text_attention_mask=text_attention_mask, + speaker_embeds=speaker_embeds, + lyric_token_idx=lyric_token_idx, + lyric_mask=lyric_mask, + lyrics_strength=lyrics_strength, + ) + + output_length = hidden_states.shape[-1] + + transformer_options = kwargs.get("transformer_options", {}) + output = self.decode( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_mask=encoder_hidden_mask, + timestep=timestep, + output_length=output_length, + block_controlnet_hidden_states=block_controlnet_hidden_states, + controlnet_scale=controlnet_scale, + transformer_options=transformer_options, + ) + + return output diff --git a/comfy/ldm/ace/vae/autoencoder_dc.py b/comfy/ldm/ace/vae/autoencoder_dc.py new file mode 100644 index 000000000..e7b1d4801 --- /dev/null +++ b/comfy/ldm/ace/vae/autoencoder_dc.py @@ -0,0 +1,644 @@ +# Rewritten from diffusers +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Tuple, Union + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +class RMSNorm(ops.RMSNorm): + def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False): + super().__init__(dim, eps=eps, elementwise_affine=elementwise_affine) + if elementwise_affine: + self.bias = nn.Parameter(torch.empty(dim)) if bias else None + + def forward(self, x): + x = super().forward(x) + if self.elementwise_affine: + if self.bias is not None: + x = x + comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device) + return x + + +def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5): + if norm_type == "batch_norm": + return nn.BatchNorm2d(num_features) + elif norm_type == "group_norm": + return ops.GroupNorm(num_groups, num_features) + elif norm_type == "layer_norm": + return ops.LayerNorm(num_features) + elif norm_type == "rms_norm": + return RMSNorm(num_features, eps=eps, elementwise_affine=True, bias=True) + else: + raise ValueError(f"Unknown normalization type: {norm_type}") + + +def get_activation(activation_type): + if activation_type == "relu": + return nn.ReLU() + elif activation_type == "relu6": + return nn.ReLU6() + elif activation_type == "silu": + return nn.SiLU() + elif activation_type == "leaky_relu": + return nn.LeakyReLU(0.2) + else: + raise ValueError(f"Unknown activation type: {activation_type}") + + +class ResBlock(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + norm_type: str = "batch_norm", + act_fn: str = "relu6", + ) -> None: + super().__init__() + + self.norm_type = norm_type + self.nonlinearity = get_activation(act_fn) if act_fn is not None else nn.Identity() + self.conv1 = ops.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = ops.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False) + self.norm = get_normalization(norm_type, out_channels) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.conv1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm(hidden_states) + + return hidden_states + residual + +class SanaMultiscaleAttentionProjection(nn.Module): + def __init__( + self, + in_channels: int, + num_attention_heads: int, + kernel_size: int, + ) -> None: + super().__init__() + + channels = 3 * in_channels + self.proj_in = ops.Conv2d( + channels, + channels, + kernel_size, + padding=kernel_size // 2, + groups=channels, + bias=False, + ) + self.proj_out = ops.Conv2d(channels, channels, 1, 1, 0, groups=3 * num_attention_heads, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.proj_in(hidden_states) + hidden_states = self.proj_out(hidden_states) + return hidden_states + +class SanaMultiscaleLinearAttention(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_attention_heads: int = None, + attention_head_dim: int = 8, + mult: float = 1.0, + norm_type: str = "batch_norm", + kernel_sizes: tuple = (5,), + eps: float = 1e-15, + residual_connection: bool = False, + ): + super().__init__() + + self.eps = eps + self.attention_head_dim = attention_head_dim + self.norm_type = norm_type + self.residual_connection = residual_connection + + num_attention_heads = ( + int(in_channels // attention_head_dim * mult) + if num_attention_heads is None + else num_attention_heads + ) + inner_dim = num_attention_heads * attention_head_dim + + self.to_q = ops.Linear(in_channels, inner_dim, bias=False) + self.to_k = ops.Linear(in_channels, inner_dim, bias=False) + self.to_v = ops.Linear(in_channels, inner_dim, bias=False) + + self.to_qkv_multiscale = nn.ModuleList() + for kernel_size in kernel_sizes: + self.to_qkv_multiscale.append( + SanaMultiscaleAttentionProjection(inner_dim, num_attention_heads, kernel_size) + ) + + self.nonlinearity = nn.ReLU() + self.to_out = ops.Linear(inner_dim * (1 + len(kernel_sizes)), out_channels, bias=False) + self.norm_out = get_normalization(norm_type, out_channels) + + def apply_linear_attention(self, query, key, value): + value = F.pad(value, (0, 0, 0, 1), mode="constant", value=1) + scores = torch.matmul(value, key.transpose(-1, -2)) + hidden_states = torch.matmul(scores, query) + + hidden_states = hidden_states.to(dtype=torch.float32) + hidden_states = hidden_states[:, :, :-1] / (hidden_states[:, :, -1:] + self.eps) + return hidden_states + + def apply_quadratic_attention(self, query, key, value): + scores = torch.matmul(key.transpose(-1, -2), query) + scores = scores.to(dtype=torch.float32) + scores = scores / (torch.sum(scores, dim=2, keepdim=True) + self.eps) + hidden_states = torch.matmul(value, scores.to(value.dtype)) + return hidden_states + + def forward(self, hidden_states): + height, width = hidden_states.shape[-2:] + if height * width > self.attention_head_dim: + use_linear_attention = True + else: + use_linear_attention = False + + residual = hidden_states + + batch_size, _, height, width = list(hidden_states.size()) + original_dtype = hidden_states.dtype + + hidden_states = hidden_states.movedim(1, -1) + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + hidden_states = torch.cat([query, key, value], dim=3) + hidden_states = hidden_states.movedim(-1, 1) + + multi_scale_qkv = [hidden_states] + for block in self.to_qkv_multiscale: + multi_scale_qkv.append(block(hidden_states)) + + hidden_states = torch.cat(multi_scale_qkv, dim=1) + + if use_linear_attention: + # for linear attention upcast hidden_states to float32 + hidden_states = hidden_states.to(dtype=torch.float32) + + hidden_states = hidden_states.reshape(batch_size, -1, 3 * self.attention_head_dim, height * width) + + query, key, value = hidden_states.chunk(3, dim=2) + query = self.nonlinearity(query) + key = self.nonlinearity(key) + + if use_linear_attention: + hidden_states = self.apply_linear_attention(query, key, value) + hidden_states = hidden_states.to(dtype=original_dtype) + else: + hidden_states = self.apply_quadratic_attention(query, key, value) + + hidden_states = torch.reshape(hidden_states, (batch_size, -1, height, width)) + hidden_states = self.to_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.norm_type == "rms_norm": + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + else: + hidden_states = self.norm_out(hidden_states) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +class EfficientViTBlock(nn.Module): + def __init__( + self, + in_channels: int, + mult: float = 1.0, + attention_head_dim: int = 32, + qkv_multiscales: tuple = (5,), + norm_type: str = "batch_norm", + ) -> None: + super().__init__() + + self.attn = SanaMultiscaleLinearAttention( + in_channels=in_channels, + out_channels=in_channels, + mult=mult, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + kernel_sizes=qkv_multiscales, + residual_connection=True, + ) + + self.conv_out = GLUMBConv( + in_channels=in_channels, + out_channels=in_channels, + norm_type="rms_norm", + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.attn(x) + x = self.conv_out(x) + return x + + +class GLUMBConv(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + expand_ratio: float = 4, + norm_type: str = None, + residual_connection: bool = True, + ) -> None: + super().__init__() + + hidden_channels = int(expand_ratio * in_channels) + self.norm_type = norm_type + self.residual_connection = residual_connection + + self.nonlinearity = nn.SiLU() + self.conv_inverted = ops.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) + self.conv_depth = ops.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) + self.conv_point = ops.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) + + self.norm = None + if norm_type == "rms_norm": + self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.residual_connection: + residual = hidden_states + + hidden_states = self.conv_inverted(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv_depth(hidden_states) + hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) + hidden_states = hidden_states * self.nonlinearity(gate) + + hidden_states = self.conv_point(hidden_states) + + if self.norm_type == "rms_norm": + # move channel to the last dimension so we apply RMSnorm across channel dimension + hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) + + if self.residual_connection: + hidden_states = hidden_states + residual + + return hidden_states + + +def get_block( + block_type: str, + in_channels: int, + out_channels: int, + attention_head_dim: int, + norm_type: str, + act_fn: str, + qkv_mutliscales: tuple = (), +): + if block_type == "ResBlock": + block = ResBlock(in_channels, out_channels, norm_type, act_fn) + elif block_type == "EfficientViTBlock": + block = EfficientViTBlock( + in_channels, + attention_head_dim=attention_head_dim, + norm_type=norm_type, + qkv_multiscales=qkv_mutliscales + ) + else: + raise ValueError(f"Block with {block_type=} is not supported.") + + return block + + +class DCDownBlock2d(nn.Module): + def __init__(self, in_channels: int, out_channels: int, downsample: bool = False, shortcut: bool = True) -> None: + super().__init__() + + self.downsample = downsample + self.factor = 2 + self.stride = 1 if downsample else 2 + self.group_size = in_channels * self.factor**2 // out_channels + self.shortcut = shortcut + + out_ratio = self.factor**2 + if downsample: + assert out_channels % out_ratio == 0 + out_channels = out_channels // out_ratio + + self.conv = ops.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=self.stride, + padding=1, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + x = self.conv(hidden_states) + if self.downsample: + x = F.pixel_unshuffle(x, self.factor) + + if self.shortcut: + y = F.pixel_unshuffle(hidden_states, self.factor) + y = y.unflatten(1, (-1, self.group_size)) + y = y.mean(dim=2) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class DCUpBlock2d(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + interpolate: bool = False, + shortcut: bool = True, + interpolation_mode: str = "nearest", + ) -> None: + super().__init__() + + self.interpolate = interpolate + self.interpolation_mode = interpolation_mode + self.shortcut = shortcut + self.factor = 2 + self.repeats = out_channels * self.factor**2 // in_channels + + out_ratio = self.factor**2 + if not interpolate: + out_channels = out_channels * out_ratio + + self.conv = ops.Conv2d(in_channels, out_channels, 3, 1, 1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.interpolate: + x = F.interpolate(hidden_states, scale_factor=self.factor, mode=self.interpolation_mode) + x = self.conv(x) + else: + x = self.conv(hidden_states) + x = F.pixel_shuffle(x, self.factor) + + if self.shortcut: + y = hidden_states.repeat_interleave(self.repeats, dim=1, output_size=hidden_states.shape[1] * self.repeats) + y = F.pixel_shuffle(y, self.factor) + hidden_states = x + y + else: + hidden_states = x + + return hidden_states + + +class Encoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + downsample_block_type: str = "pixel_unshuffle", + out_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + + if layers_per_block[0] > 0: + self.conv_in = ops.Conv2d( + in_channels, + block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + kernel_size=3, + stride=1, + padding=1, + ) + else: + self.conv_in = DCDownBlock2d( + in_channels=in_channels, + out_channels=block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=False, + ) + + down_blocks = [] + for i, (out_channel, num_layers) in enumerate(zip(block_out_channels, layers_per_block)): + down_block_list = [] + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type="rms_norm", + act_fn="silu", + qkv_mutliscales=qkv_multiscales[i], + ) + down_block_list.append(block) + + if i < num_blocks - 1 and num_layers > 0: + downsample_block = DCDownBlock2d( + in_channels=out_channel, + out_channels=block_out_channels[i + 1], + downsample=downsample_block_type == "pixel_unshuffle", + shortcut=True, + ) + down_block_list.append(downsample_block) + + down_blocks.append(nn.Sequential(*down_block_list)) + + self.down_blocks = nn.ModuleList(down_blocks) + + self.conv_out = ops.Conv2d(block_out_channels[-1], latent_channels, 3, 1, 1) + + self.out_shortcut = out_shortcut + if out_shortcut: + self.out_shortcut_average_group_size = block_out_channels[-1] // latent_channels + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.conv_in(hidden_states) + for down_block in self.down_blocks: + hidden_states = down_block(hidden_states) + + if self.out_shortcut: + x = hidden_states.unflatten(1, (-1, self.out_shortcut_average_group_size)) + x = x.mean(dim=2) + hidden_states = self.conv_out(hidden_states) + x + else: + hidden_states = self.conv_out(hidden_states) + + return hidden_states + + +class Decoder(nn.Module): + def __init__( + self, + in_channels: int, + latent_channels: int, + attention_head_dim: int = 32, + block_type: str or tuple = "ResBlock", + block_out_channels: tuple = (128, 256, 512, 512, 1024, 1024), + layers_per_block: tuple = (2, 2, 2, 2, 2, 2), + qkv_multiscales: tuple = ((), (), (), (5,), (5,), (5,)), + norm_type: str or tuple = "rms_norm", + act_fn: str or tuple = "silu", + upsample_block_type: str = "pixel_shuffle", + in_shortcut: bool = True, + ): + super().__init__() + + num_blocks = len(block_out_channels) + + if isinstance(block_type, str): + block_type = (block_type,) * num_blocks + if isinstance(norm_type, str): + norm_type = (norm_type,) * num_blocks + if isinstance(act_fn, str): + act_fn = (act_fn,) * num_blocks + + self.conv_in = ops.Conv2d(latent_channels, block_out_channels[-1], 3, 1, 1) + + self.in_shortcut = in_shortcut + if in_shortcut: + self.in_shortcut_repeats = block_out_channels[-1] // latent_channels + + up_blocks = [] + for i, (out_channel, num_layers) in reversed(list(enumerate(zip(block_out_channels, layers_per_block)))): + up_block_list = [] + + if i < num_blocks - 1 and num_layers > 0: + upsample_block = DCUpBlock2d( + block_out_channels[i + 1], + out_channel, + interpolate=upsample_block_type == "interpolate", + shortcut=True, + ) + up_block_list.append(upsample_block) + + for _ in range(num_layers): + block = get_block( + block_type[i], + out_channel, + out_channel, + attention_head_dim=attention_head_dim, + norm_type=norm_type[i], + act_fn=act_fn[i], + qkv_mutliscales=qkv_multiscales[i], + ) + up_block_list.append(block) + + up_blocks.insert(0, nn.Sequential(*up_block_list)) + + self.up_blocks = nn.ModuleList(up_blocks) + + channels = block_out_channels[0] if layers_per_block[0] > 0 else block_out_channels[1] + + self.norm_out = RMSNorm(channels, 1e-5, elementwise_affine=True, bias=True) + self.conv_act = nn.ReLU() + self.conv_out = None + + if layers_per_block[0] > 0: + self.conv_out = ops.Conv2d(channels, in_channels, 3, 1, 1) + else: + self.conv_out = DCUpBlock2d( + channels, in_channels, interpolate=upsample_block_type == "interpolate", shortcut=False + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + if self.in_shortcut: + x = hidden_states.repeat_interleave( + self.in_shortcut_repeats, dim=1, output_size=hidden_states.shape[1] * self.in_shortcut_repeats + ) + hidden_states = self.conv_in(hidden_states) + x + else: + hidden_states = self.conv_in(hidden_states) + + for up_block in reversed(self.up_blocks): + hidden_states = up_block(hidden_states) + + hidden_states = self.norm_out(hidden_states.movedim(1, -1)).movedim(-1, 1) + hidden_states = self.conv_act(hidden_states) + hidden_states = self.conv_out(hidden_states) + return hidden_states + + +class AutoencoderDC(nn.Module): + def __init__( + self, + in_channels: int = 2, + latent_channels: int = 8, + attention_head_dim: int = 32, + encoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + decoder_block_types: Union[str, Tuple[str]] = ["ResBlock", "ResBlock", "ResBlock", "EfficientViTBlock"], + encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024), + encoder_layers_per_block: Tuple[int] = (2, 2, 3, 3), + decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3), + encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (5,), (5,)), + upsample_block_type: str = "interpolate", + downsample_block_type: str = "Conv", + decoder_norm_types: Union[str, Tuple[str]] = "rms_norm", + decoder_act_fns: Union[str, Tuple[str]] = "silu", + scaling_factor: float = 0.41407, + ) -> None: + super().__init__() + + self.encoder = Encoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=encoder_block_types, + block_out_channels=encoder_block_out_channels, + layers_per_block=encoder_layers_per_block, + qkv_multiscales=encoder_qkv_multiscales, + downsample_block_type=downsample_block_type, + ) + + self.decoder = Decoder( + in_channels=in_channels, + latent_channels=latent_channels, + attention_head_dim=attention_head_dim, + block_type=decoder_block_types, + block_out_channels=decoder_block_out_channels, + layers_per_block=decoder_layers_per_block, + qkv_multiscales=decoder_qkv_multiscales, + norm_type=decoder_norm_types, + act_fn=decoder_act_fns, + upsample_block_type=upsample_block_type, + ) + + self.scaling_factor = scaling_factor + self.spatial_compression_ratio = 2 ** (len(encoder_block_out_channels) - 1) + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Internal encoding function.""" + encoded = self.encoder(x) + return encoded * self.scaling_factor + + def decode(self, z: torch.Tensor) -> torch.Tensor: + # Scale the latents back + z = z / self.scaling_factor + decoded = self.decoder(z) + return decoded + + def forward(self, x: torch.Tensor) -> torch.Tensor: + z = self.encode(x) + return self.decode(z) + diff --git a/comfy/ldm/ace/vae/music_dcae_pipeline.py b/comfy/ldm/ace/vae/music_dcae_pipeline.py new file mode 100644 index 000000000..3c8830c17 --- /dev/null +++ b/comfy/ldm/ace/vae/music_dcae_pipeline.py @@ -0,0 +1,98 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py +import torch +from .autoencoder_dc import AutoencoderDC +import logging +try: + import torchaudio +except: + logging.warning("torchaudio missing, ACE model will be broken") + +import torchvision.transforms as transforms +from .music_vocoder import ADaMoSHiFiGANV1 + + +class MusicDCAE(torch.nn.Module): + def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}): + super(MusicDCAE, self).__init__() + + self.dcae = AutoencoderDC(**dcae_config) + self.vocoder = ADaMoSHiFiGANV1(**vocoder_config) + + if source_sample_rate is None: + self.source_sample_rate = 48000 + else: + self.source_sample_rate = source_sample_rate + + self.transform = transforms.Compose([ + transforms.Normalize(0.5, 0.5), + ]) + self.min_mel_value = -11.0 + self.max_mel_value = 3.0 + self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000))) + self.mel_chunk_size = 1024 + self.time_dimention_multiple = 8 + self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple + self.scale_factor = 0.1786 + self.shift_factor = -1.9091 + + def forward_mel(self, audios): + mels = [] + for i in range(len(audios)): + image = self.vocoder.mel_transform(audios[i]) + mels.append(image) + mels = torch.stack(mels) + return mels + + @torch.no_grad() + def encode(self, audios, audio_lengths=None, sr=None): + if audio_lengths is None: + audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0]) + audio_lengths = audio_lengths.to(audios.device) + + if sr is None: + sr = self.source_sample_rate + + if sr != 44100: + audios = torchaudio.functional.resample(audios, sr, 44100) + + max_audio_len = audios.shape[-1] + if max_audio_len % (8 * 512) != 0: + audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512))) + + mels = self.forward_mel(audios) + mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value) + mels = self.transform(mels) + latents = [] + for mel in mels: + latent = self.dcae.encoder(mel.unsqueeze(0)) + latents.append(latent) + latents = torch.cat(latents, dim=0) + latents = (latents - self.shift_factor) * self.scale_factor + return latents + + @torch.no_grad() + def decode(self, latents, audio_lengths=None, sr=None): + latents = latents / self.scale_factor + self.shift_factor + + pred_wavs = [] + + for latent in latents: + mels = self.dcae.decoder(latent.unsqueeze(0)) + mels = mels * 0.5 + 0.5 + mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value + wav = self.vocoder.decode(mels[0]).squeeze(1) + + if sr is not None: + wav = torchaudio.functional.resample(wav, 44100, sr) + else: + sr = 44100 + pred_wavs.append(wav) + + if audio_lengths is not None: + pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] + return torch.stack(pred_wavs) + + def forward(self, audios, audio_lengths=None, sr=None): + latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) + sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr) + return sr, pred_wavs, latents, latent_lengths diff --git a/comfy/ldm/ace/vae/music_log_mel.py b/comfy/ldm/ace/vae/music_log_mel.py new file mode 100755 index 000000000..9c584eb7f --- /dev/null +++ b/comfy/ldm/ace/vae/music_log_mel.py @@ -0,0 +1,113 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_log_mel.py +import torch +import torch.nn as nn +from torch import Tensor +import logging +try: + from torchaudio.transforms import MelScale +except: + logging.warning("torchaudio missing, ACE model will be broken") + +import comfy.model_management + +class LinearSpectrogram(nn.Module): + def __init__( + self, + n_fft=2048, + win_length=2048, + hop_length=512, + center=False, + mode="pow2_sqrt", + ): + super().__init__() + + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.mode = mode + + self.register_buffer("window", torch.hann_window(win_length)) + + def forward(self, y: Tensor) -> Tensor: + if y.ndim == 3: + y = y.squeeze(1) + + y = torch.nn.functional.pad( + y.unsqueeze(1), + ( + (self.win_length - self.hop_length) // 2, + (self.win_length - self.hop_length + 1) // 2, + ), + mode="reflect", + ).squeeze(1) + dtype = y.dtype + spec = torch.stft( + y.float(), + self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=comfy.model_management.cast_to(self.window, dtype=torch.float32, device=y.device), + center=self.center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + + if self.mode == "pow2_sqrt": + spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) + spec = spec.to(dtype) + return spec + + +class LogMelSpectrogram(nn.Module): + def __init__( + self, + sample_rate=44100, + n_fft=2048, + win_length=2048, + hop_length=512, + n_mels=128, + center=False, + f_min=0.0, + f_max=None, + ): + super().__init__() + + self.sample_rate = sample_rate + self.n_fft = n_fft + self.win_length = win_length + self.hop_length = hop_length + self.center = center + self.n_mels = n_mels + self.f_min = f_min + self.f_max = f_max or sample_rate // 2 + + self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center) + self.mel_scale = MelScale( + self.n_mels, + self.sample_rate, + self.f_min, + self.f_max, + self.n_fft // 2 + 1, + "slaney", + "slaney", + ) + + def compress(self, x: Tensor) -> Tensor: + return torch.log(torch.clamp(x, min=1e-5)) + + def decompress(self, x: Tensor) -> Tensor: + return torch.exp(x) + + def forward(self, x: Tensor, return_linear: bool = False) -> Tensor: + linear = self.spectrogram(x) + x = self.mel_scale(linear) + x = self.compress(x) + # print(x.shape) + if return_linear: + return x, self.compress(linear) + + return x diff --git a/comfy/ldm/ace/vae/music_vocoder.py b/comfy/ldm/ace/vae/music_vocoder.py new file mode 100755 index 000000000..2f989fa86 --- /dev/null +++ b/comfy/ldm/ace/vae/music_vocoder.py @@ -0,0 +1,538 @@ +# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_vocoder.py +import torch +from torch import nn + +from functools import partial +from math import prod +from typing import Callable, Tuple, List + +import numpy as np +import torch.nn.functional as F +from torch.nn.utils.parametrize import remove_parametrizations as remove_weight_norm + +from .music_log_mel import LogMelSpectrogram + +import comfy.model_management +import comfy.ops +ops = comfy.ops.disable_weight_init + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ # noqa: E501 + + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" + + +class LayerNorm(nn.Module): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with + shape (batch_size, height, width, channels) while channels_first corresponds to inputs + with shape (batch_size, channels, height, width). + """ # noqa: E501 + + def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): + super().__init__() + self.weight = nn.Parameter(torch.ones(normalized_shape)) + self.bias = nn.Parameter(torch.zeros(normalized_shape)) + self.eps = eps + self.data_format = data_format + if self.data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError + self.normalized_shape = (normalized_shape,) + + def forward(self, x): + if self.data_format == "channels_last": + return F.layer_norm( + x, self.normalized_shape, comfy.model_management.cast_to(self.weight, dtype=x.dtype, device=x.device), comfy.model_management.cast_to(self.bias, dtype=x.dtype, device=x.device), self.eps + ) + elif self.data_format == "channels_first": + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = comfy.model_management.cast_to(self.weight[:, None], dtype=x.dtype, device=x.device) * x + comfy.model_management.cast_to(self.bias[:, None], dtype=x.dtype, device=x.device) + return x + + +class ConvNeXtBlock(nn.Module): + r"""ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. + kernel_size (int): Kernel size for depthwise conv. Default: 7. + dilation (int): Dilation for depthwise conv. Default: 1. + """ # noqa: E501 + + def __init__( + self, + dim: int, + drop_path: float = 0.0, + layer_scale_init_value: float = 1e-6, + mlp_ratio: float = 4.0, + kernel_size: int = 7, + dilation: int = 1, + ): + super().__init__() + + self.dwconv = ops.Conv1d( + dim, + dim, + kernel_size=kernel_size, + padding=int(dilation * (kernel_size - 1) / 2), + groups=dim, + ) # depthwise conv + self.norm = LayerNorm(dim, eps=1e-6) + self.pwconv1 = ops.Linear( + dim, int(mlp_ratio * dim) + ) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = ops.Linear(int(mlp_ratio * dim), dim) + self.gamma = ( + nn.Parameter(torch.empty((dim)), requires_grad=False) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = DropPath( + drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x, apply_residual: bool = True): + input = x + + x = self.dwconv(x) + x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) + x = self.norm(x) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + + if self.gamma is not None: + x = comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device) * x + + x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) + x = self.drop_path(x) + + if apply_residual: + x = input + x + + return x + + +class ParallelConvNeXtBlock(nn.Module): + def __init__(self, kernel_sizes: List[int], *args, **kwargs): + super().__init__() + self.blocks = nn.ModuleList( + [ + ConvNeXtBlock(kernel_size=kernel_size, *args, **kwargs) + for kernel_size in kernel_sizes + ] + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return torch.stack( + [block(x, apply_residual=False) for block in self.blocks] + [x], + dim=1, + ).sum(dim=1) + + +class ConvNeXtEncoder(nn.Module): + def __init__( + self, + input_channels=3, + depths=[3, 3, 9, 3], + dims=[96, 192, 384, 768], + drop_path_rate=0.0, + layer_scale_init_value=1e-6, + kernel_sizes: Tuple[int] = (7,), + ): + super().__init__() + assert len(depths) == len(dims) + + self.channel_layers = nn.ModuleList() + stem = nn.Sequential( + ops.Conv1d( + input_channels, + dims[0], + kernel_size=7, + padding=3, + padding_mode="replicate", + ), + LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), + ) + self.channel_layers.append(stem) + + for i in range(len(depths) - 1): + mid_layer = nn.Sequential( + LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), + ops.Conv1d(dims[i], dims[i + 1], kernel_size=1), + ) + self.channel_layers.append(mid_layer) + + block_fn = ( + partial(ConvNeXtBlock, kernel_size=kernel_sizes[0]) + if len(kernel_sizes) == 1 + else partial(ParallelConvNeXtBlock, kernel_sizes=kernel_sizes) + ) + + self.stages = nn.ModuleList() + drop_path_rates = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] + + cur = 0 + for i in range(len(depths)): + stage = nn.Sequential( + *[ + block_fn( + dim=dims[i], + drop_path=drop_path_rates[cur + j], + layer_scale_init_value=layer_scale_init_value, + ) + for j in range(depths[i]) + ] + ) + self.stages.append(stage) + cur += depths[i] + + self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") + + def forward( + self, + x: torch.Tensor, + ) -> torch.Tensor: + for channel_layer, stage in zip(self.channel_layers, self.stages): + x = channel_layer(x) + x = stage(x) + + return self.norm(x) + + +def get_padding(kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + +class ResBlock1(torch.nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.silu(x) + xt = c1(xt) + xt = F.silu(xt) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for conv in self.convs1: + remove_weight_norm(conv) + for conv in self.convs2: + remove_weight_norm(conv) + + +class HiFiGANGenerator(nn.Module): + def __init__( + self, + *, + hop_length: int = 512, + upsample_rates: Tuple[int] = (8, 8, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (16, 16, 8, 2, 2), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 128, + upsample_initial_channel: int = 512, + use_template: bool = True, + pre_conv_kernel_size: int = 7, + post_conv_kernel_size: int = 7, + post_activation: Callable = partial(nn.SiLU, inplace=True), + ): + super().__init__() + + assert ( + prod(upsample_rates) == hop_length + ), f"hop_length must be {prod(upsample_rates)}" + + self.conv_pre = torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + num_mels, + upsample_initial_channel, + pre_conv_kernel_size, + 1, + padding=get_padding(pre_conv_kernel_size), + ) + ) + + self.num_upsamples = len(upsample_rates) + self.num_kernels = len(resblock_kernel_sizes) + + self.noise_convs = nn.ModuleList() + self.use_template = use_template + self.ups = nn.ModuleList() + + for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): + c_cur = upsample_initial_channel // (2 ** (i + 1)) + self.ups.append( + torch.nn.utils.parametrizations.weight_norm( + ops.ConvTranspose1d( + upsample_initial_channel // (2**i), + upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + if not use_template: + continue + + if i + 1 < len(upsample_rates): + stride_f0 = np.prod(upsample_rates[i + 1:]) + self.noise_convs.append( + ops.Conv1d( + 1, + c_cur, + kernel_size=stride_f0 * 2, + stride=stride_f0, + padding=stride_f0 // 2, + ) + ) + else: + self.noise_convs.append(ops.Conv1d(1, c_cur, kernel_size=1)) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = upsample_initial_channel // (2 ** (i + 1)) + for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes): + self.resblocks.append(ResBlock1(ch, k, d)) + + self.activation_post = post_activation() + self.conv_post = torch.nn.utils.parametrizations.weight_norm( + ops.Conv1d( + ch, + 1, + post_conv_kernel_size, + 1, + padding=get_padding(post_conv_kernel_size), + ) + ) + + def forward(self, x, template=None): + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + x = F.silu(x, inplace=True) + x = self.ups[i](x) + + if self.use_template: + x = x + self.noise_convs[i](template) + + xs = None + + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + + x = xs / self.num_kernels + + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + for up in self.ups: + remove_weight_norm(up) + for block in self.resblocks: + block.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class ADaMoSHiFiGANV1(nn.Module): + def __init__( + self, + input_channels: int = 128, + depths: List[int] = [3, 3, 9, 3], + dims: List[int] = [128, 256, 384, 512], + drop_path_rate: float = 0.0, + kernel_sizes: Tuple[int] = (7,), + upsample_rates: Tuple[int] = (4, 4, 2, 2, 2, 2, 2), + upsample_kernel_sizes: Tuple[int] = (8, 8, 4, 4, 4, 4, 4), + resblock_kernel_sizes: Tuple[int] = (3, 7, 11, 13), + resblock_dilation_sizes: Tuple[Tuple[int]] = ( + (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5)), + num_mels: int = 512, + upsample_initial_channel: int = 1024, + use_template: bool = False, + pre_conv_kernel_size: int = 13, + post_conv_kernel_size: int = 13, + sampling_rate: int = 44100, + n_fft: int = 2048, + win_length: int = 2048, + hop_length: int = 512, + f_min: int = 40, + f_max: int = 16000, + n_mels: int = 128, + ): + super().__init__() + + self.backbone = ConvNeXtEncoder( + input_channels=input_channels, + depths=depths, + dims=dims, + drop_path_rate=drop_path_rate, + kernel_sizes=kernel_sizes, + ) + + self.head = HiFiGANGenerator( + hop_length=hop_length, + upsample_rates=upsample_rates, + upsample_kernel_sizes=upsample_kernel_sizes, + resblock_kernel_sizes=resblock_kernel_sizes, + resblock_dilation_sizes=resblock_dilation_sizes, + num_mels=num_mels, + upsample_initial_channel=upsample_initial_channel, + use_template=use_template, + pre_conv_kernel_size=pre_conv_kernel_size, + post_conv_kernel_size=post_conv_kernel_size, + ) + self.sampling_rate = sampling_rate + self.mel_transform = LogMelSpectrogram( + sample_rate=sampling_rate, + n_fft=n_fft, + win_length=win_length, + hop_length=hop_length, + f_min=f_min, + f_max=f_max, + n_mels=n_mels, + ) + self.eval() + + @torch.no_grad() + def decode(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y + + @torch.no_grad() + def encode(self, x): + return self.mel_transform(x) + + def forward(self, mel): + y = self.backbone(mel) + y = self.head(y) + return y diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py index 9e7e7c876..78ed6ffa6 100644 --- a/comfy/ldm/audio/autoencoder.py +++ b/comfy/ldm/audio/autoencoder.py @@ -75,16 +75,10 @@ class SnakeBeta(nn.Module): return x def WNConv1d(*args, **kwargs): - try: - return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) - except: - return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older + return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs)) def WNConvTranspose1d(*args, **kwargs): - try: - return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) - except: - return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older + return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module: if activation == "elu": diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py index 179c5b67e..ca865189e 100644 --- a/comfy/ldm/audio/dit.py +++ b/comfy/ldm/audio/dit.py @@ -298,7 +298,8 @@ class Attention(nn.Module): mask = None, context_mask = None, rotary_pos_emb = None, - causal = None + causal = None, + transformer_options={}, ): h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None @@ -363,7 +364,7 @@ class Attention(nn.Module): heads_per_kv_head = h // kv_h k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v)) - out = optimized_attention(q, k, v, h, skip_reshape=True) + out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options) out = self.to_out(out) if mask is not None: @@ -488,7 +489,8 @@ class TransformerBlock(nn.Module): global_cond=None, mask = None, context_mask = None, - rotary_pos_emb = None + rotary_pos_emb = None, + transformer_options={} ): if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None: @@ -498,12 +500,12 @@ class TransformerBlock(nn.Module): residual = x x = self.pre_norm(x) x = x * (1 + scale_self) + shift_self - x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb) + x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) x = x * torch.sigmoid(1 - gate_self) x = x + residual if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) @@ -517,10 +519,10 @@ class TransformerBlock(nn.Module): x = x + residual else: - x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb) + x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options) if context is not None: - x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask) + x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options) if self.conformer is not None: x = x + self.conformer(x) @@ -606,7 +608,8 @@ class ContinuousTransformer(nn.Module): return_info = False, **kwargs ): - patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {}) + transformer_options = kwargs.get("transformer_options", {}) + patches_replace = transformer_options.get("patches_replace", {}) batch, seq, device = *x.shape[:2], x.device context = kwargs["context"] @@ -632,7 +635,7 @@ class ContinuousTransformer(nn.Module): # Attention layers if self.rotary_pos_emb is not None: - rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device) + rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device) else: rotary_pos_emb = None @@ -645,13 +648,13 @@ class ContinuousTransformer(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"]) + out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: - x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context) + x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options) # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs) if return_info: diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py index 1258ae11f..66d9613b6 100644 --- a/comfy/ldm/aura/mmdit.py +++ b/comfy/ldm/aura/mmdit.py @@ -9,6 +9,7 @@ import torch.nn.functional as F from comfy.ldm.modules.attention import optimized_attention import comfy.ops +import comfy.patcher_extension import comfy.ldm.common_dit def modulate(x, shift, scale): @@ -84,7 +85,7 @@ class SingleAttention(nn.Module): ) #@torch.compile() - def forward(self, c): + def forward(self, c, transformer_options={}): bsz, seqlen1, _ = c.shape @@ -94,7 +95,7 @@ class SingleAttention(nn.Module): v = v.view(bsz, seqlen1, self.n_heads, self.head_dim) q, k = self.q_norm1(q), self.k_norm1(k) - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c = self.w1o(output) return c @@ -143,7 +144,7 @@ class DoubleAttention(nn.Module): #@torch.compile() - def forward(self, c, x): + def forward(self, c, x, transformer_options={}): bsz, seqlen1, _ = c.shape bsz, seqlen2, _ = x.shape @@ -167,7 +168,7 @@ class DoubleAttention(nn.Module): torch.cat([cv, xv], dim=1), ) - output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True) + output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options) c, x = output.split([seqlen1, seqlen2], dim=1) c = self.w1o(c) @@ -206,7 +207,7 @@ class MMDiTBlock(nn.Module): self.is_last = is_last #@torch.compile() - def forward(self, c, x, global_cond, **kwargs): + def forward(self, c, x, global_cond, transformer_options={}, **kwargs): cres, xres = c, x @@ -224,7 +225,7 @@ class MMDiTBlock(nn.Module): x = modulate(self.normX1(x), xshift_msa, xscale_msa) # attention - c, x = self.attn(c, x) + c, x = self.attn(c, x, transformer_options=transformer_options) c = self.normC2(cres + cgate_msa.unsqueeze(1) * c) @@ -254,13 +255,13 @@ class DiTBlock(nn.Module): self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations) #@torch.compile() - def forward(self, cx, global_cond, **kwargs): + def forward(self, cx, global_cond, transformer_options={}, **kwargs): cxres = cx shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX( global_cond ).chunk(6, dim=1) cx = modulate(self.norm1(cx), shift_msa, scale_msa) - cx = self.attn(cx) + cx = self.attn(cx, transformer_options=transformer_options) cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx) mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp)) cx = gate_mlp.unsqueeze(1) * mlpout @@ -436,6 +437,13 @@ class MMDiT(nn.Module): return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1]) def forward(self, x, timestep, context, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, transformer_options={}, **kwargs): patches_replace = transformer_options.get("patches_replace", {}) # patchify x, add PE b, c, h, w = x.shape @@ -465,13 +473,14 @@ class MMDiT(nn.Module): out = {} out["txt"], out["img"] = layer(args["txt"], args["img"], - args["vec"]) + args["vec"], + transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) c = out["txt"] x = out["img"] else: - c, x = layer(c, x, global_cond, **kwargs) + c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs) if len(self.single_layers) > 0: c_len = c.size(1) @@ -480,13 +489,13 @@ class MMDiT(nn.Module): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = layer(args["img"], args["vec"]) + out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap}) + out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap}) cx = out["img"] else: - cx = layer(cx, global_cond, **kwargs) + cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs) x = cx[:, c_len:] diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py index 3eaa0c821..42ef98c7a 100644 --- a/comfy/ldm/cascade/common.py +++ b/comfy/ldm/cascade/common.py @@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module): self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device) - def forward(self, q, k, v): + def forward(self, q, k, v, transformer_options={}): q = self.to_q(q) k = self.to_k(k) v = self.to_v(v) - out = optimized_attention(q, k, v, self.heads) + out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options) return self.out_proj(out) @@ -47,13 +47,13 @@ class Attention2D(nn.Module): self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations) # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device) - def forward(self, x, kv, self_attn=False): + def forward(self, x, kv, self_attn=False, transformer_options={}): orig_shape = x.shape x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4 if self_attn: kv = torch.cat([x, kv], dim=1) # x = self.attn(x, kv, kv, need_weights=False)[0] - x = self.attn(x, kv, kv) + x = self.attn(x, kv, kv, transformer_options=transformer_options) x = x.permute(0, 2, 1).view(*orig_shape) return x @@ -114,9 +114,9 @@ class AttnBlock(nn.Module): operations.Linear(c_cond, c, dtype=dtype, device=device) ) - def forward(self, x, kv): + def forward(self, x, kv, transformer_options={}): kv = self.kv_mapper(kv) - x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn) + x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options) return x diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py index ca8867eaf..145e6e69a 100644 --- a/comfy/ldm/cascade/stage_a.py +++ b/comfy/ldm/cascade/stage_a.py @@ -19,6 +19,10 @@ import torch from torch import nn from torch.autograd import Function +import comfy.ops + +ops = comfy.ops.disable_weight_init + class vector_quantize(Function): @staticmethod @@ -121,15 +125,15 @@ class ResBlock(nn.Module): self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.depthwise = nn.Sequential( nn.ReplicationPad2d(1), - nn.Conv2d(c, c, kernel_size=3, groups=c) + ops.Conv2d(c, c, kernel_size=3, groups=c) ) # channelwise self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6) self.channelwise = nn.Sequential( - nn.Linear(c, c_hidden), + ops.Linear(c, c_hidden), nn.GELU(), - nn.Linear(c_hidden, c), + ops.Linear(c_hidden, c), ) self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True) @@ -171,16 +175,16 @@ class StageA(nn.Module): # Encoder blocks self.in_block = nn.Sequential( nn.PixelUnshuffle(2), - nn.Conv2d(3 * 4, c_levels[0], kernel_size=1) + ops.Conv2d(3 * 4, c_levels[0], kernel_size=1) ) down_blocks = [] for i in range(levels): if i > 0: - down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) + down_blocks.append(ops.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1)) block = ResBlock(c_levels[i], c_levels[i] * 4) down_blocks.append(block) down_blocks.append(nn.Sequential( - nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), + ops.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 )) self.down_blocks = nn.Sequential(*down_blocks) @@ -191,7 +195,7 @@ class StageA(nn.Module): # Decoder blocks up_blocks = [nn.Sequential( - nn.Conv2d(c_latent, c_levels[-1], kernel_size=1) + ops.Conv2d(c_latent, c_levels[-1], kernel_size=1) )] for i in range(levels): for j in range(bottleneck_blocks if i == 0 else 1): @@ -199,11 +203,11 @@ class StageA(nn.Module): up_blocks.append(block) if i < levels - 1: up_blocks.append( - nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, + ops.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2, padding=1)) self.up_blocks = nn.Sequential(*up_blocks) self.out_block = nn.Sequential( - nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1), + ops.Conv2d(c_levels[0], 3 * 4, kernel_size=1), nn.PixelShuffle(2), ) @@ -232,17 +236,17 @@ class Discriminator(nn.Module): super().__init__() d = max(depth - 3, 3) layers = [ - nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), + nn.utils.spectral_norm(ops.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)), nn.LeakyReLU(0.2), ] for i in range(depth - 1): c_in = c_hidden // (2 ** max((d - i), 0)) c_out = c_hidden // (2 ** max((d - 1 - i), 0)) - layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) + layers.append(nn.utils.spectral_norm(ops.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) layers.append(nn.InstanceNorm2d(c_out)) layers.append(nn.LeakyReLU(0.2)) self.encoder = nn.Sequential(*layers) - self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) + self.shuffle = ops.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1) self.logits = nn.Sigmoid() def forward(self, x, cond=None): diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py index 773830956..428c67fdf 100644 --- a/comfy/ldm/cascade/stage_b.py +++ b/comfy/ldm/cascade/stage_b.py @@ -173,7 +173,7 @@ class StageB(nn.Module): clip = self.clip_norm(clip) return clip - def _down_encode(self, x, r_embed, clip): + def _down_encode(self, x, r_embed, clip, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: @@ -187,7 +187,7 @@ class StageB(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -199,7 +199,7 @@ class StageB(nn.Module): level_outputs.insert(0, x) return level_outputs - def _up_decode(self, level_outputs, r_embed, clip): + def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): @@ -216,7 +216,7 @@ class StageB(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -228,7 +228,7 @@ class StageB(nn.Module): x = upscaler(x) return x - def forward(self, x, r, effnet, clip, pixels=None, **kwargs): + def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs): if pixels is None: pixels = x.new_zeros(x.size(0), 3, 8, 8) @@ -245,8 +245,8 @@ class StageB(nn.Module): nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True)) x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear', align_corners=True) - level_outputs = self._down_encode(x, r_embed, clip) - x = self._up_decode(level_outputs, r_embed, clip) + level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options) + x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py index b952d0349..ebc4434e2 100644 --- a/comfy/ldm/cascade/stage_c.py +++ b/comfy/ldm/cascade/stage_c.py @@ -182,7 +182,7 @@ class StageC(nn.Module): clip = self.clip_norm(clip) return clip - def _down_encode(self, x, r_embed, clip, cnet=None): + def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}): level_outputs = [] block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers) for down_block, downscaler, repmap in block_group: @@ -201,7 +201,7 @@ class StageC(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -213,7 +213,7 @@ class StageC(nn.Module): level_outputs.insert(0, x) return level_outputs - def _up_decode(self, level_outputs, r_embed, clip, cnet=None): + def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}): x = level_outputs[0] block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers) for i, (up_block, upscaler, repmap) in enumerate(block_group): @@ -235,7 +235,7 @@ class StageC(nn.Module): elif isinstance(block, AttnBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, AttnBlock)): - x = block(x, clip) + x = block(x, clip, transformer_options=transformer_options) elif isinstance(block, TimestepBlock) or ( hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module, TimestepBlock)): @@ -247,7 +247,7 @@ class StageC(nn.Module): x = upscaler(x) return x - def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs): + def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs): # Process the conditioning embeddings r_embed = self.gen_r_embedding(r).to(dtype=x.dtype) for c in self.t_conds: @@ -262,8 +262,8 @@ class StageC(nn.Module): # Model Blocks x = self.embedding(x) - level_outputs = self._down_encode(x, r_embed, clip, cnet) - x = self._up_decode(level_outputs, r_embed, clip, cnet) + level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options) + x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options) return self.clf(x) def update_weights_ema(self, src_model, beta=0.999): diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py index 0cb7c49fc..b467a70a8 100644 --- a/comfy/ldm/cascade/stage_c_coder.py +++ b/comfy/ldm/cascade/stage_c_coder.py @@ -19,6 +19,9 @@ import torch import torchvision from torch import nn +import comfy.ops + +ops = comfy.ops.disable_weight_init # EfficientNet class EfficientNetEncoder(nn.Module): @@ -26,7 +29,7 @@ class EfficientNetEncoder(nn.Module): super().__init__() self.backbone = torchvision.models.efficientnet_v2_s().features.eval() self.mapper = nn.Sequential( - nn.Conv2d(1280, c_latent, kernel_size=1, bias=False), + ops.Conv2d(1280, c_latent, kernel_size=1, bias=False), nn.BatchNorm2d(c_latent, affine=False), # then normalize them to have mean 0 and std 1 ) self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406])) @@ -34,7 +37,7 @@ class EfficientNetEncoder(nn.Module): def forward(self, x): x = x * 0.5 + 0.5 - x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1]) + x = (x - self.mean.view([3,1,1]).to(device=x.device, dtype=x.dtype)) / self.std.view([3,1,1]).to(device=x.device, dtype=x.dtype) o = self.mapper(self.backbone(x)) return o @@ -44,39 +47,39 @@ class Previewer(nn.Module): def __init__(self, c_in=16, c_hidden=512, c_out=3): super().__init__() self.blocks = nn.Sequential( - nn.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels + ops.Conv2d(c_in, c_hidden, kernel_size=1), # 16 channels to 512 channels nn.GELU(), nn.BatchNorm2d(c_hidden), - nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), + ops.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden), - nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 + ops.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2), # 16 -> 32 nn.GELU(), nn.BatchNorm2d(c_hidden // 2), - nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), + ops.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 2), - nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 + ops.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2), # 32 -> 64 nn.GELU(), nn.BatchNorm2d(c_hidden // 4), - nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 4), - nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 + ops.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2), # 64 -> 128 nn.GELU(), nn.BatchNorm2d(c_hidden // 4), - nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), + ops.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1), nn.GELU(), nn.BatchNorm2d(c_hidden // 4), - nn.Conv2d(c_hidden // 4, c_out, kernel_size=1), + ops.Conv2d(c_hidden // 4, c_out, kernel_size=1), ) def forward(self, x): diff --git a/comfy/ldm/chroma/layers.py b/comfy/ldm/chroma/layers.py new file mode 100644 index 000000000..fc7110cce --- /dev/null +++ b/comfy/ldm/chroma/layers.py @@ -0,0 +1,181 @@ +import torch +from torch import Tensor, nn + +from comfy.ldm.flux.math import attention +from comfy.ldm.flux.layers import ( + MLPEmbedder, + RMSNorm, + QKNorm, + SelfAttention, + ModulationOut, +) + + + +class ChromaModulationOut(ModulationOut): + @classmethod + def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut: + return cls( + shift=tensor[:, offset : offset + 1, :], + scale=tensor[:, offset + 1 : offset + 2, :], + gate=tensor[:, offset + 2 : offset + 3, :], + ) + + + + +class Approximator(nn.Module): + def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None): + super().__init__() + self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device) + self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)]) + self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) + + @property + def device(self): + # Get the device of the module (assumes all parameters are on the same device) + return next(self.parameters()).device + + def forward(self, x: Tensor) -> Tensor: + x = self.in_proj(x) + + for layer, norms in zip(self.layers, self.norms): + x = x + layer(norms(x)) + + x = self.out_proj(x) + + return x + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.img_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations) + + self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.txt_mlp = nn.Sequential( + operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device), + nn.GELU(approximate="tanh"), + operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + self.flipped_img_txt = flipped_img_txt + + def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}): + (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec + + # prepare image for attention + img_modulated = torch.addcmul(img_mod1.shift, 1 + img_mod1.scale, self.img_norm1(img)) + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = torch.addcmul(txt_mod1.shift, 1 + txt_mod1.scale, self.txt_norm1(txt)) + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + attn = attention(torch.cat((txt_q, img_q), dim=2), + torch.cat((txt_k, img_k), dim=2), + torch.cat((txt_v, img_v), dim=2), + pe=pe, mask=attn_mask, transformer_options=transformer_options) + + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img.addcmul_(img_mod1.gate, self.img_attn.proj(img_attn)) + img.addcmul_(img_mod2.gate, self.img_mlp(torch.addcmul(img_mod2.shift, 1 + img_mod2.scale, self.img_norm2(img)))) + + # calculate the txt bloks + txt.addcmul_(txt_mod1.gate, self.txt_attn.proj(txt_attn)) + txt.addcmul_(txt_mod2.gate, self.txt_mlp(torch.addcmul(txt_mod2.shift, 1 + txt_mod2.scale, self.txt_norm2(txt)))) + + if txt.dtype == torch.float16: + txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) + + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device) + # proj and mlp_out + self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device) + + self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations) + + self.hidden_size = hidden_size + self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + + self.mlp_act = nn.GELU(approximate="tanh") + + def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor: + mod = vec + x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x)) + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + x.addcmul_(mod.gate, output) + if x.dtype == torch.float16: + x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) + return x + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.linear = operations.Linear(hidden_size, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = vec + shift = shift.squeeze(1) + scale = scale.squeeze(1) + x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x)) + x = self.linear(x) + return x diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py new file mode 100644 index 000000000..ad1c523fe --- /dev/null +++ b/comfy/ldm/chroma/model.py @@ -0,0 +1,285 @@ +#Original code can be found on: https://github.com/black-forest-labs/flux + +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from einops import rearrange, repeat +import comfy.patcher_extension +import comfy.ldm.common_dit + +from comfy.ldm.flux.layers import ( + EmbedND, + timestep_embedding, +) + +from .layers import ( + DoubleStreamBlock, + LastLayer, + SingleStreamBlock, + Approximator, + ChromaModulationOut, +) + + +@dataclass +class ChromaParams: + in_channels: int + out_channels: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list + theta: int + patch_size: int + qkv_bias: bool + in_dim: int + out_dim: int + hidden_dim: int + n_layers: int + + + + +class Chroma(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + params = ChromaParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.in_dim = params.in_dim + self.out_dim = params.out_dim + self.hidden_dim = params.hidden_dim + self.n_layers = params.n_layers + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + # set as nn identity for now, will overwrite it later. + self.distilled_guidance_layer = Approximator( + in_dim=self.in_dim, + hidden_dim=self.hidden_dim, + out_dim=self.out_dim, + n_layers=self.n_layers, + dtype=dtype, device=device, operations=operations + ) + + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations) + for _ in range(params.depth_single_blocks) + ] + ) + + if final_layer: + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations) + + self.skip_mmdit = [] + self.skip_dit = [] + self.lite = False + + def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0): + # This function slices up the modulations tensor which has the following layout: + # single : num_single_blocks * 3 elements + # double_img : num_double_blocks * 6 elements + # double_txt : num_double_blocks * 6 elements + # final : 2 elements + if block_type == "final": + return (tensor[:, -2:-1, :], tensor[:, -1:, :]) + single_block_count = self.params.depth_single_blocks + double_block_count = self.params.depth + offset = 3 * idx + if block_type == "single": + return ChromaModulationOut.from_offset(tensor, offset) + # Double block modulations are 6 elements so we double 3 * idx. + offset *= 2 + if block_type in {"double_img", "double_txt"}: + # Advance past the single block modulations. + offset += 3 * single_block_count + if block_type == "double_txt": + # Advance past the double block img modulations. + offset += 6 * double_block_count + return ( + ChromaModulationOut.from_offset(tensor, offset), + ChromaModulationOut.from_offset(tensor, offset + 3), + ) + raise ValueError("Bad block_type") + + + def forward_orig( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + guidance: Tensor = None, + control = None, + transformer_options={}, + attn_mask: Tensor = None, + ) -> Tensor: + patches_replace = transformer_options.get("patches_replace", {}) + + # running on sequences img + img = self.img_in(img) + + # distilled vector guidance + mod_index_length = 344 + distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype) + # guidance = guidance * + distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype) + + # get all modulation index + modulation_index = timestep_embedding(torch.arange(mod_index_length, device=img.device), 32).to(img.device, img.dtype) + # we need to broadcast the modulation index here so each batch has all of the index + modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype) + # and we need to broadcast timestep and guidance along too + timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype) + # then and only then we could concatenate it together + input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype) + + mod_vectors = self.distilled_guidance_layer(input_vec) + + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if i not in self.skip_mmdit: + double_mod = ( + self.get_modulations(mod_vectors, "double_img", idx=i), + self.get_modulations(mod_vectors, "double_txt", idx=i), + ) + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": double_mod, + "pe": pe, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=double_mod, + pe=pe, + attn_mask=attn_mask, + transformer_options=transformer_options) + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + img += add + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if i not in self.skip_dit: + single_mod = self.get_modulations(mod_vectors, "single", idx=i) + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": single_mod, + "pe": pe, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) + + if control is not None: # Controlnet + control_o = control.get("output") + if i < len(control_o): + add = control_o[i] + if add is not None: + img[:, txt.shape[1] :, ...] += add + + img = img[:, txt.shape[1] :, ...] + if hasattr(self, "final_layer"): + final_mod = self.get_modulations(mod_vectors, "final") + img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels) + return img + + def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, guidance, control, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs): + bs, c, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + + img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size) + + if img.ndim != 3 or context.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + h_len = ((h + (self.patch_size // 2)) // self.patch_size) + w_len = ((w + (self.patch_size // 2)) // self.patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + out = self.forward_orig(img, img_ids, context, txt_ids, timestep, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h,:w] diff --git a/comfy/ldm/chroma_radiance/layers.py b/comfy/ldm/chroma_radiance/layers.py new file mode 100644 index 000000000..3c7bc9b6b --- /dev/null +++ b/comfy/ldm/chroma_radiance/layers.py @@ -0,0 +1,206 @@ +# Adapted from https://github.com/lodestone-rock/flow +from functools import lru_cache + +import torch +from torch import nn + +from comfy.ldm.flux.layers import RMSNorm + + +class NerfEmbedder(nn.Module): + """ + An embedder module that combines input features with a 2D positional + encoding that mimics the Discrete Cosine Transform (DCT). + + This module takes an input tensor of shape (B, P^2, C), where P is the + patch size, and enriches it with positional information before projecting + it to a new hidden size. + """ + def __init__( + self, + in_channels: int, + hidden_size_input: int, + max_freqs: int, + dtype=None, + device=None, + operations=None, + ): + """ + Initializes the NerfEmbedder. + + Args: + in_channels (int): The number of channels in the input tensor. + hidden_size_input (int): The desired dimension of the output embedding. + max_freqs (int): The number of frequency components to use for both + the x and y dimensions of the positional encoding. + The total number of positional features will be max_freqs^2. + """ + super().__init__() + self.dtype = dtype + self.max_freqs = max_freqs + self.hidden_size_input = hidden_size_input + + # A linear layer to project the concatenated input features and + # positional encodings to the final output dimension. + self.embedder = nn.Sequential( + operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device) + ) + + @lru_cache(maxsize=4) + def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor: + """ + Generates and caches 2D DCT-like positional embeddings for a given patch size. + + The LRU cache is a performance optimization that avoids recomputing the + same positional grid on every forward pass. + + Args: + patch_size (int): The side length of the square input patch. + device: The torch device to create the tensors on. + dtype: The torch dtype for the tensors. + + Returns: + A tensor of shape (1, patch_size^2, max_freqs^2) containing the + positional embeddings. + """ + # Create normalized 1D coordinate grids from 0 to 1. + pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) + pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype) + + # Create a 2D meshgrid of coordinates. + pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij") + + # Reshape positions to be broadcastable with frequencies. + # Shape becomes (patch_size^2, 1, 1). + pos_x = pos_x.reshape(-1, 1, 1) + pos_y = pos_y.reshape(-1, 1, 1) + + # Create a 1D tensor of frequency values from 0 to max_freqs-1. + freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device) + + # Reshape frequencies to be broadcastable for creating 2D basis functions. + # freqs_x shape: (1, max_freqs, 1) + # freqs_y shape: (1, 1, max_freqs) + freqs_x = freqs[None, :, None] + freqs_y = freqs[None, None, :] + + # A custom weighting coefficient, not part of standard DCT. + # This seems to down-weight the contribution of higher-frequency interactions. + coeffs = (1 + freqs_x * freqs_y) ** -1 + + # Calculate the 1D cosine basis functions for x and y coordinates. + # This is the core of the DCT formulation. + dct_x = torch.cos(pos_x * freqs_x * torch.pi) + dct_y = torch.cos(pos_y * freqs_y * torch.pi) + + # Combine the 1D basis functions to create 2D basis functions by element-wise + # multiplication, and apply the custom coefficients. Broadcasting handles the + # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y). + # The result is flattened into a feature vector for each position. + dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2) + + return dct + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + """ + Forward pass for the embedder. + + Args: + inputs (Tensor): The input tensor of shape (B, P^2, C). + + Returns: + Tensor: The output tensor of shape (B, P^2, hidden_size_input). + """ + # Get the batch size, number of pixels, and number of channels. + B, P2, C = inputs.shape + + # Infer the patch side length from the number of pixels (P^2). + patch_size = int(P2 ** 0.5) + + input_dtype = inputs.dtype + inputs = inputs.to(dtype=self.dtype) + + # Fetch the pre-computed or cached positional embeddings. + dct = self.fetch_pos(patch_size, inputs.device, self.dtype) + + # Repeat the positional embeddings for each item in the batch. + dct = dct.repeat(B, 1, 1) + + # Concatenate the original input features with the positional embeddings + # along the feature dimension. + inputs = torch.cat((inputs, dct), dim=-1) + + # Project the combined tensor to the target hidden size. + return self.embedder(inputs).to(dtype=input_dtype) + + +class NerfGLUBlock(nn.Module): + """ + A NerfBlock using a Gated Linear Unit (GLU) like MLP. + """ + def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None): + super().__init__() + # The total number of parameters for the MLP is increased to accommodate + # the gate, value, and output projection matrices. + # We now need to generate parameters for 3 matrices. + total_params = 3 * hidden_size_x**2 * mlp_ratio + self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device) + self.norm = RMSNorm(hidden_size_x, dtype=dtype, device=device, operations=operations) + self.mlp_ratio = mlp_ratio + + + def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor: + batch_size, num_x, hidden_size_x = x.shape + mlp_params = self.param_generator(s) + + # Split the generated parameters into three parts for the gate, value, and output projection. + fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1) + + # Reshape the parameters into matrices for batch matrix multiplication. + fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio) + fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio) + fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x) + + # Normalize the generated weight matrices as in the original implementation. + fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2) + fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2) + fc2 = torch.nn.functional.normalize(fc2, dim=-2) + + res_x = x + x = self.norm(x) + + # Apply the final output projection. + x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2) + + return x + res_x + + +class NerfFinalLayer(nn.Module): + def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None): + super().__init__() + self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations) + self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1. + # So we temporarily move the channel dimension to the end for the norm operation. + return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1) + + +class NerfFinalLayerConv(nn.Module): + def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None): + super().__init__() + self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations) + self.conv = operations.Conv2d( + in_channels=hidden_size, + out_channels=out_channels, + kernel_size=3, + padding=1, + dtype=dtype, + device=device, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1. + # So we temporarily move the channel dimension to the end for the norm operation. + return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1)) diff --git a/comfy/ldm/chroma_radiance/model.py b/comfy/ldm/chroma_radiance/model.py new file mode 100644 index 000000000..7d7be80f5 --- /dev/null +++ b/comfy/ldm/chroma_radiance/model.py @@ -0,0 +1,320 @@ +# Credits: +# Original Flux code can be found on: https://github.com/black-forest-labs/flux +# Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow + +from dataclasses import dataclass +from typing import Optional + +import torch +from torch import Tensor, nn +from einops import repeat +import comfy.ldm.common_dit + +from comfy.ldm.flux.layers import EmbedND + +from comfy.ldm.chroma.model import Chroma, ChromaParams +from comfy.ldm.chroma.layers import ( + DoubleStreamBlock, + SingleStreamBlock, + Approximator, +) +from .layers import ( + NerfEmbedder, + NerfGLUBlock, + NerfFinalLayer, + NerfFinalLayerConv, +) + + +@dataclass +class ChromaRadianceParams(ChromaParams): + patch_size: int + nerf_hidden_size: int + nerf_mlp_ratio: int + nerf_depth: int + nerf_max_freqs: int + # Setting nerf_tile_size to 0 disables tiling. + nerf_tile_size: int + # Currently one of linear (legacy) or conv. + nerf_final_head_type: str + # None means use the same dtype as the model. + nerf_embedder_dtype: Optional[torch.dtype] + + +class ChromaRadiance(Chroma): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs): + if operations is None: + raise RuntimeError("Attempt to create ChromaRadiance object without setting operations") + nn.Module.__init__(self) + self.dtype = dtype + params = ChromaRadianceParams(**kwargs) + self.params = params + self.patch_size = params.patch_size + self.in_channels = params.in_channels + self.out_channels = params.out_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.in_dim = params.in_dim + self.out_dim = params.out_dim + self.hidden_dim = params.hidden_dim + self.n_layers = params.n_layers + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in_patch = operations.Conv2d( + params.in_channels, + params.hidden_size, + kernel_size=params.patch_size, + stride=params.patch_size, + bias=True, + dtype=dtype, + device=device, + ) + self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device) + # set as nn identity for now, will overwrite it later. + self.distilled_guidance_layer = Approximator( + in_dim=self.in_dim, + hidden_dim=self.hidden_dim, + out_dim=self.out_dim, + n_layers=self.n_layers, + dtype=dtype, device=device, operations=operations + ) + + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + dtype=dtype, device=device, operations=operations, + ) + for _ in range(params.depth_single_blocks) + ] + ) + + # pixel channel concat with DCT + self.nerf_image_embedder = NerfEmbedder( + in_channels=params.in_channels, + hidden_size_input=params.nerf_hidden_size, + max_freqs=params.nerf_max_freqs, + dtype=params.nerf_embedder_dtype or dtype, + device=device, + operations=operations, + ) + + self.nerf_blocks = nn.ModuleList([ + NerfGLUBlock( + hidden_size_s=params.hidden_size, + hidden_size_x=params.nerf_hidden_size, + mlp_ratio=params.nerf_mlp_ratio, + dtype=dtype, + device=device, + operations=operations, + ) for _ in range(params.nerf_depth) + ]) + + if params.nerf_final_head_type == "linear": + self.nerf_final_layer = NerfFinalLayer( + params.nerf_hidden_size, + out_channels=params.in_channels, + dtype=dtype, + device=device, + operations=operations, + ) + elif params.nerf_final_head_type == "conv": + self.nerf_final_layer_conv = NerfFinalLayerConv( + params.nerf_hidden_size, + out_channels=params.in_channels, + dtype=dtype, + device=device, + operations=operations, + ) + else: + errstr = f"Unsupported nerf_final_head_type {params.nerf_final_head_type}" + raise ValueError(errstr) + + self.skip_mmdit = [] + self.skip_dit = [] + self.lite = False + + @property + def _nerf_final_layer(self) -> nn.Module: + if self.params.nerf_final_head_type == "linear": + return self.nerf_final_layer + if self.params.nerf_final_head_type == "conv": + return self.nerf_final_layer_conv + # Impossible to get here as we raise an error on unexpected types on initialization. + raise NotImplementedError + + def img_in(self, img: Tensor) -> Tensor: + img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P] + # flatten into a sequence for the transformer. + return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden] + + def forward_nerf( + self, + img_orig: Tensor, + img_out: Tensor, + params: ChromaRadianceParams, + ) -> Tensor: + B, C, H, W = img_orig.shape + num_patches = img_out.shape[1] + patch_size = params.patch_size + + # Store the raw pixel values of each patch for the NeRF head later. + # unfold creates patches: [B, C * P * P, NumPatches] + nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size) + nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P] + + # Reshape for per-patch processing + nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size) + nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2) + + if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size: + # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than + # the tile size. + img_dct = self.forward_tiled_nerf(nerf_hidden, nerf_pixels, B, C, num_patches, patch_size, params) + else: + # Get DCT-encoded pixel embeddings [pixel-dct] + img_dct = self.nerf_image_embedder(nerf_pixels) + + # Pass through the dynamic MLP blocks (the NeRF) + for block in self.nerf_blocks: + img_dct = block(img_dct, nerf_hidden) + + # Reassemble the patches into the final image. + img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P] + # Reshape to combine with batch dimension for fold + img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P] + img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches] + img_dct = nn.functional.fold( + img_dct, + output_size=(H, W), + kernel_size=patch_size, + stride=patch_size, + ) + return self._nerf_final_layer(img_dct) + + def forward_tiled_nerf( + self, + nerf_hidden: Tensor, + nerf_pixels: Tensor, + batch: int, + channels: int, + num_patches: int, + patch_size: int, + params: ChromaRadianceParams, + ) -> Tensor: + """ + Processes the NeRF head in tiles to save memory. + nerf_hidden has shape [B, L, D] + nerf_pixels has shape [B, L, C * P * P] + """ + tile_size = params.nerf_tile_size + output_tiles = [] + # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1. + for i in range(0, num_patches, tile_size): + end = min(i + tile_size, num_patches) + + # Slice the current tile from the input tensors + nerf_hidden_tile = nerf_hidden[i * batch:end * batch] + nerf_pixels_tile = nerf_pixels[i * batch:end * batch] + + # get DCT-encoded pixel embeddings [pixel-dct] + img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile) + + # pass through the dynamic MLP blocks (the NeRF) + for block in self.nerf_blocks: + img_dct_tile = block(img_dct_tile, nerf_hidden_tile) + + output_tiles.append(img_dct_tile) + + # Concatenate the processed tiles along the patch dimension + return torch.cat(output_tiles, dim=0) + + def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams: + params = self.params + if not overrides: + return params + params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__} + nullable_keys = frozenset(("nerf_embedder_dtype",)) + bad_keys = tuple(k for k in overrides if k not in params_dict) + if bad_keys: + e = f"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}" + raise ValueError(e) + bad_keys = tuple( + k + for k, v in overrides.items() + if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys) + ) + if bad_keys: + e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}" + raise ValueError(e) + # At this point it's all valid keys and values so we can merge with the existing params. + params_dict |= overrides + return params.__class__(**params_dict) + + def _forward( + self, + x: Tensor, + timestep: Tensor, + context: Tensor, + guidance: Optional[Tensor], + control: Optional[dict]=None, + transformer_options: dict={}, + **kwargs: dict, + ) -> Tensor: + bs, c, h, w = x.shape + img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + + if img.ndim != 4: + raise ValueError("Input img tensor must be in [B, C, H, W] format.") + if context.ndim != 3: + raise ValueError("Input txt tensors must have 3 dimensions.") + + params = self.radiance_get_override_params(transformer_options.get("chroma_radiance_options", {})) + + h_len = (img.shape[-2] // self.patch_size) + w_len = (img.shape[-1] // self.patch_size) + + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + + img_out = self.forward_orig( + img, + img_ids, + context, + txt_ids, + timestep, + guidance, + control, + transformer_options, + attn_mask=kwargs.get("attention_mask", None), + ) + return self.forward_nerf(img, img_out, params)[:, :, :h, :w] diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py index e0f3057f7..f7f56b72c 100644 --- a/comfy/ldm/common_dit.py +++ b/comfy/ldm/common_dit.py @@ -1,5 +1,6 @@ import torch -import comfy.ops +import comfy.rmsnorm + def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()): @@ -11,20 +12,5 @@ def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"): return torch.nn.functional.pad(img, pad, mode=padding_mode) -try: - rms_norm_torch = torch.nn.functional.rms_norm -except: - rms_norm_torch = None -def rms_norm(x, weight=None, eps=1e-6): - if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): - if weight is None: - return rms_norm_torch(x, (x.shape[-1],), eps=eps) - else: - return rms_norm_torch(x, weight.shape, weight=comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) - else: - r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) - if weight is None: - return r - else: - return r * comfy.ops.cast_to(weight, dtype=x.dtype, device=x.device) +rms_norm = comfy.rmsnorm.rms_norm diff --git a/comfy/ldm/cosmos/blocks.py b/comfy/ldm/cosmos/blocks.py new file mode 100644 index 000000000..afb43d469 --- /dev/null +++ b/comfy/ldm/cosmos/blocks.py @@ -0,0 +1,805 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import Optional +import logging + +import numpy as np +import torch +from einops import rearrange, repeat +from einops.layers.torch import Rearrange +from torch import nn + +from comfy.ldm.modules.attention import optimized_attention + + +def get_normalization(name: str, channels: int, weight_args={}, operations=None): + if name == "I": + return nn.Identity() + elif name == "R": + return operations.RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args) + else: + raise ValueError(f"Normalization {name} not found") + + +class BaseAttentionOp(nn.Module): + def __init__(self): + super().__init__() + + +class Attention(nn.Module): + """ + Generalized attention impl. + + Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided. + If `context_dim` is None, self-attention is assumed. + + Parameters: + query_dim (int): Dimension of each query vector. + context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed. + heads (int, optional): Number of attention heads. Defaults to 8. + dim_head (int, optional): Dimension of each head. Defaults to 64. + dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0. + attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default. + qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False. + out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False. + qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections. + Defaults to "SSI". + qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections. + Defaults to 'per_head'. Only support 'per_head'. + + Examples: + >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1) + >>> query = torch.randn(10, 128) # Batch size of 10 + >>> context = torch.randn(10, 256) # Batch size of 10 + >>> output = attn(query, context) # Perform the attention operation + + Note: + https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + """ + + def __init__( + self, + query_dim: int, + context_dim=None, + heads=8, + dim_head=64, + dropout=0.0, + attn_op: Optional[BaseAttentionOp] = None, + qkv_bias: bool = False, + out_bias: bool = False, + qkv_norm: str = "SSI", + qkv_norm_mode: str = "per_head", + backend: str = "transformer_engine", + qkv_format: str = "bshd", + weight_args={}, + operations=None, + ) -> None: + super().__init__() + + self.is_selfattn = context_dim is None # self attention + + inner_dim = dim_head * heads + context_dim = query_dim if context_dim is None else context_dim + + self.heads = heads + self.dim_head = dim_head + self.qkv_norm_mode = qkv_norm_mode + self.qkv_format = qkv_format + + if self.qkv_norm_mode == "per_head": + norm_dim = dim_head + else: + raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") + + self.backend = backend + + self.to_q = nn.Sequential( + operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[0], norm_dim, weight_args=weight_args, operations=operations), + ) + self.to_k = nn.Sequential( + operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[1], norm_dim, weight_args=weight_args, operations=operations), + ) + self.to_v = nn.Sequential( + operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args), + get_normalization(qkv_norm[2], norm_dim, weight_args=weight_args, operations=operations), + ) + + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args), + nn.Dropout(dropout), + ) + + def cal_qkv( + self, x, context=None, mask=None, rope_emb=None, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + del kwargs + + + """ + self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers. + Before 07/24/2024, these modules normalize across all heads. + After 07/24/2024, to support tensor parallelism and follow the common practice in the community, + we support to normalize per head. + To keep the checkpoint copatibility with the previous code, + we keep the nn.Sequential but call the projection and the normalization layers separately. + We use a flag `self.qkv_norm_mode` to control the normalization behavior. + The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head. + """ + if self.qkv_norm_mode == "per_head": + q = self.to_q[0](x) + context = x if context is None else context + k = self.to_k[0](context) + v = self.to_v[0](context) + q, k, v = map( + lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head), + (q, k, v), + ) + else: + raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'") + + q = self.to_q[1](q) + k = self.to_k[1](k) + v = self.to_v[1](v) + if self.is_selfattn and rope_emb is not None: # only apply to self-attention! + # apply_rotary_pos_emb inlined + q_shape = q.shape + q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) + q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1] + q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype) + + # apply_rotary_pos_emb inlined + k_shape = k.shape + k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2) + k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1] + k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype) + return q, k, v + + def forward( + self, + x, + context=None, + mask=None, + rope_emb=None, + transformer_options={}, + **kwargs, + ): + """ + Args: + x (Tensor): The query tensor of shape [B, Mq, K] + context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None + """ + q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs) + out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options) + del q, k, v + out = rearrange(out, " b n s c -> s b (n c)") + return self.to_out(out) + + +class FeedForward(nn.Module): + """ + Transformer FFN with optional gating + + Parameters: + d_model (int): Dimensionality of input features. + d_ff (int): Dimensionality of the hidden layer. + dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1. + activation (callable, optional): The activation function applied after the first linear layer. + Defaults to nn.ReLU(). + is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer. + Defaults to False. + bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True. + + Example: + >>> ff = FeedForward(d_model=512, d_ff=2048) + >>> x = torch.randn(64, 10, 512) # Example input tensor + >>> output = ff(x) + >>> print(output.shape) # Expected shape: (64, 10, 512) + """ + + def __init__( + self, + d_model: int, + d_ff: int, + dropout: float = 0.1, + activation=nn.ReLU(), + is_gated: bool = False, + bias: bool = False, + weight_args={}, + operations=None, + ) -> None: + super().__init__() + + self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args) + self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args) + + self.dropout = nn.Dropout(dropout) + self.activation = activation + self.is_gated = is_gated + if is_gated: + self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args) + + def forward(self, x: torch.Tensor): + g = self.activation(self.layer1(x)) + if self.is_gated: + x = g * self.linear_gate(x) + else: + x = g + assert self.dropout.p == 0.0, "we skip dropout" + return self.layer2(x) + + +class GPT2FeedForward(FeedForward): + def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None): + super().__init__( + d_model=d_model, + d_ff=d_ff, + dropout=dropout, + activation=nn.GELU(), + is_gated=False, + bias=bias, + weight_args=weight_args, + operations=operations, + ) + + def forward(self, x: torch.Tensor): + assert self.dropout.p == 0.0, "we skip dropout" + + x = self.layer1(x) + x = self.activation(x) + x = self.layer2(x) + + return x + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class Timesteps(nn.Module): + def __init__(self, num_channels): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps): + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - 0.0) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + sin_emb = torch.sin(emb) + cos_emb = torch.cos(emb) + emb = torch.cat([cos_emb, sin_emb], dim=-1) + + return emb + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None): + super().__init__() + logging.debug( + f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." + ) + self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args) + self.activation = nn.SiLU() + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args) + else: + self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args) + + def forward(self, sample: torch.Tensor) -> torch.Tensor: + emb = self.linear_1(sample) + emb = self.activation(emb) + emb = self.linear_2(emb) + + if self.use_adaln_lora: + adaln_lora_B_3D = emb + emb_B_D = sample + else: + emb_B_D = emb + adaln_lora_B_3D = None + + return emb_B_D, adaln_lora_B_3D + + +class FourierFeatures(nn.Module): + """ + Implements a layer that generates Fourier features from input tensors, based on randomly sampled + frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems. + + [B] -> [B, D] + + Parameters: + num_channels (int): The number of Fourier features to generate. + bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1. + normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize + the variance of the features. Defaults to False. + + Example: + >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True) + >>> x = torch.randn(10, 256) # Example input tensor + >>> output = layer(x) + >>> print(output.shape) # Expected shape: (10, 256) + """ + + def __init__(self, num_channels, bandwidth=1, normalize=False): + super().__init__() + self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True) + self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True) + self.gain = np.sqrt(2) if normalize else 1 + + def forward(self, x, gain: float = 1.0): + """ + Apply the Fourier feature transformation to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1. + + Returns: + torch.Tensor: The transformed tensor, with Fourier features applied. + """ + in_dtype = x.dtype + x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32)) + x = x.cos().mul(self.gain * gain).to(in_dtype) + return x + + +class PatchEmbed(nn.Module): + """ + PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, + depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, + making it suitable for video and image processing tasks. It supports dividing the input into patches + and embedding each patch into a vector of size `out_channels`. + + Parameters: + - spatial_patch_size (int): The size of each spatial patch. + - temporal_patch_size (int): The size of each temporal patch. + - in_channels (int): Number of input channels. Default: 3. + - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. + - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. + """ + + def __init__( + self, + spatial_patch_size, + temporal_patch_size, + in_channels=3, + out_channels=768, + bias=True, + weight_args={}, + operations=None, + ): + super().__init__() + self.spatial_patch_size = spatial_patch_size + self.temporal_patch_size = temporal_patch_size + + self.proj = nn.Sequential( + Rearrange( + "b c (t r) (h m) (w n) -> b t h w (c r m n)", + r=temporal_patch_size, + m=spatial_patch_size, + n=spatial_patch_size, + ), + operations.Linear( + in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args + ), + ) + self.out = nn.Identity() + + def forward(self, x): + """ + Forward pass of the PatchEmbed module. + + Parameters: + - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where + B is the batch size, + C is the number of channels, + T is the temporal dimension, + H is the height, and + W is the width of the input. + + Returns: + - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. + """ + assert x.dim() == 5 + _, _, T, H, W = x.shape + assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 + assert T % self.temporal_patch_size == 0 + x = self.proj(x) + return self.out(x) + + +class FinalLayer(nn.Module): + """ + The final layer of video DiT. + """ + + def __init__( + self, + hidden_size, + spatial_patch_size, + temporal_patch_size, + out_channels, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None, + ): + super().__init__() + self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args) + self.linear = operations.Linear( + hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args + ) + self.hidden_size = hidden_size + self.n_adaln_chunks = 2 + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args), + ) + else: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args) + ) + + def forward( + self, + x_BT_HW_D, + emb_B_D, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ): + if self.use_adaln_lora: + assert adaln_lora_B_3D is not None + shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk( + 2, dim=1 + ) + else: + shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1) + + B = emb_B_D.shape[0] + T = x_BT_HW_D.shape[0] // B + shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T) + x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D) + + x_BT_HW_D = self.linear(x_BT_HW_D) + return x_BT_HW_D + + +class VideoAttn(nn.Module): + """ + Implements video attention with optional cross-attention capabilities. + + This module processes video features while maintaining their spatio-temporal structure. It can perform + self-attention within the video features or cross-attention with external context features. + + Parameters: + x_dim (int): Dimension of input feature vectors + context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention + num_heads (int): Number of attention heads + bias (bool): Whether to include bias in attention projections. Default: False + qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head" + x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD" + + Input shape: + - x: (T, H, W, B, D) video features + - context (optional): (M, B, D) context features for cross-attention + where: + T: temporal dimension + H: height + W: width + B: batch size + D: feature dimension + M: context sequence length + """ + + def __init__( + self, + x_dim: int, + context_dim: Optional[int], + num_heads: int, + bias: bool = False, + qkv_norm_mode: str = "per_head", + x_format: str = "BTHWD", + weight_args={}, + operations=None, + ) -> None: + super().__init__() + self.x_format = x_format + + self.attn = Attention( + x_dim, + context_dim, + num_heads, + x_dim // num_heads, + qkv_bias=bias, + qkv_norm="RRI", + out_bias=bias, + qkv_norm_mode=qkv_norm_mode, + qkv_format="sbhd", + weight_args=weight_args, + operations=operations, + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + """ + Forward pass for video attention. + + Args: + x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data. + context (Tensor): Context tensor of shape (B, M, D) or (M, B, D), + where M is the sequence length of the context. + crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms. + rope_emb_L_1_1_D (Optional[Tensor]): + Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. + + Returns: + Tensor: The output tensor with applied attention, maintaining the input shape. + """ + + x_T_H_W_B_D = x + context_M_B_D = context + T, H, W, B, D = x_T_H_W_B_D.shape + x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d") + x_THW_B_D = self.attn( + x_THW_B_D, + context_M_B_D, + crossattn_mask, + rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, + ) + x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W) + return x_T_H_W_B_D + + +def adaln_norm_state(norm_state, x, scale, shift): + normalized = norm_state(x) + return normalized * (1 + scale) + shift + + +class DITBuildingBlock(nn.Module): + """ + A building block for the DiT (Diffusion Transformer) architecture that supports different types of + attention and MLP operations with adaptive layer normalization. + + Parameters: + block_type (str): Type of block - one of: + - "cross_attn"/"ca": Cross-attention + - "full_attn"/"fa": Full self-attention + - "mlp"/"ff": MLP/feedforward block + x_dim (int): Dimension of input features + context_dim (Optional[int]): Dimension of context features for cross-attention + num_heads (int): Number of attention heads + mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 + bias (bool): Whether to use bias in layers. Default: False + mlp_dropout (float): Dropout rate for MLP. Default: 0.0 + qkv_norm_mode (str): QKV normalization mode. Default: "per_head" + x_format (str): Input tensor format. Default: "BTHWD" + use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False + adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 + """ + + def __init__( + self, + block_type: str, + x_dim: int, + context_dim: Optional[int], + num_heads: int, + mlp_ratio: float = 4.0, + bias: bool = False, + mlp_dropout: float = 0.0, + qkv_norm_mode: str = "per_head", + x_format: str = "BTHWD", + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None + ) -> None: + block_type = block_type.lower() + + super().__init__() + self.x_format = x_format + if block_type in ["cross_attn", "ca"]: + self.block = VideoAttn( + x_dim, + context_dim, + num_heads, + bias=bias, + qkv_norm_mode=qkv_norm_mode, + x_format=self.x_format, + weight_args=weight_args, + operations=operations, + ) + elif block_type in ["full_attn", "fa"]: + self.block = VideoAttn( + x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations + ) + elif block_type in ["mlp", "ff"]: + self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations) + else: + raise ValueError(f"Unknown block type: {block_type}") + + self.block_type = block_type + self.use_adaln_lora = use_adaln_lora + + self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6) + self.n_adaln_chunks = 3 + if use_adaln_lora: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args), + ) + else: + self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args)) + + def forward( + self, + x: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + """ + Forward pass for dynamically configured blocks with adaptive normalization. + + Args: + x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D). + emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation. + crossattn_emb (Tensor): Tensor for cross-attention blocks. + crossattn_mask (Optional[Tensor]): Optional mask for cross-attention. + rope_emb_L_1_1_D (Optional[Tensor]): + Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training. + + Returns: + Tensor: The output tensor after processing through the configured block and adaptive normalization. + """ + if self.use_adaln_lora: + shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk( + self.n_adaln_chunks, dim=1 + ) + else: + shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1) + + shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = ( + shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0), + ) + + if self.block_type in ["mlp", "ff"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + ) + elif self.block_type in ["full_attn", "fa"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + context=None, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + transformer_options=transformer_options, + ) + elif self.block_type in ["cross_attn", "ca"]: + x = x + gate_1_1_1_B_D * self.block( + adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D), + context=crossattn_emb, + crossattn_mask=crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + transformer_options=transformer_options, + ) + else: + raise ValueError(f"Unknown block type: {self.block_type}") + + return x + + +class GeneralDITTransformerBlock(nn.Module): + """ + A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer. + Each block in the sequence is specified by a block configuration string. + + Parameters: + x_dim (int): Dimension of input features + context_dim (int): Dimension of context features for cross-attention blocks + num_heads (int): Number of attention heads + block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention, + full-attention, then MLP) + mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0 + x_format (str): Input tensor format. Default: "BTHWD" + use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False + adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256 + + The block_config string uses "-" to separate block types: + - "ca"/"cross_attn": Cross-attention block + - "fa"/"full_attn": Full self-attention block + - "mlp"/"ff": MLP/feedforward block + + Example: + block_config = "ca-fa-mlp" creates a sequence of: + 1. Cross-attention block + 2. Full self-attention block + 3. MLP block + """ + + def __init__( + self, + x_dim: int, + context_dim: int, + num_heads: int, + block_config: str, + mlp_ratio: float = 4.0, + x_format: str = "BTHWD", + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + weight_args={}, + operations=None + ): + super().__init__() + self.blocks = nn.ModuleList() + self.x_format = x_format + for block_type in block_config.split("-"): + self.blocks.append( + DITBuildingBlock( + block_type, + x_dim, + context_dim, + num_heads, + mlp_ratio, + x_format=self.x_format, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + ) + + def forward( + self, + x: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + for block in self.blocks: + x = block( + x, + emb_B_D, + crossattn_emb, + crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + adaln_lora_B_3D=adaln_lora_B_3D, + transformer_options=transformer_options, + ) + return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py b/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py new file mode 100644 index 000000000..9a3ebed6a --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py @@ -0,0 +1,1041 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The model definition for 3D layers + +Adapted from: https://github.com/lucidrains/magvit2-pytorch/blob/ +9f49074179c912736e617d61b32be367eb5f993a/magvit2_pytorch/magvit2_pytorch.py#L889 + +[MIT License Copyright (c) 2023 Phil Wang] +https://github.com/lucidrains/magvit2-pytorch/blob/ +9f49074179c912736e617d61b32be367eb5f993a/LICENSE +""" +import math +from typing import Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import logging + +from comfy.ldm.modules.diffusionmodules.model import vae_attention + +from .patching import ( + Patcher, + Patcher3D, + UnPatcher, + UnPatcher3D, +) +from .utils import ( + CausalNormalize, + batch2space, + batch2time, + cast_tuple, + is_odd, + nonlinearity, + replication_pad, + space2batch, + time2batch, +) + +import comfy.ops +ops = comfy.ops.disable_weight_init + +_LEGACY_NUM_GROUPS = 32 + + +class CausalConv3d(nn.Module): + def __init__( + self, + chan_in: int = 1, + chan_out: int = 1, + kernel_size: Union[int, Tuple[int, int, int]] = 3, + pad_mode: str = "constant", + **kwargs, + ): + super().__init__() + kernel_size = cast_tuple(kernel_size, 3) + + time_kernel_size, height_kernel_size, width_kernel_size = kernel_size + + assert is_odd(height_kernel_size) and is_odd(width_kernel_size) + + dilation = kwargs.pop("dilation", 1) + stride = kwargs.pop("stride", 1) + time_stride = kwargs.pop("time_stride", 1) + time_dilation = kwargs.pop("time_dilation", 1) + padding = kwargs.pop("padding", 1) + + self.pad_mode = pad_mode + time_pad = time_dilation * (time_kernel_size - 1) + (1 - time_stride) + self.time_pad = time_pad + + self.spatial_pad = (padding, padding, padding, padding) + + stride = (time_stride, stride, stride) + dilation = (time_dilation, dilation, dilation) + self.conv3d = ops.Conv3d( + chan_in, + chan_out, + kernel_size, + stride=stride, + dilation=dilation, + **kwargs, + ) + + def _replication_pad(self, x: torch.Tensor) -> torch.Tensor: + x_prev = x[:, :, :1, ...].repeat(1, 1, self.time_pad, 1, 1) + x = torch.cat([x_prev, x], dim=2) + padding = self.spatial_pad + (0, 0) + return F.pad(x, padding, mode=self.pad_mode, value=0.0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self._replication_pad(x) + return self.conv3d(x) + + +class CausalUpsample3d(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.conv = CausalConv3d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) + time_factor = 1.0 + 1.0 * (x.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + x = x.repeat_interleave(int(time_factor), dim=2) + # TODO(freda): Check if this causes temporal inconsistency. + # Shoule reverse the order of the following two ops, + # better perf and better temporal smoothness. + x = self.conv(x) + return x[..., int(time_factor - 1) :, :, :] + + +class CausalDownsample3d(nn.Module): + def __init__(self, in_channels: int) -> None: + super().__init__() + self.conv = CausalConv3d( + in_channels, + in_channels, + kernel_size=3, + stride=2, + time_stride=2, + padding=0, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + pad = (0, 1, 0, 1, 0, 0) + x = F.pad(x, pad, mode="constant", value=0) + x = replication_pad(x) + x = self.conv(x) + return x + + +class CausalHybridUpsample3d(nn.Module): + def __init__( + self, + in_channels: int, + spatial_up: bool = True, + temporal_up: bool = True, + **kwargs, + ) -> None: + super().__init__() + self.spatial_up = spatial_up + self.temporal_up = temporal_up + if not self.spatial_up and not self.temporal_up: + return + + self.conv1 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(3, 1, 1), + stride=1, + time_stride=1, + padding=0, + ) + self.conv2 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(1, 3, 3), + stride=1, + time_stride=1, + padding=1, + ) + self.conv3 = CausalConv3d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + time_stride=1, + padding=0, + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.spatial_up and not self.temporal_up: + return x + + # hybrid upsample temporally. + if self.temporal_up: + time_factor = 1.0 + 1.0 * (x.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + x = x.repeat_interleave(int(time_factor), dim=2) + x = x[..., int(time_factor - 1) :, :, :] + x = self.conv1(x) + x + + # hybrid upsample spatially. + if self.spatial_up: + x = x.repeat_interleave(2, dim=3).repeat_interleave(2, dim=4) + x = self.conv2(x) + x + + # final 1x1x1 conv. + x = self.conv3(x) + return x + + +class CausalHybridDownsample3d(nn.Module): + def __init__( + self, + in_channels: int, + spatial_down: bool = True, + temporal_down: bool = True, + **kwargs, + ) -> None: + super().__init__() + self.spatial_down = spatial_down + self.temporal_down = temporal_down + if not self.spatial_down and not self.temporal_down: + return + + self.conv1 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(1, 3, 3), + stride=2, + time_stride=1, + padding=0, + ) + self.conv2 = CausalConv3d( + in_channels, + in_channels, + kernel_size=(3, 1, 1), + stride=1, + time_stride=2, + padding=0, + ) + self.conv3 = CausalConv3d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + time_stride=1, + padding=0, + ) + + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if not self.spatial_down and not self.temporal_down: + return x + + # hybrid downsample spatially. + if self.spatial_down: + pad = (0, 1, 0, 1, 0, 0) + x = F.pad(x, pad, mode="constant", value=0) + x1 = self.conv1(x) + x2 = F.avg_pool3d(x, kernel_size=(1, 2, 2), stride=(1, 2, 2)) + x = x1 + x2 + + # hybrid downsample temporally. + if self.temporal_down: + x = replication_pad(x) + x1 = self.conv2(x) + x2 = F.avg_pool3d(x, kernel_size=(2, 1, 1), stride=(2, 1, 1)) + x = x1 + x2 + + # final 1x1x1 conv. + x = self.conv3(x) + return x + + +class CausalResnetBlock3d(nn.Module): + def __init__( + self, + *, + in_channels: int, + out_channels: int = None, + dropout: float, + num_groups: int, + ) -> None: + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + + self.norm1 = CausalNormalize(in_channels, num_groups=num_groups) + self.conv1 = CausalConv3d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = CausalConv3d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + self.nin_shortcut = ( + CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + if in_channels != out_channels + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + x = self.nin_shortcut(x) + + return x + h + + +class CausalResnetBlockFactorized3d(nn.Module): + def __init__( + self, + *, + in_channels: int, + out_channels: int = None, + dropout: float, + num_groups: int, + ) -> None: + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + + self.norm1 = CausalNormalize(in_channels, num_groups=1) + self.conv1 = nn.Sequential( + CausalConv3d( + in_channels, + out_channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + out_channels, + out_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + self.norm2 = CausalNormalize(out_channels, num_groups=num_groups) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = nn.Sequential( + CausalConv3d( + out_channels, + out_channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + out_channels, + out_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + self.nin_shortcut = ( + CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + if in_channels != out_channels + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + x = self.nin_shortcut(x) + + return x + h + + +class CausalAttnBlock(nn.Module): + def __init__(self, in_channels: int, num_groups: int) -> None: + super().__init__() + + self.norm = CausalNormalize(in_channels, num_groups=num_groups) + self.q = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + self.optimized_attention = vae_attention() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + q, batch_size = time2batch(q) + k, batch_size = time2batch(k) + v, batch_size = time2batch(v) + + b, c, h, w = q.shape + h_ = self.optimized_attention(q, k, v) + + h_ = batch2time(h_, batch_size) + h_ = self.proj_out(h_) + return x + h_ + + +class CausalTemporalAttnBlock(nn.Module): + def __init__(self, in_channels: int, num_groups: int) -> None: + super().__init__() + + self.norm = CausalNormalize(in_channels, num_groups=num_groups) + self.q = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = CausalConv3d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + q, batch_size, height = space2batch(q) + k, _, _ = space2batch(k) + v, _, _ = space2batch(v) + + bhw, c, t = q.shape + q = q.permute(0, 2, 1) # (bhw, t, c) + k = k.permute(0, 2, 1) # (bhw, t, c) + v = v.permute(0, 2, 1) # (bhw, t, c) + + w_ = torch.bmm(q, k.permute(0, 2, 1)) # (bhw, t, t) + w_ = w_ * (int(c) ** (-0.5)) + + # Apply causal mask + mask = torch.tril(torch.ones_like(w_)) + w_ = w_.masked_fill(mask == 0, float("-inf")) + w_ = F.softmax(w_, dim=2) + + # attend to values + h_ = torch.bmm(w_, v) # (bhw, t, c) + h_ = h_.permute(0, 2, 1).reshape(bhw, c, t) # (bhw, c, t) + + h_ = batch2space(h_, batch_size, height) + h_ = self.proj_out(h_) + return x + h_ + + +class EncoderBase(nn.Module): + def __init__( + self, + in_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + **ignore_kwargs, + ) -> None: + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # Patcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.patcher = Patcher( + patch_size, ignore_kwargs.get("patch_method", "rearrange") + ) + in_channels = in_channels * patch_size * patch_size + + # downsampling + self.conv_in = CausalConv3d( + in_channels, channels, kernel_size=3, stride=1, padding=1 + ) + + # num of groups for GroupNorm, num_groups=1 for LayerNorm. + num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) + curr_res = resolution // patch_size + in_ch_mult = (1,) + tuple(channels_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = channels * in_ch_mult[i_level] + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks): + block.append( + CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=num_groups, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = CausalDownsample3d(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) + self.mid.block_2 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + + # end + self.norm_out = CausalNormalize(block_in, num_groups=num_groups) + self.conv_out = CausalConv3d( + block_in, z_channels, kernel_size=3, stride=1, padding=1 + ) + + def patcher3d(self, x: torch.Tensor) -> torch.Tensor: + x, batch_size = time2batch(x) + x = self.patcher(x) + x = batch2time(x, batch_size) + return x + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patcher3d(x) + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + else: + # temporal downsample (last level) + time_factor = 1 + 1 * (hs[-1].shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + hs[-1] = replication_pad(hs[-1]) + hs.append( + F.avg_pool3d( + hs[-1], + kernel_size=[time_factor, 1, 1], + stride=[2, 1, 1], + ) + ) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class DecoderBase(nn.Module): + def __init__( + self, + out_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + **ignore_kwargs, + ): + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # UnPatcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.unpatcher = UnPatcher( + patch_size, ignore_kwargs.get("patch_method", "rearrange") + ) + out_ch = out_channels * patch_size * patch_size + + block_in = channels * channels_mult[self.num_resolutions - 1] + curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logging.debug( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = CausalConv3d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # num of groups for GroupNorm, num_groups=1 for LayerNorm. + num_groups = ignore_kwargs.get("num_groups", _LEGACY_NUM_GROUPS) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + self.mid.attn_1 = CausalAttnBlock(block_in, num_groups=num_groups) + self.mid.block_2 = CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=num_groups, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append( + CausalResnetBlock3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=num_groups, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(CausalAttnBlock(block_in, num_groups=num_groups)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = CausalUpsample3d(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = CausalNormalize(block_in, num_groups=num_groups) + self.conv_out = CausalConv3d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor: + x, batch_size = time2batch(x) + x = self.unpatcher(x) + x = batch2time(x, batch_size) + + return x + + def forward(self, z): + h = self.conv_in(z) + + # middle block. + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # decoder blocks. + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + else: + # temporal upsample (last level) + time_factor = 1.0 + 1.0 * (h.shape[2] > 1) + if isinstance(time_factor, torch.Tensor): + time_factor = time_factor.item() + h = h.repeat_interleave(int(time_factor), dim=2) + h = h[..., int(time_factor - 1) :, :, :] + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + h = self.unpatcher3d(h) + return h + + +class EncoderFactorized(nn.Module): + def __init__( + self, + in_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + spatial_compression: int = 8, + temporal_compression: int = 8, + **ignore_kwargs, + ) -> None: + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # Patcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.patcher3d = Patcher3D( + patch_size, ignore_kwargs.get("patch_method", "haar") + ) + in_channels = in_channels * patch_size * patch_size * patch_size + + # calculate the number of downsample operations + self.num_spatial_downs = int(math.log2(spatial_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_spatial_downs <= self.num_resolutions + ), f"Spatially downsample {self.num_resolutions} times at most" + + self.num_temporal_downs = int(math.log2(temporal_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_temporal_downs <= self.num_resolutions + ), f"Temporally downsample {self.num_resolutions} times at most" + + # downsampling + self.conv_in = nn.Sequential( + CausalConv3d( + in_channels, + channels, + kernel_size=(1, 3, 3), + stride=1, + padding=1, + ), + CausalConv3d( + channels, channels, kernel_size=(3, 1, 1), stride=1, padding=0 + ), + ) + + curr_res = resolution // patch_size + in_ch_mult = (1,) + tuple(channels_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = channels * in_ch_mult[i_level] + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks): + block.append( + CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=1, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append( + nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + ) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + spatial_down = i_level < self.num_spatial_downs + temporal_down = i_level < self.num_temporal_downs + down.downsample = CausalHybridDownsample3d( + block_in, + spatial_down=spatial_down, + temporal_down=temporal_down, + ) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + self.mid.attn_1 = nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + self.mid.block_2 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + + # end + self.norm_out = CausalNormalize(block_in, num_groups=1) + self.conv_out = nn.Sequential( + CausalConv3d( + block_in, z_channels, kernel_size=(1, 3, 3), stride=1, padding=1 + ), + CausalConv3d( + z_channels, + z_channels, + kernel_size=(3, 1, 1), + stride=1, + padding=0, + ), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patcher3d(x) + + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions - 1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class DecoderFactorized(nn.Module): + def __init__( + self, + out_channels: int, + channels: int, + channels_mult: list[int], + num_res_blocks: int, + attn_resolutions: list[int], + dropout: float, + resolution: int, + z_channels: int, + spatial_compression: int = 8, + temporal_compression: int = 8, + **ignore_kwargs, + ): + super().__init__() + self.num_resolutions = len(channels_mult) + self.num_res_blocks = num_res_blocks + + # UnPatcher. + patch_size = ignore_kwargs.get("patch_size", 1) + self.unpatcher3d = UnPatcher3D( + patch_size, ignore_kwargs.get("patch_method", "haar") + ) + out_ch = out_channels * patch_size * patch_size * patch_size + + # calculate the number of upsample operations + self.num_spatial_ups = int(math.log2(spatial_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_spatial_ups <= self.num_resolutions + ), f"Spatially upsample {self.num_resolutions} times at most" + self.num_temporal_ups = int(math.log2(temporal_compression)) - int( + math.log2(patch_size) + ) + assert ( + self.num_temporal_ups <= self.num_resolutions + ), f"Temporally upsample {self.num_resolutions} times at most" + + block_in = channels * channels_mult[self.num_resolutions - 1] + curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + logging.debug( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = nn.Sequential( + CausalConv3d( + z_channels, block_in, kernel_size=(1, 3, 3), stride=1, padding=1 + ), + CausalConv3d( + block_in, block_in, kernel_size=(3, 1, 1), stride=1, padding=0 + ), + ) + + # middle + self.mid = nn.Module() + self.mid.block_1 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + self.mid.attn_1 = nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + self.mid.block_2 = CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_in, + dropout=dropout, + num_groups=1, + ) + + legacy_mode = ignore_kwargs.get("legacy_mode", False) + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = channels * channels_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append( + CausalResnetBlockFactorized3d( + in_channels=block_in, + out_channels=block_out, + dropout=dropout, + num_groups=1, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append( + nn.Sequential( + CausalAttnBlock(block_in, num_groups=1), + CausalTemporalAttnBlock(block_in, num_groups=1), + ) + ) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + # The layer index for temporal/spatial downsampling performed + # in the encoder should correspond to the layer index in + # reverse order where upsampling is performed in the decoder. + # If you've a pre-trained model, you can simply finetune. + i_level_reverse = self.num_resolutions - i_level - 1 + if legacy_mode: + temporal_up = i_level_reverse < self.num_temporal_ups + else: + temporal_up = 0 < i_level_reverse < self.num_temporal_ups + 1 + spatial_up = temporal_up or ( + i_level_reverse < self.num_spatial_ups + and self.num_spatial_ups > self.num_temporal_ups + ) + up.upsample = CausalHybridUpsample3d( + block_in, spatial_up=spatial_up, temporal_up=temporal_up + ) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = CausalNormalize(block_in, num_groups=1) + self.conv_out = nn.Sequential( + CausalConv3d(block_in, out_ch, kernel_size=(1, 3, 3), stride=1, padding=1), + CausalConv3d(out_ch, out_ch, kernel_size=(3, 1, 1), stride=1, padding=0), + ) + + def forward(self, z): + h = self.conv_in(z) + + # middle block. + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # decoder blocks. + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + h = self.unpatcher3d(h) + return h diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/patching.py b/comfy/ldm/cosmos/cosmos_tokenizer/patching.py new file mode 100644 index 000000000..87a53a1d9 --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/patching.py @@ -0,0 +1,377 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The patcher and unpatcher implementation for 2D and 3D data. + +The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions. +One on the rows and one on the columns. +For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2. +We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component. +For H component, we can use a 1D convolution with kernel [1, -1] and stride 2. +Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all + as we need to support downsampling for more than 2x. +For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be. + [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64] +""" + +import torch +import torch.nn.functional as F +from einops import rearrange + +_WAVELETS = { + "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]), + "rearrange": torch.tensor([1.0, 1.0]), +} +_PERSISTENT = False + + +class Patcher(torch.nn.Module): + """A module to convert image tensors into patches using torch operations. + + The main difference from `class Patching` is that this module implements + all operations using torch, rather than python or numpy, for efficiency purpose. + + It's bit-wise identical to the Patching module outputs, with the added + benefit of being torch.jit scriptable. + """ + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__() + self.patch_size = patch_size + self.patch_method = patch_method + self.register_buffer( + "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT + ) + self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) + self.register_buffer( + "_arange", + torch.arange(_WAVELETS[patch_method].shape[0]), + persistent=_PERSISTENT, + ) + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + if self.patch_method == "haar": + return self._haar(x) + elif self.patch_method == "rearrange": + return self._arrange(x) + else: + raise ValueError("Unknown patch method: " + self.patch_method) + + def _dwt(self, x, mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + n = h.shape[0] + g = x.shape[1] + hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype) + xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2)) + xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2)) + xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1)) + xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1)) + xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1)) + xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1)) + + out = torch.cat([xll, xlh, xhl, xhh], dim=1) + if rescale: + out = out / 2 + return out + + def _haar(self, x): + for _ in self.range: + x = self._dwt(x, rescale=True) + return x + + def _arrange(self, x): + x = rearrange( + x, + "b c (h p1) (w p2) -> b (c p1 p2) h w", + p1=self.patch_size, + p2=self.patch_size, + ).contiguous() + return x + + +class Patcher3D(Patcher): + """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos.""" + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__(patch_method=patch_method, patch_size=patch_size) + self.register_buffer( + "patch_size_buffer", + patch_size * torch.ones([1], dtype=torch.int32), + persistent=_PERSISTENT, + ) + + def _dwt(self, x, wavelet, mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + n = h.shape[0] + g = x.shape[1] + hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + # Handles temporal axis. + x = F.pad( + x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode + ).to(dtype) + xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) + xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)) + + # Handles spatial axes. + xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)) + + xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)) + + out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1) + if rescale: + out = out / (2 * torch.sqrt(torch.tensor(2.0))) + return out + + def _haar(self, x): + xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) + x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) + for _ in self.range: + x = self._dwt(x, "haar", rescale=True) + return x + + def _arrange(self, x): + xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2) + x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2) + x = rearrange( + x, + "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w", + p1=self.patch_size, + p2=self.patch_size, + p3=self.patch_size, + ).contiguous() + return x + + +class UnPatcher(torch.nn.Module): + """A module to convert patches into image tensorsusing torch operations. + + The main difference from `class Unpatching` is that this module implements + all operations using torch, rather than python or numpy, for efficiency purpose. + + It's bit-wise identical to the Unpatching module outputs, with the added + benefit of being torch.jit scriptable. + """ + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__() + self.patch_size = patch_size + self.patch_method = patch_method + self.register_buffer( + "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT + ) + self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item())) + self.register_buffer( + "_arange", + torch.arange(_WAVELETS[patch_method].shape[0]), + persistent=_PERSISTENT, + ) + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x): + if self.patch_method == "haar": + return self._ihaar(x) + elif self.patch_method == "rearrange": + return self._iarrange(x) + else: + raise ValueError("Unknown patch method: " + self.patch_method) + + def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + n = h.shape[0] + + g = x.shape[1] // 4 + hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hh = hh.to(dtype=dtype) + hl = hl.to(dtype=dtype) + + xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1) + + # Inverse transform. + yl = torch.nn.functional.conv_transpose2d( + xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yl += torch.nn.functional.conv_transpose2d( + xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yh = torch.nn.functional.conv_transpose2d( + xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + yh += torch.nn.functional.conv_transpose2d( + xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0) + ) + y = torch.nn.functional.conv_transpose2d( + yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) + ) + y += torch.nn.functional.conv_transpose2d( + yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2) + ) + + if rescale: + y = y * 2 + return y + + def _ihaar(self, x): + for _ in self.range: + x = self._idwt(x, "haar", rescale=True) + return x + + def _iarrange(self, x): + x = rearrange( + x, + "b (c p1 p2) h w -> b c (h p1) (w p2)", + p1=self.patch_size, + p2=self.patch_size, + ) + return x + + +class UnPatcher3D(UnPatcher): + """A 3D inverse discrete wavelet transform for video wavelet decompositions.""" + + def __init__(self, patch_size=1, patch_method="haar"): + super().__init__(patch_method=patch_method, patch_size=patch_size) + + def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False): + dtype = x.dtype + h = self.wavelets.to(device=x.device) + + g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors. + hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1]) + hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1) + hl = hl.to(dtype=dtype) + hh = hh.to(dtype=dtype) + + xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1) + del x + + # Height height transposed convolutions. + xll = F.conv_transpose3d( + xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlll + + xll += F.conv_transpose3d( + xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xllh + + xlh = F.conv_transpose3d( + xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlhl + + xlh += F.conv_transpose3d( + xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xlhh + + xhl = F.conv_transpose3d( + xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhll + + xhl += F.conv_transpose3d( + xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhlh + + xhh = F.conv_transpose3d( + xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhhl + + xhh += F.conv_transpose3d( + xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2) + ) + del xhhh + + # Handles width transposed convolutions. + xl = F.conv_transpose3d( + xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xll + + xl += F.conv_transpose3d( + xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xlh + + xh = F.conv_transpose3d( + xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xhl + + xh += F.conv_transpose3d( + xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1) + ) + del xhh + + # Handles time axis transposed convolutions. + x = F.conv_transpose3d( + xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) + ) + del xl + + x += F.conv_transpose3d( + xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1) + ) + + if rescale: + x = x * (2 * torch.sqrt(torch.tensor(2.0))) + return x + + def _ihaar(self, x): + for _ in self.range: + x = self._idwt(x, "haar", rescale=True) + x = x[:, :, self.patch_size - 1 :, ...] + return x + + def _iarrange(self, x): + x = rearrange( + x, + "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)", + p1=self.patch_size, + p2=self.patch_size, + p3=self.patch_size, + ) + x = x[:, :, self.patch_size - 1 :, ...] + return x diff --git a/comfy/ldm/cosmos/cosmos_tokenizer/utils.py b/comfy/ldm/cosmos/cosmos_tokenizer/utils.py new file mode 100644 index 000000000..ca993006f --- /dev/null +++ b/comfy/ldm/cosmos/cosmos_tokenizer/utils.py @@ -0,0 +1,113 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Shared utilities for the networks module.""" + +from typing import Any + +import torch +from einops import rearrange + + +import comfy.ops +ops = comfy.ops.disable_weight_init + +def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: + batch_size = x.shape[0] + return rearrange(x, "b c t h w -> (b t) c h w"), batch_size + + +def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor: + return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size) + + +def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]: + batch_size, height = x.shape[0], x.shape[-2] + return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height + + +def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor: + return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height) + + +def cast_tuple(t: Any, length: int = 1) -> Any: + return t if isinstance(t, tuple) else ((t,) * length) + + +def replication_pad(x): + return torch.cat([x[:, :, :1, ...], x], dim=2) + + +def divisible_by(num: int, den: int) -> bool: + return (num % den) == 0 + + +def is_odd(n: int) -> bool: + return not divisible_by(n, 2) + + +def nonlinearity(x): + # x * sigmoid(x) + return torch.nn.functional.silu(x) + + +def Normalize(in_channels, num_groups=32): + return ops.GroupNorm( + num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class CausalNormalize(torch.nn.Module): + def __init__(self, in_channels, num_groups=1): + super().__init__() + self.norm = ops.GroupNorm( + num_groups=num_groups, + num_channels=in_channels, + eps=1e-6, + affine=True, + ) + self.num_groups = num_groups + + def forward(self, x): + # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose. + # All new models should use num_groups=1, otherwise causality is not guaranteed. + if self.num_groups == 1: + x, batch_size = time2batch(x) + return batch2time(self.norm(x), batch_size) + return self.norm(x) + + +def exists(v): + return v is not None + + +def default(*args): + for arg in args: + if exists(arg): + return arg + return None + + +def round_ste(z: torch.Tensor) -> torch.Tensor: + """Round with straight through gradients.""" + zhat = z.round() + return z + (zhat - z).detach() + + +def log(t, eps=1e-5): + return t.clamp(min=eps).log() + + +def entropy(prob): + return (-prob * log(prob)).sum(dim=-1) diff --git a/comfy/ldm/cosmos/model.py b/comfy/ldm/cosmos/model.py new file mode 100644 index 000000000..52ef7ef43 --- /dev/null +++ b/comfy/ldm/cosmos/model.py @@ -0,0 +1,552 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. +""" + +from typing import Optional, Tuple + +import torch +from einops import rearrange +from torch import nn +from torchvision import transforms + +from enum import Enum +import logging + +import comfy.patcher_extension + +from .blocks import ( + FinalLayer, + GeneralDITTransformerBlock, + PatchEmbed, + TimestepEmbedding, + Timesteps, +) + +from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb + + +class DataType(Enum): + IMAGE = "image" + VIDEO = "video" + + +class GeneralDIT(nn.Module): + """ + A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. + + Args: + max_img_h (int): Maximum height of the input images. + max_img_w (int): Maximum width of the input images. + max_frames (int): Maximum number of frames in the video sequence. + in_channels (int): Number of input channels (e.g., RGB channels for color images). + out_channels (int): Number of output channels. + patch_spatial (tuple): Spatial resolution of patches for input processing. + patch_temporal (int): Temporal resolution of patches for input processing. + concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. + block_config (str): Configuration of the transformer block. See Notes for supported block types. + model_channels (int): Base number of channels used throughout the model. + num_blocks (int): Number of transformer blocks. + num_heads (int): Number of heads in the multi-head attention layers. + mlp_ratio (float): Expansion ratio for MLP blocks. + block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD'). + crossattn_emb_channels (int): Number of embedding channels for cross-attention. + use_cross_attn_mask (bool): Whether to use mask in cross-attention. + pos_emb_cls (str): Type of positional embeddings. + pos_emb_learnable (bool): Whether positional embeddings are learnable. + pos_emb_interpolation (str): Method for interpolating positional embeddings. + affline_emb_norm (bool): Whether to normalize affine embeddings. + use_adaln_lora (bool): Whether to use AdaLN-LoRA. + adaln_lora_dim (int): Dimension for AdaLN-LoRA. + rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. + rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. + rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. + extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. + extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings. + extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. + extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. + extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. + + Notes: + Supported block types in block_config: + * cross_attn, ca: Cross attention + * full_attn: Full attention on all flattened tokens + * mlp, ff: Feed forward block + """ + + def __init__( + self, + max_img_h: int, + max_img_w: int, + max_frames: int, + in_channels: int, + out_channels: int, + patch_spatial: tuple, + patch_temporal: int, + concat_padding_mask: bool = True, + # attention settings + block_config: str = "FA-CA-MLP", + model_channels: int = 768, + num_blocks: int = 10, + num_heads: int = 16, + mlp_ratio: float = 4.0, + block_x_format: str = "BTHWD", + # cross attention settings + crossattn_emb_channels: int = 1024, + use_cross_attn_mask: bool = False, + # positional embedding settings + pos_emb_cls: str = "sincos", + pos_emb_learnable: bool = False, + pos_emb_interpolation: str = "crop", + affline_emb_norm: bool = False, # whether or not to normalize the affine embedding + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + rope_h_extrapolation_ratio: float = 1.0, + rope_w_extrapolation_ratio: float = 1.0, + rope_t_extrapolation_ratio: float = 1.0, + extra_per_block_abs_pos_emb: bool = False, + extra_per_block_abs_pos_emb_type: str = "sincos", + extra_h_extrapolation_ratio: float = 1.0, + extra_w_extrapolation_ratio: float = 1.0, + extra_t_extrapolation_ratio: float = 1.0, + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.max_img_h = max_img_h + self.max_img_w = max_img_w + self.max_frames = max_frames + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_spatial = patch_spatial + self.patch_temporal = patch_temporal + self.num_heads = num_heads + self.num_blocks = num_blocks + self.model_channels = model_channels + self.use_cross_attn_mask = use_cross_attn_mask + self.concat_padding_mask = concat_padding_mask + # positional embedding settings + self.pos_emb_cls = pos_emb_cls + self.pos_emb_learnable = pos_emb_learnable + self.pos_emb_interpolation = pos_emb_interpolation + self.affline_emb_norm = affline_emb_norm + self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio + self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio + self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio + self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb + self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower() + self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio + self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio + self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio + self.dtype = dtype + weight_args = {"device": device, "dtype": dtype} + + in_channels = in_channels + 1 if concat_padding_mask else in_channels + self.x_embedder = PatchEmbed( + spatial_patch_size=patch_spatial, + temporal_patch_size=patch_temporal, + in_channels=in_channels, + out_channels=model_channels, + bias=False, + weight_args=weight_args, + operations=operations, + ) + + self.build_pos_embed(device=device, dtype=dtype) + self.block_x_format = block_x_format + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + self.t_embedder = nn.ModuleList( + [Timesteps(model_channels), + TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),] + ) + + self.blocks = nn.ModuleDict() + + for idx in range(num_blocks): + self.blocks[f"block{idx}"] = GeneralDITTransformerBlock( + x_dim=model_channels, + context_dim=crossattn_emb_channels, + num_heads=num_heads, + block_config=block_config, + mlp_ratio=mlp_ratio, + x_format=self.block_x_format, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + + if self.affline_emb_norm: + logging.debug("Building affine embedding normalization layer") + self.affline_norm = operations.RMSNorm(model_channels, elementwise_affine=True, eps=1e-6, device=device, dtype=dtype) + else: + self.affline_norm = nn.Identity() + + self.final_layer = FinalLayer( + hidden_size=self.model_channels, + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + out_channels=self.out_channels, + use_adaln_lora=self.use_adaln_lora, + adaln_lora_dim=self.adaln_lora_dim, + weight_args=weight_args, + operations=operations, + ) + + def build_pos_embed(self, device=None, dtype=None): + if self.pos_emb_cls == "rope3d": + cls_type = VideoRopePosition3DEmb + else: + raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") + + logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") + kwargs = dict( + model_channels=self.model_channels, + len_h=self.max_img_h // self.patch_spatial, + len_w=self.max_img_w // self.patch_spatial, + len_t=self.max_frames // self.patch_temporal, + is_learnable=self.pos_emb_learnable, + interpolation=self.pos_emb_interpolation, + head_dim=self.model_channels // self.num_heads, + h_extrapolation_ratio=self.rope_h_extrapolation_ratio, + w_extrapolation_ratio=self.rope_w_extrapolation_ratio, + t_extrapolation_ratio=self.rope_t_extrapolation_ratio, + device=device, + ) + self.pos_embedder = cls_type( + **kwargs, + ) + + if self.extra_per_block_abs_pos_emb: + assert self.extra_per_block_abs_pos_emb_type in [ + "learnable", + ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}" + kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio + kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio + kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio + kwargs["device"] = device + kwargs["dtype"] = dtype + self.extra_pos_embedder = LearnablePosEmbAxis( + **kwargs, + ) + + def prepare_embedded_sequence( + self, + x_B_C_T_H_W: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. + + Args: + x_B_C_T_H_W (torch.Tensor): video + fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. + If None, a default value (`self.base_fps`) will be used. + padding_mask (Optional[torch.Tensor]): current it is not used + + Returns: + Tuple[torch.Tensor, Optional[torch.Tensor]]: + - A tensor of shape (B, T, H, W, D) with the embedded sequence. + - An optional positional embedding tensor, returned only if the positional embedding class + (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. + + Notes: + - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. + - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. + - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using + the `self.pos_embedder` with the shape [T, H, W]. + - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the + `self.pos_embedder` with the fps tensor. + - Otherwise, the positional embeddings are generated without considering fps. + """ + if self.concat_padding_mask: + if padding_mask is not None: + padding_mask = transforms.functional.resize( + padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST + ) + else: + padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) + + x_B_C_T_H_W = torch.cat( + [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 + ) + x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) + + if self.extra_per_block_abs_pos_emb: + extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) + else: + extra_pos_emb = None + + if "rope" in self.pos_emb_cls.lower(): + return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb + + if "fps_aware" in self.pos_emb_cls: + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + else: + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + + return x_B_T_H_W_D, None, extra_pos_emb + + def decoder_head( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_D: torch.Tensor, + crossattn_emb: torch.Tensor, + origin_shape: Tuple[int, int, int, int, int], # [B, C, T, H, W] + crossattn_mask: Optional[torch.Tensor] = None, + adaln_lora_B_3D: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + del crossattn_emb, crossattn_mask + B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape + x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D") + x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D) + # This is to ensure x_BT_HW_D has the correct shape because + # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D). + x_BT_HW_D = x_BT_HW_D.view( + B * T_before_patchify // self.patch_temporal, + H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial, + -1, + ) + x_B_D_T_H_W = rearrange( + x_BT_HW_D, + "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)", + p1=self.patch_spatial, + p2=self.patch_spatial, + H=H_before_patchify // self.patch_spatial, + W=W_before_patchify // self.patch_spatial, + t=self.patch_temporal, + B=B, + ) + return x_B_D_T_H_W + + def forward_before_blocks( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + crossattn_emb: torch.Tensor, + crossattn_mask: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + image_size: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + scalar_feature: Optional[torch.Tensor] = None, + data_type: Optional[DataType] = DataType.VIDEO, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + crossattn_mask: (B, N) tensor of cross-attention masks + """ + del kwargs + assert isinstance( + data_type, DataType + ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later." + original_shape = x.shape + x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( + x, + fps=fps, + padding_mask=padding_mask, + latent_condition=latent_condition, + latent_condition_sigma=latent_condition_sigma, + ) + # logging affline scale information + affline_scale_log_info = {} + + timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype)) + affline_emb_B_D = timesteps_B_D + affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach() + + if scalar_feature is not None: + raise NotImplementedError("Scalar feature is not implemented yet.") + + affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach() + affline_emb_B_D = self.affline_norm(affline_emb_B_D) + + if self.use_cross_attn_mask: + if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask): + crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max + crossattn_mask = crossattn_mask[:, None, None, :] # .to(dtype=torch.bool) # [B, 1, 1, length] + else: + crossattn_mask = None + + if self.blocks["block0"].x_format == "THWBD": + x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D") + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange( + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D" + ) + crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D") + + if crossattn_mask: + crossattn_mask = rearrange(crossattn_mask, "B M -> M B") + + elif self.blocks["block0"].x_format == "BTHWD": + x = x_B_T_H_W_D + else: + raise ValueError(f"Unknown x_format {self.blocks[0].x_format}") + output = { + "x": x, + "affline_emb_B_D": affline_emb_B_D, + "crossattn_emb": crossattn_emb, + "crossattn_mask": crossattn_mask, + "rope_emb_L_1_1_D": rope_emb_L_1_1_D, + "adaln_lora_B_3D": adaln_lora_B_3D, + "original_shape": original_shape, + "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, + } + return output + + def forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + # crossattn_emb: torch.Tensor, + # crossattn_mask: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + image_size: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + scalar_feature: Optional[torch.Tensor] = None, + data_type: Optional[DataType] = DataType.VIDEO, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + condition_video_augment_sigma: Optional[torch.Tensor] = None, + **kwargs, + ): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) + ).execute(x, + timesteps, + context, + attention_mask, + fps, + image_size, + padding_mask, + scalar_feature, + data_type, + latent_condition, + latent_condition_sigma, + condition_video_augment_sigma, + **kwargs) + + def _forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + # crossattn_emb: torch.Tensor, + # crossattn_mask: Optional[torch.Tensor] = None, + fps: Optional[torch.Tensor] = None, + image_size: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + scalar_feature: Optional[torch.Tensor] = None, + data_type: Optional[DataType] = DataType.VIDEO, + latent_condition: Optional[torch.Tensor] = None, + latent_condition_sigma: Optional[torch.Tensor] = None, + condition_video_augment_sigma: Optional[torch.Tensor] = None, + **kwargs, + ): + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + crossattn_mask: (B, N) tensor of cross-attention masks + condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to + augment condition input, the lvg model will condition on the condition_video_augment_sigma value; + we need forward_before_blocks pass to the forward_before_blocks function. + """ + + crossattn_emb = context + crossattn_mask = attention_mask + + inputs = self.forward_before_blocks( + x=x, + timesteps=timesteps, + crossattn_emb=crossattn_emb, + crossattn_mask=crossattn_mask, + fps=fps, + image_size=image_size, + padding_mask=padding_mask, + scalar_feature=scalar_feature, + data_type=data_type, + latent_condition=latent_condition, + latent_condition_sigma=latent_condition_sigma, + condition_video_augment_sigma=condition_video_augment_sigma, + **kwargs, + ) + x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = ( + inputs["x"], + inputs["affline_emb_B_D"], + inputs["crossattn_emb"], + inputs["crossattn_mask"], + inputs["rope_emb_L_1_1_D"], + inputs["adaln_lora_B_3D"], + inputs["original_shape"], + ) + extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype) + del inputs + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + assert ( + x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape + ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}" + + transformer_options = kwargs.get("transformer_options", {}) + for _, block in self.blocks.items(): + assert ( + self.blocks["block0"].x_format == block.x_format + ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}" + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D + x = block( + x, + affline_emb_B_D, + crossattn_emb, + crossattn_mask, + rope_emb_L_1_1_D=rope_emb_L_1_1_D, + adaln_lora_B_3D=adaln_lora_B_3D, + transformer_options=transformer_options, + ) + + x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D") + + x_B_D_T_H_W = self.decoder_head( + x_B_T_H_W_D=x_B_T_H_W_D, + emb_B_D=affline_emb_B_D, + crossattn_emb=None, + origin_shape=original_shape, + crossattn_mask=None, + adaln_lora_B_3D=adaln_lora_B_3D, + ) + + return x_B_D_T_H_W diff --git a/comfy/ldm/cosmos/position_embedding.py b/comfy/ldm/cosmos/position_embedding.py new file mode 100644 index 000000000..c925811d4 --- /dev/null +++ b/comfy/ldm/cosmos/position_embedding.py @@ -0,0 +1,207 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional + +import torch +from einops import rearrange, repeat +from torch import nn +import math + + +def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor: + """ + Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted. + + Args: + x (torch.Tensor): The input tensor to normalize. + dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first. + eps (float, optional): A small constant to ensure numerical stability during division. + + Returns: + torch.Tensor: The normalized tensor. + """ + if dim is None: + dim = list(range(1, x.ndim)) + norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) + norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) + return x / norm.to(x.dtype) + + +class VideoPositionEmb(nn.Module): + def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: + """ + It delegates the embedding generation to generate_embeddings function. + """ + B_T_H_W_C = x_B_T_H_W_C.shape + embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device, dtype=dtype) + + return embeddings + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None): + raise NotImplementedError + + +class VideoRopePosition3DEmb(VideoPositionEmb): + def __init__( + self, + *, # enforce keyword arguments + head_dim: int, + len_h: int, + len_w: int, + len_t: int, + base_fps: int = 24, + h_extrapolation_ratio: float = 1.0, + w_extrapolation_ratio: float = 1.0, + t_extrapolation_ratio: float = 1.0, + enable_fps_modulation: bool = True, + device=None, + **kwargs, # used for compatibility with other positional embeddings; unused in this class + ): + del kwargs + super().__init__() + self.base_fps = base_fps + self.max_h = len_h + self.max_w = len_w + self.enable_fps_modulation = enable_fps_modulation + + dim = head_dim + dim_h = dim // 6 * 2 + dim_w = dim_h + dim_t = dim - 2 * dim_h + assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}" + self.register_buffer( + "dim_spatial_range", + torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h, + persistent=False, + ) + self.register_buffer( + "dim_temporal_range", + torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t, + persistent=False, + ) + + self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2)) + self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2)) + self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2)) + + def generate_embeddings( + self, + B_T_H_W_C: torch.Size, + fps: Optional[torch.Tensor] = None, + h_ntk_factor: Optional[float] = None, + w_ntk_factor: Optional[float] = None, + t_ntk_factor: Optional[float] = None, + device=None, + dtype=None, + ): + """ + Generate embeddings for the given input size. + + Args: + B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels). + fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None. + h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor. + w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor. + t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor. + + Returns: + Not specified in the original code snippet. + """ + h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor + w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor + t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor + + h_theta = 10000.0 * h_ntk_factor + w_theta = 10000.0 * w_ntk_factor + t_theta = 10000.0 * t_ntk_factor + + h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device)) + w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device)) + temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device)) + + B, T, H, W, _ = B_T_H_W_C + seq = torch.arange(max(H, W, T), dtype=torch.float, device=device) + uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max()) + assert ( + uniform_fps or B == 1 or T == 1 + ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1" + half_emb_h = torch.outer(seq[:H].to(device=device), h_spatial_freqs) + half_emb_w = torch.outer(seq[:W].to(device=device), w_spatial_freqs) + + # apply sequence scaling in temporal dimension + if fps is None or self.enable_fps_modulation is False: # image case + half_emb_t = torch.outer(seq[:T].to(device=device), temporal_freqs) + else: + half_emb_t = torch.outer(seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs) + + half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1) + half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1) + half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1) + + em_T_H_W_D = torch.cat( + [ + repeat(half_emb_t, "t d x -> t h w d x", h=H, w=W), + repeat(half_emb_h, "h d x -> t h w d x", t=T, w=W), + repeat(half_emb_w, "w d x -> t h w d x", t=T, h=H), + ] + , dim=-2, + ) + + return rearrange(em_T_H_W_D, "t h w d (i j) -> (t h w) d i j", i=2, j=2).float() + + +class LearnablePosEmbAxis(VideoPositionEmb): + def __init__( + self, + *, # enforce keyword arguments + interpolation: str, + model_channels: int, + len_h: int, + len_w: int, + len_t: int, + device=None, + dtype=None, + **kwargs, + ): + """ + Args: + interpolation (str): we curretly only support "crop", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet. + """ + del kwargs # unused + super().__init__() + self.interpolation = interpolation + assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}" + + self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device, dtype=dtype)) + self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype)) + self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype)) + + def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=None) -> torch.Tensor: + B, T, H, W, _ = B_T_H_W_C + if self.interpolation == "crop": + emb_h_H = self.pos_emb_h[:H].to(device=device, dtype=dtype) + emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype) + emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype) + emb = ( + repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W) + + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W) + + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H) + ) + assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}" + else: + raise ValueError(f"Unknown interpolation method {self.interpolation}") + + return normalize(emb, dim=-1, eps=1e-6) diff --git a/comfy/ldm/cosmos/predict2.py b/comfy/ldm/cosmos/predict2.py new file mode 100644 index 000000000..07a4fc79f --- /dev/null +++ b/comfy/ldm/cosmos/predict2.py @@ -0,0 +1,886 @@ +# original code from: https://github.com/nvidia-cosmos/cosmos-predict2 + +import torch +from torch import nn +from einops import rearrange +from einops.layers.torch import Rearrange +import logging +from typing import Callable, Optional, Tuple +import math + +from .position_embedding import VideoRopePosition3DEmb, LearnablePosEmbAxis +from torchvision import transforms + +import comfy.patcher_extension +from comfy.ldm.modules.attention import optimized_attention + +def apply_rotary_pos_emb( + t: torch.Tensor, + freqs: torch.Tensor, +) -> torch.Tensor: + t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float() + t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1] + t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t) + return t_out + + +# ---------------------- Feed Forward Network ----------------------- +class GPT2FeedForward(nn.Module): + def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, operations=None) -> None: + super().__init__() + self.activation = nn.GELU() + self.layer1 = operations.Linear(d_model, d_ff, bias=False, device=device, dtype=dtype) + self.layer2 = operations.Linear(d_ff, d_model, bias=False, device=device, dtype=dtype) + + self._layer_id = None + self._dim = d_model + self._hidden_dim = d_ff + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.layer1(x) + + x = self.activation(x) + x = self.layer2(x) + return x + + +def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: + """Computes multi-head attention using PyTorch's native implementation. + + This function provides a PyTorch backend alternative to Transformer Engine's attention operation. + It rearranges the input tensors to match PyTorch's expected format, computes scaled dot-product + attention, and rearranges the output back to the original format. + + The input tensor names use the following dimension conventions: + + - B: batch size + - S: sequence length + - H: number of attention heads + - D: head dimension + + Args: + q_B_S_H_D: Query tensor with shape (batch, seq_len, n_heads, head_dim) + k_B_S_H_D: Key tensor with shape (batch, seq_len, n_heads, head_dim) + v_B_S_H_D: Value tensor with shape (batch, seq_len, n_heads, head_dim) + + Returns: + Attention output tensor with shape (batch, seq_len, n_heads * head_dim) + """ + in_q_shape = q_B_S_H_D.shape + in_k_shape = k_B_S_H_D.shape + q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1]) + k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) + v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1]) + return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options) + + +class Attention(nn.Module): + """ + A flexible attention module supporting both self-attention and cross-attention mechanisms. + + This module implements a multi-head attention layer that can operate in either self-attention + or cross-attention mode. The mode is determined by whether a context dimension is provided. + The implementation uses scaled dot-product attention and supports optional bias terms and + dropout regularization. + + Args: + query_dim (int): The dimensionality of the query vectors. + context_dim (int, optional): The dimensionality of the context (key/value) vectors. + If None, the module operates in self-attention mode using query_dim. Default: None + n_heads (int, optional): Number of attention heads for multi-head attention. Default: 8 + head_dim (int, optional): The dimension of each attention head. Default: 64 + dropout (float, optional): Dropout probability applied to the output. Default: 0.0 + qkv_format (str, optional): Format specification for QKV tensors. Default: "bshd" + backend (str, optional): Backend to use for the attention operation. Default: "transformer_engine" + + Examples: + >>> # Self-attention with 512 dimensions and 8 heads + >>> self_attn = Attention(query_dim=512) + >>> x = torch.randn(32, 16, 512) # (batch_size, seq_len, dim) + >>> out = self_attn(x) # (32, 16, 512) + + >>> # Cross-attention + >>> cross_attn = Attention(query_dim=512, context_dim=256) + >>> query = torch.randn(32, 16, 512) + >>> context = torch.randn(32, 8, 256) + >>> out = cross_attn(query, context) # (32, 16, 512) + """ + + def __init__( + self, + query_dim: int, + context_dim: Optional[int] = None, + n_heads: int = 8, + head_dim: int = 64, + dropout: float = 0.0, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + logging.debug( + f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using " + f"{n_heads} heads with a dimension of {head_dim}." + ) + self.is_selfattn = context_dim is None # self attention + + context_dim = query_dim if context_dim is None else context_dim + inner_dim = head_dim * n_heads + + self.n_heads = n_heads + self.head_dim = head_dim + self.query_dim = query_dim + self.context_dim = context_dim + + self.q_proj = operations.Linear(query_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.q_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) + + self.k_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.k_norm = operations.RMSNorm(self.head_dim, eps=1e-6, device=device, dtype=dtype) + + self.v_proj = operations.Linear(context_dim, inner_dim, bias=False, device=device, dtype=dtype) + self.v_norm = nn.Identity() + + self.output_proj = operations.Linear(inner_dim, query_dim, bias=False, device=device, dtype=dtype) + self.output_dropout = nn.Dropout(dropout) if dropout > 1e-4 else nn.Identity() + + self.attn_op = torch_attention_op + + self._query_dim = query_dim + self._context_dim = context_dim + self._inner_dim = inner_dim + + def compute_qkv( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + q = self.q_proj(x) + context = x if context is None else context + k = self.k_proj(context) + v = self.v_proj(context) + q, k, v = map( + lambda t: rearrange(t, "b ... (h d) -> b ... h d", h=self.n_heads, d=self.head_dim), + (q, k, v), + ) + + def apply_norm_and_rotary_pos_emb( + q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, rope_emb: Optional[torch.Tensor] + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + q = self.q_norm(q) + k = self.k_norm(k) + v = self.v_norm(v) + if self.is_selfattn and rope_emb is not None: # only apply to self-attention! + q = apply_rotary_pos_emb(q, rope_emb) + k = apply_rotary_pos_emb(k, rope_emb) + return q, k, v + + q, k, v = apply_norm_and_rotary_pos_emb(q, k, v, rope_emb) + + return q, k, v + + def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor: + result = self.attn_op(q, k, v, transformer_options=transformer_options) # [B, S, H, D] + return self.output_dropout(self.output_proj(result)) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + rope_emb: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + """ + Args: + x (Tensor): The query tensor of shape [B, Mq, K] + context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None + """ + q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb) + return self.compute_attention(q, k, v, transformer_options=transformer_options) + + +class Timesteps(nn.Module): + def __init__(self, num_channels: int): + super().__init__() + self.num_channels = num_channels + + def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor: + assert timesteps_B_T.ndim == 2, f"Expected 2D input, got {timesteps_B_T.ndim}" + timesteps = timesteps_B_T.flatten().float() + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) + exponent = exponent / (half_dim - 0.0) + + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + + sin_emb = torch.sin(emb) + cos_emb = torch.cos(emb) + emb = torch.cat([cos_emb, sin_emb], dim=-1) + + return rearrange(emb, "(b t) d -> b t d", b=timesteps_B_T.shape[0], t=timesteps_B_T.shape[1]) + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, device=None, dtype=None, operations=None): + super().__init__() + logging.debug( + f"Using AdaLN LoRA Flag: {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility." + ) + self.in_dim = in_features + self.out_dim = out_features + self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, device=device, dtype=dtype) + self.activation = nn.SiLU() + self.use_adaln_lora = use_adaln_lora + if use_adaln_lora: + self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, device=device, dtype=dtype) + else: + self.linear_2 = operations.Linear(out_features, out_features, bias=False, device=device, dtype=dtype) + + def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + emb = self.linear_1(sample) + emb = self.activation(emb) + emb = self.linear_2(emb) + + if self.use_adaln_lora: + adaln_lora_B_T_3D = emb + emb_B_T_D = sample + else: + adaln_lora_B_T_3D = None + emb_B_T_D = emb + + return emb_B_T_D, adaln_lora_B_T_3D + + +class PatchEmbed(nn.Module): + """ + PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers, + depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions, + making it suitable for video and image processing tasks. It supports dividing the input into patches + and embedding each patch into a vector of size `out_channels`. + + Parameters: + - spatial_patch_size (int): The size of each spatial patch. + - temporal_patch_size (int): The size of each temporal patch. + - in_channels (int): Number of input channels. Default: 3. + - out_channels (int): The dimension of the embedding vector for each patch. Default: 768. + - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True. + """ + + def __init__( + self, + spatial_patch_size: int, + temporal_patch_size: int, + in_channels: int = 3, + out_channels: int = 768, + device=None, dtype=None, operations=None + ): + super().__init__() + self.spatial_patch_size = spatial_patch_size + self.temporal_patch_size = temporal_patch_size + + self.proj = nn.Sequential( + Rearrange( + "b c (t r) (h m) (w n) -> b t h w (c r m n)", + r=temporal_patch_size, + m=spatial_patch_size, + n=spatial_patch_size, + ), + operations.Linear( + in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=False, device=device, dtype=dtype + ), + ) + self.dim = in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass of the PatchEmbed module. + + Parameters: + - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where + B is the batch size, + C is the number of channels, + T is the temporal dimension, + H is the height, and + W is the width of the input. + + Returns: + - torch.Tensor: The embedded patches as a tensor, with shape b t h w c. + """ + assert x.dim() == 5 + _, _, T, H, W = x.shape + assert ( + H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0 + ), f"H,W {(H, W)} should be divisible by spatial_patch_size {self.spatial_patch_size}" + assert T % self.temporal_patch_size == 0 + x = self.proj(x) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of video DiT. + """ + + def __init__( + self, + hidden_size: int, + spatial_patch_size: int, + temporal_patch_size: int, + out_channels: int, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + device=None, dtype=None, operations=None + ): + super().__init__() + self.layer_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = operations.Linear( + hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, device=device, dtype=dtype + ) + self.hidden_size = hidden_size + self.n_adaln_chunks = 2 + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + if use_adaln_lora: + self.adaln_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype), + ) + else: + self.adaln_modulation = nn.Sequential( + nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, device=device, dtype=dtype) + ) + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + ): + if self.use_adaln_lora: + assert adaln_lora_B_T_3D is not None + shift_B_T_D, scale_B_T_D = ( + self.adaln_modulation(emb_B_T_D) + adaln_lora_B_T_3D[:, :, : 2 * self.hidden_size] + ).chunk(2, dim=-1) + else: + shift_B_T_D, scale_B_T_D = self.adaln_modulation(emb_B_T_D).chunk(2, dim=-1) + + shift_B_T_1_1_D, scale_B_T_1_1_D = rearrange(shift_B_T_D, "b t d -> b t 1 1 d"), rearrange( + scale_B_T_D, "b t d -> b t 1 1 d" + ) + + def _fn( + _x_B_T_H_W_D: torch.Tensor, + _norm_layer: nn.Module, + _scale_B_T_1_1_D: torch.Tensor, + _shift_B_T_1_1_D: torch.Tensor, + ) -> torch.Tensor: + return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D + + x_B_T_H_W_D = _fn(x_B_T_H_W_D, self.layer_norm, scale_B_T_1_1_D, shift_B_T_1_1_D) + x_B_T_H_W_O = self.linear(x_B_T_H_W_D) + return x_B_T_H_W_O + + +class Block(nn.Module): + """ + A transformer block that combines self-attention, cross-attention and MLP layers with AdaLN modulation. + Each component (self-attention, cross-attention, MLP) has its own layer normalization and AdaLN modulation. + + Parameters: + x_dim (int): Dimension of input features + context_dim (int): Dimension of context features for cross-attention + num_heads (int): Number of attention heads + mlp_ratio (float): Multiplier for MLP hidden dimension. Default: 4.0 + use_adaln_lora (bool): Whether to use AdaLN-LoRA modulation. Default: False + adaln_lora_dim (int): Hidden dimension for AdaLN-LoRA layers. Default: 256 + + The block applies the following sequence: + 1. Self-attention with AdaLN modulation + 2. Cross-attention with AdaLN modulation + 3. MLP with AdaLN modulation + + Each component uses skip connections and layer normalization. + """ + + def __init__( + self, + x_dim: int, + context_dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + self.x_dim = x_dim + self.layer_norm_self_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.self_attn = Attention(x_dim, None, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations) + + self.layer_norm_cross_attn = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.cross_attn = Attention( + x_dim, context_dim, num_heads, x_dim // num_heads, device=device, dtype=dtype, operations=operations + ) + + self.layer_norm_mlp = operations.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6, device=device, dtype=dtype) + self.mlp = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), device=device, dtype=dtype, operations=operations) + + self.use_adaln_lora = use_adaln_lora + if self.use_adaln_lora: + self.adaln_modulation_self_attn = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + self.adaln_modulation_cross_attn = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + self.adaln_modulation_mlp = nn.Sequential( + nn.SiLU(), + operations.Linear(x_dim, adaln_lora_dim, bias=False, device=device, dtype=dtype), + operations.Linear(adaln_lora_dim, 3 * x_dim, bias=False, device=device, dtype=dtype), + ) + else: + self.adaln_modulation_self_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + self.adaln_modulation_cross_attn = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + self.adaln_modulation_mlp = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, 3 * x_dim, bias=False, device=device, dtype=dtype)) + + def forward( + self, + x_B_T_H_W_D: torch.Tensor, + emb_B_T_D: torch.Tensor, + crossattn_emb: torch.Tensor, + rope_emb_L_1_1_D: Optional[torch.Tensor] = None, + adaln_lora_B_T_3D: Optional[torch.Tensor] = None, + extra_per_block_pos_emb: Optional[torch.Tensor] = None, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + if extra_per_block_pos_emb is not None: + x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb + + if self.use_adaln_lora: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = ( + self.adaln_modulation_self_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = ( + self.adaln_modulation_cross_attn(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = ( + self.adaln_modulation_mlp(emb_B_T_D) + adaln_lora_B_T_3D + ).chunk(3, dim=-1) + else: + shift_self_attn_B_T_D, scale_self_attn_B_T_D, gate_self_attn_B_T_D = self.adaln_modulation_self_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_cross_attn_B_T_D, scale_cross_attn_B_T_D, gate_cross_attn_B_T_D = self.adaln_modulation_cross_attn( + emb_B_T_D + ).chunk(3, dim=-1) + shift_mlp_B_T_D, scale_mlp_B_T_D, gate_mlp_B_T_D = self.adaln_modulation_mlp(emb_B_T_D).chunk(3, dim=-1) + + # Reshape tensors from (B, T, D) to (B, T, 1, 1, D) for broadcasting + shift_self_attn_B_T_1_1_D = rearrange(shift_self_attn_B_T_D, "b t d -> b t 1 1 d") + scale_self_attn_B_T_1_1_D = rearrange(scale_self_attn_B_T_D, "b t d -> b t 1 1 d") + gate_self_attn_B_T_1_1_D = rearrange(gate_self_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_cross_attn_B_T_1_1_D = rearrange(shift_cross_attn_B_T_D, "b t d -> b t 1 1 d") + scale_cross_attn_B_T_1_1_D = rearrange(scale_cross_attn_B_T_D, "b t d -> b t 1 1 d") + gate_cross_attn_B_T_1_1_D = rearrange(gate_cross_attn_B_T_D, "b t d -> b t 1 1 d") + + shift_mlp_B_T_1_1_D = rearrange(shift_mlp_B_T_D, "b t d -> b t 1 1 d") + scale_mlp_B_T_1_1_D = rearrange(scale_mlp_B_T_D, "b t d -> b t 1 1 d") + gate_mlp_B_T_1_1_D = rearrange(gate_mlp_B_T_D, "b t d -> b t 1 1 d") + + B, T, H, W, D = x_B_T_H_W_D.shape + + def _fn(_x_B_T_H_W_D, _norm_layer, _scale_B_T_1_1_D, _shift_B_T_1_1_D): + return _norm_layer(_x_B_T_H_W_D) * (1 + _scale_B_T_1_1_D) + _shift_B_T_1_1_D + + normalized_x_B_T_H_W_D = _fn( + x_B_T_H_W_D, + self.layer_norm_self_attn, + scale_self_attn_B_T_1_1_D, + shift_self_attn_B_T_1_1_D, + ) + result_B_T_H_W_D = rearrange( + self.self_attn( + # normalized_x_B_T_HW_D, + rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), + None, + rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + x_B_T_H_W_D = x_B_T_H_W_D + gate_self_attn_B_T_1_1_D * result_B_T_H_W_D + + def _x_fn( + _x_B_T_H_W_D: torch.Tensor, + layer_norm_cross_attn: Callable, + _scale_cross_attn_B_T_1_1_D: torch.Tensor, + _shift_cross_attn_B_T_1_1_D: torch.Tensor, + transformer_options: Optional[dict] = {}, + ) -> torch.Tensor: + _normalized_x_B_T_H_W_D = _fn( + _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D + ) + _result_B_T_H_W_D = rearrange( + self.cross_attn( + rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"), + crossattn_emb, + rope_emb=rope_emb_L_1_1_D, + transformer_options=transformer_options, + ), + "b (t h w) d -> b t h w d", + t=T, + h=H, + w=W, + ) + return _result_B_T_H_W_D + + result_B_T_H_W_D = _x_fn( + x_B_T_H_W_D, + self.layer_norm_cross_attn, + scale_cross_attn_B_T_1_1_D, + shift_cross_attn_B_T_1_1_D, + transformer_options=transformer_options, + ) + x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D + + normalized_x_B_T_H_W_D = _fn( + x_B_T_H_W_D, + self.layer_norm_mlp, + scale_mlp_B_T_1_1_D, + shift_mlp_B_T_1_1_D, + ) + result_B_T_H_W_D = self.mlp(normalized_x_B_T_H_W_D) + x_B_T_H_W_D = x_B_T_H_W_D + gate_mlp_B_T_1_1_D * result_B_T_H_W_D + return x_B_T_H_W_D + + +class MiniTrainDIT(nn.Module): + """ + A clean impl of DIT that can load and reproduce the training results of the original DIT model in~(cosmos 1) + A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing. + + Args: + max_img_h (int): Maximum height of the input images. + max_img_w (int): Maximum width of the input images. + max_frames (int): Maximum number of frames in the video sequence. + in_channels (int): Number of input channels (e.g., RGB channels for color images). + out_channels (int): Number of output channels. + patch_spatial (tuple): Spatial resolution of patches for input processing. + patch_temporal (int): Temporal resolution of patches for input processing. + concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding. + model_channels (int): Base number of channels used throughout the model. + num_blocks (int): Number of transformer blocks. + num_heads (int): Number of heads in the multi-head attention layers. + mlp_ratio (float): Expansion ratio for MLP blocks. + crossattn_emb_channels (int): Number of embedding channels for cross-attention. + pos_emb_cls (str): Type of positional embeddings. + pos_emb_learnable (bool): Whether positional embeddings are learnable. + pos_emb_interpolation (str): Method for interpolating positional embeddings. + min_fps (int): Minimum frames per second. + max_fps (int): Maximum frames per second. + use_adaln_lora (bool): Whether to use AdaLN-LoRA. + adaln_lora_dim (int): Dimension for AdaLN-LoRA. + rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE. + rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE. + rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE. + extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings. + extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings. + extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings. + extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings. + """ + + def __init__( + self, + max_img_h: int, + max_img_w: int, + max_frames: int, + in_channels: int, + out_channels: int, + patch_spatial: int, # tuple, + patch_temporal: int, + concat_padding_mask: bool = True, + # attention settings + model_channels: int = 768, + num_blocks: int = 10, + num_heads: int = 16, + mlp_ratio: float = 4.0, + # cross attention settings + crossattn_emb_channels: int = 1024, + # positional embedding settings + pos_emb_cls: str = "sincos", + pos_emb_learnable: bool = False, + pos_emb_interpolation: str = "crop", + min_fps: int = 1, + max_fps: int = 30, + use_adaln_lora: bool = False, + adaln_lora_dim: int = 256, + rope_h_extrapolation_ratio: float = 1.0, + rope_w_extrapolation_ratio: float = 1.0, + rope_t_extrapolation_ratio: float = 1.0, + extra_per_block_abs_pos_emb: bool = False, + extra_h_extrapolation_ratio: float = 1.0, + extra_w_extrapolation_ratio: float = 1.0, + extra_t_extrapolation_ratio: float = 1.0, + rope_enable_fps_modulation: bool = True, + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.dtype = dtype + self.max_img_h = max_img_h + self.max_img_w = max_img_w + self.max_frames = max_frames + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_spatial = patch_spatial + self.patch_temporal = patch_temporal + self.num_heads = num_heads + self.num_blocks = num_blocks + self.model_channels = model_channels + self.concat_padding_mask = concat_padding_mask + # positional embedding settings + self.pos_emb_cls = pos_emb_cls + self.pos_emb_learnable = pos_emb_learnable + self.pos_emb_interpolation = pos_emb_interpolation + self.min_fps = min_fps + self.max_fps = max_fps + self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio + self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio + self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio + self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb + self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio + self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio + self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio + self.rope_enable_fps_modulation = rope_enable_fps_modulation + + self.build_pos_embed(device=device, dtype=dtype) + self.use_adaln_lora = use_adaln_lora + self.adaln_lora_dim = adaln_lora_dim + self.t_embedder = nn.Sequential( + Timesteps(model_channels), + TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, device=device, dtype=dtype, operations=operations,), + ) + + in_channels = in_channels + 1 if concat_padding_mask else in_channels + self.x_embedder = PatchEmbed( + spatial_patch_size=patch_spatial, + temporal_patch_size=patch_temporal, + in_channels=in_channels, + out_channels=model_channels, + device=device, dtype=dtype, operations=operations, + ) + + self.blocks = nn.ModuleList( + [ + Block( + x_dim=model_channels, + context_dim=crossattn_emb_channels, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + use_adaln_lora=use_adaln_lora, + adaln_lora_dim=adaln_lora_dim, + device=device, dtype=dtype, operations=operations, + ) + for _ in range(num_blocks) + ] + ) + + self.final_layer = FinalLayer( + hidden_size=self.model_channels, + spatial_patch_size=self.patch_spatial, + temporal_patch_size=self.patch_temporal, + out_channels=self.out_channels, + use_adaln_lora=self.use_adaln_lora, + adaln_lora_dim=self.adaln_lora_dim, + device=device, dtype=dtype, operations=operations, + ) + + self.t_embedding_norm = operations.RMSNorm(model_channels, eps=1e-6, device=device, dtype=dtype) + + def build_pos_embed(self, device=None, dtype=None) -> None: + if self.pos_emb_cls == "rope3d": + cls_type = VideoRopePosition3DEmb + else: + raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}") + + logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}") + kwargs = dict( + model_channels=self.model_channels, + len_h=self.max_img_h // self.patch_spatial, + len_w=self.max_img_w // self.patch_spatial, + len_t=self.max_frames // self.patch_temporal, + max_fps=self.max_fps, + min_fps=self.min_fps, + is_learnable=self.pos_emb_learnable, + interpolation=self.pos_emb_interpolation, + head_dim=self.model_channels // self.num_heads, + h_extrapolation_ratio=self.rope_h_extrapolation_ratio, + w_extrapolation_ratio=self.rope_w_extrapolation_ratio, + t_extrapolation_ratio=self.rope_t_extrapolation_ratio, + enable_fps_modulation=self.rope_enable_fps_modulation, + device=device, + ) + self.pos_embedder = cls_type( + **kwargs, # type: ignore + ) + + if self.extra_per_block_abs_pos_emb: + kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio + kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio + kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio + kwargs["device"] = device + kwargs["dtype"] = dtype + self.extra_pos_embedder = LearnablePosEmbAxis( + **kwargs, # type: ignore + ) + + def prepare_embedded_sequence( + self, + x_B_C_T_H_W: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + """ + Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks. + + Args: + x_B_C_T_H_W (torch.Tensor): video + fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required. + If None, a default value (`self.base_fps`) will be used. + padding_mask (Optional[torch.Tensor]): current it is not used + + Returns: + Tuple[torch.Tensor, Optional[torch.Tensor]]: + - A tensor of shape (B, T, H, W, D) with the embedded sequence. + - An optional positional embedding tensor, returned only if the positional embedding class + (`self.pos_emb_cls`) includes 'rope'. Otherwise, None. + + Notes: + - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor. + - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`. + - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using + the `self.pos_embedder` with the shape [T, H, W]. + - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the + `self.pos_embedder` with the fps tensor. + - Otherwise, the positional embeddings are generated without considering fps. + """ + if self.concat_padding_mask: + if padding_mask is None: + padding_mask = torch.zeros(x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[3], x_B_C_T_H_W.shape[4], dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device) + else: + padding_mask = transforms.functional.resize( + padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST + ) + x_B_C_T_H_W = torch.cat( + [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1 + ) + x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W) + + if self.extra_per_block_abs_pos_emb: + extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device, dtype=x_B_C_T_H_W.dtype) + else: + extra_pos_emb = None + + if "rope" in self.pos_emb_cls.lower(): + return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb + x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device) # [B, T, H, W, D] + + return x_B_T_H_W_D, None, extra_pos_emb + + def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor: + x_B_C_Tt_Hp_Wp = rearrange( + x_B_T_H_W_M, + "B T H W (p1 p2 t C) -> B C (T t) (H p1) (W p2)", + p1=self.patch_spatial, + p2=self.patch_spatial, + t=self.patch_temporal, + ) + return x_B_C_Tt_Hp_Wp + + def forward(self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + **kwargs, + ): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) + ).execute(x, timesteps, context, fps, padding_mask, **kwargs) + + def _forward( + self, + x: torch.Tensor, + timesteps: torch.Tensor, + context: torch.Tensor, + fps: Optional[torch.Tensor] = None, + padding_mask: Optional[torch.Tensor] = None, + **kwargs, + ): + x_B_C_T_H_W = x + timesteps_B_T = timesteps + crossattn_emb = context + """ + Args: + x: (B, C, T, H, W) tensor of spatial-temp inputs + timesteps: (B, ) tensor of timesteps + crossattn_emb: (B, N, D) tensor of cross-attention embeddings + """ + x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence( + x_B_C_T_H_W, + fps=fps, + padding_mask=padding_mask, + ) + + if timesteps_B_T.ndim == 1: + timesteps_B_T = timesteps_B_T.unsqueeze(1) + t_embedding_B_T_D, adaln_lora_B_T_3D = self.t_embedder[1](self.t_embedder[0](timesteps_B_T).to(x_B_T_H_W_D.dtype)) + t_embedding_B_T_D = self.t_embedding_norm(t_embedding_B_T_D) + + # for logging purpose + affline_scale_log_info = {} + affline_scale_log_info["t_embedding_B_T_D"] = t_embedding_B_T_D.detach() + self.affline_scale_log_info = affline_scale_log_info + self.affline_emb = t_embedding_B_T_D + self.crossattn_emb = crossattn_emb + + if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None: + assert ( + x_B_T_H_W_D.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape + ), f"{x_B_T_H_W_D.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape}" + + block_kwargs = { + "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0), + "adaln_lora_B_T_3D": adaln_lora_B_T_3D, + "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, + "transformer_options": kwargs.get("transformer_options", {}), + } + for block in self.blocks: + x_B_T_H_W_D = block( + x_B_T_H_W_D, + t_embedding_B_T_D, + crossattn_emb, + **block_kwargs, + ) + + x_B_T_H_W_O = self.final_layer(x_B_T_H_W_D, t_embedding_B_T_D, adaln_lora_B_T_3D=adaln_lora_B_T_3D) + x_B_C_Tt_Hp_Wp = self.unpatchify(x_B_T_H_W_O) + return x_B_C_Tt_Hp_Wp diff --git a/comfy/ldm/cosmos/vae.py b/comfy/ldm/cosmos/vae.py new file mode 100644 index 000000000..d64f292de --- /dev/null +++ b/comfy/ldm/cosmos/vae.py @@ -0,0 +1,131 @@ +# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""The causal continuous video tokenizer with VAE or AE formulation for 3D data..""" + +import logging +import torch +from torch import nn +from enum import Enum +import math + +from .cosmos_tokenizer.layers3d import ( + EncoderFactorized, + DecoderFactorized, + CausalConv3d, +) + + +class IdentityDistribution(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, parameters): + return parameters, (torch.tensor([0.0]), torch.tensor([0.0])) + + +class GaussianDistribution(torch.nn.Module): + def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0): + super().__init__() + self.min_logvar = min_logvar + self.max_logvar = max_logvar + + def sample(self, mean, logvar): + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + + def forward(self, parameters): + mean, logvar = torch.chunk(parameters, 2, dim=1) + logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar) + return self.sample(mean, logvar), (mean, logvar) + + +class ContinuousFormulation(Enum): + VAE = GaussianDistribution + AE = IdentityDistribution + + +class CausalContinuousVideoTokenizer(nn.Module): + def __init__( + self, z_channels: int, z_factor: int, latent_channels: int, **kwargs + ) -> None: + super().__init__() + self.name = kwargs.get("name", "CausalContinuousVideoTokenizer") + self.latent_channels = latent_channels + self.sigma_data = 0.5 + + # encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name) + self.encoder = EncoderFactorized( + z_channels=z_factor * z_channels, **kwargs + ) + if kwargs.get("temporal_compression", 4) == 4: + kwargs["channels_mult"] = [2, 4] + # decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name) + self.decoder = DecoderFactorized( + z_channels=z_channels, **kwargs + ) + + self.quant_conv = CausalConv3d( + z_factor * z_channels, + z_factor * latent_channels, + kernel_size=1, + padding=0, + ) + self.post_quant_conv = CausalConv3d( + latent_channels, z_channels, kernel_size=1, padding=0 + ) + + # formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name) + self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value() + + num_parameters = sum(param.numel() for param in self.parameters()) + logging.debug(f"model={self.name}, num_parameters={num_parameters:,}") + logging.debug( + f"z_channels={z_channels}, latent_channels={self.latent_channels}." + ) + + latent_temporal_chunk = 16 + self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) + self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32)) + + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + z, posteriors = self.distribution(moments) + latent_ch = z.shape[1] + latent_t = z.shape[2] + in_dtype = z.dtype + mean = self.latent_mean.view(latent_ch, -1) + std = self.latent_std.view(latent_ch, -1) + + mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + return ((z - mean) / std) * self.sigma_data + + def decode(self, z): + in_dtype = z.dtype + latent_ch = z.shape[1] + latent_t = z.shape[2] + mean = self.latent_mean.view(latent_ch, -1) + std = self.latent_std.view(latent_ch, -1) + + mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device) + + z = z / self.sigma_data + z = z * std + mean + z = self.post_quant_conv(z) + return self.decoder(z) + diff --git a/comfy/ldm/flux/controlnet.py b/comfy/ldm/flux/controlnet.py index 5322c4891..7dcf82bbf 100644 --- a/comfy/ldm/flux/controlnet.py +++ b/comfy/ldm/flux/controlnet.py @@ -121,6 +121,11 @@ class ControlNetFlux(Flux): if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + else: + y = y[:, :self.params.vec_in_dim] + # running on sequences img img = self.img_in(img) @@ -174,7 +179,7 @@ class ControlNetFlux(Flux): out["output"] = out_output[:self.main_model_single] return out - def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs): + def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs): patch_size = 2 if self.latent_input: hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size)) diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py index 8e055151f..ef21b416b 100644 --- a/comfy/ldm/flux/layers.py +++ b/comfy/ldm/flux/layers.py @@ -105,7 +105,9 @@ class Modulation(nn.Module): self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device) def forward(self, vec: Tensor) -> tuple: - out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + if vec.ndim == 2: + vec = vec[:, None, :] + out = self.lin(nn.functional.silu(vec)).chunk(self.multiplier, dim=-1) return ( ModulationOut(*out[:3]), @@ -113,6 +115,20 @@ class Modulation(nn.Module): ) +def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None): + if modulation_dims is None: + if m_add is not None: + return torch.addcmul(m_add, tensor, m_mult) + else: + return tensor * m_mult + else: + for d in modulation_dims: + tensor[:, d[0]:d[1]] *= m_mult[:, d[2]] + if m_add is not None: + tensor[:, d[0]:d[1]] += m_add[:, d[2]] + return tensor + + class DoubleStreamBlock(nn.Module): def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, dtype=None, device=None, operations=None): super().__init__() @@ -143,20 +159,20 @@ class DoubleStreamBlock(nn.Module): ) self.flipped_img_txt = flipped_img_txt - def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None): + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}): img_mod1, img_mod2 = self.img_mod(vec) txt_mod1, txt_mod2 = self.txt_mod(vec) # prepare image for attention img_modulated = self.img_norm1(img) - img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_modulated = apply_mod(img_modulated, (1 + img_mod1.scale), img_mod1.shift, modulation_dims_img) img_qkv = self.img_attn.qkv(img_modulated) img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) # prepare txt for attention txt_modulated = self.txt_norm1(txt) - txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_modulated = apply_mod(txt_modulated, (1 + txt_mod1.scale), txt_mod1.shift, modulation_dims_txt) txt_qkv = self.txt_attn.qkv(txt_modulated) txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) @@ -166,7 +182,7 @@ class DoubleStreamBlock(nn.Module): attn = attention(torch.cat((img_q, txt_q), dim=2), torch.cat((img_k, txt_k), dim=2), torch.cat((img_v, txt_v), dim=2), - pe=pe, mask=attn_mask) + pe=pe, mask=attn_mask, transformer_options=transformer_options) img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:] else: @@ -174,17 +190,17 @@ class DoubleStreamBlock(nn.Module): attn = attention(torch.cat((txt_q, img_q), dim=2), torch.cat((txt_k, img_k), dim=2), torch.cat((txt_v, img_v), dim=2), - pe=pe, mask=attn_mask) + pe=pe, mask=attn_mask, transformer_options=transformer_options) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] # calculate the img bloks - img = img + img_mod1.gate * self.img_attn.proj(img_attn) - img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + img = img + apply_mod(self.img_attn.proj(img_attn), img_mod1.gate, None, modulation_dims_img) + img = img + apply_mod(self.img_mlp(apply_mod(self.img_norm2(img), (1 + img_mod2.scale), img_mod2.shift, modulation_dims_img)), img_mod2.gate, None, modulation_dims_img) # calculate the txt bloks - txt += txt_mod1.gate * self.txt_attn.proj(txt_attn) - txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + txt += apply_mod(self.txt_attn.proj(txt_attn), txt_mod1.gate, None, modulation_dims_txt) + txt += apply_mod(self.txt_mlp(apply_mod(self.txt_norm2(txt), (1 + txt_mod2.scale), txt_mod2.shift, modulation_dims_txt)), txt_mod2.gate, None, modulation_dims_txt) if txt.dtype == torch.float16: txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504) @@ -228,19 +244,18 @@ class SingleStreamBlock(nn.Module): self.mlp_act = nn.GELU(approximate="tanh") self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations) - def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None) -> Tensor: + def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor: mod, _ = self.modulation(vec) - x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift - qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k = self.norm(q, k, v) # compute attention - attn = attention(q, k, v, pe=pe, mask=attn_mask) + attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) - x += mod.gate * output + x += apply_mod(output, mod.gate, None, modulation_dims) if x.dtype == torch.float16: x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504) return x @@ -253,8 +268,11 @@ class LastLayer(nn.Module): self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device) self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)) - def forward(self, x: Tensor, vec: Tensor) -> Tensor: - shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) - x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Tensor: + if vec.ndim == 2: + vec = vec[:, None, :] + + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=-1) + x = apply_mod(self.norm_final(x), (1 + scale), shift, modulation_dims) x = self.linear(x) return x diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py index b6549585a..8deda0d4a 100644 --- a/comfy/ldm/flux/math.py +++ b/comfy/ldm/flux/math.py @@ -5,17 +5,25 @@ from torch import Tensor from comfy.ldm.modules.attention import optimized_attention import comfy.model_management -def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor: - q, k = apply_rope(q, k, pe) + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor: + q_shape = q.shape + k_shape = k.shape + + if pe is not None: + q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2) + k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2) + q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v) + k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v) heads = q.shape[1] - x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask) + x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options) return x def rope(pos: Tensor, dim: int, theta: int) -> Tensor: assert dim % 2 == 0 - if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu(): + if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): device = torch.device("cpu") else: device = pos.device @@ -27,11 +35,13 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor: out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) return out.to(dtype=torch.float32, device=pos.device) +def apply_rope1(x: Tensor, freqs_cis: Tensor): + x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2) + + x_out = freqs_cis[..., 0] * x_[..., 0] + x_out.addcmul_(freqs_cis[..., 1], x_[..., 1]) + + return x_out.reshape(*x.shape).type_as(x) def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor): - xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) - xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) - xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] - xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] - return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) - + return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis) diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py index dead87de8..14f90cea5 100644 --- a/comfy/ldm/flux/model.py +++ b/comfy/ldm/flux/model.py @@ -6,6 +6,7 @@ import torch from torch import Tensor, nn from einops import rearrange, repeat import comfy.ldm.common_dit +import comfy.patcher_extension from .layers import ( DoubleStreamBlock, @@ -101,6 +102,11 @@ class Flux(nn.Module): transformer_options={}, attn_mask: Tensor = None, ) -> Tensor: + + if y is None: + y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype) + + patches = transformer_options.get("patches", {}) patches_replace = transformer_options.get("patches_replace", {}) if img.ndim != 3 or txt.ndim != 3: raise ValueError("Input img and txt tensors must have 3 dimensions.") @@ -109,15 +115,25 @@ class Flux(nn.Module): img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype)) if self.params.guidance_embed: - if guidance is None: - raise ValueError("Didn't get guidance strength for guidance distilled model.") - vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) - vec = vec + self.vector_in(y[:,:self.params.vec_in_dim]) + vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) txt = self.txt_in(txt) - ids = torch.cat((txt_ids, img_ids), dim=1) - pe = self.pe_embedder(ids) + if "post_input" in patches: + for p in patches["post_input"]: + out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids}) + img = out["img"] + txt = out["txt"] + img_ids = out["img_ids"] + txt_ids = out["txt_ids"] + + if img_ids is not None: + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + else: + pe = None blocks_replace = patches_replace.get("dit", {}) for i, block in enumerate(self.double_blocks): @@ -128,14 +144,16 @@ class Flux(nn.Module): txt=args["txt"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] @@ -144,14 +162,18 @@ class Flux(nn.Module): txt=txt, vec=vec, pe=pe, - attn_mask=attn_mask) + attn_mask=attn_mask, + transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") if i < len(control_i): add = control_i[i] if add is not None: - img += add + img[:, :add.shape[1]] += add + + if img.dtype == torch.float16: + img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504) img = torch.cat((txt, img), 1) @@ -162,44 +184,97 @@ class Flux(nn.Module): out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], - attn_mask=args.get("attn_mask")) + attn_mask=args.get("attn_mask"), + transformer_options=args.get("transformer_options")) return out out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, - "attn_mask": attn_mask}, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") if i < len(control_o): add = control_o[i] if add is not None: - img[:, txt.shape[1] :, ...] += add + img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add img = img[:, txt.shape[1] :, ...] img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) return img - def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs): + def process_img(self, x, index=0, h_offset=0, w_offset=0): bs, c, h, w = x.shape patch_size = self.patch_size x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size)) img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size) - h_len = ((h + (patch_size // 2)) // patch_size) w_len = ((w + (patch_size // 2)) // patch_size) + + h_offset = ((h_offset + (patch_size // 2)) // patch_size) + w_offset = ((w_offset + (patch_size // 2)) // patch_size) + img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype) - img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs) + img_ids[:, :, 0] = img_ids[:, :, 1] + index + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + return img, repeat(img_ids, "h w c -> b (h w) c", b=bs) + + def forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, y, guidance, ref_latents, control, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, y=None, guidance=None, ref_latents=None, control=None, transformer_options={}, **kwargs): + bs, c, h_orig, w_orig = x.shape + patch_size = self.patch_size + + h_len = ((h_orig + (patch_size // 2)) // patch_size) + w_len = ((w_orig + (patch_size // 2)) // patch_size) + img, img_ids = self.process_img(x) + img_tokens = img.shape[1] + if ref_latents is not None: + h = 0 + w = 0 + index = 0 + ref_latents_method = kwargs.get("ref_latents_method", "offset") + for ref in ref_latents: + if ref_latents_method == "index": + index += 1 + h_offset = 0 + w_offset = 0 + elif ref_latents_method == "uxo": + index = 0 + h_offset = h_len * patch_size + h + w_offset = w_len * patch_size + w + h += ref.shape[-2] + w += ref.shape[-1] + else: + index = 1 + h_offset = 0 + w_offset = 0 + if ref.shape[-2] + h > ref.shape[-1] + w: + w_offset = w + else: + h_offset = h + h = max(h, ref.shape[-2] + h_offset) + w = max(w, ref.shape[-1] + w_offset) + + kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) + img = torch.cat([img, kontext], dim=1) + img_ids = torch.cat([img_ids, kontext_ids], dim=1) txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None)) - return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w] + out = out[:, :img_tokens] + return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig] diff --git a/comfy/ldm/genmo/joint_model/asymm_models_joint.py b/comfy/ldm/genmo/joint_model/asymm_models_joint.py index 2c46c24bf..5c1bb4d42 100644 --- a/comfy/ldm/genmo/joint_model/asymm_models_joint.py +++ b/comfy/ldm/genmo/joint_model/asymm_models_joint.py @@ -13,7 +13,6 @@ from comfy.ldm.modules.attention import optimized_attention from .layers import ( FeedForward, PatchEmbed, - RMSNorm, TimestepEmbedder, ) @@ -90,10 +89,10 @@ class AsymmetricAttention(nn.Module): # Query and key normalization for stability. assert qk_norm - self.q_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.k_norm_x = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.q_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) - self.k_norm_y = RMSNorm(self.head_dim, device=device, dtype=dtype) + self.q_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_x = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.q_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) + self.k_norm_y = operations.RMSNorm(self.head_dim, eps=1e-5, device=device, dtype=dtype) # Output layers. y features go back down from dim_x -> dim_y. self.proj_x = operations.Linear(dim_x, dim_x, bias=out_bias, device=device, dtype=dtype) @@ -110,6 +109,7 @@ class AsymmetricAttention(nn.Module): scale_x: torch.Tensor, # (B, dim_x), modulation for pre-RMSNorm. scale_y: torch.Tensor, # (B, dim_y), modulation for pre-RMSNorm. crop_y, + transformer_options={}, **rope_rotation, ) -> Tuple[torch.Tensor, torch.Tensor]: rope_cos = rope_rotation.get("rope_cos") @@ -144,7 +144,7 @@ class AsymmetricAttention(nn.Module): xy = optimized_attention(q, k, - v, self.num_heads, skip_reshape=True) + v, self.num_heads, skip_reshape=True, transformer_options=transformer_options) x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1) x = self.proj_x(x) @@ -225,6 +225,7 @@ class AsymmetricJointBlock(nn.Module): x: torch.Tensor, c: torch.Tensor, y: torch.Tensor, + transformer_options={}, **attn_kwargs, ): """Forward pass of a block. @@ -257,6 +258,7 @@ class AsymmetricJointBlock(nn.Module): y, scale_x=scale_msa_x, scale_y=scale_msa_y, + transformer_options=transformer_options, **attn_kwargs, ) @@ -525,10 +527,11 @@ class AsymmDiTJoint(nn.Module): args["txt"], rope_cos=args["rope_cos"], rope_sin=args["rope_sin"], - crop_y=args["num_tokens"] + crop_y=args["num_tokens"], + transformer_options=args["transformer_options"] ) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap}) y_feat = out["txt"] x = out["img"] else: @@ -539,6 +542,7 @@ class AsymmDiTJoint(nn.Module): rope_cos=rope_cos, rope_sin=rope_sin, crop_y=num_tokens, + transformer_options=transformer_options, ) # (B, M, D), (B, L, D) del y_feat # Final layers don't use dense text features. diff --git a/comfy/ldm/genmo/joint_model/layers.py b/comfy/ldm/genmo/joint_model/layers.py index 51d979559..e310bd717 100644 --- a/comfy/ldm/genmo/joint_model/layers.py +++ b/comfy/ldm/genmo/joint_model/layers.py @@ -151,14 +151,3 @@ class PatchEmbed(nn.Module): x = self.norm(x) return x - - -class RMSNorm(torch.nn.Module): - def __init__(self, hidden_size, eps=1e-5, device=None, dtype=None): - super().__init__() - self.eps = eps - self.weight = torch.nn.Parameter(torch.empty(hidden_size, device=device, dtype=dtype)) - self.register_parameter("bias", None) - - def forward(self, x): - return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) diff --git a/comfy/ldm/hidream/model.py b/comfy/ldm/hidream/model.py new file mode 100644 index 000000000..28d81c79e --- /dev/null +++ b/comfy/ldm/hidream/model.py @@ -0,0 +1,829 @@ +from typing import Optional, Tuple, List + +import torch +import torch.nn as nn +import einops +from einops import repeat + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +import torch.nn.functional as F + +from comfy.ldm.flux.math import apply_rope, rope +from comfy.ldm.flux.layers import LastLayer + +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management +import comfy.patcher_extension +import comfy.ldm.common_dit + + +# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py +class EmbedND(nn.Module): + def __init__(self, theta: int, axes_dim: List[int]): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + return emb.unsqueeze(2) + + +class PatchEmbed(nn.Module): + def __init__( + self, + patch_size=2, + in_channels=4, + out_channels=1024, + dtype=None, device=None, operations=None + ): + super().__init__() + self.patch_size = patch_size + self.out_channels = out_channels + self.proj = operations.Linear(in_channels * patch_size * patch_size, out_channels, bias=True, dtype=dtype, device=device) + + def forward(self, latent): + latent = self.proj(latent) + return latent + + +class PooledEmbed(nn.Module): + def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.pooled_embedder = TimestepEmbedding(in_channels=text_emb_dim, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) + + def forward(self, pooled_embed): + return self.pooled_embedder(pooled_embed) + + +class TimestepEmbed(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None): + super().__init__() + self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0) + self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size, dtype=dtype, device=device, operations=operations) + + def forward(self, timesteps, wdtype): + t_emb = self.time_proj(timesteps).to(dtype=wdtype) + t_emb = self.timestep_embedder(t_emb) + return t_emb + + +def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}): + return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options) + + +class HiDreamAttnProcessor_flashattn: + """Attention processor used typically in processing the SD3-like self-attention projections.""" + + def __call__( + self, + attn, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + transformer_options={}, + *args, + **kwargs, + ) -> torch.FloatTensor: + dtype = image_tokens.dtype + batch_size = image_tokens.shape[0] + + query_i = attn.q_rms_norm(attn.to_q(image_tokens)).to(dtype=dtype) + key_i = attn.k_rms_norm(attn.to_k(image_tokens)).to(dtype=dtype) + value_i = attn.to_v(image_tokens) + + inner_dim = key_i.shape[-1] + head_dim = inner_dim // attn.heads + + query_i = query_i.view(batch_size, -1, attn.heads, head_dim) + key_i = key_i.view(batch_size, -1, attn.heads, head_dim) + value_i = value_i.view(batch_size, -1, attn.heads, head_dim) + if image_tokens_masks is not None: + key_i = key_i * image_tokens_masks.view(batch_size, -1, 1, 1) + + if not attn.single: + query_t = attn.q_rms_norm_t(attn.to_q_t(text_tokens)).to(dtype=dtype) + key_t = attn.k_rms_norm_t(attn.to_k_t(text_tokens)).to(dtype=dtype) + value_t = attn.to_v_t(text_tokens) + + query_t = query_t.view(batch_size, -1, attn.heads, head_dim) + key_t = key_t.view(batch_size, -1, attn.heads, head_dim) + value_t = value_t.view(batch_size, -1, attn.heads, head_dim) + + num_image_tokens = query_i.shape[1] + num_text_tokens = query_t.shape[1] + query = torch.cat([query_i, query_t], dim=1) + key = torch.cat([key_i, key_t], dim=1) + value = torch.cat([value_i, value_t], dim=1) + else: + query = query_i + key = key_i + value = value_i + + if query.shape[-1] == rope.shape[-3] * 2: + query, key = apply_rope(query, key, rope) + else: + query_1, query_2 = query.chunk(2, dim=-1) + key_1, key_2 = key.chunk(2, dim=-1) + query_1, key_1 = apply_rope(query_1, key_1, rope) + query = torch.cat([query_1, query_2], dim=-1) + key = torch.cat([key_1, key_2], dim=-1) + + hidden_states = attention(query, key, value, transformer_options=transformer_options) + + if not attn.single: + hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1) + hidden_states_i = attn.to_out(hidden_states_i) + hidden_states_t = attn.to_out_t(hidden_states_t) + return hidden_states_i, hidden_states_t + else: + hidden_states = attn.to_out(hidden_states) + return hidden_states + +class HiDreamAttention(nn.Module): + def __init__( + self, + query_dim: int, + heads: int = 8, + dim_head: int = 64, + upcast_attention: bool = False, + upcast_softmax: bool = False, + scale_qk: bool = True, + eps: float = 1e-5, + processor = None, + out_dim: int = None, + single: bool = False, + dtype=None, device=None, operations=None + ): + # super(Attention, self).__init__() + super().__init__() + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.query_dim = query_dim + self.upcast_attention = upcast_attention + self.upcast_softmax = upcast_softmax + self.out_dim = out_dim if out_dim is not None else query_dim + + self.scale_qk = scale_qk + self.scale = dim_head**-0.5 if self.scale_qk else 1.0 + + self.heads = out_dim // dim_head if out_dim is not None else heads + self.sliceable_head_dim = heads + self.single = single + + linear_cls = operations.Linear + self.linear_cls = linear_cls + self.to_q = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) + self.to_k = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_v = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_out = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) + self.q_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + self.k_rms_norm = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + + if not single: + self.to_q_t = linear_cls(query_dim, self.inner_dim, dtype=dtype, device=device) + self.to_k_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_v_t = linear_cls(self.inner_dim, self.inner_dim, dtype=dtype, device=device) + self.to_out_t = linear_cls(self.inner_dim, self.out_dim, dtype=dtype, device=device) + self.q_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + self.k_rms_norm_t = operations.RMSNorm(self.inner_dim, eps, dtype=dtype, device=device) + + self.processor = processor + + def forward( + self, + norm_image_tokens: torch.FloatTensor, + image_tokens_masks: torch.FloatTensor = None, + norm_text_tokens: torch.FloatTensor = None, + rope: torch.FloatTensor = None, + transformer_options={}, + ) -> torch.Tensor: + return self.processor( + self, + image_tokens = norm_image_tokens, + image_tokens_masks = image_tokens_masks, + text_tokens = norm_text_tokens, + rope = rope, + transformer_options=transformer_options, + ) + + +class FeedForwardSwiGLU(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + dtype=None, device=None, operations=None + ): + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ( + (hidden_dim + multiple_of - 1) // multiple_of + ) + + self.w1 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) + self.w2 = operations.Linear(hidden_dim, dim, bias=False, dtype=dtype, device=device) + self.w3 = operations.Linear(dim, hidden_dim, bias=False, dtype=dtype, device=device) + + def forward(self, x): + return self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x)) + + +# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py +class MoEGate(nn.Module): + def __init__(self, embed_dim, num_routed_experts=4, num_activated_experts=2, aux_loss_alpha=0.01, dtype=None, device=None, operations=None): + super().__init__() + self.top_k = num_activated_experts + self.n_routed_experts = num_routed_experts + + self.scoring_func = 'softmax' + self.alpha = aux_loss_alpha + self.seq_aux = False + + # topk selection algorithm + self.norm_topk_prob = False + self.gating_dim = embed_dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), dtype=dtype, device=device)) + self.reset_parameters() + + def reset_parameters(self) -> None: + pass + # import torch.nn.init as init + # init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + + ### compute gating score + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + ### select top-k experts + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + ### norm gate to sum 1 + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + aux_loss = None + return topk_idx, topk_weight, aux_loss + + +# Modified from https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/model.py +class MOEFeedForwardSwiGLU(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + num_routed_experts: int, + num_activated_experts: int, + dtype=None, device=None, operations=None + ): + super().__init__() + self.shared_experts = FeedForwardSwiGLU(dim, hidden_dim // 2, dtype=dtype, device=device, operations=operations) + self.experts = nn.ModuleList([FeedForwardSwiGLU(dim, hidden_dim, dtype=dtype, device=device, operations=operations) for i in range(num_routed_experts)]) + self.gate = MoEGate( + embed_dim = dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + self.num_activated_experts = num_activated_experts + + def forward(self, x): + wtype = x.dtype + identity = x + orig_shape = x.shape + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if True: # self.training: # TODO: check which branch performs faster + x = x.repeat_interleave(self.num_activated_experts, dim=0) + y = torch.empty_like(x, dtype=wtype) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(dtype=wtype) + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape).to(dtype=wtype) + #y = AddAuxiliaryLoss.apply(y, aux_loss) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + y = y + self.shared_experts(identity) + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.num_activated_experts + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i-1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + + # for fp16 and other dtype + expert_cache = expert_cache.to(expert_out.dtype) + expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') + return expert_cache + + +class TextProjection(nn.Module): + def __init__(self, in_features, hidden_size, dtype=None, device=None, operations=None): + super().__init__() + self.linear = operations.Linear(in_features=in_features, out_features=hidden_size, bias=False, dtype=dtype, device=device) + + def forward(self, caption): + hidden_states = self.linear(caption) + return hidden_states + + +class BlockType: + TransformerBlock = 1 + SingleTransformerBlock = 2 + + +class HiDreamImageSingleTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + dtype=None, device=None, operations=None + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device) + ) + + # 1. Attention + self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.attn1 = HiDreamAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + processor = HiDreamAttnProcessor_flashattn(), + single = True, + dtype=dtype, device=device, operations=operations + ) + + # 3. Feed-forward + self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + if num_routed_experts > 0: + self.ff_i = MOEFeedForwardSwiGLU( + dim = dim, + hidden_dim = 4 * dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + else: + self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + transformer_options={}, + ) -> torch.FloatTensor: + wtype = image_tokens.dtype + shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \ + self.adaLN_modulation(adaln_input)[:,None].chunk(6, dim=-1) + + # 1. MM-Attention + norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i + attn_output_i = self.attn1( + norm_image_tokens, + image_tokens_masks, + rope = rope, + transformer_options=transformer_options, + ) + image_tokens = gate_msa_i * attn_output_i + image_tokens + + # 2. Feed-forward + norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i + ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens.to(dtype=wtype)) + image_tokens = ff_output_i + image_tokens + return image_tokens + + +class HiDreamImageTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + dtype=None, device=None, operations=None + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 12 * dim, bias=True, dtype=dtype, device=device) + ) + # nn.init.zeros_(self.adaLN_modulation[1].weight) + # nn.init.zeros_(self.adaLN_modulation[1].bias) + + # 1. Attention + self.norm1_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.norm1_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + self.attn1 = HiDreamAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + processor = HiDreamAttnProcessor_flashattn(), + single = False, + dtype=dtype, device=device, operations=operations + ) + + # 3. Feed-forward + self.norm3_i = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False, dtype=dtype, device=device) + if num_routed_experts > 0: + self.ff_i = MOEFeedForwardSwiGLU( + dim = dim, + hidden_dim = 4 * dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + else: + self.ff_i = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + self.norm3_t = operations.LayerNorm(dim, eps = 1e-06, elementwise_affine = False) + self.ff_t = FeedForwardSwiGLU(dim = dim, hidden_dim = 4 * dim, dtype=dtype, device=device, operations=operations) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: Optional[torch.FloatTensor] = None, + rope: torch.FloatTensor = None, + transformer_options={}, + ) -> torch.FloatTensor: + wtype = image_tokens.dtype + shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \ + shift_msa_t, scale_msa_t, gate_msa_t, shift_mlp_t, scale_mlp_t, gate_mlp_t = \ + self.adaLN_modulation(adaln_input)[:,None].chunk(12, dim=-1) + + # 1. MM-Attention + norm_image_tokens = self.norm1_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_msa_i) + shift_msa_i + norm_text_tokens = self.norm1_t(text_tokens).to(dtype=wtype) + norm_text_tokens = norm_text_tokens * (1 + scale_msa_t) + shift_msa_t + + attn_output_i, attn_output_t = self.attn1( + norm_image_tokens, + image_tokens_masks, + norm_text_tokens, + rope = rope, + transformer_options=transformer_options, + ) + + image_tokens = gate_msa_i * attn_output_i + image_tokens + text_tokens = gate_msa_t * attn_output_t + text_tokens + + # 2. Feed-forward + norm_image_tokens = self.norm3_i(image_tokens).to(dtype=wtype) + norm_image_tokens = norm_image_tokens * (1 + scale_mlp_i) + shift_mlp_i + norm_text_tokens = self.norm3_t(text_tokens).to(dtype=wtype) + norm_text_tokens = norm_text_tokens * (1 + scale_mlp_t) + shift_mlp_t + + ff_output_i = gate_mlp_i * self.ff_i(norm_image_tokens) + ff_output_t = gate_mlp_t * self.ff_t(norm_text_tokens) + image_tokens = ff_output_i + image_tokens + text_tokens = ff_output_t + text_tokens + return image_tokens, text_tokens + + +class HiDreamImageBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + block_type: BlockType = BlockType.TransformerBlock, + dtype=None, device=None, operations=None + ): + super().__init__() + block_classes = { + BlockType.TransformerBlock: HiDreamImageTransformerBlock, + BlockType.SingleTransformerBlock: HiDreamImageSingleTransformerBlock, + } + self.block = block_classes[block_type]( + dim, + num_attention_heads, + attention_head_dim, + num_routed_experts, + num_activated_experts, + dtype=dtype, device=device, operations=operations + ) + + def forward( + self, + image_tokens: torch.FloatTensor, + image_tokens_masks: Optional[torch.FloatTensor] = None, + text_tokens: Optional[torch.FloatTensor] = None, + adaln_input: torch.FloatTensor = None, + rope: torch.FloatTensor = None, + transformer_options={}, + ) -> torch.FloatTensor: + return self.block( + image_tokens, + image_tokens_masks, + text_tokens, + adaln_input, + rope, + transformer_options=transformer_options, + ) + + +class HiDreamImageTransformer2DModel(nn.Module): + def __init__( + self, + patch_size: Optional[int] = None, + in_channels: int = 64, + out_channels: Optional[int] = None, + num_layers: int = 16, + num_single_layers: int = 32, + attention_head_dim: int = 128, + num_attention_heads: int = 20, + caption_channels: List[int] = None, + text_emb_dim: int = 2048, + num_routed_experts: int = 4, + num_activated_experts: int = 2, + axes_dims_rope: Tuple[int, int] = (32, 32), + max_resolution: Tuple[int, int] = (128, 128), + llama_layers: List[int] = None, + image_model=None, + dtype=None, device=None, operations=None + ): + self.patch_size = patch_size + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + self.num_layers = num_layers + self.num_single_layers = num_single_layers + + self.gradient_checkpointing = False + + super().__init__() + self.dtype = dtype + self.out_channels = out_channels or in_channels + self.inner_dim = self.num_attention_heads * self.attention_head_dim + self.llama_layers = llama_layers + + self.t_embedder = TimestepEmbed(self.inner_dim, dtype=dtype, device=device, operations=operations) + self.p_embedder = PooledEmbed(text_emb_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) + self.x_embedder = PatchEmbed( + patch_size = patch_size, + in_channels = in_channels, + out_channels = self.inner_dim, + dtype=dtype, device=device, operations=operations + ) + self.pe_embedder = EmbedND(theta=10000, axes_dim=axes_dims_rope) + + self.double_stream_blocks = nn.ModuleList( + [ + HiDreamImageBlock( + dim = self.inner_dim, + num_attention_heads = self.num_attention_heads, + attention_head_dim = self.attention_head_dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + block_type = BlockType.TransformerBlock, + dtype=dtype, device=device, operations=operations + ) + for i in range(self.num_layers) + ] + ) + + self.single_stream_blocks = nn.ModuleList( + [ + HiDreamImageBlock( + dim = self.inner_dim, + num_attention_heads = self.num_attention_heads, + attention_head_dim = self.attention_head_dim, + num_routed_experts = num_routed_experts, + num_activated_experts = num_activated_experts, + block_type = BlockType.SingleTransformerBlock, + dtype=dtype, device=device, operations=operations + ) + for i in range(self.num_single_layers) + ] + ) + + self.final_layer = LastLayer(self.inner_dim, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations) + + caption_channels = [caption_channels[1], ] * (num_layers + num_single_layers) + [caption_channels[0], ] + caption_projection = [] + for caption_channel in caption_channels: + caption_projection.append(TextProjection(in_features=caption_channel, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations)) + self.caption_projection = nn.ModuleList(caption_projection) + self.max_seq = max_resolution[0] * max_resolution[1] // (patch_size * patch_size) + + def expand_timesteps(self, timesteps, batch_size, device): + if not torch.is_tensor(timesteps): + is_mps = device.type == "mps" + if isinstance(timesteps, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(device) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(batch_size) + return timesteps + + def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]) -> List[torch.Tensor]: + x_arr = [] + for i, img_size in enumerate(img_sizes): + pH, pW = img_size + x_arr.append( + einops.rearrange(x[i, :pH*pW].reshape(1, pH, pW, -1), 'B H W (p1 p2 C) -> B C (H p1) (W p2)', + p1=self.patch_size, p2=self.patch_size) + ) + x = torch.cat(x_arr, dim=0) + return x + + def patchify(self, x, max_seq, img_sizes=None): + pz2 = self.patch_size * self.patch_size + if isinstance(x, torch.Tensor): + B = x.shape[0] + device = x.device + dtype = x.dtype + else: + B = len(x) + device = x[0].device + dtype = x[0].dtype + x_masks = torch.zeros((B, max_seq), dtype=dtype, device=device) + + if img_sizes is not None: + for i, img_size in enumerate(img_sizes): + x_masks[i, 0:img_size[0] * img_size[1]] = 1 + x = einops.rearrange(x, 'B C S p -> B S (p C)', p=pz2) + elif isinstance(x, torch.Tensor): + pH, pW = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size + x = einops.rearrange(x, 'B C (H p1) (W p2) -> B (H W) (p1 p2 C)', p1=self.patch_size, p2=self.patch_size) + img_sizes = [[pH, pW]] * B + x_masks = None + else: + raise NotImplementedError + return x, x_masks, img_sizes + + def forward(self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + encoder_hidden_states_llama3=None, + image_cond=None, + control = None, + transformer_options = {}, + ): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, t, y, context, encoder_hidden_states_llama3, image_cond, control, transformer_options) + + def _forward( + self, + x: torch.Tensor, + t: torch.Tensor, + y: Optional[torch.Tensor] = None, + context: Optional[torch.Tensor] = None, + encoder_hidden_states_llama3=None, + image_cond=None, + control = None, + transformer_options = {}, + ) -> torch.Tensor: + bs, c, h, w = x.shape + if image_cond is not None: + x = torch.cat([x, image_cond], dim=-1) + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + timesteps = t + pooled_embeds = y + T5_encoder_hidden_states = context + + img_sizes = None + + # spatial forward + batch_size = hidden_states.shape[0] + hidden_states_type = hidden_states.dtype + + # 0. time + timesteps = self.expand_timesteps(timesteps, batch_size, hidden_states.device) + timesteps = self.t_embedder(timesteps, hidden_states_type) + p_embedder = self.p_embedder(pooled_embeds) + adaln_input = timesteps + p_embedder + + hidden_states, image_tokens_masks, img_sizes = self.patchify(hidden_states, self.max_seq, img_sizes) + if image_tokens_masks is None: + pH, pW = img_sizes[0] + img_ids = torch.zeros(pH, pW, 3, device=hidden_states.device) + img_ids[..., 1] = img_ids[..., 1] + torch.arange(pH, device=hidden_states.device)[:, None] + img_ids[..., 2] = img_ids[..., 2] + torch.arange(pW, device=hidden_states.device)[None, :] + img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size) + hidden_states = self.x_embedder(hidden_states) + + # T5_encoder_hidden_states = encoder_hidden_states[0] + encoder_hidden_states = encoder_hidden_states_llama3.movedim(1, 0) + encoder_hidden_states = [encoder_hidden_states[k] for k in self.llama_layers] + + if self.caption_projection is not None: + new_encoder_hidden_states = [] + for i, enc_hidden_state in enumerate(encoder_hidden_states): + enc_hidden_state = self.caption_projection[i](enc_hidden_state) + enc_hidden_state = enc_hidden_state.view(batch_size, -1, hidden_states.shape[-1]) + new_encoder_hidden_states.append(enc_hidden_state) + encoder_hidden_states = new_encoder_hidden_states + T5_encoder_hidden_states = self.caption_projection[-1](T5_encoder_hidden_states) + T5_encoder_hidden_states = T5_encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) + encoder_hidden_states.append(T5_encoder_hidden_states) + + txt_ids = torch.zeros( + batch_size, + encoder_hidden_states[-1].shape[1] + encoder_hidden_states[-2].shape[1] + encoder_hidden_states[0].shape[1], + 3, + device=img_ids.device, dtype=img_ids.dtype + ) + ids = torch.cat((img_ids, txt_ids), dim=1) + rope = self.pe_embedder(ids) + + # 2. Blocks + block_id = 0 + initial_encoder_hidden_states = torch.cat([encoder_hidden_states[-1], encoder_hidden_states[-2]], dim=1) + initial_encoder_hidden_states_seq_len = initial_encoder_hidden_states.shape[1] + for bid, block in enumerate(self.double_stream_blocks): + cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] + cur_encoder_hidden_states = torch.cat([initial_encoder_hidden_states, cur_llama31_encoder_hidden_states], dim=1) + hidden_states, initial_encoder_hidden_states = block( + image_tokens = hidden_states, + image_tokens_masks = image_tokens_masks, + text_tokens = cur_encoder_hidden_states, + adaln_input = adaln_input, + rope = rope, + transformer_options=transformer_options, + ) + initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len] + block_id += 1 + + image_tokens_seq_len = hidden_states.shape[1] + hidden_states = torch.cat([hidden_states, initial_encoder_hidden_states], dim=1) + hidden_states_seq_len = hidden_states.shape[1] + if image_tokens_masks is not None: + encoder_attention_mask_ones = torch.ones( + (batch_size, initial_encoder_hidden_states.shape[1] + cur_llama31_encoder_hidden_states.shape[1]), + device=image_tokens_masks.device, dtype=image_tokens_masks.dtype + ) + image_tokens_masks = torch.cat([image_tokens_masks, encoder_attention_mask_ones], dim=1) + + for bid, block in enumerate(self.single_stream_blocks): + cur_llama31_encoder_hidden_states = encoder_hidden_states[block_id] + hidden_states = torch.cat([hidden_states, cur_llama31_encoder_hidden_states], dim=1) + hidden_states = block( + image_tokens=hidden_states, + image_tokens_masks=image_tokens_masks, + text_tokens=None, + adaln_input=adaln_input, + rope=rope, + transformer_options=transformer_options, + ) + hidden_states = hidden_states[:, :hidden_states_seq_len] + block_id += 1 + + hidden_states = hidden_states[:, :image_tokens_seq_len, ...] + output = self.final_layer(hidden_states, adaln_input) + output = self.unpatchify(output, img_sizes) + return -output[:, :, :h, :w] diff --git a/comfy/ldm/hunyuan3d/model.py b/comfy/ldm/hunyuan3d/model.py new file mode 100644 index 000000000..4991b1645 --- /dev/null +++ b/comfy/ldm/hunyuan3d/model.py @@ -0,0 +1,148 @@ +import torch +from torch import nn +from comfy.ldm.flux.layers import ( + DoubleStreamBlock, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) +import comfy.patcher_extension + + +class Hunyuan3Dv2(nn.Module): + def __init__( + self, + in_channels=64, + context_in_dim=1536, + hidden_size=1024, + mlp_ratio=4.0, + num_heads=16, + depth=16, + depth_single_blocks=32, + qkv_bias=True, + guidance_embed=False, + image_model=None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.dtype = dtype + + if hidden_size % num_heads != 0: + raise ValueError( + f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" + ) + + self.max_period = 1000 # While reimplementing the model I noticed that they messed up. This 1000 value was meant to be the time_factor but they set the max_period instead + self.latent_in = operations.Linear(in_channels, hidden_size, bias=True, dtype=dtype, device=device) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=hidden_size, dtype=dtype, device=device, operations=operations) if guidance_embed else None + ) + self.cond_in = operations.Linear(context_in_dim, hidden_size, dtype=dtype, device=device) + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + dtype=dtype, device=device, operations=operations + ) + for _ in range(depth) + ] + ) + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + dtype=dtype, device=device, operations=operations + ) + for _ in range(depth_single_blocks) + ] + ) + self.final_layer = LastLayer(hidden_size, 1, in_channels, dtype=dtype, device=device, operations=operations) + + def forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, guidance, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, guidance=None, transformer_options={}, **kwargs): + x = x.movedim(-1, -2) + timestep = 1.0 - timestep + txt = context + img = self.latent_in(x) + + vec = self.time_in(timestep_embedding(timestep, 256, self.max_period).to(dtype=img.dtype)) + if self.guidance_in is not None: + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.max_period).to(img.dtype)) + + txt = self.cond_in(txt) + pe = None + attn_mask = None + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.double_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"], out["txt"] = block(img=args["img"], + txt=args["txt"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask"), + transformer_options=args["transformer_options"]) + return out + + out = blocks_replace[("double_block", i)]({"img": img, + "txt": txt, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, + {"original_block": block_wrap}) + txt = out["txt"] + img = out["img"] + else: + img, txt = block(img=img, + txt=txt, + vec=vec, + pe=pe, + attn_mask=attn_mask, + transformer_options=transformer_options) + + img = torch.cat((txt, img), 1) + + for i, block in enumerate(self.single_blocks): + if ("single_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], + vec=args["vec"], + pe=args["pe"], + attn_mask=args.get("attn_mask"), + transformer_options=args["transformer_options"]) + return out + + out = blocks_replace[("single_block", i)]({"img": img, + "vec": vec, + "pe": pe, + "attn_mask": attn_mask, + "transformer_options": transformer_options}, + {"original_block": block_wrap}) + img = out["img"] + else: + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options) + + img = img[:, txt.shape[1]:, ...] + img = self.final_layer(img, vec) + return img.movedim(-2, -1) * (-1.0) diff --git a/comfy/ldm/hunyuan3d/vae.py b/comfy/ldm/hunyuan3d/vae.py new file mode 100644 index 000000000..760944827 --- /dev/null +++ b/comfy/ldm/hunyuan3d/vae.py @@ -0,0 +1,988 @@ +# Original: https://github.com/Tencent/Hunyuan3D-2/blob/main/hy3dgen/shapegen/models/autoencoders/model.py +# Since the header on their VAE source file was a bit confusing we asked for permission to use this code from tencent under the GPL license used in ComfyUI. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import math +from tqdm import tqdm + +from typing import Optional + +import logging + +import comfy.ops +ops = comfy.ops.disable_weight_init + +def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, start_random: bool = True): + + # manually create the pointer vector + assert src.size(0) == batch.numel() + + batch_size = int(batch.max()) + 1 + deg = src.new_zeros(batch_size, dtype = torch.long) + + deg.scatter_add_(0, batch, torch.ones_like(batch)) + + ptr_vec = deg.new_zeros(batch_size + 1) + torch.cumsum(deg, 0, out=ptr_vec[1:]) + + #return fps_sampling(src, ptr_vec, ratio) + sampled_indicies = [] + + for b in range(batch_size): + # start and the end of each batch + start, end = ptr_vec[b].item(), ptr_vec[b + 1].item() + # points from the point cloud + points = src[start:end] + + num_points = points.size(0) + num_samples = max(1, math.ceil(num_points * sampling_ratio)) + + selected = torch.zeros(num_samples, device = src.device, dtype = torch.long) + distances = torch.full((num_points,), float("inf"), device = src.device) + + # select a random start point + if start_random: + farthest = torch.randint(0, num_points, (1,), device = src.device) + else: + farthest = torch.tensor([0], device = src.device, dtype = torch.long) + + for i in range(num_samples): + selected[i] = farthest + centroid = points[farthest].squeeze(0) + dist = torch.norm(points - centroid, dim = 1) # compute euclidean distance + distances = torch.minimum(distances, dist) + farthest = torch.argmax(distances) + + sampled_indicies.append(torch.arange(start, end)[selected]) + + return torch.cat(sampled_indicies, dim = 0) +class PointCrossAttention(nn.Module): + def __init__(self, + num_latents: int, + downsample_ratio: float, + pc_size: int, + pc_sharpedge_size: int, + point_feats: int, + width: int, + heads: int, + layers: int, + fourier_embedder, + normal_pe: bool = False, + qkv_bias: bool = False, + use_ln_post: bool = True, + qk_norm: bool = True): + + super().__init__() + + self.fourier_embedder = fourier_embedder + + self.pc_size = pc_size + self.normal_pe = normal_pe + self.downsample_ratio = downsample_ratio + self.pc_sharpedge_size = pc_sharpedge_size + self.num_latents = num_latents + self.point_feats = point_feats + + self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width) + + self.cross_attn = ResidualCrossAttentionBlock( + width = width, + heads = heads, + qkv_bias = qkv_bias, + qk_norm = qk_norm + ) + + self.self_attn = None + if layers > 0: + self.self_attn = Transformer( + width = width, + heads = heads, + qkv_bias = qkv_bias, + qk_norm = qk_norm, + layers = layers + ) + + if use_ln_post: + self.ln_post = nn.LayerNorm(width) + else: + self.ln_post = None + + def sample_points_and_latents(self, point_cloud: torch.Tensor, features: torch.Tensor): + + """ + Subsample points randomly from the point cloud (input_pc) + Further sample the subsampled points to get query_pc + take the fourier embeddings for both input and query pc + + Mental Note: FPS-sampled points (query_pc) act as latent tokens that attend to and learn from the broader context in input_pc. + Goal: get a smaller represenation (query_pc) to represent the entire scence structure by learning from a broader subset (input_pc). + More computationally efficient. + + Features are additional information for each point in the cloud + """ + + B, _, D = point_cloud.shape + + num_latents = int(self.num_latents) + + num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents + num_sharpedge_query = num_latents - num_random_query + + # Split random and sharpedge surface points + random_pc, sharpedge_pc = torch.split(point_cloud, [self.pc_size, self.pc_sharpedge_size], dim=1) + + # assert statements + assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size" + assert sharpedge_pc.shape[1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size" + + input_random_pc_size = int(num_random_query * self.downsample_ratio) + random_query_pc, random_input_pc, random_idx_pc, random_idx_query = \ + self.subsample(pc = random_pc, num_query = num_random_query, input_pc_size = input_random_pc_size) + + input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio) + + if input_sharpedge_pc_size == 0: + sharpedge_input_pc = torch.zeros(B, 0, D, dtype = random_input_pc.dtype).to(point_cloud.device) + sharpedge_query_pc = torch.zeros(B, 0, D, dtype= random_query_pc.dtype).to(point_cloud.device) + + else: + sharpedge_query_pc, sharpedge_input_pc, sharpedge_idx_pc, sharpedge_idx_query = \ + self.subsample(pc = sharpedge_pc, num_query = num_sharpedge_query, input_pc_size = input_sharpedge_pc_size) + + # concat the random and sharpedges + query_pc = torch.cat([random_query_pc, sharpedge_query_pc], dim = 1) + input_pc = torch.cat([random_input_pc, sharpedge_input_pc], dim = 1) + + query = self.fourier_embedder(query_pc) + data = self.fourier_embedder(input_pc) + + if self.point_feats > 0: + random_surface_features, sharpedge_surface_features = torch.split(features, [self.pc_size, self.pc_sharpedge_size], dim = 1) + + input_random_surface_features, query_random_features = \ + self.handle_features(features = random_surface_features, idx_pc = random_idx_pc, batch_size = B, + input_pc_size = input_random_pc_size, idx_query = random_idx_query) + + if input_sharpedge_pc_size == 0: + input_sharpedge_surface_features = torch.zeros(B, 0, self.point_feats, + dtype = input_random_surface_features.dtype, device = point_cloud.device) + + query_sharpedge_features = torch.zeros(B, 0, self.point_feats, + dtype = query_random_features.dtype, device = point_cloud.device) + else: + + input_sharpedge_surface_features, query_sharpedge_features = \ + self.handle_features(idx_pc = sharpedge_idx_pc, features = sharpedge_surface_features, + batch_size = B, idx_query = sharpedge_idx_query, input_pc_size = input_sharpedge_pc_size) + + query_features = torch.cat([query_random_features, query_sharpedge_features], dim = 1) + input_features = torch.cat([input_random_surface_features, input_sharpedge_surface_features], dim = 1) + + if self.normal_pe: + # apply the fourier embeddings on the first 3 dims (xyz) + input_features_pe = self.fourier_embedder(input_features[..., :3]) + query_features_pe = self.fourier_embedder(query_features[..., :3]) + # replace the first 3 dims with the new PE ones + input_features = torch.cat([input_features_pe, input_features[..., :3]], dim = -1) + query_features = torch.cat([query_features_pe, query_features[..., :3]], dim = -1) + + # concat at the channels dim + query = torch.cat([query, query_features], dim = -1) + data = torch.cat([data, input_features], dim = -1) + + # don't return pc_info to avoid unnecessary memory usuage + return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1]) + + def forward(self, point_cloud: torch.Tensor, features: torch.Tensor): + + query, data = self.sample_points_and_latents(point_cloud = point_cloud, features = features) + + # apply projections + query = self.input_proj(query) + data = self.input_proj(data) + + # apply cross attention between query and data + latents = self.cross_attn(query, data) + + if self.self_attn is not None: + latents = self.self_attn(latents) + + if self.ln_post is not None: + latents = self.ln_post(latents) + + return latents + + + def subsample(self, pc, num_query, input_pc_size: int): + + """ + num_query: number of points to keep after FPS + input_pc_size: number of points to select before FPS + """ + + B, _, D = pc.shape + query_ratio = num_query / input_pc_size + + # random subsampling of points inside the point cloud + idx_pc = torch.randperm(pc.shape[1], device = pc.device)[:input_pc_size] + input_pc = pc[:, idx_pc, :] + + # flatten to allow applying fps across the whole batch + flattent_input_pc = input_pc.view(B * input_pc_size, D) + + # construct a batch_down tensor to tell fps + # which points belong to which batch + N_down = int(flattent_input_pc.shape[0] / B) + batch_down = torch.arange(B).to(pc.device) + batch_down = torch.repeat_interleave(batch_down, N_down) + + idx_query = fps(flattent_input_pc, batch_down, sampling_ratio = query_ratio) + query_pc = flattent_input_pc[idx_query].view(B, -1, D) + + return query_pc, input_pc, idx_pc, idx_query + + def handle_features(self, features, idx_pc, input_pc_size, batch_size: int, idx_query): + + B = batch_size + + input_surface_features = features[:, idx_pc, :] + flattent_input_features = input_surface_features.view(B * input_pc_size, -1) + query_features = flattent_input_features[idx_query].view(B, -1, + flattent_input_features.shape[-1]) + + return input_surface_features, query_features + +def normalize_mesh(mesh, scale = 0.9999): + """Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]""" + + bbox = mesh.bounds + center = (bbox[1] + bbox[0]) / 2 + + max_extent = (bbox[1] - bbox[0]).max() + mesh.apply_translation(-center) + mesh.apply_scale((2 * scale) / max_extent) + + return mesh + +def sample_pointcloud(mesh, num = 200000): + """ Uniformly sample points from the surface of the mesh """ + + points, face_idx = mesh.sample(num, return_index = True) + normals = mesh.face_normals[face_idx] + return torch.from_numpy(points.astype(np.float32)), torch.from_numpy(normals.astype(np.float32)) + +def detect_sharp_edges(mesh, threshold=0.985): + """Return edge indices (a, b) that lie on sharp boundaries of the mesh.""" + + V, F = mesh.vertices, mesh.faces + VN, FN = mesh.vertex_normals, mesh.face_normals + + sharp_mask = np.ones(V.shape[0]) + for i in range(3): + indices = F[:, i] + alignment = np.einsum('ij,ij->i', VN[indices], FN) + dot_stack = np.stack((sharp_mask[indices], alignment), axis=-1) + sharp_mask[indices] = np.min(dot_stack, axis=-1) + + edge_a = np.concatenate([F[:, 0], F[:, 1], F[:, 2]]) + edge_b = np.concatenate([F[:, 1], F[:, 2], F[:, 0]]) + sharp_edges = (sharp_mask[edge_a] < threshold) & (sharp_mask[edge_b] < threshold) + + return edge_a[sharp_edges], edge_b[sharp_edges] + + +def sharp_sample_pointcloud(mesh, num = 16384): + """ Sample points preferentially from sharp edges in the mesh. """ + + edge_a, edge_b = detect_sharp_edges(mesh) + V, VN = mesh.vertices, mesh.vertex_normals + + va, vb = V[edge_a], V[edge_b] + na, nb = VN[edge_a], VN[edge_b] + + edge_lengths = np.linalg.norm(vb - va, axis=-1) + weights = edge_lengths / edge_lengths.sum() + + indices = np.searchsorted(np.cumsum(weights), np.random.rand(num)) + t = np.random.rand(num, 1) + + samples = t * va[indices] + (1 - t) * vb[indices] + normals = t * na[indices] + (1 - t) * nb[indices] + + return samples.astype(np.float32), normals.astype(np.float32) + +def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"): + """Load a surface with optional sharp-edge annotations from a trimesh mesh.""" + + import trimesh + + try: + mesh_full = trimesh.util.concatenate(mesh.dump()) + except Exception: + mesh_full = trimesh.util.concatenate(mesh) + + mesh_full = normalize_mesh(mesh_full) + + faces = mesh_full.faces + vertices = mesh_full.vertices + origin_face_count = faces.shape[0] + + mesh_surface = trimesh.Trimesh(vertices=vertices, faces=faces[:origin_face_count]) + mesh_fill = trimesh.Trimesh(vertices=vertices, faces=faces[origin_face_count:]) + + area_surface = mesh_surface.area + area_fill = mesh_fill.area + total_area = area_surface + area_fill + + sample_num = 499712 // 2 + fill_ratio = area_fill / total_area if total_area > 0 else 0 + + num_fill = int(sample_num * fill_ratio) + num_surface = sample_num - num_fill + + surf_pts, surf_normals = sample_pointcloud(mesh_surface, num_surface) + fill_pts, fill_normals = (torch.zeros(0, 3), torch.zeros(0, 3)) if num_fill == 0 else sample_pointcloud(mesh_fill, num_fill) + + sharp_pts, sharp_normals = sharp_sample_pointcloud(mesh_surface, sample_num) + + def assemble_tensor(points, normals, label=None): + + data = torch.cat([points, normals], dim=1).half().to(device) + + if label is not None: + label_tensor = torch.full((data.shape[0], 1), float(label), dtype=torch.float16).to(device) + data = torch.cat([data, label_tensor], dim=1) + + return data + + surface = assemble_tensor(torch.cat([surf_pts.to(device), fill_pts.to(device)], dim=0), + torch.cat([surf_normals.to(device), fill_normals.to(device)], dim=0), + label = 0 if sharpedge_flag else None) + + sharp_surface = assemble_tensor(torch.from_numpy(sharp_pts), torch.from_numpy(sharp_normals), + label = 1 if sharpedge_flag else None) + + rng = np.random.default_rng() + + surface = surface[rng.choice(surface.shape[0], num_points, replace = False)] + sharp_surface = sharp_surface[rng.choice(sharp_surface.shape[0], num_sharp_points, replace = False)] + + full = torch.cat([surface, sharp_surface], dim = 0).unsqueeze(0) + + return full + +class SharpEdgeSurfaceLoader: + """ Load mesh surface and sharp edge samples. """ + + def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192): + + self.num_uniform_points = num_uniform_points + self.num_sharp_points = num_sharp_points + self.total_points = num_uniform_points + num_sharp_points + + def __call__(self, mesh_input, device = "cuda"): + mesh = self._load_mesh(mesh_input) + return load_surface_sharpedge(mesh, self.num_uniform_points, self.num_sharp_points, device = device) + + @staticmethod + def _load_mesh(mesh_input): + import trimesh + + if isinstance(mesh_input, str): + mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True) + else: + mesh = mesh_input + + if isinstance(mesh, trimesh.Scene): + combined = None + for obj in mesh.geometry.values(): + combined = obj if combined is None else combined + obj + return combined + + return mesh + +class DiagonalGaussianDistribution: + def __init__(self, params: torch.Tensor, feature_dim: int = -1): + + # divide quant channels (8) into mean and log variance + self.mean, self.logvar = torch.chunk(params, 2, dim = feature_dim) + + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.std = torch.exp(0.5 * self.logvar) + + def sample(self): + + eps = torch.randn_like(self.std) + z = self.mean + eps * self.std + + return z + +################################################ +# Volume Decoder +################################################ + +class VanillaVolumeDecoder(): + @torch.no_grad() + def __call__(self, latents: torch.Tensor, geo_decoder: callable, octree_resolution: int, bounds = 1.01, + num_chunks: int = 10_000, enable_pbar: bool = True, **kwargs): + + if isinstance(bounds, float): + bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] + + bbox_min, bbox_max = torch.tensor(bounds[:3]), torch.tensor(bounds[3:]) + + x = torch.linspace(bbox_min[0], bbox_max[0], int(octree_resolution) + 1, dtype = torch.float32) + y = torch.linspace(bbox_min[1], bbox_max[1], int(octree_resolution) + 1, dtype = torch.float32) + z = torch.linspace(bbox_min[2], bbox_max[2], int(octree_resolution) + 1, dtype = torch.float32) + + [xs, ys, zs] = torch.meshgrid(x, y, z, indexing = "ij") + xyz = torch.stack((xs, ys, zs), axis=-1).to(latents.device, dtype = latents.dtype).contiguous().reshape(-1, 3) + grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1] + + batch_logits = [] + for start in tqdm(range(0, xyz.shape[0], num_chunks), desc="Volume Decoding", + disable=not enable_pbar): + + chunk_queries = xyz[start: start + num_chunks, :] + chunk_queries = chunk_queries.unsqueeze(0).repeat(latents.shape[0], 1, 1) + logits = geo_decoder(queries = chunk_queries, latents = latents) + batch_logits.append(logits) + + grid_logits = torch.cat(batch_logits, dim = 1) + grid_logits = grid_logits.view((latents.shape[0], *grid_size)).float() + + return grid_logits + +class FourierEmbedder(nn.Module): + """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts + each feature dimension of `x[..., i]` into: + [ + sin(x[..., i]), + sin(f_1*x[..., i]), + sin(f_2*x[..., i]), + ... + sin(f_N * x[..., i]), + cos(x[..., i]), + cos(f_1*x[..., i]), + cos(f_2*x[..., i]), + ... + cos(f_N * x[..., i]), + x[..., i] # only present if include_input is True. + ], here f_i is the frequency. + + Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. + If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; + Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. + + Args: + num_freqs (int): the number of frequencies, default is 6; + logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; + input_dim (int): the input dimension, default is 3; + include_input (bool): include the input tensor or not, default is True. + + Attributes: + frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], + otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); + + out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), + otherwise, it is input_dim * num_freqs * 2. + + """ + + def __init__(self, + num_freqs: int = 6, + logspace: bool = True, + input_dim: int = 3, + include_input: bool = True, + include_pi: bool = True) -> None: + + """The initialization""" + + super().__init__() + + if logspace: + frequencies = 2.0 ** torch.arange( + num_freqs, + dtype=torch.float32 + ) + else: + frequencies = torch.linspace( + 1.0, + 2.0 ** (num_freqs - 1), + num_freqs, + dtype=torch.float32 + ) + + if include_pi: + frequencies *= torch.pi + + self.register_buffer("frequencies", frequencies, persistent=False) + self.include_input = include_input + self.num_freqs = num_freqs + + self.out_dim = self.get_dims(input_dim) + + def get_dims(self, input_dim): + temp = 1 if self.include_input or self.num_freqs == 0 else 0 + out_dim = input_dim * (self.num_freqs * 2 + temp) + + return out_dim + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ Forward process. + + Args: + x: tensor of shape [..., dim] + + Returns: + embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] + where temp is 1 if include_input is True and 0 otherwise. + """ + + if self.num_freqs > 0: + embed = (x[..., None].contiguous() * self.frequencies.to(device=x.device, dtype=x.dtype)).view(*x.shape[:-1], -1) + if self.include_input: + return torch.cat((x, embed.sin(), embed.cos()), dim=-1) + else: + return torch.cat((embed.sin(), embed.cos()), dim=-1) + else: + return x + +class CrossAttentionProcessor: + def __call__(self, attn, q, k, v): + out = comfy.ops.scaled_dot_product_attention(q, k, v) + return out + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + + def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if self.drop_prob == 0. or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and self.scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + def extra_repr(self): + return f'drop_prob={round(self.drop_prob, 3):0.3f}' + + +class MLP(nn.Module): + def __init__( + self, *, + width: int, + expand_ratio: int = 4, + output_width: int = None, + drop_path_rate: float = 0.0 + ): + super().__init__() + self.width = width + self.c_fc = ops.Linear(width, width * expand_ratio) + self.c_proj = ops.Linear(width * expand_ratio, output_width if output_width is not None else width) + self.gelu = nn.GELU() + self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + def forward(self, x): + return self.drop_path(self.c_proj(self.gelu(self.c_fc(x)))) + +class QKVMultiheadCrossAttention(nn.Module): + def __init__( + self, + heads: int, + n_data = None, + width=None, + qk_norm=False, + norm_layer=ops.LayerNorm + ): + super().__init__() + self.heads = heads + self.n_data = n_data + self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + def forward(self, q, kv): + + _, n_ctx, _ = q.shape + bs, n_data, width = kv.shape + + attn_ch = width // self.heads // 2 + q = q.view(bs, n_ctx, self.heads, -1) + + kv = kv.view(bs, n_data, self.heads, -1) + k, v = torch.split(kv, attn_ch, dim=-1) + + q = self.q_norm(q) + k = self.k_norm(k) + + q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)] + out = F.scaled_dot_product_attention(q, k, v) + + out = out.transpose(1, 2).reshape(bs, n_ctx, -1) + + return out + +class MultiheadCrossAttention(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool = True, + data_width: Optional[int] = None, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + kv_cache: bool = False, + ): + super().__init__() + self.width = width + self.heads = heads + self.data_width = width if data_width is None else data_width + self.c_q = ops.Linear(width, width, bias=qkv_bias) + self.c_kv = ops.Linear(self.data_width, width * 2, bias=qkv_bias) + self.c_proj = ops.Linear(width, width) + self.attention = QKVMultiheadCrossAttention( + heads=heads, + width=width, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.kv_cache = kv_cache + self.data = None + + def forward(self, x, data): + x = self.c_q(x) + if self.kv_cache: + if self.data is None: + self.data = self.c_kv(data) + logging.info('Save kv cache,this should be called only once for one mesh') + data = self.data + else: + data = self.c_kv(data) + x = self.attention(x, data) + x = self.c_proj(x) + return x + +class ResidualCrossAttentionBlock(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + mlp_expand_ratio: int = 4, + data_width: Optional[int] = None, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False + ): + super().__init__() + + if data_width is None: + data_width = width + + self.attn = MultiheadCrossAttention( + width=width, + heads=heads, + data_width=data_width, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) + self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) + + def forward(self, x: torch.Tensor, data: torch.Tensor): + x = x + self.attn(self.ln_1(x), self.ln_2(data)) + x = x + self.mlp(self.ln_3(x)) + return x + + +class QKVMultiheadAttention(nn.Module): + def __init__( + self, + *, + heads: int, + width=None, + qk_norm=False, + norm_layer=ops.LayerNorm + ): + super().__init__() + self.heads = heads + self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() + + def forward(self, qkv): + bs, n_ctx, width = qkv.shape + attn_ch = width // self.heads // 3 + qkv = qkv.view(bs, n_ctx, self.heads, -1) + q, k, v = torch.split(qkv, attn_ch, dim=-1) + + q = self.q_norm(q) + k = self.k_norm(k) + + q, k, v = [t.permute(0, 2, 1, 3) for t in (q, k, v)] + out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) + return out + + +class MultiheadAttention(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0 + ): + super().__init__() + + self.c_qkv = ops.Linear(width, width * 3, bias=qkv_bias) + self.c_proj = ops.Linear(width, width) + self.attention = QKVMultiheadAttention( + heads=heads, + width=width, + norm_layer=norm_layer, + qk_norm=qk_norm + ) + self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() + + def forward(self, x): + x = self.c_qkv(x) + x = self.attention(x) + x = self.drop_path(self.c_proj(x)) + return x + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + *, + width: int, + heads: int, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0, + ): + super().__init__() + self.attn = MultiheadAttention( + width=width, + heads=heads, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) + self.mlp = MLP(width=width, drop_path_rate=drop_path_rate) + self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6) + + def forward(self, x: torch.Tensor): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__( + self, + *, + width: int, + layers: int, + heads: int, + qkv_bias: bool = True, + norm_layer=ops.LayerNorm, + qk_norm: bool = False, + drop_path_rate: float = 0.0 + ): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.ModuleList( + [ + ResidualAttentionBlock( + width=width, + heads=heads, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + for _ in range(layers) + ] + ) + + def forward(self, x: torch.Tensor): + for block in self.resblocks: + x = block(x) + return x + + +class CrossAttentionDecoder(nn.Module): + + def __init__( + self, + *, + out_channels: int, + fourier_embedder: FourierEmbedder, + width: int, + heads: int, + mlp_expand_ratio: int = 4, + downsample_ratio: int = 1, + enable_ln_post: bool = True, + qkv_bias: bool = True, + qk_norm: bool = False, + label_type: str = "binary" + ): + super().__init__() + + self.enable_ln_post = enable_ln_post + self.fourier_embedder = fourier_embedder + self.downsample_ratio = downsample_ratio + self.query_proj = ops.Linear(self.fourier_embedder.out_dim, width) + if self.downsample_ratio != 1: + self.latents_proj = ops.Linear(width * downsample_ratio, width) + if not self.enable_ln_post: + qk_norm = False + self.cross_attn_decoder = ResidualCrossAttentionBlock( + width=width, + mlp_expand_ratio=mlp_expand_ratio, + heads=heads, + qkv_bias=qkv_bias, + qk_norm=qk_norm + ) + + if self.enable_ln_post: + self.ln_post = ops.LayerNorm(width) + self.output_proj = ops.Linear(width, out_channels) + self.label_type = label_type + self.count = 0 + + def forward(self, queries=None, query_embeddings=None, latents=None): + if query_embeddings is None: + query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) + self.count += query_embeddings.shape[1] + if self.downsample_ratio != 1: + latents = self.latents_proj(latents) + x = self.cross_attn_decoder(query_embeddings, latents) + if self.enable_ln_post: + x = self.ln_post(x) + occ = self.output_proj(x) + return occ + + +class ShapeVAE(nn.Module): + def __init__( + self, + *, + num_latents: int = 4096, + embed_dim: int = 64, + width: int = 1024, + heads: int = 16, + num_decoder_layers: int = 16, + num_encoder_layers: int = 8, + pc_size: int = 81920, + pc_sharpedge_size: int = 0, + point_feats: int = 4, + downsample_ratio: int = 20, + geo_decoder_downsample_ratio: int = 1, + geo_decoder_mlp_expand_ratio: int = 4, + geo_decoder_ln_post: bool = True, + num_freqs: int = 8, + qkv_bias: bool = False, + qk_norm: bool = True, + drop_path_rate: float = 0.0, + include_pi: bool = False, + scale_factor: float = 1.0039506158752403, + label_type: str = "binary", + ): + super().__init__() + self.geo_decoder_ln_post = geo_decoder_ln_post + + self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) + + self.encoder = PointCrossAttention(layers = num_encoder_layers, + num_latents = num_latents, + downsample_ratio = downsample_ratio, + heads = heads, + pc_size = pc_size, + width = width, + point_feats = point_feats, + fourier_embedder = self.fourier_embedder, + pc_sharpedge_size = pc_sharpedge_size) + + self.post_kl = ops.Linear(embed_dim, width) + + self.transformer = Transformer( + width=width, + layers=num_decoder_layers, + heads=heads, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + drop_path_rate=drop_path_rate + ) + + self.geo_decoder = CrossAttentionDecoder( + fourier_embedder=self.fourier_embedder, + out_channels=1, + mlp_expand_ratio=geo_decoder_mlp_expand_ratio, + downsample_ratio=geo_decoder_downsample_ratio, + enable_ln_post=self.geo_decoder_ln_post, + width=width // geo_decoder_downsample_ratio, + heads=heads // geo_decoder_downsample_ratio, + qkv_bias=qkv_bias, + qk_norm=qk_norm, + label_type=label_type, + ) + + self.volume_decoder = VanillaVolumeDecoder() + self.scale_factor = scale_factor + + def decode(self, latents, **kwargs): + latents = self.post_kl(latents.movedim(-2, -1)) + latents = self.transformer(latents) + + bounds = kwargs.get("bounds", 1.01) + num_chunks = kwargs.get("num_chunks", 8000) + octree_resolution = kwargs.get("octree_resolution", 256) + enable_pbar = kwargs.get("enable_pbar", True) + + grid_logits = self.volume_decoder(latents, self.geo_decoder, bounds=bounds, num_chunks=num_chunks, octree_resolution=octree_resolution, enable_pbar=enable_pbar) + return grid_logits.movedim(-2, -1) + + def encode(self, surface): + + pc, feats = surface[:, :, :3], surface[:, :, 3:] + latents = self.encoder(pc, feats) + + moments = self.pre_kl(latents) + posterior = DiagonalGaussianDistribution(moments, feature_dim = -1) + + latents = posterior.sample() + + return latents diff --git a/comfy/ldm/hunyuan3dv2_1/hunyuandit.py b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py new file mode 100644 index 000000000..d48d9d642 --- /dev/null +++ b/comfy/ldm/hunyuan3dv2_1/hunyuandit.py @@ -0,0 +1,659 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + +class GELU(nn.Module): + + def __init__(self, dim_in: int, dim_out: int, operations, device, dtype): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out, device = device, dtype = dtype) + + def gelu(self, gate: torch.Tensor) -> torch.Tensor: + + if gate.device.type == "mps": + return F.gelu(gate.to(dtype = torch.float32)).to(dtype = gate.dtype) + + return F.gelu(gate) + + def forward(self, hidden_states): + + hidden_states = self.proj(hidden_states) + hidden_states = self.gelu(hidden_states) + + return hidden_states + +class FeedForward(nn.Module): + + def __init__(self, dim: int, dim_out = None, mult: int = 4, + dropout: float = 0.0, inner_dim = None, operations = None, device = None, dtype = None): + + super().__init__() + if inner_dim is None: + inner_dim = int(dim * mult) + + dim_out = dim_out if dim_out is not None else dim + + act_fn = GELU(dim, inner_dim, operations = operations, device = device, dtype = dtype) + + self.net = nn.ModuleList([]) + self.net.append(act_fn) + + self.net.append(nn.Dropout(dropout)) + self.net.append(operations.Linear(inner_dim, dim_out, device = device, dtype = dtype)) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + +class AddAuxLoss(torch.autograd.Function): + + @staticmethod + def forward(ctx, x, loss): + # do nothing in forward (no computation) + ctx.requires_aux_loss = loss.requires_grad + ctx.dtype = loss.dtype + + return x + + @staticmethod + def backward(ctx, grad_output): + # add the aux loss gradients + grad_loss = None + # put the aux grad the same as the main grad loss + # aux grad contributes equally + if ctx.requires_aux_loss: + grad_loss = torch.ones(1, dtype = ctx.dtype, device = grad_output.device) + + return grad_output, grad_loss + +class MoEGate(nn.Module): + + def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01, device = None, dtype = None): + + super().__init__() + self.top_k = num_experts_per_tok + self.n_routed_experts = num_experts + + self.alpha = aux_loss_alpha + + self.gating_dim = embed_dim + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim), device = device, dtype = dtype)) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + + # flatten hidden states + hidden_states = hidden_states.view(-1, hidden_states.size(-1)) + + # get logits and pass it to softmax + logits = F.linear(hidden_states, comfy.model_management.cast_to(self.weight, dtype=hidden_states.dtype, device=hidden_states.device), bias = None) + scores = logits.softmax(dim = -1) + + topk_weight, topk_idx = torch.topk(scores, k = self.top_k, dim = -1, sorted = False) + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + + # used bincount instead of one hot encoding + counts = torch.bincount(topk_idx.view(-1), minlength = self.n_routed_experts).float() + ce = counts / topk_idx.numel() # normalized expert usage + + # mean expert score + Pi = scores_for_aux.mean(0) + + # expert balance loss + aux_loss = (Pi * ce * self.n_routed_experts).sum() * self.alpha + else: + aux_loss = None + + return topk_idx, topk_weight, aux_loss + +class MoEBlock(nn.Module): + def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, dropout: float = 0.0, + ff_inner_dim: int = None, operations = None, device = None, dtype = None): + super().__init__() + + self.moe_top_k = moe_top_k + self.num_experts = num_experts + + self.experts = nn.ModuleList([ + FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype) + for _ in range(num_experts) + ]) + + self.gate = MoEGate(dim, num_experts = num_experts, num_experts_per_tok = moe_top_k, device = device, dtype = dtype) + self.shared_experts = FeedForward(dim, dropout = dropout, inner_dim = ff_inner_dim, operations = operations, device = device, dtype = dtype) + + def forward(self, hidden_states) -> torch.Tensor: + + identity = hidden_states + orig_shape = hidden_states.shape + topk_idx, topk_weight, aux_loss = self.gate(hidden_states) + + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + + if self.training: + + hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim = 0) + y = torch.empty_like(hidden_states, dtype = hidden_states.dtype) + + for i, expert in enumerate(self.experts): + tmp = expert(hidden_states[flat_topk_idx == i]) + y[flat_topk_idx == i] = tmp.to(hidden_states.dtype) + + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim = 1) + y = y.view(*orig_shape) + + y = AddAuxLoss.apply(y, aux_loss) + else: + y = self.moe_infer(hidden_states, flat_expert_indices = flat_topk_idx,flat_expert_weights = topk_weight.view(-1, 1)).view(*orig_shape) + + y = y + self.shared_experts(identity) + + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + + # no need for .numpy().cpu() here + tokens_per_expert = flat_expert_indices.bincount().cumsum(0) + token_idxs = idxs // self.moe_top_k + + for i, end_idx in enumerate(tokens_per_expert): + + start_idx = 0 if i == 0 else tokens_per_expert[i-1] + + if start_idx == end_idx: + continue + + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens) + + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + + # use index_add_ with a 1-D index tensor directly avoids building a large [N, D] index map and extra memcopy required by scatter_reduce_ + # + avoid dtype conversion + expert_cache.index_add_(0, exp_token_idx, expert_out) + + return expert_cache + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0, + scale: float = 1.0, max_period: int = 10000): + super().__init__() + + self.num_channels = num_channels + half_dim = num_channels // 2 + + # precompute the “inv_freq” vector once + exponent = -math.log(max_period) * torch.arange( + half_dim, dtype=torch.float32 + ) / (half_dim - downscale_freq_shift) + + inv_freq = torch.exp(exponent) + + # pad + if num_channels % 2 == 1: + # we’ll pad a zero at the end of the cos-half + inv_freq = torch.cat([inv_freq, inv_freq.new_zeros(1)]) + + # register to buffer so it moves with the device + self.register_buffer("inv_freq", inv_freq, persistent = False) + self.scale = scale + + def forward(self, timesteps: torch.Tensor): + + x = timesteps.float().unsqueeze(1) * self.inv_freq.to(timesteps.device).unsqueeze(0) + + + # fused CUDA kernels for sin and cos + sin_emb = x.sin() + cos_emb = x.cos() + + emb = torch.cat([sin_emb, cos_emb], dim = 1) + + # scale factor + if self.scale != 1.0: + emb = emb * self.scale + + # If we padded inv_freq for odd, emb is already wide enough; otherwise: + if emb.shape[1] > self.num_channels: + emb = emb[:, :self.num_channels] + + return emb + +class TimestepEmbedder(nn.Module): + def __init__(self, hidden_size, frequency_embedding_size = 256, cond_proj_dim = None, operations = None, device = None, dtype = None): + super().__init__() + + self.mlp = nn.Sequential( + operations.Linear(hidden_size, frequency_embedding_size, bias=True, device = device, dtype = dtype), + nn.GELU(), + operations.Linear(frequency_embedding_size, hidden_size, bias=True, device = device, dtype = dtype), + ) + self.frequency_embedding_size = frequency_embedding_size + + if cond_proj_dim is not None: + self.cond_proj = operations.Linear(cond_proj_dim, frequency_embedding_size, bias=False, device = device, dtype = dtype) + + self.time_embed = Timesteps(hidden_size) + + def forward(self, timesteps, condition): + + timestep_embed = self.time_embed(timesteps).type(self.mlp[0].weight.dtype) + + if condition is not None: + cond_embed = self.cond_proj(condition) + timestep_embed = timestep_embed + cond_embed + + time_conditioned = self.mlp(timestep_embed) + + # for broadcasting with image tokens + return time_conditioned.unsqueeze(1) + +class MLP(nn.Module): + def __init__(self, *, width: int, operations = None, device = None, dtype = None): + super().__init__() + self.width = width + self.fc1 = operations.Linear(width, width * 4, device = device, dtype = dtype) + self.fc2 = operations.Linear(width * 4, width, device = device, dtype = dtype) + self.gelu = nn.GELU() + + def forward(self, x): + return self.fc2(self.gelu(self.fc1(x))) + +class CrossAttention(nn.Module): + def __init__( + self, + qdim, + kdim, + num_heads, + qkv_bias=True, + qk_norm=False, + norm_layer=nn.LayerNorm, + use_fp16: bool = False, + operations = None, + dtype = None, + device = None, + **kwargs, + ): + super().__init__() + self.qdim = qdim + self.kdim = kdim + + self.num_heads = num_heads + self.head_dim = self.qdim // num_heads + + self.scale = self.head_dim ** -0.5 + + self.to_q = operations.Linear(qdim, qdim, bias=qkv_bias, device = device, dtype = dtype) + self.to_k = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype) + self.to_v = operations.Linear(kdim, qdim, bias=qkv_bias, device = device, dtype = dtype) + + if use_fp16: + eps = 1.0 / 65504 + else: + eps = 1e-6 + + if norm_layer == nn.LayerNorm: + norm_layer = operations.LayerNorm + else: + norm_layer = operations.RMSNorm + + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() + self.out_proj = operations.Linear(qdim, qdim, bias=True, device = device, dtype = dtype) + + def forward(self, x, y): + + b, s1, _ = x.shape + _, s2, _ = y.shape + + y = y.to(next(self.to_k.parameters()).dtype) + + q = self.to_q(x) + k = self.to_k(y) + v = self.to_v(y) + + kv = torch.cat((k, v), dim=-1) + split_size = kv.shape[-1] // self.num_heads // 2 + + kv = kv.view(1, -1, self.num_heads, split_size * 2) + k, v = torch.split(kv, split_size, dim=-1) + + q = q.view(b, s1, self.num_heads, self.head_dim) + k = k.view(b, s2, self.num_heads, self.head_dim) + v = v.reshape(b, s2, self.num_heads * self.head_dim) + + q = self.q_norm(q) + k = self.k_norm(k) + + x = optimized_attention( + q.reshape(b, s1, self.num_heads * self.head_dim), + k.reshape(b, s2, self.num_heads * self.head_dim), + v, + heads=self.num_heads, + ) + + out = self.out_proj(x) + + return out + +class Attention(nn.Module): + + def __init__( + self, + dim, + num_heads, + qkv_bias = True, + qk_norm = False, + norm_layer = nn.LayerNorm, + use_fp16: bool = False, + operations = None, + device = None, + dtype = None + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = self.dim // num_heads + self.scale = self.head_dim ** -0.5 + + self.to_q = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) + self.to_k = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) + self.to_v = operations.Linear(dim, dim, bias = qkv_bias, device = device, dtype = dtype) + + if use_fp16: + eps = 1.0 / 65504 + else: + eps = 1e-6 + + if norm_layer == nn.LayerNorm: + norm_layer = operations.LayerNorm + else: + norm_layer = operations.RMSNorm + + self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() + self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps = eps, device = device, dtype = dtype) if qk_norm else nn.Identity() + self.out_proj = operations.Linear(dim, dim, device = device, dtype = dtype) + + def forward(self, x): + B, N, _ = x.shape + + query = self.to_q(x) + key = self.to_k(x) + value = self.to_v(x) + + qkv_combined = torch.cat((query, key, value), dim=-1) + split_size = qkv_combined.shape[-1] // self.num_heads // 3 + + qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3) + query, key, value = torch.split(qkv, split_size, dim=-1) + + query = query.reshape(B, N, self.num_heads, self.head_dim) + key = key.reshape(B, N, self.num_heads, self.head_dim) + value = value.reshape(B, N, self.num_heads * self.head_dim) + + query = self.q_norm(query) + key = self.k_norm(key) + + x = optimized_attention( + query.reshape(B, N, self.num_heads * self.head_dim), + key.reshape(B, N, self.num_heads * self.head_dim), + value, + heads=self.num_heads, + ) + + x = self.out_proj(x) + return x + +class HunYuanDiTBlock(nn.Module): + def __init__( + self, + hidden_size, + c_emb_size, + num_heads, + text_states_dim=1024, + qk_norm=False, + norm_layer=nn.LayerNorm, + qk_norm_layer=True, + qkv_bias=True, + skip_connection=True, + timested_modulate=False, + use_moe: bool = False, + num_experts: int = 8, + moe_top_k: int = 2, + use_fp16: bool = False, + operations = None, + device = None, dtype = None + ): + super().__init__() + + # eps can't be 1e-6 in fp16 mode because of numerical stability issues + if use_fp16: + eps = 1.0 / 65504 + else: + eps = 1e-6 + + self.norm1 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) + + self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, + norm_layer=qk_norm_layer, use_fp16 = use_fp16, device = device, dtype = dtype, operations = operations) + + self.norm2 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) + + self.timested_modulate = timested_modulate + if self.timested_modulate: + self.default_modulation = nn.Sequential( + nn.SiLU(), + operations.Linear(c_emb_size, hidden_size, bias=True, device = device, dtype = dtype) + ) + + self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias, + qk_norm=qk_norm, norm_layer=qk_norm_layer, use_fp16 = use_fp16, + device = device, dtype = dtype, operations = operations) + + self.norm3 = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) + + if skip_connection: + self.skip_norm = norm_layer(hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) + self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, device = device, dtype = dtype) + else: + self.skip_linear = None + + self.use_moe = use_moe + + if self.use_moe: + self.moe = MoEBlock( + hidden_size, + num_experts = num_experts, + moe_top_k = moe_top_k, + dropout = 0.0, + ff_inner_dim = int(hidden_size * 4.0), + device = device, dtype = dtype, + operations = operations + ) + else: + self.mlp = MLP(width=hidden_size, operations=operations, device = device, dtype = dtype) + + def forward(self, hidden_states, conditioning=None, text_states=None, skip_tensor=None): + + if self.skip_linear is not None: + combined = torch.cat([skip_tensor, hidden_states], dim=-1) + hidden_states = self.skip_linear(combined) + hidden_states = self.skip_norm(hidden_states) + + # self attention + if self.timested_modulate: + modulation_shift = self.default_modulation(conditioning).unsqueeze(dim=1) + hidden_states = hidden_states + modulation_shift + + self_attn_out = self.attn1(self.norm1(hidden_states)) + hidden_states = hidden_states + self_attn_out + + # cross attention + hidden_states = hidden_states + self.attn2(self.norm2(hidden_states), text_states) + + # MLP Layer + mlp_input = self.norm3(hidden_states) + + if self.use_moe: + hidden_states = hidden_states + self.moe(mlp_input) + else: + hidden_states = hidden_states + self.mlp(mlp_input) + + return hidden_states + +class FinalLayer(nn.Module): + + def __init__(self, final_hidden_size, out_channels, operations, use_fp16: bool = False, device = None, dtype = None): + super().__init__() + + if use_fp16: + eps = 1.0 / 65504 + else: + eps = 1e-6 + + self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine = True, eps = eps, device = device, dtype = dtype) + self.linear = operations.Linear(final_hidden_size, out_channels, bias = True, device = device, dtype = dtype) + + def forward(self, x): + x = self.norm_final(x) + x = x[:, 1:] + x = self.linear(x) + return x + +class HunYuanDiTPlain(nn.Module): + + # init with the defaults values from https://huggingface.co/tencent/Hunyuan3D-2.1/blob/main/hunyuan3d-dit-v2-1/config.yaml + def __init__( + self, + in_channels: int = 64, + hidden_size: int = 2048, + context_dim: int = 1024, + depth: int = 21, + num_heads: int = 16, + qk_norm: bool = True, + qkv_bias: bool = False, + num_moe_layers: int = 6, + guidance_cond_proj_dim = 2048, + norm_type = 'layer', + num_experts: int = 8, + moe_top_k: int = 2, + use_fp16: bool = False, + dtype = None, + device = None, + operations = None, + **kwargs + ): + + self.dtype = dtype + + super().__init__() + + self.depth = depth + + self.in_channels = in_channels + self.out_channels = in_channels + + self.num_heads = num_heads + self.hidden_size = hidden_size + + norm = operations.LayerNorm if norm_type == 'layer' else operations.RMSNorm + qk_norm = operations.RMSNorm + + self.context_dim = context_dim + self.guidance_cond_proj_dim = guidance_cond_proj_dim + + self.x_embedder = operations.Linear(in_channels, hidden_size, bias = True, device = device, dtype = dtype) + self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim = guidance_cond_proj_dim, device = device, dtype = dtype, operations = operations) + + + # HUnYuanDiT Blocks + self.blocks = nn.ModuleList([ + HunYuanDiTBlock(hidden_size=hidden_size, + c_emb_size=hidden_size, + num_heads=num_heads, + text_states_dim=context_dim, + qk_norm=qk_norm, + norm_layer = norm, + qk_norm_layer = qk_norm, + skip_connection=layer > depth // 2, + qkv_bias=qkv_bias, + use_moe=True if depth - layer <= num_moe_layers else False, + num_experts=num_experts, + moe_top_k=moe_top_k, + use_fp16 = use_fp16, + device = device, dtype = dtype, operations = operations) + for layer in range(depth) + ]) + + self.depth = depth + + self.final_layer = FinalLayer(hidden_size, self.out_channels, use_fp16 = use_fp16, operations = operations, device = device, dtype = dtype) + + def forward(self, x, t, context, transformer_options = {}, **kwargs): + + x = x.movedim(-1, -2) + uncond_emb, cond_emb = context.chunk(2, dim = 0) + + context = torch.cat([cond_emb, uncond_emb], dim = 0) + main_condition = context + + t = 1.0 - t + + time_embedded = self.t_embedder(t, condition = kwargs.get('guidance_cond')) + + x = x.to(dtype = next(self.x_embedder.parameters()).dtype) + x_embedded = self.x_embedder(x) + + combined = torch.cat([time_embedded, x_embedded], dim=1) + + def block_wrap(args): + return block( + args["x"], + args["t"], + args["cond"], + skip_tensor=args.get("skip"),) + + skip_stack = [] + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for idx, block in enumerate(self.blocks): + if idx <= self.depth // 2: + skip_input = None + else: + skip_input = skip_stack.pop() + + if ("block", idx) in blocks_replace: + + combined = blocks_replace[("block", idx)]( + { + "x": combined, + "t": time_embedded, + "cond": main_condition, + "skip": skip_input, + }, + {"original_block": block_wrap}, + ) + else: + combined = block(combined, time_embedded, main_condition, skip_tensor=skip_input) + + if idx < self.depth // 2: + skip_stack.append(combined) + + output = self.final_layer(combined) + output = output.movedim(-2, -1) * (-1.0) + + cond_emb, uncond_emb = output.chunk(2, dim = 0) + return torch.cat([uncond_emb, cond_emb]) diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py index d6d854089..5132e6c07 100644 --- a/comfy/ldm/hunyuan_video/model.py +++ b/comfy/ldm/hunyuan_video/model.py @@ -1,6 +1,7 @@ #Based on Flux code because of weird hunyuan video code license. import torch +import comfy.patcher_extension import comfy.ldm.flux.layers import comfy.ldm.modules.diffusionmodules.mmdit from comfy.ldm.modules.attention import optimized_attention @@ -39,6 +40,8 @@ class HunyuanVideoParams: patch_size: list qkv_bias: bool guidance_embed: bool + byt5: bool + meanflow: bool class SelfAttentionRef(nn.Module): @@ -77,13 +80,13 @@ class TokenRefinerBlock(nn.Module): operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device), ) - def forward(self, x, c, mask): + def forward(self, x, c, mask, transformer_options={}): mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1) norm_x = self.norm1(x) qkv = self.self_attn.qkv(norm_x) q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4) - attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True) + attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options) x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1) x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1) @@ -114,14 +117,14 @@ class IndividualTokenRefiner(nn.Module): ] ) - def forward(self, x, c, mask): + def forward(self, x, c, mask, transformer_options={}): m = None if mask is not None: m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1) m = m + m.transpose(2, 3) for block in self.blocks: - x = block(x, c, m) + x = block(x, c, m, transformer_options=transformer_options) return x @@ -149,6 +152,7 @@ class TokenRefiner(nn.Module): x, timesteps, mask, + transformer_options={}, ): t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype)) # m = mask.float().unsqueeze(-1) @@ -157,9 +161,33 @@ class TokenRefiner(nn.Module): c = t + self.c_embedder(c.to(x.dtype)) x = self.input_embedder(x) - x = self.individual_token_refiner(x, c, mask) + x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options) return x + +class ByT5Mapper(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None): + super().__init__() + self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device) + self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device) + self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device) + self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device) + self.use_res = use_res + self.act_fn = nn.GELU() + + def forward(self, x): + if self.use_res: + res = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + x2 = self.act_fn(x) + x2 = self.fc3(x2) + if self.use_res: + x2 = x2 + res + return x2 + class HunyuanVideo(nn.Module): """ Transformer model for flow matching on sequences. @@ -184,9 +212,13 @@ class HunyuanVideo(nn.Module): self.num_heads = params.num_heads self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) - self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, dtype=dtype, device=device, operations=operations) + self.img_in = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) - self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + if params.vec_in_dim is not None: + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations) + else: + self.vector_in = None + self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity() ) @@ -214,6 +246,23 @@ class HunyuanVideo(nn.Module): ] ) + if params.byt5: + self.byt5_in = ByT5Mapper( + in_dim=1472, + out_dim=2048, + hidden_dim=2048, + out_dim1=self.hidden_size, + use_res=False, + dtype=dtype, device=device, operations=operations + ) + else: + self.byt5_in = None + + if params.meanflow: + self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) + else: + self.time_r_in = None + if final_layer: self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations) @@ -225,8 +274,12 @@ class HunyuanVideo(nn.Module): txt_ids: Tensor, txt_mask: Tensor, timesteps: Tensor, - y: Tensor, + y: Tensor = None, + txt_byt5=None, guidance: Tensor = None, + guiding_frame_index=None, + ref_latent=None, + disable_time_r=False, control=None, transformer_options={}, ) -> Tensor: @@ -237,17 +290,52 @@ class HunyuanVideo(nn.Module): img = self.img_in(img) vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype)) - vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + if (self.time_r_in is not None) and (not disable_time_r): + w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved + if len(w) > 0: + timesteps_r = transformer_options['sample_sigmas'][w[0] + 1] + timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype) + vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype)) + vec = (vec + vec_r) / 2 + + if ref_latent is not None: + ref_latent_ids = self.img_ids(ref_latent) + ref_latent = self.img_in(ref_latent) + img = torch.cat([ref_latent, img], dim=-2) + ref_latent_ids[..., 0] = -1 + ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1]) + img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2) + + if guiding_frame_index is not None: + token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0)) + if self.vector_in is not None: + vec_ = self.vector_in(y[:, :self.params.vec_in_dim]) + vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1) + else: + vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1) + frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2]) + modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)] + modulation_dims_txt = [(0, None, 1)] + else: + if self.vector_in is not None: + vec = vec + self.vector_in(y[:, :self.params.vec_in_dim]) + modulation_dims = None + modulation_dims_txt = None if self.params.guidance_embed: - if guidance is None: - raise ValueError("Didn't get guidance strength for guidance distilled model.") - vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) + if guidance is not None: + vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype)) if txt_mask is not None and not torch.is_floating_point(txt_mask): txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max - txt = self.txt_in(txt, timesteps, txt_mask) + txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options) + + if self.byt5_in is not None and txt_byt5 is not None: + txt_byt5 = self.byt5_in(txt_byt5) + txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype) + txt = torch.cat((txt, txt_byt5), dim=1) + txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1) ids = torch.cat((img_ids, txt_ids), dim=1) pe = self.pe_embedder(ids) @@ -265,14 +353,14 @@ class HunyuanVideo(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"]) + out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap}) txt = out["txt"] img = out["img"] else: - img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask) + img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options) if control is not None: # Controlnet control_i = control.get("input") @@ -287,13 +375,13 @@ class HunyuanVideo(nn.Module): if ("single_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"]) + out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap}) + out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap}) img = out["img"] else: - img = block(img, vec=vec, pe=pe, attn_mask=attn_mask) + img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options) if control is not None: # Controlnet control_o = control.get("output") @@ -303,18 +391,24 @@ class HunyuanVideo(nn.Module): img[:, : img_len] += add img = img[:, : img_len] + if ref_latent is not None: + img = img[:, ref_latent.shape[1]:] - img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels) - shape = initial_shape[-3:] + shape = initial_shape[-len(self.patch_size):] for i in range(len(shape)): shape[i] = shape[i] // self.patch_size[i] img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size) - img = img.permute(0, 4, 1, 5, 2, 6, 3, 7) - img = img.reshape(initial_shape) + if img.ndim == 8: + img = img.permute(0, 4, 1, 5, 2, 6, 3, 7) + img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4]) + else: + img = img.permute(0, 3, 1, 4, 2, 5) + img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3]) return img - def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs): + def img_ids(self, x): bs, c, t, h, w = x.shape patch_size = self.patch_size t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) @@ -324,7 +418,32 @@ class HunyuanVideo(nn.Module): img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1) img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1) img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1) - img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs) - txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) - out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, control, transformer_options) + return repeat(img_ids, "t h w c -> b (t h w) c", b=bs) + + def img_ids_2d(self, x): + bs, c, h, w = x.shape + patch_size = self.patch_size + h_len = ((h + (patch_size[0] // 2)) // patch_size[0]) + w_len = ((w + (patch_size[1] // 2)) // patch_size[1]) + img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype) + img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) + return repeat(img_ids, "h w c -> b (h w) c", b=bs) + + def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs): + bs = x.shape[0] + if len(self.patch_size) == 3: + img_ids = self.img_ids(x) + txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype) + else: + img_ids = self.img_ids_2d(x) + txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype) + out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options) return out diff --git a/comfy/ldm/hunyuan_video/vae.py b/comfy/ldm/hunyuan_video/vae.py new file mode 100644 index 000000000..40c12b183 --- /dev/null +++ b/comfy/ldm/hunyuan_video/vae.py @@ -0,0 +1,136 @@ +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock +import comfy.ops +ops = comfy.ops.disable_weight_init + + +class PixelShuffle2D(nn.Module): + def __init__(self, in_dim, out_dim, op=ops.Conv2d): + super().__init__() + self.conv = op(in_dim, out_dim >> 2, 3, 1, 1) + self.ratio = (in_dim << 2) // out_dim + + def forward(self, x): + b, c, h, w = x.shape + h2, w2 = h >> 1, w >> 1 + y = self.conv(x).view(b, -1, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, -1, h2, w2) + r = x.view(b, c, h2, 2, w2, 2).permute(0, 3, 5, 1, 2, 4).reshape(b, c << 2, h2, w2) + return y + r.view(b, y.shape[1], self.ratio, h2, w2).mean(2) + + +class PixelUnshuffle2D(nn.Module): + def __init__(self, in_dim, out_dim, op=ops.Conv2d): + super().__init__() + self.conv = op(in_dim, out_dim << 2, 3, 1, 1) + self.scale = (out_dim << 2) // in_dim + + def forward(self, x): + b, c, h, w = x.shape + h2, w2 = h << 1, w << 1 + y = self.conv(x).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2) + r = x.repeat_interleave(self.scale, 1).view(b, 2, 2, -1, h, w).permute(0, 3, 4, 1, 5, 2).reshape(b, -1, h2, w2) + return y + r + + +class Encoder(nn.Module): + def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, + ffactor_spatial, downsample_match_channel=True, **_): + super().__init__() + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + self.conv_in = ops.Conv2d(in_channels, block_out_channels[0], 3, 1, 1) + + self.down = nn.ModuleList() + ch = block_out_channels[0] + depth = (ffactor_spatial >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=ops.Conv2d) + for j in range(num_res_blocks)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch + stage.downsample = PixelShuffle2D(ch, nxt, ops.Conv2d) + ch = nxt + self.down.append(stage) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) + + self.norm_out = ops.GroupNorm(32, ch, 1e-6, True) + self.conv_out = ops.Conv2d(ch, z_channels << 1, 3, 1, 1) + + def forward(self, x): + x = self.conv_in(x) + + for stage in self.down: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'downsample'): + x = stage.downsample(x) + + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + b, c, h, w = x.shape + grp = c // (self.z_channels << 1) + skip = x.view(b, c // grp, grp, h, w).mean(2) + + return self.conv_out(F.silu(self.norm_out(x))) + skip + + +class Decoder(nn.Module): + def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks, + ffactor_spatial, upsample_match_channel=True, **_): + super().__init__() + block_out_channels = block_out_channels[::-1] + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + + ch = block_out_channels[0] + self.conv_in = ops.Conv2d(z_channels, ch, 3, 1, 1) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv2d) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=ops.Conv2d) + + self.up = nn.ModuleList() + depth = (ffactor_spatial >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=ops.Conv2d) + for j in range(num_res_blocks + 1)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch + stage.upsample = PixelUnshuffle2D(ch, nxt, ops.Conv2d) + ch = nxt + self.up.append(stage) + + self.norm_out = ops.GroupNorm(32, ch, 1e-6, True) + self.conv_out = ops.Conv2d(ch, out_channels, 3, 1, 1) + + def forward(self, z): + x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + for stage in self.up: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'upsample'): + x = stage.upsample(x) + + return self.conv_out(F.silu(self.norm_out(x))) diff --git a/comfy/ldm/hunyuan_video/vae_refiner.py b/comfy/ldm/hunyuan_video/vae_refiner.py new file mode 100644 index 000000000..c2a0b507d --- /dev/null +++ b/comfy/ldm/hunyuan_video/vae_refiner.py @@ -0,0 +1,301 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize +import comfy.ops +import comfy.ldm.models.autoencoder +ops = comfy.ops.disable_weight_init + +class RMS_norm(nn.Module): + def __init__(self, dim): + super().__init__() + shape = (dim, 1, 1, 1) + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.empty(shape)) + + def forward(self, x): + return F.normalize(x, dim=1) * self.scale * self.gamma + +class DnSmpl(nn.Module): + def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d): + super().__init__() + fct = 2 * 2 * 2 if tds else 1 * 2 * 2 + assert oc % fct == 0 + self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1) + self.refiner_vae = refiner_vae + + self.tds = tds + self.gs = fct * ic // oc + + def forward(self, x): + r1 = 2 if self.tds else 1 + h = self.conv(x) + + if self.tds and self.refiner_vae: + hf = h[:, :, :1, :, :] + b, c, f, ht, wd = hf.shape + hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2) + hf = hf.permute(0, 4, 6, 1, 2, 3, 5) + hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2) + hf = torch.cat([hf, hf], dim=1) + + hn = h[:, :, 1:, :, :] + b, c, frms, ht, wd = hn.shape + nf = frms // r1 + hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) + hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6) + hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) + + h = torch.cat([hf, hn], dim=2) + + xf = x[:, :, :1, :, :] + b, ci, f, ht, wd = xf.shape + xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2) + xf = xf.permute(0, 4, 6, 1, 2, 3, 5) + xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2) + B, C, T, H, W = xf.shape + xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2) + + xn = x[:, :, 1:, :, :] + b, ci, frms, ht, wd = xn.shape + nf = frms // r1 + xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) + xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6) + xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) + B, C, T, H, W = xn.shape + xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + sc = torch.cat([xf, xn], dim=2) + else: + b, c, frms, ht, wd = h.shape + + nf = frms // r1 + h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2) + h = h.permute(0, 3, 5, 7, 1, 2, 4, 6) + h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2) + + b, ci, frms, ht, wd = x.shape + nf = frms // r1 + sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2) + sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6) + sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2) + B, C, T, H, W = sc.shape + sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2) + + return h + sc + + +class UpSmpl(nn.Module): + def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d): + super().__init__() + fct = 2 * 2 * 2 if tus else 1 * 2 * 2 + self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1) + self.refiner_vae = refiner_vae + + self.tus = tus + self.rp = fct * oc // ic + + def forward(self, x): + r1 = 2 if self.tus else 1 + h = self.conv(x) + + if self.tus and self.refiner_vae: + hf = h[:, :, :1, :, :] + b, c, f, ht, wd = hf.shape + nc = c // (2 * 2) + hf = hf.reshape(b, 2, 2, nc, f, ht, wd) + hf = hf.permute(0, 3, 4, 5, 1, 6, 2) + hf = hf.reshape(b, nc, f, ht * 2, wd * 2) + hf = hf[:, : hf.shape[1] // 2] + + hn = h[:, :, 1:, :, :] + b, c, frms, ht, wd = hn.shape + nc = c // (r1 * 2 * 2) + hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd) + hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3) + hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + h = torch.cat([hf, hn], dim=2) + + xf = x[:, :, :1, :, :] + b, ci, f, ht, wd = xf.shape + xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1) + b, c, f, ht, wd = xf.shape + nc = c // (2 * 2) + xf = xf.reshape(b, 2, 2, nc, f, ht, wd) + xf = xf.permute(0, 3, 4, 5, 1, 6, 2) + xf = xf.reshape(b, nc, f, ht * 2, wd * 2) + + xn = x[:, :, 1:, :, :] + xn = xn.repeat_interleave(repeats=self.rp, dim=1) + b, c, frms, ht, wd = xn.shape + nc = c // (r1 * 2 * 2) + xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd) + xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3) + xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2) + sc = torch.cat([xf, xn], dim=2) + else: + b, c, frms, ht, wd = h.shape + nc = c // (r1 * 2 * 2) + h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd) + h = h.permute(0, 4, 5, 1, 6, 2, 7, 3) + h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + sc = x.repeat_interleave(repeats=self.rp, dim=1) + b, c, frms, ht, wd = sc.shape + nc = c // (r1 * 2 * 2) + sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd) + sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3) + sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2) + + return h + sc + +class Encoder(nn.Module): + def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks, + ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_): + super().__init__() + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + self.ffactor_temporal = ffactor_temporal + + self.refiner_vae = refiner_vae + if self.refiner_vae: + conv_op = VideoConv3d + norm_op = RMS_norm + else: + conv_op = ops.Conv3d + norm_op = Normalize + + self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1) + + self.down = nn.ModuleList() + ch = block_out_channels[0] + depth = (ffactor_spatial >> 1).bit_length() + depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=conv_op, norm_op=norm_op) + for j in range(num_res_blocks)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch + stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op) + ch = nxt + self.down.append(stage) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + + self.norm_out = norm_op(ch) + self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1) + + self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer() + + def forward(self, x): + if not self.refiner_vae and x.shape[2] == 1: + x = x.expand(-1, -1, self.ffactor_temporal, -1, -1) + + x = self.conv_in(x) + + for stage in self.down: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'downsample'): + x = stage.downsample(x) + + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + b, c, t, h, w = x.shape + grp = c // (self.z_channels << 1) + skip = x.view(b, c // grp, grp, t, h, w).mean(2) + + out = self.conv_out(F.silu(self.norm_out(x))) + skip + + if self.refiner_vae: + out = self.regul(out)[0] + + out = torch.cat((out[:, :, :1], out), dim=2) + out = out.permute(0, 2, 1, 3, 4) + b, f_times_2, c, h, w = out.shape + out = out.reshape(b, f_times_2 // 2, 2 * c, h, w) + out = out.permute(0, 2, 1, 3, 4).contiguous() + + return out + +class Decoder(nn.Module): + def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks, + ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_): + super().__init__() + block_out_channels = block_out_channels[::-1] + self.z_channels = z_channels + self.block_out_channels = block_out_channels + self.num_res_blocks = num_res_blocks + + self.refiner_vae = refiner_vae + if self.refiner_vae: + conv_op = VideoConv3d + norm_op = RMS_norm + else: + conv_op = ops.Conv3d + norm_op = Normalize + + ch = block_out_channels[0] + self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1) + + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op) + self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op) + + self.up = nn.ModuleList() + depth = (ffactor_spatial >> 1).bit_length() + depth_temporal = (ffactor_temporal >> 1).bit_length() + + for i, tgt in enumerate(block_out_channels): + stage = nn.Module() + stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt, + out_channels=tgt, + temb_channels=0, + conv_op=conv_op, norm_op=norm_op) + for j in range(num_res_blocks + 1)]) + ch = tgt + if i < depth: + nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch + stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op) + ch = nxt + self.up.append(stage) + + self.norm_out = norm_op(ch) + self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1) + + def forward(self, z): + if self.refiner_vae: + z = z.permute(0, 2, 1, 3, 4) + b, f, c, h, w = z.shape + z = z.reshape(b, f, 2, c // 2, h, w) + z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w) + z = z.permute(0, 2, 1, 3, 4) + z = z[:, :, 1:] + + x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1) + x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x))) + + for stage in self.up: + for blk in stage.block: + x = blk(x) + if hasattr(stage, 'upsample'): + x = stage.upsample(x) + + out = self.conv_out(F.silu(self.norm_out(x))) + + if not self.refiner_vae: + if z.shape[-3] == 1: + out = out[:, :, -1:] + + return out diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py index 359f6a965..5ba2b76e0 100644 --- a/comfy/ldm/hydit/models.py +++ b/comfy/ldm/hydit/models.py @@ -3,7 +3,7 @@ import torch import torch.nn as nn import comfy.ops -from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm +from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed from comfy.ldm.modules.diffusionmodules.util import timestep_embedding from torch.utils import checkpoint @@ -51,7 +51,7 @@ class HunYuanDiTBlock(nn.Module): if norm_type == "layer": norm_layer = operations.LayerNorm elif norm_type == "rms": - norm_layer = RMSNorm + norm_layer = operations.RMSNorm else: raise ValueError(f"Unknown norm_type: {norm_type}") diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py index eeeeaea04..def365ba7 100644 --- a/comfy/ldm/lightricks/model.py +++ b/comfy/ldm/lightricks/model.py @@ -1,13 +1,13 @@ import torch from torch import nn +import comfy.patcher_extension import comfy.ldm.modules.attention -from comfy.ldm.genmo.joint_model.layers import RMSNorm import comfy.ldm.common_dit from einops import rearrange import math from typing import Dict, Optional, Tuple -from .symmetric_patchifier import SymmetricPatchifier +from .symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords def get_timestep_embedding( @@ -262,8 +262,8 @@ class CrossAttention(nn.Module): self.heads = heads self.dim_head = dim_head - self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device) - self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device) + self.q_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) + self.k_norm = operations.RMSNorm(inner_dim, eps=1e-5, dtype=dtype, device=device) self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device) self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device) @@ -271,7 +271,7 @@ class CrossAttention(nn.Module): self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - def forward(self, x, context=None, mask=None, pe=None): + def forward(self, x, context=None, mask=None, pe=None, transformer_options={}): q = self.to_q(x) context = x if context is None else context k = self.to_k(context) @@ -285,9 +285,9 @@ class CrossAttention(nn.Module): k = apply_rotary_emb(k, pe) if mask is None: - out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: - out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) return self.to_out(out) @@ -303,12 +303,12 @@ class BasicTransformerBlock(nn.Module): self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype)) - def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None): + def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}): shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2) - x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa + x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa - x += self.attn2(x, context=context, mask=attention_mask) + x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options) y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp x += self.ff(y) * gate_mlp @@ -377,12 +377,16 @@ class LTXVModel(torch.nn.Module): positional_embedding_theta=10000.0, positional_embedding_max_pos=[20, 2048, 2048], + causal_temporal_positioning=False, + vae_scale_factors=(8, 32, 32), dtype=None, device=None, operations=None, **kwargs): super().__init__() self.generator = None + self.vae_scale_factors = vae_scale_factors self.dtype = dtype self.out_channels = in_channels self.inner_dim = num_attention_heads * attention_head_dim + self.causal_temporal_positioning = causal_temporal_positioning self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device) @@ -416,51 +420,38 @@ class LTXVModel(torch.nn.Module): self.patchifier = SymmetricPatchifier(1) - def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, guiding_latent_noise_scale=0, transformer_options={}, **kwargs): + def forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, **kwargs) + + def _forward(self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, **kwargs): patches_replace = transformer_options.get("patches_replace", {}) - indices_grid = self.patchifier.get_grid( - orig_num_frames=x.shape[2], - orig_height=x.shape[3], - orig_width=x.shape[4], - batch_size=x.shape[0], - scale_grid=((1 / frame_rate) * 8, 32, 32), - device=x.device, - ) - - if guiding_latent is not None: - ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype) - input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1)) - ts *= input_ts - ts[:, :, 0] = guiding_latent_noise_scale * (input_ts[:, :, 0] ** 2) - timestep = self.patchifier.patchify(ts) - input_x = x.clone() - x[:, :, 0] = guiding_latent[:, :, 0] - if guiding_latent_noise_scale > 0: - if self.generator is None: - self.generator = torch.Generator(device=x.device).manual_seed(42) - elif self.generator.device != x.device: - self.generator = torch.Generator(device=x.device).set_state(self.generator.get_state()) - - noise_shape = [guiding_latent.shape[0], guiding_latent.shape[1], 1, guiding_latent.shape[3], guiding_latent.shape[4]] - scale = guiding_latent_noise_scale * (input_ts ** 2) - guiding_noise = scale * torch.randn(size=noise_shape, device=x.device, generator=self.generator) - - x[:, :, 0] = guiding_noise[:, :, 0] + x[:, :, 0] * (1.0 - scale[:, :, 0]) - - orig_shape = list(x.shape) - x = self.patchifier.patchify(x) + x, latent_coords = self.patchifier.patchify(x) + pixel_coords = latent_to_pixel_coords( + latent_coords=latent_coords, + scale_factors=self.vae_scale_factors, + causal_fix=self.causal_temporal_positioning, + ) + + if keyframe_idxs is not None: + pixel_coords[:, :, -keyframe_idxs.shape[2]:] = keyframe_idxs + + fractional_coords = pixel_coords.to(torch.float32) + fractional_coords[:, 0] = fractional_coords[:, 0] * (1.0 / frame_rate) x = self.patchify_proj(x) timestep = timestep * 1000.0 - attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) - attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf")) # not sure about this - # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype) + if attention_mask is not None and not torch.is_floating_point(attention_mask): + attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max - pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype) + pe = precompute_freqs_cis(fractional_coords, dim=self.inner_dim, out_dtype=x.dtype) batch_size = x.shape[0] timestep, embedded_timestep = self.adaln_single( @@ -488,10 +479,10 @@ class LTXVModel(torch.nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"]) + out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap}) x = out["img"] else: x = block( @@ -499,7 +490,8 @@ class LTXVModel(torch.nn.Module): context=context, attention_mask=attention_mask, timestep=timestep, - pe=pe + pe=pe, + transformer_options=transformer_options, ) # 3. Output @@ -520,8 +512,4 @@ class LTXVModel(torch.nn.Module): out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size), ) - if guiding_latent is not None: - x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0] - - # print("res", x) return x diff --git a/comfy/ldm/lightricks/symmetric_patchifier.py b/comfy/ldm/lightricks/symmetric_patchifier.py index c58dfb20b..4b9972b9f 100644 --- a/comfy/ldm/lightricks/symmetric_patchifier.py +++ b/comfy/ldm/lightricks/symmetric_patchifier.py @@ -6,16 +6,29 @@ from einops import rearrange from torch import Tensor -def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor: - """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" - dims_to_append = target_dims - x.ndim - if dims_to_append < 0: - raise ValueError( - f"input has {x.ndim} dims but target_dims is {target_dims}, which is less" - ) - elif dims_to_append == 0: - return x - return x[(...,) + (None,) * dims_to_append] +def latent_to_pixel_coords( + latent_coords: Tensor, scale_factors: Tuple[int, int, int], causal_fix: bool = False +) -> Tensor: + """ + Converts latent coordinates to pixel coordinates by scaling them according to the VAE's + configuration. + Args: + latent_coords (Tensor): A tensor of shape [batch_size, 3, num_latents] + containing the latent corner coordinates of each token. + scale_factors (Tuple[int, int, int]): The scale factors of the VAE's latent space. + causal_fix (bool): Whether to take into account the different temporal scale + of the first frame. Default = False for backwards compatibility. + Returns: + Tensor: A tensor of pixel coordinates corresponding to the input latent coordinates. + """ + pixel_coords = ( + latent_coords + * torch.tensor(scale_factors, device=latent_coords.device)[None, :, None] + ) + if causal_fix: + # Fix temporal scale for first frame to 1 due to causality + pixel_coords[:, 0] = (pixel_coords[:, 0] + 1 - scale_factors[0]).clamp(min=0) + return pixel_coords class Patchifier(ABC): @@ -44,29 +57,26 @@ class Patchifier(ABC): def patch_size(self): return self._patch_size - def get_grid( - self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device + def get_latent_coords( + self, latent_num_frames, latent_height, latent_width, batch_size, device ): - f = orig_num_frames // self._patch_size[0] - h = orig_height // self._patch_size[1] - w = orig_width // self._patch_size[2] - grid_h = torch.arange(h, dtype=torch.float32, device=device) - grid_w = torch.arange(w, dtype=torch.float32, device=device) - grid_f = torch.arange(f, dtype=torch.float32, device=device) - grid = torch.meshgrid(grid_f, grid_h, grid_w, indexing='ij') - grid = torch.stack(grid, dim=0) - grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) - - if scale_grid is not None: - for i in range(3): - if isinstance(scale_grid[i], Tensor): - scale = append_dims(scale_grid[i], grid.ndim - 1) - else: - scale = scale_grid[i] - grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i] - - grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size) - return grid + """ + Return a tensor of shape [batch_size, 3, num_patches] containing the + top-left corner latent coordinates of each latent patch. + The tensor is repeated for each batch element. + """ + latent_sample_coords = torch.meshgrid( + torch.arange(0, latent_num_frames, self._patch_size[0], device=device), + torch.arange(0, latent_height, self._patch_size[1], device=device), + torch.arange(0, latent_width, self._patch_size[2], device=device), + indexing="ij", + ) + latent_sample_coords = torch.stack(latent_sample_coords, dim=0) + latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) + latent_coords = rearrange( + latent_coords, "b c f h w -> b c (f h w)", b=batch_size + ) + return latent_coords class SymmetricPatchifier(Patchifier): @@ -74,6 +84,8 @@ class SymmetricPatchifier(Patchifier): self, latents: Tensor, ) -> Tuple[Tensor, Tensor]: + b, _, f, h, w = latents.shape + latent_coords = self.get_latent_coords(f, h, w, b, latents.device) latents = rearrange( latents, "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)", @@ -81,7 +93,7 @@ class SymmetricPatchifier(Patchifier): p2=self._patch_size[1], p3=self._patch_size[2], ) - return latents + return latents, latent_coords def unpatchify( self, diff --git a/comfy/ldm/lightricks/vae/causal_conv3d.py b/comfy/ldm/lightricks/vae/causal_conv3d.py index c572e7e86..70d612e86 100644 --- a/comfy/ldm/lightricks/vae/causal_conv3d.py +++ b/comfy/ldm/lightricks/vae/causal_conv3d.py @@ -15,6 +15,7 @@ class CausalConv3d(nn.Module): stride: Union[int, Tuple[int]] = 1, dilation: int = 1, groups: int = 1, + spatial_padding_mode: str = "zeros", **kwargs, ): super().__init__() @@ -38,7 +39,7 @@ class CausalConv3d(nn.Module): stride=stride, dilation=dilation, padding=padding, - padding_mode="zeros", + padding_mode=spatial_padding_mode, groups=groups, ) diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py index e0344deec..75ed069ad 100644 --- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py +++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py @@ -1,13 +1,15 @@ +from __future__ import annotations import torch from torch import nn from functools import partial import math from einops import rearrange -from typing import Optional, Tuple, Union +from typing import List, Optional, Tuple, Union from .conv_nd_factory import make_conv_nd, make_linear_nd from .pixel_norm import PixelNorm from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings import comfy.ops + ops = comfy.ops.disable_weight_init class Encoder(nn.Module): @@ -32,7 +34,7 @@ class Encoder(nn.Module): norm_layer (`str`, *optional*, defaults to `group_norm`): The normalization layer to use. Can be either `group_norm` or `pixel_norm`. latent_log_var (`str`, *optional*, defaults to `per_channel`): - The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`. + The number of channels for the log variance. Can be either `per_channel`, `uniform`, `constant` or `none`. """ def __init__( @@ -40,12 +42,13 @@ class Encoder(nn.Module): dims: Union[int, Tuple[int, int]] = 3, in_channels: int = 3, out_channels: int = 3, - blocks=[("res_x", 1)], + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, norm_num_groups: int = 32, patch_size: Union[int, Tuple[int]] = 1, norm_layer: str = "group_norm", # group_norm, pixel_norm latent_log_var: str = "per_channel", + spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size @@ -65,6 +68,7 @@ class Encoder(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.down_blocks = nn.ModuleList([]) @@ -82,6 +86,7 @@ class Encoder(nn.Module): resnet_eps=1e-6, resnet_groups=norm_num_groups, norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = block_params.get("multiplier", 2) * output_channel @@ -92,6 +97,7 @@ class Encoder(nn.Module): eps=1e-6, groups=norm_num_groups, norm_layer=norm_layer, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": block = make_conv_nd( @@ -101,6 +107,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 1, 1), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": block = make_conv_nd( @@ -110,6 +117,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(1, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": block = make_conv_nd( @@ -119,6 +127,7 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all_x_y": output_channel = block_params.get("multiplier", 2) * output_channel @@ -129,6 +138,34 @@ class Encoder(nn.Module): kernel_size=3, stride=(2, 2, 2), causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_all_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_space_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, + ) + elif block_name == "compress_time_res": + output_channel = block_params.get("multiplier", 2) * output_channel + block = SpaceToDepthDownsample( + dims=dims, + in_channels=input_channel, + out_channels=output_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown block: {block_name}") @@ -152,10 +189,18 @@ class Encoder(nn.Module): conv_out_channels *= 2 elif latent_log_var == "uniform": conv_out_channels += 1 + elif latent_log_var == "constant": + conv_out_channels += 1 elif latent_log_var != "none": raise ValueError(f"Invalid latent_log_var: {latent_log_var}") self.conv_out = make_conv_nd( - dims, output_channel, conv_out_channels, 3, padding=1, causal=True + dims, + output_channel, + conv_out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False @@ -197,6 +242,15 @@ class Encoder(nn.Module): sample = torch.cat([sample, repeated_last_channel], dim=1) else: raise ValueError(f"Invalid input shape: {sample.shape}") + elif self.latent_log_var == "constant": + sample = sample[:, :-1, ...] + approx_ln_0 = ( + -30 + ) # this is the minimal clamp value in DiagonalGaussianDistribution objects + sample = torch.cat( + [sample, torch.ones_like(sample, device=sample.device) * approx_ln_0], + dim=1, + ) return sample @@ -231,7 +285,7 @@ class Decoder(nn.Module): dims, in_channels: int = 3, out_channels: int = 3, - blocks=[("res_x", 1)], + blocks: List[Tuple[str, int | dict]] = [("res_x", 1)], base_channels: int = 128, layers_per_block: int = 2, norm_num_groups: int = 32, @@ -239,6 +293,7 @@ class Decoder(nn.Module): norm_layer: str = "group_norm", causal: bool = True, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() self.patch_size = patch_size @@ -264,6 +319,7 @@ class Decoder(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.up_blocks = nn.ModuleList([]) @@ -283,6 +339,7 @@ class Decoder(nn.Module): norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "attn_res_x": block = UNetMidBlock3D( @@ -294,6 +351,7 @@ class Decoder(nn.Module): inject_noise=block_params.get("inject_noise", False), timestep_conditioning=timestep_conditioning, attention_head_dim=block_params["attention_head_dim"], + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "res_x_y": output_channel = output_channel // block_params.get("multiplier", 2) @@ -306,14 +364,21 @@ class Decoder(nn.Module): norm_layer=norm_layer, inject_noise=block_params.get("inject_noise", False), timestep_conditioning=False, + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_time": block = DepthToSpaceUpsample( - dims=dims, in_channels=input_channel, stride=(2, 1, 1) + dims=dims, + in_channels=input_channel, + stride=(2, 1, 1), + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_space": block = DepthToSpaceUpsample( - dims=dims, in_channels=input_channel, stride=(1, 2, 2) + dims=dims, + in_channels=input_channel, + stride=(1, 2, 2), + spatial_padding_mode=spatial_padding_mode, ) elif block_name == "compress_all": output_channel = output_channel // block_params.get("multiplier", 1) @@ -323,6 +388,7 @@ class Decoder(nn.Module): stride=(2, 2, 2), residual=block_params.get("residual", False), out_channels_reduction_factor=block_params.get("multiplier", 1), + spatial_padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unknown layer: {block_name}") @@ -340,7 +406,13 @@ class Decoder(nn.Module): self.conv_act = nn.SiLU() self.conv_out = make_conv_nd( - dims, output_channel, out_channels, 3, padding=1, causal=True + dims, + output_channel, + out_channels, + 3, + padding=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.gradient_checkpointing = False @@ -433,6 +505,12 @@ class UNetMidBlock3D(nn.Module): resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks. + norm_layer (`str`, *optional*, defaults to `group_norm`): + The normalization layer to use. Can be either `group_norm` or `pixel_norm`. + inject_noise (`bool`, *optional*, defaults to `False`): + Whether to inject noise into the hidden states. + timestep_conditioning (`bool`, *optional*, defaults to `False`): + Whether to condition the hidden states on the timestep. Returns: `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, @@ -451,6 +529,7 @@ class UNetMidBlock3D(nn.Module): norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() resnet_groups = ( @@ -476,13 +555,17 @@ class UNetMidBlock3D(nn.Module): norm_layer=norm_layer, inject_noise=inject_noise, timestep_conditioning=timestep_conditioning, + spatial_padding_mode=spatial_padding_mode, ) for _ in range(num_layers) ] ) def forward( - self, hidden_states: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None + self, + hidden_states: torch.FloatTensor, + causal: bool = True, + timestep: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: timestep_embed = None if self.timestep_conditioning: @@ -507,9 +590,62 @@ class UNetMidBlock3D(nn.Module): return hidden_states +class SpaceToDepthDownsample(nn.Module): + def __init__(self, dims, in_channels, out_channels, stride, spatial_padding_mode): + super().__init__() + self.stride = stride + self.group_size = in_channels * math.prod(stride) // out_channels + self.conv = make_conv_nd( + dims=dims, + in_channels=in_channels, + out_channels=out_channels // math.prod(stride), + kernel_size=3, + stride=1, + causal=True, + spatial_padding_mode=spatial_padding_mode, + ) + + def forward(self, x, causal: bool = True): + if self.stride[0] == 2: + x = torch.cat( + [x[:, :, :1, :, :], x], dim=2 + ) # duplicate first frames for padding + + # skip connection + x_in = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + x_in = rearrange(x_in, "b (c g) d h w -> b c g d h w", g=self.group_size) + x_in = x_in.mean(dim=2) + + # conv + x = self.conv(x, causal=causal) + x = rearrange( + x, + "b c (d p1) (h p2) (w p3) -> b (c p1 p2 p3) d h w", + p1=self.stride[0], + p2=self.stride[1], + p3=self.stride[2], + ) + + x = x + x_in + + return x + + class DepthToSpaceUpsample(nn.Module): def __init__( - self, dims, in_channels, stride, residual=False, out_channels_reduction_factor=1 + self, + dims, + in_channels, + stride, + residual=False, + out_channels_reduction_factor=1, + spatial_padding_mode="zeros", ): super().__init__() self.stride = stride @@ -523,6 +659,7 @@ class DepthToSpaceUpsample(nn.Module): kernel_size=3, stride=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) self.residual = residual self.out_channels_reduction_factor = out_channels_reduction_factor @@ -558,7 +695,7 @@ class DepthToSpaceUpsample(nn.Module): class LayerNorm(nn.Module): def __init__(self, dim, eps, elementwise_affine=True) -> None: super().__init__() - self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) + self.norm = ops.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine) def forward(self, x): x = rearrange(x, "b c d h w -> b d h w c") @@ -591,6 +728,7 @@ class ResnetBlock3D(nn.Module): norm_layer: str = "group_norm", inject_noise: bool = False, timestep_conditioning: bool = False, + spatial_padding_mode: str = "zeros", ): super().__init__() self.in_channels = in_channels @@ -617,6 +755,7 @@ class ResnetBlock3D(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) if inject_noise: @@ -641,6 +780,7 @@ class ResnetBlock3D(nn.Module): stride=1, padding=1, causal=True, + spatial_padding_mode=spatial_padding_mode, ) if inject_noise: @@ -801,9 +941,44 @@ class processor(nn.Module): return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x) class VideoVAE(nn.Module): - def __init__(self, version=0): + def __init__(self, version=0, config=None): super().__init__() + if config is None: + config = self.guess_config(version) + + self.timestep_conditioning = config.get("timestep_conditioning", False) + double_z = config.get("double_z", True) + latent_log_var = config.get( + "latent_log_var", "per_channel" if double_z else "none" + ) + + self.encoder = Encoder( + dims=config["dims"], + in_channels=config.get("in_channels", 3), + out_channels=config["latent_channels"], + blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + latent_log_var=latent_log_var, + norm_layer=config.get("norm_layer", "group_norm"), + spatial_padding_mode=config.get("spatial_padding_mode", "zeros"), + ) + + self.decoder = Decoder( + dims=config["dims"], + in_channels=config["latent_channels"], + out_channels=config.get("out_channels", 3), + blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), + patch_size=config.get("patch_size", 1), + norm_layer=config.get("norm_layer", "group_norm"), + causal=config.get("causal_decoder", False), + timestep_conditioning=self.timestep_conditioning, + spatial_padding_mode=config.get("spatial_padding_mode", "reflect"), + ) + + self.per_channel_statistics = processor() + + def guess_config(self, version): if version == 0: config = { "_class_name": "CausalVideoAutoencoder", @@ -830,7 +1005,7 @@ class VideoVAE(nn.Module): "use_quant_conv": False, "causal_decoder": False, } - else: + elif version == 1: config = { "_class_name": "CausalVideoAutoencoder", "dims": 3, @@ -866,37 +1041,47 @@ class VideoVAE(nn.Module): "causal_decoder": False, "timestep_conditioning": True, } - - double_z = config.get("double_z", True) - latent_log_var = config.get( - "latent_log_var", "per_channel" if double_z else "none" - ) - - self.encoder = Encoder( - dims=config["dims"], - in_channels=config.get("in_channels", 3), - out_channels=config["latent_channels"], - blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - latent_log_var=latent_log_var, - norm_layer=config.get("norm_layer", "group_norm"), - ) - - self.decoder = Decoder( - dims=config["dims"], - in_channels=config["latent_channels"], - out_channels=config.get("out_channels", 3), - blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))), - patch_size=config.get("patch_size", 1), - norm_layer=config.get("norm_layer", "group_norm"), - causal=config.get("causal_decoder", False), - timestep_conditioning=config.get("timestep_conditioning", False), - ) - - self.timestep_conditioning = config.get("timestep_conditioning", False) - self.per_channel_statistics = processor() + else: + config = { + "_class_name": "CausalVideoAutoencoder", + "dims": 3, + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "encoder_blocks": [ + ["res_x", {"num_layers": 4}], + ["compress_space_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_time_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}] + ], + "decoder_blocks": [ + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}], + ["compress_all", {"residual": True, "multiplier": 2}], + ["res_x", {"num_layers": 5, "inject_noise": False}] + ], + "scaling_factor": 1.0, + "norm_layer": "pixel_norm", + "patch_size": 4, + "latent_log_var": "uniform", + "use_quant_conv": False, + "causal_decoder": False, + "timestep_conditioning": True + } + return config def encode(self, x): + frames_count = x.shape[2] + if ((frames_count - 1) % 8) != 0: + raise ValueError("Invalid number of frames: Encode input must have 1 + 8 * x frames (e.g., 1, 9, 17, ...). Please check your input.") means, logvar = torch.chunk(self.encoder(x), 2, dim=1) return self.per_channel_statistics.normalize(means) diff --git a/comfy/ldm/lightricks/vae/conv_nd_factory.py b/comfy/ldm/lightricks/vae/conv_nd_factory.py index 52df4ee22..b4026b14f 100644 --- a/comfy/ldm/lightricks/vae/conv_nd_factory.py +++ b/comfy/ldm/lightricks/vae/conv_nd_factory.py @@ -17,7 +17,11 @@ def make_conv_nd( groups=1, bias=True, causal=False, + spatial_padding_mode="zeros", + temporal_padding_mode="zeros", ): + if not (spatial_padding_mode == temporal_padding_mode or causal): + raise NotImplementedError("spatial and temporal padding modes must be equal") if dims == 2: return ops.Conv2d( in_channels=in_channels, @@ -28,6 +32,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + padding_mode=spatial_padding_mode, ) elif dims == 3: if causal: @@ -40,6 +45,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + spatial_padding_mode=spatial_padding_mode, ) return ops.Conv3d( in_channels=in_channels, @@ -50,6 +56,7 @@ def make_conv_nd( dilation=dilation, groups=groups, bias=bias, + padding_mode=spatial_padding_mode, ) elif dims == (2, 1): return DualConv3d( @@ -59,6 +66,7 @@ def make_conv_nd( stride=stride, padding=padding, bias=bias, + padding_mode=spatial_padding_mode, ) else: raise ValueError(f"unsupported dimensions: {dims}") diff --git a/comfy/ldm/lightricks/vae/dual_conv3d.py b/comfy/ldm/lightricks/vae/dual_conv3d.py index 6bd54c0a6..dcf889296 100644 --- a/comfy/ldm/lightricks/vae/dual_conv3d.py +++ b/comfy/ldm/lightricks/vae/dual_conv3d.py @@ -18,11 +18,13 @@ class DualConv3d(nn.Module): dilation: Union[int, Tuple[int, int, int]] = 1, groups=1, bias=True, + padding_mode="zeros", ): super(DualConv3d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels + self.padding_mode = padding_mode # Ensure kernel_size, stride, padding, and dilation are tuples of length 3 if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size, kernel_size) @@ -108,6 +110,7 @@ class DualConv3d(nn.Module): self.padding1, self.dilation1, self.groups, + padding_mode=self.padding_mode, ) if skip_time_conv: @@ -122,6 +125,7 @@ class DualConv3d(nn.Module): self.padding2, self.dilation2, self.groups, + padding_mode=self.padding_mode, ) return x @@ -137,7 +141,16 @@ class DualConv3d(nn.Module): stride1 = (self.stride1[1], self.stride1[2]) padding1 = (self.padding1[1], self.padding1[2]) dilation1 = (self.dilation1[1], self.dilation1[2]) - x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups) + x = F.conv2d( + x, + weight1, + self.bias1, + stride1, + padding1, + dilation1, + self.groups, + padding_mode=self.padding_mode, + ) _, _, h, w = x.shape @@ -154,7 +167,16 @@ class DualConv3d(nn.Module): stride2 = self.stride2[0] padding2 = self.padding2[0] dilation2 = self.dilation2[0] - x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups) + x = F.conv1d( + x, + weight2, + self.bias2, + stride2, + padding2, + dilation2, + self.groups, + padding_mode=self.padding_mode, + ) x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w) return x diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py new file mode 100644 index 000000000..f87d98ac0 --- /dev/null +++ b/comfy/ldm/lumina/model.py @@ -0,0 +1,635 @@ +# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py +from __future__ import annotations + +from typing import List, Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +import comfy.ldm.common_dit + +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder +from comfy.ldm.modules.attention import optimized_attention_masked +from comfy.ldm.flux.layers import EmbedND +import comfy.patcher_extension + + +def modulate(x, scale): + return x * (1 + scale.unsqueeze(1)) + +############################################################################# +# Core NextDiT Model # +############################################################################# + + +class JointAttention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + operation_settings={}, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_local_heads = n_heads + self.n_local_kv_heads = self.n_kv_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = dim // n_heads + + self.qkv = operation_settings.get("operations").Linear( + dim, + (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.out = operation_settings.get("operations").Linear( + n_heads * self.head_dim, + dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + if qk_norm: + self.q_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.k_norm = operation_settings.get("operations").RMSNorm(self.head_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + else: + self.q_norm = self.k_norm = nn.Identity() + + @staticmethod + def apply_rotary_emb( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + + t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x_in.shape) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + transformer_options={}, + ) -> torch.Tensor: + """ + + Args: + x: + x_mask: + freqs_cis: + + Returns: + + """ + bsz, seqlen, _ = x.shape + + xq, xk, xv = torch.split( + self.qkv(x), + [ + self.n_local_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + self.n_local_kv_heads * self.head_dim, + ], + dim=-1, + ) + xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis) + xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + + n_rep = self.n_local_heads // self.n_local_kv_heads + if n_rep >= 1: + xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options) + + return self.out(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + operation_settings={}, + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + """ + super().__init__() + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = operation_settings.get("operations").Linear( + dim, + hidden_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.w2 = operation_settings.get("operations").Linear( + hidden_dim, + dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.w3 = operation_settings.get("operations").Linear( + dim, + hidden_dim, + bias=False, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class JointTransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + modulation=True, + operation_settings={}, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + operation_settings=operation_settings, + ) + self.layer_id = layer_id + self.attention_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm1 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + self.attention_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn_norm2 = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + self.modulation = modulation + if modulation: + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operation_settings.get("operations").Linear( + min(dim, 1024), + 4 * dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + adaln_input: Optional[torch.Tensor]=None, + transformer_options={}, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if self.modulation: + assert adaln_input is not None + scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) + + x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( + self.attention( + modulate(self.attention_norm1(x), scale_msa), + x_mask, + freqs_cis, + transformer_options=transformer_options, + ) + ) + x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( + self.feed_forward( + modulate(self.ffn_norm1(x), scale_mlp), + ) + ) + else: + assert adaln_input is None + x = x + self.attention_norm2( + self.attention( + self.attention_norm1(x), + x_mask, + freqs_cis, + transformer_options=transformer_options, + ) + ) + x = x + self.ffn_norm2( + self.feed_forward( + self.ffn_norm1(x), + ) + ) + return x + + +class FinalLayer(nn.Module): + """ + The final layer of NextDiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}): + super().__init__() + self.norm_final = operation_settings.get("operations").LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + self.linear = operation_settings.get("operations").Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + operation_settings.get("operations").Linear( + min(hidden_size, 1024), + hidden_size, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + def forward(self, x, c): + scale = self.adaLN_modulation(c) + x = modulate(self.norm_final(x), scale) + x = self.linear(x) + return x + + +class NextDiT(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_refiner_layers: int = 2, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + axes_dims: List[int] = (16, 56, 56), + axes_lens: List[int] = (1, 512, 512), + image_model=None, + device=None, + dtype=None, + operations=None, + ) -> None: + super().__init__() + self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + + self.x_embedder = operation_settings.get("operations").Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ) + + self.noise_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=True, + operation_settings=operation_settings, + ) + for layer_id in range(n_refiner_layers) + ] + ) + self.context_refiner = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + modulation=False, + operation_settings=operation_settings, + ) + for layer_id in range(n_refiner_layers) + ] + ) + + self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings) + self.cap_embedder = nn.Sequential( + operation_settings.get("operations").RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + operation_settings.get("operations").Linear( + cap_feat_dim, + dim, + bias=True, + device=operation_settings.get("device"), + dtype=operation_settings.get("dtype"), + ), + ) + + self.layers = nn.ModuleList( + [ + JointTransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + operation_settings=operation_settings, + ) + for layer_id in range(n_layers) + ] + ) + self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings) + + assert (dim // n_heads) == sum(axes_dims) + self.axes_dims = axes_dims + self.axes_lens = axes_lens + self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims) + self.dim = dim + self.n_heads = n_heads + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + begin = cap_size[i] + end = begin + (H // pH) * (W // pW) + imgs.append( + x[i][begin:end] + .view(H // pH, W // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + + if return_tensor: + imgs = torch.stack(imgs, dim=0) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={} + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]: + bsz = len(x) + pH = pW = self.patch_size + device = x[0].device + dtype = x[0].dtype + + if cap_mask is not None: + l_effective_cap_len = cap_mask.sum(dim=1).tolist() + else: + l_effective_cap_len = [num_tokens] * bsz + + if cap_mask is not None and not torch.is_floating_point(cap_mask): + cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max + + img_sizes = [(img.size(1), img.size(2)) for img in x] + l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes] + + max_seq_len = max( + (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len)) + ) + max_cap_len = max(l_effective_cap_len) + max_img_len = max(l_effective_img_len) + + position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device) + + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + H, W = img_sizes[i] + H_tokens, W_tokens = H // pH, W // pW + assert H_tokens * W_tokens == img_len + + position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device) + position_ids[i, cap_len:cap_len+img_len, 0] = cap_len + row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten() + col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten() + position_ids[i, cap_len:cap_len+img_len, 1] = row_ids + position_ids[i, cap_len:cap_len+img_len, 2] = col_ids + + freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype) + + # build freqs_cis for cap and image individually + cap_freqs_cis_shape = list(freqs_cis.shape) + # cap_freqs_cis_shape[1] = max_cap_len + cap_freqs_cis_shape[1] = cap_feats.shape[1] + cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + img_freqs_cis_shape = list(freqs_cis.shape) + img_freqs_cis_shape[1] = max_img_len + img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] + img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len] + + # refine context + for layer in self.context_refiner: + cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options) + + # refine image + flat_x = [] + for i in range(bsz): + img = x[i] + C, H, W = img.size() + img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1) + flat_x.append(img) + x = flat_x + padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype) + padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device) + for i in range(bsz): + padded_img_embed[i, :l_effective_img_len[i]] = x[i] + padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max + + padded_img_embed = self.x_embedder(padded_img_embed) + padded_img_mask = padded_img_mask.unsqueeze(1) + for layer in self.noise_refiner: + padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options) + + if cap_mask is not None: + mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device) + mask[:, :max_cap_len] = cap_mask[:, :max_cap_len] + else: + mask = None + + padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype) + for i in range(bsz): + cap_len = l_effective_cap_len[i] + img_len = l_effective_img_len[i] + + padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len] + padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len] + + return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis + + def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {})) + ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs) + + # def forward(self, x, t, cap_feats, cap_mask): + def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs): + t = 1.0 - timesteps + cap_feats = context + cap_mask = attention_mask + bs, c, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + """ + Forward pass of NextDiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of text tokens/features + """ + + t = self.t_embedder(t, dtype=x.dtype) # (N, D) + adaln_input = t + + cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute + + transformer_options = kwargs.get("transformer_options", {}) + x_is_tensor = isinstance(x, torch.Tensor) + x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options) + freqs_cis = freqs_cis.to(x.device) + + for layer in self.layers: + x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options) + + x = self.final_layer(x, adaln_input) + x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w] + + return -x + diff --git a/comfy/ldm/mmaudio/vae/__init__.py b/comfy/ldm/mmaudio/vae/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy/ldm/mmaudio/vae/activations.py b/comfy/ldm/mmaudio/vae/activations.py new file mode 100644 index 000000000..db9192e3e --- /dev/null +++ b/comfy/ldm/mmaudio/vae/activations.py @@ -0,0 +1,120 @@ +# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license. +# LICENSE is in incl_licenses directory. + +import torch +from torch import nn, sin, pow +from torch.nn import Parameter +import comfy.model_management + +class Snake(nn.Module): + ''' + Implementation of a sine-based periodic activation function + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter + References: + - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snake(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha: trainable parameter + alpha is initialized to 1 by default, higher values = higher-frequency. + alpha will be trained along with the rest of your model. + ''' + super(Snake, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: + self.alpha = Parameter(torch.empty(in_features)) + else: + self.alpha = Parameter(torch.empty(in_features)) + + self.alpha.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + Snake ∶= x + 1/a * sin^2 (xa) + ''' + alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + if self.alpha_logscale: + alpha = torch.exp(alpha) + x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x + + +class SnakeBeta(nn.Module): + ''' + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + ''' + def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False): + ''' + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + ''' + super(SnakeBeta, self).__init__() + self.in_features = in_features + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: + self.alpha = Parameter(torch.empty(in_features)) + self.beta = Parameter(torch.empty(in_features)) + else: + self.alpha = Parameter(torch.empty(in_features)) + self.beta = Parameter(torch.empty(in_features)) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + ''' + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + ''' + alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) + if self.alpha_logscale: + alpha = torch.exp(alpha) + beta = torch.exp(beta) + x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2) + + return x diff --git a/comfy/ldm/mmaudio/vae/alias_free_torch.py b/comfy/ldm/mmaudio/vae/alias_free_torch.py new file mode 100644 index 000000000..35c70b897 --- /dev/null +++ b/comfy/ldm/mmaudio/vae/alias_free_torch.py @@ -0,0 +1,157 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +import comfy.model_management + +if 'sinc' in dir(torch): + sinc = torch.sinc +else: + # This code is adopted from adefossez's julius.core.sinc under the MIT License + # https://adefossez.github.io/julius/julius/core.html + # LICENSE is in incl_licenses directory. + def sinc(x: torch.Tensor): + """ + Implementation of sinc, i.e. sin(pi * x) / (pi * x) + __Warning__: Different to julius.sinc, the input is multiplied by `pi`! + """ + return torch.where(x == 0, + torch.tensor(1., device=x.device, dtype=x.dtype), + torch.sin(math.pi * x) / math.pi / x) + + +# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License +# https://adefossez.github.io/julius/julius/lowpass.html +# LICENSE is in incl_licenses directory. +def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size] + even = (kernel_size % 2 == 0) + half_size = kernel_size // 2 + + #For kaiser window + delta_f = 4 * half_width + A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if A > 50.: + beta = 0.1102 * (A - 8.7) + elif A >= 21.: + beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.) + else: + beta = 0. + window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) + + # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio + if even: + time = (torch.arange(-half_size, half_size) + 0.5) + else: + time = torch.arange(kernel_size) - half_size + if cutoff == 0: + filter_ = torch.zeros_like(time) + else: + filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) + # Normalize filter to have sum = 1, otherwise we will have a small leakage + # of the constant component in the input signal. + filter_ /= filter_.sum() + filter = filter_.view(1, 1, kernel_size) + + return filter + + +class LowPassFilter1d(nn.Module): + def __init__(self, + cutoff=0.5, + half_width=0.6, + stride: int = 1, + padding: bool = True, + padding_mode: str = 'replicate', + kernel_size: int = 12): + # kernel_size should be even number for stylegan3 setup, + # in this implementation, odd number is also possible. + super().__init__() + if cutoff < -0.: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + self.kernel_size = kernel_size + self.even = (kernel_size % 2 == 0) + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + self.padding = padding + self.padding_mode = padding_mode + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter) + + #input [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + if self.padding: + x = F.pad(x, (self.pad_left, self.pad_right), + mode=self.padding_mode) + out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), + stride=self.stride, groups=C) + + return out + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + kernel_size=self.kernel_size) + self.register_buffer("filter", filter) + + # x: [B, C, T] + def forward(self, x): + _, C, _ = x.shape + + x = F.pad(x, (self.pad, self.pad), mode='replicate') + x = self.ratio * F.conv_transpose1d( + x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C) + x = x[..., self.pad_left:-self.pad_right] + + return x + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=self.kernel_size) + + def forward(self, x): + xx = self.lowpass(x) + + return xx + +class Activation1d(nn.Module): + def __init__(self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12): + super().__init__() + self.up_ratio = up_ratio + self.down_ratio = down_ratio + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + # x: [B,C,T] + def forward(self, x): + x = self.upsample(x) + x = self.act(x) + x = self.downsample(x) + + return x diff --git a/comfy/ldm/mmaudio/vae/autoencoder.py b/comfy/ldm/mmaudio/vae/autoencoder.py new file mode 100644 index 000000000..cbb9de302 --- /dev/null +++ b/comfy/ldm/mmaudio/vae/autoencoder.py @@ -0,0 +1,156 @@ +from typing import Literal + +import torch +import torch.nn as nn + +from .distributions import DiagonalGaussianDistribution +from .vae import VAE_16k +from .bigvgan import BigVGANVocoder +import logging + +try: + import torchaudio +except: + logging.warning("torchaudio missing, MMAudio VAE model will be broken") + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn): + return norm_fn(torch.clamp(x, min=clip_val) * C) + + +def spectral_normalize_torch(magnitudes, norm_fn): + output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn) + return output + +class MelConverter(nn.Module): + + def __init__( + self, + *, + sampling_rate: float, + n_fft: int, + num_mels: int, + hop_size: int, + win_size: int, + fmin: float, + fmax: float, + norm_fn, + ): + super().__init__() + self.sampling_rate = sampling_rate + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.norm_fn = norm_fn + + # mel = librosa_mel_fn(sr=self.sampling_rate, + # n_fft=self.n_fft, + # n_mels=self.num_mels, + # fmin=self.fmin, + # fmax=self.fmax) + # mel_basis = torch.from_numpy(mel).float() + mel_basis = torch.empty((num_mels, 1 + n_fft // 2)) + hann_window = torch.hann_window(self.win_size) + + self.register_buffer('mel_basis', mel_basis) + self.register_buffer('hann_window', hann_window) + + @property + def device(self): + return self.mel_basis.device + + def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor: + waveform = waveform.clamp(min=-1., max=1.).to(self.device) + + waveform = torch.nn.functional.pad( + waveform.unsqueeze(1), + [int((self.n_fft - self.hop_size) / 2), + int((self.n_fft - self.hop_size) / 2)], + mode='reflect') + waveform = waveform.squeeze(1) + + spec = torch.stft(waveform, + self.n_fft, + hop_length=self.hop_size, + win_length=self.win_size, + window=self.hann_window, + center=center, + pad_mode='reflect', + normalized=False, + onesided=True, + return_complex=True) + + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + spec = torch.matmul(self.mel_basis, spec) + spec = spectral_normalize_torch(spec, self.norm_fn) + + return spec + +class AudioAutoencoder(nn.Module): + + def __init__( + self, + *, + # ckpt_path: str, + mode=Literal['16k', '44k'], + need_vae_encoder: bool = True, + ): + super().__init__() + + assert mode == "16k", "Only 16k mode is supported currently." + self.mel_converter = MelConverter(sampling_rate=16_000, + n_fft=1024, + num_mels=80, + hop_size=256, + win_size=1024, + fmin=0, + fmax=8_000, + norm_fn=torch.log10) + + self.vae = VAE_16k().eval() + + bigvgan_config = { + "resblock": "1", + "num_mels": 80, + "upsample_rates": [4, 4, 2, 2, 2, 2], + "upsample_kernel_sizes": [8, 8, 4, 4, 4, 4], + "upsample_initial_channel": 1536, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [ + [1, 3, 5], + [1, 3, 5], + [1, 3, 5], + ], + "activation": "snakebeta", + "snake_logscale": True, + } + + self.vocoder = BigVGANVocoder( + bigvgan_config + ).eval() + + @torch.inference_mode() + def encode_audio(self, x) -> DiagonalGaussianDistribution: + # x: (B * L) + mel = self.mel_converter(x) + dist = self.vae.encode(mel) + + return dist + + @torch.no_grad() + def decode(self, z): + mel_decoded = self.vae.decode(z) + audio = self.vocoder(mel_decoded) + + audio = torchaudio.functional.resample(audio, 16000, 44100) + return audio + + @torch.no_grad() + def encode(self, audio): + audio = audio.mean(dim=1) + audio = torchaudio.functional.resample(audio, 44100, 16000) + dist = self.encode_audio(audio) + return dist.mean diff --git a/comfy/ldm/mmaudio/vae/bigvgan.py b/comfy/ldm/mmaudio/vae/bigvgan.py new file mode 100644 index 000000000..3a24337f6 --- /dev/null +++ b/comfy/ldm/mmaudio/vae/bigvgan.py @@ -0,0 +1,219 @@ +# Copyright (c) 2022 NVIDIA CORPORATION. +# Licensed under the MIT license. + +# Adapted from https://github.com/jik876/hifi-gan under the MIT license. +# LICENSE is in incl_licenses directory. + +import torch +import torch.nn as nn +from types import SimpleNamespace +from . import activations +from .alias_free_torch import Activation1d +import comfy.ops +ops = comfy.ops.disable_weight_init + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + +class AMPBlock1(torch.nn.Module): + + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): + super(AMPBlock1, self).__init__() + self.h = h + + self.convs1 = nn.ModuleList([ + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0])), + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1])), + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2])) + ]) + + self.convs2 = nn.ModuleList([ + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1)), + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1)), + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1)) + ]) + + self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers + + if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): + xt = a1(x) + xt = c1(xt) + xt = a2(xt) + xt = c2(xt) + x = xt + x + + return x + + +class AMPBlock2(torch.nn.Module): + + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): + super(AMPBlock2, self).__init__() + self.h = h + + self.convs = nn.ModuleList([ + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0])), + ops.Conv1d(channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1])) + ]) + + self.num_layers = len(self.convs) # total number of conv layers + + if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.Snake(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing + self.activations = nn.ModuleList([ + Activation1d( + activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale)) + for _ in range(self.num_layers) + ]) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + def forward(self, x): + for c, a in zip(self.convs, self.activations): + xt = a(x) + xt = c(xt) + x = xt + x + + return x + + +class BigVGANVocoder(torch.nn.Module): + # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. + def __init__(self, h): + super().__init__() + if isinstance(h, dict): + h = SimpleNamespace(**h) + self.h = h + + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + + # pre conv + self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3) + + # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default + resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 + + # transposed conv-based upsamplers. does not apply anti-aliasing + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + nn.ModuleList([ + ops.ConvTranspose1d(h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2**(i + 1)), + k, + u, + padding=(k - u) // 2) + ])) + + # residual blocks using anti-aliased multi-periodicity composition modules (AMP) + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2**(i + 1)) + for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) + + # post conv + if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing + activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale) + self.activation_post = Activation1d(activation=activation_post) + elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing + activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale) + self.activation_post = Activation1d(activation=activation_post) + else: + raise NotImplementedError( + "activation incorrectly specified. check the config file and look for 'activation'." + ) + + self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3) + + + def forward(self, x): + # pre conv + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + # upsampling + for i_up in range(len(self.ups[i])): + x = self.ups[i][i_up](x) + # AMP blocks + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + + # post conv + x = self.activation_post(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x diff --git a/comfy/ldm/mmaudio/vae/distributions.py b/comfy/ldm/mmaudio/vae/distributions.py new file mode 100644 index 000000000..df987c5ec --- /dev/null +++ b/comfy/ldm/mmaudio/vae/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/comfy/ldm/mmaudio/vae/vae.py b/comfy/ldm/mmaudio/vae/vae.py new file mode 100644 index 000000000..62f24606c --- /dev/null +++ b/comfy/ldm/mmaudio/vae/vae.py @@ -0,0 +1,358 @@ +import logging +from typing import Optional + +import torch +import torch.nn as nn + +from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D, + Upsample1D, nonlinearity) +from .distributions import DiagonalGaussianDistribution + +import comfy.ops +ops = comfy.ops.disable_weight_init + +log = logging.getLogger() + +DATA_MEAN_80D = [ + -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927, + -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728, + -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131, + -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280, + -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643, + -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436, + -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282, + -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673 +] + +DATA_STD_80D = [ + 1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263, + 0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194, + 0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043, + 0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973, + 0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939, + 0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604, + 1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070 +] + +DATA_MEAN_128D = [ + -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597, + -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033, + -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157, + -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782, + -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647, + -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795, + -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121, + -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960, + -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712, + -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120, + -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663, + -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628, + -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861 +] + +DATA_STD_128D = [ + 2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659, + 2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557, + 2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182, + 2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991, + 2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900, + 2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817, + 2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609, + 2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812, + 2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451, + 2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877, + 2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164 +] + + +class VAE(nn.Module): + + def __init__( + self, + *, + data_dim: int, + embed_dim: int, + hidden_dim: int, + ): + super().__init__() + + if data_dim == 80: + self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32)) + self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32)) + elif data_dim == 128: + self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32)) + self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32)) + + self.data_mean = self.data_mean.view(1, -1, 1) + self.data_std = self.data_std.view(1, -1, 1) + + self.encoder = Encoder1D( + dim=hidden_dim, + ch_mult=(1, 2, 4), + num_res_blocks=2, + attn_layers=[3], + down_layers=[0], + in_dim=data_dim, + embed_dim=embed_dim, + ) + self.decoder = Decoder1D( + dim=hidden_dim, + ch_mult=(1, 2, 4), + num_res_blocks=2, + attn_layers=[3], + down_layers=[0], + in_dim=data_dim, + out_dim=data_dim, + embed_dim=embed_dim, + ) + + self.embed_dim = embed_dim + # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1) + # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1) + + self.initialize_weights() + + def initialize_weights(self): + pass + + def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution: + if normalize: + x = self.normalize(x) + moments = self.encoder(x) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor: + dec = self.decoder(z) + if unnormalize: + dec = self.unnormalize(dec) + return dec + + def normalize(self, x: torch.Tensor) -> torch.Tensor: + return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + + def unnormalize(self, x: torch.Tensor) -> torch.Tensor: + return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device) + + def forward( + self, + x: torch.Tensor, + sample_posterior: bool = True, + rng: Optional[torch.Generator] = None, + normalize: bool = True, + unnormalize: bool = True, + ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]: + + posterior = self.encode(x, normalize=normalize) + if sample_posterior: + z = posterior.sample(rng) + else: + z = posterior.mode() + dec = self.decode(z, unnormalize=unnormalize) + return dec, posterior + + def load_weights(self, src_dict) -> None: + self.load_state_dict(src_dict, strict=True) + + @property + def device(self) -> torch.device: + return next(self.parameters()).device + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def remove_weight_norm(self): + return self + + +class Encoder1D(nn.Module): + + def __init__(self, + *, + dim: int, + ch_mult: tuple[int] = (1, 2, 4, 8), + num_res_blocks: int, + attn_layers: list[int] = [], + down_layers: list[int] = [], + resamp_with_conv: bool = True, + in_dim: int, + embed_dim: int, + double_z: bool = True, + kernel_size: int = 3, + clip_act: float = 256.0): + super().__init__() + self.dim = dim + self.num_layers = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_dim + self.clip_act = clip_act + self.down_layers = down_layers + self.attn_layers = attn_layers + self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + + in_ch_mult = (1, ) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + # downsampling + self.down = nn.ModuleList() + for i_level in range(self.num_layers): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = dim * in_ch_mult[i_level] + block_out = dim * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock1D(in_dim=block_in, + out_dim=block_out, + kernel_size=kernel_size, + use_norm=True)) + block_in = block_out + if i_level in attn_layers: + attn.append(AttnBlock1D(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level in down_layers: + down.downsample = Downsample1D(block_in, resamp_with_conv) + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock1D(in_dim=block_in, + out_dim=block_in, + kernel_size=kernel_size, + use_norm=True) + self.mid.attn_1 = AttnBlock1D(block_in) + self.mid.block_2 = ResnetBlock1D(in_dim=block_in, + out_dim=block_in, + kernel_size=kernel_size, + use_norm=True) + + # end + self.conv_out = ops.Conv1d(block_in, + 2 * embed_dim if double_z else embed_dim, + kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + + self.learnable_gain = nn.Parameter(torch.zeros([])) + + def forward(self, x): + + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_layers): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + h = h.clamp(-self.clip_act, self.clip_act) + if i_level in self.down_layers: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + h = h.clamp(-self.clip_act, self.clip_act) + + # end + h = nonlinearity(h) + h = self.conv_out(h) * (self.learnable_gain + 1) + return h + + +class Decoder1D(nn.Module): + + def __init__(self, + *, + dim: int, + out_dim: int, + ch_mult: tuple[int] = (1, 2, 4, 8), + num_res_blocks: int, + attn_layers: list[int] = [], + down_layers: list[int] = [], + kernel_size: int = 3, + resamp_with_conv: bool = True, + in_dim: int, + embed_dim: int, + clip_act: float = 256.0): + super().__init__() + self.ch = dim + self.num_layers = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.in_channels = in_dim + self.clip_act = clip_act + self.down_layers = [i + 1 for i in down_layers] # each downlayer add one + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = dim * ch_mult[self.num_layers - 1] + + # z to block_in + self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) + self.mid.attn_1 = AttnBlock1D(block_in) + self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_layers)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = dim * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True)) + block_in = block_out + if i_level in attn_layers: + attn.append(AttnBlock1D(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level in self.down_layers: + up.upsample = Upsample1D(block_in, resamp_with_conv) + self.up.insert(0, up) # prepend to get consistent order + + # end + self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + self.learnable_gain = nn.Parameter(torch.zeros([])) + + def forward(self, z): + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + h = h.clamp(-self.clip_act, self.clip_act) + + # upsampling + for i_level in reversed(range(self.num_layers)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + h = h.clamp(-self.clip_act, self.clip_act) + if i_level in self.down_layers: + h = self.up[i_level].upsample(h) + + h = nonlinearity(h) + h = self.conv_out(h) * (self.learnable_gain + 1) + return h + + +def VAE_16k(**kwargs) -> VAE: + return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs) + + +def VAE_44k(**kwargs) -> VAE: + return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs) + + +def get_my_vae(name: str, **kwargs) -> VAE: + if name == '16k': + return VAE_16k(**kwargs) + if name == '44k': + return VAE_44k(**kwargs) + raise ValueError(f'Unknown model: {name}') + diff --git a/comfy/ldm/mmaudio/vae/vae_modules.py b/comfy/ldm/mmaudio/vae/vae_modules.py new file mode 100644 index 000000000..3ad05134b --- /dev/null +++ b/comfy/ldm/mmaudio/vae/vae_modules.py @@ -0,0 +1,121 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from comfy.ldm.modules.diffusionmodules.model import vae_attention +import math +import comfy.ops +ops = comfy.ops.disable_weight_init + +def nonlinearity(x): + # swish + return torch.nn.functional.silu(x) / 0.596 + +def mp_sum(a, b, t=0.5): + return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2) + +def normalize(x, dim=None, eps=1e-4): + if dim is None: + dim = list(range(1, x.ndim)) + norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32) + norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel())) + return x / norm.to(x.dtype) + +class ResnetBlock1D(nn.Module): + + def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True): + super().__init__() + self.in_dim = in_dim + out_dim = in_dim if out_dim is None else out_dim + self.out_dim = out_dim + self.use_conv_shortcut = conv_shortcut + self.use_norm = use_norm + + self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + if self.in_dim != self.out_dim: + if self.use_conv_shortcut: + self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False) + else: + self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + + # pixel norm + if self.use_norm: + x = normalize(x, dim=1) + + h = x + h = nonlinearity(h) + h = self.conv1(h) + + h = nonlinearity(h) + h = self.conv2(h) + + if self.in_dim != self.out_dim: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return mp_sum(x, h, t=0.3) + + +class AttnBlock1D(nn.Module): + + def __init__(self, in_channels, num_heads=1): + super().__init__() + self.in_channels = in_channels + + self.num_heads = num_heads + self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False) + self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) + self.optimized_attention = vae_attention() + + def forward(self, x): + h = x + y = self.qkv(h) + y = y.reshape(y.shape[0], -1, 3, y.shape[-1]) + q, k, v = normalize(y, dim=1).unbind(2) + + h = self.optimized_attention(q, k, v) + h = self.proj_out(h) + + return mp_sum(x, h, t=0.3) + + +class Upsample1D(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = ops.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact') # support 3D tensor(B,C,T) + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample1D(nn.Module): + + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) + self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False) + + def forward(self, x): + + if self.with_conv: + x = self.conv1(x) + + x = F.avg_pool1d(x, kernel_size=2, stride=2) + + if self.with_conv: + x = self.conv2(x) + + return x diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index e6493155e..611d36a1b 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -11,7 +11,7 @@ from comfy.ldm.modules.ema import LitEma import comfy.ops class DiagonalGaussianRegularizer(torch.nn.Module): - def __init__(self, sample: bool = True): + def __init__(self, sample: bool = False): super().__init__() self.sample = sample @@ -19,17 +19,19 @@ class DiagonalGaussianRegularizer(torch.nn.Module): yield from () def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: - log = dict() posterior = DiagonalGaussianDistribution(z) if self.sample: z = posterior.sample() else: z = posterior.mode() - kl_loss = posterior.kl() - kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] - log["kl_loss"] = kl_loss - return z, log + return z, None +class EmptyRegularizer(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + return z, None class AbstractAutoencoder(torch.nn.Module): """ diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index 0d54e6bec..7437e0567 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -1,10 +1,13 @@ import math +import sys + import torch import torch.nn.functional as F from torch import nn, einsum from einops import rearrange, repeat -from typing import Optional +from typing import Optional, Any, Callable, Union import logging +import functools from .diffusionmodules.util import AlphaBlender, timestep_embedding from .sub_quadratic_attention import efficient_dot_product_attention @@ -15,8 +18,44 @@ if model_management.xformers_enabled(): import xformers import xformers.ops -if model_management.sage_attention_enabled(): +SAGE_ATTENTION_IS_AVAILABLE = False +try: from sageattention import sageattn + SAGE_ATTENTION_IS_AVAILABLE = True +except ImportError as e: + if model_management.sage_attention_enabled(): + if e.name == "sageattention": + logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention") + else: + raise e + exit(-1) + +FLASH_ATTENTION_IS_AVAILABLE = False +try: + from flash_attn import flash_attn_func + FLASH_ATTENTION_IS_AVAILABLE = True +except ImportError: + if model_management.flash_attention_enabled(): + logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn") + exit(-1) + +REGISTERED_ATTENTION_FUNCTIONS = {} +def register_attention_function(name: str, func: Callable): + # avoid replacing existing functions + if name not in REGISTERED_ATTENTION_FUNCTIONS: + REGISTERED_ATTENTION_FUNCTIONS[name] = func + else: + logging.warning(f"Attention function {name} already registered, skipping registration.") + +def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]: + if name == "optimized": + return optimized_attention + elif name not in REGISTERED_ATTENTION_FUNCTIONS: + if default is ...: + raise KeyError(f"Attention function {name} not found.") + else: + return default + return REGISTERED_ATTENTION_FUNCTIONS[name] from comfy.cli_args import args import comfy.ops @@ -24,38 +63,24 @@ ops = comfy.ops.disable_weight_init FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype() -def get_attn_precision(attn_precision): +def get_attn_precision(attn_precision, current_dtype): if args.dont_upcast_attention: return None - if FORCE_UPCAST_ATTENTION_DTYPE is not None: - return FORCE_UPCAST_ATTENTION_DTYPE + + if FORCE_UPCAST_ATTENTION_DTYPE is not None and current_dtype in FORCE_UPCAST_ATTENTION_DTYPE: + return FORCE_UPCAST_ATTENTION_DTYPE[current_dtype] return attn_precision def exists(val): return val is not None -def uniq(arr): - return{el: True for el in arr}.keys() - - def default(val, d): if exists(val): return val return d -def max_neg_value(t): - return -torch.finfo(t.dtype).max - - -def init_(tensor): - dim = tensor.shape[-1] - std = 1 / math.sqrt(dim) - tensor.uniform_(-std, std) - return tensor - - # feedforward class GEGLU(nn.Module): def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): @@ -89,8 +114,28 @@ class FeedForward(nn.Module): def Normalize(in_channels, dtype=None, device=None): return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) -def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): - attn_precision = get_attn_precision(attn_precision) + +def wrap_attn(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + remove_attn_wrapper_key = False + try: + if "_inside_attn_wrapper" not in kwargs: + transformer_options = kwargs.get("transformer_options", None) + remove_attn_wrapper_key = True + kwargs["_inside_attn_wrapper"] = True + if transformer_options is not None: + if "optimized_attention_override" in transformer_options: + return transformer_options["optimized_attention_override"](func, *args, **kwargs) + return func(*args, **kwargs) + finally: + if remove_attn_wrapper_key: + del kwargs["_inside_attn_wrapper"] + return wrapper + +@wrap_attn +def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): + attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: b, _, _, dim_head = q.shape @@ -142,17 +187,24 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape sim = sim.softmax(dim=-1) out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) - out = ( - out.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) + + if skip_output_reshape: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + ) + else: + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) return out - -def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False): - attn_precision = get_attn_precision(attn_precision) +@wrap_attn +def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): + attn_precision = get_attn_precision(attn_precision, query.dtype) if skip_reshape: b, _, _, dim_head = query.shape @@ -215,12 +267,15 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, ) hidden_states = hidden_states.to(dtype) - - hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) + if skip_output_reshape: + hidden_states = hidden_states.unflatten(0, (-1, heads)) + else: + hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) return hidden_states -def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): - attn_precision = get_attn_precision(attn_precision) +@wrap_attn +def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): + attn_precision = get_attn_precision(attn_precision, q.dtype) if skip_reshape: b, _, _, dim_head = q.shape @@ -326,12 +381,18 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape del q, k, v - r1 = ( - r1.unsqueeze(0) - .reshape(b, heads, -1, dim_head) - .permute(0, 2, 1, 3) - .reshape(b, -1, heads * dim_head) - ) + if skip_output_reshape: + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + ) + else: + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) return r1 BROKEN_XFORMERS = False @@ -342,7 +403,8 @@ try: except: pass -def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): +@wrap_attn +def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): b = q.shape[0] dim_head = q.shape[-1] # check to make sure xformers isn't broken @@ -357,7 +419,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh disabled_xformers = True if disabled_xformers: - return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape) + return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs) if skip_reshape: # b h k d -> b k h d @@ -395,9 +457,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) - out = ( - out.reshape(b, -1, heads * dim_head) - ) + if skip_output_reshape: + out = out.permute(0, 2, 1, 3) + else: + out = ( + out.reshape(b, -1, heads * dim_head) + ) return out @@ -407,8 +472,8 @@ else: #TODO: other GPUs ? SDP_BATCH_LIMIT = 2**31 - -def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): +@wrap_attn +def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape else: @@ -428,10 +493,11 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha mask = mask.unsqueeze(1) if SDP_BATCH_LIMIT >= b: - out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) - out = ( - out.transpose(1, 2).reshape(b, -1, heads * dim_head) - ) + out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) else: out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device) for i in range(0, b, SDP_BATCH_LIMIT): @@ -440,7 +506,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha if mask.shape[0] > 1: m = mask[i : i + SDP_BATCH_LIMIT] - out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention( + out[i : i + SDP_BATCH_LIMIT] = comfy.ops.scaled_dot_product_attention( q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], @@ -449,11 +515,11 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) return out - -def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False): +@wrap_attn +def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): if skip_reshape: b, _, _, dim_head = q.shape - tensor_layout="HND" + tensor_layout = "HND" else: b, _, dim_head = q.shape dim_head //= heads @@ -461,7 +527,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape= lambda t: t.view(b, -1, heads, dim_head), (q, k, v), ) - tensor_layout="NHD" + tensor_layout = "NHD" if mask is not None: # add a batch dimension if there isn't already one @@ -471,13 +537,85 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape= if mask.ndim == 3: mask = mask.unsqueeze(1) - out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) + try: + out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout) + except Exception as e: + logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e)) + if tensor_layout == "NHD": + q, k, v = map( + lambda t: t.transpose(1, 2), + (q, k, v), + ) + return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs) + if tensor_layout == "HND": + if not skip_output_reshape: + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + else: + if skip_output_reshape: + out = out.transpose(1, 2) + else: + out = out.reshape(b, -1, heads * dim_head) + return out + + +try: + @torch.library.custom_op("flash_attention::flash_attn", mutates_args=()) + def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, + dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: + return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal) + + + @flash_attn_wrapper.register_fake + def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False): + # Output shape is the same as q + return q.new_empty(q.shape) +except AttributeError as error: + FLASH_ATTN_ERROR = error + + def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, + dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor: + assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}" + +@wrap_attn +def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs): + if skip_reshape: + b, _, _, dim_head = q.shape + else: + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + if mask is not None: + # add a batch dimension if there isn't already one + if mask.ndim == 2: + mask = mask.unsqueeze(0) + # add a heads dimension if there isn't already one + if mask.ndim == 3: + mask = mask.unsqueeze(1) + + try: + if mask is not None: + raise RuntimeError("Mask must not be set for Flash attention") + out = flash_attn_wrapper( + q.transpose(1, 2), + k.transpose(1, 2), + v.transpose(1, 2), + dropout_p=0.0, + causal=False, + ).transpose(1, 2) + except Exception as e: + logging.warning(f"Flash Attention failed, using default SDPA: {e}") + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + if not skip_output_reshape: out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) ) - else: - out = out.reshape(b, -1, heads * dim_head) return out @@ -489,6 +627,9 @@ if model_management.sage_attention_enabled(): elif model_management.xformers_enabled(): logging.info("Using xformers attention") optimized_attention = attention_xformers +elif model_management.flash_attention_enabled(): + logging.info("Using Flash Attention") + optimized_attention = attention_flash elif model_management.pytorch_attention_enabled(): logging.info("Using pytorch attention") optimized_attention = attention_pytorch @@ -502,6 +643,19 @@ else: optimized_attention_masked = optimized_attention + +# register core-supported attention functions +if SAGE_ATTENTION_IS_AVAILABLE: + register_attention_function("sage", attention_sage) +if FLASH_ATTENTION_IS_AVAILABLE: + register_attention_function("flash", attention_flash) +if model_management.xformers_enabled(): + register_attention_function("xformers", attention_xformers) +register_attention_function("pytorch", attention_pytorch) +register_attention_function("sub_quad", attention_sub_quad) +register_attention_function("split", attention_split) + + def optimized_attention_for_device(device, mask=False, small_input=False): if small_input: if model_management.pytorch_attention_enabled(): @@ -534,7 +688,7 @@ class CrossAttention(nn.Module): self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) - def forward(self, x, context=None, value=None, mask=None): + def forward(self, x, context=None, value=None, mask=None, transformer_options={}): q = self.to_q(x) context = default(context, x) k = self.to_k(context) @@ -545,9 +699,9 @@ class CrossAttention(nn.Module): v = self.to_v(context) if mask is None: - out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision) + out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options) else: - out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision) + out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options) return self.to_out(out) @@ -651,14 +805,14 @@ class BasicTransformerBlock(nn.Module): n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) n = self.attn1.to_out(n) else: - n = self.attn1(n, context=context_attn1, value=value_attn1) + n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options) if "attn1_output_patch" in transformer_patches: patch = transformer_patches["attn1_output_patch"] for p in patch: n = p(n, extra_options) - x += n + x = n + x if "middle_patch" in transformer_patches: patch = transformer_patches["middle_patch"] for p in patch: @@ -691,19 +845,19 @@ class BasicTransformerBlock(nn.Module): n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) n = self.attn2.to_out(n) else: - n = self.attn2(n, context=context_attn2, value=value_attn2) + n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options) if "attn2_output_patch" in transformer_patches: patch = transformer_patches["attn2_output_patch"] for p in patch: n = p(n, extra_options) - x += n + x = n + x if self.is_res: x_skip = x x = self.ff(self.norm3(x)) if self.is_res: - x += x_skip + x = x_skip + x return x @@ -755,6 +909,7 @@ class SpatialTransformer(nn.Module): if not isinstance(context, list): context = [context] * len(self.transformer_blocks) b, c, h, w = x.shape + transformer_options["activations_shape"] = list(x.shape) x_in = x x = self.norm(x) if not self.use_linear: @@ -870,6 +1025,7 @@ class SpatialVideoTransformer(SpatialTransformer): transformer_options={} ) -> torch.Tensor: _, _, h, w = x.shape + transformer_options["activations_shape"] = list(x.shape) x_in = x spatial_context = None if exists(context): @@ -920,7 +1076,7 @@ class SpatialVideoTransformer(SpatialTransformer): B, S, C = x_mix.shape x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) - x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options) x_mix = rearrange( x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps ) diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py index e70f4431f..42f406f1a 100644 --- a/comfy/ldm/modules/diffusionmodules/mmdit.py +++ b/comfy/ldm/modules/diffusionmodules/mmdit.py @@ -109,7 +109,7 @@ class PatchEmbed(nn.Module): def modulate(x, shift, scale): if shift is None: shift = torch.zeros_like(scale) - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return torch.addcmul(shift.unsqueeze(1), x, 1+ scale.unsqueeze(1)) ################################################################################# @@ -321,7 +321,7 @@ class SelfAttention(nn.Module): class RMSNorm(torch.nn.Module): def __init__( - self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None + self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs ): """ Initialize the RMSNorm normalization layer. @@ -564,10 +564,7 @@ class DismantledBlock(nn.Module): assert not self.pre_only attn1 = self.attn.post_attention(attn) attn2 = self.attn2.post_attention(attn2) - out1 = gate_msa.unsqueeze(1) * attn1 - out2 = gate_msa2.unsqueeze(1) * attn2 - x = x + out1 - x = x + out2 + x = gate_cat(x, gate_msa, gate_msa2, attn1, attn2) x = x + gate_mlp.unsqueeze(1) * self.mlp( modulate(self.norm2(x), shift_mlp, scale_mlp) ) @@ -594,6 +591,11 @@ class DismantledBlock(nn.Module): ) return self.post_attention(attn, *intermediates) +def gate_cat(x, gate_msa, gate_msa2, attn1, attn2): + out1 = gate_msa.unsqueeze(1) * attn1 + out2 = gate_msa2.unsqueeze(1) * attn2 + x = torch.stack([x, out1, out2], dim=0).sum(dim=0) + return x def block_mixing(*args, use_checkpoint=True, **kwargs): if use_checkpoint: @@ -604,7 +606,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs): return _block_mixing(*args, **kwargs) -def _block_mixing(context, x, context_block, x_block, c): +def _block_mixing(context, x, context_block, x_block, c, transformer_options={}): context_qkv, context_intermediates = context_block.pre_attention(context, c) if x_block.x_block_self_attn: @@ -620,6 +622,7 @@ def _block_mixing(context, x, context_block, x_block, c): attn = optimized_attention( qkv[0], qkv[1], qkv[2], heads=x_block.attn.num_heads, + transformer_options=transformer_options, ) context_attn, x_attn = ( attn[:, : context_qkv[0].shape[1]], @@ -635,6 +638,7 @@ def _block_mixing(context, x, context_block, x_block, c): attn2 = optimized_attention( x_qkv2[0], x_qkv2[1], x_qkv2[2], heads=x_block.attn2.num_heads, + transformer_options=transformer_options, ) x = x_block.post_attention_x(x_attn, attn2, *x_intermediates) else: @@ -956,10 +960,10 @@ class MMDiT(nn.Module): if ("double_block", i) in blocks_replace: def block_wrap(args): out = {} - out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"]) + out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"]) return out - out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap}) + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap}) context = out["txt"] x = out["img"] else: @@ -968,6 +972,7 @@ class MMDiT(nn.Module): x, c=c_mod, use_checkpoint=self.use_checkpoint, + transformer_options=transformer_options, ) if control is not None: control_o = control.get("output") diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py index ed1e88212..4245eedca 100644 --- a/comfy/ldm/modules/diffusionmodules/model.py +++ b/comfy/ldm/modules/diffusionmodules/model.py @@ -36,7 +36,7 @@ def get_timestep_embedding(timesteps, embedding_dim): def nonlinearity(x): # swish - return x*torch.sigmoid(x) + return torch.nn.functional.silu(x) def Normalize(in_channels, num_groups=32): @@ -145,7 +145,7 @@ class Downsample(nn.Module): class ResnetBlock(nn.Module): def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, - dropout, temb_channels=512, conv_op=ops.Conv2d): + dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels out_channels = in_channels if out_channels is None else out_channels @@ -153,7 +153,7 @@ class ResnetBlock(nn.Module): self.use_conv_shortcut = conv_shortcut self.swish = torch.nn.SiLU(inplace=True) - self.norm1 = Normalize(in_channels) + self.norm1 = norm_op(in_channels) self.conv1 = conv_op(in_channels, out_channels, kernel_size=3, @@ -162,7 +162,7 @@ class ResnetBlock(nn.Module): if temb_channels > 0: self.temb_proj = ops.Linear(temb_channels, out_channels) - self.norm2 = Normalize(out_channels) + self.norm2 = norm_op(out_channels) self.dropout = torch.nn.Dropout(dropout, inplace=True) self.conv2 = conv_op(out_channels, out_channels, @@ -183,7 +183,7 @@ class ResnetBlock(nn.Module): stride=1, padding=0) - def forward(self, x, temb): + def forward(self, x, temb=None): h = x h = self.norm1(h) h = self.swish(h) @@ -285,7 +285,7 @@ def pytorch_attention(q, k, v): ) try: - out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) out = out.transpose(2, 3).reshape(orig_shape) except model_management.OOM_EXCEPTION: logging.warning("scaled_dot_product_attention OOMed: switched to slice attention") @@ -293,12 +293,23 @@ def pytorch_attention(q, k, v): return out +def vae_attention(): + if model_management.xformers_enabled_vae(): + logging.info("Using xformers attention in VAE") + return xformers_attention + elif model_management.pytorch_attention_enabled_vae(): + logging.info("Using pytorch attention in VAE") + return pytorch_attention + else: + logging.info("Using split attention in VAE") + return normal_attention + class AttnBlock(nn.Module): - def __init__(self, in_channels, conv_op=ops.Conv2d): + def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize): super().__init__() self.in_channels = in_channels - self.norm = Normalize(in_channels) + self.norm = norm_op(in_channels) self.q = conv_op(in_channels, in_channels, kernel_size=1, @@ -320,15 +331,7 @@ class AttnBlock(nn.Module): stride=1, padding=0) - if model_management.xformers_enabled_vae(): - logging.info("Using xformers attention in VAE") - self.optimized_attention = xformers_attention - elif model_management.pytorch_attention_enabled(): - logging.info("Using pytorch attention in VAE") - self.optimized_attention = pytorch_attention - else: - logging.info("Using split attention in VAE") - self.optimized_attention = normal_attention + self.optimized_attention = vae_attention() def forward(self, x): h_ = x @@ -699,9 +702,6 @@ class Decoder(nn.Module): padding=1) def forward(self, z, **kwargs): - #assert z.shape[1:] == self.z_shape[1:] - self.last_z_shape = z.shape - # timestep embedding temb = None diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py index 21c72373f..fab145f1c 100644 --- a/comfy/ldm/modules/sub_quadratic_attention.py +++ b/comfy/ldm/modules/sub_quadratic_attention.py @@ -31,7 +31,7 @@ def dynamic_slice( starts: List[int], sizes: List[int], ) -> Tensor: - slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] + slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes)) return x[slicing] class AttnChunk(NamedTuple): diff --git a/comfy/ldm/omnigen/omnigen2.py b/comfy/ldm/omnigen/omnigen2.py new file mode 100644 index 000000000..82edc92da --- /dev/null +++ b/comfy/ldm/omnigen/omnigen2.py @@ -0,0 +1,470 @@ +# Original code: https://github.com/VectorSpaceLab/OmniGen2 + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat +from comfy.ldm.lightricks.model import Timesteps +from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.modules.attention import optimized_attention_masked +import comfy.model_management +import comfy.ldm.common_dit + + +def apply_rotary_emb(x, freqs_cis): + if x.shape[1] == 0: + return x + + t_ = x.reshape(*x.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x.shape).to(dtype=x.dtype) + + +def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: + return F.silu(x) * y + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, device=None, operations=None): + super().__init__() + self.linear_1 = operations.Linear(in_channels, time_embed_dim, dtype=dtype, device=device) + self.act = nn.SiLU() + self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device) + + def forward(self, sample: torch.Tensor) -> torch.Tensor: + sample = self.linear_1(sample) + sample = self.act(sample) + sample = self.linear_2(sample) + return sample + + +class LuminaRMSNormZero(nn.Module): + def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=None, device=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear = operations.Linear(min(embedding_dim, 1024), 4 * embedding_dim, dtype=dtype, device=device) + self.norm = operations.RMSNorm(embedding_dim, eps=norm_eps, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + emb = self.linear(self.silu(emb)) + scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + return x, gate_msa, scale_mlp, gate_mlp + + +class LuminaLayerNormContinuous(nn.Module): + def __init__(self, embedding_dim: int, conditioning_embedding_dim: int, elementwise_affine: bool = False, eps: float = 1e-6, out_dim: Optional[int] = None, dtype=None, device=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear_1 = operations.Linear(conditioning_embedding_dim, embedding_dim, dtype=dtype, device=device) + self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine, dtype=dtype, device=device) + self.linear_2 = operations.Linear(embedding_dim, out_dim, bias=True, dtype=dtype, device=device) if out_dim is not None else None + + def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: + emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) + x = self.norm(x) * (1 + emb)[:, None, :] + if self.linear_2 is not None: + x = self.linear_2(x) + return x + + +class LuminaFeedForward(nn.Module): + def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, dtype=None, device=None, operations=None): + super().__init__() + inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of) + self.linear_1 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) + self.linear_2 = operations.Linear(inner_dim, dim, bias=False, dtype=dtype, device=device) + self.linear_3 = operations.Linear(dim, inner_dim, bias=False, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + h1, h2 = self.linear_1(x), self.linear_3(x) + return self.linear_2(swiglu(h1, h2)) + + +class Lumina2CombinedTimestepCaptionEmbedding(nn.Module): + def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048, frequency_embedding_size: int = 256, norm_eps: float = 1e-5, timestep_scale: float = 1.0, dtype=None, device=None, operations=None): + super().__init__() + self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0, scale=timestep_scale) + self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=min(hidden_size, 1024), dtype=dtype, device=device, operations=operations) + self.caption_embedder = nn.Sequential( + operations.RMSNorm(text_feat_dim, eps=norm_eps, dtype=dtype, device=device), + operations.Linear(text_feat_dim, hidden_size, bias=True, dtype=dtype, device=device), + ) + + def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Tensor, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor]: + timestep_proj = self.time_proj(timestep).to(dtype=dtype) + time_embed = self.timestep_embedder(timestep_proj) + caption_embed = self.caption_embedder(text_hidden_states) + return time_embed, caption_embed + + +class Attention(nn.Module): + def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads: int, eps: float = 1e-5, bias: bool = False, dtype=None, device=None, operations=None): + super().__init__() + self.heads = heads + self.kv_heads = kv_heads + self.dim_head = dim_head + self.scale = dim_head ** -0.5 + + self.to_q = operations.Linear(query_dim, heads * dim_head, bias=bias, dtype=dtype, device=device) + self.to_k = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(query_dim, kv_heads * dim_head, bias=bias, dtype=dtype, device=device) + + self.norm_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + self.norm_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + + self.to_out = nn.Sequential( + operations.Linear(heads * dim_head, query_dim, bias=bias, dtype=dtype, device=device), + nn.Dropout(0.0) + ) + + def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor: + batch_size, sequence_length, _ = hidden_states.shape + + query = self.to_q(hidden_states) + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + query = query.view(batch_size, -1, self.heads, self.dim_head) + key = key.view(batch_size, -1, self.kv_heads, self.dim_head) + value = value.view(batch_size, -1, self.kv_heads, self.dim_head) + + query = self.norm_q(query) + key = self.norm_k(key) + + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + key = apply_rotary_emb(key, image_rotary_emb) + + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + if self.kv_heads < self.heads: + key = key.repeat_interleave(self.heads // self.kv_heads, dim=1) + value = value.repeat_interleave(self.heads // self.kv_heads, dim=1) + + hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options) + hidden_states = self.to_out[0](hidden_states) + return hidden_states + + +class OmniGen2TransformerBlock(nn.Module): + def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, modulation: bool = True, dtype=None, device=None, operations=None): + super().__init__() + self.modulation = modulation + + self.attn = Attention( + query_dim=dim, + dim_head=dim // num_attention_heads, + heads=num_attention_heads, + kv_heads=num_kv_heads, + eps=1e-5, + bias=False, + dtype=dtype, device=device, operations=operations, + ) + + self.feed_forward = LuminaFeedForward( + dim=dim, + inner_dim=4 * dim, + multiple_of=multiple_of, + dtype=dtype, device=device, operations=operations + ) + + if modulation: + self.norm1 = LuminaRMSNormZero(embedding_dim=dim, norm_eps=norm_eps, dtype=dtype, device=device, operations=operations) + else: + self.norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + self.ffn_norm1 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device) + + def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor: + if self.modulation: + norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) + hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) + else: + norm_hidden_states = self.norm1(hidden_states) + attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options) + hidden_states = hidden_states + self.norm2(attn_output) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) + hidden_states = hidden_states + self.ffn_norm2(mlp_output) + return hidden_states + + +class OmniGen2RotaryPosEmbed(nn.Module): + def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_lens: Tuple[int, int, int] = (300, 512, 512), patch_size: int = 2): + super().__init__() + self.theta = theta + self.axes_dim = axes_dim + self.axes_lens = axes_lens + self.patch_size = patch_size + self.rope_embedder = EmbedND(dim=sum(axes_dim), theta=self.theta, axes_dim=axes_dim) + + def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, ref_img_sizes, img_sizes, device): + p = self.patch_size + + seq_lengths = [cap_len + sum(ref_img_len) + img_len for cap_len, ref_img_len, img_len in zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len)] + + max_seq_len = max(seq_lengths) + max_ref_img_len = max([sum(ref_img_len) for ref_img_len in l_effective_ref_img_len]) + max_img_len = max(l_effective_img_len) + + position_ids = torch.zeros(batch_size, max_seq_len, 3, dtype=torch.int32, device=device) + + for i, (cap_seq_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): + position_ids[i, :cap_seq_len] = repeat(torch.arange(cap_seq_len, dtype=torch.int32, device=device), "l -> l 3") + + pe_shift = cap_seq_len + pe_shift_len = cap_seq_len + + if ref_img_sizes[i] is not None: + for ref_img_size, ref_img_len in zip(ref_img_sizes[i], l_effective_ref_img_len[i]): + H, W = ref_img_size + ref_H_tokens, ref_W_tokens = H // p, W // p + + row_ids = repeat(torch.arange(ref_H_tokens, dtype=torch.int32, device=device), "h -> h w", w=ref_W_tokens).flatten() + col_ids = repeat(torch.arange(ref_W_tokens, dtype=torch.int32, device=device), "w -> h w", h=ref_H_tokens).flatten() + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 0] = pe_shift + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 1] = row_ids + position_ids[i, pe_shift_len:pe_shift_len + ref_img_len, 2] = col_ids + + pe_shift += max(ref_H_tokens, ref_W_tokens) + pe_shift_len += ref_img_len + + H, W = img_sizes[i] + H_tokens, W_tokens = H // p, W // p + + row_ids = repeat(torch.arange(H_tokens, dtype=torch.int32, device=device), "h -> h w", w=W_tokens).flatten() + col_ids = repeat(torch.arange(W_tokens, dtype=torch.int32, device=device), "w -> h w", h=H_tokens).flatten() + + position_ids[i, pe_shift_len: seq_len, 0] = pe_shift + position_ids[i, pe_shift_len: seq_len, 1] = row_ids + position_ids[i, pe_shift_len: seq_len, 2] = col_ids + + freqs_cis = self.rope_embedder(position_ids).movedim(1, 2) + + cap_freqs_cis_shape = list(freqs_cis.shape) + cap_freqs_cis_shape[1] = encoder_seq_len + cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + ref_img_freqs_cis_shape = list(freqs_cis.shape) + ref_img_freqs_cis_shape[1] = max_ref_img_len + ref_img_freqs_cis = torch.zeros(*ref_img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + img_freqs_cis_shape = list(freqs_cis.shape) + img_freqs_cis_shape[1] = max_img_len + img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype) + + for i, (cap_seq_len, ref_img_len, img_len, seq_len) in enumerate(zip(l_effective_cap_len, l_effective_ref_img_len, l_effective_img_len, seq_lengths)): + cap_freqs_cis[i, :cap_seq_len] = freqs_cis[i, :cap_seq_len] + ref_img_freqs_cis[i, :sum(ref_img_len)] = freqs_cis[i, cap_seq_len:cap_seq_len + sum(ref_img_len)] + img_freqs_cis[i, :img_len] = freqs_cis[i, cap_seq_len + sum(ref_img_len):cap_seq_len + sum(ref_img_len) + img_len] + + return cap_freqs_cis, ref_img_freqs_cis, img_freqs_cis, freqs_cis, l_effective_cap_len, seq_lengths + + +class OmniGen2Transformer2DModel(nn.Module): + def __init__( + self, + patch_size: int = 2, + in_channels: int = 16, + out_channels: Optional[int] = None, + hidden_size: int = 2304, + num_layers: int = 26, + num_refiner_layers: int = 2, + num_attention_heads: int = 24, + num_kv_heads: int = 8, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + axes_dim_rope: Tuple[int, int, int] = (32, 32, 32), + axes_lens: Tuple[int, int, int] = (300, 512, 512), + text_feat_dim: int = 1024, + timestep_scale: float = 1.0, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + + self.patch_size = patch_size + self.out_channels = out_channels or in_channels + self.hidden_size = hidden_size + self.dtype = dtype + + self.rope_embedder = OmniGen2RotaryPosEmbed( + theta=10000, + axes_dim=axes_dim_rope, + axes_lens=axes_lens, + patch_size=patch_size, + ) + + self.x_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + self.ref_image_patch_embedder = operations.Linear(patch_size * patch_size * in_channels, hidden_size, dtype=dtype, device=device) + + self.time_caption_embed = Lumina2CombinedTimestepCaptionEmbedding( + hidden_size=hidden_size, + text_feat_dim=text_feat_dim, + norm_eps=norm_eps, + timestep_scale=timestep_scale, dtype=dtype, device=device, operations=operations + ) + + self.noise_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.ref_image_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.context_refiner = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=False, dtype=dtype, device=device, operations=operations + ) for _ in range(num_refiner_layers) + ]) + + self.layers = nn.ModuleList([ + OmniGen2TransformerBlock( + hidden_size, num_attention_heads, num_kv_heads, + multiple_of, ffn_dim_multiplier, norm_eps, modulation=True, dtype=dtype, device=device, operations=operations + ) for _ in range(num_layers) + ]) + + self.norm_out = LuminaLayerNormContinuous( + embedding_dim=hidden_size, + conditioning_embedding_dim=min(hidden_size, 1024), + elementwise_affine=False, + eps=1e-6, + out_dim=patch_size * patch_size * self.out_channels, dtype=dtype, device=device, operations=operations + ) + + self.image_index_embedding = nn.Parameter(torch.empty(5, hidden_size, device=device, dtype=dtype)) + + def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states): + batch_size = len(hidden_states) + p = self.patch_size + + img_sizes = [(img.size(1), img.size(2)) for img in hidden_states] + l_effective_img_len = [(H // p) * (W // p) for (H, W) in img_sizes] + + if ref_image_hidden_states is not None: + ref_image_hidden_states = list(map(lambda ref: comfy.ldm.common_dit.pad_to_patch_size(ref, (p, p)), ref_image_hidden_states)) + ref_img_sizes = [[(imgs.size(2), imgs.size(3)) if imgs is not None else None for imgs in ref_image_hidden_states]] * batch_size + l_effective_ref_img_len = [[(ref_img_size[0] // p) * (ref_img_size[1] // p) for ref_img_size in _ref_img_sizes] if _ref_img_sizes is not None else [0] for _ref_img_sizes in ref_img_sizes] + else: + ref_img_sizes = [None for _ in range(batch_size)] + l_effective_ref_img_len = [[0] for _ in range(batch_size)] + + flat_ref_img_hidden_states = None + if ref_image_hidden_states is not None: + imgs = [] + for ref_img in ref_image_hidden_states: + B, C, H, W = ref_img.size() + ref_img = rearrange(ref_img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) + imgs.append(ref_img) + flat_ref_img_hidden_states = torch.cat(imgs, dim=1) + + img = hidden_states + B, C, H, W = img.size() + flat_hidden_states = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) + + return ( + flat_hidden_states, flat_ref_img_hidden_states, + None, None, + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, + ) + + def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}): + batch_size = len(hidden_states) + + hidden_states = self.x_embedder(hidden_states) + if ref_image_hidden_states is not None: + ref_image_hidden_states = self.ref_image_patch_embedder(ref_image_hidden_states) + image_index_embedding = comfy.model_management.cast_to(self.image_index_embedding, dtype=hidden_states.dtype, device=hidden_states.device) + + for i in range(batch_size): + shift = 0 + for j, ref_img_len in enumerate(l_effective_ref_img_len[i]): + ref_image_hidden_states[i, shift:shift + ref_img_len, :] = ref_image_hidden_states[i, shift:shift + ref_img_len, :] + image_index_embedding[j] + shift += ref_img_len + + for layer in self.noise_refiner: + hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options) + + if ref_image_hidden_states is not None: + for layer in self.ref_image_refiner: + ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options) + + hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1) + + return hidden_states + + def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs): + B, C, H, W = x.shape + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size)) + _, _, H_padded, W_padded = hidden_states.shape + timestep = 1.0 - timesteps + text_hidden_states = context + text_attention_mask = attention_mask + ref_image_hidden_states = ref_latents + device = hidden_states.device + + temb, text_hidden_states = self.time_caption_embed(timestep, text_hidden_states, hidden_states[0].dtype) + + ( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, + ) = self.flat_and_pad_to_seq(hidden_states, ref_image_hidden_states) + + ( + context_rotary_emb, ref_img_rotary_emb, noise_rotary_emb, + rotary_emb, encoder_seq_lengths, seq_lengths, + ) = self.rope_embedder( + hidden_states.shape[0], text_hidden_states.shape[1], [num_tokens] * text_hidden_states.shape[0], + l_effective_ref_img_len, l_effective_img_len, + ref_img_sizes, img_sizes, device, + ) + + for layer in self.context_refiner: + text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options) + + img_len = hidden_states.shape[1] + combined_img_hidden_states = self.img_patch_embed_and_refine( + hidden_states, ref_image_hidden_states, + img_mask, ref_img_mask, + noise_rotary_emb, ref_img_rotary_emb, + l_effective_ref_img_len, l_effective_img_len, + temb, + transformer_options=transformer_options, + ) + + hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1) + attention_mask = None + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options) + + hidden_states = self.norm_out(hidden_states, temb) + + p = self.patch_size + output = rearrange(hidden_states[:, -img_len:], 'b (h w) (p1 p2 c) -> b c (h p1) (w p2)', h=H_padded // p, w=W_padded// p, p1=p, p2=p)[:, :, :H, :W] + + return -output diff --git a/comfy/ldm/pixart/pixartms.py b/comfy/ldm/pixart/pixartms.py index 7d4eebdce..d1ac49d84 100644 --- a/comfy/ldm/pixart/pixartms.py +++ b/comfy/ldm/pixart/pixartms.py @@ -1,256 +1,256 @@ -# Based on: -# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] -# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] -import torch -import torch.nn as nn - -from .blocks import ( - t2i_modulate, - CaptionEmbedder, - AttentionKVCompress, - MultiHeadCrossAttention, - T2IFinalLayer, - SizeEmbedder, -) -from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch - - -def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32): - grid_h, grid_w = torch.meshgrid( - torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation, - torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation, - indexing='ij' - ) - emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) - emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) - emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) - return emb - -class PixArtMSBlock(nn.Module): - """ - A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. - """ - def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, - sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): - super().__init__() - self.hidden_size = hidden_size - self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - self.attn = AttentionKVCompress( - hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, - qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs - ) - self.cross_attn = MultiHeadCrossAttention( - hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs - ) - self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) - # to be compatible with lower version pytorch - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.mlp = Mlp( - in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, - dtype=dtype, device=device, operations=operations - ) - self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) - - def forward(self, x, y, t, mask=None, HW=None, **kwargs): - B, N, C = x.shape - - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1) - x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) - x = x + self.cross_attn(x, y, mask) - x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) - - return x - - -### Core PixArt Model ### -class PixArtMS(nn.Module): - """ - Diffusion model with a Transformer backbone. - """ - def __init__( - self, - input_size=32, - patch_size=2, - in_channels=4, - hidden_size=1152, - depth=28, - num_heads=16, - mlp_ratio=4.0, - class_dropout_prob=0.1, - learn_sigma=True, - pred_sigma=True, - drop_path: float = 0., - caption_channels=4096, - pe_interpolation=None, - pe_precision=None, - config=None, - model_max_length=120, - micro_condition=True, - qk_norm=False, - kv_compress_config=None, - dtype=None, - device=None, - operations=None, - **kwargs, - ): - nn.Module.__init__(self) - self.dtype = dtype - self.pred_sigma = pred_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if pred_sigma else in_channels - self.patch_size = patch_size - self.num_heads = num_heads - self.pe_interpolation = pe_interpolation - self.pe_precision = pe_precision - self.hidden_size = hidden_size - self.depth = depth - - approx_gelu = lambda: nn.GELU(approximate="tanh") - self.t_block = nn.Sequential( - nn.SiLU(), - operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) - ) - self.x_embedder = PatchEmbed( - patch_size=patch_size, - in_chans=in_channels, - embed_dim=hidden_size, - bias=True, - dtype=dtype, - device=device, - operations=operations - ) - self.t_embedder = TimestepEmbedder( - hidden_size, dtype=dtype, device=device, operations=operations, - ) - self.y_embedder = CaptionEmbedder( - in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, - act_layer=approx_gelu, token_num=model_max_length, - dtype=dtype, device=device, operations=operations, - ) - - self.micro_conditioning = micro_condition - if self.micro_conditioning: - self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) - self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) - - # For fixed sin-cos embedding: - # num_patches = (input_size // patch_size) * (input_size // patch_size) - # self.base_size = input_size // self.patch_size - # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) - - drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule - if kv_compress_config is None: - kv_compress_config = { - 'sampling': None, - 'scale_factor': 1, - 'kv_compress_layer': [], - } - self.blocks = nn.ModuleList([ - PixArtMSBlock( - hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], - sampling=kv_compress_config['sampling'], - sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, - qk_norm=qk_norm, - dtype=dtype, - device=device, - operations=operations, - ) - for i in range(depth) - ]) - self.final_layer = T2IFinalLayer( - hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations - ) - - def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs): - """ - Original forward pass of PixArt. - x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) - t: (N,) tensor of diffusion timesteps - y: (N, 1, 120, C) conditioning - ar: (N, 1): aspect ratio - cs: (N ,2) size conditioning for height/width - """ - B, C, H, W = x.shape - c_res = (H + W) // 2 - pe_interpolation = self.pe_interpolation - if pe_interpolation is None or self.pe_precision is not None: - # calculate pe_interpolation on-the-fly - pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0) - - pos_embed = get_2d_sincos_pos_embed_torch( - self.hidden_size, - h=(H // self.patch_size), - w=(W // self.patch_size), - pe_interpolation=pe_interpolation, - base_size=((round(c_res / 64) * 64) // self.patch_size), - device=x.device, - dtype=x.dtype, - ).unsqueeze(0) - - x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 - t = self.t_embedder(timestep, x.dtype) # (N, D) - - if self.micro_conditioning and (c_size is not None and c_ar is not None): - bs = x.shape[0] - c_size = self.csize_embedder(c_size, bs) # (N, D) - c_ar = self.ar_embedder(c_ar, bs) # (N, D) - t = t + torch.cat([c_size, c_ar], dim=1) - - t0 = self.t_block(t) - y = self.y_embedder(y, self.training) # (N, D) - - if mask is not None: - if mask.shape[0] != y.shape[0]: - mask = mask.repeat(y.shape[0] // mask.shape[0], 1) - mask = mask.squeeze(1).squeeze(1) - y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) - y_lens = mask.sum(dim=1).tolist() - else: - y_lens = None - y = y.squeeze(1).view(1, -1, x.shape[-1]) - for block in self.blocks: - x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D) - - x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) - x = self.unpatchify(x, H, W) # (N, out_channels, H, W) - - return x - - def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs): - B, C, H, W = x.shape - - # Fallback for missing microconds - if self.micro_conditioning: - if c_size is None: - c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) - - if c_ar is None: - c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) - - ## Still accepts the input w/o that dim but returns garbage - if len(context.shape) == 3: - context = context.unsqueeze(1) - - ## run original forward pass - out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar) - - ## only return EPS - if self.pred_sigma: - return out[:, :self.in_channels] - return out - - def unpatchify(self, x, h, w): - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - c = self.out_channels - p = self.x_embedder.patch_size[0] - h = h // self.patch_size - w = w // self.patch_size - assert h * w == x.shape[1] - - x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) - x = torch.einsum('nhwpqc->nchpwq', x) - imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) - return imgs +# Based on: +# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license] +# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license] +import torch +import torch.nn as nn + +from .blocks import ( + t2i_modulate, + CaptionEmbedder, + AttentionKVCompress, + MultiHeadCrossAttention, + T2IFinalLayer, + SizeEmbedder, +) +from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch + + +def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32): + grid_h, grid_w = torch.meshgrid( + torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation, + torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation, + indexing='ij' + ) + emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype) + emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype) + emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D) + return emb + +class PixArtMSBlock(nn.Module): + """ + A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None, + sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs): + super().__init__() + self.hidden_size = hidden_size + self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + self.attn = AttentionKVCompress( + hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio, + qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs + ) + self.cross_attn = MultiHeadCrossAttention( + hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs + ) + self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device) + # to be compatible with lower version pytorch + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, + dtype=dtype, device=device, operations=operations + ) + self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5) + + def forward(self, x, y, t, mask=None, HW=None, **kwargs): + B, N, C = x.shape + + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1) + x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW)) + x = x + self.cross_attn(x, y, mask) + x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) + + return x + + +### Core PixArt Model ### +class PixArtMS(nn.Module): + """ + Diffusion model with a Transformer backbone. + """ + def __init__( + self, + input_size=32, + patch_size=2, + in_channels=4, + hidden_size=1152, + depth=28, + num_heads=16, + mlp_ratio=4.0, + class_dropout_prob=0.1, + learn_sigma=True, + pred_sigma=True, + drop_path: float = 0., + caption_channels=4096, + pe_interpolation=None, + pe_precision=None, + config=None, + model_max_length=120, + micro_condition=True, + qk_norm=False, + kv_compress_config=None, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + nn.Module.__init__(self) + self.dtype = dtype + self.pred_sigma = pred_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if pred_sigma else in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.pe_interpolation = pe_interpolation + self.pe_precision = pe_precision + self.hidden_size = hidden_size + self.depth = depth + + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.t_block = nn.Sequential( + nn.SiLU(), + operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device) + ) + self.x_embedder = PatchEmbed( + patch_size=patch_size, + in_chans=in_channels, + embed_dim=hidden_size, + bias=True, + dtype=dtype, + device=device, + operations=operations + ) + self.t_embedder = TimestepEmbedder( + hidden_size, dtype=dtype, device=device, operations=operations, + ) + self.y_embedder = CaptionEmbedder( + in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, + act_layer=approx_gelu, token_num=model_max_length, + dtype=dtype, device=device, operations=operations, + ) + + self.micro_conditioning = micro_condition + if self.micro_conditioning: + self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations) + + # For fixed sin-cos embedding: + # num_patches = (input_size // patch_size) * (input_size // patch_size) + # self.base_size = input_size // self.patch_size + # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) + + drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule + if kv_compress_config is None: + kv_compress_config = { + 'sampling': None, + 'scale_factor': 1, + 'kv_compress_layer': [], + } + self.blocks = nn.ModuleList([ + PixArtMSBlock( + hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], + sampling=kv_compress_config['sampling'], + sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1, + qk_norm=qk_norm, + dtype=dtype, + device=device, + operations=operations, + ) + for i in range(depth) + ]) + self.final_layer = T2IFinalLayer( + hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations + ) + + def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs): + """ + Original forward pass of PixArt. + x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) + t: (N,) tensor of diffusion timesteps + y: (N, 1, 120, C) conditioning + ar: (N, 1): aspect ratio + cs: (N ,2) size conditioning for height/width + """ + B, C, H, W = x.shape + c_res = (H + W) // 2 + pe_interpolation = self.pe_interpolation + if pe_interpolation is None or self.pe_precision is not None: + # calculate pe_interpolation on-the-fly + pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0) + + pos_embed = get_2d_sincos_pos_embed_torch( + self.hidden_size, + h=(H // self.patch_size), + w=(W // self.patch_size), + pe_interpolation=pe_interpolation, + base_size=((round(c_res / 64) * 64) // self.patch_size), + device=x.device, + dtype=x.dtype, + ).unsqueeze(0) + + x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 + t = self.t_embedder(timestep, x.dtype) # (N, D) + + if self.micro_conditioning and (c_size is not None and c_ar is not None): + bs = x.shape[0] + c_size = self.csize_embedder(c_size, bs) # (N, D) + c_ar = self.ar_embedder(c_ar, bs) # (N, D) + t = t + torch.cat([c_size, c_ar], dim=1) + + t0 = self.t_block(t) + y = self.y_embedder(y, self.training) # (N, D) + + if mask is not None: + if mask.shape[0] != y.shape[0]: + mask = mask.repeat(y.shape[0] // mask.shape[0], 1) + mask = mask.squeeze(1).squeeze(1) + y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) + y_lens = mask.sum(dim=1).tolist() + else: + y_lens = None + y = y.squeeze(1).view(1, -1, x.shape[-1]) + for block in self.blocks: + x = block(x, y, t0, y_lens, (H, W), **kwargs) # (N, T, D) + + x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) + x = self.unpatchify(x, H, W) # (N, out_channels, H, W) + + return x + + def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs): + B, C, H, W = x.shape + + # Fallback for missing microconds + if self.micro_conditioning: + if c_size is None: + c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1) + + if c_ar is None: + c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1) + + ## Still accepts the input w/o that dim but returns garbage + if len(context.shape) == 3: + context = context.unsqueeze(1) + + ## run original forward pass + out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar) + + ## only return EPS + if self.pred_sigma: + return out[:, :self.in_channels] + return out + + def unpatchify(self, x, h, w): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + p = self.x_embedder.patch_size[0] + h = h // self.patch_size + w = w // self.patch_size + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p)) + return imgs diff --git a/comfy/ldm/qwen_image/controlnet.py b/comfy/ldm/qwen_image/controlnet.py new file mode 100644 index 000000000..92ac3cf0a --- /dev/null +++ b/comfy/ldm/qwen_image/controlnet.py @@ -0,0 +1,77 @@ +import torch +import math + +from .model import QwenImageTransformer2DModel + + +class QwenImageControlNetModel(QwenImageTransformer2DModel): + def __init__( + self, + extra_condition_channels=0, + dtype=None, + device=None, + operations=None, + **kwargs + ): + super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs) + self.main_model_double = 60 + + # controlnet_blocks + self.controlnet_blocks = torch.nn.ModuleList([]) + for _ in range(len(self.transformer_blocks)): + self.controlnet_blocks.append(operations.Linear(self.inner_dim, self.inner_dim, device=device, dtype=dtype)) + self.controlnet_x_embedder = operations.Linear(self.in_channels + extra_condition_channels, self.inner_dim, device=device, dtype=dtype) + + def forward( + self, + x, + timesteps, + context, + attention_mask=None, + guidance: torch.Tensor = None, + ref_latents=None, + hint=None, + transformer_options={}, + **kwargs + ): + timestep = timesteps + encoder_hidden_states = context + encoder_hidden_states_mask = attention_mask + + hidden_states, img_ids, orig_shape = self.process_img(x) + hint, _, _ = self.process_img(hint) + + txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) + txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) + ids = torch.cat((txt_ids, img_ids), dim=1) + image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype) + del ids, txt_ids, img_ids + + hidden_states = self.img_in(hidden_states) + self.controlnet_x_embedder(hint) + encoder_hidden_states = self.txt_norm(encoder_hidden_states) + encoder_hidden_states = self.txt_in(encoder_hidden_states) + + if guidance is not None: + guidance = guidance * 1000 + + temb = ( + self.time_text_embed(timestep, hidden_states) + if guidance is None + else self.time_text_embed(timestep, guidance, hidden_states) + ) + + repeat = math.ceil(self.main_model_double / len(self.controlnet_blocks)) + + controlnet_block_samples = () + for i, block in enumerate(self.transformer_blocks): + encoder_hidden_states, hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_mask=encoder_hidden_states_mask, + temb=temb, + image_rotary_emb=image_rotary_emb, + ) + + controlnet_block_samples = controlnet_block_samples + (self.controlnet_blocks[i](hidden_states),) * repeat + + return {"input": controlnet_block_samples[:self.main_model_double]} diff --git a/comfy/ldm/qwen_image/model.py b/comfy/ldm/qwen_image/model.py new file mode 100644 index 000000000..b9f60c2b7 --- /dev/null +++ b/comfy/ldm/qwen_image/model.py @@ -0,0 +1,473 @@ +# https://github.com/QwenLM/Qwen-Image (Apache 2.0) +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Optional, Tuple +from einops import repeat + +from comfy.ldm.lightricks.model import TimestepEmbedding, Timesteps +from comfy.ldm.modules.attention import optimized_attention_masked +from comfy.ldm.flux.layers import EmbedND +import comfy.ldm.common_dit +import comfy.patcher_extension + +class GELU(nn.Module): + def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True, dtype=None, device=None, operations=None): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out, bias=bias, dtype=dtype, device=device) + self.approximate = approximate + + def forward(self, hidden_states): + hidden_states = self.proj(hidden_states) + hidden_states = F.gelu(hidden_states, approximate=self.approximate) + return hidden_states + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + inner_dim=None, + bias: bool = True, + dtype=None, device=None, operations=None + ): + super().__init__() + if inner_dim is None: + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + self.net = nn.ModuleList([]) + self.net.append(GELU(dim, inner_dim, approximate="tanh", bias=bias, dtype=dtype, device=device, operations=operations)) + self.net.append(nn.Dropout(dropout)) + self.net.append(operations.Linear(inner_dim, dim_out, bias=bias, dtype=dtype, device=device)) + + def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor: + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +def apply_rotary_emb(x, freqs_cis): + if x.shape[1] == 0: + return x + + t_ = x.reshape(*x.shape[:-1], -1, 1, 2) + t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1] + return t_out.reshape(*x.shape) + + +class QwenTimestepProjEmbeddings(nn.Module): + def __init__(self, embedding_dim, pooled_projection_dim, dtype=None, device=None, operations=None): + super().__init__() + self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000) + self.timestep_embedder = TimestepEmbedding( + in_channels=256, + time_embed_dim=embedding_dim, + dtype=dtype, + device=device, + operations=operations + ) + + def forward(self, timestep, hidden_states): + timesteps_proj = self.time_proj(timestep) + timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype)) + return timesteps_emb + + +class Attention(nn.Module): + def __init__( + self, + query_dim: int, + dim_head: int = 64, + heads: int = 8, + dropout: float = 0.0, + bias: bool = False, + eps: float = 1e-5, + out_bias: bool = True, + out_dim: int = None, + out_context_dim: int = None, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.inner_kv_dim = self.inner_dim + self.heads = heads + self.dim_head = dim_head + self.out_dim = out_dim if out_dim is not None else query_dim + self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim + self.dropout = dropout + + # Q/K normalization + self.norm_q = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) + self.norm_k = operations.RMSNorm(dim_head, eps=eps, elementwise_affine=True, dtype=dtype, device=device) + self.norm_added_q = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + self.norm_added_k = operations.RMSNorm(dim_head, eps=eps, dtype=dtype, device=device) + + # Image stream projections + self.to_q = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) + self.to_k = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + self.to_v = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + + # Text stream projections + self.add_q_proj = operations.Linear(query_dim, self.inner_dim, bias=bias, dtype=dtype, device=device) + self.add_k_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + self.add_v_proj = operations.Linear(query_dim, self.inner_kv_dim, bias=bias, dtype=dtype, device=device) + + # Output projections + self.to_out = nn.ModuleList([ + operations.Linear(self.inner_dim, self.out_dim, bias=out_bias, dtype=dtype, device=device), + nn.Dropout(dropout) + ]) + self.to_add_out = operations.Linear(self.inner_dim, self.out_context_dim, bias=out_bias, dtype=dtype, device=device) + + def forward( + self, + hidden_states: torch.FloatTensor, # Image stream + encoder_hidden_states: torch.FloatTensor = None, # Text stream + encoder_hidden_states_mask: torch.FloatTensor = None, + attention_mask: Optional[torch.FloatTensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + transformer_options={}, + ) -> Tuple[torch.Tensor, torch.Tensor]: + seq_txt = encoder_hidden_states.shape[1] + + img_query = self.to_q(hidden_states).unflatten(-1, (self.heads, -1)) + img_key = self.to_k(hidden_states).unflatten(-1, (self.heads, -1)) + img_value = self.to_v(hidden_states).unflatten(-1, (self.heads, -1)) + + txt_query = self.add_q_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) + txt_key = self.add_k_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) + txt_value = self.add_v_proj(encoder_hidden_states).unflatten(-1, (self.heads, -1)) + + img_query = self.norm_q(img_query) + img_key = self.norm_k(img_key) + txt_query = self.norm_added_q(txt_query) + txt_key = self.norm_added_k(txt_key) + + joint_query = torch.cat([txt_query, img_query], dim=1) + joint_key = torch.cat([txt_key, img_key], dim=1) + joint_value = torch.cat([txt_value, img_value], dim=1) + + joint_query = apply_rotary_emb(joint_query, image_rotary_emb) + joint_key = apply_rotary_emb(joint_key, image_rotary_emb) + + joint_query = joint_query.flatten(start_dim=2) + joint_key = joint_key.flatten(start_dim=2) + joint_value = joint_value.flatten(start_dim=2) + + joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options) + + txt_attn_output = joint_hidden_states[:, :seq_txt, :] + img_attn_output = joint_hidden_states[:, seq_txt:, :] + + img_attn_output = self.to_out[0](img_attn_output) + img_attn_output = self.to_out[1](img_attn_output) + txt_attn_output = self.to_add_out(txt_attn_output) + + return img_attn_output, txt_attn_output + + +class QwenImageTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + eps: float = 1e-6, + dtype=None, + device=None, + operations=None + ): + super().__init__() + self.dim = dim + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + + self.img_mod = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), + ) + self.img_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) + self.img_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) + self.img_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) + + self.txt_mod = nn.Sequential( + nn.SiLU(), + operations.Linear(dim, 6 * dim, bias=True, dtype=dtype, device=device), + ) + self.txt_norm1 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) + self.txt_norm2 = operations.LayerNorm(dim, elementwise_affine=False, eps=eps, dtype=dtype, device=device) + self.txt_mlp = FeedForward(dim=dim, dim_out=dim, dtype=dtype, device=device, operations=operations) + + self.attn = Attention( + query_dim=dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + out_dim=dim, + bias=True, + eps=eps, + dtype=dtype, + device=device, + operations=operations, + ) + + def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + shift, scale, gate = torch.chunk(mod_params, 3, dim=-1) + return torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)), gate.unsqueeze(1) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor, + encoder_hidden_states_mask: torch.Tensor, + temb: torch.Tensor, + image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + transformer_options={}, + ) -> Tuple[torch.Tensor, torch.Tensor]: + img_mod_params = self.img_mod(temb) + txt_mod_params = self.txt_mod(temb) + img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1) + txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1) + + img_normed = self.img_norm1(hidden_states) + img_modulated, img_gate1 = self._modulate(img_normed, img_mod1) + txt_normed = self.txt_norm1(encoder_hidden_states) + txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1) + + img_attn_output, txt_attn_output = self.attn( + hidden_states=img_modulated, + encoder_hidden_states=txt_modulated, + encoder_hidden_states_mask=encoder_hidden_states_mask, + image_rotary_emb=image_rotary_emb, + transformer_options=transformer_options, + ) + + hidden_states = hidden_states + img_gate1 * img_attn_output + encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output + + img_normed2 = self.img_norm2(hidden_states) + img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2) + hidden_states = torch.addcmul(hidden_states, img_gate2, self.img_mlp(img_modulated2)) + + txt_normed2 = self.txt_norm2(encoder_hidden_states) + txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2) + encoder_hidden_states = torch.addcmul(encoder_hidden_states, txt_gate2, self.txt_mlp(txt_modulated2)) + + return encoder_hidden_states, hidden_states + + +class LastLayer(nn.Module): + def __init__( + self, + embedding_dim: int, + conditioning_embedding_dim: int, + elementwise_affine=False, + eps=1e-6, + bias=True, + dtype=None, device=None, operations=None + ): + super().__init__() + self.silu = nn.SiLU() + self.linear = operations.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias, dtype=dtype, device=device) + self.norm = operations.LayerNorm(embedding_dim, eps, elementwise_affine=False, bias=bias, dtype=dtype, device=device) + + def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor: + emb = self.linear(self.silu(conditioning_embedding)) + scale, shift = torch.chunk(emb, 2, dim=1) + x = torch.addcmul(shift[:, None, :], self.norm(x), (1 + scale)[:, None, :]) + return x + + +class QwenImageTransformer2DModel(nn.Module): + def __init__( + self, + patch_size: int = 2, + in_channels: int = 64, + out_channels: Optional[int] = 16, + num_layers: int = 60, + attention_head_dim: int = 128, + num_attention_heads: int = 24, + joint_attention_dim: int = 3584, + pooled_projection_dim: int = 768, + guidance_embeds: bool = False, + axes_dims_rope: Tuple[int, int, int] = (16, 56, 56), + image_model=None, + final_layer=True, + dtype=None, + device=None, + operations=None, + ): + super().__init__() + self.dtype = dtype + self.patch_size = patch_size + self.in_channels = in_channels + self.out_channels = out_channels or in_channels + self.inner_dim = num_attention_heads * attention_head_dim + + self.pe_embedder = EmbedND(dim=attention_head_dim, theta=10000, axes_dim=list(axes_dims_rope)) + + self.time_text_embed = QwenTimestepProjEmbeddings( + embedding_dim=self.inner_dim, + pooled_projection_dim=pooled_projection_dim, + dtype=dtype, + device=device, + operations=operations + ) + + self.txt_norm = operations.RMSNorm(joint_attention_dim, eps=1e-6, dtype=dtype, device=device) + self.img_in = operations.Linear(in_channels, self.inner_dim, dtype=dtype, device=device) + self.txt_in = operations.Linear(joint_attention_dim, self.inner_dim, dtype=dtype, device=device) + + self.transformer_blocks = nn.ModuleList([ + QwenImageTransformerBlock( + dim=self.inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dtype=dtype, + device=device, + operations=operations + ) + for _ in range(num_layers) + ]) + + if final_layer: + self.norm_out = LastLayer(self.inner_dim, self.inner_dim, dtype=dtype, device=device, operations=operations) + self.proj_out = operations.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True, dtype=dtype, device=device) + + def process_img(self, x, index=0, h_offset=0, w_offset=0): + bs, c, t, h, w = x.shape + patch_size = self.patch_size + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (1, self.patch_size, self.patch_size)) + orig_shape = hidden_states.shape + hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2) + hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5) + hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4) + h_len = ((h + (patch_size // 2)) // patch_size) + w_len = ((w + (patch_size // 2)) // patch_size) + + h_offset = ((h_offset + (patch_size // 2)) // patch_size) + w_offset = ((w_offset + (patch_size // 2)) // patch_size) + + img_ids = torch.zeros((h_len, w_len, 3), device=x.device) + img_ids[:, :, 0] = img_ids[:, :, 1] + index + img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1) - (h_len // 2) + img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0) - (w_len // 2) + return hidden_states, repeat(img_ids, "h w c -> b (h w) c", b=bs), orig_shape + + def forward(self, x, timestep, context, attention_mask=None, guidance=None, ref_latents=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, attention_mask, guidance, ref_latents, transformer_options, **kwargs) + + def _forward( + self, + x, + timesteps, + context, + attention_mask=None, + guidance: torch.Tensor = None, + ref_latents=None, + transformer_options={}, + control=None, + **kwargs + ): + timestep = timesteps + encoder_hidden_states = context + encoder_hidden_states_mask = attention_mask + + hidden_states, img_ids, orig_shape = self.process_img(x) + num_embeds = hidden_states.shape[1] + + if ref_latents is not None: + h = 0 + w = 0 + index = 0 + index_ref_method = kwargs.get("ref_latents_method", "index") == "index" + for ref in ref_latents: + if index_ref_method: + index += 1 + h_offset = 0 + w_offset = 0 + else: + index = 1 + h_offset = 0 + w_offset = 0 + if ref.shape[-2] + h > ref.shape[-1] + w: + w_offset = w + else: + h_offset = h + h = max(h, ref.shape[-2] + h_offset) + w = max(w, ref.shape[-1] + w_offset) + + kontext, kontext_ids, _ = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset) + hidden_states = torch.cat([hidden_states, kontext], dim=1) + img_ids = torch.cat([img_ids, kontext_ids], dim=1) + + txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size) // 2, ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size) // 2)) + txt_ids = torch.arange(txt_start, txt_start + context.shape[1], device=x.device).reshape(1, -1, 1).repeat(x.shape[0], 1, 3) + ids = torch.cat((txt_ids, img_ids), dim=1) + image_rotary_emb = self.pe_embedder(ids).squeeze(1).unsqueeze(2).to(x.dtype) + del ids, txt_ids, img_ids + + hidden_states = self.img_in(hidden_states) + encoder_hidden_states = self.txt_norm(encoder_hidden_states) + encoder_hidden_states = self.txt_in(encoder_hidden_states) + + if guidance is not None: + guidance = guidance * 1000 + + temb = ( + self.time_text_embed(timestep, hidden_states) + if guidance is None + else self.time_text_embed(timestep, guidance, hidden_states) + ) + + patches_replace = transformer_options.get("patches_replace", {}) + patches = transformer_options.get("patches", {}) + blocks_replace = patches_replace.get("dit", {}) + + for i, block in enumerate(self.transformer_blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap}) + hidden_states = out["img"] + encoder_hidden_states = out["txt"] + else: + encoder_hidden_states, hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states_mask=encoder_hidden_states_mask, + temb=temb, + image_rotary_emb=image_rotary_emb, + transformer_options=transformer_options, + ) + + if "double_block" in patches: + for p in patches["double_block"]: + out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options}) + hidden_states = out["img"] + encoder_hidden_states = out["txt"] + + if control is not None: # Controlnet + control_i = control.get("input") + if i < len(control_i): + add = control_i[i] + if add is not None: + hidden_states[:, :add.shape[1]] += add + + hidden_states = self.norm_out(hidden_states, temb) + hidden_states = self.proj_out(hidden_states) + + hidden_states = hidden_states[:, :num_embeds].view(orig_shape[0], orig_shape[-2] // 2, orig_shape[-1] // 2, orig_shape[1], 2, 2) + hidden_states = hidden_states.permute(0, 3, 1, 4, 2, 5) + return hidden_states.reshape(orig_shape)[:, :, :, :x.shape[-2], :x.shape[-1]] diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py new file mode 100644 index 000000000..90c347d3d --- /dev/null +++ b/comfy/ldm/wan/model.py @@ -0,0 +1,1580 @@ +# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. +import math + +import torch +import torch.nn as nn +from einops import rearrange + +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.flux.layers import EmbedND +from comfy.ldm.flux.math import apply_rope1 +import comfy.ldm.common_dit +import comfy.model_management +import comfy.patcher_extension + + +def sinusoidal_embedding_1d(dim, position): + # preprocess + assert dim % 2 == 0 + half = dim // 2 + position = position.type(torch.float32) + + # calculation + sinusoid = torch.outer( + position, torch.pow(10000, -torch.arange(half).to(position).div(half))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x + + +class WanSelfAttention(nn.Module): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6, + kv_dim=None, + operation_settings={}): + assert dim % num_heads == 0 + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.eps = eps + if kv_dim is None: + kv_dim = dim + + # layers + self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.k = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.v = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + + def forward(self, x, freqs, transformer_options={}): + r""" + Args: + x(Tensor): Shape [B, L, num_heads, C / num_heads] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim + + def qkv_fn_q(x): + q = self.norm_q(self.q(x)).view(b, s, n, d) + return apply_rope1(q, freqs) + + def qkv_fn_k(x): + k = self.norm_k(self.k(x)).view(b, s, n, d) + return apply_rope1(k, freqs) + + #These two are VRAM hogs, so we want to do all of q computation and + #have pytorch garbage collect the intermediates on the sub function + #return before we touch k + q = qkv_fn_q(x) + k = qkv_fn_k(x) + + x = optimized_attention( + q.view(b, s, n * d), + k.view(b, s, n * d), + self.v(x).view(b, s, n * d), + heads=self.num_heads, + transformer_options=transformer_options, + ) + + x = self.o(x) + return x + + +class WanT2VCrossAttention(WanSelfAttention): + + def forward(self, x, context, transformer_options={}, **kwargs): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + # compute query, key, value + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(context)) + v = self.v(context) + + # compute attention + x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) + + x = self.o(x) + return x + + +class WanI2VCrossAttention(WanSelfAttention): + + def __init__(self, + dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + eps=1e-6, operation_settings={}): + super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings) + + self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + # self.alpha = nn.Parameter(torch.zeros((1, ))) + self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity() + + def forward(self, x, context, context_img_len, transformer_options={}): + r""" + Args: + x(Tensor): Shape [B, L1, C] + context(Tensor): Shape [B, L2, C] + """ + context_img = context[:, :context_img_len] + context = context[:, context_img_len:] + + # compute query, key, value + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(context)) + v = self.v(context) + k_img = self.norm_k_img(self.k_img(context_img)) + v_img = self.v_img(context_img) + img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options) + # compute attention + x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options) + + # output + x = x + img_x + x = self.o(x) + return x + + +WAN_CROSSATTENTION_CLASSES = { + 't2v_cross_attn': WanT2VCrossAttention, + 'i2v_cross_attn': WanI2VCrossAttention, +} + + +def repeat_e(e, x): + repeats = 1 + if e.size(1) > 1: + repeats = x.size(1) // e.size(1) + if repeats == 1: + return e + if repeats * e.size(1) == x.size(1): + return torch.repeat_interleave(e, repeats, dim=1) + else: + return torch.repeat_interleave(e, repeats + 1, dim=1)[:, :x.size(1)] + + +class WanAttentionBlock(nn.Module): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, operation_settings={}): + super().__init__() + self.dim = dim + self.ffn_dim = ffn_dim + self.num_heads = num_heads + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # layers + self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, + eps, operation_settings=operation_settings) + self.norm3 = operation_settings.get("operations").LayerNorm( + dim, eps, + elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity() + self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, + num_heads, + (-1, -1), + qk_norm, + eps, operation_settings=operation_settings) + self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.ffn = nn.Sequential( + operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), + operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + # modulation + self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + def forward( + self, + x, + e, + freqs, + context, + context_img_len=257, + transformer_options={}, + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, 6, C] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + # assert e.dtype == torch.float32 + + if e.ndim < 4: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) + else: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2) + # assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), + freqs, transformer_options=transformer_options) + + x = torch.addcmul(x, y, repeat_e(e[2], x)) + del y + + # cross-attention & ffn + x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options) + y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) + x = torch.addcmul(x, y, repeat_e(e[5], x)) + return x + + +class VaceWanAttentionBlock(WanAttentionBlock): + def __init__( + self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, + block_id=0, + operation_settings={} + ): + super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) + self.block_id = block_id + if block_id == 0: + self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, c, x, **kwargs): + if self.block_id == 0: + c = self.before_proj(c) + x + c = super().forward(c, **kwargs) + c_skip = self.after_proj(c) + return c_skip, c + + +class WanCamAdapter(nn.Module): + def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}): + super(WanCamAdapter, self).__init__() + + # Pixel Unshuffle: reduce spatial dimensions by a factor of 8 + self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8) + + # Convolution: reduce spatial dimensions by a factor + # of 2 (without overlap) + self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + # Residual blocks for feature extraction + self.residual_blocks = nn.Sequential( + *[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)] + ) + + def forward(self, x): + # Reshape to merge the frame dimension into batch + bs, c, f, h, w = x.size() + x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w) + + # Pixel Unshuffle operation + x_unshuffled = self.pixel_unshuffle(x) + + # Convolution operation + x_conv = self.conv(x_unshuffled) + + # Feature extraction with residual blocks + out = self.residual_blocks(x_conv) + + # Reshape to restore original bf dimension + out = out.view(bs, f, out.size(1), out.size(2), out.size(3)) + + # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames + out = out.permute(0, 2, 1, 3, 4) + + return out + + +class WanCamResidualBlock(nn.Module): + def __init__(self, dim, operation_settings={}): + super(WanCamResidualBlock, self).__init__() + self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.relu = nn.ReLU(inplace=True) + self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, x): + residual = x + out = self.relu(self.conv1(x)) + out = self.conv2(out) + out += residual + return out + + +class Head(nn.Module): + + def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}): + super().__init__() + self.dim = dim + self.out_dim = out_dim + self.patch_size = patch_size + self.eps = eps + + # layers + out_dim = math.prod(patch_size) * out_dim + self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + # modulation + self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + def forward(self, x, e): + r""" + Args: + x(Tensor): Shape [B, L1, C] + e(Tensor): Shape [B, C] + """ + # assert e.dtype == torch.float32 + if e.ndim < 3: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1) + else: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e.unsqueeze(2)).unbind(2) + + x = (self.head(torch.addcmul(repeat_e(e[0], x), self.norm(x), 1 + repeat_e(e[1], x)))) + return x + + +class MLPProj(torch.nn.Module): + + def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}): + super().__init__() + + self.proj = torch.nn.Sequential( + operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), + operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + if flf_pos_embed_token_number is not None: + self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + else: + self.emb_pos = None + + def forward(self, image_embeds): + if self.emb_pos is not None: + image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device) + + clip_extra_context_tokens = self.proj(image_embeds) + return clip_extra_context_tokens + + +class WanModel(torch.nn.Module): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='t2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + in_dim_ref_conv=None, + wan_attn_block_class=WanAttentionBlock, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + r""" + Initialize the diffusion model backbone. + + Args: + model_type (`str`, *optional*, defaults to 't2v'): + Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) + patch_size (`tuple`, *optional*, defaults to (1, 2, 2)): + 3D patch dimensions for video embedding (t_patch, h_patch, w_patch) + text_len (`int`, *optional*, defaults to 512): + Fixed length for text embeddings + in_dim (`int`, *optional*, defaults to 16): + Input video channels (C_in) + dim (`int`, *optional*, defaults to 2048): + Hidden dimension of the transformer + ffn_dim (`int`, *optional*, defaults to 8192): + Intermediate dimension in feed-forward network + freq_dim (`int`, *optional*, defaults to 256): + Dimension for sinusoidal time embeddings + text_dim (`int`, *optional*, defaults to 4096): + Input dimension for text embeddings + out_dim (`int`, *optional*, defaults to 16): + Output video channels (C_out) + num_heads (`int`, *optional*, defaults to 16): + Number of attention heads + num_layers (`int`, *optional*, defaults to 32): + Number of transformer blocks + window_size (`tuple`, *optional*, defaults to (-1, -1)): + Window size for local attention (-1 indicates global attention) + qk_norm (`bool`, *optional*, defaults to True): + Enable query/key normalization + cross_attn_norm (`bool`, *optional*, defaults to False): + Enable cross-attention normalization + eps (`float`, *optional*, defaults to 1e-6): + Epsilon value for normalization layers + """ + + super().__init__() + self.dtype = dtype + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + assert model_type in ['t2v', 'i2v'] + self.model_type = model_type + + self.patch_size = patch_size + self.text_len = text_len + self.in_dim = in_dim + self.dim = dim + self.ffn_dim = ffn_dim + self.freq_dim = freq_dim + self.text_dim = text_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.num_layers = num_layers + self.window_size = window_size + self.qk_norm = qk_norm + self.cross_attn_norm = cross_attn_norm + self.eps = eps + + # embeddings + self.patch_embedding = operations.Conv3d( + in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32) + self.text_embedding = nn.Sequential( + operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'), + operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + self.time_embedding = nn.Sequential( + operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))) + + # blocks + cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn' + self.blocks = nn.ModuleList([ + wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads, + window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings) + for _ in range(num_layers) + ]) + + # head + self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings) + + d = dim // num_heads + self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]) + + if model_type == 'i2v': + self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings) + else: + self.img_emb = None + + if in_dim_ref_conv is not None: + self.ref_conv = operations.Conv2d(in_dim_ref_conv, dim, kernel_size=patch_size[1:], stride=patch_size[1:], device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + else: + self.ref_conv = None + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + r""" + Forward pass through the diffusion model + + Args: + x (Tensor): + List of input video tensors with shape [B, C_in, F, H, W] + t (Tensor): + Diffusion timesteps tensor of shape [B] + context (List[Tensor]): + List of text embeddings each with shape [B, L, C] + seq_len (`int`): + Maximum sequence length for positional encoding + clip_fea (Tensor, *optional*): + CLIP image features for image-to-video mode + y (List[Tensor], *optional*): + Conditional video inputs for image-to-video mode, same shape as x + + Returns: + List[Tensor]: + List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8] + """ + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) + e = e.reshape(t.shape[0], -1, e.shape[-1]) + e0 = self.time_projection(e).unflatten(2, (6, self.dim)) + + full_ref = None + if self.ref_conv is not None: + full_ref = kwargs.get("reference_latent", None) + if full_ref is not None: + full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) + x = torch.concat((full_ref, x), dim=1) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + + # head + x = self.head(x, e) + + if full_ref is not None: + x = x[:, full_ref.shape[1]:] + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + + def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None): + patch_size = self.patch_size + t_len = ((t + (patch_size[0] // 2)) // patch_size[0]) + h_len = ((h + (patch_size[1] // 2)) // patch_size[1]) + w_len = ((w + (patch_size[2] // 2)) // patch_size[2]) + + if steps_t is None: + steps_t = t_len + if steps_h is None: + steps_h = h_len + if steps_w is None: + steps_w = w_len + + img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype) + img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1) + img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1) + img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1) + img_ids = img_ids.reshape(1, -1, img_ids.shape[-1]) + + freqs = self.rope_embedder(img_ids).movedim(1, 2) + return freqs + + def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, clip_fea, time_dim_concat, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **kwargs): + bs, c, t, h, w = x.shape + x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size) + + t_len = t + if time_dim_concat is not None: + time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size) + x = torch.cat([x, time_dim_concat], dim=2) + t_len = x.shape[2] + + if self.ref_conv is not None and "reference_latent" in kwargs: + t_len += 1 + + freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype) + return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, **kwargs)[:, :, :t, :h, :w] + + def unpatchify(self, x, grid_sizes): + r""" + Reconstruct video tensors from patch embeddings. + + Args: + x (List[Tensor]): + List of patchified features, each with shape [L, C_out * prod(patch_size)] + grid_sizes (Tensor): + Original spatial-temporal grid dimensions before patching, + shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches) + + Returns: + List[Tensor]: + Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8] + """ + + c = self.out_dim + u = x + b = u.shape[0] + u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c) + u = torch.einsum('bfhwpqrc->bcfphqwr', u) + u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)]) + return u + + +class VaceWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='vace', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + vace_layers=None, + vace_in_dim=None, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + # Vace + if vace_layers is not None: + self.vace_layers = vace_layers + self.vace_in_dim = vace_in_dim + # vace blocks + self.vace_blocks = nn.ModuleList([ + VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm, self.cross_attn_norm, self.eps, block_id=i, operation_settings=operation_settings) + for i in range(self.vace_layers) + ]) + + self.vace_layers_mapping = {i: n for n, i in enumerate(range(0, self.num_layers, self.num_layers // self.vace_layers))} + # vace patch embeddings + self.vace_patch_embedding = operations.Conv3d( + self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size, device=device, dtype=torch.float32 + ) + + def forward_orig( + self, + x, + t, + context, + vace_context, + vace_strength, + clip_fea=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + orig_shape = list(vace_context.shape) + vace_context = vace_context.movedim(0, 1).reshape([-1] + orig_shape[2:]) + c = self.vace_patch_embedding(vace_context.float()).to(vace_context.dtype) + c = c.flatten(2).transpose(1, 2) + c = list(c.split(orig_shape[0], dim=0)) + + # arguments + x_orig = x + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + + ii = self.vace_layers_mapping.get(i, None) + if ii is not None: + for iii in range(len(c)): + c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + x += c_skip * vace_strength[iii] + del c_skip + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + +class CameraWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='camera', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + in_dim_control_adapter=24, + device=None, + dtype=None, + operations=None, + ): + + if model_type == 'camera': + model_type = 'i2v' + else: + model_type = 't2v' + + super().__init__(model_type=model_type, patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + operation_settings = {"operations": operations, "device": device, "dtype": dtype} + + self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings) + + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + freqs=None, + camera_conditions = None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + if self.control_adapter is not None and camera_conditions is not None: + x = x + self.control_adapter(camera_conditions).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype)) + e0 = self.time_projection(e).unflatten(1, (6, self.dim)) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + + +class CausalConv1d(nn.Module): + + def __init__(self, + chan_in, + chan_out, + kernel_size=3, + stride=1, + dilation=1, + pad_mode='replicate', + operations=None, + **kwargs): + super().__init__() + + self.pad_mode = pad_mode + padding = (kernel_size - 1, 0) # T + self.time_causal_padding = padding + + self.conv = operations.Conv1d( + chan_in, + chan_out, + kernel_size, + stride=stride, + dilation=dilation, + **kwargs) + + def forward(self, x): + x = torch.nn.functional.pad(x, self.time_causal_padding, mode=self.pad_mode) + return self.conv(x) + + +class MotionEncoder_tc(nn.Module): + + def __init__(self, + in_dim: int, + hidden_dim: int, + num_heads: int, + need_global=True, + dtype=None, + device=None, + operations=None,): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + + self.num_heads = num_heads + self.need_global = need_global + self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1, operations=operations, **factory_kwargs) + if need_global: + self.conv1_global = CausalConv1d( + in_dim, hidden_dim // 4, 3, stride=1, operations=operations, **factory_kwargs) + self.norm1 = operations.LayerNorm( + hidden_dim // 4, + elementwise_affine=False, + eps=1e-6, + **factory_kwargs) + self.act = nn.SiLU() + self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2, operations=operations, **factory_kwargs) + self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2, operations=operations, **factory_kwargs) + + if need_global: + self.final_linear = operations.Linear(hidden_dim, hidden_dim, **factory_kwargs) + + self.norm1 = operations.LayerNorm( + hidden_dim // 4, + elementwise_affine=False, + eps=1e-6, + **factory_kwargs) + + self.norm2 = operations.LayerNorm( + hidden_dim // 2, + elementwise_affine=False, + eps=1e-6, + **factory_kwargs) + + self.norm3 = operations.LayerNorm( + hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs)) + + def forward(self, x): + x = rearrange(x, 'b t c -> b c t') + x_ori = x.clone() + b, c, t = x.shape + x = self.conv1_local(x) + x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads) + x = self.norm1(x) + x = self.act(x) + x = rearrange(x, 'b t c -> b c t') + x = self.conv2(x) + x = rearrange(x, 'b c t -> b t c') + x = self.norm2(x) + x = self.act(x) + x = rearrange(x, 'b t c -> b c t') + x = self.conv3(x) + x = rearrange(x, 'b c t -> b t c') + x = self.norm3(x) + x = self.act(x) + x = rearrange(x, '(b n) t c -> b t n c', b=b) + padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1) + x = torch.cat([x, padding], dim=-2) + x_local = x.clone() + + if not self.need_global: + return x_local + + x = self.conv1_global(x_ori) + x = rearrange(x, 'b c t -> b t c') + x = self.norm1(x) + x = self.act(x) + x = rearrange(x, 'b t c -> b c t') + x = self.conv2(x) + x = rearrange(x, 'b c t -> b t c') + x = self.norm2(x) + x = self.act(x) + x = rearrange(x, 'b t c -> b c t') + x = self.conv3(x) + x = rearrange(x, 'b c t -> b t c') + x = self.norm3(x) + x = self.act(x) + x = self.final_linear(x) + x = rearrange(x, '(b n) t c -> b t n c', b=b) + + return x, x_local + + +class CausalAudioEncoder(nn.Module): + + def __init__(self, + dim=5120, + num_layers=25, + out_dim=2048, + video_rate=8, + num_token=4, + need_global=False, + dtype=None, + device=None, + operations=None): + super().__init__() + self.encoder = MotionEncoder_tc( + in_dim=dim, + hidden_dim=out_dim, + num_heads=num_token, + need_global=need_global, dtype=dtype, device=device, operations=operations) + weight = torch.empty((1, num_layers, 1, 1), dtype=dtype, device=device) + + self.weights = torch.nn.Parameter(weight) + self.act = torch.nn.SiLU() + + def forward(self, features): + # features B * num_layers * dim * video_length + weights = self.act(comfy.model_management.cast_to(self.weights, dtype=features.dtype, device=features.device)) + weights_sum = weights.sum(dim=1, keepdims=True) + weighted_feat = ((features * weights) / weights_sum).sum( + dim=1) # b dim f + weighted_feat = weighted_feat.permute(0, 2, 1) # b f dim + res = self.encoder(weighted_feat) # b f n dim + return res # b f n dim + + +class AdaLayerNorm(nn.Module): + def __init__(self, embedding_dim, output_dim=None, norm_elementwise_affine=False, norm_eps=1e-5, dtype=None, device=None, operations=None): + super().__init__() + + output_dim = output_dim or embedding_dim * 2 + + self.silu = nn.SiLU() + self.linear = operations.Linear(embedding_dim, output_dim, dtype=dtype, device=device) + self.norm = operations.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine, dtype=dtype, device=device) + + def forward(self, x, temb): + temb = self.linear(self.silu(temb)) + shift, scale = temb.chunk(2, dim=1) + shift = shift[:, None, :] + scale = scale[:, None, :] + x = self.norm(x) * (1 + scale) + shift + return x + + +class AudioInjector_WAN(nn.Module): + + def __init__(self, + dim=2048, + num_heads=32, + inject_layer=[0, 27], + root_net=None, + enable_adain=False, + adain_dim=2048, + adain_mode=None, + dtype=None, + device=None, + operations=None): + super().__init__() + self.enable_adain = enable_adain + self.adain_mode = adain_mode + self.injected_block_id = {} + audio_injector_id = 0 + for inject_id in inject_layer: + self.injected_block_id[inject_id] = audio_injector_id + audio_injector_id += 1 + + self.injector = nn.ModuleList([ + WanT2VCrossAttention( + dim=dim, + num_heads=num_heads, + qk_norm=True, operation_settings={"operations": operations, "device": device, "dtype": dtype} + ) for _ in range(audio_injector_id) + ]) + self.injector_pre_norm_feat = nn.ModuleList([ + operations.LayerNorm( + dim, + elementwise_affine=False, + eps=1e-6, dtype=dtype, device=device + ) for _ in range(audio_injector_id) + ]) + self.injector_pre_norm_vec = nn.ModuleList([ + operations.LayerNorm( + dim, + elementwise_affine=False, + eps=1e-6, dtype=dtype, device=device + ) for _ in range(audio_injector_id) + ]) + if enable_adain: + self.injector_adain_layers = nn.ModuleList([ + AdaLayerNorm( + output_dim=dim * 2, embedding_dim=adain_dim, dtype=dtype, device=device, operations=operations) + for _ in range(audio_injector_id) + ]) + if adain_mode != "attn_norm": + self.injector_adain_output_layers = nn.ModuleList( + [operations.Linear(dim, dim, dtype=dtype, device=device) for _ in range(audio_injector_id)]) + + def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len): + audio_attn_id = self.injected_block_id.get(block_id, None) + if audio_attn_id is None: + return x + + num_frames = audio_emb.shape[1] + input_hidden_states = rearrange(x[:, :seq_len], "b (t n) c -> (b t) n c", t=num_frames) + if self.enable_adain and self.adain_mode == "attn_norm": + audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c") + adain_hidden_states = self.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0]) + attn_hidden_states = adain_hidden_states + else: + attn_hidden_states = self.injector_pre_norm_feat[audio_attn_id](input_hidden_states) + audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames) + attn_audio_emb = audio_emb + residual_out = self.injector[audio_attn_id](x=attn_hidden_states, context=attn_audio_emb) + residual_out = rearrange( + residual_out, "(b t) n c -> b (t n) c", t=num_frames) + x[:, :seq_len] = x[:, :seq_len] + residual_out + return x + + +class FramePackMotioner(nn.Module): + def __init__( + self, + inner_dim=1024, + num_heads=16, # Used to indicate the number of heads in the backbone network; unrelated to this module's design + zip_frame_buckets=[ + 1, 2, 16 + ], # Three numbers representing the number of frames sampled for patch operations from the nearest to the farthest frames + drop_mode="drop", # If not "drop", it will use "padd", meaning padding instead of deletion + dtype=None, + device=None, + operations=None): + super().__init__() + self.proj = operations.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2), dtype=dtype, device=device) + self.proj_2x = operations.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4), dtype=dtype, device=device) + self.proj_4x = operations.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8), dtype=dtype, device=device) + self.zip_frame_buckets = zip_frame_buckets + + self.inner_dim = inner_dim + self.num_heads = num_heads + + self.drop_mode = drop_mode + + def forward(self, motion_latents, rope_embedder, add_last_motion=2): + lat_height, lat_width = motion_latents.shape[3], motion_latents.shape[4] + padd_lat = torch.zeros(motion_latents.shape[0], 16, sum(self.zip_frame_buckets), lat_height, lat_width).to(device=motion_latents.device, dtype=motion_latents.dtype) + overlap_frame = min(padd_lat.shape[2], motion_latents.shape[2]) + if overlap_frame > 0: + padd_lat[:, :, -overlap_frame:] = motion_latents[:, :, -overlap_frame:] + + if add_last_motion < 2 and self.drop_mode != "drop": + zero_end_frame = sum(self.zip_frame_buckets[:len(self.zip_frame_buckets) - add_last_motion - 1]) + padd_lat[:, :, -zero_end_frame:] = 0 + + clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -sum(self.zip_frame_buckets):, :, :].split(self.zip_frame_buckets[::-1], dim=2) # 16, 2 ,1 + + # patchfy + clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2) + clean_latents_2x = self.proj_2x(clean_latents_2x) + l_2x_shape = clean_latents_2x.shape + clean_latents_2x = clean_latents_2x.flatten(2).transpose(1, 2) + clean_latents_4x = self.proj_4x(clean_latents_4x) + l_4x_shape = clean_latents_4x.shape + clean_latents_4x = clean_latents_4x.flatten(2).transpose(1, 2) + + if add_last_motion < 2 and self.drop_mode == "drop": + clean_latents_post = clean_latents_post[:, : + 0] if add_last_motion < 2 else clean_latents_post + clean_latents_2x = clean_latents_2x[:, : + 0] if add_last_motion < 1 else clean_latents_2x + + motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1) + + rope_post = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-1, device=motion_latents.device, dtype=motion_latents.dtype) + rope_2x = rope_embedder.rope_encode(1, lat_height, lat_width, t_start=-3, steps_h=l_2x_shape[-2], steps_w=l_2x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) + rope_4x = rope_embedder.rope_encode(4, lat_height, lat_width, t_start=-19, steps_h=l_4x_shape[-2], steps_w=l_4x_shape[-1], device=motion_latents.device, dtype=motion_latents.dtype) + + rope = torch.cat([rope_post, rope_2x, rope_4x], dim=1) + return motion_lat, rope + + +class WanModel_S2V(WanModel): + def __init__(self, + model_type='s2v', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + audio_dim=1024, + num_audio_token=4, + enable_adain=True, + cond_dim=16, + audio_inject_layers=[0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39], + adain_mode="attn_norm", + framepack_drop_mode="padd", + image_model=None, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model, device=device, dtype=dtype, operations=operations) + + self.trainable_cond_mask = operations.Embedding(3, self.dim, device=device, dtype=dtype) + + self.casual_audio_encoder = CausalAudioEncoder( + dim=audio_dim, + out_dim=self.dim, + num_token=num_audio_token, + need_global=enable_adain, dtype=dtype, device=device, operations=operations) + + if cond_dim > 0: + self.cond_encoder = operations.Conv3d( + cond_dim, + self.dim, + kernel_size=self.patch_size, + stride=self.patch_size, device=device, dtype=dtype) + + self.audio_injector = AudioInjector_WAN( + dim=self.dim, + num_heads=self.num_heads, + inject_layer=audio_inject_layers, + root_net=self, + enable_adain=enable_adain, + adain_dim=self.dim, + adain_mode=adain_mode, + dtype=dtype, device=device, operations=operations + ) + + self.frame_packer = FramePackMotioner( + inner_dim=self.dim, + num_heads=self.num_heads, + zip_frame_buckets=[1, 2, 16], + drop_mode=framepack_drop_mode, + dtype=dtype, device=device, operations=operations) + + def forward_orig( + self, + x, + t, + context, + audio_embed=None, + reference_latent=None, + control_video=None, + reference_motion=None, + clip_fea=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + if audio_embed is not None: + num_embeds = x.shape[-3] * 4 + audio_emb_global, audio_emb = self.casual_audio_encoder(audio_embed[:, :, :, :num_embeds]) + else: + audio_emb = None + + # embeddings + bs, _, time, height, width = x.shape + x = self.patch_embedding(x.float()).to(x.dtype) + if control_video is not None: + x = x + self.cond_encoder(control_video) + + if t.ndim == 1: + t = t.unsqueeze(1).repeat(1, x.shape[2]) + + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + seq_len = x.size(1) + + cond_mask_weight = comfy.model_management.cast_to(self.trainable_cond_mask.weight, dtype=x.dtype, device=x.device).unsqueeze(1).unsqueeze(1) + x = x + cond_mask_weight[0] + + if reference_latent is not None: + ref = self.patch_embedding(reference_latent.float()).to(x.dtype) + ref = ref.flatten(2).transpose(1, 2) + freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype) + ref = ref + cond_mask_weight[1] + x = torch.cat([x, ref], dim=1) + freqs = torch.cat([freqs, freqs_ref], dim=1) + t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1) + del ref, freqs_ref + + if reference_motion is not None: + motion_encoded, freqs_motion = self.frame_packer(reference_motion, self) + motion_encoded = motion_encoded + cond_mask_weight[2] + x = torch.cat([x, motion_encoded], dim=1) + freqs = torch.cat([freqs, freqs_motion], dim=1) + + t = torch.repeat_interleave(t, 2, dim=1) + t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1) + del motion_encoded, freqs_motion + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) + e = e.reshape(t.shape[0], -1, e.shape[-1]) + e0 = self.time_projection(e).unflatten(2, (6, self.dim)) + + # context + context = self.text_embedding(context) + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context) + if audio_emb is not None: + x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len) + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x + + +class WanT2VCrossAttentionGather(WanSelfAttention): + + def forward(self, x, context, transformer_options={}, **kwargs): + r""" + Args: + x(Tensor): Shape [B, L1, C] - video tokens + context(Tensor): Shape [B, L2, C] - audio tokens with shape [B, frames*16, 1536] + """ + b, n, d = x.size(0), self.num_heads, self.head_dim + + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(context)) + v = self.v(context) + + # Handle audio temporal structure (16 tokens per frame) + k = k.reshape(-1, 16, n, d).transpose(1, 2) + v = v.reshape(-1, 16, n, d).transpose(1, 2) + + # Handle video spatial structure + q = q.reshape(k.shape[0], -1, n, d).transpose(1, 2) + + x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options) + + x = x.transpose(1, 2).reshape(b, -1, n * d) + x = self.o(x) + return x + + +class AudioCrossAttentionWrapper(nn.Module): + def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, operation_settings={}): + super().__init__() + + self.audio_cross_attn = WanT2VCrossAttentionGather(dim, num_heads, qk_norm=qk_norm, kv_dim=kv_dim, eps=eps, operation_settings=operation_settings) + self.norm1_audio = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) + + def forward(self, x, audio, transformer_options={}): + x = x + self.audio_cross_attn(self.norm1_audio(x), audio, transformer_options=transformer_options) + return x + + +class WanAttentionBlockAudio(WanAttentionBlock): + + def __init__(self, + cross_attn_type, + dim, + ffn_dim, + num_heads, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=False, + eps=1e-6, operation_settings={}): + super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings) + self.audio_cross_attn_wrapper = AudioCrossAttentionWrapper(dim, 1536, num_heads, qk_norm, eps, operation_settings=operation_settings) + + def forward( + self, + x, + e, + freqs, + context, + context_img_len=257, + audio=None, + transformer_options={}, + ): + r""" + Args: + x(Tensor): Shape [B, L, C] + e(Tensor): Shape [B, 6, C] + freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] + """ + # assert e.dtype == torch.float32 + + if e.ndim < 4: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1) + else: + e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2) + # assert e[0].dtype == torch.float32 + + # self-attention + y = self.self_attn( + torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)), + freqs, transformer_options=transformer_options) + + x = torch.addcmul(x, y, repeat_e(e[2], x)) + + # cross-attention & ffn + x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options) + if audio is not None: + x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options) + y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x))) + x = torch.addcmul(x, y, repeat_e(e[5], x)) + return x + +class DummyAdapterLayer(nn.Module): + def __init__(self, layer): + super().__init__() + self.layer = layer + + def forward(self, *args, **kwargs): + return self.layer(*args, **kwargs) + + +class AudioProjModel(nn.Module): + def __init__( + self, + seq_len=5, + blocks=13, # add a new parameter blocks + channels=768, # add a new parameter channels + intermediate_dim=512, + output_dim=1536, + context_tokens=16, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + + self.seq_len = seq_len + self.blocks = blocks + self.channels = channels + self.input_dim = seq_len * blocks * channels # update input_dim to be the product of blocks and channels. + self.intermediate_dim = intermediate_dim + self.context_tokens = context_tokens + self.output_dim = output_dim + + # define multiple linear layers + self.audio_proj_glob_1 = DummyAdapterLayer(operations.Linear(self.input_dim, intermediate_dim, dtype=dtype, device=device)) + self.audio_proj_glob_2 = DummyAdapterLayer(operations.Linear(intermediate_dim, intermediate_dim, dtype=dtype, device=device)) + self.audio_proj_glob_3 = DummyAdapterLayer(operations.Linear(intermediate_dim, context_tokens * output_dim, dtype=dtype, device=device)) + + self.audio_proj_glob_norm = DummyAdapterLayer(operations.LayerNorm(output_dim, dtype=dtype, device=device)) + + def forward(self, audio_embeds): + video_length = audio_embeds.shape[1] + audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c") + batch_size, window_size, blocks, channels = audio_embeds.shape + audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels) + + audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds)) + audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds)) + + context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim) + + context_tokens = self.audio_proj_glob_norm(context_tokens) + context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length) + + return context_tokens + + +class HumoWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='humo', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + image_model=None, + audio_token_num=16, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, wan_attn_block_class=WanAttentionBlockAudio, image_model=image_model, device=device, dtype=dtype, operations=operations) + + self.audio_proj = AudioProjModel(seq_len=8, blocks=5, channels=1280, intermediate_dim=512, output_dim=1536, context_tokens=audio_token_num, dtype=dtype, device=device, operations=operations) + + def forward_orig( + self, + x, + t, + context, + freqs=None, + audio_embed=None, + reference_latent=None, + transformer_options={}, + **kwargs, + ): + bs, _, time, height, width = x.shape + + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) + e = e.reshape(t.shape[0], -1, e.shape[-1]) + e0 = self.time_projection(e).unflatten(2, (6, self.dim)) + + if reference_latent is not None: + ref = self.patch_embedding(reference_latent.float()).to(x.dtype) + ref = ref.flatten(2).transpose(1, 2) + freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=time, device=x.device, dtype=x.dtype) + x = torch.cat([x, ref], dim=1) + freqs = torch.cat([freqs, freqs_ref], dim=1) + del ref, freqs_ref + + # context + context = self.text_embedding(context) + context_img_len = None + + if audio_embed is not None: + if reference_latent is not None: + zero_audio_pad = torch.zeros(audio_embed.shape[0], reference_latent.shape[-3], *audio_embed.shape[2:], device=audio_embed.device, dtype=audio_embed.dtype) + audio_embed = torch.cat([audio_embed, zero_audio_pad], dim=1) + audio = self.audio_proj(audio_embed).permute(0, 3, 1, 2).flatten(2).transpose(1, 2) + else: + audio = None + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, audio=audio, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options) + + # head + x = self.head(x, e) + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x diff --git a/comfy/ldm/wan/model_animate.py b/comfy/ldm/wan/model_animate.py new file mode 100644 index 000000000..7c87835d4 --- /dev/null +++ b/comfy/ldm/wan/model_animate.py @@ -0,0 +1,548 @@ +from torch import nn +import torch +from typing import Tuple, Optional +from einops import rearrange +import torch.nn.functional as F +import math +from .model import WanModel, sinusoidal_embedding_1d +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + +class CausalConv1d(nn.Module): + + def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs): + super().__init__() + + self.pad_mode = pad_mode + padding = (kernel_size - 1, 0) # T + self.time_causal_padding = padding + + self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs) + + def forward(self, x): + x = F.pad(x, self.time_causal_padding, mode=self.pad_mode) + return self.conv(x) + + +class FaceEncoder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None): + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + + self.num_heads = num_heads + self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs) + self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs) + self.act = nn.SiLU() + self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs) + self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs) + + self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs) + self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs)) + + def forward(self, x): + + x = rearrange(x, "b t c -> b c t") + b, c, t = x.shape + + x = self.conv1_local(x) + x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads) + + x = self.norm1(x) + x = self.act(x) + x = rearrange(x, "b t c -> b c t") + x = self.conv2(x) + x = rearrange(x, "b c t -> b t c") + x = self.norm2(x) + x = self.act(x) + x = rearrange(x, "b t c -> b c t") + x = self.conv3(x) + x = rearrange(x, "b c t -> b t c") + x = self.norm3(x) + x = self.act(x) + x = self.out_proj(x) + x = rearrange(x, "(b n) t c -> b t n c", b=b) + padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1) + x = torch.cat([x, padding], dim=-2) + x_local = x.clone() + + return x_local + + +def get_norm_layer(norm_layer, operations=None): + """ + Get the normalization layer. + + Args: + norm_layer (str): The type of normalization layer. + + Returns: + norm_layer (nn.Module): The normalization layer. + """ + if norm_layer == "layer": + return operations.LayerNorm + elif norm_layer == "rms": + return operations.RMSNorm + else: + raise NotImplementedError(f"Norm layer {norm_layer} is not implemented") + + +class FaceAdapter(nn.Module): + def __init__( + self, + hidden_dim: int, + heads_num: int, + qk_norm: bool = True, + qk_norm_type: str = "rms", + num_adapter_layers: int = 1, + dtype=None, device=None, operations=None + ): + + factory_kwargs = {"dtype": dtype, "device": device} + super().__init__() + self.hidden_size = hidden_dim + self.heads_num = heads_num + self.fuser_blocks = nn.ModuleList( + [ + FaceBlock( + self.hidden_size, + self.heads_num, + qk_norm=qk_norm, + qk_norm_type=qk_norm_type, + operations=operations, + **factory_kwargs, + ) + for _ in range(num_adapter_layers) + ] + ) + + def forward( + self, + x: torch.Tensor, + motion_embed: torch.Tensor, + idx: int, + freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None, + freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None, + ) -> torch.Tensor: + + return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k) + + + +class FaceBlock(nn.Module): + def __init__( + self, + hidden_size: int, + heads_num: int, + qk_norm: bool = True, + qk_norm_type: str = "rms", + qk_scale: float = None, + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + operations=None + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + + self.deterministic = False + self.hidden_size = hidden_size + self.heads_num = heads_num + head_dim = hidden_size // heads_num + self.scale = qk_scale or head_dim**-0.5 + + self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs) + self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs) + + self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs) + + qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations) + self.q_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() + ) + self.k_norm = ( + qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity() + ) + + self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs) + + def forward( + self, + x: torch.Tensor, + motion_vec: torch.Tensor, + motion_mask: Optional[torch.Tensor] = None, + # use_context_parallel=False, + ) -> torch.Tensor: + + B, T, N, C = motion_vec.shape + T_comp = T + + x_motion = self.pre_norm_motion(motion_vec) + x_feat = self.pre_norm_feat(x) + + kv = self.linear1_kv(x_motion) + q = self.linear1_q(x_feat) + + k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num) + q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num) + + # Apply QK-Norm if needed. + q = self.q_norm(q).to(v) + k = self.k_norm(k).to(v) + + k = rearrange(k, "B L N H D -> (B L) N H D") + v = rearrange(v, "B L N H D -> (B L) N H D") + + q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp) + + attn = optimized_attention(q, k, v, heads=self.heads_num) + + attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp) + + output = self.linear2(attn) + + if motion_mask is not None: + output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1) + + return output + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162 +def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): + _, minor, in_h, in_w = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, minor, in_h, 1, in_w, 1) + out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0]) + out = out.view(-1, minor, in_h * up_y, in_w * up_x) + + out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) + out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)] + + out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1) + return out[:, :, ::down_y, ::down_x] + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81 +class FusedLeakyReLU(torch.nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None): + super().__init__() + self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale) + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): + return F.leaky_relu(input + bias, negative_slope) * scale + +class Blur(torch.nn.Module): + def __init__(self, kernel, pad, dtype=None, device=None): + super().__init__() + kernel = torch.tensor(kernel, dtype=dtype, device=device) + kernel = kernel[None, :] * kernel[:, None] + kernel = kernel / kernel.sum() + self.register_buffer('kernel', kernel) + self.pad = pad + + def forward(self, input): + return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad) + +#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590 +class ScaledLeakyReLU(torch.nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + self.negative_slope = negative_slope + + def forward(self, input): + return F.leaky_relu(input, negative_slope=self.negative_slope) + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605 +class EqualConv2d(torch.nn.Module): + def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype)) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + self.stride = stride + self.padding = padding + self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None + + def forward(self, input): + if self.bias is None: + bias = None + else: + bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) + + return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding) + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134 +class EqualLinear(torch.nn.Module): + def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype)) + self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None + self.activation = activation + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def forward(self, input): + if self.bias is None: + bias = None + else: + bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul + + if self.activation: + out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale) + return fused_leaky_relu(out, bias) + return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias) + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654 +class ConvLayer(torch.nn.Sequential): + def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2))) + stride, padding = 2, 0 + else: + stride, padding = 1, kernel_size // 2 + + layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations)) + + if activate: + layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + +# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704 +class ResBlock(torch.nn.Module): + def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None): + super().__init__() + self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations) + self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations) + self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations) + + def forward(self, input): + out = self.conv2(self.conv1(input)) + skip = self.skip(input) + return (out + skip) / math.sqrt(2) + + +class EncoderApp(torch.nn.Module): + def __init__(self, w_dim=512, dtype=None, device=None, operations=None): + super().__init__() + kwargs = {"device": device, "dtype": dtype, "operations": operations} + + self.convs = torch.nn.ModuleList([ + ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs), + ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs), + ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs), + ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs), + EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs) + ]) + + def forward(self, x): + h = x + for conv in self.convs: + h = conv(h) + return h.squeeze(-1).squeeze(-1) + +class Encoder(torch.nn.Module): + def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None): + super().__init__() + self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations) + self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)]) + + def encode_motion(self, x): + return self.fc(self.net_app(x)) + +class Direction(torch.nn.Module): + def __init__(self, motion_dim, dtype=None, device=None, operations=None): + super().__init__() + self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype)) + self.motion_dim = motion_dim + + def forward(self, input): + stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype) + Q, _ = torch.linalg.qr(stabilized_weight.float()) + if input is None: + return Q + return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1) + +class Synthesis(torch.nn.Module): + def __init__(self, motion_dim, dtype=None, device=None, operations=None): + super().__init__() + self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations) + +class Generator(torch.nn.Module): + def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None): + super().__init__() + self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations) + self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations) + + def get_motion(self, img): + motion_feat = self.enc.encode_motion(img) + return self.dec.direction(motion_feat) + +class AnimateWanModel(WanModel): + r""" + Wan diffusion backbone supporting both text-to-video and image-to-video. + """ + + def __init__(self, + model_type='animate', + patch_size=(1, 2, 2), + text_len=512, + in_dim=16, + dim=2048, + ffn_dim=8192, + freq_dim=256, + text_dim=4096, + out_dim=16, + num_heads=16, + num_layers=32, + window_size=(-1, -1), + qk_norm=True, + cross_attn_norm=True, + eps=1e-6, + flf_pos_embed_token_number=None, + motion_encoder_dim=512, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + + super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations) + + self.pose_patch_embedding = operations.Conv3d( + 16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype + ) + + self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations) + + self.face_adapter = FaceAdapter( + heads_num=self.num_heads, + hidden_dim=self.dim, + num_adapter_layers=self.num_layers // 5, + device=device, dtype=dtype, operations=operations + ) + + self.face_encoder = FaceEncoder( + in_dim=motion_encoder_dim, + hidden_dim=self.dim, + num_heads=4, + device=device, dtype=dtype, operations=operations + ) + + def after_patch_embedding(self, x, pose_latents, face_pixel_values): + if pose_latents is not None: + pose_latents = self.pose_patch_embedding(pose_latents) + x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1] + + if face_pixel_values is None: + return x, None + + b, c, T, h, w = face_pixel_values.shape + face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w") + encode_bs = 8 + face_pixel_values_tmp = [] + for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)): + face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs])) + + motion_vec = torch.cat(face_pixel_values_tmp) + + motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T) + motion_vec = self.face_encoder(motion_vec) + + B, L, H, C = motion_vec.shape + pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec) + motion_vec = torch.cat([pad_face, motion_vec], dim=1) + + if motion_vec.shape[1] < x.shape[2]: + B, L, H, C = motion_vec.shape + pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec) + motion_vec = torch.cat([motion_vec, pad], dim=1) + else: + motion_vec = motion_vec[:, :x.shape[2]] + return x, motion_vec + + def forward_orig( + self, + x, + t, + context, + clip_fea=None, + pose_latents=None, + face_pixel_values=None, + freqs=None, + transformer_options={}, + **kwargs, + ): + # embeddings + x = self.patch_embedding(x.float()).to(x.dtype) + x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values) + grid_sizes = x.shape[2:] + x = x.flatten(2).transpose(1, 2) + + # time embeddings + e = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype)) + e = e.reshape(t.shape[0], -1, e.shape[-1]) + e0 = self.time_projection(e).unflatten(2, (6, self.dim)) + + full_ref = None + if self.ref_conv is not None: + full_ref = kwargs.get("reference_latent", None) + if full_ref is not None: + full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2) + x = torch.concat((full_ref, x), dim=1) + + # context + context = self.text_embedding(context) + + context_img_len = None + if clip_fea is not None: + if self.img_emb is not None: + context_clip = self.img_emb(clip_fea) # bs x 257 x dim + context = torch.concat([context_clip, context], dim=1) + context_img_len = clip_fea.shape[-2] + + patches_replace = transformer_options.get("patches_replace", {}) + blocks_replace = patches_replace.get("dit", {}) + for i, block in enumerate(self.blocks): + if ("double_block", i) in blocks_replace: + def block_wrap(args): + out = {} + out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"]) + return out + out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap}) + x = out["img"] + else: + x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options) + + if i % 5 == 0 and motion_vec is not None: + x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec) + + # head + x = self.head(x, e) + + if full_ref is not None: + x = x[:, full_ref.shape[1]:] + + # unpatchify + x = self.unpatchify(x, grid_sizes) + return x diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py new file mode 100644 index 000000000..ccbb25822 --- /dev/null +++ b/comfy/ldm/wan/vae.py @@ -0,0 +1,513 @@ +# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/vae.py +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from comfy.ldm.modules.diffusionmodules.model import vae_attention + +import comfy.ops +ops = comfy.ops.disable_weight_init + +CACHE_T = 2 + + +class CausalConv3d(ops.Conv3d): + """ + Causal 3d convolusion. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._padding = (self.padding[2], self.padding[2], self.padding[1], + self.padding[1], 2 * self.padding[0], 0) + self.padding = (0, 0, 0) + + def forward(self, x, cache_x=None, cache_list=None, cache_idx=None): + if cache_list is not None: + cache_x = cache_list[cache_idx] + cache_list[cache_idx] = None + + padding = list(self._padding) + if cache_x is not None and self._padding[4] > 0: + cache_x = cache_x.to(x.device) + x = torch.cat([cache_x, x], dim=2) + padding[4] -= cache_x.shape[2] + del cache_x + x = F.pad(x, padding) + + return super().forward(x) + + +class RMS_norm(nn.Module): + + def __init__(self, dim, channel_first=True, images=True, bias=False): + super().__init__() + broadcastable_dims = (1, 1, 1) if not images else (1, 1) + shape = (dim, *broadcastable_dims) if channel_first else (dim,) + + self.channel_first = channel_first + self.scale = dim**0.5 + self.gamma = nn.Parameter(torch.ones(shape)) + self.bias = nn.Parameter(torch.zeros(shape)) if bias else None + + def forward(self, x): + return F.normalize( + x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0) + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', + 'downsample3d') + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == 'upsample2d': + self.resample = nn.Sequential( + nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + ops.Conv2d(dim, dim // 2, 3, padding=1)) + elif mode == 'upsample3d': + self.resample = nn.Sequential( + nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + ops.Conv2d(dim, dim // 2, 3, padding=1)) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + + elif mode == 'downsample2d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == 'downsample3d': + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == 'upsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = 'Rep' + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] != 'Rep': + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + if cache_x.shape[2] < 2 and feat_cache[ + idx] is not None and feat_cache[idx] == 'Rep': + cache_x = torch.cat([ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2) + if feat_cache[idx] == 'Rep': + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.resample(x) + x = rearrange(x, '(b t) c h w -> b c t h w', t=t) + + if self.mode == 'downsample3d': + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + + cache_x = x[:, :, -1:, :, :].clone() + # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep': + # # cache last frame of last two chunk + # cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2) + + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1)) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) \ + if in_dim != out_dim else nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + old_x = x + for layer in self.residual: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, cache_list=feat_cache, cache_idx=idx) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + self.shortcut(old_x) + + +class AttentionBlock(nn.Module): + """ + Causal self-attention with a single head. + """ + + def __init__(self, dim): + super().__init__() + self.dim = dim + + # layers + self.norm = RMS_norm(dim) + self.to_qkv = ops.Conv2d(dim, dim * 3, 1) + self.proj = ops.Conv2d(dim, dim, 1) + self.optimized_attention = vae_attention() + + def forward(self, x): + identity = x + b, c, t, h, w = x.size() + x = rearrange(x, 'b c t h w -> (b t) c h w') + x = self.norm(x) + # compute query, key, value + + q, k, v = self.to_qkv(x).chunk(3, dim=1) + x = self.optimized_attention(q, k, v) + + # output + x = self.proj(x) + x = rearrange(x, '(b t) c h w-> b c t h w', t=t) + return x + identity + + +class Encoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(3, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + for _ in range(num_res_blocks): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + downsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # downsample block + if i != len(dim_mult) - 1: + mode = 'downsample3d' if temperal_downsample[ + i] else 'downsample2d' + downsamples.append(Resample(out_dim, mode=mode)) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout)) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +class Decoder3d(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + scale = 1.0 / 2**(len(dim_mult) - 2) + + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout)) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + # residual (+attention) blocks + if i == 1 or i == 2 or i == 3: + in_dim = in_dim // 2 + for _ in range(num_res_blocks + 1): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + if scale in attn_scales: + upsamples.append(AttentionBlock(out_dim)) + in_dim = out_dim + + # upsample block + if i != len(dim_mult) - 1: + mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d' + upsamples.append(Resample(out_dim, mode=mode)) + scale *= 2.0 + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), nn.SiLU(), + CausalConv3d(out_dim, 3, 3, padding=1)) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + ## conv1 + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat([ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), cache_x + ], + dim=2) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, CausalConv3d): + count += 1 + return count + + +class WanVAE(nn.Module): + + def __init__(self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, + attn_scales, self.temperal_downsample, dropout) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks, + attn_scales, self.temperal_upsample, dropout) + + def encode(self, x): + conv_idx = [0] + feat_map = [None] * count_conv3d(self.decoder) + ## cache + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + ## 对encode输入的x,按时间拆分为1、4、4、4.... + for i in range(iter_): + conv_idx = [0] + if i == 0: + out = self.encoder( + x[:, :, :1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=feat_map, + feat_idx=conv_idx) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + return mu + + def decode(self, z): + conv_idx = [0] + feat_map = [None] * count_conv3d(self.decoder) + # z: [b,c,t,h,w] + + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + conv_idx = [0] + if i == 0: + out = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx) + else: + out_ = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx) + out = torch.cat([out, out_], 2) + return out diff --git a/comfy/ldm/wan/vae2_2.py b/comfy/ldm/wan/vae2_2.py new file mode 100644 index 000000000..8e1593a54 --- /dev/null +++ b/comfy/ldm/wan/vae2_2.py @@ -0,0 +1,717 @@ +# original version: https://github.com/Wan-Video/Wan2.2/blob/main/wan/modules/vae2_2.py +# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from .vae import AttentionBlock, CausalConv3d, RMS_norm + +import comfy.ops +ops = comfy.ops.disable_weight_init + +CACHE_T = 2 + + +class Resample(nn.Module): + + def __init__(self, dim, mode): + assert mode in ( + "none", + "upsample2d", + "upsample3d", + "downsample2d", + "downsample3d", + ) + super().__init__() + self.dim = dim + self.mode = mode + + # layers + if mode == "upsample2d": + self.resample = nn.Sequential( + nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), + ops.Conv2d(dim, dim, 3, padding=1), + ) + elif mode == "upsample3d": + self.resample = nn.Sequential( + nn.Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), + ops.Conv2d(dim, dim, 3, padding=1), + # ops.Conv2d(dim, dim//2, 3, padding=1) + ) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + elif mode == "downsample2d": + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + elif mode == "downsample3d": + self.resample = nn.Sequential( + nn.ZeroPad2d((0, 1, 0, 1)), + ops.Conv2d(dim, dim, 3, stride=(2, 2))) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + else: + self.resample = nn.Identity() + + def forward(self, x, feat_cache=None, feat_idx=[0]): + b, c, t, h, w = x.size() + if self.mode == "upsample3d": + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = "Rep" + feat_idx[0] += 1 + else: + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and + feat_cache[idx] != "Rep"): + # cache last frame of last two chunk + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + if (cache_x.shape[2] < 2 and feat_cache[idx] is not None and + feat_cache[idx] == "Rep"): + cache_x = torch.cat( + [ + torch.zeros_like(cache_x).to(cache_x.device), + cache_x + ], + dim=2, + ) + if feat_cache[idx] == "Rep": + x = self.time_conv(x) + else: + x = self.time_conv(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + x = x.reshape(b, 2, c, t, h, w) + x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), + 3) + x = x.reshape(b, c, t * 2, h, w) + t = x.shape[2] + x = rearrange(x, "b c t h w -> (b t) c h w") + x = self.resample(x) + x = rearrange(x, "(b t) c h w -> b c t h w", t=t) + + if self.mode == "downsample3d": + if feat_cache is not None: + idx = feat_idx[0] + if feat_cache[idx] is None: + feat_cache[idx] = x.clone() + feat_idx[0] += 1 + else: + cache_x = x[:, :, -1:, :, :].clone() + x = self.time_conv( + torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2)) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + return x + + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, out_dim, dropout=0.0): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + # layers + self.residual = nn.Sequential( + RMS_norm(in_dim, images=False), + nn.SiLU(), + CausalConv3d(in_dim, out_dim, 3, padding=1), + RMS_norm(out_dim, images=False), + nn.SiLU(), + nn.Dropout(dropout), + CausalConv3d(out_dim, out_dim, 3, padding=1), + ) + self.shortcut = ( + CausalConv3d(in_dim, out_dim, 1) + if in_dim != out_dim else nn.Identity()) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + old_x = x + for layer in self.residual: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + # cache last frame of last two chunk + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + x = layer(x, cache_list=feat_cache, cache_idx=idx) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + self.shortcut(old_x) + + +def patchify(x, patch_size): + if patch_size == 1: + return x + if x.dim() == 4: + x = rearrange( + x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size) + elif x.dim() == 5: + x = rearrange( + x, + "b c f (h q) (w r) -> b (c r q) f h w", + q=patch_size, + r=patch_size, + ) + else: + raise ValueError(f"Invalid input shape: {x.shape}") + + return x + + +def unpatchify(x, patch_size): + if patch_size == 1: + return x + + if x.dim() == 4: + x = rearrange( + x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size) + elif x.dim() == 5: + x = rearrange( + x, + "b (c r q) f h w -> b c f (h q) (w r)", + q=patch_size, + r=patch_size, + ) + return x + + +class AvgDown3D(nn.Module): + + def __init__( + self, + in_channels, + out_channels, + factor_t, + factor_s=1, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.factor_t = factor_t + self.factor_s = factor_s + self.factor = self.factor_t * self.factor_s * self.factor_s + + assert in_channels * self.factor % out_channels == 0 + self.group_size = in_channels * self.factor // out_channels + + def forward(self, x: torch.Tensor) -> torch.Tensor: + pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t + pad = (0, 0, 0, 0, pad_t, 0) + x = F.pad(x, pad) + B, C, T, H, W = x.shape + x = x.view( + B, + C, + T // self.factor_t, + self.factor_t, + H // self.factor_s, + self.factor_s, + W // self.factor_s, + self.factor_s, + ) + x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous() + x = x.view( + B, + C * self.factor, + T // self.factor_t, + H // self.factor_s, + W // self.factor_s, + ) + x = x.view( + B, + self.out_channels, + self.group_size, + T // self.factor_t, + H // self.factor_s, + W // self.factor_s, + ) + x = x.mean(dim=2) + return x + + +class DupUp3D(nn.Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + factor_t, + factor_s=1, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + + self.factor_t = factor_t + self.factor_s = factor_s + self.factor = self.factor_t * self.factor_s * self.factor_s + + assert out_channels * self.factor % in_channels == 0 + self.repeats = out_channels * self.factor // in_channels + + def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor: + x = x.repeat_interleave(self.repeats, dim=1) + x = x.view( + x.size(0), + self.out_channels, + self.factor_t, + self.factor_s, + self.factor_s, + x.size(2), + x.size(3), + x.size(4), + ) + x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous() + x = x.view( + x.size(0), + self.out_channels, + x.size(2) * self.factor_t, + x.size(4) * self.factor_s, + x.size(6) * self.factor_s, + ) + if first_chunk: + x = x[:, :, self.factor_t - 1:, :, :] + return x + + +class Down_ResidualBlock(nn.Module): + + def __init__(self, + in_dim, + out_dim, + dropout, + mult, + temperal_downsample=False, + down_flag=False): + super().__init__() + + # Shortcut path with downsample + self.avg_shortcut = AvgDown3D( + in_dim, + out_dim, + factor_t=2 if temperal_downsample else 1, + factor_s=2 if down_flag else 1, + ) + + # Main path with residual blocks and downsample + downsamples = [] + for _ in range(mult): + downsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + in_dim = out_dim + + # Add the final downsample block + if down_flag: + mode = "downsample3d" if temperal_downsample else "downsample2d" + downsamples.append(Resample(out_dim, mode=mode)) + + self.downsamples = nn.Sequential(*downsamples) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + x_copy = x + for module in self.downsamples: + x = module(x, feat_cache, feat_idx) + + return x + self.avg_shortcut(x_copy) + + +class Up_ResidualBlock(nn.Module): + + def __init__(self, + in_dim, + out_dim, + dropout, + mult, + temperal_upsample=False, + up_flag=False): + super().__init__() + # Shortcut path with upsample + if up_flag: + self.avg_shortcut = DupUp3D( + in_dim, + out_dim, + factor_t=2 if temperal_upsample else 1, + factor_s=2 if up_flag else 1, + ) + else: + self.avg_shortcut = None + + # Main path with residual blocks and upsample + upsamples = [] + for _ in range(mult): + upsamples.append(ResidualBlock(in_dim, out_dim, dropout)) + in_dim = out_dim + + # Add the final upsample block + if up_flag: + mode = "upsample3d" if temperal_upsample else "upsample2d" + upsamples.append(Resample(out_dim, mode=mode)) + + self.upsamples = nn.Sequential(*upsamples) + + def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): + x_main = x + for module in self.upsamples: + x_main = module(x_main, feat_cache, feat_idx) + if self.avg_shortcut is not None: + x_shortcut = self.avg_shortcut(x, first_chunk) + return x_main + x_shortcut + else: + return x_main + + +class Encoder3d(nn.Module): + + def __init__( + self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0, + ): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + + # dimensions + dims = [dim * u for u in [1] + dim_mult] + scale = 1.0 + + # init block + self.conv1 = CausalConv3d(12, dims[0], 3, padding=1) + + # downsample blocks + downsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + t_down_flag = ( + temperal_downsample[i] + if i < len(temperal_downsample) else False) + downsamples.append( + Down_ResidualBlock( + in_dim=in_dim, + out_dim=out_dim, + dropout=dropout, + mult=num_res_blocks, + temperal_downsample=t_down_flag, + down_flag=i != len(dim_mult) - 1, + )) + scale /= 2.0 + self.downsamples = nn.Sequential(*downsamples) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(out_dim, out_dim, dropout), + AttentionBlock(out_dim), + ResidualBlock(out_dim, out_dim, dropout), + ) + + # # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), + nn.SiLU(), + CausalConv3d(out_dim, z_dim, 3, padding=1), + ) + + def forward(self, x, feat_cache=None, feat_idx=[0]): + + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + ## downsamples + for layer in self.downsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## middle + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + + return x + + +class Decoder3d(nn.Module): + + def __init__( + self, + dim=128, + z_dim=4, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_upsample=[False, True, True], + dropout=0.0, + ): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_upsample = temperal_upsample + + # dimensions + dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + # init block + self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1) + + # middle blocks + self.middle = nn.Sequential( + ResidualBlock(dims[0], dims[0], dropout), + AttentionBlock(dims[0]), + ResidualBlock(dims[0], dims[0], dropout), + ) + + # upsample blocks + upsamples = [] + for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): + t_up_flag = temperal_upsample[i] if i < len( + temperal_upsample) else False + upsamples.append( + Up_ResidualBlock( + in_dim=in_dim, + out_dim=out_dim, + dropout=dropout, + mult=num_res_blocks + 1, + temperal_upsample=t_up_flag, + up_flag=i != len(dim_mult) - 1, + )) + self.upsamples = nn.Sequential(*upsamples) + + # output blocks + self.head = nn.Sequential( + RMS_norm(out_dim, images=False), + nn.SiLU(), + CausalConv3d(out_dim, 12, 3, padding=1), + ) + + def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False): + if feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + x = self.conv1(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = self.conv1(x) + + for layer in self.middle: + if isinstance(layer, ResidualBlock) and feat_cache is not None: + x = layer(x, feat_cache, feat_idx) + else: + x = layer(x) + + ## upsamples + for layer in self.upsamples: + if feat_cache is not None: + x = layer(x, feat_cache, feat_idx, first_chunk) + else: + x = layer(x) + + ## head + for layer in self.head: + if isinstance(layer, CausalConv3d) and feat_cache is not None: + idx = feat_idx[0] + cache_x = x[:, :, -CACHE_T:, :, :].clone() + if cache_x.shape[2] < 2 and feat_cache[idx] is not None: + cache_x = torch.cat( + [ + feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to( + cache_x.device), + cache_x, + ], + dim=2, + ) + x = layer(x, feat_cache[idx]) + feat_cache[idx] = cache_x + feat_idx[0] += 1 + else: + x = layer(x) + return x + + +def count_conv3d(model): + count = 0 + for m in model.modules(): + if isinstance(m, CausalConv3d): + count += 1 + return count + + +class WanVAE(nn.Module): + + def __init__( + self, + dim=160, + dec_dim=256, + z_dim=16, + dim_mult=[1, 2, 4, 4], + num_res_blocks=2, + attn_scales=[], + temperal_downsample=[True, True, False], + dropout=0.0, + ): + super().__init__() + self.dim = dim + self.z_dim = z_dim + self.dim_mult = dim_mult + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.temperal_downsample = temperal_downsample + self.temperal_upsample = temperal_downsample[::-1] + + # modules + self.encoder = Encoder3d( + dim, + z_dim * 2, + dim_mult, + num_res_blocks, + attn_scales, + self.temperal_downsample, + dropout, + ) + self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1) + self.conv2 = CausalConv3d(z_dim, z_dim, 1) + self.decoder = Decoder3d( + dec_dim, + z_dim, + dim_mult, + num_res_blocks, + attn_scales, + self.temperal_upsample, + dropout, + ) + + def encode(self, x): + conv_idx = [0] + feat_map = [None] * count_conv3d(self.encoder) + x = patchify(x, patch_size=2) + t = x.shape[2] + iter_ = 1 + (t - 1) // 4 + for i in range(iter_): + conv_idx = [0] + if i == 0: + out = self.encoder( + x[:, :, :1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx, + ) + else: + out_ = self.encoder( + x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :], + feat_cache=feat_map, + feat_idx=conv_idx, + ) + out = torch.cat([out, out_], 2) + mu, log_var = self.conv1(out).chunk(2, dim=1) + return mu + + def decode(self, z): + conv_idx = [0] + feat_map = [None] * count_conv3d(self.decoder) + iter_ = z.shape[2] + x = self.conv2(z) + for i in range(iter_): + conv_idx = [0] + if i == 0: + out = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx, + first_chunk=True, + ) + else: + out_ = self.decoder( + x[:, :, i:i + 1, :, :], + feat_cache=feat_map, + feat_idx=conv_idx, + ) + out = torch.cat([out, out_], 2) + out = unpatchify(out, patch_size=2) + return out + + def reparameterize(self, mu, log_var): + std = torch.exp(0.5 * log_var) + eps = torch.randn_like(std) + return eps * std + mu + + def sample(self, imgs, deterministic=False): + mu, log_var = self.encode(imgs) + if deterministic: + return mu + std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0)) + return mu + std * torch.randn_like(std) diff --git a/comfy/lora.py b/comfy/lora.py index ec3da6f4c..36d26293a 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -20,6 +20,7 @@ from __future__ import annotations import comfy.utils import comfy.model_management import comfy.model_base +import comfy.weight_adapter as weight_adapter import logging import torch @@ -49,139 +50,12 @@ def load_lora(lora, to_load, log_missing=True): dora_scale = lora[dora_scale_name] loaded_keys.add(dora_scale_name) - reshape_name = "{}.reshape_weight".format(x) - reshape = None - if reshape_name in lora.keys(): - try: - reshape = lora[reshape_name].tolist() - loaded_keys.add(reshape_name) - except: - pass - - regular_lora = "{}.lora_up.weight".format(x) - diffusers_lora = "{}_lora.up.weight".format(x) - diffusers2_lora = "{}.lora_B.weight".format(x) - diffusers3_lora = "{}.lora.up.weight".format(x) - mochi_lora = "{}.lora_B".format(x) - transformers_lora = "{}.lora_linear_layer.up.weight".format(x) - A_name = None - - if regular_lora in lora.keys(): - A_name = regular_lora - B_name = "{}.lora_down.weight".format(x) - mid_name = "{}.lora_mid.weight".format(x) - elif diffusers_lora in lora.keys(): - A_name = diffusers_lora - B_name = "{}_lora.down.weight".format(x) - mid_name = None - elif diffusers2_lora in lora.keys(): - A_name = diffusers2_lora - B_name = "{}.lora_A.weight".format(x) - mid_name = None - elif diffusers3_lora in lora.keys(): - A_name = diffusers3_lora - B_name = "{}.lora.down.weight".format(x) - mid_name = None - elif mochi_lora in lora.keys(): - A_name = mochi_lora - B_name = "{}.lora_A".format(x) - mid_name = None - elif transformers_lora in lora.keys(): - A_name = transformers_lora - B_name ="{}.lora_linear_layer.down.weight".format(x) - mid_name = None - - if A_name is not None: - mid = None - if mid_name is not None and mid_name in lora.keys(): - mid = lora[mid_name] - loaded_keys.add(mid_name) - patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape)) - loaded_keys.add(A_name) - loaded_keys.add(B_name) - - - ######## loha - hada_w1_a_name = "{}.hada_w1_a".format(x) - hada_w1_b_name = "{}.hada_w1_b".format(x) - hada_w2_a_name = "{}.hada_w2_a".format(x) - hada_w2_b_name = "{}.hada_w2_b".format(x) - hada_t1_name = "{}.hada_t1".format(x) - hada_t2_name = "{}.hada_t2".format(x) - if hada_w1_a_name in lora.keys(): - hada_t1 = None - hada_t2 = None - if hada_t1_name in lora.keys(): - hada_t1 = lora[hada_t1_name] - hada_t2 = lora[hada_t2_name] - loaded_keys.add(hada_t1_name) - loaded_keys.add(hada_t2_name) - - patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale)) - loaded_keys.add(hada_w1_a_name) - loaded_keys.add(hada_w1_b_name) - loaded_keys.add(hada_w2_a_name) - loaded_keys.add(hada_w2_b_name) - - - ######## lokr - lokr_w1_name = "{}.lokr_w1".format(x) - lokr_w2_name = "{}.lokr_w2".format(x) - lokr_w1_a_name = "{}.lokr_w1_a".format(x) - lokr_w1_b_name = "{}.lokr_w1_b".format(x) - lokr_t2_name = "{}.lokr_t2".format(x) - lokr_w2_a_name = "{}.lokr_w2_a".format(x) - lokr_w2_b_name = "{}.lokr_w2_b".format(x) - - lokr_w1 = None - if lokr_w1_name in lora.keys(): - lokr_w1 = lora[lokr_w1_name] - loaded_keys.add(lokr_w1_name) - - lokr_w2 = None - if lokr_w2_name in lora.keys(): - lokr_w2 = lora[lokr_w2_name] - loaded_keys.add(lokr_w2_name) - - lokr_w1_a = None - if lokr_w1_a_name in lora.keys(): - lokr_w1_a = lora[lokr_w1_a_name] - loaded_keys.add(lokr_w1_a_name) - - lokr_w1_b = None - if lokr_w1_b_name in lora.keys(): - lokr_w1_b = lora[lokr_w1_b_name] - loaded_keys.add(lokr_w1_b_name) - - lokr_w2_a = None - if lokr_w2_a_name in lora.keys(): - lokr_w2_a = lora[lokr_w2_a_name] - loaded_keys.add(lokr_w2_a_name) - - lokr_w2_b = None - if lokr_w2_b_name in lora.keys(): - lokr_w2_b = lora[lokr_w2_b_name] - loaded_keys.add(lokr_w2_b_name) - - lokr_t2 = None - if lokr_t2_name in lora.keys(): - lokr_t2 = lora[lokr_t2_name] - loaded_keys.add(lokr_t2_name) - - if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): - patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale)) - - #glora - a1_name = "{}.a1.weight".format(x) - a2_name = "{}.a2.weight".format(x) - b1_name = "{}.b1.weight".format(x) - b2_name = "{}.b2.weight".format(x) - if a1_name in lora: - patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale)) - loaded_keys.add(a1_name) - loaded_keys.add(a2_name) - loaded_keys.add(b1_name) - loaded_keys.add(b2_name) + for adapter_cls in weight_adapter.adapters: + adapter = adapter_cls.load(x, lora, alpha, dora_scale, loaded_keys) + if adapter is not None: + patch_dict[to_load[x]] = adapter + loaded_keys.update(adapter.loaded_keys) + continue w_norm_name = "{}.w_norm".format(x) b_norm_name = "{}.b_norm".format(x) @@ -307,7 +181,6 @@ def model_lora_keys_unet(model, key_map={}): if k.endswith(".weight"): key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") key_map["lora_unet_{}".format(key_lora)] = k - key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names else: key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names @@ -327,6 +200,13 @@ def model_lora_keys_unet(model, key_map={}): diffusers_lora_key = diffusers_lora_key[:-2] key_map[diffusers_lora_key] = unet_key + if isinstance(model, comfy.model_base.StableCascade_C): + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_prior_unet_{}".format(key_lora)] = k + if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.") for k in diffusers_keys: @@ -380,6 +260,10 @@ def model_lora_keys_unet(model, key_map={}): key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer + for k in sdk: + hidden_size = model.model_config.unet_config.get("hidden_size", 0) + if k.endswith(".weight") and ".linear1." in k: + key_map["{}".format(k.replace(".linear1.weight", ".linear1_qkv"))] = (k, (0, 0, hidden_size * 3)) if isinstance(model, comfy.model_base.GenmoMochi): for k in sdk: @@ -399,29 +283,39 @@ def model_lora_keys_unet(model, key_map={}): key_map["transformer.{}".format(key_lora)] = k key_map["diffusion_model.{}".format(key_lora)] = k # Old loras + if isinstance(model, comfy.model_base.HiDream): + for k in sdk: + if k.startswith("diffusion_model."): + if k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format + key_map["transformer.{}".format(key_lora)] = k #SimpleTuner regular format + + if isinstance(model, comfy.model_base.ACEStep): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official ACE step lora format + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + + if isinstance(model, comfy.model_base.Omnigen2): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["{}".format(key_lora)] = k + + if isinstance(model, comfy.model_base.QwenImage): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): #QwenImage lora format + key_lora = k[len("diffusion_model."):-len(".weight")] + # Direct mapping for transformer_blocks format (QwenImage LoRA format) + key_map["{}".format(key_lora)] = k + # Support transformer prefix format + key_map["transformer.{}".format(key_lora)] = k + key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k #SimpleTuner lycoris format + return key_map -def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): - dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) - lora_diff *= alpha - weight_calc = weight + function(lora_diff).type(weight.dtype) - weight_norm = ( - weight_calc.transpose(0, 1) - .reshape(weight_calc.shape[1], -1) - .norm(dim=1, keepdim=True) - .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) - .transpose(0, 1) - ) - - weight_calc *= (dora_scale / weight_norm).type(weight.dtype) - if strength != 1.0: - weight_calc -= weight - weight += strength * (weight_calc) - else: - weight[:] = weight_calc - return weight - def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: """ Pad a tensor to a new shape with zeros. @@ -476,6 +370,16 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori if isinstance(v, list): v = (calculate_weight(v[1:], v[0][1](comfy.model_management.cast_to_device(v[0][0], weight.device, intermediate_dtype, copy=True), inplace=True), key, intermediate_dtype=intermediate_dtype), ) + if isinstance(v, weight_adapter.WeightAdapterBase): + output = v.calculate_weight(weight, key, strength, strength_model, offset, function, intermediate_dtype, original_weights) + if output is None: + logging.warning("Calculate Weight Failed: {} {}".format(v.name, key)) + else: + weight = output + if old_weight is not None: + weight = old_weight + continue + if len(v) == 1: patch_type = "diff" elif len(v) == 2: @@ -502,157 +406,6 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori diff_weight = comfy.model_management.cast_to_device(target_weight, weight.device, intermediate_dtype) - \ comfy.model_management.cast_to_device(original_weights[key][0][0], weight.device, intermediate_dtype) weight += function(strength * comfy.model_management.cast_to_device(diff_weight, weight.device, weight.dtype)) - elif patch_type == "lora": #lora/locon - mat1 = comfy.model_management.cast_to_device(v[0], weight.device, intermediate_dtype) - mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype) - dora_scale = v[4] - reshape = v[5] - - if reshape is not None: - weight = pad_tensor_to_shape(weight, reshape) - - if v[2] is not None: - alpha = v[2] / mat2.shape[0] - else: - alpha = 1.0 - - if v[3] is not None: - #locon mid weights, hopefully the math is fine because I didn't properly test it - mat3 = comfy.model_management.cast_to_device(v[3], weight.device, intermediate_dtype) - final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] - mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) - try: - lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "lokr": - w1 = v[0] - w2 = v[1] - w1_a = v[3] - w1_b = v[4] - w2_a = v[5] - w2_b = v[6] - t2 = v[7] - dora_scale = v[8] - dim = None - - if w1 is None: - dim = w1_b.shape[0] - w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) - else: - w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) - - if w2 is None: - dim = w2_b.shape[0] - if t2 is None: - w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) - else: - w2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) - else: - w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) - - if len(w2.shape) == 4: - w1 = w1.unsqueeze(2).unsqueeze(2) - if v[2] is not None and dim is not None: - alpha = v[2] / dim - else: - alpha = 1.0 - - try: - lora_diff = torch.kron(w1, w2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "loha": - w1a = v[0] - w1b = v[1] - if v[2] is not None: - alpha = v[2] / w1b.shape[0] - else: - alpha = 1.0 - - w2a = v[3] - w2b = v[4] - dora_scale = v[7] - if v[5] is not None: #cp decomposition - t1 = v[5] - t2 = v[6] - m1 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) - - m2 = torch.einsum('i j k l, j r, i p -> p r k l', - comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) - else: - m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) - m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), - comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) - - try: - lora_diff = (m1 * m2).reshape(weight.shape) - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) - elif patch_type == "glora": - dora_scale = v[5] - - old_glora = False - if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: - rank = v[0].shape[0] - old_glora = True - - if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: - if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: - pass - else: - old_glora = False - rank = v[1].shape[0] - - a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) - a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) - b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) - b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) - - if v[4] is not None: - alpha = v[4] / rank - else: - alpha = 1.0 - - try: - if old_glora: - lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora - else: - if weight.dim() > 2: - lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - else: - lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) - lora_diff += torch.mm(b1, b2).reshape(weight.shape) - - if dora_scale is not None: - weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) - else: - weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) - except Exception as e: - logging.error("ERROR {} {} {}".format(patch_type, key, e)) else: logging.warning("patch type not recognized {} {}".format(patch_type, key)) diff --git a/comfy/lora_convert.py b/comfy/lora_convert.py index 05032c690..9d8d21efe 100644 --- a/comfy/lora_convert.py +++ b/comfy/lora_convert.py @@ -1,4 +1,5 @@ import torch +import comfy.utils def convert_lora_bfl_control(sd): #BFL loras for Flux @@ -11,7 +12,32 @@ def convert_lora_bfl_control(sd): #BFL loras for Flux return sd_out +def convert_lora_wan_fun(sd): #Wan Fun loras + return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"}) + +def convert_uso_lora(sd): + sd_out = {} + for k in sd: + tensor = sd[k] + k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight") + .replace(".up.weight", ".lora_up.weight") + .replace(".qkv_lora2.", ".txt_attn.qkv.") + .replace(".qkv_lora1.", ".img_attn.qkv.") + .replace(".proj_lora1.", ".img_attn.proj.") + .replace(".proj_lora2.", ".txt_attn.proj.") + .replace(".qkv_lora.", ".linear1_qkv.") + .replace(".proj_lora.", ".linear2.") + .replace(".processor.", ".") + ) + sd_out[k_to] = tensor + return sd_out + + def convert_lora(sd): if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd: return convert_lora_bfl_control(sd) + if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd: + return convert_lora_wan_fun(sd) + if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd: + return convert_uso_lora(sd) return sd diff --git a/comfy/model_base.py b/comfy/model_base.py index 141f3f407..e877f19ac 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -16,6 +16,8 @@ along with this program. If not, see . """ +import comfy.ldm.hunyuan3dv2_1 +import comfy.ldm.hunyuan3dv2_1.hunyuandit import torch import logging from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep @@ -33,6 +35,18 @@ import comfy.ldm.audio.embedders import comfy.ldm.flux.model import comfy.ldm.lightricks.model import comfy.ldm.hunyuan_video.model +import comfy.ldm.cosmos.model +import comfy.ldm.cosmos.predict2 +import comfy.ldm.lumina.model +import comfy.ldm.wan.model +import comfy.ldm.wan.model_animate +import comfy.ldm.hunyuan3d.model +import comfy.ldm.hidream.model +import comfy.ldm.chroma.model +import comfy.ldm.chroma_radiance.model +import comfy.ldm.ace.model +import comfy.ldm.omnigen.omnigen2 +import comfy.ldm.qwen_image.model import comfy.model_management import comfy.patcher_extension @@ -41,6 +55,7 @@ import comfy.ops from enum import Enum from . import utils import comfy.latent_formats +import comfy.model_sampling import math from typing import TYPE_CHECKING if TYPE_CHECKING: @@ -55,36 +70,40 @@ class ModelType(Enum): FLOW = 6 V_PREDICTION_CONTINUOUS = 7 FLUX = 8 - - -from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV + IMG_TO_IMG = 9 + FLOW_COSMOS = 10 def model_sampling(model_config, model_type): - s = ModelSamplingDiscrete + s = comfy.model_sampling.ModelSamplingDiscrete if model_type == ModelType.EPS: - c = EPS + c = comfy.model_sampling.EPS elif model_type == ModelType.V_PREDICTION: - c = V_PREDICTION + c = comfy.model_sampling.V_PREDICTION elif model_type == ModelType.V_PREDICTION_EDM: - c = V_PREDICTION - s = ModelSamplingContinuousEDM + c = comfy.model_sampling.V_PREDICTION + s = comfy.model_sampling.ModelSamplingContinuousEDM elif model_type == ModelType.FLOW: c = comfy.model_sampling.CONST s = comfy.model_sampling.ModelSamplingDiscreteFlow elif model_type == ModelType.STABLE_CASCADE: - c = EPS - s = StableCascadeSampling + c = comfy.model_sampling.EPS + s = comfy.model_sampling.StableCascadeSampling elif model_type == ModelType.EDM: - c = EDM - s = ModelSamplingContinuousEDM + c = comfy.model_sampling.EDM + s = comfy.model_sampling.ModelSamplingContinuousEDM elif model_type == ModelType.V_PREDICTION_CONTINUOUS: - c = V_PREDICTION - s = ModelSamplingContinuousV + c = comfy.model_sampling.V_PREDICTION + s = comfy.model_sampling.ModelSamplingContinuousV elif model_type == ModelType.FLUX: c = comfy.model_sampling.CONST s = comfy.model_sampling.ModelSamplingFlux + elif model_type == ModelType.IMG_TO_IMG: + c = comfy.model_sampling.IMG_TO_IMG + elif model_type == ModelType.FLOW_COSMOS: + c = comfy.model_sampling.COSMOS_RFLOW + s = comfy.model_sampling.ModelSamplingCosmosRFlow class ModelSampling(s, c): pass @@ -92,6 +111,15 @@ def model_sampling(model_config, model_type): return ModelSampling(model_config) +def convert_tensor(extra, dtype, device): + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = comfy.model_management.cast_to_device(extra, device, dtype) + else: + extra = comfy.model_management.cast_to_device(extra, device, None) + return extra + + class BaseModel(torch.nn.Module): def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel): super().__init__() @@ -105,11 +133,12 @@ class BaseModel(torch.nn.Module): if not unet_config.get("disable_unet_model_creation", False): if model_config.custom_operations is None: - fp8 = model_config.optimizations.get("fp8", model_config.scaled_fp8 is not None) + fp8 = model_config.optimizations.get("fp8", False) operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8) else: operations = model_config.custom_operations self.diffusion_model = unet_model(**unet_config, device=device, operations=operations) + self.diffusion_model.eval() if comfy.model_management.force_channels_last(): self.diffusion_model.to(memory_format=torch.channels_last) logging.debug("using channels last mode for diffusion model") @@ -125,6 +154,8 @@ class BaseModel(torch.nn.Module): logging.info("model_type {}".format(model_type.name)) logging.debug("adm {}".format(self.adm_channels)) self.memory_usage_factor = model_config.memory_usage_factor + self.memory_usage_factor_conds = () + self.memory_usage_shape_process = {} def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): return comfy.patcher_extension.WrapperExecutor.new_class_executor( @@ -136,8 +167,9 @@ class BaseModel(torch.nn.Module): def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): sigma = t xc = self.model_sampling.calculate_input(sigma, x) + if c_concat is not None: - xc = torch.cat([xc] + [c_concat], dim=1) + xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1) context = c_crossattn dtype = self.get_dtype() @@ -146,25 +178,40 @@ class BaseModel(torch.nn.Module): dtype = self.manual_cast_dtype xc = xc.to(dtype) + device = xc.device t = self.model_sampling.timestep(t).float() - context = context.to(dtype) + if context is not None: + context = comfy.model_management.cast_to_device(context, device, dtype) + extra_conds = {} for o in kwargs: extra = kwargs[o] + if hasattr(extra, "dtype"): - if extra.dtype != torch.int and extra.dtype != torch.long: - extra = extra.to(dtype) + extra = convert_tensor(extra, dtype, device) + elif isinstance(extra, list): + ex = [] + for ext in extra: + ex.append(convert_tensor(ext, dtype, device)) + extra = ex extra_conds[o] = extra - model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() - return self.model_sampling.calculate_denoised(sigma, model_output, x) + t = self.process_timestep(t, x=x, **extra_conds) + if "latent_shapes" in extra_conds: + xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes")) + + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds) + if len(model_output) > 1 and not torch.is_tensor(model_output): + model_output, _ = utils.pack_latents(model_output) + + return self.model_sampling.calculate_denoised(sigma, model_output.float(), x) + + def process_timestep(self, timestep, **kwargs): + return timestep def get_dtype(self): return self.diffusion_model.dtype - def is_adm(self): - return self.adm_channels > 0 - def encode_adm(self, **kwargs): return None @@ -183,14 +230,20 @@ class BaseModel(torch.nn.Module): if concat_latent_image.shape[1:] != noise.shape[1:]: concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + if noise.ndim == 5: + if concat_latent_image.shape[-3] < noise.shape[-3]: + concat_latent_image = torch.nn.functional.pad(concat_latent_image, (0, 0, 0, 0, 0, noise.shape[-3] - concat_latent_image.shape[-3]), "constant", 0) + else: + concat_latent_image = concat_latent_image[:, :, :noise.shape[-3]] concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) if denoise_mask is not None: if len(denoise_mask.shape) == len(noise.shape): - denoise_mask = denoise_mask[:,:1] + denoise_mask = denoise_mask[:, :1] - denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) + num_dim = noise.ndim - 2 + denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:])) if denoise_mask.shape[-2:] != noise.shape[-2:]: denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) @@ -200,12 +253,21 @@ class BaseModel(torch.nn.Module): if ck == "mask": cond_concat.append(denoise_mask.to(device)) elif ck == "masked_image": - cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space + cond_concat.append(concat_latent_image.to(device)) # NOTE: the latent_image should be masked by the mask in pixel space + elif ck == "mask_inverted": + cond_concat.append(1.0 - denoise_mask.to(device)) else: if ck == "mask": - cond_concat.append(torch.ones_like(noise)[:,:1]) + cond_concat.append(torch.ones_like(noise)[:, :1]) elif ck == "masked_image": cond_concat.append(self.blank_inpaint_image_like(noise)) + elif ck == "mask_inverted": + cond_concat.append(torch.zeros_like(noise)[:, :1]) + if ck == "concat_image": + if concat_latent_image is not None: + cond_concat.append(concat_latent_image.to(device)) + else: + cond_concat.append(torch.zeros_like(noise)) data = torch.cat(cond_concat, dim=1) return data return None @@ -293,19 +355,38 @@ class BaseModel(torch.nn.Module): return blank_image self.blank_inpaint_image_like = blank_inpaint_image_like - def memory_required(self, input_shape): + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image) + + def memory_required(self, input_shape, cond_shapes={}): + input_shapes = [input_shape] + for c in self.memory_usage_factor_conds: + shape = cond_shapes.get(c, None) + if shape is not None: + if c in self.memory_usage_shape_process: + out = [] + for s in shape: + out.append(self.memory_usage_shape_process[c](s)) + shape = out + + if len(shape) > 0: + input_shapes += shape + if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention(): dtype = self.get_dtype() if self.manual_cast_dtype is not None: dtype = self.manual_cast_dtype #TODO: this needs to be tweaked - area = input_shape[0] * math.prod(input_shape[2:]) + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024) else: #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. - area = input_shape[0] * math.prod(input_shape[2:]) + area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes)) return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024) + def extra_conds_shapes(self, **kwargs): + return {} + def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): adm_inputs = [] @@ -340,7 +421,7 @@ class SD21UNCLIP(BaseModel): unclip_conditioning = kwargs.get("unclip_conditioning", None) device = kwargs["device"] if unclip_conditioning is None: - return torch.zeros((1, self.adm_channels)) + return torch.zeros((1, self.adm_channels), device=device) else: return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) @@ -540,6 +621,10 @@ class SD_X4Upscaler(BaseModel): out['c_concat'] = comfy.conds.CONDNoiseShape(image) out['y'] = comfy.conds.CONDRegular(noise_level) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) return out class IP2P: @@ -550,9 +635,11 @@ class IP2P: if image is None: image = torch.zeros_like(noise) + else: + image = image.to(device=device) if image.shape[1:] != noise.shape[1:]: - image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center") image = utils.resize_to_batch_size(image, noise.shape[0]) return self.process_ip2p_image_in(image) @@ -572,11 +659,23 @@ class SDXL_instructpix2pix(IP2P, SDXL): else: self.process_ip2p_image_in = lambda image: image #diffusers ip2p +class Lotus(BaseModel): + def extra_conds(self, **kwargs): + out = {} + cross_attn = kwargs.get("cross_attn", None) + out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn) + device = kwargs["device"] + task_emb = torch.tensor([1, 0]).float().to(device) + task_emb = torch.cat([torch.sin(task_emb), torch.cos(task_emb)]).unsqueeze(0) + out['y'] = comfy.conds.CONDRegular(task_emb) + return out + + def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, device=None): + super().__init__(model_config, model_type, device=device) class StableCascade_C(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageC) - self.diffusion_model.eval().requires_grad_(False) def extra_conds(self, **kwargs): out = {} @@ -605,7 +704,6 @@ class StableCascade_C(BaseModel): class StableCascade_B(BaseModel): def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None): super().__init__(model_config, model_type, device=device, unet_model=StageB) - self.diffusion_model.eval().requires_grad_(False) def extra_conds(self, **kwargs): out = {} @@ -618,7 +716,7 @@ class StableCascade_B(BaseModel): #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device)) - out["effnet"] = comfy.conds.CONDRegular(prior) + out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device)) out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,))) return out @@ -739,8 +837,9 @@ class PixArt(BaseModel): return out class Flux(BaseModel): - def __init__(self, model_config, model_type=ModelType.FLUX, device=None): - super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux) + def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.flux.model.Flux): + super().__init__(model_config, model_type, device=device, unet_model=unet_model) + self.memory_usage_factor_conds = ("ref_latents",) def concat_cond(self, **kwargs): try: @@ -797,9 +896,31 @@ class Flux(BaseModel): (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size)) attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok)) out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)])) + + guidance = kwargs.get("guidance", 3.5) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + latents = [] + for lat in ref_latents: + latents.append(self.process_latent_in(lat)) + out['ref_latents'] = comfy.conds.CONDList(latents) + + ref_latents_method = kwargs.get("reference_latents_method", None) + if ref_latents_method is not None: + out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) return out + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + return out + + class GenmoMochi(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.genmo.joint_model.asymm_models_joint.AsymmDiTJoint) @@ -828,17 +949,26 @@ class LTXV(BaseModel): if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - guiding_latent = kwargs.get("guiding_latent", None) - if guiding_latent is not None: - out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent) - - guiding_latent_noise_scale = kwargs.get("guiding_latent_noise_scale", None) - if guiding_latent_noise_scale is not None: - out["guiding_latent_noise_scale"] = comfy.conds.CONDConstant(guiding_latent_noise_scale) - out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25)) + + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + + keyframe_idxs = kwargs.get("keyframe_idxs", None) + if keyframe_idxs is not None: + out['keyframe_idxs'] = comfy.conds.CONDRegular(keyframe_idxs) + return out + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + return self.diffusion_model.patchifier.patchify(((denoise_mask) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1)))[:, :1])[0] + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return latent_image + class HunyuanVideo(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) @@ -854,5 +984,547 @@ class HunyuanVideo(BaseModel): cross_attn = kwargs.get("cross_attn", None) if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) - out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)])) + + guidance = kwargs.get("guidance", 6.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + guiding_frame_index = kwargs.get("guiding_frame_index", None) + if guiding_frame_index is not None: + out['guiding_frame_index'] = comfy.conds.CONDRegular(torch.FloatTensor([guiding_frame_index])) + + ref_latent = kwargs.get("ref_latent", None) + if ref_latent is not None: + out['ref_latent'] = comfy.conds.CONDRegular(self.process_latent_in(ref_latent)) + + return out + + def scale_latent_inpaint(self, latent_image, **kwargs): + return latent_image + +class HunyuanVideoI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image", "mask_inverted") + + def scale_latent_inpaint(self, latent_image, **kwargs): + return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device) + self.concat_keys = ("concat_image",) + + def scale_latent_inpaint(self, latent_image, **kwargs): + return super().scale_latent_inpaint(latent_image=latent_image, **kwargs) + +class CosmosVideo(BaseModel): + def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT) + self.image_to_video = image_to_video + if self.image_to_video: + self.concat_keys = ("mask_inverted",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) + return out + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) + sigma_noise_augmentation = 0 #TODO + if sigma_noise_augmentation != 0: + latent_image = latent_image + noise + latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) + return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5) + +class CosmosPredict2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.predict2.MiniTrainDIT) + self.image_to_video = image_to_video + if self.image_to_video: + self.concat_keys = ("mask_inverted",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + + out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None)) + return out + + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + if denoise_mask.ndim <= 4: + return timestep + condition_video_mask_B_1_T_1_1 = denoise_mask.mean(dim=[1, 3, 4], keepdim=True) + c_noise_B_1_T_1_1 = 0.0 * (1.0 - condition_video_mask_B_1_T_1_1) + timestep.reshape(timestep.shape[0], 1, 1, 1, 1) * condition_video_mask_B_1_T_1_1 + out = c_noise_B_1_T_1_1.squeeze(dim=[1, 3, 4]) + return out + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)) + sigma_noise_augmentation = 0 #TODO + if sigma_noise_augmentation != 0: + latent_image = latent_image + noise + latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image) + sigma = (sigma / (sigma + 1)) + return latent_image / (1.0 - sigma) + +class Lumina2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out + +class WAN21(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) + self.image_to_video = image_to_video + + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1] + if extra_channels == 0: + return None + + image = kwargs.get("concat_latent_image", None) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + shape_image[1] = extra_channels + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + latent_dim = self.latent_format.latent_channels + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + for i in range(0, image.shape[1], latent_dim): + image[:, i: i + latent_dim] = self.process_latent_in(image[:, i: i + latent_dim]) + image = utils.resize_to_batch_size(image, noise.shape[0]) + + if extra_channels != image.shape[1] + 4: + if not self.image_to_video or extra_channels == image.shape[1]: + return image + + if image.shape[1] > (extra_channels - 4): + image = image[:, :(extra_channels - 4)] + + mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + if mask is None: + mask = torch.zeros_like(noise)[:, :4] + else: + if mask.shape[1] != 4: + mask = torch.mean(mask, dim=1, keepdim=True) + mask = 1.0 - mask + mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + if mask.shape[-3] < noise.shape[-3]: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0) + if mask.shape[1] == 1: + mask = mask.repeat(1, 4, 1, 1, 1) + mask = utils.resize_to_batch_size(mask, noise.shape[0]) + + concat_mask_index = kwargs.get("concat_mask_index", 0) + if concat_mask_index != 0: + return torch.cat((image[:, :concat_mask_index], mask, image[:, concat_mask_index:]), dim=1) + else: + return torch.cat((mask, image), dim=1) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + clip_vision_output = kwargs.get("clip_vision_output", None) + if clip_vision_output is not None: + out['clip_fea'] = comfy.conds.CONDRegular(clip_vision_output.penultimate_hidden_states) + + time_dim_concat = kwargs.get("time_dim_concat", None) + if time_dim_concat is not None: + out['time_dim_concat'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_concat)) + + reference_latents = kwargs.get("reference_latents", None) + if reference_latents is not None: + out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])[:, :, 0]) + + return out + + +class WAN21_Vace(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.VaceWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + noise_shape = list(noise.shape) + vace_frames = kwargs.get("vace_frames", None) + if vace_frames is None: + noise_shape[1] = 32 + vace_frames = [torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)] + + mask = kwargs.get("vace_mask", None) + if mask is None: + noise_shape[1] = 64 + mask = [torch.ones(noise_shape, device=noise.device, dtype=noise.dtype)] * len(vace_frames) + + vace_frames_out = [] + for j in range(len(vace_frames)): + vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True) + for i in range(0, vf.shape[1], 16): + vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16]) + vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1) + vace_frames_out.append(vf) + + vace_frames = torch.stack(vace_frames_out, dim=1) + out['vace_context'] = comfy.conds.CONDRegular(vace_frames) + + vace_strength = kwargs.get("vace_strength", [1.0] * len(vace_frames_out)) + out['vace_strength'] = comfy.conds.CONDConstant(vace_strength) + return out + +class WAN21_Camera(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.CameraWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + camera_conditions = kwargs.get("camera_conditions", None) + if camera_conditions is not None: + out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions) + return out + +class WAN21_HuMo(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.HumoWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + + audio_embed = kwargs.get("audio_embed", None) + if audio_embed is not None: + out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) + + if "c_concat" not in out: # 1.7B model + reference_latents = kwargs.get("reference_latents", None) + if reference_latents is not None: + out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])) + else: + noise_shape = list(noise.shape) + noise_shape[1] += 4 + concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype) + zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1) + zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1) + zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1) + concat_latent[:, 4:] = zero_vae_values + concat_latent[:, 4:, :1] = zero_vae_values_first + concat_latent[:, 4:, 1:2] = zero_vae_values_second + out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent) + reference_latents = kwargs.get("reference_latents", None) + if reference_latents is not None: + ref_latent = self.process_latent_in(reference_latents[-1]) + ref_latent_shape = list(ref_latent.shape) + ref_latent_shape[1] += 4 + ref_latent_shape[1] + ref_latent_full = torch.zeros(ref_latent_shape, device=ref_latent.device, dtype=ref_latent.dtype) + ref_latent_full[:, 20:] = ref_latent + ref_latent_full[:, 16:20] = 1.0 + out['reference_latent'] = comfy.conds.CONDRegular(ref_latent_full) + + return out + +class WAN22_Animate(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + face_video_pixels = kwargs.get("face_video_pixels", None) + if face_video_pixels is not None: + out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels) + + pose_latents = kwargs.get("pose_video_latent", None) + if pose_latents is not None: + out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents)) + return out + +class WAN22_S2V(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V) + self.memory_usage_factor_conds = ("reference_latent", "reference_motion") + self.memory_usage_shape_process = {"reference_motion": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]} + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + audio_embed = kwargs.get("audio_embed", None) + if audio_embed is not None: + out['audio_embed'] = comfy.conds.CONDRegular(audio_embed) + + reference_latents = kwargs.get("reference_latents", None) + if reference_latents is not None: + out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1])) + + reference_motion = kwargs.get("reference_motion", None) + if reference_motion is not None: + out['reference_motion'] = comfy.conds.CONDRegular(self.process_latent_in(reference_motion)) + + control_video = kwargs.get("control_video", None) + if control_video is not None: + out['control_video'] = comfy.conds.CONDRegular(self.process_latent_in(control_video)) + return out + + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['reference_latent'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + + reference_motion = kwargs.get("reference_motion", None) + if reference_motion is not None: + out['reference_motion'] = reference_motion.shape + return out + +class WAN22(WAN21): + def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None): + super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel) + self.image_to_video = image_to_video + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + denoise_mask = kwargs.get("denoise_mask", None) + if denoise_mask is not None: + out["denoise_mask"] = comfy.conds.CONDRegular(denoise_mask) + return out + + def process_timestep(self, timestep, x, denoise_mask=None, **kwargs): + if denoise_mask is None: + return timestep + temp_ts = (torch.mean(denoise_mask[:, :, :, :, :], dim=(1, 3, 4), keepdim=True) * timestep.view([timestep.shape[0]] + [1] * (denoise_mask.ndim - 1))).reshape(timestep.shape[0], -1) + return temp_ts + + def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs): + return latent_image + +class Hunyuan3Dv2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3d.model.Hunyuan3Dv2) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + guidance = kwargs.get("guidance", 5.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out + +class Hunyuan3Dv2_1(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan3dv2_1.hunyuandit.HunYuanDiTPlain) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + guidance = kwargs.get("guidance", 5.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out + +class HiDream(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hidream.model.HiDreamImageTransformer2DModel) + + def encode_adm(self, **kwargs): + return kwargs["pooled_output"] + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + conditioning_llama3 = kwargs.get("conditioning_llama3", None) + if conditioning_llama3 is not None: + out['encoder_hidden_states_llama3'] = comfy.conds.CONDRegular(conditioning_llama3) + image_cond = kwargs.get("concat_latent_image", None) + if image_cond is not None: + out['image_cond'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_cond)) + return out + +class Chroma(Flux): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma): + super().__init__(model_config, model_type, device=device, unet_model=unet_model) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + + guidance = kwargs.get("guidance", 0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + return out + +class ChromaRadiance(Chroma): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma_radiance.model.ChromaRadiance) + +class ACEStep(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + noise = kwargs.get("noise", None) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + conditioning_lyrics = kwargs.get("conditioning_lyrics", None) + if cross_attn is not None: + out['lyric_token_idx'] = comfy.conds.CONDRegular(conditioning_lyrics) + out['speaker_embeds'] = comfy.conds.CONDRegular(torch.zeros(noise.shape[0], 512, device=noise.device, dtype=noise.dtype)) + out['lyrics_strength'] = comfy.conds.CONDConstant(kwargs.get("lyrics_strength", 1.0)) + return out + +class Omnigen2(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.omnigen.omnigen2.OmniGen2Transformer2DModel) + self.memory_usage_factor_conds = ("ref_latents",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item())) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + latents = [] + for lat in ref_latents: + latents.append(self.process_latent_in(lat)) + out['ref_latents'] = comfy.conds.CONDList(latents) + return out + + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + return out + +class QwenImage(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLUX, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.qwen_image.model.QwenImageTransformer2DModel) + self.memory_usage_factor_conds = ("ref_latents",) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + latents = [] + for lat in ref_latents: + latents.append(self.process_latent_in(lat)) + out['ref_latents'] = comfy.conds.CONDList(latents) + + ref_latents_method = kwargs.get("reference_latents_method", None) + if ref_latents_method is not None: + out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method) + return out + + def extra_conds_shapes(self, **kwargs): + out = {} + ref_latents = kwargs.get("reference_latents", None) + if ref_latents is not None: + out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16]) + return out + +class HunyuanImage21(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hunyuan_video.model.HunyuanVideo) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + attention_mask = kwargs.get("attention_mask", None) + if attention_mask is not None: + if torch.numel(attention_mask) != attention_mask.sum(): + out['attention_mask'] = comfy.conds.CONDRegular(attention_mask) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + + conditioning_byt5small = kwargs.get("conditioning_byt5small", None) + if conditioning_byt5small is not None: + out['txt_byt5'] = comfy.conds.CONDRegular(conditioning_byt5small) + + guidance = kwargs.get("guidance", 6.0) + if guidance is not None: + out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance])) + + return out + +class HunyuanImage21Refiner(HunyuanImage21): + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + image = kwargs.get("concat_latent_image", None) + noise_augmentation = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + + if image is None: + shape_image = list(noise.shape) + image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device) + else: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + image = self.process_latent_in(image) + image = utils.resize_to_batch_size(image, noise.shape[0]) + if noise_augmentation > 0: + generator = torch.Generator(device="cpu") + generator.manual_seed(kwargs.get("seed", 0) - 10) + noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device) + image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image + else: + image = 0.75 * image + return image + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + out['disable_time_r'] = comfy.conds.CONDConstant(True) return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index f5a33cd9b..141f1e164 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -1,3 +1,4 @@ +import json import comfy.supported_models import comfy.supported_models_base import comfy.utils @@ -33,7 +34,7 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict): return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross return None -def detect_unet_config(state_dict, key_prefix): +def detect_unet_config(state_dict, key_prefix, metadata=None): state_dict_keys = list(state_dict.keys()) if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model @@ -135,25 +136,45 @@ def detect_unet_config(state_dict, key_prefix): if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video dit_config = {} + in_w = state_dict['{}img_in.proj.weight'.format(key_prefix)] + out_w = state_dict['{}final_layer.linear.weight'.format(key_prefix)] dit_config["image_model"] = "hunyuan_video" - dit_config["in_channels"] = 16 - dit_config["patch_size"] = [1, 2, 2] - dit_config["out_channels"] = 16 - dit_config["vec_in_dim"] = 768 - dit_config["context_in_dim"] = 4096 - dit_config["hidden_size"] = 3072 + dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels + dit_config["patch_size"] = list(in_w.shape[2:]) + dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"]) + if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys): + dit_config["vec_in_dim"] = 768 + else: + dit_config["vec_in_dim"] = None + + if len(dit_config["patch_size"]) == 2: + dit_config["axes_dim"] = [64, 64] + else: + dit_config["axes_dim"] = [16, 56, 56] + + if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys): + dit_config["meanflow"] = True + else: + dit_config["meanflow"] = False + + dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1] + dit_config["hidden_size"] = in_w.shape[0] dit_config["mlp_ratio"] = 4.0 - dit_config["num_heads"] = 24 + dit_config["num_heads"] = in_w.shape[0] // 128 dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') - dit_config["axes_dim"] = [16, 56, 56] dit_config["theta"] = 256 dit_config["qkv_bias"] = True + if '{}byt5_in.fc1.weight'.format(key_prefix) in state_dict: + dit_config["byt5"] = True + else: + dit_config["byt5"] = False + guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys)) dit_config["guidance_embed"] = len(guidance_keys) > 0 return dit_config - if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux + if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight) dit_config = {} dit_config["image_model"] = "flux" dit_config["in_channels"] = 16 @@ -163,7 +184,9 @@ def detect_unet_config(state_dict, key_prefix): if in_key in state_dict_keys: dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size) dit_config["out_channels"] = 16 - dit_config["vec_in_dim"] = 768 + vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix) + if vec_in_key in state_dict_keys: + dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1] dit_config["context_in_dim"] = 4096 dit_config["hidden_size"] = 3072 dit_config["mlp_ratio"] = 4.0 @@ -173,7 +196,28 @@ def detect_unet_config(state_dict, key_prefix): dit_config["axes_dim"] = [16, 56, 56] dit_config["theta"] = 10000 dit_config["qkv_bias"] = True - dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma + dit_config["image_model"] = "chroma" + dit_config["in_channels"] = 64 + dit_config["out_channels"] = 64 + dit_config["in_dim"] = 64 + dit_config["out_dim"] = 3072 + dit_config["hidden_dim"] = 5120 + dit_config["n_layers"] = 5 + if f"{key_prefix}nerf_blocks.0.norm.scale" in state_dict_keys: #Chroma Radiance + dit_config["image_model"] = "chroma_radiance" + dit_config["in_channels"] = 3 + dit_config["out_channels"] = 3 + dit_config["patch_size"] = 16 + dit_config["nerf_hidden_size"] = 64 + dit_config["nerf_mlp_ratio"] = 4 + dit_config["nerf_depth"] = 4 + dit_config["nerf_max_freqs"] = 8 + dit_config["nerf_tile_size"] = 512 + dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear" + dit_config["nerf_embedder_dtype"] = torch.float32 + else: + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys return dit_config if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview @@ -210,6 +254,37 @@ def detect_unet_config(state_dict, key_prefix): if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv dit_config = {} dit_config["image_model"] = "ltxv" + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') + shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape + dit_config["attention_head_dim"] = shape[0] // 32 + dit_config["cross_attention_dim"] = shape[1] + if metadata is not None and "config" in metadata: + dit_config.update(json.loads(metadata["config"]).get("transformer", {})) + return dit_config + + if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model + dit_config = {} + dit_config["audio_model"] = "ace" + dit_config["attention_head_dim"] = 128 + dit_config["in_channels"] = 8 + dit_config["inner_dim"] = 2560 + dit_config["max_height"] = 16 + dit_config["max_position"] = 32768 + dit_config["max_width"] = 32768 + dit_config["mlp_ratio"] = 2.5 + dit_config["num_attention_heads"] = 20 + dit_config["num_layers"] = 24 + dit_config["out_channels"] = 8 + dit_config["patch_size"] = [16, 1] + dit_config["rope_theta"] = 1000000.0 + dit_config["speaker_embedding_dim"] = 512 + dit_config["text_embedding_dim"] = 768 + + dit_config["ssl_encoder_depths"] = [8, 8] + dit_config["ssl_latent_dims"] = [1024, 768] + dit_config["ssl_names"] = ["mert", "m-hubert"] + dit_config["lyric_encoder_vocab_size"] = 6693 + dit_config["lyric_hidden_size"] = 1024 return dit_config if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt @@ -239,6 +314,240 @@ def detect_unet_config(state_dict, key_prefix): dit_config["micro_condition"] = False return dit_config + if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos + dit_config = {} + dit_config["image_model"] = "cosmos" + dit_config["max_img_h"] = 240 + dit_config["max_img_w"] = 240 + dit_config["max_frames"] = 128 + concat_padding_mask = True + dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) + dit_config["out_channels"] = 16 + dit_config["patch_spatial"] = 2 + dit_config["patch_temporal"] = 1 + dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0] + dit_config["block_config"] = "FA-CA-MLP" + dit_config["concat_padding_mask"] = concat_padding_mask + dit_config["pos_emb_cls"] = "rope3d" + dit_config["pos_emb_learnable"] = False + dit_config["pos_emb_interpolation"] = "crop" + dit_config["block_x_format"] = "THWBD" + dit_config["affline_emb_norm"] = True + dit_config["use_adaln_lora"] = True + dit_config["adaln_lora_dim"] = 256 + + if dit_config["model_channels"] == 4096: + # 7B + dit_config["num_blocks"] = 28 + dit_config["num_heads"] = 32 + dit_config["extra_per_block_abs_pos_emb"] = True + dit_config["rope_h_extrapolation_ratio"] = 1.0 + dit_config["rope_w_extrapolation_ratio"] = 1.0 + dit_config["rope_t_extrapolation_ratio"] = 2.0 + dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" + else: # 5120 + # 14B + dit_config["num_blocks"] = 36 + dit_config["num_heads"] = 40 + dit_config["extra_per_block_abs_pos_emb"] = True + dit_config["rope_h_extrapolation_ratio"] = 2.0 + dit_config["rope_w_extrapolation_ratio"] = 2.0 + dit_config["rope_t_extrapolation_ratio"] = 2.0 + dit_config["extra_h_extrapolation_ratio"] = 2.0 + dit_config["extra_w_extrapolation_ratio"] = 2.0 + dit_config["extra_t_extrapolation_ratio"] = 2.0 + dit_config["extra_per_block_abs_pos_emb_type"] = "learnable" + return dit_config + + if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys: # Lumina 2 + dit_config = {} + dit_config["image_model"] = "lumina2" + dit_config["patch_size"] = 2 + dit_config["in_channels"] = 16 + dit_config["dim"] = 2304 + dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1] + dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.') + dit_config["n_heads"] = 24 + dit_config["n_kv_heads"] = 8 + dit_config["qk_norm"] = True + dit_config["axes_dims"] = [32, 32, 32] + dit_config["axes_lens"] = [300, 512, 512] + return dit_config + + if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 + dit_config = {} + dit_config["image_model"] = "wan2.1" + dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1] + out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4 + dit_config["dim"] = dim + dit_config["out_dim"] = out_dim + dit_config["num_heads"] = dim // 128 + dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0] + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + dit_config["patch_size"] = (1, 2, 2) + dit_config["freq_dim"] = 256 + dit_config["window_size"] = (-1, -1) + dit_config["qk_norm"] = True + dit_config["cross_attn_norm"] = True + dit_config["eps"] = 1e-6 + dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1] + if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "vace" + dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1] + dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.') + elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys: + if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "camera" + else: + dit_config["model_type"] = "camera_2.2" + elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "s2v" + elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "humo" + elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "animate" + else: + if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys: + dit_config["model_type"] = "i2v" + else: + dit_config["model_type"] = "t2v" + flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix)) + if flf_weight is not None: + dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1] + + ref_conv_weight = state_dict.get('{}ref_conv.weight'.format(key_prefix)) + if ref_conv_weight is not None: + dit_config["in_dim_ref_conv"] = ref_conv_weight.shape[1] + + return dit_config + + if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D + in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape + dit_config = {} + dit_config["image_model"] = "hunyuan3d2" + dit_config["in_channels"] = in_shape[1] + dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1] + dit_config["hidden_size"] = in_shape[0] + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 16 + dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.') + dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.') + dit_config["qkv_bias"] = True + dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys + return dit_config + + if f"{key_prefix}t_embedder.mlp.2.weight" in state_dict_keys: # Hunyuan 3D 2.1 + + dit_config = {} + dit_config["image_model"] = "hunyuan3d2_1" + dit_config["in_channels"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[1] + dit_config["context_dim"] = 1024 + dit_config["hidden_size"] = state_dict[f"{key_prefix}x_embedder.weight"].shape[0] + dit_config["mlp_ratio"] = 4.0 + dit_config["num_heads"] = 16 + dit_config["depth"] = count_blocks(state_dict_keys, f"{key_prefix}blocks.{{}}") + dit_config["qkv_bias"] = False + dit_config["guidance_cond_proj_dim"] = None#f"{key_prefix}t_embedder.cond_proj.weight" in state_dict_keys + return dit_config + + if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream + dit_config = {} + dit_config["image_model"] = "hidream" + dit_config["attention_head_dim"] = 128 + dit_config["axes_dims_rope"] = [64, 32, 32] + dit_config["caption_channels"] = [4096, 4096] + dit_config["max_resolution"] = [128, 128] + dit_config["in_channels"] = 16 + dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31] + dit_config["num_attention_heads"] = 20 + dit_config["num_routed_experts"] = 4 + dit_config["num_activated_experts"] = 2 + dit_config["num_layers"] = 16 + dit_config["num_single_layers"] = 32 + dit_config["out_channels"] = 16 + dit_config["patch_size"] = 2 + dit_config["text_emb_dim"] = 2048 + return dit_config + + if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2 + dit_config = {} + dit_config["image_model"] = "cosmos_predict2" + dit_config["max_img_h"] = 240 + dit_config["max_img_w"] = 240 + dit_config["max_frames"] = 128 + concat_padding_mask = True + dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask) + dit_config["out_channels"] = 16 + dit_config["patch_spatial"] = 2 + dit_config["patch_temporal"] = 1 + dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0] + dit_config["concat_padding_mask"] = concat_padding_mask + dit_config["crossattn_emb_channels"] = 1024 + dit_config["pos_emb_cls"] = "rope3d" + dit_config["pos_emb_learnable"] = True + dit_config["pos_emb_interpolation"] = "crop" + dit_config["min_fps"] = 1 + dit_config["max_fps"] = 30 + + dit_config["use_adaln_lora"] = True + dit_config["adaln_lora_dim"] = 256 + if dit_config["model_channels"] == 2048: + dit_config["num_blocks"] = 28 + dit_config["num_heads"] = 16 + elif dit_config["model_channels"] == 5120: + dit_config["num_blocks"] = 36 + dit_config["num_heads"] = 40 + + if dit_config["in_channels"] == 16: + dit_config["extra_per_block_abs_pos_emb"] = False + dit_config["rope_h_extrapolation_ratio"] = 4.0 + dit_config["rope_w_extrapolation_ratio"] = 4.0 + dit_config["rope_t_extrapolation_ratio"] = 1.0 + elif dit_config["in_channels"] == 17: # img to video + if dit_config["model_channels"] == 2048: + dit_config["extra_per_block_abs_pos_emb"] = False + dit_config["rope_h_extrapolation_ratio"] = 3.0 + dit_config["rope_w_extrapolation_ratio"] = 3.0 + dit_config["rope_t_extrapolation_ratio"] = 1.0 + elif dit_config["model_channels"] == 5120: + dit_config["rope_h_extrapolation_ratio"] = 2.0 + dit_config["rope_w_extrapolation_ratio"] = 2.0 + dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334 + + dit_config["extra_h_extrapolation_ratio"] = 1.0 + dit_config["extra_w_extrapolation_ratio"] = 1.0 + dit_config["extra_t_extrapolation_ratio"] = 1.0 + dit_config["rope_enable_fps_modulation"] = False + + return dit_config + + if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2 + dit_config = {} + dit_config["image_model"] = "omnigen2" + dit_config["axes_dim_rope"] = [40, 40, 40] + dit_config["axes_lens"] = [1024, 1664, 1664] + dit_config["ffn_dim_multiplier"] = None + dit_config["hidden_size"] = 2520 + dit_config["in_channels"] = 16 + dit_config["multiple_of"] = 256 + dit_config["norm_eps"] = 1e-05 + dit_config["num_attention_heads"] = 21 + dit_config["num_kv_heads"] = 7 + dit_config["num_layers"] = 32 + dit_config["num_refiner_layers"] = 2 + dit_config["out_channels"] = None + dit_config["patch_size"] = 2 + dit_config["text_feat_dim"] = 2048 + dit_config["timestep_scale"] = 1000.0 + return dit_config + + if '{}txt_norm.weight'.format(key_prefix) in state_dict_keys: # Qwen Image + dit_config = {} + dit_config["image_model"] = "qwen_image" + dit_config["in_channels"] = state_dict['{}img_in.weight'.format(key_prefix)].shape[1] + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.') + return dit_config + if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: return None @@ -373,8 +682,8 @@ def model_config_from_unet_config(unet_config, state_dict=None): logging.error("no match {}".format(unet_config)) return None -def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False): - unet_config = detect_unet_config(state_dict, unet_key_prefix) +def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None): + unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata) if unet_config is None: return None model_config = model_config_from_unet_config(unet_config, state_dict) @@ -387,12 +696,17 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal model_config.scaled_fp8 = scaled_fp8_weight.dtype if model_config.scaled_fp8 == torch.float32: model_config.scaled_fp8 = torch.float8_e4m3fn + if scaled_fp8_weight.nelement() == 2: + model_config.optimizations["fp8"] = False + else: + model_config.optimizations["fp8"] = True return model_config def unet_prefix_from_state_dict(state_dict): candidates = ["model.diffusion_model.", #ldm/sgm models "model.model.", #audio models + "net.", #cosmos ] counts = {k: 0 for k in candidates} for k in state_dict: @@ -447,6 +761,9 @@ def convert_config(unet_config): def unet_config_from_diffusers_unet(state_dict, dtype=None): + if "conv_in.weight" not in state_dict: + return None + match = {} transformer_depth = [] @@ -578,8 +895,13 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None): 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], 'use_temporal_attention': False, 'use_temporal_resblock': False} + LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4, + 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} - supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] + supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint] for unet_config in supported_models: matches = True @@ -612,7 +934,7 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""): depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.') hidden_size = state_dict["x_embedder.bias"].shape[0] sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix) - elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3 + elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict and 'pos_embed.proj.weight' in state_dict: #SD3 num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.') depth = state_dict["pos_embed.proj.weight"].shape[0] // 64 sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix) diff --git a/comfy/model_management.py b/comfy/model_management.py index f6dfc18b0..afe78f36e 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -19,9 +19,10 @@ import psutil import logging from enum import Enum -from comfy.cli_args import args +from comfy.cli_args import args, PerformanceFeature import torch import sys +import importlib import platform import weakref import gc @@ -46,11 +47,38 @@ cpu_state = CPUState.GPU total_vram = 0 +def get_supported_float8_types(): + float8_types = [] + try: + float8_types.append(torch.float8_e4m3fn) + except: + pass + try: + float8_types.append(torch.float8_e4m3fnuz) + except: + pass + try: + float8_types.append(torch.float8_e5m2) + except: + pass + try: + float8_types.append(torch.float8_e5m2fnuz) + except: + pass + try: + float8_types.append(torch.float8_e8m0fnu) + except: + pass + return float8_types + +FLOAT8_TYPES = get_supported_float8_types() + xpu_available = False torch_version = "" try: torch_version = torch.version.__version__ - xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available() + temp = torch_version.split(".") + torch_version_numeric = (int(temp[0]), int(temp[1])) except: pass @@ -61,6 +89,7 @@ if args.deterministic: directml_enabled = False if args.directml is not None: + logging.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.") import torch_directml directml_enabled = True device_index = args.directml @@ -73,11 +102,15 @@ if args.directml is not None: lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. try: - import intel_extension_for_pytorch as ipex - _ = torch.xpu.device_count() - xpu_available = xpu_available or torch.xpu.is_available() + import intel_extension_for_pytorch as ipex # noqa: F401 except: - xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available()) + pass + +try: + _ = torch.xpu.device_count() + xpu_available = torch.xpu.is_available() +except: + xpu_available = False try: if torch.backends.mps.is_available(): @@ -93,6 +126,18 @@ try: except: npu_available = False +try: + import torch_mlu # noqa: F401 + _ = torch.mlu.device_count() + mlu_available = torch.mlu.is_available() +except: + mlu_available = False + +try: + ixuca_available = hasattr(torch, "corex") +except: + ixuca_available = False + if args.cpu: cpu_state = CPUState.CPU @@ -110,6 +155,18 @@ def is_ascend_npu(): return True return False +def is_mlu(): + global mlu_available + if mlu_available: + return True + return False + +def is_ixuca(): + global ixuca_available + if ixuca_available: + return True + return False + def get_torch_device(): global directml_enabled global cpu_state @@ -125,6 +182,8 @@ def get_torch_device(): return torch.device("xpu", torch.xpu.current_device()) elif is_ascend_npu(): return torch.device("npu", torch.npu.current_device()) + elif is_mlu(): + return torch.device("mlu", torch.mlu.current_device()) else: return torch.device(torch.cuda.current_device()) @@ -143,14 +202,21 @@ def get_total_memory(dev=None, torch_total_too=False): elif is_intel_xpu(): stats = torch.xpu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] + mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory mem_total_torch = mem_reserved - mem_total = torch.xpu.get_device_properties(dev).total_memory + mem_total = mem_total_xpu elif is_ascend_npu(): stats = torch.npu.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_npu = torch.npu.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_npu + elif is_mlu(): + stats = torch.mlu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_mlu = torch.mlu.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_mlu else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] @@ -163,12 +229,21 @@ def get_total_memory(dev=None, torch_total_too=False): else: return mem_total +def mac_version(): + try: + return tuple(int(n) for n in platform.mac_ver()[0].split(".")) + except: + return None + total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) try: logging.info("pytorch version: {}".format(torch_version)) + mac_ver = mac_version() + if mac_ver is not None: + logging.info("Mac Version {}".format(mac_ver)) except: pass @@ -216,9 +291,27 @@ def is_amd(): return True return False +def amd_min_version(device=None, min_rdna_version=0): + if not is_amd(): + return False + + if is_device_cpu(device): + return False + + arch = torch.cuda.get_device_properties(device).gcnArchName + if arch.startswith('gfx') and len(arch) == 7: + try: + cmp_rdna_version = int(arch[4]) + 2 + except: + cmp_rdna_version = 0 + if cmp_rdna_version >= min_rdna_version: + return True + + return False + MIN_WEIGHT_MEMORY_RATIO = 0.4 if is_nvidia(): - MIN_WEIGHT_MEMORY_RATIO = 0.2 + MIN_WEIGHT_MEMORY_RATIO = 0.0 ENABLE_PYTORCH_ATTENTION = False if args.use_pytorch_cross_attention: @@ -227,22 +320,70 @@ if args.use_pytorch_cross_attention: try: if is_nvidia(): - if int(torch_version[0]) >= 2: + if torch_version_numeric[0] >= 2: if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True - if is_intel_xpu() or is_ascend_npu(): + if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca(): if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True except: pass + +SUPPORT_FP8_OPS = args.supports_fp8_compute + +AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"] + +try: + if is_amd(): + arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName + if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)): + torch.backends.cudnn.enabled = False # Seems to improve things a lot on AMD + logging.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.") + + try: + rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2])) + except: + rocm_version = (6, -1) + + logging.info("AMD arch: {}".format(arch)) + logging.info("ROCm version: {}".format(rocm_version)) + if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: + if importlib.util.find_spec('triton') is not None: # AMD efficient attention implementation depends on triton. TODO: better way of detecting if it's compiled in or not. + if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much + if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches, TODO: gfx950 + ENABLE_PYTORCH_ATTENTION = True + if rocm_version >= (7, 0): + if any((a in arch) for a in ["gfx1201"]): + ENABLE_PYTORCH_ATTENTION = True + if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4): + if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx950"]): # TODO: more arches, "gfx942" gives error on pytorch nightly 2.10 1013 rocm7.0 + SUPPORT_FP8_OPS = True + +except: + pass + + if ENABLE_PYTORCH_ATTENTION: torch.backends.cuda.enable_math_sdp(True) torch.backends.cuda.enable_flash_sdp(True) torch.backends.cuda.enable_mem_efficient_sdp(True) + +PRIORITIZE_FP16 = False # TODO: remove and replace with something that shows exactly which dtype is faster than the other try: - if int(torch_version[0]) == 2 and int(torch_version[2]) >= 5: + if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast: + torch.backends.cuda.matmul.allow_fp16_accumulation = True + PRIORITIZE_FP16 = True # TODO: limit to cards where it actually boosts performance + logging.info("Enabled fp16 accumulation.") +except: + pass + +if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast: + torch.backends.cudnn.benchmark = True + +try: + if torch_version_numeric >= (2, 5): torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True) except: logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp") @@ -256,15 +397,10 @@ elif args.highvram or args.gpu_only: vram_state = VRAMState.HIGH_VRAM FORCE_FP32 = False -FORCE_FP16 = False if args.force_fp32: logging.info("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True -if args.force_fp16: - logging.info("Forcing FP16.") - FORCE_FP16 = True - if lowvram_available: if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): vram_state = set_vram_to @@ -291,12 +427,16 @@ def get_torch_device_name(device): except: allocator_backend = "" return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) + elif device.type == "xpu": + return "{} {}".format(device, torch.xpu.get_device_name(device)) else: return "{}".format(device.type) elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.get_device_name(device)) elif is_ascend_npu(): return "{} {}".format(device, torch.npu.get_device_name(device)) + elif is_mlu(): + return "{} {}".format(device, torch.mlu.get_device_name(device)) else: return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) @@ -424,6 +564,8 @@ WINDOWS = any(platform.win32_ver()) EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 if WINDOWS: EXTRA_RESERVED_VRAM = 600 * 1024 * 1024 #Windows is higher because of the shared vram issue + if total_vram > (15 * 1024): # more extra reserved vram on 16GB+ cards + EXTRA_RESERVED_VRAM += 100 * 1024 * 1024 if args.reserve_vram is not None: EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024 @@ -483,7 +625,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu else: minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory()) - models = set(models) + models_temp = set() + for m in models: + models_temp.add(m) + for mm in m.model_patches_models(): + models_temp.add(mm) + + models = models_temp models_to_load = [] @@ -509,7 +657,9 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu if loaded_model.model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload for i in to_unload: - current_loaded_models.pop(i).model.detach(unpatch_all=False) + model_to_unload = current_loaded_models.pop(i) + model_to_unload.model.detach(unpatch_all=False) + model_to_unload.model_finalizer.detach() total_memory_required = {} for loaded_model in models_to_load: @@ -535,14 +685,11 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu vram_set_state = vram_state lowvram_model_memory = 0 if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load: - model_size = loaded_model.model_memory_required(torch_dev) loaded_memory = loaded_model.model_loaded_memory() current_free_mem = get_free_memory(torch_dev) + loaded_memory - lowvram_model_memory = max(64 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) + lowvram_model_memory = max(128 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory())) lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory) - if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary - lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 0.1 @@ -620,7 +767,7 @@ def unet_inital_load_device(parameters, dtype): return torch_dev cpu_dev = torch.device("cpu") - if DISABLE_SMART_MEMORY: + if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM: return cpu_dev model_size = dtype_size(dtype) * parameters @@ -635,7 +782,7 @@ def unet_inital_load_device(parameters, dtype): def maximum_vram_for_weights(device=None): return (get_total_memory(device) * 0.88 - minimum_inference_memory()) -def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): +def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None): if model_params < 0: model_params = 1000000000000000000000 if args.fp32_unet: @@ -650,15 +797,12 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor return torch.float8_e4m3fn if args.fp8_e5m2_unet: return torch.float8_e5m2 + if args.fp8_e8m0fnu_unet: + return torch.float8_e8m0fnu fp8_dtype = None - try: - for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: - if dtype in supported_dtypes: - fp8_dtype = dtype - break - except: - pass + if weight_dtype in FLOAT8_TYPES: + fp8_dtype = weight_dtype if fp8_dtype is not None: if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive @@ -668,6 +812,10 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor if model_params * 2 > free_model_memory: return fp8_dtype + if PRIORITIZE_FP16 or weight_dtype == torch.float16: + if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params): + return torch.float16 + for dt in supported_dtypes: if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params): if torch.float16 in supported_dtypes: @@ -700,6 +848,9 @@ def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.flo return None fp16_supported = should_use_fp16(inference_device, prioritize_performance=True) + if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes: + return torch.float16 + for dt in supported_dtypes: if dt == torch.float16 and fp16_supported: return torch.float16 @@ -746,6 +897,8 @@ def text_encoder_dtype(device=None): return torch.float8_e5m2 elif args.fp16_text_enc: return torch.float16 + elif args.bf16_text_enc: + return torch.bfloat16 elif args.fp32_text_enc: return torch.float32 @@ -784,8 +937,7 @@ def vae_dtype(device=None, allowed_dtypes=[]): if d == torch.float16 and should_use_fp16(device): return d - # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32 - if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device): + if d == torch.bfloat16 and should_use_bf16(device): return d return torch.float32 @@ -835,9 +987,11 @@ def pick_weight_dtype(dtype, fallback_dtype, device=None): return dtype def device_supports_non_blocking(device): + if args.force_non_blocking: + return True if is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking - if is_intel_xpu(): + if is_intel_xpu(): #xpu does support non blocking but it is slower on iGPUs for some reason so disable by default until situation changes return False if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews) return False @@ -845,12 +999,6 @@ def device_supports_non_blocking(device): return False return True -def device_should_use_non_blocking(device): - if not device_supports_non_blocking(device): - return False - return False - # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others - def force_channels_last(): if args.force_channels_last: return True @@ -858,15 +1006,74 @@ def force_channels_last(): #TODO return False -def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False): + +STREAMS = {} +NUM_STREAMS = 1 +if args.async_offload: + NUM_STREAMS = 2 + logging.info("Using async weight offloading with {} streams".format(NUM_STREAMS)) + +stream_counters = {} +def get_offload_stream(device): + stream_counter = stream_counters.get(device, 0) + if NUM_STREAMS <= 1: + return None + + if device in STREAMS: + ss = STREAMS[device] + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + if is_device_cuda(device): + ss[stream_counter].wait_stream(torch.cuda.current_stream()) + elif is_device_xpu(device): + ss[stream_counter].wait_stream(torch.xpu.current_stream()) + stream_counters[device] = stream_counter + return s + elif is_device_cuda(device): + ss = [] + for k in range(NUM_STREAMS): + ss.append(torch.cuda.Stream(device=device, priority=0)) + STREAMS[device] = ss + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + stream_counters[device] = stream_counter + return s + elif is_device_xpu(device): + ss = [] + for k in range(NUM_STREAMS): + ss.append(torch.xpu.Stream(device=device, priority=0)) + STREAMS[device] = ss + s = ss[stream_counter] + stream_counter = (stream_counter + 1) % len(ss) + stream_counters[device] = stream_counter + return s + return None + +def sync_stream(device, stream): + if stream is None: + return + if is_device_cuda(device): + torch.cuda.current_stream().wait_stream(stream) + elif is_device_xpu(device): + torch.xpu.current_stream().wait_stream(stream) + +def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None): if device is None or weight.device == device: if not copy: if dtype is None or weight.dtype == dtype: return weight + if stream is not None: + with stream: + return weight.to(dtype=dtype, copy=copy) return weight.to(dtype=dtype, copy=copy) - r = torch.empty_like(weight, dtype=dtype, device=device) - r.copy_(weight, non_blocking=non_blocking) + if stream is not None: + with stream: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) + else: + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight, non_blocking=non_blocking) return r def cast_to_device(tensor, device, dtype, copy=False): @@ -876,6 +1083,9 @@ def cast_to_device(tensor, device, dtype, copy=False): def sage_attention_enabled(): return args.use_sage_attention +def flash_attention_enabled(): + return args.use_flash_attention + def xformers_enabled(): global directml_enabled global cpu_state @@ -885,6 +1095,10 @@ def xformers_enabled(): return False if is_ascend_npu(): return False + if is_mlu(): + return False + if is_ixuca(): + return False if directml_enabled: return False return XFORMERS_IS_AVAILABLE @@ -901,33 +1115,38 @@ def pytorch_attention_enabled(): global ENABLE_PYTORCH_ATTENTION return ENABLE_PYTORCH_ATTENTION +def pytorch_attention_enabled_vae(): + if is_amd(): + return False # enabling pytorch attention on AMD currently causes crash when doing high res + return pytorch_attention_enabled() + def pytorch_attention_flash_attention(): global ENABLE_PYTORCH_ATTENTION if ENABLE_PYTORCH_ATTENTION: #TODO: more reliable way of checking for flash attention? - if is_nvidia(): #pytorch flash attention only works on Nvidia + if is_nvidia(): return True if is_intel_xpu(): return True if is_ascend_npu(): return True + if is_mlu(): + return True + if is_amd(): + return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention + if is_ixuca(): + return True return False -def mac_version(): - try: - return tuple(int(n) for n in platform.mac_ver()[0].split(".")) - except: - return None - def force_upcast_attention_dtype(): upcast = args.force_upcast_attention macos_version = mac_version() - if macos_version is not None and ((14, 5) <= macos_version <= (15, 2)): # black image bug on recent versions of macOS + if macos_version is not None and ((14, 5) <= macos_version): # black image bug on recent versions of macOS, I don't think it's ever getting fixed upcast = True if upcast: - return torch.float32 + return {torch.float16: torch.float32} else: return None @@ -947,8 +1166,8 @@ def get_free_memory(dev=None, torch_free_too=False): stats = torch.xpu.memory_stats(dev) mem_active = stats['active_bytes.all.current'] mem_reserved = stats['reserved_bytes.all.current'] - mem_free_torch = mem_reserved - mem_active mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved + mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_xpu + mem_free_torch elif is_ascend_npu(): stats = torch.npu.memory_stats(dev) @@ -957,6 +1176,13 @@ def get_free_memory(dev=None, torch_free_too=False): mem_free_npu, _ = torch.npu.mem_get_info(dev) mem_free_torch = mem_reserved - mem_active mem_free_total = mem_free_npu + mem_free_torch + elif is_mlu(): + stats = torch.mlu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_mlu, _ = torch.mlu.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_mlu + mem_free_torch else: stats = torch.cuda.memory_stats(dev) mem_active = stats['active_bytes.all.current'] @@ -990,24 +1216,32 @@ def is_device_cpu(device): def is_device_mps(device): return is_device_type(device, 'mps') +def is_device_xpu(device): + return is_device_type(device, 'xpu') + def is_device_cuda(device): return is_device_type(device, 'cuda') -def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): +def is_directml_enabled(): global directml_enabled + if directml_enabled: + return True + return False + +def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): if device is not None: if is_device_cpu(device): return False - if FORCE_FP16: + if args.force_fp16: return True if FORCE_FP32: return False - if directml_enabled: - return False + if is_directml_enabled(): + return True if (device is not None and is_device_mps(device)) or mps_mode(): return True @@ -1016,11 +1250,20 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma return False if is_intel_xpu(): - return True + if torch_version_numeric < (2, 3): + return True + else: + return torch.xpu.get_device_properties(device).has_fp16 if is_ascend_npu(): return True + if is_mlu(): + return True + + if is_ixuca(): + return True + if torch.version.hip: return True @@ -1076,15 +1319,36 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma return False if is_intel_xpu(): + if torch_version_numeric < (2, 3): + return True + else: + return torch.xpu.is_bf16_supported() + + if is_ascend_npu(): return True + if is_ixuca(): + return True + + if is_amd(): + arch = torch.cuda.get_device_properties(device).gcnArchName + if any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH): # RDNA2 and older don't support bf16 + if manual_cast: + return True + return False + props = torch.cuda.get_device_properties(device) + + if is_mlu(): + if props.major > 3: + return True + if props.major >= 8: return True bf16_works = torch.cuda.is_bf16_supported() - if bf16_works or manual_cast: + if bf16_works and manual_cast: free_model_memory = maximum_vram_for_weights(device) if (not prioritize_performance) or model_params * 4 > free_model_memory: return True @@ -1092,6 +1356,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma return False def supports_fp8_compute(device=None): + if SUPPORT_FP8_OPS: + return True + if not is_nvidia(): return False @@ -1103,15 +1370,22 @@ def supports_fp8_compute(device=None): if props.minor < 9: return False - if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3): + if torch_version_numeric < (2, 3): return False if WINDOWS: - if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4): + if torch_version_numeric < (2, 4): return False return True +def extended_fp16_support(): + # TODO: check why some models work with fp16 on newer torch versions but not on older + if torch_version_numeric < (2, 7): + return False + + return True + def soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: @@ -1120,6 +1394,8 @@ def soft_empty_cache(force=False): torch.xpu.empty_cache() elif is_ascend_npu(): torch.npu.empty_cache() + elif is_mlu(): + torch.mlu.empty_cache() elif torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 4597ce11c..c0b68fb8c 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -17,23 +17,26 @@ """ from __future__ import annotations -from typing import Optional, Callable -import torch + +import collections import copy import inspect import logging -import uuid -import collections import math +import uuid +from typing import Callable, Optional + +import torch -import comfy.utils import comfy.float -import comfy.model_management -import comfy.lora import comfy.hooks +import comfy.lora +import comfy.model_management import comfy.patcher_extension -from comfy.patcher_extension import CallbacksMP, WrappersMP, PatcherInjection +import comfy.utils from comfy.comfy_types import UnetWrapperFunction +from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP + def string_to_seed(data): crc = 0xFFFFFFFF @@ -96,20 +99,54 @@ def wipe_lowvram_weight(m): if hasattr(m, "prev_comfy_cast_weights"): m.comfy_cast_weights = m.prev_comfy_cast_weights del m.prev_comfy_cast_weights - m.weight_function = None - m.bias_function = None + + if hasattr(m, "weight_function"): + m.weight_function = [] + + if hasattr(m, "bias_function"): + m.bias_function = [] + +def move_weight_functions(m, device): + if device is None: + return 0 + + memory = 0 + if hasattr(m, "weight_function"): + for f in m.weight_function: + if hasattr(f, "move_to"): + memory += f.move_to(device=device) + + if hasattr(m, "bias_function"): + for f in m.bias_function: + if hasattr(f, "move_to"): + memory += f.move_to(device=device) + return memory class LowVramPatch: - def __init__(self, key, patches): + def __init__(self, key, patches, convert_func=None, set_func=None): self.key = key self.patches = patches + self.convert_func = convert_func + self.set_func = set_func + def __call__(self, weight): intermediate_dtype = weight.dtype + if self.convert_func is not None: + weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True) + if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops intermediate_dtype = torch.float32 - return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key)) + out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype) + if self.set_func is None: + return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key)) + else: + return self.set_func(out, seed=string_to_seed(self.key), return_weight=True) - return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) + out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype) + if self.set_func is not None: + return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype) + else: + return out def get_key_weight(model, key): set_func = None @@ -192,11 +229,13 @@ class ModelPatcher: self.backup = {} self.object_patches = {} self.object_patches_backup = {} + self.weight_wrapper_patches = {} self.model_options = {"transformer_options":{}} self.model_size() self.load_device = load_device self.offload_device = offload_device self.weight_inplace_update = weight_inplace_update + self.force_cast_weights = False self.patches_uuid = uuid.uuid4() self.parent = None @@ -210,7 +249,7 @@ class ModelPatcher: self.injections: dict[str, list[PatcherInjection]] = {} self.hook_patches: dict[comfy.hooks._HookRef] = {} - self.hook_patches_backup: dict[comfy.hooks._HookRef] = {} + self.hook_patches_backup: dict[comfy.hooks._HookRef] = None self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {} self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {} self.current_hooks: Optional[comfy.hooks.HookGroup] = None @@ -250,11 +289,14 @@ class ModelPatcher: n.patches_uuid = self.patches_uuid n.object_patches = self.object_patches.copy() + n.weight_wrapper_patches = self.weight_wrapper_patches.copy() n.model_options = copy.deepcopy(self.model_options) n.backup = self.backup n.object_patches_backup = self.object_patches_backup n.parent = self + n.force_cast_weights = self.force_cast_weights + # attachments n.attachments = {} for k in self.attachments: @@ -282,7 +324,7 @@ class ModelPatcher: n.injections[k] = i.copy() # hooks n.hook_patches = create_hook_patches_clone(self.hook_patches) - n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) + n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup for group in self.cached_hook_patches: n.cached_hook_patches[group] = {} for k in self.cached_hook_patches[group]: @@ -351,6 +393,9 @@ class ModelPatcher: def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False): self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization) + def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function): + self.model_options["sampler_calc_cond_batch_function"] = sampler_calc_cond_batch_function + def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction): self.model_options["model_function_wrapper"] = unet_wrapper_function @@ -399,10 +444,39 @@ class ModelPatcher: def set_model_forward_timestep_embed_patch(self, patch): self.set_model_patch(patch, "forward_timestep_embed_patch") + def set_model_double_block_patch(self, patch): + self.set_model_patch(patch, "double_block") + + def set_model_post_input_patch(self, patch): + self.set_model_patch(patch, "post_input") + def add_object_patch(self, name, obj): self.object_patches[name] = obj - def get_model_object(self, name): + def set_model_compute_dtype(self, dtype): + self.add_object_patch("manual_cast_dtype", dtype) + if dtype is not None: + self.force_cast_weights = True + self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this + + def add_weight_wrapper(self, name, function): + self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function] + self.patches_uuid = uuid.uuid4() + + def get_model_object(self, name: str) -> torch.nn.Module: + """Retrieves a nested attribute from an object using dot notation considering + object patches. + + Args: + name (str): The attribute path using dot notation (e.g. "model.layer.weight") + + Returns: + The value of the requested attribute + + Example: + patcher = ModelPatcher() + weight = patcher.get_model_object("layer1.conv.weight") + """ if name in self.object_patches: return self.object_patches[name] else: @@ -432,6 +506,30 @@ class ModelPatcher: if hasattr(wrap_func, "to"): self.model_options["model_function_wrapper"] = wrap_func.to(device) + def model_patches_models(self): + to = self.model_options["transformer_options"] + models = [] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "models"): + models += patch_list[i].models() + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "models"): + models += patch_list[k].models() + if "model_function_wrapper" in self.model_options: + wrap_func = self.model_options["model_function_wrapper"] + if hasattr(wrap_func, "models"): + models += wrap_func.models() + + return models + def model_dtype(self): if hasattr(self.model, "get_dtype"): return self.model.get_dtype() @@ -553,6 +651,9 @@ class ModelPatcher: lowvram_weight = False + weight_key = "{}.weight".format(n) + bias_key = "{}.bias".format(n) + if not full_load and hasattr(m, "comfy_cast_weights"): if mem_counter + module_mem >= lowvram_model_memory: lowvram_weight = True @@ -560,34 +661,48 @@ class ModelPatcher: if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed continue - weight_key = "{}.weight".format(n) - bias_key = "{}.bias".format(n) - + cast_weight = self.force_cast_weights if lowvram_weight: + if hasattr(m, "comfy_cast_weights"): + m.weight_function = [] + m.bias_function = [] + if weight_key in self.patches: if force_patch_weights: self.patch_weight_to_device(weight_key) else: - m.weight_function = LowVramPatch(weight_key, self.patches) + _, set_func, convert_func = get_key_weight(self.model, weight_key) + m.weight_function = [LowVramPatch(weight_key, self.patches, convert_func, set_func)] patch_counter += 1 if bias_key in self.patches: if force_patch_weights: self.patch_weight_to_device(bias_key) else: - m.bias_function = LowVramPatch(bias_key, self.patches) + _, set_func, convert_func = get_key_weight(self.model, bias_key) + m.bias_function = [LowVramPatch(bias_key, self.patches, convert_func, set_func)] patch_counter += 1 - m.prev_comfy_cast_weights = m.comfy_cast_weights - m.comfy_cast_weights = True + cast_weight = True else: if hasattr(m, "comfy_cast_weights"): - if m.comfy_cast_weights: - wipe_lowvram_weight(m) + wipe_lowvram_weight(m) if full_load or mem_counter + module_mem < lowvram_model_memory: mem_counter += module_mem load_completely.append((module_mem, n, m, params)) + if cast_weight and hasattr(m, "comfy_cast_weights"): + m.prev_comfy_cast_weights = m.comfy_cast_weights + m.comfy_cast_weights = True + + if weight_key in self.weight_wrapper_patches: + m.weight_function.extend(self.weight_wrapper_patches[weight_key]) + + if bias_key in self.weight_wrapper_patches: + m.bias_function.extend(self.weight_wrapper_patches[bias_key]) + + mem_counter += move_weight_functions(m, device_to) + load_completely.sort(reverse=True) for x in load_completely: n = x[1] @@ -649,6 +764,7 @@ class ModelPatcher: self.unpatch_hooks() if self.model.model_lowvram: for m in self.model.modules(): + move_weight_functions(m, device_to) wipe_lowvram_weight(m) self.model.model_lowvram = False @@ -683,6 +799,7 @@ class ModelPatcher: def partially_unload(self, device_to, memory_to_free=0): with self.use_ejected(): + hooks_unpatched = False memory_freed = 0 patch_counter = 0 unload_list = self._load_list() @@ -706,6 +823,10 @@ class ModelPatcher: move_weight = False break + if not hooks_unpatched: + self.unpatch_hooks() + hooks_unpatched = True + if bk.inplace_update: comfy.utils.copy_to_param(self.model, key, bk.weight) else: @@ -715,15 +836,21 @@ class ModelPatcher: weight_key = "{}.weight".format(n) bias_key = "{}.bias".format(n) if move_weight: + cast_weight = self.force_cast_weights m.to(device_to) + module_mem += move_weight_functions(m, device_to) if lowvram_possible: if weight_key in self.patches: - m.weight_function = LowVramPatch(weight_key, self.patches) + _, set_func, convert_func = get_key_weight(self.model, weight_key) + m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func)) patch_counter += 1 if bias_key in self.patches: - m.bias_function = LowVramPatch(bias_key, self.patches) + _, set_func, convert_func = get_key_weight(self.model, bias_key) + m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func)) patch_counter += 1 + cast_weight = True + if cast_weight: m.prev_comfy_cast_weights = m.comfy_cast_weights m.comfy_cast_weights = True m.comfy_patched_weights = False @@ -842,6 +969,9 @@ class ModelPatcher: if key in self.injections: self.injections.pop(key) + def get_injections(self, key: str): + return self.injections.get(key, None) + def set_additional_models(self, key: str, models: list['ModelPatcher']): self.additional_models[key] = models @@ -912,9 +1042,9 @@ class ModelPatcher: callback(self, timestep) def restore_hook_patches(self): - if len(self.hook_patches_backup) > 0: + if self.hook_patches_backup is not None: self.hook_patches = self.hook_patches_backup - self.hook_patches_backup = {} + self.hook_patches_backup = None def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode): self.hook_mode = hook_mode @@ -940,25 +1070,26 @@ class ModelPatcher: if reset_current_hooks: self.patch_hooks(None) - def register_all_hook_patches(self, hooks_dict: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]], target: comfy.hooks.EnumWeightTarget, model_options: dict=None): + def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None, + registered: comfy.hooks.HookGroup = None): self.restore_hook_patches() - registered_hooks: list[comfy.hooks.Hook] = [] - # handle WrapperHooks, if model_options provided - if model_options is not None: - for hook in hooks_dict.get(comfy.hooks.EnumHookType.Wrappers, {}): - hook.add_hook_patches(self, model_options, target, registered_hooks) + if registered is None: + registered = comfy.hooks.HookGroup() # handle WeightHooks weight_hooks_to_register: list[comfy.hooks.WeightHook] = [] - for hook in hooks_dict.get(comfy.hooks.EnumHookType.Weight, {}): + for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight): if hook.hook_ref not in self.hook_patches: weight_hooks_to_register.append(hook) + else: + registered.add(hook) if len(weight_hooks_to_register) > 0: # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state self.hook_patches_backup = create_hook_patches_clone(self.hook_patches) for hook in weight_hooks_to_register: - hook.add_hook_patches(self, model_options, target, registered_hooks) + hook.add_hook_patches(self, model_options, target_dict, registered) for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES): - callback(self, hooks_dict, target) + callback(self, hooks, target_dict, model_options, registered) + return registered def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0): with self.use_ejected(): @@ -1009,15 +1140,14 @@ class ModelPatcher: def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False): # TODO: return transformer_options dict with any additions from hooks if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)): - return {} + return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) self.patch_hooks(hooks=hooks) for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS): callback(self, hooks) - return {} + return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options) def patch_hooks(self, hooks: comfy.hooks.HookGroup): with self.use_ejected(): - self.unpatch_hooks() if hooks is not None: model_sd_keys = list(self.model_state_dict().keys()) memory_counter = None @@ -1028,12 +1158,16 @@ class ModelPatcher: # if have cached weights for hooks, use it cached_weights = self.cached_hook_patches.get(hooks, None) if cached_weights is not None: + model_sd_keys_set = set(model_sd_keys) for key in cached_weights: if key not in model_sd_keys: logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}") continue self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter) + model_sd_keys_set.remove(key) + self.unpatch_hooks(model_sd_keys_set) else: + self.unpatch_hooks() relevant_patches = self.get_combined_hook_patches(hooks=hooks) original_weights = None if len(relevant_patches) > 0: @@ -1044,6 +1178,8 @@ class ModelPatcher: continue self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights, memory_counter=memory_counter) + else: + self.unpatch_hooks() self.current_hooks = hooks def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter): @@ -1100,17 +1236,23 @@ class ModelPatcher: del out_weight del weight - def unpatch_hooks(self) -> None: + def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None: with self.use_ejected(): if len(self.hook_backup) == 0: self.current_hooks = None return keys = list(self.hook_backup.keys()) - for k in keys: - comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) + if whitelist_keys_set: + for k in keys: + if k in whitelist_keys_set: + comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) + self.hook_backup.pop(k) + else: + for k in keys: + comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1])) - self.hook_backup.clear() - self.current_hooks = None + self.hook_backup.clear() + self.current_hooks = None def clean_hooks(self): self.unpatch_hooks() diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index 4370516b9..2a00ed819 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -21,16 +21,23 @@ def rescale_zero_terminal_snr_sigmas(sigmas): alphas_bar[-1] = 4.8973451890853435e-08 return ((1 - alphas_bar) / alphas_bar) ** 0.5 +def reshape_sigma(sigma, noise_dim): + if sigma.nelement() == 1: + return sigma.view(()) + else: + return sigma.view(sigma.shape[:1] + (1,) * (noise_dim - 1)) + class EPS: def calculate_input(self, sigma, noise): - sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + sigma = reshape_sigma(sigma, noise.ndim) return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = reshape_sigma(sigma, model_output.ndim) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = reshape_sigma(sigma, noise.ndim) if max_denoise: noise = noise * torch.sqrt(1.0 + sigma ** 2.0) else: @@ -44,12 +51,12 @@ class EPS: class V_PREDICTION(EPS): def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = reshape_sigma(sigma, model_output.ndim) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class EDM(V_PREDICTION): def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = reshape_sigma(sigma, model_output.ndim) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 class CONST: @@ -57,15 +64,45 @@ class CONST: return noise def calculate_denoised(self, sigma, model_output, model_input): - sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = reshape_sigma(sigma, model_output.ndim) return model_input - model_output * sigma def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = reshape_sigma(sigma, noise.ndim) return sigma * noise + (1.0 - sigma) * latent_image def inverse_noise_scaling(self, sigma, latent): + sigma = reshape_sigma(sigma, latent.ndim) return latent / (1.0 - sigma) +class X0(EPS): + def calculate_denoised(self, sigma, model_output, model_input): + return model_output + +class IMG_TO_IMG(X0): + def calculate_input(self, sigma, noise): + return noise + +class COSMOS_RFLOW: + def calculate_input(self, sigma, noise): + sigma = (sigma / (sigma + 1)) + sigma = reshape_sigma(sigma, noise.ndim) + return noise * (1.0 - sigma) + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = (sigma / (sigma + 1)) + sigma = reshape_sigma(sigma, model_output.ndim) + return model_input * (1.0 - sigma) - model_output * sigma + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = reshape_sigma(sigma, noise.ndim) + noise = noise * sigma + noise += latent_image + return noise + + def inverse_noise_scaling(self, sigma, latent): + return latent + class ModelSamplingDiscrete(torch.nn.Module): def __init__(self, model_config=None, zsnr=None): super().__init__() @@ -99,13 +136,14 @@ class ModelSamplingDiscrete(torch.nn.Module): self.num_timesteps = int(timesteps) self.linear_start = linear_start self.linear_end = linear_end + self.zsnr = zsnr # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 - if zsnr: + if self.zsnr: sigmas = rescale_zero_terminal_snr_sigmas(sigmas) self.set_sigmas(sigmas) @@ -337,3 +375,15 @@ class ModelSamplingFlux(torch.nn.Module): if percent >= 1.0: return 0.0 return flux_time_shift(self.shift, 1.0, 1.0 - percent) + + +class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM): + def timestep(self, sigma): + return sigma / (sigma + 1) + + def sigma(self, timestep): + sigma_max = self.sigma_max + if timestep >= (sigma_max / (sigma_max + 1)): + return sigma_max + + return timestep / (1 - timestep) diff --git a/comfy/nested_tensor.py b/comfy/nested_tensor.py new file mode 100644 index 000000000..b700816fa --- /dev/null +++ b/comfy/nested_tensor.py @@ -0,0 +1,91 @@ +import torch + +class NestedTensor: + def __init__(self, tensors): + self.tensors = list(tensors) + self.is_nested = True + + def _copy(self): + return NestedTensor(self.tensors) + + def apply_operation(self, other, operation): + o = self._copy() + if isinstance(other, NestedTensor): + for i, t in enumerate(o.tensors): + o.tensors[i] = operation(t, other.tensors[i]) + else: + for i, t in enumerate(o.tensors): + o.tensors[i] = operation(t, other) + return o + + def __add__(self, b): + return self.apply_operation(b, lambda x, y: x + y) + + def __sub__(self, b): + return self.apply_operation(b, lambda x, y: x - y) + + def __mul__(self, b): + return self.apply_operation(b, lambda x, y: x * y) + + # def __itruediv__(self, b): + # return self.apply_operation(b, lambda x, y: x / y) + + def __truediv__(self, b): + return self.apply_operation(b, lambda x, y: x / y) + + def __getitem__(self, *args, **kwargs): + return self.apply_operation(None, lambda x, y: x.__getitem__(*args, **kwargs)) + + def unbind(self): + return self.tensors + + def to(self, *args, **kwargs): + o = self._copy() + for i, t in enumerate(o.tensors): + o.tensors[i] = t.to(*args, **kwargs) + return o + + def new_ones(self, *args, **kwargs): + return self.tensors[0].new_ones(*args, **kwargs) + + def float(self): + return self.to(dtype=torch.float) + + def chunk(self, *args, **kwargs): + return self.apply_operation(None, lambda x, y: x.chunk(*args, **kwargs)) + + def size(self): + return self.tensors[0].size() + + @property + def shape(self): + return self.tensors[0].shape + + @property + def ndim(self): + dims = 0 + for t in self.tensors: + dims = max(t.ndim, dims) + return dims + + @property + def device(self): + return self.tensors[0].device + + @property + def dtype(self): + return self.tensors[0].dtype + + @property + def layout(self): + return self.tensors[0].layout + + +def cat_nested(tensors, *args, **kwargs): + cated_tensors = [] + for i in range(len(tensors[0].tensors)): + tens = [] + for j in range(len(tensors)): + tens.append(tensors[j].tensors[i]) + cated_tensors.append(torch.cat(tens, *args, **kwargs)) + return NestedTensor(cated_tensors) diff --git a/comfy/ops.py b/comfy/ops.py index 06be6b48b..934e21261 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -17,15 +17,60 @@ """ import torch +import logging import comfy.model_management -from comfy.cli_args import args +from comfy.cli_args import args, PerformanceFeature import comfy.float +import comfy.rmsnorm +import contextlib + +def run_every_op(): + if torch.compiler.is_compiling(): + return + + comfy.model_management.throw_exception_if_processing_interrupted() + +def scaled_dot_product_attention(q, k, v, *args, **kwargs): + return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) + + +try: + if torch.cuda.is_available(): + from torch.nn.attention import SDPBackend, sdpa_kernel + import inspect + if "set_priority" in inspect.signature(sdpa_kernel).parameters: + SDPA_BACKEND_PRIORITY = [ + SDPBackend.FLASH_ATTENTION, + SDPBackend.EFFICIENT_ATTENTION, + SDPBackend.MATH, + ] + + SDPA_BACKEND_PRIORITY.insert(0, SDPBackend.CUDNN_ATTENTION) + + def scaled_dot_product_attention(q, k, v, *args, **kwargs): + with sdpa_kernel(SDPA_BACKEND_PRIORITY, set_priority=True): + return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs) + else: + logging.warning("Torch version too old to set sdpa backend priority.") +except (ModuleNotFoundError, TypeError): + logging.warning("Could not set sdpa backend priority.") + +NVIDIA_MEMORY_CONV_BUG_WORKAROUND = False +try: + if comfy.model_management.is_nvidia(): + if torch.backends.cudnn.version() >= 91002 and comfy.model_management.torch_version_numeric >= (2, 9) and comfy.model_management.torch_version_numeric <= (2, 10): + #TODO: change upper bound version once it's fixed' + NVIDIA_MEMORY_CONV_BUG_WORKAROUND = True + logging.info("working around nvidia conv3d memory bug.") +except: + pass cast_to = comfy.model_management.cast_to #TODO: remove once no more references def cast_to_input(weight, input, non_blocking=False, copy=True): return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) +@torch.compiler.disable() def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if input is not None: if dtype is None: @@ -35,24 +80,37 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None): if device is None: device = input.device + offload_stream = comfy.model_management.get_offload_stream(device) + if offload_stream is not None: + wf_context = offload_stream + else: + wf_context = contextlib.nullcontext() + bias = None non_blocking = comfy.model_management.device_supports_non_blocking(device) if s.bias is not None: - has_function = s.bias_function is not None - bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function) - if has_function: - bias = s.bias_function(bias) + has_function = len(s.bias_function) > 0 + bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) - has_function = s.weight_function is not None - weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function) + if has_function: + with wf_context: + for f in s.bias_function: + bias = f(bias) + + has_function = len(s.weight_function) > 0 + weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream) if has_function: - weight = s.weight_function(weight) + with wf_context: + for f in s.weight_function: + weight = f(weight) + + comfy.model_management.sync_stream(device, offload_stream) return weight, bias class CastWeightBiasOp: comfy_cast_weights = False - weight_function = None - bias_function = None + weight_function = [] + bias_function = [] class disable_weight_init: class Linear(torch.nn.Linear, CastWeightBiasOp): @@ -64,7 +122,8 @@ class disable_weight_init: return torch.nn.functional.linear(input, weight, bias) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -78,7 +137,8 @@ class disable_weight_init: return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -92,7 +152,8 @@ class disable_weight_init: return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -101,12 +162,22 @@ class disable_weight_init: def reset_parameters(self): return None + def _conv_forward(self, input, weight, bias, *args, **kwargs): + if NVIDIA_MEMORY_CONV_BUG_WORKAROUND and weight.dtype in (torch.float16, torch.bfloat16): + out = torch.cudnn_convolution(input, weight, self.padding, self.stride, self.dilation, self.groups, benchmark=False, deterministic=False, allow_tf32=True) + if bias is not None: + out += bias.reshape((1, -1) + (1,) * (out.ndim - 2)) + return out + else: + return super()._conv_forward(input, weight, bias, *args, **kwargs) + def forward_comfy_cast_weights(self, input): weight, bias = cast_bias_weight(self, input) return self._conv_forward(input, weight, bias) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -120,12 +191,12 @@ class disable_weight_init: return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) - class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp): def reset_parameters(self): return None @@ -139,7 +210,28 @@ class disable_weight_init: return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: + return self.forward_comfy_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class RMSNorm(comfy.rmsnorm.RMSNorm, CastWeightBiasOp): + def reset_parameters(self): + self.bias = None + return None + + def forward_comfy_cast_weights(self, input): + if self.weight is not None: + weight, bias = cast_bias_weight(self, input) + else: + weight = None + return comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated + # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps) + + def forward(self, *args, **kwargs): + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -160,7 +252,8 @@ class disable_weight_init: output_padding, self.groups, self.dilation) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -181,7 +274,8 @@ class disable_weight_init: output_padding, self.groups, self.dilation) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: return super().forward(*args, **kwargs) @@ -199,7 +293,8 @@ class disable_weight_init: return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype) def forward(self, *args, **kwargs): - if self.comfy_cast_weights: + run_every_op() + if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0: return self.forward_comfy_cast_weights(*args, **kwargs) else: if "out_dtype" in kwargs: @@ -241,6 +336,9 @@ class manual_cast(disable_weight_init): class ConvTranspose1d(disable_weight_init.ConvTranspose1d): comfy_cast_weights = True + class RMSNorm(disable_weight_init.RMSNorm): + comfy_cast_weights = True + class Embedding(disable_weight_init.Embedding): comfy_cast_weights = True @@ -271,10 +369,10 @@ def fp8_linear(self, input): if scale_input is None: scale_input = torch.ones((), device=input.device, dtype=torch.float32) input = torch.clamp(input, min=-448, max=448, out=input) - input = input.reshape(-1, input_shape[2]).to(dtype) + input = input.reshape(-1, input_shape[2]).to(dtype).contiguous() else: scale_input = scale_input.to(input.device) - input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype) + input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous() if bias is not None: o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight) @@ -299,14 +397,19 @@ class fp8_ops(manual_cast): return None def forward_comfy_cast_weights(self, input): - out = fp8_linear(self, input) - if out is not None: - return out + if not self.training: + try: + out = fp8_linear(self, input) + if out is not None: + return out + except Exception as e: + logging.info("Exception during fp8 op: {}".format(e)) weight, bias = cast_bias_weight(self, input) return torch.nn.functional.linear(input, weight, bias) def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None): + logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input)) class scaled_fp8_op(manual_cast): class Linear(manual_cast.Linear): def __init__(self, *args, **kwargs): @@ -345,8 +448,10 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None else: return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype) - def set_weight(self, weight, inplace_update=False, seed=None, **kwargs): + def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs): weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed) + if return_weight: + return weight if inplace_update: self.weight.data.copy_(weight) else: @@ -354,14 +459,46 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None return scaled_fp8_op +CUBLAS_IS_AVAILABLE = False +try: + from cublas_ops import CublasLinear + CUBLAS_IS_AVAILABLE = True +except ImportError: + pass + +if CUBLAS_IS_AVAILABLE: + class cublas_ops(disable_weight_init): + class Linear(CublasLinear, disable_weight_init.Linear): + def reset_parameters(self): + return None + + def forward_comfy_cast_weights(self, input): + return super().forward(input) + + def forward(self, *args, **kwargs): + return super().forward(*args, **kwargs) + def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None): fp8_compute = comfy.model_management.supports_fp8_compute(load_device) if scaled_fp8 is not None: - return scaled_fp8_ops(fp8_matrix_mult=fp8_compute, scale_input=True, override_dtype=scaled_fp8) + return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8) - if fp8_compute and (fp8_optimizations or args.fast) and not disable_fast_fp8: + if ( + fp8_compute and + (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and + not disable_fast_fp8 + ): return fp8_ops + if ( + PerformanceFeature.CublasOps in args.fast and + CUBLAS_IS_AVAILABLE and + weight_dtype == torch.float16 and + (compute_dtype == torch.float16 or compute_dtype is None) + ): + logging.info("Using cublas ops") + return cublas_ops + if compute_dtype is None or weight_dtype == compute_dtype: return disable_weight_init diff --git a/comfy/patcher_extension.py b/comfy/patcher_extension.py index 859758244..5ee4d5ee5 100644 --- a/comfy/patcher_extension.py +++ b/comfy/patcher_extension.py @@ -48,7 +48,9 @@ def get_all_callbacks(call_type: str, transformer_options: dict, is_model_option class WrappersMP: OUTER_SAMPLE = "outer_sample" + PREPARE_SAMPLING = "prepare_sampling" SAMPLER_SAMPLE = "sampler_sample" + PREDICT_NOISE = "predict_noise" CALC_COND_BATCH = "calc_cond_batch" APPLY_MODEL = "apply_model" DIFFUSION_MODEL = "diffusion_model" @@ -148,7 +150,7 @@ def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True): for key, value in dict2.items(): if isinstance(value, dict): curr_value = merged_dict.setdefault(key, {}) - merged_dict[key] = merge_nested_dicts(value, curr_value) + merged_dict[key] = merge_nested_dicts(curr_value, value) elif isinstance(value, list): merged_dict.setdefault(key, []).extend(value) else: diff --git a/comfy/pixel_space_convert.py b/comfy/pixel_space_convert.py new file mode 100644 index 000000000..049bbcfb4 --- /dev/null +++ b/comfy/pixel_space_convert.py @@ -0,0 +1,16 @@ +import torch + + +# "Fake" VAE that converts from IMAGE B, H, W, C and values on the scale of 0..1 +# to LATENT B, C, H, W and values on the scale of -1..1. +class PixelspaceConversionVAE(torch.nn.Module): + def __init__(self): + super().__init__() + self.pixel_space_vae = torch.nn.Parameter(torch.tensor(1.0)) + + def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Tensor: + return pixels + + def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Tensor: + return samples + diff --git a/comfy/rmsnorm.py b/comfy/rmsnorm.py new file mode 100644 index 000000000..555542a46 --- /dev/null +++ b/comfy/rmsnorm.py @@ -0,0 +1,57 @@ +import torch +import comfy.model_management +import numbers +import logging + +RMSNorm = None + +try: + rms_norm_torch = torch.nn.functional.rms_norm + RMSNorm = torch.nn.RMSNorm +except: + rms_norm_torch = None + logging.warning("Please update pytorch to use native RMSNorm") + + +def rms_norm(x, weight=None, eps=1e-6): + if rms_norm_torch is not None and not (torch.jit.is_tracing() or torch.jit.is_scripting()): + if weight is None: + return rms_norm_torch(x, (x.shape[-1],), eps=eps) + else: + return rms_norm_torch(x, weight.shape, weight=comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device), eps=eps) + else: + r = x * torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps) + if weight is None: + return r + else: + return r * comfy.model_management.cast_to(weight, dtype=x.dtype, device=x.device) + + +if RMSNorm is None: + class RMSNorm(torch.nn.Module): + def __init__( + self, + normalized_shape, + eps=1e-6, + elementwise_affine=True, + device=None, + dtype=None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + if isinstance(normalized_shape, numbers.Integral): + # mypy error: incompatible types in assignment + normalized_shape = (normalized_shape,) # type: ignore[assignment] + self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type] + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = torch.nn.Parameter( + torch.empty(self.normalized_shape, **factory_kwargs) + ) + else: + self.register_parameter("weight", None) + self.bias = None + + def forward(self, x): + return rms_norm(x, self.weight, self.eps) diff --git a/comfy/sample.py b/comfy/sample.py index be5a7e246..2f8f3a51c 100644 --- a/comfy/sample.py +++ b/comfy/sample.py @@ -4,13 +4,9 @@ import comfy.samplers import comfy.utils import numpy as np import logging +import comfy.nested_tensor -def prepare_noise(latent_image, seed, noise_inds=None): - """ - creates random noise given a latent image and a seed. - optional arg skip can be used to skip and discard x number of noise generations for a given seed - """ - generator = torch.manual_seed(seed) +def prepare_noise_inner(latent_image, generator, noise_inds=None): if noise_inds is None: return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") @@ -21,10 +17,29 @@ def prepare_noise(latent_image, seed, noise_inds=None): if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] - noises = torch.cat(noises, axis=0) + return torch.cat(noises, axis=0) + +def prepare_noise(latent_image, seed, noise_inds=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.manual_seed(seed) + + if latent_image.is_nested: + tensors = latent_image.unbind() + noises = [] + for t in tensors: + noises.append(prepare_noise_inner(t, generator, noise_inds)) + noises = comfy.nested_tensor.NestedTensor(noises) + else: + noises = prepare_noise_inner(latent_image, generator, noise_inds) + return noises def fix_empty_latent_channels(model, latent_image): + if latent_image.is_nested: + return latent_image latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0: latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1) diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py index ac9735369..e46971afb 100644 --- a/comfy/sampler_helpers.py +++ b/comfy/sampler_helpers.py @@ -1,5 +1,7 @@ from __future__ import annotations import uuid +import math +import collections import comfy.model_management import comfy.conds import comfy.utils @@ -24,15 +26,13 @@ def get_models_from_cond(cond, model_type): models += [c[model_type]] return models -def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]]): +def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup): # get hooks from conds, and collect cnets so they can be checked for extra_hooks cnets: list[ControlBase] = [] for c in cond: if 'hooks' in c: for hook in c['hooks'].hooks: - hook: comfy.hooks.Hook - with_type = hooks_dict.setdefault(hook.hook_type, {}) - with_type[hook] = None + full_hooks.add(hook) if 'control' in c: cnets.append(c['control']) @@ -50,10 +50,9 @@ def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[co extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list) if extra_hooks is not None: for hook in extra_hooks.hooks: - with_type = hooks_dict.setdefault(hook.hook_type, {}) - with_type[hook] = None + full_hooks.add(hook) - return hooks_dict + return full_hooks def convert_cond(cond): out = [] @@ -61,7 +60,6 @@ def convert_cond(cond): temp = c[1].copy() model_conds = temp.get("model_conds", {}) if c[0] is not None: - model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove temp["cross_attn"] = c[0] temp["model_conds"] = model_conds temp["uuid"] = uuid.uuid4() @@ -73,13 +71,11 @@ def get_additional_models(conds, dtype): cnets: list[ControlBase] = [] gligen = [] add_models = [] - hooks: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]] = {} for k in conds: cnets += get_models_from_cond(conds[k], "control") gligen += get_models_from_cond(conds[k], "gligen") add_models += get_models_from_cond(conds[k], "additional_models") - get_hooks_from_cond(conds[k], hooks) control_nets = set(cnets) @@ -90,25 +86,56 @@ def get_additional_models(conds, dtype): inference_memory += m.inference_memory_requirements(dtype) gligen = [x[1] for x in gligen] - hook_models = [x.model for x in hooks.get(comfy.hooks.EnumHookType.AddModels, {}).keys()] - models = control_models + gligen + add_models + hook_models + models = control_models + gligen + add_models return models, inference_memory +def get_additional_models_from_model_options(model_options: dict[str]=None): + """loads additional models from registered AddModels hooks""" + models = [] + if model_options is not None and "registered_hooks" in model_options: + registered: comfy.hooks.HookGroup = model_options["registered_hooks"] + for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels): + hook: comfy.hooks.AdditionalModelsHook + models.extend(hook.models) + return models + def cleanup_additional_models(models): """cleanup additional models that were loaded""" for m in models: if hasattr(m, 'cleanup'): m.cleanup() +def estimate_memory(model, noise_shape, conds): + cond_shapes = collections.defaultdict(list) + cond_shapes_min = {} + for _, cs in conds.items(): + for cond in cs: + for k, v in model.model.extra_conds_shapes(**cond).items(): + cond_shapes[k].append(v) + if cond_shapes_min.get(k, None) is None: + cond_shapes_min[k] = [v] + elif math.prod(v) > math.prod(cond_shapes_min[k][0]): + cond_shapes_min[k] = [v] -def prepare_sampling(model: 'ModelPatcher', noise_shape, conds): - real_model: 'BaseModel' = None + memory_required = model.model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:]), cond_shapes=cond_shapes) + minimum_memory_required = model.model.memory_required([noise_shape[0]] + list(noise_shape[1:]), cond_shapes=cond_shapes_min) + return memory_required, minimum_memory_required + +def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): + executor = comfy.patcher_extension.WrapperExecutor.new_executor( + _prepare_sampling, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True) + ) + return executor.execute(model, noise_shape, conds, model_options=model_options) + +def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None): + real_model: BaseModel = None models, inference_memory = get_additional_models(conds, model.model_dtype()) + models += get_additional_models_from_model_options(model_options) models += model.get_nested_additional_models() # TODO: does this require inference_memory update? - memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory - minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory - comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required) + memory_required, minimum_memory_required = estimate_memory(model, noise_shape, conds) + comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required + inference_memory, minimum_memory_required=minimum_memory_required + inference_memory) real_model = model.model return real_model, conds, models @@ -122,13 +149,36 @@ def cleanup_models(conds, models): cleanup_additional_models(set(control_cleanup)) -def prepare_model_patcher(model: 'ModelPatcher', conds, model_options: dict): +def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict): + ''' + Registers hooks from conds. + ''' # check for hooks in conds - if not registered, see if can be applied - hooks = {} + hooks = comfy.hooks.HookGroup() for k in conds: get_hooks_from_cond(conds[k], hooks) # add wrappers and callbacks from ModelPatcher to transformer_options - model_options["transformer_options"]["wrappers"] = comfy.patcher_extension.copy_nested_dicts(model.wrappers) - model_options["transformer_options"]["callbacks"] = comfy.patcher_extension.copy_nested_dicts(model.callbacks) - # register hooks on model/model_options - model.register_all_hook_patches(hooks, comfy.hooks.EnumWeightTarget.Model, model_options) + comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("wrappers", {}), model.wrappers, copy_dict1=False) + comfy.patcher_extension.merge_nested_dicts(model_options["transformer_options"].setdefault("callbacks", {}), model.callbacks, copy_dict1=False) + # begin registering hooks + registered = comfy.hooks.HookGroup() + target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model) + # handle all TransformerOptionsHooks + for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions): + hook: comfy.hooks.TransformerOptionsHook + hook.add_hook_patches(model, model_options, target_dict, registered) + # handle all AddModelsHooks + for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels): + hook: comfy.hooks.AdditionalModelsHook + hook.add_hook_patches(model, model_options, target_dict, registered) + # handle all WeightHooks by registering on ModelPatcher + model.register_all_hook_patches(hooks, target_dict, model_options, registered) + # add registered_hooks onto model_options for further reference + if len(registered) > 0: + model_options["registered_hooks"] = registered + # merge original wrappers and callbacks with hooked wrappers and callbacks + to_load_options: dict[str] = model_options.setdefault("to_load_options", {}) + for wc_name in ["wrappers", "callbacks"]: + comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name], + copy_dict1=False) + return to_load_options diff --git a/comfy/samplers.py b/comfy/samplers.py old mode 100644 new mode 100755 index af2b8e110..fa4640842 --- a/comfy/samplers.py +++ b/comfy/samplers.py @@ -12,14 +12,21 @@ import collections from comfy import model_management import math import logging -import comfy.samplers import comfy.sampler_helpers import comfy.model_patcher import comfy.patcher_extension import comfy.hooks +import comfy.context_windows +import comfy.utils import scipy.stats import numpy + +def add_area_dims(area, num_dims): + while (len(area) // 2) < num_dims: + area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:] + return area + def get_area_and_mult(conds, x_in, timestep_in): dims = tuple(x_in.shape[2:]) area = None @@ -35,6 +42,10 @@ def get_area_and_mult(conds, x_in, timestep_in): return None if 'area' in conds: area = list(conds['area']) + area = add_area_dims(area, len(dims)) + if (len(area) // 2) > len(dims): + area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)] + if 'strength' in conds: strength = conds['strength'] @@ -51,7 +62,7 @@ def get_area_and_mult(conds, x_in, timestep_in): if "mask_strength" in conds: mask_strength = conds["mask_strength"] mask = conds['mask'] - assert(mask.shape[1:] == x_in.shape[2:]) + # assert (mask.shape[1:] == x_in.shape[2:]) mask = mask[:input_x.shape[0]] if area is not None: @@ -59,27 +70,28 @@ def get_area_and_mult(conds, x_in, timestep_in): mask = mask.narrow(i + 1, area[len(dims) + i], area[i]) mask = mask * mask_strength - mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + mask = mask.unsqueeze(1).repeat((input_x.shape[0] // mask.shape[0], input_x.shape[1]) + (1, ) * (mask.ndim - 1)) else: mask = torch.ones_like(input_x) mult = mask * strength if 'mask' not in conds and area is not None: - rr = 8 + fuzz = 8 for i in range(len(dims)): + rr = min(fuzz, mult.shape[2 + i] // 4) if area[len(dims) + i] != 0: for t in range(rr): m = mult.narrow(i + 2, t, 1) - m *= ((1.0/rr) * (t + 1)) + m *= ((1.0 / rr) * (t + 1)) if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]: for t in range(rr): m = mult.narrow(i + 2, area[i] - 1 - t, 1) - m *= ((1.0/rr) * (t + 1)) + m *= ((1.0 / rr) * (t + 1)) conditioning = {} model_conds = conds["model_conds"] for c in model_conds: - conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) + conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], area=area) hooks = conds.get('hooks', None) control = conds.get('control', None) @@ -178,7 +190,7 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H cond = default_conds[i] for x in cond: # do get_area_and_mult to get all the expected values - p = comfy.samplers.get_area_and_mult(x, x_in, timestep) + p = get_area_and_mult(x, x_in, timestep) if p is None: continue # replace p's mult with calculated mult @@ -188,14 +200,20 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H hooked_to_run.setdefault(p.hooks, list()) hooked_to_run[p.hooks] += [(p, i)] -def calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): +def calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options: dict[str]): + handler: comfy.context_windows.ContextHandlerABC = model_options.get("context_handler", None) + if handler is None or not handler.should_use_context(model, conds, x_in, timestep, model_options): + return _calc_cond_batch_outer(model, conds, x_in, timestep, model_options) + return handler.execute(_calc_cond_batch_outer, model, conds, x_in, timestep, model_options) + +def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): executor = comfy.patcher_extension.WrapperExecutor.new_executor( _calc_cond_batch, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True) ) return executor.execute(model, conds, x_in, timestep, model_options) -def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): +def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options): out_conds = [] out_counts = [] # separate conds by matching hooks @@ -215,7 +233,7 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te default_c.append(x) has_default_conds = True continue - p = comfy.samplers.get_area_and_mult(x, x_in, timestep) + p = get_area_and_mult(x, x_in, timestep) if p is None: continue if p.hooks is not None: @@ -246,7 +264,13 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te for i in range(1, len(to_batch_temp) + 1): batch_amount = to_batch_temp[:len(to_batch_temp)//i] input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] - if model.memory_required(input_shape) * 1.5 < free_memory: + cond_shapes = collections.defaultdict(list) + for tt in batch_amount: + cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()} + for k, v in to_run[tt][0].conditioning.items(): + cond_shapes[k].append(v.size()) + + if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory: to_batch = batch_amount break @@ -282,17 +306,10 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te copy_dict1=False) if patches is not None: - # TODO: replace with merge_nested_dicts function - if "patches" in transformer_options: - cur_patches = transformer_options["patches"].copy() - for p in patches: - if p in cur_patches: - cur_patches[p] = cur_patches[p] + patches[p] - else: - cur_patches[p] = patches[p] - transformer_options["patches"] = cur_patches - else: - transformer_options["patches"] = patches + transformer_options["patches"] = comfy.patcher_extension.merge_nested_dicts( + transformer_options.get("patches", {}), + patches + ) transformer_options["cond_or_uncond"] = cond_or_uncond[:] transformer_options["uuids"] = uuids[:] @@ -336,7 +353,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None): if "sampler_cfg_function" in model_options: args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, - "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} + "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond} cfg_result = x - model_options["sampler_cfg_function"](args) else: cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale @@ -357,12 +374,16 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option uncond_ = uncond conds = [cond, uncond_] - out = calc_cond_batch(model, conds, x, timestep, model_options) + if "sampler_calc_cond_batch_function" in model_options: + args = {"conds": conds, "input": x, "sigma": timestep, "model": model, "model_options": model_options} + out = model_options["sampler_calc_cond_batch_function"](args) + else: + out = calc_cond_batch(model, conds, x, timestep, model_options) for fn in model_options.get("sampler_pre_cfg_function", []): args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, "model": model, "model_options": model_options} - out = fn(args) + out = fn(args) return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_) @@ -376,7 +397,7 @@ class KSamplerX0Inpaint: if "denoise_mask_function" in model_options: denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas}) latent_mask = 1. - denoise_mask - x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask + x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask out = self.inner_model(x, sigma, model_options=model_options, seed=seed) if denoise_mask is not None: out = out * denoise_mask + self.latent_image * latent_mask @@ -526,7 +547,10 @@ def resolve_areas_and_cond_masks_multidim(conditions, dims, device): if len(mask.shape) == len(dims): mask = mask.unsqueeze(0) if mask.shape[1:] != dims: - mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1) + if mask.ndim < 4: + mask = comfy.utils.common_upscale(mask.unsqueeze(1), dims[-1], dims[-2], 'bilinear', 'none').squeeze(1) + else: + mask = comfy.utils.common_upscale(mask, dims[-1], dims[-2], 'bilinear', 'none') if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2 bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) @@ -549,25 +573,37 @@ def resolve_areas_and_cond_masks(conditions, h, w, device): logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.") return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device) -def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2 +def create_cond_with_same_area_if_none(conds, c): if 'area' not in c: return + def area_inside(a, area_cmp): + a = add_area_dims(a, len(area_cmp) // 2) + area_cmp = add_area_dims(area_cmp, len(a) // 2) + + a_l = len(a) // 2 + area_cmp_l = len(area_cmp) // 2 + for i in range(min(a_l, area_cmp_l)): + if a[a_l + i] < area_cmp[area_cmp_l + i]: + return False + for i in range(min(a_l, area_cmp_l)): + if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]): + return False + return True + c_area = c['area'] smallest = None for x in conds: if 'area' in x: a = x['area'] - if c_area[2] >= a[2] and c_area[3] >= a[3]: - if a[0] + a[2] >= c_area[0] + c_area[2]: - if a[1] + a[3] >= c_area[1] + c_area[3]: - if smallest is None: - smallest = x - elif 'area' not in smallest: - smallest = x - else: - if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: - smallest = x + if area_inside(c_area, a): + if smallest is None: + smallest = x + elif 'area' not in smallest: + smallest = x + else: + if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]): + smallest = x else: if smallest is None: smallest = x @@ -686,8 +722,9 @@ class Sampler: KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu", - "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", - "ipndm", "ipndm_v", "deis"] + "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm", + "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp", + "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"] class KSAMPLER(Sampler): def __init__(self, sampler_function, extra_options={}, inpaint_options={}): @@ -745,7 +782,7 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}): return KSAMPLER(sampler_function, extra_options, inpaint_options) -def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None): +def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None, latent_shapes=None): for k in conds: conds[k] = conds[k][:] resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device) @@ -755,7 +792,7 @@ def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=N if hasattr(model, 'extra_conds'): for k in conds: - conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed, latent_shapes=latent_shapes) #make sure each cond area has an opposite one with the same area for k in conds: @@ -810,6 +847,33 @@ def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]): for cond in conds_to_modify: cond['hooks'] = hooks +def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]): + '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for + HookGroups that have the same reference.''' + registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None) + # if None were registered, make sure all hooks are cleaned from conds + if registered is None: + for k in conds: + for kk in conds[k]: + kk.pop('hooks', None) + return + # find conds that contain hooks to be replaced - group by common HookGroup refs + hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {} + for k in conds: + for kk in conds[k]: + hooks: comfy.hooks.HookGroup = kk.get('hooks', None) + if hooks is not None: + if not hooks.is_subset_of(registered): + to_replace = hook_replacement.setdefault(hooks, []) + to_replace.append(kk) + # for each hook to replace, create a new proper HookGroup and assign to all common conds + for hooks, conds_to_modify in hook_replacement.items(): + new_hooks = hooks.new_with_common_hooks(registered) + if len(new_hooks) == 0: + new_hooks = None + for kk in conds_to_modify: + kk['hooks'] = new_hooks + def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]): hooks_set = set() @@ -819,9 +883,58 @@ def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]): return len(hooks_set) +def cast_to_load_options(model_options: dict[str], device=None, dtype=None): + ''' + If any patches from hooks, wrappers, or callbacks have .to to be called, call it. + ''' + if model_options is None: + return + to_load_options = model_options.get("to_load_options", None) + if to_load_options is None: + return + + casts = [] + if device is not None: + casts.append(device) + if dtype is not None: + casts.append(dtype) + # if nothing to apply, do nothing + if len(casts) == 0: + return + + # try to call .to on patches + if "patches" in to_load_options: + patches = to_load_options["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + for cast in casts: + patch_list[i] = patch_list[i].to(cast) + if "patches_replace" in to_load_options: + patches = to_load_options["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + for cast in casts: + patch_list[k] = patch_list[k].to(cast) + # try to call .to on any wrappers/callbacks + wrappers_and_callbacks = ["wrappers", "callbacks"] + for wc_name in wrappers_and_callbacks: + if wc_name in to_load_options: + wc: dict[str, list] = to_load_options[wc_name] + for wc_dict in wc.values(): + for wc_list in wc_dict.values(): + for i in range(len(wc_list)): + if hasattr(wc_list[i], "to"): + for cast in casts: + wc_list[i] = wc_list[i].to(cast) + + class CFGGuider: - def __init__(self, model_patcher): - self.model_patcher: 'ModelPatcher' = model_patcher + def __init__(self, model_patcher: ModelPatcher): + self.model_patcher = model_patcher self.model_options = model_patcher.model_options self.original_conds = {} self.cfg = 1.0 @@ -837,16 +950,23 @@ class CFGGuider: self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k]) def __call__(self, *args, **kwargs): - return self.predict_noise(*args, **kwargs) + return self.outer_predict_noise(*args, **kwargs) + + def outer_predict_noise(self, x, timestep, model_options={}, seed=None): + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self.predict_noise, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, self.model_options, is_model_options=True) + ).execute(x, timestep, model_options, seed) def predict_noise(self, x, timestep, model_options={}, seed=None): return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed) - def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed): + def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=None): if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image. latent_image = self.inner_model.process_latent_in(latent_image) - self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed) + self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes) extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options) extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas @@ -860,8 +980,8 @@ class CFGGuider: samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) return self.inner_model.process_latent_out(samples.to(torch.float32)) - def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None): - self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds) + def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None, latent_shapes=None): + self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options) device = self.model_patcher.load_device if denoise_mask is not None: @@ -870,10 +990,11 @@ class CFGGuider: noise = noise.to(device) latent_image = latent_image.to(device) sigmas = sigmas.to(device) + cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype()) try: self.model_patcher.pre_run() - output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) finally: self.model_patcher.cleanup() @@ -886,6 +1007,12 @@ class CFGGuider: if sigmas.shape[-1] == 0: return latent_image + if latent_image.is_nested: + latent_image, latent_shapes = comfy.utils.pack_latents(latent_image.unbind()) + noise, _ = comfy.utils.pack_latents(noise.unbind()) + else: + latent_shapes = [latent_image.shape] + self.conds = {} for k in self.original_conds: self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k])) @@ -899,18 +1026,23 @@ class CFGGuider: if get_total_hook_groups_in_conds(self.conds) <= 1: self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options) + filter_registered_hooks_on_conds(self.conds, self.model_options) executor = comfy.patcher_extension.WrapperExecutor.new_class_executor( self.outer_sample, self, comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True) ) - output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed) + output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes) finally: + cast_to_load_options(self.model_options, device=self.model_patcher.offload_device) self.model_options = orig_model_options self.model_patcher.hook_mode = orig_hook_mode self.model_patcher.restore_hook_patches() del self.conds + + if len(latent_shapes) > 1: + output = comfy.nested_tensor.NestedTensor(comfy.utils.unpack_latents(output, latent_shapes)) return output @@ -931,13 +1063,13 @@ class SchedulerHandler(NamedTuple): use_ms: bool = True SCHEDULER_HANDLERS = { - "normal": SchedulerHandler(normal_scheduler), + "simple": SchedulerHandler(simple_scheduler), + "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)), "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False), "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False), - "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)), - "simple": SchedulerHandler(simple_scheduler), "ddim_uniform": SchedulerHandler(ddim_scheduler), "beta": SchedulerHandler(beta_scheduler), + "normal": SchedulerHandler(normal_scheduler), "linear_quadratic": SchedulerHandler(linear_quadratic_schedule), "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False), } diff --git a/comfy/sd.py b/comfy/sd.py index c6d6236b1..28bee248d 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,4 +1,5 @@ from __future__ import annotations +import json import torch from enum import Enum import logging @@ -11,8 +12,17 @@ from .ldm.cascade.stage_c_coder import StageC_coder from .ldm.audio.autoencoder import AudioOobleckVAE import comfy.ldm.genmo.vae.model import comfy.ldm.lightricks.vae.causal_video_autoencoder +import comfy.ldm.cosmos.vae +import comfy.ldm.wan.vae +import comfy.ldm.wan.vae2_2 +import comfy.ldm.hunyuan3d.vae +import comfy.ldm.ace.vae.music_dcae_pipeline +import comfy.ldm.hunyuan_video.vae +import comfy.ldm.mmaudio.vae.autoencoder +import comfy.pixel_space_convert import yaml import math +import os import comfy.utils @@ -34,6 +44,14 @@ import comfy.text_encoders.long_clipl import comfy.text_encoders.genmo import comfy.text_encoders.lt import comfy.text_encoders.hunyuan_video +import comfy.text_encoders.cosmos +import comfy.text_encoders.lumina2 +import comfy.text_encoders.wan +import comfy.text_encoders.hidream +import comfy.text_encoders.ace +import comfy.text_encoders.omnigen2 +import comfy.text_encoders.qwen_image +import comfy.text_encoders.hunyuan_image import comfy.model_patcher import comfy.lora @@ -111,7 +129,8 @@ class CLIP: model_management.load_models_gpu([self.patcher], force_full_load=True) self.layer_idx = None self.use_clip_schedule = False - logging.info("CLIP model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) + logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype)) + self.tokenizer_options = {} def clone(self): n = CLIP(no_init=True) @@ -119,6 +138,7 @@ class CLIP: n.cond_stage_model = self.cond_stage_model n.tokenizer = self.tokenizer n.layer_idx = self.layer_idx + n.tokenizer_options = self.tokenizer_options.copy() n.use_clip_schedule = self.use_clip_schedule n.apply_hooks_to_conds = self.apply_hooks_to_conds return n @@ -126,11 +146,19 @@ class CLIP: def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): return self.patcher.add_patches(patches, strength_patch, strength_model) + def set_tokenizer_option(self, option_name, value): + self.tokenizer_options[option_name] = value + def clip_layer(self, layer_idx): self.layer_idx = layer_idx - def tokenize(self, text, return_word_ids=False): - return self.tokenizer.tokenize_with_weights(text, return_word_ids) + def tokenize(self, text, return_word_ids=False, **kwargs): + tokenizer_options = kwargs.get("tokenizer_options", {}) + if len(self.tokenizer_options) > 0: + tokenizer_options = {**self.tokenizer_options, **tokenizer_options} + if len(tokenizer_options) > 0: + kwargs["tokenizer_options"] = tokenizer_options + return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs) def add_hooks_to_dict(self, pooled_dict: dict[str]): if self.apply_hooks_to_conds: @@ -244,12 +272,17 @@ class CLIP: return self.patcher.get_key_patches() class VAE: - def __init__(self, sd=None, device=None, config=None, dtype=None): + def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None): if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format sd = diffusers_convert.convert_vae_state_dict(sd) - self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) - self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) + if model_management.is_amd(): + VAE_KL_MEM_RATIO = 2.73 + else: + VAE_KL_MEM_RATIO = 1.0 + + self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower) + self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO self.downscale_ratio = 8 self.upscale_ratio = 8 self.latent_channels = 4 @@ -258,9 +291,13 @@ class VAE: self.process_input = lambda image: image * 2.0 - 1.0 self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0) self.working_dtypes = [torch.bfloat16, torch.float32] + self.disable_offload = False + self.not_video = False self.downscale_index_formula = None self.upscale_index_formula = None + self.extra_1d_channel = None + self.crop_input = True if config is None: if "decoder.mid.block_1.mix_factor" in sd: @@ -303,21 +340,50 @@ class VAE: self.downscale_ratio = 32 self.latent_channels = 16 elif "decoder.conv_in.weight" in sd: - #default SD1.x/SD2.x VAE parameters - ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} - - if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE - ddconfig['ch_mult'] = [1, 2, 4] - self.downscale_ratio = 4 - self.upscale_ratio = 4 - - self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] - if 'post_quant_conv.weight' in sd: - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) - else: + if sd['decoder.conv_in.weight'].shape[1] == 64: + ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True} + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + self.downscale_ratio = 32 + self.upscale_ratio = 32 + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, - encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, - decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + encoder_config={'target': "comfy.ldm.hunyuan_video.vae.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.hunyuan_video.vae.Decoder", 'params': ddconfig}) + + self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype) + elif sd['decoder.conv_in.weight'].shape[1] == 32: + ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True, "refiner_vae": False} + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) + self.upscale_index_formula = (4, 16, 16) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) + self.downscale_index_formula = (4, 16, 16) + self.latent_dim = 3 + self.not_video = True + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) + + self.memory_used_encode = lambda shape, dtype: (2800 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (2800 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) + else: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + + if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE + ddconfig['ch_mult'] = [1, 2, 4] + self.downscale_ratio = 4 + self.upscale_ratio = 4 + + self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1] + if 'post_quant_conv.weight' in sd: + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) + else: + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) elif "decoder.layers.1.layers.0.beta" in sd: self.first_stage_model = AudioOobleckVAE() self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype) @@ -330,6 +396,7 @@ class VAE: self.process_output = lambda audio: audio self.process_input = lambda audio: audio self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + self.disable_offload = True elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae if "blocks.2.blocks.3.stack.5.weight" in sd: sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."}) @@ -352,7 +419,12 @@ class VAE: version = 0 elif tensor_conv1.shape[0] == 1024: version = 1 - self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version) + if "encoder.down_blocks.1.conv.conv.bias" in sd: + version = 2 + vae_config = None + if metadata is not None and "config" in metadata: + vae_config = json.loads(metadata["config"]).get("vae", None) + self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config) self.latent_channels = 128 self.latent_dim = 3 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) @@ -362,6 +434,23 @@ class VAE: self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32) self.downscale_index_formula = (8, 32, 32) self.working_dtypes = [torch.bfloat16, torch.float32] + elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32: + ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True} + ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1] + self.latent_channels = 64 + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) + self.upscale_index_formula = (4, 16, 16) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) + self.downscale_index_formula = (4, 16, 16) + self.latent_dim = 3 + self.not_video = True + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"}, + encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig}, + decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig}) + + self.memory_used_encode = lambda shape, dtype: (1400 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1400 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) elif "decoder.conv_in.conv.weight" in sd: ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} ddconfig["conv3d"] = True @@ -376,6 +465,109 @@ class VAE: self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype) self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype) self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + elif "decoder.unpatcher3d.wavelets" in sd: + self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8) + self.upscale_index_formula = (8, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8) + self.downscale_index_formula = (8, 8, 8) + self.latent_dim = 3 + self.latent_channels = 16 + ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8} + self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig) + #TODO: these values are a bit off because this is not a standard VAE + self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype) + self.working_dtypes = [torch.bfloat16, torch.float32] + elif "decoder.middle.0.residual.0.gamma" in sd: + if "decoder.upsamples.0.upsamples.0.residual.2.weight" in sd: # Wan 2.2 VAE + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16) + self.upscale_index_formula = (4, 16, 16) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16) + self.downscale_index_formula = (4, 16, 16) + self.latent_dim = 3 + self.latent_channels = 48 + ddconfig = {"dim": 160, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} + self.first_stage_model = comfy.ldm.wan.vae2_2.WanVAE(**ddconfig) + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + self.memory_used_encode = lambda shape, dtype: 3300 * shape[3] * shape[4] * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: 8000 * shape[3] * shape[4] * (16 * 16) * model_management.dtype_size(dtype) + else: # Wan 2.1 VAE + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) + self.upscale_index_formula = (4, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) + self.downscale_index_formula = (4, 8, 8) + self.latent_dim = 3 + self.latent_channels = 16 + ddconfig = {"dim": 96, "z_dim": self.latent_channels, "dim_mult": [1, 2, 4, 4], "num_res_blocks": 2, "attn_scales": [], "temperal_downsample": [False, True, True], "dropout": 0.0} + self.first_stage_model = comfy.ldm.wan.vae.WanVAE(**ddconfig) + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + self.memory_used_encode = lambda shape, dtype: 6000 * shape[3] * shape[4] * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: 7000 * shape[3] * shape[4] * (8 * 8) * model_management.dtype_size(dtype) + # Hunyuan 3d v2 2.0 & 2.1 + elif "geo_decoder.cross_attn_decoder.ln_1.bias" in sd: + + self.latent_dim = 1 + + def estimate_memory(shape, dtype, num_layers = 16, kv_cache_multiplier = 2): + batch, num_tokens, hidden_dim = shape + dtype_size = model_management.dtype_size(dtype) + + total_mem = batch * num_tokens * hidden_dim * dtype_size * (1 + kv_cache_multiplier * num_layers) + return total_mem + + # better memory estimations + self.memory_used_encode = lambda shape, dtype, num_layers = 8, kv_cache_multiplier = 0:\ + estimate_memory(shape, dtype, num_layers, kv_cache_multiplier) + + self.memory_used_decode = lambda shape, dtype, num_layers = 16, kv_cache_multiplier = 2: \ + estimate_memory(shape, dtype, num_layers, kv_cache_multiplier) + + self.first_stage_model = comfy.ldm.hunyuan3d.vae.ShapeVAE() + self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + + elif "vocoder.backbone.channel_layers.0.0.bias" in sd: #Ace Step Audio + self.first_stage_model = comfy.ldm.ace.vae.music_dcae_pipeline.MusicDCAE(source_sample_rate=44100) + self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) + self.latent_channels = 8 + self.output_channels = 2 + self.upscale_ratio = 4096 + self.downscale_ratio = 4096 + self.latent_dim = 2 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] + self.disable_offload = True + self.extra_1d_channel = 16 + elif "pixel_space_vae" in sd: + self.first_stage_model = comfy.pixel_space_convert.PixelspaceConversionVAE() + self.memory_used_encode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype) + self.downscale_ratio = 1 + self.upscale_ratio = 1 + self.latent_channels = 3 + self.latent_dim = 2 + self.output_channels = 3 + elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE + sample_rate = 16000 + if sample_rate == 16000: + mode = '16k' + else: + mode = '44k' + + self.first_stage_model = comfy.ldm.mmaudio.vae.autoencoder.AudioAutoencoder(mode=mode) + self.memory_used_encode = lambda shape, dtype: (30 * shape[2]) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (90 * shape[2] * 1411.2) * model_management.dtype_size(dtype) + self.latent_channels = 20 + self.output_channels = 2 + self.upscale_ratio = 512 * (44100 / sample_rate) + self.downscale_ratio = 512 * (44100 / sample_rate) + self.latent_dim = 1 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.float32] + self.crop_input = False else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None @@ -404,7 +596,14 @@ class VAE: self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype)) + def throw_exception_if_invalid(self): + if self.first_stage_model is None: + raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.") + def vae_encode_crop_pixels(self, pixels): + if not self.crop_input: + return pixels + downscale_ratio = self.spacial_compression_encode() dims = pixels.shape[1:-1] @@ -430,7 +629,13 @@ class VAE: return output def decode_tiled_1d(self, samples, tile_x=128, overlap=32): - decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + if samples.ndim == 3: + decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float() + else: + og_shape = samples.shape + samples = samples.reshape((og_shape[0], og_shape[1] * og_shape[2], -1)) + decode_fn = lambda a: self.first_stage_model.decode(a.reshape((-1, og_shape[1], og_shape[2], a.shape[-1])).to(self.vae_dtype).to(self.device)).float() + return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)) def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)): @@ -450,33 +655,57 @@ class VAE: samples /= 3.0 return samples - def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048): - encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() - return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) + def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048): + if self.latent_dim == 1: + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() + out_channels = self.latent_channels + upscale_amount = 1 / self.downscale_ratio + else: + extra_channel_size = self.extra_1d_channel + out_channels = self.latent_channels * extra_channel_size + tile_x = tile_x // extra_channel_size + overlap = overlap // extra_channel_size + upscale_amount = 1 / self.downscale_ratio + encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).reshape(1, out_channels, -1).float() + + out = comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=self.output_device) + if self.latent_dim == 1: + return out + else: + return out.reshape(samples.shape[0], self.latent_channels, extra_channel_size, -1) def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)): encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float() return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device) - def decode(self, samples_in): + def decode(self, samples_in, vae_options={}): + self.throw_exception_if_invalid() pixel_samples = None + do_tile = False try: memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) - model_management.load_models_gpu([self.patcher], memory_required=memory_used) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / memory_used) batch_number = max(1, batch_number) for x in range(0, samples_in.shape[0], batch_number): samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) - out = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float()) + out = self.process_output(self.first_stage_model.decode(samples, **vae_options).to(self.output_device).float()) if pixel_samples is None: pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) pixel_samples[x:x+batch_number] = out except model_management.OOM_EXCEPTION: logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + #NOTE: We don't know what tensors were allocated to stack variables at the time of the + #exception and the exception itself refs them all until we get out of this except block. + #So we just set a flag for tiler fallback so that tensor gc can happen once the + #exception is fully off the books. + do_tile = True + + if do_tile: dims = samples_in.ndim - 2 - if dims == 1: + if dims == 1 or self.extra_1d_channel is not None: pixel_samples = self.decode_tiled_1d(samples_in) elif dims == 2: pixel_samples = self.decode_tiled_(samples_in) @@ -489,8 +718,9 @@ class VAE: return pixel_samples def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): + self.throw_exception_if_invalid() memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile - model_management.load_models_gpu([self.patcher], memory_required=memory_used) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) dims = samples.ndim - 2 args = {} if tile_x is not None: @@ -517,13 +747,18 @@ class VAE: return output.movedim(1, -1) def encode(self, pixel_samples): + self.throw_exception_if_invalid() pixel_samples = self.vae_encode_crop_pixels(pixel_samples) pixel_samples = pixel_samples.movedim(-1, 1) - if self.latent_dim == 3: - pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + do_tile = False + if self.latent_dim == 3 and pixel_samples.ndim < 5: + if not self.not_video: + pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + else: + pixel_samples = pixel_samples.unsqueeze(2) try: memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) - model_management.load_models_gpu([self.patcher], memory_required=memory_used) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) free_memory = model_management.get_free_memory(self.device) batch_number = int(free_memory / max(1, memory_used)) batch_number = max(1, batch_number) @@ -537,11 +772,18 @@ class VAE: except model_management.OOM_EXCEPTION: logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + #NOTE: We don't know what tensors were allocated to stack variables at the time of the + #exception and the exception itself refs them all until we get out of this except block. + #So we just set a flag for tiler fallback so that tensor gc can happen once the + #exception is fully off the books. + do_tile = True + + if do_tile: if self.latent_dim == 3: tile = 256 overlap = tile // 4 samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap)) - elif self.latent_dim == 1: + elif self.latent_dim == 1 or self.extra_1d_channel is not None: samples = self.encode_tiled_1d(pixel_samples) else: samples = self.encode_tiled_(pixel_samples) @@ -549,14 +791,18 @@ class VAE: return samples def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None): + self.throw_exception_if_invalid() pixel_samples = self.vae_encode_crop_pixels(pixel_samples) dims = self.latent_dim pixel_samples = pixel_samples.movedim(-1, 1) if dims == 3: - pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + if not self.not_video: + pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0) + else: + pixel_samples = pixel_samples.unsqueeze(2) memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) # TODO: calculate mem required for tile - model_management.load_models_gpu([self.patcher], memory_required=memory_used) + model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload) args = {} if tile_x is not None: @@ -610,6 +856,7 @@ class VAE: except: return None + class StyleModel: def __init__(self, model, device="cpu"): self.model = model @@ -641,6 +888,15 @@ class CLIPType(Enum): LTXV = 8 HUNYUAN_VIDEO = 9 PIXART = 10 + COSMOS = 11 + LUMINA2 = 12 + WAN = 13 + HIDREAM = 14 + CHROMA = 15 + ACE = 16 + OMNIGEN2 = 17 + QWEN_IMAGE = 18 + HUNYUAN_IMAGE = 19 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}): @@ -658,6 +914,12 @@ class TEModel(Enum): T5_XL = 5 T5_BASE = 6 LLAMA3_8 = 7 + T5_XXL_OLD = 8 + GEMMA_2_2B = 9 + QWEN25_3B = 10 + QWEN25_7B = 11 + BYT5_SMALL_GLYPH = 12 + GEMMA_3_4B = 13 def detect_te_model(sd): if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: @@ -672,8 +934,23 @@ def detect_te_model(sd): return TEModel.T5_XXL elif weight.shape[-1] == 2048: return TEModel.T5_XL + if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd: + return TEModel.T5_XXL_OLD if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd: + weight = sd['encoder.block.0.layer.0.SelfAttention.k.weight'] + if weight.shape[0] == 384: + return TEModel.BYT5_SMALL_GLYPH return TEModel.T5_BASE + if 'model.layers.0.post_feedforward_layernorm.weight' in sd: + if 'model.layers.0.self_attn.q_norm.weight' in sd: + return TEModel.GEMMA_3_4B + return TEModel.GEMMA_2_2B + if 'model.layers.0.self_attn.k_proj.bias' in sd: + weight = sd['model.layers.0.self_attn.k_proj.bias'] + if weight.shape[0] == 256: + return TEModel.QWEN25_3B + if weight.shape[0] == 512: + return TEModel.QWEN25_7B if "model.layers.0.post_attention_layernorm.weight" in sd: return TEModel.LLAMA3_8 return None @@ -681,9 +958,10 @@ def detect_te_model(sd): def t5xxl_detect(clip_data): weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" + weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight" for sd in clip_data: - if weight_name in sd: + if weight_name in sd or weight_name_old in sd: return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd) return {} @@ -710,6 +988,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip if "text_projection" in clip_data[i]: clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node + tokenizer_data = {} clip_target = EmptyClass() clip_target.params = {} if len(clip_data) == 1: @@ -721,6 +1000,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.SD3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: clip_target.clip = sdxl_clip.SDXLRefinerClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer @@ -734,22 +1016,64 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.LTXV: clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer - elif clip_type == CLIPType.PIXART: + elif clip_type == CLIPType.PIXART or clip_type == CLIPType.CHROMA: clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer + elif clip_type == CLIPType.WAN: + clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), + clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: #CLIPType.MOCHI clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer + elif te_model == TEModel.T5_XXL_OLD: + clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer elif te_model == TEModel.T5_XL: clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer elif te_model == TEModel.T5_BASE: - clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model - clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + if clip_type == CLIPType.ACE or "spiece_model" in clip_data[0]: + clip_target.clip = comfy.text_encoders.ace.AceT5Model + clip_target.tokenizer = comfy.text_encoders.ace.AceT5Tokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + else: + clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model + clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + elif te_model == TEModel.GEMMA_2_2B: + clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + elif te_model == TEModel.GEMMA_3_4B: + clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b") + clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer + tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None) + elif te_model == TEModel.LLAMA3_8: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data), + clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + elif te_model == TEModel.QWEN25_3B: + clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.omnigen2.Omnigen2Tokenizer + elif te_model == TEModel.QWEN25_7B: + if clip_type == CLIPType.HUNYUAN_IMAGE: + clip_target.clip = comfy.text_encoders.hunyuan_image.te(byt5=False, **llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer + else: + clip_target.clip = comfy.text_encoders.qwen_image.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.qwen_image.QwenImageTokenizer else: + # clip_l if clip_type == CLIPType.SD3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif clip_type == CLIPType.HIDREAM: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer else: clip_target.clip = sd1_clip.SD1ClipModel clip_target.tokenizer = sd1_clip.SD1Tokenizer @@ -767,15 +1091,38 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif clip_type == CLIPType.HUNYUAN_VIDEO: clip_target.clip = comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer + elif clip_type == CLIPType.HIDREAM: + # Detect + hidream_dualclip_classes = [] + for hidream_te in clip_data: + te_model = detect_te_model(hidream_te) + hidream_dualclip_classes.append(te_model) + + clip_l = TEModel.CLIP_L in hidream_dualclip_classes + clip_g = TEModel.CLIP_G in hidream_dualclip_classes + t5 = TEModel.T5_XXL in hidream_dualclip_classes + llama = TEModel.LLAMA3_8 in hidream_dualclip_classes + + # Initialize t5xxl_detect and llama_detect kwargs if needed + t5_kwargs = t5xxl_detect(clip_data) if t5 else {} + llama_kwargs = llama_detect(clip_data) if llama else {} + + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, **t5_kwargs, **llama_kwargs) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer + elif clip_type == CLIPType.HUNYUAN_IMAGE: + clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer else: clip_target.clip = sdxl_clip.SDXLClipModel clip_target.tokenizer = sdxl_clip.SDXLTokenizer elif len(clip_data) == 3: clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(**t5xxl_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer + elif len(clip_data) == 4: + clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), **llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer parameters = 0 - tokenizer_data = {} for c in clip_data: parameters += comfy.utils.calculate_parameters(c) tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) @@ -797,6 +1144,12 @@ def load_gligen(ckpt_path): model = model.half() return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) +def model_detection_error_hint(path, state_dict): + filename = os.path.basename(path) + if 'lora' in filename.lower(): + return "\nHINT: This seems to be a Lora file and Lora files should be put in the lora folder and loaded with a lora loader node.." + return "" + def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.") model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True) @@ -822,13 +1175,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl return (model, clip, vae) def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): - sd = comfy.utils.load_torch_file(ckpt_path) - out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options) + sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) + out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options, te_model_options=te_model_options, metadata=metadata) if out is None: - raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) + raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(ckpt_path, model_detection_error_hint(ckpt_path, sd))) return out -def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}): +def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}, te_model_options={}, metadata=None): clip = None clipvision = None vae = None @@ -840,19 +1193,24 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix) load_device = model_management.get_torch_device() - model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix) + model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata) if model_config is None: - return None + logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.") + diffusion_model = load_diffusion_model_state_dict(sd, model_options={}) + if diffusion_model is None: + return None + return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used' + unet_weight_dtype = list(model_config.supported_inference_dtypes) - if weight_dtype is not None and model_config.scaled_fp8 is None: - unet_weight_dtype.append(weight_dtype) + if model_config.scaled_fp8 is not None: + weight_dtype = None model_config.custom_operations = model_options.get("custom_operations", None) unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) if unet_dtype is None: - unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes) model_config.set_inference_dtype(unet_dtype, manual_cast_dtype) @@ -869,7 +1227,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c if output_vae: vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True) vae_sd = model_config.process_vae_state_dict(vae_sd) - vae = VAE(sd=vae_sd) + vae = VAE(sd=vae_sd, metadata=metadata) if output_clip: clip_target = model_config.clip_target(state_dict=sd) @@ -898,13 +1256,34 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c if output_model: model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device()) if inital_load_device != torch.device("cpu"): - logging.info("loaded straight to GPU") + logging.info("loaded diffusion model directly to GPU") model_management.load_models_gpu([model_patcher], force_full_load=True) return (model_patcher, clip, vae, clipvision) -def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format +def load_diffusion_model_state_dict(sd, model_options={}): + """ + Loads a UNet diffusion model from a state dictionary, supporting both diffusers and regular formats. + + Args: + sd (dict): State dictionary containing model weights and configuration + model_options (dict, optional): Additional options for model loading. Supports: + - dtype: Override model data type + - custom_operations: Custom model operations + - fp8_optimizations: Enable FP8 optimizations + + Returns: + ModelPatcher: A wrapped model instance that handles device management and weight loading. + Returns None if the model configuration cannot be detected. + + The function: + 1. Detects and handles different model formats (regular, diffusers, mmdit) + 2. Configures model dtype based on parameters and device capabilities + 3. Handles weight conversion and device placement + 4. Manages model optimization settings + 5. Loads weights and returns a device-managed model instance + """ dtype = model_options.get("dtype", None) #Allow loading unets from checkpoint files @@ -943,11 +1322,11 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse offload_device = model_management.unet_offload_device() unet_weight_dtype = list(model_config.supported_inference_dtypes) - if weight_dtype is not None and model_config.scaled_fp8 is None: - unet_weight_dtype.append(weight_dtype) + if model_config.scaled_fp8 is not None: + weight_dtype = None if dtype is None: - unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype) + unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype) else: unet_dtype = dtype @@ -962,7 +1341,7 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse model.load_model_weights(new_sd, "") left_over = sd.keys() if len(left_over) > 0: - logging.info("left over keys in unet: {}".format(left_over)) + logging.info("left over keys in diffusion model: {}".format(left_over)) return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) @@ -970,8 +1349,8 @@ def load_diffusion_model(unet_path, model_options={}): sd = comfy.utils.load_torch_file(unet_path) model = load_diffusion_model_state_dict(sd, model_options=model_options) if model is None: - logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path)) - raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + logging.error("ERROR UNSUPPORTED DIFFUSION MODEL {}".format(unet_path)) + raise RuntimeError("ERROR: Could not detect model type of: {}\n{}".format(unet_path, model_detection_error_hint(unet_path, sd))) return model def load_unet(unet_path, dtype=None): diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py index 95d41c30f..f8a7c2a1b 100644 --- a/comfy/sd1_clip.py +++ b/comfy/sd1_clip.py @@ -82,7 +82,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): LAYERS = [ "last", "pooled", - "hidden" + "hidden", + "all" ] def __init__(self, device="cpu", max_length=77, freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel, @@ -93,6 +94,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): if textmodel_json_config is None: textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + if "model_name" not in model_options: + model_options = {**model_options, "model_name": "clip_l"} if isinstance(textmodel_json_config, dict): config = textmodel_json_config @@ -100,6 +103,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): with open(textmodel_json_config) as f: config = json.load(f) + te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {}) + for k, v in te_model_options.items(): + config[k] = v + operations = model_options.get("custom_operations", None) scaled_fp8 = None @@ -147,7 +154,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): def set_clip_options(self, options): layer_idx = options.get("layer", self.layer_idx) self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled) - if layer_idx is None or abs(layer_idx) > self.num_layers: + if self.layer == "all": + pass + elif layer_idx is None or abs(layer_idx) > self.num_layers: self.layer = "last" else: self.layer = "hidden" @@ -158,71 +167,101 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): self.layer_idx = self.options_default[1] self.return_projected_pooled = self.options_default[2] - def set_up_textual_embeddings(self, tokens, current_embeds): - out_tokens = [] - next_new_token = token_dict_size = current_embeds.weight.shape[0] - embedding_weights = [] + def process_tokens(self, tokens, device): + end_token = self.special_tokens.get("end", None) + if end_token is None: + cmp_token = self.special_tokens.get("pad", -1) + else: + cmp_token = end_token + + embeds_out = [] + attention_masks = [] + num_tokens = [] for x in tokens: + attention_mask = [] tokens_temp = [] + other_embeds = [] + eos = False + index = 0 for y in x: if isinstance(y, numbers.Integral): - tokens_temp += [int(y)] - else: - if y.shape[0] == current_embeds.weight.shape[1]: - embedding_weights += [y] - tokens_temp += [next_new_token] - next_new_token += 1 + if eos: + attention_mask.append(0) else: - logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1])) - while len(tokens_temp) < len(x): - tokens_temp += [self.special_tokens["pad"]] - out_tokens += [tokens_temp] + attention_mask.append(1) + token = int(y) + tokens_temp += [token] + if not eos and token == cmp_token: + if end_token is None: + attention_mask[-1] = 0 + eos = True + else: + other_embeds.append((index, y)) + index += 1 - n = token_dict_size - if len(embedding_weights) > 0: - new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) - new_embedding.weight[:token_dict_size] = current_embeds.weight - for x in embedding_weights: - new_embedding.weight[n] = x - n += 1 - self.transformer.set_input_embeddings(new_embedding) + tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) + tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32) + index = 0 + pad_extra = 0 + embeds_info = [] + for o in other_embeds: + emb = o[1] + if torch.is_tensor(emb): + emb = {"type": "embedding", "data": emb} - processed_tokens = [] - for x in out_tokens: - processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one + extra = None + emb_type = emb.get("type", None) + if emb_type == "embedding": + emb = emb.get("data", None) + else: + if hasattr(self.transformer, "preprocess_embed"): + emb, extra = self.transformer.preprocess_embed(emb, device=device) + else: + emb = None - return processed_tokens + if emb is None: + index += -1 + continue + + ind = index + o[0] + emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) + emb_shape = emb.shape[1] + if emb.shape[-1] == tokens_embed.shape[-1]: + tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) + attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] + index += emb_shape - 1 + embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra}) + else: + index += -1 + pad_extra += emb_shape + logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1])) + + if pad_extra > 0: + padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32) + tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1) + attention_mask = attention_mask + [0] * pad_extra + + embeds_out.append(tokens_embed) + attention_masks.append(attention_mask) + num_tokens.append(sum(attention_mask)) + + return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info def forward(self, tokens): - backup_embeds = self.transformer.get_input_embeddings() - device = backup_embeds.weight.device - tokens = self.set_up_textual_embeddings(tokens, backup_embeds) - tokens = torch.LongTensor(tokens).to(device) - - attention_mask = None - if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks: - attention_mask = torch.zeros_like(tokens) - end_token = self.special_tokens.get("end", None) - if end_token is None: - cmp_token = self.special_tokens.get("pad", -1) - else: - cmp_token = end_token - - for x in range(attention_mask.shape[0]): - for y in range(attention_mask.shape[1]): - attention_mask[x, y] = 1 - if tokens[x, y] == cmp_token: - if end_token is None: - attention_mask[x, y] = 0 - break + device = self.transformer.get_input_embeddings().weight.device + embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device) attention_mask_model = None if self.enable_attention_masks: attention_mask_model = attention_mask - outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32) - self.transformer.set_input_embeddings(backup_embeds) + if self.layer == "all": + intermediate_output = "all" + else: + intermediate_output = self.layer_idx + + outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info) if self.layer == "last": z = outputs[0].float() @@ -388,13 +427,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No import safetensors.torch embed = safetensors.torch.load_file(embed_path, device="cpu") else: - if 'weights_only' in torch.load.__code__.co_varnames: - try: - embed = torch.load(embed_path, weights_only=True, map_location="cpu") - except: - embed_out = safe_load_embed_zip(embed_path) - else: - embed = torch.load(embed_path, map_location="cpu") + try: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + except: + embed_out = safe_load_embed_zip(embed_path) except Exception: logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name)) return None @@ -424,13 +460,14 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No return embed_out class SDTokenizer: - def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}): + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}): if tokenizer_path is None: tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") - self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) - self.max_length = max_length - self.min_length = min_length + self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args) + self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length) + self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length) self.end_token = None + self.min_padding = min_padding empty = self.tokenizer('')["input_ids"] self.tokenizer_adds_end_token = has_end_token @@ -448,7 +485,8 @@ class SDTokenizer: if end_token is not None: self.end_token = end_token else: - self.end_token = empty[0] + if has_end_token: + self.end_token = empty[0] if pad_token is not None: self.pad_token = pad_token @@ -485,16 +523,21 @@ class SDTokenizer: return (embed, leftover) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs): ''' Takes a prompt and converts it to a list of (token, weight, word id) elements. Tokens can both be integer tokens and pre computed CLIP tensors. Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. Returned list has the dimensions NxM where M is the input size of CLIP ''' + min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length) + min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding) text = escape_important(text) - parsed_weights = token_weights(text, 1.0) + if kwargs.get("disable_weights", False): + parsed_weights = [(text, 1.0)] + else: + parsed_weights = token_weights(text, 1.0) # tokenize words tokens = [] @@ -570,10 +613,12 @@ class SDTokenizer: #fill last batch if self.end_token is not None: batch.append((self.end_token, 1.0, 0)) - if self.pad_to_max_length: + if min_padding is not None: + batch.extend([(self.pad_token, 1.0, 0)] * min_padding) + if self.pad_to_max_length and len(batch) < self.max_length: batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch))) - if self.min_length is not None and len(batch) < self.min_length: - batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch))) + if min_length is not None and len(batch) < min_length: + batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch))) if not return_word_ids: batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] @@ -588,22 +633,27 @@ class SDTokenizer: return {} class SD1Tokenizer: - def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer): - self.clip_name = clip_name - self.clip = "clip_{}".format(self.clip_name) + def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None): + if name is not None: + self.clip_name = name + self.clip = "{}".format(self.clip_name) + else: + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer) setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): return getattr(self, self.clip).untokenize(token_weight_pair) def state_dict(self): - return {} + return getattr(self, self.clip).state_dict() class SD1CheckpointClipModel(SDClipModel): def __init__(self, device="cpu", dtype=None, model_options={}): @@ -621,6 +671,7 @@ class SD1ClipModel(torch.nn.Module): self.clip = "clip_{}".format(self.clip_name) clip_model = model_options.get("{}_class".format(self.clip), clip_model) + model_options = {**model_options, "model_name": self.clip} setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs)) self.dtypes = set() diff --git a/comfy/sd1_tokenizer/tokenizer_config.json b/comfy/sd1_tokenizer/tokenizer_config.json index 5ba7bf706..8f7b3151d 100644 --- a/comfy/sd1_tokenizer/tokenizer_config.json +++ b/comfy/sd1_tokenizer/tokenizer_config.json @@ -18,7 +18,7 @@ "single_word": false }, "errors": "replace", - "model_max_length": 77, + "model_max_length": 8192, "name_or_path": "openai/clip-vit-large-patch14", "pad_token": "<|endoftext|>", "special_tokens_map_file": "./special_tokens_map.json", diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py index 4d0a4e8e7..c8cef14e4 100644 --- a/comfy/sdxl_clip.py +++ b/comfy/sdxl_clip.py @@ -9,6 +9,7 @@ class SDXLClipG(sd1_clip.SDClipModel): layer_idx=-2 textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + model_options = {**model_options, "model_name": "clip_g"} super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options) @@ -17,19 +18,18 @@ class SDXLClipG(sd1_clip.SDClipModel): class SDXLClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) class SDXLTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): - clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) - self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): @@ -41,8 +41,7 @@ class SDXLTokenizer: class SDXLClipModel(torch.nn.Module): def __init__(self, device="cpu", dtype=None, model_options={}): super().__init__() - clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) - self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, model_options=model_options) self.clip_g = SDXLClipG(device=device, dtype=dtype, model_options=model_options) self.dtypes = set([dtype]) @@ -75,7 +74,7 @@ class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g', tokenizer_data=tokenizer_data) class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): @@ -84,6 +83,7 @@ class StableCascadeTokenizer(sd1_clip.SD1Tokenizer): class StableCascadeClipG(sd1_clip.SDClipModel): def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + model_options = {**model_options, "model_name": "clip_g"} super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 6a2cc75ae..4064bdae1 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -14,6 +14,13 @@ import comfy.text_encoders.flux import comfy.text_encoders.genmo import comfy.text_encoders.lt import comfy.text_encoders.hunyuan_video +import comfy.text_encoders.cosmos +import comfy.text_encoders.lumina2 +import comfy.text_encoders.wan +import comfy.text_encoders.ace +import comfy.text_encoders.omnigen2 +import comfy.text_encoders.qwen_image +import comfy.text_encoders.hunyuan_image from . import supported_models_base from . import latent_formats @@ -503,6 +510,22 @@ class SDXL_instructpix2pix(SDXL): def get_model(self, state_dict, prefix="", device=None): return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device) +class LotusD(SD20): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "use_temporal_attention": False, + "adm_in_channels": 4, + "in_channels": 4, + } + + unet_extra_config = { + "num_classes": 'sequential' + } + + def get_model(self, state_dict, prefix="", device=None): + return model_base.Lotus(self, device=device) + class SD3(supported_models_base.BASE): unet_config = { "in_channels": 16, @@ -678,7 +701,7 @@ class Flux(supported_models_base.BASE): unet_extra_config = {} latent_format = latent_formats.Flux - memory_usage_factor = 2.8 + memory_usage_factor = 3.1 # TODO: debug why flux mem usage is so weird on windows. supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] @@ -759,13 +782,17 @@ class LTXV(supported_models_base.BASE): unet_extra_config = {} latent_format = latent_formats.LTXV - memory_usage_factor = 2.7 + memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid supported_inference_dtypes = [torch.bfloat16, torch.float32] vae_key_prefix = ["vae."] text_encoder_key_prefix = ["text_encoders."] + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5 + def get_model(self, state_dict, prefix="", device=None): out = model_base.LTXV(self, device=device) return out @@ -787,7 +814,7 @@ class HunyuanVideo(supported_models_base.BASE): unet_extra_config = {} latent_format = latent_formats.HunyuanVideo - memory_usage_factor = 2.0 #TODO + memory_usage_factor = 1.8 #TODO supported_inference_dtypes = [torch.bfloat16, torch.float32] @@ -823,6 +850,530 @@ class HunyuanVideo(supported_models_base.BASE): hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref)) return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect)) -models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo] +class HunyuanVideoI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 33, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoI2V(self, device=device) + return out + +class HunyuanVideoSkyreelsI2V(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "in_channels": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanVideoSkyreelsI2V(self, device=device) + return out + +class CosmosT2V(supported_models_base.BASE): + unet_config = { + "image_model": "cosmos", + "in_channels": 16, + } + + sampling_settings = { + "sigma_data": 0.5, + "sigma_max": 80.0, + "sigma_min": 0.002, + } + + unet_extra_config = {} + latent_format = latent_formats.Cosmos1CV8x8x8 + + memory_usage_factor = 1.6 #TODO + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosVideo(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) + +class CosmosI2V(CosmosT2V): + unet_config = { + "image_model": "cosmos", + "in_channels": 17, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosVideo(self, image_to_video=True, device=device) + return out + +class CosmosT2IPredict2(supported_models_base.BASE): + unet_config = { + "image_model": "cosmos_predict2", + "in_channels": 16, + } + + sampling_settings = { + "sigma_data": 1.0, + "sigma_max": 80.0, + "sigma_min": 0.002, + } + + unet_extra_config = {} + latent_format = latent_formats.Wan21 + + memory_usage_factor = 1.0 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosPredict2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect)) + +class CosmosI2VPredict2(CosmosT2IPredict2): + unet_config = { + "image_model": "cosmos_predict2", + "in_channels": 17, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.CosmosPredict2(self, image_to_video=True, device=device) + return out + +class Lumina2(supported_models_base.BASE): + unet_config = { + "image_model": "lumina2", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 6.0, + } + + memory_usage_factor = 1.2 + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Lumina2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect)) + +class WAN21_T2V(supported_models_base.BASE): + unet_config = { + "image_model": "wan2.1", + "model_type": "t2v", + } + + sampling_settings = { + "shift": 8.0, + } + + unet_extra_config = {} + latent_format = latent_formats.Wan21 + + memory_usage_factor = 0.9 + + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2222 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}umt5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect)) + +class WAN21_I2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "i2v", + "in_dim": 36, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, image_to_video=True, device=device) + return out + +class WAN21_FunControl2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "i2v", + "in_dim": 48, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21(self, image_to_video=False, device=device) + return out + +class WAN21_Camera(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "camera", + "in_dim": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Camera(self, image_to_video=False, device=device) + return out + +class WAN22_Camera(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "camera_2.2", + "in_dim": 36, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Camera(self, image_to_video=False, device=device) + return out + +class WAN21_Vace(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "vace", + } + + def __init__(self, unet_config): + super().__init__(unet_config) + self.memory_usage_factor = 1.2 * self.memory_usage_factor + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_Vace(self, image_to_video=False, device=device) + return out + +class WAN21_HuMo(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "humo", + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN21_HuMo(self, image_to_video=False, device=device) + return out + +class WAN22_S2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "s2v", + } + + def __init__(self, unet_config): + super().__init__(unet_config) + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN22_S2V(self, device=device) + return out + +class WAN22_Animate(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "animate", + } + + def __init__(self, unet_config): + super().__init__(unet_config) + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN22_Animate(self, device=device) + return out + +class WAN22_T2V(WAN21_T2V): + unet_config = { + "image_model": "wan2.1", + "model_type": "t2v", + "out_dim": 48, + } + + latent_format = latent_formats.Wan22 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.WAN22(self, image_to_video=True, device=device) + return out + +class Hunyuan3Dv2(supported_models_base.BASE): + unet_config = { + "image_model": "hunyuan3d2", + } + + unet_extra_config = {} + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.0, + } + + memory_usage_factor = 3.5 + + clip_vision_prefix = "conditioner.main_image_encoder.model." + vae_key_prefix = ["vae."] + + latent_format = latent_formats.Hunyuan3Dv2 + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Hunyuan3Dv2(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None + +class Hunyuan3Dv2_1(Hunyuan3Dv2): + unet_config = { + "image_model": "hunyuan3d2_1", + } + + latent_format = latent_formats.Hunyuan3Dv2_1 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Hunyuan3Dv2_1(self, device = device) + return out + +class Hunyuan3Dv2mini(Hunyuan3Dv2): + unet_config = { + "image_model": "hunyuan3d2", + "depth": 8, + } + + latent_format = latent_formats.Hunyuan3Dv2mini + +class HiDream(supported_models_base.BASE): + unet_config = { + "image_model": "hidream", + } + + sampling_settings = { + "shift": 3.0, + } + + sampling_settings = { + } + + # memory_usage_factor = 1.2 # TODO + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HiDream(self, device=device) + return out + + def clip_target(self, state_dict={}): + return None # TODO + +class Chroma(supported_models_base.BASE): + unet_config = { + "image_model": "chroma", + } + + unet_extra_config = { + } + + sampling_settings = { + "multiplier": 1.0, + } + + latent_format = comfy.latent_formats.Flux + + memory_usage_factor = 3.2 + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Chroma(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect)) + +class ChromaRadiance(Chroma): + unet_config = { + "image_model": "chroma_radiance", + } + + latent_format = comfy.latent_formats.ChromaRadiance + + # Pixel-space model, no spatial compression for model input. + memory_usage_factor = 0.038 + + def get_model(self, state_dict, prefix="", device=None): + return model_base.ChromaRadiance(self, device=device) + +class ACEStep(supported_models_base.BASE): + unet_config = { + "audio_model": "ace", + } + + unet_extra_config = { + } + + sampling_settings = { + "shift": 3.0, + } + + latent_format = comfy.latent_formats.ACEAudio + + memory_usage_factor = 0.5 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.ACEStep(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model) + +class Omnigen2(supported_models_base.BASE): + unet_config = { + "image_model": "omnigen2", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 2.6, + } + + memory_usage_factor = 1.65 #TODO + + unet_extra_config = {} + latent_format = latent_formats.Flux + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def __init__(self, unet_config): + super().__init__(unet_config) + if comfy.model_management.extended_fp16_support(): + self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Omnigen2(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect)) + +class QwenImage(supported_models_base.BASE): + unet_config = { + "image_model": "qwen_image", + } + + sampling_settings = { + "multiplier": 1.0, + "shift": 1.15, + } + + memory_usage_factor = 1.8 #TODO + + unet_extra_config = {} + latent_format = latent_formats.Wan21 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.QwenImage(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.qwen_image.QwenImageTokenizer, comfy.text_encoders.qwen_image.te(**hunyuan_detect)) + +class HunyuanImage21(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "vec_in_dim": None, + } + + sampling_settings = { + "shift": 5.0, + } + + latent_format = latent_formats.HunyuanImage21 + + memory_usage_factor = 7.7 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanImage21(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect)) + +class HunyuanImage21Refiner(HunyuanVideo): + unet_config = { + "image_model": "hunyuan_video", + "patch_size": [1, 1, 1], + "vec_in_dim": None, + } + + sampling_settings = { + "shift": 4.0, + } + + latent_format = latent_formats.HunyuanImage21Refiner + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.HunyuanImage21Refiner(self, device=device) + return out + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage] models += [SVD_img2vid] diff --git a/comfy/text_encoders/ace.py b/comfy/text_encoders/ace.py new file mode 100644 index 000000000..d650bb10d --- /dev/null +++ b/comfy/text_encoders/ace.py @@ -0,0 +1,153 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os +import re +import torch +import logging + +from tokenizers import Tokenizer +from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji + +SUPPORT_LANGUAGES = { + "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285, + "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293, + "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753, + "ko": 6152, "hi": 6680 +} + +structure_pattern = re.compile(r"\[.*?\]") + +DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + + +class VoiceBpeTokenizer: + def __init__(self, vocab_file=DEFAULT_VOCAB_FILE): + self.tokenizer = None + if vocab_file is not None: + self.tokenizer = Tokenizer.from_file(vocab_file) + + def preprocess_text(self, txt, lang): + txt = multilingual_cleaners(txt, lang) + return txt + + def encode(self, txt, lang='en'): + # lang = lang.split("-")[0] # remove the region + # self.check_input_length(txt, lang) + txt = self.preprocess_text(txt, lang) + lang = "zh-cn" if lang == "zh" else lang + txt = f"[{lang}]{txt}" + txt = txt.replace(" ", "[SPACE]") + return self.tokenizer.encode(txt).ids + + def get_lang(self, line): + if line.startswith("[") and line[3:4] == ']': + lang = line[1:3].lower() + if lang in SUPPORT_LANGUAGES: + return lang, line[4:] + return "en", line + + def __call__(self, string): + lines = string.split("\n") + lyric_token_idx = [261] + for line in lines: + line = line.strip() + if not line: + lyric_token_idx += [2] + continue + + lang, line = self.get_lang(line) + + if lang not in SUPPORT_LANGUAGES: + lang = "en" + if "zh" in lang: + lang = "zh" + if "spa" in lang: + lang = "es" + + try: + line_out = japanese_to_romaji(line) + if line_out != line: + lang = "ja" + line = line_out + except: + pass + + try: + if structure_pattern.match(line): + token_idx = self.encode(line, "en") + else: + token_idx = self.encode(line, lang) + lyric_token_idx = lyric_token_idx + token_idx + [2] + except Exception as e: + logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang)) + return {"input_ids": lyric_token_idx} + + @staticmethod + def from_pretrained(path, **kwargs): + return VoiceBpeTokenizer(path, **kwargs) + + def get_vocab(self): + return {} + + +class UMT5BaseModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options) + +class UMT5BaseTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class LyricsTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json") + super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data) + +class AceT5Tokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs) + out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.umt5base.untokenize(token_weight_pair) + + def state_dict(self): + return self.umt5base.state_dict() + +class AceT5Model(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__() + self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options) + self.dtypes = set() + if dtype is not None: + self.dtypes.add(dtype) + + def set_clip_options(self, options): + self.umt5base.set_clip_options(options) + + def reset_clip_options(self): + self.umt5base.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_umt5base = token_weight_pairs["umt5base"] + token_weight_pairs_lyrics = token_weight_pairs["lyrics"] + + t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base) + + lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0) + return t5_out, None, {"conditioning_lyrics": lyrics_embeds} + + def load_sd(self, sd): + return self.umt5base.load_sd(sd) diff --git a/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json new file mode 100644 index 000000000..519ed340c --- /dev/null +++ b/comfy/text_encoders/ace_lyrics_tokenizer/vocab.json @@ -0,0 +1,15535 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "special": true, + "content": "[STOP]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 1, + "special": true, + "content": "[UNK]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 2, + "special": true, + "content": "[SPACE]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 259, + "special": true, + "content": "[en]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false + }, + { + "id": 260, + "special": true, + "content": "[de]", + "single_word": 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3595, + "ря": 3596, + "между": 3597, + "ства": 3598, + "вс": 3599, + "ело": 3600, + "ше": 3601, + "мер": 3602, + "ба": 3603, + "зы": 3604, + "лу": 3605, + "аль": 3606, + "дей": 3607, + "гла": 3608, + "народ": 3609, + "кти": 3610, + "предста": 3611, + "лся": 3612, + "явля": 3613, + "ски": 3614, + "нов": 3615, + "един": 3616, + "ров": 3617, + "ис": 3618, + "нима": 3619, + "рем": 3620, + "ходи": 3621, + "также": 3622, + "дру": 3623, + "ать": 3624, + "след": 3625, + "гово": 3626, + "ная": 3627, + "ющи": 3628, + "ень": 3629, + "которы": 3630, + "хот": 3631, + "ву": 3632, + "их": 3633, + "ему": 3634, + "чит": 3635, + "важ": 3636, + "орга": 3637, + "чески": 3638, + "ще": 3639, + "ке": 3640, + "ха": 3641, + "пос": 3642, + "том": 3643, + "боль": 3644, + "мне": 3645, + "пас": 3646, + "объ": 3647, + "прав": 3648, + "конф": 3649, + "слу": 3650, + "поддер": 3651, + "стви": 3652, + "наш": 3653, + "лько": 3654, + "стоя": 3655, + "ную": 3656, + "лем": 3657, + "енных": 3658, + "кра": 3659, + "ды": 3660, + "международ": 3661, + "гда": 3662, + "необ": 3663, + "госу": 3664, + "ству": 3665, + "ении": 3666, + "государ": 3667, + "кто": 3668, + "им": 3669, + "чест": 3670, + "рет": 3671, + "вопро": 3672, + "лен": 3673, + "ели": 3674, + "рова": 3675, + "ций": 3676, + "нам": 3677, + "этой": 3678, + "жения": 3679, + "необходи": 3680, + "меня": 3681, + "было": 3682, + "сили": 3683, + "фи": 3684, + "вя": 3685, + "шь": 3686, + "этого": 3687, + "они": 3688, + "органи": 3689, + "безо": 3690, + "проб": 3691, + "име": 3692, + "реш": 3693, + "би": 3694, + "безопас": 3695, + "ются": 3696, + "оста": 3697, + "енно": 3698, + "год": 3699, + "ела": 3700, + "представ": 3701, + "ться": 3702, + "слово": 3703, + "организа": 3704, + "должны": 3705, + "этом": 3706, + "бла": 3707, + "че": 3708, + "чу": 3709, + "благо": 3710, + "этому": 3711, + "врем": 3712, + "спе": 3713, + "ном": 3714, + "ений": 3715, + "спо": 3716, + "нас": 3717, + "нет": 3718, + "зу": 3719, + "вед": 3720, + "еще": 3721, + "сказа": 3722, + 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3785, + "ек": 3786, + "чер": 3787, + "усили": 3788, + "рес": 3789, + "руд": 3790, + "единенных": 3791, + "доб": 3792, + "дости": 3793, + "ствен": 3794, + "ядер": 3795, + "годня": 3796, + "каза": 3797, + "сегодня": 3798, + "сейчас": 3799, + "только": 3800, + "вод": 3801, + "есь": 3802, + "много": 3803, + "буду": 3804, + "ев": 3805, + "есть": 3806, + "три": 3807, + "общест": 3808, + "явл": 3809, + "высту": 3810, + "ред": 3811, + "счит": 3812, + "сит": 3813, + "делега": 3814, + "лож": 3815, + "этот": 3816, + "фор": 3817, + "клю": 3818, + "возмож": 3819, + "вания": 3820, + "бли": 3821, + "или": 3822, + "вз": 3823, + "наций": 3824, + "ского": 3825, + "приня": 3826, + "пла": 3827, + "оч": 3828, + "иться": 3829, + "сте": 3830, + "наши": 3831, + "которые": 3832, + "ар": 3833, + "имеет": 3834, + "сот": 3835, + "знач": 3836, + "перь": 3837, + "следу": 3838, + "ены": 3839, + "таки": 3840, + "объединенных": 3841, + "стро": 3842, + "теперь": 3843, + "бле": 3844, + "благодар": 3845, + "разв": 3846, 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3908, + "вос": 3909, + "эта": 3910, + "перего": 3911, + "говор": 3912, + "вам": 3913, + "моло": 3914, + "время": 3915, + "дь": 3916, + "хотел": 3917, + "гру": 3918, + "заявл": 3919, + "предоста": 3920, + "поль": 3921, + "нее": 3922, + "резо": 3923, + "перегово": 3924, + "резолю": 3925, + "крет": 3926, + "поддерж": 3927, + "обеспе": 3928, + "него": 3929, + "представит": 3930, + "наде": 3931, + "кри": 3932, + "чь": 3933, + "проек": 3934, + "лет": 3935, + "други": 3936, + "_": 3937, + "،": 3938, + "؛": 3939, + "؟": 3940, + "ء": 3941, + "آ": 3942, + "أ": 3943, + "ؤ": 3944, + "إ": 3945, + "ئ": 3946, + "ا": 3947, + "ب": 3948, + "ة": 3949, + "ت": 3950, + "ث": 3951, + "ج": 3952, + "ح": 3953, + "خ": 3954, + "د": 3955, + "ذ": 3956, + "ر": 3957, + "ز": 3958, + "س": 3959, + "ش": 3960, + "ص": 3961, + "ض": 3962, + "ط": 3963, + "ظ": 3964, + "ع": 3965, + "غ": 3966, + "ـ": 3967, + "ف": 3968, + "ق": 3969, + "ك": 3970, + "ل": 3971, + "م": 3972, + "ن": 3973, + "ه": 3974, + "و": 3975, + "ى": 3976, + "ي": 3977, + "ً": 3978, + "ٌ": 3979, + "ٍ": 3980, + "َ": 3981, + "ُ": 3982, + "ِ": 3983, + "ّ": 3984, + "ْ": 3985, + "ٰ": 3986, + "چ": 3987, + "ڨ": 3988, + "ک": 3989, + "ھ": 3990, + "ی": 3991, + "ۖ": 3992, + "ۗ": 3993, + "ۘ": 3994, + "ۚ": 3995, + "ۛ": 3996, + "—": 3997, + "☭": 3998, + "ﺃ": 3999, + "ﻻ": 4000, + "ال": 4001, + "َا": 4002, + "وَ": 4003, + "َّ": 4004, + "ِي": 4005, + "أَ": 4006, + "لَ": 4007, + "نَ": 4008, + "الْ": 4009, + "هُ": 4010, + "ُو": 4011, + "ما": 4012, + "نْ": 4013, + "من": 4014, + "عَ": 4015, + "نا": 4016, + "لا": 4017, + "مَ": 4018, + "تَ": 4019, + "فَ": 4020, + "أن": 4021, + "لي": 4022, + "مِ": 4023, + "ان": 4024, + "في": 4025, + "رَ": 4026, + "يَ": 4027, + "هِ": 4028, + "مْ": 4029, + "قَ": 4030, + "بِ": 4031, + "لى": 4032, + "ين": 4033, + "إِ": 4034, + "لِ": 4035, + "وا": 4036, + "كَ": 4037, + "ها": 4038, + "ًا": 4039, + "مُ": 4040, + "ون": 4041, + "الم": 4042, + "بَ": 4043, + "يا": 4044, + "ذا": 4045, + "سا": 4046, + "الل": 4047, + "مي": 4048, + "يْ": 4049, + 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"الع": 4119, + "دْ": 4120, + "قَالَ": 4121, + "رُ": 4122, + "يم": 4123, + "ية": 4124, + "نُ": 4125, + "خَ": 4126, + "رب": 4127, + "الك": 4128, + "وَا": 4129, + "أنا": 4130, + "ةِ": 4131, + "الن": 4132, + "حد": 4133, + "عِ": 4134, + "تا": 4135, + "هو": 4136, + "فا": 4137, + "عا": 4138, + "الش": 4139, + "لُ": 4140, + "يت": 4141, + "ذَا": 4142, + "يع": 4143, + "الذ": 4144, + "حْ": 4145, + "الص": 4146, + "إِنَّ": 4147, + "جا": 4148, + "علي": 4149, + "كَا": 4150, + "بُ": 4151, + "تع": 4152, + "وق": 4153, + "مل": 4154, + "لَّ": 4155, + "يد": 4156, + "أخ": 4157, + "رف": 4158, + "تي": 4159, + "الِ": 4160, + "ّا": 4161, + "ذلك": 4162, + "أَنْ": 4163, + "سِ": 4164, + "توم": 4165, + "مر": 4166, + "مَنْ": 4167, + "بل": 4168, + "الق": 4169, + "الله": 4170, + "ِيَ": 4171, + "كم": 4172, + "ذَ": 4173, + "عل": 4174, + "حب": 4175, + "سي": 4176, + "عُ": 4177, + "الج": 4178, + "الد": 4179, + "شَ": 4180, + "تك": 4181, + "فْ": 4182, + "صَ": 4183, + "لل": 4184, + "دِ": 4185, + "بر": 4186, + "فِ": 4187, + "ته": 4188, + "أع": 4189, + "تْ": 4190, + "قْ": 4191, + "الْأَ": 4192, + "ئِ": 4193, + "عَنْ": 4194, + "ور": 4195, + "حا": 4196, + "الَّ": 4197, + "مت": 4198, + "فر": 4199, + "دُ": 4200, + "هنا": 4201, + "وَأَ": 4202, + "تب": 4203, + "ةُ": 4204, + "أي": 4205, + "سب": 4206, + "ريد": 4207, + "وج": 4208, + "كُمْ": 4209, + "حِ": 4210, + "كْ": 4211, + "در": 4212, + "َاء": 4213, + "هذه": 4214, + "الط": 4215, + "الْمُ": 4216, + "دة": 4217, + "قل": 4218, + "غَ": 4219, + "يوم": 4220, + "الَّذ": 4221, + "كر": 4222, + "تر": 4223, + "كِ": 4224, + "كي": 4225, + "عَلَى": 4226, + "رَب": 4227, + "عة": 4228, + "قُ": 4229, + "جْ": 4230, + "فض": 4231, + "لة": 4232, + "هْ": 4233, + "رَا": 4234, + "وَلَ": 4235, + "الْمَ": 4236, + "أَنَّ": 4237, + "يَا": 4238, + "أُ": 4239, + "شي": 4240, + "اللَّهُ": 4241, + "لَى": 4242, + "قِ": 4243, + "أت": 4244, + "عَلَيْ": 4245, + "اللَّهِ": 4246, + "الب": 4247, + "ضَ": 4248, + "ةً": 4249, + "قي": 4250, + "ار": 4251, + "بد": 4252, + "خْ": 4253, + "سْتَ": 4254, + "طَ": 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"men ten", + "v in", + "eer ste", + "l aat", + "gro ot", + "oo d", + "to ch", + "l aten", + "aar d", + "s le", + "de el", + "st and", + "pl aat", + "re e", + "bet re", + "d i", + "l id", + "uit en", + "ra cht", + "bel eid", + "g et", + "ar t", + "st ie", + "st aten", + "g gen", + "re ken", + "e in", + "al en", + "m ing", + "mo gelijk", + "gro te", + "al tijd", + "z or", + "en kel", + "w ik", + "pol itie", + "e igen", + "el k", + "han del", + "g t", + "k we", + "m aat", + "el en", + "i p", + "v rij", + "s om", + "je s", + "aa m", + "hu is", + "v al", + "we er", + "lid staten", + "k ing", + "k le", + "be d", + "gev al", + "stel l", + "a i", + "wik kel", + "kwe stie", + "t al", + "ste e", + "a b", + "h el", + "kom st", + "p as", + "s s", + "it u", + "i den", + "eer d", + "m in", + "c e", + "p o", + "twee de", + "proble em", + "w aren", + "us sen", + "sn el", + "t ig", + "ge w", + "j u", + "ul t", + "ne men", + "com mis", + "versch il", + "k on", + "z oek", + "k rij", + "gr aag", + "den k", + "l anden", + "re den", + "be sl", + "oe g", + "bet er", + "he den", + "m ag", + "p e", + "bo ven", + "a c", + "con t", + "f d", + "h ele", + "k r", + "v ier", + "w in", + "ge z", + "k w", + "m il", + "v or", + "he m", + "ra m", + "aa s", + "ont wikkel", + "dr ie", + "v aak", + "plaat s", + "l a", + "g ang", + "ij f", + "f in", + "nat uur", + "t ussen", + "u g", + "in e", + "d a", + "b at", + "kom t", + "w acht", + "aa d", + "u t", + "é n", + "acht er", + "geb ie", + "ver k", + "lig t", + "c es", + "nie uw", + "van d", + "s t", + "n í", + "j e", + "p o", + "c h", + "r o", + "n a", + "s e", + "t o", + "n e", + "l e", + "k o", + "l a", + "d o", + "r a", + "n o", + "t e", + "h o", + "n ě", + "v a", + "l i", + "l o", + "ř e", + "c e", + "d e", + "v e", + "b y", + "n i", + "s k", + "t a", + "n á", + "z a", + "p ro", + "v o", + "v ě", + "m e", + "v á", + "s o", + "k a", + "r á", + "v y", + "z e", + "m i", + "p a", + "t i", + "st a", + "m ě", + "n é", + "ř i", + "ř í", + "m o", + "ž e", + "m a", + "j í", + "v ý", + "j i", + "d ě", + "r e", + "d a", + "k u", + "j a", + "c i", + "r u", + "č e", + "o b", + "t ě", + "m u", + "k y", + "d i", + "š e", + "k é", + "š í", + "t u", + "v i", + "p ře", + "v í", + "s i", + "n ý", + "o d", + "so u", + "v é", + "n y", + "r i", + "d y", + "b u", + "b o", + "t y", + "l á", + "l u", + "n u", + "ž i", + "m á", + "st i", + "c í", + "z á", + "p ra", + "sk é", + "m í", + "c o", + "d u", + "d á", + "by l", + "st o", + "s a", + "t í", + "je d", + "p ří", + "p ři", + "t é", + "s í", + "č i", + "v ní", + "č a", + "d í", + "z i", + "st u", + "p e", + "b a", + "d ní", + "ro z", + "va l", + "l í", + "s po", + "k á", + "b e", + "p i", + "no u", + "ta k", + "st e", + "r y", + "l é", + "vě t", + "se m", + "p ě", + "ko n", + "ne j", + "l y", + "ko u", + "ý ch", + "b ě", + "p r", + "f i", + "p rá", + "a le", + "ja ko", + "po d", + "ž í", + "z í", + "j sou", + "j sem", + "ch o", + "l ní", + "c ké", + "t á", + "m y", + "a k", + "h u", + "va t", + "pře d", + "h la", + "k e", + "st á", + "č í", + "š i", + "s le", + "k la", + "š tě", + "lo u", + "m ů", + "z na", + "ch á", + "o r", + "p ů", + "h a", + "b i", + "ta ké", + "d ů", + "no st", + "t ře", + "te r", + "p u", + "i n", + "v r", + "ve l", + "sk u", + "v še", + "t ní", + "do b", + "by la", + "č ní", + "ja k", + "v u", + "je ho", + "b ý", + "vá ní", + "ný ch", + "po u", + "te n", + "t ři", + "v z", + "st ře", + "d va", + "h le", + "č á", + "no sti", + "c k", + "v š", + "vo u", + "s u", + "h e", + "h ra", + "je n", + "s y", + "da l", + "po z", + "s lo", + "te l", + "d ru", + "de n", + "vš ak", + "g i", + "k dy", + "by lo", + "bu de", + "st ra", + "j ší", + "m é", + "me n", + "vý ch", + "ní m", + "s m", + "ko li", + "r ů", + "t ra", + "mů že", + "ne ní", + "ho d", + "b í", + "do u", + "sk a", + "t ý", + "st ě", + "u je", + "s á", + "pě t", + "ne s", + "k rá", + "to m", + "st ví", + "v ně", + "se d", + "s vé", + "p í", + "z o", + "mu sí", + "u ž", + "tí m", + "jí cí", + "jed no", + "t r", + "ča s", + "e v", + "č ty", + "sk ý", + "ni c", + "ev ro", + "to ho", + "h y", + "k ter", + "r ní", + "st í", + "s vě", + "pa k", + "vše ch", + "k ů", + "n g", + "á d", + "chá zí", + "a ni", + "a r", + "jed na", + "bý t", + "t ro", + "k ra", + "pr vní", + "m no", + "ské ho", + "p á", + "p la", + "le m", + "ne bo", + "ke m", + "st ro", + "s la", + "né ho", + "z de", + "dal ší", + "ř a", + "čty ři", + "h rá", + "dru h", + "l ně", + "v la", + "sk ých", + "š ko", + "pů so", + "pro to", + "v ů", + "sk á", + "ve n", + "še st", + "d ně", + "je ště", + "me zi", + "te k", + "s ko", + "ch a", + "ně koli", + "be z", + "g ra", + "ji ž", + "č ně", + "j á", + "s lu", + "z ná", + "ve r", + "sed m", + "k ro", + "ta m", + "a no", + "v lá", + "o sm", + "byl y", + "vá m", + "ck ý", + "te ch", + "dě ji", + "vel mi", + "le ži", + "va la", + "l ý", + "t vo", + "spo le", + "ch u", + "stu p", + "mo ž", + "evro p", + "g e", + "sta l", + "j de", + "ch y", + "ro di", + "je jí", + "po li", + "de vět", + "s me", + "a ž", + "té to", + "re m", + "d é", + "f or", + "u ni", + "f o", + "ten to", + "a u", + "ka ž", + "nu la", + "na d", + "by ch", + "mo c", + "sto u", + "e x", + "le n", + "k do", + "z d", + "pra co", + "to mu", + "ný m", + "ži vo", + "ze m", + "f e", + "f u", + "ná sle", + "j o", + "sk y", + "ji ch", + "h á", + "mě l", + "dě la", + "j sme", + "p re", + "ni ce", + "ste j", + "ne m", + "st ní", + "he m", + "ná ro", + "z u", + "b li", + "ni t", + "pa r", + "a l", + "poz ději", + "ta ko", + "n ce", + "če r", + "ší m", + "ně co", + "vá l", + "ře j", + "krá t", + "á lní", + "u r", + ". .", + "a si", + "kter é", + "sta v", + "ma jí", + "my s", + "do bě", + "s ně", + "ce n", + "z y", + "z ku", + "t ů", + "ch od", + "s pě", + "je jich", + "sou čas", + "d r", + "va li", + "ri e", + "k te", + "pr ů", + "ze ní", + "pa t", + "a n", + "po tře", + "de m", + "d nes", + "ze mí", + "sa mo", + "zna m", + "b ra", + "má m", + "te dy", + "g o", + "hla vní", + "pou ží", + "b ní", + "ve de", + "le p", + "je k", + "pra v", + "poli ti", + "d ne", + "je m", + "le t", + "če ní", + "pro b", + "ne ž", + "dě l", + "fi l", + "č o", + "cí ch", + "st é", + "d lou", + "h i", + "a by", + "to u", + "několi k", + "d la", + "vy u", + "vi t", + "ho u", + "ck ých", + "no vé", + "či n", + "st y", + "dě lá", + "k ý", + "ob la", + "pod le", + "ra n", + "dů leži", + "ta to", + "po ku", + "ko ne", + "d ý", + "d vě", + "ž ád", + "nou t", + "t ku", + "t vr", + "cké ho", + "ro v", + "r é", + "te le", + "p sa", + "s vět", + "ti vní", + "do sta", + "te m", + "še l", + "druh é", + "s kou", + "ž o", + "jed ná", + "vý znam", + "prob lé", + "pu bli", + "vá n", + "od po", + "pod po", + "d le", + "ja ké", + "še ní", + "ví m", + "bě hem", + "na chází", + "s lou", + "pou ze", + "o tá", + "p lo", + "to vé", + "vět ši", + "ko mi", + "va jí", + "ty to", + "zá pa", + "z mě", + "mo h", + "ví ce", + "spole č", + "au to", + "pro ti", + "st ru", + "dě t", + "chá ze", + "že l", + "с т", + "е н", + "н о", + "н а", + "п р", + "т о", + "п о", + "р а", + "г о", + "к о", + "н е", + "в о", + "в а", + "е т", + "е р", + "н и", + "е л", + "и т", + "н ы", + "з а", + "р о", + "ен и", + "к а", + "л и", + "е м", + "д а", + "о б", + "л а", + "д о", + "с я", + "т ь", + "о т", + "л о", + "л ь", + "е д", + "с о", + "м и", + "р е", + "м о", + "ц и", + "пр о", + "т а", + "э то", + "к и", + "р у", + "пр и", + "т и", + "с е", + "ст а", + "в ы", + "м ы", + "в и", + "б ы", + "м а", + "е с", + "л я", + "ст и", + "л е", + "ч то", + "м е", + "р и", + "ч а", + "о д", + "е й", + "ел ь", + "ени я", + "г а", + "н у", + "с и", + "п а", + "ра з", + "б о", + "ст о", + "с у", + "с а", + "д у", + "е го", + "е ст", + "и н", + "ит ь", + "и з", + "ж е", + "м у", + "п ер", + "по д", + "ени е", + "с ь", + "к у", + "пр ед", + "но го", + "ны х", + "в ер", + "т е", + "но й", + "ци и", + "д е", + "р ы", + "д ел", + "л ю", + "в е", + "о н", + "м ен", + "г и", + "н я", + "б у", + "пр а", + "в се", + "ет ся", + "ст ь", + "ж а", + "до л", + "ж и", + "б е", + "ко н", + "с л", + "ш и", + "д и", + "ст в", + "с ко", + "ны е", + "ч и", + "ю т", + "д ер", + "ст ра", + "т ы", + "х од", + "щ и", + "з о", + "з на", + "но сти", + "ч ес", + "в ля", + "ва ть", + "о р", + "по л", + "в ет", + "та к", + "ш а", + "т у", + "с во", + "пр е", + "о на", + "ит ель", + "ны й", + "с ло", + "ка к", + "в л", + "но сть", + "х о", + "мо ж", + "п е", + "д ля", + "ни я", + "но е", + "ра с", + "дол ж", + "да р", + "т ель", + "с ка", + "п у", + "ст во", + "ко то", + "ра б", + "е е", + "ро д", + "э ти", + "с об", + "о ру", + "ж ен", + "ны м", + "ит и", + "ни е", + "ко м", + "д ет", + "ст у", + "г у", + "п и", + "ме ж", + "ени ю", + "т ер", + "раб от", + "во з", + "ци я", + "ко й", + "щ ест", + "г ра", + "з и", + "р я", + "меж ду", + "ст ва", + "в с", + "ел о", + "ш е", + "м ер", + "б а", + "з ы", + "л у", + "а ль", + "д ей", + "г ла", + "на род", + "к ти", + "пред ста", + "л ся", + "я вля", + "с ки", + "но в", + "ед ин", + "ро в", + "и с", + "ни ма", + "р ем", + "ход и", + "так же", + "д ру", + "а ть", + "сл ед", + "го во", + "на я", + "ю щи", + "ен ь", + "кото ры", + "х от", + "в у", + "и х", + "ем у", + "ч ит", + "ва ж", + "ор га", + "чес ки", + "щ е", + "к е", + "х а", + "по с", + "то м", + "бо ль", + "м не", + "па с", + "об ъ", + "пра в", + "кон ф", + "сл у", + "под дер", + "ст ви", + "на ш", + "ль ко", + "сто я", + "ну ю", + "л ем", + "ен ных", + "к ра", + "д ы", + "между народ", + "г да", + "не об", + "го су", + "ств у", + "ени и", + "госу дар", + "к то", + "и м", + "ч ест", + "р ет", + "во про", + "л ен", + "ел и", + "ро ва", + "ци й", + "на м", + "это й", + "ж ения", + "необ ходи", + "мен я", + "бы ло", + "си ли", + "ф и", + "в я", + "ш ь", + "это го", + "о ни", + "орга ни", + "бе зо", + "пр об", + "и ме", + "ре ш", + "б и", + "безо пас", + "ют ся", + "о ста", + "ен но", + "го д", + "ел а", + "предста в", + "ть ся", + "сло во", + "органи за", + "долж ны", + "это м", + "б ла", + "ч е", + "ч у", + "бла го", + "это му", + "в рем", + "с пе", + "но м", + "ени й", + "с по", + "на с", + "не т", + "з у", + "в ед", + "е ще", + "ска за", + "се й", + "ер ен", + "да н", + "са м", + "ел я", + "ра н", + "зы ва", + "явля ется", + "бу дет", + "кти в", + "т ре", + "дел е", + "м от", + "конф ерен", + "ла сь", + "ча с", + "сто ро", + "ко го", + "е з", + "не й", + "о с", + "ли сь", + "раз ору", + "пер е", + "с си", + "ны ми", + "про ц", + "го ло", + "ч ело", + "бо ле", + "чело ве", + "с ер", + "п л", + "ч ет", + "стра н", + "п я", + "бы л", + "к ла", + "то в", + "ж д", + "дел а", + "е ра", + "у же", + "со вет", + "г ен", + "безопас ности", + "ц а", + "се да", + "по з", + "от вет", + "проб лем", + "на ко", + "т ем", + "до ста", + "п ы", + "щ а", + "во й", + "су щест", + "необходи мо", + "бы ть", + "мож ет", + "д ем", + "что бы", + "е к", + "ч ер", + "у сили", + "ре с", + "ру д", + "един енных", + "д об", + "до сти", + "ств ен", + "я дер", + "год ня", + "ка за", + "се годня", + "сей час", + "то лько", + "во д", + "ес ь", + "м ного", + "бу ду", + "е в", + "ест ь", + "т ри", + "об щест", + ". .", + "я вл", + "вы сту", + "р ед", + "с чит", + "с ит", + "деле га", + "ло ж", + "это т", + "ф ор", + "к лю", + "воз мож", + "ва ния", + "б ли", + "и ли", + "в з", + "на ций", + "ско го", + "при ня", + "п ла", + "о ч", + "ить ся", + "ст е", + "на ши", + "которы е", + "а р", + "име ет", + "с от", + "зна ч", + "пер ь", + "след у", + "ен ы", + "та ки", + "объ единенных", + "ст ро", + "те перь", + "б ле", + "благо дар", + "раз в", + "а н", + "жи ва", + "оч ень", + "я т", + "бе з", + "об ес", + "г ро", + "ло сь", + "с ы", + "организа ции", + "ч лен", + "то го", + "она ль", + "ж да", + "все х", + "с вя", + "боле е", + "со в", + "ко гда", + "во т", + "к ре", + "к ры", + "по этому", + "во ль", + "о й", + "ген ера", + "ч ем", + "л ы", + "пол ити", + "в ен", + "конферен ции", + "проц ес", + "б я", + "ит е", + "от но", + "разв ити", + "а ф", + "ю щ", + "в но", + "ми р", + "ни и", + "ка я", + "а с", + "итель но", + "в то", + "ени ем", + "генера ль", + "пр от", + "вс ем", + "сам бле", + "ас самбле", + "о м", + "з д", + "с мот", + "ре ги", + "ч его", + "од нако", + "усили я", + "дей стви", + "ч но", + "у ча", + "об раз", + "во с", + "э та", + "пер его", + "гово р", + "ва м", + "мо ло", + "врем я", + "д ь", + "хот ел", + "г ру", + "за явл", + "пре доста", + "по ль", + "не е", + "ре зо", + "перего во", + "резо лю", + "к рет", + "поддер ж", + "обес пе", + "не го", + "представ ит", + "на де", + "к ри", + "ч ь", + "про ек", + "л ет", + "дру ги", + "ا ل", + "َ ا", + "و َ", + "ّ َ", + "ِ ي", + "أ َ", + "ل َ", + "ن َ", + "ال ْ", + "ه ُ", + "ُ و", + "م ا", + "ن ْ", + "م ن", + "ع َ", + "ن ا", + "ل ا", + "م َ", + "ت َ", + "ف َ", + "أ ن", + "ل ي", + "م ِ", + "ا ن", + "ف ي", + "ر َ", + "ي َ", + "ه ِ", + "م ْ", + "ق َ", + "ب ِ", + "ل ى", + "ي ن", + "إ ِ", + "ل ِ", + "و ا", + "ك َ", + "ه ا", + "ً ا", + "م ُ", + "و ن", + "ال م", + "ب َ", + "ي ا", + "ذ ا", + "س ا", + "ال ل", + "م ي", + "ي ْ", + "ر ا", + "ر ي", + "ل ك", + "م َا", + "ن َّ", + "ل م", + "إ ن", + "س ت", + "و م", + "ّ َا", + "ل َا", + "ه م", + "ّ ِ", + "ك ُ", + "ك ان", + "س َ", + "ب ا", + "د ي", + "ح َ", + "ع ْ", + "ب ي", + "ال أ", + "و ل", + "ف ِي", + "ر ِ", + "د ا", + "مِ نْ", + "ُو نَ", + "و ْ", + "ه َا", + "ّ ُ", + "ال س", + "ال َ", + "ن ي", + "ل ْ", + "ت ُ", + "ه ل", + "ر ة", + "د َ", + "س ْ", + "ت ِ", + "ن َا", + "ر ْ", + "الل َّ", + "سا مي", + "ك ن", + "ك ل", + "ه َ", + "عَ لَ", + "ع لى", + "م ع", + "إ لى", + "ق د", + "ال ر", + "ُو ا", + "ي ر", + "ع ن", + "ي ُ", + "ن ِ", + "ب ْ", + "ال ح", + "هُ مْ", + "ق ا", + "ذ ه", + "ال ت", + "ِي نَ", + "ج َ", + "ه ذا", + "ع د", + "ال ع", + "د ْ", + "قَ الَ", + "ر ُ", + "ي م", + "ي ة", + "ن ُ", + "خ َ", + "ر ب", + "ال ك", + "و َا", + "أ نا", + "ة ِ", + "ال ن", + "ح د", + "ع ِ", + "ت ا", + "ه و", + "ف ا", + "ع ا", + "ال ش", + "ل ُ", + "ي ت", + "ذ َا", + "ي ع", + "ال ذ", + "ح ْ", + "ال ص", + "إِ نَّ", + "ج ا", + "ع لي", + "ك َا", + "ب ُ", + "ت ع", + "و ق", + "م ل", + "ل َّ", + "ي د", + "أ خ", + "ر ف", + "ت ي", + "ال ِ", + "ّ ا", + "ذ لك", + "أَ نْ", + "س ِ", + "ت وم", + "م ر", + "مَ نْ", + "ب ل", + "ال ق", + "الل ه", + "ِي َ", + "ك م", + "ذ َ", + "ع ل", + "ح ب", + "س ي", + "ع ُ", + "ال ج", + "ال د", + "ش َ", + "ت ك", + "ف ْ", + "ص َ", + "ل ل", + "د ِ", + "ب ر", + "ف ِ", + "ت ه", + "أ ع", + "ت ْ", + "ق ْ", + "الْ أَ", + "ئ ِ", + "عَ نْ", + "و ر", + "ح ا", + "ال َّ", + "م ت", + "ف ر", + "د ُ", + "ه نا", + "وَ أَ", + "ت ب", + "ة ُ", + "أ ي", + "س ب", + "ري د", + "و ج", + "كُ مْ", + "ح ِ", + "ك ْ", + "د ر", + "َا ء", + "ه ذه", + "ال ط", + "الْ مُ", + "د ة", + "ق ل", + "غ َ", + "ي وم", + "الَّ ذ", + "ك ر", + "ت ر", + "ك ِ", + "ك ي", + "عَلَ ى", + "رَ ب", + "ع ة", + "ق ُ", + "ج ْ", + "ف ض", + "ل ة", + "ه ْ", + "ر َا", + "وَ لَ", + "الْ مَ", + "أَ نَّ", + "ي َا", + "أ ُ", + "ش ي", + "اللَّ هُ", + "لَ ى", + "ق ِ", + "أ ت", + "عَلَ يْ", + "اللَّ هِ", + "ال ب", + "ض َ", + "ة ً", + "ق ي", + "ا ر", + "ب د", + "خ ْ", + "سْ تَ", + "ط َ", + "قَ دْ", + "ذه ب", + "أ م", + "ما ذا", + "وَ إِ", + "ة ٌ", + "و نَ", + "لي لى", + "و لا", + "ح ُ", + "ه ي", + "ص ل", + "ال خ", + "و د", + "لي س", + "ل دي", + "ق ال", + "كَا نَ", + "م َّ", + "ح ي", + "ت م", + "ل ن", + "وَ لَا", + "ب ع", + "يم كن", + "س ُ", + "ة َ", + "ح ت", + "ر ًا", + "ك ا", + "ش ا", + "هِ مْ", + "لَ هُ", + "ز َ", + "دا ً", + "م س", + "ك ث", + "الْ عَ", + "ج ِ", + "ص ْ", + "ف َا", + "ل ه", + "و ي", + "ع َا", + "هُ وَ", + "ب ِي", + "ب َا", + "أ س", + "ث َ", + "ل ِي", + "ر ض", + "الر َّ", + "لِ كَ", + "ت َّ", + "ف ُ", + "ق ة", + "ف عل", + "مِ ن", + "ال آ", + "ث ُ", + "س م", + "م َّا", + "بِ هِ", + "ت ق", + "خ ر", + "ل قد", + "خ ل", + "ش ر", + "أن ت", + "ل َّا", + "س ن", + "الس َّ", + "الذ ي", + "س َا", + "و ما", + "ز ل", + "و ب", + "أ ْ", + "إ ذا", + "ر ِي", + "ح ة", + "ن ِي", + "الْ حَ", + "وَ قَالَ", + "ب ه", + "ة ٍ", + "س أ", + "ر ٌ", + "ب ال", + "م ة", + "ش ْ", + "و ت", + "عن د", + "ف س", + "بَ عْ", + "ه ر", + "ق ط", + "أ ح", + "إن ه", + "و ع", + "ف ت", + "غ ا", + "هنا ك", + "ب ت", + "مِ نَ", + "س ر", + "ذَ لِكَ", + "ر س", + "حد ث", + "غ ْ", + "ّ ِي", + "ال إ", + "وَ يَ", + "ج ل", + "ا ست", + "ق ِي", + "ع ب", + "و س", + "ي ش", + "الَّذ ِينَ", + "تا ب", + "د ِي", + "ج ب", + "ك ون", + "ب ن", + "ال ث", + "لَ يْ", + "ب عد", + "وَ الْ", + "فَ أَ", + "ع م", + "هُ م", + "ت ن", + "ذ ْ", + "أ ص", + "أ ين", + "رَب ِّ", + "الذ ين", + "إِ ن", + "ب ين", + "ج ُ", + "عَلَيْ هِ", + "ح َا", + "ل و", + "ست ط", + "ظ ر", + "لَ مْ", + "ء ِ", + "كُ ل", + "ط ل", + "ت َا", + "ض ُ", + "كن ت", + "ل ًا", + "م ٌ", + "ق بل", + "ـ ـ", + "ذ ِ", + "قَ وْ", + "ص ِ", + "م ًا", + "كان ت", + "ص ا", + "ي ق", + "ال ف", + "ال نا", + "م ٍ", + "إِ نْ", + "ال نَّ", + "ج د", + "وَ مَا", + "ت ت", + "ب ح", + "م كان", + "كي ف", + "ّ ة", + "ال ا", + "ج َا", + "أ و", + "سا عد", + "ض ِ", + "إ لا", + "را ً", + "ق َا", + "ر أ", + "ع ت", + "أ حد", + "ه د", + "ض ا", + "ط ر", + "أ ق", + "ما ء", + "د َّ", + "ال با", + "م ُو", + "أَ وْ", + "ط ا", + "ق ُو", + "خ ِ", + "ت ل", + "ستط يع", + "د َا", + "الن َّا", + "إ لَى", + "وَ تَ", + "هَ ذَا", + "ب ة", + "علي ك", + "ج ر", + "ال من", + "ز ا", + "ر ٍ", + "د ع", + "ّ ًا", + "س ة", + "ثُ مَّ", + "شي ء", + "ال غ", + "ت ح", + "ر ُونَ", + "ال يوم", + "م ِي", + "ن ُوا", + "أ ر", + "تُ مْ", + "ع ر", + "ي ف", + "أ ب", + "د ًا", + "ص َا", + "الت َّ", + "أ ريد", + "ال ز", + "يَ وْ", + "إ لي", + "ج ي", + "يَ عْ", + "فض ل", + "ال إن", + "أن ه", + "n g", + "i 4", + "a n", + "s h", + "z h", + "i 2", + "ng 1", + "u 4", + "i 1", + "ng 2", + "d e", + "j i", + "a o", + "x i", + "u 3", + "de 5", + "e 4", + "i 3", + "ng 4", + "an 4", + "e n", + "u o", + "sh i4", + "an 2", + "u 2", + "c h", + "u 1", + "ng 3", + "a 1", + "an 1", + "e 2", + "a 4", + "e i4", + "o ng1", + "a i4", + "ao 4", + "h u", + "a ng1", + "l i", + "y o", + "an 3", + "w ei4", + "uo 2", + "n 1", + "en 2", + "ao 3", + "e 1", + "y u", + "q i", + "e ng2", + "zh o", + "a ng3", + "a ng4", + "a ng2", + "uo 4", + "m i", + "g e4", + "y i1", + "g uo2", + "e r", + "b i", + "a 3", + "h e2", + "e 3", + "y i2", + "d i4", + "zh ong1", + "b u4", + "g u", + "a i2", + "n 2", + "z ai4", + "sh i2", + "e ng1", + "r en2", + "o ng2", + "xi an4", + "y i", + "x u", + "n 4", + "l i4", + "en 4", + "y u2", + "e i2", + "yi2 ge4", + "o u4", + "e i3", + "d i", + "u i4", + "a 2", + "yo u3", + "ao 1", + "d a4", + "ch eng2", + "en 1", + "e ng4", + "y i4", + "s i1", + "zh i4", + "ji a1", + "yu an2", + "n i", + "t a1", + "de5 yi2ge4", + "k e1", + "sh u3", + "x i1", + "j i2", + "ao 2", + "t i", + "o u3", + "o ng4", + "xi a4", + "a i1", + "g ong1", + "zh i1", + "en 3", + "w ei2", + "j u", + "xu e2", + "q u1", + "zho u1", + "er 3", + "mi ng2", + "zho ng3", + "l i3", + "w u4", + "y i3", + "uo 1", + "e 5", + "j i4", + "xi ng2", + "ji an4", + "hu a4", + "y u3", + "uo 3", + "j i1", + "a i3", + "z uo4", + "h ou4", + "hu i4", + "e i1", + "ni an2", + "q i2", + "p i", + "d ao4", + "sh eng1", + "de 2", + "d ai4", + "u an2", + "zh e4", + "zh eng4", + "b en3", + "sh ang4", + "zh u3", + "b ei4", + "y e4", + "ch u1", + "zh an4", + "l e5", + "l ai2", + "sh i3", + "n an2", + "r en4", + "yo u2", + "k e4", + "b a1", + "f u4", + "d ui4", + "y a4", + "m ei3", + "z i4", + "xi n1", + "ji ng1", + "zh u", + "n 3", + "yo ng4", + "m u4", + "ji ao4", + "y e3", + "ji n4", + "bi an4", + "l u4", + "q i1", + "sh e4", + "xi ang1", + "o ng3", + "sh u4", + "d ong4", + "s uo3", + "gu an1", + "s an1", + "b o", + "t e4", + "d uo1", + "f u2", + "mi n2", + "l a1", + "zh i2", + "zh en4", + "o u1", + "w u3", + "m a3", + "i 5", + "z i5", + "j u4", + "er 4", + "y ao4", + "xia4 de5yi2ge4", + "s i4", + "t u2", + "sh an1", + "z ui4", + "ch u", + "yi n1", + "er 2", + "t ong2", + "d ong1", + "y u4", + "y an2", + "qi an2", + "shu3 xia4de5yi2ge4", + "ju n1", + "k e3", + "w en2", + "f a3", + "l uo2", + "zh u4", + "x i4", + "k ou3", + "b ei3", + "ji an1", + "f a1", + "di an4", + "ji ang1", + "wei4 yu2", + "xi ang4", + "zh i3", + "e ng3", + "f ang1", + "l an2", + "sh u", + "r i4", + "li an2", + "sh ou3", + "m o", + "qi u2", + "ji n1", + "h uo4", + "shu3xia4de5yi2ge4 zhong3", + "f en1", + "n ei4", + "g ai1", + "mei3 guo2", + "u n2", + "g e2", + "b ao3", + "qi ng1", + "g ao1", + "t ai2", + "d u", + "xi ao3", + "ji e2", + "ti an1", + "ch ang2", + "q uan2", + "li e4", + "h ai3", + "f ei1", + "t i3", + "ju e2", + "o u2", + "c i3", + "z u2", + "n i2", + "bi ao3", + "zhong1 guo2", + "d u4", + "yu e4", + "xi ng4", + "sh eng4", + "ch e1", + "d an1", + "ji e1", + "li n2", + "pi ng2", + "f u3", + "g u3", + "ji e4", + "w o", + "v 3", + "sh eng3", + "n a4", + "yu an4", + "zh ang3", + "gu an3", + "d ao3", + "z u3", + "di ng4", + "di an3", + "c eng2", + "ren2 kou3", + "t ai4", + "t ong1", + "g uo4", + "n eng2", + "ch ang3", + "hu a2", + "li u2", + "yi ng1", + "xi ao4", + "c i4", + "bian4 hua4", + "li ang3", + "g ong4", + "zho ng4", + "de5 yi1", + "s e4", + "k ai1", + "w ang2", + "ji u4", + "sh i1", + "sh ou4", + "m ei2", + "k u", + "s u", + "f eng1", + "z e2", + "tu2 shi4", + "t i2", + "q i4", + "ji u3", + "sh en1", + "zh e3", + "ren2kou3 bian4hua4", + "ren2kou3bian4hua4 tu2shi4", + "di4 qu1", + "y ang2", + "m en", + "men 5", + "l ong2", + "bi ng4", + "ch an3", + "zh u1", + "w ei3", + "w ai4", + "xi ng1", + "bo 1", + "b i3", + "t ang2", + "hu a1", + "bo 2", + "shu i3", + "sh u1", + "d ou1", + "s ai4", + "ch ao2", + "b i4", + "li ng2", + "l ei4", + "da4 xue2", + "f en4", + "shu3 de5", + "m u3", + "ji ao1", + "d ang1", + "ch eng1", + "t ong3", + "n v3", + "q i3", + "y an3", + "mi an4", + "l uo4", + "ji ng4", + "g e1", + "r u4", + "d an4", + "ri4 ben3", + "p u3", + "yu n4", + "hu ang2", + "wo 3", + "l v", + "h ai2", + "shi4 yi1", + "xi e1", + "yi ng3", + "w u2", + "sh en2", + "w ang3", + "gu ang3", + "li u4", + "s u4", + "shi4 zhen4", + "c an1", + "c ao3", + "xi a2", + "k a3", + "d a2", + "h u4", + "b an4", + "d ang3", + "h u2", + "z ong3", + "de ng3", + "de5yi2ge4 shi4zhen4", + "ch uan2", + "mo 4", + "zh ang1", + "b an1", + "mo 2", + "ch a2", + "c e4", + "zhu3 yao4", + "t ou2", + "j u2", + "shi4 wei4yu2", + "s a4", + "u n1", + "ke3 yi3", + "d u1", + "h an4", + "li ang4", + "sh a1", + "ji a3", + "z i1", + "lv 4", + "f u1", + "xi an1", + "x u4", + "gu ang1", + "m eng2", + "b ao4", + "yo u4", + "r ong2", + "zhi1 yi1", + "w ei1", + "m ao2", + "guo2 jia1", + "c ong2", + "g ou4", + "ti e3", + "zh en1", + "d u2", + "bi an1", + "c i2", + "q u3", + "f an4", + "xi ang3", + "m en2", + "j u1", + "h ong2", + "z i3", + "ta1 men5", + "ji 3", + "z ong1", + "zhou1 de5yi2ge4shi4zhen4", + "t uan2", + "ji ng3", + "gong1 si1", + "xi e4", + "l i2", + "li4 shi3", + "b ao1", + "g ang3", + "gu i1", + "zh eng1", + "zhi2 wu4", + "ta1 de5", + "pi n3", + "zhu an1", + "ch ong2", + "shi3 yong4", + "w a3", + "sh uo1", + "chu an1", + "l ei2", + "w an1", + "h uo2", + "q u", + "s u1", + "z ao3", + "g ai3", + "q u4", + "g u4", + "l u", + "x i2", + "h ang2", + "yi ng4", + "c un1", + "g en1", + "yi ng2", + "ti ng2", + "cheng2 shi4", + "ji ang3", + "li ng3", + "l un2", + "bu4 fen4", + "de ng1", + "xu an3", + "dong4 wu4", + "de2 guo2", + "xi an3", + "f an3", + "zh e5", + "h an2", + "h ao4", + "m i4", + "r an2", + "qi n1", + "ti ao2", + "zh an3", + "h i", + "k a", + "n o", + "t e", + "s u", + "s hi", + "t a", + "t o", + "n a", + "w a", + "o u", + "r u", + "n i", + "k u", + "k i", + "g a", + "d e", + "k o", + "m a", + "r e", + "r a", + "m o", + "t su", + "w o", + "e n", + "r i", + "s a", + "d a", + "s e", + "j i", + "h a", + "c hi", + "k e", + "te ki", + "m i", + "y ou", + "s h", + "s o", + "y o", + "y a", + "na i", + "t te", + "a ru", + "b a", + "u u", + "t ta", + "ka i", + "ka n", + "shi te", + "m e", + "d o", + "mo no", + "se i", + "r o", + "ko to", + "ka ra", + "shi ta", + "b u", + "m u", + "c h", + "su ru", + "k ou", + "g o", + "ma su", + "ta i", + "f u", + "k en", + "i u", + "g en", + "wa re", + "shi n", + "z u", + "a i", + "o n", + "o ku", + "g i", + "d ou", + "n e", + "y uu", + "i ru", + "i te", + "ji ko", + "de su", + "j u", + "ra re", + "sh u", + "b e", + "sh ou", + "s ha", + "se kai", + "s ou", + "k you", + "ma shita", + "s en", + "na ra", + "sa n", + "ke i", + "i ta", + "a ri", + "i tsu", + "ko no", + "j ou", + "na ka", + "ch ou", + "so re", + "g u", + "na ru", + "ga ku", + "re ba", + "g e", + "h o", + "i n", + "hi to", + "sa i", + "na n", + "da i", + "tsu ku", + "shi ki", + "sa re", + "na ku", + "p p", + "bu n", + "ju n", + "so no", + "ka ku", + "z ai", + "b i", + "to u", + "wa ta", + "sh uu", + "i i", + "te i", + "ka re", + "y u", + "shi i", + "ma de", + "sh o", + "a n", + "ke reba", + "shi ka", + "i chi", + "ha n", + "de ki", + "ni n", + "ware ware", + "na kereba", + "o ite", + "h ou", + "ya ku", + "ra i", + "mu jun", + "l e", + "yo ku", + "bu tsu", + "o o", + "ko n", + "o mo", + "ga e", + "nara nai", + "ta chi", + "z en", + "ch uu", + "kan gae", + "ta ra", + "to ki", + "ko ro", + "mujun teki", + "z e", + "na ga", + "ji n", + "shi ma", + "te n", + "i ki", + "i ku", + "no u", + "i masu", + "r ou", + "h on", + "ka e", + "t to", + "ko re", + "ta n", + "ki ta", + "i s", + "da tta", + "ji tsu", + "ma e", + "i e", + "me i", + "da n", + "h e", + "to ku", + "dou itsu", + "ri tsu", + "k yuu", + "h you", + "rare ta", + "kei sei", + "k kan", + "rare ru", + "m ou", + "do ko", + "r you", + "da ke", + "naka tta", + "so ko", + "ta be", + "e r", + "ha na", + "c o", + "fu ku", + "p a", + "so n", + "ya su", + "ch o", + "wata ku", + "ya ma", + "z a", + "k yo", + "gen zai", + "b oku", + "a ta", + "j a", + "ka wa", + "ma sen", + "j uu", + "ro n", + "b o", + "na tte", + "wataku shi", + "yo tte", + "ma i", + "g ou", + "ha i", + "mo n", + "ba n", + "ji shin", + "c a", + "re te", + "n en", + "o ka", + "ka gaku", + "na tta", + "p o", + "ka ru", + "na ri", + "m en", + "ma ta", + "e i", + "ku ru", + "ga i", + "ka ri", + "sha kai", + "kou i", + "yo ri", + "se tsu", + "j o", + "re ru", + "to koro", + "ju tsu", + "i on", + "sa ku", + "tta i", + "c ha", + "nin gen", + "n u", + "c e", + "ta me", + "kan kyou", + "de n", + "o oku", + "i ma", + "wata shi", + "tsuku ru", + "su gi", + "b en", + "ji bun", + "shi tsu", + "ke ru", + "ki n", + "ki shi", + "shika shi", + "mo to", + "ma ri", + "i tte", + "de shita", + "n de", + "ari masu", + "te r", + "z ou", + "ko e", + "ze ttai", + "kkan teki", + "h en", + "re kishi", + "deki ru", + "tsu ka", + "l a", + "i tta", + "o i", + "ko butsu", + "mi ru", + "sh oku", + "shi masu", + "gi jutsu", + "g you", + "jou shiki", + "a tta", + "ho do", + "ko ko", + "tsuku rareta", + "z oku", + "hi tei", + "ko ku", + "rekishi teki", + "ke te", + "o ri", + "i mi", + "ka ko", + "naga ra", + "ka karu", + "shu tai", + "ha ji", + "ma n", + "ta ku", + "ra n", + "douitsu teki", + "z o", + "me te", + "re i", + "tsu u", + "sare te", + "gen jitsu", + "p e", + "s t", + "ba i", + "na wa", + "ji kan", + "wa ru", + "r t", + "a tsu", + "so ku", + "koui teki", + "a ra", + "u ma", + "a no", + "i de", + "ka ta", + "te tsu", + "ga wa", + "ke do", + "re ta", + "mi n", + "sa you", + "tte ru", + "to ri", + "p u", + "ki mi", + "b ou", + "mu ra", + "sare ru", + "ma chi", + "k ya", + "o sa", + "kon na", + "a ku", + "a l", + "sare ta", + "i pp", + "shi ku", + "u chi", + "hito tsu", + "ha tara", + "tachi ba", + "shi ro", + "ka tachi", + "to mo", + "e te", + "me ru", + "ni chi", + "da re", + "ka tta", + "e ru", + "su ki", + "a ge", + "oo ki", + "ma ru", + "mo ku", + "o ko", + "kangae rareru", + "o to", + "tan ni", + "ta da", + "tai teki", + "mo tte", + "ki nou", + "shi nai", + "k ki", + "u e", + "ta ri", + "l i", + "ra nai", + "k kou", + "mi rai", + "pp on", + "go to", + "hi n", + "hi tsu", + "te ru", + "mo chi", + "ka tsu", + "re n", + "n yuu", + "su i", + "zu ka", + "tsu ite", + "no mi", + "su gu", + "ku da", + "tetsu gaku", + "i ka", + "ron ri", + "o ki", + "ni ppon", + "p er", + "shi mashita", + "chi shiki", + "cho kkanteki", + "su ko", + "t ion", + "ku u", + "a na", + "a rou", + "ka tte", + "ku ri", + "i nai", + "hyou gen", + "i shiki", + "do ku", + "a tte", + "a tara", + "to n", + "wa ri", + "ka o", + "sei san", + "hana shi", + "s i", + "ka ke", + "na ji", + "su nawa", + "sunawa chi", + "u go", + "su u", + "ba ra", + "le v", + "hi ro", + "i wa", + "be tsu", + "yo i", + "se ru", + "shite ru", + "rare te", + "to shi", + "se ki", + "tai ritsu", + "wa kara", + "to kyo", + "k ka", + "k yoku", + "u n", + "i ro", + "mi te", + "sa ki", + "kan ji", + "mi ta", + "su be", + "r yoku", + "ma tta", + "kuda sai", + "omo i", + "ta no", + "ware ru", + "co m", + "hitsu you", + "ka shi", + "re nai", + "kan kei", + "a to", + "ga tte", + "o chi", + "mo tsu", + "in g", + "son zai", + "l l", + "o re", + "tai shite", + "a me", + "sei mei", + "ka no", + "gi ri", + "kangae ru", + "yu e", + "a sa", + "o naji", + "yo ru", + "ni ku", + "osa ka", + "suko shi", + "c k", + "ta ma", + "kano jo", + "ki te", + "mon dai", + "a mari", + "e ki", + "ko jin", + "ha ya", + "i t", + "de te", + "atara shii", + "a wa", + "ga kkou", + "tsu zu", + "shu kan", + "i mashita", + "mi na", + "ata e", + "da rou", + "hatara ku", + "ga ta", + "da chi", + "ma tsu", + "ari masen", + "sei butsu", + "mi tsu", + "he ya", + "yasu i", + "d i", + "de ni", + "no ko", + "ha ha", + "do mo", + "ka mi", + "su deni", + "na o", + "ra ku", + "i ke", + "a ki", + "me ta", + "l o", + "ko domo", + "so shite", + "ga me", + "ba kari", + "to te", + "ha tsu", + "mi se", + "moku teki", + "da kara", + "s z", + "e l", + "g y", + "e n", + "t t", + "e m", + "a n", + "a k", + "e r", + "a z", + "a l", + "e t", + "o l", + "e g", + "e k", + "m i", + "o n", + "é s", + "c s", + "a t", + "á r", + "h o", + "e z", + "á l", + "i s", + "á n", + "o r", + "a r", + "e gy", + "e s", + "é r", + "á t", + "o tt", + "e tt", + "m eg", + "t a", + "o k", + "o s", + "ho gy", + "n em", + "é g", + "n y", + "k i", + "é l", + "h a", + "á s", + "ü l", + "i n", + "mi n", + "n a", + "e d", + "o m", + "i k", + "k ö", + "m a", + "n i", + "v a", + "v ol", + "é t", + "b b", + "f el", + "i g", + "l e", + "r a", + "é n", + "t e", + "d e", + "a d", + "ó l", + "b e", + "on d", + "j a", + "r e", + "u l", + "b en", + "n ek", + "u t", + "vol t", + "b an", + "ö r", + "o g", + "a p", + "o d", + "á g", + "n k", + "é k", + "v al", + "k or", + "a m", + "i l", + "í t", + "á k", + "b a", + "u d", + "sz er", + "min d", + "o z", + "é p", + "el l", + "ér t", + "m ond", + "i t", + "sz t", + "n ak", + "a mi", + "n e", + "ő l", + "cs ak", + "n é", + "ma g", + "ol y", + "m er", + "ál l", + "án y", + "ö n", + "ö l", + "min t", + "m ár", + "ö tt", + "na gy", + "é sz", + "az t", + "el ő", + "t ud", + "o t", + "é ny", + "á z", + "m ég", + "kö z", + "el y", + "s ég", + "en t", + "s em", + "ta m", + "h et", + "h al", + "f i", + "a s", + "v an", + "ho z", + "v e", + "u k", + "k ez", + "á m", + "v el", + "b er", + "a j", + "u nk", + "i z", + "va gy", + "m os", + "sz em", + "em ber", + "f og", + "mer t", + "ü k", + "l en", + "ö s", + "e j", + "t al", + "h at", + "t ak", + "h i", + "m ás", + "s ág", + "ett e", + "l eg", + "ü nk", + "h át", + "sz a", + "on y", + "ez t", + "mind en", + "en d", + "ül t", + "h an", + "j ó", + "k is", + "á j", + "in t", + "ú gy", + "i d", + "mos t", + "ar t", + "í r", + "k er", + "i tt", + "a tt", + "el t", + "mond ta", + "k ell", + "l á", + "ak i", + "ál t", + "ér d", + "t ö", + "l an", + "v ár", + "h ol", + "t el", + "l át", + "ő k", + "v et", + "s e", + "ut án", + "k ét", + "na p", + "í v", + "ál y", + "v ég", + "ö k", + "i r", + "d ul", + "v is", + "né z", + "t er", + "á ban", + "k ül", + "ak kor", + "k ap", + "sz él", + "y en", + "ú j", + "i m", + "oly an", + "es en", + "k ed", + "h ely", + "t ör", + "b ól", + "el m", + "r á", + "ár a", + "r ó", + "l ó", + "vol na", + "t an", + "le het", + "e bb", + "t en", + "t ek", + "s ok", + "k al", + "f or", + "u g", + "ol t", + "k a", + "ek et", + "b or", + "f ej", + "g ond", + "a g", + "ak ar", + "f él", + "ú l", + "b el", + "ott a", + "mi t", + "val ami", + "j el", + "é d", + "ar c", + "u r", + "hal l", + "t i", + "f öl", + "á ba", + "ol g", + "ki r", + "ol d", + "m ar", + "k érd", + "j ár", + "ú r", + "sz e", + "z s", + "él et", + "j át", + "o v", + "u s", + "é z", + "v il", + "v er", + "ő r", + "á d", + "ö g", + "le sz", + "on t", + "b iz", + "k oz", + "á bb", + "kir ály", + "es t", + "a b", + "en g", + "ig az", + "b ar", + "ha j", + "d i", + "o b", + "k od", + "r ól", + "v ez", + "tö bb", + "sz ó", + "é ben", + "ö t", + "ny i", + "t á", + "sz ól", + "gond ol", + "eg ész", + "í gy", + "ő s", + "o bb", + "os an", + "b ől", + "a bb", + "c i", + "ő t", + "n ál", + "k ép", + "azt án", + "v i", + "t art", + "be szél", + "m en", + "elő tt", + "a szt", + "ma j", + "kö r", + "han g", + "í z", + "in cs", + "a i", + "é v", + "ó d", + "ó k", + "hoz z", + "t em", + "ok at", + "an y", + "nagy on", + "h áz", + "p er", + "p ed", + "ez te", + "et len", + "nek i", + "maj d", + "sz ony", + "án ak", + "fel é", + "egy szer", + "j e", + "ad t", + "gy er", + "ami kor", + "f oly", + "sz ak", + "ő d", + "h ú", + "á sz", + "am ely", + "h ar", + "ér e", + "il yen", + "od a", + "j ák", + "t ár", + "á val", + "l ak", + "t ó", + "m ent", + "gy an", + "él y", + "ú t", + "v ar", + "kez d", + "m ell", + "mi kor", + "h ez", + "val ó", + "k o", + "m es", + "szer et", + "r end", + "l et", + "vis sza", + "ig en", + "f ő", + "va s", + "as szony", + "r ől", + "ped ig", + "p i", + "sz ép", + "t ák", + "ö v", + "an i", + "vil ág", + "p en", + "mag a", + "t et", + "sz ik", + "é j", + "én t", + "j ött", + "s an", + "sz í", + "i de", + "g at", + "ett em", + "ul t", + "h ány", + "ás t", + "a hol", + "ők et", + "h ár", + "k el", + "n ő", + "cs i", + "tal ál", + "el te", + "lá tt", + "tör t", + "ha gy", + "e sz", + "s en", + "n él", + "p ar", + "v ál", + "k ut", + "l ány", + "ami t", + "s ő", + "ell en", + "mag át", + "in k", + "u gyan", + "kül ön", + "a sz", + "mind ig", + "l ép", + "tal án", + "u n", + "sz or", + "k e", + "il lan", + "n incs", + "z et", + "vagy ok", + "tel en", + "is mer", + "s or", + "is ten", + "ít ott", + "j obb", + "v es", + "dul t", + "j uk", + "sz en", + "r o", + "ö m", + "l ett", + "k ar", + "egy ik", + "b ár", + "sz i", + "sz ív", + "az on", + "e szt", + "föl d", + "kut y", + "p illan", + "f ér", + "k om", + "t ől", + "t ű", + "é be", + "t ött", + "bar át", + "í g", + "a hogy", + "e h", + "e p", + "s o", + "v en", + "jel ent", + "t at", + "sz eg", + "mint ha", + "f al", + "egy en", + "mi l", + "sza b", + "r i", + "é m", + "biz ony", + "j on", + "ör eg", + "d olg", + "cs ap", + "ti szt", + "áll t", + "an cs", + "id ő", + "k at", + "ü gy", + "mi ért", + "ó t", + "ü r", + "cs in", + "h az", + "b et", + "én ek", + "v ér", + "j ól", + "al att", + "m ely", + "l o", + "sem mi", + "ny ug", + "v ág", + "kö vet", + "ös sze", + "ma d", + "l i", + "a cs", + "fi ú", + "kö n", + "más ik", + "j ön", + "sz ám", + "g er", + "s ó", + "r ész", + "k ér", + "z el", + "é vel", + "e o", + "e u", + "a n", + "eu l", + "eu n", + "eo n", + "a e", + "d a", + "a l", + "s s", + "i n", + "i l", + "a g", + "an g", + "y eon", + "y eo", + "d o", + "c h", + "n g", + "j i", + "h an", + "g a", + "g o", + "u i", + "h ae", + "a m", + "u l", + "u n", + "g eo", + "s i", + "n eun", + "ss da", + "s eo", + "eon g", + "y o", + "i da", + "t t", + "k k", + "j eo", + "d eul", + "w a", + "eu m", + "g e", + "o n", + "o g", + "s al", + "m an", + "yeon g", + "geo s", + "h ag", + "an eun", + "j a", + "g i", + "s u", + "i ss", + "o l", + "d ae", + "eo b", + "h a", + "j u", + "eo l", + "g eu", + "j eong", + "s ae", + "do e", + "g eul", + "s eu", + "s in", + "eul o", + "b n", + "s ang", + "bn ida", + "h al", + "b o", + "han eun", + "m al", + "i m", + "m o", + "b u", + "jeo g", + "sae ng", + "in eun", + "an h", + "m a", + "sal am", + "j o", + "s a", + "eo m", + "n ae", + "w i", + "l o", + "g wa", + "yeo l", + "n a", + "e seo", + "y e", + "m yeon", + "tt ae", + "h w", + "j e", + "eob s", + "j ang", + "g u", + "g w", + "il eul", + "yeo g", + "j eon", + "si g", + "j ag", + "j in", + "y u", + "o e", + "s e", + "hag o", + "d eun", + "y a", + "m un", + "s eong", + "g ag", + "h am", + "d ang", + "b a", + "l eul", + "s il", + "do ng", + "kk a", + "b al", + "da l", + "han da", + "eo ssda", + "ae g", + "l i", + "ha ji", + "s eon", + "o ng", + "hae ssda", + "d e", + "i ssda", + "e ge", + "b un", + "m ul", + "ju ng", + "ji g", + "m u", + "iss neun", + "b i", + "g eun", + "seu bnida", + "w on", + "p p", + "d aneun", + "eo h", + "d eo", + "ga m", + "j al", + "hae ng", + "ag o", + "y ang", + "b ul", + "b ang", + "u m", + "s o", + "h i", + "j ae", + "si m", + "saeng gag", + "hag e", + "s og", + "eo ss", + "d an", + "ja sin", + "j il", + "eo g", + "g yeong", + "doe n", + "go ng", + "m i", + "ch i", + "d eu", + "d eon", + "hae ss", + "d u", + "n am", + "eun g", + "jo h", + "n al", + "m yeong", + "w o", + "eon a", + "i go", + "g yeol", + "y ag", + "gw an", + "ul i", + "yo ng", + "n o", + "l yeo", + "j og", + "eoh ge", + "ga t", + "b og", + "mo s", + "t ong", + "ch a", + "man h", + "jeo l", + "geo l", + "h oe", + "ag a", + "n aneun", + "g an", + "un eun", + "ch eol", + "ch e", + "do l", + "b on", + "b an", + "ba d", + "ch u", + "ham yeon", + "yeo ssda", + "i bnida", + "g ye", + "eo s", + "hw al", + "salam deul", + "ji man", + "dang sin", + "ji b", + "ttae mun", + "m ae", + "i b", + "e neun", + "eu g", + "jeo m", + "geul eon", + "h wa", + "a ssda", + "b eob", + "bu t", + "b ae", + "yeo ss", + "ch in", + "ch aeg", + "g eon", + "g ae", + "nae ga", + "i ga", + "m og", + "sig an", + "g il", + "h yeon", + "l yeog", + "gu g", + "p yeon", + "s an", + "w ae", + "j ul", + "s eul", + "deun g", + "haji man", + "eum yeon", + "p il", + "m ol", + "n eu", + "a ss", + "n yeon", + "t ae", + "h u", + "p yo", + "s ul", + "g ang", + "j ineun", + "b eon", + "ha da", + "seo l", + "si p", + "dal eun", + "a p", + "sal m", + "g yo", + "ch eon", + "hag i", + "in a", + "cheol eom", + "g al", + "il a", + "kka ji", + "anh neun", + "ha bnida", + "tt eon", + "n u", + "hae seo", + "doen da", + "s ol", + "tt al", + "l a", + "il o", + "seu b", + "b yeon", + "m yeo", + "b eol", + "s on", + "n un", + "j un", + "j am", + "j eung", + "tt o", + "e n", + "mo m", + "h o", + "ch im", + "hw ang", + "eun eun", + "jo ng", + "bo da", + "n ol", + "n eom", + "but eo", + "jig eum", + "eobs da", + "dae lo", + "i g", + "y ul", + "p yeong", + "seon eun", + "sal ang", + "seu t", + "h im", + "n an", + "h eom", + "h yang", + "p i", + "gw ang", + "eobs neun", + "hw ag", + "ge ss", + "jag i", + "il eon", + "wi hae", + "dae han", + "ga ji", + "m eog", + "j yeo", + "cha j", + "b yeong", + "eo d", + "g yeo", + "do n", + "eo ji", + "g ul", + "mo deun", + "j on", + "in saeng", + "geul ae", + "h ang", + "sa sil", + "si b", + "ch al", + "il ago", + "doe l", + "g eum", + "doe neun", + "b ol", + "ga jang", + "geul igo", + "e l", + "h yeong", + "haeng bog", + "ch ul", + "h on", + "ch ae", + "s am", + "m ang", + "in da", + "da m", + "w ol", + "ch oe", + "d ul", + "si jag", + "ch eong", + "il aneun", + "ul ineun", + "ae n", + "kk e", + "mun je", + "a do", + "t eu", + "g un", + "geun eun", + "b ge", + "ch eo", + "b aeg", + "ju g", + "t a", + "sang dae", + "geu geos", + "do g", + "eu s", + "deu s", + "ja b", + "h yeo", + "tt eohge", + "u g", + "ma j", + "ch il", + "s wi", + "j ileul", + "ch ang", + "g aneun", + "m ag", + "i ji", + "da go", + "m in", + "yo han", + "t eug", + "pp un", + "al eul", + "haeng dong", + "p o", + "m il", + "ch am", + "se sang", + "e do", + "p an", + "man deul", + "am yeon", + "a b", + "kk ae", + "b ag", + "i deul", + "p um", + "m eol", + "s un", + "n eul", + "ham kke", + "chu ng", + "da b", + "yu g", + "s ag", + "gwang ye", + "il eohge", + "bal o", + "neun de", + "ham yeo", + "go s", + "geul eoh", + "an ila", + "bang beob", + "da si", + "b yeol", + "g yeon", + "gam jeong", + "on eul", + "j aneun", + "yeo m", + "l ago", + "i gi", + "hw an", + "t eul", + "eo seo", + "si k", + "ch o", + "jag a", + "geul eom", + "geul eona", + "jeong do", + "g yeog", + "geul eohge", + "geu deul", + "eu t", + "im yeon", + "j jae", + "k eun", + "i sang", + "mal haessda", + "eu ge", + "no p", + "in gan", + "bo myeon", + "t aeg", + "seu s", + "d wi", + "s aneun", + "w an", + "anh go", + "t an", + "nu gu", + "su ng", + "da myeon", + "a deul", + "p eul", + "ttal a", + "d i", + "geos do", + "a ji", + "m eon", + "eum yeo", + "dol og", + "neun g", + "mo du", + "क े", + "ह ै", + "े ं", + "् र", + "ा र", + "न े", + "य ा", + "म ें", + "स े", + "क ी", + "क ा", + "ो ं", + "त ा", + "क र", + "स ्", + "क ि", + "क ो", + "र ्", + "न ा", + "क ्", + "ह ी", + "औ र", + "प र", + "त े", + "ह ो", + "प ्र", + "ा न", + "् य", + "ल ा", + "व ा", + "ल े", + "स ा", + "है ं", + "ल ि", + "ज ा", + "ह ा", + "भ ी", + "व ि", + "इ स", + "त ी", + "न ्", + "र ा", + "म ा", + "द े", + "द ि", + "ब ा", + "त ि", + "थ ा", + "न ि", + "क ार", + "ए क", + "ही ं", + "ह ु", + "ं ग", + "ै ं", + "न ी", + "स ी", + "अ प", + "त ्", + "न हीं", + "र ी", + "म े", + "म ु", + "ि त", + "त ो", + "प ा", + "ल ी", + "लि ए", + "ग ा", + "ल ्", + "र ह", + "र े", + "क् ष", + "म ैं", + "स म", + "उ स", + "ज ि", + "त ्र", + "म ि", + "च ा", + "ो ग", + "स ं", + "द ्", + "स ि", + "आ प", + "त ु", + "द ा", + "क ु", + "य ों", + "व े", + "ज ी", + "् या", + "उ न", + "ि क", + "य े", + "भ ा", + "् ट", + "ह म", + "स् ट", + "श ा", + "ड ़", + "ं द", + "ख ा", + "म ्", + "श ्", + "य ह", + "स क", + "प ू", + "कि या", + "अप ने", + "र ू", + "स ु", + "म ी", + "ह ि", + "ज ो", + "थ े", + "र ि", + "द ी", + "थ ी", + "ग ी", + "ल ोग", + "ग या", + "त र", + "न् ह", + "च ्", + "व ार", + "ब ी", + "प ्", + "द ो", + "ट ी", + "श ि", + "कर ने", + "ग े", + "ै से", + "इ न", + "ं ड", + "सा थ", + "प ु", + "ब े", + "ब ार", + "व ी", + "अ न", + "ह र", + "उ न्ह", + "हो ता", + "ज ब", + "कु छ", + "म ान", + "क ्र", + "ब ि", + "प ह", + "फ ि", + "स र", + "ार ी", + "र ो", + "द ू", + "क हा", + "त क", + "श न", + "ब ्", + "स् थ", + "व ह", + "बा द", + "ओ ं", + "ग ु", + "ज ्", + "्र े", + "ग र", + "रह े", + "व र्", + "ह ू", + "ार ्", + "प ी", + "ब हु", + "मु झ", + "्र ा", + "दि या", + "स ब", + "कर ते", + "अप नी", + "बहु त", + "क ह", + "ट े", + "हु ए", + "कि सी", + "र हा", + "ष ्ट", + "ज ़", + "ब ना", + "स ो", + "ड ि", + "को ई", + "व ्य", + "बा त", + "र ु", + "व ो", + "मुझ े", + "द् ध", + "च ार", + "मे रे", + "व र", + "्र ी", + "जा ता", + "न ों", + "प्र ा", + "दे ख", + "ट ा", + "क् या", + "अ ध", + "ल ग", + "ल ो", + "प ि", + "य ु", + "च े", + "जि स", + "ं त", + "ान ी", + "प ै", + "ज न", + "ार े", + "च ी", + "मि ल", + "द ु", + "दे श", + "च् छ", + "ष ्", + "स ू", + "ख े", + "च ु", + "ि या", + "ल गा", + "ब ु", + "उन के", + "ज् ञ", + "क्ष ा", + "त रह", + "्या दा", + "वा ले", + "पू र्", + "मैं ने", + "का म", + "रू प", + "हो ती", + "उ प", + "ज ान", + "प्र कार", + "भ ार", + "म न", + "हु आ", + "ट र", + "हू ँ", + "पर ि", + "पा स", + "अन ु", + "रा ज", + "लोग ों", + "अ ब", + "सम झ", + "ड ी", + "म ौ", + "श ु", + "च ि", + "प े", + "क ृ", + "सक ते", + "म ह", + "य ोग", + "द र्", + "उ से", + "ं ध", + "ड ा", + "जा ए", + "ब ो", + "ू ल", + "म ो", + "ों ने", + "ं स", + "तु म", + "पह ले", + "ब ता", + "त था", + "य ो", + "ग ई", + "उ त्", + "सक ता", + "क म", + "ज ्यादा", + "र ख", + "सम य", + "ार ा", + "अ गर", + "स् त", + "च ल", + "फि र", + "वार ा", + "कर ना", + "श ी", + "ग ए", + "ब न", + "ौ र", + "हो ने", + "चा ह", + "ख ु", + "हा ँ", + "उन्ह ें", + "उन्ह ोंने", + "छ ो", + "म् ह", + "प्र ति", + "नि क", + "व न", + "्य ू", + "र ही", + "तु म्ह", + "ज ैसे", + "ि यों", + "क् यों", + "ल ों", + "फ ़", + "ं त्र", + "हो ते", + "क् ति", + "त ्य", + "कर ्", + "क ई", + "व ं", + "कि न", + "प ो", + "कार ण", + "ड़ ी", + "भ ि", + "इस के", + "ब र", + "उस के", + "द् वारा", + "श े", + "क ॉ", + "दि न", + "न् न", + "ड़ ा", + "स् व", + "नि र्", + "मु ख", + "लि या", + "ट ि", + "ज्ञ ान", + "क् त", + "द ्र", + "ग ्", + "क् स", + "म ै", + "ग ो", + "ज े", + "ट ्र", + "म ार", + "त् व", + "ध ार", + "भा व", + "कर ता", + "ख ि", + "क ं", + "चा हि", + "य र", + "प् त", + "क ों", + "ं च", + "ज ु", + "म त", + "अ च्छ", + "हु ई", + "क भी", + "ले किन", + "भ ू", + "अप ना", + "दू स", + "चाहि ए", + "य ू", + "घ र", + "सब से", + "मे री", + "ना म", + "ढ ़", + "ं ट", + "ें गे", + "ब ै", + "फ ा", + "ए वं", + "य ी", + "ग ्र", + "क्ष े", + "आ ज", + "आप को", + "भा ग", + "ठ ा", + "क ै", + "भार त", + "उन की", + "प हु", + "स भी", + "ध ा", + "ण ा", + "स ान", + "हो गा", + "त ब", + "स ंग", + "प र्", + "अ व", + "त ना", + "ग ि", + "य न", + "स् था", + "च ित", + "ट ्", + "छ ा", + "जा ने", + "क्षे त्र", + "वा ली", + "पूर् ण", + "स मा", + "कार ी" + ] + } +} \ No newline at end of file diff --git a/comfy/text_encoders/ace_text_cleaners.py b/comfy/text_encoders/ace_text_cleaners.py new file mode 100644 index 000000000..cd31d8d8c --- /dev/null +++ b/comfy/text_encoders/ace_text_cleaners.py @@ -0,0 +1,395 @@ +# basic text cleaners for the ACE step model +# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies +# TODO: more languages than english? + +import re + +def japanese_to_romaji(japanese_text): + """ + Convert Japanese hiragana and katakana to romaji (Latin alphabet representation). + + Args: + japanese_text (str): Text containing hiragana and/or katakana characters + + Returns: + str: The romaji (Latin alphabet) equivalent + """ + # Dictionary mapping kana characters to their romaji equivalents + kana_map = { + # Katakana characters + 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o', + 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko', + 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so', + 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to', + 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no', + 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho', + 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo', + 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo', + 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro', + 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n', + + # Katakana voiced consonants + 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go', + 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo', + 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do', + 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo', + 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po', + + # Katakana combinations + 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo', + 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho', + 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho', + 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo', + 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo', + 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo', + 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo', + 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo', + 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo', + 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo', + 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo', + + # Katakana small characters and special cases + 'ッ': '', # Small tsu (doubles the following consonant) + 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo', + + # Katakana extras + 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo', + 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo', + + # Hiragana characters + 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o', + 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko', + 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so', + 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to', + 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no', + 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho', + 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo', + 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo', + 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro', + 'わ': 'wa', 'を': 'wo', 'ん': 'n', + + # Hiragana voiced consonants + 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go', + 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo', + 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do', + 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo', + 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po', + + # Hiragana combinations + 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo', + 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho', + 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho', + 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo', + 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo', + 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo', + 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo', + 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo', + 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo', + 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo', + 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo', + + # Hiragana small characters and special cases + 'っ': '', # Small tsu (doubles the following consonant) + 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo', + + # Common punctuation and spaces + ' ': ' ', # Japanese space + '、': ', ', '。': '. ', + } + + result = [] + i = 0 + + while i < len(japanese_text): + # Check for small tsu (doubling the following consonant) + if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'): + if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map: + next_romaji = kana_map[japanese_text[i+1]] + if next_romaji and next_romaji[0] not in 'aiueon': + result.append(next_romaji[0]) # Double the consonant + i += 1 + continue + + # Check for combinations with small ya, yu, yo + if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'): + combo = japanese_text[i:i+2] + if combo in kana_map: + result.append(kana_map[combo]) + i += 2 + continue + + # Regular character + if japanese_text[i] in kana_map: + result.append(kana_map[japanese_text[i]]) + else: + # If it's not in our map, keep it as is (might be kanji, romaji, etc.) + result.append(japanese_text[i]) + + i += 1 + + return ''.join(result) + +def number_to_text(num, ordinal=False): + """ + Convert a number (int or float) to its text representation. + + Args: + num: The number to convert + + Returns: + str: Text representation of the number + """ + + if not isinstance(num, (int, float)): + return "Input must be a number" + + # Handle special case of zero + if num == 0: + return "zero" + + # Handle negative numbers + negative = num < 0 + num = abs(num) + + # Handle floats + if isinstance(num, float): + # Split into integer and decimal parts + int_part = int(num) + + # Convert both parts + int_text = _int_to_text(int_part) + + # Handle decimal part (convert to string and remove '0.') + decimal_str = str(num).split('.')[1] + decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str) + + result = int_text + decimal_text + else: + # Handle integers + result = _int_to_text(num) + + # Add 'negative' prefix for negative numbers + if negative: + result = "negative " + result + + return result + + +def _int_to_text(num): + """Helper function to convert an integer to text""" + + ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", + "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", + "seventeen", "eighteen", "nineteen"] + + tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] + + if num < 20: + return ones[num] + + if num < 100: + return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "") + + if num < 1000: + return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "") + + if num < 1000000: + return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "") + + if num < 1000000000: + return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "") + + return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "") + + +def _digit_to_text(digit): + """Convert a single digit to text""" + digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] + return digits[digit] + + +_whitespace_re = re.compile(r"\s+") + + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = { + "en": [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] + ], +} + + +def expand_abbreviations_multilingual(text, lang="en"): + for regex, replacement in _abbreviations[lang]: + text = re.sub(regex, replacement, text) + return text + + +_symbols_multilingual = { + "en": [ + (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1]) + for x in [ + ("&", " and "), + ("@", " at "), + ("%", " percent "), + ("#", " hash "), + ("$", " dollar "), + ("£", " pound "), + ("°", " degree "), + ] + ], +} + + +def expand_symbols_multilingual(text, lang="en"): + for regex, replacement in _symbols_multilingual[lang]: + text = re.sub(regex, replacement, text) + text = text.replace(" ", " ") # Ensure there are no double spaces + return text.strip() + + +_ordinal_re = { + "en": re.compile(r"([0-9]+)(st|nd|rd|th)"), +} +_number_re = re.compile(r"[0-9]+") +_currency_re = { + "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"), + "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"), + "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"), +} + +_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b") +_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b") +_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)") + + +def _remove_commas(m): + text = m.group(0) + if "," in text: + text = text.replace(",", "") + return text + + +def _remove_dots(m): + text = m.group(0) + if "." in text: + text = text.replace(".", "") + return text + + +def _expand_decimal_point(m, lang="en"): + amount = m.group(1).replace(",", ".") + return number_to_text(float(amount)) + + +def _expand_currency(m, lang="en", currency="USD"): + amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", ".")))) + full_amount = number_to_text(amount) + + and_equivalents = { + "en": ", ", + "es": " con ", + "fr": " et ", + "de": " und ", + "pt": " e ", + "it": " e ", + "pl": ", ", + "cs": ", ", + "ru": ", ", + "nl": ", ", + "ar": ", ", + "tr": ", ", + "hu": ", ", + "ko": ", ", + } + + if amount.is_integer(): + last_and = full_amount.rfind(and_equivalents[lang]) + if last_and != -1: + full_amount = full_amount[:last_and] + + return full_amount + + +def _expand_ordinal(m, lang="en"): + return number_to_text(int(m.group(1)), ordinal=True) + + +def _expand_number(m, lang="en"): + return number_to_text(int(m.group(0))) + + +def expand_numbers_multilingual(text, lang="en"): + if lang in ["en", "ru"]: + text = re.sub(_comma_number_re, _remove_commas, text) + else: + text = re.sub(_dot_number_re, _remove_dots, text) + try: + text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text) + text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text) + text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text) + except: + pass + + text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text) + text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text) + text = re.sub(_number_re, lambda m: _expand_number(m, lang), text) + return text + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def multilingual_cleaners(text, lang): + text = text.replace('"', "") + if lang == "tr": + text = text.replace("İ", "i") + text = text.replace("Ö", "ö") + text = text.replace("Ü", "ü") + text = lowercase(text) + try: + text = expand_numbers_multilingual(text, lang) + except: + pass + try: + text = expand_abbreviations_multilingual(text, lang) + except: + pass + try: + text = expand_symbols_multilingual(text, lang=lang) + except: + pass + text = collapse_whitespace(text) + return text + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py index e9ad45a7f..cf4252eea 100644 --- a/comfy/text_encoders/aura_t5.py +++ b/comfy/text_encoders/aura_t5.py @@ -11,7 +11,7 @@ class PT5XlModel(sd1_clip.SDClipModel): class PT5XlTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1) + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data) class AuraT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py index fc9bac1d2..ed4638a9a 100644 --- a/comfy/text_encoders/bert.py +++ b/comfy/text_encoders/bert.py @@ -93,8 +93,11 @@ class BertEmbeddings(torch.nn.Module): self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device) - def forward(self, input_tokens, token_type_ids=None, dtype=None): - x = self.word_embeddings(input_tokens, out_dtype=dtype) + def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None): + if embeds is not None: + x = embeds + else: + x = self.word_embeddings(input_tokens, out_dtype=dtype) x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x) if token_type_ids is not None: x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype) @@ -113,12 +116,12 @@ class BertModel_(torch.nn.Module): self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations) self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations) - def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): - x = self.embeddings(input_tokens, dtype=dtype) + def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): + x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype) mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) x, i = self.encoder(x, mask, intermediate_output) return x, i diff --git a/comfy/text_encoders/byt5_config_small_glyph.json b/comfy/text_encoders/byt5_config_small_glyph.json new file mode 100644 index 000000000..0239c7164 --- /dev/null +++ b/comfy/text_encoders/byt5_config_small_glyph.json @@ -0,0 +1,22 @@ +{ + "d_ff": 3584, + "d_kv": 64, + "d_model": 1472, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 4, + "num_heads": 6, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 1510 +} diff --git a/comfy/text_encoders/byt5_tokenizer/added_tokens.json b/comfy/text_encoders/byt5_tokenizer/added_tokens.json new file mode 100644 index 000000000..93c190b56 --- /dev/null +++ b/comfy/text_encoders/byt5_tokenizer/added_tokens.json @@ -0,0 +1,127 @@ +{ + "": 259, + "": 359, + "": 360, + "": 361, + "": 362, + "": 363, + "": 364, + "": 365, + "": 366, + 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+ "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "clean_up_tokenization_spaces": false, + "eos_token": "", + "extra_ids": 0, + "extra_special_tokens": {}, + "model_max_length": 1000000000000000019884624838656, + "pad_token": "", + "tokenizer_class": "ByT5Tokenizer", + "unk_token": "" +} diff --git a/comfy/text_encoders/cosmos.py b/comfy/text_encoders/cosmos.py new file mode 100644 index 000000000..a1adb5242 --- /dev/null +++ b/comfy/text_encoders/cosmos.py @@ -0,0 +1,42 @@ +from comfy import sd1_clip +import comfy.text_encoders.t5 +import os +from transformers import T5TokenizerFast + + +class T5XXLModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json") + t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None) + if t5xxl_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options) + +class CosmosT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data) + + +class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + + +def te(dtype_t5=None, t5xxl_scaled_fp8=None): + class CosmosTEModel_(CosmosT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return CosmosTEModel_ diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py index b945b1aaa..d61ef6668 100644 --- a/comfy/text_encoders/flux.py +++ b/comfy/text_encoders/flux.py @@ -9,19 +9,18 @@ import os class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) class FluxTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): - clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) - self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): @@ -35,8 +34,7 @@ class FluxClipModel(torch.nn.Module): def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): super().__init__() dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) - clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) - self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) self.dtypes = set([dtype, dtype_t5]) diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py index 45987a480..9dcf190a2 100644 --- a/comfy/text_encoders/genmo.py +++ b/comfy/text_encoders/genmo.py @@ -18,7 +18,7 @@ class MochiT5XXL(sd1_clip.SD1ClipModel): class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) class MochiT5Tokenizer(sd1_clip.SD1Tokenizer): diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py new file mode 100644 index 000000000..dbcf52784 --- /dev/null +++ b/comfy/text_encoders/hidream.py @@ -0,0 +1,155 @@ +from . import hunyuan_video +from . import sd3_clip +from comfy import sd1_clip +from comfy import sdxl_clip +import comfy.model_management +import torch +import logging + + +class HiDreamTokenizer: + def __init__(self, embedding_directory=None, tokenizer_data={}): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data) + self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens + out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + + def state_dict(self): + return {} + + +class HiDreamTEModel(torch.nn.Module): + def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device="cpu", dtype=None, model_options={}): + super().__init__() + self.dtypes = set() + if clip_l: + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_l = None + + if clip_g: + self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options) + self.dtypes.add(dtype) + else: + self.clip_g = None + + if t5: + dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) + self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True) + self.dtypes.add(dtype_t5) + else: + self.t5xxl = None + + if llama: + dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) + if "vocab_size" not in model_options: + model_options["vocab_size"] = 128256 + self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None, special_tokens={"start": 128000, "pad": 128009}) + self.dtypes.add(dtype_llama) + else: + self.llama = None + + logging.debug("Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama)) + + def set_clip_options(self, options): + if self.clip_l is not None: + self.clip_l.set_clip_options(options) + if self.clip_g is not None: + self.clip_g.set_clip_options(options) + if self.t5xxl is not None: + self.t5xxl.set_clip_options(options) + if self.llama is not None: + self.llama.set_clip_options(options) + + def reset_clip_options(self): + if self.clip_l is not None: + self.clip_l.reset_clip_options() + if self.clip_g is not None: + self.clip_g.reset_clip_options() + if self.t5xxl is not None: + self.t5xxl.reset_clip_options() + if self.llama is not None: + self.llama.reset_clip_options() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_l = token_weight_pairs["l"] + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_t5 = token_weight_pairs["t5xxl"] + token_weight_pairs_llama = token_weight_pairs["llama"] + lg_out = None + pooled = None + extra = {} + + if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0: + if self.clip_l is not None: + lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + else: + l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device()) + + if self.clip_g is not None: + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + else: + g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device()) + + pooled = torch.cat((l_pooled, g_pooled), dim=-1) + + if self.t5xxl is not None: + t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5) + t5_out, t5_pooled = t5_output[:2] + else: + t5_out = None + + if self.llama is not None: + ll_output = self.llama.encode_token_weights(token_weight_pairs_llama) + ll_out, ll_pooled = ll_output[:2] + ll_out = ll_out[:, 1:] + else: + ll_out = None + + if t5_out is None: + t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device()) + + if ll_out is None: + ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device()) + + if pooled is None: + pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device()) + + extra["conditioning_llama3"] = ll_out + return t5_out, pooled, extra + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + return self.clip_l.load_sd(sd) + elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd: + return self.t5xxl.load_sd(sd) + else: + return self.llama.load_sd(sd) + + +def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None): + class HiDreamTEModel_(HiDreamTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["llama_scaled_fp8"] = llama_scaled_fp8 + super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options) + return HiDreamTEModel_ diff --git a/comfy/text_encoders/hunyuan_image.py b/comfy/text_encoders/hunyuan_image.py new file mode 100644 index 000000000..ff04726e1 --- /dev/null +++ b/comfy/text_encoders/hunyuan_image.py @@ -0,0 +1,103 @@ +from comfy import sd1_clip +import comfy.text_encoders.llama +from .qwen_image import QwenImageTokenizer, QwenImageTEModel +from transformers import ByT5Tokenizer +import os +import re + +class ByT5SmallTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1472, embedding_key='byt5_small', tokenizer_class=ByT5Tokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) + +class HunyuanImageTokenizer(QwenImageTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>" + # self.llama_template_images = "{}" + self.byt5 = ByT5SmallTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): + out = super().tokenize_with_weights(text, return_word_ids, **kwargs) + + # ByT5 processing for HunyuanImage + text_prompt_texts = [] + pattern_quote_double = r'\"(.*?)\"' + pattern_quote_chinese_single = r'‘(.*?)’' + pattern_quote_chinese_double = r'“(.*?)”' + + matches_quote_double = re.findall(pattern_quote_double, text) + matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text) + matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text) + + text_prompt_texts.extend(matches_quote_double) + text_prompt_texts.extend(matches_quote_chinese_single) + text_prompt_texts.extend(matches_quote_chinese_double) + + if len(text_prompt_texts) > 0: + out['byt5'] = self.byt5.tokenize_with_weights(''.join(map(lambda a: 'Text "{}". '.format(a), text_prompt_texts)), return_word_ids, **kwargs) + return out + +class Qwen25_7BVLIModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}): + llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None) + if llama_scaled_fp8 is not None: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class ByT5SmallModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "byt5_config_small_glyph.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, model_options=model_options, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True) + + +class HunyuanImageTEModel(QwenImageTEModel): + def __init__(self, byt5=True, device="cpu", dtype=None, model_options={}): + super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) + + if byt5: + self.byt5_small = ByT5SmallModel(device=device, dtype=dtype, model_options=model_options) + else: + self.byt5_small = None + + def encode_token_weights(self, token_weight_pairs): + tok_pairs = token_weight_pairs["qwen25_7b"][0] + template_end = -1 + if tok_pairs[0][0] == 27: + if len(tok_pairs) > 36: # refiner prompt uses a fixed 36 template_end + template_end = 36 + + cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=template_end) + if self.byt5_small is not None and "byt5" in token_weight_pairs: + out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"]) + extra["conditioning_byt5small"] = out[0] + return cond, p, extra + + def set_clip_options(self, options): + super().set_clip_options(options) + if self.byt5_small is not None: + self.byt5_small.set_clip_options(options) + + def reset_clip_options(self): + super().reset_clip_options() + if self.byt5_small is not None: + self.byt5_small.reset_clip_options() + + def load_sd(self, sd): + if "encoder.block.0.layer.0.SelfAttention.o.weight" in sd: + return self.byt5_small.load_sd(sd) + else: + return super().load_sd(sd) + +def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None): + class QwenImageTEModel_(HunyuanImageTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["qwen_scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options) + return QwenImageTEModel_ diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py index 7149d6878..b02148b33 100644 --- a/comfy/text_encoders/hunyuan_video.py +++ b/comfy/text_encoders/hunyuan_video.py @@ -4,6 +4,7 @@ import comfy.text_encoders.llama from transformers import LlamaTokenizerFast import torch import os +import numbers def llama_detect(state_dict, prefix=""): @@ -20,33 +21,49 @@ def llama_detect(state_dict, prefix=""): class LLAMA3Tokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256): + def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=128258, end_token=128009, min_length=min_length) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data) class LLAMAModel(sd1_clip.SDClipModel): - def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}): + def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}): llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None) if llama_scaled_fp8 is not None: model_options = model_options.copy() model_options["scaled_fp8"] = llama_scaled_fp8 - super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 128000, "pad": 128258}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + textmodel_json_config = {} + vocab_size = model_options.get("vocab_size", None) + if vocab_size is not None: + textmodel_json_config["vocab_size"] = vocab_size + + model_options = {**model_options, "model_name": "llama"} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) class HunyuanVideoTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): - clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) - self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n""" # 95 tokens - self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1) + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens + self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs): out = {} - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) - llama_text = "{}{}".format(self.llama_template, text) - out["llama"] = self.llama.tokenize_with_weights(llama_text, return_word_ids) + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs) + embed_count = 0 + for r in llama_text_tokens: + for i in range(len(r)): + if r[i][0] == 128257: + if image_embeds is not None and embed_count < image_embeds.shape[0]: + r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:] + embed_count += 1 + out["llama"] = llama_text_tokens return out def untokenize(self, token_weight_pair): @@ -60,8 +77,7 @@ class HunyuanVideoClipModel(torch.nn.Module): def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}): super().__init__() dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device) - clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) - self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) + self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options) self.dtypes = set([dtype, dtype_llama]) @@ -80,20 +96,51 @@ class HunyuanVideoClipModel(torch.nn.Module): llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama) template_end = 0 - for i, v in enumerate(token_weight_pairs_llama[0]): - if v[0] == 128007: # <|end_header_id|> - template_end = i + extra_template_end = 0 + extra_sizes = 0 + user_end = 9999999999999 + images = [] + + tok_pairs = token_weight_pairs_llama[0] + for i, v in enumerate(tok_pairs): + elem = v[0] + if not torch.is_tensor(elem): + if isinstance(elem, numbers.Integral): + if elem == 128006: + if tok_pairs[i + 1][0] == 882: + if tok_pairs[i + 2][0] == 128007: + template_end = i + 2 + user_end = -1 + if elem == 128009 and user_end == -1: + user_end = i + 1 + else: + if elem.get("original_type") == "image": + elem_size = elem.get("data").shape[0] + if template_end > 0: + if user_end == -1: + extra_template_end += elem_size - 1 + else: + image_start = i + extra_sizes + image_end = i + elem_size + extra_sizes + images.append((image_start, image_end, elem.get("image_interleave", 1))) + extra_sizes += elem_size - 1 if llama_out.shape[1] > (template_end + 2): - if token_weight_pairs_llama[0][template_end + 1][0] == 271: + if tok_pairs[template_end + 1][0] == 271: template_end += 2 - llama_out = llama_out[:, template_end:] - llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end:] + llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] + llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end] if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]): llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements + if len(images) > 0: + out = [] + for i in images: + out.append(llama_out[:, i[0]: i[1]: i[2]]) + llama_output = torch.cat(out + [llama_output], dim=1) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) - return llama_out, l_pooled, llama_extra_out + return llama_output, l_pooled, llama_extra_out def load_sd(self, sd): if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py index 7cb790f45..ac6994529 100644 --- a/comfy/text_encoders/hydit.py +++ b/comfy/text_encoders/hydit.py @@ -9,24 +9,26 @@ import torch class HyditBertModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json") + model_options = {**model_options, "model_name": "hydit_clip"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) class HyditBertTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77) + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data) class MT5XLModel(sd1_clip.SDClipModel): def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json") + model_options = {**model_options, "model_name": "mt5xl"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options) class MT5XLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model") tokenizer = tokenizer_data.get("spiece_model", None) - super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data) def state_dict(self): return {"spiece_model": self.tokenizer.serialize_model()} @@ -35,12 +37,12 @@ class HyditTokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None) self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory) - self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) + self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, "spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids) - out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids) + out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs) + out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index ad4b4623e..c050759fe 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -1,14 +1,16 @@ import torch import torch.nn as nn -import torch.nn.functional as F from dataclasses import dataclass from typing import Optional, Any +import math +import logging from comfy.ldm.modules.attention import optimized_attention_for_device import comfy.model_management import comfy.ldm.common_dit import comfy.model_management +from . import qwen_vl @dataclass class Llama2Config: @@ -21,15 +23,116 @@ class Llama2Config: max_position_embeddings: int = 8192 rms_norm_eps: float = 1e-5 rope_theta: float = 500000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = False + rope_dims = None + q_norm = None + k_norm = None + rope_scale = None + +@dataclass +class Qwen25_3BConfig: + vocab_size: int = 151936 + hidden_size: int = 2048 + intermediate_size: int = 11008 + num_hidden_layers: int = 36 + num_attention_heads: int = 16 + num_key_value_heads: int = 2 + max_position_embeddings: int = 128000 + rms_norm_eps: float = 1e-6 + rope_theta: float = 1000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = True + rope_dims = None + q_norm = None + k_norm = None + rope_scale = None + +@dataclass +class Qwen25_7BVLI_Config: + vocab_size: int = 152064 + hidden_size: int = 3584 + intermediate_size: int = 18944 + num_hidden_layers: int = 28 + num_attention_heads: int = 28 + num_key_value_heads: int = 4 + max_position_embeddings: int = 128000 + rms_norm_eps: float = 1e-6 + rope_theta: float = 1000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = True + rope_dims = [16, 24, 24] + q_norm = None + k_norm = None + rope_scale = None + +@dataclass +class Gemma2_2B_Config: + vocab_size: int = 256000 + hidden_size: int = 2304 + intermediate_size: int = 9216 + num_hidden_layers: int = 26 + num_attention_heads: int = 8 + num_key_value_heads: int = 4 + max_position_embeddings: int = 8192 + rms_norm_eps: float = 1e-6 + rope_theta: float = 10000.0 + transformer_type: str = "gemma2" + head_dim = 256 + rms_norm_add = True + mlp_activation = "gelu_pytorch_tanh" + qkv_bias = False + rope_dims = None + q_norm = None + k_norm = None + sliding_attention = None + rope_scale = None + +@dataclass +class Gemma3_4B_Config: + vocab_size: int = 262208 + hidden_size: int = 2560 + intermediate_size: int = 10240 + num_hidden_layers: int = 34 + num_attention_heads: int = 8 + num_key_value_heads: int = 4 + max_position_embeddings: int = 131072 + rms_norm_eps: float = 1e-6 + rope_theta = [10000.0, 1000000.0] + transformer_type: str = "gemma3" + head_dim = 256 + rms_norm_add = True + mlp_activation = "gelu_pytorch_tanh" + qkv_bias = False + rope_dims = None + q_norm = "gemma3" + k_norm = "gemma3" + sliding_attention = [False, False, False, False, False, 1024] + rope_scale = [1.0, 8.0] class RMSNorm(nn.Module): - def __init__(self, dim: int, eps: float = 1e-5, device=None, dtype=None): + def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype)) + self.add = add def forward(self, x: torch.Tensor): - return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps) + w = self.weight + if self.add: + w = w + 1.0 + + return comfy.ldm.common_dit.rms_norm(x, w, self.eps) + def rotate_half(x): @@ -39,27 +142,49 @@ def rotate_half(x): return torch.cat((-x2, x1), dim=-1) -def precompute_freqs_cis(head_dim, seq_len, theta, device=None): - theta_numerator = torch.arange(0, head_dim, 2, device=device).float() - inv_freq = 1.0 / (theta ** (theta_numerator / head_dim)) +def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None): + if not isinstance(theta, list): + theta = [theta] - position_ids = torch.arange(0, seq_len, device=device).unsqueeze(0) + out = [] + for index, t in enumerate(theta): + theta_numerator = torch.arange(0, head_dim, 2, device=device).float() + inv_freq = 1.0 / (t ** (theta_numerator / head_dim)) - inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) - position_ids_expanded = position_ids[:, None, :].float() - freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) - emb = torch.cat((freqs, freqs), dim=-1) - cos = emb.cos() - sin = emb.sin() - return (cos, sin) + if rope_scale is not None: + if isinstance(rope_scale, list): + inv_freq /= rope_scale[index] + else: + inv_freq /= rope_scale + + inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + if rope_dims is not None and position_ids.shape[0] > 1: + mrope_section = rope_dims * 2 + cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) + sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0) + else: + cos = cos.unsqueeze(1) + sin = sin.unsqueeze(1) + out.append((cos, sin)) + + if len(out) == 1: + return out[0] + + return out def apply_rope(xq, xk, freqs_cis): - cos = freqs_cis[0].unsqueeze(1) - sin = freqs_cis[1].unsqueeze(1) + org_dtype = xq.dtype + cos = freqs_cis[0] + sin = freqs_cis[1] q_embed = (xq * cos) + (rotate_half(xq) * sin) k_embed = (xk * cos) + (rotate_half(xk) * sin) - return q_embed, k_embed + return q_embed.to(org_dtype), k_embed.to(org_dtype) class Attention(nn.Module): @@ -68,13 +193,23 @@ class Attention(nn.Module): self.num_heads = config.num_attention_heads self.num_kv_heads = config.num_key_value_heads self.hidden_size = config.hidden_size - self.head_dim = self.hidden_size // self.num_heads + + self.head_dim = config.head_dim + self.inner_size = self.num_heads * self.head_dim ops = ops or nn - self.q_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype) - self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype) - self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype) - self.o_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype) + self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype) + self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype) + + self.q_norm = None + self.k_norm = None + + if config.q_norm == "gemma3": + self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + if config.k_norm == "gemma3": + self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) def forward( self, @@ -84,7 +219,6 @@ class Attention(nn.Module): optimized_attention=None, ): batch_size, seq_length, _ = hidden_states.shape - xq = self.q_proj(hidden_states) xk = self.k_proj(hidden_states) xv = self.v_proj(hidden_states) @@ -93,6 +227,11 @@ class Attention(nn.Module): xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2) + if self.q_norm is not None: + xq = self.q_norm(xq) + if self.k_norm is not None: + xk = self.k_norm(xk) + xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis) xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) @@ -108,12 +247,16 @@ class MLP(nn.Module): self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) + if config.mlp_activation == "silu": + self.activation = torch.nn.functional.silu + elif config.mlp_activation == "gelu_pytorch_tanh": + self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh") def forward(self, x): - return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x)) + return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x)) class TransformerBlock(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None): super().__init__() self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) @@ -146,6 +289,60 @@ class TransformerBlock(nn.Module): return x +class TransformerBlockGemma2(nn.Module): + def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None): + super().__init__() + self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops) + self.mlp = MLP(config, device=device, dtype=dtype, ops=ops) + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) + + if config.sliding_attention is not None: # TODO: implement. (Not that necessary since models are trained on less than 1024 tokens) + self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)] + else: + self.sliding_attention = False + + self.transformer_type = config.transformer_type + + def forward( + self, + x: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + freqs_cis: Optional[torch.Tensor] = None, + optimized_attention=None, + ): + if self.transformer_type == 'gemma3': + if self.sliding_attention: + if x.shape[1] > self.sliding_attention: + logging.warning("Warning: sliding attention not implemented, results may be incorrect") + freqs_cis = freqs_cis[1] + else: + freqs_cis = freqs_cis[0] + + # Self Attention + residual = x + x = self.input_layernorm(x) + x = self.self_attn( + hidden_states=x, + attention_mask=attention_mask, + freqs_cis=freqs_cis, + optimized_attention=optimized_attention, + ) + + x = self.post_attention_layernorm(x) + x = residual + x + + # MLP + residual = x + x = self.pre_feedforward_layernorm(x) + x = self.mlp(x) + x = self.post_feedforward_layernorm(x) + x = residual + x + + return x + class Llama2_(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() @@ -158,19 +355,37 @@ class Llama2_(nn.Module): device=device, dtype=dtype ) + if self.config.transformer_type == "gemma2" or self.config.transformer_type == "gemma3": + transformer = TransformerBlockGemma2 + self.normalize_in = True + else: + transformer = TransformerBlock + self.normalize_in = False + self.layers = nn.ModuleList([ - TransformerBlock(config, device=device, dtype=dtype, ops=ops) - for _ in range(config.num_hidden_layers) + transformer(config, index=i, device=device, dtype=dtype, ops=ops) + for i in range(config.num_hidden_layers) ]) - self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype) # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype) - def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): - x = self.embed_tokens(x, out_dtype=dtype) + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[]): + if embeds is not None: + x = embeds + else: + x = self.embed_tokens(x, out_dtype=dtype) - freqs_cis = precompute_freqs_cis(self.config.hidden_size // self.config.num_attention_heads, - x.shape[1], + if self.normalize_in: + x *= self.config.hidden_size ** 0.5 + + if position_ids is None: + position_ids = torch.arange(0, x.shape[1], device=x.device).unsqueeze(0) + + freqs_cis = precompute_freqs_cis(self.config.head_dim, + position_ids, self.config.rope_theta, + self.config.rope_scale, + self.config.rope_dims, device=x.device) mask = None @@ -186,11 +401,17 @@ class Llama2_(nn.Module): optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) intermediate = None + all_intermediate = None if intermediate_output is not None: - if intermediate_output < 0: + if intermediate_output == "all": + all_intermediate = [] + intermediate_output = None + elif intermediate_output < 0: intermediate_output = len(self.layers) + intermediate_output for i, layer in enumerate(self.layers): + if all_intermediate is not None: + all_intermediate.append(x.unsqueeze(1).clone()) x = layer( x=x, attention_mask=mask, @@ -201,21 +422,18 @@ class Llama2_(nn.Module): intermediate = x.clone() x = self.norm(x) + if all_intermediate is not None: + all_intermediate.append(x.unsqueeze(1).clone()) + + if all_intermediate is not None: + intermediate = torch.cat(all_intermediate, dim=1) + if intermediate is not None and final_layer_norm_intermediate: intermediate = self.norm(intermediate) return x, intermediate - -class Llama2(torch.nn.Module): - def __init__(self, config_dict, dtype, device, operations): - super().__init__() - config = Llama2Config(**config_dict) - self.num_layers = config.num_hidden_layers - - self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) - self.dtype = dtype - +class BaseLlama: def get_input_embeddings(self): return self.model.embed_tokens @@ -224,3 +442,83 @@ class Llama2(torch.nn.Module): def forward(self, input_ids, *args, **kwargs): return self.model(input_ids, *args, **kwargs) + + +class Llama2(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Llama2Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + +class Qwen25_3B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Qwen25_3BConfig(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + +class Qwen25_7BVLI(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Qwen25_7BVLI_Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.visual = qwen_vl.Qwen2VLVisionTransformer(hidden_size=1280, output_hidden_size=config.hidden_size, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + + def preprocess_embed(self, embed, device): + if embed["type"] == "image": + image, grid = qwen_vl.process_qwen2vl_images(embed["data"]) + return self.visual(image.to(device, dtype=torch.float32), grid), grid + return None, None + + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): + grid = None + position_ids = None + offset = 0 + for e in embeds_info: + if e.get("type") == "image": + grid = e.get("extra", None) + start = e.get("index") + if position_ids is None: + position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device) + position_ids[:, :start] = torch.arange(0, start, device=embeds.device) + end = e.get("size") + start + len_max = int(grid.max()) // 2 + start_next = len_max + start + position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device) + position_ids[0, start:end] = start + offset + max_d = int(grid[0][1]) // 2 + position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start] + max_d = int(grid[0][2]) // 2 + position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start] + offset += len_max - (end - start) + + if grid is None: + position_ids = None + + return super().forward(x, attention_mask=attention_mask, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=final_layer_norm_intermediate, dtype=dtype, position_ids=position_ids) + +class Gemma2_2B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Gemma2_2B_Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + +class Gemma3_4B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Gemma3_4B_Config(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype diff --git a/comfy/text_encoders/long_clipl.json b/comfy/text_encoders/long_clipl.json deleted file mode 100644 index 5e2056ff3..000000000 --- a/comfy/text_encoders/long_clipl.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "_name_or_path": "openai/clip-vit-large-patch14", - "architectures": [ - "CLIPTextModel" - ], - "attention_dropout": 0.0, - "bos_token_id": 0, - "dropout": 0.0, - "eos_token_id": 49407, - "hidden_act": "quick_gelu", - "hidden_size": 768, - "initializer_factor": 1.0, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 248, - "model_type": "clip_text_model", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "pad_token_id": 1, - "projection_dim": 768, - "torch_dtype": "float32", - "transformers_version": "4.24.0", - "vocab_size": 49408 -} diff --git a/comfy/text_encoders/long_clipl.py b/comfy/text_encoders/long_clipl.py index b81912cb3..8d4c7619d 100644 --- a/comfy/text_encoders/long_clipl.py +++ b/comfy/text_encoders/long_clipl.py @@ -1,30 +1,27 @@ -from comfy import sd1_clip -import os -class LongClipTokenizer_(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - -class LongClipModel_(sd1_clip.SDClipModel): - def __init__(self, *args, **kwargs): - textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json") - super().__init__(*args, textmodel_json_config=textmodel_json_config, **kwargs) - -class LongClipTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=LongClipTokenizer_) - -class LongClipModel(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): - super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs) def model_options_long_clip(sd, tokenizer_data, model_options): w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None) + if w is None: + w = sd.get("clip_g.text_model.embeddings.position_embedding.weight", None) + else: + model_name = "clip_g" + if w is None: w = sd.get("text_model.embeddings.position_embedding.weight", None) - if w is not None and w.shape[0] == 248: + if w is not None: + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + model_name = "clip_g" + elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd: + model_name = "clip_l" + else: + model_name = "clip_l" + + if w is not None: tokenizer_data = tokenizer_data.copy() model_options = model_options.copy() - tokenizer_data["clip_l_tokenizer_class"] = LongClipTokenizer_ - model_options["clip_l_class"] = LongClipModel_ + model_config = model_options.get("model_config", {}) + model_config["max_position_embeddings"] = w.shape[0] + model_options["{}_model_config".format(model_name)] = model_config + tokenizer_data["{}_max_length".format(model_name)] = w.shape[0] return tokenizer_data, model_options diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py index 5c2ce583f..48ea67e67 100644 --- a/comfy/text_encoders/lt.py +++ b/comfy/text_encoders/lt.py @@ -6,7 +6,7 @@ import comfy.text_encoders.genmo class T5XXLTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) #pad to 128? + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128? class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer): diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py new file mode 100644 index 000000000..fd986e2c1 --- /dev/null +++ b/comfy/text_encoders/lumina2.py @@ -0,0 +1,57 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.llama + + +class Gemma2BTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class Gemma3_4BTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + +class LuminaTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer) + +class NTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_4b", tokenizer=Gemma3_4BTokenizer) + +class Gemma2_2BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + +class Gemma3_4BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + +class LuminaModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel): + super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options) + + +def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"): + if model_type == "gemma2_2b": + model = Gemma2_2BModel + elif model_type == "gemma3_4b": + model = Gemma3_4BModel + + class LuminaTEModel_(LuminaModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model) + return LuminaTEModel_ diff --git a/comfy/text_encoders/omnigen2.py b/comfy/text_encoders/omnigen2.py new file mode 100644 index 000000000..1a01b2dd4 --- /dev/null +++ b/comfy/text_encoders/omnigen2.py @@ -0,0 +1,44 @@ +from transformers import Qwen2Tokenizer +from comfy import sd1_clip +import comfy.text_encoders.llama +import os + + +class Qwen25_3BTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer) + self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n' + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs): + if llama_template is None: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs) + +class Qwen25_3BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class Omnigen2Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options) + + +def te(dtype_llama=None, llama_scaled_fp8=None): + class Omnigen2TEModel_(Omnigen2Model): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, model_options=model_options) + return Omnigen2TEModel_ diff --git a/comfy/text_encoders/pixart_t5.py b/comfy/text_encoders/pixart_t5.py index d56d57f1b..5f383de07 100644 --- a/comfy/text_encoders/pixart_t5.py +++ b/comfy/text_encoders/pixart_t5.py @@ -1,42 +1,42 @@ -import os - -from comfy import sd1_clip -import comfy.text_encoders.t5 -import comfy.text_encoders.sd3_clip -from comfy.sd1_clip import gen_empty_tokens - -from transformers import T5TokenizerFast - -class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): - def __init__(self, **kwargs): - super().__init__(**kwargs) - - def gen_empty_tokens(self, special_tokens, *args, **kwargs): - # PixArt expects the negative to be all pad tokens - special_tokens = special_tokens.copy() - special_tokens.pop("end") - return gen_empty_tokens(special_tokens, *args, **kwargs) - -class PixArtT5XXL(sd1_clip.SD1ClipModel): - def __init__(self, device="cpu", dtype=None, model_options={}): - super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) - -class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1) # no padding - -class PixArtTokenizer(sd1_clip.SD1Tokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): - super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) - -def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None): - class PixArtTEModel_(PixArtT5XXL): - def __init__(self, device="cpu", dtype=None, model_options={}): - if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: - model_options = model_options.copy() - model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 - if dtype is None: - dtype = dtype_t5 - super().__init__(device=device, dtype=dtype, model_options=model_options) - return PixArtTEModel_ +import os + +from comfy import sd1_clip +import comfy.text_encoders.t5 +import comfy.text_encoders.sd3_clip +from comfy.sd1_clip import gen_empty_tokens + +from transformers import T5TokenizerFast + +class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def gen_empty_tokens(self, special_tokens, *args, **kwargs): + # PixArt expects the negative to be all pad tokens + special_tokens = special_tokens.copy() + special_tokens.pop("end") + return gen_empty_tokens(special_tokens, *args, **kwargs) + +class PixArtT5XXL(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options) + +class T5XXLTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding + +class PixArtTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer) + +def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None): + class PixArtTEModel_(PixArtT5XXL): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 + if dtype is None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return PixArtTEModel_ diff --git a/comfy/text_encoders/qwen25_tokenizer/merges.txt b/comfy/text_encoders/qwen25_tokenizer/merges.txt new file mode 100644 index 000000000..31349551d --- /dev/null +++ b/comfy/text_encoders/qwen25_tokenizer/merges.txt @@ -0,0 +1,151388 @@ +#version: 0.2 +Ġ Ġ +ĠĠ ĠĠ +i n +Ġ t +ĠĠĠĠ ĠĠĠĠ +e r +ĠĠ Ġ +o n +Ġ a +r e +a t +s t +e n +o r +Ġt h +Ċ Ċ +Ġ c +l e +Ġ s +i t +a n +a r +a l +Ġth e +; Ċ +Ġ p +Ġ f +o u +Ġ = +i s +ĠĠĠĠ ĠĠĠ +in g +e s +Ġ w +i on +e d +i c +Ġ b +Ġ d +e t +Ġ m +Ġ o +ĉ ĉ +r o +a s +e l +c t +n d +Ġ in +Ġ h +en t +i d +Ġ n +a m +ĠĠĠĠĠĠĠĠ ĠĠĠ +Ġt o +Ġ re +- - +Ġ { +Ġo f +o m +) ;Ċ +i m +č Ċ +Ġ ( +i l +/ / +Ġa nd +u r +s e +Ġ l +e x +Ġ S +a d +Ġ " +c h +u t +i f +* * +Ġ } +e m +o l +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ +t h +) Ċ +Ġ{ Ċ +Ġ g +i g +i v +, Ċ +c e +o d +Ġ v +at e +Ġ T +a g +a y +Ġ * +o t +u s +Ġ C +Ġ st +Ġ I +u n +u l +u e +Ġ A +o w +Ġ ' +e w +Ġ < +at ion +( ) +Ġf or +a b +or t +u m +am e +Ġ is +p e +t r +c k +â Ģ +Ġ y +i st +-- -- +. ĊĊ +h e +Ġ e +l o +Ġ M +Ġb e +er s +Ġ on +Ġc on +a p +u b +Ġ P +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠ +as s +in t +> Ċ +l y +ur n +Ġ $ +; ĊĊ +a v +p ort +i r +- > +n t +ct ion +en d +Ġd e +it h +ou t +t urn +ou r +ĠĠĠĠ Ġ +l ic +re s +p t += = +Ġth is +Ġw h +Ġ if +Ġ D +v er +ag e +Ġ B +h t +ex t += " +Ġth at +** ** +Ġ R +Ġ it +es s +Ġ F +Ġ r +o s +an d +Ġa s +e ct +k e +ro m +Ġ // +c on +Ġ L +( " +q u +l ass +Ġw ith +i z +d e +Ġ N +Ġa l +o p +u p +g et +Ġ} Ċ +i le +Ġa n +at a +o re +r i +Ġp ro +; čĊ +ĉĉ ĉĉ +t er +a in +Ġ W +Ġ E +Ġc om +Ġre turn +ar t +Ġ H +a ck +im port +ub lic +Ġ or +e st +m ent +Ġ G +ab le +Ġ - +in e +il l +in d +er e +: : +it y +Ġ + +Ġt r +el f +ig ht +( ' +or m +ul t +st r +. . +" , +Ġy ou +y pe +p l +Ġn ew +Ġ j +ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠĠĠĠ +Ġf rom +Ġ ex +Ġ O +l d +Ġ [ +o c +: Ċ +Ġs e +Ġ le +---- ---- +. s +{ Ċ +' , +an t +Ġa t +as e +. c +Ġc h +< / +av e +an g +Ġa re +Ġin t +âĢ Ļ +_ t +er t +i al +a ct +} Ċ +iv e +od e +o st +Ġc lass +Ġn ot +o g +or d +al ue +al l +f f +( );Ċ +on t +im e +a re +Ġ U +Ġp r +Ġ : +i es +iz e +u re +Ġb y +i re +Ġ} ĊĊ +. p +Ġs h +ic e +a st +pt ion +tr ing +o k +_ _ +c l +# # +Ġh e +ar d +) . +Ġ @ +i ew +ĉĉ ĉ +Ġw as +i p +th is +Ġ u +ĠT he +id e +a ce +i b +a c +r ou +Ġw e +j ect +Ġp ublic +a k +v e +at h +o id +Ġ= > +u st +q ue +Ġre s +) ) +' s +Ġ k +an s +y st +un ction +**** **** +Ġ i +Ġ us +p p +on e +a il +== == +n ame +Ġst r +Ġ / +Ġ & +a ch +d iv +yst em +el l +Ġh ave +er r +ou ld +ul l +p on +Ġ J +_ p +Ġ= = +ig n +S t +. 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×Ļ×Ĺ +еÑĩ а +Ùģ Ø§Ø¹ +×Ĵ ×Ļ×ĵ +áºŃ m +ÄĻ b +Ø´ ع +ãģı ãĤĬ +à¸ŀ ุ +ед еÑĢ +à¸Ĥ à¸Ļ +à¸Ħ าร +ĠболÑĮ ÑĪ +ãģı ãģªãĤĬ +à¸ĵ า +×ĵ ×ķ×Ĵ +Ġм н +ä¸Ĭ ãģĮ +ç¶ļ ãģį +ฤ ษ +ภĨ +Ø® ÙĬ +à¹Ģà¸Ĺ à¸ŀ +สั ม +à¹Ģส à¸Ļ +à¹Ģสà¸Ļ à¸Ń +ãĥ ´ +Ġи ÑģÑĤ +با شر +ĠÑĥ ÑĢов +×ŀ ×ķ×ĸ +ab ı +wa ż +×ķצ ×IJ×Ķ +ÑĤ веÑĢ +à¸ŀัà¸Ļà¸ĺ à¹Į +׳ ×Ĵ×ĵ +ãĤĭ ãģĵãģ¨ãģĮãģ§ãģį +ĠÑĤÑĢ ÐµÐ± +à¸ģร ุà¸ĩ +ØŃت اج +à¹Ģ à¸Ħล +ã Ĩ +ÄĻ tr +Ġszcz eg +Ġר ש +à¸Ĺ à¸ĺ +Ġн ек +Ġнек оÑĤоÑĢ +в ÑĪ +Ð ¬ +à¹Īว ย +ล ุ +б ÑĢÑı +หม ูà¹Ī +à¹ģ à¸ķà¸ģ +ר׼ ×Ļ×Ŀ +Ġí ĸī +ã i +Ùĥر Ø© +â Ń +í IJ +ã į +á ģ +â ® +â ¥ +ì ® +à ¿ +â ¿ +á Ĥ +á ¤ +â ł +í Ł +ðIJ į +ðIJ ° +ðĿ Ĩ +ðŁ Ī +Ġ×¢ ׾ +Ġع ÙĨ +ĠÙħ ع +Ġ×ĸ ×Ķ +ĠÙħ ا +Ġm Ãł +Ġd ụ +á»ĩ c +а Ñħ +s ı +íķĺ ê³ł +Ġ×ķ ×ij +ĠÐŁ о +×ķת ר +ĠÙĦ Ùħ +Ġ×ķ ׾ +ãģĹãģ¦ ãģĦãĤĭ +Ġ×ŀ ×Ļ +Ġب ÙĬÙĨ +з а +ĠÙĥ اÙĨ +Ġ×Ķ ×Ļ×Ķ +ëħ Ħ +×IJ ×ķ +д и +ĠпеÑĢ Ðµ +d ı +Ġ׾ ש +Ġש ×ŀ +ãģĮ ãģĤãĤĭ +ãģĦ ãģĦ +ÑĢ Ðµ +×§ ×ķ +и ли +м е +ÙĬ ت +ãģ§ ãģĤãĤĭ +Ġв о +à¹ĥ หม +à¹ĥหม à¹Ī +Ġש ×ij +Ġ à¹Ĥà¸Ķย +ÙĬ Ùĩ +ãģ§ãģĻ ãģĮ +ãģ¨ ãģ¯ +ר ×ķ +Ġ à¸ĭึà¹Īà¸ĩ +ãģ§ãģį ãĤĭ +м о +à¹Ģà¸ŀ ืà¹Īà¸Ń +צ ×ķ +×ĺ ×ķ +ìķ Ī +Ġh á»į +à¹Ģà¸ĩ ิà¸Ļ +ĠاÙĦ ب +Ġ มี +ë¬ ¼ +Ñģ е +ëĵ¤ ìĿ´ +Ġë§ IJ +Ġl Ỽ +a ÅĤ +×Ĺ ×ijר +Ġd á»± +ÙĬ Ø« +Ġth á»ĭ +à¸ģà¹Ī à¸Ńà¸Ļ +Ġ×ij ׼׾ +ãģ ¸ +ã썿ĢĿ ãģĦãģ¾ãģĻ +ả nh +ย า +Ùģ Ø§ +ส ี +à¸ķ า +ë² ķ +ãĥª ãĥ¼ +รา à¸Ħา +Ġ×ķ ׾×IJ +ãģ¨ ãģĵãĤį +à¹Ģล ืà¸Ń +di ÄŁi +ÙĪ Ø§ÙĨ +Ġ׾×Ķ ×ª +รว ม +פ ×Ļ×Ŀ +à¸ľ ม +ж и +c ı +ÑĢ Ð¾Ð´ +Ġkar ÅŁÄ± +×Ĵ ×ķ +ãģ« ãģ¤ +ãģ«ãģ¤ ãģĦãģ¦ +r Ãł +×Ļ×ķת ר +ĠìĨ Į +×§ ×Ķ +ÑģÑĤв о +ãģij ãģ© +g é +à¸Ķ à¹īาà¸Ļ +çļĦ ãģ« +ĠÙĬ ÙħÙĥÙĨ +ìĨ į +ÙĬ Ùĥ +à¹Ħว à¹ī +Ñģки й +ì m +Ġ׾×IJ ×Ĺר +à¸Ńา หาร +Ġà¹Ģ à¸ŀ +รา ะ +ล ูà¸ģ +ÑģÑĤ а +Ġìľ ł +ÙĤ ÙĪÙĦ +б оÑĢ +Ñģк ого +หล ัà¸ĩ +à¸Ĥ à¹Īาว +à¹Ģม ืà¸Ńà¸ĩ +ê° ģ +t Ãł +ÙĬ ÙĬÙĨ +عر ض +ë° © +Ġëı Ļ +Ġà¹Ģ à¸Ľ +Ġà¹Ģà¸Ľ à¹ĩà¸Ļ +ç i +li ÄŁi +ìĹIJ ê²Į +ãĤ¿ ãĥ¼ +Ġ׾ ת +פ ×ķת +à¸Ĥ à¸Ń +ر س +ìł IJ +à¸ľ à¹Īาà¸Ļ +ÑĦ и +ج ÙĨ +ì¢ ħ +Ġ×Ķ ×¤ +Ġn go +á»ĭ a +Ġtá» ķ +Ġê·¸ 리 +à¹Ģม ืà¹Īà¸Ń +ذ Ùĥر +ìĸ ij +ìĹ Ń +×ĺ ׾ +k ı +Ġع ÙħÙĦ +Ġع ÙĨد +à¸ĭ ืà¹īà¸Ń +Ġê± ° +в е +r ü +à¹Ģ à¸Ńา +ส à¹Į +à¸Ī à¸Ļ +ס ת +Ġgi ả +ãĤĭ ãģ¨ +à¸ģำ ลัà¸ĩ +н ей +à¸Ī ริ +à¸Īริ à¸ĩ +Ġë į +Ġëį Ķ +à¸Ħà¹Ī ะ +ì n +Ġsü re +Ġqu y +à¸ļ าà¸ĩ +åıĸ ãĤĬ +ר ×Ĺ +×ij ת +ãģĮ ãģĤãĤĬãģ¾ãģĻ +ר ש +ìĹIJ ëĬĶ +Ġ×IJ פשר +ay ı +ãģĮ ãĤī +ØŃ ب +ан Ñģ +س ÙĪ +ĠпÑĢ Ðµ +د ÙĪ +ãģ« ãĤĪ +à¹Ģà¸ģ ม +สู à¸ĩ +m akt +makt ad +maktad ır +Ġön em +×Ļ×ŀ ×Ļ×Ŀ +б о +ÙĪ ÙĬØ© +รู à¸Ľ +à¹Ĥล à¸ģ +Ùħ ÙĬع +ÑģÑĤ Ñĥп +à¹Ĥ à¸Ń +دÙĬ ÙĨ +ì¤ ij +ãģĹãģ ı +à¹Ģส ีย +в Ñĭ +Ùħ ت +íĺ Ħ +ãĥIJ ãĥ¼ +ا Ø´ +×§ ס +Ġtá» ¥ +ล à¸Ķ +Ùģ Ø© +í ijľ +ر ج +k ÅĤad +ĠÅŁ ey +ĠØ£ Ùħ +Ġà¹Ģ ม +Ġب ÙĦ +Ñģ каÑı +ãģ¨ ãģ® +Ġìĭ ¤ +ấ m +ห à¹īà¸Ńà¸ĩ +à¸Ĭ ม +d ü +Ġç ek +Ġê³ ł +×Ĵ ×ij +à¸Ĭี วิ +à¸Ĭีวิ à¸ķ +Ù쨶 ÙĦ +ภ¯ +ç ı +Ġب Ø´ +ĠÙĩ ÙĨا +ãģį ãģ¾ãģĹãģŁ +t ü +Ġìĺ ģ +ĠTür k +к ÑĤ +פר ס +ãģ¨ãģĦãģĨ ãģĵãģ¨ +í ĶĦ +à¹ģร à¸ģ +ר ×ķף +Ġar as +×ŀצ ×IJ +Ġtá» ī +س ا +à¸ŀ à¸Ń +ĠاÙĦÙħ ØŃ +ãĥ ¤ +ĠاÙĦ است +Ùģ ÙĨ +×Ļ×ŀ ×Ķ +ر ت +ãģ¨ ãĤĤ +Ġна Ñģ +п ÑĢи +Ġ×Ĺ ×ķ +и ла +ÙĬ Ø´ +Ġgö z +Ġ×ij ׳×Ļ +ım ı +ĠÑĤ еÑħ +Ġh á»Ļ +غ ر +к он +اØŃ ت +Ġ à¸ŀ +à¸Ń à¸Ńà¸Ļ +à¸Ńà¸Ńà¸Ļ à¹Ħล +à¸Ńà¸Ńà¸Ļà¹Ħล à¸Ļà¹Į +Ñħ о +Ñı в +à¹ģ สà¸Ķ +à¹ģสà¸Ķ à¸ĩ +à¹Ģà¸ŀ ียà¸ĩ +ÑĤ ов +ا ÙĬ +Ġ×Ķ ×ĵ +Ġ×ķ ׼ +ãĤī ãģĦ +×ķפ ף +Ġë ¶Ī +ล à¸Ńà¸ĩ +Ø· اÙĦ +Ġн и +ĠÙħ ست +ế c +Ġש ׼ +ĠëķĮ 문 +วัà¸Ļ à¸Ĺีà¹Ī +×Ļ׾ ×ĵ +ØŃ ا +е ÑĨ +Ġc ứ +×ĵ ×ķר +ĠÙħ ØŃ +ר׼ ×ij +بÙĬ ع +ни и +ĠاÙĦØ£ ÙĪÙĦ +à¸Ħว ร +ã썿ĢĿ ãģĨ +ĠС о +ائ ÙĬØ© +ر اء +оÑģ об +Ġب Ø£ÙĨ +×¢ ×ķ×ĵ +ĠÑĤ е +ãģĵ ãģĨ +ÑģÑĤ ÑĢа +ай н +Ġsö z +ت ÙĨا +à¸Ń ิ +ặ p +ĠìķĦ ëĭĪ +íķ Ń +Ġר×IJ ש +Ġ à¹Ħà¸Ķà¹ī +Ġ×Ĵ ×ĵ +Ġס פר +обÑī е +ĠÙĪ Ø¥ +ada ÅŁ +ãģ¡ ãĤĩ +×§ ×ķ׾ +ÑĢ ÐµÐ· +ĠdÃ¼ÅŁ ün +Ġ×ij ×IJ×ŀ +Ġìĸ´ ëĸ +ער ×ij +н ее +ĠÑģÑĤÑĢ Ð°Ð½ +س اÙĨ +yn ı +ĠاÙĦر ئÙĬس +ãģĹãģ ª +Ġ׳ ת +ãģ«ãģª ãģ£ãģŁ +g ü +åıĹ ãģij +׾ ת +ìł Ī +ëĬĶ ëį° +Ø® ÙĬر +à¸ķà¹īà¸Ńà¸ĩ à¸ģาร +ĠÙĦ Ø£ÙĨ +Ġch á»ĭ +ÙĪ Ø© +à¹ĥ ส +ë¶Ģ íĦ° +íķĺ ë©´ +ữ u +à¹Ģหม ืà¸Ńà¸Ļ +б еÑĢ +ĠìĿ´ ìļ© +ĠÑģ еб +wiÄĻ ks +Ġ׳ ×¢ +ÑĤ ÑĥÑĢ +Ġngh Ä© +ש ×ķ×ĺ +ti ÄŁi +Ġde ÄŁi +×IJ ×ij +Ġ×ŀ ×ŀ +ãĥĹ ãĥŃ +wa ÅĤ +à¸Ī ึà¸ĩ +Ø® دÙħ +×IJ ×Ŀ +Ä±ÅŁ ı +cz Äħ +ר ×ĵ +ĠÑĢ Ñĥб +خر Ùī +ãģ® æĸ¹ +Ġд енÑĮ +×Ĺ ×Ļ×Ŀ +еÑĤ е +ëĤ ľ +×IJ ×Ĵ +×¢ ×ķר +ë³ Ħ +åIJĮ ãģĺ +ãĤ ² +ר ×ļ +×ķש ×IJ +ìľ ¡ +ا Ø® +צ ×Ļ×Ķ +á»± a +ãģĪ ãģ¦ +ש×Ķ ×ķ +ан ÑĤ +ลา à¸Ķ +ин г +ë¡ ł +اع د +ÙĪ Ø³Ø· +Ġв оп +Ġвоп 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ав +ưỠ¡ +ưỡ ng +ر اÙħ +×Ļ׳ ×Ļ×Ŀ +ãĥ© ãĥ¼ +ëĦ ¤ +Ġت ع +l ke +好 ãģį +æĮģ ãģ¡ +Ġë§ İ +Ġy ük +ĠÑģоÑģÑĤ ав +енÑĤ ÑĢ +pe ÅĤ +à¹Ģà¸Ľà¸¥ ีà¹Īย +à¹Ģà¸Ľà¸¥à¸µà¹Īย à¸Ļ +íı ī +ãĤĦ ãģĻ +×Ĺ ×ĸ +×ijר ×Ķ +ë£ ¨ +ìĶ Ģ +بØŃ Ø« +à¹Ģà¸ķ à¹ĩ +ów i +ب Ùĩ +ãģį ãģ¾ãģĻ +Ġ×¢ ×ŀ +×Ĵ ×ķ׾ +ез д +ÙĬÙģ Ø© +สà¸Ļ à¹ĥà¸Ī +Ġת ׾ +Ñı Ñī +Ġس ÙĨ +ĠÙĪØ§ ØŃد +ĠÑģ м +lad ı +ı ld +×Ļר ת +ีย à¸Ļ +ת×Ĺ ×ª +Ġж из +à¸ŀ ั +à¸ŀั à¸Ĵ +à¸ŀัà¸Ĵ à¸Ļา +à¸Ĭ ิ +ا Ø®ÙĦ +ãģ£ãģ¦ ãģĦãģŁ +รั à¸IJ +ãĤģ ãĤĭ +à¹Ĥ à¸ģ +ĠT á»ķ +Ġh akk +ر Ùģ +ìł Ģ +Ñģ об +ãģª ãģijãĤĮãģ° +Ùĩ ÙĪ +Ġë² ķ +ãĤ Ĩ +ĠاÙĦس عÙĪØ¯ +Ġ×IJ תר +Ø§Ø º +Ġ׾ ×ĵ +à¹ģ à¸ķ +à¹ģà¸ķ à¹Īà¸ĩ +íĮ Į +Ñĥп иÑĤÑĮ +à¸ŀืà¹īà¸Ļ à¸Ĺีà¹Ī +×ij ת×Ļ +à¹ĩ à¸ģ +ÅĤ at +Ġê°ľ ìĿ¸ +ìłķ ë³´ +ÑĤ ал +Ġgü ven +Ġİ l +Ġê° ģ +Ġب ت +×ŀ ×ķ׳×Ķ +ĠاÙĦØŃ ÙĥÙĪÙħ +ÙĤ ات +à¹ģ à¸ģà¹Ī +ห าà¸ģ +н ÑĮ +à¸Ľ รัà¸ļ +มา à¸ĵ +Ġне Ñģк +ĠØ ¶ +สม ั +สมั à¸Ħร +ãģĮ ãģĤãĤĬ +м еÑģÑĤ +Ġ×IJ צ׾ +Ġкомп ани +ס ר +ÙĬÙħ Ø© +ĠÑħ оÑĢо +ĠÑħоÑĢо ÑĪ +Ġ×Ļ ×ķ×ĵ +ü s +×Ĵ ×Ļש +à¸ļ à¸Ĺ +تÙĨ ظ +ว าà¸ĩ +ม หา +Ġ׼ ×ķ׾ +à¸Ĥ à¹īาà¸ĩ +ë° ľ +г од +д ан +ãģĭãĤĤãģĹãĤĮ ãģ¾ãģĽãĤĵ +ãģĵ ãģ¡ãĤī +ãĥIJ ãĤ¤ +ece ÄŁi +دÙĬ دة +ÙĨ Ùī +Ġëĭ¤ ìĿĮ +ว ี +غ ا +ли з +à¹Ģà¸Ķ ิ +à¹Ģà¸Ķิ ม +ĠÙĬ ست +Ġy ılı +ko ÅĦ +ãģ§ãģĹãĤĩãģĨ ãģĭ +ãģĤ ãģª +ãģĤãģª ãģŁ +ÑĨ ен +ĠÙĪ Ø² +×IJ ×Ļש +à¹Ī à¸Ń +ر ØŃ +ê´ ij +ÑĢа ÑģÑĤ +Ġ×Ķ ×ľ +ãģĹãģ¦ ãĤĤ +×ŀר ׼ +×ŀר׼ ×ĸ +éģķ ãģĦ +ãģŁ ãģı +ĠÑģ Ñĥд +в еÑģÑĤи +ĠíķĦ ìļĶ +ãĥķ ãĤ§ +ÑĤелÑĮ но +à¹Ģà¸ŀ ืà¹Īà¸Ńà¸Ļ +ÅĤu ż +à¹Ģà¸Ķิà¸Ļ à¸Ĺาà¸ĩ +ש ×ķר +Ġ×ŀ ×ĵ +×ķ×¢ ׾ +ÙĦ اÙħ +à¹Ħ à¸ĭ +л ей +кÑĥ ÑĢ +Ạ¢ +à¸Ĺ าà¸Ļ +ì§ ij +ĠгоÑĢ Ð¾Ð´ +ר ס +׾ ×ķ×Ĵ +mas ını +Ġл ÑĥÑĩ +ล à¹Īา +ìļ ¸ +ש ×ĺ +ĠÐĺ н +í Ĥ¤ +ÙĪÙĦ ا +ìķ ł +ĠØ£ÙĬ ضا +Ùĥ ار +ĠاÙĦت ع +ส ูà¹Ī +ãĤ ¼ +×ij ×Ļ×IJ +ย à¸ģ +ĠØŃ ÙĤ +ر بÙĬ +ãģĺãĤĥ ãģªãģĦ +รัà¸ģ ษา +Ñħод иÑĤ +à¸ķ à¸Ńà¸ļ +׳ ×ĺ×Ļ +ĠاÙĦÙħ ج +تÙħ ع +ов аÑĤÑĮ +ÙĦ ÙĬÙĨ +×Ļ×ŀ ×ķת +Ġm ù +n ÄĻ +Ġد ÙĬ +׼ ש×Ļ×ķ +Ġhi ç +ë ijIJ +ÙĪ Ø§Ø¡ +ÙĪ Ø· +ĠاÙĦ بÙĦ +à¹ģม à¹ī +×§ ×ķת +ÙĪØ¬ د +å§ĭ ãĤģ +ÙĬ ئة +Ġë§ ¤ +ص بØŃ +פ ×IJ +г оÑĢ +ס ×Ķ +بÙĬ ÙĤ +ย าà¸ģ +Ġн ад +ÙĬ Ùij +Ġب ÙĪ +ס ×ķר +Ùħ ÙĥاÙĨ +ר ×ij +×Ĵ ×ĸ +צ ת +b ilit +л аг +ĠN go +×IJ ×ķר +à¸ķ à¸Ļ +íĬ ¹ +à¸Ĺีà¹Ī à¸Ķี +à¸Ľà¸£à¸° 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á»ĩc +Ġn Äĥm +Ġth ì +Ġh á»įc +ĠÙĪ Øª +t é +Ġا ÙĨ +Ġt ôi +Ġ×IJ ׳×Ļ +Ġ׾ ×Ļ +Ġ×ŀ ×ķ +Ġng Ãły +Ġn Æ°á»Ľc +Ġ×Ķ ×Ļ×IJ +Ġ×IJ ×Ļ +Ġh Æ¡n +ĠÙĩ ذÙĩ +ĠÙĪ ÙĬ +ĠاÙĦ ذÙĬ +Ġ×ķ ×ŀ +Ġgi á +Ġnh ân +Ġch ÃŃnh +Ġm ình +ĠÐĿ а +Ġth ế +Ġ×Ļ ×ķתר +Ġ×IJ ×Ŀ +Ġn ên +Ġh ợ +Ġhợ p +Ġc òn +ĠÙĩ ÙĪ +Ġc Æ¡ +Ġr ất +ĠVi á»ĩt +Ġب عد +Ġש ×Ļ +Ġth á»Ŀi +Ġc ách +ĠÄij á»ĵng +Ġн о +Ġtr ưá»Ŀng +Ø Ł +ĠÄij á»ĭnh +ĠÄiji á»ģu +×Ļ ×Ļ×Ŀ +Ġth á»±c +n ın +Ġh ình +Ġn ói +Ġc ùng +Ġ×Ķ ×Ķ +ĠØ¥ ÙĨ +Ġ×IJ ×ij׾ +Ġnh ưng +Ġbi ết +Ġж е +Ġch úng +ĠÄij ang +Ġذ ÙĦÙĥ +Ġl ên +Ġkh ách +Ġn Ãło +Ġs á»Ń +Ġkh ác +Ġë° ı +Ġl ý +×Ļ ×Ļ +ĠÄij ây +Ġ׾ ×ŀ +Ġc ần +Ġtr ình +Ġph át +ãģ« ãĤĤ +п о +Ġn Äĥng +Ġb á»Ļ +Ġv ụ +ĠÄij á»Ļ +Ñĩ е +Ġnh áºŃn +Ġtr Æ°á»Ľc +Ġ×¢ ×ĵ +Ġh Ãłnh +ĠØ® ÙĦاÙĦ +Ġl ượng +Ġc ấp +Ġtá» ± +Ġv ì +Ġt ư +Ġch ất +Ġ׼ ×ŀ×ķ +Ġg ì +Ġש ׳ +Ġt ế +ת ×ķ +Ġnghi á»ĩp +Ġm ặt +ĠÙĥ Ùħا +Ġ×ij ×Ļף +Ġר ×§ +Ġth ấy +Ġmá y +ĠÙģ Ùī +Ġd ân +Ġ×IJ ×Ĺ×ĵ +Ġt âm +Ġ׼ ×ļ +Ġ׾ ×ķ +в о +Ġt ác +Ġto Ãłn +ĠÙĪ Ùħ +Ġk ết +Ġ หรืà¸Ń +ĠÙĪØ§ÙĦ Ùħ +ĠÄiji á»ĥm +Ġ×ĸ ×ķ +Ġ×ij ×ķ +׼ ×ķת +Ġh á»Ļi +Ġb ằng +ت Ùĩا +Ġ׼ ×ĵ×Ļ +Ġ×Ķ ×Ŀ +Ġxu ất +ĠÙĤ د +Ġb ảo +Ġt á»ijt +Ġt ình +ĠÙĩ ÙĬ +ĠÄij á»iji +Ġthi ết +Ġhi á»ĩu +Ġti ếp +Ġt ạo +ת ×Ķ +Ġch á»§ +o ÅĽÄĩ +Ġgi ú +Ġgiú p +Ġà ½ +Ġqu ả +Ġlo ại +Ġc ô +Ġà ´ +Ġô ng +Ġ×Ķ ×ķ +ĠاÙĦÙĬ ÙĪÙħ +ĠtÃŃ nh +г а +Ġph òng +Ġ Äĥn +Ġع اÙħ +Ġv á»ĭ +lar ını +r ÃŃa +Ġt Ỽi +ĠÄij ưá»Ŀng +Ġgi Ỽi +Ġb ản +Ġc ầu +Ġnhi ên +Ġb á»ĩnh +Ġth ưá»Ŀng +Ġ×IJ ×Ļף +ĠÄij á»ģ +Ġh á»ĩ +Ġ×Ļש ר×IJ׾ +Ġqu á +ĠÐĹ Ð° +ãģ® ãģ§ãģĻãģĮ +ĠÐŁ ÑĢи +Ġph ần +ĠÙĪ ÙĦا +ĠlỼ n +Ġtr á»ĭ +Ġcả m +Ġм о +Ġd ùng +ĠاÙĦ Ùī +ĠعÙĦÙĬ Ùĩ +ĠìŀĪ ìĬµëĭĪëĭ¤ +ÙĬ ÙĤ +ĠÙĤ بÙĦ +Ġho ặc +ĠØŃ ÙĬØ« +Ġ à¸Ĺีà¹Ī +Ġغ ÙĬر +ĠÄij ại +Ġsá»ij ng +нÑĭ ми +Ġth ức +Ġפ ×Ļ +ĠÄiji á»ĩn +ãģª ãģĭãģ£ãģŁ +Ġgi ải +Ġv ẫn +Ġи Ñħ +Ġö nce +Ġv áºŃy +Ġmu á»ijn +Ġ ảnh +à¹ĥà¸Ļ à¸ģาร +ĠQu á»ijc +Ġk ế +׳ ×IJ +Ġס ×Ļ +Ġy êu +ãģ® ãģĭ +ĠÄij ẹ +ĠÄijẹ p +Ġch ức +Ġy ıl +ĠTür kiye +d é +ĠÙĤ اÙĦ +Ġd á»ĭch +ĠolduÄŁ u +Ġch á»įn +Ġت Ùħ +หà¸Ļ ึà¹Īà¸ĩ +ãģķãĤĮ ãģŁ +Ġph áp +ìĽ Ķ +Ġti á»ģn +ãģĹ ãģ¾ãģĹãģŁ +Ġש ׾×IJ +ÙĦ Ø© +Ġ׾פ ׳×Ļ +Ġ×ij ×Ļת +ĠH Ãł +ĠØŃ ت +ĠØŃت Ùī +Ġ×¢ ×ķ×ĵ +Ġn ó +Ġth áng +à¹Ģลืà¸Ń à¸ģ +ר ×Ķ +Ġt Äĥng +Ġcá i +Ġtri á»ĥn +Ġ×IJ×ķת ×ķ +ìłģ ìĿ¸ +ĠC ông +Ġ׾×Ķ ×Ļ×ķת +Ġг ода +и Ñİ +Ġب عض +Ġ à¸ģาร +èī¯ ãģĦ +ÙĪ Øª +Ġli ên +ĠÐĿ о +ĠÐĿ е +çļĦ ãģª +ĠÙħ ت +ĠÑĤак же +ĠкоÑĤоÑĢ Ñĭе +Ġ×Ļ ×ĵ×Ļ +Ġtr á»įng +ãĤµ ãĤ¤ãĥĪ +ìłģ ìľ¼ë¡ľ +Ġt áºŃp +Ġש ׾×Ļ +íķĺ ê²Į +Ġt Ãłi +ĠÐ ¯ +Ġr á»ĵi +ا Ùĥ +Ġth ương +Ġ×Ķ ×ĸ×Ķ +ĠÙĪ ÙħÙĨ +à¸Ĺีà¹Ī มี +Ġcu á»Ļc +Ġbü yük +ãģ¨ ãģĭ +Ġ×ij ×Ļ×ķתר +Ġl ần +Ġgö re +Ġtr ợ +Ġ×ĺ ×ķ×ij +ÑĤÑĮ ÑģÑı +Ġth á»ijng +Ġ׼ ש +Ġti êu +Ġ×ŀ×IJ ×ķ×ĵ +Ø Ľ +k Äħ +Ġ à¹ĥà¸Ļ +Ġv ấn +Ġש ׾×ķ +ĠÄij á»ģu +Ùģ Øª +Ġê²ĥ ìĿ´ +Ġh óa +ĠاÙĦع اÙħ +ĠÙĬ ÙĪÙħ +к ой +Ġbi á»ĩt +ÑģÑĤ о +Ġ×Ķ ×Ļ×ķ +à¸Ĺีà¹Ī à¸Īะ +Ġ×ĵ ×Ļ +Ġ×IJ ×ļ +Ġá n +ص ÙĪØ± +Ġtr ÃŃ +ĠÐŁÑĢ Ð¾ +Ġl á»±c +ãģĹãģ¦ ãģĦãģ¾ãģĻ +Ġb Ãłi +Ġ×ĸ ×IJת +Ġb áo +à¸ļ à¸Ļ +ĠëĮĢ íķľ +Ġti ế +Ġtiế ng +Ġb ên +ãģķãĤĮ ãĤĭ +s ión +Ġt ìm +×¢ ×ķ +m é +ни Ñı +ãģ» ãģ© +Ġà¹Ģà¸ŀ ราะ +ب Ø© +Ġë¶ Ħ +Ġ×IJ ×ĸ +à¸Ĺ à¹Īาà¸Ļ +ת ×Ŀ +Ġth êm +Ġho ạt +y ı +×ĸ ×ķ +Ġgi á»Ŀ +Ġb án +à¸Ĥ าย +Ñĩ а +Ġ à¹Ĩ +ĠاÙĦÙħ ت +ĠоÑĩ енÑĮ +Ġb ất +Ġtr ẻ +ÑĤ ÑĢ +ĠØ£ ÙĨÙĩ +ĠØ« Ùħ +Ġ׼ ×ŀ×Ķ +Ġkh ó +Ġr ằng +ĠÙĪ ÙģÙĬ +ни й +Ġho Ãłn +t ó +Ġ×IJ שר +ĠìĥĿ ê°ģ +Ñģ а +Ġ׼ ×ijר +ĠÑįÑĤ ом +lar ının +Ġch ưa +з и +Ġd ẫn +ĠÐļ ак +ج ÙĪ +ĠбÑĭ ло +ĠÙĬ ت +n ı +ÅĤ am +ĠÙĪÙĩ ÙĪ +×ij ×ķ +п и +ר ת +Ġqu á»ijc +ж д +ĠÄij Æ¡n +Ùĥت ب +Ġm ắt +ระ à¸ļ +ระà¸ļ à¸ļ +ĠÙĥ اÙĨت +Ġth ân +สิà¸Ļ à¸Ħà¹īา +×Ĵ ×Ļ +Ġph ương +à¹Ħมà¹Ī à¹Ħà¸Ķà¹ī +ĠìĦ ± +ĠC ác +Ġ×Ķ×ŀ ×ķ +ĠÑĤ ем +Ġ×ĵ ×ķ +à¸Ńะ à¹Ħร +Ġv Äĥn +ãģª ãģ®ãģ§ +ĠN á»Ļi +Ġ×¢ ×ķ +ãĤīãĤĮ ãĤĭ +Ġs áng +Ġgö ster +ãģĵãģ¨ ãĤĴ +Ġtaraf ından +Ġм а +ĠпоÑģл е +Ġ׳ ×Ļת +Ġ׳×Ļת ף +Ġл еÑĤ +Ġ׾ ׳×ķ +Ñģ Ñģ +Ġ×Ļ ×ķ +п е +ĠÙĪ ÙĦÙĥ +ĠÙĪÙĦÙĥ ÙĨ +Ġngo Ãłi +ĠÄij á»ĭa +r zÄħd +dz iaÅĤ +ĠÙħ ر +иÑĤÑĮ ÑģÑı +Ġ×IJ×Ĺר ×Ļ +Ġ׾ ׼׾ +à¸Ĥ à¹īà¸Ńม +à¸Ĥà¹īà¸Ńม ูล +Ġб ол +Ġбол ее +جÙħ ع +л еÑĤ +Ġl á»ĭch +ĠÙħ Ø«ÙĦ +Ġ그리 ê³ł +Ġth ứ +ĠdeÄŁ il +ÙĪ ØŃ +Ġש׾ ×ļ +ĠÙħ ØŃÙħد +Ġn ếu +ĠÄij á»ķi +Ġv ừa +Ġm á»įi +Ġо ни +Ġl úc +ĠÙĬ ÙĥÙĪÙĨ +ì§ Ī +Ġש׾ ׳×ķ +ĠÐĶ Ð¾ +Ġש ׳×Ļ +ล ิ +×IJ פשר +Ġs ức +ê¶ Į +Ġ ứng +à¹Ħมà¹Ī มี +Ø·ÙĦ ب +ĠÑĩ ем +Ġch uyên +Ġth ÃŃch +Ġ×ķ ×Ļ +íķ © +ĠÙħ صر +д о +ĠÄij ất +Ġch ế +à¸Ĭ ืà¹Īà¸Ń +Ġìĭ ł +ĠØ¥ ذا +Ġر ئÙĬس +Ġש ×Ļש +Ġgiả m +Ñģ ка +lar ında +Ġs ợ +ĠtÃŃ ch +ĠÙĦ ÙĥÙĨ +Ġب Ùħ +×¢ ×ķ×ij +×¢×ķ×ij ×ĵ +ÅĤÄħ cz +ları na +Ġש ×Ŀ +ĠÙĦ ت +Ġש×Ķ ×ķ×IJ +t ów +Ġëĭ¤ 른 +ĠØ£ Ùĥثر +ãģ® ãģ§ãģĻ +׼ ×Ļ×Ŀ +ĠolduÄŁ unu +ãģĭ ãģª +ãĤĤ ãģĨ +ÙĬ ØŃ +Ġnh ìn +Ġngh á»ĩ +ãģ«ãģª ãģ£ãģ¦ +п а +Ġquy ết +ÙĦ ÙĤ +t á +Ġlu ôn +ĠÄij ặc +Ġ×IJ ר +Ġtu á»ķi +s ão +ìĻ ¸ +ر د +ĠبÙĩ ا +Ġ×Ķ×Ļ ×ķ×Ŀ +×ķ ×ķ×Ļ +ãģ§ãģĻ ãģŃ +ĠÑĤ ого +Ġth á»§ +ãģĹãģŁ ãģĦ +ر ÙĤ +Ġb ắt +г Ñĥ +Ġtá» Ń +ÑĪ Ð° +Ġ à¸Ľà¸µ +Ġ×Ķ×IJ ×Ŀ +íı ¬ +ż a +Ġ×IJת ×Ķ +Ġn á»Ļi +Ġph ÃŃ +ĠÅŁek ilde +Ġl á»Ŀi +d ıģı +Ġ׼×IJ ף +Ġt üm +Ġm ạnh +ĠM ỹ +ãģĿ ãĤĵãģª +Ġnh á»ı +ãģª ãģĮãĤī +Ġb ình +ı p +à¸ŀ า +ĠÄij ánh +ĠÙĪ ÙĦ +ר ×ķת +Ġ×IJ ×Ļ×ļ +Ġch uyá»ĥn +Ùĥ ا +ãĤĮ ãĤĭ +à¹ģม à¹Ī +ãĤĪ ãģı +ĠÙĪ ÙĤد +íĸ Īëĭ¤ +Ġn Æ¡i +ãģ«ãĤĪ ãģ£ãģ¦ +Ġvi ết +Ġà¹Ģà¸ŀ ืà¹Īà¸Ń +ëIJĺ ëĬĶ +اد ÙĬ +ĠÙģ Ø¥ÙĨ +ì¦ Ŀ +ĠÄij ặt +Ġh Æ°á»Ľng +Ġx ã +Ġönem li +ãģł ãģ¨ +Ġm ẹ +Ġ×ij ×Ļ +Ġ×ĵ ×ijר +Ġv áºŃt +ĠÄij ạo +Ġdá»± ng +ĠÑĤ ом +ĠÙģÙĬ Ùĩا +Ġج ÙħÙĬع +Ġthu áºŃt +st ÄĻp +Ġti ết +Ø´ ÙĬ +Ġе Ñīе +ãģĻãĤĭ ãģ¨ +ĠmÃł u +ĠÑįÑĤ ого +Ġv ô +ĠÐŃ ÑĤо +Ġth áºŃt +Ġn ữa +Ġbi ến +Ġn ữ +Ġ׾ ׼×Ŀ +×Ļ ×Ļף +Ġس ت +ĠÐŀ ÑĤ +Ġph ụ +ê¹Į ì§Ģ +Ġ׾ ×ļ +Ġk ỳ +à¹ĥ à¸Ħร +Ġg ây +ĠÙĦ ÙĦÙħ +Ġtụ c +ت ÙĬÙĨ +Ġtr ợ +Ġ׾ פ×Ļ +Ġb á»ij +ĠÐļ а +ĠÄij ình +ow Äħ +s ında +Ġkhi ến +s ız +Ġк огда +ס ׾ +ĠбÑĭ л +à¸Ļ à¹īà¸Ńย +обÑĢаР· +Ġê²ĥ ìĿ´ëĭ¤ +ëĵ¤ ìĿĢ +ãģ¸ ãģ® +Ġà¹Ģม ืà¹Īà¸Ń +Ġph ục +Ġ׊׾ק +Ġh ết +ĠÄij a +à¹Ģà¸Ķà¹ĩ à¸ģ +íĺ ķ +l ÃŃ +ê¸ ī +Ġع دد +ĠÄij á»ĵ +Ġg ần +Ġ×Ļ ×ķ×Ŀ +Ġs Ä© +ÑĢ Ñıд +Ġquy á»ģn +Ġ×IJ ׾×IJ +Ùĩ Ùħا +׳ ×Ļ×Ķ +׾ ×ķת +Ġ×Ķר ×ij×Ķ +Ġti ên +Ġal ın +Ġd á»ħ +人 ãģĮ +но Ñģ +л ÑģÑı +ĠÄij ưa +ส าว +иÑĢов ан +Ġ×ŀס פר +×Ĵ ף +Ġki ến +ĠÐ ¨ +p é +б Ñĥ +ов ой +б а +ĠØ¥ ÙĦا +×IJ ׾×Ļ +Ġx ây +Ġb ợi +Ġש ×ķ +人 ãģ® +×§ ×Ļ×Ŀ +à¹Ģà¸Ķ ืà¸Ńà¸Ļ +Ġkh á +Ġ×ķ ׾×Ķ +×ĵ ×ķת +Ġ×¢ ×ij×ķר +Ġبش ÙĥÙĦ +ĠÙĩÙĨا Ùĥ +ÑĤ ÑĢа +Ġ íķĺëĬĶ +ร à¸Ńà¸ļ +owa ÅĤ +h é +Ġdi á»ħn +Ġ×Ķ ×Ľ×ľ +ĠØ£ س +Ġch uyá»ĩn +ระ à¸Ķัà¸ļ +ĠNh ững +Ġ×IJ ×Ĺת +ĠØŃ ÙĪÙĦ +л ов +׳ ר +Ġ×ķ ׳ +Ġch Æ¡i +Ġiç inde +ÑģÑĤв Ñĥ +Ġph á»ij +ĠÑģ Ñĥ +ç§ģ ãģ¯ +Ġch ứng +Ġv á»±c +à¹ģ à¸Ń +Ġl áºŃp +Ġtừ ng +å°ij ãģĹ +ĠNg uy +ĠNguy á»ħn +ĠÙģÙĬ Ùĩ +Ġб а +×Ļ ×Ļת +Ġ×ľ×¢ ש×ķת +Ġ×ŀ ׼ +Ġnghi á»ĩm +Ġм ного +Ġе е +ëIJĺ ìĸ´ +Ġl ợi +Ġ׾ ׾×IJ +Ġ׼ ף +Ġch ÃŃ +ãģ§ ãģ® +×Ĺ ×ķ +ש ×ķ×Ŀ +Ġ×ŀ ר +ĠÐĶ Ð»Ñı +Å ģ +Ġ׼×IJ שר +ĠM á»Ļt +ĠÙĪØ§ÙĦ ت +ĠìĿ´ 룰 +ÅŁ a +Ġchi ến +Ġaras ında +Ġ×ij ×IJתר +ãģķãĤĮ ãģ¦ãģĦãĤĭ +Ø´ ÙĥÙĦ +Ġt ượng +Ġت ت +ĠC ó +Ġb á»ı +Ġtá»ī nh +Ġkh ÃŃ +ĠпÑĢ Ð¾ÑģÑĤ +ĠпÑĢоÑģÑĤ о +ĠÙĪ ÙĤاÙĦ +Ġgi áo +ĠN ếu +×IJ ×ŀר +×¢×ł×Ļ ×Ļף +íİ ¸ +Ùĩد Ùģ +ĠB á»Ļ +Ġb Ãłn +Ġng uyên +Ġgü zel +ส าย +ì² ľ +×ŀ ×ķר +Ġph ân +ס פק +×§ ×ij׾ +ĠاÙĦÙħ تØŃ +ĠاÙĦÙħتØŃ دة +ائ د +Ġ×IJ ×ŀר +Ġki ÅŁi +ì¤ Ģ +Ġtr uyá»ģn +ĠÙĦ Ùĩا +ĠÐľ а +à¸ļริ ษ +à¸ļริษ ั +à¸ļริษั à¸Ĺ +Ġש ׳×Ļ×Ŀ +Ġмен Ñı +ÅŁ e +Ġdi á»ĩn +Ġ×IJ׳ ×Ĺ׳×ķ +k ü +Ġc á»ķ +Ġm á»Ĺi +w ä +Ùħ ÙĬ +Ġhi á»ĥu +ëĭ ¬ +Ġ×Ķ ×Ĺ׾ +Ġt ên +Ġki á»ĩn +ÙĨ ÙĤÙĦ +Ġv á»ĩ +×ĵ ת +ĠÐłÐ¾ÑģÑģ ии +л Ñĥ +ĠاÙĦع ربÙĬØ© +ĠØ· رÙĬÙĤ +Ġ×Ķ×ij ×Ļת +Ñģ еÑĢ +Ġм не +ä u +Ġtri á»ĩu +ĠÄij á»§ +Ġר ×ij +ت ÙĩÙħ +à¸ĭ ี +Ġì§Ģ ê¸Ī +li ÅĽmy +د عÙħ +ãģł ãĤįãģĨ +Ñģки е +Ġh á»ıi +Ġ×§ ×ķ +ÑĢÑĥ Ñģ +ÙĨ ظر +ãģ® ãĤĤ +Ġ×Ķ ×Ľ×Ļ +ĠìĽ IJ +ÙĪ Ùĩ +ĠÙĪ Ùİ +ĠB ạn +п лаÑĤ +Ġ×ŀ ×ŀש +лÑİ Ð± +ĠнÑĥж но +Ġth ư +ãģ µ +ãģı ãĤīãģĦ +ر Ø´ +ר ×ķ×Ĺ +ĠÙĬ تÙħ +Ġצר ×Ļ×ļ +Ġph á +ม à¸Ńà¸ĩ +Ġ×ij×IJ ×ķפף +Ġcả nh +Ġíķľ ëĭ¤ +Ġ×Ķ×ŀ ת +à¸ķà¹Īาà¸ĩ à¹Ĩ +มี à¸ģาร +Ñģки Ñħ +ĠÐĴ Ñģе +Ġا ÙĪ +ج ÙĬ +ãģĵãģ¨ ãģ¯ +Ġd Ãłi +Ġh á»ĵ +èĩªåĪĨ ãģ® +à¹Ħ หà¸Ļ +ëĵ¤ ìĿĦ +ĠV Äĥn +Ġд аж +Ġдаж е +Ñĭ ми +лаÑģ ÑĮ +ÙĬ ÙĪÙĨ +ÙĨ ÙĪ +c ó +ãģĹãģ¦ ãģĦãģŁ +ãģł ãģĭãĤī +طاÙĦ ب +Ġc á»Ńa +п ÑĢоÑģ +ãģªãģ© ãģ® +รุ à¹Īà¸Ļ +Ġchi ếc +л Ñĭ +ĠÑıвлÑı еÑĤÑģÑı +Ġn á»ķi +ãģ® ãģĬ +Ġ×IJת ×Ŀ +ĠëķĮ문 ìĹIJ +à¸ģล าà¸ĩ +ĠbaÅŁ ka +ìĦ Ŀ +ĠÑĨ ел +Ùģ ÙĤ +ãģ«ãĤĪ ãĤĭ +ÙĤ ا +Ġçı kar +Ġcứ u +Ø· ا +Ġש ת +à¹Ĥ à¸Ħ +Ġ×ŀ ׾ +Ġ×Ķ ×¤×¨ +Ġг де +ĠØ® Ø· +åīį ãģ« +c jÄĻ +Ġ׊ש×ķ×ij +ר×Ĵ ×¢ +Ġkho ảng +ĠÄij á»Ŀi +ĠÐł е +Ġо на +Ġ×IJ ׳×ķ +ãģ® ãģ« +ĠاÙĦذ ÙĬÙĨ +кÑĥ п +ãĤµ ãĥ¼ãĥ +ãĤµãĥ¼ãĥ ĵ +ãĤµãĥ¼ãĥĵ ãĤ¹ +в ал +г е +Ġgi ữa +ĠKh ông +ĠâĹ ĭ +à¸ģล ุà¹Īม +ĠÙħÙĨ ذ +à¸Ń à¹Īาà¸Ļ +ĠÑģп оÑģоб +ĠÄij á»Ļi +Ġdi ÄŁer +Ġ à¸ĸà¹īา +Ùħ Ø«ÙĦ +Ġ×Ķ×IJ ×Ļ +Ġد ÙĪÙĨ +ÙĬر اÙĨ +Ñī и +بÙĨ اء +ĠØ¢ خر +ظ Ùĩر +Ġ×ij ׼ +ĠاÙĦÙħ ع +ãĥ Ĵ +Ġt ất +Ġm ục +ĠdoÄŁ ru +ãģŁ ãĤī +Ġס ×ķ +Ġx ác +ร à¸Ń +ĠcÄĥ n +Ġон л +Ġонл айн +Ġk 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دÙĬ +ÙģÙĬدÙĬ ÙĪ +ĠмеÑģÑĤ о +Ġph út +มาà¸ģ à¸ģวà¹Īา +×IJ פ +ب ÙIJ +ĠPh ú +ì± Ħ +ĠÙĪ Ø³ÙĦÙħ +à¸Īี à¸Ļ +поÑĤ ÑĢеб +Ġ×Ĺ×ĵ ש×ķת +Ø´ ÙĪ +Ġעצ ×ŀ×ķ +ĠعÙħÙĦ ÙĬØ© +à¸Ħุà¸ĵ à¸łà¸²à¸ŀ +ãģ¾ãģĻ ãģĮ +دع ÙĪ +طر ÙĤ +à¹Ħมà¹Ī à¸ķà¹īà¸Ńà¸ĩ +ë² Ķ +ìĬ ¹ +Ġk ÃŃch +ĠìĹĨ ëĬĶ +ĠÑĤ ам +ĠÙĨ ØŃÙĪ +ĠاÙĦÙĤ اÙĨÙĪÙĨ +×Ĺ ×ķ×Ŀ +Ġk ız +Ġ×ĵ ×Ļף +ĠвÑĢем ени +ãģ£ãģŁ ãĤĬ +ĠØ´ Ùĩر +ĠìĦľ ë¹ĦìĬ¤ +×¢ ש×Ķ +Ġgi ác +ĠاÙĦسÙĦ اÙħ +Ġ×IJ ש +ĠполÑĥÑĩ а +à¸Īัà¸Ķ à¸ģาร +к оÑĢ +Ġ×Ķ×ĺ ×ķ×ij +ราย à¸ģาร +주 ìĿĺ +à¹ģà¸ķà¹Ī ละ +Ġê·¸ëŁ° ëį° +à¸Ĺีà¹Ī à¹Ģà¸Ľà¹ĩà¸Ļ +Ġת ×ķ×ļ +بÙĬ اÙĨ +Ð Ļ +oÅĽci Äħ +ÑĤ ок +ĠÃ Ķ +ĠÃĶ ng +à¹Ħมà¹Ī à¹ĥà¸Ĭà¹Ī +ãģ¿ ãģ¦ +ÐŁ о +ĠЧ ÑĤо +íĻ © +×ĺ ×ij×¢ +меÑĤ ÑĢ +Ġ×ij ×ŀ×Ķ +Ġ×ij×ŀ×Ķ ×ľ +Ġ×ij×ŀ×Ķ׾ ×ļ +Ñĩ ÑĮ +×§ ש×Ķ +з нак +знак ом +uj ÄĻ +×Ļצ ר +ĠاÙĦÙħ ÙĦÙĥ +ı yla +×IJ×ŀ ת +à¸Ľ ิà¸Ķ +×IJ ×Ĺ×ĵ +ر اد +Ġm áºŃt +ëĭ¤ ëĬĶ +Ġl ạnh +ש׾ ×ķש +ØŃ دÙĬØ« +ت ز +å¹´ ãģ® +Ġк ваÑĢ +ĠкваÑĢ ÑĤиÑĢ +ä½ľ ãĤĬ +رÙĪ Ø¨ +ов ан +ĠТ е +à¸Īำ à¸ģ +à¸Īำà¸ģ ัà¸Ķ +ب اط +×Ĵ ת +Ġм аÑĪ +ĠмаÑĪ Ð¸Ð½ +×Ļצ ×Ķ +ãģ» ãģ¨ +ãģ»ãģ¨ ãĤĵãģ© +ÃŃ do +ĠÑı зÑĭк +à¸ļ ิà¸Ļ 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×Ļת +ت Ùİ +ÙĪ Ø¨Ø± +й ÑĤи +ĠÃ¶ÄŁ ren +Ġ×Ķ×ĸ ×ķ +Ġv á»įng +ÙĤÙĪ Ø© +ĠT ây +ĠÐĿ и +Ġש ×ķ×ij +ãģ¨è¨Ģ ãĤıãĤĮ +ãģ© ãĤĵãģª +׊צ×Ļ +ï½ ľ +Ġ×ķ×Ķ ×ķ×IJ +ä¸Ģ ãģ¤ +ĠÑģÑĤо иÑĤ +ni Äħ +×ĺר ×Ļ +ĠдеÑĤ ей +нÑı ÑĤÑĮ +ĠÑģдел аÑĤÑĮ +Ġë§İ ìĿ´ +ä½ķ ãģĭ +ãģĽ ãĤĭ +à¹Ħ หม +à¸ķิà¸Ķ à¸ķà¹Īà¸Ń +Ġ×ij ת×Ĺ +Ġ×ijת×Ĺ ×ķ×Ŀ +ìĻ Ħ +ì§Ģ ëĬĶ +ÑģÑĤ аÑĤ +ÑıÑģ н +ü b +Ġth ả +Ġ×ij×IJ×ŀ ת +Ġt uyến +×ĵ ×Ļר×Ķ +Ġ×IJ ×Ļש×Ļ +×ĸ׼ ר +ãģ° ãģĭãĤĬ +Ġx ét +׼ ×Ļ×ķ +׼×Ļ×ķ ×ķף +diÄŁ ini +ĠاÙĦÙħ ÙĪØ¶ÙĪØ¹ +Ġh áºŃu +à¸Īาà¸ģ à¸ģาร +×ijס ×Ļס +Ġ×ŀ×Ĵ ×Ļ×¢ +×ij ×Ļ×¢ +ĠÙĪ Ø¬Ùĩ +à¹ģà¸Ķ à¸ĩ +à¸Ļ าà¸ĩ +ĠÅŀ a +ì ¡´ +ë¡ Ģ +à¸ķ ะ +Ġ×Ķ×Ĺ×Ļ ×Ļ×Ŀ +Ùģ ÙĬد +ãģ§ãģĻ ãģĭãĤī +ê· ľ +ź ni +ĠлÑİ Ð´ÐµÐ¹ +Ġyüz de +ıy orum +ĠاÙĦ بØŃر +e ño +п аÑĢ +ÙĬ ÙĤØ© +об ÑĢ +ר ×ķ×ļ +ت ÙĪÙĤع +ĠاÙĦØ´ ÙĬØ® +åĪĿ ãĤģãģ¦ +ĠÑĤ елеÑĦ +ĠÑĤелеÑĦ он +Ġth ôi +Ġ×Ļ׼×ķ׾ ×Ļ×Ŀ +ĠÅŁ irk +ĠÅŁirk et +Ġìļ°ë¦¬ ê°Ģ +ĠÄij ông +Ġת ×ķ×ĵ×Ķ +ÑģмоÑĤÑĢ ÐµÑĤÑĮ +ĠÙĦ ÙĩÙħ +Ġ׾ ׼ +ĠN ó +ĠØŃ اÙĦØ© +ãģĦ ãģij +קר ×ķ +az ı +ãĤ³ ãĥ¼ +ĠÙĦÙĦ ت +s ınız +ĠH ải +기 ìĪł +ยัà¸ĩ à¹Ħมà¹Ī +ëĭ¤ ê³ł +פ ×Ĺ +Ġ׾×Ĵ ×ij×Ļ +Ġع ÙĨÙĩ +Ġк аз 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Ń +íĿ ´ +íŀ ľ +ï¤ ī +ï¤ Ń +ï¤ ² +ï¤ µ +ï¤ ¼ +ï¥ Ģ +ï¥ ij +ï¥ Ĵ +ï¥ ķ +ï¥ ĺ +ï¥ Ļ +ï¥ « +ï¥ ¬ +ï¥ ° +ï ¥¿ +ï¦ ĭ +ï¦ ı +ï¦ Ķ +ï¦ ĸ +ï¦ ĺ +ï¦ Ľ +ï¦ ł +ï¦ ® +ï¦ ¯ +ï¦ º +ï¦ » +ï¦ ¾ +ï§ Ĩ +ï§ ĸ +ï§ Ľ +ï§ ŀ +ï§ Ł +ï§ § +ï§ ³ +ï§ º +ï§ ½ +ï¨ ĥ +ï¨ ļ +ï¨ ¢ +ï© Ł +ï¬ ¤ +ï¬ ¬ +ï¬ ¼ +ïŃ Ĵ +ïŃ ķ +ïŃ Ľ +ïŃ Ŀ +ïŃ ŀ +ïŃ Ł +ïŃ ¤ +ïŃ § +ïŃ ¨ +ïŃ ® +ïŃ ° +ïŃ ± +ïŃ · +ïŃ ¹ +ïŃ » +ï® Ģ +ï® ĥ +ï® Ħ +ï® ħ +ï® į +ï® Ĵ +ï® ĵ +ï® ķ +ï® ¦ +ï® ® +ï® ° +ï¯ ĵ +ï¯ ľ +ï¯ © +ï¯ ª +ï¯ ¬ +ï¯ Ń +ï¯ ® +ï¯ · +ï¯ ¹ +ï¯ » +ï¯ ¼ +ï° ĥ +ï° Į +ï° IJ +ï° ĺ +ï° Ļ +ï° ľ +ï° ŀ +ï° ¢ +ï° ® +ï° ° +ï° ¼ +ï° ¿ +ï± Ģ +ï± ģ +ï± Ī +ï± ĭ +ï± ı +ï± Ń +ï² Ģ +ï² ĩ +ï² Ī +ï² ĭ +ï² İ +ï² Ĵ +ï² ľ +ï² ł +ï² ¬ +ï² » +ï³ ĩ +ï³ Ķ +ï³ £ +ï³ « +ï´ ĺ +ï´ ° +ï´ ½ +ï ¶ +ï¶ ° +ï¸ ĸ +ï¸ ´ +ï¸ ¹ +ï¹ į +ï¹ Ĺ +ï¹ ¢ +ï¹ ¤ +ï¹ © +ï¹ ± +ï¾ ° +ï¿ Ĥ +ï¿ ® +ðIJĮ ° +ðIJĮ ¹ +ðIJĮ º +ðIJĮ ½ +ðIJį Ĥ +ðIJį ĥ +ðIJį Ħ +ðIJ İ +ðIJİ ¹ +ðIJ¤ Ĥ +ðIJ¤ į +ðIJ¤ ı +ðIJ¤ ĵ +ðIJŃ ī +ðIJŃ į +ðIJ° ĩ +ðIJ° ° +ðij Ĥ +ðijĤ Ħ +ðij ĺ +ðijĺ ģ +ðĴ Ģ +ðĴĢ ¸ +ðĴ ģ +ðĴģ º +ðĴ Ħ +ðĴĦ · +ðĴ Ĭ +ðĴĬ ij +ðĴ ĭ +ðĴĭ Ĺ +ð ĴĮ +ðĴĮ ¨ +ðĵĥ ¢ +ðĵĥ ° +ðĸ ł +ðĸł ļ +ðĿĦ ĥ +ðĿĦ ħ +ðĿĦ ķ +ðĿĦ Ļ +ðĿĦ ± +ðĿĦ ´ +ðĿĦ ¹ +ðĿħ İ +ðĿħ ª +ðĿĨ £ +ðĿĨ ³ +ðĿĨ ¹ +ðĿĩ Ĭ +ðĿĩ Ĺ +ðĿĩ ļ +ðĿĩ ľ +ðĿĩ ł +ðĿIJ ī +ðĿIJ ĸ +ðĿIJ ĺ +ðĿIJ £ +ðĿIJ ± +ðĿij Ĭ +ðĿij Ń +ðĿij ¼ +ðĿij ½ +ðĿĴ ° +ðĿĴ · +ðĿĴ ¿ +ðĿĵ ģ +ðĿĵ ĭ +ðĿĵ İ +ðĿĵ Ĵ +ðĿ ĵĺ +ðĿĵ ¢ +ðĿĵ ¦ +ðĿĵ « +ðĿĵ ¿ +ðĿĶ İ +ðĿĶ ± +ðĿĶ ´ +ðĿĶ · +ðĿĶ ¸ +ðĿĶ ½ +ðĿķ Ĥ +ðĿķ ĥ +ðĿķ ĭ +ðĿķ ı +ðĿķ IJ +ðĿķ ¥ +ðĿķ ´ +ðĿķ º +ðĿĸ IJ +ðĿĸ Ľ +ðĿĸ Ŀ +ðĿĸ ŀ +ðĿĹ © +ðĿĹ ³ +ðĿĹ ½ +ðĿĺ Ĭ +ðĿĺ ĭ +ðĿĺ Ķ +ðĿĺ ± +ðĿĺ ´ +ðĿĺ ¿ +ðĿĻ Ĵ +ðĿĻ Ŀ +ðĿĻ Ł +ðĿĻ ¬ +ðĿĻ Ń +ðĿĻ » +ðĿĻ ¾ +ðĿļ Ī +ðĿļ ĭ +ðĿļ ij +ðĿļ Ł +ðĿļ ł +ðĿļ £ +ðĿĽ ½ +ðĿľ Ĥ +ðĿľ Ķ +ðĿľ Ļ +ðŁ Ģ +ðŁĢ Ħ +ðŁĦ ² +ðŁĦ ¶ +ðŁħ IJ +ðŁħ ĸ +ðŁħ ļ +ðŁħ Ľ +ðŁħ ¦ +ðŁħ ¶ +ðŁħ » +ðŁħ ¼ +ðŁĨ ĥ +ðŁĨ Ĩ +ðŁĨ İ +ðŁĪ ¯ +ðŁĪ ² +ðŁĪ ¹ +ðŁĮ ĩ +ðŁĮ ĵ +ðŁį ĺ +ðŁİ ij +ðŁİ ¿ +ðŁı ı +ðŁı Ĵ +ðŁı © +ðŁı ¯ +ðŁIJ Ģ +ðŁij Ŀ +ðŁĴ ¹ +ðŁĴ º +ðŁĵ Ł +ðŁĵ ª +ðŁĵ ¼ +ðŁĶ Ģ +ðŁĶ Ĥ +ðŁĶ ĥ +ðŁĶ ĩ +ðŁĶ ĵ +ðŁĶ ¢ +ðŁĶ ¤ +ðŁĶ © +ðŁķ ĸ +ðŁķ ļ +ðŁķ ľ +ðŁķ Ŀ +ðŁķ ŀ +ðŁķ ł +ðŁķ ¢ +ðŁķ ³ +ðŁĸ ĩ +ðŁĸ ij +ðŁĸ ¶ +ðŁĹ ģ +Ñ ¨ +Ú İ +á¡ Į +Ḡ° +áº Ģ +á¼ ® +á½ Ŀ +âĦ ¬ +âļ § +⼠¤ +ã³ ¬ +êĻ ĭ +ê¸ ij +ëĶ ī +ëĹ į +ë¡ ij +ë¯ ij +ë» ħ +ë¼ Ŀ +ìĦ IJ +ìī ¡ +ìĭ ² +ìı ± +ìĹ ¤ +ìĿ © +ìĿ ¿ +ìŁ Ļ +ìł ° +ì¥ ī +íĬ Ń +íķ ® +ï® ı +ðŁħ ± +ðŁĨ Ĵ +ðŁķ ĭ +É ĺ +Ê ĵ +Õ ĥ +à´ ´ +འħ +áĨ º +áĪ Ĭ +áĪ ¨ +áĪ ¾ +áī IJ +áĮ ĥ +áĮ ½ +áĶ Ń +áł Ĥ +áł ¬ +ᨠ¸ +á© ĭ +á¶ ı +á¾ Ķ +á¿ IJ +á¿ ļ +âĻ Ļ +âļ Ĥ +âļ Ĺ +â¡ ¢ +⤠¦ +ëĸ ° +ë¤ Ĥ +ë§ ł +ë± ĭ +ë± IJ +ìĽ ¢ +ìľ ¾ +ì³ ħ +ì» ģ +íģ » +íĥ Ļ +íĵ ĸ +íĵ Ń +íķ ± +íĽ ľ +ï¤ ħ +ï¤ Ĩ +ï¦ ĥ +ï§ © +ï¨ Ĥ +ðIJ¤ Ķ +ðIJŃ ĵ +ðIJ° ¼ +ðĿĵ ŀ +ðĿĵ ° +ðĿĻ ľ +ðĿļ ģ +ðŁħ ¢ +ðŁı ĩ +È ² +Ê ¶ +Ô Ī +Ô ij +Ý ĵ +Ý ¥ +ठij +ॠ± +ଠī +à° ³ +à° µ +à² Ł +áĢ ı +áģ ¼ +áī ¨ +áĬ Ĵ +áĭ © +áĮ Ħ +áĮ Ķ +áIJ § +á ĴĮ +áĶ ħ +áĶ Ĭ +áł Ħ +ᨠģ +Ḡĥ +Ḡ» +âĶ ŀ +âĺ µ +âļ £ +â² ¢ +ãĪ ª +ä¶ µ +ê² Ļ +ê² ´ +ê³ Ĥ +ë¡ ¼ +ìĨ Ĭ +ì¼ ĩ +íĭ į +íĵ ¬ +íĵ ® +íĵ ¶ +íĵ » +ï¤ ¦ +ï¥ ł +ï¥ ± +ïŃ ² +ðIJŃ Ĭ +ðIJ ±ħ +ðĸ ¥ +ðĸ¥ ¨ +ðĿij ³ +ðĿĵ ķ +ðĿĵ ¬ +ðĿĵ ¹ +ðĿĵ ¾ +ðĿĶ ĵ +ðĿķ į +ðĿķ ¡ +ðĿķ ± +ðĿĸ ĸ +ðĿĺ ı +ðĿĺ IJ +ðĿĺ ļ +ðĿĻ ® +ðĿĻ ° +ðĿĻ ¸ +ðĿĻ º +ðĿĻ ¼ +ðĿĻ ½ +ðĿĻ ¿ +ðĿļ Ħ +ðĿļ ı +ðŁħ ħ +ðŁħ ĵ +Æ Ī +àł Į +áĻ ³ +á ļĮ +ἠħ +ἠIJ +ᤠĬ +ḠĬ +âĶ ½ +âķ Ĭ +⼠ĩ +⼠ı +âĿ ª +âĿ « +⣠° +ãĦ į +ãĦ ĵ +ãĦ § +ãħ ĸ +ãī « +ê¦ Ķ +ï± Ĭ +ຠĤ +áħ £ +á¥ Ķ +ᥠ¤ +âĨ ¤ +âĨ · +âĩ ŀ +âĸ ¤ +âŀ ¶ +ãĪ ¼ +ï¨ · +ðĵı § +âĶ ² +âĢ ´ +âĴ Ł +âĴ ¡ +â° Ĥ +â° į +â° İ +â° IJ +â° ij +â° Ł +â° ł +â° ¡ +â¼ Ń +ãĬ ¥ +âĴ ł +â½ º +ãĩ º +ãĩ ½ +ï¨ Ĭ +áķ · +âį ¨ +âº Ł +â½ Ĺ diff --git a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json new file mode 100644 index 000000000..67688e82c --- /dev/null +++ b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json @@ -0,0 +1,241 @@ +{ + "add_bos_token": false, + "add_prefix_space": false, + "added_tokens_decoder": { + "151643": { + "content": "<|endoftext|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151644": { + "content": "<|im_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151645": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151646": { + "content": "<|object_ref_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151647": { + "content": "<|object_ref_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151648": { + "content": "<|box_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151649": { + "content": "<|box_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151650": { + "content": "<|quad_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151651": { + "content": "<|quad_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151652": { + "content": "<|vision_start|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151653": { + "content": "<|vision_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151654": { + "content": "<|vision_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151655": { + "content": "<|image_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151656": { + "content": "<|video_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151657": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151658": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151659": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151660": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151661": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151662": { + "content": "<|fim_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151663": { + "content": "<|repo_name|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151664": { + "content": "<|file_sep|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151665": { + "content": "<|img|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151666": { + "content": "<|endofimg|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151667": { + "content": "<|meta|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151668": { + "content": "<|endofmeta|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "<|im_start|>", + "<|im_end|>", + "<|object_ref_start|>", + "<|object_ref_end|>", + "<|box_start|>", + "<|box_end|>", + "<|quad_start|>", + "<|quad_end|>", + "<|vision_start|>", + "<|vision_end|>", + "<|vision_pad|>", + "<|image_pad|>", + "<|video_pad|>" + ], + "bos_token": null, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not 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\ No newline at end of file diff --git a/comfy/text_encoders/qwen_image.py b/comfy/text_encoders/qwen_image.py new file mode 100644 index 000000000..40fa67937 --- /dev/null +++ b/comfy/text_encoders/qwen_image.py @@ -0,0 +1,95 @@ +from transformers import Qwen2Tokenizer +from comfy import sd1_clip +import comfy.text_encoders.llama +import os +import torch +import numbers + +class Qwen25_7BVLITokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer") + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=3584, embedding_key='qwen25_7b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data) + + +class QwenImageTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_7b", tokenizer=Qwen25_7BVLITokenizer) + self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs): + skip_template = False + if text.startswith('<|im_start|>'): + skip_template = True + if text.startswith('<|start_header_id|>'): + skip_template = True + + if skip_template: + llama_text = text + else: + if llama_template is None: + if len(images) > 0: + llama_text = self.llama_template_images.format(text) + else: + llama_text = self.llama_template.format(text) + else: + llama_text = llama_template.format(text) + tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + key_name = next(iter(tokens)) + embed_count = 0 + qwen_tokens = tokens[key_name] + for r in qwen_tokens: + for i in range(len(r)): + if r[i][0] == 151655: + if len(images) > embed_count: + r[i] = ({"type": "image", "data": images[embed_count], "original_type": "image"},) + r[i][1:] + embed_count += 1 + return tokens + + +class Qwen25_7BVLIModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class QwenImageTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options) + + def encode_token_weights(self, token_weight_pairs, template_end=-1): + out, pooled, extra = super().encode_token_weights(token_weight_pairs) + tok_pairs = token_weight_pairs["qwen25_7b"][0] + count_im_start = 0 + if template_end == -1: + for i, v in enumerate(tok_pairs): + elem = v[0] + if not torch.is_tensor(elem): + if isinstance(elem, numbers.Integral): + if elem == 151644 and count_im_start < 2: + template_end = i + count_im_start += 1 + + if out.shape[1] > (template_end + 3): + if tok_pairs[template_end + 1][0] == 872: + if tok_pairs[template_end + 2][0] == 198: + template_end += 3 + + out = out[:, template_end:] + + extra["attention_mask"] = extra["attention_mask"][:, template_end:] + if extra["attention_mask"].sum() == torch.numel(extra["attention_mask"]): + extra.pop("attention_mask") # attention mask is useless if no masked elements + + return out, pooled, extra + + +def te(dtype_llama=None, llama_scaled_fp8=None): + class QwenImageTEModel_(QwenImageTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = llama_scaled_fp8 + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, model_options=model_options) + return QwenImageTEModel_ diff --git a/comfy/text_encoders/qwen_vl.py b/comfy/text_encoders/qwen_vl.py new file mode 100644 index 000000000..3b18ce730 --- /dev/null +++ b/comfy/text_encoders/qwen_vl.py @@ -0,0 +1,428 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Optional, Tuple +import math +from comfy.ldm.modules.attention import optimized_attention_for_device + + +def process_qwen2vl_images( + images: torch.Tensor, + min_pixels: int = 3136, + max_pixels: int = 12845056, + patch_size: int = 14, + temporal_patch_size: int = 2, + merge_size: int = 2, + image_mean: list = None, + image_std: list = None, +): + if image_mean is None: + image_mean = [0.48145466, 0.4578275, 0.40821073] + if image_std is None: + image_std = [0.26862954, 0.26130258, 0.27577711] + + batch_size, height, width, channels = images.shape + device = images.device + # dtype = images.dtype + + images = images.permute(0, 3, 1, 2) + + grid_thw_list = [] + img = images[0] + + factor = patch_size * merge_size + + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + + img_resized = F.interpolate( + img.unsqueeze(0), + size=(h_bar, w_bar), + mode='bilinear', + align_corners=False + ).squeeze(0) + + normalized = img_resized.clone() + for c in range(3): + normalized[c] = (img_resized[c] - image_mean[c]) / image_std[c] + + grid_h = h_bar // patch_size + grid_w = w_bar // patch_size + grid_thw = torch.tensor([1, grid_h, grid_w], device=device, dtype=torch.long) + + pixel_values = normalized + grid_thw_list.append(grid_thw) + image_grid_thw = torch.stack(grid_thw_list) + + grid_t = 1 + channel = pixel_values.shape[0] + pixel_values = pixel_values.unsqueeze(0).repeat(2, 1, 1, 1) + + patches = pixel_values.reshape( + grid_t, + temporal_patch_size, + channel, + grid_h // merge_size, + merge_size, + patch_size, + grid_w // merge_size, + merge_size, + patch_size, + ) + + patches = patches.permute(0, 3, 6, 4, 7, 2, 1, 5, 8) + flatten_patches = patches.reshape( + grid_t * grid_h * grid_w, + channel * temporal_patch_size * patch_size * patch_size + ) + + return flatten_patches, image_grid_thw + + +class VisionPatchEmbed(nn.Module): + def __init__( + self, + patch_size: int = 14, + temporal_patch_size: int = 2, + in_channels: int = 3, + embed_dim: int = 3584, + device=None, + dtype=None, + ops=None, + ): + super().__init__() + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.in_channels = in_channels + self.embed_dim = embed_dim + + kernel_size = [temporal_patch_size, patch_size, patch_size] + self.proj = ops.Conv3d( + in_channels, + embed_dim, + kernel_size=kernel_size, + stride=kernel_size, + bias=False, + device=device, + dtype=dtype + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = hidden_states.view( + -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size + ) + hidden_states = self.proj(hidden_states) + return hidden_states.view(-1, self.embed_dim) + + +def rotate_half(x): + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb_vision(q, k, cos, sin): + cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class VisionRotaryEmbedding(nn.Module): + def __init__(self, dim: int, theta: float = 10000.0): + super().__init__() + self.dim = dim + self.theta = theta + + def forward(self, seqlen: int, device) -> torch.Tensor: + inv_freq = 1.0 / (self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float, device=device) / self.dim)) + seq = torch.arange(seqlen, device=inv_freq.device, dtype=inv_freq.dtype) + freqs = torch.outer(seq, inv_freq) + return freqs + + +class PatchMerger(nn.Module): + def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2, device=None, dtype=None, ops=None): + super().__init__() + self.hidden_size = context_dim * (spatial_merge_size ** 2) + self.ln_q = ops.RMSNorm(context_dim, eps=1e-6, device=device, dtype=dtype) + self.mlp = nn.Sequential( + ops.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype), + nn.GELU(), + ops.Linear(self.hidden_size, dim, device=device, dtype=dtype), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.ln_q(x).reshape(-1, self.hidden_size) + x = self.mlp(x) + return x + + +class VisionAttention(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, device=None, dtype=None, ops=None): + super().__init__() + self.hidden_size = hidden_size + self.num_heads = num_heads + self.head_dim = hidden_size // num_heads + self.scaling = self.head_dim ** -0.5 + + self.qkv = ops.Linear(hidden_size, hidden_size * 3, bias=True, device=device, dtype=dtype) + self.proj = ops.Linear(hidden_size, hidden_size, bias=True, device=device, dtype=dtype) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + cu_seqlens=None, + optimized_attention=None, + ) -> torch.Tensor: + if hidden_states.dim() == 2: + seq_length, _ = hidden_states.shape + batch_size = 1 + hidden_states = hidden_states.unsqueeze(0) + else: + batch_size, seq_length, _ = hidden_states.shape + + qkv = self.qkv(hidden_states) + qkv = qkv.reshape(batch_size, seq_length, 3, self.num_heads, self.head_dim) + query_states, key_states, value_states = qkv.reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) + + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + optimized_attention(q, k, v, self.num_heads, skip_reshape=True) + for q, k, v in zip(*splits) + ] + attn_output = torch.cat(attn_outputs, dim=1) + attn_output = attn_output.reshape(seq_length, -1) + attn_output = self.proj(attn_output) + + return attn_output + + +class VisionMLP(nn.Module): + def __init__(self, hidden_size: int, intermediate_size: int, device=None, dtype=None, ops=None): + super().__init__() + self.gate_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) + self.up_proj = ops.Linear(hidden_size, intermediate_size, bias=True, device=device, dtype=dtype) + self.down_proj = ops.Linear(intermediate_size, hidden_size, bias=True, device=device, dtype=dtype) + self.act_fn = nn.SiLU() + + def forward(self, hidden_state): + return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) + + +class VisionBlock(nn.Module): + def __init__(self, hidden_size: int, intermediate_size: int, num_heads: int, device=None, dtype=None, ops=None): + super().__init__() + self.norm1 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) + self.norm2 = ops.RMSNorm(hidden_size, eps=1e-6, device=device, dtype=dtype) + self.attn = VisionAttention(hidden_size, num_heads, device=device, dtype=dtype, ops=ops) + self.mlp = VisionMLP(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + cu_seqlens=None, + optimized_attention=None, + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.norm1(hidden_states) + hidden_states = self.attn(hidden_states, position_embeddings, cu_seqlens, optimized_attention) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class Qwen2VLVisionTransformer(nn.Module): + def __init__( + self, + hidden_size: int = 3584, + output_hidden_size: int = 3584, + intermediate_size: int = 3420, + num_heads: int = 16, + num_layers: int = 32, + patch_size: int = 14, + temporal_patch_size: int = 2, + spatial_merge_size: int = 2, + window_size: int = 112, + device=None, + dtype=None, + ops=None + ): + super().__init__() + self.hidden_size = hidden_size + self.patch_size = patch_size + self.spatial_merge_size = spatial_merge_size + self.window_size = window_size + self.fullatt_block_indexes = [7, 15, 23, 31] + + self.patch_embed = VisionPatchEmbed( + patch_size=patch_size, + temporal_patch_size=temporal_patch_size, + in_channels=3, + embed_dim=hidden_size, + device=device, + dtype=dtype, + ops=ops, + ) + + head_dim = hidden_size // num_heads + self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2) + + self.blocks = nn.ModuleList([ + VisionBlock(hidden_size, intermediate_size, num_heads, device, dtype, ops) + for _ in range(num_layers) + ]) + + self.merger = PatchMerger( + dim=output_hidden_size, + context_dim=hidden_size, + spatial_merge_size=spatial_merge_size, + device=device, + dtype=dtype, + ops=ops, + ) + + def get_window_index(self, grid_thw): + window_index = [] + cu_window_seqlens = [0] + window_index_id = 0 + vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size + + for grid_t, grid_h, grid_w in grid_thw: + llm_grid_h = grid_h // self.spatial_merge_size + llm_grid_w = grid_w // self.spatial_merge_size + + index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) + + pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size + pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size + num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size + num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size + + index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) + index_padded = index_padded.reshape( + grid_t, + num_windows_h, + vit_merger_window_size, + num_windows_w, + vit_merger_window_size, + ) + index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( + grid_t, + num_windows_h * num_windows_w, + vit_merger_window_size, + vit_merger_window_size, + ) + + seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) + index_padded = index_padded.reshape(-1) + index_new = index_padded[index_padded != -100] + window_index.append(index_new + window_index_id) + + cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_size * self.spatial_merge_size + cu_window_seqlens[-1] + cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) + window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() + + window_index = torch.cat(window_index, dim=0) + return window_index, cu_window_seqlens + + def get_position_embeddings(self, grid_thw, device): + pos_ids = [] + + for t, h, w in grid_thw: + hpos_ids = torch.arange(h, device=device).unsqueeze(1).expand(-1, w) + hpos_ids = hpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + hpos_ids = hpos_ids.permute(0, 2, 1, 3).flatten() + + wpos_ids = torch.arange(w, device=device).unsqueeze(0).expand(h, -1) + wpos_ids = wpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + wpos_ids = wpos_ids.permute(0, 2, 1, 3).flatten() + + pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) + + pos_ids = torch.cat(pos_ids, dim=0) + max_grid_size = grid_thw[:, 1:].max() + rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size, device) + return rotary_pos_emb_full[pos_ids].flatten(1) + + def forward( + self, + pixel_values: torch.Tensor, + image_grid_thw: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + optimized_attention = optimized_attention_for_device(pixel_values.device, mask=False, small_input=True) + + hidden_states = self.patch_embed(pixel_values) + + window_index, cu_window_seqlens = self.get_window_index(image_grid_thw) + cu_window_seqlens = torch.tensor(cu_window_seqlens, device=hidden_states.device) + cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) + + position_embeddings = self.get_position_embeddings(image_grid_thw, hidden_states.device) + + seq_len, _ = hidden_states.size() + spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size + + hidden_states = hidden_states.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) + hidden_states = hidden_states[window_index, :, :] + hidden_states = hidden_states.reshape(seq_len, -1) + + position_embeddings = position_embeddings.reshape(seq_len // spatial_merge_unit, spatial_merge_unit, -1) + position_embeddings = position_embeddings[window_index, :, :] + position_embeddings = position_embeddings.reshape(seq_len, -1) + position_embeddings = torch.cat((position_embeddings, position_embeddings), dim=-1) + position_embeddings = (position_embeddings.cos(), position_embeddings.sin()) + + cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum( + dim=0, + dtype=torch.int32, + ) + cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) + + for i, block in enumerate(self.blocks): + if i in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + hidden_states = block(hidden_states, position_embeddings, cu_seqlens_now, optimized_attention=optimized_attention) + + hidden_states = self.merger(hidden_states) + return hidden_states diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py index 7778ce47a..2803926ac 100644 --- a/comfy/text_encoders/sa_t5.py +++ b/comfy/text_encoders/sa_t5.py @@ -11,7 +11,7 @@ class T5BaseModel(sd1_clip.SDClipModel): class T5BaseTokenizer(sd1_clip.SDTokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) + super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) class SAT5Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): diff --git a/comfy/text_encoders/sd2_clip.py b/comfy/text_encoders/sd2_clip.py index 31fc89869..700a23bf0 100644 --- a/comfy/text_encoders/sd2_clip.py +++ b/comfy/text_encoders/sd2_clip.py @@ -12,7 +12,7 @@ class SD2ClipHModel(sd1_clip.SDClipModel): class SD2ClipHTokenizer(sd1_clip.SDTokenizer): def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}): - super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data) class SD2Tokenizer(sd1_clip.SD1Tokenizer): def __init__(self, embedding_directory=None, tokenizer_data={}): diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py index 00d7e31ad..ff5d412db 100644 --- a/comfy/text_encoders/sd3_clip.py +++ b/comfy/text_encoders/sd3_clip.py @@ -15,6 +15,7 @@ class T5XXLModel(sd1_clip.SDClipModel): model_options = model_options.copy() model_options["scaled_fp8"] = t5xxl_scaled_fp8 + model_options = {**model_options, "model_name": "t5xxl"} super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) @@ -31,23 +32,22 @@ def t5_xxl_detect(state_dict, prefix=""): return out class T5XXLTokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): + def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999): tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") - super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77) + super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data) class SD3Tokenizer: def __init__(self, embedding_directory=None, tokenizer_data={}): - clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) - self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) - self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory) - self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) + self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data) - def tokenize_with_weights(self, text:str, return_word_ids=False): + def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs): out = {} - out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) - out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) - out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs) + out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs) return out def untokenize(self, token_weight_pair): @@ -61,8 +61,7 @@ class SD3ClipModel(torch.nn.Module): super().__init__() self.dtypes = set() if clip_l: - clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) - self.clip_l = clip_l_class(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options) self.dtypes.add(dtype) else: self.clip_l = None diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py index cbaa99ba5..caccb3ca2 100644 --- a/comfy/text_encoders/spiece_tokenizer.py +++ b/comfy/text_encoders/spiece_tokenizer.py @@ -1,21 +1,24 @@ import torch +import os class SPieceTokenizer: - add_eos = True - @staticmethod - def from_pretrained(path): - return SPieceTokenizer(path) + def from_pretrained(path, **kwargs): + return SPieceTokenizer(path, **kwargs) - def __init__(self, tokenizer_path): + def __init__(self, tokenizer_path, add_bos=False, add_eos=True): + self.add_bos = add_bos + self.add_eos = add_eos import sentencepiece if torch.is_tensor(tokenizer_path): tokenizer_path = tokenizer_path.numpy().tobytes() if isinstance(tokenizer_path, bytes): - self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos) + self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) else: - self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos) + if not os.path.isfile(tokenizer_path): + raise ValueError("invalid tokenizer") + self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos) def get_vocab(self): out = {} diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py index 38d8d5234..e8588992a 100644 --- a/comfy/text_encoders/t5.py +++ b/comfy/text_encoders/t5.py @@ -146,7 +146,7 @@ class T5Attention(torch.nn.Module): ) values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads) values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) - return values + return values.contiguous() def forward(self, x, mask=None, past_bias=None, optimized_attention=None): q = self.q(x) @@ -199,11 +199,11 @@ class T5Stack(torch.nn.Module): self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations) # self.dropout = nn.Dropout(config.dropout_rate) - def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None): + def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]): mask = None if attention_mask is not None: mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) - mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max) intermediate = None optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True) @@ -227,8 +227,9 @@ class T5(torch.nn.Module): super().__init__() self.num_layers = config_dict["num_layers"] model_dim = config_dict["d_model"] + inner_dim = config_dict["d_kv"] * config_dict["num_heads"] - self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) + self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations) self.dtype = dtype self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype) @@ -238,8 +239,11 @@ class T5(torch.nn.Module): def set_input_embeddings(self, embeddings): self.shared = embeddings - def forward(self, input_ids, *args, **kwargs): - x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) + def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs): + if input_ids is None: + x = embeds + else: + x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32)) if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]: x = torch.nan_to_num(x) #Fix for fp8 T5 base - return self.encoder(x, *args, **kwargs) + return self.encoder(x, attention_mask=attention_mask, **kwargs) diff --git a/comfy/text_encoders/t5_old_config_xxl.json b/comfy/text_encoders/t5_old_config_xxl.json new file mode 100644 index 000000000..c9fdd7782 --- /dev/null +++ b/comfy/text_encoders/t5_old_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 65536, + "d_kv": 128, + "d_model": 1024, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "relu", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": false, + "layer_norm_epsilon": 1e-06, + "model_type": "t5", + "num_decoder_layers": 24, + "num_heads": 128, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 32128 +} diff --git a/comfy/text_encoders/umt5_config_base.json b/comfy/text_encoders/umt5_config_base.json new file mode 100644 index 000000000..6b3618f07 --- /dev/null +++ b/comfy/text_encoders/umt5_config_base.json @@ -0,0 +1,22 @@ +{ + "d_ff": 2048, + "d_kv": 64, + "d_model": 768, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 12, + "num_heads": 12, + "num_layers": 12, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 256384 +} diff --git a/comfy/text_encoders/umt5_config_xxl.json b/comfy/text_encoders/umt5_config_xxl.json new file mode 100644 index 000000000..dfcb4b54b --- /dev/null +++ b/comfy/text_encoders/umt5_config_xxl.json @@ -0,0 +1,22 @@ +{ + "d_ff": 10240, + "d_kv": 64, + "d_model": 4096, + "decoder_start_token_id": 0, + "dropout_rate": 0.1, + "eos_token_id": 1, + "dense_act_fn": "gelu_pytorch_tanh", + "initializer_factor": 1.0, + "is_encoder_decoder": true, + "is_gated_act": true, + "layer_norm_epsilon": 1e-06, + "model_type": "umt5", + "num_decoder_layers": 24, + "num_heads": 64, + "num_layers": 24, + "output_past": true, + "pad_token_id": 0, + "relative_attention_num_buckets": 32, + "tie_word_embeddings": false, + "vocab_size": 256384 +} diff --git a/comfy/text_encoders/wan.py b/comfy/text_encoders/wan.py new file mode 100644 index 000000000..d50fa4b28 --- /dev/null +++ b/comfy/text_encoders/wan.py @@ -0,0 +1,37 @@ +from comfy import sd1_clip +from .spiece_tokenizer import SPieceTokenizer +import comfy.text_encoders.t5 +import os + +class UMT5XXlModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}): + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_xxl.json") + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options) + +class UMT5XXlTokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer = tokenizer_data.get("spiece_model", None) + super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data) + + def state_dict(self): + return {"spiece_model": self.tokenizer.serialize_model()} + + +class WanT5Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer) + +class WanT5Model(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs) + +def te(dtype_t5=None, t5xxl_scaled_fp8=None): + class WanTEModel(WanT5Model): + def __init__(self, device="cpu", dtype=None, model_options={}): + if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options: + model_options = model_options.copy() + model_options["scaled_fp8"] = t5xxl_scaled_fp8 + if dtype_t5 is not None: + dtype = dtype_t5 + super().__init__(device=device, dtype=dtype, model_options=model_options) + return WanTEModel diff --git a/comfy/utils.py b/comfy/utils.py index ea666ae5b..4bd281057 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -28,23 +28,65 @@ import logging import itertools from torch.nn.functional import interpolate from einops import rearrange +from comfy.cli_args import args -def load_torch_file(ckpt, safe_load=False, device=None): +MMAP_TORCH_FILES = args.mmap_torch_files +DISABLE_MMAP = args.disable_mmap + +ALWAYS_SAFE_LOAD = False +if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated + class ModelCheckpoint: + pass + ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint" + + def scalar(*args, **kwargs): + from numpy.core.multiarray import scalar as sc + return sc(*args, **kwargs) + scalar.__module__ = "numpy.core.multiarray" + + from numpy import dtype + from numpy.dtypes import Float64DType + from _codecs import encode + + torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode]) + ALWAYS_SAFE_LOAD = True + logging.info("Checkpoint files will always be loaded safely.") +else: + logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.") + +def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False): if device is None: device = torch.device("cpu") + metadata = None if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"): - sd = safetensors.torch.load_file(ckpt, device=device.type) + try: + with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f: + sd = {} + for k in f.keys(): + tensor = f.get_tensor(k) + if DISABLE_MMAP: # TODO: Not sure if this is the best way to bypass the mmap issues + tensor = tensor.to(device=device, copy=True) + sd[k] = tensor + if return_metadata: + metadata = f.metadata() + except Exception as e: + if len(e.args) > 0: + message = e.args[0] + if "HeaderTooLarge" in message: + raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt)) + if "MetadataIncompleteBuffer" in message: + raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt)) + raise e else: - if safe_load: - if not 'weights_only' in torch.load.__code__.co_varnames: - logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") - safe_load = False - if safe_load: - pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + torch_args = {} + if MMAP_TORCH_FILES: + torch_args["mmap"] = True + + if safe_load or ALWAYS_SAFE_LOAD: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args) else: + logging.warning("WARNING: loading {} unsafely, upgrade your pytorch to 2.4 or newer to load this file safely.".format(ckpt)) pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle) - if "global_step" in pl_sd: - logging.debug(f"Global Step: {pl_sd['global_step']}") if "state_dict" in pl_sd: sd = pl_sd["state_dict"] else: @@ -55,7 +97,7 @@ def load_torch_file(ckpt, safe_load=False, device=None): sd = pl_sd else: sd = pl_sd - return sd + return (sd, metadata) if return_metadata else sd def save_torch_file(sd, ckpt, metadata=None): if metadata is not None: @@ -660,6 +702,26 @@ def resize_to_batch_size(tensor, batch_size): return output +def resize_list_to_batch_size(l, batch_size): + in_batch_size = len(l) + if in_batch_size == batch_size or in_batch_size == 0: + return l + + if batch_size <= 1: + return l[:batch_size] + + output = [] + if batch_size < in_batch_size: + scale = (in_batch_size - 1) / (batch_size - 1) + for i in range(batch_size): + output.append(l[min(round(i * scale), in_batch_size - 1)]) + else: + scale = in_batch_size / batch_size + for i in range(batch_size): + output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]) + + return output + def convert_sd_to(state_dict, dtype): keys = list(state_dict.keys()) for k in keys: @@ -693,7 +755,25 @@ def copy_to_param(obj, attr, value): prev = getattr(obj, attrs[-1]) prev.data.copy_(value) -def get_attr(obj, attr): +def get_attr(obj, attr: str): + """Retrieves a nested attribute from an object using dot notation. + + Args: + obj: The object to get the attribute from + attr (str): The attribute path using dot notation (e.g. "model.layer.weight") + + Returns: + The value of the requested attribute + + Example: + model = MyModel() + weight = get_attr(model, "layer1.conv.weight") + # Equivalent to: model.layer1.conv.weight + + Important: + Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when + accessing nested model objects under `ModelPatcher.model`. + """ attrs = attr.split(".") for name in attrs: obj = getattr(obj, name) @@ -946,11 +1026,12 @@ def set_progress_bar_global_hook(function): PROGRESS_BAR_HOOK = function class ProgressBar: - def __init__(self, total): + def __init__(self, total, node_id=None): global PROGRESS_BAR_HOOK self.total = total self.current = 0 self.hook = PROGRESS_BAR_HOOK + self.node_id = node_id def update_absolute(self, value, total=None, preview=None): if total is not None: @@ -959,7 +1040,7 @@ class ProgressBar: value = self.total self.current = value if self.hook is not None: - self.hook(self.current, self.total, preview) + self.hook(self.current, self.total, preview, node_id=self.node_id) def update(self, value): self.update_absolute(self.current + value) @@ -1025,3 +1106,25 @@ def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out): dim=1 ) return out + +def pack_latents(latents): + latent_shapes = [] + tensors = [] + for tensor in latents: + latent_shapes.append(tensor.shape) + tensors.append(tensor.reshape(tensor.shape[0], 1, -1)) + + latent = torch.cat(tensors, dim=-1) + return latent, latent_shapes + +def unpack_latents(combined_latent, latent_shapes): + if len(latent_shapes) > 1: + output_tensors = [] + for shape in latent_shapes: + cut = math.prod(shape[1:]) + tens = combined_latent[:, :, :cut] + combined_latent = combined_latent[:, :, cut:] + output_tensors.append(tens.reshape([tens.shape[0]] + list(shape)[1:])) + else: + output_tensors = combined_latent + return output_tensors diff --git a/comfy/weight_adapter/__init__.py b/comfy/weight_adapter/__init__.py new file mode 100644 index 000000000..b40f920e4 --- /dev/null +++ b/comfy/weight_adapter/__init__.py @@ -0,0 +1,34 @@ +from .base import WeightAdapterBase, WeightAdapterTrainBase +from .lora import LoRAAdapter +from .loha import LoHaAdapter +from .lokr import LoKrAdapter +from .glora import GLoRAAdapter +from .oft import OFTAdapter +from .boft import BOFTAdapter + + +adapters: list[type[WeightAdapterBase]] = [ + LoRAAdapter, + LoHaAdapter, + LoKrAdapter, + GLoRAAdapter, + OFTAdapter, + BOFTAdapter, +] +adapter_maps: dict[str, type[WeightAdapterBase]] = { + "LoRA": LoRAAdapter, + "LoHa": LoHaAdapter, + "LoKr": LoKrAdapter, + "OFT": OFTAdapter, + ## We disable not implemented algo for now + # "GLoRA": GLoRAAdapter, + # "BOFT": BOFTAdapter, +} + + +__all__ = [ + "WeightAdapterBase", + "WeightAdapterTrainBase", + "adapters", + "adapter_maps", +] + [a.__name__ for a in adapters] diff --git a/comfy/weight_adapter/base.py b/comfy/weight_adapter/base.py new file mode 100644 index 000000000..43644b106 --- /dev/null +++ b/comfy/weight_adapter/base.py @@ -0,0 +1,175 @@ +from typing import Optional + +import torch +import torch.nn as nn + +import comfy.model_management + + +class WeightAdapterBase: + name: str + loaded_keys: set[str] + weights: list[torch.Tensor] + + @classmethod + def load(cls, x: str, lora: dict[str, torch.Tensor], alpha: float, dora_scale: torch.Tensor) -> Optional["WeightAdapterBase"]: + raise NotImplementedError + + def to_train(self) -> "WeightAdapterTrainBase": + raise NotImplementedError + + @classmethod + def create_train(cls, weight, *args) -> "WeightAdapterTrainBase": + """ + weight: The original weight tensor to be modified. + *args: Additional arguments for configuration, such as rank, alpha etc. + """ + raise NotImplementedError + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + raise NotImplementedError + + +class WeightAdapterTrainBase(nn.Module): + # We follow the scheme of PR #7032 + def __init__(self): + super().__init__() + + def __call__(self, w): + """ + w: The original weight tensor to be modified. + """ + raise NotImplementedError + + def passive_memory_usage(self): + raise NotImplementedError("passive_memory_usage is not implemented") + + def move_to(self, device): + self.to(device) + return self.passive_memory_usage() + + +def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function): + dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype) + lora_diff *= alpha + weight_calc = weight + function(lora_diff).type(weight.dtype) + + wd_on_output_axis = dora_scale.shape[0] == weight_calc.shape[0] + if wd_on_output_axis: + weight_norm = ( + weight.reshape(weight.shape[0], -1) + .norm(dim=1, keepdim=True) + .reshape(weight.shape[0], *[1] * (weight.dim() - 1)) + ) + else: + weight_norm = ( + weight_calc.transpose(0, 1) + .reshape(weight_calc.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1)) + .transpose(0, 1) + ) + weight_norm = weight_norm + torch.finfo(weight.dtype).eps + + weight_calc *= (dora_scale / weight_norm).type(weight.dtype) + if strength != 1.0: + weight_calc -= weight + weight += strength * (weight_calc) + else: + weight[:] = weight_calc + return weight + + +def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> torch.Tensor: + """ + Pad a tensor to a new shape with zeros. + + Args: + tensor (torch.Tensor): The original tensor to be padded. + new_shape (List[int]): The desired shape of the padded tensor. + + Returns: + torch.Tensor: A new tensor padded with zeros to the specified shape. + + Note: + If the new shape is smaller than the original tensor in any dimension, + the original tensor will be truncated in that dimension. + """ + if any([new_shape[i] < tensor.shape[i] for i in range(len(new_shape))]): + raise ValueError("The new shape must be larger than the original tensor in all dimensions") + + if len(new_shape) != len(tensor.shape): + raise ValueError("The new shape must have the same number of dimensions as the original tensor") + + # Create a new tensor filled with zeros + padded_tensor = torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) + + # Create slicing tuples for both tensors + orig_slices = tuple(slice(0, dim) for dim in tensor.shape) + new_slices = tuple(slice(0, dim) for dim in tensor.shape) + + # Copy the original tensor into the new tensor + padded_tensor[new_slices] = tensor[orig_slices] + + return padded_tensor + + +def tucker_weight_from_conv(up, down, mid): + up = up.reshape(up.size(0), up.size(1)) + down = down.reshape(down.size(0), down.size(1)) + return torch.einsum("m n ..., i m, n j -> i j ...", mid, up, down) + + +def tucker_weight(wa, wb, t): + temp = torch.einsum("i j ..., j r -> i r ...", t, wb) + return torch.einsum("i j ..., i r -> r j ...", temp, wa) + + +def factorization(dimension: int, factor: int = -1) -> tuple[int, int]: + """ + return a tuple of two value of input dimension decomposed by the number closest to factor + second value is higher or equal than first value. + + examples) + factor + -1 2 4 8 16 ... + 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 + 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 + 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 + 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 + 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 + 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 + """ + + if factor > 0 and (dimension % factor) == 0 and dimension >= factor**2: + m = factor + n = dimension // factor + if m > n: + n, m = m, n + return m, n + if factor < 0: + factor = dimension + m, n = 1, dimension + length = m + n + while m < n: + new_m = m + 1 + while dimension % new_m != 0: + new_m += 1 + new_n = dimension // new_m + if new_m + new_n > length or new_m > factor: + break + else: + m, n = new_m, new_n + if m > n: + n, m = m, n + return m, n diff --git a/comfy/weight_adapter/boft.py b/comfy/weight_adapter/boft.py new file mode 100644 index 000000000..b2a2f1bd4 --- /dev/null +++ b/comfy/weight_adapter/boft.py @@ -0,0 +1,115 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class BOFTAdapter(WeightAdapterBase): + name = "boft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["BOFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.oft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 4: + loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + boft_m, block_num, boft_b, *_ = blocks.shape + + try: + # Get r + I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(-1, -2) + normed_q = q + if alpha > 0: # alpha in boft/bboft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(weight) + inp = org = weight + + r_b = boft_b//2 + for i in range(boft_m): + bi = r[i] + g = 2 + k = 2**i * r_b + if strength != 1: + bi = bi * strength + (1-strength) * I + inp = ( + inp.unflatten(0, (-1, g, k)) + .transpose(1, 2) + .flatten(0, 2) + .unflatten(0, (-1, boft_b)) + ) + inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp) + inp = ( + inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2) + ) + + if rescale is not None: + inp = inp * rescale + + lora_diff = inp - org + lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function((strength * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/glora.py b/comfy/weight_adapter/glora.py new file mode 100644 index 000000000..939abbba5 --- /dev/null +++ b/comfy/weight_adapter/glora.py @@ -0,0 +1,93 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, weight_decompose + + +class GLoRAAdapter(WeightAdapterBase): + name = "glora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["GLoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + weights = (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + dora_scale = v[5] + + old_glora = False + if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]: + rank = v[0].shape[0] + old_glora = True + + if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]: + if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]: + pass + else: + old_glora = False + rank = v[1].shape[0] + + a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype) + a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype) + b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype) + b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype) + + if v[4] is not None: + alpha = v[4] / rank + else: + alpha = 1.0 + + try: + if old_glora: + lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora + else: + if weight.dim() > 2: + lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + else: + lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape) + lora_diff += torch.mm(b1, b2).reshape(weight.shape) + + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/loha.py b/comfy/weight_adapter/loha.py new file mode 100644 index 000000000..0abb2d403 --- /dev/null +++ b/comfy/weight_adapter/loha.py @@ -0,0 +1,232 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose + + +class HadaWeight(torch.autograd.Function): + @staticmethod + def forward(ctx, w1u, w1d, w2u, w2d, scale=torch.tensor(1)): + ctx.save_for_backward(w1d, w1u, w2d, w2u, scale) + diff_weight = ((w1u @ w1d) * (w2u @ w2d)) * scale + return diff_weight + + @staticmethod + def backward(ctx, grad_out): + (w1d, w1u, w2d, w2u, scale) = ctx.saved_tensors + grad_out = grad_out * scale + temp = grad_out * (w2u @ w2d) + grad_w1u = temp @ w1d.T + grad_w1d = w1u.T @ temp + + temp = grad_out * (w1u @ w1d) + grad_w2u = temp @ w2d.T + grad_w2d = w2u.T @ temp + + del temp + return grad_w1u, grad_w1d, grad_w2u, grad_w2d, None + + +class HadaWeightTucker(torch.autograd.Function): + @staticmethod + def forward(ctx, t1, w1u, w1d, t2, w2u, w2d, scale=torch.tensor(1)): + ctx.save_for_backward(t1, w1d, w1u, t2, w2d, w2u, scale) + + rebuild1 = torch.einsum("i j ..., j r, i p -> p r ...", t1, w1d, w1u) + rebuild2 = torch.einsum("i j ..., j r, i p -> p r ...", t2, w2d, w2u) + + return rebuild1 * rebuild2 * scale + + @staticmethod + def backward(ctx, grad_out): + (t1, w1d, w1u, t2, w2d, w2u, scale) = ctx.saved_tensors + grad_out = grad_out * scale + + temp = torch.einsum("i j ..., j r -> i r ...", t2, w2d) + rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w2u) + + grad_w = rebuild * grad_out + del rebuild + + grad_w1u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) + grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w1u.T) + del grad_w, temp + + grad_w1d = torch.einsum("i r ..., i j ... -> r j", t1, grad_temp) + grad_t1 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w1d.T) + del grad_temp + + temp = torch.einsum("i j ..., j r -> i r ...", t1, w1d) + rebuild = torch.einsum("i j ..., i r -> r j ...", temp, w1u) + + grad_w = rebuild * grad_out + del rebuild + + grad_w2u = torch.einsum("r j ..., i j ... -> r i", temp, grad_w) + grad_temp = torch.einsum("i j ..., i r -> r j ...", grad_w, w2u.T) + del grad_w, temp + + grad_w2d = torch.einsum("i r ..., i j ... -> r j", t2, grad_temp) + grad_t2 = torch.einsum("i j ..., j r -> i r ...", grad_temp, w2d.T) + del grad_temp + return grad_t1, grad_w1u, grad_w1d, grad_t2, grad_w2u, grad_w2d, None + + +class LohaDiff(WeightAdapterTrainBase): + def __init__(self, weights): + super().__init__() + # Unpack weights tuple from LoHaAdapter + w1a, w1b, alpha, w2a, w2b, t1, t2, _ = weights + + # Create trainable parameters + self.hada_w1_a = torch.nn.Parameter(w1a) + self.hada_w1_b = torch.nn.Parameter(w1b) + self.hada_w2_a = torch.nn.Parameter(w2a) + self.hada_w2_b = torch.nn.Parameter(w2b) + + self.use_tucker = False + if t1 is not None and t2 is not None: + self.use_tucker = True + self.hada_t1 = torch.nn.Parameter(t1) + self.hada_t2 = torch.nn.Parameter(t2) + else: + # Keep the attributes for consistent access + self.hada_t1 = None + self.hada_t2 = None + + # Store rank and non-trainable alpha + self.rank = w1b.shape[0] + self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) + + def __call__(self, w): + org_dtype = w.dtype + + scale = self.alpha / self.rank + if self.use_tucker: + diff_weight = HadaWeightTucker.apply(self.hada_t1, self.hada_w1_a, self.hada_w1_b, self.hada_t2, self.hada_w2_a, self.hada_w2_b, scale) + else: + diff_weight = HadaWeight.apply(self.hada_w1_a, self.hada_w1_b, self.hada_w2_a, self.hada_w2_b, scale) + + # Add the scaled difference to the original weight + weight = w.to(diff_weight) + diff_weight.reshape(w.shape) + + return weight.to(org_dtype) + + def passive_memory_usage(self): + """Calculates memory usage of the trainable parameters.""" + return sum(param.numel() * param.element_size() for param in self.parameters()) + + +class LoHaAdapter(WeightAdapterBase): + name = "loha" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def create_train(cls, weight, rank=1, alpha=1.0): + out_dim = weight.shape[0] + in_dim = weight.shape[1:].numel() + mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32) + mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32) + torch.nn.init.normal_(mat1, 0.1) + torch.nn.init.constant_(mat2, 0.0) + mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32) + mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32) + torch.nn.init.normal_(mat3, 0.1) + torch.nn.init.normal_(mat4, 0.01) + return LohaDiff( + (mat1, mat2, alpha, mat3, mat4, None, None, None) + ) + + def to_train(self): + return LohaDiff(self.weights) + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoHaAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + weights = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha = v[2] / w1b.shape[0] + else: + alpha = 1.0 + + w2a = v[3] + w2b = v[4] + dora_scale = v[7] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype)) + else: + m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype)) + m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype)) + + try: + lora_diff = (m1 * m2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lokr.py b/comfy/weight_adapter/lokr.py new file mode 100644 index 000000000..9b2aff2d7 --- /dev/null +++ b/comfy/weight_adapter/lokr.py @@ -0,0 +1,220 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import ( + WeightAdapterBase, + WeightAdapterTrainBase, + weight_decompose, + factorization, +) + + +class LokrDiff(WeightAdapterTrainBase): + def __init__(self, weights): + super().__init__() + (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) = weights + self.use_tucker = False + if lokr_w1_a is not None: + _, rank_a = lokr_w1_a.shape[0], lokr_w1_a.shape[1] + rank_a, _ = lokr_w1_b.shape[0], lokr_w1_b.shape[1] + self.lokr_w1_a = torch.nn.Parameter(lokr_w1_a) + self.lokr_w1_b = torch.nn.Parameter(lokr_w1_b) + self.w1_rebuild = True + self.ranka = rank_a + + if lokr_w2_a is not None: + _, rank_b = lokr_w2_a.shape[0], lokr_w2_a.shape[1] + rank_b, _ = lokr_w2_b.shape[0], lokr_w2_b.shape[1] + self.lokr_w2_a = torch.nn.Parameter(lokr_w2_a) + self.lokr_w2_b = torch.nn.Parameter(lokr_w2_b) + if lokr_t2 is not None: + self.use_tucker = True + self.lokr_t2 = torch.nn.Parameter(lokr_t2) + self.w2_rebuild = True + self.rankb = rank_b + + if lokr_w1 is not None: + self.lokr_w1 = torch.nn.Parameter(lokr_w1) + self.w1_rebuild = False + + if lokr_w2 is not None: + self.lokr_w2 = torch.nn.Parameter(lokr_w2) + self.w2_rebuild = False + + self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) + + @property + def w1(self): + if self.w1_rebuild: + return (self.lokr_w1_a @ self.lokr_w1_b) * (self.alpha / self.ranka) + else: + return self.lokr_w1 + + @property + def w2(self): + if self.w2_rebuild: + if self.use_tucker: + w2 = torch.einsum( + 'i j k l, j r, i p -> p r k l', + self.lokr_t2, + self.lokr_w2_b, + self.lokr_w2_a + ) + else: + w2 = self.lokr_w2_a @ self.lokr_w2_b + return w2 * (self.alpha / self.rankb) + else: + return self.lokr_w2 + + def __call__(self, w): + diff = torch.kron(self.w1, self.w2) + return w + diff.reshape(w.shape).to(w) + + def passive_memory_usage(self): + return sum(param.numel() * param.element_size() for param in self.parameters()) + + +class LoKrAdapter(WeightAdapterBase): + name = "lokr" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def create_train(cls, weight, rank=1, alpha=1.0): + out_dim = weight.shape[0] + in_dim = weight.shape[1:].numel() + out1, out2 = factorization(out_dim, rank) + in1, in2 = factorization(in_dim, rank) + mat1 = torch.empty(out1, in1, device=weight.device, dtype=torch.float32) + mat2 = torch.empty(out2, in2, device=weight.device, dtype=torch.float32) + torch.nn.init.kaiming_uniform_(mat2, a=5**0.5) + torch.nn.init.constant_(mat1, 0.0) + return LokrDiff( + (mat1, mat2, alpha, None, None, None, None, None, None) + ) + + def to_train(self): + return LokrDiff(self.weights) + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoKrAdapter"]: + if loaded_keys is None: + loaded_keys = set() + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + weights = (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dora_scale = v[8] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype)) + else: + w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype), + comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype)) + else: + w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha = v[2] / dim + else: + alpha = 1.0 + + try: + lora_diff = torch.kron(w1, w2).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/lora.py b/comfy/weight_adapter/lora.py new file mode 100644 index 000000000..4db004e50 --- /dev/null +++ b/comfy/weight_adapter/lora.py @@ -0,0 +1,211 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import ( + WeightAdapterBase, + WeightAdapterTrainBase, + weight_decompose, + pad_tensor_to_shape, + tucker_weight_from_conv, +) + + +class LoraDiff(WeightAdapterTrainBase): + def __init__(self, weights): + super().__init__() + mat1, mat2, alpha, mid, dora_scale, reshape = weights + out_dim, rank = mat1.shape[0], mat1.shape[1] + rank, in_dim = mat2.shape[0], mat2.shape[1] + if mid is not None: + convdim = mid.ndim - 2 + layer = ( + torch.nn.Conv1d, + torch.nn.Conv2d, + torch.nn.Conv3d + )[convdim] + else: + layer = torch.nn.Linear + self.lora_up = layer(rank, out_dim, bias=False) + self.lora_down = layer(in_dim, rank, bias=False) + self.lora_up.weight.data.copy_(mat1) + self.lora_down.weight.data.copy_(mat2) + if mid is not None: + self.lora_mid = layer(mid, rank, bias=False) + self.lora_mid.weight.data.copy_(mid) + else: + self.lora_mid = None + self.rank = rank + self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) + + def __call__(self, w): + org_dtype = w.dtype + if self.lora_mid is None: + diff = self.lora_up.weight @ self.lora_down.weight + else: + diff = tucker_weight_from_conv( + self.lora_up.weight, self.lora_down.weight, self.lora_mid.weight + ) + scale = self.alpha / self.rank + weight = w + scale * diff.reshape(w.shape) + return weight.to(org_dtype) + + def passive_memory_usage(self): + return sum(param.numel() * param.element_size() for param in self.parameters()) + + +class LoRAAdapter(WeightAdapterBase): + name = "lora" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def create_train(cls, weight, rank=1, alpha=1.0): + out_dim = weight.shape[0] + in_dim = weight.shape[1:].numel() + mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32) + mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32) + torch.nn.init.kaiming_uniform_(mat1, a=5**0.5) + torch.nn.init.constant_(mat2, 0.0) + return LoraDiff( + (mat1, mat2, alpha, None, None, None) + ) + + def to_train(self): + return LoraDiff(self.weights) + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["LoRAAdapter"]: + if loaded_keys is None: + loaded_keys = set() + + reshape_name = "{}.reshape_weight".format(x) + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + diffusers2_lora = "{}.lora_B.weight".format(x) + diffusers3_lora = "{}.lora.up.weight".format(x) + mochi_lora = "{}.lora_B".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + qwen_default_lora = "{}.lora_B.default.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif diffusers2_lora in lora.keys(): + A_name = diffusers2_lora + B_name = "{}.lora_A.weight".format(x) + mid_name = None + elif diffusers3_lora in lora.keys(): + A_name = diffusers3_lora + B_name = "{}.lora.down.weight".format(x) + mid_name = None + elif mochi_lora in lora.keys(): + A_name = mochi_lora + B_name = "{}.lora_A".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name = "{}.lora_linear_layer.down.weight".format(x) + mid_name = None + elif qwen_default_lora in lora.keys(): + A_name = qwen_default_lora + B_name = "{}.lora_A.default.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + reshape = None + if reshape_name in lora.keys(): + try: + reshape = lora[reshape_name].tolist() + loaded_keys.add(reshape_name) + except: + pass + weights = (lora[A_name], lora[B_name], alpha, mid, dora_scale, reshape) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + return cls(loaded_keys, weights) + else: + return None + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + mat1 = comfy.model_management.cast_to_device( + v[0], weight.device, intermediate_dtype + ) + mat2 = comfy.model_management.cast_to_device( + v[1], weight.device, intermediate_dtype + ) + dora_scale = v[4] + reshape = v[5] + + if reshape is not None: + weight = pad_tensor_to_shape(weight, reshape) + + if v[2] is not None: + alpha = v[2] / mat2.shape[0] + else: + alpha = 1.0 + + if v[3] is not None: + # locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = comfy.model_management.cast_to_device( + v[3], weight.device, intermediate_dtype + ) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = ( + torch.mm( + mat2.transpose(0, 1).flatten(start_dim=1), + mat3.transpose(0, 1).flatten(start_dim=1), + ) + .reshape(final_shape) + .transpose(0, 1) + ) + try: + lora_diff = torch.mm( + mat1.flatten(start_dim=1), mat2.flatten(start_dim=1) + ).reshape(weight.shape) + if dora_scale is not None: + weight = weight_decompose( + dora_scale, + weight, + lora_diff, + alpha, + strength, + intermediate_dtype, + function, + ) + else: + weight += function(((strength * alpha) * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy/weight_adapter/oft.py b/comfy/weight_adapter/oft.py new file mode 100644 index 000000000..c0aab9635 --- /dev/null +++ b/comfy/weight_adapter/oft.py @@ -0,0 +1,161 @@ +import logging +from typing import Optional + +import torch +import comfy.model_management +from .base import WeightAdapterBase, WeightAdapterTrainBase, weight_decompose, factorization + + +class OFTDiff(WeightAdapterTrainBase): + def __init__(self, weights): + super().__init__() + # Unpack weights tuple from LoHaAdapter + blocks, rescale, alpha, _ = weights + + # Create trainable parameters + self.oft_blocks = torch.nn.Parameter(blocks) + if rescale is not None: + self.rescale = torch.nn.Parameter(rescale) + self.rescaled = True + else: + self.rescaled = False + self.block_num, self.block_size, _ = blocks.shape + self.constraint = float(alpha) + self.alpha = torch.nn.Parameter(torch.tensor(alpha), requires_grad=False) + + def __call__(self, w): + org_dtype = w.dtype + I = torch.eye(self.block_size, device=self.oft_blocks.device) + + ## generate r + # for Q = -Q^T + q = self.oft_blocks - self.oft_blocks.transpose(1, 2) + normed_q = q + if self.constraint: + q_norm = torch.norm(q) + 1e-8 + if q_norm > self.constraint: + normed_q = q * self.constraint / q_norm + # use float() to prevent unsupported type + r = (I + normed_q) @ (I - normed_q).float().inverse() + + ## Apply chunked matmul on weight + _, *shape = w.shape + org_weight = w.to(dtype=r.dtype) + org_weight = org_weight.unflatten(0, (self.block_num, self.block_size)) + # Init R=0, so add I on it to ensure the output of step0 is original model output + weight = torch.einsum( + "k n m, k n ... -> k m ...", + r, + org_weight, + ).flatten(0, 1) + if self.rescaled: + weight = self.rescale * weight + return weight.to(org_dtype) + + def passive_memory_usage(self): + """Calculates memory usage of the trainable parameters.""" + return sum(param.numel() * param.element_size() for param in self.parameters()) + + +class OFTAdapter(WeightAdapterBase): + name = "oft" + + def __init__(self, loaded_keys, weights): + self.loaded_keys = loaded_keys + self.weights = weights + + @classmethod + def create_train(cls, weight, rank=1, alpha=1.0): + out_dim = weight.shape[0] + block_size, block_num = factorization(out_dim, rank) + block = torch.zeros(block_num, block_size, block_size, device=weight.device, dtype=torch.float32) + return OFTDiff( + (block, None, alpha, None) + ) + + def to_train(self): + return OFTDiff(self.weights) + + @classmethod + def load( + cls, + x: str, + lora: dict[str, torch.Tensor], + alpha: float, + dora_scale: torch.Tensor, + loaded_keys: set[str] = None, + ) -> Optional["OFTAdapter"]: + if loaded_keys is None: + loaded_keys = set() + blocks_name = "{}.oft_blocks".format(x) + rescale_name = "{}.rescale".format(x) + + blocks = None + if blocks_name in lora.keys(): + blocks = lora[blocks_name] + if blocks.ndim == 3: + loaded_keys.add(blocks_name) + else: + blocks = None + if blocks is None: + return None + + rescale = None + if rescale_name in lora.keys(): + rescale = lora[rescale_name] + loaded_keys.add(rescale_name) + + weights = (blocks, rescale, alpha, dora_scale) + return cls(loaded_keys, weights) + + def calculate_weight( + self, + weight, + key, + strength, + strength_model, + offset, + function, + intermediate_dtype=torch.float32, + original_weight=None, + ): + v = self.weights + blocks = v[0] + rescale = v[1] + alpha = v[2] + if alpha is None: + alpha = 0 + dora_scale = v[3] + + blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype) + if rescale is not None: + rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype) + + block_num, block_size, *_ = blocks.shape + + try: + # Get r + I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype) + # for Q = -Q^T + q = blocks - blocks.transpose(1, 2) + normed_q = q + if alpha > 0: # alpha in oft/boft is for constraint + q_norm = torch.norm(q) + 1e-8 + if q_norm > alpha: + normed_q = q * alpha / q_norm + # use float() to prevent unsupported type in .inverse() + r = (I + normed_q) @ (I - normed_q).float().inverse() + r = r.to(weight) + _, *shape = weight.shape + lora_diff = torch.einsum( + "k n m, k n ... -> k m ...", + (r * strength) - strength * I, + weight.view(block_num, block_size, *shape), + ).view(-1, *shape) + if dora_scale is not None: + weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function) + else: + weight += function((strength * lora_diff).type(weight.dtype)) + except Exception as e: + logging.error("ERROR {} {} {}".format(self.name, key, e)) + return weight diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py new file mode 100644 index 000000000..0d4389a6e --- /dev/null +++ b/comfy_api/feature_flags.py @@ -0,0 +1,69 @@ +""" +Feature flags module for ComfyUI WebSocket protocol negotiation. + +This module handles capability negotiation between frontend and backend, +allowing graceful protocol evolution while maintaining backward compatibility. +""" + +from typing import Any, Dict + +from comfy.cli_args import args + +# Default server capabilities +SERVER_FEATURE_FLAGS: Dict[str, Any] = { + "supports_preview_metadata": True, + "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes +} + + +def get_connection_feature( + sockets_metadata: Dict[str, Dict[str, Any]], + sid: str, + feature_name: str, + default: Any = False +) -> Any: + """ + Get a feature flag value for a specific connection. + + Args: + sockets_metadata: Dictionary of socket metadata + sid: Session ID of the connection + feature_name: Name of the feature to check + default: Default value if feature not found + + Returns: + Feature value or default if not found + """ + if sid not in sockets_metadata: + return default + + return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default) + + +def supports_feature( + sockets_metadata: Dict[str, Dict[str, Any]], + sid: str, + feature_name: str +) -> bool: + """ + Check if a connection supports a specific feature. + + Args: + sockets_metadata: Dictionary of socket metadata + sid: Session ID of the connection + feature_name: Name of the feature to check + + Returns: + Boolean indicating if feature is supported + """ + return get_connection_feature(sockets_metadata, sid, feature_name, False) is True + + +def get_server_features() -> Dict[str, Any]: + """ + Get the server's feature flags. + + Returns: + Dictionary of server feature flags + """ + return SERVER_FEATURE_FLAGS.copy() diff --git a/comfy_api/generate_api_stubs.py b/comfy_api/generate_api_stubs.py new file mode 100644 index 000000000..604a7eced --- /dev/null +++ b/comfy_api/generate_api_stubs.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python3 +""" +Script to generate .pyi stub files for the synchronous API wrappers. +This allows generating stubs without running the full ComfyUI application. +""" + +import os +import sys +import logging +import importlib + +# Add ComfyUI to path so we can import modules +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from comfy_api.internal.async_to_sync import AsyncToSyncConverter +from comfy_api.version_list import supported_versions + + +def generate_stubs_for_module(module_name: str) -> None: + """Generate stub files for a specific module that exports ComfyAPI and ComfyAPISync.""" + try: + # Import the module + module = importlib.import_module(module_name) + + # Check if module has ComfyAPISync (the sync wrapper) + if hasattr(module, "ComfyAPISync"): + # Module already has a sync class + api_class = getattr(module, "ComfyAPI", None) + sync_class = getattr(module, "ComfyAPISync") + + if api_class: + # Generate the stub file + AsyncToSyncConverter.generate_stub_file(api_class, sync_class) + logging.info(f"Generated stub file for {module_name}") + else: + logging.warning( + f"Module {module_name} has ComfyAPISync but no ComfyAPI" + ) + + elif hasattr(module, "ComfyAPI"): + # Module only has async API, need to create sync wrapper first + from comfy_api.internal.async_to_sync import create_sync_class + + api_class = getattr(module, "ComfyAPI") + sync_class = create_sync_class(api_class) + + # Generate the stub file + AsyncToSyncConverter.generate_stub_file(api_class, sync_class) + logging.info(f"Generated stub file for {module_name}") + else: + logging.warning( + f"Module {module_name} does not export ComfyAPI or ComfyAPISync" + ) + + except Exception as e: + logging.error(f"Failed to generate stub for {module_name}: {e}") + import traceback + + traceback.print_exc() + + +def main(): + """Main function to generate all API stub files.""" + logging.basicConfig(level=logging.INFO) + + logging.info("Starting stub generation...") + + # Dynamically get module names from supported_versions + api_modules = [] + for api_class in supported_versions: + # Extract module name from the class + module_name = api_class.__module__ + if module_name not in api_modules: + api_modules.append(module_name) + + logging.info(f"Found {len(api_modules)} API modules: {api_modules}") + + # Generate stubs for each module + for module_name in api_modules: + generate_stubs_for_module(module_name) + + logging.info("Stub generation complete!") + + +if __name__ == "__main__": + main() diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py new file mode 100644 index 000000000..68ff78270 --- /dev/null +++ b/comfy_api/input/__init__.py @@ -0,0 +1,16 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._input import ( + ImageInput, + AudioInput, + MaskInput, + LatentInput, + VideoInput, +) + +__all__ = [ + "ImageInput", + "AudioInput", + "MaskInput", + "LatentInput", + "VideoInput", +] diff --git a/comfy_api/input/basic_types.py b/comfy_api/input/basic_types.py new file mode 100644 index 000000000..5eadce86a --- /dev/null +++ b/comfy_api/input/basic_types.py @@ -0,0 +1,14 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._input.basic_types import ( + ImageInput, + AudioInput, + MaskInput, + LatentInput, +) + +__all__ = [ + "ImageInput", + "AudioInput", + "MaskInput", + "LatentInput", +] diff --git a/comfy_api/input/video_types.py b/comfy_api/input/video_types.py new file mode 100644 index 000000000..9ace78cbc --- /dev/null +++ b/comfy_api/input/video_types.py @@ -0,0 +1,6 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._input.video_types import VideoInput + +__all__ = [ + "VideoInput", +] diff --git a/comfy_api/input_impl/__init__.py b/comfy_api/input_impl/__init__.py new file mode 100644 index 000000000..b78ff0c08 --- /dev/null +++ b/comfy_api/input_impl/__init__.py @@ -0,0 +1,7 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents + +__all__ = [ + "VideoFromFile", + "VideoFromComponents", +] diff --git a/comfy_api/input_impl/video_types.py b/comfy_api/input_impl/video_types.py new file mode 100644 index 000000000..bd2e56ad5 --- /dev/null +++ b/comfy_api/input_impl/video_types.py @@ -0,0 +1,2 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._input_impl.video_types import * # noqa: F403 diff --git a/comfy_api/internal/__init__.py b/comfy_api/internal/__init__.py new file mode 100644 index 000000000..4ca02e320 --- /dev/null +++ b/comfy_api/internal/__init__.py @@ -0,0 +1,150 @@ +# Internal infrastructure for ComfyAPI +from .api_registry import ( + ComfyAPIBase as ComfyAPIBase, + ComfyAPIWithVersion as ComfyAPIWithVersion, + register_versions as register_versions, + get_all_versions as get_all_versions, +) + +import asyncio +from dataclasses import asdict +from typing import Callable, Optional + + +def first_real_override(cls: type, name: str, *, base: type=None) -> Optional[Callable]: + """Return the *callable* override of `name` visible on `cls`, or None if every + implementation up to (and including) `base` is the placeholder defined on `base`. + + If base is not provided, it will assume cls has a GET_BASE_CLASS + """ + if base is None: + if not hasattr(cls, "GET_BASE_CLASS"): + raise ValueError("base is required if cls does not have a GET_BASE_CLASS; is this a valid ComfyNode subclass?") + base = cls.GET_BASE_CLASS() + base_attr = getattr(base, name, None) + if base_attr is None: + return None + base_func = base_attr.__func__ + for c in cls.mro(): # NodeB, NodeA, ComfyNode, object … + if c is base: # reached the placeholder – we're done + break + if name in c.__dict__: # first class that *defines* the attr + func = getattr(c, name).__func__ + if func is not base_func: # real override + return getattr(cls, name) # bound to *cls* + return None + + +class _ComfyNodeInternal: + """Class that all V3-based APIs inherit from for ComfyNode. + + This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward.""" + @classmethod + def GET_NODE_INFO_V1(cls): + ... + + +class _NodeOutputInternal: + """Class that all V3-based APIs inherit from for NodeOutput. + + This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward.""" + ... + + +def as_pruned_dict(dataclass_obj): + '''Return dict of dataclass object with pruned None values.''' + return prune_dict(asdict(dataclass_obj)) + +def prune_dict(d: dict): + return {k: v for k,v in d.items() if v is not None} + + +def is_class(obj): + ''' + Returns True if is a class type. + Returns False if is a class instance. + ''' + return isinstance(obj, type) + + +def copy_class(cls: type) -> type: + ''' + Copy a class and its attributes. + ''' + if cls is None: + return None + cls_dict = { + k: v for k, v in cls.__dict__.items() + if k not in ('__dict__', '__weakref__', '__module__', '__doc__') + } + # new class + new_cls = type( + cls.__name__, + (cls,), + cls_dict + ) + # metadata preservation + new_cls.__module__ = cls.__module__ + new_cls.__doc__ = cls.__doc__ + return new_cls + + +class classproperty(object): + def __init__(self, f): + self.f = f + def __get__(self, obj, owner): + return self.f(owner) + + +# NOTE: this was ai generated and validated by hand +def shallow_clone_class(cls, new_name=None): + ''' + Shallow clone a class while preserving super() functionality. + ''' + new_name = new_name or f"{cls.__name__}Clone" + # Include the original class in the bases to maintain proper inheritance + new_bases = (cls,) + cls.__bases__ + return type(new_name, new_bases, dict(cls.__dict__)) + +# NOTE: this was ai generated and validated by hand +def lock_class(cls): + ''' + Lock a class so that its top-levelattributes cannot be modified. + ''' + # Locked instance __setattr__ + def locked_instance_setattr(self, name, value): + raise AttributeError( + f"Cannot set attribute '{name}' on immutable instance of {type(self).__name__}" + ) + # Locked metaclass + class LockedMeta(type(cls)): + def __setattr__(cls_, name, value): + raise AttributeError( + f"Cannot modify class attribute '{name}' on locked class '{cls_.__name__}'" + ) + # Rebuild class with locked behavior + locked_dict = dict(cls.__dict__) + locked_dict['__setattr__'] = locked_instance_setattr + + return LockedMeta(cls.__name__, cls.__bases__, locked_dict) + + +def make_locked_method_func(type_obj, func, class_clone): + """ + Returns a function that, when called with **inputs, will execute: + getattr(type_obj, func).__func__(lock_class(class_clone), **inputs) + + Supports both synchronous and asynchronous methods. + """ + locked_class = lock_class(class_clone) + method = getattr(type_obj, func).__func__ + + # Check if the original method is async + if asyncio.iscoroutinefunction(method): + async def wrapped_async_func(**inputs): + return await method(locked_class, **inputs) + return wrapped_async_func + else: + def wrapped_func(**inputs): + return method(locked_class, **inputs) + return wrapped_func diff --git a/comfy_api/internal/api_registry.py b/comfy_api/internal/api_registry.py new file mode 100644 index 000000000..7e3375cf6 --- /dev/null +++ b/comfy_api/internal/api_registry.py @@ -0,0 +1,39 @@ +from typing import Type, List, NamedTuple +from comfy_api.internal.singleton import ProxiedSingleton +from packaging import version as packaging_version + + +class ComfyAPIBase(ProxiedSingleton): + def __init__(self): + pass + + +class ComfyAPIWithVersion(NamedTuple): + version: str + api_class: Type[ComfyAPIBase] + + +def parse_version(version_str: str) -> packaging_version.Version: + """ + Parses a version string into a packaging_version.Version object. + Raises ValueError if the version string is invalid. + """ + if version_str == "latest": + return packaging_version.parse("9999999.9999999.9999999") + return packaging_version.parse(version_str) + + +registered_versions: List[ComfyAPIWithVersion] = [] + + +def register_versions(versions: List[ComfyAPIWithVersion]): + versions.sort(key=lambda x: parse_version(x.version)) + global registered_versions + registered_versions = versions + + +def get_all_versions() -> List[ComfyAPIWithVersion]: + """ + Returns a list of all registered ComfyAPI versions. + """ + return registered_versions diff --git a/comfy_api/internal/async_to_sync.py b/comfy_api/internal/async_to_sync.py new file mode 100644 index 000000000..f5f805a62 --- /dev/null +++ b/comfy_api/internal/async_to_sync.py @@ -0,0 +1,987 @@ +import asyncio +import concurrent.futures +import contextvars +import functools +import inspect +import logging +import os +import textwrap +import threading +from enum import Enum +from typing import Optional, Type, get_origin, get_args + + +class TypeTracker: + """Tracks types discovered during stub generation for automatic import generation.""" + + def __init__(self): + self.discovered_types = {} # type_name -> (module, qualname) + self.builtin_types = { + "Any", + "Dict", + "List", + "Optional", + "Tuple", + "Union", + "Set", + "Sequence", + "cast", + "NamedTuple", + "str", + "int", + "float", + "bool", + "None", + "bytes", + "object", + "type", + "dict", + "list", + "tuple", + "set", + } + self.already_imported = ( + set() + ) # Track types already imported to avoid duplicates + + def track_type(self, annotation): + """Track a type annotation and record its module/import info.""" + if annotation is None or annotation is type(None): + return + + # Skip builtins and typing module types we already import + type_name = getattr(annotation, "__name__", None) + if type_name and ( + type_name in self.builtin_types or type_name in self.already_imported + ): + return + + # Get module and qualname + module = getattr(annotation, "__module__", None) + qualname = getattr(annotation, "__qualname__", type_name or "") + + # Skip types from typing module (they're already imported) + if module == "typing": + return + + # Skip UnionType and GenericAlias from types module as they're handled specially + if module == "types" and type_name in ("UnionType", "GenericAlias"): + return + + if module and module not in ["builtins", "__main__"]: + # Store the type info + if type_name: + self.discovered_types[type_name] = (module, qualname) + + def get_imports(self, main_module_name: str) -> list[str]: + """Generate import statements for all discovered types.""" + imports = [] + imports_by_module = {} + + for type_name, (module, qualname) in sorted(self.discovered_types.items()): + # Skip types from the main module (they're already imported) + if main_module_name and module == main_module_name: + continue + + if module not in imports_by_module: + imports_by_module[module] = [] + if type_name not in imports_by_module[module]: # Avoid duplicates + imports_by_module[module].append(type_name) + + # Generate import statements + for module, types in sorted(imports_by_module.items()): + if len(types) == 1: + imports.append(f"from {module} import {types[0]}") + else: + imports.append(f"from {module} import {', '.join(sorted(set(types)))}") + + return imports + + +class AsyncToSyncConverter: + """ + Provides utilities to convert async classes to sync classes with proper type hints. + """ + + _thread_pool: Optional[concurrent.futures.ThreadPoolExecutor] = None + _thread_pool_lock = threading.Lock() + _thread_pool_initialized = False + + @classmethod + def get_thread_pool(cls, max_workers=None) -> concurrent.futures.ThreadPoolExecutor: + """Get or create the shared thread pool with proper thread-safe initialization.""" + # Fast path - check if already initialized without acquiring lock + if cls._thread_pool_initialized: + assert cls._thread_pool is not None, "Thread pool should be initialized" + return cls._thread_pool + + # Slow path - acquire lock and create pool if needed + with cls._thread_pool_lock: + if not cls._thread_pool_initialized: + cls._thread_pool = concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers, thread_name_prefix="async_to_sync_" + ) + cls._thread_pool_initialized = True + + # This should never be None at this point, but add assertion for type checker + assert cls._thread_pool is not None + return cls._thread_pool + + @classmethod + def run_async_in_thread(cls, coro_func, *args, **kwargs): + """ + Run an async function in a separate thread from the thread pool. + Blocks until the async function completes. + Properly propagates contextvars between threads and manages event loops. + """ + # Capture current context - this includes all context variables + context = contextvars.copy_context() + + # Store the result and any exception that occurs + result_container: dict = {"result": None, "exception": None} + + # Function that runs in the thread pool + def run_in_thread(): + # Create new event loop for this thread + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + + try: + # Create the coroutine within the context + async def run_with_context(): + # The coroutine function might access context variables + return await coro_func(*args, **kwargs) + + # Run the coroutine with the captured context + # This ensures all context variables are available in the async function + result = context.run(loop.run_until_complete, run_with_context()) + result_container["result"] = result + except Exception as e: + # Store the exception to re-raise in the calling thread + result_container["exception"] = e + finally: + # Ensure event loop is properly closed to prevent warnings + try: + # Cancel any remaining tasks + pending = asyncio.all_tasks(loop) + for task in pending: + task.cancel() + + # Run the loop briefly to handle cancellations + if pending: + loop.run_until_complete( + asyncio.gather(*pending, return_exceptions=True) + ) + except Exception: + pass # Ignore errors during cleanup + + # Close the event loop + loop.close() + + # Clear the event loop from the thread + asyncio.set_event_loop(None) + + # Submit to thread pool and wait for result + thread_pool = cls.get_thread_pool() + future = thread_pool.submit(run_in_thread) + future.result() # Wait for completion + + # Re-raise any exception that occurred in the thread + if result_container["exception"] is not None: + raise result_container["exception"] + + return result_container["result"] + + @classmethod + def create_sync_class(cls, async_class: Type, thread_pool_size=10) -> Type: + """ + Creates a new class with synchronous versions of all async methods. + + Args: + async_class: The async class to convert + thread_pool_size: Size of thread pool to use + + Returns: + A new class with sync versions of all async methods + """ + sync_class_name = "ComfyAPISyncStub" + cls.get_thread_pool(thread_pool_size) + + # Create a proper class with docstrings and proper base classes + sync_class_dict = { + "__doc__": async_class.__doc__, + "__module__": async_class.__module__, + "__qualname__": sync_class_name, + "__orig_class__": async_class, # Store original class for typing references + } + + # Create __init__ method + def __init__(self, *args, **kwargs): + self._async_instance = async_class(*args, **kwargs) + + # Handle annotated class attributes (like execution: Execution) + # Get all annotations from the class hierarchy + all_annotations = {} + for base_class in reversed(inspect.getmro(async_class)): + if hasattr(base_class, "__annotations__"): + all_annotations.update(base_class.__annotations__) + + # For each annotated attribute, check if it needs to be created or wrapped + for attr_name, attr_type in all_annotations.items(): + if hasattr(self._async_instance, attr_name): + # Attribute exists on the instance + attr = getattr(self._async_instance, attr_name) + # Check if this attribute needs a sync wrapper + if hasattr(attr, "__class__"): + from comfy_api.internal.singleton import ProxiedSingleton + + if isinstance(attr, ProxiedSingleton): + # Create a sync version of this attribute + try: + sync_attr_class = cls.create_sync_class(attr.__class__) + # Create instance of the sync wrapper with the async instance + sync_attr = object.__new__(sync_attr_class) # type: ignore + sync_attr._async_instance = attr + setattr(self, attr_name, sync_attr) + except Exception: + # If we can't create a sync version, keep the original + setattr(self, attr_name, attr) + else: + # Not async, just copy the reference + setattr(self, attr_name, attr) + else: + # Attribute doesn't exist, but is annotated - create it + # This handles cases like execution: Execution + if isinstance(attr_type, type): + # Check if the type is defined as an inner class + if hasattr(async_class, attr_type.__name__): + inner_class = getattr(async_class, attr_type.__name__) + from comfy_api.internal.singleton import ProxiedSingleton + + # Create an instance of the inner class + try: + # For ProxiedSingleton classes, get or create the singleton instance + if issubclass(inner_class, ProxiedSingleton): + async_instance = inner_class.get_instance() + else: + async_instance = inner_class() + + # Create sync wrapper + sync_attr_class = cls.create_sync_class(inner_class) + sync_attr = object.__new__(sync_attr_class) # type: ignore + sync_attr._async_instance = async_instance + setattr(self, attr_name, sync_attr) + # Also set on the async instance for consistency + setattr(self._async_instance, attr_name, async_instance) + except Exception as e: + logging.warning( + f"Failed to create instance for {attr_name}: {e}" + ) + + # Handle other instance attributes that might not be annotated + for name, attr in inspect.getmembers(self._async_instance): + if name.startswith("_") or hasattr(self, name): + continue + + # If attribute is an instance of a class, and that class is defined in the original class + # we need to check if it needs a sync wrapper + if isinstance(attr, object) and not isinstance( + attr, (str, int, float, bool, list, dict, tuple) + ): + from comfy_api.internal.singleton import ProxiedSingleton + + if isinstance(attr, ProxiedSingleton): + # Create a sync version of this nested class + try: + sync_attr_class = cls.create_sync_class(attr.__class__) + # Create instance of the sync wrapper with the async instance + sync_attr = object.__new__(sync_attr_class) # type: ignore + sync_attr._async_instance = attr + setattr(self, name, sync_attr) + except Exception: + # If we can't create a sync version, keep the original + setattr(self, name, attr) + + sync_class_dict["__init__"] = __init__ + + # Process methods from the async class + for name, method in inspect.getmembers( + async_class, predicate=inspect.isfunction + ): + if name.startswith("_"): + continue + + # Extract the actual return type from a coroutine + if inspect.iscoroutinefunction(method): + # Create sync version of async method with proper signature + @functools.wraps(method) + def sync_method(self, *args, _method_name=name, **kwargs): + async_method = getattr(self._async_instance, _method_name) + return AsyncToSyncConverter.run_async_in_thread( + async_method, *args, **kwargs + ) + + # Add to the class dict + sync_class_dict[name] = sync_method + else: + # For regular methods, create a proxy method + @functools.wraps(method) + def proxy_method(self, *args, _method_name=name, **kwargs): + method = getattr(self._async_instance, _method_name) + return method(*args, **kwargs) + + # Add to the class dict + sync_class_dict[name] = proxy_method + + # Handle property access + for name, prop in inspect.getmembers( + async_class, lambda x: isinstance(x, property) + ): + + def make_property(name, prop_obj): + def getter(self): + value = getattr(self._async_instance, name) + if inspect.iscoroutinefunction(value): + + def sync_fn(*args, **kwargs): + return AsyncToSyncConverter.run_async_in_thread( + value, *args, **kwargs + ) + + return sync_fn + return value + + def setter(self, value): + setattr(self._async_instance, name, value) + + return property(getter, setter if prop_obj.fset else None) + + sync_class_dict[name] = make_property(name, prop) + + # Create the class + sync_class = type(sync_class_name, (object,), sync_class_dict) + + return sync_class + + @classmethod + def _format_type_annotation( + cls, annotation, type_tracker: Optional[TypeTracker] = None + ) -> str: + """Convert a type annotation to its string representation for stub files.""" + if ( + annotation is inspect.Parameter.empty + or annotation is inspect.Signature.empty + ): + return "Any" + + # Handle None type + if annotation is type(None): + return "None" + + # Track the type if we have a tracker + if type_tracker: + type_tracker.track_type(annotation) + + # Try using typing.get_origin/get_args for Python 3.8+ + try: + origin = get_origin(annotation) + args = get_args(annotation) + + if origin is not None: + # Track the origin type + if type_tracker: + type_tracker.track_type(origin) + + # Get the origin name + origin_name = getattr(origin, "__name__", str(origin)) + if "." in origin_name: + origin_name = origin_name.split(".")[-1] + + # Special handling for types.UnionType (Python 3.10+ pipe operator) + # Convert to old-style Union for compatibility + if str(origin) == "" or origin_name == "UnionType": + origin_name = "Union" + + # Format arguments recursively + if args: + formatted_args = [] + for arg in args: + # Track each type in the union + if type_tracker: + type_tracker.track_type(arg) + formatted_args.append(cls._format_type_annotation(arg, type_tracker)) + return f"{origin_name}[{', '.join(formatted_args)}]" + else: + return origin_name + except (AttributeError, TypeError): + # Fallback for older Python versions or non-generic types + pass + + # Handle generic types the old way for compatibility + if hasattr(annotation, "__origin__") and hasattr(annotation, "__args__"): + origin = annotation.__origin__ + origin_name = ( + origin.__name__ + if hasattr(origin, "__name__") + else str(origin).split("'")[1] + ) + + # Format each type argument + args = [] + for arg in annotation.__args__: + args.append(cls._format_type_annotation(arg, type_tracker)) + + return f"{origin_name}[{', '.join(args)}]" + + # Handle regular types with __name__ + if hasattr(annotation, "__name__"): + return annotation.__name__ + + # Handle special module types (like types from typing module) + if hasattr(annotation, "__module__") and hasattr(annotation, "__qualname__"): + # For types like typing.Literal, typing.TypedDict, etc. + return annotation.__qualname__ + + # Last resort: string conversion with cleanup + type_str = str(annotation) + + # Clean up common patterns more robustly + if type_str.startswith(""): + type_str = type_str[8:-2] # Remove "" + + # Remove module prefixes for common modules + for prefix in ["typing.", "builtins.", "types."]: + if type_str.startswith(prefix): + type_str = type_str[len(prefix) :] + + # Handle special cases + if type_str in ("_empty", "inspect._empty"): + return "None" + + # Fix NoneType (this should rarely be needed now) + if type_str == "NoneType": + return "None" + + return type_str + + @classmethod + def _extract_coroutine_return_type(cls, annotation): + """Extract the actual return type from a Coroutine annotation.""" + if hasattr(annotation, "__args__") and len(annotation.__args__) > 2: + # Coroutine[Any, Any, ReturnType] -> extract ReturnType + return annotation.__args__[2] + return annotation + + @classmethod + def _format_parameter_default(cls, default_value) -> str: + """Format a parameter's default value for stub files.""" + if default_value is inspect.Parameter.empty: + return "" + elif default_value is None: + return " = None" + elif isinstance(default_value, bool): + return f" = {default_value}" + elif default_value == {}: + return " = {}" + elif default_value == []: + return " = []" + else: + return f" = {default_value}" + + @classmethod + def _format_method_parameters( + cls, + sig: inspect.Signature, + skip_self: bool = True, + type_hints: Optional[dict] = None, + type_tracker: Optional[TypeTracker] = None, + ) -> str: + """Format method parameters for stub files.""" + params = [] + if type_hints is None: + type_hints = {} + + for i, (param_name, param) in enumerate(sig.parameters.items()): + if i == 0 and param_name == "self" and skip_self: + params.append("self") + else: + # Get type annotation from type hints if available, otherwise from signature + annotation = type_hints.get(param_name, param.annotation) + type_str = cls._format_type_annotation(annotation, type_tracker) + + # Get default value + default_str = cls._format_parameter_default(param.default) + + # Combine parameter parts + if annotation is inspect.Parameter.empty: + params.append(f"{param_name}: Any{default_str}") + else: + params.append(f"{param_name}: {type_str}{default_str}") + + return ", ".join(params) + + @classmethod + def _generate_method_signature( + cls, + method_name: str, + method, + is_async: bool = False, + type_tracker: Optional[TypeTracker] = None, + ) -> str: + """Generate a complete method signature for stub files.""" + sig = inspect.signature(method) + + # Try to get evaluated type hints to resolve string annotations + try: + from typing import get_type_hints + type_hints = get_type_hints(method) + except Exception: + # Fallback to empty dict if we can't get type hints + type_hints = {} + + # For async methods, extract the actual return type + return_annotation = type_hints.get('return', sig.return_annotation) + if is_async and inspect.iscoroutinefunction(method): + return_annotation = cls._extract_coroutine_return_type(return_annotation) + + # Format parameters with type hints + params_str = cls._format_method_parameters(sig, type_hints=type_hints, type_tracker=type_tracker) + + # Format return type + return_type = cls._format_type_annotation(return_annotation, type_tracker) + if return_annotation is inspect.Signature.empty: + return_type = "None" + + return f"def {method_name}({params_str}) -> {return_type}: ..." + + @classmethod + def _generate_imports( + cls, async_class: Type, type_tracker: TypeTracker + ) -> list[str]: + """Generate import statements for the stub file.""" + imports = [] + + # Add standard typing imports + imports.append( + "from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple" + ) + + # Add imports from the original module + if async_class.__module__ != "builtins": + module = inspect.getmodule(async_class) + additional_types = [] + + if module: + # Check if module has __all__ defined + module_all = getattr(module, "__all__", None) + + for name, obj in sorted(inspect.getmembers(module)): + if isinstance(obj, type): + # Skip if __all__ is defined and this name isn't in it + # unless it's already been tracked as used in type annotations + if module_all is not None and name not in module_all: + # Check if this type was actually used in annotations + if name not in type_tracker.discovered_types: + continue + + # Check for NamedTuple + if issubclass(obj, tuple) and hasattr(obj, "_fields"): + additional_types.append(name) + # Mark as already imported + type_tracker.already_imported.add(name) + # Check for Enum + elif issubclass(obj, Enum) and name != "Enum": + additional_types.append(name) + # Mark as already imported + type_tracker.already_imported.add(name) + + if additional_types: + type_imports = ", ".join([async_class.__name__] + additional_types) + imports.append(f"from {async_class.__module__} import {type_imports}") + else: + imports.append( + f"from {async_class.__module__} import {async_class.__name__}" + ) + + # Add imports for all discovered types + # Pass the main module name to avoid duplicate imports + imports.extend( + type_tracker.get_imports(main_module_name=async_class.__module__) + ) + + # Add base module import if needed + if hasattr(inspect.getmodule(async_class), "__name__"): + module_name = inspect.getmodule(async_class).__name__ + if "." in module_name: + base_module = module_name.split(".")[0] + # Only add if not already importing from it + if not any(imp.startswith(f"from {base_module}") for imp in imports): + imports.append(f"import {base_module}") + + return imports + + @classmethod + def _get_class_attributes(cls, async_class: Type) -> list[tuple[str, Type]]: + """Extract class attributes that are classes themselves.""" + class_attributes = [] + + # Look for class attributes that are classes + for name, attr in sorted(inspect.getmembers(async_class)): + if isinstance(attr, type) and not name.startswith("_"): + class_attributes.append((name, attr)) + elif ( + hasattr(async_class, "__annotations__") + and name in async_class.__annotations__ + ): + annotation = async_class.__annotations__[name] + if isinstance(annotation, type): + class_attributes.append((name, annotation)) + + return class_attributes + + @classmethod + def _generate_inner_class_stub( + cls, + name: str, + attr: Type, + indent: str = " ", + type_tracker: Optional[TypeTracker] = None, + ) -> list[str]: + """Generate stub for an inner class.""" + stub_lines = [] + stub_lines.append(f"{indent}class {name}Sync:") + + # Add docstring if available + if hasattr(attr, "__doc__") and attr.__doc__: + stub_lines.extend( + cls._format_docstring_for_stub(attr.__doc__, f"{indent} ") + ) + + # Add __init__ if it exists + if hasattr(attr, "__init__"): + try: + init_method = getattr(attr, "__init__") + init_sig = inspect.signature(init_method) + + # Try to get type hints + try: + from typing import get_type_hints + init_hints = get_type_hints(init_method) + except Exception: + init_hints = {} + + # Format parameters + params_str = cls._format_method_parameters( + init_sig, type_hints=init_hints, type_tracker=type_tracker + ) + # Add __init__ docstring if available (before the method) + if hasattr(init_method, "__doc__") and init_method.__doc__: + stub_lines.extend( + cls._format_docstring_for_stub( + init_method.__doc__, f"{indent} " + ) + ) + stub_lines.append( + f"{indent} def __init__({params_str}) -> None: ..." + ) + except (ValueError, TypeError): + stub_lines.append( + f"{indent} def __init__(self, *args, **kwargs) -> None: ..." + ) + + # Add methods to the inner class + has_methods = False + for method_name, method in sorted( + inspect.getmembers(attr, predicate=inspect.isfunction) + ): + if method_name.startswith("_"): + continue + + has_methods = True + try: + # Add method docstring if available (before the method signature) + if method.__doc__: + stub_lines.extend( + cls._format_docstring_for_stub(method.__doc__, f"{indent} ") + ) + + method_sig = cls._generate_method_signature( + method_name, method, is_async=True, type_tracker=type_tracker + ) + stub_lines.append(f"{indent} {method_sig}") + except (ValueError, TypeError): + stub_lines.append( + f"{indent} def {method_name}(self, *args, **kwargs): ..." + ) + + if not has_methods: + stub_lines.append(f"{indent} pass") + + return stub_lines + + @classmethod + def _format_docstring_for_stub( + cls, docstring: str, indent: str = " " + ) -> list[str]: + """Format a docstring for inclusion in a stub file with proper indentation.""" + if not docstring: + return [] + + # First, dedent the docstring to remove any existing indentation + dedented = textwrap.dedent(docstring).strip() + + # Split into lines + lines = dedented.split("\n") + + # Build the properly indented docstring + result = [] + result.append(f'{indent}"""') + + for line in lines: + if line.strip(): # Non-empty line + result.append(f"{indent}{line}") + else: # Empty line + result.append("") + + result.append(f'{indent}"""') + return result + + @classmethod + def _post_process_stub_content(cls, stub_content: list[str]) -> list[str]: + """Post-process stub content to fix any remaining issues.""" + processed = [] + + for line in stub_content: + # Skip processing imports + if line.startswith(("from ", "import ")): + processed.append(line) + continue + + # Fix method signatures missing return types + if ( + line.strip().startswith("def ") + and line.strip().endswith(": ...") + and ") -> " not in line + ): + # Add -> None for methods without return annotation + line = line.replace(": ...", " -> None: ...") + + processed.append(line) + + return processed + + @classmethod + def generate_stub_file(cls, async_class: Type, sync_class: Type) -> None: + """ + Generate a .pyi stub file for the sync class to help IDEs with type checking. + """ + try: + # Only generate stub if we can determine module path + if async_class.__module__ == "__main__": + return + + module = inspect.getmodule(async_class) + if not module: + return + + module_path = module.__file__ + if not module_path: + return + + # Create stub file path in a 'generated' subdirectory + module_dir = os.path.dirname(module_path) + stub_dir = os.path.join(module_dir, "generated") + + # Ensure the generated directory exists + os.makedirs(stub_dir, exist_ok=True) + + module_name = os.path.basename(module_path) + if module_name.endswith(".py"): + module_name = module_name[:-3] + + sync_stub_path = os.path.join(stub_dir, f"{sync_class.__name__}.pyi") + + # Create a type tracker for this stub generation + type_tracker = TypeTracker() + + stub_content = [] + + # We'll generate imports after processing all methods to capture all types + # Leave a placeholder for imports + imports_placeholder_index = len(stub_content) + stub_content.append("") # Will be replaced with imports later + + # Class definition + stub_content.append(f"class {sync_class.__name__}:") + + # Docstring + if async_class.__doc__: + stub_content.extend( + cls._format_docstring_for_stub(async_class.__doc__, " ") + ) + + # Generate __init__ + try: + init_method = async_class.__init__ + init_signature = inspect.signature(init_method) + + # Try to get type hints for __init__ + try: + from typing import get_type_hints + init_hints = get_type_hints(init_method) + except Exception: + init_hints = {} + + # Format parameters + params_str = cls._format_method_parameters( + init_signature, type_hints=init_hints, type_tracker=type_tracker + ) + # Add __init__ docstring if available (before the method) + if hasattr(init_method, "__doc__") and init_method.__doc__: + stub_content.extend( + cls._format_docstring_for_stub(init_method.__doc__, " ") + ) + stub_content.append(f" def __init__({params_str}) -> None: ...") + except (ValueError, TypeError): + stub_content.append( + " def __init__(self, *args, **kwargs) -> None: ..." + ) + + stub_content.append("") # Add newline after __init__ + + # Get class attributes + class_attributes = cls._get_class_attributes(async_class) + + # Generate inner classes + for name, attr in class_attributes: + inner_class_stub = cls._generate_inner_class_stub( + name, attr, type_tracker=type_tracker + ) + stub_content.extend(inner_class_stub) + stub_content.append("") # Add newline after the inner class + + # Add methods to the main class + processed_methods = set() # Keep track of methods we've processed + for name, method in sorted( + inspect.getmembers(async_class, predicate=inspect.isfunction) + ): + if name.startswith("_") or name in processed_methods: + continue + + processed_methods.add(name) + + try: + method_sig = cls._generate_method_signature( + name, method, is_async=True, type_tracker=type_tracker + ) + + # Add docstring if available (before the method signature for proper formatting) + if method.__doc__: + stub_content.extend( + cls._format_docstring_for_stub(method.__doc__, " ") + ) + + stub_content.append(f" {method_sig}") + + stub_content.append("") # Add newline after each method + + except (ValueError, TypeError): + # If we can't get the signature, just add a simple stub + stub_content.append(f" def {name}(self, *args, **kwargs): ...") + stub_content.append("") # Add newline + + # Add properties + for name, prop in sorted( + inspect.getmembers(async_class, lambda x: isinstance(x, property)) + ): + stub_content.append(" @property") + stub_content.append(f" def {name}(self) -> Any: ...") + if prop.fset: + stub_content.append(f" @{name}.setter") + stub_content.append( + f" def {name}(self, value: Any) -> None: ..." + ) + stub_content.append("") # Add newline after each property + + # Add placeholders for the nested class instances + # Check the actual attribute names from class annotations and attributes + attribute_mappings = {} + + # First check annotations for typed attributes (including from parent classes) + # Collect all annotations from the class hierarchy + all_annotations = {} + for base_class in reversed(inspect.getmro(async_class)): + if hasattr(base_class, "__annotations__"): + all_annotations.update(base_class.__annotations__) + + for attr_name, attr_type in sorted(all_annotations.items()): + for class_name, class_type in class_attributes: + # If the class type matches the annotated type + if ( + attr_type == class_type + or (hasattr(attr_type, "__name__") and attr_type.__name__ == class_name) + or (isinstance(attr_type, str) and attr_type == class_name) + ): + attribute_mappings[class_name] = attr_name + + # Remove the extra checking - annotations should be sufficient + + # Add the attribute declarations with proper names + for class_name, class_type in class_attributes: + # Check if there's a mapping from annotation + attr_name = attribute_mappings.get(class_name, class_name) + # Use the annotation name if it exists, even if the attribute doesn't exist yet + # This is because the attribute might be created at runtime + stub_content.append(f" {attr_name}: {class_name}Sync") + + stub_content.append("") # Add a final newline + + # Now generate imports with all discovered types + imports = cls._generate_imports(async_class, type_tracker) + + # Deduplicate imports while preserving order + seen = set() + unique_imports = [] + for imp in imports: + if imp not in seen: + seen.add(imp) + unique_imports.append(imp) + else: + logging.warning(f"Duplicate import detected: {imp}") + + # Replace the placeholder with actual imports + stub_content[imports_placeholder_index : imports_placeholder_index + 1] = ( + unique_imports + ) + + # Post-process stub content + stub_content = cls._post_process_stub_content(stub_content) + + # Write stub file + with open(sync_stub_path, "w") as f: + f.write("\n".join(stub_content)) + + logging.info(f"Generated stub file: {sync_stub_path}") + + except Exception as e: + # If stub generation fails, log the error but don't break the main functionality + logging.error( + f"Error generating stub file for {sync_class.__name__}: {str(e)}" + ) + import traceback + + logging.error(traceback.format_exc()) + + +def create_sync_class(async_class: Type, thread_pool_size=10) -> Type: + """ + Creates a sync version of an async class + + Args: + async_class: The async class to convert + thread_pool_size: Size of thread pool to use + + Returns: + A new class with sync versions of all async methods + """ + return AsyncToSyncConverter.create_sync_class(async_class, thread_pool_size) diff --git a/comfy_api/internal/singleton.py b/comfy_api/internal/singleton.py new file mode 100644 index 000000000..75f16f98e --- /dev/null +++ b/comfy_api/internal/singleton.py @@ -0,0 +1,33 @@ +from typing import Type, TypeVar + +class SingletonMetaclass(type): + T = TypeVar("T", bound="SingletonMetaclass") + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(SingletonMetaclass, cls).__call__( + *args, **kwargs + ) + return cls._instances[cls] + + def inject_instance(cls: Type[T], instance: T) -> None: + assert cls not in SingletonMetaclass._instances, ( + "Cannot inject instance after first instantiation" + ) + SingletonMetaclass._instances[cls] = instance + + def get_instance(cls: Type[T], *args, **kwargs) -> T: + """ + Gets the singleton instance of the class, creating it if it doesn't exist. + """ + if cls not in SingletonMetaclass._instances: + SingletonMetaclass._instances[cls] = super( + SingletonMetaclass, cls + ).__call__(*args, **kwargs) + return cls._instances[cls] + + +class ProxiedSingleton(object, metaclass=SingletonMetaclass): + def __init__(self): + super().__init__() diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py new file mode 100644 index 000000000..b7a3fa9c1 --- /dev/null +++ b/comfy_api/latest/__init__.py @@ -0,0 +1,132 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod +from typing import Type, TYPE_CHECKING +from comfy_api.internal import ComfyAPIBase +from comfy_api.internal.singleton import ProxiedSingleton +from comfy_api.internal.async_to_sync import create_sync_class +from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput +from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents +from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents +from . import _io as io +from . import _ui as ui +# from comfy_api.latest._resources import _RESOURCES as resources #noqa: F401 +from comfy_execution.utils import get_executing_context +from comfy_execution.progress import get_progress_state, PreviewImageTuple +from PIL import Image +from comfy.cli_args import args +import numpy as np + + +class ComfyAPI_latest(ComfyAPIBase): + VERSION = "latest" + STABLE = False + + class Execution(ProxiedSingleton): + async def set_progress( + self, + value: float, + max_value: float, + node_id: str | None = None, + preview_image: Image.Image | ImageInput | None = None, + ignore_size_limit: bool = False, + ) -> None: + """ + Update the progress bar displayed in the ComfyUI interface. + + This function allows custom nodes and API calls to report their progress + back to the user interface, providing visual feedback during long operations. + + Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK + """ + executing_context = get_executing_context() + if node_id is None and executing_context is not None: + node_id = executing_context.node_id + if node_id is None: + raise ValueError("node_id must be provided if not in executing context") + + # Convert preview_image to PreviewImageTuple if needed + to_display: PreviewImageTuple | Image.Image | ImageInput | None = preview_image + if to_display is not None: + # First convert to PIL Image if needed + if isinstance(to_display, ImageInput): + # Convert ImageInput (torch.Tensor) to PIL Image + # Handle tensor shape [B, H, W, C] -> get first image if batch + tensor = to_display + if len(tensor.shape) == 4: + tensor = tensor[0] + + # Convert to numpy array and scale to 0-255 + image_np = (tensor.cpu().numpy() * 255).astype(np.uint8) + to_display = Image.fromarray(image_np) + + if isinstance(to_display, Image.Image): + # Detect image format from PIL Image + image_format = to_display.format if to_display.format else "JPEG" + # Use None for preview_size if ignore_size_limit is True + preview_size = None if ignore_size_limit else args.preview_size + to_display = (image_format, to_display, preview_size) + + get_progress_state().update_progress( + node_id=node_id, + value=value, + max_value=max_value, + image=to_display, + ) + + execution: Execution + +class ComfyExtension(ABC): + async def on_load(self) -> None: + """ + Called when an extension is loaded. + This should be used to initialize any global resources neeeded by the extension. + """ + + @abstractmethod + async def get_node_list(self) -> list[type[io.ComfyNode]]: + """ + Returns a list of nodes that this extension provides. + """ + +class Input: + Image = ImageInput + Audio = AudioInput + Mask = MaskInput + Latent = LatentInput + Video = VideoInput + +class InputImpl: + VideoFromFile = VideoFromFile + VideoFromComponents = VideoFromComponents + +class Types: + VideoCodec = VideoCodec + VideoContainer = VideoContainer + VideoComponents = VideoComponents + +ComfyAPI = ComfyAPI_latest + +# Create a synchronous version of the API +if TYPE_CHECKING: + import comfy_api.latest.generated.ComfyAPISyncStub # type: ignore + + ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub] +ComfyAPISync = create_sync_class(ComfyAPI_latest) + +# create new aliases for io and ui +IO = io +UI = ui + +__all__ = [ + "ComfyAPI", + "ComfyAPISync", + "Input", + "InputImpl", + "Types", + "ComfyExtension", + "io", + "IO", + "ui", + "UI", +] diff --git a/comfy_api/latest/_input/__init__.py b/comfy_api/latest/_input/__init__.py new file mode 100644 index 000000000..14f0e72f4 --- /dev/null +++ b/comfy_api/latest/_input/__init__.py @@ -0,0 +1,10 @@ +from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput +from .video_types import VideoInput + +__all__ = [ + "ImageInput", + "AudioInput", + "VideoInput", + "MaskInput", + "LatentInput", +] diff --git a/comfy_api/latest/_input/basic_types.py b/comfy_api/latest/_input/basic_types.py new file mode 100644 index 000000000..245c6cbb1 --- /dev/null +++ b/comfy_api/latest/_input/basic_types.py @@ -0,0 +1,42 @@ +import torch +from typing import TypedDict, List, Optional + +ImageInput = torch.Tensor +""" +An image in format [B, H, W, C] where B is the batch size, C is the number of channels, +""" + +MaskInput = torch.Tensor +""" +A mask in format [B, H, W] where B is the batch size +""" + +class AudioInput(TypedDict): + """ + TypedDict representing audio input. + """ + + waveform: torch.Tensor + """ + Tensor in the format [B, C, T] where B is the batch size, C is the number of channels, + """ + + sample_rate: int + +class LatentInput(TypedDict): + """ + TypedDict representing latent input. + """ + + samples: torch.Tensor + """ + Tensor in the format [B, C, H, W] where B is the batch size, C is the number of channels, + H is the height, and W is the width. + """ + + noise_mask: Optional[MaskInput] + """ + Optional noise mask tensor in the same format as samples. + """ + + batch_index: Optional[List[int]] diff --git a/comfy_api/latest/_input/video_types.py b/comfy_api/latest/_input/video_types.py new file mode 100644 index 000000000..a335df4d0 --- /dev/null +++ b/comfy_api/latest/_input/video_types.py @@ -0,0 +1,85 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Optional, Union, IO +import io +import av +from comfy_api.util import VideoContainer, VideoCodec, VideoComponents + +class VideoInput(ABC): + """ + Abstract base class for video input types. + """ + + @abstractmethod + def get_components(self) -> VideoComponents: + """ + Abstract method to get the video components (images, audio, and frame rate). + + Returns: + VideoComponents containing images, audio, and frame rate + """ + pass + + @abstractmethod + def save_to( + self, + path: Union[str, IO[bytes]], + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + """ + Abstract method to save the video input to a file. + """ + pass + + def get_stream_source(self) -> Union[str, io.BytesIO]: + """ + Get a streamable source for the video. This allows processing without + loading the entire video into memory. + + Returns: + Either a file path (str) or a BytesIO object that can be opened with av. + + Default implementation creates a BytesIO buffer, but subclasses should + override this for better performance when possible. + """ + buffer = io.BytesIO() + self.save_to(buffer) + buffer.seek(0) + return buffer + + # Provide a default implementation, but subclasses can provide optimized versions + # if possible. + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + components = self.get_components() + return components.images.shape[2], components.images.shape[1] + + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + components = self.get_components() + frame_count = components.images.shape[0] + return float(frame_count / components.frame_rate) + + def get_container_format(self) -> str: + """ + Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). + + Returns: + Container format as string + """ + # Default implementation - subclasses should override for better performance + source = self.get_stream_source() + with av.open(source, mode="r") as container: + return container.format.name diff --git a/comfy_api/latest/_input_impl/__init__.py b/comfy_api/latest/_input_impl/__init__.py new file mode 100644 index 000000000..02901b8b9 --- /dev/null +++ b/comfy_api/latest/_input_impl/__init__.py @@ -0,0 +1,7 @@ +from .video_types import VideoFromFile, VideoFromComponents + +__all__ = [ + # Implementations + "VideoFromFile", + "VideoFromComponents", +] diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py new file mode 100644 index 000000000..f646504c8 --- /dev/null +++ b/comfy_api/latest/_input_impl/video_types.py @@ -0,0 +1,308 @@ +from __future__ import annotations +from av.container import InputContainer +from av.subtitles.stream import SubtitleStream +from fractions import Fraction +from typing import Optional +from comfy_api.latest._input import AudioInput, VideoInput +import av +import io +import json +import numpy as np +import math +import torch +from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents + + +def container_to_output_format(container_format: str | None) -> str | None: + """ + A container's `format` may be a comma-separated list of formats. + E.g., iso container's `format` may be `mov,mp4,m4a,3gp,3g2,mj2`. + However, writing to a file/stream with `av.open` requires a single format, + or `None` to auto-detect. + """ + if not container_format: + return None # Auto-detect + + if "," not in container_format: + return container_format + + formats = container_format.split(",") + return formats[0] + + +def get_open_write_kwargs( + dest: str | io.BytesIO, container_format: str, to_format: str | None +) -> dict: + """Get kwargs for writing a `VideoFromFile` to a file/stream with `av.open`""" + open_kwargs = { + "mode": "w", + # If isobmff, preserve custom metadata tags (workflow, prompt, extra_pnginfo) + "options": {"movflags": "use_metadata_tags"}, + } + + is_write_to_buffer = isinstance(dest, io.BytesIO) + if is_write_to_buffer: + # Set output format explicitly, since it cannot be inferred from file extension + if to_format == VideoContainer.AUTO: + to_format = container_format.lower() + elif isinstance(to_format, str): + to_format = to_format.lower() + open_kwargs["format"] = container_to_output_format(to_format) + + return open_kwargs + + +class VideoFromFile(VideoInput): + """ + Class representing video input from a file. + """ + + def __init__(self, file: str | io.BytesIO): + """ + Initialize the VideoFromFile object based off of either a path on disk or a BytesIO object + containing the file contents. + """ + self.__file = file + + def get_stream_source(self) -> str | io.BytesIO: + """ + Return the underlying file source for efficient streaming. + This avoids unnecessary memory copies when the source is already a file path. + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + return self.__file + + def get_dimensions(self) -> tuple[int, int]: + """ + Returns the dimensions of the video input. + + Returns: + Tuple of (width, height) + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + for stream in container.streams: + if stream.type == 'video': + assert isinstance(stream, av.VideoStream) + return stream.width, stream.height + raise ValueError(f"No video stream found in file '{self.__file}'") + + def get_duration(self) -> float: + """ + Returns the duration of the video in seconds. + + Returns: + Duration in seconds + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + with av.open(self.__file, mode="r") as container: + if container.duration is not None: + return float(container.duration / av.time_base) + + # Fallback: calculate from frame count and frame rate + video_stream = next( + (s for s in container.streams if s.type == "video"), None + ) + if video_stream and video_stream.frames and video_stream.average_rate: + return float(video_stream.frames / video_stream.average_rate) + + # Last resort: decode frames to count them + if video_stream and video_stream.average_rate: + frame_count = 0 + container.seek(0) + for packet in container.demux(video_stream): + for _ in packet.decode(): + frame_count += 1 + if frame_count > 0: + return float(frame_count / video_stream.average_rate) + + raise ValueError(f"Could not determine duration for file '{self.__file}'") + + def get_container_format(self) -> str: + """ + Returns the container format of the video (e.g., 'mp4', 'mov', 'avi'). + + Returns: + Container format as string + """ + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) + with av.open(self.__file, mode='r') as container: + return container.format.name + + def get_components_internal(self, container: InputContainer) -> VideoComponents: + # Get video frames + frames = [] + for frame in container.decode(video=0): + img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3) + img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3) + frames.append(img) + + images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) + + # Get frame rate + video_stream = next(s for s in container.streams if s.type == 'video') + frame_rate = Fraction(video_stream.average_rate) if video_stream and video_stream.average_rate else Fraction(1) + + # Get audio if available + audio = None + try: + container.seek(0) # Reset the container to the beginning + for stream in container.streams: + if stream.type != 'audio': + continue + assert isinstance(stream, av.AudioStream) + audio_frames = [] + for packet in container.demux(stream): + for frame in packet.decode(): + assert isinstance(frame, av.AudioFrame) + audio_frames.append(frame.to_ndarray()) # shape: (channels, samples) + if len(audio_frames) > 0: + audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) + audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) + audio = AudioInput({ + "waveform": audio_tensor, + "sample_rate": int(stream.sample_rate) if stream.sample_rate else 1, + }) + except StopIteration: + pass # No audio stream + + metadata = container.metadata + return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata) + + def get_components(self) -> VideoComponents: + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + return self.get_components_internal(container) + raise ValueError(f"No video stream found in file '{self.__file}'") + + def save_to( + self, + path: str | io.BytesIO, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if isinstance(self.__file, io.BytesIO): + self.__file.seek(0) # Reset the BytesIO object to the beginning + with av.open(self.__file, mode='r') as container: + container_format = container.format.name + video_encoding = container.streams.video[0].codec.name if len(container.streams.video) > 0 else None + reuse_streams = True + if format != VideoContainer.AUTO and format not in container_format.split(","): + reuse_streams = False + if codec != VideoCodec.AUTO and codec != video_encoding and video_encoding is not None: + reuse_streams = False + + if not reuse_streams: + components = self.get_components_internal(container) + video = VideoFromComponents(components) + return video.save_to( + path, + format=format, + codec=codec, + metadata=metadata + ) + + streams = container.streams + + open_kwargs = get_open_write_kwargs(path, container_format, format) + with av.open(path, **open_kwargs) as output_container: + # Copy over the original metadata + for key, value in container.metadata.items(): + if metadata is None or key not in metadata: + output_container.metadata[key] = value + + # Add our new metadata + if metadata is not None: + for key, value in metadata.items(): + if isinstance(value, str): + output_container.metadata[key] = value + else: + output_container.metadata[key] = json.dumps(value) + + # Add streams to the new container + stream_map = {} + for stream in streams: + if isinstance(stream, (av.VideoStream, av.AudioStream, SubtitleStream)): + out_stream = output_container.add_stream_from_template(template=stream, opaque=True) + stream_map[stream] = out_stream + + # Write packets to the new container + for packet in container.demux(): + if packet.stream in stream_map and packet.dts is not None: + packet.stream = stream_map[packet.stream] + output_container.mux(packet) + +class VideoFromComponents(VideoInput): + """ + Class representing video input from tensors. + """ + + def __init__(self, components: VideoComponents): + self.__components = components + + def get_components(self) -> VideoComponents: + return VideoComponents( + images=self.__components.images, + audio=self.__components.audio, + frame_rate=self.__components.frame_rate + ) + + def save_to( + self, + path: str, + format: VideoContainer = VideoContainer.AUTO, + codec: VideoCodec = VideoCodec.AUTO, + metadata: Optional[dict] = None + ): + if format != VideoContainer.AUTO and format != VideoContainer.MP4: + raise ValueError("Only MP4 format is supported for now") + if codec != VideoCodec.AUTO and codec != VideoCodec.H264: + raise ValueError("Only H264 codec is supported for now") + with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output: + # Add metadata before writing any streams + if metadata is not None: + for key, value in metadata.items(): + output.metadata[key] = json.dumps(value) + + frame_rate = Fraction(round(self.__components.frame_rate * 1000), 1000) + # Create a video stream + video_stream = output.add_stream('h264', rate=frame_rate) + video_stream.width = self.__components.images.shape[2] + video_stream.height = self.__components.images.shape[1] + video_stream.pix_fmt = 'yuv420p' + + # Create an audio stream + audio_sample_rate = 1 + audio_stream: Optional[av.AudioStream] = None + if self.__components.audio: + audio_sample_rate = int(self.__components.audio['sample_rate']) + audio_stream = output.add_stream('aac', rate=audio_sample_rate) + + # Encode video + for i, frame in enumerate(self.__components.images): + img = (frame * 255).clamp(0, 255).byte().cpu().numpy() # shape: (H, W, 3) + frame = av.VideoFrame.from_ndarray(img, format='rgb24') + frame = frame.reformat(format='yuv420p') # Convert to YUV420P as required by h264 + packet = video_stream.encode(frame) + output.mux(packet) + + # Flush video + packet = video_stream.encode(None) + output.mux(packet) + + if audio_stream and self.__components.audio: + waveform = self.__components.audio['waveform'] + waveform = waveform[:, :, :math.ceil((audio_sample_rate / frame_rate) * self.__components.images.shape[0])] + frame = av.AudioFrame.from_ndarray(waveform.movedim(2, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[1] == 1 else 'stereo') + frame.sample_rate = audio_sample_rate + frame.pts = 0 + output.mux(audio_stream.encode(frame)) + + # Flush encoder + output.mux(audio_stream.encode(None)) diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py new file mode 100644 index 000000000..0b701260f --- /dev/null +++ b/comfy_api/latest/_io.py @@ -0,0 +1,1659 @@ +from __future__ import annotations + +import copy +import inspect +from abc import ABC, abstractmethod +from collections import Counter +from dataclasses import asdict, dataclass +from enum import Enum +from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING +from typing_extensions import NotRequired, final + +# used for type hinting +import torch + +if TYPE_CHECKING: + from spandrel import ImageModelDescriptor + from comfy.clip_vision import ClipVisionModel + from comfy.clip_vision import Output as ClipVisionOutput_ + from comfy.controlnet import ControlNet + from comfy.hooks import HookGroup, HookKeyframeGroup + from comfy.model_patcher import ModelPatcher + from comfy.samplers import CFGGuider, Sampler + from comfy.sd import CLIP, VAE + from comfy.sd import StyleModel as StyleModel_ + from comfy_api.input import VideoInput +from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class, + prune_dict, shallow_clone_class) +from comfy_api.latest._resources import Resources, ResourcesLocal +from comfy_execution.graph_utils import ExecutionBlocker + +# from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference + +class FolderType(str, Enum): + input = "input" + output = "output" + temp = "temp" + + +class UploadType(str, Enum): + image = "image_upload" + audio = "audio_upload" + video = "video_upload" + model = "file_upload" + + +class RemoteOptions: + def __init__(self, route: str, refresh_button: bool, control_after_refresh: Literal["first", "last"]="first", + timeout: int=None, max_retries: int=None, refresh: int=None): + self.route = route + """The route to the remote source.""" + self.refresh_button = refresh_button + """Specifies whether to show a refresh button in the UI below the widget.""" + self.control_after_refresh = control_after_refresh + """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on.""" + self.timeout = timeout + """The maximum amount of time to wait for a response from the remote source in milliseconds.""" + self.max_retries = max_retries + """The maximum number of retries before aborting the request.""" + self.refresh = refresh + """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed.""" + + def as_dict(self): + return prune_dict({ + "route": self.route, + "refresh_button": self.refresh_button, + "control_after_refresh": self.control_after_refresh, + "timeout": self.timeout, + "max_retries": self.max_retries, + "refresh": self.refresh, + }) + + +class NumberDisplay(str, Enum): + number = "number" + slider = "slider" + + +class _StringIOType(str): + def __ne__(self, value: object) -> bool: + if self == "*" or value == "*": + return False + if not isinstance(value, str): + return True + a = frozenset(self.split(",")) + b = frozenset(value.split(",")) + return not (b.issubset(a) or a.issubset(b)) + +class _ComfyType(ABC): + Type = Any + io_type: str = None + +# NOTE: this is a workaround to make the decorator return the correct type +T = TypeVar("T", bound=type) +def comfytype(io_type: str, **kwargs): + ''' + Decorator to mark nested classes as ComfyType; io_type will be bound to the class. + + A ComfyType may have the following attributes: + - Type = + - class Input(Input): ... + - class Output(Output): ... + ''' + def decorator(cls: T) -> T: + if isinstance(cls, _ComfyType) or issubclass(cls, _ComfyType): + # clone Input and Output classes to avoid modifying the original class + new_cls = cls + if hasattr(new_cls, "Input"): + new_cls.Input = copy_class(new_cls.Input) + if hasattr(new_cls, "Output"): + new_cls.Output = copy_class(new_cls.Output) + else: + # copy class attributes except for special ones that shouldn't be in type() + cls_dict = { + k: v for k, v in cls.__dict__.items() + if k not in ('__dict__', '__weakref__', '__module__', '__doc__') + } + # new class + new_cls: ComfyTypeIO = type( + cls.__name__, + (cls, ComfyTypeIO), + cls_dict + ) + # metadata preservation + new_cls.__module__ = cls.__module__ + new_cls.__doc__ = cls.__doc__ + # assign ComfyType attributes, if needed + # NOTE: use __ne__ trick for io_type (see node_typing.IO.__ne__ for details) + new_cls.io_type = _StringIOType(io_type) + if hasattr(new_cls, "Input") and new_cls.Input is not None: + new_cls.Input.Parent = new_cls + if hasattr(new_cls, "Output") and new_cls.Output is not None: + new_cls.Output.Parent = new_cls + return new_cls + return decorator + +def Custom(io_type: str) -> type[ComfyTypeIO]: + '''Create a ComfyType for a custom io_type.''' + @comfytype(io_type=io_type) + class CustomComfyType(ComfyTypeIO): + ... + return CustomComfyType + +class _IO_V3: + ''' + Base class for V3 Inputs and Outputs. + ''' + Parent: _ComfyType = None + + def __init__(self): + pass + + @property + def io_type(self): + return self.Parent.io_type + + @property + def Type(self): + return self.Parent.Type + +class Input(_IO_V3): + ''' + Base class for a V3 Input. + ''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): + super().__init__() + self.id = id + self.display_name = display_name + self.optional = optional + self.tooltip = tooltip + self.lazy = lazy + self.extra_dict = extra_dict if extra_dict is not None else {} + + def as_dict(self): + return prune_dict({ + "display_name": self.display_name, + "optional": self.optional, + "tooltip": self.tooltip, + "lazy": self.lazy, + }) | prune_dict(self.extra_dict) + + def get_io_type(self): + return _StringIOType(self.io_type) + +class WidgetInput(Input): + ''' + Base class for a V3 Input with widget. + ''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + default: Any=None, + socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None): + super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) + self.default = default + self.socketless = socketless + self.widget_type = widget_type + self.force_input = force_input + + def as_dict(self): + return super().as_dict() | prune_dict({ + "default": self.default, + "socketless": self.socketless, + "widgetType": self.widget_type, + "forceInput": self.force_input, + }) + + def get_io_type(self): + return self.widget_type if self.widget_type is not None else super().get_io_type() + + +class Output(_IO_V3): + def __init__(self, id: str=None, display_name: str=None, tooltip: str=None, + is_output_list=False): + self.id = id + self.display_name = display_name + self.tooltip = tooltip + self.is_output_list = is_output_list + + def as_dict(self): + return prune_dict({ + "display_name": self.display_name, + "tooltip": self.tooltip, + "is_output_list": self.is_output_list, + }) + + def get_io_type(self): + return self.io_type + + +class ComfyTypeI(_ComfyType): + '''ComfyType subclass that only has a default Input class - intended for types that only have Inputs.''' + class Input(Input): + ... + +class ComfyTypeIO(ComfyTypeI): + '''ComfyType subclass that has default Input and Output classes; useful for types with both Inputs and Outputs.''' + class Output(Output): + ... + + +@comfytype(io_type="BOOLEAN") +class Boolean(ComfyTypeIO): + Type = bool + + class Input(WidgetInput): + '''Boolean input.''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + default: bool=None, label_on: str=None, label_off: str=None, + socketless: bool=None, force_input: bool=None): + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) + self.label_on = label_on + self.label_off = label_off + self.default: bool + + def as_dict(self): + return super().as_dict() | prune_dict({ + "label_on": self.label_on, + "label_off": self.label_off, + }) + +@comfytype(io_type="INT") +class Int(ComfyTypeIO): + Type = int + + class Input(WidgetInput): + '''Integer input.''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + default: int=None, min: int=None, max: int=None, step: int=None, control_after_generate: bool=None, + display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None): + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) + self.min = min + self.max = max + self.step = step + self.control_after_generate = control_after_generate + self.display_mode = display_mode + self.default: int + + def as_dict(self): + return super().as_dict() | prune_dict({ + "min": self.min, + "max": self.max, + "step": self.step, + "control_after_generate": self.control_after_generate, + "display": self.display_mode.value if self.display_mode else None, + }) + +@comfytype(io_type="FLOAT") +class Float(ComfyTypeIO): + Type = float + + class Input(WidgetInput): + '''Float input.''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + default: float=None, min: float=None, max: float=None, step: float=None, round: float=None, + display_mode: NumberDisplay=None, socketless: bool=None, force_input: bool=None): + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) + self.min = min + self.max = max + self.step = step + self.round = round + self.display_mode = display_mode + self.default: float + + def as_dict(self): + return super().as_dict() | prune_dict({ + "min": self.min, + "max": self.max, + "step": self.step, + "round": self.round, + "display": self.display_mode, + }) + +@comfytype(io_type="STRING") +class String(ComfyTypeIO): + Type = str + + class Input(WidgetInput): + '''String input.''' + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + multiline=False, placeholder: str=None, default: str=None, dynamic_prompts: bool=None, + socketless: bool=None, force_input: bool=None): + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, None, force_input) + self.multiline = multiline + self.placeholder = placeholder + self.dynamic_prompts = dynamic_prompts + self.default: str + + def as_dict(self): + return super().as_dict() | prune_dict({ + "multiline": self.multiline, + "placeholder": self.placeholder, + "dynamicPrompts": self.dynamic_prompts, + }) + +@comfytype(io_type="COMBO") +class Combo(ComfyTypeIO): + Type = str + class Input(WidgetInput): + """Combo input (dropdown).""" + Type = str + def __init__( + self, + id: str, + options: list[str] | list[int] | type[Enum] = None, + display_name: str=None, + optional=False, + tooltip: str=None, + lazy: bool=None, + default: str | int | Enum = None, + control_after_generate: bool=None, + upload: UploadType=None, + image_folder: FolderType=None, + remote: RemoteOptions=None, + socketless: bool=None, + ): + if isinstance(options, type) and issubclass(options, Enum): + options = [v.value for v in options] + if isinstance(default, Enum): + default = default.value + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless) + self.multiselect = False + self.options = options + self.control_after_generate = control_after_generate + self.upload = upload + self.image_folder = image_folder + self.remote = remote + self.default: str + + def as_dict(self): + return super().as_dict() | prune_dict({ + "multiselect": self.multiselect, + "options": self.options, + "control_after_generate": self.control_after_generate, + **({self.upload.value: True} if self.upload is not None else {}), + "image_folder": self.image_folder.value if self.image_folder else None, + "remote": self.remote.as_dict() if self.remote else None, + }) + + class Output(Output): + def __init__(self, id: str=None, display_name: str=None, options: list[str]=None, tooltip: str=None, is_output_list=False): + super().__init__(id, display_name, tooltip, is_output_list) + self.options = options if options is not None else [] + + @property + def io_type(self): + return self.options + +@comfytype(io_type="COMBO") +class MultiCombo(ComfyTypeI): + '''Multiselect Combo input (dropdown for selecting potentially more than one value).''' + # TODO: something is wrong with the serialization, frontend does not recognize it as multiselect + Type = list[str] + class Input(Combo.Input): + def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, + default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None, + socketless: bool=None): + super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless) + self.multiselect = True + self.placeholder = placeholder + self.chip = chip + self.default: list[str] + + def as_dict(self): + to_return = super().as_dict() | prune_dict({ + "multi_select": self.multiselect, + "placeholder": self.placeholder, + "chip": self.chip, + }) + return to_return + +@comfytype(io_type="IMAGE") +class Image(ComfyTypeIO): + Type = torch.Tensor + + +@comfytype(io_type="WAN_CAMERA_EMBEDDING") +class WanCameraEmbedding(ComfyTypeIO): + Type = torch.Tensor + + +@comfytype(io_type="WEBCAM") +class Webcam(ComfyTypeIO): + Type = str + + class Input(WidgetInput): + """Webcam input.""" + Type = str + def __init__( + self, id: str, display_name: str=None, optional=False, + tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None + ): + super().__init__(id, display_name, optional, tooltip, lazy, default, socketless) + + +@comfytype(io_type="MASK") +class Mask(ComfyTypeIO): + Type = torch.Tensor + +@comfytype(io_type="LATENT") +class Latent(ComfyTypeIO): + '''Latents are stored as a dictionary.''' + class LatentDict(TypedDict): + samples: torch.Tensor + '''Latent tensors.''' + noise_mask: NotRequired[torch.Tensor] + batch_index: NotRequired[list[int]] + type: NotRequired[str] + '''Only needed if dealing with these types: audio, hunyuan3dv2''' + Type = LatentDict + +@comfytype(io_type="CONDITIONING") +class Conditioning(ComfyTypeIO): + class PooledDict(TypedDict): + pooled_output: torch.Tensor + '''Pooled output from CLIP.''' + control: NotRequired[ControlNet] + '''ControlNet to apply to conditioning.''' + control_apply_to_uncond: NotRequired[bool] + '''Whether to apply ControlNet to matching negative conditioning at sample time, if applicable.''' + cross_attn_controlnet: NotRequired[torch.Tensor] + '''CrossAttn from CLIP to use for controlnet only.''' + pooled_output_controlnet: NotRequired[torch.Tensor] + '''Pooled output from CLIP to use for controlnet only.''' + gligen: NotRequired[tuple[str, Gligen, list[tuple[torch.Tensor, int, ...]]]] + '''GLIGEN to apply to conditioning.''' + area: NotRequired[tuple[int, ...] | tuple[str, float, ...]] + '''Set area of conditioning. First half of values apply to dimensions, the second half apply to coordinates. + By default, the dimensions are based on total pixel amount, but the first value can be set to "percentage" to use a percentage of the image size instead. + + (1024, 1024, 0, 0) would apply conditioning to the top-left 1024x1024 pixels. + + ("percentage", 0.5, 0.5, 0, 0) would apply conditioning to the top-left 50% of the image.''' # TODO: verify its actually top-left + strength: NotRequired[float] + '''Strength of conditioning. Default strength is 1.0.''' + mask: NotRequired[torch.Tensor] + '''Mask to apply conditioning to.''' + mask_strength: NotRequired[float] + '''Strength of conditioning mask. Default strength is 1.0.''' + set_area_to_bounds: NotRequired[bool] + '''Whether conditioning mask should determine bounds of area - if set to false, latents are sampled at full resolution and result is applied in mask.''' + concat_latent_image: NotRequired[torch.Tensor] + '''Used for inpainting and specific models.''' + concat_mask: NotRequired[torch.Tensor] + '''Used for inpainting and specific models.''' + concat_image: NotRequired[torch.Tensor] + '''Used by SD_4XUpscale_Conditioning.''' + noise_augmentation: NotRequired[float] + '''Used by SD_4XUpscale_Conditioning.''' + hooks: NotRequired[HookGroup] + '''Applies hooks to conditioning.''' + default: NotRequired[bool] + '''Whether to this conditioning is 'default'; default conditioning gets applied to any areas of the image that have no masks/areas applied, assuming at least one area/mask is present during sampling.''' + start_percent: NotRequired[float] + '''Determines relative step to begin applying conditioning, expressed as a float between 0.0 and 1.0.''' + end_percent: NotRequired[float] + '''Determines relative step to end applying conditioning, expressed as a float between 0.0 and 1.0.''' + clip_start_percent: NotRequired[float] + '''Internal variable for conditioning scheduling - start of application, expressed as a float between 0.0 and 1.0.''' + clip_end_percent: NotRequired[float] + '''Internal variable for conditioning scheduling - end of application, expressed as a float between 0.0 and 1.0.''' + attention_mask: NotRequired[torch.Tensor] + '''Masks text conditioning; used by StyleModel among others.''' + attention_mask_img_shape: NotRequired[tuple[int, ...]] + '''Masks text conditioning; used by StyleModel among others.''' + unclip_conditioning: NotRequired[list[dict]] + '''Used by unCLIP.''' + conditioning_lyrics: NotRequired[torch.Tensor] + '''Used by AceT5Model.''' + seconds_start: NotRequired[float] + '''Used by StableAudio.''' + seconds_total: NotRequired[float] + '''Used by StableAudio.''' + lyrics_strength: NotRequired[float] + '''Used by AceStepAudio.''' + width: NotRequired[int] + '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).''' + height: NotRequired[int] + '''Used by certain models (e.g. CLIPTextEncodeSDXL/Refiner, PixArtAlpha).''' + aesthetic_score: NotRequired[float] + '''Used by CLIPTextEncodeSDXL/Refiner.''' + crop_w: NotRequired[int] + '''Used by CLIPTextEncodeSDXL.''' + crop_h: NotRequired[int] + '''Used by CLIPTextEncodeSDXL.''' + target_width: NotRequired[int] + '''Used by CLIPTextEncodeSDXL.''' + target_height: NotRequired[int] + '''Used by CLIPTextEncodeSDXL.''' + reference_latents: NotRequired[list[torch.Tensor]] + '''Used by ReferenceLatent.''' + guidance: NotRequired[float] + '''Used by Flux-like models with guidance embed.''' + guiding_frame_index: NotRequired[int] + '''Used by Hunyuan ImageToVideo.''' + ref_latent: NotRequired[torch.Tensor] + '''Used by Hunyuan ImageToVideo.''' + keyframe_idxs: NotRequired[list[int]] + '''Used by LTXV.''' + frame_rate: NotRequired[float] + '''Used by LTXV.''' + stable_cascade_prior: NotRequired[torch.Tensor] + '''Used by StableCascade.''' + elevation: NotRequired[list[float]] + '''Used by SV3D.''' + azimuth: NotRequired[list[float]] + '''Used by SV3D.''' + motion_bucket_id: NotRequired[int] + '''Used by SVD-like models.''' + fps: NotRequired[int] + '''Used by SVD-like models.''' + augmentation_level: NotRequired[float] + '''Used by SVD-like models.''' + clip_vision_output: NotRequired[ClipVisionOutput_] + '''Used by WAN-like models.''' + vace_frames: NotRequired[torch.Tensor] + '''Used by WAN VACE.''' + vace_mask: NotRequired[torch.Tensor] + '''Used by WAN VACE.''' + vace_strength: NotRequired[float] + '''Used by WAN VACE.''' + camera_conditions: NotRequired[Any] # TODO: assign proper type once defined + '''Used by WAN Camera.''' + time_dim_concat: NotRequired[torch.Tensor] + '''Used by WAN Phantom Subject.''' + + CondList = list[tuple[torch.Tensor, PooledDict]] + Type = CondList + +@comfytype(io_type="SAMPLER") +class Sampler(ComfyTypeIO): + if TYPE_CHECKING: + Type = Sampler + +@comfytype(io_type="SIGMAS") +class Sigmas(ComfyTypeIO): + Type = torch.Tensor + +@comfytype(io_type="NOISE") +class Noise(ComfyTypeIO): + Type = torch.Tensor + +@comfytype(io_type="GUIDER") +class Guider(ComfyTypeIO): + if TYPE_CHECKING: + Type = CFGGuider + +@comfytype(io_type="CLIP") +class Clip(ComfyTypeIO): + if TYPE_CHECKING: + Type = CLIP + +@comfytype(io_type="CONTROL_NET") +class ControlNet(ComfyTypeIO): + if TYPE_CHECKING: + Type = ControlNet + +@comfytype(io_type="VAE") +class Vae(ComfyTypeIO): + if TYPE_CHECKING: + Type = VAE + +@comfytype(io_type="MODEL") +class Model(ComfyTypeIO): + if TYPE_CHECKING: + Type = ModelPatcher + +@comfytype(io_type="CLIP_VISION") +class ClipVision(ComfyTypeIO): + if TYPE_CHECKING: + Type = ClipVisionModel + +@comfytype(io_type="CLIP_VISION_OUTPUT") +class ClipVisionOutput(ComfyTypeIO): + if TYPE_CHECKING: + Type = ClipVisionOutput_ + +@comfytype(io_type="STYLE_MODEL") +class StyleModel(ComfyTypeIO): + if TYPE_CHECKING: + Type = StyleModel_ + +@comfytype(io_type="GLIGEN") +class Gligen(ComfyTypeIO): + '''ModelPatcher that wraps around a 'Gligen' model.''' + if TYPE_CHECKING: + Type = ModelPatcher + +@comfytype(io_type="UPSCALE_MODEL") +class UpscaleModel(ComfyTypeIO): + if TYPE_CHECKING: + Type = ImageModelDescriptor + +@comfytype(io_type="AUDIO") +class Audio(ComfyTypeIO): + class AudioDict(TypedDict): + waveform: torch.Tensor + sampler_rate: int + Type = AudioDict + +@comfytype(io_type="VIDEO") +class Video(ComfyTypeIO): + if TYPE_CHECKING: + Type = VideoInput + +@comfytype(io_type="SVG") +class SVG(ComfyTypeIO): + Type = Any # TODO: SVG class is defined in comfy_extras/nodes_images.py, causing circular reference; should be moved to somewhere else before referenced directly in v3 + +@comfytype(io_type="LORA_MODEL") +class LoraModel(ComfyTypeIO): + Type = dict[str, torch.Tensor] + +@comfytype(io_type="LOSS_MAP") +class LossMap(ComfyTypeIO): + class LossMapDict(TypedDict): + loss: list[torch.Tensor] + Type = LossMapDict + +@comfytype(io_type="VOXEL") +class Voxel(ComfyTypeIO): + Type = Any # TODO: VOXEL class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 + +@comfytype(io_type="MESH") +class Mesh(ComfyTypeIO): + Type = Any # TODO: MESH class is defined in comfy_extras/nodes_hunyuan3d.py; should be moved to somewhere else before referenced directly in v3 + +@comfytype(io_type="HOOKS") +class Hooks(ComfyTypeIO): + if TYPE_CHECKING: + Type = HookGroup + +@comfytype(io_type="HOOK_KEYFRAMES") +class HookKeyframes(ComfyTypeIO): + if TYPE_CHECKING: + Type = HookKeyframeGroup + +@comfytype(io_type="TIMESTEPS_RANGE") +class TimestepsRange(ComfyTypeIO): + '''Range defined by start and endpoint, between 0.0 and 1.0.''' + Type = tuple[int, int] + +@comfytype(io_type="LATENT_OPERATION") +class LatentOperation(ComfyTypeIO): + Type = Callable[[torch.Tensor], torch.Tensor] + +@comfytype(io_type="FLOW_CONTROL") +class FlowControl(ComfyTypeIO): + # NOTE: only used in testing_nodes right now + Type = tuple[str, Any] + +@comfytype(io_type="ACCUMULATION") +class Accumulation(ComfyTypeIO): + # NOTE: only used in testing_nodes right now + class AccumulationDict(TypedDict): + accum: list[Any] + Type = AccumulationDict + + +@comfytype(io_type="LOAD3D_CAMERA") +class Load3DCamera(ComfyTypeIO): + class CameraInfo(TypedDict): + position: dict[str, float | int] + target: dict[str, float | int] + zoom: int + cameraType: str + + Type = CameraInfo + + +@comfytype(io_type="LOAD_3D") +class Load3D(ComfyTypeIO): + """3D models are stored as a dictionary.""" + class Model3DDict(TypedDict): + image: str + mask: str + normal: str + camera_info: Load3DCamera.CameraInfo + recording: NotRequired[str] + + Type = Model3DDict + + +@comfytype(io_type="LOAD_3D_ANIMATION") +class Load3DAnimation(Load3D): + ... + + +@comfytype(io_type="PHOTOMAKER") +class Photomaker(ComfyTypeIO): + Type = Any + + +@comfytype(io_type="POINT") +class Point(ComfyTypeIO): + Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? + +@comfytype(io_type="FACE_ANALYSIS") +class FaceAnalysis(ComfyTypeIO): + Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? + +@comfytype(io_type="BBOX") +class BBOX(ComfyTypeIO): + Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? + +@comfytype(io_type="SEGS") +class SEGS(ComfyTypeIO): + Type = Any # NOTE: I couldn't find any references in core code to POINT io_type. Does this exist? + +@comfytype(io_type="*") +class AnyType(ComfyTypeIO): + Type = Any + +@comfytype(io_type="MODEL_PATCH") +class MODEL_PATCH(ComfyTypeIO): + Type = Any + +@comfytype(io_type="AUDIO_ENCODER") +class AudioEncoder(ComfyTypeIO): + Type = Any + +@comfytype(io_type="AUDIO_ENCODER_OUTPUT") +class AudioEncoderOutput(ComfyTypeIO): + Type = Any + +@comfytype(io_type="COMFY_MULTITYPED_V3") +class MultiType: + Type = Any + class Input(Input): + ''' + Input that permits more than one input type; if `id` is an instance of `ComfyType.Input`, then that input will be used to create a widget (if applicable) with overridden values. + ''' + def __init__(self, id: str | Input, types: list[type[_ComfyType] | _ComfyType], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): + # if id is an Input, then use that Input with overridden values + self.input_override = None + if isinstance(id, Input): + self.input_override = copy.copy(id) + optional = id.optional if id.optional is True else optional + tooltip = id.tooltip if id.tooltip is not None else tooltip + display_name = id.display_name if id.display_name is not None else display_name + lazy = id.lazy if id.lazy is not None else lazy + id = id.id + # if is a widget input, make sure widget_type is set appropriately + if isinstance(self.input_override, WidgetInput): + self.input_override.widget_type = self.input_override.get_io_type() + super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) + self._io_types = types + + @property + def io_types(self) -> list[type[Input]]: + ''' + Returns list of Input class types permitted. + ''' + io_types = [] + for x in self._io_types: + if not is_class(x): + io_types.append(type(x)) + else: + io_types.append(x) + return io_types + + def get_io_type(self): + # ensure types are unique and order is preserved + str_types = [x.io_type for x in self.io_types] + if self.input_override is not None: + str_types.insert(0, self.input_override.get_io_type()) + return ",".join(list(dict.fromkeys(str_types))) + + def as_dict(self): + if self.input_override is not None: + return self.input_override.as_dict() | super().as_dict() + else: + return super().as_dict() + +class DynamicInput(Input, ABC): + ''' + Abstract class for dynamic input registration. + ''' + @abstractmethod + def get_dynamic(self) -> list[Input]: + ... + +class DynamicOutput(Output, ABC): + ''' + Abstract class for dynamic output registration. + ''' + def __init__(self, id: str=None, display_name: str=None, tooltip: str=None, + is_output_list=False): + super().__init__(id, display_name, tooltip, is_output_list) + + @abstractmethod + def get_dynamic(self) -> list[Output]: + ... + + +@comfytype(io_type="COMFY_AUTOGROW_V3") +class AutogrowDynamic(ComfyTypeI): + Type = list[Any] + class Input(DynamicInput): + def __init__(self, id: str, template_input: Input, min: int=1, max: int=None, + display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): + super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) + self.template_input = template_input + if min is not None: + assert(min >= 1) + if max is not None: + assert(max >= 1) + self.min = min + self.max = max + + def get_dynamic(self) -> list[Input]: + curr_count = 1 + new_inputs = [] + for i in range(self.min): + new_input = copy.copy(self.template_input) + new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$" + if new_input.display_name is not None: + new_input.display_name = f"{new_input.display_name}{curr_count}" + new_input.optional = self.optional or new_input.optional + if isinstance(self.template_input, WidgetInput): + new_input.force_input = True + new_inputs.append(new_input) + curr_count += 1 + # pretend to expand up to max + for i in range(curr_count-1, self.max): + new_input = copy.copy(self.template_input) + new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$" + if new_input.display_name is not None: + new_input.display_name = f"{new_input.display_name}{curr_count}" + new_input.optional = True + if isinstance(self.template_input, WidgetInput): + new_input.force_input = True + new_inputs.append(new_input) + curr_count += 1 + return new_inputs + +@comfytype(io_type="COMFY_COMBODYNAMIC_V3") +class ComboDynamic(ComfyTypeI): + class Input(DynamicInput): + def __init__(self, id: str): + pass + +@comfytype(io_type="COMFY_MATCHTYPE_V3") +class MatchType(ComfyTypeIO): + class Template: + def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType]): + self.template_id = template_id + self.allowed_types = [allowed_types] if isinstance(allowed_types, _ComfyType) else allowed_types + + def as_dict(self): + return { + "template_id": self.template_id, + "allowed_types": "".join(t.io_type for t in self.allowed_types), + } + + class Input(DynamicInput): + def __init__(self, id: str, template: MatchType.Template, + display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None): + super().__init__(id, display_name, optional, tooltip, lazy, extra_dict) + self.template = template + + def get_dynamic(self) -> list[Input]: + return [self] + + def as_dict(self): + return super().as_dict() | prune_dict({ + "template": self.template.as_dict(), + }) + + class Output(DynamicOutput): + def __init__(self, id: str, template: MatchType.Template, display_name: str=None, tooltip: str=None, + is_output_list=False): + super().__init__(id, display_name, tooltip, is_output_list) + self.template = template + + def get_dynamic(self) -> list[Output]: + return [self] + + def as_dict(self): + return super().as_dict() | prune_dict({ + "template": self.template.as_dict(), + }) + + +class HiddenHolder: + def __init__(self, unique_id: str, prompt: Any, + extra_pnginfo: Any, dynprompt: Any, + auth_token_comfy_org: str, api_key_comfy_org: str, **kwargs): + self.unique_id = unique_id + """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" + self.prompt = prompt + """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" + self.extra_pnginfo = extra_pnginfo + """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" + self.dynprompt = dynprompt + """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" + self.auth_token_comfy_org = auth_token_comfy_org + """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" + self.api_key_comfy_org = api_key_comfy_org + """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" + + def __getattr__(self, key: str): + '''If hidden variable not found, return None.''' + return None + + @classmethod + def from_dict(cls, d: dict | None): + if d is None: + d = {} + return cls( + unique_id=d.get(Hidden.unique_id, None), + prompt=d.get(Hidden.prompt, None), + extra_pnginfo=d.get(Hidden.extra_pnginfo, None), + dynprompt=d.get(Hidden.dynprompt, None), + auth_token_comfy_org=d.get(Hidden.auth_token_comfy_org, None), + api_key_comfy_org=d.get(Hidden.api_key_comfy_org, None), + ) + +class Hidden(str, Enum): + ''' + Enumerator for requesting hidden variables in nodes. + ''' + unique_id = "UNIQUE_ID" + """UNIQUE_ID is the unique identifier of the node, and matches the id property of the node on the client side. It is commonly used in client-server communications (see messages).""" + prompt = "PROMPT" + """PROMPT is the complete prompt sent by the client to the server. See the prompt object for a full description.""" + extra_pnginfo = "EXTRA_PNGINFO" + """EXTRA_PNGINFO is a dictionary that will be copied into the metadata of any .png files saved. Custom nodes can store additional information in this dictionary for saving (or as a way to communicate with a downstream node).""" + dynprompt = "DYNPROMPT" + """DYNPROMPT is an instance of comfy_execution.graph.DynamicPrompt. It differs from PROMPT in that it may mutate during the course of execution in response to Node Expansion.""" + auth_token_comfy_org = "AUTH_TOKEN_COMFY_ORG" + """AUTH_TOKEN_COMFY_ORG is a token acquired from signing into a ComfyOrg account on frontend.""" + api_key_comfy_org = "API_KEY_COMFY_ORG" + """API_KEY_COMFY_ORG is an API Key generated by ComfyOrg that allows skipping signing into a ComfyOrg account on frontend.""" + + +@dataclass +class NodeInfoV1: + input: dict=None + input_order: dict[str, list[str]]=None + output: list[str]=None + output_is_list: list[bool]=None + output_name: list[str]=None + output_tooltips: list[str]=None + name: str=None + display_name: str=None + description: str=None + python_module: Any=None + category: str=None + output_node: bool=None + deprecated: bool=None + experimental: bool=None + api_node: bool=None + +@dataclass +class NodeInfoV3: + input: dict=None + output: dict=None + hidden: list[str]=None + name: str=None + display_name: str=None + description: str=None + category: str=None + output_node: bool=None + deprecated: bool=None + experimental: bool=None + api_node: bool=None + + +@dataclass +class Schema: + """Definition of V3 node properties.""" + + node_id: str + """ID of node - should be globally unique. If this is a custom node, add a prefix or postfix to avoid name clashes.""" + display_name: str = None + """Display name of node.""" + category: str = "sd" + """The category of the node, as per the "Add Node" menu.""" + inputs: list[Input]=None + outputs: list[Output]=None + hidden: list[Hidden]=None + description: str="" + """Node description, shown as a tooltip when hovering over the node.""" + is_input_list: bool = False + """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes. + + All inputs of ``type`` will become ``list[type]``, regardless of how many items are passed in. This also affects ``check_lazy_status``. + + From the docs: + + A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing + """ + is_output_node: bool=False + """Flags this node as an output node, causing any inputs it requires to be executed. + + If a node is not connected to any output nodes, that node will not be executed. Usage:: + + From the docs: + + By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is. + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node + """ + is_deprecated: bool=False + """Flags a node as deprecated, indicating to users that they should find alternatives to this node.""" + is_experimental: bool=False + """Flags a node as experimental, informing users that it may change or not work as expected.""" + is_api_node: bool=False + """Flags a node as an API node. See: https://docs.comfy.org/tutorials/api-nodes/overview.""" + not_idempotent: bool=False + """Flags a node as not idempotent; when True, the node will run and not reuse the cached outputs when identical inputs are provided on a different node in the graph.""" + enable_expand: bool=False + """Flags a node as expandable, allowing NodeOutput to include 'expand' property.""" + + def validate(self): + '''Validate the schema: + - verify ids on inputs and outputs are unique - both internally and in relation to each other + ''' + input_ids = [i.id for i in self.inputs] if self.inputs is not None else [] + output_ids = [o.id for o in self.outputs] if self.outputs is not None else [] + input_set = set(input_ids) + output_set = set(output_ids) + issues = [] + # verify ids are unique per list + if len(input_set) != len(input_ids): + issues.append(f"Input ids must be unique, but {[item for item, count in Counter(input_ids).items() if count > 1]} are not.") + if len(output_set) != len(output_ids): + issues.append(f"Output ids must be unique, but {[item for item, count in Counter(output_ids).items() if count > 1]} are not.") + # verify ids are unique between lists + intersection = input_set & output_set + if len(intersection) > 0: + issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.") + if len(issues) > 0: + raise ValueError("\n".join(issues)) + + def finalize(self): + """Add hidden based on selected schema options, and give outputs without ids default ids.""" + # if is an api_node, will need key-related hidden + if self.is_api_node: + if self.hidden is None: + self.hidden = [] + if Hidden.auth_token_comfy_org not in self.hidden: + self.hidden.append(Hidden.auth_token_comfy_org) + if Hidden.api_key_comfy_org not in self.hidden: + self.hidden.append(Hidden.api_key_comfy_org) + # if is an output_node, will need prompt and extra_pnginfo + if self.is_output_node: + if self.hidden is None: + self.hidden = [] + if Hidden.prompt not in self.hidden: + self.hidden.append(Hidden.prompt) + if Hidden.extra_pnginfo not in self.hidden: + self.hidden.append(Hidden.extra_pnginfo) + # give outputs without ids default ids + if self.outputs is not None: + for i, output in enumerate(self.outputs): + if output.id is None: + output.id = f"_{i}_{output.io_type}_" + + def get_v1_info(self, cls) -> NodeInfoV1: + # get V1 inputs + input = { + "required": {} + } + if self.inputs: + for i in self.inputs: + if isinstance(i, DynamicInput): + dynamic_inputs = i.get_dynamic() + for d in dynamic_inputs: + add_to_dict_v1(d, input) + else: + add_to_dict_v1(i, input) + if self.hidden: + for hidden in self.hidden: + input.setdefault("hidden", {})[hidden.name] = (hidden.value,) + # create separate lists from output fields + output = [] + output_is_list = [] + output_name = [] + output_tooltips = [] + if self.outputs: + for o in self.outputs: + output.append(o.io_type) + output_is_list.append(o.is_output_list) + output_name.append(o.display_name if o.display_name else o.io_type) + output_tooltips.append(o.tooltip if o.tooltip else None) + + info = NodeInfoV1( + input=input, + input_order={key: list(value.keys()) for (key, value) in input.items()}, + output=output, + output_is_list=output_is_list, + output_name=output_name, + output_tooltips=output_tooltips, + name=self.node_id, + display_name=self.display_name, + category=self.category, + description=self.description, + output_node=self.is_output_node, + deprecated=self.is_deprecated, + experimental=self.is_experimental, + api_node=self.is_api_node, + python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes") + ) + return info + + + def get_v3_info(self, cls) -> NodeInfoV3: + input_dict = {} + output_dict = {} + hidden_list = [] + # TODO: make sure dynamic types will be handled correctly + if self.inputs: + for input in self.inputs: + add_to_dict_v3(input, input_dict) + if self.outputs: + for output in self.outputs: + add_to_dict_v3(output, output_dict) + if self.hidden: + for hidden in self.hidden: + hidden_list.append(hidden.value) + + info = NodeInfoV3( + input=input_dict, + output=output_dict, + hidden=hidden_list, + name=self.node_id, + display_name=self.display_name, + description=self.description, + category=self.category, + output_node=self.is_output_node, + deprecated=self.is_deprecated, + experimental=self.is_experimental, + api_node=self.is_api_node, + python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes") + ) + return info + + +def add_to_dict_v1(i: Input, input: dict): + key = "optional" if i.optional else "required" + as_dict = i.as_dict() + # for v1, we don't want to include the optional key + as_dict.pop("optional", None) + input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict) + +def add_to_dict_v3(io: Input | Output, d: dict): + d[io.id] = (io.get_io_type(), io.as_dict()) + + + +class _ComfyNodeBaseInternal(_ComfyNodeInternal): + """Common base class for storing internal methods and properties; DO NOT USE for defining nodes.""" + + RELATIVE_PYTHON_MODULE = None + SCHEMA = None + + # filled in during execution + resources: Resources = None + hidden: HiddenHolder = None + + @classmethod + @abstractmethod + def define_schema(cls) -> Schema: + """Override this function with one that returns a Schema instance.""" + raise NotImplementedError + + @classmethod + @abstractmethod + def execute(cls, **kwargs) -> NodeOutput: + """Override this function with one that performs node's actions.""" + raise NotImplementedError + + @classmethod + def validate_inputs(cls, **kwargs) -> bool | str: + """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS. + + If the function returns a string, it will be used as the validation error message for the node. + """ + raise NotImplementedError + + @classmethod + def fingerprint_inputs(cls, **kwargs) -> Any: + """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED. + + If this function returns the same value as last run, the node will not be executed.""" + raise NotImplementedError + + @classmethod + def check_lazy_status(cls, **kwargs) -> list[str]: + """Optionally, define this function to return a list of input names that should be evaluated. + + This basic mixin impl. requires all inputs. + + :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ + When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. + + Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). + Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status + """ + return [name for name in kwargs if kwargs[name] is None] + + def __init__(self): + self.local_resources: ResourcesLocal = None + self.__class__.VALIDATE_CLASS() + + @classmethod + def GET_BASE_CLASS(cls): + return _ComfyNodeBaseInternal + + @final + @classmethod + def VALIDATE_CLASS(cls): + if first_real_override(cls, "define_schema") is None: + raise Exception(f"No define_schema function was defined for node class {cls.__name__}.") + if first_real_override(cls, "execute") is None: + raise Exception(f"No execute function was defined for node class {cls.__name__}.") + + @classproperty + def FUNCTION(cls): # noqa + if inspect.iscoroutinefunction(cls.execute): + return "EXECUTE_NORMALIZED_ASYNC" + return "EXECUTE_NORMALIZED" + + @final + @classmethod + def EXECUTE_NORMALIZED(cls, *args, **kwargs) -> NodeOutput: + to_return = cls.execute(*args, **kwargs) + if to_return is None: + to_return = NodeOutput() + elif isinstance(to_return, NodeOutput): + pass + elif isinstance(to_return, tuple): + to_return = NodeOutput(*to_return) + elif isinstance(to_return, dict): + to_return = NodeOutput.from_dict(to_return) + elif isinstance(to_return, ExecutionBlocker): + to_return = NodeOutput(block_execution=to_return.message) + else: + raise Exception(f"Invalid return type from node: {type(to_return)}") + if to_return.expand is not None and not cls.SCHEMA.enable_expand: + raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.") + return to_return + + @final + @classmethod + async def EXECUTE_NORMALIZED_ASYNC(cls, *args, **kwargs) -> NodeOutput: + to_return = await cls.execute(*args, **kwargs) + if to_return is None: + to_return = NodeOutput() + elif isinstance(to_return, NodeOutput): + pass + elif isinstance(to_return, tuple): + to_return = NodeOutput(*to_return) + elif isinstance(to_return, dict): + to_return = NodeOutput.from_dict(to_return) + elif isinstance(to_return, ExecutionBlocker): + to_return = NodeOutput(block_execution=to_return.message) + else: + raise Exception(f"Invalid return type from node: {type(to_return)}") + if to_return.expand is not None and not cls.SCHEMA.enable_expand: + raise Exception(f"Node {cls.__name__} is not expandable, but expand included in NodeOutput; developer should set enable_expand=True on node's Schema to allow this.") + return to_return + + @final + @classmethod + def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNode]: + """Creates clone of real node class to prevent monkey-patching.""" + c_type: type[ComfyNode] = cls if is_class(cls) else type(cls) + type_clone: type[ComfyNode] = shallow_clone_class(c_type) + # set hidden + type_clone.hidden = HiddenHolder.from_dict(hidden_inputs) + return type_clone + + @final + @classmethod + def GET_NODE_INFO_V3(cls) -> dict[str, Any]: + schema = cls.GET_SCHEMA() + info = schema.get_v3_info(cls) + return asdict(info) + ############################################# + # V1 Backwards Compatibility code + #-------------------------------------------- + @final + @classmethod + def GET_NODE_INFO_V1(cls) -> dict[str, Any]: + schema = cls.GET_SCHEMA() + info = schema.get_v1_info(cls) + return asdict(info) + + _DESCRIPTION = None + @final + @classproperty + def DESCRIPTION(cls): # noqa + if cls._DESCRIPTION is None: + cls.GET_SCHEMA() + return cls._DESCRIPTION + + _CATEGORY = None + @final + @classproperty + def CATEGORY(cls): # noqa + if cls._CATEGORY is None: + cls.GET_SCHEMA() + return cls._CATEGORY + + _EXPERIMENTAL = None + @final + @classproperty + def EXPERIMENTAL(cls): # noqa + if cls._EXPERIMENTAL is None: + cls.GET_SCHEMA() + return cls._EXPERIMENTAL + + _DEPRECATED = None + @final + @classproperty + def DEPRECATED(cls): # noqa + if cls._DEPRECATED is None: + cls.GET_SCHEMA() + return cls._DEPRECATED + + _API_NODE = None + @final + @classproperty + def API_NODE(cls): # noqa + if cls._API_NODE is None: + cls.GET_SCHEMA() + return cls._API_NODE + + _OUTPUT_NODE = None + @final + @classproperty + def OUTPUT_NODE(cls): # noqa + if cls._OUTPUT_NODE is None: + cls.GET_SCHEMA() + return cls._OUTPUT_NODE + + _INPUT_IS_LIST = None + @final + @classproperty + def INPUT_IS_LIST(cls): # noqa + if cls._INPUT_IS_LIST is None: + cls.GET_SCHEMA() + return cls._INPUT_IS_LIST + _OUTPUT_IS_LIST = None + + @final + @classproperty + def OUTPUT_IS_LIST(cls): # noqa + if cls._OUTPUT_IS_LIST is None: + cls.GET_SCHEMA() + return cls._OUTPUT_IS_LIST + + _RETURN_TYPES = None + @final + @classproperty + def RETURN_TYPES(cls): # noqa + if cls._RETURN_TYPES is None: + cls.GET_SCHEMA() + return cls._RETURN_TYPES + + _RETURN_NAMES = None + @final + @classproperty + def RETURN_NAMES(cls): # noqa + if cls._RETURN_NAMES is None: + cls.GET_SCHEMA() + return cls._RETURN_NAMES + + _OUTPUT_TOOLTIPS = None + @final + @classproperty + def OUTPUT_TOOLTIPS(cls): # noqa + if cls._OUTPUT_TOOLTIPS is None: + cls.GET_SCHEMA() + return cls._OUTPUT_TOOLTIPS + + _NOT_IDEMPOTENT = None + @final + @classproperty + def NOT_IDEMPOTENT(cls): # noqa + if cls._NOT_IDEMPOTENT is None: + cls.GET_SCHEMA() + return cls._NOT_IDEMPOTENT + + @final + @classmethod + def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], Schema]: + schema = cls.FINALIZE_SCHEMA() + info = schema.get_v1_info(cls) + input = info.input + if not include_hidden: + input.pop("hidden", None) + if return_schema: + return input, schema + return input + + @final + @classmethod + def FINALIZE_SCHEMA(cls): + """Call define_schema and finalize it.""" + schema = cls.define_schema() + schema.finalize() + return schema + + @final + @classmethod + def GET_SCHEMA(cls) -> Schema: + """Validate node class, finalize schema, validate schema, and set expected class properties.""" + cls.VALIDATE_CLASS() + schema = cls.FINALIZE_SCHEMA() + schema.validate() + if cls._DESCRIPTION is None: + cls._DESCRIPTION = schema.description + if cls._CATEGORY is None: + cls._CATEGORY = schema.category + if cls._EXPERIMENTAL is None: + cls._EXPERIMENTAL = schema.is_experimental + if cls._DEPRECATED is None: + cls._DEPRECATED = schema.is_deprecated + if cls._API_NODE is None: + cls._API_NODE = schema.is_api_node + if cls._OUTPUT_NODE is None: + cls._OUTPUT_NODE = schema.is_output_node + if cls._INPUT_IS_LIST is None: + cls._INPUT_IS_LIST = schema.is_input_list + if cls._NOT_IDEMPOTENT is None: + cls._NOT_IDEMPOTENT = schema.not_idempotent + + if cls._RETURN_TYPES is None: + output = [] + output_name = [] + output_is_list = [] + output_tooltips = [] + if schema.outputs: + for o in schema.outputs: + output.append(o.io_type) + output_name.append(o.display_name if o.display_name else o.io_type) + output_is_list.append(o.is_output_list) + output_tooltips.append(o.tooltip if o.tooltip else None) + + cls._RETURN_TYPES = output + cls._RETURN_NAMES = output_name + cls._OUTPUT_IS_LIST = output_is_list + cls._OUTPUT_TOOLTIPS = output_tooltips + cls.SCHEMA = schema + return schema + #-------------------------------------------- + ############################################# + + +class ComfyNode(_ComfyNodeBaseInternal): + """Common base class for all V3 nodes.""" + + @classmethod + @abstractmethod + def define_schema(cls) -> Schema: + """Override this function with one that returns a Schema instance.""" + raise NotImplementedError + + @classmethod + @abstractmethod + def execute(cls, **kwargs) -> NodeOutput: + """Override this function with one that performs node's actions.""" + raise NotImplementedError + + @classmethod + def validate_inputs(cls, **kwargs) -> bool: + """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS.""" + raise NotImplementedError + + @classmethod + def fingerprint_inputs(cls, **kwargs) -> Any: + """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED.""" + raise NotImplementedError + + @classmethod + def check_lazy_status(cls, **kwargs) -> list[str]: + """Optionally, define this function to return a list of input names that should be evaluated. + + This basic mixin impl. requires all inputs. + + :kwargs: All node inputs will be included here. If the input is ``None``, it should be assumed that it has not yet been evaluated. \ + When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``. + + Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name). + Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params). + + Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status + """ + return [name for name in kwargs if kwargs[name] is None] + + @final + @classmethod + def GET_BASE_CLASS(cls): + """DO NOT override this class. Will break things in execution.py.""" + return ComfyNode + + +class NodeOutput(_NodeOutputInternal): + ''' + Standardized output of a node; can pass in any number of args and/or a UIOutput into 'ui' kwarg. + ''' + def __init__(self, *args: Any, ui: _UIOutput | dict=None, expand: dict=None, block_execution: str=None): + self.args = args + self.ui = ui + self.expand = expand + self.block_execution = block_execution + + @property + def result(self): + return self.args if len(self.args) > 0 else None + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> "NodeOutput": + args = () + ui = None + expand = None + if "result" in data: + result = data["result"] + if isinstance(result, ExecutionBlocker): + return cls(block_execution=result.message) + args = result + if "ui" in data: + ui = data["ui"] + if "expand" in data: + expand = data["expand"] + return cls(args=args, ui=ui, expand=expand) + + def __getitem__(self, index) -> Any: + return self.args[index] + +class _UIOutput(ABC): + def __init__(self): + pass + + @abstractmethod + def as_dict(self) -> dict: + ... + + +__all__ = [ + "FolderType", + "UploadType", + "RemoteOptions", + "NumberDisplay", + + "comfytype", + "Custom", + "Input", + "WidgetInput", + "Output", + "ComfyTypeI", + "ComfyTypeIO", + # Supported Types + "Boolean", + "Int", + "Float", + "String", + "Combo", + "MultiCombo", + "Image", + "WanCameraEmbedding", + "Webcam", + "Mask", + "Latent", + "Conditioning", + "Sampler", + "Sigmas", + "Noise", + "Guider", + "Clip", + "ControlNet", + "Vae", + "Model", + "ClipVision", + "ClipVisionOutput", + "AudioEncoder", + "AudioEncoderOutput", + "StyleModel", + "Gligen", + "UpscaleModel", + "Audio", + "Video", + "SVG", + "LoraModel", + "LossMap", + "Voxel", + "Mesh", + "Hooks", + "HookKeyframes", + "TimestepsRange", + "LatentOperation", + "FlowControl", + "Accumulation", + "Load3DCamera", + "Load3D", + "Load3DAnimation", + "Photomaker", + "Point", + "FaceAnalysis", + "BBOX", + "SEGS", + "AnyType", + "MultiType", + # Other classes + "HiddenHolder", + "Hidden", + "NodeInfoV1", + "NodeInfoV3", + "Schema", + "ComfyNode", + "NodeOutput", + "add_to_dict_v1", + "add_to_dict_v3", +] diff --git a/comfy_api/latest/_resources.py b/comfy_api/latest/_resources.py new file mode 100644 index 000000000..a6bdda972 --- /dev/null +++ b/comfy_api/latest/_resources.py @@ -0,0 +1,72 @@ +from __future__ import annotations +import comfy.utils +import folder_paths +import logging +from abc import ABC, abstractmethod +from typing import Any +import torch + +class ResourceKey(ABC): + Type = Any + def __init__(self): + ... + +class TorchDictFolderFilename(ResourceKey): + '''Key for requesting a torch file via file_name from a folder category.''' + Type = dict[str, torch.Tensor] + def __init__(self, folder_name: str, file_name: str): + self.folder_name = folder_name + self.file_name = file_name + + def __hash__(self): + return hash((self.folder_name, self.file_name)) + + def __eq__(self, other: object) -> bool: + if not isinstance(other, TorchDictFolderFilename): + return False + return self.folder_name == other.folder_name and self.file_name == other.file_name + + def __str__(self): + return f"{self.folder_name} -> {self.file_name}" + +class Resources(ABC): + def __init__(self): + ... + + @abstractmethod + def get(self, key: ResourceKey, default: Any=...) -> Any: + pass + +class ResourcesLocal(Resources): + def __init__(self): + super().__init__() + self.local_resources: dict[ResourceKey, Any] = {} + + def get(self, key: ResourceKey, default: Any=...) -> Any: + cached = self.local_resources.get(key, None) + if cached is not None: + logging.info(f"Using cached resource '{key}'") + return cached + logging.info(f"Loading resource '{key}'") + to_return = None + if isinstance(key, TorchDictFolderFilename): + if default is ...: + to_return = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise(key.folder_name, key.file_name), safe_load=True) + else: + full_path = folder_paths.get_full_path(key.folder_name, key.file_name) + if full_path is not None: + to_return = comfy.utils.load_torch_file(full_path, safe_load=True) + + if to_return is not None: + self.local_resources[key] = to_return + return to_return + if default is not ...: + return default + raise Exception(f"Unsupported resource key type: {type(key)}") + + +class _RESOURCES: + ResourceKey = ResourceKey + TorchDictFolderFilename = TorchDictFolderFilename + Resources = Resources + ResourcesLocal = ResourcesLocal diff --git a/comfy_api/latest/_ui.py b/comfy_api/latest/_ui.py new file mode 100644 index 000000000..b0bbabe2a --- /dev/null +++ b/comfy_api/latest/_ui.py @@ -0,0 +1,464 @@ +from __future__ import annotations + +import json +import os +import random +from io import BytesIO +from typing import Type + +import av +import numpy as np +import torch +try: + import torchaudio + TORCH_AUDIO_AVAILABLE = True +except: + TORCH_AUDIO_AVAILABLE = False +from PIL import Image as PILImage +from PIL.PngImagePlugin import PngInfo + +import folder_paths + +# used for image preview +from comfy.cli_args import args +from comfy_api.latest._io import ComfyNode, FolderType, Image, _UIOutput + + +class SavedResult(dict): + def __init__(self, filename: str, subfolder: str, type: FolderType): + super().__init__(filename=filename, subfolder=subfolder,type=type.value) + + @property + def filename(self) -> str: + return self["filename"] + + @property + def subfolder(self) -> str: + return self["subfolder"] + + @property + def type(self) -> FolderType: + return FolderType(self["type"]) + + +class SavedImages(_UIOutput): + """A UI output class to represent one or more saved images, potentially animated.""" + def __init__(self, results: list[SavedResult], is_animated: bool = False): + super().__init__() + self.results = results + self.is_animated = is_animated + + def as_dict(self) -> dict: + data = {"images": self.results} + if self.is_animated: + data["animated"] = (True,) + return data + + +class SavedAudios(_UIOutput): + """UI wrapper around one or more audio files on disk (FLAC / MP3 / Opus).""" + def __init__(self, results: list[SavedResult]): + super().__init__() + self.results = results + + def as_dict(self) -> dict: + return {"audio": self.results} + + +def _get_directory_by_folder_type(folder_type: FolderType) -> str: + if folder_type == FolderType.input: + return folder_paths.get_input_directory() + if folder_type == FolderType.output: + return folder_paths.get_output_directory() + return folder_paths.get_temp_directory() + + +class ImageSaveHelper: + """A helper class with static methods to handle image saving and metadata.""" + + @staticmethod + def _convert_tensor_to_pil(image_tensor: torch.Tensor) -> PILImage.Image: + """Converts a single torch tensor to a PIL Image.""" + return PILImage.fromarray(np.clip(255.0 * image_tensor.cpu().numpy(), 0, 255).astype(np.uint8)) + + @staticmethod + def _create_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None: + """Creates a PngInfo object with prompt and extra_pnginfo.""" + if args.disable_metadata or cls is None or not cls.hidden: + return None + metadata = PngInfo() + if cls.hidden.prompt: + metadata.add_text("prompt", json.dumps(cls.hidden.prompt)) + if cls.hidden.extra_pnginfo: + for x in cls.hidden.extra_pnginfo: + metadata.add_text(x, json.dumps(cls.hidden.extra_pnginfo[x])) + return metadata + + @staticmethod + def _create_animated_png_metadata(cls: Type[ComfyNode] | None) -> PngInfo | None: + """Creates a PngInfo object with prompt and extra_pnginfo for animated PNGs (APNG).""" + if args.disable_metadata or cls is None or not cls.hidden: + return None + metadata = PngInfo() + if cls.hidden.prompt: + metadata.add( + b"comf", + "prompt".encode("latin-1", "strict") + + b"\0" + + json.dumps(cls.hidden.prompt).encode("latin-1", "strict"), + after_idat=True, + ) + if cls.hidden.extra_pnginfo: + for x in cls.hidden.extra_pnginfo: + metadata.add( + b"comf", + x.encode("latin-1", "strict") + + b"\0" + + json.dumps(cls.hidden.extra_pnginfo[x]).encode("latin-1", "strict"), + after_idat=True, + ) + return metadata + + @staticmethod + def _create_webp_metadata(pil_image: PILImage.Image, cls: Type[ComfyNode] | None) -> PILImage.Exif: + """Creates EXIF metadata bytes for WebP images.""" + exif_data = pil_image.getexif() + if args.disable_metadata or cls is None or cls.hidden is None: + return exif_data + if cls.hidden.prompt is not None: + exif_data[0x0110] = "prompt:{}".format(json.dumps(cls.hidden.prompt)) # EXIF 0x0110 = Model + if cls.hidden.extra_pnginfo is not None: + inital_exif_tag = 0x010F # EXIF 0x010f = Make + for key, value in cls.hidden.extra_pnginfo.items(): + exif_data[inital_exif_tag] = "{}:{}".format(key, json.dumps(value)) + inital_exif_tag -= 1 + return exif_data + + @staticmethod + def save_images( + images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, compress_level = 4, + ) -> list[SavedResult]: + """Saves a batch of images as individual PNG files.""" + full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( + filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] + ) + results = [] + metadata = ImageSaveHelper._create_png_metadata(cls) + for batch_number, image_tensor in enumerate(images): + img = ImageSaveHelper._convert_tensor_to_pil(image_tensor) + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level) + results.append(SavedResult(file, subfolder, folder_type)) + counter += 1 + return results + + @staticmethod + def get_save_images_ui(images, filename_prefix: str, cls: Type[ComfyNode] | None, compress_level=4) -> SavedImages: + """Saves a batch of images and returns a UI object for the node output.""" + return SavedImages( + ImageSaveHelper.save_images( + images, + filename_prefix=filename_prefix, + folder_type=FolderType.output, + cls=cls, + compress_level=compress_level, + ) + ) + + @staticmethod + def save_animated_png( + images, filename_prefix: str, folder_type: FolderType, cls: Type[ComfyNode] | None, fps: float, compress_level: int + ) -> SavedResult: + """Saves a batch of images as a single animated PNG.""" + full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( + filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] + ) + pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images] + metadata = ImageSaveHelper._create_animated_png_metadata(cls) + file = f"{filename}_{counter:05}_.png" + save_path = os.path.join(full_output_folder, file) + pil_images[0].save( + save_path, + pnginfo=metadata, + compress_level=compress_level, + save_all=True, + duration=int(1000.0 / fps), + append_images=pil_images[1:], + ) + return SavedResult(file, subfolder, folder_type) + + @staticmethod + def get_save_animated_png_ui( + images, filename_prefix: str, cls: Type[ComfyNode] | None, fps: float, compress_level: int + ) -> SavedImages: + """Saves an animated PNG and returns a UI object for the node output.""" + result = ImageSaveHelper.save_animated_png( + images, + filename_prefix=filename_prefix, + folder_type=FolderType.output, + cls=cls, + fps=fps, + compress_level=compress_level, + ) + return SavedImages([result], is_animated=len(images) > 1) + + @staticmethod + def save_animated_webp( + images, + filename_prefix: str, + folder_type: FolderType, + cls: Type[ComfyNode] | None, + fps: float, + lossless: bool, + quality: int, + method: int, + ) -> SavedResult: + """Saves a batch of images as a single animated WebP.""" + full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( + filename_prefix, _get_directory_by_folder_type(folder_type), images[0].shape[1], images[0].shape[0] + ) + pil_images = [ImageSaveHelper._convert_tensor_to_pil(img) for img in images] + pil_exif = ImageSaveHelper._create_webp_metadata(pil_images[0], cls) + file = f"{filename}_{counter:05}_.webp" + pil_images[0].save( + os.path.join(full_output_folder, file), + save_all=True, + duration=int(1000.0 / fps), + append_images=pil_images[1:], + exif=pil_exif, + lossless=lossless, + quality=quality, + method=method, + ) + return SavedResult(file, subfolder, folder_type) + + @staticmethod + def get_save_animated_webp_ui( + images, + filename_prefix: str, + cls: Type[ComfyNode] | None, + fps: float, + lossless: bool, + quality: int, + method: int, + ) -> SavedImages: + """Saves an animated WebP and returns a UI object for the node output.""" + result = ImageSaveHelper.save_animated_webp( + images, + filename_prefix=filename_prefix, + folder_type=FolderType.output, + cls=cls, + fps=fps, + lossless=lossless, + quality=quality, + method=method, + ) + return SavedImages([result], is_animated=len(images) > 1) + + +class AudioSaveHelper: + """A helper class with static methods to handle audio saving and metadata.""" + _OPUS_RATES = [8000, 12000, 16000, 24000, 48000] + + @staticmethod + def save_audio( + audio: dict, + filename_prefix: str, + folder_type: FolderType, + cls: Type[ComfyNode] | None, + format: str = "flac", + quality: str = "128k", + ) -> list[SavedResult]: + full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path( + filename_prefix, _get_directory_by_folder_type(folder_type) + ) + + metadata = {} + if not args.disable_metadata and cls is not None: + if cls.hidden.prompt is not None: + metadata["prompt"] = json.dumps(cls.hidden.prompt) + if cls.hidden.extra_pnginfo is not None: + for x in cls.hidden.extra_pnginfo: + metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x]) + + results = [] + for batch_number, waveform in enumerate(audio["waveform"].cpu()): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.{format}" + output_path = os.path.join(full_output_folder, file) + + # Use original sample rate initially + sample_rate = audio["sample_rate"] + + # Handle Opus sample rate requirements + if format == "opus": + if sample_rate > 48000: + sample_rate = 48000 + elif sample_rate not in AudioSaveHelper._OPUS_RATES: + # Find the next highest supported rate + for rate in sorted(AudioSaveHelper._OPUS_RATES): + if rate > sample_rate: + sample_rate = rate + break + if sample_rate not in AudioSaveHelper._OPUS_RATES: # Fallback if still not supported + sample_rate = 48000 + + # Resample if necessary + if sample_rate != audio["sample_rate"]: + if not TORCH_AUDIO_AVAILABLE: + raise Exception("torchaudio is not available; cannot resample audio.") + waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) + + # Create output with specified format + output_buffer = BytesIO() + output_container = av.open(output_buffer, mode="w", format=format) + + # Set metadata on the container + for key, value in metadata.items(): + output_container.metadata[key] = value + + # Set up the output stream with appropriate properties + if format == "opus": + out_stream = output_container.add_stream("libopus", rate=sample_rate) + if quality == "64k": + out_stream.bit_rate = 64000 + elif quality == "96k": + out_stream.bit_rate = 96000 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "192k": + out_stream.bit_rate = 192000 + elif quality == "320k": + out_stream.bit_rate = 320000 + elif format == "mp3": + out_stream = output_container.add_stream("libmp3lame", rate=sample_rate) + if quality == "V0": + # TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool + out_stream.codec_context.qscale = 1 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "320k": + out_stream.bit_rate = 320000 + else: # format == "flac": + out_stream = output_container.add_stream("flac", rate=sample_rate) + + frame = av.AudioFrame.from_ndarray( + waveform.movedim(0, 1).reshape(1, -1).float().numpy(), + format="flt", + layout="mono" if waveform.shape[0] == 1 else "stereo", + ) + frame.sample_rate = sample_rate + frame.pts = 0 + output_container.mux(out_stream.encode(frame)) + + # Flush encoder + output_container.mux(out_stream.encode(None)) + + # Close containers + output_container.close() + + # Write the output to file + output_buffer.seek(0) + with open(output_path, "wb") as f: + f.write(output_buffer.getbuffer()) + + results.append(SavedResult(file, subfolder, folder_type)) + counter += 1 + + return results + + @staticmethod + def get_save_audio_ui( + audio, filename_prefix: str, cls: Type[ComfyNode] | None, format: str = "flac", quality: str = "128k", + ) -> SavedAudios: + """Save and instantly wrap for UI.""" + return SavedAudios( + AudioSaveHelper.save_audio( + audio, + filename_prefix=filename_prefix, + folder_type=FolderType.output, + cls=cls, + format=format, + quality=quality, + ) + ) + + +class PreviewImage(_UIOutput): + def __init__(self, image: Image.Type, animated: bool = False, cls: Type[ComfyNode] = None, **kwargs): + self.values = ImageSaveHelper.save_images( + image, + filename_prefix="ComfyUI_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for _ in range(5)), + folder_type=FolderType.temp, + cls=cls, + compress_level=1, + ) + self.animated = animated + + def as_dict(self): + return { + "images": self.values, + "animated": (self.animated,) + } + + +class PreviewMask(PreviewImage): + def __init__(self, mask: PreviewMask.Type, animated: bool=False, cls: ComfyNode=None, **kwargs): + preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + super().__init__(preview, animated, cls, **kwargs) + + +class PreviewAudio(_UIOutput): + def __init__(self, audio: dict, cls: Type[ComfyNode] = None, **kwargs): + self.values = AudioSaveHelper.save_audio( + audio, + filename_prefix="ComfyUI_temp_" + "".join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(5)), + folder_type=FolderType.temp, + cls=cls, + format="flac", + quality="128k", + ) + + def as_dict(self) -> dict: + return {"audio": self.values} + + +class PreviewVideo(_UIOutput): + def __init__(self, values: list[SavedResult | dict], **kwargs): + self.values = values + + def as_dict(self): + return {"images": self.values, "animated": (True,)} + + +class PreviewUI3D(_UIOutput): + def __init__(self, model_file, camera_info, **kwargs): + self.model_file = model_file + self.camera_info = camera_info + + def as_dict(self): + return {"result": [self.model_file, self.camera_info]} + + +class PreviewText(_UIOutput): + def __init__(self, value: str, **kwargs): + self.value = value + + def as_dict(self): + return {"text": (self.value,)} + + +__all__ = [ + "SavedResult", + "SavedImages", + "SavedAudios", + "ImageSaveHelper", + "AudioSaveHelper", + "PreviewImage", + "PreviewMask", + "PreviewAudio", + "PreviewVideo", + "PreviewUI3D", + "PreviewText", +] diff --git a/comfy_api/latest/_util/__init__.py b/comfy_api/latest/_util/__init__.py new file mode 100644 index 000000000..9019c46db --- /dev/null +++ b/comfy_api/latest/_util/__init__.py @@ -0,0 +1,8 @@ +from .video_types import VideoContainer, VideoCodec, VideoComponents + +__all__ = [ + # Utility Types + "VideoContainer", + "VideoCodec", + "VideoComponents", +] diff --git a/comfy_api/latest/_util/video_types.py b/comfy_api/latest/_util/video_types.py new file mode 100644 index 000000000..c3e3d8e3a --- /dev/null +++ b/comfy_api/latest/_util/video_types.py @@ -0,0 +1,52 @@ +from __future__ import annotations +from dataclasses import dataclass +from enum import Enum +from fractions import Fraction +from typing import Optional +from comfy_api.latest._input import ImageInput, AudioInput + +class VideoCodec(str, Enum): + AUTO = "auto" + H264 = "h264" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of codec names that can be used as node input. + """ + return [member.value for member in cls] + +class VideoContainer(str, Enum): + AUTO = "auto" + MP4 = "mp4" + + @classmethod + def as_input(cls) -> list[str]: + """ + Returns a list of container names that can be used as node input. + """ + return [member.value for member in cls] + + @classmethod + def get_extension(cls, value) -> str: + """ + Returns the file extension for the container. + """ + if isinstance(value, str): + value = cls(value) + if value == VideoContainer.MP4 or value == VideoContainer.AUTO: + return "mp4" + return "" + +@dataclass +class VideoComponents: + """ + Dataclass representing the components of a video. + """ + + images: ImageInput + frame_rate: Fraction + audio: Optional[AudioInput] = None + metadata: Optional[dict] = None + + diff --git a/comfy_api/latest/generated/ComfyAPISyncStub.pyi b/comfy_api/latest/generated/ComfyAPISyncStub.pyi new file mode 100644 index 000000000..525c074dd --- /dev/null +++ b/comfy_api/latest/generated/ComfyAPISyncStub.pyi @@ -0,0 +1,20 @@ +from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple +from comfy_api.latest import ComfyAPI_latest +from PIL.Image import Image +from torch import Tensor +class ComfyAPISyncStub: + def __init__(self) -> None: ... + + class ExecutionSync: + def __init__(self) -> None: ... + """ + Update the progress bar displayed in the ComfyUI interface. + + This function allows custom nodes and API calls to report their progress + back to the user interface, providing visual feedback during long operations. + + Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK + """ + def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... + + execution: ExecutionSync diff --git a/comfy_api/torch_helpers/__init__.py b/comfy_api/torch_helpers/__init__.py new file mode 100644 index 000000000..be7ae7a61 --- /dev/null +++ b/comfy_api/torch_helpers/__init__.py @@ -0,0 +1,5 @@ +from .torch_compile import set_torch_compile_wrapper + +__all__ = [ + "set_torch_compile_wrapper", +] diff --git a/comfy_api/torch_helpers/torch_compile.py b/comfy_api/torch_helpers/torch_compile.py new file mode 100644 index 000000000..9223f58db --- /dev/null +++ b/comfy_api/torch_helpers/torch_compile.py @@ -0,0 +1,69 @@ +from __future__ import annotations +import torch + +import comfy.utils +from comfy.patcher_extension import WrappersMP +from typing import TYPE_CHECKING, Callable, Optional +if TYPE_CHECKING: + from comfy.model_patcher import ModelPatcher + from comfy.patcher_extension import WrapperExecutor + + +COMPILE_KEY = "torch.compile" +TORCH_COMPILE_KWARGS = "torch_compile_kwargs" + + +def apply_torch_compile_factory(compiled_module_dict: dict[str, Callable]) -> Callable: + ''' + Create a wrapper that will refer to the compiled_diffusion_model. + ''' + def apply_torch_compile_wrapper(executor: WrapperExecutor, *args, **kwargs): + try: + orig_modules = {} + for key, value in compiled_module_dict.items(): + orig_modules[key] = comfy.utils.get_attr(executor.class_obj, key) + comfy.utils.set_attr(executor.class_obj, key, value) + return executor(*args, **kwargs) + finally: + for key, value in orig_modules.items(): + comfy.utils.set_attr(executor.class_obj, key, value) + return apply_torch_compile_wrapper + + +def set_torch_compile_wrapper(model: ModelPatcher, backend: str, options: Optional[dict[str,str]]=None, + mode: Optional[str]=None, fullgraph=False, dynamic: Optional[bool]=None, + keys: list[str]=["diffusion_model"], *args, **kwargs): + ''' + Perform torch.compile that will be applied at sample time for either the whole model or specific params of the BaseModel instance. + + When keys is None, it will default to using ["diffusion_model"], compiling the whole diffusion_model. + When a list of keys is provided, it will perform torch.compile on only the selected modules. + ''' + # clear out any other torch.compile wrappers + model.remove_wrappers_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY) + # if no keys, default to 'diffusion_model' + if not keys: + keys = ["diffusion_model"] + # create kwargs dict that can be referenced later + compile_kwargs = { + "backend": backend, + "options": options, + "mode": mode, + "fullgraph": fullgraph, + "dynamic": dynamic, + } + # get a dict of compiled keys + compiled_modules = {} + for key in keys: + compiled_modules[key] = torch.compile( + model=model.get_model_object(key), + **compile_kwargs, + ) + # add torch.compile wrapper + wrapper_func = apply_torch_compile_factory( + compiled_module_dict=compiled_modules, + ) + # store wrapper to run on BaseModel's apply_model function + model.add_wrapper_with_key(WrappersMP.APPLY_MODEL, COMPILE_KEY, wrapper_func) + # keep compile kwargs for reference + model.model_options[TORCH_COMPILE_KWARGS] = compile_kwargs diff --git a/comfy_api/util.py b/comfy_api/util.py new file mode 100644 index 000000000..1aa9606d2 --- /dev/null +++ b/comfy_api/util.py @@ -0,0 +1,8 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents + +__all__ = [ + "VideoCodec", + "VideoContainer", + "VideoComponents", +] diff --git a/comfy_api/util/__init__.py b/comfy_api/util/__init__.py new file mode 100644 index 000000000..4c8a89d1e --- /dev/null +++ b/comfy_api/util/__init__.py @@ -0,0 +1,8 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._util import VideoContainer, VideoCodec, VideoComponents + +__all__ = [ + "VideoContainer", + "VideoCodec", + "VideoComponents", +] diff --git a/comfy_api/util/video_types.py b/comfy_api/util/video_types.py new file mode 100644 index 000000000..68c780d64 --- /dev/null +++ b/comfy_api/util/video_types.py @@ -0,0 +1,12 @@ +# This file only exists for backwards compatibility. +from comfy_api.latest._util.video_types import ( + VideoContainer, + VideoCodec, + VideoComponents, +) + +__all__ = [ + "VideoContainer", + "VideoCodec", + "VideoComponents", +] diff --git a/comfy_api/v0_0_1/__init__.py b/comfy_api/v0_0_1/__init__.py new file mode 100644 index 000000000..93608771d --- /dev/null +++ b/comfy_api/v0_0_1/__init__.py @@ -0,0 +1,42 @@ +from comfy_api.v0_0_2 import ( + ComfyAPIAdapter_v0_0_2, + Input as Input_v0_0_2, + InputImpl as InputImpl_v0_0_2, + Types as Types_v0_0_2, +) +from typing import Type, TYPE_CHECKING +from comfy_api.internal.async_to_sync import create_sync_class + + +# This version only exists to serve as a template for future version adapters. +# There is no reason anyone should ever use it. +class ComfyAPIAdapter_v0_0_1(ComfyAPIAdapter_v0_0_2): + VERSION = "0.0.1" + STABLE = True + +class Input(Input_v0_0_2): + pass + +class InputImpl(InputImpl_v0_0_2): + pass + +class Types(Types_v0_0_2): + pass + +ComfyAPI = ComfyAPIAdapter_v0_0_1 + +# Create a synchronous version of the API +if TYPE_CHECKING: + from comfy_api.v0_0_1.generated.ComfyAPISyncStub import ComfyAPISyncStub # type: ignore + + ComfyAPISync: Type[ComfyAPISyncStub] + +ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_1) + +__all__ = [ + "ComfyAPI", + "ComfyAPISync", + "Input", + "InputImpl", + "Types", +] diff --git a/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi new file mode 100644 index 000000000..270030324 --- /dev/null +++ b/comfy_api/v0_0_1/generated/ComfyAPISyncStub.pyi @@ -0,0 +1,20 @@ +from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple +from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1 +from PIL.Image import Image +from torch import Tensor +class ComfyAPISyncStub: + def __init__(self) -> None: ... + + class ExecutionSync: + def __init__(self) -> None: ... + """ + Update the progress bar displayed in the ComfyUI interface. + + This function allows custom nodes and API calls to report their progress + back to the user interface, providing visual feedback during long operations. + + Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK + """ + def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... + + execution: ExecutionSync diff --git a/comfy_api/v0_0_2/__init__.py b/comfy_api/v0_0_2/__init__.py new file mode 100644 index 000000000..de0f95001 --- /dev/null +++ b/comfy_api/v0_0_2/__init__.py @@ -0,0 +1,45 @@ +from comfy_api.latest import ( + ComfyAPI_latest, + Input as Input_latest, + InputImpl as InputImpl_latest, + Types as Types_latest, +) +from typing import Type, TYPE_CHECKING +from comfy_api.internal.async_to_sync import create_sync_class +from comfy_api.latest import io, ui, ComfyExtension #noqa: F401 + + +class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest): + VERSION = "0.0.2" + STABLE = False + + +class Input(Input_latest): + pass + + +class InputImpl(InputImpl_latest): + pass + + +class Types(Types_latest): + pass + + +ComfyAPI = ComfyAPIAdapter_v0_0_2 + +# Create a synchronous version of the API +if TYPE_CHECKING: + from comfy_api.v0_0_2.generated.ComfyAPISyncStub import ComfyAPISyncStub # type: ignore + + ComfyAPISync: Type[ComfyAPISyncStub] +ComfyAPISync = create_sync_class(ComfyAPIAdapter_v0_0_2) + +__all__ = [ + "ComfyAPI", + "ComfyAPISync", + "Input", + "InputImpl", + "Types", + "ComfyExtension", +] diff --git a/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi b/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi new file mode 100644 index 000000000..7fcec685e --- /dev/null +++ b/comfy_api/v0_0_2/generated/ComfyAPISyncStub.pyi @@ -0,0 +1,20 @@ +from typing import Any, Dict, List, Optional, Tuple, Union, Set, Sequence, cast, NamedTuple +from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2 +from PIL.Image import Image +from torch import Tensor +class ComfyAPISyncStub: + def __init__(self) -> None: ... + + class ExecutionSync: + def __init__(self) -> None: ... + """ + Update the progress bar displayed in the ComfyUI interface. + + This function allows custom nodes and API calls to report their progress + back to the user interface, providing visual feedback during long operations. + + Migration from previous API: comfy.utils.PROGRESS_BAR_HOOK + """ + def set_progress(self, value: float, max_value: float, node_id: Union[str, None] = None, preview_image: Union[Image, Tensor, None] = None, ignore_size_limit: bool = False) -> None: ... + + execution: ExecutionSync diff --git a/comfy_api/version_list.py b/comfy_api/version_list.py new file mode 100644 index 000000000..7cb1871d5 --- /dev/null +++ b/comfy_api/version_list.py @@ -0,0 +1,12 @@ +from comfy_api.latest import ComfyAPI_latest +from comfy_api.v0_0_2 import ComfyAPIAdapter_v0_0_2 +from comfy_api.v0_0_1 import ComfyAPIAdapter_v0_0_1 +from comfy_api.internal import ComfyAPIBase +from typing import List, Type + +supported_versions: List[Type[ComfyAPIBase]] = [ + ComfyAPI_latest, + ComfyAPIAdapter_v0_0_2, + ComfyAPIAdapter_v0_0_1, +] + diff --git a/comfy_api_nodes/README.md b/comfy_api_nodes/README.md new file mode 100644 index 000000000..f56d6c860 --- /dev/null +++ b/comfy_api_nodes/README.md @@ -0,0 +1,65 @@ +# ComfyUI API Nodes + +## Introduction + +Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview). + +## Development + +While developing, you should be testing against the Staging environment. To test against staging: + +**Install ComfyUI_frontend** + +Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication. + +> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication. + +```bash +python run main.py --comfy-api-base https://stagingapi.comfy.org +``` + +To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging. + +API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes. + +### Redocly Instructions + +**Tip** +When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet. + +Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging. + +```bash +# Download the OpenAPI file from staging server. +curl -o openapi.yaml https://stagingapi.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` + + +# Merging to Master + +Before merging to comfyanonymous/ComfyUI master, follow these steps: + +1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes. +1. Make sure the ComfyUI API is deployed to prod with your changes. +1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file. + +```bash +# Download the OpenAPI file from prod server. +curl -o openapi.yaml https://api.comfy.org/openapi + +# Filter out unneeded API definitions. +npm install -g @redocly/cli +redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components + +# Generate the pydantic datamodels for validation. +datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel + +``` diff --git a/comfy_api_nodes/__init__.py b/comfy_api_nodes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy_api_nodes/apinode_utils.py b/comfy_api_nodes/apinode_utils.py new file mode 100644 index 000000000..4182c8f80 --- /dev/null +++ b/comfy_api_nodes/apinode_utils.py @@ -0,0 +1,261 @@ +from __future__ import annotations +import aiohttp +import mimetypes +from typing import Optional, Union +from comfy.utils import common_upscale +from comfy_api_nodes.apis.client import ( + ApiClient, + ApiEndpoint, + HttpMethod, + SynchronousOperation, + UploadRequest, + UploadResponse, +) +from server import PromptServer +from comfy.cli_args import args + +import numpy as np +from PIL import Image +import torch +import math +import base64 +from .util import tensor_to_bytesio, bytesio_to_image_tensor +from io import BytesIO + + +async def validate_and_cast_response( + response, timeout: int = None, node_id: Union[str, None] = None +) -> torch.Tensor: + """Validates and casts a response to a torch.Tensor. + + Args: + response: The response to validate and cast. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + ValueError: If the response is not valid. + """ + # validate raw JSON response + data = response.data + if not data or len(data) == 0: + raise ValueError("No images returned from API endpoint") + + # Initialize list to store image tensors + image_tensors: list[torch.Tensor] = [] + + # Process each image in the data array + async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session: + for img_data in data: + img_bytes: bytes + if img_data.b64_json: + img_bytes = base64.b64decode(img_data.b64_json) + elif img_data.url: + if node_id: + PromptServer.instance.send_progress_text(f"Result URL: {img_data.url}", node_id) + async with session.get(img_data.url) as resp: + if resp.status != 200: + raise ValueError("Failed to download generated image") + img_bytes = await resp.read() + else: + raise ValueError("Invalid image payload – neither URL nor base64 data present.") + + pil_img = Image.open(BytesIO(img_bytes)).convert("RGBA") + arr = np.asarray(pil_img).astype(np.float32) / 255.0 + image_tensors.append(torch.from_numpy(arr)) + + return torch.stack(image_tensors, dim=0) + + +def validate_aspect_ratio( + aspect_ratio: str, + minimum_ratio: float, + maximum_ratio: float, + minimum_ratio_str: str, + maximum_ratio_str: str, +) -> float: + """Validates and casts an aspect ratio string to a float. + + Args: + aspect_ratio: The aspect ratio string to validate. + minimum_ratio: The minimum aspect ratio. + maximum_ratio: The maximum aspect ratio. + minimum_ratio_str: The minimum aspect ratio string. + maximum_ratio_str: The maximum aspect ratio string. + + Returns: + The validated and cast aspect ratio. + + Raises: + Exception: If the aspect ratio is not valid. + """ + # get ratio values + numbers = aspect_ratio.split(":") + if len(numbers) != 2: + raise TypeError( + f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}." + ) + try: + numerator = int(numbers[0]) + denominator = int(numbers[1]) + except ValueError as exc: + raise TypeError( + f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}." + ) from exc + calculated_ratio = numerator / denominator + # if not close to minimum and maximum, check bounds + if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose( + calculated_ratio, maximum_ratio + ): + if calculated_ratio < minimum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + if calculated_ratio > maximum_ratio: + raise TypeError( + f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})." + ) + return aspect_ratio + + +async def download_url_to_bytesio( + url: str, timeout: int = None, auth_kwargs: Optional[dict[str, str]] = None +) -> BytesIO: + """Downloads content from a URL using requests and returns it as BytesIO. + + Args: + url: The URL to download. + timeout: Request timeout in seconds. Defaults to None (no timeout). + + Returns: + BytesIO object containing the downloaded content. + """ + headers = {} + if url.startswith("/proxy/"): + url = str(args.comfy_api_base).rstrip("/") + url + auth_token = auth_kwargs.get("auth_token") + comfy_api_key = auth_kwargs.get("comfy_api_key") + if auth_token: + headers["Authorization"] = f"Bearer {auth_token}" + elif comfy_api_key: + headers["X-API-KEY"] = comfy_api_key + timeout_cfg = aiohttp.ClientTimeout(total=timeout) if timeout else None + async with aiohttp.ClientSession(timeout=timeout_cfg) as session: + async with session.get(url, headers=headers) as resp: + resp.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX) + return BytesIO(await resp.read()) + + +def process_image_response(response_content: bytes | str) -> torch.Tensor: + """Uses content from a Response object and converts it to a torch.Tensor""" + return bytesio_to_image_tensor(BytesIO(response_content)) + + +def text_filepath_to_base64_string(filepath: str) -> str: + """Converts a text file to a base64 string.""" + with open(filepath, "rb") as f: + file_content = f.read() + return base64.b64encode(file_content).decode("utf-8") + + +def text_filepath_to_data_uri(filepath: str) -> str: + """Converts a text file to a data URI.""" + base64_string = text_filepath_to_base64_string(filepath) + mime_type, _ = mimetypes.guess_type(filepath) + if mime_type is None: + mime_type = "application/octet-stream" + return f"data:{mime_type};base64,{base64_string}" + + +async def upload_file_to_comfyapi( + file_bytes_io: BytesIO, + filename: str, + upload_mime_type: Optional[str], + auth_kwargs: Optional[dict[str, str]] = None, +) -> str: + """ + Uploads a single file to ComfyUI API and returns its download URL. + + Args: + file_bytes_io: BytesIO object containing the file data. + filename: The filename of the file. + upload_mime_type: MIME type of the file. + auth_kwargs: Optional authentication token(s). + + Returns: + The download URL for the uploaded file. + """ + if upload_mime_type is None: + request_object = UploadRequest(file_name=filename) + else: + request_object = UploadRequest(file_name=filename, content_type=upload_mime_type) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/customers/storage", + method=HttpMethod.POST, + request_model=UploadRequest, + response_model=UploadResponse, + ), + request=request_object, + auth_kwargs=auth_kwargs, + ) + + response: UploadResponse = await operation.execute() + await ApiClient.upload_file(response.upload_url, file_bytes_io, content_type=upload_mime_type) + return response.download_url + + +async def upload_images_to_comfyapi( + image: torch.Tensor, + max_images=8, + auth_kwargs: Optional[dict[str, str]] = None, + mime_type: Optional[str] = None, +) -> list[str]: + """ + Uploads images to ComfyUI API and returns download URLs. + To upload multiple images, stack them in the batch dimension first. + + Args: + image: Input torch.Tensor image. + max_images: Maximum number of images to upload. + auth_kwargs: Optional authentication token(s). + mime_type: Optional MIME type for the image. + """ + # if batch, try to upload each file if max_images is greater than 0 + download_urls: list[str] = [] + is_batch = len(image.shape) > 3 + batch_len = image.shape[0] if is_batch else 1 + + for idx in range(min(batch_len, max_images)): + tensor = image[idx] if is_batch else image + img_io = tensor_to_bytesio(tensor, mime_type=mime_type) + url = await upload_file_to_comfyapi(img_io, img_io.name, mime_type, auth_kwargs) + download_urls.append(url) + return download_urls + + +def resize_mask_to_image( + mask: torch.Tensor, + image: torch.Tensor, + upscale_method="nearest-exact", + crop="disabled", + allow_gradient=True, + add_channel_dim=False, +): + """ + Resize mask to be the same dimensions as an image, while maintaining proper format for API calls. + """ + _, H, W, _ = image.shape + mask = mask.unsqueeze(-1) + mask = mask.movedim(-1, 1) + mask = common_upscale( + mask, width=W, height=H, upscale_method=upscale_method, crop=crop + ) + mask = mask.movedim(1, -1) + if not add_channel_dim: + mask = mask.squeeze(-1) + if not allow_gradient: + mask = (mask > 0.5).float() + return mask diff --git a/comfy_api_nodes/apis/PixverseController.py b/comfy_api_nodes/apis/PixverseController.py new file mode 100644 index 000000000..310c0f546 --- /dev/null +++ b/comfy_api_nodes/apis/PixverseController.py @@ -0,0 +1,17 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel + +from . import PixverseDto + + +class ResponseData(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseDto.V2OpenAPII2VResp] = None diff --git a/comfy_api_nodes/apis/PixverseDto.py b/comfy_api_nodes/apis/PixverseDto.py new file mode 100644 index 000000000..323c38e96 --- /dev/null +++ b/comfy_api_nodes/apis/PixverseDto.py @@ -0,0 +1,57 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-04-29T23:44:54+00:00 + +from __future__ import annotations + +from typing import Optional + +from pydantic import BaseModel, Field + + +class V2OpenAPII2VResp(BaseModel): + video_id: Optional[int] = Field(None, description='Video_id') + + +class V2OpenAPIT2VReq(BaseModel): + aspect_ratio: str = Field( + ..., description='Aspect ratio (16:9, 4:3, 1:1, 3:4, 9:16)', examples=['16:9'] + ) + duration: int = Field( + ..., + description='Video duration (5, 8 seconds, --model=v3.5 only allows 5,8; --quality=1080p does not support 8s)', + examples=[5], + ) + model: str = Field( + ..., description='Model version (only supports v3.5)', examples=['v3.5'] + ) + motion_mode: Optional[str] = Field( + 'normal', + description='Motion mode (normal, fast, --fast only available when duration=5; --quality=1080p does not support fast)', + examples=['normal'], + ) + negative_prompt: Optional[str] = Field( + None, description='Negative prompt\n', max_length=2048 + ) + prompt: str = Field(..., description='Prompt', max_length=2048) + quality: str = Field( + ..., + description='Video quality ("360p"(Turbo model), "540p", "720p", "1080p")', + examples=['540p'], + ) + seed: Optional[int] = Field(None, description='Random seed, range: 0 - 2147483647') + style: Optional[str] = Field( + None, + description='Style (effective when model=v3.5, "anime", "3d_animation", "clay", "comic", "cyberpunk") Do not include style parameter unless needed', + examples=['anime'], + ) + template_id: Optional[int] = Field( + None, + description='Template ID (template_id must be activated before use)', + examples=[302325299692608], + ) + water_mark: Optional[bool] = Field( + False, + description='Watermark (true: add watermark, false: no watermark)', + examples=[False], + ) diff --git a/comfy_api_nodes/apis/__init__.py b/comfy_api_nodes/apis/__init__.py new file mode 100644 index 000000000..ee2aa1ce6 --- /dev/null +++ b/comfy_api_nodes/apis/__init__.py @@ -0,0 +1,6161 @@ +# generated by datamodel-codegen: +# filename: filtered-openapi.yaml +# timestamp: 2025-07-30T08:54:00+00:00 + +# pylint: disable +from __future__ import annotations + +from datetime import date, datetime +from enum import Enum +from typing import Any, Dict, List, Literal, Optional, Union +from uuid import UUID + +from pydantic import AnyUrl, BaseModel, ConfigDict, Field, RootModel, StrictBytes + + +class APIKey(BaseModel): + created_at: Optional[datetime] = None + description: Optional[str] = None + id: Optional[str] = None + key_prefix: Optional[str] = None + name: Optional[str] = None + + +class APIKeyWithPlaintext(APIKey): + plaintext_key: Optional[str] = Field( + None, description='The full API key (only returned at creation)' + ) + + +class AuditLog(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the event was created' + ) + event_id: Optional[str] = Field(None, description='the id of the event') + event_type: Optional[str] = Field(None, description='the type of the event') + params: Optional[Dict[str, Any]] = Field( + None, description='data related to the event' + ) + + +class BFLAsyncResponse(BaseModel): + id: str = Field(..., title='Id') + polling_url: str = Field(..., title='Polling Url') + + +class BFLAsyncWebhookResponse(BaseModel): + id: str = Field(..., title='Id') + status: str = Field(..., title='Status') + webhook_url: str = Field(..., title='Webhook Url') + + +class CannyHighThreshold(RootModel[int]): + root: int = Field( + ..., + description='High threshold for Canny edge detection', + ge=0, + le=500, + title='Canny High Threshold', + ) + + +class CannyLowThreshold(RootModel[int]): + root: int = Field( + ..., + description='Low threshold for Canny edge detection', + ge=0, + le=500, + title='Canny Low Threshold', + ) + + +class Guidance(RootModel[float]): + root: float = Field( + ..., + description='Guidance strength for the image generation process', + ge=1.0, + le=100.0, + title='Guidance', + ) + + +class Steps(RootModel[int]): + root: int = Field( + ..., + description='Number of steps for the image generation process', + ge=15, + le=50, + title='Steps', + ) + + +class WebhookUrl(RootModel[AnyUrl]): + root: AnyUrl = Field( + ..., description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLFluxKontextMaxGenerateRequest(BaseModel): + guidance: Optional[float] = Field( + 3, description='The guidance scale for generation', ge=1.0, le=20.0 + ) + input_image: str = Field(..., description='Base64 encoded image to be edited') + prompt: str = Field( + ..., description='The text prompt describing what to edit on the image' + ) + steps: Optional[int] = Field( + 50, description='Number of inference steps', ge=1, le=50 + ) + + +class BFLFluxKontextMaxGenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class BFLFluxKontextProGenerateRequest(BaseModel): + guidance: Optional[float] = Field( + 3, description='The guidance scale for generation', ge=1.0, le=20.0 + ) + input_image: str = Field(..., description='Base64 encoded image to be edited') + prompt: str = Field( + ..., description='The text prompt describing what to edit on the image' + ) + steps: Optional[int] = Field( + 50, description='Number of inference steps', ge=1, le=50 + ) + + +class BFLFluxKontextProGenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class OutputFormat(str, Enum): + jpeg = 'jpeg' + png = 'png' + + +class BFLFluxPro11GenerateRequest(BaseModel): + height: int = Field(..., description='Height of the generated image') + image_prompt: Optional[str] = Field(None, description='Optional image prompt') + output_format: Optional[OutputFormat] = Field( + None, description='Output image format' + ) + prompt: str = Field(..., description='The main text prompt for image generation') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to use prompt upsampling' + ) + safety_tolerance: Optional[int] = Field(None, description='Safety tolerance level') + seed: Optional[int] = Field(None, description='Random seed for reproducibility') + webhook_secret: Optional[str] = Field( + None, description='Optional webhook secret for async processing' + ) + webhook_url: Optional[str] = Field( + None, description='Optional webhook URL for async processing' + ) + width: int = Field(..., description='Width of the generated image') + + +class BFLFluxPro11GenerateResponse(BaseModel): + id: str = Field(..., description='Job ID for tracking') + polling_url: str = Field(..., description='URL to poll for results') + + +class Bottom(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand at the bottom of the image', + ge=0, + le=2048, + title='Bottom', + ) + + +class Guidance2(RootModel[float]): + root: float = Field( + ..., + description='Guidance strength for the image generation process', + ge=1.5, + le=100.0, + title='Guidance', + ) + + +class Left(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand on the left side of the image', + ge=0, + le=2048, + title='Left', + ) + + +class Right(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand on the right side of the image', + ge=0, + le=2048, + title='Right', + ) + + +class Steps2(RootModel[int]): + root: int = Field( + ..., + description='Number of steps for the image generation process', + examples=[50], + ge=15, + le=50, + title='Steps', + ) + + +class Top(RootModel[int]): + root: int = Field( + ..., + description='Number of pixels to expand at the top of the image', + ge=0, + le=2048, + title='Top', + ) + + +class BFLFluxProGenerateRequest(BaseModel): + guidance_scale: Optional[float] = Field( + None, description='The guidance scale for generation.', ge=1.0, le=20.0 + ) + height: int = Field( + ..., description='The height of the image to generate.', ge=64, le=2048 + ) + negative_prompt: Optional[str] = Field( + None, description='The negative prompt for image generation.' + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.', ge=1, le=4 + ) + num_inference_steps: Optional[int] = Field( + None, description='The number of inference steps.', ge=1, le=100 + ) + prompt: str = Field(..., description='The text prompt for image generation.') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: int = Field( + ..., description='The width of the image to generate.', ge=64, le=2048 + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class BFLOutputFormat(str, Enum): + jpeg = 'jpeg' + png = 'png' + + +class BFLValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class Status(str, Enum): + success = 'success' + not_found = 'not_found' + error = 'error' + + +class ClaimMyNodeRequest(BaseModel): + GH_TOKEN: str = Field( + ..., description='GitHub token to verify if the user owns the repo of the node' + ) + + +class ComfyNode(BaseModel): + category: Optional[str] = Field( + None, + description='UI category where the node is listed, used for grouping nodes.', + ) + comfy_node_name: Optional[str] = Field( + None, description='Unique identifier for the node' + ) + deprecated: Optional[bool] = Field( + None, + description='Indicates if the node is deprecated. Deprecated nodes are hidden in the UI.', + ) + description: Optional[str] = Field( + None, description="Brief description of the node's functionality or purpose." + ) + experimental: Optional[bool] = Field( + None, + description='Indicates if the node is experimental, subject to changes or removal.', + ) + function: Optional[str] = Field( + None, description='Name of the entry-point function to execute the node.' + ) + input_types: Optional[str] = Field(None, description='Defines input parameters') + output_is_list: Optional[List[bool]] = Field( + None, description='Boolean values indicating if each output is a list.' + ) + return_names: Optional[str] = Field( + None, description='Names of the outputs for clarity in workflows.' + ) + return_types: Optional[str] = Field( + None, description='Specifies the types of outputs produced by the node.' + ) + + +class ComfyNodeCloudBuildInfo(BaseModel): + build_id: Optional[str] = None + location: Optional[str] = None + project_id: Optional[str] = None + project_number: Optional[str] = None + + +class Status1(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type(str, Enum): + computer_call = 'computer_call' + + +class ComputerToolCall(BaseModel): + action: Dict[str, Any] + call_id: str = Field( + ..., + description='An identifier used when responding to the tool call with output.\n', + ) + id: str = Field(..., description='The unique ID of the computer call.') + status: Status1 = Field( + ..., + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + type: Type = Field( + ..., description='The type of the computer call. Always `computer_call`.' + ) + + +class Environment(str, Enum): + windows = 'windows' + mac = 'mac' + linux = 'linux' + ubuntu = 'ubuntu' + browser = 'browser' + + +class Type1(str, Enum): + computer_use_preview = 'computer_use_preview' + + +class ComputerUsePreviewTool(BaseModel): + display_height: int = Field(..., description='The height of the computer display.') + display_width: int = Field(..., description='The width of the computer display.') + environment: Environment = Field( + ..., description='The type of computer environment to control.' + ) + type: Literal['ComputerUsePreviewTool'] = Field( + ..., + description='The type of the computer use tool. Always `computer_use_preview`.', + ) + + +class CreateAPIKeyRequest(BaseModel): + description: Optional[str] = None + name: str + + +class Customer(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the user was created' + ) + email: Optional[str] = Field(None, description='The email address for this user') + has_fund: Optional[bool] = Field(None, description='Whether the user has funds') + id: str = Field(..., description='The firebase UID of the user') + is_admin: Optional[bool] = Field(None, description='Whether the user is an admin') + metronome_id: Optional[str] = Field(None, description='The Metronome customer ID') + name: Optional[str] = Field(None, description='The name for this user') + stripe_id: Optional[str] = Field(None, description='The Stripe customer ID') + updatedAt: Optional[datetime] = Field( + None, description='The date and time the user was last updated' + ) + + +class CustomerStorageResourceResponse(BaseModel): + download_url: Optional[str] = Field( + None, + description='The signed URL to use for downloading the file from the specified path', + ) + existing_file: Optional[bool] = Field( + None, description='Whether an existing file with the same hash was found' + ) + expires_at: Optional[datetime] = Field( + None, description='When the signed URL will expire' + ) + upload_url: Optional[str] = Field( + None, + description='The signed URL to use for uploading the file to the specified path', + ) + + +class Role(str, Enum): + user = 'user' + assistant = 'assistant' + system = 'system' + developer = 'developer' + + +class Type2(str, Enum): + message = 'message' + + +class Error(BaseModel): + details: Optional[List[str]] = Field( + None, + description='Optional detailed information about the error or hints for resolving it.', + ) + message: Optional[str] = Field( + None, description='A clear and concise description of the error.' + ) + + +class ErrorResponse(BaseModel): + error: str + message: str + + +class Type3(str, Enum): + file_search = 'file_search' + + +class FileSearchTool(BaseModel): + type: Literal['FileSearchTool'] = Field(..., description='The type of tool') + vector_store_ids: List[str] = Field( + ..., description='IDs of vector stores to search in' + ) + + +class Result(BaseModel): + file_id: Optional[str] = Field(None, description='The unique ID of the file.\n') + filename: Optional[str] = Field(None, description='The name of the file.\n') + score: Optional[float] = Field( + None, description='The relevance score of the file - a value between 0 and 1.\n' + ) + text: Optional[str] = Field( + None, description='The text that was retrieved from the file.\n' + ) + + +class Status2(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + incomplete = 'incomplete' + failed = 'failed' + + +class Type4(str, Enum): + file_search_call = 'file_search_call' + + +class FileSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the file search tool call.\n') + queries: List[str] = Field( + ..., description='The queries used to search for files.\n' + ) + results: Optional[List[Result]] = Field( + None, description='The results of the file search tool call.\n' + ) + status: Status2 = Field( + ..., + description='The status of the file search tool call. One of `in_progress`, \n`searching`, `incomplete` or `failed`,\n', + ) + type: Type4 = Field( + ..., + description='The type of the file search tool call. Always `file_search_call`.\n', + ) + + +class Type5(str, Enum): + function = 'function' + + +class FunctionTool(BaseModel): + description: Optional[str] = Field( + None, description='Description of what the function does' + ) + name: str = Field(..., description='Name of the function') + parameters: Dict[str, Any] = Field( + ..., description='JSON Schema object describing the function parameters' + ) + type: Literal['FunctionTool'] = Field(..., description='The type of tool') + + +class Status3(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type6(str, Enum): + function_call = 'function_call' + + +class FunctionToolCall(BaseModel): + arguments: str = Field( + ..., description='A JSON string of the arguments to pass to the function.\n' + ) + call_id: str = Field( + ..., + description='The unique ID of the function tool call generated by the model.\n', + ) + id: Optional[str] = Field( + None, description='The unique ID of the function tool call.\n' + ) + name: str = Field(..., description='The name of the function to run.\n') + status: Optional[Status3] = Field( + None, + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + type: Type6 = Field( + ..., description='The type of the function tool call. Always `function_call`.\n' + ) + + +class GeminiCitation(BaseModel): + authors: Optional[List[str]] = None + endIndex: Optional[int] = None + license: Optional[str] = None + publicationDate: Optional[date] = None + startIndex: Optional[int] = None + title: Optional[str] = None + uri: Optional[str] = None + + +class GeminiCitationMetadata(BaseModel): + citations: Optional[List[GeminiCitation]] = None + + +class Role1(str, Enum): + user = 'user' + model = 'model' + + +class GeminiFunctionDeclaration(BaseModel): + description: Optional[str] = None + name: str + parameters: Dict[str, Any] = Field( + ..., description='JSON schema for the function parameters' + ) + + +class GeminiGenerationConfig(BaseModel): + maxOutputTokens: Optional[int] = Field( + None, + description='Maximum number of tokens that can be generated in the response. A token is approximately 4 characters. 100 tokens correspond to roughly 60-80 words.\n', + examples=[2048], + ge=16, + le=8192, + ) + seed: Optional[int] = Field( + None, + description="When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used. Available for the following models:, gemini-2.5-flash-preview-04-1, gemini-2.5-pro-preview-05-0, gemini-2.0-flash-lite-00, gemini-2.0-flash-001\n", + examples=[343940597], + ) + stopSequences: Optional[List[str]] = None + temperature: Optional[float] = Field( + 1, + description="The temperature is used for sampling during response generation, which occurs when topP and topK are applied. Temperature controls the degree of randomness in token selection. Lower temperatures are good for prompts that require a less open-ended or creative response, while higher temperatures can lead to more diverse or creative results. A temperature of 0 means that the highest probability tokens are always selected. In this case, responses for a given prompt are mostly deterministic, but a small amount of variation is still possible. If the model returns a response that's too generic, too short, or the model gives a fallback response, try increasing the temperature\n", + ge=0.0, + le=2.0, + ) + topK: Optional[int] = Field( + 40, + description="Top-K changes how the model selects tokens for output. A top-K of 1 means the next selected token is the most probable among all tokens in the model's vocabulary. A top-K of 3 means that the next token is selected from among the 3 most probable tokens by using temperature.\n", + examples=[40], + ge=1, + ) + topP: Optional[float] = Field( + 0.95, + description='If specified, nucleus sampling is used.\nTop-P changes how the model selects tokens for output. Tokens are selected from the most (see top-K) to least probable until the sum of their probabilities equals the top-P value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-P value is 0.5, then the model will select either A or B as the next token by using temperature and excludes C as a candidate.\nSpecify a lower value for less random responses and a higher value for more random responses.\n', + ge=0.0, + le=1.0, + ) + + +class GeminiMimeType(str, Enum): + application_pdf = 'application/pdf' + audio_mpeg = 'audio/mpeg' + audio_mp3 = 'audio/mp3' + audio_wav = 'audio/wav' + image_png = 'image/png' + image_jpeg = 'image/jpeg' + image_webp = 'image/webp' + text_plain = 'text/plain' + video_mov = 'video/mov' + video_mpeg = 'video/mpeg' + video_mp4 = 'video/mp4' + video_mpg = 'video/mpg' + video_avi = 'video/avi' + video_wmv = 'video/wmv' + video_mpegps = 'video/mpegps' + video_flv = 'video/flv' + + +class GeminiOffset(BaseModel): + nanos: Optional[int] = Field( + None, + description='Signed fractions of a second at nanosecond resolution. Negative second values with fractions must still have non-negative nanos values.\n', + examples=[0], + ge=0, + le=999999999, + ) + seconds: Optional[int] = Field( + None, + description='Signed seconds of the span of time. Must be from -315,576,000,000 to +315,576,000,000 inclusive.\n', + examples=[60], + ge=-315576000000, + le=315576000000, + ) + + +class GeminiSafetyCategory(str, Enum): + HARM_CATEGORY_SEXUALLY_EXPLICIT = 'HARM_CATEGORY_SEXUALLY_EXPLICIT' + HARM_CATEGORY_HATE_SPEECH = 'HARM_CATEGORY_HATE_SPEECH' + HARM_CATEGORY_HARASSMENT = 'HARM_CATEGORY_HARASSMENT' + HARM_CATEGORY_DANGEROUS_CONTENT = 'HARM_CATEGORY_DANGEROUS_CONTENT' + + +class Probability(str, Enum): + NEGLIGIBLE = 'NEGLIGIBLE' + LOW = 'LOW' + MEDIUM = 'MEDIUM' + HIGH = 'HIGH' + UNKNOWN = 'UNKNOWN' + + +class GeminiSafetyRating(BaseModel): + category: Optional[GeminiSafetyCategory] = None + probability: Optional[Probability] = Field( + None, + description='The probability that the content violates the specified safety category', + ) + + +class GeminiSafetyThreshold(str, Enum): + OFF = 'OFF' + BLOCK_NONE = 'BLOCK_NONE' + BLOCK_LOW_AND_ABOVE = 'BLOCK_LOW_AND_ABOVE' + BLOCK_MEDIUM_AND_ABOVE = 'BLOCK_MEDIUM_AND_ABOVE' + BLOCK_ONLY_HIGH = 'BLOCK_ONLY_HIGH' + + +class GeminiTextPart(BaseModel): + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Answer as concisely as possible'], + ) + + +class GeminiTool(BaseModel): + functionDeclarations: Optional[List[GeminiFunctionDeclaration]] = None + + +class GeminiVideoMetadata(BaseModel): + endOffset: Optional[GeminiOffset] = None + startOffset: Optional[GeminiOffset] = None + + +class GitCommitSummary(BaseModel): + author: Optional[str] = Field(None, description='The author of the commit') + branch_name: Optional[str] = Field( + None, description='The branch where the commit was made' + ) + commit_hash: Optional[str] = Field(None, description='The hash of the commit') + commit_name: Optional[str] = Field(None, description='The name of the commit') + status_summary: Optional[Dict[str, str]] = Field( + None, description='A map of operating system to status pairs' + ) + timestamp: Optional[datetime] = Field( + None, description='The timestamp when the commit was made' + ) + + +class GithubEnterprise(BaseModel): + avatar_url: str = Field(..., description='URL to the enterprise avatar') + created_at: datetime = Field(..., description='When the enterprise was created') + description: Optional[str] = Field(None, description='The enterprise description') + html_url: str = Field(..., description='The HTML URL of the enterprise') + id: int = Field(..., description='The enterprise ID') + name: str = Field(..., description='The enterprise name') + node_id: str = Field(..., description='The enterprise node ID') + slug: str = Field(..., description='The enterprise slug') + updated_at: datetime = Field( + ..., description='When the enterprise was last updated' + ) + website_url: Optional[str] = Field(None, description='The enterprise website URL') + + +class RepositorySelection(str, Enum): + selected = 'selected' + all = 'all' + + +class GithubOrganization(BaseModel): + avatar_url: str = Field(..., description="URL to the organization's avatar") + description: Optional[str] = Field(None, description='The organization description') + events_url: str = Field(..., description="The API URL of the organization's events") + hooks_url: str = Field(..., description="The API URL of the organization's hooks") + id: int = Field(..., description='The organization ID') + issues_url: str = Field(..., description="The API URL of the organization's issues") + login: str = Field(..., description="The organization's login name") + members_url: str = Field( + ..., description="The API URL of the organization's members" + ) + node_id: str = Field(..., description='The organization node ID') + public_members_url: str = Field( + ..., description="The API URL of the organization's public members" + ) + repos_url: str = Field( + ..., description="The API URL of the organization's repositories" + ) + url: str = Field(..., description='The API URL of the organization') + + +class State(str, Enum): + uploaded = 'uploaded' + open = 'open' + + +class Action(str, Enum): + published = 'published' + unpublished = 'unpublished' + created = 'created' + edited = 'edited' + deleted = 'deleted' + prereleased = 'prereleased' + released = 'released' + + +class Type7(str, Enum): + Bot = 'Bot' + User = 'User' + Organization = 'Organization' + + +class GithubUser(BaseModel): + avatar_url: str = Field(..., description="URL to the user's avatar") + gravatar_id: Optional[str] = Field(None, description="The user's gravatar ID") + html_url: str = Field(..., description='The HTML URL of the user') + id: int = Field(..., description="The user's ID") + login: str = Field(..., description="The user's login name") + node_id: str = Field(..., description="The user's node ID") + site_admin: bool = Field(..., description='Whether the user is a site admin') + type: Type7 = Field(..., description='The type of user') + url: str = Field(..., description='The API URL of the user') + + +class IdeogramColorPalette1(BaseModel): + name: str = Field(..., description='Name of the preset color palette') + + +class Member(BaseModel): + color: Optional[str] = Field( + None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$' + ) + weight: Optional[float] = Field( + None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0 + ) + + +class IdeogramColorPalette2(BaseModel): + members: List[Member] = Field( + ..., description='Array of color definitions with optional weights' + ) + + +class IdeogramColorPalette( + RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]] +): + root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field( + ..., + description='A color palette specification that can either use a preset name or explicit color definitions with weights', + ) + + +class ImageRequest(BaseModel): + aspect_ratio: Optional[str] = Field( + None, + description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.", + ) + color_palette: Optional[Dict[str, Any]] = Field( + None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.' + ) + magic_prompt_option: Optional[str] = Field( + None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')." + ) + model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')") + negative_prompt: Optional[str] = Field( + None, + description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.', + ) + num_images: Optional[int] = Field( + 1, + description='Optional. Number of images to generate (1-8). Defaults to 1.', + ge=1, + le=8, + ) + prompt: str = Field( + ..., description='Required. The prompt to use to generate the image.' + ) + resolution: Optional[str] = Field( + None, + description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.", + ) + seed: Optional[int] = Field( + None, + description='Optional. A number between 0 and 2147483647.', + ge=0, + le=2147483647, + ) + style_type: Optional[str] = Field( + None, + description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.", + ) + + +class IdeogramGenerateRequest(BaseModel): + image_request: ImageRequest = Field( + ..., description='The image generation request parameters.' + ) + + +class Datum(BaseModel): + is_image_safe: Optional[bool] = Field( + None, description='Indicates whether the image is considered safe.' + ) + prompt: Optional[str] = Field( + None, description='The prompt used to generate this image.' + ) + resolution: Optional[str] = Field( + None, description="The resolution of the generated image (e.g., '1024x1024')." + ) + seed: Optional[int] = Field( + None, description='The seed value used for this generation.' + ) + style_type: Optional[str] = Field( + None, + description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').", + ) + url: Optional[str] = Field(None, description='URL to the generated image.') + + +class IdeogramGenerateResponse(BaseModel): + created: Optional[datetime] = Field( + None, description='Timestamp when the generation was created.' + ) + data: Optional[List[Datum]] = Field( + None, description='Array of generated image information.' + ) + + +class StyleCode(RootModel[str]): + root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$') + + +class Datum1(BaseModel): + is_image_safe: Optional[bool] = None + prompt: Optional[str] = None + resolution: Optional[str] = None + seed: Optional[int] = None + style_type: Optional[str] = None + url: Optional[str] = None + + +class IdeogramV3IdeogramResponse(BaseModel): + created: Optional[datetime] = None + data: Optional[List[Datum1]] = None + + +class RenderingSpeed1(str, Enum): + TURBO = 'TURBO' + DEFAULT = 'DEFAULT' + QUALITY = 'QUALITY' + + +class IdeogramV3ReframeRequest(BaseModel): + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + rendering_speed: Optional[RenderingSpeed1] = None + resolution: str + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class MagicPrompt(str, Enum): + AUTO = 'AUTO' + ON = 'ON' + OFF = 'OFF' + + +class StyleType(str, Enum): + AUTO = 'AUTO' + GENERAL = 'GENERAL' + REALISTIC = 'REALISTIC' + DESIGN = 'DESIGN' + + +class IdeogramV3RemixRequest(BaseModel): + aspect_ratio: Optional[str] = None + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + image_weight: Optional[int] = Field(50, ge=1, le=100) + magic_prompt: Optional[MagicPrompt] = None + negative_prompt: Optional[str] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + resolution: Optional[str] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + style_type: Optional[StyleType] = None + + +class IdeogramV3ReplaceBackgroundRequest(BaseModel): + color_palette: Optional[Dict[str, Any]] = None + image: Optional[StrictBytes] = None + magic_prompt: Optional[MagicPrompt] = None + num_images: Optional[int] = Field(None, ge=1, le=8) + prompt: str + rendering_speed: Optional[RenderingSpeed1] = None + seed: Optional[int] = Field(None, ge=0, le=2147483647) + style_codes: Optional[List[str]] = None + style_reference_images: Optional[List[StrictBytes]] = None + + +class ColorPalette(BaseModel): + name: str = Field(..., description='Name of the color palette', examples=['PASTEL']) + + +class MagicPrompt2(str, Enum): + ON = 'ON' + OFF = 'OFF' + + +class StyleType1(str, Enum): + AUTO = 'AUTO' + GENERAL = 'GENERAL' + REALISTIC = 'REALISTIC' + DESIGN = 'DESIGN' + FICTION = 'FICTION' + + +class ImagenImageGenerationInstance(BaseModel): + prompt: str = Field(..., description='Text prompt for image generation') + + +class AspectRatio(str, Enum): + field_1_1 = '1:1' + field_9_16 = '9:16' + field_16_9 = '16:9' + field_3_4 = '3:4' + field_4_3 = '4:3' + + +class PersonGeneration(str, Enum): + dont_allow = 'dont_allow' + allow_adult = 'allow_adult' + allow_all = 'allow_all' + + +class SafetySetting(str, Enum): + block_most = 'block_most' + block_some = 'block_some' + block_few = 'block_few' + block_fewest = 'block_fewest' + + +class ImagenImagePrediction(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded image content' + ) + mimeType: Optional[str] = Field( + None, description='MIME type of the generated image' + ) + prompt: Optional[str] = Field( + None, description='Enhanced or rewritten prompt used to generate this image' + ) + + +class MimeType(str, Enum): + image_png = 'image/png' + image_jpeg = 'image/jpeg' + + +class ImagenOutputOptions(BaseModel): + compressionQuality: Optional[int] = Field(None, ge=0, le=100) + mimeType: Optional[MimeType] = None + + +class Includable(str, Enum): + file_search_call_results = 'file_search_call.results' + message_input_image_image_url = 'message.input_image.image_url' + computer_call_output_output_image_url = 'computer_call_output.output.image_url' + + +class Type8(str, Enum): + input_file = 'input_file' + + +class InputFileContent(BaseModel): + file_data: Optional[str] = Field( + None, description='The content of the file to be sent to the model.\n' + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + filename: Optional[str] = Field( + None, description='The name of the file to be sent to the model.' + ) + type: Type8 = Field( + ..., description='The type of the input item. Always `input_file`.' + ) + + +class Detail(str, Enum): + low = 'low' + high = 'high' + auto = 'auto' + + +class Type9(str, Enum): + input_image = 'input_image' + + +class InputImageContent(BaseModel): + detail: Detail = Field( + ..., + description='The detail level of the image to be sent to the model. One of `high`, `low`, or `auto`. Defaults to `auto`.', + ) + file_id: Optional[str] = Field( + None, description='The ID of the file to be sent to the model.' + ) + image_url: Optional[str] = Field( + None, + description='The URL of the image to be sent to the model. A fully qualified URL or base64 encoded image in a data URL.', + ) + type: Type9 = Field( + ..., description='The type of the input item. Always `input_image`.' + ) + + +class Role3(str, Enum): + user = 'user' + system = 'system' + developer = 'developer' + + +class Type10(str, Enum): + message = 'message' + + +class Type11(str, Enum): + input_text = 'input_text' + + +class InputTextContent(BaseModel): + text: str = Field(..., description='The text input to the model.') + type: Type11 = Field( + ..., description='The type of the input item. Always `input_text`.' + ) + + +class KlingAudioUploadType(str, Enum): + file = 'file' + url = 'url' + + +class KlingCameraConfig(BaseModel): + horizontal: Optional[float] = Field( + None, + description="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right.", + ge=-10.0, + le=10.0, + ) + pan: Optional[float] = Field( + None, + description="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + ge=-10.0, + le=10.0, + ) + roll: Optional[float] = Field( + None, + description="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ge=-10.0, + le=10.0, + ) + tilt: Optional[float] = Field( + None, + description="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ge=-10.0, + le=10.0, + ) + vertical: Optional[float] = Field( + None, + description="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", + ge=-10.0, + le=10.0, + ) + zoom: Optional[float] = Field( + None, + description="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ge=-10.0, + le=10.0, + ) + + +class KlingCameraControlType(str, Enum): + simple = 'simple' + down_back = 'down_back' + forward_up = 'forward_up' + right_turn_forward = 'right_turn_forward' + left_turn_forward = 'left_turn_forward' + + +class KlingCharacterEffectModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + + +class KlingDualCharacterEffectsScene(str, Enum): + hug = 'hug' + kiss = 'kiss' + heart_gesture = 'heart_gesture' + + +class KlingDualCharacterImages(RootModel[List[str]]): + root: List[str] = Field(..., max_length=2, min_length=2) + + +class KlingErrorResponse(BaseModel): + code: int = Field( + ..., + description='- 1000: Authentication failed\n- 1001: Authorization is empty\n- 1002: Authorization is invalid\n- 1003: Authorization is not yet valid\n- 1004: Authorization has expired\n- 1100: Account exception\n- 1101: Account in arrears (postpaid scenario)\n- 1102: Resource pack depleted or expired (prepaid scenario)\n- 1103: Unauthorized access to requested resource\n- 1200: Invalid request parameters\n- 1201: Invalid parameters\n- 1202: Invalid request method\n- 1203: Requested resource does not exist\n- 1300: Trigger platform strategy\n- 1301: Trigger content security policy\n- 1302: API request too frequent\n- 1303: Concurrency/QPS exceeds limit\n- 1304: Trigger IP whitelist policy\n- 5000: Internal server error\n- 5001: Service temporarily unavailable\n- 5002: Server internal timeout\n', + ) + message: str = Field(..., description='Human-readable error message') + request_id: str = Field( + ..., description='Request ID for tracking and troubleshooting' + ) + + +class Trajectory(BaseModel): + x: Optional[int] = Field( + None, + description='The horizontal coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + y: Optional[int] = Field( + None, + description='The vertical coordinate of trajectory point. Based on bottom-left corner of image as origin (0,0).', + ) + + +class DynamicMask(BaseModel): + mask: Optional[AnyUrl] = Field( + None, + description='Dynamic Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + trajectories: Optional[List[Trajectory]] = None + + +class TaskInfo(BaseModel): + external_task_id: Optional[str] = None + + +class KlingImageGenAspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_3_2 = '3:2' + field_2_3 = '2:3' + field_21_9 = '21:9' + + +class KlingImageGenImageReferenceType(str, Enum): + subject = 'subject' + face = 'face' + + +class KlingImageGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v2 = 'kling-v2' + + +class KlingImageGenerationsRequest(BaseModel): + aspect_ratio: Optional[KlingImageGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + human_fidelity: Optional[float] = Field( + 0.45, description='Subject reference similarity', ge=0.0, le=1.0 + ) + image: Optional[str] = Field( + None, description='Reference Image - Base64 encoded string or image URL' + ) + image_fidelity: Optional[float] = Field( + 0.5, description='Reference intensity for user-uploaded images', ge=0.0, le=1.0 + ) + image_reference: Optional[KlingImageGenImageReferenceType] = None + model_name: Optional[KlingImageGenModelName] = 'kling-v1' + n: Optional[int] = Field(1, description='Number of generated images', ge=1, le=9) + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=200 + ) + prompt: str = Field(..., description='Positive text prompt', max_length=500) + + +class KlingImageResult(BaseModel): + index: Optional[int] = Field(None, description='Image Number (0-9)') + url: Optional[AnyUrl] = Field(None, description='URL for generated image') + + +class KlingLipSyncMode(str, Enum): + text2video = 'text2video' + audio2video = 'audio2video' + + +class KlingLipSyncVoiceLanguage(str, Enum): + zh = 'zh' + en = 'en' + + +class ResourcePackType(str, Enum): + decreasing_total = 'decreasing_total' + constant_period = 'constant_period' + + +class Status5(str, Enum): + toBeOnline = 'toBeOnline' + online = 'online' + expired = 'expired' + runOut = 'runOut' + + +class ResourcePackSubscribeInfo(BaseModel): + effective_time: Optional[int] = Field( + None, description='Effective time, Unix timestamp in ms' + ) + invalid_time: Optional[int] = Field( + None, description='Expiration time, Unix timestamp in ms' + ) + purchase_time: Optional[int] = Field( + None, description='Purchase time, Unix timestamp in ms' + ) + remaining_quantity: Optional[float] = Field( + None, description='Remaining quantity (updated with a 12-hour delay)' + ) + resource_pack_id: Optional[str] = Field(None, description='Resource package ID') + resource_pack_name: Optional[str] = Field(None, description='Resource package name') + resource_pack_type: Optional[ResourcePackType] = Field( + None, + description='Resource package type (decreasing_total=decreasing total, constant_period=constant periodicity)', + ) + status: Optional[Status5] = Field(None, description='Resource Package Status') + total_quantity: Optional[float] = Field(None, description='Total quantity') + + +class Data3(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + msg: Optional[str] = Field(None, description='Error information') + resource_pack_subscribe_infos: Optional[List[ResourcePackSubscribeInfo]] = Field( + None, description='Resource package list' + ) + + +class KlingResourcePackageResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code; 0 indicates success') + data: Optional[Data3] = None + message: Optional[str] = Field(None, description='Error information') + request_id: Optional[str] = Field( + None, + description='Request ID, generated by the system, used to track requests and troubleshoot problems', + ) + + +class KlingSingleImageEffectDuration(str, Enum): + field_5 = '5' + + +class KlingSingleImageEffectModelName(str, Enum): + kling_v1_6 = 'kling-v1-6' + + +class KlingSingleImageEffectsScene(str, Enum): + bloombloom = 'bloombloom' + dizzydizzy = 'dizzydizzy' + fuzzyfuzzy = 'fuzzyfuzzy' + squish = 'squish' + expansion = 'expansion' + + +class KlingTaskStatus(str, Enum): + submitted = 'submitted' + processing = 'processing' + succeed = 'succeed' + failed = 'failed' + + +class KlingTextToVideoModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_6 = 'kling-v1-6' + kling_v2_1_master = 'kling-v2-1-master' + kling_v2_5_turbo = 'kling-v2-5-turbo' + + +class KlingVideoGenAspectRatio(str, Enum): + field_16_9 = '16:9' + field_9_16 = '9:16' + field_1_1 = '1:1' + + +class KlingVideoGenCfgScale(RootModel[float]): + root: float = Field( + ..., + description="Flexibility in video generation. The higher the value, the lower the model's degree of flexibility, and the stronger the relevance to the user's prompt.", + ge=0.0, + le=1.0, + ) + + +class KlingVideoGenDuration(str, Enum): + field_5 = '5' + field_10 = '10' + + +class KlingVideoGenMode(str, Enum): + std = 'std' + pro = 'pro' + + +class KlingVideoGenModelName(str, Enum): + kling_v1 = 'kling-v1' + kling_v1_5 = 'kling-v1-5' + kling_v1_6 = 'kling-v1-6' + kling_v2_master = 'kling-v2-master' + kling_v2_1 = 'kling-v2-1' + kling_v2_1_master = 'kling-v2-1-master' + kling_v2_5_turbo = 'kling-v2-5-turbo' + + +class KlingVideoResult(BaseModel): + duration: Optional[str] = Field(None, description='Total video duration') + id: Optional[str] = Field(None, description='Generated video ID') + url: Optional[AnyUrl] = Field(None, description='URL for generated video') + + +class KlingVirtualTryOnModelName(str, Enum): + kolors_virtual_try_on_v1 = 'kolors-virtual-try-on-v1' + kolors_virtual_try_on_v1_5 = 'kolors-virtual-try-on-v1-5' + + +class KlingVirtualTryOnRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + cloth_image: Optional[str] = Field( + None, + description='Reference clothing image - Base64 encoded string or image URL', + ) + human_image: str = Field( + ..., description='Reference human image - Base64 encoded string or image URL' + ) + model_name: Optional[KlingVirtualTryOnModelName] = 'kolors-virtual-try-on-v1' + + +class TaskResult6(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data7(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult6] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVirtualTryOnResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data7] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class LumaAspectRatio(str, Enum): + field_1_1 = '1:1' + field_16_9 = '16:9' + field_9_16 = '9:16' + field_4_3 = '4:3' + field_3_4 = '3:4' + field_21_9 = '21:9' + field_9_21 = '9:21' + + +class LumaAssets(BaseModel): + image: Optional[AnyUrl] = Field(None, description='The URL of the image') + progress_video: Optional[AnyUrl] = Field( + None, description='The URL of the progress video' + ) + video: Optional[AnyUrl] = Field(None, description='The URL of the video') + + +class GenerationType(str, Enum): + add_audio = 'add_audio' + + +class LumaAudioGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the audio' + ) + generation_type: Optional[GenerationType] = 'add_audio' + negative_prompt: Optional[str] = Field( + None, description='The negative prompt of the audio' + ) + prompt: Optional[str] = Field(None, description='The prompt of the audio') + + +class LumaError(BaseModel): + detail: Optional[str] = Field(None, description='The error message') + + +class Type12(str, Enum): + generation = 'generation' + + +class LumaGenerationReference(BaseModel): + id: UUID = Field(..., description='The ID of the generation') + type: Literal['generation'] + + +class GenerationType1(str, Enum): + video = 'video' + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class GenerationType2(str, Enum): + image = 'image' + + +class LumaImageIdentity(BaseModel): + images: Optional[List[AnyUrl]] = Field( + None, description='The URLs of the image identity' + ) + + +class LumaImageModel(str, Enum): + photon_1 = 'photon-1' + photon_flash_1 = 'photon-flash-1' + + +class LumaImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the image reference' + ) + + +class Type13(str, Enum): + image = 'image' + + +class LumaImageReference(BaseModel): + type: Literal['image'] + url: AnyUrl = Field(..., description='The URL of the image') + + +class LumaKeyframe(RootModel[Union[LumaGenerationReference, LumaImageReference]]): + root: Union[LumaGenerationReference, LumaImageReference] = Field( + ..., + description='A keyframe can be either a Generation reference, an Image, or a Video', + discriminator='type', + ) + + +class LumaKeyframes(BaseModel): + frame0: Optional[LumaKeyframe] = None + frame1: Optional[LumaKeyframe] = None + + +class LumaModifyImageRef(BaseModel): + url: Optional[AnyUrl] = Field(None, description='The URL of the image reference') + weight: Optional[float] = Field( + None, description='The weight of the modify image reference' + ) + + +class LumaState(str, Enum): + queued = 'queued' + dreaming = 'dreaming' + completed = 'completed' + failed = 'failed' + + +class GenerationType3(str, Enum): + upscale_video = 'upscale_video' + + +class LumaVideoModel(str, Enum): + ray_2 = 'ray-2' + ray_flash_2 = 'ray-flash-2' + ray_1_6 = 'ray-1-6' + + +class LumaVideoModelOutputDuration1(str, Enum): + field_5s = '5s' + field_9s = '9s' + + +class LumaVideoModelOutputDuration( + RootModel[Union[LumaVideoModelOutputDuration1, str]] +): + root: Union[LumaVideoModelOutputDuration1, str] + + +class LumaVideoModelOutputResolution1(str, Enum): + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + field_4k = '4k' + + +class LumaVideoModelOutputResolution( + RootModel[Union[LumaVideoModelOutputResolution1, str]] +): + root: Union[LumaVideoModelOutputResolution1, str] + + +class MachineStats(BaseModel): + cpu_capacity: Optional[str] = Field(None, description='Total CPU on the machine.') + disk_capacity: Optional[str] = Field( + None, description='Total disk capacity on the machine.' + ) + gpu_type: Optional[str] = Field( + None, description='The GPU type. eg. NVIDIA Tesla K80' + ) + initial_cpu: Optional[str] = Field( + None, description='Initial CPU available before the job starts.' + ) + initial_disk: Optional[str] = Field( + None, description='Initial disk available before the job starts.' + ) + initial_ram: Optional[str] = Field( + None, description='Initial RAM available before the job starts.' + ) + machine_name: Optional[str] = Field(None, description='Name of the machine.') + memory_capacity: Optional[str] = Field( + None, description='Total memory on the machine.' + ) + os_version: Optional[str] = Field( + None, description='The operating system version. eg. Ubuntu Linux 20.04' + ) + pip_freeze: Optional[str] = Field(None, description='The pip freeze output') + vram_time_series: Optional[Dict[str, Any]] = Field( + None, description='Time series of VRAM usage.' + ) + + +class MinimaxBaseResponse(BaseModel): + status_code: int = Field( + ..., + description='Status code. 0 indicates success, other values indicate errors.', + ) + status_msg: str = Field( + ..., description='Specific error details or success message.' + ) + + +class File(BaseModel): + bytes: Optional[int] = Field(None, description='File size in bytes') + created_at: Optional[int] = Field( + None, description='Unix timestamp when the file was created, in seconds' + ) + download_url: Optional[str] = Field( + None, description='The URL to download the video' + ) + file_id: Optional[int] = Field(None, description='Unique identifier for the file') + filename: Optional[str] = Field(None, description='The name of the file') + purpose: Optional[str] = Field(None, description='The purpose of using the file') + + +class MinimaxFileRetrieveResponse(BaseModel): + base_resp: MinimaxBaseResponse + file: File + + +class Status6(str, Enum): + Queueing = 'Queueing' + Preparing = 'Preparing' + Processing = 'Processing' + Success = 'Success' + Fail = 'Fail' + + +class MinimaxTaskResultResponse(BaseModel): + base_resp: MinimaxBaseResponse + file_id: Optional[str] = Field( + None, + description='After the task status changes to Success, this field returns the file ID corresponding to the generated video.', + ) + status: Status6 = Field( + ..., + description="Task status: 'Queueing' (in queue), 'Preparing' (task is preparing), 'Processing' (generating), 'Success' (task completed successfully), or 'Fail' (task failed).", + ) + task_id: str = Field(..., description='The task ID being queried.') + + +class MiniMaxModel(str, Enum): + T2V_01_Director = 'T2V-01-Director' + I2V_01_Director = 'I2V-01-Director' + S2V_01 = 'S2V-01' + I2V_01 = 'I2V-01' + I2V_01_live = 'I2V-01-live' + T2V_01 = 'T2V-01' + Hailuo_02 = 'MiniMax-Hailuo-02' + + +class SubjectReferenceItem(BaseModel): + image: Optional[str] = Field( + None, description='URL or base64 encoding of the subject reference image.' + ) + mask: Optional[str] = Field( + None, + description='URL or base64 encoding of the mask for the subject reference image.', + ) + + +class MinimaxVideoGenerationRequest(BaseModel): + callback_url: Optional[str] = Field( + None, + description='Optional. URL to receive real-time status updates about the video generation task.', + ) + first_frame_image: Optional[str] = Field( + None, + description='URL or base64 encoding of the first frame image. Required when model is I2V-01, I2V-01-Director, or I2V-01-live.', + ) + model: MiniMaxModel = Field( + ..., + description='Required. ID of model. Options: T2V-01-Director, I2V-01-Director, S2V-01, I2V-01, I2V-01-live, T2V-01', + ) + prompt: Optional[str] = Field( + None, + description='Description of the video. Should be less than 2000 characters. Supports camera movement instructions in [brackets].', + max_length=2000, + ) + prompt_optimizer: Optional[bool] = Field( + True, + description='If true (default), the model will automatically optimize the prompt. Set to false for more precise control.', + ) + subject_reference: Optional[List[SubjectReferenceItem]] = Field( + None, + description='Only available when model is S2V-01. The model will generate a video based on the subject uploaded through this parameter.', + ) + duration: Optional[int] = Field( + None, + description="The length of the output video in seconds." + ) + resolution: Optional[str] = Field( + None, + description="The dimensions of the video display. 1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels." + ) + + +class MinimaxVideoGenerationResponse(BaseModel): + base_resp: MinimaxBaseResponse + task_id: str = Field( + ..., description='The task ID for the asynchronous video generation task.' + ) + + +class Modality(str, Enum): + MODALITY_UNSPECIFIED = 'MODALITY_UNSPECIFIED' + TEXT = 'TEXT' + IMAGE = 'IMAGE' + VIDEO = 'VIDEO' + AUDIO = 'AUDIO' + DOCUMENT = 'DOCUMENT' + + +class ModalityTokenCount(BaseModel): + modality: Optional[Modality] = None + tokenCount: Optional[int] = Field( + None, description='Number of tokens for the given modality.' + ) + + +class Truncation(str, Enum): + disabled = 'disabled' + auto = 'auto' + + +class ModelResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, description='Instructions for the model on how to generate the response' + ) + max_output_tokens: Optional[int] = Field( + None, description='Maximum number of tokens to generate' + ) + model: Optional[str] = Field( + None, description='The model used to generate the response' + ) + temperature: Optional[float] = Field( + 1, description='Controls randomness in the response', ge=0.0, le=2.0 + ) + top_p: Optional[float] = Field( + 1, + description='Controls diversity of the response via nucleus sampling', + ge=0.0, + le=1.0, + ) + truncation: Optional[Truncation] = Field( + 'disabled', description='How to handle truncation of the response' + ) + + +class Keyframes(BaseModel): + image_url: Optional[str] = None + + +class MoonvalleyPromptResponse(BaseModel): + error: Optional[Dict[str, Any]] = None + frame_conditioning: Optional[Dict[str, Any]] = None + id: Optional[str] = None + inference_params: Optional[Dict[str, Any]] = None + meta: Optional[Dict[str, Any]] = None + model_params: Optional[Dict[str, Any]] = None + output_url: Optional[str] = None + prompt_text: Optional[str] = None + status: Optional[str] = None + + +class MoonvalleyTextToVideoInferenceParams(BaseModel): + add_quality_guidance: Optional[bool] = Field( + True, description='Whether to add quality guidance' + ) + caching_coefficient: Optional[float] = Field( + 0.3, description='Caching coefficient for optimization' + ) + caching_cooldown: Optional[int] = Field( + 3, description='Number of caching cooldown steps' + ) + caching_warmup: Optional[int] = Field( + 3, description='Number of caching warmup steps' + ) + clip_value: Optional[float] = Field( + 3, description='CLIP value for generation control' + ) + conditioning_frame_index: Optional[int] = Field( + 0, description='Index of the conditioning frame' + ) + cooldown_steps: Optional[int] = Field( + 75, description='Number of cooldown steps (calculated based on num_frames)' + ) + fps: Optional[int] = Field( + 24, description='Frames per second of the generated video' + ) + guidance_scale: Optional[float] = Field( + 10, description='Guidance scale for generation control' + ) + height: Optional[int] = Field( + 1080, description='Height of the generated video in pixels' + ) + negative_prompt: Optional[str] = Field(None, description='Negative prompt text') + num_frames: Optional[int] = Field(64, description='Number of frames to generate') + seed: Optional[int] = Field( + None, description='Random seed for generation (default: random)' + ) + shift_value: Optional[float] = Field( + 3, description='Shift value for generation control' + ) + steps: Optional[int] = Field(80, description='Number of denoising steps') + use_guidance_schedule: Optional[bool] = Field( + True, description='Whether to use guidance scheduling' + ) + use_negative_prompts: Optional[bool] = Field( + False, description='Whether to use negative prompts' + ) + use_timestep_transform: Optional[bool] = Field( + True, description='Whether to use timestep transformation' + ) + warmup_steps: Optional[int] = Field( + 0, description='Number of warmup steps (calculated based on num_frames)' + ) + width: Optional[int] = Field( + 1920, description='Width of the generated video in pixels' + ) + + +class MoonvalleyTextToVideoRequest(BaseModel): + image_url: Optional[str] = None + inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None + prompt_text: Optional[str] = None + webhook_url: Optional[str] = None + + +class MoonvalleyUploadFileRequest(BaseModel): + file: Optional[StrictBytes] = None + + +class MoonvalleyUploadFileResponse(BaseModel): + access_url: Optional[str] = None + + +class MoonvalleyVideoToVideoInferenceParams(BaseModel): + add_quality_guidance: Optional[bool] = Field( + True, description='Whether to add quality guidance' + ) + caching_coefficient: Optional[float] = Field( + 0.3, description='Caching coefficient for optimization' + ) + caching_cooldown: Optional[int] = Field( + 3, description='Number of caching cooldown steps' + ) + caching_warmup: Optional[int] = Field( + 3, description='Number of caching warmup steps' + ) + clip_value: Optional[float] = Field( + 3, description='CLIP value for generation control' + ) + conditioning_frame_index: Optional[int] = Field( + 0, description='Index of the conditioning frame' + ) + cooldown_steps: Optional[int] = Field( + 36, description='Number of cooldown steps (calculated based on num_frames)' + ) + guidance_scale: Optional[float] = Field( + 15, description='Guidance scale for generation control' + ) + negative_prompt: Optional[str] = Field(None, description='Negative prompt text') + seed: Optional[int] = Field( + None, description='Random seed for generation (default: random)' + ) + shift_value: Optional[float] = Field( + 3, description='Shift value for generation control' + ) + steps: Optional[int] = Field(80, description='Number of denoising steps') + use_guidance_schedule: Optional[bool] = Field( + True, description='Whether to use guidance scheduling' + ) + use_negative_prompts: Optional[bool] = Field( + False, description='Whether to use negative prompts' + ) + use_timestep_transform: Optional[bool] = Field( + True, description='Whether to use timestep transformation' + ) + warmup_steps: Optional[int] = Field( + 24, description='Number of warmup steps (calculated based on num_frames)' + ) + + +class ControlType(str, Enum): + motion_control = 'motion_control' + pose_control = 'pose_control' + + +class MoonvalleyVideoToVideoRequest(BaseModel): + control_type: ControlType = Field( + ..., description='Supported types for video control' + ) + inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None + prompt_text: str = Field(..., description='Describes the video to generate') + video_url: str = Field(..., description='Url to control video') + webhook_url: Optional[str] = Field( + None, description='Optional webhook URL for notifications' + ) + + +class NodeStatus(str, Enum): + NodeStatusActive = 'NodeStatusActive' + NodeStatusDeleted = 'NodeStatusDeleted' + NodeStatusBanned = 'NodeStatusBanned' + + +class NodeVersionIdentifier(BaseModel): + node_id: str = Field(..., description='The unique identifier of the node') + version: str = Field(..., description='The version of the node') + + +class NodeVersionStatus(str, Enum): + NodeVersionStatusActive = 'NodeVersionStatusActive' + NodeVersionStatusDeleted = 'NodeVersionStatusDeleted' + NodeVersionStatusBanned = 'NodeVersionStatusBanned' + NodeVersionStatusPending = 'NodeVersionStatusPending' + NodeVersionStatusFlagged = 'NodeVersionStatusFlagged' + + +class NodeVersionUpdateRequest(BaseModel): + changelog: Optional[str] = Field( + None, description='The changelog describing the version changes.' + ) + deprecated: Optional[bool] = Field( + None, description='Whether the version is deprecated.' + ) + + +class Moderation(str, Enum): + low = 'low' + auto = 'auto' + + +class OutputFormat1(str, Enum): + png = 'png' + webp = 'webp' + jpeg = 'jpeg' + + +class OpenAIImageEditRequest(BaseModel): + background: Optional[str] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: str = Field( + ..., description='The model to use for image editing', examples=['gpt-image-1'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, description='The number of images to generate', examples=[1] + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired edit', + examples=['Give the rocketship rainbow coloring'], + ) + quality: Optional[str] = Field( + None, description='The quality of the edited image', examples=['low'] + ) + size: Optional[str] = Field( + None, description='Size of the output image', examples=['1024x1024'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Background(str, Enum): + transparent = 'transparent' + opaque = 'opaque' + + +class Quality(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + standard = 'standard' + hd = 'hd' + + +class ResponseFormat(str, Enum): + url = 'url' + b64_json = 'b64_json' + + +class Style(str, Enum): + vivid = 'vivid' + natural = 'natural' + + +class OpenAIImageGenerationRequest(BaseModel): + background: Optional[Background] = Field( + None, description='Background transparency', examples=['opaque'] + ) + model: Optional[str] = Field( + None, description='The model to use for image generation', examples=['dall-e-3'] + ) + moderation: Optional[Moderation] = Field( + None, description='Content moderation setting', examples=['auto'] + ) + n: Optional[int] = Field( + None, + description='The number of images to generate (1-10). Only 1 supported for dall-e-3.', + examples=[1], + ) + output_compression: Optional[int] = Field( + None, description='Compression level for JPEG or WebP (0-100)', examples=[100] + ) + output_format: Optional[OutputFormat1] = Field( + None, description='Format of the output image', examples=['png'] + ) + prompt: str = Field( + ..., + description='A text description of the desired image', + examples=['Draw a rocket in front of a blackhole in deep space'], + ) + quality: Optional[Quality] = Field( + None, description='The quality of the generated image', examples=['high'] + ) + response_format: Optional[ResponseFormat] = Field( + None, description='Response format of image data', examples=['b64_json'] + ) + size: Optional[str] = Field( + None, + description='Size of the image (e.g., 1024x1024, 1536x1024, auto)', + examples=['1024x1536'], + ) + style: Optional[Style] = Field( + None, description='Style of the image (only for dall-e-3)', examples=['vivid'] + ) + user: Optional[str] = Field( + None, + description='A unique identifier for end-user monitoring', + examples=['user-1234'], + ) + + +class Datum2(BaseModel): + b64_json: Optional[str] = Field(None, description='Base64 encoded image data') + revised_prompt: Optional[str] = Field(None, description='Revised prompt') + url: Optional[str] = Field(None, description='URL of the image') + + +class InputTokensDetails(BaseModel): + image_tokens: Optional[int] = None + text_tokens: Optional[int] = None + + +class Usage(BaseModel): + input_tokens: Optional[int] = None + input_tokens_details: Optional[InputTokensDetails] = None + output_tokens: Optional[int] = None + total_tokens: Optional[int] = None + + +class OpenAIImageGenerationResponse(BaseModel): + data: Optional[List[Datum2]] = None + usage: Optional[Usage] = None + + +class OpenAIModels(str, Enum): + gpt_4 = 'gpt-4' + gpt_4_0314 = 'gpt-4-0314' + gpt_4_0613 = 'gpt-4-0613' + gpt_4_32k = 'gpt-4-32k' + gpt_4_32k_0314 = 'gpt-4-32k-0314' + gpt_4_32k_0613 = 'gpt-4-32k-0613' + gpt_4_0125_preview = 'gpt-4-0125-preview' + gpt_4_turbo = 'gpt-4-turbo' + gpt_4_turbo_2024_04_09 = 'gpt-4-turbo-2024-04-09' + gpt_4_turbo_preview = 'gpt-4-turbo-preview' + gpt_4_1106_preview = 'gpt-4-1106-preview' + gpt_4_vision_preview = 'gpt-4-vision-preview' + gpt_3_5_turbo = 'gpt-3.5-turbo' + gpt_3_5_turbo_16k = 'gpt-3.5-turbo-16k' + gpt_3_5_turbo_0301 = 'gpt-3.5-turbo-0301' + gpt_3_5_turbo_0613 = 'gpt-3.5-turbo-0613' + gpt_3_5_turbo_1106 = 'gpt-3.5-turbo-1106' + gpt_3_5_turbo_0125 = 'gpt-3.5-turbo-0125' + gpt_3_5_turbo_16k_0613 = 'gpt-3.5-turbo-16k-0613' + gpt_4_1 = 'gpt-4.1' + gpt_4_1_mini = 'gpt-4.1-mini' + gpt_4_1_nano = 'gpt-4.1-nano' + gpt_4_1_2025_04_14 = 'gpt-4.1-2025-04-14' + gpt_4_1_mini_2025_04_14 = 'gpt-4.1-mini-2025-04-14' + gpt_4_1_nano_2025_04_14 = 'gpt-4.1-nano-2025-04-14' + o1 = 'o1' + o1_mini = 'o1-mini' + o1_preview = 'o1-preview' + o1_pro = 'o1-pro' + o1_2024_12_17 = 'o1-2024-12-17' + o1_preview_2024_09_12 = 'o1-preview-2024-09-12' + o1_mini_2024_09_12 = 'o1-mini-2024-09-12' + o1_pro_2025_03_19 = 'o1-pro-2025-03-19' + o3 = 'o3' + o3_mini = 'o3-mini' + o3_2025_04_16 = 'o3-2025-04-16' + o3_mini_2025_01_31 = 'o3-mini-2025-01-31' + o4_mini = 'o4-mini' + o4_mini_2025_04_16 = 'o4-mini-2025-04-16' + gpt_4o = 'gpt-4o' + gpt_4o_mini = 'gpt-4o-mini' + gpt_4o_2024_11_20 = 'gpt-4o-2024-11-20' + gpt_4o_2024_08_06 = 'gpt-4o-2024-08-06' + gpt_4o_2024_05_13 = 'gpt-4o-2024-05-13' + gpt_4o_mini_2024_07_18 = 'gpt-4o-mini-2024-07-18' + gpt_4o_audio_preview = 'gpt-4o-audio-preview' + gpt_4o_audio_preview_2024_10_01 = 'gpt-4o-audio-preview-2024-10-01' + gpt_4o_audio_preview_2024_12_17 = 'gpt-4o-audio-preview-2024-12-17' + gpt_4o_mini_audio_preview = 'gpt-4o-mini-audio-preview' + gpt_4o_mini_audio_preview_2024_12_17 = 'gpt-4o-mini-audio-preview-2024-12-17' + gpt_4o_search_preview = 'gpt-4o-search-preview' + gpt_4o_mini_search_preview = 'gpt-4o-mini-search-preview' + gpt_4o_search_preview_2025_03_11 = 'gpt-4o-search-preview-2025-03-11' + gpt_4o_mini_search_preview_2025_03_11 = 'gpt-4o-mini-search-preview-2025-03-11' + computer_use_preview = 'computer-use-preview' + computer_use_preview_2025_03_11 = 'computer-use-preview-2025-03-11' + chatgpt_4o_latest = 'chatgpt-4o-latest' + + +class Reason(str, Enum): + max_output_tokens = 'max_output_tokens' + content_filter = 'content_filter' + + +class IncompleteDetails(BaseModel): + reason: Optional[Reason] = Field( + None, description='The reason why the response is incomplete.' + ) + + +class Object(str, Enum): + response = 'response' + + +class Status7(str, Enum): + completed = 'completed' + failed = 'failed' + in_progress = 'in_progress' + incomplete = 'incomplete' + + +class Type14(str, Enum): + output_audio = 'output_audio' + + +class OutputAudioContent(BaseModel): + data: str = Field(..., description='Base64-encoded audio data') + transcript: str = Field(..., description='Transcript of the audio') + type: Type14 = Field(..., description='The type of output content') + + +class Role4(str, Enum): + assistant = 'assistant' + + +class Type15(str, Enum): + message = 'message' + + +class Type16(str, Enum): + output_text = 'output_text' + + +class OutputTextContent(BaseModel): + text: str = Field(..., description='The text content') + type: Type16 = Field(..., description='The type of output content') + + +class PersonalAccessToken(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='[Output Only]The date and time the token was created.' + ) + description: Optional[str] = Field( + None, + description="Optional. A more detailed description of the token's intended use.", + ) + id: Optional[UUID] = Field(None, description='Unique identifier for the GitCommit') + name: Optional[str] = Field( + None, + description='Required. The name of the token. Can be a simple description.', + ) + token: Optional[str] = Field( + None, + description='[Output Only]. The personal access token. Only returned during creation.', + ) + + +class AspectRatio1(RootModel[float]): + root: float = Field( + ..., + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + + +class IngredientsMode(str, Enum): + creative = 'creative' + precise = 'precise' + + +class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): + aspectRatio: Optional[AspectRatio1] = Field( + None, description='Aspect ratio (width / height)', title='Aspectratio' + ) + duration: Optional[int] = Field(5, title='Duration') + images: Optional[List[StrictBytes]] = Field(None, title='Images') + ingredientsMode: IngredientsMode = Field(..., title='Ingredientsmode') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[str] = Field('1080p', title='Resolution') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + modifyRegionMask: Optional[StrictBytes] = Field( + None, + description='A mask image that specifies the region to modify, where the mask is white and the background is black', + title='Modifyregionmask', + ) + modifyRegionRoi: Optional[str] = Field( + None, + description='Plaintext description of the object / region to modify', + title='Modifyregionroi', + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + video: Optional[StrictBytes] = Field(None, title='Video') + + +class PikaDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class PikaGenerateResponse(BaseModel): + video_id: str = Field(..., title='Video Id') + + +class PikaResolutionEnum(str, Enum): + field_1080p = '1080p' + field_720p = '720p' + + +class PikaStatusEnum(str, Enum): + queued = 'queued' + started = 'started' + finished = 'finished' + + +class PikaValidationError(BaseModel): + loc: List[Union[str, int]] = Field(..., title='Location') + msg: str = Field(..., title='Message') + type: str = Field(..., title='Error Type') + + +class PikaVideoResponse(BaseModel): + id: str = Field(..., title='Id') + progress: Optional[int] = Field(None, title='Progress') + status: PikaStatusEnum + url: Optional[str] = Field(None, title='Url') + + +class Pikaffect(str, Enum): + Cake_ify = 'Cake-ify' + Crumble = 'Crumble' + Crush = 'Crush' + Decapitate = 'Decapitate' + Deflate = 'Deflate' + Dissolve = 'Dissolve' + Explode = 'Explode' + Eye_pop = 'Eye-pop' + Inflate = 'Inflate' + Levitate = 'Levitate' + Melt = 'Melt' + Peel = 'Peel' + Poke = 'Poke' + Squish = 'Squish' + Ta_da = 'Ta-da' + Tear = 'Tear' + + +class Resp(BaseModel): + img_id: Optional[int] = None + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp_1: Optional[Resp] = Field(None, alias='Resp') + + +class Duration(int, Enum): + integer_5 = 5 + integer_8 = 8 + + +class Model1(str, Enum): + v3_5 = 'v3.5' + + +class MotionMode(str, Enum): + normal = 'normal' + fast = 'fast' + + +class Quality1(str, Enum): + field_360p = '360p' + field_540p = '540p' + field_720p = '720p' + field_1080p = '1080p' + + +class Style1(str, Enum): + anime = 'anime' + field_3d_animation = '3d_animation' + clay = 'clay' + comic = 'comic' + cyberpunk = 'cyberpunk' + + +class PixverseImageVideoRequest(BaseModel): + duration: Duration + img_id: int + model: Model1 + motion_mode: Optional[MotionMode] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class AspectRatio2(str, Enum): + field_16_9 = '16:9' + field_4_3 = '4:3' + field_1_1 = '1:1' + field_3_4 = '3:4' + field_9_16 = '9:16' + + +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: AspectRatio2 + duration: Duration + model: Model1 + motion_mode: Optional[MotionMode] = None + negative_prompt: Optional[str] = None + prompt: str + quality: Quality1 + seed: Optional[int] = None + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class PixverseTransitionVideoRequest(BaseModel): + duration: Duration + first_frame_img: int + last_frame_img: int + model: Model1 + motion_mode: MotionMode + prompt: str + quality: Quality1 + seed: int + style: Optional[Style1] = None + template_id: Optional[int] = None + water_mark: Optional[bool] = None + + +class Resp1(BaseModel): + video_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp1] = None + + +class Status8(int, Enum): + integer_1 = 1 + integer_5 = 5 + integer_6 = 6 + integer_7 = 7 + integer_8 = 8 + + +class Resp2(BaseModel): + create_time: Optional[str] = None + id: Optional[int] = None + modify_time: Optional[str] = None + negative_prompt: Optional[str] = None + outputHeight: Optional[int] = None + outputWidth: Optional[int] = None + prompt: Optional[str] = None + resolution_ratio: Optional[int] = None + seed: Optional[int] = None + size: Optional[int] = None + status: Optional[Status8] = Field( + None, + description='Video generation status codes:\n* 1 - Generation successful\n* 5 - Generating\n* 6 - Deleted\n* 7 - Contents moderation failed\n* 8 - Generation failed\n', + ) + style: Optional[str] = None + url: Optional[str] = None + + +class PixverseVideoResultResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[Resp2] = None + + +class PublisherStatus(str, Enum): + PublisherStatusActive = 'PublisherStatusActive' + PublisherStatusBanned = 'PublisherStatusBanned' + + +class PublisherUser(BaseModel): + email: Optional[str] = Field(None, description='The email address for this user.') + id: Optional[str] = Field(None, description='The unique id for this user.') + name: Optional[str] = Field(None, description='The name for this user.') + + +class RgbItem(RootModel[int]): + root: int = Field(..., ge=0, le=255) + + +class RGBColor(BaseModel): + rgb: List[RgbItem] = Field(..., max_length=3, min_length=3) + + +class GenerateSummary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class Summary(str, Enum): + auto = 'auto' + concise = 'concise' + detailed = 'detailed' + + +class ReasoningEffort(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Status9(str, Enum): + in_progress = 'in_progress' + completed = 'completed' + incomplete = 'incomplete' + + +class Type17(str, Enum): + summary_text = 'summary_text' + + +class SummaryItem(BaseModel): + text: str = Field( + ..., + description='A short summary of the reasoning used by the model when generating\nthe response.\n', + ) + type: Type17 = Field( + ..., description='The type of the object. Always `summary_text`.\n' + ) + + +class Type18(str, Enum): + reasoning = 'reasoning' + + +class ReasoningItem(BaseModel): + id: str = Field( + ..., description='The unique identifier of the reasoning content.\n' + ) + status: Optional[Status9] = Field( + None, + description='The status of the item. One of `in_progress`, `completed`, or\n`incomplete`. Populated when items are returned via API.\n', + ) + summary: List[SummaryItem] = Field(..., description='Reasoning text contents.\n') + type: Type18 = Field( + ..., description='The type of the object. Always `reasoning`.\n' + ) + + +class RecraftImageColor(BaseModel): + rgb: Optional[List[int]] = None + std: Optional[List[float]] = None + weight: Optional[float] = None + + +class RecraftImageFeatures(BaseModel): + nsfw_score: Optional[float] = None + + +class RecraftImageFormat(str, Enum): + webp = 'webp' + png = 'png' + + +class Controls(BaseModel): + artistic_level: Optional[int] = Field( + None, + description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity.', + ge=0, + le=5, + ) + background_color: Optional[RGBColor] = None + colors: Optional[List[RGBColor]] = Field( + None, description='An array of preferable colors' + ) + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + + +class RecraftImageGenerationRequest(BaseModel): + controls: Optional[Controls] = Field( + None, description='The controls for the generated image' + ) + model: str = Field( + ..., description='The model to use for generation (e.g., "recraftv3")' + ) + n: int = Field(..., description='The number of images to generate', ge=1, le=4) + prompt: str = Field( + ..., description='The text prompt describing the image to generate' + ) + size: str = Field( + ..., description='The size of the generated image (e.g., "1024x1024")' + ) + style: Optional[str] = Field( + None, + description='The style to apply to the generated image (e.g., "digital_illustration")', + ) + style_id: Optional[str] = Field( + None, + description='The style ID to apply to the generated image (e.g., "123e4567-e89b-12d3-a456-426614174000"). If style_id is provided, style should not be provided.', + ) + + +class Datum3(BaseModel): + image_id: Optional[str] = Field( + None, description='Unique identifier for the generated image' + ) + url: Optional[str] = Field(None, description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field( + ..., description='Unix timestamp when the generation was created' + ) + credits: int = Field(..., description='Number of credits used for the generation') + data: List[Datum3] = Field(..., description='Array of generated image information') + + +class RecraftImageStyle(str, Enum): + digital_illustration = 'digital_illustration' + icon = 'icon' + realistic_image = 'realistic_image' + vector_illustration = 'vector_illustration' + + +class RecraftImageSubStyle(str, Enum): + field_2d_art_poster = '2d_art_poster' + field_3d = '3d' + field_80s = '80s' + glow = 'glow' + grain = 'grain' + hand_drawn = 'hand_drawn' + infantile_sketch = 'infantile_sketch' + kawaii = 'kawaii' + pixel_art = 'pixel_art' + psychedelic = 'psychedelic' + seamless = 'seamless' + voxel = 'voxel' + watercolor = 'watercolor' + broken_line = 'broken_line' + colored_outline = 'colored_outline' + colored_shapes = 'colored_shapes' + colored_shapes_gradient = 'colored_shapes_gradient' + doodle_fill = 'doodle_fill' + doodle_offset_fill = 'doodle_offset_fill' + offset_fill = 'offset_fill' + outline = 'outline' + outline_gradient = 'outline_gradient' + uneven_fill = 'uneven_fill' + field_70s = '70s' + cartoon = 'cartoon' + doodle_line_art = 'doodle_line_art' + engraving = 'engraving' + flat_2 = 'flat_2' + kawaii_1 = 'kawaii' + line_art = 'line_art' + linocut = 'linocut' + seamless_1 = 'seamless' + b_and_w = 'b_and_w' + enterprise = 'enterprise' + hard_flash = 'hard_flash' + hdr = 'hdr' + motion_blur = 'motion_blur' + natural_light = 'natural_light' + studio_portrait = 'studio_portrait' + line_circuit = 'line_circuit' + field_2d_art_poster_2 = '2d_art_poster_2' + engraving_color = 'engraving_color' + flat_air_art = 'flat_air_art' + hand_drawn_outline = 'hand_drawn_outline' + handmade_3d = 'handmade_3d' + stickers_drawings = 'stickers_drawings' + plastic = 'plastic' + pictogram = 'pictogram' + + +class RecraftResponseFormat(str, Enum): + url = 'url' + b64_json = 'b64_json' + + +class RecraftTextLayoutItem(BaseModel): + bbox: List[List[float]] + text: str + + +class RecraftTransformModel(str, Enum): + refm1 = 'refm1' + recraft20b = 'recraft20b' + recraftv2 = 'recraftv2' + recraftv3 = 'recraftv3' + flux1_1pro = 'flux1_1pro' + flux1dev = 'flux1dev' + imagen3 = 'imagen3' + hidream_i1_dev = 'hidream_i1_dev' + + +class RecraftUserControls(BaseModel): + artistic_level: Optional[int] = None + background_color: Optional[RecraftImageColor] = None + colors: Optional[List[RecraftImageColor]] = None + no_text: Optional[bool] = None + + +class Attention(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Project(str, Enum): + comfyui = 'comfyui' + comfyui_frontend = 'comfyui_frontend' + desktop = 'desktop' + + +class ReleaseNote(BaseModel): + attention: Attention = Field( + ..., description='The attention level for this release' + ) + content: str = Field( + ..., description='The content of the release note in markdown format' + ) + id: int = Field(..., description='Unique identifier for the release note') + project: Project = Field( + ..., description='The project this release note belongs to' + ) + published_at: datetime = Field( + ..., description='When the release note was published' + ) + version: str = Field(..., description='The version of the release') + + +class RenderingSpeed(str, Enum): + DEFAULT = 'DEFAULT' + TURBO = 'TURBO' + QUALITY = 'QUALITY' + + +class Type19(str, Enum): + response_completed = 'response.completed' + + +class Type20(str, Enum): + response_content_part_added = 'response.content_part.added' + + +class Type21(str, Enum): + response_content_part_done = 'response.content_part.done' + + +class Type22(str, Enum): + response_created = 'response.created' + + +class ResponseErrorCode(str, Enum): + server_error = 'server_error' + rate_limit_exceeded = 'rate_limit_exceeded' + invalid_prompt = 'invalid_prompt' + vector_store_timeout = 'vector_store_timeout' + invalid_image = 'invalid_image' + invalid_image_format = 'invalid_image_format' + invalid_base64_image = 'invalid_base64_image' + invalid_image_url = 'invalid_image_url' + image_too_large = 'image_too_large' + image_too_small = 'image_too_small' + image_parse_error = 'image_parse_error' + image_content_policy_violation = 'image_content_policy_violation' + invalid_image_mode = 'invalid_image_mode' + image_file_too_large = 'image_file_too_large' + unsupported_image_media_type = 'unsupported_image_media_type' + empty_image_file = 'empty_image_file' + failed_to_download_image = 'failed_to_download_image' + image_file_not_found = 'image_file_not_found' + + +class Type23(str, Enum): + error = 'error' + + +class ResponseErrorEvent(BaseModel): + code: str = Field(..., description='The error code.\n') + message: str = Field(..., description='The error message.\n') + param: str = Field(..., description='The error parameter.\n') + type: Type23 = Field(..., description='The type of the event. Always `error`.\n') + + +class Type24(str, Enum): + response_failed = 'response.failed' + + +class Type25(str, Enum): + json_object = 'json_object' + + +class ResponseFormatJsonObject(BaseModel): + type: Type25 = Field( + ..., + description='The type of response format being defined. Always `json_object`.', + ) + + +class ResponseFormatJsonSchemaSchema(BaseModel): + pass + model_config = ConfigDict( + extra='allow', + ) + + +class Type26(str, Enum): + text = 'text' + + +class ResponseFormatText(BaseModel): + type: Type26 = Field( + ..., description='The type of response format being defined. Always `text`.' + ) + + +class Type27(str, Enum): + response_in_progress = 'response.in_progress' + + +class Type28(str, Enum): + response_incomplete = 'response.incomplete' + + +class Type29(str, Enum): + response_output_item_added = 'response.output_item.added' + + +class Type30(str, Enum): + response_output_item_done = 'response.output_item.done' + + +class Truncation1(str, Enum): + auto = 'auto' + disabled = 'disabled' + + +class InputTokensDetails1(BaseModel): + cached_tokens: int = Field( + ..., + description='The number of tokens that were retrieved from the cache. \n[More on prompt caching](/docs/guides/prompt-caching).\n', + ) + + +class OutputTokensDetails(BaseModel): + reasoning_tokens: int = Field(..., description='The number of reasoning tokens.') + + +class ResponseUsage(BaseModel): + input_tokens: int = Field(..., description='The number of input tokens.') + input_tokens_details: InputTokensDetails1 = Field( + ..., description='A detailed breakdown of the input tokens.' + ) + output_tokens: int = Field(..., description='The number of output tokens.') + output_tokens_details: OutputTokensDetails = Field( + ..., description='A detailed breakdown of the output tokens.' + ) + total_tokens: int = Field(..., description='The total number of tokens used.') + + +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field( + ..., description='subscription from generate endpoint' + ) + + +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description='Task UUID') + + +class RodinGenerateJobsData(BaseModel): + subscription_key: Optional[str] = Field(None, description='Subscription Key.') + uuids: Optional[List[str]] = Field(None, description='subjobs uuid.') + + +class RodinMaterialType(str, Enum): + PBR = 'PBR' + Shaded = 'Shaded' + + +class RodinMeshModeType(str, Enum): + Quad = 'Quad' + Raw = 'Raw' + + +class RodinQualityType(str, Enum): + extra_low = 'extra-low' + low = 'low' + medium = 'medium' + high = 'high' + + +class RodinResourceItem(BaseModel): + name: Optional[str] = Field(None, description='File name') + url: Optional[str] = Field(None, description='Download url') + + +class RodinStatusOptions(str, Enum): + Done = 'Done' + Failed = 'Failed' + Generating = 'Generating' + Waiting = 'Waiting' + + +class RodinTierType(str, Enum): + Regular = 'Regular' + Sketch = 'Sketch' + Detail = 'Detail' + Smooth = 'Smooth' + + +class RunwayAspectRatioEnum(str, Enum): + field_1280_720 = '1280:720' + field_720_1280 = '720:1280' + field_1104_832 = '1104:832' + field_832_1104 = '832:1104' + field_960_960 = '960:960' + field_1584_672 = '1584:672' + field_1280_768 = '1280:768' + field_768_1280 = '768:1280' + + +class RunwayDurationEnum(int, Enum): + integer_5 = 5 + integer_10 = 10 + + +class RunwayImageToVideoResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class RunwayModelEnum(str, Enum): + gen4_turbo = 'gen4_turbo' + gen3a_turbo = 'gen3a_turbo' + + +class Position(str, Enum): + first = 'first' + last = 'last' + + +class RunwayPromptImageDetailedObject(BaseModel): + position: Position = Field( + ..., + description="The position of the image in the output video. 'last' is currently supported for gen3a_turbo only.", + ) + uri: str = Field( + ..., description='A HTTPS URL or data URI containing an encoded image.' + ) + + +class RunwayPromptImageObject( + RootModel[Union[str, List[RunwayPromptImageDetailedObject]]] +): + root: Union[str, List[RunwayPromptImageDetailedObject]] = Field( + ..., + description='Image(s) to use for the video generation. Can be a single URI or an array of image objects with positions.', + ) + + +class RunwayTaskStatusEnum(str, Enum): + SUCCEEDED = 'SUCCEEDED' + RUNNING = 'RUNNING' + FAILED = 'FAILED' + PENDING = 'PENDING' + CANCELLED = 'CANCELLED' + THROTTLED = 'THROTTLED' + + +class RunwayTaskStatusResponse(BaseModel): + createdAt: datetime = Field(..., description='Task creation timestamp') + id: str = Field(..., description='Task ID') + output: Optional[List[str]] = Field(None, description='Array of output video URLs') + progress: Optional[float] = Field( + None, + description='Float value between 0 and 1 representing the progress of the task. Only available if status is RUNNING.', + ge=0.0, + le=1.0, + ) + status: RunwayTaskStatusEnum + + +class RunwayTextToImageAspectRatioEnum(str, Enum): + field_1920_1080 = '1920:1080' + field_1080_1920 = '1080:1920' + field_1024_1024 = '1024:1024' + field_1360_768 = '1360:768' + field_1080_1080 = '1080:1080' + field_1168_880 = '1168:880' + field_1440_1080 = '1440:1080' + field_1080_1440 = '1080:1440' + field_1808_768 = '1808:768' + field_2112_912 = '2112:912' + + +class Model4(str, Enum): + gen4_image = 'gen4_image' + + +class ReferenceImage(BaseModel): + uri: Optional[str] = Field( + None, description='A HTTPS URL or data URI containing an encoded image' + ) + + +class RunwayTextToImageRequest(BaseModel): + model: Model4 = Field(..., description='Model to use for generation') + promptText: str = Field( + ..., description='Text prompt for the image generation', max_length=1000 + ) + ratio: RunwayTextToImageAspectRatioEnum + referenceImages: Optional[List[ReferenceImage]] = Field( + None, description='Array of reference images to guide the generation' + ) + + +class RunwayTextToImageResponse(BaseModel): + id: Optional[str] = Field(None, description='Task ID') + + +class Name(str, Enum): + content_moderation = 'content_moderation' + + +class StabilityContentModerationResponse(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: Name = Field( + ..., + description='Our content moderation system has flagged some part of your request and subsequently denied it. You were not charged for this request. While this may at times be frustrating, it is necessary to maintain the integrity of our platform and ensure a safe experience for all users. If you would like to provide feedback, please use the [Support Form](https://kb.stability.ai/knowledge-base/kb-tickets/new).', + ) + + +class StabilityCreativity(RootModel[float]): + root: float = Field( + ..., + description='Controls the likelihood of creating additional details not heavily conditioned by the init image.', + ge=0.2, + le=0.5, + ) + + +class StabilityError(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[[{'some-field': 'is required'}]], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new) you file, as it will greatly assist us in diagnosing the root cause of the problem.\n', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityGenerationID(RootModel[str]): + root: str = Field( + ..., + description='The `id` of a generation, typically used for async generations, that can be used to check the status of the generation or retrieve the result.', + examples=['a6dc6c6e20acda010fe14d71f180658f2896ed9b4ec25aa99a6ff06c796987c4'], + max_length=64, + min_length=64, + ) + + +class Status10(str, Enum): + in_progress = 'in-progress' + + +class StabilityGetResultResponse202(BaseModel): + id: Optional[str] = Field( + None, description='The ID of the generation result.', examples=[1234567890] + ) + status: Optional[Status10] = None + + +class AspectRatio3(str, Enum): + field_21_9 = '21:9' + field_16_9 = '16:9' + field_3_2 = '3:2' + field_5_4 = '5:4' + field_1_1 = '1:1' + field_4_5 = '4:5' + field_2_3 = '2:3' + field_9_16 = '9:16' + field_9_21 = '9:21' + + +class Mode(str, Enum): + text_to_image = 'text-to-image' + image_to_image = 'image-to-image' + + +class Model5(str, Enum): + sd3_5_large = 'sd3.5-large' + sd3_5_large_turbo = 'sd3.5-large-turbo' + sd3_5_medium = 'sd3.5-medium' + + +class OutputFormat3(str, Enum): + png = 'png' + jpeg = 'jpeg' + + +class StylePreset(str, Enum): + enhance = 'enhance' + anime = 'anime' + photographic = 'photographic' + digital_art = 'digital-art' + comic_book = 'comic-book' + fantasy_art = 'fantasy-art' + line_art = 'line-art' + analog_film = 'analog-film' + neon_punk = 'neon-punk' + isometric = 'isometric' + low_poly = 'low-poly' + origami = 'origami' + modeling_compound = 'modeling-compound' + cinematic = 'cinematic' + field_3d_model = '3d-model' + pixel_art = 'pixel-art' + tile_texture = 'tile-texture' + + +class StabilityImageGenerationSD3Request(BaseModel): + aspect_ratio: Optional[AspectRatio3] = Field( + '1:1', + description='Controls the aspect ratio of the generated image. Defaults to 1:1.\n\n> **Important:** This parameter is only valid for **text-to-image** requests.', + ) + cfg_scale: Optional[float] = Field( + None, + description='How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt). The _Large_ and _Medium_ models use a default of `4`. The _Turbo_ model uses a default of `1`.', + ge=1.0, + le=10.0, + ) + image: Optional[StrictBytes] = Field( + None, + description='The image to use as the starting point for the generation.\n\nSupported formats:\n\n\n\n - jpeg\n - png\n - webp\n\nSupported dimensions:\n\n\n\n - Every side must be at least 64 pixels\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', + ) + mode: Optional[Mode] = Field( + 'text-to-image', + description='Controls whether this is a text-to-image or image-to-image generation, which affects which parameters are required:\n- **text-to-image** requires only the `prompt` parameter\n- **image-to-image** requires the `prompt`, `image`, and `strength` parameters', + title='GenerationMode', + ) + model: Optional[Model5] = Field( + 'sd3.5-large', + description='The model to use for generation.\n\n- `sd3.5-large` requires 6.5 credits per generation\n- `sd3.5-large-turbo` requires 4 credits per generation\n- `sd3.5-medium` requires 3.5 credits per generation\n- As of the April 17, 2025, `sd3-large`, `sd3-large-turbo` and `sd3-medium`\n\n\n\n are re-routed to their `sd3.5-[model version]` equivalent, at the same price.', + ) + negative_prompt: Optional[str] = Field( + None, + description='Keywords of what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + output_format: Optional[OutputFormat3] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + prompt: str = Field( + ..., + description='What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.', + max_length=10000, + min_length=1, + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + strength: Optional[float] = Field( + None, + description='Sometimes referred to as _denoising_, this parameter controls how much influence the\n`image` parameter has on the generated image. A value of 0 would yield an image that\nis identical to the input. A value of 1 would be as if you passed in no image at all.\n\n> **Important:** This parameter is only valid for **image-to-image** requests.', + ge=0.0, + le=1.0, + ) + style_preset: Optional[StylePreset] = Field( + None, description='Guides the image model towards a particular style.' + ) + + +class FinishReason(str, Enum): + SUCCESS = 'SUCCESS' + CONTENT_FILTERED = 'CONTENT_FILTERED' + + +class StabilityImageGenrationSD3Response200(BaseModel): + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + + +class StabilityImageGenrationSD3Response400(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response413(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response422(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response429(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationSD3Response500(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class OutputFormat4(str, Enum): + jpeg = 'jpeg' + png = 'png' + webp = 'webp' + + +class StabilityImageGenrationUpscaleConservativeRequest(BaseModel): + creativity: Optional[StabilityCreativity] = Field( + default_factory=lambda: StabilityCreativity.model_validate(0.35) + ) + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 9,437,184 pixels\n- The aspect ratio must be between 1:2.5 and 2.5:1', + examples=['./some/image.png'], + ) + negative_prompt: Optional[str] = Field( + None, + description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + prompt: str = Field( + ..., + description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", + max_length=10000, + min_length=1, + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse200(BaseModel): + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse400(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse413(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse422(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse429(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleConservativeResponse500(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeRequest(BaseModel): + creativity: Optional[float] = Field( + 0.3, + description='Indicates how creative the model should be when upscaling an image.\nHigher values will result in more details being added to the image during upscaling.', + ge=0.1, + le=0.5, + ) + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Every side must be at least 64 pixels\n- Total pixel count must be between 4,096 and 1,048,576 pixels', + examples=['./some/image.png'], + ) + negative_prompt: Optional[str] = Field( + None, + description='A blurb of text describing what you **do not** wish to see in the output image.\nThis is an advanced feature.', + max_length=10000, + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + prompt: str = Field( + ..., + description="What you wish to see in the output image. A strong, descriptive prompt that clearly defines\nelements, colors, and subjects will lead to better results.\n\nTo control the weight of a given word use the format `(word:weight)`,\nwhere `word` is the word you'd like to control the weight of and `weight`\nis a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`\nwould convey a sky that was blue and green, but more green than blue.", + max_length=10000, + min_length=1, + ) + seed: Optional[float] = Field( + 0, + description="A specific value that is used to guide the 'randomness' of the generation. (Omit this parameter or pass `0` to use a random seed.)", + ge=0.0, + le=4294967294.0, + ) + style_preset: Optional[StylePreset] = Field( + None, description='Guides the image model towards a particular style.' + ) + + +class StabilityImageGenrationUpscaleCreativeResponse200(BaseModel): + id: StabilityGenerationID + + +class StabilityImageGenrationUpscaleCreativeResponse400(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse413(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse422(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse429(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleCreativeResponse500(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastRequest(BaseModel): + image: StrictBytes = Field( + ..., + description='The image you wish to upscale.\n\nSupported Formats:\n- jpeg\n- png\n- webp\n\nValidation Rules:\n- Width must be between 32 and 1,536 pixels\n- Height must be between 32 and 1,536 pixels\n- Total pixel count must be between 1,024 and 1,048,576 pixels', + examples=['./some/image.png'], + ) + output_format: Optional[OutputFormat4] = Field( + 'png', description='Dictates the `content-type` of the generated image.' + ) + + +class StabilityImageGenrationUpscaleFastResponse200(BaseModel): + finish_reason: FinishReason = Field( + ..., + description='The reason the generation finished.\n\n- `SUCCESS` = successful generation.\n- `CONTENT_FILTERED` = successful generation, however the output violated our content moderation\npolicy and has been blurred as a result.', + examples=['SUCCESS'], + ) + image: str = Field( + ..., + description='The generated image, encoded to base64.', + examples=['AAAAIGZ0eXBpc29tAAACAGlzb21pc28yYXZjMW1...'], + ) + seed: Optional[float] = Field( + 0, + description='The seed used as random noise for this generation.', + examples=[343940597], + ge=0.0, + le=4294967294.0, + ) + + +class StabilityImageGenrationUpscaleFastResponse400(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse413(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse422(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse429(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityImageGenrationUpscaleFastResponse500(BaseModel): + errors: List[str] = Field( + ..., + description='One or more error messages indicating what went wrong.', + examples=[['some-field: is required']], + min_length=1, + ) + id: str = Field( + ..., + description='A unique identifier associated with this error. Please include this in any [support tickets](https://kb.stability.ai/knowledge-base/kb-tickets/new)\nyou file, as it will greatly assist us in diagnosing the root cause of the problem.', + examples=['a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4'], + min_length=1, + ) + name: str = Field( + ..., + description='Short-hand name for an error, useful for discriminating between errors with the same status code.', + examples=['bad_request'], + min_length=1, + ) + + +class StabilityStabilityClientID(RootModel[str]): + root: str = Field( + ..., + description='The name of your application, used to help us communicate app-specific debugging or moderation issues to you.', + examples=['my-awesome-app'], + max_length=256, + ) + + +class StabilityStabilityClientUserID(RootModel[str]): + root: str = Field( + ..., + description='A unique identifier for your end user. Used to help us communicate user-specific debugging or moderation issues to you. Feel free to obfuscate this value to protect user privacy.', + examples=['DiscordUser#9999'], + max_length=256, + ) + + +class StabilityStabilityClientVersion(RootModel[str]): + root: str = Field( + ..., + description='The version of your application, used to help us communicate version-specific debugging or moderation issues to you.', + examples=['1.2.1'], + max_length=256, + ) + + +class StorageFile(BaseModel): + file_path: Optional[str] = Field(None, description='Path to the file in storage') + id: Optional[UUID] = Field( + None, description='Unique identifier for the storage file' + ) + public_url: Optional[str] = Field(None, description='Public URL') + + +class StripeAddress(BaseModel): + city: Optional[str] = None + country: Optional[str] = None + line1: Optional[str] = None + line2: Optional[str] = None + postal_code: Optional[str] = None + state: Optional[str] = None + + +class StripeAmountDetails(BaseModel): + tip: Optional[Dict[str, Any]] = None + + +class StripeBillingDetails(BaseModel): + address: Optional[StripeAddress] = None + email: Optional[str] = None + name: Optional[str] = None + phone: Optional[str] = None + tax_id: Optional[Any] = None + + +class Checks(BaseModel): + address_line1_check: Optional[Any] = None + address_postal_code_check: Optional[Any] = None + cvc_check: Optional[str] = None + + +class ExtendedAuthorization(BaseModel): + status: Optional[str] = None + + +class IncrementalAuthorization(BaseModel): + status: Optional[str] = None + + +class Multicapture(BaseModel): + status: Optional[str] = None + + +class NetworkToken(BaseModel): + used: Optional[bool] = None + + +class Overcapture(BaseModel): + maximum_amount_capturable: Optional[int] = None + status: Optional[str] = None + + +class StripeCardDetails(BaseModel): + amount_authorized: Optional[int] = None + authorization_code: Optional[Any] = None + brand: Optional[str] = None + checks: Optional[Checks] = None + country: Optional[str] = None + exp_month: Optional[int] = None + exp_year: Optional[int] = None + extended_authorization: Optional[ExtendedAuthorization] = None + fingerprint: Optional[str] = None + funding: Optional[str] = None + incremental_authorization: Optional[IncrementalAuthorization] = None + installments: Optional[Any] = None + last4: Optional[str] = None + mandate: Optional[Any] = None + multicapture: Optional[Multicapture] = None + network: Optional[str] = None + network_token: Optional[NetworkToken] = None + network_transaction_id: Optional[str] = None + overcapture: Optional[Overcapture] = None + regulated_status: Optional[str] = None + three_d_secure: Optional[Any] = None + wallet: Optional[Any] = None + + +class Object1(str, Enum): + charge = 'charge' + + +class Object2(str, Enum): + event = 'event' + + +class Type31(str, Enum): + payment_intent_succeeded = 'payment_intent.succeeded' + + +class StripeOutcome(BaseModel): + advice_code: Optional[Any] = None + network_advice_code: Optional[Any] = None + network_decline_code: Optional[Any] = None + network_status: Optional[str] = None + reason: Optional[Any] = None + risk_level: Optional[str] = None + risk_score: Optional[int] = None + seller_message: Optional[str] = None + type: Optional[str] = None + + +class Object3(str, Enum): + payment_intent = 'payment_intent' + + +class StripePaymentMethodDetails(BaseModel): + card: Optional[StripeCardDetails] = None + type: Optional[str] = None + + +class Card(BaseModel): + installments: Optional[Any] = None + mandate_options: Optional[Any] = None + network: Optional[Any] = None + request_three_d_secure: Optional[str] = None + + +class StripePaymentMethodOptions(BaseModel): + card: Optional[Card] = None + + +class StripeRefundList(BaseModel): + data: Optional[List[Dict[str, Any]]] = None + has_more: Optional[bool] = None + object: Optional[str] = None + total_count: Optional[int] = None + url: Optional[str] = None + + +class StripeRequestInfo(BaseModel): + id: Optional[str] = None + idempotency_key: Optional[str] = None + + +class StripeShipping(BaseModel): + address: Optional[StripeAddress] = None + carrier: Optional[str] = None + name: Optional[str] = None + phone: Optional[str] = None + tracking_number: Optional[str] = None + + +class Type32(str, Enum): + json_schema = 'json_schema' + + +class TextResponseFormatJsonSchema(BaseModel): + description: Optional[str] = Field( + None, + description='A description of what the response format is for, used by the model to\ndetermine how to respond in the format.\n', + ) + name: str = Field( + ..., + description='The name of the response format. Must be a-z, A-Z, 0-9, or contain\nunderscores and dashes, with a maximum length of 64.\n', + ) + schema_: ResponseFormatJsonSchemaSchema = Field(..., alias='schema') + strict: Optional[bool] = Field( + False, + description='Whether to enable strict schema adherence when generating the output.\nIf set to true, the model will always follow the exact schema defined\nin the `schema` field. Only a subset of JSON Schema is supported when\n`strict` is `true`. To learn more, read the [Structured Outputs\nguide](/docs/guides/structured-outputs).\n', + ) + type: Type32 = Field( + ..., + description='The type of response format being defined. Always `json_schema`.', + ) + + +class Type33(str, Enum): + function = 'function' + + +class ToolChoiceFunction(BaseModel): + name: str = Field(..., description='The name of the function to call.') + type: Type33 = Field( + ..., description='For function calling, the type is always `function`.' + ) + + +class ToolChoiceOptions(str, Enum): + none = 'none' + auto = 'auto' + required = 'required' + + +class Type34(str, Enum): + file_search = 'file_search' + web_search_preview = 'web_search_preview' + computer_use_preview = 'computer_use_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class ToolChoiceTypes(BaseModel): + type: Type34 = Field( + ..., + description='The type of hosted tool the model should to use. Learn more about\n[built-in tools](/docs/guides/tools).\n\nAllowed values are:\n- `file_search`\n- `web_search_preview`\n- `computer_use_preview`\n', + ) + + +class TripoAnimation(str, Enum): + preset_idle = 'preset:idle' + preset_walk = 'preset:walk' + preset_climb = 'preset:climb' + preset_jump = 'preset:jump' + preset_run = 'preset:run' + preset_slash = 'preset:slash' + preset_shoot = 'preset:shoot' + preset_hurt = 'preset:hurt' + preset_fall = 'preset:fall' + preset_turn = 'preset:turn' + + +class TripoBalance(BaseModel): + balance: float + frozen: float + + +class TripoConvertFormat(str, Enum): + GLTF = 'GLTF' + USDZ = 'USDZ' + FBX = 'FBX' + OBJ = 'OBJ' + STL = 'STL' + field_3MF = '3MF' + + +class Code(int, Enum): + integer_1001 = 1001 + integer_2000 = 2000 + integer_2001 = 2001 + integer_2002 = 2002 + integer_2003 = 2003 + integer_2004 = 2004 + integer_2006 = 2006 + integer_2007 = 2007 + integer_2008 = 2008 + integer_2010 = 2010 + + +class TripoErrorResponse(BaseModel): + code: Code + message: str + suggestion: str + + +class TripoImageToModel(str, Enum): + image_to_model = 'image_to_model' + + +class TripoModelStyle(str, Enum): + person_person2cartoon = 'person:person2cartoon' + animal_venom = 'animal:venom' + object_clay = 'object:clay' + object_steampunk = 'object:steampunk' + object_christmas = 'object:christmas' + object_barbie = 'object:barbie' + gold = 'gold' + ancient_bronze = 'ancient_bronze' + + +class TripoModelVersion(str, Enum): + v2_5_20250123 = 'v2.5-20250123' + v2_0_20240919 = 'v2.0-20240919' + v1_4_20240625 = 'v1.4-20240625' + + +class TripoMultiviewMode(str, Enum): + LEFT = 'LEFT' + RIGHT = 'RIGHT' + + +class TripoMultiviewToModel(str, Enum): + multiview_to_model = 'multiview_to_model' + + +class TripoOrientation(str, Enum): + align_image = 'align_image' + default = 'default' + + +class TripoResponseSuccessCode(RootModel[int]): + root: int = Field( + ..., + description='Standard success code for Tripo API responses. Typically 0 for success.', + examples=[0], + ) + + +class TripoSpec(str, Enum): + mixamo = 'mixamo' + tripo = 'tripo' + + +class TripoStandardFormat(str, Enum): + glb = 'glb' + fbx = 'fbx' + + +class TripoStylizeOptions(str, Enum): + lego = 'lego' + voxel = 'voxel' + voronoi = 'voronoi' + minecraft = 'minecraft' + + +class Code1(int, Enum): + integer_0 = 0 + + +class Data9(BaseModel): + task_id: str = Field(..., description='used for getTask') + + +class TripoSuccessTask(BaseModel): + code: Code1 + data: Data9 + + +class Topology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class Output(BaseModel): + base_model: Optional[str] = None + model: Optional[str] = None + pbr_model: Optional[str] = None + rendered_image: Optional[str] = None + riggable: Optional[bool] = None + topology: Optional[Topology] = None + + +class Status11(str, Enum): + queued = 'queued' + running = 'running' + success = 'success' + failed = 'failed' + cancelled = 'cancelled' + unknown = 'unknown' + banned = 'banned' + expired = 'expired' + + +class TripoTask(BaseModel): + create_time: int + input: Dict[str, Any] + output: Output + progress: int = Field(..., ge=0, le=100) + status: Status11 + task_id: str + type: str + + +class TripoTextToModel(str, Enum): + text_to_model = 'text_to_model' + + +class TripoTextureAlignment(str, Enum): + original_image = 'original_image' + geometry = 'geometry' + + +class TripoTextureFormat(str, Enum): + BMP = 'BMP' + DPX = 'DPX' + HDR = 'HDR' + JPEG = 'JPEG' + OPEN_EXR = 'OPEN_EXR' + PNG = 'PNG' + TARGA = 'TARGA' + TIFF = 'TIFF' + WEBP = 'WEBP' + + +class TripoTextureQuality(str, Enum): + standard = 'standard' + detailed = 'detailed' + + +class TripoTopology(str, Enum): + bip = 'bip' + quad = 'quad' + + +class TripoTypeAnimatePrerigcheck(str, Enum): + animate_prerigcheck = 'animate_prerigcheck' + + +class TripoTypeAnimateRetarget(str, Enum): + animate_retarget = 'animate_retarget' + + +class TripoTypeAnimateRig(str, Enum): + animate_rig = 'animate_rig' + + +class TripoTypeConvertModel(str, Enum): + convert_model = 'convert_model' + + +class TripoTypeRefineModel(str, Enum): + refine_model = 'refine_model' + + +class TripoTypeStylizeModel(str, Enum): + stylize_model = 'stylize_model' + + +class TripoTypeTextureModel(str, Enum): + texture_model = 'texture_model' + + +class User(BaseModel): + email: Optional[str] = Field(None, description='The email address for this user.') + id: Optional[str] = Field(None, description='The unique id for this user.') + isAdmin: Optional[bool] = Field( + None, description='Indicates if the user has admin privileges.' + ) + isApproved: Optional[bool] = Field( + None, description='Indicates if the user is approved.' + ) + name: Optional[str] = Field(None, description='The name for this user.') + + +class Veo2GenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Error1(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + + +class Video(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded video content' + ) + gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') + mimeType: Optional[str] = Field(None, description='Video MIME type') + + +class Response(BaseModel): + field_type: Optional[str] = Field( + None, + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], + ) + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' + ) + raiMediaFilteredReasons: Optional[List[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' + ) + videos: Optional[List[Video]] = None + + +class Veo2GenVidPollResponse(BaseModel): + done: Optional[bool] = None + error: Optional[Error1] = Field( + None, description='Error details if operation failed' + ) + name: Optional[str] = None + response: Optional[Response] = Field( + None, description='The actual prediction response if done is true' + ) + + +class Image(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image1(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance(BaseModel): + image: Optional[Union[Image, Image1]] = Field( + None, description='Optional image to guide video generation' + ) + prompt: str = Field(..., description='Text description of the video') + + +class PersonGeneration1(str, Enum): + ALLOW = 'ALLOW' + BLOCK = 'BLOCK' + + +class Parameters(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration1] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' + ) + + +class Veo2GenVidRequest(BaseModel): + instances: Optional[List[Instance]] = None + parameters: Optional[Parameters] = None + + +class Veo2GenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], + ) + + +class VeoGenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Response1(BaseModel): + field_type: Optional[str] = Field( + None, + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], + ) + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' + ) + raiMediaFilteredReasons: Optional[List[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' + ) + videos: Optional[List[Video]] = None + + +class VeoGenVidPollResponse(BaseModel): + done: Optional[bool] = None + error: Optional[Error1] = Field( + None, description='Error details if operation failed' + ) + name: Optional[str] = None + response: Optional[Response1] = Field( + None, description='The actual prediction response if done is true' + ) + + +class Image2(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image3(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance1(BaseModel): + image: Optional[Union[Image2, Image3]] = Field( + None, description='Optional image to guide video generation' + ) + prompt: str = Field(..., description='Text description of the video') + + +class Parameters1(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + generateAudio: Optional[bool] = Field( + None, + description='Generate audio for the video. Only supported by veo 3 models.', + ) + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration1] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' + ) + + +class VeoGenVidRequest(BaseModel): + instances: Optional[List[Instance1]] = None + parameters: Optional[Parameters1] = None + + +class VeoGenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], + ) + + +class SearchContextSize(str, Enum): + low = 'low' + medium = 'medium' + high = 'high' + + +class Type35(str, Enum): + web_search_preview = 'web_search_preview' + web_search_preview_2025_03_11 = 'web_search_preview_2025_03_11' + + +class WebSearchPreviewTool(BaseModel): + search_context_size: Optional[SearchContextSize] = Field( + None, + description='High level guidance for the amount of context window space to use for the search. One of `low`, `medium`, or `high`. `medium` is the default.', + ) + type: Literal['WebSearchPreviewTool'] = Field( + ..., + description='The type of the web search tool. One of `web_search_preview` or `web_search_preview_2025_03_11`.', + ) + + +class Status12(str, Enum): + in_progress = 'in_progress' + searching = 'searching' + completed = 'completed' + failed = 'failed' + + +class Type36(str, Enum): + web_search_call = 'web_search_call' + + +class WebSearchToolCall(BaseModel): + id: str = Field(..., description='The unique ID of the web search tool call.\n') + status: Status12 = Field( + ..., description='The status of the web search tool call.\n' + ) + type: Type36 = Field( + ..., + description='The type of the web search tool call. Always `web_search_call`.\n', + ) + + +class WorkflowRunStatus(str, Enum): + WorkflowRunStatusStarted = 'WorkflowRunStatusStarted' + WorkflowRunStatusFailed = 'WorkflowRunStatusFailed' + WorkflowRunStatusCompleted = 'WorkflowRunStatusCompleted' + + +class ActionJobResult(BaseModel): + action_job_id: Optional[str] = Field( + None, description='Identifier of the job this result belongs to' + ) + action_run_id: Optional[str] = Field( + None, description='Identifier of the run this result belongs to' + ) + author: Optional[str] = Field(None, description='The author of the commit') + avg_vram: Optional[int] = Field( + None, description='The average VRAM used by the job' + ) + branch_name: Optional[str] = Field( + None, description='Name of the relevant git branch' + ) + comfy_run_flags: Optional[str] = Field( + None, description='The comfy run flags. E.g. `--low-vram`' + ) + commit_hash: Optional[str] = Field(None, description='The hash of the commit') + commit_id: Optional[str] = Field(None, description='The ID of the commit') + commit_message: Optional[str] = Field(None, description='The message of the commit') + commit_time: Optional[int] = Field( + None, description='The Unix timestamp when the commit was made' + ) + cuda_version: Optional[str] = Field(None, description='CUDA version used') + end_time: Optional[int] = Field( + None, description='The end time of the job as a Unix timestamp.' + ) + git_repo: Optional[str] = Field(None, description='The repository name') + id: Optional[UUID] = Field(None, description='Unique identifier for the job result') + job_trigger_user: Optional[str] = Field( + None, description='The user who triggered the job.' + ) + machine_stats: Optional[MachineStats] = None + operating_system: Optional[str] = Field(None, description='Operating system used') + peak_vram: Optional[int] = Field(None, description='The peak VRAM used by the job') + pr_number: Optional[str] = Field(None, description='The pull request number') + python_version: Optional[str] = Field(None, description='PyTorch version used') + pytorch_version: Optional[str] = Field(None, description='PyTorch version used') + start_time: Optional[int] = Field( + None, description='The start time of the job as a Unix timestamp.' + ) + status: Optional[WorkflowRunStatus] = None + storage_file: Optional[StorageFile] = None + workflow_name: Optional[str] = Field(None, description='Name of the workflow') + + +class BFLCannyInputs(BaseModel): + canny_high_threshold: Optional[CannyHighThreshold] = Field( + default_factory=lambda: CannyHighThreshold.model_validate(200), + description='High threshold for Canny edge detection', + title='Canny High Threshold', + ) + canny_low_threshold: Optional[CannyLowThreshold] = Field( + default_factory=lambda: CannyLowThreshold.model_validate(50), + description='Low threshold for Canny edge detection', + title='Canny Low Threshold', + ) + control_image: Optional[str] = Field( + None, + description='Base64 encoded image to use as control input if no preprocessed image is provided', + title='Control Image', + ) + guidance: Optional[Guidance] = Field( + default_factory=lambda: Guidance.model_validate(30), + description='Guidance strength for the image generation process', + title='Guidance', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + preprocessed_image: Optional[str] = Field( + None, + description='Optional pre-processed image that will bypass the control preprocessing step', + title='Preprocessed Image', + ) + prompt: str = Field( + ..., + description='Text prompt for image generation', + examples=['ein fantastisches bild'], + title='Prompt', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt', + title='Prompt Upsampling', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + ge=0, + le=6, + title='Safety Tolerance', + ) + seed: Optional[int] = Field( + None, + description='Optional seed for reproducibility', + examples=[42], + title='Seed', + ) + steps: Optional[Steps] = Field( + default_factory=lambda: Steps.model_validate(50), + description='Number of steps for the image generation process', + title='Steps', + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLDepthInputs(BaseModel): + control_image: Optional[str] = Field( + None, + description='Base64 encoded image to use as control input', + title='Control Image', + ) + guidance: Optional[Guidance] = Field( + default_factory=lambda: Guidance.model_validate(15), + description='Guidance strength for the image generation process', + title='Guidance', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + preprocessed_image: Optional[str] = Field( + None, + description='Optional pre-processed image that will bypass the control preprocessing step', + title='Preprocessed Image', + ) + prompt: str = Field( + ..., + description='Text prompt for image generation', + examples=['ein fantastisches bild'], + title='Prompt', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt', + title='Prompt Upsampling', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + ge=0, + le=6, + title='Safety Tolerance', + ) + seed: Optional[int] = Field( + None, + description='Optional seed for reproducibility', + examples=[42], + title='Seed', + ) + steps: Optional[Steps] = Field( + default_factory=lambda: Steps.model_validate(50), + description='Number of steps for the image generation process', + title='Steps', + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLFluxProExpandInputs(BaseModel): + bottom: Optional[Bottom] = Field( + 0, + description='Number of pixels to expand at the bottom of the image', + title='Bottom', + ) + guidance: Optional[Guidance2] = Field( + default_factory=lambda: Guidance2.model_validate(60), + description='Guidance strength for the image generation process', + title='Guidance', + ) + image: str = Field( + ..., + description='A Base64-encoded string representing the image you wish to expand.', + title='Image', + ) + left: Optional[Left] = Field( + 0, + description='Number of pixels to expand on the left side of the image', + title='Left', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + prompt: Optional[str] = Field( + '', + description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.', + examples=['ein fantastisches bild'], + title='Prompt', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', + title='Prompt Upsampling', + ) + right: Optional[Right] = Field( + 0, + description='Number of pixels to expand on the right side of the image', + title='Right', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + examples=[2], + ge=0, + le=6, + title='Safety Tolerance', + ) + seed: Optional[int] = Field( + None, description='Optional seed for reproducibility', title='Seed' + ) + steps: Optional[Steps2] = Field( + default_factory=lambda: Steps2.model_validate(50), + description='Number of steps for the image generation process', + examples=[50], + title='Steps', + ) + top: Optional[Top] = Field( + 0, description='Number of pixels to expand at the top of the image', title='Top' + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLFluxProFillInputs(BaseModel): + guidance: Optional[Guidance2] = Field( + default_factory=lambda: Guidance2.model_validate(60), + description='Guidance strength for the image generation process', + title='Guidance', + ) + image: str = Field( + ..., + description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.', + title='Image', + ) + mask: Optional[str] = Field( + None, + description='A Base64-encoded string representing a mask for the areas you want to modify in the image. The mask should be the same dimensions as the image and in black and white. Black areas (0%) indicate no modification, while white areas (100%) specify areas for inpainting. Optional if you provide an alpha mask in the original image. Validation: The endpoint verifies that the dimensions of the mask match the original image.', + title='Mask', + ) + output_format: Optional[BFLOutputFormat] = Field( + 'jpeg', + description="Output format for the generated image. Can be 'jpeg' or 'png'.", + ) + prompt: Optional[str] = Field( + '', + description='The description of the changes you want to make. This text guides the inpainting process, allowing you to specify features, styles, or modifications for the masked area.', + examples=['ein fantastisches bild'], + title='Prompt', + ) + prompt_upsampling: Optional[bool] = Field( + False, + description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation', + title='Prompt Upsampling', + ) + safety_tolerance: Optional[int] = Field( + 2, + description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict.', + examples=[2], + ge=0, + le=6, + title='Safety Tolerance', + ) + seed: Optional[int] = Field( + None, description='Optional seed for reproducibility', title='Seed' + ) + steps: Optional[Steps2] = Field( + default_factory=lambda: Steps2.model_validate(50), + description='Number of steps for the image generation process', + examples=[50], + title='Steps', + ) + webhook_secret: Optional[str] = Field( + None, + description='Optional secret for webhook signature verification', + title='Webhook Secret', + ) + webhook_url: Optional[WebhookUrl] = Field( + None, description='URL to receive webhook notifications', title='Webhook Url' + ) + + +class BFLHTTPValidationError(BaseModel): + detail: Optional[List[BFLValidationError]] = Field(None, title='Detail') + + +class BulkNodeVersionsRequest(BaseModel): + node_versions: List[NodeVersionIdentifier] = Field( + ..., description='List of node ID and version pairs to retrieve' + ) + + +CreateModelResponseProperties = ModelResponseProperties + + +class GeminiInlineData(BaseModel): + data: Optional[str] = Field( + None, + description='The base64 encoding of the image, PDF, or video to include inline in the prompt. When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB\n', + ) + mimeType: Optional[GeminiMimeType] = None + + +class GeminiPart(BaseModel): + inlineData: Optional[GeminiInlineData] = None + text: Optional[str] = Field( + None, + description='A text prompt or code snippet.', + examples=['Write a story about a robot learning to paint'], + ) + + +class GeminiPromptFeedback(BaseModel): + blockReason: Optional[str] = None + blockReasonMessage: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiSafetySetting(BaseModel): + category: GeminiSafetyCategory + threshold: GeminiSafetyThreshold + + +class GeminiSystemInstructionContent(BaseModel): + parts: List[GeminiTextPart] = Field( + ..., + description='A list of ordered parts that make up a single message. Different parts may have different IANA MIME types. For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.\n', + ) + role: Role1 = Field( + ..., + description='The identity of the entity that creates the message. The following values are supported: user: This indicates that the message is sent by a real person, typically a user-generated message. model: This indicates that the message is generated by the model. The model value is used to insert messages from the model into the conversation during multi-turn conversations. For non-multi-turn conversations, this field can be left blank or unset.\n', + examples=['user'], + ) + + +class GeminiUsageMetadata(BaseModel): + cachedContentTokenCount: Optional[int] = Field( + None, + description='Output only. Number of tokens in the cached part in the input (the cached content).', + ) + candidatesTokenCount: Optional[int] = Field( + None, description='Number of tokens in the response(s).' + ) + candidatesTokensDetails: Optional[List[ModalityTokenCount]] = Field( + None, description='Breakdown of candidate tokens by modality.' + ) + promptTokenCount: Optional[int] = Field( + None, + description='Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.', + ) + promptTokensDetails: Optional[List[ModalityTokenCount]] = Field( + None, description='Breakdown of prompt tokens by modality.' + ) + thoughtsTokenCount: Optional[int] = Field( + None, description='Number of tokens present in thoughts output.' + ) + toolUsePromptTokenCount: Optional[int] = Field( + None, description='Number of tokens present in tool-use prompt(s).' + ) + + +class GithubInstallation(BaseModel): + access_tokens_url: str = Field(..., description='The API URL for access tokens') + account: GithubUser + app_id: int = Field(..., description='The GitHub App ID') + created_at: datetime = Field(..., description='When the installation was created') + events: List[str] = Field( + ..., description='The events the installation subscribes to' + ) + html_url: str = Field(..., description='The HTML URL of the installation') + id: int = Field(..., description='The installation ID') + permissions: Dict[str, Any] = Field(..., description='The installation permissions') + repositories_url: str = Field(..., description='The API URL for repositories') + repository_selection: RepositorySelection = Field( + ..., description='Repository selection for the installation' + ) + single_file_name: Optional[str] = Field( + None, description='The single file name if applicable' + ) + target_id: int = Field(..., description='The target ID') + target_type: str = Field(..., description='The target type') + updated_at: datetime = Field( + ..., description='When the installation was last updated' + ) + + +class GithubReleaseAsset(BaseModel): + browser_download_url: str = Field(..., description='The browser download URL') + content_type: str = Field(..., description='The content type of the asset') + created_at: datetime = Field(..., description='When the asset was created') + download_count: int = Field(..., description='The number of downloads') + id: int = Field(..., description='The asset ID') + label: Optional[str] = Field(None, description='The label of the asset') + name: str = Field(..., description='The name of the asset') + node_id: str = Field(..., description='The asset node ID') + size: int = Field(..., description='The size of the asset in bytes') + state: State = Field(..., description='The state of the asset') + updated_at: datetime = Field(..., description='When the asset was last updated') + uploader: GithubUser + + +class Release(BaseModel): + assets: List[GithubReleaseAsset] = Field(..., description='Array of release assets') + assets_url: Optional[str] = Field(None, description='The URL to the release assets') + author: GithubUser + body: Optional[str] = Field(None, description='The release notes/body') + created_at: datetime = Field(..., description='When the release was created') + draft: bool = Field(..., description='Whether the release is a draft') + html_url: str = Field(..., description='The HTML URL of the release') + id: int = Field(..., description='The ID of the release') + name: Optional[str] = Field(None, description='The name of the release') + node_id: str = Field(..., description='The node ID of the release') + prerelease: bool = Field(..., description='Whether the release is a prerelease') + published_at: Optional[datetime] = Field( + None, description='When the release was published' + ) + tag_name: str = Field(..., description='The tag name of the release') + tarball_url: str = Field(..., description='URL to the tarball') + target_commitish: str = Field( + ..., description='The branch or commit the release was created from' + ) + upload_url: Optional[str] = Field( + None, description='The URL to upload release assets' + ) + url: str = Field(..., description='The API URL of the release') + zipball_url: str = Field(..., description='URL to the zipball') + + +class GithubRepository(BaseModel): + clone_url: str = Field(..., description='The clone URL of the repository') + created_at: datetime = Field(..., description='When the repository was created') + default_branch: str = Field(..., description='The default branch of the repository') + description: Optional[str] = Field(None, description='The repository description') + fork: bool = Field(..., description='Whether the repository is a fork') + full_name: str = Field( + ..., description='The full name of the repository (owner/repo)' + ) + git_url: str = Field(..., description='The git URL of the repository') + html_url: str = Field(..., description='The HTML URL of the repository') + id: int = Field(..., description='The repository ID') + name: str = Field(..., description='The name of the repository') + node_id: str = Field(..., description='The repository node ID') + owner: GithubUser + private: bool = Field(..., description='Whether the repository is private') + pushed_at: datetime = Field( + ..., description='When the repository was last pushed to' + ) + ssh_url: str = Field(..., description='The SSH URL of the repository') + updated_at: datetime = Field( + ..., description='When the repository was last updated' + ) + url: str = Field(..., description='The API URL of the repository') + + +class IdeogramV3EditRequest(BaseModel): + color_palette: Optional[IdeogramColorPalette] = None + image: Optional[StrictBytes] = Field( + None, + description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.', + ) + magic_prompt: Optional[str] = Field( + None, + description='Determine if MagicPrompt should be used in generating the request or not.', + ) + mask: Optional[StrictBytes] = Field( + None, + description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.', + ) + num_images: Optional[int] = Field( + None, description='The number of images to generate.' + ) + prompt: str = Field( + ..., description='The prompt used to describe the edited result.' + ) + rendering_speed: RenderingSpeed + seed: Optional[int] = Field( + None, description='Random seed. Set for reproducible generation.' + ) + style_codes: Optional[List[StyleCode]] = Field( + None, + description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.', + ) + style_reference_images: Optional[List[StrictBytes]] = Field( + None, + description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.', + ) + character_reference_images: Optional[List[str]] = Field( + None, + description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.' + ) + character_reference_images_mask: Optional[List[str]] = Field( + None, + description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.' + ) + + +class IdeogramV3Request(BaseModel): + aspect_ratio: Optional[str] = Field( + None, description='Aspect ratio in format WxH', examples=['1x3'] + ) + color_palette: Optional[ColorPalette] = None + magic_prompt: Optional[MagicPrompt2] = Field( + None, description='Whether to enable magic prompt enhancement' + ) + negative_prompt: Optional[str] = Field( + None, description='Text prompt specifying what to avoid in the generation' + ) + num_images: Optional[int] = Field( + None, description='Number of images to generate', ge=1 + ) + prompt: str = Field(..., description='The text prompt for image generation') + rendering_speed: RenderingSpeed + resolution: Optional[str] = Field( + None, description='Image resolution in format WxH', examples=['1280x800'] + ) + seed: Optional[int] = Field( + None, description='Seed value for reproducible generation' + ) + style_codes: Optional[List[StyleCode]] = Field( + None, description='Array of style codes in hexadecimal format' + ) + style_reference_images: Optional[List[str]] = Field( + None, description='Array of reference image URLs or identifiers' + ) + style_type: Optional[StyleType1] = Field( + None, description='The type of style to apply' + ) + character_reference_images: Optional[List[str]] = Field( + None, + description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.' + ) + character_reference_images_mask: Optional[List[str]] = Field( + None, + description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.' + ) + + +class ImagenGenerateImageResponse(BaseModel): + predictions: Optional[List[ImagenImagePrediction]] = None + + +class ImagenImageGenerationParameters(BaseModel): + addWatermark: Optional[bool] = None + aspectRatio: Optional[AspectRatio] = None + enhancePrompt: Optional[bool] = None + includeRaiReason: Optional[bool] = None + includeSafetyAttributes: Optional[bool] = None + outputOptions: Optional[ImagenOutputOptions] = None + personGeneration: Optional[PersonGeneration] = None + safetySetting: Optional[SafetySetting] = None + sampleCount: Optional[int] = Field(None, ge=1, le=4) + seed: Optional[int] = None + storageUri: Optional[AnyUrl] = None + + +class InputContent( + RootModel[Union[InputTextContent, InputImageContent, InputFileContent]] +): + root: Union[InputTextContent, InputImageContent, InputFileContent] + + +class InputMessageContentList(RootModel[List[InputContent]]): + root: List[InputContent] = Field( + ..., + description='A list of one or many input items to the model, containing different content \ntypes.\n', + title='Input item content list', + ) + + +class KlingCameraControl(BaseModel): + config: Optional[KlingCameraConfig] = None + type: Optional[KlingCameraControlType] = None + + +class KlingDualCharacterEffectInput(BaseModel): + duration: KlingVideoGenDuration + images: KlingDualCharacterImages + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingCharacterEffectModelName] = 'kling-v1' + + +class KlingImage2VideoRequest(BaseModel): + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + dynamic_masks: Optional[List[DynamicMask]] = Field( + None, + description='Dynamic Brush Configuration List (up to 6 groups). For 5-second videos, trajectory length must not exceed 77 coordinates.', + ) + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + image: Optional[str] = Field( + None, + description='Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.', + ) + image_tail: Optional[str] = Field( + None, + description='Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.', + ) + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingVideoGenModelName] = 'kling-v2-master' + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + static_mask: Optional[str] = Field( + None, + description='Static Brush Application Area (Mask image created by users using the motion brush). The aspect ratio must match the input image.', + ) + + +class TaskResult(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImage2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class TaskResult1(BaseModel): + images: Optional[List[KlingImageResult]] = None + + +class Data1(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_result: Optional[TaskResult1] = None + task_status: Optional[KlingTaskStatus] = None + task_status_msg: Optional[str] = Field(None, description='Task status information') + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingImageGenerationsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data1] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingLipSyncInputObject(BaseModel): + audio_file: Optional[str] = Field( + None, + description='Local Path of Audio File. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB. Base64 code.', + ) + audio_type: Optional[KlingAudioUploadType] = None + audio_url: Optional[str] = Field( + None, + description='Audio File Download URL. Supported formats: .mp3/.wav/.m4a/.aac, maximum file size of 5MB.', + ) + mode: KlingLipSyncMode + text: Optional[str] = Field( + None, + description='Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.', + ) + video_id: Optional[str] = Field( + None, + description='The ID of the video generated by Kling AI. Only supports 5-second and 10-second videos generated within the last 30 days.', + ) + video_url: Optional[str] = Field( + None, + description='Get link for uploaded video. Video files support .mp4/.mov, file size does not exceed 100MB, video length between 2-10s.', + ) + voice_id: Optional[str] = Field( + None, + description='Voice ID. Required when mode is text2video. The system offers a variety of voice options to choose from.', + ) + voice_language: Optional[KlingLipSyncVoiceLanguage] = 'en' + voice_speed: Optional[float] = Field( + 1, + description='Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.', + ge=0.8, + le=2.0, + ) + + +class KlingLipSyncRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + input: KlingLipSyncInputObject + + +class TaskResult2(BaseModel): + videos: Optional[List[KlingVideoResult]] = None + + +class Data2(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingLipSyncResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data2] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingSingleImageEffectInput(BaseModel): + duration: KlingSingleImageEffectDuration + image: str = Field( + ..., + description='Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1.', + ) + model_name: KlingSingleImageEffectModelName + + +class KlingText2VideoRequest(BaseModel): + aspect_ratio: Optional[KlingVideoGenAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback notification address' + ) + camera_control: Optional[KlingCameraControl] = None + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + duration: Optional[KlingVideoGenDuration] = '5' + external_task_id: Optional[str] = Field(None, description='Customized Task ID') + mode: Optional[KlingVideoGenMode] = 'std' + model_name: Optional[KlingTextToVideoModelName] = 'kling-v1' + negative_prompt: Optional[str] = Field( + None, description='Negative text prompt', max_length=2500 + ) + prompt: Optional[str] = Field( + None, description='Positive text prompt', max_length=2500 + ) + + +class Data4(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingText2VideoResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data4] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingVideoEffectsInput( + RootModel[Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput]] +): + root: Union[KlingSingleImageEffectInput, KlingDualCharacterEffectInput] + + +class KlingVideoEffectsRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address for the result of this task.', + ) + effect_scene: Union[KlingDualCharacterEffectsScene, KlingSingleImageEffectsScene] + external_task_id: Optional[str] = Field( + None, + description='Customized Task ID. Must be unique within a single user account.', + ) + input: KlingVideoEffectsInput + + +class Data5(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVideoEffectsResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data5] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class KlingVideoExtendRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback notification address. Server will notify when the task status changes.', + ) + cfg_scale: Optional[KlingVideoGenCfgScale] = Field( + default_factory=lambda: KlingVideoGenCfgScale.model_validate(0.5) + ) + negative_prompt: Optional[str] = Field( + None, + description='Negative text prompt for elements to avoid in the extended video', + max_length=2500, + ) + prompt: Optional[str] = Field( + None, + description='Positive text prompt for guiding the video extension', + max_length=2500, + ) + video_id: Optional[str] = Field( + None, + description='The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.', + ) + + +class Data6(BaseModel): + created_at: Optional[int] = Field(None, description='Task creation time') + task_id: Optional[str] = Field(None, description='Task ID') + task_info: Optional[TaskInfo] = None + task_result: Optional[TaskResult2] = None + task_status: Optional[KlingTaskStatus] = None + updated_at: Optional[int] = Field(None, description='Task update time') + + +class KlingVideoExtendResponse(BaseModel): + code: Optional[int] = Field(None, description='Error code') + data: Optional[Data6] = None + message: Optional[str] = Field(None, description='Error message') + request_id: Optional[str] = Field(None, description='Request ID') + + +class LumaGenerationRequest(BaseModel): + aspect_ratio: LumaAspectRatio + callback_url: Optional[AnyUrl] = Field( + None, + description='The callback URL of the generation, a POST request with Generation object will be sent to the callback URL when the generation is dreaming, completed, or failed', + ) + duration: LumaVideoModelOutputDuration + generation_type: Optional[GenerationType1] = 'video' + keyframes: Optional[LumaKeyframes] = None + loop: Optional[bool] = Field(None, description='Whether to loop the video') + model: LumaVideoModel + prompt: str = Field(..., description='The prompt of the generation') + resolution: LumaVideoModelOutputResolution + + +class CharacterRef(BaseModel): + identity0: Optional[LumaImageIdentity] = None + + +class LumaImageGenerationRequest(BaseModel): + aspect_ratio: Optional[LumaAspectRatio] = '16:9' + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the generation' + ) + character_ref: Optional[CharacterRef] = None + generation_type: Optional[GenerationType2] = 'image' + image_ref: Optional[List[LumaImageRef]] = None + model: Optional[LumaImageModel] = 'photon-1' + modify_image_ref: Optional[LumaModifyImageRef] = None + prompt: Optional[str] = Field(None, description='The prompt of the generation') + style_ref: Optional[List[LumaImageRef]] = None + + +class LumaUpscaleVideoGenerationRequest(BaseModel): + callback_url: Optional[AnyUrl] = Field( + None, description='The callback URL for the upscale' + ) + generation_type: Optional[GenerationType3] = 'upscale_video' + resolution: Optional[LumaVideoModelOutputResolution] = None + + +class MoonvalleyImageToVideoRequest(MoonvalleyTextToVideoRequest): + keyframes: Optional[Dict[str, Keyframes]] = None + + +class MoonvalleyResizeVideoRequest(MoonvalleyVideoToVideoRequest): + frame_position: Optional[List[int]] = Field(None, max_length=2, min_length=2) + frame_resolution: Optional[List[int]] = Field(None, max_length=2, min_length=2) + scale: Optional[List[int]] = Field(None, max_length=2, min_length=2) + + +class MoonvalleyTextToImageRequest(BaseModel): + image_url: Optional[str] = None + inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None + prompt_text: Optional[str] = None + webhook_url: Optional[str] = None + + +class NodeVersion(BaseModel): + changelog: Optional[str] = Field( + None, description='Summary of changes made in this version' + ) + comfy_node_extract_status: Optional[str] = Field( + None, description='The status of comfy node extraction process.' + ) + createdAt: Optional[datetime] = Field( + None, description='The date and time the version was created.' + ) + dependencies: Optional[List[str]] = Field( + None, description='A list of pip dependencies required by the node.' + ) + deprecated: Optional[bool] = Field( + None, description='Indicates if this version is deprecated.' + ) + downloadUrl: Optional[str] = Field( + None, description='[Output Only] URL to download this version of the node' + ) + id: Optional[str] = None + node_id: Optional[str] = Field( + None, description='The unique identifier of the node.' + ) + status: Optional[NodeVersionStatus] = None + status_reason: Optional[str] = Field( + None, description='The reason for the status change.' + ) + supported_accelerators: Optional[List[str]] = Field( + None, + description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports', + ) + supported_comfyui_frontend_version: Optional[str] = Field( + None, description='Supported versions of ComfyUI frontend' + ) + supported_comfyui_version: Optional[str] = Field( + None, description='Supported versions of ComfyUI' + ) + supported_os: Optional[List[str]] = Field( + None, description='List of operating systems that this node supports' + ) + version: Optional[str] = Field( + None, + description='The version identifier, following semantic versioning. Must be unique for the node.', + ) + + +class OutputContent(RootModel[Union[OutputTextContent, OutputAudioContent]]): + root: Union[OutputTextContent, OutputAudioContent] + + +class OutputMessage(BaseModel): + content: List[OutputContent] = Field(..., description='The content of the message') + role: Role4 = Field(..., description='The role of the message') + type: Type15 = Field(..., description='The type of output item') + + +class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): + duration: Optional[PikaDurationEnum] = 5 + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: Optional[str] = Field(None, title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): + duration: Optional[int] = Field(None, ge=5, le=10, title='Duration') + keyFrames: Optional[List[StrictBytes]] = Field( + None, description='Array of keyframe images', title='Keyframes' + ) + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): + aspectRatio: Optional[float] = Field( + 1.7777777777777777, + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + title='Aspectratio', + ) + duration: Optional[PikaDurationEnum] = 5 + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + promptText: str = Field(..., title='Prompttext') + resolution: Optional[PikaResolutionEnum] = '1080p' + seed: Optional[int] = Field(None, title='Seed') + + +class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): + image: Optional[StrictBytes] = Field(None, title='Image') + negativePrompt: Optional[str] = Field(None, title='Negativeprompt') + pikaffect: Optional[Pikaffect] = None + promptText: Optional[str] = Field(None, title='Prompttext') + seed: Optional[int] = Field(None, title='Seed') + + +class PikaHTTPValidationError(BaseModel): + detail: Optional[List[PikaValidationError]] = Field(None, title='Detail') + + +class PublisherMember(BaseModel): + id: Optional[str] = Field( + None, description='The unique identifier for the publisher member.' + ) + role: Optional[str] = Field( + None, description='The role of the user in the publisher.' + ) + user: Optional[PublisherUser] = None + + +class Reasoning(BaseModel): + effort: Optional[ReasoningEffort] = 'medium' + generate_summary: Optional[GenerateSummary] = Field( + None, + description="**Deprecated:** use `summary` instead.\n\nA summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + summary: Optional[Summary] = Field( + None, + description="A summary of the reasoning performed by the model. This can be\nuseful for debugging and understanding the model's reasoning process.\nOne of `auto`, `concise`, or `detailed`.\n", + ) + + +class RecraftImage(BaseModel): + b64_json: Optional[str] = None + features: Optional[RecraftImageFeatures] = None + image_id: UUID + revised_prompt: Optional[str] = None + url: Optional[str] = None + + +class RecraftProcessImageRequest(BaseModel): + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + response_format: Optional[RecraftResponseFormat] = None + + +class RecraftProcessImageResponse(BaseModel): + created: int + credits: int + image: RecraftImage + + +class RecraftTextLayout(RootModel[List[RecraftTextLayoutItem]]): + root: List[RecraftTextLayoutItem] + + +class RecraftTransformImageWithMaskRequest(BaseModel): + block_nsfw: Optional[bool] = None + calculate_features: Optional[bool] = None + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + mask: StrictBytes + model: Optional[RecraftTransformModel] = None + n: Optional[int] = None + negative_prompt: Optional[str] = None + prompt: str + response_format: Optional[RecraftResponseFormat] = None + style: Optional[RecraftImageStyle] = None + style_id: Optional[UUID] = None + substyle: Optional[RecraftImageSubStyle] = None + text_layout: Optional[RecraftTextLayout] = None + + +class ResponseContentPartAddedEvent(BaseModel): + content_index: int = Field( + ..., description='The index of the content part that was added.' + ) + item_id: str = Field( + ..., description='The ID of the output item that the content part was added to.' + ) + output_index: int = Field( + ..., + description='The index of the output item that the content part was added to.', + ) + part: OutputContent + type: Type20 = Field( + ..., description='The type of the event. Always `response.content_part.added`.' + ) + + +class ResponseContentPartDoneEvent(BaseModel): + content_index: int = Field( + ..., description='The index of the content part that is done.' + ) + item_id: str = Field( + ..., description='The ID of the output item that the content part was added to.' + ) + output_index: int = Field( + ..., + description='The index of the output item that the content part was added to.', + ) + part: OutputContent + type: Type21 = Field( + ..., description='The type of the event. Always `response.content_part.done`.' + ) + + +class ResponseError(BaseModel): + code: ResponseErrorCode + message: str = Field(..., description='A human-readable description of the error.') + + +class Rodin3DDownloadResponse(BaseModel): + list: Optional[List[RodinResourceItem]] = None + + +class Rodin3DGenerateRequest(BaseModel): + images: str = Field(..., description='The reference images to generate 3D Assets.') + material: Optional[RodinMaterialType] = None + mesh_mode: Optional[RodinMeshModeType] = None + quality: Optional[RodinQualityType] = None + seed: Optional[int] = Field(None, description='Seed.') + tier: Optional[RodinTierType] = None + + +class Rodin3DGenerateResponse(BaseModel): + jobs: Optional[RodinGenerateJobsData] = None + message: Optional[str] = Field(None, description='message') + prompt: Optional[str] = Field(None, description='prompt') + submit_time: Optional[str] = Field(None, description='Time') + uuid: Optional[str] = Field(None, description='Task UUID') + + +class RodinCheckStatusJobItem(BaseModel): + status: Optional[RodinStatusOptions] = None + uuid: Optional[str] = Field(None, description='sub uuid') + + +class RunwayImageToVideoRequest(BaseModel): + duration: RunwayDurationEnum + model: RunwayModelEnum + promptImage: RunwayPromptImageObject + promptText: Optional[str] = Field( + None, description='Text prompt for the generation', max_length=1000 + ) + ratio: RunwayAspectRatioEnum + seed: int = Field( + ..., description='Random seed for generation', ge=0, le=4294967295 + ) + + +class StripeCharge(BaseModel): + amount: Optional[int] = None + amount_captured: Optional[int] = None + amount_refunded: Optional[int] = None + application: Optional[str] = None + application_fee: Optional[str] = None + application_fee_amount: Optional[int] = None + balance_transaction: Optional[str] = None + billing_details: Optional[StripeBillingDetails] = None + calculated_statement_descriptor: Optional[str] = None + captured: Optional[bool] = None + created: Optional[int] = None + currency: Optional[str] = None + customer: Optional[str] = None + description: Optional[str] = None + destination: Optional[Any] = None + dispute: Optional[Any] = None + disputed: Optional[bool] = None + failure_balance_transaction: Optional[Any] = None + failure_code: Optional[Any] = None + failure_message: Optional[Any] = None + fraud_details: Optional[Dict[str, Any]] = None + id: Optional[str] = None + invoice: Optional[Any] = None + livemode: Optional[bool] = None + metadata: Optional[Dict[str, Any]] = None + object: Optional[Object1] = None + on_behalf_of: Optional[Any] = None + order: Optional[Any] = None + outcome: Optional[StripeOutcome] = None + paid: Optional[bool] = None + payment_intent: Optional[str] = None + payment_method: Optional[str] = None + payment_method_details: Optional[StripePaymentMethodDetails] = None + radar_options: Optional[Dict[str, Any]] = None + receipt_email: Optional[str] = None + receipt_number: Optional[str] = None + receipt_url: Optional[str] = None + refunded: Optional[bool] = None + refunds: Optional[StripeRefundList] = None + review: Optional[Any] = None + shipping: Optional[StripeShipping] = None + source: Optional[Any] = None + source_transfer: Optional[Any] = None + statement_descriptor: Optional[Any] = None + statement_descriptor_suffix: Optional[Any] = None + status: Optional[str] = None + transfer_data: Optional[Any] = None + transfer_group: Optional[Any] = None + + +class StripeChargeList(BaseModel): + data: Optional[List[StripeCharge]] = None + has_more: Optional[bool] = None + object: Optional[str] = None + total_count: Optional[int] = None + url: Optional[str] = None + + +class StripePaymentIntent(BaseModel): + amount: Optional[int] = None + amount_capturable: Optional[int] = None + amount_details: Optional[StripeAmountDetails] = None + amount_received: Optional[int] = None + application: Optional[str] = None + application_fee_amount: Optional[int] = None + automatic_payment_methods: Optional[Any] = None + canceled_at: Optional[int] = None + cancellation_reason: Optional[str] = None + capture_method: Optional[str] = None + charges: Optional[StripeChargeList] = None + client_secret: Optional[str] = None + confirmation_method: Optional[str] = None + created: Optional[int] = None + currency: Optional[str] = None + customer: Optional[str] = None + description: Optional[str] = None + id: Optional[str] = None + invoice: Optional[str] = None + last_payment_error: Optional[Any] = None + latest_charge: Optional[str] = None + livemode: Optional[bool] = None + metadata: Optional[Dict[str, Any]] = None + next_action: Optional[Any] = None + object: Optional[Object3] = None + on_behalf_of: Optional[Any] = None + payment_method: Optional[str] = None + payment_method_configuration_details: Optional[Any] = None + payment_method_options: Optional[StripePaymentMethodOptions] = None + payment_method_types: Optional[List[str]] = None + processing: Optional[Any] = None + receipt_email: Optional[str] = None + review: Optional[Any] = None + setup_future_usage: Optional[Any] = None + shipping: Optional[StripeShipping] = None + source: Optional[Any] = None + statement_descriptor: Optional[Any] = None + statement_descriptor_suffix: Optional[Any] = None + status: Optional[str] = None + transfer_data: Optional[Any] = None + transfer_group: Optional[Any] = None + + +class TextResponseFormatConfiguration( + RootModel[ + Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] + ] +): + root: Union[ + ResponseFormatText, TextResponseFormatJsonSchema, ResponseFormatJsonObject + ] = Field( + ..., + description='An object specifying the format that the model must output.\n\nConfiguring `{ "type": "json_schema" }` enables Structured Outputs, \nwhich ensures the model will match your supplied JSON schema. Learn more in the \n[Structured Outputs guide](/docs/guides/structured-outputs).\n\nThe default format is `{ "type": "text" }` with no additional options.\n\n**Not recommended for gpt-4o and newer models:**\n\nSetting to `{ "type": "json_object" }` enables the older JSON mode, which\nensures the message the model generates is valid JSON. Using `json_schema`\nis preferred for models that support it.\n', + ) + + +class Tool( + RootModel[ + Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] + ] +): + root: Union[ + FileSearchTool, FunctionTool, WebSearchPreviewTool, ComputerUsePreviewTool + ] = Field(..., discriminator='type') + + +class BulkNodeVersionResult(BaseModel): + error_message: Optional[str] = Field( + None, + description='Error message if retrieval failed (only present if status is error)', + ) + identifier: NodeVersionIdentifier + node_version: Optional[NodeVersion] = None + status: Status = Field(..., description='Status of the retrieval operation') + + +class BulkNodeVersionsResponse(BaseModel): + node_versions: List[BulkNodeVersionResult] = Field( + ..., description='List of retrieved node versions with their status' + ) + + +class EasyInputMessage(BaseModel): + content: Union[str, InputMessageContentList] = Field( + ..., + description='Text, image, or audio input to the model, used to generate a response.\nCan also contain previous assistant responses.\n', + ) + role: Role = Field( + ..., + description='The role of the message input. One of `user`, `assistant`, `system`, or\n`developer`.\n', + ) + type: Optional[Type2] = Field( + None, description='The type of the message input. Always `message`.\n' + ) + + +class GeminiContent(BaseModel): + parts: List[GeminiPart] + role: Role1 = Field(..., examples=['user']) + + +class GeminiGenerateContentRequest(BaseModel): + contents: List[GeminiContent] + generationConfig: Optional[GeminiGenerationConfig] = None + safetySettings: Optional[List[GeminiSafetySetting]] = None + systemInstruction: Optional[GeminiSystemInstructionContent] = None + tools: Optional[List[GeminiTool]] = None + videoMetadata: Optional[GeminiVideoMetadata] = None + + +class GithubReleaseWebhook(BaseModel): + action: Action = Field(..., description='The action performed on the release') + enterprise: Optional[GithubEnterprise] = None + installation: Optional[GithubInstallation] = None + organization: Optional[GithubOrganization] = None + release: Release = Field(..., description='The release object') + repository: GithubRepository + sender: GithubUser + + +class ImagenGenerateImageRequest(BaseModel): + instances: List[ImagenImageGenerationInstance] + parameters: ImagenImageGenerationParameters + + +class InputMessage(BaseModel): + content: Optional[InputMessageContentList] = None + role: Optional[Role3] = None + status: Optional[Status3] = None + type: Optional[Type10] = None + + +class Item( + RootModel[ + Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + InputMessage, + OutputMessage, + FileSearchToolCall, + ComputerToolCall, + WebSearchToolCall, + FunctionToolCall, + ReasoningItem, + ] = Field(..., description='Content item used to generate a response.\n') + + +class LumaGeneration(BaseModel): + assets: Optional[LumaAssets] = None + created_at: Optional[datetime] = Field( + None, description='The date and time when the generation was created' + ) + failure_reason: Optional[str] = Field( + None, description='The reason for the state of the generation' + ) + generation_type: Optional[LumaGenerationType] = None + id: Optional[UUID] = Field(None, description='The ID of the generation') + model: Optional[str] = Field(None, description='The model used for the generation') + request: Optional[ + Union[ + LumaGenerationRequest, + LumaImageGenerationRequest, + LumaUpscaleVideoGenerationRequest, + LumaAudioGenerationRequest, + ] + ] = Field(None, description='The request of the generation') + state: Optional[LumaState] = None + + +class OutputItem( + RootModel[ + Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + ] +): + root: Union[ + OutputMessage, + FileSearchToolCall, + FunctionToolCall, + WebSearchToolCall, + ComputerToolCall, + ReasoningItem, + ] + + +class Publisher(BaseModel): + createdAt: Optional[datetime] = Field( + None, description='The date and time the publisher was created.' + ) + description: Optional[str] = None + id: Optional[str] = Field( + None, + description="The unique identifier for the publisher. It's akin to a username. Should be lowercase.", + ) + logo: Optional[str] = Field(None, description="URL to the publisher's logo.") + members: Optional[List[PublisherMember]] = Field( + None, description='A list of members in the publisher.' + ) + name: Optional[str] = None + source_code_repo: Optional[str] = None + status: Optional[PublisherStatus] = None + support: Optional[str] = None + website: Optional[str] = None + + +class RecraftGenerateImageResponse(BaseModel): + created: int + credits: int + data: List[RecraftImage] + + +class RecraftImageToImageRequest(BaseModel): + block_nsfw: Optional[bool] = None + calculate_features: Optional[bool] = None + controls: Optional[RecraftUserControls] = None + image: StrictBytes + image_format: Optional[RecraftImageFormat] = None + model: Optional[RecraftTransformModel] = None + n: Optional[int] = None + negative_prompt: Optional[str] = None + prompt: str + response_format: Optional[RecraftResponseFormat] = None + strength: float + style: Optional[RecraftImageStyle] = None + style_id: Optional[UUID] = None + substyle: Optional[RecraftImageSubStyle] = None + text_layout: Optional[RecraftTextLayout] = None + + +class ResponseOutputItemAddedEvent(BaseModel): + item: OutputItem + output_index: int = Field( + ..., description='The index of the output item that was added.\n' + ) + type: Type29 = Field( + ..., description='The type of the event. Always `response.output_item.added`.\n' + ) + + +class ResponseOutputItemDoneEvent(BaseModel): + item: OutputItem + output_index: int = Field( + ..., description='The index of the output item that was marked done.\n' + ) + type: Type30 = Field( + ..., description='The type of the event. Always `response.output_item.done`.\n' + ) + + +class Text(BaseModel): + format: Optional[TextResponseFormatConfiguration] = None + + +class ResponseProperties(BaseModel): + instructions: Optional[str] = Field( + None, + description="Inserts a system (or developer) message as the first item in the model's context.\n\nWhen using along with `previous_response_id`, the instructions from a previous\nresponse will not be carried over to the next response. This makes it simple\nto swap out system (or developer) messages in new responses.\n", + ) + max_output_tokens: Optional[int] = Field( + None, + description='An upper bound for the number of tokens that can be generated for a response, including visible output tokens and [reasoning tokens](/docs/guides/reasoning).\n', + ) + model: Optional[OpenAIModels] = None + previous_response_id: Optional[str] = Field( + None, + description='The unique ID of the previous response to the model. Use this to\ncreate multi-turn conversations. Learn more about \n[conversation state](/docs/guides/conversation-state).\n', + ) + reasoning: Optional[Reasoning] = None + text: Optional[Text] = None + tool_choice: Optional[ + Union[ToolChoiceOptions, ToolChoiceTypes, ToolChoiceFunction] + ] = Field( + None, + description='How the model should select which tool (or tools) to use when generating\na response. See the `tools` parameter to see how to specify which tools\nthe model can call.\n', + ) + tools: Optional[List[Tool]] = None + truncation: Optional[Truncation1] = Field( + 'disabled', + description="The truncation strategy to use for the model response.\n- `auto`: If the context of this response and previous ones exceeds\n the model's context window size, the model will truncate the \n response to fit the context window by dropping input items in the\n middle of the conversation. \n- `disabled` (default): If a model response will exceed the context window \n size for a model, the request will fail with a 400 error.\n", + ) + + +class Rodin3DCheckStatusResponse(BaseModel): + jobs: Optional[List[RodinCheckStatusJobItem]] = Field( + None, description='Details for the generation status.' + ) + + +class Data8(BaseModel): + object: Optional[StripePaymentIntent] = None + + +class StripeEvent(BaseModel): + api_version: Optional[str] = None + created: Optional[int] = None + data: Data8 + id: str + livemode: Optional[bool] = None + object: Object2 + pending_webhooks: Optional[int] = None + request: Optional[StripeRequestInfo] = None + type: Type31 + + +class GeminiCandidate(BaseModel): + citationMetadata: Optional[GeminiCitationMetadata] = None + content: Optional[GeminiContent] = None + finishReason: Optional[str] = None + safetyRatings: Optional[List[GeminiSafetyRating]] = None + + +class GeminiGenerateContentResponse(BaseModel): + candidates: Optional[List[GeminiCandidate]] = None + promptFeedback: Optional[GeminiPromptFeedback] = None + usageMetadata: Optional[GeminiUsageMetadata] = None + + +class InputItem(RootModel[Union[EasyInputMessage, Item]]): + root: Union[EasyInputMessage, Item] + + +class Node(BaseModel): + author: Optional[str] = None + banner_url: Optional[str] = Field(None, description="URL to the node's banner.") + category: Optional[str] = Field(None, description='The category of the node.') + created_at: Optional[datetime] = Field( + None, description='The date and time when the node was created' + ) + description: Optional[str] = None + downloads: Optional[int] = Field( + None, description='The number of downloads of the node.' + ) + github_stars: Optional[int] = Field( + None, description='Number of stars on the GitHub repository.' + ) + icon: Optional[str] = Field(None, description="URL to the node's icon.") + id: Optional[str] = Field(None, description='The unique identifier of the node.') + latest_version: Optional[NodeVersion] = None + license: Optional[str] = Field( + None, description="The path to the LICENSE file in the node's repository." + ) + name: Optional[str] = Field(None, description='The display name of the node.') + preempted_comfy_node_names: Optional[List[str]] = Field( + None, description='A list of Comfy node names that are preempted by this node.' + ) + publisher: Optional[Publisher] = None + rating: Optional[float] = Field(None, description='The average rating of the node.') + repository: Optional[str] = Field(None, description="URL to the node's repository.") + search_ranking: Optional[int] = Field( + None, + description="A numerical value representing the node's search ranking, used for sorting search results.", + ) + status: Optional[NodeStatus] = None + status_detail: Optional[str] = Field( + None, description='The status detail of the node.' + ) + supported_accelerators: Optional[List[str]] = Field( + None, + description='List of accelerators (e.g. CUDA, DirectML, ROCm) that this node supports', + ) + supported_comfyui_frontend_version: Optional[str] = Field( + None, description='Supported versions of ComfyUI frontend' + ) + supported_comfyui_version: Optional[str] = Field( + None, description='Supported versions of ComfyUI' + ) + supported_os: Optional[List[str]] = Field( + None, description='List of operating systems that this node supports' + ) + tags: Optional[List[str]] = None + translations: Optional[Dict[str, Dict[str, Any]]] = Field( + None, description='Translations of node metadata in different languages.' + ) + + +class OpenAICreateResponse(CreateModelResponseProperties, ResponseProperties): + include: Optional[List[Includable]] = Field( + None, + description='Specify additional output data to include in the model response. Currently\nsupported values are:\n- `file_search_call.results`: Include the search results of\n the file search tool call.\n- `message.input_image.image_url`: Include image urls from the input message.\n- `computer_call_output.output.image_url`: Include image urls from the computer call output.\n', + ) + input: Union[str, List[InputItem]] = Field( + ..., + description='Text, image, or file inputs to the model, used to generate a response.\n\nLearn more:\n- [Text inputs and outputs](/docs/guides/text)\n- [Image inputs](/docs/guides/images)\n- [File inputs](/docs/guides/pdf-files)\n- [Conversation state](/docs/guides/conversation-state)\n- [Function calling](/docs/guides/function-calling)\n', + ) + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' + ) + store: Optional[bool] = Field( + True, + description='Whether to store the generated model response for later retrieval via\nAPI.\n', + ) + stream: Optional[bool] = Field( + False, + description='If set to true, the model response data will be streamed to the client\nas it is generated using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format).\nSee the [Streaming section below](/docs/api-reference/responses-streaming)\nfor more information.\n', + ) + usage: Optional[ResponseUsage] = None + + +class OpenAIResponse(ModelResponseProperties, ResponseProperties): + created_at: Optional[float] = Field( + None, + description='Unix timestamp (in seconds) of when this Response was created.', + ) + error: Optional[ResponseError] = None + id: Optional[str] = Field(None, description='Unique identifier for this Response.') + incomplete_details: Optional[IncompleteDetails] = Field( + None, description='Details about why the response is incomplete.\n' + ) + object: Optional[Object] = Field( + None, description='The object type of this resource - always set to `response`.' + ) + output: Optional[List[OutputItem]] = Field( + None, + description="An array of content items generated by the model.\n\n- The length and order of items in the `output` array is dependent\n on the model's response.\n- Rather than accessing the first item in the `output` array and \n assuming it's an `assistant` message with the content generated by\n the model, you might consider using the `output_text` property where\n supported in SDKs.\n", + ) + output_text: Optional[str] = Field( + None, + description='SDK-only convenience property that contains the aggregated text output \nfrom all `output_text` items in the `output` array, if any are present. \nSupported in the Python and JavaScript SDKs.\n', + ) + parallel_tool_calls: Optional[bool] = Field( + True, description='Whether to allow the model to run tool calls in parallel.\n' + ) + status: Optional[Status7] = Field( + None, + description='The status of the response generation. One of `completed`, `failed`, `in_progress`, or `incomplete`.', + ) + usage: Optional[ResponseUsage] = None + + +class ResponseCompletedEvent(BaseModel): + response: OpenAIResponse + type: Type19 = Field( + ..., description='The type of the event. Always `response.completed`.' + ) + + +class ResponseCreatedEvent(BaseModel): + response: OpenAIResponse + type: Type22 = Field( + ..., description='The type of the event. Always `response.created`.' + ) + + +class ResponseFailedEvent(BaseModel): + response: OpenAIResponse + type: Type24 = Field( + ..., description='The type of the event. Always `response.failed`.\n' + ) + + +class ResponseInProgressEvent(BaseModel): + response: OpenAIResponse + type: Type27 = Field( + ..., description='The type of the event. Always `response.in_progress`.\n' + ) + + +class ResponseIncompleteEvent(BaseModel): + response: OpenAIResponse + type: Type28 = Field( + ..., description='The type of the event. Always `response.incomplete`.\n' + ) + + +class OpenAIResponseStreamEvent( + RootModel[ + Union[ + ResponseCreatedEvent, + ResponseInProgressEvent, + ResponseCompletedEvent, + ResponseFailedEvent, + ResponseIncompleteEvent, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent, + ResponseErrorEvent, + ] + ] +): + root: Union[ + ResponseCreatedEvent, + ResponseInProgressEvent, + ResponseCompletedEvent, + ResponseFailedEvent, + ResponseIncompleteEvent, + ResponseOutputItemAddedEvent, + ResponseOutputItemDoneEvent, + ResponseContentPartAddedEvent, + ResponseContentPartDoneEvent, + ResponseErrorEvent, + ] = Field(..., description='Events that can be emitted during response streaming') diff --git a/comfy_api_nodes/apis/bfl_api.py b/comfy_api_nodes/apis/bfl_api.py new file mode 100644 index 000000000..0fc8c0607 --- /dev/null +++ b/comfy_api_nodes/apis/bfl_api.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from enum import Enum +from typing import Any, Dict, Optional + +from pydantic import BaseModel, Field, confloat, conint + + +class BFLOutputFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + + +class BFLFluxExpandImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + top: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the top of the image') + bottom: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the bottom of the image') + left: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the left side of the image') + right: conint(ge=0, le=2048) = Field(..., description='Number of pixels to expand at the right side of the image') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to expand') + + +class BFLFluxFillImageRequest(BaseModel): + prompt: str = Field(..., description='The description of the changes you want to make. This text guides the expansion process, allowing you to specify features, styles, or modifications for the expanded areas.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + steps: conint(ge=15, le=50) = Field(..., description='Number of steps for the image generation process') + guidance: confloat(ge=1.5, le=100) = Field(..., description='Guidance strength for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image: str = Field(None, description='A Base64-encoded string representing the image you wish to modify. Can contain alpha mask if desired.') + mask: str = Field(None, description='A Base64-encoded string representing the mask of the areas you with to modify.') + + +class BFLFluxProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + width: conint(ge=256, le=1440) = Field(1024, description='Width of the generated image in pixels. Must be a multiple of 32.') + height: conint(ge=256, le=1440) = Field(768, description='Height of the generated image in pixels. Must be a multiple of 32.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + # image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + # None, description='Blend between the prompt and the image prompt.' + # ) + + +class BFLFluxKontextProGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for what you wannt to edit.') + input_image: Optional[str] = Field(None, description='Image to edit in base64 format') + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + guidance: confloat(ge=0.1, le=99.0) = Field(..., description='Guidance strength for the image generation process') + steps: conint(ge=1, le=150) = Field(..., description='Number of steps for the image generation process') + safety_tolerance: Optional[conint(ge=0, le=2)] = Field( + 2, description='Tolerance level for input and output moderation. Between 0 and 2, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + + +class BFLFluxProUltraGenerateRequest(BaseModel): + prompt: str = Field(..., description='The text prompt for image generation.') + prompt_upsampling: Optional[bool] = Field( + None, description='Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation.' + ) + seed: Optional[int] = Field(None, description='The seed value for reproducibility.') + aspect_ratio: Optional[str] = Field(None, description='Aspect ratio of the image between 21:9 and 9:21.') + safety_tolerance: Optional[conint(ge=0, le=6)] = Field( + 6, description='Tolerance level for input and output moderation. Between 0 and 6, 0 being most strict, 6 being least strict. Defaults to 2.' + ) + output_format: Optional[BFLOutputFormat] = Field( + BFLOutputFormat.png, description="Output format for the generated image. Can be 'jpeg' or 'png'.", examples=['png'] + ) + raw: Optional[bool] = Field(None, description='Generate less processed, more natural-looking images.') + image_prompt: Optional[str] = Field(None, description='Optional image to remix in base64 format') + image_prompt_strength: Optional[confloat(ge=0.0, le=1.0)] = Field( + None, description='Blend between the prompt and the image prompt.' + ) + + +class BFLFluxProGenerateResponse(BaseModel): + id: str = Field(..., description='The unique identifier for the generation task.') + polling_url: str = Field(..., description='URL to poll for the generation result.') + + +class BFLStatus(str, Enum): + task_not_found = "Task not found" + pending = "Pending" + request_moderated = "Request Moderated" + content_moderated = "Content Moderated" + ready = "Ready" + error = "Error" + + +class BFLFluxStatusResponse(BaseModel): + id: str = Field(..., description="The unique identifier for the generation task.") + status: BFLStatus = Field(..., description="The status of the task.") + result: Optional[Dict[str, Any]] = Field(None, description="The result of the task (null if not completed).") + progress: Optional[float] = Field(None, description="The progress of the task (0.0 to 1.0).", ge=0.0, le=1.0) diff --git a/comfy_api_nodes/apis/client.py b/comfy_api_nodes/apis/client.py new file mode 100644 index 000000000..bdaddcc88 --- /dev/null +++ b/comfy_api_nodes/apis/client.py @@ -0,0 +1,981 @@ +""" +API Client Framework for api.comfy.org. + +This module provides a flexible framework for making API requests from ComfyUI nodes. +It supports both synchronous and asynchronous API operations with proper type validation. + +Key Components: +-------------- +1. ApiClient - Handles HTTP requests with authentication and error handling +2. ApiEndpoint - Defines a single HTTP endpoint with its request/response models +3. ApiOperation - Executes a single synchronous API operation + +Usage Examples: +-------------- + +# Example 1: Synchronous API Operation +# ------------------------------------ +# For a simple API call that returns the result immediately: + +# 1. Create the API client +api_client = ApiClient( + base_url="https://api.example.com", + auth_token="your_auth_token_here", + comfy_api_key="your_comfy_api_key_here", + timeout=30.0, + verify_ssl=True +) + +# 2. Define the endpoint +user_info_endpoint = ApiEndpoint( + path="/v1/users/me", + method=HttpMethod.GET, + request_model=EmptyRequest, # No request body needed + response_model=UserProfile, # Pydantic model for the response + query_params=None +) + +# 3. Create the request object +request = EmptyRequest() + +# 4. Create and execute the operation +operation = ApiOperation( + endpoint=user_info_endpoint, + request=request +) +user_profile = await operation.execute(client=api_client) # Returns immediately with the result + + +# Example 2: Asynchronous API Operation with Polling +# ------------------------------------------------- +# For an API that starts a task and requires polling for completion: + +# 1. Define the endpoints (initial request and polling) +generate_image_endpoint = ApiEndpoint( + path="/v1/images/generate", + method=HttpMethod.POST, + request_model=ImageGenerationRequest, + response_model=TaskCreatedResponse, + query_params=None +) + +check_task_endpoint = ApiEndpoint( + path="/v1/tasks/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=ImageGenerationResult, + query_params=None +) + +# 2. Create the request object +request = ImageGenerationRequest( + prompt="a beautiful sunset over mountains", + width=1024, + height=1024, + num_images=1 +) + +# 3. Create and execute the polling operation +operation = PollingOperation( + initial_endpoint=generate_image_endpoint, + initial_request=request, + poll_endpoint=check_task_endpoint, + task_id_field="task_id", + status_field="status", + completed_statuses=["completed"], + failed_statuses=["failed", "error"] +) + +# This will make the initial request and then poll until completion +result = await operation.execute(client=api_client) # Returns the final ImageGenerationResult when done +""" + +from __future__ import annotations +import aiohttp +import asyncio +import logging +import io +import os +import socket +from aiohttp.client_exceptions import ClientError, ClientResponseError +from typing import Type, Optional, Any, TypeVar, Generic, Callable +from enum import Enum +import json +from urllib.parse import urljoin, urlparse +from pydantic import BaseModel, Field +import uuid # For generating unique operation IDs + +from server import PromptServer +from comfy.cli_args import args +from comfy import utils +from . import request_logger + +T = TypeVar("T", bound=BaseModel) +R = TypeVar("R", bound=BaseModel) +P = TypeVar("P", bound=BaseModel) # For poll response + +PROGRESS_BAR_MAX = 100 + + +class NetworkError(Exception): + """Base exception for network-related errors with diagnostic information.""" + pass + + +class LocalNetworkError(NetworkError): + """Exception raised when local network connectivity issues are detected.""" + pass + + +class ApiServerError(NetworkError): + """Exception raised when the API server is unreachable but internet is working.""" + pass + + +class EmptyRequest(BaseModel): + """Base class for empty request bodies. + For GET requests, fields will be sent as query parameters.""" + + pass + + +class UploadRequest(BaseModel): + file_name: str = Field(..., description="Filename to upload") + content_type: Optional[str] = Field( + None, + description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", + ) + + +class UploadResponse(BaseModel): + download_url: str = Field(..., description="URL to GET uploaded file") + upload_url: str = Field(..., description="URL to PUT file to upload") + + +class HttpMethod(str, Enum): + GET = "GET" + POST = "POST" + PUT = "PUT" + DELETE = "DELETE" + PATCH = "PATCH" + + +class ApiClient: + """ + Client for making HTTP requests to an API with authentication, error handling, and retry logic. + """ + + def __init__( + self, + base_url: str, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + timeout: float = 3600.0, + verify_ssl: bool = True, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + retry_status_codes: Optional[tuple[int, ...]] = None, + session: Optional[aiohttp.ClientSession] = None, + ): + self.base_url = base_url + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + self.timeout = timeout + self.verify_ssl = verify_ssl + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + # Default retry status codes: 408 (Request Timeout), 429 (Too Many Requests), + # 500, 502, 503, 504 (Server Errors) + self.retry_status_codes = retry_status_codes or (408, 429, 500, 502, 503, 504) + self._session: Optional[aiohttp.ClientSession] = session + self._owns_session = session is None # Track if we have to close it + + @staticmethod + def _generate_operation_id(path: str) -> str: + """Generates a unique operation ID for logging.""" + return f"{path.strip('/').replace('/', '_')}_{uuid.uuid4().hex[:8]}" + + @staticmethod + def _create_json_payload_args( + data: Optional[dict[str, Any]] = None, + headers: Optional[dict[str, str]] = None, + ) -> dict[str, Any]: + return { + "json": data, + "headers": headers, + } + + def _create_form_data_args( + self, + data: dict[str, Any] | None, + files: dict[str, Any] | None, + headers: Optional[dict[str, str]] = None, + multipart_parser: Callable | None = None, + ) -> dict[str, Any]: + if headers and "Content-Type" in headers: + del headers["Content-Type"] + + if multipart_parser and data: + data = multipart_parser(data) + + if isinstance(data, aiohttp.FormData): + form = data # If the parser already returned a FormData, pass it through + else: + form = aiohttp.FormData(default_to_multipart=True) + if data: # regular text fields + for k, v in data.items(): + if v is None: + continue # aiohttp fails to serialize "None" values + # aiohttp expects strings or bytes; convert enums etc. + form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v) + + if files: + file_iter = files if isinstance(files, list) else files.items() + for field_name, file_obj in file_iter: + if file_obj is None: + continue # aiohttp fails to serialize "None" values + # file_obj can be (filename, bytes/io.BytesIO, content_type) tuple + if isinstance(file_obj, tuple): + filename, file_value, content_type = self._unpack_tuple(file_obj) + else: + file_value = file_obj + filename = getattr(file_obj, "name", field_name) + content_type = "application/octet-stream" + + form.add_field( + name=field_name, + value=file_value, + filename=filename, + content_type=content_type, + ) + return {"data": form, "headers": headers or {}} + + @staticmethod + def _create_urlencoded_form_data_args( + data: dict[str, Any], + headers: Optional[dict[str, str]] = None, + ) -> dict[str, Any]: + headers = headers or {} + headers["Content-Type"] = "application/x-www-form-urlencoded" + return { + "data": data, + "headers": headers, + } + + def get_headers(self) -> dict[str, str]: + """Get headers for API requests, including authentication if available""" + headers = {"Content-Type": "application/json", "Accept": "application/json"} + + if self.auth_token: + headers["Authorization"] = f"Bearer {self.auth_token}" + elif self.comfy_api_key: + headers["X-API-KEY"] = self.comfy_api_key + + return headers + + async def _check_connectivity(self, target_url: str) -> dict[str, bool]: + """ + Check connectivity to determine if network issues are local or server-related. + + Args: + target_url: URL to check connectivity to + + Returns: + Dictionary with connectivity status details + """ + results = { + "internet_accessible": False, + "api_accessible": False, + "is_local_issue": False, + "is_api_issue": False, + } + timeout = aiohttp.ClientTimeout(total=5.0) + async with aiohttp.ClientSession(timeout=timeout) as session: + try: + async with session.get("https://www.google.com", ssl=self.verify_ssl) as resp: + results["internet_accessible"] = resp.status < 500 + except (ClientError, asyncio.TimeoutError, socket.gaierror): + results["is_local_issue"] = True + return results # cannot reach the internet – early exit + + # Now check API health endpoint + parsed = urlparse(target_url) + health_url = f"{parsed.scheme}://{parsed.netloc}/health" + try: + async with session.get(health_url, ssl=self.verify_ssl) as resp: + results["api_accessible"] = resp.status < 500 + except ClientError: + pass # leave as False + + results["is_api_issue"] = results["internet_accessible"] and not results["api_accessible"] + return results + + async def request( + self, + method: str, + path: str, + params: Optional[dict[str, Any]] = None, + data: Optional[dict[str, Any]] = None, + files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None, + headers: Optional[dict[str, str]] = None, + content_type: str = "application/json", + multipart_parser: Callable | None = None, + retry_count: int = 0, # Used internally for tracking retries + ) -> dict[str, Any]: + """ + Make an HTTP request to the API with automatic retries for transient errors. + + Args: + method: HTTP method (GET, POST, etc.) + path: API endpoint path (will be joined with base_url) + params: Query parameters + data: body data + files: Files to upload + headers: Additional headers + content_type: Content type of the request. Defaults to application/json. + retry_count: Internal parameter for tracking retries, do not set manually + + Returns: + Parsed JSON response + + Raises: + LocalNetworkError: If local network connectivity issues are detected + ApiServerError: If the API server is unreachable but internet is working + Exception: For other request failures + """ + + # Build full URL and merge headers + relative_path = path.lstrip("/") + url = urljoin(self.base_url, relative_path) + self._check_auth(self.auth_token, self.comfy_api_key) + + request_headers = self.get_headers() + if headers: + request_headers.update(headers) + if files: + request_headers.pop("Content-Type", None) + if params: + params = {k: v for k, v in params.items() if v is not None} # aiohttp fails to serialize None values + + logging.debug("[DEBUG] Request Headers: %s", request_headers) + logging.debug("[DEBUG] Files: %s", files) + logging.debug("[DEBUG] Params: %s", params) + logging.debug("[DEBUG] Data: %s", data) + + if content_type == "application/x-www-form-urlencoded": + payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers) + elif content_type == "multipart/form-data": + payload_args = self._create_form_data_args(data, files, request_headers, multipart_parser) + else: + payload_args = self._create_json_payload_args(data, request_headers) + + operation_id = self._generate_operation_id(path) + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=request_headers, + request_params=params, + request_data=data if content_type == "application/json" else "[form-data or other]", + ) + + session = await self._get_session() + try: + async with session.request( + method, + url, + params=params, + ssl=self.verify_ssl, + **payload_args, + ) as resp: + if resp.status >= 400: + try: + error_data = await resp.json() + except (aiohttp.ContentTypeError, json.JSONDecodeError): + error_data = await resp.text() + + return await self._handle_http_error( + ClientResponseError(resp.request_info, resp.history, status=resp.status, message=error_data), + operation_id, + method, + url, + params, + data, + files, + headers, + content_type, + multipart_parser, + retry_count=retry_count, + response_content=error_data, + ) + + # Success – parse JSON (safely) and log + try: + payload = await resp.json() + response_content_to_log = payload + except (aiohttp.ContentTypeError, json.JSONDecodeError): + payload = {} + response_content_to_log = await resp.text() + + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=response_content_to_log, + ) + return payload + + except (ClientError, asyncio.TimeoutError, socket.gaierror) as e: + # Treat as *connection* problem – optionally retry, else escalate + if retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning("Connection error. Retrying in %.2fs (%s/%s): %s", delay, retry_count + 1, + self.max_retries, str(e)) + await asyncio.sleep(delay) + return await self.request( + method, + path, + params=params, + data=data, + files=files, + headers=headers, + content_type=content_type, + multipart_parser=multipart_parser, + retry_count=retry_count + 1, + ) + # One final connectivity check for diagnostics + connectivity = await self._check_connectivity(self.base_url) + if connectivity["is_local_issue"]: + raise LocalNetworkError( + "Unable to connect to the API server due to local network issues. " + "Please check your internet connection and try again." + ) from e + raise ApiServerError( + f"The API server at {self.base_url} is currently unreachable. " + f"The service may be experiencing issues. Please try again later." + ) from e + + @staticmethod + def _check_auth(auth_token, comfy_api_key): + """Verify that an auth token is present or comfy_api_key is present""" + if auth_token is None and comfy_api_key is None: + raise Exception("Unauthorized: Please login first to use this node.") + return auth_token or comfy_api_key + + @staticmethod + async def upload_file( + upload_url: str, + file: io.BytesIO | str, + content_type: str | None = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + ) -> aiohttp.ClientResponse: + """Upload a file to the API with retry logic. + + Args: + upload_url: The URL to upload to + file: Either a file path string, BytesIO object, or tuple of (file_path, filename) + content_type: Optional mime type to set for the upload + max_retries: Maximum number of retry attempts + retry_delay: Initial delay between retries in seconds + retry_backoff_factor: Multiplier for the delay after each retry + """ + headers: dict[str, str] = {} + skip_auto_headers: set[str] = set() + if content_type: + headers["Content-Type"] = content_type + else: + # tell aiohttp not to add Content-Type that will break the request signature and result in a 403 status. + skip_auto_headers.add("Content-Type") + + # Extract file bytes + if isinstance(file, io.BytesIO): + file.seek(0) + data = file.read() + elif isinstance(file, str): + with open(file, "rb") as f: + data = f.read() + else: + raise ValueError("File must be BytesIO or str path") + + parsed = urlparse(upload_url) + basename = os.path.basename(parsed.path) or parsed.netloc or "upload" + operation_id = f"upload_{basename}_{uuid.uuid4().hex[:8]}" + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + request_headers=headers, + request_data=f"[File data {len(data)} bytes]", + ) + + delay = retry_delay + for attempt in range(max_retries + 1): + try: + timeout = aiohttp.ClientTimeout(total=None) # honour server side timeouts + async with aiohttp.ClientSession(timeout=timeout) as session: + async with session.put( + upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers, + ) as resp: + resp.raise_for_status() + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content="File uploaded successfully.", + ) + return resp + except (ClientError, asyncio.TimeoutError) as e: + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + response_status_code=e.status if hasattr(e, "status") else None, + response_headers=dict(e.headers) if hasattr(e, "headers") else None, + response_content=None, + error_message=f"{type(e).__name__}: {str(e)}", + ) + if attempt < max_retries: + logging.warning( + "Upload failed (%s/%s). Retrying in %.2fs. %s", attempt + 1, max_retries, delay, str(e) + ) + await asyncio.sleep(delay) + delay *= retry_backoff_factor + else: + raise NetworkError(f"Failed to upload file after {max_retries + 1} attempts: {e}") from e + + async def _handle_http_error( + self, + exc: ClientResponseError, + operation_id: str, + *req_meta, + retry_count: int, + response_content: dict | str = "", + ) -> dict[str, Any]: + status_code = exc.status + if status_code == 401: + user_friendly = "Unauthorized: Please login first to use this node." + elif status_code == 402: + user_friendly = "Payment Required: Please add credits to your account to use this node." + elif status_code == 409: + user_friendly = "There is a problem with your account. Please contact support@comfy.org." + elif status_code == 429: + user_friendly = "Rate Limit Exceeded: Please try again later." + else: + if isinstance(response_content, dict): + if "error" in response_content and "message" in response_content["error"]: + user_friendly = f"API Error: {response_content['error']['message']}" + if "type" in response_content["error"]: + user_friendly += f" (Type: {response_content['error']['type']})" + else: # Handle cases where error is just a JSON dict with unknown format + user_friendly = f"API Error: {json.dumps(response_content)}" + else: + if len(response_content) < 200: # Arbitrary limit for display + user_friendly = f"API Error (raw): {response_content}" + else: + user_friendly = f"API Error (raw, status {response_content})" + + request_logger.log_request_response( + operation_id=operation_id, + request_method=req_meta[0], + request_url=req_meta[1], + response_status_code=exc.status, + response_headers=dict(req_meta[5]) if req_meta[5] else None, + response_content=response_content, + error_message=f"HTTP Error {exc.status}", + ) + + logging.debug("[DEBUG] API Error: %s (Status: %s)", user_friendly, status_code) + if response_content: + logging.debug("[DEBUG] Response content: %s", response_content) + + # Retry if eligible + if status_code in self.retry_status_codes and retry_count < self.max_retries: + delay = self.retry_delay * (self.retry_backoff_factor ** retry_count) + logging.warning( + "HTTP error %s. Retrying in %.2fs (%s/%s)", + status_code, + delay, + retry_count + 1, + self.max_retries, + ) + await asyncio.sleep(delay) + return await self.request( + req_meta[0], # method + req_meta[1].replace(self.base_url, ""), # path + params=req_meta[2], + data=req_meta[3], + files=req_meta[4], + headers=req_meta[5], + content_type=req_meta[6], + multipart_parser=req_meta[7], + retry_count=retry_count + 1, + ) + + raise Exception(user_friendly) from exc + + @staticmethod + def _unpack_tuple(t): + """Helper to normalise (filename, file, content_type) tuples.""" + if len(t) == 3: + return t + elif len(t) == 2: + return t[0], t[1], "application/octet-stream" + else: + raise ValueError("files tuple must be (filename, file[, content_type])") + + async def _get_session(self) -> aiohttp.ClientSession: + if self._session is None or self._session.closed: + timeout = aiohttp.ClientTimeout(total=self.timeout) + self._session = aiohttp.ClientSession(timeout=timeout) + self._owns_session = True + return self._session + + async def close(self) -> None: + if self._owns_session and self._session and not self._session.closed: + await self._session.close() + + async def __aenter__(self) -> "ApiClient": + """Allow usage as async‑context‑manager – ensures clean teardown""" + return self + + async def __aexit__(self, exc_type, exc, tb): + await self.close() + + +class ApiEndpoint(Generic[T, R]): + """Defines an API endpoint with its request and response types""" + + def __init__( + self, + path: str, + method: HttpMethod, + request_model: Type[T], + response_model: Type[R], + query_params: Optional[dict[str, Any]] = None, + ): + """Initialize an API endpoint definition. + + Args: + path: The URL path for this endpoint, can include placeholders like {id} + method: The HTTP method to use (GET, POST, etc.) + request_model: Pydantic model class that defines the structure and validation rules for API requests to this endpoint + response_model: Pydantic model class that defines the structure and validation rules for API responses from this endpoint + query_params: Optional dictionary of query parameters to include in the request + """ + self.path = path + self.method = method + self.request_model = request_model + self.response_model = response_model + self.query_params = query_params or {} + + +class SynchronousOperation(Generic[T, R]): + """Represents a single synchronous API operation.""" + + def __init__( + self, + endpoint: ApiEndpoint[T, R], + request: T, + files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None, + api_base: str | None = None, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[dict[str, str]] = None, + timeout: float = 7200.0, + verify_ssl: bool = True, + content_type: str = "application/json", + multipart_parser: Callable | None = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + ) -> None: + self.endpoint = endpoint + self.request = request + self.files = files + self.api_base: str = api_base or args.comfy_api_base + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) + self.timeout = timeout + self.verify_ssl = verify_ssl + self.content_type = content_type + self.multipart_parser = multipart_parser + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + + async def execute(self, client: Optional[ApiClient] = None) -> R: + owns_client = client is None + if owns_client: + client = ApiClient( + base_url=self.api_base, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, + timeout=self.timeout, + verify_ssl=self.verify_ssl, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, + ) + + try: + request_dict: Optional[dict[str, Any]] + if isinstance(self.request, EmptyRequest): + request_dict = None + else: + request_dict = self.request.model_dump(exclude_none=True) + for k, v in list(request_dict.items()): + if isinstance(v, Enum): + request_dict[k] = v.value + + logging.debug("[DEBUG] API Request: %s %s", self.endpoint.method.value, self.endpoint.path) + logging.debug("[DEBUG] Request Data: %s", json.dumps(request_dict, indent=2)) + logging.debug("[DEBUG] Query Params: %s", self.endpoint.query_params) + + response_json = await client.request( + self.endpoint.method.value, + self.endpoint.path, + params=self.endpoint.query_params, + data=request_dict, + files=self.files, + content_type=self.content_type, + multipart_parser=self.multipart_parser, + ) + + logging.debug("=" * 50) + logging.debug("[DEBUG] RESPONSE DETAILS:") + logging.debug("[DEBUG] Status Code: 200 (Success)") + logging.debug("[DEBUG] Response Body: %s", json.dumps(response_json, indent=2)) + logging.debug("=" * 50) + + parsed_response = self.endpoint.response_model.model_validate(response_json) + logging.debug("[DEBUG] Parsed Response: %s", parsed_response) + return parsed_response + finally: + if owns_client: + await client.close() + + +class TaskStatus(str, Enum): + """Enum for task status values""" + + COMPLETED = "completed" + FAILED = "failed" + PENDING = "pending" + + +class PollingOperation(Generic[T, R]): + """Represents an asynchronous API operation that requires polling for completion.""" + + def __init__( + self, + poll_endpoint: ApiEndpoint[EmptyRequest, R], + completed_statuses: list[str], + failed_statuses: list[str], + *, + status_extractor: Callable[[R], Optional[str]], + progress_extractor: Callable[[R], Optional[float]] | None = None, + result_url_extractor: Callable[[R], Optional[str]] | None = None, + price_extractor: Callable[[R], Optional[float]] | None = None, + request: Optional[T] = None, + api_base: str | None = None, + auth_token: Optional[str] = None, + comfy_api_key: Optional[str] = None, + auth_kwargs: Optional[dict[str, str]] = None, + poll_interval: float = 5.0, + max_poll_attempts: int = 120, # Default max polling attempts (10 minutes with 5s interval) + max_retries: int = 3, # Max retries per individual API call + retry_delay: float = 1.0, + retry_backoff_factor: float = 2.0, + estimated_duration: Optional[float] = None, + node_id: Optional[str] = None, + ) -> None: + self.poll_endpoint = poll_endpoint + self.request = request + self.api_base: str = api_base or args.comfy_api_base + self.auth_token = auth_token + self.comfy_api_key = comfy_api_key + if auth_kwargs is not None: + self.auth_token = auth_kwargs.get("auth_token", self.auth_token) + self.comfy_api_key = auth_kwargs.get("comfy_api_key", self.comfy_api_key) + self.poll_interval = poll_interval + self.max_poll_attempts = max_poll_attempts + self.max_retries = max_retries + self.retry_delay = retry_delay + self.retry_backoff_factor = retry_backoff_factor + self.estimated_duration = estimated_duration + self.status_extractor = status_extractor or (lambda x: getattr(x, "status", None)) + self.progress_extractor = progress_extractor + self.result_url_extractor = result_url_extractor + self.price_extractor = price_extractor + self.node_id = node_id + self.completed_statuses = completed_statuses + self.failed_statuses = failed_statuses + self.final_response: Optional[R] = None + self.extracted_price: Optional[float] = None + + async def execute(self, client: Optional[ApiClient] = None) -> R: + owns_client = client is None + if owns_client: + client = ApiClient( + base_url=self.api_base, + auth_token=self.auth_token, + comfy_api_key=self.comfy_api_key, + max_retries=self.max_retries, + retry_delay=self.retry_delay, + retry_backoff_factor=self.retry_backoff_factor, + ) + try: + return await self._poll_until_complete(client) + finally: + if owns_client: + await client.close() + + def _display_text_on_node(self, text: str): + if not self.node_id: + return + if self.extracted_price is not None: + text = f"Price: ${self.extracted_price}\n{text}" + PromptServer.instance.send_progress_text(text, self.node_id) + + def _display_time_progress_on_node(self, time_completed: int | float): + if not self.node_id: + return + if self.estimated_duration is not None: + remaining = max(0, int(self.estimated_duration) - time_completed) + message = f"Task in progress: {time_completed}s (~{remaining}s remaining)" + else: + message = f"Task in progress: {time_completed}s" + self._display_text_on_node(message) + + def _check_task_status(self, response: R) -> TaskStatus: + try: + status = self.status_extractor(response) + if status in self.completed_statuses: + return TaskStatus.COMPLETED + if status in self.failed_statuses: + return TaskStatus.FAILED + return TaskStatus.PENDING + except Exception as e: + logging.error("Error extracting status: %s", e) + return TaskStatus.PENDING + + async def _poll_until_complete(self, client: ApiClient) -> R: + """Poll until the task is complete""" + consecutive_errors = 0 + max_consecutive_errors = min(5, self.max_retries * 2) # Limit consecutive errors + + if self.progress_extractor: + progress = utils.ProgressBar(PROGRESS_BAR_MAX) + + status = TaskStatus.PENDING + for poll_count in range(1, self.max_poll_attempts + 1): + try: + logging.debug("[DEBUG] Polling attempt #%s", poll_count) + + request_dict = None if self.request is None else self.request.model_dump(exclude_none=True) + + if poll_count == 1: + logging.debug( + "[DEBUG] Poll Request: %s %s", + self.poll_endpoint.method.value, + self.poll_endpoint.path, + ) + logging.debug( + "[DEBUG] Poll Request Data: %s", + json.dumps(request_dict, indent=2) if request_dict else "None", + ) + + # Query task status + resp = await client.request( + self.poll_endpoint.method.value, + self.poll_endpoint.path, + params=self.poll_endpoint.query_params, + data=request_dict, + ) + consecutive_errors = 0 # reset on success + response_obj: R = self.poll_endpoint.response_model.model_validate(resp) + + # Check if task is complete + status = self._check_task_status(response_obj) + logging.debug("[DEBUG] Task Status: %s", status) + + # If progress extractor is provided, extract progress + if self.progress_extractor: + new_progress = self.progress_extractor(response_obj) + if new_progress is not None: + progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX) + + if self.price_extractor: + price = self.price_extractor(response_obj) + if price is not None: + self.extracted_price = price + + if status == TaskStatus.COMPLETED: + message = "Task completed successfully" + if self.result_url_extractor: + result_url = self.result_url_extractor(response_obj) + if result_url: + message = f"Result URL: {result_url}" + logging.debug("[DEBUG] %s", message) + self._display_text_on_node(message) + self.final_response = response_obj + if self.progress_extractor: + progress.update(100) + return self.final_response + if status == TaskStatus.FAILED: + message = f"Task failed: {json.dumps(resp)}" + logging.error("[DEBUG] %s", message) + raise Exception(message) + logging.debug("[DEBUG] Task still pending, continuing to poll...") + # Task pending – wait + for i in range(int(self.poll_interval)): + self._display_time_progress_on_node((poll_count - 1) * self.poll_interval + i) + await asyncio.sleep(1) + + except (LocalNetworkError, ApiServerError, NetworkError) as e: + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors: + raise Exception( + f"Polling aborted after {consecutive_errors} network errors: {str(e)}" + ) from e + logging.warning( + "Network error (%s/%s): %s", + consecutive_errors, + max_consecutive_errors, + str(e), + ) + await asyncio.sleep(self.poll_interval) + except Exception as e: + # For other errors, increment count and potentially abort + consecutive_errors += 1 + if consecutive_errors >= max_consecutive_errors or status == TaskStatus.FAILED: + raise Exception( + f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}" + ) from e + + logging.error("[DEBUG] Polling error: %s", str(e)) + logging.warning( + "Error during polling (attempt %s/%s): %s. Will retry in %s seconds.", + poll_count, + self.max_poll_attempts, + str(e), + self.poll_interval, + ) + await asyncio.sleep(self.poll_interval) + + # If we've exhausted all polling attempts + raise Exception( + f"Polling timed out after {self.max_poll_attempts} attempts (" f"{self.max_poll_attempts * self.poll_interval} seconds). " + "The operation may still be running on the server but is taking longer than expected." + ) diff --git a/comfy_api_nodes/apis/gemini_api.py b/comfy_api_nodes/apis/gemini_api.py new file mode 100644 index 000000000..2bf28bf93 --- /dev/null +++ b/comfy_api_nodes/apis/gemini_api.py @@ -0,0 +1,22 @@ +from typing import Optional + +from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata +from pydantic import BaseModel + + +class GeminiImageConfig(BaseModel): + aspectRatio: Optional[str] = None + + +class GeminiImageGenerationConfig(GeminiGenerationConfig): + responseModalities: Optional[list[str]] = None + imageConfig: Optional[GeminiImageConfig] = None + + +class GeminiImageGenerateContentRequest(BaseModel): + contents: list[GeminiContent] + generationConfig: Optional[GeminiImageGenerationConfig] = None + safetySettings: Optional[list[GeminiSafetySetting]] = None + systemInstruction: Optional[GeminiSystemInstructionContent] = None + tools: Optional[list[GeminiTool]] = None + videoMetadata: Optional[GeminiVideoMetadata] = None diff --git a/comfy_api_nodes/apis/luma_api.py b/comfy_api_nodes/apis/luma_api.py new file mode 100644 index 000000000..632c4ab96 --- /dev/null +++ b/comfy_api_nodes/apis/luma_api.py @@ -0,0 +1,253 @@ +from __future__ import annotations + + +import torch + +from enum import Enum +from typing import Optional, Union + +from pydantic import BaseModel, Field, confloat + + + +class LumaIO: + LUMA_REF = "LUMA_REF" + LUMA_CONCEPTS = "LUMA_CONCEPTS" + + +class LumaReference: + def __init__(self, image: torch.Tensor, weight: float): + self.image = image + self.weight = weight + + def create_api_model(self, download_url: str): + return LumaImageRef(url=download_url, weight=self.weight) + +class LumaReferenceChain: + def __init__(self, first_ref: LumaReference=None): + self.refs: list[LumaReference] = [] + if first_ref: + self.refs.append(first_ref) + + def add(self, luma_ref: LumaReference=None): + self.refs.append(luma_ref) + + def create_api_model(self, download_urls: list[str], max_refs=4): + if len(self.refs) == 0: + return None + api_refs: list[LumaImageRef] = [] + for ref, url in zip(self.refs, download_urls): + api_ref = LumaImageRef(url=url, weight=ref.weight) + api_refs.append(api_ref) + return api_refs + + def clone(self): + c = LumaReferenceChain() + for ref in self.refs: + c.add(ref) + return c + + +class LumaConcept: + def __init__(self, key: str): + self.key = key + + +class LumaConceptChain: + def __init__(self, str_list: list[str] = None): + self.concepts: list[LumaConcept] = [] + if str_list is not None: + for c in str_list: + if c != "None": + self.add(LumaConcept(key=c)) + + def add(self, concept: LumaConcept): + self.concepts.append(concept) + + def create_api_model(self): + if len(self.concepts) == 0: + return None + api_concepts: list[LumaConceptObject] = [] + for concept in self.concepts: + if concept.key == "None": + continue + api_concepts.append(LumaConceptObject(key=concept.key)) + if len(api_concepts) == 0: + return None + return api_concepts + + def clone(self): + c = LumaConceptChain() + for concept in self.concepts: + c.add(concept) + return c + + def clone_and_merge(self, other: LumaConceptChain): + c = self.clone() + for concept in other.concepts: + c.add(concept) + return c + + +def get_luma_concepts(include_none=False): + concepts = [] + if include_none: + concepts.append("None") + return concepts + [ + "truck_left", + "pan_right", + "pedestal_down", + "low_angle", + "pedestal_up", + "selfie", + "pan_left", + "roll_right", + "zoom_in", + "over_the_shoulder", + "orbit_right", + "orbit_left", + "static", + "tiny_planet", + "high_angle", + "bolt_cam", + "dolly_zoom", + "overhead", + "zoom_out", + "handheld", + "roll_left", + "pov", + "aerial_drone", + "push_in", + "crane_down", + "truck_right", + "tilt_down", + "elevator_doors", + "tilt_up", + "ground_level", + "pull_out", + "aerial", + "crane_up", + "eye_level" + ] + + +class LumaImageModel(str, Enum): + photon_1 = "photon-1" + photon_flash_1 = "photon-flash-1" + + +class LumaVideoModel(str, Enum): + ray_2 = "ray-2" + ray_flash_2 = "ray-flash-2" + ray_1_6 = "ray-1-6" + + +class LumaAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_4_3 = "4:3" + ratio_3_4 = "3:4" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +class LumaVideoOutputResolution(str, Enum): + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + res_4k = "4k" + + +class LumaVideoModelOutputDuration(str, Enum): + dur_5s = "5s" + dur_9s = "9s" + + +class LumaGenerationType(str, Enum): + video = 'video' + image = 'image' + + +class LumaState(str, Enum): + queued = "queued" + dreaming = "dreaming" + completed = "completed" + failed = "failed" + + +class LumaAssets(BaseModel): + video: Optional[str] = Field(None, description='The URL of the video') + image: Optional[str] = Field(None, description='The URL of the image') + progress_video: Optional[str] = Field(None, description='The URL of the progress video') + + +class LumaImageRef(BaseModel): + '''Used for image gen''' + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaImageReference(BaseModel): + '''Used for video gen''' + type: Optional[str] = Field('image', description='Input type, defaults to image') + url: str = Field(..., description='The URL of the image') + + +class LumaModifyImageRef(BaseModel): + url: str = Field(..., description='The URL of the image reference') + weight: confloat(ge=0.0, le=1.0) = Field(..., description='The weight of the image reference') + + +class LumaCharacterRef(BaseModel): + identity0: LumaImageIdentity = Field(..., description='The image identity object') + + +class LumaImageIdentity(BaseModel): + images: list[str] = Field(..., description='The URLs of the image identity') + + +class LumaGenerationReference(BaseModel): + type: str = Field('generation', description='Input type, defaults to generation') + id: str = Field(..., description='The ID of the generation') + + +class LumaKeyframes(BaseModel): + frame0: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + frame1: Optional[Union[LumaImageReference, LumaGenerationReference]] = Field(None, description='') + + +class LumaConceptObject(BaseModel): + key: str = Field(..., description='Camera Concept name') + + +class LumaImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaImageModel = Field(LumaImageModel.photon_1, description='The image model used for the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(LumaAspectRatio.ratio_16_9, description='The aspect ratio of the generation') + image_ref: Optional[list[LumaImageRef]] = Field(None, description='List of image reference objects') + style_ref: Optional[list[LumaImageRef]] = Field(None, description='List of style reference objects') + character_ref: Optional[LumaCharacterRef] = Field(None, description='The image identity object') + modify_image_ref: Optional[LumaModifyImageRef] = Field(None, description='The modify image reference object') + + +class LumaGenerationRequest(BaseModel): + prompt: str = Field(..., description='The prompt of the generation') + model: LumaVideoModel = Field(LumaVideoModel.ray_2, description='The video model used for the generation') + duration: Optional[LumaVideoModelOutputDuration] = Field(None, description='The duration of the generation') + aspect_ratio: Optional[LumaAspectRatio] = Field(None, description='The aspect ratio of the generation') + resolution: Optional[LumaVideoOutputResolution] = Field(None, description='The resolution of the generation') + loop: Optional[bool] = Field(None, description='Whether to loop the video') + keyframes: Optional[LumaKeyframes] = Field(None, description='The keyframes of the generation') + concepts: Optional[list[LumaConceptObject]] = Field(None, description='Camera Concepts to apply to generation') + + +class LumaGeneration(BaseModel): + id: str = Field(..., description='The ID of the generation') + generation_type: LumaGenerationType = Field(..., description='Generation type, image or video') + state: LumaState = Field(..., description='The state of the generation') + failure_reason: Optional[str] = Field(None, description='The reason for the state of the generation') + created_at: str = Field(..., description='The date and time when the generation was created') + assets: Optional[LumaAssets] = Field(None, description='The assets of the generation') + model: str = Field(..., description='The model used for the generation') + request: Union[LumaGenerationRequest, LumaImageGenerationRequest] = Field(..., description="The request used for the generation") diff --git a/comfy_api_nodes/apis/pika_defs.py b/comfy_api_nodes/apis/pika_defs.py new file mode 100644 index 000000000..232558cd7 --- /dev/null +++ b/comfy_api_nodes/apis/pika_defs.py @@ -0,0 +1,100 @@ +from typing import Optional +from enum import Enum +from pydantic import BaseModel, Field + + +class Pikaffect(str, Enum): + Cake_ify = "Cake-ify" + Crumble = "Crumble" + Crush = "Crush" + Decapitate = "Decapitate" + Deflate = "Deflate" + Dissolve = "Dissolve" + Explode = "Explode" + Eye_pop = "Eye-pop" + Inflate = "Inflate" + Levitate = "Levitate" + Melt = "Melt" + Peel = "Peel" + Poke = "Poke" + Squish = "Squish" + Ta_da = "Ta-da" + Tear = "Tear" + + +class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel): + aspectRatio: Optional[float] = Field(None, description='Aspect ratio (width / height)') + duration: Optional[int] = Field(5) + ingredientsMode: str = Field(...) + negativePrompt: Optional[str] = Field(None) + promptText: Optional[str] = Field(None) + resolution: Optional[str] = Field('1080p') + seed: Optional[int] = Field(None) + + +class PikaGenerateResponse(BaseModel): + video_id: str = Field(...) + + +class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel): + duration: Optional[int] = 5 + negativePrompt: Optional[str] = Field(None) + promptText: Optional[str] = Field(None) + resolution: Optional[str] = '1080p' + seed: Optional[int] = Field(None) + + +class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel): + duration: Optional[int] = Field(None, ge=5, le=10) + negativePrompt: Optional[str] = Field(None) + promptText: str = Field(...) + resolution: Optional[str] = '1080p' + seed: Optional[int] = Field(None) + + +class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel): + aspectRatio: Optional[float] = Field( + 1.7777777777777777, + description='Aspect ratio (width / height)', + ge=0.4, + le=2.5, + ) + duration: Optional[int] = 5 + negativePrompt: Optional[str] = Field(None) + promptText: str = Field(...) + resolution: Optional[str] = '1080p' + seed: Optional[int] = Field(None) + + +class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel): + negativePrompt: Optional[str] = Field(None) + promptText: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + + +class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel): + negativePrompt: Optional[str] = Field(None) + pikaffect: Optional[str] = None + promptText: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + + +class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel): + negativePrompt: Optional[str] = Field(None) + promptText: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + modifyRegionRoi: Optional[str] = Field(None) + + +class PikaStatusEnum(str, Enum): + queued = "queued" + started = "started" + finished = "finished" + failed = "failed" + + +class PikaVideoResponse(BaseModel): + id: str = Field(...) + progress: Optional[int] = Field(None) + status: PikaStatusEnum + url: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/pixverse_api.py b/comfy_api_nodes/apis/pixverse_api.py new file mode 100644 index 000000000..9bb29c383 --- /dev/null +++ b/comfy_api_nodes/apis/pixverse_api.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field + + +pixverse_templates = { + "Microwave": 324641385496960, + "Suit Swagger": 328545151283968, + "Anything, Robot": 313358700761536, + "Subject 3 Fever": 327828816843648, + "kiss kiss": 315446315336768, +} + + +class PixverseIO: + TEMPLATE = "PIXVERSE_TEMPLATE" + + +class PixverseStatus(int, Enum): + successful = 1 + generating = 5 + deleted = 6 + contents_moderation = 7 + failed = 8 + + +class PixverseAspectRatio(str, Enum): + ratio_16_9 = "16:9" + ratio_4_3 = "4:3" + ratio_1_1 = "1:1" + ratio_3_4 = "3:4" + ratio_9_16 = "9:16" + + +class PixverseQuality(str, Enum): + res_360p = "360p" + res_540p = "540p" + res_720p = "720p" + res_1080p = "1080p" + + +class PixverseDuration(int, Enum): + dur_5 = 5 + dur_8 = 8 + + +class PixverseMotionMode(str, Enum): + normal = "normal" + fast = "fast" + + +class PixverseStyle(str, Enum): + anime = "anime" + animation_3d = "3d_animation" + clay = "clay" + comic = "comic" + cyberpunk = "cyberpunk" + + +# NOTE: forgoing descriptions for now in return for dev speed +class PixverseTextVideoRequest(BaseModel): + aspect_ratio: PixverseAspectRatio = Field(...) + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseImageVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + img_id: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + style: Optional[str] = Field(None) + template_id: Optional[int] = Field(None) + water_mark: Optional[bool] = Field(None) + + +class PixverseTransitionVideoRequest(BaseModel): + quality: PixverseQuality = Field(...) + duration: PixverseDuration = Field(...) + first_frame_img: int = Field(...) + last_frame_img: int = Field(...) + model: Optional[str] = Field("v3.5") + motion_mode: Optional[PixverseMotionMode] = Field(PixverseMotionMode.normal) + prompt: str = Field(...) + # negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + # style: Optional[str] = Field(None) + # template_id: Optional[int] = Field(None) + # water_mark: Optional[bool] = Field(None) + + +class PixverseImageUploadResponse(BaseModel): + ErrCode: Optional[int] = None + ErrMsg: Optional[str] = None + Resp: Optional[PixverseImgIdResponseObject] = Field(None, alias='Resp') + + +class PixverseImgIdResponseObject(BaseModel): + img_id: Optional[int] = None + + +class PixverseVideoResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseVideoIdResponseObject] = Field(None) + + +class PixverseVideoIdResponseObject(BaseModel): + video_id: int = Field(..., description='Video_id') + + +class PixverseGenerationStatusResponse(BaseModel): + ErrCode: Optional[int] = Field(None) + ErrMsg: Optional[str] = Field(None) + Resp: Optional[PixverseGenerationStatusResponseObject] = Field(None) + + +class PixverseGenerationStatusResponseObject(BaseModel): + create_time: Optional[str] = Field(None) + id: Optional[int] = Field(None) + modify_time: Optional[str] = Field(None) + negative_prompt: Optional[str] = Field(None) + outputHeight: Optional[int] = Field(None) + outputWidth: Optional[int] = Field(None) + prompt: Optional[str] = Field(None) + resolution_ratio: Optional[int] = Field(None) + seed: Optional[int] = Field(None) + size: Optional[int] = Field(None) + status: Optional[int] = Field(None) + style: Optional[str] = Field(None) + url: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/recraft_api.py b/comfy_api_nodes/apis/recraft_api.py new file mode 100644 index 000000000..c36d95f24 --- /dev/null +++ b/comfy_api_nodes/apis/recraft_api.py @@ -0,0 +1,262 @@ +from __future__ import annotations + + + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, conint, confloat + + +class RecraftColor: + def __init__(self, r: int, g: int, b: int): + self.color = [r, g, b] + + def create_api_model(self): + return RecraftColorObject(rgb=self.color) + + +class RecraftColorChain: + def __init__(self): + self.colors: list[RecraftColor] = [] + + def get_first(self): + if len(self.colors) > 0: + return self.colors[0] + return None + + def add(self, color: RecraftColor): + self.colors.append(color) + + def create_api_model(self): + if not self.colors: + return None + colors_api = [x.create_api_model() for x in self.colors] + return colors_api + + def clone(self): + c = RecraftColorChain() + for color in self.colors: + c.add(color) + return c + + def clone_and_merge(self, other: RecraftColorChain): + c = self.clone() + for color in other.colors: + c.add(color) + return c + + +class RecraftControls: + def __init__(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None, + artistic_level: int=None, no_text: bool=None): + self.colors = colors + self.background_color = background_color + self.artistic_level = artistic_level + self.no_text = no_text + + def create_api_model(self): + if self.colors is None and self.background_color is None and self.artistic_level is None and self.no_text is None: + return None + colors_api = None + background_color_api = None + if self.colors: + colors_api = self.colors.create_api_model() + if self.background_color: + first_background = self.background_color.get_first() + background_color_api = first_background.create_api_model() if first_background else None + + return RecraftControlsObject(colors=colors_api, background_color=background_color_api, + artistic_level=self.artistic_level, no_text=self.no_text) + + +class RecraftStyle: + def __init__(self, style: str=None, substyle: str=None, style_id: str=None): + self.style = style + if substyle == "None": + substyle = None + self.substyle = substyle + self.style_id = style_id + + +class RecraftIO: + STYLEV3 = "RECRAFT_V3_STYLE" + COLOR = "RECRAFT_COLOR" + CONTROLS = "RECRAFT_CONTROLS" + + +class RecraftStyleV3(str, Enum): + #any = 'any' NOTE: this does not work for some reason... why? + realistic_image = 'realistic_image' + digital_illustration = 'digital_illustration' + vector_illustration = 'vector_illustration' + logo_raster = 'logo_raster' + + +def get_v3_substyles(style_v3: str, include_none=True) -> list[str]: + substyles: list[str] = [] + if include_none: + substyles.append("None") + return substyles + dict_recraft_substyles_v3.get(style_v3, []) + + +dict_recraft_substyles_v3 = { + RecraftStyleV3.realistic_image: [ + "b_and_w", + "enterprise", + "evening_light", + "faded_nostalgia", + "forest_life", + "hard_flash", + "hdr", + "motion_blur", + "mystic_naturalism", + "natural_light", + "natural_tones", + "organic_calm", + "real_life_glow", + "retro_realism", + "retro_snapshot", + "studio_portrait", + "urban_drama", + "village_realism", + "warm_folk" + ], + RecraftStyleV3.digital_illustration: [ + "2d_art_poster", + "2d_art_poster_2", + "antiquarian", + "bold_fantasy", + "child_book", + "child_books", + "cover", + "crosshatch", + "digital_engraving", + "engraving_color", + "expressionism", + "freehand_details", + "grain", + "grain_20", + "graphic_intensity", + "hand_drawn", + "hand_drawn_outline", + "handmade_3d", + "hard_comics", + "infantile_sketch", + "long_shadow", + "modern_folk", + "multicolor", + "neon_calm", + "noir", + "nostalgic_pastel", + "outline_details", + "pastel_gradient", + "pastel_sketch", + "pixel_art", + "plastic", + "pop_art", + "pop_renaissance", + "seamless", + "street_art", + "tablet_sketch", + "urban_glow", + "urban_sketching", + "vanilla_dreams", + "young_adult_book", + "young_adult_book_2" + ], + RecraftStyleV3.vector_illustration: [ + "bold_stroke", + "chemistry", + "colored_stencil", + "contour_pop_art", + "cosmics", + "cutout", + "depressive", + "editorial", + "emotional_flat", + "engraving", + "infographical", + "line_art", + "line_circuit", + "linocut", + "marker_outline", + "mosaic", + "naivector", + "roundish_flat", + "seamless", + "segmented_colors", + "sharp_contrast", + "thin", + "vector_photo", + "vivid_shapes" + ], + RecraftStyleV3.logo_raster: [ + "emblem_graffiti", + "emblem_pop_art", + "emblem_punk", + "emblem_stamp", + "emblem_vintage" + ], +} + + +class RecraftModel(str, Enum): + recraftv3 = 'recraftv3' + recraftv2 = 'recraftv2' + + +class RecraftImageSize(str, Enum): + res_1024x1024 = '1024x1024' + res_1365x1024 = '1365x1024' + res_1024x1365 = '1024x1365' + res_1536x1024 = '1536x1024' + res_1024x1536 = '1024x1536' + res_1820x1024 = '1820x1024' + res_1024x1820 = '1024x1820' + res_1024x2048 = '1024x2048' + res_2048x1024 = '2048x1024' + res_1434x1024 = '1434x1024' + res_1024x1434 = '1024x1434' + res_1024x1280 = '1024x1280' + res_1280x1024 = '1280x1024' + res_1024x1707 = '1024x1707' + res_1707x1024 = '1707x1024' + + +class RecraftColorObject(BaseModel): + rgb: list[int] = Field(..., description='An array of 3 integer values in range of 0...255 defining RGB Color Model') + + +class RecraftControlsObject(BaseModel): + colors: Optional[list[RecraftColorObject]] = Field(None, description='An array of preferable colors') + background_color: Optional[RecraftColorObject] = Field(None, description='Use given color as a desired background color') + no_text: Optional[bool] = Field(None, description='Do not embed text layouts') + artistic_level: Optional[conint(ge=0, le=5)] = Field(None, description='Defines artistic tone of your image. At a simple level, the person looks straight at the camera in a static and clean style. Dynamic and eccentric levels introduce movement and creativity. The value should be in range [0..5].') + + +class RecraftImageGenerationRequest(BaseModel): + prompt: str = Field(..., description='The text prompt describing the image to generate') + size: Optional[RecraftImageSize] = Field(None, description='The size of the generated image (e.g., "1024x1024")') + n: conint(ge=1, le=6) = Field(..., description='The number of images to generate') + negative_prompt: Optional[str] = Field(None, description='A text description of undesired elements on an image') + model: Optional[RecraftModel] = Field(RecraftModel.recraftv3, description='The model to use for generation (e.g., "recraftv3")') + style: Optional[str] = Field(None, description='The style to apply to the generated image (e.g., "digital_illustration")') + substyle: Optional[str] = Field(None, description='The substyle to apply to the generated image, depending on the style input') + controls: Optional[RecraftControlsObject] = Field(None, description='A set of custom parameters to tweak generation process') + style_id: Optional[str] = Field(None, description='Use a previously uploaded style as a reference; UUID') + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None, description='Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity') + random_seed: Optional[int] = Field(None, description="Seed for video generation") + # text_layout + + +class RecraftReturnedObject(BaseModel): + image_id: str = Field(..., description='Unique identifier for the generated image') + url: str = Field(..., description='URL to access the generated image') + + +class RecraftImageGenerationResponse(BaseModel): + created: int = Field(..., description='Unix timestamp when the generation was created') + credits: int = Field(..., description='Number of credits used for the generation') + data: Optional[list[RecraftReturnedObject]] = Field(None, description='Array of generated image information') + image: Optional[RecraftReturnedObject] = Field(None, description='Single generated image') diff --git a/comfy_api_nodes/apis/request_logger.py b/comfy_api_nodes/apis/request_logger.py new file mode 100644 index 000000000..c6974d35c --- /dev/null +++ b/comfy_api_nodes/apis/request_logger.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import os +import datetime +import json +import logging +import re +import hashlib +from typing import Any + +import folder_paths + +# Get the logger instance +logger = logging.getLogger(__name__) + + +def get_log_directory(): + """Ensures the API log directory exists within ComfyUI's temp directory and returns its path.""" + base_temp_dir = folder_paths.get_temp_directory() + log_dir = os.path.join(base_temp_dir, "api_logs") + try: + os.makedirs(log_dir, exist_ok=True) + except Exception as e: + logger.error("Error creating API log directory %s: %s", log_dir, str(e)) + # Fallback to base temp directory if sub-directory creation fails + return base_temp_dir + return log_dir + + +def _sanitize_filename_component(name: str) -> str: + if not name: + return "log" + sanitized = re.sub(r"[^A-Za-z0-9._-]+", "_", name) # Replace disallowed characters with underscore + sanitized = sanitized.strip(" ._") # Windows: trailing dots or spaces are not allowed + if not sanitized: + sanitized = "log" + return sanitized + + +def _short_hash(*parts: str, length: int = 10) -> str: + return hashlib.sha1(("|".join(parts)).encode("utf-8")).hexdigest()[:length] + + +def _build_log_filepath(log_dir: str, operation_id: str, request_url: str) -> str: + """Build log filepath. We keep it well under common path length limits aiming for <= 240 characters total.""" + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f") + slug = _sanitize_filename_component(operation_id) # Best-effort human-readable slug from operation_id + h = _short_hash(operation_id or "", request_url or "") # Short hash ties log to the full operation and URL + + # Compute how much room we have for the slug given the directory length + # Keep total path length reasonably below ~260 on Windows. + max_total_path = 240 + prefix = f"{timestamp}_" + suffix = f"_{h}.log" + if not slug: + slug = "op" + max_filename_len = max(60, max_total_path - len(log_dir) - 1) + max_slug_len = max(8, max_filename_len - len(prefix) - len(suffix)) + if len(slug) > max_slug_len: + slug = slug[:max_slug_len].rstrip(" ._-") + return os.path.join(log_dir, f"{prefix}{slug}{suffix}") + + +def _format_data_for_logging(data: Any) -> str: + """Helper to format data (dict, str, bytes) for logging.""" + if isinstance(data, bytes): + try: + return data.decode("utf-8") # Try to decode as text + except UnicodeDecodeError: + return f"[Binary data of length {len(data)} bytes]" + elif isinstance(data, (dict, list)): + try: + return json.dumps(data, indent=2, ensure_ascii=False) + except TypeError: + return str(data) # Fallback for non-serializable objects + return str(data) + + +def log_request_response( + operation_id: str, + request_method: str, + request_url: str, + request_headers: dict | None = None, + request_params: dict | None = None, + request_data: Any = None, + response_status_code: int | None = None, + response_headers: dict | None = None, + response_content: Any = None, + error_message: str | None = None, +): + """ + Logs API request and response details to a file in the temp/api_logs directory. + Filenames are sanitized and length-limited for cross-platform safety. + If we still fail to write, we fall back to appending into api.log. + """ + log_dir = get_log_directory() + filepath = _build_log_filepath(log_dir, operation_id, request_url) + + log_content: list[str] = [] + log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}") + log_content.append(f"Operation ID: {operation_id}") + log_content.append("-" * 30 + " REQUEST " + "-" * 30) + log_content.append(f"Method: {request_method}") + log_content.append(f"URL: {request_url}") + if request_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}") + if request_params: + log_content.append(f"Params:\n{_format_data_for_logging(request_params)}") + if request_data is not None: + log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}") + + log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30) + if response_status_code is not None: + log_content.append(f"Status Code: {response_status_code}") + if response_headers: + log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}") + if response_content is not None: + log_content.append(f"Content:\n{_format_data_for_logging(response_content)}") + if error_message: + log_content.append(f"Error:\n{error_message}") + + try: + with open(filepath, "w", encoding="utf-8") as f: + f.write("\n".join(log_content)) + logger.debug("API log saved to: %s", filepath) + except Exception as e: + logger.error("Error writing API log to %s: %s", filepath, str(e)) + + +if __name__ == '__main__': + # Example usage (for testing the logger directly) + logger.setLevel(logging.DEBUG) + # Mock folder_paths for direct execution if not running within ComfyUI full context + if not hasattr(folder_paths, 'get_temp_directory'): + class MockFolderPaths: + def get_temp_directory(self): + # Create a local temp dir for testing if needed + p = os.path.join(os.path.dirname(__file__), 'temp_test_logs') + os.makedirs(p, exist_ok=True) + return p + folder_paths = MockFolderPaths() + + log_request_response( + operation_id="test_operation_get", + request_method="GET", + request_url="https://api.example.com/test", + request_headers={"Authorization": "Bearer testtoken"}, + request_params={"param1": "value1"}, + response_status_code=200, + response_content={"message": "Success!"} + ) + log_request_response( + operation_id="test_operation_post_error", + request_method="POST", + request_url="https://api.example.com/submit", + request_data={"key": "value", "nested": {"num": 123}}, + error_message="Connection timed out" + ) + log_request_response( + operation_id="test_binary_response", + request_method="GET", + request_url="https://api.example.com/image.png", + response_status_code=200, + response_content=b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR...' # Sample binary data + ) diff --git a/comfy_api_nodes/apis/rodin_api.py b/comfy_api_nodes/apis/rodin_api.py new file mode 100644 index 000000000..fc26a6e73 --- /dev/null +++ b/comfy_api_nodes/apis/rodin_api.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional, List +from pydantic import BaseModel, Field + + +class Rodin3DGenerateRequest(BaseModel): + seed: int = Field(..., description="seed_") + tier: str = Field(..., description="Tier of generation.") + material: str = Field(..., description="The material type.") + quality_override: int = Field(..., description="The poly count of the mesh.") + mesh_mode: str = Field(..., description="It controls the type of faces of generated models.") + TAPose: Optional[bool] = Field(None, description="") + +class GenerateJobsData(BaseModel): + uuids: List[str] = Field(..., description="str LIST") + subscription_key: str = Field(..., description="subscription key") + +class Rodin3DGenerateResponse(BaseModel): + message: Optional[str] = Field(None, description="Return message.") + prompt: Optional[str] = Field(None, description="Generated Prompt from image.") + submit_time: Optional[str] = Field(None, description="Submit Time") + uuid: Optional[str] = Field(None, description="Task str") + jobs: Optional[GenerateJobsData] = Field(None, description="Details of jobs") + +class JobStatus(str, Enum): + """ + Status for jobs + """ + Done = "Done" + Failed = "Failed" + Generating = "Generating" + Waiting = "Waiting" + +class Rodin3DCheckStatusRequest(BaseModel): + subscription_key: str = Field(..., description="subscription from generate endpoint") + +class JobItem(BaseModel): + uuid: str = Field(..., description="uuid") + status: JobStatus = Field(...,description="Status Currently") + +class Rodin3DCheckStatusResponse(BaseModel): + jobs: List[JobItem] = Field(..., description="Job status List") + +class Rodin3DDownloadRequest(BaseModel): + task_uuid: str = Field(..., description="Task str") + +class RodinResourceItem(BaseModel): + url: str = Field(..., description="Download Url") + name: str = Field(..., description="File name with ext") + +class Rodin3DDownloadResponse(BaseModel): + list: List[RodinResourceItem] = Field(..., description="Source List") diff --git a/comfy_api_nodes/apis/stability_api.py b/comfy_api_nodes/apis/stability_api.py new file mode 100644 index 000000000..718360187 --- /dev/null +++ b/comfy_api_nodes/apis/stability_api.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +from enum import Enum +from typing import Optional + +from pydantic import BaseModel, Field, confloat + + +class StabilityFormat(str, Enum): + png = 'png' + jpeg = 'jpeg' + webp = 'webp' + + +class StabilityAspectRatio(str, Enum): + ratio_1_1 = "1:1" + ratio_16_9 = "16:9" + ratio_9_16 = "9:16" + ratio_3_2 = "3:2" + ratio_2_3 = "2:3" + ratio_5_4 = "5:4" + ratio_4_5 = "4:5" + ratio_21_9 = "21:9" + ratio_9_21 = "9:21" + + +def get_stability_style_presets(include_none=True): + presets = [] + if include_none: + presets.append("None") + return presets + [x.value for x in StabilityStylePreset] + + +class StabilityStylePreset(str, Enum): + _3d_model = "3d-model" + analog_film = "analog-film" + anime = "anime" + cinematic = "cinematic" + comic_book = "comic-book" + digital_art = "digital-art" + enhance = "enhance" + fantasy_art = "fantasy-art" + isometric = "isometric" + line_art = "line-art" + low_poly = "low-poly" + modeling_compound = "modeling-compound" + neon_punk = "neon-punk" + origami = "origami" + photographic = "photographic" + pixel_art = "pixel-art" + tile_texture = "tile-texture" + + +class Stability_SD3_5_Model(str, Enum): + sd3_5_large = "sd3.5-large" + # sd3_5_large_turbo = "sd3.5-large-turbo" + sd3_5_medium = "sd3.5-medium" + + +class Stability_SD3_5_GenerationMode(str, Enum): + text_to_image = "text-to-image" + image_to_image = "image-to-image" + + +class StabilityStable3_5Request(BaseModel): + model: str = Field(...) + mode: str = Field(...) + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + cfg_scale: float = Field(...) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityUpscaleConservativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.2, le=0.5)] = Field(None) + + +class StabilityUpscaleCreativeRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + creativity: Optional[confloat(ge=0.1, le=0.5)] = Field(None) + style_preset: Optional[str] = Field(None) + + +class StabilityStableUltraRequest(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + aspect_ratio: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + output_format: Optional[str] = Field(StabilityFormat.png.value) + image: Optional[str] = Field(None) + style_preset: Optional[str] = Field(None) + strength: Optional[confloat(ge=0.0, le=1.0)] = Field(None) + + +class StabilityStableUltraResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + + +class StabilityResultsGetResponse(BaseModel): + image: Optional[str] = Field(None) + finish_reason: Optional[str] = Field(None) + seed: Optional[int] = Field(None) + id: Optional[str] = Field(None) + name: Optional[str] = Field(None) + errors: Optional[list[str]] = Field(None) + status: Optional[str] = Field(None) + result: Optional[str] = Field(None) + + +class StabilityAsyncResponse(BaseModel): + id: Optional[str] = Field(None) + + +class StabilityTextToAudioRequest(BaseModel): + model: str = Field(...) + prompt: str = Field(...) + duration: int = Field(190, ge=1, le=190) + seed: int = Field(0, ge=0, le=4294967294) + steps: int = Field(8, ge=4, le=8) + output_format: str = Field("wav") + + +class StabilityAudioToAudioRequest(StabilityTextToAudioRequest): + strength: float = Field(0.01, ge=0.01, le=1.0) + + +class StabilityAudioInpaintRequest(StabilityTextToAudioRequest): + mask_start: int = Field(30, ge=0, le=190) + mask_end: int = Field(190, ge=0, le=190) + + +class StabilityAudioResponse(BaseModel): + audio: Optional[str] = Field(None) diff --git a/comfy_api_nodes/apis/tripo_api.py b/comfy_api_nodes/apis/tripo_api.py new file mode 100644 index 000000000..713260e2a --- /dev/null +++ b/comfy_api_nodes/apis/tripo_api.py @@ -0,0 +1,282 @@ +from __future__ import annotations +from enum import Enum +from typing import Optional, List, Dict, Any, Union + +from pydantic import BaseModel, Field, RootModel + +class TripoModelVersion(str, Enum): + v2_5_20250123 = 'v2.5-20250123' + v2_0_20240919 = 'v2.0-20240919' + v1_4_20240625 = 'v1.4-20240625' + + +class TripoTextureQuality(str, Enum): + standard = 'standard' + detailed = 'detailed' + + +class TripoStyle(str, Enum): + PERSON_TO_CARTOON = "person:person2cartoon" + ANIMAL_VENOM = "animal:venom" + OBJECT_CLAY = "object:clay" + OBJECT_STEAMPUNK = "object:steampunk" + OBJECT_CHRISTMAS = "object:christmas" + OBJECT_BARBIE = "object:barbie" + GOLD = "gold" + ANCIENT_BRONZE = "ancient_bronze" + NONE = "None" + +class TripoTaskType(str, Enum): + TEXT_TO_MODEL = "text_to_model" + IMAGE_TO_MODEL = "image_to_model" + MULTIVIEW_TO_MODEL = "multiview_to_model" + TEXTURE_MODEL = "texture_model" + REFINE_MODEL = "refine_model" + ANIMATE_PRERIGCHECK = "animate_prerigcheck" + ANIMATE_RIG = "animate_rig" + ANIMATE_RETARGET = "animate_retarget" + STYLIZE_MODEL = "stylize_model" + CONVERT_MODEL = "convert_model" + +class TripoTextureAlignment(str, Enum): + ORIGINAL_IMAGE = "original_image" + GEOMETRY = "geometry" + +class TripoOrientation(str, Enum): + ALIGN_IMAGE = "align_image" + DEFAULT = "default" + +class TripoOutFormat(str, Enum): + GLB = "glb" + FBX = "fbx" + +class TripoTopology(str, Enum): + BIP = "bip" + QUAD = "quad" + +class TripoSpec(str, Enum): + MIXAMO = "mixamo" + TRIPO = "tripo" + +class TripoAnimation(str, Enum): + IDLE = "preset:idle" + WALK = "preset:walk" + CLIMB = "preset:climb" + JUMP = "preset:jump" + RUN = "preset:run" + SLASH = "preset:slash" + SHOOT = "preset:shoot" + HURT = "preset:hurt" + FALL = "preset:fall" + TURN = "preset:turn" + +class TripoStylizeStyle(str, Enum): + LEGO = "lego" + VOXEL = "voxel" + VORONOI = "voronoi" + MINECRAFT = "minecraft" + +class TripoConvertFormat(str, Enum): + GLTF = "GLTF" + USDZ = "USDZ" + FBX = "FBX" + OBJ = "OBJ" + STL = "STL" + _3MF = "3MF" + +class TripoTextureFormat(str, Enum): + BMP = "BMP" + DPX = "DPX" + HDR = "HDR" + JPEG = "JPEG" + OPEN_EXR = "OPEN_EXR" + PNG = "PNG" + TARGA = "TARGA" + TIFF = "TIFF" + WEBP = "WEBP" + +class TripoTaskStatus(str, Enum): + QUEUED = "queued" + RUNNING = "running" + SUCCESS = "success" + FAILED = "failed" + CANCELLED = "cancelled" + UNKNOWN = "unknown" + BANNED = "banned" + EXPIRED = "expired" + +class TripoFileTokenReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + file_token: str + +class TripoUrlReference(BaseModel): + type: Optional[str] = Field(None, description='The type of the reference') + url: str + +class TripoObjectStorage(BaseModel): + bucket: str + key: str + +class TripoObjectReference(BaseModel): + type: str + object: TripoObjectStorage + +class TripoFileEmptyReference(BaseModel): + pass + +class TripoFileReference(RootModel): + root: Union[TripoFileTokenReference, TripoUrlReference, TripoObjectReference, TripoFileEmptyReference] + +class TripoGetStsTokenRequest(BaseModel): + format: str = Field(..., description='The format of the image') + +class TripoTextToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXT_TO_MODEL, description='Type of task') + prompt: str = Field(..., description='The text prompt describing the model to generate', max_length=1024) + negative_prompt: Optional[str] = Field(None, description='The negative text prompt', max_length=1024) + model_version: Optional[TripoModelVersion] = TripoModelVersion.v2_5_20250123 + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + image_seed: Optional[int] = Field(None, description='The seed for the text') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + style: Optional[TripoStyle] = None + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoImageToModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.IMAGE_TO_MODEL, description='Type of task') + file: TripoFileReference = Field(..., description='The file reference to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + style: Optional[TripoStyle] = Field(None, description='The style to apply to the generated model') + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = TripoOrientation.DEFAULT + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoMultiviewToModelRequest(BaseModel): + type: TripoTaskType = TripoTaskType.MULTIVIEW_TO_MODEL + files: List[TripoFileReference] = Field(..., description='The file references to convert to a model') + model_version: Optional[TripoModelVersion] = Field(None, description='The model version to use for generation') + orthographic_projection: Optional[bool] = Field(False, description='Whether to use orthographic projection') + face_limit: Optional[int] = Field(None, description='The number of faces to limit the generation to') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the generated model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the generated model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = TripoTextureQuality.standard + texture_alignment: Optional[TripoTextureAlignment] = TripoTextureAlignment.ORIGINAL_IMAGE + auto_size: Optional[bool] = Field(False, description='Whether to auto-size the model') + orientation: Optional[TripoOrientation] = Field(TripoOrientation.DEFAULT, description='The orientation for the model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the generated model') + +class TripoTextureModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.TEXTURE_MODEL, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + texture: Optional[bool] = Field(True, description='Whether to apply texture to the model') + pbr: Optional[bool] = Field(True, description='Whether to apply PBR to the model') + model_seed: Optional[int] = Field(None, description='The seed for the model') + texture_seed: Optional[int] = Field(None, description='The seed for the texture') + texture_quality: Optional[TripoTextureQuality] = Field(None, description='The quality of the texture') + texture_alignment: Optional[TripoTextureAlignment] = Field(TripoTextureAlignment.ORIGINAL_IMAGE, description='The texture alignment method') + +class TripoRefineModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.REFINE_MODEL, description='Type of task') + draft_model_task_id: str = Field(..., description='The task ID of the draft model') + +class TripoAnimatePrerigcheckRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_PRERIGCHECK, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + +class TripoAnimateRigRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RIG, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + spec: Optional[TripoSpec] = Field(TripoSpec.TRIPO, description='The specification for rigging') + +class TripoAnimateRetargetRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.ANIMATE_RETARGET, description='Type of task') + original_model_task_id: str = Field(..., description='The task ID of the original model') + animation: TripoAnimation = Field(..., description='The animation to apply') + out_format: Optional[TripoOutFormat] = Field(TripoOutFormat.GLB, description='The output format') + bake_animation: Optional[bool] = Field(True, description='Whether to bake the animation') + +class TripoStylizeModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.STYLIZE_MODEL, description='Type of task') + style: TripoStylizeStyle = Field(..., description='The style to apply to the model') + original_model_task_id: str = Field(..., description='The task ID of the original model') + block_size: Optional[int] = Field(80, description='The block size for stylization') + +class TripoConvertModelRequest(BaseModel): + type: TripoTaskType = Field(TripoTaskType.CONVERT_MODEL, description='Type of task') + format: TripoConvertFormat = Field(..., description='The format to convert to') + original_model_task_id: str = Field(..., description='The task ID of the original model') + quad: Optional[bool] = Field(False, description='Whether to apply quad to the model') + force_symmetry: Optional[bool] = Field(False, description='Whether to force symmetry') + face_limit: Optional[int] = Field(10000, description='The number of faces to limit the conversion to') + flatten_bottom: Optional[bool] = Field(False, description='Whether to flatten the bottom of the model') + flatten_bottom_threshold: Optional[float] = Field(0.01, description='The threshold for flattening the bottom') + texture_size: Optional[int] = Field(4096, description='The size of the texture') + texture_format: Optional[TripoTextureFormat] = Field(TripoTextureFormat.JPEG, description='The format of the texture') + pivot_to_center_bottom: Optional[bool] = Field(False, description='Whether to pivot to the center bottom') + +class TripoTaskRequest(RootModel): + root: Union[ + TripoTextToModelRequest, + TripoImageToModelRequest, + TripoMultiviewToModelRequest, + TripoTextureModelRequest, + TripoRefineModelRequest, + TripoAnimatePrerigcheckRequest, + TripoAnimateRigRequest, + TripoAnimateRetargetRequest, + TripoStylizeModelRequest, + TripoConvertModelRequest + ] + +class TripoTaskOutput(BaseModel): + model: Optional[str] = Field(None, description='URL to the model') + base_model: Optional[str] = Field(None, description='URL to the base model') + pbr_model: Optional[str] = Field(None, description='URL to the PBR model') + rendered_image: Optional[str] = Field(None, description='URL to the rendered image') + riggable: Optional[bool] = Field(None, description='Whether the model is riggable') + +class TripoTask(BaseModel): + task_id: str = Field(..., description='The task ID') + type: Optional[str] = Field(None, description='The type of task') + status: Optional[TripoTaskStatus] = Field(None, description='The status of the task') + input: Optional[Dict[str, Any]] = Field(None, description='The input parameters for the task') + output: Optional[TripoTaskOutput] = Field(None, description='The output of the task') + progress: Optional[int] = Field(None, description='The progress of the task', ge=0, le=100) + create_time: Optional[int] = Field(None, description='The creation time of the task') + running_left_time: Optional[int] = Field(None, description='The estimated time left for the task') + queue_position: Optional[int] = Field(None, description='The position in the queue') + +class TripoTaskResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoTask = Field(..., description='The task data') + +class TripoGeneralResponse(BaseModel): + code: int = Field(0, description='The response code') + data: Dict[str, str] = Field(..., description='The task ID data') + +class TripoBalanceData(BaseModel): + balance: float = Field(..., description='The account balance') + frozen: float = Field(..., description='The frozen balance') + +class TripoBalanceResponse(BaseModel): + code: int = Field(0, description='The response code') + data: TripoBalanceData = Field(..., description='The balance data') + +class TripoErrorResponse(BaseModel): + code: int = Field(..., description='The error code') + message: str = Field(..., description='The error message') + suggestion: str = Field(..., description='The suggestion for fixing the error') diff --git a/comfy_api_nodes/apis/veo_api.py b/comfy_api_nodes/apis/veo_api.py new file mode 100644 index 000000000..a55137afb --- /dev/null +++ b/comfy_api_nodes/apis/veo_api.py @@ -0,0 +1,111 @@ +from typing import Optional, Union +from enum import Enum + +from pydantic import BaseModel, Field + + +class Image2(BaseModel): + bytesBase64Encoded: str + gcsUri: Optional[str] = None + mimeType: Optional[str] = None + + +class Image3(BaseModel): + bytesBase64Encoded: Optional[str] = None + gcsUri: str + mimeType: Optional[str] = None + + +class Instance1(BaseModel): + image: Optional[Union[Image2, Image3]] = Field( + None, description='Optional image to guide video generation' + ) + prompt: str = Field(..., description='Text description of the video') + + +class PersonGeneration1(str, Enum): + ALLOW = 'ALLOW' + BLOCK = 'BLOCK' + + +class Parameters1(BaseModel): + aspectRatio: Optional[str] = Field(None, examples=['16:9']) + durationSeconds: Optional[int] = None + enhancePrompt: Optional[bool] = None + generateAudio: Optional[bool] = Field( + None, + description='Generate audio for the video. Only supported by veo 3 models.', + ) + negativePrompt: Optional[str] = None + personGeneration: Optional[PersonGeneration1] = None + sampleCount: Optional[int] = None + seed: Optional[int] = None + storageUri: Optional[str] = Field( + None, description='Optional Cloud Storage URI to upload the video' + ) + + +class VeoGenVidRequest(BaseModel): + instances: Optional[list[Instance1]] = None + parameters: Optional[Parameters1] = None + + +class VeoGenVidResponse(BaseModel): + name: str = Field( + ..., + description='Operation resource name', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/a1b07c8e-7b5a-4aba-bb34-3e1ccb8afcc8' + ], + ) + + +class VeoGenVidPollRequest(BaseModel): + operationName: str = Field( + ..., + description='Full operation name (from predict response)', + examples=[ + 'projects/PROJECT_ID/locations/us-central1/publishers/google/models/MODEL_ID/operations/OPERATION_ID' + ], + ) + + +class Video(BaseModel): + bytesBase64Encoded: Optional[str] = Field( + None, description='Base64-encoded video content' + ) + gcsUri: Optional[str] = Field(None, description='Cloud Storage URI of the video') + mimeType: Optional[str] = Field(None, description='Video MIME type') + + +class Error1(BaseModel): + code: Optional[int] = Field(None, description='Error code') + message: Optional[str] = Field(None, description='Error message') + + +class Response1(BaseModel): + field_type: Optional[str] = Field( + None, + alias='@type', + examples=[ + 'type.googleapis.com/cloud.ai.large_models.vision.GenerateVideoResponse' + ], + ) + raiMediaFilteredCount: Optional[int] = Field( + None, description='Count of media filtered by responsible AI policies' + ) + raiMediaFilteredReasons: Optional[list[str]] = Field( + None, description='Reasons why media was filtered by responsible AI policies' + ) + videos: Optional[list[Video]] = None + + +class VeoGenVidPollResponse(BaseModel): + done: Optional[bool] = None + error: Optional[Error1] = Field( + None, description='Error details if operation failed' + ) + name: Optional[str] = None + response: Optional[Response1] = Field( + None, description='The actual prediction response if done is true' + ) diff --git a/comfy_api_nodes/canary.py b/comfy_api_nodes/canary.py new file mode 100644 index 000000000..4df7590b6 --- /dev/null +++ b/comfy_api_nodes/canary.py @@ -0,0 +1,10 @@ +import av + +ver = av.__version__.split(".") +if int(ver[0]) < 14: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +if int(ver[0]) == 14 and int(ver[1]) < 2: + raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.") + +NODE_CLASS_MAPPINGS = {} diff --git a/comfy_api_nodes/mapper_utils.py b/comfy_api_nodes/mapper_utils.py new file mode 100644 index 000000000..6fab8f4bb --- /dev/null +++ b/comfy_api_nodes/mapper_utils.py @@ -0,0 +1,116 @@ +from enum import Enum + +from pydantic.fields import FieldInfo +from pydantic import BaseModel +from pydantic_core import PydanticUndefined + +from comfy.comfy_types.node_typing import IO, InputTypeOptions + +NodeInput = tuple[IO, InputTypeOptions] + + +def _create_base_config(field_info: FieldInfo) -> InputTypeOptions: + config = {} + if hasattr(field_info, "default") and field_info.default is not PydanticUndefined: + config["default"] = field_info.default + if hasattr(field_info, "description") and field_info.description is not None: + config["tooltip"] = field_info.description + return config + + +def _get_number_constraints_config(field_info: FieldInfo) -> dict: + config = {} + if hasattr(field_info, "metadata"): + metadata = field_info.metadata + for constraint in metadata: + if hasattr(constraint, "ge"): + config["min"] = constraint.ge + if hasattr(constraint, "le"): + config["max"] = constraint.le + if hasattr(constraint, "multiple_of"): + config["step"] = constraint.multiple_of + return config + + +def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.IMAGE, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.STRING, { + **_create_base_config(field_info), + **kwargs, + } + + +def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.FLOAT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput: + return IO.INT, { + **_create_base_config(field_info), + **_get_number_constraints_config(field_info), + **kwargs, + } + + +def _model_field_to_combo_input( + field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs +) -> NodeInput: + combo_config = {} + if enum_type is not None: + combo_config["options"] = [option.value for option in enum_type] + combo_config = { + **combo_config, + **_create_base_config(field_info), + **kwargs, + } + return IO.COMBO, combo_config + + +def model_field_to_node_input( + input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs +) -> NodeInput: + """ + Maps a field from a Pydantic model to a Comfy node input. + + Args: + input_type: The type of the input. + base_model: The Pydantic model to map the field from. + field_name: The name of the field to map. + **kwargs: Additional key/values to include in the input options. + + Note: + For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically. + + Example: + >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True) + >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum) + >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True) + """ + field_info: FieldInfo = base_model.model_fields[field_name] + result: NodeInput + + if input_type == IO.IMAGE: + result = _model_field_to_image_input(field_info, **kwargs) + elif input_type == IO.STRING: + result = _model_field_to_string_input(field_info, **kwargs) + elif input_type == IO.FLOAT: + result = _model_field_to_float_input(field_info, **kwargs) + elif input_type == IO.INT: + result = _model_field_to_int_input(field_info, **kwargs) + elif input_type == IO.COMBO: + result = _model_field_to_combo_input(field_info, **kwargs) + else: + message = f"Invalid input type: {input_type}" + raise ValueError(message) + + return result diff --git a/comfy_api_nodes/nodes_bfl.py b/comfy_api_nodes/nodes_bfl.py new file mode 100644 index 000000000..baa74fd52 --- /dev/null +++ b/comfy_api_nodes/nodes_bfl.py @@ -0,0 +1,690 @@ +from inspect import cleandoc +from typing import Optional + +import torch +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apinode_utils import ( + resize_mask_to_image, + validate_aspect_ratio, +) +from comfy_api_nodes.apis.bfl_api import ( + BFLFluxExpandImageRequest, + BFLFluxFillImageRequest, + BFLFluxKontextProGenerateRequest, + BFLFluxProGenerateRequest, + BFLFluxProGenerateResponse, + BFLFluxProUltraGenerateRequest, + BFLFluxStatusResponse, + BFLStatus, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_image_tensor, + poll_op, + sync_op, + tensor_to_base64_string, + validate_string, +) + + +def convert_mask_to_image(mask: torch.Tensor): + """ + Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image. + """ + mask = mask.unsqueeze(-1) + mask = torch.cat([mask] * 3, dim=-1) + return mask + + +class FluxProUltraImageNode(IO.ComfyNode): + """ + Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxProUltraImageNode", + display_name="Flux 1.1 [pro] Ultra Image", + category="api node/image/BFL", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. " + "If active, automatically modifies the prompt for more creative generation, " + "but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.String.Input( + "aspect_ratio", + default="16:9", + tooltip="Aspect ratio of image; must be between 1:4 and 4:1.", + ), + IO.Boolean.Input( + "raw", + default=False, + tooltip="When True, generate less processed, more natural-looking images.", + ), + IO.Image.Input( + "image_prompt", + optional=True, + ), + IO.Float.Input( + "image_prompt_strength", + default=0.1, + min=0.0, + max=1.0, + step=0.01, + tooltip="Blend between the prompt and the image prompt.", + optional=True, + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + def validate_inputs(cls, aspect_ratio: str): + try: + validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + except Exception as e: + return str(e) + return True + + @classmethod + async def execute( + cls, + prompt: str, + aspect_ratio: str, + prompt_upsampling: bool = False, + raw: bool = False, + seed: int = 0, + image_prompt: Optional[torch.Tensor] = None, + image_prompt_strength: float = 0.1, + ) -> IO.NodeOutput: + if image_prompt is None: + validate_string(prompt, strip_whitespace=False) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/flux-pro-1.1-ultra/generate", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxProUltraGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + seed=seed, + aspect_ratio=validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ), + raw=raw, + image_prompt=(image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt)), + image_prompt_strength=(None if image_prompt is None else round(image_prompt_strength, 2)), + ), + ) + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class FluxKontextProImageNode(IO.ComfyNode): + """ + Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio. + """ + + MINIMUM_RATIO = 1 / 4 + MAXIMUM_RATIO = 4 / 1 + MINIMUM_RATIO_STR = "1:4" + MAXIMUM_RATIO_STR = "4:1" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id=cls.NODE_ID, + display_name=cls.DISPLAY_NAME, + category="api node/image/BFL", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation - specify what and how to edit.", + ), + IO.String.Input( + "aspect_ratio", + default="16:9", + tooltip="Aspect ratio of image; must be between 1:4 and 4:1.", + ), + IO.Float.Input( + "guidance", + default=3.0, + min=0.1, + max=99.0, + step=0.1, + tooltip="Guidance strength for the image generation process", + ), + IO.Int.Input( + "steps", + default=50, + min=1, + max=150, + tooltip="Number of steps for the image generation process", + ), + IO.Int.Input( + "seed", + default=1234, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Image.Input( + "input_image", + optional=True, + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate" + NODE_ID = "FluxKontextProImageNode" + DISPLAY_NAME = "Flux.1 Kontext [pro] Image" + + @classmethod + async def execute( + cls, + prompt: str, + aspect_ratio: str, + guidance: float, + steps: int, + input_image: Optional[torch.Tensor] = None, + seed=0, + prompt_upsampling=False, + ) -> IO.NodeOutput: + aspect_ratio = validate_aspect_ratio( + aspect_ratio, + minimum_ratio=cls.MINIMUM_RATIO, + maximum_ratio=cls.MAXIMUM_RATIO, + minimum_ratio_str=cls.MINIMUM_RATIO_STR, + maximum_ratio_str=cls.MAXIMUM_RATIO_STR, + ) + if input_image is None: + validate_string(prompt, strip_whitespace=False) + initial_response = await sync_op( + cls, + ApiEndpoint(path=cls.BFL_PATH, method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxKontextProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + guidance=round(guidance, 1), + steps=steps, + seed=seed, + aspect_ratio=aspect_ratio, + input_image=(input_image if input_image is None else tensor_to_base64_string(input_image)), + ), + ) + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class FluxKontextMaxImageNode(FluxKontextProImageNode): + """ + Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio. + """ + + DESCRIPTION = cleandoc(__doc__ or "") + BFL_PATH = "/proxy/bfl/flux-kontext-max/generate" + NODE_ID = "FluxKontextMaxImageNode" + DISPLAY_NAME = "Flux.1 Kontext [max] Image" + + +class FluxProImageNode(IO.ComfyNode): + """ + Generates images synchronously based on prompt and resolution. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxProImageNode", + display_name="Flux 1.1 [pro] Image", + category="api node/image/BFL", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. " + "If active, automatically modifies the prompt for more creative generation, " + "but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Int.Input( + "width", + default=1024, + min=256, + max=1440, + step=32, + ), + IO.Int.Input( + "height", + default=768, + min=256, + max=1440, + step=32, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.Image.Input( + "image_prompt", + optional=True, + ), + # "image_prompt_strength": ( + # IO.FLOAT, + # { + # "default": 0.1, + # "min": 0.0, + # "max": 1.0, + # "step": 0.01, + # "tooltip": "Blend between the prompt and the image prompt.", + # }, + # ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + prompt_upsampling, + width: int, + height: int, + seed=0, + image_prompt=None, + # image_prompt_strength=0.1, + ) -> IO.NodeOutput: + image_prompt = image_prompt if image_prompt is None else tensor_to_base64_string(image_prompt) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/bfl/flux-pro-1.1/generate", + method="POST", + ), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxProGenerateRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + width=width, + height=height, + seed=seed, + image_prompt=image_prompt, + ), + ) + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class FluxProExpandNode(IO.ComfyNode): + """ + Outpaints image based on prompt. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxProExpandNode", + display_name="Flux.1 Expand Image", + category="api node/image/BFL", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. " + "If active, automatically modifies the prompt for more creative generation, " + "but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Int.Input( + "top", + default=0, + min=0, + max=2048, + tooltip="Number of pixels to expand at the top of the image", + ), + IO.Int.Input( + "bottom", + default=0, + min=0, + max=2048, + tooltip="Number of pixels to expand at the bottom of the image", + ), + IO.Int.Input( + "left", + default=0, + min=0, + max=2048, + tooltip="Number of pixels to expand at the left of the image", + ), + IO.Int.Input( + "right", + default=0, + min=0, + max=2048, + tooltip="Number of pixels to expand at the right of the image", + ), + IO.Float.Input( + "guidance", + default=60, + min=1.5, + max=100, + tooltip="Guidance strength for the image generation process", + ), + IO.Int.Input( + "steps", + default=50, + min=15, + max=50, + tooltip="Number of steps for the image generation process", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + top: int, + bottom: int, + left: int, + right: int, + steps: int, + guidance: float, + seed=0, + ) -> IO.NodeOutput: + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/flux-pro-1.0-expand/generate", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxExpandImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + top=top, + bottom=bottom, + left=left, + right=right, + steps=steps, + guidance=guidance, + seed=seed, + image=tensor_to_base64_string(image), + ), + ) + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class FluxProFillNode(IO.ComfyNode): + """ + Inpaints image based on mask and prompt. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="FluxProFillNode", + display_name="Flux.1 Fill Image", + category="api node/image/BFL", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + IO.Mask.Input("mask"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "prompt_upsampling", + default=False, + tooltip="Whether to perform upsampling on the prompt. " + "If active, automatically modifies the prompt for more creative generation, " + "but results are nondeterministic (same seed will not produce exactly the same result).", + ), + IO.Float.Input( + "guidance", + default=60, + min=1.5, + max=100, + tooltip="Guidance strength for the image generation process", + ), + IO.Int.Input( + "steps", + default=50, + min=15, + max=50, + tooltip="Number of steps for the image generation process", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + prompt_upsampling: bool, + steps: int, + guidance: float, + seed=0, + ) -> IO.NodeOutput: + # prepare mask + mask = resize_mask_to_image(mask, image) + mask = tensor_to_base64_string(convert_mask_to_image(mask)) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/bfl/flux-pro-1.0-fill/generate", method="POST"), + response_model=BFLFluxProGenerateResponse, + data=BFLFluxFillImageRequest( + prompt=prompt, + prompt_upsampling=prompt_upsampling, + steps=steps, + guidance=guidance, + seed=seed, + image=tensor_to_base64_string(image[:, :, :, :3]), # make sure image will have alpha channel removed + mask=mask, + ), + ) + response = await poll_op( + cls, + ApiEndpoint(initial_response.polling_url), + response_model=BFLFluxStatusResponse, + status_extractor=lambda r: r.status, + progress_extractor=lambda r: r.progress, + completed_statuses=[BFLStatus.ready], + failed_statuses=[ + BFLStatus.request_moderated, + BFLStatus.content_moderated, + BFLStatus.error, + BFLStatus.task_not_found, + ], + queued_statuses=[], + ) + return IO.NodeOutput(await download_url_to_image_tensor(response.result["sample"])) + + +class BFLExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + FluxProUltraImageNode, + # FluxProImageNode, + FluxKontextProImageNode, + FluxKontextMaxImageNode, + FluxProExpandNode, + FluxProFillNode, + ] + + +async def comfy_entrypoint() -> BFLExtension: + return BFLExtension() diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py new file mode 100644 index 000000000..534af380d --- /dev/null +++ b/comfy_api_nodes/nodes_bytedance.py @@ -0,0 +1,1114 @@ +import logging +import math +from enum import Enum +from typing import Literal, Optional, Union + +import torch +from pydantic import BaseModel, Field +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_image_tensor, + download_url_to_video_output, + get_number_of_images, + image_tensor_pair_to_batch, + poll_op, + sync_op, + upload_images_to_comfyapi, + validate_image_aspect_ratio_range, + validate_image_dimensions, + validate_string, +) + +BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations" + +# Long-running tasks endpoints(e.g., video) +BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" +BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id} + + +class Text2ImageModelName(str, Enum): + seedream_3 = "seedream-3-0-t2i-250415" + + +class Image2ImageModelName(str, Enum): + seededit_3 = "seededit-3-0-i2i-250628" + + +class Text2VideoModelName(str, Enum): + seedance_1_pro = "seedance-1-0-pro-250528" + seedance_1_lite = "seedance-1-0-lite-t2v-250428" + + +class Image2VideoModelName(str, Enum): + """note(August 31): Pro model only supports FirstFrame: https://docs.byteplus.com/en/docs/ModelArk/1520757""" + + seedance_1_pro = "seedance-1-0-pro-250528" + seedance_1_lite = "seedance-1-0-lite-i2v-250428" + + +class Text2ImageTaskCreationRequest(BaseModel): + model: Text2ImageModelName = Text2ImageModelName.seedream_3 + prompt: str = Field(...) + response_format: Optional[str] = Field("url") + size: Optional[str] = Field(None) + seed: Optional[int] = Field(0, ge=0, le=2147483647) + guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0) + watermark: Optional[bool] = Field(True) + + +class Image2ImageTaskCreationRequest(BaseModel): + model: Image2ImageModelName = Image2ImageModelName.seededit_3 + prompt: str = Field(...) + response_format: Optional[str] = Field("url") + image: str = Field(..., description="Base64 encoded string or image URL") + size: Optional[str] = Field("adaptive") + seed: Optional[int] = Field(..., ge=0, le=2147483647) + guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0) + watermark: Optional[bool] = Field(True) + + +class Seedream4Options(BaseModel): + max_images: int = Field(15) + + +class Seedream4TaskCreationRequest(BaseModel): + model: str = Field("seedream-4-0-250828") + prompt: str = Field(...) + response_format: str = Field("url") + image: Optional[list[str]] = Field(None, description="Image URLs") + size: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + sequential_image_generation: str = Field("disabled") + sequential_image_generation_options: Seedream4Options = Field(Seedream4Options(max_images=15)) + watermark: bool = Field(True) + + +class ImageTaskCreationResponse(BaseModel): + model: str = Field(...) + created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.") + data: list = Field([], description="Contains information about the generated image(s).") + error: dict = Field({}, description="Contains `code` and `message` fields in case of error.") + + +class TaskTextContent(BaseModel): + type: str = Field("text") + text: str = Field(...) + + +class TaskImageContentUrl(BaseModel): + url: str = Field(...) + + +class TaskImageContent(BaseModel): + type: str = Field("image_url") + image_url: TaskImageContentUrl = Field(...) + role: Optional[Literal["first_frame", "last_frame", "reference_image"]] = Field(None) + + +class Text2VideoTaskCreationRequest(BaseModel): + model: Text2VideoModelName = Text2VideoModelName.seedance_1_pro + content: list[TaskTextContent] = Field(..., min_length=1) + + +class Image2VideoTaskCreationRequest(BaseModel): + model: Image2VideoModelName = Image2VideoModelName.seedance_1_pro + content: list[Union[TaskTextContent, TaskImageContent]] = Field(..., min_length=2) + + +class TaskCreationResponse(BaseModel): + id: str = Field(...) + + +class TaskStatusError(BaseModel): + code: str = Field(...) + message: str = Field(...) + + +class TaskStatusResult(BaseModel): + video_url: str = Field(...) + + +class TaskStatusResponse(BaseModel): + id: str = Field(...) + model: str = Field(...) + status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...) + error: Optional[TaskStatusError] = Field(None) + content: Optional[TaskStatusResult] = Field(None) + + +RECOMMENDED_PRESETS = [ + ("1024x1024 (1:1)", 1024, 1024), + ("864x1152 (3:4)", 864, 1152), + ("1152x864 (4:3)", 1152, 864), + ("1280x720 (16:9)", 1280, 720), + ("720x1280 (9:16)", 720, 1280), + ("832x1248 (2:3)", 832, 1248), + ("1248x832 (3:2)", 1248, 832), + ("1512x648 (21:9)", 1512, 648), + ("2048x2048 (1:1)", 2048, 2048), + ("Custom", None, None), +] + +RECOMMENDED_PRESETS_SEEDREAM_4 = [ + ("2048x2048 (1:1)", 2048, 2048), + ("2304x1728 (4:3)", 2304, 1728), + ("1728x2304 (3:4)", 1728, 2304), + ("2560x1440 (16:9)", 2560, 1440), + ("1440x2560 (9:16)", 1440, 2560), + ("2496x1664 (3:2)", 2496, 1664), + ("1664x2496 (2:3)", 1664, 2496), + ("3024x1296 (21:9)", 3024, 1296), + ("4096x4096 (1:1)", 4096, 4096), + ("Custom", None, None), +] + +# The time in this dictionary are given for 10 seconds duration. +VIDEO_TASKS_EXECUTION_TIME = { + "seedance-1-0-lite-t2v-250428": { + "480p": 40, + "720p": 60, + "1080p": 90, + }, + "seedance-1-0-lite-i2v-250428": { + "480p": 40, + "720p": 60, + "1080p": 90, + }, + "seedance-1-0-pro-250528": { + "480p": 70, + "720p": 85, + "1080p": 115, + }, +} + + +def get_image_url_from_response(response: ImageTaskCreationResponse) -> str: + if response.error: + error_msg = f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}" + logging.info(error_msg) + raise RuntimeError(error_msg) + logging.info("ByteDance task succeeded, image URL: %s", response.data[0]["url"]) + return response.data[0]["url"] + + +def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the video URL from the task status response if it exists.""" + if hasattr(response, "content") and response.content: + return response.content.video_url + return None + + +class ByteDanceImageNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceImageNode", + display_name="ByteDance Image", + category="api node/image/ByteDance", + description="Generate images using ByteDance models via api based on prompt", + inputs=[ + IO.Combo.Input( + "model", + options=Text2ImageModelName, + default=Text2ImageModelName.seedream_3, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="The text prompt used to generate the image", + ), + IO.Combo.Input( + "size_preset", + options=[label for label, _, _ in RECOMMENDED_PRESETS], + tooltip="Pick a recommended size. Select Custom to use the width and height below", + ), + IO.Int.Input( + "width", + default=1024, + min=512, + max=2048, + step=64, + tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`", + ), + IO.Int.Input( + "height", + default=1024, + min=512, + max=2048, + step=64, + tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation", + optional=True, + ), + IO.Float.Input( + "guidance_scale", + default=2.5, + min=1.0, + max=10.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + tooltip="Higher value makes the image follow the prompt more closely", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the image', + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + size_preset: str, + width: int, + height: int, + seed: int, + guidance_scale: float, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + w = h = None + for label, tw, th in RECOMMENDED_PRESETS: + if label == size_preset: + w, h = tw, th + break + + if w is None or h is None: + w, h = width, height + if not (512 <= w <= 2048) or not (512 <= h <= 2048): + raise ValueError( + f"Custom size out of range: {w}x{h}. " "Both width and height must be between 512 and 2048 pixels." + ) + + payload = Text2ImageTaskCreationRequest( + model=model, + prompt=prompt, + size=f"{w}x{h}", + seed=seed, + guidance_scale=guidance_scale, + watermark=watermark, + ) + response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"), + data=payload, + response_model=ImageTaskCreationResponse, + ) + return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response))) + + +class ByteDanceImageEditNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceImageEditNode", + display_name="ByteDance Image Edit", + category="api node/image/ByteDance", + description="Edit images using ByteDance models via api based on prompt", + inputs=[ + IO.Combo.Input( + "model", + options=Image2ImageModelName, + default=Image2ImageModelName.seededit_3, + tooltip="Model name", + ), + IO.Image.Input( + "image", + tooltip="The base image to edit", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Instruction to edit image", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation", + optional=True, + ), + IO.Float.Input( + "guidance_scale", + default=5.5, + min=1.0, + max=10.0, + step=0.01, + display_mode=IO.NumberDisplay.number, + tooltip="Higher value makes the image follow the prompt more closely", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the image', + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + image: torch.Tensor, + prompt: str, + seed: int, + guidance_scale: float, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + if get_number_of_images(image) != 1: + raise ValueError("Exactly one input image is required.") + validate_image_aspect_ratio_range(image, (1, 3), (3, 1)) + source_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0] + payload = Image2ImageTaskCreationRequest( + model=model, + prompt=prompt, + image=source_url, + seed=seed, + guidance_scale=guidance_scale, + watermark=watermark, + ) + response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"), + data=payload, + response_model=ImageTaskCreationResponse, + ) + return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response))) + + +class ByteDanceSeedreamNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceSeedreamNode", + display_name="ByteDance Seedream 4", + category="api node/image/ByteDance", + description="Unified text-to-image generation and precise single-sentence editing at up to 4K resolution.", + inputs=[ + IO.Combo.Input( + "model", + options=["seedream-4-0-250828"], + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for creating or editing an image.", + ), + IO.Image.Input( + "image", + tooltip="Input image(s) for image-to-image generation. " + "List of 1-10 images for single or multi-reference generation.", + optional=True, + ), + IO.Combo.Input( + "size_preset", + options=[label for label, _, _ in RECOMMENDED_PRESETS_SEEDREAM_4], + tooltip="Pick a recommended size. Select Custom to use the width and height below.", + ), + IO.Int.Input( + "width", + default=2048, + min=1024, + max=4096, + step=64, + tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`", + optional=True, + ), + IO.Int.Input( + "height", + default=2048, + min=1024, + max=4096, + step=64, + tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`", + optional=True, + ), + IO.Combo.Input( + "sequential_image_generation", + options=["disabled", "auto"], + tooltip="Group image generation mode. " + "'disabled' generates a single image. " + "'auto' lets the model decide whether to generate multiple related images " + "(e.g., story scenes, character variations).", + optional=True, + ), + IO.Int.Input( + "max_images", + default=1, + min=1, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Maximum number of images to generate when sequential_image_generation='auto'. " + "Total images (input + generated) cannot exceed 15.", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the image.', + optional=True, + ), + IO.Boolean.Input( + "fail_on_partial", + default=True, + tooltip="If enabled, abort execution if any requested images are missing or return an error.", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + image: torch.Tensor = None, + size_preset: str = RECOMMENDED_PRESETS_SEEDREAM_4[0][0], + width: int = 2048, + height: int = 2048, + sequential_image_generation: str = "disabled", + max_images: int = 1, + seed: int = 0, + watermark: bool = True, + fail_on_partial: bool = True, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + w = h = None + for label, tw, th in RECOMMENDED_PRESETS_SEEDREAM_4: + if label == size_preset: + w, h = tw, th + break + + if w is None or h is None: + w, h = width, height + if not (1024 <= w <= 4096) or not (1024 <= h <= 4096): + raise ValueError( + f"Custom size out of range: {w}x{h}. " "Both width and height must be between 1024 and 4096 pixels." + ) + n_input_images = get_number_of_images(image) if image is not None else 0 + if n_input_images > 10: + raise ValueError(f"Maximum of 10 reference images are supported, but {n_input_images} received.") + if sequential_image_generation == "auto" and n_input_images + max_images > 15: + raise ValueError( + "The maximum number of generated images plus the number of reference images cannot exceed 15." + ) + reference_images_urls = [] + if n_input_images: + for i in image: + validate_image_aspect_ratio_range(i, (1, 3), (3, 1)) + reference_images_urls = await upload_images_to_comfyapi( + cls, + image, + max_images=n_input_images, + mime_type="image/png", + ) + response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_IMAGE_ENDPOINT, method="POST"), + response_model=ImageTaskCreationResponse, + data=Seedream4TaskCreationRequest( + model=model, + prompt=prompt, + image=reference_images_urls, + size=f"{w}x{h}", + seed=seed, + sequential_image_generation=sequential_image_generation, + sequential_image_generation_options=Seedream4Options(max_images=max_images), + watermark=watermark, + ), + ) + if len(response.data) == 1: + return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response))) + urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d] + if fail_on_partial and len(urls) < len(response.data): + raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.") + return IO.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls])) + + +class ByteDanceTextToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceTextToVideoNode", + display_name="ByteDance Text to Video", + category="api node/video/ByteDance", + description="Generate video using ByteDance models via api based on prompt", + inputs=[ + IO.Combo.Input( + "model", + options=Text2VideoModelName, + default=Text2VideoModelName.seedance_1_pro, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="The text prompt used to generate the video.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p", "1080p"], + tooltip="The resolution of the output video.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9"], + tooltip="The aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=12, + step=1, + tooltip="The duration of the output video in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "camera_fixed", + default=False, + tooltip="Specifies whether to fix the camera. The platform appends an instruction " + "to fix the camera to your prompt, but does not guarantee the actual effect.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the video.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + resolution: str, + aspect_ratio: str, + duration: int, + seed: int, + camera_fixed: bool, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"]) + + prompt = ( + f"{prompt} " + f"--resolution {resolution} " + f"--ratio {aspect_ratio} " + f"--duration {duration} " + f"--seed {seed} " + f"--camerafixed {str(camera_fixed).lower()} " + f"--watermark {str(watermark).lower()}" + ) + return await process_video_task( + cls, + payload=Text2VideoTaskCreationRequest(model=model, content=[TaskTextContent(text=prompt)]), + estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))), + ) + + +class ByteDanceImageToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceImageToVideoNode", + display_name="ByteDance Image to Video", + category="api node/video/ByteDance", + description="Generate video using ByteDance models via api based on image and prompt", + inputs=[ + IO.Combo.Input( + "model", + options=Image2VideoModelName, + default=Image2VideoModelName.seedance_1_pro, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="The text prompt used to generate the video.", + ), + IO.Image.Input( + "image", + tooltip="First frame to be used for the video.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p", "1080p"], + tooltip="The resolution of the output video.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"], + tooltip="The aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=12, + step=1, + tooltip="The duration of the output video in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "camera_fixed", + default=False, + tooltip="Specifies whether to fix the camera. The platform appends an instruction " + "to fix the camera to your prompt, but does not guarantee the actual effect.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the video.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + image: torch.Tensor, + resolution: str, + aspect_ratio: str, + duration: int, + seed: int, + camera_fixed: bool, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"]) + validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000) + validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5 + + image_url = (await upload_images_to_comfyapi(cls, image, max_images=1))[0] + prompt = ( + f"{prompt} " + f"--resolution {resolution} " + f"--ratio {aspect_ratio} " + f"--duration {duration} " + f"--seed {seed} " + f"--camerafixed {str(camera_fixed).lower()} " + f"--watermark {str(watermark).lower()}" + ) + + return await process_video_task( + cls, + payload=Image2VideoTaskCreationRequest( + model=model, + content=[TaskTextContent(text=prompt), TaskImageContent(image_url=TaskImageContentUrl(url=image_url))], + ), + estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))), + ) + + +class ByteDanceFirstLastFrameNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceFirstLastFrameNode", + display_name="ByteDance First-Last-Frame to Video", + category="api node/video/ByteDance", + description="Generate video using prompt and first and last frames.", + inputs=[ + IO.Combo.Input( + "model", + options=[model.value for model in Image2VideoModelName], + default=Image2VideoModelName.seedance_1_lite.value, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="The text prompt used to generate the video.", + ), + IO.Image.Input( + "first_frame", + tooltip="First frame to be used for the video.", + ), + IO.Image.Input( + "last_frame", + tooltip="Last frame to be used for the video.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p", "1080p"], + tooltip="The resolution of the output video.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"], + tooltip="The aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=12, + step=1, + tooltip="The duration of the output video in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "camera_fixed", + default=False, + tooltip="Specifies whether to fix the camera. The platform appends an instruction " + "to fix the camera to your prompt, but does not guarantee the actual effect.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the video.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + first_frame: torch.Tensor, + last_frame: torch.Tensor, + resolution: str, + aspect_ratio: str, + duration: int, + seed: int, + camera_fixed: bool, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "camerafixed", "watermark"]) + for i in (first_frame, last_frame): + validate_image_dimensions(i, min_width=300, min_height=300, max_width=6000, max_height=6000) + validate_image_aspect_ratio_range(i, (2, 5), (5, 2), strict=False) # 0.4 to 2.5 + + download_urls = await upload_images_to_comfyapi( + cls, + image_tensor_pair_to_batch(first_frame, last_frame), + max_images=2, + mime_type="image/png", + ) + + prompt = ( + f"{prompt} " + f"--resolution {resolution} " + f"--ratio {aspect_ratio} " + f"--duration {duration} " + f"--seed {seed} " + f"--camerafixed {str(camera_fixed).lower()} " + f"--watermark {str(watermark).lower()}" + ) + + return await process_video_task( + cls, + payload=Image2VideoTaskCreationRequest( + model=model, + content=[ + TaskTextContent(text=prompt), + TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[0])), role="first_frame"), + TaskImageContent(image_url=TaskImageContentUrl(url=str(download_urls[1])), role="last_frame"), + ], + ), + estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))), + ) + + +class ByteDanceImageReferenceNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDanceImageReferenceNode", + display_name="ByteDance Reference Images to Video", + category="api node/video/ByteDance", + description="Generate video using prompt and reference images.", + inputs=[ + IO.Combo.Input( + "model", + options=[Image2VideoModelName.seedance_1_lite.value], + default=Image2VideoModelName.seedance_1_lite.value, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="The text prompt used to generate the video.", + ), + IO.Image.Input( + "images", + tooltip="One to four images.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p"], + tooltip="The resolution of the output video.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["adaptive", "16:9", "4:3", "1:1", "3:4", "9:16", "21:9"], + tooltip="The aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=12, + step=1, + tooltip="The duration of the output video in seconds.", + display_mode=IO.NumberDisplay.slider, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the video.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + images: torch.Tensor, + resolution: str, + aspect_ratio: str, + duration: int, + seed: int, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + raise_if_text_params(prompt, ["resolution", "ratio", "duration", "seed", "watermark"]) + for image in images: + validate_image_dimensions(image, min_width=300, min_height=300, max_width=6000, max_height=6000) + validate_image_aspect_ratio_range(image, (2, 5), (5, 2), strict=False) # 0.4 to 2.5 + + image_urls = await upload_images_to_comfyapi(cls, images, max_images=4, mime_type="image/png") + prompt = ( + f"{prompt} " + f"--resolution {resolution} " + f"--ratio {aspect_ratio} " + f"--duration {duration} " + f"--seed {seed} " + f"--watermark {str(watermark).lower()}" + ) + x = [ + TaskTextContent(text=prompt), + *[TaskImageContent(image_url=TaskImageContentUrl(url=str(i)), role="reference_image") for i in image_urls], + ] + return await process_video_task( + cls, + payload=Image2VideoTaskCreationRequest(model=model, content=x), + estimated_duration=max(1, math.ceil(VIDEO_TASKS_EXECUTION_TIME[model][resolution] * (duration / 10.0))), + ) + + +async def process_video_task( + cls: type[IO.ComfyNode], + payload: Union[Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest], + estimated_duration: Optional[int], +) -> IO.NodeOutput: + initial_response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), + data=payload, + response_model=TaskCreationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{initial_response.id}"), + status_extractor=lambda r: r.status, + estimated_duration=estimated_duration, + response_model=TaskStatusResponse, + ) + return IO.NodeOutput(await download_url_to_video_output(get_video_url_from_task_status(response))) + + +def raise_if_text_params(prompt: str, text_params: list[str]) -> None: + for i in text_params: + if f"--{i} " in prompt: + raise ValueError( + f"--{i} is not allowed in the prompt, use the appropriated widget input to change this value." + ) + + +class ByteDanceExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + ByteDanceImageNode, + ByteDanceImageEditNode, + ByteDanceSeedreamNode, + ByteDanceTextToVideoNode, + ByteDanceImageToVideoNode, + ByteDanceFirstLastFrameNode, + ByteDanceImageReferenceNode, + ] + + +async def comfy_entrypoint() -> ByteDanceExtension: + return ByteDanceExtension() diff --git a/comfy_api_nodes/nodes_gemini.py b/comfy_api_nodes/nodes_gemini.py new file mode 100644 index 000000000..67f2469ad --- /dev/null +++ b/comfy_api_nodes/nodes_gemini.py @@ -0,0 +1,563 @@ +""" +API Nodes for Gemini Multimodal LLM Usage via Remote API +See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference +""" + +from __future__ import annotations + +import base64 +import json +import os +import time +import uuid +from enum import Enum +from io import BytesIO +from typing import Literal, Optional + +import torch +from typing_extensions import override + +import folder_paths +from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api.util import VideoCodec, VideoContainer +from comfy_api_nodes.apis import ( + GeminiContent, + GeminiGenerateContentRequest, + GeminiGenerateContentResponse, + GeminiInlineData, + GeminiMimeType, + GeminiPart, +) +from comfy_api_nodes.apis.gemini_api import ( + GeminiImageConfig, + GeminiImageGenerateContentRequest, + GeminiImageGenerationConfig, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + audio_to_base64_string, + bytesio_to_image_tensor, + sync_op, + tensor_to_base64_string, + validate_string, + video_to_base64_string, +) +from server import PromptServer + +GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini" +GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB + + +class GeminiModel(str, Enum): + """ + Gemini Model Names allowed by comfy-api + """ + + gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06" + gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17" + gemini_2_5_pro = "gemini-2.5-pro" + gemini_2_5_flash = "gemini-2.5-flash" + + +class GeminiImageModel(str, Enum): + """ + Gemini Image Model Names allowed by comfy-api + """ + + gemini_2_5_flash_image_preview = "gemini-2.5-flash-image-preview" + gemini_2_5_flash_image = "gemini-2.5-flash-image" + + +def create_image_parts(image_input: torch.Tensor) -> list[GeminiPart]: + """ + Convert image tensor input to Gemini API compatible parts. + + Args: + image_input: Batch of image tensors from ComfyUI. + + Returns: + List of GeminiPart objects containing the encoded images. + """ + image_parts: list[GeminiPart] = [] + for image_index in range(image_input.shape[0]): + image_as_b64 = tensor_to_base64_string(image_input[image_index].unsqueeze(0)) + image_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.image_png, + data=image_as_b64, + ) + ) + ) + return image_parts + + +def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Literal["text"] | str) -> list[GeminiPart]: + """ + Filter response parts by their type. + + Args: + response: The API response from Gemini. + part_type: Type of parts to extract ("text" or a MIME type). + + Returns: + List of response parts matching the requested type. + """ + parts = [] + for part in response.candidates[0].content.parts: + if part_type == "text" and hasattr(part, "text") and part.text: + parts.append(part) + elif hasattr(part, "inlineData") and part.inlineData and part.inlineData.mimeType == part_type: + parts.append(part) + # Skip parts that don't match the requested type + return parts + + +def get_text_from_response(response: GeminiGenerateContentResponse) -> str: + """ + Extract and concatenate all text parts from the response. + + Args: + response: The API response from Gemini. + + Returns: + Combined text from all text parts in the response. + """ + parts = get_parts_by_type(response, "text") + return "\n".join([part.text for part in parts]) + + +def get_image_from_response(response: GeminiGenerateContentResponse) -> torch.Tensor: + image_tensors: list[torch.Tensor] = [] + parts = get_parts_by_type(response, "image/png") + for part in parts: + image_data = base64.b64decode(part.inlineData.data) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + image_tensors.append(returned_image) + if len(image_tensors) == 0: + return torch.zeros((1, 1024, 1024, 4)) + return torch.cat(image_tensors, dim=0) + + +class GeminiNode(IO.ComfyNode): + """ + Node to generate text responses from a Gemini model. + + This node allows users to interact with Google's Gemini AI models, providing + multimodal inputs (text, images, audio, video, files) to generate coherent + text responses. The node works with the latest Gemini models, handling the + API communication and response parsing. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GeminiNode", + display_name="Google Gemini", + category="api node/text/Gemini", + description="Generate text responses with Google's Gemini AI model. " + "You can provide multiple types of inputs (text, images, audio, video) " + "as context for generating more relevant and meaningful responses.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text inputs to the model, used to generate a response. " + "You can include detailed instructions, questions, or context for the model.", + ), + IO.Combo.Input( + "model", + options=GeminiModel, + default=GeminiModel.gemini_2_5_pro, + tooltip="The Gemini model to use for generating responses.", + ), + IO.Int.Input( + "seed", + default=42, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="When seed is fixed to a specific value, the model makes a best effort to provide " + "the same response for repeated requests. Deterministic output isn't guaranteed. " + "Also, changing the model or parameter settings, such as the temperature, " + "can cause variations in the response even when you use the same seed value. " + "By default, a random seed value is used.", + ), + IO.Image.Input( + "images", + optional=True, + tooltip="Optional image(s) to use as context for the model. " + "To include multiple images, you can use the Batch Images node.", + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Optional audio to use as context for the model.", + ), + IO.Video.Input( + "video", + optional=True, + tooltip="Optional video to use as context for the model.", + ), + IO.Custom("GEMINI_INPUT_FILES").Input( + "files", + optional=True, + tooltip="Optional file(s) to use as context for the model. " + "Accepts inputs from the Gemini Generate Content Input Files node.", + ), + ], + outputs=[ + IO.String.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + def create_video_parts(cls, video_input: Input.Video) -> list[GeminiPart]: + """Convert video input to Gemini API compatible parts.""" + + base_64_string = video_to_base64_string(video_input, container_format=VideoContainer.MP4, codec=VideoCodec.H264) + return [ + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.video_mp4, + data=base_64_string, + ) + ) + ] + + @classmethod + def create_audio_parts(cls, audio_input: Input.Audio) -> list[GeminiPart]: + """ + Convert audio input to Gemini API compatible parts. + + Args: + audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate. + + Returns: + List of GeminiPart objects containing the encoded audio. + """ + audio_parts: list[GeminiPart] = [] + for batch_index in range(audio_input["waveform"].shape[0]): + # Recreate an IO.AUDIO object for the given batch dimension index + audio_at_index = Input.Audio( + waveform=audio_input["waveform"][batch_index].unsqueeze(0), + sample_rate=audio_input["sample_rate"], + ) + # Convert to MP3 format for compatibility with Gemini API + audio_bytes = audio_to_base64_string( + audio_at_index, + container_format="mp3", + codec_name="libmp3lame", + ) + audio_parts.append( + GeminiPart( + inlineData=GeminiInlineData( + mimeType=GeminiMimeType.audio_mp3, + data=audio_bytes, + ) + ) + ) + return audio_parts + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + seed: int, + images: Optional[torch.Tensor] = None, + audio: Optional[Input.Audio] = None, + video: Optional[Input.Video] = None, + files: Optional[list[GeminiPart]] = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + + # Create parts list with text prompt as the first part + parts: list[GeminiPart] = [GeminiPart(text=prompt)] + + # Add other modal parts + if images is not None: + image_parts = create_image_parts(images) + parts.extend(image_parts) + if audio is not None: + parts.extend(cls.create_audio_parts(audio)) + if video is not None: + parts.extend(cls.create_video_parts(video)) + if files is not None: + parts.extend(files) + + # Create response + response = await sync_op( + cls, + endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"), + data=GeminiGenerateContentRequest( + contents=[ + GeminiContent( + role="user", + parts=parts, + ) + ] + ), + response_model=GeminiGenerateContentResponse, + ) + + # Get result output + output_text = get_text_from_response(response) + if output_text: + # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. + render_spec = { + "node_id": cls.hidden.unique_id, + "component": "ChatHistoryWidget", + "props": { + "history": json.dumps( + [ + { + "prompt": prompt, + "response": output_text, + "response_id": str(uuid.uuid4()), + "timestamp": time.time(), + } + ] + ), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + + return IO.NodeOutput(output_text or "Empty response from Gemini model...") + + +class GeminiInputFiles(IO.ComfyNode): + """ + Loads and formats input files for use with the Gemini API. + + This node allows users to include text (.txt) and PDF (.pdf) files as input + context for the Gemini model. Files are converted to the appropriate format + required by the API and can be chained together to include multiple files + in a single request. + """ + + @classmethod + def define_schema(cls): + """ + For details about the supported file input types, see: + https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return IO.Schema( + node_id="GeminiInputFiles", + display_name="Gemini Input Files", + category="api node/text/Gemini", + description="Loads and prepares input files to include as inputs for Gemini LLM nodes. " + "The files will be read by the Gemini model when generating a response. " + "The contents of the text file count toward the token limit. " + "🛈 TIP: Can be chained together with other Gemini Input File nodes.", + inputs=[ + IO.Combo.Input( + "file", + options=input_files, + default=input_files[0] if input_files else None, + tooltip="Input files to include as context for the model. " + "Only accepts text (.txt) and PDF (.pdf) files for now.", + ), + IO.Custom("GEMINI_INPUT_FILES").Input( + "GEMINI_INPUT_FILES", + optional=True, + tooltip="An optional additional file(s) to batch together with the file loaded from this node. " + "Allows chaining of input files so that a single message can include multiple input files.", + ), + ], + outputs=[ + IO.Custom("GEMINI_INPUT_FILES").Output(), + ], + ) + + @classmethod + def create_file_part(cls, file_path: str) -> GeminiPart: + mime_type = GeminiMimeType.application_pdf if file_path.endswith(".pdf") else GeminiMimeType.text_plain + # Use base64 string directly, not the data URI + with open(file_path, "rb") as f: + file_content = f.read() + base64_str = base64.b64encode(file_content).decode("utf-8") + + return GeminiPart( + inlineData=GeminiInlineData( + mimeType=mime_type, + data=base64_str, + ) + ) + + @classmethod + def execute(cls, file: str, GEMINI_INPUT_FILES: Optional[list[GeminiPart]] = None) -> IO.NodeOutput: + """Loads and formats input files for Gemini API.""" + if GEMINI_INPUT_FILES is None: + GEMINI_INPUT_FILES = [] + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = cls.create_file_part(file_path) + return IO.NodeOutput([input_file_content] + GEMINI_INPUT_FILES) + + +class GeminiImage(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="GeminiImageNode", + display_name="Google Gemini Image", + category="api node/image/Gemini", + description="Edit images synchronously via Google API.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Text prompt for generation", + default="", + ), + IO.Combo.Input( + "model", + options=GeminiImageModel, + default=GeminiImageModel.gemini_2_5_flash_image, + tooltip="The Gemini model to use for generating responses.", + ), + IO.Int.Input( + "seed", + default=42, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="When seed is fixed to a specific value, the model makes a best effort to provide " + "the same response for repeated requests. Deterministic output isn't guaranteed. " + "Also, changing the model or parameter settings, such as the temperature, " + "can cause variations in the response even when you use the same seed value. " + "By default, a random seed value is used.", + ), + IO.Image.Input( + "images", + optional=True, + tooltip="Optional image(s) to use as context for the model. " + "To include multiple images, you can use the Batch Images node.", + ), + IO.Custom("GEMINI_INPUT_FILES").Input( + "files", + optional=True, + tooltip="Optional file(s) to use as context for the model. " + "Accepts inputs from the Gemini Generate Content Input Files node.", + ), + IO.Combo.Input( + "aspect_ratio", + options=["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"], + default="auto", + tooltip="Defaults to matching the output image size to that of your input image, " + "or otherwise generates 1:1 squares.", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + IO.String.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + seed: int, + images: Optional[torch.Tensor] = None, + files: Optional[list[GeminiPart]] = None, + aspect_ratio: str = "auto", + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + parts: list[GeminiPart] = [GeminiPart(text=prompt)] + + if not aspect_ratio: + aspect_ratio = "auto" # for backward compatability with old workflows; to-do remove this in December + image_config = GeminiImageConfig(aspectRatio=aspect_ratio) + + if images is not None: + image_parts = create_image_parts(images) + parts.extend(image_parts) + if files is not None: + parts.extend(files) + + response = await sync_op( + cls, + endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"), + data=GeminiImageGenerateContentRequest( + contents=[ + GeminiContent(role="user", parts=parts), + ], + generationConfig=GeminiImageGenerationConfig( + responseModalities=["TEXT", "IMAGE"], + imageConfig=None if aspect_ratio == "auto" else image_config, + ), + ), + response_model=GeminiGenerateContentResponse, + ) + + output_image = get_image_from_response(response) + output_text = get_text_from_response(response) + if output_text: + # Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button. + render_spec = { + "node_id": cls.hidden.unique_id, + "component": "ChatHistoryWidget", + "props": { + "history": json.dumps( + [ + { + "prompt": prompt, + "response": output_text, + "response_id": str(uuid.uuid4()), + "timestamp": time.time(), + } + ] + ), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + + output_text = output_text or "Empty response from Gemini model..." + return IO.NodeOutput(output_image, output_text) + + +class GeminiExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + GeminiNode, + GeminiImage, + GeminiInputFiles, + ] + + +async def comfy_entrypoint() -> GeminiExtension: + return GeminiExtension() diff --git a/comfy_api_nodes/nodes_ideogram.py b/comfy_api_nodes/nodes_ideogram.py new file mode 100644 index 000000000..9eae5f11a --- /dev/null +++ b/comfy_api_nodes/nodes_ideogram.py @@ -0,0 +1,842 @@ +from io import BytesIO +from typing_extensions import override +from comfy_api.latest import ComfyExtension, IO +from PIL import Image +import numpy as np +import torch +from comfy_api_nodes.apis import ( + IdeogramGenerateRequest, + IdeogramGenerateResponse, + ImageRequest, + IdeogramV3Request, + IdeogramV3EditRequest, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, +) + +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + bytesio_to_image_tensor, + resize_mask_to_image, +) +from server import PromptServer + +V1_V1_RES_MAP = { + "Auto":"AUTO", + "512 x 1536":"RESOLUTION_512_1536", + "576 x 1408":"RESOLUTION_576_1408", + "576 x 1472":"RESOLUTION_576_1472", + "576 x 1536":"RESOLUTION_576_1536", + "640 x 1024":"RESOLUTION_640_1024", + "640 x 1344":"RESOLUTION_640_1344", + "640 x 1408":"RESOLUTION_640_1408", + "640 x 1472":"RESOLUTION_640_1472", + "640 x 1536":"RESOLUTION_640_1536", + "704 x 1152":"RESOLUTION_704_1152", + "704 x 1216":"RESOLUTION_704_1216", + "704 x 1280":"RESOLUTION_704_1280", + "704 x 1344":"RESOLUTION_704_1344", + "704 x 1408":"RESOLUTION_704_1408", + "704 x 1472":"RESOLUTION_704_1472", + "720 x 1280":"RESOLUTION_720_1280", + "736 x 1312":"RESOLUTION_736_1312", + "768 x 1024":"RESOLUTION_768_1024", + "768 x 1088":"RESOLUTION_768_1088", + "768 x 1152":"RESOLUTION_768_1152", + "768 x 1216":"RESOLUTION_768_1216", + "768 x 1232":"RESOLUTION_768_1232", + "768 x 1280":"RESOLUTION_768_1280", + "768 x 1344":"RESOLUTION_768_1344", + "832 x 960":"RESOLUTION_832_960", + "832 x 1024":"RESOLUTION_832_1024", + "832 x 1088":"RESOLUTION_832_1088", + "832 x 1152":"RESOLUTION_832_1152", + "832 x 1216":"RESOLUTION_832_1216", + "832 x 1248":"RESOLUTION_832_1248", + "864 x 1152":"RESOLUTION_864_1152", + "896 x 960":"RESOLUTION_896_960", + "896 x 1024":"RESOLUTION_896_1024", + "896 x 1088":"RESOLUTION_896_1088", + "896 x 1120":"RESOLUTION_896_1120", + "896 x 1152":"RESOLUTION_896_1152", + "960 x 832":"RESOLUTION_960_832", + "960 x 896":"RESOLUTION_960_896", + "960 x 1024":"RESOLUTION_960_1024", + "960 x 1088":"RESOLUTION_960_1088", + "1024 x 640":"RESOLUTION_1024_640", + "1024 x 768":"RESOLUTION_1024_768", + "1024 x 832":"RESOLUTION_1024_832", + "1024 x 896":"RESOLUTION_1024_896", + "1024 x 960":"RESOLUTION_1024_960", + "1024 x 1024":"RESOLUTION_1024_1024", + "1088 x 768":"RESOLUTION_1088_768", + "1088 x 832":"RESOLUTION_1088_832", + "1088 x 896":"RESOLUTION_1088_896", + "1088 x 960":"RESOLUTION_1088_960", + "1120 x 896":"RESOLUTION_1120_896", + "1152 x 704":"RESOLUTION_1152_704", + "1152 x 768":"RESOLUTION_1152_768", + "1152 x 832":"RESOLUTION_1152_832", + "1152 x 864":"RESOLUTION_1152_864", + "1152 x 896":"RESOLUTION_1152_896", + "1216 x 704":"RESOLUTION_1216_704", + "1216 x 768":"RESOLUTION_1216_768", + "1216 x 832":"RESOLUTION_1216_832", + "1232 x 768":"RESOLUTION_1232_768", + "1248 x 832":"RESOLUTION_1248_832", + "1280 x 704":"RESOLUTION_1280_704", + "1280 x 720":"RESOLUTION_1280_720", + "1280 x 768":"RESOLUTION_1280_768", + "1280 x 800":"RESOLUTION_1280_800", + "1312 x 736":"RESOLUTION_1312_736", + "1344 x 640":"RESOLUTION_1344_640", + "1344 x 704":"RESOLUTION_1344_704", + "1344 x 768":"RESOLUTION_1344_768", + "1408 x 576":"RESOLUTION_1408_576", + "1408 x 640":"RESOLUTION_1408_640", + "1408 x 704":"RESOLUTION_1408_704", + "1472 x 576":"RESOLUTION_1472_576", + "1472 x 640":"RESOLUTION_1472_640", + "1472 x 704":"RESOLUTION_1472_704", + "1536 x 512":"RESOLUTION_1536_512", + "1536 x 576":"RESOLUTION_1536_576", + "1536 x 640":"RESOLUTION_1536_640", +} + +V1_V2_RATIO_MAP = { + "1:1":"ASPECT_1_1", + "4:3":"ASPECT_4_3", + "3:4":"ASPECT_3_4", + "16:9":"ASPECT_16_9", + "9:16":"ASPECT_9_16", + "2:1":"ASPECT_2_1", + "1:2":"ASPECT_1_2", + "3:2":"ASPECT_3_2", + "2:3":"ASPECT_2_3", + "4:5":"ASPECT_4_5", + "5:4":"ASPECT_5_4", +} + +V3_RATIO_MAP = { + "1:3":"1x3", + "3:1":"3x1", + "1:2":"1x2", + "2:1":"2x1", + "9:16":"9x16", + "16:9":"16x9", + "10:16":"10x16", + "16:10":"16x10", + "2:3":"2x3", + "3:2":"3x2", + "3:4":"3x4", + "4:3":"4x3", + "4:5":"4x5", + "5:4":"5x4", + "1:1":"1x1", +} + +V3_RESOLUTIONS= [ + "Auto", + "512x1536", + "576x1408", + "576x1472", + "576x1536", + "640x1344", + "640x1408", + "640x1472", + "640x1536", + "704x1152", + "704x1216", + "704x1280", + "704x1344", + "704x1408", + "704x1472", + "736x1312", + "768x1088", + "768x1216", + "768x1280", + "768x1344", + "800x1280", + "832x960", + "832x1024", + "832x1088", + "832x1152", + "832x1216", + "832x1248", + "864x1152", + "896x960", + "896x1024", + "896x1088", + "896x1120", + "896x1152", + "960x832", + "960x896", + "960x1024", + "960x1088", + "1024x832", + "1024x896", + "1024x960", + "1024x1024", + "1088x768", + "1088x832", + "1088x896", + "1088x960", + "1120x896", + "1152x704", + "1152x832", + "1152x864", + "1152x896", + "1216x704", + "1216x768", + "1216x832", + "1248x832", + "1280x704", + "1280x768", + "1280x800", + "1312x736", + "1344x640", + "1344x704", + "1344x768", + "1408x576", + "1408x640", + "1408x704", + "1472x576", + "1472x640", + "1472x704", + "1536x512", + "1536x576", + "1536x640" +] + +async def download_and_process_images(image_urls): + """Helper function to download and process multiple images from URLs""" + + # Initialize list to store image tensors + image_tensors = [] + + for image_url in image_urls: + # Using functions from apinode_utils.py to handle downloading and processing + image_bytesio = await download_url_to_bytesio(image_url) # Download image content to BytesIO + img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode + image_tensors.append(img_tensor) + + # Stack tensors to match (N, width, height, channels) + if image_tensors: + stacked_tensors = torch.cat(image_tensors, dim=0) + else: + raise Exception("No valid images were processed") + + return stacked_tensors + + +def display_image_urls_on_node(image_urls, node_id): + if node_id and image_urls: + if len(image_urls) == 1: + PromptServer.instance.send_progress_text( + f"Generated Image URL:\n{image_urls[0]}", node_id + ) + else: + urls_text = "Generated Image URLs:\n" + "\n".join( + f"{i+1}. {url}" for i, url in enumerate(image_urls) + ) + PromptServer.instance.send_progress_text(urls_text, node_id) + + +class IdeogramV1(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="IdeogramV1", + display_name="Ideogram V1", + category="api node/image/Ideogram", + description="Generates images using the Ideogram V1 model.", + is_api_node=True, + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "turbo", + default=False, + tooltip="Whether to use turbo mode (faster generation, potentially lower quality)", + ), + IO.Combo.Input( + "aspect_ratio", + options=list(V1_V2_RATIO_MAP.keys()), + default="1:1", + tooltip="The aspect ratio for image generation.", + optional=True, + ), + IO.Combo.Input( + "magic_prompt_option", + options=["AUTO", "ON", "OFF"], + default="AUTO", + tooltip="Determine if MagicPrompt should be used in generation", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Description of what to exclude from the image", + optional=True, + ), + IO.Int.Input( + "num_images", + default=1, + min=1, + max=8, + step=1, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + ) + + @classmethod + async def execute( + cls, + prompt, + turbo=False, + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + negative_prompt="", + num_images=1, + ): + # Determine the model based on turbo setting + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + model = "V_1_TURBO" if turbo else "V_1" + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + negative_prompt=negative_prompt if negative_prompt else None, + ) + ), + auth_kwargs=auth, + ) + + response = await operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, cls.hidden.unique_id) + return IO.NodeOutput(await download_and_process_images(image_urls)) + + +class IdeogramV2(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="IdeogramV2", + display_name="Ideogram V2", + category="api node/image/Ideogram", + description="Generates images using the Ideogram V2 model.", + is_api_node=True, + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Boolean.Input( + "turbo", + default=False, + tooltip="Whether to use turbo mode (faster generation, potentially lower quality)", + ), + IO.Combo.Input( + "aspect_ratio", + options=list(V1_V2_RATIO_MAP.keys()), + default="1:1", + tooltip="The aspect ratio for image generation. Ignored if resolution is not set to AUTO.", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=list(V1_V1_RES_MAP.keys()), + default="Auto", + tooltip="The resolution for image generation. " + "If not set to AUTO, this overrides the aspect_ratio setting.", + optional=True, + ), + IO.Combo.Input( + "magic_prompt_option", + options=["AUTO", "ON", "OFF"], + default="AUTO", + tooltip="Determine if MagicPrompt should be used in generation", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.Combo.Input( + "style_type", + options=["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"], + default="NONE", + tooltip="Style type for generation (V2 only)", + optional=True, + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Description of what to exclude from the image", + optional=True, + ), + IO.Int.Input( + "num_images", + default=1, + min=1, + max=8, + step=1, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + #"color_palette": ( + # IO.STRING, + # { + # "multiline": False, + # "default": "", + # "tooltip": "Color palette preset name or hex colors with weights", + # }, + #), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + ) + + @classmethod + async def execute( + cls, + prompt, + turbo=False, + aspect_ratio="1:1", + resolution="Auto", + magic_prompt_option="AUTO", + seed=0, + style_type="NONE", + negative_prompt="", + num_images=1, + color_palette="", + ): + aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) + resolution = V1_V1_RES_MAP.get(resolution, None) + # Determine the model based on turbo setting + model = "V_2_TURBO" if turbo else "V_2" + + # Handle resolution vs aspect_ratio logic + # If resolution is not AUTO, it overrides aspect_ratio + final_resolution = None + final_aspect_ratio = None + + if resolution != "AUTO": + final_resolution = resolution + else: + final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/ideogram/generate", + method=HttpMethod.POST, + request_model=IdeogramGenerateRequest, + response_model=IdeogramGenerateResponse, + ), + request=IdeogramGenerateRequest( + image_request=ImageRequest( + prompt=prompt, + model=model, + num_images=num_images, + seed=seed, + aspect_ratio=final_aspect_ratio, + resolution=final_resolution, + magic_prompt_option=( + magic_prompt_option if magic_prompt_option != "AUTO" else None + ), + style_type=style_type if style_type != "NONE" else None, + negative_prompt=negative_prompt if negative_prompt else None, + color_palette=color_palette if color_palette else None, + ) + ), + auth_kwargs=auth, + ) + + response = await operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, cls.hidden.unique_id) + return IO.NodeOutput(await download_and_process_images(image_urls)) + + +class IdeogramV3(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="IdeogramV3", + display_name="Ideogram V3", + category="api node/image/Ideogram", + description="Generates images using the Ideogram V3 model. " + "Supports both regular image generation from text prompts and image editing with mask.", + is_api_node=True, + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation or editing", + ), + IO.Image.Input( + "image", + tooltip="Optional reference image for image editing.", + optional=True, + ), + IO.Mask.Input( + "mask", + tooltip="Optional mask for inpainting (white areas will be replaced)", + optional=True, + ), + IO.Combo.Input( + "aspect_ratio", + options=list(V3_RATIO_MAP.keys()), + default="1:1", + tooltip="The aspect ratio for image generation. Ignored if resolution is not set to Auto.", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=V3_RESOLUTIONS, + default="Auto", + tooltip="The resolution for image generation. " + "If not set to Auto, this overrides the aspect_ratio setting.", + optional=True, + ), + IO.Combo.Input( + "magic_prompt_option", + options=["AUTO", "ON", "OFF"], + default="AUTO", + tooltip="Determine if MagicPrompt should be used in generation", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.Int.Input( + "num_images", + default=1, + min=1, + max=8, + step=1, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.Combo.Input( + "rendering_speed", + options=["DEFAULT", "TURBO", "QUALITY"], + default="DEFAULT", + tooltip="Controls the trade-off between generation speed and quality", + optional=True, + ), + IO.Image.Input( + "character_image", + tooltip="Image to use as character reference.", + optional=True, + ), + IO.Mask.Input( + "character_mask", + tooltip="Optional mask for character reference image.", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + ) + + @classmethod + async def execute( + cls, + prompt, + image=None, + mask=None, + resolution="Auto", + aspect_ratio="1:1", + magic_prompt_option="AUTO", + seed=0, + num_images=1, + rendering_speed="DEFAULT", + character_image=None, + character_mask=None, + ): + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + if rendering_speed == "BALANCED": # for backward compatibility + rendering_speed = "DEFAULT" + + character_img_binary = None + character_mask_binary = None + + if character_image is not None: + input_tensor = character_image.squeeze().cpu() + if character_mask is not None: + character_mask = resize_mask_to_image(character_mask, character_image, allow_gradient=False) + character_mask = 1.0 - character_mask + if character_mask.shape[1:] != character_image.shape[1:-1]: + raise Exception("Character mask and image must be the same size") + + mask_np = (character_mask.squeeze().cpu().numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_byte_arr = BytesIO() + mask_img.save(mask_byte_arr, format="PNG") + mask_byte_arr.seek(0) + character_mask_binary = mask_byte_arr + character_mask_binary.name = "mask.png" + + img_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(img_np) + img_byte_arr = BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + character_img_binary = img_byte_arr + character_img_binary.name = "image.png" + elif character_mask is not None: + raise Exception("Character mask requires character image to be present") + + # Check if both image and mask are provided for editing mode + if image is not None and mask is not None: + # Edit mode + path = "/proxy/ideogram/ideogram-v3/edit" + + # Process image and mask + input_tensor = image.squeeze().cpu() + # Resize mask to match image dimension + mask = resize_mask_to_image(mask, image, allow_gradient=False) + # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention). + mask = 1.0 - mask + + # Validate mask dimensions match image + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + + # Process image + img_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(img_np) + img_byte_arr = BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr + img_binary.name = "image.png" + + # Process mask - white areas will be replaced + mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_byte_arr = BytesIO() + mask_img.save(mask_byte_arr, format="PNG") + mask_byte_arr.seek(0) + mask_binary = mask_byte_arr + mask_binary.name = "mask.png" + + # Create edit request + edit_request = IdeogramV3EditRequest( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Add optional parameters + if magic_prompt_option != "AUTO": + edit_request.magic_prompt = magic_prompt_option + if seed != 0: + edit_request.seed = seed + if num_images > 1: + edit_request.num_images = num_images + + files = { + "image": img_binary, + "mask": mask_binary, + } + if character_img_binary: + files["character_reference_images"] = character_img_binary + if character_mask_binary: + files["character_mask_binary"] = character_mask_binary + + # Execute the operation for edit mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3EditRequest, + response_model=IdeogramGenerateResponse, + ), + request=edit_request, + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + + elif image is not None or mask is not None: + # If only one of image or mask is provided, raise an error + raise Exception("Ideogram V3 image editing requires both an image AND a mask") + else: + # Generation mode + path = "/proxy/ideogram/ideogram-v3/generate" + + # Create generation request + gen_request = IdeogramV3Request( + prompt=prompt, + rendering_speed=rendering_speed, + ) + + # Handle resolution vs aspect ratio + if resolution != "Auto": + gen_request.resolution = resolution + elif aspect_ratio != "1:1": + v3_aspect = V3_RATIO_MAP.get(aspect_ratio) + if v3_aspect: + gen_request.aspect_ratio = v3_aspect + + # Add optional parameters + if magic_prompt_option != "AUTO": + gen_request.magic_prompt = magic_prompt_option + if seed != 0: + gen_request.seed = seed + if num_images > 1: + gen_request.num_images = num_images + + files = {} + if character_img_binary: + files["character_reference_images"] = character_img_binary + if character_mask_binary: + files["character_mask_binary"] = character_mask_binary + if files: + gen_request.style_type = "AUTO" + + # Execute the operation for generation mode + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=IdeogramV3Request, + response_model=IdeogramGenerateResponse, + ), + request=gen_request, + files=files if files else None, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + + # Execute the operation and process response + response = await operation.execute() + + if not response.data or len(response.data) == 0: + raise Exception("No images were generated in the response") + + image_urls = [image_data.url for image_data in response.data if image_data.url] + + if not image_urls: + raise Exception("No image URLs were generated in the response") + + display_image_urls_on_node(image_urls, cls.hidden.unique_id) + return IO.NodeOutput(await download_and_process_images(image_urls)) + + +class IdeogramExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + IdeogramV1, + IdeogramV2, + IdeogramV3, + ] + +async def comfy_entrypoint() -> IdeogramExtension: + return IdeogramExtension() diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py new file mode 100644 index 000000000..eea65c9ac --- /dev/null +++ b/comfy_api_nodes/nodes_kling.py @@ -0,0 +1,1538 @@ +"""Kling API Nodes + +For source of truth on the allowed permutations of request fields, please reference: +- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) +""" + +from __future__ import annotations +from typing import Optional, TypeVar +import math +import logging + +from typing_extensions import override + +import torch + +from comfy_api_nodes.apis import ( + KlingCameraControl, + KlingCameraConfig, + KlingCameraControlType, + KlingVideoGenDuration, + KlingVideoGenMode, + KlingVideoGenAspectRatio, + KlingVideoGenModelName, + KlingText2VideoRequest, + KlingText2VideoResponse, + KlingImage2VideoRequest, + KlingImage2VideoResponse, + KlingVideoExtendRequest, + KlingVideoExtendResponse, + KlingLipSyncVoiceLanguage, + KlingLipSyncInputObject, + KlingLipSyncRequest, + KlingLipSyncResponse, + KlingVirtualTryOnModelName, + KlingVirtualTryOnRequest, + KlingVirtualTryOnResponse, + KlingVideoResult, + KlingImageResult, + KlingImageGenerationsRequest, + KlingImageGenerationsResponse, + KlingImageGenImageReferenceType, + KlingImageGenModelName, + KlingImageGenAspectRatio, + KlingVideoEffectsRequest, + KlingVideoEffectsResponse, + KlingDualCharacterEffectsScene, + KlingSingleImageEffectsScene, + KlingDualCharacterEffectInput, + KlingSingleImageEffectInput, + KlingCharacterEffectModelName, + KlingSingleImageEffectModelName, +) +from comfy_api_nodes.util import ( + validate_image_dimensions, + validate_image_aspect_ratio, + validate_video_dimensions, + validate_video_duration, + tensor_to_base64_string, + validate_string, + upload_audio_to_comfyapi, + download_url_to_image_tensor, + upload_video_to_comfyapi, + download_url_to_video_output, + sync_op, + ApiEndpoint, + poll_op, +) +from comfy_api.input_impl import VideoFromFile +from comfy_api.input.basic_types import AudioInput +from comfy_api.input.video_types import VideoInput +from comfy_api.latest import ComfyExtension, IO + +KLING_API_VERSION = "v1" +PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video" +PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video" +PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend" +PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync" +PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects" +PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations" +PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on" +PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations" + +MAX_PROMPT_LENGTH_T2V = 2500 +MAX_PROMPT_LENGTH_I2V = 500 +MAX_PROMPT_LENGTH_IMAGE_GEN = 500 +MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200 +MAX_PROMPT_LENGTH_LIP_SYNC = 120 + +AVERAGE_DURATION_T2V = 319 +AVERAGE_DURATION_I2V = 164 +AVERAGE_DURATION_LIP_SYNC = 455 +AVERAGE_DURATION_VIRTUAL_TRY_ON = 19 +AVERAGE_DURATION_IMAGE_GEN = 32 +AVERAGE_DURATION_VIDEO_EFFECTS = 320 +AVERAGE_DURATION_VIDEO_EXTEND = 320 + +R = TypeVar("R") + + +MODE_TEXT2VIDEO = { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"), + "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"), + "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"), + "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"), + "pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"), + "standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"), + "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"), + "pro mode / 5s duration / kling-v2-1-master": ("pro", "5", "kling-v2-1-master"), + "pro mode / 10s duration / kling-v2-1-master": ("pro", "10", "kling-v2-1-master"), + "pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"), + "pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"), +} +""" +Mapping of mode strings to their corresponding (mode, duration, model_name) tuples. +Only includes config combos that support the `image_tail` request field. + +See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) +""" + + +MODE_START_END_FRAME = { + "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"), + "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"), + "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"), + "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"), + "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"), + "pro mode / 5s duration / kling-v2-1": ("pro", "5", "kling-v2-1"), + "pro mode / 10s duration / kling-v2-1": ("pro", "10", "kling-v2-1"), +} +""" +Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples. +Only includes config combos that support the `image_tail` request field. + +See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap) +""" + + +VOICES_CONFIG = { + # English voices + "Melody": ("girlfriend_4_speech02", "en"), + "Sunny": ("genshin_vindi2", "en"), + "Sage": ("zhinen_xuesheng", "en"), + "Ace": ("AOT", "en"), + "Blossom": ("ai_shatang", "en"), + "Peppy": ("genshin_klee2", "en"), + "Dove": ("genshin_kirara", "en"), + "Shine": ("ai_kaiya", "en"), + "Anchor": ("oversea_male1", "en"), + "Lyric": ("ai_chenjiahao_712", "en"), + "Tender": ("chat1_female_new-3", "en"), + "Siren": ("chat_0407_5-1", "en"), + "Zippy": ("cartoon-boy-07", "en"), + "Bud": ("uk_boy1", "en"), + "Sprite": ("cartoon-girl-01", "en"), + "Candy": ("PeppaPig_platform", "en"), + "Beacon": ("ai_huangzhong_712", "en"), + "Rock": ("ai_huangyaoshi_712", "en"), + "Titan": ("ai_laoguowang_712", "en"), + "Grace": ("chengshu_jiejie", "en"), + "Helen": ("you_pingjing", "en"), + "Lore": ("calm_story1", "en"), + "Crag": ("uk_man2", "en"), + "Prattle": ("laopopo_speech02", "en"), + "Hearth": ("heainainai_speech02", "en"), + "The Reader": ("reader_en_m-v1", "en"), + "Commercial Lady": ("commercial_lady_en_f-v1", "en"), + # Chinese voices + "阳光少年": ("genshin_vindi2", "zh"), + "懂事小弟": ("zhinen_xuesheng", "zh"), + "运动少年": ("tiyuxi_xuedi", "zh"), + "青春少女": ("ai_shatang", "zh"), + "温柔小妹": ("genshin_klee2", "zh"), + "元气少女": ("genshin_kirara", "zh"), + "阳光男生": ("ai_kaiya", "zh"), + "幽默小哥": ("tiexin_nanyou", "zh"), + "文艺小哥": ("ai_chenjiahao_712", "zh"), + "甜美邻家": ("girlfriend_1_speech02", "zh"), + "温柔姐姐": ("chat1_female_new-3", "zh"), + "职场女青": ("girlfriend_2_speech02", "zh"), + "活泼男童": ("cartoon-boy-07", "zh"), + "俏皮女童": ("cartoon-girl-01", "zh"), + "稳重老爸": ("ai_huangyaoshi_712", "zh"), + "温柔妈妈": ("you_pingjing", "zh"), + "严肃上司": ("ai_laoguowang_712", "zh"), + "优雅贵妇": ("chengshu_jiejie", "zh"), + "慈祥爷爷": ("zhuxi_speech02", "zh"), + "唠叨爷爷": ("uk_oldman3", "zh"), + "唠叨奶奶": ("laopopo_speech02", "zh"), + "和蔼奶奶": ("heainainai_speech02", "zh"), + "东北老铁": ("dongbeilaotie_speech02", "zh"), + "重庆小伙": ("chongqingxiaohuo_speech02", "zh"), + "四川妹子": ("chuanmeizi_speech02", "zh"), + "潮汕大叔": ("chaoshandashu_speech02", "zh"), + "台湾男生": ("ai_taiwan_man2_speech02", "zh"), + "西安掌柜": ("xianzhanggui_speech02", "zh"), + "天津姐姐": ("tianjinjiejie_speech02", "zh"), + "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"), + "译制片男": ("yizhipiannan-v1", "zh"), + "撒娇女友": ("tianmeixuemei-v1", "zh"), + "刀片烟嗓": ("daopianyansang-v1", "zh"), + "乖巧正太": ("mengwa-v1", "zh"), +} + + +def is_valid_camera_control_configs(configs: list[float]) -> bool: + """Verifies that at least one camera control configuration is non-zero.""" + return any(not math.isclose(value, 0.0) for value in configs) + + +def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the initial response contains a task ID.""" + return bool(response.data.task_id) + + +def is_valid_video_response(response: KlingText2VideoResponse) -> bool: + """Verifies that the response contains a task result with at least one video.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.videos is not None + and len(response.data.task_result.videos) > 0 + ) + + +def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool: + """Verifies that the response contains a task result with at least one image.""" + return ( + response.data is not None + and response.data.task_result is not None + and response.data.task_result.images is not None + and len(response.data.task_result.images) > 0 + ) + + +def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool: + """Verifies that the positive prompt is not empty and that neither promt is too long.""" + if not prompt: + raise ValueError("Positive prompt is empty") + if len(prompt) > max_length: + raise ValueError(f"Positive prompt is too long: {len(prompt)} characters") + if negative_prompt and len(negative_prompt) > max_length: + raise ValueError( + f"Negative prompt is too long: {len(negative_prompt)} characters" + ) + return True + + +def validate_task_creation_response(response) -> None: + """Validates that the Kling task creation request was successful.""" + if not is_valid_task_creation_response(response): + error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}" + logging.error(error_msg) + raise Exception(error_msg) + + +def validate_video_result_response(response) -> None: + """Validates that the Kling task result contains a video.""" + if not is_valid_video_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response." + logging.error("Error: %s.\nResponse: %s", error_msg, response) + raise Exception(error_msg) + + +def validate_image_result_response(response) -> None: + """Validates that the Kling task result contains an image.""" + if not is_valid_image_response(response): + error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response." + logging.error("Error: %s.\nResponse: %s", error_msg, response) + raise Exception(error_msg) + + +def validate_input_image(image: torch.Tensor) -> None: + """ + Validates the input image adheres to the expectations of the Kling API: + - The image resolution should not be less than 300*300px + - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1 + + See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo + """ + validate_image_dimensions(image, min_width=300, min_height=300) + validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5) + + +def get_video_from_response(response) -> KlingVideoResult: + """Returns the first video object from the Kling video generation task result. + Will raise an error if the response is not valid. + """ + video = response.data.task_result.videos[0] + logging.info( + "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url + ) + return video + + +def get_video_url_from_response(response) -> Optional[str]: + """Returns the first video url from the Kling video generation task result. + Will not raise an error if the response is not valid. + """ + if response and is_valid_video_response(response): + return str(get_video_from_response(response).url) + else: + return None + + +def get_images_from_response(response) -> list[KlingImageResult]: + """Returns the list of image objects from the Kling image generation task result. + Will raise an error if the response is not valid. + """ + images = response.data.task_result.images + logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images) + return images + + +def get_images_urls_from_response(response) -> Optional[str]: + """Returns the list of image urls from the Kling image generation task result. + Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls. + """ + if response and is_valid_image_response(response): + images = get_images_from_response(response) + image_urls = [str(image.url) for image in images] + return "\n".join(image_urls) + else: + return None + + +async def image_result_to_node_output( + images: list[KlingImageResult], +) -> torch.Tensor: + """ + Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor. + If multiple images are returned, they will be stacked along the batch dimension. + """ + if len(images) == 1: + return await download_url_to_image_tensor(str(images[0].url)) + else: + return torch.cat([await download_url_to_image_tensor(str(image.url)) for image in images]) + + +async def execute_text2video( + cls: type[IO.ComfyNode], + prompt: str, + negative_prompt: str, + cfg_scale: float, + model_name: str, + model_mode: str, + duration: str, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, +) -> IO.NodeOutput: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + task_creation_response = await sync_op( + cls, + ApiEndpoint(path=PATH_TEXT_TO_VIDEO, method="POST"), + response_model=KlingText2VideoResponse, + data=KlingText2VideoRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + duration=KlingVideoGenDuration(duration), + mode=KlingVideoGenMode(model_mode), + model_name=KlingVideoGenModelName(model_name), + cfg_scale=cfg_scale, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + camera_control=camera_control, + ), + ) + + validate_task_creation_response(task_creation_response) + + task_id = task_creation_response.data.task_id + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_TEXT_TO_VIDEO}/{task_id}"), + response_model=KlingText2VideoResponse, + estimated_duration=AVERAGE_DURATION_T2V, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration)) + + +async def execute_image2video( + cls: type[IO.ComfyNode], + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + model_name: str, + cfg_scale: float, + model_mode: str, + aspect_ratio: str, + duration: str, + camera_control: Optional[KlingCameraControl] = None, + end_frame: Optional[torch.Tensor] = None, +) -> IO.NodeOutput: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V) + validate_input_image(start_frame) + + if camera_control is not None: + # Camera control type for image 2 video is always `simple` + camera_control.type = KlingCameraControlType.simple + + if model_mode == "std" and model_name == KlingVideoGenModelName.kling_v2_5_turbo.value: + model_mode = "pro" # October 5: currently "std" mode is not supported for this model + + task_creation_response = await sync_op( + cls, + ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"), + response_model=KlingImage2VideoResponse, + data=KlingImage2VideoRequest( + model_name=KlingVideoGenModelName(model_name), + image=tensor_to_base64_string(start_frame), + image_tail=( + tensor_to_base64_string(end_frame) + if end_frame is not None + else None + ), + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + mode=KlingVideoGenMode(model_mode), + duration=KlingVideoGenDuration(duration), + camera_control=camera_control, + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}"), + response_model=KlingImage2VideoResponse, + estimated_duration=AVERAGE_DURATION_I2V, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration)) + + +async def execute_video_effect( + cls: type[IO.ComfyNode], + dual_character: bool, + effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene, + model_name: str, + duration: KlingVideoGenDuration, + image_1: torch.Tensor, + image_2: Optional[torch.Tensor] = None, + model_mode: Optional[KlingVideoGenMode] = None, +) -> tuple[VideoFromFile, str, str]: + if dual_character: + request_input_field = KlingDualCharacterEffectInput( + model_name=model_name, + mode=model_mode, + images=[ + tensor_to_base64_string(image_1), + tensor_to_base64_string(image_2), + ], + duration=duration, + ) + else: + request_input_field = KlingSingleImageEffectInput( + model_name=model_name, + image=tensor_to_base64_string(image_1), + duration=duration, + ) + + task_creation_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=PATH_VIDEO_EFFECTS, method="POST"), + response_model=KlingVideoEffectsResponse, + data=KlingVideoEffectsRequest( + effect_scene=effect_scene, + input=request_input_field, + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_VIDEO_EFFECTS}/{task_id}"), + response_model=KlingVideoEffectsResponse, + estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration) + + +async def execute_lipsync( + cls: type[IO.ComfyNode], + video: VideoInput, + audio: Optional[AudioInput] = None, + voice_language: Optional[str] = None, + model_mode: Optional[str] = None, + text: Optional[str] = None, + voice_speed: Optional[float] = None, + voice_id: Optional[str] = None, +) -> IO.NodeOutput: + if text: + validate_string(text, field_name="Text", max_length=MAX_PROMPT_LENGTH_LIP_SYNC) + validate_video_dimensions(video, 720, 1920) + validate_video_duration(video, 2, 10) + + # Upload video to Comfy API and get download URL + video_url = await upload_video_to_comfyapi(cls, video) + logging.info("Uploaded video to Comfy API. URL: %s", video_url) + + # Upload the audio file to Comfy API and get download URL + if audio: + audio_url = await upload_audio_to_comfyapi(cls, audio) + logging.info("Uploaded audio to Comfy API. URL: %s", audio_url) + else: + audio_url = None + + task_creation_response = await sync_op( + cls, + ApiEndpoint(PATH_LIP_SYNC, "POST"), + response_model=KlingLipSyncResponse, + data=KlingLipSyncRequest( + input=KlingLipSyncInputObject( + video_url=video_url, + mode=model_mode, + text=text, + voice_language=voice_language, + voice_speed=voice_speed, + audio_type="url", + audio_url=audio_url, + voice_id=voice_id, + ), + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_LIP_SYNC}/{task_id}"), + response_model=KlingLipSyncResponse, + estimated_duration=AVERAGE_DURATION_LIP_SYNC, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration)) + + +class KlingCameraControls(IO.ComfyNode): + """Kling Camera Controls Node""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingCameraControls", + display_name="Kling Camera Controls", + category="api node/video/Kling", + description="Allows specifying configuration options for Kling Camera Controls and motion control effects.", + inputs=[ + IO.Combo.Input("camera_control_type", options=KlingCameraControlType), + IO.Float.Input( + "horizontal_movement", + default=0.0, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right", + ), + IO.Float.Input( + "vertical_movement", + default=0.0, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward.", + ), + IO.Float.Input( + "pan", + default=0.5, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.", + ), + IO.Float.Input( + "tilt", + default=0.0, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.", + ), + IO.Float.Input( + "roll", + default=0.0, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.", + ), + IO.Float.Input( + "zoom", + default=0.0, + min=-10.0, + max=10.0, + step=0.25, + display_mode=IO.NumberDisplay.slider, + tooltip="Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.", + ), + ], + outputs=[IO.Custom("CAMERA_CONTROL").Output(display_name="camera_control")], + ) + + @classmethod + def validate_inputs( + cls, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> bool | str: + if not is_valid_camera_control_configs( + [ + horizontal_movement, + vertical_movement, + pan, + tilt, + roll, + zoom, + ] + ): + return "Invalid camera control configs: at least one of the values must be non-zero" + return True + + @classmethod + def execute( + cls, + camera_control_type: str, + horizontal_movement: float, + vertical_movement: float, + pan: float, + tilt: float, + roll: float, + zoom: float, + ) -> IO.NodeOutput: + return IO.NodeOutput( + KlingCameraControl( + type=KlingCameraControlType(camera_control_type), + config=KlingCameraConfig( + horizontal=horizontal_movement, + vertical=vertical_movement, + pan=pan, + roll=roll, + tilt=tilt, + zoom=zoom, + ), + ) + ) + + +class KlingTextToVideoNode(IO.ComfyNode): + """Kling Text to Video Node""" + + @classmethod + def define_schema(cls) -> IO.Schema: + modes = list(MODE_TEXT2VIDEO.keys()) + return IO.Schema( + node_id="KlingTextToVideoNode", + display_name="Kling Text to Video", + category="api node/video/Kling", + description="Kling Text to Video Node", + inputs=[ + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Float.Input("cfg_scale", default=1.0, min=0.0, max=1.0), + IO.Combo.Input( + "aspect_ratio", + options=KlingVideoGenAspectRatio, + default="16:9", + ), + IO.Combo.Input( + "mode", + options=modes, + default=modes[4], + tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + ) -> IO.NodeOutput: + model_mode, duration, model_name = MODE_TEXT2VIDEO[mode] + return await execute_text2video( + cls, + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=cfg_scale, + model_mode=model_mode, + aspect_ratio=aspect_ratio, + model_name=model_name, + duration=duration, + ) + + +class KlingCameraControlT2VNode(IO.ComfyNode): + """ + Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingCameraControlT2VNode", + display_name="Kling Text to Video (Camera Control)", + category="api node/video/Kling", + description="Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text.", + inputs=[ + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Float.Input("cfg_scale", default=0.75, min=0.0, max=1.0), + IO.Combo.Input( + "aspect_ratio", + options=KlingVideoGenAspectRatio, + default="16:9", + ), + IO.Custom("CAMERA_CONTROL").Input( + "camera_control", + tooltip="Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: Optional[KlingCameraControl] = None, + ) -> IO.NodeOutput: + return await execute_text2video( + cls, + model_name=KlingVideoGenModelName.kling_v1, + cfg_scale=cfg_scale, + model_mode=KlingVideoGenMode.std, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + ) + + +class KlingImage2VideoNode(IO.ComfyNode): + """Kling Image to Video Node""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingImage2VideoNode", + display_name="Kling Image to Video", + category="api node/video/Kling", + description="Kling Image to Video Node", + inputs=[ + IO.Image.Input("start_frame", tooltip="The reference image used to generate the video."), + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Combo.Input( + "model_name", + options=KlingVideoGenModelName, + default="kling-v2-master", + ), + IO.Float.Input("cfg_scale", default=0.8, min=0.0, max=1.0), + IO.Combo.Input("mode", options=KlingVideoGenMode, default=KlingVideoGenMode.std), + IO.Combo.Input( + "aspect_ratio", + options=KlingVideoGenAspectRatio, + default=KlingVideoGenAspectRatio.field_16_9, + ), + IO.Combo.Input("duration", options=KlingVideoGenDuration, default=KlingVideoGenDuration.field_5), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + model_name: str, + cfg_scale: float, + mode: str, + aspect_ratio: str, + duration: str, + camera_control: Optional[KlingCameraControl] = None, + end_frame: Optional[torch.Tensor] = None, + ) -> IO.NodeOutput: + return await execute_image2video( + cls, + start_frame=start_frame, + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=cfg_scale, + model_name=model_name, + aspect_ratio=aspect_ratio, + model_mode=mode, + duration=duration, + camera_control=camera_control, + end_frame=end_frame, + ) + + +class KlingCameraControlI2VNode(IO.ComfyNode): + """ + Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera. + Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingCameraControlI2VNode", + display_name="Kling Image to Video (Camera Control)", + category="api node/video/Kling", + description="Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image.", + inputs=[ + IO.Image.Input( + "start_frame", + tooltip="Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.", + ), + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Float.Input("cfg_scale", default=0.75, min=0.0, max=1.0), + IO.Combo.Input( + "aspect_ratio", + options=KlingVideoGenAspectRatio, + default=KlingVideoGenAspectRatio.field_16_9, + ), + IO.Custom("CAMERA_CONTROL").Input( + "camera_control", + tooltip="Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + start_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + camera_control: KlingCameraControl, + ) -> IO.NodeOutput: + return await execute_image2video( + cls, + model_name=KlingVideoGenModelName.kling_v1_5, + start_frame=start_frame, + cfg_scale=cfg_scale, + model_mode=KlingVideoGenMode.pro, + aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio), + duration=KlingVideoGenDuration.field_5, + prompt=prompt, + negative_prompt=negative_prompt, + camera_control=camera_control, + ) + + +class KlingStartEndFrameNode(IO.ComfyNode): + """ + Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + modes = list(MODE_START_END_FRAME.keys()) + return IO.Schema( + node_id="KlingStartEndFrameNode", + display_name="Kling Start-End Frame to Video", + category="api node/video/Kling", + description="Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last.", + inputs=[ + IO.Image.Input( + "start_frame", + tooltip="Reference Image - URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1. Base64 should not include data:image prefix.", + ), + IO.Image.Input( + "end_frame", + tooltip="Reference Image - End frame control. URL or Base64 encoded string, cannot exceed 10MB, resolution not less than 300*300px. Base64 should not include data:image prefix.", + ), + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0), + IO.Combo.Input( + "aspect_ratio", + options=[i.value for i in KlingVideoGenAspectRatio], + default="16:9", + ), + IO.Combo.Input( + "mode", + options=modes, + default=modes[2], + tooltip="The configuration to use for the video generation following the format: mode / duration / model_name.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + prompt: str, + negative_prompt: str, + cfg_scale: float, + aspect_ratio: str, + mode: str, + ) -> IO.NodeOutput: + mode, duration, model_name = MODE_START_END_FRAME[mode] + return await execute_image2video( + cls, + prompt=prompt, + negative_prompt=negative_prompt, + model_name=model_name, + start_frame=start_frame, + cfg_scale=cfg_scale, + model_mode=mode, + aspect_ratio=aspect_ratio, + duration=duration, + end_frame=end_frame, + ) + + +class KlingVideoExtendNode(IO.ComfyNode): + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingVideoExtendNode", + display_name="Kling Video Extend", + category="api node/video/Kling", + description="Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Positive text prompt for guiding the video extension", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + tooltip="Negative text prompt for elements to avoid in the extended video", + ), + IO.Float.Input("cfg_scale", default=0.5, min=0.0, max=1.0), + IO.String.Input( + "video_id", + force_input=True, + tooltip="The ID of the video to be extended. Supports videos generated by text-to-video, image-to-video, and previous video extension operations. Cannot exceed 3 minutes total duration after extension.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + cfg_scale: float, + video_id: str, + ) -> IO.NodeOutput: + validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V) + task_creation_response = await sync_op( + cls, + ApiEndpoint(path=PATH_VIDEO_EXTEND, method="POST"), + response_model=KlingVideoExtendResponse, + data=KlingVideoExtendRequest( + prompt=prompt if prompt else None, + negative_prompt=negative_prompt if negative_prompt else None, + cfg_scale=cfg_scale, + video_id=video_id, + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_VIDEO_EXTEND}/{task_id}"), + response_model=KlingVideoExtendResponse, + estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_video_result_response(final_response) + + video = get_video_from_response(final_response) + return IO.NodeOutput(await download_url_to_video_output(str(video.url)), str(video.id), str(video.duration)) + + +class KlingDualCharacterVideoEffectNode(IO.ComfyNode): + """Kling Dual Character Video Effect Node""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingDualCharacterVideoEffectNode", + display_name="Kling Dual Character Video Effects", + category="api node/video/Kling", + description="Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite.", + inputs=[ + IO.Image.Input("image_left", tooltip="Left side image"), + IO.Image.Input("image_right", tooltip="Right side image"), + IO.Combo.Input( + "effect_scene", + options=[i.value for i in KlingDualCharacterEffectsScene], + ), + IO.Combo.Input( + "model_name", + options=[i.value for i in KlingCharacterEffectModelName], + default="kling-v1", + ), + IO.Combo.Input( + "mode", + options=[i.value for i in KlingVideoGenMode], + default="std", + ), + IO.Combo.Input( + "duration", + options=[i.value for i in KlingVideoGenDuration], + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image_left: torch.Tensor, + image_right: torch.Tensor, + effect_scene: KlingDualCharacterEffectsScene, + model_name: KlingCharacterEffectModelName, + mode: KlingVideoGenMode, + duration: KlingVideoGenDuration, + ) -> IO.NodeOutput: + video, _, duration = await execute_video_effect( + cls, + dual_character=True, + effect_scene=effect_scene, + model_name=model_name, + model_mode=mode, + duration=duration, + image_1=image_left, + image_2=image_right, + ) + return IO.NodeOutput(video, duration) + + +class KlingSingleImageVideoEffectNode(IO.ComfyNode): + """Kling Single Image Video Effect Node""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingSingleImageVideoEffectNode", + display_name="Kling Video Effects", + category="api node/video/Kling", + description="Achieve different special effects when generating a video based on the effect_scene.", + inputs=[ + IO.Image.Input("image", tooltip=" Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1"), + IO.Combo.Input( + "effect_scene", + options=[i.value for i in KlingSingleImageEffectsScene], + ), + IO.Combo.Input( + "model_name", + options=[i.value for i in KlingSingleImageEffectModelName], + ), + IO.Combo.Input( + "duration", + options=[i.value for i in KlingVideoGenDuration], + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + effect_scene: KlingSingleImageEffectsScene, + model_name: KlingSingleImageEffectModelName, + duration: KlingVideoGenDuration, + ) -> IO.NodeOutput: + return IO.NodeOutput( + *( + await execute_video_effect( + cls, + dual_character=False, + effect_scene=effect_scene, + model_name=model_name, + duration=duration, + image_1=image, + ) + ) + ) + + +class KlingLipSyncAudioToVideoNode(IO.ComfyNode): + """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingLipSyncAudioToVideoNode", + display_name="Kling Lip Sync Video with Audio", + category="api node/video/Kling", + description="Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.", + inputs=[ + IO.Video.Input("video"), + IO.Audio.Input("audio"), + IO.Combo.Input( + "voice_language", + options=[i.value for i in KlingLipSyncVoiceLanguage], + default="en", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: VideoInput, + audio: AudioInput, + voice_language: str, + ) -> IO.NodeOutput: + return await execute_lipsync( + cls, + video=video, + audio=audio, + voice_language=voice_language, + model_mode="audio2video", + ) + + +class KlingLipSyncTextToVideoNode(IO.ComfyNode): + """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingLipSyncTextToVideoNode", + display_name="Kling Lip Sync Video with Text", + category="api node/video/Kling", + description="Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length.", + inputs=[ + IO.Video.Input("video"), + IO.String.Input( + "text", + multiline=True, + tooltip="Text Content for Lip-Sync Video Generation. Required when mode is text2video. Maximum length is 120 characters.", + ), + IO.Combo.Input( + "voice", + options=list(VOICES_CONFIG.keys()), + default="Melody", + ), + IO.Float.Input( + "voice_speed", + default=1, + min=0.8, + max=2.0, + display_mode=IO.NumberDisplay.slider, + tooltip="Speech Rate. Valid range: 0.8~2.0, accurate to one decimal place.", + ), + ], + outputs=[ + IO.Video.Output(), + IO.String.Output(display_name="video_id"), + IO.String.Output(display_name="duration"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: VideoInput, + text: str, + voice: str, + voice_speed: float, + ) -> IO.NodeOutput: + voice_id, voice_language = VOICES_CONFIG[voice] + return await execute_lipsync( + cls, + video=video, + text=text, + voice_language=voice_language, + voice_id=voice_id, + voice_speed=voice_speed, + model_mode="text2video", + ) + + +class KlingVirtualTryOnNode(IO.ComfyNode): + """Kling Virtual Try On Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingVirtualTryOnNode", + display_name="Kling Virtual Try On", + category="api node/image/Kling", + description="Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background.", + inputs=[ + IO.Image.Input("human_image"), + IO.Image.Input("cloth_image"), + IO.Combo.Input( + "model_name", + options=[i.value for i in KlingVirtualTryOnModelName], + default="kolors-virtual-try-on-v1", + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + human_image: torch.Tensor, + cloth_image: torch.Tensor, + model_name: KlingVirtualTryOnModelName, + ) -> IO.NodeOutput: + task_creation_response = await sync_op( + cls, + ApiEndpoint(path=PATH_VIRTUAL_TRY_ON, method="POST"), + response_model=KlingVirtualTryOnResponse, + data=KlingVirtualTryOnRequest( + human_image=tensor_to_base64_string(human_image), + cloth_image=tensor_to_base64_string(cloth_image), + model_name=model_name, + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}"), + response_model=KlingVirtualTryOnResponse, + estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return IO.NodeOutput(await image_result_to_node_output(images)) + + +class KlingImageGenerationNode(IO.ComfyNode): + """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="KlingImageGenerationNode", + display_name="Kling Image Generation", + category="api node/image/Kling", + description="Kling Image Generation Node. Generate an image from a text prompt with an optional reference image.", + inputs=[ + IO.String.Input("prompt", multiline=True, tooltip="Positive text prompt"), + IO.String.Input("negative_prompt", multiline=True, tooltip="Negative text prompt"), + IO.Combo.Input( + "image_type", + options=[i.value for i in KlingImageGenImageReferenceType], + ), + IO.Float.Input( + "image_fidelity", + default=0.5, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.slider, + tooltip="Reference intensity for user-uploaded images", + ), + IO.Float.Input( + "human_fidelity", + default=0.45, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.slider, + tooltip="Subject reference similarity", + ), + IO.Combo.Input( + "model_name", + options=[i.value for i in KlingImageGenModelName], + default="kling-v1", + ), + IO.Combo.Input( + "aspect_ratio", + options=[i.value for i in KlingImageGenAspectRatio], + default="16:9", + ), + IO.Int.Input( + "n", + default=1, + min=1, + max=9, + tooltip="Number of generated images", + ), + IO.Image.Input("image", optional=True), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model_name: KlingImageGenModelName, + prompt: str, + negative_prompt: str, + image_type: KlingImageGenImageReferenceType, + image_fidelity: float, + human_fidelity: float, + n: int, + aspect_ratio: KlingImageGenAspectRatio, + image: Optional[torch.Tensor] = None, + ) -> IO.NodeOutput: + validate_string(prompt, field_name="prompt", min_length=1, max_length=MAX_PROMPT_LENGTH_IMAGE_GEN) + validate_string(negative_prompt, field_name="negative_prompt", max_length=MAX_PROMPT_LENGTH_IMAGE_GEN) + + if image is None: + image_type = None + elif model_name == KlingImageGenModelName.kling_v1: + raise ValueError(f"The model {KlingImageGenModelName.kling_v1.value} does not support reference images.") + else: + image = tensor_to_base64_string(image) + + task_creation_response = await sync_op( + cls, + ApiEndpoint(path=PATH_IMAGE_GENERATIONS, method="POST"), + response_model=KlingImageGenerationsResponse, + data=KlingImageGenerationsRequest( + model_name=model_name, + prompt=prompt, + negative_prompt=negative_prompt, + image=image, + image_reference=image_type, + image_fidelity=image_fidelity, + human_fidelity=human_fidelity, + n=n, + aspect_ratio=aspect_ratio, + ), + ) + + validate_task_creation_response(task_creation_response) + task_id = task_creation_response.data.task_id + + final_response = await poll_op( + cls, + ApiEndpoint(path=f"{PATH_IMAGE_GENERATIONS}/{task_id}"), + response_model=KlingImageGenerationsResponse, + estimated_duration=AVERAGE_DURATION_IMAGE_GEN, + status_extractor=lambda r: (r.data.task_status.value if r.data and r.data.task_status else None), + ) + validate_image_result_response(final_response) + + images = get_images_from_response(final_response) + return IO.NodeOutput(await image_result_to_node_output(images)) + + +class KlingExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + KlingCameraControls, + KlingTextToVideoNode, + KlingImage2VideoNode, + KlingCameraControlI2VNode, + KlingCameraControlT2VNode, + KlingStartEndFrameNode, + KlingVideoExtendNode, + KlingLipSyncAudioToVideoNode, + KlingLipSyncTextToVideoNode, + KlingVirtualTryOnNode, + KlingImageGenerationNode, + KlingSingleImageVideoEffectNode, + KlingDualCharacterVideoEffectNode, + ] + + +async def comfy_entrypoint() -> KlingExtension: + return KlingExtension() diff --git a/comfy_api_nodes/nodes_ltxv.py b/comfy_api_nodes/nodes_ltxv.py new file mode 100644 index 000000000..e6ad6e27a --- /dev/null +++ b/comfy_api_nodes/nodes_ltxv.py @@ -0,0 +1,191 @@ +from io import BytesIO +from typing import Optional + +import torch +from pydantic import BaseModel, Field +from typing_extensions import override + +from comfy_api.input_impl import VideoFromFile +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.util import ( + ApiEndpoint, + get_number_of_images, + sync_op_raw, + upload_images_to_comfyapi, + validate_string, +) + +MODELS_MAP = { + "LTX-2 (Pro)": "ltx-2-pro", + "LTX-2 (Fast)": "ltx-2-fast", +} + + +class ExecuteTaskRequest(BaseModel): + prompt: str = Field(...) + model: str = Field(...) + duration: int = Field(...) + resolution: str = Field(...) + fps: Optional[int] = Field(25) + generate_audio: Optional[bool] = Field(True) + image_uri: Optional[str] = Field(None) + + +class TextToVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="LtxvApiTextToVideo", + display_name="LTXV Text To Video", + category="api node/video/LTXV", + description="Professional-quality videos with customizable duration and resolution.", + inputs=[ + IO.Combo.Input("model", options=list(MODELS_MAP.keys())), + IO.String.Input( + "prompt", + multiline=True, + default="", + ), + IO.Combo.Input("duration", options=[6, 8, 10], default=8), + IO.Combo.Input( + "resolution", + options=[ + "1920x1080", + "2560x1440", + "3840x2160", + ], + ), + IO.Combo.Input("fps", options=[25, 50], default=25), + IO.Boolean.Input( + "generate_audio", + default=False, + optional=True, + tooltip="When true, the generated video will include AI-generated audio matching the scene.", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + duration: int, + resolution: str, + fps: int = 25, + generate_audio: bool = False, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1, max_length=10000) + response = await sync_op_raw( + cls, + ApiEndpoint("/proxy/ltx/v1/text-to-video", "POST"), + data=ExecuteTaskRequest( + prompt=prompt, + model=MODELS_MAP[model], + duration=duration, + resolution=resolution, + fps=fps, + generate_audio=generate_audio, + ), + as_binary=True, + max_retries=1, + ) + return IO.NodeOutput(VideoFromFile(BytesIO(response))) + + +class ImageToVideoNode(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="LtxvApiImageToVideo", + display_name="LTXV Image To Video", + category="api node/video/LTXV", + description="Professional-quality videos with customizable duration and resolution based on start image.", + inputs=[ + IO.Image.Input("image", tooltip="First frame to be used for the video."), + IO.Combo.Input("model", options=list(MODELS_MAP.keys())), + IO.String.Input( + "prompt", + multiline=True, + default="", + ), + IO.Combo.Input("duration", options=[6, 8, 10], default=8), + IO.Combo.Input( + "resolution", + options=[ + "1920x1080", + "2560x1440", + "3840x2160", + ], + ), + IO.Combo.Input("fps", options=[25, 50], default=25), + IO.Boolean.Input( + "generate_audio", + default=False, + optional=True, + tooltip="When true, the generated video will include AI-generated audio matching the scene.", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + model: str, + prompt: str, + duration: int, + resolution: str, + fps: int = 25, + generate_audio: bool = False, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1, max_length=10000) + if get_number_of_images(image) != 1: + raise ValueError("Currently only one input image is supported.") + response = await sync_op_raw( + cls, + ApiEndpoint("/proxy/ltx/v1/image-to-video", "POST"), + data=ExecuteTaskRequest( + image_uri=(await upload_images_to_comfyapi(cls, image, max_images=1, mime_type="image/png"))[0], + prompt=prompt, + model=MODELS_MAP[model], + duration=duration, + resolution=resolution, + fps=fps, + generate_audio=generate_audio, + ), + as_binary=True, + max_retries=1, + ) + return IO.NodeOutput(VideoFromFile(BytesIO(response))) + + +class LtxvApiExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + TextToVideoNode, + ImageToVideoNode, + ] + + +async def comfy_entrypoint() -> LtxvApiExtension: + return LtxvApiExtension() diff --git a/comfy_api_nodes/nodes_luma.py b/comfy_api_nodes/nodes_luma.py new file mode 100644 index 000000000..e74441e5e --- /dev/null +++ b/comfy_api_nodes/nodes_luma.py @@ -0,0 +1,759 @@ +from __future__ import annotations +from inspect import cleandoc +from typing import Optional +from typing_extensions import override +from comfy_api.latest import ComfyExtension, IO +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis.luma_api import ( + LumaImageModel, + LumaVideoModel, + LumaVideoOutputResolution, + LumaVideoModelOutputDuration, + LumaAspectRatio, + LumaState, + LumaImageGenerationRequest, + LumaGenerationRequest, + LumaGeneration, + LumaCharacterRef, + LumaModifyImageRef, + LumaImageIdentity, + LumaReference, + LumaReferenceChain, + LumaImageReference, + LumaKeyframes, + LumaConceptChain, + LumaIO, + get_luma_concepts, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + upload_images_to_comfyapi, + process_image_response, +) +from server import PromptServer +from comfy_api_nodes.util import validate_string + +import aiohttp +import torch +from io import BytesIO + +LUMA_T2V_AVERAGE_DURATION = 105 +LUMA_I2V_AVERAGE_DURATION = 100 + +def image_result_url_extractor(response: LumaGeneration): + return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None + +def video_result_url_extractor(response: LumaGeneration): + return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None + +class LumaReferenceNode(IO.ComfyNode): + """ + Holds an image and weight for use with Luma Generate Image node. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaReferenceNode", + display_name="Luma Reference", + category="api node/image/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input( + "image", + tooltip="Image to use as reference.", + ), + IO.Float.Input( + "weight", + default=1.0, + min=0.0, + max=1.0, + step=0.01, + tooltip="Weight of image reference.", + ), + IO.Custom(LumaIO.LUMA_REF).Input( + "luma_ref", + optional=True, + ), + ], + outputs=[IO.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + ) + + @classmethod + def execute( + cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None + ) -> IO.NodeOutput: + if luma_ref is not None: + luma_ref = luma_ref.clone() + else: + luma_ref = LumaReferenceChain() + luma_ref.add(LumaReference(image=image, weight=round(weight, 2))) + return IO.NodeOutput(luma_ref) + + +class LumaConceptsNode(IO.ComfyNode): + """ + Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaConceptsNode", + display_name="Luma Concepts", + category="api node/video/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Combo.Input( + "concept1", + options=get_luma_concepts(include_none=True), + ), + IO.Combo.Input( + "concept2", + options=get_luma_concepts(include_none=True), + ), + IO.Combo.Input( + "concept3", + options=get_luma_concepts(include_none=True), + ), + IO.Combo.Input( + "concept4", + options=get_luma_concepts(include_none=True), + ), + IO.Custom(LumaIO.LUMA_CONCEPTS).Input( + "luma_concepts", + tooltip="Optional Camera Concepts to add to the ones chosen here.", + optional=True, + ), + ], + outputs=[IO.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + ) + + @classmethod + def execute( + cls, + concept1: str, + concept2: str, + concept3: str, + concept4: str, + luma_concepts: LumaConceptChain = None, + ) -> IO.NodeOutput: + chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4]) + if luma_concepts is not None: + chain = luma_concepts.clone_and_merge(chain) + return IO.NodeOutput(chain) + + +class LumaImageGenerationNode(IO.ComfyNode): + """ + Generates images synchronously based on prompt and aspect ratio. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaImageNode", + display_name="Luma Text to Image", + category="api node/image/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Combo.Input( + "model", + options=LumaImageModel, + ), + IO.Combo.Input( + "aspect_ratio", + options=LumaAspectRatio, + default=LumaAspectRatio.ratio_16_9, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + ), + IO.Float.Input( + "style_image_weight", + default=1.0, + min=0.0, + max=1.0, + step=0.01, + tooltip="Weight of style image. Ignored if no style_image provided.", + ), + IO.Custom(LumaIO.LUMA_REF).Input( + "image_luma_ref", + tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.", + optional=True, + ), + IO.Image.Input( + "style_image", + tooltip="Style reference image; only 1 image will be used.", + optional=True, + ), + IO.Image.Input( + "character_image", + tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.", + optional=True, + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + aspect_ratio: str, + seed, + style_image_weight: float, + image_luma_ref: LumaReferenceChain = None, + style_image: torch.Tensor = None, + character_image: torch.Tensor = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=3) + auth_kwargs = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + # handle image_luma_ref + api_image_ref = None + if image_luma_ref is not None: + api_image_ref = await cls._convert_luma_refs( + image_luma_ref, max_refs=4, auth_kwargs=auth_kwargs, + ) + # handle style_luma_ref + api_style_ref = None + if style_image is not None: + api_style_ref = await cls._convert_style_image( + style_image, weight=style_image_weight, auth_kwargs=auth_kwargs, + ) + # handle character_ref images + character_ref = None + if character_image is not None: + download_urls = await upload_images_to_comfyapi( + character_image, max_images=4, auth_kwargs=auth_kwargs, + ) + character_ref = LumaCharacterRef( + identity0=LumaImageIdentity(images=download_urls) + ) + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=aspect_ratio, + image_ref=api_image_ref, + style_ref=api_style_ref, + character_ref=character_ref, + ), + auth_kwargs=auth_kwargs, + ) + response_api: LumaGeneration = await operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=cls.hidden.unique_id, + auth_kwargs=auth_kwargs, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.assets.image) as img_response: + img = process_image_response(await img_response.content.read()) + return IO.NodeOutput(img) + + @classmethod + async def _convert_luma_refs( + cls, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None + ): + luma_urls = [] + ref_count = 0 + for ref in luma_ref.refs: + download_urls = await upload_images_to_comfyapi( + ref.image, max_images=1, auth_kwargs=auth_kwargs + ) + luma_urls.append(download_urls[0]) + ref_count += 1 + if ref_count >= max_refs: + break + return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs) + + @classmethod + async def _convert_style_image( + cls, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None + ): + chain = LumaReferenceChain( + first_ref=LumaReference(image=style_image, weight=weight) + ) + return await cls._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs) + + +class LumaImageModifyNode(IO.ComfyNode): + """ + Modifies images synchronously based on prompt and aspect ratio. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaImageModifyNode", + display_name="Luma Image to Image", + category="api node/image/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input( + "image", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the image generation", + ), + IO.Float.Input( + "image_weight", + default=0.1, + min=0.0, + max=0.98, + step=0.01, + tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.", + ), + IO.Combo.Input( + "model", + options=LumaImageModel, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + ), + ], + outputs=[IO.Image.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + image: torch.Tensor, + image_weight: float, + seed, + ) -> IO.NodeOutput: + auth_kwargs = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + # first, upload image + download_urls = await upload_images_to_comfyapi( + image, max_images=1, auth_kwargs=auth_kwargs, + ) + image_url = download_urls[0] + # next, make Luma call with download url provided + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations/image", + method=HttpMethod.POST, + request_model=LumaImageGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaImageGenerationRequest( + prompt=prompt, + model=model, + modify_image_ref=LumaModifyImageRef( + url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2) + ), + ), + auth_kwargs=auth_kwargs, + ) + response_api: LumaGeneration = await operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=image_result_url_extractor, + node_id=cls.hidden.unique_id, + auth_kwargs=auth_kwargs, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.assets.image) as img_response: + img = process_image_response(await img_response.content.read()) + return IO.NodeOutput(img) + + +class LumaTextToVideoGenerationNode(IO.ComfyNode): + """ + Generates videos synchronously based on prompt and output_size. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaVideoNode", + display_name="Luma Text to Video", + category="api node/video/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the video generation", + ), + IO.Combo.Input( + "model", + options=LumaVideoModel, + ), + IO.Combo.Input( + "aspect_ratio", + options=LumaAspectRatio, + default=LumaAspectRatio.ratio_16_9, + ), + IO.Combo.Input( + "resolution", + options=LumaVideoOutputResolution, + default=LumaVideoOutputResolution.res_540p, + ), + IO.Combo.Input( + "duration", + options=LumaVideoModelOutputDuration, + ), + IO.Boolean.Input( + "loop", + default=False, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + ), + IO.Custom(LumaIO.LUMA_CONCEPTS).Input( + "luma_concepts", + tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.", + optional=True, + ) + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + aspect_ratio: str, + resolution: str, + duration: str, + loop: bool, + seed, + luma_concepts: LumaConceptChain = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False, min_length=3) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + auth_kwargs = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + resolution=resolution, + aspect_ratio=aspect_ratio, + duration=duration, + loop=loop, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_kwargs=auth_kwargs, + ) + response_api: LumaGeneration = await operation.execute() + + if cls.hidden.unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id) + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=cls.hidden.unique_id, + estimated_duration=LUMA_T2V_AVERAGE_DURATION, + auth_kwargs=auth_kwargs, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.assets.video) as vid_response: + return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read()))) + + +class LumaImageToVideoGenerationNode(IO.ComfyNode): + """ + Generates videos synchronously based on prompt, input images, and output_size. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="LumaImageToVideoNode", + display_name="Luma Image to Video", + category="api node/video/Luma", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the video generation", + ), + IO.Combo.Input( + "model", + options=LumaVideoModel, + ), + # IO.Combo.Input( + # "aspect_ratio", + # options=[ratio.value for ratio in LumaAspectRatio], + # default=LumaAspectRatio.ratio_16_9, + # ), + IO.Combo.Input( + "resolution", + options=LumaVideoOutputResolution, + default=LumaVideoOutputResolution.res_540p, + ), + IO.Combo.Input( + "duration", + options=[dur.value for dur in LumaVideoModelOutputDuration], + ), + IO.Boolean.Input( + "loop", + default=False, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + ), + IO.Image.Input( + "first_image", + tooltip="First frame of generated video.", + optional=True, + ), + IO.Image.Input( + "last_image", + tooltip="Last frame of generated video.", + optional=True, + ), + IO.Custom(LumaIO.LUMA_CONCEPTS).Input( + "luma_concepts", + tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.", + optional=True, + ) + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + model: str, + resolution: str, + duration: str, + loop: bool, + seed, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + luma_concepts: LumaConceptChain = None, + ) -> IO.NodeOutput: + if first_image is None and last_image is None: + raise Exception( + "At least one of first_image and last_image requires an input." + ) + auth_kwargs = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + keyframes = await cls._convert_to_keyframes(first_image, last_image, auth_kwargs=auth_kwargs) + duration = duration if model != LumaVideoModel.ray_1_6 else None + resolution = resolution if model != LumaVideoModel.ray_1_6 else None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/luma/generations", + method=HttpMethod.POST, + request_model=LumaGenerationRequest, + response_model=LumaGeneration, + ), + request=LumaGenerationRequest( + prompt=prompt, + model=model, + aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason + resolution=resolution, + duration=duration, + loop=loop, + keyframes=keyframes, + concepts=luma_concepts.create_api_model() if luma_concepts else None, + ), + auth_kwargs=auth_kwargs, + ) + response_api: LumaGeneration = await operation.execute() + + if cls.hidden.unique_id: + PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id) + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/luma/generations/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=LumaGeneration, + ), + completed_statuses=[LumaState.completed], + failed_statuses=[LumaState.failed], + status_extractor=lambda x: x.state, + result_url_extractor=video_result_url_extractor, + node_id=cls.hidden.unique_id, + estimated_duration=LUMA_I2V_AVERAGE_DURATION, + auth_kwargs=auth_kwargs, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.assets.video) as vid_response: + return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read()))) + + @classmethod + async def _convert_to_keyframes( + cls, + first_image: torch.Tensor = None, + last_image: torch.Tensor = None, + auth_kwargs: Optional[dict[str,str]] = None, + ): + if first_image is None and last_image is None: + return None + frame0 = None + frame1 = None + if first_image is not None: + download_urls = await upload_images_to_comfyapi( + first_image, max_images=1, auth_kwargs=auth_kwargs, + ) + frame0 = LumaImageReference(type="image", url=download_urls[0]) + if last_image is not None: + download_urls = await upload_images_to_comfyapi( + last_image, max_images=1, auth_kwargs=auth_kwargs, + ) + frame1 = LumaImageReference(type="image", url=download_urls[0]) + return LumaKeyframes(frame0=frame0, frame1=frame1) + + +class LumaExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + LumaImageGenerationNode, + LumaImageModifyNode, + LumaTextToVideoGenerationNode, + LumaImageToVideoGenerationNode, + LumaReferenceNode, + LumaConceptsNode, + ] + + +async def comfy_entrypoint() -> LumaExtension: + return LumaExtension() diff --git a/comfy_api_nodes/nodes_minimax.py b/comfy_api_nodes/nodes_minimax.py new file mode 100644 index 000000000..e3722e79b --- /dev/null +++ b/comfy_api_nodes/nodes_minimax.py @@ -0,0 +1,531 @@ +from inspect import cleandoc +from typing import Optional +import logging +import torch + +from typing_extensions import override +from comfy_api.latest import ComfyExtension, IO +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api_nodes.apis import ( + MinimaxVideoGenerationRequest, + MinimaxVideoGenerationResponse, + MinimaxFileRetrieveResponse, + MinimaxTaskResultResponse, + SubjectReferenceItem, + MiniMaxModel, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + upload_images_to_comfyapi, +) +from comfy_api_nodes.util import validate_string +from server import PromptServer + + +I2V_AVERAGE_DURATION = 114 +T2V_AVERAGE_DURATION = 234 + + +async def _generate_mm_video( + *, + auth: dict[str, str], + node_id: str, + prompt_text: str, + seed: int, + model: str, + image: Optional[torch.Tensor] = None, # used for ImageToVideo + subject: Optional[torch.Tensor] = None, # used for SubjectToVideo + average_duration: Optional[int] = None, +) -> IO.NodeOutput: + if image is None: + validate_string(prompt_text, field_name="prompt_text") + # upload image, if passed in + image_url = None + if image is not None: + image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=auth))[0] + + # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model + subject_reference = None + if subject is not None: + subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=auth))[0] + subject_reference = [SubjectReferenceItem(image=subject_url)] + + + video_generate_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/video_generation", + method=HttpMethod.POST, + request_model=MinimaxVideoGenerationRequest, + response_model=MinimaxVideoGenerationResponse, + ), + request=MinimaxVideoGenerationRequest( + model=MiniMaxModel(model), + prompt=prompt_text, + callback_url=None, + first_frame_image=image_url, + subject_reference=subject_reference, + prompt_optimizer=None, + ), + auth_kwargs=auth, + ) + response = await video_generate_operation.execute() + + task_id = response.task_id + if not task_id: + raise Exception(f"MiniMax generation failed: {response.base_resp}") + + video_generate_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/minimax/query/video_generation", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxTaskResultResponse, + query_params={"task_id": task_id}, + ), + completed_statuses=["Success"], + failed_statuses=["Fail"], + status_extractor=lambda x: x.status.value, + estimated_duration=average_duration, + node_id=node_id, + auth_kwargs=auth, + ) + task_result = await video_generate_operation.execute() + + file_id = task_result.file_id + if file_id is None: + raise Exception("Request was not successful. Missing file ID.") + file_retrieve_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/files/retrieve", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxFileRetrieveResponse, + query_params={"file_id": int(file_id)}, + ), + request=EmptyRequest(), + auth_kwargs=auth, + ) + file_result = await file_retrieve_operation.execute() + + file_url = file_result.file.download_url + if file_url is None: + raise Exception( + f"No video was found in the response. Full response: {file_result.model_dump()}" + ) + logging.info("Generated video URL: %s", file_url) + if node_id: + if hasattr(file_result.file, "backup_download_url"): + message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" + else: + message = f"Result URL: {file_url}" + PromptServer.instance.send_progress_text(message, node_id) + + # Download and return as VideoFromFile + video_io = await download_url_to_bytesio(file_url) + if video_io is None: + error_msg = f"Failed to download video from {file_url}" + logging.error(error_msg) + raise Exception(error_msg) + return IO.NodeOutput(VideoFromFile(video_io)) + + +class MinimaxTextToVideoNode(IO.ComfyNode): + """ + Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MinimaxTextToVideoNode", + display_name="MiniMax Text to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + IO.Combo.Input( + "model", + options=["T2V-01", "T2V-01-Director"], + default="T2V-01", + tooltip="Model to use for video generation", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt_text: str, + model: str = "T2V-01", + seed: int = 0, + ) -> IO.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=None, + subject=None, + average_duration=T2V_AVERAGE_DURATION, + ) + + +class MinimaxImageToVideoNode(IO.ComfyNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MinimaxImageToVideoNode", + display_name="MiniMax Image to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input( + "image", + tooltip="Image to use as first frame of video generation", + ), + IO.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + IO.Combo.Input( + "model", + options=["I2V-01-Director", "I2V-01", "I2V-01-live"], + default="I2V-01", + tooltip="Model to use for video generation", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt_text: str, + model: str = "I2V-01", + seed: int = 0, + ) -> IO.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=image, + subject=None, + average_duration=I2V_AVERAGE_DURATION, + ) + + +class MinimaxSubjectToVideoNode(IO.ComfyNode): + """ + Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MinimaxSubjectToVideoNode", + display_name="MiniMax Subject to Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input( + "subject", + tooltip="Image of subject to reference for video generation", + ), + IO.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation", + ), + IO.Combo.Input( + "model", + options=["S2V-01"], + default="S2V-01", + tooltip="Model to use for video generation", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + subject: torch.Tensor, + prompt_text: str, + model: str = "S2V-01", + seed: int = 0, + ) -> IO.NodeOutput: + return await _generate_mm_video( + auth={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + node_id=cls.hidden.unique_id, + prompt_text=prompt_text, + seed=seed, + model=model, + image=None, + subject=subject, + average_duration=T2V_AVERAGE_DURATION, + ) + + +class MinimaxHailuoVideoNode(IO.ComfyNode): + """Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MinimaxHailuoVideoNode", + display_name="MiniMax Hailuo Video", + category="api node/video/MiniMax", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt_text", + multiline=True, + default="", + tooltip="Text prompt to guide the video generation.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + step=1, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + optional=True, + ), + IO.Image.Input( + "first_frame_image", + tooltip="Optional image to use as the first frame to generate a video.", + optional=True, + ), + IO.Boolean.Input( + "prompt_optimizer", + default=True, + tooltip="Optimize prompt to improve generation quality when needed.", + optional=True, + ), + IO.Combo.Input( + "duration", + options=[6, 10], + default=6, + tooltip="The length of the output video in seconds.", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=["768P", "1080P"], + default="768P", + tooltip="The dimensions of the video display. 1080p is 1920x1080, 768p is 1366x768.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt_text: str, + seed: int = 0, + first_frame_image: Optional[torch.Tensor] = None, # used for ImageToVideo + prompt_optimizer: bool = True, + duration: int = 6, + resolution: str = "768P", + model: str = "MiniMax-Hailuo-02", + ) -> IO.NodeOutput: + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + if first_frame_image is None: + validate_string(prompt_text, field_name="prompt_text") + + if model == "MiniMax-Hailuo-02" and resolution.upper() == "1080P" and duration != 6: + raise Exception( + "When model is MiniMax-Hailuo-02 and resolution is 1080P, duration is limited to 6 seconds." + ) + + # upload image, if passed in + image_url = None + if first_frame_image is not None: + image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=auth))[0] + + video_generate_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/video_generation", + method=HttpMethod.POST, + request_model=MinimaxVideoGenerationRequest, + response_model=MinimaxVideoGenerationResponse, + ), + request=MinimaxVideoGenerationRequest( + model=MiniMaxModel(model), + prompt=prompt_text, + callback_url=None, + first_frame_image=image_url, + prompt_optimizer=prompt_optimizer, + duration=duration, + resolution=resolution, + ), + auth_kwargs=auth, + ) + response = await video_generate_operation.execute() + + task_id = response.task_id + if not task_id: + raise Exception(f"MiniMax generation failed: {response.base_resp}") + + average_duration = 120 if resolution == "768P" else 240 + video_generate_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/minimax/query/video_generation", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxTaskResultResponse, + query_params={"task_id": task_id}, + ), + completed_statuses=["Success"], + failed_statuses=["Fail"], + status_extractor=lambda x: x.status.value, + estimated_duration=average_duration, + node_id=cls.hidden.unique_id, + auth_kwargs=auth, + ) + task_result = await video_generate_operation.execute() + + file_id = task_result.file_id + if file_id is None: + raise Exception("Request was not successful. Missing file ID.") + file_retrieve_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/minimax/files/retrieve", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=MinimaxFileRetrieveResponse, + query_params={"file_id": int(file_id)}, + ), + request=EmptyRequest(), + auth_kwargs=auth, + ) + file_result = await file_retrieve_operation.execute() + + file_url = file_result.file.download_url + if file_url is None: + raise Exception( + f"No video was found in the response. Full response: {file_result.model_dump()}" + ) + logging.info("Generated video URL: %s", file_url) + if cls.hidden.unique_id: + if hasattr(file_result.file, "backup_download_url"): + message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}" + else: + message = f"Result URL: {file_url}" + PromptServer.instance.send_progress_text(message, cls.hidden.unique_id) + + video_io = await download_url_to_bytesio(file_url) + if video_io is None: + error_msg = f"Failed to download video from {file_url}" + logging.error(error_msg) + raise Exception(error_msg) + return IO.NodeOutput(VideoFromFile(video_io)) + + +class MinimaxExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + MinimaxTextToVideoNode, + MinimaxImageToVideoNode, + # MinimaxSubjectToVideoNode, + MinimaxHailuoVideoNode, + ] + + +async def comfy_entrypoint() -> MinimaxExtension: + return MinimaxExtension() diff --git a/comfy_api_nodes/nodes_moonvalley.py b/comfy_api_nodes/nodes_moonvalley.py new file mode 100644 index 000000000..7c31d95b3 --- /dev/null +++ b/comfy_api_nodes/nodes_moonvalley.py @@ -0,0 +1,525 @@ +import logging +from typing import Optional + +import torch +from typing_extensions import override + +from comfy_api.input import VideoInput +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apis import ( + MoonvalleyPromptResponse, + MoonvalleyTextToVideoInferenceParams, + MoonvalleyTextToVideoRequest, + MoonvalleyVideoToVideoInferenceParams, + MoonvalleyVideoToVideoRequest, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_video_output, + poll_op, + sync_op, + trim_video, + upload_images_to_comfyapi, + upload_video_to_comfyapi, + validate_container_format_is_mp4, + validate_image_dimensions, + validate_string, +) + +API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads" +API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts" +API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video" +API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video" +API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video" + +MIN_WIDTH = 300 +MIN_HEIGHT = 300 + +MAX_WIDTH = 10000 +MAX_HEIGHT = 10000 + +MIN_VID_WIDTH = 300 +MIN_VID_HEIGHT = 300 + +MAX_VID_WIDTH = 10000 +MAX_VID_HEIGHT = 10000 + +MAX_VIDEO_SIZE = 1024 * 1024 * 1024 # 1 GB max for in-memory video processing + +MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000 + + +def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool: + """Verifies that the initial response contains a task ID.""" + return bool(response.id) + + +def validate_task_creation_response(response) -> None: + if not is_valid_task_creation_response(response): + error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}" + logging.error(error_msg) + raise RuntimeError(error_msg) + + +def validate_video_to_video_input(video: VideoInput) -> VideoInput: + """ + Validates and processes video input for Moonvalley Video-to-Video generation. + + Args: + video: Input video to validate + + Returns: + Validated and potentially trimmed video + + Raises: + ValueError: If video doesn't meet requirements + MoonvalleyApiError: If video duration is too short + """ + width, height = _get_video_dimensions(video) + _validate_video_dimensions(width, height) + validate_container_format_is_mp4(video) + + return _validate_and_trim_duration(video) + + +def _get_video_dimensions(video: VideoInput) -> tuple[int, int]: + """Extracts video dimensions with error handling.""" + try: + return video.get_dimensions() + except Exception as e: + logging.error("Error getting dimensions of video: %s", e) + raise ValueError(f"Cannot get video dimensions: {e}") from e + + +def _validate_video_dimensions(width: int, height: int) -> None: + """Validates video dimensions meet Moonvalley V2V requirements.""" + supported_resolutions = { + (1920, 1080), + (1080, 1920), + (1152, 1152), + (1536, 1152), + (1152, 1536), + } + + if (width, height) not in supported_resolutions: + supported_list = ", ".join([f"{w}x{h}" for w, h in sorted(supported_resolutions)]) + raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}") + + +def _validate_and_trim_duration(video: VideoInput) -> VideoInput: + """Validates video duration and trims to 5 seconds if needed.""" + duration = video.get_duration() + _validate_minimum_duration(duration) + return _trim_if_too_long(video, duration) + + +def _validate_minimum_duration(duration: float) -> None: + """Ensures video is at least 5 seconds long.""" + if duration < 5: + raise ValueError("Input video must be at least 5 seconds long.") + + +def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput: + """Trims video to 5 seconds if longer.""" + if duration > 5: + return trim_video(video, 5) + return video + + +def parse_width_height_from_res(resolution: str): + # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict + res_map = { + "16:9 (1920 x 1080)": {"width": 1920, "height": 1080}, + "9:16 (1080 x 1920)": {"width": 1080, "height": 1920}, + "1:1 (1152 x 1152)": {"width": 1152, "height": 1152}, + "4:3 (1536 x 1152)": {"width": 1536, "height": 1152}, + "3:4 (1152 x 1536)": {"width": 1152, "height": 1536}, + # "21:9 (2560 x 1080)": {"width": 2560, "height": 1080}, + } + return res_map.get(resolution, {"width": 1920, "height": 1080}) + + +def parse_control_parameter(value): + control_map = { + "Motion Transfer": "motion_control", + "Canny": "canny_control", + "Pose Transfer": "pose_control", + "Depth": "depth_control", + } + return control_map.get(value, control_map["Motion Transfer"]) + + +async def get_response(cls: type[IO.ComfyNode], task_id: str) -> MoonvalleyPromptResponse: + return await poll_op( + cls, + ApiEndpoint(path=f"{API_PROMPTS_ENDPOINT}/{task_id}"), + response_model=MoonvalleyPromptResponse, + status_extractor=lambda r: (r.status if r and r.status else None), + poll_interval=16.0, + max_poll_attempts=240, + ) + + +class MoonvalleyImg2VideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MoonvalleyImg2VideoNode", + display_name="Moonvalley Marey Image to Video", + category="api node/video/Moonvalley Marey", + description="Moonvalley Marey Image to Video Node", + inputs=[ + IO.Image.Input( + "image", + tooltip="The reference image used to generate the video", + ), + IO.String.Input( + "prompt", + multiline=True, + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", + ), + IO.Combo.Input( + "resolution", + options=[ + "16:9 (1920 x 1080)", + "9:16 (1080 x 1920)", + "1:1 (1152 x 1152)", + "4:3 (1536 x 1152)", + "3:4 (1152 x 1536)", + # "21:9 (2560 x 1080)", + ], + default="16:9 (1920 x 1080)", + tooltip="Resolution of the output video", + ), + IO.Float.Input( + "prompt_adherence", + default=4.5, + min=1.0, + max=20.0, + step=1.0, + tooltip="Guidance scale for generation control", + ), + IO.Int.Input( + "seed", + default=9, + min=0, + max=4294967295, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed value", + control_after_generate=True, + ), + IO.Int.Input( + "steps", + default=33, + min=1, + max=100, + step=1, + tooltip="Number of denoising steps", + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt: str, + negative_prompt: str, + resolution: str, + prompt_adherence: float, + seed: int, + steps: int, + ) -> IO.NodeOutput: + validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH) + validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + width_height = parse_width_height_from_res(resolution) + + inference_params = MoonvalleyTextToVideoInferenceParams( + negative_prompt=negative_prompt, + steps=steps, + seed=seed, + guidance_scale=prompt_adherence, + width=width_height["width"], + height=width_height["height"], + use_negative_prompts=True, + ) + + # Get MIME type from tensor - assuming PNG format for image tensors + mime_type = "image/png" + image_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type=mime_type))[0] + task_creation_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=API_IMG2VIDEO_ENDPOINT, method="POST"), + response_model=MoonvalleyPromptResponse, + data=MoonvalleyTextToVideoRequest( + image_url=image_url, prompt_text=prompt, inference_params=inference_params + ), + ) + validate_task_creation_response(task_creation_response) + final_response = await get_response(cls, task_creation_response.id) + video = await download_url_to_video_output(final_response.output_url) + return IO.NodeOutput(video) + + +class MoonvalleyVideo2VideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MoonvalleyVideo2VideoNode", + display_name="Moonvalley Marey Video to Video", + category="api node/video/Moonvalley Marey", + description="", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + tooltip="Describes the video to generate", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", + ), + IO.Int.Input( + "seed", + default=9, + min=0, + max=4294967295, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed value", + control_after_generate=False, + ), + IO.Video.Input( + "video", + tooltip="The reference video used to generate the output video. Must be at least 5 seconds long. " + "Videos longer than 5s will be automatically trimmed. Only MP4 format supported.", + ), + IO.Combo.Input( + "control_type", + options=["Motion Transfer", "Pose Transfer"], + default="Motion Transfer", + optional=True, + ), + IO.Int.Input( + "motion_intensity", + default=100, + min=0, + max=100, + step=1, + tooltip="Only used if control_type is 'Motion Transfer'", + optional=True, + ), + IO.Int.Input( + "steps", + default=33, + min=1, + max=100, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Number of inference steps", + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + seed: int, + video: Optional[VideoInput] = None, + control_type: str = "Motion Transfer", + motion_intensity: Optional[int] = 100, + steps=33, + prompt_adherence=4.5, + ) -> IO.NodeOutput: + validated_video = validate_video_to_video_input(video) + video_url = await upload_video_to_comfyapi(cls, validated_video) + validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + + # Only include motion_intensity for Motion Transfer + control_params = {} + if control_type == "Motion Transfer" and motion_intensity is not None: + control_params["motion_intensity"] = motion_intensity + + inference_params = MoonvalleyVideoToVideoInferenceParams( + negative_prompt=negative_prompt, + seed=seed, + control_params=control_params, + steps=steps, + guidance_scale=prompt_adherence, + ) + + task_creation_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=API_VIDEO2VIDEO_ENDPOINT, method="POST"), + response_model=MoonvalleyPromptResponse, + data=MoonvalleyVideoToVideoRequest( + control_type=parse_control_parameter(control_type), + video_url=video_url, + prompt_text=prompt, + inference_params=inference_params, + ), + ) + validate_task_creation_response(task_creation_response) + final_response = await get_response(cls, task_creation_response.id) + return IO.NodeOutput(await download_url_to_video_output(final_response.output_url)) + + +class MoonvalleyTxt2VideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="MoonvalleyTxt2VideoNode", + display_name="Moonvalley Marey Text to Video", + category="api node/video/Moonvalley Marey", + description="", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default=" gopro, bright, contrast, static, overexposed, vignette, " + "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " + "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " + "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " + "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " + "wobbly, weird, low quality, plastic, stock footage, video camera, boring", + tooltip="Negative prompt text", + ), + IO.Combo.Input( + "resolution", + options=[ + "16:9 (1920 x 1080)", + "9:16 (1080 x 1920)", + "1:1 (1152 x 1152)", + "4:3 (1536 x 1152)", + "3:4 (1152 x 1536)", + "21:9 (2560 x 1080)", + ], + default="16:9 (1920 x 1080)", + tooltip="Resolution of the output video", + ), + IO.Float.Input( + "prompt_adherence", + default=4.0, + min=1.0, + max=20.0, + step=1.0, + tooltip="Guidance scale for generation control", + ), + IO.Int.Input( + "seed", + default=9, + min=0, + max=4294967295, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Random seed value", + ), + IO.Int.Input( + "steps", + default=33, + min=1, + max=100, + step=1, + tooltip="Inference steps", + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: str, + resolution: str, + prompt_adherence: float, + seed: int, + steps: int, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) + width_height = parse_width_height_from_res(resolution) + + inference_params = MoonvalleyTextToVideoInferenceParams( + negative_prompt=negative_prompt, + steps=steps, + seed=seed, + guidance_scale=prompt_adherence, + num_frames=128, + width=width_height["width"], + height=width_height["height"], + ) + + task_creation_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=API_TXT2VIDEO_ENDPOINT, method="POST"), + response_model=MoonvalleyPromptResponse, + data=MoonvalleyTextToVideoRequest(prompt_text=prompt, inference_params=inference_params), + ) + validate_task_creation_response(task_creation_response) + final_response = await get_response(cls, task_creation_response.id) + return IO.NodeOutput(await download_url_to_video_output(final_response.output_url)) + + +class MoonvalleyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + MoonvalleyImg2VideoNode, + MoonvalleyTxt2VideoNode, + MoonvalleyVideo2VideoNode, + ] + + +async def comfy_entrypoint() -> MoonvalleyExtension: + return MoonvalleyExtension() diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py new file mode 100644 index 000000000..c467e840c --- /dev/null +++ b/comfy_api_nodes/nodes_openai.py @@ -0,0 +1,1002 @@ +import io +from typing import TypedDict, Optional +import json +import os +import time +import re +import uuid +from enum import Enum +from inspect import cleandoc +import numpy as np +import torch +from PIL import Image +from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict +from server import PromptServer +import folder_paths + + +from comfy_api_nodes.apis import ( + OpenAIImageGenerationRequest, + OpenAIImageEditRequest, + OpenAIImageGenerationResponse, + OpenAICreateResponse, + OpenAIResponse, + CreateModelResponseProperties, + Item, + Includable, + OutputContent, + InputImageContent, + Detail, + InputTextContent, + InputMessage, + InputMessageContentList, + InputContent, + InputFileContent, +) + +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) + +from comfy_api_nodes.apinode_utils import ( + validate_and_cast_response, + text_filepath_to_data_uri, +) +from comfy_api_nodes.mapper_utils import model_field_to_node_input +from comfy_api_nodes.util import downscale_image_tensor, validate_string, tensor_to_base64_string + + +RESPONSES_ENDPOINT = "/proxy/openai/v1/responses" +STARTING_POINT_ID_PATTERN = r"" + + +class HistoryEntry(TypedDict): + """Type definition for a single history entry in the chat.""" + + prompt: str + response: str + response_id: str + timestamp: float + + +class ChatHistory(TypedDict): + """Type definition for the chat history dictionary.""" + + __annotations__: dict[str, list[HistoryEntry]] + + +class SupportedOpenAIModel(str, Enum): + o4_mini = "o4-mini" + o1 = "o1" + o3 = "o3" + o1_pro = "o1-pro" + gpt_4o = "gpt-4o" + gpt_4_1 = "gpt-4.1" + gpt_4_1_mini = "gpt-4.1-mini" + gpt_4_1_nano = "gpt-4.1-nano" + gpt_5 = "gpt-5" + gpt_5_mini = "gpt-5-mini" + gpt_5_nano = "gpt-5-nano" + + +class OpenAIDalle2(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 2 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["256x256", "512x512", "1024x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + async def api_call( + self, + prompt, + seed=0, + image=None, + mask=None, + n=1, + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-2" + path = "/proxy/openai/images/generations" + content_type = "application/json" + request_class = OpenAIImageGenerationRequest + img_binary = None + + if image is not None and mask is not None: + path = "/proxy/openai/images/edits" + content_type = "multipart/form-data" + request_class = OpenAIImageEditRequest + + input_tensor = image.squeeze().cpu() + height, width, channels = input_tensor.shape + rgba_tensor = torch.ones(height, width, 4, device="cpu") + rgba_tensor[:, :, :channels] = input_tensor + + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu() + + rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze() + + image_np = (rgba_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + img_binary = img_byte_arr # .getvalue() + img_binary.name = "image.png" + elif image is not None or mask is not None: + raise Exception("Dall-E 2 image editing requires an image AND a mask") + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + n=n, + size=size, + seed=seed, + ), + files=( + { + "image": img_binary, + } + if img_binary + else None + ), + content_type=content_type, + auth_kwargs=kwargs, + ) + + response = await operation.execute() + + img_tensor = await validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAIDalle3(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's DALL·E 3 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for DALL·E", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["standard", "hd"], + "default": "standard", + "tooltip": "Image quality", + }, + ), + "style": ( + IO.COMBO, + { + "options": ["natural", "vivid"], + "default": "natural", + "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["1024x1024", "1024x1792", "1792x1024"], + "default": "1024x1024", + "tooltip": "Image size", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + async def api_call( + self, + prompt, + seed=0, + style="natural", + quality="standard", + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "dall-e-3" + + # build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/openai/images/generations", + method=HttpMethod.POST, + request_model=OpenAIImageGenerationRequest, + response_model=OpenAIImageGenerationResponse, + ), + request=OpenAIImageGenerationRequest( + model=model, + prompt=prompt, + quality=quality, + size=size, + style=style, + seed=seed, + ), + auth_kwargs=kwargs, + ) + + response = await operation.execute() + + img_tensor = await validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAIGPTImage1(ComfyNodeABC): + """ + Generates images synchronously via OpenAI's GPT Image 1 endpoint. + """ + + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text prompt for GPT Image 1", + }, + ), + }, + "optional": { + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 2**31 - 1, + "step": 1, + "display": "number", + "control_after_generate": True, + "tooltip": "not implemented yet in backend", + }, + ), + "quality": ( + IO.COMBO, + { + "options": ["low", "medium", "high"], + "default": "low", + "tooltip": "Image quality, affects cost and generation time.", + }, + ), + "background": ( + IO.COMBO, + { + "options": ["opaque", "transparent"], + "default": "opaque", + "tooltip": "Return image with or without background", + }, + ), + "size": ( + IO.COMBO, + { + "options": ["auto", "1024x1024", "1024x1536", "1536x1024"], + "default": "auto", + "tooltip": "Image size", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 8, + "step": 1, + "display": "number", + "tooltip": "How many images to generate", + }, + ), + "image": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional reference image for image editing.", + }, + ), + "mask": ( + IO.MASK, + { + "default": None, + "tooltip": "Optional mask for inpainting (white areas will be replaced)", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "api_call" + CATEGORY = "api node/image/OpenAI" + DESCRIPTION = cleandoc(__doc__ or "") + API_NODE = True + + async def api_call( + self, + prompt, + seed=0, + quality="low", + background="opaque", + image=None, + mask=None, + n=1, + size="1024x1024", + unique_id=None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False) + model = "gpt-image-1" + path = "/proxy/openai/images/generations" + content_type = "application/json" + request_class = OpenAIImageGenerationRequest + files = [] + + if image is not None: + path = "/proxy/openai/images/edits" + request_class = OpenAIImageEditRequest + content_type = "multipart/form-data" + + batch_size = image.shape[0] + + for i in range(batch_size): + single_image = image[i : i + 1] + scaled_image = downscale_image_tensor(single_image).squeeze() + + image_np = (scaled_image.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + img_byte_arr = io.BytesIO() + img.save(img_byte_arr, format="PNG") + img_byte_arr.seek(0) + + if batch_size == 1: + files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png"))) + else: + files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png"))) + + if mask is not None: + if image is None: + raise Exception("Cannot use a mask without an input image") + if image.shape[0] != 1: + raise Exception("Cannot use a mask with multiple image") + if mask.shape[1:] != image.shape[1:-1]: + raise Exception("Mask and Image must be the same size") + batch, height, width = mask.shape + rgba_mask = torch.zeros(height, width, 4, device="cpu") + rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu() + + scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze() + + mask_np = (scaled_mask.numpy() * 255).astype(np.uint8) + mask_img = Image.fromarray(mask_np) + mask_img_byte_arr = io.BytesIO() + mask_img.save(mask_img_byte_arr, format="PNG") + mask_img_byte_arr.seek(0) + files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png"))) + + # Build the operation + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=request_class, + response_model=OpenAIImageGenerationResponse, + ), + request=request_class( + model=model, + prompt=prompt, + quality=quality, + background=background, + n=n, + seed=seed, + size=size, + ), + files=files if files else None, + content_type=content_type, + auth_kwargs=kwargs, + ) + + response = await operation.execute() + + img_tensor = await validate_and_cast_response(response, node_id=unique_id) + return (img_tensor,) + + +class OpenAITextNode(ComfyNodeABC): + """ + Base class for OpenAI text generation nodes. + """ + + RETURN_TYPES = (IO.STRING,) + FUNCTION = "api_call" + CATEGORY = "api node/text/OpenAI" + API_NODE = True + + +class OpenAIChatNode(OpenAITextNode): + """ + Node to generate text responses from an OpenAI model. + """ + + def __init__(self) -> None: + """Initialize the chat node with a new session ID and empty history.""" + self.current_session_id: str = str(uuid.uuid4()) + self.history: dict[str, list[HistoryEntry]] = {} + self.previous_response_id: Optional[str] = None + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Text inputs to the model, used to generate a response.", + }, + ), + "persist_context": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Persist chat context between calls (multi-turn conversation)", + }, + ), + "model": model_field_to_node_input( + IO.COMBO, + OpenAICreateResponse, + "model", + enum_type=SupportedOpenAIModel, + ), + }, + "optional": { + "images": ( + IO.IMAGE, + { + "default": None, + "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.", + }, + ), + "files": ( + "OPENAI_INPUT_FILES", + { + "default": None, + "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.", + }, + ), + "advanced_options": ( + "OPENAI_CHAT_CONFIG", + { + "default": None, + "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + DESCRIPTION = "Generate text responses from an OpenAI model." + + async def get_result_response( + self, + response_id: str, + include: Optional[list[Includable]] = None, + auth_kwargs: Optional[dict[str, str]] = None, + ) -> OpenAIResponse: + """ + Retrieve a model response with the given ID from the OpenAI API. + + Args: + response_id (str): The ID of the response to retrieve. + include (Optional[List[Includable]]): Additional fields to include + in the response. See the `include` parameter for Response + creation above for more information. + + """ + return await PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{RESPONSES_ENDPOINT}/{response_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=OpenAIResponse, + query_params={"include": include}, + ), + completed_statuses=["completed"], + failed_statuses=["failed"], + status_extractor=lambda response: response.status, + auth_kwargs=auth_kwargs, + ).execute() + + def get_message_content_from_response( + self, response: OpenAIResponse + ) -> list[OutputContent]: + """Extract message content from the API response.""" + for output in response.output: + if output.root.type == "message": + return output.root.content + raise TypeError("No output message found in response") + + def get_text_from_message_content( + self, message_content: list[OutputContent] + ) -> str: + """Extract text content from message content.""" + for content_item in message_content: + if content_item.root.type == "output_text": + return str(content_item.root.text) + return "No text output found in response" + + def get_history_text(self, session_id: str) -> str: + """Convert the entire history for a given session to JSON string.""" + return json.dumps(self.history[session_id]) + + def display_history_on_node(self, session_id: str, node_id: str) -> None: + """Display formatted chat history on the node UI.""" + render_spec = { + "node_id": node_id, + "component": "ChatHistoryWidget", + "props": { + "history": self.get_history_text(session_id), + }, + } + PromptServer.instance.send_sync( + "display_component", + render_spec, + ) + + def add_to_history( + self, session_id: str, prompt: str, output_text: str, response_id: str + ) -> None: + """Add a new entry to the chat history.""" + if session_id not in self.history: + self.history[session_id] = [] + self.history[session_id].append( + { + "prompt": prompt, + "response": output_text, + "response_id": response_id, + "timestamp": time.time(), + } + ) + + def parse_output_text_from_response(self, response: OpenAIResponse) -> str: + """Extract text output from the API response.""" + message_contents = self.get_message_content_from_response(response) + return self.get_text_from_message_content(message_contents) + + def generate_new_session_id(self) -> str: + """Generate a new unique session ID.""" + return str(uuid.uuid4()) + + def get_session_id(self, persist_context: bool) -> str: + """Get the current or generate a new session ID based on context persistence.""" + return ( + self.current_session_id + if persist_context + else self.generate_new_session_id() + ) + + def tensor_to_input_image_content( + self, image: torch.Tensor, detail_level: Detail = "auto" + ) -> InputImageContent: + """Convert a tensor to an input image content object.""" + return InputImageContent( + detail=detail_level, + image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}", + type="input_image", + ) + + def create_input_message_contents( + self, + prompt: str, + image: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + ) -> InputMessageContentList: + """Create a list of input message contents from prompt and optional image.""" + content_list: list[InputContent] = [ + InputTextContent(text=prompt, type="input_text"), + ] + if image is not None: + for i in range(image.shape[0]): + content_list.append( + self.tensor_to_input_image_content(image[i].unsqueeze(0)) + ) + if files is not None: + content_list.extend(files) + + return InputMessageContentList( + root=content_list, + ) + + def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]: + """Extract response ID from prompt if it exists.""" + parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt) + return parsed_id.group(1) if parsed_id else None + + def strip_response_tag_from_prompt(self, prompt: str) -> str: + """Remove the response ID tag from the prompt.""" + return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip()) + + def delete_history_after_response_id( + self, new_start_id: str, session_id: str + ) -> None: + """Delete history entries after a specific response ID.""" + if session_id not in self.history: + return + + new_history = [] + i = 0 + while ( + i < len(self.history[session_id]) + and self.history[session_id][i]["response_id"] != new_start_id + ): + new_history.append(self.history[session_id][i]) + i += 1 + + # Since it's the new starting point (not the response being edited), we include it as well + if i < len(self.history[session_id]): + new_history.append(self.history[session_id][i]) + + self.history[session_id] = new_history + + async def api_call( + self, + prompt: str, + persist_context: bool, + model: SupportedOpenAIModel, + unique_id: Optional[str] = None, + images: Optional[torch.Tensor] = None, + files: Optional[list[InputFileContent]] = None, + advanced_options: Optional[CreateModelResponseProperties] = None, + **kwargs, + ) -> tuple[str]: + # Validate inputs + validate_string(prompt, strip_whitespace=False) + + session_id = self.get_session_id(persist_context) + response_id_override = self.parse_response_id_from_prompt(prompt) + if response_id_override: + is_starting_from_beginning = response_id_override == "start" + if is_starting_from_beginning: + self.history[session_id] = [] + previous_response_id = None + else: + previous_response_id = response_id_override + self.delete_history_after_response_id(response_id_override, session_id) + prompt = self.strip_response_tag_from_prompt(prompt) + elif persist_context: + previous_response_id = self.previous_response_id + else: + previous_response_id = None + + # Create response + create_response = await SynchronousOperation( + endpoint=ApiEndpoint( + path=RESPONSES_ENDPOINT, + method=HttpMethod.POST, + request_model=OpenAICreateResponse, + response_model=OpenAIResponse, + ), + request=OpenAICreateResponse( + input=[ + Item( + root=InputMessage( + content=self.create_input_message_contents( + prompt, images, files + ), + role="user", + ) + ), + ], + store=True, + stream=False, + model=model, + previous_response_id=previous_response_id, + **( + advanced_options.model_dump(exclude_none=True) + if advanced_options + else {} + ), + ), + auth_kwargs=kwargs, + ).execute() + response_id = create_response.id + + # Get result output + result_response = await self.get_result_response(response_id, auth_kwargs=kwargs) + output_text = self.parse_output_text_from_response(result_response) + + # Update history + self.add_to_history(session_id, prompt, output_text, response_id) + self.display_history_on_node(session_id, unique_id) + self.previous_response_id = response_id + + return (output_text,) + + +class OpenAIInputFiles(ComfyNodeABC): + """ + Loads and formats input files for OpenAI API. + """ + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + """ + For details about the supported file input types, see: + https://platform.openai.com/docs/guides/pdf-files?api-mode=responses + """ + input_dir = folder_paths.get_input_directory() + input_files = [ + f + for f in os.scandir(input_dir) + if f.is_file() + and (f.name.endswith(".txt") or f.name.endswith(".pdf")) + and f.stat().st_size < 32 * 1024 * 1024 + ] + input_files = sorted(input_files, key=lambda x: x.name) + input_files = [f.name for f in input_files] + return { + "required": { + "file": ( + IO.COMBO, + { + "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.", + "options": input_files, + "default": input_files[0] if input_files else None, + }, + ), + }, + "optional": { + "OPENAI_INPUT_FILES": ( + "OPENAI_INPUT_FILES", + { + "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.", + "default": None, + }, + ), + }, + } + + DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes." + RETURN_TYPES = ("OPENAI_INPUT_FILES",) + FUNCTION = "prepare_files" + CATEGORY = "api node/text/OpenAI" + + def create_input_file_content(self, file_path: str) -> InputFileContent: + return InputFileContent( + file_data=text_filepath_to_data_uri(file_path), + filename=os.path.basename(file_path), + type="input_file", + ) + + def prepare_files( + self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = [] + ) -> tuple[list[InputFileContent]]: + """ + Loads and formats input files for OpenAI API. + """ + file_path = folder_paths.get_annotated_filepath(file) + input_file_content = self.create_input_file_content(file_path) + files = [input_file_content] + OPENAI_INPUT_FILES + return (files,) + + +class OpenAIChatConfig(ComfyNodeABC): + """Allows setting additional configuration for the OpenAI Chat Node.""" + + RETURN_TYPES = ("OPENAI_CHAT_CONFIG",) + FUNCTION = "configure" + DESCRIPTION = ( + "Allows specifying advanced configuration options for the OpenAI Chat Nodes." + ) + CATEGORY = "api node/text/OpenAI" + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "truncation": ( + IO.COMBO, + { + "options": ["auto", "disabled"], + "default": "auto", + "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error", + }, + ), + }, + "optional": { + "max_output_tokens": model_field_to_node_input( + IO.INT, + OpenAICreateResponse, + "max_output_tokens", + min=16, + default=4096, + max=16384, + tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens", + ), + "instructions": model_field_to_node_input( + IO.STRING, OpenAICreateResponse, "instructions", multiline=True + ), + }, + } + + def configure( + self, + truncation: bool, + instructions: Optional[str] = None, + max_output_tokens: Optional[int] = None, + ) -> tuple[CreateModelResponseProperties]: + """ + Configure advanced options for the OpenAI Chat Node. + + Note: + While `top_p` and `temperature` are listed as properties in the + spec, they are not supported for all models (e.g., o4-mini). + They are not exposed as inputs at all to avoid having to manually + remove depending on model choice. + """ + return ( + CreateModelResponseProperties( + instructions=instructions, + truncation=truncation, + max_output_tokens=max_output_tokens, + ), + ) + + +NODE_CLASS_MAPPINGS = { + "OpenAIDalle2": OpenAIDalle2, + "OpenAIDalle3": OpenAIDalle3, + "OpenAIGPTImage1": OpenAIGPTImage1, + "OpenAIChatNode": OpenAIChatNode, + "OpenAIInputFiles": OpenAIInputFiles, + "OpenAIChatConfig": OpenAIChatConfig, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "OpenAIDalle2": "OpenAI DALL·E 2", + "OpenAIDalle3": "OpenAI DALL·E 3", + "OpenAIGPTImage1": "OpenAI GPT Image 1", + "OpenAIChatNode": "OpenAI ChatGPT", + "OpenAIInputFiles": "OpenAI ChatGPT Input Files", + "OpenAIChatConfig": "OpenAI ChatGPT Advanced Options", +} diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py new file mode 100644 index 000000000..5bb406a3b --- /dev/null +++ b/comfy_api_nodes/nodes_pika.py @@ -0,0 +1,635 @@ +""" +Pika x ComfyUI API Nodes + +Pika API docs: https://pika-827374fb.mintlify.app/api-reference +""" +from __future__ import annotations + +from io import BytesIO +import logging +from typing import Optional, TypeVar + +import torch + +from typing_extensions import override +from comfy_api.latest import ComfyExtension, IO +from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput +from comfy_api_nodes.apis import pika_defs +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + EmptyRequest, + HttpMethod, + PollingOperation, + SynchronousOperation, +) +from comfy_api_nodes.util import validate_string, download_url_to_video_output, tensor_to_bytesio + +R = TypeVar("R") + +PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions" +PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps" +PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects" + +PIKA_API_VERSION = "2.2" +PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v" +PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v" +PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes" +PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes" + +PATH_VIDEO_GET = "/proxy/pika/videos" + + +async def execute_task( + initial_operation: SynchronousOperation[R, pika_defs.PikaGenerateResponse], + auth_kwargs: Optional[dict[str, str]] = None, + node_id: Optional[str] = None, +) -> IO.NodeOutput: + task_id = (await initial_operation.execute()).video_id + final_response: pika_defs.PikaVideoResponse = await PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"{PATH_VIDEO_GET}/{task_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=pika_defs.PikaVideoResponse, + ), + completed_statuses=["finished"], + failed_statuses=["failed", "cancelled"], + status_extractor=lambda response: (response.status.value if response.status else None), + progress_extractor=lambda response: (response.progress if hasattr(response, "progress") else None), + auth_kwargs=auth_kwargs, + result_url_extractor=lambda response: (response.url if hasattr(response, "url") else None), + node_id=node_id, + estimated_duration=60, + max_poll_attempts=240, + ).execute() + if not final_response.url: + error_msg = f"Pika task {task_id} succeeded but no video data found in response:\n{final_response}" + logging.error(error_msg) + raise Exception(error_msg) + video_url = final_response.url + logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url) + return IO.NodeOutput(await download_url_to_video_output(video_url)) + + +def get_base_inputs_types() -> list[IO.Input]: + """Get the base required inputs types common to all Pika nodes.""" + return [ + IO.String.Input("prompt_text", multiline=True), + IO.String.Input("negative_prompt", multiline=True), + IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True), + IO.Combo.Input("resolution", options=["1080p", "720p"], default="1080p"), + IO.Combo.Input("duration", options=[5, 10], default=5), + ] + + +class PikaImageToVideo(IO.ComfyNode): + """Pika 2.2 Image to Video Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PikaImageToVideoNode2_2", + display_name="Pika Image to Video", + description="Sends an image and prompt to the Pika API v2.2 to generate a video.", + category="api node/video/Pika", + inputs=[ + IO.Image.Input("image", tooltip="The image to convert to video"), + *get_base_inputs_types(), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + ) -> IO.NodeOutput: + image_bytes_io = tensor_to_bytesio(image) + pika_files = {"image": ("image.png", image_bytes_io, "image/png")} + pika_request_data = pika_defs.PikaBodyGenerate22I2vGenerate22I2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_IMAGE_TO_VIDEO, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGenerate22I2vGenerate22I2vPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaTextToVideoNode(IO.ComfyNode): + """Pika Text2Video v2.2 Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PikaTextToVideoNode2_2", + display_name="Pika Text to Video", + description="Sends a text prompt to the Pika API v2.2 to generate a video.", + category="api node/video/Pika", + inputs=[ + *get_base_inputs_types(), + IO.Float.Input( + "aspect_ratio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + tooltip="Aspect ratio (width / height)", + ) + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + aspect_ratio: float, + ) -> IO.NodeOutput: + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_TEXT_TO_VIDEO, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_defs.PikaBodyGenerate22T2vGenerate22T2vPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ), + auth_kwargs=auth, + content_type="application/x-www-form-urlencoded", + ) + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaScenes(IO.ComfyNode): + """PikaScenes v2.2 Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PikaScenesV2_2", + display_name="Pika Scenes (Video Image Composition)", + description="Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them.", + category="api node/video/Pika", + inputs=[ + *get_base_inputs_types(), + IO.Combo.Input( + "ingredients_mode", + options=["creative", "precise"], + default="creative", + ), + IO.Float.Input( + "aspect_ratio", + step=0.001, + min=0.4, + max=2.5, + default=1.7777777777777777, + tooltip="Aspect ratio (width / height)", + ), + IO.Image.Input( + "image_ingredient_1", + optional=True, + tooltip="Image that will be used as ingredient to create a video.", + ), + IO.Image.Input( + "image_ingredient_2", + optional=True, + tooltip="Image that will be used as ingredient to create a video.", + ), + IO.Image.Input( + "image_ingredient_3", + optional=True, + tooltip="Image that will be used as ingredient to create a video.", + ), + IO.Image.Input( + "image_ingredient_4", + optional=True, + tooltip="Image that will be used as ingredient to create a video.", + ), + IO.Image.Input( + "image_ingredient_5", + optional=True, + tooltip="Image that will be used as ingredient to create a video.", + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + ingredients_mode: str, + aspect_ratio: float, + image_ingredient_1: Optional[torch.Tensor] = None, + image_ingredient_2: Optional[torch.Tensor] = None, + image_ingredient_3: Optional[torch.Tensor] = None, + image_ingredient_4: Optional[torch.Tensor] = None, + image_ingredient_5: Optional[torch.Tensor] = None, + ) -> IO.NodeOutput: + all_image_bytes_io = [] + for image in [ + image_ingredient_1, + image_ingredient_2, + image_ingredient_3, + image_ingredient_4, + image_ingredient_5, + ]: + if image is not None: + all_image_bytes_io.append(tensor_to_bytesio(image)) + + pika_files = [ + ("images", (f"image_{i}.png", image_bytes_io, "image/png")) + for i, image_bytes_io in enumerate(all_image_bytes_io) + ] + + pika_request_data = pika_defs.PikaBodyGenerate22C2vGenerate22PikascenesPost( + ingredientsMode=ingredients_mode, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + aspectRatio=aspect_ratio, + ) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKASCENES, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGenerate22C2vGenerate22PikascenesPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikAdditionsNode(IO.ComfyNode): + """Pika Pikadditions Node. Add an image into a video.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Pikadditions", + display_name="Pikadditions (Video Object Insertion)", + description="Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result.", + category="api node/video/Pika", + inputs=[ + IO.Video.Input("video", tooltip="The video to add an image to."), + IO.Image.Input("image", tooltip="The image to add to the video."), + IO.String.Input("prompt_text", multiline=True), + IO.String.Input("negative_prompt", multiline=True), + IO.Int.Input( + "seed", + min=0, + max=0xFFFFFFFF, + control_after_generate=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: VideoInput, + image: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + ) -> IO.NodeOutput: + video_bytes_io = BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + + image_bytes_io = tensor_to_bytesio(image) + pika_files = { + "video": ("video.mp4", video_bytes_io, "video/mp4"), + "image": ("image.png", image_bytes_io, "image/png"), + } + pika_request_data = pika_defs.PikaBodyGeneratePikadditionsGeneratePikadditionsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKADDITIONS, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGeneratePikadditionsGeneratePikadditionsPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaSwapsNode(IO.ComfyNode): + """Pika Pikaswaps Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Pikaswaps", + display_name="Pika Swaps (Video Object Replacement)", + description="Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates.", + category="api node/video/Pika", + inputs=[ + IO.Video.Input("video", tooltip="The video to swap an object in."), + IO.Image.Input( + "image", + tooltip="The image used to replace the masked object in the video.", + optional=True, + ), + IO.Mask.Input( + "mask", + tooltip="Use the mask to define areas in the video to replace.", + optional=True, + ), + IO.String.Input("prompt_text", multiline=True, optional=True), + IO.String.Input("negative_prompt", multiline=True, optional=True), + IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True, optional=True), + IO.String.Input( + "region_to_modify", + multiline=True, + optional=True, + tooltip="Plaintext description of the object / region to modify.", + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: VideoInput, + image: Optional[torch.Tensor] = None, + mask: Optional[torch.Tensor] = None, + prompt_text: str = "", + negative_prompt: str = "", + seed: int = 0, + region_to_modify: str = "", + ) -> IO.NodeOutput: + video_bytes_io = BytesIO() + video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264) + video_bytes_io.seek(0) + pika_files = { + "video": ("video.mp4", video_bytes_io, "video/mp4"), + } + if mask is not None: + pika_files["modifyRegionMask"] = ("mask.png", tensor_to_bytesio(mask), "image/png") + if image is not None: + pika_files["image"] = ("image.png", tensor_to_bytesio(image), "image/png") + + pika_request_data = pika_defs.PikaBodyGeneratePikaswapsGeneratePikaswapsPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + modifyRegionRoi=region_to_modify if region_to_modify else None, + ) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKASWAPS, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGeneratePikaswapsGeneratePikaswapsPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_request_data, + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaffectsNode(IO.ComfyNode): + """Pika Pikaffects Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Pikaffects", + display_name="Pikaffects (Video Effects)", + description="Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear", + category="api node/video/Pika", + inputs=[ + IO.Image.Input("image", tooltip="The reference image to apply the Pikaffect to."), + IO.Combo.Input( + "pikaffect", options=pika_defs.Pikaffect, default="Cake-ify" + ), + IO.String.Input("prompt_text", multiline=True), + IO.String.Input("negative_prompt", multiline=True), + IO.Int.Input("seed", min=0, max=0xFFFFFFFF, control_after_generate=True), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + pikaffect: str, + prompt_text: str, + negative_prompt: str, + seed: int, + ) -> IO.NodeOutput: + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFFECTS, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_defs.PikaBodyGeneratePikaffectsGeneratePikaffectsPost( + pikaffect=pikaffect, + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + ), + files={"image": ("image.png", tensor_to_bytesio(image), "image/png")}, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaStartEndFrameNode(IO.ComfyNode): + """PikaFrames v2.2 Node.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PikaStartEndFrameNode2_2", + display_name="Pika Start and End Frame to Video", + description="Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them.", + category="api node/video/Pika", + inputs=[ + IO.Image.Input("image_start", tooltip="The first image to combine."), + IO.Image.Input("image_end", tooltip="The last image to combine."), + *get_base_inputs_types(), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image_start: torch.Tensor, + image_end: torch.Tensor, + prompt_text: str, + negative_prompt: str, + seed: int, + resolution: str, + duration: int, + ) -> IO.NodeOutput: + validate_string(prompt_text, field_name="prompt_text", min_length=1) + pika_files = [ + ("keyFrames", ("image_start.png", tensor_to_bytesio(image_start), "image/png")), + ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")), + ] + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + initial_operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=PATH_PIKAFRAMES, + method=HttpMethod.POST, + request_model=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost, + response_model=pika_defs.PikaGenerateResponse, + ), + request=pika_defs.PikaBodyGenerate22KeyframeGenerate22PikaframesPost( + promptText=prompt_text, + negativePrompt=negative_prompt, + seed=seed, + resolution=resolution, + duration=duration, + ), + files=pika_files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + return await execute_task(initial_operation, auth_kwargs=auth, node_id=cls.hidden.unique_id) + + +class PikaApiNodesExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + PikaImageToVideo, + PikaTextToVideoNode, + PikaScenes, + PikAdditionsNode, + PikaSwapsNode, + PikaffectsNode, + PikaStartEndFrameNode, + ] + + +async def comfy_entrypoint() -> PikaApiNodesExtension: + return PikaApiNodesExtension() diff --git a/comfy_api_nodes/nodes_pixverse.py b/comfy_api_nodes/nodes_pixverse.py new file mode 100644 index 000000000..b2b841be8 --- /dev/null +++ b/comfy_api_nodes/nodes_pixverse.py @@ -0,0 +1,525 @@ +from inspect import cleandoc +from typing import Optional +from typing_extensions import override +from io import BytesIO +from comfy_api_nodes.apis.pixverse_api import ( + PixverseTextVideoRequest, + PixverseImageVideoRequest, + PixverseTransitionVideoRequest, + PixverseImageUploadResponse, + PixverseVideoResponse, + PixverseGenerationStatusResponse, + PixverseAspectRatio, + PixverseQuality, + PixverseDuration, + PixverseMotionMode, + PixverseStatus, + PixverseIO, + pixverse_templates, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.util import validate_string, tensor_to_bytesio +from comfy_api.input_impl import VideoFromFile +from comfy_api.latest import ComfyExtension, IO + +import torch +import aiohttp + + +AVERAGE_DURATION_T2V = 32 +AVERAGE_DURATION_I2V = 30 +AVERAGE_DURATION_T2T = 52 + + +def get_video_url_from_response( + response: PixverseGenerationStatusResponse, +) -> Optional[str]: + if response.Resp is None or response.Resp.url is None: + return None + return str(response.Resp.url) + + +async def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None): + # first, upload image to Pixverse and get image id to use in actual generation call + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/image/upload", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=PixverseImageUploadResponse, + ), + request=EmptyRequest(), + files={"image": tensor_to_bytesio(image)}, + content_type="multipart/form-data", + auth_kwargs=auth_kwargs, + ) + response_upload: PixverseImageUploadResponse = await operation.execute() + + if response_upload.Resp is None: + raise Exception(f"PixVerse image upload request failed: '{response_upload.ErrMsg}'") + + return response_upload.Resp.img_id + + +class PixverseTemplateNode(IO.ComfyNode): + """ + Select template for PixVerse Video generation. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PixverseTemplateNode", + display_name="PixVerse Template", + category="api node/video/PixVerse", + inputs=[ + IO.Combo.Input("template", options=list(pixverse_templates.keys())), + ], + outputs=[IO.Custom(PixverseIO.TEMPLATE).Output(display_name="pixverse_template")], + ) + + @classmethod + def execute(cls, template: str) -> IO.NodeOutput: + template_id = pixverse_templates.get(template, None) + if template_id is None: + raise Exception(f"Template '{template}' is not recognized.") + return IO.NodeOutput(template_id) + + +class PixverseTextToVideoNode(IO.ComfyNode): + """ + Generates videos based on prompt and output_size. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PixverseTextToVideoNode", + display_name="PixVerse Text to Video", + category="api node/video/PixVerse", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the video generation", + ), + IO.Combo.Input( + "aspect_ratio", + options=PixverseAspectRatio, + ), + IO.Combo.Input( + "quality", + options=PixverseQuality, + default=PixverseQuality.res_540p, + ), + IO.Combo.Input( + "duration_seconds", + options=PixverseDuration, + ), + IO.Combo.Input( + "motion_mode", + options=PixverseMotionMode, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed for video generation.", + ), + IO.String.Input( + "negative_prompt", + default="", + multiline=True, + tooltip="An optional text description of undesired elements on an image.", + optional=True, + ), + IO.Custom(PixverseIO.TEMPLATE).Input( + "pixverse_template", + tooltip="An optional template to influence style of generation, created by the PixVerse Template node.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + aspect_ratio: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + pixverse_template: int = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/text/generate", + method=HttpMethod.POST, + request_model=PixverseTextVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTextVideoRequest( + prompt=prompt, + aspect_ratio=aspect_ratio, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=auth, + node_id=cls.hidden.unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.Resp.url) as vid_response: + return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read()))) + + +class PixverseImageToVideoNode(IO.ComfyNode): + """ + Generates videos based on prompt and output_size. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PixverseImageToVideoNode", + display_name="PixVerse Image to Video", + category="api node/video/PixVerse", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the video generation", + ), + IO.Combo.Input( + "quality", + options=PixverseQuality, + default=PixverseQuality.res_540p, + ), + IO.Combo.Input( + "duration_seconds", + options=PixverseDuration, + ), + IO.Combo.Input( + "motion_mode", + options=PixverseMotionMode, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed for video generation.", + ), + IO.String.Input( + "negative_prompt", + default="", + multiline=True, + tooltip="An optional text description of undesired elements on an image.", + optional=True, + ), + IO.Custom(PixverseIO.TEMPLATE).Input( + "pixverse_template", + tooltip="An optional template to influence style of generation, created by the PixVerse Template node.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + pixverse_template: int = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + img_id = await upload_image_to_pixverse(image, auth_kwargs=auth) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/img/generate", + method=HttpMethod.POST, + request_model=PixverseImageVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseImageVideoRequest( + img_id=img_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + template_id=pixverse_template, + seed=seed, + ), + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=auth, + node_id=cls.hidden.unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_I2V, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.Resp.url) as vid_response: + return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read()))) + + +class PixverseTransitionVideoNode(IO.ComfyNode): + """ + Generates videos based on prompt and output_size. + """ + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="PixverseTransitionVideoNode", + display_name="PixVerse Transition Video", + category="api node/video/PixVerse", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("first_frame"), + IO.Image.Input("last_frame"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt for the video generation", + ), + IO.Combo.Input( + "quality", + options=PixverseQuality, + default=PixverseQuality.res_540p, + ), + IO.Combo.Input( + "duration_seconds", + options=PixverseDuration, + ), + IO.Combo.Input( + "motion_mode", + options=PixverseMotionMode, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + control_after_generate=True, + tooltip="Seed for video generation.", + ), + IO.String.Input( + "negative_prompt", + default="", + multiline=True, + tooltip="An optional text description of undesired elements on an image.", + optional=True, + ), + ], + outputs=[IO.Video.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + first_frame: torch.Tensor, + last_frame: torch.Tensor, + prompt: str, + quality: str, + duration_seconds: int, + motion_mode: str, + seed, + negative_prompt: str = None, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + first_frame_id = await upload_image_to_pixverse(first_frame, auth_kwargs=auth) + last_frame_id = await upload_image_to_pixverse(last_frame, auth_kwargs=auth) + + # 1080p is limited to 5 seconds duration + # only normal motion_mode supported for 1080p or for non-5 second duration + if quality == PixverseQuality.res_1080p: + motion_mode = PixverseMotionMode.normal + duration_seconds = PixverseDuration.dur_5 + elif duration_seconds != PixverseDuration.dur_5: + motion_mode = PixverseMotionMode.normal + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/pixverse/video/transition/generate", + method=HttpMethod.POST, + request_model=PixverseTransitionVideoRequest, + response_model=PixverseVideoResponse, + ), + request=PixverseTransitionVideoRequest( + first_frame_img=first_frame_id, + last_frame_img=last_frame_id, + prompt=prompt, + quality=quality, + duration=duration_seconds, + motion_mode=motion_mode, + negative_prompt=negative_prompt if negative_prompt else None, + seed=seed, + ), + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.Resp is None: + raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'") + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=PixverseGenerationStatusResponse, + ), + completed_statuses=[PixverseStatus.successful], + failed_statuses=[ + PixverseStatus.contents_moderation, + PixverseStatus.failed, + PixverseStatus.deleted, + ], + status_extractor=lambda x: x.Resp.status, + auth_kwargs=auth, + node_id=cls.hidden.unique_id, + result_url_extractor=get_video_url_from_response, + estimated_duration=AVERAGE_DURATION_T2V, + ) + response_poll = await operation.execute() + + async with aiohttp.ClientSession() as session: + async with session.get(response_poll.Resp.url) as vid_response: + return IO.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read()))) + + +class PixVerseExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + PixverseTextToVideoNode, + PixverseImageToVideoNode, + PixverseTransitionVideoNode, + PixverseTemplateNode, + ] + + +async def comfy_entrypoint() -> PixVerseExtension: + return PixVerseExtension() diff --git a/comfy_api_nodes/nodes_recraft.py b/comfy_api_nodes/nodes_recraft.py new file mode 100644 index 000000000..8ee7e55c4 --- /dev/null +++ b/comfy_api_nodes/nodes_recraft.py @@ -0,0 +1,1152 @@ +from __future__ import annotations +from inspect import cleandoc +from typing import Optional +from comfy.utils import ProgressBar +from comfy_extras.nodes_images import SVG # Added +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.apis.recraft_api import ( + RecraftImageGenerationRequest, + RecraftImageGenerationResponse, + RecraftImageSize, + RecraftModel, + RecraftStyle, + RecraftStyleV3, + RecraftColor, + RecraftColorChain, + RecraftControls, + RecraftIO, + get_v3_substyles, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + EmptyRequest, +) +from comfy_api_nodes.apinode_utils import ( + download_url_to_bytesio, + resize_mask_to_image, +) +from comfy_api_nodes.util import validate_string, tensor_to_bytesio, bytesio_to_image_tensor +from server import PromptServer + +import torch +from io import BytesIO +from PIL import UnidentifiedImageError +import aiohttp + + +async def handle_recraft_file_request( + image: torch.Tensor, + path: str, + mask: torch.Tensor=None, + total_pixels=4096*4096, + timeout=1024, + request=None, + auth_kwargs: dict[str,str] = None, +) -> list[BytesIO]: + """ + Handle sending common Recraft file-only request to get back file bytes. + """ + if request is None: + request = EmptyRequest() + + files = { + 'image': tensor_to_bytesio(image, total_pixels=total_pixels).read() + } + if mask is not None: + files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read() + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=type(request), + response_model=RecraftImageGenerationResponse, + ), + request=request, + files=files, + content_type="multipart/form-data", + auth_kwargs=auth_kwargs, + multipart_parser=recraft_multipart_parser, + ) + response: RecraftImageGenerationResponse = await operation.execute() + all_bytesio = [] + if response.image is not None: + all_bytesio.append(await download_url_to_bytesio(response.image.url, timeout=timeout)) + else: + for data in response.data: + all_bytesio.append(await download_url_to_bytesio(data.url, timeout=timeout)) + + return all_bytesio + + +def recraft_multipart_parser( + data, + parent_key=None, + formatter: callable = None, + converted_to_check: list[list] = None, + is_list: bool = False, + return_mode: str = "formdata" # "dict" | "formdata" +) -> dict | aiohttp.FormData: + """ + Formats data such that multipart/form-data will work with aiohttp library when both files and data are present. + + The OpenAI client that Recraft uses has a bizarre way of serializing lists: + + It does NOT keep track of indeces of each list, so for background_color, that must be serialized as: + 'background_color[rgb][]' = [0, 0, 255] + where the array is assigned to a key that has '[]' at the end, to signal it's an array. + + This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key: + if 1 color -> 'controls[colors][][rgb][]' = [0, 0, 255] + if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0] + if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0] + etc. + Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'. + """ + # Modification of a function that handled a different type of multipart parsing, big ups: + # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b + + def handle_converted_lists(item, parent_key, lists_to_check=tuple[list]): + # if list already exists exists, just extend list with data + for check_list in lists_to_check: + for conv_tuple in check_list: + if conv_tuple[0] == parent_key and isinstance(conv_tuple[1], list): + conv_tuple[1].append(formatter(item)) + return True + return False + + if converted_to_check is None: + converted_to_check = [] + + effective_mode = return_mode if parent_key is None else "dict" + if formatter is None: + formatter = lambda v: v # Multipart representation of value + + if not isinstance(data, dict): + # if list already exists exists, just extend list with data + added = handle_converted_lists(data, parent_key, converted_to_check) + if added: + return {} + # otherwise if is_list, create new list with data + if is_list: + return {parent_key: [formatter(data)]} + # return new key with data + return {parent_key: formatter(data)} + + converted = [] + next_check = [converted] + next_check.extend(converted_to_check) + + for key, value in data.items(): + current_key = key if parent_key is None else f"{parent_key}[{key}]" + if isinstance(value, dict): + converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items()) + elif isinstance(value, list): + for ind, list_value in enumerate(value): + iter_key = f"{current_key}[]" + converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items()) + else: + converted.append((current_key, formatter(value))) + + if effective_mode == "formdata": + fd = aiohttp.FormData() + for k, v in dict(converted).items(): + if isinstance(v, list): + for item in v: + fd.add_field(k, str(item)) + else: + fd.add_field(k, str(v)) + return fd + return dict(converted) + + +class handle_recraft_image_output: + """ + Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use. + """ + def __init__(self): + pass + + def __enter__(self): + pass + + def __exit__(self, exc_type, exc_val, exc_tb): + if exc_type is not None and exc_type is UnidentifiedImageError: + raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.") + + +class RecraftColorRGBNode: + """ + Create Recraft Color by choosing specific RGB values. + """ + + RETURN_TYPES = (RecraftIO.COLOR,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + RETURN_NAMES = ("recraft_color",) + FUNCTION = "create_color" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "r": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Red value of color." + }), + "g": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Green value of color." + }), + "b": (IO.INT, { + "default": 0, + "min": 0, + "max": 255, + "tooltip": "Blue value of color." + }), + }, + "optional": { + "recraft_color": (RecraftIO.COLOR,), + } + } + + def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None): + recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain() + recraft_color.add(RecraftColor(r, g, b)) + return (recraft_color, ) + + +class RecraftControlsNode: + """ + Create Recraft Controls for customizing Recraft generation. + """ + + RETURN_TYPES = (RecraftIO.CONTROLS,) + RETURN_NAMES = ("recraft_controls",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_controls" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + }, + "optional": { + "colors": (RecraftIO.COLOR,), + "background_color": (RecraftIO.COLOR,), + } + } + + def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None): + return (RecraftControls(colors=colors, background_color=background_color), ) + + +class RecraftStyleV3RealisticImageNode: + """ + Select realistic_image style and optional substyle. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + RECRAFT_STYLE = RecraftStyleV3.realistic_image + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE),), + } + } + + def create_style(self, substyle: str): + if substyle == "None": + substyle = None + return (RecraftStyle(self.RECRAFT_STYLE, substyle),) + + +class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select digital_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.digital_illustration + + +class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + RECRAFT_STYLE = RecraftStyleV3.vector_illustration + + +class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode): + """ + Select vector_illustration style and optional substyle. + """ + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),), + } + } + + RECRAFT_STYLE = RecraftStyleV3.logo_raster + + +class RecraftStyleInfiniteStyleLibrary: + """ + Select style based on preexisting UUID from Recraft's Infinite Style Library. + """ + + RETURN_TYPES = (RecraftIO.STYLEV3,) + RETURN_NAMES = ("recraft_style",) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "create_style" + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "style_id": (IO.STRING, { + "default": "", + "tooltip": "UUID of style from Infinite Style Library.", + }) + } + } + + def create_style(self, style_id: str): + if not style_id: + raise Exception("The style_id input cannot be empty.") + return (RecraftStyle(style_id=style_id),) + + +class RecraftTextToImageNode: + """ + Generates images synchronously based on prompt and resolution. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + async def api_call( + self, + prompt: str, + size: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ), + auth_kwargs=kwargs, + ) + response: RecraftImageGenerationResponse = await operation.execute() + images = [] + urls = [] + for data in response.data: + with handle_recraft_image_output(): + if unique_id and data.url: + urls.append(data.url) + urls_string = '\n'.join(urls) + PromptServer.instance.send_progress_text( + f"Result URL: {urls_string}", unique_id + ) + image = bytesio_to_image_tensor( + await download_url_to_bytesio(data.url, timeout=1024) + ) + if len(image.shape) < 4: + image = image.unsqueeze(0) + images.append(image) + output_image = torch.cat(images, dim=0) + + return (output_image,) + + +class RecraftImageToImageNode: + """ + Modify image based on prompt and strength. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "strength": ( + IO.FLOAT, + { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01, + "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity." + } + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + strength: float, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + strength=round(strength, 2), + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + controls=controls_api, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/imageToImage", + request=request, + auth_kwargs=kwargs, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftImageInpaintingNode: + """ + Modify image based on prompt and mask. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "mask": (IO.MASK, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + mask: torch.Tensor, + prompt: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + # prepare mask tensor + mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + mask=mask[i:i+1], + path="/proxy/recraft/images/inpaint", + request=request, + auth_kwargs=kwargs, + ) + with handle_recraft_image_output(): + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftTextToVectorNode: + """ + Generates SVG synchronously based on prompt and resolution. + """ + + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),), + "size": ( + [res.value for res in RecraftImageSize], + { + "default": RecraftImageSize.res_1024x1024, + "tooltip": "The size of the generated image.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + "recraft_controls": ( + RecraftIO.CONTROLS, + { + "tooltip": "Optional additional controls over the generation via the Recraft Controls node." + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + "unique_id": "UNIQUE_ID", + }, + } + + async def api_call( + self, + prompt: str, + substyle: str, + size: str, + n: int, + seed, + negative_prompt: str = None, + recraft_controls: RecraftControls = None, + unique_id: Optional[str] = None, + **kwargs, + ): + validate_string(prompt, strip_whitespace=False, max_length=1000) + # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None) + recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle) + + controls_api = None + if recraft_controls: + controls_api = recraft_controls.create_api_model() + + if not negative_prompt: + negative_prompt = None + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/recraft/image_generation", + method=HttpMethod.POST, + request_model=RecraftImageGenerationRequest, + response_model=RecraftImageGenerationResponse, + ), + request=RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + size=size, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + controls=controls_api, + ), + auth_kwargs=kwargs, + ) + response: RecraftImageGenerationResponse = await operation.execute() + svg_data = [] + urls = [] + for data in response.data: + if unique_id and data.url: + urls.append(data.url) + # Print result on each iteration in case of error + PromptServer.instance.send_progress_text( + f"Result URL: {' '.join(urls)}", unique_id + ) + svg_data.append(await download_url_to_bytesio(data.url, timeout=1024)) + + return (SVG(svg_data),) + + +class RecraftVectorizeImageNode: + """ + Generates SVG synchronously from an input image. + """ + + RETURN_TYPES = ("SVG",) # Changed + DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + svgs = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/vectorize", + auth_kwargs=kwargs, + ) + svgs.append(SVG(sub_bytes)) + pbar.update(1) + + return (SVG.combine_all(svgs), ) + + +class RecraftReplaceBackgroundNode: + """ + Replace background on image, based on provided prompt. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + "prompt": ( + IO.STRING, + { + "multiline": True, + "default": "", + "tooltip": "Prompt for the image generation.", + }, + ), + "n": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 6, + "tooltip": "The number of images to generate.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "control_after_generate": True, + "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.", + }, + ), + }, + "optional": { + "recraft_style": (RecraftIO.STYLEV3,), + "negative_prompt": ( + IO.STRING, + { + "default": "", + "forceInput": True, + "tooltip": "An optional text description of undesired elements on an image.", + }, + ), + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + prompt: str, + n: int, + seed, + recraft_style: RecraftStyle = None, + negative_prompt: str = None, + **kwargs, + ): + default_style = RecraftStyle(RecraftStyleV3.realistic_image) + if recraft_style is None: + recraft_style = default_style + + if not negative_prompt: + negative_prompt = None + + request = RecraftImageGenerationRequest( + prompt=prompt, + negative_prompt=negative_prompt, + model=RecraftModel.recraftv3, + n=n, + style=recraft_style.style, + substyle=recraft_style.substyle, + style_id=recraft_style.style_id, + ) + + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/replaceBackground", + request=request, + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor, ) + + +class RecraftRemoveBackgroundNode: + """ + Remove background from image, and return processed image and mask. + """ + + RETURN_TYPES = (IO.IMAGE, IO.MASK) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + path="/proxy/recraft/images/removeBackground", + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + # use alpha channel as masks, in B,H,W format + masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1) + return (images_tensor, masks_tensor) + + +class RecraftCrispUpscaleNode: + """ + Upscale image synchronously. + Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/crispUpscale" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE, ), + }, + "optional": { + }, + "hidden": { + "auth_token": "AUTH_TOKEN_COMFY_ORG", + "comfy_api_key": "API_KEY_COMFY_ORG", + }, + } + + async def api_call( + self, + image: torch.Tensor, + **kwargs, + ): + images = [] + total = image.shape[0] + pbar = ProgressBar(total) + for i in range(total): + sub_bytes = await handle_recraft_file_request( + image=image[i], + path=self.RECRAFT_PATH, + auth_kwargs=kwargs, + ) + images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0)) + pbar.update(1) + + images_tensor = torch.cat(images, dim=0) + return (images_tensor,) + + +class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode): + """ + Upscale image synchronously. + Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces. + """ + + RETURN_TYPES = (IO.IMAGE,) + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "api_call" + API_NODE = True + CATEGORY = "api node/image/Recraft" + + RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale" + + +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "RecraftTextToImageNode": RecraftTextToImageNode, + "RecraftImageToImageNode": RecraftImageToImageNode, + "RecraftImageInpaintingNode": RecraftImageInpaintingNode, + "RecraftTextToVectorNode": RecraftTextToVectorNode, + "RecraftVectorizeImageNode": RecraftVectorizeImageNode, + "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode, + "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode, + "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode, + "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode, + "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode, + "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode, + "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode, + "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary, + "RecraftColorRGB": RecraftColorRGBNode, + "RecraftControls": RecraftControlsNode, +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "RecraftTextToImageNode": "Recraft Text to Image", + "RecraftImageToImageNode": "Recraft Image to Image", + "RecraftImageInpaintingNode": "Recraft Image Inpainting", + "RecraftTextToVectorNode": "Recraft Text to Vector", + "RecraftVectorizeImageNode": "Recraft Vectorize Image", + "RecraftRemoveBackgroundNode": "Recraft Remove Background", + "RecraftReplaceBackgroundNode": "Recraft Replace Background", + "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image", + "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image", + "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image", + "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration", + "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster", + "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library", + "RecraftColorRGB": "Recraft Color RGB", + "RecraftControls": "Recraft Controls", +} diff --git a/comfy_api_nodes/nodes_rodin.py b/comfy_api_nodes/nodes_rodin.py new file mode 100644 index 000000000..cf2172bd6 --- /dev/null +++ b/comfy_api_nodes/nodes_rodin.py @@ -0,0 +1,579 @@ +""" +ComfyUI X Rodin3D(Deemos) API Nodes + +Rodin API docs: https://developer.hyper3d.ai/ + +""" + +from __future__ import annotations +from inspect import cleandoc +import folder_paths as comfy_paths +import aiohttp +import os +import asyncio +import logging +import math +from typing import Optional +from io import BytesIO +from typing_extensions import override +from PIL import Image +from comfy_api_nodes.apis.rodin_api import ( + Rodin3DGenerateRequest, + Rodin3DGenerateResponse, + Rodin3DCheckStatusRequest, + Rodin3DCheckStatusResponse, + Rodin3DDownloadRequest, + Rodin3DDownloadResponse, + JobStatus, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, +) +from comfy_api.latest import ComfyExtension, IO + + +COMMON_PARAMETERS = [ + IO.Int.Input( + "Seed", + default=0, + min=0, + max=65535, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True), + IO.Combo.Input( + "Polygon_count", + options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"], + default="18K-Quad", + optional=True, + ), +] + + +def get_quality_mode(poly_count): + polycount = poly_count.split("-") + poly = polycount[1] + count = polycount[0] + if poly == "Triangle": + mesh_mode = "Raw" + elif poly == "Quad": + mesh_mode = "Quad" + else: + mesh_mode = "Quad" + + if count == "4K": + quality_override = 4000 + elif count == "8K": + quality_override = 8000 + elif count == "18K": + quality_override = 18000 + elif count == "50K": + quality_override = 50000 + elif count == "2K": + quality_override = 2000 + elif count == "20K": + quality_override = 20000 + elif count == "150K": + quality_override = 150000 + elif count == "500K": + quality_override = 500000 + else: + quality_override = 18000 + + return mesh_mode, quality_override + + +def tensor_to_filelike(tensor, max_pixels: int = 2048*2048): + """ + Converts a PyTorch tensor to a file-like object. + + Args: + - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C) + where C is the number of channels (3 for RGB), H is height, and W is width. + + Returns: + - io.BytesIO: A file-like object containing the image data. + """ + array = tensor.cpu().numpy() + array = (array * 255).astype('uint8') + image = Image.fromarray(array, 'RGB') + + original_width, original_height = image.size + original_pixels = original_width * original_height + if original_pixels > max_pixels: + scale = math.sqrt(max_pixels / original_pixels) + new_width = int(original_width * scale) + new_height = int(original_height * scale) + else: + new_width, new_height = original_width, original_height + + if new_width != original_width or new_height != original_height: + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + + img_byte_arr = BytesIO() + image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression + img_byte_arr.seek(0) + return img_byte_arr + + +async def create_generate_task( + images=None, + seed=1, + material="PBR", + quality_override=18000, + tier="Regular", + mesh_mode="Quad", + TAPose = False, + auth_kwargs: Optional[dict[str, str]] = None, +): + if images is None: + raise Exception("Rodin 3D generate requires at least 1 image.") + if len(images) > 5: + raise Exception("Rodin 3D generate requires up to 5 image.") + + path = "/proxy/rodin/api/v2/rodin" + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path=path, + method=HttpMethod.POST, + request_model=Rodin3DGenerateRequest, + response_model=Rodin3DGenerateResponse, + ), + request=Rodin3DGenerateRequest( + seed=seed, + tier=tier, + material=material, + quality_override=quality_override, + mesh_mode=mesh_mode, + TAPose=TAPose, + ), + files=[ + ( + "images", + open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image) + ) + for image in images if image is not None + ], + content_type="multipart/form-data", + auth_kwargs=auth_kwargs, + ) + + response = await operation.execute() + + if hasattr(response, "error"): + error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}" + logging.error(error_message) + raise Exception(error_message) + + logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!") + subscription_key = response.jobs.subscription_key + task_uuid = response.uuid + logging.info("[ Rodin3D API - Submit Jobs ] UUID: %s", task_uuid) + return task_uuid, subscription_key + + +def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str: + all_done = all(job.status == JobStatus.Done for job in response.jobs) + status_list = [str(job.status) for job in response.jobs] + logging.info("[ Rodin3D API - CheckStatus ] Generate Status: %s", status_list) + if any(job.status == JobStatus.Failed for job in response.jobs): + logging.error("[ Rodin3D API - CheckStatus ] Generate Failed: %s, Please try again.", status_list) + raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.") + if all_done: + return "DONE" + return "Generating" + + +async def poll_for_task_status( + subscription_key, auth_kwargs: Optional[dict[str, str]] = None, +) -> Rodin3DCheckStatusResponse: + poll_operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path="/proxy/rodin/api/v2/status", + method=HttpMethod.POST, + request_model=Rodin3DCheckStatusRequest, + response_model=Rodin3DCheckStatusResponse, + ), + request=Rodin3DCheckStatusRequest(subscription_key=subscription_key), + completed_statuses=["DONE"], + failed_statuses=["FAILED"], + status_extractor=check_rodin_status, + poll_interval=3.0, + auth_kwargs=auth_kwargs, + ) + logging.info("[ Rodin3D API - CheckStatus ] Generate Start!") + return await poll_operation.execute() + + +async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] = None) -> Rodin3DDownloadResponse: + logging.info("[ Rodin3D API - Downloading ] Generate Successfully!") + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/rodin/api/v2/download", + method=HttpMethod.POST, + request_model=Rodin3DDownloadRequest, + response_model=Rodin3DDownloadResponse, + ), + request=Rodin3DDownloadRequest(task_uuid=uuid), + auth_kwargs=auth_kwargs, + ) + return await operation.execute() + + +async def download_files(url_list, task_uuid): + save_path = os.path.join(comfy_paths.get_output_directory(), f"Rodin3D_{task_uuid}") + os.makedirs(save_path, exist_ok=True) + model_file_path = None + async with aiohttp.ClientSession() as session: + for i in url_list.list: + url = i.url + file_name = i.name + file_path = os.path.join(save_path, file_name) + if file_path.endswith(".glb"): + model_file_path = file_path + logging.info("[ Rodin3D API - download_files ] Downloading file: %s", file_path) + max_retries = 5 + for attempt in range(max_retries): + try: + async with session.get(url) as resp: + resp.raise_for_status() + with open(file_path, "wb") as f: + async for chunk in resp.content.iter_chunked(32 * 1024): + f.write(chunk) + break + except Exception as e: + logging.info("[ Rodin3D API - download_files ] Error downloading %s:%s", file_path, str(e)) + if attempt < max_retries - 1: + logging.info("Retrying...") + await asyncio.sleep(2) + else: + logging.info( + "[ Rodin3D API - download_files ] Failed to download %s after %s attempts.", + file_path, + max_retries, + ) + return model_file_path + + +class Rodin3D_Regular(IO.ComfyNode): + """Generate 3D Assets using Rodin API""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Rodin3D_Regular", + display_name="Rodin 3D Generate - Regular Generate", + category="api node/3d/Rodin", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("Images"), + *COMMON_PARAMETERS, + ], + outputs=[IO.String.Output(display_name="3D Model Path")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + Images, + Seed, + Material_Type, + Polygon_count, + ) -> IO.NodeOutput: + tier = "Regular" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality_override = get_quality_mode(Polygon_count) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + task_uuid, subscription_key = await create_generate_task( + images=m_images, + seed=Seed, + material=Material_Type, + quality_override=quality_override, + tier=tier, + mesh_mode=mesh_mode, + auth_kwargs=auth, + ) + await poll_for_task_status(subscription_key, auth_kwargs=auth) + download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth) + model = await download_files(download_list, task_uuid) + + return IO.NodeOutput(model) + + +class Rodin3D_Detail(IO.ComfyNode): + """Generate 3D Assets using Rodin API""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Rodin3D_Detail", + display_name="Rodin 3D Generate - Detail Generate", + category="api node/3d/Rodin", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("Images"), + *COMMON_PARAMETERS, + ], + outputs=[IO.String.Output(display_name="3D Model Path")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + Images, + Seed, + Material_Type, + Polygon_count, + ) -> IO.NodeOutput: + tier = "Detail" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality_override = get_quality_mode(Polygon_count) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + task_uuid, subscription_key = await create_generate_task( + images=m_images, + seed=Seed, + material=Material_Type, + quality_override=quality_override, + tier=tier, + mesh_mode=mesh_mode, + auth_kwargs=auth, + ) + await poll_for_task_status(subscription_key, auth_kwargs=auth) + download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth) + model = await download_files(download_list, task_uuid) + + return IO.NodeOutput(model) + + +class Rodin3D_Smooth(IO.ComfyNode): + """Generate 3D Assets using Rodin API""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Rodin3D_Smooth", + display_name="Rodin 3D Generate - Smooth Generate", + category="api node/3d/Rodin", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("Images"), + *COMMON_PARAMETERS, + ], + outputs=[IO.String.Output(display_name="3D Model Path")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + Images, + Seed, + Material_Type, + Polygon_count, + ) -> IO.NodeOutput: + tier = "Smooth" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality_override = get_quality_mode(Polygon_count) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + task_uuid, subscription_key = await create_generate_task( + images=m_images, + seed=Seed, + material=Material_Type, + quality_override=quality_override, + tier=tier, + mesh_mode=mesh_mode, + auth_kwargs=auth, + ) + await poll_for_task_status(subscription_key, auth_kwargs=auth) + download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth) + model = await download_files(download_list, task_uuid) + + return IO.NodeOutput(model) + + +class Rodin3D_Sketch(IO.ComfyNode): + """Generate 3D Assets using Rodin API""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Rodin3D_Sketch", + display_name="Rodin 3D Generate - Sketch Generate", + category="api node/3d/Rodin", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("Images"), + IO.Int.Input( + "Seed", + default=0, + min=0, + max=65535, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + ], + outputs=[IO.String.Output(display_name="3D Model Path")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + Images, + Seed, + ) -> IO.NodeOutput: + tier = "Sketch" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + material_type = "PBR" + quality_override = 18000 + mesh_mode = "Quad" + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + task_uuid, subscription_key = await create_generate_task( + images=m_images, + seed=Seed, + material=material_type, + quality_override=quality_override, + tier=tier, + mesh_mode=mesh_mode, + auth_kwargs=auth, + ) + await poll_for_task_status(subscription_key, auth_kwargs=auth) + download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth) + model = await download_files(download_list, task_uuid) + + return IO.NodeOutput(model) + + +class Rodin3D_Gen2(IO.ComfyNode): + """Generate 3D Assets using Rodin API""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="Rodin3D_Gen2", + display_name="Rodin 3D Generate - Gen-2 Generate", + category="api node/3d/Rodin", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("Images"), + IO.Int.Input( + "Seed", + default=0, + min=0, + max=65535, + display_mode=IO.NumberDisplay.number, + optional=True, + ), + IO.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True), + IO.Combo.Input( + "Polygon_count", + options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"], + default="500K-Triangle", + optional=True, + ), + IO.Boolean.Input("TAPose", default=False), + ], + outputs=[IO.String.Output(display_name="3D Model Path")], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + Images, + Seed, + Material_Type, + Polygon_count, + TAPose, + ) -> IO.NodeOutput: + tier = "Gen-2" + num_images = Images.shape[0] + m_images = [] + for i in range(num_images): + m_images.append(Images[i]) + mesh_mode, quality_override = get_quality_mode(Polygon_count) + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + task_uuid, subscription_key = await create_generate_task( + images=m_images, + seed=Seed, + material=Material_Type, + quality_override=quality_override, + tier=tier, + mesh_mode=mesh_mode, + TAPose=TAPose, + auth_kwargs=auth, + ) + await poll_for_task_status(subscription_key, auth_kwargs=auth) + download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth) + model = await download_files(download_list, task_uuid) + + return IO.NodeOutput(model) + + +class Rodin3DExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + Rodin3D_Regular, + Rodin3D_Detail, + Rodin3D_Smooth, + Rodin3D_Sketch, + Rodin3D_Gen2, + ] + + +async def comfy_entrypoint() -> Rodin3DExtension: + return Rodin3DExtension() diff --git a/comfy_api_nodes/nodes_runway.py b/comfy_api_nodes/nodes_runway.py new file mode 100644 index 000000000..0543d1d0e --- /dev/null +++ b/comfy_api_nodes/nodes_runway.py @@ -0,0 +1,522 @@ +"""Runway API Nodes + +API Docs: + - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete + +User Guides: + - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha + - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video + - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo + - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3 + +""" + +from typing import Union, Optional +from typing_extensions import override +from enum import Enum + +import torch + +from comfy_api_nodes.apis import ( + RunwayImageToVideoRequest, + RunwayImageToVideoResponse, + RunwayTaskStatusResponse as TaskStatusResponse, + RunwayModelEnum as Model, + RunwayDurationEnum as Duration, + RunwayAspectRatioEnum as AspectRatio, + RunwayPromptImageObject, + RunwayPromptImageDetailedObject, + RunwayTextToImageRequest, + RunwayTextToImageResponse, + Model4, + ReferenceImage, + RunwayTextToImageAspectRatioEnum, +) +from comfy_api_nodes.util import ( + image_tensor_pair_to_batch, + validate_string, + validate_image_dimensions, + validate_image_aspect_ratio, + upload_images_to_comfyapi, + download_url_to_video_output, + download_url_to_image_tensor, + ApiEndpoint, + sync_op, + poll_op, +) +from comfy_api.input_impl import VideoFromFile +from comfy_api.latest import ComfyExtension, IO + +PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video" +PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image" +PATH_GET_TASK_STATUS = "/proxy/runway/tasks" + +AVERAGE_DURATION_I2V_SECONDS = 64 +AVERAGE_DURATION_FLF_SECONDS = 256 +AVERAGE_DURATION_T2I_SECONDS = 41 + + +class RunwayApiError(Exception): + """Base exception for Runway API errors.""" + + pass + + +class RunwayGen4TurboAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen4_turbo model.""" + + field_1280_720 = "1280:720" + field_720_1280 = "720:1280" + field_1104_832 = "1104:832" + field_832_1104 = "832:1104" + field_960_960 = "960:960" + field_1584_672 = "1584:672" + + +class RunwayGen3aAspectRatio(str, Enum): + """Aspect ratios supported for Image to Video API when using gen3a_turbo model.""" + + field_768_1280 = "768:1280" + field_1280_768 = "1280:768" + + +def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the video URL from the task status response if it exists.""" + if hasattr(response, "output") and len(response.output) > 0: + return response.output[0] + return None + + +def extract_progress_from_task_status( + response: TaskStatusResponse, +) -> Union[float, None]: + if hasattr(response, "progress") and response.progress is not None: + return response.progress * 100 + return None + + +def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]: + """Returns the image URL from the task status response if it exists.""" + if hasattr(response, "output") and len(response.output) > 0: + return response.output[0] + return None + + +async def get_response( + cls: type[IO.ComfyNode], task_id: str, estimated_duration: Optional[int] = None +) -> TaskStatusResponse: + """Poll the task status until it is finished then get the response.""" + return await poll_op( + cls, + ApiEndpoint(path=f"{PATH_GET_TASK_STATUS}/{task_id}"), + response_model=TaskStatusResponse, + status_extractor=lambda r: r.status.value, + estimated_duration=estimated_duration, + progress_extractor=extract_progress_from_task_status, + ) + + +async def generate_video( + cls: type[IO.ComfyNode], + request: RunwayImageToVideoRequest, + estimated_duration: Optional[int] = None, +) -> VideoFromFile: + initial_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=PATH_IMAGE_TO_VIDEO, method="POST"), + response_model=RunwayImageToVideoResponse, + data=request, + ) + + final_response = await get_response(cls, initial_response.id, estimated_duration) + if not final_response.output: + raise RunwayApiError("Runway task succeeded but no video data found in response.") + + video_url = get_video_url_from_task_status(final_response) + return await download_url_to_video_output(video_url) + + +class RunwayImageToVideoNodeGen3a(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayImageToVideoNodeGen3a", + display_name="Runway Image to Video (Gen3a Turbo)", + category="api node/video/Runway", + description="Generate a video from a single starting frame using Gen3a Turbo model. " + "Before diving in, review these best practices to ensure that " + "your input selections will set your generation up for success: " + "https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for the generation", + ), + IO.Image.Input( + "start_frame", + tooltip="Start frame to be used for the video", + ), + IO.Combo.Input( + "duration", + options=Duration, + ), + IO.Combo.Input( + "ratio", + options=RunwayGen3aAspectRatio, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967295, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed for generation", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1) + validate_image_dimensions(start_frame, max_width=7999, max_height=7999) + validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0) + + download_urls = await upload_images_to_comfyapi( + cls, + start_frame, + max_images=1, + mime_type="image/png", + ) + + return IO.NodeOutput( + await generate_video( + cls, + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first")] + ), + ), + ) + ) + + +class RunwayImageToVideoNodeGen4(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayImageToVideoNodeGen4", + display_name="Runway Image to Video (Gen4 Turbo)", + category="api node/video/Runway", + description="Generate a video from a single starting frame using Gen4 Turbo model. " + "Before diving in, review these best practices to ensure that " + "your input selections will set your generation up for success: " + "https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for the generation", + ), + IO.Image.Input( + "start_frame", + tooltip="Start frame to be used for the video", + ), + IO.Combo.Input( + "duration", + options=Duration, + ), + IO.Combo.Input( + "ratio", + options=RunwayGen4TurboAspectRatio, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967295, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed for generation", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + start_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1) + validate_image_dimensions(start_frame, max_width=7999, max_height=7999) + validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0) + + download_urls = await upload_images_to_comfyapi( + cls, + start_frame, + max_images=1, + mime_type="image/png", + ) + + return IO.NodeOutput( + await generate_video( + cls, + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen4_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first")] + ), + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + ) + ) + + +class RunwayFirstLastFrameNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayFirstLastFrameNode", + display_name="Runway First-Last-Frame to Video", + category="api node/video/Runway", + description="Upload first and last keyframes, draft a prompt, and generate a video. " + "More complex transitions, such as cases where the Last frame is completely different " + "from the First frame, may benefit from the longer 10s duration. " + "This would give the generation more time to smoothly transition between the two inputs. " + "Before diving in, review these best practices to ensure that your input selections " + "will set your generation up for success: " + "https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for the generation", + ), + IO.Image.Input( + "start_frame", + tooltip="Start frame to be used for the video", + ), + IO.Image.Input( + "end_frame", + tooltip="End frame to be used for the video. Supported for gen3a_turbo only.", + ), + IO.Combo.Input( + "duration", + options=Duration, + ), + IO.Combo.Input( + "ratio", + options=RunwayGen3aAspectRatio, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967295, + step=1, + control_after_generate=True, + display_mode=IO.NumberDisplay.number, + tooltip="Random seed for generation", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + start_frame: torch.Tensor, + end_frame: torch.Tensor, + duration: str, + ratio: str, + seed: int, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1) + validate_image_dimensions(start_frame, max_width=7999, max_height=7999) + validate_image_dimensions(end_frame, max_width=7999, max_height=7999) + validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0) + validate_image_aspect_ratio(end_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0) + + stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame) + download_urls = await upload_images_to_comfyapi( + cls, + stacked_input_images, + max_images=2, + mime_type="image/png", + ) + if len(download_urls) != 2: + raise RunwayApiError("Failed to upload one or more images to comfy api.") + + return IO.NodeOutput( + await generate_video( + cls, + RunwayImageToVideoRequest( + promptText=prompt, + seed=seed, + model=Model("gen3a_turbo"), + duration=Duration(duration), + ratio=AspectRatio(ratio), + promptImage=RunwayPromptImageObject( + root=[ + RunwayPromptImageDetailedObject(uri=str(download_urls[0]), position="first"), + RunwayPromptImageDetailedObject(uri=str(download_urls[1]), position="last"), + ] + ), + ), + estimated_duration=AVERAGE_DURATION_FLF_SECONDS, + ) + ) + + +class RunwayTextToImageNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="RunwayTextToImageNode", + display_name="Runway Text to Image", + category="api node/image/Runway", + description="Generate an image from a text prompt using Runway's Gen 4 model. " + "You can also include reference image to guide the generation.", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for the generation", + ), + IO.Combo.Input( + "ratio", + options=[model.value for model in RunwayTextToImageAspectRatioEnum], + ), + IO.Image.Input( + "reference_image", + tooltip="Optional reference image to guide the generation", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + ratio: str, + reference_image: Optional[torch.Tensor] = None, + ) -> IO.NodeOutput: + validate_string(prompt, min_length=1) + + # Prepare reference images if provided + reference_images = None + if reference_image is not None: + validate_image_dimensions(reference_image, max_width=7999, max_height=7999) + validate_image_aspect_ratio(reference_image, min_aspect_ratio=0.5, max_aspect_ratio=2.0) + download_urls = await upload_images_to_comfyapi( + cls, + reference_image, + max_images=1, + mime_type="image/png", + ) + reference_images = [ReferenceImage(uri=str(download_urls[0]))] + + initial_response = await sync_op( + cls, + endpoint=ApiEndpoint(path=PATH_TEXT_TO_IMAGE, method="POST"), + response_model=RunwayTextToImageResponse, + data=RunwayTextToImageRequest( + promptText=prompt, + model=Model4.gen4_image, + ratio=ratio, + referenceImages=reference_images, + ), + ) + + final_response = await get_response( + cls, + initial_response.id, + estimated_duration=AVERAGE_DURATION_T2I_SECONDS, + ) + if not final_response.output: + raise RunwayApiError("Runway task succeeded but no image data found in response.") + + return IO.NodeOutput(await download_url_to_image_tensor(get_image_url_from_task_status(final_response))) + + +class RunwayExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + RunwayFirstLastFrameNode, + RunwayImageToVideoNodeGen3a, + RunwayImageToVideoNodeGen4, + RunwayTextToImageNode, + ] + + +async def comfy_entrypoint() -> RunwayExtension: + return RunwayExtension() diff --git a/comfy_api_nodes/nodes_sora.py b/comfy_api_nodes/nodes_sora.py new file mode 100644 index 000000000..92b225d40 --- /dev/null +++ b/comfy_api_nodes/nodes_sora.py @@ -0,0 +1,151 @@ +from typing import Optional + +import torch +from pydantic import BaseModel, Field +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_video_output, + get_number_of_images, + poll_op, + sync_op, + tensor_to_bytesio, +) + + +class Sora2GenerationRequest(BaseModel): + prompt: str = Field(...) + model: str = Field(...) + seconds: str = Field(...) + size: str = Field(...) + + +class Sora2GenerationResponse(BaseModel): + id: str = Field(...) + error: Optional[dict] = Field(None) + status: Optional[str] = Field(None) + + +class OpenAIVideoSora2(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="OpenAIVideoSora2", + display_name="OpenAI Sora - Video", + category="api node/video/Sora", + description="OpenAI video and audio generation.", + inputs=[ + IO.Combo.Input( + "model", + options=["sora-2", "sora-2-pro"], + default="sora-2", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Guiding text; may be empty if an input image is present.", + ), + IO.Combo.Input( + "size", + options=[ + "720x1280", + "1280x720", + "1024x1792", + "1792x1024", + ], + default="1280x720", + ), + IO.Combo.Input( + "duration", + options=[4, 8, 12], + default=8, + ), + IO.Image.Input( + "image", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + optional=True, + tooltip="Seed to determine if node should re-run; " + "actual results are nondeterministic regardless of seed.", + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + size: str = "1280x720", + duration: int = 8, + seed: int = 0, + image: Optional[torch.Tensor] = None, + ): + if model == "sora-2" and size not in ("720x1280", "1280x720"): + raise ValueError("Invalid size for sora-2 model, only 720x1280 and 1280x720 are supported.") + files_input = None + if image is not None: + if get_number_of_images(image) != 1: + raise ValueError("Currently only one input image is supported.") + files_input = {"input_reference": ("image.png", tensor_to_bytesio(image), "image/png")} + initial_response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/openai/v1/videos", method="POST"), + data=Sora2GenerationRequest( + model=model, + prompt=prompt, + seconds=str(duration), + size=size, + ), + files=files_input, + response_model=Sora2GenerationResponse, + content_type="multipart/form-data", + ) + if initial_response.error: + raise Exception(initial_response.error["message"]) + + model_time_multiplier = 1 if model == "sora-2" else 2 + await poll_op( + cls, + poll_endpoint=ApiEndpoint(path=f"/proxy/openai/v1/videos/{initial_response.id}"), + response_model=Sora2GenerationResponse, + status_extractor=lambda x: x.status, + poll_interval=8.0, + max_poll_attempts=160, + estimated_duration=int(45 * (duration / 4) * model_time_multiplier), + ) + return IO.NodeOutput( + await download_url_to_video_output(f"/proxy/openai/v1/videos/{initial_response.id}/content", cls=cls), + ) + + +class OpenAISoraExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + OpenAIVideoSora2, + ] + + +async def comfy_entrypoint() -> OpenAISoraExtension: + return OpenAISoraExtension() diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py new file mode 100644 index 000000000..783666ddf --- /dev/null +++ b/comfy_api_nodes/nodes_stability.py @@ -0,0 +1,975 @@ +from inspect import cleandoc +from typing import Optional +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, Input, IO +from comfy_api_nodes.apis.stability_api import ( + StabilityUpscaleConservativeRequest, + StabilityUpscaleCreativeRequest, + StabilityAsyncResponse, + StabilityResultsGetResponse, + StabilityStable3_5Request, + StabilityStableUltraRequest, + StabilityStableUltraResponse, + StabilityAspectRatio, + Stability_SD3_5_Model, + Stability_SD3_5_GenerationMode, + get_stability_style_presets, + StabilityTextToAudioRequest, + StabilityAudioToAudioRequest, + StabilityAudioInpaintRequest, + StabilityAudioResponse, +) +from comfy_api_nodes.apis.client import ( + ApiEndpoint, + HttpMethod, + SynchronousOperation, + PollingOperation, + EmptyRequest, +) +from comfy_api_nodes.util import ( + validate_audio_duration, + validate_string, + audio_input_to_mp3, + bytesio_to_image_tensor, + tensor_to_bytesio, + audio_bytes_to_audio_input, +) + +import torch +import base64 +from io import BytesIO +from enum import Enum + + +class StabilityPollStatus(str, Enum): + finished = "finished" + in_progress = "in_progress" + failed = "failed" + + +def get_async_dummy_status(x: StabilityResultsGetResponse): + if x.name is not None or x.errors is not None: + return StabilityPollStatus.failed + elif x.finish_reason is not None: + return StabilityPollStatus.finished + return StabilityPollStatus.in_progress + + +class StabilityStableImageUltraNode(IO.ComfyNode): + """ + Generates images synchronously based on prompt and resolution. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityStableImageUltraNode", + display_name="Stability AI Stable Image Ultra", + category="api node/image/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines" + + "elements, colors, and subjects will lead to better results. " + + "To control the weight of a given word use the format `(word:weight)`," + + "where `word` is the word you'd like to control the weight of and `weight`" + + "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" + + "would convey a sky that was blue and green, but more green than blue.", + ), + IO.Combo.Input( + "aspect_ratio", + options=StabilityAspectRatio, + default=StabilityAspectRatio.ratio_1_1, + tooltip="Aspect ratio of generated image.", + ), + IO.Combo.Input( + "style_preset", + options=get_stability_style_presets(), + tooltip="Optional desired style of generated image.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.Image.Input( + "image", + optional=True, + ), + IO.String.Input( + "negative_prompt", + default="", + tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.", + force_input=True, + optional=True, + ), + IO.Float.Input( + "image_denoise", + default=0.5, + min=0.0, + max=1.0, + step=0.01, + tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + aspect_ratio: str, + style_preset: str, + seed: int, + image: Optional[torch.Tensor] = None, + negative_prompt: str = "", + image_denoise: Optional[float] = 0.5, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/ultra", + method=HttpMethod.POST, + request_model=StabilityStableUltraRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStableUltraRequest( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return IO.NodeOutput(returned_image) + + +class StabilityStableImageSD_3_5Node(IO.ComfyNode): + """ + Generates images synchronously based on prompt and resolution. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityStableImageSD_3_5Node", + display_name="Stability AI Stable Diffusion 3.5 Image", + category="api node/image/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.", + ), + IO.Combo.Input( + "model", + options=Stability_SD3_5_Model, + ), + IO.Combo.Input( + "aspect_ratio", + options=StabilityAspectRatio, + default=StabilityAspectRatio.ratio_1_1, + tooltip="Aspect ratio of generated image.", + ), + IO.Combo.Input( + "style_preset", + options=get_stability_style_presets(), + tooltip="Optional desired style of generated image.", + ), + IO.Float.Input( + "cfg_scale", + default=4.0, + min=1.0, + max=10.0, + step=0.1, + tooltip="How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.Image.Input( + "image", + optional=True, + ), + IO.String.Input( + "negative_prompt", + default="", + tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.", + force_input=True, + optional=True, + ), + IO.Float.Input( + "image_denoise", + default=0.5, + min=0.0, + max=1.0, + step=0.01, + tooltip="Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.", + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + aspect_ratio: str, + style_preset: str, + seed: int, + cfg_scale: float, + image: Optional[torch.Tensor] = None, + negative_prompt: str = "", + image_denoise: Optional[float] = 0.5, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + # prepare image binary if image present + image_binary = None + mode = Stability_SD3_5_GenerationMode.text_to_image + if image is not None: + image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read() + mode = Stability_SD3_5_GenerationMode.image_to_image + aspect_ratio = None + else: + image_denoise = None + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/generate/sd3", + method=HttpMethod.POST, + request_model=StabilityStable3_5Request, + response_model=StabilityStableUltraResponse, + ), + request=StabilityStable3_5Request( + prompt=prompt, + negative_prompt=negative_prompt, + aspect_ratio=aspect_ratio, + seed=seed, + strength=image_denoise, + style_preset=style_preset, + cfg_scale=cfg_scale, + model=model, + mode=mode, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return IO.NodeOutput(returned_image) + + +class StabilityUpscaleConservativeNode(IO.ComfyNode): + """ + Upscale image with minimal alterations to 4K resolution. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityUpscaleConservativeNode", + display_name="Stability AI Upscale Conservative", + category="api node/image/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.", + ), + IO.Float.Input( + "creativity", + default=0.35, + min=0.2, + max=0.5, + step=0.01, + tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.String.Input( + "negative_prompt", + default="", + tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.", + force_input=True, + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt: str, + creativity: float, + seed: int, + negative_prompt: str = "", + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + + files = { + "image": image_binary + } + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/conservative", + method=HttpMethod.POST, + request_model=StabilityUpscaleConservativeRequest, + response_model=StabilityStableUltraResponse, + ), + request=StabilityUpscaleConservativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return IO.NodeOutput(returned_image) + + +class StabilityUpscaleCreativeNode(IO.ComfyNode): + """ + Upscale image with minimal alterations to 4K resolution. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityUpscaleCreativeNode", + display_name="Stability AI Upscale Creative", + category="api node/image/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results.", + ), + IO.Float.Input( + "creativity", + default=0.3, + min=0.1, + max=0.5, + step=0.01, + tooltip="Controls the likelihood of creating additional details not heavily conditioned by the init image.", + ), + IO.Combo.Input( + "style_preset", + options=get_stability_style_presets(), + tooltip="Optional desired style of generated image.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for creating the noise.", + ), + IO.String.Input( + "negative_prompt", + default="", + tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.", + force_input=True, + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + prompt: str, + creativity: float, + style_preset: str, + seed: int, + negative_prompt: str = "", + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=False) + image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read() + + if not negative_prompt: + negative_prompt = None + if style_preset == "None": + style_preset = None + + files = { + "image": image_binary + } + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/creative", + method=HttpMethod.POST, + request_model=StabilityUpscaleCreativeRequest, + response_model=StabilityAsyncResponse, + ), + request=StabilityUpscaleCreativeRequest( + prompt=prompt, + negative_prompt=negative_prompt, + creativity=round(creativity,2), + style_preset=style_preset, + seed=seed, + ), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + response_api = await operation.execute() + + operation = PollingOperation( + poll_endpoint=ApiEndpoint( + path=f"/proxy/stability/v2beta/results/{response_api.id}", + method=HttpMethod.GET, + request_model=EmptyRequest, + response_model=StabilityResultsGetResponse, + ), + poll_interval=3, + completed_statuses=[StabilityPollStatus.finished], + failed_statuses=[StabilityPollStatus.failed], + status_extractor=lambda x: get_async_dummy_status(x), + auth_kwargs=auth, + node_id=cls.hidden.unique_id, + ) + response_poll: StabilityResultsGetResponse = await operation.execute() + + if response_poll.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.") + + image_data = base64.b64decode(response_poll.result) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return IO.NodeOutput(returned_image) + + +class StabilityUpscaleFastNode(IO.ComfyNode): + """ + Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityUpscaleFastNode", + display_name="Stability AI Upscale Fast", + category="api node/image/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Image.Input("image"), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute(cls, image: torch.Tensor) -> IO.NodeOutput: + image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read() + + files = { + "image": image_binary + } + + auth = { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + } + + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/stable-image/upscale/fast", + method=HttpMethod.POST, + request_model=EmptyRequest, + response_model=StabilityStableUltraResponse, + ), + request=EmptyRequest(), + files=files, + content_type="multipart/form-data", + auth_kwargs=auth, + ) + response_api = await operation.execute() + + if response_api.finish_reason != "SUCCESS": + raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.") + + image_data = base64.b64decode(response_api.image) + returned_image = bytesio_to_image_tensor(BytesIO(image_data)) + + return IO.NodeOutput(returned_image) + + +class StabilityTextToAudio(IO.ComfyNode): + """Generates high-quality music and sound effects from text descriptions.""" + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityTextToAudio", + display_name="Stability AI Text To Audio", + category="api node/audio/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Combo.Input( + "model", + options=["stable-audio-2.5"], + ), + IO.String.Input("prompt", multiline=True, default=""), + IO.Int.Input( + "duration", + default=190, + min=1, + max=190, + step=1, + tooltip="Controls the duration in seconds of the generated audio.", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for generation.", + optional=True, + ), + IO.Int.Input( + "steps", + default=8, + min=4, + max=8, + step=1, + tooltip="Controls the number of sampling steps.", + optional=True, + ), + ], + outputs=[ + IO.Audio.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute(cls, model: str, prompt: str, duration: int, seed: int, steps: int) -> IO.NodeOutput: + validate_string(prompt, max_length=10000) + payload = StabilityTextToAudioRequest(prompt=prompt, model=model, duration=duration, seed=seed, steps=steps) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/audio/stable-audio-2/text-to-audio", + method=HttpMethod.POST, + request_model=StabilityTextToAudioRequest, + response_model=StabilityAudioResponse, + ), + request=payload, + content_type="multipart/form-data", + auth_kwargs= { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + ) + response_api = await operation.execute() + if not response_api.audio: + raise ValueError("No audio file was received in response.") + return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio))) + + +class StabilityAudioToAudio(IO.ComfyNode): + """Transforms existing audio samples into new high-quality compositions using text instructions.""" + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityAudioToAudio", + display_name="Stability AI Audio To Audio", + category="api node/audio/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Combo.Input( + "model", + options=["stable-audio-2.5"], + ), + IO.String.Input("prompt", multiline=True, default=""), + IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."), + IO.Int.Input( + "duration", + default=190, + min=1, + max=190, + step=1, + tooltip="Controls the duration in seconds of the generated audio.", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for generation.", + optional=True, + ), + IO.Int.Input( + "steps", + default=8, + min=4, + max=8, + step=1, + tooltip="Controls the number of sampling steps.", + optional=True, + ), + IO.Float.Input( + "strength", + default=1, + min=0.01, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.slider, + tooltip="Parameter controls how much influence the audio parameter has on the generated audio.", + optional=True, + ), + ], + outputs=[ + IO.Audio.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, model: str, prompt: str, audio: Input.Audio, duration: int, seed: int, steps: int, strength: float + ) -> IO.NodeOutput: + validate_string(prompt, max_length=10000) + validate_audio_duration(audio, 6, 190) + payload = StabilityAudioToAudioRequest( + prompt=prompt, model=model, duration=duration, seed=seed, steps=steps, strength=strength + ) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/audio/stable-audio-2/audio-to-audio", + method=HttpMethod.POST, + request_model=StabilityAudioToAudioRequest, + response_model=StabilityAudioResponse, + ), + request=payload, + content_type="multipart/form-data", + files={"audio": audio_input_to_mp3(audio)}, + auth_kwargs= { + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + ) + response_api = await operation.execute() + if not response_api.audio: + raise ValueError("No audio file was received in response.") + return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio))) + + +class StabilityAudioInpaint(IO.ComfyNode): + """Transforms part of existing audio sample using text instructions.""" + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="StabilityAudioInpaint", + display_name="Stability AI Audio Inpaint", + category="api node/audio/Stability AI", + description=cleandoc(cls.__doc__ or ""), + inputs=[ + IO.Combo.Input( + "model", + options=["stable-audio-2.5"], + ), + IO.String.Input("prompt", multiline=True, default=""), + IO.Audio.Input("audio", tooltip="Audio must be between 6 and 190 seconds long."), + IO.Int.Input( + "duration", + default=190, + min=1, + max=190, + step=1, + tooltip="Controls the duration in seconds of the generated audio.", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=4294967294, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="The random seed used for generation.", + optional=True, + ), + IO.Int.Input( + "steps", + default=8, + min=4, + max=8, + step=1, + tooltip="Controls the number of sampling steps.", + optional=True, + ), + IO.Int.Input( + "mask_start", + default=30, + min=0, + max=190, + step=1, + optional=True, + ), + IO.Int.Input( + "mask_end", + default=190, + min=0, + max=190, + step=1, + optional=True, + ), + ], + outputs=[ + IO.Audio.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + audio: Input.Audio, + duration: int, + seed: int, + steps: int, + mask_start: int, + mask_end: int, + ) -> IO.NodeOutput: + validate_string(prompt, max_length=10000) + if mask_end <= mask_start: + raise ValueError(f"Value of mask_end({mask_end}) should be greater then mask_start({mask_start})") + validate_audio_duration(audio, 6, 190) + + payload = StabilityAudioInpaintRequest( + prompt=prompt, + model=model, + duration=duration, + seed=seed, + steps=steps, + mask_start=mask_start, + mask_end=mask_end, + ) + operation = SynchronousOperation( + endpoint=ApiEndpoint( + path="/proxy/stability/v2beta/audio/stable-audio-2/inpaint", + method=HttpMethod.POST, + request_model=StabilityAudioInpaintRequest, + response_model=StabilityAudioResponse, + ), + request=payload, + content_type="multipart/form-data", + files={"audio": audio_input_to_mp3(audio)}, + auth_kwargs={ + "auth_token": cls.hidden.auth_token_comfy_org, + "comfy_api_key": cls.hidden.api_key_comfy_org, + }, + ) + response_api = await operation.execute() + if not response_api.audio: + raise ValueError("No audio file was received in response.") + return IO.NodeOutput(audio_bytes_to_audio_input(base64.b64decode(response_api.audio))) + + +class StabilityExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + StabilityStableImageUltraNode, + StabilityStableImageSD_3_5Node, + StabilityUpscaleConservativeNode, + StabilityUpscaleCreativeNode, + StabilityUpscaleFastNode, + StabilityTextToAudio, + StabilityAudioToAudio, + StabilityAudioInpaint, + ] + + +async def comfy_entrypoint() -> StabilityExtension: + return StabilityExtension() diff --git a/comfy_api_nodes/nodes_tripo.py b/comfy_api_nodes/nodes_tripo.py new file mode 100644 index 000000000..697100ff2 --- /dev/null +++ b/comfy_api_nodes/nodes_tripo.py @@ -0,0 +1,645 @@ +import os +from typing import Optional + +import torch +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apis.tripo_api import ( + TripoAnimateRetargetRequest, + TripoAnimateRigRequest, + TripoConvertModelRequest, + TripoFileEmptyReference, + TripoFileReference, + TripoImageToModelRequest, + TripoModelVersion, + TripoMultiviewToModelRequest, + TripoOrientation, + TripoRefineModelRequest, + TripoStyle, + TripoTaskResponse, + TripoTaskStatus, + TripoTaskType, + TripoTextToModelRequest, + TripoTextureModelRequest, + TripoUrlReference, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_as_bytesio, + poll_op, + sync_op, + upload_images_to_comfyapi, +) +from folder_paths import get_output_directory + + +def get_model_url_from_response(response: TripoTaskResponse) -> str: + if response.data is not None: + for key in ["pbr_model", "model", "base_model"]: + if getattr(response.data.output, key, None) is not None: + return getattr(response.data.output, key) + raise RuntimeError(f"Failed to get model url from response: {response}") + + +async def poll_until_finished( + node_cls: type[IO.ComfyNode], + response: TripoTaskResponse, + average_duration: Optional[int] = None, +) -> IO.NodeOutput: + """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response.""" + if response.code != 0: + raise RuntimeError(f"Failed to generate mesh: {response.error}") + task_id = response.data.task_id + response_poll = await poll_op( + node_cls, + poll_endpoint=ApiEndpoint(path=f"/proxy/tripo/v2/openapi/task/{task_id}"), + response_model=TripoTaskResponse, + completed_statuses=[TripoTaskStatus.SUCCESS], + failed_statuses=[ + TripoTaskStatus.FAILED, + TripoTaskStatus.CANCELLED, + TripoTaskStatus.UNKNOWN, + TripoTaskStatus.BANNED, + TripoTaskStatus.EXPIRED, + ], + status_extractor=lambda x: x.data.status, + progress_extractor=lambda x: x.data.progress, + estimated_duration=average_duration, + ) + if response_poll.data.status == TripoTaskStatus.SUCCESS: + url = get_model_url_from_response(response_poll) + bytesio = await download_url_as_bytesio(url) + # Save the downloaded model file + model_file = f"tripo_model_{task_id}.glb" + with open(os.path.join(get_output_directory(), model_file), "wb") as f: + f.write(bytesio.getvalue()) + return IO.NodeOutput(model_file, task_id) + raise RuntimeError(f"Failed to generate mesh: {response_poll}") + + +class TripoTextToModelNode(IO.ComfyNode): + """ + Generates 3D models synchronously based on a text prompt using Tripo's API. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoTextToModelNode", + display_name="Tripo: Text to Model", + category="api node/3d/Tripo", + inputs=[ + IO.String.Input("prompt", multiline=True), + IO.String.Input("negative_prompt", multiline=True, optional=True), + IO.Combo.Input( + "model_version", options=TripoModelVersion, default=TripoModelVersion.v2_5_20250123, optional=True + ), + IO.Combo.Input("style", options=TripoStyle, default="None", optional=True), + IO.Boolean.Input("texture", default=True, optional=True), + IO.Boolean.Input("pbr", default=True, optional=True), + IO.Int.Input("image_seed", default=42, optional=True), + IO.Int.Input("model_seed", default=42, optional=True), + IO.Int.Input("texture_seed", default=42, optional=True), + IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True), + IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True), + IO.Boolean.Input("quad", default=False, optional=True), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute( + cls, + prompt: str, + negative_prompt: Optional[str] = None, + model_version=None, + style: Optional[str] = None, + texture: Optional[bool] = None, + pbr: Optional[bool] = None, + image_seed: Optional[int] = None, + model_seed: Optional[int] = None, + texture_seed: Optional[int] = None, + texture_quality: Optional[str] = None, + face_limit: Optional[int] = None, + quad: Optional[bool] = None, + ) -> IO.NodeOutput: + style_enum = None if style == "None" else style + if not prompt: + raise RuntimeError("Prompt is required") + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoTextToModelRequest( + type=TripoTaskType.TEXT_TO_MODEL, + prompt=prompt, + negative_prompt=negative_prompt if negative_prompt else None, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + image_seed=image_seed, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad, + ), + ) + return await poll_until_finished(cls, response, average_duration=80) + + +class TripoImageToModelNode(IO.ComfyNode): + """ + Generates 3D models synchronously based on a single image using Tripo's API. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoImageToModelNode", + display_name="Tripo: Image to Model", + category="api node/3d/Tripo", + inputs=[ + IO.Image.Input("image"), + IO.Combo.Input( + "model_version", + options=TripoModelVersion, + tooltip="The model version to use for generation", + optional=True, + ), + IO.Combo.Input("style", options=TripoStyle, default="None", optional=True), + IO.Boolean.Input("texture", default=True, optional=True), + IO.Boolean.Input("pbr", default=True, optional=True), + IO.Int.Input("model_seed", default=42, optional=True), + IO.Combo.Input( + "orientation", options=TripoOrientation, default=TripoOrientation.DEFAULT, optional=True + ), + IO.Int.Input("texture_seed", default=42, optional=True), + IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True), + IO.Combo.Input( + "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True + ), + IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True), + IO.Boolean.Input("quad", default=False, optional=True), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + model_version: Optional[str] = None, + style: Optional[str] = None, + texture: Optional[bool] = None, + pbr: Optional[bool] = None, + model_seed: Optional[int] = None, + orientation=None, + texture_seed: Optional[int] = None, + texture_quality: Optional[str] = None, + texture_alignment: Optional[str] = None, + face_limit: Optional[int] = None, + quad: Optional[bool] = None, + ) -> IO.NodeOutput: + style_enum = None if style == "None" else style + if image is None: + raise RuntimeError("Image is required") + tripo_file = TripoFileReference( + root=TripoUrlReference( + url=(await upload_images_to_comfyapi(cls, image, max_images=1))[0], + type="jpeg", + ) + ) + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoImageToModelRequest( + type=TripoTaskType.IMAGE_TO_MODEL, + file=tripo_file, + model_version=model_version, + style=style_enum, + texture=texture, + pbr=pbr, + model_seed=model_seed, + orientation=orientation, + texture_alignment=texture_alignment, + texture_seed=texture_seed, + texture_quality=texture_quality, + face_limit=face_limit, + auto_size=True, + quad=quad, + ), + ) + return await poll_until_finished(cls, response, average_duration=80) + + +class TripoMultiviewToModelNode(IO.ComfyNode): + """ + Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoMultiviewToModelNode", + display_name="Tripo: Multiview to Model", + category="api node/3d/Tripo", + inputs=[ + IO.Image.Input("image"), + IO.Image.Input("image_left", optional=True), + IO.Image.Input("image_back", optional=True), + IO.Image.Input("image_right", optional=True), + IO.Combo.Input( + "model_version", + options=TripoModelVersion, + optional=True, + tooltip="The model version to use for generation", + ), + IO.Combo.Input( + "orientation", + options=TripoOrientation, + default=TripoOrientation.DEFAULT, + optional=True, + ), + IO.Boolean.Input("texture", default=True, optional=True), + IO.Boolean.Input("pbr", default=True, optional=True), + IO.Int.Input("model_seed", default=42, optional=True), + IO.Int.Input("texture_seed", default=42, optional=True), + IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True), + IO.Combo.Input( + "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True + ), + IO.Int.Input("face_limit", default=-1, min=-1, max=500000, optional=True), + IO.Boolean.Input("quad", default=False, optional=True), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute( + cls, + image: torch.Tensor, + image_left: Optional[torch.Tensor] = None, + image_back: Optional[torch.Tensor] = None, + image_right: Optional[torch.Tensor] = None, + model_version: Optional[str] = None, + orientation: Optional[str] = None, + texture: Optional[bool] = None, + pbr: Optional[bool] = None, + model_seed: Optional[int] = None, + texture_seed: Optional[int] = None, + texture_quality: Optional[str] = None, + texture_alignment: Optional[str] = None, + face_limit: Optional[int] = None, + quad: Optional[bool] = None, + ) -> IO.NodeOutput: + if image is None: + raise RuntimeError("front image for multiview is required") + images = [] + image_dict = {"image": image, "image_left": image_left, "image_back": image_back, "image_right": image_right} + if image_left is None and image_back is None and image_right is None: + raise RuntimeError("At least one of left, back, or right image must be provided for multiview") + for image_name in ["image", "image_left", "image_back", "image_right"]: + image_ = image_dict[image_name] + if image_ is not None: + images.append( + TripoFileReference( + root=TripoUrlReference( + url=(await upload_images_to_comfyapi(cls, image_, max_images=1))[0], type="jpeg" + ) + ) + ) + else: + images.append(TripoFileEmptyReference()) + response = await sync_op( + cls, + ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoMultiviewToModelRequest( + type=TripoTaskType.MULTIVIEW_TO_MODEL, + files=images, + model_version=model_version, + orientation=orientation, + texture=texture, + pbr=pbr, + model_seed=model_seed, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment, + face_limit=face_limit, + quad=quad, + ), + ) + return await poll_until_finished(cls, response, average_duration=80) + + +class TripoTextureNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoTextureNode", + display_name="Tripo: Texture model", + category="api node/3d/Tripo", + inputs=[ + IO.Custom("MODEL_TASK_ID").Input("model_task_id"), + IO.Boolean.Input("texture", default=True, optional=True), + IO.Boolean.Input("pbr", default=True, optional=True), + IO.Int.Input("texture_seed", default=42, optional=True), + IO.Combo.Input("texture_quality", default="standard", options=["standard", "detailed"], optional=True), + IO.Combo.Input( + "texture_alignment", default="original_image", options=["original_image", "geometry"], optional=True + ), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute( + cls, + model_task_id, + texture: Optional[bool] = None, + pbr: Optional[bool] = None, + texture_seed: Optional[int] = None, + texture_quality: Optional[str] = None, + texture_alignment: Optional[str] = None, + ) -> IO.NodeOutput: + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoTextureModelRequest( + original_model_task_id=model_task_id, + texture=texture, + pbr=pbr, + texture_seed=texture_seed, + texture_quality=texture_quality, + texture_alignment=texture_alignment, + ), + ) + return await poll_until_finished(cls, response, average_duration=80) + + +class TripoRefineNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoRefineNode", + display_name="Tripo: Refine Draft model", + category="api node/3d/Tripo", + description="Refine a draft model created by v1.4 Tripo models only.", + inputs=[ + IO.Custom("MODEL_TASK_ID").Input("model_task_id", tooltip="Must be a v1.4 Tripo model"), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("MODEL_TASK_ID").Output(display_name="model task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute(cls, model_task_id) -> IO.NodeOutput: + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoRefineModelRequest(draft_model_task_id=model_task_id), + ) + return await poll_until_finished(cls, response, average_duration=240) + + +class TripoRigNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoRigNode", + display_name="Tripo: Rig model", + category="api node/3d/Tripo", + inputs=[IO.Custom("MODEL_TASK_ID").Input("original_model_task_id")], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("RIG_TASK_ID").Output(display_name="rig task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute(cls, original_model_task_id) -> IO.NodeOutput: + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoAnimateRigRequest(original_model_task_id=original_model_task_id, out_format="glb", spec="tripo"), + ) + return await poll_until_finished(cls, response, average_duration=180) + + +class TripoRetargetNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoRetargetNode", + display_name="Tripo: Retarget rigged model", + category="api node/3d/Tripo", + inputs=[ + IO.Custom("RIG_TASK_ID").Input("original_model_task_id"), + IO.Combo.Input( + "animation", + options=[ + "preset:idle", + "preset:walk", + "preset:climb", + "preset:jump", + "preset:slash", + "preset:shoot", + "preset:hurt", + "preset:fall", + "preset:turn", + ], + ), + ], + outputs=[ + IO.String.Output(display_name="model_file"), + IO.Custom("RETARGET_TASK_ID").Output(display_name="retarget task_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + async def execute(cls, original_model_task_id, animation: str) -> IO.NodeOutput: + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoAnimateRetargetRequest( + original_model_task_id=original_model_task_id, + animation=animation, + out_format="glb", + bake_animation=True, + ), + ) + return await poll_until_finished(cls, response, average_duration=30) + + +class TripoConversionNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TripoConversionNode", + display_name="Tripo: Convert model", + category="api node/3d/Tripo", + inputs=[ + IO.Custom("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID").Input("original_model_task_id"), + IO.Combo.Input("format", options=["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"]), + IO.Boolean.Input("quad", default=False, optional=True), + IO.Int.Input( + "face_limit", + default=-1, + min=-1, + max=500000, + optional=True, + ), + IO.Int.Input( + "texture_size", + default=4096, + min=128, + max=4096, + optional=True, + ), + IO.Combo.Input( + "texture_format", + options=["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], + default="JPEG", + optional=True, + ), + ], + outputs=[], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + is_output_node=True, + ) + + @classmethod + def validate_inputs(cls, input_types): + # The min and max of input1 and input2 are still validated because + # we didn't take `input1` or `input2` as arguments + if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"): + return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type" + return True + + @classmethod + async def execute( + cls, + original_model_task_id, + format: str, + quad: bool, + face_limit: int, + texture_size: int, + texture_format: str, + ) -> IO.NodeOutput: + if not original_model_task_id: + raise RuntimeError("original_model_task_id is required") + response = await sync_op( + cls, + endpoint=ApiEndpoint(path="/proxy/tripo/v2/openapi/task", method="POST"), + response_model=TripoTaskResponse, + data=TripoConvertModelRequest( + original_model_task_id=original_model_task_id, + format=format, + quad=quad if quad else None, + face_limit=face_limit if face_limit != -1 else None, + texture_size=texture_size if texture_size != 4096 else None, + texture_format=texture_format if texture_format != "JPEG" else None, + ), + ) + return await poll_until_finished(cls, response, average_duration=30) + + +class TripoExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + TripoTextToModelNode, + TripoImageToModelNode, + TripoMultiviewToModelNode, + TripoTextureNode, + TripoRefineNode, + TripoRigNode, + TripoRetargetNode, + TripoConversionNode, + ] + + +async def comfy_entrypoint() -> TripoExtension: + return TripoExtension() diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py new file mode 100644 index 000000000..d37e9e9b4 --- /dev/null +++ b/comfy_api_nodes/nodes_veo2.py @@ -0,0 +1,359 @@ +import base64 +from io import BytesIO + +from typing_extensions import override + +from comfy_api.input_impl.video_types import VideoFromFile +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.apis.veo_api import ( + VeoGenVidPollRequest, + VeoGenVidPollResponse, + VeoGenVidRequest, + VeoGenVidResponse, +) +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_video_output, + poll_op, + sync_op, + tensor_to_base64_string, +) + +AVERAGE_DURATION_VIDEO_GEN = 32 +MODELS_MAP = { + "veo-2.0-generate-001": "veo-2.0-generate-001", + "veo-3.1-generate": "veo-3.1-generate-preview", + "veo-3.1-fast-generate": "veo-3.1-fast-generate-preview", + "veo-3.0-generate-001": "veo-3.0-generate-001", + "veo-3.0-fast-generate-001": "veo-3.0-fast-generate-001", +} + + +class VeoVideoGenerationNode(IO.ComfyNode): + """ + Generates videos from text prompts using Google's Veo API. + + This node can create videos from text descriptions and optional image inputs, + with control over parameters like aspect ratio, duration, and more. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="VeoVideoGenerationNode", + display_name="Google Veo 2 Video Generation", + category="api node/video/Veo", + description="Generates videos from text prompts using Google's Veo 2 API", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text description of the video", + ), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "9:16"], + default="16:9", + tooltip="Aspect ratio of the output video", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid in the video", + optional=True, + ), + IO.Int.Input( + "duration_seconds", + default=5, + min=5, + max=8, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds", + optional=True, + ), + IO.Boolean.Input( + "enhance_prompt", + default=True, + tooltip="Whether to enhance the prompt with AI assistance", + optional=True, + ), + IO.Combo.Input( + "person_generation", + options=["ALLOW", "BLOCK"], + default="ALLOW", + tooltip="Whether to allow generating people in the video", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFF, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Image.Input( + "image", + tooltip="Optional reference image to guide video generation", + optional=True, + ), + IO.Combo.Input( + "model", + options=["veo-2.0-generate-001"], + default="veo-2.0-generate-001", + tooltip="Veo 2 model to use for video generation", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + prompt, + aspect_ratio="16:9", + negative_prompt="", + duration_seconds=5, + enhance_prompt=True, + person_generation="ALLOW", + seed=0, + image=None, + model="veo-2.0-generate-001", + generate_audio=False, + ): + model = MODELS_MAP[model] + # Prepare the instances for the request + instances = [] + + instance = {"prompt": prompt} + + # Add image if provided + if image is not None: + image_base64 = tensor_to_base64_string(image) + if image_base64: + instance["image"] = {"bytesBase64Encoded": image_base64, "mimeType": "image/png"} + + instances.append(instance) + + # Create parameters dictionary + parameters = { + "aspectRatio": aspect_ratio, + "personGeneration": person_generation, + "durationSeconds": duration_seconds, + "enhancePrompt": enhance_prompt, + } + + # Add optional parameters if provided + if negative_prompt: + parameters["negativePrompt"] = negative_prompt + if seed > 0: + parameters["seed"] = seed + # Only add generateAudio for Veo 3 models + if model.find("veo-2.0") == -1: + parameters["generateAudio"] = generate_audio + + initial_response = await sync_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"), + response_model=VeoGenVidResponse, + data=VeoGenVidRequest( + instances=instances, + parameters=parameters, + ), + ) + + def status_extractor(response): + # Only return "completed" if the operation is done, regardless of success or failure + # We'll check for errors after polling completes + return "completed" if response.done else "pending" + + poll_response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"), + response_model=VeoGenVidPollResponse, + status_extractor=status_extractor, + data=VeoGenVidPollRequest( + operationName=initial_response.name, + ), + poll_interval=5.0, + estimated_duration=AVERAGE_DURATION_VIDEO_GEN, + ) + + # Now check for errors in the final response + # Check for error in poll response + if poll_response.error: + raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})") + + # Check for RAI filtered content + if ( + hasattr(poll_response.response, "raiMediaFilteredCount") + and poll_response.response.raiMediaFilteredCount > 0 + ): + + # Extract reason message if available + if ( + hasattr(poll_response.response, "raiMediaFilteredReasons") + and poll_response.response.raiMediaFilteredReasons + ): + reason = poll_response.response.raiMediaFilteredReasons[0] + error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + else: + error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)" + + raise Exception(error_message) + + # Extract video data + if ( + poll_response.response + and hasattr(poll_response.response, "videos") + and poll_response.response.videos + and len(poll_response.response.videos) > 0 + ): + video = poll_response.response.videos[0] + + # Check if video is provided as base64 or URL + if hasattr(video, "bytesBase64Encoded") and video.bytesBase64Encoded: + return IO.NodeOutput(VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded)))) + + if hasattr(video, "gcsUri") and video.gcsUri: + return IO.NodeOutput(await download_url_to_video_output(video.gcsUri)) + + raise Exception("Video returned but no data or URL was provided") + raise Exception("Video generation completed but no video was returned") + + +class Veo3VideoGenerationNode(VeoVideoGenerationNode): + """ + Generates videos from text prompts using Google's Veo 3 API. + + Supported models: + - veo-3.0-generate-001 + - veo-3.0-fast-generate-001 + + This node extends the base Veo node with Veo 3 specific features including + audio generation and fixed 8-second duration. + """ + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Veo3VideoGenerationNode", + display_name="Google Veo 3 Video Generation", + category="api node/video/Veo", + description="Generates videos from text prompts using Google's Veo 3 API", + inputs=[ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text description of the video", + ), + IO.Combo.Input( + "aspect_ratio", + options=["16:9", "9:16"], + default="16:9", + tooltip="Aspect ratio of the output video", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid in the video", + optional=True, + ), + IO.Int.Input( + "duration_seconds", + default=8, + min=8, + max=8, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds (Veo 3 only supports 8 seconds)", + optional=True, + ), + IO.Boolean.Input( + "enhance_prompt", + default=True, + tooltip="Whether to enhance the prompt with AI assistance", + optional=True, + ), + IO.Combo.Input( + "person_generation", + options=["ALLOW", "BLOCK"], + default="ALLOW", + tooltip="Whether to allow generating people in the video", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFF, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Image.Input( + "image", + tooltip="Optional reference image to guide video generation", + optional=True, + ), + IO.Combo.Input( + "model", + options=[ + "veo-3.1-generate", + "veo-3.1-fast-generate", + "veo-3.0-generate-001", + "veo-3.0-fast-generate-001", + ], + default="veo-3.0-generate-001", + tooltip="Veo 3 model to use for video generation", + optional=True, + ), + IO.Boolean.Input( + "generate_audio", + default=False, + tooltip="Generate audio for the video. Supported by all Veo 3 models.", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + +class VeoExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + VeoVideoGenerationNode, + Veo3VideoGenerationNode, + ] + + +async def comfy_entrypoint() -> VeoExtension: + return VeoExtension() diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py new file mode 100644 index 000000000..0e0572f8c --- /dev/null +++ b/comfy_api_nodes/nodes_vidu.py @@ -0,0 +1,565 @@ +import logging +from enum import Enum +from typing import Literal, Optional, TypeVar + +import torch +from pydantic import BaseModel, Field +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension +from comfy_api_nodes.util import ( + ApiEndpoint, + download_url_to_video_output, + get_number_of_images, + poll_op, + sync_op, + upload_images_to_comfyapi, + validate_aspect_ratio_closeness, + validate_image_aspect_ratio_range, + validate_image_dimensions, +) + +VIDU_TEXT_TO_VIDEO = "/proxy/vidu/text2video" +VIDU_IMAGE_TO_VIDEO = "/proxy/vidu/img2video" +VIDU_REFERENCE_VIDEO = "/proxy/vidu/reference2video" +VIDU_START_END_VIDEO = "/proxy/vidu/start-end2video" +VIDU_GET_GENERATION_STATUS = "/proxy/vidu/tasks/%s/creations" + +R = TypeVar("R") + + +class VideoModelName(str, Enum): + vidu_q1 = "viduq1" + + +class AspectRatio(str, Enum): + r_16_9 = "16:9" + r_9_16 = "9:16" + r_1_1 = "1:1" + + +class Resolution(str, Enum): + r_1080p = "1080p" + + +class MovementAmplitude(str, Enum): + auto = "auto" + small = "small" + medium = "medium" + large = "large" + + +class TaskCreationRequest(BaseModel): + model: VideoModelName = VideoModelName.vidu_q1 + prompt: Optional[str] = Field(None, max_length=1500) + duration: Optional[Literal[5]] = 5 + seed: Optional[int] = Field(0, ge=0, le=2147483647) + aspect_ratio: Optional[AspectRatio] = AspectRatio.r_16_9 + resolution: Optional[Resolution] = Resolution.r_1080p + movement_amplitude: Optional[MovementAmplitude] = MovementAmplitude.auto + images: Optional[list[str]] = Field(None, description="Base64 encoded string or image URL") + + +class TaskCreationResponse(BaseModel): + task_id: str = Field(...) + state: str = Field(...) + created_at: str = Field(...) + code: Optional[int] = Field(None, description="Error code") + + +class TaskResult(BaseModel): + id: str = Field(..., description="Creation id") + url: str = Field(..., description="The URL of the generated results, valid for one hour") + cover_url: str = Field(..., description="The cover URL of the generated results, valid for one hour") + + +class TaskStatusResponse(BaseModel): + state: str = Field(...) + err_code: Optional[str] = Field(None) + creations: list[TaskResult] = Field(..., description="Generated results") + + +def get_video_url_from_response(response) -> Optional[str]: + if response.creations: + return response.creations[0].url + return None + + +def get_video_from_response(response) -> TaskResult: + if not response.creations: + error_msg = f"Vidu request does not contain results. State: {response.state}, Error Code: {response.err_code}" + logging.info(error_msg) + raise RuntimeError(error_msg) + logging.info("Vidu task %s succeeded. Video URL: %s", response.creations[0].id, response.creations[0].url) + return response.creations[0] + + +async def execute_task( + cls: type[IO.ComfyNode], + vidu_endpoint: str, + payload: TaskCreationRequest, + estimated_duration: int, +) -> R: + response = await sync_op( + cls, + endpoint=ApiEndpoint(path=vidu_endpoint, method="POST"), + response_model=TaskCreationResponse, + data=payload, + ) + if response.state == "failed": + error_msg = f"Vidu request failed. Code: {response.code}" + logging.error(error_msg) + raise RuntimeError(error_msg) + return await poll_op( + cls, + ApiEndpoint(path=VIDU_GET_GENERATION_STATUS % response.task_id), + response_model=TaskStatusResponse, + status_extractor=lambda r: r.state.value, + estimated_duration=estimated_duration, + ) + + +class ViduTextToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ViduTextToVideoNode", + display_name="Vidu Text To Video Generation", + category="api node/video/Vidu", + description="Generate video from text prompt", + inputs=[ + IO.Combo.Input( + "model", + options=VideoModelName, + default=VideoModelName.vidu_q1, + tooltip="Model name", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="A textual description for video generation", + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=5, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Combo.Input( + "aspect_ratio", + options=AspectRatio, + default=AspectRatio.r_16_9, + tooltip="The aspect ratio of the output video", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=Resolution, + default=Resolution.r_1080p, + tooltip="Supported values may vary by model & duration", + optional=True, + ), + IO.Combo.Input( + "movement_amplitude", + options=MovementAmplitude, + default=MovementAmplitude.auto, + tooltip="The movement amplitude of objects in the frame", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + duration: int, + seed: int, + aspect_ratio: str, + resolution: str, + movement_amplitude: str, + ) -> IO.NodeOutput: + if not prompt: + raise ValueError("The prompt field is required and cannot be empty.") + payload = TaskCreationRequest( + model_name=model, + prompt=prompt, + duration=duration, + seed=seed, + aspect_ratio=aspect_ratio, + resolution=resolution, + movement_amplitude=movement_amplitude, + ) + results = await execute_task(cls, VIDU_TEXT_TO_VIDEO, payload, 320) + return IO.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) + + +class ViduImageToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ViduImageToVideoNode", + display_name="Vidu Image To Video Generation", + category="api node/video/Vidu", + description="Generate video from image and optional prompt", + inputs=[ + IO.Combo.Input( + "model", + options=VideoModelName, + default=VideoModelName.vidu_q1, + tooltip="Model name", + ), + IO.Image.Input( + "image", + tooltip="An image to be used as the start frame of the generated video", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="A textual description for video generation", + optional=True, + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=5, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=Resolution, + default=Resolution.r_1080p, + tooltip="Supported values may vary by model & duration", + optional=True, + ), + IO.Combo.Input( + "movement_amplitude", + options=MovementAmplitude, + default=MovementAmplitude.auto.value, + tooltip="The movement amplitude of objects in the frame", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + image: torch.Tensor, + prompt: str, + duration: int, + seed: int, + resolution: str, + movement_amplitude: str, + ) -> IO.NodeOutput: + if get_number_of_images(image) > 1: + raise ValueError("Only one input image is allowed.") + validate_image_aspect_ratio_range(image, (1, 4), (4, 1)) + payload = TaskCreationRequest( + model_name=model, + prompt=prompt, + duration=duration, + seed=seed, + resolution=resolution, + movement_amplitude=movement_amplitude, + ) + payload.images = await upload_images_to_comfyapi( + cls, + image, + max_images=1, + mime_type="image/png", + ) + results = await execute_task(cls, VIDU_IMAGE_TO_VIDEO, payload, 120) + return IO.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) + + +class ViduReferenceVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ViduReferenceVideoNode", + display_name="Vidu Reference To Video Generation", + category="api node/video/Vidu", + description="Generate video from multiple images and prompt", + inputs=[ + IO.Combo.Input( + "model", + options=VideoModelName, + default=VideoModelName.vidu_q1, + tooltip="Model name", + ), + IO.Image.Input( + "images", + tooltip="Images to use as references to generate a video with consistent subjects (max 7 images).", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="A textual description for video generation", + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=5, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Combo.Input( + "aspect_ratio", + options=AspectRatio, + default=AspectRatio.r_16_9, + tooltip="The aspect ratio of the output video", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=[model.value for model in Resolution], + default=Resolution.r_1080p.value, + tooltip="Supported values may vary by model & duration", + optional=True, + ), + IO.Combo.Input( + "movement_amplitude", + options=[model.value for model in MovementAmplitude], + default=MovementAmplitude.auto.value, + tooltip="The movement amplitude of objects in the frame", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + images: torch.Tensor, + prompt: str, + duration: int, + seed: int, + aspect_ratio: str, + resolution: str, + movement_amplitude: str, + ) -> IO.NodeOutput: + if not prompt: + raise ValueError("The prompt field is required and cannot be empty.") + a = get_number_of_images(images) + if a > 7: + raise ValueError("Too many images, maximum allowed is 7.") + for image in images: + validate_image_aspect_ratio_range(image, (1, 4), (4, 1)) + validate_image_dimensions(image, min_width=128, min_height=128) + payload = TaskCreationRequest( + model_name=model, + prompt=prompt, + duration=duration, + seed=seed, + aspect_ratio=aspect_ratio, + resolution=resolution, + movement_amplitude=movement_amplitude, + ) + payload.images = await upload_images_to_comfyapi( + cls, + images, + max_images=7, + mime_type="image/png", + ) + results = await execute_task(cls, VIDU_REFERENCE_VIDEO, payload, 120) + return IO.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) + + +class ViduStartEndToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ViduStartEndToVideoNode", + display_name="Vidu Start End To Video Generation", + category="api node/video/Vidu", + description="Generate a video from start and end frames and a prompt", + inputs=[ + IO.Combo.Input( + "model", + options=[model.value for model in VideoModelName], + default=VideoModelName.vidu_q1.value, + tooltip="Model name", + ), + IO.Image.Input( + "first_frame", + tooltip="Start frame", + ), + IO.Image.Input( + "end_frame", + tooltip="End frame", + ), + IO.String.Input( + "prompt", + multiline=True, + tooltip="A textual description for video generation", + optional=True, + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=5, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Duration of the output video in seconds", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed for video generation (0 for random)", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=[model.value for model in Resolution], + default=Resolution.r_1080p.value, + tooltip="Supported values may vary by model & duration", + optional=True, + ), + IO.Combo.Input( + "movement_amplitude", + options=[model.value for model in MovementAmplitude], + default=MovementAmplitude.auto.value, + tooltip="The movement amplitude of objects in the frame", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + first_frame: torch.Tensor, + end_frame: torch.Tensor, + prompt: str, + duration: int, + seed: int, + resolution: str, + movement_amplitude: str, + ) -> IO.NodeOutput: + validate_aspect_ratio_closeness(first_frame, end_frame, min_rel=0.8, max_rel=1.25, strict=False) + payload = TaskCreationRequest( + model_name=model, + prompt=prompt, + duration=duration, + seed=seed, + resolution=resolution, + movement_amplitude=movement_amplitude, + ) + payload.images = [ + (await upload_images_to_comfyapi(cls, frame, max_images=1, mime_type="image/png"))[0] + for frame in (first_frame, end_frame) + ] + results = await execute_task(cls, VIDU_START_END_VIDEO, payload, 96) + return IO.NodeOutput(await download_url_to_video_output(get_video_from_response(results).url)) + + +class ViduExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + ViduTextToVideoNode, + ViduImageToVideoNode, + ViduReferenceVideoNode, + ViduStartEndToVideoNode, + ] + + +async def comfy_entrypoint() -> ViduExtension: + return ViduExtension() diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py new file mode 100644 index 000000000..2aab3c2ff --- /dev/null +++ b/comfy_api_nodes/nodes_wan.py @@ -0,0 +1,708 @@ +import re +from typing import Optional + +import torch +from pydantic import BaseModel, Field +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api_nodes.util import ( + ApiEndpoint, + audio_to_base64_string, + download_url_to_image_tensor, + download_url_to_video_output, + get_number_of_images, + poll_op, + sync_op, + tensor_to_base64_string, + validate_audio_duration, +) + + +class Text2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + + +class Image2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + images: list[str] = Field(..., min_length=1, max_length=2) + + +class Text2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + audio_url: Optional[str] = Field(None) + + +class Image2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: Optional[str] = Field(None) + img_url: str = Field(...) + audio_url: Optional[str] = Field(None) + + +class Txt2ImageParametersField(BaseModel): + size: str = Field(...) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + prompt_extend: bool = Field(True) + watermark: bool = Field(True) + + +class Image2ImageParametersField(BaseModel): + size: Optional[str] = Field(None) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + watermark: bool = Field(True) + + +class Text2VideoParametersField(BaseModel): + size: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=10) + prompt_extend: bool = Field(True) + watermark: bool = Field(True) + audio: bool = Field(False, description="Should be audio generated automatically") + + +class Image2VideoParametersField(BaseModel): + resolution: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=10) + prompt_extend: bool = Field(True) + watermark: bool = Field(True) + audio: bool = Field(False, description="Should be audio generated automatically") + + +class Text2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2ImageInputField = Field(...) + parameters: Txt2ImageParametersField = Field(...) + + +class Image2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2ImageInputField = Field(...) + parameters: Image2ImageParametersField = Field(...) + + +class Text2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2VideoInputField = Field(...) + parameters: Text2VideoParametersField = Field(...) + + +class Image2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2VideoInputField = Field(...) + parameters: Image2VideoParametersField = Field(...) + + +class TaskCreationOutputField(BaseModel): + task_id: str = Field(...) + task_status: str = Field(...) + + +class TaskCreationResponse(BaseModel): + output: Optional[TaskCreationOutputField] = Field(None) + request_id: str = Field(...) + code: Optional[str] = Field(None, description="The error code of the failed request.") + message: Optional[str] = Field(None, description="Details of the failed request.") + + +class TaskResult(BaseModel): + url: Optional[str] = Field(None) + code: Optional[str] = Field(None) + message: Optional[str] = Field(None) + + +class ImageTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + results: Optional[list[TaskResult]] = Field(None) + + +class VideoTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + video_url: Optional[str] = Field(None) + code: Optional[str] = Field(None) + message: Optional[str] = Field(None) + + +class ImageTaskStatusResponse(BaseModel): + output: Optional[ImageTaskStatusOutputField] = Field(None) + request_id: str = Field(...) + + +class VideoTaskStatusResponse(BaseModel): + output: Optional[VideoTaskStatusOutputField] = Field(None) + request_id: str = Field(...) + + +RES_IN_PARENS = re.compile(r"\((\d+)\s*[x×]\s*(\d+)\)") + + +class WanTextToImageApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="WanTextToImageApi", + display_name="Wan Text to Image", + category="api node/image/Wan", + description="Generates image based on text prompt.", + inputs=[ + IO.Combo.Input( + "model", + options=["wan2.5-t2i-preview"], + default="wan2.5-t2i-preview", + tooltip="Model to use.", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid.", + optional=True, + ), + IO.Int.Input( + "width", + default=1024, + min=768, + max=1440, + step=32, + optional=True, + ), + IO.Int.Input( + "height", + default=1024, + min=768, + max=1440, + step=32, + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the result.', + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + negative_prompt: str = "", + width: int = 1024, + height: int = 1024, + seed: int = 0, + prompt_extend: bool = True, + watermark: bool = True, + ): + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/text2image/image-synthesis", method="POST"), + response_model=TaskCreationResponse, + data=Text2ImageTaskCreationRequest( + model=model, + input=Text2ImageInputField(prompt=prompt, negative_prompt=negative_prompt), + parameters=Txt2ImageParametersField( + size=f"{width}*{height}", + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"Unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=ImageTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + estimated_duration=9, + poll_interval=3, + ) + return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url))) + + +class WanImageToImageApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="WanImageToImageApi", + display_name="Wan Image to Image", + category="api node/image/Wan", + description="Generates an image from one or two input images and a text prompt. " + "The output image is currently fixed at 1.6 MP; its aspect ratio matches the input image(s).", + inputs=[ + IO.Combo.Input( + "model", + options=["wan2.5-i2i-preview"], + default="wan2.5-i2i-preview", + tooltip="Model to use.", + ), + IO.Image.Input( + "image", + tooltip="Single-image editing or multi-image fusion, maximum 2 images.", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid.", + optional=True, + ), + # redo this later as an optional combo of recommended resolutions + # IO.Int.Input( + # "width", + # default=1280, + # min=384, + # max=1440, + # step=16, + # optional=True, + # ), + # IO.Int.Input( + # "height", + # default=1280, + # min=384, + # max=1440, + # step=16, + # optional=True, + # ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the result.', + optional=True, + ), + ], + outputs=[ + IO.Image.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + image: torch.Tensor, + prompt: str, + negative_prompt: str = "", + # width: int = 1024, + # height: int = 1024, + seed: int = 0, + watermark: bool = True, + ): + n_images = get_number_of_images(image) + if n_images not in (1, 2): + raise ValueError(f"Expected 1 or 2 input images, got {n_images}.") + images = [] + for i in image: + images.append("data:image/png;base64," + tensor_to_base64_string(i, total_pixels=4096 * 4096)) + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/image2image/image-synthesis", method="POST"), + response_model=TaskCreationResponse, + data=Image2ImageTaskCreationRequest( + model=model, + input=Image2ImageInputField(prompt=prompt, negative_prompt=negative_prompt, images=images), + parameters=Image2ImageParametersField( + # size=f"{width}*{height}", + seed=seed, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"Unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=ImageTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + estimated_duration=42, + poll_interval=4, + ) + return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url))) + + +class WanTextToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="WanTextToVideoApi", + display_name="Wan Text to Video", + category="api node/video/Wan", + description="Generates video based on text prompt.", + inputs=[ + IO.Combo.Input( + "model", + options=["wan2.5-t2v-preview"], + default="wan2.5-t2v-preview", + tooltip="Model to use.", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid.", + optional=True, + ), + IO.Combo.Input( + "size", + options=[ + "480p: 1:1 (624x624)", + "480p: 16:9 (832x480)", + "480p: 9:16 (480x832)", + "720p: 1:1 (960x960)", + "720p: 16:9 (1280x720)", + "720p: 9:16 (720x1280)", + "720p: 4:3 (1088x832)", + "720p: 3:4 (832x1088)", + "1080p: 1:1 (1440x1440)", + "1080p: 16:9 (1920x1080)", + "1080p: 9:16 (1080x1920)", + "1080p: 4:3 (1632x1248)", + "1080p: 3:4 (1248x1632)", + ], + default="480p: 1:1 (624x624)", + optional=True, + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=10, + step=5, + display_mode=IO.NumberDisplay.number, + tooltip="Available durations: 5 and 10 seconds", + optional=True, + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "generate_audio", + default=False, + optional=True, + tooltip="If there is no audio input, generate audio automatically.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the result.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + prompt: str, + negative_prompt: str = "", + size: str = "480p: 1:1 (624x624)", + duration: int = 5, + audio: Optional[Input.Audio] = None, + seed: int = 0, + generate_audio: bool = False, + prompt_extend: bool = True, + watermark: bool = True, + ): + width, height = RES_IN_PARENS.search(size).groups() + audio_url = None + if audio is not None: + validate_audio_duration(audio, 3.0, 29.0) + audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame") + + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"), + response_model=TaskCreationResponse, + data=Text2VideoTaskCreationRequest( + model=model, + input=Text2VideoInputField(prompt=prompt, negative_prompt=negative_prompt, audio_url=audio_url), + parameters=Text2VideoParametersField( + size=f"{width}*{height}", + duration=duration, + seed=seed, + audio=generate_audio, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"Unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + estimated_duration=120 * int(duration / 5), + poll_interval=6, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class WanImageToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="WanImageToVideoApi", + display_name="Wan Image to Video", + category="api node/video/Wan", + description="Generates video based on the first frame and text prompt.", + inputs=[ + IO.Combo.Input( + "model", + options=["wan2.5-i2v-preview"], + default="wan2.5-i2v-preview", + tooltip="Model to use.", + ), + IO.Image.Input( + "image", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative text prompt to guide what to avoid.", + optional=True, + ), + IO.Combo.Input( + "resolution", + options=[ + "480P", + "720P", + "1080P", + ], + default="480P", + optional=True, + ), + IO.Int.Input( + "duration", + default=5, + min=5, + max=10, + step=5, + display_mode=IO.NumberDisplay.number, + tooltip="Available durations: 5 and 10 seconds", + optional=True, + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + optional=True, + ), + IO.Boolean.Input( + "generate_audio", + default=False, + optional=True, + tooltip="If there is no audio input, generate audio automatically.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + optional=True, + ), + IO.Boolean.Input( + "watermark", + default=True, + tooltip='Whether to add an "AI generated" watermark to the result.', + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + ) + + @classmethod + async def execute( + cls, + model: str, + image: torch.Tensor, + prompt: str, + negative_prompt: str = "", + resolution: str = "480P", + duration: int = 5, + audio: Optional[Input.Audio] = None, + seed: int = 0, + generate_audio: bool = False, + prompt_extend: bool = True, + watermark: bool = True, + ): + if get_number_of_images(image) != 1: + raise ValueError("Exactly one input image is required.") + image_url = "data:image/png;base64," + tensor_to_base64_string(image, total_pixels=2000 * 2000) + audio_url = None + if audio is not None: + validate_audio_duration(audio, 3.0, 29.0) + audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame") + initial_response = await sync_op( + cls, + ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"), + response_model=TaskCreationResponse, + data=Image2VideoTaskCreationRequest( + model=model, + input=Image2VideoInputField( + prompt=prompt, negative_prompt=negative_prompt, img_url=image_url, audio_url=audio_url + ), + parameters=Image2VideoParametersField( + resolution=resolution, + duration=duration, + seed=seed, + audio=generate_audio, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"Unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + estimated_duration=120 * int(duration / 5), + poll_interval=6, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class WanApiExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [ + WanTextToImageApi, + WanImageToImageApi, + WanTextToVideoApi, + WanImageToVideoApi, + ] + + +async def comfy_entrypoint() -> WanApiExtension: + return WanApiExtension() diff --git a/comfy_api_nodes/redocly-dev.yaml b/comfy_api_nodes/redocly-dev.yaml new file mode 100644 index 000000000..d9e3cab70 --- /dev/null +++ b/comfy_api_nodes/redocly-dev.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. +# This is used for development purposes to generate stubs for unreleased API endpoints. +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes'] + matchStrategy: all diff --git a/comfy_api_nodes/redocly.yaml b/comfy_api_nodes/redocly.yaml new file mode 100644 index 000000000..d102345b1 --- /dev/null +++ b/comfy_api_nodes/redocly.yaml @@ -0,0 +1,10 @@ +# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes. + +apis: + filter: + root: openapi.yaml + decorators: + filter-in: + property: tags + value: ['API Nodes', 'Released'] + matchStrategy: all diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py new file mode 100644 index 000000000..0cca2b59b --- /dev/null +++ b/comfy_api_nodes/util/__init__.py @@ -0,0 +1,91 @@ +from ._helpers import get_fs_object_size +from .client import ( + ApiEndpoint, + poll_op, + poll_op_raw, + sync_op, + sync_op_raw, +) +from .conversions import ( + audio_bytes_to_audio_input, + audio_input_to_mp3, + audio_to_base64_string, + bytesio_to_image_tensor, + downscale_image_tensor, + image_tensor_pair_to_batch, + pil_to_bytesio, + tensor_to_base64_string, + tensor_to_bytesio, + tensor_to_pil, + trim_video, + video_to_base64_string, +) +from .download_helpers import ( + download_url_as_bytesio, + download_url_to_bytesio, + download_url_to_image_tensor, + download_url_to_video_output, +) +from .upload_helpers import ( + upload_audio_to_comfyapi, + upload_file_to_comfyapi, + upload_images_to_comfyapi, + upload_video_to_comfyapi, +) +from .validation_utils import ( + get_number_of_images, + validate_aspect_ratio_closeness, + validate_audio_duration, + validate_container_format_is_mp4, + validate_image_aspect_ratio, + validate_image_aspect_ratio_range, + validate_image_dimensions, + validate_string, + validate_video_dimensions, + validate_video_duration, +) + +__all__ = [ + # API client + "ApiEndpoint", + "poll_op", + "poll_op_raw", + "sync_op", + "sync_op_raw", + # Upload helpers + "upload_audio_to_comfyapi", + "upload_file_to_comfyapi", + "upload_images_to_comfyapi", + "upload_video_to_comfyapi", + # Download helpers + "download_url_as_bytesio", + "download_url_to_bytesio", + "download_url_to_image_tensor", + "download_url_to_video_output", + # Conversions + "audio_bytes_to_audio_input", + "audio_input_to_mp3", + "audio_to_base64_string", + "bytesio_to_image_tensor", + "downscale_image_tensor", + "image_tensor_pair_to_batch", + "pil_to_bytesio", + "tensor_to_base64_string", + "tensor_to_bytesio", + "tensor_to_pil", + "trim_video", + "video_to_base64_string", + # Validation utilities + "get_number_of_images", + "validate_aspect_ratio_closeness", + "validate_audio_duration", + "validate_container_format_is_mp4", + "validate_image_aspect_ratio", + "validate_image_aspect_ratio_range", + "validate_image_dimensions", + "validate_string", + "validate_video_dimensions", + "validate_video_duration", + # Misc functions + "get_fs_object_size", +] diff --git a/comfy_api_nodes/util/_helpers.py b/comfy_api_nodes/util/_helpers.py new file mode 100644 index 000000000..328fe5227 --- /dev/null +++ b/comfy_api_nodes/util/_helpers.py @@ -0,0 +1,71 @@ +import asyncio +import contextlib +import os +import time +from io import BytesIO +from typing import Callable, Optional, Union + +from comfy.cli_args import args +from comfy.model_management import processing_interrupted +from comfy_api.latest import IO + +from .common_exceptions import ProcessingInterrupted + + +def is_processing_interrupted() -> bool: + """Return True if user/runtime requested interruption.""" + return processing_interrupted() + + +def get_node_id(node_cls: type[IO.ComfyNode]) -> str: + return node_cls.hidden.unique_id + + +def get_auth_header(node_cls: type[IO.ComfyNode]) -> dict[str, str]: + if node_cls.hidden.auth_token_comfy_org: + return {"Authorization": f"Bearer {node_cls.hidden.auth_token_comfy_org}"} + if node_cls.hidden.api_key_comfy_org: + return {"X-API-KEY": node_cls.hidden.api_key_comfy_org} + return {} + + +def default_base_url() -> str: + return getattr(args, "comfy_api_base", "https://api.comfy.org") + + +async def sleep_with_interrupt( + seconds: float, + node_cls: Optional[type[IO.ComfyNode]], + label: Optional[str] = None, + start_ts: Optional[float] = None, + estimated_total: Optional[int] = None, + *, + display_callback: Optional[Callable[[type[IO.ComfyNode], str, int, Optional[int]], None]] = None, +): + """ + Sleep in 1s slices while: + - Checking for interruption (raises ProcessingInterrupted). + - Optionally emitting time progress via display_callback (if provided). + """ + end = time.monotonic() + seconds + while True: + if is_processing_interrupted(): + raise ProcessingInterrupted("Task cancelled") + now = time.monotonic() + if start_ts is not None and label and display_callback: + with contextlib.suppress(Exception): + display_callback(node_cls, label, int(now - start_ts), estimated_total) + if now >= end: + break + await asyncio.sleep(min(1.0, end - now)) + + +def mimetype_to_extension(mime_type: str) -> str: + """Converts a MIME type to a file extension.""" + return mime_type.split("/")[-1].lower() + + +def get_fs_object_size(path_or_object: Union[str, BytesIO]) -> int: + if isinstance(path_or_object, str): + return os.path.getsize(path_or_object) + return len(path_or_object.getvalue()) diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py new file mode 100644 index 000000000..9c036d64b --- /dev/null +++ b/comfy_api_nodes/util/client.py @@ -0,0 +1,936 @@ +import asyncio +import contextlib +import json +import logging +import time +import uuid +from dataclasses import dataclass +from enum import Enum +from io import BytesIO +from typing import Any, Callable, Iterable, Literal, Optional, Type, TypeVar, Union +from urllib.parse import urljoin, urlparse + +import aiohttp +from aiohttp.client_exceptions import ClientError, ContentTypeError +from pydantic import BaseModel + +from comfy import utils +from comfy_api.latest import IO +from comfy_api_nodes.apis import request_logger +from server import PromptServer + +from ._helpers import ( + default_base_url, + get_auth_header, + get_node_id, + is_processing_interrupted, + sleep_with_interrupt, +) +from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted + +M = TypeVar("M", bound=BaseModel) + + +class ApiEndpoint: + def __init__( + self, + path: str, + method: Literal["GET", "POST", "PUT", "DELETE", "PATCH"] = "GET", + *, + query_params: Optional[dict[str, Any]] = None, + headers: Optional[dict[str, str]] = None, + ): + self.path = path + self.method = method + self.query_params = query_params or {} + self.headers = headers or {} + + +@dataclass +class _RequestConfig: + node_cls: type[IO.ComfyNode] + endpoint: ApiEndpoint + timeout: float + content_type: str + data: Optional[dict[str, Any]] + files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] + multipart_parser: Optional[Callable] + max_retries: int + retry_delay: float + retry_backoff: float + wait_label: str = "Waiting" + monitor_progress: bool = True + estimated_total: Optional[int] = None + final_label_on_success: Optional[str] = "Completed" + progress_origin_ts: Optional[float] = None + + +@dataclass +class _PollUIState: + started: float + status_label: str = "Queued" + is_queued: bool = True + price: Optional[float] = None + estimated_duration: Optional[int] = None + base_processing_elapsed: float = 0.0 # sum of completed active intervals + active_since: Optional[float] = None # start time of current active interval (None if queued) + + +_RETRY_STATUS = {408, 429, 500, 502, 503, 504} +COMPLETED_STATUSES = ["succeeded", "succeed", "success", "completed"] +FAILED_STATUSES = ["cancelled", "canceled", "failed", "error"] +QUEUED_STATUSES = ["created", "queued", "queueing", "submitted"] + + +async def sync_op( + cls: type[IO.ComfyNode], + endpoint: ApiEndpoint, + *, + response_model: Type[M], + data: Optional[BaseModel] = None, + files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None, + content_type: str = "application/json", + timeout: float = 3600.0, + multipart_parser: Optional[Callable] = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff: float = 2.0, + wait_label: str = "Waiting for server", + estimated_duration: Optional[int] = None, + final_label_on_success: Optional[str] = "Completed", + progress_origin_ts: Optional[float] = None, + monitor_progress: bool = True, +) -> M: + raw = await sync_op_raw( + cls, + endpoint, + data=data, + files=files, + content_type=content_type, + timeout=timeout, + multipart_parser=multipart_parser, + max_retries=max_retries, + retry_delay=retry_delay, + retry_backoff=retry_backoff, + wait_label=wait_label, + estimated_duration=estimated_duration, + as_binary=False, + final_label_on_success=final_label_on_success, + progress_origin_ts=progress_origin_ts, + monitor_progress=monitor_progress, + ) + if not isinstance(raw, dict): + raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).") + return _validate_or_raise(response_model, raw) + + +async def poll_op( + cls: type[IO.ComfyNode], + poll_endpoint: ApiEndpoint, + *, + response_model: Type[M], + status_extractor: Callable[[M], Optional[Union[str, int]]], + progress_extractor: Optional[Callable[[M], Optional[int]]] = None, + price_extractor: Optional[Callable[[M], Optional[float]]] = None, + completed_statuses: Optional[list[Union[str, int]]] = None, + failed_statuses: Optional[list[Union[str, int]]] = None, + queued_statuses: Optional[list[Union[str, int]]] = None, + data: Optional[BaseModel] = None, + poll_interval: float = 5.0, + max_poll_attempts: int = 120, + timeout_per_poll: float = 120.0, + max_retries_per_poll: int = 3, + retry_delay_per_poll: float = 1.0, + retry_backoff_per_poll: float = 2.0, + estimated_duration: Optional[int] = None, + cancel_endpoint: Optional[ApiEndpoint] = None, + cancel_timeout: float = 10.0, +) -> M: + raw = await poll_op_raw( + cls, + poll_endpoint=poll_endpoint, + status_extractor=_wrap_model_extractor(response_model, status_extractor), + progress_extractor=_wrap_model_extractor(response_model, progress_extractor), + price_extractor=_wrap_model_extractor(response_model, price_extractor), + completed_statuses=completed_statuses, + failed_statuses=failed_statuses, + queued_statuses=queued_statuses, + data=data, + poll_interval=poll_interval, + max_poll_attempts=max_poll_attempts, + timeout_per_poll=timeout_per_poll, + max_retries_per_poll=max_retries_per_poll, + retry_delay_per_poll=retry_delay_per_poll, + retry_backoff_per_poll=retry_backoff_per_poll, + estimated_duration=estimated_duration, + cancel_endpoint=cancel_endpoint, + cancel_timeout=cancel_timeout, + ) + if not isinstance(raw, dict): + raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).") + return _validate_or_raise(response_model, raw) + + +async def sync_op_raw( + cls: type[IO.ComfyNode], + endpoint: ApiEndpoint, + *, + data: Optional[Union[dict[str, Any], BaseModel]] = None, + files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None, + content_type: str = "application/json", + timeout: float = 3600.0, + multipart_parser: Optional[Callable] = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff: float = 2.0, + wait_label: str = "Waiting for server", + estimated_duration: Optional[int] = None, + as_binary: bool = False, + final_label_on_success: Optional[str] = "Completed", + progress_origin_ts: Optional[float] = None, + monitor_progress: bool = True, +) -> Union[dict[str, Any], bytes]: + """ + Make a single network request. + - If as_binary=False (default): returns JSON dict (or {'_raw': ''} if non-JSON). + - If as_binary=True: returns bytes. + """ + if isinstance(data, BaseModel): + data = data.model_dump(exclude_none=True) + for k, v in list(data.items()): + if isinstance(v, Enum): + data[k] = v.value + cfg = _RequestConfig( + node_cls=cls, + endpoint=endpoint, + timeout=timeout, + content_type=content_type, + data=data, + files=files, + multipart_parser=multipart_parser, + max_retries=max_retries, + retry_delay=retry_delay, + retry_backoff=retry_backoff, + wait_label=wait_label, + monitor_progress=monitor_progress, + estimated_total=estimated_duration, + final_label_on_success=final_label_on_success, + progress_origin_ts=progress_origin_ts, + ) + return await _request_base(cfg, expect_binary=as_binary) + + +async def poll_op_raw( + cls: type[IO.ComfyNode], + poll_endpoint: ApiEndpoint, + *, + status_extractor: Callable[[dict[str, Any]], Optional[Union[str, int]]], + progress_extractor: Optional[Callable[[dict[str, Any]], Optional[int]]] = None, + price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None, + completed_statuses: Optional[list[Union[str, int]]] = None, + failed_statuses: Optional[list[Union[str, int]]] = None, + queued_statuses: Optional[list[Union[str, int]]] = None, + data: Optional[Union[dict[str, Any], BaseModel]] = None, + poll_interval: float = 5.0, + max_poll_attempts: int = 120, + timeout_per_poll: float = 120.0, + max_retries_per_poll: int = 3, + retry_delay_per_poll: float = 1.0, + retry_backoff_per_poll: float = 2.0, + estimated_duration: Optional[int] = None, + cancel_endpoint: Optional[ApiEndpoint] = None, + cancel_timeout: float = 10.0, +) -> dict[str, Any]: + """ + Polls an endpoint until the task reaches a terminal state. Displays time while queued/processing, + checks interruption every second, and calls Cancel endpoint (if provided) on interruption. + + Uses default complete, failed and queued states assumption. + + Returns the final JSON response from the poll endpoint. + """ + completed_states = _normalize_statuses(COMPLETED_STATUSES if completed_statuses is None else completed_statuses) + failed_states = _normalize_statuses(FAILED_STATUSES if failed_statuses is None else failed_statuses) + queued_states = _normalize_statuses(QUEUED_STATUSES if queued_statuses is None else queued_statuses) + started = time.monotonic() + consumed_attempts = 0 # counts only non-queued polls + + progress_bar = utils.ProgressBar(100) if progress_extractor else None + last_progress: Optional[int] = None + + state = _PollUIState(started=started, estimated_duration=estimated_duration) + stop_ticker = asyncio.Event() + + async def _ticker(): + """Emit a UI update every second while polling is in progress.""" + try: + while not stop_ticker.is_set(): + if is_processing_interrupted(): + break + now = time.monotonic() + proc_elapsed = state.base_processing_elapsed + ( + (now - state.active_since) if state.active_since is not None else 0.0 + ) + _display_time_progress( + cls, + status=state.status_label, + elapsed_seconds=int(now - state.started), + estimated_total=state.estimated_duration, + price=state.price, + is_queued=state.is_queued, + processing_elapsed_seconds=int(proc_elapsed), + ) + await asyncio.sleep(1.0) + except Exception as exc: + logging.debug("Polling ticker exited: %s", exc) + + ticker_task = asyncio.create_task(_ticker()) + try: + while consumed_attempts < max_poll_attempts: + try: + resp_json = await sync_op_raw( + cls, + poll_endpoint, + data=data, + timeout=timeout_per_poll, + max_retries=max_retries_per_poll, + retry_delay=retry_delay_per_poll, + retry_backoff=retry_backoff_per_poll, + wait_label="Checking", + estimated_duration=None, + as_binary=False, + final_label_on_success=None, + monitor_progress=False, + ) + if not isinstance(resp_json, dict): + raise Exception("Polling endpoint returned non-JSON response.") + except ProcessingInterrupted: + if cancel_endpoint: + with contextlib.suppress(Exception): + await sync_op_raw( + cls, + cancel_endpoint, + timeout=cancel_timeout, + max_retries=0, + wait_label="Cancelling task", + estimated_duration=None, + as_binary=False, + final_label_on_success=None, + monitor_progress=False, + ) + raise + + try: + status = _normalize_status_value(status_extractor(resp_json)) + except Exception as e: + logging.error("Status extraction failed: %s", e) + status = None + + if price_extractor: + new_price = price_extractor(resp_json) + if new_price is not None: + state.price = new_price + + if progress_extractor: + new_progress = progress_extractor(resp_json) + if new_progress is not None and last_progress != new_progress: + progress_bar.update_absolute(new_progress, total=100) + last_progress = new_progress + + now_ts = time.monotonic() + is_queued = status in queued_states + + if is_queued: + if state.active_since is not None: # If we just moved from active -> queued, close the active interval + state.base_processing_elapsed += now_ts - state.active_since + state.active_since = None + else: + if state.active_since is None: # If we just moved from queued -> active, open a new active interval + state.active_since = now_ts + + state.is_queued = is_queued + state.status_label = status or ("Queued" if is_queued else "Processing") + if status in completed_states: + if state.active_since is not None: + state.base_processing_elapsed += now_ts - state.active_since + state.active_since = None + stop_ticker.set() + with contextlib.suppress(Exception): + await ticker_task + + if progress_bar and last_progress != 100: + progress_bar.update_absolute(100, total=100) + + _display_time_progress( + cls, + status=status if status else "Completed", + elapsed_seconds=int(now_ts - started), + estimated_total=estimated_duration, + price=state.price, + is_queued=False, + processing_elapsed_seconds=int(state.base_processing_elapsed), + ) + return resp_json + + if status in failed_states: + msg = f"Task failed: {json.dumps(resp_json)}" + logging.error(msg) + raise Exception(msg) + + try: + await sleep_with_interrupt(poll_interval, cls, None, None, None) + except ProcessingInterrupted: + if cancel_endpoint: + with contextlib.suppress(Exception): + await sync_op_raw( + cls, + cancel_endpoint, + timeout=cancel_timeout, + max_retries=0, + wait_label="Cancelling task", + estimated_duration=None, + as_binary=False, + final_label_on_success=None, + monitor_progress=False, + ) + raise + if not is_queued: + consumed_attempts += 1 + + raise Exception( + f"Polling timed out after {max_poll_attempts} non-queued attempts " + f"(~{int(max_poll_attempts * poll_interval)}s of active polling)." + ) + except ProcessingInterrupted: + raise + except (LocalNetworkError, ApiServerError): + raise + except Exception as e: + raise Exception(f"Polling aborted due to error: {e}") from e + finally: + stop_ticker.set() + with contextlib.suppress(Exception): + await ticker_task + + +def _display_text( + node_cls: type[IO.ComfyNode], + text: Optional[str], + *, + status: Optional[Union[str, int]] = None, + price: Optional[float] = None, +) -> None: + display_lines: list[str] = [] + if status: + display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}") + if price is not None: + display_lines.append(f"Price: ${float(price):,.4f}") + if text is not None: + display_lines.append(text) + if display_lines: + PromptServer.instance.send_progress_text("\n".join(display_lines), get_node_id(node_cls)) + + +def _display_time_progress( + node_cls: type[IO.ComfyNode], + status: Optional[Union[str, int]], + elapsed_seconds: int, + estimated_total: Optional[int] = None, + *, + price: Optional[float] = None, + is_queued: Optional[bool] = None, + processing_elapsed_seconds: Optional[int] = None, +) -> None: + if estimated_total is not None and estimated_total > 0 and is_queued is False: + pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds + remaining = max(0, int(estimated_total) - int(pe)) + time_line = f"Time elapsed: {int(elapsed_seconds)}s (~{remaining}s remaining)" + else: + time_line = f"Time elapsed: {int(elapsed_seconds)}s" + _display_text(node_cls, time_line, status=status, price=price) + + +async def _diagnose_connectivity() -> dict[str, bool]: + """Best-effort connectivity diagnostics to distinguish local vs. server issues.""" + results = { + "internet_accessible": False, + "api_accessible": False, + } + timeout = aiohttp.ClientTimeout(total=5.0) + async with aiohttp.ClientSession(timeout=timeout) as session: + with contextlib.suppress(ClientError, OSError): + async with session.get("https://www.google.com") as resp: + results["internet_accessible"] = resp.status < 500 + if not results["internet_accessible"]: + return results + + parsed = urlparse(default_base_url()) + health_url = f"{parsed.scheme}://{parsed.netloc}/health" + with contextlib.suppress(ClientError, OSError): + async with session.get(health_url) as resp: + results["api_accessible"] = resp.status < 500 + return results + + +def _unpack_tuple(t: tuple) -> tuple[str, Any, str]: + """Normalize (filename, value, content_type).""" + if len(t) == 2: + return t[0], t[1], "application/octet-stream" + if len(t) == 3: + return t[0], t[1], t[2] + raise ValueError("files tuple must be (filename, file[, content_type])") + + +def _merge_params(endpoint_params: dict[str, Any], method: str, data: Optional[dict[str, Any]]) -> dict[str, Any]: + params = dict(endpoint_params or {}) + if method.upper() == "GET" and data: + for k, v in data.items(): + if v is not None: + params[k] = v + return params + + +def _friendly_http_message(status: int, body: Any) -> str: + if status == 401: + return "Unauthorized: Please login first to use this node." + if status == 402: + return "Payment Required: Please add credits to your account to use this node." + if status == 409: + return "There is a problem with your account. Please contact support@comfy.org." + if status == 429: + return "Rate Limit Exceeded: Please try again later." + try: + if isinstance(body, dict): + err = body.get("error") + if isinstance(err, dict): + msg = err.get("message") + typ = err.get("type") + if msg and typ: + return f"API Error: {msg} (Type: {typ})" + if msg: + return f"API Error: {msg}" + return f"API Error: {json.dumps(body)}" + else: + txt = str(body) + if len(txt) <= 200: + return f"API Error (raw): {txt}" + return f"API Error (status {status})" + except Exception: + return f"HTTP {status}: Unknown error" + + +def _generate_operation_id(method: str, path: str, attempt: int) -> str: + slug = path.strip("/").replace("/", "_") or "op" + return f"{method}_{slug}_try{attempt}_{uuid.uuid4().hex[:8]}" + + +def _snapshot_request_body_for_logging( + content_type: str, + method: str, + data: Optional[dict[str, Any]], + files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]], +) -> Optional[Union[dict[str, Any], str]]: + if method.upper() == "GET": + return None + if content_type == "multipart/form-data": + form_fields = sorted([k for k, v in (data or {}).items() if v is not None]) + file_fields: list[dict[str, str]] = [] + if files: + file_iter = files if isinstance(files, list) else list(files.items()) + for field_name, file_obj in file_iter: + if file_obj is None: + continue + if isinstance(file_obj, tuple): + filename = file_obj[0] + else: + filename = getattr(file_obj, "name", field_name) + file_fields.append({"field": field_name, "filename": str(filename or "")}) + return {"_multipart": True, "form_fields": form_fields, "file_fields": file_fields} + if content_type == "application/x-www-form-urlencoded": + return data or {} + return data or {} + + +async def _request_base(cfg: _RequestConfig, expect_binary: bool): + """Core request with retries, per-second interruption monitoring, true cancellation, and friendly errors.""" + url = cfg.endpoint.path + parsed_url = urlparse(url) + if not parsed_url.scheme and not parsed_url.netloc: # is URL relative? + url = urljoin(default_base_url().rstrip("/") + "/", url.lstrip("/")) + + method = cfg.endpoint.method + params = _merge_params(cfg.endpoint.query_params, method, cfg.data if method == "GET" else None) + + async def _monitor(stop_evt: asyncio.Event, start_ts: float): + """Every second: update elapsed time and signal interruption.""" + try: + while not stop_evt.is_set(): + if is_processing_interrupted(): + return + if cfg.monitor_progress: + _display_time_progress( + cfg.node_cls, cfg.wait_label, int(time.monotonic() - start_ts), cfg.estimated_total + ) + await asyncio.sleep(1.0) + except asyncio.CancelledError: + return # normal shutdown + + start_time = cfg.progress_origin_ts if cfg.progress_origin_ts is not None else time.monotonic() + attempt = 0 + delay = cfg.retry_delay + operation_succeeded: bool = False + final_elapsed_seconds: Optional[int] = None + while True: + attempt += 1 + stop_event = asyncio.Event() + monitor_task: Optional[asyncio.Task] = None + sess: Optional[aiohttp.ClientSession] = None + + operation_id = _generate_operation_id(method, cfg.endpoint.path, attempt) + logging.debug("[DEBUG] HTTP %s %s (attempt %d)", method, url, attempt) + + payload_headers = {"Accept": "*/*"} + if not parsed_url.scheme and not parsed_url.netloc: # is URL relative? + payload_headers.update(get_auth_header(cfg.node_cls)) + if cfg.endpoint.headers: + payload_headers.update(cfg.endpoint.headers) + + payload_kw: dict[str, Any] = {"headers": payload_headers} + if method == "GET": + payload_headers.pop("Content-Type", None) + request_body_log = _snapshot_request_body_for_logging(cfg.content_type, method, cfg.data, cfg.files) + try: + if cfg.monitor_progress: + monitor_task = asyncio.create_task(_monitor(stop_event, start_time)) + + timeout = aiohttp.ClientTimeout(total=cfg.timeout) + sess = aiohttp.ClientSession(timeout=timeout) + + if cfg.content_type == "multipart/form-data" and method != "GET": + # aiohttp will set Content-Type boundary; remove any fixed Content-Type + payload_headers.pop("Content-Type", None) + if cfg.multipart_parser and cfg.data: + form = cfg.multipart_parser(cfg.data) + if not isinstance(form, aiohttp.FormData): + raise ValueError("multipart_parser must return aiohttp.FormData") + else: + form = aiohttp.FormData(default_to_multipart=True) + if cfg.data: + for k, v in cfg.data.items(): + if v is None: + continue + form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v) + if cfg.files: + file_iter = cfg.files if isinstance(cfg.files, list) else cfg.files.items() + for field_name, file_obj in file_iter: + if file_obj is None: + continue + if isinstance(file_obj, tuple): + filename, file_value, content_type = _unpack_tuple(file_obj) + else: + filename = getattr(file_obj, "name", field_name) + file_value = file_obj + content_type = "application/octet-stream" + # Attempt to rewind BytesIO for retries + if isinstance(file_value, BytesIO): + with contextlib.suppress(Exception): + file_value.seek(0) + form.add_field(field_name, file_value, filename=filename, content_type=content_type) + payload_kw["data"] = form + elif cfg.content_type == "application/x-www-form-urlencoded" and method != "GET": + payload_headers["Content-Type"] = "application/x-www-form-urlencoded" + payload_kw["data"] = cfg.data or {} + elif method != "GET": + payload_headers["Content-Type"] = "application/json" + payload_kw["json"] = cfg.data or {} + + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=dict(payload_headers) if payload_headers else None, + request_params=dict(params) if params else None, + request_data=request_body_log, + ) + except Exception as _log_e: + logging.debug("[DEBUG] request logging failed: %s", _log_e) + + req_coro = sess.request(method, url, params=params, **payload_kw) + req_task = asyncio.create_task(req_coro) + + # Race: request vs. monitor (interruption) + tasks = {req_task} + if monitor_task: + tasks.add(monitor_task) + done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED) + + if monitor_task and monitor_task in done: + # Interrupted – cancel the request and abort + if req_task in pending: + req_task.cancel() + raise ProcessingInterrupted("Task cancelled") + + # Otherwise, request finished + resp = await req_task + async with resp: + if resp.status >= 400: + try: + body = await resp.json() + except (ContentTypeError, json.JSONDecodeError): + body = await resp.text() + if resp.status in _RETRY_STATUS and attempt <= cfg.max_retries: + logging.warning( + "HTTP %s %s -> %s. Retrying in %.2fs (retry %d of %d).", + method, + url, + resp.status, + delay, + attempt, + cfg.max_retries, + ) + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=body, + error_message=_friendly_http_message(resp.status, body), + ) + except Exception as _log_e: + logging.debug("[DEBUG] response logging failed: %s", _log_e) + + await sleep_with_interrupt( + delay, + cfg.node_cls, + cfg.wait_label if cfg.monitor_progress else None, + start_time if cfg.monitor_progress else None, + cfg.estimated_total, + display_callback=_display_time_progress if cfg.monitor_progress else None, + ) + delay *= cfg.retry_backoff + continue + msg = _friendly_http_message(resp.status, body) + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=body, + error_message=msg, + ) + except Exception as _log_e: + logging.debug("[DEBUG] response logging failed: %s", _log_e) + raise Exception(msg) + + if expect_binary: + buff = bytearray() + last_tick = time.monotonic() + async for chunk in resp.content.iter_chunked(64 * 1024): + buff.extend(chunk) + now = time.monotonic() + if now - last_tick >= 1.0: + last_tick = now + if is_processing_interrupted(): + raise ProcessingInterrupted("Task cancelled") + if cfg.monitor_progress: + _display_time_progress( + cfg.node_cls, cfg.wait_label, int(now - start_time), cfg.estimated_total + ) + bytes_payload = bytes(buff) + operation_succeeded = True + final_elapsed_seconds = int(time.monotonic() - start_time) + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=bytes_payload, + ) + except Exception as _log_e: + logging.debug("[DEBUG] response logging failed: %s", _log_e) + return bytes_payload + else: + try: + payload = await resp.json() + response_content_to_log: Any = payload + except (ContentTypeError, json.JSONDecodeError): + text = await resp.text() + try: + payload = json.loads(text) if text else {} + except json.JSONDecodeError: + payload = {"_raw": text} + response_content_to_log = payload if isinstance(payload, dict) else text + operation_succeeded = True + final_elapsed_seconds = int(time.monotonic() - start_time) + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=response_content_to_log, + ) + except Exception as _log_e: + logging.debug("[DEBUG] response logging failed: %s", _log_e) + return payload + + except ProcessingInterrupted: + logging.debug("Polling was interrupted by user") + raise + except (ClientError, OSError) as e: + if attempt <= cfg.max_retries: + logging.warning( + "Connection error calling %s %s. Retrying in %.2fs (%d/%d): %s", + method, + url, + delay, + attempt, + cfg.max_retries, + str(e), + ) + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=dict(payload_headers) if payload_headers else None, + request_params=dict(params) if params else None, + request_data=request_body_log, + error_message=f"{type(e).__name__}: {str(e)} (will retry)", + ) + except Exception as _log_e: + logging.debug("[DEBUG] request error logging failed: %s", _log_e) + await sleep_with_interrupt( + delay, + cfg.node_cls, + cfg.wait_label if cfg.monitor_progress else None, + start_time if cfg.monitor_progress else None, + cfg.estimated_total, + display_callback=_display_time_progress if cfg.monitor_progress else None, + ) + delay *= cfg.retry_backoff + continue + diag = await _diagnose_connectivity() + if not diag["internet_accessible"]: + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=dict(payload_headers) if payload_headers else None, + request_params=dict(params) if params else None, + request_data=request_body_log, + error_message=f"LocalNetworkError: {str(e)}", + ) + except Exception as _log_e: + logging.debug("[DEBUG] final error logging failed: %s", _log_e) + raise LocalNetworkError( + "Unable to connect to the API server due to local network issues. " + "Please check your internet connection and try again." + ) from e + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method=method, + request_url=url, + request_headers=dict(payload_headers) if payload_headers else None, + request_params=dict(params) if params else None, + request_data=request_body_log, + error_message=f"ApiServerError: {str(e)}", + ) + except Exception as _log_e: + logging.debug("[DEBUG] final error logging failed: %s", _log_e) + raise ApiServerError( + f"The API server at {default_base_url()} is currently unreachable. " + f"The service may be experiencing issues." + ) from e + finally: + stop_event.set() + if monitor_task: + monitor_task.cancel() + with contextlib.suppress(Exception): + await monitor_task + if sess: + with contextlib.suppress(Exception): + await sess.close() + if operation_succeeded and cfg.monitor_progress and cfg.final_label_on_success: + _display_time_progress( + cfg.node_cls, + status=cfg.final_label_on_success, + elapsed_seconds=( + final_elapsed_seconds + if final_elapsed_seconds is not None + else int(time.monotonic() - start_time) + ), + estimated_total=cfg.estimated_total, + price=None, + is_queued=False, + processing_elapsed_seconds=final_elapsed_seconds, + ) + + +def _validate_or_raise(response_model: Type[M], payload: Any) -> M: + try: + return response_model.model_validate(payload) + except Exception as e: + logging.error( + "Response validation failed for %s: %s", + getattr(response_model, "__name__", response_model), + e, + ) + raise Exception( + f"Response validation failed for {getattr(response_model, '__name__', response_model)}: {e}" + ) from e + + +def _wrap_model_extractor( + response_model: Type[M], + extractor: Optional[Callable[[M], Any]], +) -> Optional[Callable[[dict[str, Any]], Any]]: + """Wrap a typed extractor so it can be used by the dict-based poller. + Validates the dict into `response_model` before invoking `extractor`. + Uses a small per-wrapper cache keyed by `id(dict)` to avoid re-validating + the same response for multiple extractors in a single poll attempt. + """ + if extractor is None: + return None + _cache: dict[int, M] = {} + + def _wrapped(d: dict[str, Any]) -> Any: + try: + key = id(d) + model = _cache.get(key) + if model is None: + model = response_model.model_validate(d) + _cache[key] = model + return extractor(model) + except Exception as e: + logging.error("Extractor failed (typed -> dict wrapper): %s", e) + raise + + return _wrapped + + +def _normalize_statuses(values: Optional[Iterable[Union[str, int]]]) -> set[Union[str, int]]: + if not values: + return set() + out: set[Union[str, int]] = set() + for v in values: + nv = _normalize_status_value(v) + if nv is not None: + out.add(nv) + return out + + +def _normalize_status_value(val: Union[str, int, None]) -> Union[str, int, None]: + if isinstance(val, str): + return val.strip().lower() + return val diff --git a/comfy_api_nodes/util/common_exceptions.py b/comfy_api_nodes/util/common_exceptions.py new file mode 100644 index 000000000..0606a4407 --- /dev/null +++ b/comfy_api_nodes/util/common_exceptions.py @@ -0,0 +1,14 @@ +class NetworkError(Exception): + """Base exception for network-related errors with diagnostic information.""" + + +class LocalNetworkError(NetworkError): + """Exception raised when local network connectivity issues are detected.""" + + +class ApiServerError(NetworkError): + """Exception raised when the API server is unreachable but internet is working.""" + + +class ProcessingInterrupted(Exception): + """Operation was interrupted by user/runtime via processing_interrupted().""" diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py new file mode 100644 index 000000000..9f4c90c5c --- /dev/null +++ b/comfy_api_nodes/util/conversions.py @@ -0,0 +1,432 @@ +import base64 +import logging +import math +import uuid +from io import BytesIO +from typing import Optional + +import av +import numpy as np +import torch +from PIL import Image + +from comfy.utils import common_upscale +from comfy_api.latest import Input, InputImpl +from comfy_api.util import VideoContainer, VideoCodec + +from ._helpers import mimetype_to_extension + + +def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor: + """Converts image data from BytesIO to a torch.Tensor. + + Args: + image_bytesio: BytesIO object containing the image data. + mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA"). + + Returns: + A torch.Tensor representing the image (1, H, W, C). + + Raises: + PIL.UnidentifiedImageError: If the image data cannot be identified. + ValueError: If the specified mode is invalid. + """ + image = Image.open(image_bytesio) + image = image.convert(mode) + image_array = np.array(image).astype(np.float32) / 255.0 + return torch.from_numpy(image_array).unsqueeze(0) + + +def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> torch.Tensor: + """ + Converts a pair of image tensors to a batch tensor. + If the images are not the same size, the smaller image is resized to + match the larger image. + """ + if image1.shape[1:] != image2.shape[1:]: + image2 = common_upscale( + image2.movedim(-1, 1), + image1.shape[2], + image1.shape[1], + "bilinear", + "center", + ).movedim(1, -1) + return torch.cat((image1, image2), dim=0) + + +def tensor_to_bytesio( + image: torch.Tensor, + name: Optional[str] = None, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> BytesIO: + """Converts a torch.Tensor image to a named BytesIO object. + + Args: + image: Input torch.Tensor image. + name: Optional filename for the BytesIO object. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Named BytesIO object containing the image data, with pointer set to the start of buffer. + """ + if not mime_type: + mime_type = "image/png" + + pil_image = tensor_to_pil(image, total_pixels=total_pixels) + img_binary = pil_to_bytesio(pil_image, mime_type=mime_type) + img_binary.name = f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}" + return img_binary + + +def tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image: + """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling.""" + if len(image.shape) > 3: + image = image[0] + # TODO: remove alpha if not allowed and present + input_tensor = image.cpu() + input_tensor = downscale_image_tensor(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze() + image_np = (input_tensor.numpy() * 255).astype(np.uint8) + img = Image.fromarray(image_np) + return img + + +def tensor_to_base64_string( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4'). + + Returns: + Base64 encoded string of the image. + """ + pil_image = tensor_to_pil(image_tensor, total_pixels=total_pixels) + img_byte_arr = pil_to_bytesio(pil_image, mime_type=mime_type) + img_bytes = img_byte_arr.getvalue() + # Encode bytes to base64 string + base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8") + return base64_encoded_string + + +def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: + """Converts a PIL Image to a BytesIO object.""" + if not mime_type: + mime_type = "image/png" + + img_byte_arr = BytesIO() + # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG') + pil_format = mime_type.split("/")[-1].upper() + if pil_format == "JPG": + pil_format = "JPEG" + img.save(img_byte_arr, format=pil_format) + img_byte_arr.seek(0) + return img_byte_arr + + +def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor: + """Downscale input image tensor to roughly the specified total pixels.""" + samples = image.movedim(-1, 1) + total = int(total_pixels) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + if scale_by >= 1: + return image + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = common_upscale(samples, width, height, "lanczos", "disabled") + s = s.movedim(1, -1) + return s + + +def tensor_to_data_uri( + image_tensor: torch.Tensor, + total_pixels: int = 2048 * 2048, + mime_type: str = "image/png", +) -> str: + """Converts a tensor image to a Data URI string. + + Args: + image_tensor: Input torch.Tensor image. + total_pixels: Maximum total pixels for potential downscaling. + mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp'). + + Returns: + Data URI string (e.g., 'data:image/png;base64,...'). + """ + base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type) + return f"data:{mime_type};base64,{base64_string}" + + +def audio_to_base64_string(audio: Input.Audio, container_format: str = "mp4", codec_name: str = "aac") -> str: + """Converts an audio input to a base64 string.""" + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, container_format, codec_name) + audio_bytes = audio_bytes_io.getvalue() + return base64.b64encode(audio_bytes).decode("utf-8") + + +def video_to_base64_string( + video: Input.Video, + container_format: VideoContainer = None, + codec: VideoCodec = None +) -> str: + """ + Converts a video input to a base64 string. + + Args: + video: The video input to convert + container_format: Optional container format to use (defaults to video.container if available) + codec: Optional codec to use (defaults to video.codec if available) + """ + video_bytes_io = BytesIO() + + # Use provided format/codec if specified, otherwise use video's own if available + format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4) + codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264) + + video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use) + video_bytes_io.seek(0) + return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8") + + +def audio_ndarray_to_bytesio( + audio_data_np: np.ndarray, + sample_rate: int, + container_format: str = "mp4", + codec_name: str = "aac", +) -> BytesIO: + """ + Encodes a numpy array of audio data into a BytesIO object. + """ + audio_bytes_io = BytesIO() + with av.open(audio_bytes_io, mode="w", format=container_format) as output_container: + audio_stream = output_container.add_stream(codec_name, rate=sample_rate) + frame = av.AudioFrame.from_ndarray( + audio_data_np, + format="fltp", + layout="stereo" if audio_data_np.shape[0] > 1 else "mono", + ) + frame.sample_rate = sample_rate + frame.pts = 0 + + for packet in audio_stream.encode(frame): + output_container.mux(packet) + + # Flush stream + for packet in audio_stream.encode(None): + output_container.mux(packet) + + audio_bytes_io.seek(0) + return audio_bytes_io + + +def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray: + """ + Prepares audio waveform for av library by converting to a contiguous numpy array. + + Args: + waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type. + + Returns: + Contiguous numpy array of the audio waveform. If the audio was batched, + the first item is taken. + """ + if waveform.ndim != 3 or waveform.shape[0] != 1: + raise ValueError("Expected waveform tensor shape (1, channels, samples)") + + # If batch is > 1, take first item + if waveform.shape[0] > 1: + waveform = waveform[0] + + # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array + audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy() + if audio_data_np.dtype != np.float32: + audio_data_np = audio_data_np.astype(np.float32) + + return audio_data_np + + +def audio_input_to_mp3(audio: Input.Audio) -> BytesIO: + waveform = audio["waveform"].cpu() + + output_buffer = BytesIO() + output_container = av.open(output_buffer, mode="w", format="mp3") + + out_stream = output_container.add_stream("libmp3lame", rate=audio["sample_rate"]) + out_stream.bit_rate = 320000 + + frame = av.AudioFrame.from_ndarray( + waveform.movedim(0, 1).reshape(1, -1).float().numpy(), + format="flt", + layout="mono" if waveform.shape[0] == 1 else "stereo", + ) + frame.sample_rate = audio["sample_rate"] + frame.pts = 0 + output_container.mux(out_stream.encode(frame)) + output_container.mux(out_stream.encode(None)) + output_container.close() + output_buffer.seek(0) + return output_buffer + + +def trim_video(video: Input.Video, duration_sec: float) -> Input.Video: + """ + Returns a new VideoInput object trimmed from the beginning to the specified duration, + using av to avoid loading entire video into memory. + + Args: + video: Input video to trim + duration_sec: Duration in seconds to keep from the beginning + + Returns: + VideoFromFile object that owns the output buffer + """ + output_buffer = BytesIO() + input_container = None + output_container = None + + try: + # Get the stream source - this avoids loading entire video into memory + # when the source is already a file path + input_source = video.get_stream_source() + + # Open containers + input_container = av.open(input_source, mode="r") + output_container = av.open(output_buffer, mode="w", format="mp4") + + # Set up output streams for re-encoding + video_stream = None + audio_stream = None + + for stream in input_container.streams: + logging.info("Found stream: type=%s, class=%s", stream.type, type(stream)) + if isinstance(stream, av.VideoStream): + # Create output video stream with same parameters + video_stream = output_container.add_stream("h264", rate=stream.average_rate) + video_stream.width = stream.width + video_stream.height = stream.height + video_stream.pix_fmt = "yuv420p" + logging.info("Added video stream: %sx%s @ %sfps", stream.width, stream.height, stream.average_rate) + elif isinstance(stream, av.AudioStream): + # Create output audio stream with same parameters + audio_stream = output_container.add_stream("aac", rate=stream.sample_rate) + audio_stream.sample_rate = stream.sample_rate + audio_stream.layout = stream.layout + logging.info("Added audio stream: %sHz, %s channels", stream.sample_rate, stream.channels) + + # Calculate target frame count that's divisible by 16 + fps = input_container.streams.video[0].average_rate + estimated_frames = int(duration_sec * fps) + target_frames = (estimated_frames // 16) * 16 # Round down to nearest multiple of 16 + + if target_frames == 0: + raise ValueError("Video too short: need at least 16 frames for Moonvalley") + + frame_count = 0 + audio_frame_count = 0 + + # Decode and re-encode video frames + if video_stream: + for frame in input_container.decode(video=0): + if frame_count >= target_frames: + break + + # Re-encode frame + for packet in video_stream.encode(frame): + output_container.mux(packet) + frame_count += 1 + + # Flush encoder + for packet in video_stream.encode(): + output_container.mux(packet) + + logging.info("Encoded %s video frames (target: %s)", frame_count, target_frames) + + # Decode and re-encode audio frames + if audio_stream: + input_container.seek(0) # Reset to beginning for audio + for frame in input_container.decode(audio=0): + if frame.time >= duration_sec: + break + + # Re-encode frame + for packet in audio_stream.encode(frame): + output_container.mux(packet) + audio_frame_count += 1 + + # Flush encoder + for packet in audio_stream.encode(): + output_container.mux(packet) + + logging.info("Encoded %s audio frames", audio_frame_count) + + # Close containers + output_container.close() + input_container.close() + + # Return as VideoFromFile using the buffer + output_buffer.seek(0) + return InputImpl.VideoFromFile(output_buffer) + + except Exception as e: + # Clean up on error + if input_container is not None: + input_container.close() + if output_container is not None: + output_container.close() + raise RuntimeError(f"Failed to trim video: {str(e)}") from e + + +def _f32_pcm(wav: torch.Tensor) -> torch.Tensor: + """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file.""" + if wav.dtype.is_floating_point: + return wav + elif wav.dtype == torch.int16: + return wav.float() / (2**15) + elif wav.dtype == torch.int32: + return wav.float() / (2**31) + raise ValueError(f"Unsupported wav dtype: {wav.dtype}") + + +def audio_bytes_to_audio_input(audio_bytes: bytes) -> dict: + """ + Decode any common audio container from bytes using PyAV and return + a Comfy AUDIO dict: {"waveform": [1, C, T] float32, "sample_rate": int}. + """ + with av.open(BytesIO(audio_bytes)) as af: + if not af.streams.audio: + raise ValueError("No audio stream found in response.") + stream = af.streams.audio[0] + + in_sr = int(stream.codec_context.sample_rate) + out_sr = in_sr + + frames: list[torch.Tensor] = [] + n_channels = stream.channels or 1 + + for frame in af.decode(streams=stream.index): + arr = frame.to_ndarray() # shape can be [C, T] or [T, C] or [T] + buf = torch.from_numpy(arr) + if buf.ndim == 1: + buf = buf.unsqueeze(0) # [T] -> [1, T] + elif buf.shape[0] != n_channels and buf.shape[-1] == n_channels: + buf = buf.transpose(0, 1).contiguous() # [T, C] -> [C, T] + elif buf.shape[0] != n_channels: + buf = buf.reshape(-1, n_channels).t().contiguous() # fallback to [C, T] + frames.append(buf) + + if not frames: + raise ValueError("Decoded zero audio frames.") + + wav = torch.cat(frames, dim=1) # [C, T] + wav = _f32_pcm(wav) + return {"waveform": wav.unsqueeze(0).contiguous(), "sample_rate": out_sr} diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py new file mode 100644 index 000000000..f89045e12 --- /dev/null +++ b/comfy_api_nodes/util/download_helpers.py @@ -0,0 +1,261 @@ +import asyncio +import contextlib +import uuid +from io import BytesIO +from pathlib import Path +from typing import IO, Optional, Union +from urllib.parse import urljoin, urlparse + +import aiohttp +import torch +from aiohttp.client_exceptions import ClientError, ContentTypeError + +from comfy_api.input_impl import VideoFromFile +from comfy_api.latest import IO as COMFY_IO +from comfy_api_nodes.apis import request_logger + +from ._helpers import ( + default_base_url, + get_auth_header, + is_processing_interrupted, + sleep_with_interrupt, +) +from .client import _diagnose_connectivity +from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted +from .conversions import bytesio_to_image_tensor + +_RETRY_STATUS = {408, 429, 500, 502, 503, 504} + + +async def download_url_to_bytesio( + url: str, + dest: Optional[Union[BytesIO, IO[bytes], str, Path]], + *, + timeout: Optional[float] = None, + max_retries: int = 5, + retry_delay: float = 1.0, + retry_backoff: float = 2.0, + cls: type[COMFY_IO.ComfyNode] = None, +) -> None: + """Stream-download a URL to `dest`. + + `dest` must be one of: + - a BytesIO (rewound to 0 after write), + - a file-like object opened in binary write mode (must implement .write()), + - a filesystem path (str | pathlib.Path), which will be opened with 'wb'. + + If `url` starts with `/proxy/`, `cls` must be provided so the URL can be expanded + to an absolute URL and authentication headers can be applied. + + Raises: + ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception (HTTP and other errors) + """ + if not isinstance(dest, (str, Path)) and not hasattr(dest, "write"): + raise ValueError("dest must be a path (str|Path) or a binary-writable object providing .write().") + + attempt = 0 + delay = retry_delay + headers: dict[str, str] = {} + + parsed_url = urlparse(url) + if not parsed_url.scheme and not parsed_url.netloc: # is URL relative? + if cls is None: + raise ValueError("For relative 'cloud' paths, the `cls` parameter is required.") + url = urljoin(default_base_url().rstrip("/") + "/", url.lstrip("/")) + headers = get_auth_header(cls) + + while True: + attempt += 1 + op_id = _generate_operation_id("GET", url, attempt) + timeout_cfg = aiohttp.ClientTimeout(total=timeout) + + is_path_sink = isinstance(dest, (str, Path)) + fhandle = None + session: Optional[aiohttp.ClientSession] = None + stop_evt: Optional[asyncio.Event] = None + monitor_task: Optional[asyncio.Task] = None + req_task: Optional[asyncio.Task] = None + + try: + with contextlib.suppress(Exception): + request_logger.log_request_response(operation_id=op_id, request_method="GET", request_url=url) + + session = aiohttp.ClientSession(timeout=timeout_cfg) + stop_evt = asyncio.Event() + + async def _monitor(): + try: + while not stop_evt.is_set(): + if is_processing_interrupted(): + return + await asyncio.sleep(1.0) + except asyncio.CancelledError: + return + + monitor_task = asyncio.create_task(_monitor()) + + req_task = asyncio.create_task(session.get(url, headers=headers)) + done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED) + + if monitor_task in done and req_task in pending: + req_task.cancel() + with contextlib.suppress(Exception): + await req_task + raise ProcessingInterrupted("Task cancelled") + + try: + resp = await req_task + except asyncio.CancelledError: + raise ProcessingInterrupted("Task cancelled") from None + + async with resp: + if resp.status >= 400: + with contextlib.suppress(Exception): + try: + body = await resp.json() + except (ContentTypeError, ValueError): + text = await resp.text() + body = text if len(text) <= 4096 else f"[text {len(text)} bytes]" + request_logger.log_request_response( + operation_id=op_id, + request_method="GET", + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=body, + error_message=f"HTTP {resp.status}", + ) + + if resp.status in _RETRY_STATUS and attempt <= max_retries: + await sleep_with_interrupt(delay, cls, None, None, None) + delay *= retry_backoff + continue + raise Exception(f"Failed to download (HTTP {resp.status}).") + + if is_path_sink: + p = Path(str(dest)) + with contextlib.suppress(Exception): + p.parent.mkdir(parents=True, exist_ok=True) + fhandle = open(p, "wb") + sink = fhandle + else: + sink = dest # BytesIO or file-like + + written = 0 + while True: + try: + chunk = await asyncio.wait_for(resp.content.read(1024 * 1024), timeout=1.0) + except asyncio.TimeoutError: + chunk = b"" + except asyncio.CancelledError: + raise ProcessingInterrupted("Task cancelled") from None + + if is_processing_interrupted(): + raise ProcessingInterrupted("Task cancelled") + + if not chunk: + if resp.content.at_eof(): + break + continue + + sink.write(chunk) + written += len(chunk) + + if isinstance(dest, BytesIO): + with contextlib.suppress(Exception): + dest.seek(0) + + with contextlib.suppress(Exception): + request_logger.log_request_response( + operation_id=op_id, + request_method="GET", + request_url=url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=f"[streamed {written} bytes to dest]", + ) + return + except asyncio.CancelledError: + raise ProcessingInterrupted("Task cancelled") from None + except (ClientError, OSError) as e: + if attempt <= max_retries: + with contextlib.suppress(Exception): + request_logger.log_request_response( + operation_id=op_id, + request_method="GET", + request_url=url, + error_message=f"{type(e).__name__}: {str(e)} (will retry)", + ) + await sleep_with_interrupt(delay, cls, None, None, None) + delay *= retry_backoff + continue + + diag = await _diagnose_connectivity() + if not diag["internet_accessible"]: + raise LocalNetworkError( + "Unable to connect to the network. Please check your internet connection and try again." + ) from e + raise ApiServerError("The remote service appears unreachable at this time.") from e + finally: + if stop_evt is not None: + stop_evt.set() + if monitor_task: + monitor_task.cancel() + with contextlib.suppress(Exception): + await monitor_task + if req_task and not req_task.done(): + req_task.cancel() + with contextlib.suppress(Exception): + await req_task + if session: + with contextlib.suppress(Exception): + await session.close() + if fhandle: + with contextlib.suppress(Exception): + fhandle.flush() + fhandle.close() + + +async def download_url_to_image_tensor( + url: str, + *, + timeout: float = None, + cls: type[COMFY_IO.ComfyNode] = None, +) -> torch.Tensor: + """Downloads an image from a URL and returns a [B, H, W, C] tensor.""" + result = BytesIO() + await download_url_to_bytesio(url, result, timeout=timeout, cls=cls) + return bytesio_to_image_tensor(result) + + +async def download_url_to_video_output( + video_url: str, + *, + timeout: float = None, + cls: type[COMFY_IO.ComfyNode] = None, +) -> VideoFromFile: + """Downloads a video from a URL and returns a `VIDEO` output.""" + result = BytesIO() + await download_url_to_bytesio(video_url, result, timeout=timeout, cls=cls) + return VideoFromFile(result) + + +async def download_url_as_bytesio( + url: str, + *, + timeout: float = None, + cls: type[COMFY_IO.ComfyNode] = None, +) -> BytesIO: + """Downloads content from a URL and returns a new BytesIO (rewound to 0).""" + result = BytesIO() + await download_url_to_bytesio(url, result, timeout=timeout, cls=cls) + return result + + +def _generate_operation_id(method: str, url: str, attempt: int) -> str: + try: + parsed = urlparse(url) + slug = (parsed.path.rsplit("/", 1)[-1] or parsed.netloc or "download").strip("/").replace("/", "_") + except Exception: + slug = "download" + return f"{method}_{slug}_try{attempt}_{uuid.uuid4().hex[:8]}" diff --git a/comfy_api_nodes/util/upload_helpers.py b/comfy_api_nodes/util/upload_helpers.py new file mode 100644 index 000000000..7bfc61704 --- /dev/null +++ b/comfy_api_nodes/util/upload_helpers.py @@ -0,0 +1,338 @@ +import asyncio +import contextlib +import logging +import time +import uuid +from io import BytesIO +from typing import Optional, Union +from urllib.parse import urlparse + +import aiohttp +import torch +from pydantic import BaseModel, Field + +from comfy_api.latest import IO, Input +from comfy_api.util import VideoCodec, VideoContainer +from comfy_api_nodes.apis import request_logger + +from ._helpers import is_processing_interrupted, sleep_with_interrupt +from .client import ( + ApiEndpoint, + _diagnose_connectivity, + _display_time_progress, + sync_op, +) +from .common_exceptions import ApiServerError, LocalNetworkError, ProcessingInterrupted +from .conversions import ( + audio_ndarray_to_bytesio, + audio_tensor_to_contiguous_ndarray, + tensor_to_bytesio, +) + + +class UploadRequest(BaseModel): + file_name: str = Field(..., description="Filename to upload") + content_type: Optional[str] = Field( + None, + description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.", + ) + + +class UploadResponse(BaseModel): + download_url: str = Field(..., description="URL to GET uploaded file") + upload_url: str = Field(..., description="URL to PUT file to upload") + + +async def upload_images_to_comfyapi( + cls: type[IO.ComfyNode], + image: torch.Tensor, + *, + max_images: int = 8, + mime_type: Optional[str] = None, + wait_label: Optional[str] = "Uploading", +) -> list[str]: + """ + Uploads images to ComfyUI API and returns download URLs. + To upload multiple images, stack them in the batch dimension first. + """ + # if batch, try to upload each file if max_images is greater than 0 + download_urls: list[str] = [] + is_batch = len(image.shape) > 3 + batch_len = image.shape[0] if is_batch else 1 + + for idx in range(min(batch_len, max_images)): + tensor = image[idx] if is_batch else image + img_io = tensor_to_bytesio(tensor, mime_type=mime_type) + url = await upload_file_to_comfyapi(cls, img_io, img_io.name, mime_type, wait_label) + download_urls.append(url) + return download_urls + + +async def upload_audio_to_comfyapi( + cls: type[IO.ComfyNode], + audio: Input.Audio, + *, + container_format: str = "mp4", + codec_name: str = "aac", + mime_type: str = "audio/mp4", + filename: str = "uploaded_audio.mp4", +) -> str: + """ + Uploads a single audio input to ComfyUI API and returns its download URL. + Encodes the raw waveform into the specified format before uploading. + """ + sample_rate: int = audio["sample_rate"] + waveform: torch.Tensor = audio["waveform"] + audio_data_np = audio_tensor_to_contiguous_ndarray(waveform) + audio_bytes_io = audio_ndarray_to_bytesio(audio_data_np, sample_rate, container_format, codec_name) + return await upload_file_to_comfyapi(cls, audio_bytes_io, filename, mime_type) + + +async def upload_video_to_comfyapi( + cls: type[IO.ComfyNode], + video: Input.Video, + *, + container: VideoContainer = VideoContainer.MP4, + codec: VideoCodec = VideoCodec.H264, + max_duration: Optional[int] = None, +) -> str: + """ + Uploads a single video to ComfyUI API and returns its download URL. + Uses the specified container and codec for saving the video before upload. + """ + if max_duration is not None: + try: + actual_duration = video.get_duration() + if actual_duration > max_duration: + raise ValueError( + f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)." + ) + except Exception as e: + logging.error("Error getting video duration: %s", str(e)) + raise ValueError(f"Could not verify video duration from source: {e}") from e + + upload_mime_type = f"video/{container.value.lower()}" + filename = f"uploaded_video.{container.value.lower()}" + + # Convert VideoInput to BytesIO using specified container/codec + video_bytes_io = BytesIO() + video.save_to(video_bytes_io, format=container, codec=codec) + video_bytes_io.seek(0) + + return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type) + + +async def upload_file_to_comfyapi( + cls: type[IO.ComfyNode], + file_bytes_io: BytesIO, + filename: str, + upload_mime_type: Optional[str], + wait_label: Optional[str] = "Uploading", +) -> str: + """Uploads a single file to ComfyUI API and returns its download URL.""" + if upload_mime_type is None: + request_object = UploadRequest(file_name=filename) + else: + request_object = UploadRequest(file_name=filename, content_type=upload_mime_type) + create_resp = await sync_op( + cls, + endpoint=ApiEndpoint(path="/customers/storage", method="POST"), + data=request_object, + response_model=UploadResponse, + final_label_on_success=None, + monitor_progress=False, + ) + await upload_file( + cls, + create_resp.upload_url, + file_bytes_io, + content_type=upload_mime_type, + wait_label=wait_label, + ) + return create_resp.download_url + + +async def upload_file( + cls: type[IO.ComfyNode], + upload_url: str, + file: Union[BytesIO, str], + *, + content_type: Optional[str] = None, + max_retries: int = 3, + retry_delay: float = 1.0, + retry_backoff: float = 2.0, + wait_label: Optional[str] = None, +) -> None: + """ + Upload a file to a signed URL (e.g., S3 pre-signed PUT) with retries, Comfy progress display, and interruption. + + Args: + cls: Node class (provides auth context + UI progress hooks). + upload_url: Pre-signed PUT URL. + file: BytesIO or path string. + content_type: Explicit MIME type. If None, we *suppress* Content-Type. + max_retries: Maximum retry attempts. + retry_delay: Initial delay in seconds. + retry_backoff: Exponential backoff factor. + wait_label: Progress label shown in Comfy UI. + + Raises: + ProcessingInterrupted, LocalNetworkError, ApiServerError, Exception + """ + if isinstance(file, BytesIO): + with contextlib.suppress(Exception): + file.seek(0) + data = file.read() + elif isinstance(file, str): + with open(file, "rb") as f: + data = f.read() + else: + raise ValueError("file must be a BytesIO or a filesystem path string") + + headers: dict[str, str] = {} + skip_auto_headers: set[str] = set() + if content_type: + headers["Content-Type"] = content_type + else: + skip_auto_headers.add("Content-Type") # Don't let aiohttp add Content-Type, it can break the signed request + + attempt = 0 + delay = retry_delay + start_ts = time.monotonic() + op_uuid = uuid.uuid4().hex[:8] + while True: + attempt += 1 + operation_id = _generate_operation_id("PUT", upload_url, attempt, op_uuid) + timeout = aiohttp.ClientTimeout(total=None) + stop_evt = asyncio.Event() + + async def _monitor(): + try: + while not stop_evt.is_set(): + if is_processing_interrupted(): + return + if wait_label: + _display_time_progress(cls, wait_label, int(time.monotonic() - start_ts), None) + await asyncio.sleep(1.0) + except asyncio.CancelledError: + return + + monitor_task = asyncio.create_task(_monitor()) + sess: Optional[aiohttp.ClientSession] = None + try: + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + request_headers=headers or None, + request_params=None, + request_data=f"[File data {len(data)} bytes]", + ) + except Exception as e: + logging.debug("[DEBUG] upload request logging failed: %s", e) + + sess = aiohttp.ClientSession(timeout=timeout) + req = sess.put(upload_url, data=data, headers=headers, skip_auto_headers=skip_auto_headers) + req_task = asyncio.create_task(req) + + done, pending = await asyncio.wait({req_task, monitor_task}, return_when=asyncio.FIRST_COMPLETED) + + if monitor_task in done and req_task in pending: + req_task.cancel() + raise ProcessingInterrupted("Upload cancelled") + + try: + resp = await req_task + except asyncio.CancelledError: + raise ProcessingInterrupted("Upload cancelled") from None + + async with resp: + if resp.status >= 400: + with contextlib.suppress(Exception): + try: + body = await resp.json() + except Exception: + body = await resp.text() + msg = f"Upload failed with status {resp.status}" + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content=body, + error_message=msg, + ) + if resp.status in {408, 429, 500, 502, 503, 504} and attempt <= max_retries: + await sleep_with_interrupt( + delay, + cls, + wait_label, + start_ts, + None, + display_callback=_display_time_progress if wait_label else None, + ) + delay *= retry_backoff + continue + raise Exception(f"Failed to upload (HTTP {resp.status}).") + try: + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + response_status_code=resp.status, + response_headers=dict(resp.headers), + response_content="File uploaded successfully.", + ) + except Exception as e: + logging.debug("[DEBUG] upload response logging failed: %s", e) + return + except asyncio.CancelledError: + raise ProcessingInterrupted("Task cancelled") from None + except (aiohttp.ClientError, OSError) as e: + if attempt <= max_retries: + with contextlib.suppress(Exception): + request_logger.log_request_response( + operation_id=operation_id, + request_method="PUT", + request_url=upload_url, + request_headers=headers or None, + request_data=f"[File data {len(data)} bytes]", + error_message=f"{type(e).__name__}: {str(e)} (will retry)", + ) + await sleep_with_interrupt( + delay, + cls, + wait_label, + start_ts, + None, + display_callback=_display_time_progress if wait_label else None, + ) + delay *= retry_backoff + continue + + diag = await _diagnose_connectivity() + if not diag["internet_accessible"]: + raise LocalNetworkError( + "Unable to connect to the network. Please check your internet connection and try again." + ) from e + raise ApiServerError("The API service appears unreachable at this time.") from e + finally: + stop_evt.set() + if monitor_task: + monitor_task.cancel() + with contextlib.suppress(Exception): + await monitor_task + if sess: + with contextlib.suppress(Exception): + await sess.close() + + +def _generate_operation_id(method: str, url: str, attempt: int, op_uuid: str) -> str: + try: + parsed = urlparse(url) + slug = (parsed.path.rsplit("/", 1)[-1] or parsed.netloc or "upload").strip("/").replace("/", "_") + except Exception: + slug = "upload" + return f"{method}_{slug}_{op_uuid}_try{attempt}" diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py new file mode 100644 index 000000000..22da05bc1 --- /dev/null +++ b/comfy_api_nodes/util/validation_utils.py @@ -0,0 +1,185 @@ +import logging +from typing import Optional + +import torch + +from comfy_api.input.video_types import VideoInput +from comfy_api.latest import Input + + +def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]: + if len(image.shape) == 4: + return image.shape[1], image.shape[2] + elif len(image.shape) == 3: + return image.shape[0], image.shape[1] + else: + raise ValueError("Invalid image tensor shape.") + + +def validate_image_dimensions( + image: torch.Tensor, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + height, width = get_image_dimensions(image) + + if min_width is not None and width < min_width: + raise ValueError(f"Image width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Image width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError(f"Image height must be at least {min_height}px, got {height}px") + if max_height is not None and height > max_height: + raise ValueError(f"Image height must be at most {max_height}px, got {height}px") + + +def validate_image_aspect_ratio( + image: torch.Tensor, + min_aspect_ratio: Optional[float] = None, + max_aspect_ratio: Optional[float] = None, +): + width, height = get_image_dimensions(image) + aspect_ratio = width / height + + if min_aspect_ratio is not None and aspect_ratio < min_aspect_ratio: + raise ValueError(f"Image aspect ratio must be at least {min_aspect_ratio}, got {aspect_ratio}") + if max_aspect_ratio is not None and aspect_ratio > max_aspect_ratio: + raise ValueError(f"Image aspect ratio must be at most {max_aspect_ratio}, got {aspect_ratio}") + + +def validate_image_aspect_ratio_range( + image: torch.Tensor, + min_ratio: tuple[float, float], # e.g. (1, 4) + max_ratio: tuple[float, float], # e.g. (4, 1) + *, + strict: bool = True, # True -> (min, max); False -> [min, max] +) -> float: + a1, b1 = min_ratio + a2, b2 = max_ratio + if a1 <= 0 or b1 <= 0 or a2 <= 0 or b2 <= 0: + raise ValueError("Ratios must be positive, like (1, 4) or (4, 1).") + lo, hi = (a1 / b1), (a2 / b2) + if lo > hi: + lo, hi = hi, lo + a1, b1, a2, b2 = a2, b2, a1, b1 # swap only for error text + w, h = get_image_dimensions(image) + if w <= 0 or h <= 0: + raise ValueError(f"Invalid image dimensions: {w}x{h}") + ar = w / h + ok = (lo < ar < hi) if strict else (lo <= ar <= hi) + if not ok: + op = "<" if strict else "≤" + raise ValueError(f"Image aspect ratio {ar:.6g} is outside allowed range: {a1}:{b1} {op} ratio {op} {a2}:{b2}") + return ar + + +def validate_aspect_ratio_closeness( + start_img, + end_img, + min_rel: float, + max_rel: float, + *, + strict: bool = False, # True => exclusive, False => inclusive +) -> None: + w1, h1 = get_image_dimensions(start_img) + w2, h2 = get_image_dimensions(end_img) + if min(w1, h1, w2, h2) <= 0: + raise ValueError("Invalid image dimensions") + ar1 = w1 / h1 + ar2 = w2 / h2 + # Normalize so it is symmetric (no need to check both ar1/ar2 and ar2/ar1) + closeness = max(ar1, ar2) / min(ar1, ar2) + limit = max(max_rel, 1.0 / min_rel) # for 0.8..1.25 this is 1.25 + if (closeness >= limit) if strict else (closeness > limit): + raise ValueError(f"Aspect ratios must be close: start/end={ar1/ar2:.4f}, allowed range {min_rel}–{max_rel}.") + + +def validate_video_dimensions( + video: Input.Video, + min_width: Optional[int] = None, + max_width: Optional[int] = None, + min_height: Optional[int] = None, + max_height: Optional[int] = None, +): + try: + width, height = video.get_dimensions() + except Exception as e: + logging.error("Error getting dimensions of video: %s", e) + return + + if min_width is not None and width < min_width: + raise ValueError(f"Video width must be at least {min_width}px, got {width}px") + if max_width is not None and width > max_width: + raise ValueError(f"Video width must be at most {max_width}px, got {width}px") + if min_height is not None and height < min_height: + raise ValueError(f"Video height must be at least {min_height}px, got {height}px") + if max_height is not None and height > max_height: + raise ValueError(f"Video height must be at most {max_height}px, got {height}px") + + +def validate_video_duration( + video: Input.Video, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, +): + try: + duration = video.get_duration() + except Exception as e: + logging.error("Error getting duration of video: %s", e) + return + + epsilon = 0.0001 + if min_duration is not None and min_duration - epsilon > duration: + raise ValueError(f"Video duration must be at least {min_duration}s, got {duration}s") + if max_duration is not None and duration > max_duration + epsilon: + raise ValueError(f"Video duration must be at most {max_duration}s, got {duration}s") + + +def get_number_of_images(images): + if isinstance(images, torch.Tensor): + return images.shape[0] if images.ndim >= 4 else 1 + return len(images) + + +def validate_audio_duration( + audio: Input.Audio, + min_duration: Optional[float] = None, + max_duration: Optional[float] = None, +) -> None: + sr = int(audio["sample_rate"]) + dur = int(audio["waveform"].shape[-1]) / sr + eps = 1.0 / sr + if min_duration is not None and dur + eps < min_duration: + raise ValueError(f"Audio duration must be at least {min_duration}s, got {dur + eps:.2f}s") + if max_duration is not None and dur - eps > max_duration: + raise ValueError(f"Audio duration must be at most {max_duration}s, got {dur - eps:.2f}s") + + +def validate_string( + string: str, + strip_whitespace=True, + field_name="prompt", + min_length=None, + max_length=None, +): + if string is None: + raise Exception(f"Field '{field_name}' cannot be empty.") + if strip_whitespace: + string = string.strip() + if min_length and len(string) < min_length: + raise Exception( + f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long." + ) + if max_length and len(string) > max_length: + raise Exception( + f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long." + ) + + +def validate_container_format_is_mp4(video: VideoInput) -> None: + """Validates video container format is MP4.""" + container_format = video.get_container_format() + if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]: + raise ValueError(f"Only MP4 container format supported. Got: {container_format}") diff --git a/comfy_config/config_parser.py b/comfy_config/config_parser.py new file mode 100644 index 000000000..8da7bd901 --- /dev/null +++ b/comfy_config/config_parser.py @@ -0,0 +1,152 @@ +import os +from pathlib import Path +from typing import Optional + +from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource + +from comfy_config.types import ( + ComfyConfig, + ProjectConfig, + PyProjectConfig, + PyProjectSettings +) + +def validate_and_extract_os_classifiers(classifiers: list) -> list: + os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")] + if not os_classifiers: + return [] + + os_values = [c[len("Operating System :: ") :] for c in os_classifiers] + valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"} + + for os_value in os_values: + if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes): + return [] + + return os_values + + +def validate_and_extract_accelerator_classifiers(classifiers: list) -> list: + accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")] + if not accelerator_classifiers: + return [] + + accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers] + + valid_accelerators = { + "GPU :: NVIDIA CUDA", + "GPU :: AMD ROCm", + "GPU :: Intel Arc", + "NPU :: Huawei Ascend", + "GPU :: Apple Metal", + } + + for accelerator_value in accelerator_values: + if accelerator_value not in valid_accelerators: + return [] + + return accelerator_values + + +""" +Extract configuration from a custom node directory's pyproject.toml file or a Python file. + +This function reads and parses the pyproject.toml file in the specified directory +to extract project and ComfyUI-specific configuration information. If no +pyproject.toml file is found, it creates a minimal configuration using the +folder name as the project name. If a Python file is provided, it uses the +file name (without extension) as the project name. + +Args: + path (str): Path to the directory containing the pyproject.toml file, or + path to a .py file. If pyproject.toml doesn't exist in a directory, + the folder name will be used as the default project name. If a .py + file is provided, the filename (without .py extension) will be used + as the project name. + +Returns: + Optional[PyProjectConfig]: A PyProjectConfig object containing: + - project: Basic project information (name, version, dependencies, etc.) + - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.) + Returns None if configuration extraction fails or if the provided file + is not a Python file. + +Notes: + - If pyproject.toml is missing in a directory, creates a default config with folder name + - If a .py file is provided, creates a default config with filename (without extension) + - Returns None for non-Python files + +Example: + >>> from comfy_config import config_parser + >>> # For directory + >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__)) + >>> project_config = config_parser.extract_node_configuration(custom_node_dir) + >>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml + >>> + >>> # For single-file Python node file + >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py" + >>> project_config = config_parser.extract_node_configuration(py_file_path) + >>> print(project_config.project.name) # "my_node" +""" +def extract_node_configuration(path) -> Optional[PyProjectConfig]: + if os.path.isfile(path): + file_path = Path(path) + + if file_path.suffix.lower() != '.py': + return None + + project_name = file_path.stem + project = ProjectConfig(name=project_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + folder_name = os.path.basename(path) + toml_path = Path(path) / "pyproject.toml" + + if not toml_path.exists(): + project = ProjectConfig(name=folder_name) + comfy = ComfyConfig() + return PyProjectConfig(project=project, tool_comfy=comfy) + + raw_settings = load_pyproject_settings(toml_path) + + project_data = raw_settings.project + + tool_data = raw_settings.tool + comfy_data = tool_data.get("comfy", {}) if tool_data else {} + + dependencies = project_data.get("dependencies", []) + supported_comfyui_frontend_version = "" + for dep in dependencies: + if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"): + supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package") + break + + supported_comfyui_version = comfy_data.get("requires-comfyui", "") + + classifiers = project_data.get('classifiers', []) + supported_os = validate_and_extract_os_classifiers(classifiers) + supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers) + + project_data['supported_os'] = supported_os + project_data['supported_accelerators'] = supported_accelerators + project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version + project_data['supported_comfyui_version'] = supported_comfyui_version + + return PyProjectConfig(project=project_data, tool_comfy=comfy_data) + + +def load_pyproject_settings(toml_path: Path) -> PyProjectSettings: + class PyProjectLoader(PyProjectSettings): + @classmethod + def settings_customise_sources( + cls, + settings_cls, + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ): + return (TomlConfigSettingsSource(settings_cls, toml_path),) + + return PyProjectLoader() diff --git a/comfy_config/types.py b/comfy_config/types.py new file mode 100644 index 000000000..59448466b --- /dev/null +++ b/comfy_config/types.py @@ -0,0 +1,97 @@ +from pydantic import BaseModel, Field, field_validator +from pydantic_settings import BaseSettings, SettingsConfigDict +from typing import List, Optional + +# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes +# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py. +# Any changes to one must be reflected in the other to maintain consistency. + +class NodeVersion(BaseModel): + changelog: str + dependencies: List[str] + deprecated: bool + id: str + version: str + download_url: str + + +class Node(BaseModel): + id: str + name: str + description: str + author: Optional[str] = None + license: Optional[str] = None + icon: Optional[str] = None + repository: Optional[str] = None + tags: List[str] = Field(default_factory=list) + latest_version: Optional[NodeVersion] = None + + +class PublishNodeVersionResponse(BaseModel): + node_version: NodeVersion + signedUrl: str + + +class URLs(BaseModel): + homepage: str = Field(default="", alias="Homepage") + documentation: str = Field(default="", alias="Documentation") + repository: str = Field(default="", alias="Repository") + issues: str = Field(default="", alias="Issues") + + +class Model(BaseModel): + location: str + model_url: str + + +class ComfyConfig(BaseModel): + publisher_id: str = Field(default="", alias="PublisherId") + display_name: str = Field(default="", alias="DisplayName") + icon: str = Field(default="", alias="Icon") + models: List[Model] = Field(default_factory=list, alias="Models") + includes: List[str] = Field(default_factory=list) + web: Optional[str] = None + banner_url: str = "" + +class License(BaseModel): + file: str = "" + text: str = "" + + +class ProjectConfig(BaseModel): + name: str = "" + description: str = "" + version: str = "1.0.0" + requires_python: str = Field(default=">= 3.9", alias="requires-python") + dependencies: List[str] = Field(default_factory=list) + license: License = Field(default_factory=License) + urls: URLs = Field(default_factory=URLs) + supported_os: List[str] = Field(default_factory=list) + supported_accelerators: List[str] = Field(default_factory=list) + supported_comfyui_version: str = "" + supported_comfyui_frontend_version: str = "" + + @field_validator('license', mode='before') + @classmethod + def validate_license(cls, v): + if isinstance(v, str): + return License(text=v) + elif isinstance(v, dict): + return License(**v) + elif isinstance(v, License): + return v + else: + return License() + + +class PyProjectConfig(BaseModel): + project: ProjectConfig = Field(default_factory=ProjectConfig) + tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig) + + +class PyProjectSettings(BaseSettings): + project: dict = Field(default_factory=dict) + + tool: dict = Field(default_factory=dict) + + model_config = SettingsConfigDict(extra='allow') diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py index 630f280fc..566bc3f9c 100644 --- a/comfy_execution/caching.py +++ b/comfy_execution/caching.py @@ -1,6 +1,7 @@ import itertools from typing import Sequence, Mapping, Dict from comfy_execution.graph import DynamicPrompt +from abc import ABC, abstractmethod import nodes @@ -16,12 +17,13 @@ def include_unique_id_in_input(class_type: str) -> bool: NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values() return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] -class CacheKeySet: +class CacheKeySet(ABC): def __init__(self, dynprompt, node_ids, is_changed_cache): self.keys = {} self.subcache_keys = {} - def add_keys(self, node_ids): + @abstractmethod + async def add_keys(self, node_ids): raise NotImplementedError() def all_node_ids(self): @@ -60,9 +62,8 @@ class CacheKeySetID(CacheKeySet): def __init__(self, dynprompt, node_ids, is_changed_cache): super().__init__(dynprompt, node_ids, is_changed_cache) self.dynprompt = dynprompt - self.add_keys(node_ids) - def add_keys(self, node_ids): + async def add_keys(self, node_ids): for node_id in node_ids: if node_id in self.keys: continue @@ -77,37 +78,36 @@ class CacheKeySetInputSignature(CacheKeySet): super().__init__(dynprompt, node_ids, is_changed_cache) self.dynprompt = dynprompt self.is_changed_cache = is_changed_cache - self.add_keys(node_ids) def include_node_id_in_input(self) -> bool: return False - def add_keys(self, node_ids): + async def add_keys(self, node_ids): for node_id in node_ids: if node_id in self.keys: continue if not self.dynprompt.has_node(node_id): continue node = self.dynprompt.get_node(node_id) - self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id) + self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id) self.subcache_keys[node_id] = (node_id, node["class_type"]) - def get_node_signature(self, dynprompt, node_id): + async def get_node_signature(self, dynprompt, node_id): signature = [] ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id) - signature.append(self.get_immediate_node_signature(dynprompt, node_id, order_mapping)) + signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping)) for ancestor_id in ancestors: - signature.append(self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)) + signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)) return to_hashable(signature) - def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping): + async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping): if not dynprompt.has_node(node_id): # This node doesn't exist -- we can't cache it. return [float("NaN")] node = dynprompt.get_node(node_id) class_type = node["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - signature = [class_type, self.is_changed_cache.get(node_id)] + signature = [class_type, await self.is_changed_cache.get(node_id)] if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type): signature.append(node_id) inputs = node["inputs"] @@ -150,9 +150,10 @@ class BasicCache: self.cache = {} self.subcaches = {} - def set_prompt(self, dynprompt, node_ids, is_changed_cache): + async def set_prompt(self, dynprompt, node_ids, is_changed_cache): self.dynprompt = dynprompt self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache) + await self.cache_key_set.add_keys(node_ids) self.is_changed_cache = is_changed_cache self.initialized = True @@ -201,13 +202,13 @@ class BasicCache: else: return None - def _ensure_subcache(self, node_id, children_ids): + async def _ensure_subcache(self, node_id, children_ids): subcache_key = self.cache_key_set.get_subcache_key(node_id) subcache = self.subcaches.get(subcache_key, None) if subcache is None: subcache = BasicCache(self.key_class) self.subcaches[subcache_key] = subcache - subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache) + await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache) return subcache def _get_subcache(self, node_id): @@ -259,10 +260,30 @@ class HierarchicalCache(BasicCache): assert cache is not None cache._set_immediate(node_id, value) - def ensure_subcache_for(self, node_id, children_ids): + async def ensure_subcache_for(self, node_id, children_ids): cache = self._get_cache_for(node_id) assert cache is not None - return cache._ensure_subcache(node_id, children_ids) + return await cache._ensure_subcache(node_id, children_ids) + +class NullCache: + + async def set_prompt(self, dynprompt, node_ids, is_changed_cache): + pass + + def all_node_ids(self): + return [] + + def clean_unused(self): + pass + + def get(self, node_id): + return None + + def set(self, node_id, value): + pass + + async def ensure_subcache_for(self, node_id, children_ids): + return self class LRUCache(BasicCache): def __init__(self, key_class, max_size=100): @@ -273,8 +294,8 @@ class LRUCache(BasicCache): self.used_generation = {} self.children = {} - def set_prompt(self, dynprompt, node_ids, is_changed_cache): - super().set_prompt(dynprompt, node_ids, is_changed_cache) + async def set_prompt(self, dynprompt, node_ids, is_changed_cache): + await super().set_prompt(dynprompt, node_ids, is_changed_cache) self.generation += 1 for node_id in node_ids: self._mark_used(node_id) @@ -303,11 +324,11 @@ class LRUCache(BasicCache): self._mark_used(node_id) return self._set_immediate(node_id, value) - def ensure_subcache_for(self, node_id, children_ids): + async def ensure_subcache_for(self, node_id, children_ids): # Just uses subcaches for tracking 'live' nodes - super()._ensure_subcache(node_id, children_ids) + await super()._ensure_subcache(node_id, children_ids) - self.cache_key_set.add_keys(children_ids) + await self.cache_key_set.add_keys(children_ids) self._mark_used(node_id) cache_key = self.cache_key_set.get_data_key(node_id) self.children[cache_key] = [] @@ -315,4 +336,3 @@ class LRUCache(BasicCache): self._mark_used(child_id) self.children[cache_key].append(self.cache_key_set.get_data_key(child_id)) return self - diff --git a/comfy_execution/graph.py b/comfy_execution/graph.py index 59b42b746..341c9735d 100644 --- a/comfy_execution/graph.py +++ b/comfy_execution/graph.py @@ -1,6 +1,14 @@ -import nodes +from __future__ import annotations +from typing import Type, Literal -from comfy_execution.graph_utils import is_link +import nodes +import asyncio +import inspect +from comfy_execution.graph_utils import is_link, ExecutionBlocker +from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions + +# NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests +ExecutionBlocker = ExecutionBlocker class DependencyCycleError(Exception): pass @@ -54,7 +62,22 @@ class DynamicPrompt: def get_original_prompt(self): return self.original_prompt -def get_input_info(class_def, input_name, valid_inputs=None): +def get_input_info( + class_def: Type[ComfyNodeABC], + input_name: str, + valid_inputs: InputTypeDict | None = None +) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]: + """Get the input type, category, and extra info for a given input name. + + Arguments: + class_def: The class definition of the node. + input_name: The name of the input to get info for. + valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES(). + + Returns: + tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name. + """ + valid_inputs = valid_inputs or class_def.INPUT_TYPES() input_info = None input_category = None @@ -82,6 +105,8 @@ class TopologicalSort: self.pendingNodes = {} self.blockCount = {} # Number of nodes this node is directly blocked by self.blocking = {} # Which nodes are blocked by this node + self.externalBlocks = 0 + self.unblockedEvent = asyncio.Event() def get_input_info(self, unique_id, input_name): class_type = self.dynprompt.get_node(unique_id)["class_type"] @@ -126,15 +151,26 @@ class TopologicalSort: from_node_id, from_socket = value if subgraph_nodes is not None and from_node_id not in subgraph_nodes: continue - input_type, input_category, input_info = self.get_input_info(unique_id, input_name) + _, _, input_info = self.get_input_info(unique_id, input_name) is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"] - if (include_lazy or not is_lazy) and not self.is_cached(from_node_id): - node_ids.append(from_node_id) + if (include_lazy or not is_lazy): + if not self.is_cached(from_node_id): + node_ids.append(from_node_id) links.append((from_node_id, from_socket, unique_id)) for link in links: self.add_strong_link(*link) + def add_external_block(self, node_id): + assert node_id in self.blockCount, "Can't add external block to a node that isn't pending" + self.externalBlocks += 1 + self.blockCount[node_id] += 1 + def unblock(): + self.externalBlocks -= 1 + self.blockCount[node_id] -= 1 + self.unblockedEvent.set() + return unblock + def is_cached(self, node_id): return False @@ -159,15 +195,45 @@ class ExecutionList(TopologicalSort): super().__init__(dynprompt) self.output_cache = output_cache self.staged_node_id = None + self.execution_cache = {} + self.execution_cache_listeners = {} def is_cached(self, node_id): return self.output_cache.get(node_id) is not None - def stage_node_execution(self): + def cache_link(self, from_node_id, to_node_id): + if not to_node_id in self.execution_cache: + self.execution_cache[to_node_id] = {} + self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id) + if not from_node_id in self.execution_cache_listeners: + self.execution_cache_listeners[from_node_id] = set() + self.execution_cache_listeners[from_node_id].add(to_node_id) + + def get_output_cache(self, from_node_id, to_node_id): + if not to_node_id in self.execution_cache: + return None + return self.execution_cache[to_node_id].get(from_node_id) + + def cache_update(self, node_id, value): + if node_id in self.execution_cache_listeners: + for to_node_id in self.execution_cache_listeners[node_id]: + if to_node_id in self.execution_cache: + self.execution_cache[to_node_id][node_id] = value + + def add_strong_link(self, from_node_id, from_socket, to_node_id): + super().add_strong_link(from_node_id, from_socket, to_node_id) + self.cache_link(from_node_id, to_node_id) + + async def stage_node_execution(self): assert self.staged_node_id is None if self.is_empty(): return None, None, None available = self.get_ready_nodes() + while len(available) == 0 and self.externalBlocks > 0: + # Wait for an external block to be released + await self.unblockedEvent.wait() + self.unblockedEvent.clear() + available = self.get_ready_nodes() if len(available) == 0: cycled_nodes = self.get_nodes_in_cycle() # Because cycles composed entirely of static nodes are caught during initial validation, @@ -203,8 +269,15 @@ class ExecutionList(TopologicalSort): return True return False + # If an available node is async, do that first. + # This will execute the asynchronous function earlier, reducing the overall time. + def is_async(node_id): + class_type = self.dynprompt.get_node(node_id)["class_type"] + class_def = nodes.NODE_CLASS_MAPPINGS[class_type] + return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION)) + for node_id in node_list: - if is_output(node_id): + if is_output(node_id) or is_async(node_id): return node_id #This should handle the VAEDecode -> preview case @@ -230,6 +303,8 @@ class ExecutionList(TopologicalSort): def complete_node_execution(self): node_id = self.staged_node_id self.pop_node(node_id) + self.execution_cache.pop(node_id, None) + self.execution_cache_listeners.pop(node_id, None) self.staged_node_id = None def get_nodes_in_cycle(self): @@ -250,21 +325,3 @@ class ExecutionList(TopologicalSort): del blocked_by[node_id] to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] return list(blocked_by.keys()) - -class ExecutionBlocker: - """ - Return this from a node and any users will be blocked with the given error message. - If the message is None, execution will be blocked silently instead. - Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's - possible, a lazy input will be more efficient and have a better user experience. - This functionality is useful in two cases: - 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node - like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using - lazy evaluation to let it conditionally disable itself.) - 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. - (I would recommend not making nodes like this in the future -- instead, make multiple nodes with - different outputs. Unfortunately, there are several popular existing nodes using this pattern.) - """ - def __init__(self, message): - self.message = message - diff --git a/comfy_execution/graph_utils.py b/comfy_execution/graph_utils.py index 8595e942d..496d2c634 100644 --- a/comfy_execution/graph_utils.py +++ b/comfy_execution/graph_utils.py @@ -137,3 +137,19 @@ def add_graph_prefix(graph, outputs, prefix): return new_graph, tuple(new_outputs) +class ExecutionBlocker: + """ + Return this from a node and any users will be blocked with the given error message. + If the message is None, execution will be blocked silently instead. + Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's + possible, a lazy input will be more efficient and have a better user experience. + This functionality is useful in two cases: + 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node + like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using + lazy evaluation to let it conditionally disable itself.) + 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. + (I would recommend not making nodes like this in the future -- instead, make multiple nodes with + different outputs. Unfortunately, there are several popular existing nodes using this pattern.) + """ + def __init__(self, message): + self.message = message diff --git a/comfy_execution/progress.py b/comfy_execution/progress.py new file mode 100644 index 000000000..f951a3350 --- /dev/null +++ b/comfy_execution/progress.py @@ -0,0 +1,350 @@ +from __future__ import annotations + +from typing import TypedDict, Dict, Optional, Tuple +from typing_extensions import override +from PIL import Image +from enum import Enum +from abc import ABC +from tqdm import tqdm +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from comfy_execution.graph import DynamicPrompt +from protocol import BinaryEventTypes +from comfy_api import feature_flags + +PreviewImageTuple = Tuple[str, Image.Image, Optional[int]] + +class NodeState(Enum): + Pending = "pending" + Running = "running" + Finished = "finished" + Error = "error" + + +class NodeProgressState(TypedDict): + """ + A class to represent the state of a node's progress. + """ + + state: NodeState + value: float + max: float + + +class ProgressHandler(ABC): + """ + Abstract base class for progress handlers. + Progress handlers receive progress updates and display them in various ways. + """ + + def __init__(self, name: str): + self.name = name + self.enabled = True + + def set_registry(self, registry: "ProgressRegistry"): + pass + + def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + """Called when a node starts processing""" + pass + + def update_handler( + self, + node_id: str, + value: float, + max_value: float, + state: NodeProgressState, + prompt_id: str, + image: PreviewImageTuple | None = None, + ): + """Called when a node's progress is updated""" + pass + + def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + """Called when a node finishes processing""" + pass + + def reset(self): + """Called when the progress registry is reset""" + pass + + def enable(self): + """Enable this handler""" + self.enabled = True + + def disable(self): + """Disable this handler""" + self.enabled = False + + +class CLIProgressHandler(ProgressHandler): + """ + Handler that displays progress using tqdm progress bars in the CLI. + """ + + def __init__(self): + super().__init__("cli") + self.progress_bars: Dict[str, tqdm] = {} + + @override + def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + # Create a new tqdm progress bar + if node_id not in self.progress_bars: + self.progress_bars[node_id] = tqdm( + total=state["max"], + desc=f"Node {node_id}", + unit="steps", + leave=True, + position=len(self.progress_bars), + ) + + @override + def update_handler( + self, + node_id: str, + value: float, + max_value: float, + state: NodeProgressState, + prompt_id: str, + image: PreviewImageTuple | None = None, + ): + # Handle case where start_handler wasn't called + if node_id not in self.progress_bars: + self.progress_bars[node_id] = tqdm( + total=max_value, + desc=f"Node {node_id}", + unit="steps", + leave=True, + position=len(self.progress_bars), + ) + self.progress_bars[node_id].update(value) + else: + # Update existing progress bar + if max_value != self.progress_bars[node_id].total: + self.progress_bars[node_id].total = max_value + # Calculate the update amount (difference from current position) + current_position = self.progress_bars[node_id].n + update_amount = value - current_position + if update_amount > 0: + self.progress_bars[node_id].update(update_amount) + + @override + def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + # Complete and close the progress bar if it exists + if node_id in self.progress_bars: + # Ensure the bar shows 100% completion + remaining = state["max"] - self.progress_bars[node_id].n + if remaining > 0: + self.progress_bars[node_id].update(remaining) + self.progress_bars[node_id].close() + del self.progress_bars[node_id] + + @override + def reset(self): + # Close all progress bars + for bar in self.progress_bars.values(): + bar.close() + self.progress_bars.clear() + + +class WebUIProgressHandler(ProgressHandler): + """ + Handler that sends progress updates to the WebUI via WebSockets. + """ + + def __init__(self, server_instance): + super().__init__("webui") + self.server_instance = server_instance + + def set_registry(self, registry: "ProgressRegistry"): + self.registry = registry + + def _send_progress_state(self, prompt_id: str, nodes: Dict[str, NodeProgressState]): + """Send the current progress state to the client""" + if self.server_instance is None: + return + + # Only send info for non-pending nodes + active_nodes = { + node_id: { + "value": state["value"], + "max": state["max"], + "state": state["state"].value, + "node_id": node_id, + "prompt_id": prompt_id, + "display_node_id": self.registry.dynprompt.get_display_node_id(node_id), + "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id), + "real_node_id": self.registry.dynprompt.get_real_node_id(node_id), + } + for node_id, state in nodes.items() + if state["state"] != NodeState.Pending + } + + # Send a combined progress_state message with all node states + # Include client_id to ensure message is only sent to the initiating client + self.server_instance.send_sync( + "progress_state", {"prompt_id": prompt_id, "nodes": active_nodes}, self.server_instance.client_id + ) + + @override + def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + # Send progress state of all nodes + if self.registry: + self._send_progress_state(prompt_id, self.registry.nodes) + + @override + def update_handler( + self, + node_id: str, + value: float, + max_value: float, + state: NodeProgressState, + prompt_id: str, + image: PreviewImageTuple | None = None, + ): + # Send progress state of all nodes + if self.registry: + self._send_progress_state(prompt_id, self.registry.nodes) + if image: + # Only send new format if client supports it + if feature_flags.supports_feature( + self.server_instance.sockets_metadata, + self.server_instance.client_id, + "supports_preview_metadata", + ): + metadata = { + "node_id": node_id, + "prompt_id": prompt_id, + "display_node_id": self.registry.dynprompt.get_display_node_id( + node_id + ), + "parent_node_id": self.registry.dynprompt.get_parent_node_id( + node_id + ), + "real_node_id": self.registry.dynprompt.get_real_node_id(node_id), + } + self.server_instance.send_sync( + BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA, + (image, metadata), + self.server_instance.client_id, + ) + + @override + def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str): + # Send progress state of all nodes + if self.registry: + self._send_progress_state(prompt_id, self.registry.nodes) + +class ProgressRegistry: + """ + Registry that maintains node progress state and notifies registered handlers. + """ + + def __init__(self, prompt_id: str, dynprompt: "DynamicPrompt"): + self.prompt_id = prompt_id + self.dynprompt = dynprompt + self.nodes: Dict[str, NodeProgressState] = {} + self.handlers: Dict[str, ProgressHandler] = {} + + def register_handler(self, handler: ProgressHandler) -> None: + """Register a progress handler""" + self.handlers[handler.name] = handler + + def unregister_handler(self, handler_name: str) -> None: + """Unregister a progress handler""" + if handler_name in self.handlers: + # Allow handler to clean up resources + self.handlers[handler_name].reset() + del self.handlers[handler_name] + + def enable_handler(self, handler_name: str) -> None: + """Enable a progress handler""" + if handler_name in self.handlers: + self.handlers[handler_name].enable() + + def disable_handler(self, handler_name: str) -> None: + """Disable a progress handler""" + if handler_name in self.handlers: + self.handlers[handler_name].disable() + + def ensure_entry(self, node_id: str) -> NodeProgressState: + """Ensure a node entry exists""" + if node_id not in self.nodes: + self.nodes[node_id] = NodeProgressState( + state=NodeState.Pending, value=0, max=1 + ) + return self.nodes[node_id] + + def start_progress(self, node_id: str) -> None: + """Start progress tracking for a node""" + entry = self.ensure_entry(node_id) + entry["state"] = NodeState.Running + entry["value"] = 0.0 + entry["max"] = 1.0 + + # Notify all enabled handlers + for handler in self.handlers.values(): + if handler.enabled: + handler.start_handler(node_id, entry, self.prompt_id) + + def update_progress( + self, node_id: str, value: float, max_value: float, image: PreviewImageTuple | None = None + ) -> None: + """Update progress for a node""" + entry = self.ensure_entry(node_id) + entry["state"] = NodeState.Running + entry["value"] = value + entry["max"] = max_value + + # Notify all enabled handlers + for handler in self.handlers.values(): + if handler.enabled: + handler.update_handler( + node_id, value, max_value, entry, self.prompt_id, image + ) + + def finish_progress(self, node_id: str) -> None: + """Finish progress tracking for a node""" + entry = self.ensure_entry(node_id) + entry["state"] = NodeState.Finished + entry["value"] = entry["max"] + + # Notify all enabled handlers + for handler in self.handlers.values(): + if handler.enabled: + handler.finish_handler(node_id, entry, self.prompt_id) + + def reset_handlers(self) -> None: + """Reset all handlers""" + for handler in self.handlers.values(): + handler.reset() + +# Global registry instance +global_progress_registry: ProgressRegistry | None = None + +def reset_progress_state(prompt_id: str, dynprompt: "DynamicPrompt") -> None: + global global_progress_registry + + # Reset existing handlers if registry exists + if global_progress_registry is not None: + global_progress_registry.reset_handlers() + + # Create new registry + global_progress_registry = ProgressRegistry(prompt_id, dynprompt) + + +def add_progress_handler(handler: ProgressHandler) -> None: + registry = get_progress_state() + handler.set_registry(registry) + registry.register_handler(handler) + + +def get_progress_state() -> ProgressRegistry: + global global_progress_registry + if global_progress_registry is None: + from comfy_execution.graph import DynamicPrompt + + global_progress_registry = ProgressRegistry( + prompt_id="", dynprompt=DynamicPrompt({}) + ) + return global_progress_registry diff --git a/comfy_execution/utils.py b/comfy_execution/utils.py new file mode 100644 index 000000000..62d32f101 --- /dev/null +++ b/comfy_execution/utils.py @@ -0,0 +1,46 @@ +import contextvars +from typing import Optional, NamedTuple + +class ExecutionContext(NamedTuple): + """ + Context information about the currently executing node. + + Attributes: + node_id: The ID of the currently executing node + list_index: The index in a list being processed (for operations on batches/lists) + """ + prompt_id: str + node_id: str + list_index: Optional[int] + +current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None) + +def get_executing_context() -> Optional[ExecutionContext]: + return current_executing_context.get(None) + +class CurrentNodeContext: + """ + Context manager for setting the current executing node context. + + Sets the current_executing_context on enter and resets it on exit. + + Example: + with CurrentNodeContext(node_id="123", list_index=0): + # Code that should run with the current node context set + process_image() + """ + def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None): + self.context = ExecutionContext( + prompt_id= prompt_id, + node_id= node_id, + list_index= list_index + ) + self.token = None + + def __enter__(self): + self.token = current_executing_context.set(self.context) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.token is not None: + current_executing_context.reset(self.token) diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py new file mode 100644 index 000000000..1409233c9 --- /dev/null +++ b/comfy_extras/nodes_ace.py @@ -0,0 +1,63 @@ +import torch +from typing_extensions import override + +import comfy.model_management +import node_helpers +from comfy_api.latest import ComfyExtension, io + + +class TextEncodeAceStepAudio(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextEncodeAceStepAudio", + category="conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("tags", multiline=True, dynamic_prompts=True), + io.String.Input("lyrics", multiline=True, dynamic_prompts=True), + io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[io.Conditioning.Output()], + ) + + @classmethod + def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput: + tokens = clip.tokenize(tags, lyrics=lyrics) + conditioning = clip.encode_from_tokens_scheduled(tokens) + conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength}) + return io.NodeOutput(conditioning) + + +class EmptyAceStepLatentAudio(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="EmptyAceStepLatentAudio", + category="latent/audio", + inputs=[ + io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), + io.Int.Input( + "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch." + ), + ], + outputs=[io.Latent.Output()], + ) + + @classmethod + def execute(cls, seconds, batch_size) -> io.NodeOutput: + length = int(seconds * 44100 / 512 / 8) + latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples": latent, "type": "audio"}) + + +class AceExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TextEncodeAceStepAudio, + EmptyAceStepLatentAudio, + ] + +async def comfy_entrypoint() -> AceExtension: + return AceExtension() diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py index 5fbb096fb..5532ffe6a 100644 --- a/comfy_extras/nodes_advanced_samplers.py +++ b/comfy_extras/nodes_advanced_samplers.py @@ -1,8 +1,13 @@ +import numpy as np +import torch +from tqdm.auto import trange +from typing_extensions import override + +import comfy.model_patcher import comfy.samplers import comfy.utils -import torch -import numpy as np -from tqdm.auto import trange +from comfy.k_diffusion.sampling import to_d +from comfy_api.latest import ComfyExtension, io @torch.no_grad() @@ -33,30 +38,29 @@ def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable return x -class SamplerLCMUpscale: - upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] +class SamplerLCMUpscale(io.ComfyNode): + UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"] @classmethod - def INPUT_TYPES(s): - return {"required": - {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}), - "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}), - "upscale_method": (s.upscale_methods,), - } - } - RETURN_TYPES = ("SAMPLER",) - CATEGORY = "sampling/custom_sampling/samplers" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="SamplerLCMUpscale", + category="sampling/custom_sampling/samplers", + inputs=[ + io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01), + io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1), + io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS), + ], + outputs=[io.Sampler.Output()], + ) - FUNCTION = "get_sampler" - - def get_sampler(self, scale_ratio, scale_steps, upscale_method): + @classmethod + def execute(cls, scale_ratio, scale_steps, upscale_method) -> io.NodeOutput: if scale_steps < 0: scale_steps = None sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method}) - return (sampler, ) + return io.NodeOutput(sampler) -from comfy.k_diffusion.sampling import to_d -import comfy.model_patcher @torch.no_grad() def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None): @@ -82,30 +86,36 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No return x -class SamplerEulerCFGpp: +class SamplerEulerCFGpp(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"version": (["regular", "alternative"],),} - } - RETURN_TYPES = ("SAMPLER",) - # CATEGORY = "sampling/custom_sampling/samplers" - CATEGORY = "_for_testing" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="SamplerEulerCFGpp", + display_name="SamplerEulerCFG++", + category="_for_testing", # "sampling/custom_sampling/samplers" + inputs=[ + io.Combo.Input("version", options=["regular", "alternative"]), + ], + outputs=[io.Sampler.Output()], + is_experimental=True, + ) - FUNCTION = "get_sampler" - - def get_sampler(self, version): + @classmethod + def execute(cls, version) -> io.NodeOutput: if version == "alternative": sampler = comfy.samplers.KSAMPLER(sample_euler_pp) else: sampler = comfy.samplers.ksampler("euler_cfg_pp") - return (sampler, ) + return io.NodeOutput(sampler) -NODE_CLASS_MAPPINGS = { - "SamplerLCMUpscale": SamplerLCMUpscale, - "SamplerEulerCFGpp": SamplerEulerCFGpp, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "SamplerEulerCFGpp": "SamplerEulerCFG++", -} +class AdvancedSamplersExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SamplerLCMUpscale, + SamplerEulerCFGpp, + ] + +async def comfy_entrypoint() -> AdvancedSamplersExtension: + return AdvancedSamplersExtension() diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py index 8d856d0e8..edd5dadd4 100644 --- a/comfy_extras/nodes_align_your_steps.py +++ b/comfy_extras/nodes_align_your_steps.py @@ -1,6 +1,10 @@ #from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html import numpy as np import torch +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + def loglinear_interp(t_steps, num_steps): """ @@ -19,25 +23,30 @@ NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.694615152 "SDXL":[14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} -class AlignYourStepsScheduler: +class AlignYourStepsScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model_type": (["SD1", "SDXL", "SVD"], ), - "steps": ("INT", {"default": 10, "min": 1, "max": 10000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="AlignYourStepsScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]), + io.Int.Input("steps", default=10, min=1, max=10000), + io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[io.Sigmas.Output()], + ) def get_sigmas(self, model_type, steps, denoise): + # Deprecated: use the V3 schema's `execute` method instead of this. + return AlignYourStepsScheduler().execute(model_type, steps, denoise).result + + @classmethod + def execute(cls, model_type, steps, denoise) -> io.NodeOutput: total_steps = steps if denoise < 1.0: if denoise <= 0.0: - return (torch.FloatTensor([]),) + return io.NodeOutput(torch.FloatTensor([])) total_steps = round(steps * denoise) sigmas = NOISE_LEVELS[model_type][:] @@ -46,8 +55,15 @@ class AlignYourStepsScheduler: sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 - return (torch.FloatTensor(sigmas), ) + return io.NodeOutput(torch.FloatTensor(sigmas)) -NODE_CLASS_MAPPINGS = { - "AlignYourStepsScheduler": AlignYourStepsScheduler, -} + +class AlignYourStepsExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + AlignYourStepsScheduler, + ] + +async def comfy_entrypoint() -> AlignYourStepsExtension: + return AlignYourStepsExtension() diff --git a/comfy_extras/nodes_apg.py b/comfy_extras/nodes_apg.py new file mode 100644 index 000000000..f27ae7da8 --- /dev/null +++ b/comfy_extras/nodes_apg.py @@ -0,0 +1,106 @@ +import torch +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + + +def project(v0, v1): + v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3]) + v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1 + v0_orthogonal = v0 - v0_parallel + return v0_parallel, v0_orthogonal + +class APG(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="APG", + display_name="Adaptive Projected Guidance", + category="sampling/custom_sampling", + inputs=[ + io.Model.Input("model"), + io.Float.Input( + "eta", + default=1.0, + min=-10.0, + max=10.0, + step=0.01, + tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.", + ), + io.Float.Input( + "norm_threshold", + default=5.0, + min=0.0, + max=50.0, + step=0.1, + tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.", + ), + io.Float.Input( + "momentum", + default=0.0, + min=-5.0, + max=1.0, + step=0.01, + tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.", + ), + ], + outputs=[io.Model.Output()], + ) + + @classmethod + def execute(cls, model, eta, norm_threshold, momentum) -> io.NodeOutput: + running_avg = 0 + prev_sigma = None + + def pre_cfg_function(args): + nonlocal running_avg, prev_sigma + + if len(args["conds_out"]) == 1: return args["conds_out"] + + cond = args["conds_out"][0] + uncond = args["conds_out"][1] + sigma = args["sigma"][0] + cond_scale = args["cond_scale"] + + if prev_sigma is not None and sigma > prev_sigma: + running_avg = 0 + prev_sigma = sigma + + guidance = cond - uncond + + if momentum != 0: + if not torch.is_tensor(running_avg): + running_avg = guidance + else: + running_avg = momentum * running_avg + guidance + guidance = running_avg + + if norm_threshold > 0: + guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True) + scale = torch.minimum( + torch.ones_like(guidance_norm), + norm_threshold / guidance_norm + ) + guidance = guidance * scale + + guidance_parallel, guidance_orthogonal = project(guidance, cond) + modified_guidance = guidance_orthogonal + eta * guidance_parallel + + modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale + + return [modified_cond, uncond] + args["conds_out"][2:] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(pre_cfg_function) + return io.NodeOutput(m) + + +class ApgExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + APG, + ] + +async def comfy_entrypoint() -> ApgExtension: + return ApgExtension() diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py index 4747eb395..c0e494c2a 100644 --- a/comfy_extras/nodes_attention_multiply.py +++ b/comfy_extras/nodes_attention_multiply.py @@ -1,3 +1,7 @@ +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + def attention_multiply(attn, model, q, k, v, out): m = model.clone() @@ -16,57 +20,71 @@ def attention_multiply(attn, model, q, k, v, out): return m -class UNetSelfAttentionMultiply: +class UNetSelfAttentionMultiply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="UNetSelfAttentionMultiply", + category="_for_testing/attention_experiments", + inputs=[ + io.Model.Input("model"), + io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[io.Model.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/attention_experiments" - - def patch(self, model, q, k, v, out): + @classmethod + def execute(cls, model, q, k, v, out) -> io.NodeOutput: m = attention_multiply("attn1", model, q, k, v, out) - return (m, ) + return io.NodeOutput(m) -class UNetCrossAttentionMultiply: + +class UNetCrossAttentionMultiply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="UNetCrossAttentionMultiply", + category="_for_testing/attention_experiments", + inputs=[ + io.Model.Input("model"), + io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[io.Model.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/attention_experiments" - - def patch(self, model, q, k, v, out): + @classmethod + def execute(cls, model, q, k, v, out) -> io.NodeOutput: m = attention_multiply("attn2", model, q, k, v, out) - return (m, ) + return io.NodeOutput(m) -class CLIPAttentionMultiply: + +class CLIPAttentionMultiply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "clip": ("CLIP",), - "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "patch" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CLIPAttentionMultiply", + category="_for_testing/attention_experiments", + inputs=[ + io.Clip.Input("clip"), + io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[io.Clip.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/attention_experiments" - - def patch(self, clip, q, k, v, out): + @classmethod + def execute(cls, clip, q, k, v, out) -> io.NodeOutput: m = clip.clone() sd = m.patcher.model_state_dict() @@ -79,23 +97,28 @@ class CLIPAttentionMultiply: m.add_patches({key: (None,)}, 0.0, v) if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"): m.add_patches({key: (None,)}, 0.0, out) - return (m, ) + return io.NodeOutput(m) -class UNetTemporalAttentionMultiply: + +class UNetTemporalAttentionMultiply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="UNetTemporalAttentionMultiply", + category="_for_testing/attention_experiments", + inputs=[ + io.Model.Input("model"), + io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[io.Model.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/attention_experiments" - - def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal): + @classmethod + def execute(cls, model, self_structural, self_temporal, cross_structural, cross_temporal) -> io.NodeOutput: m = model.clone() sd = model.model_state_dict() @@ -110,11 +133,18 @@ class UNetTemporalAttentionMultiply: m.add_patches({k: (None,)}, 0.0, cross_temporal) else: m.add_patches({k: (None,)}, 0.0, cross_structural) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply, - "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply, - "CLIPAttentionMultiply": CLIPAttentionMultiply, - "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply, -} + +class AttentionMultiplyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + UNetSelfAttentionMultiply, + UNetCrossAttentionMultiply, + CLIPAttentionMultiply, + UNetTemporalAttentionMultiply, + ] + +async def comfy_entrypoint() -> AttentionMultiplyExtension: + return AttentionMultiplyExtension() diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index 3cb918e09..2ed7e0b22 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -1,3 +1,6 @@ +from __future__ import annotations + +import av import torchaudio import torch import comfy.model_management @@ -5,11 +8,12 @@ import folder_paths import os import io import json -import struct import random import hashlib import node_helpers +import logging from comfy.cli_args import args +from comfy.comfy_types import FileLocator class EmptyLatentAudio: def __init__(self): @@ -87,59 +91,107 @@ class VAEDecodeAudio: return ({"waveform": audio, "sample_rate": 44100}, ) -def create_vorbis_comment_block(comment_dict, last_block): - vendor_string = b'ComfyUI' - vendor_length = len(vendor_string) +def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"): - comments = [] - for key, value in comment_dict.items(): - comment = f"{key}={value}".encode('utf-8') - comments.append(struct.pack('I', len(comment_data))[1:] + comment_data + # Opus supported sample rates + OPUS_RATES = [8000, 12000, 16000, 24000, 48000] - return comment_block + for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.{format}" + output_path = os.path.join(full_output_folder, file) -def insert_or_replace_vorbis_comment(flac_io, comment_dict): - if len(comment_dict) == 0: - return flac_io + # Use original sample rate initially + sample_rate = audio["sample_rate"] - flac_io.seek(4) + # Handle Opus sample rate requirements + if format == "opus": + if sample_rate > 48000: + sample_rate = 48000 + elif sample_rate not in OPUS_RATES: + # Find the next highest supported rate + for rate in sorted(OPUS_RATES): + if rate > sample_rate: + sample_rate = rate + break + if sample_rate not in OPUS_RATES: # Fallback if still not supported + sample_rate = 48000 - blocks = [] - last_block = False + # Resample if necessary + if sample_rate != audio["sample_rate"]: + waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate) - while not last_block: - header = flac_io.read(4) - last_block = (header[0] & 0x80) != 0 - block_type = header[0] & 0x7F - block_length = struct.unpack('>I', b'\x00' + header[1:])[0] - block_data = flac_io.read(block_length) + # Create output with specified format + output_buffer = io.BytesIO() + output_container = av.open(output_buffer, mode='w', format=format) - if block_type == 4 or block_type == 1: - pass - else: - header = bytes([(header[0] & (~0x80))]) + header[1:] - blocks.append(header + block_data) + # Set metadata on the container + for key, value in metadata.items(): + output_container.metadata[key] = value - blocks.append(create_vorbis_comment_block(comment_dict, last_block=True)) + layout = 'mono' if waveform.shape[0] == 1 else 'stereo' + # Set up the output stream with appropriate properties + if format == "opus": + out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout) + if quality == "64k": + out_stream.bit_rate = 64000 + elif quality == "96k": + out_stream.bit_rate = 96000 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "192k": + out_stream.bit_rate = 192000 + elif quality == "320k": + out_stream.bit_rate = 320000 + elif format == "mp3": + out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout) + if quality == "V0": + #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool + out_stream.codec_context.qscale = 1 + elif quality == "128k": + out_stream.bit_rate = 128000 + elif quality == "320k": + out_stream.bit_rate = 320000 + else: #format == "flac": + out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout) - new_flac_io = io.BytesIO() - new_flac_io.write(b'fLaC') - for block in blocks: - new_flac_io.write(block) + frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout=layout) + frame.sample_rate = sample_rate + frame.pts = 0 + output_container.mux(out_stream.encode(frame)) - new_flac_io.write(flac_io.read()) - return new_flac_io + # Flush encoder + output_container.mux(out_stream.encode(None)) + # Close containers + output_container.close() + + # Write the output to file + output_buffer.seek(0) + with open(output_path, 'wb') as f: + f.write(output_buffer.getbuffer()) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "audio": results } } class SaveAudio: def __init__(self): @@ -150,50 +202,70 @@ class SaveAudio: @classmethod def INPUT_TYPES(s): return {"required": { "audio": ("AUDIO", ), - "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})}, + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } RETURN_TYPES = () - FUNCTION = "save_audio" + FUNCTION = "save_flac" OUTPUT_NODE = True CATEGORY = "audio" - def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): - filename_prefix += self.prefix_append - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) - results = list() + def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo) - metadata = {} - if not args.disable_metadata: - if prompt is not None: - metadata["prompt"] = json.dumps(prompt) - if extra_pnginfo is not None: - for x in extra_pnginfo: - metadata[x] = json.dumps(extra_pnginfo[x]) +class SaveAudioMP3: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" - for (batch_number, waveform) in enumerate(audio["waveform"].cpu()): - filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) - file = f"{filename_with_batch_num}_{counter:05}_.flac" + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["V0", "128k", "320k"], {"default": "V0"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } - buff = io.BytesIO() - torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC") + RETURN_TYPES = () + FUNCTION = "save_mp3" - buff = insert_or_replace_vorbis_comment(buff, metadata) + OUTPUT_NODE = True - with open(os.path.join(full_output_folder, file), 'wb') as f: - f.write(buff.getbuffer()) + CATEGORY = "audio" - results.append({ - "filename": file, - "subfolder": subfolder, - "type": self.type - }) - counter += 1 + def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) - return { "ui": { "audio": results } } +class SaveAudioOpus: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "audio": ("AUDIO", ), + "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}), + "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_opus" + + OUTPUT_NODE = True + + CATEGORY = "audio" + + def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"): + return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality) class PreviewAudio(SaveAudio): def __init__(self): @@ -208,6 +280,42 @@ class PreviewAudio(SaveAudio): "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, } +def f32_pcm(wav: torch.Tensor) -> torch.Tensor: + """Convert audio to float 32 bits PCM format.""" + if wav.dtype.is_floating_point: + return wav + elif wav.dtype == torch.int16: + return wav.float() / (2 ** 15) + elif wav.dtype == torch.int32: + return wav.float() / (2 ** 31) + raise ValueError(f"Unsupported wav dtype: {wav.dtype}") + +def load(filepath: str) -> tuple[torch.Tensor, int]: + with av.open(filepath) as af: + if not af.streams.audio: + raise ValueError("No audio stream found in the file.") + + stream = af.streams.audio[0] + sr = stream.codec_context.sample_rate + n_channels = stream.channels + + frames = [] + length = 0 + for frame in af.decode(streams=stream.index): + buf = torch.from_numpy(frame.to_ndarray()) + if buf.shape[0] != n_channels: + buf = buf.view(-1, n_channels).t() + + frames.append(buf) + length += buf.shape[1] + + if not frames: + raise ValueError("No audio frames decoded.") + + wav = torch.cat(frames, dim=1) + wav = f32_pcm(wav) + return wav, sr + class LoadAudio: @classmethod def INPUT_TYPES(s): @@ -222,7 +330,7 @@ class LoadAudio: def load(self, audio): audio_path = folder_paths.get_annotated_filepath(audio) - waveform, sample_rate = torchaudio.load(audio_path) + waveform, sample_rate = load(audio_path) audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} return (audio, ) @@ -240,12 +348,267 @@ class LoadAudio: return "Invalid audio file: {}".format(audio) return True +class RecordAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": {"audio": ("AUDIO_RECORD", {})}} + + CATEGORY = "audio" + + RETURN_TYPES = ("AUDIO", ) + FUNCTION = "load" + + def load(self, audio): + audio_path = folder_paths.get_annotated_filepath(audio) + + waveform, sample_rate = load(audio_path) + audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate} + return (audio, ) + + +class TrimAudioDuration: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "audio": ("AUDIO",), + "start_index": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Start time in seconds, can be negative to count from the end (supports sub-seconds)."}), + "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "step": 0.01, "tooltip": "Duration in seconds"}), + }, + } + + FUNCTION = "trim" + RETURN_TYPES = ("AUDIO",) + CATEGORY = "audio" + DESCRIPTION = "Trim audio tensor into chosen time range." + + def trim(self, audio, start_index, duration): + waveform = audio["waveform"] + sample_rate = audio["sample_rate"] + audio_length = waveform.shape[-1] + + if start_index < 0: + start_frame = audio_length + int(round(start_index * sample_rate)) + else: + start_frame = int(round(start_index * sample_rate)) + start_frame = max(0, min(start_frame, audio_length - 1)) + + end_frame = start_frame + int(round(duration * sample_rate)) + end_frame = max(0, min(end_frame, audio_length)) + + if start_frame >= end_frame: + raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.") + + return ({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate},) + + +class SplitAudioChannels: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "audio": ("AUDIO",), + }} + + RETURN_TYPES = ("AUDIO", "AUDIO") + RETURN_NAMES = ("left", "right") + FUNCTION = "separate" + CATEGORY = "audio" + DESCRIPTION = "Separates the audio into left and right channels." + + def separate(self, audio): + waveform = audio["waveform"] + sample_rate = audio["sample_rate"] + + if waveform.shape[1] != 2: + raise ValueError("AudioSplit: Input audio has only one channel.") + + left_channel = waveform[..., 0:1, :] + right_channel = waveform[..., 1:2, :] + + return ({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate}) + + +def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2): + if sample_rate_1 != sample_rate_2: + if sample_rate_1 > sample_rate_2: + waveform_2 = torchaudio.functional.resample(waveform_2, sample_rate_2, sample_rate_1) + output_sample_rate = sample_rate_1 + logging.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.") + else: + waveform_1 = torchaudio.functional.resample(waveform_1, sample_rate_1, sample_rate_2) + output_sample_rate = sample_rate_2 + logging.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.") + else: + output_sample_rate = sample_rate_1 + return waveform_1, waveform_2, output_sample_rate + + +class AudioConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "audio1": ("AUDIO",), + "audio2": ("AUDIO",), + "direction": (['after', 'before'], {"default": 'after', "tooltip": "Whether to append audio2 after or before audio1."}), + }} + + RETURN_TYPES = ("AUDIO",) + FUNCTION = "concat" + CATEGORY = "audio" + DESCRIPTION = "Concatenates the audio1 to audio2 in the specified direction." + + def concat(self, audio1, audio2, direction): + waveform_1 = audio1["waveform"] + waveform_2 = audio2["waveform"] + sample_rate_1 = audio1["sample_rate"] + sample_rate_2 = audio2["sample_rate"] + + if waveform_1.shape[1] == 1: + waveform_1 = waveform_1.repeat(1, 2, 1) + logging.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.") + if waveform_2.shape[1] == 1: + waveform_2 = waveform_2.repeat(1, 2, 1) + logging.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.") + + waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2) + + if direction == 'after': + concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2) + elif direction == 'before': + concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2) + + return ({"waveform": concatenated_audio, "sample_rate": output_sample_rate},) + + +class AudioMerge: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "audio1": ("AUDIO",), + "audio2": ("AUDIO",), + "merge_method": (["add", "mean", "subtract", "multiply"], {"tooltip": "The method used to combine the audio waveforms."}), + }, + } + + FUNCTION = "merge" + RETURN_TYPES = ("AUDIO",) + CATEGORY = "audio" + DESCRIPTION = "Combine two audio tracks by overlaying their waveforms." + + def merge(self, audio1, audio2, merge_method): + waveform_1 = audio1["waveform"] + waveform_2 = audio2["waveform"] + sample_rate_1 = audio1["sample_rate"] + sample_rate_2 = audio2["sample_rate"] + + waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2) + + length_1 = waveform_1.shape[-1] + length_2 = waveform_2.shape[-1] + + if length_2 > length_1: + logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.") + waveform_2 = waveform_2[..., :length_1] + elif length_2 < length_1: + logging.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.") + pad_shape = list(waveform_2.shape) + pad_shape[-1] = length_1 - length_2 + pad_tensor = torch.zeros(pad_shape, dtype=waveform_2.dtype, device=waveform_2.device) + waveform_2 = torch.cat((waveform_2, pad_tensor), dim=-1) + + if merge_method == "add": + waveform = waveform_1 + waveform_2 + elif merge_method == "subtract": + waveform = waveform_1 - waveform_2 + elif merge_method == "multiply": + waveform = waveform_1 * waveform_2 + elif merge_method == "mean": + waveform = (waveform_1 + waveform_2) / 2 + + max_val = waveform.abs().max() + if max_val > 1.0: + waveform = waveform / max_val + + return ({"waveform": waveform, "sample_rate": output_sample_rate},) + + +class AudioAdjustVolume: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "audio": ("AUDIO",), + "volume": ("INT", {"default": 1.0, "min": -100, "max": 100, "tooltip": "Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc"}), + }} + + RETURN_TYPES = ("AUDIO",) + FUNCTION = "adjust_volume" + CATEGORY = "audio" + + def adjust_volume(self, audio, volume): + if volume == 0: + return (audio,) + waveform = audio["waveform"] + sample_rate = audio["sample_rate"] + + gain = 10 ** (volume / 20) + waveform = waveform * gain + + return ({"waveform": waveform, "sample_rate": sample_rate},) + + +class EmptyAudio: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Duration of the empty audio clip in seconds"}), + "sample_rate": ("INT", {"default": 44100, "tooltip": "Sample rate of the empty audio clip."}), + "channels": ("INT", {"default": 2, "min": 1, "max": 2, "tooltip": "Number of audio channels (1 for mono, 2 for stereo)."}), + }} + + RETURN_TYPES = ("AUDIO",) + FUNCTION = "create_empty_audio" + CATEGORY = "audio" + + def create_empty_audio(self, duration, sample_rate, channels): + num_samples = int(round(duration * sample_rate)) + waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32) + return ({"waveform": waveform, "sample_rate": sample_rate},) + + NODE_CLASS_MAPPINGS = { "EmptyLatentAudio": EmptyLatentAudio, "VAEEncodeAudio": VAEEncodeAudio, "VAEDecodeAudio": VAEDecodeAudio, "SaveAudio": SaveAudio, + "SaveAudioMP3": SaveAudioMP3, + "SaveAudioOpus": SaveAudioOpus, "LoadAudio": LoadAudio, "PreviewAudio": PreviewAudio, "ConditioningStableAudio": ConditioningStableAudio, + "RecordAudio": RecordAudio, + "TrimAudioDuration": TrimAudioDuration, + "SplitAudioChannels": SplitAudioChannels, + "AudioConcat": AudioConcat, + "AudioMerge": AudioMerge, + "AudioAdjustVolume": AudioAdjustVolume, + "EmptyAudio": EmptyAudio, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "EmptyLatentAudio": "Empty Latent Audio", + "VAEEncodeAudio": "VAE Encode Audio", + "VAEDecodeAudio": "VAE Decode Audio", + "PreviewAudio": "Preview Audio", + "LoadAudio": "Load Audio", + "SaveAudio": "Save Audio (FLAC)", + "SaveAudioMP3": "Save Audio (MP3)", + "SaveAudioOpus": "Save Audio (Opus)", + "RecordAudio": "Record Audio", + "TrimAudioDuration": "Trim Audio Duration", + "SplitAudioChannels": "Split Audio Channels", + "AudioConcat": "Audio Concat", + "AudioMerge": "Audio Merge", + "AudioAdjustVolume": "Audio Adjust Volume", + "EmptyAudio": "Empty Audio", } diff --git a/comfy_extras/nodes_audio_encoder.py b/comfy_extras/nodes_audio_encoder.py new file mode 100644 index 000000000..13aacd41a --- /dev/null +++ b/comfy_extras/nodes_audio_encoder.py @@ -0,0 +1,62 @@ +import folder_paths +import comfy.audio_encoders.audio_encoders +import comfy.utils +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +class AudioEncoderLoader(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="AudioEncoderLoader", + category="loaders", + inputs=[ + io.Combo.Input( + "audio_encoder_name", + options=folder_paths.get_filename_list("audio_encoders"), + ), + ], + outputs=[io.AudioEncoder.Output()], + ) + + @classmethod + def execute(cls, audio_encoder_name) -> io.NodeOutput: + audio_encoder_name = folder_paths.get_full_path_or_raise("audio_encoders", audio_encoder_name) + sd = comfy.utils.load_torch_file(audio_encoder_name, safe_load=True) + audio_encoder = comfy.audio_encoders.audio_encoders.load_audio_encoder_from_sd(sd) + if audio_encoder is None: + raise RuntimeError("ERROR: audio encoder file is invalid and does not contain a valid model.") + return io.NodeOutput(audio_encoder) + + +class AudioEncoderEncode(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="AudioEncoderEncode", + category="conditioning", + inputs=[ + io.AudioEncoder.Input("audio_encoder"), + io.Audio.Input("audio"), + ], + outputs=[io.AudioEncoderOutput.Output()], + ) + + @classmethod + def execute(cls, audio_encoder, audio) -> io.NodeOutput: + output = audio_encoder.encode_audio(audio["waveform"], audio["sample_rate"]) + return io.NodeOutput(output) + + +class AudioEncoder(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + AudioEncoderLoader, + AudioEncoderEncode, + ] + + +async def comfy_entrypoint() -> AudioEncoder: + return AudioEncoder() diff --git a/comfy_extras/nodes_camera_trajectory.py b/comfy_extras/nodes_camera_trajectory.py new file mode 100644 index 000000000..eb7ef363c --- /dev/null +++ b/comfy_extras/nodes_camera_trajectory.py @@ -0,0 +1,239 @@ +import nodes +import torch +import numpy as np +from einops import rearrange +from typing_extensions import override +import comfy.model_management + +from comfy_api.latest import ComfyExtension, io + + +CAMERA_DICT = { + "base_T_norm": 1.5, + "base_angle": np.pi/3, + "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]}, + "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]}, + "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]}, + "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]}, + "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]}, + "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]}, + "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]}, + "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]}, + "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]}, +} + + +def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): + + def get_relative_pose(cam_params): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] + abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] + cam_to_origin = 0 + target_cam_c2w = np.array([ + [1, 0, 0, 0], + [0, 1, 0, -cam_to_origin], + [0, 0, 1, 0], + [0, 0, 0, 1] + ]) + abs2rel = target_cam_c2w @ abs_w2cs[0] + ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] + ret_poses = np.array(ret_poses, dtype=np.float32) + return ret_poses + + """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + cam_params = [Camera(cam_param) for cam_param in cam_params] + + sample_wh_ratio = width / height + pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed + + if pose_wh_ratio > sample_wh_ratio: + resized_ori_w = height * pose_wh_ratio + for cam_param in cam_params: + cam_param.fx = resized_ori_w * cam_param.fx / width + else: + resized_ori_h = width / pose_wh_ratio + for cam_param in cam_params: + cam_param.fy = resized_ori_h * cam_param.fy / height + + intrinsic = np.asarray([[cam_param.fx * width, + cam_param.fy * height, + cam_param.cx * width, + cam_param.cy * height] + for cam_param in cam_params], dtype=np.float32) + + K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] + c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere + c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] + plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W + plucker_embedding = plucker_embedding[None] + plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] + return plucker_embedding + +class Camera(object): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + def __init__(self, entry): + fx, fy, cx, cy = entry[1:5] + self.fx = fx + self.fy = fy + self.cx = cx + self.cy = cy + c2w_mat = np.array(entry[7:]).reshape(4, 4) + self.c2w_mat = c2w_mat + self.w2c_mat = np.linalg.inv(c2w_mat) + +def ray_condition(K, c2w, H, W, device): + """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py + """ + # c2w: B, V, 4, 4 + # K: B, V, 4 + + B = K.shape[0] + + j, i = torch.meshgrid( + torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), + torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), + indexing='ij' + ) + i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] + + fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 + + zs = torch.ones_like(i) # [B, HxW] + xs = (i - cx) / fx * zs + ys = (j - cy) / fy * zs + zs = zs.expand_as(ys) + + directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 + directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 + + rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW + rays_o = c2w[..., :3, 3] # B, V, 3 + rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW + # c2w @ dirctions + rays_dxo = torch.cross(rays_o, rays_d) + plucker = torch.cat([rays_dxo, rays_d], dim=-1) + plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 + # plucker = plucker.permute(0, 1, 4, 2, 3) + return plucker + +def get_camera_motion(angle, T, speed, n=81): + def compute_R_form_rad_angle(angles): + theta_x, theta_y, theta_z = angles + Rx = np.array([[1, 0, 0], + [0, np.cos(theta_x), -np.sin(theta_x)], + [0, np.sin(theta_x), np.cos(theta_x)]]) + + Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], + [0, 1, 0], + [-np.sin(theta_y), 0, np.cos(theta_y)]]) + + Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], + [np.sin(theta_z), np.cos(theta_z), 0], + [0, 0, 1]]) + + R = np.dot(Rz, np.dot(Ry, Rx)) + return R + RT = [] + for i in range(n): + _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle + R = compute_R_form_rad_angle(_angle) + _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1)) + _RT = np.concatenate([R,_T], axis=1) + RT.append(_RT) + RT = np.stack(RT) + return RT + +class WanCameraEmbedding(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanCameraEmbedding", + category="camera", + inputs=[ + io.Combo.Input( + "camera_pose", + options=[ + "Static", + "Pan Up", + "Pan Down", + "Pan Left", + "Pan Right", + "Zoom In", + "Zoom Out", + "Anti Clockwise (ACW)", + "ClockWise (CW)", + ], + default="Static", + ), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True), + io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True), + io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True), + io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True), + io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True), + ], + outputs=[ + io.WanCameraEmbedding.Output(display_name="camera_embedding"), + io.Int.Output(display_name="width"), + io.Int.Output(display_name="height"), + io.Int.Output(display_name="length"), + ], + ) + + @classmethod + def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput: + """ + Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) + Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py + """ + motion_list = [camera_pose] + speed = speed + angle = np.array(CAMERA_DICT[motion_list[0]]["angle"]) + T = np.array(CAMERA_DICT[motion_list[0]]["T"]) + RT = get_camera_motion(angle, T, speed, length) + + trajs=[] + for cp in RT.tolist(): + traj=[fx,fy,cx,cy,0,0] + traj.extend(cp[0]) + traj.extend(cp[1]) + traj.extend(cp[2]) + traj.extend([0,0,0,1]) + trajs.append(traj) + + cam_params = np.array([[float(x) for x in pose] for pose in trajs]) + cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1) + control_camera_video = process_pose_params(cam_params, width=width, height=height) + control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device()) + + control_camera_video = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + + # Reshape, transpose, and view into desired shape + b, f, c, h, w = control_camera_video.shape + control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + + return io.NodeOutput(control_camera_video, width, height, length) + + +class CameraTrajectoryExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + WanCameraEmbedding, + ] + +async def comfy_entrypoint() -> CameraTrajectoryExtension: + return CameraTrajectoryExtension() diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py index d85e6b856..576f3640a 100644 --- a/comfy_extras/nodes_canny.py +++ b/comfy_extras/nodes_canny.py @@ -1,25 +1,41 @@ from kornia.filters import canny +from typing_extensions import override + import comfy.model_management +from comfy_api.latest import ComfyExtension, io -class Canny: +class Canny(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE",), - "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), - "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) - }} + def define_schema(cls): + return io.Schema( + node_id="Canny", + category="image/preprocessors", + inputs=[ + io.Image.Input("image"), + io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01), + io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01), + ], + outputs=[io.Image.Output()], + ) - RETURN_TYPES = ("IMAGE",) - FUNCTION = "detect_edge" + @classmethod + def detect_edge(cls, image, low_threshold, high_threshold): + # Deprecated: use the V3 schema's `execute` method instead of this. + return cls.execute(image, low_threshold, high_threshold) - CATEGORY = "image/preprocessors" - - def detect_edge(self, image, low_threshold, high_threshold): + @classmethod + def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput: output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) - return (img_out,) + return io.NodeOutput(img_out) -NODE_CLASS_MAPPINGS = { - "Canny": Canny, -} + +class CannyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [Canny] + + +async def comfy_entrypoint() -> CannyExtension: + return CannyExtension() diff --git a/comfy_extras/nodes_cfg.py b/comfy_extras/nodes_cfg.py new file mode 100644 index 000000000..4ebb4b51e --- /dev/null +++ b/comfy_extras/nodes_cfg.py @@ -0,0 +1,91 @@ +from typing_extensions import override + +import torch + +from comfy_api.latest import ComfyExtension, io + + +# https://github.com/WeichenFan/CFG-Zero-star +def optimized_scale(positive, negative): + positive_flat = positive.reshape(positive.shape[0], -1) + negative_flat = negative.reshape(negative.shape[0], -1) + + # Calculate dot production + dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) + + # Squared norm of uncondition + squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8 + + # st_star = v_cond^T * v_uncond / ||v_uncond||^2 + st_star = dot_product / squared_norm + + return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1)) + +class CFGZeroStar(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CFGZeroStar", + category="advanced/guidance", + inputs=[ + io.Model.Input("model"), + ], + outputs=[io.Model.Output(display_name="patched_model")], + ) + + @classmethod + def execute(cls, model) -> io.NodeOutput: + m = model.clone() + def cfg_zero_star(args): + guidance_scale = args['cond_scale'] + x = args['input'] + cond_p = args['cond_denoised'] + uncond_p = args['uncond_denoised'] + out = args["denoised"] + alpha = optimized_scale(x - cond_p, x - uncond_p) + + return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha) + m.set_model_sampler_post_cfg_function(cfg_zero_star) + return io.NodeOutput(m) + +class CFGNorm(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CFGNorm", + category="advanced/guidance", + inputs=[ + io.Model.Input("model"), + io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[io.Model.Output(display_name="patched_model")], + is_experimental=True, + ) + + @classmethod + def execute(cls, model, strength) -> io.NodeOutput: + m = model.clone() + def cfg_norm(args): + cond_p = args['cond_denoised'] + pred_text_ = args["denoised"] + + norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True) + norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True) + scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0) + return pred_text_ * scale * strength + + m.set_model_sampler_post_cfg_function(cfg_norm) + return io.NodeOutput(m) + + +class CfgExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CFGZeroStar, + CFGNorm, + ] + + +async def comfy_entrypoint() -> CfgExtension: + return CfgExtension() diff --git a/comfy_extras/nodes_chroma_radiance.py b/comfy_extras/nodes_chroma_radiance.py new file mode 100644 index 000000000..381989818 --- /dev/null +++ b/comfy_extras/nodes_chroma_radiance.py @@ -0,0 +1,114 @@ +from typing_extensions import override +from typing import Callable + +import torch + +import comfy.model_management +from comfy_api.latest import ComfyExtension, io + +import nodes + +class EmptyChromaRadianceLatentImage(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="EmptyChromaRadianceLatentImage", + category="latent/chroma_radiance", + inputs=[ + io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input(id="batch_size", default=1, min=1, max=4096), + ], + outputs=[io.Latent().Output()], + ) + + @classmethod + def execute(cls, *, width: int, height: int, batch_size: int=1) -> io.NodeOutput: + latent = torch.zeros((batch_size, 3, height, width), device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples":latent}) + + +class ChromaRadianceOptions(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="ChromaRadianceOptions", + category="model_patches/chroma_radiance", + description="Allows setting advanced options for the Chroma Radiance model.", + inputs=[ + io.Model.Input(id="model"), + io.Boolean.Input( + id="preserve_wrapper", + default=True, + tooltip="When enabled, will delegate to an existing model function wrapper if it exists. Generally should be left enabled.", + ), + io.Float.Input( + id="start_sigma", + default=1.0, + min=0.0, + max=1.0, + tooltip="First sigma that these options will be in effect.", + ), + io.Float.Input( + id="end_sigma", + default=0.0, + min=0.0, + max=1.0, + tooltip="Last sigma that these options will be in effect.", + ), + io.Int.Input( + id="nerf_tile_size", + default=-1, + min=-1, + tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).", + ), + ], + outputs=[io.Model.Output()], + ) + + @classmethod + def execute( + cls, + *, + model: io.Model.Type, + preserve_wrapper: bool, + start_sigma: float, + end_sigma: float, + nerf_tile_size: int, + ) -> io.NodeOutput: + radiance_options = {} + if nerf_tile_size >= 0: + radiance_options["nerf_tile_size"] = nerf_tile_size + + if not radiance_options: + return io.NodeOutput(model) + + old_wrapper = model.model_options.get("model_function_wrapper") + + def model_function_wrapper(apply_model: Callable, args: dict) -> torch.Tensor: + c = args["c"].copy() + sigma = args["timestep"].max().detach().cpu().item() + if end_sigma <= sigma <= start_sigma: + transformer_options = c.get("transformer_options", {}).copy() + transformer_options["chroma_radiance_options"] = radiance_options.copy() + c["transformer_options"] = transformer_options + if not (preserve_wrapper and old_wrapper): + return apply_model(args["input"], args["timestep"], **c) + return old_wrapper(apply_model, args | {"c": c}) + + model = model.clone() + model.set_model_unet_function_wrapper(model_function_wrapper) + return io.NodeOutput(model) + + +class ChromaRadianceExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyChromaRadianceLatentImage, + ChromaRadianceOptions, + ] + + +async def comfy_entrypoint() -> ChromaRadianceExtension: + return ChromaRadianceExtension() diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py index 14269caf3..520ff0e3c 100644 --- a/comfy_extras/nodes_clip_sdxl.py +++ b/comfy_extras/nodes_clip_sdxl.py @@ -1,43 +1,52 @@ -from nodes import MAX_RESOLUTION +from typing_extensions import override -class CLIPTextEncodeSDXLRefiner: +import nodes +from comfy_api.latest import ComfyExtension, io + + +class CLIPTextEncodeSDXLRefiner(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeSDXLRefiner", + category="advanced/conditioning", + inputs=[ + io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01), + io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.String.Input("text", multiline=True, dynamic_prompts=True), + io.Clip.Input("clip"), + ], + outputs=[io.Conditioning.Output()], + ) - CATEGORY = "advanced/conditioning" - - def encode(self, clip, ascore, width, height, text): + @classmethod + def execute(cls, clip, ascore, width, height, text) -> io.NodeOutput: tokens = clip.tokenize(text) - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}), ) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height})) -class CLIPTextEncodeSDXL: +class CLIPTextEncodeSDXL(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), - "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), - "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeSDXL", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.String.Input("text_g", multiline=True, dynamic_prompts=True), + io.String.Input("text_l", multiline=True, dynamic_prompts=True), + ], + outputs=[io.Conditioning.Output()], + ) - CATEGORY = "advanced/conditioning" - - def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): + @classmethod + def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput: tokens = clip.tokenize(text_g) tokens["l"] = clip.tokenize(text_l)["l"] if len(tokens["l"]) != len(tokens["g"]): @@ -46,9 +55,17 @@ class CLIPTextEncodeSDXL: tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}), ) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height})) -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, - "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, -} + +class ClipSdxlExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeSDXLRefiner, + CLIPTextEncodeSDXL, + ] + + +async def comfy_entrypoint() -> ClipSdxlExtension: + return ClipSdxlExtension() diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py index 2f994fa11..e4e4e1cbc 100644 --- a/comfy_extras/nodes_compositing.py +++ b/comfy_extras/nodes_compositing.py @@ -1,6 +1,9 @@ import torch import comfy.utils from enum import Enum +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + def resize_mask(mask, shape): return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) @@ -101,24 +104,28 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_ return out_image, out_alpha -class PorterDuffImageComposite: +class PorterDuffImageComposite(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "source": ("IMAGE",), - "source_alpha": ("MASK",), - "destination": ("IMAGE",), - "destination_alpha": ("MASK",), - "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), - }, - } + def define_schema(cls): + return io.Schema( + node_id="PorterDuffImageComposite", + display_name="Porter-Duff Image Composite", + category="mask/compositing", + inputs=[ + io.Image.Input("source"), + io.Mask.Input("source_alpha"), + io.Image.Input("destination"), + io.Mask.Input("destination_alpha"), + io.Combo.Input("mode", options=[mode.name for mode in PorterDuffMode], default=PorterDuffMode.DST.name), + ], + outputs=[ + io.Image.Output(), + io.Mask.Output(), + ], + ) - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "composite" - CATEGORY = "mask/compositing" - - def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): + @classmethod + def execute(cls, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode) -> io.NodeOutput: batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) out_images = [] out_alphas = [] @@ -150,45 +157,48 @@ class PorterDuffImageComposite: out_images.append(out_image) out_alphas.append(out_alpha.squeeze(2)) - result = (torch.stack(out_images), torch.stack(out_alphas)) - return result + return io.NodeOutput(torch.stack(out_images), torch.stack(out_alphas)) -class SplitImageWithAlpha: +class SplitImageWithAlpha(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - } - } + def define_schema(cls): + return io.Schema( + node_id="SplitImageWithAlpha", + display_name="Split Image with Alpha", + category="mask/compositing", + inputs=[ + io.Image.Input("image"), + ], + outputs=[ + io.Image.Output(), + io.Mask.Output(), + ], + ) - CATEGORY = "mask/compositing" - RETURN_TYPES = ("IMAGE", "MASK") - FUNCTION = "split_image_with_alpha" - - def split_image_with_alpha(self, image: torch.Tensor): + @classmethod + def execute(cls, image: torch.Tensor) -> io.NodeOutput: out_images = [i[:,:,:3] for i in image] out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] - result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) - return result + return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas)) -class JoinImageWithAlpha: +class JoinImageWithAlpha(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "alpha": ("MASK",), - } - } + def define_schema(cls): + return io.Schema( + node_id="JoinImageWithAlpha", + display_name="Join Image with Alpha", + category="mask/compositing", + inputs=[ + io.Image.Input("image"), + io.Mask.Input("alpha"), + ], + outputs=[io.Image.Output()], + ) - CATEGORY = "mask/compositing" - RETURN_TYPES = ("IMAGE",) - FUNCTION = "join_image_with_alpha" - - def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): + @classmethod + def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput: batch_size = min(len(image), len(alpha)) out_images = [] @@ -196,19 +206,18 @@ class JoinImageWithAlpha: for i in range(batch_size): out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) - result = (torch.stack(out_images),) - return result + return io.NodeOutput(torch.stack(out_images)) -NODE_CLASS_MAPPINGS = { - "PorterDuffImageComposite": PorterDuffImageComposite, - "SplitImageWithAlpha": SplitImageWithAlpha, - "JoinImageWithAlpha": JoinImageWithAlpha, -} +class CompositingExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + PorterDuffImageComposite, + SplitImageWithAlpha, + JoinImageWithAlpha, + ] -NODE_DISPLAY_NAME_MAPPINGS = { - "PorterDuffImageComposite": "Porter-Duff Image Composite", - "SplitImageWithAlpha": "Split Image with Alpha", - "JoinImageWithAlpha": "Join Image with Alpha", -} +async def comfy_entrypoint() -> CompositingExtension: + return CompositingExtension() diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py index 4c3a1d5bf..8b06e3de9 100644 --- a/comfy_extras/nodes_cond.py +++ b/comfy_extras/nodes_cond.py @@ -1,15 +1,25 @@ +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io -class CLIPTextEncodeControlnet: +class CLIPTextEncodeControlnet(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CLIPTextEncodeControlnet", + category="_for_testing/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.Conditioning.Input("conditioning"), + io.String.Input("text", multiline=True, dynamic_prompts=True), + ], + outputs=[io.Conditioning.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing/conditioning" - - def encode(self, clip, conditioning, text): + @classmethod + def execute(cls, clip, conditioning, text) -> io.NodeOutput: tokens = clip.tokenize(text) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) c = [] @@ -18,8 +28,41 @@ class CLIPTextEncodeControlnet: n[1]['cross_attn_controlnet'] = cond n[1]['pooled_output_controlnet'] = pooled c.append(n) - return (c, ) + return io.NodeOutput(c) -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet -} +class T5TokenizerOptions(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="T5TokenizerOptions", + category="_for_testing/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.Int.Input("min_padding", default=0, min=0, max=10000, step=1), + io.Int.Input("min_length", default=0, min=0, max=10000, step=1), + ], + outputs=[io.Clip.Output()], + is_experimental=True, + ) + + @classmethod + def execute(cls, clip, min_padding, min_length) -> io.NodeOutput: + clip = clip.clone() + for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]: + clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding) + clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length) + + return io.NodeOutput(clip) + + +class CondExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeControlnet, + T5TokenizerOptions, + ] + + +async def comfy_entrypoint() -> CondExtension: + return CondExtension() diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py new file mode 100644 index 000000000..1c3d9e697 --- /dev/null +++ b/comfy_extras/nodes_context_windows.py @@ -0,0 +1,89 @@ +from __future__ import annotations +from comfy_api.latest import ComfyExtension, io +import comfy.context_windows +import nodes + + +class ContextWindowsManualNode(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="ContextWindowsManual", + display_name="Context Windows (Manual)", + category="context", + description="Manually set context windows.", + inputs=[ + io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), + io.Int.Input("context_length", min=1, default=16, tooltip="The length of the context window."), + io.Int.Input("context_overlap", min=0, default=4, tooltip="The overlap of the context window."), + io.Combo.Input("context_schedule", options=[ + comfy.context_windows.ContextSchedules.STATIC_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, + comfy.context_windows.ContextSchedules.BATCHED, + ], tooltip="The stride of the context window."), + io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."), + io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), + io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), + io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."), + ], + outputs=[ + io.Model.Output(tooltip="The model with context windows applied during sampling."), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int) -> io.Model: + model = model.clone() + model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler( + context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule), + fuse_method=comfy.context_windows.get_matching_fuse_method(fuse_method), + context_length=context_length, + context_overlap=context_overlap, + context_stride=context_stride, + closed_loop=closed_loop, + dim=dim) + # make memory usage calculation only take into account the context window latents + comfy.context_windows.create_prepare_sampling_wrapper(model) + return io.NodeOutput(model) + +class WanContextWindowsManualNode(ContextWindowsManualNode): + @classmethod + def define_schema(cls) -> io.Schema: + schema = super().define_schema() + schema.node_id = "WanContextWindowsManual" + schema.display_name = "WAN Context Windows (Manual)" + schema.description = "Manually set context windows for WAN-like models (dim=2)." + schema.inputs = [ + io.Model.Input("model", tooltip="The model to apply context windows to during sampling."), + io.Int.Input("context_length", min=1, max=nodes.MAX_RESOLUTION, step=4, default=81, tooltip="The length of the context window."), + io.Int.Input("context_overlap", min=0, default=30, tooltip="The overlap of the context window."), + io.Combo.Input("context_schedule", options=[ + comfy.context_windows.ContextSchedules.STATIC_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_STANDARD, + comfy.context_windows.ContextSchedules.UNIFORM_LOOPED, + comfy.context_windows.ContextSchedules.BATCHED, + ], tooltip="The stride of the context window."), + io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."), + io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."), + io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."), + ] + return schema + + @classmethod + def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str) -> io.Model: + context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1 + context_overlap = max(((context_overlap - 1) // 4) + 1, 0) # at least overlap 0 + return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2) + + +class ContextWindowsExtension(ComfyExtension): + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + ContextWindowsManualNode, + WanContextWindowsManualNode, + ] + +def comfy_entrypoint(): + return ContextWindowsExtension() diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py index 2d20e1fed..e835feed7 100644 --- a/comfy_extras/nodes_controlnet.py +++ b/comfy_extras/nodes_controlnet.py @@ -1,20 +1,26 @@ from comfy.cldm.control_types import UNION_CONTROLNET_TYPES import nodes import comfy.utils +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -class SetUnionControlNetType: +class SetUnionControlNetType(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"control_net": ("CONTROL_NET", ), - "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),) - }} + def define_schema(cls): + return io.Schema( + node_id="SetUnionControlNetType", + category="conditioning/controlnet", + inputs=[ + io.ControlNet.Input("control_net"), + io.Combo.Input("type", options=["auto"] + list(UNION_CONTROLNET_TYPES.keys())), + ], + outputs=[ + io.ControlNet.Output(), + ], + ) - CATEGORY = "conditioning/controlnet" - RETURN_TYPES = ("CONTROL_NET",) - - FUNCTION = "set_controlnet_type" - - def set_controlnet_type(self, control_net, type): + @classmethod + def execute(cls, control_net, type) -> io.NodeOutput: control_net = control_net.copy() type_number = UNION_CONTROLNET_TYPES.get(type, -1) if type_number >= 0: @@ -22,27 +28,36 @@ class SetUnionControlNetType: else: control_net.set_extra_arg("control_type", []) - return (control_net,) + return io.NodeOutput(control_net) -class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced): + set_controlnet_type = execute # TODO: remove + + +class ControlNetInpaintingAliMamaApply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "vae": ("VAE", ), - "image": ("IMAGE", ), - "mask": ("MASK", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }} + def define_schema(cls): + return io.Schema( + node_id="ControlNetInpaintingAliMamaApply", + category="conditioning/controlnet", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.ControlNet.Input("control_net"), + io.Vae.Input("vae"), + io.Image.Input("image"), + io.Mask.Input("mask"), + io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + ) - FUNCTION = "apply_inpaint_controlnet" - - CATEGORY = "conditioning/controlnet" - - def apply_inpaint_controlnet(self, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent): + @classmethod + def execute(cls, positive, negative, control_net, vae, image, mask, strength, start_percent, end_percent) -> io.NodeOutput: extra_concat = [] if control_net.concat_mask: mask = 1.0 - mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) @@ -50,11 +65,20 @@ class ControlNetInpaintingAliMamaApply(nodes.ControlNetApplyAdvanced): image = image * mask_apply.movedim(1, -1).repeat(1, 1, 1, image.shape[3]) extra_concat = [mask] - return self.apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) + result = nodes.ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent, vae=vae, extra_concat=extra_concat) + return io.NodeOutput(result[0], result[1]) + + apply_inpaint_controlnet = execute # TODO: remove +class ControlNetExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SetUnionControlNetType, + ControlNetInpaintingAliMamaApply, + ] -NODE_CLASS_MAPPINGS = { - "SetUnionControlNetType": SetUnionControlNetType, - "ControlNetInpaintingAliMamaApply": ControlNetInpaintingAliMamaApply, -} + +async def comfy_entrypoint() -> ControlNetExtension: + return ControlNetExtension() diff --git a/comfy_extras/nodes_cosmos.py b/comfy_extras/nodes_cosmos.py new file mode 100644 index 000000000..7dd129d19 --- /dev/null +++ b/comfy_extras/nodes_cosmos.py @@ -0,0 +1,143 @@ +from typing_extensions import override +import nodes +import torch +import comfy.model_management +import comfy.utils +import comfy.latent_formats + +from comfy_api.latest import ComfyExtension, io + + +class EmptyCosmosLatentVideo(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="EmptyCosmosLatentVideo", + category="latent/video", + inputs=[ + io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[io.Latent.Output()], + ) + + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples": latent}) + + +def vae_encode_with_padding(vae, image, width, height, length, padding=0): + pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + pixel_len = min(pixels.shape[0], length) + padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7) + padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5 + padded_pixels[:pixel_len] = pixels[:pixel_len] + latent_len = ((pixel_len - 1) // 8) + 1 + latent_temp = vae.encode(padded_pixels) + return latent_temp[:, :, :latent_len] + + +class CosmosImageToVideoLatent(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CosmosImageToVideoLatent", + category="conditioning/inpaint", + inputs=[ + io.Vae.Input("vae"), + io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[io.Latent.Output()], + ) + + @classmethod + def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput: + latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is None and end_image is None: + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(out_latent) + + mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) + latent[:, :, :latent_temp.shape[-3]] = latent_temp + mask[:, :, :latent_temp.shape[-3]] *= 0.0 + + if end_image is not None: + latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) + latent[:, :, -latent_temp.shape[-3]:] = latent_temp + mask[:, :, -latent_temp.shape[-3]:] *= 0.0 + + out_latent = {} + out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) + out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) + return io.NodeOutput(out_latent) + +class CosmosPredict2ImageToVideoLatent(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="CosmosPredict2ImageToVideoLatent", + category="conditioning/inpaint", + inputs=[ + io.Vae.Input("vae"), + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=93, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[io.Latent.Output()], + ) + + @classmethod + def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput: + latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is None and end_image is None: + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(out_latent) + + mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1) + latent[:, :, :latent_temp.shape[-3]] = latent_temp + mask[:, :, :latent_temp.shape[-3]] *= 0.0 + + if end_image is not None: + latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0) + latent[:, :, -latent_temp.shape[-3]:] = latent_temp + mask[:, :, -latent_temp.shape[-3]:] *= 0.0 + + out_latent = {} + latent_format = comfy.latent_formats.Wan21() + latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) + out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) + out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) + return io.NodeOutput(out_latent) + + +class CosmosExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyCosmosLatentVideo, + CosmosImageToVideoLatent, + CosmosPredict2ImageToVideoLatent, + ] + + +async def comfy_entrypoint() -> CosmosExtension: + return CosmosExtension() diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py index c7ff9a4d8..d011f433b 100644 --- a/comfy_extras/nodes_custom_sampler.py +++ b/comfy_extras/nodes_custom_sampler.py @@ -1,6 +1,9 @@ +import math import comfy.samplers import comfy.sample from comfy.k_diffusion import sampling as k_diffusion_sampling +from comfy.k_diffusion import sa_solver +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict import latent_preview import torch import comfy.utils @@ -231,6 +234,102 @@ class FlipSigmas: sigmas[0] = 0.0001 return (sigmas,) +class SetFirstSigma: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "set_first_sigma" + + def set_first_sigma(self, sigmas, sigma): + sigmas = sigmas.clone() + sigmas[0] = sigma + return (sigmas, ) + +class ExtendIntermediateSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "steps": ("INT", {"default": 2, "min": 1, "max": 100}), + "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}), + "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}), + "spacing": (['linear', 'cosine', 'sine'],), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "extend" + + def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str): + if start_at_sigma < 0: + start_at_sigma = float("inf") + + interpolator = { + 'linear': lambda x: x, + 'cosine': lambda x: torch.sin(x*math.pi/2), + 'sine': lambda x: 1 - torch.cos(x*math.pi/2) + }[spacing] + + # linear space for our interpolation function + x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1] + computed_spacing = interpolator(x) + + extended_sigmas = [] + for i in range(len(sigmas) - 1): + sigma_current = sigmas[i] + sigma_next = sigmas[i+1] + + extended_sigmas.append(sigma_current) + + if end_at_sigma <= sigma_current <= start_at_sigma: + interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current + extended_sigmas.extend(interpolated_steps.tolist()) + + # Add the last sigma value + if len(sigmas) > 0: + extended_sigmas.append(sigmas[-1]) + + extended_sigmas = torch.FloatTensor(extended_sigmas) + + return (extended_sigmas,) + + +class SamplingPercentToSigma: + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "model": (IO.MODEL, {}), + "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}), + "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}), + } + } + + RETURN_TYPES = (IO.FLOAT,) + RETURN_NAMES = ("sigma_value",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigma" + + def get_sigma(self, model, sampling_percent, return_actual_sigma): + model_sampling = model.get_model_object("model_sampling") + sigma_val = model_sampling.percent_to_sigma(sampling_percent) + if return_actual_sigma: + if sampling_percent == 0.0: + sigma_val = model_sampling.sigma_max.item() + elif sampling_percent == 1.0: + sigma_val = model_sampling.sigma_min.item() + return (sigma_val,) + + class KSamplerSelect: @classmethod def INPUT_TYPES(s): @@ -412,6 +511,89 @@ class SamplerDPMAdaptative: "s_noise":s_noise }) return (sampler, ) + +class SamplerER_SDE(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "solver_type": (IO.COMBO, {"options": ["ER-SDE", "Reverse-time SDE", "ODE"]}), + "max_stage": (IO.INT, {"default": 3, "min": 1, "max": 3}), + "eta": ( + IO.FLOAT, + {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False, "tooltip": "Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."}, + ), + "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}), + } + } + + RETURN_TYPES = (IO.SAMPLER,) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, solver_type, max_stage, eta, s_noise): + if solver_type == "ODE" or (solver_type == "Reverse-time SDE" and eta == 0): + eta = 0 + s_noise = 0 + + def reverse_time_sde_noise_scaler(x): + return x ** (eta + 1) + + if solver_type == "ER-SDE": + # Use the default one in sample_er_sde() + noise_scaler = None + else: + noise_scaler = reverse_time_sde_noise_scaler + + sampler_name = "er_sde" + sampler = comfy.samplers.ksampler(sampler_name, {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage}) + return (sampler,) + + +class SamplerSASolver(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "model": (IO.MODEL, {}), + "eta": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False},), + "sde_start_percent": (IO.FLOAT, {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001},), + "sde_end_percent": (IO.FLOAT, {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001},), + "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False},), + "predictor_order": (IO.INT, {"default": 3, "min": 1, "max": 6}), + "corrector_order": (IO.INT, {"default": 4, "min": 0, "max": 6}), + "use_pece": (IO.BOOLEAN, {}), + "simple_order_2": (IO.BOOLEAN, {}), + } + } + + RETURN_TYPES = (IO.SAMPLER,) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2): + model_sampling = model.get_model_object("model_sampling") + start_sigma = model_sampling.percent_to_sigma(sde_start_percent) + end_sigma = model_sampling.percent_to_sigma(sde_end_percent) + tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=eta) + + sampler_name = "sa_solver" + sampler = comfy.samplers.ksampler( + sampler_name, + { + "tau_func": tau_func, + "s_noise": s_noise, + "predictor_order": predictor_order, + "corrector_order": corrector_order, + "use_pece": use_pece, + "simple_order_2": simple_order_2, + }, + ) + return (sampler,) + + class Noise_EmptyNoise: def __init__(self): self.seed = 0 @@ -436,7 +618,7 @@ class SamplerCustom: return {"required": {"model": ("MODEL",), "add_noise": ("BOOLEAN", {"default": True}), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), @@ -530,9 +712,10 @@ class CFGGuider: return (guider,) class Guider_DualCFG(comfy.samplers.CFGGuider): - def set_cfg(self, cfg1, cfg2): + def set_cfg(self, cfg1, cfg2, nested=False): self.cfg1 = cfg1 self.cfg2 = cfg2 + self.nested = nested def set_conds(self, positive, middle, negative): middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"}) @@ -541,9 +724,21 @@ class Guider_DualCFG(comfy.samplers.CFGGuider): def predict_noise(self, x, timestep, model_options={}, seed=None): negative_cond = self.conds.get("negative", None) middle_cond = self.conds.get("middle", None) + positive_cond = self.conds.get("positive", None) - out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options) - return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 + if self.nested: + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options) + pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond) + return out[0] + self.cfg2 * (pred_text - out[0]) + else: + if model_options.get("disable_cfg1_optimization", False) == False: + if math.isclose(self.cfg2, 1.0): + negative_cond = None + if math.isclose(self.cfg1, 1.0): + middle_cond = None + + out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options) + return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1 class DualCFGGuider: @classmethod @@ -555,6 +750,7 @@ class DualCFGGuider: "negative": ("CONDITIONING", ), "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "style": (["regular", "nested"],), } } @@ -563,10 +759,10 @@ class DualCFGGuider: FUNCTION = "get_guider" CATEGORY = "sampling/custom_sampling/guiders" - def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative): + def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style): guider = Guider_DualCFG(model) guider.set_conds(cond1, cond2, negative) - guider.set_cfg(cfg_conds, cfg_cond2_negative) + guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested")) return (guider,) class DisableNoise: @@ -587,10 +783,16 @@ class DisableNoise: class RandomNoise(DisableNoise): @classmethod def INPUT_TYPES(s): - return {"required":{ - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), - } - } + return { + "required": { + "noise_seed": ("INT", { + "default": 0, + "min": 0, + "max": 0xffffffffffffffff, + "control_after_generate": True, + }), + } + } def get_noise(self, noise_seed): return (Noise_RandomNoise(noise_seed),) @@ -707,9 +909,14 @@ NODE_CLASS_MAPPINGS = { "SamplerDPMPP_SDE": SamplerDPMPP_SDE, "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral, "SamplerDPMAdaptative": SamplerDPMAdaptative, + "SamplerER_SDE": SamplerER_SDE, + "SamplerSASolver": SamplerSASolver, "SplitSigmas": SplitSigmas, "SplitSigmasDenoise": SplitSigmasDenoise, "FlipSigmas": FlipSigmas, + "SetFirstSigma": SetFirstSigma, + "ExtendIntermediateSigmas": ExtendIntermediateSigmas, + "SamplingPercentToSigma": SamplingPercentToSigma, "CFGGuider": CFGGuider, "DualCFGGuider": DualCFGGuider, diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py index 98dbbf102..6dfdf466c 100644 --- a/comfy_extras/nodes_differential_diffusion.py +++ b/comfy_extras/nodes_differential_diffusion.py @@ -1,23 +1,41 @@ # code adapted from https://github.com/exx8/differential-diffusion +from typing_extensions import override + import torch +from comfy_api.latest import ComfyExtension, io -class DifferentialDiffusion(): + +class DifferentialDiffusion(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "apply" - CATEGORY = "_for_testing" - INIT = False + def define_schema(cls): + return io.Schema( + node_id="DifferentialDiffusion", + display_name="Differential Diffusion", + category="_for_testing", + inputs=[ + io.Model.Input("model"), + io.Float.Input( + "strength", + default=1.0, + min=0.0, + max=1.0, + step=0.01, + optional=True, + ), + ], + outputs=[io.Model.Output()], + is_experimental=True, + ) - def apply(self, model): + @classmethod + def execute(cls, model, strength=1.0) -> io.NodeOutput: model = model.clone() - model.set_model_denoise_mask_function(self.forward) - return (model,) + model.set_model_denoise_mask_function(lambda *args, **kwargs: cls.forward(*args, **kwargs, strength=strength)) + return io.NodeOutput(model) - def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): + @classmethod + def forward(cls, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict, strength: float): model = extra_options["model"] step_sigmas = extra_options["sigmas"] sigma_to = model.inner_model.model_sampling.sigma_min @@ -31,12 +49,24 @@ class DifferentialDiffusion(): threshold = (current_ts - ts_to) / (ts_from - ts_to) - return (denoise_mask >= threshold).to(denoise_mask.dtype) + # Generate the binary mask based on the threshold + binary_mask = (denoise_mask >= threshold).to(denoise_mask.dtype) + + # Blend binary mask with the original denoise_mask using strength + if strength and strength < 1: + blended_mask = strength * binary_mask + (1 - strength) * denoise_mask + return blended_mask + else: + return binary_mask -NODE_CLASS_MAPPINGS = { - "DifferentialDiffusion": DifferentialDiffusion, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "DifferentialDiffusion": "Differential Diffusion", -} +class DifferentialDiffusionExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + DifferentialDiffusion, + ] + + +async def comfy_entrypoint() -> DifferentialDiffusionExtension: + return DifferentialDiffusionExtension() diff --git a/comfy_extras/nodes_easycache.py b/comfy_extras/nodes_easycache.py new file mode 100644 index 000000000..1359e2f99 --- /dev/null +++ b/comfy_extras/nodes_easycache.py @@ -0,0 +1,499 @@ +from __future__ import annotations +from typing import TYPE_CHECKING, Union +from comfy_api.latest import io, ComfyExtension +import comfy.patcher_extension +import logging +import torch +import comfy.model_patcher +if TYPE_CHECKING: + from uuid import UUID + + +def easycache_forward_wrapper(executor, *args, **kwargs): + # get values from args + x: torch.Tensor = args[0] + transformer_options: dict[str] = args[-1] + if not isinstance(transformer_options, dict): + transformer_options = kwargs.get("transformer_options") + if not transformer_options: + transformer_options = args[-2] + easycache: EasyCacheHolder = transformer_options["easycache"] + sigmas = transformer_options["sigmas"] + uuids = transformer_options["uuids"] + if sigmas is not None and easycache.is_past_end_timestep(sigmas): + return executor(*args, **kwargs) + # prepare next x_prev + has_first_cond_uuid = easycache.has_first_cond_uuid(uuids) + next_x_prev = x + input_change = None + do_easycache = easycache.should_do_easycache(sigmas) + if do_easycache: + easycache.check_metadata(x) + # if first cond marked this step for skipping, skip it and use appropriate cached values + if easycache.skip_current_step: + if easycache.verbose: + logging.info(f"EasyCache [verbose] - was marked to skip this step by {easycache.first_cond_uuid}. Present uuids: {uuids}") + return easycache.apply_cache_diff(x, uuids) + if easycache.initial_step: + easycache.first_cond_uuid = uuids[0] + has_first_cond_uuid = easycache.has_first_cond_uuid(uuids) + easycache.initial_step = False + if has_first_cond_uuid: + if easycache.has_x_prev_subsampled(): + input_change = (easycache.subsample(x, uuids, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean() + if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate(): + approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm + easycache.cumulative_change_rate += approx_output_change_rate + if easycache.cumulative_change_rate < easycache.reuse_threshold: + if easycache.verbose: + logging.info(f"EasyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") + # other conds should also skip this step, and instead use their cached values + easycache.skip_current_step = True + return easycache.apply_cache_diff(x, uuids) + else: + if easycache.verbose: + logging.info(f"EasyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") + easycache.cumulative_change_rate = 0.0 + + output: torch.Tensor = executor(*args, **kwargs) + if has_first_cond_uuid and easycache.has_output_prev_norm(): + output_change = (easycache.subsample(output, uuids, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean() + if easycache.verbose: + output_change_rate = output_change / easycache.output_prev_norm + easycache.output_change_rates.append(output_change_rate.item()) + if easycache.has_relative_transformation_rate(): + approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm + easycache.approx_output_change_rates.append(approx_output_change_rate.item()) + if easycache.verbose: + logging.info(f"EasyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}") + if input_change is not None: + easycache.relative_transformation_rate = output_change / input_change + if easycache.verbose: + logging.info(f"EasyCache [verbose] - output_change_rate: {output_change_rate}") + # TODO: allow cache_diff to be offloaded + easycache.update_cache_diff(output, next_x_prev, uuids) + if has_first_cond_uuid: + easycache.x_prev_subsampled = easycache.subsample(next_x_prev, uuids) + easycache.output_prev_subsampled = easycache.subsample(output, uuids) + easycache.output_prev_norm = output.flatten().abs().mean() + if easycache.verbose: + logging.info(f"EasyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}") + return output + +def lazycache_predict_noise_wrapper(executor, *args, **kwargs): + # get values from args + x: torch.Tensor = args[0] + timestep: float = args[1] + model_options: dict[str] = args[2] + easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"] + if easycache.is_past_end_timestep(timestep): + return executor(*args, **kwargs) + # prepare next x_prev + next_x_prev = x + input_change = None + do_easycache = easycache.should_do_easycache(timestep) + if do_easycache: + easycache.check_metadata(x) + if easycache.has_x_prev_subsampled(): + if easycache.has_x_prev_subsampled(): + input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean() + if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate(): + approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm + easycache.cumulative_change_rate += approx_output_change_rate + if easycache.cumulative_change_rate < easycache.reuse_threshold: + if easycache.verbose: + logging.info(f"LazyCache [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") + # other conds should also skip this step, and instead use their cached values + easycache.skip_current_step = True + return easycache.apply_cache_diff(x) + else: + if easycache.verbose: + logging.info(f"LazyCache [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}") + easycache.cumulative_change_rate = 0.0 + output: torch.Tensor = executor(*args, **kwargs) + if easycache.has_output_prev_norm(): + output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean() + if easycache.verbose: + output_change_rate = output_change / easycache.output_prev_norm + easycache.output_change_rates.append(output_change_rate.item()) + if easycache.has_relative_transformation_rate(): + approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm + easycache.approx_output_change_rates.append(approx_output_change_rate.item()) + if easycache.verbose: + logging.info(f"LazyCache [verbose] - approx_output_change_rate: {approx_output_change_rate}") + if input_change is not None: + easycache.relative_transformation_rate = output_change / input_change + if easycache.verbose: + logging.info(f"LazyCache [verbose] - output_change_rate: {output_change_rate}") + # TODO: allow cache_diff to be offloaded + easycache.update_cache_diff(output, next_x_prev) + easycache.x_prev_subsampled = easycache.subsample(next_x_prev) + easycache.output_prev_subsampled = easycache.subsample(output) + easycache.output_prev_norm = output.flatten().abs().mean() + if easycache.verbose: + logging.info(f"LazyCache [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}") + return output + +def easycache_calc_cond_batch_wrapper(executor, *args, **kwargs): + model_options = args[-1] + easycache: EasyCacheHolder = model_options["transformer_options"]["easycache"] + easycache.skip_current_step = False + # TODO: check if first_cond_uuid is active at this timestep; otherwise, EasyCache needs to be partially reset + return executor(*args, **kwargs) + +def easycache_sample_wrapper(executor, *args, **kwargs): + """ + This OUTER_SAMPLE wrapper makes sure easycache is prepped for current run, and all memory usage is cleared at the end. + """ + try: + guider = executor.class_obj + orig_model_options = guider.model_options + guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options) + # clone and prepare timesteps + guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling) + easycache: Union[EasyCacheHolder, LazyCacheHolder] = guider.model_options['transformer_options']['easycache'] + logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}") + return executor(*args, **kwargs) + finally: + easycache = guider.model_options['transformer_options']['easycache'] + output_change_rates = easycache.output_change_rates + approx_output_change_rates = easycache.approx_output_change_rates + if easycache.verbose: + logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}") + logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}") + total_steps = len(args[3])-1 + # catch division by zero for log statement; sucks to crash after all sampling is done + try: + speedup = total_steps/(total_steps-easycache.total_steps_skipped) + except ZeroDivisionError: + speedup = 1.0 + logging.info(f"{easycache.name} - skipped {easycache.total_steps_skipped}/{total_steps} steps ({speedup:.2f}x speedup).") + easycache.reset() + guider.model_options = orig_model_options + + +class EasyCacheHolder: + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + self.name = "EasyCache" + self.reuse_threshold = reuse_threshold + self.start_percent = start_percent + self.end_percent = end_percent + self.subsample_factor = subsample_factor + self.offload_cache_diff = offload_cache_diff + self.verbose = verbose + # timestep values + self.start_t = 0.0 + self.end_t = 0.0 + # control values + self.relative_transformation_rate: float = None + self.cumulative_change_rate = 0.0 + self.initial_step = True + self.skip_current_step = False + # cache values + self.first_cond_uuid = None + self.x_prev_subsampled: torch.Tensor = None + self.output_prev_subsampled: torch.Tensor = None + self.output_prev_norm: torch.Tensor = None + self.uuid_cache_diffs: dict[UUID, torch.Tensor] = {} + self.output_change_rates = [] + self.approx_output_change_rates = [] + self.total_steps_skipped = 0 + # how to deal with mismatched dims + self.allow_mismatch = True + self.cut_from_start = True + self.state_metadata = None + + def is_past_end_timestep(self, timestep: float) -> bool: + return not (timestep[0] > self.end_t).item() + + def should_do_easycache(self, timestep: float) -> bool: + return (timestep[0] <= self.start_t).item() + + def has_x_prev_subsampled(self) -> bool: + return self.x_prev_subsampled is not None + + def has_output_prev_subsampled(self) -> bool: + return self.output_prev_subsampled is not None + + def has_output_prev_norm(self) -> bool: + return self.output_prev_norm is not None + + def has_relative_transformation_rate(self) -> bool: + return self.relative_transformation_rate is not None + + def prepare_timesteps(self, model_sampling): + self.start_t = model_sampling.percent_to_sigma(self.start_percent) + self.end_t = model_sampling.percent_to_sigma(self.end_percent) + return self + + def subsample(self, x: torch.Tensor, uuids: list[UUID], clone: bool = True) -> torch.Tensor: + batch_offset = x.shape[0] // len(uuids) + uuid_idx = uuids.index(self.first_cond_uuid) + if self.subsample_factor > 1: + to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ..., ::self.subsample_factor, ::self.subsample_factor] + if clone: + return to_return.clone() + return to_return + to_return = x[uuid_idx*batch_offset:(uuid_idx+1)*batch_offset, ...] + if clone: + return to_return.clone() + return to_return + + def apply_cache_diff(self, x: torch.Tensor, uuids: list[UUID]): + if self.first_cond_uuid in uuids: + self.total_steps_skipped += 1 + batch_offset = x.shape[0] // len(uuids) + for i, uuid in enumerate(uuids): + # slice out only what is relevant to this cond + batch_slice = [slice(i*batch_offset,(i+1)*batch_offset)] + # if cached dims don't match x dims, cut off excess and hope for the best (cosmos world2video) + if x.shape[1:] != self.uuid_cache_diffs[uuid].shape[1:]: + if not self.allow_mismatch: + raise ValueError(f"Cached dims {self.uuid_cache_diffs[uuid].shape} don't match x dims {x.shape} - this is no good") + slicing = [] + skip_this_dim = True + for dim_u, dim_x in zip(self.uuid_cache_diffs[uuid].shape, x.shape): + if skip_this_dim: + skip_this_dim = False + continue + if dim_u != dim_x: + if self.cut_from_start: + slicing.append(slice(dim_x-dim_u, None)) + else: + slicing.append(slice(None, dim_u)) + else: + slicing.append(slice(None)) + batch_slice = batch_slice + slicing + x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device) + return x + + def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]): + # if output dims don't match x dims, cut off excess and hope for the best (cosmos world2video) + if output.shape[1:] != x.shape[1:]: + if not self.allow_mismatch: + raise ValueError(f"Output dims {output.shape} don't match x dims {x.shape} - this is no good") + slicing = [] + skip_dim = True + for dim_o, dim_x in zip(output.shape, x.shape): + if not skip_dim and dim_o != dim_x: + if self.cut_from_start: + slicing.append(slice(dim_x-dim_o, None)) + else: + slicing.append(slice(None, dim_o)) + else: + slicing.append(slice(None)) + skip_dim = False + x = x[slicing] + diff = output - x + batch_offset = diff.shape[0] // len(uuids) + for i, uuid in enumerate(uuids): + self.uuid_cache_diffs[uuid] = diff[i*batch_offset:(i+1)*batch_offset, ...] + + def has_first_cond_uuid(self, uuids: list[UUID]) -> bool: + return self.first_cond_uuid in uuids + + def check_metadata(self, x: torch.Tensor) -> bool: + metadata = (x.device, x.dtype, x.shape[1:]) + if self.state_metadata is None: + self.state_metadata = metadata + return True + if metadata == self.state_metadata: + return True + logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state") + self.reset() + return False + + def reset(self): + self.relative_transformation_rate = 0.0 + self.cumulative_change_rate = 0.0 + self.initial_step = True + self.skip_current_step = False + self.output_change_rates = [] + self.first_cond_uuid = None + del self.x_prev_subsampled + self.x_prev_subsampled = None + del self.output_prev_subsampled + self.output_prev_subsampled = None + del self.output_prev_norm + self.output_prev_norm = None + del self.uuid_cache_diffs + self.uuid_cache_diffs = {} + self.total_steps_skipped = 0 + self.state_metadata = None + return self + + def clone(self): + return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + + +class EasyCacheNode(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="EasyCache", + display_name="EasyCache", + description="Native EasyCache implementation.", + category="advanced/debug/model", + is_experimental=True, + inputs=[ + io.Model.Input("model", tooltip="The model to add EasyCache to."), + io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."), + io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of EasyCache."), + io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of EasyCache."), + io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."), + ], + outputs=[ + io.Model.Output(tooltip="The model with EasyCache."), + ], + ) + + @classmethod + def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: + model = model.clone() + model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper) + return io.NodeOutput(model) + + +class LazyCacheHolder: + def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False): + self.name = "LazyCache" + self.reuse_threshold = reuse_threshold + self.start_percent = start_percent + self.end_percent = end_percent + self.subsample_factor = subsample_factor + self.offload_cache_diff = offload_cache_diff + self.verbose = verbose + # timestep values + self.start_t = 0.0 + self.end_t = 0.0 + # control values + self.relative_transformation_rate: float = None + self.cumulative_change_rate = 0.0 + self.initial_step = True + # cache values + self.x_prev_subsampled: torch.Tensor = None + self.output_prev_subsampled: torch.Tensor = None + self.output_prev_norm: torch.Tensor = None + self.cache_diff: torch.Tensor = None + self.output_change_rates = [] + self.approx_output_change_rates = [] + self.total_steps_skipped = 0 + self.state_metadata = None + + def has_cache_diff(self) -> bool: + return self.cache_diff is not None + + def is_past_end_timestep(self, timestep: float) -> bool: + return not (timestep[0] > self.end_t).item() + + def should_do_easycache(self, timestep: float) -> bool: + return (timestep[0] <= self.start_t).item() + + def has_x_prev_subsampled(self) -> bool: + return self.x_prev_subsampled is not None + + def has_output_prev_subsampled(self) -> bool: + return self.output_prev_subsampled is not None + + def has_output_prev_norm(self) -> bool: + return self.output_prev_norm is not None + + def has_relative_transformation_rate(self) -> bool: + return self.relative_transformation_rate is not None + + def prepare_timesteps(self, model_sampling): + self.start_t = model_sampling.percent_to_sigma(self.start_percent) + self.end_t = model_sampling.percent_to_sigma(self.end_percent) + return self + + def subsample(self, x: torch.Tensor, clone: bool = True) -> torch.Tensor: + if self.subsample_factor > 1: + to_return = x[..., ::self.subsample_factor, ::self.subsample_factor] + if clone: + return to_return.clone() + return to_return + if clone: + return x.clone() + return x + + def apply_cache_diff(self, x: torch.Tensor): + self.total_steps_skipped += 1 + return x + self.cache_diff.to(x.device) + + def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor): + self.cache_diff = output - x + + def check_metadata(self, x: torch.Tensor) -> bool: + metadata = (x.device, x.dtype, x.shape) + if self.state_metadata is None: + self.state_metadata = metadata + return True + if metadata == self.state_metadata: + return True + logging.warn(f"{self.name} - Tensor shape, dtype or device changed, resetting state") + self.reset() + return False + + def reset(self): + self.relative_transformation_rate = 0.0 + self.cumulative_change_rate = 0.0 + self.initial_step = True + self.output_change_rates = [] + self.approx_output_change_rates = [] + del self.cache_diff + self.cache_diff = None + del self.x_prev_subsampled + self.x_prev_subsampled = None + del self.output_prev_subsampled + self.output_prev_subsampled = None + del self.output_prev_norm + self.output_prev_norm = None + self.total_steps_skipped = 0 + self.state_metadata = None + return self + + def clone(self): + return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose) + +class LazyCacheNode(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="LazyCache", + display_name="LazyCache", + description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.", + category="advanced/debug/model", + is_experimental=True, + inputs=[ + io.Model.Input("model", tooltip="The model to add LazyCache to."), + io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."), + io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of LazyCache."), + io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of LazyCache."), + io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."), + ], + outputs=[ + io.Model.Output(tooltip="The model with LazyCache."), + ], + ) + + @classmethod + def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput: + model = model.clone() + model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper) + model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper) + return io.NodeOutput(model) + + +class EasyCacheExtension(ComfyExtension): + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EasyCacheNode, + LazyCacheNode, + ] + +def comfy_entrypoint(): + return EasyCacheExtension() diff --git a/comfy_extras/nodes_edit_model.py b/comfy_extras/nodes_edit_model.py new file mode 100644 index 000000000..36da66f34 --- /dev/null +++ b/comfy_extras/nodes_edit_model.py @@ -0,0 +1,38 @@ +import node_helpers +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +class ReferenceLatent(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ReferenceLatent", + category="advanced/conditioning/edit_models", + description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.", + inputs=[ + io.Conditioning.Input("conditioning"), + io.Latent.Input("latent", optional=True), + ], + outputs=[ + io.Conditioning.Output(), + ] + ) + + @classmethod + def execute(cls, conditioning, latent=None) -> io.NodeOutput: + if latent is not None: + conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True) + return io.NodeOutput(conditioning) + + +class EditModelExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + ReferenceLatent, + ] + + +def comfy_entrypoint() -> EditModelExtension: + return EditModelExtension() diff --git a/comfy_extras/nodes_eps.py b/comfy_extras/nodes_eps.py new file mode 100644 index 000000000..4d8061741 --- /dev/null +++ b/comfy_extras/nodes_eps.py @@ -0,0 +1,169 @@ +import torch +from typing_extensions import override + +from comfy.k_diffusion.sampling import sigma_to_half_log_snr +from comfy_api.latest import ComfyExtension, io + + +class EpsilonScaling(io.ComfyNode): + """ + Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models' + (https://arxiv.org/abs/2308.15321v6). + + This method mitigates exposure bias by scaling the predicted noise during sampling, + which can significantly improve sample quality. This implementation uses the "uniform schedule" + recommended by the paper for its practicality and effectiveness. + """ + @classmethod + def define_schema(cls): + return io.Schema( + node_id="Epsilon Scaling", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Float.Input( + "scaling_factor", + default=1.005, + min=0.5, + max=1.5, + step=0.001, + display_mode=io.NumberDisplay.number, + ), + ], + outputs=[ + io.Model.Output(), + ], + ) + + @classmethod + def execute(cls, model, scaling_factor) -> io.NodeOutput: + # Prevent division by zero, though the UI's min value should prevent this. + if scaling_factor == 0: + scaling_factor = 1e-9 + + def epsilon_scaling_function(args): + """ + This function is applied after the CFG guidance has been calculated. + It recalculates the denoised latent by scaling the predicted noise. + """ + denoised = args["denoised"] + x = args["input"] + + noise_pred = x - denoised + + scaled_noise_pred = noise_pred / scaling_factor + + new_denoised = x - scaled_noise_pred + + return new_denoised + + # Clone the model patcher to avoid modifying the original model in place + model_clone = model.clone() + + model_clone.set_model_sampler_post_cfg_function(epsilon_scaling_function) + + return io.NodeOutput(model_clone) + + +def compute_tsr_rescaling_factor( + snr: torch.Tensor, tsr_k: float, tsr_variance: float +) -> torch.Tensor: + """Compute the rescaling score ratio in Temporal Score Rescaling. + + See equation (6) in https://arxiv.org/pdf/2510.01184v1. + """ + posinf_mask = torch.isposinf(snr) + rescaling_factor = (snr * tsr_variance + 1) / (snr * tsr_variance / tsr_k + 1) + return torch.where(posinf_mask, tsr_k, rescaling_factor) # when snr → inf, r = tsr_k + + +class TemporalScoreRescaling(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TemporalScoreRescaling", + display_name="TSR - Temporal Score Rescaling", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Float.Input( + "tsr_k", + tooltip=( + "Controls the rescaling strength.\n" + "Lower k produces more detailed results; higher k produces smoother results in image generation. Setting k = 1 disables rescaling." + ), + default=0.95, + min=0.01, + max=100.0, + step=0.001, + display_mode=io.NumberDisplay.number, + ), + io.Float.Input( + "tsr_sigma", + tooltip=( + "Controls how early rescaling takes effect.\n" + "Larger values take effect earlier." + ), + default=1.0, + min=0.01, + max=100.0, + step=0.001, + display_mode=io.NumberDisplay.number, + ), + ], + outputs=[ + io.Model.Output( + display_name="patched_model", + ), + ], + description=( + "[Post-CFG Function]\n" + "TSR - Temporal Score Rescaling (2510.01184)\n\n" + "Rescaling the model's score or noise to steer the sampling diversity.\n" + ), + ) + + @classmethod + def execute(cls, model, tsr_k, tsr_sigma) -> io.NodeOutput: + tsr_variance = tsr_sigma**2 + + def temporal_score_rescaling(args): + denoised = args["denoised"] + x = args["input"] + sigma = args["sigma"] + curr_model = args["model"] + + # No rescaling (r = 1) or no noise + if tsr_k == 1 or sigma == 0: + return denoised + + model_sampling = curr_model.current_patcher.get_model_object("model_sampling") + half_log_snr = sigma_to_half_log_snr(sigma, model_sampling) + snr = (2 * half_log_snr).exp() + + # No rescaling needed (r = 1) + if snr == 0: + return denoised + + rescaling_r = compute_tsr_rescaling_factor(snr, tsr_k, tsr_variance) + + # Derived from scaled_denoised = (x - r * sigma * noise) / alpha + alpha = sigma * half_log_snr.exp() + return torch.lerp(x / alpha, denoised, rescaling_r) + + m = model.clone() + m.set_model_sampler_post_cfg_function(temporal_score_rescaling) + return io.NodeOutput(m) + + +class EpsilonScalingExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EpsilonScaling, + TemporalScoreRescaling, + ] + + +async def comfy_entrypoint() -> EpsilonScalingExtension: + return EpsilonScalingExtension() diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py index 2ae23f735..ce1b2e89f 100644 --- a/comfy_extras/nodes_flux.py +++ b/comfy_extras/nodes_flux.py @@ -1,44 +1,170 @@ import node_helpers +import comfy.utils +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -class CLIPTextEncodeFlux: + +class CLIPTextEncodeFlux(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeFlux", + category="advanced/conditioning/flux", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("clip_l", multiline=True, dynamic_prompts=True), + io.String.Input("t5xxl", multiline=True, dynamic_prompts=True), + io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) - CATEGORY = "advanced/conditioning/flux" - - def encode(self, clip, clip_l, t5xxl, guidance): + @classmethod + def execute(cls, clip, clip_l, t5xxl, guidance) -> io.NodeOutput: tokens = clip.tokenize(clip_l) tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), ) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance})) -class FluxGuidance: + encode = execute # TODO: remove + + +class FluxGuidance(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "conditioning": ("CONDITIONING", ), - "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}), - }} + def define_schema(cls): + return io.Schema( + node_id="FluxGuidance", + category="advanced/conditioning/flux", + inputs=[ + io.Conditioning.Input("conditioning"), + io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "append" - - CATEGORY = "advanced/conditioning/flux" - - def append(self, conditioning, guidance): + @classmethod + def execute(cls, conditioning, guidance) -> io.NodeOutput: c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance}) - return (c, ) + return io.NodeOutput(c) + + append = execute # TODO: remove -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeFlux": CLIPTextEncodeFlux, - "FluxGuidance": FluxGuidance, -} +class FluxDisableGuidance(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FluxDisableGuidance", + category="advanced/conditioning/flux", + description="This node completely disables the guidance embed on Flux and Flux like models", + inputs=[ + io.Conditioning.Input("conditioning"), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, conditioning) -> io.NodeOutput: + c = node_helpers.conditioning_set_values(conditioning, {"guidance": None}) + return io.NodeOutput(c) + + append = execute # TODO: remove + + +PREFERED_KONTEXT_RESOLUTIONS = [ + (672, 1568), + (688, 1504), + (720, 1456), + (752, 1392), + (800, 1328), + (832, 1248), + (880, 1184), + (944, 1104), + (1024, 1024), + (1104, 944), + (1184, 880), + (1248, 832), + (1328, 800), + (1392, 752), + (1456, 720), + (1504, 688), + (1568, 672), +] + + +class FluxKontextImageScale(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FluxKontextImageScale", + category="advanced/conditioning/flux", + description="This node resizes the image to one that is more optimal for flux kontext.", + inputs=[ + io.Image.Input("image"), + ], + outputs=[ + io.Image.Output(), + ], + ) + + @classmethod + def execute(cls, image) -> io.NodeOutput: + width = image.shape[2] + height = image.shape[1] + aspect_ratio = width / height + _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS) + image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1) + return io.NodeOutput(image) + + scale = execute # TODO: remove + + +class FluxKontextMultiReferenceLatentMethod(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FluxKontextMultiReferenceLatentMethod", + category="advanced/conditioning/flux", + inputs=[ + io.Conditioning.Input("conditioning"), + io.Combo.Input( + "reference_latents_method", + options=["offset", "index", "uxo/uno"], + ), + ], + outputs=[ + io.Conditioning.Output(), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, conditioning, reference_latents_method) -> io.NodeOutput: + if "uxo" in reference_latents_method or "uso" in reference_latents_method: + reference_latents_method = "uxo" + c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method}) + return io.NodeOutput(c) + + append = execute # TODO: remove + + +class FluxExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeFlux, + FluxGuidance, + FluxDisableGuidance, + FluxKontextImageScale, + FluxKontextMultiReferenceLatentMethod, + ] + + +async def comfy_entrypoint() -> FluxExtension: + return FluxExtension() diff --git a/comfy_extras/nodes_fresca.py b/comfy_extras/nodes_fresca.py new file mode 100644 index 000000000..f308eb0c1 --- /dev/null +++ b/comfy_extras/nodes_fresca.py @@ -0,0 +1,114 @@ +# Code based on https://github.com/WikiChao/FreSca (MIT License) +import torch +import torch.fft as fft +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20): + """ + Apply frequency-dependent scaling to an image tensor using Fourier transforms. + + Parameters: + x: Input tensor of shape (B, C, H, W) + scale_low: Scaling factor for low-frequency components (default: 1.0) + scale_high: Scaling factor for high-frequency components (default: 1.5) + freq_cutoff: Number of frequency indices around center to consider as low-frequency (default: 20) + + Returns: + x_filtered: Filtered version of x in spatial domain with frequency-specific scaling applied. + """ + # Preserve input dtype and device + dtype, device = x.dtype, x.device + + # Convert to float32 for FFT computations + x = x.to(torch.float32) + + # 1) Apply FFT and shift low frequencies to center + x_freq = fft.fftn(x, dim=(-2, -1)) + x_freq = fft.fftshift(x_freq, dim=(-2, -1)) + + # Initialize mask with high-frequency scaling factor + mask = torch.ones(x_freq.shape, device=device) * scale_high + m = mask + for d in range(len(x_freq.shape) - 2): + dim = d + 2 + cc = x_freq.shape[dim] // 2 + f_c = min(freq_cutoff, cc) + m = m.narrow(dim, cc - f_c, f_c * 2) + + # Apply low-frequency scaling factor to center region + m[:] = scale_low + + # 3) Apply frequency-specific scaling + x_freq = x_freq * mask + + # 4) Convert back to spatial domain + x_freq = fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real + + # 5) Restore original dtype + x_filtered = x_filtered.to(dtype) + + return x_filtered + + +class FreSca(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FreSca", + display_name="FreSca", + category="_for_testing", + description="Applies frequency-dependent scaling to the guidance", + inputs=[ + io.Model.Input("model"), + io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01, + tooltip="Scaling factor for low-frequency components"), + io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01, + tooltip="Scaling factor for high-frequency components"), + io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1, + tooltip="Number of frequency indices around center to consider as low-frequency"), + ], + outputs=[ + io.Model.Output(), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, model, scale_low, scale_high, freq_cutoff): + def custom_cfg_function(args): + conds_out = args["conds_out"] + if len(conds_out) <= 1 or None in args["conds"][:2]: + return conds_out + cond = conds_out[0] + uncond = conds_out[1] + + guidance = cond - uncond + filtered_guidance = Fourier_filter( + guidance, + scale_low=scale_low, + scale_high=scale_high, + freq_cutoff=freq_cutoff, + ) + filtered_cond = filtered_guidance + uncond + + return [filtered_cond, uncond] + conds_out[2:] + + m = model.clone() + m.set_model_sampler_pre_cfg_function(custom_cfg_function) + + return io.NodeOutput(m) + + +class FreScaExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + FreSca, + ] + + +async def comfy_entrypoint() -> FreScaExtension: + return FreScaExtension() diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py index 47b1dd049..25367560a 100644 --- a/comfy_extras/nodes_gits.py +++ b/comfy_extras/nodes_gits.py @@ -1,6 +1,8 @@ # from https://github.com/zju-pi/diff-sampler/tree/main/gits-main import numpy as np import torch +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io def loglinear_interp(t_steps, num_steps): """ @@ -333,25 +335,28 @@ NOISE_LEVELS = { ], } -class GITSScheduler: +class GITSScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}), - "steps": ("INT", {"default": 10, "min": 2, "max": 1000}), - "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - } - } - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" + def define_schema(cls): + return io.Schema( + node_id="GITSScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05), + io.Int.Input("steps", default=10, min=2, max=1000), + io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Sigmas.Output(), + ], + ) - FUNCTION = "get_sigmas" - - def get_sigmas(self, coeff, steps, denoise): + @classmethod + def execute(cls, coeff, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: - return (torch.FloatTensor([]),) + return io.NodeOutput(torch.FloatTensor([])) total_steps = round(steps * denoise) if steps <= 20: @@ -362,8 +367,16 @@ class GITSScheduler: sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 - return (torch.FloatTensor(sigmas), ) + return io.NodeOutput(torch.FloatTensor(sigmas)) -NODE_CLASS_MAPPINGS = { - "GITSScheduler": GITSScheduler, -} + +class GITSSchedulerExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + GITSScheduler, + ] + + +async def comfy_entrypoint() -> GITSSchedulerExtension: + return GITSSchedulerExtension() diff --git a/comfy_extras/nodes_hidream.py b/comfy_extras/nodes_hidream.py new file mode 100644 index 000000000..eee683ee1 --- /dev/null +++ b/comfy_extras/nodes_hidream.py @@ -0,0 +1,73 @@ +from typing_extensions import override + +import folder_paths +import comfy.sd +import comfy.model_management +from comfy_api.latest import ComfyExtension, io + + +class QuadrupleCLIPLoader(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="QuadrupleCLIPLoader", + category="advanced/loaders", + description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct", + inputs=[ + io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), + io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), + io.Combo.Input("clip_name3", options=folder_paths.get_filename_list("text_encoders")), + io.Combo.Input("clip_name4", options=folder_paths.get_filename_list("text_encoders")), + ], + outputs=[ + io.Clip.Output(), + ] + ) + + @classmethod + def execute(cls, clip_name1, clip_name2, clip_name3, clip_name4): + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) + clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) + clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) + clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4) + clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings")) + return io.NodeOutput(clip) + +class CLIPTextEncodeHiDream(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeHiDream", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("clip_l", multiline=True, dynamic_prompts=True), + io.String.Input("clip_g", multiline=True, dynamic_prompts=True), + io.String.Input("t5xxl", multiline=True, dynamic_prompts=True), + io.String.Input("llama", multiline=True, dynamic_prompts=True), + ], + outputs=[ + io.Conditioning.Output(), + ] + ) + + @classmethod + def execute(cls, clip, clip_l, clip_g, t5xxl, llama): + tokens = clip.tokenize(clip_g) + tokens["l"] = clip.tokenize(clip_l)["l"] + tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"] + tokens["llama"] = clip.tokenize(llama)["llama"] + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens)) + + +class HiDreamExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + QuadrupleCLIPLoader, + CLIPTextEncodeHiDream, + ] + + +async def comfy_entrypoint() -> HiDreamExtension: + return HiDreamExtension() diff --git a/comfy_extras/nodes_hooks.py b/comfy_extras/nodes_hooks.py index 9d9d48378..1edc06f3d 100644 --- a/comfy_extras/nodes_hooks.py +++ b/comfy_extras/nodes_hooks.py @@ -246,7 +246,7 @@ class SetClipHooks: CATEGORY = "advanced/hooks/clip" FUNCTION = "apply_hooks" - def apply_hooks(self, clip: 'CLIP', schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None): + def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None): if hooks is not None: clip = clip.clone() if apply_to_conds: @@ -255,7 +255,7 @@ class SetClipHooks: clip.use_clip_schedule = schedule_clip if not clip.use_clip_schedule: clip.patcher.forced_hooks.set_keyframes_on_hooks(None) - clip.patcher.register_all_hook_patches(hooks.get_dict_repr(), comfy.hooks.EnumWeightTarget.Clip) + clip.patcher.register_all_hook_patches(hooks, comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Clip)) return (clip,) class ConditioningTimestepsRange: diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py index d6408269f..f7c34d059 100644 --- a/comfy_extras/nodes_hunyuan.py +++ b/comfy_extras/nodes_hunyuan.py @@ -1,44 +1,220 @@ import nodes +import node_helpers import torch import comfy.model_management +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -class CLIPTextEncodeHunyuanDiT: +class CLIPTextEncodeHunyuanDiT(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeHunyuanDiT", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("bert", multiline=True, dynamic_prompts=True), + io.String.Input("mt5xl", multiline=True, dynamic_prompts=True), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) - CATEGORY = "advanced/conditioning" - - def encode(self, clip, bert, mt5xl): + @classmethod + def execute(cls, clip, bert, mt5xl) -> io.NodeOutput: tokens = clip.tokenize(bert) tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"] - return (clip.encode_from_tokens_scheduled(tokens), ) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens)) -class EmptyHunyuanLatentVideo: + encode = execute # TODO: remove + + +class EmptyHunyuanLatentVideo(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" + def define_schema(cls): + return io.Schema( + node_id="EmptyHunyuanLatentVideo", + category="latent/video", + inputs=[ + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=25, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples":latent}, ) + return io.NodeOutput({"samples":latent}) -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT, - "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo, -} + generate = execute # TODO: remove + + +PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextEncodeHunyuanVideo_ImageToVideo", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.ClipVisionOutput.Input("clip_vision_output"), + io.String.Input("prompt", multiline=True, dynamic_prompts=True), + io.Int.Input( + "image_interleave", + default=2, + min=1, + max=512, + tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.", + ), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput: + tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens)) + + encode = execute # TODO: remove + + +class HunyuanImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="HunyuanImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Vae.Input("vae"), + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=53, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]), + io.Image.Input("start_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + out_latent = {} + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length, :, :, :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + concat_latent_image = vae.encode(start_image) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + if guidance_type == "v1 (concat)": + cond = {"concat_latent_image": concat_latent_image, "concat_mask": mask} + elif guidance_type == "v2 (replace)": + cond = {'guiding_frame_index': 0} + latent[:, :, :concat_latent_image.shape[2]] = concat_latent_image + out_latent["noise_mask"] = mask + elif guidance_type == "custom": + cond = {"ref_latent": concat_latent_image} + + positive = node_helpers.conditioning_set_values(positive, cond) + + out_latent["samples"] = latent + return io.NodeOutput(positive, out_latent) + + encode = execute # TODO: remove + + +class EmptyHunyuanImageLatent(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="EmptyHunyuanImageLatent", + category="latent", + inputs=[ + io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, width, height, batch_size=1) -> io.NodeOutput: + latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples":latent}) + + generate = execute # TODO: remove + + +class HunyuanRefinerLatent(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="HunyuanRefinerLatent", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Latent.Input("latent"), + io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01), + + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput: + latent = latent["samples"] + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation}) + out_latent = {} + out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + return io.NodeOutput(positive, negative, out_latent) + + +class HunyuanExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeHunyuanDiT, + TextEncodeHunyuanVideo_ImageToVideo, + EmptyHunyuanLatentVideo, + HunyuanImageToVideo, + EmptyHunyuanImageLatent, + HunyuanRefinerLatent, + ] + + +async def comfy_entrypoint() -> HunyuanExtension: + return HunyuanExtension() diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py new file mode 100644 index 000000000..f6e71e0a8 --- /dev/null +++ b/comfy_extras/nodes_hunyuan3d.py @@ -0,0 +1,630 @@ +import torch +import os +import json +import struct +import numpy as np +from comfy.ldm.modules.diffusionmodules.mmdit import get_1d_sincos_pos_embed_from_grid_torch +import folder_paths +import comfy.model_management +from comfy.cli_args import args + +class EmptyLatentHunyuan3Dv2: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "resolution": ("INT", {"default": 3072, "min": 1, "max": 8192}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent/3d" + + def generate(self, resolution, batch_size): + latent = torch.zeros([batch_size, 64, resolution], device=comfy.model_management.intermediate_device()) + return ({"samples": latent, "type": "hunyuan3dv2"}, ) + +class Hunyuan3Dv2Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"clip_vision_output": ("CLIP_VISION_OUTPUT",), + }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision_output): + embeds = clip_vision_output.last_hidden_state + positive = [[embeds, {}]] + negative = [[torch.zeros_like(embeds), {}]] + return (positive, negative) + + +class Hunyuan3Dv2ConditioningMultiView: + @classmethod + def INPUT_TYPES(s): + return {"required": {}, + "optional": {"front": ("CLIP_VISION_OUTPUT",), + "left": ("CLIP_VISION_OUTPUT",), + "back": ("CLIP_VISION_OUTPUT",), + "right": ("CLIP_VISION_OUTPUT",), }} + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("positive", "negative") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, front=None, left=None, back=None, right=None): + all_embeds = [front, left, back, right] + out = [] + pos_embeds = None + for i, e in enumerate(all_embeds): + if e is not None: + if pos_embeds is None: + pos_embeds = get_1d_sincos_pos_embed_from_grid_torch(e.last_hidden_state.shape[-1], torch.arange(4)) + out.append(e.last_hidden_state + pos_embeds[i].reshape(1, 1, -1)) + + embeds = torch.cat(out, dim=1) + positive = [[embeds, {}]] + negative = [[torch.zeros_like(embeds), {}]] + return (positive, negative) + + +class VOXEL: + def __init__(self, data): + self.data = data + +class VAEDecodeHunyuan3D: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), + "vae": ("VAE", ), + "num_chunks": ("INT", {"default": 8000, "min": 1000, "max": 500000}), + "octree_resolution": ("INT", {"default": 256, "min": 16, "max": 512}), + }} + RETURN_TYPES = ("VOXEL",) + FUNCTION = "decode" + + CATEGORY = "latent/3d" + + def decode(self, vae, samples, num_chunks, octree_resolution): + voxels = VOXEL(vae.decode(samples["samples"], vae_options={"num_chunks": num_chunks, "octree_resolution": octree_resolution})) + return (voxels, ) + +def voxel_to_mesh(voxels, threshold=0.5, device=None): + if device is None: + device = torch.device("cpu") + voxels = voxels.to(device) + + binary = (voxels > threshold).float() + padded = torch.nn.functional.pad(binary, (1, 1, 1, 1, 1, 1), 'constant', 0) + + D, H, W = binary.shape + + neighbors = torch.tensor([ + [0, 0, 1], + [0, 0, -1], + [0, 1, 0], + [0, -1, 0], + [1, 0, 0], + [-1, 0, 0] + ], device=device) + + z, y, x = torch.meshgrid( + torch.arange(D, device=device), + torch.arange(H, device=device), + torch.arange(W, device=device), + indexing='ij' + ) + voxel_indices = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) + + solid_mask = binary.flatten() > 0 + solid_indices = voxel_indices[solid_mask] + + corner_offsets = [ + torch.tensor([ + [0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1] + ], device=device), + torch.tensor([ + [0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0] + ], device=device), + torch.tensor([ + [0, 1, 0], [1, 1, 0], [1, 1, 1], [0, 1, 1] + ], device=device), + torch.tensor([ + [0, 0, 0], [0, 0, 1], [1, 0, 1], [1, 0, 0] + ], device=device), + torch.tensor([ + [1, 0, 1], [1, 1, 1], [1, 1, 0], [1, 0, 0] + ], device=device), + torch.tensor([ + [0, 1, 0], [0, 1, 1], [0, 0, 1], [0, 0, 0] + ], device=device) + ] + + all_vertices = [] + all_indices = [] + + vertex_count = 0 + + for face_idx, offset in enumerate(neighbors): + neighbor_indices = solid_indices + offset + + padded_indices = neighbor_indices + 1 + + is_exposed = padded[ + padded_indices[:, 0], + padded_indices[:, 1], + padded_indices[:, 2] + ] == 0 + + if not is_exposed.any(): + continue + + exposed_indices = solid_indices[is_exposed] + + corners = corner_offsets[face_idx].unsqueeze(0) + + face_vertices = exposed_indices.unsqueeze(1) + corners + + all_vertices.append(face_vertices.reshape(-1, 3)) + + num_faces = exposed_indices.shape[0] + face_indices = torch.arange( + vertex_count, + vertex_count + 4 * num_faces, + device=device + ).reshape(-1, 4) + + all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 1], face_indices[:, 2]], dim=1)) + all_indices.append(torch.stack([face_indices[:, 0], face_indices[:, 2], face_indices[:, 3]], dim=1)) + + vertex_count += 4 * num_faces + + if len(all_vertices) > 0: + vertices = torch.cat(all_vertices, dim=0) + faces = torch.cat(all_indices, dim=0) + else: + vertices = torch.zeros((1, 3)) + faces = torch.zeros((1, 3)) + + v_min = 0 + v_max = max(voxels.shape) + + vertices = vertices - (v_min + v_max) / 2 + + scale = (v_max - v_min) / 2 + if scale > 0: + vertices = vertices / scale + + vertices = torch.fliplr(vertices) + return vertices, faces + +def voxel_to_mesh_surfnet(voxels, threshold=0.5, device=None): + if device is None: + device = torch.device("cpu") + voxels = voxels.to(device) + + D, H, W = voxels.shape + + padded = torch.nn.functional.pad(voxels, (1, 1, 1, 1, 1, 1), 'constant', 0) + z, y, x = torch.meshgrid( + torch.arange(D, device=device), + torch.arange(H, device=device), + torch.arange(W, device=device), + indexing='ij' + ) + cell_positions = torch.stack([z.flatten(), y.flatten(), x.flatten()], dim=1) + + corner_offsets = torch.tensor([ + [0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], + [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1] + ], device=device) + + pos = cell_positions.unsqueeze(1) + corner_offsets.unsqueeze(0) + z_idx, y_idx, x_idx = pos.unbind(-1) + corner_values = padded[z_idx, y_idx, x_idx] + + corner_signs = corner_values > threshold + has_inside = torch.any(corner_signs, dim=1) + has_outside = torch.any(~corner_signs, dim=1) + contains_surface = has_inside & has_outside + + active_cells = cell_positions[contains_surface] + active_signs = corner_signs[contains_surface] + active_values = corner_values[contains_surface] + + if active_cells.shape[0] == 0: + return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) + + edges = torch.tensor([ + [0, 1], [0, 2], [0, 4], [1, 3], + [1, 5], [2, 3], [2, 6], [3, 7], + [4, 5], [4, 6], [5, 7], [6, 7] + ], device=device) + + cell_vertices = {} + progress = comfy.utils.ProgressBar(100) + + for edge_idx, (e1, e2) in enumerate(edges): + progress.update(1) + crossing = active_signs[:, e1] != active_signs[:, e2] + if not crossing.any(): + continue + + cell_indices = torch.nonzero(crossing, as_tuple=True)[0] + + v1 = active_values[cell_indices, e1] + v2 = active_values[cell_indices, e2] + + t = torch.zeros_like(v1, device=device) + denom = v2 - v1 + valid = denom != 0 + t[valid] = (threshold - v1[valid]) / denom[valid] + t[~valid] = 0.5 + + p1 = corner_offsets[e1].float() + p2 = corner_offsets[e2].float() + + intersection = p1.unsqueeze(0) + t.unsqueeze(1) * (p2.unsqueeze(0) - p1.unsqueeze(0)) + + for i, point in zip(cell_indices.tolist(), intersection): + if i not in cell_vertices: + cell_vertices[i] = [] + cell_vertices[i].append(point) + + # Calculate the final vertices as the average of intersection points for each cell + vertices = [] + vertex_lookup = {} + + vert_progress_mod = round(len(cell_vertices)/50) + + for i, points in cell_vertices.items(): + if not i % vert_progress_mod: + progress.update(1) + + if points: + vertex = torch.stack(points).mean(dim=0) + vertex = vertex + active_cells[i].float() + vertex_lookup[tuple(active_cells[i].tolist())] = len(vertices) + vertices.append(vertex) + + if not vertices: + return torch.zeros((0, 3), device=device), torch.zeros((0, 3), dtype=torch.long, device=device) + + final_vertices = torch.stack(vertices) + + inside_corners_mask = active_signs + outside_corners_mask = ~active_signs + + inside_counts = inside_corners_mask.sum(dim=1, keepdim=True).float() + outside_counts = outside_corners_mask.sum(dim=1, keepdim=True).float() + + inside_pos = torch.zeros((active_cells.shape[0], 3), device=device) + outside_pos = torch.zeros((active_cells.shape[0], 3), device=device) + + for i in range(8): + mask_inside = inside_corners_mask[:, i].unsqueeze(1) + mask_outside = outside_corners_mask[:, i].unsqueeze(1) + inside_pos += corner_offsets[i].float().unsqueeze(0) * mask_inside + outside_pos += corner_offsets[i].float().unsqueeze(0) * mask_outside + + inside_pos /= inside_counts + outside_pos /= outside_counts + gradients = inside_pos - outside_pos + + pos_dirs = torch.tensor([ + [1, 0, 0], + [0, 1, 0], + [0, 0, 1] + ], device=device) + + cross_products = [ + torch.linalg.cross(pos_dirs[i].float(), pos_dirs[j].float()) + for i in range(3) for j in range(i+1, 3) + ] + + faces = [] + all_keys = set(vertex_lookup.keys()) + + face_progress_mod = round(len(active_cells)/38*3) + + for pair_idx, (i, j) in enumerate([(0,1), (0,2), (1,2)]): + dir_i = pos_dirs[i] + dir_j = pos_dirs[j] + cross_product = cross_products[pair_idx] + + ni_positions = active_cells + dir_i + nj_positions = active_cells + dir_j + diag_positions = active_cells + dir_i + dir_j + + alignments = torch.matmul(gradients, cross_product) + + valid_quads = [] + quad_indices = [] + + for idx, active_cell in enumerate(active_cells): + if not idx % face_progress_mod: + progress.update(1) + cell_key = tuple(active_cell.tolist()) + ni_key = tuple(ni_positions[idx].tolist()) + nj_key = tuple(nj_positions[idx].tolist()) + diag_key = tuple(diag_positions[idx].tolist()) + + if cell_key in all_keys and ni_key in all_keys and nj_key in all_keys and diag_key in all_keys: + v0 = vertex_lookup[cell_key] + v1 = vertex_lookup[ni_key] + v2 = vertex_lookup[nj_key] + v3 = vertex_lookup[diag_key] + + valid_quads.append((v0, v1, v2, v3)) + quad_indices.append(idx) + + for q_idx, (v0, v1, v2, v3) in enumerate(valid_quads): + cell_idx = quad_indices[q_idx] + if alignments[cell_idx] > 0: + faces.append(torch.tensor([v0, v1, v3], device=device, dtype=torch.long)) + faces.append(torch.tensor([v0, v3, v2], device=device, dtype=torch.long)) + else: + faces.append(torch.tensor([v0, v3, v1], device=device, dtype=torch.long)) + faces.append(torch.tensor([v0, v2, v3], device=device, dtype=torch.long)) + + if faces: + faces = torch.stack(faces) + else: + faces = torch.zeros((0, 3), dtype=torch.long, device=device) + + v_min = 0 + v_max = max(D, H, W) + + final_vertices = final_vertices - (v_min + v_max) / 2 + + scale = (v_max - v_min) / 2 + if scale > 0: + final_vertices = final_vertices / scale + + final_vertices = torch.fliplr(final_vertices) + + return final_vertices, faces + +class MESH: + def __init__(self, vertices, faces): + self.vertices = vertices + self.faces = faces + + +class VoxelToMeshBasic: + @classmethod + def INPUT_TYPES(s): + return {"required": {"voxel": ("VOXEL", ), + "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MESH",) + FUNCTION = "decode" + + CATEGORY = "3d" + + def decode(self, voxel, threshold): + vertices = [] + faces = [] + for x in voxel.data: + v, f = voxel_to_mesh(x, threshold=threshold, device=None) + vertices.append(v) + faces.append(f) + + return (MESH(torch.stack(vertices), torch.stack(faces)), ) + +class VoxelToMesh: + @classmethod + def INPUT_TYPES(s): + return {"required": {"voxel": ("VOXEL", ), + "algorithm": (["surface net", "basic"], ), + "threshold": ("FLOAT", {"default": 0.6, "min": -1.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MESH",) + FUNCTION = "decode" + + CATEGORY = "3d" + + def decode(self, voxel, algorithm, threshold): + vertices = [] + faces = [] + + if algorithm == "basic": + mesh_function = voxel_to_mesh + elif algorithm == "surface net": + mesh_function = voxel_to_mesh_surfnet + + for x in voxel.data: + v, f = mesh_function(x, threshold=threshold, device=None) + vertices.append(v) + faces.append(f) + + return (MESH(torch.stack(vertices), torch.stack(faces)), ) + + +def save_glb(vertices, faces, filepath, metadata=None): + """ + Save PyTorch tensor vertices and faces as a GLB file without external dependencies. + + Parameters: + vertices: torch.Tensor of shape (N, 3) - The vertex coordinates + faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces) + filepath: str - Output filepath (should end with .glb) + """ + + # Convert tensors to numpy arrays + vertices_np = vertices.cpu().numpy().astype(np.float32) + faces_np = faces.cpu().numpy().astype(np.uint32) + + vertices_buffer = vertices_np.tobytes() + indices_buffer = faces_np.tobytes() + + def pad_to_4_bytes(buffer): + padding_length = (4 - (len(buffer) % 4)) % 4 + return buffer + b'\x00' * padding_length + + vertices_buffer_padded = pad_to_4_bytes(vertices_buffer) + indices_buffer_padded = pad_to_4_bytes(indices_buffer) + + buffer_data = vertices_buffer_padded + indices_buffer_padded + + vertices_byte_length = len(vertices_buffer) + vertices_byte_offset = 0 + indices_byte_length = len(indices_buffer) + indices_byte_offset = len(vertices_buffer_padded) + + gltf = { + "asset": {"version": "2.0", "generator": "ComfyUI"}, + "buffers": [ + { + "byteLength": len(buffer_data) + } + ], + "bufferViews": [ + { + "buffer": 0, + "byteOffset": vertices_byte_offset, + "byteLength": vertices_byte_length, + "target": 34962 # ARRAY_BUFFER + }, + { + "buffer": 0, + "byteOffset": indices_byte_offset, + "byteLength": indices_byte_length, + "target": 34963 # ELEMENT_ARRAY_BUFFER + } + ], + "accessors": [ + { + "bufferView": 0, + "byteOffset": 0, + "componentType": 5126, # FLOAT + "count": len(vertices_np), + "type": "VEC3", + "max": vertices_np.max(axis=0).tolist(), + "min": vertices_np.min(axis=0).tolist() + }, + { + "bufferView": 1, + "byteOffset": 0, + "componentType": 5125, # UNSIGNED_INT + "count": faces_np.size, + "type": "SCALAR" + } + ], + "meshes": [ + { + "primitives": [ + { + "attributes": { + "POSITION": 0 + }, + "indices": 1, + "mode": 4 # TRIANGLES + } + ] + } + ], + "nodes": [ + { + "mesh": 0 + } + ], + "scenes": [ + { + "nodes": [0] + } + ], + "scene": 0 + } + + if metadata is not None: + gltf["asset"]["extras"] = metadata + + # Convert the JSON to bytes + gltf_json = json.dumps(gltf).encode('utf8') + + def pad_json_to_4_bytes(buffer): + padding_length = (4 - (len(buffer) % 4)) % 4 + return buffer + b' ' * padding_length + + gltf_json_padded = pad_json_to_4_bytes(gltf_json) + + # Create the GLB header + # Magic glTF + glb_header = struct.pack('<4sII', b'glTF', 2, 12 + 8 + len(gltf_json_padded) + 8 + len(buffer_data)) + + # Create JSON chunk header (chunk type 0) + json_chunk_header = struct.pack(' int: min_value = min(min_value, value) @@ -20,25 +22,31 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: return ns[idx] -class HyperTile: +class HyperTile(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), - "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), - "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), - "scale_depth": ("BOOLEAN", {"default": False}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="HyperTile", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Int.Input("tile_size", default=256, min=1, max=2048), + io.Int.Input("swap_size", default=2, min=1, max=128), + io.Int.Input("max_depth", default=0, min=0, max=10), + io.Boolean.Input("scale_depth", default=False), + ], + outputs=[ + io.Model.Output(), + ], + ) - CATEGORY = "model_patches/unet" - - def patch(self, model, tile_size, swap_size, max_depth, scale_depth): + @classmethod + def execute(cls, model, tile_size, swap_size, max_depth, scale_depth) -> io.NodeOutput: latent_tile_size = max(32, tile_size) // 8 - self.temp = None + temp = None def hypertile_in(q, k, v, extra_options): + nonlocal temp model_chans = q.shape[-2] orig_shape = extra_options['original_shape'] apply_to = [] @@ -58,14 +66,15 @@ class HyperTile: if nh * nw > 1: q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) - self.temp = (nh, nw, h, w) + temp = (nh, nw, h, w) return q, k, v return q, k, v def hypertile_out(out, extra_options): - if self.temp is not None: - nh, nw, h, w = self.temp - self.temp = None + nonlocal temp + if temp is not None: + nh, nw, h, w = temp + temp = None out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) return out @@ -76,6 +85,14 @@ class HyperTile: m.set_model_attn1_output_patch(hypertile_out) return (m, ) -NODE_CLASS_MAPPINGS = { - "HyperTile": HyperTile, -} + +class HyperTileExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + HyperTile, + ] + + +async def comfy_entrypoint() -> HyperTileExtension: + return HyperTileExtension() diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py index af37666b2..392aea32c 100644 --- a/comfy_extras/nodes_images.py +++ b/comfy_extras/nodes_images.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import nodes import folder_paths from comfy.cli_args import args @@ -8,6 +10,14 @@ from PIL.PngImagePlugin import PngInfo import numpy as np import json import os +import re +from io import BytesIO +from inspect import cleandoc +import torch +import comfy.utils + +from comfy.comfy_types import FileLocator, IO +from server import PromptServer MAX_RESOLUTION = nodes.MAX_RESOLUTION @@ -67,6 +77,24 @@ class ImageFromBatch: s = s_in[batch_index:batch_index + length].clone() return (s,) + +class ImageAddNoise: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), + "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image" + + def repeat(self, image, seed, strength): + generator = torch.manual_seed(seed) + s = torch.clip((image + strength * torch.randn(image.size(), generator=generator, device="cpu").to(image)), min=0.0, max=1.0) + return (s,) + class SaveAnimatedWEBP: def __init__(self): self.output_dir = folder_paths.get_output_directory() @@ -99,7 +127,7 @@ class SaveAnimatedWEBP: method = self.methods.get(method) filename_prefix += self.prefix_append full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) - results = list() + results: list[FileLocator] = [] pil_images = [] for image in images: i = 255. * image.cpu().numpy() @@ -186,10 +214,461 @@ class SaveAnimatedPNG: return { "ui": { "images": results, "animated": (True,)} } +class SVG: + """ + Stores SVG representations via a list of BytesIO objects. + """ + def __init__(self, data: list[BytesIO]): + self.data = data + + def combine(self, other: 'SVG') -> 'SVG': + return SVG(self.data + other.data) + + @staticmethod + def combine_all(svgs: list['SVG']) -> 'SVG': + all_svgs_list: list[BytesIO] = [] + for svg_item in svgs: + all_svgs_list.extend(svg_item.data) + return SVG(all_svgs_list) + + +class ImageStitch: + """Upstreamed from https://github.com/kijai/ComfyUI-KJNodes""" + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "direction": (["right", "down", "left", "up"], {"default": "right"}), + "match_image_size": ("BOOLEAN", {"default": True}), + "spacing_width": ( + "INT", + {"default": 0, "min": 0, "max": 1024, "step": 2}, + ), + "spacing_color": ( + ["white", "black", "red", "green", "blue"], + {"default": "white"}, + ), + }, + "optional": { + "image2": ("IMAGE",), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "stitch" + CATEGORY = "image/transform" + DESCRIPTION = """ +Stitches image2 to image1 in the specified direction. +If image2 is not provided, returns image1 unchanged. +Optional spacing can be added between images. +""" + + def stitch( + self, + image1, + direction, + match_image_size, + spacing_width, + spacing_color, + image2=None, + ): + if image2 is None: + return (image1,) + + # Handle batch size differences + if image1.shape[0] != image2.shape[0]: + max_batch = max(image1.shape[0], image2.shape[0]) + if image1.shape[0] < max_batch: + image1 = torch.cat( + [image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)] + ) + if image2.shape[0] < max_batch: + image2 = torch.cat( + [image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)] + ) + + # Match image sizes if requested + if match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + aspect_ratio = w2 / h2 + + if direction in ["left", "right"]: + target_h, target_w = h1, int(h1 * aspect_ratio) + else: # up, down + target_w, target_h = w1, int(w1 / aspect_ratio) + + image2 = comfy.utils.common_upscale( + image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled" + ).movedim(1, -1) + + color_map = { + "white": 1.0, + "black": 0.0, + "red": (1.0, 0.0, 0.0), + "green": (0.0, 1.0, 0.0), + "blue": (0.0, 0.0, 1.0), + } + + color_val = color_map[spacing_color] + + # When not matching sizes, pad to align non-concat dimensions + if not match_image_size: + h1, w1 = image1.shape[1:3] + h2, w2 = image2.shape[1:3] + pad_value = 0.0 + if not isinstance(color_val, tuple): + pad_value = color_val + + if direction in ["left", "right"]: + # For horizontal concat, pad heights to match + if h1 != h2: + target_h = max(h1, h2) + if h1 < target_h: + pad_h = target_h - h1 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) + if h2 < target_h: + pad_h = target_h - h2 + pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=pad_value) + else: # up, down + # For vertical concat, pad widths to match + if w1 != w2: + target_w = max(w1, w2) + if w1 < target_w: + pad_w = target_w - w1 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) + if w2 < target_w: + pad_w = target_w - w2 + pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2 + image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=pad_value) + + # Ensure same number of channels + if image1.shape[-1] != image2.shape[-1]: + max_channels = max(image1.shape[-1], image2.shape[-1]) + if image1.shape[-1] < max_channels: + image1 = torch.cat( + [ + image1, + torch.ones( + *image1.shape[:-1], + max_channels - image1.shape[-1], + device=image1.device, + ), + ], + dim=-1, + ) + if image2.shape[-1] < max_channels: + image2 = torch.cat( + [ + image2, + torch.ones( + *image2.shape[:-1], + max_channels - image2.shape[-1], + device=image2.device, + ), + ], + dim=-1, + ) + + # Add spacing if specified + if spacing_width > 0: + spacing_width = spacing_width + (spacing_width % 2) # Ensure even + + if direction in ["left", "right"]: + spacing_shape = ( + image1.shape[0], + max(image1.shape[1], image2.shape[1]), + spacing_width, + image1.shape[-1], + ) + else: + spacing_shape = ( + image1.shape[0], + spacing_width, + max(image1.shape[2], image2.shape[2]), + image1.shape[-1], + ) + + spacing = torch.full(spacing_shape, 0.0, device=image1.device) + if isinstance(color_val, tuple): + for i, c in enumerate(color_val): + if i < spacing.shape[-1]: + spacing[..., i] = c + if spacing.shape[-1] == 4: # Add alpha + spacing[..., 3] = 1.0 + else: + spacing[..., : min(3, spacing.shape[-1])] = color_val + if spacing.shape[-1] == 4: + spacing[..., 3] = 1.0 + + # Concatenate images + images = [image2, image1] if direction in ["left", "up"] else [image1, image2] + if spacing_width > 0: + images.insert(1, spacing) + + concat_dim = 2 if direction in ["left", "right"] else 1 + return (torch.cat(images, dim=concat_dim),) + +class ResizeAndPadImage: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "target_width": ("INT", { + "default": 512, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1 + }), + "target_height": ("INT", { + "default": 512, + "min": 1, + "max": MAX_RESOLUTION, + "step": 1 + }), + "padding_color": (["white", "black"],), + "interpolation": (["area", "bicubic", "nearest-exact", "bilinear", "lanczos"],), + } + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "resize_and_pad" + CATEGORY = "image/transform" + + def resize_and_pad(self, image, target_width, target_height, padding_color, interpolation): + batch_size, orig_height, orig_width, channels = image.shape + + scale_w = target_width / orig_width + scale_h = target_height / orig_height + scale = min(scale_w, scale_h) + + new_width = int(orig_width * scale) + new_height = int(orig_height * scale) + + image_permuted = image.permute(0, 3, 1, 2) + + resized = comfy.utils.common_upscale(image_permuted, new_width, new_height, interpolation, "disabled") + + pad_value = 0.0 if padding_color == "black" else 1.0 + padded = torch.full( + (batch_size, channels, target_height, target_width), + pad_value, + dtype=image.dtype, + device=image.device + ) + + y_offset = (target_height - new_height) // 2 + x_offset = (target_width - new_width) // 2 + + padded[:, :, y_offset:y_offset + new_height, x_offset:x_offset + new_width] = resized + + output = padded.permute(0, 2, 3, 1) + return (output,) + +class SaveSVGNode: + """ + Save SVG files on disk. + """ + + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + self.type = "output" + self.prefix_append = "" + + RETURN_TYPES = () + DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value + FUNCTION = "save_svg" + CATEGORY = "image/save" # Changed + OUTPUT_NODE = True + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "svg": ("SVG",), # Changed + "filename_prefix": ("STRING", {"default": "svg/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}) + }, + "hidden": { + "prompt": "PROMPT", + "extra_pnginfo": "EXTRA_PNGINFO" + } + } + + def save_svg(self, svg: SVG, filename_prefix="svg/ComfyUI", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + results = list() + + # Prepare metadata JSON + metadata_dict = {} + if prompt is not None: + metadata_dict["prompt"] = prompt + if extra_pnginfo is not None: + metadata_dict.update(extra_pnginfo) + + # Convert metadata to JSON string + metadata_json = json.dumps(metadata_dict, indent=2) if metadata_dict else None + + for batch_number, svg_bytes in enumerate(svg.data): + filename_with_batch_num = filename.replace("%batch_num%", str(batch_number)) + file = f"{filename_with_batch_num}_{counter:05}_.svg" + + # Read SVG content + svg_bytes.seek(0) + svg_content = svg_bytes.read().decode('utf-8') + + # Inject metadata if available + if metadata_json: + # Create metadata element with CDATA section + metadata_element = f""" + + + """ + # Insert metadata after opening svg tag using regex with a replacement function + def replacement(match): + # match.group(1) contains the captured tag + return match.group(1) + '\n' + metadata_element + + # Apply the substitution + svg_content = re.sub(r'(]*>)', replacement, svg_content, flags=re.UNICODE) + + # Write the modified SVG to file + with open(os.path.join(full_output_folder, file), 'wb') as svg_file: + svg_file.write(svg_content.encode('utf-8')) + + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + return { "ui": { "images": results } } + +class GetImageSize: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": (IO.IMAGE,), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + } + } + + RETURN_TYPES = (IO.INT, IO.INT, IO.INT) + RETURN_NAMES = ("width", "height", "batch_size") + FUNCTION = "get_size" + + CATEGORY = "image" + DESCRIPTION = """Returns width and height of the image, and passes it through unchanged.""" + + def get_size(self, image, unique_id=None) -> tuple[int, int]: + height = image.shape[1] + width = image.shape[2] + batch_size = image.shape[0] + + # Send progress text to display size on the node + if unique_id: + PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id) + + return width, height, batch_size + +class ImageRotate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": (IO.IMAGE,), + "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), + }} + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "rotate" + + CATEGORY = "image/transform" + + def rotate(self, image, rotation): + rotate_by = 0 + if rotation.startswith("90"): + rotate_by = 1 + elif rotation.startswith("180"): + rotate_by = 2 + elif rotation.startswith("270"): + rotate_by = 3 + + image = torch.rot90(image, k=rotate_by, dims=[2, 1]) + return (image,) + +class ImageFlip: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": (IO.IMAGE,), + "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), + }} + RETURN_TYPES = (IO.IMAGE,) + FUNCTION = "flip" + + CATEGORY = "image/transform" + + def flip(self, image, flip_method): + if flip_method.startswith("x"): + image = torch.flip(image, dims=[1]) + elif flip_method.startswith("y"): + image = torch.flip(image, dims=[2]) + + return (image,) + +class ImageScaleToMaxDimension: + upscale_methods = ["area", "lanczos", "bilinear", "nearest-exact", "bilinear", "bicubic"] + + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "upscale_method": (s.upscale_methods,), + "largest_size": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1})}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, largest_size): + height = image.shape[1] + width = image.shape[2] + + if height > width: + width = round((width / height) * largest_size) + height = largest_size + elif width > height: + height = round((height / width) * largest_size) + width = largest_size + else: + height = largest_size + width = largest_size + + samples = image.movedim(-1, 1) + s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1, -1) + return (s,) + NODE_CLASS_MAPPINGS = { "ImageCrop": ImageCrop, "RepeatImageBatch": RepeatImageBatch, "ImageFromBatch": ImageFromBatch, + "ImageAddNoise": ImageAddNoise, "SaveAnimatedWEBP": SaveAnimatedWEBP, "SaveAnimatedPNG": SaveAnimatedPNG, + "SaveSVGNode": SaveSVGNode, + "ImageStitch": ImageStitch, + "ResizeAndPadImage": ResizeAndPadImage, + "GetImageSize": GetImageSize, + "ImageRotate": ImageRotate, + "ImageFlip": ImageFlip, + "ImageScaleToMaxDimension": ImageScaleToMaxDimension, } diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py index c2e70a84c..78f29915d 100644 --- a/comfy_extras/nodes_ip2p.py +++ b/comfy_extras/nodes_ip2p.py @@ -1,21 +1,30 @@ import torch -class InstructPixToPixConditioning: +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +class InstructPixToPixConditioning(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE", ), - "pixels": ("IMAGE", ), - }} + def define_schema(cls): + return io.Schema( + node_id="InstructPixToPixConditioning", + category="conditioning/instructpix2pix", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Image.Input("pixels"), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) - RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - FUNCTION = "encode" - - CATEGORY = "conditioning/instructpix2pix" - - def encode(self, positive, negative, pixels, vae): + @classmethod + def execute(cls, positive, negative, pixels, vae) -> io.NodeOutput: x = (pixels.shape[1] // 8) * 8 y = (pixels.shape[2] // 8) * 8 @@ -38,8 +47,17 @@ class InstructPixToPixConditioning: n = [t[0], d] c.append(n) out.append(c) - return (out[0], out[1], out_latent) + return io.NodeOutput(out[0], out[1], out_latent) + + +class InstructPix2PixExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + InstructPixToPixConditioning, + ] + + +async def comfy_entrypoint() -> InstructPix2PixExtension: + return InstructPix2PixExtension() -NODE_CLASS_MAPPINGS = { - "InstructPixToPixConditioning": InstructPixToPixConditioning, -} diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py index af2736818..d2df07ff9 100644 --- a/comfy_extras/nodes_latent.py +++ b/comfy_extras/nodes_latent.py @@ -1,24 +1,37 @@ import comfy.utils import comfy_extras.nodes_post_processing import torch +import nodes +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -def reshape_latent_to(target_shape, latent): + +def reshape_latent_to(target_shape, latent, repeat_batch=True): if latent.shape[1:] != target_shape[1:]: - latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") - return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) + latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center") + if repeat_batch: + return comfy.utils.repeat_to_batch_size(latent, target_shape[0]) + else: + return latent -class LatentAdd: +class LatentAdd(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + def define_schema(cls): + return io.Schema( + node_id="LatentAdd", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples1"), + io.Latent.Input("samples2"), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2): + @classmethod + def execute(cls, samples1, samples2) -> io.NodeOutput: samples_out = samples1.copy() s1 = samples1["samples"] @@ -26,19 +39,25 @@ class LatentAdd: s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 + s2 - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentSubtract: +class LatentSubtract(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + def define_schema(cls): + return io.Schema( + node_id="LatentSubtract", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples1"), + io.Latent.Input("samples2"), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2): + @classmethod + def execute(cls, samples1, samples2) -> io.NodeOutput: samples_out = samples1.copy() s1 = samples1["samples"] @@ -46,41 +65,49 @@ class LatentSubtract: s2 = reshape_latent_to(s1.shape, s2) samples_out["samples"] = s1 - s2 - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentMultiply: +class LatentMultiply(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), - }} + def define_schema(cls): + return io.Schema( + node_id="LatentMultiply", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples"), + io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples, multiplier): + @classmethod + def execute(cls, samples, multiplier) -> io.NodeOutput: samples_out = samples.copy() s1 = samples["samples"] samples_out["samples"] = s1 * multiplier - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentInterpolate: +class LatentInterpolate(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), - "samples2": ("LATENT",), - "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), - }} + def define_schema(cls): + return io.Schema( + node_id="LatentInterpolate", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples1"), + io.Latent.Input("samples2"), + io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples1, samples2, ratio): + @classmethod + def execute(cls, samples1, samples2, ratio) -> io.NodeOutput: samples_out = samples1.copy() s1 = samples1["samples"] @@ -99,42 +126,131 @@ class LatentInterpolate: st = torch.nan_to_num(t / mt) samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentBatch: +class LatentConcat(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + def define_schema(cls): + return io.Schema( + node_id="LatentConcat", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples1"), + io.Latent.Input("samples2"), + io.Combo.Input("dim", options=["x", "-x", "y", "-y", "t", "-t"]), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "batch" + @classmethod + def execute(cls, samples1, samples2, dim) -> io.NodeOutput: + samples_out = samples1.copy() - CATEGORY = "latent/batch" + s1 = samples1["samples"] + s2 = samples2["samples"] + s2 = comfy.utils.repeat_to_batch_size(s2, s1.shape[0]) - def batch(self, samples1, samples2): + if "-" in dim: + c = (s2, s1) + else: + c = (s1, s2) + + if "x" in dim: + dim = -1 + elif "y" in dim: + dim = -2 + elif "t" in dim: + dim = -3 + + samples_out["samples"] = torch.cat(c, dim=dim) + return io.NodeOutput(samples_out) + +class LatentCut(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LatentCut", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples"), + io.Combo.Input("dim", options=["x", "y", "t"]), + io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1), + io.Int.Input("amount", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, samples, dim, index, amount) -> io.NodeOutput: + samples_out = samples.copy() + + s1 = samples["samples"] + + if "x" in dim: + dim = s1.ndim - 1 + elif "y" in dim: + dim = s1.ndim - 2 + elif "t" in dim: + dim = s1.ndim - 3 + + if index >= 0: + index = min(index, s1.shape[dim] - 1) + amount = min(s1.shape[dim] - index, amount) + else: + index = max(index, -s1.shape[dim]) + amount = min(-index, amount) + + samples_out["samples"] = torch.narrow(s1, dim, index, amount) + return io.NodeOutput(samples_out) + +class LatentBatch(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LatentBatch", + category="latent/batch", + inputs=[ + io.Latent.Input("samples1"), + io.Latent.Input("samples2"), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, samples1, samples2) -> io.NodeOutput: samples_out = samples1.copy() s1 = samples1["samples"] s2 = samples2["samples"] - if s1.shape[1:] != s2.shape[1:]: - s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") + s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False) s = torch.cat((s1, s2), dim=0) samples_out["samples"] = s samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentBatchSeedBehavior: +class LatentBatchSeedBehavior(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}} + def define_schema(cls): + return io.Schema( + node_id="LatentBatchSeedBehavior", + category="latent/advanced", + inputs=[ + io.Latent.Input("samples"), + io.Combo.Input("seed_behavior", options=["random", "fixed"], default="fixed"), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced" - - def op(self, samples, seed_behavior): + @classmethod + def execute(cls, samples, seed_behavior) -> io.NodeOutput: samples_out = samples.copy() latent = samples["samples"] if seed_behavior == "random": @@ -144,41 +260,50 @@ class LatentBatchSeedBehavior: batch_number = samples_out.get("batch_index", [0])[0] samples_out["batch_index"] = [batch_number] * latent.shape[0] - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentApplyOperation: +class LatentApplyOperation(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "samples": ("LATENT",), - "operation": ("LATENT_OPERATION",), - }} + def define_schema(cls): + return io.Schema( + node_id="LatentApplyOperation", + category="latent/advanced/operations", + is_experimental=True, + inputs=[ + io.Latent.Input("samples"), + io.LatentOperation.Input("operation"), + ], + outputs=[ + io.Latent.Output(), + ], + ) - RETURN_TYPES = ("LATENT",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, samples, operation): + @classmethod + def execute(cls, samples, operation) -> io.NodeOutput: samples_out = samples.copy() s1 = samples["samples"] samples_out["samples"] = operation(latent=s1) - return (samples_out,) + return io.NodeOutput(samples_out) -class LatentApplyOperationCFG: +class LatentApplyOperationCFG(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "operation": ("LATENT_OPERATION",), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="LatentApplyOperationCFG", + category="latent/advanced/operations", + is_experimental=True, + inputs=[ + io.Model.Input("model"), + io.LatentOperation.Input("operation"), + ], + outputs=[ + io.Model.Output(), + ], + ) - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def patch(self, model, operation): + @classmethod + def execute(cls, model, operation) -> io.NodeOutput: m = model.clone() def pre_cfg_function(args): @@ -190,21 +315,25 @@ class LatentApplyOperationCFG: return conds_out m.set_model_sampler_pre_cfg_function(pre_cfg_function) - return (m, ) + return io.NodeOutput(m) -class LatentOperationTonemapReinhard: +class LatentOperationTonemapReinhard(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} + def define_schema(cls): + return io.Schema( + node_id="LatentOperationTonemapReinhard", + category="latent/advanced/operations", + is_experimental=True, + inputs=[ + io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[ + io.LatentOperation.Output(), + ], + ) - RETURN_TYPES = ("LATENT_OPERATION",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, multiplier): + @classmethod + def execute(cls, multiplier) -> io.NodeOutput: def tonemap_reinhard(latent, **kwargs): latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None] normalized_latent = latent / latent_vector_magnitude @@ -220,39 +349,27 @@ class LatentOperationTonemapReinhard: new_magnitude *= top return normalized_latent * new_magnitude - return (tonemap_reinhard,) + return io.NodeOutput(tonemap_reinhard) -class LatentOperationSharpen: +class LatentOperationSharpen(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "sharpen_radius": ("INT", { - "default": 9, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.1 - }), - "alpha": ("FLOAT", { - "default": 0.1, - "min": 0.0, - "max": 5.0, - "step": 0.01 - }), - }} + def define_schema(cls): + return io.Schema( + node_id="LatentOperationSharpen", + category="latent/advanced/operations", + is_experimental=True, + inputs=[ + io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1), + io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1), + io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01), + ], + outputs=[ + io.LatentOperation.Output(), + ], + ) - RETURN_TYPES = ("LATENT_OPERATION",) - FUNCTION = "op" - - CATEGORY = "latent/advanced/operations" - EXPERIMENTAL = True - - def op(self, sharpen_radius, sigma, alpha): + @classmethod + def execute(cls, sharpen_radius, sigma, alpha) -> io.NodeOutput: def sharpen(latent, **kwargs): luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None] normalized_latent = latent / luminance @@ -269,17 +386,27 @@ class LatentOperationSharpen: sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] return luminance * sharpened - return (sharpen,) + return io.NodeOutput(sharpen) -NODE_CLASS_MAPPINGS = { - "LatentAdd": LatentAdd, - "LatentSubtract": LatentSubtract, - "LatentMultiply": LatentMultiply, - "LatentInterpolate": LatentInterpolate, - "LatentBatch": LatentBatch, - "LatentBatchSeedBehavior": LatentBatchSeedBehavior, - "LatentApplyOperation": LatentApplyOperation, - "LatentApplyOperationCFG": LatentApplyOperationCFG, - "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard, - "LatentOperationSharpen": LatentOperationSharpen, -} + +class LatentExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + LatentAdd, + LatentSubtract, + LatentMultiply, + LatentInterpolate, + LatentConcat, + LatentCut, + LatentBatch, + LatentBatchSeedBehavior, + LatentApplyOperation, + LatentApplyOperationCFG, + LatentOperationTonemapReinhard, + LatentOperationSharpen, + ] + + +async def comfy_entrypoint() -> LatentExtension: + return LatentExtension() diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py index 3560ab78a..899608149 100644 --- a/comfy_extras/nodes_load_3d.py +++ b/comfy_extras/nodes_load_3d.py @@ -2,6 +2,12 @@ import nodes import folder_paths import os +from comfy.comfy_types import IO +from comfy_api.input_impl import VideoFromFile + +from pathlib import Path + + def normalize_path(path): return path.replace('\\', '/') @@ -12,25 +18,24 @@ class Load3D(): os.makedirs(input_dir, exist_ok=True) - files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.obj', '.mtl', '.fbx', '.stl'))] + input_path = Path(input_dir) + base_path = Path(folder_paths.get_input_directory()) + + files = [ + normalize_path(str(file_path.relative_to(base_path))) + for file_path in input_path.rglob("*") + if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'} + ] return {"required": { "model_file": (sorted(files), {"file_upload": True}), "image": ("LOAD_3D", {}), "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "show_grid": ([True, False],), - "camera_type": (["perspective", "orthographic"],), - "view": (["front", "right", "top", "isometric"],), - "material": (["original", "normal", "wireframe", "depth"],), - "bg_color": ("STRING", {"default": "#000000", "multiline": False}), - "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}), - "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],), - "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}), }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING") - RETURN_NAMES = ("image", "mask", "mesh_path") + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart", "camera_info", "recording_video") FUNCTION = "process" EXPERIMENTAL = True @@ -38,22 +43,25 @@ class Load3D(): CATEGORY = "3d" def process(self, model_file, image, **kwargs): - if isinstance(image, dict): - image_path = folder_paths.get_annotated_filepath(image['image']) - mask_path = folder_paths.get_annotated_filepath(image['mask']) + image_path = folder_paths.get_annotated_filepath(image['image']) + mask_path = folder_paths.get_annotated_filepath(image['mask']) + normal_path = folder_paths.get_annotated_filepath(image['normal']) + lineart_path = folder_paths.get_annotated_filepath(image['lineart']) - load_image_node = nodes.LoadImage() - output_image, ignore_mask = load_image_node.load_image(image=image_path) - ignore_image, output_mask = load_image_node.load_image(image=mask_path) + load_image_node = nodes.LoadImage() + output_image, ignore_mask = load_image_node.load_image(image=image_path) + ignore_image, output_mask = load_image_node.load_image(image=mask_path) + normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) + lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path) - return output_image, output_mask, model_file, - else: - # to avoid the format is not dict which will happen the FE code is not compatibility to core, - # we need to this to double-check, it can be removed after merged FE into the core - image_path = folder_paths.get_annotated_filepath(image) - load_image_node = nodes.LoadImage() - output_image, output_mask = load_image_node.load_image(image=image_path) - return output_image, output_mask, model_file, + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, lineart_image, image['camera_info'], video class Load3DAnimation(): @classmethod @@ -62,26 +70,24 @@ class Load3DAnimation(): os.makedirs(input_dir, exist_ok=True) - files = [normalize_path(os.path.join("3d", f)) for f in os.listdir(input_dir) if f.endswith(('.gltf', '.glb', '.fbx'))] + input_path = Path(input_dir) + base_path = Path(folder_paths.get_input_directory()) + + files = [ + normalize_path(str(file_path.relative_to(base_path))) + for file_path in input_path.rglob("*") + if file_path.suffix.lower() in {'.gltf', '.glb', '.fbx'} + ] return {"required": { "model_file": (sorted(files), {"file_upload": True}), "image": ("LOAD_3D_ANIMATION", {}), "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}), - "show_grid": ([True, False],), - "camera_type": (["perspective", "orthographic"],), - "view": (["front", "right", "top", "isometric"],), - "material": (["original", "normal", "wireframe", "depth"],), - "bg_color": ("STRING", {"default": "#000000", "multiline": False}), - "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}), - "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],), - "animation_speed": (["0.1", "0.5", "1", "1.5", "2"], {"default": "1"}), - "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}), }} - RETURN_TYPES = ("IMAGE", "MASK", "STRING") - RETURN_NAMES = ("image", "mask", "mesh_path") + RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO) + RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video") FUNCTION = "process" EXPERIMENTAL = True @@ -89,34 +95,32 @@ class Load3DAnimation(): CATEGORY = "3d" def process(self, model_file, image, **kwargs): - if isinstance(image, dict): - image_path = folder_paths.get_annotated_filepath(image['image']) - mask_path = folder_paths.get_annotated_filepath(image['mask']) + image_path = folder_paths.get_annotated_filepath(image['image']) + mask_path = folder_paths.get_annotated_filepath(image['mask']) + normal_path = folder_paths.get_annotated_filepath(image['normal']) - load_image_node = nodes.LoadImage() - output_image, ignore_mask = load_image_node.load_image(image=image_path) - ignore_image, output_mask = load_image_node.load_image(image=mask_path) + load_image_node = nodes.LoadImage() + output_image, ignore_mask = load_image_node.load_image(image=image_path) + ignore_image, output_mask = load_image_node.load_image(image=mask_path) + normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path) - return output_image, output_mask, model_file, - else: - image_path = folder_paths.get_annotated_filepath(image) - load_image_node = nodes.LoadImage() - output_image, output_mask = load_image_node.load_image(image=image_path) - return output_image, output_mask, model_file, + video = None + + if image['recording'] != "": + recording_video_path = folder_paths.get_annotated_filepath(image['recording']) + + video = VideoFromFile(recording_video_path) + + return output_image, output_mask, model_file, normal_image, image['camera_info'], video class Preview3D(): @classmethod def INPUT_TYPES(s): return {"required": { "model_file": ("STRING", {"default": "", "multiline": False}), - "show_grid": ([True, False],), - "camera_type": (["perspective", "orthographic"],), - "view": (["front", "right", "top", "isometric"],), - "material": (["original", "normal", "wireframe", "depth"],), - "bg_color": ("STRING", {"default": "#000000", "multiline": False}), - "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}), - "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],), - "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}), + }, + "optional": { + "camera_info": ("LOAD3D_CAMERA", {}) }} OUTPUT_NODE = True @@ -128,16 +132,51 @@ class Preview3D(): EXPERIMENTAL = True def process(self, model_file, **kwargs): - return {"ui": {"model_file": [model_file]}, "result": ()} + camera_info = kwargs.get("camera_info", None) + + return { + "ui": { + "result": [model_file, camera_info] + } + } + +class Preview3DAnimation(): + @classmethod + def INPUT_TYPES(s): + return {"required": { + "model_file": ("STRING", {"default": "", "multiline": False}), + }, + "optional": { + "camera_info": ("LOAD3D_CAMERA", {}) + }} + + OUTPUT_NODE = True + RETURN_TYPES = () + + CATEGORY = "3d" + + FUNCTION = "process" + EXPERIMENTAL = True + + def process(self, model_file, **kwargs): + camera_info = kwargs.get("camera_info", None) + + return { + "ui": { + "result": [model_file, camera_info] + } + } NODE_CLASS_MAPPINGS = { "Load3D": Load3D, "Load3DAnimation": Load3DAnimation, - "Preview3D": Preview3D + "Preview3D": Preview3D, + "Preview3DAnimation": Preview3DAnimation } NODE_DISPLAY_NAME_MAPPINGS = { "Load3D": "Load 3D", "Load3DAnimation": "Load 3D - Animation", - "Preview3D": "Preview 3D" + "Preview3D": "Preview 3D", + "Preview3DAnimation": "Preview 3D - Animation" } diff --git a/comfy_extras/nodes_lora_extract.py b/comfy_extras/nodes_lora_extract.py index dfd4fe9f4..a2375cba7 100644 --- a/comfy_extras/nodes_lora_extract.py +++ b/comfy_extras/nodes_lora_extract.py @@ -5,6 +5,8 @@ import folder_paths import os import logging from enum import Enum +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io CLAMP_QUANTILE = 0.99 @@ -71,32 +73,40 @@ def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu() return output_sd -class LoraSave: - def __init__(self): - self.output_dir = folder_paths.get_output_directory() +class LoraSave(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LoraSave", + display_name="Extract and Save Lora", + category="_for_testing", + inputs=[ + io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"), + io.Int.Input("rank", default=8, min=1, max=4096, step=1), + io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())), + io.Boolean.Input("bias_diff", default=True), + io.Model.Input( + "model_diff", + tooltip="The ModelSubtract output to be converted to a lora.", + optional=True, + ), + io.Clip.Input( + "text_encoder_diff", + tooltip="The CLIPSubtract output to be converted to a lora.", + optional=True, + ), + ], + is_experimental=True, + is_output_node=True, + ) @classmethod - def INPUT_TYPES(s): - return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}), - "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}), - "lora_type": (tuple(LORA_TYPES.keys()),), - "bias_diff": ("BOOLEAN", {"default": True}), - }, - "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}), - "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})}, - } - RETURN_TYPES = () - FUNCTION = "save" - OUTPUT_NODE = True - - CATEGORY = "_for_testing" - - def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None): + def execute(cls, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None) -> io.NodeOutput: if model_diff is None and text_encoder_diff is None: - return {} + return io.NodeOutput() lora_type = LORA_TYPES.get(lora_type) - full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir) + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory()) output_sd = {} if model_diff is not None: @@ -108,12 +118,16 @@ class LoraSave: output_checkpoint = os.path.join(full_output_folder, output_checkpoint) comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None) - return {} + return io.NodeOutput() -NODE_CLASS_MAPPINGS = { - "LoraSave": LoraSave -} -NODE_DISPLAY_NAME_MAPPINGS = { - "LoraSave": "Extract and Save Lora" -} +class LoraSaveExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + LoraSave, + ] + + +async def comfy_entrypoint() -> LoraSaveExtension: + return LoraSaveExtension() diff --git a/comfy_extras/nodes_lotus.py b/comfy_extras/nodes_lotus.py new file mode 100644 index 000000000..9f62ba2bf --- /dev/null +++ b/comfy_extras/nodes_lotus.py @@ -0,0 +1,39 @@ +from typing_extensions import override + +import torch +import comfy.model_management as mm +from comfy_api.latest import ComfyExtension, io + + +class LotusConditioning(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LotusConditioning", + category="conditioning/lotus", + inputs=[], + outputs=[io.Conditioning.Output(display_name="conditioning")], + ) + + @classmethod + def execute(cls) -> io.NodeOutput: + device = mm.get_torch_device() + #lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change + #and getting parity with the reference implementation would otherwise require inference and 800mb of tensors + prompt_embeds = torch.tensor([[[-0.3134765625, -0.447509765625, -0.00823974609375, -0.22802734375, 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[[prompt_embeds, {}]] + + return io.NodeOutput(cond) + + +class LotusExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + LotusConditioning, + ] + + +async def comfy_entrypoint() -> LotusExtension: + return LotusExtension() diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index dec912416..50da5f4eb 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -3,92 +3,354 @@ import node_helpers import torch import comfy.model_management import comfy.model_sampling +import comfy.utils import math +import numpy as np +import av +from io import BytesIO +from typing_extensions import override +from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords +from comfy_api.latest import ComfyExtension, io -class EmptyLTXVLatentVideo: +class EmptyLTXVLatentVideo(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "length": ("INT", {"default": 97, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" + def define_schema(cls): + return io.Schema( + node_id="EmptyLTXVLatentVideo", + category="latent/video/ltxv", + inputs=[ + io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("length", default=97, min=1, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) - CATEGORY = "latent/video/ltxv" - - def generate(self, width, height, length, batch_size=1): + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) - return ({"samples": latent}, ) + return io.NodeOutput({"samples": latent}) + generate = execute # TODO: remove -class LTXVImgToVideo: +class LTXVImgToVideo(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "vae": ("VAE",), - "image": ("IMAGE",), - "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}), - "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "image_noise_scale": ("FLOAT", {"default": 0.15, "min": 0, "max": 1.0, "step": 0.01, "tooltip": "Amount of noise to apply on conditioning image latent."}) - }} + def define_schema(cls): + return io.Schema( + node_id="LTXVImgToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Image.Input("image"), + io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("length", default=97, min=9, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Float.Input("strength", default=1.0, min=0.0, max=1.0), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") - - CATEGORY = "conditioning/video_models" - FUNCTION = "generate" - - def generate(self, positive, negative, image, vae, width, height, length, batch_size, image_noise_scale): + @classmethod + def execute(cls, positive, negative, image, vae, width, height, length, batch_size, strength) -> io.NodeOutput: pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) encode_pixels = pixels[:, :, :, :3] t = vae.encode(encode_pixels) - positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale}) - negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t, "guiding_latent_noise_scale": image_noise_scale}) latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device()) latent[:, :, :t.shape[2]] = t - return (positive, negative, {"samples": latent}, ) + + conditioning_latent_frames_mask = torch.ones( + (batch_size, 1, latent.shape[2], 1, 1), + dtype=torch.float32, + device=latent.device, + ) + conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength + + return io.NodeOutput(positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}) + + generate = execute # TODO: remove -class LTXVConditioning: +def conditioning_get_any_value(conditioning, key, default=None): + for t in conditioning: + if key in t[1]: + return t[1][key] + return default + + +def get_noise_mask(latent): + noise_mask = latent.get("noise_mask", None) + latent_image = latent["samples"] + if noise_mask is None: + batch_size, _, latent_length, _, _ = latent_image.shape + noise_mask = torch.ones( + (batch_size, 1, latent_length, 1, 1), + dtype=torch.float32, + device=latent_image.device, + ) + else: + noise_mask = noise_mask.clone() + return noise_mask + +def get_keyframe_idxs(cond): + keyframe_idxs = conditioning_get_any_value(cond, "keyframe_idxs", None) + if keyframe_idxs is None: + return None, 0 + num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0] + return keyframe_idxs, num_keyframes + +class LTXVAddGuide(io.ComfyNode): + NUM_PREFIX_FRAMES = 2 + PATCHIFIER = SymmetricPatchifier(1) + @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING") - RETURN_NAMES = ("positive", "negative") - FUNCTION = "append" + def define_schema(cls): + return io.Schema( + node_id="LTXVAddGuide", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Latent.Input("latent"), + io.Image.Input( + "image", + tooltip="Image or video to condition the latent video on. Must be 8*n + 1 frames. " + "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames.", + ), + io.Int.Input( + "frame_idx", + default=0, + min=-9999, + max=9999, + tooltip="Frame index to start the conditioning at. " + "For single-frame images or videos with 1-8 frames, any frame_idx value is acceptable. " + "For videos with 9+ frames, frame_idx must be divisible by 8, otherwise it will be rounded " + "down to the nearest multiple of 8. Negative values are counted from the end of the video.", + ), + io.Float.Input("strength", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) - CATEGORY = "conditioning/video_models" + @classmethod + def encode(cls, vae, latent_width, latent_height, images, scale_factors): + time_scale_factor, width_scale_factor, height_scale_factor = scale_factors + images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1] + pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1) + encode_pixels = pixels[:, :, :, :3] + t = vae.encode(encode_pixels) + return encode_pixels, t - def append(self, positive, negative, frame_rate): + @classmethod + def get_latent_index(cls, cond, latent_length, guide_length, frame_idx, scale_factors): + time_scale_factor, _, _ = scale_factors + _, num_keyframes = get_keyframe_idxs(cond) + latent_count = latent_length - num_keyframes + frame_idx = frame_idx if frame_idx >= 0 else max((latent_count - 1) * time_scale_factor + 1 + frame_idx, 0) + if guide_length > 1 and frame_idx != 0: + frame_idx = (frame_idx - 1) // time_scale_factor * time_scale_factor + 1 # frame index - 1 must be divisible by 8 or frame_idx == 0 + + latent_idx = (frame_idx + time_scale_factor - 1) // time_scale_factor + + return frame_idx, latent_idx + + @classmethod + def add_keyframe_index(cls, cond, frame_idx, guiding_latent, scale_factors): + keyframe_idxs, _ = get_keyframe_idxs(cond) + _, latent_coords = cls.PATCHIFIER.patchify(guiding_latent) + pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=frame_idx == 0) # we need the causal fix only if we're placing the new latents at index 0 + pixel_coords[:, 0] += frame_idx + if keyframe_idxs is None: + keyframe_idxs = pixel_coords + else: + keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2) + return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs}) + + @classmethod + def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors): + _, latent_idx = cls.get_latent_index( + cond=positive, + latent_length=latent_image.shape[2], + guide_length=guiding_latent.shape[2], + frame_idx=frame_idx, + scale_factors=scale_factors, + ) + noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0 + + positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors) + negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors) + + mask = torch.full( + (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = torch.cat([latent_image, guiding_latent], dim=2) + noise_mask = torch.cat([noise_mask, mask], dim=2) + return positive, negative, latent_image, noise_mask + + @classmethod + def replace_latent_frames(cls, latent_image, noise_mask, guiding_latent, latent_idx, strength): + cond_length = guiding_latent.shape[2] + assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence." + + mask = torch.full( + (noise_mask.shape[0], 1, cond_length, 1, 1), + 1.0 - strength, + dtype=noise_mask.dtype, + device=noise_mask.device, + ) + + latent_image = latent_image.clone() + noise_mask = noise_mask.clone() + + latent_image[:, :, latent_idx : latent_idx + cond_length] = guiding_latent + noise_mask[:, :, latent_idx : latent_idx + cond_length] = mask + + return latent_image, noise_mask + + @classmethod + def execute(cls, positive, negative, vae, latent, image, frame_idx, strength) -> io.NodeOutput: + scale_factors = vae.downscale_index_formula + latent_image = latent["samples"] + noise_mask = get_noise_mask(latent) + + _, _, latent_length, latent_height, latent_width = latent_image.shape + image, t = cls.encode(vae, latent_width, latent_height, image, scale_factors) + + frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors) + assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence." + + num_prefix_frames = min(cls.NUM_PREFIX_FRAMES, t.shape[2]) + + positive, negative, latent_image, noise_mask = cls.append_keyframe( + positive, + negative, + frame_idx, + latent_image, + noise_mask, + t[:, :, :num_prefix_frames], + strength, + scale_factors, + ) + + latent_idx += num_prefix_frames + + t = t[:, :, num_prefix_frames:] + if t.shape[2] == 0: + return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask}) + + latent_image, noise_mask = cls.replace_latent_frames( + latent_image, + noise_mask, + t, + latent_idx, + strength, + ) + + return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask}) + + generate = execute # TODO: remove + + +class LTXVCropGuides(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LTXVCropGuides", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Latent.Input("latent"), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, latent) -> io.NodeOutput: + latent_image = latent["samples"].clone() + noise_mask = get_noise_mask(latent) + + _, num_keyframes = get_keyframe_idxs(positive) + if num_keyframes == 0: + return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask},) + + latent_image = latent_image[:, :, :-num_keyframes] + noise_mask = noise_mask[:, :, :-num_keyframes] + + positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None}) + negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None}) + + return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask}) + + crop = execute # TODO: remove + + +class LTXVConditioning(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LTXVConditioning", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Float.Input("frame_rate", default=25.0, min=0.0, max=1000.0, step=0.01), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + ) + + @classmethod + def execute(cls, positive, negative, frame_rate) -> io.NodeOutput: positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate}) negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate}) - return (positive, negative) + return io.NodeOutput(positive, negative) -class ModelSamplingLTXV: +class ModelSamplingLTXV(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), - "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), - }, - "optional": {"latent": ("LATENT",), } - } + def define_schema(cls): + return io.Schema( + node_id="ModelSamplingLTXV", + category="advanced/model", + inputs=[ + io.Model.Input("model"), + io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01), + io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01), + io.Latent.Input("latent", optional=True), + ], + outputs=[ + io.Model.Output(), + ], + ) - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "advanced/model" - - def patch(self, model, max_shift, base_shift, latent=None): + @classmethod + def execute(cls, model, max_shift, base_shift, latent=None) -> io.NodeOutput: m = model.clone() if latent is None: @@ -112,37 +374,41 @@ class ModelSamplingLTXV: model_sampling.set_parameters(shift=shift) m.add_object_patch("model_sampling", model_sampling) - return (m, ) + return io.NodeOutput(m) -class LTXVScheduler: +class LTXVScheduler(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), - "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}), - "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}), - "stretch": ("BOOLEAN", { - "default": True, - "tooltip": "Stretch the sigmas to be in the range [terminal, 1]." - }), - "terminal": ( - "FLOAT", - { - "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01, - "tooltip": "The terminal value of the sigmas after stretching." - }, - ), - }, - "optional": {"latent": ("LATENT",), } - } + def define_schema(cls): + return io.Schema( + node_id="LTXVScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Int.Input("steps", default=20, min=1, max=10000), + io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01), + io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01), + io.Boolean.Input( + id="stretch", + default=True, + tooltip="Stretch the sigmas to be in the range [terminal, 1].", + ), + io.Float.Input( + id="terminal", + default=0.1, + min=0.0, + max=0.99, + step=0.01, + tooltip="The terminal value of the sigmas after stretching.", + ), + io.Latent.Input("latent", optional=True), + ], + outputs=[ + io.Sigmas.Output(), + ], + ) - RETURN_TYPES = ("SIGMAS",) - CATEGORY = "sampling/custom_sampling/schedulers" - - FUNCTION = "get_sigmas" - - def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None): + @classmethod + def execute(cls, steps, max_shift, base_shift, stretch, terminal, latent=None) -> io.NodeOutput: if latent is None: tokens = 4096 else: @@ -172,13 +438,89 @@ class LTXVScheduler: stretched = 1.0 - (one_minus_z / scale_factor) sigmas[non_zero_mask] = stretched - return (sigmas,) + return io.NodeOutput(sigmas) + +def encode_single_frame(output_file, image_array: np.ndarray, crf): + container = av.open(output_file, "w", format="mp4") + try: + stream = container.add_stream( + "libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"} + ) + stream.height = image_array.shape[0] + stream.width = image_array.shape[1] + av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat( + format="yuv420p" + ) + container.mux(stream.encode(av_frame)) + container.mux(stream.encode()) + finally: + container.close() -NODE_CLASS_MAPPINGS = { - "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo, - "LTXVImgToVideo": LTXVImgToVideo, - "ModelSamplingLTXV": ModelSamplingLTXV, - "LTXVConditioning": LTXVConditioning, - "LTXVScheduler": LTXVScheduler, -} +def decode_single_frame(video_file): + container = av.open(video_file) + try: + stream = next(s for s in container.streams if s.type == "video") + frame = next(container.decode(stream)) + finally: + container.close() + return frame.to_ndarray(format="rgb24") + + +def preprocess(image: torch.Tensor, crf=29): + if crf == 0: + return image + + image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy() + with BytesIO() as output_file: + encode_single_frame(output_file, image_array, crf) + video_bytes = output_file.getvalue() + with BytesIO(video_bytes) as video_file: + image_array = decode_single_frame(video_file) + tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0 + return tensor + + +class LTXVPreprocess(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="LTXVPreprocess", + category="image", + inputs=[ + io.Image.Input("image"), + io.Int.Input( + id="img_compression", default=35, min=0, max=100, tooltip="Amount of compression to apply on image." + ), + ], + outputs=[ + io.Image.Output(display_name="output_image"), + ], + ) + + @classmethod + def execute(cls, image, img_compression) -> io.NodeOutput: + output_images = [] + for i in range(image.shape[0]): + output_images.append(preprocess(image[i], img_compression)) + return io.NodeOutput(torch.stack(output_images)) + + preprocess = execute # TODO: remove + +class LtxvExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyLTXVLatentVideo, + LTXVImgToVideo, + ModelSamplingLTXV, + LTXVConditioning, + LTXVScheduler, + LTXVAddGuide, + LTXVPreprocess, + LTXVCropGuides, + ] + + +async def comfy_entrypoint() -> LtxvExtension: + return LtxvExtension() diff --git a/comfy_extras/nodes_lumina2.py b/comfy_extras/nodes_lumina2.py new file mode 100644 index 000000000..89ff2397a --- /dev/null +++ b/comfy_extras/nodes_lumina2.py @@ -0,0 +1,127 @@ +from typing_extensions import override +import torch + +from comfy_api.latest import ComfyExtension, io + + +class RenormCFG(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="RenormCFG", + category="advanced/model", + inputs=[ + io.Model.Input("model"), + io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01), + io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[ + io.Model.Output(), + ], + ) + + @classmethod + def execute(cls, model, cfg_trunc, renorm_cfg) -> io.NodeOutput: + def renorm_cfg_func(args): + cond_denoised = args["cond_denoised"] + uncond_denoised = args["uncond_denoised"] + cond_scale = args["cond_scale"] + timestep = args["timestep"] + x_orig = args["input"] + in_channels = model.model.diffusion_model.in_channels + + if timestep[0] < cfg_trunc: + cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] + cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] + half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps) + half_rest = cond_rest + + if float(renorm_cfg) > 0.0: + ori_pos_norm = torch.linalg.vector_norm(cond_eps + , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True + ) + max_new_norm = ori_pos_norm * float(renorm_cfg) + new_pos_norm = torch.linalg.vector_norm( + half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True + ) + if new_pos_norm >= max_new_norm: + half_eps = half_eps * (max_new_norm / new_pos_norm) + else: + cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels] + cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:] + half_eps = cond_eps + half_rest = cond_rest + + cfg_result = torch.cat([half_eps, half_rest], dim=1) + + # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale + + return x_orig - cfg_result + + m = model.clone() + m.set_model_sampler_cfg_function(renorm_cfg_func) + return io.NodeOutput(m) + + +class CLIPTextEncodeLumina2(io.ComfyNode): + SYSTEM_PROMPT = { + "superior": "You are an assistant designed to generate superior images with the superior "\ + "degree of image-text alignment based on textual prompts or user prompts.", + "alignment": "You are an assistant designed to generate high-quality images with the "\ + "highest degree of image-text alignment based on textual prompts." + } + SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \ + "Superior: You are an assistant designed to generate superior images with the superior "\ + "degree of image-text alignment based on textual prompts or user prompts. "\ + "Alignment: You are an assistant designed to generate high-quality images with the highest "\ + "degree of image-text alignment based on textual prompts." + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeLumina2", + display_name="CLIP Text Encode for Lumina2", + category="conditioning", + description="Encodes a system prompt and a user prompt using a CLIP model into an embedding " + "that can be used to guide the diffusion model towards generating specific images.", + inputs=[ + io.Combo.Input( + "system_prompt", + options=list(cls.SYSTEM_PROMPT.keys()), + tooltip=cls.SYSTEM_PROMPT_TIP, + ), + io.String.Input( + "user_prompt", + multiline=True, + dynamic_prompts=True, + tooltip="The text to be encoded.", + ), + io.Clip.Input("clip", tooltip="The CLIP model used for encoding the text."), + ], + outputs=[ + io.Conditioning.Output( + tooltip="A conditioning containing the embedded text used to guide the diffusion model.", + ), + ], + ) + + @classmethod + def execute(cls, clip, user_prompt, system_prompt) -> io.NodeOutput: + if clip is None: + raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") + system_prompt = cls.SYSTEM_PROMPT[system_prompt] + prompt = f'{system_prompt} {user_prompt}' + tokens = clip.tokenize(prompt) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens)) + + +class Lumina2Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodeLumina2, + RenormCFG, + ] + + +async def comfy_entrypoint() -> Lumina2Extension: + return Lumina2Extension() diff --git a/comfy_extras/nodes_mahiro.py b/comfy_extras/nodes_mahiro.py index 8fcdfba75..07b3353f4 100644 --- a/comfy_extras/nodes_mahiro.py +++ b/comfy_extras/nodes_mahiro.py @@ -1,17 +1,29 @@ +from typing_extensions import override import torch import torch.nn.functional as F -class Mahiro: +from comfy_api.latest import ComfyExtension, io + + +class Mahiro(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL",), - }} - RETURN_TYPES = ("MODEL",) - RETURN_NAMES = ("patched_model",) - FUNCTION = "patch" - CATEGORY = "_for_testing" - DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt." - def patch(self, model): + def define_schema(cls): + return io.Schema( + node_id="Mahiro", + display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)", + category="_for_testing", + description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.", + inputs=[ + io.Model.Input("model"), + ], + outputs=[ + io.Model.Output(display_name="patched_model"), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, model) -> io.NodeOutput: m = model.clone() def mahiro_normd(args): scale: float = args['cond_scale'] @@ -30,12 +42,16 @@ class Mahiro: wm = (simsc*cfg + (4-simsc)*leap) / 4 return wm m.set_model_sampler_post_cfg_function(mahiro_normd) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "Mahiro": Mahiro -} -NODE_DISPLAY_NAME_MAPPINGS = { - "Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)", -} +class MahiroExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + Mahiro, + ] + + +async def comfy_entrypoint() -> MahiroExtension: + return MahiroExtension() diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index 63fd13b9a..a5e405008 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -2,41 +2,48 @@ import numpy as np import scipy.ndimage import torch import comfy.utils +import node_helpers +import folder_paths +import random +import nodes from nodes import MAX_RESOLUTION def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): source = source.to(destination.device) if resize_source: - source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + source = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), mode="bilinear") source = comfy.utils.repeat_to_batch_size(source, destination.shape[0]) - x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) - y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) + x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * multiplier)) + y = max(-source.shape[-2] * multiplier, min(y, destination.shape[-2] * multiplier)) left, top = (x // multiplier, y // multiplier) - right, bottom = (left + source.shape[3], top + source.shape[2],) + right, bottom = (left + source.shape[-1], top + source.shape[-2],) if mask is None: mask = torch.ones_like(source) else: mask = mask.to(destination.device, copy=True) - mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), mode="bilinear") mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0]) # calculate the bounds of the source that will be overlapping the destination # this prevents the source trying to overwrite latent pixels that are out of bounds # of the destination - visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) + visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),) mask = mask[:, :, :visible_height, :visible_width] + if mask.ndim < source.ndim: + mask = mask.unsqueeze(1) + inverse_mask = torch.ones_like(mask) - mask - source_portion = mask * source[:, :, :visible_height, :visible_width] - destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] + source_portion = mask * source[..., :visible_height, :visible_width] + destination_portion = inverse_mask * destination[..., top:bottom, left:right] - destination[:, :, top:bottom, left:right] = source_portion + destination_portion + destination[..., top:bottom, left:right] = source_portion + destination_portion return destination class LatentCompositeMasked: @@ -87,6 +94,7 @@ class ImageCompositeMasked: CATEGORY = "image" def composite(self, destination, source, x, y, resize_source, mask = None): + destination, source = node_helpers.image_alpha_fix(destination, source) destination = destination.clone().movedim(-1, 1) output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) return (output,) @@ -147,7 +155,7 @@ class ImageColorToMask: def image_to_mask(self, image, color): temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] - mask = torch.where(temp == color, 255, 0).float() + mask = torch.where(temp == color, 1.0, 0).float() return (mask,) class SolidMask: @@ -242,7 +250,7 @@ class MaskComposite: visible_width, visible_height = (right - left, bottom - top,) source_portion = source[:, :visible_height, :visible_width] - destination_portion = destination[:, top:bottom, left:right] + destination_portion = output[:, top:bottom, left:right] if operation == "multiply": output[:, top:bottom, left:right] = destination_portion * source_portion @@ -360,6 +368,30 @@ class ThresholdMask: mask = (mask > value).float() return (mask,) +# Mask Preview - original implement from +# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81 +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class MaskPreview(nodes.SaveImage): + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return { + "required": {"mask": ("MASK",), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + FUNCTION = "execute" + CATEGORY = "mask" + + def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None): + preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return self.save_images(preview, filename_prefix, prompt, extra_pnginfo) + NODE_CLASS_MAPPINGS = { "LatentCompositeMasked": LatentCompositeMasked, @@ -374,6 +406,7 @@ NODE_CLASS_MAPPINGS = { "FeatherMask": FeatherMask, "GrowMask": GrowMask, "ThresholdMask": ThresholdMask, + "MaskPreview": MaskPreview } NODE_DISPLAY_NAME_MAPPINGS = { diff --git a/comfy_extras/nodes_mochi.py b/comfy_extras/nodes_mochi.py index 1c474faa9..d750194fc 100644 --- a/comfy_extras/nodes_mochi.py +++ b/comfy_extras/nodes_mochi.py @@ -1,23 +1,40 @@ -import nodes +from typing_extensions import override import torch import comfy.model_management +import nodes +from comfy_api.latest import ComfyExtension, io -class EmptyMochiLatentVideo: + +class EmptyMochiLatentVideo(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "length": ("INT", {"default": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" + def define_schema(cls): + return io.Schema( + node_id="EmptyMochiLatentVideo", + category="latent/video", + inputs=[ + io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=25, min=7, max=nodes.MAX_RESOLUTION, step=6), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) - CATEGORY = "latent/video" - - def generate(self, width, height, length, batch_size=1): + @classmethod + def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput: latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) - return ({"samples":latent}, ) + return io.NodeOutput({"samples": latent}) -NODE_CLASS_MAPPINGS = { - "EmptyMochiLatentVideo": EmptyMochiLatentVideo, -} + +class MochiExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + EmptyMochiLatentVideo, + ] + + +async def comfy_entrypoint() -> MochiExtension: + return MochiExtension() diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py index 285dbf530..ae5d2c563 100644 --- a/comfy_extras/nodes_model_advanced.py +++ b/comfy_extras/nodes_model_advanced.py @@ -3,6 +3,8 @@ import comfy.model_sampling import comfy.latent_formats import nodes import torch +import node_helpers + class LCM(comfy.model_sampling.EPS): def calculate_denoised(self, sigma, model_output, model_input): @@ -18,10 +20,6 @@ class LCM(comfy.model_sampling.EPS): return c_out * x0 + c_skip * model_input -class X0(comfy.model_sampling.EPS): - def calculate_denoised(self, sigma, model_output, model_input): - return model_output - class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete): original_timesteps = 50 @@ -54,7 +52,7 @@ class ModelSamplingDiscrete: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "sampling": (["eps", "v_prediction", "lcm", "x0"],), + "sampling": (["eps", "v_prediction", "lcm", "x0", "img_to_img"],), "zsnr": ("BOOLEAN", {"default": False}), }} @@ -75,7 +73,9 @@ class ModelSamplingDiscrete: sampling_type = LCM sampling_base = ModelSamplingDiscreteDistilled elif sampling == "x0": - sampling_type = X0 + sampling_type = comfy.model_sampling.X0 + elif sampling == "img_to_img": + sampling_type = comfy.model_sampling.IMG_TO_IMG class ModelSamplingAdvanced(sampling_base, sampling_type): pass @@ -189,7 +189,7 @@ class ModelSamplingContinuousEDM: @classmethod def INPUT_TYPES(s): return {"required": { "model": ("MODEL",), - "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],), + "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps", "cosmos_rflow"],), "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), }} @@ -202,18 +202,25 @@ class ModelSamplingContinuousEDM: def patch(self, model, sampling, sigma_max, sigma_min): m = model.clone() + sampling_base = comfy.model_sampling.ModelSamplingContinuousEDM latent_format = None sigma_data = 1.0 if sampling == "eps": sampling_type = comfy.model_sampling.EPS + elif sampling == "edm": + sampling_type = comfy.model_sampling.EDM + sigma_data = 0.5 elif sampling == "v_prediction": sampling_type = comfy.model_sampling.V_PREDICTION elif sampling == "edm_playground_v2.5": sampling_type = comfy.model_sampling.EDM sigma_data = 0.5 latent_format = comfy.latent_formats.SDXL_Playground_2_5() + elif sampling == "cosmos_rflow": + sampling_type = comfy.model_sampling.COSMOS_RFLOW + sampling_base = comfy.model_sampling.ModelSamplingCosmosRFlow - class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type): + class ModelSamplingAdvanced(sampling_base, sampling_type): pass model_sampling = ModelSamplingAdvanced(model.model.model_config) @@ -291,6 +298,24 @@ class RescaleCFG: m.set_model_sampler_cfg_function(rescale_cfg) return (m, ) +class ModelComputeDtype: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "dtype": (["default", "fp32", "fp16", "bf16"],), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/debug/model" + + def patch(self, model, dtype): + m = model.clone() + m.set_model_compute_dtype(node_helpers.string_to_torch_dtype(dtype)) + return (m, ) + + NODE_CLASS_MAPPINGS = { "ModelSamplingDiscrete": ModelSamplingDiscrete, "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, @@ -300,4 +325,5 @@ NODE_CLASS_MAPPINGS = { "ModelSamplingAuraFlow": ModelSamplingAuraFlow, "ModelSamplingFlux": ModelSamplingFlux, "RescaleCFG": RescaleCFG, + "ModelComputeDtype": ModelComputeDtype, } diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py index 49420dee9..f7ca9699d 100644 --- a/comfy_extras/nodes_model_downscale.py +++ b/comfy_extras/nodes_model_downscale.py @@ -1,24 +1,33 @@ +from typing_extensions import override import comfy.utils +from comfy_api.latest import ComfyExtension, io -class PatchModelAddDownscale: - upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] + +class PatchModelAddDownscale(io.ComfyNode): + UPSCALE_METHODS = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), - "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), - "downscale_after_skip": ("BOOLEAN", {"default": True}), - "downscale_method": (s.upscale_methods,), - "upscale_method": (s.upscale_methods,), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="PatchModelAddDownscale", + display_name="PatchModelAddDownscale (Kohya Deep Shrink)", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Int.Input("block_number", default=3, min=1, max=32, step=1), + io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001), + io.Boolean.Input("downscale_after_skip", default=True), + io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS), + io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS), + ], + outputs=[ + io.Model.Output(), + ], + ) - CATEGORY = "model_patches/unet" - - def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): + @classmethod + def execute(cls, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method) -> io.NodeOutput: model_sampling = model.get_model_object("model_sampling") sigma_start = model_sampling.percent_to_sigma(start_percent) sigma_end = model_sampling.percent_to_sigma(end_percent) @@ -41,13 +50,21 @@ class PatchModelAddDownscale: else: m.set_model_input_block_patch(input_block_patch) m.set_model_output_block_patch(output_block_patch) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "PatchModelAddDownscale": PatchModelAddDownscale, -} NODE_DISPLAY_NAME_MAPPINGS = { # Sampling - "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", + "PatchModelAddDownscale": "", } + +class ModelDownscaleExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + PatchModelAddDownscale, + ] + + +async def comfy_entrypoint() -> ModelDownscaleExtension: + return ModelDownscaleExtension() diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py index ccf601158..f20beab7d 100644 --- a/comfy_extras/nodes_model_merging.py +++ b/comfy_extras/nodes_model_merging.py @@ -209,6 +209,9 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi metadata["modelspec.predict_key"] = "epsilon" elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION: metadata["modelspec.predict_key"] = "v" + extra_keys["v_pred"] = torch.tensor([]) + if getattr(model_sampling, "zsnr", False): + extra_keys["ztsnr"] = torch.tensor([]) if not args.disable_metadata: metadata["prompt"] = prompt_info @@ -273,7 +276,7 @@ class CLIPSave: comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True) clip_sd = clip.get_sd() - for prefix in ["clip_l.", "clip_g.", ""]: + for prefix in ["clip_l.", "clip_g.", "clip_h.", "t5xxl.", "pile_t5xl.", "mt5xl.", "umt5xxl.", "t5base.", "gemma2_2b.", "llama.", "hydit_clip.", ""]: k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) current_clip_sd = {} for x in k: diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py index 7cd8f98b2..55eb3ccfe 100644 --- a/comfy_extras/nodes_model_merging_model_specific.py +++ b/comfy_extras/nodes_model_merging_model_specific.py @@ -196,6 +196,147 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks): return {"required": arg_dict} +class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["extra_pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["affline_norm."] = argument + + + for i in range(28): + arg_dict["blocks.block{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["extra_pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["affline_norm."] = argument + + + for i in range(36): + arg_dict["blocks.block{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb." + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["patch_embedding."] = argument + arg_dict["time_embedding."] = argument + arg_dict["time_projection."] = argument + arg_dict["text_embedding."] = argument + arg_dict["img_emb."] = argument + + for i in range(40): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["head."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmosPredict2_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["t_embedding_norm."] = argument + + + for i in range(28): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeCosmosPredict2_14B(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embedder."] = argument + arg_dict["x_embedder."] = argument + arg_dict["t_embedder."] = argument + arg_dict["t_embedding_norm."] = argument + + + for i in range(36): + arg_dict["blocks.{}.".format(i)] = argument + + arg_dict["final_layer."] = argument + + return {"required": arg_dict} + +class ModelMergeQwenImage(comfy_extras.nodes_model_merging.ModelMergeBlocks): + CATEGORY = "advanced/model_merging/model_specific" + + @classmethod + def INPUT_TYPES(s): + arg_dict = { "model1": ("MODEL",), + "model2": ("MODEL",)} + + argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + + arg_dict["pos_embeds."] = argument + arg_dict["img_in."] = argument + arg_dict["txt_norm."] = argument + arg_dict["txt_in."] = argument + arg_dict["time_text_embed."] = argument + + for i in range(60): + arg_dict["transformer_blocks.{}.".format(i)] = argument + + arg_dict["proj_out."] = argument + + return {"required": arg_dict} + NODE_CLASS_MAPPINGS = { "ModelMergeSD1": ModelMergeSD1, "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks @@ -206,4 +347,10 @@ NODE_CLASS_MAPPINGS = { "ModelMergeSD35_Large": ModelMergeSD35_Large, "ModelMergeMochiPreview": ModelMergeMochiPreview, "ModelMergeLTXV": ModelMergeLTXV, + "ModelMergeCosmos7B": ModelMergeCosmos7B, + "ModelMergeCosmos14B": ModelMergeCosmos14B, + "ModelMergeWAN2_1": ModelMergeWAN2_1, + "ModelMergeCosmosPredict2_2B": ModelMergeCosmosPredict2_2B, + "ModelMergeCosmosPredict2_14B": ModelMergeCosmosPredict2_14B, + "ModelMergeQwenImage": ModelMergeQwenImage, } diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py new file mode 100644 index 000000000..783c59b6b --- /dev/null +++ b/comfy_extras/nodes_model_patch.py @@ -0,0 +1,343 @@ +import torch +from torch import nn +import folder_paths +import comfy.utils +import comfy.ops +import comfy.model_management +import comfy.ldm.common_dit +import comfy.latent_formats + + +class BlockWiseControlBlock(torch.nn.Module): + # [linear, gelu, linear] + def __init__(self, dim: int = 3072, device=None, dtype=None, operations=None): + super().__init__() + self.x_rms = operations.RMSNorm(dim, eps=1e-6) + self.y_rms = operations.RMSNorm(dim, eps=1e-6) + self.input_proj = operations.Linear(dim, dim) + self.act = torch.nn.GELU() + self.output_proj = operations.Linear(dim, dim) + + def forward(self, x, y): + x, y = self.x_rms(x), self.y_rms(y) + x = self.input_proj(x + y) + x = self.act(x) + x = self.output_proj(x) + return x + + +class QwenImageBlockWiseControlNet(torch.nn.Module): + def __init__( + self, + num_layers: int = 60, + in_dim: int = 64, + additional_in_dim: int = 0, + dim: int = 3072, + device=None, dtype=None, operations=None + ): + super().__init__() + self.additional_in_dim = additional_in_dim + self.img_in = operations.Linear(in_dim + additional_in_dim, dim, device=device, dtype=dtype) + self.controlnet_blocks = torch.nn.ModuleList( + [ + BlockWiseControlBlock(dim, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ] + ) + + def process_input_latent_image(self, latent_image): + latent_image[:, :16] = comfy.latent_formats.Wan21().process_in(latent_image[:, :16]) + patch_size = 2 + hidden_states = comfy.ldm.common_dit.pad_to_patch_size(latent_image, (1, patch_size, patch_size)) + orig_shape = hidden_states.shape + hidden_states = hidden_states.view(orig_shape[0], orig_shape[1], orig_shape[-2] // 2, 2, orig_shape[-1] // 2, 2) + hidden_states = hidden_states.permute(0, 2, 4, 1, 3, 5) + hidden_states = hidden_states.reshape(orig_shape[0], (orig_shape[-2] // 2) * (orig_shape[-1] // 2), orig_shape[1] * 4) + return self.img_in(hidden_states) + + def control_block(self, img, controlnet_conditioning, block_id): + return self.controlnet_blocks[block_id](img, controlnet_conditioning) + + +class SigLIPMultiFeatProjModel(torch.nn.Module): + """ + SigLIP Multi-Feature Projection Model for processing style features from different layers + and projecting them into a unified hidden space. + + Args: + siglip_token_nums (int): Number of SigLIP tokens, default 257 + style_token_nums (int): Number of style tokens, default 256 + siglip_token_dims (int): Dimension of SigLIP tokens, default 1536 + hidden_size (int): Hidden layer size, default 3072 + context_layer_norm (bool): Whether to use context layer normalization, default False + """ + + def __init__( + self, + siglip_token_nums: int = 729, + style_token_nums: int = 64, + siglip_token_dims: int = 1152, + hidden_size: int = 3072, + context_layer_norm: bool = True, + device=None, dtype=None, operations=None + ): + super().__init__() + + # High-level feature processing (layer -2) + self.high_embedding_linear = nn.Sequential( + operations.Linear(siglip_token_nums, style_token_nums), + nn.SiLU() + ) + self.high_layer_norm = ( + operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity() + ) + self.high_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True) + + # Mid-level feature processing (layer -11) + self.mid_embedding_linear = nn.Sequential( + operations.Linear(siglip_token_nums, style_token_nums), + nn.SiLU() + ) + self.mid_layer_norm = ( + operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity() + ) + self.mid_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True) + + # Low-level feature processing (layer -20) + self.low_embedding_linear = nn.Sequential( + operations.Linear(siglip_token_nums, style_token_nums), + nn.SiLU() + ) + self.low_layer_norm = ( + operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity() + ) + self.low_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True) + + def forward(self, siglip_outputs): + """ + Forward pass function + + Args: + siglip_outputs: Output from SigLIP model, containing hidden_states + + Returns: + torch.Tensor: Concatenated multi-layer features with shape [bs, 3*style_token_nums, hidden_size] + """ + dtype = next(self.high_embedding_linear.parameters()).dtype + + # Process high-level features (layer -2) + high_embedding = self._process_layer_features( + siglip_outputs[2], + self.high_embedding_linear, + self.high_layer_norm, + self.high_projection, + dtype + ) + + # Process mid-level features (layer -11) + mid_embedding = self._process_layer_features( + siglip_outputs[1], + self.mid_embedding_linear, + self.mid_layer_norm, + self.mid_projection, + dtype + ) + + # Process low-level features (layer -20) + low_embedding = self._process_layer_features( + siglip_outputs[0], + self.low_embedding_linear, + self.low_layer_norm, + self.low_projection, + dtype + ) + + # Concatenate features from all layersmodel_patch + return torch.cat((high_embedding, mid_embedding, low_embedding), dim=1) + + def _process_layer_features( + self, + hidden_states: torch.Tensor, + embedding_linear: nn.Module, + layer_norm: nn.Module, + projection: nn.Module, + dtype: torch.dtype + ) -> torch.Tensor: + """ + Helper function to process features from a single layer + + Args: + hidden_states: Input hidden states [bs, seq_len, dim] + embedding_linear: Embedding linear layer + layer_norm: Layer normalization + projection: Projection layer + dtype: Target data type + + Returns: + torch.Tensor: Processed features [bs, style_token_nums, hidden_size] + """ + # Transform dimensions: [bs, seq_len, dim] -> [bs, dim, seq_len] -> [bs, dim, style_token_nums] -> [bs, style_token_nums, dim] + embedding = embedding_linear( + hidden_states.to(dtype).transpose(1, 2) + ).transpose(1, 2) + + # Apply layer normalization + embedding = layer_norm(embedding) + + # Project to target hidden space + embedding = projection(embedding) + + return embedding + +class ModelPatchLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "name": (folder_paths.get_filename_list("model_patches"), ), + }} + RETURN_TYPES = ("MODEL_PATCH",) + FUNCTION = "load_model_patch" + EXPERIMENTAL = True + + CATEGORY = "advanced/loaders" + + def load_model_patch(self, name): + model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name) + sd = comfy.utils.load_torch_file(model_patch_path, safe_load=True) + dtype = comfy.utils.weight_dtype(sd) + + if 'controlnet_blocks.0.y_rms.weight' in sd: + additional_in_dim = sd["img_in.weight"].shape[1] - 64 + model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast) + elif 'feature_embedder.mid_layer_norm.bias' in sd: + sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True) + model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast) + + model.load_state_dict(sd) + model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) + return (model,) + + +class DiffSynthCnetPatch: + def __init__(self, model_patch, vae, image, strength, mask=None): + self.model_patch = model_patch + self.vae = vae + self.image = image + self.strength = strength + self.mask = mask + self.encoded_image = model_patch.model.process_input_latent_image(self.encode_latent_cond(image)) + self.encoded_image_size = (image.shape[1], image.shape[2]) + + def encode_latent_cond(self, image): + latent_image = self.vae.encode(image) + if self.model_patch.model.additional_in_dim > 0: + if self.mask is None: + mask_ = torch.ones_like(latent_image)[:, :self.model_patch.model.additional_in_dim // 4] + else: + mask_ = comfy.utils.common_upscale(self.mask.mean(dim=1, keepdim=True), latent_image.shape[-1], latent_image.shape[-2], "bilinear", "none") + + return torch.cat([latent_image, mask_], dim=1) + else: + return latent_image + + def __call__(self, kwargs): + x = kwargs.get("x") + img = kwargs.get("img") + block_index = kwargs.get("block_index") + spacial_compression = self.vae.spacial_compression_encode() + if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression): + image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center") + loaded_models = comfy.model_management.loaded_models(only_currently_used=True) + self.encoded_image = self.model_patch.model.process_input_latent_image(self.encode_latent_cond(image_scaled.movedim(1, -1))) + self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1]) + comfy.model_management.load_models_gpu(loaded_models) + + img[:, :self.encoded_image.shape[1]] += (self.model_patch.model.control_block(img[:, :self.encoded_image.shape[1]], self.encoded_image.to(img.dtype), block_index) * self.strength) + kwargs['img'] = img + return kwargs + + def to(self, device_or_dtype): + if isinstance(device_or_dtype, torch.device): + self.encoded_image = self.encoded_image.to(device_or_dtype) + return self + + def models(self): + return [self.model_patch] + +class QwenImageDiffsynthControlnet: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "model_patch": ("MODEL_PATCH",), + "vae": ("VAE",), + "image": ("IMAGE",), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }, + "optional": {"mask": ("MASK",)}} + RETURN_TYPES = ("MODEL",) + FUNCTION = "diffsynth_controlnet" + EXPERIMENTAL = True + + CATEGORY = "advanced/loaders/qwen" + + def diffsynth_controlnet(self, model, model_patch, vae, image, strength, mask=None): + model_patched = model.clone() + image = image[:, :, :, :3] + if mask is not None: + if mask.ndim == 3: + mask = mask.unsqueeze(1) + if mask.ndim == 4: + mask = mask.unsqueeze(2) + mask = 1.0 - mask + + model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask)) + return (model_patched,) + + +class UsoStyleProjectorPatch: + def __init__(self, model_patch, encoded_image): + self.model_patch = model_patch + self.encoded_image = encoded_image + + def __call__(self, kwargs): + txt_ids = kwargs.get("txt_ids") + txt = kwargs.get("txt") + siglip_embedding = self.model_patch.model(self.encoded_image.to(txt.dtype)).to(txt.dtype) + txt = torch.cat([siglip_embedding, txt], dim=1) + kwargs['txt'] = txt + kwargs['txt_ids'] = torch.cat([torch.zeros(siglip_embedding.shape[0], siglip_embedding.shape[1], 3, dtype=txt_ids.dtype, device=txt_ids.device), txt_ids], dim=1) + return kwargs + + def to(self, device_or_dtype): + if isinstance(device_or_dtype, torch.device): + self.encoded_image = self.encoded_image.to(device_or_dtype) + return self + + def models(self): + return [self.model_patch] + + +class USOStyleReference: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL",), + "model_patch": ("MODEL_PATCH",), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "apply_patch" + EXPERIMENTAL = True + + CATEGORY = "advanced/model_patches/flux" + + def apply_patch(self, model, model_patch, clip_vision_output): + encoded_image = torch.stack((clip_vision_output.all_hidden_states[:, -20], clip_vision_output.all_hidden_states[:, -11], clip_vision_output.penultimate_hidden_states)) + model_patched = model.clone() + model_patched.set_model_post_input_patch(UsoStyleProjectorPatch(model_patch, encoded_image)) + return (model_patched,) + + +NODE_CLASS_MAPPINGS = { + "ModelPatchLoader": ModelPatchLoader, + "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet, + "USOStyleReference": USOStyleReference, +} diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py index b1372b8ce..67377e1bc 100644 --- a/comfy_extras/nodes_morphology.py +++ b/comfy_extras/nodes_morphology.py @@ -1,23 +1,34 @@ import torch import comfy.model_management +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat +import kornia.color -class Morphology: +class Morphology(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"image": ("IMAGE",), - "operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), - "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), - }} + def define_schema(cls): + return io.Schema( + node_id="Morphology", + display_name="ImageMorphology", + category="image/postprocessing", + inputs=[ + io.Image.Input("image"), + io.Combo.Input( + "operation", + options=["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"], + ), + io.Int.Input("kernel_size", default=3, min=3, max=999, step=1), + ], + outputs=[ + io.Image.Output(), + ], + ) - RETURN_TYPES = ("IMAGE",) - FUNCTION = "process" - - CATEGORY = "image/postprocessing" - - def process(self, image, operation, kernel_size): + @classmethod + def execute(cls, image, operation, kernel_size) -> io.NodeOutput: device = comfy.model_management.get_torch_device() kernel = torch.ones(kernel_size, kernel_size, device=device) image_k = image.to(device).movedim(-1, 1) @@ -38,12 +49,63 @@ class Morphology: else: raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) - return (img_out,) + return io.NodeOutput(img_out) -NODE_CLASS_MAPPINGS = { - "Morphology": Morphology, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "Morphology": "ImageMorphology", -} +class ImageRGBToYUV(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageRGBToYUV", + category="image/batch", + inputs=[ + io.Image.Input("image"), + ], + outputs=[ + io.Image.Output(display_name="Y"), + io.Image.Output(display_name="U"), + io.Image.Output(display_name="V"), + ], + ) + + @classmethod + def execute(cls, image) -> io.NodeOutput: + out = kornia.color.rgb_to_ycbcr(image.movedim(-1, 1)).movedim(1, -1) + return io.NodeOutput(out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image)) + +class ImageYUVToRGB(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageYUVToRGB", + category="image/batch", + inputs=[ + io.Image.Input("Y"), + io.Image.Input("U"), + io.Image.Input("V"), + ], + outputs=[ + io.Image.Output(), + ], + ) + + @classmethod + def execute(cls, Y, U, V) -> io.NodeOutput: + image = torch.cat([torch.mean(Y, dim=-1, keepdim=True), torch.mean(U, dim=-1, keepdim=True), torch.mean(V, dim=-1, keepdim=True)], dim=-1) + out = kornia.color.ycbcr_to_rgb(image.movedim(-1, 1)).movedim(1, -1) + return io.NodeOutput(out) + + +class MorphologyExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + Morphology, + ImageRGBToYUV, + ImageYUVToRGB, + ] + + +async def comfy_entrypoint() -> MorphologyExtension: + return MorphologyExtension() + diff --git a/comfy_extras/nodes_optimalsteps.py b/comfy_extras/nodes_optimalsteps.py new file mode 100644 index 000000000..73f0104d8 --- /dev/null +++ b/comfy_extras/nodes_optimalsteps.py @@ -0,0 +1,71 @@ +# from https://github.com/bebebe666/OptimalSteps + +import numpy as np +import torch + +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +def loglinear_interp(t_steps, num_steps): + """ + Performs log-linear interpolation of a given array of decreasing numbers. + """ + xs = np.linspace(0, 1, len(t_steps)) + ys = np.log(t_steps[::-1]) + + new_xs = np.linspace(0, 1, num_steps) + new_ys = np.interp(new_xs, xs, ys) + + interped_ys = np.exp(new_ys)[::-1].copy() + return interped_ys + + +NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0.8287, 0.5512, 0.2808, 0.001], +"Wan":[1.0, 0.997, 0.995, 0.993, 0.991, 0.989, 0.987, 0.985, 0.98, 0.975, 0.973, 0.968, 0.96, 0.946, 0.927, 0.902, 0.864, 0.776, 0.539, 0.208, 0.001], +"Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001], +} + +class OptimalStepsScheduler(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="OptimalStepsScheduler", + category="sampling/custom_sampling/schedulers", + inputs=[ + io.Combo.Input("model_type", options=["FLUX", "Wan", "Chroma"]), + io.Int.Input("steps", default=20, min=3, max=1000), + io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01), + ], + outputs=[ + io.Sigmas.Output(), + ], + ) + + @classmethod + def execute(cls, model_type, steps, denoise) ->io.NodeOutput: + total_steps = steps + if denoise < 1.0: + if denoise <= 0.0: + return io.NodeOutput(torch.FloatTensor([])) + total_steps = round(steps * denoise) + + sigmas = NOISE_LEVELS[model_type][:] + if (steps + 1) != len(sigmas): + sigmas = loglinear_interp(sigmas, steps + 1) + + sigmas = sigmas[-(total_steps + 1):] + sigmas[-1] = 0 + return io.NodeOutput(torch.FloatTensor(sigmas)) + + +class OptimalStepsExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + OptimalStepsScheduler, + ] + + +async def comfy_entrypoint() -> OptimalStepsExtension: + return OptimalStepsExtension() diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py index eb28196f4..79fea5f0c 100644 --- a/comfy_extras/nodes_pag.py +++ b/comfy_extras/nodes_pag.py @@ -3,25 +3,30 @@ #My modified one here is more basic but has less chances of breaking with ComfyUI updates. +from typing_extensions import override + import comfy.model_patcher import comfy.samplers +from comfy_api.latest import ComfyExtension, io -class PerturbedAttentionGuidance: + +class PerturbedAttentionGuidance(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "model": ("MODEL",), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}), - } - } + def define_schema(cls): + return io.Schema( + node_id="PerturbedAttentionGuidance", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Float.Input("scale", default=3.0, min=0.0, max=100.0, step=0.01, round=0.01), + ], + outputs=[ + io.Model.Output(), + ], + ) - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" - - CATEGORY = "model_patches/unet" - - def patch(self, model, scale): + @classmethod + def execute(cls, model, scale) -> io.NodeOutput: unet_block = "middle" unet_block_id = 0 m = model.clone() @@ -49,8 +54,16 @@ class PerturbedAttentionGuidance: m.set_model_sampler_post_cfg_function(post_cfg_function) - return (m,) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "PerturbedAttentionGuidance": PerturbedAttentionGuidance, -} + +class PAGExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + PerturbedAttentionGuidance, + ] + + +async def comfy_entrypoint() -> PAGExtension: + return PAGExtension() diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py index 6c6f71767..cd068ce9c 100644 --- a/comfy_extras/nodes_perpneg.py +++ b/comfy_extras/nodes_perpneg.py @@ -4,6 +4,10 @@ import comfy.sampler_helpers import comfy.samplers import comfy.utils import node_helpers +import math +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale): pos = noise_pred_pos - noise_pred_nocond @@ -15,20 +19,27 @@ def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, co return cfg_result #TODO: This node should be removed, it has been replaced with PerpNegGuider -class PerpNeg: +class PerpNeg(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "empty_conditioning": ("CONDITIONING", ), - "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="PerpNeg", + display_name="Perp-Neg (DEPRECATED by PerpNegGuider)", + category="_for_testing", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("empty_conditioning"), + io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[ + io.Model.Output(), + ], + is_experimental=True, + is_deprecated=True, + ) - CATEGORY = "_for_testing" - DEPRECATED = True - - def patch(self, model, empty_conditioning, neg_scale): + @classmethod + def execute(cls, model, empty_conditioning, neg_scale) -> io.NodeOutput: m = model.clone() nocond = comfy.sampler_helpers.convert_cond(empty_conditioning) @@ -49,7 +60,7 @@ class PerpNeg: m.set_model_sampler_cfg_function(cfg_function) - return (m, ) + return io.NodeOutput(m) class Guider_PerpNeg(comfy.samplers.CFGGuider): @@ -69,8 +80,23 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider): negative_cond = self.conds.get("negative", None) empty_cond = self.conds.get("empty_negative_prompt", None) - (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \ - comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options) + if model_options.get("disable_cfg1_optimization", False) == False: + if math.isclose(self.neg_scale, 0.0): + negative_cond = None + if math.isclose(self.cfg, 1.0): + empty_cond = None + + conds = [positive_cond, negative_cond, empty_cond] + + out = comfy.samplers.calc_cond_batch(self.inner_model, conds, x, timestep, model_options) + + # Apply pre_cfg_functions since sampling_function() is skipped + for fn in model_options.get("sampler_pre_cfg_function", []): + args = {"conds":conds, "conds_out": out, "cond_scale": self.cfg, "timestep": timestep, + "input": x, "sigma": timestep, "model": self.inner_model, "model_options": model_options} + out = fn(args) + + noise_pred_pos, noise_pred_neg, noise_pred_empty = out cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg) # normally this would be done in cfg_function, but we skipped @@ -82,6 +108,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider): "denoised": cfg_result, "cond": positive_cond, "uncond": negative_cond, + "cond_scale": self.cfg, "model": self.inner_model, "uncond_denoised": noise_pred_neg, "cond_denoised": noise_pred_pos, @@ -95,35 +122,42 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider): return cfg_result -class PerpNegGuider: +class PerpNegGuider(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": - {"model": ("MODEL",), - "positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "empty_conditioning": ("CONDITIONING", ), - "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), - "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}), - } - } + def define_schema(cls): + return io.Schema( + node_id="PerpNegGuider", + category="_for_testing", + inputs=[ + io.Model.Input("model"), + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Conditioning.Input("empty_conditioning"), + io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01), + io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01), + ], + outputs=[ + io.Guider.Output(), + ], + is_experimental=True, + ) - RETURN_TYPES = ("GUIDER",) - - FUNCTION = "get_guider" - CATEGORY = "_for_testing" - - def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale): + @classmethod + def execute(cls, model, positive, negative, empty_conditioning, cfg, neg_scale) -> io.NodeOutput: guider = Guider_PerpNeg(model) guider.set_conds(positive, negative, empty_conditioning) guider.set_cfg(cfg, neg_scale) - return (guider,) + return io.NodeOutput(guider) -NODE_CLASS_MAPPINGS = { - "PerpNeg": PerpNeg, - "PerpNegGuider": PerpNegGuider, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)", -} +class PerpNegExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + PerpNeg, + PerpNegGuider, + ] + + +async def comfy_entrypoint() -> PerpNegExtension: + return PerpNegExtension() diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py index d358ed6d5..228183c07 100644 --- a/comfy_extras/nodes_photomaker.py +++ b/comfy_extras/nodes_photomaker.py @@ -4,6 +4,8 @@ import folder_paths import comfy.clip_model import comfy.clip_vision import comfy.ops +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io # code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 VISION_CONFIG_DICT = { @@ -116,41 +118,52 @@ class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection): return updated_prompt_embeds -class PhotoMakerLoader: +class PhotoMakerLoader(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}} + def define_schema(cls): + return io.Schema( + node_id="PhotoMakerLoader", + category="_for_testing/photomaker", + inputs=[ + io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")), + ], + outputs=[ + io.Photomaker.Output(), + ], + is_experimental=True, + ) - RETURN_TYPES = ("PHOTOMAKER",) - FUNCTION = "load_photomaker_model" - - CATEGORY = "_for_testing/photomaker" - - def load_photomaker_model(self, photomaker_model_name): + @classmethod + def execute(cls, photomaker_model_name): photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name) photomaker_model = PhotoMakerIDEncoder() data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True) if "id_encoder" in data: data = data["id_encoder"] photomaker_model.load_state_dict(data) - return (photomaker_model,) + return io.NodeOutput(photomaker_model) -class PhotoMakerEncode: +class PhotoMakerEncode(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "photomaker": ("PHOTOMAKER",), - "image": ("IMAGE",), - "clip": ("CLIP", ), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}), - }} + def define_schema(cls): + return io.Schema( + node_id="PhotoMakerEncode", + category="_for_testing/photomaker", + inputs=[ + io.Photomaker.Input("photomaker"), + io.Image.Input("image"), + io.Clip.Input("clip"), + io.String.Input("text", multiline=True, dynamic_prompts=True, default="photograph of photomaker"), + ], + outputs=[ + io.Conditioning.Output(), + ], + is_experimental=True, + ) - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "apply_photomaker" - - CATEGORY = "_for_testing/photomaker" - - def apply_photomaker(self, photomaker, image, clip, text): + @classmethod + def execute(cls, photomaker, image, clip, text): special_token = "photomaker" pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() try: @@ -178,11 +191,16 @@ class PhotoMakerEncode: else: out = cond - return ([[out, {"pooled_output": pooled}]], ) + return io.NodeOutput([[out, {"pooled_output": pooled}]]) -NODE_CLASS_MAPPINGS = { - "PhotoMakerLoader": PhotoMakerLoader, - "PhotoMakerEncode": PhotoMakerEncode, -} +class PhotomakerExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + PhotoMakerLoader, + PhotoMakerEncode, + ] +async def comfy_entrypoint() -> PhotomakerExtension: + return PhotomakerExtension() diff --git a/comfy_extras/nodes_pixart.py b/comfy_extras/nodes_pixart.py index c7209c468..a23e87b1f 100644 --- a/comfy_extras/nodes_pixart.py +++ b/comfy_extras/nodes_pixart.py @@ -1,24 +1,38 @@ -from nodes import MAX_RESOLUTION - -class CLIPTextEncodePixArtAlpha: - @classmethod - def INPUT_TYPES(s): - return {"required": { - "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), - # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ), - }} - - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" - CATEGORY = "advanced/conditioning" - DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma." - - def encode(self, clip, width, height, text): - tokens = clip.tokenize(text) - return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),) - -NODE_CLASS_MAPPINGS = { - "CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha, -} +from typing_extensions import override +import nodes +from comfy_api.latest import ComfyExtension, io + +class CLIPTextEncodePixArtAlpha(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodePixArtAlpha", + category="advanced/conditioning", + description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.", + inputs=[ + io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION), + io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION), + # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + io.String.Input("text", multiline=True, dynamic_prompts=True), + io.Clip.Input("clip"), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, clip, width, height, text): + tokens = clip.tokenize(text) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height})) + + +class PixArtExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + CLIPTextEncodePixArtAlpha, + ] + +async def comfy_entrypoint() -> PixArtExtension: + return PixArtExtension() diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 68f6ef51e..34c388a5a 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -1,3 +1,4 @@ +from typing_extensions import override import numpy as np import torch import torch.nn.functional as F @@ -6,46 +7,42 @@ import math import comfy.utils import comfy.model_management +import node_helpers +from comfy_api.latest import ComfyExtension, io - -class Blend: - def __init__(self): - pass +class Blend(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageBlend", + category="image/postprocessing", + inputs=[ + io.Image.Input("image1"), + io.Image.Input("image2"), + io.Float.Input("blend_factor", default=0.5, min=0.0, max=1.0, step=0.01), + io.Combo.Input("blend_mode", options=["normal", "multiply", "screen", "overlay", "soft_light", "difference"]), + ], + outputs=[ + io.Image.Output(), + ], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image1": ("IMAGE",), - "image2": ("IMAGE",), - "blend_factor": ("FLOAT", { - "default": 0.5, - "min": 0.0, - "max": 1.0, - "step": 0.01 - }), - "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "blend_images" - - CATEGORY = "image/postprocessing" - - def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): + def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput: + image1, image2 = node_helpers.image_alpha_fix(image1, image2) image2 = image2.to(image1.device) if image1.shape != image2.shape: image2 = image2.permute(0, 3, 1, 2) image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = image2.permute(0, 2, 3, 1) - blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = cls.blend_mode(image1, image2, blend_mode) blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor blended_image = torch.clamp(blended_image, 0, 1) - return (blended_image,) + return io.NodeOutput(blended_image) - def blend_mode(self, img1, img2, mode): + @classmethod + def blend_mode(cls, img1, img2, mode): if mode == "normal": return img2 elif mode == "multiply": @@ -55,13 +52,13 @@ class Blend: elif mode == "overlay": return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) elif mode == "soft_light": - return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (cls.g(img1) - img1)) elif mode == "difference": return img1 - img2 - else: - raise ValueError(f"Unsupported blend mode: {mode}") + raise ValueError(f"Unsupported blend mode: {mode}") - def g(self, x): + @classmethod + def g(cls, x): return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) def gaussian_kernel(kernel_size: int, sigma: float, device=None): @@ -70,38 +67,26 @@ def gaussian_kernel(kernel_size: int, sigma: float, device=None): g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) return g / g.sum() -class Blur: - def __init__(self): - pass +class Blur(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageBlur", + category="image/postprocessing", + inputs=[ + io.Image.Input("image"), + io.Int.Input("blur_radius", default=1, min=1, max=31, step=1), + io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1), + ], + outputs=[ + io.Image.Output(), + ], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "blur_radius": ("INT", { - "default": 1, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.1 - }), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "blur" - - CATEGORY = "image/postprocessing" - - def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): + def execute(cls, image: torch.Tensor, blur_radius: int, sigma: float) -> io.NodeOutput: if blur_radius == 0: - return (image,) + return io.NodeOutput(image) image = image.to(comfy.model_management.get_torch_device()) batch_size, height, width, channels = image.shape @@ -114,32 +99,26 @@ class Blur: blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] blurred = blurred.permute(0, 2, 3, 1) - return (blurred.to(comfy.model_management.intermediate_device()),) + return io.NodeOutput(blurred.to(comfy.model_management.intermediate_device())) -class Quantize: - def __init__(self): - pass +class Quantize(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "colors": ("INT", { - "default": 256, - "min": 1, - "max": 256, - "step": 1 - }), - "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "quantize" - - CATEGORY = "image/postprocessing" + def define_schema(cls): + return io.Schema( + node_id="ImageQuantize", + category="image/postprocessing", + inputs=[ + io.Image.Input("image"), + io.Int.Input("colors", default=256, min=1, max=256, step=1), + io.Combo.Input("dither", options=["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"]), + ], + outputs=[ + io.Image.Output(), + ], + ) + @staticmethod def bayer(im, pal_im, order): def normalized_bayer_matrix(n): if n == 0: @@ -165,7 +144,8 @@ class Quantize: im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) return im - def quantize(self, image: torch.Tensor, colors: int, dither: str): + @classmethod + def execute(cls, image: torch.Tensor, colors: int, dither: str) -> io.NodeOutput: batch_size, height, width, _ = image.shape result = torch.zeros_like(image) @@ -185,52 +165,36 @@ class Quantize: quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 result[b] = quantized_array - return (result,) + return io.NodeOutput(result) -class Sharpen: - def __init__(self): - pass +class Sharpen(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageSharpen", + category="image/postprocessing", + inputs=[ + io.Image.Input("image"), + io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1), + io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01), + io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01), + ], + outputs=[ + io.Image.Output(), + ], + ) @classmethod - def INPUT_TYPES(s): - return { - "required": { - "image": ("IMAGE",), - "sharpen_radius": ("INT", { - "default": 1, - "min": 1, - "max": 31, - "step": 1 - }), - "sigma": ("FLOAT", { - "default": 1.0, - "min": 0.1, - "max": 10.0, - "step": 0.01 - }), - "alpha": ("FLOAT", { - "default": 1.0, - "min": 0.0, - "max": 5.0, - "step": 0.01 - }), - }, - } - - RETURN_TYPES = ("IMAGE",) - FUNCTION = "sharpen" - - CATEGORY = "image/postprocessing" - - def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): + def execute(cls, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float) -> io.NodeOutput: if sharpen_radius == 0: - return (image,) + return io.NodeOutput(image) batch_size, height, width, channels = image.shape image = image.to(comfy.model_management.get_torch_device()) kernel_size = sharpen_radius * 2 + 1 kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) + kernel = kernel.to(dtype=image.dtype) center = kernel_size // 2 kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) @@ -242,23 +206,29 @@ class Sharpen: result = torch.clamp(sharpened, 0, 1) - return (result.to(comfy.model_management.intermediate_device()),) + return io.NodeOutput(result.to(comfy.model_management.intermediate_device())) -class ImageScaleToTotalPixels: +class ImageScaleToTotalPixels(io.ComfyNode): upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @classmethod - def INPUT_TYPES(s): - return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), - "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" + def define_schema(cls): + return io.Schema( + node_id="ImageScaleToTotalPixels", + category="image/upscaling", + inputs=[ + io.Image.Input("image"), + io.Combo.Input("upscale_method", options=cls.upscale_methods), + io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01), + ], + outputs=[ + io.Image.Output(), + ], + ) - CATEGORY = "image/upscaling" - - def upscale(self, image, upscale_method, megapixels): + @classmethod + def execute(cls, image, upscale_method, megapixels) -> io.NodeOutput: samples = image.movedim(-1,1) total = int(megapixels * 1024 * 1024) @@ -268,12 +238,18 @@ class ImageScaleToTotalPixels: s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled") s = s.movedim(1,-1) - return (s,) + return io.NodeOutput(s) -NODE_CLASS_MAPPINGS = { - "ImageBlend": Blend, - "ImageBlur": Blur, - "ImageQuantize": Quantize, - "ImageSharpen": Sharpen, - "ImageScaleToTotalPixels": ImageScaleToTotalPixels, -} +class PostProcessingExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + Blend, + Blur, + Quantize, + Sharpen, + ImageScaleToTotalPixels, + ] + +async def comfy_entrypoint() -> PostProcessingExtension: + return PostProcessingExtension() diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py new file mode 100644 index 000000000..e749fa6ae --- /dev/null +++ b/comfy_extras/nodes_preview_any.py @@ -0,0 +1,43 @@ +import json +from comfy.comfy_types.node_typing import IO + +# Preview Any - original implement from +# https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py +# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes +class PreviewAny(): + @classmethod + def INPUT_TYPES(cls): + return { + "required": {"source": (IO.ANY, {})}, + } + + RETURN_TYPES = () + FUNCTION = "main" + OUTPUT_NODE = True + + CATEGORY = "utils" + + def main(self, source=None): + value = 'None' + if isinstance(source, str): + value = source + elif isinstance(source, (int, float, bool)): + value = str(source) + elif source is not None: + try: + value = json.dumps(source, indent=4) + except Exception: + try: + value = str(source) + except Exception: + value = 'source exists, but could not be serialized.' + + return {"ui": {"text": (value,)}} + +NODE_CLASS_MAPPINGS = { + "PreviewAny": PreviewAny, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PreviewAny": "Preview Any", +} diff --git a/comfy_extras/nodes_primitive.py b/comfy_extras/nodes_primitive.py new file mode 100644 index 000000000..5a1aeba80 --- /dev/null +++ b/comfy_extras/nodes_primitive.py @@ -0,0 +1,109 @@ +import sys +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + + +class String(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PrimitiveString", + display_name="String", + category="utils/primitive", + inputs=[ + io.String.Input("value"), + ], + outputs=[io.String.Output()], + ) + + @classmethod + def execute(cls, value: str) -> io.NodeOutput: + return io.NodeOutput(value) + + +class StringMultiline(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PrimitiveStringMultiline", + display_name="String (Multiline)", + category="utils/primitive", + inputs=[ + io.String.Input("value", multiline=True), + ], + outputs=[io.String.Output()], + ) + + @classmethod + def execute(cls, value: str) -> io.NodeOutput: + return io.NodeOutput(value) + + +class Int(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PrimitiveInt", + display_name="Int", + category="utils/primitive", + inputs=[ + io.Int.Input("value", min=-sys.maxsize, max=sys.maxsize, control_after_generate=True), + ], + outputs=[io.Int.Output()], + ) + + @classmethod + def execute(cls, value: int) -> io.NodeOutput: + return io.NodeOutput(value) + + +class Float(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PrimitiveFloat", + display_name="Float", + category="utils/primitive", + inputs=[ + io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize), + ], + outputs=[io.Float.Output()], + ) + + @classmethod + def execute(cls, value: float) -> io.NodeOutput: + return io.NodeOutput(value) + + +class Boolean(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="PrimitiveBoolean", + display_name="Boolean", + category="utils/primitive", + inputs=[ + io.Boolean.Input("value"), + ], + outputs=[io.Boolean.Output()], + ) + + @classmethod + def execute(cls, value: bool) -> io.NodeOutput: + return io.NodeOutput(value) + + +class PrimitivesExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + String, + StringMultiline, + Int, + Float, + Boolean, + ] + +async def comfy_entrypoint() -> PrimitivesExtension: + return PrimitivesExtension() diff --git a/comfy_extras/nodes_qwen.py b/comfy_extras/nodes_qwen.py new file mode 100644 index 000000000..525239ae5 --- /dev/null +++ b/comfy_extras/nodes_qwen.py @@ -0,0 +1,117 @@ +import node_helpers +import comfy.utils +import math +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + + +class TextEncodeQwenImageEdit(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextEncodeQwenImageEdit", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("prompt", multiline=True, dynamic_prompts=True), + io.Vae.Input("vae", optional=True), + io.Image.Input("image", optional=True), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput: + ref_latent = None + if image is None: + images = [] + else: + samples = image.movedim(-1, 1) + total = int(1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") + image = s.movedim(1, -1) + images = [image[:, :, :, :3]] + if vae is not None: + ref_latent = vae.encode(image[:, :, :, :3]) + + tokens = clip.tokenize(prompt, images=images) + conditioning = clip.encode_from_tokens_scheduled(tokens) + if ref_latent is not None: + conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True) + return io.NodeOutput(conditioning) + + +class TextEncodeQwenImageEditPlus(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TextEncodeQwenImageEditPlus", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("prompt", multiline=True, dynamic_prompts=True), + io.Vae.Input("vae", optional=True), + io.Image.Input("image1", optional=True), + io.Image.Input("image2", optional=True), + io.Image.Input("image3", optional=True), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) + + @classmethod + def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput: + ref_latents = [] + images = [image1, image2, image3] + images_vl = [] + llama_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + image_prompt = "" + + for i, image in enumerate(images): + if image is not None: + samples = image.movedim(-1, 1) + total = int(384 * 384) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") + images_vl.append(s.movedim(1, -1)) + if vae is not None: + total = int(1024 * 1024) + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by / 8.0) * 8 + height = round(samples.shape[2] * scale_by / 8.0) * 8 + + s = comfy.utils.common_upscale(samples, width, height, "area", "disabled") + ref_latents.append(vae.encode(s.movedim(1, -1)[:, :, :, :3])) + + image_prompt += "Picture {}: <|vision_start|><|image_pad|><|vision_end|>".format(i + 1) + + tokens = clip.tokenize(image_prompt + prompt, images=images_vl, llama_template=llama_template) + conditioning = clip.encode_from_tokens_scheduled(tokens) + if len(ref_latents) > 0: + conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True) + return io.NodeOutput(conditioning) + + +class QwenExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TextEncodeQwenImageEdit, + TextEncodeQwenImageEditPlus, + ] + + +async def comfy_entrypoint() -> QwenExtension: + return QwenExtension() diff --git a/comfy_extras/nodes_rebatch.py b/comfy_extras/nodes_rebatch.py index e29cb9ed1..5f4e82aef 100644 --- a/comfy_extras/nodes_rebatch.py +++ b/comfy_extras/nodes_rebatch.py @@ -1,18 +1,25 @@ +from typing_extensions import override import torch -class LatentRebatch: +from comfy_api.latest import ComfyExtension, io + + +class LatentRebatch(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "latents": ("LATENT",), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("LATENT",) - INPUT_IS_LIST = True - OUTPUT_IS_LIST = (True, ) - - FUNCTION = "rebatch" - - CATEGORY = "latent/batch" + def define_schema(cls): + return io.Schema( + node_id="RebatchLatents", + display_name="Rebatch Latents", + category="latent/batch", + is_input_list=True, + inputs=[ + io.Latent.Input("latents"), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(is_output_list=True), + ], + ) @staticmethod def get_batch(latents, list_ind, offset): @@ -53,7 +60,8 @@ class LatentRebatch: result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] return result - def rebatch(self, latents, batch_size): + @classmethod + def execute(cls, latents, batch_size): batch_size = batch_size[0] output_list = [] @@ -63,24 +71,24 @@ class LatentRebatch: for i in range(len(latents)): # fetch new entry of list #samples, masks, indices = self.get_batch(latents, i) - next_batch = self.get_batch(latents, i, processed) + next_batch = cls.get_batch(latents, i, processed) processed += len(next_batch[2]) # set to current if current is None if current_batch[0] is None: current_batch = next_batch # add previous to list if dimensions do not match elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: - sliced, _ = self.slice_batch(current_batch, 1, batch_size) + sliced, _ = cls.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) current_batch = next_batch # cat if everything checks out else: - current_batch = self.cat_batch(current_batch, next_batch) + current_batch = cls.cat_batch(current_batch, next_batch) # add to list if dimensions gone above target batch size if current_batch[0].shape[0] > batch_size: num = current_batch[0].shape[0] // batch_size - sliced, remainder = self.slice_batch(current_batch, num, batch_size) + sliced, remainder = cls.slice_batch(current_batch, num, batch_size) for i in range(num): output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) @@ -89,7 +97,7 @@ class LatentRebatch: #add remainder if current_batch[0] is not None: - sliced, _ = self.slice_batch(current_batch, 1, batch_size) + sliced, _ = cls.slice_batch(current_batch, 1, batch_size) output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) #get rid of empty masks @@ -97,23 +105,27 @@ class LatentRebatch: if s['noise_mask'].mean() == 1.0: del s['noise_mask'] - return (output_list,) + return io.NodeOutput(output_list) -class ImageRebatch: +class ImageRebatch(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "images": ("IMAGE",), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - }} - RETURN_TYPES = ("IMAGE",) - INPUT_IS_LIST = True - OUTPUT_IS_LIST = (True, ) + def define_schema(cls): + return io.Schema( + node_id="RebatchImages", + display_name="Rebatch Images", + category="image/batch", + is_input_list=True, + inputs=[ + io.Image.Input("images"), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Image.Output(is_output_list=True), + ], + ) - FUNCTION = "rebatch" - - CATEGORY = "image/batch" - - def rebatch(self, images, batch_size): + @classmethod + def execute(cls, images, batch_size): batch_size = batch_size[0] output_list = [] @@ -125,14 +137,17 @@ class ImageRebatch: for i in range(0, len(all_images), batch_size): output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) - return (output_list,) + return io.NodeOutput(output_list) -NODE_CLASS_MAPPINGS = { - "RebatchLatents": LatentRebatch, - "RebatchImages": ImageRebatch, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "RebatchLatents": "Rebatch Latents", - "RebatchImages": "Rebatch Images", -} +class RebatchExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + LatentRebatch, + ImageRebatch, + ] + + +async def comfy_entrypoint() -> RebatchExtension: + return RebatchExtension() diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py index 1bd8d7364..0f47db30b 100644 --- a/comfy_extras/nodes_sag.py +++ b/comfy_extras/nodes_sag.py @@ -2,10 +2,13 @@ import torch from torch import einsum import torch.nn.functional as F import math +from typing_extensions import override from einops import rearrange, repeat from comfy.ldm.modules.attention import optimized_attention import comfy.samplers +from comfy_api.latest import ComfyExtension, io + # from comfy/ldm/modules/attention.py # but modified to return attention scores as well as output @@ -104,19 +107,26 @@ def gaussian_blur_2d(img, kernel_size, sigma): img = F.conv2d(img, kernel2d, groups=img.shape[-3]) return img -class SelfAttentionGuidance: +class SelfAttentionGuidance(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}), - "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="SelfAttentionGuidance", + display_name="Self-Attention Guidance", + category="_for_testing", + inputs=[ + io.Model.Input("model"), + io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01), + io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1), + ], + outputs=[ + io.Model.Output(), + ], + is_experimental=True, + ) - CATEGORY = "_for_testing" - - def patch(self, model, scale, blur_sigma): + @classmethod + def execute(cls, model, scale, blur_sigma): m = model.clone() attn_scores = None @@ -170,12 +180,16 @@ class SelfAttentionGuidance: # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "SelfAttentionGuidance": SelfAttentionGuidance, -} -NODE_DISPLAY_NAME_MAPPINGS = { - "SelfAttentionGuidance": "Self-Attention Guidance", -} +class SagExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SelfAttentionGuidance, + ] + + +async def comfy_entrypoint() -> SagExtension: + return SagExtension() diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index d75b29e60..14782cb2b 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -3,64 +3,83 @@ import comfy.sd import comfy.model_management import nodes import torch -import comfy_extras.nodes_slg +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io +from comfy_extras.nodes_slg import SkipLayerGuidanceDiT -class TripleCLIPLoader: +class TripleCLIPLoader(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), ) - }} - RETURN_TYPES = ("CLIP",) - FUNCTION = "load_clip" + def define_schema(cls): + return io.Schema( + node_id="TripleCLIPLoader", + category="advanced/loaders", + description="[Recipes]\n\nsd3: clip-l, clip-g, t5", + inputs=[ + io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")), + io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")), + io.Combo.Input("clip_name3", options=folder_paths.get_filename_list("text_encoders")), + ], + outputs=[ + io.Clip.Output(), + ], + ) - CATEGORY = "advanced/loaders" - - DESCRIPTION = "[Recipes]\n\nsd3: clip-l, clip-g, t5" - - def load_clip(self, clip_name1, clip_name2, clip_name3): + @classmethod + def execute(cls, clip_name1, clip_name2, clip_name3) -> io.NodeOutput: clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3) clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings")) - return (clip,) + return io.NodeOutput(clip) + + load_clip = execute # TODO: remove -class EmptySD3LatentImage: - def __init__(self): - self.device = comfy.model_management.intermediate_device() +class EmptySD3LatentImage(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="EmptySD3LatentImage", + category="latent/sd3", + inputs=[ + io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(), + ], + ) @classmethod - def INPUT_TYPES(s): - return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} - RETURN_TYPES = ("LATENT",) - FUNCTION = "generate" + def execute(cls, width, height, batch_size=1) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + return io.NodeOutput({"samples":latent}) - CATEGORY = "latent/sd3" - - def generate(self, width, height, batch_size=1): - latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=self.device) - return ({"samples":latent}, ) + generate = execute # TODO: remove -class CLIPTextEncodeSD3: +class CLIPTextEncodeSD3(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { - "clip": ("CLIP", ), - "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}), - "empty_padding": (["none", "empty_prompt"], ) - }} - RETURN_TYPES = ("CONDITIONING",) - FUNCTION = "encode" + def define_schema(cls): + return io.Schema( + node_id="CLIPTextEncodeSD3", + category="advanced/conditioning", + inputs=[ + io.Clip.Input("clip"), + io.String.Input("clip_l", multiline=True, dynamic_prompts=True), + io.String.Input("clip_g", multiline=True, dynamic_prompts=True), + io.String.Input("t5xxl", multiline=True, dynamic_prompts=True), + io.Combo.Input("empty_padding", options=["none", "empty_prompt"]), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) - CATEGORY = "advanced/conditioning" - - def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding): + @classmethod + def execute(cls, clip, clip_l, clip_g, t5xxl, empty_padding) -> io.NodeOutput: no_padding = empty_padding == "none" tokens = clip.tokenize(clip_g) @@ -82,57 +101,112 @@ class CLIPTextEncodeSD3: tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] - return (clip.encode_from_tokens_scheduled(tokens), ) + return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens)) + + encode = execute # TODO: remove -class ControlNetApplySD3(nodes.ControlNetApplyAdvanced): +class ControlNetApplySD3(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": {"positive": ("CONDITIONING", ), - "negative": ("CONDITIONING", ), - "control_net": ("CONTROL_NET", ), - "vae": ("VAE", ), - "image": ("IMAGE", ), - "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - CATEGORY = "conditioning/controlnet" - DEPRECATED = True + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="ControlNetApplySD3", + display_name="Apply Controlnet with VAE", + category="conditioning/controlnet", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.ControlNet.Input("control_net"), + io.Vae.Input("vae"), + io.Image.Input("image"), + io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + ], + is_deprecated=True, + ) + + @classmethod + def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None) -> io.NodeOutput: + if strength == 0: + return io.NodeOutput(positive, negative) + + control_hint = image.movedim(-1, 1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), + vae=vae, extra_concat=[]) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return io.NodeOutput(out[0], out[1]) + + apply_controlnet = execute # TODO: remove -class SkipLayerGuidanceSD3(comfy_extras.nodes_slg.SkipLayerGuidanceDiT): +class SkipLayerGuidanceSD3(io.ComfyNode): ''' Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) Experimental implementation by Dango233@StabilityAI. ''' + @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), - "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}) - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "skip_guidance_sd3" + def define_schema(cls): + return io.Schema( + node_id="SkipLayerGuidanceSD3", + category="advanced/guidance", + description="Generic version of SkipLayerGuidance node that can be used on every DiT model.", + inputs=[ + io.Model.Input("model"), + io.String.Input("layers", default="7, 8, 9", multiline=False), + io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1), + io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001), + ], + outputs=[ + io.Model.Output(), + ], + is_experimental=True, + ) - CATEGORY = "advanced/guidance" + @classmethod + def execute(cls, model, layers, scale, start_percent, end_percent) -> io.NodeOutput: + return SkipLayerGuidanceDiT().execute(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers) - def skip_guidance_sd3(self, model, layers, scale, start_percent, end_percent): - return self.skip_guidance(model=model, scale=scale, start_percent=start_percent, end_percent=end_percent, double_layers=layers) + skip_guidance_sd3 = execute # TODO: remove -NODE_CLASS_MAPPINGS = { - "TripleCLIPLoader": TripleCLIPLoader, - "EmptySD3LatentImage": EmptySD3LatentImage, - "CLIPTextEncodeSD3": CLIPTextEncodeSD3, - "ControlNetApplySD3": ControlNetApplySD3, - "SkipLayerGuidanceSD3": SkipLayerGuidanceSD3, -} +class SD3Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TripleCLIPLoader, + EmptySD3LatentImage, + CLIPTextEncodeSD3, + ControlNetApplySD3, + SkipLayerGuidanceSD3, + ] -NODE_DISPLAY_NAME_MAPPINGS = { - # Sampling - "ControlNetApplySD3": "Apply Controlnet with VAE", -} + +async def comfy_entrypoint() -> SD3Extension: + return SD3Extension() diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py index bba67e8dd..31b373370 100644 --- a/comfy_extras/nodes_sdupscale.py +++ b/comfy_extras/nodes_sdupscale.py @@ -1,23 +1,31 @@ +from typing_extensions import override + import torch import comfy.utils +from comfy_api.latest import ComfyExtension, io -class SD_4XUpscale_Conditioning: +class SD_4XUpscale_Conditioning(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "images": ("IMAGE",), - "positive": ("CONDITIONING",), - "negative": ("CONDITIONING",), - "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), - "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") + def define_schema(cls): + return io.Schema( + node_id="SD_4XUpscale_Conditioning", + category="conditioning/upscale_diffusion", + inputs=[ + io.Image.Input("images"), + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01), + io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) - FUNCTION = "encode" - - CATEGORY = "conditioning/upscale_diffusion" - - def encode(self, images, positive, negative, scale_ratio, noise_augmentation): + @classmethod + def execute(cls, images, positive, negative, scale_ratio, noise_augmentation): width = max(1, round(images.shape[-2] * scale_ratio)) height = max(1, round(images.shape[-3] * scale_ratio)) @@ -39,8 +47,16 @@ class SD_4XUpscale_Conditioning: out_cn.append(n) latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) - return (out_cp, out_cn, {"samples":latent}) + return io.NodeOutput(out_cp, out_cn, {"samples":latent}) -NODE_CLASS_MAPPINGS = { - "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, -} + +class SdUpscaleExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SD_4XUpscale_Conditioning, + ] + + +async def comfy_entrypoint() -> SdUpscaleExtension: + return SdUpscaleExtension() diff --git a/comfy_extras/nodes_slg.py b/comfy_extras/nodes_slg.py index 2fa09e250..f462faa8f 100644 --- a/comfy_extras/nodes_slg.py +++ b/comfy_extras/nodes_slg.py @@ -1,33 +1,40 @@ import comfy.model_patcher import comfy.samplers import re +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io -class SkipLayerGuidanceDiT: +class SkipLayerGuidanceDiT(io.ComfyNode): ''' Enhance guidance towards detailed dtructure by having another set of CFG negative with skipped layers. Inspired by Perturbed Attention Guidance (https://arxiv.org/abs/2403.17377) Original experimental implementation for SD3 by Dango233@StabilityAI. ''' + @classmethod - def INPUT_TYPES(s): - return {"required": {"model": ("MODEL", ), - "double_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "single_layers": ("STRING", {"default": "7, 8, 9", "multiline": False}), - "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 10.0, "step": 0.1}), - "start_percent": ("FLOAT", {"default": 0.01, "min": 0.0, "max": 1.0, "step": 0.001}), - "end_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.001}), - "rescaling_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "skip_guidance" - EXPERIMENTAL = True + def define_schema(cls): + return io.Schema( + node_id="SkipLayerGuidanceDiT", + category="advanced/guidance", + description="Generic version of SkipLayerGuidance node that can be used on every DiT model.", + is_experimental=True, + inputs=[ + io.Model.Input("model"), + io.String.Input("double_layers", default="7, 8, 9"), + io.String.Input("single_layers", default="7, 8, 9"), + io.Float.Input("scale", default=3.0, min=0.0, max=10.0, step=0.1), + io.Float.Input("start_percent", default=0.01, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=0.15, min=0.0, max=1.0, step=0.001), + io.Float.Input("rescaling_scale", default=0.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[ + io.Model.Output(), + ], + ) - DESCRIPTION = "Generic version of SkipLayerGuidance node that can be used on every DiT model." - - CATEGORY = "advanced/guidance" - - def skip_guidance(self, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0): + @classmethod + def execute(cls, model, scale, start_percent, end_percent, double_layers="", single_layers="", rescaling_scale=0) -> io.NodeOutput: # check if layer is comma separated integers def skip(args, extra_args): return args @@ -43,7 +50,7 @@ class SkipLayerGuidanceDiT: single_layers = [int(i) for i in single_layers] if len(double_layers) == 0 and len(single_layers) == 0: - return (model, ) + return io.NodeOutput(model) def post_cfg_function(args): model = args["model"] @@ -76,9 +83,94 @@ class SkipLayerGuidanceDiT: m = model.clone() m.set_model_sampler_post_cfg_function(post_cfg_function) - return (m, ) + return io.NodeOutput(m) + + skip_guidance = execute # TODO: remove -NODE_CLASS_MAPPINGS = { - "SkipLayerGuidanceDiT": SkipLayerGuidanceDiT, -} +class SkipLayerGuidanceDiTSimple(io.ComfyNode): + ''' + Simple version of the SkipLayerGuidanceDiT node that only modifies the uncond pass. + ''' + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SkipLayerGuidanceDiTSimple", + category="advanced/guidance", + description="Simple version of the SkipLayerGuidanceDiT node that only modifies the uncond pass.", + is_experimental=True, + inputs=[ + io.Model.Input("model"), + io.String.Input("double_layers", default="7, 8, 9"), + io.String.Input("single_layers", default="7, 8, 9"), + io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001), + io.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001), + ], + outputs=[ + io.Model.Output(), + ], + ) + + @classmethod + def execute(cls, model, start_percent, end_percent, double_layers="", single_layers="") -> io.NodeOutput: + def skip(args, extra_args): + return args + + model_sampling = model.get_model_object("model_sampling") + sigma_start = model_sampling.percent_to_sigma(start_percent) + sigma_end = model_sampling.percent_to_sigma(end_percent) + + double_layers = re.findall(r'\d+', double_layers) + double_layers = [int(i) for i in double_layers] + + single_layers = re.findall(r'\d+', single_layers) + single_layers = [int(i) for i in single_layers] + + if len(double_layers) == 0 and len(single_layers) == 0: + return io.NodeOutput(model) + + def calc_cond_batch_function(args): + x = args["input"] + model = args["model"] + conds = args["conds"] + sigma = args["sigma"] + + model_options = args["model_options"] + slg_model_options = model_options.copy() + + for layer in double_layers: + slg_model_options = comfy.model_patcher.set_model_options_patch_replace(slg_model_options, skip, "dit", "double_block", layer) + + for layer in single_layers: + slg_model_options = comfy.model_patcher.set_model_options_patch_replace(slg_model_options, skip, "dit", "single_block", layer) + + cond, uncond = conds + sigma_ = sigma[0].item() + if sigma_ >= sigma_end and sigma_ <= sigma_start and uncond is not None: + cond_out, _ = comfy.samplers.calc_cond_batch(model, [cond, None], x, sigma, model_options) + _, uncond_out = comfy.samplers.calc_cond_batch(model, [None, uncond], x, sigma, slg_model_options) + out = [cond_out, uncond_out] + else: + out = comfy.samplers.calc_cond_batch(model, conds, x, sigma, model_options) + + return out + + m = model.clone() + m.set_model_sampler_calc_cond_batch_function(calc_cond_batch_function) + + return io.NodeOutput(m) + + skip_guidance = execute # TODO: remove + + +class SkipLayerGuidanceExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SkipLayerGuidanceDiT, + SkipLayerGuidanceDiTSimple, + ] + + +async def comfy_entrypoint() -> SkipLayerGuidanceExtension: + return SkipLayerGuidanceExtension() diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py index be2e34c28..c6d8a683d 100644 --- a/comfy_extras/nodes_stable3d.py +++ b/comfy_extras/nodes_stable3d.py @@ -1,6 +1,8 @@ import torch import nodes import comfy.utils +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io def camera_embeddings(elevation, azimuth): elevation = torch.as_tensor([elevation]) @@ -20,26 +22,31 @@ def camera_embeddings(elevation, azimuth): return embeddings -class StableZero123_Conditioning: +class StableZero123_Conditioning(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") + def define_schema(cls): + return io.Schema( + node_id="StableZero123_Conditioning", + category="conditioning/3d_models", + inputs=[ + io.ClipVision.Input("clip_vision"), + io.Image.Input("init_image"), + io.Vae.Input("vae"), + io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False), + io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False) + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent") + ] + ) - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): + @classmethod + def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth) -> io.NodeOutput: output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) @@ -51,30 +58,35 @@ class StableZero123_Conditioning: positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent}) + return io.NodeOutput(positive, negative, {"samples":latent}) -class StableZero123_Conditioning_Batched: +class StableZero123_Conditioning_Batched(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") + def define_schema(cls): + return io.Schema( + node_id="StableZero123_Conditioning_Batched", + category="conditioning/3d_models", + inputs=[ + io.ClipVision.Input("clip_vision"), + io.Image.Input("init_image"), + io.Vae.Input("vae"), + io.Int.Input("width", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("height", default=256, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Float.Input("elevation", default=0.0, min=-180.0, max=180.0, step=0.1, round=False), + io.Float.Input("azimuth", default=0.0, min=-180.0, max=180.0, step=0.1, round=False), + io.Float.Input("elevation_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False), + io.Float.Input("azimuth_batch_increment", default=0.0, min=-180.0, max=180.0, step=0.1, round=False) + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent") + ] + ) - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): + @classmethod + def execute(cls, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment) -> io.NodeOutput: output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) @@ -93,27 +105,32 @@ class StableZero123_Conditioning_Batched: positive = [[cond, {"concat_latent_image": t}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] latent = torch.zeros([batch_size, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) + return io.NodeOutput(positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) -class SV3D_Conditioning: +class SV3D_Conditioning(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "clip_vision": ("CLIP_VISION",), - "init_image": ("IMAGE",), - "vae": ("VAE",), - "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}), - "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}), - "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}), - }} - RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") - RETURN_NAMES = ("positive", "negative", "latent") + def define_schema(cls): + return io.Schema( + node_id="SV3D_Conditioning", + category="conditioning/3d_models", + inputs=[ + io.ClipVision.Input("clip_vision"), + io.Image.Input("init_image"), + io.Vae.Input("vae"), + io.Int.Input("width", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("height", default=576, min=16, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("video_frames", default=21, min=1, max=4096), + io.Float.Input("elevation", default=0.0, min=-90.0, max=90.0, step=0.1, round=False) + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent") + ] + ) - FUNCTION = "encode" - - CATEGORY = "conditioning/3d_models" - - def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation): + @classmethod + def execute(cls, clip_vision, init_image, vae, width, height, video_frames, elevation) -> io.NodeOutput: output = clip_vision.encode_image(init_image) pooled = output.image_embeds.unsqueeze(0) pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) @@ -133,11 +150,17 @@ class SV3D_Conditioning: positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]] negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]] latent = torch.zeros([video_frames, 4, height // 8, width // 8]) - return (positive, negative, {"samples":latent}) + return io.NodeOutput(positive, negative, {"samples":latent}) -NODE_CLASS_MAPPINGS = { - "StableZero123_Conditioning": StableZero123_Conditioning, - "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, - "SV3D_Conditioning": SV3D_Conditioning, -} +class Stable3DExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + StableZero123_Conditioning, + StableZero123_Conditioning_Batched, + SV3D_Conditioning, + ] + +async def comfy_entrypoint() -> Stable3DExtension: + return Stable3DExtension() diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py index 003403215..04c0b366a 100644 --- a/comfy_extras/nodes_stable_cascade.py +++ b/comfy_extras/nodes_stable_cascade.py @@ -17,55 +17,61 @@ """ import torch -import nodes +from typing_extensions import override + import comfy.utils +import nodes +from comfy_api.latest import ComfyExtension, io -class StableCascade_EmptyLatentImage: - def __init__(self, device="cpu"): - self.device = device +class StableCascade_EmptyLatentImage(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StableCascade_EmptyLatentImage", + category="latent/stable_cascade", + inputs=[ + io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8), + io.Int.Input("compression", default=42, min=4, max=128, step=1), + io.Int.Input("batch_size", default=1, min=1, max=4096), + ], + outputs=[ + io.Latent.Output(display_name="stage_c"), + io.Latent.Output(display_name="stage_b"), + ], + ) @classmethod - def INPUT_TYPES(s): - return {"required": { - "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), - "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}), - "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), - "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}) - }} - RETURN_TYPES = ("LATENT", "LATENT") - RETURN_NAMES = ("stage_c", "stage_b") - FUNCTION = "generate" - - CATEGORY = "latent/stable_cascade" - - def generate(self, width, height, compression, batch_size=1): + def execute(cls, width, height, compression, batch_size=1): c_latent = torch.zeros([batch_size, 16, height // compression, width // compression]) b_latent = torch.zeros([batch_size, 4, height // 4, width // 4]) - return ({ + return io.NodeOutput({ "samples": c_latent, }, { "samples": b_latent, }) -class StableCascade_StageC_VAEEncode: - def __init__(self, device="cpu"): - self.device = device + +class StableCascade_StageC_VAEEncode(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StableCascade_StageC_VAEEncode", + category="latent/stable_cascade", + inputs=[ + io.Image.Input("image"), + io.Vae.Input("vae"), + io.Int.Input("compression", default=42, min=4, max=128, step=1), + ], + outputs=[ + io.Latent.Output(display_name="stage_c"), + io.Latent.Output(display_name="stage_b"), + ], + ) @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - "vae": ("VAE", ), - "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}), - }} - RETURN_TYPES = ("LATENT", "LATENT") - RETURN_NAMES = ("stage_c", "stage_b") - FUNCTION = "generate" - - CATEGORY = "latent/stable_cascade" - - def generate(self, image, vae, compression): + def execute(cls, image, vae, compression): width = image.shape[-2] height = image.shape[-3] out_width = (width // compression) * vae.downscale_ratio @@ -75,51 +81,59 @@ class StableCascade_StageC_VAEEncode: c_latent = vae.encode(s[:,:,:,:3]) b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2]) - return ({ + return io.NodeOutput({ "samples": c_latent, }, { "samples": b_latent, }) -class StableCascade_StageB_Conditioning: + +class StableCascade_StageB_Conditioning(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "conditioning": ("CONDITIONING",), - "stage_c": ("LATENT",), - }} - RETURN_TYPES = ("CONDITIONING",) + def define_schema(cls): + return io.Schema( + node_id="StableCascade_StageB_Conditioning", + category="conditioning/stable_cascade", + inputs=[ + io.Conditioning.Input("conditioning"), + io.Latent.Input("stage_c"), + ], + outputs=[ + io.Conditioning.Output(), + ], + ) - FUNCTION = "set_prior" - - CATEGORY = "conditioning/stable_cascade" - - def set_prior(self, conditioning, stage_c): + @classmethod + def execute(cls, conditioning, stage_c): c = [] for t in conditioning: d = t[1].copy() - d['stable_cascade_prior'] = stage_c['samples'] + d["stable_cascade_prior"] = stage_c["samples"] n = [t[0], d] c.append(n) - return (c, ) + return io.NodeOutput(c) -class StableCascade_SuperResolutionControlnet: - def __init__(self, device="cpu"): - self.device = device + +class StableCascade_SuperResolutionControlnet(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StableCascade_SuperResolutionControlnet", + category="_for_testing/stable_cascade", + is_experimental=True, + inputs=[ + io.Image.Input("image"), + io.Vae.Input("vae"), + ], + outputs=[ + io.Image.Output(display_name="controlnet_input"), + io.Latent.Output(display_name="stage_c"), + io.Latent.Output(display_name="stage_b"), + ], + ) @classmethod - def INPUT_TYPES(s): - return {"required": { - "image": ("IMAGE",), - "vae": ("VAE", ), - }} - RETURN_TYPES = ("IMAGE", "LATENT", "LATENT") - RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b") - FUNCTION = "generate" - - EXPERIMENTAL = True - CATEGORY = "_for_testing/stable_cascade" - - def generate(self, image, vae): + def execute(cls, image, vae): width = image.shape[-2] height = image.shape[-3] batch_size = image.shape[0] @@ -127,15 +141,22 @@ class StableCascade_SuperResolutionControlnet: c_latent = torch.zeros([batch_size, 16, height // 16, width // 16]) b_latent = torch.zeros([batch_size, 4, height // 2, width // 2]) - return (controlnet_input, { + return io.NodeOutput(controlnet_input, { "samples": c_latent, }, { "samples": b_latent, }) -NODE_CLASS_MAPPINGS = { - "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage, - "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning, - "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode, - "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet, -} + +class StableCascadeExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + StableCascade_EmptyLatentImage, + StableCascade_StageB_Conditioning, + StableCascade_StageC_VAEEncode, + StableCascade_SuperResolutionControlnet, + ] + +async def comfy_entrypoint() -> StableCascadeExtension: + return StableCascadeExtension() diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py new file mode 100644 index 000000000..571d89f62 --- /dev/null +++ b/comfy_extras/nodes_string.py @@ -0,0 +1,385 @@ +import re +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + + +class StringConcatenate(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringConcatenate", + display_name="Concatenate", + category="utils/string", + inputs=[ + io.String.Input("string_a", multiline=True), + io.String.Input("string_b", multiline=True), + io.String.Input("delimiter", multiline=False, default=""), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string_a, string_b, delimiter): + return io.NodeOutput(delimiter.join((string_a, string_b))) + + +class StringSubstring(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringSubstring", + display_name="Substring", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.Int.Input("start"), + io.Int.Input("end"), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, start, end): + return io.NodeOutput(string[start:end]) + + +class StringLength(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringLength", + display_name="Length", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + ], + outputs=[ + io.Int.Output(display_name="length"), + ] + ) + + @classmethod + def execute(cls, string): + return io.NodeOutput(len(string)) + + +class CaseConverter(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CaseConverter", + display_name="Case Converter", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.Combo.Input("mode", options=["UPPERCASE", "lowercase", "Capitalize", "Title Case"]), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, mode): + if mode == "UPPERCASE": + result = string.upper() + elif mode == "lowercase": + result = string.lower() + elif mode == "Capitalize": + result = string.capitalize() + elif mode == "Title Case": + result = string.title() + else: + result = string + + return io.NodeOutput(result) + + +class StringTrim(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringTrim", + display_name="Trim", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.Combo.Input("mode", options=["Both", "Left", "Right"]), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, mode): + if mode == "Both": + result = string.strip() + elif mode == "Left": + result = string.lstrip() + elif mode == "Right": + result = string.rstrip() + else: + result = string + + return io.NodeOutput(result) + + +class StringReplace(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringReplace", + display_name="Replace", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.String.Input("find", multiline=True), + io.String.Input("replace", multiline=True), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, find, replace): + return io.NodeOutput(string.replace(find, replace)) + + +class StringContains(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringContains", + display_name="Contains", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.String.Input("substring", multiline=True), + io.Boolean.Input("case_sensitive", default=True), + ], + outputs=[ + io.Boolean.Output(display_name="contains"), + ] + ) + + @classmethod + def execute(cls, string, substring, case_sensitive): + if case_sensitive: + contains = substring in string + else: + contains = substring.lower() in string.lower() + + return io.NodeOutput(contains) + + +class StringCompare(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="StringCompare", + display_name="Compare", + category="utils/string", + inputs=[ + io.String.Input("string_a", multiline=True), + io.String.Input("string_b", multiline=True), + io.Combo.Input("mode", options=["Starts With", "Ends With", "Equal"]), + io.Boolean.Input("case_sensitive", default=True), + ], + outputs=[ + io.Boolean.Output(), + ] + ) + + @classmethod + def execute(cls, string_a, string_b, mode, case_sensitive): + if case_sensitive: + a = string_a + b = string_b + else: + a = string_a.lower() + b = string_b.lower() + + if mode == "Equal": + return io.NodeOutput(a == b) + elif mode == "Starts With": + return io.NodeOutput(a.startswith(b)) + elif mode == "Ends With": + return io.NodeOutput(a.endswith(b)) + + +class RegexMatch(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="RegexMatch", + display_name="Regex Match", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.String.Input("regex_pattern", multiline=True), + io.Boolean.Input("case_insensitive", default=True), + io.Boolean.Input("multiline", default=False), + io.Boolean.Input("dotall", default=False), + ], + outputs=[ + io.Boolean.Output(display_name="matches"), + ] + ) + + @classmethod + def execute(cls, string, regex_pattern, case_insensitive, multiline, dotall): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + match = re.search(regex_pattern, string, flags) + result = match is not None + + except re.error: + result = False + + return io.NodeOutput(result) + + +class RegexExtract(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="RegexExtract", + display_name="Regex Extract", + category="utils/string", + inputs=[ + io.String.Input("string", multiline=True), + io.String.Input("regex_pattern", multiline=True), + io.Combo.Input("mode", options=["First Match", "All Matches", "First Group", "All Groups"]), + io.Boolean.Input("case_insensitive", default=True), + io.Boolean.Input("multiline", default=False), + io.Boolean.Input("dotall", default=False), + io.Int.Input("group_index", default=1, min=0, max=100), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, regex_pattern, mode, case_insensitive, multiline, dotall, group_index): + join_delimiter = "\n" + + flags = 0 + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + + try: + if mode == "First Match": + match = re.search(regex_pattern, string, flags) + if match: + result = match.group(0) + else: + result = "" + + elif mode == "All Matches": + matches = re.findall(regex_pattern, string, flags) + if matches: + if isinstance(matches[0], tuple): + result = join_delimiter.join([m[0] for m in matches]) + else: + result = join_delimiter.join(matches) + else: + result = "" + + elif mode == "First Group": + match = re.search(regex_pattern, string, flags) + if match and len(match.groups()) >= group_index: + result = match.group(group_index) + else: + result = "" + + elif mode == "All Groups": + matches = re.finditer(regex_pattern, string, flags) + results = [] + for match in matches: + if match.groups() and len(match.groups()) >= group_index: + results.append(match.group(group_index)) + result = join_delimiter.join(results) + else: + result = "" + + except re.error: + result = "" + + return io.NodeOutput(result) + + +class RegexReplace(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="RegexReplace", + display_name="Regex Replace", + category="utils/string", + description="Find and replace text using regex patterns.", + inputs=[ + io.String.Input("string", multiline=True), + io.String.Input("regex_pattern", multiline=True), + io.String.Input("replace", multiline=True), + io.Boolean.Input("case_insensitive", default=True, optional=True), + io.Boolean.Input("multiline", default=False, optional=True), + io.Boolean.Input("dotall", default=False, optional=True, tooltip="When enabled, the dot (.) character will match any character including newline characters. When disabled, dots won't match newlines."), + io.Int.Input("count", default=0, min=0, max=100, optional=True, tooltip="Maximum number of replacements to make. Set to 0 to replace all occurrences (default). Set to 1 to replace only the first match, 2 for the first two matches, etc."), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, string, regex_pattern, replace, case_insensitive=True, multiline=False, dotall=False, count=0): + flags = 0 + + if case_insensitive: + flags |= re.IGNORECASE + if multiline: + flags |= re.MULTILINE + if dotall: + flags |= re.DOTALL + result = re.sub(regex_pattern, replace, string, count=count, flags=flags) + return io.NodeOutput(result) + + +class StringExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + StringConcatenate, + StringSubstring, + StringLength, + CaseConverter, + StringTrim, + StringReplace, + StringContains, + StringCompare, + RegexMatch, + RegexExtract, + RegexReplace, + ] + +async def comfy_entrypoint() -> StringExtension: + return StringExtension() diff --git a/comfy_extras/nodes_tcfg.py b/comfy_extras/nodes_tcfg.py new file mode 100644 index 000000000..1a6767770 --- /dev/null +++ b/comfy_extras/nodes_tcfg.py @@ -0,0 +1,76 @@ +# TCFG: Tangential Damping Classifier-free Guidance - (arXiv: https://arxiv.org/abs/2503.18137) + +from typing_extensions import override +import torch + +from comfy_api.latest import ComfyExtension, io + + +def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tensor) -> torch.Tensor: + """Drop tangential components from uncond score to align with cond score.""" + # (B, 1, ...) + batch_num = cond_score.shape[0] + cond_score_flat = cond_score.reshape(batch_num, 1, -1).float() + uncond_score_flat = uncond_score.reshape(batch_num, 1, -1).float() + + # Score matrix A (B, 2, ...) + score_matrix = torch.cat((uncond_score_flat, cond_score_flat), dim=1) + try: + _, _, Vh = torch.linalg.svd(score_matrix, full_matrices=False) + except RuntimeError: + # Fallback to CPU + _, _, Vh = torch.linalg.svd(score_matrix.cpu(), full_matrices=False) + + # Drop the tangential components + v1 = Vh[:, 0:1, :].to(uncond_score_flat.device) # (B, 1, ...) + uncond_score_td = (uncond_score_flat @ v1.transpose(-2, -1)) * v1 + return uncond_score_td.reshape_as(uncond_score).to(uncond_score.dtype) + + +class TCFG(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TCFG", + display_name="Tangential Damping CFG", + category="advanced/guidance", + description="TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality.", + inputs=[ + io.Model.Input("model"), + ], + outputs=[ + io.Model.Output(display_name="patched_model"), + ], + ) + + @classmethod + def execute(cls, model): + m = model.clone() + + def tangential_damping_cfg(args): + # Assume [cond, uncond, ...] + x = args["input"] + conds_out = args["conds_out"] + if len(conds_out) <= 1 or None in args["conds"][:2]: + # Skip when either cond or uncond is None + return conds_out + cond_pred = conds_out[0] + uncond_pred = conds_out[1] + uncond_td = score_tangential_damping(x - cond_pred, x - uncond_pred) + uncond_pred_td = x - uncond_td + return [cond_pred, uncond_pred_td] + conds_out[2:] + + m.set_model_sampler_pre_cfg_function(tangential_damping_cfg) + return io.NodeOutput(m) + + +class TcfgExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TCFG, + ] + + +async def comfy_entrypoint() -> TcfgExtension: + return TcfgExtension() diff --git a/comfy_extras/nodes_tomesd.py b/comfy_extras/nodes_tomesd.py index 9f77c06fc..87bf29b8f 100644 --- a/comfy_extras/nodes_tomesd.py +++ b/comfy_extras/nodes_tomesd.py @@ -1,7 +1,9 @@ #Taken from: https://github.com/dbolya/tomesd import torch -from typing import Tuple, Callable +from typing import Tuple, Callable, Optional +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io import math def do_nothing(x: torch.Tensor, mode:str=None): @@ -144,33 +146,45 @@ def get_functions(x, ratio, original_shape): -class TomePatchModel: +class TomePatchModel(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls): + return io.Schema( + node_id="TomePatchModel", + category="model_patches/unet", + inputs=[ + io.Model.Input("model"), + io.Float.Input("ratio", default=0.3, min=0.0, max=1.0, step=0.01), + ], + outputs=[io.Model.Output()], + ) - CATEGORY = "model_patches/unet" - - def patch(self, model, ratio): - self.u = None + @classmethod + def execute(cls, model, ratio) -> io.NodeOutput: + u: Optional[Callable] = None def tomesd_m(q, k, v, extra_options): + nonlocal u #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q #however from my basic testing it seems that using q instead gives better results - m, self.u = get_functions(q, ratio, extra_options["original_shape"]) + m, u = get_functions(q, ratio, extra_options["original_shape"]) return m(q), k, v def tomesd_u(n, extra_options): - return self.u(n) + nonlocal u + return u(n) m = model.clone() m.set_model_attn1_patch(tomesd_m) m.set_model_attn1_output_patch(tomesd_u) - return (m, ) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "TomePatchModel": TomePatchModel, -} +class TomePatchModelExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TomePatchModel, + ] + + +async def comfy_entrypoint() -> TomePatchModelExtension: + return TomePatchModelExtension() diff --git a/comfy_extras/nodes_torch_compile.py b/comfy_extras/nodes_torch_compile.py index 1fe6f42c7..adbeece2f 100644 --- a/comfy_extras/nodes_torch_compile.py +++ b/comfy_extras/nodes_torch_compile.py @@ -1,22 +1,39 @@ -import torch +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io +from comfy_api.torch_helpers import set_torch_compile_wrapper -class TorchCompileModel: + +class TorchCompileModel(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model": ("MODEL",), - "backend": (["inductor", "cudagraphs"],), - }} - RETURN_TYPES = ("MODEL",) - FUNCTION = "patch" + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="TorchCompileModel", + category="_for_testing", + inputs=[ + io.Model.Input("model"), + io.Combo.Input( + "backend", + options=["inductor", "cudagraphs"], + ), + ], + outputs=[io.Model.Output()], + is_experimental=True, + ) - CATEGORY = "_for_testing" - EXPERIMENTAL = True - - def patch(self, model, backend): + @classmethod + def execute(cls, model, backend) -> io.NodeOutput: m = model.clone() - m.add_object_patch("diffusion_model", torch.compile(model=m.get_model_object("diffusion_model"), backend=backend)) - return (m, ) + set_torch_compile_wrapper(model=m, backend=backend) + return io.NodeOutput(m) -NODE_CLASS_MAPPINGS = { - "TorchCompileModel": TorchCompileModel, -} + +class TorchCompileExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + TorchCompileModel, + ] + + +async def comfy_entrypoint() -> TorchCompileExtension: + return TorchCompileExtension() diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py new file mode 100644 index 000000000..9e6ec6780 --- /dev/null +++ b/comfy_extras/nodes_train.py @@ -0,0 +1,895 @@ +import datetime +import json +import logging +import os + +import numpy as np +import safetensors +import torch +from PIL import Image, ImageDraw, ImageFont +from PIL.PngImagePlugin import PngInfo +import torch.utils.checkpoint +import tqdm + +import comfy.samplers +import comfy.sd +import comfy.utils +import comfy.model_management +import comfy_extras.nodes_custom_sampler +import folder_paths +import node_helpers +from comfy.cli_args import args +from comfy.comfy_types.node_typing import IO +from comfy.weight_adapter import adapters, adapter_maps + + +def make_batch_extra_option_dict(d, indicies, full_size=None): + new_dict = {} + for k, v in d.items(): + newv = v + if isinstance(v, dict): + newv = make_batch_extra_option_dict(v, indicies, full_size=full_size) + elif isinstance(v, torch.Tensor): + if full_size is None or v.size(0) == full_size: + newv = v[indicies] + elif isinstance(v, (list, tuple)) and len(v) == full_size: + newv = [v[i] for i in indicies] + new_dict[k] = newv + return new_dict + + +def process_cond_list(d, prefix=""): + if hasattr(d, "__iter__") and not hasattr(d, "items"): + for index, item in enumerate(d): + process_cond_list(item, f"{prefix}.{index}") + return d + elif hasattr(d, "items"): + for k, v in list(d.items()): + if isinstance(v, dict): + process_cond_list(v, f"{prefix}.{k}") + elif isinstance(v, torch.Tensor): + d[k] = v.clone() + elif isinstance(v, (list, tuple)): + for index, item in enumerate(v): + process_cond_list(item, f"{prefix}.{k}.{index}") + return d + + +class TrainSampler(comfy.samplers.Sampler): + def __init__(self, loss_fn, optimizer, loss_callback=None, batch_size=1, grad_acc=1, total_steps=1, seed=0, training_dtype=torch.bfloat16): + self.loss_fn = loss_fn + self.optimizer = optimizer + self.loss_callback = loss_callback + self.batch_size = batch_size + self.total_steps = total_steps + self.grad_acc = grad_acc + self.seed = seed + self.training_dtype = training_dtype + + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + model_wrap.conds = process_cond_list(model_wrap.conds) + cond = model_wrap.conds["positive"] + dataset_size = sigmas.size(0) + torch.cuda.empty_cache() + for i in (pbar:=tqdm.trange(self.total_steps, desc="Training LoRA", smoothing=0.01, disable=not comfy.utils.PROGRESS_BAR_ENABLED)): + noisegen = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(self.seed + i * 1000) + indicies = torch.randperm(dataset_size)[:self.batch_size].tolist() + + batch_latent = torch.stack([latent_image[i] for i in indicies]) + batch_noise = noisegen.generate_noise({"samples": batch_latent}).to(batch_latent.device) + batch_sigmas = [ + model_wrap.inner_model.model_sampling.percent_to_sigma( + torch.rand((1,)).item() + ) for _ in range(min(self.batch_size, dataset_size)) + ] + batch_sigmas = torch.tensor(batch_sigmas).to(batch_latent.device) + + xt = model_wrap.inner_model.model_sampling.noise_scaling( + batch_sigmas, + batch_noise, + batch_latent, + False + ) + x0 = model_wrap.inner_model.model_sampling.noise_scaling( + torch.zeros_like(batch_sigmas), + torch.zeros_like(batch_noise), + batch_latent, + False + ) + + model_wrap.conds["positive"] = [ + cond[i] for i in indicies + ] + batch_extra_args = make_batch_extra_option_dict(extra_args, indicies, full_size=dataset_size) + + with torch.autocast(xt.device.type, dtype=self.training_dtype): + x0_pred = model_wrap(xt, batch_sigmas, **batch_extra_args) + loss = self.loss_fn(x0_pred, x0) + loss.backward() + if self.loss_callback: + self.loss_callback(loss.item()) + pbar.set_postfix({"loss": f"{loss.item():.4f}"}) + + if (i+1) % self.grad_acc == 0: + self.optimizer.step() + self.optimizer.zero_grad() + torch.cuda.empty_cache() + return torch.zeros_like(latent_image) + + +class BiasDiff(torch.nn.Module): + def __init__(self, bias): + super().__init__() + self.bias = bias + + def __call__(self, b): + org_dtype = b.dtype + return (b.to(self.bias) + self.bias).to(org_dtype) + + def passive_memory_usage(self): + return self.bias.nelement() * self.bias.element_size() + + def move_to(self, device): + self.to(device=device) + return self.passive_memory_usage() + + +def load_and_process_images(image_files, input_dir, resize_method="None", w=None, h=None): + """Utility function to load and process a list of images. + + Args: + image_files: List of image filenames + input_dir: Base directory containing the images + resize_method: How to handle images of different sizes ("None", "Stretch", "Crop", "Pad") + + Returns: + torch.Tensor: Batch of processed images + """ + if not image_files: + raise ValueError("No valid images found in input") + + output_images = [] + + for file in image_files: + image_path = os.path.join(input_dir, file) + img = node_helpers.pillow(Image.open, image_path) + + if img.mode == "I": + img = img.point(lambda i: i * (1 / 255)) + img = img.convert("RGB") + + if w is None and h is None: + w, h = img.size[0], img.size[1] + + # Resize image to first image + if img.size[0] != w or img.size[1] != h: + if resize_method == "Stretch": + img = img.resize((w, h), Image.Resampling.LANCZOS) + elif resize_method == "Crop": + img = img.crop((0, 0, w, h)) + elif resize_method == "Pad": + img = img.resize((w, h), Image.Resampling.LANCZOS) + elif resize_method == "None": + raise ValueError( + "Your input image size does not match the first image in the dataset. Either select a valid resize method or use the same size for all images." + ) + + img_array = np.array(img).astype(np.float32) / 255.0 + img_tensor = torch.from_numpy(img_array)[None,] + output_images.append(img_tensor) + + return torch.cat(output_images, dim=0) + + +class LoadImageSetNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "images": ( + [ + f + for f in os.listdir(folder_paths.get_input_directory()) + if f.endswith((".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff")) + ], + {"image_upload": True, "allow_batch": True}, + ) + }, + "optional": { + "resize_method": ( + ["None", "Stretch", "Crop", "Pad"], + {"default": "None"}, + ), + }, + } + + INPUT_IS_LIST = True + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_images" + CATEGORY = "loaders" + EXPERIMENTAL = True + DESCRIPTION = "Loads a batch of images from a directory for training." + + @classmethod + def VALIDATE_INPUTS(s, images, resize_method): + filenames = images[0] if isinstance(images[0], list) else images + + for image in filenames: + if not folder_paths.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + return True + + def load_images(self, input_files, resize_method): + input_dir = folder_paths.get_input_directory() + valid_extensions = [".png", ".jpg", ".jpeg", ".webp", ".bmp", ".gif", ".jpe", ".apng", ".tif", ".tiff"] + image_files = [ + f + for f in input_files + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + output_tensor = load_and_process_images(image_files, input_dir, resize_method) + return (output_tensor,) + + +class LoadImageSetFromFolderNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}) + }, + "optional": { + "resize_method": ( + ["None", "Stretch", "Crop", "Pad"], + {"default": "None"}, + ), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "load_images" + CATEGORY = "loaders" + EXPERIMENTAL = True + DESCRIPTION = "Loads a batch of images from a directory for training." + + def load_images(self, folder, resize_method): + sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) + valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] + image_files = [ + f + for f in os.listdir(sub_input_dir) + if any(f.lower().endswith(ext) for ext in valid_extensions) + ] + output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method) + return (output_tensor,) + + +class LoadImageTextSetFromFolderNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "folder": (folder_paths.get_input_subfolders(), {"tooltip": "The folder to load images from."}), + "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."}), + }, + "optional": { + "resize_method": ( + ["None", "Stretch", "Crop", "Pad"], + {"default": "None"}, + ), + "width": ( + IO.INT, + { + "default": -1, + "min": -1, + "max": 10000, + "step": 1, + "tooltip": "The width to resize the images to. -1 means use the original width.", + }, + ), + "height": ( + IO.INT, + { + "default": -1, + "min": -1, + "max": 10000, + "step": 1, + "tooltip": "The height to resize the images to. -1 means use the original height.", + }, + ) + }, + } + + RETURN_TYPES = ("IMAGE", IO.CONDITIONING,) + FUNCTION = "load_images" + CATEGORY = "loaders" + EXPERIMENTAL = True + DESCRIPTION = "Loads a batch of images and caption from a directory for training." + + def load_images(self, folder, clip, resize_method, width=None, height=None): + if clip is None: + raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") + + logging.info(f"Loading images from folder: {folder}") + + sub_input_dir = os.path.join(folder_paths.get_input_directory(), folder) + valid_extensions = [".png", ".jpg", ".jpeg", ".webp"] + + image_files = [] + for item in os.listdir(sub_input_dir): + path = os.path.join(sub_input_dir, item) + if any(item.lower().endswith(ext) for ext in valid_extensions): + image_files.append(path) + elif os.path.isdir(path): + # Support kohya-ss/sd-scripts folder structure + repeat = 1 + if item.split("_")[0].isdigit(): + repeat = int(item.split("_")[0]) + image_files.extend([ + os.path.join(path, f) for f in os.listdir(path) if any(f.lower().endswith(ext) for ext in valid_extensions) + ] * repeat) + + caption_file_path = [ + f.replace(os.path.splitext(f)[1], ".txt") + for f in image_files + ] + captions = [] + for caption_file in caption_file_path: + caption_path = os.path.join(sub_input_dir, caption_file) + if os.path.exists(caption_path): + with open(caption_path, "r", encoding="utf-8") as f: + caption = f.read().strip() + captions.append(caption) + else: + captions.append("") + + width = width if width != -1 else None + height = height if height != -1 else None + output_tensor = load_and_process_images(image_files, sub_input_dir, resize_method, width, height) + + logging.info(f"Loaded {len(output_tensor)} images from {sub_input_dir}.") + + logging.info(f"Encoding captions from {sub_input_dir}.") + conditions = [] + empty_cond = clip.encode_from_tokens_scheduled(clip.tokenize("")) + for text in captions: + if text == "": + conditions.append(empty_cond) + tokens = clip.tokenize(text) + conditions.extend(clip.encode_from_tokens_scheduled(tokens)) + logging.info(f"Encoded {len(conditions)} captions from {sub_input_dir}.") + return (output_tensor, conditions) + + +def draw_loss_graph(loss_map, steps): + width, height = 500, 300 + img = Image.new("RGB", (width, height), "white") + draw = ImageDraw.Draw(img) + + min_loss, max_loss = min(loss_map.values()), max(loss_map.values()) + scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_map.values()] + + prev_point = (0, height - int(scaled_loss[0] * height)) + for i, l in enumerate(scaled_loss[1:], start=1): + x = int(i / (steps - 1) * width) + y = height - int(l * height) + draw.line([prev_point, (x, y)], fill="blue", width=2) + prev_point = (x, y) + + return img + + +def find_all_highest_child_module_with_forward(model: torch.nn.Module, result = None, name = None): + if result is None: + result = [] + elif hasattr(model, "forward") and not isinstance(model, (torch.nn.ModuleList, torch.nn.Sequential, torch.nn.ModuleDict)): + result.append(model) + logging.debug(f"Found module with forward: {name} ({model.__class__.__name__})") + return result + name = name or "root" + for next_name, child in model.named_children(): + find_all_highest_child_module_with_forward(child, result, f"{name}.{next_name}") + return result + + +def patch(m): + if not hasattr(m, "forward"): + return + org_forward = m.forward + def fwd(args, kwargs): + return org_forward(*args, **kwargs) + def checkpointing_fwd(*args, **kwargs): + return torch.utils.checkpoint.checkpoint( + fwd, args, kwargs, use_reentrant=False + ) + m.org_forward = org_forward + m.forward = checkpointing_fwd + + +def unpatch(m): + if hasattr(m, "org_forward"): + m.forward = m.org_forward + del m.org_forward + + +class TrainLoraNode: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": (IO.MODEL, {"tooltip": "The model to train the LoRA on."}), + "latents": ( + "LATENT", + { + "tooltip": "The Latents to use for training, serve as dataset/input of the model." + }, + ), + "positive": ( + IO.CONDITIONING, + {"tooltip": "The positive conditioning to use for training."}, + ), + "batch_size": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 10000, + "step": 1, + "tooltip": "The batch size to use for training.", + }, + ), + "grad_accumulation_steps": ( + IO.INT, + { + "default": 1, + "min": 1, + "max": 1024, + "step": 1, + "tooltip": "The number of gradient accumulation steps to use for training.", + } + ), + "steps": ( + IO.INT, + { + "default": 16, + "min": 1, + "max": 100000, + "tooltip": "The number of steps to train the LoRA for.", + }, + ), + "learning_rate": ( + IO.FLOAT, + { + "default": 0.0005, + "min": 0.0000001, + "max": 1.0, + "step": 0.000001, + "tooltip": "The learning rate to use for training.", + }, + ), + "rank": ( + IO.INT, + { + "default": 8, + "min": 1, + "max": 128, + "tooltip": "The rank of the LoRA layers.", + }, + ), + "optimizer": ( + ["AdamW", "Adam", "SGD", "RMSprop"], + { + "default": "AdamW", + "tooltip": "The optimizer to use for training.", + }, + ), + "loss_function": ( + ["MSE", "L1", "Huber", "SmoothL1"], + { + "default": "MSE", + "tooltip": "The loss function to use for training.", + }, + ), + "seed": ( + IO.INT, + { + "default": 0, + "min": 0, + "max": 0xFFFFFFFFFFFFFFFF, + "tooltip": "The seed to use for training (used in generator for LoRA weight initialization and noise sampling)", + }, + ), + "training_dtype": ( + ["bf16", "fp32"], + {"default": "bf16", "tooltip": "The dtype to use for training."}, + ), + "lora_dtype": ( + ["bf16", "fp32"], + {"default": "bf16", "tooltip": "The dtype to use for lora."}, + ), + "algorithm": ( + list(adapter_maps.keys()), + {"default": list(adapter_maps.keys())[0], "tooltip": "The algorithm to use for training."}, + ), + "gradient_checkpointing": ( + IO.BOOLEAN, + { + "default": True, + "tooltip": "Use gradient checkpointing for training.", + } + ), + "existing_lora": ( + folder_paths.get_filename_list("loras") + ["[None]"], + { + "default": "[None]", + "tooltip": "The existing LoRA to append to. Set to None for new LoRA.", + }, + ), + }, + } + + RETURN_TYPES = (IO.MODEL, IO.LORA_MODEL, IO.LOSS_MAP, IO.INT) + RETURN_NAMES = ("model_with_lora", "lora", "loss", "steps") + FUNCTION = "train" + CATEGORY = "training" + EXPERIMENTAL = True + + def train( + self, + model, + latents, + positive, + batch_size, + steps, + grad_accumulation_steps, + learning_rate, + rank, + optimizer, + loss_function, + seed, + training_dtype, + lora_dtype, + algorithm, + gradient_checkpointing, + existing_lora, + ): + mp = model.clone() + dtype = node_helpers.string_to_torch_dtype(training_dtype) + lora_dtype = node_helpers.string_to_torch_dtype(lora_dtype) + mp.set_model_compute_dtype(dtype) + + latents = latents["samples"].to(dtype) + num_images = latents.shape[0] + logging.info(f"Total Images: {num_images}, Total Captions: {len(positive)}") + if len(positive) == 1 and num_images > 1: + positive = positive * num_images + elif len(positive) != num_images: + raise ValueError( + f"Number of positive conditions ({len(positive)}) does not match number of images ({num_images})." + ) + + with torch.inference_mode(False): + lora_sd = {} + generator = torch.Generator() + generator.manual_seed(seed) + + # Load existing LoRA weights if provided + existing_weights = {} + existing_steps = 0 + if existing_lora != "[None]": + lora_path = folder_paths.get_full_path_or_raise("loras", existing_lora) + # Extract steps from filename like "trained_lora_10_steps_20250225_203716" + existing_steps = int(existing_lora.split("_steps_")[0].split("_")[-1]) + if lora_path: + existing_weights = comfy.utils.load_torch_file(lora_path) + + all_weight_adapters = [] + for n, m in mp.model.named_modules(): + if hasattr(m, "weight_function"): + if m.weight is not None: + key = "{}.weight".format(n) + shape = m.weight.shape + if len(shape) >= 2: + alpha = float(existing_weights.get(f"{key}.alpha", 1.0)) + dora_scale = existing_weights.get( + f"{key}.dora_scale", None + ) + for adapter_cls in adapters: + existing_adapter = adapter_cls.load( + n, existing_weights, alpha, dora_scale + ) + if existing_adapter is not None: + break + else: + existing_adapter = None + adapter_cls = adapter_maps[algorithm] + + if existing_adapter is not None: + train_adapter = existing_adapter.to_train().to(lora_dtype) + else: + # Use LoRA with alpha=1.0 by default + train_adapter = adapter_cls.create_train( + m.weight, rank=rank, alpha=1.0 + ).to(lora_dtype) + for name, parameter in train_adapter.named_parameters(): + lora_sd[f"{n}.{name}"] = parameter + + mp.add_weight_wrapper(key, train_adapter) + all_weight_adapters.append(train_adapter) + else: + diff = torch.nn.Parameter( + torch.zeros( + m.weight.shape, dtype=lora_dtype, requires_grad=True + ) + ) + diff_module = BiasDiff(diff) + mp.add_weight_wrapper(key, BiasDiff(diff)) + all_weight_adapters.append(diff_module) + lora_sd["{}.diff".format(n)] = diff + if hasattr(m, "bias") and m.bias is not None: + key = "{}.bias".format(n) + bias = torch.nn.Parameter( + torch.zeros(m.bias.shape, dtype=lora_dtype, requires_grad=True) + ) + bias_module = BiasDiff(bias) + lora_sd["{}.diff_b".format(n)] = bias + mp.add_weight_wrapper(key, BiasDiff(bias)) + all_weight_adapters.append(bias_module) + + if optimizer == "Adam": + optimizer = torch.optim.Adam(lora_sd.values(), lr=learning_rate) + elif optimizer == "AdamW": + optimizer = torch.optim.AdamW(lora_sd.values(), lr=learning_rate) + elif optimizer == "SGD": + optimizer = torch.optim.SGD(lora_sd.values(), lr=learning_rate) + elif optimizer == "RMSprop": + optimizer = torch.optim.RMSprop(lora_sd.values(), lr=learning_rate) + + # Setup loss function based on selection + if loss_function == "MSE": + criterion = torch.nn.MSELoss() + elif loss_function == "L1": + criterion = torch.nn.L1Loss() + elif loss_function == "Huber": + criterion = torch.nn.HuberLoss() + elif loss_function == "SmoothL1": + criterion = torch.nn.SmoothL1Loss() + + # setup models + if gradient_checkpointing: + for m in find_all_highest_child_module_with_forward(mp.model.diffusion_model): + patch(m) + mp.model.requires_grad_(False) + comfy.model_management.load_models_gpu([mp], memory_required=1e20, force_full_load=True) + + # Setup sampler and guider like in test script + loss_map = {"loss": []} + def loss_callback(loss): + loss_map["loss"].append(loss) + train_sampler = TrainSampler( + criterion, + optimizer, + loss_callback=loss_callback, + batch_size=batch_size, + grad_acc=grad_accumulation_steps, + total_steps=steps*grad_accumulation_steps, + seed=seed, + training_dtype=dtype + ) + guider = comfy_extras.nodes_custom_sampler.Guider_Basic(mp) + guider.set_conds(positive) # Set conditioning from input + + # Training loop + try: + # Generate dummy sigmas and noise + sigmas = torch.tensor(range(num_images)) + noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(seed) + guider.sample( + noise.generate_noise({"samples": latents}), + latents, + train_sampler, + sigmas, + seed=noise.seed + ) + finally: + for m in mp.model.modules(): + unpatch(m) + del train_sampler, optimizer + + for adapter in all_weight_adapters: + adapter.requires_grad_(False) + + for param in lora_sd: + lora_sd[param] = lora_sd[param].to(lora_dtype) + + return (mp, lora_sd, loss_map, steps + existing_steps) + + +class LoraModelLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}), + "lora": (IO.LORA_MODEL, {"tooltip": "The LoRA model to apply to the diffusion model."}), + "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}), + } + } + + RETURN_TYPES = ("MODEL",) + OUTPUT_TOOLTIPS = ("The modified diffusion model.",) + FUNCTION = "load_lora_model" + + CATEGORY = "loaders" + DESCRIPTION = "Load Trained LoRA weights from Train LoRA node." + EXPERIMENTAL = True + + def load_lora_model(self, model, lora, strength_model): + if strength_model == 0: + return (model, ) + + model_lora, _ = comfy.sd.load_lora_for_models(model, None, lora, strength_model, 0) + return (model_lora, ) + + +class SaveLoRA: + def __init__(self): + self.output_dir = folder_paths.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "lora": ( + IO.LORA_MODEL, + { + "tooltip": "The LoRA model to save. Do not use the model with LoRA layers." + }, + ), + "prefix": ( + "STRING", + { + "default": "loras/ComfyUI_trained_lora", + "tooltip": "The prefix to use for the saved LoRA file.", + }, + ), + }, + "optional": { + "steps": ( + IO.INT, + { + "forceInput": True, + "tooltip": "Optional: The number of steps to LoRA has been trained for, used to name the saved file.", + }, + ), + }, + } + + RETURN_TYPES = () + FUNCTION = "save" + CATEGORY = "loaders" + EXPERIMENTAL = True + OUTPUT_NODE = True + + def save(self, lora, prefix, steps=None): + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(prefix, self.output_dir) + if steps is None: + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + else: + output_checkpoint = f"{filename}_{steps}_steps_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + safetensors.torch.save_file(lora, output_checkpoint) + return {} + + +class LossGraphNode: + def __init__(self): + self.output_dir = folder_paths.get_temp_directory() + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "loss": (IO.LOSS_MAP, {"default": {}}), + "filename_prefix": (IO.STRING, {"default": "loss_graph"}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "plot_loss" + OUTPUT_NODE = True + CATEGORY = "training" + EXPERIMENTAL = True + DESCRIPTION = "Plots the loss graph and saves it to the output directory." + + def plot_loss(self, loss, filename_prefix, prompt=None, extra_pnginfo=None): + loss_values = loss["loss"] + width, height = 800, 480 + margin = 40 + + img = Image.new( + "RGB", (width + margin, height + margin), "white" + ) # Extend canvas + draw = ImageDraw.Draw(img) + + min_loss, max_loss = min(loss_values), max(loss_values) + scaled_loss = [(l - min_loss) / (max_loss - min_loss) for l in loss_values] + + steps = len(loss_values) + + prev_point = (margin, height - int(scaled_loss[0] * height)) + for i, l in enumerate(scaled_loss[1:], start=1): + x = margin + int(i / steps * width) # Scale X properly + y = height - int(l * height) + draw.line([prev_point, (x, y)], fill="blue", width=2) + prev_point = (x, y) + + draw.line([(margin, 0), (margin, height)], fill="black", width=2) # Y-axis + draw.line( + [(margin, height), (width + margin, height)], fill="black", width=2 + ) # X-axis + + font = None + try: + font = ImageFont.truetype("arial.ttf", 12) + except IOError: + font = ImageFont.load_default() + + # Add axis labels + draw.text((5, height // 2), "Loss", font=font, fill="black") + draw.text((width // 2, height + 10), "Steps", font=font, fill="black") + + # Add min/max loss values + draw.text((margin - 30, 0), f"{max_loss:.2f}", font=font, fill="black") + draw.text( + (margin - 30, height - 10), f"{min_loss:.2f}", font=font, fill="black" + ) + + metadata = None + if not args.disable_metadata: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + img.save( + os.path.join(self.output_dir, f"{filename_prefix}_{date}.png"), + pnginfo=metadata, + ) + return { + "ui": { + "images": [ + { + "filename": f"{filename_prefix}_{date}.png", + "subfolder": "", + "type": "temp", + } + ] + } + } + + +NODE_CLASS_MAPPINGS = { + "TrainLoraNode": TrainLoraNode, + "SaveLoRANode": SaveLoRA, + "LoraModelLoader": LoraModelLoader, + "LoadImageSetFromFolderNode": LoadImageSetFromFolderNode, + "LoadImageTextSetFromFolderNode": LoadImageTextSetFromFolderNode, + "LossGraphNode": LossGraphNode, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "TrainLoraNode": "Train LoRA", + "SaveLoRANode": "Save LoRA Weights", + "LoraModelLoader": "Load LoRA Model", + "LoadImageSetFromFolderNode": "Load Image Dataset from Folder", + "LoadImageTextSetFromFolderNode": "Load Image and Text Dataset from Folder", + "LossGraphNode": "Plot Loss Graph", +} diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index 04c948341..4d62b87be 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -4,6 +4,8 @@ from comfy import model_management import torch import comfy.utils import folder_paths +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io try: from spandrel_extra_arches import EXTRA_REGISTRY @@ -13,17 +15,23 @@ try: except: pass -class UpscaleModelLoader: +class UpscaleModelLoader(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "model_name": (folder_paths.get_filename_list("upscale_models"), ), - }} - RETURN_TYPES = ("UPSCALE_MODEL",) - FUNCTION = "load_model" + def define_schema(cls): + return io.Schema( + node_id="UpscaleModelLoader", + display_name="Load Upscale Model", + category="loaders", + inputs=[ + io.Combo.Input("model_name", options=folder_paths.get_filename_list("upscale_models")), + ], + outputs=[ + io.UpscaleModel.Output(), + ], + ) - CATEGORY = "loaders" - - def load_model(self, model_name): + @classmethod + def execute(cls, model_name) -> io.NodeOutput: model_path = folder_paths.get_full_path_or_raise("upscale_models", model_name) sd = comfy.utils.load_torch_file(model_path, safe_load=True) if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: @@ -33,21 +41,29 @@ class UpscaleModelLoader: if not isinstance(out, ImageModelDescriptor): raise Exception("Upscale model must be a single-image model.") - return (out, ) + return io.NodeOutput(out) + + load_model = execute # TODO: remove -class ImageUpscaleWithModel: +class ImageUpscaleWithModel(io.ComfyNode): @classmethod - def INPUT_TYPES(s): - return {"required": { "upscale_model": ("UPSCALE_MODEL",), - "image": ("IMAGE",), - }} - RETURN_TYPES = ("IMAGE",) - FUNCTION = "upscale" + def define_schema(cls): + return io.Schema( + node_id="ImageUpscaleWithModel", + display_name="Upscale Image (using Model)", + category="image/upscaling", + inputs=[ + io.UpscaleModel.Input("upscale_model"), + io.Image.Input("image"), + ], + outputs=[ + io.Image.Output(), + ], + ) - CATEGORY = "image/upscaling" - - def upscale(self, upscale_model, image): + @classmethod + def execute(cls, upscale_model, image) -> io.NodeOutput: device = model_management.get_torch_device() memory_required = model_management.module_size(upscale_model.model) @@ -75,9 +91,19 @@ class ImageUpscaleWithModel: upscale_model.to("cpu") s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) - return (s,) + return io.NodeOutput(s) -NODE_CLASS_MAPPINGS = { - "UpscaleModelLoader": UpscaleModelLoader, - "ImageUpscaleWithModel": ImageUpscaleWithModel -} + upscale = execute # TODO: remove + + +class UpscaleModelExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + UpscaleModelLoader, + ImageUpscaleWithModel, + ] + + +async def comfy_entrypoint() -> UpscaleModelExtension: + return UpscaleModelExtension() diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py new file mode 100644 index 000000000..69fabb12e --- /dev/null +++ b/comfy_extras/nodes_video.py @@ -0,0 +1,218 @@ +from __future__ import annotations + +import os +import av +import torch +import folder_paths +import json +from typing import Optional +from typing_extensions import override +from fractions import Fraction +from comfy_api.input import AudioInput, ImageInput, VideoInput +from comfy_api.input_impl import VideoFromComponents, VideoFromFile +from comfy_api.util import VideoCodec, VideoComponents, VideoContainer +from comfy_api.latest import ComfyExtension, io, ui +from comfy.cli_args import args + +class SaveWEBM(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveWEBM", + category="image/video", + is_experimental=True, + inputs=[ + io.Image.Input("images"), + io.String.Input("filename_prefix", default="ComfyUI"), + io.Combo.Input("codec", options=["vp9", "av1"]), + io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01), + io.Float.Input("crf", default=32.0, min=0, max=63.0, step=1, tooltip="Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."), + ], + outputs=[], + hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo], + is_output_node=True, + ) + + @classmethod + def execute(cls, images, codec, fps, filename_prefix, crf) -> io.NodeOutput: + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0] + ) + + file = f"{filename}_{counter:05}_.webm" + container = av.open(os.path.join(full_output_folder, file), mode="w") + + if cls.hidden.prompt is not None: + container.metadata["prompt"] = json.dumps(cls.hidden.prompt) + + if cls.hidden.extra_pnginfo is not None: + for x in cls.hidden.extra_pnginfo: + container.metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x]) + + codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"} + stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000)) + stream.width = images.shape[-2] + stream.height = images.shape[-3] + stream.pix_fmt = "yuv420p10le" if codec == "av1" else "yuv420p" + stream.bit_rate = 0 + stream.options = {'crf': str(crf)} + if codec == "av1": + stream.options["preset"] = "6" + + for frame in images: + frame = av.VideoFrame.from_ndarray(torch.clamp(frame[..., :3] * 255, min=0, max=255).to(device=torch.device("cpu"), dtype=torch.uint8).numpy(), format="rgb24") + for packet in stream.encode(frame): + container.mux(packet) + container.mux(stream.encode()) + container.close() + + return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)])) + +class SaveVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SaveVideo", + display_name="Save Video", + category="image/video", + description="Saves the input images to your ComfyUI output directory.", + inputs=[ + io.Video.Input("video", tooltip="The video to save."), + io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."), + io.Combo.Input("format", options=VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."), + io.Combo.Input("codec", options=VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."), + ], + outputs=[], + hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo], + is_output_node=True, + ) + + @classmethod + def execute(cls, video: VideoInput, filename_prefix, format, codec) -> io.NodeOutput: + width, height = video.get_dimensions() + full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path( + filename_prefix, + folder_paths.get_output_directory(), + width, + height + ) + saved_metadata = None + if not args.disable_metadata: + metadata = {} + if cls.hidden.extra_pnginfo is not None: + metadata.update(cls.hidden.extra_pnginfo) + if cls.hidden.prompt is not None: + metadata["prompt"] = cls.hidden.prompt + if len(metadata) > 0: + saved_metadata = metadata + file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}" + video.save_to( + os.path.join(full_output_folder, file), + format=format, + codec=codec, + metadata=saved_metadata + ) + + return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)])) + + +class CreateVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="CreateVideo", + display_name="Create Video", + category="image/video", + description="Create a video from images.", + inputs=[ + io.Image.Input("images", tooltip="The images to create a video from."), + io.Float.Input("fps", default=30.0, min=1.0, max=120.0, step=1.0), + io.Audio.Input("audio", optional=True, tooltip="The audio to add to the video."), + ], + outputs=[ + io.Video.Output(), + ], + ) + + @classmethod + def execute(cls, images: ImageInput, fps: float, audio: Optional[AudioInput] = None) -> io.NodeOutput: + return io.NodeOutput( + VideoFromComponents(VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps))) + ) + +class GetVideoComponents(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="GetVideoComponents", + display_name="Get Video Components", + category="image/video", + description="Extracts all components from a video: frames, audio, and framerate.", + inputs=[ + io.Video.Input("video", tooltip="The video to extract components from."), + ], + outputs=[ + io.Image.Output(display_name="images"), + io.Audio.Output(display_name="audio"), + io.Float.Output(display_name="fps"), + ], + ) + + @classmethod + def execute(cls, video: VideoInput) -> io.NodeOutput: + components = video.get_components() + + return io.NodeOutput(components.images, components.audio, float(components.frame_rate)) + +class LoadVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + input_dir = folder_paths.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + files = folder_paths.filter_files_content_types(files, ["video"]) + return io.Schema( + node_id="LoadVideo", + display_name="Load Video", + category="image/video", + inputs=[ + io.Combo.Input("file", options=sorted(files), upload=io.UploadType.video), + ], + outputs=[ + io.Video.Output(), + ], + ) + + @classmethod + def execute(cls, file) -> io.NodeOutput: + video_path = folder_paths.get_annotated_filepath(file) + return io.NodeOutput(VideoFromFile(video_path)) + + @classmethod + def fingerprint_inputs(s, file): + video_path = folder_paths.get_annotated_filepath(file) + mod_time = os.path.getmtime(video_path) + # Instead of hashing the file, we can just use the modification time to avoid + # rehashing large files. + return mod_time + + @classmethod + def validate_inputs(s, file): + if not folder_paths.exists_annotated_filepath(file): + return "Invalid video file: {}".format(file) + + return True + + +class VideoExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SaveWEBM, + SaveVideo, + CreateVideo, + GetVideoComponents, + LoadVideo, + ] + +async def comfy_entrypoint() -> VideoExtension: + return VideoExtension() diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py index e7a7ec181..0f760aa26 100644 --- a/comfy_extras/nodes_video_model.py +++ b/comfy_extras/nodes_video_model.py @@ -4,6 +4,7 @@ import comfy.utils import comfy.sd import folder_paths import comfy_extras.nodes_model_merging +import node_helpers class ImageOnlyCheckpointLoader: @@ -121,12 +122,38 @@ class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave): comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) return {} + +class ConditioningSetAreaPercentageVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, temporal, x, y, z, strength): + c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x), + "strength": strength, + "set_area_to_bounds": False}) + return (c, ) + + NODE_CLASS_MAPPINGS = { "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, "VideoLinearCFGGuidance": VideoLinearCFGGuidance, "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance, "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, + "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo, } NODE_DISPLAY_NAME_MAPPINGS = { diff --git a/comfy_extras/nodes_wan.py b/comfy_extras/nodes_wan.py new file mode 100644 index 000000000..b0bd471bf --- /dev/null +++ b/comfy_extras/nodes_wan.py @@ -0,0 +1,1313 @@ +import math +import nodes +import node_helpers +import torch +import comfy.model_management +import comfy.utils +import comfy.latent_formats +import comfy.clip_vision +import json +import numpy as np +from typing import Tuple +from typing_extensions import override +from comfy_api.latest import ComfyExtension, io + +class WanImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Image.Input("start_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + image = torch.ones((length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 + image[:start_image.shape[0]] = start_image + + concat_latent_image = vae.encode(image[:, :, :, :3]) + mask = torch.ones((1, 1, latent.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) + mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0 + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + + +class WanFunControlToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanFunControlToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Image.Input("start_image", optional=True), + io.Image.Input("control_video", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, control_video=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + concat_latent = concat_latent.repeat(1, 2, 1, 1, 1) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,16:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(control_video[:, :, :, :3]) + concat_latent[:,:16,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + +class Wan22FunControlToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="Wan22FunControlToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("ref_image", optional=True), + io.Image.Input("control_video", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, start_image=None, control_video=None) -> io.NodeOutput: + spacial_scale = vae.spacial_compression_encode() + latent_channels = vae.latent_channels + latent = torch.zeros([batch_size, latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) + if latent_channels == 48: + concat_latent = comfy.latent_formats.Wan22().process_out(concat_latent) + else: + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + concat_latent = concat_latent.repeat(1, 2, 1, 1, 1) + mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,latent_channels:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + mask[:, :, :start_image.shape[0] + 3] = 0.0 + + ref_latent = None + if ref_image is not None: + ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + ref_latent = vae.encode(ref_image[:, :, :, :3]) + + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(control_video[:, :, :, :3]) + concat_latent[:,:latent_channels,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + + mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": latent_channels}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, "concat_mask": mask, "concat_mask_index": latent_channels}) + + if ref_latent is not None: + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + +class WanFirstLastFrameToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanFirstLastFrameToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_start_image", optional=True), + io.ClipVisionOutput.Input("clip_vision_end_image", optional=True), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_start_image=None, clip_vision_end_image=None) -> io.NodeOutput: + spacial_scale = vae.spacial_compression_encode() + latent = torch.zeros([batch_size, vae.latent_channels, ((length - 1) // 4) + 1, height // spacial_scale, width // spacial_scale], device=comfy.model_management.intermediate_device()) + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + if end_image is not None: + end_image = comfy.utils.common_upscale(end_image[-length:].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + image = torch.ones((length, height, width, 3)) * 0.5 + mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) + + if start_image is not None: + image[:start_image.shape[0]] = start_image + mask[:, :, :start_image.shape[0] + 3] = 0.0 + + if end_image is not None: + image[-end_image.shape[0]:] = end_image + mask[:, :, -end_image.shape[0]:] = 0.0 + + concat_latent_image = vae.encode(image[:, :, :, :3]) + mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + clip_vision_output = None + if clip_vision_start_image is not None: + clip_vision_output = clip_vision_start_image + + if clip_vision_end_image is not None: + if clip_vision_output is not None: + states = torch.cat([clip_vision_output.penultimate_hidden_states, clip_vision_end_image.penultimate_hidden_states], dim=-2) + clip_vision_output = comfy.clip_vision.Output() + clip_vision_output.penultimate_hidden_states = states + else: + clip_vision_output = clip_vision_end_image + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + + +class WanFunInpaintToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanFunInpaintToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Image.Input("start_image", optional=True), + io.Image.Input("end_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, end_image=None, clip_vision_output=None) -> io.NodeOutput: + flfv = WanFirstLastFrameToVideo() + return flfv.execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, end_image=end_image, clip_vision_start_image=clip_vision_output) + + +class WanVaceToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanVaceToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Float.Input("strength", default=1.0, min=0.0, max=1000.0, step=0.01), + io.Image.Input("control_video", optional=True), + io.Mask.Input("control_masks", optional=True), + io.Image.Input("reference_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + io.Int.Output(display_name="trim_latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, strength, control_video=None, control_masks=None, reference_image=None) -> io.NodeOutput: + latent_length = ((length - 1) // 4) + 1 + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + if control_video.shape[0] < length: + control_video = torch.nn.functional.pad(control_video, (0, 0, 0, 0, 0, 0, 0, length - control_video.shape[0]), value=0.5) + else: + control_video = torch.ones((length, height, width, 3)) * 0.5 + + if reference_image is not None: + reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + reference_image = vae.encode(reference_image[:, :, :, :3]) + reference_image = torch.cat([reference_image, comfy.latent_formats.Wan21().process_out(torch.zeros_like(reference_image))], dim=1) + + if control_masks is None: + mask = torch.ones((length, height, width, 1)) + else: + mask = control_masks + if mask.ndim == 3: + mask = mask.unsqueeze(1) + mask = comfy.utils.common_upscale(mask[:length], width, height, "bilinear", "center").movedim(1, -1) + if mask.shape[0] < length: + mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, 0, 0, length - mask.shape[0]), value=1.0) + + control_video = control_video - 0.5 + inactive = (control_video * (1 - mask)) + 0.5 + reactive = (control_video * mask) + 0.5 + + inactive = vae.encode(inactive[:, :, :, :3]) + reactive = vae.encode(reactive[:, :, :, :3]) + control_video_latent = torch.cat((inactive, reactive), dim=1) + if reference_image is not None: + control_video_latent = torch.cat((reference_image, control_video_latent), dim=2) + + vae_stride = 8 + height_mask = height // vae_stride + width_mask = width // vae_stride + mask = mask.view(length, height_mask, vae_stride, width_mask, vae_stride) + mask = mask.permute(2, 4, 0, 1, 3) + mask = mask.reshape(vae_stride * vae_stride, length, height_mask, width_mask) + mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=(latent_length, height_mask, width_mask), mode='nearest-exact').squeeze(0) + + trim_latent = 0 + if reference_image is not None: + mask_pad = torch.zeros_like(mask[:, :reference_image.shape[2], :, :]) + mask = torch.cat((mask_pad, mask), dim=1) + latent_length += reference_image.shape[2] + trim_latent = reference_image.shape[2] + + mask = mask.unsqueeze(0) + + positive = node_helpers.conditioning_set_values(positive, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"vace_frames": [control_video_latent], "vace_mask": [mask], "vace_strength": [strength]}, append=True) + + latent = torch.zeros([batch_size, 16, latent_length, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent, trim_latent) + +class TrimVideoLatent(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="TrimVideoLatent", + category="latent/video", + inputs=[ + io.Latent.Input("samples"), + io.Int.Input("trim_amount", default=0, min=0, max=99999), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, samples, trim_amount) -> io.NodeOutput: + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1[:, :, trim_amount:] + return io.NodeOutput(samples_out) + +class WanCameraImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanCameraImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Image.Input("start_image", optional=True), + io.WanCameraEmbedding.Input("camera_conditions", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None, clip_vision_output=None, camera_conditions=None) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + concat_latent_image = vae.encode(start_image[:, :, :, :3]) + concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]] + mask = torch.ones((1, 1, latent.shape[2] * 4, latent.shape[-2], latent.shape[-1])) + mask[:, :, :start_image.shape[0] + 3] = 0.0 + mask = mask.view(1, mask.shape[2] // 4, 4, mask.shape[3], mask.shape[4]).transpose(1, 2) + + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, "concat_mask": mask}) + + if camera_conditions is not None: + positive = node_helpers.conditioning_set_values(positive, {'camera_conditions': camera_conditions}) + negative = node_helpers.conditioning_set_values(negative, {'camera_conditions': camera_conditions}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + +class WanPhantomSubjectToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanPhantomSubjectToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("images", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative_text"), + io.Conditioning.Output(display_name="negative_img_text"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, images) -> io.NodeOutput: + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + cond2 = negative + if images is not None: + images = comfy.utils.common_upscale(images[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + latent_images = [] + for i in images: + latent_images += [vae.encode(i.unsqueeze(0)[:, :, :, :3])] + concat_latent_image = torch.cat(latent_images, dim=2) + + positive = node_helpers.conditioning_set_values(positive, {"time_dim_concat": concat_latent_image}) + cond2 = node_helpers.conditioning_set_values(negative, {"time_dim_concat": concat_latent_image}) + negative = node_helpers.conditioning_set_values(negative, {"time_dim_concat": comfy.latent_formats.Wan21().process_out(torch.zeros_like(concat_latent_image))}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, cond2, negative, out_latent) + +def parse_json_tracks(tracks): + """Parse JSON track data into a standardized format""" + tracks_data = [] + try: + # If tracks is a string, try to parse it as JSON + if isinstance(tracks, str): + parsed = json.loads(tracks.replace("'", '"')) + tracks_data.extend(parsed) + else: + # If tracks is a list of strings, parse each one + for track_str in tracks: + parsed = json.loads(track_str.replace("'", '"')) + tracks_data.append(parsed) + + # Check if we have a single track (dict with x,y) or a list of tracks + if tracks_data and isinstance(tracks_data[0], dict) and 'x' in tracks_data[0]: + # Single track detected, wrap it in a list + tracks_data = [tracks_data] + elif tracks_data and isinstance(tracks_data[0], list) and tracks_data[0] and isinstance(tracks_data[0][0], dict) and 'x' in tracks_data[0][0]: + # Already a list of tracks, nothing to do + pass + else: + # Unexpected format + pass + + except json.JSONDecodeError: + tracks_data = [] + return tracks_data + +def process_tracks(tracks_np: np.ndarray, frame_size: Tuple[int, int], num_frames, quant_multi: int = 8, **kwargs): + # tracks: shape [t, h, w, 3] => samples align with 24 fps, model trained with 16 fps. + # frame_size: tuple (W, H) + tracks = torch.from_numpy(tracks_np).float() + + if tracks.shape[1] == 121: + tracks = torch.permute(tracks, (1, 0, 2, 3)) + + tracks, visibles = tracks[..., :2], tracks[..., 2:3] + + short_edge = min(*frame_size) + + frame_center = torch.tensor([*frame_size]).type_as(tracks) / 2 + tracks = tracks - frame_center + + tracks = tracks / short_edge * 2 + + visibles = visibles * 2 - 1 + + trange = torch.linspace(-1, 1, tracks.shape[0]).view(-1, 1, 1, 1).expand(*visibles.shape) + + out_ = torch.cat([trange, tracks, visibles], dim=-1).view(121, -1, 4) + + out_0 = out_[:1] + + out_l = out_[1:] # 121 => 120 | 1 + a = 120 // math.gcd(120, num_frames) + b = num_frames // math.gcd(120, num_frames) + out_l = torch.repeat_interleave(out_l, b, dim=0)[1::a] # 120 => 120 * b => 120 * b / a == F + + final_result = torch.cat([out_0, out_l], dim=0) + + return final_result + +FIXED_LENGTH = 121 +def pad_pts(tr): + """Convert list of {x,y} to (FIXED_LENGTH,1,3) array, padding/truncating.""" + pts = np.array([[p['x'], p['y'], 1] for p in tr], dtype=np.float32) + n = pts.shape[0] + if n < FIXED_LENGTH: + pad = np.zeros((FIXED_LENGTH - n, 3), dtype=np.float32) + pts = np.vstack((pts, pad)) + else: + pts = pts[:FIXED_LENGTH] + return pts.reshape(FIXED_LENGTH, 1, 3) + +def ind_sel(target: torch.Tensor, ind: torch.Tensor, dim: int = 1): + """Index selection utility function""" + assert ( + len(ind.shape) > dim + ), "Index must have the target dim, but get dim: %d, ind shape: %s" % (dim, str(ind.shape)) + + target = target.expand( + *tuple( + [ind.shape[k] if target.shape[k] == 1 else -1 for k in range(dim)] + + [ + -1, + ] + * (len(target.shape) - dim) + ) + ) + + ind_pad = ind + + if len(target.shape) > dim + 1: + for _ in range(len(target.shape) - (dim + 1)): + ind_pad = ind_pad.unsqueeze(-1) + ind_pad = ind_pad.expand(*(-1,) * (dim + 1), *target.shape[(dim + 1) : :]) + + return torch.gather(target, dim=dim, index=ind_pad) + +def merge_final(vert_attr: torch.Tensor, weight: torch.Tensor, vert_assign: torch.Tensor): + """Merge vertex attributes with weights""" + target_dim = len(vert_assign.shape) - 1 + if len(vert_attr.shape) == 2: + assert vert_attr.shape[0] > vert_assign.max() + new_shape = [1] * target_dim + list(vert_attr.shape) + tensor = vert_attr.reshape(new_shape) + sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) + else: + assert vert_attr.shape[1] > vert_assign.max() + new_shape = [vert_attr.shape[0]] + [1] * (target_dim - 1) + list(vert_attr.shape[1:]) + tensor = vert_attr.reshape(new_shape) + sel_attr = ind_sel(tensor, vert_assign.type(torch.long), dim=target_dim) + + final_attr = torch.sum(sel_attr * weight.unsqueeze(-1), dim=-2) + return final_attr + + +def _patch_motion_single( + tracks: torch.FloatTensor, # (B, T, N, 4) + vid: torch.FloatTensor, # (C, T, H, W) + temperature: float, + vae_divide: tuple, + topk: int, +): + """Apply motion patching based on tracks""" + _, T, H, W = vid.shape + N = tracks.shape[2] + _, tracks_xy, visible = torch.split( + tracks, [1, 2, 1], dim=-1 + ) # (B, T, N, 2) | (B, T, N, 1) + tracks_n = tracks_xy / torch.tensor([W / min(H, W), H / min(H, W)], device=tracks_xy.device) + tracks_n = tracks_n.clamp(-1, 1) + visible = visible.clamp(0, 1) + + xx = torch.linspace(-W / min(H, W), W / min(H, W), W) + yy = torch.linspace(-H / min(H, W), H / min(H, W), H) + + grid = torch.stack(torch.meshgrid(yy, xx, indexing="ij")[::-1], dim=-1).to( + tracks_xy.device + ) + + tracks_pad = tracks_xy[:, 1:] + visible_pad = visible[:, 1:] + + visible_align = visible_pad.view(T - 1, 4, *visible_pad.shape[2:]).sum(1) + tracks_align = (tracks_pad * visible_pad).view(T - 1, 4, *tracks_pad.shape[2:]).sum( + 1 + ) / (visible_align + 1e-5) + dist_ = ( + (tracks_align[:, None, None] - grid[None, :, :, None]).pow(2).sum(-1) + ) # T, H, W, N + weight = torch.exp(-dist_ * temperature) * visible_align.clamp(0, 1).view( + T - 1, 1, 1, N + ) + vert_weight, vert_index = torch.topk( + weight, k=min(topk, weight.shape[-1]), dim=-1 + ) + + grid_mode = "bilinear" + point_feature = torch.nn.functional.grid_sample( + vid.permute(1, 0, 2, 3)[:1], + tracks_n[:, :1].type(vid.dtype), + mode=grid_mode, + padding_mode="zeros", + align_corners=False, + ) + point_feature = point_feature.squeeze(0).squeeze(1).permute(1, 0) # N, C=16 + + out_feature = merge_final(point_feature, vert_weight, vert_index).permute(3, 0, 1, 2) # T - 1, H, W, C => C, T - 1, H, W + out_weight = vert_weight.sum(-1) # T - 1, H, W + + # out feature -> already soft weighted + mix_feature = out_feature + vid[:, 1:] * (1 - out_weight.clamp(0, 1)) + + out_feature_full = torch.cat([vid[:, :1], mix_feature], dim=1) # C, T, H, W + out_mask_full = torch.cat([torch.ones_like(out_weight[:1]), out_weight], dim=0) # T, H, W + + return out_mask_full[None].expand(vae_divide[0], -1, -1, -1), out_feature_full + + +def patch_motion( + tracks: torch.FloatTensor, # (B, TB, T, N, 4) + vid: torch.FloatTensor, # (C, T, H, W) + temperature: float = 220.0, + vae_divide: tuple = (4, 16), + topk: int = 2, +): + B = len(tracks) + + # Process each batch separately + out_masks = [] + out_features = [] + + for b in range(B): + mask, feature = _patch_motion_single( + tracks[b], # (T, N, 4) + vid[b], # (C, T, H, W) + temperature, + vae_divide, + topk + ) + out_masks.append(mask) + out_features.append(feature) + + # Stack results: (B, C, T, H, W) + out_mask_full = torch.stack(out_masks, dim=0) + out_feature_full = torch.stack(out_features, dim=0) + + return out_mask_full, out_feature_full + +class WanTrackToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanTrackToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.String.Input("tracks", multiline=True, default="[]"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Float.Input("temperature", default=220.0, min=1.0, max=1000.0, step=0.1), + io.Int.Input("topk", default=2, min=1, max=10), + io.Image.Input("start_image"), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, tracks, width, height, length, batch_size, + temperature, topk, start_image=None, clip_vision_output=None) -> io.NodeOutput: + + tracks_data = parse_json_tracks(tracks) + + if not tracks_data: + return WanImageToVideo().execute(positive, negative, vae, width, height, length, batch_size, start_image=start_image, clip_vision_output=clip_vision_output) + + latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], + device=comfy.model_management.intermediate_device()) + + if isinstance(tracks_data[0][0], dict): + tracks_data = [tracks_data] + + processed_tracks = [] + for batch in tracks_data: + arrs = [] + for track in batch: + pts = pad_pts(track) + arrs.append(pts) + + tracks_np = np.stack(arrs, axis=0) + processed_tracks.append(process_tracks(tracks_np, (width, height), length - 1).unsqueeze(0)) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:batch_size].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + videos = torch.ones((start_image.shape[0], length, height, width, start_image.shape[-1]), device=start_image.device, dtype=start_image.dtype) * 0.5 + for i in range(start_image.shape[0]): + videos[i, 0] = start_image[i] + + latent_videos = [] + videos = comfy.utils.resize_to_batch_size(videos, batch_size) + for i in range(batch_size): + latent_videos += [vae.encode(videos[i, :, :, :, :3])] + y = torch.cat(latent_videos, dim=0) + + # Scale latent since patch_motion is non-linear + y = comfy.latent_formats.Wan21().process_in(y) + + processed_tracks = comfy.utils.resize_list_to_batch_size(processed_tracks, batch_size) + res = patch_motion( + processed_tracks, y, temperature=temperature, topk=topk, vae_divide=(4, 16) + ) + + mask, concat_latent_image = res + concat_latent_image = comfy.latent_formats.Wan21().process_out(concat_latent_image) + mask = -mask + 1.0 # Invert mask to match expected format + positive = node_helpers.conditioning_set_values(positive, + {"concat_mask": mask, + "concat_latent_image": concat_latent_image}) + negative = node_helpers.conditioning_set_values(negative, + {"concat_mask": mask, + "concat_latent_image": concat_latent_image}) + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + + +def linear_interpolation(features, input_fps, output_fps, output_len=None): + """ + features: shape=[1, T, 512] + input_fps: fps for audio, f_a + output_fps: fps for video, f_m + output_len: video length + """ + features = features.transpose(1, 2) # [1, 512, T] + seq_len = features.shape[2] / float(input_fps) # T/f_a + if output_len is None: + output_len = int(seq_len * output_fps) # f_m*T/f_a + output_features = torch.nn.functional.interpolate( + features, size=output_len, align_corners=True, + mode='linear') # [1, 512, output_len] + return output_features.transpose(1, 2) # [1, output_len, 512] + + +def get_sample_indices(original_fps, + total_frames, + target_fps, + num_sample, + fixed_start=None): + required_duration = num_sample / target_fps + required_origin_frames = int(np.ceil(required_duration * original_fps)) + if required_duration > total_frames / original_fps: + raise ValueError("required_duration must be less than video length") + + if not fixed_start is None and fixed_start >= 0: + start_frame = fixed_start + else: + max_start = total_frames - required_origin_frames + if max_start < 0: + raise ValueError("video length is too short") + start_frame = np.random.randint(0, max_start + 1) + start_time = start_frame / original_fps + + end_time = start_time + required_duration + time_points = np.linspace(start_time, end_time, num_sample, endpoint=False) + + frame_indices = np.round(np.array(time_points) * original_fps).astype(int) + frame_indices = np.clip(frame_indices, 0, total_frames - 1) + return frame_indices + + +def get_audio_embed_bucket_fps(audio_embed, fps=16, batch_frames=81, m=0, video_rate=30): + num_layers, audio_frame_num, audio_dim = audio_embed.shape + + if num_layers > 1: + return_all_layers = True + else: + return_all_layers = False + + scale = video_rate / fps + + min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1 + + bucket_num = min_batch_num * batch_frames + padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * video_rate) - audio_frame_num + batch_idx = get_sample_indices( + original_fps=video_rate, + total_frames=audio_frame_num + padd_audio_num, + target_fps=fps, + num_sample=bucket_num, + fixed_start=0) + batch_audio_eb = [] + audio_sample_stride = int(video_rate / fps) + for bi in batch_idx: + if bi < audio_frame_num: + + chosen_idx = list( + range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride)) + chosen_idx = [0 if c < 0 else c for c in chosen_idx] + chosen_idx = [ + audio_frame_num - 1 if c >= audio_frame_num else c + for c in chosen_idx + ] + + if return_all_layers: + frame_audio_embed = audio_embed[:, chosen_idx].flatten( + start_dim=-2, end_dim=-1) + else: + frame_audio_embed = audio_embed[0][chosen_idx].flatten() + else: + frame_audio_embed = torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \ + else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device) + batch_audio_eb.append(frame_audio_embed) + batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0) + + return batch_audio_eb, min_batch_num + + +def wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, frame_offset=0, ref_image=None, audio_encoder_output=None, control_video=None, ref_motion=None, ref_motion_latent=None): + latent_t = ((length - 1) // 4) + 1 + if audio_encoder_output is not None: + feat = torch.cat(audio_encoder_output["encoded_audio_all_layers"]) + video_rate = 30 + fps = 16 + feat = linear_interpolation(feat, input_fps=50, output_fps=video_rate) + batch_frames = latent_t * 4 + audio_embed_bucket, num_repeat = get_audio_embed_bucket_fps(feat, fps=fps, batch_frames=batch_frames, m=0, video_rate=video_rate) + audio_embed_bucket = audio_embed_bucket.unsqueeze(0) + if len(audio_embed_bucket.shape) == 3: + audio_embed_bucket = audio_embed_bucket.permute(0, 2, 1) + elif len(audio_embed_bucket.shape) == 4: + audio_embed_bucket = audio_embed_bucket.permute(0, 2, 3, 1) + + audio_embed_bucket = audio_embed_bucket[:, :, :, frame_offset:frame_offset + batch_frames] + if audio_embed_bucket.shape[3] > 0: + positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_embed_bucket}) + negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_embed_bucket * 0.0}) + frame_offset += batch_frames + + if ref_image is not None: + ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + ref_latent = vae.encode(ref_image[:, :, :, :3]) + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True) + + if ref_motion is not None: + if ref_motion.shape[0] > 73: + ref_motion = ref_motion[-73:] + + ref_motion = comfy.utils.common_upscale(ref_motion.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + + if ref_motion.shape[0] < 73: + r = torch.ones([73, height, width, 3]) * 0.5 + r[-ref_motion.shape[0]:] = ref_motion + ref_motion = r + + ref_motion_latent = vae.encode(ref_motion[:, :, :, :3]) + + if ref_motion_latent is not None: + ref_motion_latent = ref_motion_latent[:, :, -19:] + positive = node_helpers.conditioning_set_values(positive, {"reference_motion": ref_motion_latent}) + negative = node_helpers.conditioning_set_values(negative, {"reference_motion": ref_motion_latent}) + + latent = torch.zeros([batch_size, 16, latent_t, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + + control_video_out = comfy.latent_formats.Wan21().process_out(torch.zeros_like(latent)) + if control_video is not None: + control_video = comfy.utils.common_upscale(control_video[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + control_video = vae.encode(control_video[:, :, :, :3]) + control_video_out[:, :, :control_video.shape[2]] = control_video + + # TODO: check if zero is better than none if none provided + positive = node_helpers.conditioning_set_values(positive, {"control_video": control_video_out}) + negative = node_helpers.conditioning_set_values(negative, {"control_video": control_video_out}) + + out_latent = {} + out_latent["samples"] = latent + return positive, negative, out_latent, frame_offset + + +class WanSoundImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanSoundImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), + io.Image.Input("ref_image", optional=True), + io.Image.Input("control_video", optional=True), + io.Image.Input("ref_motion", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, audio_encoder_output=None, control_video=None, ref_motion=None) -> io.NodeOutput: + positive, negative, out_latent, frame_offset = wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, ref_image=ref_image, audio_encoder_output=audio_encoder_output, + control_video=control_video, ref_motion=ref_motion) + return io.NodeOutput(positive, negative, out_latent) + + +class WanSoundImageToVideoExtend(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanSoundImageToVideoExtend", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Latent.Input("video_latent"), + io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), + io.Image.Input("ref_image", optional=True), + io.Image.Input("control_video", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + ) + + @classmethod + def execute(cls, positive, negative, vae, length, video_latent, ref_image=None, audio_encoder_output=None, control_video=None) -> io.NodeOutput: + video_latent = video_latent["samples"] + width = video_latent.shape[-1] * 8 + height = video_latent.shape[-2] * 8 + batch_size = video_latent.shape[0] + frame_offset = video_latent.shape[-3] * 4 + positive, negative, out_latent, frame_offset = wan_sound_to_video(positive, negative, vae, width, height, length, batch_size, frame_offset=frame_offset, ref_image=ref_image, audio_encoder_output=audio_encoder_output, + control_video=control_video, ref_motion=None, ref_motion_latent=video_latent) + return io.NodeOutput(positive, negative, out_latent) + + +def get_audio_emb_window(audio_emb, frame_num, frame0_idx, audio_shift=2): + zero_audio_embed = torch.zeros((audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device) + zero_audio_embed_3 = torch.zeros((3, audio_emb.shape[1], audio_emb.shape[2]), dtype=audio_emb.dtype, device=audio_emb.device) # device=audio_emb.device + iter_ = 1 + (frame_num - 1) // 4 + audio_emb_wind = [] + for lt_i in range(iter_): + if lt_i == 0: + st = frame0_idx + lt_i - 2 + ed = frame0_idx + lt_i + 3 + wind_feat = torch.stack([ + audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed + for i in range(st, ed) + ], dim=0) + wind_feat = torch.cat((zero_audio_embed_3, wind_feat), dim=0) + else: + st = frame0_idx + 1 + 4 * (lt_i - 1) - audio_shift + ed = frame0_idx + 1 + 4 * lt_i + audio_shift + wind_feat = torch.stack([ + audio_emb[i] if (0 <= i < audio_emb.shape[0]) else zero_audio_embed + for i in range(st, ed) + ], dim=0) + audio_emb_wind.append(wind_feat) + audio_emb_wind = torch.stack(audio_emb_wind, dim=0) + + return audio_emb_wind, ed - audio_shift + + +class WanHuMoImageToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanHuMoImageToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=97, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.AudioEncoderOutput.Input("audio_encoder_output", optional=True), + io.Image.Input("ref_image", optional=True), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, ref_image=None, audio_encoder_output=None) -> io.NodeOutput: + latent_t = ((length - 1) // 4) + 1 + latent = torch.zeros([batch_size, 16, latent_t, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + + if ref_image is not None: + ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + ref_latent = vae.encode(ref_image[:, :, :, :3]) + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True) + else: + zero_latent = torch.zeros([batch_size, 16, 1, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [zero_latent]}, append=True) + negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [zero_latent]}, append=True) + + if audio_encoder_output is not None: + audio_emb = torch.stack(audio_encoder_output["encoded_audio_all_layers"], dim=2) + audio_len = audio_encoder_output["audio_samples"] // 640 + audio_emb = audio_emb[:, :audio_len * 2] + + feat0 = linear_interpolation(audio_emb[:, :, 0: 8].mean(dim=2), 50, 25) + feat1 = linear_interpolation(audio_emb[:, :, 8: 16].mean(dim=2), 50, 25) + feat2 = linear_interpolation(audio_emb[:, :, 16: 24].mean(dim=2), 50, 25) + feat3 = linear_interpolation(audio_emb[:, :, 24: 32].mean(dim=2), 50, 25) + feat4 = linear_interpolation(audio_emb[:, :, 32], 50, 25) + audio_emb = torch.stack([feat0, feat1, feat2, feat3, feat4], dim=2)[0] # [T, 5, 1280] + audio_emb, _ = get_audio_emb_window(audio_emb, length, frame0_idx=0) + + audio_emb = audio_emb.unsqueeze(0) + audio_emb_neg = torch.zeros_like(audio_emb) + positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_emb}) + negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_emb_neg}) + else: + zero_audio = torch.zeros([batch_size, latent_t + 1, 8, 5, 1280], device=comfy.model_management.intermediate_device()) + positive = node_helpers.conditioning_set_values(positive, {"audio_embed": zero_audio}) + negative = node_helpers.conditioning_set_values(negative, {"audio_embed": zero_audio}) + + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent) + +class WanAnimateToVideo(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="WanAnimateToVideo", + category="conditioning/video_models", + inputs=[ + io.Conditioning.Input("positive"), + io.Conditioning.Input("negative"), + io.Vae.Input("vae"), + io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), + io.Int.Input("length", default=77, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.ClipVisionOutput.Input("clip_vision_output", optional=True), + io.Image.Input("reference_image", optional=True), + io.Image.Input("face_video", optional=True), + io.Image.Input("pose_video", optional=True), + io.Int.Input("continue_motion_max_frames", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Image.Input("background_video", optional=True), + io.Mask.Input("character_mask", optional=True), + io.Image.Input("continue_motion", optional=True), + io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="The amount of frames to seek in all the input videos. Used for generating longer videos by chunk. Connect to the video_frame_offset output of the previous node for extending a video."), + ], + outputs=[ + io.Conditioning.Output(display_name="positive"), + io.Conditioning.Output(display_name="negative"), + io.Latent.Output(display_name="latent"), + io.Int.Output(display_name="trim_latent"), + io.Int.Output(display_name="trim_image"), + io.Int.Output(display_name="video_frame_offset"), + ], + is_experimental=True, + ) + + @classmethod + def execute(cls, positive, negative, vae, width, height, length, batch_size, continue_motion_max_frames, video_frame_offset, reference_image=None, clip_vision_output=None, face_video=None, pose_video=None, continue_motion=None, background_video=None, character_mask=None) -> io.NodeOutput: + trim_to_pose_video = False + latent_length = ((length - 1) // 4) + 1 + latent_width = width // 8 + latent_height = height // 8 + trim_latent = 0 + + if reference_image is None: + reference_image = torch.zeros((1, height, width, 3)) + + image = comfy.utils.common_upscale(reference_image[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1) + concat_latent_image = vae.encode(image[:, :, :, :3]) + mask = torch.zeros((1, 4, concat_latent_image.shape[-3], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=concat_latent_image.device, dtype=concat_latent_image.dtype) + trim_latent += concat_latent_image.shape[2] + ref_motion_latent_length = 0 + + if continue_motion is None: + image = torch.ones((length, height, width, 3)) * 0.5 + else: + continue_motion = continue_motion[-continue_motion_max_frames:] + video_frame_offset -= continue_motion.shape[0] + video_frame_offset = max(0, video_frame_offset) + continue_motion = comfy.utils.common_upscale(continue_motion[-length:].movedim(-1, 1), width, height, "area", "center").movedim(1, -1) + image = torch.ones((length, height, width, continue_motion.shape[-1]), device=continue_motion.device, dtype=continue_motion.dtype) * 0.5 + image[:continue_motion.shape[0]] = continue_motion + ref_motion_latent_length += ((continue_motion.shape[0] - 1) // 4) + 1 + + if clip_vision_output is not None: + positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output}) + negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output}) + + if pose_video is not None: + if pose_video.shape[0] <= video_frame_offset: + pose_video = None + else: + pose_video = pose_video[video_frame_offset:] + + if pose_video is not None: + pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1) + if not trim_to_pose_video: + if pose_video.shape[0] < length: + pose_video = torch.cat((pose_video,) + (pose_video[-1:],) * (length - pose_video.shape[0]), dim=0) + + pose_video_latent = vae.encode(pose_video[:, :, :, :3]) + positive = node_helpers.conditioning_set_values(positive, {"pose_video_latent": pose_video_latent}) + negative = node_helpers.conditioning_set_values(negative, {"pose_video_latent": pose_video_latent}) + + if trim_to_pose_video: + latent_length = pose_video_latent.shape[2] + length = latent_length * 4 - 3 + image = image[:length] + + if face_video is not None: + if face_video.shape[0] <= video_frame_offset: + face_video = None + else: + face_video = face_video[video_frame_offset:] + + if face_video is not None: + face_video = comfy.utils.common_upscale(face_video[:length].movedim(-1, 1), 512, 512, "area", "center") * 2.0 - 1.0 + face_video = face_video.movedim(0, 1).unsqueeze(0) + positive = node_helpers.conditioning_set_values(positive, {"face_video_pixels": face_video}) + negative = node_helpers.conditioning_set_values(negative, {"face_video_pixels": face_video * 0.0 - 1.0}) + + ref_images_num = max(0, ref_motion_latent_length * 4 - 3) + if background_video is not None: + if background_video.shape[0] > video_frame_offset: + background_video = background_video[video_frame_offset:] + background_video = comfy.utils.common_upscale(background_video[:length].movedim(-1, 1), width, height, "area", "center").movedim(1, -1) + if background_video.shape[0] > ref_images_num: + image[ref_images_num:background_video.shape[0]] = background_video[ref_images_num:] + + mask_refmotion = torch.ones((1, 1, latent_length * 4, concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=mask.device, dtype=mask.dtype) + if continue_motion is not None: + mask_refmotion[:, :, :ref_motion_latent_length * 4] = 0.0 + + if character_mask is not None: + if character_mask.shape[0] > video_frame_offset or character_mask.shape[0] == 1: + if character_mask.shape[0] == 1: + character_mask = character_mask.repeat((length,) + (1,) * (character_mask.ndim - 1)) + else: + character_mask = character_mask[video_frame_offset:] + if character_mask.ndim == 3: + character_mask = character_mask.unsqueeze(1) + character_mask = character_mask.movedim(0, 1) + if character_mask.ndim == 4: + character_mask = character_mask.unsqueeze(1) + character_mask = comfy.utils.common_upscale(character_mask[:, :, :length], concat_latent_image.shape[-1], concat_latent_image.shape[-2], "nearest-exact", "center") + if character_mask.shape[2] > ref_images_num: + mask_refmotion[:, :, ref_images_num:character_mask.shape[2]] = character_mask[:, :, ref_images_num:] + + concat_latent_image = torch.cat((concat_latent_image, vae.encode(image[:, :, :, :3])), dim=2) + + + mask_refmotion = mask_refmotion.view(1, mask_refmotion.shape[2] // 4, 4, mask_refmotion.shape[3], mask_refmotion.shape[4]).transpose(1, 2) + mask = torch.cat((mask, mask_refmotion), dim=2) + positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent_image, "concat_mask": mask}) + + latent = torch.zeros([batch_size, 16, latent_length + trim_latent, latent_height, latent_width], device=comfy.model_management.intermediate_device()) + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(positive, negative, out_latent, trim_latent, max(0, ref_motion_latent_length * 4 - 3), video_frame_offset + length) + +class Wan22ImageToVideoLatent(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="Wan22ImageToVideoLatent", + category="conditioning/inpaint", + inputs=[ + io.Vae.Input("vae"), + io.Int.Input("width", default=1280, min=32, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("height", default=704, min=32, max=nodes.MAX_RESOLUTION, step=32), + io.Int.Input("length", default=49, min=1, max=nodes.MAX_RESOLUTION, step=4), + io.Int.Input("batch_size", default=1, min=1, max=4096), + io.Image.Input("start_image", optional=True), + ], + outputs=[ + io.Latent.Output(), + ], + ) + + @classmethod + def execute(cls, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput: + latent = torch.zeros([1, 48, ((length - 1) // 4) + 1, height // 16, width // 16], device=comfy.model_management.intermediate_device()) + + if start_image is None: + out_latent = {} + out_latent["samples"] = latent + return io.NodeOutput(out_latent) + + mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device()) + + if start_image is not None: + start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1) + latent_temp = vae.encode(start_image) + latent[:, :, :latent_temp.shape[-3]] = latent_temp + mask[:, :, :latent_temp.shape[-3]] *= 0.0 + + out_latent = {} + latent_format = comfy.latent_formats.Wan22() + latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask) + out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1)) + out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1)) + return io.NodeOutput(out_latent) + + +class WanExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + WanTrackToVideo, + WanImageToVideo, + WanFunControlToVideo, + Wan22FunControlToVideo, + WanFunInpaintToVideo, + WanFirstLastFrameToVideo, + WanVaceToVideo, + TrimVideoLatent, + WanCameraImageToVideo, + WanPhantomSubjectToVideo, + WanSoundImageToVideo, + WanSoundImageToVideoExtend, + WanHuMoImageToVideo, + WanAnimateToVideo, + Wan22ImageToVideoLatent, + ] + +async def comfy_entrypoint() -> WanExtension: + return WanExtension() diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py index 31eddb2d6..5bf80b4c6 100644 --- a/comfy_extras/nodes_webcam.py +++ b/comfy_extras/nodes_webcam.py @@ -20,9 +20,13 @@ class WebcamCapture(nodes.LoadImage): CATEGORY = "image" - def load_capture(s, image, **kwargs): + def load_capture(self, image, **kwargs): return super().load_image(folder_paths.get_annotated_filepath(image)) + @classmethod + def IS_CHANGED(cls, image, width, height, capture_on_queue): + return super().IS_CHANGED(image) + NODE_CLASS_MAPPINGS = { "WebcamCapture": WebcamCapture, diff --git a/comfyui_version.py b/comfyui_version.py new file mode 100644 index 000000000..33a06bbb0 --- /dev/null +++ b/comfyui_version.py @@ -0,0 +1,3 @@ +# This file is automatically generated by the build process when version is +# updated in pyproject.toml. +__version__ = "0.3.66" diff --git a/cuda_malloc.py b/cuda_malloc.py index eb2857c5f..6520d5123 100644 --- a/cuda_malloc.py +++ b/cuda_malloc.py @@ -1,6 +1,6 @@ import os import importlib.util -from comfy.cli_args import args +from comfy.cli_args import args, PerformanceFeature import subprocess #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import. @@ -74,8 +74,10 @@ if not args.cuda_malloc: module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) version = module.__version__ - if int(version[0]) >= 2: #enable by default for torch version 2.0 and up - args.cuda_malloc = cuda_malloc_supported() + + if int(version[0]) >= 2 and "+cu" in version: # enable by default for torch version 2.0 and up only on cuda torch + if PerformanceFeature.AutoTune not in args.fast: # Autotune has issues with cuda malloc + args.cuda_malloc = cuda_malloc_supported() except: pass diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example index 29ab2aa72..779c35787 100644 --- a/custom_nodes/example_node.py.example +++ b/custom_nodes/example_node.py.example @@ -1,96 +1,70 @@ -class Example: +from typing_extensions import override + +from comfy_api.latest import ComfyExtension, io + + +class Example(io.ComfyNode): """ - A example node + An example node Class methods ------------- - INPUT_TYPES (dict): - Tell the main program input parameters of nodes. - IS_CHANGED: + define_schema (io.Schema): + Tell the main program the metadata, input, output parameters of nodes. + fingerprint_inputs: optional method to control when the node is re executed. + check_lazy_status: + optional method to control list of input names that need to be evaluated. - Attributes - ---------- - RETURN_TYPES (`tuple`): - The type of each element in the output tuple. - RETURN_NAMES (`tuple`): - Optional: The name of each output in the output tuple. - FUNCTION (`str`): - The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute() - OUTPUT_NODE ([`bool`]): - If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example. - The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected. - Assumed to be False if not present. - CATEGORY (`str`): - The category the node should appear in the UI. - DEPRECATED (`bool`): - Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain - functional in existing workflows that use them. - EXPERIMENTAL (`bool`): - Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to - significant changes or removal in future versions. Use with caution in production workflows. - execute(s) -> tuple || None: - The entry point method. The name of this method must be the same as the value of property `FUNCTION`. - For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`. """ - def __init__(self): - pass @classmethod - def INPUT_TYPES(s): + def define_schema(cls) -> io.Schema: """ - Return a dictionary which contains config for all input fields. - Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT". - Input types "INT", "STRING" or "FLOAT" are special values for fields on the node. - The type can be a list for selection. - - Returns: `dict`: - - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required` - - Value input_fields (`dict`): Contains input fields config: - * Key field_name (`string`): Name of a entry-point method's argument - * Value field_config (`tuple`): - + First value is a string indicate the type of field or a list for selection. - + Second value is a config for type "INT", "STRING" or "FLOAT". + Return a schema which contains all information about the node. + Some types: "Model", "Vae", "Clip", "Conditioning", "Latent", "Image", "Int", "String", "Float", "Combo". + For outputs the "io.Model.Output" should be used, for inputs the "io.Model.Input" can be used. + The type can be a "Combo" - this will be a list for selection. """ - return { - "required": { - "image": ("IMAGE",), - "int_field": ("INT", { - "default": 0, - "min": 0, #Minimum value - "max": 4096, #Maximum value - "step": 64, #Slider's step - "display": "number", # Cosmetic only: display as "number" or "slider" - "lazy": True # Will only be evaluated if check_lazy_status requires it - }), - "float_field": ("FLOAT", { - "default": 1.0, - "min": 0.0, - "max": 10.0, - "step": 0.01, - "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. - "display": "number", - "lazy": True - }), - "print_to_screen": (["enable", "disable"],), - "string_field": ("STRING", { - "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node - "default": "Hello World!", - "lazy": True - }), - }, - } + return io.Schema( + node_id="Example", + display_name="Example Node", + category="Example", + inputs=[ + io.Image.Input("image"), + io.Int.Input( + "int_field", + min=0, + max=4096, + step=64, # Slider's step + display_mode=io.NumberDisplay.number, # Cosmetic only: display as "number" or "slider" + lazy=True, # Will only be evaluated if check_lazy_status requires it + ), + io.Float.Input( + "float_field", + default=1.0, + min=0.0, + max=10.0, + step=0.01, + round=0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. + display_mode=io.NumberDisplay.number, + lazy=True, + ), + io.Combo.Input("print_to_screen", options=["enable", "disable"]), + io.String.Input( + "string_field", + multiline=False, # True if you want the field to look like the one on the ClipTextEncode node + default="Hello world!", + lazy=True, + ) + ], + outputs=[ + io.Image.Output(), + ], + ) - RETURN_TYPES = ("IMAGE",) - #RETURN_NAMES = ("image_output_name",) - - FUNCTION = "test" - - #OUTPUT_NODE = False - - CATEGORY = "Example" - - def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen): + @classmethod + def check_lazy_status(cls, image, string_field, int_field, float_field, print_to_screen): """ Return a list of input names that need to be evaluated. @@ -107,7 +81,8 @@ class Example: else: return [] - def test(self, image, string_field, int_field, float_field, print_to_screen): + @classmethod + def execute(cls, image, string_field, int_field, float_field, print_to_screen) -> io.NodeOutput: if print_to_screen == "enable": print(f"""Your input contains: string_field aka input text: {string_field} @@ -116,7 +91,7 @@ class Example: """) #do some processing on the image, in this example I just invert it image = 1.0 - image - return (image,) + return io.NodeOutput(image) """ The node will always be re executed if any of the inputs change but @@ -127,7 +102,7 @@ class Example: changes between executions the LoadImage node is executed again. """ #@classmethod - #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen): + #def fingerprint_inputs(s, image, string_field, int_field, float_field, print_to_screen): # return "" # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension @@ -143,13 +118,13 @@ async def get_hello(request): return web.json_response("hello") -# A dictionary that contains all nodes you want to export with their names -# NOTE: names should be globally unique -NODE_CLASS_MAPPINGS = { - "Example": Example -} +class ExampleExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + Example, + ] -# A dictionary that contains the friendly/humanly readable titles for the nodes -NODE_DISPLAY_NAME_MAPPINGS = { - "Example": "Example Node" -} + +async def comfy_entrypoint() -> ExampleExtension: # ComfyUI calls this to load your extension and its nodes. + return ExampleExtension() diff --git a/execution.py b/execution.py index 2c979205b..78c36a4b0 100644 --- a/execution.py +++ b/execution.py @@ -1,22 +1,40 @@ -import sys import copy -import logging -import threading import heapq +import inspect +import logging +import sys +import threading import time import traceback from enum import Enum -import inspect -from typing import List, Literal, NamedTuple, Optional +from typing import List, Literal, NamedTuple, Optional, Union +import asyncio import torch -import nodes import comfy.model_management -from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker -from comfy_execution.graph_utils import is_link, GraphBuilder -from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID +import nodes +from comfy_execution.caching import ( + BasicCache, + CacheKeySetID, + CacheKeySetInputSignature, + NullCache, + HierarchicalCache, + LRUCache, +) +from comfy_execution.graph import ( + DynamicPrompt, + ExecutionBlocker, + ExecutionList, + get_input_info, +) +from comfy_execution.graph_utils import GraphBuilder, is_link from comfy_execution.validation import validate_node_input +from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler +from comfy_execution.utils import CurrentNodeContext +from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func +from comfy_api.latest import io + class ExecutionResult(Enum): SUCCESS = 0 @@ -27,19 +45,28 @@ class DuplicateNodeError(Exception): pass class IsChangedCache: - def __init__(self, dynprompt, outputs_cache): + def __init__(self, prompt_id: str, dynprompt: DynamicPrompt, outputs_cache: BasicCache): + self.prompt_id = prompt_id self.dynprompt = dynprompt self.outputs_cache = outputs_cache self.is_changed = {} - def get(self, node_id): + async def get(self, node_id): if node_id in self.is_changed: return self.is_changed[node_id] node = self.dynprompt.get_node(node_id) class_type = node["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] - if not hasattr(class_def, "IS_CHANGED"): + has_is_changed = False + is_changed_name = None + if issubclass(class_def, _ComfyNodeInternal) and first_real_override(class_def, "fingerprint_inputs") is not None: + has_is_changed = True + is_changed_name = "fingerprint_inputs" + elif hasattr(class_def, "IS_CHANGED"): + has_is_changed = True + is_changed_name = "IS_CHANGED" + if not has_is_changed: self.is_changed[node_id] = False return self.is_changed[node_id] @@ -48,9 +75,10 @@ class IsChangedCache: return self.is_changed[node_id] # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED - input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None) + input_data_all, _, hidden_inputs = get_input_data(node["inputs"], class_def, node_id, None) try: - is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED") + is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name) + is_changed = await resolve_map_node_over_list_results(is_changed) node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] except Exception as e: logging.warning("WARNING: {}".format(e)) @@ -59,20 +87,27 @@ class IsChangedCache: self.is_changed[node_id] = node["is_changed"] return self.is_changed[node_id] -class CacheSet: - def __init__(self, lru_size=None): - if lru_size is None or lru_size == 0: - self.init_classic_cache() - else: - self.init_lru_cache(lru_size) - self.all = [self.outputs, self.ui, self.objects] - # Useful for those with ample RAM/VRAM -- allows experimenting without - # blowing away the cache every time - def init_lru_cache(self, cache_size): - self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) - self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) - self.objects = HierarchicalCache(CacheKeySetID) +class CacheType(Enum): + CLASSIC = 0 + LRU = 1 + NONE = 2 + + +class CacheSet: + def __init__(self, cache_type=None, cache_size=None): + if cache_type == CacheType.NONE: + self.init_null_cache() + logging.info("Disabling intermediate node cache.") + elif cache_type == CacheType.LRU: + if cache_size is None: + cache_size = 0 + self.init_lru_cache(cache_size) + logging.info("Using LRU cache") + else: + self.init_classic_cache() + + self.all = [self.outputs, self.ui, self.objects] # Performs like the old cache -- dump data ASAP def init_classic_cache(self): @@ -80,6 +115,18 @@ class CacheSet: self.ui = HierarchicalCache(CacheKeySetInputSignature) self.objects = HierarchicalCache(CacheKeySetID) + def init_lru_cache(self, cache_size): + self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) + self.objects = HierarchicalCache(CacheKeySetID) + + def init_null_cache(self): + self.outputs = NullCache() + #The UI cache is expected to be iterable at the end of each workflow + #so it must cache at least a full workflow. Use Heirachical + self.ui = HierarchicalCache(CacheKeySetInputSignature) + self.objects = NullCache() + def recursive_debug_dump(self): result = { "outputs": self.outputs.recursive_debug_dump(), @@ -87,23 +134,30 @@ class CacheSet: } return result -def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}): - valid_inputs = class_def.INPUT_TYPES() +SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org") + +def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}): + is_v3 = issubclass(class_def, _ComfyNodeInternal) + if is_v3: + valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True) + else: + valid_inputs = class_def.INPUT_TYPES() input_data_all = {} missing_keys = {} + hidden_inputs_v3 = {} for x in inputs: input_data = inputs[x] - input_type, input_category, input_info = get_input_info(class_def, x, valid_inputs) + _, input_category, input_info = get_input_info(class_def, x, valid_inputs) def mark_missing(): missing_keys[x] = True input_data_all[x] = (None,) if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): input_unique_id = input_data[0] output_index = input_data[1] - if outputs is None: + if execution_list is None: mark_missing() continue # This might be a lazily-evaluated input - cached_output = outputs.get(input_unique_id) + cached_output = execution_list.get_output_cache(input_unique_id, unique_id) if cached_output is None: mark_missing() continue @@ -115,22 +169,53 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, e elif input_category is not None: input_data_all[x] = [input_data] - if "hidden" in valid_inputs: - h = valid_inputs["hidden"] - for x in h: - if h[x] == "PROMPT": - input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] - if h[x] == "DYNPROMPT": - input_data_all[x] = [dynprompt] - if h[x] == "EXTRA_PNGINFO": - input_data_all[x] = [extra_data.get('extra_pnginfo', None)] - if h[x] == "UNIQUE_ID": - input_data_all[x] = [unique_id] - return input_data_all, missing_keys + if is_v3: + if schema.hidden: + if io.Hidden.prompt in schema.hidden: + hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {} + if io.Hidden.dynprompt in schema.hidden: + hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt + if io.Hidden.extra_pnginfo in schema.hidden: + hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None) + if io.Hidden.unique_id in schema.hidden: + hidden_inputs_v3[io.Hidden.unique_id] = unique_id + if io.Hidden.auth_token_comfy_org in schema.hidden: + hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None) + if io.Hidden.api_key_comfy_org in schema.hidden: + hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None) + else: + if "hidden" in valid_inputs: + h = valid_inputs["hidden"] + for x in h: + if h[x] == "PROMPT": + input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] + if h[x] == "DYNPROMPT": + input_data_all[x] = [dynprompt] + if h[x] == "EXTRA_PNGINFO": + input_data_all[x] = [extra_data.get('extra_pnginfo', None)] + if h[x] == "UNIQUE_ID": + input_data_all[x] = [unique_id] + if h[x] == "AUTH_TOKEN_COMFY_ORG": + input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] + if h[x] == "API_KEY_COMFY_ORG": + input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] + return input_data_all, missing_keys, hidden_inputs_v3 map_node_over_list = None #Don't hook this please -def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None): +async def resolve_map_node_over_list_results(results): + remaining = [x for x in results if isinstance(x, asyncio.Task) and not x.done()] + if len(remaining) == 0: + return [x.result() if isinstance(x, asyncio.Task) else x for x in results] + else: + done, pending = await asyncio.wait(remaining) + for task in done: + exc = task.exception() + if exc is not None: + raise exc + return [x.result() if isinstance(x, asyncio.Task) else x for x in results] + +async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None): # check if node wants the lists input_is_list = getattr(obj, "INPUT_IS_LIST", False) @@ -144,7 +229,7 @@ def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execut return {k: v[i if len(v) > i else -1] for k, v in d.items()} results = [] - def process_inputs(inputs, index=None, input_is_list=False): + async def process_inputs(inputs, index=None, input_is_list=False): if allow_interrupt: nodes.before_node_execution() execution_block = None @@ -160,20 +245,52 @@ def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execut if execution_block is None: if pre_execute_cb is not None and index is not None: pre_execute_cb(index) - results.append(getattr(obj, func)(**inputs)) + # V3 + if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)): + # if is just a class, then assign no resources or state, just create clone + if is_class(obj): + type_obj = obj + obj.VALIDATE_CLASS() + class_clone = obj.PREPARE_CLASS_CLONE(hidden_inputs) + # otherwise, use class instance to populate/reuse some fields + else: + type_obj = type(obj) + type_obj.VALIDATE_CLASS() + class_clone = type_obj.PREPARE_CLASS_CLONE(hidden_inputs) + f = make_locked_method_func(type_obj, func, class_clone) + # V1 + else: + f = getattr(obj, func) + if inspect.iscoroutinefunction(f): + async def async_wrapper(f, prompt_id, unique_id, list_index, args): + with CurrentNodeContext(prompt_id, unique_id, list_index): + return await f(**args) + task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs)) + # Give the task a chance to execute without yielding + await asyncio.sleep(0) + if task.done(): + result = task.result() + results.append(result) + else: + results.append(task) + else: + with CurrentNodeContext(prompt_id, unique_id, index): + result = f(**inputs) + results.append(result) else: results.append(execution_block) if input_is_list: - process_inputs(input_data_all, 0, input_is_list=input_is_list) + await process_inputs(input_data_all, 0, input_is_list=input_is_list) elif max_len_input == 0: - process_inputs({}) + await process_inputs({}) else: for i in range(max_len_input): input_dict = slice_dict(input_data_all, i) - process_inputs(input_dict, i) + await process_inputs(input_dict, i) return results + def merge_result_data(results, obj): # check which outputs need concatenating output = [] @@ -195,11 +312,18 @@ def merge_result_data(results, obj): output.append([o[i] for o in results]) return output -def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None): +async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None): + return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs) + has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values) + if has_pending_task: + return return_values, {}, False, has_pending_task + output, ui, has_subgraph = get_output_from_returns(return_values, obj) + return output, ui, has_subgraph, False + +def get_output_from_returns(return_values, obj): results = [] uis = [] subgraph_results = [] - return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) has_subgraph = False for i in range(len(return_values)): r = return_values[i] @@ -220,6 +344,26 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb result = tuple([result] * len(obj.RETURN_TYPES)) results.append(result) subgraph_results.append((None, result)) + elif isinstance(r, _NodeOutputInternal): + # V3 + if r.ui is not None: + if isinstance(r.ui, dict): + uis.append(r.ui) + else: + uis.append(r.ui.as_dict()) + if r.expand is not None: + has_subgraph = True + new_graph = r.expand + result = r.result + if r.block_execution is not None: + result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) + subgraph_results.append((new_graph, result)) + elif r.result is not None: + result = r.result + if r.block_execution is not None: + result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) + results.append(result) + subgraph_results.append((None, result)) else: if isinstance(r, ExecutionBlocker): r = tuple([r] * len(obj.RETURN_TYPES)) @@ -233,6 +377,10 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb else: output = [] ui = dict() + # TODO: Think there's an existing bug here + # If we're performing a subgraph expansion, we probably shouldn't be returning UI values yet. + # They'll get cached without the completed subgraphs. It's an edge case and I'm not aware of + # any nodes that use both subgraph expansion and custom UI outputs, but might be a problem in the future. if len(uis) > 0: ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} return output, ui, has_subgraph @@ -245,7 +393,7 @@ def format_value(x): else: return str(x) -def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results): +async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes): unique_id = current_item real_node_id = dynprompt.get_real_node_id(unique_id) display_node_id = dynprompt.get_display_node_id(unique_id) @@ -257,11 +405,27 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp if server.client_id is not None: cached_output = caches.ui.get(unique_id) or {} server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id) + get_progress_state().finish_progress(unique_id) + execution_list.cache_update(unique_id, caches.outputs.get(unique_id)) return (ExecutionResult.SUCCESS, None, None) input_data_all = None try: - if unique_id in pending_subgraph_results: + if unique_id in pending_async_nodes: + results = [] + for r in pending_async_nodes[unique_id]: + if isinstance(r, asyncio.Task): + try: + results.append(r.result()) + except Exception as ex: + # An async task failed - propagate the exception up + del pending_async_nodes[unique_id] + raise ex + else: + results.append(r) + del pending_async_nodes[unique_id] + output_data, output_ui, has_subgraph = get_output_from_returns(results, class_def) + elif unique_id in pending_subgraph_results: cached_results = pending_subgraph_results[unique_id] resolved_outputs = [] for is_subgraph, result in cached_results: @@ -272,7 +436,7 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp for r in result: if is_link(r): source_node, source_output = r[0], r[1] - node_output = caches.outputs.get(source_node)[source_output] + node_output = execution_list.get_output_cache(source_node, unique_id)[source_output] for o in node_output: resolved_output.append(o) @@ -283,7 +447,8 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp output_ui = [] has_subgraph = False else: - input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data) + get_progress_state().start_progress(unique_id) + input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data) if server.client_id is not None: server.last_node_id = display_node_id server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) @@ -293,8 +458,13 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp obj = class_def() caches.objects.set(unique_id, obj) - if hasattr(obj, "check_lazy_status"): - required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True) + if issubclass(class_def, _ComfyNodeInternal): + lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None + else: + lazy_status_present = getattr(obj, "check_lazy_status", None) is not None + if lazy_status_present: + required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, hidden_inputs=hidden_inputs) + required_inputs = await resolve_map_node_over_list_results(required_inputs) required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) required_inputs = [x for x in required_inputs if isinstance(x,str) and ( x not in input_data_all or x in missing_keys @@ -323,8 +493,18 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp else: return block def pre_execute_cb(call_index): + # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)? GraphBuilder.set_default_prefix(unique_id, call_index, 0) - output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) + output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs) + if has_pending_tasks: + pending_async_nodes[unique_id] = output_data + unblock = execution_list.add_external_block(unique_id) + async def await_completion(): + tasks = [x for x in output_data if isinstance(x, asyncio.Task)] + await asyncio.gather(*tasks, return_exceptions=True) + unblock() + asyncio.create_task(await_completion()) + return (ExecutionResult.PENDING, None, None) if len(output_ui) > 0: caches.ui.set(unique_id, { "meta": { @@ -367,14 +547,19 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp cached_outputs.append((True, node_outputs)) new_node_ids = set(new_node_ids) for cache in caches.all: - cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused() + subcache = await cache.ensure_subcache_for(unique_id, new_node_ids) + subcache.clean_unused() for node_id in new_output_ids: execution_list.add_node(node_id) + execution_list.cache_link(node_id, unique_id) for link in new_output_links: execution_list.add_strong_link(link[0], link[1], unique_id) pending_subgraph_results[unique_id] = cached_outputs return (ExecutionResult.PENDING, None, None) + caches.outputs.set(unique_id, output_data) + execution_list.cache_update(unique_id, output_data) + except comfy.model_management.InterruptProcessingException as iex: logging.info("Processing interrupted") @@ -395,32 +580,37 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp logging.error(f"!!! Exception during processing !!! {ex}") logging.error(traceback.format_exc()) + tips = "" + + if isinstance(ex, comfy.model_management.OOM_EXCEPTION): + tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number." + logging.error("Got an OOM, unloading all loaded models.") + comfy.model_management.unload_all_models() error_details = { "node_id": real_node_id, - "exception_message": str(ex), + "exception_message": "{}\n{}".format(ex, tips), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "current_inputs": input_data_formatted } - if isinstance(ex, comfy.model_management.OOM_EXCEPTION): - logging.error("Got an OOM, unloading all loaded models.") - comfy.model_management.unload_all_models() return (ExecutionResult.FAILURE, error_details, ex) + get_progress_state().finish_progress(unique_id) executed.add(unique_id) return (ExecutionResult.SUCCESS, None, None) class PromptExecutor: - def __init__(self, server, lru_size=None): - self.lru_size = lru_size + def __init__(self, server, cache_type=False, cache_size=None): + self.cache_size = cache_size + self.cache_type = cache_type self.server = server self.reset() def reset(self): - self.caches = CacheSet(self.lru_size) + self.caches = CacheSet(cache_type=self.cache_type, cache_size=self.cache_size) self.status_messages = [] self.success = True @@ -462,6 +652,9 @@ class PromptExecutor: self.add_message("execution_error", mes, broadcast=False) def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): + asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs)) + + async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): nodes.interrupt_processing(False) if "client_id" in extra_data: @@ -474,9 +667,11 @@ class PromptExecutor: with torch.inference_mode(): dynamic_prompt = DynamicPrompt(prompt) - is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs) + reset_progress_state(prompt_id, dynamic_prompt) + add_progress_handler(WebUIProgressHandler(self.server)) + is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs) for cache in self.caches.all: - cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) + await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) cache.clean_unused() cached_nodes = [] @@ -489,6 +684,7 @@ class PromptExecutor: { "nodes": cached_nodes, "prompt_id": prompt_id}, broadcast=False) pending_subgraph_results = {} + pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results executed = set() execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) current_outputs = self.caches.outputs.all_node_ids() @@ -496,12 +692,13 @@ class PromptExecutor: execution_list.add_node(node_id) while not execution_list.is_empty(): - node_id, error, ex = execution_list.stage_node_execution() + node_id, error, ex = await execution_list.stage_node_execution() if error is not None: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) break - result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) + assert node_id is not None, "Node ID should not be None at this point" + result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes) self.success = result != ExecutionResult.FAILURE if result == ExecutionResult.FAILURE: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) @@ -531,7 +728,7 @@ class PromptExecutor: comfy.model_management.unload_all_models() -def validate_inputs(prompt, item, validated): +async def validate_inputs(prompt_id, prompt, item, validated): unique_id = item if unique_id in validated: return validated[unique_id] @@ -548,14 +745,20 @@ def validate_inputs(prompt, item, validated): validate_function_inputs = [] validate_has_kwargs = False - if hasattr(obj_class, "VALIDATE_INPUTS"): - argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS) + if issubclass(obj_class, _ComfyNodeInternal): + validate_function_name = "validate_inputs" + validate_function = first_real_override(obj_class, validate_function_name) + else: + validate_function_name = "VALIDATE_INPUTS" + validate_function = getattr(obj_class, validate_function_name, None) + if validate_function is not None: + argspec = inspect.getfullargspec(validate_function) validate_function_inputs = argspec.args validate_has_kwargs = argspec.varkw is not None received_types = {} for x in valid_inputs: - type_input, input_category, extra_info = get_input_info(obj_class, x, class_inputs) + input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs) assert extra_info is not None if x not in inputs: if input_category == "required": @@ -571,7 +774,7 @@ def validate_inputs(prompt, item, validated): continue val = inputs[x] - info = (type_input, extra_info) + info = (input_type, extra_info) if isinstance(val, list): if len(val) != 2: error = { @@ -592,8 +795,8 @@ def validate_inputs(prompt, item, validated): r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES received_type = r[val[1]] received_types[x] = received_type - if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, type_input): - details = f"{x}, received_type({received_type}) mismatch input_type({type_input})" + if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type): + details = f"{x}, received_type({received_type}) mismatch input_type({input_type})" error = { "type": "return_type_mismatch", "message": "Return type mismatch between linked nodes", @@ -608,7 +811,7 @@ def validate_inputs(prompt, item, validated): errors.append(error) continue try: - r = validate_inputs(prompt, o_id, validated) + r = await validate_inputs(prompt_id, prompt, o_id, validated) if r[0] is False: # `r` will be set in `validated[o_id]` already valid = False @@ -634,22 +837,29 @@ def validate_inputs(prompt, item, validated): continue else: try: - if type_input == "INT": + # Unwraps values wrapped in __value__ key. This is used to pass + # list widget value to execution, as by default list value is + # reserved to represent the connection between nodes. + if isinstance(val, dict) and "__value__" in val: + val = val["__value__"] + inputs[x] = val + + if input_type == "INT": val = int(val) inputs[x] = val - if type_input == "FLOAT": + if input_type == "FLOAT": val = float(val) inputs[x] = val - if type_input == "STRING": + if input_type == "STRING": val = str(val) inputs[x] = val - if type_input == "BOOLEAN": + if input_type == "BOOLEAN": val = bool(val) inputs[x] = val except Exception as ex: error = { "type": "invalid_input_type", - "message": f"Failed to convert an input value to a {type_input} value", + "message": f"Failed to convert an input value to a {input_type} value", "details": f"{x}, {val}, {ex}", "extra_info": { "input_name": x, @@ -689,18 +899,19 @@ def validate_inputs(prompt, item, validated): errors.append(error) continue - if isinstance(type_input, list): - if val not in type_input: + if isinstance(input_type, list): + combo_options = input_type + if val not in combo_options: input_config = info list_info = "" # Don't send back gigantic lists like if they're lots of # scanned model filepaths - if len(type_input) > 20: - list_info = f"(list of length {len(type_input)})" + if len(combo_options) > 20: + list_info = f"(list of length {len(combo_options)})" input_config = None else: - list_info = str(type_input) + list_info = str(combo_options) error = { "type": "value_not_in_list", @@ -716,7 +927,7 @@ def validate_inputs(prompt, item, validated): continue if len(validate_function_inputs) > 0 or validate_has_kwargs: - input_data_all, _ = get_input_data(inputs, obj_class, unique_id) + input_data_all, _, hidden_inputs = get_input_data(inputs, obj_class, unique_id) input_filtered = {} for x in input_data_all: if x in validate_function_inputs or validate_has_kwargs: @@ -724,8 +935,8 @@ def validate_inputs(prompt, item, validated): if 'input_types' in validate_function_inputs: input_filtered['input_types'] = [received_types] - #ret = obj_class.VALIDATE_INPUTS(**input_filtered) - ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") + ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, hidden_inputs=hidden_inputs) + ret = await resolve_map_node_over_list_results(ret) for x in input_filtered: for i, r in enumerate(ret): if r is not True and not isinstance(r, ExecutionBlocker): @@ -758,7 +969,7 @@ def full_type_name(klass): return klass.__qualname__ return module + '.' + klass.__qualname__ -def validate_prompt(prompt): +async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[str], None]): outputs = set() for x in prompt: if 'class_type' not in prompt[x]: @@ -768,7 +979,7 @@ def validate_prompt(prompt): "details": f"Node ID '#{x}'", "extra_info": {} } - return (False, error, [], []) + return (False, error, [], {}) class_type = prompt[x]['class_type'] class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) @@ -779,10 +990,11 @@ def validate_prompt(prompt): "details": f"Node ID '#{x}'", "extra_info": {} } - return (False, error, [], []) + return (False, error, [], {}) if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: - outputs.add(x) + if partial_execution_list is None or x in partial_execution_list: + outputs.add(x) if len(outputs) == 0: error = { @@ -791,7 +1003,7 @@ def validate_prompt(prompt): "details": "", "extra_info": {} } - return (False, error, [], []) + return (False, error, [], {}) good_outputs = set() errors = [] @@ -801,7 +1013,7 @@ def validate_prompt(prompt): valid = False reasons = [] try: - m = validate_inputs(prompt, o, validated) + m = await validate_inputs(prompt_id, prompt, o, validated) valid = m[0] reasons = m[1] except Exception as ex: @@ -878,7 +1090,6 @@ class PromptQueue: self.currently_running = {} self.history = {} self.flags = {} - server.prompt_queue = self def put(self, item): with self.mutex: @@ -915,6 +1126,11 @@ class PromptQueue: if status is not None: status_dict = copy.deepcopy(status._asdict()) + # Remove sensitive data from extra_data before storing in history + for sensitive_val in SENSITIVE_EXTRA_DATA_KEYS: + if sensitive_val in prompt[3]: + prompt[3].pop(sensitive_val) + self.history[prompt[1]] = { "prompt": prompt, "outputs": {}, @@ -923,6 +1139,7 @@ class PromptQueue: self.history[prompt[1]].update(history_result) self.server.queue_updated() + # Note: slow def get_current_queue(self): with self.mutex: out = [] @@ -930,6 +1147,13 @@ class PromptQueue: out += [x] return (out, copy.deepcopy(self.queue)) + # read-safe as long as queue items are immutable + def get_current_queue_volatile(self): + with self.mutex: + running = [x for x in self.currently_running.values()] + queued = copy.copy(self.queue) + return (running, queued) + def get_tasks_remaining(self): with self.mutex: return len(self.queue) + len(self.currently_running) @@ -952,7 +1176,7 @@ class PromptQueue: return True return False - def get_history(self, prompt_id=None, max_items=None, offset=-1): + def get_history(self, prompt_id=None, max_items=None, offset=-1, map_function=None): with self.mutex: if prompt_id is None: out = {} @@ -961,13 +1185,21 @@ class PromptQueue: offset = len(self.history) - max_items for k in self.history: if i >= offset: - out[k] = self.history[k] + p = self.history[k] + if map_function is not None: + p = map_function(p) + out[k] = p if max_items is not None and len(out) >= max_items: break i += 1 return out elif prompt_id in self.history: - return {prompt_id: copy.deepcopy(self.history[prompt_id])} + p = self.history[prompt_id] + if map_function is None: + p = copy.deepcopy(p) + else: + p = map_function(p) + return {prompt_id: p} else: return {} diff --git a/extra_model_paths.yaml.example b/extra_model_paths.yaml.example index b55913a5a..34df01681 100644 --- a/extra_model_paths.yaml.example +++ b/extra_model_paths.yaml.example @@ -1,25 +1,5 @@ #Rename this to extra_model_paths.yaml and ComfyUI will load it - -#config for a1111 ui -#all you have to do is change the base_path to where yours is installed -a111: - base_path: path/to/stable-diffusion-webui/ - - checkpoints: models/Stable-diffusion - configs: models/Stable-diffusion - vae: models/VAE - loras: | - models/Lora - models/LyCORIS - upscale_models: | - models/ESRGAN - models/RealESRGAN - models/SwinIR - embeddings: embeddings - hypernetworks: models/hypernetworks - controlnet: models/ControlNet - #config for comfyui #your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc. @@ -28,7 +8,9 @@ a111: # # You can use is_default to mark that these folders should be listed first, and used as the default dirs for eg downloads # #is_default: true # checkpoints: models/checkpoints/ -# clip: models/clip/ +# text_encoders: | +# models/text_encoders/ +# models/clip/ # legacy location still supported # clip_vision: models/clip_vision/ # configs: models/configs/ # controlnet: models/controlnet/ @@ -39,6 +21,32 @@ a111: # loras: models/loras/ # upscale_models: models/upscale_models/ # vae: models/vae/ +# audio_encoders: models/audio_encoders/ +# model_patches: models/model_patches/ + + +#config for a1111 ui +#all you have to do is uncomment this (remove the #) and change the base_path to where yours is installed + +#a111: +# base_path: path/to/stable-diffusion-webui/ +# checkpoints: models/Stable-diffusion +# configs: models/Stable-diffusion +# vae: models/VAE +# loras: | +# models/Lora +# models/LyCORIS +# upscale_models: | +# models/ESRGAN +# models/RealESRGAN +# models/SwinIR +# embeddings: embeddings +# hypernetworks: models/hypernetworks +# controlnet: models/ControlNet + + +# For a full list of supported keys (style_models, vae_approx, hypernetworks, photomaker, +# model_patches, audio_encoders, classifiers, etc.) see folder_paths.py. #other_ui: # base_path: path/to/ui diff --git a/fix_torch.py b/fix_torch.py deleted file mode 100644 index ce117b639..000000000 --- a/fix_torch.py +++ /dev/null @@ -1,28 +0,0 @@ -import importlib.util -import shutil -import os -import ctypes -import logging - - -def fix_pytorch_libomp(): - """ - Fix PyTorch libomp DLL issue on Windows by copying the correct DLL file if needed. - """ - torch_spec = importlib.util.find_spec("torch") - for folder in torch_spec.submodule_search_locations: - lib_folder = os.path.join(folder, "lib") - test_file = os.path.join(lib_folder, "fbgemm.dll") - dest = os.path.join(lib_folder, "libomp140.x86_64.dll") - if os.path.exists(dest): - break - - with open(test_file, "rb") as f: - contents = f.read() - if b"libomp140.x86_64.dll" not in contents: - break - try: - ctypes.cdll.LoadLibrary(test_file) - except FileNotFoundError: - logging.warning("Detected pytorch version with libomp issue, patching.") - shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest) diff --git a/folder_paths.py b/folder_paths.py index 3542d2edf..f110d832b 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -4,14 +4,21 @@ import os import time import mimetypes import logging -from typing import Literal +from typing import Literal, List from collections.abc import Collection -supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'} +from comfy.cli_args import args + +supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft'} folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {} -base_path = os.path.dirname(os.path.realpath(__file__)) +# --base-directory - Resets all default paths configured in folder_paths with a new base path +if args.base_directory: + base_path = os.path.abspath(args.base_directory) +else: + base_path = os.path.dirname(os.path.realpath(__file__)) + models_dir = os.path.join(base_path, "models") folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) @@ -39,10 +46,14 @@ folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")] folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) -output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output") -temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp") -input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input") -user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user") +folder_names_and_paths["model_patches"] = ([os.path.join(models_dir, "model_patches")], supported_pt_extensions) + +folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_encoders")], supported_pt_extensions) + +output_directory = os.path.join(base_path, "output") +temp_directory = os.path.join(base_path, "temp") +input_directory = os.path.join(base_path, "input") +user_directory = os.path.join(base_path, "user") filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {} @@ -78,6 +89,7 @@ cache_helper = CacheHelper() extension_mimetypes_cache = { "webp" : "image", + "fbx" : "model", } def map_legacy(folder_name: str) -> str: @@ -133,11 +145,14 @@ def get_directory_by_type(type_name: str) -> str | None: return get_input_directory() return None -def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio"]) -> list[str]: +def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]: """ Example: files = os.listdir(folder_paths.get_input_directory()) - filter_files_content_types(files, ["image", "audio", "video"]) + videos = filter_files_content_types(files, ["video"]) + + Note: + - 'model' in MIME context refers to 3D models, not files containing trained weights and parameters """ global extension_mimetypes_cache result = [] @@ -265,6 +280,9 @@ def filter_files_extensions(files: Collection[str], extensions: Collection[str]) def get_full_path(folder_name: str, filename: str) -> str | None: + """ + Get the full path of a file in a folder, has to be a file + """ global folder_names_and_paths folder_name = map_legacy(folder_name) if folder_name not in folder_names_and_paths: @@ -282,6 +300,9 @@ def get_full_path(folder_name: str, filename: str) -> str | None: def get_full_path_or_raise(folder_name: str, filename: str) -> str: + """ + Get the full path of a file in a folder, has to be a file + """ full_path = get_full_path(folder_name, filename) if full_path is None: raise FileNotFoundError(f"Model in folder '{folder_name}' with filename '{filename}' not found.") @@ -383,3 +404,26 @@ def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, im os.makedirs(full_output_folder, exist_ok=True) counter = 1 return full_output_folder, filename, counter, subfolder, filename_prefix + +def get_input_subfolders() -> list[str]: + """Returns a list of all subfolder paths in the input directory, recursively. + + Returns: + List of folder paths relative to the input directory, excluding the root directory + """ + input_dir = get_input_directory() + folders = [] + + try: + if not os.path.exists(input_dir): + return [] + + for root, dirs, _ in os.walk(input_dir): + rel_path = os.path.relpath(root, input_dir) + if rel_path != ".": # Only include non-root directories + # Normalize path separators to forward slashes + folders.append(rel_path.replace(os.sep, '/')) + + return sorted(folders) + except FileNotFoundError: + return [] diff --git a/hook_breaker_ac10a0.py b/hook_breaker_ac10a0.py new file mode 100644 index 000000000..c3e1c0633 --- /dev/null +++ b/hook_breaker_ac10a0.py @@ -0,0 +1,17 @@ +# Prevent custom nodes from hooking anything important +import comfy.model_management + +HOOK_BREAK = [(comfy.model_management, "cast_to")] + + +SAVED_FUNCTIONS = [] + + +def save_functions(): + for f in HOOK_BREAK: + SAVED_FUNCTIONS.append((f[0], f[1], getattr(f[0], f[1]))) + + +def restore_functions(): + for f in SAVED_FUNCTIONS: + setattr(f[0], f[1], f[2]) diff --git a/latent_preview.py b/latent_preview.py index 07f9cc68e..95d3cb733 100644 --- a/latent_preview.py +++ b/latent_preview.py @@ -12,7 +12,10 @@ MAX_PREVIEW_RESOLUTION = args.preview_size def preview_to_image(latent_image): latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 .mul(0xFF) # to 0..255 - ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) + ) + if comfy.model_management.directml_enabled: + latents_ubyte = latents_ubyte.to(dtype=torch.uint8) + latents_ubyte = latents_ubyte.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) return Image.fromarray(latents_ubyte.numpy()) diff --git a/main.py b/main.py index c5c9f4e09..4b4c5dcc4 100644 --- a/main.py +++ b/main.py @@ -10,13 +10,16 @@ from app.logger import setup_logger import itertools import utils.extra_config import logging +import sys +from comfy_execution.progress import get_progress_state +from comfy_execution.utils import get_executing_context +from comfy_api import feature_flags if __name__ == "__main__": - #NOTE: These do not do anything on core ComfyUI which should already have no communication with the internet, they are for custom nodes. + #NOTE: These do not do anything on core ComfyUI, they are for custom nodes. os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['DO_NOT_TRACK'] = '1' - setup_logger(log_level=args.verbose, use_stdout=args.log_stdout) def apply_custom_paths(): @@ -55,6 +58,9 @@ def apply_custom_paths(): def execute_prestartup_script(): + if args.disable_all_custom_nodes and len(args.whitelist_custom_nodes) == 0: + return + def execute_script(script_path): module_name = os.path.splitext(script_path)[0] try: @@ -66,9 +72,6 @@ def execute_prestartup_script(): logging.error(f"Failed to execute startup-script: {script_path} / {e}") return False - if args.disable_all_custom_nodes: - return - node_paths = folder_paths.get_folder_paths("custom_nodes") for custom_node_path in node_paths: possible_modules = os.listdir(custom_node_path) @@ -81,6 +84,9 @@ def execute_prestartup_script(): script_path = os.path.join(module_path, "prestartup_script.py") if os.path.exists(script_path): + if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: + logging.info(f"Prestartup Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") + continue time_before = time.perf_counter() success = execute_script(script_path) node_prestartup_times.append((time.perf_counter() - time_before, module_path, success)) @@ -106,12 +112,23 @@ import gc if os.name == "nt": - logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + os.environ['MIMALLOC_PURGE_DELAY'] = '0' if __name__ == "__main__": + os.environ['TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL'] = '1' + if args.default_device is not None: + default_dev = args.default_device + devices = list(range(32)) + devices.remove(default_dev) + devices.insert(0, default_dev) + devices = ','.join(map(str, devices)) + os.environ['CUDA_VISIBLE_DEVICES'] = str(devices) + os.environ['HIP_VISIBLE_DEVICES'] = str(devices) + if args.cuda_device is not None: os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device) os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device) + os.environ["ASCEND_RT_VISIBLE_DEVICES"] = str(args.cuda_device) logging.info("Set cuda device to: {}".format(args.cuda_device)) if args.oneapi_device_selector is not None: @@ -124,20 +141,19 @@ if __name__ == "__main__": import cuda_malloc -if args.windows_standalone_build: - try: - from fix_torch import fix_pytorch_libomp - fix_pytorch_libomp() - except: - pass +if 'torch' in sys.modules: + logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.") import comfy.utils import execution import server -from server import BinaryEventTypes +from protocol import BinaryEventTypes import nodes import comfy.model_management +import comfyui_version +import app.logger +import hook_breaker_ac10a0 def cuda_malloc_warning(): device = comfy.model_management.get_torch_device() @@ -153,7 +169,13 @@ def cuda_malloc_warning(): def prompt_worker(q, server_instance): current_time: float = 0.0 - e = execution.PromptExecutor(server_instance, lru_size=args.cache_lru) + cache_type = execution.CacheType.CLASSIC + if args.cache_lru > 0: + cache_type = execution.CacheType.LRU + elif args.cache_none: + cache_type = execution.CacheType.NONE + + e = execution.PromptExecutor(server_instance, cache_type=cache_type, cache_size=args.cache_lru) last_gc_collect = 0 need_gc = False gc_collect_interval = 10.0 @@ -183,7 +205,13 @@ def prompt_worker(q, server_instance): current_time = time.perf_counter() execution_time = current_time - execution_start_time - logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) + + # Log Time in a more readable way after 10 minutes + if execution_time > 600: + execution_time = time.strftime("%H:%M:%S", time.gmtime(execution_time)) + logging.info(f"Prompt executed in {execution_time}") + else: + logging.info("Prompt executed in {:.2f} seconds".format(execution_time)) flags = q.get_flags() free_memory = flags.get("free_memory", False) @@ -205,6 +233,7 @@ def prompt_worker(q, server_instance): comfy.model_management.soft_empty_cache() last_gc_collect = current_time need_gc = False + hook_breaker_ac10a0.restore_functions() async def run(server_instance, address='', port=8188, verbose=True, call_on_start=None): @@ -215,15 +244,34 @@ async def run(server_instance, address='', port=8188, verbose=True, call_on_star server_instance.start_multi_address(addresses, call_on_start, verbose), server_instance.publish_loop() ) - def hijack_progress(server_instance): - def hook(value, total, preview_image): + def hook(value, total, preview_image, prompt_id=None, node_id=None): + executing_context = get_executing_context() + if prompt_id is None and executing_context is not None: + prompt_id = executing_context.prompt_id + if node_id is None and executing_context is not None: + node_id = executing_context.node_id comfy.model_management.throw_exception_if_processing_interrupted() - progress = {"value": value, "max": total, "prompt_id": server_instance.last_prompt_id, "node": server_instance.last_node_id} + if prompt_id is None: + prompt_id = server_instance.last_prompt_id + if node_id is None: + node_id = server_instance.last_node_id + progress = {"value": value, "max": total, "prompt_id": prompt_id, "node": node_id} + get_progress_state().update_progress(node_id, value, total, preview_image) server_instance.send_sync("progress", progress, server_instance.client_id) if preview_image is not None: - server_instance.send_sync(BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, preview_image, server_instance.client_id) + # Only send old method if client doesn't support preview metadata + if not feature_flags.supports_feature( + server_instance.sockets_metadata, + server_instance.client_id, + "supports_preview_metadata", + ): + server_instance.send_sync( + BinaryEventTypes.UNENCODED_PREVIEW_IMAGE, + preview_image, + server_instance.client_id, + ) comfy.utils.set_progress_bar_global_hook(hook) @@ -234,6 +282,15 @@ def cleanup_temp(): shutil.rmtree(temp_dir, ignore_errors=True) +def setup_database(): + try: + from app.database.db import init_db, dependencies_available + if dependencies_available(): + init_db() + except Exception as e: + logging.error(f"Failed to initialize database. Please ensure you have installed the latest requirements. If the error persists, please report this as in future the database will be required: {e}") + + def start_comfyui(asyncio_loop=None): """ Starts the ComfyUI server using the provided asyncio event loop or creates a new one. @@ -256,16 +313,21 @@ def start_comfyui(asyncio_loop=None): asyncio_loop = asyncio.new_event_loop() asyncio.set_event_loop(asyncio_loop) prompt_server = server.PromptServer(asyncio_loop) - q = execution.PromptQueue(prompt_server) - nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes) + hook_breaker_ac10a0.save_functions() + asyncio_loop.run_until_complete(nodes.init_extra_nodes( + init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0, + init_api_nodes=not args.disable_api_nodes + )) + hook_breaker_ac10a0.restore_functions() cuda_malloc_warning() + setup_database() prompt_server.add_routes() hijack_progress(prompt_server) - threading.Thread(target=prompt_worker, daemon=True, args=(q, prompt_server,)).start() + threading.Thread(target=prompt_worker, daemon=True, args=(prompt_server.prompt_queue, prompt_server,)).start() if args.quick_test_for_ci: exit(0) @@ -292,9 +354,17 @@ def start_comfyui(asyncio_loop=None): if __name__ == "__main__": # Running directly, just start ComfyUI. + logging.info("Python version: {}".format(sys.version)) + logging.info("ComfyUI version: {}".format(comfyui_version.__version__)) + + if sys.version_info.major == 3 and sys.version_info.minor < 10: + logging.warning("WARNING: You are using a python version older than 3.10, please upgrade to a newer one. 3.12 and above is recommended.") + event_loop, _, start_all_func = start_comfyui() try: - event_loop.run_until_complete(start_all_func()) + x = start_all_func() + app.logger.print_startup_warnings() + event_loop.run_until_complete(x) except KeyboardInterrupt: logging.info("\nStopped server") diff --git a/middleware/__init__.py b/middleware/__init__.py new file mode 100644 index 000000000..2d7c7c3a9 --- /dev/null +++ b/middleware/__init__.py @@ -0,0 +1 @@ +"""Server middleware modules""" diff --git a/middleware/cache_middleware.py b/middleware/cache_middleware.py new file mode 100644 index 000000000..f02135369 --- /dev/null +++ b/middleware/cache_middleware.py @@ -0,0 +1,53 @@ +"""Cache control middleware for ComfyUI server""" + +from aiohttp import web +from typing import Callable, Awaitable + +# Time in seconds +ONE_HOUR: int = 3600 +ONE_DAY: int = 86400 +IMG_EXTENSIONS = ( + ".jpg", + ".jpeg", + ".png", + ".ppm", + ".bmp", + ".pgm", + ".tif", + ".tiff", + ".webp", +) + + +@web.middleware +async def cache_control( + request: web.Request, handler: Callable[[web.Request], Awaitable[web.Response]] +) -> web.Response: + """Cache control middleware that sets appropriate cache headers based on file type and response status""" + response: web.Response = await handler(request) + + path_filename = request.path.rsplit("/", 1)[-1] + is_entry_point = path_filename.startswith("index") and path_filename.endswith( + ".json" + ) + + if request.path.endswith(".js") or request.path.endswith(".css") or is_entry_point: + response.headers.setdefault("Cache-Control", "no-cache") + return response + + # Early return for non-image files - no cache headers needed + if not request.path.lower().endswith(IMG_EXTENSIONS): + return response + + # Handle image files + if response.status == 404: + response.headers.setdefault("Cache-Control", f"public, max-age={ONE_HOUR}") + elif response.status in (200, 201, 202, 203, 204, 205, 206, 301, 308): + # Success responses and permanent redirects - cache for 1 day + response.headers.setdefault("Cache-Control", f"public, max-age={ONE_DAY}") + elif response.status in (302, 303, 307): + # Temporary redirects - no cache + response.headers.setdefault("Cache-Control", "no-cache") + # Note: 304 Not Modified falls through - no cache headers set + + return response diff --git a/models/audio_encoders/put_audio_encoder_models_here b/models/audio_encoders/put_audio_encoder_models_here new file mode 100644 index 000000000..e69de29bb diff --git a/models/model_patches/put_model_patches_here b/models/model_patches/put_model_patches_here new file mode 100644 index 000000000..e69de29bb diff --git a/node_helpers.py b/node_helpers.py index 4b38bfff8..4ff960ef8 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -1,15 +1,22 @@ import hashlib +import torch from comfy.cli_args import args from PIL import ImageFile, UnidentifiedImageError -def conditioning_set_values(conditioning, values={}): +def conditioning_set_values(conditioning, values={}, append=False): c = [] for t in conditioning: n = [t[0], t[1].copy()] for k in values: - n[1][k] = values[k] + val = values[k] + if append: + old_val = n[1].get(k, None) + if old_val is not None: + val = old_val + val + + n[1][k] = val c.append(n) return c @@ -35,3 +42,19 @@ def hasher(): "sha512": hashlib.sha512 } return hashfuncs[args.default_hashing_function] + +def string_to_torch_dtype(string): + if string == "fp32": + return torch.float32 + if string == "fp16": + return torch.float16 + if string == "bf16": + return torch.bfloat16 + +def image_alpha_fix(destination, source): + if destination.shape[-1] < source.shape[-1]: + source = source[...,:destination.shape[-1]] + elif destination.shape[-1] > source.shape[-1]: + destination = torch.nn.functional.pad(destination, (0, 1)) + destination[..., -1] = 1.0 + return destination, source diff --git a/nodes.py b/nodes.py index 1b796ced7..12e365ca9 100644 --- a/nodes.py +++ b/nodes.py @@ -1,10 +1,12 @@ from __future__ import annotations import torch + import os import sys import json import hashlib +import inspect import traceback import math import time @@ -25,7 +27,10 @@ import comfy.sample import comfy.sd import comfy.utils import comfy.controlnet -from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator +from comfy_api.internal import register_versions, ComfyAPIWithVersion +from comfy_api.version_list import supported_versions +from comfy_api.latest import io, ComfyExtension import comfy.clip_vision @@ -63,6 +68,8 @@ class CLIPTextEncode(ComfyNodeABC): DESCRIPTION = "Encodes a text prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images." def encode(self, clip, text): + if clip is None: + raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.") tokens = clip.tokenize(text) return (clip.encode_from_tokens_scheduled(tokens), ) @@ -244,6 +251,9 @@ class ConditioningZeroOut: pooled_output = d.get("pooled_output", None) if pooled_output is not None: d["pooled_output"] = torch.zeros_like(pooled_output) + conditioning_lyrics = d.get("conditioning_lyrics", None) + if conditioning_lyrics is not None: + d["conditioning_lyrics"] = torch.zeros_like(conditioning_lyrics) n = [torch.zeros_like(t[0]), d] c.append(n) return (c, ) @@ -477,7 +487,7 @@ class SaveLatent: file = f"{filename}_{counter:05}_.latent" - results = list() + results: list[FileLocator] = [] results.append({ "filename": file, "subfolder": subfolder, @@ -487,7 +497,7 @@ class SaveLatent: file = os.path.join(full_output_folder, file) output = {} - output["latent_tensor"] = samples["samples"] + output["latent_tensor"] = samples["samples"].contiguous() output["latent_format_version_0"] = torch.tensor([]) comfy.utils.save_torch_file(output, file, metadata=metadata) @@ -720,6 +730,7 @@ class VAELoader: vaes.append("taesd3") if f1_taesd_dec and f1_taesd_enc: vaes.append("taef1") + vaes.append("pixel_space") return vaes @staticmethod @@ -762,12 +773,16 @@ class VAELoader: #TODO: scale factor? def load_vae(self, vae_name): - if vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: + if vae_name == "pixel_space": + sd = {} + sd["pixel_space_vae"] = torch.tensor(1.0) + elif vae_name in ["taesd", "taesdxl", "taesd3", "taef1"]: sd = self.load_taesd(vae_name) else: vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) sd = comfy.utils.load_torch_file(vae_path) vae = comfy.sd.VAE(sd=sd) + vae.throw_exception_if_invalid() return (vae,) class ControlNetLoader: @@ -783,6 +798,8 @@ class ControlNetLoader: def load_controlnet(self, control_net_name): controlnet_path = folder_paths.get_full_path_or_raise("controlnet", control_net_name) controlnet = comfy.controlnet.load_controlnet(controlnet_path) + if controlnet is None: + raise RuntimeError("ERROR: controlnet file is invalid and does not contain a valid controlnet model.") return (controlnet,) class DiffControlNetLoader: @@ -912,7 +929,7 @@ class CLIPLoader: @classmethod def INPUT_TYPES(s): return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart"], ), + "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), @@ -922,23 +939,10 @@ class CLIPLoader: CATEGORY = "advanced/loaders" - DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5" + DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 xxl/ clip-g / clip-l\nstable_audio: t5 base\nmochi: t5 xxl\ncosmos: old t5 xxl\nlumina2: gemma 2 2B\nwan: umt5 xxl\n hidream: llama-3.1 (Recommend) or t5\nomnigen2: qwen vl 2.5 3B" def load_clip(self, clip_name, type="stable_diffusion", device="default"): - if type == "stable_cascade": - clip_type = comfy.sd.CLIPType.STABLE_CASCADE - elif type == "sd3": - clip_type = comfy.sd.CLIPType.SD3 - elif type == "stable_audio": - clip_type = comfy.sd.CLIPType.STABLE_AUDIO - elif type == "mochi": - clip_type = comfy.sd.CLIPType.MOCHI - elif type == "ltxv": - clip_type = comfy.sd.CLIPType.LTXV - elif type == "pixart": - clip_type = comfy.sd.CLIPType.PIXART - else: - clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) model_options = {} if device == "cpu": @@ -953,7 +957,7 @@ class DualCLIPLoader: def INPUT_TYPES(s): return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), - "type": (["sdxl", "sd3", "flux", "hunyuan_video"], ), + "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image"], ), }, "optional": { "device": (["default", "cpu"], {"advanced": True}), @@ -963,19 +967,13 @@ class DualCLIPLoader: CATEGORY = "advanced/loaders" - DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5" + DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5\nhidream: at least one of t5 or llama, recommended t5 and llama\nhunyuan_image: qwen2.5vl 7b and byt5 small" def load_clip(self, clip_name1, clip_name2, type, device="default"): + clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION) + clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1) clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2) - if type == "sdxl": - clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION - elif type == "sd3": - clip_type = comfy.sd.CLIPType.SD3 - elif type == "flux": - clip_type = comfy.sd.CLIPType.FLUX - elif type == "hunyuan_video": - clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO model_options = {} if device == "cpu": @@ -997,6 +995,8 @@ class CLIPVisionLoader: def load_clip(self, clip_name): clip_path = folder_paths.get_full_path_or_raise("clip_vision", clip_name) clip_vision = comfy.clip_vision.load(clip_path) + if clip_vision is None: + raise RuntimeError("ERROR: clip vision file is invalid and does not contain a valid vision model.") return (clip_vision,) class CLIPVisionEncode: @@ -1058,10 +1058,11 @@ class StyleModelApply: for t in conditioning: (txt, keys) = t keys = keys.copy() - if strength_type == "attn_bias" and strength != 1.0: + # even if the strength is 1.0 (i.e, no change), if there's already a mask, we have to add to it + if "attention_mask" in keys or (strength_type == "attn_bias" and strength != 1.0): # math.log raises an error if the argument is zero # torch.log returns -inf, which is what we want - attn_bias = torch.log(torch.Tensor([strength])) + attn_bias = torch.log(torch.Tensor([strength if strength_type == "attn_bias" else 1.0])) # get the size of the mask image mask_ref_size = keys.get("attention_mask_img_shape", (1, 1)) n_ref = mask_ref_size[0] * mask_ref_size[1] @@ -1111,16 +1112,7 @@ class unCLIPConditioning: if strength == 0: return (conditioning, ) - c = [] - for t in conditioning: - o = t[1].copy() - x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} - if "unclip_conditioning" in o: - o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] - else: - o["unclip_conditioning"] = [x] - n = [t[0], o] - c.append(n) + c = node_helpers.conditioning_set_values(conditioning, {"unclip_conditioning": [{"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation}]}, append=True) return (c, ) class GLIGENLoader: @@ -1241,12 +1233,12 @@ class RepeatLatentBatch: s = samples.copy() s_in = samples["samples"] - s["samples"] = s_in.repeat((amount, 1,1,1)) + s["samples"] = s_in.repeat((amount,) + ((1,) * (s_in.ndim - 1))) if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: masks = samples["noise_mask"] if masks.shape[0] < s_in.shape[0]: - masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] - s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + masks = masks.repeat((math.ceil(s_in.shape[0] / masks.shape[0]),) + ((1,) * (masks.ndim - 1)))[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount,) + ((1,) * (samples["noise_mask"].ndim - 1))) if "batch_index" in s: offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] @@ -1510,7 +1502,7 @@ class KSampler: return { "required": { "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}), - "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "The random seed used for creating the noise."}), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}), @@ -1538,7 +1530,7 @@ class KSamplerAdvanced: return {"required": {"model": ("MODEL",), "add_noise": (["enable", "disable"], ), - "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), @@ -1640,6 +1632,7 @@ class LoadImage: def INPUT_TYPES(s): input_dir = folder_paths.get_input_directory() files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + files = folder_paths.filter_files_content_types(files, ["image"]) return {"required": {"image": (sorted(files), {"image_upload": True})}, } @@ -1678,6 +1671,9 @@ class LoadImage: if 'A' in i.getbands(): mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 mask = 1. - torch.from_numpy(mask) + elif i.mode == 'P' and 'transparency' in i.info: + mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) else: mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") output_images.append(image) @@ -1756,6 +1752,29 @@ class LoadImageMask: return True + +class LoadImageOutput(LoadImage): + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("COMBO", { + "image_upload": True, + "image_folder": "output", + "remote": { + "route": "/internal/files/output", + "refresh_button": True, + "control_after_refresh": "first", + }, + }), + } + } + + DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration." + EXPERIMENTAL = True + FUNCTION = "load_image" + + class ImageScale: upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] crop_methods = ["disabled", "center"] @@ -1921,7 +1940,7 @@ class ImagePadForOutpaint: mask[top:top + d2, left:left + d3] = t - return (new_image, mask) + return (new_image, mask.unsqueeze(0)) NODE_CLASS_MAPPINGS = { @@ -1942,6 +1961,7 @@ NODE_CLASS_MAPPINGS = { "PreviewImage": PreviewImage, "LoadImage": LoadImage, "LoadImageMask": LoadImageMask, + "LoadImageOutput": LoadImageOutput, "ImageScale": ImageScale, "ImageScaleBy": ImageScaleBy, "ImageInvert": ImageInvert, @@ -2007,7 +2027,6 @@ NODE_DISPLAY_NAME_MAPPINGS = { "DiffControlNetLoader": "Load ControlNet Model (diff)", "StyleModelLoader": "Load Style Model", "CLIPVisionLoader": "Load CLIP Vision", - "UpscaleModelLoader": "Load Upscale Model", "UNETLoader": "Load Diffusion Model", # Conditioning "CLIPVisionEncode": "CLIP Vision Encode", @@ -2042,18 +2061,20 @@ NODE_DISPLAY_NAME_MAPPINGS = { "PreviewImage": "Preview Image", "LoadImage": "Load Image", "LoadImageMask": "Load Image (as Mask)", + "LoadImageOutput": "Load Image (from Outputs)", "ImageScale": "Upscale Image", "ImageScaleBy": "Upscale Image By", - "ImageUpscaleWithModel": "Upscale Image (using Model)", "ImageInvert": "Invert Image", "ImagePadForOutpaint": "Pad Image for Outpainting", "ImageBatch": "Batch Images", "ImageCrop": "Image Crop", + "ImageStitch": "Image Stitch", "ImageBlend": "Image Blend", "ImageBlur": "Image Blur", "ImageQuantize": "Image Quantize", "ImageSharpen": "Image Sharpen", "ImageScaleToTotalPixels": "Scale Image to Total Pixels", + "GetImageSize": "Get Image Size", # _for_testing "VAEDecodeTiled": "VAE Decode (Tiled)", "VAEEncodeTiled": "VAE Encode (Tiled)", @@ -2087,31 +2108,55 @@ def get_module_name(module_path: str) -> str: return base_path -def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool: - module_name = os.path.basename(module_path) +async def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool: + module_name = get_module_name(module_path) if os.path.isfile(module_path): sp = os.path.splitext(module_path) module_name = sp[0] + sys_module_name = module_name + elif os.path.isdir(module_path): + sys_module_name = module_path.replace(".", "_x_") + try: logging.debug("Trying to load custom node {}".format(module_path)) if os.path.isfile(module_path): - module_spec = importlib.util.spec_from_file_location(module_name, module_path) + module_spec = importlib.util.spec_from_file_location(sys_module_name, module_path) module_dir = os.path.split(module_path)[0] else: - module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) + module_spec = importlib.util.spec_from_file_location(sys_module_name, os.path.join(module_path, "__init__.py")) module_dir = module_path module = importlib.util.module_from_spec(module_spec) - sys.modules[module_name] = module + sys.modules[sys_module_name] = module module_spec.loader.exec_module(module) LOADED_MODULE_DIRS[module_name] = os.path.abspath(module_dir) + try: + from comfy_config import config_parser + + project_config = config_parser.extract_node_configuration(module_path) + + web_dir_name = project_config.tool_comfy.web + + if web_dir_name: + web_dir_path = os.path.join(module_path, web_dir_name) + + if os.path.isdir(web_dir_path): + project_name = project_config.project.name + + EXTENSION_WEB_DIRS[project_name] = web_dir_path + + logging.info("Automatically register web folder {} for {}".format(web_dir_name, project_name)) + except Exception as e: + logging.warning(f"Unable to parse pyproject.toml due to lack dependency pydantic-settings, please run 'pip install -r requirements.txt': {e}") + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) if os.path.isdir(web_dir): EXTENSION_WEB_DIRS[module_name] = web_dir + # V1 node definition if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: for name, node_cls in module.NODE_CLASS_MAPPINGS.items(): if name not in ignore: @@ -2120,15 +2165,45 @@ def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) return True + # V3 Extension Definition + elif hasattr(module, "comfy_entrypoint"): + entrypoint = getattr(module, "comfy_entrypoint") + if not callable(entrypoint): + logging.warning(f"comfy_entrypoint in {module_path} is not callable, skipping.") + return False + try: + if inspect.iscoroutinefunction(entrypoint): + extension = await entrypoint() + else: + extension = entrypoint() + if not isinstance(extension, ComfyExtension): + logging.warning(f"comfy_entrypoint in {module_path} did not return a ComfyExtension, skipping.") + return False + node_list = await extension.get_node_list() + if not isinstance(node_list, list): + logging.warning(f"comfy_entrypoint in {module_path} did not return a list of nodes, skipping.") + return False + for node_cls in node_list: + node_cls: io.ComfyNode + schema = node_cls.GET_SCHEMA() + if schema.node_id not in ignore: + NODE_CLASS_MAPPINGS[schema.node_id] = node_cls + node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path)) + if schema.display_name is not None: + NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name + return True + except Exception as e: + logging.warning(f"Error while calling comfy_entrypoint in {module_path}: {e}") + return False else: - logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or NODES_LIST (need one).") return False except Exception as e: logging.warning(traceback.format_exc()) logging.warning(f"Cannot import {module_path} module for custom nodes: {e}") return False -def init_external_custom_nodes(): +async def init_external_custom_nodes(): """ Initializes the external custom nodes. @@ -2150,8 +2225,11 @@ def init_external_custom_nodes(): module_path = os.path.join(custom_node_path, possible_module) if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue if module_path.endswith(".disabled"): continue + if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes: + logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes") + continue time_before = time.perf_counter() - success = load_custom_node(module_path, base_node_names, module_parent="custom_nodes") + success = await load_custom_node(module_path, base_node_names, module_parent="custom_nodes") node_import_times.append((time.perf_counter() - time_before, module_path, success)) if len(node_import_times) > 0: @@ -2164,7 +2242,7 @@ def init_external_custom_nodes(): logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1])) logging.info("") -def init_builtin_extra_nodes(): +async def init_builtin_extra_nodes(): """ Initializes the built-in extra nodes in ComfyUI. @@ -2194,6 +2272,7 @@ def init_builtin_extra_nodes(): "nodes_model_downscale.py", "nodes_images.py", "nodes_video_model.py", + "nodes_train.py", "nodes_sag.py", "nodes_perpneg.py", "nodes_stable3d.py", @@ -2216,6 +2295,7 @@ def init_builtin_extra_nodes(): "nodes_gits.py", "nodes_controlnet.py", "nodes_hunyuan.py", + "nodes_eps.py", "nodes_flux.py", "nodes_lora_extract.py", "nodes_torch_compile.py", @@ -2225,24 +2305,109 @@ def init_builtin_extra_nodes(): "nodes_lt.py", "nodes_hooks.py", "nodes_load_3d.py", + "nodes_cosmos.py", + "nodes_video.py", + "nodes_lumina2.py", + "nodes_wan.py", + "nodes_lotus.py", + "nodes_hunyuan3d.py", + "nodes_primitive.py", + "nodes_cfg.py", + "nodes_optimalsteps.py", + "nodes_hidream.py", + "nodes_fresca.py", + "nodes_apg.py", + "nodes_preview_any.py", + "nodes_ace.py", + "nodes_string.py", + "nodes_camera_trajectory.py", + "nodes_edit_model.py", + "nodes_tcfg.py", + "nodes_context_windows.py", + "nodes_qwen.py", + "nodes_chroma_radiance.py", + "nodes_model_patch.py", + "nodes_easycache.py", + "nodes_audio_encoder.py", ] import_failed = [] for node_file in extras_files: - if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): + if not await load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"): import_failed.append(node_file) return import_failed -def init_extra_nodes(init_custom_nodes=True): - import_failed = init_builtin_extra_nodes() +async def init_builtin_api_nodes(): + api_nodes_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_api_nodes") + api_nodes_files = [ + "nodes_ideogram.py", + "nodes_openai.py", + "nodes_minimax.py", + "nodes_veo2.py", + "nodes_kling.py", + "nodes_bfl.py", + "nodes_bytedance.py", + "nodes_ltxv.py", + "nodes_luma.py", + "nodes_recraft.py", + "nodes_pixverse.py", + "nodes_stability.py", + "nodes_pika.py", + "nodes_runway.py", + "nodes_sora.py", + "nodes_tripo.py", + "nodes_moonvalley.py", + "nodes_rodin.py", + "nodes_gemini.py", + "nodes_vidu.py", + "nodes_wan.py", + ] + + if not await load_custom_node(os.path.join(api_nodes_dir, "canary.py"), module_parent="comfy_api_nodes"): + return api_nodes_files + + import_failed = [] + for node_file in api_nodes_files: + if not await load_custom_node(os.path.join(api_nodes_dir, node_file), module_parent="comfy_api_nodes"): + import_failed.append(node_file) + + return import_failed + +async def init_public_apis(): + register_versions([ + ComfyAPIWithVersion( + version=getattr(v, "VERSION"), + api_class=v + ) for v in supported_versions + ]) + +async def init_extra_nodes(init_custom_nodes=True, init_api_nodes=True): + await init_public_apis() + + import_failed = await init_builtin_extra_nodes() + + import_failed_api = [] + if init_api_nodes: + import_failed_api = await init_builtin_api_nodes() if init_custom_nodes: - init_external_custom_nodes() + await init_external_custom_nodes() else: logging.info("Skipping loading of custom nodes") + if len(import_failed_api) > 0: + logging.warning("WARNING: some comfy_api_nodes/ nodes did not import correctly. This may be because they are missing some dependencies.\n") + for node in import_failed_api: + logging.warning("IMPORT FAILED: {}".format(node)) + logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.") + if args.windows_standalone_build: + logging.warning("Please run the update script: update/update_comfyui.bat") + else: + logging.warning("Please do a: pip install -r requirements.txt") + logging.warning("") + if len(import_failed) > 0: logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n") for node in import_failed: diff --git a/notebooks/comfyui_colab.ipynb b/notebooks/comfyui_colab.ipynb deleted file mode 100644 index 5560b5ff9..000000000 --- a/notebooks/comfyui_colab.ipynb +++ /dev/null @@ -1,322 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "aaaaaaaaaa" - }, - "source": [ - "Git clone the repo and install the requirements. (ignore the pip errors about protobuf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "bbbbbbbbbb" - }, - "outputs": [], - "source": [ - "#@title Environment Setup\n", - "\n", - "\n", - "OPTIONS = {}\n", - "\n", - "USE_GOOGLE_DRIVE = False #@param {type:\"boolean\"}\n", - "UPDATE_COMFY_UI = True #@param {type:\"boolean\"}\n", - "WORKSPACE = 'ComfyUI'\n", - "OPTIONS['USE_GOOGLE_DRIVE'] = USE_GOOGLE_DRIVE\n", - "OPTIONS['UPDATE_COMFY_UI'] = UPDATE_COMFY_UI\n", - "\n", - "if OPTIONS['USE_GOOGLE_DRIVE']:\n", - " !echo \"Mounting Google Drive...\"\n", - " %cd /\n", - " \n", - " from google.colab import drive\n", - " drive.mount('/content/drive')\n", - "\n", - " WORKSPACE = \"/content/drive/MyDrive/ComfyUI\"\n", - " %cd /content/drive/MyDrive\n", - "\n", - "![ ! -d $WORKSPACE ] && echo -= Initial setup ComfyUI =- && git clone https://github.com/comfyanonymous/ComfyUI\n", - "%cd $WORKSPACE\n", - "\n", - "if OPTIONS['UPDATE_COMFY_UI']:\n", - " !echo -= Updating ComfyUI =-\n", - " !git pull\n", - "\n", - "!echo -= Install dependencies =-\n", - "!pip install xformers!=0.0.18 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://download.pytorch.org/whl/cu117" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cccccccccc" - }, - "source": [ - "Download some models/checkpoints/vae or custom comfyui nodes (uncomment the commands for the ones you want)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dddddddddd" - }, - "outputs": [], - "source": [ - "# Checkpoints\n", - "\n", - "### SDXL\n", - "### I recommend these workflow examples: https://comfyanonymous.github.io/ComfyUI_examples/sdxl/\n", - "\n", - "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors -P ./models/checkpoints/\n", - "\n", - "# SDXL ReVision\n", - "#!wget -c https://huggingface.co/comfyanonymous/clip_vision_g/resolve/main/clip_vision_g.safetensors -P ./models/clip_vision/\n", - "\n", - "# SD1.5\n", - "!wget -c https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors -P ./models/checkpoints/\n", - "\n", - "# SD2\n", - "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.safetensors -P ./models/checkpoints/\n", - "\n", - "# Some SD1.5 anime style\n", - "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix2/AbyssOrangeMix2_hard.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1_orangemixs.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A3_orangemixs.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/anything-v3-fp16-pruned.safetensors -P ./models/checkpoints/\n", - "\n", - "# Waifu Diffusion 1.5 (anime style SD2.x 768-v)\n", - "#!wget -c https://huggingface.co/waifu-diffusion/wd-1-5-beta3/resolve/main/wd-illusion-fp16.safetensors -P ./models/checkpoints/\n", - "\n", - "\n", - "# unCLIP models\n", - "#!wget -c https://huggingface.co/comfyanonymous/illuminatiDiffusionV1_v11_unCLIP/resolve/main/illuminatiDiffusionV1_v11-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", - "#!wget -c https://huggingface.co/comfyanonymous/wd-1.5-beta2_unCLIP/resolve/main/wd-1-5-beta2-aesthetic-unclip-h-fp16.safetensors -P ./models/checkpoints/\n", - "\n", - "\n", - "# VAE\n", - "!wget -c https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors -P ./models/vae/\n", - "#!wget -c https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt -P ./models/vae/\n", - "#!wget -c https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt -P ./models/vae/\n", - "\n", - "\n", - "# Loras\n", - "#!wget -c https://civitai.com/api/download/models/10350 -O ./models/loras/theovercomer8sContrastFix_sd21768.safetensors #theovercomer8sContrastFix SD2.x 768-v\n", - "#!wget -c https://civitai.com/api/download/models/10638 -O ./models/loras/theovercomer8sContrastFix_sd15.safetensors #theovercomer8sContrastFix SD1.x\n", - "#!wget -c https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -P ./models/loras/ #SDXL offset noise lora\n", - "\n", - "\n", - "# T2I-Adapter\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_depth_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_seg_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_sketch_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_keypose_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_openpose_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_color_sd14v1.pth -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_canny_sd14v1.pth -P ./models/controlnet/\n", - "\n", - "# T2I Styles Model\n", - "#!wget -c https://huggingface.co/TencentARC/T2I-Adapter/resolve/main/models/t2iadapter_style_sd14v1.pth -P ./models/style_models/\n", - "\n", - "# CLIPVision model (needed for styles model)\n", - "#!wget -c https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin -O ./models/clip_vision/clip_vit14.bin\n", - "\n", - "\n", - "# ControlNet\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_seg_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors -P ./models/controlnet/\n", - "\n", - "# ControlNet SDXL\n", - "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-canny-rank256.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-depth-rank256.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-recolor-rank256.safetensors -P ./models/controlnet/\n", - "#!wget -c https://huggingface.co/stabilityai/control-lora/resolve/main/control-LoRAs-rank256/control-lora-sketch-rank256.safetensors -P ./models/controlnet/\n", - "\n", - "# Controlnet Preprocessor nodes by Fannovel16\n", - "#!cd custom_nodes && git clone https://github.com/Fannovel16/comfy_controlnet_preprocessors; cd comfy_controlnet_preprocessors && python install.py\n", - "\n", - "\n", - "# GLIGEN\n", - "#!wget -c https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/resolve/main/gligen_sd14_textbox_pruned_fp16.safetensors -P ./models/gligen/\n", - "\n", - "\n", - "# ESRGAN upscale model\n", - "#!wget -c https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./models/upscale_models/\n", - "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth -P ./models/upscale_models/\n", - "#!wget -c https://huggingface.co/sberbank-ai/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth -P ./models/upscale_models/\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kkkkkkkkkkkkkkk" - }, - "source": [ - "### Run ComfyUI with cloudflared (Recommended Way)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jjjjjjjjjjjjjj" - }, - "outputs": [], - "source": [ - "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n", - "!dpkg -i cloudflared-linux-amd64.deb\n", - "\n", - "import subprocess\n", - "import threading\n", - "import time\n", - "import socket\n", - "import urllib.request\n", - "\n", - "def iframe_thread(port):\n", - " while True:\n", - " time.sleep(0.5)\n", - " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", - " result = sock.connect_ex(('127.0.0.1', port))\n", - " if result == 0:\n", - " break\n", - " sock.close()\n", - " print(\"\\nComfyUI finished loading, trying to launch cloudflared (if it gets stuck here cloudflared is having issues)\\n\")\n", - "\n", - " p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", - " for line in p.stderr:\n", - " l = line.decode()\n", - " if \"trycloudflare.com \" in l:\n", - " print(\"This is the URL to access ComfyUI:\", l[l.find(\"http\"):], end='')\n", - " #print(l, end='')\n", - "\n", - "\n", - "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", - "\n", - "!python main.py --dont-print-server" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kkkkkkkkkkkkkk" - }, - "source": [ - "### Run ComfyUI with localtunnel\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jjjjjjjjjjjjj" - }, - "outputs": [], - "source": [ - "!npm install -g localtunnel\n", - "\n", - "import threading\n", - "\n", - "def iframe_thread(port):\n", - " while True:\n", - " time.sleep(0.5)\n", - " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", - " result = sock.connect_ex(('127.0.0.1', port))\n", - " if result == 0:\n", - " break\n", - " sock.close()\n", - " print(\"\\nComfyUI finished loading, trying to launch localtunnel (if it gets stuck here localtunnel is having issues)\\n\")\n", - "\n", - " print(\"The password/enpoint ip for localtunnel is:\", urllib.request.urlopen('https://ipv4.icanhazip.com').read().decode('utf8').strip(\"\\n\"))\n", - " p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n", - " for line in p.stdout:\n", - " print(line.decode(), end='')\n", - "\n", - "\n", - "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", - "\n", - "!python main.py --dont-print-server" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gggggggggg" - }, - "source": [ - "### Run ComfyUI with colab iframe (use only in case the previous way with localtunnel doesn't work)\n", - "\n", - "You should see the ui appear in an iframe. If you get a 403 error, it's your firefox settings or an extension that's messing things up.\n", - "\n", - "If you want to open it in another window use the link.\n", - "\n", - "Note that some UI features like live image previews won't work because the colab iframe blocks websockets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hhhhhhhhhh" - }, - "outputs": [], - "source": [ - "import threading\n", - "def iframe_thread(port):\n", - " while True:\n", - " time.sleep(0.5)\n", - " sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n", - " result = sock.connect_ex(('127.0.0.1', port))\n", - " if result == 0:\n", - " break\n", - " sock.close()\n", - " from google.colab import output\n", - " output.serve_kernel_port_as_iframe(port, height=1024)\n", - " print(\"to open it in a window you can open this link here:\")\n", - " output.serve_kernel_port_as_window(port)\n", - "\n", - "threading.Thread(target=iframe_thread, daemon=True, args=(8188,)).start()\n", - "\n", - "!python main.py --dont-print-server" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "provenance": [] - }, - "gpuClass": "standard", - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/protocol.py b/protocol.py new file mode 100644 index 000000000..038a0a840 --- /dev/null +++ b/protocol.py @@ -0,0 +1,7 @@ + +class BinaryEventTypes: + PREVIEW_IMAGE = 1 + UNENCODED_PREVIEW_IMAGE = 2 + TEXT = 3 + PREVIEW_IMAGE_WITH_METADATA = 4 + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..fcc4854a5 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,71 @@ +[project] +name = "ComfyUI" +version = "0.3.66" +readme = "README.md" +license = { file = "LICENSE" } +requires-python = ">=3.9" + +[project.urls] +homepage = "https://www.comfy.org/" +repository = "https://github.com/comfyanonymous/ComfyUI" +documentation = "https://docs.comfy.org/" + +[tool.ruff] +lint.select = [ + "N805", # invalid-first-argument-name-for-method + "S307", # suspicious-eval-usage + "S102", # exec + "T", # print-usage + "W", + # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. + # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f + "F", +] +exclude = ["*.ipynb", "**/generated/*.pyi"] + +[tool.pylint] +master.py-version = "3.9" +master.extension-pkg-allow-list = [ + "pydantic", +] +reports.output-format = "colorized" +similarities.ignore-imports = "yes" +messages_control.disable = [ + "missing-module-docstring", + "missing-class-docstring", + "missing-function-docstring", + "line-too-long", + "too-few-public-methods", + "too-many-public-methods", + "too-many-instance-attributes", + "too-many-positional-arguments", + "broad-exception-raised", + "too-many-lines", + "invalid-name", + "unused-argument", + "broad-exception-caught", + "consider-using-with", + "fixme", + "too-many-statements", + "too-many-branches", + "too-many-locals", + "too-many-arguments", + "too-many-return-statements", + "too-many-nested-blocks", + "duplicate-code", + "abstract-method", + "superfluous-parens", + "arguments-differ", + "redefined-builtin", + "unnecessary-lambda", + "dangerous-default-value", + "invalid-overridden-method", + # next warnings should be fixed in future + "bad-classmethod-argument", # Class method should have 'cls' as first argument + "wrong-import-order", # Standard imports should be placed before third party imports + "ungrouped-imports", + "unnecessary-pass", + "unnecessary-lambda-assignment", + "no-else-return", + "unused-variable", +] diff --git a/requirements.txt b/requirements.txt index 4c2c0b2b2..cc3d4ca94 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,20 +1,29 @@ +comfyui-frontend-package==1.28.7 +comfyui-workflow-templates==0.2.4 +comfyui-embedded-docs==0.3.0 torch torchsde torchvision torchaudio +numpy>=1.25.0 einops -transformers>=4.28.1 +transformers>=4.37.2 tokenizers>=0.13.3 sentencepiece safetensors>=0.4.2 -aiohttp +aiohttp>=3.11.8 +yarl>=1.18.0 pyyaml Pillow scipy tqdm psutil +alembic +SQLAlchemy +av>=14.2.0 #non essential dependencies: kornia>=0.7.1 spandrel -soundfile +pydantic~=2.0 +pydantic-settings~=2.0 diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index 660831f21..000000000 --- a/ruff.toml +++ /dev/null @@ -1,14 +0,0 @@ -# Disable all rules by default -lint.ignore = ["ALL"] - -# Enable specific rules -lint.select = [ - "S307", # suspicious-eval-usage - "T201", # print-usage - "W", - # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names. - # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f - "F", -] - -exclude = ["*.ipynb"] diff --git a/script_examples/basic_api_example.py b/script_examples/basic_api_example.py index c916e6cb9..7e20cc2c1 100644 --- a/script_examples/basic_api_example.py +++ b/script_examples/basic_api_example.py @@ -3,11 +3,7 @@ from urllib import request #This is the ComfyUI api prompt format. -#If you want it for a specific workflow you can "enable dev mode options" -#in the settings of the UI (gear beside the "Queue Size: ") this will enable -#a button on the UI to save workflows in api format. - -#keep in mind ComfyUI is pre alpha software so this format will change a bit. +#If you want it for a specific workflow you can "File -> Export (API)" in the interface. #this is the one for the default workflow prompt_text = """ @@ -101,6 +97,14 @@ prompt_text = """ def queue_prompt(prompt): p = {"prompt": prompt} + + # If the workflow contains API nodes, you can add a Comfy API key to the `extra_data`` field of the payload. + # p["extra_data"] = { + # "api_key_comfy_org": "comfyui-87d01e28d*******************************************************" # replace with real key + # } + # See: https://docs.comfy.org/tutorials/api-nodes/overview + # Generate a key here: https://platform.comfy.org/login + data = json.dumps(p).encode('utf-8') req = request.Request("http://127.0.0.1:8188/prompt", data=data) request.urlopen(req) diff --git a/script_examples/websockets_api_example.py b/script_examples/websockets_api_example.py index d696d2bba..58f26cfb6 100644 --- a/script_examples/websockets_api_example.py +++ b/script_examples/websockets_api_example.py @@ -10,11 +10,11 @@ import urllib.parse server_address = "127.0.0.1:8188" client_id = str(uuid.uuid4()) -def queue_prompt(prompt): - p = {"prompt": prompt, "client_id": client_id} +def queue_prompt(prompt, prompt_id): + p = {"prompt": prompt, "client_id": client_id, "prompt_id": prompt_id} data = json.dumps(p).encode('utf-8') - req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) - return json.loads(urllib.request.urlopen(req).read()) + req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) + urllib.request.urlopen(req).read() def get_image(filename, subfolder, folder_type): data = {"filename": filename, "subfolder": subfolder, "type": folder_type} @@ -27,7 +27,8 @@ def get_history(prompt_id): return json.loads(response.read()) def get_images(ws, prompt): - prompt_id = queue_prompt(prompt)['prompt_id'] + prompt_id = str(uuid.uuid4()) + queue_prompt(prompt, prompt_id) output_images = {} while True: out = ws.recv() diff --git a/server.py b/server.py index ceb5e83bb..fe58db286 100644 --- a/server.py +++ b/server.py @@ -26,17 +26,22 @@ import mimetypes from comfy.cli_args import args import comfy.utils import comfy.model_management +from comfy_api import feature_flags import node_helpers +from comfyui_version import __version__ from app.frontend_management import FrontendManager +from comfy_api.internal import _ComfyNodeInternal + from app.user_manager import UserManager from app.model_manager import ModelFileManager from app.custom_node_manager import CustomNodeManager -from typing import Optional +from app.subgraph_manager import SubgraphManager +from typing import Optional, Union from api_server.routes.internal.internal_routes import InternalRoutes +from protocol import BinaryEventTypes -class BinaryEventTypes: - PREVIEW_IMAGE = 1 - UNENCODED_PREVIEW_IMAGE = 2 +# Import cache control middleware +from middleware.cache_middleware import cache_control async def send_socket_catch_exception(function, message): try: @@ -44,28 +49,41 @@ async def send_socket_catch_exception(function, message): except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err: logging.warning("send error: {}".format(err)) -def get_comfyui_version(): - comfyui_version = "unknown" - repo_path = os.path.dirname(os.path.realpath(__file__)) - try: - import pygit2 - repo = pygit2.Repository(repo_path) - comfyui_version = repo.describe(describe_strategy=pygit2.GIT_DESCRIBE_TAGS) - except Exception: - try: - import subprocess - comfyui_version = subprocess.check_output(["git", "describe", "--tags"], cwd=repo_path).decode('utf-8') - except Exception as e: - logging.warning(f"Failed to get ComfyUI version: {e}") - return comfyui_version.strip() +# Track deprecated paths that have been warned about to only warn once per file +_deprecated_paths_warned = set() @web.middleware -async def cache_control(request: web.Request, handler): +async def deprecation_warning(request: web.Request, handler): + """Middleware to warn about deprecated frontend API paths""" + path = request.path + + if path.startswith("/scripts/ui") or path.startswith("/extensions/core/"): + # Only warn once per unique file path + if path not in _deprecated_paths_warned: + _deprecated_paths_warned.add(path) + logging.warning( + f"[DEPRECATION WARNING] Detected import of deprecated legacy API: {path}. " + f"This is likely caused by a custom node extension using outdated APIs. " + f"Please update your extensions or contact the extension author for an updated version." + ) + response: web.Response = await handler(request) - if request.path.endswith('.js') or request.path.endswith('.css'): - response.headers.setdefault('Cache-Control', 'no-cache') return response + +@web.middleware +async def compress_body(request: web.Request, handler): + accept_encoding = request.headers.get("Accept-Encoding", "") + response: web.Response = await handler(request) + if not isinstance(response, web.Response): + return response + if response.content_type not in ["application/json", "text/plain"]: + return response + if response.body and "gzip" in accept_encoding: + response.enable_compression() + return response + + def create_cors_middleware(allowed_origin: str): @web.middleware async def cors_middleware(request: web.Request, handler): @@ -150,20 +168,25 @@ class PromptServer(): PromptServer.instance = self mimetypes.init() - mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8' + mimetypes.add_type('application/javascript; charset=utf-8', '.js') + mimetypes.add_type('image/webp', '.webp') self.user_manager = UserManager() self.model_file_manager = ModelFileManager() self.custom_node_manager = CustomNodeManager() + self.subgraph_manager = SubgraphManager() self.internal_routes = InternalRoutes(self) self.supports = ["custom_nodes_from_web"] - self.prompt_queue = None + self.prompt_queue = execution.PromptQueue(self) self.loop = loop self.messages = asyncio.Queue() self.client_session:Optional[aiohttp.ClientSession] = None self.number = 0 - middlewares = [cache_control] + middlewares = [cache_control, deprecation_warning] + if args.enable_compress_response_body: + middlewares.append(compress_body) + if args.enable_cors_header: middlewares.append(create_cors_middleware(args.enable_cors_header)) else: @@ -172,6 +195,7 @@ class PromptServer(): max_upload_size = round(args.max_upload_size * 1024 * 1024) self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares) self.sockets = dict() + self.sockets_metadata = dict() self.web_root = ( FrontendManager.init_frontend(args.front_end_version) if args.front_end_root is None @@ -196,20 +220,53 @@ class PromptServer(): else: sid = uuid.uuid4().hex + # Store WebSocket for backward compatibility self.sockets[sid] = ws + # Store metadata separately + self.sockets_metadata[sid] = {"feature_flags": {}} try: # Send initial state to the new client - await self.send("status", { "status": self.get_queue_info(), 'sid': sid }, sid) + await self.send("status", {"status": self.get_queue_info(), "sid": sid}, sid) # On reconnect if we are the currently executing client send the current node if self.client_id == sid and self.last_node_id is not None: await self.send("executing", { "node": self.last_node_id }, sid) + # Flag to track if we've received the first message + first_message = True + async for msg in ws: if msg.type == aiohttp.WSMsgType.ERROR: logging.warning('ws connection closed with exception %s' % ws.exception()) + elif msg.type == aiohttp.WSMsgType.TEXT: + try: + data = json.loads(msg.data) + # Check if first message is feature flags + if first_message and data.get("type") == "feature_flags": + # Store client feature flags + client_flags = data.get("data", {}) + self.sockets_metadata[sid]["feature_flags"] = client_flags + + # Send server feature flags in response + await self.send( + "feature_flags", + feature_flags.get_server_features(), + sid, + ) + + logging.debug( + f"Feature flags negotiated for client {sid}: {client_flags}" + ) + first_message = False + except json.JSONDecodeError: + logging.warning( + f"Invalid JSON received from client {sid}: {msg.data}" + ) + except Exception as e: + logging.error(f"Error processing WebSocket message: {e}") finally: self.sockets.pop(sid, None) + self.sockets_metadata.pop(sid, None) return ws @routes.get("/") @@ -221,7 +278,7 @@ class PromptServer(): return response @routes.get("/embeddings") - def get_embeddings(self): + def get_embeddings(request): embeddings = folder_paths.get_filename_list("embeddings") return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings))) @@ -277,7 +334,6 @@ class PromptServer(): a.update(f.read()) b.update(image.file.read()) image.file.seek(0) - f.close() return a.hexdigest() == b.hexdigest() return False @@ -343,6 +399,9 @@ class PromptServer(): original_ref = json.loads(post.get("original_ref")) filename, output_dir = folder_paths.annotated_filepath(original_ref['filename']) + if not filename: + return web.Response(status=400) + # validation for security: prevent accessing arbitrary path if filename[0] == '/' or '..' in filename: return web.Response(status=400) @@ -382,7 +441,10 @@ class PromptServer(): async def view_image(request): if "filename" in request.rel_url.query: filename = request.rel_url.query["filename"] - filename,output_dir = folder_paths.annotated_filepath(filename) + filename, output_dir = folder_paths.annotated_filepath(filename) + + if not filename: + return web.Response(status=400) # validation for security: prevent accessing arbitrary path if filename[0] == '/' or '..' in filename: @@ -465,9 +527,8 @@ class PromptServer(): # Get content type from mimetype, defaulting to 'application/octet-stream' content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream' - # For security, force certain extensions to download instead of display - file_extension = os.path.splitext(filename)[1].lower() - if file_extension in {'.html', '.htm', '.js', '.css'}: + # For security, force certain mimetypes to download instead of display + if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}: content_type = 'application/octet-stream' # Forces download return web.FileResponse( @@ -512,13 +573,19 @@ class PromptServer(): ram_free = comfy.model_management.get_free_memory(cpu_device) vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True) vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True) + required_frontend_version = FrontendManager.get_required_frontend_version() + installed_templates_version = FrontendManager.get_installed_templates_version() + required_templates_version = FrontendManager.get_required_templates_version() system_stats = { "system": { "os": os.name, "ram_total": ram_total, "ram_free": ram_free, - "comfyui_version": get_comfyui_version(), + "comfyui_version": __version__, + "required_frontend_version": required_frontend_version, + "installed_templates_version": installed_templates_version, + "required_templates_version": required_templates_version, "python_version": sys.version, "pytorch_version": comfy.model_management.torch_version, "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded", @@ -538,12 +605,18 @@ class PromptServer(): } return web.json_response(system_stats) + @routes.get("/features") + async def get_features(request): + return web.json_response(feature_flags.get_server_features()) + @routes.get("/prompt") async def get_prompt(request): return web.json_response(self.get_queue_info()) def node_info(node_class): obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] + if issubclass(obj_class, _ComfyNodeInternal): + return obj_class.GET_NODE_INFO_V1() info = {} info['input'] = obj_class.INPUT_TYPES() info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()} @@ -570,6 +643,9 @@ class PromptServer(): info['deprecated'] = True if getattr(obj_class, "EXPERIMENTAL", False): info['experimental'] = True + + if hasattr(obj_class, 'API_NODE'): + info['api_node'] = obj_class.API_NODE return info @routes.get("/object_info") @@ -597,7 +673,14 @@ class PromptServer(): max_items = request.rel_url.query.get("max_items", None) if max_items is not None: max_items = int(max_items) - return web.json_response(self.prompt_queue.get_history(max_items=max_items)) + + offset = request.rel_url.query.get("offset", None) + if offset is not None: + offset = int(offset) + else: + offset = -1 + + return web.json_response(self.prompt_queue.get_history(max_items=max_items, offset=offset)) @routes.get("/history/{prompt_id}") async def get_history_prompt_id(request): @@ -607,7 +690,7 @@ class PromptServer(): @routes.get("/queue") async def get_queue(request): queue_info = {} - current_queue = self.prompt_queue.get_current_queue() + current_queue = self.prompt_queue.get_current_queue_volatile() queue_info['queue_running'] = current_queue[0] queue_info['queue_pending'] = current_queue[1] return web.json_response(queue_info) @@ -630,7 +713,13 @@ class PromptServer(): if "prompt" in json_data: prompt = json_data["prompt"] - valid = execution.validate_prompt(prompt) + prompt_id = str(json_data.get("prompt_id", uuid.uuid4())) + + partial_execution_targets = None + if "partial_execution_targets" in json_data: + partial_execution_targets = json_data["partial_execution_targets"] + + valid = await execution.validate_prompt(prompt_id, prompt, partial_execution_targets) extra_data = {} if "extra_data" in json_data: extra_data = json_data["extra_data"] @@ -638,7 +727,6 @@ class PromptServer(): if "client_id" in json_data: extra_data["client_id"] = json_data["client_id"] if valid[0]: - prompt_id = str(uuid.uuid4()) outputs_to_execute = valid[2] self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute)) response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]} @@ -647,7 +735,13 @@ class PromptServer(): logging.warning("invalid prompt: {}".format(valid[1])) return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400) else: - return web.json_response({"error": "no prompt", "node_errors": []}, status=400) + error = { + "type": "no_prompt", + "message": "No prompt provided", + "details": "No prompt provided", + "extra_info": {} + } + return web.json_response({"error": error, "node_errors": {}}, status=400) @routes.post("/queue") async def post_queue(request): @@ -665,7 +759,34 @@ class PromptServer(): @routes.post("/interrupt") async def post_interrupt(request): - nodes.interrupt_processing() + try: + json_data = await request.json() + except json.JSONDecodeError: + json_data = {} + + # Check if a specific prompt_id was provided for targeted interruption + prompt_id = json_data.get('prompt_id') + if prompt_id: + currently_running, _ = self.prompt_queue.get_current_queue() + + # Check if the prompt_id matches any currently running prompt + should_interrupt = False + for item in currently_running: + # item structure: (number, prompt_id, prompt, extra_data, outputs_to_execute) + if item[1] == prompt_id: + logging.info(f"Interrupting prompt {prompt_id}") + should_interrupt = True + break + + if should_interrupt: + nodes.interrupt_processing() + else: + logging.info(f"Prompt {prompt_id} is not currently running, skipping interrupt") + else: + # No prompt_id provided, do a global interrupt + logging.info("Global interrupt (no prompt_id specified)") + nodes.interrupt_processing() + return web.Response(status=200) @routes.post("/free") @@ -700,6 +821,7 @@ class PromptServer(): self.user_manager.add_routes(self.routes) self.model_file_manager.add_routes(self.routes) self.custom_node_manager.add_routes(self.routes, self.app, nodes.LOADED_MODULE_DIRS.items()) + self.subgraph_manager.add_routes(self.routes, nodes.LOADED_MODULE_DIRS.items()) self.app.add_subapp('/internal', self.internal_routes.get_app()) # Prefix every route with /api for easier matching for delegation. @@ -720,6 +842,19 @@ class PromptServer(): for name, dir in nodes.EXTENSION_WEB_DIRS.items(): self.app.add_routes([web.static('/extensions/' + name, dir)]) + workflow_templates_path = FrontendManager.templates_path() + if workflow_templates_path: + self.app.add_routes([ + web.static('/templates', workflow_templates_path) + ]) + + # Serve embedded documentation from the package + embedded_docs_path = FrontendManager.embedded_docs_path() + if embedded_docs_path: + self.app.add_routes([ + web.static('/docs', embedded_docs_path) + ]) + self.app.add_routes([ web.static('/', self.web_root), ]) @@ -734,6 +869,10 @@ class PromptServer(): async def send(self, event, data, sid=None): if event == BinaryEventTypes.UNENCODED_PREVIEW_IMAGE: await self.send_image(data, sid=sid) + elif event == BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA: + # data is (preview_image, metadata) + preview_image, metadata = data + await self.send_image_with_metadata(preview_image, metadata, sid=sid) elif isinstance(data, (bytes, bytearray)): await self.send_bytes(event, data, sid) else: @@ -756,7 +895,7 @@ class PromptServer(): if hasattr(Image, 'Resampling'): resampling = Image.Resampling.BILINEAR else: - resampling = Image.ANTIALIAS + resampling = Image.Resampling.LANCZOS image = ImageOps.contain(image, (max_size, max_size), resampling) type_num = 1 @@ -772,6 +911,43 @@ class PromptServer(): preview_bytes = bytesIO.getvalue() await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE, preview_bytes, sid=sid) + async def send_image_with_metadata(self, image_data, metadata=None, sid=None): + image_type = image_data[0] + image = image_data[1] + max_size = image_data[2] + if max_size is not None: + if hasattr(Image, 'Resampling'): + resampling = Image.Resampling.BILINEAR + else: + resampling = Image.Resampling.LANCZOS + + image = ImageOps.contain(image, (max_size, max_size), resampling) + + mimetype = "image/png" if image_type == "PNG" else "image/jpeg" + + # Prepare metadata + if metadata is None: + metadata = {} + metadata["image_type"] = mimetype + + # Serialize metadata as JSON + import json + metadata_json = json.dumps(metadata).encode('utf-8') + metadata_length = len(metadata_json) + + # Prepare image data + bytesIO = BytesIO() + image.save(bytesIO, format=image_type, quality=95, compress_level=1) + image_bytes = bytesIO.getvalue() + + # Combine metadata and image + combined_data = bytearray() + combined_data.extend(struct.pack(">I", metadata_length)) + combined_data.extend(metadata_json) + combined_data.extend(image_bytes) + + await self.send_bytes(BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA, combined_data, sid=sid) + async def send_bytes(self, event, data, sid=None): message = self.encode_bytes(event, data) @@ -813,10 +989,10 @@ class PromptServer(): ssl_ctx = None scheme = "http" if args.tls_keyfile and args.tls_certfile: - ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) - ssl_ctx.load_cert_chain(certfile=args.tls_certfile, + ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE) + ssl_ctx.load_cert_chain(certfile=args.tls_certfile, keyfile=args.tls_keyfile) - scheme = "https" + scheme = "https" if verbose: logging.info("Starting server\n") @@ -853,3 +1029,15 @@ class PromptServer(): logging.warning(traceback.format_exc()) return json_data + + def send_progress_text( + self, text: Union[bytes, bytearray, str], node_id: str, sid=None + ): + if isinstance(text, str): + text = text.encode("utf-8") + node_id_bytes = str(node_id).encode("utf-8") + + # Pack the node_id length as a 4-byte unsigned integer, followed by the node_id bytes + message = struct.pack(">I", len(node_id_bytes)) + node_id_bytes + text + + self.send_sync(BinaryEventTypes.TEXT, message, sid) diff --git a/tests-unit/app_test/custom_node_manager_test.py b/tests-unit/app_test/custom_node_manager_test.py index 89598de84..b61e25e54 100644 --- a/tests-unit/app_test/custom_node_manager_test.py +++ b/tests-unit/app_test/custom_node_manager_test.py @@ -2,39 +2,146 @@ import pytest from aiohttp import web from unittest.mock import patch from app.custom_node_manager import CustomNodeManager +import json pytestmark = ( pytest.mark.asyncio ) # This applies the asyncio mark to all test functions in the module + @pytest.fixture def custom_node_manager(): return CustomNodeManager() + @pytest.fixture def app(custom_node_manager): app = web.Application() routes = web.RouteTableDef() - custom_node_manager.add_routes(routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")]) + custom_node_manager.add_routes( + routes, app, [("ComfyUI-TestExtension1", "ComfyUI-TestExtension1")] + ) app.add_routes(routes) return app + async def test_get_workflow_templates(aiohttp_client, app, tmp_path): client = await aiohttp_client(app) # Setup temporary custom nodes file structure with 1 workflow file custom_nodes_dir = tmp_path / "custom_nodes" - example_workflows_dir = custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows" + example_workflows_dir = ( + custom_nodes_dir / "ComfyUI-TestExtension1" / "example_workflows" + ) example_workflows_dir.mkdir(parents=True) template_file = example_workflows_dir / "workflow1.json" - template_file.write_text('') + template_file.write_text("") - with patch('folder_paths.folder_names_and_paths', { - 'custom_nodes': ([str(custom_nodes_dir)], None) - }): - response = await client.get('/workflow_templates') + with patch( + "folder_paths.folder_names_and_paths", + {"custom_nodes": ([str(custom_nodes_dir)], None)}, + ): + response = await client.get("/workflow_templates") assert response.status == 200 workflows_dict = await response.json() assert isinstance(workflows_dict, dict) assert "ComfyUI-TestExtension1" in workflows_dict assert isinstance(workflows_dict["ComfyUI-TestExtension1"], list) assert workflows_dict["ComfyUI-TestExtension1"][0] == "workflow1" + + +async def test_build_translations_empty_when_no_locales(custom_node_manager, tmp_path): + custom_nodes_dir = tmp_path / "custom_nodes" + custom_nodes_dir.mkdir(parents=True) + + with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]): + translations = custom_node_manager.build_translations() + assert translations == {} + + +async def test_build_translations_loads_all_files(custom_node_manager, tmp_path): + # Setup test directory structure + custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension" + locales_dir = custom_nodes_dir / "locales" / "en" + locales_dir.mkdir(parents=True) + + # Create test translation files + main_content = {"title": "Test Extension"} + (locales_dir / "main.json").write_text(json.dumps(main_content)) + + node_defs = {"node1": "Node 1"} + (locales_dir / "nodeDefs.json").write_text(json.dumps(node_defs)) + + commands = {"cmd1": "Command 1"} + (locales_dir / "commands.json").write_text(json.dumps(commands)) + + settings = {"setting1": "Setting 1"} + (locales_dir / "settings.json").write_text(json.dumps(settings)) + + with patch( + "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"] + ): + translations = custom_node_manager.build_translations() + + assert translations == { + "en": { + "title": "Test Extension", + "nodeDefs": {"node1": "Node 1"}, + "commands": {"cmd1": "Command 1"}, + "settings": {"setting1": "Setting 1"}, + } + } + + +async def test_build_translations_handles_invalid_json(custom_node_manager, tmp_path): + # Setup test directory structure + custom_nodes_dir = tmp_path / "custom_nodes" / "test-extension" + locales_dir = custom_nodes_dir / "locales" / "en" + locales_dir.mkdir(parents=True) + + # Create valid main.json + main_content = {"title": "Test Extension"} + (locales_dir / "main.json").write_text(json.dumps(main_content)) + + # Create invalid JSON file + (locales_dir / "nodeDefs.json").write_text("invalid json{") + + with patch( + "folder_paths.get_folder_paths", return_value=[tmp_path / "custom_nodes"] + ): + translations = custom_node_manager.build_translations() + + assert translations == { + "en": { + "title": "Test Extension", + } + } + + +async def test_build_translations_merges_multiple_extensions( + custom_node_manager, tmp_path +): + # Setup test directory structure for two extensions + custom_nodes_dir = tmp_path / "custom_nodes" + ext1_dir = custom_nodes_dir / "extension1" / "locales" / "en" + ext2_dir = custom_nodes_dir / "extension2" / "locales" / "en" + ext1_dir.mkdir(parents=True) + ext2_dir.mkdir(parents=True) + + # Create translation files for extension 1 + ext1_main = {"title": "Extension 1", "shared": "Original"} + (ext1_dir / "main.json").write_text(json.dumps(ext1_main)) + + # Create translation files for extension 2 + ext2_main = {"description": "Extension 2", "shared": "Override"} + (ext2_dir / "main.json").write_text(json.dumps(ext2_main)) + + with patch("folder_paths.get_folder_paths", return_value=[str(custom_nodes_dir)]): + translations = custom_node_manager.build_translations() + + assert translations == { + "en": { + "title": "Extension 1", + "description": "Extension 2", + "shared": "Override", # Second extension should override first + } + } diff --git a/tests-unit/app_test/frontend_manager_test.py b/tests-unit/app_test/frontend_manager_test.py index a8df52484..643f04e72 100644 --- a/tests-unit/app_test/frontend_manager_test.py +++ b/tests-unit/app_test/frontend_manager_test.py @@ -1,7 +1,7 @@ import argparse import pytest from requests.exceptions import HTTPError -from unittest.mock import patch +from unittest.mock import patch, mock_open from app.frontend_management import ( FrontendManager, @@ -70,7 +70,7 @@ def test_get_release_invalid_version(mock_provider): def test_init_frontend_default(): version_string = DEFAULT_VERSION_STRING frontend_path = FrontendManager.init_frontend(version_string) - assert frontend_path == FrontendManager.DEFAULT_FRONTEND_PATH + assert frontend_path == FrontendManager.default_frontend_path() def test_init_frontend_invalid_version(): @@ -84,24 +84,29 @@ def test_init_frontend_invalid_provider(): with pytest.raises(HTTPError): FrontendManager.init_frontend_unsafe(version_string) + @pytest.fixture def mock_os_functions(): - with patch('app.frontend_management.os.makedirs') as mock_makedirs, \ - patch('app.frontend_management.os.listdir') as mock_listdir, \ - patch('app.frontend_management.os.rmdir') as mock_rmdir: + with ( + patch("app.frontend_management.os.makedirs") as mock_makedirs, + patch("app.frontend_management.os.listdir") as mock_listdir, + patch("app.frontend_management.os.rmdir") as mock_rmdir, + ): mock_listdir.return_value = [] # Simulate empty directory yield mock_makedirs, mock_listdir, mock_rmdir + @pytest.fixture def mock_download(): - with patch('app.frontend_management.download_release_asset_zip') as mock: + with patch("app.frontend_management.download_release_asset_zip") as mock: mock.side_effect = Exception("Download failed") # Simulate download failure yield mock + def test_finally_block(mock_os_functions, mock_download, mock_provider): # Arrange mock_makedirs, mock_listdir, mock_rmdir = mock_os_functions - version_string = 'test-owner/test-repo@1.0.0' + version_string = "test-owner/test-repo@1.0.0" # Act & Assert with pytest.raises(Exception): @@ -128,3 +133,146 @@ def test_parse_version_string_invalid(): version_string = "invalid" with pytest.raises(argparse.ArgumentTypeError): FrontendManager.parse_version_string(version_string) + + +def test_init_frontend_default_with_mocks(): + # Arrange + version_string = DEFAULT_VERSION_STRING + + # Act + with ( + patch("app.frontend_management.check_frontend_version") as mock_check, + patch.object( + FrontendManager, "default_frontend_path", return_value="/mocked/path" + ), + ): + frontend_path = FrontendManager.init_frontend(version_string) + + # Assert + assert frontend_path == "/mocked/path" + mock_check.assert_called_once() + + +def test_init_frontend_fallback_on_error(): + # Arrange + version_string = "test-owner/test-repo@1.0.0" + + # Act + with ( + patch.object( + FrontendManager, "init_frontend_unsafe", side_effect=Exception("Test error") + ), + patch("app.frontend_management.check_frontend_version") as mock_check, + patch.object( + FrontendManager, "default_frontend_path", return_value="/default/path" + ), + ): + frontend_path = FrontendManager.init_frontend(version_string) + + # Assert + assert frontend_path == "/default/path" + mock_check.assert_called_once() + + +def test_get_frontend_version(): + # Arrange + expected_version = "1.25.0" + mock_requirements_content = """torch +torchsde +comfyui-frontend-package==1.25.0 +other-package==1.0.0 +numpy""" + + # Act + with patch("builtins.open", mock_open(read_data=mock_requirements_content)): + version = FrontendManager.get_required_frontend_version() + + # Assert + assert version == expected_version + + +def test_get_frontend_version_invalid_semver(): + # Arrange + mock_requirements_content = """torch +torchsde +comfyui-frontend-package==1.29.3.75 +other-package==1.0.0 +numpy""" + + # Act + with patch("builtins.open", mock_open(read_data=mock_requirements_content)): + version = FrontendManager.get_required_frontend_version() + + # Assert + assert version is None + + +def test_get_templates_version(): + # Arrange + expected_version = "0.1.41" + mock_requirements_content = """torch +torchsde +comfyui-frontend-package==1.25.0 +comfyui-workflow-templates==0.1.41 +other-package==1.0.0 +numpy""" + + # Act + with patch("builtins.open", mock_open(read_data=mock_requirements_content)): + version = FrontendManager.get_required_templates_version() + + # Assert + assert version == expected_version + + +def test_get_templates_version_not_found(): + # Arrange + mock_requirements_content = """torch +torchsde +comfyui-frontend-package==1.25.0 +other-package==1.0.0 +numpy""" + + # Act + with patch("builtins.open", mock_open(read_data=mock_requirements_content)): + version = FrontendManager.get_required_templates_version() + + # Assert + assert version is None + + +def test_get_templates_version_invalid_semver(): + # Arrange + mock_requirements_content = """torch +torchsde +comfyui-workflow-templates==1.0.0.beta +other-package==1.0.0 +numpy""" + + # Act + with patch("builtins.open", mock_open(read_data=mock_requirements_content)): + version = FrontendManager.get_required_templates_version() + + # Assert + assert version is None + + +def test_get_installed_templates_version(): + # Arrange + expected_version = "0.1.40" + + # Act + with patch("app.frontend_management.version", return_value=expected_version): + version = FrontendManager.get_installed_templates_version() + + # Assert + assert version == expected_version + + +def test_get_installed_templates_version_not_installed(): + # Act + with patch("app.frontend_management.version", side_effect=Exception("Package not found")): + version = FrontendManager.get_installed_templates_version() + + # Assert + assert version is None diff --git a/tests-unit/comfy_api_nodes_test/mapper_utils_test.py b/tests-unit/comfy_api_nodes_test/mapper_utils_test.py new file mode 100644 index 000000000..69488f691 --- /dev/null +++ b/tests-unit/comfy_api_nodes_test/mapper_utils_test.py @@ -0,0 +1,297 @@ +from typing import Optional +from enum import Enum + +from pydantic import BaseModel, Field + +from comfy.comfy_types.node_typing import IO +from comfy_api_nodes.mapper_utils import model_field_to_node_input + + +def test_model_field_to_float_input(): + """Tests mapping a float field with constraints.""" + + class ModelWithFloatField(BaseModel): + cfg_scale: Optional[float] = Field( + default=0.5, + description="Flexibility in video generation", + ge=0.0, + le=1.0, + multiple_of=0.001, + ) + + expected_output = ( + IO.FLOAT, + { + "default": 0.5, + "tooltip": "Flexibility in video generation", + "min": 0.0, + "max": 1.0, + "step": 0.001, + }, + ) + + actual_output = model_field_to_node_input( + IO.FLOAT, ModelWithFloatField, "cfg_scale" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_float_input_no_constraints(): + """Tests mapping a float field with no constraints.""" + + class ModelWithFloatField(BaseModel): + cfg_scale: Optional[float] = Field(default=0.5) + + expected_output = ( + IO.FLOAT, + { + "default": 0.5, + }, + ) + + actual_output = model_field_to_node_input( + IO.FLOAT, ModelWithFloatField, "cfg_scale" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_int_input(): + """Tests mapping an int field with constraints.""" + + class ModelWithIntField(BaseModel): + num_frames: Optional[int] = Field( + default=10, + description="Number of frames to generate", + ge=1, + le=100, + multiple_of=1, + ) + + expected_output = ( + IO.INT, + { + "default": 10, + "tooltip": "Number of frames to generate", + "min": 1, + "max": 100, + "step": 1, + }, + ) + + actual_output = model_field_to_node_input(IO.INT, ModelWithIntField, "num_frames") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_string_input(): + """Tests mapping a string field.""" + + class ModelWithStringField(BaseModel): + prompt: Optional[str] = Field( + default="A beautiful sunset over a calm ocean", + description="A prompt for the video generation", + ) + + expected_output = ( + IO.STRING, + { + "default": "A beautiful sunset over a calm ocean", + "tooltip": "A prompt for the video generation", + }, + ) + + actual_output = model_field_to_node_input(IO.STRING, ModelWithStringField, "prompt") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_string_input_multiline(): + """Tests mapping a string field.""" + + class ModelWithStringField(BaseModel): + prompt: Optional[str] = Field( + default="A beautiful sunset over a calm ocean", + description="A prompt for the video generation", + ) + + expected_output = ( + IO.STRING, + { + "default": "A beautiful sunset over a calm ocean", + "tooltip": "A prompt for the video generation", + "multiline": True, + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithStringField, "prompt", multiline=True + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_combo_input(): + """Tests mapping a combo field.""" + + class MockEnum(str, Enum): + option_1 = "option 1" + option_2 = "option 2" + option_3 = "option 3" + + class ModelWithComboField(BaseModel): + model_name: Optional[MockEnum] = Field("option 1", description="Model Name") + + expected_output = ( + IO.COMBO, + { + "options": ["option 1", "option 2", "option 3"], + "default": "option 1", + "tooltip": "Model Name", + }, + ) + + actual_output = model_field_to_node_input( + IO.COMBO, ModelWithComboField, "model_name", enum_type=MockEnum + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_combo_input_no_options(): + """Tests mapping a combo field with no options.""" + + class ModelWithComboField(BaseModel): + model_name: Optional[str] = Field(description="Model Name") + + expected_output = ( + IO.COMBO, + { + "tooltip": "Model Name", + }, + ) + + actual_output = model_field_to_node_input( + IO.COMBO, ModelWithComboField, "model_name" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_image_input(): + """Tests mapping an image field.""" + + class ModelWithImageField(BaseModel): + image: Optional[str] = Field( + default=None, + description="An image for the video generation", + ) + + expected_output = ( + IO.IMAGE, + { + "default": None, + "tooltip": "An image for the video generation", + }, + ) + + actual_output = model_field_to_node_input(IO.IMAGE, ModelWithImageField, "image") + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_description(): + """Tests mapping a field with no description.""" + + class ModelWithNoDescriptionField(BaseModel): + field: Optional[str] = Field(default="default value") + + expected_output = ( + IO.STRING, + { + "default": "default value", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoDescriptionField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_default(): + """Tests mapping a field with no default.""" + + class ModelWithNoDefaultField(BaseModel): + field: Optional[str] = Field(description="A field with no default") + + expected_output = ( + IO.STRING, + { + "tooltip": "A field with no default", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoDefaultField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_no_metadata(): + """Tests mapping a field with no metadata or properties defined on the schema.""" + + class ModelWithNoMetadataField(BaseModel): + field: Optional[str] = Field() + + expected_output = ( + IO.STRING, + {}, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoMetadataField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] + + +def test_model_field_to_node_input_default_is_none(): + """ + Tests mapping a field with a default of `None`. + I.e., the default field should be included as the schema explicitly sets it to `None`. + """ + + class ModelWithNoneDefaultField(BaseModel): + field: Optional[str] = Field( + default=None, description="A field with a default of None" + ) + + expected_output = ( + IO.STRING, + { + "default": None, + "tooltip": "A field with a default of None", + }, + ) + + actual_output = model_field_to_node_input( + IO.STRING, ModelWithNoneDefaultField, "field" + ) + + assert actual_output[0] == expected_output[0] + assert actual_output[1] == expected_output[1] diff --git a/tests-unit/comfy_api_test/input_impl_test.py b/tests-unit/comfy_api_test/input_impl_test.py new file mode 100644 index 000000000..5fc21a9a7 --- /dev/null +++ b/tests-unit/comfy_api_test/input_impl_test.py @@ -0,0 +1,91 @@ +import io +from comfy_api.input_impl.video_types import ( + container_to_output_format, + get_open_write_kwargs, +) +from comfy_api.util import VideoContainer + + +def test_container_to_output_format_empty_string(): + """Test that an empty string input returns None. `None` arg allows default auto-detection.""" + assert container_to_output_format("") is None + + +def test_container_to_output_format_none(): + """Test that None input returns None.""" + assert container_to_output_format(None) is None + + +def test_container_to_output_format_comma_separated(): + """Test that a comma-separated list returns a valid singular format from the list.""" + comma_separated_format = "mp4,mov,m4a" + output_format = container_to_output_format(comma_separated_format) + assert output_format in comma_separated_format + + +def test_container_to_output_format_single(): + """Test that a single format string (not comma-separated list) is returned as is.""" + assert container_to_output_format("mp4") == "mp4" + + +def test_get_open_write_kwargs_filepath_no_format(): + """Test that 'format' kwarg is NOT set when dest is a file path.""" + kwargs_auto = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) + assert "format" not in kwargs_auto, "Format should not be set for file paths (AUTO)" + + kwargs_specific = get_open_write_kwargs("output.avi", "mp4", "avi") + fail_msg = "Format should not be set for file paths (Specific)" + assert "format" not in kwargs_specific, fail_msg + + +def test_get_open_write_kwargs_base_options_mode(): + """Test basic kwargs for file path: mode and movflags.""" + kwargs = get_open_write_kwargs("output.mp4", "mp4", VideoContainer.AUTO) + assert kwargs["mode"] == "w", "mode should be set to write" + + fail_msg = "movflags should be set to preserve custom metadata tags" + assert "movflags" in kwargs["options"], fail_msg + assert kwargs["options"]["movflags"] == "use_metadata_tags", fail_msg + + +def test_get_open_write_kwargs_bytesio_auto_format(): + """Test kwargs for BytesIO dest with AUTO format.""" + dest = io.BytesIO() + container_fmt = "mov,mp4,m4a" + kwargs = get_open_write_kwargs(dest, container_fmt, VideoContainer.AUTO) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = ( + "Format should be a valid format from the container's format list when AUTO" + ) + assert kwargs["format"] in container_fmt, fail_msg + + +def test_get_open_write_kwargs_bytesio_specific_format(): + """Test kwargs for BytesIO dest with a specific single format.""" + dest = io.BytesIO() + container_fmt = "avi" + to_fmt = VideoContainer.MP4 + kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = "Format should be the specified format (lowercased) when output format is not AUTO" + assert kwargs["format"] == "mp4", fail_msg + + +def test_get_open_write_kwargs_bytesio_specific_format_list(): + """Test kwargs for BytesIO dest with a specific comma-separated format.""" + dest = io.BytesIO() + container_fmt = "avi" + to_fmt = "mov,mp4,m4a" # A format string that is a list + kwargs = get_open_write_kwargs(dest, container_fmt, to_fmt) + + assert kwargs["mode"] == "w" + assert kwargs["options"]["movflags"] == "use_metadata_tags" + + fail_msg = "Format should be a valid format from the specified format list when output format is not AUTO" + assert kwargs["format"] in to_fmt, fail_msg diff --git a/tests-unit/comfy_api_test/video_types_test.py b/tests-unit/comfy_api_test/video_types_test.py new file mode 100644 index 000000000..b25fcb1ca --- /dev/null +++ b/tests-unit/comfy_api_test/video_types_test.py @@ -0,0 +1,239 @@ +import pytest +import torch +import tempfile +import os +import av +import io +from fractions import Fraction +from comfy_api.input_impl.video_types import VideoFromFile, VideoFromComponents +from comfy_api.util.video_types import VideoComponents +from comfy_api.input.basic_types import AudioInput +from av.error import InvalidDataError + +EPSILON = 0.0001 + + +@pytest.fixture +def sample_images(): + """3-frame 2x2 RGB video tensor""" + return torch.rand(3, 2, 2, 3) + + +@pytest.fixture +def sample_audio(): + """Stereo audio with 44.1kHz sample rate""" + return AudioInput( + { + "waveform": torch.rand(1, 2, 1000), + "sample_rate": 44100, + } + ) + + +@pytest.fixture +def video_components(sample_images, sample_audio): + """VideoComponents with images, audio, and metadata""" + return VideoComponents( + images=sample_images, + audio=sample_audio, + frame_rate=Fraction(30), + metadata={"test": "metadata"}, + ) + + +def create_test_video(width=4, height=4, frames=3, fps=30): + """Helper to create a temporary video file""" + tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) + with av.open(tmp.name, mode="w") as container: + stream = container.add_stream("h264", rate=fps) + stream.width = width + stream.height = height + stream.pix_fmt = "yuv420p" + + for i in range(frames): + frame = av.VideoFrame.from_ndarray( + torch.ones(height, width, 3, dtype=torch.uint8).numpy() * (i * 85), + format="rgb24", + ) + frame = frame.reformat(format="yuv420p") + packet = stream.encode(frame) + container.mux(packet) + + # Flush + packet = stream.encode(None) + container.mux(packet) + + return tmp.name + + +@pytest.fixture +def simple_video_file(): + """4x4 video with 3 frames at 30fps""" + file_path = create_test_video() + yield file_path + os.unlink(file_path) + + +def test_video_from_components_get_duration(video_components): + """Duration calculated correctly from frame count and frame rate""" + video = VideoFromComponents(video_components) + duration = video.get_duration() + + expected_duration = 3.0 / 30.0 + assert duration == pytest.approx(expected_duration) + + +def test_video_from_components_get_duration_different_frame_rates(sample_images): + """Duration correct for different frame rates including fractional""" + # Test with 60 fps + components_60fps = VideoComponents(images=sample_images, frame_rate=Fraction(60)) + video_60fps = VideoFromComponents(components_60fps) + assert video_60fps.get_duration() == pytest.approx(3.0 / 60.0) + + # Test with fractional frame rate (23.976fps) + components_frac = VideoComponents( + images=sample_images, frame_rate=Fraction(24000, 1001) + ) + video_frac = VideoFromComponents(components_frac) + expected_frac = 3.0 / (24000.0 / 1001.0) + assert video_frac.get_duration() == pytest.approx(expected_frac) + + +def test_video_from_components_get_duration_empty_video(): + """Duration is zero for empty video""" + empty_components = VideoComponents( + images=torch.zeros(0, 2, 2, 3), frame_rate=Fraction(30) + ) + video = VideoFromComponents(empty_components) + assert video.get_duration() == 0.0 + + +def test_video_from_components_get_dimensions(video_components): + """Dimensions returned correctly from image tensor shape""" + video = VideoFromComponents(video_components) + width, height = video.get_dimensions() + assert width == 2 + assert height == 2 + + +def test_video_from_file_get_duration(simple_video_file): + """Duration extracted from file metadata""" + video = VideoFromFile(simple_video_file) + duration = video.get_duration() + assert duration == pytest.approx(0.1, abs=0.01) + + +def test_video_from_file_get_dimensions(simple_video_file): + """Dimensions read from stream without decoding frames""" + video = VideoFromFile(simple_video_file) + width, height = video.get_dimensions() + assert width == 4 + assert height == 4 + + +def test_video_from_file_bytesio_input(): + """VideoFromFile works with BytesIO input""" + buffer = io.BytesIO() + with av.open(buffer, mode="w", format="mp4") as container: + stream = container.add_stream("h264", rate=30) + stream.width = 2 + stream.height = 2 + stream.pix_fmt = "yuv420p" + + frame = av.VideoFrame.from_ndarray( + torch.zeros(2, 2, 3, dtype=torch.uint8).numpy(), format="rgb24" + ) + frame = frame.reformat(format="yuv420p") + packet = stream.encode(frame) + container.mux(packet) + packet = stream.encode(None) + container.mux(packet) + + buffer.seek(0) + video = VideoFromFile(buffer) + + assert video.get_dimensions() == (2, 2) + assert video.get_duration() == pytest.approx(1 / 30, abs=0.01) + + +def test_video_from_file_invalid_file_error(): + """InvalidDataError raised for non-video files""" + with tempfile.NamedTemporaryFile(suffix=".txt", delete=False) as tmp: + tmp.write(b"not a video file") + tmp.flush() + tmp_name = tmp.name + + try: + with pytest.raises(InvalidDataError): + video = VideoFromFile(tmp_name) + video.get_dimensions() + finally: + os.unlink(tmp_name) + + +def test_video_from_file_audio_only_error(): + """ValueError raised for audio-only files""" + with tempfile.NamedTemporaryFile(suffix=".m4a", delete=False) as tmp: + tmp_name = tmp.name + + try: + with av.open(tmp_name, mode="w") as container: + stream = container.add_stream("aac", rate=44100) + stream.sample_rate = 44100 + stream.format = "fltp" + + audio_data = torch.zeros(1, 1024).numpy() + audio_frame = av.AudioFrame.from_ndarray( + audio_data, format="fltp", layout="mono" + ) + audio_frame.sample_rate = 44100 + audio_frame.pts = 0 + packet = stream.encode(audio_frame) + container.mux(packet) + + for packet in stream.encode(None): + container.mux(packet) + + with pytest.raises(ValueError, match="No video stream found"): + video = VideoFromFile(tmp_name) + video.get_dimensions() + finally: + os.unlink(tmp_name) + + +def test_single_frame_video(): + """Single frame video has correct duration""" + components = VideoComponents( + images=torch.rand(1, 10, 10, 3), frame_rate=Fraction(1) + ) + video = VideoFromComponents(components) + assert video.get_duration() == 1.0 + + +@pytest.mark.parametrize( + "frame_rate,expected_fps", + [ + (Fraction(24000, 1001), 24000 / 1001), + (Fraction(30000, 1001), 30000 / 1001), + (Fraction(25, 1), 25.0), + (Fraction(50, 2), 25.0), + ], +) +def test_fractional_frame_rates(frame_rate, expected_fps): + """Duration calculated correctly for various fractional frame rates""" + components = VideoComponents(images=torch.rand(100, 4, 4, 3), frame_rate=frame_rate) + video = VideoFromComponents(components) + duration = video.get_duration() + expected_duration = 100.0 / expected_fps + assert duration == pytest.approx(expected_duration) + + +def test_duration_consistency(video_components): + """get_duration() consistent with manual calculation from components""" + video = VideoFromComponents(video_components) + + duration = video.get_duration() + components = video.get_components() + manual_duration = float(components.images.shape[0] / components.frame_rate) + + assert duration == pytest.approx(manual_duration) diff --git a/tests-unit/comfy_extras_test/__init__.py b/tests-unit/comfy_extras_test/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests-unit/comfy_extras_test/image_stitch_test.py b/tests-unit/comfy_extras_test/image_stitch_test.py new file mode 100644 index 000000000..b5a0f022c --- /dev/null +++ b/tests-unit/comfy_extras_test/image_stitch_test.py @@ -0,0 +1,243 @@ +import torch +from unittest.mock import patch, MagicMock + +# Mock nodes module to prevent CUDA initialization during import +mock_nodes = MagicMock() +mock_nodes.MAX_RESOLUTION = 16384 + +# Mock server module for PromptServer +mock_server = MagicMock() + +with patch.dict('sys.modules', {'nodes': mock_nodes, 'server': mock_server}): + from comfy_extras.nodes_images import ImageStitch + + +class TestImageStitch: + + def create_test_image(self, batch_size=1, height=64, width=64, channels=3): + """Helper to create test images with specific dimensions""" + return torch.rand(batch_size, height, width, channels) + + def test_no_image2_passthrough(self): + """Test that when image2 is None, image1 is returned unchanged""" + node = ImageStitch() + image1 = self.create_test_image() + + result = node.stitch(image1, "right", True, 0, "white", image2=None) + + assert len(result) == 1 + assert torch.equal(result[0], image1) + + def test_basic_horizontal_stitch_right(self): + """Test basic horizontal stitching to the right""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + + result = node.stitch(image1, "right", False, 0, "white", image2) + + assert result[0].shape == (1, 32, 56, 3) # 32 + 24 width + + def test_basic_horizontal_stitch_left(self): + """Test basic horizontal stitching to the left""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + + result = node.stitch(image1, "left", False, 0, "white", image2) + + assert result[0].shape == (1, 32, 56, 3) # 24 + 32 width + + def test_basic_vertical_stitch_down(self): + """Test basic vertical stitching downward""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + + result = node.stitch(image1, "down", False, 0, "white", image2) + + assert result[0].shape == (1, 56, 32, 3) # 32 + 24 height + + def test_basic_vertical_stitch_up(self): + """Test basic vertical stitching upward""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + + result = node.stitch(image1, "up", False, 0, "white", image2) + + assert result[0].shape == (1, 56, 32, 3) # 24 + 32 height + + def test_size_matching_horizontal(self): + """Test size matching for horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=64) + image2 = self.create_test_image(height=32, width=32) # Different aspect ratio + + result = node.stitch(image1, "right", True, 0, "white", image2) + + # image2 should be resized to match image1's height (64) with preserved aspect ratio + expected_width = 64 + 64 # original + resized (32*64/32 = 64) + assert result[0].shape == (1, 64, expected_width, 3) + + def test_size_matching_vertical(self): + """Test size matching for vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=64) + image2 = self.create_test_image(height=32, width=32) + + result = node.stitch(image1, "down", True, 0, "white", image2) + + # image2 should be resized to match image1's width (64) with preserved aspect ratio + expected_height = 64 + 64 # original + resized (32*64/32 = 64) + assert result[0].shape == (1, expected_height, 64, 3) + + def test_padding_for_mismatched_heights_horizontal(self): + """Test padding when heights don't match in horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=64, width=32) + image2 = self.create_test_image(height=48, width=24) # Shorter height + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Both images should be padded to height 64 + assert result[0].shape == (1, 64, 56, 3) # 32 + 24 width, max(64,48) height + + def test_padding_for_mismatched_widths_vertical(self): + """Test padding when widths don't match in vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=64) + image2 = self.create_test_image(height=24, width=48) # Narrower width + + result = node.stitch(image1, "down", False, 0, "white", image2) + + # Both images should be padded to width 64 + assert result[0].shape == (1, 56, 64, 3) # 32 + 24 height, max(64,48) width + + def test_spacing_horizontal(self): + """Test spacing addition in horizontal concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=24) + spacing_width = 16 + + result = node.stitch(image1, "right", False, spacing_width, "white", image2) + + # Expected width: 32 + 16 (spacing) + 24 = 72 + assert result[0].shape == (1, 32, 72, 3) + + def test_spacing_vertical(self): + """Test spacing addition in vertical concatenation""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=24, width=32) + spacing_width = 16 + + result = node.stitch(image1, "down", False, spacing_width, "white", image2) + + # Expected height: 32 + 16 (spacing) + 24 = 72 + assert result[0].shape == (1, 72, 32, 3) + + def test_spacing_color_values(self): + """Test that spacing colors are applied correctly""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + # Test white spacing + result_white = node.stitch(image1, "right", False, 16, "white", image2) + # Check that spacing region contains white values (close to 1.0) + spacing_region = result_white[0][:, :, 32:48, :] # Middle 16 pixels + assert torch.all(spacing_region >= 0.9) # Should be close to white + + # Test black spacing + result_black = node.stitch(image1, "right", False, 16, "black", image2) + spacing_region = result_black[0][:, :, 32:48, :] + assert torch.all(spacing_region <= 0.1) # Should be close to black + + def test_odd_spacing_width_made_even(self): + """Test that odd spacing widths are made even""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + # Use odd spacing width + result = node.stitch(image1, "right", False, 15, "white", image2) + + # Should be made even (16), so total width = 32 + 16 + 32 = 80 + assert result[0].shape == (1, 32, 80, 3) + + def test_batch_size_matching(self): + """Test that different batch sizes are handled correctly""" + node = ImageStitch() + image1 = self.create_test_image(batch_size=2, height=32, width=32) + image2 = self.create_test_image(batch_size=1, height=32, width=32) + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should match larger batch size + assert result[0].shape == (2, 32, 64, 3) + + def test_channel_matching_rgb_to_rgba(self): + """Test that channel differences are handled (RGB + alpha)""" + node = ImageStitch() + image1 = self.create_test_image(channels=3) # RGB + image2 = self.create_test_image(channels=4) # RGBA + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should have 4 channels (RGBA) + assert result[0].shape[-1] == 4 + + def test_channel_matching_rgba_to_rgb(self): + """Test that channel differences are handled (RGBA + RGB)""" + node = ImageStitch() + image1 = self.create_test_image(channels=4) # RGBA + image2 = self.create_test_image(channels=3) # RGB + + result = node.stitch(image1, "right", False, 0, "white", image2) + + # Should have 4 channels (RGBA) + assert result[0].shape[-1] == 4 + + def test_all_color_options(self): + """Test all available color options""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + colors = ["white", "black", "red", "green", "blue"] + + for color in colors: + result = node.stitch(image1, "right", False, 16, color, image2) + assert result[0].shape == (1, 32, 80, 3) # Basic shape check + + def test_all_directions(self): + """Test all direction options""" + node = ImageStitch() + image1 = self.create_test_image(height=32, width=32) + image2 = self.create_test_image(height=32, width=32) + + directions = ["right", "left", "up", "down"] + + for direction in directions: + result = node.stitch(image1, direction, False, 0, "white", image2) + assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3) + + def test_batch_size_channel_spacing_integration(self): + """Test integration of batch matching, channel matching, size matching, and spacings""" + node = ImageStitch() + image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3) + image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4) + + result = node.stitch(image1, "right", True, 8, "red", image2) + + # Should handle: batch matching, size matching, channel matching, spacing + assert result[0].shape[0] == 2 # Batch size matched + assert result[0].shape[-1] == 4 # Channels matched to max + assert result[0].shape[1] == 64 # Height from image1 (size matching) + # Width should be: 48 + 8 (spacing) + resized_image2_width + expected_image2_width = int(64 * (32/32)) # Resized to height 64 + expected_total_width = 48 + 8 + expected_image2_width + assert result[0].shape[2] == expected_total_width + diff --git a/tests-unit/comfy_test/folder_path_test.py b/tests-unit/comfy_test/folder_path_test.py index f8173cdcc..775e15c36 100644 --- a/tests-unit/comfy_test/folder_path_test.py +++ b/tests-unit/comfy_test/folder_path_test.py @@ -1,19 +1,23 @@ ### 🗻 This file is created through the spirit of Mount Fuji at its peak # TODO(yoland): clean up this after I get back down +import sys import pytest import os import tempfile from unittest.mock import patch +from importlib import reload import folder_paths +import comfy.cli_args +from comfy.options import enable_args_parsing +enable_args_parsing() + @pytest.fixture() def clear_folder_paths(): - # Clear the global dictionary before each test to ensure isolation - original = folder_paths.folder_names_and_paths.copy() - folder_paths.folder_names_and_paths.clear() + # Reload the module after each test to ensure isolation yield - folder_paths.folder_names_and_paths = original + reload(folder_paths) @pytest.fixture def temp_dir(): @@ -21,7 +25,21 @@ def temp_dir(): yield tmpdirname -def test_get_directory_by_type(): +@pytest.fixture +def set_base_dir(): + def _set_base_dir(base_dir): + # Mock CLI args + with patch.object(sys, 'argv', ["main.py", "--base-directory", base_dir]): + reload(comfy.cli_args) + reload(folder_paths) + yield _set_base_dir + # Reload the modules after each test to ensure isolation + with patch.object(sys, 'argv', ["main.py"]): + reload(comfy.cli_args) + reload(folder_paths) + + +def test_get_directory_by_type(clear_folder_paths): test_dir = "/test/dir" folder_paths.set_output_directory(test_dir) assert folder_paths.get_directory_by_type("output") == test_dir @@ -96,3 +114,49 @@ def test_get_save_image_path(temp_dir): assert counter == 1 assert subfolder == "" assert filename_prefix == "test" + + +def test_base_path_changes(set_base_dir): + test_dir = os.path.abspath("/test/dir") + set_base_dir(test_dir) + + assert folder_paths.base_path == test_dir + assert folder_paths.models_dir == os.path.join(test_dir, "models") + assert folder_paths.input_directory == os.path.join(test_dir, "input") + assert folder_paths.output_directory == os.path.join(test_dir, "output") + assert folder_paths.temp_directory == os.path.join(test_dir, "temp") + assert folder_paths.user_directory == os.path.join(test_dir, "user") + + assert os.path.join(test_dir, "custom_nodes") in folder_paths.get_folder_paths("custom_nodes") + + for name in ["checkpoints", "loras", "vae", "configs", "embeddings", "controlnet", "classifiers"]: + assert folder_paths.get_folder_paths(name)[0] == os.path.join(test_dir, "models", name) + + +def test_base_path_change_clears_old(set_base_dir): + test_dir = os.path.abspath("/test/dir") + set_base_dir(test_dir) + + assert len(folder_paths.get_folder_paths("custom_nodes")) == 1 + + single_model_paths = [ + "checkpoints", + "loras", + "vae", + "configs", + "clip_vision", + "style_models", + "diffusers", + "vae_approx", + "gligen", + "upscale_models", + "embeddings", + "hypernetworks", + "photomaker", + "classifiers", + ] + for name in single_model_paths: + assert len(folder_paths.get_folder_paths(name)) == 1 + + for name in ["controlnet", "diffusion_models", "text_encoders"]: + assert len(folder_paths.get_folder_paths(name)) == 2 diff --git a/tests-unit/feature_flags_test.py b/tests-unit/feature_flags_test.py new file mode 100644 index 000000000..f2702cfc8 --- /dev/null +++ b/tests-unit/feature_flags_test.py @@ -0,0 +1,98 @@ +"""Tests for feature flags functionality.""" + +from comfy_api.feature_flags import ( + get_connection_feature, + supports_feature, + get_server_features, + SERVER_FEATURE_FLAGS, +) + + +class TestFeatureFlags: + """Test suite for feature flags functions.""" + + def test_get_server_features_returns_copy(self): + """Test that get_server_features returns a copy of the server flags.""" + features = get_server_features() + # Verify it's a copy by modifying it + features["test_flag"] = True + # Original should be unchanged + assert "test_flag" not in SERVER_FEATURE_FLAGS + + def test_get_server_features_contains_expected_flags(self): + """Test that server features contain expected flags.""" + features = get_server_features() + assert "supports_preview_metadata" in features + assert features["supports_preview_metadata"] is True + assert "max_upload_size" in features + assert isinstance(features["max_upload_size"], (int, float)) + + def test_get_connection_feature_with_missing_sid(self): + """Test getting feature for non-existent session ID.""" + sockets_metadata = {} + result = get_connection_feature(sockets_metadata, "missing_sid", "some_feature") + assert result is False # Default value + + def test_get_connection_feature_with_custom_default(self): + """Test getting feature with custom default value.""" + sockets_metadata = {} + result = get_connection_feature( + sockets_metadata, "missing_sid", "some_feature", default="custom_default" + ) + assert result == "custom_default" + + def test_get_connection_feature_with_feature_flags(self): + """Test getting feature from connection with feature flags.""" + sockets_metadata = { + "sid1": { + "feature_flags": { + "supports_preview_metadata": True, + "custom_feature": "value", + }, + } + } + result = get_connection_feature(sockets_metadata, "sid1", "supports_preview_metadata") + assert result is True + + result = get_connection_feature(sockets_metadata, "sid1", "custom_feature") + assert result == "value" + + def test_get_connection_feature_missing_feature(self): + """Test getting non-existent feature from connection.""" + sockets_metadata = { + "sid1": {"feature_flags": {"existing_feature": True}} + } + result = get_connection_feature(sockets_metadata, "sid1", "missing_feature") + assert result is False + + def test_supports_feature_returns_boolean(self): + """Test that supports_feature always returns boolean.""" + sockets_metadata = { + "sid1": { + "feature_flags": { + "bool_feature": True, + "string_feature": "value", + "none_feature": None, + }, + } + } + + # True boolean feature + assert supports_feature(sockets_metadata, "sid1", "bool_feature") is True + + # Non-boolean values should return False + assert supports_feature(sockets_metadata, "sid1", "string_feature") is False + assert supports_feature(sockets_metadata, "sid1", "none_feature") is False + assert supports_feature(sockets_metadata, "sid1", "missing_feature") is False + + def test_supports_feature_with_missing_connection(self): + """Test supports_feature with missing connection.""" + sockets_metadata = {} + assert supports_feature(sockets_metadata, "missing_sid", "any_feature") is False + + def test_empty_feature_flags_dict(self): + """Test connection with empty feature flags dictionary.""" + sockets_metadata = {"sid1": {"feature_flags": {}}} + result = get_connection_feature(sockets_metadata, "sid1", "any_feature") + assert result is False + assert supports_feature(sockets_metadata, "sid1", "any_feature") is False diff --git a/tests-unit/folder_paths_test/filter_by_content_types_test.py b/tests-unit/folder_paths_test/filter_by_content_types_test.py index 423677a60..683f9fc11 100644 --- a/tests-unit/folder_paths_test/filter_by_content_types_test.py +++ b/tests-unit/folder_paths_test/filter_by_content_types_test.py @@ -1,14 +1,17 @@ import pytest import os import tempfile -from folder_paths import filter_files_content_types +from folder_paths import filter_files_content_types, extension_mimetypes_cache +from unittest.mock import patch + @pytest.fixture(scope="module") def file_extensions(): return { 'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'], 'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'], - 'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv'] + 'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv'], + 'model': ['gltf', 'glb', 'obj', 'fbx', 'stl'] } @@ -22,7 +25,18 @@ def mock_dir(file_extensions): yield directory -def test_categorizes_all_correctly(mock_dir, file_extensions): +@pytest.fixture +def patched_mimetype_cache(file_extensions): + # Mock model file extensions since they may not be in the test-runner system's mimetype cache + new_cache = extension_mimetypes_cache.copy() + for extension in file_extensions["model"]: + new_cache[extension] = "model" + + with patch("folder_paths.extension_mimetypes_cache", new_cache): + yield + + +def test_categorizes_all_correctly(mock_dir, file_extensions, patched_mimetype_cache): files = os.listdir(mock_dir) for content_type, extensions in file_extensions.items(): filtered_files = filter_files_content_types(files, [content_type]) @@ -30,7 +44,7 @@ def test_categorizes_all_correctly(mock_dir, file_extensions): assert f"sample_{content_type}.{extension}" in filtered_files -def test_categorizes_all_uniquely(mock_dir, file_extensions): +def test_categorizes_all_uniquely(mock_dir, file_extensions, patched_mimetype_cache): files = os.listdir(mock_dir) for content_type, extensions in file_extensions.items(): filtered_files = filter_files_content_types(files, [content_type]) diff --git a/tests-unit/folder_paths_test/misc_test.py b/tests-unit/folder_paths_test/misc_test.py new file mode 100644 index 000000000..fcf667453 --- /dev/null +++ b/tests-unit/folder_paths_test/misc_test.py @@ -0,0 +1,51 @@ +import pytest +import os +import tempfile +from folder_paths import get_input_subfolders, set_input_directory + +@pytest.fixture(scope="module") +def mock_folder_structure(): + with tempfile.TemporaryDirectory() as temp_dir: + # Create a nested folder structure + folders = [ + "folder1", + "folder1/subfolder1", + "folder1/subfolder2", + "folder2", + "folder2/deep", + "folder2/deep/nested", + "empty_folder" + ] + + # Create the folders + for folder in folders: + os.makedirs(os.path.join(temp_dir, folder)) + + # Add some files to test they're not included + with open(os.path.join(temp_dir, "root_file.txt"), "w") as f: + f.write("test") + with open(os.path.join(temp_dir, "folder1", "test.txt"), "w") as f: + f.write("test") + + set_input_directory(temp_dir) + yield temp_dir + + +def test_gets_all_folders(mock_folder_structure): + folders = get_input_subfolders() + expected = ["folder1", "folder1/subfolder1", "folder1/subfolder2", + "folder2", "folder2/deep", "folder2/deep/nested", "empty_folder"] + assert sorted(folders) == sorted(expected) + + +def test_handles_nonexistent_input_directory(): + with tempfile.TemporaryDirectory() as temp_dir: + nonexistent = os.path.join(temp_dir, "nonexistent") + set_input_directory(nonexistent) + assert get_input_subfolders() == [] + + +def test_empty_input_directory(): + with tempfile.TemporaryDirectory() as temp_dir: + set_input_directory(temp_dir) + assert get_input_subfolders() == [] # Empty since we don't include root diff --git a/tests-unit/prompt_server_test/user_manager_test.py b/tests-unit/prompt_server_test/user_manager_test.py index 7e523cbf4..b939d8e68 100644 --- a/tests-unit/prompt_server_test/user_manager_test.py +++ b/tests-unit/prompt_server_test/user_manager_test.py @@ -229,3 +229,61 @@ async def test_move_userdata_full_info(aiohttp_client, app, tmp_path): assert not os.path.exists(tmp_path / "source.txt") with open(tmp_path / "dest.txt", "r") as f: assert f.read() == "test content" + + +async def test_listuserdata_v2_empty_root(aiohttp_client, app): + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata") + assert resp.status == 200 + assert await resp.json() == [] + + +async def test_listuserdata_v2_nonexistent_subdirectory(aiohttp_client, app): + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=does_not_exist") + assert resp.status == 404 + + +async def test_listuserdata_v2_default(aiohttp_client, app, tmp_path): + os.makedirs(tmp_path / "test_dir" / "subdir") + (tmp_path / "test_dir" / "file1.txt").write_text("content") + (tmp_path / "test_dir" / "subdir" / "file2.txt").write_text("content") + + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=test_dir") + assert resp.status == 200 + data = await resp.json() + file_paths = {item["path"] for item in data if item["type"] == "file"} + assert file_paths == {"test_dir/file1.txt", "test_dir/subdir/file2.txt"} + + +async def test_listuserdata_v2_normalized_separators(aiohttp_client, app, tmp_path, monkeypatch): + # Force backslash as os separator + monkeypatch.setattr(os, 'sep', '\\') + monkeypatch.setattr(os.path, 'sep', '\\') + os.makedirs(tmp_path / "test_dir" / "subdir") + (tmp_path / "test_dir" / "subdir" / "file1.txt").write_text("x") + + client = await aiohttp_client(app) + resp = await client.get("/v2/userdata?path=test_dir") + assert resp.status == 200 + data = await resp.json() + for item in data: + assert "/" in item["path"] + assert "\\" not in item["path"]\ + +async def test_listuserdata_v2_url_encoded_path(aiohttp_client, app, tmp_path): + # Create a directory with a space in its name and a file inside + os.makedirs(tmp_path / "my dir") + (tmp_path / "my dir" / "file.txt").write_text("content") + + client = await aiohttp_client(app) + # Use URL-encoded space in path parameter + resp = await client.get("/v2/userdata?path=my%20dir&recurse=false") + assert resp.status == 200 + data = await resp.json() + assert len(data) == 1 + entry = data[0] + assert entry["name"] == "file.txt" + # Ensure the path is correctly decoded and uses forward slash + assert entry["path"] == "my dir/file.txt" diff --git a/tests-unit/requirements.txt b/tests-unit/requirements.txt index d70d00f4b..3a6790ee0 100644 --- a/tests-unit/requirements.txt +++ b/tests-unit/requirements.txt @@ -1,3 +1,4 @@ pytest>=7.8.0 pytest-aiohttp pytest-asyncio +websocket-client diff --git a/tests-unit/server/routes/internal_routes_test.py b/tests-unit/server/routes/internal_routes_test.py deleted file mode 100644 index 68c846652..000000000 --- a/tests-unit/server/routes/internal_routes_test.py +++ /dev/null @@ -1,115 +0,0 @@ -import pytest -from aiohttp import web -from unittest.mock import MagicMock, patch -from api_server.routes.internal.internal_routes import InternalRoutes -from api_server.services.file_service import FileService -from folder_paths import models_dir, user_directory, output_directory - - -@pytest.fixture -def internal_routes(): - return InternalRoutes(None) - -@pytest.fixture -def aiohttp_client_factory(aiohttp_client, internal_routes): - async def _get_client(): - app = internal_routes.get_app() - return await aiohttp_client(app) - return _get_client - -@pytest.mark.asyncio -async def test_list_files_valid_directory(aiohttp_client_factory, internal_routes): - mock_file_list = [ - {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100}, - {"name": "dir1", "path": "dir1", "type": "directory"} - ] - internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list) - client = await aiohttp_client_factory() - resp = await client.get('/files?directory=models') - assert resp.status == 200 - data = await resp.json() - assert 'files' in data - assert len(data['files']) == 2 - assert data['files'] == mock_file_list - - # Check other valid directories - resp = await client.get('/files?directory=user') - assert resp.status == 200 - resp = await client.get('/files?directory=output') - assert resp.status == 200 - -@pytest.mark.asyncio -async def test_list_files_invalid_directory(aiohttp_client_factory, internal_routes): - internal_routes.file_service.list_files = MagicMock(side_effect=ValueError("Invalid directory key")) - client = await aiohttp_client_factory() - resp = await client.get('/files?directory=invalid') - assert resp.status == 400 - data = await resp.json() - assert 'error' in data - assert data['error'] == "Invalid directory key" - -@pytest.mark.asyncio -async def test_list_files_exception(aiohttp_client_factory, internal_routes): - internal_routes.file_service.list_files = MagicMock(side_effect=Exception("Unexpected error")) - client = await aiohttp_client_factory() - resp = await client.get('/files?directory=models') - assert resp.status == 500 - data = await resp.json() - assert 'error' in data - assert data['error'] == "Unexpected error" - -@pytest.mark.asyncio -async def test_list_files_no_directory_param(aiohttp_client_factory, internal_routes): - mock_file_list = [] - internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list) - client = await aiohttp_client_factory() - resp = await client.get('/files') - assert resp.status == 200 - data = await resp.json() - assert 'files' in data - assert len(data['files']) == 0 - -def test_setup_routes(internal_routes): - internal_routes.setup_routes() - routes = internal_routes.routes - assert any(route.method == 'GET' and str(route.path) == '/files' for route in routes) - -def test_get_app(internal_routes): - app = internal_routes.get_app() - assert isinstance(app, web.Application) - assert internal_routes._app is not None - -def test_get_app_reuse(internal_routes): - app1 = internal_routes.get_app() - app2 = internal_routes.get_app() - assert app1 is app2 - -@pytest.mark.asyncio -async def test_routes_added_to_app(aiohttp_client_factory, internal_routes): - client = await aiohttp_client_factory() - try: - resp = await client.get('/files') - print(f"Response received: status {resp.status}") # noqa: T201 - except Exception as e: - print(f"Exception occurred during GET request: {e}") # noqa: T201 - raise - - assert resp.status != 404, "Route /files does not exist" - -@pytest.mark.asyncio -async def test_file_service_initialization(): - with patch('api_server.routes.internal.internal_routes.FileService') as MockFileService: - # Create a mock instance - mock_file_service_instance = MagicMock(spec=FileService) - MockFileService.return_value = mock_file_service_instance - internal_routes = InternalRoutes(None) - - # Check if FileService was initialized with the correct parameters - MockFileService.assert_called_once_with({ - "models": models_dir, - "user": user_directory, - "output": output_directory - }) - - # Verify that the file_service attribute of InternalRoutes is set - assert internal_routes.file_service == mock_file_service_instance diff --git a/tests-unit/server/services/file_service_test.py b/tests-unit/server/services/file_service_test.py deleted file mode 100644 index 09c3efc9f..000000000 --- a/tests-unit/server/services/file_service_test.py +++ /dev/null @@ -1,54 +0,0 @@ -import pytest -from unittest.mock import MagicMock -from api_server.services.file_service import FileService - -@pytest.fixture -def mock_file_system_ops(): - return MagicMock() - -@pytest.fixture -def file_service(mock_file_system_ops): - allowed_directories = { - "models": "/path/to/models", - "user": "/path/to/user", - "output": "/path/to/output" - } - return FileService(allowed_directories, file_system_ops=mock_file_system_ops) - -def test_list_files_valid_directory(file_service, mock_file_system_ops): - mock_file_system_ops.walk_directory.return_value = [ - {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100}, - {"name": "dir1", "path": "dir1", "type": "directory"} - ] - - result = file_service.list_files("models") - - assert len(result) == 2 - assert result[0]["name"] == "file1.txt" - assert result[1]["name"] == "dir1" - mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models") - -def test_list_files_invalid_directory(file_service): - # Does not support walking directories outside of the allowed directories - with pytest.raises(ValueError, match="Invalid directory key"): - file_service.list_files("invalid_key") - -def test_list_files_empty_directory(file_service, mock_file_system_ops): - mock_file_system_ops.walk_directory.return_value = [] - - result = file_service.list_files("models") - - assert len(result) == 0 - mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models") - -@pytest.mark.parametrize("directory_key", ["models", "user", "output"]) -def test_list_files_all_allowed_directories(file_service, mock_file_system_ops, directory_key): - mock_file_system_ops.walk_directory.return_value = [ - {"name": f"file_{directory_key}.txt", "path": f"file_{directory_key}.txt", "type": "file", "size": 100} - ] - - result = file_service.list_files(directory_key) - - assert len(result) == 1 - assert result[0]["name"] == f"file_{directory_key}.txt" - mock_file_system_ops.walk_directory.assert_called_once_with(f"/path/to/{directory_key}") diff --git a/tests-unit/server_test/test_cache_control.py b/tests-unit/server_test/test_cache_control.py new file mode 100644 index 000000000..fa68d9408 --- /dev/null +++ b/tests-unit/server_test/test_cache_control.py @@ -0,0 +1,262 @@ +"""Tests for server cache control middleware""" + +import pytest +from aiohttp import web +from aiohttp.test_utils import make_mocked_request +from typing import Dict, Any + +from middleware.cache_middleware import cache_control, ONE_HOUR, ONE_DAY, IMG_EXTENSIONS + +pytestmark = pytest.mark.asyncio # Apply asyncio mark to all tests + +# Test configuration data +CACHE_SCENARIOS = [ + # Image file scenarios + { + "name": "image_200_status", + "path": "/test.jpg", + "status": 200, + "expected_cache": f"public, max-age={ONE_DAY}", + "should_have_header": True, + }, + { + "name": "image_404_status", + "path": "/missing.jpg", + "status": 404, + "expected_cache": f"public, max-age={ONE_HOUR}", + "should_have_header": True, + }, + # JavaScript/CSS scenarios + { + "name": "js_no_cache", + "path": "/script.js", + "status": 200, + "expected_cache": "no-cache", + "should_have_header": True, + }, + { + "name": "css_no_cache", + "path": "/styles.css", + "status": 200, + "expected_cache": "no-cache", + "should_have_header": True, + }, + { + "name": "index_json_no_cache", + "path": "/api/index.json", + "status": 200, + "expected_cache": "no-cache", + "should_have_header": True, + }, + { + "name": "localized_index_json_no_cache", + "path": "/templates/index.zh.json", + "status": 200, + "expected_cache": "no-cache", + "should_have_header": True, + }, + # Non-matching files + { + "name": "html_no_header", + "path": "/index.html", + "status": 200, + "expected_cache": None, + "should_have_header": False, + }, + { + "name": "txt_no_header", + "path": "/data.txt", + "status": 200, + "expected_cache": None, + "should_have_header": False, + }, + { + "name": "api_endpoint_no_header", + "path": "/api/endpoint", + "status": 200, + "expected_cache": None, + "should_have_header": False, + }, + { + "name": "pdf_no_header", + "path": "/file.pdf", + "status": 200, + "expected_cache": None, + "should_have_header": False, + }, +] + +# Status code scenarios for images +IMAGE_STATUS_SCENARIOS = [ + # Success statuses get long cache + {"status": 200, "expected": f"public, max-age={ONE_DAY}"}, + {"status": 201, "expected": f"public, max-age={ONE_DAY}"}, + {"status": 202, "expected": f"public, max-age={ONE_DAY}"}, + {"status": 204, "expected": f"public, max-age={ONE_DAY}"}, + {"status": 206, "expected": f"public, max-age={ONE_DAY}"}, + # Permanent redirects get long cache + {"status": 301, "expected": f"public, max-age={ONE_DAY}"}, + {"status": 308, "expected": f"public, max-age={ONE_DAY}"}, + # Temporary redirects get no cache + {"status": 302, "expected": "no-cache"}, + {"status": 303, "expected": "no-cache"}, + {"status": 307, "expected": "no-cache"}, + # 404 gets short cache + {"status": 404, "expected": f"public, max-age={ONE_HOUR}"}, +] + +# Case sensitivity test paths +CASE_SENSITIVITY_PATHS = ["/image.JPG", "/photo.PNG", "/pic.JpEg"] + +# Edge case test paths +EDGE_CASE_PATHS = [ + { + "name": "query_strings_ignored", + "path": "/image.jpg?v=123&size=large", + "expected": f"public, max-age={ONE_DAY}", + }, + { + "name": "multiple_dots_in_path", + "path": "/image.min.jpg", + "expected": f"public, max-age={ONE_DAY}", + }, + { + "name": "nested_paths_with_images", + "path": "/static/images/photo.jpg", + "expected": f"public, max-age={ONE_DAY}", + }, +] + + +class TestCacheControl: + """Test cache control middleware functionality""" + + @pytest.fixture + def status_handler_factory(self): + """Create a factory for handlers that return specific status codes""" + + def factory(status: int, headers: Dict[str, str] = None): + async def handler(request): + return web.Response(status=status, headers=headers or {}) + + return handler + + return factory + + @pytest.fixture + def mock_handler(self, status_handler_factory): + """Create a mock handler that returns a response with 200 status""" + return status_handler_factory(200) + + @pytest.fixture + def handler_with_existing_cache(self, status_handler_factory): + """Create a handler that returns response with existing Cache-Control header""" + return status_handler_factory(200, {"Cache-Control": "max-age=3600"}) + + async def assert_cache_header( + self, + response: web.Response, + expected_cache: str = None, + should_have_header: bool = True, + ): + """Helper to assert cache control headers""" + if should_have_header: + assert "Cache-Control" in response.headers + if expected_cache: + assert response.headers["Cache-Control"] == expected_cache + else: + assert "Cache-Control" not in response.headers + + # Parameterized tests + @pytest.mark.parametrize("scenario", CACHE_SCENARIOS, ids=lambda x: x["name"]) + async def test_cache_control_scenarios( + self, scenario: Dict[str, Any], status_handler_factory + ): + """Test various cache control scenarios""" + handler = status_handler_factory(scenario["status"]) + request = make_mocked_request("GET", scenario["path"]) + response = await cache_control(request, handler) + + assert response.status == scenario["status"] + await self.assert_cache_header( + response, scenario["expected_cache"], scenario["should_have_header"] + ) + + @pytest.mark.parametrize("ext", IMG_EXTENSIONS) + async def test_all_image_extensions(self, ext: str, mock_handler): + """Test all defined image extensions are handled correctly""" + request = make_mocked_request("GET", f"/image{ext}") + response = await cache_control(request, mock_handler) + + assert response.status == 200 + assert "Cache-Control" in response.headers + assert response.headers["Cache-Control"] == f"public, max-age={ONE_DAY}" + + @pytest.mark.parametrize( + "status_scenario", IMAGE_STATUS_SCENARIOS, ids=lambda x: f"status_{x['status']}" + ) + async def test_image_status_codes( + self, status_scenario: Dict[str, Any], status_handler_factory + ): + """Test different status codes for image requests""" + handler = status_handler_factory(status_scenario["status"]) + request = make_mocked_request("GET", "/image.jpg") + response = await cache_control(request, handler) + + assert response.status == status_scenario["status"] + assert "Cache-Control" in response.headers + assert response.headers["Cache-Control"] == status_scenario["expected"] + + @pytest.mark.parametrize("path", CASE_SENSITIVITY_PATHS) + async def test_case_insensitive_image_extension(self, path: str, mock_handler): + """Test that image extensions are matched case-insensitively""" + request = make_mocked_request("GET", path) + response = await cache_control(request, mock_handler) + + assert "Cache-Control" in response.headers + assert response.headers["Cache-Control"] == f"public, max-age={ONE_DAY}" + + @pytest.mark.parametrize("edge_case", EDGE_CASE_PATHS, ids=lambda x: x["name"]) + async def test_edge_cases(self, edge_case: Dict[str, str], mock_handler): + """Test edge cases like query strings, nested paths, etc.""" + request = make_mocked_request("GET", edge_case["path"]) + response = await cache_control(request, mock_handler) + + assert "Cache-Control" in response.headers + assert response.headers["Cache-Control"] == edge_case["expected"] + + # Header preservation tests (special cases not covered by parameterization) + async def test_js_preserves_existing_headers(self, handler_with_existing_cache): + """Test that .js files preserve existing Cache-Control headers""" + request = make_mocked_request("GET", "/script.js") + response = await cache_control(request, handler_with_existing_cache) + + # setdefault should preserve existing header + assert response.headers["Cache-Control"] == "max-age=3600" + + async def test_css_preserves_existing_headers(self, handler_with_existing_cache): + """Test that .css files preserve existing Cache-Control headers""" + request = make_mocked_request("GET", "/styles.css") + response = await cache_control(request, handler_with_existing_cache) + + # setdefault should preserve existing header + assert response.headers["Cache-Control"] == "max-age=3600" + + async def test_image_preserves_existing_headers(self, status_handler_factory): + """Test that image cache headers preserve existing Cache-Control""" + handler = status_handler_factory(200, {"Cache-Control": "private, no-cache"}) + request = make_mocked_request("GET", "/image.jpg") + response = await cache_control(request, handler) + + # setdefault should preserve existing header + assert response.headers["Cache-Control"] == "private, no-cache" + + async def test_304_not_modified_inherits_cache(self, status_handler_factory): + """Test that 304 Not Modified doesn't set cache headers for images""" + handler = status_handler_factory(304, {"Cache-Control": "max-age=7200"}) + request = make_mocked_request("GET", "/not-modified.jpg") + response = await cache_control(request, handler) + + assert response.status == 304 + # Should preserve existing cache header, not override + assert response.headers["Cache-Control"] == "max-age=7200" diff --git a/tests-unit/utils/extra_config_test.py b/tests-unit/utils/extra_config_test.py index 143e1f2ee..eae1aa3d3 100644 --- a/tests-unit/utils/extra_config_test.py +++ b/tests-unit/utils/extra_config_test.py @@ -1,11 +1,22 @@ import pytest import yaml import os +import sys from unittest.mock import Mock, patch, mock_open from utils.extra_config import load_extra_path_config import folder_paths + +@pytest.fixture() +def clear_folder_paths(): + # Clear the global dictionary before each test to ensure isolation + original = folder_paths.folder_names_and_paths.copy() + folder_paths.folder_names_and_paths.clear() + yield + folder_paths.folder_names_and_paths = original + + @pytest.fixture def mock_yaml_content(): return { @@ -15,10 +26,12 @@ def mock_yaml_content(): } } + @pytest.fixture def mock_expanded_home(): return '/home/user' + @pytest.fixture def yaml_config_with_appdata(): return """ @@ -27,20 +40,33 @@ def yaml_config_with_appdata(): checkpoints: 'models/checkpoints' """ + @pytest.fixture def mock_yaml_content_appdata(yaml_config_with_appdata): return yaml.safe_load(yaml_config_with_appdata) + @pytest.fixture def mock_expandvars_appdata(): mock = Mock() - mock.side_effect = lambda path: path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming') + + def expandvars(path): + if '%APPDATA%' in path: + if sys.platform == 'win32': + return path.replace('%APPDATA%', 'C:/Users/TestUser/AppData/Roaming') + else: + return path.replace('%APPDATA%', '/Users/TestUser/AppData/Roaming') + return path + + mock.side_effect = expandvars return mock + @pytest.fixture def mock_add_model_folder_path(): return Mock() + @pytest.fixture def mock_expanduser(mock_expanded_home): def _expanduser(path): @@ -49,10 +75,12 @@ def mock_expanduser(mock_expanded_home): return path return _expanduser + @pytest.fixture def mock_yaml_safe_load(mock_yaml_content): return Mock(return_value=mock_yaml_content) + @patch('builtins.open', new_callable=mock_open, read_data="dummy file content") def test_load_extra_model_paths_expands_userpath( mock_file, @@ -86,7 +114,8 @@ def test_load_extra_model_paths_expands_userpath( mock_yaml_safe_load.assert_called_once() # Check if open was called with the correct file path - mock_file.assert_called_once_with(dummy_yaml_file_name, 'r') + mock_file.assert_called_once_with(dummy_yaml_file_name, 'r', encoding='utf-8') + @patch('builtins.open', new_callable=mock_open) def test_load_extra_model_paths_expands_appdata( @@ -111,9 +140,12 @@ def test_load_extra_model_paths_expands_appdata( dummy_yaml_file_name = 'dummy_path.yaml' load_extra_path_config(dummy_yaml_file_name) - expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI' + if sys.platform == "win32": + expected_base_path = 'C:/Users/TestUser/AppData/Roaming/ComfyUI' + else: + expected_base_path = '/Users/TestUser/AppData/Roaming/ComfyUI' expected_calls = [ - ('checkpoints', os.path.join(expected_base_path, 'models/checkpoints'), False), + ('checkpoints', os.path.normpath(os.path.join(expected_base_path, 'models/checkpoints')), False), ] assert mock_add_model_folder_path.call_count == len(expected_calls) @@ -124,3 +156,148 @@ def test_load_extra_model_paths_expands_appdata( # Verify that expandvars was called assert mock_expandvars_appdata.called + + +@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") +@patch("yaml.safe_load") +def test_load_extra_path_config_relative_base_path( + mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path +): + """ + Test that when 'base_path' is a relative path in the YAML, it is joined to the YAML file directory, and then + the items in the config are correctly converted to absolute paths. + """ + sub_folder = "./my_rel_base" + config_data = { + "some_model_folder": { + "base_path": sub_folder, + "is_default": True, + "checkpoints": "checkpoints", + "some_key": "some_value" + } + } + mock_yaml_load.return_value = config_data + + dummy_yaml_name = "dummy_file.yaml" + + def fake_abspath(path): + if path == dummy_yaml_name: + # If it's the YAML path, treat it like it lives in tmp_path + return os.path.join(str(tmp_path), dummy_yaml_name) + return os.path.join(str(tmp_path), path) # Otherwise, do a normal join relative to tmp_path + + def fake_dirname(path): + # We expect path to be the result of fake_abspath(dummy_yaml_name) + if path.endswith(dummy_yaml_name): + return str(tmp_path) + return os.path.dirname(path) + + monkeypatch.setattr(os.path, "abspath", fake_abspath) + monkeypatch.setattr(os.path, "dirname", fake_dirname) + + load_extra_path_config(dummy_yaml_name) + + expected_checkpoints = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "checkpoints")) + expected_some_value = os.path.abspath(os.path.join(str(tmp_path), "my_rel_base", "some_value")) + + actual_paths = folder_paths.folder_names_and_paths["checkpoints"][0] + assert len(actual_paths) == 1, "Should have one path added for 'checkpoints'." + assert actual_paths[0] == expected_checkpoints + + actual_paths = folder_paths.folder_names_and_paths["some_key"][0] + assert len(actual_paths) == 1, "Should have one path added for 'some_key'." + assert actual_paths[0] == expected_some_value + + +@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") +@patch("yaml.safe_load") +def test_load_extra_path_config_absolute_base_path( + mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path +): + """ + Test that when 'base_path' is an absolute path, each subdirectory is joined with that absolute path, + rather than being relative to the YAML's directory. + """ + abs_base = os.path.join(str(tmp_path), "abs_base") + config_data = { + "some_absolute_folder": { + "base_path": abs_base, # <-- absolute + "is_default": True, + "loras": "loras_folder", + "embeddings": "embeddings_folder" + } + } + mock_yaml_load.return_value = config_data + + dummy_yaml_name = "dummy_abs.yaml" + + def fake_abspath(path): + if path == dummy_yaml_name: + # If it's the YAML path, treat it like it is in tmp_path + return os.path.join(str(tmp_path), dummy_yaml_name) + return path # For absolute base, we just return path directly + + def fake_dirname(path): + return str(tmp_path) if path.endswith(dummy_yaml_name) else os.path.dirname(path) + + monkeypatch.setattr(os.path, "abspath", fake_abspath) + monkeypatch.setattr(os.path, "dirname", fake_dirname) + + load_extra_path_config(dummy_yaml_name) + + # Expect the final paths to be /loras_folder and /embeddings_folder + expected_loras = os.path.join(abs_base, "loras_folder") + expected_embeddings = os.path.join(abs_base, "embeddings_folder") + + actual_loras = folder_paths.folder_names_and_paths["loras"][0] + assert len(actual_loras) == 1, "Should have one path for 'loras'." + assert actual_loras[0] == os.path.abspath(expected_loras) + + actual_embeddings = folder_paths.folder_names_and_paths["embeddings"][0] + assert len(actual_embeddings) == 1, "Should have one path for 'embeddings'." + assert actual_embeddings[0] == os.path.abspath(expected_embeddings) + + +@patch("builtins.open", new_callable=mock_open, read_data="dummy yaml content") +@patch("yaml.safe_load") +def test_load_extra_path_config_no_base_path( + mock_yaml_load, _mock_file, clear_folder_paths, monkeypatch, tmp_path +): + """ + Test that if 'base_path' is not present, each path is joined + with the directory of the YAML file (unless it's already absolute). + """ + config_data = { + "some_folder_without_base": { + "is_default": True, + "text_encoders": "clip", + "diffusion_models": "unet" + } + } + mock_yaml_load.return_value = config_data + + dummy_yaml_name = "dummy_no_base.yaml" + + def fake_abspath(path): + if path == dummy_yaml_name: + return os.path.join(str(tmp_path), dummy_yaml_name) + return os.path.join(str(tmp_path), path) + + def fake_dirname(path): + return str(tmp_path) if path.endswith(dummy_yaml_name) else os.path.dirname(path) + + monkeypatch.setattr(os.path, "abspath", fake_abspath) + monkeypatch.setattr(os.path, "dirname", fake_dirname) + + load_extra_path_config(dummy_yaml_name) + + expected_clip = os.path.join(str(tmp_path), "clip") + expected_unet = os.path.join(str(tmp_path), "unet") + + actual_text_encoders = folder_paths.folder_names_and_paths["text_encoders"][0] + assert len(actual_text_encoders) == 1, "Should have one path for 'text_encoders'." + assert actual_text_encoders[0] == os.path.abspath(expected_clip) + + actual_diffusion = folder_paths.folder_names_and_paths["diffusion_models"][0] + assert len(actual_diffusion) == 1, "Should have one path for 'diffusion_models'." + assert actual_diffusion[0] == os.path.abspath(expected_unet) diff --git a/tests-unit/utils/json_util_test.py b/tests-unit/utils/json_util_test.py new file mode 100644 index 000000000..d3089d8d1 --- /dev/null +++ b/tests-unit/utils/json_util_test.py @@ -0,0 +1,71 @@ +from utils.json_util import merge_json_recursive + + +def test_merge_simple_dicts(): + base = {"a": 1, "b": 2} + update = {"b": 3, "c": 4} + expected = {"a": 1, "b": 3, "c": 4} + assert merge_json_recursive(base, update) == expected + + +def test_merge_nested_dicts(): + base = {"a": {"x": 1, "y": 2}, "b": 3} + update = {"a": {"y": 4, "z": 5}} + expected = {"a": {"x": 1, "y": 4, "z": 5}, "b": 3} + assert merge_json_recursive(base, update) == expected + + +def test_merge_lists(): + base = {"a": [1, 2], "b": 3} + update = {"a": [3, 4]} + expected = {"a": [1, 2, 3, 4], "b": 3} + assert merge_json_recursive(base, update) == expected + + +def test_merge_nested_lists(): + base = {"a": {"x": [1, 2]}} + update = {"a": {"x": [3, 4]}} + expected = {"a": {"x": [1, 2, 3, 4]}} + assert merge_json_recursive(base, update) == expected + + +def test_merge_mixed_types(): + base = {"a": [1, 2], "b": {"x": 1}} + update = {"a": [3], "b": {"y": 2}} + expected = {"a": [1, 2, 3], "b": {"x": 1, "y": 2}} + assert merge_json_recursive(base, update) == expected + + +def test_merge_overwrite_non_dict(): + base = {"a": 1} + update = {"a": {"x": 2}} + expected = {"a": {"x": 2}} + assert merge_json_recursive(base, update) == expected + + +def test_merge_empty_dicts(): + base = {} + update = {"a": 1} + expected = {"a": 1} + assert merge_json_recursive(base, update) == expected + + +def test_merge_none_values(): + base = {"a": None} + update = {"a": {"x": 1}} + expected = {"a": {"x": 1}} + assert merge_json_recursive(base, update) == expected + + +def test_merge_different_types(): + base = {"a": [1, 2]} + update = {"a": "string"} + expected = {"a": "string"} + assert merge_json_recursive(base, update) == expected + + +def test_merge_complex_nested(): + base = {"a": [1, 2], "b": {"x": [3, 4], "y": {"p": 1}}} + update = {"a": [5], "b": {"x": [6], "y": {"q": 2}}} + expected = {"a": [1, 2, 5], "b": {"x": [3, 4, 6], "y": {"p": 1, "q": 2}}} + assert merge_json_recursive(base, update) == expected diff --git a/tests-unit/websocket_feature_flags_test.py b/tests-unit/websocket_feature_flags_test.py new file mode 100644 index 000000000..e93b2e1dd --- /dev/null +++ b/tests-unit/websocket_feature_flags_test.py @@ -0,0 +1,77 @@ +"""Simplified tests for WebSocket feature flags functionality.""" +from comfy_api import feature_flags + + +class TestWebSocketFeatureFlags: + """Test suite for WebSocket feature flags integration.""" + + def test_server_feature_flags_response(self): + """Test server feature flags are properly formatted.""" + features = feature_flags.get_server_features() + + # Check expected server features + assert "supports_preview_metadata" in features + assert features["supports_preview_metadata"] is True + assert "max_upload_size" in features + assert isinstance(features["max_upload_size"], (int, float)) + + def test_progress_py_checks_feature_flags(self): + """Test that progress.py checks feature flags before sending metadata.""" + # This simulates the check in progress.py + client_id = "test_client" + sockets_metadata = {"test_client": {"feature_flags": {}}} + + # The actual check would be in progress.py + supports_metadata = feature_flags.supports_feature( + sockets_metadata, client_id, "supports_preview_metadata" + ) + + assert supports_metadata is False + + def test_multiple_clients_different_features(self): + """Test handling multiple clients with different feature support.""" + sockets_metadata = { + "modern_client": { + "feature_flags": {"supports_preview_metadata": True} + }, + "legacy_client": { + "feature_flags": {} + } + } + + # Check modern client + assert feature_flags.supports_feature( + sockets_metadata, "modern_client", "supports_preview_metadata" + ) is True + + # Check legacy client + assert feature_flags.supports_feature( + sockets_metadata, "legacy_client", "supports_preview_metadata" + ) is False + + def test_feature_negotiation_message_format(self): + """Test the format of feature negotiation messages.""" + # Client message format + client_message = { + "type": "feature_flags", + "data": { + "supports_preview_metadata": True, + "api_version": "1.0.0" + } + } + + # Verify structure + assert client_message["type"] == "feature_flags" + assert "supports_preview_metadata" in client_message["data"] + + # Server response format (what would be sent) + server_features = feature_flags.get_server_features() + server_message = { + "type": "feature_flags", + "data": server_features + } + + # Verify structure + assert server_message["type"] == "feature_flags" + assert "supports_preview_metadata" in server_message["data"] + assert server_message["data"]["supports_preview_metadata"] is True diff --git a/tests/conftest.py b/tests/conftest.py index 4e30eb581..290e3a5c0 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -6,6 +6,7 @@ def pytest_addoption(parser): parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images') parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)") parser.addoption("--port", type=int, default=8188, help="Set the listen port.") + parser.addoption("--skip-timing-checks", action="store_true", default=False, help="Skip timing-related assertions in tests (useful for CI environments with variable performance)") # This initializes args at the beginning of the test session @pytest.fixture(scope="session", autouse=True) @@ -19,6 +20,11 @@ def args_pytest(pytestconfig): return args +@pytest.fixture(scope="session") +def skip_timing_checks(pytestconfig): + """Fixture that returns whether timing checks should be skipped.""" + return pytestconfig.getoption("--skip-timing-checks") + def pytest_collection_modifyitems(items): # Modifies items so tests run in the correct order diff --git a/tests/execution/extra_model_paths.yaml b/tests/execution/extra_model_paths.yaml new file mode 100644 index 000000000..68e056564 --- /dev/null +++ b/tests/execution/extra_model_paths.yaml @@ -0,0 +1,4 @@ +# Config for testing nodes +testing: + custom_nodes: testing_nodes + diff --git a/tests/execution/test_async_nodes.py b/tests/execution/test_async_nodes.py new file mode 100644 index 000000000..c771b4b36 --- /dev/null +++ b/tests/execution/test_async_nodes.py @@ -0,0 +1,427 @@ +import pytest +import time +import torch +import urllib.error +import numpy as np +import subprocess + +from pytest import fixture +from comfy_execution.graph_utils import GraphBuilder +from tests.execution.test_execution import ComfyClient, run_warmup + + +@pytest.mark.execution +class TestAsyncNodes: + @fixture(scope="class", autouse=True, params=[ + (False, 0), + (True, 0), + (True, 100), + ]) + def _server(self, args_pytest, request): + pargs = [ + 'python','main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml', + '--cpu', + ] + use_lru, lru_size = request.param + if use_lru: + pargs += ['--cache-lru', str(lru_size)] + # Running server with args: pargs + p = subprocess.Popen(pargs) + yield + p.kill() + torch.cuda.empty_cache() + + @fixture(scope="class", autouse=True) + def shared_client(self, args_pytest, _server): + client = ComfyClient() + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + client.connect(listen=args_pytest["listen"], port=args_pytest["port"]) + except ConnectionRefusedError: + # Retrying... + pass + else: + break + yield client + del client + torch.cuda.empty_cache() + + @fixture + def client(self, shared_client, request): + shared_client.set_test_name(f"async_nodes[{request.node.name}]") + yield shared_client + + @fixture + def builder(self, request): + yield GraphBuilder(prefix=request.node.name) + + # Happy Path Tests + + def test_basic_async_execution(self, client: ComfyClient, builder: GraphBuilder): + """Test that a basic async node executes correctly.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.1) + output = g.node("SaveImage", images=sleep_node.out(0)) + + result = client.run(g) + + # Verify execution completed + assert result.did_run(sleep_node), "Async sleep node should have executed" + assert result.did_run(output), "Output node should have executed" + + # Verify the image passed through correctly + result_images = result.get_images(output) + assert len(result_images) == 1, "Should have 1 image" + assert np.array(result_images[0]).min() == 0 and np.array(result_images[0]).max() == 0, "Image should be black" + + def test_multiple_async_parallel_execution(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + """Test that multiple async nodes execute in parallel.""" + # Warmup execution to ensure server is fully initialized + run_warmup(client) + + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # Create multiple async sleep nodes with different durations + sleep1 = g.node("TestSleep", value=image.out(0), seconds=0.3) + sleep2 = g.node("TestSleep", value=image.out(0), seconds=0.4) + sleep3 = g.node("TestSleep", value=image.out(0), seconds=0.5) + + # Add outputs for each + _output1 = g.node("PreviewImage", images=sleep1.out(0)) + _output2 = g.node("PreviewImage", images=sleep2.out(0)) + _output3 = g.node("PreviewImage", images=sleep3.out(0)) + + start_time = time.time() + result = client.run(g) + elapsed_time = time.time() - start_time + + # Should take ~0.5s (max duration) not 1.2s (sum of durations) + if not skip_timing_checks: + assert elapsed_time < 0.8, f"Parallel execution took {elapsed_time}s, expected < 0.8s" + + # Verify all nodes executed + assert result.did_run(sleep1) and result.did_run(sleep2) and result.did_run(sleep3) + + def test_async_with_dependencies(self, client: ComfyClient, builder: GraphBuilder): + """Test async nodes with proper dependency handling.""" + g = builder + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Chain of async operations + sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2) + sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2) + + # Average depends on both async results + average = g.node("TestVariadicAverage", input1=sleep1.out(0), input2=sleep2.out(0)) + output = g.node("SaveImage", images=average.out(0)) + + result = client.run(g) + + # Verify execution order + assert result.did_run(sleep1) and result.did_run(sleep2) + assert result.did_run(average) and result.did_run(output) + + # Verify averaged result + result_images = result.get_images(output) + avg_value = np.array(result_images[0]).mean() + assert abs(avg_value - 127.5) < 1, f"Average value {avg_value} should be ~127.5" + + def test_async_validate_inputs(self, client: ComfyClient, builder: GraphBuilder): + """Test async VALIDATE_INPUTS function.""" + g = builder + # Create a test node with async validation + validation_node = g.node("TestAsyncValidation", value=5.0, threshold=10.0) + g.node("SaveImage", images=validation_node.out(0)) + + # Should pass validation + result = client.run(g) + assert result.did_run(validation_node) + + # Test validation failure + validation_node.inputs['threshold'] = 3.0 # Will fail since value > threshold + with pytest.raises(urllib.error.HTTPError): + client.run(g) + + def test_async_lazy_evaluation(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + """Test async nodes with lazy evaluation.""" + # Warmup execution to ensure server is fully initialized + run_warmup(client, prefix="warmup_lazy") + + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) + + # Create async nodes that will be evaluated lazily + sleep1 = g.node("TestSleep", value=input1.out(0), seconds=0.3) + sleep2 = g.node("TestSleep", value=input2.out(0), seconds=0.3) + + # Use lazy mix that only needs sleep1 (mask=0.0) + lazy_mix = g.node("TestLazyMixImages", image1=sleep1.out(0), image2=sleep2.out(0), mask=mask.out(0)) + g.node("SaveImage", images=lazy_mix.out(0)) + + start_time = time.time() + result = client.run(g) + elapsed_time = time.time() - start_time + + # Should only execute sleep1, not sleep2 + if not skip_timing_checks: + assert elapsed_time < 0.5, f"Should skip sleep2, took {elapsed_time}s" + assert result.did_run(sleep1), "Sleep1 should have executed" + assert not result.did_run(sleep2), "Sleep2 should have been skipped" + + def test_async_check_lazy_status(self, client: ComfyClient, builder: GraphBuilder): + """Test async check_lazy_status function.""" + g = builder + # Create a node with async check_lazy_status + lazy_node = g.node("TestAsyncLazyCheck", + input1="value1", + input2="value2", + condition=True) + g.node("SaveImage", images=lazy_node.out(0)) + + result = client.run(g) + assert result.did_run(lazy_node) + + # Error Handling Tests + + def test_async_execution_error(self, client: ComfyClient, builder: GraphBuilder): + """Test that async execution errors are properly handled.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + # Create an async node that will error + error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1) + g.node("SaveImage", images=error_node.out(0)) + + try: + client.run(g) + assert False, "Should have raised an error" + except Exception as e: + assert 'prompt_id' in e.args[0], f"Did not get proper error message: {e}" + assert e.args[0]['node_id'] == error_node.id, "Error should be from async error node" + + def test_async_validation_error(self, client: ComfyClient, builder: GraphBuilder): + """Test async validation error handling.""" + g = builder + # Node with async validation that will fail + validation_node = g.node("TestAsyncValidationError", value=15.0, max_value=10.0) + g.node("SaveImage", images=validation_node.out(0)) + + with pytest.raises(urllib.error.HTTPError) as exc_info: + client.run(g) + # Verify it's a validation error + assert exc_info.value.code == 400 + + def test_async_timeout_handling(self, client: ComfyClient, builder: GraphBuilder): + """Test handling of async operations that timeout.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + # Very long sleep that would timeout + timeout_node = g.node("TestAsyncTimeout", value=image.out(0), timeout=0.5, operation_time=2.0) + g.node("SaveImage", images=timeout_node.out(0)) + + try: + client.run(g) + assert False, "Should have raised a timeout error" + except Exception as e: + assert 'timeout' in str(e).lower(), f"Expected timeout error, got: {e}" + + def test_concurrent_async_error_recovery(self, client: ComfyClient, builder: GraphBuilder): + """Test that workflow can recover after async errors.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # First run with error + error_node = g.node("TestAsyncError", value=image.out(0), error_after=0.1) + g.node("SaveImage", images=error_node.out(0)) + + try: + client.run(g) + except Exception: + pass # Expected + + # Second run should succeed + g2 = GraphBuilder(prefix="recovery_test") + image2 = g2.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + sleep_node = g2.node("TestSleep", value=image2.out(0), seconds=0.1) + g2.node("SaveImage", images=sleep_node.out(0)) + + result = client.run(g2) + assert result.did_run(sleep_node), "Should be able to run after error" + + def test_sync_error_during_async_execution(self, client: ComfyClient, builder: GraphBuilder): + """Test handling when sync node errors while async node is executing.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # Async node that takes time + sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.5) + + # Sync node that will error immediately + error_node = g.node("TestSyncError", value=image.out(0)) + + # Both feed into output + g.node("PreviewImage", images=sleep_node.out(0)) + g.node("PreviewImage", images=error_node.out(0)) + + try: + client.run(g) + assert False, "Should have raised an error" + except Exception as e: + # Verify the sync error was caught even though async was running + assert 'prompt_id' in e.args[0] + + # Edge Cases + + def test_async_with_execution_blocker(self, client: ComfyClient, builder: GraphBuilder): + """Test async nodes with execution blockers.""" + g = builder + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Async sleep nodes + sleep1 = g.node("TestSleep", value=image1.out(0), seconds=0.2) + sleep2 = g.node("TestSleep", value=image2.out(0), seconds=0.2) + + # Create list of images + image_list = g.node("TestMakeListNode", value1=sleep1.out(0), value2=sleep2.out(0)) + + # Create list of blocking conditions - [False, True] to block only the second item + int1 = g.node("StubInt", value=1) + int2 = g.node("StubInt", value=2) + block_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0)) + + # Compare each value against 2, so first is False (1 != 2) and second is True (2 == 2) + compare = g.node("TestIntConditions", a=block_list.out(0), b=2, operation="==") + + # Block based on the comparison results + blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False) + + output = g.node("PreviewImage", images=blocker.out(0)) + + result = client.run(g) + images = result.get_images(output) + assert len(images) == 1, "Should have blocked second image" + + def test_async_caching_behavior(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + """Test that async nodes are properly cached.""" + # Warmup execution to ensure server is fully initialized + run_warmup(client, prefix="warmup_cache") + + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + sleep_node = g.node("TestSleep", value=image.out(0), seconds=0.2) + g.node("SaveImage", images=sleep_node.out(0)) + + # First run + result1 = client.run(g) + assert result1.did_run(sleep_node), "Should run first time" + + # Second run - should be cached + start_time = time.time() + result2 = client.run(g) + elapsed_time = time.time() - start_time + + assert not result2.did_run(sleep_node), "Should be cached" + if not skip_timing_checks: + assert elapsed_time < 0.1, f"Cached run took {elapsed_time}s, should be instant" + + def test_async_with_dynamic_prompts(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + """Test async nodes within dynamically generated prompts.""" + # Warmup execution to ensure server is fully initialized + run_warmup(client, prefix="warmup_dynamic") + + g = builder + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Node that generates async nodes dynamically + dynamic_async = g.node("TestDynamicAsyncGeneration", + image1=image1.out(0), + image2=image2.out(0), + num_async_nodes=5, + sleep_duration=0.4) + g.node("SaveImage", images=dynamic_async.out(0)) + + start_time = time.time() + result = client.run(g) + elapsed_time = time.time() - start_time + + # Should execute async nodes in parallel within dynamic prompt + if not skip_timing_checks: + assert elapsed_time < 1.0, f"Dynamic async execution took {elapsed_time}s" + assert result.did_run(dynamic_async) + + def test_async_resource_cleanup(self, client: ComfyClient, builder: GraphBuilder): + """Test that async resources are properly cleaned up.""" + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # Create multiple async nodes that use resources + resource_nodes = [] + for i in range(5): + node = g.node("TestAsyncResourceUser", + value=image.out(0), + resource_id=f"resource_{i}", + duration=0.1) + resource_nodes.append(node) + g.node("PreviewImage", images=node.out(0)) + + result = client.run(g) + + # Verify all nodes executed + for node in resource_nodes: + assert result.did_run(node) + + # Run again to ensure resources were cleaned up + result2 = client.run(g) + # Should be cached but not error due to resource conflicts + for node in resource_nodes: + assert not result2.did_run(node), "Should be cached" + + def test_async_cancellation(self, client: ComfyClient, builder: GraphBuilder): + """Test cancellation of async operations.""" + # This would require implementing cancellation in the client + # For now, we'll test that long-running async operations can be interrupted + pass # TODO: Implement when cancellation API is available + + def test_mixed_sync_async_execution(self, client: ComfyClient, builder: GraphBuilder): + """Test workflows with both sync and async nodes.""" + g = builder + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) + + # Mix of sync and async operations + # Sync: lazy mix images + sync_op1 = g.node("TestLazyMixImages", image1=image1.out(0), image2=image2.out(0), mask=mask.out(0)) + # Async: sleep + async_op1 = g.node("TestSleep", value=sync_op1.out(0), seconds=0.2) + # Sync: custom validation + sync_op2 = g.node("TestCustomValidation1", input1=async_op1.out(0), input2=0.5) + # Async: sleep again + async_op2 = g.node("TestSleep", value=sync_op2.out(0), seconds=0.2) + + output = g.node("SaveImage", images=async_op2.out(0)) + + result = client.run(g) + + # Verify all nodes executed in correct order + assert result.did_run(sync_op1) + assert result.did_run(async_op1) + assert result.did_run(sync_op2) + assert result.did_run(async_op2) + + # Image should be a mix of black and white (gray) + result_images = result.get_images(output) + avg_value = np.array(result_images[0]).mean() + assert abs(avg_value - 63.75) < 5, f"Average value {avg_value} should be ~63.75" diff --git a/tests/inference/test_execution.py b/tests/execution/test_execution.py similarity index 54% rename from tests/inference/test_execution.py rename to tests/execution/test_execution.py index 5cda5c1ae..ace0d2279 100644 --- a/tests/inference/test_execution.py +++ b/tests/execution/test_execution.py @@ -15,10 +15,18 @@ import urllib.parse import urllib.error from comfy_execution.graph_utils import GraphBuilder, Node +def run_warmup(client, prefix="warmup"): + """Run a simple workflow to warm up the server.""" + warmup_g = GraphBuilder(prefix=prefix) + warmup_image = warmup_g.node("StubImage", content="BLACK", height=32, width=32, batch_size=1) + warmup_g.node("PreviewImage", images=warmup_image.out(0)) + client.run(warmup_g) + class RunResult: def __init__(self, prompt_id: str): self.outputs: Dict[str,Dict] = {} self.runs: Dict[str,bool] = {} + self.cached: Dict[str,bool] = {} self.prompt_id: str = prompt_id def get_output(self, node: Node): @@ -27,6 +35,13 @@ class RunResult: def did_run(self, node: Node): return self.runs.get(node.id, False) + def was_cached(self, node: Node): + return self.cached.get(node.id, False) + + def was_executed(self, node: Node): + """Returns True if node was either run or cached""" + return self.did_run(node) or self.was_cached(node) + def get_images(self, node: Node): output = self.get_output(node) if output is None: @@ -51,8 +66,10 @@ class ComfyClient: ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id)) self.ws = ws - def queue_prompt(self, prompt): + def queue_prompt(self, prompt, partial_execution_targets=None): p = {"prompt": prompt, "client_id": self.client_id} + if partial_execution_targets is not None: + p["partial_execution_targets"] = partial_execution_targets data = json.dumps(p).encode('utf-8') req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data) return json.loads(urllib.request.urlopen(req).read()) @@ -67,16 +84,31 @@ class ComfyClient: with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response: return json.loads(response.read()) + def get_all_history(self, max_items=None, offset=None): + url = "http://{}/history".format(self.server_address) + params = {} + if max_items is not None: + params["max_items"] = max_items + if offset is not None: + params["offset"] = offset + + if params: + url_values = urllib.parse.urlencode(params) + url = "{}?{}".format(url, url_values) + + with urllib.request.urlopen(url) as response: + return json.loads(response.read()) + def set_test_name(self, name): self.test_name = name - def run(self, graph): + def run(self, graph, partial_execution_targets=None): prompt = graph.finalize() for node in graph.nodes.values(): if node.class_type == 'SaveImage': node.inputs['filename_prefix'] = self.test_name - prompt_id = self.queue_prompt(prompt)['prompt_id'] + prompt_id = self.queue_prompt(prompt, partial_execution_targets)['prompt_id'] result = RunResult(prompt_id) while True: out = self.ws.recv() @@ -92,7 +124,10 @@ class ComfyClient: elif message['type'] == 'execution_error': raise Exception(message['data']) elif message['type'] == 'execution_cached': - pass # Probably want to store this off for testing + if message['data']['prompt_id'] == prompt_id: + cached_nodes = message['data'].get('nodes', []) + for node_id in cached_nodes: + result.cached[node_id] = True history = self.get_history(prompt_id)[prompt_id] for node_id in history['outputs']: @@ -117,26 +152,25 @@ class TestExecution: # Initialize server and client # @fixture(scope="class", autouse=True, params=[ - # (use_lru, lru_size) - (False, 0), - (True, 0), - (True, 100), + { "extra_args" : [], "should_cache_results" : True }, + { "extra_args" : ["--cache-lru", 0], "should_cache_results" : True }, + { "extra_args" : ["--cache-lru", 100], "should_cache_results" : True }, + { "extra_args" : ["--cache-none"], "should_cache_results" : False }, ]) - def _server(self, args_pytest, request): + def server(self, args_pytest, request): # Start server pargs = [ 'python','main.py', '--output-directory', args_pytest["output_dir"], '--listen', args_pytest["listen"], '--port', str(args_pytest["port"]), - '--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml', + '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml', + '--cpu', ] - use_lru, lru_size = request.param - if use_lru: - pargs += ['--cache-lru', str(lru_size)] + pargs += [ str(param) for param in request.param["extra_args"] ] print("Running server with args:", pargs) # noqa: T201 p = subprocess.Popen(pargs) - yield + yield request.param p.kill() torch.cuda.empty_cache() @@ -157,7 +191,7 @@ class TestExecution: return comfy_client @fixture(scope="class", autouse=True) - def shared_client(self, args_pytest, _server): + def shared_client(self, args_pytest, server): client = self.start_client(args_pytest["listen"], args_pytest["port"]) yield client del client @@ -189,7 +223,7 @@ class TestExecution: assert result.did_run(mask) assert result.did_run(lazy_mix) - def test_full_cache(self, client: ComfyClient, builder: GraphBuilder): + def test_full_cache(self, client: ComfyClient, builder: GraphBuilder, server): g = builder input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) @@ -201,9 +235,12 @@ class TestExecution: client.run(g) result2 = client.run(g) for node_id, node in g.nodes.items(): - assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached" + if server["should_cache_results"]: + assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached" + else: + assert result2.did_run(node), f"Node {node_id} was cached, but should have been run" - def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder): + def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder, server): g = builder input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) @@ -215,8 +252,12 @@ class TestExecution: client.run(g) mask.inputs['value'] = 0.4 result2 = client.run(g) - assert not result2.did_run(input1), "Input1 should have been cached" - assert not result2.did_run(input2), "Input2 should have been cached" + if server["should_cache_results"]: + assert not result2.did_run(input1), "Input1 should have been cached" + assert not result2.did_run(input2), "Input2 should have been cached" + else: + assert result2.did_run(input1), "Input1 should have been rerun" + assert result2.did_run(input2), "Input2 should have been rerun" def test_error(self, client: ComfyClient, builder: GraphBuilder): g = builder @@ -252,7 +293,7 @@ class TestExecution: @pytest.mark.parametrize("test_type, test_value", [ ("StubInt", 5), - ("StubFloat", 5.0) + ("StubMask", 5.0) ]) def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder): g = builder @@ -375,7 +416,7 @@ class TestExecution: input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1) client.run(g) - def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder): + def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder, server): g = builder # Creating the nodes in this specific order previously caused a bug save = g.node("SaveImage") @@ -391,7 +432,10 @@ class TestExecution: result3 = client.run(g) result4 = client.run(g) assert result1.did_run(is_changed), "is_changed should have been run" - assert not result2.did_run(is_changed), "is_changed should have been cached" + if server["should_cache_results"]: + assert not result2.did_run(is_changed), "is_changed should have been cached" + else: + assert result2.did_run(is_changed), "is_changed should have been re-run" assert result3.did_run(is_changed), "is_changed should have been re-run" assert result4.did_run(is_changed), "is_changed should not have been cached" @@ -477,9 +521,8 @@ class TestExecution: assert len(images1) == 1, "Should have 1 image" assert len(images2) == 1, "Should have 1 image" - # This tests that only constant outputs are used in the call to `IS_CHANGED` - def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder): + def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder, server): g = builder input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1) test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5) @@ -495,7 +538,82 @@ class TestExecution: images = result.get_images(output) assert len(images) == 1, "Should have 1 image" assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25" - assert not result.did_run(test_node), "The execution should have been cached" + if server["should_cache_results"]: + assert not result.did_run(test_node), "The execution should have been cached" + else: + assert result.did_run(test_node), "The execution should have been re-run" + + + def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + # Warmup execution to ensure server is fully initialized + run_warmup(client) + + g = builder + image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # Create sleep nodes for each duration + sleep_node1 = g.node("TestSleep", value=image.out(0), seconds=2.9) + sleep_node2 = g.node("TestSleep", value=image.out(0), seconds=3.1) + sleep_node3 = g.node("TestSleep", value=image.out(0), seconds=3.0) + + # Add outputs to verify the execution + _output1 = g.node("PreviewImage", images=sleep_node1.out(0)) + _output2 = g.node("PreviewImage", images=sleep_node2.out(0)) + _output3 = g.node("PreviewImage", images=sleep_node3.out(0)) + + start_time = time.time() + result = client.run(g) + elapsed_time = time.time() - start_time + + # The test should take around 3.0 seconds (the longest sleep duration) + # plus some overhead, but definitely less than the sum of all sleeps (9.0s) + if not skip_timing_checks: + assert elapsed_time < 8.9, f"Parallel execution took {elapsed_time}s, expected less than 8.9s" + + # Verify that all nodes executed + assert result.did_run(sleep_node1), "Sleep node 1 should have run" + assert result.did_run(sleep_node2), "Sleep node 2 should have run" + assert result.did_run(sleep_node3), "Sleep node 3 should have run" + + def test_parallel_sleep_expansion(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks): + # Warmup execution to ensure server is fully initialized + run_warmup(client) + + g = builder + # Create input images with different values + image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + image3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Create a TestParallelSleep node that expands into multiple TestSleep nodes + parallel_sleep = g.node("TestParallelSleep", + image1=image1.out(0), + image2=image2.out(0), + image3=image3.out(0), + sleep1=4.8, + sleep2=4.9, + sleep3=5.0) + output = g.node("SaveImage", images=parallel_sleep.out(0)) + + start_time = time.time() + result = client.run(g) + elapsed_time = time.time() - start_time + + # Similar to the previous test, expect parallel execution of the sleep nodes + # which should complete in less than the sum of all sleeps + # Lots of leeway here since Windows CI is slow + if not skip_timing_checks: + assert elapsed_time < 13.0, f"Expansion execution took {elapsed_time}s" + + # Verify the parallel sleep node executed + assert result.did_run(parallel_sleep), "ParallelSleep node should have run" + + # Verify we get an image as output (blend of the three input images) + result_images = result.get_images(output) + assert len(result_images) == 1, "Should have 1 image" + # Average pixel value should be around 170 (255 * 2 // 3) + avg_value = numpy.array(result_images[0]).mean() + assert avg_value == 170, f"Image average value {avg_value} should be 170" # This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker # as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node, @@ -522,3 +640,240 @@ class TestExecution: assert len(images) == 2, "Should have 2 images" assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black" assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black" + + # Output nodes included in the partial execution list are executed + def test_partial_execution_included_outputs(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Create two separate output nodes + output1 = g.node("SaveImage", images=input1.out(0)) + output2 = g.node("SaveImage", images=input2.out(0)) + + # Run with partial execution targeting only output1 + result = client.run(g, partial_execution_targets=[output1.id]) + + assert result.was_executed(input1), "Input1 should have been executed (run or cached)" + assert result.was_executed(output1), "Output1 should have been executed (run or cached)" + assert not result.did_run(input2), "Input2 should not have run" + assert not result.did_run(output2), "Output2 should not have run" + + # Verify only output1 produced results + assert len(result.get_images(output1)) == 1, "Output1 should have produced an image" + assert len(result.get_images(output2)) == 0, "Output2 should not have produced an image" + + # Output nodes NOT included in the partial execution list are NOT executed + def test_partial_execution_excluded_outputs(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + input3 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) + + # Create three output nodes + output1 = g.node("SaveImage", images=input1.out(0)) + output2 = g.node("SaveImage", images=input2.out(0)) + output3 = g.node("SaveImage", images=input3.out(0)) + + # Run with partial execution targeting only output1 and output3 + result = client.run(g, partial_execution_targets=[output1.id, output3.id]) + + assert result.was_executed(input1), "Input1 should have been executed" + assert result.was_executed(input3), "Input3 should have been executed" + assert result.was_executed(output1), "Output1 should have been executed" + assert result.was_executed(output3), "Output3 should have been executed" + assert not result.did_run(input2), "Input2 should not have run" + assert not result.did_run(output2), "Output2 should not have run" + + # Output nodes NOT in list ARE executed if necessary for nodes that are in the list + def test_partial_execution_dependencies(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + + # Create a processing chain with an OUTPUT_NODE that has socket outputs + output_with_socket = g.node("TestOutputNodeWithSocketOutput", image=input1.out(0), value=2.0) + + # Create another node that depends on the output_with_socket + dependent_node = g.node("TestLazyMixImages", + image1=output_with_socket.out(0), + image2=input1.out(0), + mask=g.node("StubMask", value=0.5, height=512, width=512, batch_size=1).out(0)) + + # Create the final output + final_output = g.node("SaveImage", images=dependent_node.out(0)) + + # Run with partial execution targeting only the final output + result = client.run(g, partial_execution_targets=[final_output.id]) + + # All nodes should have been executed because they're dependencies + assert result.was_executed(input1), "Input1 should have been executed" + assert result.was_executed(output_with_socket), "Output with socket should have been executed (dependency)" + assert result.was_executed(dependent_node), "Dependent node should have been executed" + assert result.was_executed(final_output), "Final output should have been executed" + + # Lazy execution works with partial execution + def test_partial_execution_with_lazy_nodes(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + input3 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1) + + # Create masks that will trigger different lazy execution paths + mask1 = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1) # Will only need image1 + mask2 = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1) # Will need both images + + # Create two lazy mix nodes + lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask1.out(0)) + lazy_mix2 = g.node("TestLazyMixImages", image1=input2.out(0), image2=input3.out(0), mask=mask2.out(0)) + + output1 = g.node("SaveImage", images=lazy_mix1.out(0)) + output2 = g.node("SaveImage", images=lazy_mix2.out(0)) + + # Run with partial execution targeting only output1 + result = client.run(g, partial_execution_targets=[output1.id]) + + # For output1 path - only input1 should run due to lazy evaluation (mask=0.0) + assert result.was_executed(input1), "Input1 should have been executed" + assert not result.did_run(input2), "Input2 should not have run (lazy evaluation)" + assert result.was_executed(mask1), "Mask1 should have been executed" + assert result.was_executed(lazy_mix1), "Lazy mix1 should have been executed" + assert result.was_executed(output1), "Output1 should have been executed" + + # Nothing from output2 path should run + assert not result.did_run(input3), "Input3 should not have run" + assert not result.did_run(mask2), "Mask2 should not have run" + assert not result.did_run(lazy_mix2), "Lazy mix2 should not have run" + assert not result.did_run(output2), "Output2 should not have run" + + # Multiple OUTPUT_NODEs with dependencies + def test_partial_execution_multiple_output_nodes(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1) + + # Create a chain of OUTPUT_NODEs + output_node1 = g.node("TestOutputNodeWithSocketOutput", image=input1.out(0), value=1.5) + output_node2 = g.node("TestOutputNodeWithSocketOutput", image=output_node1.out(0), value=2.0) + + # Create regular output nodes + save1 = g.node("SaveImage", images=output_node1.out(0)) + save2 = g.node("SaveImage", images=output_node2.out(0)) + save3 = g.node("SaveImage", images=input2.out(0)) + + # Run targeting only save2 + result = client.run(g, partial_execution_targets=[save2.id]) + + # Should run: input1, output_node1, output_node2, save2 + assert result.was_executed(input1), "Input1 should have been executed" + assert result.was_executed(output_node1), "Output node 1 should have been executed (dependency)" + assert result.was_executed(output_node2), "Output node 2 should have been executed (dependency)" + assert result.was_executed(save2), "Save2 should have been executed" + + # Should NOT run: input2, save1, save3 + assert not result.did_run(input2), "Input2 should not have run" + assert not result.did_run(save1), "Save1 should not have run" + assert not result.did_run(save3), "Save3 should not have run" + + # Empty partial execution list (should execute nothing) + def test_partial_execution_empty_list(self, client: ComfyClient, builder: GraphBuilder): + g = builder + input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) + _output1 = g.node("SaveImage", images=input1.out(0)) + + # Run with empty partial execution list + try: + _result = client.run(g, partial_execution_targets=[]) + # Should get an error because no outputs are selected + assert False, "Should have raised an error for empty partial execution list" + except urllib.error.HTTPError: + pass # Expected behavior + + def _create_history_item(self, client, builder): + g = GraphBuilder(prefix="offset_test") + input_node = g.node( + "StubImage", content="BLACK", height=32, width=32, batch_size=1 + ) + g.node("SaveImage", images=input_node.out(0)) + return client.run(g) + + def test_offset_returns_different_items_than_beginning_of_history( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test that offset skips items at the beginning""" + for _ in range(5): + self._create_history_item(client, builder) + + first_two = client.get_all_history(max_items=2, offset=0) + next_two = client.get_all_history(max_items=2, offset=2) + + assert set(first_two.keys()).isdisjoint( + set(next_two.keys()) + ), "Offset should skip initial items" + + def test_offset_beyond_history_length_returns_empty( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset larger than total history returns empty result""" + self._create_history_item(client, builder) + + result = client.get_all_history(offset=100) + assert len(result) == 0, "Large offset should return no items" + + def test_offset_at_exact_history_length_returns_empty( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset equal to history length returns empty""" + for _ in range(3): + self._create_history_item(client, builder) + + all_history = client.get_all_history() + result = client.get_all_history(offset=len(all_history)) + assert len(result) == 0, "Offset at history length should return empty" + + def test_offset_zero_equals_no_offset_parameter( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset=0 behaves same as omitting offset""" + self._create_history_item(client, builder) + + with_zero = client.get_all_history(offset=0) + without_offset = client.get_all_history() + + assert with_zero == without_offset, "offset=0 should equal no offset" + + def test_offset_without_max_items_skips_from_beginning( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset alone (no max_items) returns remaining items""" + for _ in range(4): + self._create_history_item(client, builder) + + all_items = client.get_all_history() + offset_items = client.get_all_history(offset=2) + + assert ( + len(offset_items) == len(all_items) - 2 + ), "Offset should skip specified number of items" + + def test_offset_with_max_items_returns_correct_window( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset + max_items returns correct slice of history""" + for _ in range(6): + self._create_history_item(client, builder) + + window = client.get_all_history(max_items=2, offset=1) + assert len(window) <= 2, "Should respect max_items limit" + + def test_offset_near_end_returns_remaining_items_only( + self, client: ComfyClient, builder: GraphBuilder + ): + """Test offset near end of history returns only remaining items""" + for _ in range(3): + self._create_history_item(client, builder) + + all_history = client.get_all_history() + # Offset to near the end + result = client.get_all_history(max_items=5, offset=len(all_history) - 1) + + assert len(result) <= 1, "Should return at most 1 item when offset is near end" diff --git a/tests/execution/test_progress_isolation.py b/tests/execution/test_progress_isolation.py new file mode 100644 index 000000000..93dc0d41b --- /dev/null +++ b/tests/execution/test_progress_isolation.py @@ -0,0 +1,233 @@ +"""Test that progress updates are properly isolated between WebSocket clients.""" + +import json +import pytest +import time +import threading +import uuid +import websocket +from typing import List, Dict, Any +from comfy_execution.graph_utils import GraphBuilder +from tests.execution.test_execution import ComfyClient + + +class ProgressTracker: + """Tracks progress messages received by a WebSocket client.""" + + def __init__(self, client_id: str): + self.client_id = client_id + self.progress_messages: List[Dict[str, Any]] = [] + self.lock = threading.Lock() + + def add_message(self, message: Dict[str, Any]): + """Thread-safe addition of progress messages.""" + with self.lock: + self.progress_messages.append(message) + + def get_messages_for_prompt(self, prompt_id: str) -> List[Dict[str, Any]]: + """Get all progress messages for a specific prompt_id.""" + with self.lock: + return [ + msg for msg in self.progress_messages + if msg.get('data', {}).get('prompt_id') == prompt_id + ] + + def has_cross_contamination(self, own_prompt_id: str) -> bool: + """Check if this client received progress for other prompts.""" + with self.lock: + for msg in self.progress_messages: + msg_prompt_id = msg.get('data', {}).get('prompt_id') + if msg_prompt_id and msg_prompt_id != own_prompt_id: + return True + return False + + +class IsolatedClient(ComfyClient): + """Extended ComfyClient that tracks all WebSocket messages.""" + + def __init__(self): + super().__init__() + self.progress_tracker = None + self.all_messages: List[Dict[str, Any]] = [] + + def connect(self, listen='127.0.0.1', port=8188, client_id=None): + """Connect with a specific client_id and set up message tracking.""" + if client_id is None: + client_id = str(uuid.uuid4()) + super().connect(listen, port, client_id) + self.progress_tracker = ProgressTracker(client_id) + + def listen_for_messages(self, duration: float = 5.0): + """Listen for WebSocket messages for a specified duration.""" + end_time = time.time() + duration + self.ws.settimeout(0.5) # Non-blocking with timeout + + while time.time() < end_time: + try: + out = self.ws.recv() + if isinstance(out, str): + message = json.loads(out) + self.all_messages.append(message) + + # Track progress_state messages + if message.get('type') == 'progress_state': + self.progress_tracker.add_message(message) + except websocket.WebSocketTimeoutException: + continue + except Exception: + # Log error silently in test context + break + + +@pytest.mark.execution +class TestProgressIsolation: + """Test suite for verifying progress update isolation between clients.""" + + @pytest.fixture(scope="class", autouse=True) + def _server(self, args_pytest): + """Start the ComfyUI server for testing.""" + import subprocess + pargs = [ + 'python', 'main.py', + '--output-directory', args_pytest["output_dir"], + '--listen', args_pytest["listen"], + '--port', str(args_pytest["port"]), + '--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml', + '--cpu', + ] + p = subprocess.Popen(pargs) + yield + p.kill() + + def start_client_with_retry(self, listen: str, port: int, client_id: str = None): + """Start client with connection retries.""" + client = IsolatedClient() + # Connect to server (with retries) + n_tries = 5 + for i in range(n_tries): + time.sleep(4) + try: + client.connect(listen, port, client_id) + return client + except ConnectionRefusedError as e: + print(e) # noqa: T201 + print(f"({i+1}/{n_tries}) Retrying...") # noqa: T201 + raise ConnectionRefusedError(f"Failed to connect after {n_tries} attempts") + + def test_progress_isolation_between_clients(self, args_pytest): + """Test that progress updates are isolated between different clients.""" + listen = args_pytest["listen"] + port = args_pytest["port"] + + # Create two separate clients with unique IDs + client_a_id = "client_a_" + str(uuid.uuid4()) + client_b_id = "client_b_" + str(uuid.uuid4()) + + try: + # Connect both clients with retries + client_a = self.start_client_with_retry(listen, port, client_a_id) + client_b = self.start_client_with_retry(listen, port, client_b_id) + + # Create simple workflows for both clients + graph_a = GraphBuilder(prefix="client_a") + image_a = graph_a.node("StubImage", content="BLACK", height=256, width=256, batch_size=1) + graph_a.node("PreviewImage", images=image_a.out(0)) + + graph_b = GraphBuilder(prefix="client_b") + image_b = graph_b.node("StubImage", content="WHITE", height=256, width=256, batch_size=1) + graph_b.node("PreviewImage", images=image_b.out(0)) + + # Submit workflows from both clients + prompt_a = graph_a.finalize() + prompt_b = graph_b.finalize() + + response_a = client_a.queue_prompt(prompt_a) + prompt_id_a = response_a['prompt_id'] + + response_b = client_b.queue_prompt(prompt_b) + prompt_id_b = response_b['prompt_id'] + + # Start threads to listen for messages on both clients + def listen_client_a(): + client_a.listen_for_messages(duration=10.0) + + def listen_client_b(): + client_b.listen_for_messages(duration=10.0) + + thread_a = threading.Thread(target=listen_client_a) + thread_b = threading.Thread(target=listen_client_b) + + thread_a.start() + thread_b.start() + + # Wait for threads to complete + thread_a.join() + thread_b.join() + + # Verify isolation + # Client A should only receive progress for prompt_id_a + assert not client_a.progress_tracker.has_cross_contamination(prompt_id_a), \ + f"Client A received progress updates for other clients' workflows. " \ + f"Expected only {prompt_id_a}, but got messages for multiple prompts." + + # Client B should only receive progress for prompt_id_b + assert not client_b.progress_tracker.has_cross_contamination(prompt_id_b), \ + f"Client B received progress updates for other clients' workflows. " \ + f"Expected only {prompt_id_b}, but got messages for multiple prompts." + + # Verify each client received their own progress updates + client_a_messages = client_a.progress_tracker.get_messages_for_prompt(prompt_id_a) + client_b_messages = client_b.progress_tracker.get_messages_for_prompt(prompt_id_b) + + assert len(client_a_messages) > 0, \ + "Client A did not receive any progress updates for its own workflow" + assert len(client_b_messages) > 0, \ + "Client B did not receive any progress updates for its own workflow" + + # Ensure no cross-contamination + client_a_other = client_a.progress_tracker.get_messages_for_prompt(prompt_id_b) + client_b_other = client_b.progress_tracker.get_messages_for_prompt(prompt_id_a) + + assert len(client_a_other) == 0, \ + f"Client A incorrectly received {len(client_a_other)} progress updates for Client B's workflow" + assert len(client_b_other) == 0, \ + f"Client B incorrectly received {len(client_b_other)} progress updates for Client A's workflow" + + finally: + # Clean up connections + if hasattr(client_a, 'ws'): + client_a.ws.close() + if hasattr(client_b, 'ws'): + client_b.ws.close() + + def test_progress_with_missing_client_id(self, args_pytest): + """Test that progress updates handle missing client_id gracefully.""" + listen = args_pytest["listen"] + port = args_pytest["port"] + + try: + # Connect client with retries + client = self.start_client_with_retry(listen, port) + + # Create a simple workflow + graph = GraphBuilder(prefix="test_missing_id") + image = graph.node("StubImage", content="BLACK", height=128, width=128, batch_size=1) + graph.node("PreviewImage", images=image.out(0)) + + # Submit workflow + prompt = graph.finalize() + response = client.queue_prompt(prompt) + prompt_id = response['prompt_id'] + + # Listen for messages + client.listen_for_messages(duration=5.0) + + # Should still receive progress updates for own workflow + messages = client.progress_tracker.get_messages_for_prompt(prompt_id) + assert len(messages) > 0, \ + "Client did not receive progress updates even though it initiated the workflow" + + finally: + if hasattr(client, 'ws'): + client.ws.close() + diff --git a/tests/inference/testing_nodes/testing-pack/__init__.py b/tests/execution/testing_nodes/testing-pack/__init__.py similarity index 74% rename from tests/inference/testing_nodes/testing-pack/__init__.py rename to tests/execution/testing_nodes/testing-pack/__init__.py index dcc71659a..3d5ac8a94 100644 --- a/tests/inference/testing_nodes/testing-pack/__init__.py +++ b/tests/execution/testing_nodes/testing-pack/__init__.py @@ -1,23 +1,28 @@ -from .specific_tests import TEST_NODE_CLASS_MAPPINGS, TEST_NODE_DISPLAY_NAME_MAPPINGS -from .flow_control import FLOW_CONTROL_NODE_CLASS_MAPPINGS, FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS -from .util import UTILITY_NODE_CLASS_MAPPINGS, UTILITY_NODE_DISPLAY_NAME_MAPPINGS -from .conditions import CONDITION_NODE_CLASS_MAPPINGS, CONDITION_NODE_DISPLAY_NAME_MAPPINGS -from .stubs import TEST_STUB_NODE_CLASS_MAPPINGS, TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS - -# NODE_CLASS_MAPPINGS = GENERAL_NODE_CLASS_MAPPINGS.update(COMPONENT_NODE_CLASS_MAPPINGS) -# NODE_DISPLAY_NAME_MAPPINGS = GENERAL_NODE_DISPLAY_NAME_MAPPINGS.update(COMPONENT_NODE_DISPLAY_NAME_MAPPINGS) - -NODE_CLASS_MAPPINGS = {} -NODE_CLASS_MAPPINGS.update(TEST_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(FLOW_CONTROL_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(UTILITY_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(CONDITION_NODE_CLASS_MAPPINGS) -NODE_CLASS_MAPPINGS.update(TEST_STUB_NODE_CLASS_MAPPINGS) - -NODE_DISPLAY_NAME_MAPPINGS = {} -NODE_DISPLAY_NAME_MAPPINGS.update(TEST_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(UTILITY_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(CONDITION_NODE_DISPLAY_NAME_MAPPINGS) -NODE_DISPLAY_NAME_MAPPINGS.update(TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS) - +from .specific_tests import TEST_NODE_CLASS_MAPPINGS, TEST_NODE_DISPLAY_NAME_MAPPINGS +from .flow_control import FLOW_CONTROL_NODE_CLASS_MAPPINGS, FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS +from .util import UTILITY_NODE_CLASS_MAPPINGS, UTILITY_NODE_DISPLAY_NAME_MAPPINGS +from .conditions import CONDITION_NODE_CLASS_MAPPINGS, CONDITION_NODE_DISPLAY_NAME_MAPPINGS +from .stubs import TEST_STUB_NODE_CLASS_MAPPINGS, TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS +from .async_test_nodes import ASYNC_TEST_NODE_CLASS_MAPPINGS, ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS +from .api_test_nodes import API_TEST_NODE_CLASS_MAPPINGS, API_TEST_NODE_DISPLAY_NAME_MAPPINGS + +# NODE_CLASS_MAPPINGS = GENERAL_NODE_CLASS_MAPPINGS.update(COMPONENT_NODE_CLASS_MAPPINGS) +# NODE_DISPLAY_NAME_MAPPINGS = GENERAL_NODE_DISPLAY_NAME_MAPPINGS.update(COMPONENT_NODE_DISPLAY_NAME_MAPPINGS) + +NODE_CLASS_MAPPINGS = {} +NODE_CLASS_MAPPINGS.update(TEST_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(FLOW_CONTROL_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(UTILITY_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(CONDITION_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(TEST_STUB_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(ASYNC_TEST_NODE_CLASS_MAPPINGS) +NODE_CLASS_MAPPINGS.update(API_TEST_NODE_CLASS_MAPPINGS) + +NODE_DISPLAY_NAME_MAPPINGS = {} +NODE_DISPLAY_NAME_MAPPINGS.update(TEST_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(FLOW_CONTROL_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(UTILITY_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(CONDITION_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS) +NODE_DISPLAY_NAME_MAPPINGS.update(API_TEST_NODE_DISPLAY_NAME_MAPPINGS) diff --git a/tests/execution/testing_nodes/testing-pack/api_test_nodes.py b/tests/execution/testing_nodes/testing-pack/api_test_nodes.py new file mode 100644 index 000000000..b2eaae05e --- /dev/null +++ b/tests/execution/testing_nodes/testing-pack/api_test_nodes.py @@ -0,0 +1,78 @@ +import asyncio +import time +from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict +from comfy_api.v0_0_2 import ComfyAPI, ComfyAPISync + +api = ComfyAPI() +api_sync = ComfyAPISync() + + +class TestAsyncProgressUpdate(ComfyNodeABC): + """Test node with async VALIDATE_INPUTS.""" + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "value": (IO.ANY, {}), + "sleep_seconds": (IO.FLOAT, {"default": 1.0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "execute" + CATEGORY = "_for_testing/async" + + async def execute(self, value, sleep_seconds): + start = time.time() + expiration = start + sleep_seconds + now = start + while now < expiration: + now = time.time() + await api.execution.set_progress( + value=(now - start) / sleep_seconds, + max_value=1.0, + ) + await asyncio.sleep(0.01) + return (value,) + + +class TestSyncProgressUpdate(ComfyNodeABC): + """Test node with async VALIDATE_INPUTS.""" + + @classmethod + def INPUT_TYPES(cls) -> InputTypeDict: + return { + "required": { + "value": (IO.ANY, {}), + "sleep_seconds": (IO.FLOAT, {"default": 1.0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "execute" + CATEGORY = "_for_testing/async" + + def execute(self, value, sleep_seconds): + start = time.time() + expiration = start + sleep_seconds + now = start + while now < expiration: + now = time.time() + api_sync.execution.set_progress( + value=(now - start) / sleep_seconds, + max_value=1.0, + ) + time.sleep(0.01) + return (value,) + + +API_TEST_NODE_CLASS_MAPPINGS = { + "TestAsyncProgressUpdate": TestAsyncProgressUpdate, + "TestSyncProgressUpdate": TestSyncProgressUpdate, +} + +API_TEST_NODE_DISPLAY_NAME_MAPPINGS = { + "TestAsyncProgressUpdate": "Async Progress Update Test Node", + "TestSyncProgressUpdate": "Sync Progress Update Test Node", +} diff --git a/tests/execution/testing_nodes/testing-pack/async_test_nodes.py b/tests/execution/testing_nodes/testing-pack/async_test_nodes.py new file mode 100644 index 000000000..547eea6f4 --- /dev/null +++ b/tests/execution/testing_nodes/testing-pack/async_test_nodes.py @@ -0,0 +1,343 @@ +import torch +import asyncio +from typing import Dict +from comfy.utils import ProgressBar +from comfy_execution.graph_utils import GraphBuilder +from comfy.comfy_types.node_typing import ComfyNodeABC +from comfy.comfy_types import IO + + +class TestAsyncValidation(ComfyNodeABC): + """Test node with async VALIDATE_INPUTS.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 5.0}), + "threshold": ("FLOAT", {"default": 10.0}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + CATEGORY = "_for_testing/async" + + @classmethod + async def VALIDATE_INPUTS(cls, value, threshold): + # Simulate async validation (e.g., checking remote service) + await asyncio.sleep(0.05) + + if value > threshold: + return f"Value {value} exceeds threshold {threshold}" + return True + + def process(self, value, threshold): + # Create image based on value + intensity = value / 10.0 + image = torch.ones([1, 512, 512, 3]) * intensity + return (image,) + + +class TestAsyncError(ComfyNodeABC): + """Test node that errors during async execution.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + "error_after": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 10.0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "error_execution" + CATEGORY = "_for_testing/async" + + async def error_execution(self, value, error_after): + await asyncio.sleep(error_after) + raise RuntimeError("Intentional async execution error for testing") + + +class TestAsyncValidationError(ComfyNodeABC): + """Test node with async validation that always fails.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 5.0}), + "max_value": ("FLOAT", {"default": 10.0}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + CATEGORY = "_for_testing/async" + + @classmethod + async def VALIDATE_INPUTS(cls, value, max_value): + await asyncio.sleep(0.05) + # Always fail validation for values > max_value + if value > max_value: + return f"Async validation failed: {value} > {max_value}" + return True + + def process(self, value, max_value): + # This won't be reached if validation fails + image = torch.ones([1, 512, 512, 3]) * (value / max_value) + return (image,) + + +class TestAsyncTimeout(ComfyNodeABC): + """Test node that simulates timeout scenarios.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + "timeout": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0}), + "operation_time": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 10.0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "timeout_execution" + CATEGORY = "_for_testing/async" + + async def timeout_execution(self, value, timeout, operation_time): + try: + # This will timeout if operation_time > timeout + await asyncio.wait_for(asyncio.sleep(operation_time), timeout=timeout) + return (value,) + except asyncio.TimeoutError: + raise RuntimeError(f"Operation timed out after {timeout} seconds") + + +class TestSyncError(ComfyNodeABC): + """Test node that errors synchronously (for mixed sync/async testing).""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "sync_error" + CATEGORY = "_for_testing/async" + + def sync_error(self, value): + raise RuntimeError("Intentional sync execution error for testing") + + +class TestAsyncLazyCheck(ComfyNodeABC): + """Test node with async check_lazy_status.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "input1": (IO.ANY, {"lazy": True}), + "input2": (IO.ANY, {"lazy": True}), + "condition": ("BOOLEAN", {"default": True}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + CATEGORY = "_for_testing/async" + + async def check_lazy_status(self, condition, input1, input2): + # Simulate async checking (e.g., querying remote service) + await asyncio.sleep(0.05) + + needed = [] + if condition and input1 is None: + needed.append("input1") + if not condition and input2 is None: + needed.append("input2") + return needed + + def process(self, input1, input2, condition): + # Return a simple image + return (torch.ones([1, 512, 512, 3]),) + + +class TestDynamicAsyncGeneration(ComfyNodeABC): + """Test node that dynamically generates async nodes.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "num_async_nodes": ("INT", {"default": 3, "min": 1, "max": 10}), + "sleep_duration": ("FLOAT", {"default": 0.2, "min": 0.1, "max": 1.0}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate_async_workflow" + CATEGORY = "_for_testing/async" + + def generate_async_workflow(self, image1, image2, num_async_nodes, sleep_duration): + g = GraphBuilder() + + # Create multiple async sleep nodes + sleep_nodes = [] + for i in range(num_async_nodes): + image = image1 if i % 2 == 0 else image2 + sleep_node = g.node("TestSleep", value=image, seconds=sleep_duration) + sleep_nodes.append(sleep_node) + + # Average all results + if len(sleep_nodes) == 1: + final_node = sleep_nodes[0] + else: + avg_inputs = {"input1": sleep_nodes[0].out(0)} + for i, node in enumerate(sleep_nodes[1:], 2): + avg_inputs[f"input{i}"] = node.out(0) + final_node = g.node("TestVariadicAverage", **avg_inputs) + + return { + "result": (final_node.out(0),), + "expand": g.finalize(), + } + + +class TestAsyncResourceUser(ComfyNodeABC): + """Test node that uses resources during async execution.""" + + # Class-level resource tracking for testing + _active_resources: Dict[str, bool] = {} + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + "resource_id": ("STRING", {"default": "resource_0"}), + "duration": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "use_resource" + CATEGORY = "_for_testing/async" + + async def use_resource(self, value, resource_id, duration): + # Check if resource is already in use + if self._active_resources.get(resource_id, False): + raise RuntimeError(f"Resource {resource_id} is already in use!") + + # Mark resource as in use + self._active_resources[resource_id] = True + + try: + # Simulate resource usage + await asyncio.sleep(duration) + return (value,) + finally: + # Always clean up resource + self._active_resources[resource_id] = False + + +class TestAsyncBatchProcessing(ComfyNodeABC): + """Test async processing of batched inputs.""" + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "images": ("IMAGE",), + "process_time_per_item": ("FLOAT", {"default": 0.1, "min": 0.01, "max": 1.0}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process_batch" + CATEGORY = "_for_testing/async" + + async def process_batch(self, images, process_time_per_item, unique_id): + batch_size = images.shape[0] + pbar = ProgressBar(batch_size, node_id=unique_id) + + # Process each image in the batch + processed = [] + for i in range(batch_size): + # Simulate async processing + await asyncio.sleep(process_time_per_item) + + # Simple processing: invert the image + processed_image = 1.0 - images[i:i+1] + processed.append(processed_image) + + pbar.update(1) + + # Stack processed images + result = torch.cat(processed, dim=0) + return (result,) + + +class TestAsyncConcurrentLimit(ComfyNodeABC): + """Test concurrent execution limits for async nodes.""" + + _semaphore = asyncio.Semaphore(2) # Only allow 2 concurrent executions + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + "duration": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 2.0}), + "node_id": ("INT", {"default": 0}), + }, + } + + RETURN_TYPES = (IO.ANY,) + FUNCTION = "limited_execution" + CATEGORY = "_for_testing/async" + + async def limited_execution(self, value, duration, node_id): + async with self._semaphore: + # Node {node_id} acquired semaphore + await asyncio.sleep(duration) + # Node {node_id} releasing semaphore + return (value,) + + +# Add node mappings +ASYNC_TEST_NODE_CLASS_MAPPINGS = { + "TestAsyncValidation": TestAsyncValidation, + "TestAsyncError": TestAsyncError, + "TestAsyncValidationError": TestAsyncValidationError, + "TestAsyncTimeout": TestAsyncTimeout, + "TestSyncError": TestSyncError, + "TestAsyncLazyCheck": TestAsyncLazyCheck, + "TestDynamicAsyncGeneration": TestDynamicAsyncGeneration, + "TestAsyncResourceUser": TestAsyncResourceUser, + "TestAsyncBatchProcessing": TestAsyncBatchProcessing, + "TestAsyncConcurrentLimit": TestAsyncConcurrentLimit, +} + +ASYNC_TEST_NODE_DISPLAY_NAME_MAPPINGS = { + "TestAsyncValidation": "Test Async Validation", + "TestAsyncError": "Test Async Error", + "TestAsyncValidationError": "Test Async Validation Error", + "TestAsyncTimeout": "Test Async Timeout", + "TestSyncError": "Test Sync Error", + "TestAsyncLazyCheck": "Test Async Lazy Check", + "TestDynamicAsyncGeneration": "Test Dynamic Async Generation", + "TestAsyncResourceUser": "Test Async Resource User", + "TestAsyncBatchProcessing": "Test Async Batch Processing", + "TestAsyncConcurrentLimit": "Test Async Concurrent Limit", +} diff --git a/tests/inference/testing_nodes/testing-pack/conditions.py b/tests/execution/testing_nodes/testing-pack/conditions.py similarity index 100% rename from tests/inference/testing_nodes/testing-pack/conditions.py rename to tests/execution/testing_nodes/testing-pack/conditions.py diff --git a/tests/inference/testing_nodes/testing-pack/flow_control.py b/tests/execution/testing_nodes/testing-pack/flow_control.py similarity index 100% rename from tests/inference/testing_nodes/testing-pack/flow_control.py rename to tests/execution/testing_nodes/testing-pack/flow_control.py diff --git a/tests/inference/testing_nodes/testing-pack/specific_tests.py b/tests/execution/testing_nodes/testing-pack/specific_tests.py similarity index 65% rename from tests/inference/testing_nodes/testing-pack/specific_tests.py rename to tests/execution/testing_nodes/testing-pack/specific_tests.py index 9d05ab14f..4f8f01ae4 100644 --- a/tests/inference/testing_nodes/testing-pack/specific_tests.py +++ b/tests/execution/testing_nodes/testing-pack/specific_tests.py @@ -1,6 +1,11 @@ import torch +import time +import asyncio +from comfy.utils import ProgressBar from .tools import VariantSupport from comfy_execution.graph_utils import GraphBuilder +from comfy.comfy_types.node_typing import ComfyNodeABC +from comfy.comfy_types import IO class TestLazyMixImages: @classmethod @@ -333,6 +338,150 @@ class TestMixedExpansionReturns: "expand": g.finalize(), } +class TestSamplingInExpansion: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 100}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 30.0}), + "prompt": ("STRING", {"multiline": True, "default": "a beautiful landscape with mountains and trees"}), + "negative_prompt": ("STRING", {"multiline": True, "default": "blurry, bad quality, worst quality"}), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sampling_in_expansion" + + CATEGORY = "Testing/Nodes" + + def sampling_in_expansion(self, model, clip, vae, seed, steps, cfg, prompt, negative_prompt): + g = GraphBuilder() + + # Create a basic image generation workflow using the input model, clip and vae + # 1. Setup text prompts using the provided CLIP model + positive_prompt = g.node("CLIPTextEncode", + text=prompt, + clip=clip) + negative_prompt = g.node("CLIPTextEncode", + text=negative_prompt, + clip=clip) + + # 2. Create empty latent with specified size + empty_latent = g.node("EmptyLatentImage", width=512, height=512, batch_size=1) + + # 3. Setup sampler and generate image latent + sampler = g.node("KSampler", + model=model, + positive=positive_prompt.out(0), + negative=negative_prompt.out(0), + latent_image=empty_latent.out(0), + seed=seed, + steps=steps, + cfg=cfg, + sampler_name="euler_ancestral", + scheduler="normal") + + # 4. Decode latent to image using VAE + output = g.node("VAEDecode", samples=sampler.out(0), vae=vae) + + return { + "result": (output.out(0),), + "expand": g.finalize(), + } + +class TestSleep(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": (IO.ANY, {}), + "seconds": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 9999.0, "step": 0.01, "tooltip": "The amount of seconds to sleep."}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + }, + } + RETURN_TYPES = (IO.ANY,) + FUNCTION = "sleep" + + CATEGORY = "_for_testing" + + async def sleep(self, value, seconds, unique_id): + pbar = ProgressBar(seconds, node_id=unique_id) + start = time.time() + expiration = start + seconds + now = start + while now < expiration: + now = time.time() + pbar.update_absolute(now - start) + await asyncio.sleep(0.01) + return (value,) + +class TestParallelSleep(ComfyNodeABC): + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image1": ("IMAGE", ), + "image2": ("IMAGE", ), + "image3": ("IMAGE", ), + "sleep1": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), + "sleep2": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), + "sleep3": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.01}), + }, + "hidden": { + "unique_id": "UNIQUE_ID", + }, + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "parallel_sleep" + CATEGORY = "_for_testing" + OUTPUT_NODE = True + + def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id): + # Create a graph dynamically with three TestSleep nodes + g = GraphBuilder() + + # Create sleep nodes for each duration and image + sleep_node1 = g.node("TestSleep", value=image1, seconds=sleep1) + sleep_node2 = g.node("TestSleep", value=image2, seconds=sleep2) + sleep_node3 = g.node("TestSleep", value=image3, seconds=sleep3) + + # Blend the results using TestVariadicAverage + blend = g.node("TestVariadicAverage", + input1=sleep_node1.out(0), + input2=sleep_node2.out(0), + input3=sleep_node3.out(0)) + + return { + "result": (blend.out(0),), + "expand": g.finalize(), + } + +class TestOutputNodeWithSocketOutput: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}), + }, + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "process" + CATEGORY = "_for_testing" + OUTPUT_NODE = True + + def process(self, image, value): + # Apply value scaling and return both as output and socket + result = image * value + return (result,) + TEST_NODE_CLASS_MAPPINGS = { "TestLazyMixImages": TestLazyMixImages, "TestVariadicAverage": TestVariadicAverage, @@ -345,6 +494,10 @@ TEST_NODE_CLASS_MAPPINGS = { "TestCustomValidation5": TestCustomValidation5, "TestDynamicDependencyCycle": TestDynamicDependencyCycle, "TestMixedExpansionReturns": TestMixedExpansionReturns, + "TestSamplingInExpansion": TestSamplingInExpansion, + "TestSleep": TestSleep, + "TestParallelSleep": TestParallelSleep, + "TestOutputNodeWithSocketOutput": TestOutputNodeWithSocketOutput, } TEST_NODE_DISPLAY_NAME_MAPPINGS = { @@ -359,4 +512,8 @@ TEST_NODE_DISPLAY_NAME_MAPPINGS = { "TestCustomValidation5": "Custom Validation 5", "TestDynamicDependencyCycle": "Dynamic Dependency Cycle", "TestMixedExpansionReturns": "Mixed Expansion Returns", + "TestSamplingInExpansion": "Sampling In Expansion", + "TestSleep": "Test Sleep", + "TestParallelSleep": "Test Parallel Sleep", + "TestOutputNodeWithSocketOutput": "Test Output Node With Socket Output", } diff --git a/tests/inference/testing_nodes/testing-pack/stubs.py b/tests/execution/testing_nodes/testing-pack/stubs.py similarity index 100% rename from tests/inference/testing_nodes/testing-pack/stubs.py rename to tests/execution/testing_nodes/testing-pack/stubs.py diff --git a/tests/inference/testing_nodes/testing-pack/tools.py b/tests/execution/testing_nodes/testing-pack/tools.py similarity index 100% rename from tests/inference/testing_nodes/testing-pack/tools.py rename to tests/execution/testing_nodes/testing-pack/tools.py diff --git a/tests/inference/testing_nodes/testing-pack/util.py b/tests/execution/testing_nodes/testing-pack/util.py similarity index 100% rename from tests/inference/testing_nodes/testing-pack/util.py rename to tests/execution/testing_nodes/testing-pack/util.py diff --git a/tests/inference/extra_model_paths.yaml b/tests/inference/extra_model_paths.yaml deleted file mode 100644 index 75b2e1ae4..000000000 --- a/tests/inference/extra_model_paths.yaml +++ /dev/null @@ -1,4 +0,0 @@ -# Config for testing nodes -testing: - custom_nodes: tests/inference/testing_nodes - diff --git a/utils/extra_config.py b/utils/extra_config.py index 415db0427..a0fcda9e8 100644 --- a/utils/extra_config.py +++ b/utils/extra_config.py @@ -4,8 +4,9 @@ import folder_paths import logging def load_extra_path_config(yaml_path): - with open(yaml_path, 'r') as stream: + with open(yaml_path, 'r', encoding='utf-8') as stream: config = yaml.safe_load(stream) + yaml_dir = os.path.dirname(os.path.abspath(yaml_path)) for c in config: conf = config[c] if conf is None: @@ -14,6 +15,8 @@ def load_extra_path_config(yaml_path): if "base_path" in conf: base_path = conf.pop("base_path") base_path = os.path.expandvars(os.path.expanduser(base_path)) + if not os.path.isabs(base_path): + base_path = os.path.abspath(os.path.join(yaml_dir, base_path)) is_default = False if "is_default" in conf: is_default = conf.pop("is_default") @@ -22,10 +25,10 @@ def load_extra_path_config(yaml_path): if len(y) == 0: continue full_path = y - if base_path is not None: + if base_path: full_path = os.path.join(base_path, full_path) elif not os.path.isabs(full_path): - yaml_dir = os.path.dirname(os.path.abspath(yaml_path)) full_path = os.path.abspath(os.path.join(yaml_dir, y)) - logging.info("Adding extra search path {} {}".format(x, full_path)) - folder_paths.add_model_folder_path(x, full_path, is_default) + normalized_path = os.path.normpath(full_path) + logging.info("Adding extra search path {} {}".format(x, normalized_path)) + folder_paths.add_model_folder_path(x, normalized_path, is_default) diff --git a/utils/install_util.py b/utils/install_util.py new file mode 100644 index 000000000..0f59bcf91 --- /dev/null +++ b/utils/install_util.py @@ -0,0 +1,18 @@ +from pathlib import Path +import sys + +# The path to the requirements.txt file +requirements_path = Path(__file__).parents[1] / "requirements.txt" + + +def get_missing_requirements_message(): + """The warning message to display when a package is missing.""" + + extra = "" + if sys.flags.no_user_site: + extra = "-s " + return f""" +Please install the updated requirements.txt file by running: +{sys.executable} {extra}-m pip install -r {requirements_path} +If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem. +""".strip() diff --git a/utils/json_util.py b/utils/json_util.py new file mode 100644 index 000000000..da45af4f7 --- /dev/null +++ b/utils/json_util.py @@ -0,0 +1,26 @@ +def merge_json_recursive(base, update): + """Recursively merge two JSON-like objects. + - Dictionaries are merged recursively + - Lists are concatenated + - Other types are overwritten by the update value + + Args: + base: Base JSON-like object + update: Update JSON-like object to merge into base + + Returns: + Merged JSON-like object + """ + if not isinstance(base, dict) or not isinstance(update, dict): + if isinstance(base, list) and isinstance(update, list): + return base + update + return update + + merged = base.copy() + for key, value in update.items(): + if key in merged: + merged[key] = merge_json_recursive(merged[key], value) + else: + merged[key] = value + + return merged diff --git a/web/assets/BaseViewTemplate-CsEJhGbv.js b/web/assets/BaseViewTemplate-CsEJhGbv.js deleted file mode 100644 index d254b402f..000000000 --- a/web/assets/BaseViewTemplate-CsEJhGbv.js +++ /dev/null @@ -1,23 +0,0 @@ -import { d as defineComponent, o as openBlock, f as createElementBlock, J as renderSlot, T as normalizeClass } from "./index-C4Fk50Nx.js"; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "BaseViewTemplate", - props: { - dark: { type: Boolean, default: false } - }, - setup(__props) { - const props = __props; - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div", { - class: normalizeClass(["font-sans w-screen h-screen flex items-center justify-center pointer-events-auto overflow-auto", [ - props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300" - ]]) - }, [ - renderSlot(_ctx.$slots, "default") - ], 2); - }; - } -}); -export { - _sfc_main as _ -}; -//# sourceMappingURL=BaseViewTemplate-CsEJhGbv.js.map diff --git a/web/assets/CREDIT.txt b/web/assets/CREDIT.txt deleted file mode 100644 index b3a9bc906..000000000 --- a/web/assets/CREDIT.txt +++ /dev/null @@ -1 +0,0 @@ -Thanks to OpenArt (https://openart.ai) for providing the sorted-custom-node-map data, captured in September 2024. \ No newline at end of file diff --git a/web/assets/DownloadGitView-DP1MIWYX.js b/web/assets/DownloadGitView-DP1MIWYX.js deleted file mode 100644 index fff3e7436..000000000 --- a/web/assets/DownloadGitView-DP1MIWYX.js +++ /dev/null @@ -1,58 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, bW as useRouter } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -const _hoisted_1 = { class: "max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding" }; -const _hoisted_2 = { class: "mt-24 text-4xl font-bold text-red-500" }; -const _hoisted_3 = { class: "space-y-4" }; -const _hoisted_4 = { class: "text-xl" }; -const _hoisted_5 = { class: "text-xl" }; -const _hoisted_6 = { class: "text-m" }; -const _hoisted_7 = { class: "flex gap-4 flex-row-reverse" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "DownloadGitView", - setup(__props) { - const openGitDownloads = /* @__PURE__ */ __name(() => { - window.open("https://git-scm.com/downloads/", "_blank"); - }, "openGitDownloads"); - const skipGit = /* @__PURE__ */ __name(() => { - console.warn("pushing"); - const router = useRouter(); - router.push("install"); - }, "skipGit"); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, null, { - default: withCtx(() => [ - createBaseVNode("div", _hoisted_1, [ - createBaseVNode("h1", _hoisted_2, toDisplayString(_ctx.$t("downloadGit.title")), 1), - createBaseVNode("div", _hoisted_3, [ - createBaseVNode("p", _hoisted_4, toDisplayString(_ctx.$t("downloadGit.message")), 1), - createBaseVNode("p", _hoisted_5, toDisplayString(_ctx.$t("downloadGit.instructions")), 1), - createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("downloadGit.warning")), 1) - ]), - createBaseVNode("div", _hoisted_7, [ - createVNode(unref(script), { - label: _ctx.$t("downloadGit.gitWebsite"), - icon: "pi pi-external-link", - "icon-pos": "right", - onClick: openGitDownloads, - severity: "primary" - }, null, 8, ["label"]), - createVNode(unref(script), { - label: _ctx.$t("downloadGit.skip"), - icon: "pi pi-exclamation-triangle", - onClick: skipGit, - severity: "secondary" - }, null, 8, ["label"]) - ]) - ]) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=DownloadGitView-DP1MIWYX.js.map diff --git a/web/assets/ExtensionPanel-CxijYN47.js b/web/assets/ExtensionPanel-CxijYN47.js deleted file mode 100644 index 6cf7706c1..000000000 --- a/web/assets/ExtensionPanel-CxijYN47.js +++ /dev/null @@ -1,183 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, ab as ref, cn as FilterMatchMode, cs as useExtensionStore, a as useSettingStore, m as onMounted, c as computed, o as openBlock, k as createBlock, M as withCtx, N as createVNode, co as SearchBox, j as unref, bZ as script, H as createBaseVNode, f as createElementBlock, E as renderList, X as toDisplayString, aE as createTextVNode, F as Fragment, l as script$1, I as createCommentVNode, aI as script$3, bO as script$4, c4 as script$5, cp as _sfc_main$1 } from "./index-C4Fk50Nx.js"; -import { s as script$2, a as script$6 } from "./index-CK0rrCYF.js"; -import "./index-lMQBwSDj.js"; -import "./index-B7ycxfFq.js"; -const _hoisted_1 = { class: "flex justify-end" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "ExtensionPanel", - setup(__props) { - const filters = ref({ - global: { value: "", matchMode: FilterMatchMode.CONTAINS } - }); - const extensionStore = useExtensionStore(); - const settingStore = useSettingStore(); - const editingEnabledExtensions = ref({}); - onMounted(() => { - extensionStore.extensions.forEach((ext) => { - editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name); - }); - }); - const changedExtensions = computed(() => { - return extensionStore.extensions.filter( - (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name) - ); - }); - const hasChanges = computed(() => { - return changedExtensions.value.length > 0; - }); - const updateExtensionStatus = /* @__PURE__ */ __name(() => { - const editingDisabledExtensionNames = Object.entries( - editingEnabledExtensions.value - ).filter(([_, enabled]) => !enabled).map(([name]) => name); - settingStore.set("Comfy.Extension.Disabled", [ - ...extensionStore.inactiveDisabledExtensionNames, - ...editingDisabledExtensionNames - ]); - }, "updateExtensionStatus"); - const enableAllExtensions = /* @__PURE__ */ __name(() => { - extensionStore.extensions.forEach((ext) => { - if (extensionStore.isExtensionReadOnly(ext.name)) return; - editingEnabledExtensions.value[ext.name] = true; - }); - updateExtensionStatus(); - }, "enableAllExtensions"); - const disableAllExtensions = /* @__PURE__ */ __name(() => { - extensionStore.extensions.forEach((ext) => { - if (extensionStore.isExtensionReadOnly(ext.name)) return; - editingEnabledExtensions.value[ext.name] = false; - }); - updateExtensionStatus(); - }, "disableAllExtensions"); - const disableThirdPartyExtensions = /* @__PURE__ */ __name(() => { - extensionStore.extensions.forEach((ext) => { - if (extensionStore.isCoreExtension(ext.name)) return; - editingEnabledExtensions.value[ext.name] = false; - }); - updateExtensionStatus(); - }, "disableThirdPartyExtensions"); - const applyChanges = /* @__PURE__ */ __name(() => { - window.location.reload(); - }, "applyChanges"); - const menu = ref(); - const contextMenuItems = [ - { - label: "Enable All", - icon: "pi pi-check", - command: enableAllExtensions - }, - { - label: "Disable All", - icon: "pi pi-times", - command: disableAllExtensions - }, - { - label: "Disable 3rd Party", - icon: "pi pi-times", - command: disableThirdPartyExtensions, - disabled: !extensionStore.hasThirdPartyExtensions - } - ]; - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, { - value: "Extension", - class: "extension-panel" - }, { - header: withCtx(() => [ - createVNode(SearchBox, { - modelValue: filters.value["global"].value, - "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event), - placeholder: _ctx.$t("g.searchExtensions") + "..." - }, null, 8, ["modelValue", "placeholder"]), - hasChanges.value ? (openBlock(), createBlock(unref(script), { - key: 0, - severity: "info", - "pt:text": "w-full", - class: "max-h-96 overflow-y-auto" - }, { - default: withCtx(() => [ - createBaseVNode("ul", null, [ - (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => { - return openBlock(), createElementBlock("li", { - key: ext.name - }, [ - createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1), - createTextVNode(" " + toDisplayString(ext.name), 1) - ]); - }), 128)) - ]), - createBaseVNode("div", _hoisted_1, [ - createVNode(unref(script$1), { - label: _ctx.$t("g.reloadToApplyChanges"), - onClick: applyChanges, - outlined: "", - severity: "danger" - }, null, 8, ["label"]) - ]) - ]), - _: 1 - })) : createCommentVNode("", true) - ]), - default: withCtx(() => [ - createVNode(unref(script$6), { - value: unref(extensionStore).extensions, - stripedRows: "", - size: "small", - filters: filters.value - }, { - default: withCtx(() => [ - createVNode(unref(script$2), { - header: _ctx.$t("g.extensionName"), - sortable: "", - field: "name" - }, { - body: withCtx((slotProps) => [ - createTextVNode(toDisplayString(slotProps.data.name) + " ", 1), - unref(extensionStore).isCoreExtension(slotProps.data.name) ? (openBlock(), createBlock(unref(script$3), { - key: 0, - value: "Core" - })) : createCommentVNode("", true) - ]), - _: 1 - }, 8, ["header"]), - createVNode(unref(script$2), { pt: { - headerCell: "flex items-center justify-end", - bodyCell: "flex items-center justify-end" - } }, { - header: withCtx(() => [ - createVNode(unref(script$1), { - icon: "pi pi-ellipsis-h", - text: "", - severity: "secondary", - onClick: _cache[1] || (_cache[1] = ($event) => menu.value.show($event)) - }), - createVNode(unref(script$4), { - ref_key: "menu", - ref: menu, - model: contextMenuItems - }, null, 512) - ]), - body: withCtx((slotProps) => [ - createVNode(unref(script$5), { - disabled: unref(extensionStore).isExtensionReadOnly(slotProps.data.name), - modelValue: editingEnabledExtensions.value[slotProps.data.name], - "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"), - onChange: updateExtensionStatus - }, null, 8, ["disabled", "modelValue", "onUpdate:modelValue"]) - ]), - _: 1 - }) - ]), - _: 1 - }, 8, ["value", "filters"]) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=ExtensionPanel-CxijYN47.js.map diff --git a/web/assets/GraphView-CIRWBKTm.css b/web/assets/GraphView-CIRWBKTm.css deleted file mode 100644 index 59d1b3d14..000000000 --- a/web/assets/GraphView-CIRWBKTm.css +++ /dev/null @@ -1,273 +0,0 @@ - -.comfy-menu-hamburger[data-v-5661bed0] { - pointer-events: auto; - position: fixed; - z-index: 9999; -} - -[data-v-e50caa15] .p-splitter-gutter { - pointer-events: auto; -} -[data-v-e50caa15] .p-splitter-gutter:hover,[data-v-e50caa15] .p-splitter-gutter[data-p-gutter-resizing='true'] { - transition: background-color 0.2s ease 300ms; - background-color: var(--p-primary-color); -} -.side-bar-panel[data-v-e50caa15] { - background-color: var(--bg-color); - pointer-events: auto; -} -.bottom-panel[data-v-e50caa15] { - background-color: var(--bg-color); - pointer-events: auto; -} -.splitter-overlay[data-v-e50caa15] { - pointer-events: none; - border-style: none; - background-color: transparent; -} -.splitter-overlay-root[data-v-e50caa15] { - position: absolute; - top: 0px; - left: 0px; - height: 100%; - width: 100%; - - /* Set it the same as the ComfyUI menu */ - /* Note: Lite-graph DOM widgets have the same z-index as the node id, so - 999 should be sufficient to make sure splitter overlays on node's DOM - widgets */ - z-index: 999; -} - -.p-buttongroup-vertical[data-v-cf40dd39] { - display: flex; - flex-direction: column; - border-radius: var(--p-button-border-radius); - overflow: hidden; - border: 1px solid var(--p-panel-border-color); -} -.p-buttongroup-vertical .p-button[data-v-cf40dd39] { - margin: 0; - border-radius: 0; -} - -.node-tooltip[data-v-46859edf] { - background: var(--comfy-input-bg); - border-radius: 5px; - box-shadow: 0 0 5px rgba(0, 0, 0, 0.4); - color: var(--input-text); - font-family: sans-serif; - left: 0; - max-width: 30vw; - padding: 4px 8px; - position: absolute; - top: 0; - transform: translate(5px, calc(-100% - 5px)); - white-space: pre-wrap; - z-index: 99999; -} - -.group-title-editor.node-title-editor[data-v-12d3fd12] { - z-index: 9999; - padding: 0.25rem; -} -[data-v-12d3fd12] .editable-text { - width: 100%; - height: 100%; -} -[data-v-12d3fd12] .editable-text input { - width: 100%; - height: 100%; - /* Override the default font size */ - font-size: inherit; -} - -[data-v-5741c9ae] .highlight { - background-color: var(--p-primary-color); - color: var(--p-primary-contrast-color); - font-weight: bold; - border-radius: 0.25rem; - padding: 0rem 0.125rem; - margin: -0.125rem 0.125rem; -} - -.invisible-dialog-root { - width: 60%; - min-width: 24rem; - max-width: 48rem; - border: 0 !important; - background-color: transparent !important; - margin-top: 25vh; - margin-left: 400px; -} -@media all and (max-width: 768px) { -.invisible-dialog-root { - margin-left: 0px; -} -} -.node-search-box-dialog-mask { - align-items: flex-start !important; -} - -.side-bar-button-icon { - font-size: var(--sidebar-icon-size) !important; -} -.side-bar-button-selected .side-bar-button-icon { - font-size: var(--sidebar-icon-size) !important; - font-weight: bold; -} - -.side-bar-button[data-v-6ab4daa6] { - width: var(--sidebar-width); - height: var(--sidebar-width); - border-radius: 0; -} -.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6], -.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover { - border-left: 4px solid var(--p-button-text-primary-color); -} -.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6], -.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover { - border-right: 4px solid var(--p-button-text-primary-color); -} - -:root { - --sidebar-width: 64px; - --sidebar-icon-size: 1.5rem; -} -:root .small-sidebar { - --sidebar-width: 40px; - --sidebar-icon-size: 1rem; -} - -.side-tool-bar-container[data-v-37d8d7b4] { - display: flex; - flex-direction: column; - align-items: center; - - pointer-events: auto; - - width: var(--sidebar-width); - height: 100%; - - background-color: var(--comfy-menu-secondary-bg); - color: var(--fg-color); - box-shadow: var(--bar-shadow); -} -.side-tool-bar-end[data-v-37d8d7b4] { - align-self: flex-end; - margin-top: auto; -} - -[data-v-b9328350] .p-inputtext { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} - -.comfyui-queue-button[data-v-7f4f551b] .p-splitbutton-dropdown { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -.actionbar[data-v-915e5456] { - pointer-events: all; - position: fixed; - z-index: 1000; -} -.actionbar.is-docked[data-v-915e5456] { - position: static; - border-style: none; - background-color: transparent; - padding: 0px; -} -.actionbar.is-dragging[data-v-915e5456] { - -webkit-user-select: none; - -moz-user-select: none; - user-select: none; -} -[data-v-915e5456] .p-panel-content { - padding: 0.25rem; -} -.is-docked[data-v-915e5456] .p-panel-content { - padding: 0px; -} -[data-v-915e5456] .p-panel-header { - display: none; -} - -.top-menubar[data-v-6fecd137] .p-menubar-item-link svg { - display: none; -} -[data-v-6fecd137] .p-menubar-submenu.dropdown-direction-up { - top: auto; - bottom: 100%; - flex-direction: column-reverse; -} -.keybinding-tag[data-v-6fecd137] { - background: var(--p-content-hover-background); - border-color: var(--p-content-border-color); - border-style: solid; -} - -.status-indicator[data-v-8d011a31] { - position: absolute; - font-weight: 700; - font-size: 1.5rem; - top: 50%; - left: 50%; - transform: translate(-50%, -50%) -} - -[data-v-d485c044] .p-togglebutton::before { - display: none -} -[data-v-d485c044] .p-togglebutton { - position: relative; - flex-shrink: 0; - border-radius: 0px; - background-color: transparent; - padding: 0px -} -[data-v-d485c044] .p-togglebutton.p-togglebutton-checked { - border-bottom-width: 2px; - border-bottom-color: var(--p-button-text-primary-color) -} -[data-v-d485c044] .p-togglebutton-checked .close-button,[data-v-d485c044] .p-togglebutton:hover .close-button { - visibility: visible -} -[data-v-d485c044] .p-togglebutton:hover .status-indicator { - display: none -} -[data-v-d485c044] .p-togglebutton .close-button { - visibility: hidden -} - -.comfyui-menu[data-v-878b63b8] { - width: 100vw; - background: var(--comfy-menu-bg); - color: var(--fg-color); - box-shadow: var(--bar-shadow); - font-family: Arial, Helvetica, sans-serif; - font-size: 0.8em; - box-sizing: border-box; - z-index: 1000; - order: 0; - grid-column: 1/-1; - max-height: 90vh; -} -.comfyui-menu.dropzone[data-v-878b63b8] { - background: var(--p-highlight-background); -} -.comfyui-menu.dropzone-active[data-v-878b63b8] { - background: var(--p-highlight-background-focus); -} -[data-v-878b63b8] .p-menubar-item-label { - line-height: revert; -} -.comfyui-logo[data-v-878b63b8] { - font-size: 1.2em; - -webkit-user-select: none; - -moz-user-select: none; - user-select: none; - cursor: default; -} diff --git a/web/assets/GraphView-DswvqURL.js b/web/assets/GraphView-DswvqURL.js deleted file mode 100644 index 14d9f1925..000000000 --- a/web/assets/GraphView-DswvqURL.js +++ /dev/null @@ -1,9894 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, u as useExecutionStore, c as computed, a as useSettingStore, b as useWorkflowStore, e as useTitle, o as openBlock, f as createElementBlock, g as useWorkspaceStore, w as watchEffect, h as app, r as resolveDirective, i as withDirectives, v as vShow, j as unref, k as createBlock, n as normalizeStyle, s as showNativeMenu, l as script$d, _ as _export_sfc, m as onMounted, p as onBeforeUnmount, B as BaseStyle, q as script$e, t as getWidth, x as getHeight, y as getOuterWidth, z as getOuterHeight, A as getVNodeProp, C as isArray, D as mergeProps, F as Fragment, E as renderList, G as resolveDynamicComponent, H as createBaseVNode, I as createCommentVNode, J as renderSlot, K as useSidebarTabStore, L as useBottomPanelStore, M as withCtx, N as createVNode, O as getAttribute, P as findSingle, Q as focus, R as equals, S as Ripple, T as normalizeClass, U as getOffset, V as script$f, W as script$g, X as toDisplayString, Y as script$h, Z as markRaw, $ as defineStore, a0 as shallowRef, a1 as useI18n, a2 as useCommandStore, a3 as LiteGraph, a4 as useColorPaletteStore, a5 as watch, a6 as useNodeDefStore, a7 as BadgePosition, a8 as LGraphBadge, a9 as _, aa as NodeBadgeMode, ab as ref, ac as useEventListener, ad as nextTick, ae as st, af as normalizeI18nKey, ag as LGraphGroup, ah as LGraphNode, ai as EditableText, aj as isNotEmpty, ak as UniqueComponentId, al as ZIndex, am as resolveFieldData, an as OverlayEventBus, ao as isEmpty, ap as addStyle, aq as relativePosition, ar as absolutePosition, as as ConnectedOverlayScrollHandler, at as isTouchDevice, au as findLastIndex, av as script$i, aw as script$j, ax as script$k, ay as script$l, az as script$m, aA as script$n, aB as resolveComponent, aC as Transition, aD as createSlots, aE as createTextVNode, aF as useNodeFrequencyStore, aG as useNodeBookmarkStore, aH as highlightQuery, aI as script$o, aJ as formatNumberWithSuffix, aK as NodeSourceType, aL as pushScopeId, aM as popScopeId, aN as NodePreview, aO as NodeSearchFilter, aP as script$p, aQ as SearchFilterChip, aR as useLitegraphService, aS as storeToRefs, aT as isRef, aU as toRaw, aV as LinkReleaseTriggerAction, aW as script$q, aX as useUserStore, aY as useDialogStore, aZ as SettingDialogHeader, a_ as SettingDialogContent, a$ as useKeybindingStore, b0 as Teleport, b1 as LinkMarkerShape, b2 as useModelToNodeStore, b3 as CanvasPointer, b4 as IS_CONTROL_WIDGET, b5 as updateControlWidgetLabel, b6 as useColorPaletteService, b7 as setStorageValue, b8 as api, b9 as usePragmaticDroppable, ba as LGraph, bb as LLink, bc as DragAndScale, bd as LGraphCanvas, be as ContextMenu, bf as ChangeTracker, bg as useWorkflowService, bh as ComfyNodeDefImpl, bi as ComfyModelDef, bj as script$r, bk as script$s, bl as script$t, bm as script$u, bn as script$v, bo as normalizeProps, bp as ToastEventBus, bq as setAttribute, br as TransitionGroup, bs as useToast, bt as useToastStore, bu as resolve, bv as nestedPosition, bw as script$w, bx as isPrintableCharacter, by as useQueueSettingsStore, bz as script$x, bA as useQueuePendingTaskCountStore, bB as useLocalStorage, bC as useDraggable, bD as watchDebounced, bE as inject, bF as useElementBounding, bG as lodashExports, bH as useEventBus, bI as script$z, bJ as guardReactiveProps, bK as useMenuItemStore, bL as usePragmaticDraggable, bM as withModifiers, bN as script$B, bO as script$C, bP as provide, bQ as script$D, bR as useDialogService, bS as LGraphEventMode, bT as useQueueStore, bU as i18n, bV as useModelStore } from "./index-C4Fk50Nx.js"; -import { s as script$y } from "./index-hdfnBvYs.js"; -import { s as script$A } from "./index-lMQBwSDj.js"; -import { u as useKeybindingService } from "./keybindingService-D48fkLBy.js"; -import { u as useServerConfigStore } from "./serverConfigStore-BawYAb1j.js"; -import "./index-B7ycxfFq.js"; -const DEFAULT_TITLE = "ComfyUI"; -const TITLE_SUFFIX = " - ComfyUI"; -const _sfc_main$t = /* @__PURE__ */ defineComponent({ - __name: "BrowserTabTitle", - setup(__props) { - const executionStore = useExecutionStore(); - const executionText = computed( - () => executionStore.isIdle ? "" : `[${executionStore.executionProgress}%]` - ); - const settingStore = useSettingStore(); - const betaMenuEnabled = computed( - () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" - ); - const workflowStore = useWorkflowStore(); - const isUnsavedText = computed( - () => workflowStore.activeWorkflow?.isModified || !workflowStore.activeWorkflow?.isPersisted ? " *" : "" - ); - const workflowNameText = computed(() => { - const workflowName = workflowStore.activeWorkflow?.filename; - return workflowName ? isUnsavedText.value + workflowName + TITLE_SUFFIX : DEFAULT_TITLE; - }); - const nodeExecutionTitle = computed( - () => executionStore.executingNode && executionStore.executingNodeProgress ? `${executionText.value}[${executionStore.executingNodeProgress}%] ${executionStore.executingNode.type}` : "" - ); - const workflowTitle = computed( - () => executionText.value + (betaMenuEnabled.value ? workflowNameText.value : DEFAULT_TITLE) - ); - const title = computed(() => nodeExecutionTitle.value || workflowTitle.value); - useTitle(title); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div"); - }; - } -}); -const _sfc_main$s = /* @__PURE__ */ defineComponent({ - __name: "MenuHamburger", - setup(__props) { - const workspaceState = useWorkspaceStore(); - const settingStore = useSettingStore(); - const exitFocusMode = /* @__PURE__ */ __name(() => { - workspaceState.focusMode = false; - }, "exitFocusMode"); - watchEffect(() => { - if (settingStore.get("Comfy.UseNewMenu") !== "Disabled") { - return; - } - if (workspaceState.focusMode) { - app.ui.menuContainer.style.display = "none"; - } else { - app.ui.menuContainer.style.display = "block"; - } - }); - const menuSetting = computed(() => settingStore.get("Comfy.UseNewMenu")); - const positionCSS = computed( - () => ( - // 'Bottom' menuSetting shows the hamburger button in the bottom right corner - // 'Disabled', 'Top' menuSetting shows the hamburger button in the top right corner - menuSetting.value === "Bottom" ? { bottom: "0px", right: "0px" } : { top: "0px", right: "0px" } - ) - ); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return withDirectives((openBlock(), createBlock(unref(script$d), { - class: "comfy-menu-hamburger", - style: normalizeStyle(positionCSS.value), - icon: "pi pi-bars", - severity: "secondary", - text: "", - size: "large", - onClick: exitFocusMode, - onContextmenu: unref(showNativeMenu) - }, null, 8, ["style", "onContextmenu"])), [ - [vShow, unref(workspaceState).focusMode], - [_directive_tooltip, { value: _ctx.$t("menu.showMenu"), showDelay: 300 }] - ]); - }; - } -}); -const MenuHamburger = /* @__PURE__ */ _export_sfc(_sfc_main$s, [["__scopeId", "data-v-5661bed0"]]); -const _sfc_main$r = /* @__PURE__ */ defineComponent({ - __name: "UnloadWindowConfirmDialog", - setup(__props) { - const settingStore = useSettingStore(); - const handleBeforeUnload = /* @__PURE__ */ __name((event) => { - if (settingStore.get("Comfy.Window.UnloadConfirmation")) { - event.preventDefault(); - return true; - } - return void 0; - }, "handleBeforeUnload"); - onMounted(() => { - window.addEventListener("beforeunload", handleBeforeUnload); - }); - onBeforeUnmount(() => { - window.removeEventListener("beforeunload", handleBeforeUnload); - }); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div"); - }; - } -}); -var theme$7 = /* @__PURE__ */ __name(function theme(_ref) { - var dt = _ref.dt; - return "\n.p-splitter {\n display: flex;\n flex-wrap: nowrap;\n border: 1px solid ".concat(dt("splitter.border.color"), ";\n background: ").concat(dt("splitter.background"), ";\n border-radius: ").concat(dt("border.radius.md"), ";\n color: ").concat(dt("splitter.color"), ";\n}\n\n.p-splitter-vertical {\n flex-direction: column;\n}\n\n.p-splitter-gutter {\n flex-grow: 0;\n flex-shrink: 0;\n display: flex;\n align-items: center;\n justify-content: center;\n z-index: 1;\n background: ").concat(dt("splitter.gutter.background"), ";\n}\n\n.p-splitter-gutter-handle {\n border-radius: ").concat(dt("splitter.handle.border.radius"), ";\n background: ").concat(dt("splitter.handle.background"), ";\n transition: outline-color ").concat(dt("splitter.transition.duration"), ", box-shadow ").concat(dt("splitter.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-splitter-gutter-handle:focus-visible {\n box-shadow: ").concat(dt("splitter.handle.focus.ring.shadow"), ";\n outline: ").concat(dt("splitter.handle.focus.ring.width"), " ").concat(dt("splitter.handle.focus.ring.style"), " ").concat(dt("splitter.handle.focus.ring.color"), ";\n outline-offset: ").concat(dt("splitter.handle.focus.ring.offset"), ";\n}\n\n.p-splitter-horizontal.p-splitter-resizing {\n cursor: col-resize;\n user-select: none;\n}\n\n.p-splitter-vertical.p-splitter-resizing {\n cursor: row-resize;\n user-select: none;\n}\n\n.p-splitter-horizontal > .p-splitter-gutter > .p-splitter-gutter-handle {\n height: ").concat(dt("splitter.handle.size"), ";\n width: 100%;\n}\n\n.p-splitter-vertical > .p-splitter-gutter > .p-splitter-gutter-handle {\n width: ").concat(dt("splitter.handle.size"), ";\n height: 100%;\n}\n\n.p-splitter-horizontal > .p-splitter-gutter {\n cursor: col-resize;\n}\n\n.p-splitter-vertical > .p-splitter-gutter {\n cursor: row-resize;\n}\n\n.p-splitterpanel {\n flex-grow: 1;\n overflow: hidden;\n}\n\n.p-splitterpanel-nested {\n display: flex;\n}\n\n.p-splitterpanel .p-splitter {\n flex-grow: 1;\n border: 0 none;\n}\n"); -}, "theme"); -var classes$a = { - root: /* @__PURE__ */ __name(function root(_ref2) { - var props = _ref2.props; - return ["p-splitter p-component", "p-splitter-" + props.layout]; - }, "root"), - gutter: "p-splitter-gutter", - gutterHandle: "p-splitter-gutter-handle" -}; -var inlineStyles$4 = { - root: /* @__PURE__ */ __name(function root2(_ref3) { - var props = _ref3.props; - return [{ - display: "flex", - "flex-wrap": "nowrap" - }, props.layout === "vertical" ? { - "flex-direction": "column" - } : ""]; - }, "root") -}; -var SplitterStyle = BaseStyle.extend({ - name: "splitter", - theme: theme$7, - classes: classes$a, - inlineStyles: inlineStyles$4 -}); -var script$1$a = { - name: "BaseSplitter", - "extends": script$e, - props: { - layout: { - type: String, - "default": "horizontal" - }, - gutterSize: { - type: Number, - "default": 4 - }, - stateKey: { - type: String, - "default": null - }, - stateStorage: { - type: String, - "default": "session" - }, - step: { - type: Number, - "default": 5 - } - }, - style: SplitterStyle, - provide: /* @__PURE__ */ __name(function provide2() { - return { - $pcSplitter: this, - $parentInstance: this - }; - }, "provide") -}; -function _toConsumableArray$2(r) { - return _arrayWithoutHoles$2(r) || _iterableToArray$2(r) || _unsupportedIterableToArray$2(r) || _nonIterableSpread$2(); -} -__name(_toConsumableArray$2, "_toConsumableArray$2"); -function _nonIterableSpread$2() { - throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method."); -} -__name(_nonIterableSpread$2, "_nonIterableSpread$2"); -function _unsupportedIterableToArray$2(r, a) { - if (r) { - if ("string" == typeof r) return _arrayLikeToArray$2(r, a); - var t = {}.toString.call(r).slice(8, -1); - return "Object" === t && r.constructor && (t = r.constructor.name), "Map" === t || "Set" === t ? Array.from(r) : "Arguments" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray$2(r, a) : void 0; - } -} -__name(_unsupportedIterableToArray$2, "_unsupportedIterableToArray$2"); -function _iterableToArray$2(r) { - if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); -} -__name(_iterableToArray$2, "_iterableToArray$2"); -function _arrayWithoutHoles$2(r) { - if (Array.isArray(r)) return _arrayLikeToArray$2(r); -} -__name(_arrayWithoutHoles$2, "_arrayWithoutHoles$2"); -function _arrayLikeToArray$2(r, a) { - (null == a || a > r.length) && (a = r.length); - for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; - return n; -} -__name(_arrayLikeToArray$2, "_arrayLikeToArray$2"); -var script$c = { - name: "Splitter", - "extends": script$1$a, - inheritAttrs: false, - emits: ["resizestart", "resizeend", "resize"], - dragging: false, - mouseMoveListener: null, - mouseUpListener: null, - touchMoveListener: null, - touchEndListener: null, - size: null, - gutterElement: null, - startPos: null, - prevPanelElement: null, - nextPanelElement: null, - nextPanelSize: null, - prevPanelSize: null, - panelSizes: null, - prevPanelIndex: null, - timer: null, - data: /* @__PURE__ */ __name(function data() { - return { - prevSize: null - }; - }, "data"), - mounted: /* @__PURE__ */ __name(function mounted() { - var _this = this; - if (this.panels && this.panels.length) { - var initialized = false; - if (this.isStateful()) { - initialized = this.restoreState(); - } - if (!initialized) { - var children = _toConsumableArray$2(this.$el.children).filter(function(child) { - return child.getAttribute("data-pc-name") === "splitterpanel"; - }); - var _panelSizes = []; - this.panels.map(function(panel, i) { - var panelInitialSize = panel.props && panel.props.size ? panel.props.size : null; - var panelSize = panelInitialSize || 100 / _this.panels.length; - _panelSizes[i] = panelSize; - children[i].style.flexBasis = "calc(" + panelSize + "% - " + (_this.panels.length - 1) * _this.gutterSize + "px)"; - }); - this.panelSizes = _panelSizes; - this.prevSize = parseFloat(_panelSizes[0]).toFixed(4); - } - } - }, "mounted"), - beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount() { - this.clear(); - this.unbindMouseListeners(); - }, "beforeUnmount"), - methods: { - isSplitterPanel: /* @__PURE__ */ __name(function isSplitterPanel(child) { - return child.type.name === "SplitterPanel"; - }, "isSplitterPanel"), - onResizeStart: /* @__PURE__ */ __name(function onResizeStart(event, index, isKeyDown) { - this.gutterElement = event.currentTarget || event.target.parentElement; - this.size = this.horizontal ? getWidth(this.$el) : getHeight(this.$el); - if (!isKeyDown) { - this.dragging = true; - this.startPos = this.layout === "horizontal" ? event.pageX || event.changedTouches[0].pageX : event.pageY || event.changedTouches[0].pageY; - } - this.prevPanelElement = this.gutterElement.previousElementSibling; - this.nextPanelElement = this.gutterElement.nextElementSibling; - if (isKeyDown) { - this.prevPanelSize = this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true); - this.nextPanelSize = this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true); - } else { - this.prevPanelSize = 100 * (this.horizontal ? getOuterWidth(this.prevPanelElement, true) : getOuterHeight(this.prevPanelElement, true)) / this.size; - this.nextPanelSize = 100 * (this.horizontal ? getOuterWidth(this.nextPanelElement, true) : getOuterHeight(this.nextPanelElement, true)) / this.size; - } - this.prevPanelIndex = index; - this.$emit("resizestart", { - originalEvent: event, - sizes: this.panelSizes - }); - this.$refs.gutter[index].setAttribute("data-p-gutter-resizing", true); - this.$el.setAttribute("data-p-resizing", true); - }, "onResizeStart"), - onResize: /* @__PURE__ */ __name(function onResize(event, step, isKeyDown) { - var newPos, newPrevPanelSize, newNextPanelSize; - if (isKeyDown) { - if (this.horizontal) { - newPrevPanelSize = 100 * (this.prevPanelSize + step) / this.size; - newNextPanelSize = 100 * (this.nextPanelSize - step) / this.size; - } else { - newPrevPanelSize = 100 * (this.prevPanelSize - step) / this.size; - newNextPanelSize = 100 * (this.nextPanelSize + step) / this.size; - } - } else { - if (this.horizontal) newPos = event.pageX * 100 / this.size - this.startPos * 100 / this.size; - else newPos = event.pageY * 100 / this.size - this.startPos * 100 / this.size; - newPrevPanelSize = this.prevPanelSize + newPos; - newNextPanelSize = this.nextPanelSize - newPos; - } - if (this.validateResize(newPrevPanelSize, newNextPanelSize)) { - this.prevPanelElement.style.flexBasis = "calc(" + newPrevPanelSize + "% - " + (this.panels.length - 1) * this.gutterSize + "px)"; - this.nextPanelElement.style.flexBasis = "calc(" + newNextPanelSize + "% - " + (this.panels.length - 1) * this.gutterSize + "px)"; - this.panelSizes[this.prevPanelIndex] = newPrevPanelSize; - this.panelSizes[this.prevPanelIndex + 1] = newNextPanelSize; - this.prevSize = parseFloat(newPrevPanelSize).toFixed(4); - } - this.$emit("resize", { - originalEvent: event, - sizes: this.panelSizes - }); - }, "onResize"), - onResizeEnd: /* @__PURE__ */ __name(function onResizeEnd(event) { - if (this.isStateful()) { - this.saveState(); - } - this.$emit("resizeend", { - originalEvent: event, - sizes: this.panelSizes - }); - this.$refs.gutter.forEach(function(gutter) { - return gutter.setAttribute("data-p-gutter-resizing", false); - }); - this.$el.setAttribute("data-p-resizing", false); - this.clear(); - }, "onResizeEnd"), - repeat: /* @__PURE__ */ __name(function repeat(event, index, step) { - this.onResizeStart(event, index, true); - this.onResize(event, step, true); - }, "repeat"), - setTimer: /* @__PURE__ */ __name(function setTimer(event, index, step) { - var _this2 = this; - if (!this.timer) { - this.timer = setInterval(function() { - _this2.repeat(event, index, step); - }, 40); - } - }, "setTimer"), - clearTimer: /* @__PURE__ */ __name(function clearTimer() { - if (this.timer) { - clearInterval(this.timer); - this.timer = null; - } - }, "clearTimer"), - onGutterKeyUp: /* @__PURE__ */ __name(function onGutterKeyUp() { - this.clearTimer(); - this.onResizeEnd(); - }, "onGutterKeyUp"), - onGutterKeyDown: /* @__PURE__ */ __name(function onGutterKeyDown(event, index) { - switch (event.code) { - case "ArrowLeft": { - if (this.layout === "horizontal") { - this.setTimer(event, index, this.step * -1); - } - event.preventDefault(); - break; - } - case "ArrowRight": { - if (this.layout === "horizontal") { - this.setTimer(event, index, this.step); - } - event.preventDefault(); - break; - } - case "ArrowDown": { - if (this.layout === "vertical") { - this.setTimer(event, index, this.step * -1); - } - event.preventDefault(); - break; - } - case "ArrowUp": { - if (this.layout === "vertical") { - this.setTimer(event, index, this.step); - } - event.preventDefault(); - break; - } - } - }, "onGutterKeyDown"), - onGutterMouseDown: /* @__PURE__ */ __name(function onGutterMouseDown(event, index) { - this.onResizeStart(event, index); - this.bindMouseListeners(); - }, "onGutterMouseDown"), - onGutterTouchStart: /* @__PURE__ */ __name(function onGutterTouchStart(event, index) { - this.onResizeStart(event, index); - this.bindTouchListeners(); - event.preventDefault(); - }, "onGutterTouchStart"), - onGutterTouchMove: /* @__PURE__ */ __name(function onGutterTouchMove(event) { - this.onResize(event); - event.preventDefault(); - }, "onGutterTouchMove"), - onGutterTouchEnd: /* @__PURE__ */ __name(function onGutterTouchEnd(event) { - this.onResizeEnd(event); - this.unbindTouchListeners(); - event.preventDefault(); - }, "onGutterTouchEnd"), - bindMouseListeners: /* @__PURE__ */ __name(function bindMouseListeners() { - var _this3 = this; - if (!this.mouseMoveListener) { - this.mouseMoveListener = function(event) { - return _this3.onResize(event); - }; - document.addEventListener("mousemove", this.mouseMoveListener); - } - if (!this.mouseUpListener) { - this.mouseUpListener = function(event) { - _this3.onResizeEnd(event); - _this3.unbindMouseListeners(); - }; - document.addEventListener("mouseup", this.mouseUpListener); - } - }, "bindMouseListeners"), - bindTouchListeners: /* @__PURE__ */ __name(function bindTouchListeners() { - var _this4 = this; - if (!this.touchMoveListener) { - this.touchMoveListener = function(event) { - return _this4.onResize(event.changedTouches[0]); - }; - document.addEventListener("touchmove", this.touchMoveListener); - } - if (!this.touchEndListener) { - this.touchEndListener = function(event) { - _this4.resizeEnd(event); - _this4.unbindTouchListeners(); - }; - document.addEventListener("touchend", this.touchEndListener); - } - }, "bindTouchListeners"), - validateResize: /* @__PURE__ */ __name(function validateResize(newPrevPanelSize, newNextPanelSize) { - if (newPrevPanelSize > 100 || newPrevPanelSize < 0) return false; - if (newNextPanelSize > 100 || newNextPanelSize < 0) return false; - var prevPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex], "minSize"); - if (this.panels[this.prevPanelIndex].props && prevPanelMinSize && prevPanelMinSize > newPrevPanelSize) { - return false; - } - var newPanelMinSize = getVNodeProp(this.panels[this.prevPanelIndex + 1], "minSize"); - if (this.panels[this.prevPanelIndex + 1].props && newPanelMinSize && newPanelMinSize > newNextPanelSize) { - return false; - } - return true; - }, "validateResize"), - unbindMouseListeners: /* @__PURE__ */ __name(function unbindMouseListeners() { - if (this.mouseMoveListener) { - document.removeEventListener("mousemove", this.mouseMoveListener); - this.mouseMoveListener = null; - } - if (this.mouseUpListener) { - document.removeEventListener("mouseup", this.mouseUpListener); - this.mouseUpListener = null; - } - }, "unbindMouseListeners"), - unbindTouchListeners: /* @__PURE__ */ __name(function unbindTouchListeners() { - if (this.touchMoveListener) { - document.removeEventListener("touchmove", this.touchMoveListener); - this.touchMoveListener = null; - } - if (this.touchEndListener) { - document.removeEventListener("touchend", this.touchEndListener); - this.touchEndListener = null; - } - }, "unbindTouchListeners"), - clear: /* @__PURE__ */ __name(function clear() { - this.dragging = false; - this.size = null; - this.startPos = null; - this.prevPanelElement = null; - this.nextPanelElement = null; - this.prevPanelSize = null; - this.nextPanelSize = null; - this.gutterElement = null; - this.prevPanelIndex = null; - }, "clear"), - isStateful: /* @__PURE__ */ __name(function isStateful() { - return this.stateKey != null; - }, "isStateful"), - getStorage: /* @__PURE__ */ __name(function getStorage() { - switch (this.stateStorage) { - case "local": - return window.localStorage; - case "session": - return window.sessionStorage; - default: - throw new Error(this.stateStorage + ' is not a valid value for the state storage, supported values are "local" and "session".'); - } - }, "getStorage"), - saveState: /* @__PURE__ */ __name(function saveState() { - if (isArray(this.panelSizes)) { - this.getStorage().setItem(this.stateKey, JSON.stringify(this.panelSizes)); - } - }, "saveState"), - restoreState: /* @__PURE__ */ __name(function restoreState() { - var _this5 = this; - var storage = this.getStorage(); - var stateString = storage.getItem(this.stateKey); - if (stateString) { - this.panelSizes = JSON.parse(stateString); - var children = _toConsumableArray$2(this.$el.children).filter(function(child) { - return child.getAttribute("data-pc-name") === "splitterpanel"; - }); - children.forEach(function(child, i) { - child.style.flexBasis = "calc(" + _this5.panelSizes[i] + "% - " + (_this5.panels.length - 1) * _this5.gutterSize + "px)"; - }); - return true; - } - return false; - }, "restoreState") - }, - computed: { - panels: /* @__PURE__ */ __name(function panels() { - var _this6 = this; - var panels2 = []; - this.$slots["default"]().forEach(function(child) { - if (_this6.isSplitterPanel(child)) { - panels2.push(child); - } else if (child.children instanceof Array) { - child.children.forEach(function(nestedChild) { - if (_this6.isSplitterPanel(nestedChild)) { - panels2.push(nestedChild); - } - }); - } - }); - return panels2; - }, "panels"), - gutterStyle: /* @__PURE__ */ __name(function gutterStyle() { - if (this.horizontal) return { - width: this.gutterSize + "px" - }; - else return { - height: this.gutterSize + "px" - }; - }, "gutterStyle"), - horizontal: /* @__PURE__ */ __name(function horizontal() { - return this.layout === "horizontal"; - }, "horizontal"), - getPTOptions: /* @__PURE__ */ __name(function getPTOptions() { - var _this$$parentInstance; - return { - context: { - nested: (_this$$parentInstance = this.$parentInstance) === null || _this$$parentInstance === void 0 ? void 0 : _this$$parentInstance.nestedState - } - }; - }, "getPTOptions") - } -}; -var _hoisted_1$m = ["onMousedown", "onTouchstart", "onTouchmove", "onTouchend"]; -var _hoisted_2$j = ["aria-orientation", "aria-valuenow", "onKeydown"]; -function render$j(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("div", mergeProps({ - "class": _ctx.cx("root"), - style: _ctx.sx("root"), - "data-p-resizing": false - }, _ctx.ptmi("root", $options.getPTOptions)), [(openBlock(true), createElementBlock(Fragment, null, renderList($options.panels, function(panel, i) { - return openBlock(), createElementBlock(Fragment, { - key: i - }, [(openBlock(), createBlock(resolveDynamicComponent(panel), { - tabindex: "-1" - })), i !== $options.panels.length - 1 ? (openBlock(), createElementBlock("div", mergeProps({ - key: 0, - ref_for: true, - ref: "gutter", - "class": _ctx.cx("gutter"), - role: "separator", - tabindex: "-1", - onMousedown: /* @__PURE__ */ __name(function onMousedown($event) { - return $options.onGutterMouseDown($event, i); - }, "onMousedown"), - onTouchstart: /* @__PURE__ */ __name(function onTouchstart($event) { - return $options.onGutterTouchStart($event, i); - }, "onTouchstart"), - onTouchmove: /* @__PURE__ */ __name(function onTouchmove($event) { - return $options.onGutterTouchMove($event, i); - }, "onTouchmove"), - onTouchend: /* @__PURE__ */ __name(function onTouchend($event) { - return $options.onGutterTouchEnd($event, i); - }, "onTouchend"), - "data-p-gutter-resizing": false - }, _ctx.ptm("gutter")), [createBaseVNode("div", mergeProps({ - "class": _ctx.cx("gutterHandle"), - tabindex: "0", - style: [$options.gutterStyle], - "aria-orientation": _ctx.layout, - "aria-valuenow": $data.prevSize, - onKeyup: _cache[0] || (_cache[0] = function() { - return $options.onGutterKeyUp && $options.onGutterKeyUp.apply($options, arguments); - }), - onKeydown: /* @__PURE__ */ __name(function onKeydown2($event) { - return $options.onGutterKeyDown($event, i); - }, "onKeydown"), - ref_for: true - }, _ctx.ptm("gutterHandle")), null, 16, _hoisted_2$j)], 16, _hoisted_1$m)) : createCommentVNode("", true)], 64); - }), 128))], 16); -} -__name(render$j, "render$j"); -script$c.render = render$j; -var classes$9 = { - root: /* @__PURE__ */ __name(function root3(_ref) { - var instance = _ref.instance; - return ["p-splitterpanel", { - "p-splitterpanel-nested": instance.isNested - }]; - }, "root") -}; -var SplitterPanelStyle = BaseStyle.extend({ - name: "splitterpanel", - classes: classes$9 -}); -var script$1$9 = { - name: "BaseSplitterPanel", - "extends": script$e, - props: { - size: { - type: Number, - "default": null - }, - minSize: { - type: Number, - "default": null - } - }, - style: SplitterPanelStyle, - provide: /* @__PURE__ */ __name(function provide3() { - return { - $pcSplitterPanel: this, - $parentInstance: this - }; - }, "provide") -}; -var script$b = { - name: "SplitterPanel", - "extends": script$1$9, - inheritAttrs: false, - data: /* @__PURE__ */ __name(function data2() { - return { - nestedState: null - }; - }, "data"), - computed: { - isNested: /* @__PURE__ */ __name(function isNested() { - var _this = this; - return this.$slots["default"]().some(function(child) { - _this.nestedState = child.type.name === "Splitter" ? true : null; - return _this.nestedState; - }); - }, "isNested"), - getPTOptions: /* @__PURE__ */ __name(function getPTOptions2() { - return { - context: { - nested: this.isNested - } - }; - }, "getPTOptions") - } -}; -function render$i(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("div", mergeProps({ - ref: "container", - "class": _ctx.cx("root") - }, _ctx.ptmi("root", $options.getPTOptions)), [renderSlot(_ctx.$slots, "default")], 16); -} -__name(render$i, "render$i"); -script$b.render = render$i; -const _sfc_main$q = /* @__PURE__ */ defineComponent({ - __name: "LiteGraphCanvasSplitterOverlay", - setup(__props) { - const settingStore = useSettingStore(); - const sidebarLocation = computed( - () => settingStore.get("Comfy.Sidebar.Location") - ); - const sidebarPanelVisible = computed( - () => useSidebarTabStore().activeSidebarTab !== null - ); - const bottomPanelVisible = computed( - () => useBottomPanelStore().bottomPanelVisible - ); - const activeSidebarTabId = computed( - () => useSidebarTabStore().activeSidebarTabId - ); - return (_ctx, _cache) => { - return openBlock(), createBlock(unref(script$c), { - class: "splitter-overlay-root splitter-overlay", - "pt:gutter": sidebarPanelVisible.value ? "" : "hidden", - key: activeSidebarTabId.value, - stateKey: activeSidebarTabId.value, - stateStorage: "local" - }, { - default: withCtx(() => [ - sidebarLocation.value === "left" ? withDirectives((openBlock(), createBlock(unref(script$b), { - key: 0, - class: "side-bar-panel", - minSize: 10, - size: 20 - }, { - default: withCtx(() => [ - renderSlot(_ctx.$slots, "side-bar-panel", {}, void 0, true) - ]), - _: 3 - }, 512)), [ - [vShow, sidebarPanelVisible.value] - ]) : createCommentVNode("", true), - createVNode(unref(script$b), { size: 100 }, { - default: withCtx(() => [ - createVNode(unref(script$c), { - class: "splitter-overlay max-w-full", - layout: "vertical", - "pt:gutter": bottomPanelVisible.value ? "" : "hidden", - stateKey: "bottom-panel-splitter", - stateStorage: "local" - }, { - default: withCtx(() => [ - createVNode(unref(script$b), { class: "graph-canvas-panel relative" }, { - default: withCtx(() => [ - renderSlot(_ctx.$slots, "graph-canvas-panel", {}, void 0, true) - ]), - _: 3 - }), - withDirectives(createVNode(unref(script$b), { class: "bottom-panel" }, { - default: withCtx(() => [ - renderSlot(_ctx.$slots, "bottom-panel", {}, void 0, true) - ]), - _: 3 - }, 512), [ - [vShow, bottomPanelVisible.value] - ]) - ]), - _: 3 - }, 8, ["pt:gutter"]) - ]), - _: 3 - }), - sidebarLocation.value === "right" ? withDirectives((openBlock(), createBlock(unref(script$b), { - key: 1, - class: "side-bar-panel", - minSize: 10, - size: 20 - }, { - default: withCtx(() => [ - renderSlot(_ctx.$slots, "side-bar-panel", {}, void 0, true) - ]), - _: 3 - }, 512)), [ - [vShow, sidebarPanelVisible.value] - ]) : createCommentVNode("", true) - ]), - _: 3 - }, 8, ["pt:gutter", "stateKey"]); - }; - } -}); -const LiteGraphCanvasSplitterOverlay = /* @__PURE__ */ _export_sfc(_sfc_main$q, [["__scopeId", "data-v-e50caa15"]]); -var classes$8 = { - root: /* @__PURE__ */ __name(function root4(_ref) { - var instance = _ref.instance, props = _ref.props; - return ["p-tab", { - "p-tab-active": instance.active, - "p-disabled": props.disabled - }]; - }, "root") -}; -var TabStyle = BaseStyle.extend({ - name: "tab", - classes: classes$8 -}); -var script$1$8 = { - name: "BaseTab", - "extends": script$e, - props: { - value: { - type: [String, Number], - "default": void 0 - }, - disabled: { - type: Boolean, - "default": false - }, - as: { - type: [String, Object], - "default": "BUTTON" - }, - asChild: { - type: Boolean, - "default": false - } - }, - style: TabStyle, - provide: /* @__PURE__ */ __name(function provide4() { - return { - $pcTab: this, - $parentInstance: this - }; - }, "provide") -}; -var script$a = { - name: "Tab", - "extends": script$1$8, - inheritAttrs: false, - inject: ["$pcTabs", "$pcTabList"], - methods: { - onFocus: /* @__PURE__ */ __name(function onFocus() { - this.$pcTabs.selectOnFocus && this.changeActiveValue(); - }, "onFocus"), - onClick: /* @__PURE__ */ __name(function onClick() { - this.changeActiveValue(); - }, "onClick"), - onKeydown: /* @__PURE__ */ __name(function onKeydown(event) { - switch (event.code) { - case "ArrowRight": - this.onArrowRightKey(event); - break; - case "ArrowLeft": - this.onArrowLeftKey(event); - break; - case "Home": - this.onHomeKey(event); - break; - case "End": - this.onEndKey(event); - break; - case "PageDown": - this.onPageDownKey(event); - break; - case "PageUp": - this.onPageUpKey(event); - break; - case "Enter": - case "NumpadEnter": - case "Space": - this.onEnterKey(event); - break; - } - }, "onKeydown"), - onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey(event) { - var nextTab = this.findNextTab(event.currentTarget); - nextTab ? this.changeFocusedTab(event, nextTab) : this.onHomeKey(event); - event.preventDefault(); - }, "onArrowRightKey"), - onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey(event) { - var prevTab = this.findPrevTab(event.currentTarget); - prevTab ? this.changeFocusedTab(event, prevTab) : this.onEndKey(event); - event.preventDefault(); - }, "onArrowLeftKey"), - onHomeKey: /* @__PURE__ */ __name(function onHomeKey(event) { - var firstTab = this.findFirstTab(); - this.changeFocusedTab(event, firstTab); - event.preventDefault(); - }, "onHomeKey"), - onEndKey: /* @__PURE__ */ __name(function onEndKey(event) { - var lastTab = this.findLastTab(); - this.changeFocusedTab(event, lastTab); - event.preventDefault(); - }, "onEndKey"), - onPageDownKey: /* @__PURE__ */ __name(function onPageDownKey(event) { - this.scrollInView(this.findLastTab()); - event.preventDefault(); - }, "onPageDownKey"), - onPageUpKey: /* @__PURE__ */ __name(function onPageUpKey(event) { - this.scrollInView(this.findFirstTab()); - event.preventDefault(); - }, "onPageUpKey"), - onEnterKey: /* @__PURE__ */ __name(function onEnterKey(event) { - this.changeActiveValue(); - event.preventDefault(); - }, "onEnterKey"), - findNextTab: /* @__PURE__ */ __name(function findNextTab(tabElement) { - var selfCheck = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : false; - var element = selfCheck ? tabElement : tabElement.nextElementSibling; - return element ? getAttribute(element, "data-p-disabled") || getAttribute(element, "data-pc-section") === "inkbar" ? this.findNextTab(element) : findSingle(element, '[data-pc-name="tab"]') : null; - }, "findNextTab"), - findPrevTab: /* @__PURE__ */ __name(function findPrevTab(tabElement) { - var selfCheck = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : false; - var element = selfCheck ? tabElement : tabElement.previousElementSibling; - return element ? getAttribute(element, "data-p-disabled") || getAttribute(element, "data-pc-section") === "inkbar" ? this.findPrevTab(element) : findSingle(element, '[data-pc-name="tab"]') : null; - }, "findPrevTab"), - findFirstTab: /* @__PURE__ */ __name(function findFirstTab() { - return this.findNextTab(this.$pcTabList.$refs.content.firstElementChild, true); - }, "findFirstTab"), - findLastTab: /* @__PURE__ */ __name(function findLastTab() { - return this.findPrevTab(this.$pcTabList.$refs.content.lastElementChild, true); - }, "findLastTab"), - changeActiveValue: /* @__PURE__ */ __name(function changeActiveValue() { - this.$pcTabs.updateValue(this.value); - }, "changeActiveValue"), - changeFocusedTab: /* @__PURE__ */ __name(function changeFocusedTab(event, element) { - focus(element); - this.scrollInView(element); - }, "changeFocusedTab"), - scrollInView: /* @__PURE__ */ __name(function scrollInView(element) { - var _element$scrollIntoVi; - element === null || element === void 0 || (_element$scrollIntoVi = element.scrollIntoView) === null || _element$scrollIntoVi === void 0 || _element$scrollIntoVi.call(element, { - block: "nearest" - }); - }, "scrollInView") - }, - computed: { - active: /* @__PURE__ */ __name(function active() { - var _this$$pcTabs; - return equals((_this$$pcTabs = this.$pcTabs) === null || _this$$pcTabs === void 0 ? void 0 : _this$$pcTabs.d_value, this.value); - }, "active"), - id: /* @__PURE__ */ __name(function id() { - var _this$$pcTabs2; - return "".concat((_this$$pcTabs2 = this.$pcTabs) === null || _this$$pcTabs2 === void 0 ? void 0 : _this$$pcTabs2.id, "_tab_").concat(this.value); - }, "id"), - ariaControls: /* @__PURE__ */ __name(function ariaControls() { - var _this$$pcTabs3; - return "".concat((_this$$pcTabs3 = this.$pcTabs) === null || _this$$pcTabs3 === void 0 ? void 0 : _this$$pcTabs3.id, "_tabpanel_").concat(this.value); - }, "ariaControls"), - attrs: /* @__PURE__ */ __name(function attrs() { - return mergeProps(this.asAttrs, this.a11yAttrs, this.ptmi("root", this.ptParams)); - }, "attrs"), - asAttrs: /* @__PURE__ */ __name(function asAttrs() { - return this.as === "BUTTON" ? { - type: "button", - disabled: this.disabled - } : void 0; - }, "asAttrs"), - a11yAttrs: /* @__PURE__ */ __name(function a11yAttrs() { - return { - id: this.id, - tabindex: this.active ? this.$pcTabs.tabindex : -1, - role: "tab", - "aria-selected": this.active, - "aria-controls": this.ariaControls, - "data-pc-name": "tab", - "data-p-disabled": this.disabled, - "data-p-active": this.active, - onFocus: this.onFocus, - onKeydown: this.onKeydown - }; - }, "a11yAttrs"), - ptParams: /* @__PURE__ */ __name(function ptParams() { - return { - context: { - active: this.active - } - }; - }, "ptParams") - }, - directives: { - ripple: Ripple - } -}; -function render$h(_ctx, _cache, $props, $setup, $data, $options) { - var _directive_ripple = resolveDirective("ripple"); - return !_ctx.asChild ? withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({ - key: 0, - "class": _ctx.cx("root"), - onClick: $options.onClick - }, $options.attrs), { - "default": withCtx(function() { - return [renderSlot(_ctx.$slots, "default")]; - }), - _: 3 - }, 16, ["class", "onClick"])), [[_directive_ripple]]) : renderSlot(_ctx.$slots, "default", { - key: 1, - "class": normalizeClass(_ctx.cx("root")), - active: $options.active, - a11yAttrs: $options.a11yAttrs, - onClick: $options.onClick - }); -} -__name(render$h, "render$h"); -script$a.render = render$h; -var classes$7 = { - root: "p-tablist", - content: /* @__PURE__ */ __name(function content(_ref) { - var instance = _ref.instance; - return ["p-tablist-content", { - "p-tablist-viewport": instance.$pcTabs.scrollable - }]; - }, "content"), - tabList: "p-tablist-tab-list", - activeBar: "p-tablist-active-bar", - prevButton: "p-tablist-prev-button p-tablist-nav-button", - nextButton: "p-tablist-next-button p-tablist-nav-button" -}; -var TabListStyle = BaseStyle.extend({ - name: "tablist", - classes: classes$7 -}); -var script$1$7 = { - name: "BaseTabList", - "extends": script$e, - props: {}, - style: TabListStyle, - provide: /* @__PURE__ */ __name(function provide5() { - return { - $pcTabList: this, - $parentInstance: this - }; - }, "provide") -}; -var script$9 = { - name: "TabList", - "extends": script$1$7, - inheritAttrs: false, - inject: ["$pcTabs"], - data: /* @__PURE__ */ __name(function data3() { - return { - isPrevButtonEnabled: false, - isNextButtonEnabled: true - }; - }, "data"), - resizeObserver: void 0, - watch: { - showNavigators: /* @__PURE__ */ __name(function showNavigators(newValue) { - newValue ? this.bindResizeObserver() : this.unbindResizeObserver(); - }, "showNavigators"), - activeValue: { - flush: "post", - handler: /* @__PURE__ */ __name(function handler() { - this.updateInkBar(); - }, "handler") - } - }, - mounted: /* @__PURE__ */ __name(function mounted2() { - var _this = this; - this.$nextTick(function() { - _this.updateInkBar(); - }); - if (this.showNavigators) { - this.updateButtonState(); - this.bindResizeObserver(); - } - }, "mounted"), - updated: /* @__PURE__ */ __name(function updated() { - this.showNavigators && this.updateButtonState(); - }, "updated"), - beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount2() { - this.unbindResizeObserver(); - }, "beforeUnmount"), - methods: { - onScroll: /* @__PURE__ */ __name(function onScroll(event) { - this.showNavigators && this.updateButtonState(); - event.preventDefault(); - }, "onScroll"), - onPrevButtonClick: /* @__PURE__ */ __name(function onPrevButtonClick() { - var content2 = this.$refs.content; - var width = getWidth(content2); - var pos = content2.scrollLeft - width; - content2.scrollLeft = pos <= 0 ? 0 : pos; - }, "onPrevButtonClick"), - onNextButtonClick: /* @__PURE__ */ __name(function onNextButtonClick() { - var content2 = this.$refs.content; - var width = getWidth(content2) - this.getVisibleButtonWidths(); - var pos = content2.scrollLeft + width; - var lastPos = content2.scrollWidth - width; - content2.scrollLeft = pos >= lastPos ? lastPos : pos; - }, "onNextButtonClick"), - bindResizeObserver: /* @__PURE__ */ __name(function bindResizeObserver() { - var _this2 = this; - this.resizeObserver = new ResizeObserver(function() { - return _this2.updateButtonState(); - }); - this.resizeObserver.observe(this.$refs.list); - }, "bindResizeObserver"), - unbindResizeObserver: /* @__PURE__ */ __name(function unbindResizeObserver() { - var _this$resizeObserver; - (_this$resizeObserver = this.resizeObserver) === null || _this$resizeObserver === void 0 || _this$resizeObserver.unobserve(this.$refs.list); - this.resizeObserver = void 0; - }, "unbindResizeObserver"), - updateInkBar: /* @__PURE__ */ __name(function updateInkBar() { - var _this$$refs = this.$refs, content2 = _this$$refs.content, inkbar = _this$$refs.inkbar, tabs = _this$$refs.tabs; - var activeTab = findSingle(content2, '[data-pc-name="tab"][data-p-active="true"]'); - if (this.$pcTabs.isVertical()) { - inkbar.style.height = getOuterHeight(activeTab) + "px"; - inkbar.style.top = getOffset(activeTab).top - getOffset(tabs).top + "px"; - } else { - inkbar.style.width = getOuterWidth(activeTab) + "px"; - inkbar.style.left = getOffset(activeTab).left - getOffset(tabs).left + "px"; - } - }, "updateInkBar"), - updateButtonState: /* @__PURE__ */ __name(function updateButtonState() { - var _this$$refs2 = this.$refs, list = _this$$refs2.list, content2 = _this$$refs2.content; - var scrollLeft = content2.scrollLeft, scrollTop = content2.scrollTop, scrollWidth = content2.scrollWidth, scrollHeight = content2.scrollHeight, offsetWidth = content2.offsetWidth, offsetHeight = content2.offsetHeight; - var _ref = [getWidth(content2), getHeight(content2)], width = _ref[0], height = _ref[1]; - if (this.$pcTabs.isVertical()) { - this.isPrevButtonEnabled = scrollTop !== 0; - this.isNextButtonEnabled = list.offsetHeight >= offsetHeight && parseInt(scrollTop) !== scrollHeight - height; - } else { - this.isPrevButtonEnabled = scrollLeft !== 0; - this.isNextButtonEnabled = list.offsetWidth >= offsetWidth && parseInt(scrollLeft) !== scrollWidth - width; - } - }, "updateButtonState"), - getVisibleButtonWidths: /* @__PURE__ */ __name(function getVisibleButtonWidths() { - var _this$$refs3 = this.$refs, prevBtn = _this$$refs3.prevBtn, nextBtn = _this$$refs3.nextBtn; - return [prevBtn, nextBtn].reduce(function(acc, el) { - return el ? acc + getWidth(el) : acc; - }, 0); - }, "getVisibleButtonWidths") - }, - computed: { - templates: /* @__PURE__ */ __name(function templates() { - return this.$pcTabs.$slots; - }, "templates"), - activeValue: /* @__PURE__ */ __name(function activeValue() { - return this.$pcTabs.d_value; - }, "activeValue"), - showNavigators: /* @__PURE__ */ __name(function showNavigators2() { - return this.$pcTabs.scrollable && this.$pcTabs.showNavigators; - }, "showNavigators"), - prevButtonAriaLabel: /* @__PURE__ */ __name(function prevButtonAriaLabel() { - return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.previous : void 0; - }, "prevButtonAriaLabel"), - nextButtonAriaLabel: /* @__PURE__ */ __name(function nextButtonAriaLabel() { - return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.next : void 0; - }, "nextButtonAriaLabel") - }, - components: { - ChevronLeftIcon: script$f, - ChevronRightIcon: script$g - }, - directives: { - ripple: Ripple - } -}; -var _hoisted_1$l = ["aria-label", "tabindex"]; -var _hoisted_2$i = ["aria-orientation"]; -var _hoisted_3$g = ["aria-label", "tabindex"]; -function render$g(_ctx, _cache, $props, $setup, $data, $options) { - var _directive_ripple = resolveDirective("ripple"); - return openBlock(), createElementBlock("div", mergeProps({ - ref: "list", - "class": _ctx.cx("root") - }, _ctx.ptmi("root")), [$options.showNavigators && $data.isPrevButtonEnabled ? withDirectives((openBlock(), createElementBlock("button", mergeProps({ - key: 0, - ref: "prevButton", - "class": _ctx.cx("prevButton"), - "aria-label": $options.prevButtonAriaLabel, - tabindex: $options.$pcTabs.tabindex, - onClick: _cache[0] || (_cache[0] = function() { - return $options.onPrevButtonClick && $options.onPrevButtonClick.apply($options, arguments); - }) - }, _ctx.ptm("prevButton"), { - "data-pc-group-section": "navigator" - }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.previcon || "ChevronLeftIcon"), mergeProps({ - "aria-hidden": "true" - }, _ctx.ptm("prevIcon")), null, 16))], 16, _hoisted_1$l)), [[_directive_ripple]]) : createCommentVNode("", true), createBaseVNode("div", mergeProps({ - ref: "content", - "class": _ctx.cx("content"), - onScroll: _cache[1] || (_cache[1] = function() { - return $options.onScroll && $options.onScroll.apply($options, arguments); - }) - }, _ctx.ptm("content")), [createBaseVNode("div", mergeProps({ - ref: "tabs", - "class": _ctx.cx("tabList"), - role: "tablist", - "aria-orientation": $options.$pcTabs.orientation || "horizontal" - }, _ctx.ptm("tabList")), [renderSlot(_ctx.$slots, "default"), createBaseVNode("span", mergeProps({ - ref: "inkbar", - "class": _ctx.cx("activeBar"), - role: "presentation", - "aria-hidden": "true" - }, _ctx.ptm("activeBar")), null, 16)], 16, _hoisted_2$i)], 16), $options.showNavigators && $data.isNextButtonEnabled ? withDirectives((openBlock(), createElementBlock("button", mergeProps({ - key: 1, - ref: "nextButton", - "class": _ctx.cx("nextButton"), - "aria-label": $options.nextButtonAriaLabel, - tabindex: $options.$pcTabs.tabindex, - onClick: _cache[2] || (_cache[2] = function() { - return $options.onNextButtonClick && $options.onNextButtonClick.apply($options, arguments); - }) - }, _ctx.ptm("nextButton"), { - "data-pc-group-section": "navigator" - }), [(openBlock(), createBlock(resolveDynamicComponent($options.templates.nexticon || "ChevronRightIcon"), mergeProps({ - "aria-hidden": "true" - }, _ctx.ptm("nextIcon")), null, 16))], 16, _hoisted_3$g)), [[_directive_ripple]]) : createCommentVNode("", true)], 16); -} -__name(render$g, "render$g"); -script$9.render = render$g; -const _sfc_main$p = /* @__PURE__ */ defineComponent({ - __name: "ExtensionSlot", - props: { - extension: {} - }, - setup(__props) { - const props = __props; - const mountCustomExtension = /* @__PURE__ */ __name((extension, el) => { - extension.render(el); - }, "mountCustomExtension"); - onBeforeUnmount(() => { - if (props.extension.type === "custom" && props.extension.destroy) { - props.extension.destroy(); - } - }); - return (_ctx, _cache) => { - return _ctx.extension.type === "vue" ? 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-}, "theme"); -var classes$6 = { - root: "p-buttongroup p-component" -}; -var ButtonGroupStyle = BaseStyle.extend({ - name: "buttongroup", - theme: theme$6, - classes: classes$6 -}); -var script$1$6 = { - name: "BaseButtonGroup", - "extends": script$e, - style: ButtonGroupStyle, - provide: /* @__PURE__ */ __name(function provide6() { - return { - $pcButtonGroup: this, - $parentInstance: this - }; - }, "provide") -}; -var script$8 = { - name: "ButtonGroup", - "extends": script$1$6, - inheritAttrs: false -}; -function render$d(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("span", mergeProps({ - "class": _ctx.cx("root"), - role: "group" - }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default")], 16); -} -__name(render$d, "render$d"); -script$8.render = render$d; -const useTitleEditorStore = defineStore("titleEditor", () => { - const titleEditorTarget = shallowRef(null); - return { - titleEditorTarget - }; -}); -const useCanvasStore = defineStore("canvas", () => { - const canvas = shallowRef(null); - return { - canvas - }; -}); -const _sfc_main$n = /* @__PURE__ */ defineComponent({ - __name: "GraphCanvasMenu", - setup(__props) { - const { t } = useI18n(); - const commandStore = useCommandStore(); - const canvasStore = useCanvasStore(); - const settingStore = useSettingStore(); - const linkHidden = computed( - () => settingStore.get("Comfy.LinkRenderMode") === LiteGraph.HIDDEN_LINK - ); - let interval = null; - const repeat2 = /* @__PURE__ */ __name((command) => { - if (interval) return; - const cmd = /* @__PURE__ */ __name(() => commandStore.execute(command), "cmd"); - cmd(); - interval = window.setInterval(cmd, 100); - }, "repeat"); - const stopRepeat = /* @__PURE__ */ __name(() => { - if (interval) { - clearInterval(interval); - interval = null; - } - }, "stopRepeat"); - return (_ctx, _cache) => { - const _component_i_material_symbols58pan_tool_outline = __unplugin_components_0$2; - const _component_i_simple_line_icons58cursor = __unplugin_components_1$2; - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createBlock(unref(script$8), { class: "p-buttongroup-vertical absolute bottom-[10px] right-[10px] z-[1000] pointer-events-auto" }, { - default: withCtx(() => [ - withDirectives(createVNode(unref(script$d), { - severity: "secondary", - icon: "pi pi-plus", - onMousedown: _cache[0] || (_cache[0] = ($event) => repeat2("Comfy.Canvas.ZoomIn")), - onMouseup: stopRepeat - }, null, 512), [ - [ - _directive_tooltip, - unref(t)("graphCanvasMenu.zoomIn"), - void 0, - { left: true } - ] - ]), - withDirectives(createVNode(unref(script$d), { - severity: "secondary", - icon: "pi pi-minus", - onMousedown: _cache[1] || (_cache[1] = ($event) => repeat2("Comfy.Canvas.ZoomOut")), - onMouseup: stopRepeat - }, null, 512), [ - [ - _directive_tooltip, - unref(t)("graphCanvasMenu.zoomOut"), - void 0, - { left: true } - ] - ]), - withDirectives(createVNode(unref(script$d), { - severity: "secondary", - icon: "pi pi-expand", - onClick: _cache[2] || (_cache[2] = () => unref(commandStore).execute("Comfy.Canvas.FitView")) - }, null, 512), [ - [ - _directive_tooltip, - unref(t)("graphCanvasMenu.fitView"), - void 0, - { left: true } - ] - ]), - withDirectives((openBlock(), createBlock(unref(script$d), { - severity: "secondary", - onClick: _cache[3] || (_cache[3] = () => unref(commandStore).execute("Comfy.Canvas.ToggleLock")) - }, { - icon: withCtx(() => [ - unref(canvasStore).canvas?.read_only ? (openBlock(), createBlock(_component_i_material_symbols58pan_tool_outline, { key: 0 })) : (openBlock(), createBlock(_component_i_simple_line_icons58cursor, { key: 1 })) - ]), - _: 1 - })), [ - [ - _directive_tooltip, - unref(t)( - "graphCanvasMenu." + (unref(canvasStore).canvas?.read_only ? "panMode" : "selectMode") - ) + " (Space)", - void 0, - { left: true } - ] - ]), - withDirectives(createVNode(unref(script$d), { - severity: "secondary", - icon: linkHidden.value ? "pi pi-eye-slash" : "pi pi-eye", - onClick: _cache[4] || (_cache[4] = () => unref(commandStore).execute("Comfy.Canvas.ToggleLinkVisibility")), - "data-testid": "toggle-link-visibility-button" - }, null, 8, ["icon"]), [ - [ - _directive_tooltip, - unref(t)("graphCanvasMenu.toggleLinkVisibility"), - void 0, - { left: true } - ] - ]) - ]), - _: 1 - }); - }; - } -}); -const GraphCanvasMenu = /* @__PURE__ */ _export_sfc(_sfc_main$n, [["__scopeId", "data-v-cf40dd39"]]); -const _sfc_main$m = /* @__PURE__ */ defineComponent({ - __name: "NodeBadge", - setup(__props) { - const settingStore = useSettingStore(); - const colorPaletteStore = useColorPaletteStore(); - const nodeSourceBadgeMode = computed( - () => settingStore.get("Comfy.NodeBadge.NodeSourceBadgeMode") - ); - const nodeIdBadgeMode = computed( - () => settingStore.get("Comfy.NodeBadge.NodeIdBadgeMode") - ); - const nodeLifeCycleBadgeMode = computed( - () => settingStore.get("Comfy.NodeBadge.NodeLifeCycleBadgeMode") - ); - watch([nodeSourceBadgeMode, nodeIdBadgeMode, nodeLifeCycleBadgeMode], () => { - app.graph?.setDirtyCanvas(true, true); - }); - const nodeDefStore = useNodeDefStore(); - function badgeTextVisible(nodeDef, badgeMode) { - return !(badgeMode === NodeBadgeMode.None || nodeDef?.isCoreNode && badgeMode === NodeBadgeMode.HideBuiltIn); - } - __name(badgeTextVisible, "badgeTextVisible"); - onMounted(() => { - app.registerExtension({ - name: "Comfy.NodeBadge", - nodeCreated(node) { - node.badgePosition = BadgePosition.TopRight; - const badge = computed(() => { - const nodeDef = nodeDefStore.fromLGraphNode(node); - return new LGraphBadge({ - text: _.truncate( - [ - badgeTextVisible(nodeDef, nodeIdBadgeMode.value) ? `#${node.id}` : "", - badgeTextVisible(nodeDef, nodeLifeCycleBadgeMode.value) ? nodeDef?.nodeLifeCycleBadgeText ?? "" : "", - badgeTextVisible(nodeDef, nodeSourceBadgeMode.value) ? nodeDef?.nodeSource?.badgeText ?? "" : "" - ].filter((s) => s.length > 0).join(" "), - { - length: 31 - } - ), - fgColor: colorPaletteStore.completedActivePalette.colors.litegraph_base.BADGE_FG_COLOR, - bgColor: colorPaletteStore.completedActivePalette.colors.litegraph_base.BADGE_BG_COLOR - }); - }); - node.badges.push(() => badge.value); - } - }); - }); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div"); - }; - } -}); -const _sfc_main$l = /* @__PURE__ */ defineComponent({ - __name: "NodeTooltip", - setup(__props) { - let idleTimeout; - const nodeDefStore = useNodeDefStore(); - const tooltipRef = ref(); - const tooltipText = ref(""); - const left = ref(); - const top = ref(); - const hideTooltip = /* @__PURE__ */ __name(() => tooltipText.value = null, "hideTooltip"); - const showTooltip = /* @__PURE__ */ __name(async (tooltip) => { - if (!tooltip) return; - left.value = app.canvas.mouse[0] + "px"; - top.value = app.canvas.mouse[1] + "px"; - tooltipText.value = tooltip; - await nextTick(); - const rect = tooltipRef.value.getBoundingClientRect(); - if (rect.right > window.innerWidth) { - left.value = app.canvas.mouse[0] - rect.width + "px"; - } - if (rect.top < 0) { - top.value = app.canvas.mouse[1] + rect.height + "px"; - } - }, "showTooltip"); - const onIdle = /* @__PURE__ */ __name(() => { - const { canvas } = app; - const node = canvas.node_over; - if (!node) return; - const ctor = node.constructor; - const nodeDef = nodeDefStore.nodeDefsByName[node.type]; - if (ctor.title_mode !== LiteGraph.NO_TITLE && canvas.graph_mouse[1] < node.pos[1]) { - return showTooltip(nodeDef.description); - } - if (node.flags?.collapsed) return; - const inputSlot = canvas.isOverNodeInput( - node, - canvas.graph_mouse[0], - canvas.graph_mouse[1], - [0, 0] - ); - if (inputSlot !== -1) { - const inputName = node.inputs[inputSlot].name; - const translatedTooltip = st( - `nodeDefs.${normalizeI18nKey(node.type)}.inputs.${normalizeI18nKey(inputName)}.tooltip`, - nodeDef.inputs.getInput(inputName)?.tooltip - ); - return showTooltip(translatedTooltip); - } - const outputSlot = canvas.isOverNodeOutput( - node, - canvas.graph_mouse[0], - canvas.graph_mouse[1], - [0, 0] - ); - if (outputSlot !== -1) { - const translatedTooltip = st( - `nodeDefs.${normalizeI18nKey(node.type)}.outputs.${outputSlot}.tooltip`, - nodeDef.outputs.all?.[outputSlot]?.tooltip - ); - return showTooltip(translatedTooltip); - } - const widget = app.canvas.getWidgetAtCursor(); - if (widget && !widget.element) { - const translatedTooltip = st( - `nodeDefs.${normalizeI18nKey(node.type)}.inputs.${normalizeI18nKey(widget.name)}.tooltip`, - nodeDef.inputs.getInput(widget.name)?.tooltip - ); - return showTooltip(widget.tooltip ?? translatedTooltip); - } - }, "onIdle"); - const onMouseMove = /* @__PURE__ */ __name((e) => { - hideTooltip(); - clearTimeout(idleTimeout); - if (e.target.nodeName !== "CANVAS") return; - idleTimeout = window.setTimeout(onIdle, 500); - }, "onMouseMove"); - useEventListener(window, "mousemove", onMouseMove); - useEventListener(window, "click", hideTooltip); - return (_ctx, _cache) => { - return tooltipText.value ? (openBlock(), createElementBlock("div", { - key: 0, - ref_key: "tooltipRef", - ref: tooltipRef, - class: "node-tooltip", - style: normalizeStyle({ left: left.value, top: top.value }) - }, toDisplayString(tooltipText.value), 5)) : createCommentVNode("", true); - }; - } -}); -const NodeTooltip = /* @__PURE__ */ _export_sfc(_sfc_main$l, [["__scopeId", "data-v-46859edf"]]); -const _sfc_main$k = /* @__PURE__ */ defineComponent({ - __name: "TitleEditor", - setup(__props) { - const settingStore = useSettingStore(); - const showInput = ref(false); - const editedTitle = ref(""); - const inputStyle = ref({ - position: "fixed", - left: "0px", - top: "0px", - width: "200px", - height: "20px", - fontSize: "12px" - }); - const titleEditorStore = useTitleEditorStore(); - const canvasStore = useCanvasStore(); - const previousCanvasDraggable = ref(true); - const onEdit = /* @__PURE__ */ __name((newValue) => { - if (titleEditorStore.titleEditorTarget && newValue.trim() !== "") { - titleEditorStore.titleEditorTarget.title = newValue.trim(); - app.graph.setDirtyCanvas(true, true); - } - showInput.value = false; - titleEditorStore.titleEditorTarget = null; - canvasStore.canvas.allow_dragcanvas = previousCanvasDraggable.value; - }, "onEdit"); - watch( - () => titleEditorStore.titleEditorTarget, - (target) => { - if (target === null) { - return; - } - editedTitle.value = target.title; - showInput.value = true; - previousCanvasDraggable.value = canvasStore.canvas.allow_dragcanvas; - canvasStore.canvas.allow_dragcanvas = false; - if (target instanceof LGraphGroup) { - const group = target; - const [x, y] = group.pos; - const [w, h] = group.size; - const [left, top] = app.canvasPosToClientPos([x, y]); - inputStyle.value.left = `${left}px`; - inputStyle.value.top = `${top}px`; - const width = w * app.canvas.ds.scale; - const height = group.titleHeight * app.canvas.ds.scale; - inputStyle.value.width = `${width}px`; - inputStyle.value.height = `${height}px`; - const fontSize = group.font_size * app.canvas.ds.scale; - inputStyle.value.fontSize = `${fontSize}px`; - } else if (target instanceof LGraphNode) { - const node = target; - const [x, y] = node.getBounding(); - const canvasWidth = node.width; - const canvasHeight = LiteGraph.NODE_TITLE_HEIGHT; - const [left, top] = app.canvasPosToClientPos([x, y]); - inputStyle.value.left = `${left}px`; - inputStyle.value.top = `${top}px`; - const width = canvasWidth * app.canvas.ds.scale; - const height = canvasHeight * app.canvas.ds.scale; - inputStyle.value.width = `${width}px`; - inputStyle.value.height = `${height}px`; - const fontSize = 12 * app.canvas.ds.scale; - inputStyle.value.fontSize = `${fontSize}px`; - } - } - ); - const canvasEventHandler = /* @__PURE__ */ __name((event) => { - if (event.detail.subType === "group-double-click") { - if (!settingStore.get("Comfy.Group.DoubleClickTitleToEdit")) { - return; - } - const group = event.detail.group; - const [x, y] = group.pos; - const e = event.detail.originalEvent; - const relativeY = e.canvasY - y; - if (relativeY <= group.titleHeight) { - titleEditorStore.titleEditorTarget = group; - } - } else if (event.detail.subType === "node-double-click") { - if (!settingStore.get("Comfy.Node.DoubleClickTitleToEdit")) { - return; - } - const node = event.detail.node; - const [x, y] = node.pos; - const e = event.detail.originalEvent; - const relativeY = e.canvasY - y; - if (relativeY <= 0) { - titleEditorStore.titleEditorTarget = node; - } - } - }, "canvasEventHandler"); - useEventListener(document, "litegraph:canvas", canvasEventHandler); - return (_ctx, _cache) => { - return showInput.value ? (openBlock(), createElementBlock("div", { - key: 0, - class: "group-title-editor node-title-editor", - style: normalizeStyle(inputStyle.value) - }, [ - createVNode(EditableText, { - isEditing: showInput.value, - modelValue: editedTitle.value, - onEdit - }, null, 8, ["isEditing", "modelValue"]) - ], 4)) : createCommentVNode("", true); - }; - } -}); -const TitleEditor = /* @__PURE__ */ _export_sfc(_sfc_main$k, [["__scopeId", "data-v-12d3fd12"]]); -const useSearchBoxStore = defineStore("searchBox", () => { - const visible = ref(false); - function toggleVisible() { - visible.value = !visible.value; - } - __name(toggleVisible, "toggleVisible"); - return { - visible, - toggleVisible - }; -}); -class ConnectingLinkImpl { - static { - __name(this, "ConnectingLinkImpl"); - } - constructor(node, slot, input, output, pos, afterRerouteId) { - this.node = node; - this.slot = slot; - this.input = input; - this.output = output; - this.pos = pos; - this.afterRerouteId = afterRerouteId; - } - static createFromPlainObject(obj) { - return new ConnectingLinkImpl( - obj.node, - obj.slot, - obj.input, - obj.output, - obj.pos, - obj.afterRerouteId - ); - } - get type() { - const result = this.input ? this.input.type : this.output?.type ?? null; - return result === -1 ? null : result; - } - /** - * Which slot type is release and need to be reconnected. - * - 'output' means we need a new node's outputs slot to connect with this link - */ - get releaseSlotType() { - return this.output ? "input" : "output"; - } - connectTo(newNode) { - const newNodeSlots = this.releaseSlotType === "output" ? newNode.outputs : newNode.inputs; - if (!newNodeSlots) return; - const newNodeSlot = newNodeSlots.findIndex( - (slot) => LiteGraph.isValidConnection(slot.type, this.type) - ); - if (newNodeSlot === -1) { - console.warn( - `Could not find slot with type ${this.type} on node ${newNode.title}. This should never happen` - ); - return; - } - if (this.releaseSlotType === "input") { - this.node.connect(this.slot, newNode, newNodeSlot, this.afterRerouteId); - } else { - newNode.connect(newNodeSlot, this.node, this.slot, this.afterRerouteId); - } - } -} -var theme$5 = /* @__PURE__ */ __name(function theme3(_ref) { - var dt = _ref.dt; - return "\n.p-autocomplete {\n display: inline-flex;\n}\n\n.p-autocomplete-loader {\n position: absolute;\n top: 50%;\n margin-top: -0.5rem;\n right: ".concat(dt("autocomplete.padding.x"), ";\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-loader {\n right: calc(").concat(dt("autocomplete.dropdown.width"), " + ").concat(dt("autocomplete.padding.x"), ");\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input {\n flex: 1 1 auto;\n width: 1%;\n}\n\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input,\n.p-autocomplete:has(.p-autocomplete-dropdown) .p-autocomplete-input-multiple {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n\n.p-autocomplete-dropdown {\n cursor: pointer;\n display: inline-flex;\n cursor: pointer;\n user-select: none;\n align-items: center;\n justify-content: center;\n overflow: hidden;\n position: relative;\n width: ").concat(dt("autocomplete.dropdown.width"), ";\n border-top-right-radius: ").concat(dt("autocomplete.dropdown.border.radius"), ";\n border-bottom-right-radius: ").concat(dt("autocomplete.dropdown.border.radius"), ";\n background: ").concat(dt("autocomplete.dropdown.background"), ";\n border: 1px solid ").concat(dt("autocomplete.dropdown.border.color"), ";\n border-left: 0 none;\n color: ").concat(dt("autocomplete.dropdown.color"), ";\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ", outline-color ").concat(dt("autocomplete.transition.duration"), ", box-shadow ").concat(dt("autocomplete.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-autocomplete-dropdown:not(:disabled):hover {\n background: ").concat(dt("autocomplete.dropdown.hover.background"), ";\n border-color: ").concat(dt("autocomplete.dropdown.hover.border.color"), ";\n color: ").concat(dt("autocomplete.dropdown.hover.color"), ";\n}\n\n.p-autocomplete-dropdown:not(:disabled):active {\n background: ").concat(dt("autocomplete.dropdown.active.background"), ";\n border-color: ").concat(dt("autocomplete.dropdown.active.border.color"), ";\n color: ").concat(dt("autocomplete.dropdown.active.color"), ";\n}\n\n.p-autocomplete-dropdown:focus-visible {\n box-shadow: ").concat(dt("autocomplete.dropdown.focus.ring.shadow"), ";\n outline: ").concat(dt("autocomplete.dropdown.focus.ring.width"), " ").concat(dt("autocomplete.dropdown.focus.ring.style"), " ").concat(dt("autocomplete.dropdown.focus.ring.color"), ";\n outline-offset: ").concat(dt("autocomplete.dropdown.focus.ring.offset"), ";\n}\n\n.p-autocomplete .p-autocomplete-overlay {\n min-width: 100%;\n}\n\n.p-autocomplete-overlay {\n position: absolute;\n overflow: auto;\n top: 0;\n left: 0;\n background: ").concat(dt("autocomplete.overlay.background"), ";\n color: ").concat(dt("autocomplete.overlay.color"), ";\n border: 1px solid ").concat(dt("autocomplete.overlay.border.color"), ";\n border-radius: ").concat(dt("autocomplete.overlay.border.radius"), ";\n box-shadow: ").concat(dt("autocomplete.overlay.shadow"), ";\n}\n\n.p-autocomplete-list {\n margin: 0;\n padding: 0;\n list-style-type: none;\n display: flex;\n flex-direction: column;\n gap: ").concat(dt("autocomplete.list.gap"), ";\n padding: ").concat(dt("autocomplete.list.padding"), ";\n}\n\n.p-autocomplete-option {\n cursor: pointer;\n white-space: nowrap;\n position: relative;\n overflow: hidden;\n display: flex;\n align-items: center;\n padding: ").concat(dt("autocomplete.option.padding"), ";\n border: 0 none;\n color: ").concat(dt("autocomplete.option.color"), ";\n background: transparent;\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ";\n border-radius: ").concat(dt("autocomplete.option.border.radius"), ";\n}\n\n.p-autocomplete-option:not(.p-autocomplete-option-selected):not(.p-disabled).p-focus {\n background: ").concat(dt("autocomplete.option.focus.background"), ";\n color: ").concat(dt("autocomplete.option.focus.color"), ";\n}\n\n.p-autocomplete-option-selected {\n background: ").concat(dt("autocomplete.option.selected.background"), ";\n color: ").concat(dt("autocomplete.option.selected.color"), ";\n}\n\n.p-autocomplete-option-selected.p-focus {\n background: ").concat(dt("autocomplete.option.selected.focus.background"), ";\n color: ").concat(dt("autocomplete.option.selected.focus.color"), ";\n}\n\n.p-autocomplete-option-group {\n margin: 0;\n padding: ").concat(dt("autocomplete.option.group.padding"), ";\n color: ").concat(dt("autocomplete.option.group.color"), ";\n background: ").concat(dt("autocomplete.option.group.background"), ";\n font-weight: ").concat(dt("autocomplete.option.group.font.weight"), ";\n}\n\n.p-autocomplete-input-multiple {\n margin: 0;\n list-style-type: none;\n cursor: text;\n overflow: hidden;\n display: flex;\n align-items: center;\n flex-wrap: wrap;\n padding: calc(").concat(dt("autocomplete.padding.y"), " / 2) ").concat(dt("autocomplete.padding.x"), ";\n gap: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n color: ").concat(dt("autocomplete.color"), ";\n background: ").concat(dt("autocomplete.background"), ";\n border: 1px solid ").concat(dt("autocomplete.border.color"), ";\n border-radius: ").concat(dt("autocomplete.border.radius"), ";\n width: 100%;\n transition: background ").concat(dt("autocomplete.transition.duration"), ", color ").concat(dt("autocomplete.transition.duration"), ", border-color ").concat(dt("autocomplete.transition.duration"), ", outline-color ").concat(dt("autocomplete.transition.duration"), ", box-shadow ").concat(dt("autocomplete.transition.duration"), ";\n outline-color: transparent;\n box-shadow: ").concat(dt("autocomplete.shadow"), ";\n}\n\n.p-autocomplete:not(.p-disabled):hover .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.hover.border.color"), ";\n}\n\n.p-autocomplete:not(.p-disabled).p-focus .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.focus.border.color"), ";\n box-shadow: ").concat(dt("autocomplete.focus.ring.shadow"), ";\n outline: ").concat(dt("autocomplete.focus.ring.width"), " ").concat(dt("autocomplete.focus.ring.style"), " ").concat(dt("autocomplete.focus.ring.color"), ";\n outline-offset: ").concat(dt("autocomplete.focus.ring.offset"), ";\n}\n\n.p-autocomplete.p-invalid .p-autocomplete-input-multiple {\n border-color: ").concat(dt("autocomplete.invalid.border.color"), ";\n}\n\n.p-variant-filled.p-autocomplete-input-multiple {\n background: ").concat(dt("autocomplete.filled.background"), ";\n}\n\n.p-autocomplete:not(.p-disabled).p-focus .p-variant-filled.p-autocomplete-input-multiple {\n background: ").concat(dt("autocomplete.filled.focus.background"), ";\n}\n\n.p-autocomplete.p-disabled .p-autocomplete-input-multiple {\n opacity: 1;\n background: ").concat(dt("autocomplete.disabled.background"), ";\n color: ").concat(dt("autocomplete.disabled.color"), ";\n}\n\n.p-autocomplete-chip.p-chip {\n padding-top: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-bottom: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n border-radius: ").concat(dt("autocomplete.chip.border.radius"), ";\n}\n\n.p-autocomplete-input-multiple:has(.p-autocomplete-chip) {\n padding-left: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-right: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n}\n\n.p-autocomplete-chip-item.p-focus .p-autocomplete-chip {\n background: ").concat(dt("inputchips.chip.focus.background"), ";\n color: ").concat(dt("inputchips.chip.focus.color"), ";\n}\n\n.p-autocomplete-input-chip {\n flex: 1 1 auto;\n display: inline-flex;\n padding-top: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n padding-bottom: calc(").concat(dt("autocomplete.padding.y"), " / 2);\n}\n\n.p-autocomplete-input-chip input {\n border: 0 none;\n outline: 0 none;\n background: transparent;\n margin: 0;\n padding: 0;\n box-shadow: none;\n border-radius: 0;\n width: 100%;\n font-family: inherit;\n font-feature-settings: inherit;\n font-size: 1rem;\n color: inherit;\n}\n\n.p-autocomplete-input-chip input::placeholder {\n color: ").concat(dt("autocomplete.placeholder.color"), ";\n}\n\n.p-autocomplete-empty-message {\n padding: ").concat(dt("autocomplete.empty.message.padding"), ";\n}\n\n.p-autocomplete-fluid {\n display: flex;\n}\n\n.p-autocomplete-fluid:has(.p-autocomplete-dropdown) .p-autocomplete-input {\n width: 1%;\n}\n"); -}, "theme"); -var inlineStyles$3 = { - root: { - position: "relative" - } -}; -var classes$5 = { - root: /* @__PURE__ */ __name(function root5(_ref2) { - var instance = _ref2.instance, props = _ref2.props; - return ["p-autocomplete p-component p-inputwrapper", { - "p-disabled": props.disabled, - "p-invalid": props.invalid, - "p-focus": instance.focused, - "p-inputwrapper-filled": props.modelValue || isNotEmpty(instance.inputValue), - "p-inputwrapper-focus": instance.focused, - "p-autocomplete-open": instance.overlayVisible, - "p-autocomplete-fluid": instance.hasFluid - }]; - }, "root"), - pcInput: "p-autocomplete-input", - inputMultiple: /* @__PURE__ */ __name(function inputMultiple(_ref3) { - var props = _ref3.props, instance = _ref3.instance; - return ["p-autocomplete-input-multiple", { - "p-variant-filled": props.variant ? props.variant === "filled" : instance.$primevue.config.inputStyle === "filled" || instance.$primevue.config.inputVariant === "filled" - }]; - }, "inputMultiple"), - chipItem: /* @__PURE__ */ __name(function chipItem(_ref4) { - var instance = _ref4.instance, i = _ref4.i; - return ["p-autocomplete-chip-item", { - "p-focus": instance.focusedMultipleOptionIndex === i - }]; - }, "chipItem"), - pcChip: "p-autocomplete-chip", - chipIcon: "p-autocomplete-chip-icon", - inputChip: "p-autocomplete-input-chip", - loader: "p-autocomplete-loader", - dropdown: "p-autocomplete-dropdown", - overlay: "p-autocomplete-overlay p-component", - list: "p-autocomplete-list", - optionGroup: "p-autocomplete-option-group", - option: /* @__PURE__ */ __name(function option(_ref5) { - var instance = _ref5.instance, _option = _ref5.option, i = _ref5.i, getItemOptions = _ref5.getItemOptions; - return ["p-autocomplete-option", { - "p-autocomplete-option-selected": instance.isSelected(_option), - "p-focus": instance.focusedOptionIndex === instance.getOptionIndex(i, getItemOptions), - "p-disabled": instance.isOptionDisabled(_option) - }]; - }, "option"), - emptyMessage: "p-autocomplete-empty-message" -}; -var AutoCompleteStyle = BaseStyle.extend({ - name: "autocomplete", - theme: theme$5, - classes: classes$5, - inlineStyles: inlineStyles$3 -}); -var script$1$5 = { - name: "BaseAutoComplete", - "extends": script$e, - props: { - modelValue: null, - suggestions: { - type: Array, - "default": null - }, - optionLabel: null, - optionDisabled: null, - optionGroupLabel: null, - optionGroupChildren: null, - scrollHeight: { - type: String, - "default": "14rem" - }, - dropdown: { - type: Boolean, - "default": false - }, - dropdownMode: { - type: String, - "default": "blank" - }, - multiple: { - type: Boolean, - "default": false - }, - loading: { - type: Boolean, - "default": false - }, - variant: { - type: String, - "default": null - }, - invalid: { - type: Boolean, - "default": false - }, - disabled: { - type: Boolean, - "default": false - }, - placeholder: { - type: String, - "default": null - }, - dataKey: { - type: String, - "default": null - }, - minLength: { - type: Number, - "default": 1 - }, - delay: { - type: Number, - "default": 300 - }, - appendTo: { - type: [String, Object], - "default": "body" - }, - forceSelection: { - type: Boolean, - "default": false - }, - completeOnFocus: { - type: Boolean, - "default": false - }, - inputId: { - type: String, - "default": null - }, - inputStyle: { - type: Object, - "default": null - }, - inputClass: { - type: [String, Object], - "default": null - }, - panelStyle: { - type: Object, - "default": null - }, - panelClass: { - type: [String, Object], - "default": null - }, - overlayStyle: { - type: Object, - "default": null - }, - overlayClass: { - type: [String, Object], - "default": null - }, - dropdownIcon: { - type: String, - "default": null - }, - dropdownClass: { - type: [String, Object], - "default": null - }, - loader: { - type: String, - "default": null - }, - loadingIcon: { - type: String, - "default": null - }, - removeTokenIcon: { - type: String, - "default": null - }, - chipIcon: { - type: String, - "default": null - }, - virtualScrollerOptions: { - type: Object, - "default": null - }, - autoOptionFocus: { - type: Boolean, - "default": false - }, - selectOnFocus: { - type: Boolean, - "default": false - }, - focusOnHover: { - type: Boolean, - "default": true - }, - searchLocale: { - type: String, - "default": void 0 - }, - searchMessage: { - type: String, - "default": null - }, - selectionMessage: { - type: String, - "default": null - }, - emptySelectionMessage: { - type: String, - "default": null - }, - emptySearchMessage: { - type: String, - "default": null - }, - tabindex: { - type: Number, - "default": 0 - }, - typeahead: { - type: Boolean, - "default": true - }, - ariaLabel: { - type: String, - "default": null - }, - ariaLabelledby: { - type: String, - "default": null - }, - fluid: { - type: Boolean, - "default": null - } - }, - style: AutoCompleteStyle, - provide: /* @__PURE__ */ __name(function provide7() { - return { - $pcAutoComplete: this, - $parentInstance: this - }; - }, "provide") -}; -function _typeof$1$1(o) { - "@babel/helpers - typeof"; - return _typeof$1$1 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { - return typeof o2; - } : function(o2) { - return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; - }, _typeof$1$1(o); -} -__name(_typeof$1$1, "_typeof$1$1"); -function _toConsumableArray$1(r) { - return _arrayWithoutHoles$1(r) || _iterableToArray$1(r) || _unsupportedIterableToArray$1(r) || _nonIterableSpread$1(); -} -__name(_toConsumableArray$1, "_toConsumableArray$1"); -function _nonIterableSpread$1() { - throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method."); -} -__name(_nonIterableSpread$1, "_nonIterableSpread$1"); -function _unsupportedIterableToArray$1(r, a) { - if (r) { - if ("string" == typeof r) return _arrayLikeToArray$1(r, a); - var t = {}.toString.call(r).slice(8, -1); - return "Object" === t && r.constructor && (t = r.constructor.name), "Map" === t || "Set" === t ? Array.from(r) : "Arguments" === t || /^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(t) ? _arrayLikeToArray$1(r, a) : void 0; - } -} -__name(_unsupportedIterableToArray$1, "_unsupportedIterableToArray$1"); -function _iterableToArray$1(r) { - if ("undefined" != typeof Symbol && null != r[Symbol.iterator] || null != r["@@iterator"]) return Array.from(r); -} -__name(_iterableToArray$1, "_iterableToArray$1"); -function _arrayWithoutHoles$1(r) { - if (Array.isArray(r)) return _arrayLikeToArray$1(r); -} -__name(_arrayWithoutHoles$1, "_arrayWithoutHoles$1"); -function _arrayLikeToArray$1(r, a) { - (null == a || a > r.length) && (a = r.length); - for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e]; - return n; -} -__name(_arrayLikeToArray$1, "_arrayLikeToArray$1"); -var script$7 = { - name: "AutoComplete", - "extends": script$1$5, - inheritAttrs: false, - emits: ["update:modelValue", "change", "focus", "blur", "item-select", "item-unselect", "option-select", "option-unselect", "dropdown-click", "clear", "complete", "before-show", "before-hide", "show", "hide"], - inject: { - $pcFluid: { - "default": null - } - }, - outsideClickListener: null, - resizeListener: null, - scrollHandler: null, - overlay: null, - virtualScroller: null, - searchTimeout: null, - dirty: false, - data: /* @__PURE__ */ __name(function data4() { - return { - id: this.$attrs.id, - clicked: false, - focused: false, - focusedOptionIndex: -1, - focusedMultipleOptionIndex: -1, - overlayVisible: false, - searching: false - }; - }, "data"), - watch: { - "$attrs.id": /* @__PURE__ */ __name(function $attrsId(newValue) { - this.id = newValue || UniqueComponentId(); - }, "$attrsId"), - suggestions: /* @__PURE__ */ __name(function suggestions() { - if (this.searching) { - this.show(); - this.focusedOptionIndex = this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; - this.searching = false; - } - this.autoUpdateModel(); - }, "suggestions") - }, - mounted: /* @__PURE__ */ __name(function mounted3() { - this.id = this.id || UniqueComponentId(); - this.autoUpdateModel(); - }, "mounted"), - updated: /* @__PURE__ */ __name(function updated2() { - if (this.overlayVisible) { - this.alignOverlay(); - } - }, "updated"), - beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount3() { - this.unbindOutsideClickListener(); - this.unbindResizeListener(); - if (this.scrollHandler) { - this.scrollHandler.destroy(); - this.scrollHandler = null; - } - if (this.overlay) { - ZIndex.clear(this.overlay); - this.overlay = null; - } - }, "beforeUnmount"), - methods: { - getOptionIndex: /* @__PURE__ */ __name(function getOptionIndex(index, fn) { - return this.virtualScrollerDisabled ? index : fn && fn(index)["index"]; - }, "getOptionIndex"), - getOptionLabel: /* @__PURE__ */ __name(function getOptionLabel(option2) { - return this.optionLabel ? resolveFieldData(option2, this.optionLabel) : option2; - }, "getOptionLabel"), - getOptionValue: /* @__PURE__ */ __name(function getOptionValue(option2) { - return option2; - }, "getOptionValue"), - getOptionRenderKey: /* @__PURE__ */ __name(function getOptionRenderKey(option2, index) { - return (this.dataKey ? resolveFieldData(option2, this.dataKey) : this.getOptionLabel(option2)) + "_" + index; - }, "getOptionRenderKey"), - getPTOptions: /* @__PURE__ */ __name(function getPTOptions3(option2, itemOptions, index, key) { - return this.ptm(key, { - context: { - selected: this.isSelected(option2), - focused: this.focusedOptionIndex === this.getOptionIndex(index, itemOptions), - disabled: this.isOptionDisabled(option2) - } - }); - }, "getPTOptions"), - isOptionDisabled: /* @__PURE__ */ __name(function isOptionDisabled(option2) { - return this.optionDisabled ? resolveFieldData(option2, this.optionDisabled) : false; - }, "isOptionDisabled"), - isOptionGroup: /* @__PURE__ */ __name(function isOptionGroup(option2) { - return this.optionGroupLabel && option2.optionGroup && option2.group; - }, "isOptionGroup"), - getOptionGroupLabel: /* @__PURE__ */ __name(function getOptionGroupLabel(optionGroup) { - return resolveFieldData(optionGroup, this.optionGroupLabel); - }, "getOptionGroupLabel"), - getOptionGroupChildren: /* @__PURE__ */ __name(function getOptionGroupChildren(optionGroup) { - return resolveFieldData(optionGroup, this.optionGroupChildren); - }, "getOptionGroupChildren"), - getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset(index) { - var _this = this; - return (this.optionGroupLabel ? index - this.visibleOptions.slice(0, index).filter(function(option2) { - return _this.isOptionGroup(option2); - }).length : index) + 1; - }, "getAriaPosInset"), - show: /* @__PURE__ */ __name(function show(isFocus) { - this.$emit("before-show"); - this.dirty = true; - this.overlayVisible = true; - this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; - isFocus && focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); - }, "show"), - hide: /* @__PURE__ */ __name(function hide(isFocus) { - var _this2 = this; - var _hide = /* @__PURE__ */ __name(function _hide2() { - _this2.$emit("before-hide"); - _this2.dirty = isFocus; - _this2.overlayVisible = false; - _this2.clicked = false; - _this2.focusedOptionIndex = -1; - isFocus && focus(_this2.multiple ? _this2.$refs.focusInput : _this2.$refs.focusInput.$el); - }, "_hide"); - setTimeout(function() { - _hide(); - }, 0); - }, "hide"), - onFocus: /* @__PURE__ */ __name(function onFocus2(event) { - if (this.disabled) { - return; - } - if (!this.dirty && this.completeOnFocus) { - this.search(event, event.target.value, "focus"); - } - this.dirty = true; - this.focused = true; - if (this.overlayVisible) { - this.focusedOptionIndex = this.focusedOptionIndex !== -1 ? this.focusedOptionIndex : this.overlayVisible && this.autoOptionFocus ? this.findFirstFocusedOptionIndex() : -1; - this.scrollInView(this.focusedOptionIndex); - } - this.$emit("focus", event); - }, "onFocus"), - onBlur: /* @__PURE__ */ __name(function onBlur(event) { - this.dirty = false; - this.focused = false; - this.focusedOptionIndex = -1; - this.$emit("blur", event); - }, "onBlur"), - onKeyDown: /* @__PURE__ */ __name(function onKeyDown(event) { - if (this.disabled) { - event.preventDefault(); - return; - } - switch (event.code) { - case "ArrowDown": - this.onArrowDownKey(event); - break; - case "ArrowUp": - this.onArrowUpKey(event); - break; - case "ArrowLeft": - this.onArrowLeftKey(event); - break; - case "ArrowRight": - this.onArrowRightKey(event); - break; - case "Home": - this.onHomeKey(event); - break; - case "End": - this.onEndKey(event); - break; - case "PageDown": - this.onPageDownKey(event); - break; - case "PageUp": - this.onPageUpKey(event); - break; - case "Enter": - case "NumpadEnter": - this.onEnterKey(event); - break; - case "Escape": - this.onEscapeKey(event); - break; - case "Tab": - this.onTabKey(event); - break; - case "Backspace": - this.onBackspaceKey(event); - break; - } - this.clicked = false; - }, "onKeyDown"), - onInput: /* @__PURE__ */ __name(function onInput(event) { - var _this3 = this; - if (this.typeahead) { - if (this.searchTimeout) { - clearTimeout(this.searchTimeout); - } - var query = event.target.value; - if (!this.multiple) { - this.updateModel(event, query); - } - if (query.length === 0) { - this.hide(); - this.$emit("clear"); - } else { - if (query.length >= this.minLength) { - this.focusedOptionIndex = -1; - this.searchTimeout = setTimeout(function() { - _this3.search(event, query, "input"); - }, this.delay); - } else { - this.hide(); - } - } - } - }, "onInput"), - onChange: /* @__PURE__ */ __name(function onChange(event) { - var _this4 = this; - if (this.forceSelection) { - var valid = false; - if (this.visibleOptions && !this.multiple) { - var value = this.multiple ? this.$refs.focusInput.value : this.$refs.focusInput.$el.value; - var matchedValue = this.visibleOptions.find(function(option2) { - return _this4.isOptionMatched(option2, value || ""); - }); - if (matchedValue !== void 0) { - valid = true; - !this.isSelected(matchedValue) && this.onOptionSelect(event, matchedValue); - } - } - if (!valid) { - if (this.multiple) this.$refs.focusInput.value = ""; - else this.$refs.focusInput.$el.value = ""; - this.$emit("clear"); - !this.multiple && this.updateModel(event, null); - } - } - }, "onChange"), - onMultipleContainerFocus: /* @__PURE__ */ __name(function onMultipleContainerFocus() { - if (this.disabled) { - return; - } - this.focused = true; - }, "onMultipleContainerFocus"), - onMultipleContainerBlur: /* @__PURE__ */ __name(function onMultipleContainerBlur() { - this.focusedMultipleOptionIndex = -1; - this.focused = false; - }, "onMultipleContainerBlur"), - onMultipleContainerKeyDown: /* @__PURE__ */ __name(function onMultipleContainerKeyDown(event) { - if (this.disabled) { - event.preventDefault(); - return; - } - switch (event.code) { - case "ArrowLeft": - this.onArrowLeftKeyOnMultiple(event); - break; - case "ArrowRight": - this.onArrowRightKeyOnMultiple(event); - break; - case "Backspace": - this.onBackspaceKeyOnMultiple(event); - break; - } - }, "onMultipleContainerKeyDown"), - onContainerClick: /* @__PURE__ */ __name(function onContainerClick(event) { - this.clicked = true; - if (this.disabled || this.searching || this.loading || this.isInputClicked(event) || this.isDropdownClicked(event)) { - return; - } - if (!this.overlay || !this.overlay.contains(event.target)) { - focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); - } - }, "onContainerClick"), - onDropdownClick: /* @__PURE__ */ __name(function onDropdownClick(event) { - var query = void 0; - if (this.overlayVisible) { - this.hide(true); - } else { - var target = this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el; - focus(target); - query = target.value; - if (this.dropdownMode === "blank") this.search(event, "", "dropdown"); - else if (this.dropdownMode === "current") this.search(event, query, "dropdown"); - } - this.$emit("dropdown-click", { - originalEvent: event, - query - }); - }, "onDropdownClick"), - onOptionSelect: /* @__PURE__ */ __name(function onOptionSelect(event, option2) { - var isHide = arguments.length > 2 && arguments[2] !== void 0 ? arguments[2] : true; - var value = this.getOptionValue(option2); - if (this.multiple) { - this.$refs.focusInput.value = ""; - if (!this.isSelected(option2)) { - this.updateModel(event, [].concat(_toConsumableArray$1(this.modelValue || []), [value])); - } - } else { - this.updateModel(event, value); - } - this.$emit("item-select", { - originalEvent: event, - value: option2 - }); - this.$emit("option-select", { - originalEvent: event, - value: option2 - }); - isHide && this.hide(true); - }, "onOptionSelect"), - onOptionMouseMove: /* @__PURE__ */ __name(function onOptionMouseMove(event, index) { - if (this.focusOnHover) { - this.changeFocusedOptionIndex(event, index); - } - }, "onOptionMouseMove"), - onOverlayClick: /* @__PURE__ */ __name(function onOverlayClick(event) { - OverlayEventBus.emit("overlay-click", { - originalEvent: event, - target: this.$el - }); - }, "onOverlayClick"), - onOverlayKeyDown: /* @__PURE__ */ __name(function onOverlayKeyDown(event) { - switch (event.code) { - case "Escape": - this.onEscapeKey(event); - break; - } - }, "onOverlayKeyDown"), - onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey(event) { - if (!this.overlayVisible) { - return; - } - var optionIndex = this.focusedOptionIndex !== -1 ? this.findNextOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findFirstOptionIndex() : this.findFirstFocusedOptionIndex(); - this.changeFocusedOptionIndex(event, optionIndex); - event.preventDefault(); - }, "onArrowDownKey"), - onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey(event) { - if (!this.overlayVisible) { - return; - } - if (event.altKey) { - if (this.focusedOptionIndex !== -1) { - this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); - } - this.overlayVisible && this.hide(); - event.preventDefault(); - } else { - var optionIndex = this.focusedOptionIndex !== -1 ? this.findPrevOptionIndex(this.focusedOptionIndex) : this.clicked ? this.findLastOptionIndex() : this.findLastFocusedOptionIndex(); - this.changeFocusedOptionIndex(event, optionIndex); - event.preventDefault(); - } - }, "onArrowUpKey"), - onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey2(event) { - var target = event.currentTarget; - this.focusedOptionIndex = -1; - if (this.multiple) { - if (isEmpty(target.value) && this.hasSelectedOption) { - focus(this.$refs.multiContainer); - this.focusedMultipleOptionIndex = this.modelValue.length; - } else { - event.stopPropagation(); - } - } - }, "onArrowLeftKey"), - onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey2(event) { - this.focusedOptionIndex = -1; - this.multiple && event.stopPropagation(); - }, "onArrowRightKey"), - onHomeKey: /* @__PURE__ */ __name(function onHomeKey2(event) { - var currentTarget = event.currentTarget; - var len = currentTarget.value.length; - currentTarget.setSelectionRange(0, event.shiftKey ? len : 0); - this.focusedOptionIndex = -1; - event.preventDefault(); - }, "onHomeKey"), - onEndKey: /* @__PURE__ */ __name(function onEndKey2(event) { - var currentTarget = event.currentTarget; - var len = currentTarget.value.length; - currentTarget.setSelectionRange(event.shiftKey ? 0 : len, len); - this.focusedOptionIndex = -1; - event.preventDefault(); - }, "onEndKey"), - onPageUpKey: /* @__PURE__ */ __name(function onPageUpKey2(event) { - this.scrollInView(0); - event.preventDefault(); - }, "onPageUpKey"), - onPageDownKey: /* @__PURE__ */ __name(function onPageDownKey2(event) { - this.scrollInView(this.visibleOptions.length - 1); - event.preventDefault(); - }, "onPageDownKey"), - onEnterKey: /* @__PURE__ */ __name(function onEnterKey2(event) { - if (!this.typeahead) { - if (this.multiple) { - this.updateModel(event, [].concat(_toConsumableArray$1(this.modelValue || []), [event.target.value])); - this.$refs.focusInput.value = ""; - } - } else { - if (!this.overlayVisible) { - this.focusedOptionIndex = -1; - this.onArrowDownKey(event); - } else { - if (this.focusedOptionIndex !== -1) { - this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); - } - this.hide(); - } - } - }, "onEnterKey"), - onEscapeKey: /* @__PURE__ */ __name(function onEscapeKey(event) { - this.overlayVisible && this.hide(true); - event.preventDefault(); - }, "onEscapeKey"), - onTabKey: /* @__PURE__ */ __name(function onTabKey(event) { - if (this.focusedOptionIndex !== -1) { - this.onOptionSelect(event, this.visibleOptions[this.focusedOptionIndex]); - } - this.overlayVisible && this.hide(); - }, "onTabKey"), - onBackspaceKey: /* @__PURE__ */ __name(function onBackspaceKey(event) { - if (this.multiple) { - if (isNotEmpty(this.modelValue) && !this.$refs.focusInput.value) { - var removedValue = this.modelValue[this.modelValue.length - 1]; - var newValue = this.modelValue.slice(0, -1); - this.$emit("update:modelValue", newValue); - this.$emit("item-unselect", { - originalEvent: event, - value: removedValue - }); - this.$emit("option-unselect", { - originalEvent: event, - value: removedValue - }); - } - event.stopPropagation(); - } - }, "onBackspaceKey"), - onArrowLeftKeyOnMultiple: /* @__PURE__ */ __name(function onArrowLeftKeyOnMultiple() { - this.focusedMultipleOptionIndex = this.focusedMultipleOptionIndex < 1 ? 0 : this.focusedMultipleOptionIndex - 1; - }, "onArrowLeftKeyOnMultiple"), - onArrowRightKeyOnMultiple: /* @__PURE__ */ __name(function onArrowRightKeyOnMultiple() { - this.focusedMultipleOptionIndex++; - if (this.focusedMultipleOptionIndex > this.modelValue.length - 1) { - this.focusedMultipleOptionIndex = -1; - focus(this.$refs.focusInput); - } - }, "onArrowRightKeyOnMultiple"), - onBackspaceKeyOnMultiple: /* @__PURE__ */ __name(function onBackspaceKeyOnMultiple(event) { - if (this.focusedMultipleOptionIndex !== -1) { - this.removeOption(event, this.focusedMultipleOptionIndex); - } - }, "onBackspaceKeyOnMultiple"), - onOverlayEnter: /* @__PURE__ */ __name(function onOverlayEnter(el) { - ZIndex.set("overlay", el, this.$primevue.config.zIndex.overlay); - addStyle(el, { - position: "absolute", - top: "0", - left: "0" - }); - this.alignOverlay(); - }, "onOverlayEnter"), - onOverlayAfterEnter: /* @__PURE__ */ __name(function onOverlayAfterEnter() { - this.bindOutsideClickListener(); - this.bindScrollListener(); - this.bindResizeListener(); - this.$emit("show"); - }, "onOverlayAfterEnter"), - onOverlayLeave: /* @__PURE__ */ __name(function onOverlayLeave() { - this.unbindOutsideClickListener(); - this.unbindScrollListener(); - this.unbindResizeListener(); - this.$emit("hide"); - this.overlay = null; - }, "onOverlayLeave"), - onOverlayAfterLeave: /* @__PURE__ */ __name(function onOverlayAfterLeave(el) { - ZIndex.clear(el); - }, "onOverlayAfterLeave"), - alignOverlay: /* @__PURE__ */ __name(function alignOverlay() { - var target = this.multiple ? this.$refs.multiContainer : this.$refs.focusInput.$el; - if (this.appendTo === "self") { - relativePosition(this.overlay, target); - } else { - this.overlay.style.minWidth = getOuterWidth(target) + "px"; - absolutePosition(this.overlay, target); - } - }, "alignOverlay"), - bindOutsideClickListener: /* @__PURE__ */ __name(function bindOutsideClickListener() { - var _this5 = this; - if (!this.outsideClickListener) { - this.outsideClickListener = function(event) { - if (_this5.overlayVisible && _this5.overlay && _this5.isOutsideClicked(event)) { - _this5.hide(); - } - }; - document.addEventListener("click", this.outsideClickListener); - } - }, "bindOutsideClickListener"), - unbindOutsideClickListener: /* @__PURE__ */ __name(function unbindOutsideClickListener() { - if (this.outsideClickListener) { - document.removeEventListener("click", this.outsideClickListener); - this.outsideClickListener = null; - } - }, "unbindOutsideClickListener"), - bindScrollListener: /* @__PURE__ */ __name(function bindScrollListener() { - var _this6 = this; - if (!this.scrollHandler) { - this.scrollHandler = new ConnectedOverlayScrollHandler(this.$refs.container, function() { - if (_this6.overlayVisible) { - _this6.hide(); - } - }); - } - this.scrollHandler.bindScrollListener(); - }, "bindScrollListener"), - unbindScrollListener: /* @__PURE__ */ __name(function unbindScrollListener() { - if (this.scrollHandler) { - this.scrollHandler.unbindScrollListener(); - } - }, "unbindScrollListener"), - bindResizeListener: /* @__PURE__ */ __name(function bindResizeListener() { - var _this7 = this; - if (!this.resizeListener) { - this.resizeListener = function() { - if (_this7.overlayVisible && !isTouchDevice()) { - _this7.hide(); - } - }; - window.addEventListener("resize", this.resizeListener); - } - }, "bindResizeListener"), - unbindResizeListener: /* @__PURE__ */ __name(function unbindResizeListener() { - if (this.resizeListener) { - window.removeEventListener("resize", this.resizeListener); - this.resizeListener = null; - } - }, "unbindResizeListener"), - isOutsideClicked: /* @__PURE__ */ __name(function isOutsideClicked(event) { - return !this.overlay.contains(event.target) && !this.isInputClicked(event) && !this.isDropdownClicked(event); - }, "isOutsideClicked"), - isInputClicked: /* @__PURE__ */ __name(function isInputClicked(event) { - if (this.multiple) return event.target === this.$refs.multiContainer || this.$refs.multiContainer.contains(event.target); - else return event.target === this.$refs.focusInput.$el; - }, "isInputClicked"), - isDropdownClicked: /* @__PURE__ */ __name(function isDropdownClicked(event) { - return this.$refs.dropdownButton ? event.target === this.$refs.dropdownButton || this.$refs.dropdownButton.contains(event.target) : false; - }, "isDropdownClicked"), - isOptionMatched: /* @__PURE__ */ __name(function isOptionMatched(option2, value) { - var _this$getOptionLabel; - return this.isValidOption(option2) && ((_this$getOptionLabel = this.getOptionLabel(option2)) === null || _this$getOptionLabel === void 0 ? void 0 : _this$getOptionLabel.toLocaleLowerCase(this.searchLocale)) === value.toLocaleLowerCase(this.searchLocale); - }, "isOptionMatched"), - isValidOption: /* @__PURE__ */ __name(function isValidOption(option2) { - return isNotEmpty(option2) && !(this.isOptionDisabled(option2) || this.isOptionGroup(option2)); - }, "isValidOption"), - isValidSelectedOption: /* @__PURE__ */ __name(function isValidSelectedOption(option2) { - return this.isValidOption(option2) && this.isSelected(option2); - }, "isValidSelectedOption"), - isEquals: /* @__PURE__ */ __name(function isEquals(value1, value2) { - return equals(value1, value2, this.equalityKey); - }, "isEquals"), - isSelected: /* @__PURE__ */ __name(function isSelected(option2) { - var _this8 = this; - var optionValue = this.getOptionValue(option2); - return this.multiple ? (this.modelValue || []).some(function(value) { - return _this8.isEquals(value, optionValue); - }) : this.isEquals(this.modelValue, this.getOptionValue(option2)); - }, "isSelected"), - findFirstOptionIndex: /* @__PURE__ */ __name(function findFirstOptionIndex() { - var _this9 = this; - return this.visibleOptions.findIndex(function(option2) { - return _this9.isValidOption(option2); - }); - }, "findFirstOptionIndex"), - findLastOptionIndex: /* @__PURE__ */ __name(function findLastOptionIndex() { - var _this10 = this; - return findLastIndex(this.visibleOptions, function(option2) { - return _this10.isValidOption(option2); - }); - }, "findLastOptionIndex"), - findNextOptionIndex: /* @__PURE__ */ __name(function findNextOptionIndex(index) { - var _this11 = this; - var matchedOptionIndex = index < this.visibleOptions.length - 1 ? this.visibleOptions.slice(index + 1).findIndex(function(option2) { - return _this11.isValidOption(option2); - }) : -1; - return matchedOptionIndex > -1 ? matchedOptionIndex + index + 1 : index; - }, "findNextOptionIndex"), - findPrevOptionIndex: /* @__PURE__ */ __name(function findPrevOptionIndex(index) { - var _this12 = this; - var matchedOptionIndex = index > 0 ? findLastIndex(this.visibleOptions.slice(0, index), function(option2) { - return _this12.isValidOption(option2); - }) : -1; - return matchedOptionIndex > -1 ? matchedOptionIndex : index; - }, "findPrevOptionIndex"), - findSelectedOptionIndex: /* @__PURE__ */ __name(function findSelectedOptionIndex() { - var _this13 = this; - return this.hasSelectedOption ? this.visibleOptions.findIndex(function(option2) { - return _this13.isValidSelectedOption(option2); - }) : -1; - }, "findSelectedOptionIndex"), - findFirstFocusedOptionIndex: /* @__PURE__ */ __name(function findFirstFocusedOptionIndex() { - var selectedIndex = this.findSelectedOptionIndex(); - return selectedIndex < 0 ? this.findFirstOptionIndex() : selectedIndex; - }, "findFirstFocusedOptionIndex"), - findLastFocusedOptionIndex: /* @__PURE__ */ __name(function findLastFocusedOptionIndex() { - var selectedIndex = this.findSelectedOptionIndex(); - return selectedIndex < 0 ? this.findLastOptionIndex() : selectedIndex; - }, "findLastFocusedOptionIndex"), - search: /* @__PURE__ */ __name(function search(event, query, source) { - if (query === void 0 || query === null) { - return; - } - if (source === "input" && query.trim().length === 0) { - return; - } - this.searching = true; - this.$emit("complete", { - originalEvent: event, - query - }); - }, "search"), - removeOption: /* @__PURE__ */ __name(function removeOption(event, index) { - var _this14 = this; - var removedOption = this.modelValue[index]; - var value = this.modelValue.filter(function(_2, i) { - return i !== index; - }).map(function(option2) { - return _this14.getOptionValue(option2); - }); - this.updateModel(event, value); - this.$emit("item-unselect", { - originalEvent: event, - value: removedOption - }); - this.$emit("option-unselect", { - originalEvent: event, - value: removedOption - }); - this.dirty = true; - focus(this.multiple ? this.$refs.focusInput : this.$refs.focusInput.$el); - }, "removeOption"), - changeFocusedOptionIndex: /* @__PURE__ */ __name(function changeFocusedOptionIndex(event, index) { - if (this.focusedOptionIndex !== index) { - this.focusedOptionIndex = index; - this.scrollInView(); - if (this.selectOnFocus) { - this.onOptionSelect(event, this.visibleOptions[index], false); - } - } - }, "changeFocusedOptionIndex"), - scrollInView: /* @__PURE__ */ __name(function scrollInView2() { - var _this15 = this; - var index = arguments.length > 0 && arguments[0] !== void 0 ? arguments[0] : -1; - this.$nextTick(function() { - var id2 = index !== -1 ? "".concat(_this15.id, "_").concat(index) : _this15.focusedOptionId; - var element = findSingle(_this15.list, 'li[id="'.concat(id2, '"]')); - if (element) { - element.scrollIntoView && element.scrollIntoView({ - block: "nearest", - inline: "start" - }); - } else if (!_this15.virtualScrollerDisabled) { - _this15.virtualScroller && _this15.virtualScroller.scrollToIndex(index !== -1 ? index : _this15.focusedOptionIndex); - } - }); - }, "scrollInView"), - autoUpdateModel: /* @__PURE__ */ __name(function autoUpdateModel() { - if (this.selectOnFocus && this.autoOptionFocus && !this.hasSelectedOption) { - this.focusedOptionIndex = this.findFirstFocusedOptionIndex(); - this.onOptionSelect(null, this.visibleOptions[this.focusedOptionIndex], false); - } - }, "autoUpdateModel"), - updateModel: /* @__PURE__ */ __name(function updateModel(event, value) { - this.$emit("update:modelValue", value); - this.$emit("change", { - originalEvent: event, - value - }); - }, "updateModel"), - flatOptions: /* @__PURE__ */ __name(function flatOptions(options) { - var _this16 = this; - return (options || []).reduce(function(result, option2, index) { - result.push({ - optionGroup: option2, - group: true, - index - }); - var optionGroupChildren = _this16.getOptionGroupChildren(option2); - optionGroupChildren && optionGroupChildren.forEach(function(o) { - return result.push(o); - }); - return result; - }, []); - }, "flatOptions"), - overlayRef: /* @__PURE__ */ __name(function overlayRef(el) { - this.overlay = el; - }, "overlayRef"), - listRef: /* @__PURE__ */ __name(function listRef(el, contentRef) { - this.list = el; - contentRef && contentRef(el); - }, "listRef"), - virtualScrollerRef: /* @__PURE__ */ __name(function virtualScrollerRef(el) { - this.virtualScroller = el; - }, "virtualScrollerRef") - }, - computed: { - visibleOptions: /* @__PURE__ */ __name(function visibleOptions() { - return this.optionGroupLabel ? this.flatOptions(this.suggestions) : this.suggestions || []; - }, "visibleOptions"), - inputValue: /* @__PURE__ */ __name(function inputValue() { - if (isNotEmpty(this.modelValue)) { - if (_typeof$1$1(this.modelValue) === "object") { - var label = this.getOptionLabel(this.modelValue); - return label != null ? label : this.modelValue; - } else { - return this.modelValue; - } - } else { - return ""; - } - }, "inputValue"), - hasSelectedOption: /* @__PURE__ */ __name(function hasSelectedOption() { - return isNotEmpty(this.modelValue); - }, "hasSelectedOption"), - equalityKey: /* @__PURE__ */ __name(function equalityKey() { - return this.dataKey; - }, "equalityKey"), - searchResultMessageText: /* @__PURE__ */ __name(function searchResultMessageText() { - return isNotEmpty(this.visibleOptions) && this.overlayVisible ? this.searchMessageText.replaceAll("{0}", this.visibleOptions.length) : this.emptySearchMessageText; - }, "searchResultMessageText"), - searchMessageText: /* @__PURE__ */ __name(function searchMessageText() { - return this.searchMessage || this.$primevue.config.locale.searchMessage || ""; - }, "searchMessageText"), - emptySearchMessageText: /* @__PURE__ */ __name(function emptySearchMessageText() { - return this.emptySearchMessage || this.$primevue.config.locale.emptySearchMessage || ""; - }, "emptySearchMessageText"), - selectionMessageText: /* @__PURE__ */ __name(function selectionMessageText() { - return this.selectionMessage || this.$primevue.config.locale.selectionMessage || ""; - }, "selectionMessageText"), - emptySelectionMessageText: /* @__PURE__ */ __name(function emptySelectionMessageText() { - return this.emptySelectionMessage || this.$primevue.config.locale.emptySelectionMessage || ""; - }, "emptySelectionMessageText"), - selectedMessageText: /* @__PURE__ */ __name(function selectedMessageText() { - return this.hasSelectedOption ? this.selectionMessageText.replaceAll("{0}", this.multiple ? this.modelValue.length : "1") : this.emptySelectionMessageText; - }, "selectedMessageText"), - listAriaLabel: /* @__PURE__ */ __name(function listAriaLabel() { - return this.$primevue.config.locale.aria ? this.$primevue.config.locale.aria.listLabel : void 0; - }, "listAriaLabel"), - focusedOptionId: /* @__PURE__ */ __name(function focusedOptionId() { - return this.focusedOptionIndex !== -1 ? "".concat(this.id, "_").concat(this.focusedOptionIndex) : null; - }, "focusedOptionId"), - focusedMultipleOptionId: /* @__PURE__ */ __name(function focusedMultipleOptionId() { - return this.focusedMultipleOptionIndex !== -1 ? "".concat(this.id, "_multiple_option_").concat(this.focusedMultipleOptionIndex) : null; - }, "focusedMultipleOptionId"), - ariaSetSize: /* @__PURE__ */ __name(function ariaSetSize() { - var _this17 = this; - return this.visibleOptions.filter(function(option2) { - return !_this17.isOptionGroup(option2); - }).length; - }, "ariaSetSize"), - virtualScrollerDisabled: /* @__PURE__ */ __name(function virtualScrollerDisabled() { - return !this.virtualScrollerOptions; - }, "virtualScrollerDisabled"), - panelId: /* @__PURE__ */ __name(function panelId() { - return this.id + "_panel"; - }, "panelId"), - hasFluid: /* @__PURE__ */ __name(function hasFluid() { - return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid; - }, "hasFluid") - }, - components: { - InputText: script$i, - VirtualScroller: script$j, - Portal: script$k, - ChevronDownIcon: script$l, - SpinnerIcon: script$m, - Chip: script$n - }, - directives: { - ripple: Ripple - } -}; -function _typeof$4(o) { - "@babel/helpers - typeof"; - return _typeof$4 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { - return typeof o2; - } : function(o2) { - return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; - }, _typeof$4(o); -} -__name(_typeof$4, "_typeof$4"); -function ownKeys$3(e, r) { - var t = Object.keys(e); - if (Object.getOwnPropertySymbols) { - var o = Object.getOwnPropertySymbols(e); - r && (o = o.filter(function(r2) { - return Object.getOwnPropertyDescriptor(e, r2).enumerable; - })), t.push.apply(t, o); - } - return t; -} -__name(ownKeys$3, "ownKeys$3"); -function _objectSpread$3(e) { - for (var r = 1; r < arguments.length; r++) { - var t = null != arguments[r] ? arguments[r] : {}; - r % 2 ? ownKeys$3(Object(t), true).forEach(function(r2) { - _defineProperty$4(e, r2, t[r2]); - }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys$3(Object(t)).forEach(function(r2) { - Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); - }); - } - return e; -} -__name(_objectSpread$3, "_objectSpread$3"); -function _defineProperty$4(e, r, t) { - return (r = _toPropertyKey$4(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; -} -__name(_defineProperty$4, "_defineProperty$4"); -function _toPropertyKey$4(t) { - var i = _toPrimitive$4(t, "string"); - return "symbol" == _typeof$4(i) ? i : i + ""; -} -__name(_toPropertyKey$4, "_toPropertyKey$4"); -function _toPrimitive$4(t, r) { - if ("object" != _typeof$4(t) || !t) return t; - var e = t[Symbol.toPrimitive]; - if (void 0 !== e) { - var i = e.call(t, r || "default"); - if ("object" != _typeof$4(i)) return i; - throw new TypeError("@@toPrimitive must return a primitive value."); - } - return ("string" === r ? 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(openBlock(), createElementBlock("ul", mergeProps({ - key: 1, - ref: "multiContainer", - "class": _ctx.cx("inputMultiple"), - tabindex: "-1", - role: "listbox", - "aria-orientation": "horizontal", - "aria-activedescendant": $data.focused ? $options.focusedMultipleOptionId : void 0, - onFocus: _cache[5] || (_cache[5] = function() { - return $options.onMultipleContainerFocus && $options.onMultipleContainerFocus.apply($options, arguments); - }), - onBlur: _cache[6] || (_cache[6] = function() { - return $options.onMultipleContainerBlur && $options.onMultipleContainerBlur.apply($options, arguments); - }), - onKeydown: _cache[7] || (_cache[7] = function() { - return $options.onMultipleContainerKeyDown && $options.onMultipleContainerKeyDown.apply($options, arguments); - }) - }, _ctx.ptm("inputMultiple")), [(openBlock(true), createElementBlock(Fragment, null, renderList(_ctx.modelValue, function(option2, i) { - return openBlock(), createElementBlock("li", mergeProps({ - key: "".concat(i, "_").concat($options.getOptionLabel(option2)), - id: $data.id + "_multiple_option_" + i, - "class": _ctx.cx("chipItem", { - i - }), - role: "option", - "aria-label": $options.getOptionLabel(option2), - "aria-selected": true, - "aria-setsize": _ctx.modelValue.length, - "aria-posinset": i + 1, - ref_for: true - }, _ctx.ptm("chipItem")), [renderSlot(_ctx.$slots, "chip", mergeProps({ - "class": _ctx.cx("pcChip"), - value: option2, - index: i, - removeCallback: /* @__PURE__ */ __name(function removeCallback(event) { - return $options.removeOption(event, i); - }, "removeCallback"), - ref_for: true - }, _ctx.ptm("pcChip")), function() { - return [createVNode(_component_Chip, { - "class": normalizeClass(_ctx.cx("pcChip")), - label: $options.getOptionLabel(option2), - removeIcon: _ctx.chipIcon || _ctx.removeTokenIcon, - removable: "", - unstyled: _ctx.unstyled, - onRemove: /* @__PURE__ */ __name(function onRemove2($event) { - return $options.removeOption($event, i); - }, "onRemove"), - pt: _ctx.ptm("pcChip") - }, { - removeicon: withCtx(function() { - return [renderSlot(_ctx.$slots, _ctx.$slots.chipicon ? "chipicon" : "removetokenicon", { - "class": normalizeClass(_ctx.cx("chipIcon")), - index: i, - removeCallback: /* @__PURE__ */ __name(function removeCallback(event) { - return $options.removeOption(event, i); - }, "removeCallback") - })]; - }), - _: 2 - }, 1032, ["class", "label", "removeIcon", "unstyled", "onRemove", "pt"])]; - })], 16, _hoisted_2$e); - }), 128)), createBaseVNode("li", mergeProps({ - "class": _ctx.cx("inputChip"), - role: "option" - }, _ctx.ptm("inputChip")), [createBaseVNode("input", mergeProps({ - ref: "focusInput", - id: _ctx.inputId, - type: "text", - style: _ctx.inputStyle, - "class": _ctx.inputClass, - placeholder: _ctx.placeholder, - tabindex: !_ctx.disabled ? _ctx.tabindex : -1, - disabled: _ctx.disabled, - autocomplete: "off", - role: "combobox", - "aria-label": _ctx.ariaLabel, - "aria-labelledby": _ctx.ariaLabelledby, - "aria-haspopup": "listbox", - "aria-autocomplete": "list", - "aria-expanded": $data.overlayVisible, - "aria-controls": $data.id + "_list", - "aria-activedescendant": $data.focused ? $options.focusedOptionId : void 0, - "aria-invalid": _ctx.invalid || void 0, - onFocus: _cache[0] || (_cache[0] = function() { - return $options.onFocus && $options.onFocus.apply($options, arguments); - }), - onBlur: _cache[1] || (_cache[1] = function() { - return $options.onBlur && $options.onBlur.apply($options, arguments); - }), - onKeydown: _cache[2] || (_cache[2] = function() { - return $options.onKeyDown && $options.onKeyDown.apply($options, arguments); - }), - onInput: _cache[3] || (_cache[3] = function() { - return $options.onInput && $options.onInput.apply($options, arguments); - }), - onChange: _cache[4] || (_cache[4] = function() { - return $options.onChange && $options.onChange.apply($options, arguments); - }) - }, _ctx.ptm("input")), null, 16, _hoisted_3$c)], 16)], 16, _hoisted_1$h)) : createCommentVNode("", true), $data.searching || _ctx.loading ? renderSlot(_ctx.$slots, _ctx.$slots.loader ? "loader" : "loadingicon", { - key: 2, - "class": normalizeClass(_ctx.cx("loader")) - }, function() { - return [_ctx.loader || _ctx.loadingIcon ? (openBlock(), createElementBlock("i", mergeProps({ - key: 0, - "class": ["pi-spin", _ctx.cx("loader"), _ctx.loader, _ctx.loadingIcon], - "aria-hidden": "true" - }, _ctx.ptm("loader")), null, 16)) : (openBlock(), createBlock(_component_SpinnerIcon, mergeProps({ - key: 1, - "class": _ctx.cx("loader"), - spin: "", - "aria-hidden": "true" - }, _ctx.ptm("loader")), null, 16, ["class"]))]; - }) : createCommentVNode("", true), renderSlot(_ctx.$slots, _ctx.$slots.dropdown ? "dropdown" : "dropdownbutton", { - toggleCallback: /* @__PURE__ */ __name(function toggleCallback(event) { - return $options.onDropdownClick(event); - }, "toggleCallback") - }, function() { - return [_ctx.dropdown ? (openBlock(), createElementBlock("button", mergeProps({ - key: 0, - ref: "dropdownButton", - type: "button", - "class": [_ctx.cx("dropdown"), _ctx.dropdownClass], - disabled: _ctx.disabled, - "aria-haspopup": "listbox", - "aria-expanded": $data.overlayVisible, - "aria-controls": $options.panelId, - onClick: _cache[8] || (_cache[8] = function() { - return $options.onDropdownClick && $options.onDropdownClick.apply($options, arguments); - }) - }, _ctx.ptm("dropdown")), [renderSlot(_ctx.$slots, "dropdownicon", { - "class": normalizeClass(_ctx.dropdownIcon) - }, function() { - return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.dropdownIcon ? "span" : "ChevronDownIcon"), mergeProps({ - "class": _ctx.dropdownIcon - }, _ctx.ptm("dropdownIcon")), null, 16, ["class"]))]; - })], 16, _hoisted_4$4)) : createCommentVNode("", true)]; - }), createBaseVNode("span", mergeProps({ - role: "status", - "aria-live": "polite", - "class": "p-hidden-accessible" - }, _ctx.ptm("hiddenSearchResult"), { - "data-p-hidden-accessible": true - }), toDisplayString($options.searchResultMessageText), 17), createVNode(_component_Portal, { - appendTo: _ctx.appendTo - }, { - "default": withCtx(function() { - return [createVNode(Transition, mergeProps({ - name: "p-connected-overlay", - onEnter: $options.onOverlayEnter, - onAfterEnter: $options.onOverlayAfterEnter, - onLeave: $options.onOverlayLeave, - onAfterLeave: $options.onOverlayAfterLeave - }, _ctx.ptm("transition")), { - "default": withCtx(function() { - return [$data.overlayVisible ? (openBlock(), createElementBlock("div", mergeProps({ - key: 0, - ref: $options.overlayRef, - id: $options.panelId, - "class": [_ctx.cx("overlay"), _ctx.panelClass, _ctx.overlayClass], - style: _objectSpread$3(_objectSpread$3(_objectSpread$3({}, _ctx.panelStyle), _ctx.overlayStyle), {}, { - "max-height": $options.virtualScrollerDisabled ? _ctx.scrollHeight : "" - }), - onClick: _cache[9] || (_cache[9] = function() { - return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments); - }), - onKeydown: _cache[10] || (_cache[10] = function() { - return $options.onOverlayKeyDown && $options.onOverlayKeyDown.apply($options, arguments); - }) - }, _ctx.ptm("overlay")), [renderSlot(_ctx.$slots, "header", { - value: _ctx.modelValue, - suggestions: $options.visibleOptions - }), createVNode(_component_VirtualScroller, mergeProps({ - ref: $options.virtualScrollerRef - }, _ctx.virtualScrollerOptions, { - style: { - height: _ctx.scrollHeight - }, - items: $options.visibleOptions, - tabindex: -1, - disabled: $options.virtualScrollerDisabled, - pt: _ctx.ptm("virtualScroller") - }), createSlots({ - content: withCtx(function(_ref) { - var styleClass = _ref.styleClass, contentRef = _ref.contentRef, items = _ref.items, getItemOptions = _ref.getItemOptions, contentStyle = _ref.contentStyle, itemSize = _ref.itemSize; - return [createBaseVNode("ul", mergeProps({ - ref: /* @__PURE__ */ __name(function ref2(el) { - return $options.listRef(el, contentRef); - }, "ref"), - id: $data.id + "_list", - "class": [_ctx.cx("list"), styleClass], - style: contentStyle, - role: "listbox", - "aria-label": $options.listAriaLabel - }, _ctx.ptm("list")), [(openBlock(true), createElementBlock(Fragment, null, renderList(items, function(option2, i) { - return openBlock(), createElementBlock(Fragment, { - key: $options.getOptionRenderKey(option2, $options.getOptionIndex(i, getItemOptions)) - }, [$options.isOptionGroup(option2) ? (openBlock(), createElementBlock("li", mergeProps({ - key: 0, - id: $data.id + "_" + $options.getOptionIndex(i, getItemOptions), - style: { - height: itemSize ? itemSize + "px" : void 0 - }, - "class": _ctx.cx("optionGroup"), - role: "option", - ref_for: true - }, _ctx.ptm("optionGroup")), [renderSlot(_ctx.$slots, "optiongroup", { - option: option2.optionGroup, - index: $options.getOptionIndex(i, getItemOptions) - }, function() { - return [createTextVNode(toDisplayString($options.getOptionGroupLabel(option2.optionGroup)), 1)]; - })], 16, _hoisted_7$1)) : withDirectives((openBlock(), createElementBlock("li", mergeProps({ - key: 1, - id: $data.id + "_" + $options.getOptionIndex(i, getItemOptions), - style: { - height: itemSize ? itemSize + "px" : void 0 - }, - "class": _ctx.cx("option", { - option: option2, - i, - getItemOptions - }), - role: "option", - "aria-label": $options.getOptionLabel(option2), - "aria-selected": $options.isSelected(option2), - "aria-disabled": $options.isOptionDisabled(option2), - "aria-setsize": $options.ariaSetSize, - "aria-posinset": $options.getAriaPosInset($options.getOptionIndex(i, getItemOptions)), - onClick: /* @__PURE__ */ __name(function onClick2($event) { - return $options.onOptionSelect($event, option2); - }, "onClick"), - onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { - return $options.onOptionMouseMove($event, $options.getOptionIndex(i, getItemOptions)); - }, "onMousemove"), - "data-p-selected": $options.isSelected(option2), - "data-p-focus": $data.focusedOptionIndex === $options.getOptionIndex(i, getItemOptions), - "data-p-disabled": $options.isOptionDisabled(option2), - ref_for: true - }, $options.getPTOptions(option2, getItemOptions, i, "option")), [renderSlot(_ctx.$slots, "option", { - option: option2, - index: $options.getOptionIndex(i, getItemOptions) - }, function() { - return [createTextVNode(toDisplayString($options.getOptionLabel(option2)), 1)]; - })], 16, _hoisted_8$1)), [[_directive_ripple]])], 64); - }), 128)), !items || items && items.length === 0 ? (openBlock(), createElementBlock("li", mergeProps({ - key: 0, - "class": _ctx.cx("emptyMessage"), - role: "option" - }, _ctx.ptm("emptyMessage")), [renderSlot(_ctx.$slots, "empty", {}, function() { - return [createTextVNode(toDisplayString($options.searchResultMessageText), 1)]; - })], 16)) : createCommentVNode("", true)], 16, _hoisted_6$2)]; - }), - _: 2 - }, [_ctx.$slots.loader ? { - name: "loader", - fn: withCtx(function(_ref2) { - var options = _ref2.options; - return [renderSlot(_ctx.$slots, "loader", { - options - })]; - }), - key: "0" - } : void 0]), 1040, ["style", "items", "disabled", "pt"]), renderSlot(_ctx.$slots, "footer", { - value: _ctx.modelValue, - suggestions: $options.visibleOptions - }), createBaseVNode("span", mergeProps({ - role: "status", - "aria-live": "polite", - "class": "p-hidden-accessible" - }, _ctx.ptm("hiddenSelectedMessage"), { - "data-p-hidden-accessible": true - }), toDisplayString($options.selectedMessageText), 17)], 16, _hoisted_5$3)) : createCommentVNode("", true)]; - }), - _: 3 - }, 16, ["onEnter", "onAfterEnter", "onLeave", "onAfterLeave"])]; - }), - _: 3 - }, 8, ["appendTo"])], 16); -} -__name(render$c, "render$c"); -script$7.render = render$c; -const _sfc_main$j = { - name: "AutoCompletePlus", - extends: script$7, - emits: ["focused-option-changed"], - mounted() { - if (typeof script$7.mounted === "function") { - script$7.mounted.call(this); - } - this.$watch( - () => this.focusedOptionIndex, - (newVal, oldVal) => { - this.$emit("focused-option-changed", newVal); - } - ); - } -}; -const _withScopeId$5 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-5741c9ae"), n = n(), popScopeId(), n), "_withScopeId$5"); -const _hoisted_1$g = { class: "option-container flex justify-between items-center px-2 py-0 cursor-pointer overflow-hidden w-full" }; -const _hoisted_2$d = { class: "option-display-name font-semibold flex flex-col" }; -const _hoisted_3$b = { key: 0 }; -const _hoisted_4$3 = /* @__PURE__ */ _withScopeId$5(() => /* @__PURE__ */ createBaseVNode("i", { class: "pi pi-bookmark-fill text-sm mr-1" }, null, -1)); -const _hoisted_5$2 = [ - _hoisted_4$3 -]; -const _hoisted_6$1 = ["innerHTML"]; -const _hoisted_7 = /* @__PURE__ */ _withScopeId$5(() => /* @__PURE__ */ createBaseVNode("span", null, " ", -1)); -const _hoisted_8 = ["innerHTML"]; -const _hoisted_9 = { - key: 0, - class: "option-category font-light text-sm text-gray-400 overflow-hidden text-ellipsis whitespace-nowrap" -}; -const _hoisted_10 = { class: "option-badges" }; -const _sfc_main$i = /* @__PURE__ */ defineComponent({ - __name: "NodeSearchItem", - props: { - nodeDef: {}, - currentQuery: {} - }, - setup(__props) { - const settingStore = useSettingStore(); - const showCategory = computed( - () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowCategory") - ); - const showIdName = computed( - () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowIdName") - ); - const showNodeFrequency = computed( - () => settingStore.get("Comfy.NodeSearchBoxImpl.ShowNodeFrequency") - ); - const nodeFrequencyStore = useNodeFrequencyStore(); - const nodeFrequency = computed( - () => nodeFrequencyStore.getNodeFrequency(props.nodeDef) - ); - const nodeBookmarkStore = useNodeBookmarkStore(); - const isBookmarked = computed( - () => nodeBookmarkStore.isBookmarked(props.nodeDef) - ); - const props = __props; - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div", _hoisted_1$g, [ - createBaseVNode("div", _hoisted_2$d, [ - createBaseVNode("div", null, [ - isBookmarked.value ? (openBlock(), createElementBlock("span", _hoisted_3$b, _hoisted_5$2)) : createCommentVNode("", true), - createBaseVNode("span", { - innerHTML: unref(highlightQuery)(_ctx.nodeDef.display_name, _ctx.currentQuery) - }, null, 8, _hoisted_6$1), - _hoisted_7, - showIdName.value ? (openBlock(), createBlock(unref(script$o), { - key: 1, - severity: "secondary" - }, { - default: withCtx(() => [ - createBaseVNode("span", { - innerHTML: unref(highlightQuery)(_ctx.nodeDef.name, _ctx.currentQuery) - }, null, 8, _hoisted_8) - ]), - _: 1 - })) : createCommentVNode("", true) - ]), - showCategory.value ? (openBlock(), createElementBlock("div", _hoisted_9, toDisplayString(_ctx.nodeDef.category.replaceAll("/", " > ")), 1)) : createCommentVNode("", true) - ]), - createBaseVNode("div", _hoisted_10, [ - _ctx.nodeDef.experimental ? (openBlock(), createBlock(unref(script$o), { - key: 0, - value: _ctx.$t("g.experimental"), - severity: "primary" - }, null, 8, ["value"])) : createCommentVNode("", true), - _ctx.nodeDef.deprecated ? (openBlock(), createBlock(unref(script$o), { - key: 1, - value: _ctx.$t("g.deprecated"), - severity: "danger" - }, null, 8, ["value"])) : createCommentVNode("", true), - showNodeFrequency.value && nodeFrequency.value > 0 ? (openBlock(), createBlock(unref(script$o), { - key: 2, - value: unref(formatNumberWithSuffix)(nodeFrequency.value, { roundToInt: true }), - severity: "secondary" - }, null, 8, ["value"])) : createCommentVNode("", true), - _ctx.nodeDef.nodeSource.type !== unref(NodeSourceType).Unknown ? (openBlock(), createBlock(unref(script$n), { - key: 3, - class: "text-sm font-light" - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.nodeDef.nodeSource.displayText), 1) - ]), - _: 1 - })) : createCommentVNode("", true) - ]) - ]); - }; - } -}); -const NodeSearchItem = /* @__PURE__ */ _export_sfc(_sfc_main$i, [["__scopeId", "data-v-5741c9ae"]]); -const _hoisted_1$f = { class: "comfy-vue-node-search-container flex justify-center items-center w-full min-w-96 pointer-events-auto" }; -const _hoisted_2$c = { - key: 0, - class: "comfy-vue-node-preview-container absolute left-[-350px] top-[50px]" -}; -const _hoisted_3$a = /* @__PURE__ */ createBaseVNode("h3", null, "Add node filter condition", -1); -const _hoisted_4$2 = { class: "_dialog-body" }; -const _sfc_main$h = /* @__PURE__ */ defineComponent({ - __name: "NodeSearchBox", - props: { - filters: {}, - searchLimit: { default: 64 } - }, - emits: ["addFilter", "removeFilter", "addNode"], - setup(__props, { emit: __emit }) { - const settingStore = useSettingStore(); - const { t } = useI18n(); - const enableNodePreview = computed( - () => settingStore.get("Comfy.NodeSearchBoxImpl.NodePreview") - ); - const props = __props; - const nodeSearchFilterVisible = ref(false); - const inputId = `comfy-vue-node-search-box-input-${Math.random()}`; - const suggestions2 = ref([]); - const hoveredSuggestion = ref(null); - const currentQuery = ref(""); - const placeholder = computed(() => { - return props.filters.length === 0 ? t("g.searchNodes") + "..." : ""; - }); - const nodeDefStore = useNodeDefStore(); - const nodeFrequencyStore = useNodeFrequencyStore(); - const search2 = /* @__PURE__ */ __name((query) => { - const queryIsEmpty = query === "" && props.filters.length === 0; - currentQuery.value = query; - suggestions2.value = queryIsEmpty ? nodeFrequencyStore.topNodeDefs : [ - ...nodeDefStore.nodeSearchService.searchNode(query, props.filters, { - limit: props.searchLimit - }) - ]; - }, "search"); - const emit = __emit; - let inputElement = null; - const reFocusInput = /* @__PURE__ */ __name(() => { - inputElement ??= document.getElementById(inputId); - if (inputElement) { - inputElement.blur(); - nextTick(() => inputElement?.focus()); - } - }, "reFocusInput"); - onMounted(reFocusInput); - const onAddFilter = /* @__PURE__ */ __name((filterAndValue) => { - nodeSearchFilterVisible.value = false; - emit("addFilter", filterAndValue); - reFocusInput(); - }, "onAddFilter"); - const onRemoveFilter = /* @__PURE__ */ __name((event, filterAndValue) => { - event.stopPropagation(); - event.preventDefault(); - emit("removeFilter", filterAndValue); - reFocusInput(); - }, "onRemoveFilter"); - const setHoverSuggestion = /* @__PURE__ */ __name((index) => { - if (index === -1) { - hoveredSuggestion.value = null; - return; - } - const value = suggestions2.value[index]; - hoveredSuggestion.value = value; - }, "setHoverSuggestion"); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div", _hoisted_1$f, [ - enableNodePreview.value ? (openBlock(), createElementBlock("div", _hoisted_2$c, [ - hoveredSuggestion.value ? (openBlock(), createBlock(NodePreview, { - nodeDef: hoveredSuggestion.value, - key: hoveredSuggestion.value?.name || "" - }, null, 8, ["nodeDef"])) : createCommentVNode("", true) - ])) : createCommentVNode("", true), - createVNode(unref(script$d), { - icon: "pi pi-filter", - severity: "secondary", - class: "filter-button z-10", - onClick: _cache[0] || (_cache[0] = ($event) => nodeSearchFilterVisible.value = true) - }), - createVNode(unref(script$p), { - visible: nodeSearchFilterVisible.value, - "onUpdate:visible": _cache[1] || (_cache[1] = ($event) => nodeSearchFilterVisible.value = $event), - class: "min-w-96" - }, { - header: withCtx(() => [ - _hoisted_3$a - ]), - default: withCtx(() => [ - createBaseVNode("div", _hoisted_4$2, [ - createVNode(NodeSearchFilter, { onAddFilter }) - ]) - ]), - _: 1 - }, 8, ["visible"]), - createVNode(_sfc_main$j, { - "model-value": props.filters, - class: "comfy-vue-node-search-box z-10 flex-grow", - scrollHeight: "40vh", - placeholder: placeholder.value, - "input-id": inputId, - "append-to": "self", - suggestions: suggestions2.value, - "min-length": 0, - delay: 100, - loading: !unref(nodeFrequencyStore).isLoaded, - onComplete: _cache[2] || (_cache[2] = ($event) => search2($event.query)), - onOptionSelect: _cache[3] || (_cache[3] = ($event) => emit("addNode", $event.value)), - onFocusedOptionChanged: _cache[4] || (_cache[4] = ($event) => setHoverSuggestion($event)), - "complete-on-focus": "", - "auto-option-focus": "", - "force-selection": "", - multiple: "", - optionLabel: "display_name" - }, { - option: withCtx(({ option: option2 }) => [ - createVNode(NodeSearchItem, { - nodeDef: option2, - currentQuery: currentQuery.value - }, null, 8, ["nodeDef", "currentQuery"]) - ]), - chip: withCtx(({ value }) => [ - (openBlock(), createBlock(SearchFilterChip, { - key: `${value[0].id}-${value[1]}`, - onRemove: /* @__PURE__ */ __name(($event) => onRemoveFilter($event, value), "onRemove"), - text: value[1], - badge: value[0].invokeSequence.toUpperCase(), - "badge-class": value[0].invokeSequence + "-badge" - }, null, 8, ["onRemove", "text", "badge", "badge-class"])) - ]), - _: 1 - }, 8, ["model-value", "placeholder", "suggestions", "loading"]) - ]); - }; - } -}); -const _sfc_main$g = /* @__PURE__ */ defineComponent({ - __name: "NodeSearchBoxPopover", - setup(__props) { - const settingStore = useSettingStore(); - const litegraphService = useLitegraphService(); - const { visible } = storeToRefs(useSearchBoxStore()); - const dismissable = ref(true); - const triggerEvent = ref(null); - const getNewNodeLocation = /* @__PURE__ */ __name(() => { - if (!triggerEvent.value) { - return litegraphService.getCanvasCenter(); - } - const originalEvent = triggerEvent.value.detail.originalEvent; - return [originalEvent.canvasX, originalEvent.canvasY]; - }, "getNewNodeLocation"); - const nodeFilters = ref([]); - const addFilter = /* @__PURE__ */ __name((filter) => { - nodeFilters.value.push(filter); - }, "addFilter"); - const removeFilter = /* @__PURE__ */ __name((filter) => { - nodeFilters.value = nodeFilters.value.filter( - (f) => toRaw(f) !== toRaw(filter) - ); - }, "removeFilter"); - const clearFilters = /* @__PURE__ */ __name(() => { - nodeFilters.value = []; - }, "clearFilters"); - const closeDialog = /* @__PURE__ */ __name(() => { - visible.value = false; - }, "closeDialog"); - const addNode = /* @__PURE__ */ __name((nodeDef) => { - const node = litegraphService.addNodeOnGraph(nodeDef, { - pos: getNewNodeLocation() - }); - const eventDetail = triggerEvent.value?.detail; - if (eventDetail && eventDetail.subType === "empty-release") { - eventDetail.linkReleaseContext.links.forEach((link) => { - ConnectingLinkImpl.createFromPlainObject(link).connectTo(node); - }); - } - window.setTimeout(() => { - closeDialog(); - }, 100); - }, "addNode"); - const newSearchBoxEnabled = computed( - () => settingStore.get("Comfy.NodeSearchBoxImpl") === "default" - ); - const showSearchBox = /* @__PURE__ */ __name((e) => { - const detail = e.detail; - if (newSearchBoxEnabled.value) { - if (detail.originalEvent?.pointerType === "touch") { - setTimeout(() => { - showNewSearchBox(e); - }, 128); - } else { - showNewSearchBox(e); - } - } else { - canvasStore.canvas.showSearchBox(detail.originalEvent); - } - }, "showSearchBox"); - const nodeDefStore = useNodeDefStore(); - const showNewSearchBox = /* @__PURE__ */ __name((e) => { - if (e.detail.subType === "empty-release") { - const links = e.detail.linkReleaseContext.links; - if (links.length === 0) { - console.warn("Empty release with no links! This should never happen"); - return; - } - const firstLink = ConnectingLinkImpl.createFromPlainObject(links[0]); - const filter = nodeDefStore.nodeSearchService.getFilterById( - firstLink.releaseSlotType - ); - const dataType = firstLink.type.toString(); - addFilter([filter, dataType]); - } - visible.value = true; - triggerEvent.value = e; - dismissable.value = false; - setTimeout(() => { - dismissable.value = true; - }, 300); - }, "showNewSearchBox"); - const showContextMenu = /* @__PURE__ */ __name((e) => { - if (e.detail.subType !== "empty-release") { - return; - } - const links = e.detail.linkReleaseContext.links; - if (links.length === 0) { - console.warn("Empty release with no links! This should never happen"); - return; - } - const firstLink = ConnectingLinkImpl.createFromPlainObject(links[0]); - const mouseEvent = e.detail.originalEvent; - const commonOptions = { - e: mouseEvent, - allow_searchbox: true, - showSearchBox: /* @__PURE__ */ __name(() => showSearchBox(e), "showSearchBox") - }; - const connectionOptions = firstLink.output ? { - nodeFrom: firstLink.node, - slotFrom: firstLink.output, - afterRerouteId: firstLink.afterRerouteId - } : { - nodeTo: firstLink.node, - slotTo: firstLink.input, - afterRerouteId: firstLink.afterRerouteId - }; - canvasStore.canvas.showConnectionMenu({ - ...connectionOptions, - ...commonOptions - }); - }, "showContextMenu"); - const canvasStore = useCanvasStore(); - watchEffect(() => { - if (canvasStore.canvas) { - LiteGraph.release_link_on_empty_shows_menu = false; - canvasStore.canvas.allow_searchbox = false; - } - }); - const canvasEventHandler = /* @__PURE__ */ __name((e) => { - if (e.detail.subType === "empty-double-click") { - showSearchBox(e); - } else if (e.detail.subType === "empty-release") { - handleCanvasEmptyRelease(e); - } else if (e.detail.subType === "group-double-click") { - const group = e.detail.group; - const [x, y] = group.pos; - const relativeY = e.detail.originalEvent.canvasY - y; - if (relativeY > group.titleHeight) { - showSearchBox(e); - } - } - }, "canvasEventHandler"); - const linkReleaseAction = computed(() => { - return settingStore.get("Comfy.LinkRelease.Action"); - }); - const linkReleaseActionShift = computed(() => { - return settingStore.get("Comfy.LinkRelease.ActionShift"); - }); - const handleCanvasEmptyRelease = /* @__PURE__ */ __name((e) => { - const detail = e.detail; - const shiftPressed = detail.originalEvent.shiftKey; - const action = shiftPressed ? linkReleaseActionShift.value : linkReleaseAction.value; - switch (action) { - case LinkReleaseTriggerAction.SEARCH_BOX: - showSearchBox(e); - break; - case LinkReleaseTriggerAction.CONTEXT_MENU: - showContextMenu(e); - break; - case LinkReleaseTriggerAction.NO_ACTION: - default: - break; - } - }, "handleCanvasEmptyRelease"); - useEventListener(document, "litegraph:canvas", canvasEventHandler); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div", null, [ - createVNode(unref(script$p), { - visible: unref(visible), - "onUpdate:visible": _cache[0] || (_cache[0] = ($event) => isRef(visible) ? visible.value = $event : null), - modal: "", - "dismissable-mask": dismissable.value, - onHide: clearFilters, - pt: { - root: { - class: "invisible-dialog-root", - role: "search" - }, - mask: { class: "node-search-box-dialog-mask" }, - transition: { - enterFromClass: "opacity-0 scale-75", - // 100ms is the duration of the transition in the dialog component - enterActiveClass: "transition-all duration-100 ease-out", - leaveActiveClass: "transition-all duration-100 ease-in", - leaveToClass: "opacity-0 scale-75" - } - } - }, { - container: withCtx(() => [ - createVNode(_sfc_main$h, { - filters: nodeFilters.value, - onAddFilter: addFilter, - onRemoveFilter: removeFilter, - onAddNode: addNode - }, null, 8, ["filters"]) - ]), - _: 1 - }, 8, ["visible", "dismissable-mask"]) - ]); - }; - } -}); -var theme$4 = /* @__PURE__ */ __name(function theme4(_ref) { - var dt = _ref.dt; - return "\n.p-overlaybadge {\n position: relative;\n}\n\n.p-overlaybadge .p-badge {\n position: absolute;\n top: 0;\n right: 0;\n transform: translate(50%, -50%);\n transform-origin: 100% 0;\n margin: 0;\n outline-width: ".concat(dt("overlaybadge.outline.width"), ";\n outline-style: solid;\n outline-color: ").concat(dt("overlaybadge.outline.color"), ";\n}\n"); -}, "theme"); -var classes$4 = { - root: "p-overlaybadge" -}; -var OverlayBadgeStyle = BaseStyle.extend({ - name: "overlaybadge", - theme: theme$4, - classes: classes$4 -}); -var script$1$4 = { - name: "OverlayBadge", - "extends": script$q, - style: OverlayBadgeStyle, - provide: /* @__PURE__ */ __name(function provide8() { - return { - $pcOverlayBadge: this, - $parentInstance: this - }; - }, "provide") -}; -var script$6 = { - name: "OverlayBadge", - "extends": script$1$4, - inheritAttrs: false, - components: { - Badge: script$q - } -}; -function render$b(_ctx, _cache, $props, $setup, $data, $options) { - var _component_Badge = resolveComponent("Badge"); - return openBlock(), createElementBlock("div", mergeProps({ - "class": _ctx.cx("root") - }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default"), createVNode(_component_Badge, mergeProps(_ctx.$props, { - pt: _ctx.ptm("pcBadge") - }), null, 16, ["pt"])], 16); -} -__name(render$b, "render$b"); -script$6.render = render$b; -const _sfc_main$f = /* @__PURE__ */ defineComponent({ - __name: "SidebarIcon", - props: { - icon: String, - selected: Boolean, - tooltip: { - type: String, - default: "" - }, - class: { - type: String, - default: "" - }, - iconBadge: { - type: [String, Function], - default: "" - } - }, - emits: ["click"], - setup(__props, { emit: __emit }) { - const props = __props; - const emit = __emit; - const overlayValue = computed( - () => typeof props.iconBadge === "function" ? props.iconBadge() || "" : props.iconBadge - ); - const shouldShowBadge = computed(() => !!overlayValue.value); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return withDirectives((openBlock(), createBlock(unref(script$d), { - class: normalizeClass(props.class), - text: "", - pt: { - root: { - class: `side-bar-button ${props.selected ? "p-button-primary side-bar-button-selected" : "p-button-secondary"}`, - "aria-label": props.tooltip - } - }, - onClick: _cache[0] || (_cache[0] = ($event) => emit("click", $event)) - }, { - icon: withCtx(() => [ - shouldShowBadge.value ? (openBlock(), createBlock(unref(script$6), { - key: 0, - value: overlayValue.value - }, { - default: withCtx(() => [ - createBaseVNode("i", { - class: normalizeClass(props.icon + " side-bar-button-icon") - }, null, 2) - ]), - _: 1 - }, 8, ["value"])) : (openBlock(), createElementBlock("i", { - key: 1, - class: normalizeClass(props.icon + " side-bar-button-icon") - }, null, 2)) - ]), - _: 1 - }, 8, ["class", "pt"])), [ - [_directive_tooltip, { value: props.tooltip, showDelay: 300, hideDelay: 300 }] - ]); - }; - } -}); -const SidebarIcon = /* @__PURE__ */ _export_sfc(_sfc_main$f, [["__scopeId", "data-v-6ab4daa6"]]); -const _sfc_main$e = /* @__PURE__ */ defineComponent({ - __name: "SidebarLogoutIcon", - setup(__props) { - const { t } = useI18n(); - const userStore = useUserStore(); - const tooltip = computed( - () => `${t("sideToolbar.logout")} (${userStore.currentUser?.username})` - ); - const logout = /* @__PURE__ */ __name(() => { - userStore.logout(); - window.location.reload(); - }, "logout"); - return (_ctx, _cache) => { - return openBlock(), createBlock(SidebarIcon, { - icon: "pi pi-sign-out", - tooltip: tooltip.value, - onClick: logout - }, null, 8, ["tooltip"]); - }; - } -}); -const _sfc_main$d = /* @__PURE__ */ defineComponent({ - __name: "SidebarSettingsToggleIcon", - setup(__props) { - const dialogStore = useDialogStore(); - const showSetting = /* @__PURE__ */ __name(() => { - dialogStore.showDialog({ - key: "global-settings", - headerComponent: SettingDialogHeader, - component: SettingDialogContent - }); - }, "showSetting"); - return (_ctx, _cache) => { - return openBlock(), createBlock(SidebarIcon, { - icon: "pi pi-cog", - class: "comfy-settings-btn", - onClick: showSetting, - tooltip: _ctx.$t("g.settings") - }, null, 8, ["tooltip"]); - }; - } -}); -const _sfc_main$c = /* @__PURE__ */ defineComponent({ - __name: "SidebarThemeToggleIcon", - setup(__props) { - const settingStore = useSettingStore(); - const currentTheme = computed(() => settingStore.get("Comfy.ColorPalette")); - const icon = computed( - () => currentTheme.value !== "light" ? "pi pi-moon" : "pi pi-sun" - ); - const commandStore = useCommandStore(); - const toggleTheme = /* @__PURE__ */ __name(() => { - commandStore.execute("Comfy.ToggleTheme"); - }, "toggleTheme"); - return (_ctx, _cache) => { - return openBlock(), createBlock(SidebarIcon, { - icon: icon.value, - onClick: toggleTheme, - tooltip: _ctx.$t("sideToolbar.themeToggle"), - class: "comfy-vue-theme-toggle" - }, null, 8, ["icon", "tooltip"]); - }; - } -}); -const _withScopeId$4 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-37d8d7b4"), n = n(), popScopeId(), n), "_withScopeId$4"); -const _hoisted_1$e = { class: "side-tool-bar-end" }; -const _hoisted_2$b = { - key: 0, - class: "sidebar-content-container h-full overflow-y-auto overflow-x-hidden" -}; -const _sfc_main$b = /* @__PURE__ */ defineComponent({ - __name: "SideToolbar", - setup(__props) { - const workspaceStore = useWorkspaceStore(); - const settingStore = useSettingStore(); - const userStore = useUserStore(); - const teleportTarget = computed( - () => settingStore.get("Comfy.Sidebar.Location") === "left" ? ".comfyui-body-left" : ".comfyui-body-right" - ); - const isSmall = computed( - () => settingStore.get("Comfy.Sidebar.Size") === "small" - ); - const tabs = computed(() => workspaceStore.getSidebarTabs()); - const selectedTab = computed(() => workspaceStore.sidebarTab.activeSidebarTab); - const onTabClick = /* @__PURE__ */ __name((item3) => { - workspaceStore.sidebarTab.toggleSidebarTab(item3.id); - }, "onTabClick"); - const keybindingStore = useKeybindingStore(); - const getTabTooltipSuffix = /* @__PURE__ */ __name((tab) => { - const keybinding = keybindingStore.getKeybindingByCommandId( - `Workspace.ToggleSidebarTab.${tab.id}` - ); - return keybinding ? ` (${keybinding.combo.toString()})` : ""; - }, "getTabTooltipSuffix"); - return (_ctx, _cache) => { - return openBlock(), createElementBlock(Fragment, null, [ - (openBlock(), createBlock(Teleport, { to: teleportTarget.value }, [ - createBaseVNode("nav", { - class: normalizeClass("side-tool-bar-container" + (isSmall.value ? " small-sidebar" : "")) - }, [ - (openBlock(true), createElementBlock(Fragment, null, renderList(tabs.value, (tab) => { - return openBlock(), createBlock(SidebarIcon, { - key: tab.id, - icon: tab.icon, - iconBadge: tab.iconBadge, - tooltip: tab.tooltip + getTabTooltipSuffix(tab), - selected: tab.id === selectedTab.value?.id, - class: normalizeClass(tab.id + "-tab-button"), - onClick: /* @__PURE__ */ __name(($event) => onTabClick(tab), "onClick") - }, null, 8, ["icon", "iconBadge", "tooltip", "selected", "class", "onClick"]); - }), 128)), - createBaseVNode("div", _hoisted_1$e, [ - unref(userStore).isMultiUserServer ? (openBlock(), createBlock(_sfc_main$e, { key: 0 })) : createCommentVNode("", true), - createVNode(_sfc_main$c), - createVNode(_sfc_main$d) - ]) - ], 2) - ], 8, ["to"])), - selectedTab.value ? (openBlock(), createElementBlock("div", _hoisted_2$b, [ - createVNode(_sfc_main$p, { extension: selectedTab.value }, null, 8, ["extension"]) - ])) : createCommentVNode("", true) - ], 64); - }; - } -}); -const SideToolbar = /* @__PURE__ */ _export_sfc(_sfc_main$b, [["__scopeId", "data-v-37d8d7b4"]]); -const CORE_SETTINGS = [ - { - id: "Comfy.Validation.Workflows", - name: "Validate workflows", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.NodeSearchBoxImpl", - category: ["Comfy", "Node Search Box", "Implementation"], - experimental: true, - name: "Node search box implementation", - type: "combo", - options: ["default", "litegraph (legacy)"], - defaultValue: "default" - }, - { - id: "Comfy.LinkRelease.Action", - category: ["LiteGraph", "LinkRelease", "Action"], - name: "Action on link release (No modifier)", - type: "combo", - options: Object.values(LinkReleaseTriggerAction), - defaultValue: LinkReleaseTriggerAction.CONTEXT_MENU - }, - { - id: "Comfy.LinkRelease.ActionShift", - category: ["LiteGraph", "LinkRelease", "ActionShift"], - name: "Action on link release (Shift)", - type: "combo", - options: Object.values(LinkReleaseTriggerAction), - defaultValue: LinkReleaseTriggerAction.SEARCH_BOX - }, - { - id: "Comfy.NodeSearchBoxImpl.NodePreview", - category: ["Comfy", "Node Search Box", "NodePreview"], - name: "Node preview", - tooltip: "Only applies to the default implementation", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.NodeSearchBoxImpl.ShowCategory", - category: ["Comfy", "Node Search Box", "ShowCategory"], - name: "Show node category in search results", - tooltip: "Only applies to the default implementation", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.NodeSearchBoxImpl.ShowIdName", - category: ["Comfy", "Node Search Box", "ShowIdName"], - name: "Show node id name in search results", - tooltip: "Only applies to the default implementation", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.NodeSearchBoxImpl.ShowNodeFrequency", - category: ["Comfy", "Node Search Box", "ShowNodeFrequency"], - name: "Show node frequency in search results", - tooltip: "Only applies to the default implementation", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.Sidebar.Location", - category: ["Appearance", "Sidebar", "Location"], - name: "Sidebar location", - type: "combo", - options: ["left", "right"], - defaultValue: "left" - }, - { - id: "Comfy.Sidebar.Size", - category: ["Appearance", "Sidebar", "Size"], - name: "Sidebar size", - type: "combo", - options: ["normal", "small"], - defaultValue: /* @__PURE__ */ __name(() => window.innerWidth < 1600 ? "small" : "normal", "defaultValue") - }, - { - id: "Comfy.TextareaWidget.FontSize", - category: ["Appearance", "Node Widget", "TextareaWidget", "FontSize"], - name: "Textarea widget font size", - type: "slider", - defaultValue: 10, - attrs: { - min: 8, - max: 24 - } - }, - { - id: "Comfy.TextareaWidget.Spellcheck", - category: ["Comfy", "Node Widget", "TextareaWidget", "Spellcheck"], - name: "Textarea widget spellcheck", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.Workflow.SortNodeIdOnSave", - name: "Sort node IDs when saving workflow", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.Graph.CanvasInfo", - category: ["LiteGraph", "Canvas", "CanvasInfo"], - name: "Show canvas info on bottom left corner (fps, etc.)", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Node.ShowDeprecated", - name: "Show deprecated nodes in search", - tooltip: "Deprecated nodes are hidden by default in the UI, but remain functional in existing workflows that use them.", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.Node.ShowExperimental", - name: "Show experimental nodes in search", - tooltip: "Experimental nodes are marked as such in the UI and may be subject to significant changes or removal in future versions. Use with caution in production workflows", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Node.Opacity", - category: ["Appearance", "Node", "Opacity"], - name: "Node opacity", - type: "slider", - defaultValue: 1, - attrs: { - min: 0.01, - max: 1, - step: 0.01 - } - }, - { - id: "Comfy.Workflow.ShowMissingNodesWarning", - name: "Show missing nodes warning", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Workflow.ShowMissingModelsWarning", - name: "Show missing models warning", - type: "boolean", - defaultValue: false, - experimental: true - }, - { - id: "Comfy.Graph.ZoomSpeed", - category: ["LiteGraph", "Canvas", "ZoomSpeed"], - name: "Canvas zoom speed", - type: "slider", - defaultValue: 1.1, - attrs: { - min: 1.01, - max: 2.5, - step: 0.01 - } - }, - // Bookmarks are stored in the settings store. - // Bookmarks are in format of category/display_name. e.g. "conditioning/CLIPTextEncode" - { - id: "Comfy.NodeLibrary.Bookmarks", - name: "Node library bookmarks with display name (deprecated)", - type: "hidden", - defaultValue: [], - deprecated: true - }, - { - id: "Comfy.NodeLibrary.Bookmarks.V2", - name: "Node library bookmarks v2 with unique name", - type: "hidden", - defaultValue: [] - }, - // Stores mapping from bookmark folder name to its customization. - { - id: "Comfy.NodeLibrary.BookmarksCustomization", - name: "Node library bookmarks customization", - type: "hidden", - defaultValue: {} - }, - // Hidden setting used by the queue for how to fit images - { - id: "Comfy.Queue.ImageFit", - name: "Queue image fit", - type: "hidden", - defaultValue: "cover" - }, - { - id: "Comfy.GroupSelectedNodes.Padding", - category: ["LiteGraph", "Group", "Padding"], - name: "Group selected nodes padding", - type: "slider", - defaultValue: 10, - attrs: { - min: 0, - max: 100 - } - }, - { - id: "Comfy.Node.DoubleClickTitleToEdit", - category: ["LiteGraph", "Node", "DoubleClickTitleToEdit"], - name: "Double click node title to edit", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Group.DoubleClickTitleToEdit", - category: ["LiteGraph", "Group", "DoubleClickTitleToEdit"], - name: "Double click group title to edit", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Window.UnloadConfirmation", - name: "Show confirmation when closing window", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.TreeExplorer.ItemPadding", - category: ["Appearance", "Tree Explorer", "ItemPadding"], - name: "Tree explorer item padding", - type: "slider", - defaultValue: 2, - attrs: { - min: 0, - max: 8, - step: 1 - } - }, - { - id: "Comfy.ModelLibrary.AutoLoadAll", - name: "Automatically load all model folders", - tooltip: "If true, all folders will load as soon as you open the model library (this may cause delays while it loads). If false, root level model folders will only load once you click on them.", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.ModelLibrary.NameFormat", - name: "What name to display in the model library tree view", - tooltip: 'Select "filename" to render a simplified view of the raw filename (without directory or ".safetensors" extension) in the model list. Select "title" to display the configurable model metadata title.', - type: "combo", - options: ["filename", "title"], - defaultValue: "title" - }, - { - id: "Comfy.Locale", - name: "Language", - type: "combo", - options: [ - { value: "en", text: "English" }, - { value: "zh", text: "中文" }, - { value: "ru", text: "Русский" }, - { value: "ja", text: "日本語" }, - { value: "ko", text: "한국어" }, - { value: "fr", text: "Français" } - ], - defaultValue: /* @__PURE__ */ __name(() => navigator.language.split("-")[0] || "en", "defaultValue") - }, - { - id: "Comfy.NodeBadge.NodeSourceBadgeMode", - category: ["LiteGraph", "Node", "NodeSourceBadgeMode"], - name: "Node source badge mode", - type: "combo", - options: Object.values(NodeBadgeMode), - defaultValue: NodeBadgeMode.HideBuiltIn - }, - { - id: "Comfy.NodeBadge.NodeIdBadgeMode", - category: ["LiteGraph", "Node", "NodeIdBadgeMode"], - name: "Node ID badge mode", - type: "combo", - options: [NodeBadgeMode.None, NodeBadgeMode.ShowAll], - defaultValue: NodeBadgeMode.ShowAll - }, - { - id: "Comfy.NodeBadge.NodeLifeCycleBadgeMode", - category: ["LiteGraph", "Node", "NodeLifeCycleBadgeMode"], - name: "Node life cycle badge mode", - type: "combo", - options: [NodeBadgeMode.None, NodeBadgeMode.ShowAll], - defaultValue: NodeBadgeMode.ShowAll - }, - { - id: "Comfy.ConfirmClear", - category: ["Comfy", "Workflow", "ConfirmClear"], - name: "Require confirmation when clearing workflow", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.PromptFilename", - category: ["Comfy", "Workflow", "PromptFilename"], - name: "Prompt for filename when saving workflow", - type: "boolean", - defaultValue: true - }, - /** - * file format for preview - * - * format;quality - * - * ex) - * webp;50 -> webp, quality 50 - * jpeg;80 -> rgb, jpeg, quality 80 - * - * @type {string} - */ - { - id: "Comfy.PreviewFormat", - category: ["LiteGraph", "Node Widget", "PreviewFormat"], - name: "Preview image format", - tooltip: "When displaying a preview in the image widget, convert it to a lightweight image, e.g. webp, jpeg, webp;50, etc.", - type: "text", - defaultValue: "" - }, - { - id: "Comfy.DisableSliders", - category: ["LiteGraph", "Node Widget", "DisableSliders"], - name: "Disable node widget sliders", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.DisableFloatRounding", - category: ["LiteGraph", "Node Widget", "DisableFloatRounding"], - name: "Disable default float widget rounding.", - tooltip: "(requires page reload) Cannot disable round when round is set by the node in the backend.", - type: "boolean", - defaultValue: false - }, - { - id: "Comfy.FloatRoundingPrecision", - category: ["LiteGraph", "Node Widget", "FloatRoundingPrecision"], - name: "Float widget rounding decimal places [0 = auto].", - tooltip: "(requires page reload)", - type: "slider", - attrs: { - min: 0, - max: 6, - step: 1 - }, - defaultValue: 0 - }, - { - id: "Comfy.EnableTooltips", - category: ["LiteGraph", "Node", "EnableTooltips"], - name: "Enable Tooltips", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.DevMode", - name: "Enable dev mode options (API save, etc.)", - type: "boolean", - defaultValue: false, - onChange: /* @__PURE__ */ __name((value) => { - const element = document.getElementById("comfy-dev-save-api-button"); - if (element) { - element.style.display = value ? "flex" : "none"; - } - }, "onChange") - }, - { - id: "Comfy.UseNewMenu", - category: ["Comfy", "Menu", "UseNewMenu"], - defaultValue: "Top", - name: "Use new menu", - type: "combo", - options: ["Disabled", "Top", "Bottom"], - migrateDeprecatedValue: /* @__PURE__ */ __name((value) => { - if (value === "Floating") { - return "Top"; - } - return value; - }, "migrateDeprecatedValue") - }, - { - id: "Comfy.Workflow.WorkflowTabsPosition", - name: "Opened workflows position", - type: "combo", - options: ["Sidebar", "Topbar"], - defaultValue: "Topbar" - }, - { - id: "Comfy.Graph.CanvasMenu", - category: ["LiteGraph", "Canvas", "CanvasMenu"], - name: "Show graph canvas menu", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.QueueButton.BatchCountLimit", - name: "Batch count limit", - tooltip: "The maximum number of tasks added to the queue at one button click", - type: "number", - defaultValue: 100, - versionAdded: "1.3.5" - }, - { - id: "Comfy.Keybinding.UnsetBindings", - name: "Keybindings unset by the user", - type: "hidden", - defaultValue: [], - versionAdded: "1.3.7" - }, - { - id: "Comfy.Keybinding.NewBindings", - name: "Keybindings set by the user", - type: "hidden", - defaultValue: [], - versionAdded: "1.3.7" - }, - { - id: "Comfy.Extension.Disabled", - name: "Disabled extension names", - type: "hidden", - defaultValue: [], - versionAdded: "1.3.11" - }, - { - id: "Comfy.Validation.NodeDefs", - name: "Validate node definitions (slow)", - type: "boolean", - tooltip: "Recommended for node developers. This will validate all node definitions on startup.", - defaultValue: false, - versionAdded: "1.3.14" - }, - { - id: "Comfy.LinkRenderMode", - category: ["LiteGraph", "Graph", "LinkRenderMode"], - name: "Link Render Mode", - defaultValue: 2, - type: "combo", - options: [ - { value: LiteGraph.STRAIGHT_LINK, text: "Straight" }, - { value: LiteGraph.LINEAR_LINK, text: "Linear" }, - { value: LiteGraph.SPLINE_LINK, text: "Spline" }, - { value: LiteGraph.HIDDEN_LINK, text: "Hidden" } - ] - }, - { - id: "Comfy.Node.AutoSnapLinkToSlot", - category: ["LiteGraph", "Node", "AutoSnapLinkToSlot"], - name: "Auto snap link to node slot", - tooltip: "When dragging a link over a node, the link automatically snap to a viable input slot on the node", - type: "boolean", - defaultValue: true, - versionAdded: "1.3.29" - }, - { - id: "Comfy.Node.SnapHighlightsNode", - category: ["LiteGraph", "Node", "SnapHighlightsNode"], - name: "Snap highlights node", - tooltip: "When dragging a link over a node with viable input slot, highlight the node", - type: "boolean", - defaultValue: true, - versionAdded: "1.3.29" - }, - { - id: "Comfy.Node.BypassAllLinksOnDelete", - category: ["LiteGraph", "Node", "BypassAllLinksOnDelete"], - name: "Keep all links when deleting nodes", - tooltip: "When deleting a node, attempt to reconnect all of its input and output links (bypassing the deleted node)", - type: "boolean", - defaultValue: true, - versionAdded: "1.3.40" - }, - { - id: "Comfy.Node.MiddleClickRerouteNode", - category: ["LiteGraph", "Node", "MiddleClickRerouteNode"], - name: "Middle-click creates a new Reroute node", - type: "boolean", - defaultValue: true, - versionAdded: "1.3.42" - }, - { - id: "Comfy.RerouteBeta", - category: ["LiteGraph", "RerouteBeta"], - name: "Opt-in to the reroute beta test", - tooltip: "Enables the new native reroutes.\n\nReroutes can be added by holding alt and dragging from a link line, or on the link menu.\n\nDisabling this option is non-destructive - reroutes are hidden.", - experimental: true, - type: "boolean", - defaultValue: false, - versionAdded: "1.3.42" - }, - { - id: "Comfy.Graph.LinkMarkers", - category: ["LiteGraph", "Link", "LinkMarkers"], - name: "Link midpoint markers", - defaultValue: LinkMarkerShape.Circle, - type: "combo", - options: [ - { value: LinkMarkerShape.None, text: "None" }, - { value: LinkMarkerShape.Circle, text: "Circle" }, - { value: LinkMarkerShape.Arrow, text: "Arrow" } - ], - versionAdded: "1.3.42" - }, - { - id: "Comfy.DOMClippingEnabled", - category: ["LiteGraph", "Node", "DOMClippingEnabled"], - name: "Enable DOM element clipping (enabling may reduce performance)", - type: "boolean", - defaultValue: true - }, - { - id: "Comfy.Graph.CtrlShiftZoom", - category: ["LiteGraph", "Canvas", "CtrlShiftZoom"], - name: "Enable fast-zoom shortcut (Ctrl + Shift + Drag)", - type: "boolean", - defaultValue: true, - versionAdded: "1.4.0" - }, - { - id: "Comfy.Pointer.ClickDrift", - category: ["LiteGraph", "Pointer", "ClickDrift"], - name: "Pointer click drift (maximum distance)", - tooltip: "If the pointer moves more than this distance while holding a button down, it is considered dragging (rather than clicking).\n\nHelps prevent objects from being unintentionally nudged if the pointer is moved whilst clicking.", - experimental: true, - type: "slider", - attrs: { - min: 0, - max: 20, - step: 1 - }, - defaultValue: 6, - versionAdded: "1.4.3" - }, - { - id: "Comfy.Pointer.ClickBufferTime", - category: ["LiteGraph", "Pointer", "ClickBufferTime"], - name: "Pointer click drift delay", - tooltip: "After pressing a pointer button down, this is the maximum time (in milliseconds) that pointer movement can be ignored for.\n\nHelps prevent objects from being unintentionally nudged if the pointer is moved whilst clicking.", - experimental: true, - type: "slider", - attrs: { - min: 0, - max: 1e3, - step: 25 - }, - defaultValue: 150, - versionAdded: "1.4.3" - }, - { - id: "Comfy.Pointer.DoubleClickTime", - category: ["LiteGraph", "Pointer", "DoubleClickTime"], - name: "Double click interval (maximum)", - tooltip: "The maximum time in milliseconds between the two clicks of a double-click. Increasing this value may assist if double-clicks are sometimes not registered.", - type: "slider", - attrs: { - min: 100, - max: 1e3, - step: 50 - }, - defaultValue: 300, - versionAdded: "1.4.3" - }, - { - id: "Comfy.SnapToGrid.GridSize", - category: ["LiteGraph", "Canvas", "GridSize"], - name: "Snap to grid size", - type: "slider", - attrs: { - min: 1, - max: 500 - }, - tooltip: "When dragging and resizing nodes while holding shift they will be aligned to the grid, this controls the size of that grid.", - defaultValue: LiteGraph.CANVAS_GRID_SIZE - }, - // Keep the 'pysssss.SnapToGrid' setting id so we don't need to migrate setting values. - // Using a new setting id can cause existing users to lose their existing settings. - { - id: "pysssss.SnapToGrid", - category: ["LiteGraph", "Canvas", "AlwaysSnapToGrid"], - name: "Always snap to grid", - type: "boolean", - defaultValue: false, - versionAdded: "1.3.13" - }, - { - id: "Comfy.Server.ServerConfigValues", - name: "Server config values for frontend display", - tooltip: "Server config values used for frontend display only", - type: "hidden", - // Mapping from server config id to value. - defaultValue: {}, - versionAdded: "1.4.8" - }, - { - id: "Comfy.Server.LaunchArgs", - name: "Server launch arguments", - tooltip: "These are the actual arguments that are passed to the server when it is launched.", - type: "hidden", - defaultValue: {}, - versionAdded: "1.4.8" - }, - { - id: "Comfy.Queue.MaxHistoryItems", - name: "Queue history size", - tooltip: "The maximum number of tasks that show in the queue history.", - type: "slider", - attrs: { - min: 16, - max: 256, - step: 16 - }, - defaultValue: 64, - versionAdded: "1.4.12" - }, - { - id: "LiteGraph.Canvas.MaximumFps", - name: "Maxium FPS", - tooltip: "The maximum frames per second that the canvas is allowed to render. Caps GPU usage at the cost of smoothness. If 0, the screen refresh rate is used. Default: 0", - type: "slider", - attrs: { - min: 0, - max: 120 - }, - defaultValue: 0, - versionAdded: "1.5.1" - }, - { - id: "Comfy.EnableWorkflowViewRestore", - category: ["Comfy", "Workflow", "EnableWorkflowViewRestore"], - name: "Save and restore canvas position and zoom level in workflows", - type: "boolean", - defaultValue: true, - versionModified: "1.5.4" - }, - { - id: "Comfy.Workflow.ConfirmDelete", - name: "Show confirmation when deleting workflows", - type: "boolean", - defaultValue: true, - versionAdded: "1.5.6" - }, - { - id: "Comfy.ColorPalette", - name: "The active color palette id", - type: "hidden", - defaultValue: "dark", - versionModified: "1.6.7", - migrateDeprecatedValue(value) { - return value.startsWith("custom_") ? value.replace("custom_", "") : value; - } - }, - { - id: "Comfy.CustomColorPalettes", - name: "Custom color palettes", - type: "hidden", - defaultValue: {}, - versionModified: "1.6.7" - }, - { - id: "Comfy.WidgetControlMode", - category: ["Comfy", "Node Widget", "WidgetControlMode"], - name: "Widget control mode", - tooltip: "Controls when widget values are updated (randomize/increment/decrement), either before the prompt is queued or after.", - type: "combo", - defaultValue: "after", - options: ["before", "after"], - versionModified: "1.6.10" - } -]; -const _sfc_main$a = /* @__PURE__ */ defineComponent({ - __name: "GraphCanvas", - emits: ["ready"], - setup(__props, { emit: __emit }) { - const emit = __emit; - const canvasRef = ref(null); - const litegraphService = useLitegraphService(); - const settingStore = useSettingStore(); - const nodeDefStore = useNodeDefStore(); - const workspaceStore = useWorkspaceStore(); - const canvasStore = useCanvasStore(); - const modelToNodeStore = useModelToNodeStore(); - const betaMenuEnabled = computed( - () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" - ); - const canvasMenuEnabled = computed( - () => settingStore.get("Comfy.Graph.CanvasMenu") - ); - const tooltipEnabled = computed(() => settingStore.get("Comfy.EnableTooltips")); - watchEffect(() => { - const canvasInfoEnabled = settingStore.get("Comfy.Graph.CanvasInfo"); - if (canvasStore.canvas) { - canvasStore.canvas.show_info = canvasInfoEnabled; - } - }); - watchEffect(() => { - const zoomSpeed = settingStore.get("Comfy.Graph.ZoomSpeed"); - if (canvasStore.canvas) { - canvasStore.canvas.zoom_speed = zoomSpeed; - } - }); - watchEffect(() => { - LiteGraph.snaps_for_comfy = settingStore.get("Comfy.Node.AutoSnapLinkToSlot"); - }); - watchEffect(() => { - LiteGraph.snap_highlights_node = settingStore.get( - "Comfy.Node.SnapHighlightsNode" - ); - }); - watchEffect(() => { - LGraphNode.keepAllLinksOnBypass = settingStore.get( - "Comfy.Node.BypassAllLinksOnDelete" - ); - }); - watchEffect(() => { - LiteGraph.middle_click_slot_add_default_node = settingStore.get( - "Comfy.Node.MiddleClickRerouteNode" - ); - }); - watchEffect(() => { - nodeDefStore.showDeprecated = settingStore.get("Comfy.Node.ShowDeprecated"); - }); - watchEffect(() => { - nodeDefStore.showExperimental = settingStore.get( - "Comfy.Node.ShowExperimental" - ); - }); - watchEffect(() => { - const spellcheckEnabled = settingStore.get("Comfy.TextareaWidget.Spellcheck"); - const textareas = document.querySelectorAll("textarea.comfy-multiline-input"); - textareas.forEach((textarea) => { - textarea.spellcheck = spellcheckEnabled; - textarea.focus(); - textarea.blur(); - }); - }); - watchEffect(() => { - const linkRenderMode = settingStore.get("Comfy.LinkRenderMode"); - if (canvasStore.canvas) { - canvasStore.canvas.links_render_mode = linkRenderMode; - canvasStore.canvas.setDirty( - /* fg */ - false, - /* bg */ - true - ); - } - }); - watchEffect(() => { - const linkMarkerShape = settingStore.get("Comfy.Graph.LinkMarkers"); - const { canvas } = canvasStore; - if (canvas) { - canvas.linkMarkerShape = linkMarkerShape; - canvas.setDirty(false, true); - } - }); - watchEffect(() => { - const reroutesEnabled = settingStore.get("Comfy.RerouteBeta"); - const { canvas } = canvasStore; - if (canvas) { - canvas.reroutesEnabled = reroutesEnabled; - canvas.setDirty(false, true); - } - }); - watchEffect(() => { - const maximumFps = settingStore.get("LiteGraph.Canvas.MaximumFps"); - const { canvas } = canvasStore; - if (canvas) canvas.maximumFps = maximumFps; - }); - watchEffect(() => { - CanvasPointer.doubleClickTime = settingStore.get( - "Comfy.Pointer.DoubleClickTime" - ); - }); - watchEffect(() => { - CanvasPointer.bufferTime = settingStore.get("Comfy.Pointer.ClickBufferTime"); - }); - watchEffect(() => { - CanvasPointer.maxClickDrift = settingStore.get("Comfy.Pointer.ClickDrift"); - }); - watchEffect(() => { - LiteGraph.CANVAS_GRID_SIZE = settingStore.get("Comfy.SnapToGrid.GridSize"); - }); - watchEffect(() => { - LiteGraph.alwaysSnapToGrid = settingStore.get("pysssss.SnapToGrid"); - }); - watch( - () => settingStore.get("Comfy.WidgetControlMode"), - () => { - if (!canvasStore.canvas) return; - for (const n of app.graph.nodes) { - if (!n.widgets) continue; - for (const w of n.widgets) { - if (w[IS_CONTROL_WIDGET]) { - updateControlWidgetLabel(w); - if (w.linkedWidgets) { - for (const l of w.linkedWidgets) { - updateControlWidgetLabel(l); - } - } - } - } - } - app.graph.setDirtyCanvas(true); - } - ); - const colorPaletteService = useColorPaletteService(); - const colorPaletteStore = useColorPaletteStore(); - watch( - [() => canvasStore.canvas, () => settingStore.get("Comfy.ColorPalette")], - ([canvas, currentPaletteId]) => { - if (!canvas) return; - colorPaletteService.loadColorPalette(currentPaletteId); - } - ); - watch( - () => colorPaletteStore.activePaletteId, - (newValue) => { - settingStore.set("Comfy.ColorPalette", newValue); - } - ); - const workflowStore = useWorkflowStore(); - const persistCurrentWorkflow = /* @__PURE__ */ __name(() => { - const workflow = JSON.stringify(app.serializeGraph()); - localStorage.setItem("workflow", workflow); - if (api.clientId) { - sessionStorage.setItem(`workflow:${api.clientId}`, workflow); - } - }, "persistCurrentWorkflow"); - watchEffect(() => { - if (workflowStore.activeWorkflow) { - const workflow = workflowStore.activeWorkflow; - setStorageValue("Comfy.PreviousWorkflow", workflow.key); - persistCurrentWorkflow(); - } - }); - api.addEventListener("graphChanged", persistCurrentWorkflow); - usePragmaticDroppable(() => canvasRef.value, { - onDrop: /* @__PURE__ */ __name((event) => { - const loc = event.location.current.input; - const dndData = event.source.data; - if (dndData.type === "tree-explorer-node") { - const node = dndData.data; - if (node.data instanceof ComfyNodeDefImpl) { - const nodeDef = node.data; - const pos = app.clientPosToCanvasPos([ - loc.clientX - 20, - loc.clientY - ]); - litegraphService.addNodeOnGraph(nodeDef, { pos }); - } else if (node.data instanceof ComfyModelDef) { - const model = node.data; - const pos = app.clientPosToCanvasPos([loc.clientX, loc.clientY]); - const nodeAtPos = app.graph.getNodeOnPos(pos[0], pos[1]); - let targetProvider = null; - let targetGraphNode = null; - if (nodeAtPos) { - const providers = modelToNodeStore.getAllNodeProviders( - model.directory - ); - for (const provider of providers) { - if (provider.nodeDef.name === nodeAtPos.comfyClass) { - targetGraphNode = nodeAtPos; - targetProvider = provider; - } - } - } - if (!targetGraphNode) { - const provider = modelToNodeStore.getNodeProvider(model.directory); - if (provider) { - targetGraphNode = litegraphService.addNodeOnGraph( - provider.nodeDef, - { - pos - } - ); - targetProvider = provider; - } - } - if (targetGraphNode) { - const widget = targetGraphNode.widgets.find( - (widget2) => widget2.name === targetProvider.key - ); - if (widget) { - widget.value = model.file_name; - } - } - } - } - }, "onDrop") - }); - const comfyAppReady = ref(false); - onMounted(async () => { - window["LiteGraph"] = LiteGraph; - window["LGraph"] = LGraph; - window["LLink"] = LLink; - window["LGraphNode"] = LGraphNode; - window["LGraphGroup"] = LGraphGroup; - window["DragAndScale"] = DragAndScale; - window["LGraphCanvas"] = LGraphCanvas; - window["ContextMenu"] = ContextMenu; - window["LGraphBadge"] = LGraphBadge; - app.vueAppReady = true; - workspaceStore.spinner = true; - ChangeTracker.init(app); - await settingStore.loadSettingValues(); - CORE_SETTINGS.forEach((setting) => { - settingStore.addSetting(setting); - }); - await app.setup(canvasRef.value); - canvasStore.canvas = app.canvas; - canvasStore.canvas.render_canvas_border = false; - workspaceStore.spinner = false; - window["app"] = app; - window["graph"] = app.graph; - comfyAppReady.value = true; - colorPaletteStore.customPalettes = settingStore.get( - "Comfy.CustomColorPalettes" - ); - watch( - () => settingStore.get("Comfy.Locale"), - async () => { - await useCommandStore().execute("Comfy.RefreshNodeDefinitions"); - useWorkflowService().reloadCurrentWorkflow(); - } - ); - emit("ready"); - }); - return (_ctx, _cache) => { - return openBlock(), createElementBlock(Fragment, null, [ - (openBlock(), createBlock(Teleport, { to: ".graph-canvas-container" }, [ - comfyAppReady.value && betaMenuEnabled.value && !unref(workspaceStore).focusMode ? (openBlock(), createBlock(LiteGraphCanvasSplitterOverlay, { key: 0 }, { - "side-bar-panel": withCtx(() => [ - createVNode(SideToolbar) - ]), - "bottom-panel": withCtx(() => [ - createVNode(_sfc_main$o) - ]), - "graph-canvas-panel": withCtx(() => [ - canvasMenuEnabled.value ? (openBlock(), createBlock(GraphCanvasMenu, { key: 0 })) : createCommentVNode("", true) - ]), - _: 1 - })) : createCommentVNode("", true), - createVNode(TitleEditor), - !betaMenuEnabled.value && canvasMenuEnabled.value ? (openBlock(), createBlock(GraphCanvasMenu, { key: 1 })) : createCommentVNode("", true), - createBaseVNode("canvas", { - ref_key: "canvasRef", - ref: canvasRef, - id: "graph-canvas", - tabindex: "1" - }, null, 512) - ])), - createVNode(_sfc_main$g), - tooltipEnabled.value ? (openBlock(), createBlock(NodeTooltip, { key: 0 })) : createCommentVNode("", true), - createVNode(_sfc_main$m) - ], 64); - }; - } -}); -function _typeof$3(o) { - "@babel/helpers - typeof"; - return _typeof$3 = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { - return typeof o2; - } : function(o2) { - return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; - }, _typeof$3(o); -} -__name(_typeof$3, "_typeof$3"); -function _defineProperty$3(e, r, t) { - return (r = _toPropertyKey$3(r)) in e ? 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String : Number)(t); -} -__name(_toPrimitive$3, "_toPrimitive$3"); -var theme$3 = /* @__PURE__ */ __name(function theme5(_ref) { - var dt = _ref.dt; - return "\n.p-toast {\n width: ".concat(dt("toast.width"), ";\n white-space: pre-line;\n word-break: break-word;\n}\n\n.p-toast-message {\n margin: 0 0 1rem 0;\n}\n\n.p-toast-message-icon {\n flex-shrink: 0;\n font-size: ").concat(dt("toast.icon.size"), ";\n width: ").concat(dt("toast.icon.size"), ";\n height: ").concat(dt("toast.icon.size"), ";\n}\n\n.p-toast-message-content {\n display: flex;\n align-items: flex-start;\n padding: ").concat(dt("toast.content.padding"), ";\n gap: ").concat(dt("toast.content.gap"), ";\n}\n\n.p-toast-message-text {\n flex: 1 1 auto;\n display: flex;\n flex-direction: column;\n gap: ").concat(dt("toast.text.gap"), ";\n}\n\n.p-toast-summary {\n font-weight: ").concat(dt("toast.summary.font.weight"), ";\n font-size: ").concat(dt("toast.summary.font.size"), ";\n}\n\n.p-toast-detail {\n font-weight: 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none;\n}\n\n.p-toast-message-info,\n.p-toast-message-success,\n.p-toast-message-warn,\n.p-toast-message-error,\n.p-toast-message-secondary,\n.p-toast-message-contrast {\n border-width: ").concat(dt("toast.border.width"), ";\n border-style: solid;\n backdrop-filter: blur(").concat(dt("toast.blur"), ");\n border-radius: ").concat(dt("toast.border.radius"), ";\n}\n\n.p-toast-close-icon {\n font-size: ").concat(dt("toast.close.icon.size"), ";\n width: ").concat(dt("toast.close.icon.size"), ";\n height: ").concat(dt("toast.close.icon.size"), ";\n}\n\n.p-toast-close-button:focus-visible {\n outline-width: ").concat(dt("focus.ring.width"), ";\n outline-style: ").concat(dt("focus.ring.style"), ";\n outline-offset: ").concat(dt("focus.ring.offset"), ";\n}\n\n.p-toast-message-info {\n background: ").concat(dt("toast.info.background"), ";\n border-color: ").concat(dt("toast.info.border.color"), ";\n color: ").concat(dt("toast.info.color"), ";\n box-shadow: ").concat(dt("toast.info.shadow"), 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").concat(dt("toast.success.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-success .p-toast-close-button:hover {\n background: ").concat(dt("toast.success.close.button.hover.background"), ";\n}\n\n.p-toast-message-warn {\n background: ").concat(dt("toast.warn.background"), ";\n border-color: ").concat(dt("toast.warn.border.color"), ";\n color: ").concat(dt("toast.warn.color"), ";\n box-shadow: ").concat(dt("toast.warn.shadow"), ";\n}\n\n.p-toast-message-warn .p-toast-detail {\n color: ").concat(dt("toast.warn.detail.color"), ";\n}\n\n.p-toast-message-warn .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.warn.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.warn.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-warn .p-toast-close-button:hover {\n background: ").concat(dt("toast.warn.close.button.hover.background"), ";\n}\n\n.p-toast-message-error {\n background: ").concat(dt("toast.error.background"), ";\n border-color: ").concat(dt("toast.error.border.color"), ";\n color: ").concat(dt("toast.error.color"), ";\n box-shadow: ").concat(dt("toast.error.shadow"), ";\n}\n\n.p-toast-message-error .p-toast-detail {\n color: ").concat(dt("toast.error.detail.color"), ";\n}\n\n.p-toast-message-error .p-toast-close-button:focus-visible {\n outline-color: ").concat(dt("toast.error.close.button.focus.ring.color"), ";\n box-shadow: ").concat(dt("toast.error.close.button.focus.ring.shadow"), ";\n}\n\n.p-toast-message-error .p-toast-close-button:hover {\n background: ").concat(dt("toast.error.close.button.hover.background"), ";\n}\n\n.p-toast-message-secondary {\n background: ").concat(dt("toast.secondary.background"), ";\n border-color: ").concat(dt("toast.secondary.border.color"), ";\n color: ").concat(dt("toast.secondary.color"), ";\n box-shadow: ").concat(dt("toast.secondary.shadow"), ";\n}\n\n.p-toast-message-secondary .p-toast-detail {\n color: ").concat(dt("toast.secondary.detail.color"), 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position === "top-right" || position === "top-left" || position === "top-center" ? "20px" : position === "center" ? "50%" : null, - right: (position === "top-right" || position === "bottom-right") && "20px", - bottom: (position === "bottom-left" || position === "bottom-right" || position === "bottom-center") && "20px", - left: position === "top-left" || position === "bottom-left" ? "20px" : position === "center" || position === "top-center" || position === "bottom-center" ? 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-}, "theme"); -var inlineStyles$1 = { - submenu: /* @__PURE__ */ __name(function submenu(_ref2) { - var instance = _ref2.instance, processedItem = _ref2.processedItem; - return { - display: instance.isItemActive(processedItem) ? "flex" : "none" - }; - }, "submenu") -}; -var classes$2 = { - root: /* @__PURE__ */ __name(function root8(_ref3) { - _ref3.instance; - var props = _ref3.props; - return ["p-tieredmenu p-component", { - "p-tieredmenu-overlay": props.popup - }]; - }, "root"), - start: "p-tieredmenu-start", - rootList: "p-tieredmenu-root-list", - item: /* @__PURE__ */ __name(function item(_ref4) { - var instance = _ref4.instance, processedItem = _ref4.processedItem; - return ["p-tieredmenu-item", { - "p-tieredmenu-item-active": instance.isItemActive(processedItem), - "p-focus": instance.isItemFocused(processedItem), - "p-disabled": instance.isItemDisabled(processedItem) - }]; - }, "item"), - itemContent: "p-tieredmenu-item-content", - itemLink: "p-tieredmenu-item-link", - itemIcon: "p-tieredmenu-item-icon", - itemLabel: "p-tieredmenu-item-label", - submenuIcon: "p-tieredmenu-submenu-icon", - submenu: "p-tieredmenu-submenu", - separator: "p-tieredmenu-separator", - end: "p-tieredmenu-end" -}; -var TieredMenuStyle = BaseStyle.extend({ - name: "tieredmenu", - theme: theme$2, - classes: classes$2, - inlineStyles: inlineStyles$1 -}); -var script$2$1 = { - name: "BaseTieredMenu", - "extends": script$e, - props: { - popup: { - type: Boolean, - "default": false - }, - model: { - type: Array, - "default": null - }, - appendTo: { - type: [String, Object], - "default": "body" - }, - autoZIndex: { - type: Boolean, - "default": true - }, - baseZIndex: { - type: Number, - "default": 0 - }, - disabled: { - type: Boolean, - "default": false - }, - tabindex: { - type: Number, - "default": 0 - }, - ariaLabelledby: { - type: String, - "default": null - }, - ariaLabel: { - type: String, - "default": null - } - }, - style: TieredMenuStyle, - provide: /* @__PURE__ */ __name(function provide10() { - return { - $pcTieredMenu: this, - $parentInstance: this - }; - }, "provide") -}; -var script$1$2 = { - name: "TieredMenuSub", - hostName: "TieredMenu", - "extends": script$e, - emits: ["item-click", "item-mouseenter", "item-mousemove"], - container: null, - props: { - menuId: { - type: String, - "default": null - }, - focusedItemId: { - type: String, - "default": null - }, - items: { - type: Array, - "default": null - }, - visible: { - type: Boolean, - "default": false - }, - level: { - type: Number, - "default": 0 - }, - templates: { - type: Object, - "default": null - }, - activeItemPath: { - type: Object, - "default": null - }, - tabindex: { - type: Number, - "default": 0 - } - }, - methods: { - getItemId: /* @__PURE__ */ __name(function getItemId(processedItem) { - return "".concat(this.menuId, "_").concat(processedItem.key); - }, "getItemId"), - getItemKey: /* @__PURE__ */ __name(function getItemKey(processedItem) { - return this.getItemId(processedItem); - }, "getItemKey"), - getItemProp: /* @__PURE__ */ __name(function getItemProp(processedItem, name, params) { - return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : void 0; - }, "getItemProp"), - getItemLabel: /* @__PURE__ */ __name(function getItemLabel(processedItem) { - return this.getItemProp(processedItem, "label"); - }, "getItemLabel"), - getItemLabelId: /* @__PURE__ */ __name(function getItemLabelId(processedItem) { - return "".concat(this.menuId, "_").concat(processedItem.key, "_label"); - }, "getItemLabelId"), - getPTOptions: /* @__PURE__ */ __name(function getPTOptions4(processedItem, index, key) { - return this.ptm(key, { - context: { - item: processedItem.item, - index, - active: this.isItemActive(processedItem), - focused: this.isItemFocused(processedItem), - disabled: this.isItemDisabled(processedItem) - } - }); - }, "getPTOptions"), - isItemActive: /* @__PURE__ */ __name(function isItemActive(processedItem) { - return this.activeItemPath.some(function(path) { - return path.key === processedItem.key; - }); - }, "isItemActive"), - isItemVisible: /* @__PURE__ */ __name(function isItemVisible(processedItem) { - return this.getItemProp(processedItem, "visible") !== false; - }, "isItemVisible"), - isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled(processedItem) { - return this.getItemProp(processedItem, "disabled"); - }, "isItemDisabled"), - isItemFocused: /* @__PURE__ */ __name(function isItemFocused(processedItem) { - return this.focusedItemId === this.getItemId(processedItem); - }, "isItemFocused"), - isItemGroup: /* @__PURE__ */ __name(function isItemGroup(processedItem) { - return isNotEmpty(processedItem.items); - }, "isItemGroup"), - onEnter: /* @__PURE__ */ __name(function onEnter2() { - nestedPosition(this.container, this.level); - }, "onEnter"), - onItemClick: /* @__PURE__ */ __name(function onItemClick(event, processedItem) { - this.getItemProp(processedItem, "command", { - originalEvent: event, - item: processedItem.item - }); - this.$emit("item-click", { - originalEvent: event, - processedItem, - isFocus: true - }); - }, "onItemClick"), - onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter(event, processedItem) { - this.$emit("item-mouseenter", { - originalEvent: event, - processedItem - }); - }, "onItemMouseEnter"), - onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove(event, processedItem) { - this.$emit("item-mousemove", { - originalEvent: event, - processedItem - }); - }, "onItemMouseMove"), - getAriaSetSize: /* @__PURE__ */ __name(function getAriaSetSize() { - var _this = this; - return this.items.filter(function(processedItem) { - return _this.isItemVisible(processedItem) && !_this.getItemProp(processedItem, "separator"); - }).length; - }, "getAriaSetSize"), - getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset2(index) { - var _this2 = this; - return index - this.items.slice(0, index).filter(function(processedItem) { - return _this2.isItemVisible(processedItem) && _this2.getItemProp(processedItem, "separator"); - }).length + 1; - }, "getAriaPosInset"), - getMenuItemProps: /* @__PURE__ */ __name(function getMenuItemProps(processedItem, index) { - return { - action: mergeProps({ - "class": this.cx("itemLink"), - tabindex: -1, - "aria-hidden": true - }, this.getPTOptions(processedItem, index, "itemLink")), - icon: mergeProps({ - "class": [this.cx("itemIcon"), this.getItemProp(processedItem, "icon")] - }, this.getPTOptions(processedItem, index, "itemIcon")), - label: mergeProps({ - "class": this.cx("itemLabel") - }, this.getPTOptions(processedItem, index, "itemLabel")), - submenuicon: mergeProps({ - "class": this.cx("submenuIcon") - }, this.getPTOptions(processedItem, index, "submenuIcon")) - }; - }, "getMenuItemProps"), - containerRef: /* @__PURE__ */ __name(function containerRef(el) { - this.container = el; - }, "containerRef") - }, - components: { - AngleRightIcon: script$w - }, - directives: { - ripple: Ripple - } -}; -var _hoisted_1$1$2 = ["tabindex"]; -var _hoisted_2$6 = ["id", "aria-label", "aria-disabled", "aria-expanded", "aria-haspopup", "aria-level", "aria-setsize", "aria-posinset", "data-p-active", "data-p-focused", "data-p-disabled"]; -var _hoisted_3$5 = ["onClick", "onMouseenter", "onMousemove"]; -var _hoisted_4$1 = ["href", "target"]; -var _hoisted_5$1 = ["id"]; -var _hoisted_6 = ["id"]; -function render$1$1(_ctx, _cache, $props, $setup, $data, $options) { - var _component_AngleRightIcon = resolveComponent("AngleRightIcon"); - var _component_TieredMenuSub = resolveComponent("TieredMenuSub", true); - var _directive_ripple = resolveDirective("ripple"); - return openBlock(), createBlock(Transition, mergeProps({ - name: "p-tieredmenu", - onEnter: $options.onEnter - }, _ctx.ptm("menu.transition")), { - "default": withCtx(function() { - return [($props.level === 0 ? true : $props.visible) ? (openBlock(), createElementBlock("ul", mergeProps({ - key: 0, - ref: $options.containerRef, - "class": $props.level === 0 ? _ctx.cx("rootList") : _ctx.cx("submenu"), - tabindex: $props.tabindex - }, $props.level === 0 ? _ctx.ptm("rootList") : _ctx.ptm("submenu")), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function(processedItem, index) { - return openBlock(), createElementBlock(Fragment, { - key: $options.getItemKey(processedItem) - }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ - key: 0, - id: $options.getItemId(processedItem), - style: $options.getItemProp(processedItem, "style"), - "class": [_ctx.cx("item", { - processedItem - }), $options.getItemProp(processedItem, "class")], - role: "menuitem", - "aria-label": $options.getItemLabel(processedItem), - "aria-disabled": $options.isItemDisabled(processedItem) || void 0, - "aria-expanded": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : void 0, - "aria-haspopup": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, "to") ? "menu" : void 0, - "aria-level": $props.level + 1, - "aria-setsize": $options.getAriaSetSize(), - "aria-posinset": $options.getAriaPosInset(index), - ref_for: true - }, $options.getPTOptions(processedItem, index, "item"), { - "data-p-active": $options.isItemActive(processedItem), - "data-p-focused": $options.isItemFocused(processedItem), - "data-p-disabled": $options.isItemDisabled(processedItem) - }), [createBaseVNode("div", mergeProps({ - "class": _ctx.cx("itemContent"), - onClick: /* @__PURE__ */ __name(function onClick2($event) { - return $options.onItemClick($event, processedItem); - }, "onClick"), - onMouseenter: /* @__PURE__ */ __name(function onMouseenter($event) { - return $options.onItemMouseEnter($event, processedItem); - }, "onMouseenter"), - onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { - return $options.onItemMouseMove($event, processedItem); - }, "onMousemove"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemContent")), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock("a", mergeProps({ - key: 0, - href: $options.getItemProp(processedItem, "url"), - "class": _ctx.cx("itemLink"), - target: $options.getItemProp(processedItem, "target"), - tabindex: "-1", - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemLink")), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), { - key: 0, - item: processedItem.item, - "class": normalizeClass(_ctx.cx("itemIcon")) - }, null, 8, ["item", "class"])) : $options.getItemProp(processedItem, "icon") ? (openBlock(), createElementBlock("span", mergeProps({ - key: 1, - "class": [_ctx.cx("itemIcon"), $options.getItemProp(processedItem, "icon")], - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemIcon")), null, 16)) : createCommentVNode("", true), createBaseVNode("span", mergeProps({ - id: $options.getItemLabelId(processedItem), - "class": _ctx.cx("itemLabel"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemLabel")), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_5$1), $options.getItemProp(processedItem, "items") ? (openBlock(), createElementBlock(Fragment, { - key: 2 - }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), mergeProps({ - key: 0, - "class": _ctx.cx("submenuIcon"), - active: $options.isItemActive(processedItem), - ref_for: true - }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class", "active"])) : (openBlock(), createBlock(_component_AngleRightIcon, mergeProps({ - key: 1, - "class": _ctx.cx("submenuIcon"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class"]))], 64)) : createCommentVNode("", true)], 16, _hoisted_4$1)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), { - key: 1, - item: processedItem.item, - hasSubmenu: $options.getItemProp(processedItem, "items"), - label: $options.getItemLabel(processedItem), - props: $options.getMenuItemProps(processedItem, index) - }, null, 8, ["item", "hasSubmenu", "label", "props"]))], 16, _hoisted_3$5), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_TieredMenuSub, { - key: 0, - id: $options.getItemId(processedItem) + "_list", - style: normalizeStyle(_ctx.sx("submenu", true, { - processedItem - })), - "aria-labelledby": $options.getItemLabelId(processedItem), - role: "menu", - menuId: $props.menuId, - focusedItemId: $props.focusedItemId, - items: processedItem.items, - templates: $props.templates, - activeItemPath: $props.activeItemPath, - level: $props.level + 1, - visible: $options.isItemActive(processedItem) && $options.isItemGroup(processedItem), - pt: _ctx.pt, - unstyled: _ctx.unstyled, - onItemClick: _cache[0] || (_cache[0] = function($event) { - return _ctx.$emit("item-click", $event); - }), - onItemMouseenter: _cache[1] || (_cache[1] = function($event) { - return _ctx.$emit("item-mouseenter", $event); - }), - onItemMousemove: _cache[2] || (_cache[2] = function($event) { - return _ctx.$emit("item-mousemove", $event); - }) - }, null, 8, ["id", "style", "aria-labelledby", "menuId", "focusedItemId", "items", "templates", "activeItemPath", "level", "visible", "pt", "unstyled"])) : createCommentVNode("", true)], 16, _hoisted_2$6)) : createCommentVNode("", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ - key: 1, - id: $options.getItemId(processedItem), - style: $options.getItemProp(processedItem, "style"), - "class": [_ctx.cx("separator"), $options.getItemProp(processedItem, "class")], - role: "separator", - ref_for: true - }, _ctx.ptm("separator")), null, 16, _hoisted_6)) : createCommentVNode("", true)], 64); - }), 128))], 16, _hoisted_1$1$2)) : createCommentVNode("", true)]; - }), - _: 1 - }, 16, ["onEnter"]); -} -__name(render$1$1, "render$1$1"); -script$1$2.render = render$1$1; -var script$4 = { - name: "TieredMenu", - "extends": script$2$1, - inheritAttrs: false, - emits: ["focus", "blur", "before-show", "before-hide", "hide", "show"], - outsideClickListener: null, - scrollHandler: null, - resizeListener: null, - target: null, - container: null, - menubar: null, - searchTimeout: null, - searchValue: null, - data: /* @__PURE__ */ __name(function data6() { - return { - id: this.$attrs.id, - focused: false, - focusedItemInfo: { - index: -1, - level: 0, - parentKey: "" - }, - activeItemPath: [], - visible: !this.popup, - submenuVisible: false, - dirty: false - }; - }, "data"), - watch: { - "$attrs.id": /* @__PURE__ */ __name(function $attrsId2(newValue) { - this.id = newValue || UniqueComponentId(); - }, "$attrsId"), - activeItemPath: /* @__PURE__ */ __name(function activeItemPath(newPath) { - if (!this.popup) { - if (isNotEmpty(newPath)) { - this.bindOutsideClickListener(); - this.bindResizeListener(); - } else { - this.unbindOutsideClickListener(); - this.unbindResizeListener(); - } - } - }, "activeItemPath") - }, - mounted: /* @__PURE__ */ __name(function mounted6() { - this.id = this.id || UniqueComponentId(); - }, "mounted"), - beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount6() { - this.unbindOutsideClickListener(); - this.unbindResizeListener(); - if (this.scrollHandler) { - this.scrollHandler.destroy(); - this.scrollHandler = null; - } - if (this.container && this.autoZIndex) { - ZIndex.clear(this.container); - } - this.target = null; - this.container = null; - }, "beforeUnmount"), - methods: { - getItemProp: /* @__PURE__ */ __name(function getItemProp2(item3, name) { - return item3 ? resolve(item3[name]) : void 0; - }, "getItemProp"), - getItemLabel: /* @__PURE__ */ __name(function getItemLabel2(item3) { - return this.getItemProp(item3, "label"); - }, "getItemLabel"), - isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled2(item3) { - return this.getItemProp(item3, "disabled"); - }, "isItemDisabled"), - isItemVisible: /* @__PURE__ */ __name(function isItemVisible2(item3) { - return this.getItemProp(item3, "visible") !== false; - }, "isItemVisible"), - isItemGroup: /* @__PURE__ */ __name(function isItemGroup2(item3) { - return isNotEmpty(this.getItemProp(item3, "items")); - }, "isItemGroup"), - isItemSeparator: /* @__PURE__ */ __name(function isItemSeparator(item3) { - return this.getItemProp(item3, "separator"); - }, "isItemSeparator"), - getProccessedItemLabel: /* @__PURE__ */ __name(function getProccessedItemLabel(processedItem) { - return processedItem ? this.getItemLabel(processedItem.item) : void 0; - }, "getProccessedItemLabel"), - isProccessedItemGroup: /* @__PURE__ */ __name(function isProccessedItemGroup(processedItem) { - return processedItem && isNotEmpty(processedItem.items); - }, "isProccessedItemGroup"), - toggle: /* @__PURE__ */ __name(function toggle(event) { - this.visible ? this.hide(event, true) : this.show(event); - }, "toggle"), - show: /* @__PURE__ */ __name(function show2(event, isFocus) { - if (this.popup) { - this.$emit("before-show"); - this.visible = true; - this.target = this.target || event.currentTarget; - this.relatedTarget = event.relatedTarget || null; - } - isFocus && focus(this.menubar); - }, "show"), - hide: /* @__PURE__ */ __name(function hide2(event, isFocus) { - if (this.popup) { - this.$emit("before-hide"); - this.visible = false; - } - this.activeItemPath = []; - this.focusedItemInfo = { - index: -1, - level: 0, - parentKey: "" - }; - isFocus && focus(this.relatedTarget || this.target || this.menubar); - this.dirty = false; - }, "hide"), - onFocus: /* @__PURE__ */ __name(function onFocus3(event) { - this.focused = true; - if (!this.popup) { - this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : { - index: this.findFirstFocusedItemIndex(), - level: 0, - parentKey: "" - }; - } - this.$emit("focus", event); - }, "onFocus"), - onBlur: /* @__PURE__ */ __name(function onBlur2(event) { - this.focused = false; - this.focusedItemInfo = { - index: -1, - level: 0, - parentKey: "" - }; - this.searchValue = ""; - this.dirty = false; - this.$emit("blur", event); - }, "onBlur"), - onKeyDown: /* @__PURE__ */ __name(function onKeyDown2(event) { - if (this.disabled) { - event.preventDefault(); - return; - } - var metaKey = event.metaKey || event.ctrlKey; - switch (event.code) { - case "ArrowDown": - this.onArrowDownKey(event); - break; - case "ArrowUp": - this.onArrowUpKey(event); - break; - case "ArrowLeft": - this.onArrowLeftKey(event); - break; - case "ArrowRight": - this.onArrowRightKey(event); - break; - case "Home": - this.onHomeKey(event); - break; - case "End": - this.onEndKey(event); - break; - case "Space": - this.onSpaceKey(event); - break; - case "Enter": - case "NumpadEnter": - this.onEnterKey(event); - break; - case "Escape": - this.onEscapeKey(event); - break; - case "Tab": - this.onTabKey(event); - break; - case "PageDown": - case "PageUp": - case "Backspace": - case "ShiftLeft": - case "ShiftRight": - break; - default: - if (!metaKey && isPrintableCharacter(event.key)) { - this.searchItems(event, event.key); - } - break; - } - }, "onKeyDown"), - onItemChange: /* @__PURE__ */ __name(function onItemChange(event) { - var processedItem = event.processedItem, isFocus = event.isFocus; - if (isEmpty(processedItem)) return; - var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey, items = processedItem.items; - var grouped = isNotEmpty(items); - var activeItemPath3 = this.activeItemPath.filter(function(p) { - return p.parentKey !== parentKey && p.parentKey !== key; - }); - if (grouped) { - activeItemPath3.push(processedItem); - this.submenuVisible = true; - } - this.focusedItemInfo = { - index, - level, - parentKey - }; - this.activeItemPath = activeItemPath3; - grouped && (this.dirty = true); - isFocus && focus(this.menubar); - }, "onItemChange"), - onOverlayClick: /* @__PURE__ */ __name(function onOverlayClick2(event) { - OverlayEventBus.emit("overlay-click", { - originalEvent: event, - target: this.target - }); - }, "onOverlayClick"), - onItemClick: /* @__PURE__ */ __name(function onItemClick2(event) { - var originalEvent = event.originalEvent, processedItem = event.processedItem; - var grouped = this.isProccessedItemGroup(processedItem); - var root11 = isEmpty(processedItem.parent); - var selected = this.isSelected(processedItem); - if (selected) { - var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey; - this.activeItemPath = this.activeItemPath.filter(function(p) { - return key !== p.key && key.startsWith(p.key); - }); - this.focusedItemInfo = { - index, - level, - parentKey - }; - this.dirty = !root11; - focus(this.menubar); - } else { - if (grouped) { - this.onItemChange(event); - } else { - var rootProcessedItem = root11 ? processedItem : this.activeItemPath.find(function(p) { - return p.parentKey === ""; - }); - this.hide(originalEvent); - this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1); - focus(this.menubar); - } - } - }, "onItemClick"), - onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter2(event) { - if (this.dirty) { - this.onItemChange(event); - } - }, "onItemMouseEnter"), - onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove2(event) { - if (this.focused) { - this.changeFocusedItemIndex(event, event.processedItem.index); - } - }, "onItemMouseMove"), - onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey2(event) { - var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); - this.changeFocusedItemIndex(event, itemIndex); - event.preventDefault(); - }, "onArrowDownKey"), - onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey2(event) { - if (event.altKey) { - if (this.focusedItemInfo.index !== -1) { - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var grouped = this.isProccessedItemGroup(processedItem); - !grouped && this.onItemChange({ - originalEvent: event, - processedItem - }); - } - this.popup && this.hide(event, true); - event.preventDefault(); - } else { - var itemIndex = this.focusedItemInfo.index !== -1 ? 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void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase())); - }, "isItemMatched"), - isValidItem: /* @__PURE__ */ __name(function isValidItem(processedItem) { - return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item); - }, "isValidItem"), - isValidSelectedItem: /* @__PURE__ */ __name(function isValidSelectedItem(processedItem) { - return this.isValidItem(processedItem) && this.isSelected(processedItem); - }, "isValidSelectedItem"), - isSelected: /* @__PURE__ */ __name(function isSelected2(processedItem) { - return this.activeItemPath.some(function(p) { - return p.key === processedItem.key; - }); - }, "isSelected"), - findFirstItemIndex: /* @__PURE__ */ __name(function findFirstItemIndex() { - var _this5 = this; - return this.visibleItems.findIndex(function(processedItem) { - return _this5.isValidItem(processedItem); - }); - }, "findFirstItemIndex"), - findLastItemIndex: /* @__PURE__ */ __name(function findLastItemIndex() { - var _this6 = this; - return findLastIndex(this.visibleItems, function(processedItem) { - return _this6.isValidItem(processedItem); - }); - }, "findLastItemIndex"), - findNextItemIndex: /* @__PURE__ */ __name(function findNextItemIndex(index) { - var _this7 = this; - var matchedItemIndex = index < this.visibleItems.length - 1 ? 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"".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? "_" + this.focusedItemInfo.parentKey : "", "_").concat(this.focusedItemInfo.index) : null; - }, "focusedItemId") - }, - components: { - TieredMenuSub: script$1$2, - Portal: script$k - } -}; -var _hoisted_1$8 = ["id"]; -function render$5(_ctx, _cache, $props, $setup, $data, $options) { - var _component_TieredMenuSub = resolveComponent("TieredMenuSub"); - var _component_Portal = resolveComponent("Portal"); - return openBlock(), createBlock(_component_Portal, { - appendTo: _ctx.appendTo, - disabled: !_ctx.popup - }, { - "default": withCtx(function() { - return [createVNode(Transition, mergeProps({ - name: "p-connected-overlay", - onEnter: $options.onEnter, - onAfterEnter: $options.onAfterEnter, - onLeave: $options.onLeave, - onAfterLeave: $options.onAfterLeave - }, _ctx.ptm("transition")), { - "default": withCtx(function() { - return [$data.visible ? (openBlock(), createElementBlock("div", mergeProps({ - key: 0, - ref: $options.containerRef, - id: $data.id, - "class": _ctx.cx("root"), - onClick: _cache[0] || (_cache[0] = function() { - return $options.onOverlayClick && $options.onOverlayClick.apply($options, arguments); - }) - }, _ctx.ptmi("root")), [_ctx.$slots.start ? (openBlock(), createElementBlock("div", mergeProps({ - key: 0, - "class": _ctx.cx("start") - }, _ctx.ptm("start")), [renderSlot(_ctx.$slots, "start")], 16)) : createCommentVNode("", true), createVNode(_component_TieredMenuSub, { - ref: $options.menubarRef, - id: $data.id + "_list", - tabindex: !_ctx.disabled ? _ctx.tabindex : -1, - role: "menubar", - "aria-label": _ctx.ariaLabel, - "aria-labelledby": _ctx.ariaLabelledby, - "aria-disabled": _ctx.disabled || void 0, - "aria-orientation": "vertical", - "aria-activedescendant": $data.focused ? $options.focusedItemId : void 0, - menuId: $data.id, - focusedItemId: $data.focused ? $options.focusedItemId : void 0, - items: $options.processedItems, - templates: _ctx.$slots, - activeItemPath: $data.activeItemPath, - level: 0, - visible: $data.submenuVisible, - pt: _ctx.pt, - unstyled: _ctx.unstyled, - onFocus: $options.onFocus, - onBlur: $options.onBlur, - onKeydown: $options.onKeyDown, - onItemClick: $options.onItemClick, - onItemMouseenter: $options.onItemMouseEnter, - onItemMousemove: $options.onItemMouseMove - }, null, 8, ["id", "tabindex", "aria-label", "aria-labelledby", "aria-disabled", "aria-activedescendant", "menuId", "focusedItemId", "items", "templates", "activeItemPath", "visible", "pt", "unstyled", "onFocus", "onBlur", "onKeydown", "onItemClick", "onItemMouseenter", "onItemMousemove"]), _ctx.$slots.end ? (openBlock(), createElementBlock("div", mergeProps({ - key: 1, - "class": _ctx.cx("end") - }, _ctx.ptm("end")), [renderSlot(_ctx.$slots, "end")], 16)) : createCommentVNode("", true)], 16, _hoisted_1$8)) : createCommentVNode("", true)]; - }), - _: 3 - }, 16, ["onEnter", "onAfterEnter", "onLeave", "onAfterLeave"])]; - }), - _: 3 - }, 8, ["appendTo", "disabled"]); -} -__name(render$5, "render$5"); -script$4.render = render$5; -var theme$1 = /* @__PURE__ */ __name(function theme7(_ref) { - var dt = _ref.dt; - return "\n.p-splitbutton {\n display: inline-flex;\n position: relative;\n border-radius: ".concat(dt("splitbutton.border.radius"), ";\n}\n\n.p-splitbutton-button {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n border-right: 0 none;\n}\n\n.p-splitbutton-button:focus-visible,\n.p-splitbutton-dropdown:focus-visible {\n z-index: 1;\n}\n\n.p-splitbutton-button:not(:disabled):hover,\n.p-splitbutton-button:not(:disabled):active {\n border-right: 0 none;\n}\n\n.p-splitbutton-dropdown {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n\n.p-splitbutton .p-menu {\n min-width: 100%;\n}\n\n.p-splitbutton-fluid {\n display: flex;\n}\n\n.p-splitbutton-rounded .p-splitbutton-dropdown {\n border-top-right-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n border-bottom-right-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n}\n\n.p-splitbutton-rounded .p-splitbutton-button {\n border-top-left-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n border-bottom-left-radius: ").concat(dt("splitbutton.rounded.border.radius"), ";\n}\n\n.p-splitbutton-raised {\n box-shadow: ").concat(dt("splitbutton.raised.shadow"), ";\n}\n"); -}, "theme"); -var classes$1 = { - root: /* @__PURE__ */ __name(function root9(_ref2) { - var instance = _ref2.instance, props = _ref2.props; - return ["p-splitbutton p-component", { - "p-splitbutton-raised": props.raised, - "p-splitbutton-rounded": props.rounded, - "p-splitbutton-fluid": instance.hasFluid - }]; - }, "root"), - pcButton: "p-splitbutton-button", - pcDropdown: "p-splitbutton-dropdown" -}; -var SplitButtonStyle = BaseStyle.extend({ - name: "splitbutton", - theme: theme$1, - classes: classes$1 -}); -var script$1$1 = { - name: "BaseSplitButton", - "extends": script$e, - props: { - label: { - type: String, - "default": null - }, - icon: { - type: String, - "default": null - }, - model: { - type: Array, - "default": null - }, - autoZIndex: { - type: Boolean, - "default": true - }, - baseZIndex: { - type: Number, - "default": 0 - }, - appendTo: { - type: [String, Object], - "default": "body" - }, - disabled: { - type: Boolean, - "default": false - }, - fluid: { - type: Boolean, - "default": null - }, - "class": { - type: null, - "default": null - }, - style: { - type: null, - "default": null - }, - buttonProps: { - type: null, - "default": null - }, - menuButtonProps: { - type: null, - "default": null - }, - menuButtonIcon: { - type: String, - "default": void 0 - }, - dropdownIcon: { - type: String, - "default": void 0 - }, - severity: { - type: String, - "default": null - }, - raised: { - type: Boolean, - "default": false - }, - rounded: { - type: Boolean, - "default": false - }, - text: { - type: Boolean, - "default": false - }, - outlined: { - type: Boolean, - "default": false - }, - size: { - type: String, - "default": null - }, - plain: { - type: Boolean, - "default": false - } - }, - style: SplitButtonStyle, - provide: /* @__PURE__ */ __name(function provide11() { - return { - $pcSplitButton: this, - $parentInstance: this - }; - }, "provide") -}; -var script$3 = { - name: "SplitButton", - "extends": script$1$1, - inheritAttrs: false, - emits: ["click"], - inject: { - $pcFluid: { - "default": null - } - }, - data: /* @__PURE__ */ __name(function data7() { - return { - id: this.$attrs.id, - isExpanded: false - }; - }, "data"), - watch: { - "$attrs.id": /* @__PURE__ */ __name(function $attrsId3(newValue) { - this.id = newValue || UniqueComponentId(); - }, "$attrsId") - }, - mounted: /* @__PURE__ */ __name(function mounted7() { - var _this = this; - this.id = this.id || UniqueComponentId(); - this.$watch("$refs.menu.visible", function(newValue) { - _this.isExpanded = newValue; - }); - }, "mounted"), - methods: { - onDropdownButtonClick: /* @__PURE__ */ __name(function onDropdownButtonClick(event) { - if (event) { - event.preventDefault(); - } - this.$refs.menu.toggle({ - currentTarget: this.$el, - relatedTarget: this.$refs.button.$el - }); - this.isExpanded = this.$refs.menu.visible; - }, "onDropdownButtonClick"), - onDropdownKeydown: /* @__PURE__ */ __name(function onDropdownKeydown(event) { - if (event.code === "ArrowDown" || event.code === "ArrowUp") { - this.onDropdownButtonClick(); - event.preventDefault(); - } - }, "onDropdownKeydown"), - onDefaultButtonClick: /* @__PURE__ */ __name(function onDefaultButtonClick(event) { - if (this.isExpanded) { - this.$refs.menu.hide(event); - } - this.$emit("click", event); - }, "onDefaultButtonClick") - }, - computed: { - containerClass: /* @__PURE__ */ __name(function containerClass() { - return [this.cx("root"), this["class"]]; - }, "containerClass"), - hasFluid: /* @__PURE__ */ __name(function hasFluid2() { - return isEmpty(this.fluid) ? !!this.$pcFluid : this.fluid; - }, "hasFluid") - }, - components: { - PVSButton: script$d, - PVSMenu: script$4, - ChevronDownIcon: script$l - } -}; -var _hoisted_1$7 = ["data-p-severity"]; -function render$4(_ctx, _cache, $props, $setup, $data, $options) { - var _component_PVSButton = resolveComponent("PVSButton"); - var _component_PVSMenu = resolveComponent("PVSMenu"); - return openBlock(), createElementBlock("div", mergeProps({ - "class": $options.containerClass, - style: _ctx.style - }, _ctx.ptmi("root"), { - "data-p-severity": _ctx.severity - }), [createVNode(_component_PVSButton, mergeProps({ - type: "button", - "class": _ctx.cx("pcButton"), - label: _ctx.label, - disabled: _ctx.disabled, - severity: _ctx.severity, - text: _ctx.text, - icon: _ctx.icon, - outlined: _ctx.outlined, - size: _ctx.size, - fluid: _ctx.fluid, - "aria-label": _ctx.label, - onClick: $options.onDefaultButtonClick - }, _ctx.buttonProps, { - pt: _ctx.ptm("pcButton"), - unstyled: _ctx.unstyled - }), createSlots({ - "default": withCtx(function() { - return [renderSlot(_ctx.$slots, "default")]; - }), - _: 2 - }, [_ctx.$slots.icon ? { - name: "icon", - fn: withCtx(function(slotProps) { - return [renderSlot(_ctx.$slots, "icon", { - "class": normalizeClass(slotProps["class"]) - }, function() { - return [createBaseVNode("span", mergeProps({ - "class": [_ctx.icon, slotProps["class"]] - }, _ctx.ptm("pcButton")["icon"], { - "data-pc-section": "buttonicon" - }), null, 16)]; - })]; - }), - key: "0" - } : void 0]), 1040, ["class", "label", "disabled", "severity", "text", "icon", "outlined", "size", "fluid", "aria-label", "onClick", "pt", "unstyled"]), createVNode(_component_PVSButton, mergeProps({ - ref: "button", - type: "button", - "class": _ctx.cx("pcDropdown"), - disabled: _ctx.disabled, - "aria-haspopup": "true", - "aria-expanded": $data.isExpanded, - "aria-controls": $data.id + "_overlay", - onClick: $options.onDropdownButtonClick, - onKeydown: $options.onDropdownKeydown, - severity: _ctx.severity, - text: _ctx.text, - outlined: _ctx.outlined, - size: _ctx.size, - unstyled: _ctx.unstyled - }, _ctx.menuButtonProps, { - pt: _ctx.ptm("pcDropdown") - }), { - icon: withCtx(function(slotProps) { - return [renderSlot(_ctx.$slots, _ctx.$slots.dropdownicon ? "dropdownicon" : "menubuttonicon", { - "class": normalizeClass(slotProps["class"]) - }, function() { - return [(openBlock(), createBlock(resolveDynamicComponent(_ctx.menuButtonIcon || _ctx.dropdownIcon ? "span" : "ChevronDownIcon"), mergeProps({ - "class": [_ctx.dropdownIcon || _ctx.menuButtonIcon, slotProps["class"]] - }, _ctx.ptm("pcDropdown")["icon"], { - "data-pc-section": "menubuttonicon" - }), null, 16, ["class"]))]; - })]; - }), - _: 3 - }, 16, ["class", "disabled", "aria-expanded", "aria-controls", "onClick", "onKeydown", "severity", "text", "outlined", "size", "unstyled", "pt"]), createVNode(_component_PVSMenu, { - ref: "menu", - id: $data.id + "_overlay", - model: _ctx.model, - popup: true, - autoZIndex: _ctx.autoZIndex, - baseZIndex: _ctx.baseZIndex, - appendTo: _ctx.appendTo, - unstyled: _ctx.unstyled, - pt: _ctx.ptm("pcMenu") - }, createSlots({ - _: 2 - }, [_ctx.$slots.menuitemicon ? { - name: "itemicon", - fn: withCtx(function(slotProps) { - return [renderSlot(_ctx.$slots, "menuitemicon", { - item: slotProps.item, - "class": normalizeClass(slotProps["class"]) - })]; - }), - key: "0" - } : void 0, _ctx.$slots.item ? { - name: "item", - fn: withCtx(function(slotProps) { - return [renderSlot(_ctx.$slots, "item", { - item: slotProps.item, - hasSubmenu: slotProps.hasSubmenu, - label: slotProps.label, - props: slotProps.props - })]; - }), - key: "1" - } : void 0]), 1032, ["id", "model", "autoZIndex", "baseZIndex", "appendTo", "unstyled", "pt"])], 16, _hoisted_1$7); -} -__name(render$4, "render$4"); -script$3.render = render$4; -const minQueueCount = 1; -const _sfc_main$8 = /* @__PURE__ */ defineComponent({ - __name: "BatchCountEdit", - props: { - class: { default: "" } - }, - setup(__props) { - const props = __props; - const queueSettingsStore = useQueueSettingsStore(); - const { batchCount } = storeToRefs(queueSettingsStore); - const settingStore = useSettingStore(); - const maxQueueCount = computed( - () => settingStore.get("Comfy.QueueButton.BatchCountLimit") - ); - const handleClick = /* @__PURE__ */ __name((increment) => { - let newCount; - if (increment) { - const originalCount = batchCount.value - 1; - newCount = Math.min(originalCount * 2, maxQueueCount.value); - } else { - const originalCount = batchCount.value + 1; - newCount = Math.floor(originalCount / 2); - } - batchCount.value = newCount; - }, "handleClick"); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return withDirectives((openBlock(), createElementBlock("div", { - class: normalizeClass(["batch-count", props.class]) - }, [ - createVNode(unref(script$x), { - class: "w-14", - modelValue: unref(batchCount), - "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => isRef(batchCount) ? batchCount.value = $event : null), - min: minQueueCount, - max: maxQueueCount.value, - fluid: "", - showButtons: "", - pt: { - incrementButton: { - class: "w-6", - onmousedown: /* @__PURE__ */ __name(() => { - handleClick(true); - }, "onmousedown") - }, - decrementButton: { - class: "w-6", - onmousedown: /* @__PURE__ */ __name(() => { - handleClick(false); - }, "onmousedown") - } - } - }, null, 8, ["modelValue", "max", "pt"]) - ], 2)), [ - [ - _directive_tooltip, - _ctx.$t("menu.batchCount"), - void 0, - { bottom: true } - ] - ]); - }; - } -}); -const BatchCountEdit = /* @__PURE__ */ _export_sfc(_sfc_main$8, [["__scopeId", "data-v-b9328350"]]); -const _withScopeId$3 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-7f4f551b"), n = n(), popScopeId(), n), "_withScopeId$3"); -const _hoisted_1$6 = { class: "queue-button-group flex" }; -const _sfc_main$7 = /* @__PURE__ */ defineComponent({ - __name: "ComfyQueueButton", - setup(__props) { - const workspaceStore = useWorkspaceStore(); - const queueCountStore = storeToRefs(useQueuePendingTaskCountStore()); - const { mode: queueMode } = storeToRefs(useQueueSettingsStore()); - const { t } = useI18n(); - const queueModeMenuItemLookup = computed(() => ({ - disabled: { - key: "disabled", - label: t("menu.queue"), - tooltip: t("menu.disabledTooltip"), - command: /* @__PURE__ */ __name(() => { - queueMode.value = "disabled"; - }, "command") - }, - instant: { - key: "instant", - label: `${t("menu.queue")} (${t("menu.instant")})`, - tooltip: t("menu.instantTooltip"), - command: /* @__PURE__ */ __name(() => { - queueMode.value = "instant"; - }, "command") - }, - change: { - key: "change", - label: `${t("menu.queue")} (${t("menu.onChange")})`, - tooltip: t("menu.onChangeTooltip"), - command: /* @__PURE__ */ __name(() => { - queueMode.value = "change"; - }, "command") - } - })); - const activeQueueModeMenuItem = computed( - () => queueModeMenuItemLookup.value[queueMode.value] - ); - const queueModeMenuItems = computed( - () => Object.values(queueModeMenuItemLookup.value) - ); - const executingPrompt = computed(() => !!queueCountStore.count.value); - const hasPendingTasks = computed(() => queueCountStore.count.value > 1); - const commandStore = useCommandStore(); - const queuePrompt = /* @__PURE__ */ __name((e) => { - const commandId = e.shiftKey ? "Comfy.QueuePromptFront" : "Comfy.QueuePrompt"; - commandStore.execute(commandId); - }, "queuePrompt"); - return (_ctx, _cache) => { - const _component_i_lucide58list_start = __unplugin_components_0$1; - const _component_i_lucide58play = __unplugin_components_1$1; - const _component_i_lucide58fast_forward = __unplugin_components_2; - const _component_i_lucide58step_forward = __unplugin_components_3; - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createElementBlock("div", _hoisted_1$6, [ - withDirectives((openBlock(), createBlock(unref(script$3), { - class: "comfyui-queue-button", - label: activeQueueModeMenuItem.value.label, - severity: "primary", - size: "small", - onClick: queuePrompt, - model: queueModeMenuItems.value, - "data-testid": "queue-button" - }, { - icon: withCtx(() => [ - unref(workspaceStore).shiftDown ? (openBlock(), createBlock(_component_i_lucide58list_start, { key: 0 })) : unref(queueMode) === "disabled" ? (openBlock(), createBlock(_component_i_lucide58play, { key: 1 })) : unref(queueMode) === "instant" ? (openBlock(), createBlock(_component_i_lucide58fast_forward, { key: 2 })) : unref(queueMode) === "change" ? (openBlock(), createBlock(_component_i_lucide58step_forward, { key: 3 })) : createCommentVNode("", true) - ]), - item: withCtx(({ item: item3 }) => [ - withDirectives(createVNode(unref(script$d), { - label: item3.label, - icon: item3.icon, - severity: item3.key === unref(queueMode) ? "primary" : "secondary", - size: "small", - text: "" - }, null, 8, ["label", "icon", "severity"]), [ - [_directive_tooltip, item3.tooltip] - ]) - ]), - _: 1 - }, 8, ["label", "model"])), [ - [ - _directive_tooltip, - unref(workspaceStore).shiftDown ? _ctx.$t("menu.queueWorkflowFront") : _ctx.$t("menu.queueWorkflow"), - void 0, - { bottom: true } - ] - ]), - createVNode(BatchCountEdit), - createVNode(unref(script$8), { class: "execution-actions flex flex-nowrap" }, { - default: withCtx(() => [ - withDirectives(createVNode(unref(script$d), { - icon: "pi pi-times", - severity: executingPrompt.value ? "danger" : "secondary", - disabled: !executingPrompt.value, - text: "", - onClick: _cache[0] || (_cache[0] = () => unref(commandStore).execute("Comfy.Interrupt")) - }, null, 8, ["severity", "disabled"]), [ - [ - _directive_tooltip, - _ctx.$t("menu.interrupt"), - void 0, - { bottom: true } - ] - ]), - withDirectives(createVNode(unref(script$d), { - icon: "pi pi-stop", - severity: hasPendingTasks.value ? "danger" : "secondary", - disabled: !hasPendingTasks.value, - text: "", - onClick: _cache[1] || (_cache[1] = () => unref(commandStore).execute("Comfy.ClearPendingTasks")) - }, null, 8, ["severity", "disabled"]), [ - [ - _directive_tooltip, - _ctx.$t("sideToolbar.queueTab.clearPendingTasks"), - void 0, - { bottom: true } - ] - ]) - ]), - _: 1 - }) - ]); - }; - } -}); -const ComfyQueueButton = /* @__PURE__ */ _export_sfc(_sfc_main$7, [["__scopeId", "data-v-7f4f551b"]]); -const overlapThreshold = 20; -const _sfc_main$6 = /* @__PURE__ */ defineComponent({ - __name: "ComfyActionbar", - setup(__props) { - const settingsStore = useSettingStore(); - const visible = computed( - () => settingsStore.get("Comfy.UseNewMenu") !== "Disabled" - ); - const panelRef = ref(null); - const dragHandleRef = ref(null); - const isDocked = useLocalStorage("Comfy.MenuPosition.Docked", false); - const storedPosition = useLocalStorage("Comfy.MenuPosition.Floating", { - x: 0, - y: 0 - }); - const { - x, - y, - style, - isDragging - } = useDraggable(panelRef, { - initialValue: { x: 0, y: 0 }, - handle: dragHandleRef, - containerElement: document.body - }); - watchDebounced( - [x, y], - ([newX, newY]) => { - storedPosition.value = { x: newX, y: newY }; - }, - { debounce: 300 } - ); - const setInitialPosition = /* @__PURE__ */ __name(() => { - if (x.value !== 0 || y.value !== 0) { - return; - } - if (storedPosition.value.x !== 0 || storedPosition.value.y !== 0) { - x.value = storedPosition.value.x; - y.value = storedPosition.value.y; - captureLastDragState(); - return; - } - if (panelRef.value) { - const screenWidth = window.innerWidth; - const screenHeight = window.innerHeight; - const menuWidth = panelRef.value.offsetWidth; - const menuHeight = panelRef.value.offsetHeight; - if (menuWidth === 0 || menuHeight === 0) { - return; - } - x.value = (screenWidth - menuWidth) / 2; - y.value = screenHeight - menuHeight - 10; - captureLastDragState(); - } - }, "setInitialPosition"); - onMounted(setInitialPosition); - watch(visible, (newVisible) => { - if (newVisible) { - nextTick(setInitialPosition); - } - }); - const lastDragState = ref({ - x: x.value, - y: y.value, - windowWidth: window.innerWidth, - windowHeight: window.innerHeight - }); - const captureLastDragState = /* @__PURE__ */ __name(() => { - lastDragState.value = { - x: x.value, - y: y.value, - windowWidth: window.innerWidth, - windowHeight: window.innerHeight - }; - }, "captureLastDragState"); - watch( - isDragging, - (newIsDragging) => { - if (!newIsDragging) { - captureLastDragState(); - } - }, - { immediate: true } - ); - const adjustMenuPosition = /* @__PURE__ */ __name(() => { - if (panelRef.value) { - const screenWidth = window.innerWidth; - const screenHeight = window.innerHeight; - const menuWidth = panelRef.value.offsetWidth; - const menuHeight = panelRef.value.offsetHeight; - const distanceLeft = lastDragState.value.x; - const distanceRight = lastDragState.value.windowWidth - (lastDragState.value.x + menuWidth); - const distanceTop = lastDragState.value.y; - const distanceBottom = lastDragState.value.windowHeight - (lastDragState.value.y + menuHeight); - const distances = [ - { edge: "left", distance: distanceLeft }, - { edge: "right", distance: distanceRight }, - { edge: "top", distance: distanceTop }, - { edge: "bottom", distance: distanceBottom } - ]; - const closestEdge = distances.reduce( - (min, curr) => curr.distance < min.distance ? curr : min - ); - const verticalRatio = lastDragState.value.y / lastDragState.value.windowHeight; - const horizontalRatio = lastDragState.value.x / lastDragState.value.windowWidth; - if (closestEdge.edge === "left") { - x.value = closestEdge.distance; - y.value = verticalRatio * screenHeight; - } else if (closestEdge.edge === "right") { - x.value = screenWidth - menuWidth - closestEdge.distance; - y.value = verticalRatio * screenHeight; - } else if (closestEdge.edge === "top") { - x.value = horizontalRatio * screenWidth; - y.value = closestEdge.distance; - } else { - x.value = horizontalRatio * screenWidth; - y.value = screenHeight - menuHeight - closestEdge.distance; - } - x.value = lodashExports.clamp(x.value, 0, screenWidth - menuWidth); - y.value = lodashExports.clamp(y.value, 0, screenHeight - menuHeight); - } - }, "adjustMenuPosition"); - useEventListener(window, "resize", adjustMenuPosition); - const topMenuRef = inject("topMenuRef"); - const topMenuBounds = useElementBounding(topMenuRef); - const isOverlappingWithTopMenu = computed(() => { - if (!panelRef.value) { - return false; - } - const { height } = panelRef.value.getBoundingClientRect(); - const actionbarBottom = y.value + height; - const topMenuBottom = topMenuBounds.bottom.value; - const overlapPixels = Math.min(actionbarBottom, topMenuBottom) - Math.max(y.value, topMenuBounds.top.value); - return overlapPixels > overlapThreshold; - }); - watch(isDragging, (newIsDragging) => { - if (!newIsDragging) { - isDocked.value = isOverlappingWithTopMenu.value; - } else { - isDocked.value = false; - } - }); - const eventBus = useEventBus("topMenu"); - watch([isDragging, isOverlappingWithTopMenu], ([dragging, overlapping]) => { - eventBus.emit("updateHighlight", { - isDragging: dragging, - isOverlapping: overlapping - }); - }); - return (_ctx, _cache) => { - return openBlock(), createBlock(unref(script$y), { - class: normalizeClass(["actionbar w-fit", { "is-dragging": unref(isDragging), "is-docked": unref(isDocked) }]), - style: normalizeStyle(unref(style)) - }, { - default: withCtx(() => [ - createBaseVNode("div", { - class: "actionbar-content flex items-center", - ref_key: "panelRef", - ref: panelRef - }, [ - createBaseVNode("span", { - class: "drag-handle cursor-move mr-2 p-0!", - ref_key: "dragHandleRef", - ref: dragHandleRef - }, null, 512), - createVNode(ComfyQueueButton) - ], 512) - ]), - _: 1 - }, 8, ["style", "class"]); - }; - } -}); -const Actionbar = /* @__PURE__ */ _export_sfc(_sfc_main$6, [["__scopeId", "data-v-915e5456"]]); -const _hoisted_1$5 = { - viewBox: "0 0 24 24", - width: "1.2em", - height: "1.2em" -}; -const _hoisted_2$5 = /* @__PURE__ */ createBaseVNode("path", { - fill: "currentColor", - d: "M5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm0-5v3h14v-3zm0-2h14V5H5zm0 2v3z" -}, null, -1); -const _hoisted_3$4 = [ - _hoisted_2$5 -]; -function render$3(_ctx, _cache) { - return openBlock(), createElementBlock("svg", _hoisted_1$5, [..._hoisted_3$4]); -} -__name(render$3, "render$3"); -const __unplugin_components_1 = markRaw({ name: "material-symbols-dock-to-bottom-outline", render: render$3 }); -const _hoisted_1$4 = { - viewBox: "0 0 24 24", - width: "1.2em", - height: "1.2em" -}; -const _hoisted_2$4 = /* @__PURE__ */ createBaseVNode("path", { - fill: "currentColor", - d: "M5 21q-.825 0-1.412-.587T3 19V5q0-.825.588-1.412T5 3h14q.825 0 1.413.588T21 5v14q0 .825-.587 1.413T19 21zm0-7h14V5H5z" -}, null, -1); -const _hoisted_3$3 = [ - _hoisted_2$4 -]; -function render$2(_ctx, _cache) { - return openBlock(), createElementBlock("svg", _hoisted_1$4, [..._hoisted_3$3]); -} -__name(render$2, "render$2"); -const __unplugin_components_0 = markRaw({ name: "material-symbols-dock-to-bottom", render: render$2 }); -const _sfc_main$5 = /* @__PURE__ */ defineComponent({ - __name: "BottomPanelToggleButton", - setup(__props) { - const bottomPanelStore = useBottomPanelStore(); - return (_ctx, _cache) => { - const _component_i_material_symbols58dock_to_bottom = __unplugin_components_0; - const _component_i_material_symbols58dock_to_bottom_outline = __unplugin_components_1; - const _directive_tooltip = resolveDirective("tooltip"); - return withDirectives((openBlock(), createBlock(unref(script$d), { - severity: "secondary", - text: "", - onClick: unref(bottomPanelStore).toggleBottomPanel - }, { - icon: withCtx(() => [ - unref(bottomPanelStore).bottomPanelVisible ? (openBlock(), createBlock(_component_i_material_symbols58dock_to_bottom, { key: 0 })) : (openBlock(), createBlock(_component_i_material_symbols58dock_to_bottom_outline, { key: 1 })) - ]), - _: 1 - }, 8, ["onClick"])), [ - [vShow, unref(bottomPanelStore).bottomPanelTabs.length > 0], - [_directive_tooltip, { value: _ctx.$t("menu.toggleBottomPanel"), showDelay: 300 }] - ]); - }; - } -}); -var theme8 = /* @__PURE__ */ __name(function theme9(_ref) { - var dt = _ref.dt; - return "\n.p-menubar {\n display: flex;\n align-items: center;\n background: ".concat(dt("menubar.background"), ";\n border: 1px solid ").concat(dt("menubar.border.color"), ";\n border-radius: ").concat(dt("menubar.border.radius"), ";\n color: ").concat(dt("menubar.color"), ";\n padding: ").concat(dt("menubar.padding"), ";\n gap: ").concat(dt("menubar.gap"), ";\n}\n\n.p-menubar-start,\n.p-megamenu-end {\n display: flex;\n align-items: center;\n}\n\n.p-menubar-root-list,\n.p-menubar-submenu {\n display: flex;\n margin: 0;\n padding: 0;\n list-style: none;\n outline: 0 none;\n}\n\n.p-menubar-root-list {\n align-items: center;\n flex-wrap: wrap;\n gap: ").concat(dt("menubar.gap"), ";\n}\n\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\n border-radius: ").concat(dt("menubar.base.item.border.radius"), ";\n}\n\n.p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\n padding: ").concat(dt("menubar.base.item.padding"), ";\n}\n\n.p-menubar-item-content {\n transition: background ").concat(dt("menubar.transition.duration"), ", color ").concat(dt("menubar.transition.duration"), ";\n border-radius: ").concat(dt("menubar.item.border.radius"), ";\n color: ").concat(dt("menubar.item.color"), ";\n}\n\n.p-menubar-item-link {\n cursor: pointer;\n display: flex;\n align-items: center;\n text-decoration: none;\n overflow: hidden;\n position: relative;\n color: inherit;\n padding: ").concat(dt("menubar.item.padding"), ";\n gap: ").concat(dt("menubar.item.gap"), ";\n user-select: none;\n outline: 0 none;\n}\n\n.p-menubar-item-label {\n line-height: 1;\n}\n\n.p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.color"), ";\n}\n\n.p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.color"), ";\n margin-left: auto;\n font-size: ").concat(dt("menubar.submenu.icon.size"), ";\n width: ").concat(dt("menubar.submenu.icon.size"), ";\n height: ").concat(dt("menubar.submenu.icon.size"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content {\n color: ").concat(dt("menubar.item.focus.color"), ";\n background: ").concat(dt("menubar.item.focus.background"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.focus.color"), ";\n}\n\n.p-menubar-item.p-focus > .p-menubar-item-content .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.focus.color"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover {\n color: ").concat(dt("menubar.item.focus.color"), ";\n background: ").concat(dt("menubar.item.focus.background"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.focus.color"), ";\n}\n\n.p-menubar-item:not(.p-disabled) > .p-menubar-item-content:hover .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.focus.color"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content {\n color: ").concat(dt("menubar.item.active.color"), ";\n background: ").concat(dt("menubar.item.active.background"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-item-icon {\n color: ").concat(dt("menubar.item.icon.active.color"), ";\n}\n\n.p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n color: ").concat(dt("menubar.submenu.icon.active.color"), ";\n}\n\n.p-menubar-submenu {\n display: none;\n position: absolute;\n min-width: 12.5rem;\n z-index: 1;\n background: ").concat(dt("menubar.submenu.background"), ";\n border: 1px solid ").concat(dt("menubar.submenu.border.color"), ";\n border-radius: ").concat(dt("menubar.border.radius"), ";\n box-shadow: ").concat(dt("menubar.submenu.shadow"), ";\n color: ").concat(dt("menubar.submenu.color"), ";\n flex-direction: column;\n padding: ").concat(dt("menubar.submenu.padding"), ";\n gap: ").concat(dt("menubar.submenu.gap"), ";\n}\n\n.p-menubar-submenu .p-menubar-separator {\n border-top: 1px solid ").concat(dt("menubar.separator.border.color"), ";\n}\n\n.p-menubar-submenu .p-menubar-item {\n position: relative;\n}\n\n .p-menubar-submenu > .p-menubar-item-active > .p-menubar-submenu {\n display: block;\n left: 100%;\n top: 0;\n}\n\n.p-menubar-end {\n margin-left: auto;\n align-self: center;\n}\n\n.p-menubar-button {\n display: none;\n justify-content: center;\n align-items: center;\n cursor: pointer;\n width: ").concat(dt("menubar.mobile.button.size"), ";\n height: ").concat(dt("menubar.mobile.button.size"), ";\n position: relative;\n color: ").concat(dt("menubar.mobile.button.color"), ";\n border: 0 none;\n background: transparent;\n border-radius: ").concat(dt("menubar.mobile.button.border.radius"), ";\n transition: background ").concat(dt("menubar.transition.duration"), ", color ").concat(dt("menubar.transition.duration"), ", outline-color ").concat(dt("menubar.transition.duration"), ";\n outline-color: transparent;\n}\n\n.p-menubar-button:hover {\n color: ").concat(dt("menubar.mobile.button.hover.color"), ";\n background: ").concat(dt("menubar.mobile.button.hover.background"), ";\n}\n\n.p-menubar-button:focus-visible {\n box-shadow: ").concat(dt("menubar.mobile.button.focus.ring.shadow"), ";\n outline: ").concat(dt("menubar.mobile.button.focus.ring.width"), " ").concat(dt("menubar.mobile.button.focus.ring.style"), " ").concat(dt("menubar.mobile.button.focus.ring.color"), ";\n outline-offset: ").concat(dt("menubar.mobile.button.focus.ring.offset"), ";\n}\n\n.p-menubar-mobile {\n position: relative;\n}\n\n.p-menubar-mobile .p-menubar-button {\n display: flex;\n}\n\n.p-menubar-mobile .p-menubar-root-list {\n position: absolute;\n display: none;\n width: 100%;\n padding: ").concat(dt("menubar.submenu.padding"), ";\n background: ").concat(dt("menubar.submenu.background"), ";\n border: 1px solid ").concat(dt("menubar.submenu.border.color"), ";\n box-shadow: ").concat(dt("menubar.submenu.shadow"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content {\n border-radius: ").concat(dt("menubar.item.border.radius"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content > .p-menubar-item-link {\n padding: ").concat(dt("menubar.item.padding"), ";\n}\n\n.p-menubar-mobile-active .p-menubar-root-list {\n display: flex;\n flex-direction: column;\n top: 100%;\n left: 0;\n z-index: 1;\n}\n\n.p-menubar-mobile .p-menubar-root-list .p-menubar-item {\n width: 100%;\n position: static;\n}\n\n.p-menubar-mobile .p-menubar-root-list .p-menubar-separator {\n border-top: 1px solid ").concat(dt("menubar.separator.border.color"), ";\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item > .p-menubar-item-content .p-menubar-submenu-icon {\n margin-left: auto;\n transition: transform 0.2s;\n}\n\n.p-menubar-mobile .p-menubar-root-list > .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n transform: rotate(-180deg);\n}\n\n.p-menubar-mobile .p-menubar-submenu .p-menubar-submenu-icon {\n transition: transform 0.2s;\n transform: rotate(90deg);\n}\n\n.p-menubar-mobile .p-menubar-item-active > .p-menubar-item-content .p-menubar-submenu-icon {\n transform: rotate(-90deg);\n}\n\n.p-menubar-mobile .p-menubar-submenu {\n width: 100%;\n position: static;\n box-shadow: none;\n border: 0 none;\n padding-left: ").concat(dt("menubar.submenu.mobile.indent"), ";\n}\n"); -}, "theme"); -var inlineStyles = { - submenu: /* @__PURE__ */ __name(function submenu2(_ref2) { - var instance = _ref2.instance, processedItem = _ref2.processedItem; - return { - display: instance.isItemActive(processedItem) ? "flex" : "none" - }; - }, "submenu") -}; -var classes = { - root: /* @__PURE__ */ __name(function root10(_ref3) { - var instance = _ref3.instance; - return ["p-menubar p-component", { - "p-menubar-mobile": instance.queryMatches, - "p-menubar-mobile-active": instance.mobileActive - }]; - }, "root"), - start: "p-menubar-start", - button: "p-menubar-button", - rootList: "p-menubar-root-list", - item: /* @__PURE__ */ __name(function item2(_ref4) { - var instance = _ref4.instance, processedItem = _ref4.processedItem; - return ["p-menubar-item", { - "p-menubar-item-active": instance.isItemActive(processedItem), - "p-focus": instance.isItemFocused(processedItem), - "p-disabled": instance.isItemDisabled(processedItem) - }]; - }, "item"), - itemContent: "p-menubar-item-content", - itemLink: "p-menubar-item-link", - itemIcon: "p-menubar-item-icon", - itemLabel: "p-menubar-item-label", - submenuIcon: "p-menubar-submenu-icon", - submenu: "p-menubar-submenu", - separator: "p-menubar-separator", - end: "p-menubar-end" -}; -var MenubarStyle = BaseStyle.extend({ - name: "menubar", - theme: theme8, - classes, - inlineStyles -}); -var script$2 = { - name: "BaseMenubar", - "extends": script$e, - props: { - model: { - type: Array, - "default": null - }, - buttonProps: { - type: null, - "default": null - }, - breakpoint: { - type: String, - "default": "960px" - }, - ariaLabelledby: { - type: String, - "default": null - }, - ariaLabel: { - type: String, - "default": null - } - }, - style: MenubarStyle, - provide: /* @__PURE__ */ __name(function provide12() { - return { - $pcMenubar: this, - $parentInstance: this - }; - }, "provide") -}; -var script$1 = { - name: "MenubarSub", - hostName: "Menubar", - "extends": script$e, - emits: ["item-mouseenter", "item-click", "item-mousemove"], - props: { - items: { - type: Array, - "default": null - }, - root: { - type: Boolean, - "default": false - }, - popup: { - type: Boolean, - "default": false - }, - mobileActive: { - type: Boolean, - "default": false - }, - templates: { - type: Object, - "default": null - }, - level: { - type: Number, - "default": 0 - }, - menuId: { - type: String, - "default": null - }, - focusedItemId: { - type: String, - "default": null - }, - activeItemPath: { - type: Object, - "default": null - } - }, - list: null, - methods: { - getItemId: /* @__PURE__ */ __name(function getItemId2(processedItem) { - return "".concat(this.menuId, "_").concat(processedItem.key); - }, "getItemId"), - getItemKey: /* @__PURE__ */ __name(function getItemKey2(processedItem) { - return this.getItemId(processedItem); - }, "getItemKey"), - getItemProp: /* @__PURE__ */ __name(function getItemProp3(processedItem, name, params) { - return processedItem && processedItem.item ? resolve(processedItem.item[name], params) : void 0; - }, "getItemProp"), - getItemLabel: /* @__PURE__ */ __name(function getItemLabel3(processedItem) { - return this.getItemProp(processedItem, "label"); - }, "getItemLabel"), - getItemLabelId: /* @__PURE__ */ __name(function getItemLabelId2(processedItem) { - return "".concat(this.menuId, "_").concat(processedItem.key, "_label"); - }, "getItemLabelId"), - getPTOptions: /* @__PURE__ */ __name(function getPTOptions5(processedItem, index, key) { - return this.ptm(key, { - context: { - item: processedItem.item, - index, - active: this.isItemActive(processedItem), - focused: this.isItemFocused(processedItem), - disabled: this.isItemDisabled(processedItem), - level: this.level - } - }); - }, "getPTOptions"), - isItemActive: /* @__PURE__ */ __name(function isItemActive2(processedItem) { - return this.activeItemPath.some(function(path) { - return path.key === processedItem.key; - }); - }, "isItemActive"), - isItemVisible: /* @__PURE__ */ __name(function isItemVisible3(processedItem) { - return this.getItemProp(processedItem, "visible") !== false; - }, "isItemVisible"), - isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled3(processedItem) { - return this.getItemProp(processedItem, "disabled"); - }, "isItemDisabled"), - isItemFocused: /* @__PURE__ */ __name(function isItemFocused2(processedItem) { - return this.focusedItemId === this.getItemId(processedItem); - }, "isItemFocused"), - isItemGroup: /* @__PURE__ */ __name(function isItemGroup3(processedItem) { - return isNotEmpty(processedItem.items); - }, "isItemGroup"), - onItemClick: /* @__PURE__ */ __name(function onItemClick3(event, processedItem) { - this.getItemProp(processedItem, "command", { - originalEvent: event, - item: processedItem.item - }); - this.$emit("item-click", { - originalEvent: event, - processedItem, - isFocus: true - }); - }, "onItemClick"), - onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter3(event, processedItem) { - this.$emit("item-mouseenter", { - originalEvent: event, - processedItem - }); - }, "onItemMouseEnter"), - onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove3(event, processedItem) { - this.$emit("item-mousemove", { - originalEvent: event, - processedItem - }); - }, "onItemMouseMove"), - getAriaPosInset: /* @__PURE__ */ __name(function getAriaPosInset3(index) { - return index - this.calculateAriaSetSize.slice(0, index).length + 1; - }, "getAriaPosInset"), - getMenuItemProps: /* @__PURE__ */ __name(function getMenuItemProps2(processedItem, index) { - return { - action: mergeProps({ - "class": this.cx("itemLink"), - tabindex: -1, - "aria-hidden": true - }, this.getPTOptions(processedItem, index, "itemLink")), - icon: mergeProps({ - "class": [this.cx("itemIcon"), this.getItemProp(processedItem, "icon")] - }, this.getPTOptions(processedItem, index, "itemIcon")), - label: mergeProps({ - "class": this.cx("itemLabel") - }, this.getPTOptions(processedItem, index, "itemLabel")), - submenuicon: mergeProps({ - "class": this.cx("submenuIcon") - }, this.getPTOptions(processedItem, index, "submenuIcon")) - }; - }, "getMenuItemProps") - }, - computed: { - calculateAriaSetSize: /* @__PURE__ */ __name(function calculateAriaSetSize() { - var _this = this; - return this.items.filter(function(processedItem) { - return _this.isItemVisible(processedItem) && _this.getItemProp(processedItem, "separator"); - }); - }, "calculateAriaSetSize"), - getAriaSetSize: /* @__PURE__ */ __name(function getAriaSetSize2() { - var _this2 = this; - return this.items.filter(function(processedItem) { - return _this2.isItemVisible(processedItem) && !_this2.getItemProp(processedItem, "separator"); - }).length; - }, "getAriaSetSize") - }, - components: { - AngleRightIcon: script$w, - AngleDownIcon: script$z - }, - directives: { - ripple: Ripple - } -}; -var _hoisted_1$1$1 = ["id", "aria-label", "aria-disabled", "aria-expanded", "aria-haspopup", "aria-level", "aria-setsize", "aria-posinset", "data-p-active", "data-p-focused", "data-p-disabled"]; -var _hoisted_2$3 = ["onClick", "onMouseenter", "onMousemove"]; -var _hoisted_3$2 = ["href", "target"]; -var _hoisted_4 = ["id"]; -var _hoisted_5 = ["id"]; -function render$1(_ctx, _cache, $props, $setup, $data, $options) { - var _component_MenubarSub = resolveComponent("MenubarSub", true); - var _directive_ripple = resolveDirective("ripple"); - return openBlock(), createElementBlock("ul", mergeProps({ - "class": $props.level === 0 ? _ctx.cx("rootList") : _ctx.cx("submenu") - }, $props.level === 0 ? _ctx.ptm("rootList") : _ctx.ptm("submenu")), [(openBlock(true), createElementBlock(Fragment, null, renderList($props.items, function(processedItem, index) { - return openBlock(), createElementBlock(Fragment, { - key: $options.getItemKey(processedItem) - }, [$options.isItemVisible(processedItem) && !$options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ - key: 0, - id: $options.getItemId(processedItem), - style: $options.getItemProp(processedItem, "style"), - "class": [_ctx.cx("item", { - processedItem - }), $options.getItemProp(processedItem, "class")], - role: "menuitem", - "aria-label": $options.getItemLabel(processedItem), - "aria-disabled": $options.isItemDisabled(processedItem) || void 0, - "aria-expanded": $options.isItemGroup(processedItem) ? $options.isItemActive(processedItem) : void 0, - "aria-haspopup": $options.isItemGroup(processedItem) && !$options.getItemProp(processedItem, "to") ? "menu" : void 0, - "aria-level": $props.level + 1, - "aria-setsize": $options.getAriaSetSize, - "aria-posinset": $options.getAriaPosInset(index), - ref_for: true - }, $options.getPTOptions(processedItem, index, "item"), { - "data-p-active": $options.isItemActive(processedItem), - "data-p-focused": $options.isItemFocused(processedItem), - "data-p-disabled": $options.isItemDisabled(processedItem) - }), [createBaseVNode("div", mergeProps({ - "class": _ctx.cx("itemContent"), - onClick: /* @__PURE__ */ __name(function onClick2($event) { - return $options.onItemClick($event, processedItem); - }, "onClick"), - onMouseenter: /* @__PURE__ */ __name(function onMouseenter($event) { - return $options.onItemMouseEnter($event, processedItem); - }, "onMouseenter"), - onMousemove: /* @__PURE__ */ __name(function onMousemove($event) { - return $options.onItemMouseMove($event, processedItem); - }, "onMousemove"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemContent")), [!$props.templates.item ? withDirectives((openBlock(), createElementBlock("a", mergeProps({ - key: 0, - href: $options.getItemProp(processedItem, "url"), - "class": _ctx.cx("itemLink"), - target: $options.getItemProp(processedItem, "target"), - tabindex: "-1", - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemLink")), [$props.templates.itemicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.itemicon), { - key: 0, - item: processedItem.item, - "class": normalizeClass(_ctx.cx("itemIcon")) - }, null, 8, ["item", "class"])) : $options.getItemProp(processedItem, "icon") ? (openBlock(), createElementBlock("span", mergeProps({ - key: 1, - "class": [_ctx.cx("itemIcon"), $options.getItemProp(processedItem, "icon")], - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemIcon")), null, 16)) : createCommentVNode("", true), createBaseVNode("span", mergeProps({ - id: $options.getItemLabelId(processedItem), - "class": _ctx.cx("itemLabel"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "itemLabel")), toDisplayString($options.getItemLabel(processedItem)), 17, _hoisted_4), $options.getItemProp(processedItem, "items") ? (openBlock(), createElementBlock(Fragment, { - key: 2 - }, [$props.templates.submenuicon ? (openBlock(), createBlock(resolveDynamicComponent($props.templates.submenuicon), { - key: 0, - root: $props.root, - active: $options.isItemActive(processedItem), - "class": normalizeClass(_ctx.cx("submenuIcon")) - }, null, 8, ["root", "active", "class"])) : (openBlock(), createBlock(resolveDynamicComponent($props.root ? "AngleDownIcon" : "AngleRightIcon"), mergeProps({ - key: 1, - "class": _ctx.cx("submenuIcon"), - ref_for: true - }, $options.getPTOptions(processedItem, index, "submenuIcon")), null, 16, ["class"]))], 64)) : createCommentVNode("", true)], 16, _hoisted_3$2)), [[_directive_ripple]]) : (openBlock(), createBlock(resolveDynamicComponent($props.templates.item), { - key: 1, - item: processedItem.item, - root: $props.root, - hasSubmenu: $options.getItemProp(processedItem, "items"), - label: $options.getItemLabel(processedItem), - props: $options.getMenuItemProps(processedItem, index) - }, null, 8, ["item", "root", "hasSubmenu", "label", "props"]))], 16, _hoisted_2$3), $options.isItemVisible(processedItem) && $options.isItemGroup(processedItem) ? (openBlock(), createBlock(_component_MenubarSub, { - key: 0, - id: $options.getItemId(processedItem) + "_list", - menuId: $props.menuId, - role: "menu", - style: normalizeStyle(_ctx.sx("submenu", true, { - processedItem - })), - focusedItemId: $props.focusedItemId, - items: processedItem.items, - mobileActive: $props.mobileActive, - activeItemPath: $props.activeItemPath, - templates: $props.templates, - level: $props.level + 1, - "aria-labelledby": $options.getItemLabelId(processedItem), - pt: _ctx.pt, - unstyled: _ctx.unstyled, - onItemClick: _cache[0] || (_cache[0] = function($event) { - return _ctx.$emit("item-click", $event); - }), - onItemMouseenter: _cache[1] || (_cache[1] = function($event) { - return _ctx.$emit("item-mouseenter", $event); - }), - onItemMousemove: _cache[2] || (_cache[2] = function($event) { - return _ctx.$emit("item-mousemove", $event); - }) - }, null, 8, ["id", "menuId", "style", "focusedItemId", "items", "mobileActive", "activeItemPath", "templates", "level", "aria-labelledby", "pt", "unstyled"])) : createCommentVNode("", true)], 16, _hoisted_1$1$1)) : createCommentVNode("", true), $options.isItemVisible(processedItem) && $options.getItemProp(processedItem, "separator") ? (openBlock(), createElementBlock("li", mergeProps({ - key: 1, - id: $options.getItemId(processedItem), - "class": [_ctx.cx("separator"), $options.getItemProp(processedItem, "class")], - style: $options.getItemProp(processedItem, "style"), - role: "separator", - ref_for: true - }, _ctx.ptm("separator")), null, 16, _hoisted_5)) : createCommentVNode("", true)], 64); - }), 128))], 16); -} -__name(render$1, "render$1"); -script$1.render = render$1; -var script = { - name: "Menubar", - "extends": script$2, - inheritAttrs: false, - emits: ["focus", "blur"], - matchMediaListener: null, - data: /* @__PURE__ */ __name(function data8() { - return { - id: this.$attrs.id, - mobileActive: false, - focused: false, - focusedItemInfo: { - index: -1, - level: 0, - parentKey: "" - }, - activeItemPath: [], - dirty: false, - query: null, - queryMatches: false - }; - }, "data"), - watch: { - "$attrs.id": /* @__PURE__ */ __name(function $attrsId4(newValue) { - this.id = newValue || UniqueComponentId(); - }, "$attrsId"), - activeItemPath: /* @__PURE__ */ __name(function activeItemPath2(newPath) { - if (isNotEmpty(newPath)) { - this.bindOutsideClickListener(); - this.bindResizeListener(); - } else { - this.unbindOutsideClickListener(); - this.unbindResizeListener(); - } - }, "activeItemPath") - }, - outsideClickListener: null, - container: null, - menubar: null, - mounted: /* @__PURE__ */ __name(function mounted8() { - this.id = this.id || UniqueComponentId(); - this.bindMatchMediaListener(); - }, "mounted"), - beforeUnmount: /* @__PURE__ */ __name(function beforeUnmount7() { - this.mobileActive = false; - this.unbindOutsideClickListener(); - this.unbindResizeListener(); - this.unbindMatchMediaListener(); - if (this.container) { - ZIndex.clear(this.container); - } - this.container = null; - }, "beforeUnmount"), - methods: { - getItemProp: /* @__PURE__ */ __name(function getItemProp4(item3, name) { - return item3 ? resolve(item3[name]) : void 0; - }, "getItemProp"), - getItemLabel: /* @__PURE__ */ __name(function getItemLabel4(item3) { - return this.getItemProp(item3, "label"); - }, "getItemLabel"), - isItemDisabled: /* @__PURE__ */ __name(function isItemDisabled4(item3) { - return this.getItemProp(item3, "disabled"); - }, "isItemDisabled"), - isItemVisible: /* @__PURE__ */ __name(function isItemVisible4(item3) { - return this.getItemProp(item3, "visible") !== false; - }, "isItemVisible"), - isItemGroup: /* @__PURE__ */ __name(function isItemGroup4(item3) { - return isNotEmpty(this.getItemProp(item3, "items")); - }, "isItemGroup"), - isItemSeparator: /* @__PURE__ */ __name(function isItemSeparator2(item3) { - return this.getItemProp(item3, "separator"); - }, "isItemSeparator"), - getProccessedItemLabel: /* @__PURE__ */ __name(function getProccessedItemLabel2(processedItem) { - return processedItem ? this.getItemLabel(processedItem.item) : void 0; - }, "getProccessedItemLabel"), - isProccessedItemGroup: /* @__PURE__ */ __name(function isProccessedItemGroup2(processedItem) { - return processedItem && isNotEmpty(processedItem.items); - }, "isProccessedItemGroup"), - toggle: /* @__PURE__ */ __name(function toggle2(event) { - var _this = this; - if (this.mobileActive) { - this.mobileActive = false; - ZIndex.clear(this.menubar); - this.hide(); - } else { - this.mobileActive = true; - ZIndex.set("menu", this.menubar, this.$primevue.config.zIndex.menu); - setTimeout(function() { - _this.show(); - }, 1); - } - this.bindOutsideClickListener(); - event.preventDefault(); - }, "toggle"), - show: /* @__PURE__ */ __name(function show3() { - focus(this.menubar); - }, "show"), - hide: /* @__PURE__ */ __name(function hide3(event, isFocus) { - var _this2 = this; - if (this.mobileActive) { - this.mobileActive = false; - setTimeout(function() { - focus(_this2.$refs.menubutton); - }, 0); - } - this.activeItemPath = []; - this.focusedItemInfo = { - index: -1, - level: 0, - parentKey: "" - }; - isFocus && focus(this.menubar); - this.dirty = false; - }, "hide"), - onFocus: /* @__PURE__ */ __name(function onFocus4(event) { - this.focused = true; - this.focusedItemInfo = this.focusedItemInfo.index !== -1 ? this.focusedItemInfo : { - index: this.findFirstFocusedItemIndex(), - level: 0, - parentKey: "" - }; - this.$emit("focus", event); - }, "onFocus"), - onBlur: /* @__PURE__ */ __name(function onBlur3(event) { - this.focused = false; - this.focusedItemInfo = { - index: -1, - level: 0, - parentKey: "" - }; - this.searchValue = ""; - this.dirty = false; - this.$emit("blur", event); - }, "onBlur"), - onKeyDown: /* @__PURE__ */ __name(function onKeyDown3(event) { - var metaKey = event.metaKey || event.ctrlKey; - switch (event.code) { - case "ArrowDown": - this.onArrowDownKey(event); - break; - case "ArrowUp": - this.onArrowUpKey(event); - break; - case "ArrowLeft": - this.onArrowLeftKey(event); - break; - case "ArrowRight": - this.onArrowRightKey(event); - break; - case "Home": - this.onHomeKey(event); - break; - case "End": - this.onEndKey(event); - break; - case "Space": - this.onSpaceKey(event); - break; - case "Enter": - case "NumpadEnter": - this.onEnterKey(event); - break; - case "Escape": - this.onEscapeKey(event); - break; - case "Tab": - this.onTabKey(event); - break; - case "PageDown": - case "PageUp": - case "Backspace": - case "ShiftLeft": - case "ShiftRight": - break; - default: - if (!metaKey && isPrintableCharacter(event.key)) { - this.searchItems(event, event.key); - } - break; - } - }, "onKeyDown"), - onItemChange: /* @__PURE__ */ __name(function onItemChange2(event) { - var processedItem = event.processedItem, isFocus = event.isFocus; - if (isEmpty(processedItem)) return; - var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey, items = processedItem.items; - var grouped = isNotEmpty(items); - var activeItemPath3 = this.activeItemPath.filter(function(p) { - return p.parentKey !== parentKey && p.parentKey !== key; - }); - grouped && activeItemPath3.push(processedItem); - this.focusedItemInfo = { - index, - level, - parentKey - }; - this.activeItemPath = activeItemPath3; - grouped && (this.dirty = true); - isFocus && focus(this.menubar); - }, "onItemChange"), - onItemClick: /* @__PURE__ */ __name(function onItemClick4(event) { - var originalEvent = event.originalEvent, processedItem = event.processedItem; - var grouped = this.isProccessedItemGroup(processedItem); - var root11 = isEmpty(processedItem.parent); - var selected = this.isSelected(processedItem); - if (selected) { - var index = processedItem.index, key = processedItem.key, level = processedItem.level, parentKey = processedItem.parentKey; - this.activeItemPath = this.activeItemPath.filter(function(p) { - return key !== p.key && key.startsWith(p.key); - }); - this.focusedItemInfo = { - index, - level, - parentKey - }; - this.dirty = !root11; - focus(this.menubar); - } else { - if (grouped) { - this.onItemChange(event); - } else { - var rootProcessedItem = root11 ? processedItem : this.activeItemPath.find(function(p) { - return p.parentKey === ""; - }); - this.hide(originalEvent); - this.changeFocusedItemIndex(originalEvent, rootProcessedItem ? rootProcessedItem.index : -1); - this.mobileActive = false; - focus(this.menubar); - } - } - }, "onItemClick"), - onItemMouseEnter: /* @__PURE__ */ __name(function onItemMouseEnter4(event) { - if (this.dirty) { - this.onItemChange(event); - } - }, "onItemMouseEnter"), - onItemMouseMove: /* @__PURE__ */ __name(function onItemMouseMove4(event) { - if (this.focused) { - this.changeFocusedItemIndex(event, event.processedItem.index); - } - }, "onItemMouseMove"), - menuButtonClick: /* @__PURE__ */ __name(function menuButtonClick(event) { - this.toggle(event); - }, "menuButtonClick"), - menuButtonKeydown: /* @__PURE__ */ __name(function menuButtonKeydown(event) { - (event.code === "Enter" || event.code === "NumpadEnter" || event.code === "Space") && this.menuButtonClick(event); - }, "menuButtonKeydown"), - onArrowDownKey: /* @__PURE__ */ __name(function onArrowDownKey3(event) { - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var root11 = processedItem ? isEmpty(processedItem.parent) : null; - if (root11) { - var grouped = this.isProccessedItemGroup(processedItem); - if (grouped) { - this.onItemChange({ - originalEvent: event, - processedItem - }); - this.focusedItemInfo = { - index: -1, - parentKey: processedItem.key - }; - this.onArrowRightKey(event); - } - } else { - var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); - this.changeFocusedItemIndex(event, itemIndex); - } - event.preventDefault(); - }, "onArrowDownKey"), - onArrowUpKey: /* @__PURE__ */ __name(function onArrowUpKey3(event) { - var _this3 = this; - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var root11 = isEmpty(processedItem.parent); - if (root11) { - var grouped = this.isProccessedItemGroup(processedItem); - if (grouped) { - this.onItemChange({ - originalEvent: event, - processedItem - }); - this.focusedItemInfo = { - index: -1, - parentKey: processedItem.key - }; - var itemIndex = this.findLastItemIndex(); - this.changeFocusedItemIndex(event, itemIndex); - } - } else { - var parentItem = this.activeItemPath.find(function(p) { - return p.key === processedItem.parentKey; - }); - if (this.focusedItemInfo.index === 0) { - this.focusedItemInfo = { - index: -1, - parentKey: parentItem ? parentItem.parentKey : "" - }; - this.searchValue = ""; - this.onArrowLeftKey(event); - this.activeItemPath = this.activeItemPath.filter(function(p) { - return p.parentKey !== _this3.focusedItemInfo.parentKey; - }); - } else { - var _itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex(); - this.changeFocusedItemIndex(event, _itemIndex); - } - } - event.preventDefault(); - }, "onArrowUpKey"), - onArrowLeftKey: /* @__PURE__ */ __name(function onArrowLeftKey4(event) { - var _this4 = this; - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var parentItem = processedItem ? this.activeItemPath.find(function(p) { - return p.key === processedItem.parentKey; - }) : null; - if (parentItem) { - this.onItemChange({ - originalEvent: event, - processedItem: parentItem - }); - this.activeItemPath = this.activeItemPath.filter(function(p) { - return p.parentKey !== _this4.focusedItemInfo.parentKey; - }); - event.preventDefault(); - } else { - var itemIndex = this.focusedItemInfo.index !== -1 ? this.findPrevItemIndex(this.focusedItemInfo.index) : this.findLastFocusedItemIndex(); - this.changeFocusedItemIndex(event, itemIndex); - event.preventDefault(); - } - }, "onArrowLeftKey"), - onArrowRightKey: /* @__PURE__ */ __name(function onArrowRightKey4(event) { - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var parentItem = processedItem ? this.activeItemPath.find(function(p) { - return p.key === processedItem.parentKey; - }) : null; - if (parentItem) { - var grouped = this.isProccessedItemGroup(processedItem); - if (grouped) { - this.onItemChange({ - originalEvent: event, - processedItem - }); - this.focusedItemInfo = { - index: -1, - parentKey: processedItem.key - }; - this.onArrowDownKey(event); - } - } else { - var itemIndex = this.focusedItemInfo.index !== -1 ? this.findNextItemIndex(this.focusedItemInfo.index) : this.findFirstFocusedItemIndex(); - this.changeFocusedItemIndex(event, itemIndex); - event.preventDefault(); - } - }, "onArrowRightKey"), - onHomeKey: /* @__PURE__ */ __name(function onHomeKey4(event) { - this.changeFocusedItemIndex(event, this.findFirstItemIndex()); - event.preventDefault(); - }, "onHomeKey"), - onEndKey: /* @__PURE__ */ __name(function onEndKey4(event) { - this.changeFocusedItemIndex(event, this.findLastItemIndex()); - event.preventDefault(); - }, "onEndKey"), - onEnterKey: /* @__PURE__ */ __name(function onEnterKey4(event) { - if (this.focusedItemInfo.index !== -1) { - var element = findSingle(this.menubar, 'li[id="'.concat("".concat(this.focusedItemId), '"]')); - var anchorElement = element && findSingle(element, 'a[data-pc-section="itemlink"]'); - anchorElement ? anchorElement.click() : element && element.click(); - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var grouped = this.isProccessedItemGroup(processedItem); - !grouped && (this.focusedItemInfo.index = this.findFirstFocusedItemIndex()); - } - event.preventDefault(); - }, "onEnterKey"), - onSpaceKey: /* @__PURE__ */ __name(function onSpaceKey2(event) { - this.onEnterKey(event); - }, "onSpaceKey"), - onEscapeKey: /* @__PURE__ */ __name(function onEscapeKey3(event) { - if (this.focusedItemInfo.level !== 0) { - var _focusedItemInfo = this.focusedItemInfo; - this.hide(event, false); - this.focusedItemInfo = { - index: Number(_focusedItemInfo.parentKey.split("_")[0]), - level: 0, - parentKey: "" - }; - } - event.preventDefault(); - }, "onEscapeKey"), - onTabKey: /* @__PURE__ */ __name(function onTabKey3(event) { - if (this.focusedItemInfo.index !== -1) { - var processedItem = this.visibleItems[this.focusedItemInfo.index]; - var grouped = this.isProccessedItemGroup(processedItem); - !grouped && this.onItemChange({ - originalEvent: event, - processedItem - }); - } - this.hide(); - }, "onTabKey"), - bindOutsideClickListener: /* @__PURE__ */ __name(function bindOutsideClickListener3() { - var _this5 = this; - if (!this.outsideClickListener) { - this.outsideClickListener = function(event) { - var isOutsideContainer = _this5.container && !_this5.container.contains(event.target); - var isOutsideTarget = !(_this5.target && (_this5.target === event.target || _this5.target.contains(event.target))); - if (isOutsideContainer && isOutsideTarget) { - _this5.hide(); - } - }; - document.addEventListener("click", this.outsideClickListener); - } - }, "bindOutsideClickListener"), - unbindOutsideClickListener: /* @__PURE__ */ __name(function unbindOutsideClickListener3() { - if (this.outsideClickListener) { - document.removeEventListener("click", this.outsideClickListener); - this.outsideClickListener = null; - } - }, "unbindOutsideClickListener"), - bindResizeListener: /* @__PURE__ */ __name(function bindResizeListener3() { - var _this6 = this; - if (!this.resizeListener) { - this.resizeListener = function(event) { - if (!isTouchDevice()) { - _this6.hide(event, true); - } - _this6.mobileActive = false; - }; - window.addEventListener("resize", this.resizeListener); - } - }, "bindResizeListener"), - unbindResizeListener: /* @__PURE__ */ __name(function unbindResizeListener3() { - if (this.resizeListener) { - window.removeEventListener("resize", this.resizeListener); - this.resizeListener = null; - } - }, "unbindResizeListener"), - bindMatchMediaListener: /* @__PURE__ */ __name(function bindMatchMediaListener() { - var _this7 = this; - if (!this.matchMediaListener) { - var query = matchMedia("(max-width: ".concat(this.breakpoint, ")")); - this.query = query; - this.queryMatches = query.matches; - this.matchMediaListener = function() { - _this7.queryMatches = query.matches; - _this7.mobileActive = false; - }; - this.query.addEventListener("change", this.matchMediaListener); - } - }, "bindMatchMediaListener"), - unbindMatchMediaListener: /* @__PURE__ */ __name(function unbindMatchMediaListener() { - if (this.matchMediaListener) { - this.query.removeEventListener("change", this.matchMediaListener); - this.matchMediaListener = null; - } - }, "unbindMatchMediaListener"), - isItemMatched: /* @__PURE__ */ __name(function isItemMatched2(processedItem) { - var _this$getProccessedIt; - return this.isValidItem(processedItem) && ((_this$getProccessedIt = this.getProccessedItemLabel(processedItem)) === null || _this$getProccessedIt === void 0 ? void 0 : _this$getProccessedIt.toLocaleLowerCase().startsWith(this.searchValue.toLocaleLowerCase())); - }, "isItemMatched"), - isValidItem: /* @__PURE__ */ __name(function isValidItem2(processedItem) { - return !!processedItem && !this.isItemDisabled(processedItem.item) && !this.isItemSeparator(processedItem.item) && this.isItemVisible(processedItem.item); - }, "isValidItem"), - isValidSelectedItem: /* @__PURE__ */ __name(function isValidSelectedItem2(processedItem) { - return this.isValidItem(processedItem) && this.isSelected(processedItem); - }, "isValidSelectedItem"), - isSelected: /* @__PURE__ */ __name(function isSelected3(processedItem) { - return this.activeItemPath.some(function(p) { - return p.key === processedItem.key; - }); - }, "isSelected"), - findFirstItemIndex: /* @__PURE__ */ __name(function findFirstItemIndex2() { - var _this8 = this; - return this.visibleItems.findIndex(function(processedItem) { - return _this8.isValidItem(processedItem); - }); - }, "findFirstItemIndex"), - findLastItemIndex: /* @__PURE__ */ __name(function findLastItemIndex2() { - var _this9 = this; - return findLastIndex(this.visibleItems, function(processedItem) { - return _this9.isValidItem(processedItem); - }); - }, "findLastItemIndex"), - findNextItemIndex: /* @__PURE__ */ __name(function findNextItemIndex2(index) { - var _this10 = this; - var matchedItemIndex = index < this.visibleItems.length - 1 ? this.visibleItems.slice(index + 1).findIndex(function(processedItem) { - return _this10.isValidItem(processedItem); - }) : -1; - return matchedItemIndex > -1 ? matchedItemIndex + index + 1 : index; - }, "findNextItemIndex"), - findPrevItemIndex: /* @__PURE__ */ __name(function findPrevItemIndex2(index) { - var _this11 = this; - var matchedItemIndex = index > 0 ? findLastIndex(this.visibleItems.slice(0, index), function(processedItem) { - return _this11.isValidItem(processedItem); - }) : -1; - return matchedItemIndex > -1 ? matchedItemIndex : index; - }, "findPrevItemIndex"), - findSelectedItemIndex: /* @__PURE__ */ __name(function findSelectedItemIndex2() { - var _this12 = this; - return this.visibleItems.findIndex(function(processedItem) { - return _this12.isValidSelectedItem(processedItem); - }); - }, "findSelectedItemIndex"), - findFirstFocusedItemIndex: /* @__PURE__ */ __name(function findFirstFocusedItemIndex2() { - var selectedIndex = this.findSelectedItemIndex(); - return selectedIndex < 0 ? this.findFirstItemIndex() : selectedIndex; - }, "findFirstFocusedItemIndex"), - findLastFocusedItemIndex: /* @__PURE__ */ __name(function findLastFocusedItemIndex2() { - var selectedIndex = this.findSelectedItemIndex(); - return selectedIndex < 0 ? this.findLastItemIndex() : selectedIndex; - }, "findLastFocusedItemIndex"), - searchItems: /* @__PURE__ */ __name(function searchItems2(event, _char) { - var _this13 = this; - this.searchValue = (this.searchValue || "") + _char; - var itemIndex = -1; - var matched = false; - if (this.focusedItemInfo.index !== -1) { - itemIndex = this.visibleItems.slice(this.focusedItemInfo.index).findIndex(function(processedItem) { - return _this13.isItemMatched(processedItem); - }); - itemIndex = itemIndex === -1 ? this.visibleItems.slice(0, this.focusedItemInfo.index).findIndex(function(processedItem) { - return _this13.isItemMatched(processedItem); - }) : itemIndex + this.focusedItemInfo.index; - } else { - itemIndex = this.visibleItems.findIndex(function(processedItem) { - return _this13.isItemMatched(processedItem); - }); - } - if (itemIndex !== -1) { - matched = true; - } - if (itemIndex === -1 && this.focusedItemInfo.index === -1) { - itemIndex = this.findFirstFocusedItemIndex(); - } - if (itemIndex !== -1) { - this.changeFocusedItemIndex(event, itemIndex); - } - if (this.searchTimeout) { - clearTimeout(this.searchTimeout); - } - this.searchTimeout = setTimeout(function() { - _this13.searchValue = ""; - _this13.searchTimeout = null; - }, 500); - return matched; - }, "searchItems"), - changeFocusedItemIndex: /* @__PURE__ */ __name(function changeFocusedItemIndex2(event, index) { - if (this.focusedItemInfo.index !== index) { - this.focusedItemInfo.index = index; - this.scrollInView(); - } - }, "changeFocusedItemIndex"), - scrollInView: /* @__PURE__ */ __name(function scrollInView4() { - var index = arguments.length > 0 && arguments[0] !== void 0 ? arguments[0] : -1; - var id2 = index !== -1 ? "".concat(this.id, "_").concat(index) : this.focusedItemId; - var element = findSingle(this.menubar, 'li[id="'.concat(id2, '"]')); - if (element) { - element.scrollIntoView && element.scrollIntoView({ - block: "nearest", - inline: "start" - }); - } - }, "scrollInView"), - createProcessedItems: /* @__PURE__ */ __name(function createProcessedItems2(items) { - var _this14 = this; - var level = arguments.length > 1 && arguments[1] !== void 0 ? arguments[1] : 0; - var parent = arguments.length > 2 && arguments[2] !== void 0 ? arguments[2] : {}; - var parentKey = arguments.length > 3 && arguments[3] !== void 0 ? arguments[3] : ""; - var processedItems3 = []; - items && items.forEach(function(item3, index) { - var key = (parentKey !== "" ? parentKey + "_" : "") + index; - var newItem = { - item: item3, - index, - level, - key, - parent, - parentKey - }; - newItem["items"] = _this14.createProcessedItems(item3.items, level + 1, newItem, key); - processedItems3.push(newItem); - }); - return processedItems3; - }, "createProcessedItems"), - containerRef: /* @__PURE__ */ __name(function containerRef3(el) { - this.container = el; - }, "containerRef"), - menubarRef: /* @__PURE__ */ __name(function menubarRef2(el) { - this.menubar = el ? el.$el : void 0; - }, "menubarRef") - }, - computed: { - processedItems: /* @__PURE__ */ __name(function processedItems2() { - return this.createProcessedItems(this.model || []); - }, "processedItems"), - visibleItems: /* @__PURE__ */ __name(function visibleItems2() { - var _this15 = this; - var processedItem = this.activeItemPath.find(function(p) { - return p.key === _this15.focusedItemInfo.parentKey; - }); - return processedItem ? processedItem.items : this.processedItems; - }, "visibleItems"), - focusedItemId: /* @__PURE__ */ __name(function focusedItemId2() { - return this.focusedItemInfo.index !== -1 ? "".concat(this.id).concat(isNotEmpty(this.focusedItemInfo.parentKey) ? "_" + this.focusedItemInfo.parentKey : "", "_").concat(this.focusedItemInfo.index) : null; - }, "focusedItemId") - }, - components: { - MenubarSub: script$1, - BarsIcon: script$A - } -}; -function _typeof(o) { - "@babel/helpers - typeof"; - return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function(o2) { - return typeof o2; - } : function(o2) { - return o2 && "function" == typeof Symbol && o2.constructor === Symbol && o2 !== Symbol.prototype ? "symbol" : typeof o2; - }, _typeof(o); -} -__name(_typeof, "_typeof"); -function ownKeys(e, r) { - var t = Object.keys(e); - if (Object.getOwnPropertySymbols) { - var o = Object.getOwnPropertySymbols(e); - r && (o = o.filter(function(r2) { - return Object.getOwnPropertyDescriptor(e, r2).enumerable; - })), t.push.apply(t, o); - } - return t; -} -__name(ownKeys, "ownKeys"); -function _objectSpread(e) { - for (var r = 1; r < arguments.length; r++) { - var t = null != arguments[r] ? arguments[r] : {}; - r % 2 ? ownKeys(Object(t), true).forEach(function(r2) { - _defineProperty(e, r2, t[r2]); - }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function(r2) { - Object.defineProperty(e, r2, Object.getOwnPropertyDescriptor(t, r2)); - }); - } - return e; -} -__name(_objectSpread, "_objectSpread"); -function _defineProperty(e, r, t) { - return (r = _toPropertyKey(r)) in e ? Object.defineProperty(e, r, { value: t, enumerable: true, configurable: true, writable: true }) : e[r] = t, e; -} -__name(_defineProperty, "_defineProperty"); -function _toPropertyKey(t) { - var i = _toPrimitive(t, "string"); - return "symbol" == _typeof(i) ? i : i + ""; -} -__name(_toPropertyKey, "_toPropertyKey"); -function _toPrimitive(t, r) { - if ("object" != _typeof(t) || !t) return t; - var e = t[Symbol.toPrimitive]; - if (void 0 !== e) { - var i = e.call(t, r || "default"); - if ("object" != _typeof(i)) return i; - throw new TypeError("@@toPrimitive must return a primitive value."); - } - return ("string" === r ? String : Number)(t); -} -__name(_toPrimitive, "_toPrimitive"); -var _hoisted_1$3 = ["aria-haspopup", "aria-expanded", "aria-controls", "aria-label"]; -function render(_ctx, _cache, $props, $setup, $data, $options) { - var _component_BarsIcon = resolveComponent("BarsIcon"); - var _component_MenubarSub = resolveComponent("MenubarSub"); - return openBlock(), createElementBlock("div", mergeProps({ - ref: $options.containerRef, - "class": _ctx.cx("root") - }, _ctx.ptmi("root")), [_ctx.$slots.start ? (openBlock(), createElementBlock("div", mergeProps({ - key: 0, - "class": _ctx.cx("start") - }, _ctx.ptm("start")), [renderSlot(_ctx.$slots, "start")], 16)) : createCommentVNode("", true), renderSlot(_ctx.$slots, _ctx.$slots.button ? "button" : "menubutton", { - id: $data.id, - "class": normalizeClass(_ctx.cx("button")), - toggleCallback: /* @__PURE__ */ __name(function toggleCallback(event) { - return $options.menuButtonClick(event); - }, "toggleCallback") - }, function() { - var _ctx$$primevue$config; - return [_ctx.model && _ctx.model.length > 0 ? (openBlock(), createElementBlock("a", mergeProps({ - key: 0, - ref: "menubutton", - role: "button", - tabindex: "0", - "class": _ctx.cx("button"), - "aria-haspopup": _ctx.model.length && _ctx.model.length > 0 ? true : false, - "aria-expanded": $data.mobileActive, - "aria-controls": $data.id, - "aria-label": (_ctx$$primevue$config = _ctx.$primevue.config.locale.aria) === null || _ctx$$primevue$config === void 0 ? void 0 : _ctx$$primevue$config.navigation, - onClick: _cache[0] || (_cache[0] = function($event) { - return $options.menuButtonClick($event); - }), - onKeydown: _cache[1] || (_cache[1] = function($event) { - return $options.menuButtonKeydown($event); - }) - }, _objectSpread(_objectSpread({}, _ctx.buttonProps), _ctx.ptm("button"))), [renderSlot(_ctx.$slots, _ctx.$slots.buttonicon ? "buttonicon" : "menubuttonicon", {}, function() { - return [createVNode(_component_BarsIcon, normalizeProps(guardReactiveProps(_ctx.ptm("buttonicon"))), null, 16)]; - })], 16, _hoisted_1$3)) : createCommentVNode("", true)]; - }), createVNode(_component_MenubarSub, { - ref: $options.menubarRef, - id: $data.id + "_list", - role: "menubar", - items: $options.processedItems, - templates: _ctx.$slots, - root: true, - mobileActive: $data.mobileActive, - tabindex: "0", - "aria-activedescendant": $data.focused ? $options.focusedItemId : void 0, - menuId: $data.id, - focusedItemId: $data.focused ? $options.focusedItemId : void 0, - activeItemPath: $data.activeItemPath, - level: 0, - "aria-labelledby": _ctx.ariaLabelledby, - "aria-label": _ctx.ariaLabel, - pt: _ctx.pt, - unstyled: _ctx.unstyled, - onFocus: $options.onFocus, - onBlur: $options.onBlur, - onKeydown: $options.onKeyDown, - onItemClick: $options.onItemClick, - onItemMouseenter: $options.onItemMouseEnter, - onItemMousemove: $options.onItemMouseMove - }, null, 8, ["id", "items", "templates", "mobileActive", "aria-activedescendant", "menuId", "focusedItemId", "activeItemPath", "aria-labelledby", "aria-label", "pt", "unstyled", "onFocus", "onBlur", "onKeydown", "onItemClick", "onItemMouseenter", "onItemMousemove"]), _ctx.$slots.end ? (openBlock(), createElementBlock("div", mergeProps({ - key: 1, - "class": _ctx.cx("end") - }, _ctx.ptm("end")), [renderSlot(_ctx.$slots, "end")], 16)) : createCommentVNode("", true)], 16); -} -__name(render, "render"); -script.render = render; -const _withScopeId$2 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-6fecd137"), n = n(), popScopeId(), n), "_withScopeId$2"); -const _hoisted_1$2 = ["href"]; -const _hoisted_2$2 = { class: "p-menubar-item-label" }; -const _hoisted_3$1 = { - key: 1, - class: "ml-auto border border-surface rounded text-muted text-xs p-1 keybinding-tag" -}; -const _sfc_main$4 = /* @__PURE__ */ defineComponent({ - __name: "CommandMenubar", - setup(__props) { - const settingStore = useSettingStore(); - const dropdownDirection = computed( - () => settingStore.get("Comfy.UseNewMenu") === "Top" ? "down" : "up" - ); - const menuItemsStore = useMenuItemStore(); - const { t } = useI18n(); - const translateMenuItem = /* @__PURE__ */ __name((item3) => { - const label = typeof item3.label === "function" ? item3.label() : item3.label; - const translatedLabel = label ? t(`menuLabels.${normalizeI18nKey(label)}`, label) : void 0; - return { - ...item3, - label: translatedLabel, - items: item3.items?.map(translateMenuItem) - }; - }, "translateMenuItem"); - const translatedItems = computed( - () => menuItemsStore.menuItems.map(translateMenuItem) - ); - return (_ctx, _cache) => { - return openBlock(), createBlock(unref(script), { - model: translatedItems.value, - class: "top-menubar border-none p-0 bg-transparent", - pt: { - rootList: "gap-0 flex-nowrap w-auto", - submenu: `dropdown-direction-${dropdownDirection.value}`, - item: "relative" - } - }, { - item: withCtx(({ item: item3, props }) => [ - createBaseVNode("a", mergeProps({ class: "p-menubar-item-link" }, props.action, { - href: item3.url, - target: "_blank" - }), [ - item3.icon ? (openBlock(), createElementBlock("span", { - key: 0, - class: normalizeClass(["p-menubar-item-icon", item3.icon]) - }, null, 2)) : createCommentVNode("", true), - createBaseVNode("span", _hoisted_2$2, toDisplayString(item3.label), 1), - item3?.comfyCommand?.keybinding ? (openBlock(), createElementBlock("span", _hoisted_3$1, toDisplayString(item3.comfyCommand.keybinding.combo.toString()), 1)) : createCommentVNode("", true) - ], 16, _hoisted_1$2) - ]), - _: 1 - }, 8, ["model", "pt"]); - }; - } -}); -const CommandMenubar = /* @__PURE__ */ _export_sfc(_sfc_main$4, [["__scopeId", "data-v-6fecd137"]]); -const _withScopeId$1 = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-8d011a31"), n = n(), popScopeId(), n), "_withScopeId$1"); -const _hoisted_1$1 = { class: "workflow-label text-sm max-w-[150px] truncate inline-block" }; -const _hoisted_2$1 = { class: "relative" }; -const _hoisted_3 = { - key: 0, - class: "status-indicator" -}; -const _sfc_main$3 = /* @__PURE__ */ defineComponent({ - __name: "WorkflowTab", - props: { - class: {}, - workflowOption: {} - }, - setup(__props) { - const props = __props; - const workspaceStore = useWorkspaceStore(); - const workflowStore = useWorkflowStore(); - const workflowTabRef = ref(null); - const closeWorkflows = /* @__PURE__ */ __name(async (options) => { - for (const opt of options) { - if (!await useWorkflowService().closeWorkflow(opt.workflow, { - warnIfUnsaved: !workspaceStore.shiftDown - })) { - break; - } - } - }, "closeWorkflows"); - const onCloseWorkflow = /* @__PURE__ */ __name((option2) => { - closeWorkflows([option2]); - }, "onCloseWorkflow"); - const tabGetter = /* @__PURE__ */ __name(() => workflowTabRef.value, "tabGetter"); - usePragmaticDraggable(tabGetter, { - getInitialData: /* @__PURE__ */ __name(() => { - return { - workflowKey: props.workflowOption.workflow.key - }; - }, "getInitialData") - }); - usePragmaticDroppable(tabGetter, { - getData: /* @__PURE__ */ __name(() => { - return { - workflowKey: props.workflowOption.workflow.key - }; - }, "getData"), - onDrop: /* @__PURE__ */ __name((e) => { - const fromIndex = workflowStore.openWorkflows.findIndex( - (wf) => wf.key === e.source.data.workflowKey - ); - const toIndex = workflowStore.openWorkflows.findIndex( - (wf) => wf.key === e.location.current.dropTargets[0]?.data.workflowKey - ); - if (fromIndex !== toIndex) { - workflowStore.reorderWorkflows(fromIndex, toIndex); - } - }, "onDrop") - }); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createElementBlock("div", mergeProps({ - class: "flex p-2 gap-2 workflow-tab", - ref_key: "workflowTabRef", - ref: workflowTabRef - }, _ctx.$attrs), [ - withDirectives((openBlock(), createElementBlock("span", _hoisted_1$1, [ - createTextVNode(toDisplayString(_ctx.workflowOption.workflow.filename), 1) - ])), [ - [ - _directive_tooltip, - _ctx.workflowOption.workflow.key, - void 0, - { bottom: true } - ] - ]), - createBaseVNode("div", _hoisted_2$1, [ - !unref(workspaceStore).shiftDown && (_ctx.workflowOption.workflow.isModified || !_ctx.workflowOption.workflow.isPersisted) ? (openBlock(), createElementBlock("span", _hoisted_3, "•")) : createCommentVNode("", true), - createVNode(unref(script$d), { - class: "close-button p-0 w-auto", - icon: "pi pi-times", - text: "", - severity: "secondary", - size: "small", - onClick: _cache[0] || (_cache[0] = withModifiers(($event) => onCloseWorkflow(_ctx.workflowOption), ["stop"])) - }) - ]) - ], 16); - }; - } -}); -const WorkflowTab = /* @__PURE__ */ _export_sfc(_sfc_main$3, [["__scopeId", "data-v-8d011a31"]]); -const _sfc_main$2 = /* @__PURE__ */ defineComponent({ - __name: "WorkflowTabs", - props: { - class: {} - }, - setup(__props) { - const props = __props; - const { t } = useI18n(); - const workspaceStore = useWorkspaceStore(); - const workflowStore = useWorkflowStore(); - const workflowService = useWorkflowService(); - const rightClickedTab = ref(null); - const menu = ref(); - const workflowToOption = /* @__PURE__ */ __name((workflow) => ({ - value: workflow.path, - workflow - }), "workflowToOption"); - const options = computed( - () => workflowStore.openWorkflows.map(workflowToOption) - ); - const selectedWorkflow = computed( - () => workflowStore.activeWorkflow ? workflowToOption(workflowStore.activeWorkflow) : null - ); - const onWorkflowChange = /* @__PURE__ */ __name((option2) => { - if (!option2) { - return; - } - if (selectedWorkflow.value?.value === option2.value) { - return; - } - workflowService.openWorkflow(option2.workflow); - }, "onWorkflowChange"); - const closeWorkflows = /* @__PURE__ */ __name(async (options2) => { - for (const opt of options2) { - if (!await workflowService.closeWorkflow(opt.workflow, { - warnIfUnsaved: !workspaceStore.shiftDown - })) { - break; - } - } - }, "closeWorkflows"); - const onCloseWorkflow = /* @__PURE__ */ __name((option2) => { - closeWorkflows([option2]); - }, "onCloseWorkflow"); - const showContextMenu = /* @__PURE__ */ __name((event, option2) => { - rightClickedTab.value = option2; - menu.value.show(event); - }, "showContextMenu"); - const contextMenuItems = computed(() => { - const tab = rightClickedTab.value; - if (!tab) return []; - const index = options.value.findIndex((v) => v.workflow === tab.workflow); - return [ - { - label: t("tabMenu.duplicateTab"), - command: /* @__PURE__ */ __name(() => { - workflowService.duplicateWorkflow(tab.workflow); - }, "command") - }, - { - separator: true - }, - { - label: t("tabMenu.closeTab"), - command: /* @__PURE__ */ __name(() => onCloseWorkflow(tab), "command") - }, - { - label: t("tabMenu.closeTabsToLeft"), - command: /* @__PURE__ */ __name(() => closeWorkflows(options.value.slice(0, index)), "command"), - disabled: index <= 0 - }, - { - label: t("tabMenu.closeTabsToRight"), - command: /* @__PURE__ */ __name(() => closeWorkflows(options.value.slice(index + 1)), "command"), - disabled: index === options.value.length - 1 - }, - { - label: t("tabMenu.closeOtherTabs"), - command: /* @__PURE__ */ __name(() => closeWorkflows([ - ...options.value.slice(index + 1), - ...options.value.slice(0, index) - ]), "command"), - disabled: options.value.length <= 1 - } - ]; - }); - const commandStore = useCommandStore(); - return (_ctx, _cache) => { - return openBlock(), createElementBlock(Fragment, null, [ - createVNode(unref(script$B), { - class: normalizeClass(["workflow-tabs bg-transparent inline", props.class]), - modelValue: selectedWorkflow.value, - "onUpdate:modelValue": onWorkflowChange, - options: options.value, - optionLabel: "label", - dataKey: "value" - }, { - option: withCtx(({ option: option2 }) => [ - createVNode(WorkflowTab, { - onContextmenu: /* @__PURE__ */ __name(($event) => showContextMenu($event, option2), "onContextmenu"), - onMouseup: withModifiers(($event) => onCloseWorkflow(option2), ["middle"]), - "workflow-option": option2 - }, null, 8, ["onContextmenu", "onMouseup", "workflow-option"]) - ]), - _: 1 - }, 8, ["class", "modelValue", "options"]), - createVNode(unref(script$d), { - class: "new-blank-workflow-button", - icon: "pi pi-plus", - text: "", - severity: "secondary", - onClick: _cache[0] || (_cache[0] = () => unref(commandStore).execute("Comfy.NewBlankWorkflow")) - }), - createVNode(unref(script$C), { - ref_key: "menu", - ref: menu, - model: contextMenuItems.value - }, null, 8, ["model"]) - ], 64); - }; - } -}); -const WorkflowTabs = /* @__PURE__ */ _export_sfc(_sfc_main$2, [["__scopeId", "data-v-d485c044"]]); -const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-878b63b8"), n = n(), popScopeId(), n), "_withScopeId"); -const _hoisted_1 = /* @__PURE__ */ _withScopeId(() => /* @__PURE__ */ createBaseVNode("h1", { class: "comfyui-logo mx-2" }, "ComfyUI", -1)); -const _hoisted_2 = { class: "flex-grow" }; -const _sfc_main$1 = /* @__PURE__ */ defineComponent({ - __name: "TopMenubar", - setup(__props) { - const workspaceState = useWorkspaceStore(); - const settingStore = useSettingStore(); - const workflowTabsPosition = computed( - () => settingStore.get("Comfy.Workflow.WorkflowTabsPosition") - ); - const betaMenuEnabled = computed( - () => settingStore.get("Comfy.UseNewMenu") !== "Disabled" - ); - const teleportTarget = computed( - () => settingStore.get("Comfy.UseNewMenu") === "Top" ? ".comfyui-body-top" : ".comfyui-body-bottom" - ); - const menuRight = ref(null); - onMounted(() => { - if (menuRight.value) { - menuRight.value.appendChild(app.menu.element); - } - }); - const topMenuRef = ref(null); - provide("topMenuRef", topMenuRef); - const eventBus = useEventBus("topMenu"); - const isDropZone = ref(false); - const isDroppable = ref(false); - eventBus.on((event, payload) => { - if (event === "updateHighlight") { - isDropZone.value = payload.isDragging; - isDroppable.value = payload.isOverlapping && payload.isDragging; - } - }); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createBlock(Teleport, { to: teleportTarget.value }, [ - withDirectives(createBaseVNode("div", { - ref_key: "topMenuRef", - ref: topMenuRef, - class: normalizeClass(["comfyui-menu flex items-center", { dropzone: isDropZone.value, "dropzone-active": isDroppable.value }]) - }, [ - _hoisted_1, - createVNode(CommandMenubar), - createVNode(unref(script$D), { - layout: "vertical", - class: "mx-2" - }), - createBaseVNode("div", _hoisted_2, [ - workflowTabsPosition.value === "Topbar" ? (openBlock(), createBlock(WorkflowTabs, { key: 0 })) : createCommentVNode("", true) - ]), - createBaseVNode("div", { - class: "comfyui-menu-right", - ref_key: "menuRight", - ref: menuRight - }, null, 512), - createVNode(Actionbar), - createVNode(_sfc_main$5), - withDirectives(createVNode(unref(script$d), { - icon: "pi pi-bars", - severity: "secondary", - text: "", - onClick: _cache[0] || (_cache[0] = ($event) => unref(workspaceState).focusMode = true), - onContextmenu: unref(showNativeMenu) - }, null, 8, ["onContextmenu"]), [ - [_directive_tooltip, { value: _ctx.$t("menu.hideMenu"), showDelay: 300 }] - ]) - ], 2), [ - [vShow, betaMenuEnabled.value && !unref(workspaceState).focusMode] - ]) - ], 8, ["to"]); - }; - } -}); -const TopMenubar = /* @__PURE__ */ _export_sfc(_sfc_main$1, [["__scopeId", "data-v-878b63b8"]]); -var LatentPreviewMethod = /* @__PURE__ */ ((LatentPreviewMethod2) => { - LatentPreviewMethod2["NoPreviews"] = "none"; - LatentPreviewMethod2["Auto"] = "auto"; - LatentPreviewMethod2["Latent2RGB"] = "latent2rgb"; - LatentPreviewMethod2["TAESD"] = "taesd"; - return LatentPreviewMethod2; -})(LatentPreviewMethod || {}); -var LogLevel = /* @__PURE__ */ ((LogLevel2) => { - LogLevel2["DEBUG"] = "DEBUG"; - LogLevel2["INFO"] = "INFO"; - LogLevel2["WARNING"] = "WARNING"; - LogLevel2["ERROR"] = "ERROR"; - LogLevel2["CRITICAL"] = "CRITICAL"; - return LogLevel2; -})(LogLevel || {}); -var HashFunction = /* @__PURE__ */ ((HashFunction2) => { - HashFunction2["MD5"] = "md5"; - HashFunction2["SHA1"] = "sha1"; - HashFunction2["SHA256"] = "sha256"; - HashFunction2["SHA512"] = "sha512"; - return HashFunction2; -})(HashFunction || {}); -var AutoLaunch = /* @__PURE__ */ ((AutoLaunch2) => { - AutoLaunch2["Auto"] = "auto"; - AutoLaunch2["Disable"] = "disable"; - AutoLaunch2["Enable"] = "enable"; - return AutoLaunch2; -})(AutoLaunch || {}); -var CudaMalloc = /* @__PURE__ */ ((CudaMalloc2) => { - CudaMalloc2["Auto"] = "auto"; - CudaMalloc2["Disable"] = "disable"; - CudaMalloc2["Enable"] = "enable"; - return CudaMalloc2; -})(CudaMalloc || {}); -var FloatingPointPrecision = /* @__PURE__ */ ((FloatingPointPrecision2) => { - FloatingPointPrecision2["AUTO"] = "auto"; - FloatingPointPrecision2["FP64"] = "fp64"; - FloatingPointPrecision2["FP32"] = "fp32"; - FloatingPointPrecision2["FP16"] = "fp16"; - FloatingPointPrecision2["BF16"] = "bf16"; - FloatingPointPrecision2["FP8E4M3FN"] = "fp8_e4m3fn"; - FloatingPointPrecision2["FP8E5M2"] = "fp8_e5m2"; - return FloatingPointPrecision2; -})(FloatingPointPrecision || {}); -var CrossAttentionMethod = /* @__PURE__ */ ((CrossAttentionMethod2) => { - CrossAttentionMethod2["Auto"] = "auto"; - CrossAttentionMethod2["Split"] = "split"; - CrossAttentionMethod2["Quad"] = "quad"; - CrossAttentionMethod2["Pytorch"] = "pytorch"; - return CrossAttentionMethod2; -})(CrossAttentionMethod || {}); -var VramManagement = /* @__PURE__ */ ((VramManagement2) => { - VramManagement2["Auto"] = "auto"; - VramManagement2["GPUOnly"] = "gpu-only"; - VramManagement2["HighVram"] = "highvram"; - VramManagement2["NormalVram"] = "normalvram"; - VramManagement2["LowVram"] = "lowvram"; - VramManagement2["NoVram"] = "novram"; - VramManagement2["CPU"] = "cpu"; - return VramManagement2; -})(VramManagement || {}); -const WEB_ONLY_CONFIG_ITEMS = [ - // Launch behavior - { - id: "auto-launch", - name: "Automatically opens in the browser on startup", - category: ["Launch"], - type: "combo", - options: Object.values(AutoLaunch), - defaultValue: AutoLaunch.Auto, - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case AutoLaunch.Auto: - return {}; - case AutoLaunch.Enable: - return { - ["auto-launch"]: true - }; - case AutoLaunch.Disable: - return { - ["disable-auto-launch"]: true - }; - } - }, "getValue") - } -]; -const SERVER_CONFIG_ITEMS = [ - // Network settings - { - id: "listen", - name: "Host: The IP address to listen on", - category: ["Network"], - type: "text", - defaultValue: "127.0.0.1" - }, - { - id: "port", - name: "Port: The port to listen on", - category: ["Network"], - type: "number", - // The default launch port for desktop app is 8000 instead of 8188. - defaultValue: 8e3 - }, - { - id: "tls-keyfile", - name: "TLS Key File: Path to TLS key file for HTTPS", - category: ["Network"], - type: "text", - defaultValue: "" - }, - { - id: "tls-certfile", - name: "TLS Certificate File: Path to TLS certificate file for HTTPS", - category: ["Network"], - type: "text", - defaultValue: "" - }, - { - id: "enable-cors-header", - name: 'Enable CORS header: Use "*" for all origins or specify domain', - category: ["Network"], - type: "text", - defaultValue: "" - }, - { - id: "max-upload-size", - name: "Maximum upload size (MB)", - category: ["Network"], - type: "number", - defaultValue: 100 - }, - // CUDA settings - { - id: "cuda-device", - name: "CUDA device index to use", - category: ["CUDA"], - type: "number", - defaultValue: null - }, - { - id: "cuda-malloc", - name: "Use CUDA malloc for memory allocation", - category: ["CUDA"], - type: "combo", - options: Object.values(CudaMalloc), - defaultValue: CudaMalloc.Auto, - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case CudaMalloc.Auto: - return {}; - case CudaMalloc.Enable: - return { - ["cuda-malloc"]: true - }; - case CudaMalloc.Disable: - return { - ["disable-cuda-malloc"]: true - }; - } - }, "getValue") - }, - // Precision settings - { - id: "global-precision", - name: "Global floating point precision", - category: ["Inference"], - type: "combo", - options: [ - FloatingPointPrecision.AUTO, - FloatingPointPrecision.FP32, - FloatingPointPrecision.FP16 - ], - defaultValue: FloatingPointPrecision.AUTO, - tooltip: "Global floating point precision", - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case FloatingPointPrecision.AUTO: - return {}; - case FloatingPointPrecision.FP32: - return { - ["force-fp32"]: true - }; - case FloatingPointPrecision.FP16: - return { - ["force-fp16"]: true - }; - default: - return {}; - } - }, "getValue") - }, - // UNET precision - { - id: "unet-precision", - name: "UNET precision", - category: ["Inference"], - type: "combo", - options: [ - FloatingPointPrecision.AUTO, - FloatingPointPrecision.FP64, - FloatingPointPrecision.FP32, - FloatingPointPrecision.FP16, - FloatingPointPrecision.BF16, - FloatingPointPrecision.FP8E4M3FN, - FloatingPointPrecision.FP8E5M2 - ], - defaultValue: FloatingPointPrecision.AUTO, - tooltip: "UNET precision", - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case FloatingPointPrecision.AUTO: - return {}; - default: - return { - [`${value.toLowerCase()}-unet`]: true - }; - } - }, "getValue") - }, - // VAE settings - { - id: "vae-precision", - name: "VAE precision", - category: ["Inference"], - type: "combo", - options: [ - FloatingPointPrecision.AUTO, - FloatingPointPrecision.FP16, - FloatingPointPrecision.FP32, - FloatingPointPrecision.BF16 - ], - defaultValue: FloatingPointPrecision.AUTO, - tooltip: "VAE precision", - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case FloatingPointPrecision.AUTO: - return {}; - default: - return { - [`${value.toLowerCase()}-vae`]: true - }; - } - }, "getValue") - }, - { - id: "cpu-vae", - name: "Run VAE on CPU", - category: ["Inference"], - type: "boolean", - defaultValue: false - }, - // Text Encoder settings - { - id: "text-encoder-precision", - name: "Text Encoder precision", - category: ["Inference"], - type: "combo", - options: [ - FloatingPointPrecision.AUTO, - FloatingPointPrecision.FP8E4M3FN, - FloatingPointPrecision.FP8E5M2, - FloatingPointPrecision.FP16, - FloatingPointPrecision.FP32 - ], - defaultValue: FloatingPointPrecision.AUTO, - tooltip: "Text Encoder precision", - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case FloatingPointPrecision.AUTO: - return {}; - default: - return { - [`${value.toLowerCase()}-text-enc`]: true - }; - } - }, "getValue") - }, - // Memory and performance settings - { - id: "force-channels-last", - name: "Force channels-last memory format", - category: ["Memory"], - type: "boolean", - defaultValue: false - }, - { - id: "directml", - name: "DirectML device index", - category: ["Memory"], - type: "number", - defaultValue: null - }, - { - id: "disable-ipex-optimize", - name: "Disable IPEX optimization", - category: ["Memory"], - type: "boolean", - defaultValue: false - }, - // Preview settings - { - id: "preview-method", - name: "Method used for latent previews", - category: ["Preview"], - type: "combo", - options: Object.values(LatentPreviewMethod), - defaultValue: LatentPreviewMethod.NoPreviews - }, - { - id: "preview-size", - name: "Size of preview images", - category: ["Preview"], - type: "slider", - defaultValue: 512, - attrs: { - min: 128, - max: 2048, - step: 128 - } - }, - // Cache settings - { - id: "cache-classic", - name: "Use classic cache system", - category: ["Cache"], - type: "boolean", - defaultValue: false - }, - { - id: "cache-lru", - name: "Use LRU caching with a maximum of N node results cached.", - category: ["Cache"], - type: "number", - defaultValue: null, - tooltip: "May use more RAM/VRAM." - }, - // Attention settings - { - id: "cross-attention-method", - name: "Cross attention method", - category: ["Attention"], - type: "combo", - options: Object.values(CrossAttentionMethod), - defaultValue: CrossAttentionMethod.Auto, - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case CrossAttentionMethod.Auto: - return {}; - default: - return { - [`use-${value.toLowerCase()}-cross-attention`]: true - }; - } - }, "getValue") - }, - { - id: "disable-xformers", - name: "Disable xFormers optimization", - type: "boolean", - defaultValue: false - }, - { - id: "force-upcast-attention", - name: "Force attention upcast", - category: ["Attention"], - type: "boolean", - defaultValue: false - }, - { - id: "dont-upcast-attention", - name: "Prevent attention upcast", - category: ["Attention"], - type: "boolean", - defaultValue: false - }, - // VRAM management - { - id: "vram-management", - name: "VRAM management mode", - category: ["Memory"], - type: "combo", - options: Object.values(VramManagement), - defaultValue: VramManagement.Auto, - getValue: /* @__PURE__ */ __name((value) => { - switch (value) { - case VramManagement.Auto: - return {}; - default: - return { - [value]: true - }; - } - }, "getValue") - }, - { - id: "reserve-vram", - name: "Reserved VRAM (GB)", - category: ["Memory"], - type: "number", - defaultValue: null, - tooltip: "Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reverved depending on your OS." - }, - // Misc settings - { - id: "default-hashing-function", - name: "Default hashing function for model files", - type: "combo", - options: Object.values(HashFunction), - defaultValue: HashFunction.SHA256 - }, - { - id: "disable-smart-memory", - name: "Disable smart memory management", - tooltip: "Force ComfyUI to aggressively offload to regular ram instead of keeping models in vram when it can.", - category: ["Memory"], - type: "boolean", - defaultValue: false - }, - { - id: "deterministic", - name: "Make pytorch use slower deterministic algorithms when it can.", - type: "boolean", - defaultValue: false, - tooltip: "Note that this might not make images deterministic in all cases." - }, - { - id: "fast", - name: "Enable some untested and potentially quality deteriorating optimizations.", - type: "boolean", - defaultValue: false - }, - { - id: "dont-print-server", - name: "Don't print server output to console.", - type: "boolean", - defaultValue: false - }, - { - id: "disable-metadata", - name: "Disable saving prompt metadata in files.", - type: "boolean", - defaultValue: false - }, - { - id: "disable-all-custom-nodes", - name: "Disable loading all custom nodes.", - type: "boolean", - defaultValue: false - }, - { - id: "log-level", - name: "Logging verbosity level", - type: "combo", - options: Object.values(LogLevel), - defaultValue: LogLevel.INFO, - getValue: /* @__PURE__ */ __name((value) => { - return { - verbose: value - }; - }, "getValue") - }, - // Directories - { - id: "input-directory", - name: "Input directory", - category: ["Directories"], - type: "text", - defaultValue: "" - }, - { - id: "output-directory", - name: "Output directory", - category: ["Directories"], - type: "text", - defaultValue: "" - } -]; -function useCoreCommands() { - const workflowService = useWorkflowService(); - const workflowStore = useWorkflowStore(); - const dialogService = useDialogService(); - const getTracker = /* @__PURE__ */ __name(() => workflowStore.activeWorkflow?.changeTracker, "getTracker"); - const getSelectedNodes = /* @__PURE__ */ __name(() => { - const selectedNodes = app.canvas.selected_nodes; - const result = []; - if (selectedNodes) { - for (const i in selectedNodes) { - const node = selectedNodes[i]; - result.push(node); - } - } - return result; - }, "getSelectedNodes"); - const toggleSelectedNodesMode = /* @__PURE__ */ __name((mode) => { - getSelectedNodes().forEach((node) => { - if (node.mode === mode) { - node.mode = LGraphEventMode.ALWAYS; - } else { - node.mode = mode; - } - }); - }, "toggleSelectedNodesMode"); - return [ - { - id: "Comfy.NewBlankWorkflow", - icon: "pi pi-plus", - label: "New Blank Workflow", - menubarLabel: "New", - function: /* @__PURE__ */ __name(() => workflowService.loadBlankWorkflow(), "function") - }, - { - id: "Comfy.OpenWorkflow", - icon: "pi pi-folder-open", - label: "Open Workflow", - menubarLabel: "Open", - function: /* @__PURE__ */ __name(() => { - app.ui.loadFile(); - }, "function") - }, - { - id: "Comfy.LoadDefaultWorkflow", - icon: "pi pi-code", - label: "Load Default Workflow", - function: /* @__PURE__ */ __name(() => workflowService.loadDefaultWorkflow(), "function") - }, - { - id: "Comfy.SaveWorkflow", - icon: "pi pi-save", - label: "Save Workflow", - menubarLabel: "Save", - function: /* @__PURE__ */ __name(async () => { - const workflow = useWorkflowStore().activeWorkflow; - if (!workflow) return; - await workflowService.saveWorkflow(workflow); - }, "function") - }, - { - id: "Comfy.SaveWorkflowAs", - icon: "pi pi-save", - label: "Save Workflow As", - menubarLabel: "Save As", - function: /* @__PURE__ */ __name(async () => { - const workflow = useWorkflowStore().activeWorkflow; - if (!workflow) return; - await workflowService.saveWorkflowAs(workflow); - }, "function") - }, - { - id: "Comfy.ExportWorkflow", - icon: "pi pi-download", - label: "Export Workflow", - menubarLabel: "Export", - function: /* @__PURE__ */ __name(() => { - workflowService.exportWorkflow("workflow", "workflow"); - }, "function") - }, - { - id: "Comfy.ExportWorkflowAPI", - icon: "pi pi-download", - label: "Export Workflow (API Format)", - menubarLabel: "Export (API)", - function: /* @__PURE__ */ __name(() => { - workflowService.exportWorkflow("workflow_api", "output"); - }, "function") - }, - { - id: "Comfy.Undo", - icon: "pi pi-undo", - label: "Undo", - function: /* @__PURE__ */ __name(async () => { - await getTracker()?.undo?.(); - }, "function") - }, - { - id: "Comfy.Redo", - icon: "pi pi-refresh", - label: "Redo", - function: /* @__PURE__ */ __name(async () => { - await getTracker()?.redo?.(); - }, "function") - }, - { - id: "Comfy.ClearWorkflow", - icon: "pi pi-trash", - label: "Clear Workflow", - function: /* @__PURE__ */ __name(() => { - const settingStore = useSettingStore(); - if (!settingStore.get("Comfy.ComfirmClear") || confirm("Clear workflow?")) { - app.clean(); - app.graph.clear(); - api.dispatchCustomEvent("graphCleared"); - } - }, "function") - }, - { - id: "Comfy.Canvas.ResetView", - icon: "pi pi-expand", - label: "Reset View", - function: /* @__PURE__ */ __name(() => { - app.resetView(); - }, "function") - }, - { - id: "Comfy.OpenClipspace", - icon: "pi pi-clipboard", - label: "Clipspace", - function: /* @__PURE__ */ __name(() => { - app.openClipspace(); - }, "function") - }, - { - id: "Comfy.RefreshNodeDefinitions", - icon: "pi pi-refresh", - label: "Refresh Node Definitions", - function: /* @__PURE__ */ __name(async () => { - await app.refreshComboInNodes(); - }, "function") - }, - { - id: "Comfy.Interrupt", - icon: "pi pi-stop", - label: "Interrupt", - function: /* @__PURE__ */ __name(async () => { - await api.interrupt(); - useToastStore().add({ - severity: "info", - summary: "Interrupted", - detail: "Execution has been interrupted", - life: 1e3 - }); - }, "function") - }, - { - id: "Comfy.ClearPendingTasks", - icon: "pi pi-stop", - label: "Clear Pending Tasks", - function: /* @__PURE__ */ __name(async () => { - await useQueueStore().clear(["queue"]); - useToastStore().add({ - severity: "info", - summary: "Confirmed", - detail: "Pending tasks deleted", - life: 3e3 - }); - }, "function") - }, - { - id: "Comfy.BrowseTemplates", - icon: "pi pi-folder-open", - label: "Browse Templates", - function: /* @__PURE__ */ __name(() => { - dialogService.showTemplateWorkflowsDialog(); - }, "function") - }, - { - id: "Comfy.Canvas.ZoomIn", - icon: "pi pi-plus", - label: "Zoom In", - function: /* @__PURE__ */ __name(() => { - const ds = app.canvas.ds; - ds.changeScale( - ds.scale * 1.1, - ds.element ? [ds.element.width / 2, ds.element.height / 2] : void 0 - ); - app.canvas.setDirty(true, true); - }, "function") - }, - { - id: "Comfy.Canvas.ZoomOut", - icon: "pi pi-minus", - label: "Zoom Out", - function: /* @__PURE__ */ __name(() => { - const ds = app.canvas.ds; - ds.changeScale( - ds.scale / 1.1, - ds.element ? [ds.element.width / 2, ds.element.height / 2] : void 0 - ); - app.canvas.setDirty(true, true); - }, "function") - }, - { - id: "Comfy.Canvas.FitView", - icon: "pi pi-expand", - label: "Fit view to selected nodes", - function: /* @__PURE__ */ __name(() => { - if (app.canvas.empty) { - useToastStore().add({ - severity: "error", - summary: "Empty canvas", - life: 3e3 - }); - return; - } - app.canvas.fitViewToSelectionAnimated(); - }, "function") - }, - { - id: "Comfy.Canvas.ToggleLock", - icon: "pi pi-lock", - label: "Canvas Toggle Lock", - function: /* @__PURE__ */ __name(() => { - app.canvas["read_only"] = !app.canvas["read_only"]; - }, "function") - }, - { - id: "Comfy.Canvas.ToggleLinkVisibility", - icon: "pi pi-eye", - label: "Canvas Toggle Link Visibility", - versionAdded: "1.3.6", - function: (() => { - const settingStore = useSettingStore(); - let lastLinksRenderMode = LiteGraph.SPLINE_LINK; - return () => { - const currentMode = settingStore.get("Comfy.LinkRenderMode"); - if (currentMode === LiteGraph.HIDDEN_LINK) { - settingStore.set("Comfy.LinkRenderMode", lastLinksRenderMode); - } else { - lastLinksRenderMode = currentMode; - settingStore.set("Comfy.LinkRenderMode", LiteGraph.HIDDEN_LINK); - } - }; - })() - }, - { - id: "Comfy.QueuePrompt", - icon: "pi pi-play", - label: "Queue Prompt", - versionAdded: "1.3.7", - function: /* @__PURE__ */ __name(() => { - const batchCount = useQueueSettingsStore().batchCount; - app.queuePrompt(0, batchCount); - }, "function") - }, - { - id: "Comfy.QueuePromptFront", - icon: "pi pi-play", - label: "Queue Prompt (Front)", - versionAdded: "1.3.7", - function: /* @__PURE__ */ __name(() => { - const batchCount = useQueueSettingsStore().batchCount; - app.queuePrompt(-1, batchCount); - }, "function") - }, - { - id: "Comfy.ShowSettingsDialog", - icon: "pi pi-cog", - label: "Show Settings Dialog", - versionAdded: "1.3.7", - function: /* @__PURE__ */ __name(() => { - dialogService.showSettingsDialog(); - }, "function") - }, - { - id: "Comfy.Graph.GroupSelectedNodes", - icon: "pi pi-sitemap", - label: "Group Selected Nodes", - versionAdded: "1.3.7", - function: /* @__PURE__ */ __name(() => { - const { canvas } = app; - if (!canvas.selectedItems?.size) { - useToastStore().add({ - severity: "error", - summary: "Nothing to group", - detail: "Please select the nodes (or other groups) to create a group for", - life: 3e3 - }); - return; - } - const group = new LGraphGroup(); - const padding = useSettingStore().get( - "Comfy.GroupSelectedNodes.Padding" - ); - group.resizeTo(canvas.selectedItems, padding); - canvas.graph.add(group); - useTitleEditorStore().titleEditorTarget = group; - }, "function") - }, - { - id: "Workspace.NextOpenedWorkflow", - icon: "pi pi-step-forward", - label: "Next Opened Workflow", - versionAdded: "1.3.9", - function: /* @__PURE__ */ __name(() => { - workflowService.loadNextOpenedWorkflow(); - }, "function") - }, - { - id: "Workspace.PreviousOpenedWorkflow", - icon: "pi pi-step-backward", - label: "Previous Opened Workflow", - versionAdded: "1.3.9", - function: /* @__PURE__ */ __name(() => { - workflowService.loadPreviousOpenedWorkflow(); - }, "function") - }, - { - id: "Comfy.Canvas.ToggleSelectedNodes.Mute", - icon: "pi pi-volume-off", - label: "Mute/Unmute Selected Nodes", - versionAdded: "1.3.11", - function: /* @__PURE__ */ __name(() => { - toggleSelectedNodesMode(LGraphEventMode.NEVER); - }, "function") - }, - { - id: "Comfy.Canvas.ToggleSelectedNodes.Bypass", - icon: "pi pi-shield", - label: "Bypass/Unbypass Selected Nodes", - versionAdded: "1.3.11", - function: /* @__PURE__ */ __name(() => { - toggleSelectedNodesMode(LGraphEventMode.BYPASS); - }, "function") - }, - { - id: "Comfy.Canvas.ToggleSelectedNodes.Pin", - icon: "pi pi-pin", - label: "Pin/Unpin Selected Nodes", - versionAdded: "1.3.11", - function: /* @__PURE__ */ __name(() => { - getSelectedNodes().forEach((node) => { - node.pin(!node.pinned); - }); - }, "function") - }, - { - id: "Comfy.Canvas.ToggleSelected.Pin", - icon: "pi pi-pin", - label: "Pin/Unpin Selected Items", - versionAdded: "1.3.33", - function: /* @__PURE__ */ __name(() => { - for (const item3 of app.canvas.selectedItems) { - if (item3 instanceof LGraphNode || item3 instanceof LGraphGroup) { - item3.pin(!item3.pinned); - } - } - }, "function") - }, - { - id: "Comfy.Canvas.ToggleSelectedNodes.Collapse", - icon: "pi pi-minus", - label: "Collapse/Expand Selected Nodes", - versionAdded: "1.3.11", - function: /* @__PURE__ */ __name(() => { - getSelectedNodes().forEach((node) => { - node.collapse(); - }); - }, "function") - }, - { - id: "Comfy.ToggleTheme", - icon: "pi pi-moon", - label: "Toggle Theme (Dark/Light)", - versionAdded: "1.3.12", - function: /* @__PURE__ */ (() => { - let previousDarkTheme = "dark"; - const isDarkMode = /* @__PURE__ */ __name((themeId) => themeId !== "light", "isDarkMode"); - return () => { - const settingStore = useSettingStore(); - const currentTheme = settingStore.get("Comfy.ColorPalette"); - if (isDarkMode(currentTheme)) { - previousDarkTheme = currentTheme; - settingStore.set("Comfy.ColorPalette", "light"); - } else { - settingStore.set("Comfy.ColorPalette", previousDarkTheme); - } - }; - })() - }, - { - id: "Workspace.ToggleBottomPanel", - icon: "pi pi-list", - label: "Toggle Bottom Panel", - versionAdded: "1.3.22", - function: /* @__PURE__ */ __name(() => { - useBottomPanelStore().toggleBottomPanel(); - }, "function") - }, - { - id: "Workspace.ToggleFocusMode", - icon: "pi pi-eye", - label: "Toggle Focus Mode", - versionAdded: "1.3.27", - function: /* @__PURE__ */ __name(() => { - useWorkspaceStore().toggleFocusMode(); - }, "function") - }, - { - id: "Comfy.Graph.FitGroupToContents", - icon: "pi pi-expand", - label: "Fit Group To Contents", - versionAdded: "1.4.9", - function: /* @__PURE__ */ __name(() => { - for (const group of app.canvas.selectedItems) { - if (group instanceof LGraphGroup) { - group.recomputeInsideNodes(); - const padding = useSettingStore().get( - "Comfy.GroupSelectedNodes.Padding" - ); - group.resizeTo(group.children, padding); - app.graph.change(); - } - } - }, "function") - }, - { - id: "Comfy.Help.OpenComfyUIIssues", - icon: "pi pi-github", - label: "Open ComfyUI Issues", - menubarLabel: "ComfyUI Issues", - versionAdded: "1.5.5", - function: /* @__PURE__ */ __name(() => { - window.open( - "https://github.com/comfyanonymous/ComfyUI/issues", - "_blank" - ); - }, "function") - }, - { - id: "Comfy.Help.OpenComfyUIDocs", - icon: "pi pi-info-circle", - label: "Open ComfyUI Docs", - menubarLabel: "ComfyUI Docs", - versionAdded: "1.5.5", - function: /* @__PURE__ */ __name(() => { - window.open("https://docs.comfy.org/", "_blank"); - }, "function") - }, - { - id: "Comfy.Help.OpenComfyOrgDiscord", - icon: "pi pi-discord", - label: "Open Comfy-Org Discord", - menubarLabel: "Comfy-Org Discord", - versionAdded: "1.5.5", - function: /* @__PURE__ */ __name(() => { - window.open("https://www.comfy.org/discord", "_blank"); - }, "function") - }, - { - id: "Workspace.SearchBox.Toggle", - icon: "pi pi-search", - label: "Toggle Search Box", - versionAdded: "1.5.7", - function: /* @__PURE__ */ __name(() => { - useSearchBoxStore().toggleVisible(); - }, "function") - }, - { - id: "Comfy.Help.AboutComfyUI", - icon: "pi pi-info-circle", - label: "Open About ComfyUI", - menubarLabel: "About ComfyUI", - versionAdded: "1.6.4", - function: /* @__PURE__ */ __name(() => { - dialogService.showSettingsDialog("about"); - }, "function") - }, - { - id: "Comfy.DuplicateWorkflow", - icon: "pi pi-clone", - label: "Duplicate Current Workflow", - versionAdded: "1.6.15", - function: /* @__PURE__ */ __name(() => { - workflowService.duplicateWorkflow(workflowStore.activeWorkflow); - }, "function") - } - ]; -} -__name(useCoreCommands, "useCoreCommands"); -function setupAutoQueueHandler() { - const queueCountStore = useQueuePendingTaskCountStore(); - const queueSettingsStore = useQueueSettingsStore(); - let graphHasChanged = false; - let internalCount = 0; - api.addEventListener("graphChanged", () => { - if (queueSettingsStore.mode === "change") { - if (internalCount) { - graphHasChanged = true; - } else { - graphHasChanged = false; - app.queuePrompt(0, queueSettingsStore.batchCount); - internalCount++; - } - } - }); - queueCountStore.$subscribe( - () => { - internalCount = queueCountStore.count; - if (!internalCount && !app.lastExecutionError) { - if (queueSettingsStore.mode === "instant" || queueSettingsStore.mode === "change" && graphHasChanged) { - graphHasChanged = false; - app.queuePrompt(0, queueSettingsStore.batchCount); - } - } - }, - { detached: true } - ); -} -__name(setupAutoQueueHandler, "setupAutoQueueHandler"); -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "GraphView", - setup(__props) { - setupAutoQueueHandler(); - const { t } = useI18n(); - const toast = useToast(); - const settingStore = useSettingStore(); - const executionStore = useExecutionStore(); - const theme10 = computed(() => settingStore.get("Comfy.ColorPalette")); - watch( - theme10, - (newTheme) => { - const DARK_THEME_CLASS = "dark-theme"; - const isDarkTheme = newTheme !== "light"; - if (isDarkTheme) { - document.body.classList.add(DARK_THEME_CLASS); - } else { - document.body.classList.remove(DARK_THEME_CLASS); - } - }, - { immediate: true } - ); - watchEffect(() => { - const fontSize = settingStore.get("Comfy.TextareaWidget.FontSize"); - document.documentElement.style.setProperty( - "--comfy-textarea-font-size", - `${fontSize}px` - ); - }); - watchEffect(() => { - const padding = settingStore.get("Comfy.TreeExplorer.ItemPadding"); - document.documentElement.style.setProperty( - "--comfy-tree-explorer-item-padding", - `${padding}px` - ); - }); - watchEffect(() => { - const locale = settingStore.get("Comfy.Locale"); - if (locale) { - i18n.global.locale.value = locale; - } - }); - watchEffect(() => { - const useNewMenu = settingStore.get("Comfy.UseNewMenu"); - if (useNewMenu === "Disabled") { - app.ui.menuContainer.style.setProperty("display", "block"); - app.ui.restoreMenuPosition(); - } else { - app.ui.menuContainer.style.setProperty("display", "none"); - } - }); - watchEffect(() => { - useQueueStore().maxHistoryItems = settingStore.get( - "Comfy.Queue.MaxHistoryItems" - ); - }); - const init = /* @__PURE__ */ __name(() => { - const coreCommands = useCoreCommands(); - useCommandStore().registerCommands(coreCommands); - useMenuItemStore().registerCoreMenuCommands(); - useKeybindingService().registerCoreKeybindings(); - useSidebarTabStore().registerCoreSidebarTabs(); - useBottomPanelStore().registerCoreBottomPanelTabs(); - app.extensionManager = useWorkspaceStore(); - }, "init"); - const queuePendingTaskCountStore = useQueuePendingTaskCountStore(); - const onStatus = /* @__PURE__ */ __name((e) => { - queuePendingTaskCountStore.update(e); - }, "onStatus"); - const reconnectingMessage = { - severity: "error", - summary: t("g.reconnecting") - }; - const onReconnecting = /* @__PURE__ */ __name(() => { - toast.remove(reconnectingMessage); - toast.add(reconnectingMessage); - }, "onReconnecting"); - const onReconnected = /* @__PURE__ */ __name(() => { - toast.remove(reconnectingMessage); - toast.add({ - severity: "success", - summary: t("g.reconnected"), - life: 2e3 - }); - }, "onReconnected"); - onMounted(() => { - api.addEventListener("status", onStatus); - api.addEventListener("reconnecting", onReconnecting); - api.addEventListener("reconnected", onReconnected); - executionStore.bindExecutionEvents(); - try { - init(); - } catch (e) { - console.error("Failed to init ComfyUI frontend", e); - } - }); - onBeforeUnmount(() => { - api.removeEventListener("status", onStatus); - api.removeEventListener("reconnecting", onReconnecting); - api.removeEventListener("reconnected", onReconnected); - executionStore.unbindExecutionEvents(); - }); - useEventListener(window, "keydown", useKeybindingService().keybindHandler); - const onGraphReady = /* @__PURE__ */ __name(() => { - requestIdleCallback( - () => { - useKeybindingService().registerUserKeybindings(); - useServerConfigStore().loadServerConfig( - SERVER_CONFIG_ITEMS, - settingStore.get("Comfy.Server.ServerConfigValues") - ); - useModelStore().loadModelFolders(); - useNodeDefStore().nodeSearchService.endsWithFilterStartSequence(""); - useNodeFrequencyStore().loadNodeFrequencies(); - }, - { timeout: 1e3 } - ); - }, "onGraphReady"); - return (_ctx, _cache) => { - return openBlock(), createElementBlock(Fragment, null, [ - createVNode(TopMenubar), - createVNode(_sfc_main$a, { onReady: onGraphReady }), - createVNode(_sfc_main$9), - createVNode(_sfc_main$r), - createVNode(_sfc_main$t), - createVNode(MenuHamburger) - ], 64); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=GraphView-DswvqURL.js.map diff --git a/web/assets/InstallView-AV2llYNm.js b/web/assets/InstallView-AV2llYNm.js deleted file mode 100644 index 0b455d93f..000000000 --- a/web/assets/InstallView-AV2llYNm.js +++ /dev/null @@ -1,1288 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value2) => __defProp(target, "name", { value: value2, configurable: true }); -import { B as BaseStyle, q as script$6, o as openBlock, f as createElementBlock, D as mergeProps, c1 as findIndexInList, c2 as find, aB as resolveComponent, k as createBlock, G as resolveDynamicComponent, M as withCtx, H as createBaseVNode, X as toDisplayString, J as renderSlot, I as createCommentVNode, T as normalizeClass, P as findSingle, F as Fragment, aC as Transition, i as withDirectives, v as vShow, ak as UniqueComponentId, d as defineComponent, ab as ref, c3 as useModel, N as createVNode, j as unref, c4 as script$7, bQ as script$8, bM as withModifiers, aP as script$9, a1 as useI18n, c as computed, aI as script$a, aE as createTextVNode, c0 as electronAPI, m as onMounted, r as resolveDirective, av as script$b, c5 as script$c, c6 as script$d, l as script$e, bZ as script$f, c7 as MigrationItems, w as watchEffect, E as renderList, c8 as script$g, bW as useRouter, aL as pushScopeId, aM as popScopeId, aU as toRaw, _ as _export_sfc } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$5 } from "./BaseViewTemplate-CsEJhGbv.js"; -var classes$4 = { - root: /* @__PURE__ */ __name(function root(_ref) { - var instance = _ref.instance; - return ["p-step", { - "p-step-active": instance.active, - "p-disabled": instance.isStepDisabled - }]; - }, "root"), - header: "p-step-header", - number: "p-step-number", - title: "p-step-title" -}; -var StepStyle = BaseStyle.extend({ - name: "step", - classes: classes$4 -}); -var script$2$2 = { - name: "StepperSeparator", - hostName: "Stepper", - "extends": script$6 -}; -function render$1$2(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("span", mergeProps({ - "class": _ctx.cx("separator") - }, _ctx.ptm("separator")), null, 16); -} -__name(render$1$2, "render$1$2"); -script$2$2.render = render$1$2; -var script$1$4 = { - name: "BaseStep", - "extends": script$6, - props: { - value: { - type: [String, Number], - "default": void 0 - }, - disabled: { - type: Boolean, - "default": false - }, - asChild: { - type: Boolean, - "default": false - }, - as: { - type: [String, Object], - "default": "DIV" - } - }, - style: StepStyle, - provide: /* @__PURE__ */ __name(function provide() { - return { - $pcStep: this, - $parentInstance: this - }; - }, "provide") -}; -var script$5 = { - name: "Step", - "extends": script$1$4, - inheritAttrs: false, - inject: { - $pcStepper: { - "default": null - }, - $pcStepList: { - "default": null - }, - $pcStepItem: { - "default": null - } - }, - data: /* @__PURE__ */ __name(function data() { - return { - isSeparatorVisible: false - }; - }, "data"), - mounted: /* @__PURE__ */ __name(function mounted() { - if (this.$el && this.$pcStepList) { - var index = findIndexInList(this.$el, find(this.$pcStepper.$el, '[data-pc-name="step"]')); - var stepLen = find(this.$pcStepper.$el, '[data-pc-name="step"]').length; - this.isSeparatorVisible = index !== stepLen - 1; - } - }, "mounted"), - methods: { - getPTOptions: /* @__PURE__ */ __name(function getPTOptions(key) { - var _ptm = key === "root" ? this.ptmi : this.ptm; - return _ptm(key, { - context: { - active: this.active, - disabled: this.isStepDisabled - } - }); - }, "getPTOptions"), - onStepClick: /* @__PURE__ */ __name(function onStepClick() { - this.$pcStepper.updateValue(this.activeValue); - }, "onStepClick") - }, - computed: { - active: /* @__PURE__ */ __name(function active() { - return this.$pcStepper.isStepActive(this.activeValue); - }, "active"), - activeValue: /* @__PURE__ */ __name(function activeValue() { - var _this$$pcStepItem; - return !!this.$pcStepItem ? (_this$$pcStepItem = this.$pcStepItem) === null || _this$$pcStepItem === void 0 ? void 0 : _this$$pcStepItem.value : this.value; - }, "activeValue"), - isStepDisabled: /* @__PURE__ */ __name(function isStepDisabled() { - return !this.active && (this.$pcStepper.isStepDisabled() || this.disabled); - }, "isStepDisabled"), - id: /* @__PURE__ */ __name(function id() { - var _this$$pcStepper; - return "".concat((_this$$pcStepper = this.$pcStepper) === null || _this$$pcStepper === void 0 ? void 0 : _this$$pcStepper.id, "_step_").concat(this.activeValue); - }, "id"), - ariaControls: /* @__PURE__ */ __name(function ariaControls() { - var _this$$pcStepper2; - return "".concat((_this$$pcStepper2 = this.$pcStepper) === null || _this$$pcStepper2 === void 0 ? void 0 : _this$$pcStepper2.id, "_steppanel_").concat(this.activeValue); - }, "ariaControls"), - a11yAttrs: /* @__PURE__ */ __name(function a11yAttrs() { - return { - root: { - role: "presentation", - "aria-current": this.active ? "step" : void 0, - "data-pc-name": "step", - "data-pc-section": "root", - "data-p-disabled": this.disabled, - "data-p-active": this.active - }, - header: { - id: this.id, - role: "tab", - taindex: this.disabled ? -1 : void 0, - "aria-controls": this.ariaControls, - "data-pc-section": "header", - disabled: this.disabled, - onClick: this.onStepClick - } - }; - }, "a11yAttrs") - }, - components: { - StepperSeparator: script$2$2 - } -}; -var _hoisted_1$5 = ["id", "tabindex", "aria-controls", "disabled"]; -function render$4(_ctx, _cache, $props, $setup, $data, $options) { - var _component_StepperSeparator = resolveComponent("StepperSeparator"); - return !_ctx.asChild ? (openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({ - key: 0, - "class": _ctx.cx("root"), - "aria-current": $options.active ? "step" : void 0, - role: "presentation", - "data-p-active": $options.active, - "data-p-disabled": $options.isStepDisabled - }, $options.getPTOptions("root")), { - "default": withCtx(function() { - return [createBaseVNode("button", mergeProps({ - id: $options.id, - "class": _ctx.cx("header"), - role: "tab", - type: "button", - tabindex: $options.isStepDisabled ? -1 : void 0, - "aria-controls": $options.ariaControls, - disabled: $options.isStepDisabled, - onClick: _cache[0] || (_cache[0] = function() { - return $options.onStepClick && $options.onStepClick.apply($options, arguments); - }) - }, $options.getPTOptions("header")), [createBaseVNode("span", mergeProps({ - "class": _ctx.cx("number") - }, $options.getPTOptions("number")), toDisplayString($options.activeValue), 17), createBaseVNode("span", mergeProps({ - "class": _ctx.cx("title") - }, $options.getPTOptions("title")), [renderSlot(_ctx.$slots, "default")], 16)], 16, _hoisted_1$5), $data.isSeparatorVisible ? (openBlock(), createBlock(_component_StepperSeparator, { - key: 0 - })) : createCommentVNode("", true)]; - }), - _: 3 - }, 16, ["class", "aria-current", "data-p-active", "data-p-disabled"])) : renderSlot(_ctx.$slots, "default", { - key: 1, - "class": normalizeClass(_ctx.cx("root")), - active: $options.active, - value: _ctx.value, - a11yAttrs: $options.a11yAttrs, - activateCallback: $options.onStepClick - }); -} -__name(render$4, "render$4"); -script$5.render = render$4; -var classes$3 = { - root: "p-steplist" -}; -var StepListStyle = BaseStyle.extend({ - name: "steplist", - classes: classes$3 -}); -var script$1$3 = { - name: "BaseStepList", - "extends": script$6, - style: StepListStyle, - provide: /* @__PURE__ */ __name(function provide2() { - return { - $pcStepList: this, - $parentInstance: this - }; - }, "provide") -}; -var script$4 = { - name: "StepList", - "extends": script$1$3, - inheritAttrs: false -}; -function render$3(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("div", mergeProps({ - "class": _ctx.cx("root") - }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default")], 16); -} -__name(render$3, "render$3"); -script$4.render = render$3; -var classes$2 = { - root: /* @__PURE__ */ __name(function root2(_ref) { - var instance = _ref.instance; - return ["p-steppanel", { - "p-steppanel-active": instance.isVertical && instance.active - }]; - }, "root"), - content: "p-steppanel-content" -}; -var StepPanelStyle = BaseStyle.extend({ - name: "steppanel", - classes: classes$2 -}); -var script$2$1 = { - name: "StepperSeparator", - hostName: "Stepper", - "extends": script$6 -}; -function render$1$1(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("span", mergeProps({ - "class": _ctx.cx("separator") - }, _ctx.ptm("separator")), null, 16); -} -__name(render$1$1, "render$1$1"); -script$2$1.render = render$1$1; -var script$1$2 = { - name: "BaseStepPanel", - "extends": script$6, - props: { - value: { - type: [String, Number], - "default": void 0 - }, - asChild: { - type: Boolean, - "default": false - }, - as: { - type: [String, Object], - "default": "DIV" - } - }, - style: StepPanelStyle, - provide: /* @__PURE__ */ __name(function provide3() { - return { - $pcStepPanel: this, - $parentInstance: this - }; - }, "provide") -}; -var script$3 = { - name: "StepPanel", - "extends": script$1$2, - inheritAttrs: false, - inject: { - $pcStepper: { - "default": null - }, - $pcStepItem: { - "default": null - }, - $pcStepList: { - "default": null - } - }, - data: /* @__PURE__ */ __name(function data2() { - return { - isSeparatorVisible: false - }; - }, "data"), - mounted: /* @__PURE__ */ __name(function mounted2() { - if (this.$el) { - var _this$$pcStepItem, _this$$pcStepList; - var stepElements = find(this.$pcStepper.$el, '[data-pc-name="step"]'); - var stepPanelEl = findSingle(this.isVertical ? (_this$$pcStepItem = this.$pcStepItem) === null || _this$$pcStepItem === void 0 ? void 0 : _this$$pcStepItem.$el : (_this$$pcStepList = this.$pcStepList) === null || _this$$pcStepList === void 0 ? void 0 : _this$$pcStepList.$el, '[data-pc-name="step"]'); - var stepPanelIndex = findIndexInList(stepPanelEl, stepElements); - this.isSeparatorVisible = this.isVertical && stepPanelIndex !== stepElements.length - 1; - } - }, "mounted"), - methods: { - getPTOptions: /* @__PURE__ */ __name(function getPTOptions2(key) { - var _ptm = key === "root" ? this.ptmi : this.ptm; - return _ptm(key, { - context: { - active: this.active - } - }); - }, "getPTOptions"), - updateValue: /* @__PURE__ */ __name(function updateValue(val) { - this.$pcStepper.updateValue(val); - }, "updateValue") - }, - computed: { - active: /* @__PURE__ */ __name(function active2() { - var _this$$pcStepItem2, _this$$pcStepper; - var activeValue3 = !!this.$pcStepItem ? (_this$$pcStepItem2 = this.$pcStepItem) === null || _this$$pcStepItem2 === void 0 ? void 0 : _this$$pcStepItem2.value : this.value; - return activeValue3 === ((_this$$pcStepper = this.$pcStepper) === null || _this$$pcStepper === void 0 ? void 0 : _this$$pcStepper.d_value); - }, "active"), - isVertical: /* @__PURE__ */ __name(function isVertical() { - return !!this.$pcStepItem; - }, "isVertical"), - activeValue: /* @__PURE__ */ __name(function activeValue2() { - var _this$$pcStepItem3; - return this.isVertical ? (_this$$pcStepItem3 = this.$pcStepItem) === null || _this$$pcStepItem3 === void 0 ? void 0 : _this$$pcStepItem3.value : this.value; - }, "activeValue"), - id: /* @__PURE__ */ __name(function id2() { - var _this$$pcStepper2; - return "".concat((_this$$pcStepper2 = this.$pcStepper) === null || _this$$pcStepper2 === void 0 ? void 0 : _this$$pcStepper2.id, "_steppanel_").concat(this.activeValue); - }, "id"), - ariaControls: /* @__PURE__ */ __name(function ariaControls2() { - var _this$$pcStepper3; - return "".concat((_this$$pcStepper3 = this.$pcStepper) === null || _this$$pcStepper3 === void 0 ? void 0 : _this$$pcStepper3.id, "_step_").concat(this.activeValue); - }, "ariaControls"), - a11yAttrs: /* @__PURE__ */ __name(function a11yAttrs2() { - return { - id: this.id, - role: "tabpanel", - "aria-controls": this.ariaControls, - "data-pc-name": "steppanel", - "data-p-active": this.active - }; - }, "a11yAttrs") - }, - components: { - StepperSeparator: script$2$1 - } -}; -function render$2(_ctx, _cache, $props, $setup, $data, $options) { - var _component_StepperSeparator = resolveComponent("StepperSeparator"); - return $options.isVertical ? (openBlock(), createElementBlock(Fragment, { - key: 0 - }, [!_ctx.asChild ? (openBlock(), createBlock(Transition, mergeProps({ - key: 0, - name: "p-toggleable-content" - }, _ctx.ptm("transition")), { - "default": withCtx(function() { - return [withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({ - id: $options.id, - "class": _ctx.cx("root"), - role: "tabpanel", - "aria-controls": $options.ariaControls - }, $options.getPTOptions("root")), { - "default": withCtx(function() { - return [$data.isSeparatorVisible ? (openBlock(), createBlock(_component_StepperSeparator, { - key: 0 - })) : createCommentVNode("", true), createBaseVNode("div", mergeProps({ - "class": _ctx.cx("content") - }, $options.getPTOptions("content")), [renderSlot(_ctx.$slots, "default", { - active: $options.active, - activateCallback: /* @__PURE__ */ __name(function activateCallback(val) { - return $options.updateValue(val); - }, "activateCallback") - })], 16)]; - }), - _: 3 - }, 16, ["id", "class", "aria-controls"])), [[vShow, $options.active]])]; - }), - _: 3 - }, 16)) : renderSlot(_ctx.$slots, "default", { - key: 1, - active: $options.active, - a11yAttrs: $options.a11yAttrs, - activateCallback: /* @__PURE__ */ __name(function activateCallback(val) { - return $options.updateValue(val); - }, "activateCallback") - })], 64)) : (openBlock(), createElementBlock(Fragment, { - key: 1 - }, [!_ctx.asChild ? withDirectives((openBlock(), createBlock(resolveDynamicComponent(_ctx.as), mergeProps({ - key: 0, - id: $options.id, - "class": _ctx.cx("root"), - role: "tabpanel", - "aria-controls": $options.ariaControls - }, $options.getPTOptions("root")), { - "default": withCtx(function() { - return [renderSlot(_ctx.$slots, "default", { - active: $options.active, - activateCallback: /* @__PURE__ */ __name(function activateCallback(val) { - return $options.updateValue(val); - }, "activateCallback") - })]; - }), - _: 3 - }, 16, ["id", "class", "aria-controls"])), [[vShow, $options.active]]) : _ctx.asChild && $options.active ? renderSlot(_ctx.$slots, "default", { - key: 1, - active: $options.active, - a11yAttrs: $options.a11yAttrs, - activateCallback: /* @__PURE__ */ __name(function activateCallback(val) { - return $options.updateValue(val); - }, "activateCallback") - }) : createCommentVNode("", true)], 64)); -} -__name(render$2, "render$2"); -script$3.render = render$2; -var classes$1 = { - root: "p-steppanels" -}; -var StepPanelsStyle = BaseStyle.extend({ - name: "steppanels", - classes: classes$1 -}); -var script$1$1 = { - name: "BaseStepPanels", - "extends": script$6, - style: StepPanelsStyle, - provide: /* @__PURE__ */ __name(function provide4() { - return { - $pcStepPanels: this, - $parentInstance: this - }; - }, "provide") -}; -var script$2 = { - name: "StepPanels", - "extends": script$1$1, - inheritAttrs: false -}; -function render$1(_ctx, _cache, $props, $setup, $data, $options) { - return openBlock(), createElementBlock("div", mergeProps({ - "class": _ctx.cx("root") - }, _ctx.ptmi("root")), [renderSlot(_ctx.$slots, "default")], 16); -} -__name(render$1, "render$1"); -script$2.render = render$1; -var theme = /* @__PURE__ */ __name(function theme2(_ref) { - var dt = _ref.dt; - return "\n.p-steplist {\n position: relative;\n display: flex;\n justify-content: space-between;\n align-items: center;\n margin: 0;\n padding: 0;\n list-style-type: none;\n overflow-x: auto;\n}\n\n.p-step {\n position: relative;\n display: flex;\n flex: 1 1 auto;\n align-items: center;\n gap: ".concat(dt("stepper.step.gap"), ";\n padding: ").concat(dt("stepper.step.padding"), ";\n}\n\n.p-step:last-of-type {\n flex: initial;\n}\n\n.p-step-header {\n border: 0 none;\n display: inline-flex;\n align-items: center;\n text-decoration: none;\n cursor: pointer;\n transition: background ").concat(dt("stepper.transition.duration"), ", color ").concat(dt("stepper.transition.duration"), ", border-color ").concat(dt("stepper.transition.duration"), ", outline-color ").concat(dt("stepper.transition.duration"), ", box-shadow ").concat(dt("stepper.transition.duration"), ";\n border-radius: ").concat(dt("stepper.step.header.border.radius"), ";\n outline-color: transparent;\n background: transparent;\n padding: ").concat(dt("stepper.step.header.padding"), ";\n gap: ").concat(dt("stepper.step.header.gap"), ";\n}\n\n.p-step-header:focus-visible {\n box-shadow: ").concat(dt("stepper.step.header.focus.ring.shadow"), ";\n outline: ").concat(dt("stepper.step.header.focus.ring.width"), " ").concat(dt("stepper.step.header.focus.ring.style"), " ").concat(dt("stepper.step.header.focus.ring.color"), ";\n outline-offset: ").concat(dt("stepper.step.header.focus.ring.offset"), ";\n}\n\n.p-stepper.p-stepper-readonly .p-step {\n cursor: auto;\n}\n\n.p-step-title {\n display: block;\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n max-width: 100%;\n color: ").concat(dt("stepper.step.title.color"), ";\n font-weight: ").concat(dt("stepper.step.title.font.weight"), ";\n transition: background ").concat(dt("stepper.transition.duration"), ", color ").concat(dt("stepper.transition.duration"), ", border-color ").concat(dt("stepper.transition.duration"), ", box-shadow ").concat(dt("stepper.transition.duration"), ", outline-color ").concat(dt("stepper.transition.duration"), ";\n}\n\n.p-step-number {\n display: flex;\n align-items: center;\n justify-content: center;\n color: ").concat(dt("stepper.step.number.color"), ";\n border: 2px solid ").concat(dt("stepper.step.number.border.color"), ";\n background: ").concat(dt("stepper.step.number.background"), ";\n min-width: ").concat(dt("stepper.step.number.size"), ";\n height: ").concat(dt("stepper.step.number.size"), ";\n line-height: ").concat(dt("stepper.step.number.size"), ";\n font-size: ").concat(dt("stepper.step.number.font.size"), ";\n z-index: 1;\n border-radius: ").concat(dt("stepper.step.number.border.radius"), ";\n position: relative;\n font-weight: ").concat(dt("stepper.step.number.font.weight"), ';\n}\n\n.p-step-number::after {\n content: " ";\n position: absolute;\n width: 100%;\n height: 100%;\n border-radius: ').concat(dt("stepper.step.number.border.radius"), ";\n box-shadow: ").concat(dt("stepper.step.number.shadow"), ";\n}\n\n.p-step-active .p-step-header {\n cursor: default;\n}\n\n.p-step-active .p-step-number {\n background: ").concat(dt("stepper.step.number.active.background"), ";\n border-color: ").concat(dt("stepper.step.number.active.border.color"), ";\n color: ").concat(dt("stepper.step.number.active.color"), ";\n}\n\n.p-step-active .p-step-title {\n color: ").concat(dt("stepper.step.title.active.color"), ";\n}\n\n.p-step:not(.p-disabled):focus-visible {\n outline: ").concat(dt("focus.ring.width"), " ").concat(dt("focus.ring.style"), " ").concat(dt("focus.ring.color"), ";\n outline-offset: ").concat(dt("focus.ring.offset"), ";\n}\n\n.p-step:has(~ .p-step-active) .p-stepper-separator {\n background: ").concat(dt("stepper.separator.active.background"), ";\n}\n\n.p-stepper-separator {\n flex: 1 1 0;\n background: ").concat(dt("stepper.separator.background"), ";\n width: 100%;\n height: ").concat(dt("stepper.separator.size"), ";\n transition: background ").concat(dt("stepper.transition.duration"), ", color ").concat(dt("stepper.transition.duration"), ", border-color ").concat(dt("stepper.transition.duration"), ", box-shadow ").concat(dt("stepper.transition.duration"), ", outline-color ").concat(dt("stepper.transition.duration"), ";\n}\n\n.p-steppanels {\n padding: ").concat(dt("stepper.steppanels.padding"), ";\n}\n\n.p-steppanel {\n background: ").concat(dt("stepper.steppanel.background"), ";\n color: ").concat(dt("stepper.steppanel.color"), ";\n}\n\n.p-stepper:has(.p-stepitem) {\n display: flex;\n flex-direction: column;\n}\n\n.p-stepitem {\n display: flex;\n flex-direction: column;\n flex: initial;\n}\n\n.p-stepitem.p-stepitem-active {\n flex: 1 1 auto;\n}\n\n.p-stepitem .p-step {\n flex: initial;\n}\n\n.p-stepitem .p-steppanel-content {\n width: 100%;\n padding: ").concat(dt("stepper.steppanel.padding"), ";\n}\n\n.p-stepitem .p-steppanel {\n display: flex;\n flex: 1 1 auto;\n}\n\n.p-stepitem .p-stepper-separator {\n flex: 0 0 auto;\n width: ").concat(dt("stepper.separator.size"), ";\n height: auto;\n margin: ").concat(dt("stepper.separator.margin"), ";\n position: relative;\n left: calc(-1 * ").concat(dt("stepper.separator.size"), ");\n}\n\n.p-stepitem:has(~ .p-stepitem-active) .p-stepper-separator {\n background: ").concat(dt("stepper.separator.active.background"), ";\n}\n\n.p-stepitem:last-of-type .p-steppanel {\n padding-inline-start: ").concat(dt("stepper.step.number.size"), ";\n}\n"); -}, "theme"); -var classes = { - root: /* @__PURE__ */ __name(function root3(_ref2) { - var props = _ref2.props; - return ["p-stepper p-component", { - "p-readonly": props.linear - }]; - }, "root"), - separator: "p-stepper-separator" -}; -var StepperStyle = BaseStyle.extend({ - name: "stepper", - theme, - classes -}); -var script$1 = { - name: "BaseStepper", - "extends": script$6, - props: { - value: { - type: [String, Number], - "default": void 0 - }, - linear: { - type: Boolean, - "default": false - } - }, - style: StepperStyle, - provide: /* @__PURE__ */ __name(function provide5() { - return { - $pcStepper: this, - $parentInstance: this - }; - }, "provide") -}; -var script = { - name: "Stepper", - "extends": script$1, - inheritAttrs: false, - emits: ["update:value"], - data: /* @__PURE__ */ 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(openBlock(), createBlock(unref(script$f), { - key: 1, - severity: "warn" - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.$t("install.pathExists")), 1) - ]), - _: 1 - })) : createCommentVNode("", true) - ]), - createBaseVNode("div", _hoisted_6$1, [ - createBaseVNode("h3", _hoisted_7$1, toDisplayString(_ctx.$t("install.systemLocations")), 1), - createBaseVNode("div", _hoisted_8$1, [ - createBaseVNode("div", _hoisted_9$1, [ - _hoisted_10$1, - _hoisted_11$1, - createBaseVNode("span", _hoisted_12$1, toDisplayString(appData.value), 1), - withDirectives(createBaseVNode("span", _hoisted_13, null, 512), [ - [_directive_tooltip, _ctx.$t("install.appDataLocationTooltip")] - ]) - ]), - createBaseVNode("div", _hoisted_14, [ - _hoisted_15, - _hoisted_16, - createBaseVNode("span", _hoisted_17, toDisplayString(appPath.value), 1), - withDirectives(createBaseVNode("span", _hoisted_18, null, 512), [ - [_directive_tooltip, _ctx.$t("install.appPathLocationTooltip")] - ]) - ]) - ]) - ]) - ]); - }; - } -}); -const _hoisted_1$1 = { class: "flex flex-col gap-6 w-[600px]" }; -const _hoisted_2$1 = { class: "flex flex-col gap-4" }; -const _hoisted_3$1 = { class: "text-2xl font-semibold text-neutral-100" }; -const _hoisted_4$1 = { class: "text-neutral-400 my-0" }; -const _hoisted_5 = { class: "flex gap-2" }; -const _hoisted_6 = { - key: 0, - class: "flex flex-col gap-4 bg-neutral-800 p-4 rounded-lg" -}; -const _hoisted_7 = { class: "text-lg mt-0 font-medium text-neutral-100" }; -const _hoisted_8 = { class: "flex flex-col gap-3" }; -const _hoisted_9 = ["onClick"]; -const _hoisted_10 = ["for"]; -const _hoisted_11 = { class: "text-sm text-neutral-400 my-1" }; -const _hoisted_12 = { - key: 1, - class: "text-neutral-400 italic" -}; -const _sfc_main$1 = /* @__PURE__ */ defineComponent({ - __name: "MigrationPicker", - props: { - "sourcePath": { required: false }, - "sourcePathModifiers": {}, - "migrationItemIds": { - required: false - }, - "migrationItemIdsModifiers": {} - }, - emits: ["update:sourcePath", "update:migrationItemIds"], - setup(__props) { - const { t } = useI18n(); - const electron = electronAPI(); - const sourcePath = useModel(__props, "sourcePath"); - const migrationItemIds = useModel(__props, "migrationItemIds"); - const migrationItems = ref( - MigrationItems.map((item) => ({ - ...item, - selected: true - })) - ); - const pathError = ref(""); - const isValidSource = computed( - () => sourcePath.value !== "" && pathError.value === "" - ); - const validateSource = /* @__PURE__ */ __name(async (sourcePath2) => { - if (!sourcePath2) { - pathError.value = ""; - return; - } - try { - pathError.value = ""; - const validation = await electron.validateComfyUISource(sourcePath2); - if (!validation.isValid) pathError.value = validation.error; - } catch (error) { - console.error(error); - pathError.value = t("install.pathValidationFailed"); - } - }, "validateSource"); - const browsePath = /* @__PURE__ */ __name(async () => { - try { - const result = await electron.showDirectoryPicker(); - if (result) { - sourcePath.value = result; - await validateSource(result); - } - } catch (error) { - console.error(error); - pathError.value = t("install.failedToSelectDirectory"); - } - }, "browsePath"); - watchEffect(() => { - migrationItemIds.value = migrationItems.value.filter((item) => item.selected).map((item) => item.id); - }); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("div", _hoisted_1$1, [ - createBaseVNode("div", _hoisted_2$1, [ - createBaseVNode("h2", _hoisted_3$1, toDisplayString(_ctx.$t("install.migrateFromExistingInstallation")), 1), - createBaseVNode("p", _hoisted_4$1, toDisplayString(_ctx.$t("install.migrationSourcePathDescription")), 1), - createBaseVNode("div", _hoisted_5, [ - createVNode(unref(script$b), { - modelValue: sourcePath.value, - "onUpdate:modelValue": [ - _cache[0] || (_cache[0] = ($event) => sourcePath.value = $event), - validateSource - ], - placeholder: "Select existing ComfyUI installation (optional)", - class: normalizeClass(["flex-1", { "p-invalid": pathError.value }]) - }, null, 8, ["modelValue", "class"]), - createVNode(unref(script$e), { - icon: "pi pi-folder", - onClick: browsePath, - class: "w-12" - }) - ]), - pathError.value ? (openBlock(), createBlock(unref(script$f), { - key: 0, - severity: "error" - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(pathError.value), 1) - ]), - _: 1 - })) : createCommentVNode("", true) - ]), - isValidSource.value ? (openBlock(), createElementBlock("div", _hoisted_6, [ - createBaseVNode("h3", _hoisted_7, toDisplayString(_ctx.$t("install.selectItemsToMigrate")), 1), - createBaseVNode("div", _hoisted_8, [ - (openBlock(true), createElementBlock(Fragment, null, renderList(migrationItems.value, (item) => { - return openBlock(), createElementBlock("div", { - key: item.id, - class: "flex items-center gap-3 p-2 hover:bg-neutral-700 rounded", - onClick: /* @__PURE__ */ __name(($event) => item.selected = !item.selected, "onClick") - }, [ - createVNode(unref(script$g), { - modelValue: item.selected, - "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => item.selected = $event, "onUpdate:modelValue"), - inputId: item.id, - binary: true, - onClick: _cache[1] || (_cache[1] = withModifiers(() => { - }, ["stop"])) - }, null, 8, ["modelValue", "onUpdate:modelValue", "inputId"]), - createBaseVNode("div", null, [ - createBaseVNode("label", { - for: item.id, - class: "text-neutral-200 font-medium" - }, toDisplayString(item.label), 9, _hoisted_10), - createBaseVNode("p", _hoisted_11, toDisplayString(item.description), 1) - ]) - ], 8, _hoisted_9); - }), 128)) - ]) - ])) : (openBlock(), createElementBlock("div", _hoisted_12, toDisplayString(_ctx.$t("install.migrationOptional")), 1)) - ]); - }; - } -}); -const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-de33872d"), n = n(), popScopeId(), n), "_withScopeId"); -const _hoisted_1 = { class: "flex pt-6 justify-end" }; -const _hoisted_2 = { class: "flex pt-6 justify-between" }; -const _hoisted_3 = { class: "flex pt-6 justify-between" }; -const _hoisted_4 = { class: "flex pt-6 justify-between" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "InstallView", - setup(__props) { - const device = ref(null); - const installPath = ref(""); - const pathError = ref(""); - const migrationSourcePath = ref(""); - const migrationItemIds = ref([]); - const autoUpdate = ref(true); - const allowMetrics = ref(true); - const highestStep = ref(0); - const setHighestStep = /* @__PURE__ */ __name((value2) => { - const int = typeof value2 === "number" ? value2 : parseInt(value2, 10); - if (!isNaN(int) && int > highestStep.value) highestStep.value = int; - }, "setHighestStep"); - const hasError = computed(() => pathError.value !== ""); - const noGpu = computed(() => typeof device.value !== "string"); - const electron = electronAPI(); - const router = useRouter(); - const install = /* @__PURE__ */ __name(() => { - const options = { - installPath: installPath.value, - autoUpdate: autoUpdate.value, - allowMetrics: allowMetrics.value, - migrationSourcePath: migrationSourcePath.value, - migrationItemIds: toRaw(migrationItemIds.value), - device: device.value - }; - electron.installComfyUI(options); - const nextPage = options.device === "unsupported" ? "/manual-configuration" : "/server-start"; - router.push(nextPage); - }, "install"); - onMounted(async () => { - if (!electron) return; - const detectedGpu = await electron.Config.getDetectedGpu(); - if (detectedGpu === "mps" || detectedGpu === "nvidia") - device.value = detectedGpu; - }); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$5, { dark: "" }, { - default: withCtx(() => [ - createVNode(unref(script), { - class: "h-full p-8 2xl:p-16", - value: "0", - "onUpdate:value": setHighestStep - }, { - default: withCtx(() => [ - createVNode(unref(script$4), { class: "select-none" }, { - default: withCtx(() => [ - createVNode(unref(script$5), { value: "0" }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.$t("install.gpu")), 1) - ]), - _: 1 - }), - createVNode(unref(script$5), { - value: "1", - disabled: noGpu.value - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.$t("install.installLocation")), 1) - ]), - _: 1 - }, 8, ["disabled"]), - createVNode(unref(script$5), { - value: "2", - disabled: noGpu.value || hasError.value || highestStep.value < 1 - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.$t("install.migration")), 1) - ]), - _: 1 - }, 8, ["disabled"]), - createVNode(unref(script$5), { - value: "3", - disabled: noGpu.value || hasError.value || highestStep.value < 2 - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(_ctx.$t("install.desktopSettings")), 1) - ]), - _: 1 - }, 8, ["disabled"]) - ]), - _: 1 - }), - createVNode(unref(script$2), null, { - default: withCtx(() => [ - createVNode(unref(script$3), { value: "0" }, { - default: withCtx(({ activateCallback }) => [ - createVNode(_sfc_main$3, { - device: device.value, - "onUpdate:device": _cache[0] || (_cache[0] = ($event) => device.value = $event) - }, null, 8, ["device"]), - createBaseVNode("div", _hoisted_1, [ - createVNode(unref(script$e), { - label: _ctx.$t("g.next"), - icon: "pi pi-arrow-right", - iconPos: "right", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick"), - disabled: typeof device.value !== "string" - }, null, 8, ["label", "onClick", "disabled"]) - ]) - ]), - _: 1 - }), - createVNode(unref(script$3), { value: "1" }, { - default: withCtx(({ activateCallback }) => [ - createVNode(_sfc_main$2, { - installPath: installPath.value, - "onUpdate:installPath": _cache[1] || (_cache[1] = ($event) => installPath.value = $event), - pathError: pathError.value, - "onUpdate:pathError": _cache[2] || (_cache[2] = ($event) => pathError.value = $event) - }, null, 8, ["installPath", "pathError"]), - createBaseVNode("div", _hoisted_2, [ - createVNode(unref(script$e), { - label: _ctx.$t("g.back"), - severity: "secondary", - icon: "pi pi-arrow-left", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("0"), "onClick") - }, null, 8, ["label", "onClick"]), - createVNode(unref(script$e), { - label: _ctx.$t("g.next"), - icon: "pi pi-arrow-right", - iconPos: "right", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick"), - disabled: pathError.value !== "" - }, null, 8, ["label", "onClick", "disabled"]) - ]) - ]), - _: 1 - }), - createVNode(unref(script$3), { value: "2" }, { - default: withCtx(({ activateCallback }) => [ - createVNode(_sfc_main$1, { - sourcePath: migrationSourcePath.value, - "onUpdate:sourcePath": _cache[3] || (_cache[3] = ($event) => migrationSourcePath.value = $event), - migrationItemIds: migrationItemIds.value, - "onUpdate:migrationItemIds": _cache[4] || (_cache[4] = ($event) => migrationItemIds.value = $event) - }, null, 8, ["sourcePath", "migrationItemIds"]), - createBaseVNode("div", _hoisted_3, [ - createVNode(unref(script$e), { - label: _ctx.$t("g.back"), - severity: "secondary", - icon: "pi pi-arrow-left", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("1"), "onClick") - }, null, 8, ["label", "onClick"]), - createVNode(unref(script$e), { - label: _ctx.$t("g.next"), - icon: "pi pi-arrow-right", - iconPos: "right", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("3"), "onClick") - }, null, 8, ["label", "onClick"]) - ]) - ]), - _: 1 - }), - createVNode(unref(script$3), { value: "3" }, { - default: withCtx(({ activateCallback }) => [ - createVNode(_sfc_main$4, { - autoUpdate: autoUpdate.value, - "onUpdate:autoUpdate": _cache[5] || (_cache[5] = ($event) => autoUpdate.value = $event), - allowMetrics: allowMetrics.value, - "onUpdate:allowMetrics": _cache[6] || (_cache[6] = ($event) => allowMetrics.value = $event) - }, null, 8, ["autoUpdate", "allowMetrics"]), - createBaseVNode("div", _hoisted_4, [ - createVNode(unref(script$e), { - label: _ctx.$t("g.back"), - severity: "secondary", - icon: "pi pi-arrow-left", - onClick: /* @__PURE__ */ __name(($event) => activateCallback("2"), "onClick") - }, null, 8, ["label", "onClick"]), - createVNode(unref(script$e), { - label: _ctx.$t("g.install"), - icon: "pi pi-check", - iconPos: "right", - disabled: hasError.value, - onClick: _cache[7] || (_cache[7] = ($event) => install()) - }, null, 8, ["label", "disabled"]) - ]) - ]), - _: 1 - }) - ]), - _: 1 - }) - ]), - _: 1 - }) - ]), - _: 1 - }); - }; - } -}); -const InstallView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-de33872d"]]); -export { - InstallView as default -}; -//# sourceMappingURL=InstallView-AV2llYNm.js.map diff --git a/web/assets/InstallView-CwQdoH-C.css b/web/assets/InstallView-CwQdoH-C.css deleted file mode 100644 index df5787787..000000000 --- a/web/assets/InstallView-CwQdoH-C.css +++ /dev/null @@ -1,79 +0,0 @@ - -:root { - --p-tag-gap: 0.5rem; -} -.hover-brighten { - transition-property: color, background-color, border-color, text-decoration-color, fill, stroke; - transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); - transition-duration: 150ms; - transition-property: filter, box-shadow; -&:hover { - filter: brightness(107%) contrast(105%); - box-shadow: 0 0 0.25rem #ffffff79; -} -} -.p-accordioncontent-content { - border-radius: 0.5rem; - --tw-bg-opacity: 1; - background-color: rgb(23 23 23 / var(--tw-bg-opacity)); - transition-property: color, background-color, border-color, text-decoration-color, fill, stroke; - transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); - transition-duration: 150ms; -} -div.selected { -.gpu-button:not(.selected) { - opacity: 0.5; -} -.gpu-button:not(.selected):hover { - opacity: 1; -} -} -.gpu-button { - margin: 0px; - display: flex; - width: 50%; - cursor: pointer; - flex-direction: column; - align-items: center; - justify-content: space-around; - border-radius: 0.5rem; - background-color: rgb(38 38 38 / var(--tw-bg-opacity)); - --tw-bg-opacity: 0.5; - transition-property: color, background-color, border-color, text-decoration-color, fill, stroke; - transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); - transition-duration: 150ms; -} -.gpu-button:hover { - --tw-bg-opacity: 0.75; -} -.gpu-button { -&.selected { - --tw-bg-opacity: 1; - background-color: rgb(64 64 64 / var(--tw-bg-opacity)); -} -&.selected { - --tw-bg-opacity: 0.5; -} -&.selected { - opacity: 1; -} -&.selected:hover { - --tw-bg-opacity: 0.6; -} -} -.disabled { - pointer-events: none; - opacity: 0.4; -} -.p-card-header { - flex-grow: 1; - text-align: center; -} -.p-card-body { - padding-top: 0px; - text-align: center; -} - -[data-v-de33872d] .p-steppanel { - background-color: transparent -} diff --git a/web/assets/KeybindingPanel-CxaJ1IiJ.js b/web/assets/KeybindingPanel-CxaJ1IiJ.js deleted file mode 100644 index f165f9a51..000000000 --- a/web/assets/KeybindingPanel-CxaJ1IiJ.js +++ /dev/null @@ -1,284 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, E as renderList, N as createVNode, M as withCtx, aE as createTextVNode, X as toDisplayString, j as unref, aI as script, I as createCommentVNode, ab as ref, cn as FilterMatchMode, a$ as useKeybindingStore, a2 as useCommandStore, a1 as useI18n, af as normalizeI18nKey, w as watchEffect, bs as useToast, r as resolveDirective, k as createBlock, co as SearchBox, H as createBaseVNode, l as script$2, av as script$4, bM as withModifiers, bZ as script$5, aP as script$6, i as withDirectives, cp as _sfc_main$2, aL as pushScopeId, aM as popScopeId, cq as KeyComboImpl, cr as KeybindingImpl, _ as _export_sfc } from "./index-C4Fk50Nx.js"; -import { s as script$1, a as script$3 } from "./index-CK0rrCYF.js"; -import { u as useKeybindingService } from "./keybindingService-D48fkLBy.js"; -import "./index-lMQBwSDj.js"; -import "./index-B7ycxfFq.js"; -const _hoisted_1$1 = { - key: 0, - class: "px-2" -}; -const _sfc_main$1 = /* @__PURE__ */ defineComponent({ - __name: "KeyComboDisplay", - props: { - keyCombo: {}, - isModified: { type: Boolean, default: false } - }, - setup(__props) { - const props = __props; - const keySequences = computed(() => props.keyCombo.getKeySequences()); - return (_ctx, _cache) => { - return openBlock(), createElementBlock("span", null, [ - (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => { - return openBlock(), createElementBlock(Fragment, { key: index }, [ - createVNode(unref(script), { - severity: _ctx.isModified ? "info" : "secondary" - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(sequence), 1) - ]), - _: 2 - }, 1032, ["severity"]), - index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true) - ], 64); - }), 128)) - ]); - }; - } -}); -const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2554ab36"), n = n(), popScopeId(), n), "_withScopeId"); -const _hoisted_1 = { class: "actions invisible flex flex-row" }; -const _hoisted_2 = ["title"]; -const _hoisted_3 = { key: 1 }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "KeybindingPanel", - setup(__props) { - const filters = ref({ - global: { value: "", matchMode: FilterMatchMode.CONTAINS } - }); - const keybindingStore = useKeybindingStore(); - const keybindingService = useKeybindingService(); - const commandStore = useCommandStore(); - const { t } = useI18n(); - const commandsData = computed(() => { - return Object.values(commandStore.commands).map((command) => ({ - id: command.id, - label: t(`commands.${normalizeI18nKey(command.id)}.label`, command.label), - keybinding: keybindingStore.getKeybindingByCommandId(command.id) - })); - }); - const selectedCommandData = ref(null); - const editDialogVisible = ref(false); - const newBindingKeyCombo = ref(null); - const currentEditingCommand = ref(null); - const keybindingInput = ref(null); - const existingKeybindingOnCombo = computed(() => { - if (!currentEditingCommand.value) { - return null; - } - if (currentEditingCommand.value.keybinding?.combo?.equals( - newBindingKeyCombo.value - )) { - return null; - } - if (!newBindingKeyCombo.value) { - return null; - } - return keybindingStore.getKeybinding(newBindingKeyCombo.value); - }); - function editKeybinding(commandData) { - currentEditingCommand.value = commandData; - newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null; - editDialogVisible.value = true; - } - __name(editKeybinding, "editKeybinding"); - watchEffect(() => { - if (editDialogVisible.value) { - setTimeout(() => { - keybindingInput.value?.$el?.focus(); - }, 300); - } - }); - function removeKeybinding(commandData) { - if (commandData.keybinding) { - keybindingStore.unsetKeybinding(commandData.keybinding); - keybindingService.persistUserKeybindings(); - } - } - __name(removeKeybinding, "removeKeybinding"); - function captureKeybinding(event) { - const keyCombo = KeyComboImpl.fromEvent(event); - newBindingKeyCombo.value = keyCombo; - } - __name(captureKeybinding, "captureKeybinding"); - function cancelEdit() { - editDialogVisible.value = false; - currentEditingCommand.value = null; - newBindingKeyCombo.value = null; - } - __name(cancelEdit, "cancelEdit"); - function saveKeybinding() { - if (currentEditingCommand.value && newBindingKeyCombo.value) { - const updated = keybindingStore.updateKeybindingOnCommand( - new KeybindingImpl({ - commandId: currentEditingCommand.value.id, - combo: newBindingKeyCombo.value - }) - ); - if (updated) { - keybindingService.persistUserKeybindings(); - } - } - cancelEdit(); - } - __name(saveKeybinding, "saveKeybinding"); - const toast = useToast(); - async function resetKeybindings() { - keybindingStore.resetKeybindings(); - await keybindingService.persistUserKeybindings(); - toast.add({ - severity: "info", - summary: "Info", - detail: "Keybindings reset", - life: 3e3 - }); - } - __name(resetKeybindings, "resetKeybindings"); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createBlock(_sfc_main$2, { - value: "Keybinding", - class: "keybinding-panel" - }, { - header: withCtx(() => [ - createVNode(SearchBox, { - modelValue: filters.value["global"].value, - "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event), - placeholder: _ctx.$t("g.searchKeybindings") + "..." - }, null, 8, ["modelValue", "placeholder"]) - ]), - default: withCtx(() => [ - createVNode(unref(script$3), { - value: commandsData.value, - selection: selectedCommandData.value, - "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event), - "global-filter-fields": ["id"], - filters: filters.value, - selectionMode: "single", - stripedRows: "", - pt: { - header: "px-0" - } - }, { - default: withCtx(() => [ - createVNode(unref(script$1), { - field: "actions", - header: "" - }, { - body: withCtx((slotProps) => [ - createBaseVNode("div", _hoisted_1, [ - createVNode(unref(script$2), { - icon: "pi pi-pencil", - class: "p-button-text", - onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick") - }, null, 8, ["onClick"]), - createVNode(unref(script$2), { - icon: "pi pi-trash", - class: "p-button-text p-button-danger", - onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"), - disabled: !slotProps.data.keybinding - }, null, 8, ["onClick", "disabled"]) - ]) - ]), - _: 1 - }), - createVNode(unref(script$1), { - field: "id", - header: _ctx.$t("g.command"), - sortable: "", - class: "max-w-64 2xl:max-w-full" - }, { - body: withCtx((slotProps) => [ - createBaseVNode("div", { - class: "overflow-hidden text-ellipsis whitespace-nowrap", - title: slotProps.data.id - }, toDisplayString(slotProps.data.label), 9, _hoisted_2) - ]), - _: 1 - }, 8, ["header"]), - createVNode(unref(script$1), { - field: "keybinding", - header: _ctx.$t("g.keybinding") - }, { - body: withCtx((slotProps) => [ - slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, { - key: 0, - keyCombo: slotProps.data.keybinding.combo, - isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id) - }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-")) - ]), - _: 1 - }, 8, ["header"]) - ]), - _: 1 - }, 8, ["value", "selection", "filters"]), - createVNode(unref(script$6), { - class: "min-w-96", - visible: editDialogVisible.value, - "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event), - modal: "", - header: currentEditingCommand.value?.id, - onHide: cancelEdit - }, { - footer: withCtx(() => [ - createVNode(unref(script$2), { - label: "Save", - icon: "pi pi-check", - onClick: saveKeybinding, - disabled: !!existingKeybindingOnCombo.value, - autofocus: "" - }, null, 8, ["disabled"]) - ]), - default: withCtx(() => [ - createBaseVNode("div", null, [ - createVNode(unref(script$4), { - class: "mb-2 text-center", - ref_key: "keybindingInput", - ref: keybindingInput, - modelValue: newBindingKeyCombo.value?.toString() ?? "", - placeholder: "Press keys for new binding", - onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]), - autocomplete: "off", - fluid: "", - invalid: !!existingKeybindingOnCombo.value - }, null, 8, ["modelValue", "invalid"]), - existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), { - key: 0, - severity: "error" - }, { - default: withCtx(() => [ - createTextVNode(" Keybinding already exists on "), - createVNode(unref(script), { - severity: "secondary", - value: existingKeybindingOnCombo.value.commandId - }, null, 8, ["value"]) - ]), - _: 1 - })) : createCommentVNode("", true) - ]) - ]), - _: 1 - }, 8, ["visible", "header"]), - withDirectives(createVNode(unref(script$2), { - class: "mt-4", - label: _ctx.$t("g.reset"), - icon: "pi pi-trash", - severity: "danger", - fluid: "", - text: "", - onClick: resetKeybindings - }, null, 8, ["label"]), [ - [_directive_tooltip, _ctx.$t("g.resetKeybindingsTooltip")] - ]) - ]), - _: 1 - }); - }; - } -}); -const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-2554ab36"]]); -export { - KeybindingPanel as default -}; -//# sourceMappingURL=KeybindingPanel-CxaJ1IiJ.js.map diff --git a/web/assets/KeybindingPanel-DvrUYZ4S.css b/web/assets/KeybindingPanel-DvrUYZ4S.css deleted file mode 100644 index 8f714bcdb..000000000 --- a/web/assets/KeybindingPanel-DvrUYZ4S.css +++ /dev/null @@ -1,8 +0,0 @@ - -[data-v-2554ab36] .p-datatable-tbody > tr > td { - padding: 0.25rem; - min-height: 2rem -} -[data-v-2554ab36] .p-datatable-row-selected .actions,[data-v-2554ab36] .p-datatable-selectable-row:hover .actions { - visibility: visible -} diff --git a/web/assets/ManualConfigurationView-B6ecEClB.css b/web/assets/ManualConfigurationView-B6ecEClB.css deleted file mode 100644 index 06a5cc3e8..000000000 --- a/web/assets/ManualConfigurationView-B6ecEClB.css +++ /dev/null @@ -1,7 +0,0 @@ - -:root { - --p-tag-gap: 0.5rem; -} -.comfy-installer { - margin-top: max(1rem, max(0px, calc((100vh - 42rem) * 0.5))); -} diff --git a/web/assets/ManualConfigurationView-BA4Vtud8.js b/web/assets/ManualConfigurationView-BA4Vtud8.js deleted file mode 100644 index 122da4e96..000000000 --- a/web/assets/ManualConfigurationView-BA4Vtud8.js +++ /dev/null @@ -1,75 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, a1 as useI18n, ab as ref, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, aI as script, l as script$2, c0 as electronAPI } from "./index-C4Fk50Nx.js"; -import { s as script$1 } from "./index-hdfnBvYs.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -import "./index-B7ycxfFq.js"; -const _hoisted_1 = { class: "comfy-installer grow flex flex-col gap-4 text-neutral-300 max-w-110" }; -const _hoisted_2 = { class: "text-2xl font-semibold text-neutral-100" }; -const _hoisted_3 = { class: "m-1 text-neutral-300" }; -const _hoisted_4 = { class: "ml-2" }; -const _hoisted_5 = { class: "m-1 mb-4" }; -const _hoisted_6 = { class: "m-0" }; -const _hoisted_7 = { class: "m-1" }; -const _hoisted_8 = { class: "font-mono" }; -const _hoisted_9 = { class: "m-1" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "ManualConfigurationView", - setup(__props) { - const { t } = useI18n(); - const electron = electronAPI(); - const basePath = ref(null); - const sep = ref("/"); - const restartApp = /* @__PURE__ */ __name((message) => electron.restartApp(message), "restartApp"); - onMounted(async () => { - basePath.value = await electron.getBasePath(); - if (basePath.value.indexOf("/") === -1) sep.value = "\\"; - }); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, { dark: "" }, { - default: withCtx(() => [ - createBaseVNode("div", _hoisted_1, [ - createBaseVNode("h2", _hoisted_2, toDisplayString(_ctx.$t("install.manualConfiguration.title")), 1), - createBaseVNode("p", _hoisted_3, [ - createVNode(unref(script), { - icon: "pi pi-exclamation-triangle", - severity: "warn", - value: unref(t)("icon.exclamation-triangle") - }, null, 8, ["value"]), - createBaseVNode("strong", _hoisted_4, toDisplayString(_ctx.$t("install.gpuSelection.customComfyNeedsPython")), 1) - ]), - createBaseVNode("div", null, [ - createBaseVNode("p", _hoisted_5, toDisplayString(_ctx.$t("install.manualConfiguration.requirements")) + ": ", 1), - createBaseVNode("ul", _hoisted_6, [ - createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customManualVenv")), 1), - createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customInstallRequirements")), 1) - ]) - ]), - createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("install.manualConfiguration.createVenv")) + ":", 1), - createVNode(unref(script$1), { - header: unref(t)("install.manualConfiguration.virtualEnvironmentPath") - }, { - default: withCtx(() => [ - createBaseVNode("span", _hoisted_8, toDisplayString(`${basePath.value}${sep.value}.venv${sep.value}`), 1) - ]), - _: 1 - }, 8, ["header"]), - createBaseVNode("p", _hoisted_9, toDisplayString(_ctx.$t("install.manualConfiguration.restartWhenFinished")), 1), - createVNode(unref(script$2), { - class: "place-self-end", - label: unref(t)("menuLabels.Restart"), - severity: "warn", - icon: "pi pi-refresh", - onClick: _cache[0] || (_cache[0] = ($event) => restartApp("Manual configuration complete")) - }, null, 8, ["label"]) - ]) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=ManualConfigurationView-BA4Vtud8.js.map diff --git a/web/assets/NotSupportedView-CRaD8u74.js b/web/assets/NotSupportedView-CRaD8u74.js deleted file mode 100644 index 07e3eca64..000000000 --- a/web/assets/NotSupportedView-CRaD8u74.js +++ /dev/null @@ -1,86 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, bW as useRouter, r as resolveDirective, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, i as withDirectives } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href; -const _hoisted_1 = { class: "sad-container" }; -const _hoisted_2 = /* @__PURE__ */ createBaseVNode("img", { - class: "sad-girl", - src: _imports_0, - alt: "Sad girl illustration" -}, null, -1); -const _hoisted_3 = { class: "no-drag sad-text flex items-center" }; -const _hoisted_4 = { class: "flex flex-col gap-8 p-8 min-w-110" }; -const _hoisted_5 = { class: "text-4xl font-bold text-red-500" }; -const _hoisted_6 = { class: "space-y-4" }; -const _hoisted_7 = { class: "text-xl" }; -const _hoisted_8 = { class: "list-disc list-inside space-y-1 text-neutral-800" }; -const _hoisted_9 = { class: "flex gap-4" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "NotSupportedView", - setup(__props) { - const openDocs = /* @__PURE__ */ __name(() => { - window.open( - "https://github.com/Comfy-Org/desktop#currently-supported-platforms", - "_blank" - ); - }, "openDocs"); - const reportIssue = /* @__PURE__ */ __name(() => { - window.open("https://forum.comfy.org/c/v1-feedback/", "_blank"); - }, "reportIssue"); - const router = useRouter(); - const continueToInstall = /* @__PURE__ */ __name(() => { - router.push("/install"); - }, "continueToInstall"); - return (_ctx, _cache) => { - const _directive_tooltip = resolveDirective("tooltip"); - return openBlock(), createBlock(_sfc_main$1, null, { - default: withCtx(() => [ - createBaseVNode("div", _hoisted_1, [ - _hoisted_2, - createBaseVNode("div", _hoisted_3, [ - createBaseVNode("div", _hoisted_4, [ - createBaseVNode("h1", _hoisted_5, toDisplayString(_ctx.$t("notSupported.title")), 1), - createBaseVNode("div", _hoisted_6, [ - createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("notSupported.message")), 1), - createBaseVNode("ul", _hoisted_8, [ - createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1), - createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1) - ]) - ]), - createBaseVNode("div", _hoisted_9, [ - createVNode(unref(script), { - label: _ctx.$t("notSupported.learnMore"), - icon: "pi pi-github", - onClick: openDocs, - severity: "secondary" - }, null, 8, ["label"]), - createVNode(unref(script), { - label: _ctx.$t("notSupported.reportIssue"), - icon: "pi pi-flag", - onClick: reportIssue, - severity: "secondary" - }, null, 8, ["label"]), - withDirectives(createVNode(unref(script), { - label: _ctx.$t("notSupported.continue"), - icon: "pi pi-arrow-right", - iconPos: "right", - onClick: continueToInstall, - severity: "danger" - }, null, 8, ["label"]), [ - [_directive_tooltip, _ctx.$t("notSupported.continueTooltip")] - ]) - ]) - ]) - ]) - ]) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=NotSupportedView-CRaD8u74.js.map diff --git a/web/assets/NotSupportedView-bFzHmqNj.css b/web/assets/NotSupportedView-bFzHmqNj.css deleted file mode 100644 index 80ac32982..000000000 --- a/web/assets/NotSupportedView-bFzHmqNj.css +++ /dev/null @@ -1,17 +0,0 @@ - -.sad-container { - display: grid; - align-items: center; - justify-content: space-evenly; - grid-template-columns: 25rem 1fr; -& > * { - grid-row: 1; -} -} -.sad-text { - grid-column: 1/3; -} -.sad-girl { - grid-column: 2/3; - width: min(75vw, 100vh); -} diff --git a/web/assets/ServerConfigPanel-TLv4HMGK.js b/web/assets/ServerConfigPanel-TLv4HMGK.js deleted file mode 100644 index 6489eb665..000000000 --- a/web/assets/ServerConfigPanel-TLv4HMGK.js +++ /dev/null @@ -1,158 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { H as createBaseVNode, o as openBlock, f as createElementBlock, Z as markRaw, d as defineComponent, a as useSettingStore, aS as storeToRefs, a5 as watch, cO as useCopyToClipboard, a1 as useI18n, k as createBlock, M as withCtx, j as unref, bZ as script, X as toDisplayString, E as renderList, F as Fragment, N as createVNode, l as script$1, I as createCommentVNode, bQ as script$2, cP as FormItem, cp as _sfc_main$1, c0 as electronAPI } from "./index-C4Fk50Nx.js"; -import { u as useServerConfigStore } from "./serverConfigStore-BawYAb1j.js"; -const _hoisted_1$1 = { - viewBox: "0 0 24 24", - width: "1.2em", - height: "1.2em" -}; -const _hoisted_2$1 = /* @__PURE__ */ createBaseVNode("path", { - fill: "none", - stroke: "currentColor", - "stroke-linecap": "round", - "stroke-linejoin": "round", - "stroke-width": "2", - d: "m4 17l6-6l-6-6m8 14h8" -}, null, -1); -const _hoisted_3$1 = [ - _hoisted_2$1 -]; -function render(_ctx, _cache) { - return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3$1]); -} -__name(render, "render"); -const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render }); -const _hoisted_1 = { class: "flex flex-col gap-2" }; -const _hoisted_2 = { class: "flex justify-end gap-2" }; -const _hoisted_3 = { class: "flex items-center justify-between" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "ServerConfigPanel", - setup(__props) { - const settingStore = useSettingStore(); - const serverConfigStore = useServerConfigStore(); - const { - serverConfigsByCategory, - serverConfigValues, - launchArgs, - commandLineArgs, - modifiedConfigs - } = storeToRefs(serverConfigStore); - const revertChanges = /* @__PURE__ */ __name(() => { - serverConfigStore.revertChanges(); - }, "revertChanges"); - const restartApp = /* @__PURE__ */ __name(() => { - electronAPI().restartApp(); - }, "restartApp"); - watch(launchArgs, (newVal) => { - settingStore.set("Comfy.Server.LaunchArgs", newVal); - }); - watch(serverConfigValues, (newVal) => { - settingStore.set("Comfy.Server.ServerConfigValues", newVal); - }); - const { copyToClipboard } = useCopyToClipboard(); - const copyCommandLineArgs = /* @__PURE__ */ __name(async () => { - await copyToClipboard(commandLineArgs.value); - }, "copyCommandLineArgs"); - const { t } = useI18n(); - const translateItem = /* @__PURE__ */ __name((item) => { - return { - ...item, - name: t(`serverConfigItems.${item.id}.name`, item.name), - tooltip: item.tooltip ? t(`serverConfigItems.${item.id}.tooltip`, item.tooltip) : void 0 - }; - }, "translateItem"); - return (_ctx, _cache) => { - const _component_i_lucide58terminal = __unplugin_components_0; - return openBlock(), createBlock(_sfc_main$1, { - value: "Server-Config", - class: "server-config-panel" - }, { - header: withCtx(() => [ - createBaseVNode("div", _hoisted_1, [ - unref(modifiedConfigs).length > 0 ? (openBlock(), createBlock(unref(script), { - key: 0, - severity: "info", - "pt:text": "w-full" - }, { - default: withCtx(() => [ - createBaseVNode("p", null, toDisplayString(_ctx.$t("serverConfig.modifiedConfigs")), 1), - createBaseVNode("ul", null, [ - (openBlock(true), createElementBlock(Fragment, null, renderList(unref(modifiedConfigs), (config) => { - return openBlock(), createElementBlock("li", { - key: config.id - }, toDisplayString(config.name) + ": " + toDisplayString(config.initialValue) + " → " + toDisplayString(config.value), 1); - }), 128)) - ]), - createBaseVNode("div", _hoisted_2, [ - createVNode(unref(script$1), { - label: _ctx.$t("serverConfig.revertChanges"), - onClick: revertChanges, - outlined: "" - }, null, 8, ["label"]), - createVNode(unref(script$1), { - label: _ctx.$t("serverConfig.restart"), - onClick: restartApp, - outlined: "", - severity: "danger" - }, null, 8, ["label"]) - ]) - ]), - _: 1 - })) : createCommentVNode("", true), - unref(commandLineArgs) ? (openBlock(), createBlock(unref(script), { - key: 1, - severity: "secondary", - "pt:text": "w-full" - }, { - icon: withCtx(() => [ - createVNode(_component_i_lucide58terminal, { class: "text-xl font-bold" }) - ]), - default: withCtx(() => [ - createBaseVNode("div", _hoisted_3, [ - createBaseVNode("p", null, toDisplayString(unref(commandLineArgs)), 1), - createVNode(unref(script$1), { - icon: "pi pi-clipboard", - onClick: copyCommandLineArgs, - severity: "secondary", - text: "" - }) - ]) - ]), - _: 1 - })) : createCommentVNode("", true) - ]) - ]), - default: withCtx(() => [ - (openBlock(true), createElementBlock(Fragment, null, renderList(Object.entries(unref(serverConfigsByCategory)), ([label, items], i) => { - return openBlock(), createElementBlock("div", { key: label }, [ - i > 0 ? (openBlock(), createBlock(unref(script$2), { key: 0 })) : createCommentVNode("", true), - createBaseVNode("h3", null, toDisplayString(_ctx.$t(`serverConfigCategories.${label}`, label)), 1), - (openBlock(true), createElementBlock(Fragment, null, renderList(items, (item) => { - return openBlock(), createElementBlock("div", { - key: item.name, - class: "mb-4" - }, [ - createVNode(FormItem, { - item: translateItem(item), - formValue: item.value, - "onUpdate:formValue": /* @__PURE__ */ __name(($event) => item.value = $event, "onUpdate:formValue"), - id: item.id, - labelClass: { - "text-highlight": item.initialValue !== item.value - } - }, null, 8, ["item", "formValue", "onUpdate:formValue", "id", "labelClass"]) - ]); - }), 128)) - ]); - }), 128)) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=ServerConfigPanel-TLv4HMGK.js.map diff --git a/web/assets/ServerStartView-CiO_acWT.js b/web/assets/ServerStartView-CiO_acWT.js deleted file mode 100644 index 1abcb36af..000000000 --- a/web/assets/ServerStartView-CiO_acWT.js +++ /dev/null @@ -1,98 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, a1 as useI18n, ab as ref, b_ as ProgressStatus, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, aE as createTextVNode, X as toDisplayString, j as unref, f as createElementBlock, I as createCommentVNode, N as createVNode, l as script, i as withDirectives, v as vShow, b$ as BaseTerminal, aL as pushScopeId, aM as popScopeId, c0 as electronAPI, _ as _export_sfc } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-42c1131d"), n = n(), popScopeId(), n), "_withScopeId"); -const _hoisted_1 = { class: "text-2xl font-bold" }; -const _hoisted_2 = { key: 0 }; -const _hoisted_3 = { - key: 0, - class: "flex flex-col items-center gap-4" -}; -const _hoisted_4 = { class: "flex items-center my-4 gap-2" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "ServerStartView", - setup(__props) { - const electron = electronAPI(); - const { t } = useI18n(); - const status = ref(ProgressStatus.INITIAL_STATE); - const electronVersion = ref(""); - let xterm; - const terminalVisible = ref(true); - const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => { - status.value = newStatus; - if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false; - else xterm?.clear(); - }, "updateProgress"); - const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => { - xterm = terminal; - useAutoSize(root, true, true); - electron.onLogMessage((message) => { - terminal.write(message); - }); - terminal.options.cursorBlink = false; - terminal.options.disableStdin = true; - terminal.options.cursorInactiveStyle = "block"; - }, "terminalCreated"); - const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall"); - const reportIssue = /* @__PURE__ */ __name(() => { - window.open("https://forum.comfy.org/c/v1-feedback/", "_blank"); - }, "reportIssue"); - const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs"); - onMounted(async () => { - electron.sendReady(); - electron.onProgressUpdate(updateProgress); - electronVersion.value = await electron.getElectronVersion(); - }); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, { - dark: "", - class: "flex-col" - }, { - default: withCtx(() => [ - createBaseVNode("h2", _hoisted_1, [ - createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1), - status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_2, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true) - ]), - status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_3, [ - createBaseVNode("div", _hoisted_4, [ - createVNode(unref(script), { - icon: "pi pi-flag", - severity: "secondary", - label: unref(t)("serverStart.reportIssue"), - onClick: reportIssue - }, null, 8, ["label"]), - createVNode(unref(script), { - icon: "pi pi-file", - severity: "secondary", - label: unref(t)("serverStart.openLogs"), - onClick: openLogs - }, null, 8, ["label"]), - createVNode(unref(script), { - icon: "pi pi-refresh", - label: unref(t)("serverStart.reinstall"), - onClick: reinstall - }, null, 8, ["label"]) - ]), - !terminalVisible.value ? (openBlock(), createBlock(unref(script), { - key: 0, - icon: "pi pi-search", - severity: "secondary", - label: unref(t)("serverStart.showTerminal"), - onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true) - }, null, 8, ["label"])) : createCommentVNode("", true) - ])) : createCommentVNode("", true), - withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [ - [vShow, terminalVisible.value] - ]) - ]), - _: 1 - }); - }; - } -}); -const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-42c1131d"]]); -export { - ServerStartView as default -}; -//# sourceMappingURL=ServerStartView-CiO_acWT.js.map diff --git a/web/assets/ServerStartView-CnyN4Ib6.css b/web/assets/ServerStartView-CnyN4Ib6.css deleted file mode 100644 index 60a63414f..000000000 --- a/web/assets/ServerStartView-CnyN4Ib6.css +++ /dev/null @@ -1,5 +0,0 @@ - -[data-v-42c1131d] .xterm-helper-textarea { - /* Hide this as it moves all over when uv is running */ - display: none; -} diff --git a/web/assets/UserSelectView-2l9Kbchu.js b/web/assets/UserSelectView-2l9Kbchu.js deleted file mode 100644 index 554218e54..000000000 --- a/web/assets/UserSelectView-2l9Kbchu.js +++ /dev/null @@ -1,102 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, aX as useUserStore, bW as useRouter, ab as ref, c as computed, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, bX as withKeys, j as unref, av as script, bQ as script$1, bY as script$2, bZ as script$3, aE as createTextVNode, I as createCommentVNode, l as script$4 } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -const _hoisted_1 = { - id: "comfy-user-selection", - class: "min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg" -}; -const _hoisted_2 = /* @__PURE__ */ createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1); -const _hoisted_3 = { class: "flex w-full flex-col items-center" }; -const _hoisted_4 = { class: "flex w-full flex-col gap-2" }; -const _hoisted_5 = { for: "new-user-input" }; -const _hoisted_6 = { class: "flex w-full flex-col gap-2" }; -const _hoisted_7 = { for: "existing-user-select" }; -const _hoisted_8 = { class: "mt-5" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "UserSelectView", - setup(__props) { - const userStore = useUserStore(); - const router = useRouter(); - const selectedUser = ref(null); - const newUsername = ref(""); - const loginError = ref(""); - const createNewUser = computed(() => newUsername.value.trim() !== ""); - const newUserExistsError = computed(() => { - return userStore.users.find((user) => user.username === newUsername.value) ? `User "${newUsername.value}" already exists` : ""; - }); - const error = computed(() => newUserExistsError.value || loginError.value); - const login = /* @__PURE__ */ __name(async () => { - try { - const user = createNewUser.value ? await userStore.createUser(newUsername.value) : selectedUser.value; - if (!user) { - throw new Error("No user selected"); - } - userStore.login(user); - router.push("/"); - } catch (err) { - loginError.value = err.message ?? JSON.stringify(err); - } - }, "login"); - onMounted(async () => { - if (!userStore.initialized) { - await userStore.initialize(); - } - }); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, { dark: "" }, { - default: withCtx(() => [ - createBaseVNode("main", _hoisted_1, [ - _hoisted_2, - createBaseVNode("div", _hoisted_3, [ - createBaseVNode("div", _hoisted_4, [ - createBaseVNode("label", _hoisted_5, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1), - createVNode(unref(script), { - id: "new-user-input", - modelValue: newUsername.value, - "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => newUsername.value = $event), - placeholder: _ctx.$t("userSelect.enterUsername"), - onKeyup: withKeys(login, ["enter"]) - }, null, 8, ["modelValue", "placeholder"]) - ]), - createVNode(unref(script$1)), - createBaseVNode("div", _hoisted_6, [ - createBaseVNode("label", _hoisted_7, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1), - createVNode(unref(script$2), { - modelValue: selectedUser.value, - "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event), - class: "w-full", - inputId: "existing-user-select", - options: unref(userStore).users, - "option-label": "username", - placeholder: _ctx.$t("userSelect.selectUser"), - disabled: createNewUser.value - }, null, 8, ["modelValue", "options", "placeholder", "disabled"]), - error.value ? (openBlock(), createBlock(unref(script$3), { - key: 0, - severity: "error" - }, { - default: withCtx(() => [ - createTextVNode(toDisplayString(error.value), 1) - ]), - _: 1 - })) : createCommentVNode("", true) - ]), - createBaseVNode("footer", _hoisted_8, [ - createVNode(unref(script$4), { - label: _ctx.$t("userSelect.next"), - onClick: login - }, null, 8, ["label"]) - ]) - ]) - ]) - ]), - _: 1 - }); - }; - } -}); -export { - _sfc_main as default -}; -//# sourceMappingURL=UserSelectView-2l9Kbchu.js.map diff --git a/web/assets/WelcomeView-Brz3-luE.css b/web/assets/WelcomeView-Brz3-luE.css deleted file mode 100644 index 522f34388..000000000 --- a/web/assets/WelcomeView-Brz3-luE.css +++ /dev/null @@ -1,36 +0,0 @@ - -.animated-gradient-text[data-v-7dfaf74c] { - font-weight: 700; - font-size: clamp(2rem, 8vw, 4rem); - background: linear-gradient(to right, #12c2e9, #c471ed, #f64f59, #12c2e9); - background-size: 300% auto; - background-clip: text; - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - animation: gradient-7dfaf74c 8s linear infinite; -} -.text-glow[data-v-7dfaf74c] { - filter: drop-shadow(0 0 8px rgba(255, 255, 255, 0.3)); -} -@keyframes gradient-7dfaf74c { -0% { - background-position: 0% center; -} -100% { - background-position: 300% center; -} -} -.fade-in-up[data-v-7dfaf74c] { - animation: fadeInUp-7dfaf74c 1.5s ease-out; - animation-fill-mode: both; -} -@keyframes fadeInUp-7dfaf74c { -0% { - opacity: 0; - transform: translateY(20px); -} -100% { - opacity: 1; - transform: translateY(0); -} -} diff --git a/web/assets/WelcomeView-CB7Th_kO.js b/web/assets/WelcomeView-CB7Th_kO.js deleted file mode 100644 index d5adbd558..000000000 --- a/web/assets/WelcomeView-CB7Th_kO.js +++ /dev/null @@ -1,40 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { d as defineComponent, bW as useRouter, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, aL as pushScopeId, aM as popScopeId, _ as _export_sfc } from "./index-C4Fk50Nx.js"; -import { _ as _sfc_main$1 } from "./BaseViewTemplate-CsEJhGbv.js"; -const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-7dfaf74c"), n = n(), popScopeId(), n), "_withScopeId"); -const _hoisted_1 = { class: "flex flex-col items-center justify-center gap-8 p-8" }; -const _hoisted_2 = { class: "animated-gradient-text text-glow select-none" }; -const _sfc_main = /* @__PURE__ */ defineComponent({ - __name: "WelcomeView", - setup(__props) { - const router = useRouter(); - const navigateTo = /* @__PURE__ */ __name((path) => { - router.push(path); - }, "navigateTo"); - return (_ctx, _cache) => { - return openBlock(), createBlock(_sfc_main$1, { dark: "" }, { - default: withCtx(() => [ - createBaseVNode("div", _hoisted_1, [ - createBaseVNode("h1", _hoisted_2, toDisplayString(_ctx.$t("welcome.title")), 1), - createVNode(unref(script), { - label: _ctx.$t("welcome.getStarted"), - icon: "pi pi-arrow-right", - iconPos: "right", - size: "large", - rounded: "", - onClick: _cache[0] || (_cache[0] = ($event) => navigateTo("/install")), - class: "p-4 text-lg fade-in-up" - }, null, 8, ["label"]) - ]) - ]), - _: 1 - }); - }; - } -}); -const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-7dfaf74c"]]); -export { - WelcomeView as default -}; -//# sourceMappingURL=WelcomeView-CB7Th_kO.js.map diff --git a/web/assets/images/Git-Logo-White.svg b/web/assets/images/Git-Logo-White.svg deleted file mode 100644 index f2961b944..000000000 --- a/web/assets/images/Git-Logo-White.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/web/assets/images/apple-mps-logo.png b/web/assets/images/apple-mps-logo.png deleted file mode 100644 index 261edbfd6..000000000 Binary files a/web/assets/images/apple-mps-logo.png and /dev/null differ diff --git a/web/assets/images/manual-configuration.svg b/web/assets/images/manual-configuration.svg deleted file mode 100644 index bc90c6470..000000000 --- a/web/assets/images/manual-configuration.svg +++ /dev/null @@ -1,5 +0,0 @@ - - - - - diff --git a/web/assets/images/nvidia-logo.svg b/web/assets/images/nvidia-logo.svg deleted file mode 100644 index 71f15b53b..000000000 --- a/web/assets/images/nvidia-logo.svg +++ /dev/null @@ -1,6 +0,0 @@ - Artificial Intelligence Computing Leadership from NVIDIA - - - - - \ No newline at end of file diff --git a/web/assets/images/sad_girl.png b/web/assets/images/sad_girl.png deleted file mode 100644 index 2b0925a63..000000000 Binary files a/web/assets/images/sad_girl.png and /dev/null differ diff --git a/web/assets/index-5Sv744Dr.js b/web/assets/index-5Sv744Dr.js deleted file mode 100644 index 5ec1cf8e6..000000000 --- a/web/assets/index-5Sv744Dr.js +++ /dev/null @@ -1,53214 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { ca as ComfyDialog, cb as $el, cc as ComfyApp, h as app, a3 as LiteGraph, bd as LGraphCanvas, cd as useExtensionService, ce as processDynamicPrompt, cf as isElectron, c0 as electronAPI, bR as useDialogService, cg as t, ch as DraggableList, bt as useToastStore, ah as LGraphNode, ci as applyTextReplacements, cj as ComfyWidgets, ck as addValueControlWidgets, a6 as useNodeDefStore, cl as serialise, cm as deserialiseAndCreate, b8 as api, a as useSettingStore, ag as LGraphGroup, ad as nextTick } from "./index-C4Fk50Nx.js"; -class ClipspaceDialog extends ComfyDialog { - static { - __name(this, "ClipspaceDialog"); - } - static items = []; - static instance = null; - static registerButton(name, contextPredicate, callback) { - const item = $el("button", { - type: "button", - textContent: name, - contextPredicate, - onclick: callback - }); - ClipspaceDialog.items.push(item); - } - static invalidatePreview() { - if (ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0) { - const img_preview = document.getElementById( - "clipspace_preview" - ); - if (img_preview) { - img_preview.src = ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]].src; - img_preview.style.maxHeight = "100%"; - img_preview.style.maxWidth = "100%"; - } - } - } - static invalidate() { - if (ClipspaceDialog.instance) { - const self2 = ClipspaceDialog.instance; - const children = $el("div.comfy-modal-content", [ - self2.createImgSettings(), - ...self2.createButtons() - ]); - if (self2.element) { - if (self2.element.firstChild) { - self2.element.removeChild(self2.element.firstChild); - } - self2.element.appendChild(children); - } else { - self2.element = $el("div.comfy-modal", { parent: document.body }, [ - children - ]); - } - if (self2.element.children[0].children.length <= 1) { - self2.element.children[0].appendChild( - $el("p", {}, [ - "Unable to find the features to edit content of a format stored in the current Clipspace." - ]) - ); - } - ClipspaceDialog.invalidatePreview(); - } - } - constructor() { - super(); - } - createButtons() { - const buttons = []; - for (let idx in ClipspaceDialog.items) { - const item = ClipspaceDialog.items[idx]; - if (!item.contextPredicate || item.contextPredicate()) - buttons.push(ClipspaceDialog.items[idx]); - } - buttons.push( - $el("button", { - type: "button", - textContent: "Close", - onclick: /* @__PURE__ */ __name(() => { - this.close(); - }, "onclick") - }) - ); - return buttons; - } - createImgSettings() { - if (ComfyApp.clipspace?.imgs) { - const combo_items = []; - const imgs = ComfyApp.clipspace.imgs; - for (let i = 0; i < imgs.length; i++) { - combo_items.push($el("option", { value: i }, [`${i}`])); - } - const combo1 = $el( - "select", - { - id: "clipspace_img_selector", - onchange: /* @__PURE__ */ __name((event) => { - if (event.target && ComfyApp.clipspace) { - ComfyApp.clipspace["selectedIndex"] = event.target.selectedIndex; - ClipspaceDialog.invalidatePreview(); - } - }, "onchange") - }, - combo_items - ); - const row1 = $el("tr", {}, [ - $el("td", {}, [$el("font", { color: "white" }, ["Select Image"])]), - $el("td", {}, [combo1]) - ]); - const combo2 = $el( - "select", - { - id: "clipspace_img_paste_mode", - onchange: /* @__PURE__ */ __name((event) => { - if (event.target && ComfyApp.clipspace) { - ComfyApp.clipspace["img_paste_mode"] = event.target.value; - } - }, "onchange") - }, - [ - $el("option", { value: "selected" }, "selected"), - $el("option", { value: "all" }, "all") - ] - ); - combo2.value = ComfyApp.clipspace["img_paste_mode"]; - const row2 = $el("tr", {}, [ - $el("td", {}, [$el("font", { color: "white" }, ["Paste Mode"])]), - $el("td", {}, [combo2]) - ]); - const td2 = $el( - "td", - { align: "center", width: "100px", height: "100px", colSpan: "2" }, - [$el("img", { id: "clipspace_preview", ondragstart: /* @__PURE__ */ __name(() => false, "ondragstart") }, [])] - ); - const row3 = $el("tr", {}, [td2]); - return $el("table", {}, [row1, row2, row3]); - } else { - return []; - } - } - createImgPreview() { - if (ComfyApp.clipspace?.imgs) { - return $el("img", { id: "clipspace_preview", ondragstart: /* @__PURE__ */ __name(() => false, "ondragstart") }); - } else return []; - } - show() { - const img_preview = document.getElementById("clipspace_preview"); - ClipspaceDialog.invalidate(); - this.element.style.display = "block"; - } -} -app.registerExtension({ - name: "Comfy.Clipspace", - init(app2) { - app2.openClipspace = function() { - if (!ClipspaceDialog.instance) { - ClipspaceDialog.instance = new ClipspaceDialog(); - ComfyApp.clipspace_invalidate_handler = ClipspaceDialog.invalidate; - } - if (ComfyApp.clipspace) { - ClipspaceDialog.instance.show(); - } else app2.ui.dialog.show("Clipspace is Empty!"); - }; - } -}); -window.comfyAPI = window.comfyAPI || {}; -window.comfyAPI.clipspace = window.comfyAPI.clipspace || {}; -window.comfyAPI.clipspace.ClipspaceDialog = ClipspaceDialog; -const ext$1 = { - name: "Comfy.ContextMenuFilter", - init() { - const ctxMenu = LiteGraph.ContextMenu; - LiteGraph.ContextMenu = function(values, options) { - const ctx = new ctxMenu(values, options); - if (options?.className === "dark" && values?.length > 4) { - const filter = document.createElement("input"); - filter.classList.add("comfy-context-menu-filter"); - filter.placeholder = "Filter list"; - ctx.root.prepend(filter); - const items = Array.from( - ctx.root.querySelectorAll(".litemenu-entry") - ); - let displayedItems = [...items]; - let itemCount = displayedItems.length; - requestAnimationFrame(() => { - const currentNode = LGraphCanvas.active_canvas.current_node; - const clickedComboValue = currentNode?.widgets?.filter( - (w) => w.type === "combo" && w.options.values?.length === values.length - ).find( - (w) => w.options.values?.every((v, i) => v === values[i]) - )?.value; - let selectedIndex = clickedComboValue ? values.findIndex((v) => v === clickedComboValue) : 0; - if (selectedIndex < 0) { - selectedIndex = 0; - } - let selectedItem = displayedItems[selectedIndex]; - updateSelected(); - function updateSelected() { - selectedItem?.style.setProperty("background-color", ""); - selectedItem?.style.setProperty("color", ""); - selectedItem = displayedItems[selectedIndex]; - selectedItem?.style.setProperty( - "background-color", - "#ccc", - "important" - ); - selectedItem?.style.setProperty("color", "#000", "important"); - } - __name(updateSelected, "updateSelected"); - const positionList = /* @__PURE__ */ __name(() => { - const rect = ctx.root.getBoundingClientRect(); - if (rect.top < 0) { - const scale = 1 - ctx.root.getBoundingClientRect().height / ctx.root.clientHeight; - const shift = ctx.root.clientHeight * scale / 2; - ctx.root.style.top = -shift + "px"; - } - }, "positionList"); - filter.addEventListener("keydown", (event) => { - switch (event.key) { - case "ArrowUp": - event.preventDefault(); - if (selectedIndex === 0) { - selectedIndex = itemCount - 1; - } else { - selectedIndex--; - } - updateSelected(); - break; - case "ArrowRight": - event.preventDefault(); - selectedIndex = itemCount - 1; - updateSelected(); - break; - case "ArrowDown": - event.preventDefault(); - if (selectedIndex === itemCount - 1) { - selectedIndex = 0; - } else { - selectedIndex++; - } - updateSelected(); - break; - case "ArrowLeft": - event.preventDefault(); - selectedIndex = 0; - updateSelected(); - break; - case "Enter": - selectedItem?.click(); - break; - case "Escape": - ctx.close(); - break; - } - }); - filter.addEventListener("input", () => { - const term = filter.value.toLocaleLowerCase(); - displayedItems = items.filter((item) => { - const isVisible = !term || item.textContent?.toLocaleLowerCase().includes(term); - item.style.display = isVisible ? "block" : "none"; - return isVisible; - }); - selectedIndex = 0; - if (displayedItems.includes(selectedItem)) { - selectedIndex = displayedItems.findIndex( - (d) => d === selectedItem - ); - } - itemCount = displayedItems.length; - updateSelected(); - if (options.event) { - let top = options.event.clientY - 10; - const bodyRect = document.body.getBoundingClientRect(); - const rootRect = ctx.root.getBoundingClientRect(); - if (bodyRect.height && top > bodyRect.height - rootRect.height - 10) { - top = Math.max(0, bodyRect.height - rootRect.height - 10); - } - ctx.root.style.top = top + "px"; - positionList(); - } - }); - requestAnimationFrame(() => { - filter.focus(); - positionList(); - }); - }); - } - return ctx; - }; - LiteGraph.ContextMenu.prototype = ctxMenu.prototype; - } -}; -app.registerExtension(ext$1); -useExtensionService().registerExtension({ - name: "Comfy.DynamicPrompts", - nodeCreated(node) { - if (node.widgets) { - const widgets = node.widgets.filter((n) => n.dynamicPrompts); - for (const widget of widgets) { - widget.serializeValue = (workflowNode, widgetIndex) => { - if (typeof widget.value !== "string") return widget.value; - const prompt = processDynamicPrompt(widget.value); - if (workflowNode?.widgets_values) - workflowNode.widgets_values[widgetIndex] = prompt; - return prompt; - }; - } - } - } -}); -app.registerExtension({ - name: "Comfy.EditAttention", - init() { - const editAttentionDelta = app.ui.settings.addSetting({ - id: "Comfy.EditAttention.Delta", - category: ["Comfy", "EditTokenWeight", "Delta"], - name: "Ctrl+up/down precision", - type: "slider", - attrs: { - min: 0.01, - max: 0.5, - step: 0.01 - }, - defaultValue: 0.05 - }); - function incrementWeight(weight, delta) { - const floatWeight = parseFloat(weight); - if (isNaN(floatWeight)) return weight; - const newWeight = floatWeight + delta; - return String(Number(newWeight.toFixed(10))); - } - __name(incrementWeight, "incrementWeight"); - function findNearestEnclosure(text, cursorPos) { - let start = cursorPos, end = cursorPos; - let openCount = 0, closeCount = 0; - while (start >= 0) { - start--; - if (text[start] === "(" && openCount === closeCount) break; - if (text[start] === "(") openCount++; - if (text[start] === ")") closeCount++; - } - if (start < 0) return null; - openCount = 0; - closeCount = 0; - while (end < text.length) { - if (text[end] === ")" && openCount === closeCount) break; - if (text[end] === "(") openCount++; - if (text[end] === ")") closeCount++; - end++; - } - if (end === text.length) return null; - return { start: start + 1, end }; - } - __name(findNearestEnclosure, "findNearestEnclosure"); - function addWeightToParentheses(text) { - const parenRegex = /^\((.*)\)$/; - const parenMatch = text.match(parenRegex); - const floatRegex = /:([+-]?(\d*\.)?\d+([eE][+-]?\d+)?)/; - const floatMatch = text.match(floatRegex); - if (parenMatch && !floatMatch) { - return `(${parenMatch[1]}:1.0)`; - } else { - return text; - } - } - __name(addWeightToParentheses, "addWeightToParentheses"); - function editAttention(event) { - const inputField = event.composedPath()[0]; - const delta = parseFloat(editAttentionDelta.value); - if (inputField.tagName !== "TEXTAREA") return; - if (!(event.key === "ArrowUp" || event.key === "ArrowDown")) return; - if (!event.ctrlKey && !event.metaKey) return; - event.preventDefault(); - let start = inputField.selectionStart; - let end = inputField.selectionEnd; - let selectedText = inputField.value.substring(start, end); - if (!selectedText) { - const nearestEnclosure = findNearestEnclosure(inputField.value, start); - if (nearestEnclosure) { - start = nearestEnclosure.start; - end = nearestEnclosure.end; - selectedText = inputField.value.substring(start, end); - } else { - const delimiters = " .,\\/!?%^*;:{}=-_`~()\r\n "; - while (!delimiters.includes(inputField.value[start - 1]) && start > 0) { - start--; - } - while (!delimiters.includes(inputField.value[end]) && end < inputField.value.length) { - end++; - } - selectedText = inputField.value.substring(start, end); - if (!selectedText) return; - } - } - if (selectedText[selectedText.length - 1] === " ") { - selectedText = selectedText.substring(0, selectedText.length - 1); - end -= 1; - } - if (inputField.value[start - 1] === "(" && inputField.value[end] === ")") { - start -= 1; - end += 1; - selectedText = inputField.value.substring(start, end); - } - if (selectedText[0] !== "(" || selectedText[selectedText.length - 1] !== ")") { - selectedText = `(${selectedText})`; - } - selectedText = addWeightToParentheses(selectedText); - const weightDelta = event.key === "ArrowUp" ? delta : -delta; - const updatedText = selectedText.replace( - /\((.*):([+-]?\d+(?:\.\d+)?)\)/, - (match, text, weight) => { - weight = incrementWeight(weight, weightDelta); - if (weight == 1) { - return text; - } else { - return `(${text}:${weight})`; - } - } - ); - inputField.setSelectionRange(start, end); - document.execCommand("insertText", false, updatedText); - inputField.setSelectionRange(start, start + updatedText.length); - } - __name(editAttention, "editAttention"); - window.addEventListener("keydown", editAttention); - } -}); -(async () => { - if (!isElectron()) return; - const electronAPI$1 = electronAPI(); - const desktopAppVersion = await electronAPI$1.getElectronVersion(); - const onChangeRestartApp = /* @__PURE__ */ __name((newValue, oldValue) => { - if (oldValue !== void 0 && newValue !== oldValue) { - electronAPI$1.restartApp("Restart ComfyUI to apply changes.", 1500); - } - }, "onChangeRestartApp"); - app.registerExtension({ - name: "Comfy.ElectronAdapter", - settings: [ - { - id: "Comfy-Desktop.AutoUpdate", - category: ["Comfy-Desktop", "General", "AutoUpdate"], - name: "Automatically check for updates", - type: "boolean", - defaultValue: true, - onChange: onChangeRestartApp - }, - { - id: "Comfy-Desktop.SendStatistics", - category: ["Comfy-Desktop", "General", "Send Statistics"], - name: "Send anonymous crash reports", - type: "boolean", - defaultValue: true, - onChange: onChangeRestartApp - } - ], - commands: [ - { - id: "Comfy-Desktop.Folders.OpenLogsFolder", - label: "Open Logs Folder", - icon: "pi pi-folder-open", - function() { - electronAPI$1.openLogsFolder(); - } - }, - { - id: "Comfy-Desktop.Folders.OpenModelsFolder", - label: "Open Models Folder", - icon: "pi pi-folder-open", - function() { - electronAPI$1.openModelsFolder(); - } - }, - { - id: "Comfy-Desktop.Folders.OpenOutputsFolder", - label: "Open Outputs Folder", - icon: "pi pi-folder-open", - function() { - electronAPI$1.openOutputsFolder(); - } - }, - { - id: "Comfy-Desktop.Folders.OpenInputsFolder", - label: "Open Inputs Folder", - icon: "pi pi-folder-open", - function() { - electronAPI$1.openInputsFolder(); - } - }, - { - id: "Comfy-Desktop.Folders.OpenCustomNodesFolder", - label: "Open Custom Nodes Folder", - icon: "pi pi-folder-open", - function() { - electronAPI$1.openCustomNodesFolder(); - } - }, - { - id: "Comfy-Desktop.Folders.OpenModelConfig", - label: "Open extra_model_paths.yaml", - icon: "pi pi-file", - function() { - electronAPI$1.openModelConfig(); - } - }, - { - id: "Comfy-Desktop.OpenDevTools", - label: "Open DevTools", - icon: "pi pi-code", - function() { - electronAPI$1.openDevTools(); - } - }, - { - id: "Comfy-Desktop.OpenFeedbackPage", - label: "Feedback", - icon: "pi pi-envelope", - function() { - window.open("https://forum.comfy.org/c/v1-feedback/", "_blank"); - } - }, - { - id: "Comfy-Desktop.OpenUserGuide", - label: "Desktop User Guide", - icon: "pi pi-book", - function() { - window.open("https://comfyorg.notion.site/", "_blank"); - } - }, - { - id: "Comfy-Desktop.Reinstall", - label: "Reinstall", - icon: "pi pi-refresh", - async function() { - const proceed = await useDialogService().confirm({ - message: t("desktopMenu.confirmReinstall"), - title: t("desktopMenu.reinstall"), - type: "reinstall" - }); - if (proceed) electronAPI$1.reinstall(); - } - }, - { - id: "Comfy-Desktop.Restart", - label: "Restart", - icon: "pi pi-refresh", - function() { - electronAPI$1.restartApp(); - } - } - ], - menuCommands: [ - { - path: ["Help"], - commands: [ - "Comfy-Desktop.OpenUserGuide", - "Comfy-Desktop.OpenFeedbackPage" - ] - }, - { - path: ["Help"], - commands: ["Comfy-Desktop.OpenDevTools"] - }, - { - path: ["Help", "Open Folder"], - commands: [ - "Comfy-Desktop.Folders.OpenLogsFolder", - "Comfy-Desktop.Folders.OpenModelsFolder", - "Comfy-Desktop.Folders.OpenOutputsFolder", - "Comfy-Desktop.Folders.OpenInputsFolder", - "Comfy-Desktop.Folders.OpenCustomNodesFolder", - "Comfy-Desktop.Folders.OpenModelConfig" - ] - }, - { - path: ["Help"], - commands: ["Comfy-Desktop.Reinstall"] - } - ], - aboutPageBadges: [ - { - label: "ComfyUI_desktop v" + desktopAppVersion, - url: "https://github.com/Comfy-Org/electron", - icon: "pi pi-github" - } - ] - }); -})(); -const ORDER = Symbol(); -const PREFIX$1 = "workflow"; -const SEPARATOR$1 = ">"; -function merge(target, source) { - if (typeof target === "object" && typeof source === "object") { - for (const key in source) { - const sv = source[key]; - if (typeof sv === "object") { - let tv = target[key]; - if (!tv) tv = target[key] = {}; - merge(tv, source[key]); - } else { - target[key] = sv; - } - } - } - return target; -} -__name(merge, "merge"); -class ManageGroupDialog extends ComfyDialog { - static { - __name(this, "ManageGroupDialog"); - } - tabs; - selectedNodeIndex; - selectedTab = "Inputs"; - selectedGroup; - modifications = {}; - nodeItems; - app; - groupNodeType; - groupNodeDef; - groupData; - innerNodesList; - widgetsPage; - inputsPage; - outputsPage; - draggable; - get selectedNodeInnerIndex() { - return +this.nodeItems[this.selectedNodeIndex].dataset.nodeindex; - } - constructor(app2) { - super(); - this.app = app2; - this.element = $el("dialog.comfy-group-manage", { - parent: document.body - }); - } - changeTab(tab) { - this.tabs[this.selectedTab].tab.classList.remove("active"); - this.tabs[this.selectedTab].page.classList.remove("active"); - this.tabs[tab].tab.classList.add("active"); - this.tabs[tab].page.classList.add("active"); - this.selectedTab = tab; - } - changeNode(index, force) { - if (!force && this.selectedNodeIndex === index) return; - if (this.selectedNodeIndex != null) { - this.nodeItems[this.selectedNodeIndex].classList.remove("selected"); - } - this.nodeItems[index].classList.add("selected"); - this.selectedNodeIndex = index; - if (!this.buildInputsPage() && this.selectedTab === "Inputs") { - this.changeTab("Widgets"); - } - if (!this.buildWidgetsPage() && this.selectedTab === "Widgets") { - this.changeTab("Outputs"); - } - if (!this.buildOutputsPage() && this.selectedTab === "Outputs") { - this.changeTab("Inputs"); - } - this.changeTab(this.selectedTab); - } - getGroupData() { - this.groupNodeType = LiteGraph.registered_node_types[`${PREFIX$1}${SEPARATOR$1}` + this.selectedGroup]; - this.groupNodeDef = this.groupNodeType.nodeData; - this.groupData = GroupNodeHandler.getGroupData(this.groupNodeType); - } - changeGroup(group, reset = true) { - this.selectedGroup = group; - this.getGroupData(); - const nodes = this.groupData.nodeData.nodes; - this.nodeItems = nodes.map( - (n, i) => $el( - "li.draggable-item", - { - dataset: { - nodeindex: n.index + "" - }, - onclick: /* @__PURE__ */ __name(() => { - this.changeNode(i); - }, "onclick") - }, - [ - $el("span.drag-handle"), - $el( - "div", - { - textContent: n.title ?? n.type - }, - n.title ? $el("span", { - textContent: n.type - }) : [] - ) - ] - ) - ); - this.innerNodesList.replaceChildren(...this.nodeItems); - if (reset) { - this.selectedNodeIndex = null; - this.changeNode(0); - } else { - const items = this.draggable.getAllItems(); - let index = items.findIndex((item) => item.classList.contains("selected")); - if (index === -1) index = this.selectedNodeIndex; - this.changeNode(index, true); - } - const ordered = [...nodes]; - this.draggable?.dispose(); - this.draggable = new DraggableList(this.innerNodesList, "li"); - this.draggable.addEventListener( - "dragend", - ({ detail: { oldPosition, newPosition } }) => { - if (oldPosition === newPosition) return; - ordered.splice(newPosition, 0, ordered.splice(oldPosition, 1)[0]); - for (let i = 0; i < ordered.length; i++) { - this.storeModification({ - nodeIndex: ordered[i].index, - section: ORDER, - prop: "order", - value: i - }); - } - } - ); - } - storeModification(props) { - const { nodeIndex, section, prop, value } = props; - const groupMod = this.modifications[this.selectedGroup] ??= {}; - const nodesMod = groupMod.nodes ??= {}; - const nodeMod = nodesMod[nodeIndex ?? this.selectedNodeInnerIndex] ??= {}; - const typeMod = nodeMod[section] ??= {}; - if (typeof value === "object") { - const objMod = typeMod[prop] ??= {}; - Object.assign(objMod, value); - } else { - typeMod[prop] = value; - } - } - getEditElement(section, prop, value, placeholder, checked, checkable = true) { - if (value === placeholder) value = ""; - const mods = this.modifications[this.selectedGroup]?.nodes?.[this.selectedNodeInnerIndex]?.[section]?.[prop]; - if (mods) { - if (mods.name != null) { - value = mods.name; - } - if (mods.visible != null) { - checked = mods.visible; - } - } - return $el("div", [ - $el("input", { - value, - placeholder, - type: "text", - onchange: /* @__PURE__ */ __name((e) => { - this.storeModification({ - section, - prop, - value: { name: e.target.value } - }); - }, "onchange") - }), - $el("label", { textContent: "Visible" }, [ - $el("input", { - type: "checkbox", - checked, - disabled: !checkable, - onchange: /* @__PURE__ */ __name((e) => { - this.storeModification({ - section, - prop, - value: { visible: !!e.target.checked } - }); - }, "onchange") - }) - ]) - ]); - } - buildWidgetsPage() { - const widgets = this.groupData.oldToNewWidgetMap[this.selectedNodeInnerIndex]; - const items = Object.keys(widgets ?? {}); - const type = app.graph.extra.groupNodes[this.selectedGroup]; - const config = type.config?.[this.selectedNodeInnerIndex]?.input; - this.widgetsPage.replaceChildren( - ...items.map((oldName) => { - return this.getEditElement( - "input", - oldName, - widgets[oldName], - oldName, - config?.[oldName]?.visible !== false - ); - }) - ); - return !!items.length; - } - buildInputsPage() { - const inputs = this.groupData.nodeInputs[this.selectedNodeInnerIndex]; - const items = Object.keys(inputs ?? {}); - const type = app.graph.extra.groupNodes[this.selectedGroup]; - const config = type.config?.[this.selectedNodeInnerIndex]?.input; - this.inputsPage.replaceChildren( - ...items.map((oldName) => { - let value = inputs[oldName]; - if (!value) { - return; - } - return this.getEditElement( - "input", - oldName, - value, - oldName, - config?.[oldName]?.visible !== false - ); - }).filter(Boolean) - ); - return !!items.length; - } - buildOutputsPage() { - const nodes = this.groupData.nodeData.nodes; - const innerNodeDef = this.groupData.getNodeDef( - nodes[this.selectedNodeInnerIndex] - ); - const outputs = innerNodeDef?.output ?? []; - const groupOutputs = this.groupData.oldToNewOutputMap[this.selectedNodeInnerIndex]; - const type = app.graph.extra.groupNodes[this.selectedGroup]; - const config = type.config?.[this.selectedNodeInnerIndex]?.output; - const node = this.groupData.nodeData.nodes[this.selectedNodeInnerIndex]; - const checkable = node.type !== "PrimitiveNode"; - this.outputsPage.replaceChildren( - ...outputs.map((type2, slot) => { - const groupOutputIndex = groupOutputs?.[slot]; - const oldName = innerNodeDef.output_name?.[slot] ?? type2; - let value = config?.[slot]?.name; - const visible = config?.[slot]?.visible || groupOutputIndex != null; - if (!value || value === oldName) { - value = ""; - } - return this.getEditElement( - "output", - slot, - value, - oldName, - visible, - checkable - ); - }).filter(Boolean) - ); - return !!outputs.length; - } - show(type) { - const groupNodes = Object.keys(app.graph.extra?.groupNodes ?? {}).sort( - (a, b) => a.localeCompare(b) - ); - this.innerNodesList = $el( - "ul.comfy-group-manage-list-items" - ); - this.widgetsPage = $el("section.comfy-group-manage-node-page"); - this.inputsPage = $el("section.comfy-group-manage-node-page"); - this.outputsPage = $el("section.comfy-group-manage-node-page"); - const pages = $el("div", [ - this.widgetsPage, - this.inputsPage, - this.outputsPage - ]); - this.tabs = [ - ["Inputs", this.inputsPage], - ["Widgets", this.widgetsPage], - ["Outputs", this.outputsPage] - ].reduce((p, [name, page]) => { - p[name] = { - tab: $el("a", { - onclick: /* @__PURE__ */ __name(() => { - this.changeTab(name); - }, "onclick"), - textContent: name - }), - page - }; - return p; - }, {}); - const outer = $el("div.comfy-group-manage-outer", [ - $el("header", [ - $el("h2", "Group Nodes"), - $el( - "select", - { - onchange: /* @__PURE__ */ __name((e) => { - this.changeGroup(e.target.value); - }, "onchange") - }, - groupNodes.map( - (g) => $el("option", { - textContent: g, - selected: `${PREFIX$1}${SEPARATOR$1}${g}` === type, - value: g - }) - ) - ) - ]), - $el("main", [ - $el("section.comfy-group-manage-list", this.innerNodesList), - $el("section.comfy-group-manage-node", [ - $el( - "header", - Object.values(this.tabs).map((t2) => t2.tab) - ), - pages - ]) - ]), - $el("footer", [ - $el( - "button.comfy-btn", - { - onclick: /* @__PURE__ */ __name((e) => { - const node = app.graph.nodes.find( - (n) => n.type === `${PREFIX$1}${SEPARATOR$1}` + this.selectedGroup - ); - if (node) { - useToastStore().addAlert( - "This group node is in use in the current workflow, please first remove these." - ); - return; - } - if (confirm( - `Are you sure you want to remove the node: "${this.selectedGroup}"` - )) { - delete app.graph.extra.groupNodes[this.selectedGroup]; - LiteGraph.unregisterNodeType( - `${PREFIX$1}${SEPARATOR$1}` + this.selectedGroup - ); - } - this.show(); - }, "onclick") - }, - "Delete Group Node" - ), - $el( - "button.comfy-btn", - { - onclick: /* @__PURE__ */ __name(async () => { - let nodesByType; - let recreateNodes = []; - const types = {}; - for (const g in this.modifications) { - const type2 = app.graph.extra.groupNodes[g]; - let config = type2.config ??= {}; - let nodeMods = this.modifications[g]?.nodes; - if (nodeMods) { - const keys = Object.keys(nodeMods); - if (nodeMods[keys[0]][ORDER]) { - const orderedNodes = []; - const orderedMods = {}; - const orderedConfig = {}; - for (const n of keys) { - const order = nodeMods[n][ORDER].order; - orderedNodes[order] = type2.nodes[+n]; - orderedMods[order] = nodeMods[n]; - orderedNodes[order].index = order; - } - for (const l of type2.links) { - if (l[0] != null) l[0] = type2.nodes[l[0]].index; - if (l[2] != null) l[2] = type2.nodes[l[2]].index; - } - if (type2.external) { - for (const ext2 of type2.external) { - ext2[0] = type2.nodes[ext2[0]]; - } - } - for (const id2 of keys) { - if (config[id2]) { - orderedConfig[type2.nodes[id2].index] = config[id2]; - } - delete config[id2]; - } - type2.nodes = orderedNodes; - nodeMods = orderedMods; - type2.config = config = orderedConfig; - } - merge(config, nodeMods); - } - types[g] = type2; - if (!nodesByType) { - nodesByType = app.graph.nodes.reduce((p, n) => { - p[n.type] ??= []; - p[n.type].push(n); - return p; - }, {}); - } - const nodes = nodesByType[`${PREFIX$1}${SEPARATOR$1}` + g]; - if (nodes) recreateNodes.push(...nodes); - } - await GroupNodeConfig.registerFromWorkflow(types, {}); - for (const node of recreateNodes) { - node.recreate(); - } - this.modifications = {}; - this.app.graph.setDirtyCanvas(true, true); - this.changeGroup(this.selectedGroup, false); - }, "onclick") - }, - "Save" - ), - $el( - "button.comfy-btn", - { onclick: /* @__PURE__ */ __name(() => this.element.close(), "onclick") }, - "Close" - ) - ]) - ]); - this.element.replaceChildren(outer); - this.changeGroup( - type ? groupNodes.find((g) => `${PREFIX$1}${SEPARATOR$1}${g}` === type) ?? groupNodes[0] : groupNodes[0] - ); - this.element.showModal(); - this.element.addEventListener("close", () => { - this.draggable?.dispose(); - this.element.remove(); - }); - } -} -window.comfyAPI = window.comfyAPI || {}; -window.comfyAPI.groupNodeManage = window.comfyAPI.groupNodeManage || {}; -window.comfyAPI.groupNodeManage.ManageGroupDialog = ManageGroupDialog; -const CONVERTED_TYPE = "converted-widget"; -const VALID_TYPES = [ - "STRING", - "combo", - "number", - "toggle", - "BOOLEAN", - "text", - "string" -]; -const CONFIG = Symbol(); -const GET_CONFIG = Symbol(); -const TARGET = Symbol(); -const replacePropertyName = "Run widget replace on values"; -class PrimitiveNode extends LGraphNode { - static { - __name(this, "PrimitiveNode"); - } - controlValues; - lastType; - static category; - constructor(title) { - super(title); - this.addOutput("connect to widget input", "*"); - this.serialize_widgets = true; - this.isVirtualNode = true; - if (!this.properties || !(replacePropertyName in this.properties)) { - this.addProperty(replacePropertyName, false, "boolean"); - } - } - applyToGraph(extraLinks = []) { - if (!this.outputs[0].links?.length) return; - function get_links(node) { - let links2 = []; - for (const l of node.outputs[0].links) { - const linkInfo = app.graph.links[l]; - const n = node.graph.getNodeById(linkInfo.target_id); - if (n.type == "Reroute") { - links2 = links2.concat(get_links(n)); - } else { - links2.push(l); - } - } - return links2; - } - __name(get_links, "get_links"); - let links = [ - ...get_links(this).map((l) => app.graph.links[l]), - ...extraLinks - ]; - let v = this.widgets?.[0].value; - if (v && this.properties[replacePropertyName]) { - v = applyTextReplacements(app, v); - } - for (const linkInfo of links) { - const node = this.graph.getNodeById(linkInfo.target_id); - const input = node.inputs[linkInfo.target_slot]; - let widget; - if (input.widget[TARGET]) { - widget = input.widget[TARGET]; - } else { - const widgetName = input.widget.name; - if (widgetName) { - widget = node.widgets.find((w) => w.name === widgetName); - } - } - if (widget) { - widget.value = v; - if (widget.callback) { - widget.callback( - widget.value, - app.canvas, - node, - app.canvas.graph_mouse, - {} - ); - } - } - } - } - refreshComboInNode() { - const widget = this.widgets?.[0]; - if (widget?.type === "combo") { - widget.options.values = this.outputs[0].widget[GET_CONFIG]()[0]; - if (!widget.options.values.includes(widget.value)) { - widget.value = widget.options.values[0]; - widget.callback(widget.value); - } - } - } - onAfterGraphConfigured() { - if (this.outputs[0].links?.length && !this.widgets?.length) { - if (!this.#onFirstConnection()) return; - if (this.widgets) { - for (let i = 0; i < this.widgets_values.length; i++) { - const w = this.widgets[i]; - if (w) { - w.value = this.widgets_values[i]; - } - } - } - this.#mergeWidgetConfig(); - } - } - onConnectionsChange(_, index, connected) { - if (app.configuringGraph) { - return; - } - const links = this.outputs[0].links; - if (connected) { - if (links?.length && !this.widgets?.length) { - this.#onFirstConnection(); - } - } else { - this.#mergeWidgetConfig(); - if (!links?.length) { - this.onLastDisconnect(); - } - } - } - onConnectOutput(slot, type, input, target_node, target_slot) { - if (!input.widget) { - if (!(input.type in ComfyWidgets)) return false; - } - if (this.outputs[slot].links?.length) { - const valid = this.#isValidConnection(input); - if (valid) { - this.applyToGraph([{ target_id: target_node.id, target_slot }]); - } - return valid; - } - } - #onFirstConnection(recreating) { - if (!this.outputs[0].links) { - this.onLastDisconnect(); - return; - } - const linkId = this.outputs[0].links[0]; - const link = this.graph.links[linkId]; - if (!link) return; - const theirNode = this.graph.getNodeById(link.target_id); - if (!theirNode || !theirNode.inputs) return; - const input = theirNode.inputs[link.target_slot]; - if (!input) return; - let widget; - if (!input.widget) { - if (!(input.type in ComfyWidgets)) return; - widget = { name: input.name, [GET_CONFIG]: () => [input.type, {}] }; - } else { - widget = input.widget; - } - const config = widget[GET_CONFIG]?.(); - if (!config) return; - const { type } = getWidgetType(config); - this.outputs[0].type = type; - this.outputs[0].name = type; - this.outputs[0].widget = widget; - this.#createWidget( - widget[CONFIG] ?? config, - theirNode, - widget.name, - recreating, - widget[TARGET] - ); - } - #createWidget(inputData, node, widgetName, recreating, targetWidget) { - let type = inputData[0]; - if (type instanceof Array) { - type = "COMBO"; - } - const [oldWidth, oldHeight] = this.size; - let widget; - if (type in ComfyWidgets) { - widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget; - } else { - widget = this.addWidget(type, "value", null, () => { - }, {}); - } - if (targetWidget) { - widget.value = targetWidget.value; - } else if (node?.widgets && widget) { - const theirWidget = node.widgets.find((w) => w.name === widgetName); - if (theirWidget) { - widget.value = theirWidget.value; - } - } - if (!inputData?.[1]?.control_after_generate && (widget.type === "number" || widget.type === "combo")) { - let control_value = this.widgets_values?.[1]; - if (!control_value) { - control_value = "fixed"; - } - addValueControlWidgets( - this, - widget, - control_value, - void 0, - inputData - ); - let filter = this.widgets_values?.[2]; - if (filter && this.widgets.length === 3) { - this.widgets[2].value = filter; - } - } - const controlValues = this.controlValues; - if (this.lastType === this.widgets[0].type && controlValues?.length === this.widgets.length - 1) { - for (let i = 0; i < controlValues.length; i++) { - this.widgets[i + 1].value = controlValues[i]; - } - } - const callback = widget.callback; - const self2 = this; - widget.callback = function() { - const r = callback ? callback.apply(this, arguments) : void 0; - self2.applyToGraph(); - return r; - }; - this.size = [ - Math.max(this.size[0], oldWidth), - Math.max(this.size[1], oldHeight) - ]; - if (!recreating) { - const sz = this.computeSize(); - if (this.size[0] < sz[0]) { - this.size[0] = sz[0]; - } - if (this.size[1] < sz[1]) { - this.size[1] = sz[1]; - } - requestAnimationFrame(() => { - if (this.onResize) { - this.onResize(this.size); - } - }); - } - } - recreateWidget() { - const values = this.widgets?.map((w) => w.value); - this.#removeWidgets(); - this.#onFirstConnection(true); - if (values?.length) { - for (let i = 0; i < this.widgets?.length; i++) - this.widgets[i].value = values[i]; - } - return this.widgets?.[0]; - } - #mergeWidgetConfig() { - const output = this.outputs[0]; - const links = output.links; - const hasConfig = !!output.widget[CONFIG]; - if (hasConfig) { - delete output.widget[CONFIG]; - } - if (links?.length < 2 && hasConfig) { - if (links.length) { - this.recreateWidget(); - } - return; - } - const config1 = output.widget[GET_CONFIG](); - const isNumber = config1[0] === "INT" || config1[0] === "FLOAT"; - if (!isNumber) return; - for (const linkId of links) { - const link = app.graph.links[linkId]; - if (!link) continue; - const theirNode = app.graph.getNodeById(link.target_id); - const theirInput = theirNode.inputs[link.target_slot]; - this.#isValidConnection(theirInput, hasConfig); - } - } - isValidWidgetLink(originSlot, targetNode, targetWidget) { - const config2 = getConfig.call(targetNode, targetWidget.name) ?? [ - targetWidget.type, - targetWidget.options || {} - ]; - if (!isConvertibleWidget(targetWidget, config2)) return false; - const output = this.outputs[originSlot]; - if (!(output.widget?.[CONFIG] ?? output.widget?.[GET_CONFIG]())) { - return true; - } - return !!mergeIfValid.call(this, output, config2); - } - #isValidConnection(input, forceUpdate) { - const output = this.outputs[0]; - const config2 = input.widget[GET_CONFIG](); - return !!mergeIfValid.call( - this, - output, - config2, - forceUpdate, - this.recreateWidget - ); - } - #removeWidgets() { - if (this.widgets) { - for (const w of this.widgets) { - if (w.onRemove) { - w.onRemove(); - } - } - this.controlValues = []; - this.lastType = this.widgets[0]?.type; - for (let i = 1; i < this.widgets.length; i++) { - this.controlValues.push(this.widgets[i].value); - } - setTimeout(() => { - delete this.lastType; - delete this.controlValues; - }, 15); - this.widgets.length = 0; - } - } - onLastDisconnect() { - this.outputs[0].type = "*"; - this.outputs[0].name = "connect to widget input"; - delete this.outputs[0].widget; - this.#removeWidgets(); - } -} -function getWidgetConfig(slot) { - return slot.widget[CONFIG] ?? slot.widget[GET_CONFIG]?.() ?? ["*", {}]; -} -__name(getWidgetConfig, "getWidgetConfig"); -function getConfig(widgetName) { - const { nodeData } = this.constructor; - return nodeData?.input?.required?.[widgetName] ?? nodeData?.input?.optional?.[widgetName]; -} -__name(getConfig, "getConfig"); -function isConvertibleWidget(widget, config) { - return (VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0])) && !widget.options?.forceInput; -} -__name(isConvertibleWidget, "isConvertibleWidget"); -function hideWidget(node, widget, suffix = "") { - if (widget.type?.startsWith(CONVERTED_TYPE)) return; - widget.origType = widget.type; - widget.origComputeSize = widget.computeSize; - widget.origSerializeValue = widget.serializeValue; - widget.computeSize = () => [0, -4]; - widget.type = CONVERTED_TYPE + suffix; - widget.serializeValue = () => { - if (!node.inputs) { - return void 0; - } - let node_input = node.inputs.find((i) => i.widget?.name === widget.name); - if (!node_input || !node_input.link) { - return void 0; - } - return widget.origSerializeValue ? widget.origSerializeValue() : widget.value; - }; - if (widget.linkedWidgets) { - for (const w of widget.linkedWidgets) { - hideWidget(node, w, ":" + widget.name); - } - } -} -__name(hideWidget, "hideWidget"); -function showWidget(widget) { - widget.type = widget.origType; - widget.computeSize = widget.origComputeSize; - widget.serializeValue = widget.origSerializeValue; - delete widget.origType; - delete widget.origComputeSize; - delete widget.origSerializeValue; - if (widget.linkedWidgets) { - for (const w of widget.linkedWidgets) { - showWidget(w); - } - } -} -__name(showWidget, "showWidget"); -function convertToInput(node, widget, config) { - hideWidget(node, widget); - const { type } = getWidgetType(config); - const [oldWidth, oldHeight] = node.size; - const inputIsOptional = !!widget.options?.inputIsOptional; - const input = node.addInput(widget.name, type, { - widget: { name: widget.name, [GET_CONFIG]: () => config }, - ...inputIsOptional ? { shape: LiteGraph.SlotShape.HollowCircle } : {} - }); - for (const widget2 of node.widgets) { - widget2.last_y += LiteGraph.NODE_SLOT_HEIGHT; - } - node.setSize([ - Math.max(oldWidth, node.size[0]), - Math.max(oldHeight, node.size[1]) - ]); - return input; -} -__name(convertToInput, "convertToInput"); -function convertToWidget(node, widget) { - showWidget(widget); - const [oldWidth, oldHeight] = node.size; - node.removeInput(node.inputs.findIndex((i) => i.widget?.name === widget.name)); - for (const widget2 of node.widgets) { - widget2.last_y -= LiteGraph.NODE_SLOT_HEIGHT; - } - node.setSize([ - Math.max(oldWidth, node.size[0]), - Math.max(oldHeight, node.size[1]) - ]); -} -__name(convertToWidget, "convertToWidget"); -function getWidgetType(config) { - let type = config[0]; - if (type instanceof Array) { - type = "COMBO"; - } - return { type }; -} -__name(getWidgetType, "getWidgetType"); -function isValidCombo(combo, obj) { - if (!(obj instanceof Array)) { - console.log(`connection rejected: tried to connect combo to ${obj}`); - return false; - } - if (combo.length !== obj.length) { - console.log(`connection rejected: combo lists dont match`); - return false; - } - if (combo.find((v, i) => obj[i] !== v)) { - console.log(`connection rejected: combo lists dont match`); - return false; - } - return true; -} -__name(isValidCombo, "isValidCombo"); -function isPrimitiveNode(node) { - return node.type === "PrimitiveNode"; -} -__name(isPrimitiveNode, "isPrimitiveNode"); -function setWidgetConfig(slot, config, target) { - if (!slot.widget) return; - if (config) { - slot.widget[GET_CONFIG] = () => config; - slot.widget[TARGET] = target; - } else { - delete slot.widget; - } - if (slot.link) { - const link = app.graph.links[slot.link]; - if (link) { - const originNode = app.graph.getNodeById(link.origin_id); - if (isPrimitiveNode(originNode)) { - if (config) { - originNode.recreateWidget(); - } else if (!app.configuringGraph) { - originNode.disconnectOutput(0); - originNode.onLastDisconnect(); - } - } - } - } -} -__name(setWidgetConfig, "setWidgetConfig"); -function mergeIfValid(output, config2, forceUpdate, recreateWidget, config1) { - if (!config1) { - config1 = getWidgetConfig(output); - } - if (config1[0] instanceof Array) { - if (!isValidCombo(config1[0], config2[0])) return; - } else if (config1[0] !== config2[0]) { - console.log(`connection rejected: types dont match`, config1[0], config2[0]); - return; - } - const keys = /* @__PURE__ */ new Set([ - ...Object.keys(config1[1] ?? {}), - ...Object.keys(config2[1] ?? {}) - ]); - let customConfig; - const getCustomConfig = /* @__PURE__ */ __name(() => { - if (!customConfig) { - if (typeof structuredClone === "undefined") { - customConfig = JSON.parse(JSON.stringify(config1[1] ?? {})); - } else { - customConfig = structuredClone(config1[1] ?? {}); - } - } - return customConfig; - }, "getCustomConfig"); - const isNumber = config1[0] === "INT" || config1[0] === "FLOAT"; - for (const k of keys.values()) { - if (k !== "default" && k !== "forceInput" && k !== "defaultInput" && k !== "control_after_generate" && k !== "multiline" && k !== "tooltip" && k !== "dynamicPrompts") { - let v1 = config1[1][k]; - let v2 = config2[1]?.[k]; - if (v1 === v2 || !v1 && !v2) continue; - if (isNumber) { - if (k === "min") { - const theirMax = config2[1]?.["max"]; - if (theirMax != null && v1 > theirMax) { - console.log("connection rejected: min > max", v1, theirMax); - return; - } - getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.max(v1, v2); - continue; - } else if (k === "max") { - const theirMin = config2[1]?.["min"]; - if (theirMin != null && v1 < theirMin) { - console.log("connection rejected: max < min", v1, theirMin); - return; - } - getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.min(v1, v2); - continue; - } else if (k === "step") { - let step; - if (v1 == null) { - step = v2; - } else if (v2 == null) { - step = v1; - } else { - if (v1 < v2) { - const a = v2; - v2 = v1; - v1 = a; - } - if (v1 % v2) { - console.log( - "connection rejected: steps not divisible", - "current:", - v1, - "new:", - v2 - ); - return; - } - step = v1; - } - getCustomConfig()[k] = step; - continue; - } - } - console.log(`connection rejected: config ${k} values dont match`, v1, v2); - return; - } - } - if (customConfig || forceUpdate) { - if (customConfig) { - output.widget[CONFIG] = [config1[0], customConfig]; - } - const widget = recreateWidget?.call(this); - if (widget) { - const min = widget.options.min; - const max2 = widget.options.max; - if (min != null && widget.value < min) widget.value = min; - if (max2 != null && widget.value > max2) widget.value = max2; - widget.callback(widget.value); - } - } - return { customConfig }; -} -__name(mergeIfValid, "mergeIfValid"); -let useConversionSubmenusSetting; -app.registerExtension({ - name: "Comfy.WidgetInputs", - init() { - useConversionSubmenusSetting = app.ui.settings.addSetting({ - id: "Comfy.NodeInputConversionSubmenus", - name: "In the node context menu, place the entries that convert between input/widget in sub-menus.", - type: "boolean", - defaultValue: true - }); - }, - setup() { - app.canvas.getWidgetLinkType = function(widget, node) { - const nodeDefStore = useNodeDefStore(); - const nodeDef = nodeDefStore.nodeDefsByName[node.type]; - const input = nodeDef.inputs.getInput(widget.name); - return input?.type; - }; - document.addEventListener( - "litegraph:canvas", - async (e) => { - if (e.detail.subType === "connectingWidgetLink") { - const { node, link, widget } = e.detail; - if (!node || !link || !widget) return; - const nodeData = node.constructor.nodeData; - if (!nodeData) return; - const all = { - ...nodeData?.input?.required, - ...nodeData?.input?.optional - }; - const inputSpec = all[widget.name]; - if (!inputSpec) return; - const input = convertToInput(node, widget, inputSpec); - if (!input) return; - const originNode = link.node; - originNode.connect(link.slot, node, node.inputs.lastIndexOf(input)); - } - } - ); - }, - async beforeRegisterNodeDef(nodeType, nodeData, app2) { - const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions; - nodeType.prototype.convertWidgetToInput = function(widget) { - const config = getConfig.call(this, widget.name) ?? [ - widget.type, - widget.options || {} - ]; - if (!isConvertibleWidget(widget, config)) return false; - if (widget.type?.startsWith(CONVERTED_TYPE)) return false; - convertToInput(this, widget, config); - return true; - }; - nodeType.prototype.getExtraMenuOptions = function(_, options) { - const r = origGetExtraMenuOptions ? origGetExtraMenuOptions.apply(this, arguments) : void 0; - if (this.widgets) { - let toInput = []; - let toWidget = []; - for (const w of this.widgets) { - if (w.options?.forceInput) { - continue; - } - if (w.type === CONVERTED_TYPE) { - toWidget.push({ - // @ts-expect-error never - content: `Convert ${w.name} to widget`, - callback: /* @__PURE__ */ __name(() => convertToWidget(this, w), "callback") - }); - } else { - const config = getConfig.call(this, w.name) ?? [ - w.type, - w.options || {} - ]; - if (isConvertibleWidget(w, config)) { - toInput.push({ - content: `Convert ${w.name} to input`, - callback: /* @__PURE__ */ __name(() => convertToInput(this, w, config), "callback") - }); - } - } - } - if (toInput.length) { - if (useConversionSubmenusSetting.value) { - options.push({ - content: "Convert Widget to Input", - submenu: { - options: toInput - } - }); - } else { - options.push(...toInput, null); - } - } - if (toWidget.length) { - if (useConversionSubmenusSetting.value) { - options.push({ - content: "Convert Input to Widget", - submenu: { - options: toWidget - } - }); - } else { - options.push(...toWidget, null); - } - } - } - return r; - }; - nodeType.prototype.onGraphConfigured = function() { - if (!this.inputs) return; - this.widgets ??= []; - for (const input of this.inputs) { - if (input.widget) { - if (!input.widget[GET_CONFIG]) { - input.widget[GET_CONFIG] = () => getConfig.call(this, input.widget.name); - } - if (input.widget.config) { - if (input.widget.config[0] instanceof Array) { - input.type = "COMBO"; - const link = app2.graph.links[input.link]; - if (link) { - link.type = input.type; - } - } - delete input.widget.config; - } - const w = this.widgets.find((w2) => w2.name === input.widget.name); - if (w) { - hideWidget(this, w); - } else { - convertToWidget(this, input); - } - } - } - }; - const origOnNodeCreated = nodeType.prototype.onNodeCreated; - nodeType.prototype.onNodeCreated = function() { - const r = origOnNodeCreated ? origOnNodeCreated.apply(this) : void 0; - if (!app2.configuringGraph && this.widgets) { - for (const w of this.widgets) { - if (w?.options?.forceInput || w?.options?.defaultInput) { - const config = getConfig.call(this, w.name) ?? [ - w.type, - w.options || {} - ]; - convertToInput(this, w, config); - } - } - } - return r; - }; - const origOnConfigure = nodeType.prototype.onConfigure; - nodeType.prototype.onConfigure = function() { - const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : void 0; - if (!app2.configuringGraph && this.inputs) { - for (const input of this.inputs) { - if (input.widget && !input.widget[GET_CONFIG]) { - input.widget[GET_CONFIG] = () => ( - // @ts-expect-error input.widget has unknown type - getConfig.call(this, input.widget.name) - ); - const w = this.widgets.find((w2) => w2.name === input.widget.name); - if (w) { - hideWidget(this, w); - } - } - } - } - return r; - }; - function isNodeAtPos(pos) { - for (const n of app2.graph.nodes) { - if (n.pos[0] === pos[0] && n.pos[1] === pos[1]) { - return true; - } - } - return false; - } - __name(isNodeAtPos, "isNodeAtPos"); - const origOnInputDblClick = nodeType.prototype.onInputDblClick; - const ignoreDblClick = Symbol(); - nodeType.prototype.onInputDblClick = function(slot) { - const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : void 0; - const input = this.inputs[slot]; - if (!input.widget || !input[ignoreDblClick]) { - if (!(input.type in ComfyWidgets) && !(input.widget?.[GET_CONFIG]?.()?.[0] instanceof Array)) { - return r; - } - } - const node = LiteGraph.createNode("PrimitiveNode"); - app2.graph.add(node); - const pos = [ - this.pos[0] - node.size[0] - 30, - this.pos[1] - ]; - while (isNodeAtPos(pos)) { - pos[1] += LiteGraph.NODE_TITLE_HEIGHT; - } - node.pos = pos; - node.connect(0, this, slot); - node.title = input.name; - input[ignoreDblClick] = true; - setTimeout(() => { - delete input[ignoreDblClick]; - }, 300); - return r; - }; - const onConnectInput = nodeType.prototype.onConnectInput; - nodeType.prototype.onConnectInput = function(targetSlot, type, output, originNode, originSlot) { - const v = onConnectInput?.(this, arguments); - if (type !== "COMBO") return v; - if (originNode.outputs[originSlot].widget) return v; - const targetCombo = this.inputs[targetSlot].widget?.[GET_CONFIG]?.()?.[0]; - if (!targetCombo || !(targetCombo instanceof Array)) return v; - const originConfig = originNode.constructor?.nodeData?.output?.[originSlot]; - if (!originConfig || !isValidCombo(targetCombo, originConfig)) { - return false; - } - return v; - }; - }, - registerCustomNodes() { - LiteGraph.registerNodeType( - "PrimitiveNode", - Object.assign(PrimitiveNode, { - title: "Primitive" - }) - ); - PrimitiveNode.category = "utils"; - } -}); -window.comfyAPI = window.comfyAPI || {}; -window.comfyAPI.widgetInputs = window.comfyAPI.widgetInputs || {}; -window.comfyAPI.widgetInputs.getWidgetConfig = getWidgetConfig; -window.comfyAPI.widgetInputs.convertToInput = convertToInput; -window.comfyAPI.widgetInputs.setWidgetConfig = setWidgetConfig; -window.comfyAPI.widgetInputs.mergeIfValid = mergeIfValid; -const GROUP = Symbol(); -const PREFIX = "workflow"; -const SEPARATOR = ">"; -const Workflow = { - InUse: { - Free: 0, - Registered: 1, - InWorkflow: 2 - }, - isInUseGroupNode(name) { - const id2 = `${PREFIX}${SEPARATOR}${name}`; - if (app.graph.extra?.groupNodes?.[name]) { - if (app.graph.nodes.find((n) => n.type === id2)) { - return Workflow.InUse.InWorkflow; - } else { - return Workflow.InUse.Registered; - } - } - return Workflow.InUse.Free; - }, - storeGroupNode(name, data) { - let extra = app.graph.extra; - if (!extra) app.graph.extra = extra = {}; - let groupNodes = extra.groupNodes; - if (!groupNodes) extra.groupNodes = groupNodes = {}; - groupNodes[name] = data; - } -}; -class GroupNodeBuilder { - static { - __name(this, "GroupNodeBuilder"); - } - nodes; - nodeData; - constructor(nodes) { - this.nodes = nodes; - } - async build() { - const name = await this.getName(); - if (!name) return; - this.sortNodes(); - this.nodeData = this.getNodeData(); - Workflow.storeGroupNode(name, this.nodeData); - return { name, nodeData: this.nodeData }; - } - async getName() { - const name = await useDialogService().prompt({ - title: t("groupNode.create"), - message: t("groupNode.enterName"), - defaultValue: "" - }); - if (!name) return; - const used = Workflow.isInUseGroupNode(name); - switch (used) { - case Workflow.InUse.InWorkflow: - useToastStore().addAlert( - "An in use group node with this name already exists embedded in this workflow, please remove any instances or use a new name." - ); - return; - case Workflow.InUse.Registered: - if (!confirm( - "A group node with this name already exists embedded in this workflow, are you sure you want to overwrite it?" - )) { - return; - } - break; - } - return name; - } - sortNodes() { - const nodesInOrder = app.graph.computeExecutionOrder(false); - this.nodes = this.nodes.map((node) => ({ index: nodesInOrder.indexOf(node), node })).sort((a, b) => a.index - b.index || a.node.id - b.node.id).map(({ node }) => node); - } - getNodeData() { - const storeLinkTypes = /* @__PURE__ */ __name((config) => { - for (const link of config.links) { - const origin = app.graph.getNodeById(link[4]); - const type = origin.outputs[link[1]].type; - link.push(type); - } - }, "storeLinkTypes"); - const storeExternalLinks = /* @__PURE__ */ __name((config) => { - config.external = []; - for (let i = 0; i < this.nodes.length; i++) { - const node = this.nodes[i]; - if (!node.outputs?.length) continue; - for (let slot = 0; slot < node.outputs.length; slot++) { - let hasExternal = false; - const output = node.outputs[slot]; - let type = output.type; - if (!output.links?.length) continue; - for (const l of output.links) { - const link = app.graph.links[l]; - if (!link) continue; - if (type === "*") type = link.type; - if (!app.canvas.selected_nodes[link.target_id]) { - hasExternal = true; - break; - } - } - if (hasExternal) { - config.external.push([i, slot, type]); - } - } - } - }, "storeExternalLinks"); - try { - const serialised = serialise(this.nodes, app.canvas.graph); - const config = JSON.parse(serialised); - storeLinkTypes(config); - storeExternalLinks(config); - return config; - } finally { - } - } -} -class GroupNodeConfig { - static { - __name(this, "GroupNodeConfig"); - } - name; - nodeData; - inputCount; - oldToNewOutputMap; - newToOldOutputMap; - oldToNewInputMap; - oldToNewWidgetMap; - newToOldWidgetMap; - primitiveDefs; - widgetToPrimitive; - primitiveToWidget; - nodeInputs; - outputVisibility; - nodeDef; - inputs; - linksFrom; - linksTo; - externalFrom; - constructor(name, nodeData) { - this.name = name; - this.nodeData = nodeData; - this.getLinks(); - this.inputCount = 0; - this.oldToNewOutputMap = {}; - this.newToOldOutputMap = {}; - this.oldToNewInputMap = {}; - this.oldToNewWidgetMap = {}; - this.newToOldWidgetMap = {}; - this.primitiveDefs = {}; - this.widgetToPrimitive = {}; - this.primitiveToWidget = {}; - this.nodeInputs = {}; - this.outputVisibility = []; - } - async registerType(source = PREFIX) { - this.nodeDef = { - output: [], - output_name: [], - output_is_list: [], - // @ts-expect-error Unused, doesn't exist - output_is_hidden: [], - name: source + SEPARATOR + this.name, - display_name: this.name, - category: "group nodes" + (SEPARATOR + source), - input: { required: {} }, - description: `Group node combining ${this.nodeData.nodes.map((n) => n.type).join(", ")}`, - python_module: "custom_nodes." + this.name, - [GROUP]: this - }; - this.inputs = []; - const seenInputs = {}; - const seenOutputs = {}; - for (let i = 0; i < this.nodeData.nodes.length; i++) { - const node = this.nodeData.nodes[i]; - node.index = i; - this.processNode(node, seenInputs, seenOutputs); - } - for (const p of this.#convertedToProcess) { - p(); - } - this.#convertedToProcess = null; - await app.registerNodeDef(`${PREFIX}${SEPARATOR}` + this.name, this.nodeDef); - useNodeDefStore().addNodeDef(this.nodeDef); - } - getLinks() { - this.linksFrom = {}; - this.linksTo = {}; - this.externalFrom = {}; - for (const l of this.nodeData.links) { - const [sourceNodeId, sourceNodeSlot, targetNodeId, targetNodeSlot] = l; - if (sourceNodeId == null) continue; - if (!this.linksFrom[sourceNodeId]) { - this.linksFrom[sourceNodeId] = {}; - } - if (!this.linksFrom[sourceNodeId][sourceNodeSlot]) { - this.linksFrom[sourceNodeId][sourceNodeSlot] = []; - } - this.linksFrom[sourceNodeId][sourceNodeSlot].push(l); - if (!this.linksTo[targetNodeId]) { - this.linksTo[targetNodeId] = {}; - } - this.linksTo[targetNodeId][targetNodeSlot] = l; - } - if (this.nodeData.external) { - for (const ext2 of this.nodeData.external) { - if (!this.externalFrom[ext2[0]]) { - this.externalFrom[ext2[0]] = { [ext2[1]]: ext2[2] }; - } else { - this.externalFrom[ext2[0]][ext2[1]] = ext2[2]; - } - } - } - } - processNode(node, seenInputs, seenOutputs) { - const def = this.getNodeDef(node); - if (!def) return; - const inputs = { ...def.input?.required, ...def.input?.optional }; - this.inputs.push(this.processNodeInputs(node, seenInputs, inputs)); - if (def.output?.length) this.processNodeOutputs(node, seenOutputs, def); - } - getNodeDef(node) { - const def = globalDefs[node.type]; - if (def) return def; - const linksFrom = this.linksFrom[node.index]; - if (node.type === "PrimitiveNode") { - if (!linksFrom) return; - let type = linksFrom["0"][0][5]; - if (type === "COMBO") { - const source = node.outputs[0].widget.name; - const fromTypeName = this.nodeData.nodes[linksFrom["0"][0][2]].type; - const fromType = globalDefs[fromTypeName]; - const input = fromType.input.required[source] ?? fromType.input.optional[source]; - type = input[0]; - } - const def2 = this.primitiveDefs[node.index] = { - input: { - required: { - value: [type, {}] - } - }, - output: [type], - output_name: [], - output_is_list: [] - }; - return def2; - } else if (node.type === "Reroute") { - const linksTo = this.linksTo[node.index]; - if (linksTo && linksFrom && !this.externalFrom[node.index]?.[0]) { - return null; - } - let config = {}; - let rerouteType = "*"; - if (linksFrom) { - for (const [, , id2, slot] of linksFrom["0"]) { - const node2 = this.nodeData.nodes[id2]; - const input = node2.inputs[slot]; - if (rerouteType === "*") { - rerouteType = input.type; - } - if (input.widget) { - const targetDef = globalDefs[node2.type]; - const targetWidget = targetDef.input.required[input.widget.name] ?? targetDef.input.optional[input.widget.name]; - const widget = [targetWidget[0], config]; - const res = mergeIfValid( - { - widget - }, - targetWidget, - false, - null, - widget - ); - config = res?.customConfig ?? config; - } - } - } else if (linksTo) { - const [id2, slot] = linksTo["0"]; - rerouteType = this.nodeData.nodes[id2].outputs[slot].type; - } else { - for (const l of this.nodeData.links) { - if (l[2] === node.index) { - rerouteType = l[5]; - break; - } - } - if (rerouteType === "*") { - const t2 = this.externalFrom[node.index]?.[0]; - if (t2) { - rerouteType = t2; - } - } - } - config.forceInput = true; - return { - input: { - required: { - [rerouteType]: [rerouteType, config] - } - }, - output: [rerouteType], - output_name: [], - output_is_list: [] - }; - } - console.warn( - "Skipping virtual node " + node.type + " when building group node " + this.name - ); - } - getInputConfig(node, inputName, seenInputs, config, extra) { - const customConfig = this.nodeData.config?.[node.index]?.input?.[inputName]; - let name = customConfig?.name ?? node.inputs?.find((inp) => inp.name === inputName)?.label ?? inputName; - let key = name; - let prefix = ""; - if (node.type === "PrimitiveNode" && node.title || name in seenInputs) { - prefix = `${node.title ?? node.type} `; - key = name = `${prefix}${inputName}`; - if (name in seenInputs) { - name = `${prefix}${seenInputs[name]} ${inputName}`; - } - } - seenInputs[key] = (seenInputs[key] ?? 1) + 1; - if (inputName === "seed" || inputName === "noise_seed") { - if (!extra) extra = {}; - extra.control_after_generate = `${prefix}control_after_generate`; - } - if (config[0] === "IMAGEUPLOAD") { - if (!extra) extra = {}; - extra.widget = this.oldToNewWidgetMap[node.index]?.[config[1]?.widget ?? "image"] ?? "image"; - } - if (extra) { - config = [config[0], { ...config[1], ...extra }]; - } - return { name, config, customConfig }; - } - processWidgetInputs(inputs, node, inputNames, seenInputs) { - const slots = []; - const converted = /* @__PURE__ */ new Map(); - const widgetMap = this.oldToNewWidgetMap[node.index] = {}; - for (const inputName of inputNames) { - let widgetType = app.getWidgetType(inputs[inputName], inputName); - if (widgetType) { - const convertedIndex = node.inputs?.findIndex( - (inp) => inp.name === inputName && inp.widget?.name === inputName - ); - if (convertedIndex > -1) { - converted.set(convertedIndex, inputName); - widgetMap[inputName] = null; - } else { - const { name, config } = this.getInputConfig( - node, - inputName, - seenInputs, - inputs[inputName] - ); - this.nodeDef.input.required[name] = config; - widgetMap[inputName] = name; - this.newToOldWidgetMap[name] = { node, inputName }; - } - } else { - slots.push(inputName); - } - } - return { converted, slots }; - } - checkPrimitiveConnection(link, inputName, inputs) { - const sourceNode = this.nodeData.nodes[link[0]]; - if (sourceNode.type === "PrimitiveNode") { - const [sourceNodeId, _, targetNodeId, __] = link; - const primitiveDef = this.primitiveDefs[sourceNodeId]; - const targetWidget = inputs[inputName]; - const primitiveConfig = primitiveDef.input.required.value; - const output = { widget: primitiveConfig }; - const config = mergeIfValid( - output, - targetWidget, - false, - null, - primitiveConfig - ); - primitiveConfig[1] = config?.customConfig ?? inputs[inputName][1] ? { ...inputs[inputName][1] } : {}; - let name = this.oldToNewWidgetMap[sourceNodeId]["value"]; - name = name.substr(0, name.length - 6); - primitiveConfig[1].control_after_generate = true; - primitiveConfig[1].control_prefix = name; - let toPrimitive = this.widgetToPrimitive[targetNodeId]; - if (!toPrimitive) { - toPrimitive = this.widgetToPrimitive[targetNodeId] = {}; - } - if (toPrimitive[inputName]) { - toPrimitive[inputName].push(sourceNodeId); - } - toPrimitive[inputName] = sourceNodeId; - let toWidget = this.primitiveToWidget[sourceNodeId]; - if (!toWidget) { - toWidget = this.primitiveToWidget[sourceNodeId] = []; - } - toWidget.push({ nodeId: targetNodeId, inputName }); - } - } - processInputSlots(inputs, node, slots, linksTo, inputMap, seenInputs) { - this.nodeInputs[node.index] = {}; - for (let i = 0; i < slots.length; i++) { - const inputName = slots[i]; - if (linksTo[i]) { - this.checkPrimitiveConnection(linksTo[i], inputName, inputs); - continue; - } - const { name, config, customConfig } = this.getInputConfig( - node, - inputName, - seenInputs, - inputs[inputName] - ); - this.nodeInputs[node.index][inputName] = name; - if (customConfig?.visible === false) continue; - this.nodeDef.input.required[name] = config; - inputMap[i] = this.inputCount++; - } - } - processConvertedWidgets(inputs, node, slots, converted, linksTo, inputMap, seenInputs) { - const convertedSlots = [...converted.keys()].sort().map((k) => converted.get(k)); - for (let i = 0; i < convertedSlots.length; i++) { - const inputName = convertedSlots[i]; - if (linksTo[slots.length + i]) { - this.checkPrimitiveConnection( - linksTo[slots.length + i], - inputName, - inputs - ); - continue; - } - const { name, config } = this.getInputConfig( - node, - inputName, - seenInputs, - inputs[inputName], - { - defaultInput: true - } - ); - this.nodeDef.input.required[name] = config; - this.newToOldWidgetMap[name] = { node, inputName }; - if (!this.oldToNewWidgetMap[node.index]) { - this.oldToNewWidgetMap[node.index] = {}; - } - this.oldToNewWidgetMap[node.index][inputName] = name; - inputMap[slots.length + i] = this.inputCount++; - } - } - #convertedToProcess = []; - processNodeInputs(node, seenInputs, inputs) { - const inputMapping = []; - const inputNames = Object.keys(inputs); - if (!inputNames.length) return; - const { converted, slots } = this.processWidgetInputs( - inputs, - node, - inputNames, - seenInputs - ); - const linksTo = this.linksTo[node.index] ?? {}; - const inputMap = this.oldToNewInputMap[node.index] = {}; - this.processInputSlots(inputs, node, slots, linksTo, inputMap, seenInputs); - this.#convertedToProcess.push( - () => this.processConvertedWidgets( - inputs, - node, - slots, - converted, - linksTo, - inputMap, - seenInputs - ) - ); - return inputMapping; - } - processNodeOutputs(node, seenOutputs, def) { - const oldToNew = this.oldToNewOutputMap[node.index] = {}; - for (let outputId = 0; outputId < def.output.length; outputId++) { - const linksFrom = this.linksFrom[node.index]; - const hasLink = linksFrom?.[outputId] && !this.externalFrom[node.index]?.[outputId]; - const customConfig = this.nodeData.config?.[node.index]?.output?.[outputId]; - const visible = customConfig?.visible ?? !hasLink; - this.outputVisibility.push(visible); - if (!visible) { - continue; - } - oldToNew[outputId] = this.nodeDef.output.length; - this.newToOldOutputMap[this.nodeDef.output.length] = { - node, - slot: outputId - }; - this.nodeDef.output.push(def.output[outputId]); - this.nodeDef.output_is_list.push(def.output_is_list[outputId]); - let label = customConfig?.name; - if (!label) { - label = def.output_name?.[outputId] ?? def.output[outputId]; - const output = node.outputs.find((o) => o.name === label); - if (output?.label) { - label = output.label; - } - } - let name = label; - if (name in seenOutputs) { - const prefix = `${node.title ?? node.type} `; - name = `${prefix}${label}`; - if (name in seenOutputs) { - name = `${prefix}${node.index} ${label}`; - } - } - seenOutputs[name] = 1; - this.nodeDef.output_name.push(name); - } - } - static async registerFromWorkflow(groupNodes, missingNodeTypes) { - for (const g in groupNodes) { - const groupData = groupNodes[g]; - let hasMissing = false; - for (const n of groupData.nodes) { - if (!(n.type in LiteGraph.registered_node_types)) { - missingNodeTypes.push({ - type: n.type, - hint: ` (In group node '${PREFIX}${SEPARATOR}${g}')` - }); - missingNodeTypes.push({ - type: `${PREFIX}${SEPARATOR}` + g, - action: { - text: "Remove from workflow", - callback: /* @__PURE__ */ __name((e) => { - delete groupNodes[g]; - e.target.textContent = "Removed"; - e.target.style.pointerEvents = "none"; - e.target.style.opacity = 0.7; - }, "callback") - } - }); - hasMissing = true; - } - } - if (hasMissing) continue; - const config = new GroupNodeConfig(g, groupData); - await config.registerType(); - } - } -} -class GroupNodeHandler { - static { - __name(this, "GroupNodeHandler"); - } - node; - groupData; - innerNodes; - constructor(node) { - this.node = node; - this.groupData = node.constructor?.nodeData?.[GROUP]; - this.node.setInnerNodes = (innerNodes) => { - this.innerNodes = innerNodes; - for (let innerNodeIndex = 0; innerNodeIndex < this.innerNodes.length; innerNodeIndex++) { - const innerNode = this.innerNodes[innerNodeIndex]; - for (const w of innerNode.widgets ?? []) { - if (w.type === "converted-widget") { - w.serializeValue = w.origSerializeValue; - } - } - innerNode.index = innerNodeIndex; - innerNode.getInputNode = (slot) => { - const externalSlot = this.groupData.oldToNewInputMap[innerNode.index]?.[slot]; - if (externalSlot != null) { - return this.node.getInputNode(externalSlot); - } - const innerLink = this.groupData.linksTo[innerNode.index]?.[slot]; - if (!innerLink) return null; - const inputNode = innerNodes[innerLink[0]]; - if (inputNode.type === "PrimitiveNode") return null; - return inputNode; - }; - innerNode.getInputLink = (slot) => { - const externalSlot = this.groupData.oldToNewInputMap[innerNode.index]?.[slot]; - if (externalSlot != null) { - const linkId = this.node.inputs[externalSlot].link; - let link2 = app.graph.links[linkId]; - link2 = { - ...link2, - target_id: innerNode.id, - target_slot: +slot - }; - return link2; - } - let link = this.groupData.linksTo[innerNode.index]?.[slot]; - if (!link) return null; - link = { - origin_id: innerNodes[link[0]].id, - origin_slot: link[1], - target_id: innerNode.id, - target_slot: +slot - }; - return link; - }; - } - }; - this.node.updateLink = (link) => { - link = { ...link }; - const output = this.groupData.newToOldOutputMap[link.origin_slot]; - let innerNode = this.innerNodes[output.node.index]; - let l; - while (innerNode?.type === "Reroute") { - l = innerNode.getInputLink(0); - innerNode = innerNode.getInputNode(0); - } - if (!innerNode) { - return null; - } - if (l && GroupNodeHandler.isGroupNode(innerNode)) { - return innerNode.updateLink(l); - } - link.origin_id = innerNode.id; - link.origin_slot = l?.origin_slot ?? output.slot; - return link; - }; - this.node.getInnerNodes = () => { - if (!this.innerNodes) { - this.node.setInnerNodes( - this.groupData.nodeData.nodes.map((n, i) => { - const innerNode = LiteGraph.createNode(n.type); - innerNode.configure(n); - innerNode.id = `${this.node.id}:${i}`; - return innerNode; - }) - ); - } - this.updateInnerWidgets(); - return this.innerNodes; - }; - this.node.recreate = async () => { - const id2 = this.node.id; - const sz = this.node.size; - const nodes = this.node.convertToNodes(); - const groupNode = LiteGraph.createNode(this.node.type); - groupNode.id = id2; - groupNode.setInnerNodes(nodes); - groupNode[GROUP].populateWidgets(); - app.graph.add(groupNode); - groupNode.size = [ - Math.max(groupNode.size[0], sz[0]), - Math.max(groupNode.size[1], sz[1]) - ]; - const builder = new GroupNodeBuilder(nodes); - const nodeData = builder.getNodeData(); - groupNode[GROUP].groupData.nodeData.links = nodeData.links; - groupNode[GROUP].replaceNodes(nodes); - return groupNode; - }; - this.node.convertToNodes = () => { - const addInnerNodes = /* @__PURE__ */ __name(() => { - const c = { ...this.groupData.nodeData }; - c.nodes = [...c.nodes]; - const innerNodes = this.node.getInnerNodes(); - let ids = []; - for (let i = 0; i < c.nodes.length; i++) { - let id2 = innerNodes?.[i]?.id; - if (id2 == null || isNaN(id2)) { - id2 = void 0; - } else { - ids.push(id2); - } - c.nodes[i] = { ...c.nodes[i], id: id2 }; - } - deserialiseAndCreate(JSON.stringify(c), app.canvas); - const [x, y] = this.node.pos; - let top; - let left; - const selectedIds = ids.length ? ids : Object.keys(app.canvas.selected_nodes); - const newNodes = []; - for (let i = 0; i < selectedIds.length; i++) { - const id2 = selectedIds[i]; - const newNode = app.graph.getNodeById(id2); - const innerNode = innerNodes[i]; - newNodes.push(newNode); - if (left == null || newNode.pos[0] < left) { - left = newNode.pos[0]; - } - if (top == null || newNode.pos[1] < top) { - top = newNode.pos[1]; - } - if (!newNode.widgets) continue; - const map = this.groupData.oldToNewWidgetMap[innerNode.index]; - if (map) { - const widgets = Object.keys(map); - for (const oldName of widgets) { - const newName = map[oldName]; - if (!newName) continue; - const widgetIndex = this.node.widgets.findIndex( - (w) => w.name === newName - ); - if (widgetIndex === -1) continue; - if (innerNode.type === "PrimitiveNode") { - for (let i2 = 0; i2 < newNode.widgets.length; i2++) { - newNode.widgets[i2].value = this.node.widgets[widgetIndex + i2].value; - } - } else { - const outerWidget = this.node.widgets[widgetIndex]; - const newWidget = newNode.widgets.find( - (w) => w.name === oldName - ); - if (!newWidget) continue; - newWidget.value = outerWidget.value; - for (let w = 0; w < outerWidget.linkedWidgets?.length; w++) { - newWidget.linkedWidgets[w].value = outerWidget.linkedWidgets[w].value; - } - } - } - } - } - for (const newNode of newNodes) { - newNode.pos[0] -= left - x; - newNode.pos[1] -= top - y; - } - return { newNodes, selectedIds }; - }, "addInnerNodes"); - const reconnectInputs = /* @__PURE__ */ __name((selectedIds) => { - for (const innerNodeIndex in this.groupData.oldToNewInputMap) { - const id2 = selectedIds[innerNodeIndex]; - const newNode = app.graph.getNodeById(id2); - const map = this.groupData.oldToNewInputMap[innerNodeIndex]; - for (const innerInputId in map) { - const groupSlotId = map[innerInputId]; - if (groupSlotId == null) continue; - const slot = node.inputs[groupSlotId]; - if (slot.link == null) continue; - const link = app.graph.links[slot.link]; - if (!link) continue; - const originNode = app.graph.getNodeById(link.origin_id); - originNode.connect(link.origin_slot, newNode, +innerInputId); - } - } - }, "reconnectInputs"); - const reconnectOutputs = /* @__PURE__ */ __name((selectedIds) => { - for (let groupOutputId = 0; groupOutputId < node.outputs?.length; groupOutputId++) { - const output = node.outputs[groupOutputId]; - if (!output.links) continue; - const links = [...output.links]; - for (const l of links) { - const slot = this.groupData.newToOldOutputMap[groupOutputId]; - const link = app.graph.links[l]; - const targetNode = app.graph.getNodeById(link.target_id); - const newNode = app.graph.getNodeById(selectedIds[slot.node.index]); - newNode.connect(slot.slot, targetNode, link.target_slot); - } - } - }, "reconnectOutputs"); - app.canvas.emitBeforeChange(); - try { - const { newNodes, selectedIds } = addInnerNodes(); - reconnectInputs(selectedIds); - reconnectOutputs(selectedIds); - app.graph.remove(this.node); - return newNodes; - } finally { - app.canvas.emitAfterChange(); - } - }; - const getExtraMenuOptions = this.node.getExtraMenuOptions; - this.node.getExtraMenuOptions = function(_, options) { - getExtraMenuOptions?.apply(this, arguments); - let optionIndex = options.findIndex((o) => o.content === "Outputs"); - if (optionIndex === -1) optionIndex = options.length; - else optionIndex++; - options.splice( - optionIndex, - 0, - null, - { - content: "Convert to nodes", - // @ts-expect-error - callback: /* @__PURE__ */ __name(() => { - return this.convertToNodes(); - }, "callback") - }, - { - content: "Manage Group Node", - callback: /* @__PURE__ */ __name(() => manageGroupNodes(this.type), "callback") - } - ); - }; - const onDrawTitleBox = this.node.onDrawTitleBox; - this.node.onDrawTitleBox = function(ctx, height, size, scale) { - onDrawTitleBox?.apply(this, arguments); - const fill2 = ctx.fillStyle; - ctx.beginPath(); - ctx.rect(11, -height + 11, 2, 2); - ctx.rect(14, -height + 11, 2, 2); - ctx.rect(17, -height + 11, 2, 2); - ctx.rect(11, -height + 14, 2, 2); - ctx.rect(14, -height + 14, 2, 2); - ctx.rect(17, -height + 14, 2, 2); - ctx.rect(11, -height + 17, 2, 2); - ctx.rect(14, -height + 17, 2, 2); - ctx.rect(17, -height + 17, 2, 2); - ctx.fillStyle = this.boxcolor || LiteGraph.NODE_DEFAULT_BOXCOLOR; - ctx.fill(); - ctx.fillStyle = fill2; - }; - const onDrawForeground = node.onDrawForeground; - const groupData = this.groupData.nodeData; - node.onDrawForeground = function(ctx) { - const r = onDrawForeground?.apply?.(this, arguments); - if (+app.runningNodeId === this.id && this.runningInternalNodeId !== null) { - const n = groupData.nodes[this.runningInternalNodeId]; - if (!n) return; - const message = `Running ${n.title || n.type} (${this.runningInternalNodeId}/${groupData.nodes.length})`; - ctx.save(); - ctx.font = "12px sans-serif"; - const sz = ctx.measureText(message); - ctx.fillStyle = node.boxcolor || LiteGraph.NODE_DEFAULT_BOXCOLOR; - ctx.beginPath(); - ctx.roundRect( - 0, - -LiteGraph.NODE_TITLE_HEIGHT - 20, - sz.width + 12, - 20, - 5 - ); - ctx.fill(); - ctx.fillStyle = "#fff"; - ctx.fillText(message, 6, -LiteGraph.NODE_TITLE_HEIGHT - 6); - ctx.restore(); - } - }; - const onExecutionStart = this.node.onExecutionStart; - this.node.onExecutionStart = function() { - this.resetExecution = true; - return onExecutionStart?.apply(this, arguments); - }; - const self2 = this; - const onNodeCreated = this.node.onNodeCreated; - this.node.onNodeCreated = function() { - if (!this.widgets) { - return; - } - const config = self2.groupData.nodeData.config; - if (config) { - for (const n in config) { - const inputs = config[n]?.input; - for (const w in inputs) { - if (inputs[w].visible !== false) continue; - const widgetName = self2.groupData.oldToNewWidgetMap[n][w]; - const widget = this.widgets.find((w2) => w2.name === widgetName); - if (widget) { - widget.type = "hidden"; - widget.computeSize = () => [0, -4]; - } - } - } - } - return onNodeCreated?.apply(this, arguments); - }; - function handleEvent(type, getId, getEvent) { - const handler = /* @__PURE__ */ __name(({ detail }) => { - const id2 = getId(detail); - if (!id2) return; - const node2 = app.graph.getNodeById(id2); - if (node2) return; - const innerNodeIndex = this.innerNodes?.findIndex((n) => n.id == id2); - if (innerNodeIndex > -1) { - this.node.runningInternalNodeId = innerNodeIndex; - api.dispatchCustomEvent( - type, - getEvent(detail, `${this.node.id}`, this.node) - ); - } - }, "handler"); - api.addEventListener(type, handler); - return handler; - } - __name(handleEvent, "handleEvent"); - const executing = handleEvent.call( - this, - "executing", - (d) => d, - (d, id2, node2) => id2 - ); - const executed = handleEvent.call( - this, - "executed", - (d) => d?.display_node || d?.node, - (d, id2, node2) => ({ - ...d, - node: id2, - display_node: id2, - merge: !node2.resetExecution - }) - ); - const onRemoved = node.onRemoved; - this.node.onRemoved = function() { - onRemoved?.apply(this, arguments); - api.removeEventListener("executing", executing); - api.removeEventListener("executed", executed); - }; - this.node.refreshComboInNode = (defs) => { - for (const widgetName in this.groupData.newToOldWidgetMap) { - const widget = this.node.widgets.find((w) => w.name === widgetName); - if (widget?.type === "combo") { - const old = this.groupData.newToOldWidgetMap[widgetName]; - const def = defs[old.node.type]; - const input = def?.input?.required?.[old.inputName] ?? def?.input?.optional?.[old.inputName]; - if (!input) continue; - widget.options.values = input[0]; - if (old.inputName !== "image" && // @ts-expect-error Widget values - !widget.options.values.includes(widget.value)) { - widget.value = widget.options.values[0]; - widget.callback(widget.value); - } - } - } - }; - } - updateInnerWidgets() { - for (const newWidgetName in this.groupData.newToOldWidgetMap) { - const newWidget = this.node.widgets.find((w) => w.name === newWidgetName); - if (!newWidget) continue; - const newValue = newWidget.value; - const old = this.groupData.newToOldWidgetMap[newWidgetName]; - let innerNode = this.innerNodes[old.node.index]; - if (innerNode.type === "PrimitiveNode") { - innerNode.primitiveValue = newValue; - const primitiveLinked = this.groupData.primitiveToWidget[old.node.index]; - for (const linked of primitiveLinked ?? []) { - const node = this.innerNodes[linked.nodeId]; - const widget2 = node.widgets.find((w) => w.name === linked.inputName); - if (widget2) { - widget2.value = newValue; - } - } - continue; - } else if (innerNode.type === "Reroute") { - const rerouteLinks = this.groupData.linksFrom[old.node.index]; - if (rerouteLinks) { - for (const [_, , targetNodeId, targetSlot] of rerouteLinks["0"]) { - const node = this.innerNodes[targetNodeId]; - const input = node.inputs[targetSlot]; - if (input.widget) { - const widget2 = node.widgets?.find( - (w) => w.name === input.widget.name - ); - if (widget2) { - widget2.value = newValue; - } - } - } - } - } - const widget = innerNode.widgets?.find((w) => w.name === old.inputName); - if (widget) { - widget.value = newValue; - } - } - } - populatePrimitive(node, nodeId, oldName, i, linkedShift) { - const primitiveId = this.groupData.widgetToPrimitive[nodeId]?.[oldName]; - if (primitiveId == null) return; - const targetWidgetName = this.groupData.oldToNewWidgetMap[primitiveId]["value"]; - const targetWidgetIndex = this.node.widgets.findIndex( - (w) => w.name === targetWidgetName - ); - if (targetWidgetIndex > -1) { - const primitiveNode = this.innerNodes[primitiveId]; - let len = primitiveNode.widgets.length; - if (len - 1 !== this.node.widgets[targetWidgetIndex].linkedWidgets?.length) { - len = 1; - } - for (let i2 = 0; i2 < len; i2++) { - this.node.widgets[targetWidgetIndex + i2].value = primitiveNode.widgets[i2].value; - } - } - return true; - } - populateReroute(node, nodeId, map) { - if (node.type !== "Reroute") return; - const link = this.groupData.linksFrom[nodeId]?.[0]?.[0]; - if (!link) return; - const [, , targetNodeId, targetNodeSlot] = link; - const targetNode = this.groupData.nodeData.nodes[targetNodeId]; - const inputs = targetNode.inputs; - const targetWidget = inputs?.[targetNodeSlot]?.widget; - if (!targetWidget) return; - const offset = inputs.length - (targetNode.widgets_values?.length ?? 0); - const v = targetNode.widgets_values?.[targetNodeSlot - offset]; - if (v == null) return; - const widgetName = Object.values(map)[0]; - const widget = this.node.widgets.find((w) => w.name === widgetName); - if (widget) { - widget.value = v; - } - } - populateWidgets() { - if (!this.node.widgets) return; - for (let nodeId = 0; nodeId < this.groupData.nodeData.nodes.length; nodeId++) { - const node = this.groupData.nodeData.nodes[nodeId]; - const map = this.groupData.oldToNewWidgetMap[nodeId] ?? {}; - const widgets = Object.keys(map); - if (!node.widgets_values?.length) { - this.populateReroute(node, nodeId, map); - continue; - } - let linkedShift = 0; - for (let i = 0; i < widgets.length; i++) { - const oldName = widgets[i]; - const newName = map[oldName]; - const widgetIndex = this.node.widgets.findIndex( - (w) => w.name === newName - ); - const mainWidget = this.node.widgets[widgetIndex]; - if (this.populatePrimitive(node, nodeId, oldName, i, linkedShift) || widgetIndex === -1) { - const innerWidget = this.innerNodes[nodeId].widgets?.find( - (w) => w.name === oldName - ); - linkedShift += innerWidget?.linkedWidgets?.length ?? 0; - } - if (widgetIndex === -1) { - continue; - } - mainWidget.value = node.widgets_values[i + linkedShift]; - for (let w = 0; w < mainWidget.linkedWidgets?.length; w++) { - this.node.widgets[widgetIndex + w + 1].value = node.widgets_values[i + ++linkedShift]; - } - } - } - } - replaceNodes(nodes) { - let top; - let left; - for (let i = 0; i < nodes.length; i++) { - const node = nodes[i]; - if (left == null || node.pos[0] < left) { - left = node.pos[0]; - } - if (top == null || node.pos[1] < top) { - top = node.pos[1]; - } - this.linkOutputs(node, i); - app.graph.remove(node); - } - this.linkInputs(); - this.node.pos = [left, top]; - } - linkOutputs(originalNode, nodeId) { - if (!originalNode.outputs) return; - for (const output of originalNode.outputs) { - if (!output.links) continue; - const links = [...output.links]; - for (const l of links) { - const link = app.graph.links[l]; - if (!link) continue; - const targetNode = app.graph.getNodeById(link.target_id); - const newSlot = this.groupData.oldToNewOutputMap[nodeId]?.[link.origin_slot]; - if (newSlot != null) { - this.node.connect(newSlot, targetNode, link.target_slot); - } - } - } - } - linkInputs() { - for (const link of this.groupData.nodeData.links ?? []) { - const [, originSlot, targetId, targetSlot, actualOriginId] = link; - const originNode = app.graph.getNodeById(actualOriginId); - if (!originNode) continue; - originNode.connect( - originSlot, - // @ts-expect-error Valid - uses deprecated interface. Required check: if (graph.getNodeById(this.node.id) !== this.node) report() - this.node.id, - this.groupData.oldToNewInputMap[targetId][targetSlot] - ); - } - } - static getGroupData(node) { - return (node.nodeData ?? node.constructor?.nodeData)?.[GROUP]; - } - static isGroupNode(node) { - return !!node.constructor?.nodeData?.[GROUP]; - } - static async fromNodes(nodes) { - const builder = new GroupNodeBuilder(nodes); - const res = await builder.build(); - if (!res) return; - const { name, nodeData } = res; - const config = new GroupNodeConfig(name, nodeData); - await config.registerType(); - const groupNode = LiteGraph.createNode(`${PREFIX}${SEPARATOR}${name}`); - groupNode.setInnerNodes(builder.nodes); - groupNode[GROUP].populateWidgets(); - app.graph.add(groupNode); - groupNode[GROUP].replaceNodes(builder.nodes); - return groupNode; - } -} -function addConvertToGroupOptions() { - function addConvertOption(options, index) { - const selected = Object.values(app.canvas.selected_nodes ?? {}); - const disabled = selected.length < 2 || selected.find((n) => GroupNodeHandler.isGroupNode(n)); - options.splice(index + 1, null, { - content: `Convert to Group Node`, - disabled, - callback: convertSelectedNodesToGroupNode - }); - } - __name(addConvertOption, "addConvertOption"); - function addManageOption(options, index) { - const groups = app.graph.extra?.groupNodes; - const disabled = !groups || !Object.keys(groups).length; - options.splice(index + 1, null, { - content: `Manage Group Nodes`, - disabled, - callback: /* @__PURE__ */ __name(() => manageGroupNodes(), "callback") - }); - } - __name(addManageOption, "addManageOption"); - const getCanvasMenuOptions = LGraphCanvas.prototype.getCanvasMenuOptions; - LGraphCanvas.prototype.getCanvasMenuOptions = function() { - const options = getCanvasMenuOptions.apply(this, arguments); - const index = options.findIndex((o) => o?.content === "Add Group") + 1 || options.length; - addConvertOption(options, index); - addManageOption(options, index + 1); - return options; - }; - const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions; - LGraphCanvas.prototype.getNodeMenuOptions = function(node) { - const options = getNodeMenuOptions.apply(this, arguments); - if (!GroupNodeHandler.isGroupNode(node)) { - const index = options.findIndex((o) => o?.content === "Outputs") + 1 || options.length - 1; - addConvertOption(options, index); - } - return options; - }; -} -__name(addConvertToGroupOptions, "addConvertToGroupOptions"); -const replaceLegacySeparators = /* @__PURE__ */ __name((nodes) => { - for (const node of nodes) { - if (typeof node.type === "string" && node.type.startsWith("workflow/")) { - node.type = node.type.replace(/^workflow\//, `${PREFIX}${SEPARATOR}`); - } - } -}, "replaceLegacySeparators"); -async function convertSelectedNodesToGroupNode() { - const nodes = Object.values(app.canvas.selected_nodes ?? {}); - if (nodes.length === 0) { - throw new Error("No nodes selected"); - } - if (nodes.length === 1) { - throw new Error("Please select multiple nodes to convert to group node"); - } - if (nodes.some((n) => GroupNodeHandler.isGroupNode(n))) { - throw new Error("Selected nodes contain a group node"); - } - return await GroupNodeHandler.fromNodes(nodes); -} -__name(convertSelectedNodesToGroupNode, "convertSelectedNodesToGroupNode"); -function ungroupSelectedGroupNodes() { - const nodes = Object.values(app.canvas.selected_nodes ?? {}); - for (const node of nodes) { - if (GroupNodeHandler.isGroupNode(node)) { - node.convertToNodes?.(); - } - } -} -__name(ungroupSelectedGroupNodes, "ungroupSelectedGroupNodes"); -function manageGroupNodes(type) { - new ManageGroupDialog(app).show(type); -} -__name(manageGroupNodes, "manageGroupNodes"); -const id$2 = "Comfy.GroupNode"; -let globalDefs; -const ext = { - name: id$2, - commands: [ - { - id: "Comfy.GroupNode.ConvertSelectedNodesToGroupNode", - label: "Convert selected nodes to group node", - icon: "pi pi-sitemap", - versionAdded: "1.3.17", - function: convertSelectedNodesToGroupNode - }, - { - id: "Comfy.GroupNode.UngroupSelectedGroupNodes", - label: "Ungroup selected group nodes", - icon: "pi pi-sitemap", - versionAdded: "1.3.17", - function: ungroupSelectedGroupNodes - }, - { - id: "Comfy.GroupNode.ManageGroupNodes", - label: "Manage group nodes", - icon: "pi pi-cog", - versionAdded: "1.3.17", - function: manageGroupNodes - } - ], - keybindings: [ - { - commandId: "Comfy.GroupNode.ConvertSelectedNodesToGroupNode", - combo: { - alt: true, - key: "g" - } - }, - { - commandId: "Comfy.GroupNode.UngroupSelectedGroupNodes", - combo: { - alt: true, - shift: true, - key: "G" - } - } - ], - setup() { - addConvertToGroupOptions(); - }, - async beforeConfigureGraph(graphData, missingNodeTypes) { - const nodes = graphData?.extra?.groupNodes; - if (nodes) { - replaceLegacySeparators(graphData.nodes); - await GroupNodeConfig.registerFromWorkflow(nodes, missingNodeTypes); - } - }, - addCustomNodeDefs(defs) { - globalDefs = defs; - }, - nodeCreated(node) { - if (GroupNodeHandler.isGroupNode(node)) { - node[GROUP] = new GroupNodeHandler(node); - if (node.title && node[GROUP]?.groupData?.nodeData) { - Workflow.storeGroupNode(node.title, node[GROUP].groupData.nodeData); - } - } - }, - async refreshComboInNodes(defs) { - Object.assign(globalDefs, defs); - const nodes = app.graph.extra?.groupNodes; - if (nodes) { - await GroupNodeConfig.registerFromWorkflow(nodes, {}); - } - } -}; -app.registerExtension(ext); -window.comfyAPI = window.comfyAPI || {}; -window.comfyAPI.groupNode = window.comfyAPI.groupNode || {}; -window.comfyAPI.groupNode.GroupNodeConfig = GroupNodeConfig; -window.comfyAPI.groupNode.GroupNodeHandler = GroupNodeHandler; -function setNodeMode(node, mode) { - node.mode = mode; - node.graph?.change(); -} -__name(setNodeMode, "setNodeMode"); -function addNodesToGroup(group, items) { - const padding = useSettingStore().get("Comfy.GroupSelectedNodes.Padding"); - group.resizeTo([...group.children, ...items], padding); -} -__name(addNodesToGroup, "addNodesToGroup"); -app.registerExtension({ - name: "Comfy.GroupOptions", - setup() { - const orig = LGraphCanvas.prototype.getCanvasMenuOptions; - LGraphCanvas.prototype.getCanvasMenuOptions = function() { - const options = orig.apply(this, arguments); - const group = this.graph.getGroupOnPos( - this.graph_mouse[0], - this.graph_mouse[1] - ); - if (!group) { - options.push({ - content: "Add Group For Selected Nodes", - disabled: !this.selectedItems?.size, - callback: /* @__PURE__ */ __name(() => { - const group2 = new LGraphGroup(); - addNodesToGroup(group2, this.selectedItems); - this.graph.add(group2); - this.graph.change(); - }, "callback") - }); - return options; - } - group.recomputeInsideNodes(); - const nodesInGroup = group.nodes; - options.push({ - content: "Add Selected Nodes To Group", - disabled: !this.selectedItems?.size, - callback: /* @__PURE__ */ __name(() => { - addNodesToGroup(group, this.selectedItems); - this.graph.change(); - }, "callback") - }); - if (nodesInGroup.length === 0) { - return options; - } else { - options.push(null); - } - let allNodesAreSameMode = true; - for (let i = 1; i < nodesInGroup.length; i++) { - if (nodesInGroup[i].mode !== nodesInGroup[0].mode) { - allNodesAreSameMode = false; - break; - } - } - options.push({ - content: "Fit Group To Nodes", - callback: /* @__PURE__ */ __name(() => { - group.recomputeInsideNodes(); - const padding = useSettingStore().get( - "Comfy.GroupSelectedNodes.Padding" - ); - group.resizeTo(group.children, padding); - this.graph.change(); - }, "callback") - }); - options.push({ - content: "Select Nodes", - callback: /* @__PURE__ */ __name(() => { - this.selectNodes(nodesInGroup); - this.graph.change(); - this.canvas.focus(); - }, "callback") - }); - if (allNodesAreSameMode) { - const mode = nodesInGroup[0].mode; - switch (mode) { - case 0: - options.push({ - content: "Set Group Nodes to Never", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); - } - }, "callback") - }); - options.push({ - content: "Bypass Group Nodes", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); - } - }, "callback") - }); - break; - case 2: - options.push({ - content: "Set Group Nodes to Always", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); - } - }, "callback") - }); - options.push({ - content: "Bypass Group Nodes", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); - } - }, "callback") - }); - break; - case 4: - options.push({ - content: "Set Group Nodes to Always", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); - } - }, "callback") - }); - options.push({ - content: "Set Group Nodes to Never", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); - } - }, "callback") - }); - break; - default: - options.push({ - content: "Set Group Nodes to Always", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); - } - }, "callback") - }); - options.push({ - content: "Set Group Nodes to Never", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); - } - }, "callback") - }); - options.push({ - content: "Bypass Group Nodes", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); - } - }, "callback") - }); - break; - } - } else { - options.push({ - content: "Set Group Nodes to Always", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 0); - } - }, "callback") - }); - options.push({ - content: "Set Group Nodes to Never", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 2); - } - }, "callback") - }); - options.push({ - content: "Bypass Group Nodes", - callback: /* @__PURE__ */ __name(() => { - for (const node of nodesInGroup) { - setNodeMode(node, 4); - } - }, "callback") - }); - } - return options; - }; - } -}); -const id$1 = "Comfy.InvertMenuScrolling"; -app.registerExtension({ - name: id$1, - init() { - const ctxMenu = LiteGraph.ContextMenu; - const replace = /* @__PURE__ */ __name(() => { - LiteGraph.ContextMenu = function(values, options) { - options = options || {}; - if (options.scroll_speed) { - options.scroll_speed *= -1; - } else { - options.scroll_speed = -0.1; - } - return ctxMenu.call(this, values, options); - }; - LiteGraph.ContextMenu.prototype = ctxMenu.prototype; - }, "replace"); - app.ui.settings.addSetting({ - id: id$1, - category: ["LiteGraph", "Menu", "InvertMenuScrolling"], - name: "Invert Context Menu Scrolling", - type: "boolean", - defaultValue: false, - onChange(value) { - if (value) { - replace(); - } else { - LiteGraph.ContextMenu = ctxMenu; - } - } - }); - } -}); -/** - * @license - * Copyright 2010-2024 Three.js Authors - * SPDX-License-Identifier: MIT - */ -const REVISION = "170"; -const MOUSE = { LEFT: 0, MIDDLE: 1, RIGHT: 2, ROTATE: 0, DOLLY: 1, PAN: 2 }; -const TOUCH = { ROTATE: 0, PAN: 1, DOLLY_PAN: 2, DOLLY_ROTATE: 3 }; -const CullFaceNone = 0; -const CullFaceBack = 1; -const CullFaceFront = 2; -const CullFaceFrontBack = 3; -const BasicShadowMap = 0; -const PCFShadowMap = 1; -const PCFSoftShadowMap = 2; -const VSMShadowMap = 3; -const FrontSide = 0; -const BackSide = 1; -const DoubleSide = 2; -const NoBlending = 0; -const NormalBlending = 1; -const AdditiveBlending = 2; -const SubtractiveBlending = 3; -const MultiplyBlending = 4; -const CustomBlending = 5; -const AddEquation = 100; -const SubtractEquation = 101; -const ReverseSubtractEquation = 102; -const MinEquation = 103; -const MaxEquation = 104; -const ZeroFactor = 200; -const OneFactor = 201; -const SrcColorFactor = 202; -const OneMinusSrcColorFactor = 203; -const SrcAlphaFactor = 204; -const OneMinusSrcAlphaFactor = 205; -const DstAlphaFactor = 206; -const OneMinusDstAlphaFactor = 207; -const DstColorFactor = 208; -const OneMinusDstColorFactor = 209; -const SrcAlphaSaturateFactor = 210; -const ConstantColorFactor = 211; -const OneMinusConstantColorFactor = 212; -const ConstantAlphaFactor = 213; -const OneMinusConstantAlphaFactor = 214; -const NeverDepth = 0; -const AlwaysDepth = 1; -const LessDepth = 2; -const LessEqualDepth = 3; -const EqualDepth = 4; -const GreaterEqualDepth = 5; -const GreaterDepth = 6; -const NotEqualDepth = 7; -const MultiplyOperation = 0; -const MixOperation = 1; -const AddOperation = 2; -const NoToneMapping = 0; -const LinearToneMapping = 1; -const ReinhardToneMapping = 2; -const CineonToneMapping = 3; -const ACESFilmicToneMapping = 4; -const CustomToneMapping = 5; -const AgXToneMapping = 6; -const NeutralToneMapping = 7; -const AttachedBindMode = "attached"; -const DetachedBindMode = "detached"; -const UVMapping = 300; -const CubeReflectionMapping = 301; -const CubeRefractionMapping = 302; -const EquirectangularReflectionMapping = 303; -const EquirectangularRefractionMapping = 304; -const CubeUVReflectionMapping = 306; -const RepeatWrapping = 1e3; -const ClampToEdgeWrapping = 1001; -const MirroredRepeatWrapping = 1002; -const NearestFilter = 1003; -const NearestMipmapNearestFilter = 1004; -const NearestMipMapNearestFilter = 1004; -const NearestMipmapLinearFilter = 1005; -const NearestMipMapLinearFilter = 1005; -const LinearFilter = 1006; -const LinearMipmapNearestFilter = 1007; -const LinearMipMapNearestFilter = 1007; -const LinearMipmapLinearFilter = 1008; -const LinearMipMapLinearFilter = 1008; -const UnsignedByteType = 1009; -const ByteType = 1010; -const ShortType = 1011; -const UnsignedShortType = 1012; -const IntType = 1013; -const UnsignedIntType = 1014; -const FloatType = 1015; -const HalfFloatType = 1016; -const UnsignedShort4444Type = 1017; -const UnsignedShort5551Type = 1018; -const UnsignedInt248Type = 1020; -const UnsignedInt5999Type = 35902; -const AlphaFormat = 1021; -const RGBFormat = 1022; -const RGBAFormat = 1023; -const LuminanceFormat = 1024; -const LuminanceAlphaFormat = 1025; -const DepthFormat = 1026; -const DepthStencilFormat = 1027; -const RedFormat = 1028; -const RedIntegerFormat = 1029; -const RGFormat = 1030; -const RGIntegerFormat = 1031; -const RGBIntegerFormat = 1032; -const RGBAIntegerFormat = 1033; -const RGB_S3TC_DXT1_Format = 33776; -const RGBA_S3TC_DXT1_Format = 33777; -const RGBA_S3TC_DXT3_Format = 33778; -const RGBA_S3TC_DXT5_Format = 33779; -const RGB_PVRTC_4BPPV1_Format = 35840; -const RGB_PVRTC_2BPPV1_Format = 35841; -const RGBA_PVRTC_4BPPV1_Format = 35842; -const RGBA_PVRTC_2BPPV1_Format = 35843; -const RGB_ETC1_Format = 36196; -const RGB_ETC2_Format = 37492; -const RGBA_ETC2_EAC_Format = 37496; -const RGBA_ASTC_4x4_Format = 37808; -const RGBA_ASTC_5x4_Format = 37809; -const RGBA_ASTC_5x5_Format = 37810; -const RGBA_ASTC_6x5_Format = 37811; -const RGBA_ASTC_6x6_Format = 37812; -const RGBA_ASTC_8x5_Format = 37813; -const RGBA_ASTC_8x6_Format = 37814; -const RGBA_ASTC_8x8_Format = 37815; -const RGBA_ASTC_10x5_Format = 37816; -const RGBA_ASTC_10x6_Format = 37817; -const RGBA_ASTC_10x8_Format = 37818; -const RGBA_ASTC_10x10_Format = 37819; -const RGBA_ASTC_12x10_Format = 37820; -const RGBA_ASTC_12x12_Format = 37821; -const RGBA_BPTC_Format = 36492; -const RGB_BPTC_SIGNED_Format = 36494; -const RGB_BPTC_UNSIGNED_Format = 36495; -const RED_RGTC1_Format = 36283; -const SIGNED_RED_RGTC1_Format = 36284; -const RED_GREEN_RGTC2_Format = 36285; -const SIGNED_RED_GREEN_RGTC2_Format = 36286; -const LoopOnce = 2200; -const LoopRepeat = 2201; -const LoopPingPong = 2202; -const InterpolateDiscrete = 2300; -const InterpolateLinear = 2301; -const InterpolateSmooth = 2302; -const ZeroCurvatureEnding = 2400; -const ZeroSlopeEnding = 2401; -const WrapAroundEnding = 2402; -const NormalAnimationBlendMode = 2500; -const AdditiveAnimationBlendMode = 2501; -const TrianglesDrawMode = 0; -const TriangleStripDrawMode = 1; -const TriangleFanDrawMode = 2; -const BasicDepthPacking = 3200; -const RGBADepthPacking = 3201; -const RGBDepthPacking = 3202; -const RGDepthPacking = 3203; -const TangentSpaceNormalMap = 0; -const ObjectSpaceNormalMap = 1; -const NoColorSpace = ""; -const SRGBColorSpace = "srgb"; -const LinearSRGBColorSpace = "srgb-linear"; -const LinearTransfer = "linear"; -const SRGBTransfer = "srgb"; -const ZeroStencilOp = 0; -const KeepStencilOp = 7680; -const ReplaceStencilOp = 7681; -const IncrementStencilOp = 7682; -const DecrementStencilOp = 7683; -const IncrementWrapStencilOp = 34055; -const DecrementWrapStencilOp = 34056; -const InvertStencilOp = 5386; -const NeverStencilFunc = 512; -const LessStencilFunc = 513; -const EqualStencilFunc = 514; -const LessEqualStencilFunc = 515; -const GreaterStencilFunc = 516; -const NotEqualStencilFunc = 517; -const GreaterEqualStencilFunc = 518; -const AlwaysStencilFunc = 519; -const NeverCompare = 512; -const LessCompare = 513; -const EqualCompare = 514; -const LessEqualCompare = 515; -const GreaterCompare = 516; -const NotEqualCompare = 517; -const GreaterEqualCompare = 518; -const AlwaysCompare = 519; -const StaticDrawUsage = 35044; -const DynamicDrawUsage = 35048; -const StreamDrawUsage = 35040; -const StaticReadUsage = 35045; -const DynamicReadUsage = 35049; -const StreamReadUsage = 35041; -const StaticCopyUsage = 35046; -const DynamicCopyUsage = 35050; -const StreamCopyUsage = 35042; -const GLSL1 = "100"; -const GLSL3 = "300 es"; -const WebGLCoordinateSystem = 2e3; -const WebGPUCoordinateSystem = 2001; -class EventDispatcher { - static { - __name(this, "EventDispatcher"); - } - addEventListener(type, listener) { - if (this._listeners === void 0) this._listeners = {}; - const listeners = this._listeners; - if (listeners[type] === void 0) { - listeners[type] = []; - } - if (listeners[type].indexOf(listener) === -1) { - listeners[type].push(listener); - } - } - hasEventListener(type, listener) { - if (this._listeners === void 0) return false; - const listeners = this._listeners; - return listeners[type] !== void 0 && listeners[type].indexOf(listener) !== -1; - } - removeEventListener(type, listener) { - if (this._listeners === void 0) return; - const listeners = this._listeners; - const listenerArray = listeners[type]; - if (listenerArray !== void 0) { - const index = listenerArray.indexOf(listener); - if (index !== -1) { - listenerArray.splice(index, 1); - } - } - } - dispatchEvent(event) { - if (this._listeners === void 0) return; - const listeners = this._listeners; - const listenerArray = listeners[event.type]; - if (listenerArray !== void 0) { - event.target = this; - const array = listenerArray.slice(0); - for (let i = 0, l = array.length; i < l; i++) { - array[i].call(this, event); - } - event.target = null; - } - } -} -const _lut = ["00", "01", "02", "03", "04", "05", "06", "07", "08", "09", "0a", "0b", "0c", "0d", "0e", "0f", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "1a", "1b", "1c", "1d", "1e", "1f", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "2a", "2b", "2c", "2d", "2e", "2f", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "3a", "3b", "3c", "3d", "3e", "3f", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "4a", "4b", "4c", "4d", "4e", "4f", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "5a", "5b", "5c", "5d", "5e", "5f", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "6a", "6b", "6c", "6d", "6e", "6f", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "7a", "7b", "7c", "7d", "7e", "7f", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "8a", "8b", "8c", "8d", "8e", "8f", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "9a", "9b", "9c", "9d", "9e", "9f", "a0", "a1", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9", "aa", "ab", "ac", "ad", "ae", "af", "b0", "b1", "b2", "b3", "b4", "b5", "b6", "b7", "b8", "b9", "ba", "bb", "bc", "bd", "be", "bf", "c0", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8", "c9", "ca", "cb", "cc", "cd", "ce", "cf", "d0", "d1", "d2", "d3", "d4", "d5", "d6", "d7", "d8", "d9", "da", "db", "dc", "dd", "de", "df", "e0", "e1", "e2", "e3", "e4", "e5", "e6", "e7", "e8", "e9", "ea", "eb", "ec", "ed", "ee", "ef", "f0", "f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9", "fa", "fb", "fc", "fd", "fe", "ff"]; -let _seed = 1234567; -const DEG2RAD = Math.PI / 180; -const RAD2DEG = 180 / Math.PI; -function generateUUID() { - const d0 = Math.random() * 4294967295 | 0; - const d1 = Math.random() * 4294967295 | 0; - const d2 = Math.random() * 4294967295 | 0; - const d3 = Math.random() * 4294967295 | 0; - const uuid = _lut[d0 & 255] + _lut[d0 >> 8 & 255] + _lut[d0 >> 16 & 255] + _lut[d0 >> 24 & 255] + "-" + _lut[d1 & 255] + _lut[d1 >> 8 & 255] + "-" + _lut[d1 >> 16 & 15 | 64] + _lut[d1 >> 24 & 255] + "-" + _lut[d2 & 63 | 128] + _lut[d2 >> 8 & 255] + "-" + _lut[d2 >> 16 & 255] + _lut[d2 >> 24 & 255] + _lut[d3 & 255] + _lut[d3 >> 8 & 255] + _lut[d3 >> 16 & 255] + _lut[d3 >> 24 & 255]; - return uuid.toLowerCase(); -} -__name(generateUUID, "generateUUID"); -function clamp(value, min, max2) { - return Math.max(min, Math.min(max2, value)); -} -__name(clamp, "clamp"); -function euclideanModulo(n, m) { - return (n % m + m) % m; -} -__name(euclideanModulo, "euclideanModulo"); -function mapLinear(x, a1, a2, b1, b22) { - return b1 + (x - a1) * (b22 - b1) / (a2 - a1); -} -__name(mapLinear, "mapLinear"); -function inverseLerp(x, y, value) { - if (x !== y) { - return (value - x) / (y - x); - } else { - return 0; - } -} -__name(inverseLerp, "inverseLerp"); -function lerp(x, y, t2) { - return (1 - t2) * x + t2 * y; -} -__name(lerp, "lerp"); -function damp(x, y, lambda, dt) { - return lerp(x, y, 1 - Math.exp(-lambda * dt)); -} -__name(damp, "damp"); -function pingpong(x, length = 1) { - return length - Math.abs(euclideanModulo(x, length * 2) - length); -} -__name(pingpong, "pingpong"); -function smoothstep(x, min, max2) { - if (x <= min) return 0; - if (x >= max2) return 1; - x = (x - min) / (max2 - min); - return x * x * (3 - 2 * x); -} -__name(smoothstep, "smoothstep"); -function smootherstep(x, min, max2) { - if (x <= min) return 0; - if (x >= max2) return 1; - x = (x - min) / (max2 - min); - return x * x * x * (x * (x * 6 - 15) + 10); -} -__name(smootherstep, "smootherstep"); -function randInt(low, high) { - return low + Math.floor(Math.random() * (high - low + 1)); -} -__name(randInt, "randInt"); -function randFloat(low, high) { - return low + Math.random() * (high - low); -} -__name(randFloat, "randFloat"); -function randFloatSpread(range) { - return range * (0.5 - Math.random()); -} -__name(randFloatSpread, "randFloatSpread"); -function seededRandom(s) { - if (s !== void 0) _seed = s; - let t2 = _seed += 1831565813; - t2 = Math.imul(t2 ^ t2 >>> 15, t2 | 1); - t2 ^= t2 + Math.imul(t2 ^ t2 >>> 7, t2 | 61); - return ((t2 ^ t2 >>> 14) >>> 0) / 4294967296; -} -__name(seededRandom, "seededRandom"); -function degToRad(degrees) { - return degrees * DEG2RAD; -} -__name(degToRad, "degToRad"); -function radToDeg(radians) { - return radians * RAD2DEG; -} -__name(radToDeg, "radToDeg"); -function isPowerOfTwo(value) { - return (value & value - 1) === 0 && value !== 0; -} -__name(isPowerOfTwo, "isPowerOfTwo"); -function ceilPowerOfTwo(value) { - return Math.pow(2, Math.ceil(Math.log(value) / Math.LN2)); -} -__name(ceilPowerOfTwo, "ceilPowerOfTwo"); -function floorPowerOfTwo(value) { - return Math.pow(2, Math.floor(Math.log(value) / Math.LN2)); -} -__name(floorPowerOfTwo, "floorPowerOfTwo"); -function setQuaternionFromProperEuler(q, a, b, c, order) { - const cos = Math.cos; - const sin = Math.sin; - const c2 = cos(b / 2); - const s2 = sin(b / 2); - const c13 = cos((a + c) / 2); - const s13 = sin((a + c) / 2); - const c1_3 = cos((a - c) / 2); - const s1_3 = sin((a - c) / 2); - const c3_1 = cos((c - a) / 2); - const s3_1 = sin((c - a) / 2); - switch (order) { - case "XYX": - q.set(c2 * s13, s2 * c1_3, s2 * s1_3, c2 * c13); - break; - case "YZY": - q.set(s2 * s1_3, c2 * s13, s2 * c1_3, c2 * c13); - break; - case "ZXZ": - q.set(s2 * c1_3, s2 * s1_3, c2 * s13, c2 * c13); - break; - case "XZX": - q.set(c2 * s13, s2 * s3_1, s2 * c3_1, c2 * c13); - break; - case "YXY": - q.set(s2 * c3_1, c2 * s13, s2 * s3_1, c2 * c13); - break; - case "ZYZ": - q.set(s2 * s3_1, s2 * c3_1, c2 * s13, c2 * c13); - break; - default: - console.warn("THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: " + order); - } -} -__name(setQuaternionFromProperEuler, "setQuaternionFromProperEuler"); -function denormalize(value, array) { - switch (array.constructor) { - case Float32Array: - return value; - case Uint32Array: - return value / 4294967295; - case Uint16Array: - return value / 65535; - case Uint8Array: - return value / 255; - case Int32Array: - return Math.max(value / 2147483647, -1); - case Int16Array: - return Math.max(value / 32767, -1); - case Int8Array: - return Math.max(value / 127, -1); - default: - throw new Error("Invalid component type."); - } -} -__name(denormalize, "denormalize"); -function normalize(value, array) { - switch (array.constructor) { - case Float32Array: - return value; - case Uint32Array: - return Math.round(value * 4294967295); - case Uint16Array: - return Math.round(value * 65535); - case Uint8Array: - return Math.round(value * 255); - case Int32Array: - return Math.round(value * 2147483647); - case Int16Array: - return Math.round(value * 32767); - case Int8Array: - return Math.round(value * 127); - default: - throw new Error("Invalid component type."); - } -} -__name(normalize, "normalize"); -const MathUtils = { - DEG2RAD, - RAD2DEG, - generateUUID, - clamp, - euclideanModulo, - mapLinear, - inverseLerp, - lerp, - damp, - pingpong, - smoothstep, - smootherstep, - randInt, - randFloat, - randFloatSpread, - seededRandom, - degToRad, - radToDeg, - isPowerOfTwo, - ceilPowerOfTwo, - floorPowerOfTwo, - setQuaternionFromProperEuler, - normalize, - denormalize -}; -class Vector2 { - static { - __name(this, "Vector2"); - } - constructor(x = 0, y = 0) { - Vector2.prototype.isVector2 = true; - this.x = x; - this.y = y; - } - get width() { - return this.x; - } - set width(value) { - this.x = value; - } - get height() { - return this.y; - } - set height(value) { - this.y = value; - } - set(x, y) { - this.x = x; - this.y = y; - return this; - } - setScalar(scalar) { - this.x = scalar; - this.y = scalar; - return this; - } - setX(x) { - this.x = x; - return this; - } - setY(y) { - this.y = y; - return this; - } - setComponent(index, value) { - switch (index) { - case 0: - this.x = value; - break; - case 1: - this.y = value; - break; - default: - throw new Error("index is out of range: " + index); - } - return this; - } - getComponent(index) { - switch (index) { - case 0: - return this.x; - case 1: - return this.y; - default: - throw new Error("index is out of range: " + index); - } - } - clone() { - return new this.constructor(this.x, this.y); - } - copy(v) { - this.x = v.x; - this.y = v.y; - return this; - } - add(v) { - this.x += v.x; - this.y += v.y; - return this; - } - addScalar(s) { - this.x += s; - this.y += s; - return this; - } - addVectors(a, b) { - this.x = a.x + b.x; - this.y = a.y + b.y; - return this; - } - addScaledVector(v, s) { - this.x += v.x * s; - this.y += v.y * s; - return this; - } - sub(v) { - this.x -= v.x; - this.y -= v.y; - return this; - } - subScalar(s) { - this.x -= s; - this.y -= s; - return this; - } - subVectors(a, b) { - this.x = a.x - b.x; - this.y = a.y - b.y; - return this; - } - multiply(v) { - this.x *= v.x; - this.y *= v.y; - return this; - } - multiplyScalar(scalar) { - this.x *= scalar; - this.y *= scalar; - return this; - } - divide(v) { - this.x /= v.x; - this.y /= v.y; - return this; - } - divideScalar(scalar) { - return this.multiplyScalar(1 / scalar); - } - applyMatrix3(m) { - const x = this.x, y = this.y; - const e = m.elements; - this.x = e[0] * x + e[3] * y + e[6]; - this.y = e[1] * x + e[4] * y + e[7]; - return this; - } - min(v) { - this.x = Math.min(this.x, v.x); - this.y = Math.min(this.y, v.y); - return this; - } - max(v) { - this.x = Math.max(this.x, v.x); - this.y = Math.max(this.y, v.y); - return this; - } - clamp(min, max2) { - this.x = Math.max(min.x, Math.min(max2.x, this.x)); - this.y = Math.max(min.y, Math.min(max2.y, this.y)); - return this; - } - clampScalar(minVal, maxVal) { - this.x = Math.max(minVal, Math.min(maxVal, this.x)); - this.y = Math.max(minVal, Math.min(maxVal, this.y)); - return this; - } - clampLength(min, max2) { - const length = this.length(); - return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max2, length))); - } - floor() { - this.x = Math.floor(this.x); - this.y = Math.floor(this.y); - return this; - } - ceil() { - this.x = Math.ceil(this.x); - this.y = Math.ceil(this.y); - return this; - } - round() { - this.x = Math.round(this.x); - this.y = Math.round(this.y); - return this; - } - roundToZero() { - this.x = Math.trunc(this.x); - this.y = Math.trunc(this.y); - return this; - } - negate() { - this.x = -this.x; - this.y = -this.y; - return this; - } - dot(v) { - return this.x * v.x + this.y * v.y; - } - cross(v) { - return this.x * v.y - this.y * v.x; - } - lengthSq() { - return this.x * this.x + this.y * this.y; - } - length() { - return Math.sqrt(this.x * this.x + this.y * this.y); - } - manhattanLength() { - return Math.abs(this.x) + Math.abs(this.y); - } - normalize() { - return this.divideScalar(this.length() || 1); - } - angle() { - const angle = Math.atan2(-this.y, -this.x) + Math.PI; - return angle; - } - angleTo(v) { - const denominator = Math.sqrt(this.lengthSq() * v.lengthSq()); - if (denominator === 0) return Math.PI / 2; - const theta = this.dot(v) / denominator; - return Math.acos(clamp(theta, -1, 1)); - } - distanceTo(v) { - return Math.sqrt(this.distanceToSquared(v)); - } - distanceToSquared(v) { - const dx = this.x - v.x, dy = this.y - v.y; - return dx * dx + dy * dy; - } - manhattanDistanceTo(v) { - return Math.abs(this.x - v.x) + Math.abs(this.y - v.y); - } - setLength(length) { - return this.normalize().multiplyScalar(length); - } - lerp(v, alpha) { - this.x += (v.x - this.x) * alpha; - this.y += (v.y - this.y) * alpha; - return this; - } - lerpVectors(v1, v2, alpha) { - this.x = v1.x + (v2.x - v1.x) * alpha; - this.y = v1.y + (v2.y - v1.y) * alpha; - return this; - } - equals(v) { - return v.x === this.x && v.y === this.y; - } - fromArray(array, offset = 0) { - this.x = array[offset]; - this.y = array[offset + 1]; - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this.x; - array[offset + 1] = this.y; - return array; - } - fromBufferAttribute(attribute, index) { - this.x = attribute.getX(index); - this.y = attribute.getY(index); - return this; - } - rotateAround(center, angle) { - const c = Math.cos(angle), s = Math.sin(angle); - const x = this.x - center.x; - const y = this.y - center.y; - this.x = x * c - y * s + center.x; - this.y = x * s + y * c + center.y; - return this; - } - random() { - this.x = Math.random(); - this.y = Math.random(); - return this; - } - *[Symbol.iterator]() { - yield this.x; - yield this.y; - } -} -class Matrix3 { - static { - __name(this, "Matrix3"); - } - constructor(n11, n12, n13, n21, n22, n23, n31, n32, n33) { - Matrix3.prototype.isMatrix3 = true; - this.elements = [ - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1 - ]; - if (n11 !== void 0) { - this.set(n11, n12, n13, n21, n22, n23, n31, n32, n33); - } - } - set(n11, n12, n13, n21, n22, n23, n31, n32, n33) { - const te2 = this.elements; - te2[0] = n11; - te2[1] = n21; - te2[2] = n31; - te2[3] = n12; - te2[4] = n22; - te2[5] = n32; - te2[6] = n13; - te2[7] = n23; - te2[8] = n33; - return this; - } - identity() { - this.set( - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1 - ); - return this; - } - copy(m) { - const te2 = this.elements; - const me = m.elements; - te2[0] = me[0]; - te2[1] = me[1]; - te2[2] = me[2]; - te2[3] = me[3]; - te2[4] = me[4]; - te2[5] = me[5]; - te2[6] = me[6]; - te2[7] = me[7]; - te2[8] = me[8]; - return this; - } - extractBasis(xAxis, yAxis, zAxis) { - xAxis.setFromMatrix3Column(this, 0); - yAxis.setFromMatrix3Column(this, 1); - zAxis.setFromMatrix3Column(this, 2); - return this; - } - setFromMatrix4(m) { - const me = m.elements; - this.set( - me[0], - me[4], - me[8], - me[1], - me[5], - me[9], - me[2], - me[6], - me[10] - ); - return this; - } - multiply(m) { - return this.multiplyMatrices(this, m); - } - premultiply(m) { - return this.multiplyMatrices(m, this); - } - multiplyMatrices(a, b) { - const ae = a.elements; - const be = b.elements; - const te2 = this.elements; - const a11 = ae[0], a12 = ae[3], a13 = ae[6]; - const a21 = ae[1], a22 = ae[4], a23 = ae[7]; - const a31 = ae[2], a32 = ae[5], a33 = ae[8]; - const b11 = be[0], b12 = be[3], b13 = be[6]; - const b21 = be[1], b22 = be[4], b23 = be[7]; - const b31 = be[2], b32 = be[5], b33 = be[8]; - te2[0] = a11 * b11 + a12 * b21 + a13 * b31; - te2[3] = a11 * b12 + a12 * b22 + a13 * b32; - te2[6] = a11 * b13 + a12 * b23 + a13 * b33; - te2[1] = a21 * b11 + a22 * b21 + a23 * b31; - te2[4] = a21 * b12 + a22 * b22 + a23 * b32; - te2[7] = a21 * b13 + a22 * b23 + a23 * b33; - te2[2] = a31 * b11 + a32 * b21 + a33 * b31; - te2[5] = a31 * b12 + a32 * b22 + a33 * b32; - te2[8] = a31 * b13 + a32 * b23 + a33 * b33; - return this; - } - multiplyScalar(s) { - const te2 = this.elements; - te2[0] *= s; - te2[3] *= s; - te2[6] *= s; - te2[1] *= s; - te2[4] *= s; - te2[7] *= s; - te2[2] *= s; - te2[5] *= s; - te2[8] *= s; - return this; - } - determinant() { - const te2 = this.elements; - const a = te2[0], b = te2[1], c = te2[2], d = te2[3], e = te2[4], f = te2[5], g = te2[6], h = te2[7], i = te2[8]; - return a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g; - } - invert() { - const te2 = this.elements, n11 = te2[0], n21 = te2[1], n31 = te2[2], n12 = te2[3], n22 = te2[4], n32 = te2[5], n13 = te2[6], n23 = te2[7], n33 = te2[8], t11 = n33 * n22 - n32 * n23, t12 = n32 * n13 - n33 * n12, t13 = n23 * n12 - n22 * n13, det = n11 * t11 + n21 * t12 + n31 * t13; - if (det === 0) return this.set(0, 0, 0, 0, 0, 0, 0, 0, 0); - const detInv = 1 / det; - te2[0] = t11 * detInv; - te2[1] = (n31 * n23 - n33 * n21) * detInv; - te2[2] = (n32 * n21 - n31 * n22) * detInv; - te2[3] = t12 * detInv; - te2[4] = (n33 * n11 - n31 * n13) * detInv; - te2[5] = (n31 * n12 - n32 * n11) * detInv; - te2[6] = t13 * detInv; - te2[7] = (n21 * n13 - n23 * n11) * detInv; - te2[8] = (n22 * n11 - n21 * n12) * detInv; - return this; - } - transpose() { - let tmp2; - const m = this.elements; - tmp2 = m[1]; - m[1] = m[3]; - m[3] = tmp2; - tmp2 = m[2]; - m[2] = m[6]; - m[6] = tmp2; - tmp2 = m[5]; - m[5] = m[7]; - m[7] = tmp2; - return this; - } - getNormalMatrix(matrix4) { - return this.setFromMatrix4(matrix4).invert().transpose(); - } - transposeIntoArray(r) { - const m = this.elements; - r[0] = m[0]; - r[1] = m[3]; - r[2] = m[6]; - r[3] = m[1]; - r[4] = m[4]; - r[5] = m[7]; - r[6] = m[2]; - r[7] = m[5]; - r[8] = m[8]; - return this; - } - setUvTransform(tx, ty, sx, sy, rotation, cx, cy) { - const c = Math.cos(rotation); - const s = Math.sin(rotation); - this.set( - sx * c, - sx * s, - -sx * (c * cx + s * cy) + cx + tx, - -sy * s, - sy * c, - -sy * (-s * cx + c * cy) + cy + ty, - 0, - 0, - 1 - ); - return this; - } - // - scale(sx, sy) { - this.premultiply(_m3.makeScale(sx, sy)); - return this; - } - rotate(theta) { - this.premultiply(_m3.makeRotation(-theta)); - return this; - } - translate(tx, ty) { - this.premultiply(_m3.makeTranslation(tx, ty)); - return this; - } - // for 2D Transforms - makeTranslation(x, y) { - if (x.isVector2) { - this.set( - 1, - 0, - x.x, - 0, - 1, - x.y, - 0, - 0, - 1 - ); - } else { - this.set( - 1, - 0, - x, - 0, - 1, - y, - 0, - 0, - 1 - ); - } - return this; - } - makeRotation(theta) { - const c = Math.cos(theta); - const s = Math.sin(theta); - this.set( - c, - -s, - 0, - s, - c, - 0, - 0, - 0, - 1 - ); - return this; - } - makeScale(x, y) { - this.set( - x, - 0, - 0, - 0, - y, - 0, - 0, - 0, - 1 - ); - return this; - } - // - equals(matrix) { - const te2 = this.elements; - const me = matrix.elements; - for (let i = 0; i < 9; i++) { - if (te2[i] !== me[i]) return false; - } - return true; - } - fromArray(array, offset = 0) { - for (let i = 0; i < 9; i++) { - this.elements[i] = array[i + offset]; - } - return this; - } - toArray(array = [], offset = 0) { - const te2 = this.elements; - array[offset] = te2[0]; - array[offset + 1] = te2[1]; - array[offset + 2] = te2[2]; - array[offset + 3] = te2[3]; - array[offset + 4] = te2[4]; - array[offset + 5] = te2[5]; - array[offset + 6] = te2[6]; - array[offset + 7] = te2[7]; - array[offset + 8] = te2[8]; - return array; - } - clone() { - return new this.constructor().fromArray(this.elements); - } -} -const _m3 = /* @__PURE__ */ new Matrix3(); -function arrayNeedsUint32(array) { - for (let i = array.length - 1; i >= 0; --i) { - if (array[i] >= 65535) return true; - } - return false; -} -__name(arrayNeedsUint32, "arrayNeedsUint32"); -const TYPED_ARRAYS = { - Int8Array, - Uint8Array, - Uint8ClampedArray, - Int16Array, - Uint16Array, - Int32Array, - Uint32Array, - Float32Array, - Float64Array -}; -function getTypedArray(type, buffer) { - return new TYPED_ARRAYS[type](buffer); -} -__name(getTypedArray, "getTypedArray"); -function createElementNS(name) { - return document.createElementNS("http://www.w3.org/1999/xhtml", name); -} -__name(createElementNS, "createElementNS"); -function createCanvasElement() { - const canvas = createElementNS("canvas"); - canvas.style.display = "block"; - return canvas; -} -__name(createCanvasElement, "createCanvasElement"); -const _cache = {}; -function warnOnce(message) { - if (message in _cache) return; - _cache[message] = true; - console.warn(message); -} -__name(warnOnce, "warnOnce"); -function probeAsync(gl, sync, interval) { - return new Promise(function(resolve, reject) { - function probe() { - switch (gl.clientWaitSync(sync, gl.SYNC_FLUSH_COMMANDS_BIT, 0)) { - case gl.WAIT_FAILED: - reject(); - break; - case gl.TIMEOUT_EXPIRED: - setTimeout(probe, interval); - break; - default: - resolve(); - } - } - __name(probe, "probe"); - setTimeout(probe, interval); - }); -} -__name(probeAsync, "probeAsync"); -function toNormalizedProjectionMatrix(projectionMatrix) { - const m = projectionMatrix.elements; - m[2] = 0.5 * m[2] + 0.5 * m[3]; - m[6] = 0.5 * m[6] + 0.5 * m[7]; - m[10] = 0.5 * m[10] + 0.5 * m[11]; - m[14] = 0.5 * m[14] + 0.5 * m[15]; -} -__name(toNormalizedProjectionMatrix, "toNormalizedProjectionMatrix"); -function toReversedProjectionMatrix(projectionMatrix) { - const m = projectionMatrix.elements; - const isPerspectiveMatrix = m[11] === -1; - if (isPerspectiveMatrix) { - m[10] = -m[10] - 1; - m[14] = -m[14]; - } else { - m[10] = -m[10]; - m[14] = -m[14] + 1; - } -} -__name(toReversedProjectionMatrix, "toReversedProjectionMatrix"); -const ColorManagement = { - enabled: true, - workingColorSpace: LinearSRGBColorSpace, - /** - * Implementations of supported color spaces. - * - * Required: - * - primaries: chromaticity coordinates [ rx ry gx gy bx by ] - * - whitePoint: reference white [ x y ] - * - transfer: transfer function (pre-defined) - * - toXYZ: Matrix3 RGB to XYZ transform - * - fromXYZ: Matrix3 XYZ to RGB transform - * - luminanceCoefficients: RGB luminance coefficients - * - * Optional: - * - outputColorSpaceConfig: { drawingBufferColorSpace: ColorSpace } - * - workingColorSpaceConfig: { unpackColorSpace: ColorSpace } - * - * Reference: - * - https://www.russellcottrell.com/photo/matrixCalculator.htm - */ - spaces: {}, - convert: /* @__PURE__ */ __name(function(color, sourceColorSpace, targetColorSpace) { - if (this.enabled === false || sourceColorSpace === targetColorSpace || !sourceColorSpace || !targetColorSpace) { - return color; - } - if (this.spaces[sourceColorSpace].transfer === SRGBTransfer) { - color.r = SRGBToLinear(color.r); - color.g = SRGBToLinear(color.g); - color.b = SRGBToLinear(color.b); - } - if (this.spaces[sourceColorSpace].primaries !== this.spaces[targetColorSpace].primaries) { - color.applyMatrix3(this.spaces[sourceColorSpace].toXYZ); - color.applyMatrix3(this.spaces[targetColorSpace].fromXYZ); - } - if (this.spaces[targetColorSpace].transfer === SRGBTransfer) { - color.r = LinearToSRGB(color.r); - color.g = LinearToSRGB(color.g); - color.b = LinearToSRGB(color.b); - } - return color; - }, "convert"), - fromWorkingColorSpace: /* @__PURE__ */ __name(function(color, targetColorSpace) { - return this.convert(color, this.workingColorSpace, targetColorSpace); - }, "fromWorkingColorSpace"), - toWorkingColorSpace: /* @__PURE__ */ __name(function(color, sourceColorSpace) { - return this.convert(color, sourceColorSpace, this.workingColorSpace); - }, "toWorkingColorSpace"), - getPrimaries: /* @__PURE__ */ __name(function(colorSpace) { - return this.spaces[colorSpace].primaries; - }, "getPrimaries"), - getTransfer: /* @__PURE__ */ __name(function(colorSpace) { - if (colorSpace === NoColorSpace) return LinearTransfer; - return this.spaces[colorSpace].transfer; - }, "getTransfer"), - getLuminanceCoefficients: /* @__PURE__ */ __name(function(target, colorSpace = this.workingColorSpace) { - return target.fromArray(this.spaces[colorSpace].luminanceCoefficients); - }, "getLuminanceCoefficients"), - define: /* @__PURE__ */ __name(function(colorSpaces) { - Object.assign(this.spaces, colorSpaces); - }, "define"), - // Internal APIs - _getMatrix: /* @__PURE__ */ __name(function(targetMatrix, sourceColorSpace, targetColorSpace) { - return targetMatrix.copy(this.spaces[sourceColorSpace].toXYZ).multiply(this.spaces[targetColorSpace].fromXYZ); - }, "_getMatrix"), - _getDrawingBufferColorSpace: /* @__PURE__ */ __name(function(colorSpace) { - return this.spaces[colorSpace].outputColorSpaceConfig.drawingBufferColorSpace; - }, "_getDrawingBufferColorSpace"), - _getUnpackColorSpace: /* @__PURE__ */ __name(function(colorSpace = this.workingColorSpace) { - return this.spaces[colorSpace].workingColorSpaceConfig.unpackColorSpace; - }, "_getUnpackColorSpace") -}; -function SRGBToLinear(c) { - return c < 0.04045 ? c * 0.0773993808 : Math.pow(c * 0.9478672986 + 0.0521327014, 2.4); -} -__name(SRGBToLinear, "SRGBToLinear"); -function LinearToSRGB(c) { - return c < 31308e-7 ? c * 12.92 : 1.055 * Math.pow(c, 0.41666) - 0.055; -} -__name(LinearToSRGB, "LinearToSRGB"); -const REC709_PRIMARIES = [0.64, 0.33, 0.3, 0.6, 0.15, 0.06]; -const REC709_LUMINANCE_COEFFICIENTS = [0.2126, 0.7152, 0.0722]; -const D65 = [0.3127, 0.329]; -const LINEAR_REC709_TO_XYZ = /* @__PURE__ */ new Matrix3().set( - 0.4123908, - 0.3575843, - 0.1804808, - 0.212639, - 0.7151687, - 0.0721923, - 0.0193308, - 0.1191948, - 0.9505322 -); -const XYZ_TO_LINEAR_REC709 = /* @__PURE__ */ new Matrix3().set( - 3.2409699, - -1.5373832, - -0.4986108, - -0.9692436, - 1.8759675, - 0.0415551, - 0.0556301, - -0.203977, - 1.0569715 -); -ColorManagement.define({ - [LinearSRGBColorSpace]: { - primaries: REC709_PRIMARIES, - whitePoint: D65, - transfer: LinearTransfer, - toXYZ: LINEAR_REC709_TO_XYZ, - fromXYZ: XYZ_TO_LINEAR_REC709, - luminanceCoefficients: REC709_LUMINANCE_COEFFICIENTS, - workingColorSpaceConfig: { unpackColorSpace: SRGBColorSpace }, - outputColorSpaceConfig: { drawingBufferColorSpace: SRGBColorSpace } - }, - [SRGBColorSpace]: { - primaries: REC709_PRIMARIES, - whitePoint: D65, - transfer: SRGBTransfer, - toXYZ: LINEAR_REC709_TO_XYZ, - fromXYZ: XYZ_TO_LINEAR_REC709, - luminanceCoefficients: REC709_LUMINANCE_COEFFICIENTS, - outputColorSpaceConfig: { drawingBufferColorSpace: SRGBColorSpace } - } -}); -let _canvas; -class ImageUtils { - static { - __name(this, "ImageUtils"); - } - static getDataURL(image) { - if (/^data:/i.test(image.src)) { - return image.src; - } - if (typeof HTMLCanvasElement === "undefined") { - return image.src; - } - let canvas; - if (image instanceof HTMLCanvasElement) { - canvas = image; - } else { - if (_canvas === void 0) _canvas = createElementNS("canvas"); - _canvas.width = image.width; - _canvas.height = image.height; - const context = _canvas.getContext("2d"); - if (image instanceof ImageData) { - context.putImageData(image, 0, 0); - } else { - context.drawImage(image, 0, 0, image.width, image.height); - } - canvas = _canvas; - } - if (canvas.width > 2048 || canvas.height > 2048) { - console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons", image); - return canvas.toDataURL("image/jpeg", 0.6); - } else { - return canvas.toDataURL("image/png"); - } - } - static sRGBToLinear(image) { - if (typeof HTMLImageElement !== "undefined" && image instanceof HTMLImageElement || typeof HTMLCanvasElement !== "undefined" && image instanceof HTMLCanvasElement || typeof ImageBitmap !== "undefined" && image instanceof ImageBitmap) { - const canvas = createElementNS("canvas"); - canvas.width = image.width; - canvas.height = image.height; - const context = canvas.getContext("2d"); - context.drawImage(image, 0, 0, image.width, image.height); - const imageData = context.getImageData(0, 0, image.width, image.height); - const data = imageData.data; - for (let i = 0; i < data.length; i++) { - data[i] = SRGBToLinear(data[i] / 255) * 255; - } - context.putImageData(imageData, 0, 0); - return canvas; - } else if (image.data) { - const data = image.data.slice(0); - for (let i = 0; i < data.length; i++) { - if (data instanceof Uint8Array || data instanceof Uint8ClampedArray) { - data[i] = Math.floor(SRGBToLinear(data[i] / 255) * 255); - } else { - data[i] = SRGBToLinear(data[i]); - } - } - return { - data, - width: image.width, - height: image.height - }; - } else { - console.warn("THREE.ImageUtils.sRGBToLinear(): Unsupported image type. No color space conversion applied."); - return image; - } - } -} -let _sourceId = 0; -class Source { - static { - __name(this, "Source"); - } - constructor(data = null) { - this.isSource = true; - Object.defineProperty(this, "id", { value: _sourceId++ }); - this.uuid = generateUUID(); - this.data = data; - this.dataReady = true; - this.version = 0; - } - set needsUpdate(value) { - if (value === true) this.version++; - } - toJSON(meta) { - const isRootObject = meta === void 0 || typeof meta === "string"; - if (!isRootObject && meta.images[this.uuid] !== void 0) { - return meta.images[this.uuid]; - } - const output = { - uuid: this.uuid, - url: "" - }; - const data = this.data; - if (data !== null) { - let url; - if (Array.isArray(data)) { - url = []; - for (let i = 0, l = data.length; i < l; i++) { - if (data[i].isDataTexture) { - url.push(serializeImage(data[i].image)); - } else { - url.push(serializeImage(data[i])); - } - } - } else { - url = serializeImage(data); - } - output.url = url; - } - if (!isRootObject) { - meta.images[this.uuid] = output; - } - return output; - } -} -function serializeImage(image) { - if (typeof HTMLImageElement !== "undefined" && image instanceof HTMLImageElement || typeof HTMLCanvasElement !== "undefined" && image instanceof HTMLCanvasElement || typeof ImageBitmap !== "undefined" && image instanceof ImageBitmap) { - return ImageUtils.getDataURL(image); - } else { - if (image.data) { - return { - data: Array.from(image.data), - width: image.width, - height: image.height, - type: image.data.constructor.name - }; - } else { - console.warn("THREE.Texture: Unable to serialize Texture."); - return {}; - } - } -} -__name(serializeImage, "serializeImage"); -let _textureId = 0; -class Texture extends EventDispatcher { - static { - __name(this, "Texture"); - } - constructor(image = Texture.DEFAULT_IMAGE, mapping = Texture.DEFAULT_MAPPING, wrapS = ClampToEdgeWrapping, wrapT = ClampToEdgeWrapping, magFilter = LinearFilter, minFilter = LinearMipmapLinearFilter, format = RGBAFormat, type = UnsignedByteType, anisotropy = Texture.DEFAULT_ANISOTROPY, colorSpace = NoColorSpace) { - super(); - this.isTexture = true; - Object.defineProperty(this, "id", { value: _textureId++ }); - this.uuid = generateUUID(); - this.name = ""; - this.source = new Source(image); - this.mipmaps = []; - this.mapping = mapping; - this.channel = 0; - this.wrapS = wrapS; - this.wrapT = wrapT; - this.magFilter = magFilter; - this.minFilter = minFilter; - this.anisotropy = anisotropy; - this.format = format; - this.internalFormat = null; - this.type = type; - this.offset = new Vector2(0, 0); - this.repeat = new Vector2(1, 1); - this.center = new Vector2(0, 0); - this.rotation = 0; - this.matrixAutoUpdate = true; - this.matrix = new Matrix3(); - this.generateMipmaps = true; - this.premultiplyAlpha = false; - this.flipY = true; - this.unpackAlignment = 4; - this.colorSpace = colorSpace; - this.userData = {}; - this.version = 0; - this.onUpdate = null; - this.isRenderTargetTexture = false; - this.pmremVersion = 0; - } - get image() { - return this.source.data; - } - set image(value = null) { - this.source.data = value; - } - updateMatrix() { - this.matrix.setUvTransform(this.offset.x, this.offset.y, this.repeat.x, this.repeat.y, this.rotation, this.center.x, this.center.y); - } - clone() { - return new this.constructor().copy(this); - } - copy(source) { - this.name = source.name; - this.source = source.source; - this.mipmaps = source.mipmaps.slice(0); - this.mapping = source.mapping; - this.channel = source.channel; - this.wrapS = source.wrapS; - this.wrapT = source.wrapT; - this.magFilter = source.magFilter; - this.minFilter = source.minFilter; - this.anisotropy = source.anisotropy; - this.format = source.format; - this.internalFormat = source.internalFormat; - this.type = source.type; - this.offset.copy(source.offset); - this.repeat.copy(source.repeat); - this.center.copy(source.center); - this.rotation = source.rotation; - this.matrixAutoUpdate = source.matrixAutoUpdate; - this.matrix.copy(source.matrix); - this.generateMipmaps = source.generateMipmaps; - this.premultiplyAlpha = source.premultiplyAlpha; - this.flipY = source.flipY; - this.unpackAlignment = source.unpackAlignment; - this.colorSpace = source.colorSpace; - this.userData = JSON.parse(JSON.stringify(source.userData)); - this.needsUpdate = true; - return this; - } - toJSON(meta) { - const isRootObject = meta === void 0 || typeof meta === "string"; - if (!isRootObject && meta.textures[this.uuid] !== void 0) { - return meta.textures[this.uuid]; - } - const output = { - metadata: { - version: 4.6, - type: "Texture", - generator: "Texture.toJSON" - }, - uuid: this.uuid, - name: this.name, - image: this.source.toJSON(meta).uuid, - mapping: this.mapping, - channel: this.channel, - repeat: [this.repeat.x, this.repeat.y], - offset: [this.offset.x, this.offset.y], - center: [this.center.x, this.center.y], - rotation: this.rotation, - wrap: [this.wrapS, this.wrapT], - format: this.format, - internalFormat: this.internalFormat, - type: this.type, - colorSpace: this.colorSpace, - minFilter: this.minFilter, - magFilter: this.magFilter, - anisotropy: this.anisotropy, - flipY: this.flipY, - generateMipmaps: this.generateMipmaps, - premultiplyAlpha: this.premultiplyAlpha, - unpackAlignment: this.unpackAlignment - }; - if (Object.keys(this.userData).length > 0) output.userData = this.userData; - if (!isRootObject) { - meta.textures[this.uuid] = output; - } - return output; - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - } - transformUv(uv) { - if (this.mapping !== UVMapping) return uv; - uv.applyMatrix3(this.matrix); - if (uv.x < 0 || uv.x > 1) { - switch (this.wrapS) { - case RepeatWrapping: - uv.x = uv.x - Math.floor(uv.x); - break; - case ClampToEdgeWrapping: - uv.x = uv.x < 0 ? 0 : 1; - break; - case MirroredRepeatWrapping: - if (Math.abs(Math.floor(uv.x) % 2) === 1) { - uv.x = Math.ceil(uv.x) - uv.x; - } else { - uv.x = uv.x - Math.floor(uv.x); - } - break; - } - } - if (uv.y < 0 || uv.y > 1) { - switch (this.wrapT) { - case RepeatWrapping: - uv.y = uv.y - Math.floor(uv.y); - break; - case ClampToEdgeWrapping: - uv.y = uv.y < 0 ? 0 : 1; - break; - case MirroredRepeatWrapping: - if (Math.abs(Math.floor(uv.y) % 2) === 1) { - uv.y = Math.ceil(uv.y) - uv.y; - } else { - uv.y = uv.y - Math.floor(uv.y); - } - break; - } - } - if (this.flipY) { - uv.y = 1 - uv.y; - } - return uv; - } - set needsUpdate(value) { - if (value === true) { - this.version++; - this.source.needsUpdate = true; - } - } - set needsPMREMUpdate(value) { - if (value === true) { - this.pmremVersion++; - } - } -} -Texture.DEFAULT_IMAGE = null; -Texture.DEFAULT_MAPPING = UVMapping; -Texture.DEFAULT_ANISOTROPY = 1; -class Vector4 { - static { - __name(this, "Vector4"); - } - constructor(x = 0, y = 0, z = 0, w = 1) { - Vector4.prototype.isVector4 = true; - this.x = x; - this.y = y; - this.z = z; - this.w = w; - } - get width() { - return this.z; - } - set width(value) { - this.z = value; - } - get height() { - return this.w; - } - set height(value) { - this.w = value; - } - set(x, y, z, w) { - this.x = x; - this.y = y; - this.z = z; - this.w = w; - return this; - } - setScalar(scalar) { - this.x = scalar; - this.y = scalar; - this.z = scalar; - this.w = scalar; - return this; - } - setX(x) { - this.x = x; - return this; - } - setY(y) { - this.y = y; - return this; - } - setZ(z) { - this.z = z; - return this; - } - setW(w) { - this.w = w; - return this; - } - setComponent(index, value) { - switch (index) { - case 0: - this.x = value; - break; - case 1: - this.y = value; - break; - case 2: - this.z = value; - break; - case 3: - this.w = value; - break; - default: - throw new Error("index is out of range: " + index); - } - return this; - } - getComponent(index) { - switch (index) { - case 0: - return this.x; - case 1: - return this.y; - case 2: - return this.z; - case 3: - return this.w; - default: - throw new Error("index is out of range: " + index); - } - } - clone() { - return new this.constructor(this.x, this.y, this.z, this.w); - } - copy(v) { - this.x = v.x; - this.y = v.y; - this.z = v.z; - this.w = v.w !== void 0 ? v.w : 1; - return this; - } - add(v) { - this.x += v.x; - this.y += v.y; - this.z += v.z; - this.w += v.w; - return this; - } - addScalar(s) { - this.x += s; - this.y += s; - this.z += s; - this.w += s; - return this; - } - addVectors(a, b) { - this.x = a.x + b.x; - this.y = a.y + b.y; - this.z = a.z + b.z; - this.w = a.w + b.w; - return this; - } - addScaledVector(v, s) { - this.x += v.x * s; - this.y += v.y * s; - this.z += v.z * s; - this.w += v.w * s; - return this; - } - sub(v) { - this.x -= v.x; - this.y -= v.y; - this.z -= v.z; - this.w -= v.w; - return this; - } - subScalar(s) { - this.x -= s; - this.y -= s; - this.z -= s; - this.w -= s; - return this; - } - subVectors(a, b) { - this.x = a.x - b.x; - this.y = a.y - b.y; - this.z = a.z - b.z; - this.w = a.w - b.w; - return this; - } - multiply(v) { - this.x *= v.x; - this.y *= v.y; - this.z *= v.z; - this.w *= v.w; - return this; - } - multiplyScalar(scalar) { - this.x *= scalar; - this.y *= scalar; - this.z *= scalar; - this.w *= scalar; - return this; - } - applyMatrix4(m) { - const x = this.x, y = this.y, z = this.z, w = this.w; - const e = m.elements; - this.x = e[0] * x + e[4] * y + e[8] * z + e[12] * w; - this.y = e[1] * x + e[5] * y + e[9] * z + e[13] * w; - this.z = e[2] * x + e[6] * y + e[10] * z + e[14] * w; - this.w = e[3] * x + e[7] * y + e[11] * z + e[15] * w; - return this; - } - divide(v) { - this.x /= v.x; - this.y /= v.y; - this.z /= v.z; - this.w /= v.w; - return this; - } - divideScalar(scalar) { - return this.multiplyScalar(1 / scalar); - } - setAxisAngleFromQuaternion(q) { - this.w = 2 * Math.acos(q.w); - const s = Math.sqrt(1 - q.w * q.w); - if (s < 1e-4) { - this.x = 1; - this.y = 0; - this.z = 0; - } else { - this.x = q.x / s; - this.y = q.y / s; - this.z = q.z / s; - } - return this; - } - setAxisAngleFromRotationMatrix(m) { - let angle, x, y, z; - const epsilon = 0.01, epsilon2 = 0.1, te2 = m.elements, m11 = te2[0], m12 = te2[4], m13 = te2[8], m21 = te2[1], m22 = te2[5], m23 = te2[9], m31 = te2[2], m32 = te2[6], m33 = te2[10]; - if (Math.abs(m12 - m21) < epsilon && Math.abs(m13 - m31) < epsilon && Math.abs(m23 - m32) < epsilon) { - if (Math.abs(m12 + m21) < epsilon2 && Math.abs(m13 + m31) < epsilon2 && Math.abs(m23 + m32) < epsilon2 && Math.abs(m11 + m22 + m33 - 3) < epsilon2) { - this.set(1, 0, 0, 0); - return this; - } - angle = Math.PI; - const xx = (m11 + 1) / 2; - const yy = (m22 + 1) / 2; - const zz = (m33 + 1) / 2; - const xy = (m12 + m21) / 4; - const xz = (m13 + m31) / 4; - const yz = (m23 + m32) / 4; - if (xx > yy && xx > zz) { - if (xx < epsilon) { - x = 0; - y = 0.707106781; - z = 0.707106781; - } else { - x = Math.sqrt(xx); - y = xy / x; - z = xz / x; - } - } else if (yy > zz) { - if (yy < epsilon) { - x = 0.707106781; - y = 0; - z = 0.707106781; - } else { - y = Math.sqrt(yy); - x = xy / y; - z = yz / y; - } - } else { - if (zz < epsilon) { - x = 0.707106781; - y = 0.707106781; - z = 0; - } else { - z = Math.sqrt(zz); - x = xz / z; - y = yz / z; - } - } - this.set(x, y, z, angle); - return this; - } - let s = Math.sqrt((m32 - m23) * (m32 - m23) + (m13 - m31) * (m13 - m31) + (m21 - m12) * (m21 - m12)); - if (Math.abs(s) < 1e-3) s = 1; - this.x = (m32 - m23) / s; - this.y = (m13 - m31) / s; - this.z = (m21 - m12) / s; - this.w = Math.acos((m11 + m22 + m33 - 1) / 2); - return this; - } - setFromMatrixPosition(m) { - const e = m.elements; - this.x = e[12]; - this.y = e[13]; - this.z = e[14]; - this.w = e[15]; - return this; - } - min(v) { - this.x = Math.min(this.x, v.x); - this.y = Math.min(this.y, v.y); - this.z = Math.min(this.z, v.z); - this.w = Math.min(this.w, v.w); - return this; - } - max(v) { - this.x = Math.max(this.x, v.x); - this.y = Math.max(this.y, v.y); - this.z = Math.max(this.z, v.z); - this.w = Math.max(this.w, v.w); - return this; - } - clamp(min, max2) { - this.x = Math.max(min.x, Math.min(max2.x, this.x)); - this.y = Math.max(min.y, Math.min(max2.y, this.y)); - this.z = Math.max(min.z, Math.min(max2.z, this.z)); - this.w = Math.max(min.w, Math.min(max2.w, this.w)); - return this; - } - clampScalar(minVal, maxVal) { - this.x = Math.max(minVal, Math.min(maxVal, this.x)); - this.y = Math.max(minVal, Math.min(maxVal, this.y)); - this.z = Math.max(minVal, Math.min(maxVal, this.z)); - this.w = Math.max(minVal, Math.min(maxVal, this.w)); - return this; - } - clampLength(min, max2) { - const length = this.length(); - return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max2, length))); - } - floor() { - this.x = Math.floor(this.x); - this.y = Math.floor(this.y); - this.z = Math.floor(this.z); - this.w = Math.floor(this.w); - return this; - } - ceil() { - this.x = Math.ceil(this.x); - this.y = Math.ceil(this.y); - this.z = Math.ceil(this.z); - this.w = Math.ceil(this.w); - return this; - } - round() { - this.x = Math.round(this.x); - this.y = Math.round(this.y); - this.z = Math.round(this.z); - this.w = Math.round(this.w); - return this; - } - roundToZero() { - this.x = Math.trunc(this.x); - this.y = Math.trunc(this.y); - this.z = Math.trunc(this.z); - this.w = Math.trunc(this.w); - return this; - } - negate() { - this.x = -this.x; - this.y = -this.y; - this.z = -this.z; - this.w = -this.w; - return this; - } - dot(v) { - return this.x * v.x + this.y * v.y + this.z * v.z + this.w * v.w; - } - lengthSq() { - return this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w; - } - length() { - return Math.sqrt(this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w); - } - manhattanLength() { - return Math.abs(this.x) + Math.abs(this.y) + Math.abs(this.z) + Math.abs(this.w); - } - normalize() { - return this.divideScalar(this.length() || 1); - } - setLength(length) { - return this.normalize().multiplyScalar(length); - } - lerp(v, alpha) { - this.x += (v.x - this.x) * alpha; - this.y += (v.y - this.y) * alpha; - this.z += (v.z - this.z) * alpha; - this.w += (v.w - this.w) * alpha; - return this; - } - lerpVectors(v1, v2, alpha) { - this.x = v1.x + (v2.x - v1.x) * alpha; - this.y = v1.y + (v2.y - v1.y) * alpha; - this.z = v1.z + (v2.z - v1.z) * alpha; - this.w = v1.w + (v2.w - v1.w) * alpha; - return this; - } - equals(v) { - return v.x === this.x && v.y === this.y && v.z === this.z && v.w === this.w; - } - fromArray(array, offset = 0) { - this.x = array[offset]; - this.y = array[offset + 1]; - this.z = array[offset + 2]; - this.w = array[offset + 3]; - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this.x; - array[offset + 1] = this.y; - array[offset + 2] = this.z; - array[offset + 3] = this.w; - return array; - } - fromBufferAttribute(attribute, index) { - this.x = attribute.getX(index); - this.y = attribute.getY(index); - this.z = attribute.getZ(index); - this.w = attribute.getW(index); - return this; - } - random() { - this.x = Math.random(); - this.y = Math.random(); - this.z = Math.random(); - this.w = Math.random(); - return this; - } - *[Symbol.iterator]() { - yield this.x; - yield this.y; - yield this.z; - yield this.w; - } -} -class RenderTarget extends EventDispatcher { - static { - __name(this, "RenderTarget"); - } - constructor(width = 1, height = 1, options = {}) { - super(); - this.isRenderTarget = true; - this.width = width; - this.height = height; - this.depth = 1; - this.scissor = new Vector4(0, 0, width, height); - this.scissorTest = false; - this.viewport = new Vector4(0, 0, width, height); - const image = { width, height, depth: 1 }; - options = Object.assign({ - generateMipmaps: false, - internalFormat: null, - minFilter: LinearFilter, - depthBuffer: true, - stencilBuffer: false, - resolveDepthBuffer: true, - resolveStencilBuffer: true, - depthTexture: null, - samples: 0, - count: 1 - }, options); - const texture = new Texture(image, options.mapping, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.colorSpace); - texture.flipY = false; - texture.generateMipmaps = options.generateMipmaps; - texture.internalFormat = options.internalFormat; - this.textures = []; - const count = options.count; - for (let i = 0; i < count; i++) { - this.textures[i] = texture.clone(); - this.textures[i].isRenderTargetTexture = true; - } - this.depthBuffer = options.depthBuffer; - this.stencilBuffer = options.stencilBuffer; - this.resolveDepthBuffer = options.resolveDepthBuffer; - this.resolveStencilBuffer = options.resolveStencilBuffer; - this.depthTexture = options.depthTexture; - this.samples = options.samples; - } - get texture() { - return this.textures[0]; - } - set texture(value) { - this.textures[0] = value; - } - setSize(width, height, depth = 1) { - if (this.width !== width || this.height !== height || this.depth !== depth) { - this.width = width; - this.height = height; - this.depth = depth; - for (let i = 0, il = this.textures.length; i < il; i++) { - this.textures[i].image.width = width; - this.textures[i].image.height = height; - this.textures[i].image.depth = depth; - } - this.dispose(); - } - this.viewport.set(0, 0, width, height); - this.scissor.set(0, 0, width, height); - } - clone() { - return new this.constructor().copy(this); - } - copy(source) { - this.width = source.width; - this.height = source.height; - this.depth = source.depth; - this.scissor.copy(source.scissor); - this.scissorTest = source.scissorTest; - this.viewport.copy(source.viewport); - this.textures.length = 0; - for (let i = 0, il = source.textures.length; i < il; i++) { - this.textures[i] = source.textures[i].clone(); - this.textures[i].isRenderTargetTexture = true; - } - const image = Object.assign({}, source.texture.image); - this.texture.source = new Source(image); - this.depthBuffer = source.depthBuffer; - this.stencilBuffer = source.stencilBuffer; - this.resolveDepthBuffer = source.resolveDepthBuffer; - this.resolveStencilBuffer = source.resolveStencilBuffer; - if (source.depthTexture !== null) this.depthTexture = source.depthTexture.clone(); - this.samples = source.samples; - return this; - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - } -} -class WebGLRenderTarget extends RenderTarget { - static { - __name(this, "WebGLRenderTarget"); - } - constructor(width = 1, height = 1, options = {}) { - super(width, height, options); - this.isWebGLRenderTarget = true; - } -} -class DataArrayTexture extends Texture { - static { - __name(this, "DataArrayTexture"); - } - constructor(data = null, width = 1, height = 1, depth = 1) { - super(null); - this.isDataArrayTexture = true; - this.image = { data, width, height, depth }; - this.magFilter = NearestFilter; - this.minFilter = NearestFilter; - this.wrapR = ClampToEdgeWrapping; - this.generateMipmaps = false; - this.flipY = false; - this.unpackAlignment = 1; - this.layerUpdates = /* @__PURE__ */ new Set(); - } - addLayerUpdate(layerIndex) { - this.layerUpdates.add(layerIndex); - } - clearLayerUpdates() { - this.layerUpdates.clear(); - } -} -class WebGLArrayRenderTarget extends WebGLRenderTarget { - static { - __name(this, "WebGLArrayRenderTarget"); - } - constructor(width = 1, height = 1, depth = 1, options = {}) { - super(width, height, options); - this.isWebGLArrayRenderTarget = true; - this.depth = depth; - this.texture = new DataArrayTexture(null, width, height, depth); - this.texture.isRenderTargetTexture = true; - } -} -class Data3DTexture extends Texture { - static { - __name(this, "Data3DTexture"); - } - constructor(data = null, width = 1, height = 1, depth = 1) { - super(null); - this.isData3DTexture = true; - this.image = { data, width, height, depth }; - this.magFilter = NearestFilter; - this.minFilter = NearestFilter; - this.wrapR = ClampToEdgeWrapping; - this.generateMipmaps = false; - this.flipY = false; - this.unpackAlignment = 1; - } -} -class WebGL3DRenderTarget extends WebGLRenderTarget { - static { - __name(this, "WebGL3DRenderTarget"); - } - constructor(width = 1, height = 1, depth = 1, options = {}) { - super(width, height, options); - this.isWebGL3DRenderTarget = true; - this.depth = depth; - this.texture = new Data3DTexture(null, width, height, depth); - this.texture.isRenderTargetTexture = true; - } -} -class Quaternion { - static { - __name(this, "Quaternion"); - } - constructor(x = 0, y = 0, z = 0, w = 1) { - this.isQuaternion = true; - this._x = x; - this._y = y; - this._z = z; - this._w = w; - } - static slerpFlat(dst, dstOffset, src0, srcOffset0, src1, srcOffset1, t2) { - let x0 = src0[srcOffset0 + 0], y0 = src0[srcOffset0 + 1], z0 = src0[srcOffset0 + 2], w0 = src0[srcOffset0 + 3]; - const x1 = src1[srcOffset1 + 0], y1 = src1[srcOffset1 + 1], z1 = src1[srcOffset1 + 2], w1 = src1[srcOffset1 + 3]; - if (t2 === 0) { - dst[dstOffset + 0] = x0; - dst[dstOffset + 1] = y0; - dst[dstOffset + 2] = z0; - dst[dstOffset + 3] = w0; - return; - } - if (t2 === 1) { - dst[dstOffset + 0] = x1; - dst[dstOffset + 1] = y1; - dst[dstOffset + 2] = z1; - dst[dstOffset + 3] = w1; - return; - } - if (w0 !== w1 || x0 !== x1 || y0 !== y1 || z0 !== z1) { - let s = 1 - t2; - const cos = x0 * x1 + y0 * y1 + z0 * z1 + w0 * w1, dir = cos >= 0 ? 1 : -1, sqrSin = 1 - cos * cos; - if (sqrSin > Number.EPSILON) { - const sin = Math.sqrt(sqrSin), len = Math.atan2(sin, cos * dir); - s = Math.sin(s * len) / sin; - t2 = Math.sin(t2 * len) / sin; - } - const tDir = t2 * dir; - x0 = x0 * s + x1 * tDir; - y0 = y0 * s + y1 * tDir; - z0 = z0 * s + z1 * tDir; - w0 = w0 * s + w1 * tDir; - if (s === 1 - t2) { - const f = 1 / Math.sqrt(x0 * x0 + y0 * y0 + z0 * z0 + w0 * w0); - x0 *= f; - y0 *= f; - z0 *= f; - w0 *= f; - } - } - dst[dstOffset] = x0; - dst[dstOffset + 1] = y0; - dst[dstOffset + 2] = z0; - dst[dstOffset + 3] = w0; - } - static multiplyQuaternionsFlat(dst, dstOffset, src0, srcOffset0, src1, srcOffset1) { - const x0 = src0[srcOffset0]; - const y0 = src0[srcOffset0 + 1]; - const z0 = src0[srcOffset0 + 2]; - const w0 = src0[srcOffset0 + 3]; - const x1 = src1[srcOffset1]; - const y1 = src1[srcOffset1 + 1]; - const z1 = src1[srcOffset1 + 2]; - const w1 = src1[srcOffset1 + 3]; - dst[dstOffset] = x0 * w1 + w0 * x1 + y0 * z1 - z0 * y1; - dst[dstOffset + 1] = y0 * w1 + w0 * y1 + z0 * x1 - x0 * z1; - dst[dstOffset + 2] = z0 * w1 + w0 * z1 + x0 * y1 - y0 * x1; - dst[dstOffset + 3] = w0 * w1 - x0 * x1 - y0 * y1 - z0 * z1; - return dst; - } - get x() { - return this._x; - } - set x(value) { - this._x = value; - this._onChangeCallback(); - } - get y() { - return this._y; - } - set y(value) { - this._y = value; - this._onChangeCallback(); - } - get z() { - return this._z; - } - set z(value) { - this._z = value; - this._onChangeCallback(); - } - get w() { - return this._w; - } - set w(value) { - this._w = value; - this._onChangeCallback(); - } - set(x, y, z, w) { - this._x = x; - this._y = y; - this._z = z; - this._w = w; - this._onChangeCallback(); - return this; - } - clone() { - return new this.constructor(this._x, this._y, this._z, this._w); - } - copy(quaternion) { - this._x = quaternion.x; - this._y = quaternion.y; - this._z = quaternion.z; - this._w = quaternion.w; - this._onChangeCallback(); - return this; - } - setFromEuler(euler, update = true) { - const x = euler._x, y = euler._y, z = euler._z, order = euler._order; - const cos = Math.cos; - const sin = Math.sin; - const c1 = cos(x / 2); - const c2 = cos(y / 2); - const c3 = cos(z / 2); - const s1 = sin(x / 2); - const s2 = sin(y / 2); - const s3 = sin(z / 2); - switch (order) { - case "XYZ": - this._x = s1 * c2 * c3 + c1 * s2 * s3; - this._y = c1 * s2 * c3 - s1 * c2 * s3; - this._z = c1 * c2 * s3 + s1 * s2 * c3; - this._w = c1 * c2 * c3 - s1 * s2 * s3; - break; - case "YXZ": - this._x = s1 * c2 * c3 + c1 * s2 * s3; - this._y = c1 * s2 * c3 - s1 * c2 * s3; - this._z = c1 * c2 * s3 - s1 * s2 * c3; - this._w = c1 * c2 * c3 + s1 * s2 * s3; - break; - case "ZXY": - this._x = s1 * c2 * c3 - c1 * s2 * s3; - this._y = c1 * s2 * c3 + s1 * c2 * s3; - this._z = c1 * c2 * s3 + s1 * s2 * c3; - this._w = c1 * c2 * c3 - s1 * s2 * s3; - break; - case "ZYX": - this._x = s1 * c2 * c3 - c1 * s2 * s3; - this._y = c1 * s2 * c3 + s1 * c2 * s3; - this._z = c1 * c2 * s3 - s1 * s2 * c3; - this._w = c1 * c2 * c3 + s1 * s2 * s3; - break; - case "YZX": - this._x = s1 * c2 * c3 + c1 * s2 * s3; - this._y = c1 * s2 * c3 + s1 * c2 * s3; - this._z = c1 * c2 * s3 - s1 * s2 * c3; - this._w = c1 * c2 * c3 - s1 * s2 * s3; - break; - case "XZY": - this._x = s1 * c2 * c3 - c1 * s2 * s3; - this._y = c1 * s2 * c3 - s1 * c2 * s3; - this._z = c1 * c2 * s3 + s1 * s2 * c3; - this._w = c1 * c2 * c3 + s1 * s2 * s3; - break; - default: - console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: " + order); - } - if (update === true) this._onChangeCallback(); - return this; - } - setFromAxisAngle(axis, angle) { - const halfAngle = angle / 2, s = Math.sin(halfAngle); - this._x = axis.x * s; - this._y = axis.y * s; - this._z = axis.z * s; - this._w = Math.cos(halfAngle); - this._onChangeCallback(); - return this; - } - setFromRotationMatrix(m) { - const te2 = m.elements, m11 = te2[0], m12 = te2[4], m13 = te2[8], m21 = te2[1], m22 = te2[5], m23 = te2[9], m31 = te2[2], m32 = te2[6], m33 = te2[10], trace = m11 + m22 + m33; - if (trace > 0) { - const s = 0.5 / Math.sqrt(trace + 1); - this._w = 0.25 / s; - this._x = (m32 - m23) * s; - this._y = (m13 - m31) * s; - this._z = (m21 - m12) * s; - } else if (m11 > m22 && m11 > m33) { - const s = 2 * Math.sqrt(1 + m11 - m22 - m33); - this._w = (m32 - m23) / s; - this._x = 0.25 * s; - this._y = (m12 + m21) / s; - this._z = (m13 + m31) / s; - } else if (m22 > m33) { - const s = 2 * Math.sqrt(1 + m22 - m11 - m33); - this._w = (m13 - m31) / s; - this._x = (m12 + m21) / s; - this._y = 0.25 * s; - this._z = (m23 + m32) / s; - } else { - const s = 2 * Math.sqrt(1 + m33 - m11 - m22); - this._w = (m21 - m12) / s; - this._x = (m13 + m31) / s; - this._y = (m23 + m32) / s; - this._z = 0.25 * s; - } - this._onChangeCallback(); - return this; - } - setFromUnitVectors(vFrom, vTo) { - let r = vFrom.dot(vTo) + 1; - if (r < Number.EPSILON) { - r = 0; - if (Math.abs(vFrom.x) > Math.abs(vFrom.z)) { - this._x = -vFrom.y; - this._y = vFrom.x; - this._z = 0; - this._w = r; - } else { - this._x = 0; - this._y = -vFrom.z; - this._z = vFrom.y; - this._w = r; - } - } else { - this._x = vFrom.y * vTo.z - vFrom.z * vTo.y; - this._y = vFrom.z * vTo.x - vFrom.x * vTo.z; - this._z = vFrom.x * vTo.y - vFrom.y * vTo.x; - this._w = r; - } - return this.normalize(); - } - angleTo(q) { - return 2 * Math.acos(Math.abs(clamp(this.dot(q), -1, 1))); - } - rotateTowards(q, step) { - const angle = this.angleTo(q); - if (angle === 0) return this; - const t2 = Math.min(1, step / angle); - this.slerp(q, t2); - return this; - } - identity() { - return this.set(0, 0, 0, 1); - } - invert() { - return this.conjugate(); - } - conjugate() { - this._x *= -1; - this._y *= -1; - this._z *= -1; - this._onChangeCallback(); - return this; - } - dot(v) { - return this._x * v._x + this._y * v._y + this._z * v._z + this._w * v._w; - } - lengthSq() { - return this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w; - } - length() { - return Math.sqrt(this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w); - } - normalize() { - let l = this.length(); - if (l === 0) { - this._x = 0; - this._y = 0; - this._z = 0; - this._w = 1; - } else { - l = 1 / l; - this._x = this._x * l; - this._y = this._y * l; - this._z = this._z * l; - this._w = this._w * l; - } - this._onChangeCallback(); - return this; - } - multiply(q) { - return this.multiplyQuaternions(this, q); - } - premultiply(q) { - return this.multiplyQuaternions(q, this); - } - multiplyQuaternions(a, b) { - const qax = a._x, qay = a._y, qaz = a._z, qaw = a._w; - const qbx = b._x, qby = b._y, qbz = b._z, qbw = b._w; - this._x = qax * qbw + qaw * qbx + qay * qbz - qaz * qby; - this._y = qay * qbw + qaw * qby + qaz * qbx - qax * qbz; - this._z = qaz * qbw + qaw * qbz + qax * qby - qay * qbx; - this._w = qaw * qbw - qax * qbx - qay * qby - qaz * qbz; - this._onChangeCallback(); - return this; - } - slerp(qb, t2) { - if (t2 === 0) return this; - if (t2 === 1) return this.copy(qb); - const x = this._x, y = this._y, z = this._z, w = this._w; - let cosHalfTheta = w * qb._w + x * qb._x + y * qb._y + z * qb._z; - if (cosHalfTheta < 0) { - this._w = -qb._w; - this._x = -qb._x; - this._y = -qb._y; - this._z = -qb._z; - cosHalfTheta = -cosHalfTheta; - } else { - this.copy(qb); - } - if (cosHalfTheta >= 1) { - this._w = w; - this._x = x; - this._y = y; - this._z = z; - return this; - } - const sqrSinHalfTheta = 1 - cosHalfTheta * cosHalfTheta; - if (sqrSinHalfTheta <= Number.EPSILON) { - const s = 1 - t2; - this._w = s * w + t2 * this._w; - this._x = s * x + t2 * this._x; - this._y = s * y + t2 * this._y; - this._z = s * z + t2 * this._z; - this.normalize(); - return this; - } - const sinHalfTheta = Math.sqrt(sqrSinHalfTheta); - const halfTheta = Math.atan2(sinHalfTheta, cosHalfTheta); - const ratioA = Math.sin((1 - t2) * halfTheta) / sinHalfTheta, ratioB = Math.sin(t2 * halfTheta) / sinHalfTheta; - this._w = w * ratioA + this._w * ratioB; - this._x = x * ratioA + this._x * ratioB; - this._y = y * ratioA + this._y * ratioB; - this._z = z * ratioA + this._z * ratioB; - this._onChangeCallback(); - return this; - } - slerpQuaternions(qa, qb, t2) { - return this.copy(qa).slerp(qb, t2); - } - random() { - const theta1 = 2 * Math.PI * Math.random(); - const theta2 = 2 * Math.PI * Math.random(); - const x0 = Math.random(); - const r1 = Math.sqrt(1 - x0); - const r2 = Math.sqrt(x0); - return this.set( - r1 * Math.sin(theta1), - r1 * Math.cos(theta1), - r2 * Math.sin(theta2), - r2 * Math.cos(theta2) - ); - } - equals(quaternion) { - return quaternion._x === this._x && quaternion._y === this._y && quaternion._z === this._z && quaternion._w === this._w; - } - fromArray(array, offset = 0) { - this._x = array[offset]; - this._y = array[offset + 1]; - this._z = array[offset + 2]; - this._w = array[offset + 3]; - this._onChangeCallback(); - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this._x; - array[offset + 1] = this._y; - array[offset + 2] = this._z; - array[offset + 3] = this._w; - return array; - } - fromBufferAttribute(attribute, index) { - this._x = attribute.getX(index); - this._y = attribute.getY(index); - this._z = attribute.getZ(index); - this._w = attribute.getW(index); - this._onChangeCallback(); - return this; - } - toJSON() { - return this.toArray(); - } - _onChange(callback) { - this._onChangeCallback = callback; - return this; - } - _onChangeCallback() { - } - *[Symbol.iterator]() { - yield this._x; - yield this._y; - yield this._z; - yield this._w; - } -} -class Vector3 { - static { - __name(this, "Vector3"); - } - constructor(x = 0, y = 0, z = 0) { - Vector3.prototype.isVector3 = true; - this.x = x; - this.y = y; - this.z = z; - } - set(x, y, z) { - if (z === void 0) z = this.z; - this.x = x; - this.y = y; - this.z = z; - return this; - } - setScalar(scalar) { - this.x = scalar; - this.y = scalar; - this.z = scalar; - return this; - } - setX(x) { - this.x = x; - return this; - } - setY(y) { - this.y = y; - return this; - } - setZ(z) { - this.z = z; - return this; - } - setComponent(index, value) { - switch (index) { - case 0: - this.x = value; - break; - case 1: - this.y = value; - break; - case 2: - this.z = value; - break; - default: - throw new Error("index is out of range: " + index); - } - return this; - } - getComponent(index) { - switch (index) { - case 0: - return this.x; - case 1: - return this.y; - case 2: - return this.z; - default: - throw new Error("index is out of range: " + index); - } - } - clone() { - return new this.constructor(this.x, this.y, this.z); - } - copy(v) { - this.x = v.x; - this.y = v.y; - this.z = v.z; - return this; - } - add(v) { - this.x += v.x; - this.y += v.y; - this.z += v.z; - return this; - } - addScalar(s) { - this.x += s; - this.y += s; - this.z += s; - return this; - } - addVectors(a, b) { - this.x = a.x + b.x; - this.y = a.y + b.y; - this.z = a.z + b.z; - return this; - } - addScaledVector(v, s) { - this.x += v.x * s; - this.y += v.y * s; - this.z += v.z * s; - return this; - } - sub(v) { - this.x -= v.x; - this.y -= v.y; - this.z -= v.z; - return this; - } - subScalar(s) { - this.x -= s; - this.y -= s; - this.z -= s; - return this; - } - subVectors(a, b) { - this.x = a.x - b.x; - this.y = a.y - b.y; - this.z = a.z - b.z; - return this; - } - multiply(v) { - this.x *= v.x; - this.y *= v.y; - this.z *= v.z; - return this; - } - multiplyScalar(scalar) { - this.x *= scalar; - this.y *= scalar; - this.z *= scalar; - return this; - } - multiplyVectors(a, b) { - this.x = a.x * b.x; - this.y = a.y * b.y; - this.z = a.z * b.z; - return this; - } - applyEuler(euler) { - return this.applyQuaternion(_quaternion$4.setFromEuler(euler)); - } - applyAxisAngle(axis, angle) { - return this.applyQuaternion(_quaternion$4.setFromAxisAngle(axis, angle)); - } - applyMatrix3(m) { - const x = this.x, y = this.y, z = this.z; - const e = m.elements; - this.x = e[0] * x + e[3] * y + e[6] * z; - this.y = e[1] * x + e[4] * y + e[7] * z; - this.z = e[2] * x + e[5] * y + e[8] * z; - return this; - } - applyNormalMatrix(m) { - return this.applyMatrix3(m).normalize(); - } - applyMatrix4(m) { - const x = this.x, y = this.y, z = this.z; - const e = m.elements; - const w = 1 / (e[3] * x + e[7] * y + e[11] * z + e[15]); - this.x = (e[0] * x + e[4] * y + e[8] * z + e[12]) * w; - this.y = (e[1] * x + e[5] * y + e[9] * z + e[13]) * w; - this.z = (e[2] * x + e[6] * y + e[10] * z + e[14]) * w; - return this; - } - applyQuaternion(q) { - const vx = this.x, vy = this.y, vz = this.z; - const qx = q.x, qy = q.y, qz = q.z, qw = q.w; - const tx = 2 * (qy * vz - qz * vy); - const ty = 2 * (qz * vx - qx * vz); - const tz = 2 * (qx * vy - qy * vx); - this.x = vx + qw * tx + qy * tz - qz * ty; - this.y = vy + qw * ty + qz * tx - qx * tz; - this.z = vz + qw * tz + qx * ty - qy * tx; - return this; - } - project(camera) { - return this.applyMatrix4(camera.matrixWorldInverse).applyMatrix4(camera.projectionMatrix); - } - unproject(camera) { - return this.applyMatrix4(camera.projectionMatrixInverse).applyMatrix4(camera.matrixWorld); - } - transformDirection(m) { - const x = this.x, y = this.y, z = this.z; - const e = m.elements; - this.x = e[0] * x + e[4] * y + e[8] * z; - this.y = e[1] * x + e[5] * y + e[9] * z; - this.z = e[2] * x + e[6] * y + e[10] * z; - return this.normalize(); - } - divide(v) { - this.x /= v.x; - this.y /= v.y; - this.z /= v.z; - return this; - } - divideScalar(scalar) { - return this.multiplyScalar(1 / scalar); - } - min(v) { - this.x = Math.min(this.x, v.x); - this.y = Math.min(this.y, v.y); - this.z = Math.min(this.z, v.z); - return this; - } - max(v) { - this.x = Math.max(this.x, v.x); - this.y = Math.max(this.y, v.y); - this.z = Math.max(this.z, v.z); - return this; - } - clamp(min, max2) { - this.x = Math.max(min.x, Math.min(max2.x, this.x)); - this.y = Math.max(min.y, Math.min(max2.y, this.y)); - this.z = Math.max(min.z, Math.min(max2.z, this.z)); - return this; - } - clampScalar(minVal, maxVal) { - this.x = Math.max(minVal, Math.min(maxVal, this.x)); - this.y = Math.max(minVal, Math.min(maxVal, this.y)); - this.z = Math.max(minVal, Math.min(maxVal, this.z)); - return this; - } - clampLength(min, max2) { - const length = this.length(); - return this.divideScalar(length || 1).multiplyScalar(Math.max(min, Math.min(max2, length))); - } - floor() { - this.x = Math.floor(this.x); - this.y = Math.floor(this.y); - this.z = Math.floor(this.z); - return this; - } - ceil() { - this.x = Math.ceil(this.x); - this.y = Math.ceil(this.y); - this.z = Math.ceil(this.z); - return this; - } - round() { - this.x = Math.round(this.x); - this.y = Math.round(this.y); - this.z = Math.round(this.z); - return this; - } - roundToZero() { - this.x = Math.trunc(this.x); - this.y = Math.trunc(this.y); - this.z = Math.trunc(this.z); - return this; - } - negate() { - this.x = -this.x; - this.y = -this.y; - this.z = -this.z; - return this; - } - dot(v) { - return this.x * v.x + this.y * v.y + this.z * v.z; - } - // TODO lengthSquared? - lengthSq() { - return this.x * this.x + this.y * this.y + this.z * this.z; - } - length() { - return Math.sqrt(this.x * this.x + this.y * this.y + this.z * this.z); - } - manhattanLength() { - return Math.abs(this.x) + Math.abs(this.y) + Math.abs(this.z); - } - normalize() { - return this.divideScalar(this.length() || 1); - } - setLength(length) { - return this.normalize().multiplyScalar(length); - } - lerp(v, alpha) { - this.x += (v.x - this.x) * alpha; - this.y += (v.y - this.y) * alpha; - this.z += (v.z - this.z) * alpha; - return this; - } - lerpVectors(v1, v2, alpha) { - this.x = v1.x + (v2.x - v1.x) * alpha; - this.y = v1.y + (v2.y - v1.y) * alpha; - this.z = v1.z + (v2.z - v1.z) * alpha; - return this; - } - cross(v) { - return this.crossVectors(this, v); - } - crossVectors(a, b) { - const ax = a.x, ay = a.y, az = a.z; - const bx = b.x, by = b.y, bz = b.z; - this.x = ay * bz - az * by; - this.y = az * bx - ax * bz; - this.z = ax * by - ay * bx; - return this; - } - projectOnVector(v) { - const denominator = v.lengthSq(); - if (denominator === 0) return this.set(0, 0, 0); - const scalar = v.dot(this) / denominator; - return this.copy(v).multiplyScalar(scalar); - } - projectOnPlane(planeNormal) { - _vector$c.copy(this).projectOnVector(planeNormal); - return this.sub(_vector$c); - } - reflect(normal) { - return this.sub(_vector$c.copy(normal).multiplyScalar(2 * this.dot(normal))); - } - angleTo(v) { - const denominator = Math.sqrt(this.lengthSq() * v.lengthSq()); - if (denominator === 0) return Math.PI / 2; - const theta = this.dot(v) / denominator; - return Math.acos(clamp(theta, -1, 1)); - } - distanceTo(v) { - return Math.sqrt(this.distanceToSquared(v)); - } - distanceToSquared(v) { - const dx = this.x - v.x, dy = this.y - v.y, dz = this.z - v.z; - return dx * dx + dy * dy + dz * dz; - } - manhattanDistanceTo(v) { - return Math.abs(this.x - v.x) + Math.abs(this.y - v.y) + Math.abs(this.z - v.z); - } - setFromSpherical(s) { - return this.setFromSphericalCoords(s.radius, s.phi, s.theta); - } - setFromSphericalCoords(radius, phi, theta) { - const sinPhiRadius = Math.sin(phi) * radius; - this.x = sinPhiRadius * Math.sin(theta); - this.y = Math.cos(phi) * radius; - this.z = sinPhiRadius * Math.cos(theta); - return this; - } - setFromCylindrical(c) { - return this.setFromCylindricalCoords(c.radius, c.theta, c.y); - } - setFromCylindricalCoords(radius, theta, y) { - this.x = radius * Math.sin(theta); - this.y = y; - this.z = radius * Math.cos(theta); - return this; - } - setFromMatrixPosition(m) { - const e = m.elements; - this.x = e[12]; - this.y = e[13]; - this.z = e[14]; - return this; - } - setFromMatrixScale(m) { - const sx = this.setFromMatrixColumn(m, 0).length(); - const sy = this.setFromMatrixColumn(m, 1).length(); - const sz = this.setFromMatrixColumn(m, 2).length(); - this.x = sx; - this.y = sy; - this.z = sz; - return this; - } - setFromMatrixColumn(m, index) { - return this.fromArray(m.elements, index * 4); - } - setFromMatrix3Column(m, index) { - return this.fromArray(m.elements, index * 3); - } - setFromEuler(e) { - this.x = e._x; - this.y = e._y; - this.z = e._z; - return this; - } - setFromColor(c) { - this.x = c.r; - this.y = c.g; - this.z = c.b; - return this; - } - equals(v) { - return v.x === this.x && v.y === this.y && v.z === this.z; - } - fromArray(array, offset = 0) { - this.x = array[offset]; - this.y = array[offset + 1]; - this.z = array[offset + 2]; - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this.x; - array[offset + 1] = this.y; - array[offset + 2] = this.z; - return array; - } - fromBufferAttribute(attribute, index) { - this.x = attribute.getX(index); - this.y = attribute.getY(index); - this.z = attribute.getZ(index); - return this; - } - random() { - this.x = Math.random(); - this.y = Math.random(); - this.z = Math.random(); - return this; - } - randomDirection() { - const theta = Math.random() * Math.PI * 2; - const u = Math.random() * 2 - 1; - const c = Math.sqrt(1 - u * u); - this.x = c * Math.cos(theta); - this.y = u; - this.z = c * Math.sin(theta); - return this; - } - *[Symbol.iterator]() { - yield this.x; - yield this.y; - yield this.z; - } -} -const _vector$c = /* @__PURE__ */ new Vector3(); -const _quaternion$4 = /* @__PURE__ */ new Quaternion(); -class Box3 { - static { - __name(this, "Box3"); - } - constructor(min = new Vector3(Infinity, Infinity, Infinity), max2 = new Vector3(-Infinity, -Infinity, -Infinity)) { - this.isBox3 = true; - this.min = min; - this.max = max2; - } - set(min, max2) { - this.min.copy(min); - this.max.copy(max2); - return this; - } - setFromArray(array) { - this.makeEmpty(); - for (let i = 0, il = array.length; i < il; i += 3) { - this.expandByPoint(_vector$b.fromArray(array, i)); - } - return this; - } - setFromBufferAttribute(attribute) { - this.makeEmpty(); - for (let i = 0, il = attribute.count; i < il; i++) { - this.expandByPoint(_vector$b.fromBufferAttribute(attribute, i)); - } - return this; - } - setFromPoints(points) { - this.makeEmpty(); - for (let i = 0, il = points.length; i < il; i++) { - this.expandByPoint(points[i]); - } - return this; - } - setFromCenterAndSize(center, size) { - const halfSize = _vector$b.copy(size).multiplyScalar(0.5); - this.min.copy(center).sub(halfSize); - this.max.copy(center).add(halfSize); - return this; - } - setFromObject(object, precise = false) { - this.makeEmpty(); - return this.expandByObject(object, precise); - } - clone() { - return new this.constructor().copy(this); - } - copy(box) { - this.min.copy(box.min); - this.max.copy(box.max); - return this; - } - makeEmpty() { - this.min.x = this.min.y = this.min.z = Infinity; - this.max.x = this.max.y = this.max.z = -Infinity; - return this; - } - isEmpty() { - return this.max.x < this.min.x || this.max.y < this.min.y || this.max.z < this.min.z; - } - getCenter(target) { - return this.isEmpty() ? target.set(0, 0, 0) : target.addVectors(this.min, this.max).multiplyScalar(0.5); - } - getSize(target) { - return this.isEmpty() ? target.set(0, 0, 0) : target.subVectors(this.max, this.min); - } - expandByPoint(point) { - this.min.min(point); - this.max.max(point); - return this; - } - expandByVector(vector) { - this.min.sub(vector); - this.max.add(vector); - return this; - } - expandByScalar(scalar) { - this.min.addScalar(-scalar); - this.max.addScalar(scalar); - return this; - } - expandByObject(object, precise = false) { - object.updateWorldMatrix(false, false); - const geometry = object.geometry; - if (geometry !== void 0) { - const positionAttribute = geometry.getAttribute("position"); - if (precise === true && positionAttribute !== void 0 && object.isInstancedMesh !== true) { - for (let i = 0, l = positionAttribute.count; i < l; i++) { - if (object.isMesh === true) { - object.getVertexPosition(i, _vector$b); - } else { - _vector$b.fromBufferAttribute(positionAttribute, i); - } - _vector$b.applyMatrix4(object.matrixWorld); - this.expandByPoint(_vector$b); - } - } else { - if (object.boundingBox !== void 0) { - if (object.boundingBox === null) { - object.computeBoundingBox(); - } - _box$4.copy(object.boundingBox); - } else { - if (geometry.boundingBox === null) { - geometry.computeBoundingBox(); - } - _box$4.copy(geometry.boundingBox); - } - _box$4.applyMatrix4(object.matrixWorld); - this.union(_box$4); - } - } - const children = object.children; - for (let i = 0, l = children.length; i < l; i++) { - this.expandByObject(children[i], precise); - } - return this; - } - containsPoint(point) { - return point.x >= this.min.x && point.x <= this.max.x && point.y >= this.min.y && point.y <= this.max.y && point.z >= this.min.z && point.z <= this.max.z; - } - containsBox(box) { - return this.min.x <= box.min.x && box.max.x <= this.max.x && this.min.y <= box.min.y && box.max.y <= this.max.y && this.min.z <= box.min.z && box.max.z <= this.max.z; - } - getParameter(point, target) { - return target.set( - (point.x - this.min.x) / (this.max.x - this.min.x), - (point.y - this.min.y) / (this.max.y - this.min.y), - (point.z - this.min.z) / (this.max.z - this.min.z) - ); - } - intersectsBox(box) { - return box.max.x >= this.min.x && box.min.x <= this.max.x && box.max.y >= this.min.y && box.min.y <= this.max.y && box.max.z >= this.min.z && box.min.z <= this.max.z; - } - intersectsSphere(sphere) { - this.clampPoint(sphere.center, _vector$b); - return _vector$b.distanceToSquared(sphere.center) <= sphere.radius * sphere.radius; - } - intersectsPlane(plane) { - let min, max2; - if (plane.normal.x > 0) { - min = plane.normal.x * this.min.x; - max2 = plane.normal.x * this.max.x; - } else { - min = plane.normal.x * this.max.x; - max2 = plane.normal.x * this.min.x; - } - if (plane.normal.y > 0) { - min += plane.normal.y * this.min.y; - max2 += plane.normal.y * this.max.y; - } else { - min += plane.normal.y * this.max.y; - max2 += plane.normal.y * this.min.y; - } - if (plane.normal.z > 0) { - min += plane.normal.z * this.min.z; - max2 += plane.normal.z * this.max.z; - } else { - min += plane.normal.z * this.max.z; - max2 += plane.normal.z * this.min.z; - } - return min <= -plane.constant && max2 >= -plane.constant; - } - intersectsTriangle(triangle) { - if (this.isEmpty()) { - return false; - } - this.getCenter(_center); - _extents.subVectors(this.max, _center); - _v0$3.subVectors(triangle.a, _center); - _v1$7.subVectors(triangle.b, _center); - _v2$4.subVectors(triangle.c, _center); - _f0.subVectors(_v1$7, _v0$3); - _f1.subVectors(_v2$4, _v1$7); - _f2.subVectors(_v0$3, _v2$4); - let axes = [ - 0, - -_f0.z, - _f0.y, - 0, - -_f1.z, - _f1.y, - 0, - -_f2.z, - _f2.y, - _f0.z, - 0, - -_f0.x, - _f1.z, - 0, - -_f1.x, - _f2.z, - 0, - -_f2.x, - -_f0.y, - _f0.x, - 0, - -_f1.y, - _f1.x, - 0, - -_f2.y, - _f2.x, - 0 - ]; - if (!satForAxes(axes, _v0$3, _v1$7, _v2$4, _extents)) { - return false; - } - axes = [1, 0, 0, 0, 1, 0, 0, 0, 1]; - if (!satForAxes(axes, _v0$3, _v1$7, _v2$4, _extents)) { - return false; - } - _triangleNormal.crossVectors(_f0, _f1); - axes = [_triangleNormal.x, _triangleNormal.y, _triangleNormal.z]; - return satForAxes(axes, _v0$3, _v1$7, _v2$4, _extents); - } - clampPoint(point, target) { - return target.copy(point).clamp(this.min, this.max); - } - distanceToPoint(point) { - return this.clampPoint(point, _vector$b).distanceTo(point); - } - getBoundingSphere(target) { - if (this.isEmpty()) { - target.makeEmpty(); - } else { - this.getCenter(target.center); - target.radius = this.getSize(_vector$b).length() * 0.5; - } - return target; - } - intersect(box) { - this.min.max(box.min); - this.max.min(box.max); - if (this.isEmpty()) this.makeEmpty(); - return this; - } - union(box) { - this.min.min(box.min); - this.max.max(box.max); - return this; - } - applyMatrix4(matrix) { - if (this.isEmpty()) return this; - _points[0].set(this.min.x, this.min.y, this.min.z).applyMatrix4(matrix); - _points[1].set(this.min.x, this.min.y, this.max.z).applyMatrix4(matrix); - _points[2].set(this.min.x, this.max.y, this.min.z).applyMatrix4(matrix); - _points[3].set(this.min.x, this.max.y, this.max.z).applyMatrix4(matrix); - _points[4].set(this.max.x, this.min.y, this.min.z).applyMatrix4(matrix); - _points[5].set(this.max.x, this.min.y, this.max.z).applyMatrix4(matrix); - _points[6].set(this.max.x, this.max.y, this.min.z).applyMatrix4(matrix); - _points[7].set(this.max.x, this.max.y, this.max.z).applyMatrix4(matrix); - this.setFromPoints(_points); - return this; - } - translate(offset) { - this.min.add(offset); - this.max.add(offset); - return this; - } - equals(box) { - return box.min.equals(this.min) && box.max.equals(this.max); - } -} -const _points = [ - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3(), - /* @__PURE__ */ new Vector3() -]; -const _vector$b = /* @__PURE__ */ new Vector3(); -const _box$4 = /* @__PURE__ */ new Box3(); -const _v0$3 = /* @__PURE__ */ new Vector3(); -const _v1$7 = /* @__PURE__ */ new Vector3(); -const _v2$4 = /* @__PURE__ */ new Vector3(); -const _f0 = /* @__PURE__ */ new Vector3(); -const _f1 = /* @__PURE__ */ new Vector3(); -const _f2 = /* @__PURE__ */ new Vector3(); -const _center = /* @__PURE__ */ new Vector3(); -const _extents = /* @__PURE__ */ new Vector3(); -const _triangleNormal = /* @__PURE__ */ new Vector3(); -const _testAxis = /* @__PURE__ */ new Vector3(); -function satForAxes(axes, v0, v1, v2, extents) { - for (let i = 0, j = axes.length - 3; i <= j; i += 3) { - _testAxis.fromArray(axes, i); - const r = extents.x * Math.abs(_testAxis.x) + extents.y * Math.abs(_testAxis.y) + extents.z * Math.abs(_testAxis.z); - const p0 = v0.dot(_testAxis); - const p1 = v1.dot(_testAxis); - const p2 = v2.dot(_testAxis); - if (Math.max(-Math.max(p0, p1, p2), Math.min(p0, p1, p2)) > r) { - return false; - } - } - return true; -} -__name(satForAxes, "satForAxes"); -const _box$3 = /* @__PURE__ */ new Box3(); -const _v1$6 = /* @__PURE__ */ new Vector3(); -const _v2$3 = /* @__PURE__ */ new Vector3(); -class Sphere { - static { - __name(this, "Sphere"); - } - constructor(center = new Vector3(), radius = -1) { - this.isSphere = true; - this.center = center; - this.radius = radius; - } - set(center, radius) { - this.center.copy(center); - this.radius = radius; - return this; - } - setFromPoints(points, optionalCenter) { - const center = this.center; - if (optionalCenter !== void 0) { - center.copy(optionalCenter); - } else { - _box$3.setFromPoints(points).getCenter(center); - } - let maxRadiusSq = 0; - for (let i = 0, il = points.length; i < il; i++) { - maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(points[i])); - } - this.radius = Math.sqrt(maxRadiusSq); - return this; - } - copy(sphere) { - this.center.copy(sphere.center); - this.radius = sphere.radius; - return this; - } - isEmpty() { - return this.radius < 0; - } - makeEmpty() { - this.center.set(0, 0, 0); - this.radius = -1; - return this; - } - containsPoint(point) { - return point.distanceToSquared(this.center) <= this.radius * this.radius; - } - distanceToPoint(point) { - return point.distanceTo(this.center) - this.radius; - } - intersectsSphere(sphere) { - const radiusSum = this.radius + sphere.radius; - return sphere.center.distanceToSquared(this.center) <= radiusSum * radiusSum; - } - intersectsBox(box) { - return box.intersectsSphere(this); - } - intersectsPlane(plane) { - return Math.abs(plane.distanceToPoint(this.center)) <= this.radius; - } - clampPoint(point, target) { - const deltaLengthSq = this.center.distanceToSquared(point); - target.copy(point); - if (deltaLengthSq > this.radius * this.radius) { - target.sub(this.center).normalize(); - target.multiplyScalar(this.radius).add(this.center); - } - return target; - } - getBoundingBox(target) { - if (this.isEmpty()) { - target.makeEmpty(); - return target; - } - target.set(this.center, this.center); - target.expandByScalar(this.radius); - return target; - } - applyMatrix4(matrix) { - this.center.applyMatrix4(matrix); - this.radius = this.radius * matrix.getMaxScaleOnAxis(); - return this; - } - translate(offset) { - this.center.add(offset); - return this; - } - expandByPoint(point) { - if (this.isEmpty()) { - this.center.copy(point); - this.radius = 0; - return this; - } - _v1$6.subVectors(point, this.center); - const lengthSq = _v1$6.lengthSq(); - if (lengthSq > this.radius * this.radius) { - const length = Math.sqrt(lengthSq); - const delta = (length - this.radius) * 0.5; - this.center.addScaledVector(_v1$6, delta / length); - this.radius += delta; - } - return this; - } - union(sphere) { - if (sphere.isEmpty()) { - return this; - } - if (this.isEmpty()) { - this.copy(sphere); - return this; - } - if (this.center.equals(sphere.center) === true) { - this.radius = Math.max(this.radius, sphere.radius); - } else { - _v2$3.subVectors(sphere.center, this.center).setLength(sphere.radius); - this.expandByPoint(_v1$6.copy(sphere.center).add(_v2$3)); - this.expandByPoint(_v1$6.copy(sphere.center).sub(_v2$3)); - } - return this; - } - equals(sphere) { - return sphere.center.equals(this.center) && sphere.radius === this.radius; - } - clone() { - return new this.constructor().copy(this); - } -} -const _vector$a = /* @__PURE__ */ new Vector3(); -const _segCenter = /* @__PURE__ */ new Vector3(); -const _segDir = /* @__PURE__ */ new Vector3(); -const _diff = /* @__PURE__ */ new Vector3(); -const _edge1 = /* @__PURE__ */ new Vector3(); -const _edge2 = /* @__PURE__ */ new Vector3(); -const _normal$1 = /* @__PURE__ */ new Vector3(); -class Ray { - static { - __name(this, "Ray"); - } - constructor(origin = new Vector3(), direction = new Vector3(0, 0, -1)) { - this.origin = origin; - this.direction = direction; - } - set(origin, direction) { - this.origin.copy(origin); - this.direction.copy(direction); - return this; - } - copy(ray) { - this.origin.copy(ray.origin); - this.direction.copy(ray.direction); - return this; - } - at(t2, target) { - return target.copy(this.origin).addScaledVector(this.direction, t2); - } - lookAt(v) { - this.direction.copy(v).sub(this.origin).normalize(); - return this; - } - recast(t2) { - this.origin.copy(this.at(t2, _vector$a)); - return this; - } - closestPointToPoint(point, target) { - target.subVectors(point, this.origin); - const directionDistance = target.dot(this.direction); - if (directionDistance < 0) { - return target.copy(this.origin); - } - return target.copy(this.origin).addScaledVector(this.direction, directionDistance); - } - distanceToPoint(point) { - return Math.sqrt(this.distanceSqToPoint(point)); - } - distanceSqToPoint(point) { - const directionDistance = _vector$a.subVectors(point, this.origin).dot(this.direction); - if (directionDistance < 0) { - return this.origin.distanceToSquared(point); - } - _vector$a.copy(this.origin).addScaledVector(this.direction, directionDistance); - return _vector$a.distanceToSquared(point); - } - distanceSqToSegment(v0, v1, optionalPointOnRay, optionalPointOnSegment) { - _segCenter.copy(v0).add(v1).multiplyScalar(0.5); - _segDir.copy(v1).sub(v0).normalize(); - _diff.copy(this.origin).sub(_segCenter); - const segExtent = v0.distanceTo(v1) * 0.5; - const a01 = -this.direction.dot(_segDir); - const b0 = _diff.dot(this.direction); - const b1 = -_diff.dot(_segDir); - const c = _diff.lengthSq(); - const det = Math.abs(1 - a01 * a01); - let s0, s1, sqrDist, extDet; - if (det > 0) { - s0 = a01 * b1 - b0; - s1 = a01 * b0 - b1; - extDet = segExtent * det; - if (s0 >= 0) { - if (s1 >= -extDet) { - if (s1 <= extDet) { - const invDet = 1 / det; - s0 *= invDet; - s1 *= invDet; - sqrDist = s0 * (s0 + a01 * s1 + 2 * b0) + s1 * (a01 * s0 + s1 + 2 * b1) + c; - } else { - s1 = segExtent; - s0 = Math.max(0, -(a01 * s1 + b0)); - sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c; - } - } else { - s1 = -segExtent; - s0 = Math.max(0, -(a01 * s1 + b0)); - sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c; - } - } else { - if (s1 <= -extDet) { - s0 = Math.max(0, -(-a01 * segExtent + b0)); - s1 = s0 > 0 ? -segExtent : Math.min(Math.max(-segExtent, -b1), segExtent); - sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c; - } else if (s1 <= extDet) { - s0 = 0; - s1 = Math.min(Math.max(-segExtent, -b1), segExtent); - sqrDist = s1 * (s1 + 2 * b1) + c; - } else { - s0 = Math.max(0, -(a01 * segExtent + b0)); - s1 = s0 > 0 ? segExtent : Math.min(Math.max(-segExtent, -b1), segExtent); - sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c; - } - } - } else { - s1 = a01 > 0 ? -segExtent : segExtent; - s0 = Math.max(0, -(a01 * s1 + b0)); - sqrDist = -s0 * s0 + s1 * (s1 + 2 * b1) + c; - } - if (optionalPointOnRay) { - optionalPointOnRay.copy(this.origin).addScaledVector(this.direction, s0); - } - if (optionalPointOnSegment) { - optionalPointOnSegment.copy(_segCenter).addScaledVector(_segDir, s1); - } - return sqrDist; - } - intersectSphere(sphere, target) { - _vector$a.subVectors(sphere.center, this.origin); - const tca = _vector$a.dot(this.direction); - const d2 = _vector$a.dot(_vector$a) - tca * tca; - const radius2 = sphere.radius * sphere.radius; - if (d2 > radius2) return null; - const thc = Math.sqrt(radius2 - d2); - const t0 = tca - thc; - const t1 = tca + thc; - if (t1 < 0) return null; - if (t0 < 0) return this.at(t1, target); - return this.at(t0, target); - } - intersectsSphere(sphere) { - return this.distanceSqToPoint(sphere.center) <= sphere.radius * sphere.radius; - } - distanceToPlane(plane) { - const denominator = plane.normal.dot(this.direction); - if (denominator === 0) { - if (plane.distanceToPoint(this.origin) === 0) { - return 0; - } - return null; - } - const t2 = -(this.origin.dot(plane.normal) + plane.constant) / denominator; - return t2 >= 0 ? t2 : null; - } - intersectPlane(plane, target) { - const t2 = this.distanceToPlane(plane); - if (t2 === null) { - return null; - } - return this.at(t2, target); - } - intersectsPlane(plane) { - const distToPoint = plane.distanceToPoint(this.origin); - if (distToPoint === 0) { - return true; - } - const denominator = plane.normal.dot(this.direction); - if (denominator * distToPoint < 0) { - return true; - } - return false; - } - intersectBox(box, target) { - let tmin, tmax, tymin, tymax, tzmin, tzmax; - const invdirx = 1 / this.direction.x, invdiry = 1 / this.direction.y, invdirz = 1 / this.direction.z; - const origin = this.origin; - if (invdirx >= 0) { - tmin = (box.min.x - origin.x) * invdirx; - tmax = (box.max.x - origin.x) * invdirx; - } else { - tmin = (box.max.x - origin.x) * invdirx; - tmax = (box.min.x - origin.x) * invdirx; - } - if (invdiry >= 0) { - tymin = (box.min.y - origin.y) * invdiry; - tymax = (box.max.y - origin.y) * invdiry; - } else { - tymin = (box.max.y - origin.y) * invdiry; - tymax = (box.min.y - origin.y) * invdiry; - } - if (tmin > tymax || tymin > tmax) return null; - if (tymin > tmin || isNaN(tmin)) tmin = tymin; - if (tymax < tmax || isNaN(tmax)) tmax = tymax; - if (invdirz >= 0) { - tzmin = (box.min.z - origin.z) * invdirz; - tzmax = (box.max.z - origin.z) * invdirz; - } else { - tzmin = (box.max.z - origin.z) * invdirz; - tzmax = (box.min.z - origin.z) * invdirz; - } - if (tmin > tzmax || tzmin > tmax) return null; - if (tzmin > tmin || tmin !== tmin) tmin = tzmin; - if (tzmax < tmax || tmax !== tmax) tmax = tzmax; - if (tmax < 0) return null; - return this.at(tmin >= 0 ? tmin : tmax, target); - } - intersectsBox(box) { - return this.intersectBox(box, _vector$a) !== null; - } - intersectTriangle(a, b, c, backfaceCulling, target) { - _edge1.subVectors(b, a); - _edge2.subVectors(c, a); - _normal$1.crossVectors(_edge1, _edge2); - let DdN = this.direction.dot(_normal$1); - let sign2; - if (DdN > 0) { - if (backfaceCulling) return null; - sign2 = 1; - } else if (DdN < 0) { - sign2 = -1; - DdN = -DdN; - } else { - return null; - } - _diff.subVectors(this.origin, a); - const DdQxE2 = sign2 * this.direction.dot(_edge2.crossVectors(_diff, _edge2)); - if (DdQxE2 < 0) { - return null; - } - const DdE1xQ = sign2 * this.direction.dot(_edge1.cross(_diff)); - if (DdE1xQ < 0) { - return null; - } - if (DdQxE2 + DdE1xQ > DdN) { - return null; - } - const QdN = -sign2 * _diff.dot(_normal$1); - if (QdN < 0) { - return null; - } - return this.at(QdN / DdN, target); - } - applyMatrix4(matrix4) { - this.origin.applyMatrix4(matrix4); - this.direction.transformDirection(matrix4); - return this; - } - equals(ray) { - return ray.origin.equals(this.origin) && ray.direction.equals(this.direction); - } - clone() { - return new this.constructor().copy(this); - } -} -class Matrix4 { - static { - __name(this, "Matrix4"); - } - constructor(n11, n12, n13, n14, n21, n22, n23, n24, n31, n32, n33, n34, n41, n42, n43, n44) { - Matrix4.prototype.isMatrix4 = true; - this.elements = [ - 1, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 1 - ]; - if (n11 !== void 0) { - this.set(n11, n12, n13, n14, n21, n22, n23, n24, n31, n32, n33, n34, n41, n42, n43, n44); - } - } - set(n11, n12, n13, n14, n21, n22, n23, n24, n31, n32, n33, n34, n41, n42, n43, n44) { - const te2 = this.elements; - te2[0] = n11; - te2[4] = n12; - te2[8] = n13; - te2[12] = n14; - te2[1] = n21; - te2[5] = n22; - te2[9] = n23; - te2[13] = n24; - te2[2] = n31; - te2[6] = n32; - te2[10] = n33; - te2[14] = n34; - te2[3] = n41; - te2[7] = n42; - te2[11] = n43; - te2[15] = n44; - return this; - } - identity() { - this.set( - 1, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - clone() { - return new Matrix4().fromArray(this.elements); - } - copy(m) { - const te2 = this.elements; - const me = m.elements; - te2[0] = me[0]; - te2[1] = me[1]; - te2[2] = me[2]; - te2[3] = me[3]; - te2[4] = me[4]; - te2[5] = me[5]; - te2[6] = me[6]; - te2[7] = me[7]; - te2[8] = me[8]; - te2[9] = me[9]; - te2[10] = me[10]; - te2[11] = me[11]; - te2[12] = me[12]; - te2[13] = me[13]; - te2[14] = me[14]; - te2[15] = me[15]; - return this; - } - copyPosition(m) { - const te2 = this.elements, me = m.elements; - te2[12] = me[12]; - te2[13] = me[13]; - te2[14] = me[14]; - return this; - } - setFromMatrix3(m) { - const me = m.elements; - this.set( - me[0], - me[3], - me[6], - 0, - me[1], - me[4], - me[7], - 0, - me[2], - me[5], - me[8], - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - extractBasis(xAxis, yAxis, zAxis) { - xAxis.setFromMatrixColumn(this, 0); - yAxis.setFromMatrixColumn(this, 1); - zAxis.setFromMatrixColumn(this, 2); - return this; - } - makeBasis(xAxis, yAxis, zAxis) { - this.set( - xAxis.x, - yAxis.x, - zAxis.x, - 0, - xAxis.y, - yAxis.y, - zAxis.y, - 0, - xAxis.z, - yAxis.z, - zAxis.z, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - extractRotation(m) { - const te2 = this.elements; - const me = m.elements; - const scaleX = 1 / _v1$5.setFromMatrixColumn(m, 0).length(); - const scaleY = 1 / _v1$5.setFromMatrixColumn(m, 1).length(); - const scaleZ = 1 / _v1$5.setFromMatrixColumn(m, 2).length(); - te2[0] = me[0] * scaleX; - te2[1] = me[1] * scaleX; - te2[2] = me[2] * scaleX; - te2[3] = 0; - te2[4] = me[4] * scaleY; - te2[5] = me[5] * scaleY; - te2[6] = me[6] * scaleY; - te2[7] = 0; - te2[8] = me[8] * scaleZ; - te2[9] = me[9] * scaleZ; - te2[10] = me[10] * scaleZ; - te2[11] = 0; - te2[12] = 0; - te2[13] = 0; - te2[14] = 0; - te2[15] = 1; - return this; - } - makeRotationFromEuler(euler) { - const te2 = this.elements; - const x = euler.x, y = euler.y, z = euler.z; - const a = Math.cos(x), b = Math.sin(x); - const c = Math.cos(y), d = Math.sin(y); - const e = Math.cos(z), f = Math.sin(z); - if (euler.order === "XYZ") { - const ae = a * e, af = a * f, be = b * e, bf = b * f; - te2[0] = c * e; - te2[4] = -c * f; - te2[8] = d; - te2[1] = af + be * d; - te2[5] = ae - bf * d; - te2[9] = -b * c; - te2[2] = bf - ae * d; - te2[6] = be + af * d; - te2[10] = a * c; - } else if (euler.order === "YXZ") { - const ce = c * e, cf = c * f, de = d * e, df = d * f; - te2[0] = ce + df * b; - te2[4] = de * b - cf; - te2[8] = a * d; - te2[1] = a * f; - te2[5] = a * e; - te2[9] = -b; - te2[2] = cf * b - de; - te2[6] = df + ce * b; - te2[10] = a * c; - } else if (euler.order === "ZXY") { - const ce = c * e, cf = c * f, de = d * e, df = d * f; - te2[0] = ce - df * b; - te2[4] = -a * f; - te2[8] = de + cf * b; - te2[1] = cf + de * b; - te2[5] = a * e; - te2[9] = df - ce * b; - te2[2] = -a * d; - te2[6] = b; - te2[10] = a * c; - } else if (euler.order === "ZYX") { - const ae = a * e, af = a * f, be = b * e, bf = b * f; - te2[0] = c * e; - te2[4] = be * d - af; - te2[8] = ae * d + bf; - te2[1] = c * f; - te2[5] = bf * d + ae; - te2[9] = af * d - be; - te2[2] = -d; - te2[6] = b * c; - te2[10] = a * c; - } else if (euler.order === "YZX") { - const ac = a * c, ad = a * d, bc = b * c, bd = b * d; - te2[0] = c * e; - te2[4] = bd - ac * f; - te2[8] = bc * f + ad; - te2[1] = f; - te2[5] = a * e; - te2[9] = -b * e; - te2[2] = -d * e; - te2[6] = ad * f + bc; - te2[10] = ac - bd * f; - } else if (euler.order === "XZY") { - const ac = a * c, ad = a * d, bc = b * c, bd = b * d; - te2[0] = c * e; - te2[4] = -f; - te2[8] = d * e; - te2[1] = ac * f + bd; - te2[5] = a * e; - te2[9] = ad * f - bc; - te2[2] = bc * f - ad; - te2[6] = b * e; - te2[10] = bd * f + ac; - } - te2[3] = 0; - te2[7] = 0; - te2[11] = 0; - te2[12] = 0; - te2[13] = 0; - te2[14] = 0; - te2[15] = 1; - return this; - } - makeRotationFromQuaternion(q) { - return this.compose(_zero, q, _one); - } - lookAt(eye, target, up) { - const te2 = this.elements; - _z.subVectors(eye, target); - if (_z.lengthSq() === 0) { - _z.z = 1; - } - _z.normalize(); - _x.crossVectors(up, _z); - if (_x.lengthSq() === 0) { - if (Math.abs(up.z) === 1) { - _z.x += 1e-4; - } else { - _z.z += 1e-4; - } - _z.normalize(); - _x.crossVectors(up, _z); - } - _x.normalize(); - _y.crossVectors(_z, _x); - te2[0] = _x.x; - te2[4] = _y.x; - te2[8] = _z.x; - te2[1] = _x.y; - te2[5] = _y.y; - te2[9] = _z.y; - te2[2] = _x.z; - te2[6] = _y.z; - te2[10] = _z.z; - return this; - } - multiply(m) { - return this.multiplyMatrices(this, m); - } - premultiply(m) { - return this.multiplyMatrices(m, this); - } - multiplyMatrices(a, b) { - const ae = a.elements; - const be = b.elements; - const te2 = this.elements; - const a11 = ae[0], a12 = ae[4], a13 = ae[8], a14 = ae[12]; - const a21 = ae[1], a22 = ae[5], a23 = ae[9], a24 = ae[13]; - const a31 = ae[2], a32 = ae[6], a33 = ae[10], a34 = ae[14]; - const a41 = ae[3], a42 = ae[7], a43 = ae[11], a44 = ae[15]; - const b11 = be[0], b12 = be[4], b13 = be[8], b14 = be[12]; - const b21 = be[1], b22 = be[5], b23 = be[9], b24 = be[13]; - const b31 = be[2], b32 = be[6], b33 = be[10], b34 = be[14]; - const b41 = be[3], b42 = be[7], b43 = be[11], b44 = be[15]; - te2[0] = a11 * b11 + a12 * b21 + a13 * b31 + a14 * b41; - te2[4] = a11 * b12 + a12 * b22 + a13 * b32 + a14 * b42; - te2[8] = a11 * b13 + a12 * b23 + a13 * b33 + a14 * b43; - te2[12] = a11 * b14 + a12 * b24 + a13 * b34 + a14 * b44; - te2[1] = a21 * b11 + a22 * b21 + a23 * b31 + a24 * b41; - te2[5] = a21 * b12 + a22 * b22 + a23 * b32 + a24 * b42; - te2[9] = a21 * b13 + a22 * b23 + a23 * b33 + a24 * b43; - te2[13] = a21 * b14 + a22 * b24 + a23 * b34 + a24 * b44; - te2[2] = a31 * b11 + a32 * b21 + a33 * b31 + a34 * b41; - te2[6] = a31 * b12 + a32 * b22 + a33 * b32 + a34 * b42; - te2[10] = a31 * b13 + a32 * b23 + a33 * b33 + a34 * b43; - te2[14] = a31 * b14 + a32 * b24 + a33 * b34 + a34 * b44; - te2[3] = a41 * b11 + a42 * b21 + a43 * b31 + a44 * b41; - te2[7] = a41 * b12 + a42 * b22 + a43 * b32 + a44 * b42; - te2[11] = a41 * b13 + a42 * b23 + a43 * b33 + a44 * b43; - te2[15] = a41 * b14 + a42 * b24 + a43 * b34 + a44 * b44; - return this; - } - multiplyScalar(s) { - const te2 = this.elements; - te2[0] *= s; - te2[4] *= s; - te2[8] *= s; - te2[12] *= s; - te2[1] *= s; - te2[5] *= s; - te2[9] *= s; - te2[13] *= s; - te2[2] *= s; - te2[6] *= s; - te2[10] *= s; - te2[14] *= s; - te2[3] *= s; - te2[7] *= s; - te2[11] *= s; - te2[15] *= s; - return this; - } - determinant() { - const te2 = this.elements; - const n11 = te2[0], n12 = te2[4], n13 = te2[8], n14 = te2[12]; - const n21 = te2[1], n22 = te2[5], n23 = te2[9], n24 = te2[13]; - const n31 = te2[2], n32 = te2[6], n33 = te2[10], n34 = te2[14]; - const n41 = te2[3], n42 = te2[7], n43 = te2[11], n44 = te2[15]; - return n41 * (+n14 * n23 * n32 - n13 * n24 * n32 - n14 * n22 * n33 + n12 * n24 * n33 + n13 * n22 * n34 - n12 * n23 * n34) + n42 * (+n11 * n23 * n34 - n11 * n24 * n33 + n14 * n21 * n33 - n13 * n21 * n34 + n13 * n24 * n31 - n14 * n23 * n31) + n43 * (+n11 * n24 * n32 - n11 * n22 * n34 - n14 * n21 * n32 + n12 * n21 * n34 + n14 * n22 * n31 - n12 * n24 * n31) + n44 * (-n13 * n22 * n31 - n11 * n23 * n32 + n11 * n22 * n33 + n13 * n21 * n32 - n12 * n21 * n33 + n12 * n23 * n31); - } - transpose() { - const te2 = this.elements; - let tmp2; - tmp2 = te2[1]; - te2[1] = te2[4]; - te2[4] = tmp2; - tmp2 = te2[2]; - te2[2] = te2[8]; - te2[8] = tmp2; - tmp2 = te2[6]; - te2[6] = te2[9]; - te2[9] = tmp2; - tmp2 = te2[3]; - te2[3] = te2[12]; - te2[12] = tmp2; - tmp2 = te2[7]; - te2[7] = te2[13]; - te2[13] = tmp2; - tmp2 = te2[11]; - te2[11] = te2[14]; - te2[14] = tmp2; - return this; - } - setPosition(x, y, z) { - const te2 = this.elements; - if (x.isVector3) { - te2[12] = x.x; - te2[13] = x.y; - te2[14] = x.z; - } else { - te2[12] = x; - te2[13] = y; - te2[14] = z; - } - return this; - } - invert() { - const te2 = this.elements, n11 = te2[0], n21 = te2[1], n31 = te2[2], n41 = te2[3], n12 = te2[4], n22 = te2[5], n32 = te2[6], n42 = te2[7], n13 = te2[8], n23 = te2[9], n33 = te2[10], n43 = te2[11], n14 = te2[12], n24 = te2[13], n34 = te2[14], n44 = te2[15], t11 = n23 * n34 * n42 - n24 * n33 * n42 + n24 * n32 * n43 - n22 * n34 * n43 - n23 * n32 * n44 + n22 * n33 * n44, t12 = n14 * n33 * n42 - n13 * n34 * n42 - n14 * n32 * n43 + n12 * n34 * n43 + n13 * n32 * n44 - n12 * n33 * n44, t13 = n13 * n24 * n42 - n14 * n23 * n42 + n14 * n22 * n43 - n12 * n24 * n43 - n13 * n22 * n44 + n12 * n23 * n44, t14 = n14 * n23 * n32 - n13 * n24 * n32 - n14 * n22 * n33 + n12 * n24 * n33 + n13 * n22 * n34 - n12 * n23 * n34; - const det = n11 * t11 + n21 * t12 + n31 * t13 + n41 * t14; - if (det === 0) return this.set(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0); - const detInv = 1 / det; - te2[0] = t11 * detInv; - te2[1] = (n24 * n33 * n41 - n23 * n34 * n41 - n24 * n31 * n43 + n21 * n34 * n43 + n23 * n31 * n44 - n21 * n33 * n44) * detInv; - te2[2] = (n22 * n34 * n41 - n24 * n32 * n41 + n24 * n31 * n42 - n21 * n34 * n42 - n22 * n31 * n44 + n21 * n32 * n44) * detInv; - te2[3] = (n23 * n32 * n41 - n22 * n33 * n41 - n23 * n31 * n42 + n21 * n33 * n42 + n22 * n31 * n43 - n21 * n32 * n43) * detInv; - te2[4] = t12 * detInv; - te2[5] = (n13 * n34 * n41 - n14 * n33 * n41 + n14 * n31 * n43 - n11 * n34 * n43 - n13 * n31 * n44 + n11 * n33 * n44) * detInv; - te2[6] = (n14 * n32 * n41 - n12 * n34 * n41 - n14 * n31 * n42 + n11 * n34 * n42 + n12 * n31 * n44 - n11 * n32 * n44) * detInv; - te2[7] = (n12 * n33 * n41 - n13 * n32 * n41 + n13 * n31 * n42 - n11 * n33 * n42 - n12 * n31 * n43 + n11 * n32 * n43) * detInv; - te2[8] = t13 * detInv; - te2[9] = (n14 * n23 * n41 - n13 * n24 * n41 - n14 * n21 * n43 + n11 * n24 * n43 + n13 * n21 * n44 - n11 * n23 * n44) * detInv; - te2[10] = (n12 * n24 * n41 - n14 * n22 * n41 + n14 * n21 * n42 - n11 * n24 * n42 - n12 * n21 * n44 + n11 * n22 * n44) * detInv; - te2[11] = (n13 * n22 * n41 - n12 * n23 * n41 - n13 * n21 * n42 + n11 * n23 * n42 + n12 * n21 * n43 - n11 * n22 * n43) * detInv; - te2[12] = t14 * detInv; - te2[13] = (n13 * n24 * n31 - n14 * n23 * n31 + n14 * n21 * n33 - n11 * n24 * n33 - n13 * n21 * n34 + n11 * n23 * n34) * detInv; - te2[14] = (n14 * n22 * n31 - n12 * n24 * n31 - n14 * n21 * n32 + n11 * n24 * n32 + n12 * n21 * n34 - n11 * n22 * n34) * detInv; - te2[15] = (n12 * n23 * n31 - n13 * n22 * n31 + n13 * n21 * n32 - n11 * n23 * n32 - n12 * n21 * n33 + n11 * n22 * n33) * detInv; - return this; - } - scale(v) { - const te2 = this.elements; - const x = v.x, y = v.y, z = v.z; - te2[0] *= x; - te2[4] *= y; - te2[8] *= z; - te2[1] *= x; - te2[5] *= y; - te2[9] *= z; - te2[2] *= x; - te2[6] *= y; - te2[10] *= z; - te2[3] *= x; - te2[7] *= y; - te2[11] *= z; - return this; - } - getMaxScaleOnAxis() { - const te2 = this.elements; - const scaleXSq = te2[0] * te2[0] + te2[1] * te2[1] + te2[2] * te2[2]; - const scaleYSq = te2[4] * te2[4] + te2[5] * te2[5] + te2[6] * te2[6]; - const scaleZSq = te2[8] * te2[8] + te2[9] * te2[9] + te2[10] * te2[10]; - return Math.sqrt(Math.max(scaleXSq, scaleYSq, scaleZSq)); - } - makeTranslation(x, y, z) { - if (x.isVector3) { - this.set( - 1, - 0, - 0, - x.x, - 0, - 1, - 0, - x.y, - 0, - 0, - 1, - x.z, - 0, - 0, - 0, - 1 - ); - } else { - this.set( - 1, - 0, - 0, - x, - 0, - 1, - 0, - y, - 0, - 0, - 1, - z, - 0, - 0, - 0, - 1 - ); - } - return this; - } - makeRotationX(theta) { - const c = Math.cos(theta), s = Math.sin(theta); - this.set( - 1, - 0, - 0, - 0, - 0, - c, - -s, - 0, - 0, - s, - c, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - makeRotationY(theta) { - const c = Math.cos(theta), s = Math.sin(theta); - this.set( - c, - 0, - s, - 0, - 0, - 1, - 0, - 0, - -s, - 0, - c, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - makeRotationZ(theta) { - const c = Math.cos(theta), s = Math.sin(theta); - this.set( - c, - -s, - 0, - 0, - s, - c, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - makeRotationAxis(axis, angle) { - const c = Math.cos(angle); - const s = Math.sin(angle); - const t2 = 1 - c; - const x = axis.x, y = axis.y, z = axis.z; - const tx = t2 * x, ty = t2 * y; - this.set( - tx * x + c, - tx * y - s * z, - tx * z + s * y, - 0, - tx * y + s * z, - ty * y + c, - ty * z - s * x, - 0, - tx * z - s * y, - ty * z + s * x, - t2 * z * z + c, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - makeScale(x, y, z) { - this.set( - x, - 0, - 0, - 0, - 0, - y, - 0, - 0, - 0, - 0, - z, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - makeShear(xy, xz, yx, yz, zx, zy) { - this.set( - 1, - yx, - zx, - 0, - xy, - 1, - zy, - 0, - xz, - yz, - 1, - 0, - 0, - 0, - 0, - 1 - ); - return this; - } - compose(position, quaternion, scale) { - const te2 = this.elements; - const x = quaternion._x, y = quaternion._y, z = quaternion._z, w = quaternion._w; - const x2 = x + x, y2 = y + y, z2 = z + z; - const xx = x * x2, xy = x * y2, xz = x * z2; - const yy = y * y2, yz = y * z2, zz = z * z2; - const wx = w * x2, wy = w * y2, wz = w * z2; - const sx = scale.x, sy = scale.y, sz = scale.z; - te2[0] = (1 - (yy + zz)) * sx; - te2[1] = (xy + wz) * sx; - te2[2] = (xz - wy) * sx; - te2[3] = 0; - te2[4] = (xy - wz) * sy; - te2[5] = (1 - (xx + zz)) * sy; - te2[6] = (yz + wx) * sy; - te2[7] = 0; - te2[8] = (xz + wy) * sz; - te2[9] = (yz - wx) * sz; - te2[10] = (1 - (xx + yy)) * sz; - te2[11] = 0; - te2[12] = position.x; - te2[13] = position.y; - te2[14] = position.z; - te2[15] = 1; - return this; - } - decompose(position, quaternion, scale) { - const te2 = this.elements; - let sx = _v1$5.set(te2[0], te2[1], te2[2]).length(); - const sy = _v1$5.set(te2[4], te2[5], te2[6]).length(); - const sz = _v1$5.set(te2[8], te2[9], te2[10]).length(); - const det = this.determinant(); - if (det < 0) sx = -sx; - position.x = te2[12]; - position.y = te2[13]; - position.z = te2[14]; - _m1$4.copy(this); - const invSX = 1 / sx; - const invSY = 1 / sy; - const invSZ = 1 / sz; - _m1$4.elements[0] *= invSX; - _m1$4.elements[1] *= invSX; - _m1$4.elements[2] *= invSX; - _m1$4.elements[4] *= invSY; - _m1$4.elements[5] *= invSY; - _m1$4.elements[6] *= invSY; - _m1$4.elements[8] *= invSZ; - _m1$4.elements[9] *= invSZ; - _m1$4.elements[10] *= invSZ; - quaternion.setFromRotationMatrix(_m1$4); - scale.x = sx; - scale.y = sy; - scale.z = sz; - return this; - } - makePerspective(left, right, top, bottom, near, far, coordinateSystem = WebGLCoordinateSystem) { - const te2 = this.elements; - const x = 2 * near / (right - left); - const y = 2 * near / (top - bottom); - const a = (right + left) / (right - left); - const b = (top + bottom) / (top - bottom); - let c, d; - if (coordinateSystem === WebGLCoordinateSystem) { - c = -(far + near) / (far - near); - d = -2 * far * near / (far - near); - } else if (coordinateSystem === WebGPUCoordinateSystem) { - c = -far / (far - near); - d = -far * near / (far - near); - } else { - throw new Error("THREE.Matrix4.makePerspective(): Invalid coordinate system: " + coordinateSystem); - } - te2[0] = x; - te2[4] = 0; - te2[8] = a; - te2[12] = 0; - te2[1] = 0; - te2[5] = y; - te2[9] = b; - te2[13] = 0; - te2[2] = 0; - te2[6] = 0; - te2[10] = c; - te2[14] = d; - te2[3] = 0; - te2[7] = 0; - te2[11] = -1; - te2[15] = 0; - return this; - } - makeOrthographic(left, right, top, bottom, near, far, coordinateSystem = WebGLCoordinateSystem) { - const te2 = this.elements; - const w = 1 / (right - left); - const h = 1 / (top - bottom); - const p = 1 / (far - near); - const x = (right + left) * w; - const y = (top + bottom) * h; - let z, zInv; - if (coordinateSystem === WebGLCoordinateSystem) { - z = (far + near) * p; - zInv = -2 * p; - } else if (coordinateSystem === WebGPUCoordinateSystem) { - z = near * p; - zInv = -1 * p; - } else { - throw new Error("THREE.Matrix4.makeOrthographic(): Invalid coordinate system: " + coordinateSystem); - } - te2[0] = 2 * w; - te2[4] = 0; - te2[8] = 0; - te2[12] = -x; - te2[1] = 0; - te2[5] = 2 * h; - te2[9] = 0; - te2[13] = -y; - te2[2] = 0; - te2[6] = 0; - te2[10] = zInv; - te2[14] = -z; - te2[3] = 0; - te2[7] = 0; - te2[11] = 0; - te2[15] = 1; - return this; - } - equals(matrix) { - const te2 = this.elements; - const me = matrix.elements; - for (let i = 0; i < 16; i++) { - if (te2[i] !== me[i]) return false; - } - return true; - } - fromArray(array, offset = 0) { - for (let i = 0; i < 16; i++) { - this.elements[i] = array[i + offset]; - } - return this; - } - toArray(array = [], offset = 0) { - const te2 = this.elements; - array[offset] = te2[0]; - array[offset + 1] = te2[1]; - array[offset + 2] = te2[2]; - array[offset + 3] = te2[3]; - array[offset + 4] = te2[4]; - array[offset + 5] = te2[5]; - array[offset + 6] = te2[6]; - array[offset + 7] = te2[7]; - array[offset + 8] = te2[8]; - array[offset + 9] = te2[9]; - array[offset + 10] = te2[10]; - array[offset + 11] = te2[11]; - array[offset + 12] = te2[12]; - array[offset + 13] = te2[13]; - array[offset + 14] = te2[14]; - array[offset + 15] = te2[15]; - return array; - } -} -const _v1$5 = /* @__PURE__ */ new Vector3(); -const _m1$4 = /* @__PURE__ */ new Matrix4(); -const _zero = /* @__PURE__ */ new Vector3(0, 0, 0); -const _one = /* @__PURE__ */ new Vector3(1, 1, 1); -const _x = /* @__PURE__ */ new Vector3(); -const _y = /* @__PURE__ */ new Vector3(); -const _z = /* @__PURE__ */ new Vector3(); -const _matrix$2 = /* @__PURE__ */ new Matrix4(); -const _quaternion$3 = /* @__PURE__ */ new Quaternion(); -class Euler { - static { - __name(this, "Euler"); - } - constructor(x = 0, y = 0, z = 0, order = Euler.DEFAULT_ORDER) { - this.isEuler = true; - this._x = x; - this._y = y; - this._z = z; - this._order = order; - } - get x() { - return this._x; - } - set x(value) { - this._x = value; - this._onChangeCallback(); - } - get y() { - return this._y; - } - set y(value) { - this._y = value; - this._onChangeCallback(); - } - get z() { - return this._z; - } - set z(value) { - this._z = value; - this._onChangeCallback(); - } - get order() { - return this._order; - } - set order(value) { - this._order = value; - this._onChangeCallback(); - } - set(x, y, z, order = this._order) { - this._x = x; - this._y = y; - this._z = z; - this._order = order; - this._onChangeCallback(); - return this; - } - clone() { - return new this.constructor(this._x, this._y, this._z, this._order); - } - copy(euler) { - this._x = euler._x; - this._y = euler._y; - this._z = euler._z; - this._order = euler._order; - this._onChangeCallback(); - return this; - } - setFromRotationMatrix(m, order = this._order, update = true) { - const te2 = m.elements; - const m11 = te2[0], m12 = te2[4], m13 = te2[8]; - const m21 = te2[1], m22 = te2[5], m23 = te2[9]; - const m31 = te2[2], m32 = te2[6], m33 = te2[10]; - switch (order) { - case "XYZ": - this._y = Math.asin(clamp(m13, -1, 1)); - if (Math.abs(m13) < 0.9999999) { - this._x = Math.atan2(-m23, m33); - this._z = Math.atan2(-m12, m11); - } else { - this._x = Math.atan2(m32, m22); - this._z = 0; - } - break; - case "YXZ": - this._x = Math.asin(-clamp(m23, -1, 1)); - if (Math.abs(m23) < 0.9999999) { - this._y = Math.atan2(m13, m33); - this._z = Math.atan2(m21, m22); - } else { - this._y = Math.atan2(-m31, m11); - this._z = 0; - } - break; - case "ZXY": - this._x = Math.asin(clamp(m32, -1, 1)); - if (Math.abs(m32) < 0.9999999) { - this._y = Math.atan2(-m31, m33); - this._z = Math.atan2(-m12, m22); - } else { - this._y = 0; - this._z = Math.atan2(m21, m11); - } - break; - case "ZYX": - this._y = Math.asin(-clamp(m31, -1, 1)); - if (Math.abs(m31) < 0.9999999) { - this._x = Math.atan2(m32, m33); - this._z = Math.atan2(m21, m11); - } else { - this._x = 0; - this._z = Math.atan2(-m12, m22); - } - break; - case "YZX": - this._z = Math.asin(clamp(m21, -1, 1)); - if (Math.abs(m21) < 0.9999999) { - this._x = Math.atan2(-m23, m22); - this._y = Math.atan2(-m31, m11); - } else { - this._x = 0; - this._y = Math.atan2(m13, m33); - } - break; - case "XZY": - this._z = Math.asin(-clamp(m12, -1, 1)); - if (Math.abs(m12) < 0.9999999) { - this._x = Math.atan2(m32, m22); - this._y = Math.atan2(m13, m11); - } else { - this._x = Math.atan2(-m23, m33); - this._y = 0; - } - break; - default: - console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: " + order); - } - this._order = order; - if (update === true) this._onChangeCallback(); - return this; - } - setFromQuaternion(q, order, update) { - _matrix$2.makeRotationFromQuaternion(q); - return this.setFromRotationMatrix(_matrix$2, order, update); - } - setFromVector3(v, order = this._order) { - return this.set(v.x, v.y, v.z, order); - } - reorder(newOrder) { - _quaternion$3.setFromEuler(this); - return this.setFromQuaternion(_quaternion$3, newOrder); - } - equals(euler) { - return euler._x === this._x && euler._y === this._y && euler._z === this._z && euler._order === this._order; - } - fromArray(array) { - this._x = array[0]; - this._y = array[1]; - this._z = array[2]; - if (array[3] !== void 0) this._order = array[3]; - this._onChangeCallback(); - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this._x; - array[offset + 1] = this._y; - array[offset + 2] = this._z; - array[offset + 3] = this._order; - return array; - } - _onChange(callback) { - this._onChangeCallback = callback; - return this; - } - _onChangeCallback() { - } - *[Symbol.iterator]() { - yield this._x; - yield this._y; - yield this._z; - yield this._order; - } -} -Euler.DEFAULT_ORDER = "XYZ"; -class Layers { - static { - __name(this, "Layers"); - } - constructor() { - this.mask = 1 | 0; - } - set(channel) { - this.mask = (1 << channel | 0) >>> 0; - } - enable(channel) { - this.mask |= 1 << channel | 0; - } - enableAll() { - this.mask = 4294967295 | 0; - } - toggle(channel) { - this.mask ^= 1 << channel | 0; - } - disable(channel) { - this.mask &= ~(1 << channel | 0); - } - disableAll() { - this.mask = 0; - } - test(layers) { - return (this.mask & layers.mask) !== 0; - } - isEnabled(channel) { - return (this.mask & (1 << channel | 0)) !== 0; - } -} -let _object3DId = 0; -const _v1$4 = /* @__PURE__ */ new Vector3(); -const _q1 = /* @__PURE__ */ new Quaternion(); -const _m1$3 = /* @__PURE__ */ new Matrix4(); -const _target = /* @__PURE__ */ new Vector3(); -const _position$3 = /* @__PURE__ */ new Vector3(); -const _scale$2 = /* @__PURE__ */ new Vector3(); -const _quaternion$2 = /* @__PURE__ */ new Quaternion(); -const _xAxis = /* @__PURE__ */ new Vector3(1, 0, 0); -const _yAxis = /* @__PURE__ */ new Vector3(0, 1, 0); -const _zAxis = /* @__PURE__ */ new Vector3(0, 0, 1); -const _addedEvent = { type: "added" }; -const _removedEvent = { type: "removed" }; -const _childaddedEvent = { type: "childadded", child: null }; -const _childremovedEvent = { type: "childremoved", child: null }; -class Object3D extends EventDispatcher { - static { - __name(this, "Object3D"); - } - constructor() { - super(); - this.isObject3D = true; - Object.defineProperty(this, "id", { value: _object3DId++ }); - this.uuid = generateUUID(); - this.name = ""; - this.type = "Object3D"; - this.parent = null; - this.children = []; - this.up = Object3D.DEFAULT_UP.clone(); - const position = new Vector3(); - const rotation = new Euler(); - const quaternion = new Quaternion(); - const scale = new Vector3(1, 1, 1); - function onRotationChange() { - quaternion.setFromEuler(rotation, false); - } - __name(onRotationChange, "onRotationChange"); - function onQuaternionChange() { - rotation.setFromQuaternion(quaternion, void 0, false); - } - __name(onQuaternionChange, "onQuaternionChange"); - rotation._onChange(onRotationChange); - quaternion._onChange(onQuaternionChange); - Object.defineProperties(this, { - position: { - configurable: true, - enumerable: true, - value: position - }, - rotation: { - configurable: true, - enumerable: true, - value: rotation - }, - quaternion: { - configurable: true, - enumerable: true, - value: quaternion - }, - scale: { - configurable: true, - enumerable: true, - value: scale - }, - modelViewMatrix: { - value: new Matrix4() - }, - normalMatrix: { - value: new Matrix3() - } - }); - this.matrix = new Matrix4(); - this.matrixWorld = new Matrix4(); - this.matrixAutoUpdate = Object3D.DEFAULT_MATRIX_AUTO_UPDATE; - this.matrixWorldAutoUpdate = Object3D.DEFAULT_MATRIX_WORLD_AUTO_UPDATE; - this.matrixWorldNeedsUpdate = false; - this.layers = new Layers(); - this.visible = true; - this.castShadow = false; - this.receiveShadow = false; - this.frustumCulled = true; - this.renderOrder = 0; - this.animations = []; - this.userData = {}; - } - onBeforeShadow() { - } - onAfterShadow() { - } - onBeforeRender() { - } - onAfterRender() { - } - applyMatrix4(matrix) { - if (this.matrixAutoUpdate) this.updateMatrix(); - this.matrix.premultiply(matrix); - this.matrix.decompose(this.position, this.quaternion, this.scale); - } - applyQuaternion(q) { - this.quaternion.premultiply(q); - return this; - } - setRotationFromAxisAngle(axis, angle) { - this.quaternion.setFromAxisAngle(axis, angle); - } - setRotationFromEuler(euler) { - this.quaternion.setFromEuler(euler, true); - } - setRotationFromMatrix(m) { - this.quaternion.setFromRotationMatrix(m); - } - setRotationFromQuaternion(q) { - this.quaternion.copy(q); - } - rotateOnAxis(axis, angle) { - _q1.setFromAxisAngle(axis, angle); - this.quaternion.multiply(_q1); - return this; - } - rotateOnWorldAxis(axis, angle) { - _q1.setFromAxisAngle(axis, angle); - this.quaternion.premultiply(_q1); - return this; - } - rotateX(angle) { - return this.rotateOnAxis(_xAxis, angle); - } - rotateY(angle) { - return this.rotateOnAxis(_yAxis, angle); - } - rotateZ(angle) { - return this.rotateOnAxis(_zAxis, angle); - } - translateOnAxis(axis, distance) { - _v1$4.copy(axis).applyQuaternion(this.quaternion); - this.position.add(_v1$4.multiplyScalar(distance)); - return this; - } - translateX(distance) { - return this.translateOnAxis(_xAxis, distance); - } - translateY(distance) { - return this.translateOnAxis(_yAxis, distance); - } - translateZ(distance) { - return this.translateOnAxis(_zAxis, distance); - } - localToWorld(vector) { - this.updateWorldMatrix(true, false); - return vector.applyMatrix4(this.matrixWorld); - } - worldToLocal(vector) { - this.updateWorldMatrix(true, false); - return vector.applyMatrix4(_m1$3.copy(this.matrixWorld).invert()); - } - lookAt(x, y, z) { - if (x.isVector3) { - _target.copy(x); - } else { - _target.set(x, y, z); - } - const parent = this.parent; - this.updateWorldMatrix(true, false); - _position$3.setFromMatrixPosition(this.matrixWorld); - if (this.isCamera || this.isLight) { - _m1$3.lookAt(_position$3, _target, this.up); - } else { - _m1$3.lookAt(_target, _position$3, this.up); - } - this.quaternion.setFromRotationMatrix(_m1$3); - if (parent) { - _m1$3.extractRotation(parent.matrixWorld); - _q1.setFromRotationMatrix(_m1$3); - this.quaternion.premultiply(_q1.invert()); - } - } - add(object) { - if (arguments.length > 1) { - for (let i = 0; i < arguments.length; i++) { - this.add(arguments[i]); - } - return this; - } - if (object === this) { - console.error("THREE.Object3D.add: object can't be added as a child of itself.", object); - return this; - } - if (object && object.isObject3D) { - object.removeFromParent(); - object.parent = this; - this.children.push(object); - object.dispatchEvent(_addedEvent); - _childaddedEvent.child = object; - this.dispatchEvent(_childaddedEvent); - _childaddedEvent.child = null; - } else { - console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.", object); - } - return this; - } - remove(object) { - if (arguments.length > 1) { - for (let i = 0; i < arguments.length; i++) { - this.remove(arguments[i]); - } - return this; - } - const index = this.children.indexOf(object); - if (index !== -1) { - object.parent = null; - this.children.splice(index, 1); - object.dispatchEvent(_removedEvent); - _childremovedEvent.child = object; - this.dispatchEvent(_childremovedEvent); - _childremovedEvent.child = null; - } - return this; - } - removeFromParent() { - const parent = this.parent; - if (parent !== null) { - parent.remove(this); - } - return this; - } - clear() { - return this.remove(...this.children); - } - attach(object) { - this.updateWorldMatrix(true, false); - _m1$3.copy(this.matrixWorld).invert(); - if (object.parent !== null) { - object.parent.updateWorldMatrix(true, false); - _m1$3.multiply(object.parent.matrixWorld); - } - object.applyMatrix4(_m1$3); - object.removeFromParent(); - object.parent = this; - this.children.push(object); - object.updateWorldMatrix(false, true); - object.dispatchEvent(_addedEvent); - _childaddedEvent.child = object; - this.dispatchEvent(_childaddedEvent); - _childaddedEvent.child = null; - return this; - } - getObjectById(id2) { - return this.getObjectByProperty("id", id2); - } - getObjectByName(name) { - return this.getObjectByProperty("name", name); - } - getObjectByProperty(name, value) { - if (this[name] === value) return this; - for (let i = 0, l = this.children.length; i < l; i++) { - const child = this.children[i]; - const object = child.getObjectByProperty(name, value); - if (object !== void 0) { - return object; - } - } - return void 0; - } - getObjectsByProperty(name, value, result = []) { - if (this[name] === value) result.push(this); - const children = this.children; - for (let i = 0, l = children.length; i < l; i++) { - children[i].getObjectsByProperty(name, value, result); - } - return result; - } - getWorldPosition(target) { - this.updateWorldMatrix(true, false); - return target.setFromMatrixPosition(this.matrixWorld); - } - getWorldQuaternion(target) { - this.updateWorldMatrix(true, false); - this.matrixWorld.decompose(_position$3, target, _scale$2); - return target; - } - getWorldScale(target) { - this.updateWorldMatrix(true, false); - this.matrixWorld.decompose(_position$3, _quaternion$2, target); - return target; - } - getWorldDirection(target) { - this.updateWorldMatrix(true, false); - const e = this.matrixWorld.elements; - return target.set(e[8], e[9], e[10]).normalize(); - } - raycast() { - } - traverse(callback) { - callback(this); - const children = this.children; - for (let i = 0, l = children.length; i < l; i++) { - children[i].traverse(callback); - } - } - traverseVisible(callback) { - if (this.visible === false) return; - callback(this); - const children = this.children; - for (let i = 0, l = children.length; i < l; i++) { - children[i].traverseVisible(callback); - } - } - traverseAncestors(callback) { - const parent = this.parent; - if (parent !== null) { - callback(parent); - parent.traverseAncestors(callback); - } - } - updateMatrix() { - this.matrix.compose(this.position, this.quaternion, this.scale); - this.matrixWorldNeedsUpdate = true; - } - updateMatrixWorld(force) { - if (this.matrixAutoUpdate) this.updateMatrix(); - if (this.matrixWorldNeedsUpdate || force) { - if (this.matrixWorldAutoUpdate === true) { - if (this.parent === null) { - this.matrixWorld.copy(this.matrix); - } else { - this.matrixWorld.multiplyMatrices(this.parent.matrixWorld, this.matrix); - } - } - this.matrixWorldNeedsUpdate = false; - force = true; - } - const children = this.children; - for (let i = 0, l = children.length; i < l; i++) { - const child = children[i]; - child.updateMatrixWorld(force); - } - } - updateWorldMatrix(updateParents, updateChildren) { - const parent = this.parent; - if (updateParents === true && parent !== null) { - parent.updateWorldMatrix(true, false); - } - if (this.matrixAutoUpdate) this.updateMatrix(); - if (this.matrixWorldAutoUpdate === true) { - if (this.parent === null) { - this.matrixWorld.copy(this.matrix); - } else { - this.matrixWorld.multiplyMatrices(this.parent.matrixWorld, this.matrix); - } - } - if (updateChildren === true) { - const children = this.children; - for (let i = 0, l = children.length; i < l; i++) { - const child = children[i]; - child.updateWorldMatrix(false, true); - } - } - } - toJSON(meta) { - const isRootObject = meta === void 0 || typeof meta === "string"; - const output = {}; - if (isRootObject) { - meta = { - geometries: {}, - materials: {}, - textures: {}, - images: {}, - shapes: {}, - skeletons: {}, - animations: {}, - nodes: {} - }; - output.metadata = { - version: 4.6, - type: "Object", - generator: "Object3D.toJSON" - }; - } - const object = {}; - object.uuid = this.uuid; - object.type = this.type; - if (this.name !== "") object.name = this.name; - if (this.castShadow === true) object.castShadow = true; - if (this.receiveShadow === true) object.receiveShadow = true; - if (this.visible === false) object.visible = false; - if (this.frustumCulled === false) object.frustumCulled = false; - if (this.renderOrder !== 0) object.renderOrder = this.renderOrder; - if (Object.keys(this.userData).length > 0) object.userData = this.userData; - object.layers = this.layers.mask; - object.matrix = this.matrix.toArray(); - object.up = this.up.toArray(); - if (this.matrixAutoUpdate === false) object.matrixAutoUpdate = false; - if (this.isInstancedMesh) { - object.type = "InstancedMesh"; - object.count = this.count; - object.instanceMatrix = this.instanceMatrix.toJSON(); - if (this.instanceColor !== null) object.instanceColor = this.instanceColor.toJSON(); - } - if (this.isBatchedMesh) { - object.type = "BatchedMesh"; - object.perObjectFrustumCulled = this.perObjectFrustumCulled; - object.sortObjects = this.sortObjects; - object.drawRanges = this._drawRanges; - object.reservedRanges = this._reservedRanges; - object.visibility = this._visibility; - object.active = this._active; - object.bounds = this._bounds.map((bound) => ({ - boxInitialized: bound.boxInitialized, - boxMin: bound.box.min.toArray(), - boxMax: bound.box.max.toArray(), - sphereInitialized: bound.sphereInitialized, - sphereRadius: bound.sphere.radius, - sphereCenter: bound.sphere.center.toArray() - })); - object.maxInstanceCount = this._maxInstanceCount; - object.maxVertexCount = this._maxVertexCount; - object.maxIndexCount = this._maxIndexCount; - object.geometryInitialized = this._geometryInitialized; - object.geometryCount = this._geometryCount; - object.matricesTexture = this._matricesTexture.toJSON(meta); - if (this._colorsTexture !== null) object.colorsTexture = this._colorsTexture.toJSON(meta); - if (this.boundingSphere !== null) { - object.boundingSphere = { - center: object.boundingSphere.center.toArray(), - radius: object.boundingSphere.radius - }; - } - if (this.boundingBox !== null) { - object.boundingBox = { - min: object.boundingBox.min.toArray(), - max: object.boundingBox.max.toArray() - }; - } - } - function serialize(library, element) { - if (library[element.uuid] === void 0) { - library[element.uuid] = element.toJSON(meta); - } - return element.uuid; - } - __name(serialize, "serialize"); - if (this.isScene) { - if (this.background) { - if (this.background.isColor) { - object.background = this.background.toJSON(); - } else if (this.background.isTexture) { - object.background = this.background.toJSON(meta).uuid; - } - } - if (this.environment && this.environment.isTexture && this.environment.isRenderTargetTexture !== true) { - object.environment = this.environment.toJSON(meta).uuid; - } - } else if (this.isMesh || this.isLine || this.isPoints) { - object.geometry = serialize(meta.geometries, this.geometry); - const parameters = this.geometry.parameters; - if (parameters !== void 0 && parameters.shapes !== void 0) { - const shapes = parameters.shapes; - if (Array.isArray(shapes)) { - for (let i = 0, l = shapes.length; i < l; i++) { - const shape = shapes[i]; - serialize(meta.shapes, shape); - } - } else { - serialize(meta.shapes, shapes); - } - } - } - if (this.isSkinnedMesh) { - object.bindMode = this.bindMode; - object.bindMatrix = this.bindMatrix.toArray(); - if (this.skeleton !== void 0) { - serialize(meta.skeletons, this.skeleton); - object.skeleton = this.skeleton.uuid; - } - } - if (this.material !== void 0) { - if (Array.isArray(this.material)) { - const uuids = []; - for (let i = 0, l = this.material.length; i < l; i++) { - uuids.push(serialize(meta.materials, this.material[i])); - } - object.material = uuids; - } else { - object.material = serialize(meta.materials, this.material); - } - } - if (this.children.length > 0) { - object.children = []; - for (let i = 0; i < this.children.length; i++) { - object.children.push(this.children[i].toJSON(meta).object); - } - } - if (this.animations.length > 0) { - object.animations = []; - for (let i = 0; i < this.animations.length; i++) { - const animation = this.animations[i]; - object.animations.push(serialize(meta.animations, animation)); - } - } - if (isRootObject) { - const geometries = extractFromCache(meta.geometries); - const materials = extractFromCache(meta.materials); - const textures = extractFromCache(meta.textures); - const images = extractFromCache(meta.images); - const shapes = extractFromCache(meta.shapes); - const skeletons = extractFromCache(meta.skeletons); - const animations = extractFromCache(meta.animations); - const nodes = extractFromCache(meta.nodes); - if (geometries.length > 0) output.geometries = geometries; - if (materials.length > 0) output.materials = materials; - if (textures.length > 0) output.textures = textures; - if (images.length > 0) output.images = images; - if (shapes.length > 0) output.shapes = shapes; - if (skeletons.length > 0) output.skeletons = skeletons; - if (animations.length > 0) output.animations = animations; - if (nodes.length > 0) output.nodes = nodes; - } - output.object = object; - return output; - function extractFromCache(cache) { - const values = []; - for (const key in cache) { - const data = cache[key]; - delete data.metadata; - values.push(data); - } - return values; - } - __name(extractFromCache, "extractFromCache"); - } - clone(recursive) { - return new this.constructor().copy(this, recursive); - } - copy(source, recursive = true) { - this.name = source.name; - this.up.copy(source.up); - this.position.copy(source.position); - this.rotation.order = source.rotation.order; - this.quaternion.copy(source.quaternion); - this.scale.copy(source.scale); - this.matrix.copy(source.matrix); - this.matrixWorld.copy(source.matrixWorld); - this.matrixAutoUpdate = source.matrixAutoUpdate; - this.matrixWorldAutoUpdate = source.matrixWorldAutoUpdate; - this.matrixWorldNeedsUpdate = source.matrixWorldNeedsUpdate; - this.layers.mask = source.layers.mask; - this.visible = source.visible; - this.castShadow = source.castShadow; - this.receiveShadow = source.receiveShadow; - this.frustumCulled = source.frustumCulled; - this.renderOrder = source.renderOrder; - this.animations = source.animations.slice(); - this.userData = JSON.parse(JSON.stringify(source.userData)); - if (recursive === true) { - for (let i = 0; i < source.children.length; i++) { - const child = source.children[i]; - this.add(child.clone()); - } - } - return this; - } -} -Object3D.DEFAULT_UP = /* @__PURE__ */ new Vector3(0, 1, 0); -Object3D.DEFAULT_MATRIX_AUTO_UPDATE = true; -Object3D.DEFAULT_MATRIX_WORLD_AUTO_UPDATE = true; -const _v0$2 = /* @__PURE__ */ new Vector3(); -const _v1$3 = /* @__PURE__ */ new Vector3(); -const _v2$2 = /* @__PURE__ */ new Vector3(); -const _v3$2 = /* @__PURE__ */ new Vector3(); -const _vab = /* @__PURE__ */ new Vector3(); -const _vac = /* @__PURE__ */ new Vector3(); -const _vbc = /* @__PURE__ */ new Vector3(); -const _vap = /* @__PURE__ */ new Vector3(); -const _vbp = /* @__PURE__ */ new Vector3(); -const _vcp = /* @__PURE__ */ new Vector3(); -const _v40 = /* @__PURE__ */ new Vector4(); -const _v41 = /* @__PURE__ */ new Vector4(); -const _v42 = /* @__PURE__ */ new Vector4(); -class Triangle { - static { - __name(this, "Triangle"); - } - constructor(a = new Vector3(), b = new Vector3(), c = new Vector3()) { - this.a = a; - this.b = b; - this.c = c; - } - static getNormal(a, b, c, target) { - target.subVectors(c, b); - _v0$2.subVectors(a, b); - target.cross(_v0$2); - const targetLengthSq = target.lengthSq(); - if (targetLengthSq > 0) { - return target.multiplyScalar(1 / Math.sqrt(targetLengthSq)); - } - return target.set(0, 0, 0); - } - // static/instance method to calculate barycentric coordinates - // based on: http://www.blackpawn.com/texts/pointinpoly/default.html - static getBarycoord(point, a, b, c, target) { - _v0$2.subVectors(c, a); - _v1$3.subVectors(b, a); - _v2$2.subVectors(point, a); - const dot00 = _v0$2.dot(_v0$2); - const dot01 = _v0$2.dot(_v1$3); - const dot02 = _v0$2.dot(_v2$2); - const dot11 = _v1$3.dot(_v1$3); - const dot12 = _v1$3.dot(_v2$2); - const denom = dot00 * dot11 - dot01 * dot01; - if (denom === 0) { - target.set(0, 0, 0); - return null; - } - const invDenom = 1 / denom; - const u = (dot11 * dot02 - dot01 * dot12) * invDenom; - const v = (dot00 * dot12 - dot01 * dot02) * invDenom; - return target.set(1 - u - v, v, u); - } - static containsPoint(point, a, b, c) { - if (this.getBarycoord(point, a, b, c, _v3$2) === null) { - return false; - } - return _v3$2.x >= 0 && _v3$2.y >= 0 && _v3$2.x + _v3$2.y <= 1; - } - static getInterpolation(point, p1, p2, p3, v1, v2, v3, target) { - if (this.getBarycoord(point, p1, p2, p3, _v3$2) === null) { - target.x = 0; - target.y = 0; - if ("z" in target) target.z = 0; - if ("w" in target) target.w = 0; - return null; - } - target.setScalar(0); - target.addScaledVector(v1, _v3$2.x); - target.addScaledVector(v2, _v3$2.y); - target.addScaledVector(v3, _v3$2.z); - return target; - } - static getInterpolatedAttribute(attr, i1, i2, i3, barycoord, target) { - _v40.setScalar(0); - _v41.setScalar(0); - _v42.setScalar(0); - _v40.fromBufferAttribute(attr, i1); - _v41.fromBufferAttribute(attr, i2); - _v42.fromBufferAttribute(attr, i3); - target.setScalar(0); - target.addScaledVector(_v40, barycoord.x); - target.addScaledVector(_v41, barycoord.y); - target.addScaledVector(_v42, barycoord.z); - return target; - } - static isFrontFacing(a, b, c, direction) { - _v0$2.subVectors(c, b); - _v1$3.subVectors(a, b); - return _v0$2.cross(_v1$3).dot(direction) < 0 ? true : false; - } - set(a, b, c) { - this.a.copy(a); - this.b.copy(b); - this.c.copy(c); - return this; - } - setFromPointsAndIndices(points, i0, i1, i2) { - this.a.copy(points[i0]); - this.b.copy(points[i1]); - this.c.copy(points[i2]); - return this; - } - setFromAttributeAndIndices(attribute, i0, i1, i2) { - this.a.fromBufferAttribute(attribute, i0); - this.b.fromBufferAttribute(attribute, i1); - this.c.fromBufferAttribute(attribute, i2); - return this; - } - clone() { - return new this.constructor().copy(this); - } - copy(triangle) { - this.a.copy(triangle.a); - this.b.copy(triangle.b); - this.c.copy(triangle.c); - return this; - } - getArea() { - _v0$2.subVectors(this.c, this.b); - _v1$3.subVectors(this.a, this.b); - return _v0$2.cross(_v1$3).length() * 0.5; - } - getMidpoint(target) { - return target.addVectors(this.a, this.b).add(this.c).multiplyScalar(1 / 3); - } - getNormal(target) { - return Triangle.getNormal(this.a, this.b, this.c, target); - } - getPlane(target) { - return target.setFromCoplanarPoints(this.a, this.b, this.c); - } - getBarycoord(point, target) { - return Triangle.getBarycoord(point, this.a, this.b, this.c, target); - } - getInterpolation(point, v1, v2, v3, target) { - return Triangle.getInterpolation(point, this.a, this.b, this.c, v1, v2, v3, target); - } - containsPoint(point) { - return Triangle.containsPoint(point, this.a, this.b, this.c); - } - isFrontFacing(direction) { - return Triangle.isFrontFacing(this.a, this.b, this.c, direction); - } - intersectsBox(box) { - return box.intersectsTriangle(this); - } - closestPointToPoint(p, target) { - const a = this.a, b = this.b, c = this.c; - let v, w; - _vab.subVectors(b, a); - _vac.subVectors(c, a); - _vap.subVectors(p, a); - const d1 = _vab.dot(_vap); - const d2 = _vac.dot(_vap); - if (d1 <= 0 && d2 <= 0) { - return target.copy(a); - } - _vbp.subVectors(p, b); - const d3 = _vab.dot(_vbp); - const d4 = _vac.dot(_vbp); - if (d3 >= 0 && d4 <= d3) { - return target.copy(b); - } - const vc = d1 * d4 - d3 * d2; - if (vc <= 0 && d1 >= 0 && d3 <= 0) { - v = d1 / (d1 - d3); - return target.copy(a).addScaledVector(_vab, v); - } - _vcp.subVectors(p, c); - const d5 = _vab.dot(_vcp); - const d6 = _vac.dot(_vcp); - if (d6 >= 0 && d5 <= d6) { - return target.copy(c); - } - const vb = d5 * d2 - d1 * d6; - if (vb <= 0 && d2 >= 0 && d6 <= 0) { - w = d2 / (d2 - d6); - return target.copy(a).addScaledVector(_vac, w); - } - const va = d3 * d6 - d5 * d4; - if (va <= 0 && d4 - d3 >= 0 && d5 - d6 >= 0) { - _vbc.subVectors(c, b); - w = (d4 - d3) / (d4 - d3 + (d5 - d6)); - return target.copy(b).addScaledVector(_vbc, w); - } - const denom = 1 / (va + vb + vc); - v = vb * denom; - w = vc * denom; - return target.copy(a).addScaledVector(_vab, v).addScaledVector(_vac, w); - } - equals(triangle) { - return triangle.a.equals(this.a) && triangle.b.equals(this.b) && triangle.c.equals(this.c); - } -} -const _colorKeywords = { - "aliceblue": 15792383, - "antiquewhite": 16444375, - "aqua": 65535, - "aquamarine": 8388564, - "azure": 15794175, - "beige": 16119260, - "bisque": 16770244, - "black": 0, - "blanchedalmond": 16772045, - "blue": 255, - "blueviolet": 9055202, - "brown": 10824234, - "burlywood": 14596231, - "cadetblue": 6266528, - "chartreuse": 8388352, - "chocolate": 13789470, - "coral": 16744272, - "cornflowerblue": 6591981, - "cornsilk": 16775388, - "crimson": 14423100, - "cyan": 65535, - "darkblue": 139, - "darkcyan": 35723, - "darkgoldenrod": 12092939, - "darkgray": 11119017, - "darkgreen": 25600, - "darkgrey": 11119017, - "darkkhaki": 12433259, - "darkmagenta": 9109643, - "darkolivegreen": 5597999, - "darkorange": 16747520, - "darkorchid": 10040012, - "darkred": 9109504, - "darksalmon": 15308410, - "darkseagreen": 9419919, - "darkslateblue": 4734347, - "darkslategray": 3100495, - "darkslategrey": 3100495, - "darkturquoise": 52945, - "darkviolet": 9699539, - "deeppink": 16716947, - "deepskyblue": 49151, - "dimgray": 6908265, - "dimgrey": 6908265, - "dodgerblue": 2003199, - "firebrick": 11674146, - "floralwhite": 16775920, - "forestgreen": 2263842, - "fuchsia": 16711935, - "gainsboro": 14474460, - "ghostwhite": 16316671, - "gold": 16766720, - "goldenrod": 14329120, - "gray": 8421504, - "green": 32768, - "greenyellow": 11403055, - "grey": 8421504, - "honeydew": 15794160, - "hotpink": 16738740, - "indianred": 13458524, - "indigo": 4915330, - "ivory": 16777200, - "khaki": 15787660, - "lavender": 15132410, - "lavenderblush": 16773365, - "lawngreen": 8190976, - "lemonchiffon": 16775885, - "lightblue": 11393254, - "lightcoral": 15761536, - "lightcyan": 14745599, - "lightgoldenrodyellow": 16448210, - "lightgray": 13882323, - "lightgreen": 9498256, - "lightgrey": 13882323, - "lightpink": 16758465, - "lightsalmon": 16752762, - "lightseagreen": 2142890, - "lightskyblue": 8900346, - "lightslategray": 7833753, - "lightslategrey": 7833753, - "lightsteelblue": 11584734, - "lightyellow": 16777184, - "lime": 65280, - "limegreen": 3329330, - "linen": 16445670, - "magenta": 16711935, - "maroon": 8388608, - "mediumaquamarine": 6737322, - "mediumblue": 205, - "mediumorchid": 12211667, - "mediumpurple": 9662683, - "mediumseagreen": 3978097, - "mediumslateblue": 8087790, - "mediumspringgreen": 64154, - "mediumturquoise": 4772300, - "mediumvioletred": 13047173, - "midnightblue": 1644912, - "mintcream": 16121850, - "mistyrose": 16770273, - "moccasin": 16770229, - "navajowhite": 16768685, - "navy": 128, - "oldlace": 16643558, - "olive": 8421376, - "olivedrab": 7048739, - "orange": 16753920, - "orangered": 16729344, - "orchid": 14315734, - "palegoldenrod": 15657130, - "palegreen": 10025880, - "paleturquoise": 11529966, - "palevioletred": 14381203, - "papayawhip": 16773077, - "peachpuff": 16767673, - "peru": 13468991, - "pink": 16761035, - "plum": 14524637, - "powderblue": 11591910, - "purple": 8388736, - "rebeccapurple": 6697881, - "red": 16711680, - "rosybrown": 12357519, - "royalblue": 4286945, - "saddlebrown": 9127187, - "salmon": 16416882, - "sandybrown": 16032864, - "seagreen": 3050327, - "seashell": 16774638, - "sienna": 10506797, - "silver": 12632256, - "skyblue": 8900331, - "slateblue": 6970061, - "slategray": 7372944, - "slategrey": 7372944, - "snow": 16775930, - "springgreen": 65407, - "steelblue": 4620980, - "tan": 13808780, - "teal": 32896, - "thistle": 14204888, - "tomato": 16737095, - "turquoise": 4251856, - "violet": 15631086, - "wheat": 16113331, - "white": 16777215, - "whitesmoke": 16119285, - "yellow": 16776960, - "yellowgreen": 10145074 -}; -const _hslA = { h: 0, s: 0, l: 0 }; -const _hslB = { h: 0, s: 0, l: 0 }; -function hue2rgb(p, q, t2) { - if (t2 < 0) t2 += 1; - if (t2 > 1) t2 -= 1; - if (t2 < 1 / 6) return p + (q - p) * 6 * t2; - if (t2 < 1 / 2) return q; - if (t2 < 2 / 3) return p + (q - p) * 6 * (2 / 3 - t2); - return p; -} -__name(hue2rgb, "hue2rgb"); -class Color { - static { - __name(this, "Color"); - } - constructor(r, g, b) { - this.isColor = true; - this.r = 1; - this.g = 1; - this.b = 1; - return this.set(r, g, b); - } - set(r, g, b) { - if (g === void 0 && b === void 0) { - const value = r; - if (value && value.isColor) { - this.copy(value); - } else if (typeof value === "number") { - this.setHex(value); - } else if (typeof value === "string") { - this.setStyle(value); - } - } else { - this.setRGB(r, g, b); - } - return this; - } - setScalar(scalar) { - this.r = scalar; - this.g = scalar; - this.b = scalar; - return this; - } - setHex(hex, colorSpace = SRGBColorSpace) { - hex = Math.floor(hex); - this.r = (hex >> 16 & 255) / 255; - this.g = (hex >> 8 & 255) / 255; - this.b = (hex & 255) / 255; - ColorManagement.toWorkingColorSpace(this, colorSpace); - return this; - } - setRGB(r, g, b, colorSpace = ColorManagement.workingColorSpace) { - this.r = r; - this.g = g; - this.b = b; - ColorManagement.toWorkingColorSpace(this, colorSpace); - return this; - } - setHSL(h, s, l, colorSpace = ColorManagement.workingColorSpace) { - h = euclideanModulo(h, 1); - s = clamp(s, 0, 1); - l = clamp(l, 0, 1); - if (s === 0) { - this.r = this.g = this.b = l; - } else { - const p = l <= 0.5 ? l * (1 + s) : l + s - l * s; - const q = 2 * l - p; - this.r = hue2rgb(q, p, h + 1 / 3); - this.g = hue2rgb(q, p, h); - this.b = hue2rgb(q, p, h - 1 / 3); - } - ColorManagement.toWorkingColorSpace(this, colorSpace); - return this; - } - setStyle(style, colorSpace = SRGBColorSpace) { - function handleAlpha(string) { - if (string === void 0) return; - if (parseFloat(string) < 1) { - console.warn("THREE.Color: Alpha component of " + style + " will be ignored."); - } - } - __name(handleAlpha, "handleAlpha"); - let m; - if (m = /^(\w+)\(([^\)]*)\)/.exec(style)) { - let color; - const name = m[1]; - const components = m[2]; - switch (name) { - case "rgb": - case "rgba": - if (color = /^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(components)) { - handleAlpha(color[4]); - return this.setRGB( - Math.min(255, parseInt(color[1], 10)) / 255, - Math.min(255, parseInt(color[2], 10)) / 255, - Math.min(255, parseInt(color[3], 10)) / 255, - colorSpace - ); - } - if (color = /^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(components)) { - handleAlpha(color[4]); - return this.setRGB( - Math.min(100, parseInt(color[1], 10)) / 100, - Math.min(100, parseInt(color[2], 10)) / 100, - Math.min(100, parseInt(color[3], 10)) / 100, - colorSpace - ); - } - break; - case "hsl": - case "hsla": - if (color = /^\s*(\d*\.?\d+)\s*,\s*(\d*\.?\d+)\%\s*,\s*(\d*\.?\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(components)) { - handleAlpha(color[4]); - return this.setHSL( - parseFloat(color[1]) / 360, - parseFloat(color[2]) / 100, - parseFloat(color[3]) / 100, - colorSpace - ); - } - break; - default: - console.warn("THREE.Color: Unknown color model " + style); - } - } else if (m = /^\#([A-Fa-f\d]+)$/.exec(style)) { - const hex = m[1]; - const size = hex.length; - if (size === 3) { - return this.setRGB( - parseInt(hex.charAt(0), 16) / 15, - parseInt(hex.charAt(1), 16) / 15, - parseInt(hex.charAt(2), 16) / 15, - colorSpace - ); - } else if (size === 6) { - return this.setHex(parseInt(hex, 16), colorSpace); - } else { - console.warn("THREE.Color: Invalid hex color " + style); - } - } else if (style && style.length > 0) { - return this.setColorName(style, colorSpace); - } - return this; - } - setColorName(style, colorSpace = SRGBColorSpace) { - const hex = _colorKeywords[style.toLowerCase()]; - if (hex !== void 0) { - this.setHex(hex, colorSpace); - } else { - console.warn("THREE.Color: Unknown color " + style); - } - return this; - } - clone() { - return new this.constructor(this.r, this.g, this.b); - } - copy(color) { - this.r = color.r; - this.g = color.g; - this.b = color.b; - return this; - } - copySRGBToLinear(color) { - this.r = SRGBToLinear(color.r); - this.g = SRGBToLinear(color.g); - this.b = SRGBToLinear(color.b); - return this; - } - copyLinearToSRGB(color) { - this.r = LinearToSRGB(color.r); - this.g = LinearToSRGB(color.g); - this.b = LinearToSRGB(color.b); - return this; - } - convertSRGBToLinear() { - this.copySRGBToLinear(this); - return this; - } - convertLinearToSRGB() { - this.copyLinearToSRGB(this); - return this; - } - getHex(colorSpace = SRGBColorSpace) { - ColorManagement.fromWorkingColorSpace(_color$1.copy(this), colorSpace); - return Math.round(clamp(_color$1.r * 255, 0, 255)) * 65536 + Math.round(clamp(_color$1.g * 255, 0, 255)) * 256 + Math.round(clamp(_color$1.b * 255, 0, 255)); - } - getHexString(colorSpace = SRGBColorSpace) { - return ("000000" + this.getHex(colorSpace).toString(16)).slice(-6); - } - getHSL(target, colorSpace = ColorManagement.workingColorSpace) { - ColorManagement.fromWorkingColorSpace(_color$1.copy(this), colorSpace); - const r = _color$1.r, g = _color$1.g, b = _color$1.b; - const max2 = Math.max(r, g, b); - const min = Math.min(r, g, b); - let hue, saturation; - const lightness = (min + max2) / 2; - if (min === max2) { - hue = 0; - saturation = 0; - } else { - const delta = max2 - min; - saturation = lightness <= 0.5 ? delta / (max2 + min) : delta / (2 - max2 - min); - switch (max2) { - case r: - hue = (g - b) / delta + (g < b ? 6 : 0); - break; - case g: - hue = (b - r) / delta + 2; - break; - case b: - hue = (r - g) / delta + 4; - break; - } - hue /= 6; - } - target.h = hue; - target.s = saturation; - target.l = lightness; - return target; - } - getRGB(target, colorSpace = ColorManagement.workingColorSpace) { - ColorManagement.fromWorkingColorSpace(_color$1.copy(this), colorSpace); - target.r = _color$1.r; - target.g = _color$1.g; - target.b = _color$1.b; - return target; - } - getStyle(colorSpace = SRGBColorSpace) { - ColorManagement.fromWorkingColorSpace(_color$1.copy(this), colorSpace); - const r = _color$1.r, g = _color$1.g, b = _color$1.b; - if (colorSpace !== SRGBColorSpace) { - return `color(${colorSpace} ${r.toFixed(3)} ${g.toFixed(3)} ${b.toFixed(3)})`; - } - return `rgb(${Math.round(r * 255)},${Math.round(g * 255)},${Math.round(b * 255)})`; - } - offsetHSL(h, s, l) { - this.getHSL(_hslA); - return this.setHSL(_hslA.h + h, _hslA.s + s, _hslA.l + l); - } - add(color) { - this.r += color.r; - this.g += color.g; - this.b += color.b; - return this; - } - addColors(color1, color2) { - this.r = color1.r + color2.r; - this.g = color1.g + color2.g; - this.b = color1.b + color2.b; - return this; - } - addScalar(s) { - this.r += s; - this.g += s; - this.b += s; - return this; - } - sub(color) { - this.r = Math.max(0, this.r - color.r); - this.g = Math.max(0, this.g - color.g); - this.b = Math.max(0, this.b - color.b); - return this; - } - multiply(color) { - this.r *= color.r; - this.g *= color.g; - this.b *= color.b; - return this; - } - multiplyScalar(s) { - this.r *= s; - this.g *= s; - this.b *= s; - return this; - } - lerp(color, alpha) { - this.r += (color.r - this.r) * alpha; - this.g += (color.g - this.g) * alpha; - this.b += (color.b - this.b) * alpha; - return this; - } - lerpColors(color1, color2, alpha) { - this.r = color1.r + (color2.r - color1.r) * alpha; - this.g = color1.g + (color2.g - color1.g) * alpha; - this.b = color1.b + (color2.b - color1.b) * alpha; - return this; - } - lerpHSL(color, alpha) { - this.getHSL(_hslA); - color.getHSL(_hslB); - const h = lerp(_hslA.h, _hslB.h, alpha); - const s = lerp(_hslA.s, _hslB.s, alpha); - const l = lerp(_hslA.l, _hslB.l, alpha); - this.setHSL(h, s, l); - return this; - } - setFromVector3(v) { - this.r = v.x; - this.g = v.y; - this.b = v.z; - return this; - } - applyMatrix3(m) { - const r = this.r, g = this.g, b = this.b; - const e = m.elements; - this.r = e[0] * r + e[3] * g + e[6] * b; - this.g = e[1] * r + e[4] * g + e[7] * b; - this.b = e[2] * r + e[5] * g + e[8] * b; - return this; - } - equals(c) { - return c.r === this.r && c.g === this.g && c.b === this.b; - } - fromArray(array, offset = 0) { - this.r = array[offset]; - this.g = array[offset + 1]; - this.b = array[offset + 2]; - return this; - } - toArray(array = [], offset = 0) { - array[offset] = this.r; - array[offset + 1] = this.g; - array[offset + 2] = this.b; - return array; - } - fromBufferAttribute(attribute, index) { - this.r = attribute.getX(index); - this.g = attribute.getY(index); - this.b = attribute.getZ(index); - return this; - } - toJSON() { - return this.getHex(); - } - *[Symbol.iterator]() { - yield this.r; - yield this.g; - yield this.b; - } -} -const _color$1 = /* @__PURE__ */ new Color(); -Color.NAMES = _colorKeywords; -let _materialId = 0; -class Material extends EventDispatcher { - static { - __name(this, "Material"); - } - static get type() { - return "Material"; - } - get type() { - return this.constructor.type; - } - set type(_value) { - } - constructor() { - super(); - this.isMaterial = true; - Object.defineProperty(this, "id", { value: _materialId++ }); - this.uuid = generateUUID(); - this.name = ""; - this.blending = NormalBlending; - this.side = FrontSide; - this.vertexColors = false; - this.opacity = 1; - this.transparent = false; - this.alphaHash = false; - this.blendSrc = SrcAlphaFactor; - this.blendDst = OneMinusSrcAlphaFactor; - this.blendEquation = AddEquation; - this.blendSrcAlpha = null; - this.blendDstAlpha = null; - this.blendEquationAlpha = null; - this.blendColor = new Color(0, 0, 0); - this.blendAlpha = 0; - this.depthFunc = LessEqualDepth; - this.depthTest = true; - this.depthWrite = true; - this.stencilWriteMask = 255; - this.stencilFunc = AlwaysStencilFunc; - this.stencilRef = 0; - this.stencilFuncMask = 255; - this.stencilFail = KeepStencilOp; - this.stencilZFail = KeepStencilOp; - this.stencilZPass = KeepStencilOp; - this.stencilWrite = false; - this.clippingPlanes = null; - this.clipIntersection = false; - this.clipShadows = false; - this.shadowSide = null; - this.colorWrite = true; - this.precision = null; - this.polygonOffset = false; - this.polygonOffsetFactor = 0; - this.polygonOffsetUnits = 0; - this.dithering = false; - this.alphaToCoverage = false; - this.premultipliedAlpha = false; - this.forceSinglePass = false; - this.visible = true; - this.toneMapped = true; - this.userData = {}; - this.version = 0; - this._alphaTest = 0; - } - get alphaTest() { - return this._alphaTest; - } - set alphaTest(value) { - if (this._alphaTest > 0 !== value > 0) { - this.version++; - } - this._alphaTest = value; - } - // onBeforeRender and onBeforeCompile only supported in WebGLRenderer - onBeforeRender() { - } - onBeforeCompile() { - } - customProgramCacheKey() { - return this.onBeforeCompile.toString(); - } - setValues(values) { - if (values === void 0) return; - for (const key in values) { - const newValue = values[key]; - if (newValue === void 0) { - console.warn(`THREE.Material: parameter '${key}' has value of undefined.`); - continue; - } - const currentValue = this[key]; - if (currentValue === void 0) { - console.warn(`THREE.Material: '${key}' is not a property of THREE.${this.type}.`); - continue; - } - if (currentValue && currentValue.isColor) { - currentValue.set(newValue); - } else if (currentValue && currentValue.isVector3 && (newValue && newValue.isVector3)) { - currentValue.copy(newValue); - } else { - this[key] = newValue; - } - } - } - toJSON(meta) { - const isRootObject = meta === void 0 || typeof meta === "string"; - if (isRootObject) { - meta = { - textures: {}, - images: {} - }; - } - const data = { - metadata: { - version: 4.6, - type: "Material", - generator: "Material.toJSON" - } - }; - data.uuid = this.uuid; - data.type = this.type; - if (this.name !== "") data.name = this.name; - if (this.color && this.color.isColor) data.color = this.color.getHex(); - if (this.roughness !== void 0) data.roughness = this.roughness; - if (this.metalness !== void 0) data.metalness = this.metalness; - if (this.sheen !== void 0) data.sheen = this.sheen; - if (this.sheenColor && this.sheenColor.isColor) data.sheenColor = this.sheenColor.getHex(); - if (this.sheenRoughness !== void 0) data.sheenRoughness = this.sheenRoughness; - if (this.emissive && this.emissive.isColor) data.emissive = this.emissive.getHex(); - if (this.emissiveIntensity !== void 0 && this.emissiveIntensity !== 1) data.emissiveIntensity = this.emissiveIntensity; - if (this.specular && this.specular.isColor) data.specular = this.specular.getHex(); - if (this.specularIntensity !== void 0) data.specularIntensity = this.specularIntensity; - if (this.specularColor && this.specularColor.isColor) data.specularColor = this.specularColor.getHex(); - if (this.shininess !== void 0) data.shininess = this.shininess; - if (this.clearcoat !== void 0) data.clearcoat = this.clearcoat; - if (this.clearcoatRoughness !== void 0) data.clearcoatRoughness = this.clearcoatRoughness; - if (this.clearcoatMap && this.clearcoatMap.isTexture) { - data.clearcoatMap = this.clearcoatMap.toJSON(meta).uuid; - } - if (this.clearcoatRoughnessMap && this.clearcoatRoughnessMap.isTexture) { - data.clearcoatRoughnessMap = this.clearcoatRoughnessMap.toJSON(meta).uuid; - } - if (this.clearcoatNormalMap && this.clearcoatNormalMap.isTexture) { - data.clearcoatNormalMap = this.clearcoatNormalMap.toJSON(meta).uuid; - data.clearcoatNormalScale = this.clearcoatNormalScale.toArray(); - } - if (this.dispersion !== void 0) data.dispersion = this.dispersion; - if (this.iridescence !== void 0) data.iridescence = this.iridescence; - if (this.iridescenceIOR !== void 0) data.iridescenceIOR = this.iridescenceIOR; - if (this.iridescenceThicknessRange !== void 0) data.iridescenceThicknessRange = this.iridescenceThicknessRange; - if (this.iridescenceMap && this.iridescenceMap.isTexture) { - data.iridescenceMap = this.iridescenceMap.toJSON(meta).uuid; - } - if (this.iridescenceThicknessMap && this.iridescenceThicknessMap.isTexture) { - data.iridescenceThicknessMap = this.iridescenceThicknessMap.toJSON(meta).uuid; - } - if (this.anisotropy !== void 0) data.anisotropy = this.anisotropy; - if (this.anisotropyRotation !== void 0) data.anisotropyRotation = this.anisotropyRotation; - if (this.anisotropyMap && this.anisotropyMap.isTexture) { - data.anisotropyMap = this.anisotropyMap.toJSON(meta).uuid; - } - if (this.map && this.map.isTexture) data.map = this.map.toJSON(meta).uuid; - if (this.matcap && this.matcap.isTexture) data.matcap = this.matcap.toJSON(meta).uuid; - if (this.alphaMap && this.alphaMap.isTexture) data.alphaMap = this.alphaMap.toJSON(meta).uuid; - if (this.lightMap && this.lightMap.isTexture) { - data.lightMap = this.lightMap.toJSON(meta).uuid; - data.lightMapIntensity = this.lightMapIntensity; - } - if (this.aoMap && this.aoMap.isTexture) { - data.aoMap = this.aoMap.toJSON(meta).uuid; - data.aoMapIntensity = this.aoMapIntensity; - } - if (this.bumpMap && this.bumpMap.isTexture) { - data.bumpMap = this.bumpMap.toJSON(meta).uuid; - data.bumpScale = this.bumpScale; - } - if (this.normalMap && this.normalMap.isTexture) { - data.normalMap = this.normalMap.toJSON(meta).uuid; - data.normalMapType = this.normalMapType; - data.normalScale = this.normalScale.toArray(); - } - if (this.displacementMap && this.displacementMap.isTexture) { - data.displacementMap = this.displacementMap.toJSON(meta).uuid; - data.displacementScale = this.displacementScale; - data.displacementBias = this.displacementBias; - } - if (this.roughnessMap && this.roughnessMap.isTexture) data.roughnessMap = this.roughnessMap.toJSON(meta).uuid; - if (this.metalnessMap && this.metalnessMap.isTexture) data.metalnessMap = this.metalnessMap.toJSON(meta).uuid; - if (this.emissiveMap && this.emissiveMap.isTexture) data.emissiveMap = this.emissiveMap.toJSON(meta).uuid; - if (this.specularMap && this.specularMap.isTexture) data.specularMap = this.specularMap.toJSON(meta).uuid; - if (this.specularIntensityMap && this.specularIntensityMap.isTexture) data.specularIntensityMap = this.specularIntensityMap.toJSON(meta).uuid; - if (this.specularColorMap && this.specularColorMap.isTexture) data.specularColorMap = this.specularColorMap.toJSON(meta).uuid; - if (this.envMap && this.envMap.isTexture) { - data.envMap = this.envMap.toJSON(meta).uuid; - if (this.combine !== void 0) data.combine = this.combine; - } - if (this.envMapRotation !== void 0) data.envMapRotation = this.envMapRotation.toArray(); - if (this.envMapIntensity !== void 0) data.envMapIntensity = this.envMapIntensity; - if (this.reflectivity !== void 0) data.reflectivity = this.reflectivity; - if (this.refractionRatio !== void 0) data.refractionRatio = this.refractionRatio; - if (this.gradientMap && this.gradientMap.isTexture) { - data.gradientMap = this.gradientMap.toJSON(meta).uuid; - } - if (this.transmission !== void 0) data.transmission = this.transmission; - if (this.transmissionMap && this.transmissionMap.isTexture) data.transmissionMap = this.transmissionMap.toJSON(meta).uuid; - if (this.thickness !== void 0) data.thickness = this.thickness; - if (this.thicknessMap && this.thicknessMap.isTexture) data.thicknessMap = this.thicknessMap.toJSON(meta).uuid; - if (this.attenuationDistance !== void 0 && this.attenuationDistance !== Infinity) data.attenuationDistance = this.attenuationDistance; - if (this.attenuationColor !== void 0) data.attenuationColor = this.attenuationColor.getHex(); - if (this.size !== void 0) data.size = this.size; - if (this.shadowSide !== null) data.shadowSide = this.shadowSide; - if (this.sizeAttenuation !== void 0) data.sizeAttenuation = this.sizeAttenuation; - if (this.blending !== NormalBlending) data.blending = this.blending; - if (this.side !== FrontSide) data.side = this.side; - if (this.vertexColors === true) data.vertexColors = true; - if (this.opacity < 1) data.opacity = this.opacity; - if (this.transparent === true) data.transparent = true; - if (this.blendSrc !== SrcAlphaFactor) data.blendSrc = this.blendSrc; - if (this.blendDst !== OneMinusSrcAlphaFactor) data.blendDst = this.blendDst; - if (this.blendEquation !== AddEquation) data.blendEquation = this.blendEquation; - if (this.blendSrcAlpha !== null) data.blendSrcAlpha = this.blendSrcAlpha; - if (this.blendDstAlpha !== null) data.blendDstAlpha = this.blendDstAlpha; - if (this.blendEquationAlpha !== null) data.blendEquationAlpha = this.blendEquationAlpha; - if (this.blendColor && this.blendColor.isColor) data.blendColor = this.blendColor.getHex(); - if (this.blendAlpha !== 0) data.blendAlpha = this.blendAlpha; - if (this.depthFunc !== LessEqualDepth) data.depthFunc = this.depthFunc; - if (this.depthTest === false) data.depthTest = this.depthTest; - if (this.depthWrite === false) data.depthWrite = this.depthWrite; - if (this.colorWrite === false) data.colorWrite = this.colorWrite; - if (this.stencilWriteMask !== 255) data.stencilWriteMask = this.stencilWriteMask; - if (this.stencilFunc !== AlwaysStencilFunc) data.stencilFunc = this.stencilFunc; - if (this.stencilRef !== 0) data.stencilRef = this.stencilRef; - if (this.stencilFuncMask !== 255) data.stencilFuncMask = this.stencilFuncMask; - if (this.stencilFail !== KeepStencilOp) data.stencilFail = this.stencilFail; - if (this.stencilZFail !== KeepStencilOp) data.stencilZFail = this.stencilZFail; - if (this.stencilZPass !== KeepStencilOp) data.stencilZPass = this.stencilZPass; - if (this.stencilWrite === true) data.stencilWrite = this.stencilWrite; - if (this.rotation !== void 0 && this.rotation !== 0) data.rotation = this.rotation; - if (this.polygonOffset === true) data.polygonOffset = true; - if (this.polygonOffsetFactor !== 0) data.polygonOffsetFactor = this.polygonOffsetFactor; - if (this.polygonOffsetUnits !== 0) data.polygonOffsetUnits = this.polygonOffsetUnits; - if (this.linewidth !== void 0 && this.linewidth !== 1) data.linewidth = this.linewidth; - if (this.dashSize !== void 0) data.dashSize = this.dashSize; - if (this.gapSize !== void 0) data.gapSize = this.gapSize; - if (this.scale !== void 0) data.scale = this.scale; - if (this.dithering === true) data.dithering = true; - if (this.alphaTest > 0) data.alphaTest = this.alphaTest; - if (this.alphaHash === true) data.alphaHash = true; - if (this.alphaToCoverage === true) data.alphaToCoverage = true; - if (this.premultipliedAlpha === true) data.premultipliedAlpha = true; - if (this.forceSinglePass === true) data.forceSinglePass = true; - if (this.wireframe === true) data.wireframe = true; - if (this.wireframeLinewidth > 1) data.wireframeLinewidth = this.wireframeLinewidth; - if (this.wireframeLinecap !== "round") data.wireframeLinecap = this.wireframeLinecap; - if (this.wireframeLinejoin !== "round") data.wireframeLinejoin = this.wireframeLinejoin; - if (this.flatShading === true) data.flatShading = true; - if (this.visible === false) data.visible = false; - if (this.toneMapped === false) data.toneMapped = false; - if (this.fog === false) data.fog = false; - if (Object.keys(this.userData).length > 0) data.userData = this.userData; - function extractFromCache(cache) { - const values = []; - for (const key in cache) { - const data2 = cache[key]; - delete data2.metadata; - values.push(data2); - } - return values; - } - __name(extractFromCache, "extractFromCache"); - if (isRootObject) { - const textures = extractFromCache(meta.textures); - const images = extractFromCache(meta.images); - if (textures.length > 0) data.textures = textures; - if (images.length > 0) data.images = images; - } - return data; - } - clone() { - return new this.constructor().copy(this); - } - copy(source) { - this.name = source.name; - this.blending = source.blending; - this.side = source.side; - this.vertexColors = source.vertexColors; - this.opacity = source.opacity; - this.transparent = source.transparent; - this.blendSrc = source.blendSrc; - this.blendDst = source.blendDst; - this.blendEquation = source.blendEquation; - this.blendSrcAlpha = source.blendSrcAlpha; - this.blendDstAlpha = source.blendDstAlpha; - this.blendEquationAlpha = source.blendEquationAlpha; - this.blendColor.copy(source.blendColor); - this.blendAlpha = source.blendAlpha; - this.depthFunc = source.depthFunc; - this.depthTest = source.depthTest; - this.depthWrite = source.depthWrite; - this.stencilWriteMask = source.stencilWriteMask; - this.stencilFunc = source.stencilFunc; - this.stencilRef = source.stencilRef; - this.stencilFuncMask = source.stencilFuncMask; - this.stencilFail = source.stencilFail; - this.stencilZFail = source.stencilZFail; - this.stencilZPass = source.stencilZPass; - this.stencilWrite = source.stencilWrite; - const srcPlanes = source.clippingPlanes; - let dstPlanes = null; - if (srcPlanes !== null) { - const n = srcPlanes.length; - dstPlanes = new Array(n); - for (let i = 0; i !== n; ++i) { - dstPlanes[i] = srcPlanes[i].clone(); - } - } - this.clippingPlanes = dstPlanes; - this.clipIntersection = source.clipIntersection; - this.clipShadows = source.clipShadows; - this.shadowSide = source.shadowSide; - this.colorWrite = source.colorWrite; - this.precision = source.precision; - this.polygonOffset = source.polygonOffset; - this.polygonOffsetFactor = source.polygonOffsetFactor; - this.polygonOffsetUnits = source.polygonOffsetUnits; - this.dithering = source.dithering; - this.alphaTest = source.alphaTest; - this.alphaHash = source.alphaHash; - this.alphaToCoverage = source.alphaToCoverage; - this.premultipliedAlpha = source.premultipliedAlpha; - this.forceSinglePass = source.forceSinglePass; - this.visible = source.visible; - this.toneMapped = source.toneMapped; - this.userData = JSON.parse(JSON.stringify(source.userData)); - return this; - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - } - set needsUpdate(value) { - if (value === true) this.version++; - } - onBuild() { - console.warn("Material: onBuild() has been removed."); - } -} -class MeshBasicMaterial extends Material { - static { - __name(this, "MeshBasicMaterial"); - } - static get type() { - return "MeshBasicMaterial"; - } - constructor(parameters) { - super(); - this.isMeshBasicMaterial = true; - this.color = new Color(16777215); - this.map = null; - this.lightMap = null; - this.lightMapIntensity = 1; - this.aoMap = null; - this.aoMapIntensity = 1; - this.specularMap = null; - this.alphaMap = null; - this.envMap = null; - this.envMapRotation = new Euler(); - this.combine = MultiplyOperation; - this.reflectivity = 1; - this.refractionRatio = 0.98; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.wireframeLinecap = "round"; - this.wireframeLinejoin = "round"; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.lightMap = source.lightMap; - this.lightMapIntensity = source.lightMapIntensity; - this.aoMap = source.aoMap; - this.aoMapIntensity = source.aoMapIntensity; - this.specularMap = source.specularMap; - this.alphaMap = source.alphaMap; - this.envMap = source.envMap; - this.envMapRotation.copy(source.envMapRotation); - this.combine = source.combine; - this.reflectivity = source.reflectivity; - this.refractionRatio = source.refractionRatio; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.wireframeLinecap = source.wireframeLinecap; - this.wireframeLinejoin = source.wireframeLinejoin; - this.fog = source.fog; - return this; - } -} -const _tables = /* @__PURE__ */ _generateTables(); -function _generateTables() { - const buffer = new ArrayBuffer(4); - const floatView = new Float32Array(buffer); - const uint32View = new Uint32Array(buffer); - const baseTable = new Uint32Array(512); - const shiftTable = new Uint32Array(512); - for (let i = 0; i < 256; ++i) { - const e = i - 127; - if (e < -27) { - baseTable[i] = 0; - baseTable[i | 256] = 32768; - shiftTable[i] = 24; - shiftTable[i | 256] = 24; - } else if (e < -14) { - baseTable[i] = 1024 >> -e - 14; - baseTable[i | 256] = 1024 >> -e - 14 | 32768; - shiftTable[i] = -e - 1; - shiftTable[i | 256] = -e - 1; - } else if (e <= 15) { - baseTable[i] = e + 15 << 10; - baseTable[i | 256] = e + 15 << 10 | 32768; - shiftTable[i] = 13; - shiftTable[i | 256] = 13; - } else if (e < 128) { - baseTable[i] = 31744; - baseTable[i | 256] = 64512; - shiftTable[i] = 24; - shiftTable[i | 256] = 24; - } else { - baseTable[i] = 31744; - baseTable[i | 256] = 64512; - shiftTable[i] = 13; - shiftTable[i | 256] = 13; - } - } - const mantissaTable = new Uint32Array(2048); - const exponentTable = new Uint32Array(64); - const offsetTable = new Uint32Array(64); - for (let i = 1; i < 1024; ++i) { - let m = i << 13; - let e = 0; - while ((m & 8388608) === 0) { - m <<= 1; - e -= 8388608; - } - m &= ~8388608; - e += 947912704; - mantissaTable[i] = m | e; - } - for (let i = 1024; i < 2048; ++i) { - mantissaTable[i] = 939524096 + (i - 1024 << 13); - } - for (let i = 1; i < 31; ++i) { - exponentTable[i] = i << 23; - } - exponentTable[31] = 1199570944; - exponentTable[32] = 2147483648; - for (let i = 33; i < 63; ++i) { - exponentTable[i] = 2147483648 + (i - 32 << 23); - } - exponentTable[63] = 3347054592; - for (let i = 1; i < 64; ++i) { - if (i !== 32) { - offsetTable[i] = 1024; - } - } - return { - floatView, - uint32View, - baseTable, - shiftTable, - mantissaTable, - exponentTable, - offsetTable - }; -} -__name(_generateTables, "_generateTables"); -function toHalfFloat(val) { - if (Math.abs(val) > 65504) console.warn("THREE.DataUtils.toHalfFloat(): Value out of range."); - val = clamp(val, -65504, 65504); - _tables.floatView[0] = val; - const f = _tables.uint32View[0]; - const e = f >> 23 & 511; - return _tables.baseTable[e] + ((f & 8388607) >> _tables.shiftTable[e]); -} -__name(toHalfFloat, "toHalfFloat"); -function fromHalfFloat(val) { - const m = val >> 10; - _tables.uint32View[0] = _tables.mantissaTable[_tables.offsetTable[m] + (val & 1023)] + _tables.exponentTable[m]; - return _tables.floatView[0]; -} -__name(fromHalfFloat, "fromHalfFloat"); -const DataUtils = { - toHalfFloat, - fromHalfFloat -}; -const _vector$9 = /* @__PURE__ */ new Vector3(); -const _vector2$1 = /* @__PURE__ */ new Vector2(); -class BufferAttribute { - static { - __name(this, "BufferAttribute"); - } - constructor(array, itemSize, normalized = false) { - if (Array.isArray(array)) { - throw new TypeError("THREE.BufferAttribute: array should be a Typed Array."); - } - this.isBufferAttribute = true; - this.name = ""; - this.array = array; - this.itemSize = itemSize; - this.count = array !== void 0 ? array.length / itemSize : 0; - this.normalized = normalized; - this.usage = StaticDrawUsage; - this.updateRanges = []; - this.gpuType = FloatType; - this.version = 0; - } - onUploadCallback() { - } - set needsUpdate(value) { - if (value === true) this.version++; - } - setUsage(value) { - this.usage = value; - return this; - } - addUpdateRange(start, count) { - this.updateRanges.push({ start, count }); - } - clearUpdateRanges() { - this.updateRanges.length = 0; - } - copy(source) { - this.name = source.name; - this.array = new source.array.constructor(source.array); - this.itemSize = source.itemSize; - this.count = source.count; - this.normalized = source.normalized; - this.usage = source.usage; - this.gpuType = source.gpuType; - return this; - } - copyAt(index1, attribute, index2) { - index1 *= this.itemSize; - index2 *= attribute.itemSize; - for (let i = 0, l = this.itemSize; i < l; i++) { - this.array[index1 + i] = attribute.array[index2 + i]; - } - return this; - } - copyArray(array) { - this.array.set(array); - return this; - } - applyMatrix3(m) { - if (this.itemSize === 2) { - for (let i = 0, l = this.count; i < l; i++) { - _vector2$1.fromBufferAttribute(this, i); - _vector2$1.applyMatrix3(m); - this.setXY(i, _vector2$1.x, _vector2$1.y); - } - } else if (this.itemSize === 3) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$9.fromBufferAttribute(this, i); - _vector$9.applyMatrix3(m); - this.setXYZ(i, _vector$9.x, _vector$9.y, _vector$9.z); - } - } - return this; - } - applyMatrix4(m) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$9.fromBufferAttribute(this, i); - _vector$9.applyMatrix4(m); - this.setXYZ(i, _vector$9.x, _vector$9.y, _vector$9.z); - } - return this; - } - applyNormalMatrix(m) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$9.fromBufferAttribute(this, i); - _vector$9.applyNormalMatrix(m); - this.setXYZ(i, _vector$9.x, _vector$9.y, _vector$9.z); - } - return this; - } - transformDirection(m) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$9.fromBufferAttribute(this, i); - _vector$9.transformDirection(m); - this.setXYZ(i, _vector$9.x, _vector$9.y, _vector$9.z); - } - return this; - } - set(value, offset = 0) { - this.array.set(value, offset); - return this; - } - getComponent(index, component) { - let value = this.array[index * this.itemSize + component]; - if (this.normalized) value = denormalize(value, this.array); - return value; - } - setComponent(index, component, value) { - if (this.normalized) value = normalize(value, this.array); - this.array[index * this.itemSize + component] = value; - return this; - } - getX(index) { - let x = this.array[index * this.itemSize]; - if (this.normalized) x = denormalize(x, this.array); - return x; - } - setX(index, x) { - if (this.normalized) x = normalize(x, this.array); - this.array[index * this.itemSize] = x; - return this; - } - getY(index) { - let y = this.array[index * this.itemSize + 1]; - if (this.normalized) y = denormalize(y, this.array); - return y; - } - setY(index, y) { - if (this.normalized) y = normalize(y, this.array); - this.array[index * this.itemSize + 1] = y; - return this; - } - getZ(index) { - let z = this.array[index * this.itemSize + 2]; - if (this.normalized) z = denormalize(z, this.array); - return z; - } - setZ(index, z) { - if (this.normalized) z = normalize(z, this.array); - this.array[index * this.itemSize + 2] = z; - return this; - } - getW(index) { - let w = this.array[index * this.itemSize + 3]; - if (this.normalized) w = denormalize(w, this.array); - return w; - } - setW(index, w) { - if (this.normalized) w = normalize(w, this.array); - this.array[index * this.itemSize + 3] = w; - return this; - } - setXY(index, x, y) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - } - this.array[index + 0] = x; - this.array[index + 1] = y; - return this; - } - setXYZ(index, x, y, z) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - } - this.array[index + 0] = x; - this.array[index + 1] = y; - this.array[index + 2] = z; - return this; - } - setXYZW(index, x, y, z, w) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - w = normalize(w, this.array); - } - this.array[index + 0] = x; - this.array[index + 1] = y; - this.array[index + 2] = z; - this.array[index + 3] = w; - return this; - } - onUpload(callback) { - this.onUploadCallback = callback; - return this; - } - clone() { - return new this.constructor(this.array, this.itemSize).copy(this); - } - toJSON() { - const data = { - itemSize: this.itemSize, - type: this.array.constructor.name, - array: Array.from(this.array), - normalized: this.normalized - }; - if (this.name !== "") data.name = this.name; - if (this.usage !== StaticDrawUsage) data.usage = this.usage; - return data; - } -} -class Int8BufferAttribute extends BufferAttribute { - static { - __name(this, "Int8BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Int8Array(array), itemSize, normalized); - } -} -class Uint8BufferAttribute extends BufferAttribute { - static { - __name(this, "Uint8BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Uint8Array(array), itemSize, normalized); - } -} -class Uint8ClampedBufferAttribute extends BufferAttribute { - static { - __name(this, "Uint8ClampedBufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Uint8ClampedArray(array), itemSize, normalized); - } -} -class Int16BufferAttribute extends BufferAttribute { - static { - __name(this, "Int16BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Int16Array(array), itemSize, normalized); - } -} -class Uint16BufferAttribute extends BufferAttribute { - static { - __name(this, "Uint16BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Uint16Array(array), itemSize, normalized); - } -} -class Int32BufferAttribute extends BufferAttribute { - static { - __name(this, "Int32BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Int32Array(array), itemSize, normalized); - } -} -class Uint32BufferAttribute extends BufferAttribute { - static { - __name(this, "Uint32BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Uint32Array(array), itemSize, normalized); - } -} -class Float16BufferAttribute extends BufferAttribute { - static { - __name(this, "Float16BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Uint16Array(array), itemSize, normalized); - this.isFloat16BufferAttribute = true; - } - getX(index) { - let x = fromHalfFloat(this.array[index * this.itemSize]); - if (this.normalized) x = denormalize(x, this.array); - return x; - } - setX(index, x) { - if (this.normalized) x = normalize(x, this.array); - this.array[index * this.itemSize] = toHalfFloat(x); - return this; - } - getY(index) { - let y = fromHalfFloat(this.array[index * this.itemSize + 1]); - if (this.normalized) y = denormalize(y, this.array); - return y; - } - setY(index, y) { - if (this.normalized) y = normalize(y, this.array); - this.array[index * this.itemSize + 1] = toHalfFloat(y); - return this; - } - getZ(index) { - let z = fromHalfFloat(this.array[index * this.itemSize + 2]); - if (this.normalized) z = denormalize(z, this.array); - return z; - } - setZ(index, z) { - if (this.normalized) z = normalize(z, this.array); - this.array[index * this.itemSize + 2] = toHalfFloat(z); - return this; - } - getW(index) { - let w = fromHalfFloat(this.array[index * this.itemSize + 3]); - if (this.normalized) w = denormalize(w, this.array); - return w; - } - setW(index, w) { - if (this.normalized) w = normalize(w, this.array); - this.array[index * this.itemSize + 3] = toHalfFloat(w); - return this; - } - setXY(index, x, y) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - } - this.array[index + 0] = toHalfFloat(x); - this.array[index + 1] = toHalfFloat(y); - return this; - } - setXYZ(index, x, y, z) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - } - this.array[index + 0] = toHalfFloat(x); - this.array[index + 1] = toHalfFloat(y); - this.array[index + 2] = toHalfFloat(z); - return this; - } - setXYZW(index, x, y, z, w) { - index *= this.itemSize; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - w = normalize(w, this.array); - } - this.array[index + 0] = toHalfFloat(x); - this.array[index + 1] = toHalfFloat(y); - this.array[index + 2] = toHalfFloat(z); - this.array[index + 3] = toHalfFloat(w); - return this; - } -} -class Float32BufferAttribute extends BufferAttribute { - static { - __name(this, "Float32BufferAttribute"); - } - constructor(array, itemSize, normalized) { - super(new Float32Array(array), itemSize, normalized); - } -} -let _id$2 = 0; -const _m1$2 = /* @__PURE__ */ new Matrix4(); -const _obj = /* @__PURE__ */ new Object3D(); -const _offset = /* @__PURE__ */ new Vector3(); -const _box$2 = /* @__PURE__ */ new Box3(); -const _boxMorphTargets = /* @__PURE__ */ new Box3(); -const _vector$8 = /* @__PURE__ */ new Vector3(); -class BufferGeometry extends EventDispatcher { - static { - __name(this, "BufferGeometry"); - } - constructor() { - super(); - this.isBufferGeometry = true; - Object.defineProperty(this, "id", { value: _id$2++ }); - this.uuid = generateUUID(); - this.name = ""; - this.type = "BufferGeometry"; - this.index = null; - this.indirect = null; - this.attributes = {}; - this.morphAttributes = {}; - this.morphTargetsRelative = false; - this.groups = []; - this.boundingBox = null; - this.boundingSphere = null; - this.drawRange = { start: 0, count: Infinity }; - this.userData = {}; - } - getIndex() { - return this.index; - } - setIndex(index) { - if (Array.isArray(index)) { - this.index = new (arrayNeedsUint32(index) ? Uint32BufferAttribute : Uint16BufferAttribute)(index, 1); - } else { - this.index = index; - } - return this; - } - setIndirect(indirect) { - this.indirect = indirect; - return this; - } - getIndirect() { - return this.indirect; - } - getAttribute(name) { - return this.attributes[name]; - } - setAttribute(name, attribute) { - this.attributes[name] = attribute; - return this; - } - deleteAttribute(name) { - delete this.attributes[name]; - return this; - } - hasAttribute(name) { - return this.attributes[name] !== void 0; - } - addGroup(start, count, materialIndex = 0) { - this.groups.push({ - start, - count, - materialIndex - }); - } - clearGroups() { - this.groups = []; - } - setDrawRange(start, count) { - this.drawRange.start = start; - this.drawRange.count = count; - } - applyMatrix4(matrix) { - const position = this.attributes.position; - if (position !== void 0) { - position.applyMatrix4(matrix); - position.needsUpdate = true; - } - const normal = this.attributes.normal; - if (normal !== void 0) { - const normalMatrix = new Matrix3().getNormalMatrix(matrix); - normal.applyNormalMatrix(normalMatrix); - normal.needsUpdate = true; - } - const tangent = this.attributes.tangent; - if (tangent !== void 0) { - tangent.transformDirection(matrix); - tangent.needsUpdate = true; - } - if (this.boundingBox !== null) { - this.computeBoundingBox(); - } - if (this.boundingSphere !== null) { - this.computeBoundingSphere(); - } - return this; - } - applyQuaternion(q) { - _m1$2.makeRotationFromQuaternion(q); - this.applyMatrix4(_m1$2); - return this; - } - rotateX(angle) { - _m1$2.makeRotationX(angle); - this.applyMatrix4(_m1$2); - return this; - } - rotateY(angle) { - _m1$2.makeRotationY(angle); - this.applyMatrix4(_m1$2); - return this; - } - rotateZ(angle) { - _m1$2.makeRotationZ(angle); - this.applyMatrix4(_m1$2); - return this; - } - translate(x, y, z) { - _m1$2.makeTranslation(x, y, z); - this.applyMatrix4(_m1$2); - return this; - } - scale(x, y, z) { - _m1$2.makeScale(x, y, z); - this.applyMatrix4(_m1$2); - return this; - } - lookAt(vector) { - _obj.lookAt(vector); - _obj.updateMatrix(); - this.applyMatrix4(_obj.matrix); - return this; - } - center() { - this.computeBoundingBox(); - this.boundingBox.getCenter(_offset).negate(); - this.translate(_offset.x, _offset.y, _offset.z); - return this; - } - setFromPoints(points) { - const positionAttribute = this.getAttribute("position"); - if (positionAttribute === void 0) { - const position = []; - for (let i = 0, l = points.length; i < l; i++) { - const point = points[i]; - position.push(point.x, point.y, point.z || 0); - } - this.setAttribute("position", new Float32BufferAttribute(position, 3)); - } else { - for (let i = 0, l = positionAttribute.count; i < l; i++) { - const point = points[i]; - positionAttribute.setXYZ(i, point.x, point.y, point.z || 0); - } - if (points.length > positionAttribute.count) { - console.warn("THREE.BufferGeometry: Buffer size too small for points data. Use .dispose() and create a new geometry."); - } - positionAttribute.needsUpdate = true; - } - return this; - } - computeBoundingBox() { - if (this.boundingBox === null) { - this.boundingBox = new Box3(); - } - const position = this.attributes.position; - const morphAttributesPosition = this.morphAttributes.position; - if (position && position.isGLBufferAttribute) { - console.error("THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box.", this); - this.boundingBox.set( - new Vector3(-Infinity, -Infinity, -Infinity), - new Vector3(Infinity, Infinity, Infinity) - ); - return; - } - if (position !== void 0) { - this.boundingBox.setFromBufferAttribute(position); - if (morphAttributesPosition) { - for (let i = 0, il = morphAttributesPosition.length; i < il; i++) { - const morphAttribute = morphAttributesPosition[i]; - _box$2.setFromBufferAttribute(morphAttribute); - if (this.morphTargetsRelative) { - _vector$8.addVectors(this.boundingBox.min, _box$2.min); - this.boundingBox.expandByPoint(_vector$8); - _vector$8.addVectors(this.boundingBox.max, _box$2.max); - this.boundingBox.expandByPoint(_vector$8); - } else { - this.boundingBox.expandByPoint(_box$2.min); - this.boundingBox.expandByPoint(_box$2.max); - } - } - } - } else { - this.boundingBox.makeEmpty(); - } - if (isNaN(this.boundingBox.min.x) || isNaN(this.boundingBox.min.y) || isNaN(this.boundingBox.min.z)) { - console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.', this); - } - } - computeBoundingSphere() { - if (this.boundingSphere === null) { - this.boundingSphere = new Sphere(); - } - const position = this.attributes.position; - const morphAttributesPosition = this.morphAttributes.position; - if (position && position.isGLBufferAttribute) { - console.error("THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere.", this); - this.boundingSphere.set(new Vector3(), Infinity); - return; - } - if (position) { - const center = this.boundingSphere.center; - _box$2.setFromBufferAttribute(position); - if (morphAttributesPosition) { - for (let i = 0, il = morphAttributesPosition.length; i < il; i++) { - const morphAttribute = morphAttributesPosition[i]; - _boxMorphTargets.setFromBufferAttribute(morphAttribute); - if (this.morphTargetsRelative) { - _vector$8.addVectors(_box$2.min, _boxMorphTargets.min); - _box$2.expandByPoint(_vector$8); - _vector$8.addVectors(_box$2.max, _boxMorphTargets.max); - _box$2.expandByPoint(_vector$8); - } else { - _box$2.expandByPoint(_boxMorphTargets.min); - _box$2.expandByPoint(_boxMorphTargets.max); - } - } - } - _box$2.getCenter(center); - let maxRadiusSq = 0; - for (let i = 0, il = position.count; i < il; i++) { - _vector$8.fromBufferAttribute(position, i); - maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(_vector$8)); - } - if (morphAttributesPosition) { - for (let i = 0, il = morphAttributesPosition.length; i < il; i++) { - const morphAttribute = morphAttributesPosition[i]; - const morphTargetsRelative = this.morphTargetsRelative; - for (let j = 0, jl = morphAttribute.count; j < jl; j++) { - _vector$8.fromBufferAttribute(morphAttribute, j); - if (morphTargetsRelative) { - _offset.fromBufferAttribute(position, j); - _vector$8.add(_offset); - } - maxRadiusSq = Math.max(maxRadiusSq, center.distanceToSquared(_vector$8)); - } - } - } - this.boundingSphere.radius = Math.sqrt(maxRadiusSq); - if (isNaN(this.boundingSphere.radius)) { - console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.', this); - } - } - } - computeTangents() { - const index = this.index; - const attributes = this.attributes; - if (index === null || attributes.position === void 0 || attributes.normal === void 0 || attributes.uv === void 0) { - console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)"); - return; - } - const positionAttribute = attributes.position; - const normalAttribute = attributes.normal; - const uvAttribute = attributes.uv; - if (this.hasAttribute("tangent") === false) { - this.setAttribute("tangent", new BufferAttribute(new Float32Array(4 * positionAttribute.count), 4)); - } - const tangentAttribute = this.getAttribute("tangent"); - const tan1 = [], tan2 = []; - for (let i = 0; i < positionAttribute.count; i++) { - tan1[i] = new Vector3(); - tan2[i] = new Vector3(); - } - const vA = new Vector3(), vB = new Vector3(), vC = new Vector3(), uvA = new Vector2(), uvB = new Vector2(), uvC = new Vector2(), sdir = new Vector3(), tdir = new Vector3(); - function handleTriangle(a, b, c) { - vA.fromBufferAttribute(positionAttribute, a); - vB.fromBufferAttribute(positionAttribute, b); - vC.fromBufferAttribute(positionAttribute, c); - uvA.fromBufferAttribute(uvAttribute, a); - uvB.fromBufferAttribute(uvAttribute, b); - uvC.fromBufferAttribute(uvAttribute, c); - vB.sub(vA); - vC.sub(vA); - uvB.sub(uvA); - uvC.sub(uvA); - const r = 1 / (uvB.x * uvC.y - uvC.x * uvB.y); - if (!isFinite(r)) return; - sdir.copy(vB).multiplyScalar(uvC.y).addScaledVector(vC, -uvB.y).multiplyScalar(r); - tdir.copy(vC).multiplyScalar(uvB.x).addScaledVector(vB, -uvC.x).multiplyScalar(r); - tan1[a].add(sdir); - tan1[b].add(sdir); - tan1[c].add(sdir); - tan2[a].add(tdir); - tan2[b].add(tdir); - tan2[c].add(tdir); - } - __name(handleTriangle, "handleTriangle"); - let groups = this.groups; - if (groups.length === 0) { - groups = [{ - start: 0, - count: index.count - }]; - } - for (let i = 0, il = groups.length; i < il; ++i) { - const group = groups[i]; - const start = group.start; - const count = group.count; - for (let j = start, jl = start + count; j < jl; j += 3) { - handleTriangle( - index.getX(j + 0), - index.getX(j + 1), - index.getX(j + 2) - ); - } - } - const tmp2 = new Vector3(), tmp22 = new Vector3(); - const n = new Vector3(), n2 = new Vector3(); - function handleVertex(v) { - n.fromBufferAttribute(normalAttribute, v); - n2.copy(n); - const t2 = tan1[v]; - tmp2.copy(t2); - tmp2.sub(n.multiplyScalar(n.dot(t2))).normalize(); - tmp22.crossVectors(n2, t2); - const test = tmp22.dot(tan2[v]); - const w = test < 0 ? -1 : 1; - tangentAttribute.setXYZW(v, tmp2.x, tmp2.y, tmp2.z, w); - } - __name(handleVertex, "handleVertex"); - for (let i = 0, il = groups.length; i < il; ++i) { - const group = groups[i]; - const start = group.start; - const count = group.count; - for (let j = start, jl = start + count; j < jl; j += 3) { - handleVertex(index.getX(j + 0)); - handleVertex(index.getX(j + 1)); - handleVertex(index.getX(j + 2)); - } - } - } - computeVertexNormals() { - const index = this.index; - const positionAttribute = this.getAttribute("position"); - if (positionAttribute !== void 0) { - let normalAttribute = this.getAttribute("normal"); - if (normalAttribute === void 0) { - normalAttribute = new BufferAttribute(new Float32Array(positionAttribute.count * 3), 3); - this.setAttribute("normal", normalAttribute); - } else { - for (let i = 0, il = normalAttribute.count; i < il; i++) { - normalAttribute.setXYZ(i, 0, 0, 0); - } - } - const pA = new Vector3(), pB = new Vector3(), pC = new Vector3(); - const nA = new Vector3(), nB = new Vector3(), nC = new Vector3(); - const cb = new Vector3(), ab = new Vector3(); - if (index) { - for (let i = 0, il = index.count; i < il; i += 3) { - const vA = index.getX(i + 0); - const vB = index.getX(i + 1); - const vC = index.getX(i + 2); - pA.fromBufferAttribute(positionAttribute, vA); - pB.fromBufferAttribute(positionAttribute, vB); - pC.fromBufferAttribute(positionAttribute, vC); - cb.subVectors(pC, pB); - ab.subVectors(pA, pB); - cb.cross(ab); - nA.fromBufferAttribute(normalAttribute, vA); - nB.fromBufferAttribute(normalAttribute, vB); - nC.fromBufferAttribute(normalAttribute, vC); - nA.add(cb); - nB.add(cb); - nC.add(cb); - normalAttribute.setXYZ(vA, nA.x, nA.y, nA.z); - normalAttribute.setXYZ(vB, nB.x, nB.y, nB.z); - normalAttribute.setXYZ(vC, nC.x, nC.y, nC.z); - } - } else { - for (let i = 0, il = positionAttribute.count; i < il; i += 3) { - pA.fromBufferAttribute(positionAttribute, i + 0); - pB.fromBufferAttribute(positionAttribute, i + 1); - pC.fromBufferAttribute(positionAttribute, i + 2); - cb.subVectors(pC, pB); - ab.subVectors(pA, pB); - cb.cross(ab); - normalAttribute.setXYZ(i + 0, cb.x, cb.y, cb.z); - normalAttribute.setXYZ(i + 1, cb.x, cb.y, cb.z); - normalAttribute.setXYZ(i + 2, cb.x, cb.y, cb.z); - } - } - this.normalizeNormals(); - normalAttribute.needsUpdate = true; - } - } - normalizeNormals() { - const normals = this.attributes.normal; - for (let i = 0, il = normals.count; i < il; i++) { - _vector$8.fromBufferAttribute(normals, i); - _vector$8.normalize(); - normals.setXYZ(i, _vector$8.x, _vector$8.y, _vector$8.z); - } - } - toNonIndexed() { - function convertBufferAttribute(attribute, indices2) { - const array = attribute.array; - const itemSize = attribute.itemSize; - const normalized = attribute.normalized; - const array2 = new array.constructor(indices2.length * itemSize); - let index = 0, index2 = 0; - for (let i = 0, l = indices2.length; i < l; i++) { - if (attribute.isInterleavedBufferAttribute) { - index = indices2[i] * attribute.data.stride + attribute.offset; - } else { - index = indices2[i] * itemSize; - } - for (let j = 0; j < itemSize; j++) { - array2[index2++] = array[index++]; - } - } - return new BufferAttribute(array2, itemSize, normalized); - } - __name(convertBufferAttribute, "convertBufferAttribute"); - if (this.index === null) { - console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."); - return this; - } - const geometry2 = new BufferGeometry(); - const indices = this.index.array; - const attributes = this.attributes; - for (const name in attributes) { - const attribute = attributes[name]; - const newAttribute = convertBufferAttribute(attribute, indices); - geometry2.setAttribute(name, newAttribute); - } - const morphAttributes = this.morphAttributes; - for (const name in morphAttributes) { - const morphArray = []; - const morphAttribute = morphAttributes[name]; - for (let i = 0, il = morphAttribute.length; i < il; i++) { - const attribute = morphAttribute[i]; - const newAttribute = convertBufferAttribute(attribute, indices); - morphArray.push(newAttribute); - } - geometry2.morphAttributes[name] = morphArray; - } - geometry2.morphTargetsRelative = this.morphTargetsRelative; - const groups = this.groups; - for (let i = 0, l = groups.length; i < l; i++) { - const group = groups[i]; - geometry2.addGroup(group.start, group.count, group.materialIndex); - } - return geometry2; - } - toJSON() { - const data = { - metadata: { - version: 4.6, - type: "BufferGeometry", - generator: "BufferGeometry.toJSON" - } - }; - data.uuid = this.uuid; - data.type = this.type; - if (this.name !== "") data.name = this.name; - if (Object.keys(this.userData).length > 0) data.userData = this.userData; - if (this.parameters !== void 0) { - const parameters = this.parameters; - for (const key in parameters) { - if (parameters[key] !== void 0) data[key] = parameters[key]; - } - return data; - } - data.data = { attributes: {} }; - const index = this.index; - if (index !== null) { - data.data.index = { - type: index.array.constructor.name, - array: Array.prototype.slice.call(index.array) - }; - } - const attributes = this.attributes; - for (const key in attributes) { - const attribute = attributes[key]; - data.data.attributes[key] = attribute.toJSON(data.data); - } - const morphAttributes = {}; - let hasMorphAttributes = false; - for (const key in this.morphAttributes) { - const attributeArray = this.morphAttributes[key]; - const array = []; - for (let i = 0, il = attributeArray.length; i < il; i++) { - const attribute = attributeArray[i]; - array.push(attribute.toJSON(data.data)); - } - if (array.length > 0) { - morphAttributes[key] = array; - hasMorphAttributes = true; - } - } - if (hasMorphAttributes) { - data.data.morphAttributes = morphAttributes; - data.data.morphTargetsRelative = this.morphTargetsRelative; - } - const groups = this.groups; - if (groups.length > 0) { - data.data.groups = JSON.parse(JSON.stringify(groups)); - } - const boundingSphere = this.boundingSphere; - if (boundingSphere !== null) { - data.data.boundingSphere = { - center: boundingSphere.center.toArray(), - radius: boundingSphere.radius - }; - } - return data; - } - clone() { - return new this.constructor().copy(this); - } - copy(source) { - this.index = null; - this.attributes = {}; - this.morphAttributes = {}; - this.groups = []; - this.boundingBox = null; - this.boundingSphere = null; - const data = {}; - this.name = source.name; - const index = source.index; - if (index !== null) { - this.setIndex(index.clone(data)); - } - const attributes = source.attributes; - for (const name in attributes) { - const attribute = attributes[name]; - this.setAttribute(name, attribute.clone(data)); - } - const morphAttributes = source.morphAttributes; - for (const name in morphAttributes) { - const array = []; - const morphAttribute = morphAttributes[name]; - for (let i = 0, l = morphAttribute.length; i < l; i++) { - array.push(morphAttribute[i].clone(data)); - } - this.morphAttributes[name] = array; - } - this.morphTargetsRelative = source.morphTargetsRelative; - const groups = source.groups; - for (let i = 0, l = groups.length; i < l; i++) { - const group = groups[i]; - this.addGroup(group.start, group.count, group.materialIndex); - } - const boundingBox = source.boundingBox; - if (boundingBox !== null) { - this.boundingBox = boundingBox.clone(); - } - const boundingSphere = source.boundingSphere; - if (boundingSphere !== null) { - this.boundingSphere = boundingSphere.clone(); - } - this.drawRange.start = source.drawRange.start; - this.drawRange.count = source.drawRange.count; - this.userData = source.userData; - return this; - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - } -} -const _inverseMatrix$3 = /* @__PURE__ */ new Matrix4(); -const _ray$3 = /* @__PURE__ */ new Ray(); -const _sphere$6 = /* @__PURE__ */ new Sphere(); -const _sphereHitAt = /* @__PURE__ */ new Vector3(); -const _vA$1 = /* @__PURE__ */ new Vector3(); -const _vB$1 = /* @__PURE__ */ new Vector3(); -const _vC$1 = /* @__PURE__ */ new Vector3(); -const _tempA = /* @__PURE__ */ new Vector3(); -const _morphA = /* @__PURE__ */ new Vector3(); -const _intersectionPoint = /* @__PURE__ */ new Vector3(); -const _intersectionPointWorld = /* @__PURE__ */ new Vector3(); -class Mesh extends Object3D { - static { - __name(this, "Mesh"); - } - constructor(geometry = new BufferGeometry(), material = new MeshBasicMaterial()) { - super(); - this.isMesh = true; - this.type = "Mesh"; - this.geometry = geometry; - this.material = material; - this.updateMorphTargets(); - } - copy(source, recursive) { - super.copy(source, recursive); - if (source.morphTargetInfluences !== void 0) { - this.morphTargetInfluences = source.morphTargetInfluences.slice(); - } - if (source.morphTargetDictionary !== void 0) { - this.morphTargetDictionary = Object.assign({}, source.morphTargetDictionary); - } - this.material = Array.isArray(source.material) ? source.material.slice() : source.material; - this.geometry = source.geometry; - return this; - } - updateMorphTargets() { - const geometry = this.geometry; - const morphAttributes = geometry.morphAttributes; - const keys = Object.keys(morphAttributes); - if (keys.length > 0) { - const morphAttribute = morphAttributes[keys[0]]; - if (morphAttribute !== void 0) { - this.morphTargetInfluences = []; - this.morphTargetDictionary = {}; - for (let m = 0, ml = morphAttribute.length; m < ml; m++) { - const name = morphAttribute[m].name || String(m); - this.morphTargetInfluences.push(0); - this.morphTargetDictionary[name] = m; - } - } - } - } - getVertexPosition(index, target) { - const geometry = this.geometry; - const position = geometry.attributes.position; - const morphPosition = geometry.morphAttributes.position; - const morphTargetsRelative = geometry.morphTargetsRelative; - target.fromBufferAttribute(position, index); - const morphInfluences = this.morphTargetInfluences; - if (morphPosition && morphInfluences) { - _morphA.set(0, 0, 0); - for (let i = 0, il = morphPosition.length; i < il; i++) { - const influence = morphInfluences[i]; - const morphAttribute = morphPosition[i]; - if (influence === 0) continue; - _tempA.fromBufferAttribute(morphAttribute, index); - if (morphTargetsRelative) { - _morphA.addScaledVector(_tempA, influence); - } else { - _morphA.addScaledVector(_tempA.sub(target), influence); - } - } - target.add(_morphA); - } - return target; - } - raycast(raycaster, intersects2) { - const geometry = this.geometry; - const material = this.material; - const matrixWorld = this.matrixWorld; - if (material === void 0) return; - if (geometry.boundingSphere === null) geometry.computeBoundingSphere(); - _sphere$6.copy(geometry.boundingSphere); - _sphere$6.applyMatrix4(matrixWorld); - _ray$3.copy(raycaster.ray).recast(raycaster.near); - if (_sphere$6.containsPoint(_ray$3.origin) === false) { - if (_ray$3.intersectSphere(_sphere$6, _sphereHitAt) === null) return; - if (_ray$3.origin.distanceToSquared(_sphereHitAt) > (raycaster.far - raycaster.near) ** 2) return; - } - _inverseMatrix$3.copy(matrixWorld).invert(); - _ray$3.copy(raycaster.ray).applyMatrix4(_inverseMatrix$3); - if (geometry.boundingBox !== null) { - if (_ray$3.intersectsBox(geometry.boundingBox) === false) return; - } - this._computeIntersections(raycaster, intersects2, _ray$3); - } - _computeIntersections(raycaster, intersects2, rayLocalSpace) { - let intersection; - const geometry = this.geometry; - const material = this.material; - const index = geometry.index; - const position = geometry.attributes.position; - const uv = geometry.attributes.uv; - const uv1 = geometry.attributes.uv1; - const normal = geometry.attributes.normal; - const groups = geometry.groups; - const drawRange = geometry.drawRange; - if (index !== null) { - if (Array.isArray(material)) { - for (let i = 0, il = groups.length; i < il; i++) { - const group = groups[i]; - const groupMaterial = material[group.materialIndex]; - const start = Math.max(group.start, drawRange.start); - const end = Math.min(index.count, Math.min(group.start + group.count, drawRange.start + drawRange.count)); - for (let j = start, jl = end; j < jl; j += 3) { - const a = index.getX(j); - const b = index.getX(j + 1); - const c = index.getX(j + 2); - intersection = checkGeometryIntersection(this, groupMaterial, raycaster, rayLocalSpace, uv, uv1, normal, a, b, c); - if (intersection) { - intersection.faceIndex = Math.floor(j / 3); - intersection.face.materialIndex = group.materialIndex; - intersects2.push(intersection); - } - } - } - } else { - const start = Math.max(0, drawRange.start); - const end = Math.min(index.count, drawRange.start + drawRange.count); - for (let i = start, il = end; i < il; i += 3) { - const a = index.getX(i); - const b = index.getX(i + 1); - const c = index.getX(i + 2); - intersection = checkGeometryIntersection(this, material, raycaster, rayLocalSpace, uv, uv1, normal, a, b, c); - if (intersection) { - intersection.faceIndex = Math.floor(i / 3); - intersects2.push(intersection); - } - } - } - } else if (position !== void 0) { - if (Array.isArray(material)) { - for (let i = 0, il = groups.length; i < il; i++) { - const group = groups[i]; - const groupMaterial = material[group.materialIndex]; - const start = Math.max(group.start, drawRange.start); - const end = Math.min(position.count, Math.min(group.start + group.count, drawRange.start + drawRange.count)); - for (let j = start, jl = end; j < jl; j += 3) { - const a = j; - const b = j + 1; - const c = j + 2; - intersection = checkGeometryIntersection(this, groupMaterial, raycaster, rayLocalSpace, uv, uv1, normal, a, b, c); - if (intersection) { - intersection.faceIndex = Math.floor(j / 3); - intersection.face.materialIndex = group.materialIndex; - intersects2.push(intersection); - } - } - } - } else { - const start = Math.max(0, drawRange.start); - const end = Math.min(position.count, drawRange.start + drawRange.count); - for (let i = start, il = end; i < il; i += 3) { - const a = i; - const b = i + 1; - const c = i + 2; - intersection = checkGeometryIntersection(this, material, raycaster, rayLocalSpace, uv, uv1, normal, a, b, c); - if (intersection) { - intersection.faceIndex = Math.floor(i / 3); - intersects2.push(intersection); - } - } - } - } - } -} -function checkIntersection$1(object, material, raycaster, ray, pA, pB, pC, point) { - let intersect2; - if (material.side === BackSide) { - intersect2 = ray.intersectTriangle(pC, pB, pA, true, point); - } else { - intersect2 = ray.intersectTriangle(pA, pB, pC, material.side === FrontSide, point); - } - if (intersect2 === null) return null; - _intersectionPointWorld.copy(point); - _intersectionPointWorld.applyMatrix4(object.matrixWorld); - const distance = raycaster.ray.origin.distanceTo(_intersectionPointWorld); - if (distance < raycaster.near || distance > raycaster.far) return null; - return { - distance, - point: _intersectionPointWorld.clone(), - object - }; -} -__name(checkIntersection$1, "checkIntersection$1"); -function checkGeometryIntersection(object, material, raycaster, ray, uv, uv1, normal, a, b, c) { - object.getVertexPosition(a, _vA$1); - object.getVertexPosition(b, _vB$1); - object.getVertexPosition(c, _vC$1); - const intersection = checkIntersection$1(object, material, raycaster, ray, _vA$1, _vB$1, _vC$1, _intersectionPoint); - if (intersection) { - const barycoord = new Vector3(); - Triangle.getBarycoord(_intersectionPoint, _vA$1, _vB$1, _vC$1, barycoord); - if (uv) { - intersection.uv = Triangle.getInterpolatedAttribute(uv, a, b, c, barycoord, new Vector2()); - } - if (uv1) { - intersection.uv1 = Triangle.getInterpolatedAttribute(uv1, a, b, c, barycoord, new Vector2()); - } - if (normal) { - intersection.normal = Triangle.getInterpolatedAttribute(normal, a, b, c, barycoord, new Vector3()); - if (intersection.normal.dot(ray.direction) > 0) { - intersection.normal.multiplyScalar(-1); - } - } - const face = { - a, - b, - c, - normal: new Vector3(), - materialIndex: 0 - }; - Triangle.getNormal(_vA$1, _vB$1, _vC$1, face.normal); - intersection.face = face; - intersection.barycoord = barycoord; - } - return intersection; -} -__name(checkGeometryIntersection, "checkGeometryIntersection"); -class BoxGeometry extends BufferGeometry { - static { - __name(this, "BoxGeometry"); - } - constructor(width = 1, height = 1, depth = 1, widthSegments = 1, heightSegments = 1, depthSegments = 1) { - super(); - this.type = "BoxGeometry"; - this.parameters = { - width, - height, - depth, - widthSegments, - heightSegments, - depthSegments - }; - const scope = this; - widthSegments = Math.floor(widthSegments); - heightSegments = Math.floor(heightSegments); - depthSegments = Math.floor(depthSegments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - let numberOfVertices = 0; - let groupStart = 0; - buildPlane("z", "y", "x", -1, -1, depth, height, width, depthSegments, heightSegments, 0); - buildPlane("z", "y", "x", 1, -1, depth, height, -width, depthSegments, heightSegments, 1); - buildPlane("x", "z", "y", 1, 1, width, depth, height, widthSegments, depthSegments, 2); - buildPlane("x", "z", "y", 1, -1, width, depth, -height, widthSegments, depthSegments, 3); - buildPlane("x", "y", "z", 1, -1, width, height, depth, widthSegments, heightSegments, 4); - buildPlane("x", "y", "z", -1, -1, width, height, -depth, widthSegments, heightSegments, 5); - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - function buildPlane(u, v, w, udir, vdir, width2, height2, depth2, gridX, gridY, materialIndex) { - const segmentWidth = width2 / gridX; - const segmentHeight = height2 / gridY; - const widthHalf = width2 / 2; - const heightHalf = height2 / 2; - const depthHalf = depth2 / 2; - const gridX1 = gridX + 1; - const gridY1 = gridY + 1; - let vertexCounter = 0; - let groupCount = 0; - const vector = new Vector3(); - for (let iy = 0; iy < gridY1; iy++) { - const y = iy * segmentHeight - heightHalf; - for (let ix = 0; ix < gridX1; ix++) { - const x = ix * segmentWidth - widthHalf; - vector[u] = x * udir; - vector[v] = y * vdir; - vector[w] = depthHalf; - vertices.push(vector.x, vector.y, vector.z); - vector[u] = 0; - vector[v] = 0; - vector[w] = depth2 > 0 ? 1 : -1; - normals.push(vector.x, vector.y, vector.z); - uvs.push(ix / gridX); - uvs.push(1 - iy / gridY); - vertexCounter += 1; - } - } - for (let iy = 0; iy < gridY; iy++) { - for (let ix = 0; ix < gridX; ix++) { - const a = numberOfVertices + ix + gridX1 * iy; - const b = numberOfVertices + ix + gridX1 * (iy + 1); - const c = numberOfVertices + (ix + 1) + gridX1 * (iy + 1); - const d = numberOfVertices + (ix + 1) + gridX1 * iy; - indices.push(a, b, d); - indices.push(b, c, d); - groupCount += 6; - } - } - scope.addGroup(groupStart, groupCount, materialIndex); - groupStart += groupCount; - numberOfVertices += vertexCounter; - } - __name(buildPlane, "buildPlane"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new BoxGeometry(data.width, data.height, data.depth, data.widthSegments, data.heightSegments, data.depthSegments); - } -} -function cloneUniforms(src) { - const dst = {}; - for (const u in src) { - dst[u] = {}; - for (const p in src[u]) { - const property = src[u][p]; - if (property && (property.isColor || property.isMatrix3 || property.isMatrix4 || property.isVector2 || property.isVector3 || property.isVector4 || property.isTexture || property.isQuaternion)) { - if (property.isRenderTargetTexture) { - console.warn("UniformsUtils: Textures of render targets cannot be cloned via cloneUniforms() or mergeUniforms()."); - dst[u][p] = null; - } else { - dst[u][p] = property.clone(); - } - } else if (Array.isArray(property)) { - dst[u][p] = property.slice(); - } else { - dst[u][p] = property; - } - } - } - return dst; -} -__name(cloneUniforms, "cloneUniforms"); -function mergeUniforms(uniforms) { - const merged = {}; - for (let u = 0; u < uniforms.length; u++) { - const tmp2 = cloneUniforms(uniforms[u]); - for (const p in tmp2) { - merged[p] = tmp2[p]; - } - } - return merged; -} -__name(mergeUniforms, "mergeUniforms"); -function cloneUniformsGroups(src) { - const dst = []; - for (let u = 0; u < src.length; u++) { - dst.push(src[u].clone()); - } - return dst; -} -__name(cloneUniformsGroups, "cloneUniformsGroups"); -function getUnlitUniformColorSpace(renderer) { - const currentRenderTarget = renderer.getRenderTarget(); - if (currentRenderTarget === null) { - return renderer.outputColorSpace; - } - if (currentRenderTarget.isXRRenderTarget === true) { - return currentRenderTarget.texture.colorSpace; - } - return ColorManagement.workingColorSpace; -} -__name(getUnlitUniformColorSpace, "getUnlitUniformColorSpace"); -const UniformsUtils = { clone: cloneUniforms, merge: mergeUniforms }; -var default_vertex = "void main() {\n gl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}"; -var default_fragment = "void main() {\n gl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}"; -class ShaderMaterial extends Material { - static { - __name(this, "ShaderMaterial"); - } - static get type() { - return "ShaderMaterial"; - } - constructor(parameters) { - super(); - this.isShaderMaterial = true; - this.defines = {}; - this.uniforms = {}; - this.uniformsGroups = []; - this.vertexShader = default_vertex; - this.fragmentShader = default_fragment; - this.linewidth = 1; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.fog = false; - this.lights = false; - this.clipping = false; - this.forceSinglePass = true; - this.extensions = { - clipCullDistance: false, - // set to use vertex shader clipping - multiDraw: false - // set to use vertex shader multi_draw / enable gl_DrawID - }; - this.defaultAttributeValues = { - "color": [1, 1, 1], - "uv": [0, 0], - "uv1": [0, 0] - }; - this.index0AttributeName = void 0; - this.uniformsNeedUpdate = false; - this.glslVersion = null; - if (parameters !== void 0) { - this.setValues(parameters); - } - } - copy(source) { - super.copy(source); - this.fragmentShader = source.fragmentShader; - this.vertexShader = source.vertexShader; - this.uniforms = cloneUniforms(source.uniforms); - this.uniformsGroups = cloneUniformsGroups(source.uniformsGroups); - this.defines = Object.assign({}, source.defines); - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.fog = source.fog; - this.lights = source.lights; - this.clipping = source.clipping; - this.extensions = Object.assign({}, source.extensions); - this.glslVersion = source.glslVersion; - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - data.glslVersion = this.glslVersion; - data.uniforms = {}; - for (const name in this.uniforms) { - const uniform = this.uniforms[name]; - const value = uniform.value; - if (value && value.isTexture) { - data.uniforms[name] = { - type: "t", - value: value.toJSON(meta).uuid - }; - } else if (value && value.isColor) { - data.uniforms[name] = { - type: "c", - value: value.getHex() - }; - } else if (value && value.isVector2) { - data.uniforms[name] = { - type: "v2", - value: value.toArray() - }; - } else if (value && value.isVector3) { - data.uniforms[name] = { - type: "v3", - value: value.toArray() - }; - } else if (value && value.isVector4) { - data.uniforms[name] = { - type: "v4", - value: value.toArray() - }; - } else if (value && value.isMatrix3) { - data.uniforms[name] = { - type: "m3", - value: value.toArray() - }; - } else if (value && value.isMatrix4) { - data.uniforms[name] = { - type: "m4", - value: value.toArray() - }; - } else { - data.uniforms[name] = { - value - }; - } - } - if (Object.keys(this.defines).length > 0) data.defines = this.defines; - data.vertexShader = this.vertexShader; - data.fragmentShader = this.fragmentShader; - data.lights = this.lights; - data.clipping = this.clipping; - const extensions = {}; - for (const key in this.extensions) { - if (this.extensions[key] === true) extensions[key] = true; - } - if (Object.keys(extensions).length > 0) data.extensions = extensions; - return data; - } -} -class Camera extends Object3D { - static { - __name(this, "Camera"); - } - constructor() { - super(); - this.isCamera = true; - this.type = "Camera"; - this.matrixWorldInverse = new Matrix4(); - this.projectionMatrix = new Matrix4(); - this.projectionMatrixInverse = new Matrix4(); - this.coordinateSystem = WebGLCoordinateSystem; - } - copy(source, recursive) { - super.copy(source, recursive); - this.matrixWorldInverse.copy(source.matrixWorldInverse); - this.projectionMatrix.copy(source.projectionMatrix); - this.projectionMatrixInverse.copy(source.projectionMatrixInverse); - this.coordinateSystem = source.coordinateSystem; - return this; - } - getWorldDirection(target) { - return super.getWorldDirection(target).negate(); - } - updateMatrixWorld(force) { - super.updateMatrixWorld(force); - this.matrixWorldInverse.copy(this.matrixWorld).invert(); - } - updateWorldMatrix(updateParents, updateChildren) { - super.updateWorldMatrix(updateParents, updateChildren); - this.matrixWorldInverse.copy(this.matrixWorld).invert(); - } - clone() { - return new this.constructor().copy(this); - } -} -const _v3$1 = /* @__PURE__ */ new Vector3(); -const _minTarget = /* @__PURE__ */ new Vector2(); -const _maxTarget = /* @__PURE__ */ new Vector2(); -class PerspectiveCamera extends Camera { - static { - __name(this, "PerspectiveCamera"); - } - constructor(fov2 = 50, aspect2 = 1, near = 0.1, far = 2e3) { - super(); - this.isPerspectiveCamera = true; - this.type = "PerspectiveCamera"; - this.fov = fov2; - this.zoom = 1; - this.near = near; - this.far = far; - this.focus = 10; - this.aspect = aspect2; - this.view = null; - this.filmGauge = 35; - this.filmOffset = 0; - this.updateProjectionMatrix(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.fov = source.fov; - this.zoom = source.zoom; - this.near = source.near; - this.far = source.far; - this.focus = source.focus; - this.aspect = source.aspect; - this.view = source.view === null ? null : Object.assign({}, source.view); - this.filmGauge = source.filmGauge; - this.filmOffset = source.filmOffset; - return this; - } - /** - * Sets the FOV by focal length in respect to the current .filmGauge. - * - * The default film gauge is 35, so that the focal length can be specified for - * a 35mm (full frame) camera. - * - * Values for focal length and film gauge must have the same unit. - */ - setFocalLength(focalLength) { - const vExtentSlope = 0.5 * this.getFilmHeight() / focalLength; - this.fov = RAD2DEG * 2 * Math.atan(vExtentSlope); - this.updateProjectionMatrix(); - } - /** - * Calculates the focal length from the current .fov and .filmGauge. - */ - getFocalLength() { - const vExtentSlope = Math.tan(DEG2RAD * 0.5 * this.fov); - return 0.5 * this.getFilmHeight() / vExtentSlope; - } - getEffectiveFOV() { - return RAD2DEG * 2 * Math.atan( - Math.tan(DEG2RAD * 0.5 * this.fov) / this.zoom - ); - } - getFilmWidth() { - return this.filmGauge * Math.min(this.aspect, 1); - } - getFilmHeight() { - return this.filmGauge / Math.max(this.aspect, 1); - } - /** - * Computes the 2D bounds of the camera's viewable rectangle at a given distance along the viewing direction. - * Sets minTarget and maxTarget to the coordinates of the lower-left and upper-right corners of the view rectangle. - */ - getViewBounds(distance, minTarget, maxTarget) { - _v3$1.set(-1, -1, 0.5).applyMatrix4(this.projectionMatrixInverse); - minTarget.set(_v3$1.x, _v3$1.y).multiplyScalar(-distance / _v3$1.z); - _v3$1.set(1, 1, 0.5).applyMatrix4(this.projectionMatrixInverse); - maxTarget.set(_v3$1.x, _v3$1.y).multiplyScalar(-distance / _v3$1.z); - } - /** - * Computes the width and height of the camera's viewable rectangle at a given distance along the viewing direction. - * Copies the result into the target Vector2, where x is width and y is height. - */ - getViewSize(distance, target) { - this.getViewBounds(distance, _minTarget, _maxTarget); - return target.subVectors(_maxTarget, _minTarget); - } - /** - * Sets an offset in a larger frustum. This is useful for multi-window or - * multi-monitor/multi-machine setups. - * - * For example, if you have 3x2 monitors and each monitor is 1920x1080 and - * the monitors are in grid like this - * - * +---+---+---+ - * | A | B | C | - * +---+---+---+ - * | D | E | F | - * +---+---+---+ - * - * then for each monitor you would call it like this - * - * const w = 1920; - * const h = 1080; - * const fullWidth = w * 3; - * const fullHeight = h * 2; - * - * --A-- - * camera.setViewOffset( fullWidth, fullHeight, w * 0, h * 0, w, h ); - * --B-- - * camera.setViewOffset( fullWidth, fullHeight, w * 1, h * 0, w, h ); - * --C-- - * camera.setViewOffset( fullWidth, fullHeight, w * 2, h * 0, w, h ); - * --D-- - * camera.setViewOffset( fullWidth, fullHeight, w * 0, h * 1, w, h ); - * --E-- - * camera.setViewOffset( fullWidth, fullHeight, w * 1, h * 1, w, h ); - * --F-- - * camera.setViewOffset( fullWidth, fullHeight, w * 2, h * 1, w, h ); - * - * Note there is no reason monitors have to be the same size or in a grid. - */ - setViewOffset(fullWidth, fullHeight, x, y, width, height) { - this.aspect = fullWidth / fullHeight; - if (this.view === null) { - this.view = { - enabled: true, - fullWidth: 1, - fullHeight: 1, - offsetX: 0, - offsetY: 0, - width: 1, - height: 1 - }; - } - this.view.enabled = true; - this.view.fullWidth = fullWidth; - this.view.fullHeight = fullHeight; - this.view.offsetX = x; - this.view.offsetY = y; - this.view.width = width; - this.view.height = height; - this.updateProjectionMatrix(); - } - clearViewOffset() { - if (this.view !== null) { - this.view.enabled = false; - } - this.updateProjectionMatrix(); - } - updateProjectionMatrix() { - const near = this.near; - let top = near * Math.tan(DEG2RAD * 0.5 * this.fov) / this.zoom; - let height = 2 * top; - let width = this.aspect * height; - let left = -0.5 * width; - const view = this.view; - if (this.view !== null && this.view.enabled) { - const fullWidth = view.fullWidth, fullHeight = view.fullHeight; - left += view.offsetX * width / fullWidth; - top -= view.offsetY * height / fullHeight; - width *= view.width / fullWidth; - height *= view.height / fullHeight; - } - const skew = this.filmOffset; - if (skew !== 0) left += near * skew / this.getFilmWidth(); - this.projectionMatrix.makePerspective(left, left + width, top, top - height, near, this.far, this.coordinateSystem); - this.projectionMatrixInverse.copy(this.projectionMatrix).invert(); - } - toJSON(meta) { - const data = super.toJSON(meta); - data.object.fov = this.fov; - data.object.zoom = this.zoom; - data.object.near = this.near; - data.object.far = this.far; - data.object.focus = this.focus; - data.object.aspect = this.aspect; - if (this.view !== null) data.object.view = Object.assign({}, this.view); - data.object.filmGauge = this.filmGauge; - data.object.filmOffset = this.filmOffset; - return data; - } -} -const fov = -90; -const aspect = 1; -class CubeCamera extends Object3D { - static { - __name(this, "CubeCamera"); - } - constructor(near, far, renderTarget) { - super(); - this.type = "CubeCamera"; - this.renderTarget = renderTarget; - this.coordinateSystem = null; - this.activeMipmapLevel = 0; - const cameraPX = new PerspectiveCamera(fov, aspect, near, far); - cameraPX.layers = this.layers; - this.add(cameraPX); - const cameraNX = new PerspectiveCamera(fov, aspect, near, far); - cameraNX.layers = this.layers; - this.add(cameraNX); - const cameraPY = new PerspectiveCamera(fov, aspect, near, far); - cameraPY.layers = this.layers; - this.add(cameraPY); - const cameraNY = new PerspectiveCamera(fov, aspect, near, far); - cameraNY.layers = this.layers; - this.add(cameraNY); - const cameraPZ = new PerspectiveCamera(fov, aspect, near, far); - cameraPZ.layers = this.layers; - this.add(cameraPZ); - const cameraNZ = new PerspectiveCamera(fov, aspect, near, far); - cameraNZ.layers = this.layers; - this.add(cameraNZ); - } - updateCoordinateSystem() { - const coordinateSystem = this.coordinateSystem; - const cameras = this.children.concat(); - const [cameraPX, cameraNX, cameraPY, cameraNY, cameraPZ, cameraNZ] = cameras; - for (const camera of cameras) this.remove(camera); - if (coordinateSystem === WebGLCoordinateSystem) { - cameraPX.up.set(0, 1, 0); - cameraPX.lookAt(1, 0, 0); - cameraNX.up.set(0, 1, 0); - cameraNX.lookAt(-1, 0, 0); - cameraPY.up.set(0, 0, -1); - cameraPY.lookAt(0, 1, 0); - cameraNY.up.set(0, 0, 1); - cameraNY.lookAt(0, -1, 0); - cameraPZ.up.set(0, 1, 0); - cameraPZ.lookAt(0, 0, 1); - cameraNZ.up.set(0, 1, 0); - cameraNZ.lookAt(0, 0, -1); - } else if (coordinateSystem === WebGPUCoordinateSystem) { - cameraPX.up.set(0, -1, 0); - cameraPX.lookAt(-1, 0, 0); - cameraNX.up.set(0, -1, 0); - cameraNX.lookAt(1, 0, 0); - cameraPY.up.set(0, 0, 1); - cameraPY.lookAt(0, 1, 0); - cameraNY.up.set(0, 0, -1); - cameraNY.lookAt(0, -1, 0); - cameraPZ.up.set(0, -1, 0); - cameraPZ.lookAt(0, 0, 1); - cameraNZ.up.set(0, -1, 0); - cameraNZ.lookAt(0, 0, -1); - } else { - throw new Error("THREE.CubeCamera.updateCoordinateSystem(): Invalid coordinate system: " + coordinateSystem); - } - for (const camera of cameras) { - this.add(camera); - camera.updateMatrixWorld(); - } - } - update(renderer, scene) { - if (this.parent === null) this.updateMatrixWorld(); - const { renderTarget, activeMipmapLevel } = this; - if (this.coordinateSystem !== renderer.coordinateSystem) { - this.coordinateSystem = renderer.coordinateSystem; - this.updateCoordinateSystem(); - } - const [cameraPX, cameraNX, cameraPY, cameraNY, cameraPZ, cameraNZ] = this.children; - const currentRenderTarget = renderer.getRenderTarget(); - const currentActiveCubeFace = renderer.getActiveCubeFace(); - const currentActiveMipmapLevel = renderer.getActiveMipmapLevel(); - const currentXrEnabled = renderer.xr.enabled; - renderer.xr.enabled = false; - const generateMipmaps = renderTarget.texture.generateMipmaps; - renderTarget.texture.generateMipmaps = false; - renderer.setRenderTarget(renderTarget, 0, activeMipmapLevel); - renderer.render(scene, cameraPX); - renderer.setRenderTarget(renderTarget, 1, activeMipmapLevel); - renderer.render(scene, cameraNX); - renderer.setRenderTarget(renderTarget, 2, activeMipmapLevel); - renderer.render(scene, cameraPY); - renderer.setRenderTarget(renderTarget, 3, activeMipmapLevel); - renderer.render(scene, cameraNY); - renderer.setRenderTarget(renderTarget, 4, activeMipmapLevel); - renderer.render(scene, cameraPZ); - renderTarget.texture.generateMipmaps = generateMipmaps; - renderer.setRenderTarget(renderTarget, 5, activeMipmapLevel); - renderer.render(scene, cameraNZ); - renderer.setRenderTarget(currentRenderTarget, currentActiveCubeFace, currentActiveMipmapLevel); - renderer.xr.enabled = currentXrEnabled; - renderTarget.texture.needsPMREMUpdate = true; - } -} -class CubeTexture extends Texture { - static { - __name(this, "CubeTexture"); - } - constructor(images, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, colorSpace) { - images = images !== void 0 ? images : []; - mapping = mapping !== void 0 ? mapping : CubeReflectionMapping; - super(images, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, colorSpace); - this.isCubeTexture = true; - this.flipY = false; - } - get images() { - return this.image; - } - set images(value) { - this.image = value; - } -} -class WebGLCubeRenderTarget extends WebGLRenderTarget { - static { - __name(this, "WebGLCubeRenderTarget"); - } - constructor(size = 1, options = {}) { - super(size, size, options); - this.isWebGLCubeRenderTarget = true; - const image = { width: size, height: size, depth: 1 }; - const images = [image, image, image, image, image, image]; - this.texture = new CubeTexture(images, options.mapping, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.colorSpace); - this.texture.isRenderTargetTexture = true; - this.texture.generateMipmaps = options.generateMipmaps !== void 0 ? options.generateMipmaps : false; - this.texture.minFilter = options.minFilter !== void 0 ? options.minFilter : LinearFilter; - } - fromEquirectangularTexture(renderer, texture) { - this.texture.type = texture.type; - this.texture.colorSpace = texture.colorSpace; - this.texture.generateMipmaps = texture.generateMipmaps; - this.texture.minFilter = texture.minFilter; - this.texture.magFilter = texture.magFilter; - const shader = { - uniforms: { - tEquirect: { value: null } - }, - vertexShader: ( - /* glsl */ - ` - - varying vec3 vWorldDirection; - - vec3 transformDirection( in vec3 dir, in mat4 matrix ) { - - return normalize( ( matrix * vec4( dir, 0.0 ) ).xyz ); - - } - - void main() { - - vWorldDirection = transformDirection( position, modelMatrix ); - - #include - #include - - } - ` - ), - fragmentShader: ( - /* glsl */ - ` - - uniform sampler2D tEquirect; - - varying vec3 vWorldDirection; - - #include - - void main() { - - vec3 direction = normalize( vWorldDirection ); - - vec2 sampleUV = equirectUv( direction ); - - gl_FragColor = texture2D( tEquirect, sampleUV ); - - } - ` - ) - }; - const geometry = new BoxGeometry(5, 5, 5); - const material = new ShaderMaterial({ - name: "CubemapFromEquirect", - uniforms: cloneUniforms(shader.uniforms), - vertexShader: shader.vertexShader, - fragmentShader: shader.fragmentShader, - side: BackSide, - blending: NoBlending - }); - material.uniforms.tEquirect.value = texture; - const mesh = new Mesh(geometry, material); - const currentMinFilter = texture.minFilter; - if (texture.minFilter === LinearMipmapLinearFilter) texture.minFilter = LinearFilter; - const camera = new CubeCamera(1, 10, this); - camera.update(renderer, mesh); - texture.minFilter = currentMinFilter; - mesh.geometry.dispose(); - mesh.material.dispose(); - return this; - } - clear(renderer, color, depth, stencil) { - const currentRenderTarget = renderer.getRenderTarget(); - for (let i = 0; i < 6; i++) { - renderer.setRenderTarget(this, i); - renderer.clear(color, depth, stencil); - } - renderer.setRenderTarget(currentRenderTarget); - } -} -const _vector1 = /* @__PURE__ */ new Vector3(); -const _vector2 = /* @__PURE__ */ new Vector3(); -const _normalMatrix = /* @__PURE__ */ new Matrix3(); -class Plane { - static { - __name(this, "Plane"); - } - constructor(normal = new Vector3(1, 0, 0), constant = 0) { - this.isPlane = true; - this.normal = normal; - this.constant = constant; - } - set(normal, constant) { - this.normal.copy(normal); - this.constant = constant; - return this; - } - setComponents(x, y, z, w) { - this.normal.set(x, y, z); - this.constant = w; - return this; - } - setFromNormalAndCoplanarPoint(normal, point) { - this.normal.copy(normal); - this.constant = -point.dot(this.normal); - return this; - } - setFromCoplanarPoints(a, b, c) { - const normal = _vector1.subVectors(c, b).cross(_vector2.subVectors(a, b)).normalize(); - this.setFromNormalAndCoplanarPoint(normal, a); - return this; - } - copy(plane) { - this.normal.copy(plane.normal); - this.constant = plane.constant; - return this; - } - normalize() { - const inverseNormalLength = 1 / this.normal.length(); - this.normal.multiplyScalar(inverseNormalLength); - this.constant *= inverseNormalLength; - return this; - } - negate() { - this.constant *= -1; - this.normal.negate(); - return this; - } - distanceToPoint(point) { - return this.normal.dot(point) + this.constant; - } - distanceToSphere(sphere) { - return this.distanceToPoint(sphere.center) - sphere.radius; - } - projectPoint(point, target) { - return target.copy(point).addScaledVector(this.normal, -this.distanceToPoint(point)); - } - intersectLine(line, target) { - const direction = line.delta(_vector1); - const denominator = this.normal.dot(direction); - if (denominator === 0) { - if (this.distanceToPoint(line.start) === 0) { - return target.copy(line.start); - } - return null; - } - const t2 = -(line.start.dot(this.normal) + this.constant) / denominator; - if (t2 < 0 || t2 > 1) { - return null; - } - return target.copy(line.start).addScaledVector(direction, t2); - } - intersectsLine(line) { - const startSign = this.distanceToPoint(line.start); - const endSign = this.distanceToPoint(line.end); - return startSign < 0 && endSign > 0 || endSign < 0 && startSign > 0; - } - intersectsBox(box) { - return box.intersectsPlane(this); - } - intersectsSphere(sphere) { - return sphere.intersectsPlane(this); - } - coplanarPoint(target) { - return target.copy(this.normal).multiplyScalar(-this.constant); - } - applyMatrix4(matrix, optionalNormalMatrix) { - const normalMatrix = optionalNormalMatrix || _normalMatrix.getNormalMatrix(matrix); - const referencePoint = this.coplanarPoint(_vector1).applyMatrix4(matrix); - const normal = this.normal.applyMatrix3(normalMatrix).normalize(); - this.constant = -referencePoint.dot(normal); - return this; - } - translate(offset) { - this.constant -= offset.dot(this.normal); - return this; - } - equals(plane) { - return plane.normal.equals(this.normal) && plane.constant === this.constant; - } - clone() { - return new this.constructor().copy(this); - } -} -const _sphere$5 = /* @__PURE__ */ new Sphere(); -const _vector$7 = /* @__PURE__ */ new Vector3(); -class Frustum { - static { - __name(this, "Frustum"); - } - constructor(p0 = new Plane(), p1 = new Plane(), p2 = new Plane(), p3 = new Plane(), p4 = new Plane(), p5 = new Plane()) { - this.planes = [p0, p1, p2, p3, p4, p5]; - } - set(p0, p1, p2, p3, p4, p5) { - const planes = this.planes; - planes[0].copy(p0); - planes[1].copy(p1); - planes[2].copy(p2); - planes[3].copy(p3); - planes[4].copy(p4); - planes[5].copy(p5); - return this; - } - copy(frustum) { - const planes = this.planes; - for (let i = 0; i < 6; i++) { - planes[i].copy(frustum.planes[i]); - } - return this; - } - setFromProjectionMatrix(m, coordinateSystem = WebGLCoordinateSystem) { - const planes = this.planes; - const me = m.elements; - const me0 = me[0], me1 = me[1], me2 = me[2], me3 = me[3]; - const me4 = me[4], me5 = me[5], me6 = me[6], me7 = me[7]; - const me8 = me[8], me9 = me[9], me10 = me[10], me11 = me[11]; - const me12 = me[12], me13 = me[13], me14 = me[14], me15 = me[15]; - planes[0].setComponents(me3 - me0, me7 - me4, me11 - me8, me15 - me12).normalize(); - planes[1].setComponents(me3 + me0, me7 + me4, me11 + me8, me15 + me12).normalize(); - planes[2].setComponents(me3 + me1, me7 + me5, me11 + me9, me15 + me13).normalize(); - planes[3].setComponents(me3 - me1, me7 - me5, me11 - me9, me15 - me13).normalize(); - planes[4].setComponents(me3 - me2, me7 - me6, me11 - me10, me15 - me14).normalize(); - if (coordinateSystem === WebGLCoordinateSystem) { - planes[5].setComponents(me3 + me2, me7 + me6, me11 + me10, me15 + me14).normalize(); - } else if (coordinateSystem === WebGPUCoordinateSystem) { - planes[5].setComponents(me2, me6, me10, me14).normalize(); - } else { - throw new Error("THREE.Frustum.setFromProjectionMatrix(): Invalid coordinate system: " + coordinateSystem); - } - return this; - } - intersectsObject(object) { - if (object.boundingSphere !== void 0) { - if (object.boundingSphere === null) object.computeBoundingSphere(); - _sphere$5.copy(object.boundingSphere).applyMatrix4(object.matrixWorld); - } else { - const geometry = object.geometry; - if (geometry.boundingSphere === null) geometry.computeBoundingSphere(); - _sphere$5.copy(geometry.boundingSphere).applyMatrix4(object.matrixWorld); - } - return this.intersectsSphere(_sphere$5); - } - intersectsSprite(sprite) { - _sphere$5.center.set(0, 0, 0); - _sphere$5.radius = 0.7071067811865476; - _sphere$5.applyMatrix4(sprite.matrixWorld); - return this.intersectsSphere(_sphere$5); - } - intersectsSphere(sphere) { - const planes = this.planes; - const center = sphere.center; - const negRadius = -sphere.radius; - for (let i = 0; i < 6; i++) { - const distance = planes[i].distanceToPoint(center); - if (distance < negRadius) { - return false; - } - } - return true; - } - intersectsBox(box) { - const planes = this.planes; - for (let i = 0; i < 6; i++) { - const plane = planes[i]; - _vector$7.x = plane.normal.x > 0 ? box.max.x : box.min.x; - _vector$7.y = plane.normal.y > 0 ? box.max.y : box.min.y; - _vector$7.z = plane.normal.z > 0 ? box.max.z : box.min.z; - if (plane.distanceToPoint(_vector$7) < 0) { - return false; - } - } - return true; - } - containsPoint(point) { - const planes = this.planes; - for (let i = 0; i < 6; i++) { - if (planes[i].distanceToPoint(point) < 0) { - return false; - } - } - return true; - } - clone() { - return new this.constructor().copy(this); - } -} -function WebGLAnimation() { - let context = null; - let isAnimating = false; - let animationLoop = null; - let requestId = null; - function onAnimationFrame(time, frame) { - animationLoop(time, frame); - requestId = context.requestAnimationFrame(onAnimationFrame); - } - __name(onAnimationFrame, "onAnimationFrame"); - return { - start: /* @__PURE__ */ __name(function() { - if (isAnimating === true) return; - if (animationLoop === null) return; - requestId = context.requestAnimationFrame(onAnimationFrame); - isAnimating = true; - }, "start"), - stop: /* @__PURE__ */ __name(function() { - context.cancelAnimationFrame(requestId); - isAnimating = false; - }, "stop"), - setAnimationLoop: /* @__PURE__ */ __name(function(callback) { - animationLoop = callback; - }, "setAnimationLoop"), - setContext: /* @__PURE__ */ __name(function(value) { - context = value; - }, "setContext") - }; -} -__name(WebGLAnimation, "WebGLAnimation"); -function WebGLAttributes(gl) { - const buffers = /* @__PURE__ */ new WeakMap(); - function createBuffer(attribute, bufferType) { - const array = attribute.array; - const usage = attribute.usage; - const size = array.byteLength; - const buffer = gl.createBuffer(); - gl.bindBuffer(bufferType, buffer); - gl.bufferData(bufferType, array, usage); - attribute.onUploadCallback(); - let type; - if (array instanceof Float32Array) { - type = gl.FLOAT; - } else if (array instanceof Uint16Array) { - if (attribute.isFloat16BufferAttribute) { - type = gl.HALF_FLOAT; - } else { - type = gl.UNSIGNED_SHORT; - } - } else if (array instanceof Int16Array) { - type = gl.SHORT; - } else if (array instanceof Uint32Array) { - type = gl.UNSIGNED_INT; - } else if (array instanceof Int32Array) { - type = gl.INT; - } else if (array instanceof Int8Array) { - type = gl.BYTE; - } else if (array instanceof Uint8Array) { - type = gl.UNSIGNED_BYTE; - } else if (array instanceof Uint8ClampedArray) { - type = gl.UNSIGNED_BYTE; - } else { - throw new Error("THREE.WebGLAttributes: Unsupported buffer data format: " + array); - } - return { - buffer, - type, - bytesPerElement: array.BYTES_PER_ELEMENT, - version: attribute.version, - size - }; - } - __name(createBuffer, "createBuffer"); - function updateBuffer(buffer, attribute, bufferType) { - const array = attribute.array; - const updateRanges = attribute.updateRanges; - gl.bindBuffer(bufferType, buffer); - if (updateRanges.length === 0) { - gl.bufferSubData(bufferType, 0, array); - } else { - updateRanges.sort((a, b) => a.start - b.start); - let mergeIndex = 0; - for (let i = 1; i < updateRanges.length; i++) { - const previousRange = updateRanges[mergeIndex]; - const range = updateRanges[i]; - if (range.start <= previousRange.start + previousRange.count + 1) { - previousRange.count = Math.max( - previousRange.count, - range.start + range.count - previousRange.start - ); - } else { - ++mergeIndex; - updateRanges[mergeIndex] = range; - } - } - updateRanges.length = mergeIndex + 1; - for (let i = 0, l = updateRanges.length; i < l; i++) { - const range = updateRanges[i]; - gl.bufferSubData( - bufferType, - range.start * array.BYTES_PER_ELEMENT, - array, - range.start, - range.count - ); - } - attribute.clearUpdateRanges(); - } - attribute.onUploadCallback(); - } - __name(updateBuffer, "updateBuffer"); - function get(attribute) { - if (attribute.isInterleavedBufferAttribute) attribute = attribute.data; - return buffers.get(attribute); - } - __name(get, "get"); - function remove(attribute) { - if (attribute.isInterleavedBufferAttribute) attribute = attribute.data; - const data = buffers.get(attribute); - if (data) { - gl.deleteBuffer(data.buffer); - buffers.delete(attribute); - } - } - __name(remove, "remove"); - function update(attribute, bufferType) { - if (attribute.isInterleavedBufferAttribute) attribute = attribute.data; - if (attribute.isGLBufferAttribute) { - const cached = buffers.get(attribute); - if (!cached || cached.version < attribute.version) { - buffers.set(attribute, { - buffer: attribute.buffer, - type: attribute.type, - bytesPerElement: attribute.elementSize, - version: attribute.version - }); - } - return; - } - const data = buffers.get(attribute); - if (data === void 0) { - buffers.set(attribute, createBuffer(attribute, bufferType)); - } else if (data.version < attribute.version) { - if (data.size !== attribute.array.byteLength) { - throw new Error("THREE.WebGLAttributes: The size of the buffer attribute's array buffer does not match the original size. Resizing buffer attributes is not supported."); - } - updateBuffer(data.buffer, attribute, bufferType); - data.version = attribute.version; - } - } - __name(update, "update"); - return { - get, - remove, - update - }; -} -__name(WebGLAttributes, "WebGLAttributes"); -class PlaneGeometry extends BufferGeometry { - static { - __name(this, "PlaneGeometry"); - } - constructor(width = 1, height = 1, widthSegments = 1, heightSegments = 1) { - super(); - this.type = "PlaneGeometry"; - this.parameters = { - width, - height, - widthSegments, - heightSegments - }; - const width_half = width / 2; - const height_half = height / 2; - const gridX = Math.floor(widthSegments); - const gridY = Math.floor(heightSegments); - const gridX1 = gridX + 1; - const gridY1 = gridY + 1; - const segment_width = width / gridX; - const segment_height = height / gridY; - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - for (let iy = 0; iy < gridY1; iy++) { - const y = iy * segment_height - height_half; - for (let ix = 0; ix < gridX1; ix++) { - const x = ix * segment_width - width_half; - vertices.push(x, -y, 0); - normals.push(0, 0, 1); - uvs.push(ix / gridX); - uvs.push(1 - iy / gridY); - } - } - for (let iy = 0; iy < gridY; iy++) { - for (let ix = 0; ix < gridX; ix++) { - const a = ix + gridX1 * iy; - const b = ix + gridX1 * (iy + 1); - const c = ix + 1 + gridX1 * (iy + 1); - const d = ix + 1 + gridX1 * iy; - indices.push(a, b, d); - indices.push(b, c, d); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new PlaneGeometry(data.width, data.height, data.widthSegments, data.heightSegments); - } -} -var alphahash_fragment = "#ifdef USE_ALPHAHASH\n if ( diffuseColor.a < getAlphaHashThreshold( vPosition ) ) discard;\n#endif"; -var alphahash_pars_fragment = "#ifdef USE_ALPHAHASH\n const float ALPHA_HASH_SCALE = 0.05;\n float hash2D( vec2 value ) {\n return fract( 1.0e4 * sin( 17.0 * value.x + 0.1 * value.y ) * ( 0.1 + abs( sin( 13.0 * value.y + value.x ) ) ) );\n }\n float hash3D( vec3 value ) {\n return hash2D( vec2( hash2D( value.xy ), value.z ) );\n }\n float getAlphaHashThreshold( vec3 position ) {\n float maxDeriv = max(\n length( dFdx( position.xyz ) ),\n length( dFdy( position.xyz ) )\n );\n float pixScale = 1.0 / ( ALPHA_HASH_SCALE * maxDeriv );\n vec2 pixScales = vec2(\n exp2( floor( log2( pixScale ) ) ),\n exp2( ceil( log2( pixScale ) ) )\n );\n vec2 alpha = vec2(\n hash3D( floor( pixScales.x * position.xyz ) ),\n hash3D( floor( pixScales.y * position.xyz ) )\n );\n float lerpFactor = fract( log2( pixScale ) );\n float x = ( 1.0 - lerpFactor ) * alpha.x + lerpFactor * alpha.y;\n float a = min( lerpFactor, 1.0 - lerpFactor );\n vec3 cases = vec3(\n x * x / ( 2.0 * a * ( 1.0 - a ) ),\n ( x - 0.5 * a ) / ( 1.0 - a ),\n 1.0 - ( ( 1.0 - x ) * ( 1.0 - x ) / ( 2.0 * a * ( 1.0 - a ) ) )\n );\n float threshold = ( x < ( 1.0 - a ) )\n ? ( ( x < a ) ? cases.x : cases.y )\n : cases.z;\n return clamp( threshold , 1.0e-6, 1.0 );\n }\n#endif"; -var alphamap_fragment = "#ifdef USE_ALPHAMAP\n diffuseColor.a *= texture2D( alphaMap, vAlphaMapUv ).g;\n#endif"; -var alphamap_pars_fragment = "#ifdef USE_ALPHAMAP\n uniform sampler2D alphaMap;\n#endif"; -var alphatest_fragment = "#ifdef USE_ALPHATEST\n #ifdef ALPHA_TO_COVERAGE\n diffuseColor.a = smoothstep( alphaTest, alphaTest + fwidth( diffuseColor.a ), diffuseColor.a );\n if ( diffuseColor.a == 0.0 ) discard;\n #else\n if ( diffuseColor.a < alphaTest ) discard;\n #endif\n#endif"; -var alphatest_pars_fragment = "#ifdef USE_ALPHATEST\n uniform float alphaTest;\n#endif"; -var aomap_fragment = "#ifdef USE_AOMAP\n float ambientOcclusion = ( texture2D( aoMap, vAoMapUv ).r - 1.0 ) * aoMapIntensity + 1.0;\n reflectedLight.indirectDiffuse *= ambientOcclusion;\n #if defined( USE_CLEARCOAT ) \n clearcoatSpecularIndirect *= ambientOcclusion;\n #endif\n #if defined( USE_SHEEN ) \n sheenSpecularIndirect *= ambientOcclusion;\n #endif\n #if defined( USE_ENVMAP ) && defined( STANDARD )\n float dotNV = saturate( dot( geometryNormal, geometryViewDir ) );\n reflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.roughness );\n #endif\n#endif"; -var aomap_pars_fragment = "#ifdef USE_AOMAP\n uniform sampler2D aoMap;\n uniform float aoMapIntensity;\n#endif"; -var batching_pars_vertex = "#ifdef USE_BATCHING\n #if ! defined( GL_ANGLE_multi_draw )\n #define gl_DrawID _gl_DrawID\n uniform int _gl_DrawID;\n #endif\n uniform highp sampler2D batchingTexture;\n uniform highp usampler2D batchingIdTexture;\n mat4 getBatchingMatrix( const in float i ) {\n int size = textureSize( batchingTexture, 0 ).x;\n int j = int( i ) * 4;\n int x = j % size;\n int y = j / size;\n vec4 v1 = texelFetch( batchingTexture, ivec2( x, y ), 0 );\n vec4 v2 = texelFetch( batchingTexture, ivec2( x + 1, y ), 0 );\n vec4 v3 = texelFetch( batchingTexture, ivec2( x + 2, y ), 0 );\n vec4 v4 = texelFetch( batchingTexture, ivec2( x + 3, y ), 0 );\n return mat4( v1, v2, v3, v4 );\n }\n float getIndirectIndex( const in int i ) {\n int size = textureSize( batchingIdTexture, 0 ).x;\n int x = i % size;\n int y = i / size;\n return float( texelFetch( batchingIdTexture, ivec2( x, y ), 0 ).r );\n }\n#endif\n#ifdef USE_BATCHING_COLOR\n uniform sampler2D batchingColorTexture;\n vec3 getBatchingColor( const in float i ) {\n int size = textureSize( batchingColorTexture, 0 ).x;\n int j = int( i );\n int x = j % size;\n int y = j / size;\n return texelFetch( batchingColorTexture, ivec2( x, y ), 0 ).rgb;\n }\n#endif"; -var batching_vertex = "#ifdef USE_BATCHING\n mat4 batchingMatrix = getBatchingMatrix( getIndirectIndex( gl_DrawID ) );\n#endif"; -var begin_vertex = "vec3 transformed = vec3( position );\n#ifdef USE_ALPHAHASH\n vPosition = vec3( position );\n#endif"; -var beginnormal_vertex = "vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n vec3 objectTangent = vec3( tangent.xyz );\n#endif"; -var bsdfs = "float G_BlinnPhong_Implicit( ) {\n return 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n return RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_BlinnPhong( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float shininess ) {\n vec3 halfDir = normalize( lightDir + viewDir );\n float dotNH = saturate( dot( normal, halfDir ) );\n float dotVH = saturate( dot( viewDir, halfDir ) );\n vec3 F = F_Schlick( specularColor, 1.0, dotVH );\n float G = G_BlinnPhong_Implicit( );\n float D = D_BlinnPhong( shininess, dotNH );\n return F * ( G * D );\n} // validated"; -var iridescence_fragment = "#ifdef USE_IRIDESCENCE\n const mat3 XYZ_TO_REC709 = mat3(\n 3.2404542, -0.9692660, 0.0556434,\n -1.5371385, 1.8760108, -0.2040259,\n -0.4985314, 0.0415560, 1.0572252\n );\n vec3 Fresnel0ToIor( vec3 fresnel0 ) {\n vec3 sqrtF0 = sqrt( fresnel0 );\n return ( vec3( 1.0 ) + sqrtF0 ) / ( vec3( 1.0 ) - sqrtF0 );\n }\n vec3 IorToFresnel0( vec3 transmittedIor, float incidentIor ) {\n return pow2( ( transmittedIor - vec3( incidentIor ) ) / ( transmittedIor + vec3( incidentIor ) ) );\n }\n float IorToFresnel0( float transmittedIor, float incidentIor ) {\n return pow2( ( transmittedIor - incidentIor ) / ( transmittedIor + incidentIor ));\n }\n vec3 evalSensitivity( float OPD, vec3 shift ) {\n float phase = 2.0 * PI * OPD * 1.0e-9;\n vec3 val = vec3( 5.4856e-13, 4.4201e-13, 5.2481e-13 );\n vec3 pos = vec3( 1.6810e+06, 1.7953e+06, 2.2084e+06 );\n vec3 var = vec3( 4.3278e+09, 9.3046e+09, 6.6121e+09 );\n vec3 xyz = val * sqrt( 2.0 * PI * var ) * cos( pos * phase + shift ) * exp( - pow2( phase ) * var );\n xyz.x += 9.7470e-14 * sqrt( 2.0 * PI * 4.5282e+09 ) * cos( 2.2399e+06 * phase + shift[ 0 ] ) * exp( - 4.5282e+09 * pow2( phase ) );\n xyz /= 1.0685e-7;\n vec3 rgb = XYZ_TO_REC709 * xyz;\n return rgb;\n }\n vec3 evalIridescence( float outsideIOR, float eta2, float cosTheta1, float thinFilmThickness, vec3 baseF0 ) {\n vec3 I;\n float iridescenceIOR = mix( outsideIOR, eta2, smoothstep( 0.0, 0.03, thinFilmThickness ) );\n float sinTheta2Sq = pow2( outsideIOR / iridescenceIOR ) * ( 1.0 - pow2( cosTheta1 ) );\n float cosTheta2Sq = 1.0 - sinTheta2Sq;\n if ( cosTheta2Sq < 0.0 ) {\n return vec3( 1.0 );\n }\n float cosTheta2 = sqrt( cosTheta2Sq );\n float R0 = IorToFresnel0( iridescenceIOR, outsideIOR );\n float R12 = F_Schlick( R0, 1.0, cosTheta1 );\n float T121 = 1.0 - R12;\n float phi12 = 0.0;\n if ( iridescenceIOR < outsideIOR ) phi12 = PI;\n float phi21 = PI - phi12;\n vec3 baseIOR = Fresnel0ToIor( clamp( baseF0, 0.0, 0.9999 ) ); vec3 R1 = IorToFresnel0( baseIOR, iridescenceIOR );\n vec3 R23 = F_Schlick( R1, 1.0, cosTheta2 );\n vec3 phi23 = vec3( 0.0 );\n if ( baseIOR[ 0 ] < iridescenceIOR ) phi23[ 0 ] = PI;\n if ( baseIOR[ 1 ] < iridescenceIOR ) phi23[ 1 ] = PI;\n if ( baseIOR[ 2 ] < iridescenceIOR ) phi23[ 2 ] = PI;\n float OPD = 2.0 * iridescenceIOR * thinFilmThickness * cosTheta2;\n vec3 phi = vec3( phi21 ) + phi23;\n vec3 R123 = clamp( R12 * R23, 1e-5, 0.9999 );\n vec3 r123 = sqrt( R123 );\n vec3 Rs = pow2( T121 ) * R23 / ( vec3( 1.0 ) - R123 );\n vec3 C0 = R12 + Rs;\n I = C0;\n vec3 Cm = Rs - T121;\n for ( int m = 1; m <= 2; ++ m ) {\n Cm *= r123;\n vec3 Sm = 2.0 * evalSensitivity( float( m ) * OPD, float( m ) * phi );\n I += Cm * Sm;\n }\n return max( I, vec3( 0.0 ) );\n }\n#endif"; -var bumpmap_pars_fragment = "#ifdef USE_BUMPMAP\n uniform sampler2D bumpMap;\n uniform float bumpScale;\n vec2 dHdxy_fwd() {\n vec2 dSTdx = dFdx( vBumpMapUv );\n vec2 dSTdy = dFdy( vBumpMapUv );\n float Hll = bumpScale * texture2D( bumpMap, vBumpMapUv ).x;\n float dBx = bumpScale * texture2D( bumpMap, vBumpMapUv + dSTdx ).x - Hll;\n float dBy = bumpScale * texture2D( bumpMap, vBumpMapUv + dSTdy ).x - Hll;\n return vec2( dBx, dBy );\n }\n vec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n vec3 vSigmaX = normalize( dFdx( surf_pos.xyz ) );\n vec3 vSigmaY = normalize( dFdy( surf_pos.xyz ) );\n vec3 vN = surf_norm;\n vec3 R1 = cross( vSigmaY, vN );\n vec3 R2 = cross( vN, vSigmaX );\n float fDet = dot( vSigmaX, R1 ) * faceDirection;\n vec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n return normalize( abs( fDet ) * surf_norm - vGrad );\n }\n#endif"; -var clipping_planes_fragment = "#if NUM_CLIPPING_PLANES > 0\n vec4 plane;\n #ifdef ALPHA_TO_COVERAGE\n float distanceToPlane, distanceGradient;\n float clipOpacity = 1.0;\n #pragma unroll_loop_start\n for ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n plane = clippingPlanes[ i ];\n distanceToPlane = - dot( vClipPosition, plane.xyz ) + plane.w;\n distanceGradient = fwidth( distanceToPlane ) / 2.0;\n clipOpacity *= smoothstep( - distanceGradient, distanceGradient, distanceToPlane );\n if ( clipOpacity == 0.0 ) discard;\n }\n #pragma unroll_loop_end\n #if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n float unionClipOpacity = 1.0;\n #pragma unroll_loop_start\n for ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n plane = clippingPlanes[ i ];\n distanceToPlane = - dot( vClipPosition, plane.xyz ) + plane.w;\n distanceGradient = fwidth( distanceToPlane ) / 2.0;\n unionClipOpacity *= 1.0 - smoothstep( - distanceGradient, distanceGradient, distanceToPlane );\n }\n #pragma unroll_loop_end\n clipOpacity *= 1.0 - unionClipOpacity;\n #endif\n diffuseColor.a *= clipOpacity;\n if ( diffuseColor.a == 0.0 ) discard;\n #else\n #pragma unroll_loop_start\n for ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n plane = clippingPlanes[ i ];\n if ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n }\n #pragma unroll_loop_end\n #if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n bool clipped = true;\n #pragma unroll_loop_start\n for ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n plane = clippingPlanes[ i ];\n clipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n }\n #pragma unroll_loop_end\n if ( clipped ) discard;\n #endif\n #endif\n#endif"; -var clipping_planes_pars_fragment = "#if NUM_CLIPPING_PLANES > 0\n varying vec3 vClipPosition;\n uniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif"; -var clipping_planes_pars_vertex = "#if NUM_CLIPPING_PLANES > 0\n varying vec3 vClipPosition;\n#endif"; -var clipping_planes_vertex = "#if NUM_CLIPPING_PLANES > 0\n vClipPosition = - mvPosition.xyz;\n#endif"; -var color_fragment = "#if defined( USE_COLOR_ALPHA )\n diffuseColor *= vColor;\n#elif defined( USE_COLOR )\n diffuseColor.rgb *= vColor;\n#endif"; -var color_pars_fragment = "#if defined( USE_COLOR_ALPHA )\n varying vec4 vColor;\n#elif defined( USE_COLOR )\n varying vec3 vColor;\n#endif"; -var color_pars_vertex = "#if defined( USE_COLOR_ALPHA )\n varying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR ) || defined( USE_BATCHING_COLOR )\n varying vec3 vColor;\n#endif"; -var color_vertex = "#if defined( USE_COLOR_ALPHA )\n vColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR ) || defined( USE_BATCHING_COLOR )\n vColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n vColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n vColor.xyz *= instanceColor.xyz;\n#endif\n#ifdef USE_BATCHING_COLOR\n vec3 batchingColor = getBatchingColor( getIndirectIndex( gl_DrawID ) );\n vColor.xyz *= batchingColor.xyz;\n#endif"; -var common = "#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement( a ) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nvec3 pow2( const in vec3 x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat max3( const in vec3 v ) { return max( max( v.x, v.y ), v.z ); }\nfloat average( const in vec3 v ) { return dot( v, vec3( 0.3333333 ) ); }\nhighp float rand( const in vec2 uv ) {\n const highp float a = 12.9898, b = 78.233, c = 43758.5453;\n highp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n return fract( sin( sn ) * c );\n}\n#ifdef HIGH_PRECISION\n float precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n float precisionSafeLength( vec3 v ) {\n float maxComponent = max3( abs( v ) );\n return length( v / maxComponent ) * maxComponent;\n }\n#endif\nstruct IncidentLight {\n vec3 color;\n vec3 direction;\n bool visible;\n};\nstruct ReflectedLight {\n vec3 directDiffuse;\n vec3 directSpecular;\n vec3 indirectDiffuse;\n vec3 indirectSpecular;\n};\n#ifdef USE_ALPHAHASH\n varying vec3 vPosition;\n#endif\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n return normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n return normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nmat3 transposeMat3( const in mat3 m ) {\n mat3 tmp;\n tmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n tmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n tmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n return tmp;\n}\nbool isPerspectiveMatrix( mat4 m ) {\n return m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n float u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n float v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n return vec2( u, v );\n}\nvec3 BRDF_Lambert( const in vec3 diffuseColor ) {\n return RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in float f90, const in float dotVH ) {\n float fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n return f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n}\nfloat F_Schlick( const in float f0, const in float f90, const in float dotVH ) {\n float fresnel = exp2( ( - 5.55473 * dotVH - 6.98316 ) * dotVH );\n return f0 * ( 1.0 - fresnel ) + ( f90 * fresnel );\n} // validated"; -var cube_uv_reflection_fragment = "#ifdef ENVMAP_TYPE_CUBE_UV\n #define cubeUV_minMipLevel 4.0\n #define cubeUV_minTileSize 16.0\n float getFace( vec3 direction ) {\n vec3 absDirection = abs( direction );\n float face = - 1.0;\n if ( absDirection.x > absDirection.z ) {\n if ( absDirection.x > absDirection.y )\n face = direction.x > 0.0 ? 0.0 : 3.0;\n else\n face = direction.y > 0.0 ? 1.0 : 4.0;\n } else {\n if ( absDirection.z > absDirection.y )\n face = direction.z > 0.0 ? 2.0 : 5.0;\n else\n face = direction.y > 0.0 ? 1.0 : 4.0;\n }\n return face;\n }\n vec2 getUV( vec3 direction, float face ) {\n vec2 uv;\n if ( face == 0.0 ) {\n uv = vec2( direction.z, direction.y ) / abs( direction.x );\n } else if ( face == 1.0 ) {\n uv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n } else if ( face == 2.0 ) {\n uv = vec2( - direction.x, direction.y ) / abs( direction.z );\n } else if ( face == 3.0 ) {\n uv = vec2( - direction.z, direction.y ) / abs( direction.x );\n } else if ( face == 4.0 ) {\n uv = vec2( - direction.x, direction.z ) / abs( direction.y );\n } else {\n uv = vec2( direction.x, direction.y ) / abs( direction.z );\n }\n return 0.5 * ( uv + 1.0 );\n }\n vec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n float face = getFace( direction );\n float filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n mipInt = max( mipInt, cubeUV_minMipLevel );\n float faceSize = exp2( mipInt );\n highp vec2 uv = getUV( direction, face ) * ( faceSize - 2.0 ) + 1.0;\n if ( face > 2.0 ) {\n uv.y += faceSize;\n face -= 3.0;\n }\n uv.x += face * faceSize;\n uv.x += filterInt * 3.0 * cubeUV_minTileSize;\n uv.y += 4.0 * ( exp2( CUBEUV_MAX_MIP ) - faceSize );\n uv.x *= CUBEUV_TEXEL_WIDTH;\n uv.y *= CUBEUV_TEXEL_HEIGHT;\n #ifdef texture2DGradEXT\n return texture2DGradEXT( envMap, uv, vec2( 0.0 ), vec2( 0.0 ) ).rgb;\n #else\n return texture2D( envMap, uv ).rgb;\n #endif\n }\n #define cubeUV_r0 1.0\n #define cubeUV_m0 - 2.0\n #define cubeUV_r1 0.8\n #define cubeUV_m1 - 1.0\n #define cubeUV_r4 0.4\n #define cubeUV_m4 2.0\n #define cubeUV_r5 0.305\n #define cubeUV_m5 3.0\n #define cubeUV_r6 0.21\n #define cubeUV_m6 4.0\n float roughnessToMip( float roughness ) {\n float mip = 0.0;\n if ( roughness >= cubeUV_r1 ) {\n mip = ( cubeUV_r0 - roughness ) * ( cubeUV_m1 - cubeUV_m0 ) / ( cubeUV_r0 - cubeUV_r1 ) + cubeUV_m0;\n } else if ( roughness >= cubeUV_r4 ) {\n mip = ( cubeUV_r1 - roughness ) * ( cubeUV_m4 - cubeUV_m1 ) / ( cubeUV_r1 - cubeUV_r4 ) + cubeUV_m1;\n } else if ( roughness >= cubeUV_r5 ) {\n mip = ( cubeUV_r4 - roughness ) * ( cubeUV_m5 - cubeUV_m4 ) / ( cubeUV_r4 - cubeUV_r5 ) + cubeUV_m4;\n } else if ( roughness >= cubeUV_r6 ) {\n mip = ( cubeUV_r5 - roughness ) * ( cubeUV_m6 - cubeUV_m5 ) / ( cubeUV_r5 - cubeUV_r6 ) + cubeUV_m5;\n } else {\n mip = - 2.0 * log2( 1.16 * roughness ); }\n return mip;\n }\n vec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n float mip = clamp( roughnessToMip( roughness ), cubeUV_m0, CUBEUV_MAX_MIP );\n float mipF = fract( mip );\n float mipInt = floor( mip );\n vec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n if ( mipF == 0.0 ) {\n return vec4( color0, 1.0 );\n } else {\n vec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n return vec4( mix( color0, color1, mipF ), 1.0 );\n }\n }\n#endif"; -var defaultnormal_vertex = "vec3 transformedNormal = objectNormal;\n#ifdef USE_TANGENT\n vec3 transformedTangent = objectTangent;\n#endif\n#ifdef USE_BATCHING\n mat3 bm = mat3( batchingMatrix );\n transformedNormal /= vec3( dot( bm[ 0 ], bm[ 0 ] ), dot( bm[ 1 ], bm[ 1 ] ), dot( bm[ 2 ], bm[ 2 ] ) );\n transformedNormal = bm * transformedNormal;\n #ifdef USE_TANGENT\n transformedTangent = bm * transformedTangent;\n #endif\n#endif\n#ifdef USE_INSTANCING\n mat3 im = mat3( instanceMatrix );\n transformedNormal /= vec3( dot( im[ 0 ], im[ 0 ] ), dot( im[ 1 ], im[ 1 ] ), dot( im[ 2 ], im[ 2 ] ) );\n transformedNormal = im * transformedNormal;\n #ifdef USE_TANGENT\n transformedTangent = im * transformedTangent;\n #endif\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n transformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n transformedTangent = ( modelViewMatrix * vec4( transformedTangent, 0.0 ) ).xyz;\n #ifdef FLIP_SIDED\n transformedTangent = - transformedTangent;\n #endif\n#endif"; -var displacementmap_pars_vertex = "#ifdef USE_DISPLACEMENTMAP\n uniform sampler2D displacementMap;\n uniform float displacementScale;\n uniform float displacementBias;\n#endif"; -var displacementmap_vertex = "#ifdef USE_DISPLACEMENTMAP\n transformed += normalize( objectNormal ) * ( texture2D( displacementMap, vDisplacementMapUv ).x * displacementScale + displacementBias );\n#endif"; -var emissivemap_fragment = "#ifdef USE_EMISSIVEMAP\n vec4 emissiveColor = texture2D( emissiveMap, vEmissiveMapUv );\n #ifdef DECODE_VIDEO_TEXTURE_EMISSIVE\n emissiveColor = sRGBTransferEOTF( emissiveColor );\n #endif\n totalEmissiveRadiance *= emissiveColor.rgb;\n#endif"; -var emissivemap_pars_fragment = "#ifdef USE_EMISSIVEMAP\n uniform sampler2D emissiveMap;\n#endif"; -var colorspace_fragment = "gl_FragColor = linearToOutputTexel( gl_FragColor );"; -var colorspace_pars_fragment = "vec4 LinearTransferOETF( in vec4 value ) {\n return value;\n}\nvec4 sRGBTransferEOTF( in vec4 value ) {\n return vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 sRGBTransferOETF( in vec4 value ) {\n return vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}"; -var envmap_fragment = "#ifdef USE_ENVMAP\n #ifdef ENV_WORLDPOS\n vec3 cameraToFrag;\n if ( isOrthographic ) {\n cameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n } else {\n cameraToFrag = normalize( vWorldPosition - cameraPosition );\n }\n vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n #ifdef ENVMAP_MODE_REFLECTION\n vec3 reflectVec = reflect( cameraToFrag, worldNormal );\n #else\n vec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n #endif\n #else\n vec3 reflectVec = vReflect;\n #endif\n #ifdef ENVMAP_TYPE_CUBE\n vec4 envColor = textureCube( envMap, envMapRotation * vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n #else\n vec4 envColor = vec4( 0.0 );\n #endif\n #ifdef ENVMAP_BLENDING_MULTIPLY\n outgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n #elif defined( ENVMAP_BLENDING_MIX )\n outgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n #elif defined( ENVMAP_BLENDING_ADD )\n outgoingLight += envColor.xyz * specularStrength * reflectivity;\n #endif\n#endif"; -var envmap_common_pars_fragment = "#ifdef USE_ENVMAP\n uniform float envMapIntensity;\n uniform float flipEnvMap;\n uniform mat3 envMapRotation;\n #ifdef ENVMAP_TYPE_CUBE\n uniform samplerCube envMap;\n #else\n uniform sampler2D envMap;\n #endif\n \n#endif"; -var envmap_pars_fragment = "#ifdef USE_ENVMAP\n uniform float reflectivity;\n #if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n #define ENV_WORLDPOS\n #endif\n #ifdef ENV_WORLDPOS\n varying vec3 vWorldPosition;\n uniform float refractionRatio;\n #else\n varying vec3 vReflect;\n #endif\n#endif"; -var envmap_pars_vertex = "#ifdef USE_ENVMAP\n #if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( LAMBERT )\n #define ENV_WORLDPOS\n #endif\n #ifdef ENV_WORLDPOS\n \n varying vec3 vWorldPosition;\n #else\n varying vec3 vReflect;\n uniform float refractionRatio;\n #endif\n#endif"; -var envmap_vertex = "#ifdef USE_ENVMAP\n #ifdef ENV_WORLDPOS\n vWorldPosition = worldPosition.xyz;\n #else\n vec3 cameraToVertex;\n if ( isOrthographic ) {\n cameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n } else {\n cameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n }\n vec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n #ifdef ENVMAP_MODE_REFLECTION\n vReflect = reflect( cameraToVertex, worldNormal );\n #else\n vReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n #endif\n #endif\n#endif"; -var fog_vertex = "#ifdef USE_FOG\n vFogDepth = - mvPosition.z;\n#endif"; -var fog_pars_vertex = "#ifdef USE_FOG\n varying float vFogDepth;\n#endif"; -var fog_fragment = "#ifdef USE_FOG\n #ifdef FOG_EXP2\n float fogFactor = 1.0 - exp( - fogDensity * fogDensity * vFogDepth * vFogDepth );\n #else\n float fogFactor = smoothstep( fogNear, fogFar, vFogDepth );\n #endif\n gl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif"; -var fog_pars_fragment = "#ifdef USE_FOG\n uniform vec3 fogColor;\n varying float vFogDepth;\n #ifdef FOG_EXP2\n uniform float fogDensity;\n #else\n uniform float fogNear;\n uniform float fogFar;\n #endif\n#endif"; -var gradientmap_pars_fragment = "#ifdef USE_GRADIENTMAP\n uniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n float dotNL = dot( normal, lightDirection );\n vec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n #ifdef USE_GRADIENTMAP\n return vec3( texture2D( gradientMap, coord ).r );\n #else\n vec2 fw = fwidth( coord ) * 0.5;\n return mix( vec3( 0.7 ), vec3( 1.0 ), smoothstep( 0.7 - fw.x, 0.7 + fw.x, coord.x ) );\n #endif\n}"; -var lightmap_pars_fragment = "#ifdef USE_LIGHTMAP\n uniform sampler2D lightMap;\n uniform float lightMapIntensity;\n#endif"; -var lights_lambert_fragment = "LambertMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularStrength = specularStrength;"; -var lights_lambert_pars_fragment = "varying vec3 vViewPosition;\nstruct LambertMaterial {\n vec3 diffuseColor;\n float specularStrength;\n};\nvoid RE_Direct_Lambert( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n float dotNL = saturate( dot( geometryNormal, directLight.direction ) );\n vec3 irradiance = dotNL * directLight.color;\n reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Lambert( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in LambertMaterial material, inout ReflectedLight reflectedLight ) {\n reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct RE_Direct_Lambert\n#define RE_IndirectDiffuse RE_IndirectDiffuse_Lambert"; -var lights_pars_begin = "uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\n#if defined( USE_LIGHT_PROBES )\n uniform vec3 lightProbe[ 9 ];\n#endif\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n float x = normal.x, y = normal.y, z = normal.z;\n vec3 result = shCoefficients[ 0 ] * 0.886227;\n result += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n result += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n result += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n result += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n result += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n result += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n result += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n result += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n return result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in vec3 normal ) {\n vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n vec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n return irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n vec3 irradiance = ambientLightColor;\n return irradiance;\n}\nfloat getDistanceAttenuation( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n float distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n if ( cutoffDistance > 0.0 ) {\n distanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n }\n return distanceFalloff;\n}\nfloat getSpotAttenuation( const in float coneCosine, const in float penumbraCosine, const in float angleCosine ) {\n return smoothstep( coneCosine, penumbraCosine, angleCosine );\n}\n#if NUM_DIR_LIGHTS > 0\n struct DirectionalLight {\n vec3 direction;\n vec3 color;\n };\n uniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n void getDirectionalLightInfo( const in DirectionalLight directionalLight, out IncidentLight light ) {\n light.color = directionalLight.color;\n light.direction = directionalLight.direction;\n light.visible = true;\n }\n#endif\n#if NUM_POINT_LIGHTS > 0\n struct PointLight {\n vec3 position;\n vec3 color;\n float distance;\n float decay;\n };\n uniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n void getPointLightInfo( const in PointLight pointLight, const in vec3 geometryPosition, out IncidentLight light ) {\n vec3 lVector = pointLight.position - geometryPosition;\n light.direction = normalize( lVector );\n float lightDistance = length( lVector );\n light.color = pointLight.color;\n light.color *= getDistanceAttenuation( lightDistance, pointLight.distance, pointLight.decay );\n light.visible = ( light.color != vec3( 0.0 ) );\n }\n#endif\n#if NUM_SPOT_LIGHTS > 0\n struct SpotLight {\n vec3 position;\n vec3 direction;\n vec3 color;\n float distance;\n float decay;\n float coneCos;\n float penumbraCos;\n };\n uniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n void getSpotLightInfo( const in SpotLight spotLight, const in vec3 geometryPosition, out IncidentLight light ) {\n vec3 lVector = spotLight.position - geometryPosition;\n light.direction = normalize( lVector );\n float angleCos = dot( light.direction, spotLight.direction );\n float spotAttenuation = getSpotAttenuation( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n if ( spotAttenuation > 0.0 ) {\n float lightDistance = length( lVector );\n light.color = spotLight.color * spotAttenuation;\n light.color *= getDistanceAttenuation( lightDistance, spotLight.distance, spotLight.decay );\n light.visible = ( light.color != vec3( 0.0 ) );\n } else {\n light.color = vec3( 0.0 );\n light.visible = false;\n }\n }\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n struct RectAreaLight {\n vec3 color;\n vec3 position;\n vec3 halfWidth;\n vec3 halfHeight;\n };\n uniform sampler2D ltc_1; uniform sampler2D ltc_2;\n uniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n struct HemisphereLight {\n vec3 direction;\n vec3 skyColor;\n vec3 groundColor;\n };\n uniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n vec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in vec3 normal ) {\n float dotNL = dot( normal, hemiLight.direction );\n float hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n vec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n return irradiance;\n }\n#endif"; -var envmap_physical_pars_fragment = "#ifdef USE_ENVMAP\n vec3 getIBLIrradiance( const in vec3 normal ) {\n #ifdef ENVMAP_TYPE_CUBE_UV\n vec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n vec4 envMapColor = textureCubeUV( envMap, envMapRotation * worldNormal, 1.0 );\n return PI * envMapColor.rgb * envMapIntensity;\n #else\n return vec3( 0.0 );\n #endif\n }\n vec3 getIBLRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness ) {\n #ifdef ENVMAP_TYPE_CUBE_UV\n vec3 reflectVec = reflect( - viewDir, normal );\n reflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n reflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n vec4 envMapColor = textureCubeUV( envMap, envMapRotation * reflectVec, roughness );\n return envMapColor.rgb * envMapIntensity;\n #else\n return vec3( 0.0 );\n #endif\n }\n #ifdef USE_ANISOTROPY\n vec3 getIBLAnisotropyRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in vec3 bitangent, const in float anisotropy ) {\n #ifdef ENVMAP_TYPE_CUBE_UV\n vec3 bentNormal = cross( bitangent, viewDir );\n bentNormal = normalize( cross( bentNormal, bitangent ) );\n bentNormal = normalize( mix( bentNormal, normal, pow2( pow2( 1.0 - anisotropy * ( 1.0 - roughness ) ) ) ) );\n return getIBLRadiance( viewDir, bentNormal, roughness );\n #else\n return vec3( 0.0 );\n #endif\n }\n #endif\n#endif"; -var lights_toon_fragment = "ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;"; -var lights_toon_pars_fragment = "varying vec3 vViewPosition;\nstruct ToonMaterial {\n vec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n vec3 irradiance = getGradientIrradiance( geometryNormal, directLight.direction ) * directLight.color;\n reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct RE_Direct_Toon\n#define RE_IndirectDiffuse RE_IndirectDiffuse_Toon"; -var lights_phong_fragment = "BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;"; -var lights_phong_pars_fragment = "varying vec3 vViewPosition;\nstruct BlinnPhongMaterial {\n vec3 diffuseColor;\n vec3 specularColor;\n float specularShininess;\n float specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n float dotNL = saturate( dot( geometryNormal, directLight.direction ) );\n vec3 irradiance = dotNL * directLight.color;\n reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n reflectedLight.directSpecular += irradiance * BRDF_BlinnPhong( directLight.direction, geometryViewDir, geometryNormal, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\n#define RE_Direct RE_Direct_BlinnPhong\n#define RE_IndirectDiffuse RE_IndirectDiffuse_BlinnPhong"; -var lights_physical_fragment = "PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( nonPerturbedNormal ) ), abs( dFdy( nonPerturbedNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.roughness = max( roughnessFactor, 0.0525 );material.roughness += geometryRoughness;\nmaterial.roughness = min( material.roughness, 1.0 );\n#ifdef IOR\n material.ior = ior;\n #ifdef USE_SPECULAR\n float specularIntensityFactor = specularIntensity;\n vec3 specularColorFactor = specularColor;\n #ifdef USE_SPECULAR_COLORMAP\n specularColorFactor *= texture2D( specularColorMap, vSpecularColorMapUv ).rgb;\n #endif\n #ifdef USE_SPECULAR_INTENSITYMAP\n specularIntensityFactor *= texture2D( specularIntensityMap, vSpecularIntensityMapUv ).a;\n #endif\n material.specularF90 = mix( specularIntensityFactor, 1.0, metalnessFactor );\n #else\n float specularIntensityFactor = 1.0;\n vec3 specularColorFactor = vec3( 1.0 );\n material.specularF90 = 1.0;\n #endif\n material.specularColor = mix( min( pow2( ( material.ior - 1.0 ) / ( material.ior + 1.0 ) ) * specularColorFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n material.specularColor = mix( vec3( 0.04 ), diffuseColor.rgb, metalnessFactor );\n material.specularF90 = 1.0;\n#endif\n#ifdef USE_CLEARCOAT\n material.clearcoat = clearcoat;\n material.clearcoatRoughness = clearcoatRoughness;\n material.clearcoatF0 = vec3( 0.04 );\n material.clearcoatF90 = 1.0;\n #ifdef USE_CLEARCOATMAP\n material.clearcoat *= texture2D( clearcoatMap, vClearcoatMapUv ).x;\n #endif\n #ifdef USE_CLEARCOAT_ROUGHNESSMAP\n material.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vClearcoatRoughnessMapUv ).y;\n #endif\n material.clearcoat = saturate( material.clearcoat ); material.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n material.clearcoatRoughness += geometryRoughness;\n material.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_DISPERSION\n material.dispersion = dispersion;\n#endif\n#ifdef USE_IRIDESCENCE\n material.iridescence = iridescence;\n material.iridescenceIOR = iridescenceIOR;\n #ifdef USE_IRIDESCENCEMAP\n material.iridescence *= texture2D( iridescenceMap, vIridescenceMapUv ).r;\n #endif\n #ifdef USE_IRIDESCENCE_THICKNESSMAP\n material.iridescenceThickness = (iridescenceThicknessMaximum - iridescenceThicknessMinimum) * texture2D( iridescenceThicknessMap, vIridescenceThicknessMapUv ).g + iridescenceThicknessMinimum;\n #else\n material.iridescenceThickness = iridescenceThicknessMaximum;\n #endif\n#endif\n#ifdef USE_SHEEN\n material.sheenColor = sheenColor;\n #ifdef USE_SHEEN_COLORMAP\n material.sheenColor *= texture2D( sheenColorMap, vSheenColorMapUv ).rgb;\n #endif\n material.sheenRoughness = clamp( sheenRoughness, 0.07, 1.0 );\n #ifdef USE_SHEEN_ROUGHNESSMAP\n material.sheenRoughness *= texture2D( sheenRoughnessMap, vSheenRoughnessMapUv ).a;\n #endif\n#endif\n#ifdef USE_ANISOTROPY\n #ifdef USE_ANISOTROPYMAP\n mat2 anisotropyMat = mat2( anisotropyVector.x, anisotropyVector.y, - anisotropyVector.y, anisotropyVector.x );\n vec3 anisotropyPolar = texture2D( anisotropyMap, vAnisotropyMapUv ).rgb;\n vec2 anisotropyV = anisotropyMat * normalize( 2.0 * anisotropyPolar.rg - vec2( 1.0 ) ) * anisotropyPolar.b;\n #else\n vec2 anisotropyV = anisotropyVector;\n #endif\n material.anisotropy = length( anisotropyV );\n if( material.anisotropy == 0.0 ) {\n anisotropyV = vec2( 1.0, 0.0 );\n } else {\n anisotropyV /= material.anisotropy;\n material.anisotropy = saturate( material.anisotropy );\n }\n material.alphaT = mix( pow2( material.roughness ), 1.0, pow2( material.anisotropy ) );\n material.anisotropyT = tbn[ 0 ] * anisotropyV.x + tbn[ 1 ] * anisotropyV.y;\n material.anisotropyB = tbn[ 1 ] * anisotropyV.x - tbn[ 0 ] * anisotropyV.y;\n#endif"; -var lights_physical_pars_fragment = "struct PhysicalMaterial {\n vec3 diffuseColor;\n float roughness;\n vec3 specularColor;\n float specularF90;\n float dispersion;\n #ifdef USE_CLEARCOAT\n float clearcoat;\n float clearcoatRoughness;\n vec3 clearcoatF0;\n float clearcoatF90;\n #endif\n #ifdef USE_IRIDESCENCE\n float iridescence;\n float iridescenceIOR;\n float iridescenceThickness;\n vec3 iridescenceFresnel;\n vec3 iridescenceF0;\n #endif\n #ifdef USE_SHEEN\n vec3 sheenColor;\n float sheenRoughness;\n #endif\n #ifdef IOR\n float ior;\n #endif\n #ifdef USE_TRANSMISSION\n float transmission;\n float transmissionAlpha;\n float thickness;\n float attenuationDistance;\n vec3 attenuationColor;\n #endif\n #ifdef USE_ANISOTROPY\n float anisotropy;\n float alphaT;\n vec3 anisotropyT;\n vec3 anisotropyB;\n #endif\n};\nvec3 clearcoatSpecularDirect = vec3( 0.0 );\nvec3 clearcoatSpecularIndirect = vec3( 0.0 );\nvec3 sheenSpecularDirect = vec3( 0.0 );\nvec3 sheenSpecularIndirect = vec3(0.0 );\nvec3 Schlick_to_F0( const in vec3 f, const in float f90, const in float dotVH ) {\n float x = clamp( 1.0 - dotVH, 0.0, 1.0 );\n float x2 = x * x;\n float x5 = clamp( x * x2 * x2, 0.0, 0.9999 );\n return ( f - vec3( f90 ) * x5 ) / ( 1.0 - x5 );\n}\nfloat V_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n float a2 = pow2( alpha );\n float gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n float gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n return 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n float a2 = pow2( alpha );\n float denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n return RECIPROCAL_PI * a2 / pow2( denom );\n}\n#ifdef USE_ANISOTROPY\n float V_GGX_SmithCorrelated_Anisotropic( const in float alphaT, const in float alphaB, const in float dotTV, const in float dotBV, const in float dotTL, const in float dotBL, const in float dotNV, const in float dotNL ) {\n float gv = dotNL * length( vec3( alphaT * dotTV, alphaB * dotBV, dotNV ) );\n float gl = dotNV * length( vec3( alphaT * dotTL, alphaB * dotBL, dotNL ) );\n float v = 0.5 / ( gv + gl );\n return saturate(v);\n }\n float D_GGX_Anisotropic( const in float alphaT, const in float alphaB, const in float dotNH, const in float dotTH, const in float dotBH ) {\n float a2 = alphaT * alphaB;\n highp vec3 v = vec3( alphaB * dotTH, alphaT * dotBH, a2 * dotNH );\n highp float v2 = dot( v, v );\n float w2 = a2 / v2;\n return RECIPROCAL_PI * a2 * pow2 ( w2 );\n }\n#endif\n#ifdef USE_CLEARCOAT\n vec3 BRDF_GGX_Clearcoat( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in PhysicalMaterial material) {\n vec3 f0 = material.clearcoatF0;\n float f90 = material.clearcoatF90;\n float roughness = material.clearcoatRoughness;\n float alpha = pow2( roughness );\n vec3 halfDir = normalize( lightDir + viewDir );\n float dotNL = saturate( dot( normal, lightDir ) );\n float dotNV = saturate( dot( normal, viewDir ) );\n float dotNH = saturate( dot( normal, halfDir ) );\n float dotVH = saturate( dot( viewDir, halfDir ) );\n vec3 F = F_Schlick( f0, f90, dotVH );\n float V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n float D = D_GGX( alpha, dotNH );\n return F * ( V * D );\n }\n#endif\nvec3 BRDF_GGX( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, const in PhysicalMaterial material ) {\n vec3 f0 = material.specularColor;\n float f90 = material.specularF90;\n float roughness = material.roughness;\n float alpha = pow2( roughness );\n vec3 halfDir = normalize( lightDir + viewDir );\n float dotNL = saturate( dot( normal, lightDir ) );\n float dotNV = saturate( dot( normal, viewDir ) );\n float dotNH = saturate( dot( normal, halfDir ) );\n float dotVH = saturate( dot( viewDir, halfDir ) );\n vec3 F = F_Schlick( f0, f90, dotVH );\n #ifdef USE_IRIDESCENCE\n F = mix( F, material.iridescenceFresnel, material.iridescence );\n #endif\n #ifdef USE_ANISOTROPY\n float dotTL = dot( material.anisotropyT, lightDir );\n float dotTV = dot( material.anisotropyT, viewDir );\n float dotTH = dot( material.anisotropyT, halfDir );\n float dotBL = dot( material.anisotropyB, lightDir );\n float dotBV = dot( material.anisotropyB, viewDir );\n float dotBH = dot( material.anisotropyB, halfDir );\n float V = V_GGX_SmithCorrelated_Anisotropic( material.alphaT, alpha, dotTV, dotBV, dotTL, dotBL, dotNV, dotNL );\n float D = D_GGX_Anisotropic( material.alphaT, alpha, dotNH, dotTH, dotBH );\n #else\n float V = V_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n float D = D_GGX( alpha, dotNH );\n #endif\n return F * ( V * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n const float LUT_SIZE = 64.0;\n const float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n const float LUT_BIAS = 0.5 / LUT_SIZE;\n float dotNV = saturate( dot( N, V ) );\n vec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n uv = uv * LUT_SCALE + LUT_BIAS;\n return uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n float l = length( f );\n return max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n float x = dot( v1, v2 );\n float y = abs( x );\n float a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n float b = 3.4175940 + ( 4.1616724 + y ) * y;\n float v = a / b;\n float theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n return cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n vec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n vec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n vec3 lightNormal = cross( v1, v2 );\n if( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n vec3 T1, T2;\n T1 = normalize( V - N * dot( V, N ) );\n T2 = - cross( N, T1 );\n mat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n vec3 coords[ 4 ];\n coords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n coords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n coords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n coords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n coords[ 0 ] = normalize( coords[ 0 ] );\n coords[ 1 ] = normalize( coords[ 1 ] );\n coords[ 2 ] = normalize( coords[ 2 ] );\n coords[ 3 ] = normalize( coords[ 3 ] );\n vec3 vectorFormFactor = vec3( 0.0 );\n vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n vectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n float result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n return vec3( result );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie( float roughness, float dotNH ) {\n float alpha = pow2( roughness );\n float invAlpha = 1.0 / alpha;\n float cos2h = dotNH * dotNH;\n float sin2h = max( 1.0 - cos2h, 0.0078125 );\n return ( 2.0 + invAlpha ) * pow( sin2h, invAlpha * 0.5 ) / ( 2.0 * PI );\n}\nfloat V_Neubelt( float dotNV, float dotNL ) {\n return saturate( 1.0 / ( 4.0 * ( dotNL + dotNV - dotNL * dotNV ) ) );\n}\nvec3 BRDF_Sheen( const in vec3 lightDir, const in vec3 viewDir, const in vec3 normal, vec3 sheenColor, const in float sheenRoughness ) {\n vec3 halfDir = normalize( lightDir + viewDir );\n float dotNL = saturate( dot( normal, lightDir ) );\n float dotNV = saturate( dot( normal, viewDir ) );\n float dotNH = saturate( dot( normal, halfDir ) );\n float D = D_Charlie( sheenRoughness, dotNH );\n float V = V_Neubelt( dotNV, dotNL );\n return sheenColor * ( D * V );\n}\n#endif\nfloat IBLSheenBRDF( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n float dotNV = saturate( dot( normal, viewDir ) );\n float r2 = roughness * roughness;\n float a = roughness < 0.25 ? -339.2 * r2 + 161.4 * roughness - 25.9 : -8.48 * r2 + 14.3 * roughness - 9.95;\n float b = roughness < 0.25 ? 44.0 * r2 - 23.7 * roughness + 3.26 : 1.97 * r2 - 3.27 * roughness + 0.72;\n float DG = exp( a * dotNV + b ) + ( roughness < 0.25 ? 0.0 : 0.1 * ( roughness - 0.25 ) );\n return saturate( DG * RECIPROCAL_PI );\n}\nvec2 DFGApprox( const in vec3 normal, const in vec3 viewDir, const in float roughness ) {\n float dotNV = saturate( dot( normal, viewDir ) );\n const vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n const vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n vec4 r = roughness * c0 + c1;\n float a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n vec2 fab = vec2( - 1.04, 1.04 ) * a004 + r.zw;\n return fab;\n}\nvec3 EnvironmentBRDF( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness ) {\n vec2 fab = DFGApprox( normal, viewDir, roughness );\n return specularColor * fab.x + specularF90 * fab.y;\n}\n#ifdef USE_IRIDESCENCE\nvoid computeMultiscatteringIridescence( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float iridescence, const in vec3 iridescenceF0, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#else\nvoid computeMultiscattering( const in vec3 normal, const in vec3 viewDir, const in vec3 specularColor, const in float specularF90, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n#endif\n vec2 fab = DFGApprox( normal, viewDir, roughness );\n #ifdef USE_IRIDESCENCE\n vec3 Fr = mix( specularColor, iridescenceF0, iridescence );\n #else\n vec3 Fr = specularColor;\n #endif\n vec3 FssEss = Fr * fab.x + specularF90 * fab.y;\n float Ess = fab.x + fab.y;\n float Ems = 1.0 - Ess;\n vec3 Favg = Fr + ( 1.0 - Fr ) * 0.047619; vec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n singleScatter += FssEss;\n multiScatter += Fms * Ems;\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n void RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n vec3 normal = geometryNormal;\n vec3 viewDir = geometryViewDir;\n vec3 position = geometryPosition;\n vec3 lightPos = rectAreaLight.position;\n vec3 halfWidth = rectAreaLight.halfWidth;\n vec3 halfHeight = rectAreaLight.halfHeight;\n vec3 lightColor = rectAreaLight.color;\n float roughness = material.roughness;\n vec3 rectCoords[ 4 ];\n rectCoords[ 0 ] = lightPos + halfWidth - halfHeight; rectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n rectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n rectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n vec2 uv = LTC_Uv( normal, viewDir, roughness );\n vec4 t1 = texture2D( ltc_1, uv );\n vec4 t2 = texture2D( ltc_2, uv );\n mat3 mInv = mat3(\n vec3( t1.x, 0, t1.y ),\n vec3( 0, 1, 0 ),\n vec3( t1.z, 0, t1.w )\n );\n vec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n reflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n reflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n }\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n float dotNL = saturate( dot( geometryNormal, directLight.direction ) );\n vec3 irradiance = dotNL * directLight.color;\n #ifdef USE_CLEARCOAT\n float dotNLcc = saturate( dot( geometryClearcoatNormal, directLight.direction ) );\n vec3 ccIrradiance = dotNLcc * directLight.color;\n clearcoatSpecularDirect += ccIrradiance * BRDF_GGX_Clearcoat( directLight.direction, geometryViewDir, geometryClearcoatNormal, material );\n #endif\n #ifdef USE_SHEEN\n sheenSpecularDirect += irradiance * BRDF_Sheen( directLight.direction, geometryViewDir, geometryNormal, material.sheenColor, material.sheenRoughness );\n #endif\n reflectedLight.directSpecular += irradiance * BRDF_GGX( directLight.direction, geometryViewDir, geometryNormal, material );\n reflectedLight.directDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n reflectedLight.indirectDiffuse += irradiance * BRDF_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in vec3 geometryPosition, const in vec3 geometryNormal, const in vec3 geometryViewDir, const in vec3 geometryClearcoatNormal, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n #ifdef USE_CLEARCOAT\n clearcoatSpecularIndirect += clearcoatRadiance * EnvironmentBRDF( geometryClearcoatNormal, geometryViewDir, material.clearcoatF0, material.clearcoatF90, material.clearcoatRoughness );\n #endif\n #ifdef USE_SHEEN\n sheenSpecularIndirect += irradiance * material.sheenColor * IBLSheenBRDF( geometryNormal, geometryViewDir, material.sheenRoughness );\n #endif\n vec3 singleScattering = vec3( 0.0 );\n vec3 multiScattering = vec3( 0.0 );\n vec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n #ifdef USE_IRIDESCENCE\n computeMultiscatteringIridescence( geometryNormal, geometryViewDir, material.specularColor, material.specularF90, material.iridescence, material.iridescenceFresnel, material.roughness, singleScattering, multiScattering );\n #else\n computeMultiscattering( geometryNormal, geometryViewDir, material.specularColor, material.specularF90, material.roughness, singleScattering, multiScattering );\n #endif\n vec3 totalScattering = singleScattering + multiScattering;\n vec3 diffuse = material.diffuseColor * ( 1.0 - max( max( totalScattering.r, totalScattering.g ), totalScattering.b ) );\n reflectedLight.indirectSpecular += radiance * singleScattering;\n reflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n reflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct RE_Direct_Physical\n#define RE_Direct_RectArea RE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse RE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular RE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n return saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}"; -var lights_fragment_begin = "\nvec3 geometryPosition = - vViewPosition;\nvec3 geometryNormal = normal;\nvec3 geometryViewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\nvec3 geometryClearcoatNormal = vec3( 0.0 );\n#ifdef USE_CLEARCOAT\n geometryClearcoatNormal = clearcoatNormal;\n#endif\n#ifdef USE_IRIDESCENCE\n float dotNVi = saturate( dot( normal, geometryViewDir ) );\n if ( material.iridescenceThickness == 0.0 ) {\n material.iridescence = 0.0;\n } else {\n material.iridescence = saturate( material.iridescence );\n }\n if ( material.iridescence > 0.0 ) {\n material.iridescenceFresnel = evalIridescence( 1.0, material.iridescenceIOR, dotNVi, material.iridescenceThickness, material.specularColor );\n material.iridescenceF0 = Schlick_to_F0( material.iridescenceFresnel, 1.0, dotNVi );\n }\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n PointLight pointLight;\n #if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n PointLightShadow pointLightShadow;\n #endif\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n pointLight = pointLights[ i ];\n getPointLightInfo( pointLight, geometryPosition, directLight );\n #if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n pointLightShadow = pointLightShadows[ i ];\n directLight.color *= ( directLight.visible && receiveShadow ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowIntensity, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n #endif\n RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n }\n #pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n SpotLight spotLight;\n vec4 spotColor;\n vec3 spotLightCoord;\n bool inSpotLightMap;\n #if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n SpotLightShadow spotLightShadow;\n #endif\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n spotLight = spotLights[ i ];\n getSpotLightInfo( spotLight, geometryPosition, directLight );\n #if ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n #define SPOT_LIGHT_MAP_INDEX UNROLLED_LOOP_INDEX\n #elif ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n #define SPOT_LIGHT_MAP_INDEX NUM_SPOT_LIGHT_MAPS\n #else\n #define SPOT_LIGHT_MAP_INDEX ( UNROLLED_LOOP_INDEX - NUM_SPOT_LIGHT_SHADOWS + NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS )\n #endif\n #if ( SPOT_LIGHT_MAP_INDEX < NUM_SPOT_LIGHT_MAPS )\n spotLightCoord = vSpotLightCoord[ i ].xyz / vSpotLightCoord[ i ].w;\n inSpotLightMap = all( lessThan( abs( spotLightCoord * 2. - 1. ), vec3( 1.0 ) ) );\n spotColor = texture2D( spotLightMap[ SPOT_LIGHT_MAP_INDEX ], spotLightCoord.xy );\n directLight.color = inSpotLightMap ? directLight.color * spotColor.rgb : directLight.color;\n #endif\n #undef SPOT_LIGHT_MAP_INDEX\n #if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n spotLightShadow = spotLightShadows[ i ];\n directLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowIntensity, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n #endif\n RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n }\n #pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n DirectionalLight directionalLight;\n #if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n DirectionalLightShadow directionalLightShadow;\n #endif\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n directionalLight = directionalLights[ i ];\n getDirectionalLightInfo( directionalLight, directLight );\n #if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n directionalLightShadow = directionalLightShadows[ i ];\n directLight.color *= ( directLight.visible && receiveShadow ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowIntensity, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n #endif\n RE_Direct( directLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n }\n #pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n RectAreaLight rectAreaLight;\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n rectAreaLight = rectAreaLights[ i ];\n RE_Direct_RectArea( rectAreaLight, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n }\n #pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n vec3 iblIrradiance = vec3( 0.0 );\n vec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n #if defined( USE_LIGHT_PROBES )\n irradiance += getLightProbeIrradiance( lightProbe, geometryNormal );\n #endif\n #if ( NUM_HEMI_LIGHTS > 0 )\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n irradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometryNormal );\n }\n #pragma unroll_loop_end\n #endif\n#endif\n#if defined( RE_IndirectSpecular )\n vec3 radiance = vec3( 0.0 );\n vec3 clearcoatRadiance = vec3( 0.0 );\n#endif"; -var lights_fragment_maps = "#if defined( RE_IndirectDiffuse )\n #ifdef USE_LIGHTMAP\n vec4 lightMapTexel = texture2D( lightMap, vLightMapUv );\n vec3 lightMapIrradiance = lightMapTexel.rgb * lightMapIntensity;\n irradiance += lightMapIrradiance;\n #endif\n #if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n iblIrradiance += getIBLIrradiance( geometryNormal );\n #endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n #ifdef USE_ANISOTROPY\n radiance += getIBLAnisotropyRadiance( geometryViewDir, geometryNormal, material.roughness, material.anisotropyB, material.anisotropy );\n #else\n radiance += getIBLRadiance( geometryViewDir, geometryNormal, material.roughness );\n #endif\n #ifdef USE_CLEARCOAT\n clearcoatRadiance += getIBLRadiance( geometryViewDir, geometryClearcoatNormal, material.clearcoatRoughness );\n #endif\n#endif"; -var lights_fragment_end = "#if defined( RE_IndirectDiffuse )\n RE_IndirectDiffuse( irradiance, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n RE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometryPosition, geometryNormal, geometryViewDir, geometryClearcoatNormal, material, reflectedLight );\n#endif"; -var logdepthbuf_fragment = "#if defined( USE_LOGDEPTHBUF )\n gl_FragDepth = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif"; -var logdepthbuf_pars_fragment = "#if defined( USE_LOGDEPTHBUF )\n uniform float logDepthBufFC;\n varying float vFragDepth;\n varying float vIsPerspective;\n#endif"; -var logdepthbuf_pars_vertex = "#ifdef USE_LOGDEPTHBUF\n varying float vFragDepth;\n varying float vIsPerspective;\n#endif"; -var logdepthbuf_vertex = "#ifdef USE_LOGDEPTHBUF\n vFragDepth = 1.0 + gl_Position.w;\n vIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n#endif"; -var map_fragment = "#ifdef USE_MAP\n vec4 sampledDiffuseColor = texture2D( map, vMapUv );\n #ifdef DECODE_VIDEO_TEXTURE\n sampledDiffuseColor = sRGBTransferEOTF( sampledDiffuseColor );\n #endif\n diffuseColor *= sampledDiffuseColor;\n#endif"; -var map_pars_fragment = "#ifdef USE_MAP\n uniform sampler2D map;\n#endif"; -var map_particle_fragment = "#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n #if defined( USE_POINTS_UV )\n vec2 uv = vUv;\n #else\n vec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n #endif\n#endif\n#ifdef USE_MAP\n diffuseColor *= texture2D( map, uv );\n#endif\n#ifdef USE_ALPHAMAP\n diffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif"; -var map_particle_pars_fragment = "#if defined( USE_POINTS_UV )\n varying vec2 vUv;\n#else\n #if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n uniform mat3 uvTransform;\n #endif\n#endif\n#ifdef USE_MAP\n uniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n uniform sampler2D alphaMap;\n#endif"; -var metalnessmap_fragment = "float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n vec4 texelMetalness = texture2D( metalnessMap, vMetalnessMapUv );\n metalnessFactor *= texelMetalness.b;\n#endif"; -var metalnessmap_pars_fragment = "#ifdef USE_METALNESSMAP\n uniform sampler2D metalnessMap;\n#endif"; -var morphinstance_vertex = "#ifdef USE_INSTANCING_MORPH\n float morphTargetInfluences[ MORPHTARGETS_COUNT ];\n float morphTargetBaseInfluence = texelFetch( morphTexture, ivec2( 0, gl_InstanceID ), 0 ).r;\n for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n morphTargetInfluences[i] = texelFetch( morphTexture, ivec2( i + 1, gl_InstanceID ), 0 ).r;\n }\n#endif"; -var morphcolor_vertex = "#if defined( USE_MORPHCOLORS )\n vColor *= morphTargetBaseInfluence;\n for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n #if defined( USE_COLOR_ALPHA )\n if ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ) * morphTargetInfluences[ i ];\n #elif defined( USE_COLOR )\n if ( morphTargetInfluences[ i ] != 0.0 ) vColor += getMorph( gl_VertexID, i, 2 ).rgb * morphTargetInfluences[ i ];\n #endif\n }\n#endif"; -var morphnormal_vertex = "#ifdef USE_MORPHNORMALS\n objectNormal *= morphTargetBaseInfluence;\n for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n if ( morphTargetInfluences[ i ] != 0.0 ) objectNormal += getMorph( gl_VertexID, i, 1 ).xyz * morphTargetInfluences[ i ];\n }\n#endif"; -var morphtarget_pars_vertex = "#ifdef USE_MORPHTARGETS\n #ifndef USE_INSTANCING_MORPH\n uniform float morphTargetBaseInfluence;\n uniform float morphTargetInfluences[ MORPHTARGETS_COUNT ];\n #endif\n uniform sampler2DArray morphTargetsTexture;\n uniform ivec2 morphTargetsTextureSize;\n vec4 getMorph( const in int vertexIndex, const in int morphTargetIndex, const in int offset ) {\n int texelIndex = vertexIndex * MORPHTARGETS_TEXTURE_STRIDE + offset;\n int y = texelIndex / morphTargetsTextureSize.x;\n int x = texelIndex - y * morphTargetsTextureSize.x;\n ivec3 morphUV = ivec3( x, y, morphTargetIndex );\n return texelFetch( morphTargetsTexture, morphUV, 0 );\n }\n#endif"; -var morphtarget_vertex = "#ifdef USE_MORPHTARGETS\n transformed *= morphTargetBaseInfluence;\n for ( int i = 0; i < MORPHTARGETS_COUNT; i ++ ) {\n if ( morphTargetInfluences[ i ] != 0.0 ) transformed += getMorph( gl_VertexID, i, 0 ).xyz * morphTargetInfluences[ i ];\n }\n#endif"; -var normal_fragment_begin = "float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n vec3 fdx = dFdx( vViewPosition );\n vec3 fdy = dFdy( vViewPosition );\n vec3 normal = normalize( cross( fdx, fdy ) );\n#else\n vec3 normal = normalize( vNormal );\n #ifdef DOUBLE_SIDED\n normal *= faceDirection;\n #endif\n#endif\n#if defined( USE_NORMALMAP_TANGENTSPACE ) || defined( USE_CLEARCOAT_NORMALMAP ) || defined( USE_ANISOTROPY )\n #ifdef USE_TANGENT\n mat3 tbn = mat3( normalize( vTangent ), normalize( vBitangent ), normal );\n #else\n mat3 tbn = getTangentFrame( - vViewPosition, normal,\n #if defined( USE_NORMALMAP )\n vNormalMapUv\n #elif defined( USE_CLEARCOAT_NORMALMAP )\n vClearcoatNormalMapUv\n #else\n vUv\n #endif\n );\n #endif\n #if defined( DOUBLE_SIDED ) && ! defined( FLAT_SHADED )\n tbn[0] *= faceDirection;\n tbn[1] *= faceDirection;\n #endif\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n #ifdef USE_TANGENT\n mat3 tbn2 = mat3( normalize( vTangent ), normalize( vBitangent ), normal );\n #else\n mat3 tbn2 = getTangentFrame( - vViewPosition, normal, vClearcoatNormalMapUv );\n #endif\n #if defined( DOUBLE_SIDED ) && ! defined( FLAT_SHADED )\n tbn2[0] *= faceDirection;\n tbn2[1] *= faceDirection;\n #endif\n#endif\nvec3 nonPerturbedNormal = normal;"; -var normal_fragment_maps = "#ifdef USE_NORMALMAP_OBJECTSPACE\n normal = texture2D( normalMap, vNormalMapUv ).xyz * 2.0 - 1.0;\n #ifdef FLIP_SIDED\n normal = - normal;\n #endif\n #ifdef DOUBLE_SIDED\n normal = normal * faceDirection;\n #endif\n normal = normalize( normalMatrix * normal );\n#elif defined( USE_NORMALMAP_TANGENTSPACE )\n vec3 mapN = texture2D( normalMap, vNormalMapUv ).xyz * 2.0 - 1.0;\n mapN.xy *= normalScale;\n normal = normalize( tbn * mapN );\n#elif defined( USE_BUMPMAP )\n normal = perturbNormalArb( - vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif"; -var normal_pars_fragment = "#ifndef FLAT_SHADED\n varying vec3 vNormal;\n #ifdef USE_TANGENT\n varying vec3 vTangent;\n varying vec3 vBitangent;\n #endif\n#endif"; -var normal_pars_vertex = "#ifndef FLAT_SHADED\n varying vec3 vNormal;\n #ifdef USE_TANGENT\n varying vec3 vTangent;\n varying vec3 vBitangent;\n #endif\n#endif"; -var normal_vertex = "#ifndef FLAT_SHADED\n vNormal = normalize( transformedNormal );\n #ifdef USE_TANGENT\n vTangent = normalize( transformedTangent );\n vBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n #endif\n#endif"; -var normalmap_pars_fragment = "#ifdef USE_NORMALMAP\n uniform sampler2D normalMap;\n uniform vec2 normalScale;\n#endif\n#ifdef USE_NORMALMAP_OBJECTSPACE\n uniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( USE_NORMALMAP_TANGENTSPACE ) || defined ( USE_CLEARCOAT_NORMALMAP ) || defined( USE_ANISOTROPY ) )\n mat3 getTangentFrame( vec3 eye_pos, vec3 surf_norm, vec2 uv ) {\n vec3 q0 = dFdx( eye_pos.xyz );\n vec3 q1 = dFdy( eye_pos.xyz );\n vec2 st0 = dFdx( uv.st );\n vec2 st1 = dFdy( uv.st );\n vec3 N = surf_norm;\n vec3 q1perp = cross( q1, N );\n vec3 q0perp = cross( N, q0 );\n vec3 T = q1perp * st0.x + q0perp * st1.x;\n vec3 B = q1perp * st0.y + q0perp * st1.y;\n float det = max( dot( T, T ), dot( B, B ) );\n float scale = ( det == 0.0 ) ? 0.0 : inversesqrt( det );\n return mat3( T * scale, B * scale, N );\n }\n#endif"; -var clearcoat_normal_fragment_begin = "#ifdef USE_CLEARCOAT\n vec3 clearcoatNormal = nonPerturbedNormal;\n#endif"; -var clearcoat_normal_fragment_maps = "#ifdef USE_CLEARCOAT_NORMALMAP\n vec3 clearcoatMapN = texture2D( clearcoatNormalMap, vClearcoatNormalMapUv ).xyz * 2.0 - 1.0;\n clearcoatMapN.xy *= clearcoatNormalScale;\n clearcoatNormal = normalize( tbn2 * clearcoatMapN );\n#endif"; -var clearcoat_pars_fragment = "#ifdef USE_CLEARCOATMAP\n uniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n uniform sampler2D clearcoatNormalMap;\n uniform vec2 clearcoatNormalScale;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n uniform sampler2D clearcoatRoughnessMap;\n#endif"; -var iridescence_pars_fragment = "#ifdef USE_IRIDESCENCEMAP\n uniform sampler2D iridescenceMap;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n uniform sampler2D iridescenceThicknessMap;\n#endif"; -var opaque_fragment = "#ifdef OPAQUE\ndiffuseColor.a = 1.0;\n#endif\n#ifdef USE_TRANSMISSION\ndiffuseColor.a *= material.transmissionAlpha;\n#endif\ngl_FragColor = vec4( outgoingLight, diffuseColor.a );"; -var packing = "vec3 packNormalToRGB( const in vec3 normal ) {\n return normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n return 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;const float ShiftRight8 = 1. / 256.;\nconst float Inv255 = 1. / 255.;\nconst vec4 PackFactors = vec4( 1.0, 256.0, 256.0 * 256.0, 256.0 * 256.0 * 256.0 );\nconst vec2 UnpackFactors2 = vec2( UnpackDownscale, 1.0 / PackFactors.g );\nconst vec3 UnpackFactors3 = vec3( UnpackDownscale / PackFactors.rg, 1.0 / PackFactors.b );\nconst vec4 UnpackFactors4 = vec4( UnpackDownscale / PackFactors.rgb, 1.0 / PackFactors.a );\nvec4 packDepthToRGBA( const in float v ) {\n if( v <= 0.0 )\n return vec4( 0., 0., 0., 0. );\n if( v >= 1.0 )\n return vec4( 1., 1., 1., 1. );\n float vuf;\n float af = modf( v * PackFactors.a, vuf );\n float bf = modf( vuf * ShiftRight8, vuf );\n float gf = modf( vuf * ShiftRight8, vuf );\n return vec4( vuf * Inv255, gf * PackUpscale, bf * PackUpscale, af );\n}\nvec3 packDepthToRGB( const in float v ) {\n if( v <= 0.0 )\n return vec3( 0., 0., 0. );\n if( v >= 1.0 )\n return vec3( 1., 1., 1. );\n float vuf;\n float bf = modf( v * PackFactors.b, vuf );\n float gf = modf( vuf * ShiftRight8, vuf );\n return vec3( vuf * Inv255, gf * PackUpscale, bf );\n}\nvec2 packDepthToRG( const in float v ) {\n if( v <= 0.0 )\n return vec2( 0., 0. );\n if( v >= 1.0 )\n return vec2( 1., 1. );\n float vuf;\n float gf = modf( v * 256., vuf );\n return vec2( vuf * Inv255, gf );\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n return dot( v, UnpackFactors4 );\n}\nfloat unpackRGBToDepth( const in vec3 v ) {\n return dot( v, UnpackFactors3 );\n}\nfloat unpackRGToDepth( const in vec2 v ) {\n return v.r * UnpackFactors2.r + v.g * UnpackFactors2.g;\n}\nvec4 pack2HalfToRGBA( const in vec2 v ) {\n vec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ) );\n return vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w );\n}\nvec2 unpackRGBATo2Half( const in vec4 v ) {\n return vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n return ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float depth, const in float near, const in float far ) {\n return depth * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n return ( ( near + viewZ ) * far ) / ( ( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float depth, const in float near, const in float far ) {\n return ( near * far ) / ( ( far - near ) * depth - far );\n}"; -var premultiplied_alpha_fragment = "#ifdef PREMULTIPLIED_ALPHA\n gl_FragColor.rgb *= gl_FragColor.a;\n#endif"; -var project_vertex = "vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_BATCHING\n mvPosition = batchingMatrix * mvPosition;\n#endif\n#ifdef USE_INSTANCING\n mvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;"; -var dithering_fragment = "#ifdef DITHERING\n gl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif"; -var dithering_pars_fragment = "#ifdef DITHERING\n vec3 dithering( vec3 color ) {\n float grid_position = rand( gl_FragCoord.xy );\n vec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n dither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n return color + dither_shift_RGB;\n }\n#endif"; -var roughnessmap_fragment = "float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n vec4 texelRoughness = texture2D( roughnessMap, vRoughnessMapUv );\n roughnessFactor *= texelRoughness.g;\n#endif"; -var roughnessmap_pars_fragment = "#ifdef USE_ROUGHNESSMAP\n uniform sampler2D roughnessMap;\n#endif"; -var shadowmap_pars_fragment = "#if NUM_SPOT_LIGHT_COORDS > 0\n varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#if NUM_SPOT_LIGHT_MAPS > 0\n uniform sampler2D spotLightMap[ NUM_SPOT_LIGHT_MAPS ];\n#endif\n#ifdef USE_SHADOWMAP\n #if NUM_DIR_LIGHT_SHADOWS > 0\n uniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n varying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n struct DirectionalLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n };\n uniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n #endif\n #if NUM_SPOT_LIGHT_SHADOWS > 0\n uniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n struct SpotLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n };\n uniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n #endif\n #if NUM_POINT_LIGHT_SHADOWS > 0\n uniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n varying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n struct PointLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n float shadowCameraNear;\n float shadowCameraFar;\n };\n uniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n #endif\n float texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n return step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n }\n vec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n return unpackRGBATo2Half( texture2D( shadow, uv ) );\n }\n float VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n float occlusion = 1.0;\n vec2 distribution = texture2DDistribution( shadow, uv );\n float hard_shadow = step( compare , distribution.x );\n if (hard_shadow != 1.0 ) {\n float distance = compare - distribution.x ;\n float variance = max( 0.00000, distribution.y * distribution.y );\n float softness_probability = variance / (variance + distance * distance ); softness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 ); occlusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n }\n return occlusion;\n }\n float getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowIntensity, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n float shadow = 1.0;\n shadowCoord.xyz /= shadowCoord.w;\n shadowCoord.z += shadowBias;\n bool inFrustum = shadowCoord.x >= 0.0 && shadowCoord.x <= 1.0 && shadowCoord.y >= 0.0 && shadowCoord.y <= 1.0;\n bool frustumTest = inFrustum && shadowCoord.z <= 1.0;\n if ( frustumTest ) {\n #if defined( SHADOWMAP_TYPE_PCF )\n vec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n float dx0 = - texelSize.x * shadowRadius;\n float dy0 = - texelSize.y * shadowRadius;\n float dx1 = + texelSize.x * shadowRadius;\n float dy1 = + texelSize.y * shadowRadius;\n float dx2 = dx0 / 2.0;\n float dy2 = dy0 / 2.0;\n float dx3 = dx1 / 2.0;\n float dy3 = dy1 / 2.0;\n shadow = (\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n ) * ( 1.0 / 17.0 );\n #elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n vec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n float dx = texelSize.x;\n float dy = texelSize.y;\n vec2 uv = shadowCoord.xy;\n vec2 f = fract( uv * shadowMapSize + 0.5 );\n uv -= f * texelSize;\n shadow = (\n texture2DCompare( shadowMap, uv, shadowCoord.z ) +\n texture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n texture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n texture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n mix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n f.x ) +\n mix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n f.x ) +\n mix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n f.y ) +\n mix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n f.y ) +\n mix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n f.x ),\n mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ),\n texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n f.x ),\n f.y )\n ) * ( 1.0 / 9.0 );\n #elif defined( SHADOWMAP_TYPE_VSM )\n shadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n #else\n shadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n #endif\n }\n return mix( 1.0, shadow, shadowIntensity );\n }\n vec2 cubeToUV( vec3 v, float texelSizeY ) {\n vec3 absV = abs( v );\n float scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n absV *= scaleToCube;\n v *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n vec2 planar = v.xy;\n float almostATexel = 1.5 * texelSizeY;\n float almostOne = 1.0 - almostATexel;\n if ( absV.z >= almostOne ) {\n if ( v.z > 0.0 )\n planar.x = 4.0 - v.x;\n } else if ( absV.x >= almostOne ) {\n float signX = sign( v.x );\n planar.x = v.z * signX + 2.0 * signX;\n } else if ( absV.y >= almostOne ) {\n float signY = sign( v.y );\n planar.x = v.x + 2.0 * signY + 2.0;\n planar.y = v.z * signY - 2.0;\n }\n return vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n }\n float getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowIntensity, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n float shadow = 1.0;\n vec3 lightToPosition = shadowCoord.xyz;\n \n float lightToPositionLength = length( lightToPosition );\n if ( lightToPositionLength - shadowCameraFar <= 0.0 && lightToPositionLength - shadowCameraNear >= 0.0 ) {\n float dp = ( lightToPositionLength - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear ); dp += shadowBias;\n vec3 bd3D = normalize( lightToPosition );\n vec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n #if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n vec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n shadow = (\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n texture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n ) * ( 1.0 / 9.0 );\n #else\n shadow = texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n #endif\n }\n return mix( 1.0, shadow, shadowIntensity );\n }\n#endif"; -var shadowmap_pars_vertex = "#if NUM_SPOT_LIGHT_COORDS > 0\n uniform mat4 spotLightMatrix[ NUM_SPOT_LIGHT_COORDS ];\n varying vec4 vSpotLightCoord[ NUM_SPOT_LIGHT_COORDS ];\n#endif\n#ifdef USE_SHADOWMAP\n #if NUM_DIR_LIGHT_SHADOWS > 0\n uniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n varying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n struct DirectionalLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n };\n uniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n #endif\n #if NUM_SPOT_LIGHT_SHADOWS > 0\n struct SpotLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n };\n uniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n #endif\n #if NUM_POINT_LIGHT_SHADOWS > 0\n uniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n varying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n struct PointLightShadow {\n float shadowIntensity;\n float shadowBias;\n float shadowNormalBias;\n float shadowRadius;\n vec2 shadowMapSize;\n float shadowCameraNear;\n float shadowCameraFar;\n };\n uniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n #endif\n#endif"; -var shadowmap_vertex = "#if ( defined( USE_SHADOWMAP ) && ( NUM_DIR_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0 ) ) || ( NUM_SPOT_LIGHT_COORDS > 0 )\n vec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n vec4 shadowWorldPosition;\n#endif\n#if defined( USE_SHADOWMAP )\n #if NUM_DIR_LIGHT_SHADOWS > 0\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n shadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n vDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n }\n #pragma unroll_loop_end\n #endif\n #if NUM_POINT_LIGHT_SHADOWS > 0\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n shadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n vPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n }\n #pragma unroll_loop_end\n #endif\n#endif\n#if NUM_SPOT_LIGHT_COORDS > 0\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_SPOT_LIGHT_COORDS; i ++ ) {\n shadowWorldPosition = worldPosition;\n #if ( defined( USE_SHADOWMAP ) && UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n shadowWorldPosition.xyz += shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias;\n #endif\n vSpotLightCoord[ i ] = spotLightMatrix[ i ] * shadowWorldPosition;\n }\n #pragma unroll_loop_end\n#endif"; -var shadowmask_pars_fragment = "float getShadowMask() {\n float shadow = 1.0;\n #ifdef USE_SHADOWMAP\n #if NUM_DIR_LIGHT_SHADOWS > 0\n DirectionalLightShadow directionalLight;\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n directionalLight = directionalLightShadows[ i ];\n shadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowIntensity, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n }\n #pragma unroll_loop_end\n #endif\n #if NUM_SPOT_LIGHT_SHADOWS > 0\n SpotLightShadow spotLight;\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n spotLight = spotLightShadows[ i ];\n shadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowIntensity, spotLight.shadowBias, spotLight.shadowRadius, vSpotLightCoord[ i ] ) : 1.0;\n }\n #pragma unroll_loop_end\n #endif\n #if NUM_POINT_LIGHT_SHADOWS > 0\n PointLightShadow pointLight;\n #pragma unroll_loop_start\n for ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n pointLight = pointLightShadows[ i ];\n shadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowIntensity, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n }\n #pragma unroll_loop_end\n #endif\n #endif\n return shadow;\n}"; -var skinbase_vertex = "#ifdef USE_SKINNING\n mat4 boneMatX = getBoneMatrix( skinIndex.x );\n mat4 boneMatY = getBoneMatrix( skinIndex.y );\n mat4 boneMatZ = getBoneMatrix( skinIndex.z );\n mat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif"; -var skinning_pars_vertex = "#ifdef USE_SKINNING\n uniform mat4 bindMatrix;\n uniform mat4 bindMatrixInverse;\n uniform highp sampler2D boneTexture;\n mat4 getBoneMatrix( const in float i ) {\n int size = textureSize( boneTexture, 0 ).x;\n int j = int( i ) * 4;\n int x = j % size;\n int y = j / size;\n vec4 v1 = texelFetch( boneTexture, ivec2( x, y ), 0 );\n vec4 v2 = texelFetch( boneTexture, ivec2( x + 1, y ), 0 );\n vec4 v3 = texelFetch( boneTexture, ivec2( x + 2, y ), 0 );\n vec4 v4 = texelFetch( boneTexture, ivec2( x + 3, y ), 0 );\n return mat4( v1, v2, v3, v4 );\n }\n#endif"; -var skinning_vertex = "#ifdef USE_SKINNING\n vec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n vec4 skinned = vec4( 0.0 );\n skinned += boneMatX * skinVertex * skinWeight.x;\n skinned += boneMatY * skinVertex * skinWeight.y;\n skinned += boneMatZ * skinVertex * skinWeight.z;\n skinned += boneMatW * skinVertex * skinWeight.w;\n transformed = ( bindMatrixInverse * skinned ).xyz;\n#endif"; -var skinnormal_vertex = "#ifdef USE_SKINNING\n mat4 skinMatrix = mat4( 0.0 );\n skinMatrix += skinWeight.x * boneMatX;\n skinMatrix += skinWeight.y * boneMatY;\n skinMatrix += skinWeight.z * boneMatZ;\n skinMatrix += skinWeight.w * boneMatW;\n skinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n objectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n #ifdef USE_TANGENT\n objectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n #endif\n#endif"; -var specularmap_fragment = "float specularStrength;\n#ifdef USE_SPECULARMAP\n vec4 texelSpecular = texture2D( specularMap, vSpecularMapUv );\n specularStrength = texelSpecular.r;\n#else\n specularStrength = 1.0;\n#endif"; -var specularmap_pars_fragment = "#ifdef USE_SPECULARMAP\n uniform sampler2D specularMap;\n#endif"; -var tonemapping_fragment = "#if defined( TONE_MAPPING )\n gl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif"; -var tonemapping_pars_fragment = "#ifndef saturate\n#define saturate( a ) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n return saturate( toneMappingExposure * color );\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n color *= toneMappingExposure;\n return saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 CineonToneMapping( vec3 color ) {\n color *= toneMappingExposure;\n color = max( vec3( 0.0 ), color - 0.004 );\n return pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n vec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n vec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n return a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n const mat3 ACESInputMat = mat3(\n vec3( 0.59719, 0.07600, 0.02840 ), vec3( 0.35458, 0.90834, 0.13383 ),\n vec3( 0.04823, 0.01566, 0.83777 )\n );\n const mat3 ACESOutputMat = mat3(\n vec3( 1.60475, -0.10208, -0.00327 ), vec3( -0.53108, 1.10813, -0.07276 ),\n vec3( -0.07367, -0.00605, 1.07602 )\n );\n color *= toneMappingExposure / 0.6;\n color = ACESInputMat * color;\n color = RRTAndODTFit( color );\n color = ACESOutputMat * color;\n return saturate( color );\n}\nconst mat3 LINEAR_REC2020_TO_LINEAR_SRGB = mat3(\n vec3( 1.6605, - 0.1246, - 0.0182 ),\n vec3( - 0.5876, 1.1329, - 0.1006 ),\n vec3( - 0.0728, - 0.0083, 1.1187 )\n);\nconst mat3 LINEAR_SRGB_TO_LINEAR_REC2020 = mat3(\n vec3( 0.6274, 0.0691, 0.0164 ),\n vec3( 0.3293, 0.9195, 0.0880 ),\n vec3( 0.0433, 0.0113, 0.8956 )\n);\nvec3 agxDefaultContrastApprox( vec3 x ) {\n vec3 x2 = x * x;\n vec3 x4 = x2 * x2;\n return + 15.5 * x4 * x2\n - 40.14 * x4 * x\n + 31.96 * x4\n - 6.868 * x2 * x\n + 0.4298 * x2\n + 0.1191 * x\n - 0.00232;\n}\nvec3 AgXToneMapping( vec3 color ) {\n const mat3 AgXInsetMatrix = mat3(\n vec3( 0.856627153315983, 0.137318972929847, 0.11189821299995 ),\n vec3( 0.0951212405381588, 0.761241990602591, 0.0767994186031903 ),\n vec3( 0.0482516061458583, 0.101439036467562, 0.811302368396859 )\n );\n const mat3 AgXOutsetMatrix = mat3(\n vec3( 1.1271005818144368, - 0.1413297634984383, - 0.14132976349843826 ),\n vec3( - 0.11060664309660323, 1.157823702216272, - 0.11060664309660294 ),\n vec3( - 0.016493938717834573, - 0.016493938717834257, 1.2519364065950405 )\n );\n const float AgxMinEv = - 12.47393; const float AgxMaxEv = 4.026069;\n color *= toneMappingExposure;\n color = LINEAR_SRGB_TO_LINEAR_REC2020 * color;\n color = AgXInsetMatrix * color;\n color = max( color, 1e-10 ); color = log2( color );\n color = ( color - AgxMinEv ) / ( AgxMaxEv - AgxMinEv );\n color = clamp( color, 0.0, 1.0 );\n color = agxDefaultContrastApprox( color );\n color = AgXOutsetMatrix * color;\n color = pow( max( vec3( 0.0 ), color ), vec3( 2.2 ) );\n color = LINEAR_REC2020_TO_LINEAR_SRGB * color;\n color = clamp( color, 0.0, 1.0 );\n return color;\n}\nvec3 NeutralToneMapping( vec3 color ) {\n const float StartCompression = 0.8 - 0.04;\n const float Desaturation = 0.15;\n color *= toneMappingExposure;\n float x = min( color.r, min( color.g, color.b ) );\n float offset = x < 0.08 ? x - 6.25 * x * x : 0.04;\n color -= offset;\n float peak = max( color.r, max( color.g, color.b ) );\n if ( peak < StartCompression ) return color;\n float d = 1. - StartCompression;\n float newPeak = 1. - d * d / ( peak + d - StartCompression );\n color *= newPeak / peak;\n float g = 1. - 1. / ( Desaturation * ( peak - newPeak ) + 1. );\n return mix( color, vec3( newPeak ), g );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }"; -var transmission_fragment = "#ifdef USE_TRANSMISSION\n material.transmission = transmission;\n material.transmissionAlpha = 1.0;\n material.thickness = thickness;\n material.attenuationDistance = attenuationDistance;\n material.attenuationColor = attenuationColor;\n #ifdef USE_TRANSMISSIONMAP\n material.transmission *= texture2D( transmissionMap, vTransmissionMapUv ).r;\n #endif\n #ifdef USE_THICKNESSMAP\n material.thickness *= texture2D( thicknessMap, vThicknessMapUv ).g;\n #endif\n vec3 pos = vWorldPosition;\n vec3 v = normalize( cameraPosition - pos );\n vec3 n = inverseTransformDirection( normal, viewMatrix );\n vec4 transmitted = getIBLVolumeRefraction(\n n, v, material.roughness, material.diffuseColor, material.specularColor, material.specularF90,\n pos, modelMatrix, viewMatrix, projectionMatrix, material.dispersion, material.ior, material.thickness,\n material.attenuationColor, material.attenuationDistance );\n material.transmissionAlpha = mix( material.transmissionAlpha, transmitted.a, material.transmission );\n totalDiffuse = mix( totalDiffuse, transmitted.rgb, material.transmission );\n#endif"; -var transmission_pars_fragment = "#ifdef USE_TRANSMISSION\n uniform float transmission;\n uniform float thickness;\n uniform float attenuationDistance;\n uniform vec3 attenuationColor;\n #ifdef USE_TRANSMISSIONMAP\n uniform sampler2D transmissionMap;\n #endif\n #ifdef USE_THICKNESSMAP\n uniform sampler2D thicknessMap;\n #endif\n uniform vec2 transmissionSamplerSize;\n uniform sampler2D transmissionSamplerMap;\n uniform mat4 modelMatrix;\n uniform mat4 projectionMatrix;\n varying vec3 vWorldPosition;\n float w0( float a ) {\n return ( 1.0 / 6.0 ) * ( a * ( a * ( - a + 3.0 ) - 3.0 ) + 1.0 );\n }\n float w1( float a ) {\n return ( 1.0 / 6.0 ) * ( a * a * ( 3.0 * a - 6.0 ) + 4.0 );\n }\n float w2( float a ){\n return ( 1.0 / 6.0 ) * ( a * ( a * ( - 3.0 * a + 3.0 ) + 3.0 ) + 1.0 );\n }\n float w3( float a ) {\n return ( 1.0 / 6.0 ) * ( a * a * a );\n }\n float g0( float a ) {\n return w0( a ) + w1( a );\n }\n float g1( float a ) {\n return w2( a ) + w3( a );\n }\n float h0( float a ) {\n return - 1.0 + w1( a ) / ( w0( a ) + w1( a ) );\n }\n float h1( float a ) {\n return 1.0 + w3( a ) / ( w2( a ) + w3( a ) );\n }\n vec4 bicubic( sampler2D tex, vec2 uv, vec4 texelSize, float lod ) {\n uv = uv * texelSize.zw + 0.5;\n vec2 iuv = floor( uv );\n vec2 fuv = fract( uv );\n float g0x = g0( fuv.x );\n float g1x = g1( fuv.x );\n float h0x = h0( fuv.x );\n float h1x = h1( fuv.x );\n float h0y = h0( fuv.y );\n float h1y = h1( fuv.y );\n vec2 p0 = ( vec2( iuv.x + h0x, iuv.y + h0y ) - 0.5 ) * texelSize.xy;\n vec2 p1 = ( vec2( iuv.x + h1x, iuv.y + h0y ) - 0.5 ) * texelSize.xy;\n vec2 p2 = ( vec2( iuv.x + h0x, iuv.y + h1y ) - 0.5 ) * texelSize.xy;\n vec2 p3 = ( vec2( iuv.x + h1x, iuv.y + h1y ) - 0.5 ) * texelSize.xy;\n return g0( fuv.y ) * ( g0x * textureLod( tex, p0, lod ) + g1x * textureLod( tex, p1, lod ) ) +\n g1( fuv.y ) * ( g0x * textureLod( tex, p2, lod ) + g1x * textureLod( tex, p3, lod ) );\n }\n vec4 textureBicubic( sampler2D sampler, vec2 uv, float lod ) {\n vec2 fLodSize = vec2( textureSize( sampler, int( lod ) ) );\n vec2 cLodSize = vec2( textureSize( sampler, int( lod + 1.0 ) ) );\n vec2 fLodSizeInv = 1.0 / fLodSize;\n vec2 cLodSizeInv = 1.0 / cLodSize;\n vec4 fSample = bicubic( sampler, uv, vec4( fLodSizeInv, fLodSize ), floor( lod ) );\n vec4 cSample = bicubic( sampler, uv, vec4( cLodSizeInv, cLodSize ), ceil( lod ) );\n return mix( fSample, cSample, fract( lod ) );\n }\n vec3 getVolumeTransmissionRay( const in vec3 n, const in vec3 v, const in float thickness, const in float ior, const in mat4 modelMatrix ) {\n vec3 refractionVector = refract( - v, normalize( n ), 1.0 / ior );\n vec3 modelScale;\n modelScale.x = length( vec3( modelMatrix[ 0 ].xyz ) );\n modelScale.y = length( vec3( modelMatrix[ 1 ].xyz ) );\n modelScale.z = length( vec3( modelMatrix[ 2 ].xyz ) );\n return normalize( refractionVector ) * thickness * modelScale;\n }\n float applyIorToRoughness( const in float roughness, const in float ior ) {\n return roughness * clamp( ior * 2.0 - 2.0, 0.0, 1.0 );\n }\n vec4 getTransmissionSample( const in vec2 fragCoord, const in float roughness, const in float ior ) {\n float lod = log2( transmissionSamplerSize.x ) * applyIorToRoughness( roughness, ior );\n return textureBicubic( transmissionSamplerMap, fragCoord.xy, lod );\n }\n vec3 volumeAttenuation( const in float transmissionDistance, const in vec3 attenuationColor, const in float attenuationDistance ) {\n if ( isinf( attenuationDistance ) ) {\n return vec3( 1.0 );\n } else {\n vec3 attenuationCoefficient = -log( attenuationColor ) / attenuationDistance;\n vec3 transmittance = exp( - attenuationCoefficient * transmissionDistance ); return transmittance;\n }\n }\n vec4 getIBLVolumeRefraction( const in vec3 n, const in vec3 v, const in float roughness, const in vec3 diffuseColor,\n const in vec3 specularColor, const in float specularF90, const in vec3 position, const in mat4 modelMatrix,\n const in mat4 viewMatrix, const in mat4 projMatrix, const in float dispersion, const in float ior, const in float thickness,\n const in vec3 attenuationColor, const in float attenuationDistance ) {\n vec4 transmittedLight;\n vec3 transmittance;\n #ifdef USE_DISPERSION\n float halfSpread = ( ior - 1.0 ) * 0.025 * dispersion;\n vec3 iors = vec3( ior - halfSpread, ior, ior + halfSpread );\n for ( int i = 0; i < 3; i ++ ) {\n vec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, iors[ i ], modelMatrix );\n vec3 refractedRayExit = position + transmissionRay;\n \n vec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\n vec2 refractionCoords = ndcPos.xy / ndcPos.w;\n refractionCoords += 1.0;\n refractionCoords /= 2.0;\n \n vec4 transmissionSample = getTransmissionSample( refractionCoords, roughness, iors[ i ] );\n transmittedLight[ i ] = transmissionSample[ i ];\n transmittedLight.a += transmissionSample.a;\n transmittance[ i ] = diffuseColor[ i ] * volumeAttenuation( length( transmissionRay ), attenuationColor, attenuationDistance )[ i ];\n }\n transmittedLight.a /= 3.0;\n \n #else\n \n vec3 transmissionRay = getVolumeTransmissionRay( n, v, thickness, ior, modelMatrix );\n vec3 refractedRayExit = position + transmissionRay;\n vec4 ndcPos = projMatrix * viewMatrix * vec4( refractedRayExit, 1.0 );\n vec2 refractionCoords = ndcPos.xy / ndcPos.w;\n refractionCoords += 1.0;\n refractionCoords /= 2.0;\n transmittedLight = getTransmissionSample( refractionCoords, roughness, ior );\n transmittance = diffuseColor * volumeAttenuation( length( transmissionRay ), attenuationColor, attenuationDistance );\n \n #endif\n vec3 attenuatedColor = transmittance * transmittedLight.rgb;\n vec3 F = EnvironmentBRDF( n, v, specularColor, specularF90, roughness );\n float transmittanceFactor = ( transmittance.r + transmittance.g + transmittance.b ) / 3.0;\n return vec4( ( 1.0 - F ) * attenuatedColor, 1.0 - ( 1.0 - transmittedLight.a ) * transmittanceFactor );\n }\n#endif"; -var uv_pars_fragment = "#if defined( USE_UV ) || defined( USE_ANISOTROPY )\n varying vec2 vUv;\n#endif\n#ifdef USE_MAP\n varying vec2 vMapUv;\n#endif\n#ifdef USE_ALPHAMAP\n varying vec2 vAlphaMapUv;\n#endif\n#ifdef USE_LIGHTMAP\n varying vec2 vLightMapUv;\n#endif\n#ifdef USE_AOMAP\n varying vec2 vAoMapUv;\n#endif\n#ifdef USE_BUMPMAP\n varying vec2 vBumpMapUv;\n#endif\n#ifdef USE_NORMALMAP\n varying vec2 vNormalMapUv;\n#endif\n#ifdef USE_EMISSIVEMAP\n varying vec2 vEmissiveMapUv;\n#endif\n#ifdef USE_METALNESSMAP\n varying vec2 vMetalnessMapUv;\n#endif\n#ifdef USE_ROUGHNESSMAP\n varying vec2 vRoughnessMapUv;\n#endif\n#ifdef USE_ANISOTROPYMAP\n varying vec2 vAnisotropyMapUv;\n#endif\n#ifdef USE_CLEARCOATMAP\n varying vec2 vClearcoatMapUv;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n varying vec2 vClearcoatNormalMapUv;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n varying vec2 vClearcoatRoughnessMapUv;\n#endif\n#ifdef USE_IRIDESCENCEMAP\n varying vec2 vIridescenceMapUv;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n varying vec2 vIridescenceThicknessMapUv;\n#endif\n#ifdef USE_SHEEN_COLORMAP\n varying vec2 vSheenColorMapUv;\n#endif\n#ifdef USE_SHEEN_ROUGHNESSMAP\n varying vec2 vSheenRoughnessMapUv;\n#endif\n#ifdef USE_SPECULARMAP\n varying vec2 vSpecularMapUv;\n#endif\n#ifdef USE_SPECULAR_COLORMAP\n varying vec2 vSpecularColorMapUv;\n#endif\n#ifdef USE_SPECULAR_INTENSITYMAP\n varying vec2 vSpecularIntensityMapUv;\n#endif\n#ifdef USE_TRANSMISSIONMAP\n uniform mat3 transmissionMapTransform;\n varying vec2 vTransmissionMapUv;\n#endif\n#ifdef USE_THICKNESSMAP\n uniform mat3 thicknessMapTransform;\n varying vec2 vThicknessMapUv;\n#endif"; -var uv_pars_vertex = "#if defined( USE_UV ) || defined( USE_ANISOTROPY )\n varying vec2 vUv;\n#endif\n#ifdef USE_MAP\n uniform mat3 mapTransform;\n varying vec2 vMapUv;\n#endif\n#ifdef USE_ALPHAMAP\n uniform mat3 alphaMapTransform;\n varying vec2 vAlphaMapUv;\n#endif\n#ifdef USE_LIGHTMAP\n uniform mat3 lightMapTransform;\n varying vec2 vLightMapUv;\n#endif\n#ifdef USE_AOMAP\n uniform mat3 aoMapTransform;\n varying vec2 vAoMapUv;\n#endif\n#ifdef USE_BUMPMAP\n uniform mat3 bumpMapTransform;\n varying vec2 vBumpMapUv;\n#endif\n#ifdef USE_NORMALMAP\n uniform mat3 normalMapTransform;\n varying vec2 vNormalMapUv;\n#endif\n#ifdef USE_DISPLACEMENTMAP\n uniform mat3 displacementMapTransform;\n varying vec2 vDisplacementMapUv;\n#endif\n#ifdef USE_EMISSIVEMAP\n uniform mat3 emissiveMapTransform;\n varying vec2 vEmissiveMapUv;\n#endif\n#ifdef USE_METALNESSMAP\n uniform mat3 metalnessMapTransform;\n varying vec2 vMetalnessMapUv;\n#endif\n#ifdef USE_ROUGHNESSMAP\n uniform mat3 roughnessMapTransform;\n varying vec2 vRoughnessMapUv;\n#endif\n#ifdef USE_ANISOTROPYMAP\n uniform mat3 anisotropyMapTransform;\n varying vec2 vAnisotropyMapUv;\n#endif\n#ifdef USE_CLEARCOATMAP\n uniform mat3 clearcoatMapTransform;\n varying vec2 vClearcoatMapUv;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n uniform mat3 clearcoatNormalMapTransform;\n varying vec2 vClearcoatNormalMapUv;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n uniform mat3 clearcoatRoughnessMapTransform;\n varying vec2 vClearcoatRoughnessMapUv;\n#endif\n#ifdef USE_SHEEN_COLORMAP\n uniform mat3 sheenColorMapTransform;\n varying vec2 vSheenColorMapUv;\n#endif\n#ifdef USE_SHEEN_ROUGHNESSMAP\n uniform mat3 sheenRoughnessMapTransform;\n varying vec2 vSheenRoughnessMapUv;\n#endif\n#ifdef USE_IRIDESCENCEMAP\n uniform mat3 iridescenceMapTransform;\n varying vec2 vIridescenceMapUv;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n uniform mat3 iridescenceThicknessMapTransform;\n varying vec2 vIridescenceThicknessMapUv;\n#endif\n#ifdef USE_SPECULARMAP\n uniform mat3 specularMapTransform;\n varying vec2 vSpecularMapUv;\n#endif\n#ifdef USE_SPECULAR_COLORMAP\n uniform mat3 specularColorMapTransform;\n varying vec2 vSpecularColorMapUv;\n#endif\n#ifdef USE_SPECULAR_INTENSITYMAP\n uniform mat3 specularIntensityMapTransform;\n varying vec2 vSpecularIntensityMapUv;\n#endif\n#ifdef USE_TRANSMISSIONMAP\n uniform mat3 transmissionMapTransform;\n varying vec2 vTransmissionMapUv;\n#endif\n#ifdef USE_THICKNESSMAP\n uniform mat3 thicknessMapTransform;\n varying vec2 vThicknessMapUv;\n#endif"; -var uv_vertex = "#if defined( USE_UV ) || defined( USE_ANISOTROPY )\n vUv = vec3( uv, 1 ).xy;\n#endif\n#ifdef USE_MAP\n vMapUv = ( mapTransform * vec3( MAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_ALPHAMAP\n vAlphaMapUv = ( alphaMapTransform * vec3( ALPHAMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_LIGHTMAP\n vLightMapUv = ( lightMapTransform * vec3( LIGHTMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_AOMAP\n vAoMapUv = ( aoMapTransform * vec3( AOMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_BUMPMAP\n vBumpMapUv = ( bumpMapTransform * vec3( BUMPMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_NORMALMAP\n vNormalMapUv = ( normalMapTransform * vec3( NORMALMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_DISPLACEMENTMAP\n vDisplacementMapUv = ( displacementMapTransform * vec3( DISPLACEMENTMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_EMISSIVEMAP\n vEmissiveMapUv = ( emissiveMapTransform * vec3( EMISSIVEMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_METALNESSMAP\n vMetalnessMapUv = ( metalnessMapTransform * vec3( METALNESSMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_ROUGHNESSMAP\n vRoughnessMapUv = ( roughnessMapTransform * vec3( ROUGHNESSMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_ANISOTROPYMAP\n vAnisotropyMapUv = ( anisotropyMapTransform * vec3( ANISOTROPYMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_CLEARCOATMAP\n vClearcoatMapUv = ( clearcoatMapTransform * vec3( CLEARCOATMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n vClearcoatNormalMapUv = ( clearcoatNormalMapTransform * vec3( CLEARCOAT_NORMALMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n vClearcoatRoughnessMapUv = ( clearcoatRoughnessMapTransform * vec3( CLEARCOAT_ROUGHNESSMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_IRIDESCENCEMAP\n vIridescenceMapUv = ( iridescenceMapTransform * vec3( IRIDESCENCEMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_IRIDESCENCE_THICKNESSMAP\n vIridescenceThicknessMapUv = ( iridescenceThicknessMapTransform * vec3( IRIDESCENCE_THICKNESSMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_SHEEN_COLORMAP\n vSheenColorMapUv = ( sheenColorMapTransform * vec3( SHEEN_COLORMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_SHEEN_ROUGHNESSMAP\n vSheenRoughnessMapUv = ( sheenRoughnessMapTransform * vec3( SHEEN_ROUGHNESSMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_SPECULARMAP\n vSpecularMapUv = ( specularMapTransform * vec3( SPECULARMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_SPECULAR_COLORMAP\n vSpecularColorMapUv = ( specularColorMapTransform * vec3( SPECULAR_COLORMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_SPECULAR_INTENSITYMAP\n vSpecularIntensityMapUv = ( specularIntensityMapTransform * vec3( SPECULAR_INTENSITYMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_TRANSMISSIONMAP\n vTransmissionMapUv = ( transmissionMapTransform * vec3( TRANSMISSIONMAP_UV, 1 ) ).xy;\n#endif\n#ifdef USE_THICKNESSMAP\n vThicknessMapUv = ( thicknessMapTransform * vec3( THICKNESSMAP_UV, 1 ) ).xy;\n#endif"; -var worldpos_vertex = "#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION ) || NUM_SPOT_LIGHT_COORDS > 0\n vec4 worldPosition = vec4( transformed, 1.0 );\n #ifdef USE_BATCHING\n worldPosition = batchingMatrix * worldPosition;\n #endif\n #ifdef USE_INSTANCING\n worldPosition = instanceMatrix * worldPosition;\n #endif\n worldPosition = modelMatrix * worldPosition;\n#endif"; -const vertex$h = "varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n vUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n gl_Position = vec4( position.xy, 1.0, 1.0 );\n}"; -const fragment$h = "uniform sampler2D t2D;\nuniform float backgroundIntensity;\nvarying vec2 vUv;\nvoid main() {\n vec4 texColor = texture2D( t2D, vUv );\n #ifdef DECODE_VIDEO_TEXTURE\n texColor = vec4( mix( pow( texColor.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), texColor.rgb * 0.0773993808, vec3( lessThanEqual( texColor.rgb, vec3( 0.04045 ) ) ) ), texColor.w );\n #endif\n texColor.rgb *= backgroundIntensity;\n gl_FragColor = texColor;\n #include \n #include \n}"; -const vertex$g = "varying vec3 vWorldDirection;\n#include \nvoid main() {\n vWorldDirection = transformDirection( position, modelMatrix );\n #include \n #include \n gl_Position.z = gl_Position.w;\n}"; -const fragment$g = "#ifdef ENVMAP_TYPE_CUBE\n uniform samplerCube envMap;\n#elif defined( ENVMAP_TYPE_CUBE_UV )\n uniform sampler2D envMap;\n#endif\nuniform float flipEnvMap;\nuniform float backgroundBlurriness;\nuniform float backgroundIntensity;\nuniform mat3 backgroundRotation;\nvarying vec3 vWorldDirection;\n#include \nvoid main() {\n #ifdef ENVMAP_TYPE_CUBE\n vec4 texColor = textureCube( envMap, backgroundRotation * vec3( flipEnvMap * vWorldDirection.x, vWorldDirection.yz ) );\n #elif defined( ENVMAP_TYPE_CUBE_UV )\n vec4 texColor = textureCubeUV( envMap, backgroundRotation * vWorldDirection, backgroundBlurriness );\n #else\n vec4 texColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n #endif\n texColor.rgb *= backgroundIntensity;\n gl_FragColor = texColor;\n #include \n #include \n}"; -const vertex$f = "varying vec3 vWorldDirection;\n#include \nvoid main() {\n vWorldDirection = transformDirection( position, modelMatrix );\n #include \n #include \n gl_Position.z = gl_Position.w;\n}"; -const fragment$f = "uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n vec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n gl_FragColor = texColor;\n gl_FragColor.a *= opacity;\n #include \n #include \n}"; -const vertex$e = "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvarying vec2 vHighPrecisionZW;\nvoid main() {\n #include \n #include \n #include \n #include \n #ifdef USE_DISPLACEMENTMAP\n #include \n #include \n #include \n #endif\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vHighPrecisionZW = gl_Position.zw;\n}"; -const fragment$e = "#if DEPTH_PACKING == 3200\n uniform float opacity;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvarying vec2 vHighPrecisionZW;\nvoid main() {\n vec4 diffuseColor = vec4( 1.0 );\n #include \n #if DEPTH_PACKING == 3200\n diffuseColor.a = opacity;\n #endif\n #include \n #include \n #include \n #include \n #include \n float fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n #if DEPTH_PACKING == 3200\n gl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n #elif DEPTH_PACKING == 3201\n gl_FragColor = packDepthToRGBA( fragCoordZ );\n #elif DEPTH_PACKING == 3202\n gl_FragColor = vec4( packDepthToRGB( fragCoordZ ), 1.0 );\n #elif DEPTH_PACKING == 3203\n gl_FragColor = vec4( packDepthToRG( fragCoordZ ), 0.0, 1.0 );\n #endif\n}"; -const vertex$d = "#define DISTANCE\nvarying vec3 vWorldPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #ifdef USE_DISPLACEMENTMAP\n #include \n #include \n #include \n #endif\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vWorldPosition = worldPosition.xyz;\n}"; -const fragment$d = "#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main () {\n vec4 diffuseColor = vec4( 1.0 );\n #include \n #include \n #include \n #include \n #include \n float dist = length( vWorldPosition - referencePosition );\n dist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n dist = saturate( dist );\n gl_FragColor = packDepthToRGBA( dist );\n}"; -const vertex$c = "varying vec3 vWorldDirection;\n#include \nvoid main() {\n vWorldDirection = transformDirection( position, modelMatrix );\n #include \n #include \n}"; -const fragment$c = "uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include \nvoid main() {\n vec3 direction = normalize( vWorldDirection );\n vec2 sampleUV = equirectUv( direction );\n gl_FragColor = texture2D( tEquirect, sampleUV );\n #include \n #include \n}"; -const vertex$b = "uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vLineDistance = scale * lineDistance;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const fragment$b = "uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n if ( mod( vLineDistance, totalSize ) > dashSize ) {\n discard;\n }\n vec3 outgoingLight = vec3( 0.0 );\n #include \n #include \n #include \n outgoingLight = diffuseColor.rgb;\n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$a = "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n #include \n #include \n #include \n #include \n #include \n #endif\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const fragment$a = "uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n varying vec3 vNormal;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n #ifdef USE_LIGHTMAP\n vec4 lightMapTexel = texture2D( lightMap, vLightMapUv );\n reflectedLight.indirectDiffuse += lightMapTexel.rgb * lightMapIntensity * RECIPROCAL_PI;\n #else\n reflectedLight.indirectDiffuse += vec3( 1.0 );\n #endif\n #include \n reflectedLight.indirectDiffuse *= diffuseColor.rgb;\n vec3 outgoingLight = reflectedLight.indirectDiffuse;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$9 = "#define LAMBERT\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vViewPosition = - mvPosition.xyz;\n #include \n #include \n #include \n #include \n}"; -const fragment$9 = "#define LAMBERT\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n vec3 totalEmissiveRadiance = emissive;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$8 = "#define MATCAP\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vViewPosition = - mvPosition.xyz;\n}"; -const fragment$8 = "#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vec3 viewDir = normalize( vViewPosition );\n vec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n vec3 y = cross( viewDir, x );\n vec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n #ifdef USE_MATCAP\n vec4 matcapColor = texture2D( matcap, uv );\n #else\n vec4 matcapColor = vec4( vec3( mix( 0.2, 0.8, uv.y ) ), 1.0 );\n #endif\n vec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$7 = "#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )\n varying vec3 vViewPosition;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )\n vViewPosition = - mvPosition.xyz;\n#endif\n}"; -const fragment$7 = "#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP_TANGENTSPACE )\n varying vec3 vViewPosition;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( 0.0, 0.0, 0.0, opacity );\n #include \n #include \n #include \n #include \n gl_FragColor = vec4( packNormalToRGB( normal ), diffuseColor.a );\n #ifdef OPAQUE\n gl_FragColor.a = 1.0;\n #endif\n}"; -const vertex$6 = "#define PHONG\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vViewPosition = - mvPosition.xyz;\n #include \n #include \n #include \n #include \n}"; -const fragment$6 = "#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n vec3 totalEmissiveRadiance = emissive;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$5 = "#define STANDARD\nvarying vec3 vViewPosition;\n#ifdef USE_TRANSMISSION\n varying vec3 vWorldPosition;\n#endif\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vViewPosition = - mvPosition.xyz;\n #include \n #include \n #include \n#ifdef USE_TRANSMISSION\n vWorldPosition = worldPosition.xyz;\n#endif\n}"; -const fragment$5 = "#define STANDARD\n#ifdef PHYSICAL\n #define IOR\n #define USE_SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef IOR\n uniform float ior;\n#endif\n#ifdef USE_SPECULAR\n uniform float specularIntensity;\n uniform vec3 specularColor;\n #ifdef USE_SPECULAR_COLORMAP\n uniform sampler2D specularColorMap;\n #endif\n #ifdef USE_SPECULAR_INTENSITYMAP\n uniform sampler2D specularIntensityMap;\n #endif\n#endif\n#ifdef USE_CLEARCOAT\n uniform float clearcoat;\n uniform float clearcoatRoughness;\n#endif\n#ifdef USE_DISPERSION\n uniform float dispersion;\n#endif\n#ifdef USE_IRIDESCENCE\n uniform float iridescence;\n uniform float iridescenceIOR;\n uniform float iridescenceThicknessMinimum;\n uniform float iridescenceThicknessMaximum;\n#endif\n#ifdef USE_SHEEN\n uniform vec3 sheenColor;\n uniform float sheenRoughness;\n #ifdef USE_SHEEN_COLORMAP\n uniform sampler2D sheenColorMap;\n #endif\n #ifdef USE_SHEEN_ROUGHNESSMAP\n uniform sampler2D sheenRoughnessMap;\n #endif\n#endif\n#ifdef USE_ANISOTROPY\n uniform vec2 anisotropyVector;\n #ifdef USE_ANISOTROPYMAP\n uniform sampler2D anisotropyMap;\n #endif\n#endif\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n vec3 totalEmissiveRadiance = emissive;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n vec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n #include \n vec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n #ifdef USE_SHEEN\n float sheenEnergyComp = 1.0 - 0.157 * max3( material.sheenColor );\n outgoingLight = outgoingLight * sheenEnergyComp + sheenSpecularDirect + sheenSpecularIndirect;\n #endif\n #ifdef USE_CLEARCOAT\n float dotNVcc = saturate( dot( geometryClearcoatNormal, geometryViewDir ) );\n vec3 Fcc = F_Schlick( material.clearcoatF0, material.clearcoatF90, dotNVcc );\n outgoingLight = outgoingLight * ( 1.0 - material.clearcoat * Fcc ) + ( clearcoatSpecularDirect + clearcoatSpecularIndirect ) * material.clearcoat;\n #endif\n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$4 = "#define TOON\nvarying vec3 vViewPosition;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vViewPosition = - mvPosition.xyz;\n #include \n #include \n #include \n}"; -const fragment$4 = "#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n ReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n vec3 totalEmissiveRadiance = emissive;\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n vec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$3 = "uniform float size;\nuniform float scale;\n#include \n#include \n#include \n#include \n#include \n#include \n#ifdef USE_POINTS_UV\n varying vec2 vUv;\n uniform mat3 uvTransform;\n#endif\nvoid main() {\n #ifdef USE_POINTS_UV\n vUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n #endif\n #include \n #include \n #include \n #include \n #include \n #include \n gl_PointSize = size;\n #ifdef USE_SIZEATTENUATION\n bool isPerspective = isPerspectiveMatrix( projectionMatrix );\n if ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n #endif\n #include \n #include \n #include \n #include \n}"; -const fragment$3 = "uniform vec3 diffuse;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n vec3 outgoingLight = vec3( 0.0 );\n #include \n #include \n #include \n #include \n #include \n outgoingLight = diffuseColor.rgb;\n #include \n #include \n #include \n #include \n #include \n}"; -const vertex$2 = "#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n #include \n}"; -const fragment$2 = "uniform vec3 color;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n gl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n #include \n #include \n #include \n}"; -const vertex$1 = "uniform float rotation;\nuniform vec2 center;\n#include \n#include \n#include \n#include \n#include \nvoid main() {\n #include \n vec4 mvPosition = modelViewMatrix[ 3 ];\n vec2 scale = vec2( length( modelMatrix[ 0 ].xyz ), length( modelMatrix[ 1 ].xyz ) );\n #ifndef USE_SIZEATTENUATION\n bool isPerspective = isPerspectiveMatrix( projectionMatrix );\n if ( isPerspective ) scale *= - mvPosition.z;\n #endif\n vec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n vec2 rotatedPosition;\n rotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n rotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n mvPosition.xy += rotatedPosition;\n gl_Position = projectionMatrix * mvPosition;\n #include \n #include \n #include \n}"; -const fragment$1 = "uniform vec3 diffuse;\nuniform float opacity;\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nvoid main() {\n vec4 diffuseColor = vec4( diffuse, opacity );\n #include \n vec3 outgoingLight = vec3( 0.0 );\n #include \n #include \n #include \n #include \n #include \n outgoingLight = diffuseColor.rgb;\n #include \n #include \n #include \n #include \n}"; -const ShaderChunk = { - alphahash_fragment, - alphahash_pars_fragment, - alphamap_fragment, - alphamap_pars_fragment, - alphatest_fragment, - alphatest_pars_fragment, - aomap_fragment, - aomap_pars_fragment, - batching_pars_vertex, - batching_vertex, - begin_vertex, - beginnormal_vertex, - bsdfs, - iridescence_fragment, - bumpmap_pars_fragment, - clipping_planes_fragment, - clipping_planes_pars_fragment, - clipping_planes_pars_vertex, - clipping_planes_vertex, - color_fragment, - color_pars_fragment, - color_pars_vertex, - color_vertex, - common, - cube_uv_reflection_fragment, - defaultnormal_vertex, - displacementmap_pars_vertex, - displacementmap_vertex, - emissivemap_fragment, - emissivemap_pars_fragment, - colorspace_fragment, - colorspace_pars_fragment, - envmap_fragment, - envmap_common_pars_fragment, - envmap_pars_fragment, - envmap_pars_vertex, - envmap_physical_pars_fragment, - envmap_vertex, - fog_vertex, - fog_pars_vertex, - fog_fragment, - fog_pars_fragment, - gradientmap_pars_fragment, - lightmap_pars_fragment, - lights_lambert_fragment, - lights_lambert_pars_fragment, - lights_pars_begin, - lights_toon_fragment, - lights_toon_pars_fragment, - lights_phong_fragment, - lights_phong_pars_fragment, - lights_physical_fragment, - lights_physical_pars_fragment, - lights_fragment_begin, - lights_fragment_maps, - lights_fragment_end, - logdepthbuf_fragment, - logdepthbuf_pars_fragment, - logdepthbuf_pars_vertex, - logdepthbuf_vertex, - map_fragment, - map_pars_fragment, - map_particle_fragment, - map_particle_pars_fragment, - metalnessmap_fragment, - metalnessmap_pars_fragment, - morphinstance_vertex, - morphcolor_vertex, - morphnormal_vertex, - morphtarget_pars_vertex, - morphtarget_vertex, - normal_fragment_begin, - normal_fragment_maps, - normal_pars_fragment, - normal_pars_vertex, - normal_vertex, - normalmap_pars_fragment, - clearcoat_normal_fragment_begin, - clearcoat_normal_fragment_maps, - clearcoat_pars_fragment, - iridescence_pars_fragment, - opaque_fragment, - packing, - premultiplied_alpha_fragment, - project_vertex, - dithering_fragment, - dithering_pars_fragment, - roughnessmap_fragment, - roughnessmap_pars_fragment, - shadowmap_pars_fragment, - shadowmap_pars_vertex, - shadowmap_vertex, - shadowmask_pars_fragment, - skinbase_vertex, - skinning_pars_vertex, - skinning_vertex, - skinnormal_vertex, - specularmap_fragment, - specularmap_pars_fragment, - tonemapping_fragment, - tonemapping_pars_fragment, - transmission_fragment, - transmission_pars_fragment, - uv_pars_fragment, - uv_pars_vertex, - uv_vertex, - worldpos_vertex, - background_vert: vertex$h, - background_frag: fragment$h, - backgroundCube_vert: vertex$g, - backgroundCube_frag: fragment$g, - cube_vert: vertex$f, - cube_frag: fragment$f, - depth_vert: vertex$e, - depth_frag: fragment$e, - distanceRGBA_vert: vertex$d, - distanceRGBA_frag: fragment$d, - equirect_vert: vertex$c, - equirect_frag: fragment$c, - linedashed_vert: vertex$b, - linedashed_frag: fragment$b, - meshbasic_vert: vertex$a, - meshbasic_frag: fragment$a, - meshlambert_vert: vertex$9, - meshlambert_frag: fragment$9, - meshmatcap_vert: vertex$8, - meshmatcap_frag: fragment$8, - meshnormal_vert: vertex$7, - meshnormal_frag: fragment$7, - meshphong_vert: vertex$6, - meshphong_frag: fragment$6, - meshphysical_vert: vertex$5, - meshphysical_frag: fragment$5, - meshtoon_vert: vertex$4, - meshtoon_frag: fragment$4, - points_vert: vertex$3, - points_frag: fragment$3, - shadow_vert: vertex$2, - shadow_frag: fragment$2, - sprite_vert: vertex$1, - sprite_frag: fragment$1 -}; -const UniformsLib = { - common: { - diffuse: { value: /* @__PURE__ */ new Color(16777215) }, - opacity: { value: 1 }, - map: { value: null }, - mapTransform: { value: /* @__PURE__ */ new Matrix3() }, - alphaMap: { value: null }, - alphaMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - alphaTest: { value: 0 } - }, - specularmap: { - specularMap: { value: null }, - specularMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - envmap: { - envMap: { value: null }, - envMapRotation: { value: /* @__PURE__ */ new Matrix3() }, - flipEnvMap: { value: -1 }, - reflectivity: { value: 1 }, - // basic, lambert, phong - ior: { value: 1.5 }, - // physical - refractionRatio: { value: 0.98 } - // basic, lambert, phong - }, - aomap: { - aoMap: { value: null }, - aoMapIntensity: { value: 1 }, - aoMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - lightmap: { - lightMap: { value: null }, - lightMapIntensity: { value: 1 }, - lightMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - bumpmap: { - bumpMap: { value: null }, - bumpMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - bumpScale: { value: 1 } - }, - normalmap: { - normalMap: { value: null }, - normalMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - normalScale: { value: /* @__PURE__ */ new Vector2(1, 1) } - }, - displacementmap: { - displacementMap: { value: null }, - displacementMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - displacementScale: { value: 1 }, - displacementBias: { value: 0 } - }, - emissivemap: { - emissiveMap: { value: null }, - emissiveMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - metalnessmap: { - metalnessMap: { value: null }, - metalnessMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - roughnessmap: { - roughnessMap: { value: null }, - roughnessMapTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - gradientmap: { - gradientMap: { value: null } - }, - fog: { - fogDensity: { value: 25e-5 }, - fogNear: { value: 1 }, - fogFar: { value: 2e3 }, - fogColor: { value: /* @__PURE__ */ new Color(16777215) } - }, - lights: { - ambientLightColor: { value: [] }, - lightProbe: { value: [] }, - directionalLights: { value: [], properties: { - direction: {}, - color: {} - } }, - directionalLightShadows: { value: [], properties: { - shadowIntensity: 1, - shadowBias: {}, - shadowNormalBias: {}, - shadowRadius: {}, - shadowMapSize: {} - } }, - directionalShadowMap: { value: [] }, - directionalShadowMatrix: { value: [] }, - spotLights: { value: [], properties: { - color: {}, - position: {}, - direction: {}, - distance: {}, - coneCos: {}, - penumbraCos: {}, - decay: {} - } }, - spotLightShadows: { value: [], properties: { - shadowIntensity: 1, - shadowBias: {}, - shadowNormalBias: {}, - shadowRadius: {}, - shadowMapSize: {} - } }, - spotLightMap: { value: [] }, - spotShadowMap: { value: [] }, - spotLightMatrix: { value: [] }, - pointLights: { value: [], properties: { - color: {}, - position: {}, - decay: {}, - distance: {} - } }, - pointLightShadows: { value: [], properties: { - shadowIntensity: 1, - shadowBias: {}, - shadowNormalBias: {}, - shadowRadius: {}, - shadowMapSize: {}, - shadowCameraNear: {}, - shadowCameraFar: {} - } }, - pointShadowMap: { value: [] }, - pointShadowMatrix: { value: [] }, - hemisphereLights: { value: [], properties: { - direction: {}, - skyColor: {}, - groundColor: {} - } }, - // TODO (abelnation): RectAreaLight BRDF data needs to be moved from example to main src - rectAreaLights: { value: [], properties: { - color: {}, - position: {}, - width: {}, - height: {} - } }, - ltc_1: { value: null }, - ltc_2: { value: null } - }, - points: { - diffuse: { value: /* @__PURE__ */ new Color(16777215) }, - opacity: { value: 1 }, - size: { value: 1 }, - scale: { value: 1 }, - map: { value: null }, - alphaMap: { value: null }, - alphaMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - alphaTest: { value: 0 }, - uvTransform: { value: /* @__PURE__ */ new Matrix3() } - }, - sprite: { - diffuse: { value: /* @__PURE__ */ new Color(16777215) }, - opacity: { value: 1 }, - center: { value: /* @__PURE__ */ new Vector2(0.5, 0.5) }, - rotation: { value: 0 }, - map: { value: null }, - mapTransform: { value: /* @__PURE__ */ new Matrix3() }, - alphaMap: { value: null }, - alphaMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - alphaTest: { value: 0 } - } -}; -const ShaderLib = { - basic: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.specularmap, - UniformsLib.envmap, - UniformsLib.aomap, - UniformsLib.lightmap, - UniformsLib.fog - ]), - vertexShader: ShaderChunk.meshbasic_vert, - fragmentShader: ShaderChunk.meshbasic_frag - }, - lambert: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.specularmap, - UniformsLib.envmap, - UniformsLib.aomap, - UniformsLib.lightmap, - UniformsLib.emissivemap, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - UniformsLib.fog, - UniformsLib.lights, - { - emissive: { value: /* @__PURE__ */ new Color(0) } - } - ]), - vertexShader: ShaderChunk.meshlambert_vert, - fragmentShader: ShaderChunk.meshlambert_frag - }, - phong: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.specularmap, - UniformsLib.envmap, - UniformsLib.aomap, - UniformsLib.lightmap, - UniformsLib.emissivemap, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - UniformsLib.fog, - UniformsLib.lights, - { - emissive: { value: /* @__PURE__ */ new Color(0) }, - specular: { value: /* @__PURE__ */ new Color(1118481) }, - shininess: { value: 30 } - } - ]), - vertexShader: ShaderChunk.meshphong_vert, - fragmentShader: ShaderChunk.meshphong_frag - }, - standard: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.envmap, - UniformsLib.aomap, - UniformsLib.lightmap, - UniformsLib.emissivemap, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - UniformsLib.roughnessmap, - UniformsLib.metalnessmap, - UniformsLib.fog, - UniformsLib.lights, - { - emissive: { value: /* @__PURE__ */ new Color(0) }, - roughness: { value: 1 }, - metalness: { value: 0 }, - envMapIntensity: { value: 1 } - } - ]), - vertexShader: ShaderChunk.meshphysical_vert, - fragmentShader: ShaderChunk.meshphysical_frag - }, - toon: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.aomap, - UniformsLib.lightmap, - UniformsLib.emissivemap, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - UniformsLib.gradientmap, - UniformsLib.fog, - UniformsLib.lights, - { - emissive: { value: /* @__PURE__ */ new Color(0) } - } - ]), - vertexShader: ShaderChunk.meshtoon_vert, - fragmentShader: ShaderChunk.meshtoon_frag - }, - matcap: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - UniformsLib.fog, - { - matcap: { value: null } - } - ]), - vertexShader: ShaderChunk.meshmatcap_vert, - fragmentShader: ShaderChunk.meshmatcap_frag - }, - points: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.points, - UniformsLib.fog - ]), - vertexShader: ShaderChunk.points_vert, - fragmentShader: ShaderChunk.points_frag - }, - dashed: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.fog, - { - scale: { value: 1 }, - dashSize: { value: 1 }, - totalSize: { value: 2 } - } - ]), - vertexShader: ShaderChunk.linedashed_vert, - fragmentShader: ShaderChunk.linedashed_frag - }, - depth: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.displacementmap - ]), - vertexShader: ShaderChunk.depth_vert, - fragmentShader: ShaderChunk.depth_frag - }, - normal: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.bumpmap, - UniformsLib.normalmap, - UniformsLib.displacementmap, - { - opacity: { value: 1 } - } - ]), - vertexShader: ShaderChunk.meshnormal_vert, - fragmentShader: ShaderChunk.meshnormal_frag - }, - sprite: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.sprite, - UniformsLib.fog - ]), - vertexShader: ShaderChunk.sprite_vert, - fragmentShader: ShaderChunk.sprite_frag - }, - background: { - uniforms: { - uvTransform: { value: /* @__PURE__ */ new Matrix3() }, - t2D: { value: null }, - backgroundIntensity: { value: 1 } - }, - vertexShader: ShaderChunk.background_vert, - fragmentShader: ShaderChunk.background_frag - }, - backgroundCube: { - uniforms: { - envMap: { value: null }, - flipEnvMap: { value: -1 }, - backgroundBlurriness: { value: 0 }, - backgroundIntensity: { value: 1 }, - backgroundRotation: { value: /* @__PURE__ */ new Matrix3() } - }, - vertexShader: ShaderChunk.backgroundCube_vert, - fragmentShader: ShaderChunk.backgroundCube_frag - }, - cube: { - uniforms: { - tCube: { value: null }, - tFlip: { value: -1 }, - opacity: { value: 1 } - }, - vertexShader: ShaderChunk.cube_vert, - fragmentShader: ShaderChunk.cube_frag - }, - equirect: { - uniforms: { - tEquirect: { value: null } - }, - vertexShader: ShaderChunk.equirect_vert, - fragmentShader: ShaderChunk.equirect_frag - }, - distanceRGBA: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.common, - UniformsLib.displacementmap, - { - referencePosition: { value: /* @__PURE__ */ new Vector3() }, - nearDistance: { value: 1 }, - farDistance: { value: 1e3 } - } - ]), - vertexShader: ShaderChunk.distanceRGBA_vert, - fragmentShader: ShaderChunk.distanceRGBA_frag - }, - shadow: { - uniforms: /* @__PURE__ */ mergeUniforms([ - UniformsLib.lights, - UniformsLib.fog, - { - color: { value: /* @__PURE__ */ new Color(0) }, - opacity: { value: 1 } - } - ]), - vertexShader: ShaderChunk.shadow_vert, - fragmentShader: ShaderChunk.shadow_frag - } -}; -ShaderLib.physical = { - uniforms: /* @__PURE__ */ mergeUniforms([ - ShaderLib.standard.uniforms, - { - clearcoat: { value: 0 }, - clearcoatMap: { value: null }, - clearcoatMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - clearcoatNormalMap: { value: null }, - clearcoatNormalMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - clearcoatNormalScale: { value: /* @__PURE__ */ new Vector2(1, 1) }, - clearcoatRoughness: { value: 0 }, - clearcoatRoughnessMap: { value: null }, - clearcoatRoughnessMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - dispersion: { value: 0 }, - iridescence: { value: 0 }, - iridescenceMap: { value: null }, - iridescenceMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - iridescenceIOR: { value: 1.3 }, - iridescenceThicknessMinimum: { value: 100 }, - iridescenceThicknessMaximum: { value: 400 }, - iridescenceThicknessMap: { value: null }, - iridescenceThicknessMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - sheen: { value: 0 }, - sheenColor: { value: /* @__PURE__ */ new Color(0) }, - sheenColorMap: { value: null }, - sheenColorMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - sheenRoughness: { value: 1 }, - sheenRoughnessMap: { value: null }, - sheenRoughnessMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - transmission: { value: 0 }, - transmissionMap: { value: null }, - transmissionMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - transmissionSamplerSize: { value: /* @__PURE__ */ new Vector2() }, - transmissionSamplerMap: { value: null }, - thickness: { value: 0 }, - thicknessMap: { value: null }, - thicknessMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - attenuationDistance: { value: 0 }, - attenuationColor: { value: /* @__PURE__ */ new Color(0) }, - specularColor: { value: /* @__PURE__ */ new Color(1, 1, 1) }, - specularColorMap: { value: null }, - specularColorMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - specularIntensity: { value: 1 }, - specularIntensityMap: { value: null }, - specularIntensityMapTransform: { value: /* @__PURE__ */ new Matrix3() }, - anisotropyVector: { value: /* @__PURE__ */ new Vector2() }, - anisotropyMap: { value: null }, - anisotropyMapTransform: { value: /* @__PURE__ */ new Matrix3() } - } - ]), - vertexShader: ShaderChunk.meshphysical_vert, - fragmentShader: ShaderChunk.meshphysical_frag -}; -const _rgb = { r: 0, b: 0, g: 0 }; -const _e1$1 = /* @__PURE__ */ new Euler(); -const _m1$1 = /* @__PURE__ */ new Matrix4(); -function WebGLBackground(renderer, cubemaps, cubeuvmaps, state, objects, alpha, premultipliedAlpha) { - const clearColor = new Color(0); - let clearAlpha = alpha === true ? 0 : 1; - let planeMesh; - let boxMesh; - let currentBackground = null; - let currentBackgroundVersion = 0; - let currentTonemapping = null; - function getBackground(scene) { - let background = scene.isScene === true ? scene.background : null; - if (background && background.isTexture) { - const usePMREM = scene.backgroundBlurriness > 0; - background = (usePMREM ? cubeuvmaps : cubemaps).get(background); - } - return background; - } - __name(getBackground, "getBackground"); - function render(scene) { - let forceClear = false; - const background = getBackground(scene); - if (background === null) { - setClear(clearColor, clearAlpha); - } else if (background && background.isColor) { - setClear(background, 1); - forceClear = true; - } - const environmentBlendMode = renderer.xr.getEnvironmentBlendMode(); - if (environmentBlendMode === "additive") { - state.buffers.color.setClear(0, 0, 0, 1, premultipliedAlpha); - } else if (environmentBlendMode === "alpha-blend") { - state.buffers.color.setClear(0, 0, 0, 0, premultipliedAlpha); - } - if (renderer.autoClear || forceClear) { - state.buffers.depth.setTest(true); - state.buffers.depth.setMask(true); - state.buffers.color.setMask(true); - renderer.clear(renderer.autoClearColor, renderer.autoClearDepth, renderer.autoClearStencil); - } - } - __name(render, "render"); - function addToRenderList(renderList, scene) { - const background = getBackground(scene); - if (background && (background.isCubeTexture || background.mapping === CubeUVReflectionMapping)) { - if (boxMesh === void 0) { - boxMesh = new Mesh( - new BoxGeometry(1, 1, 1), - new ShaderMaterial({ - name: "BackgroundCubeMaterial", - uniforms: cloneUniforms(ShaderLib.backgroundCube.uniforms), - vertexShader: ShaderLib.backgroundCube.vertexShader, - fragmentShader: ShaderLib.backgroundCube.fragmentShader, - side: BackSide, - depthTest: false, - depthWrite: false, - fog: false - }) - ); - boxMesh.geometry.deleteAttribute("normal"); - boxMesh.geometry.deleteAttribute("uv"); - boxMesh.onBeforeRender = function(renderer2, scene2, camera) { - this.matrixWorld.copyPosition(camera.matrixWorld); - }; - Object.defineProperty(boxMesh.material, "envMap", { - get: /* @__PURE__ */ __name(function() { - return this.uniforms.envMap.value; - }, "get") - }); - objects.update(boxMesh); - } - _e1$1.copy(scene.backgroundRotation); - _e1$1.x *= -1; - _e1$1.y *= -1; - _e1$1.z *= -1; - if (background.isCubeTexture && background.isRenderTargetTexture === false) { - _e1$1.y *= -1; - _e1$1.z *= -1; - } - boxMesh.material.uniforms.envMap.value = background; - boxMesh.material.uniforms.flipEnvMap.value = background.isCubeTexture && background.isRenderTargetTexture === false ? -1 : 1; - boxMesh.material.uniforms.backgroundBlurriness.value = scene.backgroundBlurriness; - boxMesh.material.uniforms.backgroundIntensity.value = scene.backgroundIntensity; - boxMesh.material.uniforms.backgroundRotation.value.setFromMatrix4(_m1$1.makeRotationFromEuler(_e1$1)); - boxMesh.material.toneMapped = ColorManagement.getTransfer(background.colorSpace) !== SRGBTransfer; - if (currentBackground !== background || currentBackgroundVersion !== background.version || currentTonemapping !== renderer.toneMapping) { - boxMesh.material.needsUpdate = true; - currentBackground = background; - currentBackgroundVersion = background.version; - currentTonemapping = renderer.toneMapping; - } - boxMesh.layers.enableAll(); - renderList.unshift(boxMesh, boxMesh.geometry, boxMesh.material, 0, 0, null); - } else if (background && background.isTexture) { - if (planeMesh === void 0) { - planeMesh = new Mesh( - new PlaneGeometry(2, 2), - new ShaderMaterial({ - name: "BackgroundMaterial", - uniforms: cloneUniforms(ShaderLib.background.uniforms), - vertexShader: ShaderLib.background.vertexShader, - fragmentShader: ShaderLib.background.fragmentShader, - side: FrontSide, - depthTest: false, - depthWrite: false, - fog: false - }) - ); - planeMesh.geometry.deleteAttribute("normal"); - Object.defineProperty(planeMesh.material, "map", { - get: /* @__PURE__ */ __name(function() { - return this.uniforms.t2D.value; - }, "get") - }); - objects.update(planeMesh); - } - planeMesh.material.uniforms.t2D.value = background; - planeMesh.material.uniforms.backgroundIntensity.value = scene.backgroundIntensity; - planeMesh.material.toneMapped = ColorManagement.getTransfer(background.colorSpace) !== SRGBTransfer; - if (background.matrixAutoUpdate === true) { - background.updateMatrix(); - } - planeMesh.material.uniforms.uvTransform.value.copy(background.matrix); - if (currentBackground !== background || currentBackgroundVersion !== background.version || currentTonemapping !== renderer.toneMapping) { - planeMesh.material.needsUpdate = true; - currentBackground = background; - currentBackgroundVersion = background.version; - currentTonemapping = renderer.toneMapping; - } - planeMesh.layers.enableAll(); - renderList.unshift(planeMesh, planeMesh.geometry, planeMesh.material, 0, 0, null); - } - } - __name(addToRenderList, "addToRenderList"); - function setClear(color, alpha2) { - color.getRGB(_rgb, getUnlitUniformColorSpace(renderer)); - state.buffers.color.setClear(_rgb.r, _rgb.g, _rgb.b, alpha2, premultipliedAlpha); - } - __name(setClear, "setClear"); - return { - getClearColor: /* @__PURE__ */ __name(function() { - return clearColor; - }, "getClearColor"), - setClearColor: /* @__PURE__ */ __name(function(color, alpha2 = 1) { - clearColor.set(color); - clearAlpha = alpha2; - setClear(clearColor, clearAlpha); - }, "setClearColor"), - getClearAlpha: /* @__PURE__ */ __name(function() { - return clearAlpha; - }, "getClearAlpha"), - setClearAlpha: /* @__PURE__ */ __name(function(alpha2) { - clearAlpha = alpha2; - setClear(clearColor, clearAlpha); - }, "setClearAlpha"), - render, - addToRenderList - }; -} -__name(WebGLBackground, "WebGLBackground"); -function WebGLBindingStates(gl, attributes) { - const maxVertexAttributes = gl.getParameter(gl.MAX_VERTEX_ATTRIBS); - const bindingStates = {}; - const defaultState = createBindingState(null); - let currentState = defaultState; - let forceUpdate = false; - function setup(object, material, program, geometry, index) { - let updateBuffers = false; - const state = getBindingState(geometry, program, material); - if (currentState !== state) { - currentState = state; - bindVertexArrayObject(currentState.object); - } - updateBuffers = needsUpdate(object, geometry, program, index); - if (updateBuffers) saveCache(object, geometry, program, index); - if (index !== null) { - attributes.update(index, gl.ELEMENT_ARRAY_BUFFER); - } - if (updateBuffers || forceUpdate) { - forceUpdate = false; - setupVertexAttributes(object, material, program, geometry); - if (index !== null) { - gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, attributes.get(index).buffer); - } - } - } - __name(setup, "setup"); - function createVertexArrayObject() { - return gl.createVertexArray(); - } - __name(createVertexArrayObject, "createVertexArrayObject"); - function bindVertexArrayObject(vao) { - return gl.bindVertexArray(vao); - } - __name(bindVertexArrayObject, "bindVertexArrayObject"); - function deleteVertexArrayObject(vao) { - return gl.deleteVertexArray(vao); - } - __name(deleteVertexArrayObject, "deleteVertexArrayObject"); - function getBindingState(geometry, program, material) { - const wireframe = material.wireframe === true; - let programMap = bindingStates[geometry.id]; - if (programMap === void 0) { - programMap = {}; - bindingStates[geometry.id] = programMap; - } - let stateMap = programMap[program.id]; - if (stateMap === void 0) { - stateMap = {}; - programMap[program.id] = stateMap; - } - let state = stateMap[wireframe]; - if (state === void 0) { - state = createBindingState(createVertexArrayObject()); - stateMap[wireframe] = state; - } - return state; - } - __name(getBindingState, "getBindingState"); - function createBindingState(vao) { - const newAttributes = []; - const enabledAttributes = []; - const attributeDivisors = []; - for (let i = 0; i < maxVertexAttributes; i++) { - newAttributes[i] = 0; - enabledAttributes[i] = 0; - attributeDivisors[i] = 0; - } - return { - // for backward compatibility on non-VAO support browser - geometry: null, - program: null, - wireframe: false, - newAttributes, - enabledAttributes, - attributeDivisors, - object: vao, - attributes: {}, - index: null - }; - } - __name(createBindingState, "createBindingState"); - function needsUpdate(object, geometry, program, index) { - const cachedAttributes = currentState.attributes; - const geometryAttributes = geometry.attributes; - let attributesNum = 0; - const programAttributes = program.getAttributes(); - for (const name in programAttributes) { - const programAttribute = programAttributes[name]; - if (programAttribute.location >= 0) { - const cachedAttribute = cachedAttributes[name]; - let geometryAttribute = geometryAttributes[name]; - if (geometryAttribute === void 0) { - if (name === "instanceMatrix" && object.instanceMatrix) geometryAttribute = object.instanceMatrix; - if (name === "instanceColor" && object.instanceColor) geometryAttribute = object.instanceColor; - } - if (cachedAttribute === void 0) return true; - if (cachedAttribute.attribute !== geometryAttribute) return true; - if (geometryAttribute && cachedAttribute.data !== geometryAttribute.data) return true; - attributesNum++; - } - } - if (currentState.attributesNum !== attributesNum) return true; - if (currentState.index !== index) return true; - return false; - } - __name(needsUpdate, "needsUpdate"); - function saveCache(object, geometry, program, index) { - const cache = {}; - const attributes2 = geometry.attributes; - let attributesNum = 0; - const programAttributes = program.getAttributes(); - for (const name in programAttributes) { - const programAttribute = programAttributes[name]; - if (programAttribute.location >= 0) { - let attribute = attributes2[name]; - if (attribute === void 0) { - if (name === "instanceMatrix" && object.instanceMatrix) attribute = object.instanceMatrix; - if (name === "instanceColor" && object.instanceColor) attribute = object.instanceColor; - } - const data = {}; - data.attribute = attribute; - if (attribute && attribute.data) { - data.data = attribute.data; - } - cache[name] = data; - attributesNum++; - } - } - currentState.attributes = cache; - currentState.attributesNum = attributesNum; - currentState.index = index; - } - __name(saveCache, "saveCache"); - function initAttributes() { - const newAttributes = currentState.newAttributes; - for (let i = 0, il = newAttributes.length; i < il; i++) { - newAttributes[i] = 0; - } - } - __name(initAttributes, "initAttributes"); - function enableAttribute(attribute) { - enableAttributeAndDivisor(attribute, 0); - } - __name(enableAttribute, "enableAttribute"); - function enableAttributeAndDivisor(attribute, meshPerAttribute) { - const newAttributes = currentState.newAttributes; - const enabledAttributes = currentState.enabledAttributes; - const attributeDivisors = currentState.attributeDivisors; - newAttributes[attribute] = 1; - if (enabledAttributes[attribute] === 0) { - gl.enableVertexAttribArray(attribute); - enabledAttributes[attribute] = 1; - } - if (attributeDivisors[attribute] !== meshPerAttribute) { - gl.vertexAttribDivisor(attribute, meshPerAttribute); - attributeDivisors[attribute] = meshPerAttribute; - } - } - __name(enableAttributeAndDivisor, "enableAttributeAndDivisor"); - function disableUnusedAttributes() { - const newAttributes = currentState.newAttributes; - const enabledAttributes = currentState.enabledAttributes; - for (let i = 0, il = enabledAttributes.length; i < il; i++) { - if (enabledAttributes[i] !== newAttributes[i]) { - gl.disableVertexAttribArray(i); - enabledAttributes[i] = 0; - } - } - } - __name(disableUnusedAttributes, "disableUnusedAttributes"); - function vertexAttribPointer(index, size, type, normalized, stride, offset, integer) { - if (integer === true) { - gl.vertexAttribIPointer(index, size, type, stride, offset); - } else { - gl.vertexAttribPointer(index, size, type, normalized, stride, offset); - } - } - __name(vertexAttribPointer, "vertexAttribPointer"); - function setupVertexAttributes(object, material, program, geometry) { - initAttributes(); - const geometryAttributes = geometry.attributes; - const programAttributes = program.getAttributes(); - const materialDefaultAttributeValues = material.defaultAttributeValues; - for (const name in programAttributes) { - const programAttribute = programAttributes[name]; - if (programAttribute.location >= 0) { - let geometryAttribute = geometryAttributes[name]; - if (geometryAttribute === void 0) { - if (name === "instanceMatrix" && object.instanceMatrix) geometryAttribute = object.instanceMatrix; - if (name === "instanceColor" && object.instanceColor) geometryAttribute = object.instanceColor; - } - if (geometryAttribute !== void 0) { - const normalized = geometryAttribute.normalized; - const size = geometryAttribute.itemSize; - const attribute = attributes.get(geometryAttribute); - if (attribute === void 0) continue; - const buffer = attribute.buffer; - const type = attribute.type; - const bytesPerElement = attribute.bytesPerElement; - const integer = type === gl.INT || type === gl.UNSIGNED_INT || geometryAttribute.gpuType === IntType; - if (geometryAttribute.isInterleavedBufferAttribute) { - const data = geometryAttribute.data; - const stride = data.stride; - const offset = geometryAttribute.offset; - if (data.isInstancedInterleavedBuffer) { - for (let i = 0; i < programAttribute.locationSize; i++) { - enableAttributeAndDivisor(programAttribute.location + i, data.meshPerAttribute); - } - if (object.isInstancedMesh !== true && geometry._maxInstanceCount === void 0) { - geometry._maxInstanceCount = data.meshPerAttribute * data.count; - } - } else { - for (let i = 0; i < programAttribute.locationSize; i++) { - enableAttribute(programAttribute.location + i); - } - } - gl.bindBuffer(gl.ARRAY_BUFFER, buffer); - for (let i = 0; i < programAttribute.locationSize; i++) { - vertexAttribPointer( - programAttribute.location + i, - size / programAttribute.locationSize, - type, - normalized, - stride * bytesPerElement, - (offset + size / programAttribute.locationSize * i) * bytesPerElement, - integer - ); - } - } else { - if (geometryAttribute.isInstancedBufferAttribute) { - for (let i = 0; i < programAttribute.locationSize; i++) { - enableAttributeAndDivisor(programAttribute.location + i, geometryAttribute.meshPerAttribute); - } - if (object.isInstancedMesh !== true && geometry._maxInstanceCount === void 0) { - geometry._maxInstanceCount = geometryAttribute.meshPerAttribute * geometryAttribute.count; - } - } else { - for (let i = 0; i < programAttribute.locationSize; i++) { - enableAttribute(programAttribute.location + i); - } - } - gl.bindBuffer(gl.ARRAY_BUFFER, buffer); - for (let i = 0; i < programAttribute.locationSize; i++) { - vertexAttribPointer( - programAttribute.location + i, - size / programAttribute.locationSize, - type, - normalized, - size * bytesPerElement, - size / programAttribute.locationSize * i * bytesPerElement, - integer - ); - } - } - } else if (materialDefaultAttributeValues !== void 0) { - const value = materialDefaultAttributeValues[name]; - if (value !== void 0) { - switch (value.length) { - case 2: - gl.vertexAttrib2fv(programAttribute.location, value); - break; - case 3: - gl.vertexAttrib3fv(programAttribute.location, value); - break; - case 4: - gl.vertexAttrib4fv(programAttribute.location, value); - break; - default: - gl.vertexAttrib1fv(programAttribute.location, value); - } - } - } - } - } - disableUnusedAttributes(); - } - __name(setupVertexAttributes, "setupVertexAttributes"); - function dispose() { - reset(); - for (const geometryId in bindingStates) { - const programMap = bindingStates[geometryId]; - for (const programId in programMap) { - const stateMap = programMap[programId]; - for (const wireframe in stateMap) { - deleteVertexArrayObject(stateMap[wireframe].object); - delete stateMap[wireframe]; - } - delete programMap[programId]; - } - delete bindingStates[geometryId]; - } - } - __name(dispose, "dispose"); - function releaseStatesOfGeometry(geometry) { - if (bindingStates[geometry.id] === void 0) return; - const programMap = bindingStates[geometry.id]; - for (const programId in programMap) { - const stateMap = programMap[programId]; - for (const wireframe in stateMap) { - deleteVertexArrayObject(stateMap[wireframe].object); - delete stateMap[wireframe]; - } - delete programMap[programId]; - } - delete bindingStates[geometry.id]; - } - __name(releaseStatesOfGeometry, "releaseStatesOfGeometry"); - function releaseStatesOfProgram(program) { - for (const geometryId in bindingStates) { - const programMap = bindingStates[geometryId]; - if (programMap[program.id] === void 0) continue; - const stateMap = programMap[program.id]; - for (const wireframe in stateMap) { - deleteVertexArrayObject(stateMap[wireframe].object); - delete stateMap[wireframe]; - } - delete programMap[program.id]; - } - } - __name(releaseStatesOfProgram, "releaseStatesOfProgram"); - function reset() { - resetDefaultState(); - forceUpdate = true; - if (currentState === defaultState) return; - currentState = defaultState; - bindVertexArrayObject(currentState.object); - } - __name(reset, "reset"); - function resetDefaultState() { - defaultState.geometry = null; - defaultState.program = null; - defaultState.wireframe = false; - } - __name(resetDefaultState, "resetDefaultState"); - return { - setup, - reset, - resetDefaultState, - dispose, - releaseStatesOfGeometry, - releaseStatesOfProgram, - initAttributes, - enableAttribute, - disableUnusedAttributes - }; -} -__name(WebGLBindingStates, "WebGLBindingStates"); -function WebGLBufferRenderer(gl, extensions, info) { - let mode; - function setMode(value) { - mode = value; - } - __name(setMode, "setMode"); - function render(start, count) { - gl.drawArrays(mode, start, count); - info.update(count, mode, 1); - } - __name(render, "render"); - function renderInstances(start, count, primcount) { - if (primcount === 0) return; - gl.drawArraysInstanced(mode, start, count, primcount); - info.update(count, mode, primcount); - } - __name(renderInstances, "renderInstances"); - function renderMultiDraw(starts, counts, drawCount) { - if (drawCount === 0) return; - const extension = extensions.get("WEBGL_multi_draw"); - extension.multiDrawArraysWEBGL(mode, starts, 0, counts, 0, drawCount); - let elementCount = 0; - for (let i = 0; i < drawCount; i++) { - elementCount += counts[i]; - } - info.update(elementCount, mode, 1); - } - __name(renderMultiDraw, "renderMultiDraw"); - function renderMultiDrawInstances(starts, counts, drawCount, primcount) { - if (drawCount === 0) return; - const extension = extensions.get("WEBGL_multi_draw"); - if (extension === null) { - for (let i = 0; i < starts.length; i++) { - renderInstances(starts[i], counts[i], primcount[i]); - } - } else { - extension.multiDrawArraysInstancedWEBGL(mode, starts, 0, counts, 0, primcount, 0, drawCount); - let elementCount = 0; - for (let i = 0; i < drawCount; i++) { - elementCount += counts[i] * primcount[i]; - } - info.update(elementCount, mode, 1); - } - } - __name(renderMultiDrawInstances, "renderMultiDrawInstances"); - this.setMode = setMode; - this.render = render; - this.renderInstances = renderInstances; - this.renderMultiDraw = renderMultiDraw; - this.renderMultiDrawInstances = renderMultiDrawInstances; -} -__name(WebGLBufferRenderer, "WebGLBufferRenderer"); -function WebGLCapabilities(gl, extensions, parameters, utils) { - let maxAnisotropy; - function getMaxAnisotropy() { - if (maxAnisotropy !== void 0) return maxAnisotropy; - if (extensions.has("EXT_texture_filter_anisotropic") === true) { - const extension = extensions.get("EXT_texture_filter_anisotropic"); - maxAnisotropy = gl.getParameter(extension.MAX_TEXTURE_MAX_ANISOTROPY_EXT); - } else { - maxAnisotropy = 0; - } - return maxAnisotropy; - } - __name(getMaxAnisotropy, "getMaxAnisotropy"); - function textureFormatReadable(textureFormat) { - if (textureFormat !== RGBAFormat && utils.convert(textureFormat) !== gl.getParameter(gl.IMPLEMENTATION_COLOR_READ_FORMAT)) { - return false; - } - return true; - } - __name(textureFormatReadable, "textureFormatReadable"); - function textureTypeReadable(textureType) { - const halfFloatSupportedByExt = textureType === HalfFloatType && (extensions.has("EXT_color_buffer_half_float") || extensions.has("EXT_color_buffer_float")); - if (textureType !== UnsignedByteType && utils.convert(textureType) !== gl.getParameter(gl.IMPLEMENTATION_COLOR_READ_TYPE) && // Edge and Chrome Mac < 52 (#9513) - textureType !== FloatType && !halfFloatSupportedByExt) { - return false; - } - return true; - } - __name(textureTypeReadable, "textureTypeReadable"); - function getMaxPrecision(precision2) { - if (precision2 === "highp") { - if (gl.getShaderPrecisionFormat(gl.VERTEX_SHADER, gl.HIGH_FLOAT).precision > 0 && gl.getShaderPrecisionFormat(gl.FRAGMENT_SHADER, gl.HIGH_FLOAT).precision > 0) { - return "highp"; - } - precision2 = "mediump"; - } - if (precision2 === "mediump") { - if (gl.getShaderPrecisionFormat(gl.VERTEX_SHADER, gl.MEDIUM_FLOAT).precision > 0 && gl.getShaderPrecisionFormat(gl.FRAGMENT_SHADER, gl.MEDIUM_FLOAT).precision > 0) { - return "mediump"; - } - } - return "lowp"; - } - __name(getMaxPrecision, "getMaxPrecision"); - let precision = parameters.precision !== void 0 ? parameters.precision : "highp"; - const maxPrecision = getMaxPrecision(precision); - if (maxPrecision !== precision) { - console.warn("THREE.WebGLRenderer:", precision, "not supported, using", maxPrecision, "instead."); - precision = maxPrecision; - } - const logarithmicDepthBuffer = parameters.logarithmicDepthBuffer === true; - const reverseDepthBuffer = parameters.reverseDepthBuffer === true && extensions.has("EXT_clip_control"); - const maxTextures = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS); - const maxVertexTextures = gl.getParameter(gl.MAX_VERTEX_TEXTURE_IMAGE_UNITS); - const maxTextureSize = gl.getParameter(gl.MAX_TEXTURE_SIZE); - const maxCubemapSize = gl.getParameter(gl.MAX_CUBE_MAP_TEXTURE_SIZE); - const maxAttributes = gl.getParameter(gl.MAX_VERTEX_ATTRIBS); - const maxVertexUniforms = gl.getParameter(gl.MAX_VERTEX_UNIFORM_VECTORS); - const maxVaryings = gl.getParameter(gl.MAX_VARYING_VECTORS); - const maxFragmentUniforms = gl.getParameter(gl.MAX_FRAGMENT_UNIFORM_VECTORS); - const vertexTextures = maxVertexTextures > 0; - const maxSamples = gl.getParameter(gl.MAX_SAMPLES); - return { - isWebGL2: true, - // keeping this for backwards compatibility - getMaxAnisotropy, - getMaxPrecision, - textureFormatReadable, - textureTypeReadable, - precision, - logarithmicDepthBuffer, - reverseDepthBuffer, - maxTextures, - maxVertexTextures, - maxTextureSize, - maxCubemapSize, - maxAttributes, - maxVertexUniforms, - maxVaryings, - maxFragmentUniforms, - vertexTextures, - maxSamples - }; -} -__name(WebGLCapabilities, "WebGLCapabilities"); -function WebGLClipping(properties) { - const scope = this; - let globalState = null, numGlobalPlanes = 0, localClippingEnabled = false, renderingShadows = false; - const plane = new Plane(), viewNormalMatrix = new Matrix3(), uniform = { value: null, needsUpdate: false }; - this.uniform = uniform; - this.numPlanes = 0; - this.numIntersection = 0; - this.init = function(planes, enableLocalClipping) { - const enabled = planes.length !== 0 || enableLocalClipping || // enable state of previous frame - the clipping code has to - // run another frame in order to reset the state: - numGlobalPlanes !== 0 || localClippingEnabled; - localClippingEnabled = enableLocalClipping; - numGlobalPlanes = planes.length; - return enabled; - }; - this.beginShadows = function() { - renderingShadows = true; - projectPlanes(null); - }; - this.endShadows = function() { - renderingShadows = false; - }; - this.setGlobalState = function(planes, camera) { - globalState = projectPlanes(planes, camera, 0); - }; - this.setState = function(material, camera, useCache) { - const planes = material.clippingPlanes, clipIntersection = material.clipIntersection, clipShadows = material.clipShadows; - const materialProperties = properties.get(material); - if (!localClippingEnabled || planes === null || planes.length === 0 || renderingShadows && !clipShadows) { - if (renderingShadows) { - projectPlanes(null); - } else { - resetGlobalState(); - } - } else { - const nGlobal = renderingShadows ? 0 : numGlobalPlanes, lGlobal = nGlobal * 4; - let dstArray = materialProperties.clippingState || null; - uniform.value = dstArray; - dstArray = projectPlanes(planes, camera, lGlobal, useCache); - for (let i = 0; i !== lGlobal; ++i) { - dstArray[i] = globalState[i]; - } - materialProperties.clippingState = dstArray; - this.numIntersection = clipIntersection ? this.numPlanes : 0; - this.numPlanes += nGlobal; - } - }; - function resetGlobalState() { - if (uniform.value !== globalState) { - uniform.value = globalState; - uniform.needsUpdate = numGlobalPlanes > 0; - } - scope.numPlanes = numGlobalPlanes; - scope.numIntersection = 0; - } - __name(resetGlobalState, "resetGlobalState"); - function projectPlanes(planes, camera, dstOffset, skipTransform) { - const nPlanes = planes !== null ? planes.length : 0; - let dstArray = null; - if (nPlanes !== 0) { - dstArray = uniform.value; - if (skipTransform !== true || dstArray === null) { - const flatSize = dstOffset + nPlanes * 4, viewMatrix = camera.matrixWorldInverse; - viewNormalMatrix.getNormalMatrix(viewMatrix); - if (dstArray === null || dstArray.length < flatSize) { - dstArray = new Float32Array(flatSize); - } - for (let i = 0, i4 = dstOffset; i !== nPlanes; ++i, i4 += 4) { - plane.copy(planes[i]).applyMatrix4(viewMatrix, viewNormalMatrix); - plane.normal.toArray(dstArray, i4); - dstArray[i4 + 3] = plane.constant; - } - } - uniform.value = dstArray; - uniform.needsUpdate = true; - } - scope.numPlanes = nPlanes; - scope.numIntersection = 0; - return dstArray; - } - __name(projectPlanes, "projectPlanes"); -} -__name(WebGLClipping, "WebGLClipping"); -function WebGLCubeMaps(renderer) { - let cubemaps = /* @__PURE__ */ new WeakMap(); - function mapTextureMapping(texture, mapping) { - if (mapping === EquirectangularReflectionMapping) { - texture.mapping = CubeReflectionMapping; - } else if (mapping === EquirectangularRefractionMapping) { - texture.mapping = CubeRefractionMapping; - } - return texture; - } - __name(mapTextureMapping, "mapTextureMapping"); - function get(texture) { - if (texture && texture.isTexture) { - const mapping = texture.mapping; - if (mapping === EquirectangularReflectionMapping || mapping === EquirectangularRefractionMapping) { - if (cubemaps.has(texture)) { - const cubemap = cubemaps.get(texture).texture; - return mapTextureMapping(cubemap, texture.mapping); - } else { - const image = texture.image; - if (image && image.height > 0) { - const renderTarget = new WebGLCubeRenderTarget(image.height); - renderTarget.fromEquirectangularTexture(renderer, texture); - cubemaps.set(texture, renderTarget); - texture.addEventListener("dispose", onTextureDispose); - return mapTextureMapping(renderTarget.texture, texture.mapping); - } else { - return null; - } - } - } - } - return texture; - } - __name(get, "get"); - function onTextureDispose(event) { - const texture = event.target; - texture.removeEventListener("dispose", onTextureDispose); - const cubemap = cubemaps.get(texture); - if (cubemap !== void 0) { - cubemaps.delete(texture); - cubemap.dispose(); - } - } - __name(onTextureDispose, "onTextureDispose"); - function dispose() { - cubemaps = /* @__PURE__ */ new WeakMap(); - } - __name(dispose, "dispose"); - return { - get, - dispose - }; -} -__name(WebGLCubeMaps, "WebGLCubeMaps"); -class OrthographicCamera extends Camera { - static { - __name(this, "OrthographicCamera"); - } - constructor(left = -1, right = 1, top = 1, bottom = -1, near = 0.1, far = 2e3) { - super(); - this.isOrthographicCamera = true; - this.type = "OrthographicCamera"; - this.zoom = 1; - this.view = null; - this.left = left; - this.right = right; - this.top = top; - this.bottom = bottom; - this.near = near; - this.far = far; - this.updateProjectionMatrix(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.left = source.left; - this.right = source.right; - this.top = source.top; - this.bottom = source.bottom; - this.near = source.near; - this.far = source.far; - this.zoom = source.zoom; - this.view = source.view === null ? null : Object.assign({}, source.view); - return this; - } - setViewOffset(fullWidth, fullHeight, x, y, width, height) { - if (this.view === null) { - this.view = { - enabled: true, - fullWidth: 1, - fullHeight: 1, - offsetX: 0, - offsetY: 0, - width: 1, - height: 1 - }; - } - this.view.enabled = true; - this.view.fullWidth = fullWidth; - this.view.fullHeight = fullHeight; - this.view.offsetX = x; - this.view.offsetY = y; - this.view.width = width; - this.view.height = height; - this.updateProjectionMatrix(); - } - clearViewOffset() { - if (this.view !== null) { - this.view.enabled = false; - } - this.updateProjectionMatrix(); - } - updateProjectionMatrix() { - const dx = (this.right - this.left) / (2 * this.zoom); - const dy = (this.top - this.bottom) / (2 * this.zoom); - const cx = (this.right + this.left) / 2; - const cy = (this.top + this.bottom) / 2; - let left = cx - dx; - let right = cx + dx; - let top = cy + dy; - let bottom = cy - dy; - if (this.view !== null && this.view.enabled) { - const scaleW = (this.right - this.left) / this.view.fullWidth / this.zoom; - const scaleH = (this.top - this.bottom) / this.view.fullHeight / this.zoom; - left += scaleW * this.view.offsetX; - right = left + scaleW * this.view.width; - top -= scaleH * this.view.offsetY; - bottom = top - scaleH * this.view.height; - } - this.projectionMatrix.makeOrthographic(left, right, top, bottom, this.near, this.far, this.coordinateSystem); - this.projectionMatrixInverse.copy(this.projectionMatrix).invert(); - } - toJSON(meta) { - const data = super.toJSON(meta); - data.object.zoom = this.zoom; - data.object.left = this.left; - data.object.right = this.right; - data.object.top = this.top; - data.object.bottom = this.bottom; - data.object.near = this.near; - data.object.far = this.far; - if (this.view !== null) data.object.view = Object.assign({}, this.view); - return data; - } -} -const LOD_MIN = 4; -const EXTRA_LOD_SIGMA = [0.125, 0.215, 0.35, 0.446, 0.526, 0.582]; -const MAX_SAMPLES = 20; -const _flatCamera = /* @__PURE__ */ new OrthographicCamera(); -const _clearColor = /* @__PURE__ */ new Color(); -let _oldTarget = null; -let _oldActiveCubeFace = 0; -let _oldActiveMipmapLevel = 0; -let _oldXrEnabled = false; -const PHI = (1 + Math.sqrt(5)) / 2; -const INV_PHI = 1 / PHI; -const _axisDirections = [ - /* @__PURE__ */ new Vector3(-PHI, INV_PHI, 0), - /* @__PURE__ */ new Vector3(PHI, INV_PHI, 0), - /* @__PURE__ */ new Vector3(-INV_PHI, 0, PHI), - /* @__PURE__ */ new Vector3(INV_PHI, 0, PHI), - /* @__PURE__ */ new Vector3(0, PHI, -INV_PHI), - /* @__PURE__ */ new Vector3(0, PHI, INV_PHI), - /* @__PURE__ */ new Vector3(-1, 1, -1), - /* @__PURE__ */ new Vector3(1, 1, -1), - /* @__PURE__ */ new Vector3(-1, 1, 1), - /* @__PURE__ */ new Vector3(1, 1, 1) -]; -class PMREMGenerator { - static { - __name(this, "PMREMGenerator"); - } - constructor(renderer) { - this._renderer = renderer; - this._pingPongRenderTarget = null; - this._lodMax = 0; - this._cubeSize = 0; - this._lodPlanes = []; - this._sizeLods = []; - this._sigmas = []; - this._blurMaterial = null; - this._cubemapMaterial = null; - this._equirectMaterial = null; - this._compileMaterial(this._blurMaterial); - } - /** - * Generates a PMREM from a supplied Scene, which can be faster than using an - * image if networking bandwidth is low. Optional sigma specifies a blur radius - * in radians to be applied to the scene before PMREM generation. Optional near - * and far planes ensure the scene is rendered in its entirety (the cubeCamera - * is placed at the origin). - */ - fromScene(scene, sigma = 0, near = 0.1, far = 100) { - _oldTarget = this._renderer.getRenderTarget(); - _oldActiveCubeFace = this._renderer.getActiveCubeFace(); - _oldActiveMipmapLevel = this._renderer.getActiveMipmapLevel(); - _oldXrEnabled = this._renderer.xr.enabled; - this._renderer.xr.enabled = false; - this._setSize(256); - const cubeUVRenderTarget = this._allocateTargets(); - cubeUVRenderTarget.depthBuffer = true; - this._sceneToCubeUV(scene, near, far, cubeUVRenderTarget); - if (sigma > 0) { - this._blur(cubeUVRenderTarget, 0, 0, sigma); - } - this._applyPMREM(cubeUVRenderTarget); - this._cleanup(cubeUVRenderTarget); - return cubeUVRenderTarget; - } - /** - * Generates a PMREM from an equirectangular texture, which can be either LDR - * or HDR. The ideal input image size is 1k (1024 x 512), - * as this matches best with the 256 x 256 cubemap output. - * The smallest supported equirectangular image size is 64 x 32. - */ - fromEquirectangular(equirectangular, renderTarget = null) { - return this._fromTexture(equirectangular, renderTarget); - } - /** - * Generates a PMREM from an cubemap texture, which can be either LDR - * or HDR. The ideal input cube size is 256 x 256, - * as this matches best with the 256 x 256 cubemap output. - * The smallest supported cube size is 16 x 16. - */ - fromCubemap(cubemap, renderTarget = null) { - return this._fromTexture(cubemap, renderTarget); - } - /** - * Pre-compiles the cubemap shader. You can get faster start-up by invoking this method during - * your texture's network fetch for increased concurrency. - */ - compileCubemapShader() { - if (this._cubemapMaterial === null) { - this._cubemapMaterial = _getCubemapMaterial(); - this._compileMaterial(this._cubemapMaterial); - } - } - /** - * Pre-compiles the equirectangular shader. You can get faster start-up by invoking this method during - * your texture's network fetch for increased concurrency. - */ - compileEquirectangularShader() { - if (this._equirectMaterial === null) { - this._equirectMaterial = _getEquirectMaterial(); - this._compileMaterial(this._equirectMaterial); - } - } - /** - * Disposes of the PMREMGenerator's internal memory. Note that PMREMGenerator is a static class, - * so you should not need more than one PMREMGenerator object. If you do, calling dispose() on - * one of them will cause any others to also become unusable. - */ - dispose() { - this._dispose(); - if (this._cubemapMaterial !== null) this._cubemapMaterial.dispose(); - if (this._equirectMaterial !== null) this._equirectMaterial.dispose(); - } - // private interface - _setSize(cubeSize) { - this._lodMax = Math.floor(Math.log2(cubeSize)); - this._cubeSize = Math.pow(2, this._lodMax); - } - _dispose() { - if (this._blurMaterial !== null) this._blurMaterial.dispose(); - if (this._pingPongRenderTarget !== null) this._pingPongRenderTarget.dispose(); - for (let i = 0; i < this._lodPlanes.length; i++) { - this._lodPlanes[i].dispose(); - } - } - _cleanup(outputTarget) { - this._renderer.setRenderTarget(_oldTarget, _oldActiveCubeFace, _oldActiveMipmapLevel); - this._renderer.xr.enabled = _oldXrEnabled; - outputTarget.scissorTest = false; - _setViewport(outputTarget, 0, 0, outputTarget.width, outputTarget.height); - } - _fromTexture(texture, renderTarget) { - if (texture.mapping === CubeReflectionMapping || texture.mapping === CubeRefractionMapping) { - this._setSize(texture.image.length === 0 ? 16 : texture.image[0].width || texture.image[0].image.width); - } else { - this._setSize(texture.image.width / 4); - } - _oldTarget = this._renderer.getRenderTarget(); - _oldActiveCubeFace = this._renderer.getActiveCubeFace(); - _oldActiveMipmapLevel = this._renderer.getActiveMipmapLevel(); - _oldXrEnabled = this._renderer.xr.enabled; - this._renderer.xr.enabled = false; - const cubeUVRenderTarget = renderTarget || this._allocateTargets(); - this._textureToCubeUV(texture, cubeUVRenderTarget); - this._applyPMREM(cubeUVRenderTarget); - this._cleanup(cubeUVRenderTarget); - return cubeUVRenderTarget; - } - _allocateTargets() { - const width = 3 * Math.max(this._cubeSize, 16 * 7); - const height = 4 * this._cubeSize; - const params = { - magFilter: LinearFilter, - minFilter: LinearFilter, - generateMipmaps: false, - type: HalfFloatType, - format: RGBAFormat, - colorSpace: LinearSRGBColorSpace, - depthBuffer: false - }; - const cubeUVRenderTarget = _createRenderTarget(width, height, params); - if (this._pingPongRenderTarget === null || this._pingPongRenderTarget.width !== width || this._pingPongRenderTarget.height !== height) { - if (this._pingPongRenderTarget !== null) { - this._dispose(); - } - this._pingPongRenderTarget = _createRenderTarget(width, height, params); - const { _lodMax } = this; - ({ sizeLods: this._sizeLods, lodPlanes: this._lodPlanes, sigmas: this._sigmas } = _createPlanes(_lodMax)); - this._blurMaterial = _getBlurShader(_lodMax, width, height); - } - return cubeUVRenderTarget; - } - _compileMaterial(material) { - const tmpMesh = new Mesh(this._lodPlanes[0], material); - this._renderer.compile(tmpMesh, _flatCamera); - } - _sceneToCubeUV(scene, near, far, cubeUVRenderTarget) { - const fov2 = 90; - const aspect2 = 1; - const cubeCamera = new PerspectiveCamera(fov2, aspect2, near, far); - const upSign = [1, -1, 1, 1, 1, 1]; - const forwardSign = [1, 1, 1, -1, -1, -1]; - const renderer = this._renderer; - const originalAutoClear = renderer.autoClear; - const toneMapping = renderer.toneMapping; - renderer.getClearColor(_clearColor); - renderer.toneMapping = NoToneMapping; - renderer.autoClear = false; - const backgroundMaterial = new MeshBasicMaterial({ - name: "PMREM.Background", - side: BackSide, - depthWrite: false, - depthTest: false - }); - const backgroundBox = new Mesh(new BoxGeometry(), backgroundMaterial); - let useSolidColor = false; - const background = scene.background; - if (background) { - if (background.isColor) { - backgroundMaterial.color.copy(background); - scene.background = null; - useSolidColor = true; - } - } else { - backgroundMaterial.color.copy(_clearColor); - useSolidColor = true; - } - for (let i = 0; i < 6; i++) { - const col = i % 3; - if (col === 0) { - cubeCamera.up.set(0, upSign[i], 0); - cubeCamera.lookAt(forwardSign[i], 0, 0); - } else if (col === 1) { - cubeCamera.up.set(0, 0, upSign[i]); - cubeCamera.lookAt(0, forwardSign[i], 0); - } else { - cubeCamera.up.set(0, upSign[i], 0); - cubeCamera.lookAt(0, 0, forwardSign[i]); - } - const size = this._cubeSize; - _setViewport(cubeUVRenderTarget, col * size, i > 2 ? size : 0, size, size); - renderer.setRenderTarget(cubeUVRenderTarget); - if (useSolidColor) { - renderer.render(backgroundBox, cubeCamera); - } - renderer.render(scene, cubeCamera); - } - backgroundBox.geometry.dispose(); - backgroundBox.material.dispose(); - renderer.toneMapping = toneMapping; - renderer.autoClear = originalAutoClear; - scene.background = background; - } - _textureToCubeUV(texture, cubeUVRenderTarget) { - const renderer = this._renderer; - const isCubeTexture = texture.mapping === CubeReflectionMapping || texture.mapping === CubeRefractionMapping; - if (isCubeTexture) { - if (this._cubemapMaterial === null) { - this._cubemapMaterial = _getCubemapMaterial(); - } - this._cubemapMaterial.uniforms.flipEnvMap.value = texture.isRenderTargetTexture === false ? -1 : 1; - } else { - if (this._equirectMaterial === null) { - this._equirectMaterial = _getEquirectMaterial(); - } - } - const material = isCubeTexture ? this._cubemapMaterial : this._equirectMaterial; - const mesh = new Mesh(this._lodPlanes[0], material); - const uniforms = material.uniforms; - uniforms["envMap"].value = texture; - const size = this._cubeSize; - _setViewport(cubeUVRenderTarget, 0, 0, 3 * size, 2 * size); - renderer.setRenderTarget(cubeUVRenderTarget); - renderer.render(mesh, _flatCamera); - } - _applyPMREM(cubeUVRenderTarget) { - const renderer = this._renderer; - const autoClear = renderer.autoClear; - renderer.autoClear = false; - const n = this._lodPlanes.length; - for (let i = 1; i < n; i++) { - const sigma = Math.sqrt(this._sigmas[i] * this._sigmas[i] - this._sigmas[i - 1] * this._sigmas[i - 1]); - const poleAxis = _axisDirections[(n - i - 1) % _axisDirections.length]; - this._blur(cubeUVRenderTarget, i - 1, i, sigma, poleAxis); - } - renderer.autoClear = autoClear; - } - /** - * This is a two-pass Gaussian blur for a cubemap. Normally this is done - * vertically and horizontally, but this breaks down on a cube. Here we apply - * the blur latitudinally (around the poles), and then longitudinally (towards - * the poles) to approximate the orthogonally-separable blur. It is least - * accurate at the poles, but still does a decent job. - */ - _blur(cubeUVRenderTarget, lodIn, lodOut, sigma, poleAxis) { - const pingPongRenderTarget = this._pingPongRenderTarget; - this._halfBlur( - cubeUVRenderTarget, - pingPongRenderTarget, - lodIn, - lodOut, - sigma, - "latitudinal", - poleAxis - ); - this._halfBlur( - pingPongRenderTarget, - cubeUVRenderTarget, - lodOut, - lodOut, - sigma, - "longitudinal", - poleAxis - ); - } - _halfBlur(targetIn, targetOut, lodIn, lodOut, sigmaRadians, direction, poleAxis) { - const renderer = this._renderer; - const blurMaterial = this._blurMaterial; - if (direction !== "latitudinal" && direction !== "longitudinal") { - console.error( - "blur direction must be either latitudinal or longitudinal!" - ); - } - const STANDARD_DEVIATIONS = 3; - const blurMesh = new Mesh(this._lodPlanes[lodOut], blurMaterial); - const blurUniforms = blurMaterial.uniforms; - const pixels = this._sizeLods[lodIn] - 1; - const radiansPerPixel = isFinite(sigmaRadians) ? Math.PI / (2 * pixels) : 2 * Math.PI / (2 * MAX_SAMPLES - 1); - const sigmaPixels = sigmaRadians / radiansPerPixel; - const samples = isFinite(sigmaRadians) ? 1 + Math.floor(STANDARD_DEVIATIONS * sigmaPixels) : MAX_SAMPLES; - if (samples > MAX_SAMPLES) { - console.warn(`sigmaRadians, ${sigmaRadians}, is too large and will clip, as it requested ${samples} samples when the maximum is set to ${MAX_SAMPLES}`); - } - const weights = []; - let sum = 0; - for (let i = 0; i < MAX_SAMPLES; ++i) { - const x2 = i / sigmaPixels; - const weight = Math.exp(-x2 * x2 / 2); - weights.push(weight); - if (i === 0) { - sum += weight; - } else if (i < samples) { - sum += 2 * weight; - } - } - for (let i = 0; i < weights.length; i++) { - weights[i] = weights[i] / sum; - } - blurUniforms["envMap"].value = targetIn.texture; - blurUniforms["samples"].value = samples; - blurUniforms["weights"].value = weights; - blurUniforms["latitudinal"].value = direction === "latitudinal"; - if (poleAxis) { - blurUniforms["poleAxis"].value = poleAxis; - } - const { _lodMax } = this; - blurUniforms["dTheta"].value = radiansPerPixel; - blurUniforms["mipInt"].value = _lodMax - lodIn; - const outputSize = this._sizeLods[lodOut]; - const x = 3 * outputSize * (lodOut > _lodMax - LOD_MIN ? lodOut - _lodMax + LOD_MIN : 0); - const y = 4 * (this._cubeSize - outputSize); - _setViewport(targetOut, x, y, 3 * outputSize, 2 * outputSize); - renderer.setRenderTarget(targetOut); - renderer.render(blurMesh, _flatCamera); - } -} -function _createPlanes(lodMax) { - const lodPlanes = []; - const sizeLods = []; - const sigmas = []; - let lod = lodMax; - const totalLods = lodMax - LOD_MIN + 1 + EXTRA_LOD_SIGMA.length; - for (let i = 0; i < totalLods; i++) { - const sizeLod = Math.pow(2, lod); - sizeLods.push(sizeLod); - let sigma = 1 / sizeLod; - if (i > lodMax - LOD_MIN) { - sigma = EXTRA_LOD_SIGMA[i - lodMax + LOD_MIN - 1]; - } else if (i === 0) { - sigma = 0; - } - sigmas.push(sigma); - const texelSize = 1 / (sizeLod - 2); - const min = -texelSize; - const max2 = 1 + texelSize; - const uv1 = [min, min, max2, min, max2, max2, min, min, max2, max2, min, max2]; - const cubeFaces = 6; - const vertices = 6; - const positionSize = 3; - const uvSize = 2; - const faceIndexSize = 1; - const position = new Float32Array(positionSize * vertices * cubeFaces); - const uv = new Float32Array(uvSize * vertices * cubeFaces); - const faceIndex = new Float32Array(faceIndexSize * vertices * cubeFaces); - for (let face = 0; face < cubeFaces; face++) { - const x = face % 3 * 2 / 3 - 1; - const y = face > 2 ? 0 : -1; - const coordinates = [ - x, - y, - 0, - x + 2 / 3, - y, - 0, - x + 2 / 3, - y + 1, - 0, - x, - y, - 0, - x + 2 / 3, - y + 1, - 0, - x, - y + 1, - 0 - ]; - position.set(coordinates, positionSize * vertices * face); - uv.set(uv1, uvSize * vertices * face); - const fill2 = [face, face, face, face, face, face]; - faceIndex.set(fill2, faceIndexSize * vertices * face); - } - const planes = new BufferGeometry(); - planes.setAttribute("position", new BufferAttribute(position, positionSize)); - planes.setAttribute("uv", new BufferAttribute(uv, uvSize)); - planes.setAttribute("faceIndex", new BufferAttribute(faceIndex, faceIndexSize)); - lodPlanes.push(planes); - if (lod > LOD_MIN) { - lod--; - } - } - return { lodPlanes, sizeLods, sigmas }; -} -__name(_createPlanes, "_createPlanes"); -function _createRenderTarget(width, height, params) { - const cubeUVRenderTarget = new WebGLRenderTarget(width, height, params); - cubeUVRenderTarget.texture.mapping = CubeUVReflectionMapping; - cubeUVRenderTarget.texture.name = "PMREM.cubeUv"; - cubeUVRenderTarget.scissorTest = true; - return cubeUVRenderTarget; -} -__name(_createRenderTarget, "_createRenderTarget"); -function _setViewport(target, x, y, width, height) { - target.viewport.set(x, y, width, height); - target.scissor.set(x, y, width, height); -} -__name(_setViewport, "_setViewport"); -function _getBlurShader(lodMax, width, height) { - const weights = new Float32Array(MAX_SAMPLES); - const poleAxis = new Vector3(0, 1, 0); - const shaderMaterial = new ShaderMaterial({ - name: "SphericalGaussianBlur", - defines: { - "n": MAX_SAMPLES, - "CUBEUV_TEXEL_WIDTH": 1 / width, - "CUBEUV_TEXEL_HEIGHT": 1 / height, - "CUBEUV_MAX_MIP": `${lodMax}.0` - }, - uniforms: { - "envMap": { value: null }, - "samples": { value: 1 }, - "weights": { value: weights }, - "latitudinal": { value: false }, - "dTheta": { value: 0 }, - "mipInt": { value: 0 }, - "poleAxis": { value: poleAxis } - }, - vertexShader: _getCommonVertexShader(), - fragmentShader: ( - /* glsl */ - ` - - precision mediump float; - precision mediump int; - - varying vec3 vOutputDirection; - - uniform sampler2D envMap; - uniform int samples; - uniform float weights[ n ]; - uniform bool latitudinal; - uniform float dTheta; - uniform float mipInt; - uniform vec3 poleAxis; - - #define ENVMAP_TYPE_CUBE_UV - #include - - vec3 getSample( float theta, vec3 axis ) { - - float cosTheta = cos( theta ); - // Rodrigues' axis-angle rotation - vec3 sampleDirection = vOutputDirection * cosTheta - + cross( axis, vOutputDirection ) * sin( theta ) - + axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta ); - - return bilinearCubeUV( envMap, sampleDirection, mipInt ); - - } - - void main() { - - vec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection ); - - if ( all( equal( axis, vec3( 0.0 ) ) ) ) { - - axis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x ); - - } - - axis = normalize( axis ); - - gl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 ); - gl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis ); - - for ( int i = 1; i < n; i++ ) { - - if ( i >= samples ) { - - break; - - } - - float theta = dTheta * float( i ); - gl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis ); - gl_FragColor.rgb += weights[ i ] * getSample( theta, axis ); - - } - - } - ` - ), - blending: NoBlending, - depthTest: false, - depthWrite: false - }); - return shaderMaterial; -} -__name(_getBlurShader, "_getBlurShader"); -function _getEquirectMaterial() { - return new ShaderMaterial({ - name: "EquirectangularToCubeUV", - uniforms: { - "envMap": { value: null } - }, - vertexShader: _getCommonVertexShader(), - fragmentShader: ( - /* glsl */ - ` - - precision mediump float; - precision mediump int; - - varying vec3 vOutputDirection; - - uniform sampler2D envMap; - - #include - - void main() { - - vec3 outputDirection = normalize( vOutputDirection ); - vec2 uv = equirectUv( outputDirection ); - - gl_FragColor = vec4( texture2D ( envMap, uv ).rgb, 1.0 ); - - } - ` - ), - blending: NoBlending, - depthTest: false, - depthWrite: false - }); -} -__name(_getEquirectMaterial, "_getEquirectMaterial"); -function _getCubemapMaterial() { - return new ShaderMaterial({ - name: "CubemapToCubeUV", - uniforms: { - "envMap": { value: null }, - "flipEnvMap": { value: -1 } - }, - vertexShader: _getCommonVertexShader(), - fragmentShader: ( - /* glsl */ - ` - - precision mediump float; - precision mediump int; - - uniform float flipEnvMap; - - varying vec3 vOutputDirection; - - uniform samplerCube envMap; - - void main() { - - gl_FragColor = textureCube( envMap, vec3( flipEnvMap * vOutputDirection.x, vOutputDirection.yz ) ); - - } - ` - ), - blending: NoBlending, - depthTest: false, - depthWrite: false - }); -} -__name(_getCubemapMaterial, "_getCubemapMaterial"); -function _getCommonVertexShader() { - return ( - /* glsl */ - ` - - precision mediump float; - precision mediump int; - - attribute float faceIndex; - - varying vec3 vOutputDirection; - - // RH coordinate system; PMREM face-indexing convention - vec3 getDirection( vec2 uv, float face ) { - - uv = 2.0 * uv - 1.0; - - vec3 direction = vec3( uv, 1.0 ); - - if ( face == 0.0 ) { - - direction = direction.zyx; // ( 1, v, u ) pos x - - } else if ( face == 1.0 ) { - - direction = direction.xzy; - direction.xz *= -1.0; // ( -u, 1, -v ) pos y - - } else if ( face == 2.0 ) { - - direction.x *= -1.0; // ( -u, v, 1 ) pos z - - } else if ( face == 3.0 ) { - - direction = direction.zyx; - direction.xz *= -1.0; // ( -1, v, -u ) neg x - - } else if ( face == 4.0 ) { - - direction = direction.xzy; - direction.xy *= -1.0; // ( -u, -1, v ) neg y - - } else if ( face == 5.0 ) { - - direction.z *= -1.0; // ( u, v, -1 ) neg z - - } - - return direction; - - } - - void main() { - - vOutputDirection = getDirection( uv, faceIndex ); - gl_Position = vec4( position, 1.0 ); - - } - ` - ); -} -__name(_getCommonVertexShader, "_getCommonVertexShader"); -function WebGLCubeUVMaps(renderer) { - let cubeUVmaps = /* @__PURE__ */ new WeakMap(); - let pmremGenerator = null; - function get(texture) { - if (texture && texture.isTexture) { - const mapping = texture.mapping; - const isEquirectMap = mapping === EquirectangularReflectionMapping || mapping === EquirectangularRefractionMapping; - const isCubeMap = mapping === CubeReflectionMapping || mapping === CubeRefractionMapping; - if (isEquirectMap || isCubeMap) { - let renderTarget = cubeUVmaps.get(texture); - const currentPMREMVersion = renderTarget !== void 0 ? renderTarget.texture.pmremVersion : 0; - if (texture.isRenderTargetTexture && texture.pmremVersion !== currentPMREMVersion) { - if (pmremGenerator === null) pmremGenerator = new PMREMGenerator(renderer); - renderTarget = isEquirectMap ? pmremGenerator.fromEquirectangular(texture, renderTarget) : pmremGenerator.fromCubemap(texture, renderTarget); - renderTarget.texture.pmremVersion = texture.pmremVersion; - cubeUVmaps.set(texture, renderTarget); - return renderTarget.texture; - } else { - if (renderTarget !== void 0) { - return renderTarget.texture; - } else { - const image = texture.image; - if (isEquirectMap && image && image.height > 0 || isCubeMap && image && isCubeTextureComplete(image)) { - if (pmremGenerator === null) pmremGenerator = new PMREMGenerator(renderer); - renderTarget = isEquirectMap ? pmremGenerator.fromEquirectangular(texture) : pmremGenerator.fromCubemap(texture); - renderTarget.texture.pmremVersion = texture.pmremVersion; - cubeUVmaps.set(texture, renderTarget); - texture.addEventListener("dispose", onTextureDispose); - return renderTarget.texture; - } else { - return null; - } - } - } - } - } - return texture; - } - __name(get, "get"); - function isCubeTextureComplete(image) { - let count = 0; - const length = 6; - for (let i = 0; i < length; i++) { - if (image[i] !== void 0) count++; - } - return count === length; - } - __name(isCubeTextureComplete, "isCubeTextureComplete"); - function onTextureDispose(event) { - const texture = event.target; - texture.removeEventListener("dispose", onTextureDispose); - const cubemapUV = cubeUVmaps.get(texture); - if (cubemapUV !== void 0) { - cubeUVmaps.delete(texture); - cubemapUV.dispose(); - } - } - __name(onTextureDispose, "onTextureDispose"); - function dispose() { - cubeUVmaps = /* @__PURE__ */ new WeakMap(); - if (pmremGenerator !== null) { - pmremGenerator.dispose(); - pmremGenerator = null; - } - } - __name(dispose, "dispose"); - return { - get, - dispose - }; -} -__name(WebGLCubeUVMaps, "WebGLCubeUVMaps"); -function WebGLExtensions(gl) { - const extensions = {}; - function getExtension(name) { - if (extensions[name] !== void 0) { - return extensions[name]; - } - let extension; - switch (name) { - case "WEBGL_depth_texture": - extension = gl.getExtension("WEBGL_depth_texture") || gl.getExtension("MOZ_WEBGL_depth_texture") || gl.getExtension("WEBKIT_WEBGL_depth_texture"); - break; - case "EXT_texture_filter_anisotropic": - extension = gl.getExtension("EXT_texture_filter_anisotropic") || gl.getExtension("MOZ_EXT_texture_filter_anisotropic") || gl.getExtension("WEBKIT_EXT_texture_filter_anisotropic"); - break; - case "WEBGL_compressed_texture_s3tc": - extension = gl.getExtension("WEBGL_compressed_texture_s3tc") || gl.getExtension("MOZ_WEBGL_compressed_texture_s3tc") || gl.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc"); - break; - case "WEBGL_compressed_texture_pvrtc": - extension = gl.getExtension("WEBGL_compressed_texture_pvrtc") || gl.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc"); - break; - default: - extension = gl.getExtension(name); - } - extensions[name] = extension; - return extension; - } - __name(getExtension, "getExtension"); - return { - has: /* @__PURE__ */ __name(function(name) { - return getExtension(name) !== null; - }, "has"), - init: /* @__PURE__ */ __name(function() { - getExtension("EXT_color_buffer_float"); - getExtension("WEBGL_clip_cull_distance"); - getExtension("OES_texture_float_linear"); - getExtension("EXT_color_buffer_half_float"); - getExtension("WEBGL_multisampled_render_to_texture"); - getExtension("WEBGL_render_shared_exponent"); - }, "init"), - get: /* @__PURE__ */ __name(function(name) { - const extension = getExtension(name); - if (extension === null) { - warnOnce("THREE.WebGLRenderer: " + name + " extension not supported."); - } - return extension; - }, "get") - }; -} -__name(WebGLExtensions, "WebGLExtensions"); -function WebGLGeometries(gl, attributes, info, bindingStates) { - const geometries = {}; - const wireframeAttributes = /* @__PURE__ */ new WeakMap(); - function onGeometryDispose(event) { - const geometry = event.target; - if (geometry.index !== null) { - attributes.remove(geometry.index); - } - for (const name in geometry.attributes) { - attributes.remove(geometry.attributes[name]); - } - for (const name in geometry.morphAttributes) { - const array = geometry.morphAttributes[name]; - for (let i = 0, l = array.length; i < l; i++) { - attributes.remove(array[i]); - } - } - geometry.removeEventListener("dispose", onGeometryDispose); - delete geometries[geometry.id]; - const attribute = wireframeAttributes.get(geometry); - if (attribute) { - attributes.remove(attribute); - wireframeAttributes.delete(geometry); - } - bindingStates.releaseStatesOfGeometry(geometry); - if (geometry.isInstancedBufferGeometry === true) { - delete geometry._maxInstanceCount; - } - info.memory.geometries--; - } - __name(onGeometryDispose, "onGeometryDispose"); - function get(object, geometry) { - if (geometries[geometry.id] === true) return geometry; - geometry.addEventListener("dispose", onGeometryDispose); - geometries[geometry.id] = true; - info.memory.geometries++; - return geometry; - } - __name(get, "get"); - function update(geometry) { - const geometryAttributes = geometry.attributes; - for (const name in geometryAttributes) { - attributes.update(geometryAttributes[name], gl.ARRAY_BUFFER); - } - const morphAttributes = geometry.morphAttributes; - for (const name in morphAttributes) { - const array = morphAttributes[name]; - for (let i = 0, l = array.length; i < l; i++) { - attributes.update(array[i], gl.ARRAY_BUFFER); - } - } - } - __name(update, "update"); - function updateWireframeAttribute(geometry) { - const indices = []; - const geometryIndex = geometry.index; - const geometryPosition = geometry.attributes.position; - let version = 0; - if (geometryIndex !== null) { - const array = geometryIndex.array; - version = geometryIndex.version; - for (let i = 0, l = array.length; i < l; i += 3) { - const a = array[i + 0]; - const b = array[i + 1]; - const c = array[i + 2]; - indices.push(a, b, b, c, c, a); - } - } else if (geometryPosition !== void 0) { - const array = geometryPosition.array; - version = geometryPosition.version; - for (let i = 0, l = array.length / 3 - 1; i < l; i += 3) { - const a = i + 0; - const b = i + 1; - const c = i + 2; - indices.push(a, b, b, c, c, a); - } - } else { - return; - } - const attribute = new (arrayNeedsUint32(indices) ? Uint32BufferAttribute : Uint16BufferAttribute)(indices, 1); - attribute.version = version; - const previousAttribute = wireframeAttributes.get(geometry); - if (previousAttribute) attributes.remove(previousAttribute); - wireframeAttributes.set(geometry, attribute); - } - __name(updateWireframeAttribute, "updateWireframeAttribute"); - function getWireframeAttribute(geometry) { - const currentAttribute = wireframeAttributes.get(geometry); - if (currentAttribute) { - const geometryIndex = geometry.index; - if (geometryIndex !== null) { - if (currentAttribute.version < geometryIndex.version) { - updateWireframeAttribute(geometry); - } - } - } else { - updateWireframeAttribute(geometry); - } - return wireframeAttributes.get(geometry); - } - __name(getWireframeAttribute, "getWireframeAttribute"); - return { - get, - update, - getWireframeAttribute - }; -} -__name(WebGLGeometries, "WebGLGeometries"); -function WebGLIndexedBufferRenderer(gl, extensions, info) { - let mode; - function setMode(value) { - mode = value; - } - __name(setMode, "setMode"); - let type, bytesPerElement; - function setIndex(value) { - type = value.type; - bytesPerElement = value.bytesPerElement; - } - __name(setIndex, "setIndex"); - function render(start, count) { - gl.drawElements(mode, count, type, start * bytesPerElement); - info.update(count, mode, 1); - } - __name(render, "render"); - function renderInstances(start, count, primcount) { - if (primcount === 0) return; - gl.drawElementsInstanced(mode, count, type, start * bytesPerElement, primcount); - info.update(count, mode, primcount); - } - __name(renderInstances, "renderInstances"); - function renderMultiDraw(starts, counts, drawCount) { - if (drawCount === 0) return; - const extension = extensions.get("WEBGL_multi_draw"); - extension.multiDrawElementsWEBGL(mode, counts, 0, type, starts, 0, drawCount); - let elementCount = 0; - for (let i = 0; i < drawCount; i++) { - elementCount += counts[i]; - } - info.update(elementCount, mode, 1); - } - __name(renderMultiDraw, "renderMultiDraw"); - function renderMultiDrawInstances(starts, counts, drawCount, primcount) { - if (drawCount === 0) return; - const extension = extensions.get("WEBGL_multi_draw"); - if (extension === null) { - for (let i = 0; i < starts.length; i++) { - renderInstances(starts[i] / bytesPerElement, counts[i], primcount[i]); - } - } else { - extension.multiDrawElementsInstancedWEBGL(mode, counts, 0, type, starts, 0, primcount, 0, drawCount); - let elementCount = 0; - for (let i = 0; i < drawCount; i++) { - elementCount += counts[i] * primcount[i]; - } - info.update(elementCount, mode, 1); - } - } - __name(renderMultiDrawInstances, "renderMultiDrawInstances"); - this.setMode = setMode; - this.setIndex = setIndex; - this.render = render; - this.renderInstances = renderInstances; - this.renderMultiDraw = renderMultiDraw; - this.renderMultiDrawInstances = renderMultiDrawInstances; -} -__name(WebGLIndexedBufferRenderer, "WebGLIndexedBufferRenderer"); -function WebGLInfo(gl) { - const memory = { - geometries: 0, - textures: 0 - }; - const render = { - frame: 0, - calls: 0, - triangles: 0, - points: 0, - lines: 0 - }; - function update(count, mode, instanceCount) { - render.calls++; - switch (mode) { - case gl.TRIANGLES: - render.triangles += instanceCount * (count / 3); - break; - case gl.LINES: - render.lines += instanceCount * (count / 2); - break; - case gl.LINE_STRIP: - render.lines += instanceCount * (count - 1); - break; - case gl.LINE_LOOP: - render.lines += instanceCount * count; - break; - case gl.POINTS: - render.points += instanceCount * count; - break; - default: - console.error("THREE.WebGLInfo: Unknown draw mode:", mode); - break; - } - } - __name(update, "update"); - function reset() { - render.calls = 0; - render.triangles = 0; - render.points = 0; - render.lines = 0; - } - __name(reset, "reset"); - return { - memory, - render, - programs: null, - autoReset: true, - reset, - update - }; -} -__name(WebGLInfo, "WebGLInfo"); -function WebGLMorphtargets(gl, capabilities, textures) { - const morphTextures = /* @__PURE__ */ new WeakMap(); - const morph = new Vector4(); - function update(object, geometry, program) { - const objectInfluences = object.morphTargetInfluences; - const morphAttribute = geometry.morphAttributes.position || geometry.morphAttributes.normal || geometry.morphAttributes.color; - const morphTargetsCount = morphAttribute !== void 0 ? morphAttribute.length : 0; - let entry = morphTextures.get(geometry); - if (entry === void 0 || entry.count !== morphTargetsCount) { - let disposeTexture = function() { - texture.dispose(); - morphTextures.delete(geometry); - geometry.removeEventListener("dispose", disposeTexture); - }; - __name(disposeTexture, "disposeTexture"); - if (entry !== void 0) entry.texture.dispose(); - const hasMorphPosition = geometry.morphAttributes.position !== void 0; - const hasMorphNormals = geometry.morphAttributes.normal !== void 0; - const hasMorphColors = geometry.morphAttributes.color !== void 0; - const morphTargets = geometry.morphAttributes.position || []; - const morphNormals = geometry.morphAttributes.normal || []; - const morphColors = geometry.morphAttributes.color || []; - let vertexDataCount = 0; - if (hasMorphPosition === true) vertexDataCount = 1; - if (hasMorphNormals === true) vertexDataCount = 2; - if (hasMorphColors === true) vertexDataCount = 3; - let width = geometry.attributes.position.count * vertexDataCount; - let height = 1; - if (width > capabilities.maxTextureSize) { - height = Math.ceil(width / capabilities.maxTextureSize); - width = capabilities.maxTextureSize; - } - const buffer = new Float32Array(width * height * 4 * morphTargetsCount); - const texture = new DataArrayTexture(buffer, width, height, morphTargetsCount); - texture.type = FloatType; - texture.needsUpdate = true; - const vertexDataStride = vertexDataCount * 4; - for (let i = 0; i < morphTargetsCount; i++) { - const morphTarget = morphTargets[i]; - const morphNormal = morphNormals[i]; - const morphColor = morphColors[i]; - const offset = width * height * 4 * i; - for (let j = 0; j < morphTarget.count; j++) { - const stride = j * vertexDataStride; - if (hasMorphPosition === true) { - morph.fromBufferAttribute(morphTarget, j); - buffer[offset + stride + 0] = morph.x; - buffer[offset + stride + 1] = morph.y; - buffer[offset + stride + 2] = morph.z; - buffer[offset + stride + 3] = 0; - } - if (hasMorphNormals === true) { - morph.fromBufferAttribute(morphNormal, j); - buffer[offset + stride + 4] = morph.x; - buffer[offset + stride + 5] = morph.y; - buffer[offset + stride + 6] = morph.z; - buffer[offset + stride + 7] = 0; - } - if (hasMorphColors === true) { - morph.fromBufferAttribute(morphColor, j); - buffer[offset + stride + 8] = morph.x; - buffer[offset + stride + 9] = morph.y; - buffer[offset + stride + 10] = morph.z; - buffer[offset + stride + 11] = morphColor.itemSize === 4 ? morph.w : 1; - } - } - } - entry = { - count: morphTargetsCount, - texture, - size: new Vector2(width, height) - }; - morphTextures.set(geometry, entry); - geometry.addEventListener("dispose", disposeTexture); - } - if (object.isInstancedMesh === true && object.morphTexture !== null) { - program.getUniforms().setValue(gl, "morphTexture", object.morphTexture, textures); - } else { - let morphInfluencesSum = 0; - for (let i = 0; i < objectInfluences.length; i++) { - morphInfluencesSum += objectInfluences[i]; - } - const morphBaseInfluence = geometry.morphTargetsRelative ? 1 : 1 - morphInfluencesSum; - program.getUniforms().setValue(gl, "morphTargetBaseInfluence", morphBaseInfluence); - program.getUniforms().setValue(gl, "morphTargetInfluences", objectInfluences); - } - program.getUniforms().setValue(gl, "morphTargetsTexture", entry.texture, textures); - program.getUniforms().setValue(gl, "morphTargetsTextureSize", entry.size); - } - __name(update, "update"); - return { - update - }; -} -__name(WebGLMorphtargets, "WebGLMorphtargets"); -function WebGLObjects(gl, geometries, attributes, info) { - let updateMap = /* @__PURE__ */ new WeakMap(); - function update(object) { - const frame = info.render.frame; - const geometry = object.geometry; - const buffergeometry = geometries.get(object, geometry); - if (updateMap.get(buffergeometry) !== frame) { - geometries.update(buffergeometry); - updateMap.set(buffergeometry, frame); - } - if (object.isInstancedMesh) { - if (object.hasEventListener("dispose", onInstancedMeshDispose) === false) { - object.addEventListener("dispose", onInstancedMeshDispose); - } - if (updateMap.get(object) !== frame) { - attributes.update(object.instanceMatrix, gl.ARRAY_BUFFER); - if (object.instanceColor !== null) { - attributes.update(object.instanceColor, gl.ARRAY_BUFFER); - } - updateMap.set(object, frame); - } - } - if (object.isSkinnedMesh) { - const skeleton = object.skeleton; - if (updateMap.get(skeleton) !== frame) { - skeleton.update(); - updateMap.set(skeleton, frame); - } - } - return buffergeometry; - } - __name(update, "update"); - function dispose() { - updateMap = /* @__PURE__ */ new WeakMap(); - } - __name(dispose, "dispose"); - function onInstancedMeshDispose(event) { - const instancedMesh = event.target; - instancedMesh.removeEventListener("dispose", onInstancedMeshDispose); - attributes.remove(instancedMesh.instanceMatrix); - if (instancedMesh.instanceColor !== null) attributes.remove(instancedMesh.instanceColor); - } - __name(onInstancedMeshDispose, "onInstancedMeshDispose"); - return { - update, - dispose - }; -} -__name(WebGLObjects, "WebGLObjects"); -class DepthTexture extends Texture { - static { - __name(this, "DepthTexture"); - } - constructor(width, height, type, mapping, wrapS, wrapT, magFilter, minFilter, anisotropy, format = DepthFormat) { - if (format !== DepthFormat && format !== DepthStencilFormat) { - throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat"); - } - if (type === void 0 && format === DepthFormat) type = UnsignedIntType; - if (type === void 0 && format === DepthStencilFormat) type = UnsignedInt248Type; - super(null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy); - this.isDepthTexture = true; - this.image = { width, height }; - this.magFilter = magFilter !== void 0 ? magFilter : NearestFilter; - this.minFilter = minFilter !== void 0 ? minFilter : NearestFilter; - this.flipY = false; - this.generateMipmaps = false; - this.compareFunction = null; - } - copy(source) { - super.copy(source); - this.compareFunction = source.compareFunction; - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - if (this.compareFunction !== null) data.compareFunction = this.compareFunction; - return data; - } -} -const emptyTexture = /* @__PURE__ */ new Texture(); -const emptyShadowTexture = /* @__PURE__ */ new DepthTexture(1, 1); -const emptyArrayTexture = /* @__PURE__ */ new DataArrayTexture(); -const empty3dTexture = /* @__PURE__ */ new Data3DTexture(); -const emptyCubeTexture = /* @__PURE__ */ new CubeTexture(); -const arrayCacheF32 = []; -const arrayCacheI32 = []; -const mat4array = new Float32Array(16); -const mat3array = new Float32Array(9); -const mat2array = new Float32Array(4); -function flatten(array, nBlocks, blockSize) { - const firstElem = array[0]; - if (firstElem <= 0 || firstElem > 0) return array; - const n = nBlocks * blockSize; - let r = arrayCacheF32[n]; - if (r === void 0) { - r = new Float32Array(n); - arrayCacheF32[n] = r; - } - if (nBlocks !== 0) { - firstElem.toArray(r, 0); - for (let i = 1, offset = 0; i !== nBlocks; ++i) { - offset += blockSize; - array[i].toArray(r, offset); - } - } - return r; -} -__name(flatten, "flatten"); -function arraysEqual(a, b) { - if (a.length !== b.length) return false; - for (let i = 0, l = a.length; i < l; i++) { - if (a[i] !== b[i]) return false; - } - return true; -} -__name(arraysEqual, "arraysEqual"); -function copyArray(a, b) { - for (let i = 0, l = b.length; i < l; i++) { - a[i] = b[i]; - } -} -__name(copyArray, "copyArray"); -function allocTexUnits(textures, n) { - let r = arrayCacheI32[n]; - if (r === void 0) { - r = new Int32Array(n); - arrayCacheI32[n] = r; - } - for (let i = 0; i !== n; ++i) { - r[i] = textures.allocateTextureUnit(); - } - return r; -} -__name(allocTexUnits, "allocTexUnits"); -function setValueV1f(gl, v) { - const cache = this.cache; - if (cache[0] === v) return; - gl.uniform1f(this.addr, v); - cache[0] = v; -} -__name(setValueV1f, "setValueV1f"); -function setValueV2f(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y) { - gl.uniform2f(this.addr, v.x, v.y); - cache[0] = v.x; - cache[1] = v.y; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform2fv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV2f, "setValueV2f"); -function setValueV3f(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z) { - gl.uniform3f(this.addr, v.x, v.y, v.z); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - } - } else if (v.r !== void 0) { - if (cache[0] !== v.r || cache[1] !== v.g || cache[2] !== v.b) { - gl.uniform3f(this.addr, v.r, v.g, v.b); - cache[0] = v.r; - cache[1] = v.g; - cache[2] = v.b; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform3fv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV3f, "setValueV3f"); -function setValueV4f(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z || cache[3] !== v.w) { - gl.uniform4f(this.addr, v.x, v.y, v.z, v.w); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - cache[3] = v.w; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform4fv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV4f, "setValueV4f"); -function setValueM2(gl, v) { - const cache = this.cache; - const elements = v.elements; - if (elements === void 0) { - if (arraysEqual(cache, v)) return; - gl.uniformMatrix2fv(this.addr, false, v); - copyArray(cache, v); - } else { - if (arraysEqual(cache, elements)) return; - mat2array.set(elements); - gl.uniformMatrix2fv(this.addr, false, mat2array); - copyArray(cache, elements); - } -} -__name(setValueM2, "setValueM2"); -function setValueM3(gl, v) { - const cache = this.cache; - const elements = v.elements; - if (elements === void 0) { - if (arraysEqual(cache, v)) return; - gl.uniformMatrix3fv(this.addr, false, v); - copyArray(cache, v); - } else { - if (arraysEqual(cache, elements)) return; - mat3array.set(elements); - gl.uniformMatrix3fv(this.addr, false, mat3array); - copyArray(cache, elements); - } -} -__name(setValueM3, "setValueM3"); -function setValueM4(gl, v) { - const cache = this.cache; - const elements = v.elements; - if (elements === void 0) { - if (arraysEqual(cache, v)) return; - gl.uniformMatrix4fv(this.addr, false, v); - copyArray(cache, v); - } else { - if (arraysEqual(cache, elements)) return; - mat4array.set(elements); - gl.uniformMatrix4fv(this.addr, false, mat4array); - copyArray(cache, elements); - } -} -__name(setValueM4, "setValueM4"); -function setValueV1i(gl, v) { - const cache = this.cache; - if (cache[0] === v) return; - gl.uniform1i(this.addr, v); - cache[0] = v; -} -__name(setValueV1i, "setValueV1i"); -function setValueV2i(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y) { - gl.uniform2i(this.addr, v.x, v.y); - cache[0] = v.x; - cache[1] = v.y; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform2iv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV2i, "setValueV2i"); -function setValueV3i(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z) { - gl.uniform3i(this.addr, v.x, v.y, v.z); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform3iv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV3i, "setValueV3i"); -function setValueV4i(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z || cache[3] !== v.w) { - gl.uniform4i(this.addr, v.x, v.y, v.z, v.w); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - cache[3] = v.w; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform4iv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV4i, "setValueV4i"); -function setValueV1ui(gl, v) { - const cache = this.cache; - if (cache[0] === v) return; - gl.uniform1ui(this.addr, v); - cache[0] = v; -} -__name(setValueV1ui, "setValueV1ui"); -function setValueV2ui(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y) { - gl.uniform2ui(this.addr, v.x, v.y); - cache[0] = v.x; - cache[1] = v.y; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform2uiv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV2ui, "setValueV2ui"); -function setValueV3ui(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z) { - gl.uniform3ui(this.addr, v.x, v.y, v.z); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform3uiv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV3ui, "setValueV3ui"); -function setValueV4ui(gl, v) { - const cache = this.cache; - if (v.x !== void 0) { - if (cache[0] !== v.x || cache[1] !== v.y || cache[2] !== v.z || cache[3] !== v.w) { - gl.uniform4ui(this.addr, v.x, v.y, v.z, v.w); - cache[0] = v.x; - cache[1] = v.y; - cache[2] = v.z; - cache[3] = v.w; - } - } else { - if (arraysEqual(cache, v)) return; - gl.uniform4uiv(this.addr, v); - copyArray(cache, v); - } -} -__name(setValueV4ui, "setValueV4ui"); -function setValueT1(gl, v, textures) { - const cache = this.cache; - const unit = textures.allocateTextureUnit(); - if (cache[0] !== unit) { - gl.uniform1i(this.addr, unit); - cache[0] = unit; - } - let emptyTexture2D; - if (this.type === gl.SAMPLER_2D_SHADOW) { - emptyShadowTexture.compareFunction = LessEqualCompare; - emptyTexture2D = emptyShadowTexture; - } else { - emptyTexture2D = emptyTexture; - } - textures.setTexture2D(v || emptyTexture2D, unit); -} -__name(setValueT1, "setValueT1"); -function setValueT3D1(gl, v, textures) { - const cache = this.cache; - const unit = textures.allocateTextureUnit(); - if (cache[0] !== unit) { - gl.uniform1i(this.addr, unit); - cache[0] = unit; - } - textures.setTexture3D(v || empty3dTexture, unit); -} -__name(setValueT3D1, "setValueT3D1"); -function setValueT6(gl, v, textures) { - const cache = this.cache; - const unit = textures.allocateTextureUnit(); - if (cache[0] !== unit) { - gl.uniform1i(this.addr, unit); - cache[0] = unit; - } - textures.setTextureCube(v || emptyCubeTexture, unit); -} -__name(setValueT6, "setValueT6"); -function setValueT2DArray1(gl, v, textures) { - const cache = this.cache; - const unit = textures.allocateTextureUnit(); - if (cache[0] !== unit) { - gl.uniform1i(this.addr, unit); - cache[0] = unit; - } - textures.setTexture2DArray(v || emptyArrayTexture, unit); -} -__name(setValueT2DArray1, "setValueT2DArray1"); -function getSingularSetter(type) { - switch (type) { - case 5126: - return setValueV1f; - case 35664: - return setValueV2f; - case 35665: - return setValueV3f; - case 35666: - return setValueV4f; - case 35674: - return setValueM2; - case 35675: - return setValueM3; - case 35676: - return setValueM4; - case 5124: - case 35670: - return setValueV1i; - case 35667: - case 35671: - return setValueV2i; - case 35668: - case 35672: - return setValueV3i; - case 35669: - case 35673: - return setValueV4i; - case 5125: - return setValueV1ui; - case 36294: - return setValueV2ui; - case 36295: - return setValueV3ui; - case 36296: - return setValueV4ui; - case 35678: - case 36198: - case 36298: - case 36306: - case 35682: - return setValueT1; - case 35679: - case 36299: - case 36307: - return setValueT3D1; - case 35680: - case 36300: - case 36308: - case 36293: - return setValueT6; - case 36289: - case 36303: - case 36311: - case 36292: - return setValueT2DArray1; - } -} -__name(getSingularSetter, "getSingularSetter"); -function setValueV1fArray(gl, v) { - gl.uniform1fv(this.addr, v); -} -__name(setValueV1fArray, "setValueV1fArray"); -function setValueV2fArray(gl, v) { - const data = flatten(v, this.size, 2); - gl.uniform2fv(this.addr, data); -} -__name(setValueV2fArray, "setValueV2fArray"); -function setValueV3fArray(gl, v) { - const data = flatten(v, this.size, 3); - gl.uniform3fv(this.addr, data); -} -__name(setValueV3fArray, "setValueV3fArray"); -function setValueV4fArray(gl, v) { - const data = flatten(v, this.size, 4); - gl.uniform4fv(this.addr, data); -} -__name(setValueV4fArray, "setValueV4fArray"); -function setValueM2Array(gl, v) { - const data = flatten(v, this.size, 4); - gl.uniformMatrix2fv(this.addr, false, data); -} -__name(setValueM2Array, "setValueM2Array"); -function setValueM3Array(gl, v) { - const data = flatten(v, this.size, 9); - gl.uniformMatrix3fv(this.addr, false, data); -} -__name(setValueM3Array, "setValueM3Array"); -function setValueM4Array(gl, v) { - const data = flatten(v, this.size, 16); - gl.uniformMatrix4fv(this.addr, false, data); -} -__name(setValueM4Array, "setValueM4Array"); -function setValueV1iArray(gl, v) { - gl.uniform1iv(this.addr, v); -} -__name(setValueV1iArray, "setValueV1iArray"); -function setValueV2iArray(gl, v) { - gl.uniform2iv(this.addr, v); -} -__name(setValueV2iArray, "setValueV2iArray"); -function setValueV3iArray(gl, v) { - gl.uniform3iv(this.addr, v); -} -__name(setValueV3iArray, "setValueV3iArray"); -function setValueV4iArray(gl, v) { - gl.uniform4iv(this.addr, v); -} -__name(setValueV4iArray, "setValueV4iArray"); -function setValueV1uiArray(gl, v) { - gl.uniform1uiv(this.addr, v); -} -__name(setValueV1uiArray, "setValueV1uiArray"); -function setValueV2uiArray(gl, v) { - gl.uniform2uiv(this.addr, v); -} -__name(setValueV2uiArray, "setValueV2uiArray"); -function setValueV3uiArray(gl, v) { - gl.uniform3uiv(this.addr, v); -} -__name(setValueV3uiArray, "setValueV3uiArray"); -function setValueV4uiArray(gl, v) { - gl.uniform4uiv(this.addr, v); -} -__name(setValueV4uiArray, "setValueV4uiArray"); -function setValueT1Array(gl, v, textures) { - const cache = this.cache; - const n = v.length; - const units = allocTexUnits(textures, n); - if (!arraysEqual(cache, units)) { - gl.uniform1iv(this.addr, units); - copyArray(cache, units); - } - for (let i = 0; i !== n; ++i) { - textures.setTexture2D(v[i] || emptyTexture, units[i]); - } -} -__name(setValueT1Array, "setValueT1Array"); -function setValueT3DArray(gl, v, textures) { - const cache = this.cache; - const n = v.length; - const units = allocTexUnits(textures, n); - if (!arraysEqual(cache, units)) { - gl.uniform1iv(this.addr, units); - copyArray(cache, units); - } - for (let i = 0; i !== n; ++i) { - textures.setTexture3D(v[i] || empty3dTexture, units[i]); - } -} -__name(setValueT3DArray, "setValueT3DArray"); -function setValueT6Array(gl, v, textures) { - const cache = this.cache; - const n = v.length; - const units = allocTexUnits(textures, n); - if (!arraysEqual(cache, units)) { - gl.uniform1iv(this.addr, units); - copyArray(cache, units); - } - for (let i = 0; i !== n; ++i) { - textures.setTextureCube(v[i] || emptyCubeTexture, units[i]); - } -} -__name(setValueT6Array, "setValueT6Array"); -function setValueT2DArrayArray(gl, v, textures) { - const cache = this.cache; - const n = v.length; - const units = allocTexUnits(textures, n); - if (!arraysEqual(cache, units)) { - gl.uniform1iv(this.addr, units); - copyArray(cache, units); - } - for (let i = 0; i !== n; ++i) { - textures.setTexture2DArray(v[i] || emptyArrayTexture, units[i]); - } -} -__name(setValueT2DArrayArray, "setValueT2DArrayArray"); -function getPureArraySetter(type) { - switch (type) { - case 5126: - return setValueV1fArray; - case 35664: - return setValueV2fArray; - case 35665: - return setValueV3fArray; - case 35666: - return setValueV4fArray; - case 35674: - return setValueM2Array; - case 35675: - return setValueM3Array; - case 35676: - return setValueM4Array; - case 5124: - case 35670: - return setValueV1iArray; - case 35667: - case 35671: - return setValueV2iArray; - case 35668: - case 35672: - return setValueV3iArray; - case 35669: - case 35673: - return setValueV4iArray; - case 5125: - return setValueV1uiArray; - case 36294: - return setValueV2uiArray; - case 36295: - return setValueV3uiArray; - case 36296: - return setValueV4uiArray; - case 35678: - case 36198: - case 36298: - case 36306: - case 35682: - return setValueT1Array; - case 35679: - case 36299: - case 36307: - return setValueT3DArray; - case 35680: - case 36300: - case 36308: - case 36293: - return setValueT6Array; - case 36289: - case 36303: - case 36311: - case 36292: - return setValueT2DArrayArray; - } -} -__name(getPureArraySetter, "getPureArraySetter"); -class SingleUniform { - static { - __name(this, "SingleUniform"); - } - constructor(id2, activeInfo, addr) { - this.id = id2; - this.addr = addr; - this.cache = []; - this.type = activeInfo.type; - this.setValue = getSingularSetter(activeInfo.type); - } -} -class PureArrayUniform { - static { - __name(this, "PureArrayUniform"); - } - constructor(id2, activeInfo, addr) { - this.id = id2; - this.addr = addr; - this.cache = []; - this.type = activeInfo.type; - this.size = activeInfo.size; - this.setValue = getPureArraySetter(activeInfo.type); - } -} -class StructuredUniform { - static { - __name(this, "StructuredUniform"); - } - constructor(id2) { - this.id = id2; - this.seq = []; - this.map = {}; - } - setValue(gl, value, textures) { - const seq = this.seq; - for (let i = 0, n = seq.length; i !== n; ++i) { - const u = seq[i]; - u.setValue(gl, value[u.id], textures); - } - } -} -const RePathPart = /(\w+)(\])?(\[|\.)?/g; -function addUniform(container, uniformObject) { - container.seq.push(uniformObject); - container.map[uniformObject.id] = uniformObject; -} -__name(addUniform, "addUniform"); -function parseUniform(activeInfo, addr, container) { - const path = activeInfo.name, pathLength = path.length; - RePathPart.lastIndex = 0; - while (true) { - const match = RePathPart.exec(path), matchEnd = RePathPart.lastIndex; - let id2 = match[1]; - const idIsIndex = match[2] === "]", subscript = match[3]; - if (idIsIndex) id2 = id2 | 0; - if (subscript === void 0 || subscript === "[" && matchEnd + 2 === pathLength) { - addUniform(container, subscript === void 0 ? new SingleUniform(id2, activeInfo, addr) : new PureArrayUniform(id2, activeInfo, addr)); - break; - } else { - const map = container.map; - let next = map[id2]; - if (next === void 0) { - next = new StructuredUniform(id2); - addUniform(container, next); - } - container = next; - } - } -} -__name(parseUniform, "parseUniform"); -class WebGLUniforms { - static { - __name(this, "WebGLUniforms"); - } - constructor(gl, program) { - this.seq = []; - this.map = {}; - const n = gl.getProgramParameter(program, gl.ACTIVE_UNIFORMS); - for (let i = 0; i < n; ++i) { - const info = gl.getActiveUniform(program, i), addr = gl.getUniformLocation(program, info.name); - parseUniform(info, addr, this); - } - } - setValue(gl, name, value, textures) { - const u = this.map[name]; - if (u !== void 0) u.setValue(gl, value, textures); - } - setOptional(gl, object, name) { - const v = object[name]; - if (v !== void 0) this.setValue(gl, name, v); - } - static upload(gl, seq, values, textures) { - for (let i = 0, n = seq.length; i !== n; ++i) { - const u = seq[i], v = values[u.id]; - if (v.needsUpdate !== false) { - u.setValue(gl, v.value, textures); - } - } - } - static seqWithValue(seq, values) { - const r = []; - for (let i = 0, n = seq.length; i !== n; ++i) { - const u = seq[i]; - if (u.id in values) r.push(u); - } - return r; - } -} -function WebGLShader(gl, type, string) { - const shader = gl.createShader(type); - gl.shaderSource(shader, string); - gl.compileShader(shader); - return shader; -} -__name(WebGLShader, "WebGLShader"); -const COMPLETION_STATUS_KHR = 37297; -let programIdCount = 0; -function handleSource(string, errorLine) { - const lines = string.split("\n"); - const lines2 = []; - const from = Math.max(errorLine - 6, 0); - const to = Math.min(errorLine + 6, lines.length); - for (let i = from; i < to; i++) { - const line = i + 1; - lines2.push(`${line === errorLine ? ">" : " "} ${line}: ${lines[i]}`); - } - return lines2.join("\n"); -} -__name(handleSource, "handleSource"); -const _m0 = /* @__PURE__ */ new Matrix3(); -function getEncodingComponents(colorSpace) { - ColorManagement._getMatrix(_m0, ColorManagement.workingColorSpace, colorSpace); - const encodingMatrix = `mat3( ${_m0.elements.map((v) => v.toFixed(4))} )`; - switch (ColorManagement.getTransfer(colorSpace)) { - case LinearTransfer: - return [encodingMatrix, "LinearTransferOETF"]; - case SRGBTransfer: - return [encodingMatrix, "sRGBTransferOETF"]; - default: - console.warn("THREE.WebGLProgram: Unsupported color space: ", colorSpace); - return [encodingMatrix, "LinearTransferOETF"]; - } -} -__name(getEncodingComponents, "getEncodingComponents"); -function getShaderErrors(gl, shader, type) { - const status = gl.getShaderParameter(shader, gl.COMPILE_STATUS); - const errors = gl.getShaderInfoLog(shader).trim(); - if (status && errors === "") return ""; - const errorMatches = /ERROR: 0:(\d+)/.exec(errors); - if (errorMatches) { - const errorLine = parseInt(errorMatches[1]); - return type.toUpperCase() + "\n\n" + errors + "\n\n" + handleSource(gl.getShaderSource(shader), errorLine); - } else { - return errors; - } -} -__name(getShaderErrors, "getShaderErrors"); -function getTexelEncodingFunction(functionName, colorSpace) { - const components = getEncodingComponents(colorSpace); - return [ - `vec4 ${functionName}( vec4 value ) {`, - ` return ${components[1]}( vec4( value.rgb * ${components[0]}, value.a ) );`, - "}" - ].join("\n"); -} -__name(getTexelEncodingFunction, "getTexelEncodingFunction"); -function getToneMappingFunction(functionName, toneMapping) { - let toneMappingName; - switch (toneMapping) { - case LinearToneMapping: - toneMappingName = "Linear"; - break; - case ReinhardToneMapping: - toneMappingName = "Reinhard"; - break; - case CineonToneMapping: - toneMappingName = "Cineon"; - break; - case ACESFilmicToneMapping: - toneMappingName = "ACESFilmic"; - break; - case AgXToneMapping: - toneMappingName = "AgX"; - break; - case NeutralToneMapping: - toneMappingName = "Neutral"; - break; - case CustomToneMapping: - toneMappingName = "Custom"; - break; - default: - console.warn("THREE.WebGLProgram: Unsupported toneMapping:", toneMapping); - toneMappingName = "Linear"; - } - return "vec3 " + functionName + "( vec3 color ) { return " + toneMappingName + "ToneMapping( color ); }"; -} -__name(getToneMappingFunction, "getToneMappingFunction"); -const _v0$1 = /* @__PURE__ */ new Vector3(); -function getLuminanceFunction() { - ColorManagement.getLuminanceCoefficients(_v0$1); - const r = _v0$1.x.toFixed(4); - const g = _v0$1.y.toFixed(4); - const b = _v0$1.z.toFixed(4); - return [ - "float luminance( const in vec3 rgb ) {", - ` const vec3 weights = vec3( ${r}, ${g}, ${b} );`, - " return dot( weights, rgb );", - "}" - ].join("\n"); -} -__name(getLuminanceFunction, "getLuminanceFunction"); -function generateVertexExtensions(parameters) { - const chunks = [ - parameters.extensionClipCullDistance ? "#extension GL_ANGLE_clip_cull_distance : require" : "", - parameters.extensionMultiDraw ? "#extension GL_ANGLE_multi_draw : require" : "" - ]; - return chunks.filter(filterEmptyLine).join("\n"); -} -__name(generateVertexExtensions, "generateVertexExtensions"); -function generateDefines(defines) { - const chunks = []; - for (const name in defines) { - const value = defines[name]; - if (value === false) continue; - chunks.push("#define " + name + " " + value); - } - return chunks.join("\n"); -} -__name(generateDefines, "generateDefines"); -function fetchAttributeLocations(gl, program) { - const attributes = {}; - const n = gl.getProgramParameter(program, gl.ACTIVE_ATTRIBUTES); - for (let i = 0; i < n; i++) { - const info = gl.getActiveAttrib(program, i); - const name = info.name; - let locationSize = 1; - if (info.type === gl.FLOAT_MAT2) locationSize = 2; - if (info.type === gl.FLOAT_MAT3) locationSize = 3; - if (info.type === gl.FLOAT_MAT4) locationSize = 4; - attributes[name] = { - type: info.type, - location: gl.getAttribLocation(program, name), - locationSize - }; - } - return attributes; -} -__name(fetchAttributeLocations, "fetchAttributeLocations"); -function filterEmptyLine(string) { - return string !== ""; -} -__name(filterEmptyLine, "filterEmptyLine"); -function replaceLightNums(string, parameters) { - const numSpotLightCoords = parameters.numSpotLightShadows + parameters.numSpotLightMaps - parameters.numSpotLightShadowsWithMaps; - return string.replace(/NUM_DIR_LIGHTS/g, parameters.numDirLights).replace(/NUM_SPOT_LIGHTS/g, parameters.numSpotLights).replace(/NUM_SPOT_LIGHT_MAPS/g, parameters.numSpotLightMaps).replace(/NUM_SPOT_LIGHT_COORDS/g, numSpotLightCoords).replace(/NUM_RECT_AREA_LIGHTS/g, parameters.numRectAreaLights).replace(/NUM_POINT_LIGHTS/g, parameters.numPointLights).replace(/NUM_HEMI_LIGHTS/g, parameters.numHemiLights).replace(/NUM_DIR_LIGHT_SHADOWS/g, parameters.numDirLightShadows).replace(/NUM_SPOT_LIGHT_SHADOWS_WITH_MAPS/g, parameters.numSpotLightShadowsWithMaps).replace(/NUM_SPOT_LIGHT_SHADOWS/g, parameters.numSpotLightShadows).replace(/NUM_POINT_LIGHT_SHADOWS/g, parameters.numPointLightShadows); -} -__name(replaceLightNums, "replaceLightNums"); -function replaceClippingPlaneNums(string, parameters) { - return string.replace(/NUM_CLIPPING_PLANES/g, parameters.numClippingPlanes).replace(/UNION_CLIPPING_PLANES/g, parameters.numClippingPlanes - parameters.numClipIntersection); -} -__name(replaceClippingPlaneNums, "replaceClippingPlaneNums"); -const includePattern = /^[ \t]*#include +<([\w\d./]+)>/gm; -function resolveIncludes(string) { - return string.replace(includePattern, includeReplacer); -} -__name(resolveIncludes, "resolveIncludes"); -const shaderChunkMap = /* @__PURE__ */ new Map(); -function includeReplacer(match, include) { - let string = ShaderChunk[include]; - if (string === void 0) { - const newInclude = shaderChunkMap.get(include); - if (newInclude !== void 0) { - string = ShaderChunk[newInclude]; - console.warn('THREE.WebGLRenderer: Shader chunk "%s" has been deprecated. Use "%s" instead.', include, newInclude); - } else { - throw new Error("Can not resolve #include <" + include + ">"); - } - } - return resolveIncludes(string); -} -__name(includeReplacer, "includeReplacer"); -const unrollLoopPattern = /#pragma unroll_loop_start\s+for\s*\(\s*int\s+i\s*=\s*(\d+)\s*;\s*i\s*<\s*(\d+)\s*;\s*i\s*\+\+\s*\)\s*{([\s\S]+?)}\s+#pragma unroll_loop_end/g; -function unrollLoops(string) { - return string.replace(unrollLoopPattern, loopReplacer); -} -__name(unrollLoops, "unrollLoops"); -function loopReplacer(match, start, end, snippet) { - let string = ""; - for (let i = parseInt(start); i < parseInt(end); i++) { - string += snippet.replace(/\[\s*i\s*\]/g, "[ " + i + " ]").replace(/UNROLLED_LOOP_INDEX/g, i); - } - return string; -} -__name(loopReplacer, "loopReplacer"); -function generatePrecision(parameters) { - let precisionstring = `precision ${parameters.precision} float; - precision ${parameters.precision} int; - precision ${parameters.precision} sampler2D; - precision ${parameters.precision} samplerCube; - precision ${parameters.precision} sampler3D; - precision ${parameters.precision} sampler2DArray; - precision ${parameters.precision} sampler2DShadow; - precision ${parameters.precision} samplerCubeShadow; - precision ${parameters.precision} sampler2DArrayShadow; - precision ${parameters.precision} isampler2D; - precision ${parameters.precision} isampler3D; - precision ${parameters.precision} isamplerCube; - precision ${parameters.precision} isampler2DArray; - precision ${parameters.precision} usampler2D; - precision ${parameters.precision} usampler3D; - precision ${parameters.precision} usamplerCube; - precision ${parameters.precision} usampler2DArray; - `; - if (parameters.precision === "highp") { - precisionstring += "\n#define HIGH_PRECISION"; - } else if (parameters.precision === "mediump") { - precisionstring += "\n#define MEDIUM_PRECISION"; - } else if (parameters.precision === "lowp") { - precisionstring += "\n#define LOW_PRECISION"; - } - return precisionstring; -} -__name(generatePrecision, "generatePrecision"); -function generateShadowMapTypeDefine(parameters) { - let shadowMapTypeDefine = "SHADOWMAP_TYPE_BASIC"; - if (parameters.shadowMapType === PCFShadowMap) { - shadowMapTypeDefine = "SHADOWMAP_TYPE_PCF"; - } else if (parameters.shadowMapType === PCFSoftShadowMap) { - shadowMapTypeDefine = "SHADOWMAP_TYPE_PCF_SOFT"; - } else if (parameters.shadowMapType === VSMShadowMap) { - shadowMapTypeDefine = "SHADOWMAP_TYPE_VSM"; - } - return shadowMapTypeDefine; -} -__name(generateShadowMapTypeDefine, "generateShadowMapTypeDefine"); -function generateEnvMapTypeDefine(parameters) { - let envMapTypeDefine = "ENVMAP_TYPE_CUBE"; - if (parameters.envMap) { - switch (parameters.envMapMode) { - case CubeReflectionMapping: - case CubeRefractionMapping: - envMapTypeDefine = "ENVMAP_TYPE_CUBE"; - break; - case CubeUVReflectionMapping: - envMapTypeDefine = "ENVMAP_TYPE_CUBE_UV"; - break; - } - } - return envMapTypeDefine; -} -__name(generateEnvMapTypeDefine, "generateEnvMapTypeDefine"); -function generateEnvMapModeDefine(parameters) { - let envMapModeDefine = "ENVMAP_MODE_REFLECTION"; - if (parameters.envMap) { - switch (parameters.envMapMode) { - case CubeRefractionMapping: - envMapModeDefine = "ENVMAP_MODE_REFRACTION"; - break; - } - } - return envMapModeDefine; -} -__name(generateEnvMapModeDefine, "generateEnvMapModeDefine"); -function generateEnvMapBlendingDefine(parameters) { - let envMapBlendingDefine = "ENVMAP_BLENDING_NONE"; - if (parameters.envMap) { - switch (parameters.combine) { - case MultiplyOperation: - envMapBlendingDefine = "ENVMAP_BLENDING_MULTIPLY"; - break; - case MixOperation: - envMapBlendingDefine = "ENVMAP_BLENDING_MIX"; - break; - case AddOperation: - envMapBlendingDefine = "ENVMAP_BLENDING_ADD"; - break; - } - } - return envMapBlendingDefine; -} -__name(generateEnvMapBlendingDefine, "generateEnvMapBlendingDefine"); -function generateCubeUVSize(parameters) { - const imageHeight = parameters.envMapCubeUVHeight; - if (imageHeight === null) return null; - const maxMip = Math.log2(imageHeight) - 2; - const texelHeight = 1 / imageHeight; - const texelWidth = 1 / (3 * Math.max(Math.pow(2, maxMip), 7 * 16)); - return { texelWidth, texelHeight, maxMip }; -} -__name(generateCubeUVSize, "generateCubeUVSize"); -function WebGLProgram(renderer, cacheKey, parameters, bindingStates) { - const gl = renderer.getContext(); - const defines = parameters.defines; - let vertexShader = parameters.vertexShader; - let fragmentShader = parameters.fragmentShader; - const shadowMapTypeDefine = generateShadowMapTypeDefine(parameters); - const envMapTypeDefine = generateEnvMapTypeDefine(parameters); - const envMapModeDefine = generateEnvMapModeDefine(parameters); - const envMapBlendingDefine = generateEnvMapBlendingDefine(parameters); - const envMapCubeUVSize = generateCubeUVSize(parameters); - const customVertexExtensions = generateVertexExtensions(parameters); - const customDefines = generateDefines(defines); - const program = gl.createProgram(); - let prefixVertex, prefixFragment; - let versionString = parameters.glslVersion ? "#version " + parameters.glslVersion + "\n" : ""; - if (parameters.isRawShaderMaterial) { - prefixVertex = [ - "#define SHADER_TYPE " + parameters.shaderType, - "#define SHADER_NAME " + parameters.shaderName, - customDefines - ].filter(filterEmptyLine).join("\n"); - if (prefixVertex.length > 0) { - prefixVertex += "\n"; - } - prefixFragment = [ - "#define SHADER_TYPE " + parameters.shaderType, - "#define SHADER_NAME " + parameters.shaderName, - customDefines - ].filter(filterEmptyLine).join("\n"); - if (prefixFragment.length > 0) { - prefixFragment += "\n"; - } - } else { - prefixVertex = [ - generatePrecision(parameters), - "#define SHADER_TYPE " + parameters.shaderType, - "#define SHADER_NAME " + parameters.shaderName, - customDefines, - parameters.extensionClipCullDistance ? "#define USE_CLIP_DISTANCE" : "", - parameters.batching ? "#define USE_BATCHING" : "", - parameters.batchingColor ? "#define USE_BATCHING_COLOR" : "", - parameters.instancing ? "#define USE_INSTANCING" : "", - parameters.instancingColor ? "#define USE_INSTANCING_COLOR" : "", - parameters.instancingMorph ? "#define USE_INSTANCING_MORPH" : "", - parameters.useFog && parameters.fog ? "#define USE_FOG" : "", - parameters.useFog && parameters.fogExp2 ? "#define FOG_EXP2" : "", - parameters.map ? "#define USE_MAP" : "", - parameters.envMap ? "#define USE_ENVMAP" : "", - parameters.envMap ? "#define " + envMapModeDefine : "", - parameters.lightMap ? "#define USE_LIGHTMAP" : "", - parameters.aoMap ? "#define USE_AOMAP" : "", - parameters.bumpMap ? "#define USE_BUMPMAP" : "", - parameters.normalMap ? "#define USE_NORMALMAP" : "", - parameters.normalMapObjectSpace ? "#define USE_NORMALMAP_OBJECTSPACE" : "", - parameters.normalMapTangentSpace ? "#define USE_NORMALMAP_TANGENTSPACE" : "", - parameters.displacementMap ? "#define USE_DISPLACEMENTMAP" : "", - parameters.emissiveMap ? "#define USE_EMISSIVEMAP" : "", - parameters.anisotropy ? "#define USE_ANISOTROPY" : "", - parameters.anisotropyMap ? "#define USE_ANISOTROPYMAP" : "", - parameters.clearcoatMap ? "#define USE_CLEARCOATMAP" : "", - parameters.clearcoatRoughnessMap ? "#define USE_CLEARCOAT_ROUGHNESSMAP" : "", - parameters.clearcoatNormalMap ? "#define USE_CLEARCOAT_NORMALMAP" : "", - parameters.iridescenceMap ? "#define USE_IRIDESCENCEMAP" : "", - parameters.iridescenceThicknessMap ? "#define USE_IRIDESCENCE_THICKNESSMAP" : "", - parameters.specularMap ? "#define USE_SPECULARMAP" : "", - parameters.specularColorMap ? "#define USE_SPECULAR_COLORMAP" : "", - parameters.specularIntensityMap ? "#define USE_SPECULAR_INTENSITYMAP" : "", - parameters.roughnessMap ? "#define USE_ROUGHNESSMAP" : "", - parameters.metalnessMap ? "#define USE_METALNESSMAP" : "", - parameters.alphaMap ? "#define USE_ALPHAMAP" : "", - parameters.alphaHash ? "#define USE_ALPHAHASH" : "", - parameters.transmission ? "#define USE_TRANSMISSION" : "", - parameters.transmissionMap ? "#define USE_TRANSMISSIONMAP" : "", - parameters.thicknessMap ? "#define USE_THICKNESSMAP" : "", - parameters.sheenColorMap ? "#define USE_SHEEN_COLORMAP" : "", - parameters.sheenRoughnessMap ? "#define USE_SHEEN_ROUGHNESSMAP" : "", - // - parameters.mapUv ? "#define MAP_UV " + parameters.mapUv : "", - parameters.alphaMapUv ? "#define ALPHAMAP_UV " + parameters.alphaMapUv : "", - parameters.lightMapUv ? "#define LIGHTMAP_UV " + parameters.lightMapUv : "", - parameters.aoMapUv ? "#define AOMAP_UV " + parameters.aoMapUv : "", - parameters.emissiveMapUv ? "#define EMISSIVEMAP_UV " + parameters.emissiveMapUv : "", - parameters.bumpMapUv ? "#define BUMPMAP_UV " + parameters.bumpMapUv : "", - parameters.normalMapUv ? "#define NORMALMAP_UV " + parameters.normalMapUv : "", - parameters.displacementMapUv ? "#define DISPLACEMENTMAP_UV " + parameters.displacementMapUv : "", - parameters.metalnessMapUv ? "#define METALNESSMAP_UV " + parameters.metalnessMapUv : "", - parameters.roughnessMapUv ? "#define ROUGHNESSMAP_UV " + parameters.roughnessMapUv : "", - parameters.anisotropyMapUv ? "#define ANISOTROPYMAP_UV " + parameters.anisotropyMapUv : "", - parameters.clearcoatMapUv ? "#define CLEARCOATMAP_UV " + parameters.clearcoatMapUv : "", - parameters.clearcoatNormalMapUv ? "#define CLEARCOAT_NORMALMAP_UV " + parameters.clearcoatNormalMapUv : "", - parameters.clearcoatRoughnessMapUv ? "#define CLEARCOAT_ROUGHNESSMAP_UV " + parameters.clearcoatRoughnessMapUv : "", - parameters.iridescenceMapUv ? "#define IRIDESCENCEMAP_UV " + parameters.iridescenceMapUv : "", - parameters.iridescenceThicknessMapUv ? "#define IRIDESCENCE_THICKNESSMAP_UV " + parameters.iridescenceThicknessMapUv : "", - parameters.sheenColorMapUv ? "#define SHEEN_COLORMAP_UV " + parameters.sheenColorMapUv : "", - parameters.sheenRoughnessMapUv ? "#define SHEEN_ROUGHNESSMAP_UV " + parameters.sheenRoughnessMapUv : "", - parameters.specularMapUv ? "#define SPECULARMAP_UV " + parameters.specularMapUv : "", - parameters.specularColorMapUv ? "#define SPECULAR_COLORMAP_UV " + parameters.specularColorMapUv : "", - parameters.specularIntensityMapUv ? "#define SPECULAR_INTENSITYMAP_UV " + parameters.specularIntensityMapUv : "", - parameters.transmissionMapUv ? "#define TRANSMISSIONMAP_UV " + parameters.transmissionMapUv : "", - parameters.thicknessMapUv ? "#define THICKNESSMAP_UV " + parameters.thicknessMapUv : "", - // - parameters.vertexTangents && parameters.flatShading === false ? "#define USE_TANGENT" : "", - parameters.vertexColors ? "#define USE_COLOR" : "", - parameters.vertexAlphas ? "#define USE_COLOR_ALPHA" : "", - parameters.vertexUv1s ? "#define USE_UV1" : "", - parameters.vertexUv2s ? "#define USE_UV2" : "", - parameters.vertexUv3s ? "#define USE_UV3" : "", - parameters.pointsUvs ? "#define USE_POINTS_UV" : "", - parameters.flatShading ? "#define FLAT_SHADED" : "", - parameters.skinning ? "#define USE_SKINNING" : "", - parameters.morphTargets ? "#define USE_MORPHTARGETS" : "", - parameters.morphNormals && parameters.flatShading === false ? "#define USE_MORPHNORMALS" : "", - parameters.morphColors ? "#define USE_MORPHCOLORS" : "", - parameters.morphTargetsCount > 0 ? "#define MORPHTARGETS_TEXTURE_STRIDE " + parameters.morphTextureStride : "", - parameters.morphTargetsCount > 0 ? "#define MORPHTARGETS_COUNT " + parameters.morphTargetsCount : "", - parameters.doubleSided ? "#define DOUBLE_SIDED" : "", - parameters.flipSided ? "#define FLIP_SIDED" : "", - parameters.shadowMapEnabled ? "#define USE_SHADOWMAP" : "", - parameters.shadowMapEnabled ? "#define " + shadowMapTypeDefine : "", - parameters.sizeAttenuation ? "#define USE_SIZEATTENUATION" : "", - parameters.numLightProbes > 0 ? "#define USE_LIGHT_PROBES" : "", - parameters.logarithmicDepthBuffer ? "#define USE_LOGDEPTHBUF" : "", - parameters.reverseDepthBuffer ? "#define USE_REVERSEDEPTHBUF" : "", - "uniform mat4 modelMatrix;", - "uniform mat4 modelViewMatrix;", - "uniform mat4 projectionMatrix;", - "uniform mat4 viewMatrix;", - "uniform mat3 normalMatrix;", - "uniform vec3 cameraPosition;", - "uniform bool isOrthographic;", - "#ifdef USE_INSTANCING", - " attribute mat4 instanceMatrix;", - "#endif", - "#ifdef USE_INSTANCING_COLOR", - " attribute vec3 instanceColor;", - "#endif", - "#ifdef USE_INSTANCING_MORPH", - " uniform sampler2D morphTexture;", - "#endif", - "attribute vec3 position;", - "attribute vec3 normal;", - "attribute vec2 uv;", - "#ifdef USE_UV1", - " attribute vec2 uv1;", - "#endif", - "#ifdef USE_UV2", - " attribute vec2 uv2;", - "#endif", - "#ifdef USE_UV3", - " attribute vec2 uv3;", - "#endif", - "#ifdef USE_TANGENT", - " attribute vec4 tangent;", - "#endif", - "#if defined( USE_COLOR_ALPHA )", - " attribute vec4 color;", - "#elif defined( USE_COLOR )", - " attribute vec3 color;", - "#endif", - "#ifdef USE_SKINNING", - " attribute vec4 skinIndex;", - " attribute vec4 skinWeight;", - "#endif", - "\n" - ].filter(filterEmptyLine).join("\n"); - prefixFragment = [ - generatePrecision(parameters), - "#define SHADER_TYPE " + parameters.shaderType, - "#define SHADER_NAME " + parameters.shaderName, - customDefines, - parameters.useFog && parameters.fog ? "#define USE_FOG" : "", - parameters.useFog && parameters.fogExp2 ? "#define FOG_EXP2" : "", - parameters.alphaToCoverage ? "#define ALPHA_TO_COVERAGE" : "", - parameters.map ? "#define USE_MAP" : "", - parameters.matcap ? "#define USE_MATCAP" : "", - parameters.envMap ? "#define USE_ENVMAP" : "", - parameters.envMap ? "#define " + envMapTypeDefine : "", - parameters.envMap ? "#define " + envMapModeDefine : "", - parameters.envMap ? "#define " + envMapBlendingDefine : "", - envMapCubeUVSize ? "#define CUBEUV_TEXEL_WIDTH " + envMapCubeUVSize.texelWidth : "", - envMapCubeUVSize ? "#define CUBEUV_TEXEL_HEIGHT " + envMapCubeUVSize.texelHeight : "", - envMapCubeUVSize ? "#define CUBEUV_MAX_MIP " + envMapCubeUVSize.maxMip + ".0" : "", - parameters.lightMap ? "#define USE_LIGHTMAP" : "", - parameters.aoMap ? "#define USE_AOMAP" : "", - parameters.bumpMap ? "#define USE_BUMPMAP" : "", - parameters.normalMap ? "#define USE_NORMALMAP" : "", - parameters.normalMapObjectSpace ? "#define USE_NORMALMAP_OBJECTSPACE" : "", - parameters.normalMapTangentSpace ? "#define USE_NORMALMAP_TANGENTSPACE" : "", - parameters.emissiveMap ? "#define USE_EMISSIVEMAP" : "", - parameters.anisotropy ? "#define USE_ANISOTROPY" : "", - parameters.anisotropyMap ? "#define USE_ANISOTROPYMAP" : "", - parameters.clearcoat ? "#define USE_CLEARCOAT" : "", - parameters.clearcoatMap ? "#define USE_CLEARCOATMAP" : "", - parameters.clearcoatRoughnessMap ? "#define USE_CLEARCOAT_ROUGHNESSMAP" : "", - parameters.clearcoatNormalMap ? "#define USE_CLEARCOAT_NORMALMAP" : "", - parameters.dispersion ? "#define USE_DISPERSION" : "", - parameters.iridescence ? "#define USE_IRIDESCENCE" : "", - parameters.iridescenceMap ? "#define USE_IRIDESCENCEMAP" : "", - parameters.iridescenceThicknessMap ? "#define USE_IRIDESCENCE_THICKNESSMAP" : "", - parameters.specularMap ? "#define USE_SPECULARMAP" : "", - parameters.specularColorMap ? "#define USE_SPECULAR_COLORMAP" : "", - parameters.specularIntensityMap ? "#define USE_SPECULAR_INTENSITYMAP" : "", - parameters.roughnessMap ? "#define USE_ROUGHNESSMAP" : "", - parameters.metalnessMap ? "#define USE_METALNESSMAP" : "", - parameters.alphaMap ? "#define USE_ALPHAMAP" : "", - parameters.alphaTest ? "#define USE_ALPHATEST" : "", - parameters.alphaHash ? "#define USE_ALPHAHASH" : "", - parameters.sheen ? "#define USE_SHEEN" : "", - parameters.sheenColorMap ? "#define USE_SHEEN_COLORMAP" : "", - parameters.sheenRoughnessMap ? "#define USE_SHEEN_ROUGHNESSMAP" : "", - parameters.transmission ? "#define USE_TRANSMISSION" : "", - parameters.transmissionMap ? "#define USE_TRANSMISSIONMAP" : "", - parameters.thicknessMap ? "#define USE_THICKNESSMAP" : "", - parameters.vertexTangents && parameters.flatShading === false ? "#define USE_TANGENT" : "", - parameters.vertexColors || parameters.instancingColor || parameters.batchingColor ? "#define USE_COLOR" : "", - parameters.vertexAlphas ? "#define USE_COLOR_ALPHA" : "", - parameters.vertexUv1s ? "#define USE_UV1" : "", - parameters.vertexUv2s ? "#define USE_UV2" : "", - parameters.vertexUv3s ? "#define USE_UV3" : "", - parameters.pointsUvs ? "#define USE_POINTS_UV" : "", - parameters.gradientMap ? "#define USE_GRADIENTMAP" : "", - parameters.flatShading ? "#define FLAT_SHADED" : "", - parameters.doubleSided ? "#define DOUBLE_SIDED" : "", - parameters.flipSided ? "#define FLIP_SIDED" : "", - parameters.shadowMapEnabled ? "#define USE_SHADOWMAP" : "", - parameters.shadowMapEnabled ? "#define " + shadowMapTypeDefine : "", - parameters.premultipliedAlpha ? "#define PREMULTIPLIED_ALPHA" : "", - parameters.numLightProbes > 0 ? "#define USE_LIGHT_PROBES" : "", - parameters.decodeVideoTexture ? "#define DECODE_VIDEO_TEXTURE" : "", - parameters.decodeVideoTextureEmissive ? "#define DECODE_VIDEO_TEXTURE_EMISSIVE" : "", - parameters.logarithmicDepthBuffer ? "#define USE_LOGDEPTHBUF" : "", - parameters.reverseDepthBuffer ? "#define USE_REVERSEDEPTHBUF" : "", - "uniform mat4 viewMatrix;", - "uniform vec3 cameraPosition;", - "uniform bool isOrthographic;", - parameters.toneMapping !== NoToneMapping ? "#define TONE_MAPPING" : "", - parameters.toneMapping !== NoToneMapping ? ShaderChunk["tonemapping_pars_fragment"] : "", - // this code is required here because it is used by the toneMapping() function defined below - parameters.toneMapping !== NoToneMapping ? getToneMappingFunction("toneMapping", parameters.toneMapping) : "", - parameters.dithering ? "#define DITHERING" : "", - parameters.opaque ? "#define OPAQUE" : "", - ShaderChunk["colorspace_pars_fragment"], - // this code is required here because it is used by the various encoding/decoding function defined below - getTexelEncodingFunction("linearToOutputTexel", parameters.outputColorSpace), - getLuminanceFunction(), - parameters.useDepthPacking ? "#define DEPTH_PACKING " + parameters.depthPacking : "", - "\n" - ].filter(filterEmptyLine).join("\n"); - } - vertexShader = resolveIncludes(vertexShader); - vertexShader = replaceLightNums(vertexShader, parameters); - vertexShader = replaceClippingPlaneNums(vertexShader, parameters); - fragmentShader = resolveIncludes(fragmentShader); - fragmentShader = replaceLightNums(fragmentShader, parameters); - fragmentShader = replaceClippingPlaneNums(fragmentShader, parameters); - vertexShader = unrollLoops(vertexShader); - fragmentShader = unrollLoops(fragmentShader); - if (parameters.isRawShaderMaterial !== true) { - versionString = "#version 300 es\n"; - prefixVertex = [ - customVertexExtensions, - "#define attribute in", - "#define varying out", - "#define texture2D texture" - ].join("\n") + "\n" + prefixVertex; - prefixFragment = [ - "#define varying in", - parameters.glslVersion === GLSL3 ? "" : "layout(location = 0) out highp vec4 pc_fragColor;", - parameters.glslVersion === GLSL3 ? "" : "#define gl_FragColor pc_fragColor", - "#define gl_FragDepthEXT gl_FragDepth", - "#define texture2D texture", - "#define textureCube texture", - "#define texture2DProj textureProj", - "#define texture2DLodEXT textureLod", - "#define texture2DProjLodEXT textureProjLod", - "#define textureCubeLodEXT textureLod", - "#define texture2DGradEXT textureGrad", - "#define texture2DProjGradEXT textureProjGrad", - "#define textureCubeGradEXT textureGrad" - ].join("\n") + "\n" + prefixFragment; - } - const vertexGlsl = versionString + prefixVertex + vertexShader; - const fragmentGlsl = versionString + prefixFragment + fragmentShader; - const glVertexShader = WebGLShader(gl, gl.VERTEX_SHADER, vertexGlsl); - const glFragmentShader = WebGLShader(gl, gl.FRAGMENT_SHADER, fragmentGlsl); - gl.attachShader(program, glVertexShader); - gl.attachShader(program, glFragmentShader); - if (parameters.index0AttributeName !== void 0) { - gl.bindAttribLocation(program, 0, parameters.index0AttributeName); - } else if (parameters.morphTargets === true) { - gl.bindAttribLocation(program, 0, "position"); - } - gl.linkProgram(program); - function onFirstUse(self2) { - if (renderer.debug.checkShaderErrors) { - const programLog = gl.getProgramInfoLog(program).trim(); - const vertexLog = gl.getShaderInfoLog(glVertexShader).trim(); - const fragmentLog = gl.getShaderInfoLog(glFragmentShader).trim(); - let runnable = true; - let haveDiagnostics = true; - if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) { - runnable = false; - if (typeof renderer.debug.onShaderError === "function") { - renderer.debug.onShaderError(gl, program, glVertexShader, glFragmentShader); - } else { - const vertexErrors = getShaderErrors(gl, glVertexShader, "vertex"); - const fragmentErrors = getShaderErrors(gl, glFragmentShader, "fragment"); - console.error( - "THREE.WebGLProgram: Shader Error " + gl.getError() + " - VALIDATE_STATUS " + gl.getProgramParameter(program, gl.VALIDATE_STATUS) + "\n\nMaterial Name: " + self2.name + "\nMaterial Type: " + self2.type + "\n\nProgram Info Log: " + programLog + "\n" + vertexErrors + "\n" + fragmentErrors - ); - } - } else if (programLog !== "") { - console.warn("THREE.WebGLProgram: Program Info Log:", programLog); - } else if (vertexLog === "" || fragmentLog === "") { - haveDiagnostics = false; - } - if (haveDiagnostics) { - self2.diagnostics = { - runnable, - programLog, - vertexShader: { - log: vertexLog, - prefix: prefixVertex - }, - fragmentShader: { - log: fragmentLog, - prefix: prefixFragment - } - }; - } - } - gl.deleteShader(glVertexShader); - gl.deleteShader(glFragmentShader); - cachedUniforms = new WebGLUniforms(gl, program); - cachedAttributes = fetchAttributeLocations(gl, program); - } - __name(onFirstUse, "onFirstUse"); - let cachedUniforms; - this.getUniforms = function() { - if (cachedUniforms === void 0) { - onFirstUse(this); - } - return cachedUniforms; - }; - let cachedAttributes; - this.getAttributes = function() { - if (cachedAttributes === void 0) { - onFirstUse(this); - } - return cachedAttributes; - }; - let programReady = parameters.rendererExtensionParallelShaderCompile === false; - this.isReady = function() { - if (programReady === false) { - programReady = gl.getProgramParameter(program, COMPLETION_STATUS_KHR); - } - return programReady; - }; - this.destroy = function() { - bindingStates.releaseStatesOfProgram(this); - gl.deleteProgram(program); - this.program = void 0; - }; - this.type = parameters.shaderType; - this.name = parameters.shaderName; - this.id = programIdCount++; - this.cacheKey = cacheKey; - this.usedTimes = 1; - this.program = program; - this.vertexShader = glVertexShader; - this.fragmentShader = glFragmentShader; - return this; -} -__name(WebGLProgram, "WebGLProgram"); -let _id$1 = 0; -class WebGLShaderCache { - static { - __name(this, "WebGLShaderCache"); - } - constructor() { - this.shaderCache = /* @__PURE__ */ new Map(); - this.materialCache = /* @__PURE__ */ new Map(); - } - update(material) { - const vertexShader = material.vertexShader; - const fragmentShader = material.fragmentShader; - const vertexShaderStage = this._getShaderStage(vertexShader); - const fragmentShaderStage = this._getShaderStage(fragmentShader); - const materialShaders = this._getShaderCacheForMaterial(material); - if (materialShaders.has(vertexShaderStage) === false) { - materialShaders.add(vertexShaderStage); - vertexShaderStage.usedTimes++; - } - if (materialShaders.has(fragmentShaderStage) === false) { - materialShaders.add(fragmentShaderStage); - fragmentShaderStage.usedTimes++; - } - return this; - } - remove(material) { - const materialShaders = this.materialCache.get(material); - for (const shaderStage of materialShaders) { - shaderStage.usedTimes--; - if (shaderStage.usedTimes === 0) this.shaderCache.delete(shaderStage.code); - } - this.materialCache.delete(material); - return this; - } - getVertexShaderID(material) { - return this._getShaderStage(material.vertexShader).id; - } - getFragmentShaderID(material) { - return this._getShaderStage(material.fragmentShader).id; - } - dispose() { - this.shaderCache.clear(); - this.materialCache.clear(); - } - _getShaderCacheForMaterial(material) { - const cache = this.materialCache; - let set = cache.get(material); - if (set === void 0) { - set = /* @__PURE__ */ new Set(); - cache.set(material, set); - } - return set; - } - _getShaderStage(code) { - const cache = this.shaderCache; - let stage = cache.get(code); - if (stage === void 0) { - stage = new WebGLShaderStage(code); - cache.set(code, stage); - } - return stage; - } -} -class WebGLShaderStage { - static { - __name(this, "WebGLShaderStage"); - } - constructor(code) { - this.id = _id$1++; - this.code = code; - this.usedTimes = 0; - } -} -function WebGLPrograms(renderer, cubemaps, cubeuvmaps, extensions, capabilities, bindingStates, clipping) { - const _programLayers = new Layers(); - const _customShaders = new WebGLShaderCache(); - const _activeChannels = /* @__PURE__ */ new Set(); - const programs = []; - const logarithmicDepthBuffer = capabilities.logarithmicDepthBuffer; - const SUPPORTS_VERTEX_TEXTURES = capabilities.vertexTextures; - let precision = capabilities.precision; - const shaderIDs = { - MeshDepthMaterial: "depth", - MeshDistanceMaterial: "distanceRGBA", - MeshNormalMaterial: "normal", - MeshBasicMaterial: "basic", - MeshLambertMaterial: "lambert", - MeshPhongMaterial: "phong", - MeshToonMaterial: "toon", - MeshStandardMaterial: "physical", - MeshPhysicalMaterial: "physical", - MeshMatcapMaterial: "matcap", - LineBasicMaterial: "basic", - LineDashedMaterial: "dashed", - PointsMaterial: "points", - ShadowMaterial: "shadow", - SpriteMaterial: "sprite" - }; - function getChannel(value) { - _activeChannels.add(value); - if (value === 0) return "uv"; - return `uv${value}`; - } - __name(getChannel, "getChannel"); - function getParameters(material, lights, shadows, scene, object) { - const fog = scene.fog; - const geometry = object.geometry; - const environment = material.isMeshStandardMaterial ? scene.environment : null; - const envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || environment); - const envMapCubeUVHeight = !!envMap && envMap.mapping === CubeUVReflectionMapping ? envMap.image.height : null; - const shaderID = shaderIDs[material.type]; - if (material.precision !== null) { - precision = capabilities.getMaxPrecision(material.precision); - if (precision !== material.precision) { - console.warn("THREE.WebGLProgram.getParameters:", material.precision, "not supported, using", precision, "instead."); - } - } - const morphAttribute = geometry.morphAttributes.position || geometry.morphAttributes.normal || geometry.morphAttributes.color; - const morphTargetsCount = morphAttribute !== void 0 ? morphAttribute.length : 0; - let morphTextureStride = 0; - if (geometry.morphAttributes.position !== void 0) morphTextureStride = 1; - if (geometry.morphAttributes.normal !== void 0) morphTextureStride = 2; - if (geometry.morphAttributes.color !== void 0) morphTextureStride = 3; - let vertexShader, fragmentShader; - let customVertexShaderID, customFragmentShaderID; - if (shaderID) { - const shader = ShaderLib[shaderID]; - vertexShader = shader.vertexShader; - fragmentShader = shader.fragmentShader; - } else { - vertexShader = material.vertexShader; - fragmentShader = material.fragmentShader; - _customShaders.update(material); - customVertexShaderID = _customShaders.getVertexShaderID(material); - customFragmentShaderID = _customShaders.getFragmentShaderID(material); - } - const currentRenderTarget = renderer.getRenderTarget(); - const reverseDepthBuffer = renderer.state.buffers.depth.getReversed(); - const IS_INSTANCEDMESH = object.isInstancedMesh === true; - const IS_BATCHEDMESH = object.isBatchedMesh === true; - const HAS_MAP = !!material.map; - const HAS_MATCAP = !!material.matcap; - const HAS_ENVMAP = !!envMap; - const HAS_AOMAP = !!material.aoMap; - const HAS_LIGHTMAP = !!material.lightMap; - const HAS_BUMPMAP = !!material.bumpMap; - const HAS_NORMALMAP = !!material.normalMap; - const HAS_DISPLACEMENTMAP = !!material.displacementMap; - const HAS_EMISSIVEMAP = !!material.emissiveMap; - const HAS_METALNESSMAP = !!material.metalnessMap; - const HAS_ROUGHNESSMAP = !!material.roughnessMap; - const HAS_ANISOTROPY = material.anisotropy > 0; - const HAS_CLEARCOAT = material.clearcoat > 0; - const HAS_DISPERSION = material.dispersion > 0; - const HAS_IRIDESCENCE = material.iridescence > 0; - const HAS_SHEEN = material.sheen > 0; - const HAS_TRANSMISSION = material.transmission > 0; - const HAS_ANISOTROPYMAP = HAS_ANISOTROPY && !!material.anisotropyMap; - const HAS_CLEARCOATMAP = HAS_CLEARCOAT && !!material.clearcoatMap; - const HAS_CLEARCOAT_NORMALMAP = HAS_CLEARCOAT && !!material.clearcoatNormalMap; - const HAS_CLEARCOAT_ROUGHNESSMAP = HAS_CLEARCOAT && !!material.clearcoatRoughnessMap; - const HAS_IRIDESCENCEMAP = HAS_IRIDESCENCE && !!material.iridescenceMap; - const HAS_IRIDESCENCE_THICKNESSMAP = HAS_IRIDESCENCE && !!material.iridescenceThicknessMap; - const HAS_SHEEN_COLORMAP = HAS_SHEEN && !!material.sheenColorMap; - const HAS_SHEEN_ROUGHNESSMAP = HAS_SHEEN && !!material.sheenRoughnessMap; - const HAS_SPECULARMAP = !!material.specularMap; - const HAS_SPECULAR_COLORMAP = !!material.specularColorMap; - const HAS_SPECULAR_INTENSITYMAP = !!material.specularIntensityMap; - const HAS_TRANSMISSIONMAP = HAS_TRANSMISSION && !!material.transmissionMap; - const HAS_THICKNESSMAP = HAS_TRANSMISSION && !!material.thicknessMap; - const HAS_GRADIENTMAP = !!material.gradientMap; - const HAS_ALPHAMAP = !!material.alphaMap; - const HAS_ALPHATEST = material.alphaTest > 0; - const HAS_ALPHAHASH = !!material.alphaHash; - const HAS_EXTENSIONS = !!material.extensions; - let toneMapping = NoToneMapping; - if (material.toneMapped) { - if (currentRenderTarget === null || currentRenderTarget.isXRRenderTarget === true) { - toneMapping = renderer.toneMapping; - } - } - const parameters = { - shaderID, - shaderType: material.type, - shaderName: material.name, - vertexShader, - fragmentShader, - defines: material.defines, - customVertexShaderID, - customFragmentShaderID, - isRawShaderMaterial: material.isRawShaderMaterial === true, - glslVersion: material.glslVersion, - precision, - batching: IS_BATCHEDMESH, - batchingColor: IS_BATCHEDMESH && object._colorsTexture !== null, - instancing: IS_INSTANCEDMESH, - instancingColor: IS_INSTANCEDMESH && object.instanceColor !== null, - instancingMorph: IS_INSTANCEDMESH && object.morphTexture !== null, - supportsVertexTextures: SUPPORTS_VERTEX_TEXTURES, - outputColorSpace: currentRenderTarget === null ? renderer.outputColorSpace : currentRenderTarget.isXRRenderTarget === true ? currentRenderTarget.texture.colorSpace : LinearSRGBColorSpace, - alphaToCoverage: !!material.alphaToCoverage, - map: HAS_MAP, - matcap: HAS_MATCAP, - envMap: HAS_ENVMAP, - envMapMode: HAS_ENVMAP && envMap.mapping, - envMapCubeUVHeight, - aoMap: HAS_AOMAP, - lightMap: HAS_LIGHTMAP, - bumpMap: HAS_BUMPMAP, - normalMap: HAS_NORMALMAP, - displacementMap: SUPPORTS_VERTEX_TEXTURES && HAS_DISPLACEMENTMAP, - emissiveMap: HAS_EMISSIVEMAP, - normalMapObjectSpace: HAS_NORMALMAP && material.normalMapType === ObjectSpaceNormalMap, - normalMapTangentSpace: HAS_NORMALMAP && material.normalMapType === TangentSpaceNormalMap, - metalnessMap: HAS_METALNESSMAP, - roughnessMap: HAS_ROUGHNESSMAP, - anisotropy: HAS_ANISOTROPY, - anisotropyMap: HAS_ANISOTROPYMAP, - clearcoat: HAS_CLEARCOAT, - clearcoatMap: HAS_CLEARCOATMAP, - clearcoatNormalMap: HAS_CLEARCOAT_NORMALMAP, - clearcoatRoughnessMap: HAS_CLEARCOAT_ROUGHNESSMAP, - dispersion: HAS_DISPERSION, - iridescence: HAS_IRIDESCENCE, - iridescenceMap: HAS_IRIDESCENCEMAP, - iridescenceThicknessMap: HAS_IRIDESCENCE_THICKNESSMAP, - sheen: HAS_SHEEN, - sheenColorMap: HAS_SHEEN_COLORMAP, - sheenRoughnessMap: HAS_SHEEN_ROUGHNESSMAP, - specularMap: HAS_SPECULARMAP, - specularColorMap: HAS_SPECULAR_COLORMAP, - specularIntensityMap: HAS_SPECULAR_INTENSITYMAP, - transmission: HAS_TRANSMISSION, - transmissionMap: HAS_TRANSMISSIONMAP, - thicknessMap: HAS_THICKNESSMAP, - gradientMap: HAS_GRADIENTMAP, - opaque: material.transparent === false && material.blending === NormalBlending && material.alphaToCoverage === false, - alphaMap: HAS_ALPHAMAP, - alphaTest: HAS_ALPHATEST, - alphaHash: HAS_ALPHAHASH, - combine: material.combine, - // - mapUv: HAS_MAP && getChannel(material.map.channel), - aoMapUv: HAS_AOMAP && getChannel(material.aoMap.channel), - lightMapUv: HAS_LIGHTMAP && getChannel(material.lightMap.channel), - bumpMapUv: HAS_BUMPMAP && getChannel(material.bumpMap.channel), - normalMapUv: HAS_NORMALMAP && getChannel(material.normalMap.channel), - displacementMapUv: HAS_DISPLACEMENTMAP && getChannel(material.displacementMap.channel), - emissiveMapUv: HAS_EMISSIVEMAP && getChannel(material.emissiveMap.channel), - metalnessMapUv: HAS_METALNESSMAP && getChannel(material.metalnessMap.channel), - roughnessMapUv: HAS_ROUGHNESSMAP && getChannel(material.roughnessMap.channel), - anisotropyMapUv: HAS_ANISOTROPYMAP && getChannel(material.anisotropyMap.channel), - clearcoatMapUv: HAS_CLEARCOATMAP && getChannel(material.clearcoatMap.channel), - clearcoatNormalMapUv: HAS_CLEARCOAT_NORMALMAP && getChannel(material.clearcoatNormalMap.channel), - clearcoatRoughnessMapUv: HAS_CLEARCOAT_ROUGHNESSMAP && getChannel(material.clearcoatRoughnessMap.channel), - iridescenceMapUv: HAS_IRIDESCENCEMAP && getChannel(material.iridescenceMap.channel), - iridescenceThicknessMapUv: HAS_IRIDESCENCE_THICKNESSMAP && getChannel(material.iridescenceThicknessMap.channel), - sheenColorMapUv: HAS_SHEEN_COLORMAP && getChannel(material.sheenColorMap.channel), - sheenRoughnessMapUv: HAS_SHEEN_ROUGHNESSMAP && getChannel(material.sheenRoughnessMap.channel), - specularMapUv: HAS_SPECULARMAP && getChannel(material.specularMap.channel), - specularColorMapUv: HAS_SPECULAR_COLORMAP && getChannel(material.specularColorMap.channel), - specularIntensityMapUv: HAS_SPECULAR_INTENSITYMAP && getChannel(material.specularIntensityMap.channel), - transmissionMapUv: HAS_TRANSMISSIONMAP && getChannel(material.transmissionMap.channel), - thicknessMapUv: HAS_THICKNESSMAP && getChannel(material.thicknessMap.channel), - alphaMapUv: HAS_ALPHAMAP && getChannel(material.alphaMap.channel), - // - vertexTangents: !!geometry.attributes.tangent && (HAS_NORMALMAP || HAS_ANISOTROPY), - vertexColors: material.vertexColors, - vertexAlphas: material.vertexColors === true && !!geometry.attributes.color && geometry.attributes.color.itemSize === 4, - pointsUvs: object.isPoints === true && !!geometry.attributes.uv && (HAS_MAP || HAS_ALPHAMAP), - fog: !!fog, - useFog: material.fog === true, - fogExp2: !!fog && fog.isFogExp2, - flatShading: material.flatShading === true, - sizeAttenuation: material.sizeAttenuation === true, - logarithmicDepthBuffer, - reverseDepthBuffer, - skinning: object.isSkinnedMesh === true, - morphTargets: geometry.morphAttributes.position !== void 0, - morphNormals: geometry.morphAttributes.normal !== void 0, - morphColors: geometry.morphAttributes.color !== void 0, - morphTargetsCount, - morphTextureStride, - numDirLights: lights.directional.length, - numPointLights: lights.point.length, - numSpotLights: lights.spot.length, - numSpotLightMaps: lights.spotLightMap.length, - numRectAreaLights: lights.rectArea.length, - numHemiLights: lights.hemi.length, - numDirLightShadows: lights.directionalShadowMap.length, - numPointLightShadows: lights.pointShadowMap.length, - numSpotLightShadows: lights.spotShadowMap.length, - numSpotLightShadowsWithMaps: lights.numSpotLightShadowsWithMaps, - numLightProbes: lights.numLightProbes, - numClippingPlanes: clipping.numPlanes, - numClipIntersection: clipping.numIntersection, - dithering: material.dithering, - shadowMapEnabled: renderer.shadowMap.enabled && shadows.length > 0, - shadowMapType: renderer.shadowMap.type, - toneMapping, - decodeVideoTexture: HAS_MAP && material.map.isVideoTexture === true && ColorManagement.getTransfer(material.map.colorSpace) === SRGBTransfer, - decodeVideoTextureEmissive: HAS_EMISSIVEMAP && material.emissiveMap.isVideoTexture === true && ColorManagement.getTransfer(material.emissiveMap.colorSpace) === SRGBTransfer, - premultipliedAlpha: material.premultipliedAlpha, - doubleSided: material.side === DoubleSide, - flipSided: material.side === BackSide, - useDepthPacking: material.depthPacking >= 0, - depthPacking: material.depthPacking || 0, - index0AttributeName: material.index0AttributeName, - extensionClipCullDistance: HAS_EXTENSIONS && material.extensions.clipCullDistance === true && extensions.has("WEBGL_clip_cull_distance"), - extensionMultiDraw: (HAS_EXTENSIONS && material.extensions.multiDraw === true || IS_BATCHEDMESH) && extensions.has("WEBGL_multi_draw"), - rendererExtensionParallelShaderCompile: extensions.has("KHR_parallel_shader_compile"), - customProgramCacheKey: material.customProgramCacheKey() - }; - parameters.vertexUv1s = _activeChannels.has(1); - parameters.vertexUv2s = _activeChannels.has(2); - parameters.vertexUv3s = _activeChannels.has(3); - _activeChannels.clear(); - return parameters; - } - __name(getParameters, "getParameters"); - function getProgramCacheKey(parameters) { - const array = []; - if (parameters.shaderID) { - array.push(parameters.shaderID); - } else { - array.push(parameters.customVertexShaderID); - array.push(parameters.customFragmentShaderID); - } - if (parameters.defines !== void 0) { - for (const name in parameters.defines) { - array.push(name); - array.push(parameters.defines[name]); - } - } - if (parameters.isRawShaderMaterial === false) { - getProgramCacheKeyParameters(array, parameters); - getProgramCacheKeyBooleans(array, parameters); - array.push(renderer.outputColorSpace); - } - array.push(parameters.customProgramCacheKey); - return array.join(); - } - __name(getProgramCacheKey, "getProgramCacheKey"); - function getProgramCacheKeyParameters(array, parameters) { - array.push(parameters.precision); - array.push(parameters.outputColorSpace); - array.push(parameters.envMapMode); - array.push(parameters.envMapCubeUVHeight); - array.push(parameters.mapUv); - array.push(parameters.alphaMapUv); - array.push(parameters.lightMapUv); - array.push(parameters.aoMapUv); - array.push(parameters.bumpMapUv); - array.push(parameters.normalMapUv); - array.push(parameters.displacementMapUv); - array.push(parameters.emissiveMapUv); - array.push(parameters.metalnessMapUv); - array.push(parameters.roughnessMapUv); - array.push(parameters.anisotropyMapUv); - array.push(parameters.clearcoatMapUv); - array.push(parameters.clearcoatNormalMapUv); - array.push(parameters.clearcoatRoughnessMapUv); - array.push(parameters.iridescenceMapUv); - array.push(parameters.iridescenceThicknessMapUv); - array.push(parameters.sheenColorMapUv); - array.push(parameters.sheenRoughnessMapUv); - array.push(parameters.specularMapUv); - array.push(parameters.specularColorMapUv); - array.push(parameters.specularIntensityMapUv); - array.push(parameters.transmissionMapUv); - array.push(parameters.thicknessMapUv); - array.push(parameters.combine); - array.push(parameters.fogExp2); - array.push(parameters.sizeAttenuation); - array.push(parameters.morphTargetsCount); - array.push(parameters.morphAttributeCount); - array.push(parameters.numDirLights); - array.push(parameters.numPointLights); - array.push(parameters.numSpotLights); - array.push(parameters.numSpotLightMaps); - array.push(parameters.numHemiLights); - array.push(parameters.numRectAreaLights); - array.push(parameters.numDirLightShadows); - array.push(parameters.numPointLightShadows); - array.push(parameters.numSpotLightShadows); - array.push(parameters.numSpotLightShadowsWithMaps); - array.push(parameters.numLightProbes); - array.push(parameters.shadowMapType); - array.push(parameters.toneMapping); - array.push(parameters.numClippingPlanes); - array.push(parameters.numClipIntersection); - array.push(parameters.depthPacking); - } - __name(getProgramCacheKeyParameters, "getProgramCacheKeyParameters"); - function getProgramCacheKeyBooleans(array, parameters) { - _programLayers.disableAll(); - if (parameters.supportsVertexTextures) - _programLayers.enable(0); - if (parameters.instancing) - _programLayers.enable(1); - if (parameters.instancingColor) - _programLayers.enable(2); - if (parameters.instancingMorph) - _programLayers.enable(3); - if (parameters.matcap) - _programLayers.enable(4); - if (parameters.envMap) - _programLayers.enable(5); - if (parameters.normalMapObjectSpace) - _programLayers.enable(6); - if (parameters.normalMapTangentSpace) - _programLayers.enable(7); - if (parameters.clearcoat) - _programLayers.enable(8); - if (parameters.iridescence) - _programLayers.enable(9); - if (parameters.alphaTest) - _programLayers.enable(10); - if (parameters.vertexColors) - _programLayers.enable(11); - if (parameters.vertexAlphas) - _programLayers.enable(12); - if (parameters.vertexUv1s) - _programLayers.enable(13); - if (parameters.vertexUv2s) - _programLayers.enable(14); - if (parameters.vertexUv3s) - _programLayers.enable(15); - if (parameters.vertexTangents) - _programLayers.enable(16); - if (parameters.anisotropy) - _programLayers.enable(17); - if (parameters.alphaHash) - _programLayers.enable(18); - if (parameters.batching) - _programLayers.enable(19); - if (parameters.dispersion) - _programLayers.enable(20); - if (parameters.batchingColor) - _programLayers.enable(21); - array.push(_programLayers.mask); - _programLayers.disableAll(); - if (parameters.fog) - _programLayers.enable(0); - if (parameters.useFog) - _programLayers.enable(1); - if (parameters.flatShading) - _programLayers.enable(2); - if (parameters.logarithmicDepthBuffer) - _programLayers.enable(3); - if (parameters.reverseDepthBuffer) - _programLayers.enable(4); - if (parameters.skinning) - _programLayers.enable(5); - if (parameters.morphTargets) - _programLayers.enable(6); - if (parameters.morphNormals) - _programLayers.enable(7); - if (parameters.morphColors) - _programLayers.enable(8); - if (parameters.premultipliedAlpha) - _programLayers.enable(9); - if (parameters.shadowMapEnabled) - _programLayers.enable(10); - if (parameters.doubleSided) - _programLayers.enable(11); - if (parameters.flipSided) - _programLayers.enable(12); - if (parameters.useDepthPacking) - _programLayers.enable(13); - if (parameters.dithering) - _programLayers.enable(14); - if (parameters.transmission) - _programLayers.enable(15); - if (parameters.sheen) - _programLayers.enable(16); - if (parameters.opaque) - _programLayers.enable(17); - if (parameters.pointsUvs) - _programLayers.enable(18); - if (parameters.decodeVideoTexture) - _programLayers.enable(19); - if (parameters.decodeVideoTextureEmissive) - _programLayers.enable(20); - if (parameters.alphaToCoverage) - _programLayers.enable(21); - array.push(_programLayers.mask); - } - __name(getProgramCacheKeyBooleans, "getProgramCacheKeyBooleans"); - function getUniforms(material) { - const shaderID = shaderIDs[material.type]; - let uniforms; - if (shaderID) { - const shader = ShaderLib[shaderID]; - uniforms = UniformsUtils.clone(shader.uniforms); - } else { - uniforms = material.uniforms; - } - return uniforms; - } - __name(getUniforms, "getUniforms"); - function acquireProgram(parameters, cacheKey) { - let program; - for (let p = 0, pl = programs.length; p < pl; p++) { - const preexistingProgram = programs[p]; - if (preexistingProgram.cacheKey === cacheKey) { - program = preexistingProgram; - ++program.usedTimes; - break; - } - } - if (program === void 0) { - program = new WebGLProgram(renderer, cacheKey, parameters, bindingStates); - programs.push(program); - } - return program; - } - __name(acquireProgram, "acquireProgram"); - function releaseProgram(program) { - if (--program.usedTimes === 0) { - const i = programs.indexOf(program); - programs[i] = programs[programs.length - 1]; - programs.pop(); - program.destroy(); - } - } - __name(releaseProgram, "releaseProgram"); - function releaseShaderCache(material) { - _customShaders.remove(material); - } - __name(releaseShaderCache, "releaseShaderCache"); - function dispose() { - _customShaders.dispose(); - } - __name(dispose, "dispose"); - return { - getParameters, - getProgramCacheKey, - getUniforms, - acquireProgram, - releaseProgram, - releaseShaderCache, - // Exposed for resource monitoring & error feedback via renderer.info: - programs, - dispose - }; -} -__name(WebGLPrograms, "WebGLPrograms"); -function WebGLProperties() { - let properties = /* @__PURE__ */ new WeakMap(); - function has(object) { - return properties.has(object); - } - __name(has, "has"); - function get(object) { - let map = properties.get(object); - if (map === void 0) { - map = {}; - properties.set(object, map); - } - return map; - } - __name(get, "get"); - function remove(object) { - properties.delete(object); - } - __name(remove, "remove"); - function update(object, key, value) { - properties.get(object)[key] = value; - } - __name(update, "update"); - function dispose() { - properties = /* @__PURE__ */ new WeakMap(); - } - __name(dispose, "dispose"); - return { - has, - get, - remove, - update, - dispose - }; -} -__name(WebGLProperties, "WebGLProperties"); -function painterSortStable(a, b) { - if (a.groupOrder !== b.groupOrder) { - return a.groupOrder - b.groupOrder; - } else if (a.renderOrder !== b.renderOrder) { - return a.renderOrder - b.renderOrder; - } else if (a.material.id !== b.material.id) { - return a.material.id - b.material.id; - } else if (a.z !== b.z) { - return a.z - b.z; - } else { - return a.id - b.id; - } -} -__name(painterSortStable, "painterSortStable"); -function reversePainterSortStable(a, b) { - if (a.groupOrder !== b.groupOrder) { - return a.groupOrder - b.groupOrder; - } else if (a.renderOrder !== b.renderOrder) { - return a.renderOrder - b.renderOrder; - } else if (a.z !== b.z) { - return b.z - a.z; - } else { - return a.id - b.id; - } -} -__name(reversePainterSortStable, "reversePainterSortStable"); -function WebGLRenderList() { - const renderItems = []; - let renderItemsIndex = 0; - const opaque = []; - const transmissive = []; - const transparent = []; - function init() { - renderItemsIndex = 0; - opaque.length = 0; - transmissive.length = 0; - transparent.length = 0; - } - __name(init, "init"); - function getNextRenderItem(object, geometry, material, groupOrder, z, group) { - let renderItem = renderItems[renderItemsIndex]; - if (renderItem === void 0) { - renderItem = { - id: object.id, - object, - geometry, - material, - groupOrder, - renderOrder: object.renderOrder, - z, - group - }; - renderItems[renderItemsIndex] = renderItem; - } else { - renderItem.id = object.id; - renderItem.object = object; - renderItem.geometry = geometry; - renderItem.material = material; - renderItem.groupOrder = groupOrder; - renderItem.renderOrder = object.renderOrder; - renderItem.z = z; - renderItem.group = group; - } - renderItemsIndex++; - return renderItem; - } - __name(getNextRenderItem, "getNextRenderItem"); - function push(object, geometry, material, groupOrder, z, group) { - const renderItem = getNextRenderItem(object, geometry, material, groupOrder, z, group); - if (material.transmission > 0) { - transmissive.push(renderItem); - } else if (material.transparent === true) { - transparent.push(renderItem); - } else { - opaque.push(renderItem); - } - } - __name(push, "push"); - function unshift(object, geometry, material, groupOrder, z, group) { - const renderItem = getNextRenderItem(object, geometry, material, groupOrder, z, group); - if (material.transmission > 0) { - transmissive.unshift(renderItem); - } else if (material.transparent === true) { - transparent.unshift(renderItem); - } else { - opaque.unshift(renderItem); - } - } - __name(unshift, "unshift"); - function sort(customOpaqueSort, customTransparentSort) { - if (opaque.length > 1) opaque.sort(customOpaqueSort || painterSortStable); - if (transmissive.length > 1) transmissive.sort(customTransparentSort || reversePainterSortStable); - if (transparent.length > 1) transparent.sort(customTransparentSort || reversePainterSortStable); - } - __name(sort, "sort"); - function finish() { - for (let i = renderItemsIndex, il = renderItems.length; i < il; i++) { - const renderItem = renderItems[i]; - if (renderItem.id === null) break; - renderItem.id = null; - renderItem.object = null; - renderItem.geometry = null; - renderItem.material = null; - renderItem.group = null; - } - } - __name(finish, "finish"); - return { - opaque, - transmissive, - transparent, - init, - push, - unshift, - finish, - sort - }; -} -__name(WebGLRenderList, "WebGLRenderList"); -function WebGLRenderLists() { - let lists = /* @__PURE__ */ new WeakMap(); - function get(scene, renderCallDepth) { - const listArray = lists.get(scene); - let list; - if (listArray === void 0) { - list = new WebGLRenderList(); - lists.set(scene, [list]); - } else { - if (renderCallDepth >= listArray.length) { - list = new WebGLRenderList(); - listArray.push(list); - } else { - list = listArray[renderCallDepth]; - } - } - return list; - } - __name(get, "get"); - function dispose() { - lists = /* @__PURE__ */ new WeakMap(); - } - __name(dispose, "dispose"); - return { - get, - dispose - }; -} -__name(WebGLRenderLists, "WebGLRenderLists"); -function UniformsCache() { - const lights = {}; - return { - get: /* @__PURE__ */ __name(function(light) { - if (lights[light.id] !== void 0) { - return lights[light.id]; - } - let uniforms; - switch (light.type) { - case "DirectionalLight": - uniforms = { - direction: new Vector3(), - color: new Color() - }; - break; - case "SpotLight": - uniforms = { - position: new Vector3(), - direction: new Vector3(), - color: new Color(), - distance: 0, - coneCos: 0, - penumbraCos: 0, - decay: 0 - }; - break; - case "PointLight": - uniforms = { - position: new Vector3(), - color: new Color(), - distance: 0, - decay: 0 - }; - break; - case "HemisphereLight": - uniforms = { - direction: new Vector3(), - skyColor: new Color(), - groundColor: new Color() - }; - break; - case "RectAreaLight": - uniforms = { - color: new Color(), - position: new Vector3(), - halfWidth: new Vector3(), - halfHeight: new Vector3() - }; - break; - } - lights[light.id] = uniforms; - return uniforms; - }, "get") - }; -} -__name(UniformsCache, "UniformsCache"); -function ShadowUniformsCache() { - const lights = {}; - return { - get: /* @__PURE__ */ __name(function(light) { - if (lights[light.id] !== void 0) { - return lights[light.id]; - } - let uniforms; - switch (light.type) { - case "DirectionalLight": - uniforms = { - shadowIntensity: 1, - shadowBias: 0, - shadowNormalBias: 0, - shadowRadius: 1, - shadowMapSize: new Vector2() - }; - break; - case "SpotLight": - uniforms = { - shadowIntensity: 1, - shadowBias: 0, - shadowNormalBias: 0, - shadowRadius: 1, - shadowMapSize: new Vector2() - }; - break; - case "PointLight": - uniforms = { - shadowIntensity: 1, - shadowBias: 0, - shadowNormalBias: 0, - shadowRadius: 1, - shadowMapSize: new Vector2(), - shadowCameraNear: 1, - shadowCameraFar: 1e3 - }; - break; - } - lights[light.id] = uniforms; - return uniforms; - }, "get") - }; -} -__name(ShadowUniformsCache, "ShadowUniformsCache"); -let nextVersion = 0; -function shadowCastingAndTexturingLightsFirst(lightA, lightB) { - return (lightB.castShadow ? 2 : 0) - (lightA.castShadow ? 2 : 0) + (lightB.map ? 1 : 0) - (lightA.map ? 1 : 0); -} -__name(shadowCastingAndTexturingLightsFirst, "shadowCastingAndTexturingLightsFirst"); -function WebGLLights(extensions) { - const cache = new UniformsCache(); - const shadowCache = ShadowUniformsCache(); - const state = { - version: 0, - hash: { - directionalLength: -1, - pointLength: -1, - spotLength: -1, - rectAreaLength: -1, - hemiLength: -1, - numDirectionalShadows: -1, - numPointShadows: -1, - numSpotShadows: -1, - numSpotMaps: -1, - numLightProbes: -1 - }, - ambient: [0, 0, 0], - probe: [], - directional: [], - directionalShadow: [], - directionalShadowMap: [], - directionalShadowMatrix: [], - spot: [], - spotLightMap: [], - spotShadow: [], - spotShadowMap: [], - spotLightMatrix: [], - rectArea: [], - rectAreaLTC1: null, - rectAreaLTC2: null, - point: [], - pointShadow: [], - pointShadowMap: [], - pointShadowMatrix: [], - hemi: [], - numSpotLightShadowsWithMaps: 0, - numLightProbes: 0 - }; - for (let i = 0; i < 9; i++) state.probe.push(new Vector3()); - const vector3 = new Vector3(); - const matrix4 = new Matrix4(); - const matrix42 = new Matrix4(); - function setup(lights) { - let r = 0, g = 0, b = 0; - for (let i = 0; i < 9; i++) state.probe[i].set(0, 0, 0); - let directionalLength = 0; - let pointLength = 0; - let spotLength = 0; - let rectAreaLength = 0; - let hemiLength = 0; - let numDirectionalShadows = 0; - let numPointShadows = 0; - let numSpotShadows = 0; - let numSpotMaps = 0; - let numSpotShadowsWithMaps = 0; - let numLightProbes = 0; - lights.sort(shadowCastingAndTexturingLightsFirst); - for (let i = 0, l = lights.length; i < l; i++) { - const light = lights[i]; - const color = light.color; - const intensity = light.intensity; - const distance = light.distance; - const shadowMap = light.shadow && light.shadow.map ? light.shadow.map.texture : null; - if (light.isAmbientLight) { - r += color.r * intensity; - g += color.g * intensity; - b += color.b * intensity; - } else if (light.isLightProbe) { - for (let j = 0; j < 9; j++) { - state.probe[j].addScaledVector(light.sh.coefficients[j], intensity); - } - numLightProbes++; - } else if (light.isDirectionalLight) { - const uniforms = cache.get(light); - uniforms.color.copy(light.color).multiplyScalar(light.intensity); - if (light.castShadow) { - const shadow = light.shadow; - const shadowUniforms = shadowCache.get(light); - shadowUniforms.shadowIntensity = shadow.intensity; - shadowUniforms.shadowBias = shadow.bias; - shadowUniforms.shadowNormalBias = shadow.normalBias; - shadowUniforms.shadowRadius = shadow.radius; - shadowUniforms.shadowMapSize = shadow.mapSize; - state.directionalShadow[directionalLength] = shadowUniforms; - state.directionalShadowMap[directionalLength] = shadowMap; - state.directionalShadowMatrix[directionalLength] = light.shadow.matrix; - numDirectionalShadows++; - } - state.directional[directionalLength] = uniforms; - directionalLength++; - } else if (light.isSpotLight) { - const uniforms = cache.get(light); - uniforms.position.setFromMatrixPosition(light.matrixWorld); - uniforms.color.copy(color).multiplyScalar(intensity); - uniforms.distance = distance; - uniforms.coneCos = Math.cos(light.angle); - uniforms.penumbraCos = Math.cos(light.angle * (1 - light.penumbra)); - uniforms.decay = light.decay; - state.spot[spotLength] = uniforms; - const shadow = light.shadow; - if (light.map) { - state.spotLightMap[numSpotMaps] = light.map; - numSpotMaps++; - shadow.updateMatrices(light); - if (light.castShadow) numSpotShadowsWithMaps++; - } - state.spotLightMatrix[spotLength] = shadow.matrix; - if (light.castShadow) { - const shadowUniforms = shadowCache.get(light); - shadowUniforms.shadowIntensity = shadow.intensity; - shadowUniforms.shadowBias = shadow.bias; - shadowUniforms.shadowNormalBias = shadow.normalBias; - shadowUniforms.shadowRadius = shadow.radius; - shadowUniforms.shadowMapSize = shadow.mapSize; - state.spotShadow[spotLength] = shadowUniforms; - state.spotShadowMap[spotLength] = shadowMap; - numSpotShadows++; - } - spotLength++; - } else if (light.isRectAreaLight) { - const uniforms = cache.get(light); - uniforms.color.copy(color).multiplyScalar(intensity); - uniforms.halfWidth.set(light.width * 0.5, 0, 0); - uniforms.halfHeight.set(0, light.height * 0.5, 0); - state.rectArea[rectAreaLength] = uniforms; - rectAreaLength++; - } else if (light.isPointLight) { - const uniforms = cache.get(light); - uniforms.color.copy(light.color).multiplyScalar(light.intensity); - uniforms.distance = light.distance; - uniforms.decay = light.decay; - if (light.castShadow) { - const shadow = light.shadow; - const shadowUniforms = shadowCache.get(light); - shadowUniforms.shadowIntensity = shadow.intensity; - shadowUniforms.shadowBias = shadow.bias; - shadowUniforms.shadowNormalBias = shadow.normalBias; - shadowUniforms.shadowRadius = shadow.radius; - shadowUniforms.shadowMapSize = shadow.mapSize; - shadowUniforms.shadowCameraNear = shadow.camera.near; - shadowUniforms.shadowCameraFar = shadow.camera.far; - state.pointShadow[pointLength] = shadowUniforms; - state.pointShadowMap[pointLength] = shadowMap; - state.pointShadowMatrix[pointLength] = light.shadow.matrix; - numPointShadows++; - } - state.point[pointLength] = uniforms; - pointLength++; - } else if (light.isHemisphereLight) { - const uniforms = cache.get(light); - uniforms.skyColor.copy(light.color).multiplyScalar(intensity); - uniforms.groundColor.copy(light.groundColor).multiplyScalar(intensity); - state.hemi[hemiLength] = uniforms; - hemiLength++; - } - } - if (rectAreaLength > 0) { - if (extensions.has("OES_texture_float_linear") === true) { - state.rectAreaLTC1 = UniformsLib.LTC_FLOAT_1; - state.rectAreaLTC2 = UniformsLib.LTC_FLOAT_2; - } else { - state.rectAreaLTC1 = UniformsLib.LTC_HALF_1; - state.rectAreaLTC2 = UniformsLib.LTC_HALF_2; - } - } - state.ambient[0] = r; - state.ambient[1] = g; - state.ambient[2] = b; - const hash = state.hash; - if (hash.directionalLength !== directionalLength || hash.pointLength !== pointLength || hash.spotLength !== spotLength || hash.rectAreaLength !== rectAreaLength || hash.hemiLength !== hemiLength || hash.numDirectionalShadows !== numDirectionalShadows || hash.numPointShadows !== numPointShadows || hash.numSpotShadows !== numSpotShadows || hash.numSpotMaps !== numSpotMaps || hash.numLightProbes !== numLightProbes) { - state.directional.length = directionalLength; - state.spot.length = spotLength; - state.rectArea.length = rectAreaLength; - state.point.length = pointLength; - state.hemi.length = hemiLength; - state.directionalShadow.length = numDirectionalShadows; - state.directionalShadowMap.length = numDirectionalShadows; - state.pointShadow.length = numPointShadows; - state.pointShadowMap.length = numPointShadows; - state.spotShadow.length = numSpotShadows; - state.spotShadowMap.length = numSpotShadows; - state.directionalShadowMatrix.length = numDirectionalShadows; - state.pointShadowMatrix.length = numPointShadows; - state.spotLightMatrix.length = numSpotShadows + numSpotMaps - numSpotShadowsWithMaps; - state.spotLightMap.length = numSpotMaps; - state.numSpotLightShadowsWithMaps = numSpotShadowsWithMaps; - state.numLightProbes = numLightProbes; - hash.directionalLength = directionalLength; - hash.pointLength = pointLength; - hash.spotLength = spotLength; - hash.rectAreaLength = rectAreaLength; - hash.hemiLength = hemiLength; - hash.numDirectionalShadows = numDirectionalShadows; - hash.numPointShadows = numPointShadows; - hash.numSpotShadows = numSpotShadows; - hash.numSpotMaps = numSpotMaps; - hash.numLightProbes = numLightProbes; - state.version = nextVersion++; - } - } - __name(setup, "setup"); - function setupView(lights, camera) { - let directionalLength = 0; - let pointLength = 0; - let spotLength = 0; - let rectAreaLength = 0; - let hemiLength = 0; - const viewMatrix = camera.matrixWorldInverse; - for (let i = 0, l = lights.length; i < l; i++) { - const light = lights[i]; - if (light.isDirectionalLight) { - const uniforms = state.directional[directionalLength]; - uniforms.direction.setFromMatrixPosition(light.matrixWorld); - vector3.setFromMatrixPosition(light.target.matrixWorld); - uniforms.direction.sub(vector3); - uniforms.direction.transformDirection(viewMatrix); - directionalLength++; - } else if (light.isSpotLight) { - const uniforms = state.spot[spotLength]; - uniforms.position.setFromMatrixPosition(light.matrixWorld); - uniforms.position.applyMatrix4(viewMatrix); - uniforms.direction.setFromMatrixPosition(light.matrixWorld); - vector3.setFromMatrixPosition(light.target.matrixWorld); - uniforms.direction.sub(vector3); - uniforms.direction.transformDirection(viewMatrix); - spotLength++; - } else if (light.isRectAreaLight) { - const uniforms = state.rectArea[rectAreaLength]; - uniforms.position.setFromMatrixPosition(light.matrixWorld); - uniforms.position.applyMatrix4(viewMatrix); - matrix42.identity(); - matrix4.copy(light.matrixWorld); - matrix4.premultiply(viewMatrix); - matrix42.extractRotation(matrix4); - uniforms.halfWidth.set(light.width * 0.5, 0, 0); - uniforms.halfHeight.set(0, light.height * 0.5, 0); - uniforms.halfWidth.applyMatrix4(matrix42); - uniforms.halfHeight.applyMatrix4(matrix42); - rectAreaLength++; - } else if (light.isPointLight) { - const uniforms = state.point[pointLength]; - uniforms.position.setFromMatrixPosition(light.matrixWorld); - uniforms.position.applyMatrix4(viewMatrix); - pointLength++; - } else if (light.isHemisphereLight) { - const uniforms = state.hemi[hemiLength]; - uniforms.direction.setFromMatrixPosition(light.matrixWorld); - uniforms.direction.transformDirection(viewMatrix); - hemiLength++; - } - } - } - __name(setupView, "setupView"); - return { - setup, - setupView, - state - }; -} -__name(WebGLLights, "WebGLLights"); -function WebGLRenderState(extensions) { - const lights = new WebGLLights(extensions); - const lightsArray = []; - const shadowsArray = []; - function init(camera) { - state.camera = camera; - lightsArray.length = 0; - shadowsArray.length = 0; - } - __name(init, "init"); - function pushLight(light) { - lightsArray.push(light); - } - __name(pushLight, "pushLight"); - function pushShadow(shadowLight) { - shadowsArray.push(shadowLight); - } - __name(pushShadow, "pushShadow"); - function setupLights() { - lights.setup(lightsArray); - } - __name(setupLights, "setupLights"); - function setupLightsView(camera) { - lights.setupView(lightsArray, camera); - } - __name(setupLightsView, "setupLightsView"); - const state = { - lightsArray, - shadowsArray, - camera: null, - lights, - transmissionRenderTarget: {} - }; - return { - init, - state, - setupLights, - setupLightsView, - pushLight, - pushShadow - }; -} -__name(WebGLRenderState, "WebGLRenderState"); -function WebGLRenderStates(extensions) { - let renderStates = /* @__PURE__ */ new WeakMap(); - function get(scene, renderCallDepth = 0) { - const renderStateArray = renderStates.get(scene); - let renderState; - if (renderStateArray === void 0) { - renderState = new WebGLRenderState(extensions); - renderStates.set(scene, [renderState]); - } else { - if (renderCallDepth >= renderStateArray.length) { - renderState = new WebGLRenderState(extensions); - renderStateArray.push(renderState); - } else { - renderState = renderStateArray[renderCallDepth]; - } - } - return renderState; - } - __name(get, "get"); - function dispose() { - renderStates = /* @__PURE__ */ new WeakMap(); - } - __name(dispose, "dispose"); - return { - get, - dispose - }; -} -__name(WebGLRenderStates, "WebGLRenderStates"); -class MeshDepthMaterial extends Material { - static { - __name(this, "MeshDepthMaterial"); - } - static get type() { - return "MeshDepthMaterial"; - } - constructor(parameters) { - super(); - this.isMeshDepthMaterial = true; - this.depthPacking = BasicDepthPacking; - this.map = null; - this.alphaMap = null; - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.depthPacking = source.depthPacking; - this.map = source.map; - this.alphaMap = source.alphaMap; - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - return this; - } -} -class MeshDistanceMaterial extends Material { - static { - __name(this, "MeshDistanceMaterial"); - } - static get type() { - return "MeshDistanceMaterial"; - } - constructor(parameters) { - super(); - this.isMeshDistanceMaterial = true; - this.map = null; - this.alphaMap = null; - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.map = source.map; - this.alphaMap = source.alphaMap; - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - return this; - } -} -const vertex = "void main() {\n gl_Position = vec4( position, 1.0 );\n}"; -const fragment = "uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include \nvoid main() {\n const float samples = float( VSM_SAMPLES );\n float mean = 0.0;\n float squared_mean = 0.0;\n float uvStride = samples <= 1.0 ? 0.0 : 2.0 / ( samples - 1.0 );\n float uvStart = samples <= 1.0 ? 0.0 : - 1.0;\n for ( float i = 0.0; i < samples; i ++ ) {\n float uvOffset = uvStart + i * uvStride;\n #ifdef HORIZONTAL_PASS\n vec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( uvOffset, 0.0 ) * radius ) / resolution ) );\n mean += distribution.x;\n squared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n #else\n float depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, uvOffset ) * radius ) / resolution ) );\n mean += depth;\n squared_mean += depth * depth;\n #endif\n }\n mean = mean / samples;\n squared_mean = squared_mean / samples;\n float std_dev = sqrt( squared_mean - mean * mean );\n gl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"; -function WebGLShadowMap(renderer, objects, capabilities) { - let _frustum2 = new Frustum(); - const _shadowMapSize = new Vector2(), _viewportSize = new Vector2(), _viewport = new Vector4(), _depthMaterial = new MeshDepthMaterial({ depthPacking: RGBADepthPacking }), _distanceMaterial = new MeshDistanceMaterial(), _materialCache = {}, _maxTextureSize = capabilities.maxTextureSize; - const shadowSide = { [FrontSide]: BackSide, [BackSide]: FrontSide, [DoubleSide]: DoubleSide }; - const shadowMaterialVertical = new ShaderMaterial({ - defines: { - VSM_SAMPLES: 8 - }, - uniforms: { - shadow_pass: { value: null }, - resolution: { value: new Vector2() }, - radius: { value: 4 } - }, - vertexShader: vertex, - fragmentShader: fragment - }); - const shadowMaterialHorizontal = shadowMaterialVertical.clone(); - shadowMaterialHorizontal.defines.HORIZONTAL_PASS = 1; - const fullScreenTri = new BufferGeometry(); - fullScreenTri.setAttribute( - "position", - new BufferAttribute( - new Float32Array([-1, -1, 0.5, 3, -1, 0.5, -1, 3, 0.5]), - 3 - ) - ); - const fullScreenMesh = new Mesh(fullScreenTri, shadowMaterialVertical); - const scope = this; - this.enabled = false; - this.autoUpdate = true; - this.needsUpdate = false; - this.type = PCFShadowMap; - let _previousType = this.type; - this.render = function(lights, scene, camera) { - if (scope.enabled === false) return; - if (scope.autoUpdate === false && scope.needsUpdate === false) return; - if (lights.length === 0) return; - const currentRenderTarget = renderer.getRenderTarget(); - const activeCubeFace = renderer.getActiveCubeFace(); - const activeMipmapLevel = renderer.getActiveMipmapLevel(); - const _state = renderer.state; - _state.setBlending(NoBlending); - _state.buffers.color.setClear(1, 1, 1, 1); - _state.buffers.depth.setTest(true); - _state.setScissorTest(false); - const toVSM = _previousType !== VSMShadowMap && this.type === VSMShadowMap; - const fromVSM = _previousType === VSMShadowMap && this.type !== VSMShadowMap; - for (let i = 0, il = lights.length; i < il; i++) { - const light = lights[i]; - const shadow = light.shadow; - if (shadow === void 0) { - console.warn("THREE.WebGLShadowMap:", light, "has no shadow."); - continue; - } - if (shadow.autoUpdate === false && shadow.needsUpdate === false) continue; - _shadowMapSize.copy(shadow.mapSize); - const shadowFrameExtents = shadow.getFrameExtents(); - _shadowMapSize.multiply(shadowFrameExtents); - _viewportSize.copy(shadow.mapSize); - if (_shadowMapSize.x > _maxTextureSize || _shadowMapSize.y > _maxTextureSize) { - if (_shadowMapSize.x > _maxTextureSize) { - _viewportSize.x = Math.floor(_maxTextureSize / shadowFrameExtents.x); - _shadowMapSize.x = _viewportSize.x * shadowFrameExtents.x; - shadow.mapSize.x = _viewportSize.x; - } - if (_shadowMapSize.y > _maxTextureSize) { - _viewportSize.y = Math.floor(_maxTextureSize / shadowFrameExtents.y); - _shadowMapSize.y = _viewportSize.y * shadowFrameExtents.y; - shadow.mapSize.y = _viewportSize.y; - } - } - if (shadow.map === null || toVSM === true || fromVSM === true) { - const pars = this.type !== VSMShadowMap ? { minFilter: NearestFilter, magFilter: NearestFilter } : {}; - if (shadow.map !== null) { - shadow.map.dispose(); - } - shadow.map = new WebGLRenderTarget(_shadowMapSize.x, _shadowMapSize.y, pars); - shadow.map.texture.name = light.name + ".shadowMap"; - shadow.camera.updateProjectionMatrix(); - } - renderer.setRenderTarget(shadow.map); - renderer.clear(); - const viewportCount = shadow.getViewportCount(); - for (let vp = 0; vp < viewportCount; vp++) { - const viewport = shadow.getViewport(vp); - _viewport.set( - _viewportSize.x * viewport.x, - _viewportSize.y * viewport.y, - _viewportSize.x * viewport.z, - _viewportSize.y * viewport.w - ); - _state.viewport(_viewport); - shadow.updateMatrices(light, vp); - _frustum2 = shadow.getFrustum(); - renderObject(scene, camera, shadow.camera, light, this.type); - } - if (shadow.isPointLightShadow !== true && this.type === VSMShadowMap) { - VSMPass(shadow, camera); - } - shadow.needsUpdate = false; - } - _previousType = this.type; - scope.needsUpdate = false; - renderer.setRenderTarget(currentRenderTarget, activeCubeFace, activeMipmapLevel); - }; - function VSMPass(shadow, camera) { - const geometry = objects.update(fullScreenMesh); - if (shadowMaterialVertical.defines.VSM_SAMPLES !== shadow.blurSamples) { - shadowMaterialVertical.defines.VSM_SAMPLES = shadow.blurSamples; - shadowMaterialHorizontal.defines.VSM_SAMPLES = shadow.blurSamples; - shadowMaterialVertical.needsUpdate = true; - shadowMaterialHorizontal.needsUpdate = true; - } - if (shadow.mapPass === null) { - shadow.mapPass = new WebGLRenderTarget(_shadowMapSize.x, _shadowMapSize.y); - } - shadowMaterialVertical.uniforms.shadow_pass.value = shadow.map.texture; - shadowMaterialVertical.uniforms.resolution.value = shadow.mapSize; - shadowMaterialVertical.uniforms.radius.value = shadow.radius; - renderer.setRenderTarget(shadow.mapPass); - renderer.clear(); - renderer.renderBufferDirect(camera, null, geometry, shadowMaterialVertical, fullScreenMesh, null); - shadowMaterialHorizontal.uniforms.shadow_pass.value = shadow.mapPass.texture; - shadowMaterialHorizontal.uniforms.resolution.value = shadow.mapSize; - shadowMaterialHorizontal.uniforms.radius.value = shadow.radius; - renderer.setRenderTarget(shadow.map); - renderer.clear(); - renderer.renderBufferDirect(camera, null, geometry, shadowMaterialHorizontal, fullScreenMesh, null); - } - __name(VSMPass, "VSMPass"); - function getDepthMaterial(object, material, light, type) { - let result = null; - const customMaterial = light.isPointLight === true ? object.customDistanceMaterial : object.customDepthMaterial; - if (customMaterial !== void 0) { - result = customMaterial; - } else { - result = light.isPointLight === true ? _distanceMaterial : _depthMaterial; - if (renderer.localClippingEnabled && material.clipShadows === true && Array.isArray(material.clippingPlanes) && material.clippingPlanes.length !== 0 || material.displacementMap && material.displacementScale !== 0 || material.alphaMap && material.alphaTest > 0 || material.map && material.alphaTest > 0) { - const keyA = result.uuid, keyB = material.uuid; - let materialsForVariant = _materialCache[keyA]; - if (materialsForVariant === void 0) { - materialsForVariant = {}; - _materialCache[keyA] = materialsForVariant; - } - let cachedMaterial = materialsForVariant[keyB]; - if (cachedMaterial === void 0) { - cachedMaterial = result.clone(); - materialsForVariant[keyB] = cachedMaterial; - material.addEventListener("dispose", onMaterialDispose); - } - result = cachedMaterial; - } - } - result.visible = material.visible; - result.wireframe = material.wireframe; - if (type === VSMShadowMap) { - result.side = material.shadowSide !== null ? material.shadowSide : material.side; - } else { - result.side = material.shadowSide !== null ? material.shadowSide : shadowSide[material.side]; - } - result.alphaMap = material.alphaMap; - result.alphaTest = material.alphaTest; - result.map = material.map; - result.clipShadows = material.clipShadows; - result.clippingPlanes = material.clippingPlanes; - result.clipIntersection = material.clipIntersection; - result.displacementMap = material.displacementMap; - result.displacementScale = material.displacementScale; - result.displacementBias = material.displacementBias; - result.wireframeLinewidth = material.wireframeLinewidth; - result.linewidth = material.linewidth; - if (light.isPointLight === true && result.isMeshDistanceMaterial === true) { - const materialProperties = renderer.properties.get(result); - materialProperties.light = light; - } - return result; - } - __name(getDepthMaterial, "getDepthMaterial"); - function renderObject(object, camera, shadowCamera, light, type) { - if (object.visible === false) return; - const visible = object.layers.test(camera.layers); - if (visible && (object.isMesh || object.isLine || object.isPoints)) { - if ((object.castShadow || object.receiveShadow && type === VSMShadowMap) && (!object.frustumCulled || _frustum2.intersectsObject(object))) { - object.modelViewMatrix.multiplyMatrices(shadowCamera.matrixWorldInverse, object.matrixWorld); - const geometry = objects.update(object); - const material = object.material; - if (Array.isArray(material)) { - const groups = geometry.groups; - for (let k = 0, kl = groups.length; k < kl; k++) { - const group = groups[k]; - const groupMaterial = material[group.materialIndex]; - if (groupMaterial && groupMaterial.visible) { - const depthMaterial = getDepthMaterial(object, groupMaterial, light, type); - object.onBeforeShadow(renderer, object, camera, shadowCamera, geometry, depthMaterial, group); - renderer.renderBufferDirect(shadowCamera, null, geometry, depthMaterial, object, group); - object.onAfterShadow(renderer, object, camera, shadowCamera, geometry, depthMaterial, group); - } - } - } else if (material.visible) { - const depthMaterial = getDepthMaterial(object, material, light, type); - object.onBeforeShadow(renderer, object, camera, shadowCamera, geometry, depthMaterial, null); - renderer.renderBufferDirect(shadowCamera, null, geometry, depthMaterial, object, null); - object.onAfterShadow(renderer, object, camera, shadowCamera, geometry, depthMaterial, null); - } - } - } - const children = object.children; - for (let i = 0, l = children.length; i < l; i++) { - renderObject(children[i], camera, shadowCamera, light, type); - } - } - __name(renderObject, "renderObject"); - function onMaterialDispose(event) { - const material = event.target; - material.removeEventListener("dispose", onMaterialDispose); - for (const id2 in _materialCache) { - const cache = _materialCache[id2]; - const uuid = event.target.uuid; - if (uuid in cache) { - const shadowMaterial = cache[uuid]; - shadowMaterial.dispose(); - delete cache[uuid]; - } - } - } - __name(onMaterialDispose, "onMaterialDispose"); -} -__name(WebGLShadowMap, "WebGLShadowMap"); -const reversedFuncs = { - [NeverDepth]: AlwaysDepth, - [LessDepth]: GreaterDepth, - [EqualDepth]: NotEqualDepth, - [LessEqualDepth]: GreaterEqualDepth, - [AlwaysDepth]: NeverDepth, - [GreaterDepth]: LessDepth, - [NotEqualDepth]: EqualDepth, - [GreaterEqualDepth]: LessEqualDepth -}; -function WebGLState(gl, extensions) { - function ColorBuffer() { - let locked = false; - const color = new Vector4(); - let currentColorMask = null; - const currentColorClear = new Vector4(0, 0, 0, 0); - return { - setMask: /* @__PURE__ */ __name(function(colorMask) { - if (currentColorMask !== colorMask && !locked) { - gl.colorMask(colorMask, colorMask, colorMask, colorMask); - currentColorMask = colorMask; - } - }, "setMask"), - setLocked: /* @__PURE__ */ __name(function(lock) { - locked = lock; - }, "setLocked"), - setClear: /* @__PURE__ */ __name(function(r, g, b, a, premultipliedAlpha) { - if (premultipliedAlpha === true) { - r *= a; - g *= a; - b *= a; - } - color.set(r, g, b, a); - if (currentColorClear.equals(color) === false) { - gl.clearColor(r, g, b, a); - currentColorClear.copy(color); - } - }, "setClear"), - reset: /* @__PURE__ */ __name(function() { - locked = false; - currentColorMask = null; - currentColorClear.set(-1, 0, 0, 0); - }, "reset") - }; - } - __name(ColorBuffer, "ColorBuffer"); - function DepthBuffer() { - let locked = false; - let reversed = false; - let currentDepthMask = null; - let currentDepthFunc = null; - let currentDepthClear = null; - return { - setReversed: /* @__PURE__ */ __name(function(value) { - if (reversed !== value) { - const ext2 = extensions.get("EXT_clip_control"); - if (reversed) { - ext2.clipControlEXT(ext2.LOWER_LEFT_EXT, ext2.ZERO_TO_ONE_EXT); - } else { - ext2.clipControlEXT(ext2.LOWER_LEFT_EXT, ext2.NEGATIVE_ONE_TO_ONE_EXT); - } - const oldDepth = currentDepthClear; - currentDepthClear = null; - this.setClear(oldDepth); - } - reversed = value; - }, "setReversed"), - getReversed: /* @__PURE__ */ __name(function() { - return reversed; - }, "getReversed"), - setTest: /* @__PURE__ */ __name(function(depthTest) { - if (depthTest) { - enable(gl.DEPTH_TEST); - } else { - disable(gl.DEPTH_TEST); - } - }, "setTest"), - setMask: /* @__PURE__ */ __name(function(depthMask) { - if (currentDepthMask !== depthMask && !locked) { - gl.depthMask(depthMask); - currentDepthMask = depthMask; - } - }, "setMask"), - setFunc: /* @__PURE__ */ __name(function(depthFunc) { - if (reversed) depthFunc = reversedFuncs[depthFunc]; - if (currentDepthFunc !== depthFunc) { - switch (depthFunc) { - case NeverDepth: - gl.depthFunc(gl.NEVER); - break; - case AlwaysDepth: - gl.depthFunc(gl.ALWAYS); - break; - case LessDepth: - gl.depthFunc(gl.LESS); - break; - case LessEqualDepth: - gl.depthFunc(gl.LEQUAL); - break; - case EqualDepth: - gl.depthFunc(gl.EQUAL); - break; - case GreaterEqualDepth: - gl.depthFunc(gl.GEQUAL); - break; - case GreaterDepth: - gl.depthFunc(gl.GREATER); - break; - case NotEqualDepth: - gl.depthFunc(gl.NOTEQUAL); - break; - default: - gl.depthFunc(gl.LEQUAL); - } - currentDepthFunc = depthFunc; - } - }, "setFunc"), - setLocked: /* @__PURE__ */ __name(function(lock) { - locked = lock; - }, "setLocked"), - setClear: /* @__PURE__ */ __name(function(depth) { - if (currentDepthClear !== depth) { - if (reversed) { - depth = 1 - depth; - } - gl.clearDepth(depth); - currentDepthClear = depth; - } - }, "setClear"), - reset: /* @__PURE__ */ __name(function() { - locked = false; - currentDepthMask = null; - currentDepthFunc = null; - currentDepthClear = null; - reversed = false; - }, "reset") - }; - } - __name(DepthBuffer, "DepthBuffer"); - function StencilBuffer() { - let locked = false; - let currentStencilMask = null; - let currentStencilFunc = null; - let currentStencilRef = null; - let currentStencilFuncMask = null; - let currentStencilFail = null; - let currentStencilZFail = null; - let currentStencilZPass = null; - let currentStencilClear = null; - return { - setTest: /* @__PURE__ */ __name(function(stencilTest) { - if (!locked) { - if (stencilTest) { - enable(gl.STENCIL_TEST); - } else { - disable(gl.STENCIL_TEST); - } - } - }, "setTest"), - setMask: /* @__PURE__ */ __name(function(stencilMask) { - if (currentStencilMask !== stencilMask && !locked) { - gl.stencilMask(stencilMask); - currentStencilMask = stencilMask; - } - }, "setMask"), - setFunc: /* @__PURE__ */ __name(function(stencilFunc, stencilRef, stencilMask) { - if (currentStencilFunc !== stencilFunc || currentStencilRef !== stencilRef || currentStencilFuncMask !== stencilMask) { - gl.stencilFunc(stencilFunc, stencilRef, stencilMask); - currentStencilFunc = stencilFunc; - currentStencilRef = stencilRef; - currentStencilFuncMask = stencilMask; - } - }, "setFunc"), - setOp: /* @__PURE__ */ __name(function(stencilFail, stencilZFail, stencilZPass) { - if (currentStencilFail !== stencilFail || currentStencilZFail !== stencilZFail || currentStencilZPass !== stencilZPass) { - gl.stencilOp(stencilFail, stencilZFail, stencilZPass); - currentStencilFail = stencilFail; - currentStencilZFail = stencilZFail; - currentStencilZPass = stencilZPass; - } - }, "setOp"), - setLocked: /* @__PURE__ */ __name(function(lock) { - locked = lock; - }, "setLocked"), - setClear: /* @__PURE__ */ __name(function(stencil) { - if (currentStencilClear !== stencil) { - gl.clearStencil(stencil); - currentStencilClear = stencil; - } - }, "setClear"), - reset: /* @__PURE__ */ __name(function() { - locked = false; - currentStencilMask = null; - currentStencilFunc = null; - currentStencilRef = null; - currentStencilFuncMask = null; - currentStencilFail = null; - currentStencilZFail = null; - currentStencilZPass = null; - currentStencilClear = null; - }, "reset") - }; - } - __name(StencilBuffer, "StencilBuffer"); - const colorBuffer = new ColorBuffer(); - const depthBuffer = new DepthBuffer(); - const stencilBuffer = new StencilBuffer(); - const uboBindings = /* @__PURE__ */ new WeakMap(); - const uboProgramMap = /* @__PURE__ */ new WeakMap(); - let enabledCapabilities = {}; - let currentBoundFramebuffers = {}; - let currentDrawbuffers = /* @__PURE__ */ new WeakMap(); - let defaultDrawbuffers = []; - let currentProgram = null; - let currentBlendingEnabled = false; - let currentBlending = null; - let currentBlendEquation = null; - let currentBlendSrc = null; - let currentBlendDst = null; - let currentBlendEquationAlpha = null; - let currentBlendSrcAlpha = null; - let currentBlendDstAlpha = null; - let currentBlendColor = new Color(0, 0, 0); - let currentBlendAlpha = 0; - let currentPremultipledAlpha = false; - let currentFlipSided = null; - let currentCullFace = null; - let currentLineWidth = null; - let currentPolygonOffsetFactor = null; - let currentPolygonOffsetUnits = null; - const maxTextures = gl.getParameter(gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS); - let lineWidthAvailable = false; - let version = 0; - const glVersion = gl.getParameter(gl.VERSION); - if (glVersion.indexOf("WebGL") !== -1) { - version = parseFloat(/^WebGL (\d)/.exec(glVersion)[1]); - lineWidthAvailable = version >= 1; - } else if (glVersion.indexOf("OpenGL ES") !== -1) { - version = parseFloat(/^OpenGL ES (\d)/.exec(glVersion)[1]); - lineWidthAvailable = version >= 2; - } - let currentTextureSlot = null; - let currentBoundTextures = {}; - const scissorParam = gl.getParameter(gl.SCISSOR_BOX); - const viewportParam = gl.getParameter(gl.VIEWPORT); - const currentScissor = new Vector4().fromArray(scissorParam); - const currentViewport = new Vector4().fromArray(viewportParam); - function createTexture(type, target, count, dimensions) { - const data = new Uint8Array(4); - const texture = gl.createTexture(); - gl.bindTexture(type, texture); - gl.texParameteri(type, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(type, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - for (let i = 0; i < count; i++) { - if (type === gl.TEXTURE_3D || type === gl.TEXTURE_2D_ARRAY) { - gl.texImage3D(target, 0, gl.RGBA, 1, 1, dimensions, 0, gl.RGBA, gl.UNSIGNED_BYTE, data); - } else { - gl.texImage2D(target + i, 0, gl.RGBA, 1, 1, 0, gl.RGBA, gl.UNSIGNED_BYTE, data); - } - } - return texture; - } - __name(createTexture, "createTexture"); - const emptyTextures = {}; - emptyTextures[gl.TEXTURE_2D] = createTexture(gl.TEXTURE_2D, gl.TEXTURE_2D, 1); - emptyTextures[gl.TEXTURE_CUBE_MAP] = createTexture(gl.TEXTURE_CUBE_MAP, gl.TEXTURE_CUBE_MAP_POSITIVE_X, 6); - emptyTextures[gl.TEXTURE_2D_ARRAY] = createTexture(gl.TEXTURE_2D_ARRAY, gl.TEXTURE_2D_ARRAY, 1, 1); - emptyTextures[gl.TEXTURE_3D] = createTexture(gl.TEXTURE_3D, gl.TEXTURE_3D, 1, 1); - colorBuffer.setClear(0, 0, 0, 1); - depthBuffer.setClear(1); - stencilBuffer.setClear(0); - enable(gl.DEPTH_TEST); - depthBuffer.setFunc(LessEqualDepth); - setFlipSided(false); - setCullFace(CullFaceBack); - enable(gl.CULL_FACE); - setBlending(NoBlending); - function enable(id2) { - if (enabledCapabilities[id2] !== true) { - gl.enable(id2); - enabledCapabilities[id2] = true; - } - } - __name(enable, "enable"); - function disable(id2) { - if (enabledCapabilities[id2] !== false) { - gl.disable(id2); - enabledCapabilities[id2] = false; - } - } - __name(disable, "disable"); - function bindFramebuffer(target, framebuffer) { - if (currentBoundFramebuffers[target] !== framebuffer) { - gl.bindFramebuffer(target, framebuffer); - currentBoundFramebuffers[target] = framebuffer; - if (target === gl.DRAW_FRAMEBUFFER) { - currentBoundFramebuffers[gl.FRAMEBUFFER] = framebuffer; - } - if (target === gl.FRAMEBUFFER) { - currentBoundFramebuffers[gl.DRAW_FRAMEBUFFER] = framebuffer; - } - return true; - } - return false; - } - __name(bindFramebuffer, "bindFramebuffer"); - function drawBuffers(renderTarget, framebuffer) { - let drawBuffers2 = defaultDrawbuffers; - let needsUpdate = false; - if (renderTarget) { - drawBuffers2 = currentDrawbuffers.get(framebuffer); - if (drawBuffers2 === void 0) { - drawBuffers2 = []; - currentDrawbuffers.set(framebuffer, drawBuffers2); - } - const textures = renderTarget.textures; - if (drawBuffers2.length !== textures.length || drawBuffers2[0] !== gl.COLOR_ATTACHMENT0) { - for (let i = 0, il = textures.length; i < il; i++) { - drawBuffers2[i] = gl.COLOR_ATTACHMENT0 + i; - } - drawBuffers2.length = textures.length; - needsUpdate = true; - } - } else { - if (drawBuffers2[0] !== gl.BACK) { - drawBuffers2[0] = gl.BACK; - needsUpdate = true; - } - } - if (needsUpdate) { - gl.drawBuffers(drawBuffers2); - } - } - __name(drawBuffers, "drawBuffers"); - function useProgram(program) { - if (currentProgram !== program) { - gl.useProgram(program); - currentProgram = program; - return true; - } - return false; - } - __name(useProgram, "useProgram"); - const equationToGL = { - [AddEquation]: gl.FUNC_ADD, - [SubtractEquation]: gl.FUNC_SUBTRACT, - [ReverseSubtractEquation]: gl.FUNC_REVERSE_SUBTRACT - }; - equationToGL[MinEquation] = gl.MIN; - equationToGL[MaxEquation] = gl.MAX; - const factorToGL = { - [ZeroFactor]: gl.ZERO, - [OneFactor]: gl.ONE, - [SrcColorFactor]: gl.SRC_COLOR, - [SrcAlphaFactor]: gl.SRC_ALPHA, - [SrcAlphaSaturateFactor]: gl.SRC_ALPHA_SATURATE, - [DstColorFactor]: gl.DST_COLOR, - [DstAlphaFactor]: gl.DST_ALPHA, - [OneMinusSrcColorFactor]: gl.ONE_MINUS_SRC_COLOR, - [OneMinusSrcAlphaFactor]: gl.ONE_MINUS_SRC_ALPHA, - [OneMinusDstColorFactor]: gl.ONE_MINUS_DST_COLOR, - [OneMinusDstAlphaFactor]: gl.ONE_MINUS_DST_ALPHA, - [ConstantColorFactor]: gl.CONSTANT_COLOR, - [OneMinusConstantColorFactor]: gl.ONE_MINUS_CONSTANT_COLOR, - [ConstantAlphaFactor]: gl.CONSTANT_ALPHA, - [OneMinusConstantAlphaFactor]: gl.ONE_MINUS_CONSTANT_ALPHA - }; - function setBlending(blending, blendEquation, blendSrc, blendDst, blendEquationAlpha, blendSrcAlpha, blendDstAlpha, blendColor, blendAlpha, premultipliedAlpha) { - if (blending === NoBlending) { - if (currentBlendingEnabled === true) { - disable(gl.BLEND); - currentBlendingEnabled = false; - } - return; - } - if (currentBlendingEnabled === false) { - enable(gl.BLEND); - currentBlendingEnabled = true; - } - if (blending !== CustomBlending) { - if (blending !== currentBlending || premultipliedAlpha !== currentPremultipledAlpha) { - if (currentBlendEquation !== AddEquation || currentBlendEquationAlpha !== AddEquation) { - gl.blendEquation(gl.FUNC_ADD); - currentBlendEquation = AddEquation; - currentBlendEquationAlpha = AddEquation; - } - if (premultipliedAlpha) { - switch (blending) { - case NormalBlending: - gl.blendFuncSeparate(gl.ONE, gl.ONE_MINUS_SRC_ALPHA, gl.ONE, gl.ONE_MINUS_SRC_ALPHA); - break; - case AdditiveBlending: - gl.blendFunc(gl.ONE, gl.ONE); - break; - case SubtractiveBlending: - gl.blendFuncSeparate(gl.ZERO, gl.ONE_MINUS_SRC_COLOR, gl.ZERO, gl.ONE); - break; - case MultiplyBlending: - gl.blendFuncSeparate(gl.ZERO, gl.SRC_COLOR, gl.ZERO, gl.SRC_ALPHA); - break; - default: - console.error("THREE.WebGLState: Invalid blending: ", blending); - break; - } - } else { - switch (blending) { - case NormalBlending: - gl.blendFuncSeparate(gl.SRC_ALPHA, gl.ONE_MINUS_SRC_ALPHA, gl.ONE, gl.ONE_MINUS_SRC_ALPHA); - break; - case AdditiveBlending: - gl.blendFunc(gl.SRC_ALPHA, gl.ONE); - break; - case SubtractiveBlending: - gl.blendFuncSeparate(gl.ZERO, gl.ONE_MINUS_SRC_COLOR, gl.ZERO, gl.ONE); - break; - case MultiplyBlending: - gl.blendFunc(gl.ZERO, gl.SRC_COLOR); - break; - default: - console.error("THREE.WebGLState: Invalid blending: ", blending); - break; - } - } - currentBlendSrc = null; - currentBlendDst = null; - currentBlendSrcAlpha = null; - currentBlendDstAlpha = null; - currentBlendColor.set(0, 0, 0); - currentBlendAlpha = 0; - currentBlending = blending; - currentPremultipledAlpha = premultipliedAlpha; - } - return; - } - blendEquationAlpha = blendEquationAlpha || blendEquation; - blendSrcAlpha = blendSrcAlpha || blendSrc; - blendDstAlpha = blendDstAlpha || blendDst; - if (blendEquation !== currentBlendEquation || blendEquationAlpha !== currentBlendEquationAlpha) { - gl.blendEquationSeparate(equationToGL[blendEquation], equationToGL[blendEquationAlpha]); - currentBlendEquation = blendEquation; - currentBlendEquationAlpha = blendEquationAlpha; - } - if (blendSrc !== currentBlendSrc || blendDst !== currentBlendDst || blendSrcAlpha !== currentBlendSrcAlpha || blendDstAlpha !== currentBlendDstAlpha) { - gl.blendFuncSeparate(factorToGL[blendSrc], factorToGL[blendDst], factorToGL[blendSrcAlpha], factorToGL[blendDstAlpha]); - currentBlendSrc = blendSrc; - currentBlendDst = blendDst; - currentBlendSrcAlpha = blendSrcAlpha; - currentBlendDstAlpha = blendDstAlpha; - } - if (blendColor.equals(currentBlendColor) === false || blendAlpha !== currentBlendAlpha) { - gl.blendColor(blendColor.r, blendColor.g, blendColor.b, blendAlpha); - currentBlendColor.copy(blendColor); - currentBlendAlpha = blendAlpha; - } - currentBlending = blending; - currentPremultipledAlpha = false; - } - __name(setBlending, "setBlending"); - function setMaterial(material, frontFaceCW) { - material.side === DoubleSide ? disable(gl.CULL_FACE) : enable(gl.CULL_FACE); - let flipSided = material.side === BackSide; - if (frontFaceCW) flipSided = !flipSided; - setFlipSided(flipSided); - material.blending === NormalBlending && material.transparent === false ? setBlending(NoBlending) : setBlending(material.blending, material.blendEquation, material.blendSrc, material.blendDst, material.blendEquationAlpha, material.blendSrcAlpha, material.blendDstAlpha, material.blendColor, material.blendAlpha, material.premultipliedAlpha); - depthBuffer.setFunc(material.depthFunc); - depthBuffer.setTest(material.depthTest); - depthBuffer.setMask(material.depthWrite); - colorBuffer.setMask(material.colorWrite); - const stencilWrite = material.stencilWrite; - stencilBuffer.setTest(stencilWrite); - if (stencilWrite) { - stencilBuffer.setMask(material.stencilWriteMask); - stencilBuffer.setFunc(material.stencilFunc, material.stencilRef, material.stencilFuncMask); - stencilBuffer.setOp(material.stencilFail, material.stencilZFail, material.stencilZPass); - } - setPolygonOffset(material.polygonOffset, material.polygonOffsetFactor, material.polygonOffsetUnits); - material.alphaToCoverage === true ? enable(gl.SAMPLE_ALPHA_TO_COVERAGE) : disable(gl.SAMPLE_ALPHA_TO_COVERAGE); - } - __name(setMaterial, "setMaterial"); - function setFlipSided(flipSided) { - if (currentFlipSided !== flipSided) { - if (flipSided) { - gl.frontFace(gl.CW); - } else { - gl.frontFace(gl.CCW); - } - currentFlipSided = flipSided; - } - } - __name(setFlipSided, "setFlipSided"); - function setCullFace(cullFace) { - if (cullFace !== CullFaceNone) { - enable(gl.CULL_FACE); - if (cullFace !== currentCullFace) { - if (cullFace === CullFaceBack) { - gl.cullFace(gl.BACK); - } else if (cullFace === CullFaceFront) { - gl.cullFace(gl.FRONT); - } else { - gl.cullFace(gl.FRONT_AND_BACK); - } - } - } else { - disable(gl.CULL_FACE); - } - currentCullFace = cullFace; - } - __name(setCullFace, "setCullFace"); - function setLineWidth(width) { - if (width !== currentLineWidth) { - if (lineWidthAvailable) gl.lineWidth(width); - currentLineWidth = width; - } - } - __name(setLineWidth, "setLineWidth"); - function setPolygonOffset(polygonOffset, factor, units) { - if (polygonOffset) { - enable(gl.POLYGON_OFFSET_FILL); - if (currentPolygonOffsetFactor !== factor || currentPolygonOffsetUnits !== units) { - gl.polygonOffset(factor, units); - currentPolygonOffsetFactor = factor; - currentPolygonOffsetUnits = units; - } - } else { - disable(gl.POLYGON_OFFSET_FILL); - } - } - __name(setPolygonOffset, "setPolygonOffset"); - function setScissorTest(scissorTest) { - if (scissorTest) { - enable(gl.SCISSOR_TEST); - } else { - disable(gl.SCISSOR_TEST); - } - } - __name(setScissorTest, "setScissorTest"); - function activeTexture(webglSlot) { - if (webglSlot === void 0) webglSlot = gl.TEXTURE0 + maxTextures - 1; - if (currentTextureSlot !== webglSlot) { - gl.activeTexture(webglSlot); - currentTextureSlot = webglSlot; - } - } - __name(activeTexture, "activeTexture"); - function bindTexture(webglType, webglTexture, webglSlot) { - if (webglSlot === void 0) { - if (currentTextureSlot === null) { - webglSlot = gl.TEXTURE0 + maxTextures - 1; - } else { - webglSlot = currentTextureSlot; - } - } - let boundTexture = currentBoundTextures[webglSlot]; - if (boundTexture === void 0) { - boundTexture = { type: void 0, texture: void 0 }; - currentBoundTextures[webglSlot] = boundTexture; - } - if (boundTexture.type !== webglType || boundTexture.texture !== webglTexture) { - if (currentTextureSlot !== webglSlot) { - gl.activeTexture(webglSlot); - currentTextureSlot = webglSlot; - } - gl.bindTexture(webglType, webglTexture || emptyTextures[webglType]); - boundTexture.type = webglType; - boundTexture.texture = webglTexture; - } - } - __name(bindTexture, "bindTexture"); - function unbindTexture() { - const boundTexture = currentBoundTextures[currentTextureSlot]; - if (boundTexture !== void 0 && boundTexture.type !== void 0) { - gl.bindTexture(boundTexture.type, null); - boundTexture.type = void 0; - boundTexture.texture = void 0; - } - } - __name(unbindTexture, "unbindTexture"); - function compressedTexImage2D() { - try { - gl.compressedTexImage2D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(compressedTexImage2D, "compressedTexImage2D"); - function compressedTexImage3D() { - try { - gl.compressedTexImage3D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(compressedTexImage3D, "compressedTexImage3D"); - function texSubImage2D() { - try { - gl.texSubImage2D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texSubImage2D, "texSubImage2D"); - function texSubImage3D() { - try { - gl.texSubImage3D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texSubImage3D, "texSubImage3D"); - function compressedTexSubImage2D() { - try { - gl.compressedTexSubImage2D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(compressedTexSubImage2D, "compressedTexSubImage2D"); - function compressedTexSubImage3D() { - try { - gl.compressedTexSubImage3D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(compressedTexSubImage3D, "compressedTexSubImage3D"); - function texStorage2D() { - try { - gl.texStorage2D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texStorage2D, "texStorage2D"); - function texStorage3D() { - try { - gl.texStorage3D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texStorage3D, "texStorage3D"); - function texImage2D() { - try { - gl.texImage2D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texImage2D, "texImage2D"); - function texImage3D() { - try { - gl.texImage3D.apply(gl, arguments); - } catch (error) { - console.error("THREE.WebGLState:", error); - } - } - __name(texImage3D, "texImage3D"); - function scissor(scissor2) { - if (currentScissor.equals(scissor2) === false) { - gl.scissor(scissor2.x, scissor2.y, scissor2.z, scissor2.w); - currentScissor.copy(scissor2); - } - } - __name(scissor, "scissor"); - function viewport(viewport2) { - if (currentViewport.equals(viewport2) === false) { - gl.viewport(viewport2.x, viewport2.y, viewport2.z, viewport2.w); - currentViewport.copy(viewport2); - } - } - __name(viewport, "viewport"); - function updateUBOMapping(uniformsGroup, program) { - let mapping = uboProgramMap.get(program); - if (mapping === void 0) { - mapping = /* @__PURE__ */ new WeakMap(); - uboProgramMap.set(program, mapping); - } - let blockIndex = mapping.get(uniformsGroup); - if (blockIndex === void 0) { - blockIndex = gl.getUniformBlockIndex(program, uniformsGroup.name); - mapping.set(uniformsGroup, blockIndex); - } - } - __name(updateUBOMapping, "updateUBOMapping"); - function uniformBlockBinding(uniformsGroup, program) { - const mapping = uboProgramMap.get(program); - const blockIndex = mapping.get(uniformsGroup); - if (uboBindings.get(program) !== blockIndex) { - gl.uniformBlockBinding(program, blockIndex, uniformsGroup.__bindingPointIndex); - uboBindings.set(program, blockIndex); - } - } - __name(uniformBlockBinding, "uniformBlockBinding"); - function reset() { - gl.disable(gl.BLEND); - gl.disable(gl.CULL_FACE); - gl.disable(gl.DEPTH_TEST); - gl.disable(gl.POLYGON_OFFSET_FILL); - gl.disable(gl.SCISSOR_TEST); - gl.disable(gl.STENCIL_TEST); - gl.disable(gl.SAMPLE_ALPHA_TO_COVERAGE); - gl.blendEquation(gl.FUNC_ADD); - gl.blendFunc(gl.ONE, gl.ZERO); - gl.blendFuncSeparate(gl.ONE, gl.ZERO, gl.ONE, gl.ZERO); - gl.blendColor(0, 0, 0, 0); - gl.colorMask(true, true, true, true); - gl.clearColor(0, 0, 0, 0); - gl.depthMask(true); - gl.depthFunc(gl.LESS); - depthBuffer.setReversed(false); - gl.clearDepth(1); - gl.stencilMask(4294967295); - gl.stencilFunc(gl.ALWAYS, 0, 4294967295); - gl.stencilOp(gl.KEEP, gl.KEEP, gl.KEEP); - gl.clearStencil(0); - gl.cullFace(gl.BACK); - gl.frontFace(gl.CCW); - gl.polygonOffset(0, 0); - gl.activeTexture(gl.TEXTURE0); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - gl.bindFramebuffer(gl.DRAW_FRAMEBUFFER, null); - gl.bindFramebuffer(gl.READ_FRAMEBUFFER, null); - gl.useProgram(null); - gl.lineWidth(1); - gl.scissor(0, 0, gl.canvas.width, gl.canvas.height); - gl.viewport(0, 0, gl.canvas.width, gl.canvas.height); - enabledCapabilities = {}; - currentTextureSlot = null; - currentBoundTextures = {}; - currentBoundFramebuffers = {}; - currentDrawbuffers = /* @__PURE__ */ new WeakMap(); - defaultDrawbuffers = []; - currentProgram = null; - currentBlendingEnabled = false; - currentBlending = null; - currentBlendEquation = null; - currentBlendSrc = null; - currentBlendDst = null; - currentBlendEquationAlpha = null; - currentBlendSrcAlpha = null; - currentBlendDstAlpha = null; - currentBlendColor = new Color(0, 0, 0); - currentBlendAlpha = 0; - currentPremultipledAlpha = false; - currentFlipSided = null; - currentCullFace = null; - currentLineWidth = null; - currentPolygonOffsetFactor = null; - currentPolygonOffsetUnits = null; - currentScissor.set(0, 0, gl.canvas.width, gl.canvas.height); - currentViewport.set(0, 0, gl.canvas.width, gl.canvas.height); - colorBuffer.reset(); - depthBuffer.reset(); - stencilBuffer.reset(); - } - __name(reset, "reset"); - return { - buffers: { - color: colorBuffer, - depth: depthBuffer, - stencil: stencilBuffer - }, - enable, - disable, - bindFramebuffer, - drawBuffers, - useProgram, - setBlending, - setMaterial, - setFlipSided, - setCullFace, - setLineWidth, - setPolygonOffset, - setScissorTest, - activeTexture, - bindTexture, - unbindTexture, - compressedTexImage2D, - compressedTexImage3D, - texImage2D, - texImage3D, - updateUBOMapping, - uniformBlockBinding, - texStorage2D, - texStorage3D, - texSubImage2D, - texSubImage3D, - compressedTexSubImage2D, - compressedTexSubImage3D, - scissor, - viewport, - reset - }; -} -__name(WebGLState, "WebGLState"); -function contain(texture, aspect2) { - const imageAspect = texture.image && texture.image.width ? texture.image.width / texture.image.height : 1; - if (imageAspect > aspect2) { - texture.repeat.x = 1; - texture.repeat.y = imageAspect / aspect2; - texture.offset.x = 0; - texture.offset.y = (1 - texture.repeat.y) / 2; - } else { - texture.repeat.x = aspect2 / imageAspect; - texture.repeat.y = 1; - texture.offset.x = (1 - texture.repeat.x) / 2; - texture.offset.y = 0; - } - return texture; -} -__name(contain, "contain"); -function cover(texture, aspect2) { - const imageAspect = texture.image && texture.image.width ? texture.image.width / texture.image.height : 1; - if (imageAspect > aspect2) { - texture.repeat.x = aspect2 / imageAspect; - texture.repeat.y = 1; - texture.offset.x = (1 - texture.repeat.x) / 2; - texture.offset.y = 0; - } else { - texture.repeat.x = 1; - texture.repeat.y = imageAspect / aspect2; - texture.offset.x = 0; - texture.offset.y = (1 - texture.repeat.y) / 2; - } - return texture; -} -__name(cover, "cover"); -function fill(texture) { - texture.repeat.x = 1; - texture.repeat.y = 1; - texture.offset.x = 0; - texture.offset.y = 0; - return texture; -} -__name(fill, "fill"); -function getByteLength(width, height, format, type) { - const typeByteLength = getTextureTypeByteLength(type); - switch (format) { - case AlphaFormat: - return width * height; - case LuminanceFormat: - return width * height; - case LuminanceAlphaFormat: - return width * height * 2; - case RedFormat: - return width * height / typeByteLength.components * typeByteLength.byteLength; - case RedIntegerFormat: - return width * height / typeByteLength.components * typeByteLength.byteLength; - case RGFormat: - return width * height * 2 / typeByteLength.components * typeByteLength.byteLength; - case RGIntegerFormat: - return width * height * 2 / typeByteLength.components * typeByteLength.byteLength; - case RGBFormat: - return width * height * 3 / typeByteLength.components * typeByteLength.byteLength; - case RGBAFormat: - return width * height * 4 / typeByteLength.components * typeByteLength.byteLength; - case RGBAIntegerFormat: - return width * height * 4 / typeByteLength.components * typeByteLength.byteLength; - case RGB_S3TC_DXT1_Format: - case RGBA_S3TC_DXT1_Format: - return Math.floor((width + 3) / 4) * Math.floor((height + 3) / 4) * 8; - case RGBA_S3TC_DXT3_Format: - case RGBA_S3TC_DXT5_Format: - return Math.floor((width + 3) / 4) * Math.floor((height + 3) / 4) * 16; - case RGB_PVRTC_2BPPV1_Format: - case RGBA_PVRTC_2BPPV1_Format: - return Math.max(width, 16) * Math.max(height, 8) / 4; - case RGB_PVRTC_4BPPV1_Format: - case RGBA_PVRTC_4BPPV1_Format: - return Math.max(width, 8) * Math.max(height, 8) / 2; - case RGB_ETC1_Format: - case RGB_ETC2_Format: - return Math.floor((width + 3) / 4) * Math.floor((height + 3) / 4) * 8; - case RGBA_ETC2_EAC_Format: - return Math.floor((width + 3) / 4) * Math.floor((height + 3) / 4) * 16; - case RGBA_ASTC_4x4_Format: - return Math.floor((width + 3) / 4) * Math.floor((height + 3) / 4) * 16; - case RGBA_ASTC_5x4_Format: - return Math.floor((width + 4) / 5) * Math.floor((height + 3) / 4) * 16; - case RGBA_ASTC_5x5_Format: - return Math.floor((width + 4) / 5) * Math.floor((height + 4) / 5) * 16; - case RGBA_ASTC_6x5_Format: - return Math.floor((width + 5) / 6) * Math.floor((height + 4) / 5) * 16; - case RGBA_ASTC_6x6_Format: - return Math.floor((width + 5) / 6) * Math.floor((height + 5) / 6) * 16; - case RGBA_ASTC_8x5_Format: - return Math.floor((width + 7) / 8) * Math.floor((height + 4) / 5) * 16; - case RGBA_ASTC_8x6_Format: - return Math.floor((width + 7) / 8) * Math.floor((height + 5) / 6) * 16; - case RGBA_ASTC_8x8_Format: - return Math.floor((width + 7) / 8) * Math.floor((height + 7) / 8) * 16; - case RGBA_ASTC_10x5_Format: - return Math.floor((width + 9) / 10) * Math.floor((height + 4) / 5) * 16; - case RGBA_ASTC_10x6_Format: - return Math.floor((width + 9) / 10) * Math.floor((height + 5) / 6) * 16; - case RGBA_ASTC_10x8_Format: - return Math.floor((width + 9) / 10) * Math.floor((height + 7) / 8) * 16; - case RGBA_ASTC_10x10_Format: - return Math.floor((width + 9) / 10) * Math.floor((height + 9) / 10) * 16; - case RGBA_ASTC_12x10_Format: - return Math.floor((width + 11) / 12) * Math.floor((height + 9) / 10) * 16; - case RGBA_ASTC_12x12_Format: - return Math.floor((width + 11) / 12) * Math.floor((height + 11) / 12) * 16; - case RGBA_BPTC_Format: - case RGB_BPTC_SIGNED_Format: - case RGB_BPTC_UNSIGNED_Format: - return Math.ceil(width / 4) * Math.ceil(height / 4) * 16; - case RED_RGTC1_Format: - case SIGNED_RED_RGTC1_Format: - return Math.ceil(width / 4) * Math.ceil(height / 4) * 8; - case RED_GREEN_RGTC2_Format: - case SIGNED_RED_GREEN_RGTC2_Format: - return Math.ceil(width / 4) * Math.ceil(height / 4) * 16; - } - throw new Error( - `Unable to determine texture byte length for ${format} format.` - ); -} -__name(getByteLength, "getByteLength"); -function getTextureTypeByteLength(type) { - switch (type) { - case UnsignedByteType: - case ByteType: - return { byteLength: 1, components: 1 }; - case UnsignedShortType: - case ShortType: - case HalfFloatType: - return { byteLength: 2, components: 1 }; - case UnsignedShort4444Type: - case UnsignedShort5551Type: - return { byteLength: 2, components: 4 }; - case UnsignedIntType: - case IntType: - case FloatType: - return { byteLength: 4, components: 1 }; - case UnsignedInt5999Type: - return { byteLength: 4, components: 3 }; - } - throw new Error(`Unknown texture type ${type}.`); -} -__name(getTextureTypeByteLength, "getTextureTypeByteLength"); -const TextureUtils = { - contain, - cover, - fill, - getByteLength -}; -function WebGLTextures(_gl, extensions, state, properties, capabilities, utils, info) { - const multisampledRTTExt = extensions.has("WEBGL_multisampled_render_to_texture") ? extensions.get("WEBGL_multisampled_render_to_texture") : null; - const supportsInvalidateFramebuffer = typeof navigator === "undefined" ? false : /OculusBrowser/g.test(navigator.userAgent); - const _imageDimensions = new Vector2(); - const _videoTextures = /* @__PURE__ */ new WeakMap(); - let _canvas2; - const _sources = /* @__PURE__ */ new WeakMap(); - let useOffscreenCanvas = false; - try { - useOffscreenCanvas = typeof OffscreenCanvas !== "undefined" && new OffscreenCanvas(1, 1).getContext("2d") !== null; - } catch (err2) { - } - function createCanvas(width, height) { - return useOffscreenCanvas ? ( - // eslint-disable-next-line compat/compat - new OffscreenCanvas(width, height) - ) : createElementNS("canvas"); - } - __name(createCanvas, "createCanvas"); - function resizeImage(image, needsNewCanvas, maxSize) { - let scale = 1; - const dimensions = getDimensions(image); - if (dimensions.width > maxSize || dimensions.height > maxSize) { - scale = maxSize / Math.max(dimensions.width, dimensions.height); - } - if (scale < 1) { - if (typeof HTMLImageElement !== "undefined" && image instanceof HTMLImageElement || typeof HTMLCanvasElement !== "undefined" && image instanceof HTMLCanvasElement || typeof ImageBitmap !== "undefined" && image instanceof ImageBitmap || typeof VideoFrame !== "undefined" && image instanceof VideoFrame) { - const width = Math.floor(scale * dimensions.width); - const height = Math.floor(scale * dimensions.height); - if (_canvas2 === void 0) _canvas2 = createCanvas(width, height); - const canvas = needsNewCanvas ? createCanvas(width, height) : _canvas2; - canvas.width = width; - canvas.height = height; - const context = canvas.getContext("2d"); - context.drawImage(image, 0, 0, width, height); - console.warn("THREE.WebGLRenderer: Texture has been resized from (" + dimensions.width + "x" + dimensions.height + ") to (" + width + "x" + height + ")."); - return canvas; - } else { - if ("data" in image) { - console.warn("THREE.WebGLRenderer: Image in DataTexture is too big (" + dimensions.width + "x" + dimensions.height + ")."); - } - return image; - } - } - return image; - } - __name(resizeImage, "resizeImage"); - function textureNeedsGenerateMipmaps(texture) { - return texture.generateMipmaps; - } - __name(textureNeedsGenerateMipmaps, "textureNeedsGenerateMipmaps"); - function generateMipmap(target) { - _gl.generateMipmap(target); - } - __name(generateMipmap, "generateMipmap"); - function getTargetType(texture) { - if (texture.isWebGLCubeRenderTarget) return _gl.TEXTURE_CUBE_MAP; - if (texture.isWebGL3DRenderTarget) return _gl.TEXTURE_3D; - if (texture.isWebGLArrayRenderTarget || texture.isCompressedArrayTexture) return _gl.TEXTURE_2D_ARRAY; - return _gl.TEXTURE_2D; - } - __name(getTargetType, "getTargetType"); - function getInternalFormat(internalFormatName, glFormat, glType, colorSpace, forceLinearTransfer = false) { - if (internalFormatName !== null) { - if (_gl[internalFormatName] !== void 0) return _gl[internalFormatName]; - console.warn("THREE.WebGLRenderer: Attempt to use non-existing WebGL internal format '" + internalFormatName + "'"); - } - let internalFormat = glFormat; - if (glFormat === _gl.RED) { - if (glType === _gl.FLOAT) internalFormat = _gl.R32F; - if (glType === _gl.HALF_FLOAT) internalFormat = _gl.R16F; - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.R8; - } - if (glFormat === _gl.RED_INTEGER) { - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.R8UI; - if (glType === _gl.UNSIGNED_SHORT) internalFormat = _gl.R16UI; - if (glType === _gl.UNSIGNED_INT) internalFormat = _gl.R32UI; - if (glType === _gl.BYTE) internalFormat = _gl.R8I; - if (glType === _gl.SHORT) internalFormat = _gl.R16I; - if (glType === _gl.INT) internalFormat = _gl.R32I; - } - if (glFormat === _gl.RG) { - if (glType === _gl.FLOAT) internalFormat = _gl.RG32F; - if (glType === _gl.HALF_FLOAT) internalFormat = _gl.RG16F; - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.RG8; - } - if (glFormat === _gl.RG_INTEGER) { - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.RG8UI; - if (glType === _gl.UNSIGNED_SHORT) internalFormat = _gl.RG16UI; - if (glType === _gl.UNSIGNED_INT) internalFormat = _gl.RG32UI; - if (glType === _gl.BYTE) internalFormat = _gl.RG8I; - if (glType === _gl.SHORT) internalFormat = _gl.RG16I; - if (glType === _gl.INT) internalFormat = _gl.RG32I; - } - if (glFormat === _gl.RGB_INTEGER) { - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.RGB8UI; - if (glType === _gl.UNSIGNED_SHORT) internalFormat = _gl.RGB16UI; - if (glType === _gl.UNSIGNED_INT) internalFormat = _gl.RGB32UI; - if (glType === _gl.BYTE) internalFormat = _gl.RGB8I; - if (glType === _gl.SHORT) internalFormat = _gl.RGB16I; - if (glType === _gl.INT) internalFormat = _gl.RGB32I; - } - if (glFormat === _gl.RGBA_INTEGER) { - if (glType === _gl.UNSIGNED_BYTE) internalFormat = _gl.RGBA8UI; - if (glType === _gl.UNSIGNED_SHORT) internalFormat = _gl.RGBA16UI; - if (glType === _gl.UNSIGNED_INT) internalFormat = _gl.RGBA32UI; - if (glType === _gl.BYTE) internalFormat = _gl.RGBA8I; - if (glType === _gl.SHORT) internalFormat = _gl.RGBA16I; - if (glType === _gl.INT) internalFormat = _gl.RGBA32I; - } - if (glFormat === _gl.RGB) { - if (glType === _gl.UNSIGNED_INT_5_9_9_9_REV) internalFormat = _gl.RGB9_E5; - } - if (glFormat === _gl.RGBA) { - const transfer = forceLinearTransfer ? LinearTransfer : ColorManagement.getTransfer(colorSpace); - if (glType === _gl.FLOAT) internalFormat = _gl.RGBA32F; - if (glType === _gl.HALF_FLOAT) internalFormat = _gl.RGBA16F; - if (glType === _gl.UNSIGNED_BYTE) internalFormat = transfer === SRGBTransfer ? _gl.SRGB8_ALPHA8 : _gl.RGBA8; - if (glType === _gl.UNSIGNED_SHORT_4_4_4_4) internalFormat = _gl.RGBA4; - if (glType === _gl.UNSIGNED_SHORT_5_5_5_1) internalFormat = _gl.RGB5_A1; - } - if (internalFormat === _gl.R16F || internalFormat === _gl.R32F || internalFormat === _gl.RG16F || internalFormat === _gl.RG32F || internalFormat === _gl.RGBA16F || internalFormat === _gl.RGBA32F) { - extensions.get("EXT_color_buffer_float"); - } - return internalFormat; - } - __name(getInternalFormat, "getInternalFormat"); - function getInternalDepthFormat(useStencil, depthType) { - let glInternalFormat; - if (useStencil) { - if (depthType === null || depthType === UnsignedIntType || depthType === UnsignedInt248Type) { - glInternalFormat = _gl.DEPTH24_STENCIL8; - } else if (depthType === FloatType) { - glInternalFormat = _gl.DEPTH32F_STENCIL8; - } else if (depthType === UnsignedShortType) { - glInternalFormat = _gl.DEPTH24_STENCIL8; - console.warn("DepthTexture: 16 bit depth attachment is not supported with stencil. Using 24-bit attachment."); - } - } else { - if (depthType === null || depthType === UnsignedIntType || depthType === UnsignedInt248Type) { - glInternalFormat = _gl.DEPTH_COMPONENT24; - } else if (depthType === FloatType) { - glInternalFormat = _gl.DEPTH_COMPONENT32F; - } else if (depthType === UnsignedShortType) { - glInternalFormat = _gl.DEPTH_COMPONENT16; - } - } - return glInternalFormat; - } - __name(getInternalDepthFormat, "getInternalDepthFormat"); - function getMipLevels(texture, image) { - if (textureNeedsGenerateMipmaps(texture) === true || texture.isFramebufferTexture && texture.minFilter !== NearestFilter && texture.minFilter !== LinearFilter) { - return Math.log2(Math.max(image.width, image.height)) + 1; - } else if (texture.mipmaps !== void 0 && texture.mipmaps.length > 0) { - return texture.mipmaps.length; - } else if (texture.isCompressedTexture && Array.isArray(texture.image)) { - return image.mipmaps.length; - } else { - return 1; - } - } - __name(getMipLevels, "getMipLevels"); - function onTextureDispose(event) { - const texture = event.target; - texture.removeEventListener("dispose", onTextureDispose); - deallocateTexture(texture); - if (texture.isVideoTexture) { - _videoTextures.delete(texture); - } - } - __name(onTextureDispose, "onTextureDispose"); - function onRenderTargetDispose(event) { - const renderTarget = event.target; - renderTarget.removeEventListener("dispose", onRenderTargetDispose); - deallocateRenderTarget(renderTarget); - } - __name(onRenderTargetDispose, "onRenderTargetDispose"); - function deallocateTexture(texture) { - const textureProperties = properties.get(texture); - if (textureProperties.__webglInit === void 0) return; - const source = texture.source; - const webglTextures = _sources.get(source); - if (webglTextures) { - const webglTexture = webglTextures[textureProperties.__cacheKey]; - webglTexture.usedTimes--; - if (webglTexture.usedTimes === 0) { - deleteTexture(texture); - } - if (Object.keys(webglTextures).length === 0) { - _sources.delete(source); - } - } - properties.remove(texture); - } - __name(deallocateTexture, "deallocateTexture"); - function deleteTexture(texture) { - const textureProperties = properties.get(texture); - _gl.deleteTexture(textureProperties.__webglTexture); - const source = texture.source; - const webglTextures = _sources.get(source); - delete webglTextures[textureProperties.__cacheKey]; - info.memory.textures--; - } - __name(deleteTexture, "deleteTexture"); - function deallocateRenderTarget(renderTarget) { - const renderTargetProperties = properties.get(renderTarget); - if (renderTarget.depthTexture) { - renderTarget.depthTexture.dispose(); - properties.remove(renderTarget.depthTexture); - } - if (renderTarget.isWebGLCubeRenderTarget) { - for (let i = 0; i < 6; i++) { - if (Array.isArray(renderTargetProperties.__webglFramebuffer[i])) { - for (let level = 0; level < renderTargetProperties.__webglFramebuffer[i].length; level++) _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer[i][level]); - } else { - _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer[i]); - } - if (renderTargetProperties.__webglDepthbuffer) _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthbuffer[i]); - } - } else { - if (Array.isArray(renderTargetProperties.__webglFramebuffer)) { - for (let level = 0; level < renderTargetProperties.__webglFramebuffer.length; level++) _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer[level]); - } else { - _gl.deleteFramebuffer(renderTargetProperties.__webglFramebuffer); - } - if (renderTargetProperties.__webglDepthbuffer) _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthbuffer); - if (renderTargetProperties.__webglMultisampledFramebuffer) _gl.deleteFramebuffer(renderTargetProperties.__webglMultisampledFramebuffer); - if (renderTargetProperties.__webglColorRenderbuffer) { - for (let i = 0; i < renderTargetProperties.__webglColorRenderbuffer.length; i++) { - if (renderTargetProperties.__webglColorRenderbuffer[i]) _gl.deleteRenderbuffer(renderTargetProperties.__webglColorRenderbuffer[i]); - } - } - if (renderTargetProperties.__webglDepthRenderbuffer) _gl.deleteRenderbuffer(renderTargetProperties.__webglDepthRenderbuffer); - } - const textures = renderTarget.textures; - for (let i = 0, il = textures.length; i < il; i++) { - const attachmentProperties = properties.get(textures[i]); - if (attachmentProperties.__webglTexture) { - _gl.deleteTexture(attachmentProperties.__webglTexture); - info.memory.textures--; - } - properties.remove(textures[i]); - } - properties.remove(renderTarget); - } - __name(deallocateRenderTarget, "deallocateRenderTarget"); - let textureUnits = 0; - function resetTextureUnits() { - textureUnits = 0; - } - __name(resetTextureUnits, "resetTextureUnits"); - function allocateTextureUnit() { - const textureUnit = textureUnits; - if (textureUnit >= capabilities.maxTextures) { - console.warn("THREE.WebGLTextures: Trying to use " + textureUnit + " texture units while this GPU supports only " + capabilities.maxTextures); - } - textureUnits += 1; - return textureUnit; - } - __name(allocateTextureUnit, "allocateTextureUnit"); - function getTextureCacheKey(texture) { - const array = []; - array.push(texture.wrapS); - array.push(texture.wrapT); - array.push(texture.wrapR || 0); - array.push(texture.magFilter); - array.push(texture.minFilter); - array.push(texture.anisotropy); - array.push(texture.internalFormat); - array.push(texture.format); - array.push(texture.type); - array.push(texture.generateMipmaps); - array.push(texture.premultiplyAlpha); - array.push(texture.flipY); - array.push(texture.unpackAlignment); - array.push(texture.colorSpace); - return array.join(); - } - __name(getTextureCacheKey, "getTextureCacheKey"); - function setTexture2D(texture, slot) { - const textureProperties = properties.get(texture); - if (texture.isVideoTexture) updateVideoTexture(texture); - if (texture.isRenderTargetTexture === false && texture.version > 0 && textureProperties.__version !== texture.version) { - const image = texture.image; - if (image === null) { - console.warn("THREE.WebGLRenderer: Texture marked for update but no image data found."); - } else if (image.complete === false) { - console.warn("THREE.WebGLRenderer: Texture marked for update but image is incomplete"); - } else { - uploadTexture(textureProperties, texture, slot); - return; - } - } - state.bindTexture(_gl.TEXTURE_2D, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - } - __name(setTexture2D, "setTexture2D"); - function setTexture2DArray(texture, slot) { - const textureProperties = properties.get(texture); - if (texture.version > 0 && textureProperties.__version !== texture.version) { - uploadTexture(textureProperties, texture, slot); - return; - } - state.bindTexture(_gl.TEXTURE_2D_ARRAY, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - } - __name(setTexture2DArray, "setTexture2DArray"); - function setTexture3D(texture, slot) { - const textureProperties = properties.get(texture); - if (texture.version > 0 && textureProperties.__version !== texture.version) { - uploadTexture(textureProperties, texture, slot); - return; - } - state.bindTexture(_gl.TEXTURE_3D, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - } - __name(setTexture3D, "setTexture3D"); - function setTextureCube(texture, slot) { - const textureProperties = properties.get(texture); - if (texture.version > 0 && textureProperties.__version !== texture.version) { - uploadCubeTexture(textureProperties, texture, slot); - return; - } - state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - } - __name(setTextureCube, "setTextureCube"); - const wrappingToGL = { - [RepeatWrapping]: _gl.REPEAT, - [ClampToEdgeWrapping]: _gl.CLAMP_TO_EDGE, - [MirroredRepeatWrapping]: _gl.MIRRORED_REPEAT - }; - const filterToGL = { - [NearestFilter]: _gl.NEAREST, - [NearestMipmapNearestFilter]: _gl.NEAREST_MIPMAP_NEAREST, - [NearestMipmapLinearFilter]: _gl.NEAREST_MIPMAP_LINEAR, - [LinearFilter]: _gl.LINEAR, - [LinearMipmapNearestFilter]: _gl.LINEAR_MIPMAP_NEAREST, - [LinearMipmapLinearFilter]: _gl.LINEAR_MIPMAP_LINEAR - }; - const compareToGL = { - [NeverCompare]: _gl.NEVER, - [AlwaysCompare]: _gl.ALWAYS, - [LessCompare]: _gl.LESS, - [LessEqualCompare]: _gl.LEQUAL, - [EqualCompare]: _gl.EQUAL, - [GreaterEqualCompare]: _gl.GEQUAL, - [GreaterCompare]: _gl.GREATER, - [NotEqualCompare]: _gl.NOTEQUAL - }; - function setTextureParameters(textureType, texture) { - if (texture.type === FloatType && extensions.has("OES_texture_float_linear") === false && (texture.magFilter === LinearFilter || texture.magFilter === LinearMipmapNearestFilter || texture.magFilter === NearestMipmapLinearFilter || texture.magFilter === LinearMipmapLinearFilter || texture.minFilter === LinearFilter || texture.minFilter === LinearMipmapNearestFilter || texture.minFilter === NearestMipmapLinearFilter || texture.minFilter === LinearMipmapLinearFilter)) { - console.warn("THREE.WebGLRenderer: Unable to use linear filtering with floating point textures. OES_texture_float_linear not supported on this device."); - } - _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_S, wrappingToGL[texture.wrapS]); - _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_T, wrappingToGL[texture.wrapT]); - if (textureType === _gl.TEXTURE_3D || textureType === _gl.TEXTURE_2D_ARRAY) { - _gl.texParameteri(textureType, _gl.TEXTURE_WRAP_R, wrappingToGL[texture.wrapR]); - } - _gl.texParameteri(textureType, _gl.TEXTURE_MAG_FILTER, filterToGL[texture.magFilter]); - _gl.texParameteri(textureType, _gl.TEXTURE_MIN_FILTER, filterToGL[texture.minFilter]); - if (texture.compareFunction) { - _gl.texParameteri(textureType, _gl.TEXTURE_COMPARE_MODE, _gl.COMPARE_REF_TO_TEXTURE); - _gl.texParameteri(textureType, _gl.TEXTURE_COMPARE_FUNC, compareToGL[texture.compareFunction]); - } - if (extensions.has("EXT_texture_filter_anisotropic") === true) { - if (texture.magFilter === NearestFilter) return; - if (texture.minFilter !== NearestMipmapLinearFilter && texture.minFilter !== LinearMipmapLinearFilter) return; - if (texture.type === FloatType && extensions.has("OES_texture_float_linear") === false) return; - if (texture.anisotropy > 1 || properties.get(texture).__currentAnisotropy) { - const extension = extensions.get("EXT_texture_filter_anisotropic"); - _gl.texParameterf(textureType, extension.TEXTURE_MAX_ANISOTROPY_EXT, Math.min(texture.anisotropy, capabilities.getMaxAnisotropy())); - properties.get(texture).__currentAnisotropy = texture.anisotropy; - } - } - } - __name(setTextureParameters, "setTextureParameters"); - function initTexture(textureProperties, texture) { - let forceUpload = false; - if (textureProperties.__webglInit === void 0) { - textureProperties.__webglInit = true; - texture.addEventListener("dispose", onTextureDispose); - } - const source = texture.source; - let webglTextures = _sources.get(source); - if (webglTextures === void 0) { - webglTextures = {}; - _sources.set(source, webglTextures); - } - const textureCacheKey = getTextureCacheKey(texture); - if (textureCacheKey !== textureProperties.__cacheKey) { - if (webglTextures[textureCacheKey] === void 0) { - webglTextures[textureCacheKey] = { - texture: _gl.createTexture(), - usedTimes: 0 - }; - info.memory.textures++; - forceUpload = true; - } - webglTextures[textureCacheKey].usedTimes++; - const webglTexture = webglTextures[textureProperties.__cacheKey]; - if (webglTexture !== void 0) { - webglTextures[textureProperties.__cacheKey].usedTimes--; - if (webglTexture.usedTimes === 0) { - deleteTexture(texture); - } - } - textureProperties.__cacheKey = textureCacheKey; - textureProperties.__webglTexture = webglTextures[textureCacheKey].texture; - } - return forceUpload; - } - __name(initTexture, "initTexture"); - function uploadTexture(textureProperties, texture, slot) { - let textureType = _gl.TEXTURE_2D; - if (texture.isDataArrayTexture || texture.isCompressedArrayTexture) textureType = _gl.TEXTURE_2D_ARRAY; - if (texture.isData3DTexture) textureType = _gl.TEXTURE_3D; - const forceUpload = initTexture(textureProperties, texture); - const source = texture.source; - state.bindTexture(textureType, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - const sourceProperties = properties.get(source); - if (source.version !== sourceProperties.__version || forceUpload === true) { - state.activeTexture(_gl.TEXTURE0 + slot); - const workingPrimaries = ColorManagement.getPrimaries(ColorManagement.workingColorSpace); - const texturePrimaries = texture.colorSpace === NoColorSpace ? null : ColorManagement.getPrimaries(texture.colorSpace); - const unpackConversion = texture.colorSpace === NoColorSpace || workingPrimaries === texturePrimaries ? _gl.NONE : _gl.BROWSER_DEFAULT_WEBGL; - _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, texture.flipY); - _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, texture.premultiplyAlpha); - _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, texture.unpackAlignment); - _gl.pixelStorei(_gl.UNPACK_COLORSPACE_CONVERSION_WEBGL, unpackConversion); - let image = resizeImage(texture.image, false, capabilities.maxTextureSize); - image = verifyColorSpace(texture, image); - const glFormat = utils.convert(texture.format, texture.colorSpace); - const glType = utils.convert(texture.type); - let glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.colorSpace, texture.isVideoTexture); - setTextureParameters(textureType, texture); - let mipmap; - const mipmaps = texture.mipmaps; - const useTexStorage = texture.isVideoTexture !== true; - const allocateMemory = sourceProperties.__version === void 0 || forceUpload === true; - const dataReady = source.dataReady; - const levels = getMipLevels(texture, image); - if (texture.isDepthTexture) { - glInternalFormat = getInternalDepthFormat(texture.format === DepthStencilFormat, texture.type); - if (allocateMemory) { - if (useTexStorage) { - state.texStorage2D(_gl.TEXTURE_2D, 1, glInternalFormat, image.width, image.height); - } else { - state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, image.width, image.height, 0, glFormat, glType, null); - } - } - } else if (texture.isDataTexture) { - if (mipmaps.length > 0) { - if (useTexStorage && allocateMemory) { - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, mipmaps[0].width, mipmaps[0].height); - } - for (let i = 0, il = mipmaps.length; i < il; i++) { - mipmap = mipmaps[i]; - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_2D, i, 0, 0, mipmap.width, mipmap.height, glFormat, glType, mipmap.data); - } - } else { - state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data); - } - } - texture.generateMipmaps = false; - } else { - if (useTexStorage) { - if (allocateMemory) { - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, image.width, image.height); - } - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_2D, 0, 0, 0, image.width, image.height, glFormat, glType, image.data); - } - } else { - state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, image.width, image.height, 0, glFormat, glType, image.data); - } - } - } else if (texture.isCompressedTexture) { - if (texture.isCompressedArrayTexture) { - if (useTexStorage && allocateMemory) { - state.texStorage3D(_gl.TEXTURE_2D_ARRAY, levels, glInternalFormat, mipmaps[0].width, mipmaps[0].height, image.depth); - } - for (let i = 0, il = mipmaps.length; i < il; i++) { - mipmap = mipmaps[i]; - if (texture.format !== RGBAFormat) { - if (glFormat !== null) { - if (useTexStorage) { - if (dataReady) { - if (texture.layerUpdates.size > 0) { - const layerByteLength = getByteLength(mipmap.width, mipmap.height, texture.format, texture.type); - for (const layerIndex of texture.layerUpdates) { - const layerData = mipmap.data.subarray( - layerIndex * layerByteLength / mipmap.data.BYTES_PER_ELEMENT, - (layerIndex + 1) * layerByteLength / mipmap.data.BYTES_PER_ELEMENT - ); - state.compressedTexSubImage3D(_gl.TEXTURE_2D_ARRAY, i, 0, 0, layerIndex, mipmap.width, mipmap.height, 1, glFormat, layerData); - } - texture.clearLayerUpdates(); - } else { - state.compressedTexSubImage3D(_gl.TEXTURE_2D_ARRAY, i, 0, 0, 0, mipmap.width, mipmap.height, image.depth, glFormat, mipmap.data); - } - } - } else { - state.compressedTexImage3D(_gl.TEXTURE_2D_ARRAY, i, glInternalFormat, mipmap.width, mipmap.height, image.depth, 0, mipmap.data, 0, 0); - } - } else { - console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"); - } - } else { - if (useTexStorage) { - if (dataReady) { - state.texSubImage3D(_gl.TEXTURE_2D_ARRAY, i, 0, 0, 0, mipmap.width, mipmap.height, image.depth, glFormat, glType, mipmap.data); - } - } else { - state.texImage3D(_gl.TEXTURE_2D_ARRAY, i, glInternalFormat, mipmap.width, mipmap.height, image.depth, 0, glFormat, glType, mipmap.data); - } - } - } - } else { - if (useTexStorage && allocateMemory) { - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, mipmaps[0].width, mipmaps[0].height); - } - for (let i = 0, il = mipmaps.length; i < il; i++) { - mipmap = mipmaps[i]; - if (texture.format !== RGBAFormat) { - if (glFormat !== null) { - if (useTexStorage) { - if (dataReady) { - state.compressedTexSubImage2D(_gl.TEXTURE_2D, i, 0, 0, mipmap.width, mipmap.height, glFormat, mipmap.data); - } - } else { - state.compressedTexImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, mipmap.data); - } - } else { - console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"); - } - } else { - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_2D, i, 0, 0, mipmap.width, mipmap.height, glFormat, glType, mipmap.data); - } - } else { - state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data); - } - } - } - } - } else if (texture.isDataArrayTexture) { - if (useTexStorage) { - if (allocateMemory) { - state.texStorage3D(_gl.TEXTURE_2D_ARRAY, levels, glInternalFormat, image.width, image.height, image.depth); - } - if (dataReady) { - if (texture.layerUpdates.size > 0) { - const layerByteLength = getByteLength(image.width, image.height, texture.format, texture.type); - for (const layerIndex of texture.layerUpdates) { - const layerData = image.data.subarray( - layerIndex * layerByteLength / image.data.BYTES_PER_ELEMENT, - (layerIndex + 1) * layerByteLength / image.data.BYTES_PER_ELEMENT - ); - state.texSubImage3D(_gl.TEXTURE_2D_ARRAY, 0, 0, 0, layerIndex, image.width, image.height, 1, glFormat, glType, layerData); - } - texture.clearLayerUpdates(); - } else { - state.texSubImage3D(_gl.TEXTURE_2D_ARRAY, 0, 0, 0, 0, image.width, image.height, image.depth, glFormat, glType, image.data); - } - } - } else { - state.texImage3D(_gl.TEXTURE_2D_ARRAY, 0, glInternalFormat, image.width, image.height, image.depth, 0, glFormat, glType, image.data); - } - } else if (texture.isData3DTexture) { - if (useTexStorage) { - if (allocateMemory) { - state.texStorage3D(_gl.TEXTURE_3D, levels, glInternalFormat, image.width, image.height, image.depth); - } - if (dataReady) { - state.texSubImage3D(_gl.TEXTURE_3D, 0, 0, 0, 0, image.width, image.height, image.depth, glFormat, glType, image.data); - } - } else { - state.texImage3D(_gl.TEXTURE_3D, 0, glInternalFormat, image.width, image.height, image.depth, 0, glFormat, glType, image.data); - } - } else if (texture.isFramebufferTexture) { - if (allocateMemory) { - if (useTexStorage) { - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, image.width, image.height); - } else { - let width = image.width, height = image.height; - for (let i = 0; i < levels; i++) { - state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, width, height, 0, glFormat, glType, null); - width >>= 1; - height >>= 1; - } - } - } - } else { - if (mipmaps.length > 0) { - if (useTexStorage && allocateMemory) { - const dimensions = getDimensions(mipmaps[0]); - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, dimensions.width, dimensions.height); - } - for (let i = 0, il = mipmaps.length; i < il; i++) { - mipmap = mipmaps[i]; - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_2D, i, 0, 0, glFormat, glType, mipmap); - } - } else { - state.texImage2D(_gl.TEXTURE_2D, i, glInternalFormat, glFormat, glType, mipmap); - } - } - texture.generateMipmaps = false; - } else { - if (useTexStorage) { - if (allocateMemory) { - const dimensions = getDimensions(image); - state.texStorage2D(_gl.TEXTURE_2D, levels, glInternalFormat, dimensions.width, dimensions.height); - } - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_2D, 0, 0, 0, glFormat, glType, image); - } - } else { - state.texImage2D(_gl.TEXTURE_2D, 0, glInternalFormat, glFormat, glType, image); - } - } - } - if (textureNeedsGenerateMipmaps(texture)) { - generateMipmap(textureType); - } - sourceProperties.__version = source.version; - if (texture.onUpdate) texture.onUpdate(texture); - } - textureProperties.__version = texture.version; - } - __name(uploadTexture, "uploadTexture"); - function uploadCubeTexture(textureProperties, texture, slot) { - if (texture.image.length !== 6) return; - const forceUpload = initTexture(textureProperties, texture); - const source = texture.source; - state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture, _gl.TEXTURE0 + slot); - const sourceProperties = properties.get(source); - if (source.version !== sourceProperties.__version || forceUpload === true) { - state.activeTexture(_gl.TEXTURE0 + slot); - const workingPrimaries = ColorManagement.getPrimaries(ColorManagement.workingColorSpace); - const texturePrimaries = texture.colorSpace === NoColorSpace ? null : ColorManagement.getPrimaries(texture.colorSpace); - const unpackConversion = texture.colorSpace === NoColorSpace || workingPrimaries === texturePrimaries ? _gl.NONE : _gl.BROWSER_DEFAULT_WEBGL; - _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, texture.flipY); - _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, texture.premultiplyAlpha); - _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, texture.unpackAlignment); - _gl.pixelStorei(_gl.UNPACK_COLORSPACE_CONVERSION_WEBGL, unpackConversion); - const isCompressed = texture.isCompressedTexture || texture.image[0].isCompressedTexture; - const isDataTexture = texture.image[0] && texture.image[0].isDataTexture; - const cubeImage = []; - for (let i = 0; i < 6; i++) { - if (!isCompressed && !isDataTexture) { - cubeImage[i] = resizeImage(texture.image[i], true, capabilities.maxCubemapSize); - } else { - cubeImage[i] = isDataTexture ? texture.image[i].image : texture.image[i]; - } - cubeImage[i] = verifyColorSpace(texture, cubeImage[i]); - } - const image = cubeImage[0], glFormat = utils.convert(texture.format, texture.colorSpace), glType = utils.convert(texture.type), glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.colorSpace); - const useTexStorage = texture.isVideoTexture !== true; - const allocateMemory = sourceProperties.__version === void 0 || forceUpload === true; - const dataReady = source.dataReady; - let levels = getMipLevels(texture, image); - setTextureParameters(_gl.TEXTURE_CUBE_MAP, texture); - let mipmaps; - if (isCompressed) { - if (useTexStorage && allocateMemory) { - state.texStorage2D(_gl.TEXTURE_CUBE_MAP, levels, glInternalFormat, image.width, image.height); - } - for (let i = 0; i < 6; i++) { - mipmaps = cubeImage[i].mipmaps; - for (let j = 0; j < mipmaps.length; j++) { - const mipmap = mipmaps[j]; - if (texture.format !== RGBAFormat) { - if (glFormat !== null) { - if (useTexStorage) { - if (dataReady) { - state.compressedTexSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, 0, 0, mipmap.width, mipmap.height, glFormat, mipmap.data); - } - } else { - state.compressedTexImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, glInternalFormat, mipmap.width, mipmap.height, 0, mipmap.data); - } - } else { - console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"); - } - } else { - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, 0, 0, mipmap.width, mipmap.height, glFormat, glType, mipmap.data); - } - } else { - state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j, glInternalFormat, mipmap.width, mipmap.height, 0, glFormat, glType, mipmap.data); - } - } - } - } - } else { - mipmaps = texture.mipmaps; - if (useTexStorage && allocateMemory) { - if (mipmaps.length > 0) levels++; - const dimensions = getDimensions(cubeImage[0]); - state.texStorage2D(_gl.TEXTURE_CUBE_MAP, levels, glInternalFormat, dimensions.width, dimensions.height); - } - for (let i = 0; i < 6; i++) { - if (isDataTexture) { - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, 0, 0, cubeImage[i].width, cubeImage[i].height, glFormat, glType, cubeImage[i].data); - } - } else { - state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, glInternalFormat, cubeImage[i].width, cubeImage[i].height, 0, glFormat, glType, cubeImage[i].data); - } - for (let j = 0; j < mipmaps.length; j++) { - const mipmap = mipmaps[j]; - const mipmapImage = mipmap.image[i].image; - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, 0, 0, mipmapImage.width, mipmapImage.height, glFormat, glType, mipmapImage.data); - } - } else { - state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, glInternalFormat, mipmapImage.width, mipmapImage.height, 0, glFormat, glType, mipmapImage.data); - } - } - } else { - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, 0, 0, glFormat, glType, cubeImage[i]); - } - } else { - state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0, glInternalFormat, glFormat, glType, cubeImage[i]); - } - for (let j = 0; j < mipmaps.length; j++) { - const mipmap = mipmaps[j]; - if (useTexStorage) { - if (dataReady) { - state.texSubImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, 0, 0, glFormat, glType, mipmap.image[i]); - } - } else { - state.texImage2D(_gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, j + 1, glInternalFormat, glFormat, glType, mipmap.image[i]); - } - } - } - } - } - if (textureNeedsGenerateMipmaps(texture)) { - generateMipmap(_gl.TEXTURE_CUBE_MAP); - } - sourceProperties.__version = source.version; - if (texture.onUpdate) texture.onUpdate(texture); - } - textureProperties.__version = texture.version; - } - __name(uploadCubeTexture, "uploadCubeTexture"); - function setupFrameBufferTexture(framebuffer, renderTarget, texture, attachment, textureTarget, level) { - const glFormat = utils.convert(texture.format, texture.colorSpace); - const glType = utils.convert(texture.type); - const glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.colorSpace); - const renderTargetProperties = properties.get(renderTarget); - const textureProperties = properties.get(texture); - textureProperties.__renderTarget = renderTarget; - if (!renderTargetProperties.__hasExternalTextures) { - const width = Math.max(1, renderTarget.width >> level); - const height = Math.max(1, renderTarget.height >> level); - if (textureTarget === _gl.TEXTURE_3D || textureTarget === _gl.TEXTURE_2D_ARRAY) { - state.texImage3D(textureTarget, level, glInternalFormat, width, height, renderTarget.depth, 0, glFormat, glType, null); - } else { - state.texImage2D(textureTarget, level, glInternalFormat, width, height, 0, glFormat, glType, null); - } - } - state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer); - if (useMultisampledRTT(renderTarget)) { - multisampledRTTExt.framebufferTexture2DMultisampleEXT(_gl.FRAMEBUFFER, attachment, textureTarget, textureProperties.__webglTexture, 0, getRenderTargetSamples(renderTarget)); - } else if (textureTarget === _gl.TEXTURE_2D || textureTarget >= _gl.TEXTURE_CUBE_MAP_POSITIVE_X && textureTarget <= _gl.TEXTURE_CUBE_MAP_NEGATIVE_Z) { - _gl.framebufferTexture2D(_gl.FRAMEBUFFER, attachment, textureTarget, textureProperties.__webglTexture, level); - } - state.bindFramebuffer(_gl.FRAMEBUFFER, null); - } - __name(setupFrameBufferTexture, "setupFrameBufferTexture"); - function setupRenderBufferStorage(renderbuffer, renderTarget, isMultisample) { - _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderbuffer); - if (renderTarget.depthBuffer) { - const depthTexture = renderTarget.depthTexture; - const depthType = depthTexture && depthTexture.isDepthTexture ? depthTexture.type : null; - const glInternalFormat = getInternalDepthFormat(renderTarget.stencilBuffer, depthType); - const glAttachmentType = renderTarget.stencilBuffer ? _gl.DEPTH_STENCIL_ATTACHMENT : _gl.DEPTH_ATTACHMENT; - const samples = getRenderTargetSamples(renderTarget); - const isUseMultisampledRTT = useMultisampledRTT(renderTarget); - if (isUseMultisampledRTT) { - multisampledRTTExt.renderbufferStorageMultisampleEXT(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height); - } else if (isMultisample) { - _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height); - } else { - _gl.renderbufferStorage(_gl.RENDERBUFFER, glInternalFormat, renderTarget.width, renderTarget.height); - } - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, glAttachmentType, _gl.RENDERBUFFER, renderbuffer); - } else { - const textures = renderTarget.textures; - for (let i = 0; i < textures.length; i++) { - const texture = textures[i]; - const glFormat = utils.convert(texture.format, texture.colorSpace); - const glType = utils.convert(texture.type); - const glInternalFormat = getInternalFormat(texture.internalFormat, glFormat, glType, texture.colorSpace); - const samples = getRenderTargetSamples(renderTarget); - if (isMultisample && useMultisampledRTT(renderTarget) === false) { - _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height); - } else if (useMultisampledRTT(renderTarget)) { - multisampledRTTExt.renderbufferStorageMultisampleEXT(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height); - } else { - _gl.renderbufferStorage(_gl.RENDERBUFFER, glInternalFormat, renderTarget.width, renderTarget.height); - } - } - } - _gl.bindRenderbuffer(_gl.RENDERBUFFER, null); - } - __name(setupRenderBufferStorage, "setupRenderBufferStorage"); - function setupDepthTexture(framebuffer, renderTarget) { - const isCube = renderTarget && renderTarget.isWebGLCubeRenderTarget; - if (isCube) throw new Error("Depth Texture with cube render targets is not supported"); - state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer); - if (!(renderTarget.depthTexture && renderTarget.depthTexture.isDepthTexture)) { - throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture"); - } - const textureProperties = properties.get(renderTarget.depthTexture); - textureProperties.__renderTarget = renderTarget; - if (!textureProperties.__webglTexture || renderTarget.depthTexture.image.width !== renderTarget.width || renderTarget.depthTexture.image.height !== renderTarget.height) { - renderTarget.depthTexture.image.width = renderTarget.width; - renderTarget.depthTexture.image.height = renderTarget.height; - renderTarget.depthTexture.needsUpdate = true; - } - setTexture2D(renderTarget.depthTexture, 0); - const webglDepthTexture = textureProperties.__webglTexture; - const samples = getRenderTargetSamples(renderTarget); - if (renderTarget.depthTexture.format === DepthFormat) { - if (useMultisampledRTT(renderTarget)) { - multisampledRTTExt.framebufferTexture2DMultisampleEXT(_gl.FRAMEBUFFER, _gl.DEPTH_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0, samples); - } else { - _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.DEPTH_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0); - } - } else if (renderTarget.depthTexture.format === DepthStencilFormat) { - if (useMultisampledRTT(renderTarget)) { - multisampledRTTExt.framebufferTexture2DMultisampleEXT(_gl.FRAMEBUFFER, _gl.DEPTH_STENCIL_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0, samples); - } else { - _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.DEPTH_STENCIL_ATTACHMENT, _gl.TEXTURE_2D, webglDepthTexture, 0); - } - } else { - throw new Error("Unknown depthTexture format"); - } - } - __name(setupDepthTexture, "setupDepthTexture"); - function setupDepthRenderbuffer(renderTarget) { - const renderTargetProperties = properties.get(renderTarget); - const isCube = renderTarget.isWebGLCubeRenderTarget === true; - if (renderTargetProperties.__boundDepthTexture !== renderTarget.depthTexture) { - const depthTexture = renderTarget.depthTexture; - if (renderTargetProperties.__depthDisposeCallback) { - renderTargetProperties.__depthDisposeCallback(); - } - if (depthTexture) { - const disposeEvent = /* @__PURE__ */ __name(() => { - delete renderTargetProperties.__boundDepthTexture; - delete renderTargetProperties.__depthDisposeCallback; - depthTexture.removeEventListener("dispose", disposeEvent); - }, "disposeEvent"); - depthTexture.addEventListener("dispose", disposeEvent); - renderTargetProperties.__depthDisposeCallback = disposeEvent; - } - renderTargetProperties.__boundDepthTexture = depthTexture; - } - if (renderTarget.depthTexture && !renderTargetProperties.__autoAllocateDepthBuffer) { - if (isCube) throw new Error("target.depthTexture not supported in Cube render targets"); - setupDepthTexture(renderTargetProperties.__webglFramebuffer, renderTarget); - } else { - if (isCube) { - renderTargetProperties.__webglDepthbuffer = []; - for (let i = 0; i < 6; i++) { - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer[i]); - if (renderTargetProperties.__webglDepthbuffer[i] === void 0) { - renderTargetProperties.__webglDepthbuffer[i] = _gl.createRenderbuffer(); - setupRenderBufferStorage(renderTargetProperties.__webglDepthbuffer[i], renderTarget, false); - } else { - const glAttachmentType = renderTarget.stencilBuffer ? _gl.DEPTH_STENCIL_ATTACHMENT : _gl.DEPTH_ATTACHMENT; - const renderbuffer = renderTargetProperties.__webglDepthbuffer[i]; - _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderbuffer); - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, glAttachmentType, _gl.RENDERBUFFER, renderbuffer); - } - } - } else { - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer); - if (renderTargetProperties.__webglDepthbuffer === void 0) { - renderTargetProperties.__webglDepthbuffer = _gl.createRenderbuffer(); - setupRenderBufferStorage(renderTargetProperties.__webglDepthbuffer, renderTarget, false); - } else { - const glAttachmentType = renderTarget.stencilBuffer ? _gl.DEPTH_STENCIL_ATTACHMENT : _gl.DEPTH_ATTACHMENT; - const renderbuffer = renderTargetProperties.__webglDepthbuffer; - _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderbuffer); - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, glAttachmentType, _gl.RENDERBUFFER, renderbuffer); - } - } - } - state.bindFramebuffer(_gl.FRAMEBUFFER, null); - } - __name(setupDepthRenderbuffer, "setupDepthRenderbuffer"); - function rebindTextures(renderTarget, colorTexture, depthTexture) { - const renderTargetProperties = properties.get(renderTarget); - if (colorTexture !== void 0) { - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer, renderTarget, renderTarget.texture, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_2D, 0); - } - if (depthTexture !== void 0) { - setupDepthRenderbuffer(renderTarget); - } - } - __name(rebindTextures, "rebindTextures"); - function setupRenderTarget(renderTarget) { - const texture = renderTarget.texture; - const renderTargetProperties = properties.get(renderTarget); - const textureProperties = properties.get(texture); - renderTarget.addEventListener("dispose", onRenderTargetDispose); - const textures = renderTarget.textures; - const isCube = renderTarget.isWebGLCubeRenderTarget === true; - const isMultipleRenderTargets = textures.length > 1; - if (!isMultipleRenderTargets) { - if (textureProperties.__webglTexture === void 0) { - textureProperties.__webglTexture = _gl.createTexture(); - } - textureProperties.__version = texture.version; - info.memory.textures++; - } - if (isCube) { - renderTargetProperties.__webglFramebuffer = []; - for (let i = 0; i < 6; i++) { - if (texture.mipmaps && texture.mipmaps.length > 0) { - renderTargetProperties.__webglFramebuffer[i] = []; - for (let level = 0; level < texture.mipmaps.length; level++) { - renderTargetProperties.__webglFramebuffer[i][level] = _gl.createFramebuffer(); - } - } else { - renderTargetProperties.__webglFramebuffer[i] = _gl.createFramebuffer(); - } - } - } else { - if (texture.mipmaps && texture.mipmaps.length > 0) { - renderTargetProperties.__webglFramebuffer = []; - for (let level = 0; level < texture.mipmaps.length; level++) { - renderTargetProperties.__webglFramebuffer[level] = _gl.createFramebuffer(); - } - } else { - renderTargetProperties.__webglFramebuffer = _gl.createFramebuffer(); - } - if (isMultipleRenderTargets) { - for (let i = 0, il = textures.length; i < il; i++) { - const attachmentProperties = properties.get(textures[i]); - if (attachmentProperties.__webglTexture === void 0) { - attachmentProperties.__webglTexture = _gl.createTexture(); - info.memory.textures++; - } - } - } - if (renderTarget.samples > 0 && useMultisampledRTT(renderTarget) === false) { - renderTargetProperties.__webglMultisampledFramebuffer = _gl.createFramebuffer(); - renderTargetProperties.__webglColorRenderbuffer = []; - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer); - for (let i = 0; i < textures.length; i++) { - const texture2 = textures[i]; - renderTargetProperties.__webglColorRenderbuffer[i] = _gl.createRenderbuffer(); - _gl.bindRenderbuffer(_gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer[i]); - const glFormat = utils.convert(texture2.format, texture2.colorSpace); - const glType = utils.convert(texture2.type); - const glInternalFormat = getInternalFormat(texture2.internalFormat, glFormat, glType, texture2.colorSpace, renderTarget.isXRRenderTarget === true); - const samples = getRenderTargetSamples(renderTarget); - _gl.renderbufferStorageMultisample(_gl.RENDERBUFFER, samples, glInternalFormat, renderTarget.width, renderTarget.height); - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0 + i, _gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer[i]); - } - _gl.bindRenderbuffer(_gl.RENDERBUFFER, null); - if (renderTarget.depthBuffer) { - renderTargetProperties.__webglDepthRenderbuffer = _gl.createRenderbuffer(); - setupRenderBufferStorage(renderTargetProperties.__webglDepthRenderbuffer, renderTarget, true); - } - state.bindFramebuffer(_gl.FRAMEBUFFER, null); - } - } - if (isCube) { - state.bindTexture(_gl.TEXTURE_CUBE_MAP, textureProperties.__webglTexture); - setTextureParameters(_gl.TEXTURE_CUBE_MAP, texture); - for (let i = 0; i < 6; i++) { - if (texture.mipmaps && texture.mipmaps.length > 0) { - for (let level = 0; level < texture.mipmaps.length; level++) { - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer[i][level], renderTarget, texture, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, level); - } - } else { - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer[i], renderTarget, texture, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_CUBE_MAP_POSITIVE_X + i, 0); - } - } - if (textureNeedsGenerateMipmaps(texture)) { - generateMipmap(_gl.TEXTURE_CUBE_MAP); - } - state.unbindTexture(); - } else if (isMultipleRenderTargets) { - for (let i = 0, il = textures.length; i < il; i++) { - const attachment = textures[i]; - const attachmentProperties = properties.get(attachment); - state.bindTexture(_gl.TEXTURE_2D, attachmentProperties.__webglTexture); - setTextureParameters(_gl.TEXTURE_2D, attachment); - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer, renderTarget, attachment, _gl.COLOR_ATTACHMENT0 + i, _gl.TEXTURE_2D, 0); - if (textureNeedsGenerateMipmaps(attachment)) { - generateMipmap(_gl.TEXTURE_2D); - } - } - state.unbindTexture(); - } else { - let glTextureType = _gl.TEXTURE_2D; - if (renderTarget.isWebGL3DRenderTarget || renderTarget.isWebGLArrayRenderTarget) { - glTextureType = renderTarget.isWebGL3DRenderTarget ? _gl.TEXTURE_3D : _gl.TEXTURE_2D_ARRAY; - } - state.bindTexture(glTextureType, textureProperties.__webglTexture); - setTextureParameters(glTextureType, texture); - if (texture.mipmaps && texture.mipmaps.length > 0) { - for (let level = 0; level < texture.mipmaps.length; level++) { - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer[level], renderTarget, texture, _gl.COLOR_ATTACHMENT0, glTextureType, level); - } - } else { - setupFrameBufferTexture(renderTargetProperties.__webglFramebuffer, renderTarget, texture, _gl.COLOR_ATTACHMENT0, glTextureType, 0); - } - if (textureNeedsGenerateMipmaps(texture)) { - generateMipmap(glTextureType); - } - state.unbindTexture(); - } - if (renderTarget.depthBuffer) { - setupDepthRenderbuffer(renderTarget); - } - } - __name(setupRenderTarget, "setupRenderTarget"); - function updateRenderTargetMipmap(renderTarget) { - const textures = renderTarget.textures; - for (let i = 0, il = textures.length; i < il; i++) { - const texture = textures[i]; - if (textureNeedsGenerateMipmaps(texture)) { - const targetType = getTargetType(renderTarget); - const webglTexture = properties.get(texture).__webglTexture; - state.bindTexture(targetType, webglTexture); - generateMipmap(targetType); - state.unbindTexture(); - } - } - } - __name(updateRenderTargetMipmap, "updateRenderTargetMipmap"); - const invalidationArrayRead = []; - const invalidationArrayDraw = []; - function updateMultisampleRenderTarget(renderTarget) { - if (renderTarget.samples > 0) { - if (useMultisampledRTT(renderTarget) === false) { - const textures = renderTarget.textures; - const width = renderTarget.width; - const height = renderTarget.height; - let mask = _gl.COLOR_BUFFER_BIT; - const depthStyle = renderTarget.stencilBuffer ? _gl.DEPTH_STENCIL_ATTACHMENT : _gl.DEPTH_ATTACHMENT; - const renderTargetProperties = properties.get(renderTarget); - const isMultipleRenderTargets = textures.length > 1; - if (isMultipleRenderTargets) { - for (let i = 0; i < textures.length; i++) { - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer); - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0 + i, _gl.RENDERBUFFER, null); - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer); - _gl.framebufferTexture2D(_gl.DRAW_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0 + i, _gl.TEXTURE_2D, null, 0); - } - } - state.bindFramebuffer(_gl.READ_FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer); - state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, renderTargetProperties.__webglFramebuffer); - for (let i = 0; i < textures.length; i++) { - if (renderTarget.resolveDepthBuffer) { - if (renderTarget.depthBuffer) mask |= _gl.DEPTH_BUFFER_BIT; - if (renderTarget.stencilBuffer && renderTarget.resolveStencilBuffer) mask |= _gl.STENCIL_BUFFER_BIT; - } - if (isMultipleRenderTargets) { - _gl.framebufferRenderbuffer(_gl.READ_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, _gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer[i]); - const webglTexture = properties.get(textures[i]).__webglTexture; - _gl.framebufferTexture2D(_gl.DRAW_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_2D, webglTexture, 0); - } - _gl.blitFramebuffer(0, 0, width, height, 0, 0, width, height, mask, _gl.NEAREST); - if (supportsInvalidateFramebuffer === true) { - invalidationArrayRead.length = 0; - invalidationArrayDraw.length = 0; - invalidationArrayRead.push(_gl.COLOR_ATTACHMENT0 + i); - if (renderTarget.depthBuffer && renderTarget.resolveDepthBuffer === false) { - invalidationArrayRead.push(depthStyle); - invalidationArrayDraw.push(depthStyle); - _gl.invalidateFramebuffer(_gl.DRAW_FRAMEBUFFER, invalidationArrayDraw); - } - _gl.invalidateFramebuffer(_gl.READ_FRAMEBUFFER, invalidationArrayRead); - } - } - state.bindFramebuffer(_gl.READ_FRAMEBUFFER, null); - state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, null); - if (isMultipleRenderTargets) { - for (let i = 0; i < textures.length; i++) { - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer); - _gl.framebufferRenderbuffer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0 + i, _gl.RENDERBUFFER, renderTargetProperties.__webglColorRenderbuffer[i]); - const webglTexture = properties.get(textures[i]).__webglTexture; - state.bindFramebuffer(_gl.FRAMEBUFFER, renderTargetProperties.__webglFramebuffer); - _gl.framebufferTexture2D(_gl.DRAW_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0 + i, _gl.TEXTURE_2D, webglTexture, 0); - } - } - state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, renderTargetProperties.__webglMultisampledFramebuffer); - } else { - if (renderTarget.depthBuffer && renderTarget.resolveDepthBuffer === false && supportsInvalidateFramebuffer) { - const depthStyle = renderTarget.stencilBuffer ? _gl.DEPTH_STENCIL_ATTACHMENT : _gl.DEPTH_ATTACHMENT; - _gl.invalidateFramebuffer(_gl.DRAW_FRAMEBUFFER, [depthStyle]); - } - } - } - } - __name(updateMultisampleRenderTarget, "updateMultisampleRenderTarget"); - function getRenderTargetSamples(renderTarget) { - return Math.min(capabilities.maxSamples, renderTarget.samples); - } - __name(getRenderTargetSamples, "getRenderTargetSamples"); - function useMultisampledRTT(renderTarget) { - const renderTargetProperties = properties.get(renderTarget); - return renderTarget.samples > 0 && extensions.has("WEBGL_multisampled_render_to_texture") === true && renderTargetProperties.__useRenderToTexture !== false; - } - __name(useMultisampledRTT, "useMultisampledRTT"); - function updateVideoTexture(texture) { - const frame = info.render.frame; - if (_videoTextures.get(texture) !== frame) { - _videoTextures.set(texture, frame); - texture.update(); - } - } - __name(updateVideoTexture, "updateVideoTexture"); - function verifyColorSpace(texture, image) { - const colorSpace = texture.colorSpace; - const format = texture.format; - const type = texture.type; - if (texture.isCompressedTexture === true || texture.isVideoTexture === true) return image; - if (colorSpace !== LinearSRGBColorSpace && colorSpace !== NoColorSpace) { - if (ColorManagement.getTransfer(colorSpace) === SRGBTransfer) { - if (format !== RGBAFormat || type !== UnsignedByteType) { - console.warn("THREE.WebGLTextures: sRGB encoded textures have to use RGBAFormat and UnsignedByteType."); - } - } else { - console.error("THREE.WebGLTextures: Unsupported texture color space:", colorSpace); - } - } - return image; - } - __name(verifyColorSpace, "verifyColorSpace"); - function getDimensions(image) { - if (typeof HTMLImageElement !== "undefined" && image instanceof HTMLImageElement) { - _imageDimensions.width = image.naturalWidth || image.width; - _imageDimensions.height = image.naturalHeight || image.height; - } else if (typeof VideoFrame !== "undefined" && image instanceof VideoFrame) { - _imageDimensions.width = image.displayWidth; - _imageDimensions.height = image.displayHeight; - } else { - _imageDimensions.width = image.width; - _imageDimensions.height = image.height; - } - return _imageDimensions; - } - __name(getDimensions, "getDimensions"); - this.allocateTextureUnit = allocateTextureUnit; - this.resetTextureUnits = resetTextureUnits; - this.setTexture2D = setTexture2D; - this.setTexture2DArray = setTexture2DArray; - this.setTexture3D = setTexture3D; - this.setTextureCube = setTextureCube; - this.rebindTextures = rebindTextures; - this.setupRenderTarget = setupRenderTarget; - this.updateRenderTargetMipmap = updateRenderTargetMipmap; - this.updateMultisampleRenderTarget = updateMultisampleRenderTarget; - this.setupDepthRenderbuffer = setupDepthRenderbuffer; - this.setupFrameBufferTexture = setupFrameBufferTexture; - this.useMultisampledRTT = useMultisampledRTT; -} -__name(WebGLTextures, "WebGLTextures"); -function WebGLUtils(gl, extensions) { - function convert(p, colorSpace = NoColorSpace) { - let extension; - const transfer = ColorManagement.getTransfer(colorSpace); - if (p === UnsignedByteType) return gl.UNSIGNED_BYTE; - if (p === UnsignedShort4444Type) return gl.UNSIGNED_SHORT_4_4_4_4; - if (p === UnsignedShort5551Type) return gl.UNSIGNED_SHORT_5_5_5_1; - if (p === UnsignedInt5999Type) return gl.UNSIGNED_INT_5_9_9_9_REV; - if (p === ByteType) return gl.BYTE; - if (p === ShortType) return gl.SHORT; - if (p === UnsignedShortType) return gl.UNSIGNED_SHORT; - if (p === IntType) return gl.INT; - if (p === UnsignedIntType) return gl.UNSIGNED_INT; - if (p === FloatType) return gl.FLOAT; - if (p === HalfFloatType) return gl.HALF_FLOAT; - if (p === AlphaFormat) return gl.ALPHA; - if (p === RGBFormat) return gl.RGB; - if (p === RGBAFormat) return gl.RGBA; - if (p === LuminanceFormat) return gl.LUMINANCE; - if (p === LuminanceAlphaFormat) return gl.LUMINANCE_ALPHA; - if (p === DepthFormat) return gl.DEPTH_COMPONENT; - if (p === DepthStencilFormat) return gl.DEPTH_STENCIL; - if (p === RedFormat) return gl.RED; - if (p === RedIntegerFormat) return gl.RED_INTEGER; - if (p === RGFormat) return gl.RG; - if (p === RGIntegerFormat) return gl.RG_INTEGER; - if (p === RGBAIntegerFormat) return gl.RGBA_INTEGER; - if (p === RGB_S3TC_DXT1_Format || p === RGBA_S3TC_DXT1_Format || p === RGBA_S3TC_DXT3_Format || p === RGBA_S3TC_DXT5_Format) { - if (transfer === SRGBTransfer) { - extension = extensions.get("WEBGL_compressed_texture_s3tc_srgb"); - if (extension !== null) { - if (p === RGB_S3TC_DXT1_Format) return extension.COMPRESSED_SRGB_S3TC_DXT1_EXT; - if (p === RGBA_S3TC_DXT1_Format) return extension.COMPRESSED_SRGB_ALPHA_S3TC_DXT1_EXT; - if (p === RGBA_S3TC_DXT3_Format) return extension.COMPRESSED_SRGB_ALPHA_S3TC_DXT3_EXT; - if (p === RGBA_S3TC_DXT5_Format) return extension.COMPRESSED_SRGB_ALPHA_S3TC_DXT5_EXT; - } else { - return null; - } - } else { - extension = extensions.get("WEBGL_compressed_texture_s3tc"); - if (extension !== null) { - if (p === RGB_S3TC_DXT1_Format) return extension.COMPRESSED_RGB_S3TC_DXT1_EXT; - if (p === RGBA_S3TC_DXT1_Format) return extension.COMPRESSED_RGBA_S3TC_DXT1_EXT; - if (p === RGBA_S3TC_DXT3_Format) return extension.COMPRESSED_RGBA_S3TC_DXT3_EXT; - if (p === RGBA_S3TC_DXT5_Format) return extension.COMPRESSED_RGBA_S3TC_DXT5_EXT; - } else { - return null; - } - } - } - if (p === RGB_PVRTC_4BPPV1_Format || p === RGB_PVRTC_2BPPV1_Format || p === RGBA_PVRTC_4BPPV1_Format || p === RGBA_PVRTC_2BPPV1_Format) { - extension = extensions.get("WEBGL_compressed_texture_pvrtc"); - if (extension !== null) { - if (p === RGB_PVRTC_4BPPV1_Format) return extension.COMPRESSED_RGB_PVRTC_4BPPV1_IMG; - if (p === RGB_PVRTC_2BPPV1_Format) return extension.COMPRESSED_RGB_PVRTC_2BPPV1_IMG; - if (p === RGBA_PVRTC_4BPPV1_Format) return extension.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG; - if (p === RGBA_PVRTC_2BPPV1_Format) return extension.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG; - } else { - return null; - } - } - if (p === RGB_ETC1_Format || p === RGB_ETC2_Format || p === RGBA_ETC2_EAC_Format) { - extension = extensions.get("WEBGL_compressed_texture_etc"); - if (extension !== null) { - if (p === RGB_ETC1_Format || p === RGB_ETC2_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ETC2 : extension.COMPRESSED_RGB8_ETC2; - if (p === RGBA_ETC2_EAC_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ETC2_EAC : extension.COMPRESSED_RGBA8_ETC2_EAC; - } else { - return null; - } - } - if (p === RGBA_ASTC_4x4_Format || p === RGBA_ASTC_5x4_Format || p === RGBA_ASTC_5x5_Format || p === RGBA_ASTC_6x5_Format || p === RGBA_ASTC_6x6_Format || p === RGBA_ASTC_8x5_Format || p === RGBA_ASTC_8x6_Format || p === RGBA_ASTC_8x8_Format || p === RGBA_ASTC_10x5_Format || p === RGBA_ASTC_10x6_Format || p === RGBA_ASTC_10x8_Format || p === RGBA_ASTC_10x10_Format || p === RGBA_ASTC_12x10_Format || p === RGBA_ASTC_12x12_Format) { - extension = extensions.get("WEBGL_compressed_texture_astc"); - if (extension !== null) { - if (p === RGBA_ASTC_4x4_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_4x4_KHR : extension.COMPRESSED_RGBA_ASTC_4x4_KHR; - if (p === RGBA_ASTC_5x4_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_5x4_KHR : extension.COMPRESSED_RGBA_ASTC_5x4_KHR; - if (p === RGBA_ASTC_5x5_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_5x5_KHR : extension.COMPRESSED_RGBA_ASTC_5x5_KHR; - if (p === RGBA_ASTC_6x5_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_6x5_KHR : extension.COMPRESSED_RGBA_ASTC_6x5_KHR; - if (p === RGBA_ASTC_6x6_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_6x6_KHR : extension.COMPRESSED_RGBA_ASTC_6x6_KHR; - if (p === RGBA_ASTC_8x5_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_8x5_KHR : extension.COMPRESSED_RGBA_ASTC_8x5_KHR; - if (p === RGBA_ASTC_8x6_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_8x6_KHR : extension.COMPRESSED_RGBA_ASTC_8x6_KHR; - if (p === RGBA_ASTC_8x8_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_8x8_KHR : extension.COMPRESSED_RGBA_ASTC_8x8_KHR; - if (p === RGBA_ASTC_10x5_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_10x5_KHR : extension.COMPRESSED_RGBA_ASTC_10x5_KHR; - if (p === RGBA_ASTC_10x6_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_10x6_KHR : extension.COMPRESSED_RGBA_ASTC_10x6_KHR; - if (p === RGBA_ASTC_10x8_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_10x8_KHR : extension.COMPRESSED_RGBA_ASTC_10x8_KHR; - if (p === RGBA_ASTC_10x10_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_10x10_KHR : extension.COMPRESSED_RGBA_ASTC_10x10_KHR; - if (p === RGBA_ASTC_12x10_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_12x10_KHR : extension.COMPRESSED_RGBA_ASTC_12x10_KHR; - if (p === RGBA_ASTC_12x12_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB8_ALPHA8_ASTC_12x12_KHR : extension.COMPRESSED_RGBA_ASTC_12x12_KHR; - } else { - return null; - } - } - if (p === RGBA_BPTC_Format || p === RGB_BPTC_SIGNED_Format || p === RGB_BPTC_UNSIGNED_Format) { - extension = extensions.get("EXT_texture_compression_bptc"); - if (extension !== null) { - if (p === RGBA_BPTC_Format) return transfer === SRGBTransfer ? extension.COMPRESSED_SRGB_ALPHA_BPTC_UNORM_EXT : extension.COMPRESSED_RGBA_BPTC_UNORM_EXT; - if (p === RGB_BPTC_SIGNED_Format) return extension.COMPRESSED_RGB_BPTC_SIGNED_FLOAT_EXT; - if (p === RGB_BPTC_UNSIGNED_Format) return extension.COMPRESSED_RGB_BPTC_UNSIGNED_FLOAT_EXT; - } else { - return null; - } - } - if (p === RED_RGTC1_Format || p === SIGNED_RED_RGTC1_Format || p === RED_GREEN_RGTC2_Format || p === SIGNED_RED_GREEN_RGTC2_Format) { - extension = extensions.get("EXT_texture_compression_rgtc"); - if (extension !== null) { - if (p === RGBA_BPTC_Format) return extension.COMPRESSED_RED_RGTC1_EXT; - if (p === SIGNED_RED_RGTC1_Format) return extension.COMPRESSED_SIGNED_RED_RGTC1_EXT; - if (p === RED_GREEN_RGTC2_Format) return extension.COMPRESSED_RED_GREEN_RGTC2_EXT; - if (p === SIGNED_RED_GREEN_RGTC2_Format) return extension.COMPRESSED_SIGNED_RED_GREEN_RGTC2_EXT; - } else { - return null; - } - } - if (p === UnsignedInt248Type) return gl.UNSIGNED_INT_24_8; - return gl[p] !== void 0 ? gl[p] : null; - } - __name(convert, "convert"); - return { convert }; -} -__name(WebGLUtils, "WebGLUtils"); -class ArrayCamera extends PerspectiveCamera { - static { - __name(this, "ArrayCamera"); - } - constructor(array = []) { - super(); - this.isArrayCamera = true; - this.cameras = array; - } -} -class Group extends Object3D { - static { - __name(this, "Group"); - } - constructor() { - super(); - this.isGroup = true; - this.type = "Group"; - } -} -const _moveEvent = { type: "move" }; -class WebXRController { - static { - __name(this, "WebXRController"); - } - constructor() { - this._targetRay = null; - this._grip = null; - this._hand = null; - } - getHandSpace() { - if (this._hand === null) { - this._hand = new Group(); - this._hand.matrixAutoUpdate = false; - this._hand.visible = false; - this._hand.joints = {}; - this._hand.inputState = { pinching: false }; - } - return this._hand; - } - getTargetRaySpace() { - if (this._targetRay === null) { - this._targetRay = new Group(); - this._targetRay.matrixAutoUpdate = false; - this._targetRay.visible = false; - this._targetRay.hasLinearVelocity = false; - this._targetRay.linearVelocity = new Vector3(); - this._targetRay.hasAngularVelocity = false; - this._targetRay.angularVelocity = new Vector3(); - } - return this._targetRay; - } - getGripSpace() { - if (this._grip === null) { - this._grip = new Group(); - this._grip.matrixAutoUpdate = false; - this._grip.visible = false; - this._grip.hasLinearVelocity = false; - this._grip.linearVelocity = new Vector3(); - this._grip.hasAngularVelocity = false; - this._grip.angularVelocity = new Vector3(); - } - return this._grip; - } - dispatchEvent(event) { - if (this._targetRay !== null) { - this._targetRay.dispatchEvent(event); - } - if (this._grip !== null) { - this._grip.dispatchEvent(event); - } - if (this._hand !== null) { - this._hand.dispatchEvent(event); - } - return this; - } - connect(inputSource) { - if (inputSource && inputSource.hand) { - const hand = this._hand; - if (hand) { - for (const inputjoint of inputSource.hand.values()) { - this._getHandJoint(hand, inputjoint); - } - } - } - this.dispatchEvent({ type: "connected", data: inputSource }); - return this; - } - disconnect(inputSource) { - this.dispatchEvent({ type: "disconnected", data: inputSource }); - if (this._targetRay !== null) { - this._targetRay.visible = false; - } - if (this._grip !== null) { - this._grip.visible = false; - } - if (this._hand !== null) { - this._hand.visible = false; - } - return this; - } - update(inputSource, frame, referenceSpace) { - let inputPose = null; - let gripPose = null; - let handPose = null; - const targetRay = this._targetRay; - const grip = this._grip; - const hand = this._hand; - if (inputSource && frame.session.visibilityState !== "visible-blurred") { - if (hand && inputSource.hand) { - handPose = true; - for (const inputjoint of inputSource.hand.values()) { - const jointPose = frame.getJointPose(inputjoint, referenceSpace); - const joint = this._getHandJoint(hand, inputjoint); - if (jointPose !== null) { - joint.matrix.fromArray(jointPose.transform.matrix); - joint.matrix.decompose(joint.position, joint.rotation, joint.scale); - joint.matrixWorldNeedsUpdate = true; - joint.jointRadius = jointPose.radius; - } - joint.visible = jointPose !== null; - } - const indexTip = hand.joints["index-finger-tip"]; - const thumbTip = hand.joints["thumb-tip"]; - const distance = indexTip.position.distanceTo(thumbTip.position); - const distanceToPinch = 0.02; - const threshold = 5e-3; - if (hand.inputState.pinching && distance > distanceToPinch + threshold) { - hand.inputState.pinching = false; - this.dispatchEvent({ - type: "pinchend", - handedness: inputSource.handedness, - target: this - }); - } else if (!hand.inputState.pinching && distance <= distanceToPinch - threshold) { - hand.inputState.pinching = true; - this.dispatchEvent({ - type: "pinchstart", - handedness: inputSource.handedness, - target: this - }); - } - } else { - if (grip !== null && inputSource.gripSpace) { - gripPose = frame.getPose(inputSource.gripSpace, referenceSpace); - if (gripPose !== null) { - grip.matrix.fromArray(gripPose.transform.matrix); - grip.matrix.decompose(grip.position, grip.rotation, grip.scale); - grip.matrixWorldNeedsUpdate = true; - if (gripPose.linearVelocity) { - grip.hasLinearVelocity = true; - grip.linearVelocity.copy(gripPose.linearVelocity); - } else { - grip.hasLinearVelocity = false; - } - if (gripPose.angularVelocity) { - grip.hasAngularVelocity = true; - grip.angularVelocity.copy(gripPose.angularVelocity); - } else { - grip.hasAngularVelocity = false; - } - } - } - } - if (targetRay !== null) { - inputPose = frame.getPose(inputSource.targetRaySpace, referenceSpace); - if (inputPose === null && gripPose !== null) { - inputPose = gripPose; - } - if (inputPose !== null) { - targetRay.matrix.fromArray(inputPose.transform.matrix); - targetRay.matrix.decompose(targetRay.position, targetRay.rotation, targetRay.scale); - targetRay.matrixWorldNeedsUpdate = true; - if (inputPose.linearVelocity) { - targetRay.hasLinearVelocity = true; - targetRay.linearVelocity.copy(inputPose.linearVelocity); - } else { - targetRay.hasLinearVelocity = false; - } - if (inputPose.angularVelocity) { - targetRay.hasAngularVelocity = true; - targetRay.angularVelocity.copy(inputPose.angularVelocity); - } else { - targetRay.hasAngularVelocity = false; - } - this.dispatchEvent(_moveEvent); - } - } - } - if (targetRay !== null) { - targetRay.visible = inputPose !== null; - } - if (grip !== null) { - grip.visible = gripPose !== null; - } - if (hand !== null) { - hand.visible = handPose !== null; - } - return this; - } - // private method - _getHandJoint(hand, inputjoint) { - if (hand.joints[inputjoint.jointName] === void 0) { - const joint = new Group(); - joint.matrixAutoUpdate = false; - joint.visible = false; - hand.joints[inputjoint.jointName] = joint; - hand.add(joint); - } - return hand.joints[inputjoint.jointName]; - } -} -const _occlusion_vertex = ` -void main() { - - gl_Position = vec4( position, 1.0 ); - -}`; -const _occlusion_fragment = ` -uniform sampler2DArray depthColor; -uniform float depthWidth; -uniform float depthHeight; - -void main() { - - vec2 coord = vec2( gl_FragCoord.x / depthWidth, gl_FragCoord.y / depthHeight ); - - if ( coord.x >= 1.0 ) { - - gl_FragDepth = texture( depthColor, vec3( coord.x - 1.0, coord.y, 1 ) ).r; - - } else { - - gl_FragDepth = texture( depthColor, vec3( coord.x, coord.y, 0 ) ).r; - - } - -}`; -class WebXRDepthSensing { - static { - __name(this, "WebXRDepthSensing"); - } - constructor() { - this.texture = null; - this.mesh = null; - this.depthNear = 0; - this.depthFar = 0; - } - init(renderer, depthData, renderState) { - if (this.texture === null) { - const texture = new Texture(); - const texProps = renderer.properties.get(texture); - texProps.__webglTexture = depthData.texture; - if (depthData.depthNear != renderState.depthNear || depthData.depthFar != renderState.depthFar) { - this.depthNear = depthData.depthNear; - this.depthFar = depthData.depthFar; - } - this.texture = texture; - } - } - getMesh(cameraXR) { - if (this.texture !== null) { - if (this.mesh === null) { - const viewport = cameraXR.cameras[0].viewport; - const material = new ShaderMaterial({ - vertexShader: _occlusion_vertex, - fragmentShader: _occlusion_fragment, - uniforms: { - depthColor: { value: this.texture }, - depthWidth: { value: viewport.z }, - depthHeight: { value: viewport.w } - } - }); - this.mesh = new Mesh(new PlaneGeometry(20, 20), material); - } - } - return this.mesh; - } - reset() { - this.texture = null; - this.mesh = null; - } - getDepthTexture() { - return this.texture; - } -} -class WebXRManager extends EventDispatcher { - static { - __name(this, "WebXRManager"); - } - constructor(renderer, gl) { - super(); - const scope = this; - let session = null; - let framebufferScaleFactor = 1; - let referenceSpace = null; - let referenceSpaceType = "local-floor"; - let foveation = 1; - let customReferenceSpace = null; - let pose = null; - let glBinding = null; - let glProjLayer = null; - let glBaseLayer = null; - let xrFrame = null; - const depthSensing = new WebXRDepthSensing(); - const attributes = gl.getContextAttributes(); - let initialRenderTarget = null; - let newRenderTarget = null; - const controllers = []; - const controllerInputSources = []; - const currentSize = new Vector2(); - let currentPixelRatio = null; - const cameraL = new PerspectiveCamera(); - cameraL.viewport = new Vector4(); - const cameraR = new PerspectiveCamera(); - cameraR.viewport = new Vector4(); - const cameras = [cameraL, cameraR]; - const cameraXR = new ArrayCamera(); - let _currentDepthNear = null; - let _currentDepthFar = null; - this.cameraAutoUpdate = true; - this.enabled = false; - this.isPresenting = false; - this.getController = function(index) { - let controller = controllers[index]; - if (controller === void 0) { - controller = new WebXRController(); - controllers[index] = controller; - } - return controller.getTargetRaySpace(); - }; - this.getControllerGrip = function(index) { - let controller = controllers[index]; - if (controller === void 0) { - controller = new WebXRController(); - controllers[index] = controller; - } - return controller.getGripSpace(); - }; - this.getHand = function(index) { - let controller = controllers[index]; - if (controller === void 0) { - controller = new WebXRController(); - controllers[index] = controller; - } - return controller.getHandSpace(); - }; - function onSessionEvent(event) { - const controllerIndex = controllerInputSources.indexOf(event.inputSource); - if (controllerIndex === -1) { - return; - } - const controller = controllers[controllerIndex]; - if (controller !== void 0) { - controller.update(event.inputSource, event.frame, customReferenceSpace || referenceSpace); - controller.dispatchEvent({ type: event.type, data: event.inputSource }); - } - } - __name(onSessionEvent, "onSessionEvent"); - function onSessionEnd() { - session.removeEventListener("select", onSessionEvent); - session.removeEventListener("selectstart", onSessionEvent); - session.removeEventListener("selectend", onSessionEvent); - session.removeEventListener("squeeze", onSessionEvent); - session.removeEventListener("squeezestart", onSessionEvent); - session.removeEventListener("squeezeend", onSessionEvent); - session.removeEventListener("end", onSessionEnd); - session.removeEventListener("inputsourceschange", onInputSourcesChange); - for (let i = 0; i < controllers.length; i++) { - const inputSource = controllerInputSources[i]; - if (inputSource === null) continue; - controllerInputSources[i] = null; - controllers[i].disconnect(inputSource); - } - _currentDepthNear = null; - _currentDepthFar = null; - depthSensing.reset(); - renderer.setRenderTarget(initialRenderTarget); - glBaseLayer = null; - glProjLayer = null; - glBinding = null; - session = null; - newRenderTarget = null; - animation.stop(); - scope.isPresenting = false; - renderer.setPixelRatio(currentPixelRatio); - renderer.setSize(currentSize.width, currentSize.height, false); - scope.dispatchEvent({ type: "sessionend" }); - } - __name(onSessionEnd, "onSessionEnd"); - this.setFramebufferScaleFactor = function(value) { - framebufferScaleFactor = value; - if (scope.isPresenting === true) { - console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting."); - } - }; - this.setReferenceSpaceType = function(value) { - referenceSpaceType = value; - if (scope.isPresenting === true) { - console.warn("THREE.WebXRManager: Cannot change reference space type while presenting."); - } - }; - this.getReferenceSpace = function() { - return customReferenceSpace || referenceSpace; - }; - this.setReferenceSpace = function(space) { - customReferenceSpace = space; - }; - this.getBaseLayer = function() { - return glProjLayer !== null ? glProjLayer : glBaseLayer; - }; - this.getBinding = function() { - return glBinding; - }; - this.getFrame = function() { - return xrFrame; - }; - this.getSession = function() { - return session; - }; - this.setSession = async function(value) { - session = value; - if (session !== null) { - initialRenderTarget = renderer.getRenderTarget(); - session.addEventListener("select", onSessionEvent); - session.addEventListener("selectstart", onSessionEvent); - session.addEventListener("selectend", onSessionEvent); - session.addEventListener("squeeze", onSessionEvent); - session.addEventListener("squeezestart", onSessionEvent); - session.addEventListener("squeezeend", onSessionEvent); - session.addEventListener("end", onSessionEnd); - session.addEventListener("inputsourceschange", onInputSourcesChange); - if (attributes.xrCompatible !== true) { - await gl.makeXRCompatible(); - } - currentPixelRatio = renderer.getPixelRatio(); - renderer.getSize(currentSize); - if (session.renderState.layers === void 0) { - const layerInit = { - antialias: attributes.antialias, - alpha: true, - depth: attributes.depth, - stencil: attributes.stencil, - framebufferScaleFactor - }; - glBaseLayer = new XRWebGLLayer(session, gl, layerInit); - session.updateRenderState({ baseLayer: glBaseLayer }); - renderer.setPixelRatio(1); - renderer.setSize(glBaseLayer.framebufferWidth, glBaseLayer.framebufferHeight, false); - newRenderTarget = new WebGLRenderTarget( - glBaseLayer.framebufferWidth, - glBaseLayer.framebufferHeight, - { - format: RGBAFormat, - type: UnsignedByteType, - colorSpace: renderer.outputColorSpace, - stencilBuffer: attributes.stencil - } - ); - } else { - let depthFormat = null; - let depthType = null; - let glDepthFormat = null; - if (attributes.depth) { - glDepthFormat = attributes.stencil ? gl.DEPTH24_STENCIL8 : gl.DEPTH_COMPONENT24; - depthFormat = attributes.stencil ? DepthStencilFormat : DepthFormat; - depthType = attributes.stencil ? UnsignedInt248Type : UnsignedIntType; - } - const projectionlayerInit = { - colorFormat: gl.RGBA8, - depthFormat: glDepthFormat, - scaleFactor: framebufferScaleFactor - }; - glBinding = new XRWebGLBinding(session, gl); - glProjLayer = glBinding.createProjectionLayer(projectionlayerInit); - session.updateRenderState({ layers: [glProjLayer] }); - renderer.setPixelRatio(1); - renderer.setSize(glProjLayer.textureWidth, glProjLayer.textureHeight, false); - newRenderTarget = new WebGLRenderTarget( - glProjLayer.textureWidth, - glProjLayer.textureHeight, - { - format: RGBAFormat, - type: UnsignedByteType, - depthTexture: new DepthTexture(glProjLayer.textureWidth, glProjLayer.textureHeight, depthType, void 0, void 0, void 0, void 0, void 0, void 0, depthFormat), - stencilBuffer: attributes.stencil, - colorSpace: renderer.outputColorSpace, - samples: attributes.antialias ? 4 : 0, - resolveDepthBuffer: glProjLayer.ignoreDepthValues === false - } - ); - } - newRenderTarget.isXRRenderTarget = true; - this.setFoveation(foveation); - customReferenceSpace = null; - referenceSpace = await session.requestReferenceSpace(referenceSpaceType); - animation.setContext(session); - animation.start(); - scope.isPresenting = true; - scope.dispatchEvent({ type: "sessionstart" }); - } - }; - this.getEnvironmentBlendMode = function() { - if (session !== null) { - return session.environmentBlendMode; - } - }; - this.getDepthTexture = function() { - return depthSensing.getDepthTexture(); - }; - function onInputSourcesChange(event) { - for (let i = 0; i < event.removed.length; i++) { - const inputSource = event.removed[i]; - const index = controllerInputSources.indexOf(inputSource); - if (index >= 0) { - controllerInputSources[index] = null; - controllers[index].disconnect(inputSource); - } - } - for (let i = 0; i < event.added.length; i++) { - const inputSource = event.added[i]; - let controllerIndex = controllerInputSources.indexOf(inputSource); - if (controllerIndex === -1) { - for (let i2 = 0; i2 < controllers.length; i2++) { - if (i2 >= controllerInputSources.length) { - controllerInputSources.push(inputSource); - controllerIndex = i2; - break; - } else if (controllerInputSources[i2] === null) { - controllerInputSources[i2] = inputSource; - controllerIndex = i2; - break; - } - } - if (controllerIndex === -1) break; - } - const controller = controllers[controllerIndex]; - if (controller) { - controller.connect(inputSource); - } - } - } - __name(onInputSourcesChange, "onInputSourcesChange"); - const cameraLPos = new Vector3(); - const cameraRPos = new Vector3(); - function setProjectionFromUnion(camera, cameraL2, cameraR2) { - cameraLPos.setFromMatrixPosition(cameraL2.matrixWorld); - cameraRPos.setFromMatrixPosition(cameraR2.matrixWorld); - const ipd = cameraLPos.distanceTo(cameraRPos); - const projL = cameraL2.projectionMatrix.elements; - const projR = cameraR2.projectionMatrix.elements; - const near = projL[14] / (projL[10] - 1); - const far = projL[14] / (projL[10] + 1); - const topFov = (projL[9] + 1) / projL[5]; - const bottomFov = (projL[9] - 1) / projL[5]; - const leftFov = (projL[8] - 1) / projL[0]; - const rightFov = (projR[8] + 1) / projR[0]; - const left = near * leftFov; - const right = near * rightFov; - const zOffset = ipd / (-leftFov + rightFov); - const xOffset = zOffset * -leftFov; - cameraL2.matrixWorld.decompose(camera.position, camera.quaternion, camera.scale); - camera.translateX(xOffset); - camera.translateZ(zOffset); - camera.matrixWorld.compose(camera.position, camera.quaternion, camera.scale); - camera.matrixWorldInverse.copy(camera.matrixWorld).invert(); - if (projL[10] === -1) { - camera.projectionMatrix.copy(cameraL2.projectionMatrix); - camera.projectionMatrixInverse.copy(cameraL2.projectionMatrixInverse); - } else { - const near2 = near + zOffset; - const far2 = far + zOffset; - const left2 = left - xOffset; - const right2 = right + (ipd - xOffset); - const top2 = topFov * far / far2 * near2; - const bottom2 = bottomFov * far / far2 * near2; - camera.projectionMatrix.makePerspective(left2, right2, top2, bottom2, near2, far2); - camera.projectionMatrixInverse.copy(camera.projectionMatrix).invert(); - } - } - __name(setProjectionFromUnion, "setProjectionFromUnion"); - function updateCamera(camera, parent) { - if (parent === null) { - camera.matrixWorld.copy(camera.matrix); - } else { - camera.matrixWorld.multiplyMatrices(parent.matrixWorld, camera.matrix); - } - camera.matrixWorldInverse.copy(camera.matrixWorld).invert(); - } - __name(updateCamera, "updateCamera"); - this.updateCamera = function(camera) { - if (session === null) return; - let depthNear = camera.near; - let depthFar = camera.far; - if (depthSensing.texture !== null) { - if (depthSensing.depthNear > 0) depthNear = depthSensing.depthNear; - if (depthSensing.depthFar > 0) depthFar = depthSensing.depthFar; - } - cameraXR.near = cameraR.near = cameraL.near = depthNear; - cameraXR.far = cameraR.far = cameraL.far = depthFar; - if (_currentDepthNear !== cameraXR.near || _currentDepthFar !== cameraXR.far) { - session.updateRenderState({ - depthNear: cameraXR.near, - depthFar: cameraXR.far - }); - _currentDepthNear = cameraXR.near; - _currentDepthFar = cameraXR.far; - } - cameraL.layers.mask = camera.layers.mask | 2; - cameraR.layers.mask = camera.layers.mask | 4; - cameraXR.layers.mask = cameraL.layers.mask | cameraR.layers.mask; - const parent = camera.parent; - const cameras2 = cameraXR.cameras; - updateCamera(cameraXR, parent); - for (let i = 0; i < cameras2.length; i++) { - updateCamera(cameras2[i], parent); - } - if (cameras2.length === 2) { - setProjectionFromUnion(cameraXR, cameraL, cameraR); - } else { - cameraXR.projectionMatrix.copy(cameraL.projectionMatrix); - } - updateUserCamera(camera, cameraXR, parent); - }; - function updateUserCamera(camera, cameraXR2, parent) { - if (parent === null) { - camera.matrix.copy(cameraXR2.matrixWorld); - } else { - camera.matrix.copy(parent.matrixWorld); - camera.matrix.invert(); - camera.matrix.multiply(cameraXR2.matrixWorld); - } - camera.matrix.decompose(camera.position, camera.quaternion, camera.scale); - camera.updateMatrixWorld(true); - camera.projectionMatrix.copy(cameraXR2.projectionMatrix); - camera.projectionMatrixInverse.copy(cameraXR2.projectionMatrixInverse); - if (camera.isPerspectiveCamera) { - camera.fov = RAD2DEG * 2 * Math.atan(1 / camera.projectionMatrix.elements[5]); - camera.zoom = 1; - } - } - __name(updateUserCamera, "updateUserCamera"); - this.getCamera = function() { - return cameraXR; - }; - this.getFoveation = function() { - if (glProjLayer === null && glBaseLayer === null) { - return void 0; - } - return foveation; - }; - this.setFoveation = function(value) { - foveation = value; - if (glProjLayer !== null) { - glProjLayer.fixedFoveation = value; - } - if (glBaseLayer !== null && glBaseLayer.fixedFoveation !== void 0) { - glBaseLayer.fixedFoveation = value; - } - }; - this.hasDepthSensing = function() { - return depthSensing.texture !== null; - }; - this.getDepthSensingMesh = function() { - return depthSensing.getMesh(cameraXR); - }; - let onAnimationFrameCallback = null; - function onAnimationFrame(time, frame) { - pose = frame.getViewerPose(customReferenceSpace || referenceSpace); - xrFrame = frame; - if (pose !== null) { - const views = pose.views; - if (glBaseLayer !== null) { - renderer.setRenderTargetFramebuffer(newRenderTarget, glBaseLayer.framebuffer); - renderer.setRenderTarget(newRenderTarget); - } - let cameraXRNeedsUpdate = false; - if (views.length !== cameraXR.cameras.length) { - cameraXR.cameras.length = 0; - cameraXRNeedsUpdate = true; - } - for (let i = 0; i < views.length; i++) { - const view = views[i]; - let viewport = null; - if (glBaseLayer !== null) { - viewport = glBaseLayer.getViewport(view); - } else { - const glSubImage = glBinding.getViewSubImage(glProjLayer, view); - viewport = glSubImage.viewport; - if (i === 0) { - renderer.setRenderTargetTextures( - newRenderTarget, - glSubImage.colorTexture, - glProjLayer.ignoreDepthValues ? void 0 : glSubImage.depthStencilTexture - ); - renderer.setRenderTarget(newRenderTarget); - } - } - let camera = cameras[i]; - if (camera === void 0) { - camera = new PerspectiveCamera(); - camera.layers.enable(i); - camera.viewport = new Vector4(); - cameras[i] = camera; - } - camera.matrix.fromArray(view.transform.matrix); - camera.matrix.decompose(camera.position, camera.quaternion, camera.scale); - camera.projectionMatrix.fromArray(view.projectionMatrix); - camera.projectionMatrixInverse.copy(camera.projectionMatrix).invert(); - camera.viewport.set(viewport.x, viewport.y, viewport.width, viewport.height); - if (i === 0) { - cameraXR.matrix.copy(camera.matrix); - cameraXR.matrix.decompose(cameraXR.position, cameraXR.quaternion, cameraXR.scale); - } - if (cameraXRNeedsUpdate === true) { - cameraXR.cameras.push(camera); - } - } - const enabledFeatures = session.enabledFeatures; - if (enabledFeatures && enabledFeatures.includes("depth-sensing")) { - const depthData = glBinding.getDepthInformation(views[0]); - if (depthData && depthData.isValid && depthData.texture) { - depthSensing.init(renderer, depthData, session.renderState); - } - } - } - for (let i = 0; i < controllers.length; i++) { - const inputSource = controllerInputSources[i]; - const controller = controllers[i]; - if (inputSource !== null && controller !== void 0) { - controller.update(inputSource, frame, customReferenceSpace || referenceSpace); - } - } - if (onAnimationFrameCallback) onAnimationFrameCallback(time, frame); - if (frame.detectedPlanes) { - scope.dispatchEvent({ type: "planesdetected", data: frame }); - } - xrFrame = null; - } - __name(onAnimationFrame, "onAnimationFrame"); - const animation = new WebGLAnimation(); - animation.setAnimationLoop(onAnimationFrame); - this.setAnimationLoop = function(callback) { - onAnimationFrameCallback = callback; - }; - this.dispose = function() { - }; - } -} -const _e1 = /* @__PURE__ */ new Euler(); -const _m1 = /* @__PURE__ */ new Matrix4(); -function WebGLMaterials(renderer, properties) { - function refreshTransformUniform(map, uniform) { - if (map.matrixAutoUpdate === true) { - map.updateMatrix(); - } - uniform.value.copy(map.matrix); - } - __name(refreshTransformUniform, "refreshTransformUniform"); - function refreshFogUniforms(uniforms, fog) { - fog.color.getRGB(uniforms.fogColor.value, getUnlitUniformColorSpace(renderer)); - if (fog.isFog) { - uniforms.fogNear.value = fog.near; - uniforms.fogFar.value = fog.far; - } else if (fog.isFogExp2) { - uniforms.fogDensity.value = fog.density; - } - } - __name(refreshFogUniforms, "refreshFogUniforms"); - function refreshMaterialUniforms(uniforms, material, pixelRatio, height, transmissionRenderTarget) { - if (material.isMeshBasicMaterial) { - refreshUniformsCommon(uniforms, material); - } else if (material.isMeshLambertMaterial) { - refreshUniformsCommon(uniforms, material); - } else if (material.isMeshToonMaterial) { - refreshUniformsCommon(uniforms, material); - refreshUniformsToon(uniforms, material); - } else if (material.isMeshPhongMaterial) { - refreshUniformsCommon(uniforms, material); - refreshUniformsPhong(uniforms, material); - } else if (material.isMeshStandardMaterial) { - refreshUniformsCommon(uniforms, material); - refreshUniformsStandard(uniforms, material); - if (material.isMeshPhysicalMaterial) { - refreshUniformsPhysical(uniforms, material, transmissionRenderTarget); - } - } else if (material.isMeshMatcapMaterial) { - refreshUniformsCommon(uniforms, material); - refreshUniformsMatcap(uniforms, material); - } else if (material.isMeshDepthMaterial) { - refreshUniformsCommon(uniforms, material); - } else if (material.isMeshDistanceMaterial) { - refreshUniformsCommon(uniforms, material); - refreshUniformsDistance(uniforms, material); - } else if (material.isMeshNormalMaterial) { - refreshUniformsCommon(uniforms, material); - } else if (material.isLineBasicMaterial) { - refreshUniformsLine(uniforms, material); - if (material.isLineDashedMaterial) { - refreshUniformsDash(uniforms, material); - } - } else if (material.isPointsMaterial) { - refreshUniformsPoints(uniforms, material, pixelRatio, height); - } else if (material.isSpriteMaterial) { - refreshUniformsSprites(uniforms, material); - } else if (material.isShadowMaterial) { - uniforms.color.value.copy(material.color); - uniforms.opacity.value = material.opacity; - } else if (material.isShaderMaterial) { - material.uniformsNeedUpdate = false; - } - } - __name(refreshMaterialUniforms, "refreshMaterialUniforms"); - function refreshUniformsCommon(uniforms, material) { - uniforms.opacity.value = material.opacity; - if (material.color) { - uniforms.diffuse.value.copy(material.color); - } - if (material.emissive) { - uniforms.emissive.value.copy(material.emissive).multiplyScalar(material.emissiveIntensity); - } - if (material.map) { - uniforms.map.value = material.map; - refreshTransformUniform(material.map, uniforms.mapTransform); - } - if (material.alphaMap) { - uniforms.alphaMap.value = material.alphaMap; - refreshTransformUniform(material.alphaMap, uniforms.alphaMapTransform); - } - if (material.bumpMap) { - uniforms.bumpMap.value = material.bumpMap; - refreshTransformUniform(material.bumpMap, uniforms.bumpMapTransform); - uniforms.bumpScale.value = material.bumpScale; - if (material.side === BackSide) { - uniforms.bumpScale.value *= -1; - } - } - if (material.normalMap) { - uniforms.normalMap.value = material.normalMap; - refreshTransformUniform(material.normalMap, uniforms.normalMapTransform); - uniforms.normalScale.value.copy(material.normalScale); - if (material.side === BackSide) { - uniforms.normalScale.value.negate(); - } - } - if (material.displacementMap) { - uniforms.displacementMap.value = material.displacementMap; - refreshTransformUniform(material.displacementMap, uniforms.displacementMapTransform); - uniforms.displacementScale.value = material.displacementScale; - uniforms.displacementBias.value = material.displacementBias; - } - if (material.emissiveMap) { - uniforms.emissiveMap.value = material.emissiveMap; - refreshTransformUniform(material.emissiveMap, uniforms.emissiveMapTransform); - } - if (material.specularMap) { - uniforms.specularMap.value = material.specularMap; - refreshTransformUniform(material.specularMap, uniforms.specularMapTransform); - } - if (material.alphaTest > 0) { - uniforms.alphaTest.value = material.alphaTest; - } - const materialProperties = properties.get(material); - const envMap = materialProperties.envMap; - const envMapRotation = materialProperties.envMapRotation; - if (envMap) { - uniforms.envMap.value = envMap; - _e1.copy(envMapRotation); - _e1.x *= -1; - _e1.y *= -1; - _e1.z *= -1; - if (envMap.isCubeTexture && envMap.isRenderTargetTexture === false) { - _e1.y *= -1; - _e1.z *= -1; - } - uniforms.envMapRotation.value.setFromMatrix4(_m1.makeRotationFromEuler(_e1)); - uniforms.flipEnvMap.value = envMap.isCubeTexture && envMap.isRenderTargetTexture === false ? -1 : 1; - uniforms.reflectivity.value = material.reflectivity; - uniforms.ior.value = material.ior; - uniforms.refractionRatio.value = material.refractionRatio; - } - if (material.lightMap) { - uniforms.lightMap.value = material.lightMap; - uniforms.lightMapIntensity.value = material.lightMapIntensity; - refreshTransformUniform(material.lightMap, uniforms.lightMapTransform); - } - if (material.aoMap) { - uniforms.aoMap.value = material.aoMap; - uniforms.aoMapIntensity.value = material.aoMapIntensity; - refreshTransformUniform(material.aoMap, uniforms.aoMapTransform); - } - } - __name(refreshUniformsCommon, "refreshUniformsCommon"); - function refreshUniformsLine(uniforms, material) { - uniforms.diffuse.value.copy(material.color); - uniforms.opacity.value = material.opacity; - if (material.map) { - uniforms.map.value = material.map; - refreshTransformUniform(material.map, uniforms.mapTransform); - } - } - __name(refreshUniformsLine, "refreshUniformsLine"); - function refreshUniformsDash(uniforms, material) { - uniforms.dashSize.value = material.dashSize; - uniforms.totalSize.value = material.dashSize + material.gapSize; - uniforms.scale.value = material.scale; - } - __name(refreshUniformsDash, "refreshUniformsDash"); - function refreshUniformsPoints(uniforms, material, pixelRatio, height) { - uniforms.diffuse.value.copy(material.color); - uniforms.opacity.value = material.opacity; - uniforms.size.value = material.size * pixelRatio; - uniforms.scale.value = height * 0.5; - if (material.map) { - uniforms.map.value = material.map; - refreshTransformUniform(material.map, uniforms.uvTransform); - } - if (material.alphaMap) { - uniforms.alphaMap.value = material.alphaMap; - refreshTransformUniform(material.alphaMap, uniforms.alphaMapTransform); - } - if (material.alphaTest > 0) { - uniforms.alphaTest.value = material.alphaTest; - } - } - __name(refreshUniformsPoints, "refreshUniformsPoints"); - function refreshUniformsSprites(uniforms, material) { - uniforms.diffuse.value.copy(material.color); - uniforms.opacity.value = material.opacity; - uniforms.rotation.value = material.rotation; - if (material.map) { - uniforms.map.value = material.map; - refreshTransformUniform(material.map, uniforms.mapTransform); - } - if (material.alphaMap) { - uniforms.alphaMap.value = material.alphaMap; - refreshTransformUniform(material.alphaMap, uniforms.alphaMapTransform); - } - if (material.alphaTest > 0) { - uniforms.alphaTest.value = material.alphaTest; - } - } - __name(refreshUniformsSprites, "refreshUniformsSprites"); - function refreshUniformsPhong(uniforms, material) { - uniforms.specular.value.copy(material.specular); - uniforms.shininess.value = Math.max(material.shininess, 1e-4); - } - __name(refreshUniformsPhong, "refreshUniformsPhong"); - function refreshUniformsToon(uniforms, material) { - if (material.gradientMap) { - uniforms.gradientMap.value = material.gradientMap; - } - } - __name(refreshUniformsToon, "refreshUniformsToon"); - function refreshUniformsStandard(uniforms, material) { - uniforms.metalness.value = material.metalness; - if (material.metalnessMap) { - uniforms.metalnessMap.value = material.metalnessMap; - refreshTransformUniform(material.metalnessMap, uniforms.metalnessMapTransform); - } - uniforms.roughness.value = material.roughness; - if (material.roughnessMap) { - uniforms.roughnessMap.value = material.roughnessMap; - refreshTransformUniform(material.roughnessMap, uniforms.roughnessMapTransform); - } - if (material.envMap) { - uniforms.envMapIntensity.value = material.envMapIntensity; - } - } - __name(refreshUniformsStandard, "refreshUniformsStandard"); - function refreshUniformsPhysical(uniforms, material, transmissionRenderTarget) { - uniforms.ior.value = material.ior; - if (material.sheen > 0) { - uniforms.sheenColor.value.copy(material.sheenColor).multiplyScalar(material.sheen); - uniforms.sheenRoughness.value = material.sheenRoughness; - if (material.sheenColorMap) { - uniforms.sheenColorMap.value = material.sheenColorMap; - refreshTransformUniform(material.sheenColorMap, uniforms.sheenColorMapTransform); - } - if (material.sheenRoughnessMap) { - uniforms.sheenRoughnessMap.value = material.sheenRoughnessMap; - refreshTransformUniform(material.sheenRoughnessMap, uniforms.sheenRoughnessMapTransform); - } - } - if (material.clearcoat > 0) { - uniforms.clearcoat.value = material.clearcoat; - uniforms.clearcoatRoughness.value = material.clearcoatRoughness; - if (material.clearcoatMap) { - uniforms.clearcoatMap.value = material.clearcoatMap; - refreshTransformUniform(material.clearcoatMap, uniforms.clearcoatMapTransform); - } - if (material.clearcoatRoughnessMap) { - uniforms.clearcoatRoughnessMap.value = material.clearcoatRoughnessMap; - refreshTransformUniform(material.clearcoatRoughnessMap, uniforms.clearcoatRoughnessMapTransform); - } - if (material.clearcoatNormalMap) { - uniforms.clearcoatNormalMap.value = material.clearcoatNormalMap; - refreshTransformUniform(material.clearcoatNormalMap, uniforms.clearcoatNormalMapTransform); - uniforms.clearcoatNormalScale.value.copy(material.clearcoatNormalScale); - if (material.side === BackSide) { - uniforms.clearcoatNormalScale.value.negate(); - } - } - } - if (material.dispersion > 0) { - uniforms.dispersion.value = material.dispersion; - } - if (material.iridescence > 0) { - uniforms.iridescence.value = material.iridescence; - uniforms.iridescenceIOR.value = material.iridescenceIOR; - uniforms.iridescenceThicknessMinimum.value = material.iridescenceThicknessRange[0]; - uniforms.iridescenceThicknessMaximum.value = material.iridescenceThicknessRange[1]; - if (material.iridescenceMap) { - uniforms.iridescenceMap.value = material.iridescenceMap; - refreshTransformUniform(material.iridescenceMap, uniforms.iridescenceMapTransform); - } - if (material.iridescenceThicknessMap) { - uniforms.iridescenceThicknessMap.value = material.iridescenceThicknessMap; - refreshTransformUniform(material.iridescenceThicknessMap, uniforms.iridescenceThicknessMapTransform); - } - } - if (material.transmission > 0) { - uniforms.transmission.value = material.transmission; - uniforms.transmissionSamplerMap.value = transmissionRenderTarget.texture; - uniforms.transmissionSamplerSize.value.set(transmissionRenderTarget.width, transmissionRenderTarget.height); - if (material.transmissionMap) { - uniforms.transmissionMap.value = material.transmissionMap; - refreshTransformUniform(material.transmissionMap, uniforms.transmissionMapTransform); - } - uniforms.thickness.value = material.thickness; - if (material.thicknessMap) { - uniforms.thicknessMap.value = material.thicknessMap; - refreshTransformUniform(material.thicknessMap, uniforms.thicknessMapTransform); - } - uniforms.attenuationDistance.value = material.attenuationDistance; - uniforms.attenuationColor.value.copy(material.attenuationColor); - } - if (material.anisotropy > 0) { - uniforms.anisotropyVector.value.set(material.anisotropy * Math.cos(material.anisotropyRotation), material.anisotropy * Math.sin(material.anisotropyRotation)); - if (material.anisotropyMap) { - uniforms.anisotropyMap.value = material.anisotropyMap; - refreshTransformUniform(material.anisotropyMap, uniforms.anisotropyMapTransform); - } - } - uniforms.specularIntensity.value = material.specularIntensity; - uniforms.specularColor.value.copy(material.specularColor); - if (material.specularColorMap) { - uniforms.specularColorMap.value = material.specularColorMap; - refreshTransformUniform(material.specularColorMap, uniforms.specularColorMapTransform); - } - if (material.specularIntensityMap) { - uniforms.specularIntensityMap.value = material.specularIntensityMap; - refreshTransformUniform(material.specularIntensityMap, uniforms.specularIntensityMapTransform); - } - } - __name(refreshUniformsPhysical, "refreshUniformsPhysical"); - function refreshUniformsMatcap(uniforms, material) { - if (material.matcap) { - uniforms.matcap.value = material.matcap; - } - } - __name(refreshUniformsMatcap, "refreshUniformsMatcap"); - function refreshUniformsDistance(uniforms, material) { - const light = properties.get(material).light; - uniforms.referencePosition.value.setFromMatrixPosition(light.matrixWorld); - uniforms.nearDistance.value = light.shadow.camera.near; - uniforms.farDistance.value = light.shadow.camera.far; - } - __name(refreshUniformsDistance, "refreshUniformsDistance"); - return { - refreshFogUniforms, - refreshMaterialUniforms - }; -} -__name(WebGLMaterials, "WebGLMaterials"); -function WebGLUniformsGroups(gl, info, capabilities, state) { - let buffers = {}; - let updateList = {}; - let allocatedBindingPoints = []; - const maxBindingPoints = gl.getParameter(gl.MAX_UNIFORM_BUFFER_BINDINGS); - function bind(uniformsGroup, program) { - const webglProgram = program.program; - state.uniformBlockBinding(uniformsGroup, webglProgram); - } - __name(bind, "bind"); - function update(uniformsGroup, program) { - let buffer = buffers[uniformsGroup.id]; - if (buffer === void 0) { - prepareUniformsGroup(uniformsGroup); - buffer = createBuffer(uniformsGroup); - buffers[uniformsGroup.id] = buffer; - uniformsGroup.addEventListener("dispose", onUniformsGroupsDispose); - } - const webglProgram = program.program; - state.updateUBOMapping(uniformsGroup, webglProgram); - const frame = info.render.frame; - if (updateList[uniformsGroup.id] !== frame) { - updateBufferData(uniformsGroup); - updateList[uniformsGroup.id] = frame; - } - } - __name(update, "update"); - function createBuffer(uniformsGroup) { - const bindingPointIndex = allocateBindingPointIndex(); - uniformsGroup.__bindingPointIndex = bindingPointIndex; - const buffer = gl.createBuffer(); - const size = uniformsGroup.__size; - const usage = uniformsGroup.usage; - gl.bindBuffer(gl.UNIFORM_BUFFER, buffer); - gl.bufferData(gl.UNIFORM_BUFFER, size, usage); - gl.bindBuffer(gl.UNIFORM_BUFFER, null); - gl.bindBufferBase(gl.UNIFORM_BUFFER, bindingPointIndex, buffer); - return buffer; - } - __name(createBuffer, "createBuffer"); - function allocateBindingPointIndex() { - for (let i = 0; i < maxBindingPoints; i++) { - if (allocatedBindingPoints.indexOf(i) === -1) { - allocatedBindingPoints.push(i); - return i; - } - } - console.error("THREE.WebGLRenderer: Maximum number of simultaneously usable uniforms groups reached."); - return 0; - } - __name(allocateBindingPointIndex, "allocateBindingPointIndex"); - function updateBufferData(uniformsGroup) { - const buffer = buffers[uniformsGroup.id]; - const uniforms = uniformsGroup.uniforms; - const cache = uniformsGroup.__cache; - gl.bindBuffer(gl.UNIFORM_BUFFER, buffer); - for (let i = 0, il = uniforms.length; i < il; i++) { - const uniformArray = Array.isArray(uniforms[i]) ? uniforms[i] : [uniforms[i]]; - for (let j = 0, jl = uniformArray.length; j < jl; j++) { - const uniform = uniformArray[j]; - if (hasUniformChanged(uniform, i, j, cache) === true) { - const offset = uniform.__offset; - const values = Array.isArray(uniform.value) ? uniform.value : [uniform.value]; - let arrayOffset = 0; - for (let k = 0; k < values.length; k++) { - const value = values[k]; - const info2 = getUniformSize(value); - if (typeof value === "number" || typeof value === "boolean") { - uniform.__data[0] = value; - gl.bufferSubData(gl.UNIFORM_BUFFER, offset + arrayOffset, uniform.__data); - } else if (value.isMatrix3) { - uniform.__data[0] = value.elements[0]; - uniform.__data[1] = value.elements[1]; - uniform.__data[2] = value.elements[2]; - uniform.__data[3] = 0; - uniform.__data[4] = value.elements[3]; - uniform.__data[5] = value.elements[4]; - uniform.__data[6] = value.elements[5]; - uniform.__data[7] = 0; - uniform.__data[8] = value.elements[6]; - uniform.__data[9] = value.elements[7]; - uniform.__data[10] = value.elements[8]; - uniform.__data[11] = 0; - } else { - value.toArray(uniform.__data, arrayOffset); - arrayOffset += info2.storage / Float32Array.BYTES_PER_ELEMENT; - } - } - gl.bufferSubData(gl.UNIFORM_BUFFER, offset, uniform.__data); - } - } - } - gl.bindBuffer(gl.UNIFORM_BUFFER, null); - } - __name(updateBufferData, "updateBufferData"); - function hasUniformChanged(uniform, index, indexArray, cache) { - const value = uniform.value; - const indexString = index + "_" + indexArray; - if (cache[indexString] === void 0) { - if (typeof value === "number" || typeof value === "boolean") { - cache[indexString] = value; - } else { - cache[indexString] = value.clone(); - } - return true; - } else { - const cachedObject = cache[indexString]; - if (typeof value === "number" || typeof value === "boolean") { - if (cachedObject !== value) { - cache[indexString] = value; - return true; - } - } else { - if (cachedObject.equals(value) === false) { - cachedObject.copy(value); - return true; - } - } - } - return false; - } - __name(hasUniformChanged, "hasUniformChanged"); - function prepareUniformsGroup(uniformsGroup) { - const uniforms = uniformsGroup.uniforms; - let offset = 0; - const chunkSize = 16; - for (let i = 0, l = uniforms.length; i < l; i++) { - const uniformArray = Array.isArray(uniforms[i]) ? uniforms[i] : [uniforms[i]]; - for (let j = 0, jl = uniformArray.length; j < jl; j++) { - const uniform = uniformArray[j]; - const values = Array.isArray(uniform.value) ? uniform.value : [uniform.value]; - for (let k = 0, kl = values.length; k < kl; k++) { - const value = values[k]; - const info2 = getUniformSize(value); - const chunkOffset2 = offset % chunkSize; - const chunkPadding = chunkOffset2 % info2.boundary; - const chunkStart = chunkOffset2 + chunkPadding; - offset += chunkPadding; - if (chunkStart !== 0 && chunkSize - chunkStart < info2.storage) { - offset += chunkSize - chunkStart; - } - uniform.__data = new Float32Array(info2.storage / Float32Array.BYTES_PER_ELEMENT); - uniform.__offset = offset; - offset += info2.storage; - } - } - } - const chunkOffset = offset % chunkSize; - if (chunkOffset > 0) offset += chunkSize - chunkOffset; - uniformsGroup.__size = offset; - uniformsGroup.__cache = {}; - return this; - } - __name(prepareUniformsGroup, "prepareUniformsGroup"); - function getUniformSize(value) { - const info2 = { - boundary: 0, - // bytes - storage: 0 - // bytes - }; - if (typeof value === "number" || typeof value === "boolean") { - info2.boundary = 4; - info2.storage = 4; - } else if (value.isVector2) { - info2.boundary = 8; - info2.storage = 8; - } else if (value.isVector3 || value.isColor) { - info2.boundary = 16; - info2.storage = 12; - } else if (value.isVector4) { - info2.boundary = 16; - info2.storage = 16; - } else if (value.isMatrix3) { - info2.boundary = 48; - info2.storage = 48; - } else if (value.isMatrix4) { - info2.boundary = 64; - info2.storage = 64; - } else if (value.isTexture) { - console.warn("THREE.WebGLRenderer: Texture samplers can not be part of an uniforms group."); - } else { - console.warn("THREE.WebGLRenderer: Unsupported uniform value type.", value); - } - return info2; - } - __name(getUniformSize, "getUniformSize"); - function onUniformsGroupsDispose(event) { - const uniformsGroup = event.target; - uniformsGroup.removeEventListener("dispose", onUniformsGroupsDispose); - const index = allocatedBindingPoints.indexOf(uniformsGroup.__bindingPointIndex); - allocatedBindingPoints.splice(index, 1); - gl.deleteBuffer(buffers[uniformsGroup.id]); - delete buffers[uniformsGroup.id]; - delete updateList[uniformsGroup.id]; - } - __name(onUniformsGroupsDispose, "onUniformsGroupsDispose"); - function dispose() { - for (const id2 in buffers) { - gl.deleteBuffer(buffers[id2]); - } - allocatedBindingPoints = []; - buffers = {}; - updateList = {}; - } - __name(dispose, "dispose"); - return { - bind, - update, - dispose - }; -} -__name(WebGLUniformsGroups, "WebGLUniformsGroups"); -class WebGLRenderer { - static { - __name(this, "WebGLRenderer"); - } - constructor(parameters = {}) { - const { - canvas = createCanvasElement(), - context = null, - depth = true, - stencil = false, - alpha = false, - antialias = false, - premultipliedAlpha = true, - preserveDrawingBuffer = false, - powerPreference = "default", - failIfMajorPerformanceCaveat = false, - reverseDepthBuffer = false - } = parameters; - this.isWebGLRenderer = true; - let _alpha; - if (context !== null) { - if (typeof WebGLRenderingContext !== "undefined" && context instanceof WebGLRenderingContext) { - throw new Error("THREE.WebGLRenderer: WebGL 1 is not supported since r163."); - } - _alpha = context.getContextAttributes().alpha; - } else { - _alpha = alpha; - } - const uintClearColor = new Uint32Array(4); - const intClearColor = new Int32Array(4); - let currentRenderList = null; - let currentRenderState = null; - const renderListStack = []; - const renderStateStack = []; - this.domElement = canvas; - this.debug = { - /** - * Enables error checking and reporting when shader programs are being compiled - * @type {boolean} - */ - checkShaderErrors: true, - /** - * Callback for custom error reporting. - * @type {?Function} - */ - onShaderError: null - }; - this.autoClear = true; - this.autoClearColor = true; - this.autoClearDepth = true; - this.autoClearStencil = true; - this.sortObjects = true; - this.clippingPlanes = []; - this.localClippingEnabled = false; - this._outputColorSpace = SRGBColorSpace; - this.toneMapping = NoToneMapping; - this.toneMappingExposure = 1; - const _this = this; - let _isContextLost = false; - let _currentActiveCubeFace = 0; - let _currentActiveMipmapLevel = 0; - let _currentRenderTarget = null; - let _currentMaterialId = -1; - let _currentCamera = null; - const _currentViewport = new Vector4(); - const _currentScissor = new Vector4(); - let _currentScissorTest = null; - const _currentClearColor = new Color(0); - let _currentClearAlpha = 0; - let _width = canvas.width; - let _height = canvas.height; - let _pixelRatio = 1; - let _opaqueSort = null; - let _transparentSort = null; - const _viewport = new Vector4(0, 0, _width, _height); - const _scissor = new Vector4(0, 0, _width, _height); - let _scissorTest = false; - const _frustum2 = new Frustum(); - let _clippingEnabled = false; - let _localClippingEnabled = false; - const _currentProjectionMatrix = new Matrix4(); - const _projScreenMatrix2 = new Matrix4(); - const _vector32 = new Vector3(); - const _vector4 = new Vector4(); - const _emptyScene = { background: null, fog: null, environment: null, overrideMaterial: null, isScene: true }; - let _renderBackground = false; - function getTargetPixelRatio() { - return _currentRenderTarget === null ? _pixelRatio : 1; - } - __name(getTargetPixelRatio, "getTargetPixelRatio"); - let _gl = context; - function getContext(contextName, contextAttributes) { - return canvas.getContext(contextName, contextAttributes); - } - __name(getContext, "getContext"); - try { - const contextAttributes = { - alpha: true, - depth, - stencil, - antialias, - premultipliedAlpha, - preserveDrawingBuffer, - powerPreference, - failIfMajorPerformanceCaveat - }; - if ("setAttribute" in canvas) canvas.setAttribute("data-engine", `three.js r${REVISION}`); - canvas.addEventListener("webglcontextlost", onContextLost, false); - canvas.addEventListener("webglcontextrestored", onContextRestore, false); - canvas.addEventListener("webglcontextcreationerror", onContextCreationError, false); - if (_gl === null) { - const contextName = "webgl2"; - _gl = getContext(contextName, contextAttributes); - if (_gl === null) { - if (getContext(contextName)) { - throw new Error("Error creating WebGL context with your selected attributes."); - } else { - throw new Error("Error creating WebGL context."); - } - } - } - } catch (error) { - console.error("THREE.WebGLRenderer: " + error.message); - throw error; - } - let extensions, capabilities, state, info; - let properties, textures, cubemaps, cubeuvmaps, attributes, geometries, objects; - let programCache, materials, renderLists, renderStates, clipping, shadowMap; - let background, morphtargets, bufferRenderer, indexedBufferRenderer; - let utils, bindingStates, uniformsGroups; - function initGLContext() { - extensions = new WebGLExtensions(_gl); - extensions.init(); - utils = new WebGLUtils(_gl, extensions); - capabilities = new WebGLCapabilities(_gl, extensions, parameters, utils); - state = new WebGLState(_gl, extensions); - if (capabilities.reverseDepthBuffer && reverseDepthBuffer) { - state.buffers.depth.setReversed(true); - } - info = new WebGLInfo(_gl); - properties = new WebGLProperties(); - textures = new WebGLTextures(_gl, extensions, state, properties, capabilities, utils, info); - cubemaps = new WebGLCubeMaps(_this); - cubeuvmaps = new WebGLCubeUVMaps(_this); - attributes = new WebGLAttributes(_gl); - bindingStates = new WebGLBindingStates(_gl, attributes); - geometries = new WebGLGeometries(_gl, attributes, info, bindingStates); - objects = new WebGLObjects(_gl, geometries, attributes, info); - morphtargets = new WebGLMorphtargets(_gl, capabilities, textures); - clipping = new WebGLClipping(properties); - programCache = new WebGLPrograms(_this, cubemaps, cubeuvmaps, extensions, capabilities, bindingStates, clipping); - materials = new WebGLMaterials(_this, properties); - renderLists = new WebGLRenderLists(); - renderStates = new WebGLRenderStates(extensions); - background = new WebGLBackground(_this, cubemaps, cubeuvmaps, state, objects, _alpha, premultipliedAlpha); - shadowMap = new WebGLShadowMap(_this, objects, capabilities); - uniformsGroups = new WebGLUniformsGroups(_gl, info, capabilities, state); - bufferRenderer = new WebGLBufferRenderer(_gl, extensions, info); - indexedBufferRenderer = new WebGLIndexedBufferRenderer(_gl, extensions, info); - info.programs = programCache.programs; - _this.capabilities = capabilities; - _this.extensions = extensions; - _this.properties = properties; - _this.renderLists = renderLists; - _this.shadowMap = shadowMap; - _this.state = state; - _this.info = info; - } - __name(initGLContext, "initGLContext"); - initGLContext(); - const xr = new WebXRManager(_this, _gl); - this.xr = xr; - this.getContext = function() { - return _gl; - }; - this.getContextAttributes = function() { - return _gl.getContextAttributes(); - }; - this.forceContextLoss = function() { - const extension = extensions.get("WEBGL_lose_context"); - if (extension) extension.loseContext(); - }; - this.forceContextRestore = function() { - const extension = extensions.get("WEBGL_lose_context"); - if (extension) extension.restoreContext(); - }; - this.getPixelRatio = function() { - return _pixelRatio; - }; - this.setPixelRatio = function(value) { - if (value === void 0) return; - _pixelRatio = value; - this.setSize(_width, _height, false); - }; - this.getSize = function(target) { - return target.set(_width, _height); - }; - this.setSize = function(width, height, updateStyle = true) { - if (xr.isPresenting) { - console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting."); - return; - } - _width = width; - _height = height; - canvas.width = Math.floor(width * _pixelRatio); - canvas.height = Math.floor(height * _pixelRatio); - if (updateStyle === true) { - canvas.style.width = width + "px"; - canvas.style.height = height + "px"; - } - this.setViewport(0, 0, width, height); - }; - this.getDrawingBufferSize = function(target) { - return target.set(_width * _pixelRatio, _height * _pixelRatio).floor(); - }; - this.setDrawingBufferSize = function(width, height, pixelRatio) { - _width = width; - _height = height; - _pixelRatio = pixelRatio; - canvas.width = Math.floor(width * pixelRatio); - canvas.height = Math.floor(height * pixelRatio); - this.setViewport(0, 0, width, height); - }; - this.getCurrentViewport = function(target) { - return target.copy(_currentViewport); - }; - this.getViewport = function(target) { - return target.copy(_viewport); - }; - this.setViewport = function(x, y, width, height) { - if (x.isVector4) { - _viewport.set(x.x, x.y, x.z, x.w); - } else { - _viewport.set(x, y, width, height); - } - state.viewport(_currentViewport.copy(_viewport).multiplyScalar(_pixelRatio).round()); - }; - this.getScissor = function(target) { - return target.copy(_scissor); - }; - this.setScissor = function(x, y, width, height) { - if (x.isVector4) { - _scissor.set(x.x, x.y, x.z, x.w); - } else { - _scissor.set(x, y, width, height); - } - state.scissor(_currentScissor.copy(_scissor).multiplyScalar(_pixelRatio).round()); - }; - this.getScissorTest = function() { - return _scissorTest; - }; - this.setScissorTest = function(boolean) { - state.setScissorTest(_scissorTest = boolean); - }; - this.setOpaqueSort = function(method) { - _opaqueSort = method; - }; - this.setTransparentSort = function(method) { - _transparentSort = method; - }; - this.getClearColor = function(target) { - return target.copy(background.getClearColor()); - }; - this.setClearColor = function() { - background.setClearColor.apply(background, arguments); - }; - this.getClearAlpha = function() { - return background.getClearAlpha(); - }; - this.setClearAlpha = function() { - background.setClearAlpha.apply(background, arguments); - }; - this.clear = function(color = true, depth2 = true, stencil2 = true) { - let bits2 = 0; - if (color) { - let isIntegerFormat = false; - if (_currentRenderTarget !== null) { - const targetFormat = _currentRenderTarget.texture.format; - isIntegerFormat = targetFormat === RGBAIntegerFormat || targetFormat === RGIntegerFormat || targetFormat === RedIntegerFormat; - } - if (isIntegerFormat) { - const targetType = _currentRenderTarget.texture.type; - const isUnsignedType = targetType === UnsignedByteType || targetType === UnsignedIntType || targetType === UnsignedShortType || targetType === UnsignedInt248Type || targetType === UnsignedShort4444Type || targetType === UnsignedShort5551Type; - const clearColor = background.getClearColor(); - const a = background.getClearAlpha(); - const r = clearColor.r; - const g = clearColor.g; - const b = clearColor.b; - if (isUnsignedType) { - uintClearColor[0] = r; - uintClearColor[1] = g; - uintClearColor[2] = b; - uintClearColor[3] = a; - _gl.clearBufferuiv(_gl.COLOR, 0, uintClearColor); - } else { - intClearColor[0] = r; - intClearColor[1] = g; - intClearColor[2] = b; - intClearColor[3] = a; - _gl.clearBufferiv(_gl.COLOR, 0, intClearColor); - } - } else { - bits2 |= _gl.COLOR_BUFFER_BIT; - } - } - if (depth2) { - bits2 |= _gl.DEPTH_BUFFER_BIT; - } - if (stencil2) { - bits2 |= _gl.STENCIL_BUFFER_BIT; - this.state.buffers.stencil.setMask(4294967295); - } - _gl.clear(bits2); - }; - this.clearColor = function() { - this.clear(true, false, false); - }; - this.clearDepth = function() { - this.clear(false, true, false); - }; - this.clearStencil = function() { - this.clear(false, false, true); - }; - this.dispose = function() { - canvas.removeEventListener("webglcontextlost", onContextLost, false); - canvas.removeEventListener("webglcontextrestored", onContextRestore, false); - canvas.removeEventListener("webglcontextcreationerror", onContextCreationError, false); - renderLists.dispose(); - renderStates.dispose(); - properties.dispose(); - cubemaps.dispose(); - cubeuvmaps.dispose(); - objects.dispose(); - bindingStates.dispose(); - uniformsGroups.dispose(); - programCache.dispose(); - xr.dispose(); - xr.removeEventListener("sessionstart", onXRSessionStart); - xr.removeEventListener("sessionend", onXRSessionEnd); - animation.stop(); - }; - function onContextLost(event) { - event.preventDefault(); - console.log("THREE.WebGLRenderer: Context Lost."); - _isContextLost = true; - } - __name(onContextLost, "onContextLost"); - function onContextRestore() { - console.log("THREE.WebGLRenderer: Context Restored."); - _isContextLost = false; - const infoAutoReset = info.autoReset; - const shadowMapEnabled = shadowMap.enabled; - const shadowMapAutoUpdate = shadowMap.autoUpdate; - const shadowMapNeedsUpdate = shadowMap.needsUpdate; - const shadowMapType = shadowMap.type; - initGLContext(); - info.autoReset = infoAutoReset; - shadowMap.enabled = shadowMapEnabled; - shadowMap.autoUpdate = shadowMapAutoUpdate; - shadowMap.needsUpdate = shadowMapNeedsUpdate; - shadowMap.type = shadowMapType; - } - __name(onContextRestore, "onContextRestore"); - function onContextCreationError(event) { - console.error("THREE.WebGLRenderer: A WebGL context could not be created. Reason: ", event.statusMessage); - } - __name(onContextCreationError, "onContextCreationError"); - function onMaterialDispose(event) { - const material = event.target; - material.removeEventListener("dispose", onMaterialDispose); - deallocateMaterial(material); - } - __name(onMaterialDispose, "onMaterialDispose"); - function deallocateMaterial(material) { - releaseMaterialProgramReferences(material); - properties.remove(material); - } - __name(deallocateMaterial, "deallocateMaterial"); - function releaseMaterialProgramReferences(material) { - const programs = properties.get(material).programs; - if (programs !== void 0) { - programs.forEach(function(program) { - programCache.releaseProgram(program); - }); - if (material.isShaderMaterial) { - programCache.releaseShaderCache(material); - } - } - } - __name(releaseMaterialProgramReferences, "releaseMaterialProgramReferences"); - this.renderBufferDirect = function(camera, scene, geometry, material, object, group) { - if (scene === null) scene = _emptyScene; - const frontFaceCW = object.isMesh && object.matrixWorld.determinant() < 0; - const program = setProgram(camera, scene, geometry, material, object); - state.setMaterial(material, frontFaceCW); - let index = geometry.index; - let rangeFactor = 1; - if (material.wireframe === true) { - index = geometries.getWireframeAttribute(geometry); - if (index === void 0) return; - rangeFactor = 2; - } - const drawRange = geometry.drawRange; - const position = geometry.attributes.position; - let drawStart = drawRange.start * rangeFactor; - let drawEnd = (drawRange.start + drawRange.count) * rangeFactor; - if (group !== null) { - drawStart = Math.max(drawStart, group.start * rangeFactor); - drawEnd = Math.min(drawEnd, (group.start + group.count) * rangeFactor); - } - if (index !== null) { - drawStart = Math.max(drawStart, 0); - drawEnd = Math.min(drawEnd, index.count); - } else if (position !== void 0 && position !== null) { - drawStart = Math.max(drawStart, 0); - drawEnd = Math.min(drawEnd, position.count); - } - const drawCount = drawEnd - drawStart; - if (drawCount < 0 || drawCount === Infinity) return; - bindingStates.setup(object, material, program, geometry, index); - let attribute; - let renderer = bufferRenderer; - if (index !== null) { - attribute = attributes.get(index); - renderer = indexedBufferRenderer; - renderer.setIndex(attribute); - } - if (object.isMesh) { - if (material.wireframe === true) { - state.setLineWidth(material.wireframeLinewidth * getTargetPixelRatio()); - renderer.setMode(_gl.LINES); - } else { - renderer.setMode(_gl.TRIANGLES); - } - } else if (object.isLine) { - let lineWidth = material.linewidth; - if (lineWidth === void 0) lineWidth = 1; - state.setLineWidth(lineWidth * getTargetPixelRatio()); - if (object.isLineSegments) { - renderer.setMode(_gl.LINES); - } else if (object.isLineLoop) { - renderer.setMode(_gl.LINE_LOOP); - } else { - renderer.setMode(_gl.LINE_STRIP); - } - } else if (object.isPoints) { - renderer.setMode(_gl.POINTS); - } else if (object.isSprite) { - renderer.setMode(_gl.TRIANGLES); - } - if (object.isBatchedMesh) { - if (object._multiDrawInstances !== null) { - renderer.renderMultiDrawInstances(object._multiDrawStarts, object._multiDrawCounts, object._multiDrawCount, object._multiDrawInstances); - } else { - if (!extensions.get("WEBGL_multi_draw")) { - const starts = object._multiDrawStarts; - const counts = object._multiDrawCounts; - const drawCount2 = object._multiDrawCount; - const bytesPerElement = index ? attributes.get(index).bytesPerElement : 1; - const uniforms = properties.get(material).currentProgram.getUniforms(); - for (let i = 0; i < drawCount2; i++) { - uniforms.setValue(_gl, "_gl_DrawID", i); - renderer.render(starts[i] / bytesPerElement, counts[i]); - } - } else { - renderer.renderMultiDraw(object._multiDrawStarts, object._multiDrawCounts, object._multiDrawCount); - } - } - } else if (object.isInstancedMesh) { - renderer.renderInstances(drawStart, drawCount, object.count); - } else if (geometry.isInstancedBufferGeometry) { - const maxInstanceCount = geometry._maxInstanceCount !== void 0 ? geometry._maxInstanceCount : Infinity; - const instanceCount = Math.min(geometry.instanceCount, maxInstanceCount); - renderer.renderInstances(drawStart, drawCount, instanceCount); - } else { - renderer.render(drawStart, drawCount); - } - }; - function prepareMaterial(material, scene, object) { - if (material.transparent === true && material.side === DoubleSide && material.forceSinglePass === false) { - material.side = BackSide; - material.needsUpdate = true; - getProgram(material, scene, object); - material.side = FrontSide; - material.needsUpdate = true; - getProgram(material, scene, object); - material.side = DoubleSide; - } else { - getProgram(material, scene, object); - } - } - __name(prepareMaterial, "prepareMaterial"); - this.compile = function(scene, camera, targetScene = null) { - if (targetScene === null) targetScene = scene; - currentRenderState = renderStates.get(targetScene); - currentRenderState.init(camera); - renderStateStack.push(currentRenderState); - targetScene.traverseVisible(function(object) { - if (object.isLight && object.layers.test(camera.layers)) { - currentRenderState.pushLight(object); - if (object.castShadow) { - currentRenderState.pushShadow(object); - } - } - }); - if (scene !== targetScene) { - scene.traverseVisible(function(object) { - if (object.isLight && object.layers.test(camera.layers)) { - currentRenderState.pushLight(object); - if (object.castShadow) { - currentRenderState.pushShadow(object); - } - } - }); - } - currentRenderState.setupLights(); - const materials2 = /* @__PURE__ */ new Set(); - scene.traverse(function(object) { - if (!(object.isMesh || object.isPoints || object.isLine || object.isSprite)) { - return; - } - const material = object.material; - if (material) { - if (Array.isArray(material)) { - for (let i = 0; i < material.length; i++) { - const material2 = material[i]; - prepareMaterial(material2, targetScene, object); - materials2.add(material2); - } - } else { - prepareMaterial(material, targetScene, object); - materials2.add(material); - } - } - }); - renderStateStack.pop(); - currentRenderState = null; - return materials2; - }; - this.compileAsync = function(scene, camera, targetScene = null) { - const materials2 = this.compile(scene, camera, targetScene); - return new Promise((resolve) => { - function checkMaterialsReady() { - materials2.forEach(function(material) { - const materialProperties = properties.get(material); - const program = materialProperties.currentProgram; - if (program.isReady()) { - materials2.delete(material); - } - }); - if (materials2.size === 0) { - resolve(scene); - return; - } - setTimeout(checkMaterialsReady, 10); - } - __name(checkMaterialsReady, "checkMaterialsReady"); - if (extensions.get("KHR_parallel_shader_compile") !== null) { - checkMaterialsReady(); - } else { - setTimeout(checkMaterialsReady, 10); - } - }); - }; - let onAnimationFrameCallback = null; - function onAnimationFrame(time) { - if (onAnimationFrameCallback) onAnimationFrameCallback(time); - } - __name(onAnimationFrame, "onAnimationFrame"); - function onXRSessionStart() { - animation.stop(); - } - __name(onXRSessionStart, "onXRSessionStart"); - function onXRSessionEnd() { - animation.start(); - } - __name(onXRSessionEnd, "onXRSessionEnd"); - const animation = new WebGLAnimation(); - animation.setAnimationLoop(onAnimationFrame); - if (typeof self !== "undefined") animation.setContext(self); - this.setAnimationLoop = function(callback) { - onAnimationFrameCallback = callback; - xr.setAnimationLoop(callback); - callback === null ? animation.stop() : animation.start(); - }; - xr.addEventListener("sessionstart", onXRSessionStart); - xr.addEventListener("sessionend", onXRSessionEnd); - this.render = function(scene, camera) { - if (camera !== void 0 && camera.isCamera !== true) { - console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera."); - return; - } - if (_isContextLost === true) return; - if (scene.matrixWorldAutoUpdate === true) scene.updateMatrixWorld(); - if (camera.parent === null && camera.matrixWorldAutoUpdate === true) camera.updateMatrixWorld(); - if (xr.enabled === true && xr.isPresenting === true) { - if (xr.cameraAutoUpdate === true) xr.updateCamera(camera); - camera = xr.getCamera(); - } - if (scene.isScene === true) scene.onBeforeRender(_this, scene, camera, _currentRenderTarget); - currentRenderState = renderStates.get(scene, renderStateStack.length); - currentRenderState.init(camera); - renderStateStack.push(currentRenderState); - _projScreenMatrix2.multiplyMatrices(camera.projectionMatrix, camera.matrixWorldInverse); - _frustum2.setFromProjectionMatrix(_projScreenMatrix2); - _localClippingEnabled = this.localClippingEnabled; - _clippingEnabled = clipping.init(this.clippingPlanes, _localClippingEnabled); - currentRenderList = renderLists.get(scene, renderListStack.length); - currentRenderList.init(); - renderListStack.push(currentRenderList); - if (xr.enabled === true && xr.isPresenting === true) { - const depthSensingMesh = _this.xr.getDepthSensingMesh(); - if (depthSensingMesh !== null) { - projectObject(depthSensingMesh, camera, -Infinity, _this.sortObjects); - } - } - projectObject(scene, camera, 0, _this.sortObjects); - currentRenderList.finish(); - if (_this.sortObjects === true) { - currentRenderList.sort(_opaqueSort, _transparentSort); - } - _renderBackground = xr.enabled === false || xr.isPresenting === false || xr.hasDepthSensing() === false; - if (_renderBackground) { - background.addToRenderList(currentRenderList, scene); - } - this.info.render.frame++; - if (_clippingEnabled === true) clipping.beginShadows(); - const shadowsArray = currentRenderState.state.shadowsArray; - shadowMap.render(shadowsArray, scene, camera); - if (_clippingEnabled === true) clipping.endShadows(); - if (this.info.autoReset === true) this.info.reset(); - const opaqueObjects = currentRenderList.opaque; - const transmissiveObjects = currentRenderList.transmissive; - currentRenderState.setupLights(); - if (camera.isArrayCamera) { - const cameras = camera.cameras; - if (transmissiveObjects.length > 0) { - for (let i = 0, l = cameras.length; i < l; i++) { - const camera2 = cameras[i]; - renderTransmissionPass(opaqueObjects, transmissiveObjects, scene, camera2); - } - } - if (_renderBackground) background.render(scene); - for (let i = 0, l = cameras.length; i < l; i++) { - const camera2 = cameras[i]; - renderScene(currentRenderList, scene, camera2, camera2.viewport); - } - } else { - if (transmissiveObjects.length > 0) renderTransmissionPass(opaqueObjects, transmissiveObjects, scene, camera); - if (_renderBackground) background.render(scene); - renderScene(currentRenderList, scene, camera); - } - if (_currentRenderTarget !== null) { - textures.updateMultisampleRenderTarget(_currentRenderTarget); - textures.updateRenderTargetMipmap(_currentRenderTarget); - } - if (scene.isScene === true) scene.onAfterRender(_this, scene, camera); - bindingStates.resetDefaultState(); - _currentMaterialId = -1; - _currentCamera = null; - renderStateStack.pop(); - if (renderStateStack.length > 0) { - currentRenderState = renderStateStack[renderStateStack.length - 1]; - if (_clippingEnabled === true) clipping.setGlobalState(_this.clippingPlanes, currentRenderState.state.camera); - } else { - currentRenderState = null; - } - renderListStack.pop(); - if (renderListStack.length > 0) { - currentRenderList = renderListStack[renderListStack.length - 1]; - } else { - currentRenderList = null; - } - }; - function projectObject(object, camera, groupOrder, sortObjects) { - if (object.visible === false) return; - const visible = object.layers.test(camera.layers); - if (visible) { - if (object.isGroup) { - groupOrder = object.renderOrder; - } else if (object.isLOD) { - if (object.autoUpdate === true) object.update(camera); - } else if (object.isLight) { - currentRenderState.pushLight(object); - if (object.castShadow) { - currentRenderState.pushShadow(object); - } - } else if (object.isSprite) { - if (!object.frustumCulled || _frustum2.intersectsSprite(object)) { - if (sortObjects) { - _vector4.setFromMatrixPosition(object.matrixWorld).applyMatrix4(_projScreenMatrix2); - } - const geometry = objects.update(object); - const material = object.material; - if (material.visible) { - currentRenderList.push(object, geometry, material, groupOrder, _vector4.z, null); - } - } - } else if (object.isMesh || object.isLine || object.isPoints) { - if (!object.frustumCulled || _frustum2.intersectsObject(object)) { - const geometry = objects.update(object); - const material = object.material; - if (sortObjects) { - if (object.boundingSphere !== void 0) { - if (object.boundingSphere === null) object.computeBoundingSphere(); - _vector4.copy(object.boundingSphere.center); - } else { - if (geometry.boundingSphere === null) geometry.computeBoundingSphere(); - _vector4.copy(geometry.boundingSphere.center); - } - _vector4.applyMatrix4(object.matrixWorld).applyMatrix4(_projScreenMatrix2); - } - if (Array.isArray(material)) { - const groups = geometry.groups; - for (let i = 0, l = groups.length; i < l; i++) { - const group = groups[i]; - const groupMaterial = material[group.materialIndex]; - if (groupMaterial && groupMaterial.visible) { - currentRenderList.push(object, geometry, groupMaterial, groupOrder, _vector4.z, group); - } - } - } else if (material.visible) { - currentRenderList.push(object, geometry, material, groupOrder, _vector4.z, null); - } - } - } - } - const children = object.children; - for (let i = 0, l = children.length; i < l; i++) { - projectObject(children[i], camera, groupOrder, sortObjects); - } - } - __name(projectObject, "projectObject"); - function renderScene(currentRenderList2, scene, camera, viewport) { - const opaqueObjects = currentRenderList2.opaque; - const transmissiveObjects = currentRenderList2.transmissive; - const transparentObjects = currentRenderList2.transparent; - currentRenderState.setupLightsView(camera); - if (_clippingEnabled === true) clipping.setGlobalState(_this.clippingPlanes, camera); - if (viewport) state.viewport(_currentViewport.copy(viewport)); - if (opaqueObjects.length > 0) renderObjects(opaqueObjects, scene, camera); - if (transmissiveObjects.length > 0) renderObjects(transmissiveObjects, scene, camera); - if (transparentObjects.length > 0) renderObjects(transparentObjects, scene, camera); - state.buffers.depth.setTest(true); - state.buffers.depth.setMask(true); - state.buffers.color.setMask(true); - state.setPolygonOffset(false); - } - __name(renderScene, "renderScene"); - function renderTransmissionPass(opaqueObjects, transmissiveObjects, scene, camera) { - const overrideMaterial = scene.isScene === true ? scene.overrideMaterial : null; - if (overrideMaterial !== null) { - return; - } - if (currentRenderState.state.transmissionRenderTarget[camera.id] === void 0) { - currentRenderState.state.transmissionRenderTarget[camera.id] = new WebGLRenderTarget(1, 1, { - generateMipmaps: true, - type: extensions.has("EXT_color_buffer_half_float") || extensions.has("EXT_color_buffer_float") ? HalfFloatType : UnsignedByteType, - minFilter: LinearMipmapLinearFilter, - samples: 4, - stencilBuffer: stencil, - resolveDepthBuffer: false, - resolveStencilBuffer: false, - colorSpace: ColorManagement.workingColorSpace - }); - } - const transmissionRenderTarget = currentRenderState.state.transmissionRenderTarget[camera.id]; - const activeViewport = camera.viewport || _currentViewport; - transmissionRenderTarget.setSize(activeViewport.z, activeViewport.w); - const currentRenderTarget = _this.getRenderTarget(); - _this.setRenderTarget(transmissionRenderTarget); - _this.getClearColor(_currentClearColor); - _currentClearAlpha = _this.getClearAlpha(); - if (_currentClearAlpha < 1) _this.setClearColor(16777215, 0.5); - _this.clear(); - if (_renderBackground) background.render(scene); - const currentToneMapping = _this.toneMapping; - _this.toneMapping = NoToneMapping; - const currentCameraViewport = camera.viewport; - if (camera.viewport !== void 0) camera.viewport = void 0; - currentRenderState.setupLightsView(camera); - if (_clippingEnabled === true) clipping.setGlobalState(_this.clippingPlanes, camera); - renderObjects(opaqueObjects, scene, camera); - textures.updateMultisampleRenderTarget(transmissionRenderTarget); - textures.updateRenderTargetMipmap(transmissionRenderTarget); - if (extensions.has("WEBGL_multisampled_render_to_texture") === false) { - let renderTargetNeedsUpdate = false; - for (let i = 0, l = transmissiveObjects.length; i < l; i++) { - const renderItem = transmissiveObjects[i]; - const object = renderItem.object; - const geometry = renderItem.geometry; - const material = renderItem.material; - const group = renderItem.group; - if (material.side === DoubleSide && object.layers.test(camera.layers)) { - const currentSide = material.side; - material.side = BackSide; - material.needsUpdate = true; - renderObject(object, scene, camera, geometry, material, group); - material.side = currentSide; - material.needsUpdate = true; - renderTargetNeedsUpdate = true; - } - } - if (renderTargetNeedsUpdate === true) { - textures.updateMultisampleRenderTarget(transmissionRenderTarget); - textures.updateRenderTargetMipmap(transmissionRenderTarget); - } - } - _this.setRenderTarget(currentRenderTarget); - _this.setClearColor(_currentClearColor, _currentClearAlpha); - if (currentCameraViewport !== void 0) camera.viewport = currentCameraViewport; - _this.toneMapping = currentToneMapping; - } - __name(renderTransmissionPass, "renderTransmissionPass"); - function renderObjects(renderList, scene, camera) { - const overrideMaterial = scene.isScene === true ? scene.overrideMaterial : null; - for (let i = 0, l = renderList.length; i < l; i++) { - const renderItem = renderList[i]; - const object = renderItem.object; - const geometry = renderItem.geometry; - const material = overrideMaterial === null ? renderItem.material : overrideMaterial; - const group = renderItem.group; - if (object.layers.test(camera.layers)) { - renderObject(object, scene, camera, geometry, material, group); - } - } - } - __name(renderObjects, "renderObjects"); - function renderObject(object, scene, camera, geometry, material, group) { - object.onBeforeRender(_this, scene, camera, geometry, material, group); - object.modelViewMatrix.multiplyMatrices(camera.matrixWorldInverse, object.matrixWorld); - object.normalMatrix.getNormalMatrix(object.modelViewMatrix); - material.onBeforeRender(_this, scene, camera, geometry, object, group); - if (material.transparent === true && material.side === DoubleSide && material.forceSinglePass === false) { - material.side = BackSide; - material.needsUpdate = true; - _this.renderBufferDirect(camera, scene, geometry, material, object, group); - material.side = FrontSide; - material.needsUpdate = true; - _this.renderBufferDirect(camera, scene, geometry, material, object, group); - material.side = DoubleSide; - } else { - _this.renderBufferDirect(camera, scene, geometry, material, object, group); - } - object.onAfterRender(_this, scene, camera, geometry, material, group); - } - __name(renderObject, "renderObject"); - function getProgram(material, scene, object) { - if (scene.isScene !== true) scene = _emptyScene; - const materialProperties = properties.get(material); - const lights = currentRenderState.state.lights; - const shadowsArray = currentRenderState.state.shadowsArray; - const lightsStateVersion = lights.state.version; - const parameters2 = programCache.getParameters(material, lights.state, shadowsArray, scene, object); - const programCacheKey = programCache.getProgramCacheKey(parameters2); - let programs = materialProperties.programs; - materialProperties.environment = material.isMeshStandardMaterial ? scene.environment : null; - materialProperties.fog = scene.fog; - materialProperties.envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || materialProperties.environment); - materialProperties.envMapRotation = materialProperties.environment !== null && material.envMap === null ? scene.environmentRotation : material.envMapRotation; - if (programs === void 0) { - material.addEventListener("dispose", onMaterialDispose); - programs = /* @__PURE__ */ new Map(); - materialProperties.programs = programs; - } - let program = programs.get(programCacheKey); - if (program !== void 0) { - if (materialProperties.currentProgram === program && materialProperties.lightsStateVersion === lightsStateVersion) { - updateCommonMaterialProperties(material, parameters2); - return program; - } - } else { - parameters2.uniforms = programCache.getUniforms(material); - material.onBeforeCompile(parameters2, _this); - program = programCache.acquireProgram(parameters2, programCacheKey); - programs.set(programCacheKey, program); - materialProperties.uniforms = parameters2.uniforms; - } - const uniforms = materialProperties.uniforms; - if (!material.isShaderMaterial && !material.isRawShaderMaterial || material.clipping === true) { - uniforms.clippingPlanes = clipping.uniform; - } - updateCommonMaterialProperties(material, parameters2); - materialProperties.needsLights = materialNeedsLights(material); - materialProperties.lightsStateVersion = lightsStateVersion; - if (materialProperties.needsLights) { - uniforms.ambientLightColor.value = lights.state.ambient; - uniforms.lightProbe.value = lights.state.probe; - uniforms.directionalLights.value = lights.state.directional; - uniforms.directionalLightShadows.value = lights.state.directionalShadow; - uniforms.spotLights.value = lights.state.spot; - uniforms.spotLightShadows.value = lights.state.spotShadow; - uniforms.rectAreaLights.value = lights.state.rectArea; - uniforms.ltc_1.value = lights.state.rectAreaLTC1; - uniforms.ltc_2.value = lights.state.rectAreaLTC2; - uniforms.pointLights.value = lights.state.point; - uniforms.pointLightShadows.value = lights.state.pointShadow; - uniforms.hemisphereLights.value = lights.state.hemi; - uniforms.directionalShadowMap.value = lights.state.directionalShadowMap; - uniforms.directionalShadowMatrix.value = lights.state.directionalShadowMatrix; - uniforms.spotShadowMap.value = lights.state.spotShadowMap; - uniforms.spotLightMatrix.value = lights.state.spotLightMatrix; - uniforms.spotLightMap.value = lights.state.spotLightMap; - uniforms.pointShadowMap.value = lights.state.pointShadowMap; - uniforms.pointShadowMatrix.value = lights.state.pointShadowMatrix; - } - materialProperties.currentProgram = program; - materialProperties.uniformsList = null; - return program; - } - __name(getProgram, "getProgram"); - function getUniformList(materialProperties) { - if (materialProperties.uniformsList === null) { - const progUniforms = materialProperties.currentProgram.getUniforms(); - materialProperties.uniformsList = WebGLUniforms.seqWithValue(progUniforms.seq, materialProperties.uniforms); - } - return materialProperties.uniformsList; - } - __name(getUniformList, "getUniformList"); - function updateCommonMaterialProperties(material, parameters2) { - const materialProperties = properties.get(material); - materialProperties.outputColorSpace = parameters2.outputColorSpace; - materialProperties.batching = parameters2.batching; - materialProperties.batchingColor = parameters2.batchingColor; - materialProperties.instancing = parameters2.instancing; - materialProperties.instancingColor = parameters2.instancingColor; - materialProperties.instancingMorph = parameters2.instancingMorph; - materialProperties.skinning = parameters2.skinning; - materialProperties.morphTargets = parameters2.morphTargets; - materialProperties.morphNormals = parameters2.morphNormals; - materialProperties.morphColors = parameters2.morphColors; - materialProperties.morphTargetsCount = parameters2.morphTargetsCount; - materialProperties.numClippingPlanes = parameters2.numClippingPlanes; - materialProperties.numIntersection = parameters2.numClipIntersection; - materialProperties.vertexAlphas = parameters2.vertexAlphas; - materialProperties.vertexTangents = parameters2.vertexTangents; - materialProperties.toneMapping = parameters2.toneMapping; - } - __name(updateCommonMaterialProperties, "updateCommonMaterialProperties"); - function setProgram(camera, scene, geometry, material, object) { - if (scene.isScene !== true) scene = _emptyScene; - textures.resetTextureUnits(); - const fog = scene.fog; - const environment = material.isMeshStandardMaterial ? scene.environment : null; - const colorSpace = _currentRenderTarget === null ? _this.outputColorSpace : _currentRenderTarget.isXRRenderTarget === true ? _currentRenderTarget.texture.colorSpace : LinearSRGBColorSpace; - const envMap = (material.isMeshStandardMaterial ? cubeuvmaps : cubemaps).get(material.envMap || environment); - const vertexAlphas = material.vertexColors === true && !!geometry.attributes.color && geometry.attributes.color.itemSize === 4; - const vertexTangents = !!geometry.attributes.tangent && (!!material.normalMap || material.anisotropy > 0); - const morphTargets = !!geometry.morphAttributes.position; - const morphNormals = !!geometry.morphAttributes.normal; - const morphColors = !!geometry.morphAttributes.color; - let toneMapping = NoToneMapping; - if (material.toneMapped) { - if (_currentRenderTarget === null || _currentRenderTarget.isXRRenderTarget === true) { - toneMapping = _this.toneMapping; - } - } - const morphAttribute = geometry.morphAttributes.position || geometry.morphAttributes.normal || geometry.morphAttributes.color; - const morphTargetsCount = morphAttribute !== void 0 ? morphAttribute.length : 0; - const materialProperties = properties.get(material); - const lights = currentRenderState.state.lights; - if (_clippingEnabled === true) { - if (_localClippingEnabled === true || camera !== _currentCamera) { - const useCache = camera === _currentCamera && material.id === _currentMaterialId; - clipping.setState(material, camera, useCache); - } - } - let needsProgramChange = false; - if (material.version === materialProperties.__version) { - if (materialProperties.needsLights && materialProperties.lightsStateVersion !== lights.state.version) { - needsProgramChange = true; - } else if (materialProperties.outputColorSpace !== colorSpace) { - needsProgramChange = true; - } else if (object.isBatchedMesh && materialProperties.batching === false) { - needsProgramChange = true; - } else if (!object.isBatchedMesh && materialProperties.batching === true) { - needsProgramChange = true; - } else if (object.isBatchedMesh && materialProperties.batchingColor === true && object.colorTexture === null) { - needsProgramChange = true; - } else if (object.isBatchedMesh && materialProperties.batchingColor === false && object.colorTexture !== null) { - needsProgramChange = true; - } else if (object.isInstancedMesh && materialProperties.instancing === false) { - needsProgramChange = true; - } else if (!object.isInstancedMesh && materialProperties.instancing === true) { - needsProgramChange = true; - } else if (object.isSkinnedMesh && materialProperties.skinning === false) { - needsProgramChange = true; - } else if (!object.isSkinnedMesh && materialProperties.skinning === true) { - needsProgramChange = true; - } else if (object.isInstancedMesh && materialProperties.instancingColor === true && object.instanceColor === null) { - needsProgramChange = true; - } else if (object.isInstancedMesh && materialProperties.instancingColor === false && object.instanceColor !== null) { - needsProgramChange = true; - } else if (object.isInstancedMesh && materialProperties.instancingMorph === true && object.morphTexture === null) { - needsProgramChange = true; - } else if (object.isInstancedMesh && materialProperties.instancingMorph === false && object.morphTexture !== null) { - needsProgramChange = true; - } else if (materialProperties.envMap !== envMap) { - needsProgramChange = true; - } else if (material.fog === true && materialProperties.fog !== fog) { - needsProgramChange = true; - } else if (materialProperties.numClippingPlanes !== void 0 && (materialProperties.numClippingPlanes !== clipping.numPlanes || materialProperties.numIntersection !== clipping.numIntersection)) { - needsProgramChange = true; - } else if (materialProperties.vertexAlphas !== vertexAlphas) { - needsProgramChange = true; - } else if (materialProperties.vertexTangents !== vertexTangents) { - needsProgramChange = true; - } else if (materialProperties.morphTargets !== morphTargets) { - needsProgramChange = true; - } else if (materialProperties.morphNormals !== morphNormals) { - needsProgramChange = true; - } else if (materialProperties.morphColors !== morphColors) { - needsProgramChange = true; - } else if (materialProperties.toneMapping !== toneMapping) { - needsProgramChange = true; - } else if (materialProperties.morphTargetsCount !== morphTargetsCount) { - needsProgramChange = true; - } - } else { - needsProgramChange = true; - materialProperties.__version = material.version; - } - let program = materialProperties.currentProgram; - if (needsProgramChange === true) { - program = getProgram(material, scene, object); - } - let refreshProgram = false; - let refreshMaterial = false; - let refreshLights = false; - const p_uniforms = program.getUniforms(), m_uniforms = materialProperties.uniforms; - if (state.useProgram(program.program)) { - refreshProgram = true; - refreshMaterial = true; - refreshLights = true; - } - if (material.id !== _currentMaterialId) { - _currentMaterialId = material.id; - refreshMaterial = true; - } - if (refreshProgram || _currentCamera !== camera) { - const reverseDepthBuffer2 = state.buffers.depth.getReversed(); - if (reverseDepthBuffer2) { - _currentProjectionMatrix.copy(camera.projectionMatrix); - toNormalizedProjectionMatrix(_currentProjectionMatrix); - toReversedProjectionMatrix(_currentProjectionMatrix); - p_uniforms.setValue(_gl, "projectionMatrix", _currentProjectionMatrix); - } else { - p_uniforms.setValue(_gl, "projectionMatrix", camera.projectionMatrix); - } - p_uniforms.setValue(_gl, "viewMatrix", camera.matrixWorldInverse); - const uCamPos = p_uniforms.map.cameraPosition; - if (uCamPos !== void 0) { - uCamPos.setValue(_gl, _vector32.setFromMatrixPosition(camera.matrixWorld)); - } - if (capabilities.logarithmicDepthBuffer) { - p_uniforms.setValue( - _gl, - "logDepthBufFC", - 2 / (Math.log(camera.far + 1) / Math.LN2) - ); - } - if (material.isMeshPhongMaterial || material.isMeshToonMaterial || material.isMeshLambertMaterial || material.isMeshBasicMaterial || material.isMeshStandardMaterial || material.isShaderMaterial) { - p_uniforms.setValue(_gl, "isOrthographic", camera.isOrthographicCamera === true); - } - if (_currentCamera !== camera) { - _currentCamera = camera; - refreshMaterial = true; - refreshLights = true; - } - } - if (object.isSkinnedMesh) { - p_uniforms.setOptional(_gl, object, "bindMatrix"); - p_uniforms.setOptional(_gl, object, "bindMatrixInverse"); - const skeleton = object.skeleton; - if (skeleton) { - if (skeleton.boneTexture === null) skeleton.computeBoneTexture(); - p_uniforms.setValue(_gl, "boneTexture", skeleton.boneTexture, textures); - } - } - if (object.isBatchedMesh) { - p_uniforms.setOptional(_gl, object, "batchingTexture"); - p_uniforms.setValue(_gl, "batchingTexture", object._matricesTexture, textures); - p_uniforms.setOptional(_gl, object, "batchingIdTexture"); - p_uniforms.setValue(_gl, "batchingIdTexture", object._indirectTexture, textures); - p_uniforms.setOptional(_gl, object, "batchingColorTexture"); - if (object._colorsTexture !== null) { - p_uniforms.setValue(_gl, "batchingColorTexture", object._colorsTexture, textures); - } - } - const morphAttributes = geometry.morphAttributes; - if (morphAttributes.position !== void 0 || morphAttributes.normal !== void 0 || morphAttributes.color !== void 0) { - morphtargets.update(object, geometry, program); - } - if (refreshMaterial || materialProperties.receiveShadow !== object.receiveShadow) { - materialProperties.receiveShadow = object.receiveShadow; - p_uniforms.setValue(_gl, "receiveShadow", object.receiveShadow); - } - if (material.isMeshGouraudMaterial && material.envMap !== null) { - m_uniforms.envMap.value = envMap; - m_uniforms.flipEnvMap.value = envMap.isCubeTexture && envMap.isRenderTargetTexture === false ? -1 : 1; - } - if (material.isMeshStandardMaterial && material.envMap === null && scene.environment !== null) { - m_uniforms.envMapIntensity.value = scene.environmentIntensity; - } - if (refreshMaterial) { - p_uniforms.setValue(_gl, "toneMappingExposure", _this.toneMappingExposure); - if (materialProperties.needsLights) { - markUniformsLightsNeedsUpdate(m_uniforms, refreshLights); - } - if (fog && material.fog === true) { - materials.refreshFogUniforms(m_uniforms, fog); - } - materials.refreshMaterialUniforms(m_uniforms, material, _pixelRatio, _height, currentRenderState.state.transmissionRenderTarget[camera.id]); - WebGLUniforms.upload(_gl, getUniformList(materialProperties), m_uniforms, textures); - } - if (material.isShaderMaterial && material.uniformsNeedUpdate === true) { - WebGLUniforms.upload(_gl, getUniformList(materialProperties), m_uniforms, textures); - material.uniformsNeedUpdate = false; - } - if (material.isSpriteMaterial) { - p_uniforms.setValue(_gl, "center", object.center); - } - p_uniforms.setValue(_gl, "modelViewMatrix", object.modelViewMatrix); - p_uniforms.setValue(_gl, "normalMatrix", object.normalMatrix); - p_uniforms.setValue(_gl, "modelMatrix", object.matrixWorld); - if (material.isShaderMaterial || material.isRawShaderMaterial) { - const groups = material.uniformsGroups; - for (let i = 0, l = groups.length; i < l; i++) { - const group = groups[i]; - uniformsGroups.update(group, program); - uniformsGroups.bind(group, program); - } - } - return program; - } - __name(setProgram, "setProgram"); - function markUniformsLightsNeedsUpdate(uniforms, value) { - uniforms.ambientLightColor.needsUpdate = value; - uniforms.lightProbe.needsUpdate = value; - uniforms.directionalLights.needsUpdate = value; - uniforms.directionalLightShadows.needsUpdate = value; - uniforms.pointLights.needsUpdate = value; - uniforms.pointLightShadows.needsUpdate = value; - uniforms.spotLights.needsUpdate = value; - uniforms.spotLightShadows.needsUpdate = value; - uniforms.rectAreaLights.needsUpdate = value; - uniforms.hemisphereLights.needsUpdate = value; - } - __name(markUniformsLightsNeedsUpdate, "markUniformsLightsNeedsUpdate"); - function materialNeedsLights(material) { - return material.isMeshLambertMaterial || material.isMeshToonMaterial || material.isMeshPhongMaterial || material.isMeshStandardMaterial || material.isShadowMaterial || material.isShaderMaterial && material.lights === true; - } - __name(materialNeedsLights, "materialNeedsLights"); - this.getActiveCubeFace = function() { - return _currentActiveCubeFace; - }; - this.getActiveMipmapLevel = function() { - return _currentActiveMipmapLevel; - }; - this.getRenderTarget = function() { - return _currentRenderTarget; - }; - this.setRenderTargetTextures = function(renderTarget, colorTexture, depthTexture) { - properties.get(renderTarget.texture).__webglTexture = colorTexture; - properties.get(renderTarget.depthTexture).__webglTexture = depthTexture; - const renderTargetProperties = properties.get(renderTarget); - renderTargetProperties.__hasExternalTextures = true; - renderTargetProperties.__autoAllocateDepthBuffer = depthTexture === void 0; - if (!renderTargetProperties.__autoAllocateDepthBuffer) { - if (extensions.has("WEBGL_multisampled_render_to_texture") === true) { - console.warn("THREE.WebGLRenderer: Render-to-texture extension was disabled because an external texture was provided"); - renderTargetProperties.__useRenderToTexture = false; - } - } - }; - this.setRenderTargetFramebuffer = function(renderTarget, defaultFramebuffer) { - const renderTargetProperties = properties.get(renderTarget); - renderTargetProperties.__webglFramebuffer = defaultFramebuffer; - renderTargetProperties.__useDefaultFramebuffer = defaultFramebuffer === void 0; - }; - this.setRenderTarget = function(renderTarget, activeCubeFace = 0, activeMipmapLevel = 0) { - _currentRenderTarget = renderTarget; - _currentActiveCubeFace = activeCubeFace; - _currentActiveMipmapLevel = activeMipmapLevel; - let useDefaultFramebuffer = true; - let framebuffer = null; - let isCube = false; - let isRenderTarget3D = false; - if (renderTarget) { - const renderTargetProperties = properties.get(renderTarget); - if (renderTargetProperties.__useDefaultFramebuffer !== void 0) { - state.bindFramebuffer(_gl.FRAMEBUFFER, null); - useDefaultFramebuffer = false; - } else if (renderTargetProperties.__webglFramebuffer === void 0) { - textures.setupRenderTarget(renderTarget); - } else if (renderTargetProperties.__hasExternalTextures) { - textures.rebindTextures(renderTarget, properties.get(renderTarget.texture).__webglTexture, properties.get(renderTarget.depthTexture).__webglTexture); - } else if (renderTarget.depthBuffer) { - const depthTexture = renderTarget.depthTexture; - if (renderTargetProperties.__boundDepthTexture !== depthTexture) { - if (depthTexture !== null && properties.has(depthTexture) && (renderTarget.width !== depthTexture.image.width || renderTarget.height !== depthTexture.image.height)) { - throw new Error("WebGLRenderTarget: Attached DepthTexture is initialized to the incorrect size."); - } - textures.setupDepthRenderbuffer(renderTarget); - } - } - const texture = renderTarget.texture; - if (texture.isData3DTexture || texture.isDataArrayTexture || texture.isCompressedArrayTexture) { - isRenderTarget3D = true; - } - const __webglFramebuffer = properties.get(renderTarget).__webglFramebuffer; - if (renderTarget.isWebGLCubeRenderTarget) { - if (Array.isArray(__webglFramebuffer[activeCubeFace])) { - framebuffer = __webglFramebuffer[activeCubeFace][activeMipmapLevel]; - } else { - framebuffer = __webglFramebuffer[activeCubeFace]; - } - isCube = true; - } else if (renderTarget.samples > 0 && textures.useMultisampledRTT(renderTarget) === false) { - framebuffer = properties.get(renderTarget).__webglMultisampledFramebuffer; - } else { - if (Array.isArray(__webglFramebuffer)) { - framebuffer = __webglFramebuffer[activeMipmapLevel]; - } else { - framebuffer = __webglFramebuffer; - } - } - _currentViewport.copy(renderTarget.viewport); - _currentScissor.copy(renderTarget.scissor); - _currentScissorTest = renderTarget.scissorTest; - } else { - _currentViewport.copy(_viewport).multiplyScalar(_pixelRatio).floor(); - _currentScissor.copy(_scissor).multiplyScalar(_pixelRatio).floor(); - _currentScissorTest = _scissorTest; - } - const framebufferBound = state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer); - if (framebufferBound && useDefaultFramebuffer) { - state.drawBuffers(renderTarget, framebuffer); - } - state.viewport(_currentViewport); - state.scissor(_currentScissor); - state.setScissorTest(_currentScissorTest); - if (isCube) { - const textureProperties = properties.get(renderTarget.texture); - _gl.framebufferTexture2D(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, _gl.TEXTURE_CUBE_MAP_POSITIVE_X + activeCubeFace, textureProperties.__webglTexture, activeMipmapLevel); - } else if (isRenderTarget3D) { - const textureProperties = properties.get(renderTarget.texture); - const layer = activeCubeFace || 0; - _gl.framebufferTextureLayer(_gl.FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, textureProperties.__webglTexture, activeMipmapLevel || 0, layer); - } - _currentMaterialId = -1; - }; - this.readRenderTargetPixels = function(renderTarget, x, y, width, height, buffer, activeCubeFaceIndex) { - if (!(renderTarget && renderTarget.isWebGLRenderTarget)) { - console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget."); - return; - } - let framebuffer = properties.get(renderTarget).__webglFramebuffer; - if (renderTarget.isWebGLCubeRenderTarget && activeCubeFaceIndex !== void 0) { - framebuffer = framebuffer[activeCubeFaceIndex]; - } - if (framebuffer) { - state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer); - try { - const texture = renderTarget.texture; - const textureFormat = texture.format; - const textureType = texture.type; - if (!capabilities.textureFormatReadable(textureFormat)) { - console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format."); - return; - } - if (!capabilities.textureTypeReadable(textureType)) { - console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type."); - return; - } - if (x >= 0 && x <= renderTarget.width - width && (y >= 0 && y <= renderTarget.height - height)) { - _gl.readPixels(x, y, width, height, utils.convert(textureFormat), utils.convert(textureType), buffer); - } - } finally { - const framebuffer2 = _currentRenderTarget !== null ? properties.get(_currentRenderTarget).__webglFramebuffer : null; - state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer2); - } - } - }; - this.readRenderTargetPixelsAsync = async function(renderTarget, x, y, width, height, buffer, activeCubeFaceIndex) { - if (!(renderTarget && renderTarget.isWebGLRenderTarget)) { - throw new Error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget."); - } - let framebuffer = properties.get(renderTarget).__webglFramebuffer; - if (renderTarget.isWebGLCubeRenderTarget && activeCubeFaceIndex !== void 0) { - framebuffer = framebuffer[activeCubeFaceIndex]; - } - if (framebuffer) { - const texture = renderTarget.texture; - const textureFormat = texture.format; - const textureType = texture.type; - if (!capabilities.textureFormatReadable(textureFormat)) { - throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: renderTarget is not in RGBA or implementation defined format."); - } - if (!capabilities.textureTypeReadable(textureType)) { - throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: renderTarget is not in UnsignedByteType or implementation defined type."); - } - if (x >= 0 && x <= renderTarget.width - width && (y >= 0 && y <= renderTarget.height - height)) { - state.bindFramebuffer(_gl.FRAMEBUFFER, framebuffer); - const glBuffer = _gl.createBuffer(); - _gl.bindBuffer(_gl.PIXEL_PACK_BUFFER, glBuffer); - _gl.bufferData(_gl.PIXEL_PACK_BUFFER, buffer.byteLength, _gl.STREAM_READ); - _gl.readPixels(x, y, width, height, utils.convert(textureFormat), utils.convert(textureType), 0); - const currFramebuffer = _currentRenderTarget !== null ? properties.get(_currentRenderTarget).__webglFramebuffer : null; - state.bindFramebuffer(_gl.FRAMEBUFFER, currFramebuffer); - const sync = _gl.fenceSync(_gl.SYNC_GPU_COMMANDS_COMPLETE, 0); - _gl.flush(); - await probeAsync(_gl, sync, 4); - _gl.bindBuffer(_gl.PIXEL_PACK_BUFFER, glBuffer); - _gl.getBufferSubData(_gl.PIXEL_PACK_BUFFER, 0, buffer); - _gl.deleteBuffer(glBuffer); - _gl.deleteSync(sync); - return buffer; - } else { - throw new Error("THREE.WebGLRenderer.readRenderTargetPixelsAsync: requested read bounds are out of range."); - } - } - }; - this.copyFramebufferToTexture = function(texture, position = null, level = 0) { - if (texture.isTexture !== true) { - warnOnce("WebGLRenderer: copyFramebufferToTexture function signature has changed."); - position = arguments[0] || null; - texture = arguments[1]; - } - const levelScale = Math.pow(2, -level); - const width = Math.floor(texture.image.width * levelScale); - const height = Math.floor(texture.image.height * levelScale); - const x = position !== null ? position.x : 0; - const y = position !== null ? position.y : 0; - textures.setTexture2D(texture, 0); - _gl.copyTexSubImage2D(_gl.TEXTURE_2D, level, 0, 0, x, y, width, height); - state.unbindTexture(); - }; - this.copyTextureToTexture = function(srcTexture, dstTexture, srcRegion = null, dstPosition = null, level = 0) { - if (srcTexture.isTexture !== true) { - warnOnce("WebGLRenderer: copyTextureToTexture function signature has changed."); - dstPosition = arguments[0] || null; - srcTexture = arguments[1]; - dstTexture = arguments[2]; - level = arguments[3] || 0; - srcRegion = null; - } - let width, height, depth2, minX, minY, minZ; - let dstX, dstY, dstZ; - const image = srcTexture.isCompressedTexture ? srcTexture.mipmaps[level] : srcTexture.image; - if (srcRegion !== null) { - width = srcRegion.max.x - srcRegion.min.x; - height = srcRegion.max.y - srcRegion.min.y; - depth2 = srcRegion.isBox3 ? srcRegion.max.z - srcRegion.min.z : 1; - minX = srcRegion.min.x; - minY = srcRegion.min.y; - minZ = srcRegion.isBox3 ? srcRegion.min.z : 0; - } else { - width = image.width; - height = image.height; - depth2 = image.depth || 1; - minX = 0; - minY = 0; - minZ = 0; - } - if (dstPosition !== null) { - dstX = dstPosition.x; - dstY = dstPosition.y; - dstZ = dstPosition.z; - } else { - dstX = 0; - dstY = 0; - dstZ = 0; - } - const glFormat = utils.convert(dstTexture.format); - const glType = utils.convert(dstTexture.type); - let glTarget; - if (dstTexture.isData3DTexture) { - textures.setTexture3D(dstTexture, 0); - glTarget = _gl.TEXTURE_3D; - } else if (dstTexture.isDataArrayTexture || dstTexture.isCompressedArrayTexture) { - textures.setTexture2DArray(dstTexture, 0); - glTarget = _gl.TEXTURE_2D_ARRAY; - } else { - textures.setTexture2D(dstTexture, 0); - glTarget = _gl.TEXTURE_2D; - } - _gl.pixelStorei(_gl.UNPACK_FLIP_Y_WEBGL, dstTexture.flipY); - _gl.pixelStorei(_gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, dstTexture.premultiplyAlpha); - _gl.pixelStorei(_gl.UNPACK_ALIGNMENT, dstTexture.unpackAlignment); - const currentUnpackRowLen = _gl.getParameter(_gl.UNPACK_ROW_LENGTH); - const currentUnpackImageHeight = _gl.getParameter(_gl.UNPACK_IMAGE_HEIGHT); - const currentUnpackSkipPixels = _gl.getParameter(_gl.UNPACK_SKIP_PIXELS); - const currentUnpackSkipRows = _gl.getParameter(_gl.UNPACK_SKIP_ROWS); - const currentUnpackSkipImages = _gl.getParameter(_gl.UNPACK_SKIP_IMAGES); - _gl.pixelStorei(_gl.UNPACK_ROW_LENGTH, image.width); - _gl.pixelStorei(_gl.UNPACK_IMAGE_HEIGHT, image.height); - _gl.pixelStorei(_gl.UNPACK_SKIP_PIXELS, minX); - _gl.pixelStorei(_gl.UNPACK_SKIP_ROWS, minY); - _gl.pixelStorei(_gl.UNPACK_SKIP_IMAGES, minZ); - const isSrc3D = srcTexture.isDataArrayTexture || srcTexture.isData3DTexture; - const isDst3D = dstTexture.isDataArrayTexture || dstTexture.isData3DTexture; - if (srcTexture.isRenderTargetTexture || srcTexture.isDepthTexture) { - const srcTextureProperties = properties.get(srcTexture); - const dstTextureProperties = properties.get(dstTexture); - const srcRenderTargetProperties = properties.get(srcTextureProperties.__renderTarget); - const dstRenderTargetProperties = properties.get(dstTextureProperties.__renderTarget); - state.bindFramebuffer(_gl.READ_FRAMEBUFFER, srcRenderTargetProperties.__webglFramebuffer); - state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, dstRenderTargetProperties.__webglFramebuffer); - for (let i = 0; i < depth2; i++) { - if (isSrc3D) { - _gl.framebufferTextureLayer(_gl.READ_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, properties.get(srcTexture).__webglTexture, level, minZ + i); - } - if (srcTexture.isDepthTexture) { - if (isDst3D) { - _gl.framebufferTextureLayer(_gl.DRAW_FRAMEBUFFER, _gl.COLOR_ATTACHMENT0, properties.get(dstTexture).__webglTexture, level, dstZ + i); - } - _gl.blitFramebuffer(minX, minY, width, height, dstX, dstY, width, height, _gl.DEPTH_BUFFER_BIT, _gl.NEAREST); - } else if (isDst3D) { - _gl.copyTexSubImage3D(glTarget, level, dstX, dstY, dstZ + i, minX, minY, width, height); - } else { - _gl.copyTexSubImage2D(glTarget, level, dstX, dstY, dstZ + i, minX, minY, width, height); - } - } - state.bindFramebuffer(_gl.READ_FRAMEBUFFER, null); - state.bindFramebuffer(_gl.DRAW_FRAMEBUFFER, null); - } else { - if (isDst3D) { - if (srcTexture.isDataTexture || srcTexture.isData3DTexture) { - _gl.texSubImage3D(glTarget, level, dstX, dstY, dstZ, width, height, depth2, glFormat, glType, image.data); - } else if (dstTexture.isCompressedArrayTexture) { - _gl.compressedTexSubImage3D(glTarget, level, dstX, dstY, dstZ, width, height, depth2, glFormat, image.data); - } else { - _gl.texSubImage3D(glTarget, level, dstX, dstY, dstZ, width, height, depth2, glFormat, glType, image); - } - } else { - if (srcTexture.isDataTexture) { - _gl.texSubImage2D(_gl.TEXTURE_2D, level, dstX, dstY, width, height, glFormat, glType, image.data); - } else if (srcTexture.isCompressedTexture) { - _gl.compressedTexSubImage2D(_gl.TEXTURE_2D, level, dstX, dstY, image.width, image.height, glFormat, image.data); - } else { - _gl.texSubImage2D(_gl.TEXTURE_2D, level, dstX, dstY, width, height, glFormat, glType, image); - } - } - } - _gl.pixelStorei(_gl.UNPACK_ROW_LENGTH, currentUnpackRowLen); - _gl.pixelStorei(_gl.UNPACK_IMAGE_HEIGHT, currentUnpackImageHeight); - _gl.pixelStorei(_gl.UNPACK_SKIP_PIXELS, currentUnpackSkipPixels); - _gl.pixelStorei(_gl.UNPACK_SKIP_ROWS, currentUnpackSkipRows); - _gl.pixelStorei(_gl.UNPACK_SKIP_IMAGES, currentUnpackSkipImages); - if (level === 0 && dstTexture.generateMipmaps) { - _gl.generateMipmap(glTarget); - } - state.unbindTexture(); - }; - this.copyTextureToTexture3D = function(srcTexture, dstTexture, srcRegion = null, dstPosition = null, level = 0) { - if (srcTexture.isTexture !== true) { - warnOnce("WebGLRenderer: copyTextureToTexture3D function signature has changed."); - srcRegion = arguments[0] || null; - dstPosition = arguments[1] || null; - srcTexture = arguments[2]; - dstTexture = arguments[3]; - level = arguments[4] || 0; - } - warnOnce('WebGLRenderer: copyTextureToTexture3D function has been deprecated. Use "copyTextureToTexture" instead.'); - return this.copyTextureToTexture(srcTexture, dstTexture, srcRegion, dstPosition, level); - }; - this.initRenderTarget = function(target) { - if (properties.get(target).__webglFramebuffer === void 0) { - textures.setupRenderTarget(target); - } - }; - this.initTexture = function(texture) { - if (texture.isCubeTexture) { - textures.setTextureCube(texture, 0); - } else if (texture.isData3DTexture) { - textures.setTexture3D(texture, 0); - } else if (texture.isDataArrayTexture || texture.isCompressedArrayTexture) { - textures.setTexture2DArray(texture, 0); - } else { - textures.setTexture2D(texture, 0); - } - state.unbindTexture(); - }; - this.resetState = function() { - _currentActiveCubeFace = 0; - _currentActiveMipmapLevel = 0; - _currentRenderTarget = null; - state.reset(); - bindingStates.reset(); - }; - if (typeof __THREE_DEVTOOLS__ !== "undefined") { - __THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe", { detail: this })); - } - } - get coordinateSystem() { - return WebGLCoordinateSystem; - } - get outputColorSpace() { - return this._outputColorSpace; - } - set outputColorSpace(colorSpace) { - this._outputColorSpace = colorSpace; - const gl = this.getContext(); - gl.drawingBufferColorspace = ColorManagement._getDrawingBufferColorSpace(colorSpace); - gl.unpackColorSpace = ColorManagement._getUnpackColorSpace(); - } -} -class FogExp2 { - static { - __name(this, "FogExp2"); - } - constructor(color, density = 25e-5) { - this.isFogExp2 = true; - this.name = ""; - this.color = new Color(color); - this.density = density; - } - clone() { - return new FogExp2(this.color, this.density); - } - toJSON() { - return { - type: "FogExp2", - name: this.name, - color: this.color.getHex(), - density: this.density - }; - } -} -class Fog { - static { - __name(this, "Fog"); - } - constructor(color, near = 1, far = 1e3) { - this.isFog = true; - this.name = ""; - this.color = new Color(color); - this.near = near; - this.far = far; - } - clone() { - return new Fog(this.color, this.near, this.far); - } - toJSON() { - return { - type: "Fog", - name: this.name, - color: this.color.getHex(), - near: this.near, - far: this.far - }; - } -} -class Scene extends Object3D { - static { - __name(this, "Scene"); - } - constructor() { - super(); - this.isScene = true; - this.type = "Scene"; - this.background = null; - this.environment = null; - this.fog = null; - this.backgroundBlurriness = 0; - this.backgroundIntensity = 1; - this.backgroundRotation = new Euler(); - this.environmentIntensity = 1; - this.environmentRotation = new Euler(); - this.overrideMaterial = null; - if (typeof __THREE_DEVTOOLS__ !== "undefined") { - __THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe", { detail: this })); - } - } - copy(source, recursive) { - super.copy(source, recursive); - if (source.background !== null) this.background = source.background.clone(); - if (source.environment !== null) this.environment = source.environment.clone(); - if (source.fog !== null) this.fog = source.fog.clone(); - this.backgroundBlurriness = source.backgroundBlurriness; - this.backgroundIntensity = source.backgroundIntensity; - this.backgroundRotation.copy(source.backgroundRotation); - this.environmentIntensity = source.environmentIntensity; - this.environmentRotation.copy(source.environmentRotation); - if (source.overrideMaterial !== null) this.overrideMaterial = source.overrideMaterial.clone(); - this.matrixAutoUpdate = source.matrixAutoUpdate; - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - if (this.fog !== null) data.object.fog = this.fog.toJSON(); - if (this.backgroundBlurriness > 0) data.object.backgroundBlurriness = this.backgroundBlurriness; - if (this.backgroundIntensity !== 1) data.object.backgroundIntensity = this.backgroundIntensity; - data.object.backgroundRotation = this.backgroundRotation.toArray(); - if (this.environmentIntensity !== 1) data.object.environmentIntensity = this.environmentIntensity; - data.object.environmentRotation = this.environmentRotation.toArray(); - return data; - } -} -class InterleavedBuffer { - static { - __name(this, "InterleavedBuffer"); - } - constructor(array, stride) { - this.isInterleavedBuffer = true; - this.array = array; - this.stride = stride; - this.count = array !== void 0 ? array.length / stride : 0; - this.usage = StaticDrawUsage; - this.updateRanges = []; - this.version = 0; - this.uuid = generateUUID(); - } - onUploadCallback() { - } - set needsUpdate(value) { - if (value === true) this.version++; - } - setUsage(value) { - this.usage = value; - return this; - } - addUpdateRange(start, count) { - this.updateRanges.push({ start, count }); - } - clearUpdateRanges() { - this.updateRanges.length = 0; - } - copy(source) { - this.array = new source.array.constructor(source.array); - this.count = source.count; - this.stride = source.stride; - this.usage = source.usage; - return this; - } - copyAt(index1, attribute, index2) { - index1 *= this.stride; - index2 *= attribute.stride; - for (let i = 0, l = this.stride; i < l; i++) { - this.array[index1 + i] = attribute.array[index2 + i]; - } - return this; - } - set(value, offset = 0) { - this.array.set(value, offset); - return this; - } - clone(data) { - if (data.arrayBuffers === void 0) { - data.arrayBuffers = {}; - } - if (this.array.buffer._uuid === void 0) { - this.array.buffer._uuid = generateUUID(); - } - if (data.arrayBuffers[this.array.buffer._uuid] === void 0) { - data.arrayBuffers[this.array.buffer._uuid] = this.array.slice(0).buffer; - } - const array = new this.array.constructor(data.arrayBuffers[this.array.buffer._uuid]); - const ib = new this.constructor(array, this.stride); - ib.setUsage(this.usage); - return ib; - } - onUpload(callback) { - this.onUploadCallback = callback; - return this; - } - toJSON(data) { - if (data.arrayBuffers === void 0) { - data.arrayBuffers = {}; - } - if (this.array.buffer._uuid === void 0) { - this.array.buffer._uuid = generateUUID(); - } - if (data.arrayBuffers[this.array.buffer._uuid] === void 0) { - data.arrayBuffers[this.array.buffer._uuid] = Array.from(new Uint32Array(this.array.buffer)); - } - return { - uuid: this.uuid, - buffer: this.array.buffer._uuid, - type: this.array.constructor.name, - stride: this.stride - }; - } -} -const _vector$6 = /* @__PURE__ */ new Vector3(); -class InterleavedBufferAttribute { - static { - __name(this, "InterleavedBufferAttribute"); - } - constructor(interleavedBuffer, itemSize, offset, normalized = false) { - this.isInterleavedBufferAttribute = true; - this.name = ""; - this.data = interleavedBuffer; - this.itemSize = itemSize; - this.offset = offset; - this.normalized = normalized; - } - get count() { - return this.data.count; - } - get array() { - return this.data.array; - } - set needsUpdate(value) { - this.data.needsUpdate = value; - } - applyMatrix4(m) { - for (let i = 0, l = this.data.count; i < l; i++) { - _vector$6.fromBufferAttribute(this, i); - _vector$6.applyMatrix4(m); - this.setXYZ(i, _vector$6.x, _vector$6.y, _vector$6.z); - } - return this; - } - applyNormalMatrix(m) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$6.fromBufferAttribute(this, i); - _vector$6.applyNormalMatrix(m); - this.setXYZ(i, _vector$6.x, _vector$6.y, _vector$6.z); - } - return this; - } - transformDirection(m) { - for (let i = 0, l = this.count; i < l; i++) { - _vector$6.fromBufferAttribute(this, i); - _vector$6.transformDirection(m); - this.setXYZ(i, _vector$6.x, _vector$6.y, _vector$6.z); - } - return this; - } - getComponent(index, component) { - let value = this.array[index * this.data.stride + this.offset + component]; - if (this.normalized) value = denormalize(value, this.array); - return value; - } - setComponent(index, component, value) { - if (this.normalized) value = normalize(value, this.array); - this.data.array[index * this.data.stride + this.offset + component] = value; - return this; - } - setX(index, x) { - if (this.normalized) x = normalize(x, this.array); - this.data.array[index * this.data.stride + this.offset] = x; - return this; - } - setY(index, y) { - if (this.normalized) y = normalize(y, this.array); - this.data.array[index * this.data.stride + this.offset + 1] = y; - return this; - } - setZ(index, z) { - if (this.normalized) z = normalize(z, this.array); - this.data.array[index * this.data.stride + this.offset + 2] = z; - return this; - } - setW(index, w) { - if (this.normalized) w = normalize(w, this.array); - this.data.array[index * this.data.stride + this.offset + 3] = w; - return this; - } - getX(index) { - let x = this.data.array[index * this.data.stride + this.offset]; - if (this.normalized) x = denormalize(x, this.array); - return x; - } - getY(index) { - let y = this.data.array[index * this.data.stride + this.offset + 1]; - if (this.normalized) y = denormalize(y, this.array); - return y; - } - getZ(index) { - let z = this.data.array[index * this.data.stride + this.offset + 2]; - if (this.normalized) z = denormalize(z, this.array); - return z; - } - getW(index) { - let w = this.data.array[index * this.data.stride + this.offset + 3]; - if (this.normalized) w = denormalize(w, this.array); - return w; - } - setXY(index, x, y) { - index = index * this.data.stride + this.offset; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - } - this.data.array[index + 0] = x; - this.data.array[index + 1] = y; - return this; - } - setXYZ(index, x, y, z) { - index = index * this.data.stride + this.offset; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - } - this.data.array[index + 0] = x; - this.data.array[index + 1] = y; - this.data.array[index + 2] = z; - return this; - } - setXYZW(index, x, y, z, w) { - index = index * this.data.stride + this.offset; - if (this.normalized) { - x = normalize(x, this.array); - y = normalize(y, this.array); - z = normalize(z, this.array); - w = normalize(w, this.array); - } - this.data.array[index + 0] = x; - this.data.array[index + 1] = y; - this.data.array[index + 2] = z; - this.data.array[index + 3] = w; - return this; - } - clone(data) { - if (data === void 0) { - console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interleaved buffer attribute will de-interleave buffer data."); - const array = []; - for (let i = 0; i < this.count; i++) { - const index = i * this.data.stride + this.offset; - for (let j = 0; j < this.itemSize; j++) { - array.push(this.data.array[index + j]); - } - } - return new BufferAttribute(new this.array.constructor(array), this.itemSize, this.normalized); - } else { - if (data.interleavedBuffers === void 0) { - data.interleavedBuffers = {}; - } - if (data.interleavedBuffers[this.data.uuid] === void 0) { - data.interleavedBuffers[this.data.uuid] = this.data.clone(data); - } - return new InterleavedBufferAttribute(data.interleavedBuffers[this.data.uuid], this.itemSize, this.offset, this.normalized); - } - } - toJSON(data) { - if (data === void 0) { - console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interleaved buffer attribute will de-interleave buffer data."); - const array = []; - for (let i = 0; i < this.count; i++) { - const index = i * this.data.stride + this.offset; - for (let j = 0; j < this.itemSize; j++) { - array.push(this.data.array[index + j]); - } - } - return { - itemSize: this.itemSize, - type: this.array.constructor.name, - array, - normalized: this.normalized - }; - } else { - if (data.interleavedBuffers === void 0) { - data.interleavedBuffers = {}; - } - if (data.interleavedBuffers[this.data.uuid] === void 0) { - data.interleavedBuffers[this.data.uuid] = this.data.toJSON(data); - } - return { - isInterleavedBufferAttribute: true, - itemSize: this.itemSize, - data: this.data.uuid, - offset: this.offset, - normalized: this.normalized - }; - } - } -} -class SpriteMaterial extends Material { - static { - __name(this, "SpriteMaterial"); - } - static get type() { - return "SpriteMaterial"; - } - constructor(parameters) { - super(); - this.isSpriteMaterial = true; - this.color = new Color(16777215); - this.map = null; - this.alphaMap = null; - this.rotation = 0; - this.sizeAttenuation = true; - this.transparent = true; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.alphaMap = source.alphaMap; - this.rotation = source.rotation; - this.sizeAttenuation = source.sizeAttenuation; - this.fog = source.fog; - return this; - } -} -let _geometry; -const _intersectPoint = /* @__PURE__ */ new Vector3(); -const _worldScale = /* @__PURE__ */ new Vector3(); -const _mvPosition = /* @__PURE__ */ new Vector3(); -const _alignedPosition = /* @__PURE__ */ new Vector2(); -const _rotatedPosition = /* @__PURE__ */ new Vector2(); -const _viewWorldMatrix = /* @__PURE__ */ new Matrix4(); -const _vA$2 = /* @__PURE__ */ new Vector3(); -const _vB$2 = /* @__PURE__ */ new Vector3(); -const _vC$2 = /* @__PURE__ */ new Vector3(); -const _uvA = /* @__PURE__ */ new Vector2(); -const _uvB = /* @__PURE__ */ new Vector2(); -const _uvC = /* @__PURE__ */ new Vector2(); -class Sprite extends Object3D { - static { - __name(this, "Sprite"); - } - constructor(material = new SpriteMaterial()) { - super(); - this.isSprite = true; - this.type = "Sprite"; - if (_geometry === void 0) { - _geometry = new BufferGeometry(); - const float32Array = new Float32Array([ - -0.5, - -0.5, - 0, - 0, - 0, - 0.5, - -0.5, - 0, - 1, - 0, - 0.5, - 0.5, - 0, - 1, - 1, - -0.5, - 0.5, - 0, - 0, - 1 - ]); - const interleavedBuffer = new InterleavedBuffer(float32Array, 5); - _geometry.setIndex([0, 1, 2, 0, 2, 3]); - _geometry.setAttribute("position", new InterleavedBufferAttribute(interleavedBuffer, 3, 0, false)); - _geometry.setAttribute("uv", new InterleavedBufferAttribute(interleavedBuffer, 2, 3, false)); - } - this.geometry = _geometry; - this.material = material; - this.center = new Vector2(0.5, 0.5); - } - raycast(raycaster, intersects2) { - if (raycaster.camera === null) { - console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'); - } - _worldScale.setFromMatrixScale(this.matrixWorld); - _viewWorldMatrix.copy(raycaster.camera.matrixWorld); - this.modelViewMatrix.multiplyMatrices(raycaster.camera.matrixWorldInverse, this.matrixWorld); - _mvPosition.setFromMatrixPosition(this.modelViewMatrix); - if (raycaster.camera.isPerspectiveCamera && this.material.sizeAttenuation === false) { - _worldScale.multiplyScalar(-_mvPosition.z); - } - const rotation = this.material.rotation; - let sin, cos; - if (rotation !== 0) { - cos = Math.cos(rotation); - sin = Math.sin(rotation); - } - const center = this.center; - transformVertex(_vA$2.set(-0.5, -0.5, 0), _mvPosition, center, _worldScale, sin, cos); - transformVertex(_vB$2.set(0.5, -0.5, 0), _mvPosition, center, _worldScale, sin, cos); - transformVertex(_vC$2.set(0.5, 0.5, 0), _mvPosition, center, _worldScale, sin, cos); - _uvA.set(0, 0); - _uvB.set(1, 0); - _uvC.set(1, 1); - let intersect2 = raycaster.ray.intersectTriangle(_vA$2, _vB$2, _vC$2, false, _intersectPoint); - if (intersect2 === null) { - transformVertex(_vB$2.set(-0.5, 0.5, 0), _mvPosition, center, _worldScale, sin, cos); - _uvB.set(0, 1); - intersect2 = raycaster.ray.intersectTriangle(_vA$2, _vC$2, _vB$2, false, _intersectPoint); - if (intersect2 === null) { - return; - } - } - const distance = raycaster.ray.origin.distanceTo(_intersectPoint); - if (distance < raycaster.near || distance > raycaster.far) return; - intersects2.push({ - distance, - point: _intersectPoint.clone(), - uv: Triangle.getInterpolation(_intersectPoint, _vA$2, _vB$2, _vC$2, _uvA, _uvB, _uvC, new Vector2()), - face: null, - object: this - }); - } - copy(source, recursive) { - super.copy(source, recursive); - if (source.center !== void 0) this.center.copy(source.center); - this.material = source.material; - return this; - } -} -function transformVertex(vertexPosition, mvPosition, center, scale, sin, cos) { - _alignedPosition.subVectors(vertexPosition, center).addScalar(0.5).multiply(scale); - if (sin !== void 0) { - _rotatedPosition.x = cos * _alignedPosition.x - sin * _alignedPosition.y; - _rotatedPosition.y = sin * _alignedPosition.x + cos * _alignedPosition.y; - } else { - _rotatedPosition.copy(_alignedPosition); - } - vertexPosition.copy(mvPosition); - vertexPosition.x += _rotatedPosition.x; - vertexPosition.y += _rotatedPosition.y; - vertexPosition.applyMatrix4(_viewWorldMatrix); -} -__name(transformVertex, "transformVertex"); -const _v1$2 = /* @__PURE__ */ new Vector3(); -const _v2$1 = /* @__PURE__ */ new Vector3(); -class LOD extends Object3D { - static { - __name(this, "LOD"); - } - constructor() { - super(); - this._currentLevel = 0; - this.type = "LOD"; - Object.defineProperties(this, { - levels: { - enumerable: true, - value: [] - }, - isLOD: { - value: true - } - }); - this.autoUpdate = true; - } - copy(source) { - super.copy(source, false); - const levels = source.levels; - for (let i = 0, l = levels.length; i < l; i++) { - const level = levels[i]; - this.addLevel(level.object.clone(), level.distance, level.hysteresis); - } - this.autoUpdate = source.autoUpdate; - return this; - } - addLevel(object, distance = 0, hysteresis = 0) { - distance = Math.abs(distance); - const levels = this.levels; - let l; - for (l = 0; l < levels.length; l++) { - if (distance < levels[l].distance) { - break; - } - } - levels.splice(l, 0, { distance, hysteresis, object }); - this.add(object); - return this; - } - removeLevel(distance) { - const levels = this.levels; - for (let i = 0; i < levels.length; i++) { - if (levels[i].distance === distance) { - const removedElements = levels.splice(i, 1); - this.remove(removedElements[0].object); - return true; - } - } - return false; - } - getCurrentLevel() { - return this._currentLevel; - } - getObjectForDistance(distance) { - const levels = this.levels; - if (levels.length > 0) { - let i, l; - for (i = 1, l = levels.length; i < l; i++) { - let levelDistance = levels[i].distance; - if (levels[i].object.visible) { - levelDistance -= levelDistance * levels[i].hysteresis; - } - if (distance < levelDistance) { - break; - } - } - return levels[i - 1].object; - } - return null; - } - raycast(raycaster, intersects2) { - const levels = this.levels; - if (levels.length > 0) { - _v1$2.setFromMatrixPosition(this.matrixWorld); - const distance = raycaster.ray.origin.distanceTo(_v1$2); - this.getObjectForDistance(distance).raycast(raycaster, intersects2); - } - } - update(camera) { - const levels = this.levels; - if (levels.length > 1) { - _v1$2.setFromMatrixPosition(camera.matrixWorld); - _v2$1.setFromMatrixPosition(this.matrixWorld); - const distance = _v1$2.distanceTo(_v2$1) / camera.zoom; - levels[0].object.visible = true; - let i, l; - for (i = 1, l = levels.length; i < l; i++) { - let levelDistance = levels[i].distance; - if (levels[i].object.visible) { - levelDistance -= levelDistance * levels[i].hysteresis; - } - if (distance >= levelDistance) { - levels[i - 1].object.visible = false; - levels[i].object.visible = true; - } else { - break; - } - } - this._currentLevel = i - 1; - for (; i < l; i++) { - levels[i].object.visible = false; - } - } - } - toJSON(meta) { - const data = super.toJSON(meta); - if (this.autoUpdate === false) data.object.autoUpdate = false; - data.object.levels = []; - const levels = this.levels; - for (let i = 0, l = levels.length; i < l; i++) { - const level = levels[i]; - data.object.levels.push({ - object: level.object.uuid, - distance: level.distance, - hysteresis: level.hysteresis - }); - } - return data; - } -} -const _basePosition = /* @__PURE__ */ new Vector3(); -const _skinIndex = /* @__PURE__ */ new Vector4(); -const _skinWeight = /* @__PURE__ */ new Vector4(); -const _vector3 = /* @__PURE__ */ new Vector3(); -const _matrix4 = /* @__PURE__ */ new Matrix4(); -const _vertex = /* @__PURE__ */ new Vector3(); -const _sphere$4 = /* @__PURE__ */ new Sphere(); -const _inverseMatrix$2 = /* @__PURE__ */ new Matrix4(); -const _ray$2 = /* @__PURE__ */ new Ray(); -class SkinnedMesh extends Mesh { - static { - __name(this, "SkinnedMesh"); - } - constructor(geometry, material) { - super(geometry, material); - this.isSkinnedMesh = true; - this.type = "SkinnedMesh"; - this.bindMode = AttachedBindMode; - this.bindMatrix = new Matrix4(); - this.bindMatrixInverse = new Matrix4(); - this.boundingBox = null; - this.boundingSphere = null; - } - computeBoundingBox() { - const geometry = this.geometry; - if (this.boundingBox === null) { - this.boundingBox = new Box3(); - } - this.boundingBox.makeEmpty(); - const positionAttribute = geometry.getAttribute("position"); - for (let i = 0; i < positionAttribute.count; i++) { - this.getVertexPosition(i, _vertex); - this.boundingBox.expandByPoint(_vertex); - } - } - computeBoundingSphere() { - const geometry = this.geometry; - if (this.boundingSphere === null) { - this.boundingSphere = new Sphere(); - } - this.boundingSphere.makeEmpty(); - const positionAttribute = geometry.getAttribute("position"); - for (let i = 0; i < positionAttribute.count; i++) { - this.getVertexPosition(i, _vertex); - this.boundingSphere.expandByPoint(_vertex); - } - } - copy(source, recursive) { - super.copy(source, recursive); - this.bindMode = source.bindMode; - this.bindMatrix.copy(source.bindMatrix); - this.bindMatrixInverse.copy(source.bindMatrixInverse); - this.skeleton = source.skeleton; - if (source.boundingBox !== null) this.boundingBox = source.boundingBox.clone(); - if (source.boundingSphere !== null) this.boundingSphere = source.boundingSphere.clone(); - return this; - } - raycast(raycaster, intersects2) { - const material = this.material; - const matrixWorld = this.matrixWorld; - if (material === void 0) return; - if (this.boundingSphere === null) this.computeBoundingSphere(); - _sphere$4.copy(this.boundingSphere); - _sphere$4.applyMatrix4(matrixWorld); - if (raycaster.ray.intersectsSphere(_sphere$4) === false) return; - _inverseMatrix$2.copy(matrixWorld).invert(); - _ray$2.copy(raycaster.ray).applyMatrix4(_inverseMatrix$2); - if (this.boundingBox !== null) { - if (_ray$2.intersectsBox(this.boundingBox) === false) return; - } - this._computeIntersections(raycaster, intersects2, _ray$2); - } - getVertexPosition(index, target) { - super.getVertexPosition(index, target); - this.applyBoneTransform(index, target); - return target; - } - bind(skeleton, bindMatrix) { - this.skeleton = skeleton; - if (bindMatrix === void 0) { - this.updateMatrixWorld(true); - this.skeleton.calculateInverses(); - bindMatrix = this.matrixWorld; - } - this.bindMatrix.copy(bindMatrix); - this.bindMatrixInverse.copy(bindMatrix).invert(); - } - pose() { - this.skeleton.pose(); - } - normalizeSkinWeights() { - const vector = new Vector4(); - const skinWeight = this.geometry.attributes.skinWeight; - for (let i = 0, l = skinWeight.count; i < l; i++) { - vector.fromBufferAttribute(skinWeight, i); - const scale = 1 / vector.manhattanLength(); - if (scale !== Infinity) { - vector.multiplyScalar(scale); - } else { - vector.set(1, 0, 0, 0); - } - skinWeight.setXYZW(i, vector.x, vector.y, vector.z, vector.w); - } - } - updateMatrixWorld(force) { - super.updateMatrixWorld(force); - if (this.bindMode === AttachedBindMode) { - this.bindMatrixInverse.copy(this.matrixWorld).invert(); - } else if (this.bindMode === DetachedBindMode) { - this.bindMatrixInverse.copy(this.bindMatrix).invert(); - } else { - console.warn("THREE.SkinnedMesh: Unrecognized bindMode: " + this.bindMode); - } - } - applyBoneTransform(index, vector) { - const skeleton = this.skeleton; - const geometry = this.geometry; - _skinIndex.fromBufferAttribute(geometry.attributes.skinIndex, index); - _skinWeight.fromBufferAttribute(geometry.attributes.skinWeight, index); - _basePosition.copy(vector).applyMatrix4(this.bindMatrix); - vector.set(0, 0, 0); - for (let i = 0; i < 4; i++) { - const weight = _skinWeight.getComponent(i); - if (weight !== 0) { - const boneIndex = _skinIndex.getComponent(i); - _matrix4.multiplyMatrices(skeleton.bones[boneIndex].matrixWorld, skeleton.boneInverses[boneIndex]); - vector.addScaledVector(_vector3.copy(_basePosition).applyMatrix4(_matrix4), weight); - } - } - return vector.applyMatrix4(this.bindMatrixInverse); - } -} -class Bone extends Object3D { - static { - __name(this, "Bone"); - } - constructor() { - super(); - this.isBone = true; - this.type = "Bone"; - } -} -class DataTexture extends Texture { - static { - __name(this, "DataTexture"); - } - constructor(data = null, width = 1, height = 1, format, type, mapping, wrapS, wrapT, magFilter = NearestFilter, minFilter = NearestFilter, anisotropy, colorSpace) { - super(null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, colorSpace); - this.isDataTexture = true; - this.image = { data, width, height }; - this.generateMipmaps = false; - this.flipY = false; - this.unpackAlignment = 1; - } -} -const _offsetMatrix = /* @__PURE__ */ new Matrix4(); -const _identityMatrix$1 = /* @__PURE__ */ new Matrix4(); -class Skeleton { - static { - __name(this, "Skeleton"); - } - constructor(bones = [], boneInverses = []) { - this.uuid = generateUUID(); - this.bones = bones.slice(0); - this.boneInverses = boneInverses; - this.boneMatrices = null; - this.boneTexture = null; - this.init(); - } - init() { - const bones = this.bones; - const boneInverses = this.boneInverses; - this.boneMatrices = new Float32Array(bones.length * 16); - if (boneInverses.length === 0) { - this.calculateInverses(); - } else { - if (bones.length !== boneInverses.length) { - console.warn("THREE.Skeleton: Number of inverse bone matrices does not match amount of bones."); - this.boneInverses = []; - for (let i = 0, il = this.bones.length; i < il; i++) { - this.boneInverses.push(new Matrix4()); - } - } - } - } - calculateInverses() { - this.boneInverses.length = 0; - for (let i = 0, il = this.bones.length; i < il; i++) { - const inverse = new Matrix4(); - if (this.bones[i]) { - inverse.copy(this.bones[i].matrixWorld).invert(); - } - this.boneInverses.push(inverse); - } - } - pose() { - for (let i = 0, il = this.bones.length; i < il; i++) { - const bone = this.bones[i]; - if (bone) { - bone.matrixWorld.copy(this.boneInverses[i]).invert(); - } - } - for (let i = 0, il = this.bones.length; i < il; i++) { - const bone = this.bones[i]; - if (bone) { - if (bone.parent && bone.parent.isBone) { - bone.matrix.copy(bone.parent.matrixWorld).invert(); - bone.matrix.multiply(bone.matrixWorld); - } else { - bone.matrix.copy(bone.matrixWorld); - } - bone.matrix.decompose(bone.position, bone.quaternion, bone.scale); - } - } - } - update() { - const bones = this.bones; - const boneInverses = this.boneInverses; - const boneMatrices = this.boneMatrices; - const boneTexture = this.boneTexture; - for (let i = 0, il = bones.length; i < il; i++) { - const matrix = bones[i] ? bones[i].matrixWorld : _identityMatrix$1; - _offsetMatrix.multiplyMatrices(matrix, boneInverses[i]); - _offsetMatrix.toArray(boneMatrices, i * 16); - } - if (boneTexture !== null) { - boneTexture.needsUpdate = true; - } - } - clone() { - return new Skeleton(this.bones, this.boneInverses); - } - computeBoneTexture() { - let size = Math.sqrt(this.bones.length * 4); - size = Math.ceil(size / 4) * 4; - size = Math.max(size, 4); - const boneMatrices = new Float32Array(size * size * 4); - boneMatrices.set(this.boneMatrices); - const boneTexture = new DataTexture(boneMatrices, size, size, RGBAFormat, FloatType); - boneTexture.needsUpdate = true; - this.boneMatrices = boneMatrices; - this.boneTexture = boneTexture; - return this; - } - getBoneByName(name) { - for (let i = 0, il = this.bones.length; i < il; i++) { - const bone = this.bones[i]; - if (bone.name === name) { - return bone; - } - } - return void 0; - } - dispose() { - if (this.boneTexture !== null) { - this.boneTexture.dispose(); - this.boneTexture = null; - } - } - fromJSON(json, bones) { - this.uuid = json.uuid; - for (let i = 0, l = json.bones.length; i < l; i++) { - const uuid = json.bones[i]; - let bone = bones[uuid]; - if (bone === void 0) { - console.warn("THREE.Skeleton: No bone found with UUID:", uuid); - bone = new Bone(); - } - this.bones.push(bone); - this.boneInverses.push(new Matrix4().fromArray(json.boneInverses[i])); - } - this.init(); - return this; - } - toJSON() { - const data = { - metadata: { - version: 4.6, - type: "Skeleton", - generator: "Skeleton.toJSON" - }, - bones: [], - boneInverses: [] - }; - data.uuid = this.uuid; - const bones = this.bones; - const boneInverses = this.boneInverses; - for (let i = 0, l = bones.length; i < l; i++) { - const bone = bones[i]; - data.bones.push(bone.uuid); - const boneInverse = boneInverses[i]; - data.boneInverses.push(boneInverse.toArray()); - } - return data; - } -} -class InstancedBufferAttribute extends BufferAttribute { - static { - __name(this, "InstancedBufferAttribute"); - } - constructor(array, itemSize, normalized, meshPerAttribute = 1) { - super(array, itemSize, normalized); - this.isInstancedBufferAttribute = true; - this.meshPerAttribute = meshPerAttribute; - } - copy(source) { - super.copy(source); - this.meshPerAttribute = source.meshPerAttribute; - return this; - } - toJSON() { - const data = super.toJSON(); - data.meshPerAttribute = this.meshPerAttribute; - data.isInstancedBufferAttribute = true; - return data; - } -} -const _instanceLocalMatrix = /* @__PURE__ */ new Matrix4(); -const _instanceWorldMatrix = /* @__PURE__ */ new Matrix4(); -const _instanceIntersects = []; -const _box3 = /* @__PURE__ */ new Box3(); -const _identity = /* @__PURE__ */ new Matrix4(); -const _mesh$1 = /* @__PURE__ */ new Mesh(); -const _sphere$3 = /* @__PURE__ */ new Sphere(); -class InstancedMesh extends Mesh { - static { - __name(this, "InstancedMesh"); - } - constructor(geometry, material, count) { - super(geometry, material); - this.isInstancedMesh = true; - this.instanceMatrix = new InstancedBufferAttribute(new Float32Array(count * 16), 16); - this.instanceColor = null; - this.morphTexture = null; - this.count = count; - this.boundingBox = null; - this.boundingSphere = null; - for (let i = 0; i < count; i++) { - this.setMatrixAt(i, _identity); - } - } - computeBoundingBox() { - const geometry = this.geometry; - const count = this.count; - if (this.boundingBox === null) { - this.boundingBox = new Box3(); - } - if (geometry.boundingBox === null) { - geometry.computeBoundingBox(); - } - this.boundingBox.makeEmpty(); - for (let i = 0; i < count; i++) { - this.getMatrixAt(i, _instanceLocalMatrix); - _box3.copy(geometry.boundingBox).applyMatrix4(_instanceLocalMatrix); - this.boundingBox.union(_box3); - } - } - computeBoundingSphere() { - const geometry = this.geometry; - const count = this.count; - if (this.boundingSphere === null) { - this.boundingSphere = new Sphere(); - } - if (geometry.boundingSphere === null) { - geometry.computeBoundingSphere(); - } - this.boundingSphere.makeEmpty(); - for (let i = 0; i < count; i++) { - this.getMatrixAt(i, _instanceLocalMatrix); - _sphere$3.copy(geometry.boundingSphere).applyMatrix4(_instanceLocalMatrix); - this.boundingSphere.union(_sphere$3); - } - } - copy(source, recursive) { - super.copy(source, recursive); - this.instanceMatrix.copy(source.instanceMatrix); - if (source.morphTexture !== null) this.morphTexture = source.morphTexture.clone(); - if (source.instanceColor !== null) this.instanceColor = source.instanceColor.clone(); - this.count = source.count; - if (source.boundingBox !== null) this.boundingBox = source.boundingBox.clone(); - if (source.boundingSphere !== null) this.boundingSphere = source.boundingSphere.clone(); - return this; - } - getColorAt(index, color) { - color.fromArray(this.instanceColor.array, index * 3); - } - getMatrixAt(index, matrix) { - matrix.fromArray(this.instanceMatrix.array, index * 16); - } - getMorphAt(index, object) { - const objectInfluences = object.morphTargetInfluences; - const array = this.morphTexture.source.data.data; - const len = objectInfluences.length + 1; - const dataIndex = index * len + 1; - for (let i = 0; i < objectInfluences.length; i++) { - objectInfluences[i] = array[dataIndex + i]; - } - } - raycast(raycaster, intersects2) { - const matrixWorld = this.matrixWorld; - const raycastTimes = this.count; - _mesh$1.geometry = this.geometry; - _mesh$1.material = this.material; - if (_mesh$1.material === void 0) return; - if (this.boundingSphere === null) this.computeBoundingSphere(); - _sphere$3.copy(this.boundingSphere); - _sphere$3.applyMatrix4(matrixWorld); - if (raycaster.ray.intersectsSphere(_sphere$3) === false) return; - for (let instanceId = 0; instanceId < raycastTimes; instanceId++) { - this.getMatrixAt(instanceId, _instanceLocalMatrix); - _instanceWorldMatrix.multiplyMatrices(matrixWorld, _instanceLocalMatrix); - _mesh$1.matrixWorld = _instanceWorldMatrix; - _mesh$1.raycast(raycaster, _instanceIntersects); - for (let i = 0, l = _instanceIntersects.length; i < l; i++) { - const intersect2 = _instanceIntersects[i]; - intersect2.instanceId = instanceId; - intersect2.object = this; - intersects2.push(intersect2); - } - _instanceIntersects.length = 0; - } - } - setColorAt(index, color) { - if (this.instanceColor === null) { - this.instanceColor = new InstancedBufferAttribute(new Float32Array(this.instanceMatrix.count * 3).fill(1), 3); - } - color.toArray(this.instanceColor.array, index * 3); - } - setMatrixAt(index, matrix) { - matrix.toArray(this.instanceMatrix.array, index * 16); - } - setMorphAt(index, object) { - const objectInfluences = object.morphTargetInfluences; - const len = objectInfluences.length + 1; - if (this.morphTexture === null) { - this.morphTexture = new DataTexture(new Float32Array(len * this.count), len, this.count, RedFormat, FloatType); - } - const array = this.morphTexture.source.data.data; - let morphInfluencesSum = 0; - for (let i = 0; i < objectInfluences.length; i++) { - morphInfluencesSum += objectInfluences[i]; - } - const morphBaseInfluence = this.geometry.morphTargetsRelative ? 1 : 1 - morphInfluencesSum; - const dataIndex = len * index; - array[dataIndex] = morphBaseInfluence; - array.set(objectInfluences, dataIndex + 1); - } - updateMorphTargets() { - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - if (this.morphTexture !== null) { - this.morphTexture.dispose(); - this.morphTexture = null; - } - return this; - } -} -function ascIdSort(a, b) { - return a - b; -} -__name(ascIdSort, "ascIdSort"); -function sortOpaque(a, b) { - return a.z - b.z; -} -__name(sortOpaque, "sortOpaque"); -function sortTransparent(a, b) { - return b.z - a.z; -} -__name(sortTransparent, "sortTransparent"); -class MultiDrawRenderList { - static { - __name(this, "MultiDrawRenderList"); - } - constructor() { - this.index = 0; - this.pool = []; - this.list = []; - } - push(start, count, z, index) { - const pool = this.pool; - const list = this.list; - if (this.index >= pool.length) { - pool.push({ - start: -1, - count: -1, - z: -1, - index: -1 - }); - } - const item = pool[this.index]; - list.push(item); - this.index++; - item.start = start; - item.count = count; - item.z = z; - item.index = index; - } - reset() { - this.list.length = 0; - this.index = 0; - } -} -const _matrix$1 = /* @__PURE__ */ new Matrix4(); -const _whiteColor = /* @__PURE__ */ new Color(1, 1, 1); -const _frustum = /* @__PURE__ */ new Frustum(); -const _box$1 = /* @__PURE__ */ new Box3(); -const _sphere$2 = /* @__PURE__ */ new Sphere(); -const _vector$5 = /* @__PURE__ */ new Vector3(); -const _forward = /* @__PURE__ */ new Vector3(); -const _temp = /* @__PURE__ */ new Vector3(); -const _renderList = /* @__PURE__ */ new MultiDrawRenderList(); -const _mesh = /* @__PURE__ */ new Mesh(); -const _batchIntersects = []; -function copyAttributeData(src, target, targetOffset = 0) { - const itemSize = target.itemSize; - if (src.isInterleavedBufferAttribute || src.array.constructor !== target.array.constructor) { - const vertexCount = src.count; - for (let i = 0; i < vertexCount; i++) { - for (let c = 0; c < itemSize; c++) { - target.setComponent(i + targetOffset, c, src.getComponent(i, c)); - } - } - } else { - target.array.set(src.array, targetOffset * itemSize); - } - target.needsUpdate = true; -} -__name(copyAttributeData, "copyAttributeData"); -function copyArrayContents(src, target) { - if (src.constructor !== target.constructor) { - const len = Math.min(src.length, target.length); - for (let i = 0; i < len; i++) { - target[i] = src[i]; - } - } else { - const len = Math.min(src.length, target.length); - target.set(new src.constructor(src.buffer, 0, len)); - } -} -__name(copyArrayContents, "copyArrayContents"); -class BatchedMesh extends Mesh { - static { - __name(this, "BatchedMesh"); - } - get maxInstanceCount() { - return this._maxInstanceCount; - } - get instanceCount() { - return this._instanceInfo.length - this._availableInstanceIds.length; - } - get unusedVertexCount() { - return this._maxVertexCount - this._nextVertexStart; - } - get unusedIndexCount() { - return this._maxIndexCount - this._nextIndexStart; - } - constructor(maxInstanceCount, maxVertexCount, maxIndexCount = maxVertexCount * 2, material) { - super(new BufferGeometry(), material); - this.isBatchedMesh = true; - this.perObjectFrustumCulled = true; - this.sortObjects = true; - this.boundingBox = null; - this.boundingSphere = null; - this.customSort = null; - this._instanceInfo = []; - this._geometryInfo = []; - this._availableInstanceIds = []; - this._availableGeometryIds = []; - this._nextIndexStart = 0; - this._nextVertexStart = 0; - this._geometryCount = 0; - this._visibilityChanged = true; - this._geometryInitialized = false; - this._maxInstanceCount = maxInstanceCount; - this._maxVertexCount = maxVertexCount; - this._maxIndexCount = maxIndexCount; - this._multiDrawCounts = new Int32Array(maxInstanceCount); - this._multiDrawStarts = new Int32Array(maxInstanceCount); - this._multiDrawCount = 0; - this._multiDrawInstances = null; - this._matricesTexture = null; - this._indirectTexture = null; - this._colorsTexture = null; - this._initMatricesTexture(); - this._initIndirectTexture(); - } - _initMatricesTexture() { - let size = Math.sqrt(this._maxInstanceCount * 4); - size = Math.ceil(size / 4) * 4; - size = Math.max(size, 4); - const matricesArray = new Float32Array(size * size * 4); - const matricesTexture = new DataTexture(matricesArray, size, size, RGBAFormat, FloatType); - this._matricesTexture = matricesTexture; - } - _initIndirectTexture() { - let size = Math.sqrt(this._maxInstanceCount); - size = Math.ceil(size); - const indirectArray = new Uint32Array(size * size); - const indirectTexture = new DataTexture(indirectArray, size, size, RedIntegerFormat, UnsignedIntType); - this._indirectTexture = indirectTexture; - } - _initColorsTexture() { - let size = Math.sqrt(this._maxInstanceCount); - size = Math.ceil(size); - const colorsArray = new Float32Array(size * size * 4).fill(1); - const colorsTexture = new DataTexture(colorsArray, size, size, RGBAFormat, FloatType); - colorsTexture.colorSpace = ColorManagement.workingColorSpace; - this._colorsTexture = colorsTexture; - } - _initializeGeometry(reference) { - const geometry = this.geometry; - const maxVertexCount = this._maxVertexCount; - const maxIndexCount = this._maxIndexCount; - if (this._geometryInitialized === false) { - for (const attributeName in reference.attributes) { - const srcAttribute = reference.getAttribute(attributeName); - const { array, itemSize, normalized } = srcAttribute; - const dstArray = new array.constructor(maxVertexCount * itemSize); - const dstAttribute = new BufferAttribute(dstArray, itemSize, normalized); - geometry.setAttribute(attributeName, dstAttribute); - } - if (reference.getIndex() !== null) { - const indexArray = maxVertexCount > 65535 ? new Uint32Array(maxIndexCount) : new Uint16Array(maxIndexCount); - geometry.setIndex(new BufferAttribute(indexArray, 1)); - } - this._geometryInitialized = true; - } - } - // Make sure the geometry is compatible with the existing combined geometry attributes - _validateGeometry(geometry) { - const batchGeometry = this.geometry; - if (Boolean(geometry.getIndex()) !== Boolean(batchGeometry.getIndex())) { - throw new Error('BatchedMesh: All geometries must consistently have "index".'); - } - for (const attributeName in batchGeometry.attributes) { - if (!geometry.hasAttribute(attributeName)) { - throw new Error(`BatchedMesh: Added geometry missing "${attributeName}". All geometries must have consistent attributes.`); - } - const srcAttribute = geometry.getAttribute(attributeName); - const dstAttribute = batchGeometry.getAttribute(attributeName); - if (srcAttribute.itemSize !== dstAttribute.itemSize || srcAttribute.normalized !== dstAttribute.normalized) { - throw new Error("BatchedMesh: All attributes must have a consistent itemSize and normalized value."); - } - } - } - setCustomSort(func) { - this.customSort = func; - return this; - } - computeBoundingBox() { - if (this.boundingBox === null) { - this.boundingBox = new Box3(); - } - const boundingBox = this.boundingBox; - const instanceInfo = this._instanceInfo; - boundingBox.makeEmpty(); - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (instanceInfo[i].active === false) continue; - const geometryId = instanceInfo[i].geometryIndex; - this.getMatrixAt(i, _matrix$1); - this.getBoundingBoxAt(geometryId, _box$1).applyMatrix4(_matrix$1); - boundingBox.union(_box$1); - } - } - computeBoundingSphere() { - if (this.boundingSphere === null) { - this.boundingSphere = new Sphere(); - } - const boundingSphere = this.boundingSphere; - const instanceInfo = this._instanceInfo; - boundingSphere.makeEmpty(); - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (instanceInfo[i].active === false) continue; - const geometryId = instanceInfo[i].geometryIndex; - this.getMatrixAt(i, _matrix$1); - this.getBoundingSphereAt(geometryId, _sphere$2).applyMatrix4(_matrix$1); - boundingSphere.union(_sphere$2); - } - } - addInstance(geometryId) { - const atCapacity = this._instanceInfo.length >= this.maxInstanceCount; - if (atCapacity && this._availableInstanceIds.length === 0) { - throw new Error("BatchedMesh: Maximum item count reached."); - } - const instanceInfo = { - visible: true, - active: true, - geometryIndex: geometryId - }; - let drawId = null; - if (this._availableInstanceIds.length > 0) { - this._availableInstanceIds.sort(ascIdSort); - drawId = this._availableInstanceIds.shift(); - this._instanceInfo[drawId] = instanceInfo; - } else { - drawId = this._instanceInfo.length; - this._instanceInfo.push(instanceInfo); - } - const matricesTexture = this._matricesTexture; - _matrix$1.identity().toArray(matricesTexture.image.data, drawId * 16); - matricesTexture.needsUpdate = true; - const colorsTexture = this._colorsTexture; - if (colorsTexture) { - _whiteColor.toArray(colorsTexture.image.data, drawId * 4); - colorsTexture.needsUpdate = true; - } - this._visibilityChanged = true; - return drawId; - } - addGeometry(geometry, reservedVertexCount = -1, reservedIndexCount = -1) { - this._initializeGeometry(geometry); - this._validateGeometry(geometry); - const geometryInfo = { - // geometry information - vertexStart: -1, - vertexCount: -1, - reservedVertexCount: -1, - indexStart: -1, - indexCount: -1, - reservedIndexCount: -1, - // draw range information - start: -1, - count: -1, - // state - boundingBox: null, - boundingSphere: null, - active: true - }; - const geometryInfoList = this._geometryInfo; - geometryInfo.vertexStart = this._nextVertexStart; - geometryInfo.reservedVertexCount = reservedVertexCount === -1 ? geometry.getAttribute("position").count : reservedVertexCount; - const index = geometry.getIndex(); - const hasIndex = index !== null; - if (hasIndex) { - geometryInfo.indexStart = this._nextIndexStart; - geometryInfo.reservedIndexCount = reservedIndexCount === -1 ? index.count : reservedIndexCount; - } - if (geometryInfo.indexStart !== -1 && geometryInfo.indexStart + geometryInfo.reservedIndexCount > this._maxIndexCount || geometryInfo.vertexStart + geometryInfo.reservedVertexCount > this._maxVertexCount) { - throw new Error("BatchedMesh: Reserved space request exceeds the maximum buffer size."); - } - let geometryId; - if (this._availableGeometryIds.length > 0) { - this._availableGeometryIds.sort(ascIdSort); - geometryId = this._availableGeometryIds.shift(); - geometryInfoList[geometryId] = geometryInfo; - } else { - geometryId = this._geometryCount; - this._geometryCount++; - geometryInfoList.push(geometryInfo); - } - this.setGeometryAt(geometryId, geometry); - this._nextIndexStart = geometryInfo.indexStart + geometryInfo.reservedIndexCount; - this._nextVertexStart = geometryInfo.vertexStart + geometryInfo.reservedVertexCount; - return geometryId; - } - setGeometryAt(geometryId, geometry) { - if (geometryId >= this._geometryCount) { - throw new Error("BatchedMesh: Maximum geometry count reached."); - } - this._validateGeometry(geometry); - const batchGeometry = this.geometry; - const hasIndex = batchGeometry.getIndex() !== null; - const dstIndex = batchGeometry.getIndex(); - const srcIndex = geometry.getIndex(); - const geometryInfo = this._geometryInfo[geometryId]; - if (hasIndex && srcIndex.count > geometryInfo.reservedIndexCount || geometry.attributes.position.count > geometryInfo.reservedVertexCount) { - throw new Error("BatchedMesh: Reserved space not large enough for provided geometry."); - } - const vertexStart = geometryInfo.vertexStart; - const reservedVertexCount = geometryInfo.reservedVertexCount; - geometryInfo.vertexCount = geometry.getAttribute("position").count; - for (const attributeName in batchGeometry.attributes) { - const srcAttribute = geometry.getAttribute(attributeName); - const dstAttribute = batchGeometry.getAttribute(attributeName); - copyAttributeData(srcAttribute, dstAttribute, vertexStart); - const itemSize = srcAttribute.itemSize; - for (let i = srcAttribute.count, l = reservedVertexCount; i < l; i++) { - const index = vertexStart + i; - for (let c = 0; c < itemSize; c++) { - dstAttribute.setComponent(index, c, 0); - } - } - dstAttribute.needsUpdate = true; - dstAttribute.addUpdateRange(vertexStart * itemSize, reservedVertexCount * itemSize); - } - if (hasIndex) { - const indexStart = geometryInfo.indexStart; - const reservedIndexCount = geometryInfo.reservedIndexCount; - geometryInfo.indexCount = geometry.getIndex().count; - for (let i = 0; i < srcIndex.count; i++) { - dstIndex.setX(indexStart + i, vertexStart + srcIndex.getX(i)); - } - for (let i = srcIndex.count, l = reservedIndexCount; i < l; i++) { - dstIndex.setX(indexStart + i, vertexStart); - } - dstIndex.needsUpdate = true; - dstIndex.addUpdateRange(indexStart, geometryInfo.reservedIndexCount); - } - geometryInfo.start = hasIndex ? geometryInfo.indexStart : geometryInfo.vertexStart; - geometryInfo.count = hasIndex ? geometryInfo.indexCount : geometryInfo.vertexCount; - geometryInfo.boundingBox = null; - if (geometry.boundingBox !== null) { - geometryInfo.boundingBox = geometry.boundingBox.clone(); - } - geometryInfo.boundingSphere = null; - if (geometry.boundingSphere !== null) { - geometryInfo.boundingSphere = geometry.boundingSphere.clone(); - } - this._visibilityChanged = true; - return geometryId; - } - deleteGeometry(geometryId) { - const geometryInfoList = this._geometryInfo; - if (geometryId >= geometryInfoList.length || geometryInfoList[geometryId].active === false) { - return this; - } - const instanceInfo = this._instanceInfo; - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (instanceInfo[i].geometryIndex === geometryId) { - this.deleteInstance(i); - } - } - geometryInfoList[geometryId].active = false; - this._availableGeometryIds.push(geometryId); - this._visibilityChanged = true; - return this; - } - deleteInstance(instanceId) { - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return this; - } - instanceInfo[instanceId].active = false; - this._availableInstanceIds.push(instanceId); - this._visibilityChanged = true; - return this; - } - optimize() { - let nextVertexStart = 0; - let nextIndexStart = 0; - const geometryInfoList = this._geometryInfo; - const indices = geometryInfoList.map((e, i) => i).sort((a, b) => { - return geometryInfoList[a].vertexStart - geometryInfoList[b].vertexStart; - }); - const geometry = this.geometry; - for (let i = 0, l = geometryInfoList.length; i < l; i++) { - const index = indices[i]; - const geometryInfo = geometryInfoList[index]; - if (geometryInfo.active === false) { - continue; - } - if (geometry.index !== null) { - if (geometryInfo.indexStart !== nextIndexStart) { - const { indexStart, vertexStart, reservedIndexCount } = geometryInfo; - const index2 = geometry.index; - const array = index2.array; - const elementDelta = nextVertexStart - vertexStart; - for (let j = indexStart; j < indexStart + reservedIndexCount; j++) { - array[j] = array[j] + elementDelta; - } - index2.array.copyWithin(nextIndexStart, indexStart, indexStart + reservedIndexCount); - index2.addUpdateRange(nextIndexStart, reservedIndexCount); - geometryInfo.indexStart = nextIndexStart; - } - nextIndexStart += geometryInfo.reservedIndexCount; - } - if (geometryInfo.vertexStart !== nextVertexStart) { - const { vertexStart, reservedVertexCount } = geometryInfo; - const attributes = geometry.attributes; - for (const key in attributes) { - const attribute = attributes[key]; - const { array, itemSize } = attribute; - array.copyWithin(nextVertexStart * itemSize, vertexStart * itemSize, (vertexStart + reservedVertexCount) * itemSize); - attribute.addUpdateRange(nextVertexStart * itemSize, reservedVertexCount * itemSize); - } - geometryInfo.vertexStart = nextVertexStart; - } - nextVertexStart += geometryInfo.reservedVertexCount; - geometryInfo.start = geometry.index ? geometryInfo.indexStart : geometryInfo.vertexStart; - this._nextIndexStart = geometry.index ? geometryInfo.indexStart + geometryInfo.reservedIndexCount : 0; - this._nextVertexStart = geometryInfo.vertexStart + geometryInfo.reservedVertexCount; - } - return this; - } - // get bounding box and compute it if it doesn't exist - getBoundingBoxAt(geometryId, target) { - if (geometryId >= this._geometryCount) { - return null; - } - const geometry = this.geometry; - const geometryInfo = this._geometryInfo[geometryId]; - if (geometryInfo.boundingBox === null) { - const box = new Box3(); - const index = geometry.index; - const position = geometry.attributes.position; - for (let i = geometryInfo.start, l = geometryInfo.start + geometryInfo.count; i < l; i++) { - let iv = i; - if (index) { - iv = index.getX(iv); - } - box.expandByPoint(_vector$5.fromBufferAttribute(position, iv)); - } - geometryInfo.boundingBox = box; - } - target.copy(geometryInfo.boundingBox); - return target; - } - // get bounding sphere and compute it if it doesn't exist - getBoundingSphereAt(geometryId, target) { - if (geometryId >= this._geometryCount) { - return null; - } - const geometry = this.geometry; - const geometryInfo = this._geometryInfo[geometryId]; - if (geometryInfo.boundingSphere === null) { - const sphere = new Sphere(); - this.getBoundingBoxAt(geometryId, _box$1); - _box$1.getCenter(sphere.center); - const index = geometry.index; - const position = geometry.attributes.position; - let maxRadiusSq = 0; - for (let i = geometryInfo.start, l = geometryInfo.start + geometryInfo.count; i < l; i++) { - let iv = i; - if (index) { - iv = index.getX(iv); - } - _vector$5.fromBufferAttribute(position, iv); - maxRadiusSq = Math.max(maxRadiusSq, sphere.center.distanceToSquared(_vector$5)); - } - sphere.radius = Math.sqrt(maxRadiusSq); - geometryInfo.boundingSphere = sphere; - } - target.copy(geometryInfo.boundingSphere); - return target; - } - setMatrixAt(instanceId, matrix) { - const instanceInfo = this._instanceInfo; - const matricesTexture = this._matricesTexture; - const matricesArray = this._matricesTexture.image.data; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return this; - } - matrix.toArray(matricesArray, instanceId * 16); - matricesTexture.needsUpdate = true; - return this; - } - getMatrixAt(instanceId, matrix) { - const instanceInfo = this._instanceInfo; - const matricesArray = this._matricesTexture.image.data; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return null; - } - return matrix.fromArray(matricesArray, instanceId * 16); - } - setColorAt(instanceId, color) { - if (this._colorsTexture === null) { - this._initColorsTexture(); - } - const colorsTexture = this._colorsTexture; - const colorsArray = this._colorsTexture.image.data; - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return this; - } - color.toArray(colorsArray, instanceId * 4); - colorsTexture.needsUpdate = true; - return this; - } - getColorAt(instanceId, color) { - const colorsArray = this._colorsTexture.image.data; - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return null; - } - return color.fromArray(colorsArray, instanceId * 4); - } - setVisibleAt(instanceId, value) { - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false || instanceInfo[instanceId].visible === value) { - return this; - } - instanceInfo[instanceId].visible = value; - this._visibilityChanged = true; - return this; - } - getVisibleAt(instanceId) { - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return false; - } - return instanceInfo[instanceId].visible; - } - setGeometryIdAt(instanceId, geometryId) { - const instanceInfo = this._instanceInfo; - const geometryInfoList = this._geometryInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return null; - } - if (geometryId >= geometryInfoList.length || geometryInfoList[geometryId].active === false) { - return null; - } - instanceInfo[instanceId].geometryIndex = geometryId; - return this; - } - getGeometryIdAt(instanceId) { - const instanceInfo = this._instanceInfo; - if (instanceId >= instanceInfo.length || instanceInfo[instanceId].active === false) { - return -1; - } - return instanceInfo[instanceId].geometryIndex; - } - getGeometryRangeAt(geometryId, target = {}) { - if (geometryId < 0 || geometryId >= this._geometryCount) { - return null; - } - const geometryInfo = this._geometryInfo[geometryId]; - target.vertexStart = geometryInfo.vertexStart; - target.vertexCount = geometryInfo.vertexCount; - target.reservedVertexCount = geometryInfo.reservedVertexCount; - target.indexStart = geometryInfo.indexStart; - target.indexCount = geometryInfo.indexCount; - target.reservedIndexCount = geometryInfo.reservedIndexCount; - target.start = geometryInfo.start; - target.count = geometryInfo.count; - return target; - } - setInstanceCount(maxInstanceCount) { - const availableInstanceIds = this._availableInstanceIds; - const instanceInfo = this._instanceInfo; - availableInstanceIds.sort(ascIdSort); - while (availableInstanceIds[availableInstanceIds.length - 1] === instanceInfo.length) { - instanceInfo.pop(); - availableInstanceIds.pop(); - } - if (maxInstanceCount < instanceInfo.length) { - throw new Error(`BatchedMesh: Instance ids outside the range ${maxInstanceCount} are being used. Cannot shrink instance count.`); - } - const multiDrawCounts = new Int32Array(maxInstanceCount); - const multiDrawStarts = new Int32Array(maxInstanceCount); - copyArrayContents(this._multiDrawCounts, multiDrawCounts); - copyArrayContents(this._multiDrawStarts, multiDrawStarts); - this._multiDrawCounts = multiDrawCounts; - this._multiDrawStarts = multiDrawStarts; - this._maxInstanceCount = maxInstanceCount; - const indirectTexture = this._indirectTexture; - const matricesTexture = this._matricesTexture; - const colorsTexture = this._colorsTexture; - indirectTexture.dispose(); - this._initIndirectTexture(); - copyArrayContents(indirectTexture.image.data, this._indirectTexture.image.data); - matricesTexture.dispose(); - this._initMatricesTexture(); - copyArrayContents(matricesTexture.image.data, this._matricesTexture.image.data); - if (colorsTexture) { - colorsTexture.dispose(); - this._initColorsTexture(); - copyArrayContents(colorsTexture.image.data, this._colorsTexture.image.data); - } - } - setGeometrySize(maxVertexCount, maxIndexCount) { - const validRanges = [...this._geometryInfo].filter((info) => info.active); - const requiredVertexLength = Math.max(...validRanges.map((range) => range.vertexStart + range.reservedVertexCount)); - if (requiredVertexLength > maxVertexCount) { - throw new Error(`BatchedMesh: Geometry vertex values are being used outside the range ${maxIndexCount}. Cannot shrink further.`); - } - if (this.geometry.index) { - const requiredIndexLength = Math.max(...validRanges.map((range) => range.indexStart + range.reservedIndexCount)); - if (requiredIndexLength > maxIndexCount) { - throw new Error(`BatchedMesh: Geometry index values are being used outside the range ${maxIndexCount}. Cannot shrink further.`); - } - } - const oldGeometry = this.geometry; - oldGeometry.dispose(); - this._maxVertexCount = maxVertexCount; - this._maxIndexCount = maxIndexCount; - if (this._geometryInitialized) { - this._geometryInitialized = false; - this.geometry = new BufferGeometry(); - this._initializeGeometry(oldGeometry); - } - const geometry = this.geometry; - if (oldGeometry.index) { - copyArrayContents(oldGeometry.index.array, geometry.index.array); - } - for (const key in oldGeometry.attributes) { - copyArrayContents(oldGeometry.attributes[key].array, geometry.attributes[key].array); - } - } - raycast(raycaster, intersects2) { - const instanceInfo = this._instanceInfo; - const geometryInfoList = this._geometryInfo; - const matrixWorld = this.matrixWorld; - const batchGeometry = this.geometry; - _mesh.material = this.material; - _mesh.geometry.index = batchGeometry.index; - _mesh.geometry.attributes = batchGeometry.attributes; - if (_mesh.geometry.boundingBox === null) { - _mesh.geometry.boundingBox = new Box3(); - } - if (_mesh.geometry.boundingSphere === null) { - _mesh.geometry.boundingSphere = new Sphere(); - } - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (!instanceInfo[i].visible || !instanceInfo[i].active) { - continue; - } - const geometryId = instanceInfo[i].geometryIndex; - const geometryInfo = geometryInfoList[geometryId]; - _mesh.geometry.setDrawRange(geometryInfo.start, geometryInfo.count); - this.getMatrixAt(i, _mesh.matrixWorld).premultiply(matrixWorld); - this.getBoundingBoxAt(geometryId, _mesh.geometry.boundingBox); - this.getBoundingSphereAt(geometryId, _mesh.geometry.boundingSphere); - _mesh.raycast(raycaster, _batchIntersects); - for (let j = 0, l2 = _batchIntersects.length; j < l2; j++) { - const intersect2 = _batchIntersects[j]; - intersect2.object = this; - intersect2.batchId = i; - intersects2.push(intersect2); - } - _batchIntersects.length = 0; - } - _mesh.material = null; - _mesh.geometry.index = null; - _mesh.geometry.attributes = {}; - _mesh.geometry.setDrawRange(0, Infinity); - } - copy(source) { - super.copy(source); - this.geometry = source.geometry.clone(); - this.perObjectFrustumCulled = source.perObjectFrustumCulled; - this.sortObjects = source.sortObjects; - this.boundingBox = source.boundingBox !== null ? source.boundingBox.clone() : null; - this.boundingSphere = source.boundingSphere !== null ? source.boundingSphere.clone() : null; - this._geometryInfo = source._geometryInfo.map((info) => ({ - ...info, - boundingBox: info.boundingBox !== null ? info.boundingBox.clone() : null, - boundingSphere: info.boundingSphere !== null ? info.boundingSphere.clone() : null - })); - this._instanceInfo = source._instanceInfo.map((info) => ({ ...info })); - this._maxInstanceCount = source._maxInstanceCount; - this._maxVertexCount = source._maxVertexCount; - this._maxIndexCount = source._maxIndexCount; - this._geometryInitialized = source._geometryInitialized; - this._geometryCount = source._geometryCount; - this._multiDrawCounts = source._multiDrawCounts.slice(); - this._multiDrawStarts = source._multiDrawStarts.slice(); - this._matricesTexture = source._matricesTexture.clone(); - this._matricesTexture.image.data = this._matricesTexture.image.data.slice(); - if (this._colorsTexture !== null) { - this._colorsTexture = source._colorsTexture.clone(); - this._colorsTexture.image.data = this._colorsTexture.image.data.slice(); - } - return this; - } - dispose() { - this.geometry.dispose(); - this._matricesTexture.dispose(); - this._matricesTexture = null; - this._indirectTexture.dispose(); - this._indirectTexture = null; - if (this._colorsTexture !== null) { - this._colorsTexture.dispose(); - this._colorsTexture = null; - } - return this; - } - onBeforeRender(renderer, scene, camera, geometry, material) { - if (!this._visibilityChanged && !this.perObjectFrustumCulled && !this.sortObjects) { - return; - } - const index = geometry.getIndex(); - const bytesPerElement = index === null ? 1 : index.array.BYTES_PER_ELEMENT; - const instanceInfo = this._instanceInfo; - const multiDrawStarts = this._multiDrawStarts; - const multiDrawCounts = this._multiDrawCounts; - const geometryInfoList = this._geometryInfo; - const perObjectFrustumCulled = this.perObjectFrustumCulled; - const indirectTexture = this._indirectTexture; - const indirectArray = indirectTexture.image.data; - if (perObjectFrustumCulled) { - _matrix$1.multiplyMatrices(camera.projectionMatrix, camera.matrixWorldInverse).multiply(this.matrixWorld); - _frustum.setFromProjectionMatrix( - _matrix$1, - renderer.coordinateSystem - ); - } - let multiDrawCount = 0; - if (this.sortObjects) { - _matrix$1.copy(this.matrixWorld).invert(); - _vector$5.setFromMatrixPosition(camera.matrixWorld).applyMatrix4(_matrix$1); - _forward.set(0, 0, -1).transformDirection(camera.matrixWorld).transformDirection(_matrix$1); - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (instanceInfo[i].visible && instanceInfo[i].active) { - const geometryId = instanceInfo[i].geometryIndex; - this.getMatrixAt(i, _matrix$1); - this.getBoundingSphereAt(geometryId, _sphere$2).applyMatrix4(_matrix$1); - let culled = false; - if (perObjectFrustumCulled) { - culled = !_frustum.intersectsSphere(_sphere$2); - } - if (!culled) { - const geometryInfo = geometryInfoList[geometryId]; - const z = _temp.subVectors(_sphere$2.center, _vector$5).dot(_forward); - _renderList.push(geometryInfo.start, geometryInfo.count, z, i); - } - } - } - const list = _renderList.list; - const customSort = this.customSort; - if (customSort === null) { - list.sort(material.transparent ? sortTransparent : sortOpaque); - } else { - customSort.call(this, list, camera); - } - for (let i = 0, l = list.length; i < l; i++) { - const item = list[i]; - multiDrawStarts[multiDrawCount] = item.start * bytesPerElement; - multiDrawCounts[multiDrawCount] = item.count; - indirectArray[multiDrawCount] = item.index; - multiDrawCount++; - } - _renderList.reset(); - } else { - for (let i = 0, l = instanceInfo.length; i < l; i++) { - if (instanceInfo[i].visible && instanceInfo[i].active) { - const geometryId = instanceInfo[i].geometryIndex; - let culled = false; - if (perObjectFrustumCulled) { - this.getMatrixAt(i, _matrix$1); - this.getBoundingSphereAt(geometryId, _sphere$2).applyMatrix4(_matrix$1); - culled = !_frustum.intersectsSphere(_sphere$2); - } - if (!culled) { - const geometryInfo = geometryInfoList[geometryId]; - multiDrawStarts[multiDrawCount] = geometryInfo.start * bytesPerElement; - multiDrawCounts[multiDrawCount] = geometryInfo.count; - indirectArray[multiDrawCount] = i; - multiDrawCount++; - } - } - } - } - indirectTexture.needsUpdate = true; - this._multiDrawCount = multiDrawCount; - this._visibilityChanged = false; - } - onBeforeShadow(renderer, object, camera, shadowCamera, geometry, depthMaterial) { - this.onBeforeRender(renderer, null, shadowCamera, geometry, depthMaterial); - } -} -class LineBasicMaterial extends Material { - static { - __name(this, "LineBasicMaterial"); - } - static get type() { - return "LineBasicMaterial"; - } - constructor(parameters) { - super(); - this.isLineBasicMaterial = true; - this.color = new Color(16777215); - this.map = null; - this.linewidth = 1; - this.linecap = "round"; - this.linejoin = "round"; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.linewidth = source.linewidth; - this.linecap = source.linecap; - this.linejoin = source.linejoin; - this.fog = source.fog; - return this; - } -} -const _vStart = /* @__PURE__ */ new Vector3(); -const _vEnd = /* @__PURE__ */ new Vector3(); -const _inverseMatrix$1 = /* @__PURE__ */ new Matrix4(); -const _ray$1 = /* @__PURE__ */ new Ray(); -const _sphere$1 = /* @__PURE__ */ new Sphere(); -const _intersectPointOnRay = /* @__PURE__ */ new Vector3(); -const _intersectPointOnSegment = /* @__PURE__ */ new Vector3(); -class Line extends Object3D { - static { - __name(this, "Line"); - } - constructor(geometry = new BufferGeometry(), material = new LineBasicMaterial()) { - super(); - this.isLine = true; - this.type = "Line"; - this.geometry = geometry; - this.material = material; - this.updateMorphTargets(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.material = Array.isArray(source.material) ? source.material.slice() : source.material; - this.geometry = source.geometry; - return this; - } - computeLineDistances() { - const geometry = this.geometry; - if (geometry.index === null) { - const positionAttribute = geometry.attributes.position; - const lineDistances = [0]; - for (let i = 1, l = positionAttribute.count; i < l; i++) { - _vStart.fromBufferAttribute(positionAttribute, i - 1); - _vEnd.fromBufferAttribute(positionAttribute, i); - lineDistances[i] = lineDistances[i - 1]; - lineDistances[i] += _vStart.distanceTo(_vEnd); - } - geometry.setAttribute("lineDistance", new Float32BufferAttribute(lineDistances, 1)); - } else { - console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry."); - } - return this; - } - raycast(raycaster, intersects2) { - const geometry = this.geometry; - const matrixWorld = this.matrixWorld; - const threshold = raycaster.params.Line.threshold; - const drawRange = geometry.drawRange; - if (geometry.boundingSphere === null) geometry.computeBoundingSphere(); - _sphere$1.copy(geometry.boundingSphere); - _sphere$1.applyMatrix4(matrixWorld); - _sphere$1.radius += threshold; - if (raycaster.ray.intersectsSphere(_sphere$1) === false) return; - _inverseMatrix$1.copy(matrixWorld).invert(); - _ray$1.copy(raycaster.ray).applyMatrix4(_inverseMatrix$1); - const localThreshold = threshold / ((this.scale.x + this.scale.y + this.scale.z) / 3); - const localThresholdSq = localThreshold * localThreshold; - const step = this.isLineSegments ? 2 : 1; - const index = geometry.index; - const attributes = geometry.attributes; - const positionAttribute = attributes.position; - if (index !== null) { - const start = Math.max(0, drawRange.start); - const end = Math.min(index.count, drawRange.start + drawRange.count); - for (let i = start, l = end - 1; i < l; i += step) { - const a = index.getX(i); - const b = index.getX(i + 1); - const intersect2 = checkIntersection(this, raycaster, _ray$1, localThresholdSq, a, b); - if (intersect2) { - intersects2.push(intersect2); - } - } - if (this.isLineLoop) { - const a = index.getX(end - 1); - const b = index.getX(start); - const intersect2 = checkIntersection(this, raycaster, _ray$1, localThresholdSq, a, b); - if (intersect2) { - intersects2.push(intersect2); - } - } - } else { - const start = Math.max(0, drawRange.start); - const end = Math.min(positionAttribute.count, drawRange.start + drawRange.count); - for (let i = start, l = end - 1; i < l; i += step) { - const intersect2 = checkIntersection(this, raycaster, _ray$1, localThresholdSq, i, i + 1); - if (intersect2) { - intersects2.push(intersect2); - } - } - if (this.isLineLoop) { - const intersect2 = checkIntersection(this, raycaster, _ray$1, localThresholdSq, end - 1, start); - if (intersect2) { - intersects2.push(intersect2); - } - } - } - } - updateMorphTargets() { - const geometry = this.geometry; - const morphAttributes = geometry.morphAttributes; - const keys = Object.keys(morphAttributes); - if (keys.length > 0) { - const morphAttribute = morphAttributes[keys[0]]; - if (morphAttribute !== void 0) { - this.morphTargetInfluences = []; - this.morphTargetDictionary = {}; - for (let m = 0, ml = morphAttribute.length; m < ml; m++) { - const name = morphAttribute[m].name || String(m); - this.morphTargetInfluences.push(0); - this.morphTargetDictionary[name] = m; - } - } - } - } -} -function checkIntersection(object, raycaster, ray, thresholdSq, a, b) { - const positionAttribute = object.geometry.attributes.position; - _vStart.fromBufferAttribute(positionAttribute, a); - _vEnd.fromBufferAttribute(positionAttribute, b); - const distSq = ray.distanceSqToSegment(_vStart, _vEnd, _intersectPointOnRay, _intersectPointOnSegment); - if (distSq > thresholdSq) return; - _intersectPointOnRay.applyMatrix4(object.matrixWorld); - const distance = raycaster.ray.origin.distanceTo(_intersectPointOnRay); - if (distance < raycaster.near || distance > raycaster.far) return; - return { - distance, - // What do we want? intersection point on the ray or on the segment?? - // point: raycaster.ray.at( distance ), - point: _intersectPointOnSegment.clone().applyMatrix4(object.matrixWorld), - index: a, - face: null, - faceIndex: null, - barycoord: null, - object - }; -} -__name(checkIntersection, "checkIntersection"); -const _start = /* @__PURE__ */ new Vector3(); -const _end = /* @__PURE__ */ new Vector3(); -class LineSegments extends Line { - static { - __name(this, "LineSegments"); - } - constructor(geometry, material) { - super(geometry, material); - this.isLineSegments = true; - this.type = "LineSegments"; - } - computeLineDistances() { - const geometry = this.geometry; - if (geometry.index === null) { - const positionAttribute = geometry.attributes.position; - const lineDistances = []; - for (let i = 0, l = positionAttribute.count; i < l; i += 2) { - _start.fromBufferAttribute(positionAttribute, i); - _end.fromBufferAttribute(positionAttribute, i + 1); - lineDistances[i] = i === 0 ? 0 : lineDistances[i - 1]; - lineDistances[i + 1] = lineDistances[i] + _start.distanceTo(_end); - } - geometry.setAttribute("lineDistance", new Float32BufferAttribute(lineDistances, 1)); - } else { - console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry."); - } - return this; - } -} -class LineLoop extends Line { - static { - __name(this, "LineLoop"); - } - constructor(geometry, material) { - super(geometry, material); - this.isLineLoop = true; - this.type = "LineLoop"; - } -} -class PointsMaterial extends Material { - static { - __name(this, "PointsMaterial"); - } - static get type() { - return "PointsMaterial"; - } - constructor(parameters) { - super(); - this.isPointsMaterial = true; - this.color = new Color(16777215); - this.map = null; - this.alphaMap = null; - this.size = 1; - this.sizeAttenuation = true; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.alphaMap = source.alphaMap; - this.size = source.size; - this.sizeAttenuation = source.sizeAttenuation; - this.fog = source.fog; - return this; - } -} -const _inverseMatrix = /* @__PURE__ */ new Matrix4(); -const _ray$4 = /* @__PURE__ */ new Ray(); -const _sphere = /* @__PURE__ */ new Sphere(); -const _position$2 = /* @__PURE__ */ new Vector3(); -class Points extends Object3D { - static { - __name(this, "Points"); - } - constructor(geometry = new BufferGeometry(), material = new PointsMaterial()) { - super(); - this.isPoints = true; - this.type = "Points"; - this.geometry = geometry; - this.material = material; - this.updateMorphTargets(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.material = Array.isArray(source.material) ? source.material.slice() : source.material; - this.geometry = source.geometry; - return this; - } - raycast(raycaster, intersects2) { - const geometry = this.geometry; - const matrixWorld = this.matrixWorld; - const threshold = raycaster.params.Points.threshold; - const drawRange = geometry.drawRange; - if (geometry.boundingSphere === null) geometry.computeBoundingSphere(); - _sphere.copy(geometry.boundingSphere); - _sphere.applyMatrix4(matrixWorld); - _sphere.radius += threshold; - if (raycaster.ray.intersectsSphere(_sphere) === false) return; - _inverseMatrix.copy(matrixWorld).invert(); - _ray$4.copy(raycaster.ray).applyMatrix4(_inverseMatrix); - const localThreshold = threshold / ((this.scale.x + this.scale.y + this.scale.z) / 3); - const localThresholdSq = localThreshold * localThreshold; - const index = geometry.index; - const attributes = geometry.attributes; - const positionAttribute = attributes.position; - if (index !== null) { - const start = Math.max(0, drawRange.start); - const end = Math.min(index.count, drawRange.start + drawRange.count); - for (let i = start, il = end; i < il; i++) { - const a = index.getX(i); - _position$2.fromBufferAttribute(positionAttribute, a); - testPoint(_position$2, a, localThresholdSq, matrixWorld, raycaster, intersects2, this); - } - } else { - const start = Math.max(0, drawRange.start); - const end = Math.min(positionAttribute.count, drawRange.start + drawRange.count); - for (let i = start, l = end; i < l; i++) { - _position$2.fromBufferAttribute(positionAttribute, i); - testPoint(_position$2, i, localThresholdSq, matrixWorld, raycaster, intersects2, this); - } - } - } - updateMorphTargets() { - const geometry = this.geometry; - const morphAttributes = geometry.morphAttributes; - const keys = Object.keys(morphAttributes); - if (keys.length > 0) { - const morphAttribute = morphAttributes[keys[0]]; - if (morphAttribute !== void 0) { - this.morphTargetInfluences = []; - this.morphTargetDictionary = {}; - for (let m = 0, ml = morphAttribute.length; m < ml; m++) { - const name = morphAttribute[m].name || String(m); - this.morphTargetInfluences.push(0); - this.morphTargetDictionary[name] = m; - } - } - } - } -} -function testPoint(point, index, localThresholdSq, matrixWorld, raycaster, intersects2, object) { - const rayPointDistanceSq = _ray$4.distanceSqToPoint(point); - if (rayPointDistanceSq < localThresholdSq) { - const intersectPoint = new Vector3(); - _ray$4.closestPointToPoint(point, intersectPoint); - intersectPoint.applyMatrix4(matrixWorld); - const distance = raycaster.ray.origin.distanceTo(intersectPoint); - if (distance < raycaster.near || distance > raycaster.far) return; - intersects2.push({ - distance, - distanceToRay: Math.sqrt(rayPointDistanceSq), - point: intersectPoint, - index, - face: null, - faceIndex: null, - barycoord: null, - object - }); - } -} -__name(testPoint, "testPoint"); -class VideoTexture extends Texture { - static { - __name(this, "VideoTexture"); - } - constructor(video, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy) { - super(video, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy); - this.isVideoTexture = true; - this.minFilter = minFilter !== void 0 ? minFilter : LinearFilter; - this.magFilter = magFilter !== void 0 ? magFilter : LinearFilter; - this.generateMipmaps = false; - const scope = this; - function updateVideo() { - scope.needsUpdate = true; - video.requestVideoFrameCallback(updateVideo); - } - __name(updateVideo, "updateVideo"); - if ("requestVideoFrameCallback" in video) { - video.requestVideoFrameCallback(updateVideo); - } - } - clone() { - return new this.constructor(this.image).copy(this); - } - update() { - const video = this.image; - const hasVideoFrameCallback = "requestVideoFrameCallback" in video; - if (hasVideoFrameCallback === false && video.readyState >= video.HAVE_CURRENT_DATA) { - this.needsUpdate = true; - } - } -} -class FramebufferTexture extends Texture { - static { - __name(this, "FramebufferTexture"); - } - constructor(width, height) { - super({ width, height }); - this.isFramebufferTexture = true; - this.magFilter = NearestFilter; - this.minFilter = NearestFilter; - this.generateMipmaps = false; - this.needsUpdate = true; - } -} -class CompressedTexture extends Texture { - static { - __name(this, "CompressedTexture"); - } - constructor(mipmaps, width, height, format, type, mapping, wrapS, wrapT, magFilter, minFilter, anisotropy, colorSpace) { - super(null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, colorSpace); - this.isCompressedTexture = true; - this.image = { width, height }; - this.mipmaps = mipmaps; - this.flipY = false; - this.generateMipmaps = false; - } -} -class CompressedArrayTexture extends CompressedTexture { - static { - __name(this, "CompressedArrayTexture"); - } - constructor(mipmaps, width, height, depth, format, type) { - super(mipmaps, width, height, format, type); - this.isCompressedArrayTexture = true; - this.image.depth = depth; - this.wrapR = ClampToEdgeWrapping; - this.layerUpdates = /* @__PURE__ */ new Set(); - } - addLayerUpdate(layerIndex) { - this.layerUpdates.add(layerIndex); - } - clearLayerUpdates() { - this.layerUpdates.clear(); - } -} -class CompressedCubeTexture extends CompressedTexture { - static { - __name(this, "CompressedCubeTexture"); - } - constructor(images, format, type) { - super(void 0, images[0].width, images[0].height, format, type, CubeReflectionMapping); - this.isCompressedCubeTexture = true; - this.isCubeTexture = true; - this.image = images; - } -} -class CanvasTexture extends Texture { - static { - __name(this, "CanvasTexture"); - } - constructor(canvas, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy) { - super(canvas, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy); - this.isCanvasTexture = true; - this.needsUpdate = true; - } -} -class Curve { - static { - __name(this, "Curve"); - } - constructor() { - this.type = "Curve"; - this.arcLengthDivisions = 200; - } - // Virtual base class method to overwrite and implement in subclasses - // - t [0 .. 1] - getPoint() { - console.warn("THREE.Curve: .getPoint() not implemented."); - return null; - } - // Get point at relative position in curve according to arc length - // - u [0 .. 1] - getPointAt(u, optionalTarget) { - const t2 = this.getUtoTmapping(u); - return this.getPoint(t2, optionalTarget); - } - // Get sequence of points using getPoint( t ) - getPoints(divisions = 5) { - const points = []; - for (let d = 0; d <= divisions; d++) { - points.push(this.getPoint(d / divisions)); - } - return points; - } - // Get sequence of points using getPointAt( u ) - getSpacedPoints(divisions = 5) { - const points = []; - for (let d = 0; d <= divisions; d++) { - points.push(this.getPointAt(d / divisions)); - } - return points; - } - // Get total curve arc length - getLength() { - const lengths = this.getLengths(); - return lengths[lengths.length - 1]; - } - // Get list of cumulative segment lengths - getLengths(divisions = this.arcLengthDivisions) { - if (this.cacheArcLengths && this.cacheArcLengths.length === divisions + 1 && !this.needsUpdate) { - return this.cacheArcLengths; - } - this.needsUpdate = false; - const cache = []; - let current, last = this.getPoint(0); - let sum = 0; - cache.push(0); - for (let p = 1; p <= divisions; p++) { - current = this.getPoint(p / divisions); - sum += current.distanceTo(last); - cache.push(sum); - last = current; - } - this.cacheArcLengths = cache; - return cache; - } - updateArcLengths() { - this.needsUpdate = true; - this.getLengths(); - } - // Given u ( 0 .. 1 ), get a t to find p. This gives you points which are equidistant - getUtoTmapping(u, distance) { - const arcLengths = this.getLengths(); - let i = 0; - const il = arcLengths.length; - let targetArcLength; - if (distance) { - targetArcLength = distance; - } else { - targetArcLength = u * arcLengths[il - 1]; - } - let low = 0, high = il - 1, comparison; - while (low <= high) { - i = Math.floor(low + (high - low) / 2); - comparison = arcLengths[i] - targetArcLength; - if (comparison < 0) { - low = i + 1; - } else if (comparison > 0) { - high = i - 1; - } else { - high = i; - break; - } - } - i = high; - if (arcLengths[i] === targetArcLength) { - return i / (il - 1); - } - const lengthBefore = arcLengths[i]; - const lengthAfter = arcLengths[i + 1]; - const segmentLength = lengthAfter - lengthBefore; - const segmentFraction = (targetArcLength - lengthBefore) / segmentLength; - const t2 = (i + segmentFraction) / (il - 1); - return t2; - } - // Returns a unit vector tangent at t - // In case any sub curve does not implement its tangent derivation, - // 2 points a small delta apart will be used to find its gradient - // which seems to give a reasonable approximation - getTangent(t2, optionalTarget) { - const delta = 1e-4; - let t1 = t2 - delta; - let t22 = t2 + delta; - if (t1 < 0) t1 = 0; - if (t22 > 1) t22 = 1; - const pt1 = this.getPoint(t1); - const pt2 = this.getPoint(t22); - const tangent = optionalTarget || (pt1.isVector2 ? new Vector2() : new Vector3()); - tangent.copy(pt2).sub(pt1).normalize(); - return tangent; - } - getTangentAt(u, optionalTarget) { - const t2 = this.getUtoTmapping(u); - return this.getTangent(t2, optionalTarget); - } - computeFrenetFrames(segments, closed) { - const normal = new Vector3(); - const tangents = []; - const normals = []; - const binormals = []; - const vec = new Vector3(); - const mat = new Matrix4(); - for (let i = 0; i <= segments; i++) { - const u = i / segments; - tangents[i] = this.getTangentAt(u, new Vector3()); - } - normals[0] = new Vector3(); - binormals[0] = new Vector3(); - let min = Number.MAX_VALUE; - const tx = Math.abs(tangents[0].x); - const ty = Math.abs(tangents[0].y); - const tz = Math.abs(tangents[0].z); - if (tx <= min) { - min = tx; - normal.set(1, 0, 0); - } - if (ty <= min) { - min = ty; - normal.set(0, 1, 0); - } - if (tz <= min) { - normal.set(0, 0, 1); - } - vec.crossVectors(tangents[0], normal).normalize(); - normals[0].crossVectors(tangents[0], vec); - binormals[0].crossVectors(tangents[0], normals[0]); - for (let i = 1; i <= segments; i++) { - normals[i] = normals[i - 1].clone(); - binormals[i] = binormals[i - 1].clone(); - vec.crossVectors(tangents[i - 1], tangents[i]); - if (vec.length() > Number.EPSILON) { - vec.normalize(); - const theta = Math.acos(clamp(tangents[i - 1].dot(tangents[i]), -1, 1)); - normals[i].applyMatrix4(mat.makeRotationAxis(vec, theta)); - } - binormals[i].crossVectors(tangents[i], normals[i]); - } - if (closed === true) { - let theta = Math.acos(clamp(normals[0].dot(normals[segments]), -1, 1)); - theta /= segments; - if (tangents[0].dot(vec.crossVectors(normals[0], normals[segments])) > 0) { - theta = -theta; - } - for (let i = 1; i <= segments; i++) { - normals[i].applyMatrix4(mat.makeRotationAxis(tangents[i], theta * i)); - binormals[i].crossVectors(tangents[i], normals[i]); - } - } - return { - tangents, - normals, - binormals - }; - } - clone() { - return new this.constructor().copy(this); - } - copy(source) { - this.arcLengthDivisions = source.arcLengthDivisions; - return this; - } - toJSON() { - const data = { - metadata: { - version: 4.6, - type: "Curve", - generator: "Curve.toJSON" - } - }; - data.arcLengthDivisions = this.arcLengthDivisions; - data.type = this.type; - return data; - } - fromJSON(json) { - this.arcLengthDivisions = json.arcLengthDivisions; - return this; - } -} -class EllipseCurve extends Curve { - static { - __name(this, "EllipseCurve"); - } - constructor(aX = 0, aY = 0, xRadius = 1, yRadius = 1, aStartAngle = 0, aEndAngle = Math.PI * 2, aClockwise = false, aRotation = 0) { - super(); - this.isEllipseCurve = true; - this.type = "EllipseCurve"; - this.aX = aX; - this.aY = aY; - this.xRadius = xRadius; - this.yRadius = yRadius; - this.aStartAngle = aStartAngle; - this.aEndAngle = aEndAngle; - this.aClockwise = aClockwise; - this.aRotation = aRotation; - } - getPoint(t2, optionalTarget = new Vector2()) { - const point = optionalTarget; - const twoPi = Math.PI * 2; - let deltaAngle = this.aEndAngle - this.aStartAngle; - const samePoints = Math.abs(deltaAngle) < Number.EPSILON; - while (deltaAngle < 0) deltaAngle += twoPi; - while (deltaAngle > twoPi) deltaAngle -= twoPi; - if (deltaAngle < Number.EPSILON) { - if (samePoints) { - deltaAngle = 0; - } else { - deltaAngle = twoPi; - } - } - if (this.aClockwise === true && !samePoints) { - if (deltaAngle === twoPi) { - deltaAngle = -twoPi; - } else { - deltaAngle = deltaAngle - twoPi; - } - } - const angle = this.aStartAngle + t2 * deltaAngle; - let x = this.aX + this.xRadius * Math.cos(angle); - let y = this.aY + this.yRadius * Math.sin(angle); - if (this.aRotation !== 0) { - const cos = Math.cos(this.aRotation); - const sin = Math.sin(this.aRotation); - const tx = x - this.aX; - const ty = y - this.aY; - x = tx * cos - ty * sin + this.aX; - y = tx * sin + ty * cos + this.aY; - } - return point.set(x, y); - } - copy(source) { - super.copy(source); - this.aX = source.aX; - this.aY = source.aY; - this.xRadius = source.xRadius; - this.yRadius = source.yRadius; - this.aStartAngle = source.aStartAngle; - this.aEndAngle = source.aEndAngle; - this.aClockwise = source.aClockwise; - this.aRotation = source.aRotation; - return this; - } - toJSON() { - const data = super.toJSON(); - data.aX = this.aX; - data.aY = this.aY; - data.xRadius = this.xRadius; - data.yRadius = this.yRadius; - data.aStartAngle = this.aStartAngle; - data.aEndAngle = this.aEndAngle; - data.aClockwise = this.aClockwise; - data.aRotation = this.aRotation; - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.aX = json.aX; - this.aY = json.aY; - this.xRadius = json.xRadius; - this.yRadius = json.yRadius; - this.aStartAngle = json.aStartAngle; - this.aEndAngle = json.aEndAngle; - this.aClockwise = json.aClockwise; - this.aRotation = json.aRotation; - return this; - } -} -class ArcCurve extends EllipseCurve { - static { - __name(this, "ArcCurve"); - } - constructor(aX, aY, aRadius, aStartAngle, aEndAngle, aClockwise) { - super(aX, aY, aRadius, aRadius, aStartAngle, aEndAngle, aClockwise); - this.isArcCurve = true; - this.type = "ArcCurve"; - } -} -function CubicPoly() { - let c0 = 0, c1 = 0, c2 = 0, c3 = 0; - function init(x0, x1, t0, t1) { - c0 = x0; - c1 = t0; - c2 = -3 * x0 + 3 * x1 - 2 * t0 - t1; - c3 = 2 * x0 - 2 * x1 + t0 + t1; - } - __name(init, "init"); - return { - initCatmullRom: /* @__PURE__ */ __name(function(x0, x1, x2, x3, tension) { - init(x1, x2, tension * (x2 - x0), tension * (x3 - x1)); - }, "initCatmullRom"), - initNonuniformCatmullRom: /* @__PURE__ */ __name(function(x0, x1, x2, x3, dt0, dt1, dt2) { - let t1 = (x1 - x0) / dt0 - (x2 - x0) / (dt0 + dt1) + (x2 - x1) / dt1; - let t2 = (x2 - x1) / dt1 - (x3 - x1) / (dt1 + dt2) + (x3 - x2) / dt2; - t1 *= dt1; - t2 *= dt1; - init(x1, x2, t1, t2); - }, "initNonuniformCatmullRom"), - calc: /* @__PURE__ */ __name(function(t2) { - const t22 = t2 * t2; - const t3 = t22 * t2; - return c0 + c1 * t2 + c2 * t22 + c3 * t3; - }, "calc") - }; -} -__name(CubicPoly, "CubicPoly"); -const tmp = /* @__PURE__ */ new Vector3(); -const px = /* @__PURE__ */ new CubicPoly(); -const py = /* @__PURE__ */ new CubicPoly(); -const pz = /* @__PURE__ */ new CubicPoly(); -class CatmullRomCurve3 extends Curve { - static { - __name(this, "CatmullRomCurve3"); - } - constructor(points = [], closed = false, curveType = "centripetal", tension = 0.5) { - super(); - this.isCatmullRomCurve3 = true; - this.type = "CatmullRomCurve3"; - this.points = points; - this.closed = closed; - this.curveType = curveType; - this.tension = tension; - } - getPoint(t2, optionalTarget = new Vector3()) { - const point = optionalTarget; - const points = this.points; - const l = points.length; - const p = (l - (this.closed ? 0 : 1)) * t2; - let intPoint = Math.floor(p); - let weight = p - intPoint; - if (this.closed) { - intPoint += intPoint > 0 ? 0 : (Math.floor(Math.abs(intPoint) / l) + 1) * l; - } else if (weight === 0 && intPoint === l - 1) { - intPoint = l - 2; - weight = 1; - } - let p0, p3; - if (this.closed || intPoint > 0) { - p0 = points[(intPoint - 1) % l]; - } else { - tmp.subVectors(points[0], points[1]).add(points[0]); - p0 = tmp; - } - const p1 = points[intPoint % l]; - const p2 = points[(intPoint + 1) % l]; - if (this.closed || intPoint + 2 < l) { - p3 = points[(intPoint + 2) % l]; - } else { - tmp.subVectors(points[l - 1], points[l - 2]).add(points[l - 1]); - p3 = tmp; - } - if (this.curveType === "centripetal" || this.curveType === "chordal") { - const pow = this.curveType === "chordal" ? 0.5 : 0.25; - let dt0 = Math.pow(p0.distanceToSquared(p1), pow); - let dt1 = Math.pow(p1.distanceToSquared(p2), pow); - let dt2 = Math.pow(p2.distanceToSquared(p3), pow); - if (dt1 < 1e-4) dt1 = 1; - if (dt0 < 1e-4) dt0 = dt1; - if (dt2 < 1e-4) dt2 = dt1; - px.initNonuniformCatmullRom(p0.x, p1.x, p2.x, p3.x, dt0, dt1, dt2); - py.initNonuniformCatmullRom(p0.y, p1.y, p2.y, p3.y, dt0, dt1, dt2); - pz.initNonuniformCatmullRom(p0.z, p1.z, p2.z, p3.z, dt0, dt1, dt2); - } else if (this.curveType === "catmullrom") { - px.initCatmullRom(p0.x, p1.x, p2.x, p3.x, this.tension); - py.initCatmullRom(p0.y, p1.y, p2.y, p3.y, this.tension); - pz.initCatmullRom(p0.z, p1.z, p2.z, p3.z, this.tension); - } - point.set( - px.calc(weight), - py.calc(weight), - pz.calc(weight) - ); - return point; - } - copy(source) { - super.copy(source); - this.points = []; - for (let i = 0, l = source.points.length; i < l; i++) { - const point = source.points[i]; - this.points.push(point.clone()); - } - this.closed = source.closed; - this.curveType = source.curveType; - this.tension = source.tension; - return this; - } - toJSON() { - const data = super.toJSON(); - data.points = []; - for (let i = 0, l = this.points.length; i < l; i++) { - const point = this.points[i]; - data.points.push(point.toArray()); - } - data.closed = this.closed; - data.curveType = this.curveType; - data.tension = this.tension; - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.points = []; - for (let i = 0, l = json.points.length; i < l; i++) { - const point = json.points[i]; - this.points.push(new Vector3().fromArray(point)); - } - this.closed = json.closed; - this.curveType = json.curveType; - this.tension = json.tension; - return this; - } -} -function CatmullRom(t2, p0, p1, p2, p3) { - const v0 = (p2 - p0) * 0.5; - const v1 = (p3 - p1) * 0.5; - const t22 = t2 * t2; - const t3 = t2 * t22; - return (2 * p1 - 2 * p2 + v0 + v1) * t3 + (-3 * p1 + 3 * p2 - 2 * v0 - v1) * t22 + v0 * t2 + p1; -} -__name(CatmullRom, "CatmullRom"); -function QuadraticBezierP0(t2, p) { - const k = 1 - t2; - return k * k * p; -} -__name(QuadraticBezierP0, "QuadraticBezierP0"); -function QuadraticBezierP1(t2, p) { - return 2 * (1 - t2) * t2 * p; -} -__name(QuadraticBezierP1, "QuadraticBezierP1"); -function QuadraticBezierP2(t2, p) { - return t2 * t2 * p; -} -__name(QuadraticBezierP2, "QuadraticBezierP2"); -function QuadraticBezier(t2, p0, p1, p2) { - return QuadraticBezierP0(t2, p0) + QuadraticBezierP1(t2, p1) + QuadraticBezierP2(t2, p2); -} -__name(QuadraticBezier, "QuadraticBezier"); -function CubicBezierP0(t2, p) { - const k = 1 - t2; - return k * k * k * p; -} -__name(CubicBezierP0, "CubicBezierP0"); -function CubicBezierP1(t2, p) { - const k = 1 - t2; - return 3 * k * k * t2 * p; -} -__name(CubicBezierP1, "CubicBezierP1"); -function CubicBezierP2(t2, p) { - return 3 * (1 - t2) * t2 * t2 * p; -} -__name(CubicBezierP2, "CubicBezierP2"); -function CubicBezierP3(t2, p) { - return t2 * t2 * t2 * p; -} -__name(CubicBezierP3, "CubicBezierP3"); -function CubicBezier(t2, p0, p1, p2, p3) { - return CubicBezierP0(t2, p0) + CubicBezierP1(t2, p1) + CubicBezierP2(t2, p2) + CubicBezierP3(t2, p3); -} -__name(CubicBezier, "CubicBezier"); -class CubicBezierCurve extends Curve { - static { - __name(this, "CubicBezierCurve"); - } - constructor(v0 = new Vector2(), v1 = new Vector2(), v2 = new Vector2(), v3 = new Vector2()) { - super(); - this.isCubicBezierCurve = true; - this.type = "CubicBezierCurve"; - this.v0 = v0; - this.v1 = v1; - this.v2 = v2; - this.v3 = v3; - } - getPoint(t2, optionalTarget = new Vector2()) { - const point = optionalTarget; - const v0 = this.v0, v1 = this.v1, v2 = this.v2, v3 = this.v3; - point.set( - CubicBezier(t2, v0.x, v1.x, v2.x, v3.x), - CubicBezier(t2, v0.y, v1.y, v2.y, v3.y) - ); - return point; - } - copy(source) { - super.copy(source); - this.v0.copy(source.v0); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - this.v3.copy(source.v3); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v0 = this.v0.toArray(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - data.v3 = this.v3.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v0.fromArray(json.v0); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - this.v3.fromArray(json.v3); - return this; - } -} -class CubicBezierCurve3 extends Curve { - static { - __name(this, "CubicBezierCurve3"); - } - constructor(v0 = new Vector3(), v1 = new Vector3(), v2 = new Vector3(), v3 = new Vector3()) { - super(); - this.isCubicBezierCurve3 = true; - this.type = "CubicBezierCurve3"; - this.v0 = v0; - this.v1 = v1; - this.v2 = v2; - this.v3 = v3; - } - getPoint(t2, optionalTarget = new Vector3()) { - const point = optionalTarget; - const v0 = this.v0, v1 = this.v1, v2 = this.v2, v3 = this.v3; - point.set( - CubicBezier(t2, v0.x, v1.x, v2.x, v3.x), - CubicBezier(t2, v0.y, v1.y, v2.y, v3.y), - CubicBezier(t2, v0.z, v1.z, v2.z, v3.z) - ); - return point; - } - copy(source) { - super.copy(source); - this.v0.copy(source.v0); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - this.v3.copy(source.v3); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v0 = this.v0.toArray(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - data.v3 = this.v3.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v0.fromArray(json.v0); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - this.v3.fromArray(json.v3); - return this; - } -} -class LineCurve extends Curve { - static { - __name(this, "LineCurve"); - } - constructor(v1 = new Vector2(), v2 = new Vector2()) { - super(); - this.isLineCurve = true; - this.type = "LineCurve"; - this.v1 = v1; - this.v2 = v2; - } - getPoint(t2, optionalTarget = new Vector2()) { - const point = optionalTarget; - if (t2 === 1) { - point.copy(this.v2); - } else { - point.copy(this.v2).sub(this.v1); - point.multiplyScalar(t2).add(this.v1); - } - return point; - } - // Line curve is linear, so we can overwrite default getPointAt - getPointAt(u, optionalTarget) { - return this.getPoint(u, optionalTarget); - } - getTangent(t2, optionalTarget = new Vector2()) { - return optionalTarget.subVectors(this.v2, this.v1).normalize(); - } - getTangentAt(u, optionalTarget) { - return this.getTangent(u, optionalTarget); - } - copy(source) { - super.copy(source); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - return this; - } -} -class LineCurve3 extends Curve { - static { - __name(this, "LineCurve3"); - } - constructor(v1 = new Vector3(), v2 = new Vector3()) { - super(); - this.isLineCurve3 = true; - this.type = "LineCurve3"; - this.v1 = v1; - this.v2 = v2; - } - getPoint(t2, optionalTarget = new Vector3()) { - const point = optionalTarget; - if (t2 === 1) { - point.copy(this.v2); - } else { - point.copy(this.v2).sub(this.v1); - point.multiplyScalar(t2).add(this.v1); - } - return point; - } - // Line curve is linear, so we can overwrite default getPointAt - getPointAt(u, optionalTarget) { - return this.getPoint(u, optionalTarget); - } - getTangent(t2, optionalTarget = new Vector3()) { - return optionalTarget.subVectors(this.v2, this.v1).normalize(); - } - getTangentAt(u, optionalTarget) { - return this.getTangent(u, optionalTarget); - } - copy(source) { - super.copy(source); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - return this; - } -} -class QuadraticBezierCurve extends Curve { - static { - __name(this, "QuadraticBezierCurve"); - } - constructor(v0 = new Vector2(), v1 = new Vector2(), v2 = new Vector2()) { - super(); - this.isQuadraticBezierCurve = true; - this.type = "QuadraticBezierCurve"; - this.v0 = v0; - this.v1 = v1; - this.v2 = v2; - } - getPoint(t2, optionalTarget = new Vector2()) { - const point = optionalTarget; - const v0 = this.v0, v1 = this.v1, v2 = this.v2; - point.set( - QuadraticBezier(t2, v0.x, v1.x, v2.x), - QuadraticBezier(t2, v0.y, v1.y, v2.y) - ); - return point; - } - copy(source) { - super.copy(source); - this.v0.copy(source.v0); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v0 = this.v0.toArray(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v0.fromArray(json.v0); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - return this; - } -} -class QuadraticBezierCurve3 extends Curve { - static { - __name(this, "QuadraticBezierCurve3"); - } - constructor(v0 = new Vector3(), v1 = new Vector3(), v2 = new Vector3()) { - super(); - this.isQuadraticBezierCurve3 = true; - this.type = "QuadraticBezierCurve3"; - this.v0 = v0; - this.v1 = v1; - this.v2 = v2; - } - getPoint(t2, optionalTarget = new Vector3()) { - const point = optionalTarget; - const v0 = this.v0, v1 = this.v1, v2 = this.v2; - point.set( - QuadraticBezier(t2, v0.x, v1.x, v2.x), - QuadraticBezier(t2, v0.y, v1.y, v2.y), - QuadraticBezier(t2, v0.z, v1.z, v2.z) - ); - return point; - } - copy(source) { - super.copy(source); - this.v0.copy(source.v0); - this.v1.copy(source.v1); - this.v2.copy(source.v2); - return this; - } - toJSON() { - const data = super.toJSON(); - data.v0 = this.v0.toArray(); - data.v1 = this.v1.toArray(); - data.v2 = this.v2.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.v0.fromArray(json.v0); - this.v1.fromArray(json.v1); - this.v2.fromArray(json.v2); - return this; - } -} -class SplineCurve extends Curve { - static { - __name(this, "SplineCurve"); - } - constructor(points = []) { - super(); - this.isSplineCurve = true; - this.type = "SplineCurve"; - this.points = points; - } - getPoint(t2, optionalTarget = new Vector2()) { - const point = optionalTarget; - const points = this.points; - const p = (points.length - 1) * t2; - const intPoint = Math.floor(p); - const weight = p - intPoint; - const p0 = points[intPoint === 0 ? intPoint : intPoint - 1]; - const p1 = points[intPoint]; - const p2 = points[intPoint > points.length - 2 ? points.length - 1 : intPoint + 1]; - const p3 = points[intPoint > points.length - 3 ? points.length - 1 : intPoint + 2]; - point.set( - CatmullRom(weight, p0.x, p1.x, p2.x, p3.x), - CatmullRom(weight, p0.y, p1.y, p2.y, p3.y) - ); - return point; - } - copy(source) { - super.copy(source); - this.points = []; - for (let i = 0, l = source.points.length; i < l; i++) { - const point = source.points[i]; - this.points.push(point.clone()); - } - return this; - } - toJSON() { - const data = super.toJSON(); - data.points = []; - for (let i = 0, l = this.points.length; i < l; i++) { - const point = this.points[i]; - data.points.push(point.toArray()); - } - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.points = []; - for (let i = 0, l = json.points.length; i < l; i++) { - const point = json.points[i]; - this.points.push(new Vector2().fromArray(point)); - } - return this; - } -} -var Curves = /* @__PURE__ */ Object.freeze({ - __proto__: null, - ArcCurve, - CatmullRomCurve3, - CubicBezierCurve, - CubicBezierCurve3, - EllipseCurve, - LineCurve, - LineCurve3, - QuadraticBezierCurve, - QuadraticBezierCurve3, - SplineCurve -}); -class CurvePath extends Curve { - static { - __name(this, "CurvePath"); - } - constructor() { - super(); - this.type = "CurvePath"; - this.curves = []; - this.autoClose = false; - } - add(curve) { - this.curves.push(curve); - } - closePath() { - const startPoint = this.curves[0].getPoint(0); - const endPoint = this.curves[this.curves.length - 1].getPoint(1); - if (!startPoint.equals(endPoint)) { - const lineType = startPoint.isVector2 === true ? "LineCurve" : "LineCurve3"; - this.curves.push(new Curves[lineType](endPoint, startPoint)); - } - return this; - } - // To get accurate point with reference to - // entire path distance at time t, - // following has to be done: - // 1. Length of each sub path have to be known - // 2. Locate and identify type of curve - // 3. Get t for the curve - // 4. Return curve.getPointAt(t') - getPoint(t2, optionalTarget) { - const d = t2 * this.getLength(); - const curveLengths = this.getCurveLengths(); - let i = 0; - while (i < curveLengths.length) { - if (curveLengths[i] >= d) { - const diff = curveLengths[i] - d; - const curve = this.curves[i]; - const segmentLength = curve.getLength(); - const u = segmentLength === 0 ? 0 : 1 - diff / segmentLength; - return curve.getPointAt(u, optionalTarget); - } - i++; - } - return null; - } - // We cannot use the default THREE.Curve getPoint() with getLength() because in - // THREE.Curve, getLength() depends on getPoint() but in THREE.CurvePath - // getPoint() depends on getLength - getLength() { - const lens = this.getCurveLengths(); - return lens[lens.length - 1]; - } - // cacheLengths must be recalculated. - updateArcLengths() { - this.needsUpdate = true; - this.cacheLengths = null; - this.getCurveLengths(); - } - // Compute lengths and cache them - // We cannot overwrite getLengths() because UtoT mapping uses it. - getCurveLengths() { - if (this.cacheLengths && this.cacheLengths.length === this.curves.length) { - return this.cacheLengths; - } - const lengths = []; - let sums = 0; - for (let i = 0, l = this.curves.length; i < l; i++) { - sums += this.curves[i].getLength(); - lengths.push(sums); - } - this.cacheLengths = lengths; - return lengths; - } - getSpacedPoints(divisions = 40) { - const points = []; - for (let i = 0; i <= divisions; i++) { - points.push(this.getPoint(i / divisions)); - } - if (this.autoClose) { - points.push(points[0]); - } - return points; - } - getPoints(divisions = 12) { - const points = []; - let last; - for (let i = 0, curves = this.curves; i < curves.length; i++) { - const curve = curves[i]; - const resolution = curve.isEllipseCurve ? divisions * 2 : curve.isLineCurve || curve.isLineCurve3 ? 1 : curve.isSplineCurve ? divisions * curve.points.length : divisions; - const pts = curve.getPoints(resolution); - for (let j = 0; j < pts.length; j++) { - const point = pts[j]; - if (last && last.equals(point)) continue; - points.push(point); - last = point; - } - } - if (this.autoClose && points.length > 1 && !points[points.length - 1].equals(points[0])) { - points.push(points[0]); - } - return points; - } - copy(source) { - super.copy(source); - this.curves = []; - for (let i = 0, l = source.curves.length; i < l; i++) { - const curve = source.curves[i]; - this.curves.push(curve.clone()); - } - this.autoClose = source.autoClose; - return this; - } - toJSON() { - const data = super.toJSON(); - data.autoClose = this.autoClose; - data.curves = []; - for (let i = 0, l = this.curves.length; i < l; i++) { - const curve = this.curves[i]; - data.curves.push(curve.toJSON()); - } - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.autoClose = json.autoClose; - this.curves = []; - for (let i = 0, l = json.curves.length; i < l; i++) { - const curve = json.curves[i]; - this.curves.push(new Curves[curve.type]().fromJSON(curve)); - } - return this; - } -} -class Path extends CurvePath { - static { - __name(this, "Path"); - } - constructor(points) { - super(); - this.type = "Path"; - this.currentPoint = new Vector2(); - if (points) { - this.setFromPoints(points); - } - } - setFromPoints(points) { - this.moveTo(points[0].x, points[0].y); - for (let i = 1, l = points.length; i < l; i++) { - this.lineTo(points[i].x, points[i].y); - } - return this; - } - moveTo(x, y) { - this.currentPoint.set(x, y); - return this; - } - lineTo(x, y) { - const curve = new LineCurve(this.currentPoint.clone(), new Vector2(x, y)); - this.curves.push(curve); - this.currentPoint.set(x, y); - return this; - } - quadraticCurveTo(aCPx, aCPy, aX, aY) { - const curve = new QuadraticBezierCurve( - this.currentPoint.clone(), - new Vector2(aCPx, aCPy), - new Vector2(aX, aY) - ); - this.curves.push(curve); - this.currentPoint.set(aX, aY); - return this; - } - bezierCurveTo(aCP1x, aCP1y, aCP2x, aCP2y, aX, aY) { - const curve = new CubicBezierCurve( - this.currentPoint.clone(), - new Vector2(aCP1x, aCP1y), - new Vector2(aCP2x, aCP2y), - new Vector2(aX, aY) - ); - this.curves.push(curve); - this.currentPoint.set(aX, aY); - return this; - } - splineThru(pts) { - const npts = [this.currentPoint.clone()].concat(pts); - const curve = new SplineCurve(npts); - this.curves.push(curve); - this.currentPoint.copy(pts[pts.length - 1]); - return this; - } - arc(aX, aY, aRadius, aStartAngle, aEndAngle, aClockwise) { - const x0 = this.currentPoint.x; - const y0 = this.currentPoint.y; - this.absarc( - aX + x0, - aY + y0, - aRadius, - aStartAngle, - aEndAngle, - aClockwise - ); - return this; - } - absarc(aX, aY, aRadius, aStartAngle, aEndAngle, aClockwise) { - this.absellipse(aX, aY, aRadius, aRadius, aStartAngle, aEndAngle, aClockwise); - return this; - } - ellipse(aX, aY, xRadius, yRadius, aStartAngle, aEndAngle, aClockwise, aRotation) { - const x0 = this.currentPoint.x; - const y0 = this.currentPoint.y; - this.absellipse(aX + x0, aY + y0, xRadius, yRadius, aStartAngle, aEndAngle, aClockwise, aRotation); - return this; - } - absellipse(aX, aY, xRadius, yRadius, aStartAngle, aEndAngle, aClockwise, aRotation) { - const curve = new EllipseCurve(aX, aY, xRadius, yRadius, aStartAngle, aEndAngle, aClockwise, aRotation); - if (this.curves.length > 0) { - const firstPoint = curve.getPoint(0); - if (!firstPoint.equals(this.currentPoint)) { - this.lineTo(firstPoint.x, firstPoint.y); - } - } - this.curves.push(curve); - const lastPoint = curve.getPoint(1); - this.currentPoint.copy(lastPoint); - return this; - } - copy(source) { - super.copy(source); - this.currentPoint.copy(source.currentPoint); - return this; - } - toJSON() { - const data = super.toJSON(); - data.currentPoint = this.currentPoint.toArray(); - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.currentPoint.fromArray(json.currentPoint); - return this; - } -} -class LatheGeometry extends BufferGeometry { - static { - __name(this, "LatheGeometry"); - } - constructor(points = [new Vector2(0, -0.5), new Vector2(0.5, 0), new Vector2(0, 0.5)], segments = 12, phiStart = 0, phiLength = Math.PI * 2) { - super(); - this.type = "LatheGeometry"; - this.parameters = { - points, - segments, - phiStart, - phiLength - }; - segments = Math.floor(segments); - phiLength = clamp(phiLength, 0, Math.PI * 2); - const indices = []; - const vertices = []; - const uvs = []; - const initNormals = []; - const normals = []; - const inverseSegments = 1 / segments; - const vertex2 = new Vector3(); - const uv = new Vector2(); - const normal = new Vector3(); - const curNormal = new Vector3(); - const prevNormal = new Vector3(); - let dx = 0; - let dy = 0; - for (let j = 0; j <= points.length - 1; j++) { - switch (j) { - case 0: - dx = points[j + 1].x - points[j].x; - dy = points[j + 1].y - points[j].y; - normal.x = dy * 1; - normal.y = -dx; - normal.z = dy * 0; - prevNormal.copy(normal); - normal.normalize(); - initNormals.push(normal.x, normal.y, normal.z); - break; - case points.length - 1: - initNormals.push(prevNormal.x, prevNormal.y, prevNormal.z); - break; - default: - dx = points[j + 1].x - points[j].x; - dy = points[j + 1].y - points[j].y; - normal.x = dy * 1; - normal.y = -dx; - normal.z = dy * 0; - curNormal.copy(normal); - normal.x += prevNormal.x; - normal.y += prevNormal.y; - normal.z += prevNormal.z; - normal.normalize(); - initNormals.push(normal.x, normal.y, normal.z); - prevNormal.copy(curNormal); - } - } - for (let i = 0; i <= segments; i++) { - const phi = phiStart + i * inverseSegments * phiLength; - const sin = Math.sin(phi); - const cos = Math.cos(phi); - for (let j = 0; j <= points.length - 1; j++) { - vertex2.x = points[j].x * sin; - vertex2.y = points[j].y; - vertex2.z = points[j].x * cos; - vertices.push(vertex2.x, vertex2.y, vertex2.z); - uv.x = i / segments; - uv.y = j / (points.length - 1); - uvs.push(uv.x, uv.y); - const x = initNormals[3 * j + 0] * sin; - const y = initNormals[3 * j + 1]; - const z = initNormals[3 * j + 0] * cos; - normals.push(x, y, z); - } - } - for (let i = 0; i < segments; i++) { - for (let j = 0; j < points.length - 1; j++) { - const base = j + i * points.length; - const a = base; - const b = base + points.length; - const c = base + points.length + 1; - const d = base + 1; - indices.push(a, b, d); - indices.push(c, d, b); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new LatheGeometry(data.points, data.segments, data.phiStart, data.phiLength); - } -} -class CapsuleGeometry extends LatheGeometry { - static { - __name(this, "CapsuleGeometry"); - } - constructor(radius = 1, length = 1, capSegments = 4, radialSegments = 8) { - const path = new Path(); - path.absarc(0, -length / 2, radius, Math.PI * 1.5, 0); - path.absarc(0, length / 2, radius, 0, Math.PI * 0.5); - super(path.getPoints(capSegments), radialSegments); - this.type = "CapsuleGeometry"; - this.parameters = { - radius, - length, - capSegments, - radialSegments - }; - } - static fromJSON(data) { - return new CapsuleGeometry(data.radius, data.length, data.capSegments, data.radialSegments); - } -} -class CircleGeometry extends BufferGeometry { - static { - __name(this, "CircleGeometry"); - } - constructor(radius = 1, segments = 32, thetaStart = 0, thetaLength = Math.PI * 2) { - super(); - this.type = "CircleGeometry"; - this.parameters = { - radius, - segments, - thetaStart, - thetaLength - }; - segments = Math.max(3, segments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - const vertex2 = new Vector3(); - const uv = new Vector2(); - vertices.push(0, 0, 0); - normals.push(0, 0, 1); - uvs.push(0.5, 0.5); - for (let s = 0, i = 3; s <= segments; s++, i += 3) { - const segment = thetaStart + s / segments * thetaLength; - vertex2.x = radius * Math.cos(segment); - vertex2.y = radius * Math.sin(segment); - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normals.push(0, 0, 1); - uv.x = (vertices[i] / radius + 1) / 2; - uv.y = (vertices[i + 1] / radius + 1) / 2; - uvs.push(uv.x, uv.y); - } - for (let i = 1; i <= segments; i++) { - indices.push(i, i + 1, 0); - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new CircleGeometry(data.radius, data.segments, data.thetaStart, data.thetaLength); - } -} -class CylinderGeometry extends BufferGeometry { - static { - __name(this, "CylinderGeometry"); - } - constructor(radiusTop = 1, radiusBottom = 1, height = 1, radialSegments = 32, heightSegments = 1, openEnded = false, thetaStart = 0, thetaLength = Math.PI * 2) { - super(); - this.type = "CylinderGeometry"; - this.parameters = { - radiusTop, - radiusBottom, - height, - radialSegments, - heightSegments, - openEnded, - thetaStart, - thetaLength - }; - const scope = this; - radialSegments = Math.floor(radialSegments); - heightSegments = Math.floor(heightSegments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - let index = 0; - const indexArray = []; - const halfHeight = height / 2; - let groupStart = 0; - generateTorso(); - if (openEnded === false) { - if (radiusTop > 0) generateCap(true); - if (radiusBottom > 0) generateCap(false); - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - function generateTorso() { - const normal = new Vector3(); - const vertex2 = new Vector3(); - let groupCount = 0; - const slope = (radiusBottom - radiusTop) / height; - for (let y = 0; y <= heightSegments; y++) { - const indexRow = []; - const v = y / heightSegments; - const radius = v * (radiusBottom - radiusTop) + radiusTop; - for (let x = 0; x <= radialSegments; x++) { - const u = x / radialSegments; - const theta = u * thetaLength + thetaStart; - const sinTheta = Math.sin(theta); - const cosTheta = Math.cos(theta); - vertex2.x = radius * sinTheta; - vertex2.y = -v * height + halfHeight; - vertex2.z = radius * cosTheta; - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normal.set(sinTheta, slope, cosTheta).normalize(); - normals.push(normal.x, normal.y, normal.z); - uvs.push(u, 1 - v); - indexRow.push(index++); - } - indexArray.push(indexRow); - } - for (let x = 0; x < radialSegments; x++) { - for (let y = 0; y < heightSegments; y++) { - const a = indexArray[y][x]; - const b = indexArray[y + 1][x]; - const c = indexArray[y + 1][x + 1]; - const d = indexArray[y][x + 1]; - if (radiusTop > 0 || y !== 0) { - indices.push(a, b, d); - groupCount += 3; - } - if (radiusBottom > 0 || y !== heightSegments - 1) { - indices.push(b, c, d); - groupCount += 3; - } - } - } - scope.addGroup(groupStart, groupCount, 0); - groupStart += groupCount; - } - __name(generateTorso, "generateTorso"); - function generateCap(top) { - const centerIndexStart = index; - const uv = new Vector2(); - const vertex2 = new Vector3(); - let groupCount = 0; - const radius = top === true ? radiusTop : radiusBottom; - const sign2 = top === true ? 1 : -1; - for (let x = 1; x <= radialSegments; x++) { - vertices.push(0, halfHeight * sign2, 0); - normals.push(0, sign2, 0); - uvs.push(0.5, 0.5); - index++; - } - const centerIndexEnd = index; - for (let x = 0; x <= radialSegments; x++) { - const u = x / radialSegments; - const theta = u * thetaLength + thetaStart; - const cosTheta = Math.cos(theta); - const sinTheta = Math.sin(theta); - vertex2.x = radius * sinTheta; - vertex2.y = halfHeight * sign2; - vertex2.z = radius * cosTheta; - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normals.push(0, sign2, 0); - uv.x = cosTheta * 0.5 + 0.5; - uv.y = sinTheta * 0.5 * sign2 + 0.5; - uvs.push(uv.x, uv.y); - index++; - } - for (let x = 0; x < radialSegments; x++) { - const c = centerIndexStart + x; - const i = centerIndexEnd + x; - if (top === true) { - indices.push(i, i + 1, c); - } else { - indices.push(i + 1, i, c); - } - groupCount += 3; - } - scope.addGroup(groupStart, groupCount, top === true ? 1 : 2); - groupStart += groupCount; - } - __name(generateCap, "generateCap"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new CylinderGeometry(data.radiusTop, data.radiusBottom, data.height, data.radialSegments, data.heightSegments, data.openEnded, data.thetaStart, data.thetaLength); - } -} -class ConeGeometry extends CylinderGeometry { - static { - __name(this, "ConeGeometry"); - } - constructor(radius = 1, height = 1, radialSegments = 32, heightSegments = 1, openEnded = false, thetaStart = 0, thetaLength = Math.PI * 2) { - super(0, radius, height, radialSegments, heightSegments, openEnded, thetaStart, thetaLength); - this.type = "ConeGeometry"; - this.parameters = { - radius, - height, - radialSegments, - heightSegments, - openEnded, - thetaStart, - thetaLength - }; - } - static fromJSON(data) { - return new ConeGeometry(data.radius, data.height, data.radialSegments, data.heightSegments, data.openEnded, data.thetaStart, data.thetaLength); - } -} -class PolyhedronGeometry extends BufferGeometry { - static { - __name(this, "PolyhedronGeometry"); - } - constructor(vertices = [], indices = [], radius = 1, detail = 0) { - super(); - this.type = "PolyhedronGeometry"; - this.parameters = { - vertices, - indices, - radius, - detail - }; - const vertexBuffer = []; - const uvBuffer = []; - subdivide(detail); - applyRadius(radius); - generateUVs(); - this.setAttribute("position", new Float32BufferAttribute(vertexBuffer, 3)); - this.setAttribute("normal", new Float32BufferAttribute(vertexBuffer.slice(), 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvBuffer, 2)); - if (detail === 0) { - this.computeVertexNormals(); - } else { - this.normalizeNormals(); - } - function subdivide(detail2) { - const a = new Vector3(); - const b = new Vector3(); - const c = new Vector3(); - for (let i = 0; i < indices.length; i += 3) { - getVertexByIndex(indices[i + 0], a); - getVertexByIndex(indices[i + 1], b); - getVertexByIndex(indices[i + 2], c); - subdivideFace(a, b, c, detail2); - } - } - __name(subdivide, "subdivide"); - function subdivideFace(a, b, c, detail2) { - const cols = detail2 + 1; - const v = []; - for (let i = 0; i <= cols; i++) { - v[i] = []; - const aj = a.clone().lerp(c, i / cols); - const bj = b.clone().lerp(c, i / cols); - const rows = cols - i; - for (let j = 0; j <= rows; j++) { - if (j === 0 && i === cols) { - v[i][j] = aj; - } else { - v[i][j] = aj.clone().lerp(bj, j / rows); - } - } - } - for (let i = 0; i < cols; i++) { - for (let j = 0; j < 2 * (cols - i) - 1; j++) { - const k = Math.floor(j / 2); - if (j % 2 === 0) { - pushVertex(v[i][k + 1]); - pushVertex(v[i + 1][k]); - pushVertex(v[i][k]); - } else { - pushVertex(v[i][k + 1]); - pushVertex(v[i + 1][k + 1]); - pushVertex(v[i + 1][k]); - } - } - } - } - __name(subdivideFace, "subdivideFace"); - function applyRadius(radius2) { - const vertex2 = new Vector3(); - for (let i = 0; i < vertexBuffer.length; i += 3) { - vertex2.x = vertexBuffer[i + 0]; - vertex2.y = vertexBuffer[i + 1]; - vertex2.z = vertexBuffer[i + 2]; - vertex2.normalize().multiplyScalar(radius2); - vertexBuffer[i + 0] = vertex2.x; - vertexBuffer[i + 1] = vertex2.y; - vertexBuffer[i + 2] = vertex2.z; - } - } - __name(applyRadius, "applyRadius"); - function generateUVs() { - const vertex2 = new Vector3(); - for (let i = 0; i < vertexBuffer.length; i += 3) { - vertex2.x = vertexBuffer[i + 0]; - vertex2.y = vertexBuffer[i + 1]; - vertex2.z = vertexBuffer[i + 2]; - const u = azimuth(vertex2) / 2 / Math.PI + 0.5; - const v = inclination(vertex2) / Math.PI + 0.5; - uvBuffer.push(u, 1 - v); - } - correctUVs(); - correctSeam(); - } - __name(generateUVs, "generateUVs"); - function correctSeam() { - for (let i = 0; i < uvBuffer.length; i += 6) { - const x0 = uvBuffer[i + 0]; - const x1 = uvBuffer[i + 2]; - const x2 = uvBuffer[i + 4]; - const max2 = Math.max(x0, x1, x2); - const min = Math.min(x0, x1, x2); - if (max2 > 0.9 && min < 0.1) { - if (x0 < 0.2) uvBuffer[i + 0] += 1; - if (x1 < 0.2) uvBuffer[i + 2] += 1; - if (x2 < 0.2) uvBuffer[i + 4] += 1; - } - } - } - __name(correctSeam, "correctSeam"); - function pushVertex(vertex2) { - vertexBuffer.push(vertex2.x, vertex2.y, vertex2.z); - } - __name(pushVertex, "pushVertex"); - function getVertexByIndex(index, vertex2) { - const stride = index * 3; - vertex2.x = vertices[stride + 0]; - vertex2.y = vertices[stride + 1]; - vertex2.z = vertices[stride + 2]; - } - __name(getVertexByIndex, "getVertexByIndex"); - function correctUVs() { - const a = new Vector3(); - const b = new Vector3(); - const c = new Vector3(); - const centroid = new Vector3(); - const uvA = new Vector2(); - const uvB = new Vector2(); - const uvC = new Vector2(); - for (let i = 0, j = 0; i < vertexBuffer.length; i += 9, j += 6) { - a.set(vertexBuffer[i + 0], vertexBuffer[i + 1], vertexBuffer[i + 2]); - b.set(vertexBuffer[i + 3], vertexBuffer[i + 4], vertexBuffer[i + 5]); - c.set(vertexBuffer[i + 6], vertexBuffer[i + 7], vertexBuffer[i + 8]); - uvA.set(uvBuffer[j + 0], uvBuffer[j + 1]); - uvB.set(uvBuffer[j + 2], uvBuffer[j + 3]); - uvC.set(uvBuffer[j + 4], uvBuffer[j + 5]); - centroid.copy(a).add(b).add(c).divideScalar(3); - const azi = azimuth(centroid); - correctUV(uvA, j + 0, a, azi); - correctUV(uvB, j + 2, b, azi); - correctUV(uvC, j + 4, c, azi); - } - } - __name(correctUVs, "correctUVs"); - function correctUV(uv, stride, vector, azimuth2) { - if (azimuth2 < 0 && uv.x === 1) { - uvBuffer[stride] = uv.x - 1; - } - if (vector.x === 0 && vector.z === 0) { - uvBuffer[stride] = azimuth2 / 2 / Math.PI + 0.5; - } - } - __name(correctUV, "correctUV"); - function azimuth(vector) { - return Math.atan2(vector.z, -vector.x); - } - __name(azimuth, "azimuth"); - function inclination(vector) { - return Math.atan2(-vector.y, Math.sqrt(vector.x * vector.x + vector.z * vector.z)); - } - __name(inclination, "inclination"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new PolyhedronGeometry(data.vertices, data.indices, data.radius, data.details); - } -} -class DodecahedronGeometry extends PolyhedronGeometry { - static { - __name(this, "DodecahedronGeometry"); - } - constructor(radius = 1, detail = 0) { - const t2 = (1 + Math.sqrt(5)) / 2; - const r = 1 / t2; - const vertices = [ - // (±1, ±1, ±1) - -1, - -1, - -1, - -1, - -1, - 1, - -1, - 1, - -1, - -1, - 1, - 1, - 1, - -1, - -1, - 1, - -1, - 1, - 1, - 1, - -1, - 1, - 1, - 1, - // (0, ±1/φ, ±φ) - 0, - -r, - -t2, - 0, - -r, - t2, - 0, - r, - -t2, - 0, - r, - t2, - // (±1/φ, ±φ, 0) - -r, - -t2, - 0, - -r, - t2, - 0, - r, - -t2, - 0, - r, - t2, - 0, - // (±φ, 0, ±1/φ) - -t2, - 0, - -r, - t2, - 0, - -r, - -t2, - 0, - r, - t2, - 0, - r - ]; - const indices = [ - 3, - 11, - 7, - 3, - 7, - 15, - 3, - 15, - 13, - 7, - 19, - 17, - 7, - 17, - 6, - 7, - 6, - 15, - 17, - 4, - 8, - 17, - 8, - 10, - 17, - 10, - 6, - 8, - 0, - 16, - 8, - 16, - 2, - 8, - 2, - 10, - 0, - 12, - 1, - 0, - 1, - 18, - 0, - 18, - 16, - 6, - 10, - 2, - 6, - 2, - 13, - 6, - 13, - 15, - 2, - 16, - 18, - 2, - 18, - 3, - 2, - 3, - 13, - 18, - 1, - 9, - 18, - 9, - 11, - 18, - 11, - 3, - 4, - 14, - 12, - 4, - 12, - 0, - 4, - 0, - 8, - 11, - 9, - 5, - 11, - 5, - 19, - 11, - 19, - 7, - 19, - 5, - 14, - 19, - 14, - 4, - 19, - 4, - 17, - 1, - 12, - 14, - 1, - 14, - 5, - 1, - 5, - 9 - ]; - super(vertices, indices, radius, detail); - this.type = "DodecahedronGeometry"; - this.parameters = { - radius, - detail - }; - } - static fromJSON(data) { - return new DodecahedronGeometry(data.radius, data.detail); - } -} -const _v0 = /* @__PURE__ */ new Vector3(); -const _v1$1 = /* @__PURE__ */ new Vector3(); -const _normal = /* @__PURE__ */ new Vector3(); -const _triangle = /* @__PURE__ */ new Triangle(); -class EdgesGeometry extends BufferGeometry { - static { - __name(this, "EdgesGeometry"); - } - constructor(geometry = null, thresholdAngle = 1) { - super(); - this.type = "EdgesGeometry"; - this.parameters = { - geometry, - thresholdAngle - }; - if (geometry !== null) { - const precisionPoints = 4; - const precision = Math.pow(10, precisionPoints); - const thresholdDot = Math.cos(DEG2RAD * thresholdAngle); - const indexAttr = geometry.getIndex(); - const positionAttr = geometry.getAttribute("position"); - const indexCount = indexAttr ? indexAttr.count : positionAttr.count; - const indexArr = [0, 0, 0]; - const vertKeys = ["a", "b", "c"]; - const hashes = new Array(3); - const edgeData = {}; - const vertices = []; - for (let i = 0; i < indexCount; i += 3) { - if (indexAttr) { - indexArr[0] = indexAttr.getX(i); - indexArr[1] = indexAttr.getX(i + 1); - indexArr[2] = indexAttr.getX(i + 2); - } else { - indexArr[0] = i; - indexArr[1] = i + 1; - indexArr[2] = i + 2; - } - const { a, b, c } = _triangle; - a.fromBufferAttribute(positionAttr, indexArr[0]); - b.fromBufferAttribute(positionAttr, indexArr[1]); - c.fromBufferAttribute(positionAttr, indexArr[2]); - _triangle.getNormal(_normal); - hashes[0] = `${Math.round(a.x * precision)},${Math.round(a.y * precision)},${Math.round(a.z * precision)}`; - hashes[1] = `${Math.round(b.x * precision)},${Math.round(b.y * precision)},${Math.round(b.z * precision)}`; - hashes[2] = `${Math.round(c.x * precision)},${Math.round(c.y * precision)},${Math.round(c.z * precision)}`; - if (hashes[0] === hashes[1] || hashes[1] === hashes[2] || hashes[2] === hashes[0]) { - continue; - } - for (let j = 0; j < 3; j++) { - const jNext = (j + 1) % 3; - const vecHash0 = hashes[j]; - const vecHash1 = hashes[jNext]; - const v0 = _triangle[vertKeys[j]]; - const v1 = _triangle[vertKeys[jNext]]; - const hash = `${vecHash0}_${vecHash1}`; - const reverseHash = `${vecHash1}_${vecHash0}`; - if (reverseHash in edgeData && edgeData[reverseHash]) { - if (_normal.dot(edgeData[reverseHash].normal) <= thresholdDot) { - vertices.push(v0.x, v0.y, v0.z); - vertices.push(v1.x, v1.y, v1.z); - } - edgeData[reverseHash] = null; - } else if (!(hash in edgeData)) { - edgeData[hash] = { - index0: indexArr[j], - index1: indexArr[jNext], - normal: _normal.clone() - }; - } - } - } - for (const key in edgeData) { - if (edgeData[key]) { - const { index0, index1 } = edgeData[key]; - _v0.fromBufferAttribute(positionAttr, index0); - _v1$1.fromBufferAttribute(positionAttr, index1); - vertices.push(_v0.x, _v0.y, _v0.z); - vertices.push(_v1$1.x, _v1$1.y, _v1$1.z); - } - } - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - } - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } -} -class Shape extends Path { - static { - __name(this, "Shape"); - } - constructor(points) { - super(points); - this.uuid = generateUUID(); - this.type = "Shape"; - this.holes = []; - } - getPointsHoles(divisions) { - const holesPts = []; - for (let i = 0, l = this.holes.length; i < l; i++) { - holesPts[i] = this.holes[i].getPoints(divisions); - } - return holesPts; - } - // get points of shape and holes (keypoints based on segments parameter) - extractPoints(divisions) { - return { - shape: this.getPoints(divisions), - holes: this.getPointsHoles(divisions) - }; - } - copy(source) { - super.copy(source); - this.holes = []; - for (let i = 0, l = source.holes.length; i < l; i++) { - const hole = source.holes[i]; - this.holes.push(hole.clone()); - } - return this; - } - toJSON() { - const data = super.toJSON(); - data.uuid = this.uuid; - data.holes = []; - for (let i = 0, l = this.holes.length; i < l; i++) { - const hole = this.holes[i]; - data.holes.push(hole.toJSON()); - } - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.uuid = json.uuid; - this.holes = []; - for (let i = 0, l = json.holes.length; i < l; i++) { - const hole = json.holes[i]; - this.holes.push(new Path().fromJSON(hole)); - } - return this; - } -} -const Earcut = { - triangulate: /* @__PURE__ */ __name(function(data, holeIndices, dim = 2) { - const hasHoles = holeIndices && holeIndices.length; - const outerLen = hasHoles ? holeIndices[0] * dim : data.length; - let outerNode = linkedList(data, 0, outerLen, dim, true); - const triangles = []; - if (!outerNode || outerNode.next === outerNode.prev) return triangles; - let minX, minY, maxX, maxY, x, y, invSize; - if (hasHoles) outerNode = eliminateHoles(data, holeIndices, outerNode, dim); - if (data.length > 80 * dim) { - minX = maxX = data[0]; - minY = maxY = data[1]; - for (let i = dim; i < outerLen; i += dim) { - x = data[i]; - y = data[i + 1]; - if (x < minX) minX = x; - if (y < minY) minY = y; - if (x > maxX) maxX = x; - if (y > maxY) maxY = y; - } - invSize = Math.max(maxX - minX, maxY - minY); - invSize = invSize !== 0 ? 32767 / invSize : 0; - } - earcutLinked(outerNode, triangles, dim, minX, minY, invSize, 0); - return triangles; - }, "triangulate") -}; -function linkedList(data, start, end, dim, clockwise) { - let i, last; - if (clockwise === signedArea(data, start, end, dim) > 0) { - for (i = start; i < end; i += dim) last = insertNode(i, data[i], data[i + 1], last); - } else { - for (i = end - dim; i >= start; i -= dim) last = insertNode(i, data[i], data[i + 1], last); - } - if (last && equals(last, last.next)) { - removeNode(last); - last = last.next; - } - return last; -} -__name(linkedList, "linkedList"); -function filterPoints(start, end) { - if (!start) return start; - if (!end) end = start; - let p = start, again; - do { - again = false; - if (!p.steiner && (equals(p, p.next) || area(p.prev, p, p.next) === 0)) { - removeNode(p); - p = end = p.prev; - if (p === p.next) break; - again = true; - } else { - p = p.next; - } - } while (again || p !== end); - return end; -} -__name(filterPoints, "filterPoints"); -function earcutLinked(ear, triangles, dim, minX, minY, invSize, pass) { - if (!ear) return; - if (!pass && invSize) indexCurve(ear, minX, minY, invSize); - let stop = ear, prev, next; - while (ear.prev !== ear.next) { - prev = ear.prev; - next = ear.next; - if (invSize ? isEarHashed(ear, minX, minY, invSize) : isEar(ear)) { - triangles.push(prev.i / dim | 0); - triangles.push(ear.i / dim | 0); - triangles.push(next.i / dim | 0); - removeNode(ear); - ear = next.next; - stop = next.next; - continue; - } - ear = next; - if (ear === stop) { - if (!pass) { - earcutLinked(filterPoints(ear), triangles, dim, minX, minY, invSize, 1); - } else if (pass === 1) { - ear = cureLocalIntersections(filterPoints(ear), triangles, dim); - earcutLinked(ear, triangles, dim, minX, minY, invSize, 2); - } else if (pass === 2) { - splitEarcut(ear, triangles, dim, minX, minY, invSize); - } - break; - } - } -} -__name(earcutLinked, "earcutLinked"); -function isEar(ear) { - const a = ear.prev, b = ear, c = ear.next; - if (area(a, b, c) >= 0) return false; - const ax = a.x, bx = b.x, cx = c.x, ay = a.y, by = b.y, cy = c.y; - const x0 = ax < bx ? ax < cx ? ax : cx : bx < cx ? bx : cx, y0 = ay < by ? ay < cy ? ay : cy : by < cy ? by : cy, x1 = ax > bx ? ax > cx ? ax : cx : bx > cx ? bx : cx, y1 = ay > by ? ay > cy ? ay : cy : by > cy ? by : cy; - let p = c.next; - while (p !== a) { - if (p.x >= x0 && p.x <= x1 && p.y >= y0 && p.y <= y1 && pointInTriangle(ax, ay, bx, by, cx, cy, p.x, p.y) && area(p.prev, p, p.next) >= 0) return false; - p = p.next; - } - return true; -} -__name(isEar, "isEar"); -function isEarHashed(ear, minX, minY, invSize) { - const a = ear.prev, b = ear, c = ear.next; - if (area(a, b, c) >= 0) return false; - const ax = a.x, bx = b.x, cx = c.x, ay = a.y, by = b.y, cy = c.y; - const x0 = ax < bx ? ax < cx ? ax : cx : bx < cx ? bx : cx, y0 = ay < by ? ay < cy ? ay : cy : by < cy ? by : cy, x1 = ax > bx ? ax > cx ? ax : cx : bx > cx ? bx : cx, y1 = ay > by ? ay > cy ? ay : cy : by > cy ? by : cy; - const minZ = zOrder(x0, y0, minX, minY, invSize), maxZ = zOrder(x1, y1, minX, minY, invSize); - let p = ear.prevZ, n = ear.nextZ; - while (p && p.z >= minZ && n && n.z <= maxZ) { - if (p.x >= x0 && p.x <= x1 && p.y >= y0 && p.y <= y1 && p !== a && p !== c && pointInTriangle(ax, ay, bx, by, cx, cy, p.x, p.y) && area(p.prev, p, p.next) >= 0) return false; - p = p.prevZ; - if (n.x >= x0 && n.x <= x1 && n.y >= y0 && n.y <= y1 && n !== a && n !== c && pointInTriangle(ax, ay, bx, by, cx, cy, n.x, n.y) && area(n.prev, n, n.next) >= 0) return false; - n = n.nextZ; - } - while (p && p.z >= minZ) { - if (p.x >= x0 && p.x <= x1 && p.y >= y0 && p.y <= y1 && p !== a && p !== c && pointInTriangle(ax, ay, bx, by, cx, cy, p.x, p.y) && area(p.prev, p, p.next) >= 0) return false; - p = p.prevZ; - } - while (n && n.z <= maxZ) { - if (n.x >= x0 && n.x <= x1 && n.y >= y0 && n.y <= y1 && n !== a && n !== c && pointInTriangle(ax, ay, bx, by, cx, cy, n.x, n.y) && area(n.prev, n, n.next) >= 0) return false; - n = n.nextZ; - } - return true; -} -__name(isEarHashed, "isEarHashed"); -function cureLocalIntersections(start, triangles, dim) { - let p = start; - do { - const a = p.prev, b = p.next.next; - if (!equals(a, b) && intersects(a, p, p.next, b) && locallyInside(a, b) && locallyInside(b, a)) { - triangles.push(a.i / dim | 0); - triangles.push(p.i / dim | 0); - triangles.push(b.i / dim | 0); - removeNode(p); - removeNode(p.next); - p = start = b; - } - p = p.next; - } while (p !== start); - return filterPoints(p); -} -__name(cureLocalIntersections, "cureLocalIntersections"); -function splitEarcut(start, triangles, dim, minX, minY, invSize) { - let a = start; - do { - let b = a.next.next; - while (b !== a.prev) { - if (a.i !== b.i && isValidDiagonal(a, b)) { - let c = splitPolygon(a, b); - a = filterPoints(a, a.next); - c = filterPoints(c, c.next); - earcutLinked(a, triangles, dim, minX, minY, invSize, 0); - earcutLinked(c, triangles, dim, minX, minY, invSize, 0); - return; - } - b = b.next; - } - a = a.next; - } while (a !== start); -} -__name(splitEarcut, "splitEarcut"); -function eliminateHoles(data, holeIndices, outerNode, dim) { - const queue = []; - let i, len, start, end, list; - for (i = 0, len = holeIndices.length; i < len; i++) { - start = holeIndices[i] * dim; - end = i < len - 1 ? holeIndices[i + 1] * dim : data.length; - list = linkedList(data, start, end, dim, false); - if (list === list.next) list.steiner = true; - queue.push(getLeftmost(list)); - } - queue.sort(compareX); - for (i = 0; i < queue.length; i++) { - outerNode = eliminateHole(queue[i], outerNode); - } - return outerNode; -} -__name(eliminateHoles, "eliminateHoles"); -function compareX(a, b) { - return a.x - b.x; -} -__name(compareX, "compareX"); -function eliminateHole(hole, outerNode) { - const bridge = findHoleBridge(hole, outerNode); - if (!bridge) { - return outerNode; - } - const bridgeReverse = splitPolygon(bridge, hole); - filterPoints(bridgeReverse, bridgeReverse.next); - return filterPoints(bridge, bridge.next); -} -__name(eliminateHole, "eliminateHole"); -function findHoleBridge(hole, outerNode) { - let p = outerNode, qx = -Infinity, m; - const hx = hole.x, hy = hole.y; - do { - if (hy <= p.y && hy >= p.next.y && p.next.y !== p.y) { - const x = p.x + (hy - p.y) * (p.next.x - p.x) / (p.next.y - p.y); - if (x <= hx && x > qx) { - qx = x; - m = p.x < p.next.x ? p : p.next; - if (x === hx) return m; - } - } - p = p.next; - } while (p !== outerNode); - if (!m) return null; - const stop = m, mx = m.x, my = m.y; - let tanMin = Infinity, tan; - p = m; - do { - if (hx >= p.x && p.x >= mx && hx !== p.x && pointInTriangle(hy < my ? hx : qx, hy, mx, my, hy < my ? qx : hx, hy, p.x, p.y)) { - tan = Math.abs(hy - p.y) / (hx - p.x); - if (locallyInside(p, hole) && (tan < tanMin || tan === tanMin && (p.x > m.x || p.x === m.x && sectorContainsSector(m, p)))) { - m = p; - tanMin = tan; - } - } - p = p.next; - } while (p !== stop); - return m; -} -__name(findHoleBridge, "findHoleBridge"); -function sectorContainsSector(m, p) { - return area(m.prev, m, p.prev) < 0 && area(p.next, m, m.next) < 0; -} -__name(sectorContainsSector, "sectorContainsSector"); -function indexCurve(start, minX, minY, invSize) { - let p = start; - do { - if (p.z === 0) p.z = zOrder(p.x, p.y, minX, minY, invSize); - p.prevZ = p.prev; - p.nextZ = p.next; - p = p.next; - } while (p !== start); - p.prevZ.nextZ = null; - p.prevZ = null; - sortLinked(p); -} -__name(indexCurve, "indexCurve"); -function sortLinked(list) { - let i, p, q, e, tail, numMerges, pSize, qSize, inSize = 1; - do { - p = list; - list = null; - tail = null; - numMerges = 0; - while (p) { - numMerges++; - q = p; - pSize = 0; - for (i = 0; i < inSize; i++) { - pSize++; - q = q.nextZ; - if (!q) break; - } - qSize = inSize; - while (pSize > 0 || qSize > 0 && q) { - if (pSize !== 0 && (qSize === 0 || !q || p.z <= q.z)) { - e = p; - p = p.nextZ; - pSize--; - } else { - e = q; - q = q.nextZ; - qSize--; - } - if (tail) tail.nextZ = e; - else list = e; - e.prevZ = tail; - tail = e; - } - p = q; - } - tail.nextZ = null; - inSize *= 2; - } while (numMerges > 1); - return list; -} -__name(sortLinked, "sortLinked"); -function zOrder(x, y, minX, minY, invSize) { - x = (x - minX) * invSize | 0; - y = (y - minY) * invSize | 0; - x = (x | x << 8) & 16711935; - x = (x | x << 4) & 252645135; - x = (x | x << 2) & 858993459; - x = (x | x << 1) & 1431655765; - y = (y | y << 8) & 16711935; - y = (y | y << 4) & 252645135; - y = (y | y << 2) & 858993459; - y = (y | y << 1) & 1431655765; - return x | y << 1; -} -__name(zOrder, "zOrder"); -function getLeftmost(start) { - let p = start, leftmost = start; - do { - if (p.x < leftmost.x || p.x === leftmost.x && p.y < leftmost.y) leftmost = p; - p = p.next; - } while (p !== start); - return leftmost; -} -__name(getLeftmost, "getLeftmost"); -function pointInTriangle(ax, ay, bx, by, cx, cy, px2, py2) { - return (cx - px2) * (ay - py2) >= (ax - px2) * (cy - py2) && (ax - px2) * (by - py2) >= (bx - px2) * (ay - py2) && (bx - px2) * (cy - py2) >= (cx - px2) * (by - py2); -} -__name(pointInTriangle, "pointInTriangle"); -function isValidDiagonal(a, b) { - return a.next.i !== b.i && a.prev.i !== b.i && !intersectsPolygon(a, b) && // dones't intersect other edges - (locallyInside(a, b) && locallyInside(b, a) && middleInside(a, b) && // locally visible - (area(a.prev, a, b.prev) || area(a, b.prev, b)) || // does not create opposite-facing sectors - equals(a, b) && area(a.prev, a, a.next) > 0 && area(b.prev, b, b.next) > 0); -} -__name(isValidDiagonal, "isValidDiagonal"); -function area(p, q, r) { - return (q.y - p.y) * (r.x - q.x) - (q.x - p.x) * (r.y - q.y); -} -__name(area, "area"); -function equals(p1, p2) { - return p1.x === p2.x && p1.y === p2.y; -} -__name(equals, "equals"); -function intersects(p1, q1, p2, q2) { - const o1 = sign(area(p1, q1, p2)); - const o2 = sign(area(p1, q1, q2)); - const o3 = sign(area(p2, q2, p1)); - const o4 = sign(area(p2, q2, q1)); - if (o1 !== o2 && o3 !== o4) return true; - if (o1 === 0 && onSegment(p1, p2, q1)) return true; - if (o2 === 0 && onSegment(p1, q2, q1)) return true; - if (o3 === 0 && onSegment(p2, p1, q2)) return true; - if (o4 === 0 && onSegment(p2, q1, q2)) return true; - return false; -} -__name(intersects, "intersects"); -function onSegment(p, q, r) { - return q.x <= Math.max(p.x, r.x) && q.x >= Math.min(p.x, r.x) && q.y <= Math.max(p.y, r.y) && q.y >= Math.min(p.y, r.y); -} -__name(onSegment, "onSegment"); -function sign(num) { - return num > 0 ? 1 : num < 0 ? -1 : 0; -} -__name(sign, "sign"); -function intersectsPolygon(a, b) { - let p = a; - do { - if (p.i !== a.i && p.next.i !== a.i && p.i !== b.i && p.next.i !== b.i && intersects(p, p.next, a, b)) return true; - p = p.next; - } while (p !== a); - return false; -} -__name(intersectsPolygon, "intersectsPolygon"); -function locallyInside(a, b) { - return area(a.prev, a, a.next) < 0 ? area(a, b, a.next) >= 0 && area(a, a.prev, b) >= 0 : area(a, b, a.prev) < 0 || area(a, a.next, b) < 0; -} -__name(locallyInside, "locallyInside"); -function middleInside(a, b) { - let p = a, inside = false; - const px2 = (a.x + b.x) / 2, py2 = (a.y + b.y) / 2; - do { - if (p.y > py2 !== p.next.y > py2 && p.next.y !== p.y && px2 < (p.next.x - p.x) * (py2 - p.y) / (p.next.y - p.y) + p.x) - inside = !inside; - p = p.next; - } while (p !== a); - return inside; -} -__name(middleInside, "middleInside"); -function splitPolygon(a, b) { - const a2 = new Node(a.i, a.x, a.y), b22 = new Node(b.i, b.x, b.y), an = a.next, bp = b.prev; - a.next = b; - b.prev = a; - a2.next = an; - an.prev = a2; - b22.next = a2; - a2.prev = b22; - bp.next = b22; - b22.prev = bp; - return b22; -} -__name(splitPolygon, "splitPolygon"); -function insertNode(i, x, y, last) { - const p = new Node(i, x, y); - if (!last) { - p.prev = p; - p.next = p; - } else { - p.next = last.next; - p.prev = last; - last.next.prev = p; - last.next = p; - } - return p; -} -__name(insertNode, "insertNode"); -function removeNode(p) { - p.next.prev = p.prev; - p.prev.next = p.next; - if (p.prevZ) p.prevZ.nextZ = p.nextZ; - if (p.nextZ) p.nextZ.prevZ = p.prevZ; -} -__name(removeNode, "removeNode"); -function Node(i, x, y) { - this.i = i; - this.x = x; - this.y = y; - this.prev = null; - this.next = null; - this.z = 0; - this.prevZ = null; - this.nextZ = null; - this.steiner = false; -} -__name(Node, "Node"); -function signedArea(data, start, end, dim) { - let sum = 0; - for (let i = start, j = end - dim; i < end; i += dim) { - sum += (data[j] - data[i]) * (data[i + 1] + data[j + 1]); - j = i; - } - return sum; -} -__name(signedArea, "signedArea"); -class ShapeUtils { - static { - __name(this, "ShapeUtils"); - } - // calculate area of the contour polygon - static area(contour) { - const n = contour.length; - let a = 0; - for (let p = n - 1, q = 0; q < n; p = q++) { - a += contour[p].x * contour[q].y - contour[q].x * contour[p].y; - } - return a * 0.5; - } - static isClockWise(pts) { - return ShapeUtils.area(pts) < 0; - } - static triangulateShape(contour, holes) { - const vertices = []; - const holeIndices = []; - const faces = []; - removeDupEndPts(contour); - addContour(vertices, contour); - let holeIndex = contour.length; - holes.forEach(removeDupEndPts); - for (let i = 0; i < holes.length; i++) { - holeIndices.push(holeIndex); - holeIndex += holes[i].length; - addContour(vertices, holes[i]); - } - const triangles = Earcut.triangulate(vertices, holeIndices); - for (let i = 0; i < triangles.length; i += 3) { - faces.push(triangles.slice(i, i + 3)); - } - return faces; - } -} -function removeDupEndPts(points) { - const l = points.length; - if (l > 2 && points[l - 1].equals(points[0])) { - points.pop(); - } -} -__name(removeDupEndPts, "removeDupEndPts"); -function addContour(vertices, contour) { - for (let i = 0; i < contour.length; i++) { - vertices.push(contour[i].x); - vertices.push(contour[i].y); - } -} -__name(addContour, "addContour"); -class ExtrudeGeometry extends BufferGeometry { - static { - __name(this, "ExtrudeGeometry"); - } - constructor(shapes = new Shape([new Vector2(0.5, 0.5), new Vector2(-0.5, 0.5), new Vector2(-0.5, -0.5), new Vector2(0.5, -0.5)]), options = {}) { - super(); - this.type = "ExtrudeGeometry"; - this.parameters = { - shapes, - options - }; - shapes = Array.isArray(shapes) ? shapes : [shapes]; - const scope = this; - const verticesArray = []; - const uvArray = []; - for (let i = 0, l = shapes.length; i < l; i++) { - const shape = shapes[i]; - addShape(shape); - } - this.setAttribute("position", new Float32BufferAttribute(verticesArray, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvArray, 2)); - this.computeVertexNormals(); - function addShape(shape) { - const placeholder = []; - const curveSegments = options.curveSegments !== void 0 ? options.curveSegments : 12; - const steps = options.steps !== void 0 ? options.steps : 1; - const depth = options.depth !== void 0 ? options.depth : 1; - let bevelEnabled = options.bevelEnabled !== void 0 ? options.bevelEnabled : true; - let bevelThickness = options.bevelThickness !== void 0 ? options.bevelThickness : 0.2; - let bevelSize = options.bevelSize !== void 0 ? options.bevelSize : bevelThickness - 0.1; - let bevelOffset = options.bevelOffset !== void 0 ? options.bevelOffset : 0; - let bevelSegments = options.bevelSegments !== void 0 ? options.bevelSegments : 3; - const extrudePath = options.extrudePath; - const uvgen = options.UVGenerator !== void 0 ? options.UVGenerator : WorldUVGenerator; - let extrudePts, extrudeByPath = false; - let splineTube, binormal, normal, position2; - if (extrudePath) { - extrudePts = extrudePath.getSpacedPoints(steps); - extrudeByPath = true; - bevelEnabled = false; - splineTube = extrudePath.computeFrenetFrames(steps, false); - binormal = new Vector3(); - normal = new Vector3(); - position2 = new Vector3(); - } - if (!bevelEnabled) { - bevelSegments = 0; - bevelThickness = 0; - bevelSize = 0; - bevelOffset = 0; - } - const shapePoints = shape.extractPoints(curveSegments); - let vertices = shapePoints.shape; - const holes = shapePoints.holes; - const reverse = !ShapeUtils.isClockWise(vertices); - if (reverse) { - vertices = vertices.reverse(); - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - if (ShapeUtils.isClockWise(ahole)) { - holes[h] = ahole.reverse(); - } - } - } - const faces = ShapeUtils.triangulateShape(vertices, holes); - const contour = vertices; - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - vertices = vertices.concat(ahole); - } - function scalePt2(pt, vec, size) { - if (!vec) console.error("THREE.ExtrudeGeometry: vec does not exist"); - return pt.clone().addScaledVector(vec, size); - } - __name(scalePt2, "scalePt2"); - const vlen = vertices.length, flen = faces.length; - function getBevelVec(inPt, inPrev, inNext) { - let v_trans_x, v_trans_y, shrink_by; - const v_prev_x = inPt.x - inPrev.x, v_prev_y = inPt.y - inPrev.y; - const v_next_x = inNext.x - inPt.x, v_next_y = inNext.y - inPt.y; - const v_prev_lensq = v_prev_x * v_prev_x + v_prev_y * v_prev_y; - const collinear0 = v_prev_x * v_next_y - v_prev_y * v_next_x; - if (Math.abs(collinear0) > Number.EPSILON) { - const v_prev_len = Math.sqrt(v_prev_lensq); - const v_next_len = Math.sqrt(v_next_x * v_next_x + v_next_y * v_next_y); - const ptPrevShift_x = inPrev.x - v_prev_y / v_prev_len; - const ptPrevShift_y = inPrev.y + v_prev_x / v_prev_len; - const ptNextShift_x = inNext.x - v_next_y / v_next_len; - const ptNextShift_y = inNext.y + v_next_x / v_next_len; - const sf = ((ptNextShift_x - ptPrevShift_x) * v_next_y - (ptNextShift_y - ptPrevShift_y) * v_next_x) / (v_prev_x * v_next_y - v_prev_y * v_next_x); - v_trans_x = ptPrevShift_x + v_prev_x * sf - inPt.x; - v_trans_y = ptPrevShift_y + v_prev_y * sf - inPt.y; - const v_trans_lensq = v_trans_x * v_trans_x + v_trans_y * v_trans_y; - if (v_trans_lensq <= 2) { - return new Vector2(v_trans_x, v_trans_y); - } else { - shrink_by = Math.sqrt(v_trans_lensq / 2); - } - } else { - let direction_eq = false; - if (v_prev_x > Number.EPSILON) { - if (v_next_x > Number.EPSILON) { - direction_eq = true; - } - } else { - if (v_prev_x < -Number.EPSILON) { - if (v_next_x < -Number.EPSILON) { - direction_eq = true; - } - } else { - if (Math.sign(v_prev_y) === Math.sign(v_next_y)) { - direction_eq = true; - } - } - } - if (direction_eq) { - v_trans_x = -v_prev_y; - v_trans_y = v_prev_x; - shrink_by = Math.sqrt(v_prev_lensq); - } else { - v_trans_x = v_prev_x; - v_trans_y = v_prev_y; - shrink_by = Math.sqrt(v_prev_lensq / 2); - } - } - return new Vector2(v_trans_x / shrink_by, v_trans_y / shrink_by); - } - __name(getBevelVec, "getBevelVec"); - const contourMovements = []; - for (let i = 0, il = contour.length, j = il - 1, k = i + 1; i < il; i++, j++, k++) { - if (j === il) j = 0; - if (k === il) k = 0; - contourMovements[i] = getBevelVec(contour[i], contour[j], contour[k]); - } - const holesMovements = []; - let oneHoleMovements, verticesMovements = contourMovements.concat(); - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - oneHoleMovements = []; - for (let i = 0, il = ahole.length, j = il - 1, k = i + 1; i < il; i++, j++, k++) { - if (j === il) j = 0; - if (k === il) k = 0; - oneHoleMovements[i] = getBevelVec(ahole[i], ahole[j], ahole[k]); - } - holesMovements.push(oneHoleMovements); - verticesMovements = verticesMovements.concat(oneHoleMovements); - } - for (let b = 0; b < bevelSegments; b++) { - const t2 = b / bevelSegments; - const z = bevelThickness * Math.cos(t2 * Math.PI / 2); - const bs2 = bevelSize * Math.sin(t2 * Math.PI / 2) + bevelOffset; - for (let i = 0, il = contour.length; i < il; i++) { - const vert = scalePt2(contour[i], contourMovements[i], bs2); - v(vert.x, vert.y, -z); - } - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - oneHoleMovements = holesMovements[h]; - for (let i = 0, il = ahole.length; i < il; i++) { - const vert = scalePt2(ahole[i], oneHoleMovements[i], bs2); - v(vert.x, vert.y, -z); - } - } - } - const bs = bevelSize + bevelOffset; - for (let i = 0; i < vlen; i++) { - const vert = bevelEnabled ? scalePt2(vertices[i], verticesMovements[i], bs) : vertices[i]; - if (!extrudeByPath) { - v(vert.x, vert.y, 0); - } else { - normal.copy(splineTube.normals[0]).multiplyScalar(vert.x); - binormal.copy(splineTube.binormals[0]).multiplyScalar(vert.y); - position2.copy(extrudePts[0]).add(normal).add(binormal); - v(position2.x, position2.y, position2.z); - } - } - for (let s = 1; s <= steps; s++) { - for (let i = 0; i < vlen; i++) { - const vert = bevelEnabled ? scalePt2(vertices[i], verticesMovements[i], bs) : vertices[i]; - if (!extrudeByPath) { - v(vert.x, vert.y, depth / steps * s); - } else { - normal.copy(splineTube.normals[s]).multiplyScalar(vert.x); - binormal.copy(splineTube.binormals[s]).multiplyScalar(vert.y); - position2.copy(extrudePts[s]).add(normal).add(binormal); - v(position2.x, position2.y, position2.z); - } - } - } - for (let b = bevelSegments - 1; b >= 0; b--) { - const t2 = b / bevelSegments; - const z = bevelThickness * Math.cos(t2 * Math.PI / 2); - const bs2 = bevelSize * Math.sin(t2 * Math.PI / 2) + bevelOffset; - for (let i = 0, il = contour.length; i < il; i++) { - const vert = scalePt2(contour[i], contourMovements[i], bs2); - v(vert.x, vert.y, depth + z); - } - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - oneHoleMovements = holesMovements[h]; - for (let i = 0, il = ahole.length; i < il; i++) { - const vert = scalePt2(ahole[i], oneHoleMovements[i], bs2); - if (!extrudeByPath) { - v(vert.x, vert.y, depth + z); - } else { - v(vert.x, vert.y + extrudePts[steps - 1].y, extrudePts[steps - 1].x + z); - } - } - } - } - buildLidFaces(); - buildSideFaces(); - function buildLidFaces() { - const start = verticesArray.length / 3; - if (bevelEnabled) { - let layer = 0; - let offset = vlen * layer; - for (let i = 0; i < flen; i++) { - const face = faces[i]; - f3(face[2] + offset, face[1] + offset, face[0] + offset); - } - layer = steps + bevelSegments * 2; - offset = vlen * layer; - for (let i = 0; i < flen; i++) { - const face = faces[i]; - f3(face[0] + offset, face[1] + offset, face[2] + offset); - } - } else { - for (let i = 0; i < flen; i++) { - const face = faces[i]; - f3(face[2], face[1], face[0]); - } - for (let i = 0; i < flen; i++) { - const face = faces[i]; - f3(face[0] + vlen * steps, face[1] + vlen * steps, face[2] + vlen * steps); - } - } - scope.addGroup(start, verticesArray.length / 3 - start, 0); - } - __name(buildLidFaces, "buildLidFaces"); - function buildSideFaces() { - const start = verticesArray.length / 3; - let layeroffset = 0; - sidewalls(contour, layeroffset); - layeroffset += contour.length; - for (let h = 0, hl = holes.length; h < hl; h++) { - const ahole = holes[h]; - sidewalls(ahole, layeroffset); - layeroffset += ahole.length; - } - scope.addGroup(start, verticesArray.length / 3 - start, 1); - } - __name(buildSideFaces, "buildSideFaces"); - function sidewalls(contour2, layeroffset) { - let i = contour2.length; - while (--i >= 0) { - const j = i; - let k = i - 1; - if (k < 0) k = contour2.length - 1; - for (let s = 0, sl = steps + bevelSegments * 2; s < sl; s++) { - const slen1 = vlen * s; - const slen2 = vlen * (s + 1); - const a = layeroffset + j + slen1, b = layeroffset + k + slen1, c = layeroffset + k + slen2, d = layeroffset + j + slen2; - f4(a, b, c, d); - } - } - } - __name(sidewalls, "sidewalls"); - function v(x, y, z) { - placeholder.push(x); - placeholder.push(y); - placeholder.push(z); - } - __name(v, "v"); - function f3(a, b, c) { - addVertex(a); - addVertex(b); - addVertex(c); - const nextIndex = verticesArray.length / 3; - const uvs = uvgen.generateTopUV(scope, verticesArray, nextIndex - 3, nextIndex - 2, nextIndex - 1); - addUV(uvs[0]); - addUV(uvs[1]); - addUV(uvs[2]); - } - __name(f3, "f3"); - function f4(a, b, c, d) { - addVertex(a); - addVertex(b); - addVertex(d); - addVertex(b); - addVertex(c); - addVertex(d); - const nextIndex = verticesArray.length / 3; - const uvs = uvgen.generateSideWallUV(scope, verticesArray, nextIndex - 6, nextIndex - 3, nextIndex - 2, nextIndex - 1); - addUV(uvs[0]); - addUV(uvs[1]); - addUV(uvs[3]); - addUV(uvs[1]); - addUV(uvs[2]); - addUV(uvs[3]); - } - __name(f4, "f4"); - function addVertex(index) { - verticesArray.push(placeholder[index * 3 + 0]); - verticesArray.push(placeholder[index * 3 + 1]); - verticesArray.push(placeholder[index * 3 + 2]); - } - __name(addVertex, "addVertex"); - function addUV(vector2) { - uvArray.push(vector2.x); - uvArray.push(vector2.y); - } - __name(addUV, "addUV"); - } - __name(addShape, "addShape"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - toJSON() { - const data = super.toJSON(); - const shapes = this.parameters.shapes; - const options = this.parameters.options; - return toJSON$1(shapes, options, data); - } - static fromJSON(data, shapes) { - const geometryShapes = []; - for (let j = 0, jl = data.shapes.length; j < jl; j++) { - const shape = shapes[data.shapes[j]]; - geometryShapes.push(shape); - } - const extrudePath = data.options.extrudePath; - if (extrudePath !== void 0) { - data.options.extrudePath = new Curves[extrudePath.type]().fromJSON(extrudePath); - } - return new ExtrudeGeometry(geometryShapes, data.options); - } -} -const WorldUVGenerator = { - generateTopUV: /* @__PURE__ */ __name(function(geometry, vertices, indexA, indexB, indexC) { - const a_x = vertices[indexA * 3]; - const a_y = vertices[indexA * 3 + 1]; - const b_x = vertices[indexB * 3]; - const b_y = vertices[indexB * 3 + 1]; - const c_x = vertices[indexC * 3]; - const c_y = vertices[indexC * 3 + 1]; - return [ - new Vector2(a_x, a_y), - new Vector2(b_x, b_y), - new Vector2(c_x, c_y) - ]; - }, "generateTopUV"), - generateSideWallUV: /* @__PURE__ */ __name(function(geometry, vertices, indexA, indexB, indexC, indexD) { - const a_x = vertices[indexA * 3]; - const a_y = vertices[indexA * 3 + 1]; - const a_z = vertices[indexA * 3 + 2]; - const b_x = vertices[indexB * 3]; - const b_y = vertices[indexB * 3 + 1]; - const b_z = vertices[indexB * 3 + 2]; - const c_x = vertices[indexC * 3]; - const c_y = vertices[indexC * 3 + 1]; - const c_z = vertices[indexC * 3 + 2]; - const d_x = vertices[indexD * 3]; - const d_y = vertices[indexD * 3 + 1]; - const d_z = vertices[indexD * 3 + 2]; - if (Math.abs(a_y - b_y) < Math.abs(a_x - b_x)) { - return [ - new Vector2(a_x, 1 - a_z), - new Vector2(b_x, 1 - b_z), - new Vector2(c_x, 1 - c_z), - new Vector2(d_x, 1 - d_z) - ]; - } else { - return [ - new Vector2(a_y, 1 - a_z), - new Vector2(b_y, 1 - b_z), - new Vector2(c_y, 1 - c_z), - new Vector2(d_y, 1 - d_z) - ]; - } - }, "generateSideWallUV") -}; -function toJSON$1(shapes, options, data) { - data.shapes = []; - if (Array.isArray(shapes)) { - for (let i = 0, l = shapes.length; i < l; i++) { - const shape = shapes[i]; - data.shapes.push(shape.uuid); - } - } else { - data.shapes.push(shapes.uuid); - } - data.options = Object.assign({}, options); - if (options.extrudePath !== void 0) data.options.extrudePath = options.extrudePath.toJSON(); - return data; -} -__name(toJSON$1, "toJSON$1"); -class IcosahedronGeometry extends PolyhedronGeometry { - static { - __name(this, "IcosahedronGeometry"); - } - constructor(radius = 1, detail = 0) { - const t2 = (1 + Math.sqrt(5)) / 2; - const vertices = [ - -1, - t2, - 0, - 1, - t2, - 0, - -1, - -t2, - 0, - 1, - -t2, - 0, - 0, - -1, - t2, - 0, - 1, - t2, - 0, - -1, - -t2, - 0, - 1, - -t2, - t2, - 0, - -1, - t2, - 0, - 1, - -t2, - 0, - -1, - -t2, - 0, - 1 - ]; - const indices = [ - 0, - 11, - 5, - 0, - 5, - 1, - 0, - 1, - 7, - 0, - 7, - 10, - 0, - 10, - 11, - 1, - 5, - 9, - 5, - 11, - 4, - 11, - 10, - 2, - 10, - 7, - 6, - 7, - 1, - 8, - 3, - 9, - 4, - 3, - 4, - 2, - 3, - 2, - 6, - 3, - 6, - 8, - 3, - 8, - 9, - 4, - 9, - 5, - 2, - 4, - 11, - 6, - 2, - 10, - 8, - 6, - 7, - 9, - 8, - 1 - ]; - super(vertices, indices, radius, detail); - this.type = "IcosahedronGeometry"; - this.parameters = { - radius, - detail - }; - } - static fromJSON(data) { - return new IcosahedronGeometry(data.radius, data.detail); - } -} -class OctahedronGeometry extends PolyhedronGeometry { - static { - __name(this, "OctahedronGeometry"); - } - constructor(radius = 1, detail = 0) { - const vertices = [ - 1, - 0, - 0, - -1, - 0, - 0, - 0, - 1, - 0, - 0, - -1, - 0, - 0, - 0, - 1, - 0, - 0, - -1 - ]; - const indices = [ - 0, - 2, - 4, - 0, - 4, - 3, - 0, - 3, - 5, - 0, - 5, - 2, - 1, - 2, - 5, - 1, - 5, - 3, - 1, - 3, - 4, - 1, - 4, - 2 - ]; - super(vertices, indices, radius, detail); - this.type = "OctahedronGeometry"; - this.parameters = { - radius, - detail - }; - } - static fromJSON(data) { - return new OctahedronGeometry(data.radius, data.detail); - } -} -class RingGeometry extends BufferGeometry { - static { - __name(this, "RingGeometry"); - } - constructor(innerRadius = 0.5, outerRadius = 1, thetaSegments = 32, phiSegments = 1, thetaStart = 0, thetaLength = Math.PI * 2) { - super(); - this.type = "RingGeometry"; - this.parameters = { - innerRadius, - outerRadius, - thetaSegments, - phiSegments, - thetaStart, - thetaLength - }; - thetaSegments = Math.max(3, thetaSegments); - phiSegments = Math.max(1, phiSegments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - let radius = innerRadius; - const radiusStep = (outerRadius - innerRadius) / phiSegments; - const vertex2 = new Vector3(); - const uv = new Vector2(); - for (let j = 0; j <= phiSegments; j++) { - for (let i = 0; i <= thetaSegments; i++) { - const segment = thetaStart + i / thetaSegments * thetaLength; - vertex2.x = radius * Math.cos(segment); - vertex2.y = radius * Math.sin(segment); - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normals.push(0, 0, 1); - uv.x = (vertex2.x / outerRadius + 1) / 2; - uv.y = (vertex2.y / outerRadius + 1) / 2; - uvs.push(uv.x, uv.y); - } - radius += radiusStep; - } - for (let j = 0; j < phiSegments; j++) { - const thetaSegmentLevel = j * (thetaSegments + 1); - for (let i = 0; i < thetaSegments; i++) { - const segment = i + thetaSegmentLevel; - const a = segment; - const b = segment + thetaSegments + 1; - const c = segment + thetaSegments + 2; - const d = segment + 1; - indices.push(a, b, d); - indices.push(b, c, d); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new RingGeometry(data.innerRadius, data.outerRadius, data.thetaSegments, data.phiSegments, data.thetaStart, data.thetaLength); - } -} -class ShapeGeometry extends BufferGeometry { - static { - __name(this, "ShapeGeometry"); - } - constructor(shapes = new Shape([new Vector2(0, 0.5), new Vector2(-0.5, -0.5), new Vector2(0.5, -0.5)]), curveSegments = 12) { - super(); - this.type = "ShapeGeometry"; - this.parameters = { - shapes, - curveSegments - }; - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - let groupStart = 0; - let groupCount = 0; - if (Array.isArray(shapes) === false) { - addShape(shapes); - } else { - for (let i = 0; i < shapes.length; i++) { - addShape(shapes[i]); - this.addGroup(groupStart, groupCount, i); - groupStart += groupCount; - groupCount = 0; - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - function addShape(shape) { - const indexOffset = vertices.length / 3; - const points = shape.extractPoints(curveSegments); - let shapeVertices = points.shape; - const shapeHoles = points.holes; - if (ShapeUtils.isClockWise(shapeVertices) === false) { - shapeVertices = shapeVertices.reverse(); - } - for (let i = 0, l = shapeHoles.length; i < l; i++) { - const shapeHole = shapeHoles[i]; - if (ShapeUtils.isClockWise(shapeHole) === true) { - shapeHoles[i] = shapeHole.reverse(); - } - } - const faces = ShapeUtils.triangulateShape(shapeVertices, shapeHoles); - for (let i = 0, l = shapeHoles.length; i < l; i++) { - const shapeHole = shapeHoles[i]; - shapeVertices = shapeVertices.concat(shapeHole); - } - for (let i = 0, l = shapeVertices.length; i < l; i++) { - const vertex2 = shapeVertices[i]; - vertices.push(vertex2.x, vertex2.y, 0); - normals.push(0, 0, 1); - uvs.push(vertex2.x, vertex2.y); - } - for (let i = 0, l = faces.length; i < l; i++) { - const face = faces[i]; - const a = face[0] + indexOffset; - const b = face[1] + indexOffset; - const c = face[2] + indexOffset; - indices.push(a, b, c); - groupCount += 3; - } - } - __name(addShape, "addShape"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - toJSON() { - const data = super.toJSON(); - const shapes = this.parameters.shapes; - return toJSON(shapes, data); - } - static fromJSON(data, shapes) { - const geometryShapes = []; - for (let j = 0, jl = data.shapes.length; j < jl; j++) { - const shape = shapes[data.shapes[j]]; - geometryShapes.push(shape); - } - return new ShapeGeometry(geometryShapes, data.curveSegments); - } -} -function toJSON(shapes, data) { - data.shapes = []; - if (Array.isArray(shapes)) { - for (let i = 0, l = shapes.length; i < l; i++) { - const shape = shapes[i]; - data.shapes.push(shape.uuid); - } - } else { - data.shapes.push(shapes.uuid); - } - return data; -} -__name(toJSON, "toJSON"); -class SphereGeometry extends BufferGeometry { - static { - __name(this, "SphereGeometry"); - } - constructor(radius = 1, widthSegments = 32, heightSegments = 16, phiStart = 0, phiLength = Math.PI * 2, thetaStart = 0, thetaLength = Math.PI) { - super(); - this.type = "SphereGeometry"; - this.parameters = { - radius, - widthSegments, - heightSegments, - phiStart, - phiLength, - thetaStart, - thetaLength - }; - widthSegments = Math.max(3, Math.floor(widthSegments)); - heightSegments = Math.max(2, Math.floor(heightSegments)); - const thetaEnd = Math.min(thetaStart + thetaLength, Math.PI); - let index = 0; - const grid = []; - const vertex2 = new Vector3(); - const normal = new Vector3(); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - for (let iy = 0; iy <= heightSegments; iy++) { - const verticesRow = []; - const v = iy / heightSegments; - let uOffset = 0; - if (iy === 0 && thetaStart === 0) { - uOffset = 0.5 / widthSegments; - } else if (iy === heightSegments && thetaEnd === Math.PI) { - uOffset = -0.5 / widthSegments; - } - for (let ix = 0; ix <= widthSegments; ix++) { - const u = ix / widthSegments; - vertex2.x = -radius * Math.cos(phiStart + u * phiLength) * Math.sin(thetaStart + v * thetaLength); - vertex2.y = radius * Math.cos(thetaStart + v * thetaLength); - vertex2.z = radius * Math.sin(phiStart + u * phiLength) * Math.sin(thetaStart + v * thetaLength); - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normal.copy(vertex2).normalize(); - normals.push(normal.x, normal.y, normal.z); - uvs.push(u + uOffset, 1 - v); - verticesRow.push(index++); - } - grid.push(verticesRow); - } - for (let iy = 0; iy < heightSegments; iy++) { - for (let ix = 0; ix < widthSegments; ix++) { - const a = grid[iy][ix + 1]; - const b = grid[iy][ix]; - const c = grid[iy + 1][ix]; - const d = grid[iy + 1][ix + 1]; - if (iy !== 0 || thetaStart > 0) indices.push(a, b, d); - if (iy !== heightSegments - 1 || thetaEnd < Math.PI) indices.push(b, c, d); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new SphereGeometry(data.radius, data.widthSegments, data.heightSegments, data.phiStart, data.phiLength, data.thetaStart, data.thetaLength); - } -} -class TetrahedronGeometry extends PolyhedronGeometry { - static { - __name(this, "TetrahedronGeometry"); - } - constructor(radius = 1, detail = 0) { - const vertices = [ - 1, - 1, - 1, - -1, - -1, - 1, - -1, - 1, - -1, - 1, - -1, - -1 - ]; - const indices = [ - 2, - 1, - 0, - 0, - 3, - 2, - 1, - 3, - 0, - 2, - 3, - 1 - ]; - super(vertices, indices, radius, detail); - this.type = "TetrahedronGeometry"; - this.parameters = { - radius, - detail - }; - } - static fromJSON(data) { - return new TetrahedronGeometry(data.radius, data.detail); - } -} -class TorusGeometry extends BufferGeometry { - static { - __name(this, "TorusGeometry"); - } - constructor(radius = 1, tube = 0.4, radialSegments = 12, tubularSegments = 48, arc = Math.PI * 2) { - super(); - this.type = "TorusGeometry"; - this.parameters = { - radius, - tube, - radialSegments, - tubularSegments, - arc - }; - radialSegments = Math.floor(radialSegments); - tubularSegments = Math.floor(tubularSegments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - const center = new Vector3(); - const vertex2 = new Vector3(); - const normal = new Vector3(); - for (let j = 0; j <= radialSegments; j++) { - for (let i = 0; i <= tubularSegments; i++) { - const u = i / tubularSegments * arc; - const v = j / radialSegments * Math.PI * 2; - vertex2.x = (radius + tube * Math.cos(v)) * Math.cos(u); - vertex2.y = (radius + tube * Math.cos(v)) * Math.sin(u); - vertex2.z = tube * Math.sin(v); - vertices.push(vertex2.x, vertex2.y, vertex2.z); - center.x = radius * Math.cos(u); - center.y = radius * Math.sin(u); - normal.subVectors(vertex2, center).normalize(); - normals.push(normal.x, normal.y, normal.z); - uvs.push(i / tubularSegments); - uvs.push(j / radialSegments); - } - } - for (let j = 1; j <= radialSegments; j++) { - for (let i = 1; i <= tubularSegments; i++) { - const a = (tubularSegments + 1) * j + i - 1; - const b = (tubularSegments + 1) * (j - 1) + i - 1; - const c = (tubularSegments + 1) * (j - 1) + i; - const d = (tubularSegments + 1) * j + i; - indices.push(a, b, d); - indices.push(b, c, d); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new TorusGeometry(data.radius, data.tube, data.radialSegments, data.tubularSegments, data.arc); - } -} -class TorusKnotGeometry extends BufferGeometry { - static { - __name(this, "TorusKnotGeometry"); - } - constructor(radius = 1, tube = 0.4, tubularSegments = 64, radialSegments = 8, p = 2, q = 3) { - super(); - this.type = "TorusKnotGeometry"; - this.parameters = { - radius, - tube, - tubularSegments, - radialSegments, - p, - q - }; - tubularSegments = Math.floor(tubularSegments); - radialSegments = Math.floor(radialSegments); - const indices = []; - const vertices = []; - const normals = []; - const uvs = []; - const vertex2 = new Vector3(); - const normal = new Vector3(); - const P1 = new Vector3(); - const P2 = new Vector3(); - const B = new Vector3(); - const T = new Vector3(); - const N = new Vector3(); - for (let i = 0; i <= tubularSegments; ++i) { - const u = i / tubularSegments * p * Math.PI * 2; - calculatePositionOnCurve(u, p, q, radius, P1); - calculatePositionOnCurve(u + 0.01, p, q, radius, P2); - T.subVectors(P2, P1); - N.addVectors(P2, P1); - B.crossVectors(T, N); - N.crossVectors(B, T); - B.normalize(); - N.normalize(); - for (let j = 0; j <= radialSegments; ++j) { - const v = j / radialSegments * Math.PI * 2; - const cx = -tube * Math.cos(v); - const cy = tube * Math.sin(v); - vertex2.x = P1.x + (cx * N.x + cy * B.x); - vertex2.y = P1.y + (cx * N.y + cy * B.y); - vertex2.z = P1.z + (cx * N.z + cy * B.z); - vertices.push(vertex2.x, vertex2.y, vertex2.z); - normal.subVectors(vertex2, P1).normalize(); - normals.push(normal.x, normal.y, normal.z); - uvs.push(i / tubularSegments); - uvs.push(j / radialSegments); - } - } - for (let j = 1; j <= tubularSegments; j++) { - for (let i = 1; i <= radialSegments; i++) { - const a = (radialSegments + 1) * (j - 1) + (i - 1); - const b = (radialSegments + 1) * j + (i - 1); - const c = (radialSegments + 1) * j + i; - const d = (radialSegments + 1) * (j - 1) + i; - indices.push(a, b, d); - indices.push(b, c, d); - } - } - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - function calculatePositionOnCurve(u, p2, q2, radius2, position) { - const cu = Math.cos(u); - const su = Math.sin(u); - const quOverP = q2 / p2 * u; - const cs = Math.cos(quOverP); - position.x = radius2 * (2 + cs) * 0.5 * cu; - position.y = radius2 * (2 + cs) * su * 0.5; - position.z = radius2 * Math.sin(quOverP) * 0.5; - } - __name(calculatePositionOnCurve, "calculatePositionOnCurve"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - static fromJSON(data) { - return new TorusKnotGeometry(data.radius, data.tube, data.tubularSegments, data.radialSegments, data.p, data.q); - } -} -class TubeGeometry extends BufferGeometry { - static { - __name(this, "TubeGeometry"); - } - constructor(path = new QuadraticBezierCurve3(new Vector3(-1, -1, 0), new Vector3(-1, 1, 0), new Vector3(1, 1, 0)), tubularSegments = 64, radius = 1, radialSegments = 8, closed = false) { - super(); - this.type = "TubeGeometry"; - this.parameters = { - path, - tubularSegments, - radius, - radialSegments, - closed - }; - const frames = path.computeFrenetFrames(tubularSegments, closed); - this.tangents = frames.tangents; - this.normals = frames.normals; - this.binormals = frames.binormals; - const vertex2 = new Vector3(); - const normal = new Vector3(); - const uv = new Vector2(); - let P = new Vector3(); - const vertices = []; - const normals = []; - const uvs = []; - const indices = []; - generateBufferData(); - this.setIndex(indices); - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - this.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - this.setAttribute("uv", new Float32BufferAttribute(uvs, 2)); - function generateBufferData() { - for (let i = 0; i < tubularSegments; i++) { - generateSegment(i); - } - generateSegment(closed === false ? tubularSegments : 0); - generateUVs(); - generateIndices(); - } - __name(generateBufferData, "generateBufferData"); - function generateSegment(i) { - P = path.getPointAt(i / tubularSegments, P); - const N = frames.normals[i]; - const B = frames.binormals[i]; - for (let j = 0; j <= radialSegments; j++) { - const v = j / radialSegments * Math.PI * 2; - const sin = Math.sin(v); - const cos = -Math.cos(v); - normal.x = cos * N.x + sin * B.x; - normal.y = cos * N.y + sin * B.y; - normal.z = cos * N.z + sin * B.z; - normal.normalize(); - normals.push(normal.x, normal.y, normal.z); - vertex2.x = P.x + radius * normal.x; - vertex2.y = P.y + radius * normal.y; - vertex2.z = P.z + radius * normal.z; - vertices.push(vertex2.x, vertex2.y, vertex2.z); - } - } - __name(generateSegment, "generateSegment"); - function generateIndices() { - for (let j = 1; j <= tubularSegments; j++) { - for (let i = 1; i <= radialSegments; i++) { - const a = (radialSegments + 1) * (j - 1) + (i - 1); - const b = (radialSegments + 1) * j + (i - 1); - const c = (radialSegments + 1) * j + i; - const d = (radialSegments + 1) * (j - 1) + i; - indices.push(a, b, d); - indices.push(b, c, d); - } - } - } - __name(generateIndices, "generateIndices"); - function generateUVs() { - for (let i = 0; i <= tubularSegments; i++) { - for (let j = 0; j <= radialSegments; j++) { - uv.x = i / tubularSegments; - uv.y = j / radialSegments; - uvs.push(uv.x, uv.y); - } - } - } - __name(generateUVs, "generateUVs"); - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } - toJSON() { - const data = super.toJSON(); - data.path = this.parameters.path.toJSON(); - return data; - } - static fromJSON(data) { - return new TubeGeometry( - new Curves[data.path.type]().fromJSON(data.path), - data.tubularSegments, - data.radius, - data.radialSegments, - data.closed - ); - } -} -class WireframeGeometry extends BufferGeometry { - static { - __name(this, "WireframeGeometry"); - } - constructor(geometry = null) { - super(); - this.type = "WireframeGeometry"; - this.parameters = { - geometry - }; - if (geometry !== null) { - const vertices = []; - const edges = /* @__PURE__ */ new Set(); - const start = new Vector3(); - const end = new Vector3(); - if (geometry.index !== null) { - const position = geometry.attributes.position; - const indices = geometry.index; - let groups = geometry.groups; - if (groups.length === 0) { - groups = [{ start: 0, count: indices.count, materialIndex: 0 }]; - } - for (let o = 0, ol = groups.length; o < ol; ++o) { - const group = groups[o]; - const groupStart = group.start; - const groupCount = group.count; - for (let i = groupStart, l = groupStart + groupCount; i < l; i += 3) { - for (let j = 0; j < 3; j++) { - const index1 = indices.getX(i + j); - const index2 = indices.getX(i + (j + 1) % 3); - start.fromBufferAttribute(position, index1); - end.fromBufferAttribute(position, index2); - if (isUniqueEdge(start, end, edges) === true) { - vertices.push(start.x, start.y, start.z); - vertices.push(end.x, end.y, end.z); - } - } - } - } - } else { - const position = geometry.attributes.position; - for (let i = 0, l = position.count / 3; i < l; i++) { - for (let j = 0; j < 3; j++) { - const index1 = 3 * i + j; - const index2 = 3 * i + (j + 1) % 3; - start.fromBufferAttribute(position, index1); - end.fromBufferAttribute(position, index2); - if (isUniqueEdge(start, end, edges) === true) { - vertices.push(start.x, start.y, start.z); - vertices.push(end.x, end.y, end.z); - } - } - } - } - this.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - } - } - copy(source) { - super.copy(source); - this.parameters = Object.assign({}, source.parameters); - return this; - } -} -function isUniqueEdge(start, end, edges) { - const hash1 = `${start.x},${start.y},${start.z}-${end.x},${end.y},${end.z}`; - const hash2 = `${end.x},${end.y},${end.z}-${start.x},${start.y},${start.z}`; - if (edges.has(hash1) === true || edges.has(hash2) === true) { - return false; - } else { - edges.add(hash1); - edges.add(hash2); - return true; - } -} -__name(isUniqueEdge, "isUniqueEdge"); -var Geometries = /* @__PURE__ */ Object.freeze({ - __proto__: null, - BoxGeometry, - CapsuleGeometry, - CircleGeometry, - ConeGeometry, - CylinderGeometry, - DodecahedronGeometry, - EdgesGeometry, - ExtrudeGeometry, - IcosahedronGeometry, - LatheGeometry, - OctahedronGeometry, - PlaneGeometry, - PolyhedronGeometry, - RingGeometry, - ShapeGeometry, - SphereGeometry, - TetrahedronGeometry, - TorusGeometry, - TorusKnotGeometry, - TubeGeometry, - WireframeGeometry -}); -class ShadowMaterial extends Material { - static { - __name(this, "ShadowMaterial"); - } - static get type() { - return "ShadowMaterial"; - } - constructor(parameters) { - super(); - this.isShadowMaterial = true; - this.color = new Color(0); - this.transparent = true; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.fog = source.fog; - return this; - } -} -class RawShaderMaterial extends ShaderMaterial { - static { - __name(this, "RawShaderMaterial"); - } - static get type() { - return "RawShaderMaterial"; - } - constructor(parameters) { - super(parameters); - this.isRawShaderMaterial = true; - } -} -class MeshStandardMaterial extends Material { - static { - __name(this, "MeshStandardMaterial"); - } - static get type() { - return "MeshStandardMaterial"; - } - constructor(parameters) { - super(); - this.isMeshStandardMaterial = true; - this.defines = { "STANDARD": "" }; - this.color = new Color(16777215); - this.roughness = 1; - this.metalness = 0; - this.map = null; - this.lightMap = null; - this.lightMapIntensity = 1; - this.aoMap = null; - this.aoMapIntensity = 1; - this.emissive = new Color(0); - this.emissiveIntensity = 1; - this.emissiveMap = null; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.roughnessMap = null; - this.metalnessMap = null; - this.alphaMap = null; - this.envMap = null; - this.envMapRotation = new Euler(); - this.envMapIntensity = 1; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.wireframeLinecap = "round"; - this.wireframeLinejoin = "round"; - this.flatShading = false; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.defines = { "STANDARD": "" }; - this.color.copy(source.color); - this.roughness = source.roughness; - this.metalness = source.metalness; - this.map = source.map; - this.lightMap = source.lightMap; - this.lightMapIntensity = source.lightMapIntensity; - this.aoMap = source.aoMap; - this.aoMapIntensity = source.aoMapIntensity; - this.emissive.copy(source.emissive); - this.emissiveMap = source.emissiveMap; - this.emissiveIntensity = source.emissiveIntensity; - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.roughnessMap = source.roughnessMap; - this.metalnessMap = source.metalnessMap; - this.alphaMap = source.alphaMap; - this.envMap = source.envMap; - this.envMapRotation.copy(source.envMapRotation); - this.envMapIntensity = source.envMapIntensity; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.wireframeLinecap = source.wireframeLinecap; - this.wireframeLinejoin = source.wireframeLinejoin; - this.flatShading = source.flatShading; - this.fog = source.fog; - return this; - } -} -class MeshPhysicalMaterial extends MeshStandardMaterial { - static { - __name(this, "MeshPhysicalMaterial"); - } - static get type() { - return "MeshPhysicalMaterial"; - } - constructor(parameters) { - super(); - this.isMeshPhysicalMaterial = true; - this.defines = { - "STANDARD": "", - "PHYSICAL": "" - }; - this.anisotropyRotation = 0; - this.anisotropyMap = null; - this.clearcoatMap = null; - this.clearcoatRoughness = 0; - this.clearcoatRoughnessMap = null; - this.clearcoatNormalScale = new Vector2(1, 1); - this.clearcoatNormalMap = null; - this.ior = 1.5; - Object.defineProperty(this, "reflectivity", { - get: /* @__PURE__ */ __name(function() { - return clamp(2.5 * (this.ior - 1) / (this.ior + 1), 0, 1); - }, "get"), - set: /* @__PURE__ */ __name(function(reflectivity) { - this.ior = (1 + 0.4 * reflectivity) / (1 - 0.4 * reflectivity); - }, "set") - }); - this.iridescenceMap = null; - this.iridescenceIOR = 1.3; - this.iridescenceThicknessRange = [100, 400]; - this.iridescenceThicknessMap = null; - this.sheenColor = new Color(0); - this.sheenColorMap = null; - this.sheenRoughness = 1; - this.sheenRoughnessMap = null; - this.transmissionMap = null; - this.thickness = 0; - this.thicknessMap = null; - this.attenuationDistance = Infinity; - this.attenuationColor = new Color(1, 1, 1); - this.specularIntensity = 1; - this.specularIntensityMap = null; - this.specularColor = new Color(1, 1, 1); - this.specularColorMap = null; - this._anisotropy = 0; - this._clearcoat = 0; - this._dispersion = 0; - this._iridescence = 0; - this._sheen = 0; - this._transmission = 0; - this.setValues(parameters); - } - get anisotropy() { - return this._anisotropy; - } - set anisotropy(value) { - if (this._anisotropy > 0 !== value > 0) { - this.version++; - } - this._anisotropy = value; - } - get clearcoat() { - return this._clearcoat; - } - set clearcoat(value) { - if (this._clearcoat > 0 !== value > 0) { - this.version++; - } - this._clearcoat = value; - } - get iridescence() { - return this._iridescence; - } - set iridescence(value) { - if (this._iridescence > 0 !== value > 0) { - this.version++; - } - this._iridescence = value; - } - get dispersion() { - return this._dispersion; - } - set dispersion(value) { - if (this._dispersion > 0 !== value > 0) { - this.version++; - } - this._dispersion = value; - } - get sheen() { - return this._sheen; - } - set sheen(value) { - if (this._sheen > 0 !== value > 0) { - this.version++; - } - this._sheen = value; - } - get transmission() { - return this._transmission; - } - set transmission(value) { - if (this._transmission > 0 !== value > 0) { - this.version++; - } - this._transmission = value; - } - copy(source) { - super.copy(source); - this.defines = { - "STANDARD": "", - "PHYSICAL": "" - }; - this.anisotropy = source.anisotropy; - this.anisotropyRotation = source.anisotropyRotation; - this.anisotropyMap = source.anisotropyMap; - this.clearcoat = source.clearcoat; - this.clearcoatMap = source.clearcoatMap; - this.clearcoatRoughness = source.clearcoatRoughness; - this.clearcoatRoughnessMap = source.clearcoatRoughnessMap; - this.clearcoatNormalMap = source.clearcoatNormalMap; - this.clearcoatNormalScale.copy(source.clearcoatNormalScale); - this.dispersion = source.dispersion; - this.ior = source.ior; - this.iridescence = source.iridescence; - this.iridescenceMap = source.iridescenceMap; - this.iridescenceIOR = source.iridescenceIOR; - this.iridescenceThicknessRange = [...source.iridescenceThicknessRange]; - this.iridescenceThicknessMap = source.iridescenceThicknessMap; - this.sheen = source.sheen; - this.sheenColor.copy(source.sheenColor); - this.sheenColorMap = source.sheenColorMap; - this.sheenRoughness = source.sheenRoughness; - this.sheenRoughnessMap = source.sheenRoughnessMap; - this.transmission = source.transmission; - this.transmissionMap = source.transmissionMap; - this.thickness = source.thickness; - this.thicknessMap = source.thicknessMap; - this.attenuationDistance = source.attenuationDistance; - this.attenuationColor.copy(source.attenuationColor); - this.specularIntensity = source.specularIntensity; - this.specularIntensityMap = source.specularIntensityMap; - this.specularColor.copy(source.specularColor); - this.specularColorMap = source.specularColorMap; - return this; - } -} -class MeshPhongMaterial extends Material { - static { - __name(this, "MeshPhongMaterial"); - } - static get type() { - return "MeshPhongMaterial"; - } - constructor(parameters) { - super(); - this.isMeshPhongMaterial = true; - this.color = new Color(16777215); - this.specular = new Color(1118481); - this.shininess = 30; - this.map = null; - this.lightMap = null; - this.lightMapIntensity = 1; - this.aoMap = null; - this.aoMapIntensity = 1; - this.emissive = new Color(0); - this.emissiveIntensity = 1; - this.emissiveMap = null; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.specularMap = null; - this.alphaMap = null; - this.envMap = null; - this.envMapRotation = new Euler(); - this.combine = MultiplyOperation; - this.reflectivity = 1; - this.refractionRatio = 0.98; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.wireframeLinecap = "round"; - this.wireframeLinejoin = "round"; - this.flatShading = false; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.specular.copy(source.specular); - this.shininess = source.shininess; - this.map = source.map; - this.lightMap = source.lightMap; - this.lightMapIntensity = source.lightMapIntensity; - this.aoMap = source.aoMap; - this.aoMapIntensity = source.aoMapIntensity; - this.emissive.copy(source.emissive); - this.emissiveMap = source.emissiveMap; - this.emissiveIntensity = source.emissiveIntensity; - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.specularMap = source.specularMap; - this.alphaMap = source.alphaMap; - this.envMap = source.envMap; - this.envMapRotation.copy(source.envMapRotation); - this.combine = source.combine; - this.reflectivity = source.reflectivity; - this.refractionRatio = source.refractionRatio; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.wireframeLinecap = source.wireframeLinecap; - this.wireframeLinejoin = source.wireframeLinejoin; - this.flatShading = source.flatShading; - this.fog = source.fog; - return this; - } -} -class MeshToonMaterial extends Material { - static { - __name(this, "MeshToonMaterial"); - } - static get type() { - return "MeshToonMaterial"; - } - constructor(parameters) { - super(); - this.isMeshToonMaterial = true; - this.defines = { "TOON": "" }; - this.color = new Color(16777215); - this.map = null; - this.gradientMap = null; - this.lightMap = null; - this.lightMapIntensity = 1; - this.aoMap = null; - this.aoMapIntensity = 1; - this.emissive = new Color(0); - this.emissiveIntensity = 1; - this.emissiveMap = null; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.alphaMap = null; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.wireframeLinecap = "round"; - this.wireframeLinejoin = "round"; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.gradientMap = source.gradientMap; - this.lightMap = source.lightMap; - this.lightMapIntensity = source.lightMapIntensity; - this.aoMap = source.aoMap; - this.aoMapIntensity = source.aoMapIntensity; - this.emissive.copy(source.emissive); - this.emissiveMap = source.emissiveMap; - this.emissiveIntensity = source.emissiveIntensity; - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.alphaMap = source.alphaMap; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.wireframeLinecap = source.wireframeLinecap; - this.wireframeLinejoin = source.wireframeLinejoin; - this.fog = source.fog; - return this; - } -} -class MeshNormalMaterial extends Material { - static { - __name(this, "MeshNormalMaterial"); - } - static get type() { - return "MeshNormalMaterial"; - } - constructor(parameters) { - super(); - this.isMeshNormalMaterial = true; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.flatShading = false; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.flatShading = source.flatShading; - return this; - } -} -class MeshLambertMaterial extends Material { - static { - __name(this, "MeshLambertMaterial"); - } - static get type() { - return "MeshLambertMaterial"; - } - constructor(parameters) { - super(); - this.isMeshLambertMaterial = true; - this.color = new Color(16777215); - this.map = null; - this.lightMap = null; - this.lightMapIntensity = 1; - this.aoMap = null; - this.aoMapIntensity = 1; - this.emissive = new Color(0); - this.emissiveIntensity = 1; - this.emissiveMap = null; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.specularMap = null; - this.alphaMap = null; - this.envMap = null; - this.envMapRotation = new Euler(); - this.combine = MultiplyOperation; - this.reflectivity = 1; - this.refractionRatio = 0.98; - this.wireframe = false; - this.wireframeLinewidth = 1; - this.wireframeLinecap = "round"; - this.wireframeLinejoin = "round"; - this.flatShading = false; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.color.copy(source.color); - this.map = source.map; - this.lightMap = source.lightMap; - this.lightMapIntensity = source.lightMapIntensity; - this.aoMap = source.aoMap; - this.aoMapIntensity = source.aoMapIntensity; - this.emissive.copy(source.emissive); - this.emissiveMap = source.emissiveMap; - this.emissiveIntensity = source.emissiveIntensity; - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.specularMap = source.specularMap; - this.alphaMap = source.alphaMap; - this.envMap = source.envMap; - this.envMapRotation.copy(source.envMapRotation); - this.combine = source.combine; - this.reflectivity = source.reflectivity; - this.refractionRatio = source.refractionRatio; - this.wireframe = source.wireframe; - this.wireframeLinewidth = source.wireframeLinewidth; - this.wireframeLinecap = source.wireframeLinecap; - this.wireframeLinejoin = source.wireframeLinejoin; - this.flatShading = source.flatShading; - this.fog = source.fog; - return this; - } -} -class MeshMatcapMaterial extends Material { - static { - __name(this, "MeshMatcapMaterial"); - } - static get type() { - return "MeshMatcapMaterial"; - } - constructor(parameters) { - super(); - this.isMeshMatcapMaterial = true; - this.defines = { "MATCAP": "" }; - this.color = new Color(16777215); - this.matcap = null; - this.map = null; - this.bumpMap = null; - this.bumpScale = 1; - this.normalMap = null; - this.normalMapType = TangentSpaceNormalMap; - this.normalScale = new Vector2(1, 1); - this.displacementMap = null; - this.displacementScale = 1; - this.displacementBias = 0; - this.alphaMap = null; - this.flatShading = false; - this.fog = true; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.defines = { "MATCAP": "" }; - this.color.copy(source.color); - this.matcap = source.matcap; - this.map = source.map; - this.bumpMap = source.bumpMap; - this.bumpScale = source.bumpScale; - this.normalMap = source.normalMap; - this.normalMapType = source.normalMapType; - this.normalScale.copy(source.normalScale); - this.displacementMap = source.displacementMap; - this.displacementScale = source.displacementScale; - this.displacementBias = source.displacementBias; - this.alphaMap = source.alphaMap; - this.flatShading = source.flatShading; - this.fog = source.fog; - return this; - } -} -class LineDashedMaterial extends LineBasicMaterial { - static { - __name(this, "LineDashedMaterial"); - } - static get type() { - return "LineDashedMaterial"; - } - constructor(parameters) { - super(); - this.isLineDashedMaterial = true; - this.scale = 1; - this.dashSize = 3; - this.gapSize = 1; - this.setValues(parameters); - } - copy(source) { - super.copy(source); - this.scale = source.scale; - this.dashSize = source.dashSize; - this.gapSize = source.gapSize; - return this; - } -} -function convertArray(array, type, forceClone) { - if (!array || // let 'undefined' and 'null' pass - !forceClone && array.constructor === type) return array; - if (typeof type.BYTES_PER_ELEMENT === "number") { - return new type(array); - } - return Array.prototype.slice.call(array); -} -__name(convertArray, "convertArray"); -function isTypedArray(object) { - return ArrayBuffer.isView(object) && !(object instanceof DataView); -} -__name(isTypedArray, "isTypedArray"); -function getKeyframeOrder(times) { - function compareTime(i, j) { - return times[i] - times[j]; - } - __name(compareTime, "compareTime"); - const n = times.length; - const result = new Array(n); - for (let i = 0; i !== n; ++i) result[i] = i; - result.sort(compareTime); - return result; -} -__name(getKeyframeOrder, "getKeyframeOrder"); -function sortedArray(values, stride, order) { - const nValues = values.length; - const result = new values.constructor(nValues); - for (let i = 0, dstOffset = 0; dstOffset !== nValues; ++i) { - const srcOffset = order[i] * stride; - for (let j = 0; j !== stride; ++j) { - result[dstOffset++] = values[srcOffset + j]; - } - } - return result; -} -__name(sortedArray, "sortedArray"); -function flattenJSON(jsonKeys, times, values, valuePropertyName) { - let i = 1, key = jsonKeys[0]; - while (key !== void 0 && key[valuePropertyName] === void 0) { - key = jsonKeys[i++]; - } - if (key === void 0) return; - let value = key[valuePropertyName]; - if (value === void 0) return; - if (Array.isArray(value)) { - do { - value = key[valuePropertyName]; - if (value !== void 0) { - times.push(key.time); - values.push.apply(values, value); - } - key = jsonKeys[i++]; - } while (key !== void 0); - } else if (value.toArray !== void 0) { - do { - value = key[valuePropertyName]; - if (value !== void 0) { - times.push(key.time); - value.toArray(values, values.length); - } - key = jsonKeys[i++]; - } while (key !== void 0); - } else { - do { - value = key[valuePropertyName]; - if (value !== void 0) { - times.push(key.time); - values.push(value); - } - key = jsonKeys[i++]; - } while (key !== void 0); - } -} -__name(flattenJSON, "flattenJSON"); -function subclip(sourceClip, name, startFrame, endFrame, fps = 30) { - const clip = sourceClip.clone(); - clip.name = name; - const tracks = []; - for (let i = 0; i < clip.tracks.length; ++i) { - const track = clip.tracks[i]; - const valueSize = track.getValueSize(); - const times = []; - const values = []; - for (let j = 0; j < track.times.length; ++j) { - const frame = track.times[j] * fps; - if (frame < startFrame || frame >= endFrame) continue; - times.push(track.times[j]); - for (let k = 0; k < valueSize; ++k) { - values.push(track.values[j * valueSize + k]); - } - } - if (times.length === 0) continue; - track.times = convertArray(times, track.times.constructor); - track.values = convertArray(values, track.values.constructor); - tracks.push(track); - } - clip.tracks = tracks; - let minStartTime = Infinity; - for (let i = 0; i < clip.tracks.length; ++i) { - if (minStartTime > clip.tracks[i].times[0]) { - minStartTime = clip.tracks[i].times[0]; - } - } - for (let i = 0; i < clip.tracks.length; ++i) { - clip.tracks[i].shift(-1 * minStartTime); - } - clip.resetDuration(); - return clip; -} -__name(subclip, "subclip"); -function makeClipAdditive(targetClip, referenceFrame = 0, referenceClip = targetClip, fps = 30) { - if (fps <= 0) fps = 30; - const numTracks = referenceClip.tracks.length; - const referenceTime = referenceFrame / fps; - for (let i = 0; i < numTracks; ++i) { - const referenceTrack = referenceClip.tracks[i]; - const referenceTrackType = referenceTrack.ValueTypeName; - if (referenceTrackType === "bool" || referenceTrackType === "string") continue; - const targetTrack = targetClip.tracks.find(function(track) { - return track.name === referenceTrack.name && track.ValueTypeName === referenceTrackType; - }); - if (targetTrack === void 0) continue; - let referenceOffset = 0; - const referenceValueSize = referenceTrack.getValueSize(); - if (referenceTrack.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline) { - referenceOffset = referenceValueSize / 3; - } - let targetOffset = 0; - const targetValueSize = targetTrack.getValueSize(); - if (targetTrack.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline) { - targetOffset = targetValueSize / 3; - } - const lastIndex = referenceTrack.times.length - 1; - let referenceValue; - if (referenceTime <= referenceTrack.times[0]) { - const startIndex = referenceOffset; - const endIndex = referenceValueSize - referenceOffset; - referenceValue = referenceTrack.values.slice(startIndex, endIndex); - } else if (referenceTime >= referenceTrack.times[lastIndex]) { - const startIndex = lastIndex * referenceValueSize + referenceOffset; - const endIndex = startIndex + referenceValueSize - referenceOffset; - referenceValue = referenceTrack.values.slice(startIndex, endIndex); - } else { - const interpolant = referenceTrack.createInterpolant(); - const startIndex = referenceOffset; - const endIndex = referenceValueSize - referenceOffset; - interpolant.evaluate(referenceTime); - referenceValue = interpolant.resultBuffer.slice(startIndex, endIndex); - } - if (referenceTrackType === "quaternion") { - const referenceQuat = new Quaternion().fromArray(referenceValue).normalize().conjugate(); - referenceQuat.toArray(referenceValue); - } - const numTimes = targetTrack.times.length; - for (let j = 0; j < numTimes; ++j) { - const valueStart = j * targetValueSize + targetOffset; - if (referenceTrackType === "quaternion") { - Quaternion.multiplyQuaternionsFlat( - targetTrack.values, - valueStart, - referenceValue, - 0, - targetTrack.values, - valueStart - ); - } else { - const valueEnd = targetValueSize - targetOffset * 2; - for (let k = 0; k < valueEnd; ++k) { - targetTrack.values[valueStart + k] -= referenceValue[k]; - } - } - } - } - targetClip.blendMode = AdditiveAnimationBlendMode; - return targetClip; -} -__name(makeClipAdditive, "makeClipAdditive"); -const AnimationUtils = { - convertArray, - isTypedArray, - getKeyframeOrder, - sortedArray, - flattenJSON, - subclip, - makeClipAdditive -}; -class Interpolant { - static { - __name(this, "Interpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - this.parameterPositions = parameterPositions; - this._cachedIndex = 0; - this.resultBuffer = resultBuffer !== void 0 ? resultBuffer : new sampleValues.constructor(sampleSize); - this.sampleValues = sampleValues; - this.valueSize = sampleSize; - this.settings = null; - this.DefaultSettings_ = {}; - } - evaluate(t2) { - const pp = this.parameterPositions; - let i1 = this._cachedIndex, t1 = pp[i1], t0 = pp[i1 - 1]; - validate_interval: { - seek: { - let right; - linear_scan: { - forward_scan: if (!(t2 < t1)) { - for (let giveUpAt = i1 + 2; ; ) { - if (t1 === void 0) { - if (t2 < t0) break forward_scan; - i1 = pp.length; - this._cachedIndex = i1; - return this.copySampleValue_(i1 - 1); - } - if (i1 === giveUpAt) break; - t0 = t1; - t1 = pp[++i1]; - if (t2 < t1) { - break seek; - } - } - right = pp.length; - break linear_scan; - } - if (!(t2 >= t0)) { - const t1global = pp[1]; - if (t2 < t1global) { - i1 = 2; - t0 = t1global; - } - for (let giveUpAt = i1 - 2; ; ) { - if (t0 === void 0) { - this._cachedIndex = 0; - return this.copySampleValue_(0); - } - if (i1 === giveUpAt) break; - t1 = t0; - t0 = pp[--i1 - 1]; - if (t2 >= t0) { - break seek; - } - } - right = i1; - i1 = 0; - break linear_scan; - } - break validate_interval; - } - while (i1 < right) { - const mid = i1 + right >>> 1; - if (t2 < pp[mid]) { - right = mid; - } else { - i1 = mid + 1; - } - } - t1 = pp[i1]; - t0 = pp[i1 - 1]; - if (t0 === void 0) { - this._cachedIndex = 0; - return this.copySampleValue_(0); - } - if (t1 === void 0) { - i1 = pp.length; - this._cachedIndex = i1; - return this.copySampleValue_(i1 - 1); - } - } - this._cachedIndex = i1; - this.intervalChanged_(i1, t0, t1); - } - return this.interpolate_(i1, t0, t2, t1); - } - getSettings_() { - return this.settings || this.DefaultSettings_; - } - copySampleValue_(index) { - const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, offset = index * stride; - for (let i = 0; i !== stride; ++i) { - result[i] = values[offset + i]; - } - return result; - } - // Template methods for derived classes: - interpolate_() { - throw new Error("call to abstract method"); - } - intervalChanged_() { - } -} -class CubicInterpolant extends Interpolant { - static { - __name(this, "CubicInterpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - super(parameterPositions, sampleValues, sampleSize, resultBuffer); - this._weightPrev = -0; - this._offsetPrev = -0; - this._weightNext = -0; - this._offsetNext = -0; - this.DefaultSettings_ = { - endingStart: ZeroCurvatureEnding, - endingEnd: ZeroCurvatureEnding - }; - } - intervalChanged_(i1, t0, t1) { - const pp = this.parameterPositions; - let iPrev = i1 - 2, iNext = i1 + 1, tPrev = pp[iPrev], tNext = pp[iNext]; - if (tPrev === void 0) { - switch (this.getSettings_().endingStart) { - case ZeroSlopeEnding: - iPrev = i1; - tPrev = 2 * t0 - t1; - break; - case WrapAroundEnding: - iPrev = pp.length - 2; - tPrev = t0 + pp[iPrev] - pp[iPrev + 1]; - break; - default: - iPrev = i1; - tPrev = t1; - } - } - if (tNext === void 0) { - switch (this.getSettings_().endingEnd) { - case ZeroSlopeEnding: - iNext = i1; - tNext = 2 * t1 - t0; - break; - case WrapAroundEnding: - iNext = 1; - tNext = t1 + pp[1] - pp[0]; - break; - default: - iNext = i1 - 1; - tNext = t0; - } - } - const halfDt = (t1 - t0) * 0.5, stride = this.valueSize; - this._weightPrev = halfDt / (t0 - tPrev); - this._weightNext = halfDt / (tNext - t1); - this._offsetPrev = iPrev * stride; - this._offsetNext = iNext * stride; - } - interpolate_(i1, t0, t2, t1) { - const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, o1 = i1 * stride, o0 = o1 - stride, oP = this._offsetPrev, oN = this._offsetNext, wP = this._weightPrev, wN = this._weightNext, p = (t2 - t0) / (t1 - t0), pp = p * p, ppp = pp * p; - const sP = -wP * ppp + 2 * wP * pp - wP * p; - const s0 = (1 + wP) * ppp + (-1.5 - 2 * wP) * pp + (-0.5 + wP) * p + 1; - const s1 = (-1 - wN) * ppp + (1.5 + wN) * pp + 0.5 * p; - const sN = wN * ppp - wN * pp; - for (let i = 0; i !== stride; ++i) { - result[i] = sP * values[oP + i] + s0 * values[o0 + i] + s1 * values[o1 + i] + sN * values[oN + i]; - } - return result; - } -} -class LinearInterpolant extends Interpolant { - static { - __name(this, "LinearInterpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - super(parameterPositions, sampleValues, sampleSize, resultBuffer); - } - interpolate_(i1, t0, t2, t1) { - const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, offset1 = i1 * stride, offset0 = offset1 - stride, weight1 = (t2 - t0) / (t1 - t0), weight0 = 1 - weight1; - for (let i = 0; i !== stride; ++i) { - result[i] = values[offset0 + i] * weight0 + values[offset1 + i] * weight1; - } - return result; - } -} -class DiscreteInterpolant extends Interpolant { - static { - __name(this, "DiscreteInterpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - super(parameterPositions, sampleValues, sampleSize, resultBuffer); - } - interpolate_(i1) { - return this.copySampleValue_(i1 - 1); - } -} -class KeyframeTrack { - static { - __name(this, "KeyframeTrack"); - } - constructor(name, times, values, interpolation) { - if (name === void 0) throw new Error("THREE.KeyframeTrack: track name is undefined"); - if (times === void 0 || times.length === 0) throw new Error("THREE.KeyframeTrack: no keyframes in track named " + name); - this.name = name; - this.times = convertArray(times, this.TimeBufferType); - this.values = convertArray(values, this.ValueBufferType); - this.setInterpolation(interpolation || this.DefaultInterpolation); - } - // Serialization (in static context, because of constructor invocation - // and automatic invocation of .toJSON): - static toJSON(track) { - const trackType = track.constructor; - let json; - if (trackType.toJSON !== this.toJSON) { - json = trackType.toJSON(track); - } else { - json = { - "name": track.name, - "times": convertArray(track.times, Array), - "values": convertArray(track.values, Array) - }; - const interpolation = track.getInterpolation(); - if (interpolation !== track.DefaultInterpolation) { - json.interpolation = interpolation; - } - } - json.type = track.ValueTypeName; - return json; - } - InterpolantFactoryMethodDiscrete(result) { - return new DiscreteInterpolant(this.times, this.values, this.getValueSize(), result); - } - InterpolantFactoryMethodLinear(result) { - return new LinearInterpolant(this.times, this.values, this.getValueSize(), result); - } - InterpolantFactoryMethodSmooth(result) { - return new CubicInterpolant(this.times, this.values, this.getValueSize(), result); - } - setInterpolation(interpolation) { - let factoryMethod; - switch (interpolation) { - case InterpolateDiscrete: - factoryMethod = this.InterpolantFactoryMethodDiscrete; - break; - case InterpolateLinear: - factoryMethod = this.InterpolantFactoryMethodLinear; - break; - case InterpolateSmooth: - factoryMethod = this.InterpolantFactoryMethodSmooth; - break; - } - if (factoryMethod === void 0) { - const message = "unsupported interpolation for " + this.ValueTypeName + " keyframe track named " + this.name; - if (this.createInterpolant === void 0) { - if (interpolation !== this.DefaultInterpolation) { - this.setInterpolation(this.DefaultInterpolation); - } else { - throw new Error(message); - } - } - console.warn("THREE.KeyframeTrack:", message); - return this; - } - this.createInterpolant = factoryMethod; - return this; - } - getInterpolation() { - switch (this.createInterpolant) { - case this.InterpolantFactoryMethodDiscrete: - return InterpolateDiscrete; - case this.InterpolantFactoryMethodLinear: - return InterpolateLinear; - case this.InterpolantFactoryMethodSmooth: - return InterpolateSmooth; - } - } - getValueSize() { - return this.values.length / this.times.length; - } - // move all keyframes either forwards or backwards in time - shift(timeOffset) { - if (timeOffset !== 0) { - const times = this.times; - for (let i = 0, n = times.length; i !== n; ++i) { - times[i] += timeOffset; - } - } - return this; - } - // scale all keyframe times by a factor (useful for frame <-> seconds conversions) - scale(timeScale) { - if (timeScale !== 1) { - const times = this.times; - for (let i = 0, n = times.length; i !== n; ++i) { - times[i] *= timeScale; - } - } - return this; - } - // removes keyframes before and after animation without changing any values within the range [startTime, endTime]. - // IMPORTANT: We do not shift around keys to the start of the track time, because for interpolated keys this will change their values - trim(startTime, endTime) { - const times = this.times, nKeys = times.length; - let from = 0, to = nKeys - 1; - while (from !== nKeys && times[from] < startTime) { - ++from; - } - while (to !== -1 && times[to] > endTime) { - --to; - } - ++to; - if (from !== 0 || to !== nKeys) { - if (from >= to) { - to = Math.max(to, 1); - from = to - 1; - } - const stride = this.getValueSize(); - this.times = times.slice(from, to); - this.values = this.values.slice(from * stride, to * stride); - } - return this; - } - // ensure we do not get a GarbageInGarbageOut situation, make sure tracks are at least minimally viable - validate() { - let valid = true; - const valueSize = this.getValueSize(); - if (valueSize - Math.floor(valueSize) !== 0) { - console.error("THREE.KeyframeTrack: Invalid value size in track.", this); - valid = false; - } - const times = this.times, values = this.values, nKeys = times.length; - if (nKeys === 0) { - console.error("THREE.KeyframeTrack: Track is empty.", this); - valid = false; - } - let prevTime = null; - for (let i = 0; i !== nKeys; i++) { - const currTime = times[i]; - if (typeof currTime === "number" && isNaN(currTime)) { - console.error("THREE.KeyframeTrack: Time is not a valid number.", this, i, currTime); - valid = false; - break; - } - if (prevTime !== null && prevTime > currTime) { - console.error("THREE.KeyframeTrack: Out of order keys.", this, i, currTime, prevTime); - valid = false; - break; - } - prevTime = currTime; - } - if (values !== void 0) { - if (isTypedArray(values)) { - for (let i = 0, n = values.length; i !== n; ++i) { - const value = values[i]; - if (isNaN(value)) { - console.error("THREE.KeyframeTrack: Value is not a valid number.", this, i, value); - valid = false; - break; - } - } - } - } - return valid; - } - // removes equivalent sequential keys as common in morph target sequences - // (0,0,0,0,1,1,1,0,0,0,0,0,0,0) --> (0,0,1,1,0,0) - optimize() { - const times = this.times.slice(), values = this.values.slice(), stride = this.getValueSize(), smoothInterpolation = this.getInterpolation() === InterpolateSmooth, lastIndex = times.length - 1; - let writeIndex = 1; - for (let i = 1; i < lastIndex; ++i) { - let keep = false; - const time = times[i]; - const timeNext = times[i + 1]; - if (time !== timeNext && (i !== 1 || time !== times[0])) { - if (!smoothInterpolation) { - const offset = i * stride, offsetP = offset - stride, offsetN = offset + stride; - for (let j = 0; j !== stride; ++j) { - const value = values[offset + j]; - if (value !== values[offsetP + j] || value !== values[offsetN + j]) { - keep = true; - break; - } - } - } else { - keep = true; - } - } - if (keep) { - if (i !== writeIndex) { - times[writeIndex] = times[i]; - const readOffset = i * stride, writeOffset = writeIndex * stride; - for (let j = 0; j !== stride; ++j) { - values[writeOffset + j] = values[readOffset + j]; - } - } - ++writeIndex; - } - } - if (lastIndex > 0) { - times[writeIndex] = times[lastIndex]; - for (let readOffset = lastIndex * stride, writeOffset = writeIndex * stride, j = 0; j !== stride; ++j) { - values[writeOffset + j] = values[readOffset + j]; - } - ++writeIndex; - } - if (writeIndex !== times.length) { - this.times = times.slice(0, writeIndex); - this.values = values.slice(0, writeIndex * stride); - } else { - this.times = times; - this.values = values; - } - return this; - } - clone() { - const times = this.times.slice(); - const values = this.values.slice(); - const TypedKeyframeTrack = this.constructor; - const track = new TypedKeyframeTrack(this.name, times, values); - track.createInterpolant = this.createInterpolant; - return track; - } -} -KeyframeTrack.prototype.TimeBufferType = Float32Array; -KeyframeTrack.prototype.ValueBufferType = Float32Array; -KeyframeTrack.prototype.DefaultInterpolation = InterpolateLinear; -class BooleanKeyframeTrack extends KeyframeTrack { - static { - __name(this, "BooleanKeyframeTrack"); - } - // No interpolation parameter because only InterpolateDiscrete is valid. - constructor(name, times, values) { - super(name, times, values); - } -} -BooleanKeyframeTrack.prototype.ValueTypeName = "bool"; -BooleanKeyframeTrack.prototype.ValueBufferType = Array; -BooleanKeyframeTrack.prototype.DefaultInterpolation = InterpolateDiscrete; -BooleanKeyframeTrack.prototype.InterpolantFactoryMethodLinear = void 0; -BooleanKeyframeTrack.prototype.InterpolantFactoryMethodSmooth = void 0; -class ColorKeyframeTrack extends KeyframeTrack { - static { - __name(this, "ColorKeyframeTrack"); - } -} -ColorKeyframeTrack.prototype.ValueTypeName = "color"; -class NumberKeyframeTrack extends KeyframeTrack { - static { - __name(this, "NumberKeyframeTrack"); - } -} -NumberKeyframeTrack.prototype.ValueTypeName = "number"; -class QuaternionLinearInterpolant extends Interpolant { - static { - __name(this, "QuaternionLinearInterpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - super(parameterPositions, sampleValues, sampleSize, resultBuffer); - } - interpolate_(i1, t0, t2, t1) { - const result = this.resultBuffer, values = this.sampleValues, stride = this.valueSize, alpha = (t2 - t0) / (t1 - t0); - let offset = i1 * stride; - for (let end = offset + stride; offset !== end; offset += 4) { - Quaternion.slerpFlat(result, 0, values, offset - stride, values, offset, alpha); - } - return result; - } -} -class QuaternionKeyframeTrack extends KeyframeTrack { - static { - __name(this, "QuaternionKeyframeTrack"); - } - InterpolantFactoryMethodLinear(result) { - return new QuaternionLinearInterpolant(this.times, this.values, this.getValueSize(), result); - } -} -QuaternionKeyframeTrack.prototype.ValueTypeName = "quaternion"; -QuaternionKeyframeTrack.prototype.InterpolantFactoryMethodSmooth = void 0; -class StringKeyframeTrack extends KeyframeTrack { - static { - __name(this, "StringKeyframeTrack"); - } - // No interpolation parameter because only InterpolateDiscrete is valid. - constructor(name, times, values) { - super(name, times, values); - } -} -StringKeyframeTrack.prototype.ValueTypeName = "string"; -StringKeyframeTrack.prototype.ValueBufferType = Array; -StringKeyframeTrack.prototype.DefaultInterpolation = InterpolateDiscrete; -StringKeyframeTrack.prototype.InterpolantFactoryMethodLinear = void 0; -StringKeyframeTrack.prototype.InterpolantFactoryMethodSmooth = void 0; -class VectorKeyframeTrack extends KeyframeTrack { - static { - __name(this, "VectorKeyframeTrack"); - } -} -VectorKeyframeTrack.prototype.ValueTypeName = "vector"; -class AnimationClip { - static { - __name(this, "AnimationClip"); - } - constructor(name = "", duration = -1, tracks = [], blendMode = NormalAnimationBlendMode) { - this.name = name; - this.tracks = tracks; - this.duration = duration; - this.blendMode = blendMode; - this.uuid = generateUUID(); - if (this.duration < 0) { - this.resetDuration(); - } - } - static parse(json) { - const tracks = [], jsonTracks = json.tracks, frameTime = 1 / (json.fps || 1); - for (let i = 0, n = jsonTracks.length; i !== n; ++i) { - tracks.push(parseKeyframeTrack(jsonTracks[i]).scale(frameTime)); - } - const clip = new this(json.name, json.duration, tracks, json.blendMode); - clip.uuid = json.uuid; - return clip; - } - static toJSON(clip) { - const tracks = [], clipTracks = clip.tracks; - const json = { - "name": clip.name, - "duration": clip.duration, - "tracks": tracks, - "uuid": clip.uuid, - "blendMode": clip.blendMode - }; - for (let i = 0, n = clipTracks.length; i !== n; ++i) { - tracks.push(KeyframeTrack.toJSON(clipTracks[i])); - } - return json; - } - static CreateFromMorphTargetSequence(name, morphTargetSequence, fps, noLoop) { - const numMorphTargets = morphTargetSequence.length; - const tracks = []; - for (let i = 0; i < numMorphTargets; i++) { - let times = []; - let values = []; - times.push( - (i + numMorphTargets - 1) % numMorphTargets, - i, - (i + 1) % numMorphTargets - ); - values.push(0, 1, 0); - const order = getKeyframeOrder(times); - times = sortedArray(times, 1, order); - values = sortedArray(values, 1, order); - if (!noLoop && times[0] === 0) { - times.push(numMorphTargets); - values.push(values[0]); - } - tracks.push( - new NumberKeyframeTrack( - ".morphTargetInfluences[" + morphTargetSequence[i].name + "]", - times, - values - ).scale(1 / fps) - ); - } - return new this(name, -1, tracks); - } - static findByName(objectOrClipArray, name) { - let clipArray = objectOrClipArray; - if (!Array.isArray(objectOrClipArray)) { - const o = objectOrClipArray; - clipArray = o.geometry && o.geometry.animations || o.animations; - } - for (let i = 0; i < clipArray.length; i++) { - if (clipArray[i].name === name) { - return clipArray[i]; - } - } - return null; - } - static CreateClipsFromMorphTargetSequences(morphTargets, fps, noLoop) { - const animationToMorphTargets = {}; - const pattern = /^([\w-]*?)([\d]+)$/; - for (let i = 0, il = morphTargets.length; i < il; i++) { - const morphTarget = morphTargets[i]; - const parts = morphTarget.name.match(pattern); - if (parts && parts.length > 1) { - const name = parts[1]; - let animationMorphTargets = animationToMorphTargets[name]; - if (!animationMorphTargets) { - animationToMorphTargets[name] = animationMorphTargets = []; - } - animationMorphTargets.push(morphTarget); - } - } - const clips = []; - for (const name in animationToMorphTargets) { - clips.push(this.CreateFromMorphTargetSequence(name, animationToMorphTargets[name], fps, noLoop)); - } - return clips; - } - // parse the animation.hierarchy format - static parseAnimation(animation, bones) { - if (!animation) { - console.error("THREE.AnimationClip: No animation in JSONLoader data."); - return null; - } - const addNonemptyTrack = /* @__PURE__ */ __name(function(trackType, trackName, animationKeys, propertyName, destTracks) { - if (animationKeys.length !== 0) { - const times = []; - const values = []; - flattenJSON(animationKeys, times, values, propertyName); - if (times.length !== 0) { - destTracks.push(new trackType(trackName, times, values)); - } - } - }, "addNonemptyTrack"); - const tracks = []; - const clipName = animation.name || "default"; - const fps = animation.fps || 30; - const blendMode = animation.blendMode; - let duration = animation.length || -1; - const hierarchyTracks = animation.hierarchy || []; - for (let h = 0; h < hierarchyTracks.length; h++) { - const animationKeys = hierarchyTracks[h].keys; - if (!animationKeys || animationKeys.length === 0) continue; - if (animationKeys[0].morphTargets) { - const morphTargetNames = {}; - let k; - for (k = 0; k < animationKeys.length; k++) { - if (animationKeys[k].morphTargets) { - for (let m = 0; m < animationKeys[k].morphTargets.length; m++) { - morphTargetNames[animationKeys[k].morphTargets[m]] = -1; - } - } - } - for (const morphTargetName in morphTargetNames) { - const times = []; - const values = []; - for (let m = 0; m !== animationKeys[k].morphTargets.length; ++m) { - const animationKey = animationKeys[k]; - times.push(animationKey.time); - values.push(animationKey.morphTarget === morphTargetName ? 1 : 0); - } - tracks.push(new NumberKeyframeTrack(".morphTargetInfluence[" + morphTargetName + "]", times, values)); - } - duration = morphTargetNames.length * fps; - } else { - const boneName = ".bones[" + bones[h].name + "]"; - addNonemptyTrack( - VectorKeyframeTrack, - boneName + ".position", - animationKeys, - "pos", - tracks - ); - addNonemptyTrack( - QuaternionKeyframeTrack, - boneName + ".quaternion", - animationKeys, - "rot", - tracks - ); - addNonemptyTrack( - VectorKeyframeTrack, - boneName + ".scale", - animationKeys, - "scl", - tracks - ); - } - } - if (tracks.length === 0) { - return null; - } - const clip = new this(clipName, duration, tracks, blendMode); - return clip; - } - resetDuration() { - const tracks = this.tracks; - let duration = 0; - for (let i = 0, n = tracks.length; i !== n; ++i) { - const track = this.tracks[i]; - duration = Math.max(duration, track.times[track.times.length - 1]); - } - this.duration = duration; - return this; - } - trim() { - for (let i = 0; i < this.tracks.length; i++) { - this.tracks[i].trim(0, this.duration); - } - return this; - } - validate() { - let valid = true; - for (let i = 0; i < this.tracks.length; i++) { - valid = valid && this.tracks[i].validate(); - } - return valid; - } - optimize() { - for (let i = 0; i < this.tracks.length; i++) { - this.tracks[i].optimize(); - } - return this; - } - clone() { - const tracks = []; - for (let i = 0; i < this.tracks.length; i++) { - tracks.push(this.tracks[i].clone()); - } - return new this.constructor(this.name, this.duration, tracks, this.blendMode); - } - toJSON() { - return this.constructor.toJSON(this); - } -} -function getTrackTypeForValueTypeName(typeName) { - switch (typeName.toLowerCase()) { - case "scalar": - case "double": - case "float": - case "number": - case "integer": - return NumberKeyframeTrack; - case "vector": - case "vector2": - case "vector3": - case "vector4": - return VectorKeyframeTrack; - case "color": - return ColorKeyframeTrack; - case "quaternion": - return QuaternionKeyframeTrack; - case "bool": - case "boolean": - return BooleanKeyframeTrack; - case "string": - return StringKeyframeTrack; - } - throw new Error("THREE.KeyframeTrack: Unsupported typeName: " + typeName); -} -__name(getTrackTypeForValueTypeName, "getTrackTypeForValueTypeName"); -function parseKeyframeTrack(json) { - if (json.type === void 0) { - throw new Error("THREE.KeyframeTrack: track type undefined, can not parse"); - } - const trackType = getTrackTypeForValueTypeName(json.type); - if (json.times === void 0) { - const times = [], values = []; - flattenJSON(json.keys, times, values, "value"); - json.times = times; - json.values = values; - } - if (trackType.parse !== void 0) { - return trackType.parse(json); - } else { - return new trackType(json.name, json.times, json.values, json.interpolation); - } -} -__name(parseKeyframeTrack, "parseKeyframeTrack"); -const Cache = { - enabled: false, - files: {}, - add: /* @__PURE__ */ __name(function(key, file2) { - if (this.enabled === false) return; - this.files[key] = file2; - }, "add"), - get: /* @__PURE__ */ __name(function(key) { - if (this.enabled === false) return; - return this.files[key]; - }, "get"), - remove: /* @__PURE__ */ __name(function(key) { - delete this.files[key]; - }, "remove"), - clear: /* @__PURE__ */ __name(function() { - this.files = {}; - }, "clear") -}; -class LoadingManager { - static { - __name(this, "LoadingManager"); - } - constructor(onLoad, onProgress, onError) { - const scope = this; - let isLoading = false; - let itemsLoaded = 0; - let itemsTotal = 0; - let urlModifier = void 0; - const handlers = []; - this.onStart = void 0; - this.onLoad = onLoad; - this.onProgress = onProgress; - this.onError = onError; - this.itemStart = function(url) { - itemsTotal++; - if (isLoading === false) { - if (scope.onStart !== void 0) { - scope.onStart(url, itemsLoaded, itemsTotal); - } - } - isLoading = true; - }; - this.itemEnd = function(url) { - itemsLoaded++; - if (scope.onProgress !== void 0) { - scope.onProgress(url, itemsLoaded, itemsTotal); - } - if (itemsLoaded === itemsTotal) { - isLoading = false; - if (scope.onLoad !== void 0) { - scope.onLoad(); - } - } - }; - this.itemError = function(url) { - if (scope.onError !== void 0) { - scope.onError(url); - } - }; - this.resolveURL = function(url) { - if (urlModifier) { - return urlModifier(url); - } - return url; - }; - this.setURLModifier = function(transform) { - urlModifier = transform; - return this; - }; - this.addHandler = function(regex, loader) { - handlers.push(regex, loader); - return this; - }; - this.removeHandler = function(regex) { - const index = handlers.indexOf(regex); - if (index !== -1) { - handlers.splice(index, 2); - } - return this; - }; - this.getHandler = function(file2) { - for (let i = 0, l = handlers.length; i < l; i += 2) { - const regex = handlers[i]; - const loader = handlers[i + 1]; - if (regex.global) regex.lastIndex = 0; - if (regex.test(file2)) { - return loader; - } - } - return null; - }; - } -} -const DefaultLoadingManager = /* @__PURE__ */ new LoadingManager(); -class Loader { - static { - __name(this, "Loader"); - } - constructor(manager) { - this.manager = manager !== void 0 ? manager : DefaultLoadingManager; - this.crossOrigin = "anonymous"; - this.withCredentials = false; - this.path = ""; - this.resourcePath = ""; - this.requestHeader = {}; - } - load() { - } - loadAsync(url, onProgress) { - const scope = this; - return new Promise(function(resolve, reject) { - scope.load(url, resolve, onProgress, reject); - }); - } - parse() { - } - setCrossOrigin(crossOrigin) { - this.crossOrigin = crossOrigin; - return this; - } - setWithCredentials(value) { - this.withCredentials = value; - return this; - } - setPath(path) { - this.path = path; - return this; - } - setResourcePath(resourcePath) { - this.resourcePath = resourcePath; - return this; - } - setRequestHeader(requestHeader) { - this.requestHeader = requestHeader; - return this; - } -} -Loader.DEFAULT_MATERIAL_NAME = "__DEFAULT"; -const loading = {}; -class HttpError extends Error { - static { - __name(this, "HttpError"); - } - constructor(message, response) { - super(message); - this.response = response; - } -} -class FileLoader extends Loader { - static { - __name(this, "FileLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - if (url === void 0) url = ""; - if (this.path !== void 0) url = this.path + url; - url = this.manager.resolveURL(url); - const cached = Cache.get(url); - if (cached !== void 0) { - this.manager.itemStart(url); - setTimeout(() => { - if (onLoad) onLoad(cached); - this.manager.itemEnd(url); - }, 0); - return cached; - } - if (loading[url] !== void 0) { - loading[url].push({ - onLoad, - onProgress, - onError - }); - return; - } - loading[url] = []; - loading[url].push({ - onLoad, - onProgress, - onError - }); - const req = new Request(url, { - headers: new Headers(this.requestHeader), - credentials: this.withCredentials ? "include" : "same-origin" - // An abort controller could be added within a future PR - }); - const mimeType = this.mimeType; - const responseType = this.responseType; - fetch(req).then((response) => { - if (response.status === 200 || response.status === 0) { - if (response.status === 0) { - console.warn("THREE.FileLoader: HTTP Status 0 received."); - } - if (typeof ReadableStream === "undefined" || response.body === void 0 || response.body.getReader === void 0) { - return response; - } - const callbacks = loading[url]; - const reader = response.body.getReader(); - const contentLength = response.headers.get("X-File-Size") || response.headers.get("Content-Length"); - const total = contentLength ? parseInt(contentLength) : 0; - const lengthComputable = total !== 0; - let loaded = 0; - const stream = new ReadableStream({ - start(controller) { - readData(); - function readData() { - reader.read().then(({ done, value }) => { - if (done) { - controller.close(); - } else { - loaded += value.byteLength; - const event = new ProgressEvent("progress", { lengthComputable, loaded, total }); - for (let i = 0, il = callbacks.length; i < il; i++) { - const callback = callbacks[i]; - if (callback.onProgress) callback.onProgress(event); - } - controller.enqueue(value); - readData(); - } - }, (e) => { - controller.error(e); - }); - } - __name(readData, "readData"); - } - }); - return new Response(stream); - } else { - throw new HttpError(`fetch for "${response.url}" responded with ${response.status}: ${response.statusText}`, response); - } - }).then((response) => { - switch (responseType) { - case "arraybuffer": - return response.arrayBuffer(); - case "blob": - return response.blob(); - case "document": - return response.text().then((text) => { - const parser = new DOMParser(); - return parser.parseFromString(text, mimeType); - }); - case "json": - return response.json(); - default: - if (mimeType === void 0) { - return response.text(); - } else { - const re = /charset="?([^;"\s]*)"?/i; - const exec = re.exec(mimeType); - const label = exec && exec[1] ? exec[1].toLowerCase() : void 0; - const decoder = new TextDecoder(label); - return response.arrayBuffer().then((ab) => decoder.decode(ab)); - } - } - }).then((data) => { - Cache.add(url, data); - const callbacks = loading[url]; - delete loading[url]; - for (let i = 0, il = callbacks.length; i < il; i++) { - const callback = callbacks[i]; - if (callback.onLoad) callback.onLoad(data); - } - }).catch((err2) => { - const callbacks = loading[url]; - if (callbacks === void 0) { - this.manager.itemError(url); - throw err2; - } - delete loading[url]; - for (let i = 0, il = callbacks.length; i < il; i++) { - const callback = callbacks[i]; - if (callback.onError) callback.onError(err2); - } - this.manager.itemError(url); - }).finally(() => { - this.manager.itemEnd(url); - }); - this.manager.itemStart(url); - } - setResponseType(value) { - this.responseType = value; - return this; - } - setMimeType(value) { - this.mimeType = value; - return this; - } -} -class AnimationLoader extends Loader { - static { - __name(this, "AnimationLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(JSON.parse(text))); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - parse(json) { - const animations = []; - for (let i = 0; i < json.length; i++) { - const clip = AnimationClip.parse(json[i]); - animations.push(clip); - } - return animations; - } -} -class CompressedTextureLoader extends Loader { - static { - __name(this, "CompressedTextureLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const images = []; - const texture = new CompressedTexture(); - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setResponseType("arraybuffer"); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(scope.withCredentials); - let loaded = 0; - function loadTexture(i) { - loader.load(url[i], function(buffer) { - const texDatas = scope.parse(buffer, true); - images[i] = { - width: texDatas.width, - height: texDatas.height, - format: texDatas.format, - mipmaps: texDatas.mipmaps - }; - loaded += 1; - if (loaded === 6) { - if (texDatas.mipmapCount === 1) texture.minFilter = LinearFilter; - texture.image = images; - texture.format = texDatas.format; - texture.needsUpdate = true; - if (onLoad) onLoad(texture); - } - }, onProgress, onError); - } - __name(loadTexture, "loadTexture"); - if (Array.isArray(url)) { - for (let i = 0, il = url.length; i < il; ++i) { - loadTexture(i); - } - } else { - loader.load(url, function(buffer) { - const texDatas = scope.parse(buffer, true); - if (texDatas.isCubemap) { - const faces = texDatas.mipmaps.length / texDatas.mipmapCount; - for (let f = 0; f < faces; f++) { - images[f] = { mipmaps: [] }; - for (let i = 0; i < texDatas.mipmapCount; i++) { - images[f].mipmaps.push(texDatas.mipmaps[f * texDatas.mipmapCount + i]); - images[f].format = texDatas.format; - images[f].width = texDatas.width; - images[f].height = texDatas.height; - } - } - texture.image = images; - } else { - texture.image.width = texDatas.width; - texture.image.height = texDatas.height; - texture.mipmaps = texDatas.mipmaps; - } - if (texDatas.mipmapCount === 1) { - texture.minFilter = LinearFilter; - } - texture.format = texDatas.format; - texture.needsUpdate = true; - if (onLoad) onLoad(texture); - }, onProgress, onError); - } - return texture; - } -} -class ImageLoader extends Loader { - static { - __name(this, "ImageLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - if (this.path !== void 0) url = this.path + url; - url = this.manager.resolveURL(url); - const scope = this; - const cached = Cache.get(url); - if (cached !== void 0) { - scope.manager.itemStart(url); - setTimeout(function() { - if (onLoad) onLoad(cached); - scope.manager.itemEnd(url); - }, 0); - return cached; - } - const image = createElementNS("img"); - function onImageLoad() { - removeEventListeners(); - Cache.add(url, this); - if (onLoad) onLoad(this); - scope.manager.itemEnd(url); - } - __name(onImageLoad, "onImageLoad"); - function onImageError(event) { - removeEventListeners(); - if (onError) onError(event); - scope.manager.itemError(url); - scope.manager.itemEnd(url); - } - __name(onImageError, "onImageError"); - function removeEventListeners() { - image.removeEventListener("load", onImageLoad, false); - image.removeEventListener("error", onImageError, false); - } - __name(removeEventListeners, "removeEventListeners"); - image.addEventListener("load", onImageLoad, false); - image.addEventListener("error", onImageError, false); - if (url.slice(0, 5) !== "data:") { - if (this.crossOrigin !== void 0) image.crossOrigin = this.crossOrigin; - } - scope.manager.itemStart(url); - image.src = url; - return image; - } -} -class CubeTextureLoader extends Loader { - static { - __name(this, "CubeTextureLoader"); - } - constructor(manager) { - super(manager); - } - load(urls, onLoad, onProgress, onError) { - const texture = new CubeTexture(); - texture.colorSpace = SRGBColorSpace; - const loader = new ImageLoader(this.manager); - loader.setCrossOrigin(this.crossOrigin); - loader.setPath(this.path); - let loaded = 0; - function loadTexture(i) { - loader.load(urls[i], function(image) { - texture.images[i] = image; - loaded++; - if (loaded === 6) { - texture.needsUpdate = true; - if (onLoad) onLoad(texture); - } - }, void 0, onError); - } - __name(loadTexture, "loadTexture"); - for (let i = 0; i < urls.length; ++i) { - loadTexture(i); - } - return texture; - } -} -class DataTextureLoader extends Loader { - static { - __name(this, "DataTextureLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const texture = new DataTexture(); - const loader = new FileLoader(this.manager); - loader.setResponseType("arraybuffer"); - loader.setRequestHeader(this.requestHeader); - loader.setPath(this.path); - loader.setWithCredentials(scope.withCredentials); - loader.load(url, function(buffer) { - let texData; - try { - texData = scope.parse(buffer); - } catch (error) { - if (onError !== void 0) { - onError(error); - } else { - console.error(error); - return; - } - } - if (texData.image !== void 0) { - texture.image = texData.image; - } else if (texData.data !== void 0) { - texture.image.width = texData.width; - texture.image.height = texData.height; - texture.image.data = texData.data; - } - texture.wrapS = texData.wrapS !== void 0 ? texData.wrapS : ClampToEdgeWrapping; - texture.wrapT = texData.wrapT !== void 0 ? texData.wrapT : ClampToEdgeWrapping; - texture.magFilter = texData.magFilter !== void 0 ? texData.magFilter : LinearFilter; - texture.minFilter = texData.minFilter !== void 0 ? texData.minFilter : LinearFilter; - texture.anisotropy = texData.anisotropy !== void 0 ? texData.anisotropy : 1; - if (texData.colorSpace !== void 0) { - texture.colorSpace = texData.colorSpace; - } - if (texData.flipY !== void 0) { - texture.flipY = texData.flipY; - } - if (texData.format !== void 0) { - texture.format = texData.format; - } - if (texData.type !== void 0) { - texture.type = texData.type; - } - if (texData.mipmaps !== void 0) { - texture.mipmaps = texData.mipmaps; - texture.minFilter = LinearMipmapLinearFilter; - } - if (texData.mipmapCount === 1) { - texture.minFilter = LinearFilter; - } - if (texData.generateMipmaps !== void 0) { - texture.generateMipmaps = texData.generateMipmaps; - } - texture.needsUpdate = true; - if (onLoad) onLoad(texture, texData); - }, onProgress, onError); - return texture; - } -} -class TextureLoader extends Loader { - static { - __name(this, "TextureLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const texture = new Texture(); - const loader = new ImageLoader(this.manager); - loader.setCrossOrigin(this.crossOrigin); - loader.setPath(this.path); - loader.load(url, function(image) { - texture.image = image; - texture.needsUpdate = true; - if (onLoad !== void 0) { - onLoad(texture); - } - }, onProgress, onError); - return texture; - } -} -class Light extends Object3D { - static { - __name(this, "Light"); - } - constructor(color, intensity = 1) { - super(); - this.isLight = true; - this.type = "Light"; - this.color = new Color(color); - this.intensity = intensity; - } - dispose() { - } - copy(source, recursive) { - super.copy(source, recursive); - this.color.copy(source.color); - this.intensity = source.intensity; - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - data.object.color = this.color.getHex(); - data.object.intensity = this.intensity; - if (this.groundColor !== void 0) data.object.groundColor = this.groundColor.getHex(); - if (this.distance !== void 0) data.object.distance = this.distance; - if (this.angle !== void 0) data.object.angle = this.angle; - if (this.decay !== void 0) data.object.decay = this.decay; - if (this.penumbra !== void 0) data.object.penumbra = this.penumbra; - if (this.shadow !== void 0) data.object.shadow = this.shadow.toJSON(); - if (this.target !== void 0) data.object.target = this.target.uuid; - return data; - } -} -class HemisphereLight extends Light { - static { - __name(this, "HemisphereLight"); - } - constructor(skyColor, groundColor, intensity) { - super(skyColor, intensity); - this.isHemisphereLight = true; - this.type = "HemisphereLight"; - this.position.copy(Object3D.DEFAULT_UP); - this.updateMatrix(); - this.groundColor = new Color(groundColor); - } - copy(source, recursive) { - super.copy(source, recursive); - this.groundColor.copy(source.groundColor); - return this; - } -} -const _projScreenMatrix$1 = /* @__PURE__ */ new Matrix4(); -const _lightPositionWorld$1 = /* @__PURE__ */ new Vector3(); -const _lookTarget$1 = /* @__PURE__ */ new Vector3(); -class LightShadow { - static { - __name(this, "LightShadow"); - } - constructor(camera) { - this.camera = camera; - this.intensity = 1; - this.bias = 0; - this.normalBias = 0; - this.radius = 1; - this.blurSamples = 8; - this.mapSize = new Vector2(512, 512); - this.map = null; - this.mapPass = null; - this.matrix = new Matrix4(); - this.autoUpdate = true; - this.needsUpdate = false; - this._frustum = new Frustum(); - this._frameExtents = new Vector2(1, 1); - this._viewportCount = 1; - this._viewports = [ - new Vector4(0, 0, 1, 1) - ]; - } - getViewportCount() { - return this._viewportCount; - } - getFrustum() { - return this._frustum; - } - updateMatrices(light) { - const shadowCamera = this.camera; - const shadowMatrix = this.matrix; - _lightPositionWorld$1.setFromMatrixPosition(light.matrixWorld); - shadowCamera.position.copy(_lightPositionWorld$1); - _lookTarget$1.setFromMatrixPosition(light.target.matrixWorld); - shadowCamera.lookAt(_lookTarget$1); - shadowCamera.updateMatrixWorld(); - _projScreenMatrix$1.multiplyMatrices(shadowCamera.projectionMatrix, shadowCamera.matrixWorldInverse); - this._frustum.setFromProjectionMatrix(_projScreenMatrix$1); - shadowMatrix.set( - 0.5, - 0, - 0, - 0.5, - 0, - 0.5, - 0, - 0.5, - 0, - 0, - 0.5, - 0.5, - 0, - 0, - 0, - 1 - ); - shadowMatrix.multiply(_projScreenMatrix$1); - } - getViewport(viewportIndex) { - return this._viewports[viewportIndex]; - } - getFrameExtents() { - return this._frameExtents; - } - dispose() { - if (this.map) { - this.map.dispose(); - } - if (this.mapPass) { - this.mapPass.dispose(); - } - } - copy(source) { - this.camera = source.camera.clone(); - this.intensity = source.intensity; - this.bias = source.bias; - this.radius = source.radius; - this.mapSize.copy(source.mapSize); - return this; - } - clone() { - return new this.constructor().copy(this); - } - toJSON() { - const object = {}; - if (this.intensity !== 1) object.intensity = this.intensity; - if (this.bias !== 0) object.bias = this.bias; - if (this.normalBias !== 0) object.normalBias = this.normalBias; - if (this.radius !== 1) object.radius = this.radius; - if (this.mapSize.x !== 512 || this.mapSize.y !== 512) object.mapSize = this.mapSize.toArray(); - object.camera = this.camera.toJSON(false).object; - delete object.camera.matrix; - return object; - } -} -class SpotLightShadow extends LightShadow { - static { - __name(this, "SpotLightShadow"); - } - constructor() { - super(new PerspectiveCamera(50, 1, 0.5, 500)); - this.isSpotLightShadow = true; - this.focus = 1; - } - updateMatrices(light) { - const camera = this.camera; - const fov2 = RAD2DEG * 2 * light.angle * this.focus; - const aspect2 = this.mapSize.width / this.mapSize.height; - const far = light.distance || camera.far; - if (fov2 !== camera.fov || aspect2 !== camera.aspect || far !== camera.far) { - camera.fov = fov2; - camera.aspect = aspect2; - camera.far = far; - camera.updateProjectionMatrix(); - } - super.updateMatrices(light); - } - copy(source) { - super.copy(source); - this.focus = source.focus; - return this; - } -} -class SpotLight extends Light { - static { - __name(this, "SpotLight"); - } - constructor(color, intensity, distance = 0, angle = Math.PI / 3, penumbra = 0, decay = 2) { - super(color, intensity); - this.isSpotLight = true; - this.type = "SpotLight"; - this.position.copy(Object3D.DEFAULT_UP); - this.updateMatrix(); - this.target = new Object3D(); - this.distance = distance; - this.angle = angle; - this.penumbra = penumbra; - this.decay = decay; - this.map = null; - this.shadow = new SpotLightShadow(); - } - get power() { - return this.intensity * Math.PI; - } - set power(power) { - this.intensity = power / Math.PI; - } - dispose() { - this.shadow.dispose(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.distance = source.distance; - this.angle = source.angle; - this.penumbra = source.penumbra; - this.decay = source.decay; - this.target = source.target.clone(); - this.shadow = source.shadow.clone(); - return this; - } -} -const _projScreenMatrix = /* @__PURE__ */ new Matrix4(); -const _lightPositionWorld = /* @__PURE__ */ new Vector3(); -const _lookTarget = /* @__PURE__ */ new Vector3(); -class PointLightShadow extends LightShadow { - static { - __name(this, "PointLightShadow"); - } - constructor() { - super(new PerspectiveCamera(90, 1, 0.5, 500)); - this.isPointLightShadow = true; - this._frameExtents = new Vector2(4, 2); - this._viewportCount = 6; - this._viewports = [ - // These viewports map a cube-map onto a 2D texture with the - // following orientation: - // - // xzXZ - // y Y - // - // X - Positive x direction - // x - Negative x direction - // Y - Positive y direction - // y - Negative y direction - // Z - Positive z direction - // z - Negative z direction - // positive X - new Vector4(2, 1, 1, 1), - // negative X - new Vector4(0, 1, 1, 1), - // positive Z - new Vector4(3, 1, 1, 1), - // negative Z - new Vector4(1, 1, 1, 1), - // positive Y - new Vector4(3, 0, 1, 1), - // negative Y - new Vector4(1, 0, 1, 1) - ]; - this._cubeDirections = [ - new Vector3(1, 0, 0), - new Vector3(-1, 0, 0), - new Vector3(0, 0, 1), - new Vector3(0, 0, -1), - new Vector3(0, 1, 0), - new Vector3(0, -1, 0) - ]; - this._cubeUps = [ - new Vector3(0, 1, 0), - new Vector3(0, 1, 0), - new Vector3(0, 1, 0), - new Vector3(0, 1, 0), - new Vector3(0, 0, 1), - new Vector3(0, 0, -1) - ]; - } - updateMatrices(light, viewportIndex = 0) { - const camera = this.camera; - const shadowMatrix = this.matrix; - const far = light.distance || camera.far; - if (far !== camera.far) { - camera.far = far; - camera.updateProjectionMatrix(); - } - _lightPositionWorld.setFromMatrixPosition(light.matrixWorld); - camera.position.copy(_lightPositionWorld); - _lookTarget.copy(camera.position); - _lookTarget.add(this._cubeDirections[viewportIndex]); - camera.up.copy(this._cubeUps[viewportIndex]); - camera.lookAt(_lookTarget); - camera.updateMatrixWorld(); - shadowMatrix.makeTranslation(-_lightPositionWorld.x, -_lightPositionWorld.y, -_lightPositionWorld.z); - _projScreenMatrix.multiplyMatrices(camera.projectionMatrix, camera.matrixWorldInverse); - this._frustum.setFromProjectionMatrix(_projScreenMatrix); - } -} -class PointLight extends Light { - static { - __name(this, "PointLight"); - } - constructor(color, intensity, distance = 0, decay = 2) { - super(color, intensity); - this.isPointLight = true; - this.type = "PointLight"; - this.distance = distance; - this.decay = decay; - this.shadow = new PointLightShadow(); - } - get power() { - return this.intensity * 4 * Math.PI; - } - set power(power) { - this.intensity = power / (4 * Math.PI); - } - dispose() { - this.shadow.dispose(); - } - copy(source, recursive) { - super.copy(source, recursive); - this.distance = source.distance; - this.decay = source.decay; - this.shadow = source.shadow.clone(); - return this; - } -} -class DirectionalLightShadow extends LightShadow { - static { - __name(this, "DirectionalLightShadow"); - } - constructor() { - super(new OrthographicCamera(-5, 5, 5, -5, 0.5, 500)); - this.isDirectionalLightShadow = true; - } -} -class DirectionalLight extends Light { - static { - __name(this, "DirectionalLight"); - } - constructor(color, intensity) { - super(color, intensity); - this.isDirectionalLight = true; - this.type = "DirectionalLight"; - this.position.copy(Object3D.DEFAULT_UP); - this.updateMatrix(); - this.target = new Object3D(); - this.shadow = new DirectionalLightShadow(); - } - dispose() { - this.shadow.dispose(); - } - copy(source) { - super.copy(source); - this.target = source.target.clone(); - this.shadow = source.shadow.clone(); - return this; - } -} -class AmbientLight extends Light { - static { - __name(this, "AmbientLight"); - } - constructor(color, intensity) { - super(color, intensity); - this.isAmbientLight = true; - this.type = "AmbientLight"; - } -} -class RectAreaLight extends Light { - static { - __name(this, "RectAreaLight"); - } - constructor(color, intensity, width = 10, height = 10) { - super(color, intensity); - this.isRectAreaLight = true; - this.type = "RectAreaLight"; - this.width = width; - this.height = height; - } - get power() { - return this.intensity * this.width * this.height * Math.PI; - } - set power(power) { - this.intensity = power / (this.width * this.height * Math.PI); - } - copy(source) { - super.copy(source); - this.width = source.width; - this.height = source.height; - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - data.object.width = this.width; - data.object.height = this.height; - return data; - } -} -class SphericalHarmonics3 { - static { - __name(this, "SphericalHarmonics3"); - } - constructor() { - this.isSphericalHarmonics3 = true; - this.coefficients = []; - for (let i = 0; i < 9; i++) { - this.coefficients.push(new Vector3()); - } - } - set(coefficients) { - for (let i = 0; i < 9; i++) { - this.coefficients[i].copy(coefficients[i]); - } - return this; - } - zero() { - for (let i = 0; i < 9; i++) { - this.coefficients[i].set(0, 0, 0); - } - return this; - } - // get the radiance in the direction of the normal - // target is a Vector3 - getAt(normal, target) { - const x = normal.x, y = normal.y, z = normal.z; - const coeff = this.coefficients; - target.copy(coeff[0]).multiplyScalar(0.282095); - target.addScaledVector(coeff[1], 0.488603 * y); - target.addScaledVector(coeff[2], 0.488603 * z); - target.addScaledVector(coeff[3], 0.488603 * x); - target.addScaledVector(coeff[4], 1.092548 * (x * y)); - target.addScaledVector(coeff[5], 1.092548 * (y * z)); - target.addScaledVector(coeff[6], 0.315392 * (3 * z * z - 1)); - target.addScaledVector(coeff[7], 1.092548 * (x * z)); - target.addScaledVector(coeff[8], 0.546274 * (x * x - y * y)); - return target; - } - // get the irradiance (radiance convolved with cosine lobe) in the direction of the normal - // target is a Vector3 - // https://graphics.stanford.edu/papers/envmap/envmap.pdf - getIrradianceAt(normal, target) { - const x = normal.x, y = normal.y, z = normal.z; - const coeff = this.coefficients; - target.copy(coeff[0]).multiplyScalar(0.886227); - target.addScaledVector(coeff[1], 2 * 0.511664 * y); - target.addScaledVector(coeff[2], 2 * 0.511664 * z); - target.addScaledVector(coeff[3], 2 * 0.511664 * x); - target.addScaledVector(coeff[4], 2 * 0.429043 * x * y); - target.addScaledVector(coeff[5], 2 * 0.429043 * y * z); - target.addScaledVector(coeff[6], 0.743125 * z * z - 0.247708); - target.addScaledVector(coeff[7], 2 * 0.429043 * x * z); - target.addScaledVector(coeff[8], 0.429043 * (x * x - y * y)); - return target; - } - add(sh) { - for (let i = 0; i < 9; i++) { - this.coefficients[i].add(sh.coefficients[i]); - } - return this; - } - addScaledSH(sh, s) { - for (let i = 0; i < 9; i++) { - this.coefficients[i].addScaledVector(sh.coefficients[i], s); - } - return this; - } - scale(s) { - for (let i = 0; i < 9; i++) { - this.coefficients[i].multiplyScalar(s); - } - return this; - } - lerp(sh, alpha) { - for (let i = 0; i < 9; i++) { - this.coefficients[i].lerp(sh.coefficients[i], alpha); - } - return this; - } - equals(sh) { - for (let i = 0; i < 9; i++) { - if (!this.coefficients[i].equals(sh.coefficients[i])) { - return false; - } - } - return true; - } - copy(sh) { - return this.set(sh.coefficients); - } - clone() { - return new this.constructor().copy(this); - } - fromArray(array, offset = 0) { - const coefficients = this.coefficients; - for (let i = 0; i < 9; i++) { - coefficients[i].fromArray(array, offset + i * 3); - } - return this; - } - toArray(array = [], offset = 0) { - const coefficients = this.coefficients; - for (let i = 0; i < 9; i++) { - coefficients[i].toArray(array, offset + i * 3); - } - return array; - } - // evaluate the basis functions - // shBasis is an Array[ 9 ] - static getBasisAt(normal, shBasis) { - const x = normal.x, y = normal.y, z = normal.z; - shBasis[0] = 0.282095; - shBasis[1] = 0.488603 * y; - shBasis[2] = 0.488603 * z; - shBasis[3] = 0.488603 * x; - shBasis[4] = 1.092548 * x * y; - shBasis[5] = 1.092548 * y * z; - shBasis[6] = 0.315392 * (3 * z * z - 1); - shBasis[7] = 1.092548 * x * z; - shBasis[8] = 0.546274 * (x * x - y * y); - } -} -class LightProbe extends Light { - static { - __name(this, "LightProbe"); - } - constructor(sh = new SphericalHarmonics3(), intensity = 1) { - super(void 0, intensity); - this.isLightProbe = true; - this.sh = sh; - } - copy(source) { - super.copy(source); - this.sh.copy(source.sh); - return this; - } - fromJSON(json) { - this.intensity = json.intensity; - this.sh.fromArray(json.sh); - return this; - } - toJSON(meta) { - const data = super.toJSON(meta); - data.object.sh = this.sh.toArray(); - return data; - } -} -class MaterialLoader extends Loader { - static { - __name(this, "MaterialLoader"); - } - constructor(manager) { - super(manager); - this.textures = {}; - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(scope.manager); - loader.setPath(scope.path); - loader.setRequestHeader(scope.requestHeader); - loader.setWithCredentials(scope.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(JSON.parse(text))); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - parse(json) { - const textures = this.textures; - function getTexture(name) { - if (textures[name] === void 0) { - console.warn("THREE.MaterialLoader: Undefined texture", name); - } - return textures[name]; - } - __name(getTexture, "getTexture"); - const material = this.createMaterialFromType(json.type); - if (json.uuid !== void 0) material.uuid = json.uuid; - if (json.name !== void 0) material.name = json.name; - if (json.color !== void 0 && material.color !== void 0) material.color.setHex(json.color); - if (json.roughness !== void 0) material.roughness = json.roughness; - if (json.metalness !== void 0) material.metalness = json.metalness; - if (json.sheen !== void 0) material.sheen = json.sheen; - if (json.sheenColor !== void 0) material.sheenColor = new Color().setHex(json.sheenColor); - if (json.sheenRoughness !== void 0) material.sheenRoughness = json.sheenRoughness; - if (json.emissive !== void 0 && material.emissive !== void 0) material.emissive.setHex(json.emissive); - if (json.specular !== void 0 && material.specular !== void 0) material.specular.setHex(json.specular); - if (json.specularIntensity !== void 0) material.specularIntensity = json.specularIntensity; - if (json.specularColor !== void 0 && material.specularColor !== void 0) material.specularColor.setHex(json.specularColor); - if (json.shininess !== void 0) material.shininess = json.shininess; - if (json.clearcoat !== void 0) material.clearcoat = json.clearcoat; - if (json.clearcoatRoughness !== void 0) material.clearcoatRoughness = json.clearcoatRoughness; - if (json.dispersion !== void 0) material.dispersion = json.dispersion; - if (json.iridescence !== void 0) material.iridescence = json.iridescence; - if (json.iridescenceIOR !== void 0) material.iridescenceIOR = json.iridescenceIOR; - if (json.iridescenceThicknessRange !== void 0) material.iridescenceThicknessRange = json.iridescenceThicknessRange; - if (json.transmission !== void 0) material.transmission = json.transmission; - if (json.thickness !== void 0) material.thickness = json.thickness; - if (json.attenuationDistance !== void 0) material.attenuationDistance = json.attenuationDistance; - if (json.attenuationColor !== void 0 && material.attenuationColor !== void 0) material.attenuationColor.setHex(json.attenuationColor); - if (json.anisotropy !== void 0) material.anisotropy = json.anisotropy; - if (json.anisotropyRotation !== void 0) material.anisotropyRotation = json.anisotropyRotation; - if (json.fog !== void 0) material.fog = json.fog; - if (json.flatShading !== void 0) material.flatShading = json.flatShading; - if (json.blending !== void 0) material.blending = json.blending; - if (json.combine !== void 0) material.combine = json.combine; - if (json.side !== void 0) material.side = json.side; - if (json.shadowSide !== void 0) material.shadowSide = json.shadowSide; - if (json.opacity !== void 0) material.opacity = json.opacity; - if (json.transparent !== void 0) material.transparent = json.transparent; - if (json.alphaTest !== void 0) material.alphaTest = json.alphaTest; - if (json.alphaHash !== void 0) material.alphaHash = json.alphaHash; - if (json.depthFunc !== void 0) material.depthFunc = json.depthFunc; - if (json.depthTest !== void 0) material.depthTest = json.depthTest; - if (json.depthWrite !== void 0) material.depthWrite = json.depthWrite; - if (json.colorWrite !== void 0) material.colorWrite = json.colorWrite; - if (json.blendSrc !== void 0) material.blendSrc = json.blendSrc; - if (json.blendDst !== void 0) material.blendDst = json.blendDst; - if (json.blendEquation !== void 0) material.blendEquation = json.blendEquation; - if (json.blendSrcAlpha !== void 0) material.blendSrcAlpha = json.blendSrcAlpha; - if (json.blendDstAlpha !== void 0) material.blendDstAlpha = json.blendDstAlpha; - if (json.blendEquationAlpha !== void 0) material.blendEquationAlpha = json.blendEquationAlpha; - if (json.blendColor !== void 0 && material.blendColor !== void 0) material.blendColor.setHex(json.blendColor); - if (json.blendAlpha !== void 0) material.blendAlpha = json.blendAlpha; - if (json.stencilWriteMask !== void 0) material.stencilWriteMask = json.stencilWriteMask; - if (json.stencilFunc !== void 0) material.stencilFunc = json.stencilFunc; - if (json.stencilRef !== void 0) material.stencilRef = json.stencilRef; - if (json.stencilFuncMask !== void 0) material.stencilFuncMask = json.stencilFuncMask; - if (json.stencilFail !== void 0) material.stencilFail = json.stencilFail; - if (json.stencilZFail !== void 0) material.stencilZFail = json.stencilZFail; - if (json.stencilZPass !== void 0) material.stencilZPass = json.stencilZPass; - if (json.stencilWrite !== void 0) material.stencilWrite = json.stencilWrite; - if (json.wireframe !== void 0) material.wireframe = json.wireframe; - if (json.wireframeLinewidth !== void 0) material.wireframeLinewidth = json.wireframeLinewidth; - if (json.wireframeLinecap !== void 0) material.wireframeLinecap = json.wireframeLinecap; - if (json.wireframeLinejoin !== void 0) material.wireframeLinejoin = json.wireframeLinejoin; - if (json.rotation !== void 0) material.rotation = json.rotation; - if (json.linewidth !== void 0) material.linewidth = json.linewidth; - if (json.dashSize !== void 0) material.dashSize = json.dashSize; - if (json.gapSize !== void 0) material.gapSize = json.gapSize; - if (json.scale !== void 0) material.scale = json.scale; - if (json.polygonOffset !== void 0) material.polygonOffset = json.polygonOffset; - if (json.polygonOffsetFactor !== void 0) material.polygonOffsetFactor = json.polygonOffsetFactor; - if (json.polygonOffsetUnits !== void 0) material.polygonOffsetUnits = json.polygonOffsetUnits; - if (json.dithering !== void 0) material.dithering = json.dithering; - if (json.alphaToCoverage !== void 0) material.alphaToCoverage = json.alphaToCoverage; - if (json.premultipliedAlpha !== void 0) material.premultipliedAlpha = json.premultipliedAlpha; - if (json.forceSinglePass !== void 0) material.forceSinglePass = json.forceSinglePass; - if (json.visible !== void 0) material.visible = json.visible; - if (json.toneMapped !== void 0) material.toneMapped = json.toneMapped; - if (json.userData !== void 0) material.userData = json.userData; - if (json.vertexColors !== void 0) { - if (typeof json.vertexColors === "number") { - material.vertexColors = json.vertexColors > 0 ? true : false; - } else { - material.vertexColors = json.vertexColors; - } - } - if (json.uniforms !== void 0) { - for (const name in json.uniforms) { - const uniform = json.uniforms[name]; - material.uniforms[name] = {}; - switch (uniform.type) { - case "t": - material.uniforms[name].value = getTexture(uniform.value); - break; - case "c": - material.uniforms[name].value = new Color().setHex(uniform.value); - break; - case "v2": - material.uniforms[name].value = new Vector2().fromArray(uniform.value); - break; - case "v3": - material.uniforms[name].value = new Vector3().fromArray(uniform.value); - break; - case "v4": - material.uniforms[name].value = new Vector4().fromArray(uniform.value); - break; - case "m3": - material.uniforms[name].value = new Matrix3().fromArray(uniform.value); - break; - case "m4": - material.uniforms[name].value = new Matrix4().fromArray(uniform.value); - break; - default: - material.uniforms[name].value = uniform.value; - } - } - } - if (json.defines !== void 0) material.defines = json.defines; - if (json.vertexShader !== void 0) material.vertexShader = json.vertexShader; - if (json.fragmentShader !== void 0) material.fragmentShader = json.fragmentShader; - if (json.glslVersion !== void 0) material.glslVersion = json.glslVersion; - if (json.extensions !== void 0) { - for (const key in json.extensions) { - material.extensions[key] = json.extensions[key]; - } - } - if (json.lights !== void 0) material.lights = json.lights; - if (json.clipping !== void 0) material.clipping = json.clipping; - if (json.size !== void 0) material.size = json.size; - if (json.sizeAttenuation !== void 0) material.sizeAttenuation = json.sizeAttenuation; - if (json.map !== void 0) material.map = getTexture(json.map); - if (json.matcap !== void 0) material.matcap = getTexture(json.matcap); - if (json.alphaMap !== void 0) material.alphaMap = getTexture(json.alphaMap); - if (json.bumpMap !== void 0) material.bumpMap = getTexture(json.bumpMap); - if (json.bumpScale !== void 0) material.bumpScale = json.bumpScale; - if (json.normalMap !== void 0) material.normalMap = getTexture(json.normalMap); - if (json.normalMapType !== void 0) material.normalMapType = json.normalMapType; - if (json.normalScale !== void 0) { - let normalScale = json.normalScale; - if (Array.isArray(normalScale) === false) { - normalScale = [normalScale, normalScale]; - } - material.normalScale = new Vector2().fromArray(normalScale); - } - if (json.displacementMap !== void 0) material.displacementMap = getTexture(json.displacementMap); - if (json.displacementScale !== void 0) material.displacementScale = json.displacementScale; - if (json.displacementBias !== void 0) material.displacementBias = json.displacementBias; - if (json.roughnessMap !== void 0) material.roughnessMap = getTexture(json.roughnessMap); - if (json.metalnessMap !== void 0) material.metalnessMap = getTexture(json.metalnessMap); - if (json.emissiveMap !== void 0) material.emissiveMap = getTexture(json.emissiveMap); - if (json.emissiveIntensity !== void 0) material.emissiveIntensity = json.emissiveIntensity; - if (json.specularMap !== void 0) material.specularMap = getTexture(json.specularMap); - if (json.specularIntensityMap !== void 0) material.specularIntensityMap = getTexture(json.specularIntensityMap); - if (json.specularColorMap !== void 0) material.specularColorMap = getTexture(json.specularColorMap); - if (json.envMap !== void 0) material.envMap = getTexture(json.envMap); - if (json.envMapRotation !== void 0) material.envMapRotation.fromArray(json.envMapRotation); - if (json.envMapIntensity !== void 0) material.envMapIntensity = json.envMapIntensity; - if (json.reflectivity !== void 0) material.reflectivity = json.reflectivity; - if (json.refractionRatio !== void 0) material.refractionRatio = json.refractionRatio; - if (json.lightMap !== void 0) material.lightMap = getTexture(json.lightMap); - if (json.lightMapIntensity !== void 0) material.lightMapIntensity = json.lightMapIntensity; - if (json.aoMap !== void 0) material.aoMap = getTexture(json.aoMap); - if (json.aoMapIntensity !== void 0) material.aoMapIntensity = json.aoMapIntensity; - if (json.gradientMap !== void 0) material.gradientMap = getTexture(json.gradientMap); - if (json.clearcoatMap !== void 0) material.clearcoatMap = getTexture(json.clearcoatMap); - if (json.clearcoatRoughnessMap !== void 0) material.clearcoatRoughnessMap = getTexture(json.clearcoatRoughnessMap); - if (json.clearcoatNormalMap !== void 0) material.clearcoatNormalMap = getTexture(json.clearcoatNormalMap); - if (json.clearcoatNormalScale !== void 0) material.clearcoatNormalScale = new Vector2().fromArray(json.clearcoatNormalScale); - if (json.iridescenceMap !== void 0) material.iridescenceMap = getTexture(json.iridescenceMap); - if (json.iridescenceThicknessMap !== void 0) material.iridescenceThicknessMap = getTexture(json.iridescenceThicknessMap); - if (json.transmissionMap !== void 0) material.transmissionMap = getTexture(json.transmissionMap); - if (json.thicknessMap !== void 0) material.thicknessMap = getTexture(json.thicknessMap); - if (json.anisotropyMap !== void 0) material.anisotropyMap = getTexture(json.anisotropyMap); - if (json.sheenColorMap !== void 0) material.sheenColorMap = getTexture(json.sheenColorMap); - if (json.sheenRoughnessMap !== void 0) material.sheenRoughnessMap = getTexture(json.sheenRoughnessMap); - return material; - } - setTextures(value) { - this.textures = value; - return this; - } - createMaterialFromType(type) { - return MaterialLoader.createMaterialFromType(type); - } - static createMaterialFromType(type) { - const materialLib = { - ShadowMaterial, - SpriteMaterial, - RawShaderMaterial, - ShaderMaterial, - PointsMaterial, - MeshPhysicalMaterial, - MeshStandardMaterial, - MeshPhongMaterial, - MeshToonMaterial, - MeshNormalMaterial, - MeshLambertMaterial, - MeshDepthMaterial, - MeshDistanceMaterial, - MeshBasicMaterial, - MeshMatcapMaterial, - LineDashedMaterial, - LineBasicMaterial, - Material - }; - return new materialLib[type](); - } -} -class LoaderUtils { - static { - __name(this, "LoaderUtils"); - } - static decodeText(array) { - console.warn("THREE.LoaderUtils: decodeText() has been deprecated with r165 and will be removed with r175. Use TextDecoder instead."); - if (typeof TextDecoder !== "undefined") { - return new TextDecoder().decode(array); - } - let s = ""; - for (let i = 0, il = array.length; i < il; i++) { - s += String.fromCharCode(array[i]); - } - try { - return decodeURIComponent(escape(s)); - } catch (e) { - return s; - } - } - static extractUrlBase(url) { - const index = url.lastIndexOf("/"); - if (index === -1) return "./"; - return url.slice(0, index + 1); - } - static resolveURL(url, path) { - if (typeof url !== "string" || url === "") return ""; - if (/^https?:\/\//i.test(path) && /^\//.test(url)) { - path = path.replace(/(^https?:\/\/[^\/]+).*/i, "$1"); - } - if (/^(https?:)?\/\//i.test(url)) return url; - if (/^data:.*,.*$/i.test(url)) return url; - if (/^blob:.*$/i.test(url)) return url; - return path + url; - } -} -class InstancedBufferGeometry extends BufferGeometry { - static { - __name(this, "InstancedBufferGeometry"); - } - constructor() { - super(); - this.isInstancedBufferGeometry = true; - this.type = "InstancedBufferGeometry"; - this.instanceCount = Infinity; - } - copy(source) { - super.copy(source); - this.instanceCount = source.instanceCount; - return this; - } - toJSON() { - const data = super.toJSON(); - data.instanceCount = this.instanceCount; - data.isInstancedBufferGeometry = true; - return data; - } -} -class BufferGeometryLoader extends Loader { - static { - __name(this, "BufferGeometryLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(scope.manager); - loader.setPath(scope.path); - loader.setRequestHeader(scope.requestHeader); - loader.setWithCredentials(scope.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(JSON.parse(text))); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - parse(json) { - const interleavedBufferMap = {}; - const arrayBufferMap = {}; - function getInterleavedBuffer(json2, uuid) { - if (interleavedBufferMap[uuid] !== void 0) return interleavedBufferMap[uuid]; - const interleavedBuffers = json2.interleavedBuffers; - const interleavedBuffer = interleavedBuffers[uuid]; - const buffer = getArrayBuffer(json2, interleavedBuffer.buffer); - const array = getTypedArray(interleavedBuffer.type, buffer); - const ib = new InterleavedBuffer(array, interleavedBuffer.stride); - ib.uuid = interleavedBuffer.uuid; - interleavedBufferMap[uuid] = ib; - return ib; - } - __name(getInterleavedBuffer, "getInterleavedBuffer"); - function getArrayBuffer(json2, uuid) { - if (arrayBufferMap[uuid] !== void 0) return arrayBufferMap[uuid]; - const arrayBuffers = json2.arrayBuffers; - const arrayBuffer = arrayBuffers[uuid]; - const ab = new Uint32Array(arrayBuffer).buffer; - arrayBufferMap[uuid] = ab; - return ab; - } - __name(getArrayBuffer, "getArrayBuffer"); - const geometry = json.isInstancedBufferGeometry ? new InstancedBufferGeometry() : new BufferGeometry(); - const index = json.data.index; - if (index !== void 0) { - const typedArray = getTypedArray(index.type, index.array); - geometry.setIndex(new BufferAttribute(typedArray, 1)); - } - const attributes = json.data.attributes; - for (const key in attributes) { - const attribute = attributes[key]; - let bufferAttribute; - if (attribute.isInterleavedBufferAttribute) { - const interleavedBuffer = getInterleavedBuffer(json.data, attribute.data); - bufferAttribute = new InterleavedBufferAttribute(interleavedBuffer, attribute.itemSize, attribute.offset, attribute.normalized); - } else { - const typedArray = getTypedArray(attribute.type, attribute.array); - const bufferAttributeConstr = attribute.isInstancedBufferAttribute ? InstancedBufferAttribute : BufferAttribute; - bufferAttribute = new bufferAttributeConstr(typedArray, attribute.itemSize, attribute.normalized); - } - if (attribute.name !== void 0) bufferAttribute.name = attribute.name; - if (attribute.usage !== void 0) bufferAttribute.setUsage(attribute.usage); - geometry.setAttribute(key, bufferAttribute); - } - const morphAttributes = json.data.morphAttributes; - if (morphAttributes) { - for (const key in morphAttributes) { - const attributeArray = morphAttributes[key]; - const array = []; - for (let i = 0, il = attributeArray.length; i < il; i++) { - const attribute = attributeArray[i]; - let bufferAttribute; - if (attribute.isInterleavedBufferAttribute) { - const interleavedBuffer = getInterleavedBuffer(json.data, attribute.data); - bufferAttribute = new InterleavedBufferAttribute(interleavedBuffer, attribute.itemSize, attribute.offset, attribute.normalized); - } else { - const typedArray = getTypedArray(attribute.type, attribute.array); - bufferAttribute = new BufferAttribute(typedArray, attribute.itemSize, attribute.normalized); - } - if (attribute.name !== void 0) bufferAttribute.name = attribute.name; - array.push(bufferAttribute); - } - geometry.morphAttributes[key] = array; - } - } - const morphTargetsRelative = json.data.morphTargetsRelative; - if (morphTargetsRelative) { - geometry.morphTargetsRelative = true; - } - const groups = json.data.groups || json.data.drawcalls || json.data.offsets; - if (groups !== void 0) { - for (let i = 0, n = groups.length; i !== n; ++i) { - const group = groups[i]; - geometry.addGroup(group.start, group.count, group.materialIndex); - } - } - const boundingSphere = json.data.boundingSphere; - if (boundingSphere !== void 0) { - const center = new Vector3(); - if (boundingSphere.center !== void 0) { - center.fromArray(boundingSphere.center); - } - geometry.boundingSphere = new Sphere(center, boundingSphere.radius); - } - if (json.name) geometry.name = json.name; - if (json.userData) geometry.userData = json.userData; - return geometry; - } -} -class ObjectLoader extends Loader { - static { - __name(this, "ObjectLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const path = this.path === "" ? LoaderUtils.extractUrlBase(url) : this.path; - this.resourcePath = this.resourcePath || path; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(text) { - let json = null; - try { - json = JSON.parse(text); - } catch (error) { - if (onError !== void 0) onError(error); - console.error("THREE:ObjectLoader: Can't parse " + url + ".", error.message); - return; - } - const metadata = json.metadata; - if (metadata === void 0 || metadata.type === void 0 || metadata.type.toLowerCase() === "geometry") { - if (onError !== void 0) onError(new Error("THREE.ObjectLoader: Can't load " + url)); - console.error("THREE.ObjectLoader: Can't load " + url); - return; - } - scope.parse(json, onLoad); - }, onProgress, onError); - } - async loadAsync(url, onProgress) { - const scope = this; - const path = this.path === "" ? LoaderUtils.extractUrlBase(url) : this.path; - this.resourcePath = this.resourcePath || path; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - const text = await loader.loadAsync(url, onProgress); - const json = JSON.parse(text); - const metadata = json.metadata; - if (metadata === void 0 || metadata.type === void 0 || metadata.type.toLowerCase() === "geometry") { - throw new Error("THREE.ObjectLoader: Can't load " + url); - } - return await scope.parseAsync(json); - } - parse(json, onLoad) { - const animations = this.parseAnimations(json.animations); - const shapes = this.parseShapes(json.shapes); - const geometries = this.parseGeometries(json.geometries, shapes); - const images = this.parseImages(json.images, function() { - if (onLoad !== void 0) onLoad(object); - }); - const textures = this.parseTextures(json.textures, images); - const materials = this.parseMaterials(json.materials, textures); - const object = this.parseObject(json.object, geometries, materials, textures, animations); - const skeletons = this.parseSkeletons(json.skeletons, object); - this.bindSkeletons(object, skeletons); - this.bindLightTargets(object); - if (onLoad !== void 0) { - let hasImages = false; - for (const uuid in images) { - if (images[uuid].data instanceof HTMLImageElement) { - hasImages = true; - break; - } - } - if (hasImages === false) onLoad(object); - } - return object; - } - async parseAsync(json) { - const animations = this.parseAnimations(json.animations); - const shapes = this.parseShapes(json.shapes); - const geometries = this.parseGeometries(json.geometries, shapes); - const images = await this.parseImagesAsync(json.images); - const textures = this.parseTextures(json.textures, images); - const materials = this.parseMaterials(json.materials, textures); - const object = this.parseObject(json.object, geometries, materials, textures, animations); - const skeletons = this.parseSkeletons(json.skeletons, object); - this.bindSkeletons(object, skeletons); - this.bindLightTargets(object); - return object; - } - parseShapes(json) { - const shapes = {}; - if (json !== void 0) { - for (let i = 0, l = json.length; i < l; i++) { - const shape = new Shape().fromJSON(json[i]); - shapes[shape.uuid] = shape; - } - } - return shapes; - } - parseSkeletons(json, object) { - const skeletons = {}; - const bones = {}; - object.traverse(function(child) { - if (child.isBone) bones[child.uuid] = child; - }); - if (json !== void 0) { - for (let i = 0, l = json.length; i < l; i++) { - const skeleton = new Skeleton().fromJSON(json[i], bones); - skeletons[skeleton.uuid] = skeleton; - } - } - return skeletons; - } - parseGeometries(json, shapes) { - const geometries = {}; - if (json !== void 0) { - const bufferGeometryLoader = new BufferGeometryLoader(); - for (let i = 0, l = json.length; i < l; i++) { - let geometry; - const data = json[i]; - switch (data.type) { - case "BufferGeometry": - case "InstancedBufferGeometry": - geometry = bufferGeometryLoader.parse(data); - break; - default: - if (data.type in Geometries) { - geometry = Geometries[data.type].fromJSON(data, shapes); - } else { - console.warn(`THREE.ObjectLoader: Unsupported geometry type "${data.type}"`); - } - } - geometry.uuid = data.uuid; - if (data.name !== void 0) geometry.name = data.name; - if (data.userData !== void 0) geometry.userData = data.userData; - geometries[data.uuid] = geometry; - } - } - return geometries; - } - parseMaterials(json, textures) { - const cache = {}; - const materials = {}; - if (json !== void 0) { - const loader = new MaterialLoader(); - loader.setTextures(textures); - for (let i = 0, l = json.length; i < l; i++) { - const data = json[i]; - if (cache[data.uuid] === void 0) { - cache[data.uuid] = loader.parse(data); - } - materials[data.uuid] = cache[data.uuid]; - } - } - return materials; - } - parseAnimations(json) { - const animations = {}; - if (json !== void 0) { - for (let i = 0; i < json.length; i++) { - const data = json[i]; - const clip = AnimationClip.parse(data); - animations[clip.uuid] = clip; - } - } - return animations; - } - parseImages(json, onLoad) { - const scope = this; - const images = {}; - let loader; - function loadImage2(url) { - scope.manager.itemStart(url); - return loader.load(url, function() { - scope.manager.itemEnd(url); - }, void 0, function() { - scope.manager.itemError(url); - scope.manager.itemEnd(url); - }); - } - __name(loadImage2, "loadImage"); - function deserializeImage(image) { - if (typeof image === "string") { - const url = image; - const path = /^(\/\/)|([a-z]+:(\/\/)?)/i.test(url) ? url : scope.resourcePath + url; - return loadImage2(path); - } else { - if (image.data) { - return { - data: getTypedArray(image.type, image.data), - width: image.width, - height: image.height - }; - } else { - return null; - } - } - } - __name(deserializeImage, "deserializeImage"); - if (json !== void 0 && json.length > 0) { - const manager = new LoadingManager(onLoad); - loader = new ImageLoader(manager); - loader.setCrossOrigin(this.crossOrigin); - for (let i = 0, il = json.length; i < il; i++) { - const image = json[i]; - const url = image.url; - if (Array.isArray(url)) { - const imageArray = []; - for (let j = 0, jl = url.length; j < jl; j++) { - const currentUrl = url[j]; - const deserializedImage = deserializeImage(currentUrl); - if (deserializedImage !== null) { - if (deserializedImage instanceof HTMLImageElement) { - imageArray.push(deserializedImage); - } else { - imageArray.push(new DataTexture(deserializedImage.data, deserializedImage.width, deserializedImage.height)); - } - } - } - images[image.uuid] = new Source(imageArray); - } else { - const deserializedImage = deserializeImage(image.url); - images[image.uuid] = new Source(deserializedImage); - } - } - } - return images; - } - async parseImagesAsync(json) { - const scope = this; - const images = {}; - let loader; - async function deserializeImage(image) { - if (typeof image === "string") { - const url = image; - const path = /^(\/\/)|([a-z]+:(\/\/)?)/i.test(url) ? url : scope.resourcePath + url; - return await loader.loadAsync(path); - } else { - if (image.data) { - return { - data: getTypedArray(image.type, image.data), - width: image.width, - height: image.height - }; - } else { - return null; - } - } - } - __name(deserializeImage, "deserializeImage"); - if (json !== void 0 && json.length > 0) { - loader = new ImageLoader(this.manager); - loader.setCrossOrigin(this.crossOrigin); - for (let i = 0, il = json.length; i < il; i++) { - const image = json[i]; - const url = image.url; - if (Array.isArray(url)) { - const imageArray = []; - for (let j = 0, jl = url.length; j < jl; j++) { - const currentUrl = url[j]; - const deserializedImage = await deserializeImage(currentUrl); - if (deserializedImage !== null) { - if (deserializedImage instanceof HTMLImageElement) { - imageArray.push(deserializedImage); - } else { - imageArray.push(new DataTexture(deserializedImage.data, deserializedImage.width, deserializedImage.height)); - } - } - } - images[image.uuid] = new Source(imageArray); - } else { - const deserializedImage = await deserializeImage(image.url); - images[image.uuid] = new Source(deserializedImage); - } - } - } - return images; - } - parseTextures(json, images) { - function parseConstant(value, type) { - if (typeof value === "number") return value; - console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.", value); - return type[value]; - } - __name(parseConstant, "parseConstant"); - const textures = {}; - if (json !== void 0) { - for (let i = 0, l = json.length; i < l; i++) { - const data = json[i]; - if (data.image === void 0) { - console.warn('THREE.ObjectLoader: No "image" specified for', data.uuid); - } - if (images[data.image] === void 0) { - console.warn("THREE.ObjectLoader: Undefined image", data.image); - } - const source = images[data.image]; - const image = source.data; - let texture; - if (Array.isArray(image)) { - texture = new CubeTexture(); - if (image.length === 6) texture.needsUpdate = true; - } else { - if (image && image.data) { - texture = new DataTexture(); - } else { - texture = new Texture(); - } - if (image) texture.needsUpdate = true; - } - texture.source = source; - texture.uuid = data.uuid; - if (data.name !== void 0) texture.name = data.name; - if (data.mapping !== void 0) texture.mapping = parseConstant(data.mapping, TEXTURE_MAPPING); - if (data.channel !== void 0) texture.channel = data.channel; - if (data.offset !== void 0) texture.offset.fromArray(data.offset); - if (data.repeat !== void 0) texture.repeat.fromArray(data.repeat); - if (data.center !== void 0) texture.center.fromArray(data.center); - if (data.rotation !== void 0) texture.rotation = data.rotation; - if (data.wrap !== void 0) { - texture.wrapS = parseConstant(data.wrap[0], TEXTURE_WRAPPING); - texture.wrapT = parseConstant(data.wrap[1], TEXTURE_WRAPPING); - } - if (data.format !== void 0) texture.format = data.format; - if (data.internalFormat !== void 0) texture.internalFormat = data.internalFormat; - if (data.type !== void 0) texture.type = data.type; - if (data.colorSpace !== void 0) texture.colorSpace = data.colorSpace; - if (data.minFilter !== void 0) texture.minFilter = parseConstant(data.minFilter, TEXTURE_FILTER); - if (data.magFilter !== void 0) texture.magFilter = parseConstant(data.magFilter, TEXTURE_FILTER); - if (data.anisotropy !== void 0) texture.anisotropy = data.anisotropy; - if (data.flipY !== void 0) texture.flipY = data.flipY; - if (data.generateMipmaps !== void 0) texture.generateMipmaps = data.generateMipmaps; - if (data.premultiplyAlpha !== void 0) texture.premultiplyAlpha = data.premultiplyAlpha; - if (data.unpackAlignment !== void 0) texture.unpackAlignment = data.unpackAlignment; - if (data.compareFunction !== void 0) texture.compareFunction = data.compareFunction; - if (data.userData !== void 0) texture.userData = data.userData; - textures[data.uuid] = texture; - } - } - return textures; - } - parseObject(data, geometries, materials, textures, animations) { - let object; - function getGeometry(name) { - if (geometries[name] === void 0) { - console.warn("THREE.ObjectLoader: Undefined geometry", name); - } - return geometries[name]; - } - __name(getGeometry, "getGeometry"); - function getMaterial(name) { - if (name === void 0) return void 0; - if (Array.isArray(name)) { - const array = []; - for (let i = 0, l = name.length; i < l; i++) { - const uuid = name[i]; - if (materials[uuid] === void 0) { - console.warn("THREE.ObjectLoader: Undefined material", uuid); - } - array.push(materials[uuid]); - } - return array; - } - if (materials[name] === void 0) { - console.warn("THREE.ObjectLoader: Undefined material", name); - } - return materials[name]; - } - __name(getMaterial, "getMaterial"); - function getTexture(uuid) { - if (textures[uuid] === void 0) { - console.warn("THREE.ObjectLoader: Undefined texture", uuid); - } - return textures[uuid]; - } - __name(getTexture, "getTexture"); - let geometry, material; - switch (data.type) { - case "Scene": - object = new Scene(); - if (data.background !== void 0) { - if (Number.isInteger(data.background)) { - object.background = new Color(data.background); - } else { - object.background = getTexture(data.background); - } - } - if (data.environment !== void 0) { - object.environment = getTexture(data.environment); - } - if (data.fog !== void 0) { - if (data.fog.type === "Fog") { - object.fog = new Fog(data.fog.color, data.fog.near, data.fog.far); - } else if (data.fog.type === "FogExp2") { - object.fog = new FogExp2(data.fog.color, data.fog.density); - } - if (data.fog.name !== "") { - object.fog.name = data.fog.name; - } - } - if (data.backgroundBlurriness !== void 0) object.backgroundBlurriness = data.backgroundBlurriness; - if (data.backgroundIntensity !== void 0) object.backgroundIntensity = data.backgroundIntensity; - if (data.backgroundRotation !== void 0) object.backgroundRotation.fromArray(data.backgroundRotation); - if (data.environmentIntensity !== void 0) object.environmentIntensity = data.environmentIntensity; - if (data.environmentRotation !== void 0) object.environmentRotation.fromArray(data.environmentRotation); - break; - case "PerspectiveCamera": - object = new PerspectiveCamera(data.fov, data.aspect, data.near, data.far); - if (data.focus !== void 0) object.focus = data.focus; - if (data.zoom !== void 0) object.zoom = data.zoom; - if (data.filmGauge !== void 0) object.filmGauge = data.filmGauge; - if (data.filmOffset !== void 0) object.filmOffset = data.filmOffset; - if (data.view !== void 0) object.view = Object.assign({}, data.view); - break; - case "OrthographicCamera": - object = new OrthographicCamera(data.left, data.right, data.top, data.bottom, data.near, data.far); - if (data.zoom !== void 0) object.zoom = data.zoom; - if (data.view !== void 0) object.view = Object.assign({}, data.view); - break; - case "AmbientLight": - object = new AmbientLight(data.color, data.intensity); - break; - case "DirectionalLight": - object = new DirectionalLight(data.color, data.intensity); - object.target = data.target || ""; - break; - case "PointLight": - object = new PointLight(data.color, data.intensity, data.distance, data.decay); - break; - case "RectAreaLight": - object = new RectAreaLight(data.color, data.intensity, data.width, data.height); - break; - case "SpotLight": - object = new SpotLight(data.color, data.intensity, data.distance, data.angle, data.penumbra, data.decay); - object.target = data.target || ""; - break; - case "HemisphereLight": - object = new HemisphereLight(data.color, data.groundColor, data.intensity); - break; - case "LightProbe": - object = new LightProbe().fromJSON(data); - break; - case "SkinnedMesh": - geometry = getGeometry(data.geometry); - material = getMaterial(data.material); - object = new SkinnedMesh(geometry, material); - if (data.bindMode !== void 0) object.bindMode = data.bindMode; - if (data.bindMatrix !== void 0) object.bindMatrix.fromArray(data.bindMatrix); - if (data.skeleton !== void 0) object.skeleton = data.skeleton; - break; - case "Mesh": - geometry = getGeometry(data.geometry); - material = getMaterial(data.material); - object = new Mesh(geometry, material); - break; - case "InstancedMesh": - geometry = getGeometry(data.geometry); - material = getMaterial(data.material); - const count = data.count; - const instanceMatrix = data.instanceMatrix; - const instanceColor = data.instanceColor; - object = new InstancedMesh(geometry, material, count); - object.instanceMatrix = new InstancedBufferAttribute(new Float32Array(instanceMatrix.array), 16); - if (instanceColor !== void 0) object.instanceColor = new InstancedBufferAttribute(new Float32Array(instanceColor.array), instanceColor.itemSize); - break; - case "BatchedMesh": - geometry = getGeometry(data.geometry); - material = getMaterial(data.material); - object = new BatchedMesh(data.maxInstanceCount, data.maxVertexCount, data.maxIndexCount, material); - object.geometry = geometry; - object.perObjectFrustumCulled = data.perObjectFrustumCulled; - object.sortObjects = data.sortObjects; - object._drawRanges = data.drawRanges; - object._reservedRanges = data.reservedRanges; - object._visibility = data.visibility; - object._active = data.active; - object._bounds = data.bounds.map((bound) => { - const box = new Box3(); - box.min.fromArray(bound.boxMin); - box.max.fromArray(bound.boxMax); - const sphere = new Sphere(); - sphere.radius = bound.sphereRadius; - sphere.center.fromArray(bound.sphereCenter); - return { - boxInitialized: bound.boxInitialized, - box, - sphereInitialized: bound.sphereInitialized, - sphere - }; - }); - object._maxInstanceCount = data.maxInstanceCount; - object._maxVertexCount = data.maxVertexCount; - object._maxIndexCount = data.maxIndexCount; - object._geometryInitialized = data.geometryInitialized; - object._geometryCount = data.geometryCount; - object._matricesTexture = getTexture(data.matricesTexture.uuid); - if (data.colorsTexture !== void 0) object._colorsTexture = getTexture(data.colorsTexture.uuid); - break; - case "LOD": - object = new LOD(); - break; - case "Line": - object = new Line(getGeometry(data.geometry), getMaterial(data.material)); - break; - case "LineLoop": - object = new LineLoop(getGeometry(data.geometry), getMaterial(data.material)); - break; - case "LineSegments": - object = new LineSegments(getGeometry(data.geometry), getMaterial(data.material)); - break; - case "PointCloud": - case "Points": - object = new Points(getGeometry(data.geometry), getMaterial(data.material)); - break; - case "Sprite": - object = new Sprite(getMaterial(data.material)); - break; - case "Group": - object = new Group(); - break; - case "Bone": - object = new Bone(); - break; - default: - object = new Object3D(); - } - object.uuid = data.uuid; - if (data.name !== void 0) object.name = data.name; - if (data.matrix !== void 0) { - object.matrix.fromArray(data.matrix); - if (data.matrixAutoUpdate !== void 0) object.matrixAutoUpdate = data.matrixAutoUpdate; - if (object.matrixAutoUpdate) object.matrix.decompose(object.position, object.quaternion, object.scale); - } else { - if (data.position !== void 0) object.position.fromArray(data.position); - if (data.rotation !== void 0) object.rotation.fromArray(data.rotation); - if (data.quaternion !== void 0) object.quaternion.fromArray(data.quaternion); - if (data.scale !== void 0) object.scale.fromArray(data.scale); - } - if (data.up !== void 0) object.up.fromArray(data.up); - if (data.castShadow !== void 0) object.castShadow = data.castShadow; - if (data.receiveShadow !== void 0) object.receiveShadow = data.receiveShadow; - if (data.shadow) { - if (data.shadow.intensity !== void 0) object.shadow.intensity = data.shadow.intensity; - if (data.shadow.bias !== void 0) object.shadow.bias = data.shadow.bias; - if (data.shadow.normalBias !== void 0) object.shadow.normalBias = data.shadow.normalBias; - if (data.shadow.radius !== void 0) object.shadow.radius = data.shadow.radius; - if (data.shadow.mapSize !== void 0) object.shadow.mapSize.fromArray(data.shadow.mapSize); - if (data.shadow.camera !== void 0) object.shadow.camera = this.parseObject(data.shadow.camera); - } - if (data.visible !== void 0) object.visible = data.visible; - if (data.frustumCulled !== void 0) object.frustumCulled = data.frustumCulled; - if (data.renderOrder !== void 0) object.renderOrder = data.renderOrder; - if (data.userData !== void 0) object.userData = data.userData; - if (data.layers !== void 0) object.layers.mask = data.layers; - if (data.children !== void 0) { - const children = data.children; - for (let i = 0; i < children.length; i++) { - object.add(this.parseObject(children[i], geometries, materials, textures, animations)); - } - } - if (data.animations !== void 0) { - const objectAnimations = data.animations; - for (let i = 0; i < objectAnimations.length; i++) { - const uuid = objectAnimations[i]; - object.animations.push(animations[uuid]); - } - } - if (data.type === "LOD") { - if (data.autoUpdate !== void 0) object.autoUpdate = data.autoUpdate; - const levels = data.levels; - for (let l = 0; l < levels.length; l++) { - const level = levels[l]; - const child = object.getObjectByProperty("uuid", level.object); - if (child !== void 0) { - object.addLevel(child, level.distance, level.hysteresis); - } - } - } - return object; - } - bindSkeletons(object, skeletons) { - if (Object.keys(skeletons).length === 0) return; - object.traverse(function(child) { - if (child.isSkinnedMesh === true && child.skeleton !== void 0) { - const skeleton = skeletons[child.skeleton]; - if (skeleton === void 0) { - console.warn("THREE.ObjectLoader: No skeleton found with UUID:", child.skeleton); - } else { - child.bind(skeleton, child.bindMatrix); - } - } - }); - } - bindLightTargets(object) { - object.traverse(function(child) { - if (child.isDirectionalLight || child.isSpotLight) { - const uuid = child.target; - const target = object.getObjectByProperty("uuid", uuid); - if (target !== void 0) { - child.target = target; - } else { - child.target = new Object3D(); - } - } - }); - } -} -const TEXTURE_MAPPING = { - UVMapping, - CubeReflectionMapping, - CubeRefractionMapping, - EquirectangularReflectionMapping, - EquirectangularRefractionMapping, - CubeUVReflectionMapping -}; -const TEXTURE_WRAPPING = { - RepeatWrapping, - ClampToEdgeWrapping, - MirroredRepeatWrapping -}; -const TEXTURE_FILTER = { - NearestFilter, - NearestMipmapNearestFilter, - NearestMipmapLinearFilter, - LinearFilter, - LinearMipmapNearestFilter, - LinearMipmapLinearFilter -}; -class ImageBitmapLoader extends Loader { - static { - __name(this, "ImageBitmapLoader"); - } - constructor(manager) { - super(manager); - this.isImageBitmapLoader = true; - if (typeof createImageBitmap === "undefined") { - console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."); - } - if (typeof fetch === "undefined") { - console.warn("THREE.ImageBitmapLoader: fetch() not supported."); - } - this.options = { premultiplyAlpha: "none" }; - } - setOptions(options) { - this.options = options; - return this; - } - load(url, onLoad, onProgress, onError) { - if (url === void 0) url = ""; - if (this.path !== void 0) url = this.path + url; - url = this.manager.resolveURL(url); - const scope = this; - const cached = Cache.get(url); - if (cached !== void 0) { - scope.manager.itemStart(url); - if (cached.then) { - cached.then((imageBitmap) => { - if (onLoad) onLoad(imageBitmap); - scope.manager.itemEnd(url); - }).catch((e) => { - if (onError) onError(e); - }); - return; - } - setTimeout(function() { - if (onLoad) onLoad(cached); - scope.manager.itemEnd(url); - }, 0); - return cached; - } - const fetchOptions = {}; - fetchOptions.credentials = this.crossOrigin === "anonymous" ? "same-origin" : "include"; - fetchOptions.headers = this.requestHeader; - const promise = fetch(url, fetchOptions).then(function(res) { - return res.blob(); - }).then(function(blob) { - return createImageBitmap(blob, Object.assign(scope.options, { colorSpaceConversion: "none" })); - }).then(function(imageBitmap) { - Cache.add(url, imageBitmap); - if (onLoad) onLoad(imageBitmap); - scope.manager.itemEnd(url); - return imageBitmap; - }).catch(function(e) { - if (onError) onError(e); - Cache.remove(url); - scope.manager.itemError(url); - scope.manager.itemEnd(url); - }); - Cache.add(url, promise); - scope.manager.itemStart(url); - } -} -let _context; -class AudioContext { - static { - __name(this, "AudioContext"); - } - static getContext() { - if (_context === void 0) { - _context = new (window.AudioContext || window.webkitAudioContext)(); - } - return _context; - } - static setContext(value) { - _context = value; - } -} -class AudioLoader extends Loader { - static { - __name(this, "AudioLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(this.manager); - loader.setResponseType("arraybuffer"); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(buffer) { - try { - const bufferCopy = buffer.slice(0); - const context = AudioContext.getContext(); - context.decodeAudioData(bufferCopy, function(audioBuffer) { - onLoad(audioBuffer); - }).catch(handleError); - } catch (e) { - handleError(e); - } - }, onProgress, onError); - function handleError(e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - __name(handleError, "handleError"); - } -} -const _eyeRight = /* @__PURE__ */ new Matrix4(); -const _eyeLeft = /* @__PURE__ */ new Matrix4(); -const _projectionMatrix = /* @__PURE__ */ new Matrix4(); -class StereoCamera { - static { - __name(this, "StereoCamera"); - } - constructor() { - this.type = "StereoCamera"; - this.aspect = 1; - this.eyeSep = 0.064; - this.cameraL = new PerspectiveCamera(); - this.cameraL.layers.enable(1); - this.cameraL.matrixAutoUpdate = false; - this.cameraR = new PerspectiveCamera(); - this.cameraR.layers.enable(2); - this.cameraR.matrixAutoUpdate = false; - this._cache = { - focus: null, - fov: null, - aspect: null, - near: null, - far: null, - zoom: null, - eyeSep: null - }; - } - update(camera) { - const cache = this._cache; - const needsUpdate = cache.focus !== camera.focus || cache.fov !== camera.fov || cache.aspect !== camera.aspect * this.aspect || cache.near !== camera.near || cache.far !== camera.far || cache.zoom !== camera.zoom || cache.eyeSep !== this.eyeSep; - if (needsUpdate) { - cache.focus = camera.focus; - cache.fov = camera.fov; - cache.aspect = camera.aspect * this.aspect; - cache.near = camera.near; - cache.far = camera.far; - cache.zoom = camera.zoom; - cache.eyeSep = this.eyeSep; - _projectionMatrix.copy(camera.projectionMatrix); - const eyeSepHalf = cache.eyeSep / 2; - const eyeSepOnProjection = eyeSepHalf * cache.near / cache.focus; - const ymax = cache.near * Math.tan(DEG2RAD * cache.fov * 0.5) / cache.zoom; - let xmin, xmax; - _eyeLeft.elements[12] = -eyeSepHalf; - _eyeRight.elements[12] = eyeSepHalf; - xmin = -ymax * cache.aspect + eyeSepOnProjection; - xmax = ymax * cache.aspect + eyeSepOnProjection; - _projectionMatrix.elements[0] = 2 * cache.near / (xmax - xmin); - _projectionMatrix.elements[8] = (xmax + xmin) / (xmax - xmin); - this.cameraL.projectionMatrix.copy(_projectionMatrix); - xmin = -ymax * cache.aspect - eyeSepOnProjection; - xmax = ymax * cache.aspect - eyeSepOnProjection; - _projectionMatrix.elements[0] = 2 * cache.near / (xmax - xmin); - _projectionMatrix.elements[8] = (xmax + xmin) / (xmax - xmin); - this.cameraR.projectionMatrix.copy(_projectionMatrix); - } - this.cameraL.matrixWorld.copy(camera.matrixWorld).multiply(_eyeLeft); - this.cameraR.matrixWorld.copy(camera.matrixWorld).multiply(_eyeRight); - } -} -class Clock { - static { - __name(this, "Clock"); - } - constructor(autoStart = true) { - this.autoStart = autoStart; - this.startTime = 0; - this.oldTime = 0; - this.elapsedTime = 0; - this.running = false; - } - start() { - this.startTime = now(); - this.oldTime = this.startTime; - this.elapsedTime = 0; - this.running = true; - } - stop() { - this.getElapsedTime(); - this.running = false; - this.autoStart = false; - } - getElapsedTime() { - this.getDelta(); - return this.elapsedTime; - } - getDelta() { - let diff = 0; - if (this.autoStart && !this.running) { - this.start(); - return 0; - } - if (this.running) { - const newTime = now(); - diff = (newTime - this.oldTime) / 1e3; - this.oldTime = newTime; - this.elapsedTime += diff; - } - return diff; - } -} -function now() { - return performance.now(); -} -__name(now, "now"); -const _position$1 = /* @__PURE__ */ new Vector3(); -const _quaternion$1 = /* @__PURE__ */ new Quaternion(); -const _scale$1 = /* @__PURE__ */ new Vector3(); -const _orientation$1 = /* @__PURE__ */ new Vector3(); -class AudioListener extends Object3D { - static { - __name(this, "AudioListener"); - } - constructor() { - super(); - this.type = "AudioListener"; - this.context = AudioContext.getContext(); - this.gain = this.context.createGain(); - this.gain.connect(this.context.destination); - this.filter = null; - this.timeDelta = 0; - this._clock = new Clock(); - } - getInput() { - return this.gain; - } - removeFilter() { - if (this.filter !== null) { - this.gain.disconnect(this.filter); - this.filter.disconnect(this.context.destination); - this.gain.connect(this.context.destination); - this.filter = null; - } - return this; - } - getFilter() { - return this.filter; - } - setFilter(value) { - if (this.filter !== null) { - this.gain.disconnect(this.filter); - this.filter.disconnect(this.context.destination); - } else { - this.gain.disconnect(this.context.destination); - } - this.filter = value; - this.gain.connect(this.filter); - this.filter.connect(this.context.destination); - return this; - } - getMasterVolume() { - return this.gain.gain.value; - } - setMasterVolume(value) { - this.gain.gain.setTargetAtTime(value, this.context.currentTime, 0.01); - return this; - } - updateMatrixWorld(force) { - super.updateMatrixWorld(force); - const listener = this.context.listener; - const up = this.up; - this.timeDelta = this._clock.getDelta(); - this.matrixWorld.decompose(_position$1, _quaternion$1, _scale$1); - _orientation$1.set(0, 0, -1).applyQuaternion(_quaternion$1); - if (listener.positionX) { - const endTime = this.context.currentTime + this.timeDelta; - listener.positionX.linearRampToValueAtTime(_position$1.x, endTime); - listener.positionY.linearRampToValueAtTime(_position$1.y, endTime); - listener.positionZ.linearRampToValueAtTime(_position$1.z, endTime); - listener.forwardX.linearRampToValueAtTime(_orientation$1.x, endTime); - listener.forwardY.linearRampToValueAtTime(_orientation$1.y, endTime); - listener.forwardZ.linearRampToValueAtTime(_orientation$1.z, endTime); - listener.upX.linearRampToValueAtTime(up.x, endTime); - listener.upY.linearRampToValueAtTime(up.y, endTime); - listener.upZ.linearRampToValueAtTime(up.z, endTime); - } else { - listener.setPosition(_position$1.x, _position$1.y, _position$1.z); - listener.setOrientation(_orientation$1.x, _orientation$1.y, _orientation$1.z, up.x, up.y, up.z); - } - } -} -class Audio extends Object3D { - static { - __name(this, "Audio"); - } - constructor(listener) { - super(); - this.type = "Audio"; - this.listener = listener; - this.context = listener.context; - this.gain = this.context.createGain(); - this.gain.connect(listener.getInput()); - this.autoplay = false; - this.buffer = null; - this.detune = 0; - this.loop = false; - this.loopStart = 0; - this.loopEnd = 0; - this.offset = 0; - this.duration = void 0; - this.playbackRate = 1; - this.isPlaying = false; - this.hasPlaybackControl = true; - this.source = null; - this.sourceType = "empty"; - this._startedAt = 0; - this._progress = 0; - this._connected = false; - this.filters = []; - } - getOutput() { - return this.gain; - } - setNodeSource(audioNode) { - this.hasPlaybackControl = false; - this.sourceType = "audioNode"; - this.source = audioNode; - this.connect(); - return this; - } - setMediaElementSource(mediaElement) { - this.hasPlaybackControl = false; - this.sourceType = "mediaNode"; - this.source = this.context.createMediaElementSource(mediaElement); - this.connect(); - return this; - } - setMediaStreamSource(mediaStream) { - this.hasPlaybackControl = false; - this.sourceType = "mediaStreamNode"; - this.source = this.context.createMediaStreamSource(mediaStream); - this.connect(); - return this; - } - setBuffer(audioBuffer) { - this.buffer = audioBuffer; - this.sourceType = "buffer"; - if (this.autoplay) this.play(); - return this; - } - play(delay = 0) { - if (this.isPlaying === true) { - console.warn("THREE.Audio: Audio is already playing."); - return; - } - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return; - } - this._startedAt = this.context.currentTime + delay; - const source = this.context.createBufferSource(); - source.buffer = this.buffer; - source.loop = this.loop; - source.loopStart = this.loopStart; - source.loopEnd = this.loopEnd; - source.onended = this.onEnded.bind(this); - source.start(this._startedAt, this._progress + this.offset, this.duration); - this.isPlaying = true; - this.source = source; - this.setDetune(this.detune); - this.setPlaybackRate(this.playbackRate); - return this.connect(); - } - pause() { - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return; - } - if (this.isPlaying === true) { - this._progress += Math.max(this.context.currentTime - this._startedAt, 0) * this.playbackRate; - if (this.loop === true) { - this._progress = this._progress % (this.duration || this.buffer.duration); - } - this.source.stop(); - this.source.onended = null; - this.isPlaying = false; - } - return this; - } - stop(delay = 0) { - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return; - } - this._progress = 0; - if (this.source !== null) { - this.source.stop(this.context.currentTime + delay); - this.source.onended = null; - } - this.isPlaying = false; - return this; - } - connect() { - if (this.filters.length > 0) { - this.source.connect(this.filters[0]); - for (let i = 1, l = this.filters.length; i < l; i++) { - this.filters[i - 1].connect(this.filters[i]); - } - this.filters[this.filters.length - 1].connect(this.getOutput()); - } else { - this.source.connect(this.getOutput()); - } - this._connected = true; - return this; - } - disconnect() { - if (this._connected === false) { - return; - } - if (this.filters.length > 0) { - this.source.disconnect(this.filters[0]); - for (let i = 1, l = this.filters.length; i < l; i++) { - this.filters[i - 1].disconnect(this.filters[i]); - } - this.filters[this.filters.length - 1].disconnect(this.getOutput()); - } else { - this.source.disconnect(this.getOutput()); - } - this._connected = false; - return this; - } - getFilters() { - return this.filters; - } - setFilters(value) { - if (!value) value = []; - if (this._connected === true) { - this.disconnect(); - this.filters = value.slice(); - this.connect(); - } else { - this.filters = value.slice(); - } - return this; - } - setDetune(value) { - this.detune = value; - if (this.isPlaying === true && this.source.detune !== void 0) { - this.source.detune.setTargetAtTime(this.detune, this.context.currentTime, 0.01); - } - return this; - } - getDetune() { - return this.detune; - } - getFilter() { - return this.getFilters()[0]; - } - setFilter(filter) { - return this.setFilters(filter ? [filter] : []); - } - setPlaybackRate(value) { - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return; - } - this.playbackRate = value; - if (this.isPlaying === true) { - this.source.playbackRate.setTargetAtTime(this.playbackRate, this.context.currentTime, 0.01); - } - return this; - } - getPlaybackRate() { - return this.playbackRate; - } - onEnded() { - this.isPlaying = false; - } - getLoop() { - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return false; - } - return this.loop; - } - setLoop(value) { - if (this.hasPlaybackControl === false) { - console.warn("THREE.Audio: this Audio has no playback control."); - return; - } - this.loop = value; - if (this.isPlaying === true) { - this.source.loop = this.loop; - } - return this; - } - setLoopStart(value) { - this.loopStart = value; - return this; - } - setLoopEnd(value) { - this.loopEnd = value; - return this; - } - getVolume() { - return this.gain.gain.value; - } - setVolume(value) { - this.gain.gain.setTargetAtTime(value, this.context.currentTime, 0.01); - return this; - } -} -const _position = /* @__PURE__ */ new Vector3(); -const _quaternion = /* @__PURE__ */ new Quaternion(); -const _scale = /* @__PURE__ */ new Vector3(); -const _orientation = /* @__PURE__ */ new Vector3(); -class PositionalAudio extends Audio { - static { - __name(this, "PositionalAudio"); - } - constructor(listener) { - super(listener); - this.panner = this.context.createPanner(); - this.panner.panningModel = "HRTF"; - this.panner.connect(this.gain); - } - connect() { - super.connect(); - this.panner.connect(this.gain); - } - disconnect() { - super.disconnect(); - this.panner.disconnect(this.gain); - } - getOutput() { - return this.panner; - } - getRefDistance() { - return this.panner.refDistance; - } - setRefDistance(value) { - this.panner.refDistance = value; - return this; - } - getRolloffFactor() { - return this.panner.rolloffFactor; - } - setRolloffFactor(value) { - this.panner.rolloffFactor = value; - return this; - } - getDistanceModel() { - return this.panner.distanceModel; - } - setDistanceModel(value) { - this.panner.distanceModel = value; - return this; - } - getMaxDistance() { - return this.panner.maxDistance; - } - setMaxDistance(value) { - this.panner.maxDistance = value; - return this; - } - setDirectionalCone(coneInnerAngle, coneOuterAngle, coneOuterGain) { - this.panner.coneInnerAngle = coneInnerAngle; - this.panner.coneOuterAngle = coneOuterAngle; - this.panner.coneOuterGain = coneOuterGain; - return this; - } - updateMatrixWorld(force) { - super.updateMatrixWorld(force); - if (this.hasPlaybackControl === true && this.isPlaying === false) return; - this.matrixWorld.decompose(_position, _quaternion, _scale); - _orientation.set(0, 0, 1).applyQuaternion(_quaternion); - const panner = this.panner; - if (panner.positionX) { - const endTime = this.context.currentTime + this.listener.timeDelta; - panner.positionX.linearRampToValueAtTime(_position.x, endTime); - panner.positionY.linearRampToValueAtTime(_position.y, endTime); - panner.positionZ.linearRampToValueAtTime(_position.z, endTime); - panner.orientationX.linearRampToValueAtTime(_orientation.x, endTime); - panner.orientationY.linearRampToValueAtTime(_orientation.y, endTime); - panner.orientationZ.linearRampToValueAtTime(_orientation.z, endTime); - } else { - panner.setPosition(_position.x, _position.y, _position.z); - panner.setOrientation(_orientation.x, _orientation.y, _orientation.z); - } - } -} -class AudioAnalyser { - static { - __name(this, "AudioAnalyser"); - } - constructor(audio, fftSize = 2048) { - this.analyser = audio.context.createAnalyser(); - this.analyser.fftSize = fftSize; - this.data = new Uint8Array(this.analyser.frequencyBinCount); - audio.getOutput().connect(this.analyser); - } - getFrequencyData() { - this.analyser.getByteFrequencyData(this.data); - return this.data; - } - getAverageFrequency() { - let value = 0; - const data = this.getFrequencyData(); - for (let i = 0; i < data.length; i++) { - value += data[i]; - } - return value / data.length; - } -} -class PropertyMixer { - static { - __name(this, "PropertyMixer"); - } - constructor(binding, typeName, valueSize) { - this.binding = binding; - this.valueSize = valueSize; - let mixFunction, mixFunctionAdditive, setIdentity; - switch (typeName) { - case "quaternion": - mixFunction = this._slerp; - mixFunctionAdditive = this._slerpAdditive; - setIdentity = this._setAdditiveIdentityQuaternion; - this.buffer = new Float64Array(valueSize * 6); - this._workIndex = 5; - break; - case "string": - case "bool": - mixFunction = this._select; - mixFunctionAdditive = this._select; - setIdentity = this._setAdditiveIdentityOther; - this.buffer = new Array(valueSize * 5); - break; - default: - mixFunction = this._lerp; - mixFunctionAdditive = this._lerpAdditive; - setIdentity = this._setAdditiveIdentityNumeric; - this.buffer = new Float64Array(valueSize * 5); - } - this._mixBufferRegion = mixFunction; - this._mixBufferRegionAdditive = mixFunctionAdditive; - this._setIdentity = setIdentity; - this._origIndex = 3; - this._addIndex = 4; - this.cumulativeWeight = 0; - this.cumulativeWeightAdditive = 0; - this.useCount = 0; - this.referenceCount = 0; - } - // accumulate data in the 'incoming' region into 'accu' - accumulate(accuIndex, weight) { - const buffer = this.buffer, stride = this.valueSize, offset = accuIndex * stride + stride; - let currentWeight = this.cumulativeWeight; - if (currentWeight === 0) { - for (let i = 0; i !== stride; ++i) { - buffer[offset + i] = buffer[i]; - } - currentWeight = weight; - } else { - currentWeight += weight; - const mix = weight / currentWeight; - this._mixBufferRegion(buffer, offset, 0, mix, stride); - } - this.cumulativeWeight = currentWeight; - } - // accumulate data in the 'incoming' region into 'add' - accumulateAdditive(weight) { - const buffer = this.buffer, stride = this.valueSize, offset = stride * this._addIndex; - if (this.cumulativeWeightAdditive === 0) { - this._setIdentity(); - } - this._mixBufferRegionAdditive(buffer, offset, 0, weight, stride); - this.cumulativeWeightAdditive += weight; - } - // apply the state of 'accu' to the binding when accus differ - apply(accuIndex) { - const stride = this.valueSize, buffer = this.buffer, offset = accuIndex * stride + stride, weight = this.cumulativeWeight, weightAdditive = this.cumulativeWeightAdditive, binding = this.binding; - this.cumulativeWeight = 0; - this.cumulativeWeightAdditive = 0; - if (weight < 1) { - const originalValueOffset = stride * this._origIndex; - this._mixBufferRegion( - buffer, - offset, - originalValueOffset, - 1 - weight, - stride - ); - } - if (weightAdditive > 0) { - this._mixBufferRegionAdditive(buffer, offset, this._addIndex * stride, 1, stride); - } - for (let i = stride, e = stride + stride; i !== e; ++i) { - if (buffer[i] !== buffer[i + stride]) { - binding.setValue(buffer, offset); - break; - } - } - } - // remember the state of the bound property and copy it to both accus - saveOriginalState() { - const binding = this.binding; - const buffer = this.buffer, stride = this.valueSize, originalValueOffset = stride * this._origIndex; - binding.getValue(buffer, originalValueOffset); - for (let i = stride, e = originalValueOffset; i !== e; ++i) { - buffer[i] = buffer[originalValueOffset + i % stride]; - } - this._setIdentity(); - this.cumulativeWeight = 0; - this.cumulativeWeightAdditive = 0; - } - // apply the state previously taken via 'saveOriginalState' to the binding - restoreOriginalState() { - const originalValueOffset = this.valueSize * 3; - this.binding.setValue(this.buffer, originalValueOffset); - } - _setAdditiveIdentityNumeric() { - const startIndex = this._addIndex * this.valueSize; - const endIndex = startIndex + this.valueSize; - for (let i = startIndex; i < endIndex; i++) { - this.buffer[i] = 0; - } - } - _setAdditiveIdentityQuaternion() { - this._setAdditiveIdentityNumeric(); - this.buffer[this._addIndex * this.valueSize + 3] = 1; - } - _setAdditiveIdentityOther() { - const startIndex = this._origIndex * this.valueSize; - const targetIndex = this._addIndex * this.valueSize; - for (let i = 0; i < this.valueSize; i++) { - this.buffer[targetIndex + i] = this.buffer[startIndex + i]; - } - } - // mix functions - _select(buffer, dstOffset, srcOffset, t2, stride) { - if (t2 >= 0.5) { - for (let i = 0; i !== stride; ++i) { - buffer[dstOffset + i] = buffer[srcOffset + i]; - } - } - } - _slerp(buffer, dstOffset, srcOffset, t2) { - Quaternion.slerpFlat(buffer, dstOffset, buffer, dstOffset, buffer, srcOffset, t2); - } - _slerpAdditive(buffer, dstOffset, srcOffset, t2, stride) { - const workOffset = this._workIndex * stride; - Quaternion.multiplyQuaternionsFlat(buffer, workOffset, buffer, dstOffset, buffer, srcOffset); - Quaternion.slerpFlat(buffer, dstOffset, buffer, dstOffset, buffer, workOffset, t2); - } - _lerp(buffer, dstOffset, srcOffset, t2, stride) { - const s = 1 - t2; - for (let i = 0; i !== stride; ++i) { - const j = dstOffset + i; - buffer[j] = buffer[j] * s + buffer[srcOffset + i] * t2; - } - } - _lerpAdditive(buffer, dstOffset, srcOffset, t2, stride) { - for (let i = 0; i !== stride; ++i) { - const j = dstOffset + i; - buffer[j] = buffer[j] + buffer[srcOffset + i] * t2; - } - } -} -const _RESERVED_CHARS_RE = "\\[\\]\\.:\\/"; -const _reservedRe = new RegExp("[" + _RESERVED_CHARS_RE + "]", "g"); -const _wordChar = "[^" + _RESERVED_CHARS_RE + "]"; -const _wordCharOrDot = "[^" + _RESERVED_CHARS_RE.replace("\\.", "") + "]"; -const _directoryRe = /* @__PURE__ */ /((?:WC+[\/:])*)/.source.replace("WC", _wordChar); -const _nodeRe = /* @__PURE__ */ /(WCOD+)?/.source.replace("WCOD", _wordCharOrDot); -const _objectRe = /* @__PURE__ */ /(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC", _wordChar); -const _propertyRe = /* @__PURE__ */ /\.(WC+)(?:\[(.+)\])?/.source.replace("WC", _wordChar); -const _trackRe = new RegExp( - "^" + _directoryRe + _nodeRe + _objectRe + _propertyRe + "$" -); -const _supportedObjectNames = ["material", "materials", "bones", "map"]; -class Composite { - static { - __name(this, "Composite"); - } - constructor(targetGroup, path, optionalParsedPath) { - const parsedPath = optionalParsedPath || PropertyBinding.parseTrackName(path); - this._targetGroup = targetGroup; - this._bindings = targetGroup.subscribe_(path, parsedPath); - } - getValue(array, offset) { - this.bind(); - const firstValidIndex = this._targetGroup.nCachedObjects_, binding = this._bindings[firstValidIndex]; - if (binding !== void 0) binding.getValue(array, offset); - } - setValue(array, offset) { - const bindings = this._bindings; - for (let i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++i) { - bindings[i].setValue(array, offset); - } - } - bind() { - const bindings = this._bindings; - for (let i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++i) { - bindings[i].bind(); - } - } - unbind() { - const bindings = this._bindings; - for (let i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++i) { - bindings[i].unbind(); - } - } -} -class PropertyBinding { - static { - __name(this, "PropertyBinding"); - } - constructor(rootNode, path, parsedPath) { - this.path = path; - this.parsedPath = parsedPath || PropertyBinding.parseTrackName(path); - this.node = PropertyBinding.findNode(rootNode, this.parsedPath.nodeName); - this.rootNode = rootNode; - this.getValue = this._getValue_unbound; - this.setValue = this._setValue_unbound; - } - static create(root, path, parsedPath) { - if (!(root && root.isAnimationObjectGroup)) { - return new PropertyBinding(root, path, parsedPath); - } else { - return new PropertyBinding.Composite(root, path, parsedPath); - } - } - /** - * Replaces spaces with underscores and removes unsupported characters from - * node names, to ensure compatibility with parseTrackName(). - * - * @param {string} name Node name to be sanitized. - * @return {string} - */ - static sanitizeNodeName(name) { - return name.replace(/\s/g, "_").replace(_reservedRe, ""); - } - static parseTrackName(trackName) { - const matches = _trackRe.exec(trackName); - if (matches === null) { - throw new Error("PropertyBinding: Cannot parse trackName: " + trackName); - } - const results = { - // directoryName: matches[ 1 ], // (tschw) currently unused - nodeName: matches[2], - objectName: matches[3], - objectIndex: matches[4], - propertyName: matches[5], - // required - propertyIndex: matches[6] - }; - const lastDot = results.nodeName && results.nodeName.lastIndexOf("."); - if (lastDot !== void 0 && lastDot !== -1) { - const objectName = results.nodeName.substring(lastDot + 1); - if (_supportedObjectNames.indexOf(objectName) !== -1) { - results.nodeName = results.nodeName.substring(0, lastDot); - results.objectName = objectName; - } - } - if (results.propertyName === null || results.propertyName.length === 0) { - throw new Error("PropertyBinding: can not parse propertyName from trackName: " + trackName); - } - return results; - } - static findNode(root, nodeName) { - if (nodeName === void 0 || nodeName === "" || nodeName === "." || nodeName === -1 || nodeName === root.name || nodeName === root.uuid) { - return root; - } - if (root.skeleton) { - const bone = root.skeleton.getBoneByName(nodeName); - if (bone !== void 0) { - return bone; - } - } - if (root.children) { - const searchNodeSubtree = /* @__PURE__ */ __name(function(children) { - for (let i = 0; i < children.length; i++) { - const childNode = children[i]; - if (childNode.name === nodeName || childNode.uuid === nodeName) { - return childNode; - } - const result = searchNodeSubtree(childNode.children); - if (result) return result; - } - return null; - }, "searchNodeSubtree"); - const subTreeNode = searchNodeSubtree(root.children); - if (subTreeNode) { - return subTreeNode; - } - } - return null; - } - // these are used to "bind" a nonexistent property - _getValue_unavailable() { - } - _setValue_unavailable() { - } - // Getters - _getValue_direct(buffer, offset) { - buffer[offset] = this.targetObject[this.propertyName]; - } - _getValue_array(buffer, offset) { - const source = this.resolvedProperty; - for (let i = 0, n = source.length; i !== n; ++i) { - buffer[offset++] = source[i]; - } - } - _getValue_arrayElement(buffer, offset) { - buffer[offset] = this.resolvedProperty[this.propertyIndex]; - } - _getValue_toArray(buffer, offset) { - this.resolvedProperty.toArray(buffer, offset); - } - // Direct - _setValue_direct(buffer, offset) { - this.targetObject[this.propertyName] = buffer[offset]; - } - _setValue_direct_setNeedsUpdate(buffer, offset) { - this.targetObject[this.propertyName] = buffer[offset]; - this.targetObject.needsUpdate = true; - } - _setValue_direct_setMatrixWorldNeedsUpdate(buffer, offset) { - this.targetObject[this.propertyName] = buffer[offset]; - this.targetObject.matrixWorldNeedsUpdate = true; - } - // EntireArray - _setValue_array(buffer, offset) { - const dest = this.resolvedProperty; - for (let i = 0, n = dest.length; i !== n; ++i) { - dest[i] = buffer[offset++]; - } - } - _setValue_array_setNeedsUpdate(buffer, offset) { - const dest = this.resolvedProperty; - for (let i = 0, n = dest.length; i !== n; ++i) { - dest[i] = buffer[offset++]; - } - this.targetObject.needsUpdate = true; - } - _setValue_array_setMatrixWorldNeedsUpdate(buffer, offset) { - const dest = this.resolvedProperty; - for (let i = 0, n = dest.length; i !== n; ++i) { - dest[i] = buffer[offset++]; - } - this.targetObject.matrixWorldNeedsUpdate = true; - } - // ArrayElement - _setValue_arrayElement(buffer, offset) { - this.resolvedProperty[this.propertyIndex] = buffer[offset]; - } - _setValue_arrayElement_setNeedsUpdate(buffer, offset) { - this.resolvedProperty[this.propertyIndex] = buffer[offset]; - this.targetObject.needsUpdate = true; - } - _setValue_arrayElement_setMatrixWorldNeedsUpdate(buffer, offset) { - this.resolvedProperty[this.propertyIndex] = buffer[offset]; - this.targetObject.matrixWorldNeedsUpdate = true; - } - // HasToFromArray - _setValue_fromArray(buffer, offset) { - this.resolvedProperty.fromArray(buffer, offset); - } - _setValue_fromArray_setNeedsUpdate(buffer, offset) { - this.resolvedProperty.fromArray(buffer, offset); - this.targetObject.needsUpdate = true; - } - _setValue_fromArray_setMatrixWorldNeedsUpdate(buffer, offset) { - this.resolvedProperty.fromArray(buffer, offset); - this.targetObject.matrixWorldNeedsUpdate = true; - } - _getValue_unbound(targetArray, offset) { - this.bind(); - this.getValue(targetArray, offset); - } - _setValue_unbound(sourceArray, offset) { - this.bind(); - this.setValue(sourceArray, offset); - } - // create getter / setter pair for a property in the scene graph - bind() { - let targetObject = this.node; - const parsedPath = this.parsedPath; - const objectName = parsedPath.objectName; - const propertyName = parsedPath.propertyName; - let propertyIndex = parsedPath.propertyIndex; - if (!targetObject) { - targetObject = PropertyBinding.findNode(this.rootNode, parsedPath.nodeName); - this.node = targetObject; - } - this.getValue = this._getValue_unavailable; - this.setValue = this._setValue_unavailable; - if (!targetObject) { - console.warn("THREE.PropertyBinding: No target node found for track: " + this.path + "."); - return; - } - if (objectName) { - let objectIndex = parsedPath.objectIndex; - switch (objectName) { - case "materials": - if (!targetObject.material) { - console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.", this); - return; - } - if (!targetObject.material.materials) { - console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.", this); - return; - } - targetObject = targetObject.material.materials; - break; - case "bones": - if (!targetObject.skeleton) { - console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.", this); - return; - } - targetObject = targetObject.skeleton.bones; - for (let i = 0; i < targetObject.length; i++) { - if (targetObject[i].name === objectIndex) { - objectIndex = i; - break; - } - } - break; - case "map": - if ("map" in targetObject) { - targetObject = targetObject.map; - break; - } - if (!targetObject.material) { - console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.", this); - return; - } - if (!targetObject.material.map) { - console.error("THREE.PropertyBinding: Can not bind to material.map as node.material does not have a map.", this); - return; - } - targetObject = targetObject.material.map; - break; - default: - if (targetObject[objectName] === void 0) { - console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.", this); - return; - } - targetObject = targetObject[objectName]; - } - if (objectIndex !== void 0) { - if (targetObject[objectIndex] === void 0) { - console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.", this, targetObject); - return; - } - targetObject = targetObject[objectIndex]; - } - } - const nodeProperty = targetObject[propertyName]; - if (nodeProperty === void 0) { - const nodeName = parsedPath.nodeName; - console.error("THREE.PropertyBinding: Trying to update property for track: " + nodeName + "." + propertyName + " but it wasn't found.", targetObject); - return; - } - let versioning = this.Versioning.None; - this.targetObject = targetObject; - if (targetObject.needsUpdate !== void 0) { - versioning = this.Versioning.NeedsUpdate; - } else if (targetObject.matrixWorldNeedsUpdate !== void 0) { - versioning = this.Versioning.MatrixWorldNeedsUpdate; - } - let bindingType = this.BindingType.Direct; - if (propertyIndex !== void 0) { - if (propertyName === "morphTargetInfluences") { - if (!targetObject.geometry) { - console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.", this); - return; - } - if (!targetObject.geometry.morphAttributes) { - console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.", this); - return; - } - if (targetObject.morphTargetDictionary[propertyIndex] !== void 0) { - propertyIndex = targetObject.morphTargetDictionary[propertyIndex]; - } - } - bindingType = this.BindingType.ArrayElement; - this.resolvedProperty = nodeProperty; - this.propertyIndex = propertyIndex; - } else if (nodeProperty.fromArray !== void 0 && nodeProperty.toArray !== void 0) { - bindingType = this.BindingType.HasFromToArray; - this.resolvedProperty = nodeProperty; - } else if (Array.isArray(nodeProperty)) { - bindingType = this.BindingType.EntireArray; - this.resolvedProperty = nodeProperty; - } else { - this.propertyName = propertyName; - } - this.getValue = this.GetterByBindingType[bindingType]; - this.setValue = this.SetterByBindingTypeAndVersioning[bindingType][versioning]; - } - unbind() { - this.node = null; - this.getValue = this._getValue_unbound; - this.setValue = this._setValue_unbound; - } -} -PropertyBinding.Composite = Composite; -PropertyBinding.prototype.BindingType = { - Direct: 0, - EntireArray: 1, - ArrayElement: 2, - HasFromToArray: 3 -}; -PropertyBinding.prototype.Versioning = { - None: 0, - NeedsUpdate: 1, - MatrixWorldNeedsUpdate: 2 -}; -PropertyBinding.prototype.GetterByBindingType = [ - PropertyBinding.prototype._getValue_direct, - PropertyBinding.prototype._getValue_array, - PropertyBinding.prototype._getValue_arrayElement, - PropertyBinding.prototype._getValue_toArray -]; -PropertyBinding.prototype.SetterByBindingTypeAndVersioning = [ - [ - // Direct - PropertyBinding.prototype._setValue_direct, - PropertyBinding.prototype._setValue_direct_setNeedsUpdate, - PropertyBinding.prototype._setValue_direct_setMatrixWorldNeedsUpdate - ], - [ - // EntireArray - PropertyBinding.prototype._setValue_array, - PropertyBinding.prototype._setValue_array_setNeedsUpdate, - PropertyBinding.prototype._setValue_array_setMatrixWorldNeedsUpdate - ], - [ - // ArrayElement - PropertyBinding.prototype._setValue_arrayElement, - PropertyBinding.prototype._setValue_arrayElement_setNeedsUpdate, - PropertyBinding.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate - ], - [ - // HasToFromArray - PropertyBinding.prototype._setValue_fromArray, - PropertyBinding.prototype._setValue_fromArray_setNeedsUpdate, - PropertyBinding.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate - ] -]; -class AnimationObjectGroup { - static { - __name(this, "AnimationObjectGroup"); - } - constructor() { - this.isAnimationObjectGroup = true; - this.uuid = generateUUID(); - this._objects = Array.prototype.slice.call(arguments); - this.nCachedObjects_ = 0; - const indices = {}; - this._indicesByUUID = indices; - for (let i = 0, n = arguments.length; i !== n; ++i) { - indices[arguments[i].uuid] = i; - } - this._paths = []; - this._parsedPaths = []; - this._bindings = []; - this._bindingsIndicesByPath = {}; - const scope = this; - this.stats = { - objects: { - get total() { - return scope._objects.length; - }, - get inUse() { - return this.total - scope.nCachedObjects_; - } - }, - get bindingsPerObject() { - return scope._bindings.length; - } - }; - } - add() { - const objects = this._objects, indicesByUUID = this._indicesByUUID, paths = this._paths, parsedPaths = this._parsedPaths, bindings = this._bindings, nBindings = bindings.length; - let knownObject = void 0, nObjects = objects.length, nCachedObjects = this.nCachedObjects_; - for (let i = 0, n = arguments.length; i !== n; ++i) { - const object = arguments[i], uuid = object.uuid; - let index = indicesByUUID[uuid]; - if (index === void 0) { - index = nObjects++; - indicesByUUID[uuid] = index; - objects.push(object); - for (let j = 0, m = nBindings; j !== m; ++j) { - bindings[j].push(new PropertyBinding(object, paths[j], parsedPaths[j])); - } - } else if (index < nCachedObjects) { - knownObject = objects[index]; - const firstActiveIndex = --nCachedObjects, lastCachedObject = objects[firstActiveIndex]; - indicesByUUID[lastCachedObject.uuid] = index; - objects[index] = lastCachedObject; - indicesByUUID[uuid] = firstActiveIndex; - objects[firstActiveIndex] = object; - for (let j = 0, m = nBindings; j !== m; ++j) { - const bindingsForPath = bindings[j], lastCached = bindingsForPath[firstActiveIndex]; - let binding = bindingsForPath[index]; - bindingsForPath[index] = lastCached; - if (binding === void 0) { - binding = new PropertyBinding(object, paths[j], parsedPaths[j]); - } - bindingsForPath[firstActiveIndex] = binding; - } - } else if (objects[index] !== knownObject) { - console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes."); - } - } - this.nCachedObjects_ = nCachedObjects; - } - remove() { - const objects = this._objects, indicesByUUID = this._indicesByUUID, bindings = this._bindings, nBindings = bindings.length; - let nCachedObjects = this.nCachedObjects_; - for (let i = 0, n = arguments.length; i !== n; ++i) { - const object = arguments[i], uuid = object.uuid, index = indicesByUUID[uuid]; - if (index !== void 0 && index >= nCachedObjects) { - const lastCachedIndex = nCachedObjects++, firstActiveObject = objects[lastCachedIndex]; - indicesByUUID[firstActiveObject.uuid] = index; - objects[index] = firstActiveObject; - indicesByUUID[uuid] = lastCachedIndex; - objects[lastCachedIndex] = object; - for (let j = 0, m = nBindings; j !== m; ++j) { - const bindingsForPath = bindings[j], firstActive = bindingsForPath[lastCachedIndex], binding = bindingsForPath[index]; - bindingsForPath[index] = firstActive; - bindingsForPath[lastCachedIndex] = binding; - } - } - } - this.nCachedObjects_ = nCachedObjects; - } - // remove & forget - uncache() { - const objects = this._objects, indicesByUUID = this._indicesByUUID, bindings = this._bindings, nBindings = bindings.length; - let nCachedObjects = this.nCachedObjects_, nObjects = objects.length; - for (let i = 0, n = arguments.length; i !== n; ++i) { - const object = arguments[i], uuid = object.uuid, index = indicesByUUID[uuid]; - if (index !== void 0) { - delete indicesByUUID[uuid]; - if (index < nCachedObjects) { - const firstActiveIndex = --nCachedObjects, lastCachedObject = objects[firstActiveIndex], lastIndex = --nObjects, lastObject = objects[lastIndex]; - indicesByUUID[lastCachedObject.uuid] = index; - objects[index] = lastCachedObject; - indicesByUUID[lastObject.uuid] = firstActiveIndex; - objects[firstActiveIndex] = lastObject; - objects.pop(); - for (let j = 0, m = nBindings; j !== m; ++j) { - const bindingsForPath = bindings[j], lastCached = bindingsForPath[firstActiveIndex], last = bindingsForPath[lastIndex]; - bindingsForPath[index] = lastCached; - bindingsForPath[firstActiveIndex] = last; - bindingsForPath.pop(); - } - } else { - const lastIndex = --nObjects, lastObject = objects[lastIndex]; - if (lastIndex > 0) { - indicesByUUID[lastObject.uuid] = index; - } - objects[index] = lastObject; - objects.pop(); - for (let j = 0, m = nBindings; j !== m; ++j) { - const bindingsForPath = bindings[j]; - bindingsForPath[index] = bindingsForPath[lastIndex]; - bindingsForPath.pop(); - } - } - } - } - this.nCachedObjects_ = nCachedObjects; - } - // Internal interface used by befriended PropertyBinding.Composite: - subscribe_(path, parsedPath) { - const indicesByPath = this._bindingsIndicesByPath; - let index = indicesByPath[path]; - const bindings = this._bindings; - if (index !== void 0) return bindings[index]; - const paths = this._paths, parsedPaths = this._parsedPaths, objects = this._objects, nObjects = objects.length, nCachedObjects = this.nCachedObjects_, bindingsForPath = new Array(nObjects); - index = bindings.length; - indicesByPath[path] = index; - paths.push(path); - parsedPaths.push(parsedPath); - bindings.push(bindingsForPath); - for (let i = nCachedObjects, n = objects.length; i !== n; ++i) { - const object = objects[i]; - bindingsForPath[i] = new PropertyBinding(object, path, parsedPath); - } - return bindingsForPath; - } - unsubscribe_(path) { - const indicesByPath = this._bindingsIndicesByPath, index = indicesByPath[path]; - if (index !== void 0) { - const paths = this._paths, parsedPaths = this._parsedPaths, bindings = this._bindings, lastBindingsIndex = bindings.length - 1, lastBindings = bindings[lastBindingsIndex], lastBindingsPath = path[lastBindingsIndex]; - indicesByPath[lastBindingsPath] = index; - bindings[index] = lastBindings; - bindings.pop(); - parsedPaths[index] = parsedPaths[lastBindingsIndex]; - parsedPaths.pop(); - paths[index] = paths[lastBindingsIndex]; - paths.pop(); - } - } -} -class AnimationAction { - static { - __name(this, "AnimationAction"); - } - constructor(mixer, clip, localRoot = null, blendMode = clip.blendMode) { - this._mixer = mixer; - this._clip = clip; - this._localRoot = localRoot; - this.blendMode = blendMode; - const tracks = clip.tracks, nTracks = tracks.length, interpolants = new Array(nTracks); - const interpolantSettings = { - endingStart: ZeroCurvatureEnding, - endingEnd: ZeroCurvatureEnding - }; - for (let i = 0; i !== nTracks; ++i) { - const interpolant = tracks[i].createInterpolant(null); - interpolants[i] = interpolant; - interpolant.settings = interpolantSettings; - } - this._interpolantSettings = interpolantSettings; - this._interpolants = interpolants; - this._propertyBindings = new Array(nTracks); - this._cacheIndex = null; - this._byClipCacheIndex = null; - this._timeScaleInterpolant = null; - this._weightInterpolant = null; - this.loop = LoopRepeat; - this._loopCount = -1; - this._startTime = null; - this.time = 0; - this.timeScale = 1; - this._effectiveTimeScale = 1; - this.weight = 1; - this._effectiveWeight = 1; - this.repetitions = Infinity; - this.paused = false; - this.enabled = true; - this.clampWhenFinished = false; - this.zeroSlopeAtStart = true; - this.zeroSlopeAtEnd = true; - } - // State & Scheduling - play() { - this._mixer._activateAction(this); - return this; - } - stop() { - this._mixer._deactivateAction(this); - return this.reset(); - } - reset() { - this.paused = false; - this.enabled = true; - this.time = 0; - this._loopCount = -1; - this._startTime = null; - return this.stopFading().stopWarping(); - } - isRunning() { - return this.enabled && !this.paused && this.timeScale !== 0 && this._startTime === null && this._mixer._isActiveAction(this); - } - // return true when play has been called - isScheduled() { - return this._mixer._isActiveAction(this); - } - startAt(time) { - this._startTime = time; - return this; - } - setLoop(mode, repetitions) { - this.loop = mode; - this.repetitions = repetitions; - return this; - } - // Weight - // set the weight stopping any scheduled fading - // although .enabled = false yields an effective weight of zero, this - // method does *not* change .enabled, because it would be confusing - setEffectiveWeight(weight) { - this.weight = weight; - this._effectiveWeight = this.enabled ? weight : 0; - return this.stopFading(); - } - // return the weight considering fading and .enabled - getEffectiveWeight() { - return this._effectiveWeight; - } - fadeIn(duration) { - return this._scheduleFading(duration, 0, 1); - } - fadeOut(duration) { - return this._scheduleFading(duration, 1, 0); - } - crossFadeFrom(fadeOutAction, duration, warp) { - fadeOutAction.fadeOut(duration); - this.fadeIn(duration); - if (warp) { - const fadeInDuration = this._clip.duration, fadeOutDuration = fadeOutAction._clip.duration, startEndRatio = fadeOutDuration / fadeInDuration, endStartRatio = fadeInDuration / fadeOutDuration; - fadeOutAction.warp(1, startEndRatio, duration); - this.warp(endStartRatio, 1, duration); - } - return this; - } - crossFadeTo(fadeInAction, duration, warp) { - return fadeInAction.crossFadeFrom(this, duration, warp); - } - stopFading() { - const weightInterpolant = this._weightInterpolant; - if (weightInterpolant !== null) { - this._weightInterpolant = null; - this._mixer._takeBackControlInterpolant(weightInterpolant); - } - return this; - } - // Time Scale Control - // set the time scale stopping any scheduled warping - // although .paused = true yields an effective time scale of zero, this - // method does *not* change .paused, because it would be confusing - setEffectiveTimeScale(timeScale) { - this.timeScale = timeScale; - this._effectiveTimeScale = this.paused ? 0 : timeScale; - return this.stopWarping(); - } - // return the time scale considering warping and .paused - getEffectiveTimeScale() { - return this._effectiveTimeScale; - } - setDuration(duration) { - this.timeScale = this._clip.duration / duration; - return this.stopWarping(); - } - syncWith(action) { - this.time = action.time; - this.timeScale = action.timeScale; - return this.stopWarping(); - } - halt(duration) { - return this.warp(this._effectiveTimeScale, 0, duration); - } - warp(startTimeScale, endTimeScale, duration) { - const mixer = this._mixer, now2 = mixer.time, timeScale = this.timeScale; - let interpolant = this._timeScaleInterpolant; - if (interpolant === null) { - interpolant = mixer._lendControlInterpolant(); - this._timeScaleInterpolant = interpolant; - } - const times = interpolant.parameterPositions, values = interpolant.sampleValues; - times[0] = now2; - times[1] = now2 + duration; - values[0] = startTimeScale / timeScale; - values[1] = endTimeScale / timeScale; - return this; - } - stopWarping() { - const timeScaleInterpolant = this._timeScaleInterpolant; - if (timeScaleInterpolant !== null) { - this._timeScaleInterpolant = null; - this._mixer._takeBackControlInterpolant(timeScaleInterpolant); - } - return this; - } - // Object Accessors - getMixer() { - return this._mixer; - } - getClip() { - return this._clip; - } - getRoot() { - return this._localRoot || this._mixer._root; - } - // Interna - _update(time, deltaTime, timeDirection, accuIndex) { - if (!this.enabled) { - this._updateWeight(time); - return; - } - const startTime = this._startTime; - if (startTime !== null) { - const timeRunning = (time - startTime) * timeDirection; - if (timeRunning < 0 || timeDirection === 0) { - deltaTime = 0; - } else { - this._startTime = null; - deltaTime = timeDirection * timeRunning; - } - } - deltaTime *= this._updateTimeScale(time); - const clipTime = this._updateTime(deltaTime); - const weight = this._updateWeight(time); - if (weight > 0) { - const interpolants = this._interpolants; - const propertyMixers = this._propertyBindings; - switch (this.blendMode) { - case AdditiveAnimationBlendMode: - for (let j = 0, m = interpolants.length; j !== m; ++j) { - interpolants[j].evaluate(clipTime); - propertyMixers[j].accumulateAdditive(weight); - } - break; - case NormalAnimationBlendMode: - default: - for (let j = 0, m = interpolants.length; j !== m; ++j) { - interpolants[j].evaluate(clipTime); - propertyMixers[j].accumulate(accuIndex, weight); - } - } - } - } - _updateWeight(time) { - let weight = 0; - if (this.enabled) { - weight = this.weight; - const interpolant = this._weightInterpolant; - if (interpolant !== null) { - const interpolantValue = interpolant.evaluate(time)[0]; - weight *= interpolantValue; - if (time > interpolant.parameterPositions[1]) { - this.stopFading(); - if (interpolantValue === 0) { - this.enabled = false; - } - } - } - } - this._effectiveWeight = weight; - return weight; - } - _updateTimeScale(time) { - let timeScale = 0; - if (!this.paused) { - timeScale = this.timeScale; - const interpolant = this._timeScaleInterpolant; - if (interpolant !== null) { - const interpolantValue = interpolant.evaluate(time)[0]; - timeScale *= interpolantValue; - if (time > interpolant.parameterPositions[1]) { - this.stopWarping(); - if (timeScale === 0) { - this.paused = true; - } else { - this.timeScale = timeScale; - } - } - } - } - this._effectiveTimeScale = timeScale; - return timeScale; - } - _updateTime(deltaTime) { - const duration = this._clip.duration; - const loop = this.loop; - let time = this.time + deltaTime; - let loopCount = this._loopCount; - const pingPong = loop === LoopPingPong; - if (deltaTime === 0) { - if (loopCount === -1) return time; - return pingPong && (loopCount & 1) === 1 ? duration - time : time; - } - if (loop === LoopOnce) { - if (loopCount === -1) { - this._loopCount = 0; - this._setEndings(true, true, false); - } - handle_stop: { - if (time >= duration) { - time = duration; - } else if (time < 0) { - time = 0; - } else { - this.time = time; - break handle_stop; - } - if (this.clampWhenFinished) this.paused = true; - else this.enabled = false; - this.time = time; - this._mixer.dispatchEvent({ - type: "finished", - action: this, - direction: deltaTime < 0 ? -1 : 1 - }); - } - } else { - if (loopCount === -1) { - if (deltaTime >= 0) { - loopCount = 0; - this._setEndings(true, this.repetitions === 0, pingPong); - } else { - this._setEndings(this.repetitions === 0, true, pingPong); - } - } - if (time >= duration || time < 0) { - const loopDelta = Math.floor(time / duration); - time -= duration * loopDelta; - loopCount += Math.abs(loopDelta); - const pending = this.repetitions - loopCount; - if (pending <= 0) { - if (this.clampWhenFinished) this.paused = true; - else this.enabled = false; - time = deltaTime > 0 ? duration : 0; - this.time = time; - this._mixer.dispatchEvent({ - type: "finished", - action: this, - direction: deltaTime > 0 ? 1 : -1 - }); - } else { - if (pending === 1) { - const atStart = deltaTime < 0; - this._setEndings(atStart, !atStart, pingPong); - } else { - this._setEndings(false, false, pingPong); - } - this._loopCount = loopCount; - this.time = time; - this._mixer.dispatchEvent({ - type: "loop", - action: this, - loopDelta - }); - } - } else { - this.time = time; - } - if (pingPong && (loopCount & 1) === 1) { - return duration - time; - } - } - return time; - } - _setEndings(atStart, atEnd, pingPong) { - const settings = this._interpolantSettings; - if (pingPong) { - settings.endingStart = ZeroSlopeEnding; - settings.endingEnd = ZeroSlopeEnding; - } else { - if (atStart) { - settings.endingStart = this.zeroSlopeAtStart ? ZeroSlopeEnding : ZeroCurvatureEnding; - } else { - settings.endingStart = WrapAroundEnding; - } - if (atEnd) { - settings.endingEnd = this.zeroSlopeAtEnd ? ZeroSlopeEnding : ZeroCurvatureEnding; - } else { - settings.endingEnd = WrapAroundEnding; - } - } - } - _scheduleFading(duration, weightNow, weightThen) { - const mixer = this._mixer, now2 = mixer.time; - let interpolant = this._weightInterpolant; - if (interpolant === null) { - interpolant = mixer._lendControlInterpolant(); - this._weightInterpolant = interpolant; - } - const times = interpolant.parameterPositions, values = interpolant.sampleValues; - times[0] = now2; - values[0] = weightNow; - times[1] = now2 + duration; - values[1] = weightThen; - return this; - } -} -const _controlInterpolantsResultBuffer = new Float32Array(1); -class AnimationMixer extends EventDispatcher { - static { - __name(this, "AnimationMixer"); - } - constructor(root) { - super(); - this._root = root; - this._initMemoryManager(); - this._accuIndex = 0; - this.time = 0; - this.timeScale = 1; - } - _bindAction(action, prototypeAction) { - const root = action._localRoot || this._root, tracks = action._clip.tracks, nTracks = tracks.length, bindings = action._propertyBindings, interpolants = action._interpolants, rootUuid = root.uuid, bindingsByRoot = this._bindingsByRootAndName; - let bindingsByName = bindingsByRoot[rootUuid]; - if (bindingsByName === void 0) { - bindingsByName = {}; - bindingsByRoot[rootUuid] = bindingsByName; - } - for (let i = 0; i !== nTracks; ++i) { - const track = tracks[i], trackName = track.name; - let binding = bindingsByName[trackName]; - if (binding !== void 0) { - ++binding.referenceCount; - bindings[i] = binding; - } else { - binding = bindings[i]; - if (binding !== void 0) { - if (binding._cacheIndex === null) { - ++binding.referenceCount; - this._addInactiveBinding(binding, rootUuid, trackName); - } - continue; - } - const path = prototypeAction && prototypeAction._propertyBindings[i].binding.parsedPath; - binding = new PropertyMixer( - PropertyBinding.create(root, trackName, path), - track.ValueTypeName, - track.getValueSize() - ); - ++binding.referenceCount; - this._addInactiveBinding(binding, rootUuid, trackName); - bindings[i] = binding; - } - interpolants[i].resultBuffer = binding.buffer; - } - } - _activateAction(action) { - if (!this._isActiveAction(action)) { - if (action._cacheIndex === null) { - const rootUuid = (action._localRoot || this._root).uuid, clipUuid = action._clip.uuid, actionsForClip = this._actionsByClip[clipUuid]; - this._bindAction( - action, - actionsForClip && actionsForClip.knownActions[0] - ); - this._addInactiveAction(action, clipUuid, rootUuid); - } - const bindings = action._propertyBindings; - for (let i = 0, n = bindings.length; i !== n; ++i) { - const binding = bindings[i]; - if (binding.useCount++ === 0) { - this._lendBinding(binding); - binding.saveOriginalState(); - } - } - this._lendAction(action); - } - } - _deactivateAction(action) { - if (this._isActiveAction(action)) { - const bindings = action._propertyBindings; - for (let i = 0, n = bindings.length; i !== n; ++i) { - const binding = bindings[i]; - if (--binding.useCount === 0) { - binding.restoreOriginalState(); - this._takeBackBinding(binding); - } - } - this._takeBackAction(action); - } - } - // Memory manager - _initMemoryManager() { - this._actions = []; - this._nActiveActions = 0; - this._actionsByClip = {}; - this._bindings = []; - this._nActiveBindings = 0; - this._bindingsByRootAndName = {}; - this._controlInterpolants = []; - this._nActiveControlInterpolants = 0; - const scope = this; - this.stats = { - actions: { - get total() { - return scope._actions.length; - }, - get inUse() { - return scope._nActiveActions; - } - }, - bindings: { - get total() { - return scope._bindings.length; - }, - get inUse() { - return scope._nActiveBindings; - } - }, - controlInterpolants: { - get total() { - return scope._controlInterpolants.length; - }, - get inUse() { - return scope._nActiveControlInterpolants; - } - } - }; - } - // Memory management for AnimationAction objects - _isActiveAction(action) { - const index = action._cacheIndex; - return index !== null && index < this._nActiveActions; - } - _addInactiveAction(action, clipUuid, rootUuid) { - const actions = this._actions, actionsByClip = this._actionsByClip; - let actionsForClip = actionsByClip[clipUuid]; - if (actionsForClip === void 0) { - actionsForClip = { - knownActions: [action], - actionByRoot: {} - }; - action._byClipCacheIndex = 0; - actionsByClip[clipUuid] = actionsForClip; - } else { - const knownActions = actionsForClip.knownActions; - action._byClipCacheIndex = knownActions.length; - knownActions.push(action); - } - action._cacheIndex = actions.length; - actions.push(action); - actionsForClip.actionByRoot[rootUuid] = action; - } - _removeInactiveAction(action) { - const actions = this._actions, lastInactiveAction = actions[actions.length - 1], cacheIndex = action._cacheIndex; - lastInactiveAction._cacheIndex = cacheIndex; - actions[cacheIndex] = lastInactiveAction; - actions.pop(); - action._cacheIndex = null; - const clipUuid = action._clip.uuid, actionsByClip = this._actionsByClip, actionsForClip = actionsByClip[clipUuid], knownActionsForClip = actionsForClip.knownActions, lastKnownAction = knownActionsForClip[knownActionsForClip.length - 1], byClipCacheIndex = action._byClipCacheIndex; - lastKnownAction._byClipCacheIndex = byClipCacheIndex; - knownActionsForClip[byClipCacheIndex] = lastKnownAction; - knownActionsForClip.pop(); - action._byClipCacheIndex = null; - const actionByRoot = actionsForClip.actionByRoot, rootUuid = (action._localRoot || this._root).uuid; - delete actionByRoot[rootUuid]; - if (knownActionsForClip.length === 0) { - delete actionsByClip[clipUuid]; - } - this._removeInactiveBindingsForAction(action); - } - _removeInactiveBindingsForAction(action) { - const bindings = action._propertyBindings; - for (let i = 0, n = bindings.length; i !== n; ++i) { - const binding = bindings[i]; - if (--binding.referenceCount === 0) { - this._removeInactiveBinding(binding); - } - } - } - _lendAction(action) { - const actions = this._actions, prevIndex = action._cacheIndex, lastActiveIndex = this._nActiveActions++, firstInactiveAction = actions[lastActiveIndex]; - action._cacheIndex = lastActiveIndex; - actions[lastActiveIndex] = action; - firstInactiveAction._cacheIndex = prevIndex; - actions[prevIndex] = firstInactiveAction; - } - _takeBackAction(action) { - const actions = this._actions, prevIndex = action._cacheIndex, firstInactiveIndex = --this._nActiveActions, lastActiveAction = actions[firstInactiveIndex]; - action._cacheIndex = firstInactiveIndex; - actions[firstInactiveIndex] = action; - lastActiveAction._cacheIndex = prevIndex; - actions[prevIndex] = lastActiveAction; - } - // Memory management for PropertyMixer objects - _addInactiveBinding(binding, rootUuid, trackName) { - const bindingsByRoot = this._bindingsByRootAndName, bindings = this._bindings; - let bindingByName = bindingsByRoot[rootUuid]; - if (bindingByName === void 0) { - bindingByName = {}; - bindingsByRoot[rootUuid] = bindingByName; - } - bindingByName[trackName] = binding; - binding._cacheIndex = bindings.length; - bindings.push(binding); - } - _removeInactiveBinding(binding) { - const bindings = this._bindings, propBinding = binding.binding, rootUuid = propBinding.rootNode.uuid, trackName = propBinding.path, bindingsByRoot = this._bindingsByRootAndName, bindingByName = bindingsByRoot[rootUuid], lastInactiveBinding = bindings[bindings.length - 1], cacheIndex = binding._cacheIndex; - lastInactiveBinding._cacheIndex = cacheIndex; - bindings[cacheIndex] = lastInactiveBinding; - bindings.pop(); - delete bindingByName[trackName]; - if (Object.keys(bindingByName).length === 0) { - delete bindingsByRoot[rootUuid]; - } - } - _lendBinding(binding) { - const bindings = this._bindings, prevIndex = binding._cacheIndex, lastActiveIndex = this._nActiveBindings++, firstInactiveBinding = bindings[lastActiveIndex]; - binding._cacheIndex = lastActiveIndex; - bindings[lastActiveIndex] = binding; - firstInactiveBinding._cacheIndex = prevIndex; - bindings[prevIndex] = firstInactiveBinding; - } - _takeBackBinding(binding) { - const bindings = this._bindings, prevIndex = binding._cacheIndex, firstInactiveIndex = --this._nActiveBindings, lastActiveBinding = bindings[firstInactiveIndex]; - binding._cacheIndex = firstInactiveIndex; - bindings[firstInactiveIndex] = binding; - lastActiveBinding._cacheIndex = prevIndex; - bindings[prevIndex] = lastActiveBinding; - } - // Memory management of Interpolants for weight and time scale - _lendControlInterpolant() { - const interpolants = this._controlInterpolants, lastActiveIndex = this._nActiveControlInterpolants++; - let interpolant = interpolants[lastActiveIndex]; - if (interpolant === void 0) { - interpolant = new LinearInterpolant( - new Float32Array(2), - new Float32Array(2), - 1, - _controlInterpolantsResultBuffer - ); - interpolant.__cacheIndex = lastActiveIndex; - interpolants[lastActiveIndex] = interpolant; - } - return interpolant; - } - _takeBackControlInterpolant(interpolant) { - const interpolants = this._controlInterpolants, prevIndex = interpolant.__cacheIndex, firstInactiveIndex = --this._nActiveControlInterpolants, lastActiveInterpolant = interpolants[firstInactiveIndex]; - interpolant.__cacheIndex = firstInactiveIndex; - interpolants[firstInactiveIndex] = interpolant; - lastActiveInterpolant.__cacheIndex = prevIndex; - interpolants[prevIndex] = lastActiveInterpolant; - } - // return an action for a clip optionally using a custom root target - // object (this method allocates a lot of dynamic memory in case a - // previously unknown clip/root combination is specified) - clipAction(clip, optionalRoot, blendMode) { - const root = optionalRoot || this._root, rootUuid = root.uuid; - let clipObject = typeof clip === "string" ? AnimationClip.findByName(root, clip) : clip; - const clipUuid = clipObject !== null ? clipObject.uuid : clip; - const actionsForClip = this._actionsByClip[clipUuid]; - let prototypeAction = null; - if (blendMode === void 0) { - if (clipObject !== null) { - blendMode = clipObject.blendMode; - } else { - blendMode = NormalAnimationBlendMode; - } - } - if (actionsForClip !== void 0) { - const existingAction = actionsForClip.actionByRoot[rootUuid]; - if (existingAction !== void 0 && existingAction.blendMode === blendMode) { - return existingAction; - } - prototypeAction = actionsForClip.knownActions[0]; - if (clipObject === null) - clipObject = prototypeAction._clip; - } - if (clipObject === null) return null; - const newAction = new AnimationAction(this, clipObject, optionalRoot, blendMode); - this._bindAction(newAction, prototypeAction); - this._addInactiveAction(newAction, clipUuid, rootUuid); - return newAction; - } - // get an existing action - existingAction(clip, optionalRoot) { - const root = optionalRoot || this._root, rootUuid = root.uuid, clipObject = typeof clip === "string" ? AnimationClip.findByName(root, clip) : clip, clipUuid = clipObject ? clipObject.uuid : clip, actionsForClip = this._actionsByClip[clipUuid]; - if (actionsForClip !== void 0) { - return actionsForClip.actionByRoot[rootUuid] || null; - } - return null; - } - // deactivates all previously scheduled actions - stopAllAction() { - const actions = this._actions, nActions = this._nActiveActions; - for (let i = nActions - 1; i >= 0; --i) { - actions[i].stop(); - } - return this; - } - // advance the time and update apply the animation - update(deltaTime) { - deltaTime *= this.timeScale; - const actions = this._actions, nActions = this._nActiveActions, time = this.time += deltaTime, timeDirection = Math.sign(deltaTime), accuIndex = this._accuIndex ^= 1; - for (let i = 0; i !== nActions; ++i) { - const action = actions[i]; - action._update(time, deltaTime, timeDirection, accuIndex); - } - const bindings = this._bindings, nBindings = this._nActiveBindings; - for (let i = 0; i !== nBindings; ++i) { - bindings[i].apply(accuIndex); - } - return this; - } - // Allows you to seek to a specific time in an animation. - setTime(timeInSeconds) { - this.time = 0; - for (let i = 0; i < this._actions.length; i++) { - this._actions[i].time = 0; - } - return this.update(timeInSeconds); - } - // return this mixer's root target object - getRoot() { - return this._root; - } - // free all resources specific to a particular clip - uncacheClip(clip) { - const actions = this._actions, clipUuid = clip.uuid, actionsByClip = this._actionsByClip, actionsForClip = actionsByClip[clipUuid]; - if (actionsForClip !== void 0) { - const actionsToRemove = actionsForClip.knownActions; - for (let i = 0, n = actionsToRemove.length; i !== n; ++i) { - const action = actionsToRemove[i]; - this._deactivateAction(action); - const cacheIndex = action._cacheIndex, lastInactiveAction = actions[actions.length - 1]; - action._cacheIndex = null; - action._byClipCacheIndex = null; - lastInactiveAction._cacheIndex = cacheIndex; - actions[cacheIndex] = lastInactiveAction; - actions.pop(); - this._removeInactiveBindingsForAction(action); - } - delete actionsByClip[clipUuid]; - } - } - // free all resources specific to a particular root target object - uncacheRoot(root) { - const rootUuid = root.uuid, actionsByClip = this._actionsByClip; - for (const clipUuid in actionsByClip) { - const actionByRoot = actionsByClip[clipUuid].actionByRoot, action = actionByRoot[rootUuid]; - if (action !== void 0) { - this._deactivateAction(action); - this._removeInactiveAction(action); - } - } - const bindingsByRoot = this._bindingsByRootAndName, bindingByName = bindingsByRoot[rootUuid]; - if (bindingByName !== void 0) { - for (const trackName in bindingByName) { - const binding = bindingByName[trackName]; - binding.restoreOriginalState(); - this._removeInactiveBinding(binding); - } - } - } - // remove a targeted clip from the cache - uncacheAction(clip, optionalRoot) { - const action = this.existingAction(clip, optionalRoot); - if (action !== null) { - this._deactivateAction(action); - this._removeInactiveAction(action); - } - } -} -class Uniform { - static { - __name(this, "Uniform"); - } - constructor(value) { - this.value = value; - } - clone() { - return new Uniform(this.value.clone === void 0 ? this.value : this.value.clone()); - } -} -let _id = 0; -class UniformsGroup extends EventDispatcher { - static { - __name(this, "UniformsGroup"); - } - constructor() { - super(); - this.isUniformsGroup = true; - Object.defineProperty(this, "id", { value: _id++ }); - this.name = ""; - this.usage = StaticDrawUsage; - this.uniforms = []; - } - add(uniform) { - this.uniforms.push(uniform); - return this; - } - remove(uniform) { - const index = this.uniforms.indexOf(uniform); - if (index !== -1) this.uniforms.splice(index, 1); - return this; - } - setName(name) { - this.name = name; - return this; - } - setUsage(value) { - this.usage = value; - return this; - } - dispose() { - this.dispatchEvent({ type: "dispose" }); - return this; - } - copy(source) { - this.name = source.name; - this.usage = source.usage; - const uniformsSource = source.uniforms; - this.uniforms.length = 0; - for (let i = 0, l = uniformsSource.length; i < l; i++) { - const uniforms = Array.isArray(uniformsSource[i]) ? uniformsSource[i] : [uniformsSource[i]]; - for (let j = 0; j < uniforms.length; j++) { - this.uniforms.push(uniforms[j].clone()); - } - } - return this; - } - clone() { - return new this.constructor().copy(this); - } -} -class InstancedInterleavedBuffer extends InterleavedBuffer { - static { - __name(this, "InstancedInterleavedBuffer"); - } - constructor(array, stride, meshPerAttribute = 1) { - super(array, stride); - this.isInstancedInterleavedBuffer = true; - this.meshPerAttribute = meshPerAttribute; - } - copy(source) { - super.copy(source); - this.meshPerAttribute = source.meshPerAttribute; - return this; - } - clone(data) { - const ib = super.clone(data); - ib.meshPerAttribute = this.meshPerAttribute; - return ib; - } - toJSON(data) { - const json = super.toJSON(data); - json.isInstancedInterleavedBuffer = true; - json.meshPerAttribute = this.meshPerAttribute; - return json; - } -} -class GLBufferAttribute { - static { - __name(this, "GLBufferAttribute"); - } - constructor(buffer, type, itemSize, elementSize, count) { - this.isGLBufferAttribute = true; - this.name = ""; - this.buffer = buffer; - this.type = type; - this.itemSize = itemSize; - this.elementSize = elementSize; - this.count = count; - this.version = 0; - } - set needsUpdate(value) { - if (value === true) this.version++; - } - setBuffer(buffer) { - this.buffer = buffer; - return this; - } - setType(type, elementSize) { - this.type = type; - this.elementSize = elementSize; - return this; - } - setItemSize(itemSize) { - this.itemSize = itemSize; - return this; - } - setCount(count) { - this.count = count; - return this; - } -} -const _matrix = /* @__PURE__ */ new Matrix4(); -class Raycaster { - static { - __name(this, "Raycaster"); - } - constructor(origin, direction, near = 0, far = Infinity) { - this.ray = new Ray(origin, direction); - this.near = near; - this.far = far; - this.camera = null; - this.layers = new Layers(); - this.params = { - Mesh: {}, - Line: { threshold: 1 }, - LOD: {}, - Points: { threshold: 1 }, - Sprite: {} - }; - } - set(origin, direction) { - this.ray.set(origin, direction); - } - setFromCamera(coords, camera) { - if (camera.isPerspectiveCamera) { - this.ray.origin.setFromMatrixPosition(camera.matrixWorld); - this.ray.direction.set(coords.x, coords.y, 0.5).unproject(camera).sub(this.ray.origin).normalize(); - this.camera = camera; - } else if (camera.isOrthographicCamera) { - this.ray.origin.set(coords.x, coords.y, (camera.near + camera.far) / (camera.near - camera.far)).unproject(camera); - this.ray.direction.set(0, 0, -1).transformDirection(camera.matrixWorld); - this.camera = camera; - } else { - console.error("THREE.Raycaster: Unsupported camera type: " + camera.type); - } - } - setFromXRController(controller) { - _matrix.identity().extractRotation(controller.matrixWorld); - this.ray.origin.setFromMatrixPosition(controller.matrixWorld); - this.ray.direction.set(0, 0, -1).applyMatrix4(_matrix); - return this; - } - intersectObject(object, recursive = true, intersects2 = []) { - intersect(object, this, intersects2, recursive); - intersects2.sort(ascSort); - return intersects2; - } - intersectObjects(objects, recursive = true, intersects2 = []) { - for (let i = 0, l = objects.length; i < l; i++) { - intersect(objects[i], this, intersects2, recursive); - } - intersects2.sort(ascSort); - return intersects2; - } -} -function ascSort(a, b) { - return a.distance - b.distance; -} -__name(ascSort, "ascSort"); -function intersect(object, raycaster, intersects2, recursive) { - let propagate = true; - if (object.layers.test(raycaster.layers)) { - const result = object.raycast(raycaster, intersects2); - if (result === false) propagate = false; - } - if (propagate === true && recursive === true) { - const children = object.children; - for (let i = 0, l = children.length; i < l; i++) { - intersect(children[i], raycaster, intersects2, true); - } - } -} -__name(intersect, "intersect"); -class Spherical { - static { - __name(this, "Spherical"); - } - constructor(radius = 1, phi = 0, theta = 0) { - this.radius = radius; - this.phi = phi; - this.theta = theta; - return this; - } - set(radius, phi, theta) { - this.radius = radius; - this.phi = phi; - this.theta = theta; - return this; - } - copy(other) { - this.radius = other.radius; - this.phi = other.phi; - this.theta = other.theta; - return this; - } - // restrict phi to be between EPS and PI-EPS - makeSafe() { - const EPS = 1e-6; - this.phi = Math.max(EPS, Math.min(Math.PI - EPS, this.phi)); - return this; - } - setFromVector3(v) { - return this.setFromCartesianCoords(v.x, v.y, v.z); - } - setFromCartesianCoords(x, y, z) { - this.radius = Math.sqrt(x * x + y * y + z * z); - if (this.radius === 0) { - this.theta = 0; - this.phi = 0; - } else { - this.theta = Math.atan2(x, z); - this.phi = Math.acos(clamp(y / this.radius, -1, 1)); - } - return this; - } - clone() { - return new this.constructor().copy(this); - } -} -class Cylindrical { - static { - __name(this, "Cylindrical"); - } - constructor(radius = 1, theta = 0, y = 0) { - this.radius = radius; - this.theta = theta; - this.y = y; - return this; - } - set(radius, theta, y) { - this.radius = radius; - this.theta = theta; - this.y = y; - return this; - } - copy(other) { - this.radius = other.radius; - this.theta = other.theta; - this.y = other.y; - return this; - } - setFromVector3(v) { - return this.setFromCartesianCoords(v.x, v.y, v.z); - } - setFromCartesianCoords(x, y, z) { - this.radius = Math.sqrt(x * x + z * z); - this.theta = Math.atan2(x, z); - this.y = y; - return this; - } - clone() { - return new this.constructor().copy(this); - } -} -class Matrix2 { - static { - __name(this, "Matrix2"); - } - constructor(n11, n12, n21, n22) { - Matrix2.prototype.isMatrix2 = true; - this.elements = [ - 1, - 0, - 0, - 1 - ]; - if (n11 !== void 0) { - this.set(n11, n12, n21, n22); - } - } - identity() { - this.set( - 1, - 0, - 0, - 1 - ); - return this; - } - fromArray(array, offset = 0) { - for (let i = 0; i < 4; i++) { - this.elements[i] = array[i + offset]; - } - return this; - } - set(n11, n12, n21, n22) { - const te2 = this.elements; - te2[0] = n11; - te2[2] = n12; - te2[1] = n21; - te2[3] = n22; - return this; - } -} -const _vector$4 = /* @__PURE__ */ new Vector2(); -class Box2 { - static { - __name(this, "Box2"); - } - constructor(min = new Vector2(Infinity, Infinity), max2 = new Vector2(-Infinity, -Infinity)) { - this.isBox2 = true; - this.min = min; - this.max = max2; - } - set(min, max2) { - this.min.copy(min); - this.max.copy(max2); - return this; - } - setFromPoints(points) { - this.makeEmpty(); - for (let i = 0, il = points.length; i < il; i++) { - this.expandByPoint(points[i]); - } - return this; - } - setFromCenterAndSize(center, size) { - const halfSize = _vector$4.copy(size).multiplyScalar(0.5); - this.min.copy(center).sub(halfSize); - this.max.copy(center).add(halfSize); - return this; - } - clone() { - return new this.constructor().copy(this); - } - copy(box) { - this.min.copy(box.min); - this.max.copy(box.max); - return this; - } - makeEmpty() { - this.min.x = this.min.y = Infinity; - this.max.x = this.max.y = -Infinity; - return this; - } - isEmpty() { - return this.max.x < this.min.x || this.max.y < this.min.y; - } - getCenter(target) { - return this.isEmpty() ? target.set(0, 0) : target.addVectors(this.min, this.max).multiplyScalar(0.5); - } - getSize(target) { - return this.isEmpty() ? target.set(0, 0) : target.subVectors(this.max, this.min); - } - expandByPoint(point) { - this.min.min(point); - this.max.max(point); - return this; - } - expandByVector(vector) { - this.min.sub(vector); - this.max.add(vector); - return this; - } - expandByScalar(scalar) { - this.min.addScalar(-scalar); - this.max.addScalar(scalar); - return this; - } - containsPoint(point) { - return point.x >= this.min.x && point.x <= this.max.x && point.y >= this.min.y && point.y <= this.max.y; - } - containsBox(box) { - return this.min.x <= box.min.x && box.max.x <= this.max.x && this.min.y <= box.min.y && box.max.y <= this.max.y; - } - getParameter(point, target) { - return target.set( - (point.x - this.min.x) / (this.max.x - this.min.x), - (point.y - this.min.y) / (this.max.y - this.min.y) - ); - } - intersectsBox(box) { - return box.max.x >= this.min.x && box.min.x <= this.max.x && box.max.y >= this.min.y && box.min.y <= this.max.y; - } - clampPoint(point, target) { - return target.copy(point).clamp(this.min, this.max); - } - distanceToPoint(point) { - return this.clampPoint(point, _vector$4).distanceTo(point); - } - intersect(box) { - this.min.max(box.min); - this.max.min(box.max); - if (this.isEmpty()) this.makeEmpty(); - return this; - } - union(box) { - this.min.min(box.min); - this.max.max(box.max); - return this; - } - translate(offset) { - this.min.add(offset); - this.max.add(offset); - return this; - } - equals(box) { - return box.min.equals(this.min) && box.max.equals(this.max); - } -} -const _startP = /* @__PURE__ */ new Vector3(); -const _startEnd = /* @__PURE__ */ new Vector3(); -class Line3 { - static { - __name(this, "Line3"); - } - constructor(start = new Vector3(), end = new Vector3()) { - this.start = start; - this.end = end; - } - set(start, end) { - this.start.copy(start); - this.end.copy(end); - return this; - } - copy(line) { - this.start.copy(line.start); - this.end.copy(line.end); - return this; - } - getCenter(target) { - return target.addVectors(this.start, this.end).multiplyScalar(0.5); - } - delta(target) { - return target.subVectors(this.end, this.start); - } - distanceSq() { - return this.start.distanceToSquared(this.end); - } - distance() { - return this.start.distanceTo(this.end); - } - at(t2, target) { - return this.delta(target).multiplyScalar(t2).add(this.start); - } - closestPointToPointParameter(point, clampToLine) { - _startP.subVectors(point, this.start); - _startEnd.subVectors(this.end, this.start); - const startEnd2 = _startEnd.dot(_startEnd); - const startEnd_startP = _startEnd.dot(_startP); - let t2 = startEnd_startP / startEnd2; - if (clampToLine) { - t2 = clamp(t2, 0, 1); - } - return t2; - } - closestPointToPoint(point, clampToLine, target) { - const t2 = this.closestPointToPointParameter(point, clampToLine); - return this.delta(target).multiplyScalar(t2).add(this.start); - } - applyMatrix4(matrix) { - this.start.applyMatrix4(matrix); - this.end.applyMatrix4(matrix); - return this; - } - equals(line) { - return line.start.equals(this.start) && line.end.equals(this.end); - } - clone() { - return new this.constructor().copy(this); - } -} -const _vector$3 = /* @__PURE__ */ new Vector3(); -class SpotLightHelper extends Object3D { - static { - __name(this, "SpotLightHelper"); - } - constructor(light, color) { - super(); - this.light = light; - this.matrixAutoUpdate = false; - this.color = color; - this.type = "SpotLightHelper"; - const geometry = new BufferGeometry(); - const positions = [ - 0, - 0, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 1, - 0, - 0, - 0, - -1, - 0, - 1, - 0, - 0, - 0, - 0, - 1, - 1, - 0, - 0, - 0, - 0, - -1, - 1 - ]; - for (let i = 0, j = 1, l = 32; i < l; i++, j++) { - const p1 = i / l * Math.PI * 2; - const p2 = j / l * Math.PI * 2; - positions.push( - Math.cos(p1), - Math.sin(p1), - 1, - Math.cos(p2), - Math.sin(p2), - 1 - ); - } - geometry.setAttribute("position", new Float32BufferAttribute(positions, 3)); - const material = new LineBasicMaterial({ fog: false, toneMapped: false }); - this.cone = new LineSegments(geometry, material); - this.add(this.cone); - this.update(); - } - dispose() { - this.cone.geometry.dispose(); - this.cone.material.dispose(); - } - update() { - this.light.updateWorldMatrix(true, false); - this.light.target.updateWorldMatrix(true, false); - if (this.parent) { - this.parent.updateWorldMatrix(true); - this.matrix.copy(this.parent.matrixWorld).invert().multiply(this.light.matrixWorld); - } else { - this.matrix.copy(this.light.matrixWorld); - } - this.matrixWorld.copy(this.light.matrixWorld); - const coneLength = this.light.distance ? this.light.distance : 1e3; - const coneWidth = coneLength * Math.tan(this.light.angle); - this.cone.scale.set(coneWidth, coneWidth, coneLength); - _vector$3.setFromMatrixPosition(this.light.target.matrixWorld); - this.cone.lookAt(_vector$3); - if (this.color !== void 0) { - this.cone.material.color.set(this.color); - } else { - this.cone.material.color.copy(this.light.color); - } - } -} -const _vector$2 = /* @__PURE__ */ new Vector3(); -const _boneMatrix = /* @__PURE__ */ new Matrix4(); -const _matrixWorldInv = /* @__PURE__ */ new Matrix4(); -class SkeletonHelper extends LineSegments { - static { - __name(this, "SkeletonHelper"); - } - constructor(object) { - const bones = getBoneList(object); - const geometry = new BufferGeometry(); - const vertices = []; - const colors = []; - const color1 = new Color(0, 0, 1); - const color2 = new Color(0, 1, 0); - for (let i = 0; i < bones.length; i++) { - const bone = bones[i]; - if (bone.parent && bone.parent.isBone) { - vertices.push(0, 0, 0); - vertices.push(0, 0, 0); - colors.push(color1.r, color1.g, color1.b); - colors.push(color2.r, color2.g, color2.b); - } - } - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("color", new Float32BufferAttribute(colors, 3)); - const material = new LineBasicMaterial({ vertexColors: true, depthTest: false, depthWrite: false, toneMapped: false, transparent: true }); - super(geometry, material); - this.isSkeletonHelper = true; - this.type = "SkeletonHelper"; - this.root = object; - this.bones = bones; - this.matrix = object.matrixWorld; - this.matrixAutoUpdate = false; - } - updateMatrixWorld(force) { - const bones = this.bones; - const geometry = this.geometry; - const position = geometry.getAttribute("position"); - _matrixWorldInv.copy(this.root.matrixWorld).invert(); - for (let i = 0, j = 0; i < bones.length; i++) { - const bone = bones[i]; - if (bone.parent && bone.parent.isBone) { - _boneMatrix.multiplyMatrices(_matrixWorldInv, bone.matrixWorld); - _vector$2.setFromMatrixPosition(_boneMatrix); - position.setXYZ(j, _vector$2.x, _vector$2.y, _vector$2.z); - _boneMatrix.multiplyMatrices(_matrixWorldInv, bone.parent.matrixWorld); - _vector$2.setFromMatrixPosition(_boneMatrix); - position.setXYZ(j + 1, _vector$2.x, _vector$2.y, _vector$2.z); - j += 2; - } - } - geometry.getAttribute("position").needsUpdate = true; - super.updateMatrixWorld(force); - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -function getBoneList(object) { - const boneList = []; - if (object.isBone === true) { - boneList.push(object); - } - for (let i = 0; i < object.children.length; i++) { - boneList.push.apply(boneList, getBoneList(object.children[i])); - } - return boneList; -} -__name(getBoneList, "getBoneList"); -class PointLightHelper extends Mesh { - static { - __name(this, "PointLightHelper"); - } - constructor(light, sphereSize, color) { - const geometry = new SphereGeometry(sphereSize, 4, 2); - const material = new MeshBasicMaterial({ wireframe: true, fog: false, toneMapped: false }); - super(geometry, material); - this.light = light; - this.color = color; - this.type = "PointLightHelper"; - this.matrix = this.light.matrixWorld; - this.matrixAutoUpdate = false; - this.update(); - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } - update() { - this.light.updateWorldMatrix(true, false); - if (this.color !== void 0) { - this.material.color.set(this.color); - } else { - this.material.color.copy(this.light.color); - } - } -} -const _vector$1 = /* @__PURE__ */ new Vector3(); -const _color1 = /* @__PURE__ */ new Color(); -const _color2 = /* @__PURE__ */ new Color(); -class HemisphereLightHelper extends Object3D { - static { - __name(this, "HemisphereLightHelper"); - } - constructor(light, size, color) { - super(); - this.light = light; - this.matrix = light.matrixWorld; - this.matrixAutoUpdate = false; - this.color = color; - this.type = "HemisphereLightHelper"; - const geometry = new OctahedronGeometry(size); - geometry.rotateY(Math.PI * 0.5); - this.material = new MeshBasicMaterial({ wireframe: true, fog: false, toneMapped: false }); - if (this.color === void 0) this.material.vertexColors = true; - const position = geometry.getAttribute("position"); - const colors = new Float32Array(position.count * 3); - geometry.setAttribute("color", new BufferAttribute(colors, 3)); - this.add(new Mesh(geometry, this.material)); - this.update(); - } - dispose() { - this.children[0].geometry.dispose(); - this.children[0].material.dispose(); - } - update() { - const mesh = this.children[0]; - if (this.color !== void 0) { - this.material.color.set(this.color); - } else { - const colors = mesh.geometry.getAttribute("color"); - _color1.copy(this.light.color); - _color2.copy(this.light.groundColor); - for (let i = 0, l = colors.count; i < l; i++) { - const color = i < l / 2 ? _color1 : _color2; - colors.setXYZ(i, color.r, color.g, color.b); - } - colors.needsUpdate = true; - } - this.light.updateWorldMatrix(true, false); - mesh.lookAt(_vector$1.setFromMatrixPosition(this.light.matrixWorld).negate()); - } -} -class GridHelper extends LineSegments { - static { - __name(this, "GridHelper"); - } - constructor(size = 10, divisions = 10, color1 = 4473924, color2 = 8947848) { - color1 = new Color(color1); - color2 = new Color(color2); - const center = divisions / 2; - const step = size / divisions; - const halfSize = size / 2; - const vertices = [], colors = []; - for (let i = 0, j = 0, k = -halfSize; i <= divisions; i++, k += step) { - vertices.push(-halfSize, 0, k, halfSize, 0, k); - vertices.push(k, 0, -halfSize, k, 0, halfSize); - const color = i === center ? color1 : color2; - color.toArray(colors, j); - j += 3; - color.toArray(colors, j); - j += 3; - color.toArray(colors, j); - j += 3; - color.toArray(colors, j); - j += 3; - } - const geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("color", new Float32BufferAttribute(colors, 3)); - const material = new LineBasicMaterial({ vertexColors: true, toneMapped: false }); - super(geometry, material); - this.type = "GridHelper"; - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -class PolarGridHelper extends LineSegments { - static { - __name(this, "PolarGridHelper"); - } - constructor(radius = 10, sectors = 16, rings = 8, divisions = 64, color1 = 4473924, color2 = 8947848) { - color1 = new Color(color1); - color2 = new Color(color2); - const vertices = []; - const colors = []; - if (sectors > 1) { - for (let i = 0; i < sectors; i++) { - const v = i / sectors * (Math.PI * 2); - const x = Math.sin(v) * radius; - const z = Math.cos(v) * radius; - vertices.push(0, 0, 0); - vertices.push(x, 0, z); - const color = i & 1 ? color1 : color2; - colors.push(color.r, color.g, color.b); - colors.push(color.r, color.g, color.b); - } - } - for (let i = 0; i < rings; i++) { - const color = i & 1 ? color1 : color2; - const r = radius - radius / rings * i; - for (let j = 0; j < divisions; j++) { - let v = j / divisions * (Math.PI * 2); - let x = Math.sin(v) * r; - let z = Math.cos(v) * r; - vertices.push(x, 0, z); - colors.push(color.r, color.g, color.b); - v = (j + 1) / divisions * (Math.PI * 2); - x = Math.sin(v) * r; - z = Math.cos(v) * r; - vertices.push(x, 0, z); - colors.push(color.r, color.g, color.b); - } - } - const geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("color", new Float32BufferAttribute(colors, 3)); - const material = new LineBasicMaterial({ vertexColors: true, toneMapped: false }); - super(geometry, material); - this.type = "PolarGridHelper"; - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -const _v1 = /* @__PURE__ */ new Vector3(); -const _v2 = /* @__PURE__ */ new Vector3(); -const _v3 = /* @__PURE__ */ new Vector3(); -class DirectionalLightHelper extends Object3D { - static { - __name(this, "DirectionalLightHelper"); - } - constructor(light, size, color) { - super(); - this.light = light; - this.matrix = light.matrixWorld; - this.matrixAutoUpdate = false; - this.color = color; - this.type = "DirectionalLightHelper"; - if (size === void 0) size = 1; - let geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute([ - -size, - size, - 0, - size, - size, - 0, - size, - -size, - 0, - -size, - -size, - 0, - -size, - size, - 0 - ], 3)); - const material = new LineBasicMaterial({ fog: false, toneMapped: false }); - this.lightPlane = new Line(geometry, material); - this.add(this.lightPlane); - geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute([0, 0, 0, 0, 0, 1], 3)); - this.targetLine = new Line(geometry, material); - this.add(this.targetLine); - this.update(); - } - dispose() { - this.lightPlane.geometry.dispose(); - this.lightPlane.material.dispose(); - this.targetLine.geometry.dispose(); - this.targetLine.material.dispose(); - } - update() { - this.light.updateWorldMatrix(true, false); - this.light.target.updateWorldMatrix(true, false); - _v1.setFromMatrixPosition(this.light.matrixWorld); - _v2.setFromMatrixPosition(this.light.target.matrixWorld); - _v3.subVectors(_v2, _v1); - this.lightPlane.lookAt(_v2); - if (this.color !== void 0) { - this.lightPlane.material.color.set(this.color); - this.targetLine.material.color.set(this.color); - } else { - this.lightPlane.material.color.copy(this.light.color); - this.targetLine.material.color.copy(this.light.color); - } - this.targetLine.lookAt(_v2); - this.targetLine.scale.z = _v3.length(); - } -} -const _vector = /* @__PURE__ */ new Vector3(); -const _camera = /* @__PURE__ */ new Camera(); -class CameraHelper extends LineSegments { - static { - __name(this, "CameraHelper"); - } - constructor(camera) { - const geometry = new BufferGeometry(); - const material = new LineBasicMaterial({ color: 16777215, vertexColors: true, toneMapped: false }); - const vertices = []; - const colors = []; - const pointMap = {}; - addLine("n1", "n2"); - addLine("n2", "n4"); - addLine("n4", "n3"); - addLine("n3", "n1"); - addLine("f1", "f2"); - addLine("f2", "f4"); - addLine("f4", "f3"); - addLine("f3", "f1"); - addLine("n1", "f1"); - addLine("n2", "f2"); - addLine("n3", "f3"); - addLine("n4", "f4"); - addLine("p", "n1"); - addLine("p", "n2"); - addLine("p", "n3"); - addLine("p", "n4"); - addLine("u1", "u2"); - addLine("u2", "u3"); - addLine("u3", "u1"); - addLine("c", "t"); - addLine("p", "c"); - addLine("cn1", "cn2"); - addLine("cn3", "cn4"); - addLine("cf1", "cf2"); - addLine("cf3", "cf4"); - function addLine(a, b) { - addPoint(a); - addPoint(b); - } - __name(addLine, "addLine"); - function addPoint(id2) { - vertices.push(0, 0, 0); - colors.push(0, 0, 0); - if (pointMap[id2] === void 0) { - pointMap[id2] = []; - } - pointMap[id2].push(vertices.length / 3 - 1); - } - __name(addPoint, "addPoint"); - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("color", new Float32BufferAttribute(colors, 3)); - super(geometry, material); - this.type = "CameraHelper"; - this.camera = camera; - if (this.camera.updateProjectionMatrix) this.camera.updateProjectionMatrix(); - this.matrix = camera.matrixWorld; - this.matrixAutoUpdate = false; - this.pointMap = pointMap; - this.update(); - const colorFrustum = new Color(16755200); - const colorCone = new Color(16711680); - const colorUp = new Color(43775); - const colorTarget = new Color(16777215); - const colorCross = new Color(3355443); - this.setColors(colorFrustum, colorCone, colorUp, colorTarget, colorCross); - } - setColors(frustum, cone, up, target, cross) { - const geometry = this.geometry; - const colorAttribute = geometry.getAttribute("color"); - colorAttribute.setXYZ(0, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(1, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(2, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(3, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(4, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(5, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(6, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(7, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(8, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(9, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(10, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(11, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(12, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(13, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(14, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(15, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(16, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(17, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(18, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(19, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(20, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(21, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(22, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(23, frustum.r, frustum.g, frustum.b); - colorAttribute.setXYZ(24, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(25, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(26, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(27, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(28, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(29, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(30, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(31, cone.r, cone.g, cone.b); - colorAttribute.setXYZ(32, up.r, up.g, up.b); - colorAttribute.setXYZ(33, up.r, up.g, up.b); - colorAttribute.setXYZ(34, up.r, up.g, up.b); - colorAttribute.setXYZ(35, up.r, up.g, up.b); - colorAttribute.setXYZ(36, up.r, up.g, up.b); - colorAttribute.setXYZ(37, up.r, up.g, up.b); - colorAttribute.setXYZ(38, target.r, target.g, target.b); - colorAttribute.setXYZ(39, target.r, target.g, target.b); - colorAttribute.setXYZ(40, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(41, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(42, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(43, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(44, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(45, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(46, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(47, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(48, cross.r, cross.g, cross.b); - colorAttribute.setXYZ(49, cross.r, cross.g, cross.b); - colorAttribute.needsUpdate = true; - } - update() { - const geometry = this.geometry; - const pointMap = this.pointMap; - const w = 1, h = 1; - _camera.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse); - setPoint("c", pointMap, geometry, _camera, 0, 0, -1); - setPoint("t", pointMap, geometry, _camera, 0, 0, 1); - setPoint("n1", pointMap, geometry, _camera, -w, -h, -1); - setPoint("n2", pointMap, geometry, _camera, w, -h, -1); - setPoint("n3", pointMap, geometry, _camera, -w, h, -1); - setPoint("n4", pointMap, geometry, _camera, w, h, -1); - setPoint("f1", pointMap, geometry, _camera, -w, -h, 1); - setPoint("f2", pointMap, geometry, _camera, w, -h, 1); - setPoint("f3", pointMap, geometry, _camera, -w, h, 1); - setPoint("f4", pointMap, geometry, _camera, w, h, 1); - setPoint("u1", pointMap, geometry, _camera, w * 0.7, h * 1.1, -1); - setPoint("u2", pointMap, geometry, _camera, -w * 0.7, h * 1.1, -1); - setPoint("u3", pointMap, geometry, _camera, 0, h * 2, -1); - setPoint("cf1", pointMap, geometry, _camera, -w, 0, 1); - setPoint("cf2", pointMap, geometry, _camera, w, 0, 1); - setPoint("cf3", pointMap, geometry, _camera, 0, -h, 1); - setPoint("cf4", pointMap, geometry, _camera, 0, h, 1); - setPoint("cn1", pointMap, geometry, _camera, -w, 0, -1); - setPoint("cn2", pointMap, geometry, _camera, w, 0, -1); - setPoint("cn3", pointMap, geometry, _camera, 0, -h, -1); - setPoint("cn4", pointMap, geometry, _camera, 0, h, -1); - geometry.getAttribute("position").needsUpdate = true; - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -function setPoint(point, pointMap, geometry, camera, x, y, z) { - _vector.set(x, y, z).unproject(camera); - const points = pointMap[point]; - if (points !== void 0) { - const position = geometry.getAttribute("position"); - for (let i = 0, l = points.length; i < l; i++) { - position.setXYZ(points[i], _vector.x, _vector.y, _vector.z); - } - } -} -__name(setPoint, "setPoint"); -const _box = /* @__PURE__ */ new Box3(); -class BoxHelper extends LineSegments { - static { - __name(this, "BoxHelper"); - } - constructor(object, color = 16776960) { - const indices = new Uint16Array([0, 1, 1, 2, 2, 3, 3, 0, 4, 5, 5, 6, 6, 7, 7, 4, 0, 4, 1, 5, 2, 6, 3, 7]); - const positions = new Float32Array(8 * 3); - const geometry = new BufferGeometry(); - geometry.setIndex(new BufferAttribute(indices, 1)); - geometry.setAttribute("position", new BufferAttribute(positions, 3)); - super(geometry, new LineBasicMaterial({ color, toneMapped: false })); - this.object = object; - this.type = "BoxHelper"; - this.matrixAutoUpdate = false; - this.update(); - } - update(object) { - if (object !== void 0) { - console.warn("THREE.BoxHelper: .update() has no longer arguments."); - } - if (this.object !== void 0) { - _box.setFromObject(this.object); - } - if (_box.isEmpty()) return; - const min = _box.min; - const max2 = _box.max; - const position = this.geometry.attributes.position; - const array = position.array; - array[0] = max2.x; - array[1] = max2.y; - array[2] = max2.z; - array[3] = min.x; - array[4] = max2.y; - array[5] = max2.z; - array[6] = min.x; - array[7] = min.y; - array[8] = max2.z; - array[9] = max2.x; - array[10] = min.y; - array[11] = max2.z; - array[12] = max2.x; - array[13] = max2.y; - array[14] = min.z; - array[15] = min.x; - array[16] = max2.y; - array[17] = min.z; - array[18] = min.x; - array[19] = min.y; - array[20] = min.z; - array[21] = max2.x; - array[22] = min.y; - array[23] = min.z; - position.needsUpdate = true; - this.geometry.computeBoundingSphere(); - } - setFromObject(object) { - this.object = object; - this.update(); - return this; - } - copy(source, recursive) { - super.copy(source, recursive); - this.object = source.object; - return this; - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -class Box3Helper extends LineSegments { - static { - __name(this, "Box3Helper"); - } - constructor(box, color = 16776960) { - const indices = new Uint16Array([0, 1, 1, 2, 2, 3, 3, 0, 4, 5, 5, 6, 6, 7, 7, 4, 0, 4, 1, 5, 2, 6, 3, 7]); - const positions = [1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1]; - const geometry = new BufferGeometry(); - geometry.setIndex(new BufferAttribute(indices, 1)); - geometry.setAttribute("position", new Float32BufferAttribute(positions, 3)); - super(geometry, new LineBasicMaterial({ color, toneMapped: false })); - this.box = box; - this.type = "Box3Helper"; - this.geometry.computeBoundingSphere(); - } - updateMatrixWorld(force) { - const box = this.box; - if (box.isEmpty()) return; - box.getCenter(this.position); - box.getSize(this.scale); - this.scale.multiplyScalar(0.5); - super.updateMatrixWorld(force); - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -class PlaneHelper extends Line { - static { - __name(this, "PlaneHelper"); - } - constructor(plane, size = 1, hex = 16776960) { - const color = hex; - const positions = [1, -1, 0, -1, 1, 0, -1, -1, 0, 1, 1, 0, -1, 1, 0, -1, -1, 0, 1, -1, 0, 1, 1, 0]; - const geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute(positions, 3)); - geometry.computeBoundingSphere(); - super(geometry, new LineBasicMaterial({ color, toneMapped: false })); - this.type = "PlaneHelper"; - this.plane = plane; - this.size = size; - const positions2 = [1, 1, 0, -1, 1, 0, -1, -1, 0, 1, 1, 0, -1, -1, 0, 1, -1, 0]; - const geometry2 = new BufferGeometry(); - geometry2.setAttribute("position", new Float32BufferAttribute(positions2, 3)); - geometry2.computeBoundingSphere(); - this.add(new Mesh(geometry2, new MeshBasicMaterial({ color, opacity: 0.2, transparent: true, depthWrite: false, toneMapped: false }))); - } - updateMatrixWorld(force) { - this.position.set(0, 0, 0); - this.scale.set(0.5 * this.size, 0.5 * this.size, 1); - this.lookAt(this.plane.normal); - this.translateZ(-this.plane.constant); - super.updateMatrixWorld(force); - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - this.children[0].geometry.dispose(); - this.children[0].material.dispose(); - } -} -const _axis = /* @__PURE__ */ new Vector3(); -let _lineGeometry, _coneGeometry; -class ArrowHelper extends Object3D { - static { - __name(this, "ArrowHelper"); - } - // dir is assumed to be normalized - constructor(dir = new Vector3(0, 0, 1), origin = new Vector3(0, 0, 0), length = 1, color = 16776960, headLength = length * 0.2, headWidth = headLength * 0.2) { - super(); - this.type = "ArrowHelper"; - if (_lineGeometry === void 0) { - _lineGeometry = new BufferGeometry(); - _lineGeometry.setAttribute("position", new Float32BufferAttribute([0, 0, 0, 0, 1, 0], 3)); - _coneGeometry = new CylinderGeometry(0, 0.5, 1, 5, 1); - _coneGeometry.translate(0, -0.5, 0); - } - this.position.copy(origin); - this.line = new Line(_lineGeometry, new LineBasicMaterial({ color, toneMapped: false })); - this.line.matrixAutoUpdate = false; - this.add(this.line); - this.cone = new Mesh(_coneGeometry, new MeshBasicMaterial({ color, toneMapped: false })); - this.cone.matrixAutoUpdate = false; - this.add(this.cone); - this.setDirection(dir); - this.setLength(length, headLength, headWidth); - } - setDirection(dir) { - if (dir.y > 0.99999) { - this.quaternion.set(0, 0, 0, 1); - } else if (dir.y < -0.99999) { - this.quaternion.set(1, 0, 0, 0); - } else { - _axis.set(dir.z, 0, -dir.x).normalize(); - const radians = Math.acos(dir.y); - this.quaternion.setFromAxisAngle(_axis, radians); - } - } - setLength(length, headLength = length * 0.2, headWidth = headLength * 0.2) { - this.line.scale.set(1, Math.max(1e-4, length - headLength), 1); - this.line.updateMatrix(); - this.cone.scale.set(headWidth, headLength, headWidth); - this.cone.position.y = length; - this.cone.updateMatrix(); - } - setColor(color) { - this.line.material.color.set(color); - this.cone.material.color.set(color); - } - copy(source) { - super.copy(source, false); - this.line.copy(source.line); - this.cone.copy(source.cone); - return this; - } - dispose() { - this.line.geometry.dispose(); - this.line.material.dispose(); - this.cone.geometry.dispose(); - this.cone.material.dispose(); - } -} -class AxesHelper extends LineSegments { - static { - __name(this, "AxesHelper"); - } - constructor(size = 1) { - const vertices = [ - 0, - 0, - 0, - size, - 0, - 0, - 0, - 0, - 0, - 0, - size, - 0, - 0, - 0, - 0, - 0, - 0, - size - ]; - const colors = [ - 1, - 0, - 0, - 1, - 0.6, - 0, - 0, - 1, - 0, - 0.6, - 1, - 0, - 0, - 0, - 1, - 0, - 0.6, - 1 - ]; - const geometry = new BufferGeometry(); - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("color", new Float32BufferAttribute(colors, 3)); - const material = new LineBasicMaterial({ vertexColors: true, toneMapped: false }); - super(geometry, material); - this.type = "AxesHelper"; - } - setColors(xAxisColor, yAxisColor, zAxisColor) { - const color = new Color(); - const array = this.geometry.attributes.color.array; - color.set(xAxisColor); - color.toArray(array, 0); - color.toArray(array, 3); - color.set(yAxisColor); - color.toArray(array, 6); - color.toArray(array, 9); - color.set(zAxisColor); - color.toArray(array, 12); - color.toArray(array, 15); - this.geometry.attributes.color.needsUpdate = true; - return this; - } - dispose() { - this.geometry.dispose(); - this.material.dispose(); - } -} -class ShapePath { - static { - __name(this, "ShapePath"); - } - constructor() { - this.type = "ShapePath"; - this.color = new Color(); - this.subPaths = []; - this.currentPath = null; - } - moveTo(x, y) { - this.currentPath = new Path(); - this.subPaths.push(this.currentPath); - this.currentPath.moveTo(x, y); - return this; - } - lineTo(x, y) { - this.currentPath.lineTo(x, y); - return this; - } - quadraticCurveTo(aCPx, aCPy, aX, aY) { - this.currentPath.quadraticCurveTo(aCPx, aCPy, aX, aY); - return this; - } - bezierCurveTo(aCP1x, aCP1y, aCP2x, aCP2y, aX, aY) { - this.currentPath.bezierCurveTo(aCP1x, aCP1y, aCP2x, aCP2y, aX, aY); - return this; - } - splineThru(pts) { - this.currentPath.splineThru(pts); - return this; - } - toShapes(isCCW) { - function toShapesNoHoles(inSubpaths) { - const shapes2 = []; - for (let i = 0, l = inSubpaths.length; i < l; i++) { - const tmpPath2 = inSubpaths[i]; - const tmpShape2 = new Shape(); - tmpShape2.curves = tmpPath2.curves; - shapes2.push(tmpShape2); - } - return shapes2; - } - __name(toShapesNoHoles, "toShapesNoHoles"); - function isPointInsidePolygon(inPt, inPolygon) { - const polyLen = inPolygon.length; - let inside = false; - for (let p = polyLen - 1, q = 0; q < polyLen; p = q++) { - let edgeLowPt = inPolygon[p]; - let edgeHighPt = inPolygon[q]; - let edgeDx = edgeHighPt.x - edgeLowPt.x; - let edgeDy = edgeHighPt.y - edgeLowPt.y; - if (Math.abs(edgeDy) > Number.EPSILON) { - if (edgeDy < 0) { - edgeLowPt = inPolygon[q]; - edgeDx = -edgeDx; - edgeHighPt = inPolygon[p]; - edgeDy = -edgeDy; - } - if (inPt.y < edgeLowPt.y || inPt.y > edgeHighPt.y) continue; - if (inPt.y === edgeLowPt.y) { - if (inPt.x === edgeLowPt.x) return true; - } else { - const perpEdge = edgeDy * (inPt.x - edgeLowPt.x) - edgeDx * (inPt.y - edgeLowPt.y); - if (perpEdge === 0) return true; - if (perpEdge < 0) continue; - inside = !inside; - } - } else { - if (inPt.y !== edgeLowPt.y) continue; - if (edgeHighPt.x <= inPt.x && inPt.x <= edgeLowPt.x || edgeLowPt.x <= inPt.x && inPt.x <= edgeHighPt.x) return true; - } - } - return inside; - } - __name(isPointInsidePolygon, "isPointInsidePolygon"); - const isClockWise = ShapeUtils.isClockWise; - const subPaths = this.subPaths; - if (subPaths.length === 0) return []; - let solid, tmpPath, tmpShape; - const shapes = []; - if (subPaths.length === 1) { - tmpPath = subPaths[0]; - tmpShape = new Shape(); - tmpShape.curves = tmpPath.curves; - shapes.push(tmpShape); - return shapes; - } - let holesFirst = !isClockWise(subPaths[0].getPoints()); - holesFirst = isCCW ? !holesFirst : holesFirst; - const betterShapeHoles = []; - const newShapes = []; - let newShapeHoles = []; - let mainIdx = 0; - let tmpPoints; - newShapes[mainIdx] = void 0; - newShapeHoles[mainIdx] = []; - for (let i = 0, l = subPaths.length; i < l; i++) { - tmpPath = subPaths[i]; - tmpPoints = tmpPath.getPoints(); - solid = isClockWise(tmpPoints); - solid = isCCW ? !solid : solid; - if (solid) { - if (!holesFirst && newShapes[mainIdx]) mainIdx++; - newShapes[mainIdx] = { s: new Shape(), p: tmpPoints }; - newShapes[mainIdx].s.curves = tmpPath.curves; - if (holesFirst) mainIdx++; - newShapeHoles[mainIdx] = []; - } else { - newShapeHoles[mainIdx].push({ h: tmpPath, p: tmpPoints[0] }); - } - } - if (!newShapes[0]) return toShapesNoHoles(subPaths); - if (newShapes.length > 1) { - let ambiguous = false; - let toChange = 0; - for (let sIdx = 0, sLen = newShapes.length; sIdx < sLen; sIdx++) { - betterShapeHoles[sIdx] = []; - } - for (let sIdx = 0, sLen = newShapes.length; sIdx < sLen; sIdx++) { - const sho = newShapeHoles[sIdx]; - for (let hIdx = 0; hIdx < sho.length; hIdx++) { - const ho = sho[hIdx]; - let hole_unassigned = true; - for (let s2Idx = 0; s2Idx < newShapes.length; s2Idx++) { - if (isPointInsidePolygon(ho.p, newShapes[s2Idx].p)) { - if (sIdx !== s2Idx) toChange++; - if (hole_unassigned) { - hole_unassigned = false; - betterShapeHoles[s2Idx].push(ho); - } else { - ambiguous = true; - } - } - } - if (hole_unassigned) { - betterShapeHoles[sIdx].push(ho); - } - } - } - if (toChange > 0 && ambiguous === false) { - newShapeHoles = betterShapeHoles; - } - } - let tmpHoles; - for (let i = 0, il = newShapes.length; i < il; i++) { - tmpShape = newShapes[i].s; - shapes.push(tmpShape); - tmpHoles = newShapeHoles[i]; - for (let j = 0, jl = tmpHoles.length; j < jl; j++) { - tmpShape.holes.push(tmpHoles[j].h); - } - } - return shapes; - } -} -class Controls extends EventDispatcher { - static { - __name(this, "Controls"); - } - constructor(object, domElement = null) { - super(); - this.object = object; - this.domElement = domElement; - this.enabled = true; - this.state = -1; - this.keys = {}; - this.mouseButtons = { LEFT: null, MIDDLE: null, RIGHT: null }; - this.touches = { ONE: null, TWO: null }; - } - connect() { - } - disconnect() { - } - dispose() { - } - update() { - } -} -class WebGLMultipleRenderTargets extends WebGLRenderTarget { - static { - __name(this, "WebGLMultipleRenderTargets"); - } - // @deprecated, r162 - constructor(width = 1, height = 1, count = 1, options = {}) { - console.warn('THREE.WebGLMultipleRenderTargets has been deprecated and will be removed in r172. Use THREE.WebGLRenderTarget and set the "count" parameter to enable MRT.'); - super(width, height, { ...options, count }); - this.isWebGLMultipleRenderTargets = true; - } - get texture() { - return this.textures; - } -} -if (typeof __THREE_DEVTOOLS__ !== "undefined") { - __THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register", { detail: { - revision: REVISION - } })); -} -if (typeof window !== "undefined") { - if (window.__THREE__) { - console.warn("WARNING: Multiple instances of Three.js being imported."); - } else { - window.__THREE__ = REVISION; - } -} -const _changeEvent = { type: "change" }; -const _startEvent = { type: "start" }; -const _endEvent = { type: "end" }; -const _ray = new Ray(); -const _plane = new Plane(); -const _TILT_LIMIT = Math.cos(70 * MathUtils.DEG2RAD); -const _v = new Vector3(); -const _twoPI = 2 * Math.PI; -const _STATE = { - NONE: -1, - ROTATE: 0, - DOLLY: 1, - PAN: 2, - TOUCH_ROTATE: 3, - TOUCH_PAN: 4, - TOUCH_DOLLY_PAN: 5, - TOUCH_DOLLY_ROTATE: 6 -}; -const _EPS = 1e-6; -class OrbitControls extends Controls { - static { - __name(this, "OrbitControls"); - } - constructor(object, domElement = null) { - super(object, domElement); - this.state = _STATE.NONE; - this.enabled = true; - this.target = new Vector3(); - this.cursor = new Vector3(); - this.minDistance = 0; - this.maxDistance = Infinity; - this.minZoom = 0; - this.maxZoom = Infinity; - this.minTargetRadius = 0; - this.maxTargetRadius = Infinity; - this.minPolarAngle = 0; - this.maxPolarAngle = Math.PI; - this.minAzimuthAngle = -Infinity; - this.maxAzimuthAngle = Infinity; - this.enableDamping = false; - this.dampingFactor = 0.05; - this.enableZoom = true; - this.zoomSpeed = 1; - this.enableRotate = true; - this.rotateSpeed = 1; - this.enablePan = true; - this.panSpeed = 1; - this.screenSpacePanning = true; - this.keyPanSpeed = 7; - this.zoomToCursor = false; - this.autoRotate = false; - this.autoRotateSpeed = 2; - this.keys = { LEFT: "ArrowLeft", UP: "ArrowUp", RIGHT: "ArrowRight", BOTTOM: "ArrowDown" }; - this.mouseButtons = { LEFT: MOUSE.ROTATE, MIDDLE: MOUSE.DOLLY, RIGHT: MOUSE.PAN }; - this.touches = { ONE: TOUCH.ROTATE, TWO: TOUCH.DOLLY_PAN }; - this.target0 = this.target.clone(); - this.position0 = this.object.position.clone(); - this.zoom0 = this.object.zoom; - this._domElementKeyEvents = null; - this._lastPosition = new Vector3(); - this._lastQuaternion = new Quaternion(); - this._lastTargetPosition = new Vector3(); - this._quat = new Quaternion().setFromUnitVectors(object.up, new Vector3(0, 1, 0)); - this._quatInverse = this._quat.clone().invert(); - this._spherical = new Spherical(); - this._sphericalDelta = new Spherical(); - this._scale = 1; - this._panOffset = new Vector3(); - this._rotateStart = new Vector2(); - this._rotateEnd = new Vector2(); - this._rotateDelta = new Vector2(); - this._panStart = new Vector2(); - this._panEnd = new Vector2(); - this._panDelta = new Vector2(); - this._dollyStart = new Vector2(); - this._dollyEnd = new Vector2(); - this._dollyDelta = new Vector2(); - this._dollyDirection = new Vector3(); - this._mouse = new Vector2(); - this._performCursorZoom = false; - this._pointers = []; - this._pointerPositions = {}; - this._controlActive = false; - this._onPointerMove = onPointerMove.bind(this); - this._onPointerDown = onPointerDown.bind(this); - this._onPointerUp = onPointerUp.bind(this); - this._onContextMenu = onContextMenu.bind(this); - this._onMouseWheel = onMouseWheel.bind(this); - this._onKeyDown = onKeyDown.bind(this); - this._onTouchStart = onTouchStart.bind(this); - this._onTouchMove = onTouchMove.bind(this); - this._onMouseDown = onMouseDown.bind(this); - this._onMouseMove = onMouseMove.bind(this); - this._interceptControlDown = interceptControlDown.bind(this); - this._interceptControlUp = interceptControlUp.bind(this); - if (this.domElement !== null) { - this.connect(); - } - this.update(); - } - connect() { - this.domElement.addEventListener("pointerdown", this._onPointerDown); - this.domElement.addEventListener("pointercancel", this._onPointerUp); - this.domElement.addEventListener("contextmenu", this._onContextMenu); - this.domElement.addEventListener("wheel", this._onMouseWheel, { passive: false }); - const document2 = this.domElement.getRootNode(); - document2.addEventListener("keydown", this._interceptControlDown, { passive: true, capture: true }); - this.domElement.style.touchAction = "none"; - } - disconnect() { - this.domElement.removeEventListener("pointerdown", this._onPointerDown); - this.domElement.removeEventListener("pointermove", this._onPointerMove); - this.domElement.removeEventListener("pointerup", this._onPointerUp); - this.domElement.removeEventListener("pointercancel", this._onPointerUp); - this.domElement.removeEventListener("wheel", this._onMouseWheel); - this.domElement.removeEventListener("contextmenu", this._onContextMenu); - this.stopListenToKeyEvents(); - const document2 = this.domElement.getRootNode(); - document2.removeEventListener("keydown", this._interceptControlDown, { capture: true }); - this.domElement.style.touchAction = "auto"; - } - dispose() { - this.disconnect(); - } - getPolarAngle() { - return this._spherical.phi; - } - getAzimuthalAngle() { - return this._spherical.theta; - } - getDistance() { - return this.object.position.distanceTo(this.target); - } - listenToKeyEvents(domElement) { - domElement.addEventListener("keydown", this._onKeyDown); - this._domElementKeyEvents = domElement; - } - stopListenToKeyEvents() { - if (this._domElementKeyEvents !== null) { - this._domElementKeyEvents.removeEventListener("keydown", this._onKeyDown); - this._domElementKeyEvents = null; - } - } - saveState() { - this.target0.copy(this.target); - this.position0.copy(this.object.position); - this.zoom0 = this.object.zoom; - } - reset() { - this.target.copy(this.target0); - this.object.position.copy(this.position0); - this.object.zoom = this.zoom0; - this.object.updateProjectionMatrix(); - this.dispatchEvent(_changeEvent); - this.update(); - this.state = _STATE.NONE; - } - update(deltaTime = null) { - const position = this.object.position; - _v.copy(position).sub(this.target); - _v.applyQuaternion(this._quat); - this._spherical.setFromVector3(_v); - if (this.autoRotate && this.state === _STATE.NONE) { - this._rotateLeft(this._getAutoRotationAngle(deltaTime)); - } - if (this.enableDamping) { - this._spherical.theta += this._sphericalDelta.theta * this.dampingFactor; - this._spherical.phi += this._sphericalDelta.phi * this.dampingFactor; - } else { - this._spherical.theta += this._sphericalDelta.theta; - this._spherical.phi += this._sphericalDelta.phi; - } - let min = this.minAzimuthAngle; - let max2 = this.maxAzimuthAngle; - if (isFinite(min) && isFinite(max2)) { - if (min < -Math.PI) min += _twoPI; - else if (min > Math.PI) min -= _twoPI; - if (max2 < -Math.PI) max2 += _twoPI; - else if (max2 > Math.PI) max2 -= _twoPI; - if (min <= max2) { - this._spherical.theta = Math.max(min, Math.min(max2, this._spherical.theta)); - } else { - this._spherical.theta = this._spherical.theta > (min + max2) / 2 ? Math.max(min, this._spherical.theta) : Math.min(max2, this._spherical.theta); - } - } - this._spherical.phi = Math.max(this.minPolarAngle, Math.min(this.maxPolarAngle, this._spherical.phi)); - this._spherical.makeSafe(); - if (this.enableDamping === true) { - this.target.addScaledVector(this._panOffset, this.dampingFactor); - } else { - this.target.add(this._panOffset); - } - this.target.sub(this.cursor); - this.target.clampLength(this.minTargetRadius, this.maxTargetRadius); - this.target.add(this.cursor); - let zoomChanged = false; - if (this.zoomToCursor && this._performCursorZoom || this.object.isOrthographicCamera) { - this._spherical.radius = this._clampDistance(this._spherical.radius); - } else { - const prevRadius = this._spherical.radius; - this._spherical.radius = this._clampDistance(this._spherical.radius * this._scale); - zoomChanged = prevRadius != this._spherical.radius; - } - _v.setFromSpherical(this._spherical); - _v.applyQuaternion(this._quatInverse); - position.copy(this.target).add(_v); - this.object.lookAt(this.target); - if (this.enableDamping === true) { - this._sphericalDelta.theta *= 1 - this.dampingFactor; - this._sphericalDelta.phi *= 1 - this.dampingFactor; - this._panOffset.multiplyScalar(1 - this.dampingFactor); - } else { - this._sphericalDelta.set(0, 0, 0); - this._panOffset.set(0, 0, 0); - } - if (this.zoomToCursor && this._performCursorZoom) { - let newRadius = null; - if (this.object.isPerspectiveCamera) { - const prevRadius = _v.length(); - newRadius = this._clampDistance(prevRadius * this._scale); - const radiusDelta = prevRadius - newRadius; - this.object.position.addScaledVector(this._dollyDirection, radiusDelta); - this.object.updateMatrixWorld(); - zoomChanged = !!radiusDelta; - } else if (this.object.isOrthographicCamera) { - const mouseBefore = new Vector3(this._mouse.x, this._mouse.y, 0); - mouseBefore.unproject(this.object); - const prevZoom = this.object.zoom; - this.object.zoom = Math.max(this.minZoom, Math.min(this.maxZoom, this.object.zoom / this._scale)); - this.object.updateProjectionMatrix(); - zoomChanged = prevZoom !== this.object.zoom; - const mouseAfter = new Vector3(this._mouse.x, this._mouse.y, 0); - mouseAfter.unproject(this.object); - this.object.position.sub(mouseAfter).add(mouseBefore); - this.object.updateMatrixWorld(); - newRadius = _v.length(); - } else { - console.warn("WARNING: OrbitControls.js encountered an unknown camera type - zoom to cursor disabled."); - this.zoomToCursor = false; - } - if (newRadius !== null) { - if (this.screenSpacePanning) { - this.target.set(0, 0, -1).transformDirection(this.object.matrix).multiplyScalar(newRadius).add(this.object.position); - } else { - _ray.origin.copy(this.object.position); - _ray.direction.set(0, 0, -1).transformDirection(this.object.matrix); - if (Math.abs(this.object.up.dot(_ray.direction)) < _TILT_LIMIT) { - this.object.lookAt(this.target); - } else { - _plane.setFromNormalAndCoplanarPoint(this.object.up, this.target); - _ray.intersectPlane(_plane, this.target); - } - } - } - } else if (this.object.isOrthographicCamera) { - const prevZoom = this.object.zoom; - this.object.zoom = Math.max(this.minZoom, Math.min(this.maxZoom, this.object.zoom / this._scale)); - if (prevZoom !== this.object.zoom) { - this.object.updateProjectionMatrix(); - zoomChanged = true; - } - } - this._scale = 1; - this._performCursorZoom = false; - if (zoomChanged || this._lastPosition.distanceToSquared(this.object.position) > _EPS || 8 * (1 - this._lastQuaternion.dot(this.object.quaternion)) > _EPS || this._lastTargetPosition.distanceToSquared(this.target) > _EPS) { - this.dispatchEvent(_changeEvent); - this._lastPosition.copy(this.object.position); - this._lastQuaternion.copy(this.object.quaternion); - this._lastTargetPosition.copy(this.target); - return true; - } - return false; - } - _getAutoRotationAngle(deltaTime) { - if (deltaTime !== null) { - return _twoPI / 60 * this.autoRotateSpeed * deltaTime; - } else { - return _twoPI / 60 / 60 * this.autoRotateSpeed; - } - } - _getZoomScale(delta) { - const normalizedDelta = Math.abs(delta * 0.01); - return Math.pow(0.95, this.zoomSpeed * normalizedDelta); - } - _rotateLeft(angle) { - this._sphericalDelta.theta -= angle; - } - _rotateUp(angle) { - this._sphericalDelta.phi -= angle; - } - _panLeft(distance, objectMatrix) { - _v.setFromMatrixColumn(objectMatrix, 0); - _v.multiplyScalar(-distance); - this._panOffset.add(_v); - } - _panUp(distance, objectMatrix) { - if (this.screenSpacePanning === true) { - _v.setFromMatrixColumn(objectMatrix, 1); - } else { - _v.setFromMatrixColumn(objectMatrix, 0); - _v.crossVectors(this.object.up, _v); - } - _v.multiplyScalar(distance); - this._panOffset.add(_v); - } - // deltaX and deltaY are in pixels; right and down are positive - _pan(deltaX, deltaY) { - const element = this.domElement; - if (this.object.isPerspectiveCamera) { - const position = this.object.position; - _v.copy(position).sub(this.target); - let targetDistance = _v.length(); - targetDistance *= Math.tan(this.object.fov / 2 * Math.PI / 180); - this._panLeft(2 * deltaX * targetDistance / element.clientHeight, this.object.matrix); - this._panUp(2 * deltaY * targetDistance / element.clientHeight, this.object.matrix); - } else if (this.object.isOrthographicCamera) { - this._panLeft(deltaX * (this.object.right - this.object.left) / this.object.zoom / element.clientWidth, this.object.matrix); - this._panUp(deltaY * (this.object.top - this.object.bottom) / this.object.zoom / element.clientHeight, this.object.matrix); - } else { - console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."); - this.enablePan = false; - } - } - _dollyOut(dollyScale) { - if (this.object.isPerspectiveCamera || this.object.isOrthographicCamera) { - this._scale /= dollyScale; - } else { - console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."); - this.enableZoom = false; - } - } - _dollyIn(dollyScale) { - if (this.object.isPerspectiveCamera || this.object.isOrthographicCamera) { - this._scale *= dollyScale; - } else { - console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."); - this.enableZoom = false; - } - } - _updateZoomParameters(x, y) { - if (!this.zoomToCursor) { - return; - } - this._performCursorZoom = true; - const rect = this.domElement.getBoundingClientRect(); - const dx = x - rect.left; - const dy = y - rect.top; - const w = rect.width; - const h = rect.height; - this._mouse.x = dx / w * 2 - 1; - this._mouse.y = -(dy / h) * 2 + 1; - this._dollyDirection.set(this._mouse.x, this._mouse.y, 1).unproject(this.object).sub(this.object.position).normalize(); - } - _clampDistance(dist) { - return Math.max(this.minDistance, Math.min(this.maxDistance, dist)); - } - // - // event callbacks - update the object state - // - _handleMouseDownRotate(event) { - this._rotateStart.set(event.clientX, event.clientY); - } - _handleMouseDownDolly(event) { - this._updateZoomParameters(event.clientX, event.clientX); - this._dollyStart.set(event.clientX, event.clientY); - } - _handleMouseDownPan(event) { - this._panStart.set(event.clientX, event.clientY); - } - _handleMouseMoveRotate(event) { - this._rotateEnd.set(event.clientX, event.clientY); - this._rotateDelta.subVectors(this._rotateEnd, this._rotateStart).multiplyScalar(this.rotateSpeed); - const element = this.domElement; - this._rotateLeft(_twoPI * this._rotateDelta.x / element.clientHeight); - this._rotateUp(_twoPI * this._rotateDelta.y / element.clientHeight); - this._rotateStart.copy(this._rotateEnd); - this.update(); - } - _handleMouseMoveDolly(event) { - this._dollyEnd.set(event.clientX, event.clientY); - this._dollyDelta.subVectors(this._dollyEnd, this._dollyStart); - if (this._dollyDelta.y > 0) { - this._dollyOut(this._getZoomScale(this._dollyDelta.y)); - } else if (this._dollyDelta.y < 0) { - this._dollyIn(this._getZoomScale(this._dollyDelta.y)); - } - this._dollyStart.copy(this._dollyEnd); - this.update(); - } - _handleMouseMovePan(event) { - this._panEnd.set(event.clientX, event.clientY); - this._panDelta.subVectors(this._panEnd, this._panStart).multiplyScalar(this.panSpeed); - this._pan(this._panDelta.x, this._panDelta.y); - this._panStart.copy(this._panEnd); - this.update(); - } - _handleMouseWheel(event) { - this._updateZoomParameters(event.clientX, event.clientY); - if (event.deltaY < 0) { - this._dollyIn(this._getZoomScale(event.deltaY)); - } else if (event.deltaY > 0) { - this._dollyOut(this._getZoomScale(event.deltaY)); - } - this.update(); - } - _handleKeyDown(event) { - let needsUpdate = false; - switch (event.code) { - case this.keys.UP: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - this._rotateUp(_twoPI * this.rotateSpeed / this.domElement.clientHeight); - } else { - this._pan(0, this.keyPanSpeed); - } - needsUpdate = true; - break; - case this.keys.BOTTOM: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - this._rotateUp(-_twoPI * this.rotateSpeed / this.domElement.clientHeight); - } else { - this._pan(0, -this.keyPanSpeed); - } - needsUpdate = true; - break; - case this.keys.LEFT: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - this._rotateLeft(_twoPI * this.rotateSpeed / this.domElement.clientHeight); - } else { - this._pan(this.keyPanSpeed, 0); - } - needsUpdate = true; - break; - case this.keys.RIGHT: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - this._rotateLeft(-_twoPI * this.rotateSpeed / this.domElement.clientHeight); - } else { - this._pan(-this.keyPanSpeed, 0); - } - needsUpdate = true; - break; - } - if (needsUpdate) { - event.preventDefault(); - this.update(); - } - } - _handleTouchStartRotate(event) { - if (this._pointers.length === 1) { - this._rotateStart.set(event.pageX, event.pageY); - } else { - const position = this._getSecondPointerPosition(event); - const x = 0.5 * (event.pageX + position.x); - const y = 0.5 * (event.pageY + position.y); - this._rotateStart.set(x, y); - } - } - _handleTouchStartPan(event) { - if (this._pointers.length === 1) { - this._panStart.set(event.pageX, event.pageY); - } else { - const position = this._getSecondPointerPosition(event); - const x = 0.5 * (event.pageX + position.x); - const y = 0.5 * (event.pageY + position.y); - this._panStart.set(x, y); - } - } - _handleTouchStartDolly(event) { - const position = this._getSecondPointerPosition(event); - const dx = event.pageX - position.x; - const dy = event.pageY - position.y; - const distance = Math.sqrt(dx * dx + dy * dy); - this._dollyStart.set(0, distance); - } - _handleTouchStartDollyPan(event) { - if (this.enableZoom) this._handleTouchStartDolly(event); - if (this.enablePan) this._handleTouchStartPan(event); - } - _handleTouchStartDollyRotate(event) { - if (this.enableZoom) this._handleTouchStartDolly(event); - if (this.enableRotate) this._handleTouchStartRotate(event); - } - _handleTouchMoveRotate(event) { - if (this._pointers.length == 1) { - this._rotateEnd.set(event.pageX, event.pageY); - } else { - const position = this._getSecondPointerPosition(event); - const x = 0.5 * (event.pageX + position.x); - const y = 0.5 * (event.pageY + position.y); - this._rotateEnd.set(x, y); - } - this._rotateDelta.subVectors(this._rotateEnd, this._rotateStart).multiplyScalar(this.rotateSpeed); - const element = this.domElement; - this._rotateLeft(_twoPI * this._rotateDelta.x / element.clientHeight); - this._rotateUp(_twoPI * this._rotateDelta.y / element.clientHeight); - this._rotateStart.copy(this._rotateEnd); - } - _handleTouchMovePan(event) { - if (this._pointers.length === 1) { - this._panEnd.set(event.pageX, event.pageY); - } else { - const position = this._getSecondPointerPosition(event); - const x = 0.5 * (event.pageX + position.x); - const y = 0.5 * (event.pageY + position.y); - this._panEnd.set(x, y); - } - this._panDelta.subVectors(this._panEnd, this._panStart).multiplyScalar(this.panSpeed); - this._pan(this._panDelta.x, this._panDelta.y); - this._panStart.copy(this._panEnd); - } - _handleTouchMoveDolly(event) { - const position = this._getSecondPointerPosition(event); - const dx = event.pageX - position.x; - const dy = event.pageY - position.y; - const distance = Math.sqrt(dx * dx + dy * dy); - this._dollyEnd.set(0, distance); - this._dollyDelta.set(0, Math.pow(this._dollyEnd.y / this._dollyStart.y, this.zoomSpeed)); - this._dollyOut(this._dollyDelta.y); - this._dollyStart.copy(this._dollyEnd); - const centerX = (event.pageX + position.x) * 0.5; - const centerY = (event.pageY + position.y) * 0.5; - this._updateZoomParameters(centerX, centerY); - } - _handleTouchMoveDollyPan(event) { - if (this.enableZoom) this._handleTouchMoveDolly(event); - if (this.enablePan) this._handleTouchMovePan(event); - } - _handleTouchMoveDollyRotate(event) { - if (this.enableZoom) this._handleTouchMoveDolly(event); - if (this.enableRotate) this._handleTouchMoveRotate(event); - } - // pointers - _addPointer(event) { - this._pointers.push(event.pointerId); - } - _removePointer(event) { - delete this._pointerPositions[event.pointerId]; - for (let i = 0; i < this._pointers.length; i++) { - if (this._pointers[i] == event.pointerId) { - this._pointers.splice(i, 1); - return; - } - } - } - _isTrackingPointer(event) { - for (let i = 0; i < this._pointers.length; i++) { - if (this._pointers[i] == event.pointerId) return true; - } - return false; - } - _trackPointer(event) { - let position = this._pointerPositions[event.pointerId]; - if (position === void 0) { - position = new Vector2(); - this._pointerPositions[event.pointerId] = position; - } - position.set(event.pageX, event.pageY); - } - _getSecondPointerPosition(event) { - const pointerId = event.pointerId === this._pointers[0] ? this._pointers[1] : this._pointers[0]; - return this._pointerPositions[pointerId]; - } - // - _customWheelEvent(event) { - const mode = event.deltaMode; - const newEvent = { - clientX: event.clientX, - clientY: event.clientY, - deltaY: event.deltaY - }; - switch (mode) { - case 1: - newEvent.deltaY *= 16; - break; - case 2: - newEvent.deltaY *= 100; - break; - } - if (event.ctrlKey && !this._controlActive) { - newEvent.deltaY *= 10; - } - return newEvent; - } -} -function onPointerDown(event) { - if (this.enabled === false) return; - if (this._pointers.length === 0) { - this.domElement.setPointerCapture(event.pointerId); - this.domElement.addEventListener("pointermove", this._onPointerMove); - this.domElement.addEventListener("pointerup", this._onPointerUp); - } - if (this._isTrackingPointer(event)) return; - this._addPointer(event); - if (event.pointerType === "touch") { - this._onTouchStart(event); - } else { - this._onMouseDown(event); - } -} -__name(onPointerDown, "onPointerDown"); -function onPointerMove(event) { - if (this.enabled === false) return; - if (event.pointerType === "touch") { - this._onTouchMove(event); - } else { - this._onMouseMove(event); - } -} -__name(onPointerMove, "onPointerMove"); -function onPointerUp(event) { - this._removePointer(event); - switch (this._pointers.length) { - case 0: - this.domElement.releasePointerCapture(event.pointerId); - this.domElement.removeEventListener("pointermove", this._onPointerMove); - this.domElement.removeEventListener("pointerup", this._onPointerUp); - this.dispatchEvent(_endEvent); - this.state = _STATE.NONE; - break; - case 1: - const pointerId = this._pointers[0]; - const position = this._pointerPositions[pointerId]; - this._onTouchStart({ pointerId, pageX: position.x, pageY: position.y }); - break; - } -} -__name(onPointerUp, "onPointerUp"); -function onMouseDown(event) { - let mouseAction; - switch (event.button) { - case 0: - mouseAction = this.mouseButtons.LEFT; - break; - case 1: - mouseAction = this.mouseButtons.MIDDLE; - break; - case 2: - mouseAction = this.mouseButtons.RIGHT; - break; - default: - mouseAction = -1; - } - switch (mouseAction) { - case MOUSE.DOLLY: - if (this.enableZoom === false) return; - this._handleMouseDownDolly(event); - this.state = _STATE.DOLLY; - break; - case MOUSE.ROTATE: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - if (this.enablePan === false) return; - this._handleMouseDownPan(event); - this.state = _STATE.PAN; - } else { - if (this.enableRotate === false) return; - this._handleMouseDownRotate(event); - this.state = _STATE.ROTATE; - } - break; - case MOUSE.PAN: - if (event.ctrlKey || event.metaKey || event.shiftKey) { - if (this.enableRotate === false) return; - this._handleMouseDownRotate(event); - this.state = _STATE.ROTATE; - } else { - if (this.enablePan === false) return; - this._handleMouseDownPan(event); - this.state = _STATE.PAN; - } - break; - default: - this.state = _STATE.NONE; - } - if (this.state !== _STATE.NONE) { - this.dispatchEvent(_startEvent); - } -} -__name(onMouseDown, "onMouseDown"); -function onMouseMove(event) { - switch (this.state) { - case _STATE.ROTATE: - if (this.enableRotate === false) return; - this._handleMouseMoveRotate(event); - break; - case _STATE.DOLLY: - if (this.enableZoom === false) return; - this._handleMouseMoveDolly(event); - break; - case _STATE.PAN: - if (this.enablePan === false) return; - this._handleMouseMovePan(event); - break; - } -} -__name(onMouseMove, "onMouseMove"); -function onMouseWheel(event) { - if (this.enabled === false || this.enableZoom === false || this.state !== _STATE.NONE) return; - event.preventDefault(); - this.dispatchEvent(_startEvent); - this._handleMouseWheel(this._customWheelEvent(event)); - this.dispatchEvent(_endEvent); -} -__name(onMouseWheel, "onMouseWheel"); -function onKeyDown(event) { - if (this.enabled === false || this.enablePan === false) return; - this._handleKeyDown(event); -} -__name(onKeyDown, "onKeyDown"); -function onTouchStart(event) { - this._trackPointer(event); - switch (this._pointers.length) { - case 1: - switch (this.touches.ONE) { - case TOUCH.ROTATE: - if (this.enableRotate === false) return; - this._handleTouchStartRotate(event); - this.state = _STATE.TOUCH_ROTATE; - break; - case TOUCH.PAN: - if (this.enablePan === false) return; - this._handleTouchStartPan(event); - this.state = _STATE.TOUCH_PAN; - break; - default: - this.state = _STATE.NONE; - } - break; - case 2: - switch (this.touches.TWO) { - case TOUCH.DOLLY_PAN: - if (this.enableZoom === false && this.enablePan === false) return; - this._handleTouchStartDollyPan(event); - this.state = _STATE.TOUCH_DOLLY_PAN; - break; - case TOUCH.DOLLY_ROTATE: - if (this.enableZoom === false && this.enableRotate === false) return; - this._handleTouchStartDollyRotate(event); - this.state = _STATE.TOUCH_DOLLY_ROTATE; - break; - default: - this.state = _STATE.NONE; - } - break; - default: - this.state = _STATE.NONE; - } - if (this.state !== _STATE.NONE) { - this.dispatchEvent(_startEvent); - } -} -__name(onTouchStart, "onTouchStart"); -function onTouchMove(event) { - this._trackPointer(event); - switch (this.state) { - case _STATE.TOUCH_ROTATE: - if (this.enableRotate === false) return; - this._handleTouchMoveRotate(event); - this.update(); - break; - case _STATE.TOUCH_PAN: - if (this.enablePan === false) return; - this._handleTouchMovePan(event); - this.update(); - break; - case _STATE.TOUCH_DOLLY_PAN: - if (this.enableZoom === false && this.enablePan === false) return; - this._handleTouchMoveDollyPan(event); - this.update(); - break; - case _STATE.TOUCH_DOLLY_ROTATE: - if (this.enableZoom === false && this.enableRotate === false) return; - this._handleTouchMoveDollyRotate(event); - this.update(); - break; - default: - this.state = _STATE.NONE; - } -} -__name(onTouchMove, "onTouchMove"); -function onContextMenu(event) { - if (this.enabled === false) return; - event.preventDefault(); -} -__name(onContextMenu, "onContextMenu"); -function interceptControlDown(event) { - if (event.key === "Control") { - this._controlActive = true; - const document2 = this.domElement.getRootNode(); - document2.addEventListener("keyup", this._interceptControlUp, { passive: true, capture: true }); - } -} -__name(interceptControlDown, "interceptControlDown"); -function interceptControlUp(event) { - if (event.key === "Control") { - this._controlActive = false; - const document2 = this.domElement.getRootNode(); - document2.removeEventListener("keyup", this._interceptControlUp, { passive: true, capture: true }); - } -} -__name(interceptControlUp, "interceptControlUp"); -/*! -fflate - fast JavaScript compression/decompression - -Licensed under MIT. https://github.com/101arrowz/fflate/blob/master/LICENSE -version 0.8.2 -*/ -var ch2 = {}; -var wk = /* @__PURE__ */ __name(function(c, id2, msg, transfer, cb) { - var w = new Worker(ch2[id2] || (ch2[id2] = URL.createObjectURL(new Blob([ - c + ';addEventListener("error",function(e){e=e.error;postMessage({$e$:[e.message,e.code,e.stack]})})' - ], { type: "text/javascript" })))); - w.onmessage = function(e) { - var d = e.data, ed = d.$e$; - if (ed) { - var err2 = new Error(ed[0]); - err2["code"] = ed[1]; - err2.stack = ed[2]; - cb(err2, null); 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- e.code = ind; - if (Error.captureStackTrace) - Error.captureStackTrace(e, err); - if (!nt) - throw e; - return e; -}, "err"); -var inflt = /* @__PURE__ */ __name(function(dat, st, buf, dict) { - var sl = dat.length, dl = dict ? dict.length : 0; - if (!sl || st.f && !st.l) - return buf || new u8(0); - var noBuf = !buf; - var resize = noBuf || st.i != 2; - var noSt = st.i; - if (noBuf) - buf = new u8(sl * 3); - var cbuf = /* @__PURE__ */ __name(function(l2) { - var bl = buf.length; - if (l2 > bl) { - var nbuf = new u8(Math.max(bl * 2, l2)); - nbuf.set(buf); - buf = nbuf; - } - }, "cbuf"); - var final = st.f || 0, pos = st.p || 0, bt = st.b || 0, lm = st.l, dm = st.d, lbt = st.m, dbt = st.n; - var tbts = sl * 8; - do { - if (!lm) { - final = bits(dat, pos, 1); - var type = bits(dat, pos + 1, 3); - pos += 3; - if (!type) { - var s = shft(pos) + 4, l = dat[s - 4] | dat[s - 3] << 8, t2 = s + l; - if (t2 > sl) { - if (noSt) - err(0); - break; - } - if (resize) - cbuf(bt + l); - buf.set(dat.subarray(s, t2), bt); - st.b = bt += l, st.p = pos = t2 * 8, st.f = final; - continue; - } else if (type == 1) - lm = flrm, dm = fdrm, lbt = 9, dbt = 5; - else if (type == 2) { - var hLit = bits(dat, pos, 31) + 257, hcLen = bits(dat, pos + 10, 15) + 4; - var tl = hLit + bits(dat, pos + 5, 31) + 1; - pos += 14; - var ldt = new u8(tl); - var clt = new u8(19); - for (var i = 0; i < hcLen; ++i) { - clt[clim[i]] = bits(dat, pos + i * 3, 7); - } - pos += hcLen * 3; - var clb = max(clt), clbmsk = (1 << clb) - 1; - var clm = hMap(clt, clb, 1); - for (var i = 0; i < tl; ) { - var r = clm[bits(dat, pos, clbmsk)]; - pos += r & 15; - var s = r >> 4; - if (s < 16) { - ldt[i++] = s; - } else { - var c = 0, n = 0; - if (s == 16) - n = 3 + bits(dat, pos, 3), pos += 2, c = ldt[i - 1]; - else if (s == 17) - n = 3 + bits(dat, pos, 7), pos += 3; - else if (s == 18) - n = 11 + bits(dat, pos, 127), pos += 7; - while (n--) - ldt[i++] = c; - } - } - var lt = ldt.subarray(0, hLit), dt = ldt.subarray(hLit); - lbt = max(lt); - dbt = max(dt); - lm = hMap(lt, lbt, 1); - dm = hMap(dt, dbt, 1); - } else - err(1); - if (pos > tbts) { - if (noSt) - err(0); - break; - } - } - if (resize) - cbuf(bt + 131072); - var lms = (1 << lbt) - 1, dms = (1 << dbt) - 1; - var lpos = pos; - for (; ; lpos = pos) { - var c = lm[bits16(dat, pos) & lms], sym = c >> 4; - pos += c & 15; - if (pos > tbts) { - if (noSt) - err(0); - break; - } - if (!c) - err(2); - if (sym < 256) - buf[bt++] = sym; - else if (sym == 256) { - lpos = pos, lm = null; - break; - } else { - var add = sym - 254; - if (sym > 264) { - var i = sym - 257, b = fleb[i]; - add = bits(dat, pos, (1 << b) - 1) + fl[i]; - pos += b; - } - var d = dm[bits16(dat, pos) & dms], dsym = d >> 4; - if (!d) - err(3); - pos += d & 15; - var dt = fd[dsym]; - if (dsym > 3) { - var b = fdeb[dsym]; - dt += bits16(dat, pos) & (1 << b) - 1, pos += b; - } - if (pos > tbts) { - if (noSt) - err(0); - break; - } - if (resize) - cbuf(bt + 131072); - var end = bt + add; - if (bt < dt) { - var shift = dl - dt, dend = Math.min(dt, end); - if (shift + bt < 0) - err(3); - for (; bt < dend; ++bt) - buf[bt] = dict[shift + bt]; - } - for (; bt < end; ++bt) - buf[bt] = buf[bt - dt]; - } - } - st.l = lm, st.p = lpos, st.b = bt, st.f = final; - if (lm) - final = 1, st.m = lbt, st.d = dm, st.n = dbt; - } while (!final); - return bt != buf.length && noBuf ? slc(buf, 0, bt) : buf.subarray(0, bt); -}, "inflt"); -var wbits = /* @__PURE__ */ __name(function(d, p, v) { - v <<= p & 7; - var o = p / 8 | 0; - d[o] |= v; - d[o + 1] |= v >> 8; -}, "wbits"); -var wbits16 = /* @__PURE__ */ __name(function(d, p, v) { - v <<= p & 7; - var o = p / 8 | 0; - d[o] |= v; - d[o + 1] |= v >> 8; - d[o + 2] |= v >> 16; -}, "wbits16"); -var hTree = /* @__PURE__ */ __name(function(d, mb) { - var t2 = []; - for (var i = 0; i < d.length; ++i) { - if (d[i]) - t2.push({ s: i, f: d[i] }); - } - var s = t2.length; - var t22 = t2.slice(); - if (!s) - return { t: et, l: 0 }; - if (s == 1) { - var v = new u8(t2[0].s + 1); - v[t2[0].s] = 1; - return { t: v, l: 1 }; - } - t2.sort(function(a, b) { - return a.f - b.f; - }); - t2.push({ s: -1, f: 25001 }); - var l = t2[0], r = t2[1], i0 = 0, i1 = 1, i2 = 2; - t2[0] = { s: -1, f: l.f + r.f, l, r }; - while (i1 != s - 1) { - l = t2[t2[i0].f < t2[i2].f ? i0++ : i2++]; - r = t2[i0 != i1 && t2[i0].f < t2[i2].f ? i0++ : i2++]; - t2[i1++] = { s: -1, f: l.f + r.f, l, r }; - } - var maxSym = t22[0].s; - for (var i = 1; i < s; ++i) { - if (t22[i].s > maxSym) - maxSym = t22[i].s; - } - var tr = new u16(maxSym + 1); - var mbt = ln(t2[i1 - 1], tr, 0); - if (mbt > mb) { - var i = 0, dt = 0; - var lft = mbt - mb, cst = 1 << lft; - t22.sort(function(a, b) { - return tr[b.s] - tr[a.s] || a.f - b.f; - }); - for (; i < s; ++i) { - var i2_1 = t22[i].s; - if (tr[i2_1] > mb) { - dt += cst - (1 << mbt - tr[i2_1]); - tr[i2_1] = mb; - } else - break; - } - dt >>= lft; - while (dt > 0) { - var i2_2 = t22[i].s; - if (tr[i2_2] < mb) - dt -= 1 << mb - tr[i2_2]++ - 1; - else - ++i; - } - for (; i >= 0 && dt; --i) { - var i2_3 = t22[i].s; - if (tr[i2_3] == mb) { - --tr[i2_3]; - ++dt; - } - } - mbt = mb; - } - return { t: new u8(tr), l: mbt }; -}, "hTree"); -var ln = /* @__PURE__ */ __name(function(n, l, d) { - return n.s == -1 ? Math.max(ln(n.l, l, d + 1), ln(n.r, l, d + 1)) : l[n.s] = d; -}, "ln"); -var lc = /* @__PURE__ */ __name(function(c) { - var s = c.length; - while (s && !c[--s]) - ; - var cl = new u16(++s); - var cli = 0, cln = c[0], cls = 1; - var w = /* @__PURE__ */ __name(function(v) { - cl[cli++] = v; - }, "w"); - for (var i = 1; i <= s; ++i) { - if (c[i] == cln && i != s) - ++cls; - else { - if (!cln && cls > 2) { - for (; cls > 138; cls -= 138) - w(32754); - if (cls > 2) { - w(cls > 10 ? cls - 11 << 5 | 28690 : cls - 3 << 5 | 12305); - cls = 0; - } - } else if (cls > 3) { - w(cln), --cls; - for (; cls > 6; cls -= 6) - w(8304); - if (cls > 2) - w(cls - 3 << 5 | 8208), cls = 0; - } - while (cls--) - w(cln); - cls = 1; - cln = c[i]; - } - } - return { c: cl.subarray(0, cli), n: s }; -}, "lc"); -var clen = /* @__PURE__ */ __name(function(cf, cl) { - var l = 0; - for (var i = 0; i < cl.length; ++i) - l += cf[i] * cl[i]; - return l; -}, "clen"); -var wfblk = /* @__PURE__ */ __name(function(out, pos, dat) { - var s = dat.length; - var o = shft(pos + 2); - out[o] = s & 255; - out[o + 1] = s >> 8; - out[o + 2] = out[o] ^ 255; - out[o + 3] = out[o + 1] ^ 255; - for (var i = 0; i < s; ++i) - out[o + i + 4] = dat[i]; - return (o + 4 + s) * 8; -}, "wfblk"); -var wblk = /* @__PURE__ */ __name(function(dat, out, final, syms, lf, df, eb, li, bs, bl, p) { - wbits(out, p++, final); - ++lf[256]; - var _a2 = hTree(lf, 15), dlt = _a2.t, mlb = _a2.l; - var _b2 = hTree(df, 15), ddt = _b2.t, mdb = _b2.l; - var _c = lc(dlt), lclt = _c.c, nlc = _c.n; - var _d = lc(ddt), lcdt = _d.c, ndc = _d.n; - var lcfreq = new u16(19); - for (var i = 0; i < lclt.length; ++i) - ++lcfreq[lclt[i] & 31]; - for (var i = 0; i < lcdt.length; ++i) - ++lcfreq[lcdt[i] & 31]; - var _e = hTree(lcfreq, 7), lct = _e.t, mlcb = _e.l; - var nlcc = 19; - for (; nlcc > 4 && !lct[clim[nlcc - 1]]; --nlcc) - ; - var flen = bl + 5 << 3; - var ftlen = clen(lf, flt) + clen(df, fdt) + eb; - var dtlen = clen(lf, dlt) + clen(df, ddt) + eb + 14 + 3 * nlcc + clen(lcfreq, lct) + 2 * lcfreq[16] + 3 * lcfreq[17] + 7 * lcfreq[18]; - if (bs >= 0 && flen <= ftlen && flen <= dtlen) - return wfblk(out, p, dat.subarray(bs, bs + bl)); - var lm, ll, dm, dl; - wbits(out, p, 1 + (dtlen < ftlen)), p += 2; - if (dtlen < ftlen) { - lm = hMap(dlt, mlb, 0), ll = dlt, dm = hMap(ddt, mdb, 0), dl = ddt; - var llm = hMap(lct, mlcb, 0); - wbits(out, p, nlc - 257); - wbits(out, p + 5, ndc - 1); - wbits(out, p + 10, nlcc - 4); - p += 14; - for (var i = 0; i < nlcc; ++i) - wbits(out, p + 3 * i, lct[clim[i]]); - p += 3 * nlcc; - var lcts = [lclt, lcdt]; - for (var it = 0; it < 2; ++it) { - var clct = lcts[it]; - for (var i = 0; i < clct.length; ++i) { - var len = clct[i] & 31; - wbits(out, p, llm[len]), p += lct[len]; - if (len > 15) - wbits(out, p, clct[i] >> 5 & 127), p += clct[i] >> 12; - } - } - } else { - lm = flm, ll = flt, dm = fdm, dl = fdt; - } - for (var i = 0; i < li; ++i) { - var sym = syms[i]; - if (sym > 255) { - var len = sym >> 18 & 31; - wbits16(out, p, lm[len + 257]), p += ll[len + 257]; - if (len > 7) - wbits(out, p, sym >> 23 & 31), p += fleb[len]; - var dst = sym & 31; - wbits16(out, p, dm[dst]), p += dl[dst]; - if (dst > 3) - wbits16(out, p, sym >> 5 & 8191), p += fdeb[dst]; - } else { - wbits16(out, p, lm[sym]), p += ll[sym]; - } - } - wbits16(out, p, lm[256]); - return p + ll[256]; -}, "wblk"); -var deo = /* @__PURE__ */ new i32([65540, 131080, 131088, 131104, 262176, 1048704, 1048832, 2114560, 2117632]); -var et = /* @__PURE__ */ new u8(0); -var dflt = /* @__PURE__ */ __name(function(dat, lvl, plvl, pre, post, st) { - var s = st.z || dat.length; - var o = new u8(pre + s + 5 * (1 + Math.ceil(s / 7e3)) + post); - var w = o.subarray(pre, o.length - post); - var lst = st.l; - var pos = (st.r || 0) & 7; - if (lvl) { - if (pos) - w[0] = st.r >> 3; - var opt = deo[lvl - 1]; - var n = opt >> 13, c = opt & 8191; - var msk_1 = (1 << plvl) - 1; - var prev = st.p || new u16(32768), head = st.h || new u16(msk_1 + 1); - var bs1_1 = Math.ceil(plvl / 3), bs2_1 = 2 * bs1_1; - var hsh = /* @__PURE__ */ __name(function(i2) { - return (dat[i2] ^ dat[i2 + 1] << bs1_1 ^ dat[i2 + 2] << bs2_1) & msk_1; - }, "hsh"); - var syms = new i32(25e3); - var lf = new u16(288), df = new u16(32); - var lc_1 = 0, eb = 0, i = st.i || 0, li = 0, wi = st.w || 0, bs = 0; - for (; i + 2 < s; ++i) { - var hv = hsh(i); - var imod = i & 32767, pimod = head[hv]; - prev[imod] = pimod; - head[hv] = imod; - if (wi <= i) { - var rem = s - i; - if ((lc_1 > 7e3 || li > 24576) && (rem > 423 || !lst)) { - pos = wblk(dat, w, 0, syms, lf, df, eb, li, bs, i - bs, pos); - li = lc_1 = eb = 0, bs = i; - for (var j = 0; j < 286; ++j) - lf[j] = 0; - for (var j = 0; j < 30; ++j) - df[j] = 0; - } - var l = 2, d = 0, ch_1 = c, dif = imod - pimod & 32767; - if (rem > 2 && hv == hsh(i - dif)) { - var maxn = Math.min(n, rem) - 1; - var maxd = Math.min(32767, i); - var ml = Math.min(258, rem); - while (dif <= maxd && --ch_1 && imod != pimod) { - if (dat[i + l] == dat[i + l - dif]) { - var nl = 0; - for (; nl < ml && dat[i + nl] == dat[i + nl - dif]; ++nl) - ; - if (nl > l) { - l = nl, d = dif; - if (nl > maxn) - break; - var mmd = Math.min(dif, nl - 2); - var md = 0; - for (var j = 0; j < mmd; ++j) { - var ti = i - dif + j & 32767; - var pti = prev[ti]; - var cd = ti - pti & 32767; - if (cd > md) - md = cd, pimod = ti; - } - } - } - imod = pimod, pimod = prev[imod]; - dif += imod - pimod & 32767; - } - } - if (d) { - syms[li++] = 268435456 | revfl[l] << 18 | revfd[d]; - var lin = revfl[l] & 31, din = revfd[d] & 31; - eb += fleb[lin] + fdeb[din]; - ++lf[257 + lin]; - ++df[din]; - wi = i + l; - ++lc_1; - } else { - syms[li++] = dat[i]; - ++lf[dat[i]]; - } - } - } - for (i = Math.max(i, wi); i < s; ++i) { - syms[li++] = dat[i]; - ++lf[dat[i]]; - } - pos = wblk(dat, w, lst, syms, lf, df, eb, li, bs, i - bs, pos); - if (!lst) { - st.r = pos & 7 | w[pos / 8 | 0] << 3; - pos -= 7; - st.h = head, st.p = prev, st.i = i, st.w = wi; - } - } else { - for (var i = st.w || 0; i < s + lst; i += 65535) { - var e = i + 65535; - if (e >= s) { - w[pos / 8 | 0] = lst; - e = s; - } - pos = wfblk(w, pos + 1, dat.subarray(i, e)); - } - st.i = s; - } - return slc(o, 0, pre + shft(pos) + post); -}, "dflt"); -var crct = /* @__PURE__ */ function() { - var t2 = new Int32Array(256); - for (var i = 0; i < 256; ++i) { - var c = i, k = 9; - while (--k) - c = (c & 1 && -306674912) ^ c >>> 1; - t2[i] = c; - } - return t2; -}(); -var crc = /* @__PURE__ */ __name(function() { - var c = -1; - return { - p: /* @__PURE__ */ __name(function(d) { - var cr = c; - for (var i = 0; i < d.length; ++i) - cr = crct[cr & 255 ^ d[i]] ^ cr >>> 8; - c = cr; - }, "p"), - d: /* @__PURE__ */ __name(function() { - return ~c; - }, "d") - }; -}, "crc"); -var adler = /* @__PURE__ */ __name(function() { - var a = 1, b = 0; - return { - p: /* @__PURE__ */ __name(function(d) { - var n = a, m = b; - var l = d.length | 0; - for (var i = 0; i != l; ) { - var e = Math.min(i + 2655, l); - for (; i < e; ++i) - m += n += d[i]; - n = (n & 65535) + 15 * (n >> 16), m = (m & 65535) + 15 * (m >> 16); - } - a = n, b = m; - }, "p"), - d: /* @__PURE__ */ __name(function() { - a %= 65521, b %= 65521; - return (a & 255) << 24 | (a & 65280) << 8 | (b & 255) << 8 | b >> 8; - }, "d") - }; -}, "adler"); -; -var dopt = /* @__PURE__ */ __name(function(dat, opt, pre, post, st) { - if (!st) { - st = { l: 1 }; - if (opt.dictionary) { - var dict = opt.dictionary.subarray(-32768); - var newDat = new u8(dict.length + dat.length); - newDat.set(dict); - newDat.set(dat, dict.length); - dat = newDat; - st.w = dict.length; - } - } - return dflt(dat, opt.level == null ? 6 : opt.level, opt.mem == null ? st.l ? Math.ceil(Math.max(8, Math.min(13, Math.log(dat.length))) * 1.5) : 20 : 12 + opt.mem, pre, post, st); -}, "dopt"); -var mrg = /* @__PURE__ */ __name(function(a, b) { - var o = {}; - for (var k in a) - o[k] = a[k]; - for (var k in b) - o[k] = b[k]; - return o; -}, "mrg"); -var wcln = /* @__PURE__ */ __name(function(fn, fnStr, td2) { - var dt = fn(); - var st = fn.toString(); - var ks = st.slice(st.indexOf("[") + 1, st.lastIndexOf("]")).replace(/\s+/g, "").split(","); - for (var i = 0; i < dt.length; ++i) { - var v = dt[i], k = ks[i]; - if (typeof v == "function") { - fnStr += ";" + k + "="; - var st_1 = v.toString(); - if (v.prototype) { - if (st_1.indexOf("[native code]") != -1) { - var spInd = st_1.indexOf(" ", 8) + 1; - fnStr += st_1.slice(spInd, st_1.indexOf("(", spInd)); - } else { - fnStr += st_1; - for (var t2 in v.prototype) - fnStr += ";" + k + ".prototype." + t2 + "=" + v.prototype[t2].toString(); - } - } else - fnStr += st_1; - } else - td2[k] = v; - } - return fnStr; -}, "wcln"); -var ch = []; -var cbfs = /* @__PURE__ */ __name(function(v) { - var tl = []; - for (var k in v) { - if (v[k].buffer) { - tl.push((v[k] = new v[k].constructor(v[k])).buffer); - } - } - return tl; -}, "cbfs"); -var wrkr = /* @__PURE__ */ __name(function(fns, init, id2, cb) { - if (!ch[id2]) { - var fnStr = "", td_1 = {}, m = fns.length - 1; - for (var i = 0; i < m; ++i) - fnStr = wcln(fns[i], fnStr, td_1); - ch[id2] = { c: wcln(fns[m], fnStr, td_1), e: td_1 }; - } - var td2 = mrg({}, ch[id2].e); - return wk(ch[id2].c + ";onmessage=function(e){for(var k in e.data)self[k]=e.data[k];onmessage=" + init.toString() + "}", id2, td2, cbfs(td2), cb); -}, "wrkr"); -var bInflt = /* @__PURE__ */ __name(function() { - return [u8, u16, i32, fleb, fdeb, clim, fl, fd, flrm, fdrm, rev, ec, hMap, max, bits, bits16, shft, slc, err, inflt, inflateSync, pbf, gopt]; -}, "bInflt"); -var bDflt = /* @__PURE__ */ __name(function() { - return [u8, u16, i32, fleb, fdeb, clim, revfl, revfd, flm, flt, fdm, fdt, rev, deo, et, hMap, wbits, wbits16, hTree, ln, lc, clen, wfblk, wblk, shft, slc, dflt, dopt, deflateSync, pbf]; -}, "bDflt"); -var gze = /* @__PURE__ */ __name(function() { - return [gzh, gzhl, wbytes, crc, crct]; -}, "gze"); -var guze = /* @__PURE__ */ __name(function() { - return [gzs, gzl]; -}, "guze"); -var zle = /* @__PURE__ */ __name(function() { - return [zlh, wbytes, adler]; -}, "zle"); -var zule = /* @__PURE__ */ __name(function() { - return [zls]; -}, "zule"); -var pbf = /* @__PURE__ */ __name(function(msg) { - return postMessage(msg, [msg.buffer]); -}, "pbf"); -var gopt = /* @__PURE__ */ __name(function(o) { - return o && { - out: o.size && new u8(o.size), - dictionary: o.dictionary - }; -}, "gopt"); -var cbify = /* @__PURE__ */ __name(function(dat, opts, fns, init, id2, cb) { - var w = wrkr(fns, init, id2, function(err2, dat2) { - w.terminate(); - cb(err2, dat2); - }); - w.postMessage([dat, opts], opts.consume ? [dat.buffer] : []); - return function() { - w.terminate(); - }; -}, "cbify"); -var astrm = /* @__PURE__ */ __name(function(strm) { - strm.ondata = function(dat, final) { - return postMessage([dat, final], [dat.buffer]); - }; - return function(ev) { - if (ev.data.length) { - strm.push(ev.data[0], ev.data[1]); - postMessage([ev.data[0].length]); - } else - strm.flush(); - }; -}, "astrm"); -var astrmify = /* @__PURE__ */ __name(function(fns, strm, opts, init, id2, flush, ext2) { - var t2; - var w = wrkr(fns, init, id2, function(err2, dat) { - if (err2) - w.terminate(), strm.ondata.call(strm, err2); - else if (!Array.isArray(dat)) - ext2(dat); - else if (dat.length == 1) { - strm.queuedSize -= dat[0]; - if (strm.ondrain) - strm.ondrain(dat[0]); - } else { - if (dat[1]) - w.terminate(); - strm.ondata.call(strm, err2, dat[0], dat[1]); - } - }); - w.postMessage(opts); - strm.queuedSize = 0; - strm.push = function(d, f) { - if (!strm.ondata) - err(5); - if (t2) - strm.ondata(err(4, 0, 1), null, !!f); - strm.queuedSize += d.length; - w.postMessage([d, t2 = f], [d.buffer]); - }; - strm.terminate = function() { - w.terminate(); - }; - if (flush) { - strm.flush = function() { - w.postMessage([]); - }; - } -}, "astrmify"); -var b2 = /* @__PURE__ */ __name(function(d, b) { - return d[b] | d[b + 1] << 8; -}, "b2"); -var b4 = /* @__PURE__ */ __name(function(d, b) { - return (d[b] | d[b + 1] << 8 | d[b + 2] << 16 | d[b + 3] << 24) >>> 0; -}, "b4"); -var b8 = /* @__PURE__ */ __name(function(d, b) { - return b4(d, b) + b4(d, b + 4) * 4294967296; -}, "b8"); -var wbytes = /* @__PURE__ */ __name(function(d, b, v) { - for (; v; ++b) - d[b] = v, v >>>= 8; -}, "wbytes"); -var gzh = /* @__PURE__ */ __name(function(c, o) { - var fn = o.filename; - c[0] = 31, c[1] = 139, c[2] = 8, c[8] = o.level < 2 ? 4 : o.level == 9 ? 2 : 0, c[9] = 3; - if (o.mtime != 0) - wbytes(c, 4, Math.floor(new Date(o.mtime || Date.now()) / 1e3)); - if (fn) { - c[3] = 8; - for (var i = 0; i <= fn.length; ++i) - c[i + 10] = fn.charCodeAt(i); - } -}, "gzh"); -var gzs = /* @__PURE__ */ __name(function(d) { - if (d[0] != 31 || d[1] != 139 || d[2] != 8) - err(6, "invalid gzip data"); - var flg = d[3]; - var st = 10; - if (flg & 4) - st += (d[10] | d[11] << 8) + 2; - for (var zs = (flg >> 3 & 1) + (flg >> 4 & 1); zs > 0; zs -= !d[st++]) - ; - return st + (flg & 2); -}, "gzs"); -var gzl = /* @__PURE__ */ __name(function(d) { - var l = d.length; - return (d[l - 4] | d[l - 3] << 8 | d[l - 2] << 16 | d[l - 1] << 24) >>> 0; -}, "gzl"); -var gzhl = /* @__PURE__ */ __name(function(o) { - return 10 + (o.filename ? o.filename.length + 1 : 0); -}, "gzhl"); -var zlh = /* @__PURE__ */ __name(function(c, o) { - var lv = o.level, fl2 = lv == 0 ? 0 : lv < 6 ? 1 : lv == 9 ? 3 : 2; - c[0] = 120, c[1] = fl2 << 6 | (o.dictionary && 32); - c[1] |= 31 - (c[0] << 8 | c[1]) % 31; - if (o.dictionary) { - var h = adler(); - h.p(o.dictionary); - wbytes(c, 2, h.d()); - } -}, "zlh"); -var zls = /* @__PURE__ */ __name(function(d, dict) { - if ((d[0] & 15) != 8 || d[0] >> 4 > 7 || (d[0] << 8 | d[1]) % 31) - err(6, "invalid zlib data"); - if ((d[1] >> 5 & 1) == +!dict) - err(6, "invalid zlib data: " + (d[1] & 32 ? "need" : "unexpected") + " dictionary"); - return (d[1] >> 3 & 4) + 2; -}, "zls"); -function StrmOpt(opts, cb) { - if (typeof opts == "function") - cb = opts, opts = {}; - this.ondata = cb; - return opts; -} -__name(StrmOpt, "StrmOpt"); -var Deflate = /* @__PURE__ */ function() { - function Deflate2(opts, cb) { - if (typeof opts == "function") - cb = opts, opts = {}; - this.ondata = cb; - this.o = opts || {}; - this.s = { l: 0, i: 32768, w: 32768, z: 32768 }; - this.b = new u8(98304); - if (this.o.dictionary) { - var dict = this.o.dictionary.subarray(-32768); - this.b.set(dict, 32768 - dict.length); - this.s.i = 32768 - dict.length; - } - } - __name(Deflate2, "Deflate"); - Deflate2.prototype.p = function(c, f) { - this.ondata(dopt(c, this.o, 0, 0, this.s), f); - }; - Deflate2.prototype.push = function(chunk, final) { - if (!this.ondata) - err(5); - if (this.s.l) - err(4); - var endLen = chunk.length + this.s.z; - if (endLen > this.b.length) { - if (endLen > 2 * this.b.length - 32768) { - var newBuf = new u8(endLen & -32768); - newBuf.set(this.b.subarray(0, this.s.z)); - this.b = newBuf; - } - var split = this.b.length - this.s.z; - this.b.set(chunk.subarray(0, split), this.s.z); - this.s.z = this.b.length; - this.p(this.b, false); - this.b.set(this.b.subarray(-32768)); - this.b.set(chunk.subarray(split), 32768); - this.s.z = chunk.length - split + 32768; - this.s.i = 32766, this.s.w = 32768; - } else { - this.b.set(chunk, this.s.z); - this.s.z += chunk.length; - } - this.s.l = final & 1; - if (this.s.z > this.s.w + 8191 || final) { - this.p(this.b, final || false); - this.s.w = this.s.i, this.s.i -= 2; - } - }; - Deflate2.prototype.flush = function() { - if (!this.ondata) - err(5); - if (this.s.l) - err(4); - this.p(this.b, false); - this.s.w = this.s.i, this.s.i -= 2; - }; - return Deflate2; -}(); -var AsyncDeflate = /* @__PURE__ */ function() { - function AsyncDeflate2(opts, cb) { - astrmify([ - bDflt, - function() { - return [astrm, Deflate]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Deflate(ev.data); - onmessage = astrm(strm); - }, 6, 1); - } - __name(AsyncDeflate2, "AsyncDeflate"); - return AsyncDeflate2; -}(); -function deflate(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bDflt - ], function(ev) { - return pbf(deflateSync(ev.data[0], ev.data[1])); - }, 0, cb); -} -__name(deflate, "deflate"); -function deflateSync(data, opts) { - return dopt(data, opts || {}, 0, 0); -} -__name(deflateSync, "deflateSync"); -var Inflate = /* @__PURE__ */ function() { - function Inflate2(opts, cb) { - if (typeof opts == "function") - cb = opts, opts = {}; - this.ondata = cb; - var dict = opts && opts.dictionary && opts.dictionary.subarray(-32768); - this.s = { i: 0, b: dict ? dict.length : 0 }; - this.o = new u8(32768); - this.p = new u8(0); - if (dict) - this.o.set(dict); - } - __name(Inflate2, "Inflate"); - Inflate2.prototype.e = function(c) { - if (!this.ondata) - err(5); - if (this.d) - err(4); - if (!this.p.length) - this.p = c; - else if (c.length) { - var n = new u8(this.p.length + c.length); - n.set(this.p), n.set(c, this.p.length), this.p = n; - } - }; - Inflate2.prototype.c = function(final) { - this.s.i = +(this.d = final || false); - var bts = this.s.b; - var dt = inflt(this.p, this.s, this.o); - this.ondata(slc(dt, bts, this.s.b), this.d); - this.o = slc(dt, this.s.b - 32768), this.s.b = this.o.length; - this.p = slc(this.p, this.s.p / 8 | 0), this.s.p &= 7; - }; - Inflate2.prototype.push = function(chunk, final) { - this.e(chunk), this.c(final); - }; - return Inflate2; -}(); -var AsyncInflate = /* @__PURE__ */ function() { - function AsyncInflate2(opts, cb) { - astrmify([ - bInflt, - function() { - return [astrm, Inflate]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Inflate(ev.data); - onmessage = astrm(strm); - }, 7, 0); - } - __name(AsyncInflate2, "AsyncInflate"); - return AsyncInflate2; -}(); -function inflate(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bInflt - ], function(ev) { - return pbf(inflateSync(ev.data[0], gopt(ev.data[1]))); - }, 1, cb); -} -__name(inflate, "inflate"); -function inflateSync(data, opts) { - return inflt(data, { i: 2 }, opts && opts.out, opts && opts.dictionary); -} -__name(inflateSync, "inflateSync"); -var Gzip = /* @__PURE__ */ function() { - function Gzip2(opts, cb) { - this.c = crc(); - this.l = 0; - this.v = 1; - Deflate.call(this, opts, cb); - } - __name(Gzip2, "Gzip"); - Gzip2.prototype.push = function(chunk, final) { - this.c.p(chunk); - this.l += chunk.length; - Deflate.prototype.push.call(this, chunk, final); - }; - Gzip2.prototype.p = function(c, f) { - var raw = dopt(c, this.o, this.v && gzhl(this.o), f && 8, this.s); - if (this.v) - gzh(raw, this.o), this.v = 0; - if (f) - wbytes(raw, raw.length - 8, this.c.d()), wbytes(raw, raw.length - 4, this.l); - this.ondata(raw, f); - }; - Gzip2.prototype.flush = function() { - Deflate.prototype.flush.call(this); - }; - return Gzip2; -}(); -var AsyncGzip = /* @__PURE__ */ function() { - function AsyncGzip2(opts, cb) { - astrmify([ - bDflt, - gze, - function() { - return [astrm, Deflate, Gzip]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Gzip(ev.data); - onmessage = astrm(strm); - }, 8, 1); - } - __name(AsyncGzip2, "AsyncGzip"); - return AsyncGzip2; -}(); -function gzip(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bDflt, - gze, - function() { - return [gzipSync]; - } - ], function(ev) { - return pbf(gzipSync(ev.data[0], ev.data[1])); - }, 2, cb); -} -__name(gzip, "gzip"); -function gzipSync(data, opts) { - if (!opts) - opts = {}; - var c = crc(), l = data.length; - c.p(data); - var d = dopt(data, opts, gzhl(opts), 8), s = d.length; - return gzh(d, opts), wbytes(d, s - 8, c.d()), wbytes(d, s - 4, l), d; -} -__name(gzipSync, "gzipSync"); -var Gunzip = /* @__PURE__ */ function() { - function Gunzip2(opts, cb) { - this.v = 1; - this.r = 0; - Inflate.call(this, opts, cb); - } - __name(Gunzip2, "Gunzip"); - Gunzip2.prototype.push = function(chunk, final) { - Inflate.prototype.e.call(this, chunk); - this.r += chunk.length; - if (this.v) { - var p = this.p.subarray(this.v - 1); - var s = p.length > 3 ? gzs(p) : 4; - if (s > p.length) { - if (!final) - return; - } else if (this.v > 1 && this.onmember) { - this.onmember(this.r - p.length); - } - this.p = p.subarray(s), this.v = 0; - } - Inflate.prototype.c.call(this, final); - if (this.s.f && !this.s.l && !final) { - this.v = shft(this.s.p) + 9; - this.s = { i: 0 }; - this.o = new u8(0); - this.push(new u8(0), final); - } - }; - return Gunzip2; -}(); -var AsyncGunzip = /* @__PURE__ */ function() { - function AsyncGunzip2(opts, cb) { - var _this = this; - astrmify([ - bInflt, - guze, - function() { - return [astrm, Inflate, Gunzip]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Gunzip(ev.data); - strm.onmember = function(offset) { - return postMessage(offset); - }; - onmessage = astrm(strm); - }, 9, 0, function(offset) { - return _this.onmember && _this.onmember(offset); - }); - } - __name(AsyncGunzip2, "AsyncGunzip"); - return AsyncGunzip2; -}(); -function gunzip(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bInflt, - guze, - function() { - return [gunzipSync]; - } - ], function(ev) { - return pbf(gunzipSync(ev.data[0], ev.data[1])); - }, 3, cb); -} -__name(gunzip, "gunzip"); -function gunzipSync(data, opts) { - var st = gzs(data); - if (st + 8 > data.length) - err(6, "invalid gzip data"); - return inflt(data.subarray(st, -8), { i: 2 }, opts && opts.out || new u8(gzl(data)), opts && opts.dictionary); -} -__name(gunzipSync, "gunzipSync"); -var Zlib = /* @__PURE__ */ function() { - function Zlib2(opts, cb) { - this.c = adler(); - this.v = 1; - Deflate.call(this, opts, cb); - } - __name(Zlib2, "Zlib"); - Zlib2.prototype.push = function(chunk, final) { - this.c.p(chunk); - Deflate.prototype.push.call(this, chunk, final); - }; - Zlib2.prototype.p = function(c, f) { - var raw = dopt(c, this.o, this.v && (this.o.dictionary ? 6 : 2), f && 4, this.s); - if (this.v) - zlh(raw, this.o), this.v = 0; - if (f) - wbytes(raw, raw.length - 4, this.c.d()); - this.ondata(raw, f); - }; - Zlib2.prototype.flush = function() { - Deflate.prototype.flush.call(this); - }; - return Zlib2; -}(); -var AsyncZlib = /* @__PURE__ */ function() { - function AsyncZlib2(opts, cb) { - astrmify([ - bDflt, - zle, - function() { - return [astrm, Deflate, Zlib]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Zlib(ev.data); - onmessage = astrm(strm); - }, 10, 1); - } - __name(AsyncZlib2, "AsyncZlib"); - return AsyncZlib2; -}(); -function zlib(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bDflt, - zle, - function() { - return [zlibSync]; - } - ], function(ev) { - return pbf(zlibSync(ev.data[0], ev.data[1])); - }, 4, cb); -} -__name(zlib, "zlib"); -function zlibSync(data, opts) { - if (!opts) - opts = {}; - var a = adler(); - a.p(data); - var d = dopt(data, opts, opts.dictionary ? 6 : 2, 4); - return zlh(d, opts), wbytes(d, d.length - 4, a.d()), d; -} -__name(zlibSync, "zlibSync"); -var Unzlib = /* @__PURE__ */ function() { - function Unzlib2(opts, cb) { - Inflate.call(this, opts, cb); - this.v = opts && opts.dictionary ? 2 : 1; - } - __name(Unzlib2, "Unzlib"); - Unzlib2.prototype.push = function(chunk, final) { - Inflate.prototype.e.call(this, chunk); - if (this.v) { - if (this.p.length < 6 && !final) - return; - this.p = this.p.subarray(zls(this.p, this.v - 1)), this.v = 0; - } - if (final) { - if (this.p.length < 4) - err(6, "invalid zlib data"); - this.p = this.p.subarray(0, -4); - } - Inflate.prototype.c.call(this, final); - }; - return Unzlib2; -}(); -var AsyncUnzlib = /* @__PURE__ */ function() { - function AsyncUnzlib2(opts, cb) { - astrmify([ - bInflt, - zule, - function() { - return [astrm, Inflate, Unzlib]; - } - ], this, StrmOpt.call(this, opts, cb), function(ev) { - var strm = new Unzlib(ev.data); - onmessage = astrm(strm); - }, 11, 0); - } - __name(AsyncUnzlib2, "AsyncUnzlib"); - return AsyncUnzlib2; -}(); -function unzlib(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return cbify(data, opts, [ - bInflt, - zule, - function() { - return [unzlibSync]; - } - ], function(ev) { - return pbf(unzlibSync(ev.data[0], gopt(ev.data[1]))); - }, 5, cb); -} -__name(unzlib, "unzlib"); -function unzlibSync(data, opts) { - return inflt(data.subarray(zls(data, opts && opts.dictionary), -4), { i: 2 }, opts && opts.out, opts && opts.dictionary); -} -__name(unzlibSync, "unzlibSync"); -var Decompress = /* @__PURE__ */ function() { - function Decompress2(opts, cb) { - this.o = StrmOpt.call(this, opts, cb) || {}; - this.G = Gunzip; - this.I = Inflate; - this.Z = Unzlib; - } - __name(Decompress2, "Decompress"); - Decompress2.prototype.i = function() { - var _this = this; - this.s.ondata = function(dat, final) { - _this.ondata(dat, final); - }; - }; - Decompress2.prototype.push = function(chunk, final) { - if (!this.ondata) - err(5); - if (!this.s) { - if (this.p && this.p.length) { - var n = new u8(this.p.length + chunk.length); - n.set(this.p), n.set(chunk, this.p.length); - } else - this.p = chunk; - if (this.p.length > 2) { - this.s = this.p[0] == 31 && this.p[1] == 139 && this.p[2] == 8 ? new this.G(this.o) : (this.p[0] & 15) != 8 || this.p[0] >> 4 > 7 || (this.p[0] << 8 | this.p[1]) % 31 ? new this.I(this.o) : new this.Z(this.o); - this.i(); - this.s.push(this.p, final); - this.p = null; - } - } else - this.s.push(chunk, final); - }; - return Decompress2; -}(); -var AsyncDecompress = /* @__PURE__ */ function() { - function AsyncDecompress2(opts, cb) { - Decompress.call(this, opts, cb); - this.queuedSize = 0; - this.G = AsyncGunzip; - this.I = AsyncInflate; - this.Z = AsyncUnzlib; - } - __name(AsyncDecompress2, "AsyncDecompress"); - AsyncDecompress2.prototype.i = function() { - var _this = this; - this.s.ondata = function(err2, dat, final) { - _this.ondata(err2, dat, final); - }; - this.s.ondrain = function(size) { - _this.queuedSize -= size; - if (_this.ondrain) - _this.ondrain(size); - }; - }; - AsyncDecompress2.prototype.push = function(chunk, final) { - this.queuedSize += chunk.length; - Decompress.prototype.push.call(this, chunk, final); - }; - return AsyncDecompress2; -}(); -function decompress(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - return data[0] == 31 && data[1] == 139 && data[2] == 8 ? gunzip(data, opts, cb) : (data[0] & 15) != 8 || data[0] >> 4 > 7 || (data[0] << 8 | data[1]) % 31 ? inflate(data, opts, cb) : unzlib(data, opts, cb); -} -__name(decompress, "decompress"); -function decompressSync(data, opts) { - return data[0] == 31 && data[1] == 139 && data[2] == 8 ? gunzipSync(data, opts) : (data[0] & 15) != 8 || data[0] >> 4 > 7 || (data[0] << 8 | data[1]) % 31 ? inflateSync(data, opts) : unzlibSync(data, opts); -} -__name(decompressSync, "decompressSync"); -var fltn = /* @__PURE__ */ __name(function(d, p, t2, o) { - for (var k in d) { - var val = d[k], n = p + k, op = o; - if (Array.isArray(val)) - op = mrg(o, val[1]), val = val[0]; - if (val instanceof u8) - t2[n] = [val, op]; - else { - t2[n += "/"] = [new u8(0), op]; - fltn(val, n, t2, o); - } - } -}, "fltn"); -var te = typeof TextEncoder != "undefined" && /* @__PURE__ */ new TextEncoder(); -var td = typeof TextDecoder != "undefined" && /* @__PURE__ */ new TextDecoder(); -var tds = 0; -try { - td.decode(et, { stream: true }); - tds = 1; -} catch (e) { -} -var dutf8 = /* @__PURE__ */ __name(function(d) { - for (var r = "", i = 0; ; ) { - var c = d[i++]; - var eb = (c > 127) + (c > 223) + (c > 239); - if (i + eb > d.length) - return { s: r, r: slc(d, i - 1) }; - if (!eb) - r += String.fromCharCode(c); - else if (eb == 3) { - c = ((c & 15) << 18 | (d[i++] & 63) << 12 | (d[i++] & 63) << 6 | d[i++] & 63) - 65536, r += String.fromCharCode(55296 | c >> 10, 56320 | c & 1023); - } else if (eb & 1) - r += String.fromCharCode((c & 31) << 6 | d[i++] & 63); - else - r += String.fromCharCode((c & 15) << 12 | (d[i++] & 63) << 6 | d[i++] & 63); - } -}, "dutf8"); -var DecodeUTF8 = /* @__PURE__ */ function() { - function DecodeUTF82(cb) { - this.ondata = cb; - if (tds) - this.t = new TextDecoder(); - else - this.p = et; - } - __name(DecodeUTF82, "DecodeUTF8"); - DecodeUTF82.prototype.push = function(chunk, final) { - if (!this.ondata) - err(5); - final = !!final; - if (this.t) { - this.ondata(this.t.decode(chunk, { stream: true }), final); - if (final) { - if (this.t.decode().length) - err(8); - this.t = null; - } - return; - } - if (!this.p) - err(4); - var dat = new u8(this.p.length + chunk.length); - dat.set(this.p); - dat.set(chunk, this.p.length); - var _a2 = dutf8(dat), s = _a2.s, r = _a2.r; - if (final) { - if (r.length) - err(8); - this.p = null; - } else - this.p = r; - this.ondata(s, final); - }; - return DecodeUTF82; -}(); -var EncodeUTF8 = /* @__PURE__ */ function() { - function EncodeUTF82(cb) { - this.ondata = cb; - } - __name(EncodeUTF82, "EncodeUTF8"); - EncodeUTF82.prototype.push = function(chunk, final) { - if (!this.ondata) - err(5); - if (this.d) - err(4); - this.ondata(strToU8(chunk), this.d = final || false); - }; - return EncodeUTF82; -}(); -function strToU8(str, latin1) { - if (latin1) { - var ar_1 = new u8(str.length); - for (var i = 0; i < str.length; ++i) - ar_1[i] = str.charCodeAt(i); - return ar_1; - } - if (te) - return te.encode(str); - var l = str.length; - var ar = new u8(str.length + (str.length >> 1)); - var ai = 0; - var w = /* @__PURE__ */ __name(function(v) { - ar[ai++] = v; - }, "w"); - for (var i = 0; i < l; ++i) { - if (ai + 5 > ar.length) { - var n = new u8(ai + 8 + (l - i << 1)); - n.set(ar); - ar = n; - } - var c = str.charCodeAt(i); - if (c < 128 || latin1) - w(c); - else if (c < 2048) - w(192 | c >> 6), w(128 | c & 63); - else if (c > 55295 && c < 57344) - c = 65536 + (c & 1023 << 10) | str.charCodeAt(++i) & 1023, w(240 | c >> 18), w(128 | c >> 12 & 63), w(128 | c >> 6 & 63), w(128 | c & 63); - else - w(224 | c >> 12), w(128 | c >> 6 & 63), w(128 | c & 63); - } - return slc(ar, 0, ai); -} -__name(strToU8, "strToU8"); -function strFromU8(dat, latin1) { - if (latin1) { - var r = ""; - for (var i = 0; i < dat.length; i += 16384) - r += String.fromCharCode.apply(null, dat.subarray(i, i + 16384)); - return r; - } else if (td) { - return td.decode(dat); - } else { - var _a2 = dutf8(dat), s = _a2.s, r = _a2.r; - if (r.length) - err(8); - return s; - } -} -__name(strFromU8, "strFromU8"); -; -var dbf = /* @__PURE__ */ __name(function(l) { - return l == 1 ? 3 : l < 6 ? 2 : l == 9 ? 1 : 0; -}, "dbf"); -var slzh = /* @__PURE__ */ __name(function(d, b) { - return b + 30 + b2(d, b + 26) + b2(d, b + 28); -}, "slzh"); -var zh = /* @__PURE__ */ __name(function(d, b, z) { - var fnl = b2(d, b + 28), fn = strFromU8(d.subarray(b + 46, b + 46 + fnl), !(b2(d, b + 8) & 2048)), es = b + 46 + fnl, bs = b4(d, b + 20); - var _a2 = z && bs == 4294967295 ? z64e(d, es) : [bs, b4(d, b + 24), b4(d, b + 42)], sc = _a2[0], su = _a2[1], off = _a2[2]; - return [b2(d, b + 10), sc, su, fn, es + b2(d, b + 30) + b2(d, b + 32), off]; -}, "zh"); -var z64e = /* @__PURE__ */ __name(function(d, b) { - for (; b2(d, b) != 1; b += 4 + b2(d, b + 2)) - ; - return [b8(d, b + 12), b8(d, b + 4), b8(d, b + 20)]; -}, "z64e"); -var exfl = /* @__PURE__ */ __name(function(ex) { - var le = 0; - if (ex) { - for (var k in ex) { - var l = ex[k].length; - if (l > 65535) - err(9); - le += l + 4; - } - } - return le; -}, "exfl"); -var wzh = /* @__PURE__ */ __name(function(d, b, f, fn, u, c, ce, co) { - var fl2 = fn.length, ex = f.extra, col = co && co.length; - var exl = exfl(ex); - wbytes(d, b, ce != null ? 33639248 : 67324752), b += 4; - if (ce != null) - d[b++] = 20, d[b++] = f.os; - d[b] = 20, b += 2; - d[b++] = f.flag << 1 | (c < 0 && 8), d[b++] = u && 8; - d[b++] = f.compression & 255, d[b++] = f.compression >> 8; - var dt = new Date(f.mtime == null ? Date.now() : f.mtime), y = dt.getFullYear() - 1980; - if (y < 0 || y > 119) - err(10); - wbytes(d, b, y << 25 | dt.getMonth() + 1 << 21 | dt.getDate() << 16 | dt.getHours() << 11 | dt.getMinutes() << 5 | dt.getSeconds() >> 1), b += 4; - if (c != -1) { - wbytes(d, b, f.crc); - wbytes(d, b + 4, c < 0 ? -c - 2 : c); - wbytes(d, b + 8, f.size); - } - wbytes(d, b + 12, fl2); - wbytes(d, b + 14, exl), b += 16; - if (ce != null) { - wbytes(d, b, col); - wbytes(d, b + 6, f.attrs); - wbytes(d, b + 10, ce), b += 14; - } - d.set(fn, b); - b += fl2; - if (exl) { - for (var k in ex) { - var exf = ex[k], l = exf.length; - wbytes(d, b, +k); - wbytes(d, b + 2, l); - d.set(exf, b + 4), b += 4 + l; - } - } - if (col) - d.set(co, b), b += col; - return b; -}, "wzh"); -var wzf = /* @__PURE__ */ __name(function(o, b, c, d, e) { - wbytes(o, b, 101010256); - wbytes(o, b + 8, c); - wbytes(o, b + 10, c); - wbytes(o, b + 12, d); - wbytes(o, b + 16, e); -}, "wzf"); -var ZipPassThrough = /* @__PURE__ */ function() { - function ZipPassThrough2(filename) { - this.filename = filename; - this.c = crc(); - this.size = 0; - this.compression = 0; - } - __name(ZipPassThrough2, "ZipPassThrough"); - ZipPassThrough2.prototype.process = function(chunk, final) { - this.ondata(null, chunk, final); - }; - ZipPassThrough2.prototype.push = function(chunk, final) { - if (!this.ondata) - err(5); - this.c.p(chunk); - this.size += chunk.length; - if (final) - this.crc = this.c.d(); - this.process(chunk, final || false); - }; - return ZipPassThrough2; -}(); -var ZipDeflate = /* @__PURE__ */ function() { - function ZipDeflate2(filename, opts) { - var _this = this; - if (!opts) - opts = {}; - ZipPassThrough.call(this, filename); - this.d = new Deflate(opts, function(dat, final) { - _this.ondata(null, dat, final); - }); - this.compression = 8; - this.flag = dbf(opts.level); - } - __name(ZipDeflate2, "ZipDeflate"); - ZipDeflate2.prototype.process = function(chunk, final) { - try { - this.d.push(chunk, final); - } catch (e) { - this.ondata(e, null, final); - } - }; - ZipDeflate2.prototype.push = function(chunk, final) { - ZipPassThrough.prototype.push.call(this, chunk, final); - }; - return ZipDeflate2; -}(); -var AsyncZipDeflate = /* @__PURE__ */ function() { - function AsyncZipDeflate2(filename, opts) { - var _this = this; - if (!opts) - opts = {}; - ZipPassThrough.call(this, filename); - this.d = new AsyncDeflate(opts, function(err2, dat, final) { - _this.ondata(err2, dat, final); - }); - this.compression = 8; - this.flag = dbf(opts.level); - this.terminate = this.d.terminate; - } - __name(AsyncZipDeflate2, "AsyncZipDeflate"); - AsyncZipDeflate2.prototype.process = function(chunk, final) { - this.d.push(chunk, final); - }; - AsyncZipDeflate2.prototype.push = function(chunk, final) { - ZipPassThrough.prototype.push.call(this, chunk, final); - }; - return AsyncZipDeflate2; -}(); -var Zip = /* @__PURE__ */ function() { - function Zip2(cb) { - this.ondata = cb; - this.u = []; - this.d = 1; - } - __name(Zip2, "Zip"); - Zip2.prototype.add = function(file2) { - var _this = this; - if (!this.ondata) - err(5); - if (this.d & 2) - this.ondata(err(4 + (this.d & 1) * 8, 0, 1), null, false); - else { - var f = strToU8(file2.filename), fl_1 = f.length; - var com = file2.comment, o = com && strToU8(com); - var u = fl_1 != file2.filename.length || o && com.length != o.length; - var hl_1 = fl_1 + exfl(file2.extra) + 30; - if (fl_1 > 65535) - this.ondata(err(11, 0, 1), null, false); - var header = new u8(hl_1); - wzh(header, 0, file2, f, u, -1); - var chks_1 = [header]; - var pAll_1 = /* @__PURE__ */ __name(function() { - for (var _i = 0, chks_2 = chks_1; _i < chks_2.length; _i++) { - var chk = chks_2[_i]; - _this.ondata(null, chk, false); - } - chks_1 = []; - }, "pAll_1"); - var tr_1 = this.d; - this.d = 0; - var ind_1 = this.u.length; - var uf_1 = mrg(file2, { - f, - u, - o, - t: /* @__PURE__ */ __name(function() { - if (file2.terminate) - file2.terminate(); - }, "t"), - r: /* @__PURE__ */ __name(function() { - pAll_1(); - if (tr_1) { - var nxt = _this.u[ind_1 + 1]; - if (nxt) - nxt.r(); - else - _this.d = 1; - } - tr_1 = 1; - }, "r") - }); - var cl_1 = 0; - file2.ondata = function(err2, dat, final) { - if (err2) { - _this.ondata(err2, dat, final); - _this.terminate(); - } else { - cl_1 += dat.length; - chks_1.push(dat); - if (final) { - var dd = new u8(16); - wbytes(dd, 0, 134695760); - wbytes(dd, 4, file2.crc); - wbytes(dd, 8, cl_1); - wbytes(dd, 12, file2.size); - chks_1.push(dd); - uf_1.c = cl_1, uf_1.b = hl_1 + cl_1 + 16, uf_1.crc = file2.crc, uf_1.size = file2.size; - if (tr_1) - uf_1.r(); - tr_1 = 1; - } else if (tr_1) - pAll_1(); - } - }; - this.u.push(uf_1); - } - }; - Zip2.prototype.end = function() { - var _this = this; - if (this.d & 2) { - this.ondata(err(4 + (this.d & 1) * 8, 0, 1), null, true); - return; - } - if (this.d) - this.e(); - else - this.u.push({ - r: /* @__PURE__ */ __name(function() { - if (!(_this.d & 1)) - return; - _this.u.splice(-1, 1); - _this.e(); - }, "r"), - t: /* @__PURE__ */ __name(function() { - }, "t") - }); - this.d = 3; - }; - Zip2.prototype.e = function() { - var bt = 0, l = 0, tl = 0; - for (var _i = 0, _a2 = this.u; _i < _a2.length; _i++) { - var f = _a2[_i]; - tl += 46 + f.f.length + exfl(f.extra) + (f.o ? f.o.length : 0); - } - var out = new u8(tl + 22); - for (var _b2 = 0, _c = this.u; _b2 < _c.length; _b2++) { - var f = _c[_b2]; - wzh(out, bt, f, f.f, f.u, -f.c - 2, l, f.o); - bt += 46 + f.f.length + exfl(f.extra) + (f.o ? f.o.length : 0), l += f.b; - } - wzf(out, bt, this.u.length, tl, l); - this.ondata(null, out, true); - this.d = 2; - }; - Zip2.prototype.terminate = function() { - for (var _i = 0, _a2 = this.u; _i < _a2.length; _i++) { - var f = _a2[_i]; - f.t(); - } - this.d = 2; - }; - return Zip2; -}(); -function zip(data, opts, cb) { - if (!cb) - cb = opts, opts = {}; - if (typeof cb != "function") - err(7); - var r = {}; - fltn(data, "", r, opts); - var k = Object.keys(r); - var lft = k.length, o = 0, tot = 0; - var slft = lft, files = new Array(lft); - var term = []; - var tAll = /* @__PURE__ */ __name(function() { - for (var i2 = 0; i2 < term.length; ++i2) - term[i2](); - }, "tAll"); - var cbd = /* @__PURE__ */ __name(function(a, b) { - mt(function() { - cb(a, b); - }); - }, "cbd"); - mt(function() { - cbd = cb; - }); - var cbf = /* @__PURE__ */ __name(function() { - var out = new u8(tot + 22), oe = o, cdl = tot - o; - tot = 0; - for (var i2 = 0; i2 < slft; ++i2) { - var f = files[i2]; - try { - var l = f.c.length; - wzh(out, tot, f, f.f, f.u, l); - var badd = 30 + f.f.length + exfl(f.extra); - var loc = tot + badd; - out.set(f.c, loc); - wzh(out, o, f, f.f, f.u, l, tot, f.m), o += 16 + badd + (f.m ? f.m.length : 0), tot = loc + l; - } catch (e) { - return cbd(e, null); - } - } - wzf(out, o, files.length, cdl, oe); - cbd(null, out); - }, "cbf"); - if (!lft) - cbf(); - var _loop_1 = /* @__PURE__ */ __name(function(i2) { - var fn = k[i2]; - var _a2 = r[fn], file2 = _a2[0], p = _a2[1]; - var c = crc(), size = file2.length; - c.p(file2); - var f = strToU8(fn), s = f.length; - var com = p.comment, m = com && strToU8(com), ms = m && m.length; - var exl = exfl(p.extra); - var compression = p.level == 0 ? 0 : 8; - var cbl = /* @__PURE__ */ __name(function(e, d) { - if (e) { - tAll(); - cbd(e, null); - } else { - var l = d.length; - files[i2] = mrg(p, { - size, - crc: c.d(), - c: d, - f, - m, - u: s != fn.length || m && com.length != ms, - compression - }); - o += 30 + s + exl + l; - tot += 76 + 2 * (s + exl) + (ms || 0) + l; - if (!--lft) - cbf(); - } - }, "cbl"); - if (s > 65535) - cbl(err(11, 0, 1), null); - if (!compression) - cbl(null, file2); - else if (size < 16e4) { - try { - cbl(null, deflateSync(file2, p)); - } catch (e) { - cbl(e, null); - } - } else - term.push(deflate(file2, p, cbl)); - }, "_loop_1"); - for (var i = 0; i < slft; ++i) { - _loop_1(i); - } - return tAll; -} -__name(zip, "zip"); -function zipSync(data, opts) { - if (!opts) - opts = {}; - var r = {}; - var files = []; - fltn(data, "", r, opts); - var o = 0; - var tot = 0; - for (var fn in r) { - var _a2 = r[fn], file2 = _a2[0], p = _a2[1]; - var compression = p.level == 0 ? 0 : 8; - var f = strToU8(fn), s = f.length; - var com = p.comment, m = com && strToU8(com), ms = m && m.length; - var exl = exfl(p.extra); - if (s > 65535) - err(11); 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- } else { - low = mid; - } - mid = Math.floor((low + high) / 2); - } - return mid; -} -__name(findSpan, "findSpan"); -function calcBasisFunctions(span, u, p, U) { - const N = []; - const left = []; - const right = []; - N[0] = 1; - for (let j = 1; j <= p; ++j) { - left[j] = u - U[span + 1 - j]; - right[j] = U[span + j] - u; - let saved = 0; - for (let r = 0; r < j; ++r) { - const rv = right[r + 1]; - const lv = left[j - r]; - const temp = N[r] / (rv + lv); - N[r] = saved + rv * temp; - saved = lv * temp; - } - N[j] = saved; - } - return N; -} -__name(calcBasisFunctions, "calcBasisFunctions"); -function calcBSplinePoint(p, U, P, u) { - const span = findSpan(p, u, U); - const N = calcBasisFunctions(span, u, p, U); - const C = new Vector4(0, 0, 0, 0); - for (let j = 0; j <= p; ++j) { - const point = P[span - p + j]; - const Nj = N[j]; - const wNj = point.w * Nj; - C.x += point.x * wNj; - C.y += point.y * wNj; - C.z += point.z * wNj; - C.w += point.w * Nj; - } - return C; -} -__name(calcBSplinePoint, "calcBSplinePoint"); -function calcBasisFunctionDerivatives(span, u, p, n, U) { - const zeroArr = []; - for (let i = 0; i <= p; ++i) - zeroArr[i] = 0; - const ders = []; - for (let i = 0; i <= n; ++i) - ders[i] = zeroArr.slice(0); - const ndu = []; - for (let i = 0; i <= p; ++i) - ndu[i] = zeroArr.slice(0); - ndu[0][0] = 1; - const left = zeroArr.slice(0); - const right = zeroArr.slice(0); - for (let j = 1; j <= p; ++j) { - left[j] = u - U[span + 1 - j]; - right[j] = U[span + j] - u; - let saved = 0; - for (let r2 = 0; r2 < j; ++r2) { - const rv = right[r2 + 1]; - const lv = left[j - r2]; - ndu[j][r2] = rv + lv; - const temp = ndu[r2][j - 1] / ndu[j][r2]; - ndu[r2][j] = saved + rv * temp; - saved = lv * temp; - } - ndu[j][j] = saved; - } - for (let j = 0; j <= p; ++j) { - ders[0][j] = ndu[j][p]; - } - for (let r2 = 0; r2 <= p; ++r2) { - let s1 = 0; - let s2 = 1; - const a = []; - for (let i = 0; i <= p; ++i) { - a[i] = zeroArr.slice(0); - } - a[0][0] = 1; - for (let k = 1; k <= n; ++k) { - let d = 0; - const rk = r2 - k; - const pk = p - k; - if (r2 >= k) { - a[s2][0] = a[s1][0] / ndu[pk + 1][rk]; - d = a[s2][0] * ndu[rk][pk]; - } - const j1 = rk >= -1 ? 1 : -rk; - const j2 = r2 - 1 <= pk ? k - 1 : p - r2; - for (let j3 = j1; j3 <= j2; ++j3) { - a[s2][j3] = (a[s1][j3] - a[s1][j3 - 1]) / ndu[pk + 1][rk + j3]; - d += a[s2][j3] * ndu[rk + j3][pk]; - } - if (r2 <= pk) { - a[s2][k] = -a[s1][k - 1] / ndu[pk + 1][r2]; - d += a[s2][k] * ndu[r2][pk]; - } - ders[k][r2] = d; - const j = s1; - s1 = s2; - s2 = j; - } - } - let r = p; - for (let k = 1; k <= n; ++k) { - for (let j = 0; j <= p; ++j) { - ders[k][j] *= r; - } - r *= p - k; - } - return ders; -} -__name(calcBasisFunctionDerivatives, "calcBasisFunctionDerivatives"); -function calcBSplineDerivatives(p, U, P, u, nd) { - const du = nd < p ? nd : p; - const CK = []; - const span = findSpan(p, u, U); - const nders = calcBasisFunctionDerivatives(span, u, p, du, U); - const Pw = []; - for (let i = 0; i < P.length; ++i) { - const point = P[i].clone(); - const w = point.w; - point.x *= w; - point.y *= w; - point.z *= w; - Pw[i] = point; - } - for (let k = 0; k <= du; ++k) { - const point = Pw[span - p].clone().multiplyScalar(nders[k][0]); - for (let j = 1; j <= p; ++j) { - point.add(Pw[span - p + j].clone().multiplyScalar(nders[k][j])); - } - CK[k] = point; - } - for (let k = du + 1; k <= nd + 1; ++k) { - CK[k] = new Vector4(0, 0, 0); - } - return CK; -} -__name(calcBSplineDerivatives, "calcBSplineDerivatives"); -function calcKoverI(k, i) { - let nom = 1; - for (let j = 2; j <= k; ++j) { - nom *= j; - } - let denom = 1; - for (let j = 2; j <= i; ++j) { - denom *= j; - } - for (let j = 2; j <= k - i; ++j) { - denom *= j; - } - return nom / denom; -} -__name(calcKoverI, "calcKoverI"); -function calcRationalCurveDerivatives(Pders) { - const nd = Pders.length; - const Aders = []; - const wders = []; - for (let i = 0; i < nd; ++i) { - const point = Pders[i]; - Aders[i] = new Vector3(point.x, point.y, point.z); - wders[i] = point.w; - } - const CK = []; - for (let k = 0; k < nd; ++k) { - const v = Aders[k].clone(); - for (let i = 1; i <= k; ++i) { - v.sub(CK[k - i].clone().multiplyScalar(calcKoverI(k, i) * wders[i])); - } - CK[k] = v.divideScalar(wders[0]); - } - return CK; -} -__name(calcRationalCurveDerivatives, "calcRationalCurveDerivatives"); -function calcNURBSDerivatives(p, U, P, u, nd) { - const Pders = calcBSplineDerivatives(p, U, P, u, nd); - return calcRationalCurveDerivatives(Pders); -} -__name(calcNURBSDerivatives, "calcNURBSDerivatives"); -function calcSurfacePoint(p, q, U, V, P, u, v, target) { - const uspan = findSpan(p, u, U); - const vspan = findSpan(q, v, V); - const Nu = calcBasisFunctions(uspan, u, p, U); - const Nv = calcBasisFunctions(vspan, v, q, V); - const temp = []; - for (let l = 0; l <= q; ++l) { - temp[l] = new Vector4(0, 0, 0, 0); - for (let k = 0; k <= p; ++k) { - const point = P[uspan - p + k][vspan - q + l].clone(); - const w = point.w; - point.x *= w; - point.y *= w; - point.z *= w; - temp[l].add(point.multiplyScalar(Nu[k])); - } - } - const Sw = new Vector4(0, 0, 0, 0); - for (let l = 0; l <= q; ++l) { - Sw.add(temp[l].multiplyScalar(Nv[l])); - } - Sw.divideScalar(Sw.w); - target.set(Sw.x, Sw.y, Sw.z); -} -__name(calcSurfacePoint, "calcSurfacePoint"); -function calcVolumePoint(p, q, r, U, V, W, P, u, v, w, target) { - const uspan = findSpan(p, u, U); - const vspan = findSpan(q, v, V); - const wspan = findSpan(r, w, W); - const Nu = calcBasisFunctions(uspan, u, p, U); - const Nv = calcBasisFunctions(vspan, v, q, V); - const Nw = calcBasisFunctions(wspan, w, r, W); - const temp = []; - for (let m = 0; m <= r; ++m) { - temp[m] = []; - for (let l = 0; l <= q; ++l) { - temp[m][l] = new Vector4(0, 0, 0, 0); - for (let k = 0; k <= p; ++k) { - const point = P[uspan - p + k][vspan - q + l][wspan - r + m].clone(); - const w2 = point.w; - point.x *= w2; - point.y *= w2; - point.z *= w2; - temp[m][l].add(point.multiplyScalar(Nu[k])); - } - } - } - const Sw = new Vector4(0, 0, 0, 0); - for (let m = 0; m <= r; ++m) { - for (let l = 0; l <= q; ++l) { - Sw.add(temp[m][l].multiplyScalar(Nw[m]).multiplyScalar(Nv[l])); - } - } - Sw.divideScalar(Sw.w); - target.set(Sw.x, Sw.y, Sw.z); -} -__name(calcVolumePoint, "calcVolumePoint"); -class NURBSCurve extends Curve { - static { - __name(this, "NURBSCurve"); - } - constructor(degree, knots, controlPoints, startKnot, endKnot) { - super(); - const knotsLength = knots ? knots.length - 1 : 0; - const pointsLength = controlPoints ? controlPoints.length : 0; - this.degree = degree; - this.knots = knots; - this.controlPoints = []; - this.startKnot = startKnot || 0; - this.endKnot = endKnot || knotsLength; - for (let i = 0; i < pointsLength; ++i) { - const point = controlPoints[i]; - this.controlPoints[i] = new Vector4(point.x, point.y, point.z, point.w); - } - } - getPoint(t2, optionalTarget = new Vector3()) { - const point = optionalTarget; - const u = this.knots[this.startKnot] + t2 * (this.knots[this.endKnot] - this.knots[this.startKnot]); - const hpoint = calcBSplinePoint(this.degree, this.knots, this.controlPoints, u); - if (hpoint.w !== 1) { - hpoint.divideScalar(hpoint.w); - } - return point.set(hpoint.x, hpoint.y, hpoint.z); - } - getTangent(t2, optionalTarget = new Vector3()) { - const tangent = optionalTarget; - const u = this.knots[0] + t2 * (this.knots[this.knots.length - 1] - this.knots[0]); - const ders = calcNURBSDerivatives(this.degree, this.knots, this.controlPoints, u, 1); - tangent.copy(ders[1]).normalize(); - return tangent; - } - toJSON() { - const data = super.toJSON(); - data.degree = this.degree; - data.knots = [...this.knots]; - data.controlPoints = this.controlPoints.map((p) => p.toArray()); - data.startKnot = this.startKnot; - data.endKnot = this.endKnot; - return data; - } - fromJSON(json) { - super.fromJSON(json); - this.degree = json.degree; - this.knots = [...json.knots]; - this.controlPoints = json.controlPoints.map((p) => new Vector4(p[0], p[1], p[2], p[3])); - this.startKnot = json.startKnot; - this.endKnot = json.endKnot; - return this; - } -} -let fbxTree; -let connections; -let sceneGraph; -class FBXLoader extends Loader { - static { - __name(this, "FBXLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const path = scope.path === "" ? LoaderUtils.extractUrlBase(url) : scope.path; - const loader = new FileLoader(this.manager); - loader.setPath(scope.path); - loader.setResponseType("arraybuffer"); - loader.setRequestHeader(scope.requestHeader); - loader.setWithCredentials(scope.withCredentials); - loader.load(url, function(buffer) { - try { - onLoad(scope.parse(buffer, path)); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - parse(FBXBuffer, path) { - if (isFbxFormatBinary(FBXBuffer)) { - fbxTree = new BinaryParser().parse(FBXBuffer); - } else { - const FBXText = convertArrayBufferToString(FBXBuffer); - if (!isFbxFormatASCII(FBXText)) { - throw new Error("THREE.FBXLoader: Unknown format."); - } - if (getFbxVersion(FBXText) < 7e3) { - throw new Error("THREE.FBXLoader: FBX version not supported, FileVersion: " + getFbxVersion(FBXText)); - } - fbxTree = new TextParser().parse(FBXText); - } - const textureLoader = new TextureLoader(this.manager).setPath(this.resourcePath || path).setCrossOrigin(this.crossOrigin); - return new FBXTreeParser(textureLoader, this.manager).parse(fbxTree); - } -} -class FBXTreeParser { - static { - __name(this, "FBXTreeParser"); - } - constructor(textureLoader, manager) { - this.textureLoader = textureLoader; - this.manager = manager; - } - parse() { - connections = this.parseConnections(); - const images = this.parseImages(); - const textures = this.parseTextures(images); - const materials = this.parseMaterials(textures); - const deformers = this.parseDeformers(); - const geometryMap = new GeometryParser().parse(deformers); - this.parseScene(deformers, geometryMap, materials); - return sceneGraph; - } - // Parses FBXTree.Connections which holds parent-child connections between objects (e.g. material -> texture, model->geometry ) - // and details the connection type - parseConnections() { - const connectionMap = /* @__PURE__ */ new Map(); - if ("Connections" in fbxTree) { - const rawConnections = fbxTree.Connections.connections; - rawConnections.forEach(function(rawConnection) { - const fromID = rawConnection[0]; - const toID = rawConnection[1]; - const relationship = rawConnection[2]; - if (!connectionMap.has(fromID)) { - connectionMap.set(fromID, { - parents: [], - children: [] - }); - } - const parentRelationship = { ID: toID, relationship }; - connectionMap.get(fromID).parents.push(parentRelationship); - if (!connectionMap.has(toID)) { - connectionMap.set(toID, { - parents: [], - children: [] - }); - } - const childRelationship = { ID: fromID, relationship }; - connectionMap.get(toID).children.push(childRelationship); - }); - } - return connectionMap; - } - // Parse FBXTree.Objects.Video for embedded image data - // These images are connected to textures in FBXTree.Objects.Textures - // via FBXTree.Connections. - parseImages() { - const images = {}; - const blobs = {}; - if ("Video" in fbxTree.Objects) { - const videoNodes = fbxTree.Objects.Video; - for (const nodeID in videoNodes) { - const videoNode = videoNodes[nodeID]; - const id2 = parseInt(nodeID); - images[id2] = videoNode.RelativeFilename || videoNode.Filename; - if ("Content" in videoNode) { - const arrayBufferContent = videoNode.Content instanceof ArrayBuffer && videoNode.Content.byteLength > 0; - const base64Content = typeof videoNode.Content === "string" && videoNode.Content !== ""; - if (arrayBufferContent || base64Content) { - const image = this.parseImage(videoNodes[nodeID]); - blobs[videoNode.RelativeFilename || videoNode.Filename] = image; - } - } - } - } - for (const id2 in images) { - const filename = images[id2]; - if (blobs[filename] !== void 0) images[id2] = blobs[filename]; - else images[id2] = images[id2].split("\\").pop(); - } - return images; - } - // Parse embedded image data in FBXTree.Video.Content - parseImage(videoNode) { - const content = videoNode.Content; - const fileName = videoNode.RelativeFilename || videoNode.Filename; - const extension = fileName.slice(fileName.lastIndexOf(".") + 1).toLowerCase(); - let type; - switch (extension) { - case "bmp": - type = "image/bmp"; - break; - case "jpg": - case "jpeg": - type = "image/jpeg"; - break; - case "png": - type = "image/png"; - break; - case "tif": - type = "image/tiff"; - break; - case "tga": - if (this.manager.getHandler(".tga") === null) { - console.warn("FBXLoader: TGA loader not found, skipping ", fileName); - } - type = "image/tga"; - break; - default: - console.warn('FBXLoader: Image type "' + extension + '" is not supported.'); - return; - } - if (typeof content === "string") { - return "data:" + type + ";base64," + content; - } else { - const array = new Uint8Array(content); - return window.URL.createObjectURL(new Blob([array], { type })); - } - } - // Parse nodes in FBXTree.Objects.Texture - // These contain details such as UV scaling, cropping, rotation etc and are connected - // to images in FBXTree.Objects.Video - parseTextures(images) { - const textureMap = /* @__PURE__ */ new Map(); - if ("Texture" in fbxTree.Objects) { - const textureNodes = fbxTree.Objects.Texture; - for (const nodeID in textureNodes) { - const texture = this.parseTexture(textureNodes[nodeID], images); - textureMap.set(parseInt(nodeID), texture); - } - } - return textureMap; - } - // Parse individual node in FBXTree.Objects.Texture - parseTexture(textureNode, images) { - const texture = this.loadTexture(textureNode, images); - texture.ID = textureNode.id; - texture.name = textureNode.attrName; - const wrapModeU = textureNode.WrapModeU; - const wrapModeV = textureNode.WrapModeV; - const valueU = wrapModeU !== void 0 ? wrapModeU.value : 0; - const valueV = wrapModeV !== void 0 ? wrapModeV.value : 0; - texture.wrapS = valueU === 0 ? RepeatWrapping : ClampToEdgeWrapping; - texture.wrapT = valueV === 0 ? RepeatWrapping : ClampToEdgeWrapping; - if ("Scaling" in textureNode) { - const values = textureNode.Scaling.value; - texture.repeat.x = values[0]; - texture.repeat.y = values[1]; - } - if ("Translation" in textureNode) { - const values = textureNode.Translation.value; - texture.offset.x = values[0]; - texture.offset.y = values[1]; - } - return texture; - } - // load a texture specified as a blob or data URI, or via an external URL using TextureLoader - loadTexture(textureNode, images) { - const nonNativeExtensions = /* @__PURE__ */ new Set(["tga", "tif", "tiff", "exr", "dds", "hdr", "ktx2"]); - const extension = textureNode.FileName.split(".").pop().toLowerCase(); - const loader = nonNativeExtensions.has(extension) ? this.manager.getHandler(`.${extension}`) : this.textureLoader; - if (!loader) { - console.warn( - `FBXLoader: ${extension.toUpperCase()} loader not found, creating placeholder texture for`, - textureNode.RelativeFilename - ); - return new Texture(); - } - const loaderPath = loader.path; - if (!loaderPath) { - loader.setPath(this.textureLoader.path); - } - const children = connections.get(textureNode.id).children; - let fileName; - if (children !== void 0 && children.length > 0 && images[children[0].ID] !== void 0) { - fileName = images[children[0].ID]; - if (fileName.indexOf("blob:") === 0 || fileName.indexOf("data:") === 0) { - loader.setPath(void 0); - } - } - const texture = loader.load(fileName); - loader.setPath(loaderPath); - return texture; - } - // Parse nodes in FBXTree.Objects.Material - parseMaterials(textureMap) { - const materialMap = /* @__PURE__ */ new Map(); - if ("Material" in fbxTree.Objects) { - const materialNodes = fbxTree.Objects.Material; - for (const nodeID in materialNodes) { - const material = this.parseMaterial(materialNodes[nodeID], textureMap); - if (material !== null) materialMap.set(parseInt(nodeID), material); - } - } - return materialMap; - } - // Parse single node in FBXTree.Objects.Material - // Materials are connected to texture maps in FBXTree.Objects.Textures - // FBX format currently only supports Lambert and Phong shading models - parseMaterial(materialNode, textureMap) { - const ID = materialNode.id; - const name = materialNode.attrName; - let type = materialNode.ShadingModel; - if (typeof type === "object") { - type = type.value; - } - if (!connections.has(ID)) return null; - const parameters = this.parseParameters(materialNode, textureMap, ID); - let material; - switch (type.toLowerCase()) { - case "phong": - material = new MeshPhongMaterial(); - break; - case "lambert": - material = new MeshLambertMaterial(); - break; - default: - console.warn('THREE.FBXLoader: unknown material type "%s". Defaulting to MeshPhongMaterial.', type); - material = new MeshPhongMaterial(); - break; - } - material.setValues(parameters); - material.name = name; - return material; - } - // Parse FBX material and return parameters suitable for a three.js material - // Also parse the texture map and return any textures associated with the material - parseParameters(materialNode, textureMap, ID) { - const parameters = {}; - if (materialNode.BumpFactor) { - parameters.bumpScale = materialNode.BumpFactor.value; - } - if (materialNode.Diffuse) { - parameters.color = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.Diffuse.value), SRGBColorSpace); - } else if (materialNode.DiffuseColor && (materialNode.DiffuseColor.type === "Color" || materialNode.DiffuseColor.type === "ColorRGB")) { - parameters.color = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.DiffuseColor.value), SRGBColorSpace); - } - if (materialNode.DisplacementFactor) { - parameters.displacementScale = materialNode.DisplacementFactor.value; - } - if (materialNode.Emissive) { - parameters.emissive = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.Emissive.value), SRGBColorSpace); - } else if (materialNode.EmissiveColor && (materialNode.EmissiveColor.type === "Color" || materialNode.EmissiveColor.type === "ColorRGB")) { - parameters.emissive = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.EmissiveColor.value), SRGBColorSpace); - } - if (materialNode.EmissiveFactor) { - parameters.emissiveIntensity = parseFloat(materialNode.EmissiveFactor.value); - } - parameters.opacity = 1 - (materialNode.TransparencyFactor ? parseFloat(materialNode.TransparencyFactor.value) : 0); - if (parameters.opacity === 1 || parameters.opacity === 0) { - parameters.opacity = materialNode.Opacity ? parseFloat(materialNode.Opacity.value) : null; - if (parameters.opacity === null) { - parameters.opacity = 1 - (materialNode.TransparentColor ? parseFloat(materialNode.TransparentColor.value[0]) : 0); - } - } - if (parameters.opacity < 1) { - parameters.transparent = true; - } - if (materialNode.ReflectionFactor) { - parameters.reflectivity = materialNode.ReflectionFactor.value; - } - if (materialNode.Shininess) { - parameters.shininess = materialNode.Shininess.value; - } - if (materialNode.Specular) { - parameters.specular = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.Specular.value), SRGBColorSpace); - } else if (materialNode.SpecularColor && materialNode.SpecularColor.type === "Color") { - parameters.specular = ColorManagement.toWorkingColorSpace(new Color().fromArray(materialNode.SpecularColor.value), SRGBColorSpace); - } - const scope = this; - connections.get(ID).children.forEach(function(child) { - const type = child.relationship; - switch (type) { - case "Bump": - parameters.bumpMap = scope.getTexture(textureMap, child.ID); - break; - case "Maya|TEX_ao_map": - parameters.aoMap = scope.getTexture(textureMap, child.ID); - break; - case "DiffuseColor": - case "Maya|TEX_color_map": - parameters.map = scope.getTexture(textureMap, child.ID); - if (parameters.map !== void 0) { - parameters.map.colorSpace = SRGBColorSpace; - } - break; - case "DisplacementColor": - parameters.displacementMap = scope.getTexture(textureMap, child.ID); - break; - case "EmissiveColor": - parameters.emissiveMap = scope.getTexture(textureMap, child.ID); - if (parameters.emissiveMap !== void 0) { - parameters.emissiveMap.colorSpace = SRGBColorSpace; - } - break; - case "NormalMap": - case "Maya|TEX_normal_map": - parameters.normalMap = scope.getTexture(textureMap, child.ID); - break; - case "ReflectionColor": - parameters.envMap = scope.getTexture(textureMap, child.ID); - if (parameters.envMap !== void 0) { - parameters.envMap.mapping = EquirectangularReflectionMapping; - parameters.envMap.colorSpace = SRGBColorSpace; - } - break; - case "SpecularColor": - parameters.specularMap = scope.getTexture(textureMap, child.ID); - if (parameters.specularMap !== void 0) { - parameters.specularMap.colorSpace = SRGBColorSpace; - } - break; - case "TransparentColor": - case "TransparencyFactor": - parameters.alphaMap = scope.getTexture(textureMap, child.ID); - parameters.transparent = true; - break; - case "AmbientColor": - case "ShininessExponent": - case "SpecularFactor": - case "VectorDisplacementColor": - default: - console.warn("THREE.FBXLoader: %s map is not supported in three.js, skipping texture.", type); - break; - } - }); - return parameters; - } - // get a texture from the textureMap for use by a material. - getTexture(textureMap, id2) { - if ("LayeredTexture" in fbxTree.Objects && id2 in fbxTree.Objects.LayeredTexture) { - console.warn("THREE.FBXLoader: layered textures are not supported in three.js. Discarding all but first layer."); - id2 = connections.get(id2).children[0].ID; - } - return textureMap.get(id2); - } - // Parse nodes in FBXTree.Objects.Deformer - // Deformer node can contain skinning or Vertex Cache animation data, however only skinning is supported here - // Generates map of Skeleton-like objects for use later when generating and binding skeletons. - parseDeformers() { - const skeletons = {}; - const morphTargets = {}; - if ("Deformer" in fbxTree.Objects) { - const DeformerNodes = fbxTree.Objects.Deformer; - for (const nodeID in DeformerNodes) { - const deformerNode = DeformerNodes[nodeID]; - const relationships = connections.get(parseInt(nodeID)); - if (deformerNode.attrType === "Skin") { - const skeleton = this.parseSkeleton(relationships, DeformerNodes); - skeleton.ID = nodeID; - if (relationships.parents.length > 1) console.warn("THREE.FBXLoader: skeleton attached to more than one geometry is not supported."); - skeleton.geometryID = relationships.parents[0].ID; - skeletons[nodeID] = skeleton; - } else if (deformerNode.attrType === "BlendShape") { - const morphTarget = { - id: nodeID - }; - morphTarget.rawTargets = this.parseMorphTargets(relationships, DeformerNodes); - morphTarget.id = nodeID; - if (relationships.parents.length > 1) console.warn("THREE.FBXLoader: morph target attached to more than one geometry is not supported."); - morphTargets[nodeID] = morphTarget; - } - } - } - return { - skeletons, - morphTargets - }; - } - // Parse single nodes in FBXTree.Objects.Deformer - // The top level skeleton node has type 'Skin' and sub nodes have type 'Cluster' - // Each skin node represents a skeleton and each cluster node represents a bone - parseSkeleton(relationships, deformerNodes) { - const rawBones = []; - relationships.children.forEach(function(child) { - const boneNode = deformerNodes[child.ID]; - if (boneNode.attrType !== "Cluster") return; - const rawBone = { - ID: child.ID, - indices: [], - weights: [], - transformLink: new Matrix4().fromArray(boneNode.TransformLink.a) - // transform: new Matrix4().fromArray( boneNode.Transform.a ), - // linkMode: boneNode.Mode, - }; - if ("Indexes" in boneNode) { - rawBone.indices = boneNode.Indexes.a; - rawBone.weights = boneNode.Weights.a; - } - rawBones.push(rawBone); - }); - return { - rawBones, - bones: [] - }; - } - // The top level morph deformer node has type "BlendShape" and sub nodes have type "BlendShapeChannel" - parseMorphTargets(relationships, deformerNodes) { - const rawMorphTargets = []; - for (let i = 0; i < relationships.children.length; i++) { - const child = relationships.children[i]; - const morphTargetNode = deformerNodes[child.ID]; - const rawMorphTarget = { - name: morphTargetNode.attrName, - initialWeight: morphTargetNode.DeformPercent, - id: morphTargetNode.id, - fullWeights: morphTargetNode.FullWeights.a - }; - if (morphTargetNode.attrType !== "BlendShapeChannel") return; - rawMorphTarget.geoID = connections.get(parseInt(child.ID)).children.filter(function(child2) { - return child2.relationship === void 0; - })[0].ID; - rawMorphTargets.push(rawMorphTarget); - } - return rawMorphTargets; - } - // create the main Group() to be returned by the loader - parseScene(deformers, geometryMap, materialMap) { - sceneGraph = new Group(); - const modelMap = this.parseModels(deformers.skeletons, geometryMap, materialMap); - const modelNodes = fbxTree.Objects.Model; - const scope = this; - modelMap.forEach(function(model) { - const modelNode = modelNodes[model.ID]; - scope.setLookAtProperties(model, modelNode); - const parentConnections = connections.get(model.ID).parents; - parentConnections.forEach(function(connection) { - const parent = modelMap.get(connection.ID); - if (parent !== void 0) parent.add(model); - }); - if (model.parent === null) { - sceneGraph.add(model); - } - }); - this.bindSkeleton(deformers.skeletons, geometryMap, modelMap); - this.addGlobalSceneSettings(); - sceneGraph.traverse(function(node) { - if (node.userData.transformData) { - if (node.parent) { - node.userData.transformData.parentMatrix = node.parent.matrix; - node.userData.transformData.parentMatrixWorld = node.parent.matrixWorld; - } - const transform = generateTransform(node.userData.transformData); - node.applyMatrix4(transform); - node.updateWorldMatrix(); - } - }); - const animations = new AnimationParser().parse(); - if (sceneGraph.children.length === 1 && sceneGraph.children[0].isGroup) { - sceneGraph.children[0].animations = animations; - sceneGraph = sceneGraph.children[0]; - } - sceneGraph.animations = animations; - } - // parse nodes in FBXTree.Objects.Model - parseModels(skeletons, geometryMap, materialMap) { - const modelMap = /* @__PURE__ */ new Map(); - const modelNodes = fbxTree.Objects.Model; - for (const nodeID in modelNodes) { - const id2 = parseInt(nodeID); - const node = modelNodes[nodeID]; - const relationships = connections.get(id2); - let model = this.buildSkeleton(relationships, skeletons, id2, node.attrName); - if (!model) { - switch (node.attrType) { - case "Camera": - model = this.createCamera(relationships); - break; - case "Light": - model = this.createLight(relationships); - break; - case "Mesh": - model = this.createMesh(relationships, geometryMap, materialMap); - break; - case "NurbsCurve": - model = this.createCurve(relationships, geometryMap); - break; - case "LimbNode": - case "Root": - model = new Bone(); - break; - case "Null": - default: - model = new Group(); - break; - } - model.name = node.attrName ? PropertyBinding.sanitizeNodeName(node.attrName) : ""; - model.userData.originalName = node.attrName; - model.ID = id2; - } - this.getTransformData(model, node); - modelMap.set(id2, model); - } - return modelMap; - } - buildSkeleton(relationships, skeletons, id2, name) { - let bone = null; - relationships.parents.forEach(function(parent) { - for (const ID in skeletons) { - const skeleton = skeletons[ID]; - skeleton.rawBones.forEach(function(rawBone, i) { - if (rawBone.ID === parent.ID) { - const subBone = bone; - bone = new Bone(); - bone.matrixWorld.copy(rawBone.transformLink); - bone.name = name ? PropertyBinding.sanitizeNodeName(name) : ""; - bone.userData.originalName = name; - bone.ID = id2; - skeleton.bones[i] = bone; - if (subBone !== null) { - bone.add(subBone); - } - } - }); - } - }); - return bone; - } - // create a PerspectiveCamera or OrthographicCamera - createCamera(relationships) { - let model; - let cameraAttribute; - relationships.children.forEach(function(child) { - const attr = fbxTree.Objects.NodeAttribute[child.ID]; - if (attr !== void 0) { - cameraAttribute = attr; - } - }); - if (cameraAttribute === void 0) { - model = new Object3D(); - } else { - let type = 0; - if (cameraAttribute.CameraProjectionType !== void 0 && cameraAttribute.CameraProjectionType.value === 1) { - type = 1; - } - let nearClippingPlane = 1; - if (cameraAttribute.NearPlane !== void 0) { - nearClippingPlane = cameraAttribute.NearPlane.value / 1e3; - } - let farClippingPlane = 1e3; - if (cameraAttribute.FarPlane !== void 0) { - farClippingPlane = cameraAttribute.FarPlane.value / 1e3; - } - let width = window.innerWidth; - let height = window.innerHeight; - if (cameraAttribute.AspectWidth !== void 0 && cameraAttribute.AspectHeight !== void 0) { - width = cameraAttribute.AspectWidth.value; - height = cameraAttribute.AspectHeight.value; - } - const aspect2 = width / height; - let fov2 = 45; - if (cameraAttribute.FieldOfView !== void 0) { - fov2 = cameraAttribute.FieldOfView.value; - } - const focalLength = cameraAttribute.FocalLength ? cameraAttribute.FocalLength.value : null; - switch (type) { - case 0: - model = new PerspectiveCamera(fov2, aspect2, nearClippingPlane, farClippingPlane); - if (focalLength !== null) model.setFocalLength(focalLength); - break; - case 1: - console.warn("THREE.FBXLoader: Orthographic cameras not supported yet."); - model = new Object3D(); - break; - default: - console.warn("THREE.FBXLoader: Unknown camera type " + type + "."); - model = new Object3D(); - break; - } - } - return model; - } - // Create a DirectionalLight, PointLight or SpotLight - createLight(relationships) { - let model; - let lightAttribute; - relationships.children.forEach(function(child) { - const attr = fbxTree.Objects.NodeAttribute[child.ID]; - if (attr !== void 0) { - lightAttribute = attr; - } - }); - if (lightAttribute === void 0) { - model = new Object3D(); - } else { - let type; - if (lightAttribute.LightType === void 0) { - type = 0; - } else { - type = lightAttribute.LightType.value; - } - let color = 16777215; - if (lightAttribute.Color !== void 0) { - color = ColorManagement.toWorkingColorSpace(new Color().fromArray(lightAttribute.Color.value), SRGBColorSpace); - } - let intensity = lightAttribute.Intensity === void 0 ? 1 : lightAttribute.Intensity.value / 100; - if (lightAttribute.CastLightOnObject !== void 0 && lightAttribute.CastLightOnObject.value === 0) { - intensity = 0; - } - let distance = 0; - if (lightAttribute.FarAttenuationEnd !== void 0) { - if (lightAttribute.EnableFarAttenuation !== void 0 && lightAttribute.EnableFarAttenuation.value === 0) { - distance = 0; - } else { - distance = lightAttribute.FarAttenuationEnd.value; - } - } - const decay = 1; - switch (type) { - case 0: - model = new PointLight(color, intensity, distance, decay); - break; - case 1: - model = new DirectionalLight(color, intensity); - break; - case 2: - let angle = Math.PI / 3; - if (lightAttribute.InnerAngle !== void 0) { - angle = MathUtils.degToRad(lightAttribute.InnerAngle.value); - } - let penumbra = 0; - if (lightAttribute.OuterAngle !== void 0) { - penumbra = MathUtils.degToRad(lightAttribute.OuterAngle.value); - penumbra = Math.max(penumbra, 1); - } - model = new SpotLight(color, intensity, distance, angle, penumbra, decay); - break; - default: - console.warn("THREE.FBXLoader: Unknown light type " + lightAttribute.LightType.value + ", defaulting to a PointLight."); - model = new PointLight(color, intensity); - break; - } - if (lightAttribute.CastShadows !== void 0 && lightAttribute.CastShadows.value === 1) { - model.castShadow = true; - } - } - return model; - } - createMesh(relationships, geometryMap, materialMap) { - let model; - let geometry = null; - let material = null; - const materials = []; - relationships.children.forEach(function(child) { - if (geometryMap.has(child.ID)) { - geometry = geometryMap.get(child.ID); - } - if (materialMap.has(child.ID)) { - materials.push(materialMap.get(child.ID)); - } - }); - if (materials.length > 1) { - material = materials; - } else if (materials.length > 0) { - material = materials[0]; - } else { - material = new MeshPhongMaterial({ - name: Loader.DEFAULT_MATERIAL_NAME, - color: 13421772 - }); - materials.push(material); - } - if ("color" in geometry.attributes) { - materials.forEach(function(material2) { - material2.vertexColors = true; - }); - } - if (geometry.FBX_Deformer) { - model = new SkinnedMesh(geometry, material); - model.normalizeSkinWeights(); - } else { - model = new Mesh(geometry, material); - } - return model; - } - createCurve(relationships, geometryMap) { - const geometry = relationships.children.reduce(function(geo, child) { - if (geometryMap.has(child.ID)) geo = geometryMap.get(child.ID); - return geo; - }, null); - const material = new LineBasicMaterial({ - name: Loader.DEFAULT_MATERIAL_NAME, - color: 3342591, - linewidth: 1 - }); - return new Line(geometry, material); - } - // parse the model node for transform data - getTransformData(model, modelNode) { - const transformData = {}; - if ("InheritType" in modelNode) transformData.inheritType = parseInt(modelNode.InheritType.value); - if ("RotationOrder" in modelNode) transformData.eulerOrder = getEulerOrder(modelNode.RotationOrder.value); - else transformData.eulerOrder = getEulerOrder(0); - if ("Lcl_Translation" in modelNode) transformData.translation = modelNode.Lcl_Translation.value; - if ("PreRotation" in modelNode) transformData.preRotation = modelNode.PreRotation.value; - if ("Lcl_Rotation" in modelNode) transformData.rotation = modelNode.Lcl_Rotation.value; - if ("PostRotation" in modelNode) transformData.postRotation = modelNode.PostRotation.value; - if ("Lcl_Scaling" in modelNode) transformData.scale = modelNode.Lcl_Scaling.value; - if ("ScalingOffset" in modelNode) transformData.scalingOffset = modelNode.ScalingOffset.value; - if ("ScalingPivot" in modelNode) transformData.scalingPivot = modelNode.ScalingPivot.value; - if ("RotationOffset" in modelNode) transformData.rotationOffset = modelNode.RotationOffset.value; - if ("RotationPivot" in modelNode) transformData.rotationPivot = modelNode.RotationPivot.value; - model.userData.transformData = transformData; - } - setLookAtProperties(model, modelNode) { - if ("LookAtProperty" in modelNode) { - const children = connections.get(model.ID).children; - children.forEach(function(child) { - if (child.relationship === "LookAtProperty") { - const lookAtTarget = fbxTree.Objects.Model[child.ID]; - if ("Lcl_Translation" in lookAtTarget) { - const pos = lookAtTarget.Lcl_Translation.value; - if (model.target !== void 0) { - model.target.position.fromArray(pos); - sceneGraph.add(model.target); - } else { - model.lookAt(new Vector3().fromArray(pos)); - } - } - } - }); - } - } - bindSkeleton(skeletons, geometryMap, modelMap) { - const bindMatrices = this.parsePoseNodes(); - for (const ID in skeletons) { - const skeleton = skeletons[ID]; - const parents = connections.get(parseInt(skeleton.ID)).parents; - parents.forEach(function(parent) { - if (geometryMap.has(parent.ID)) { - const geoID = parent.ID; - const geoRelationships = connections.get(geoID); - geoRelationships.parents.forEach(function(geoConnParent) { - if (modelMap.has(geoConnParent.ID)) { - const model = modelMap.get(geoConnParent.ID); - model.bind(new Skeleton(skeleton.bones), bindMatrices[geoConnParent.ID]); - } - }); - } - }); - } - } - parsePoseNodes() { - const bindMatrices = {}; - if ("Pose" in fbxTree.Objects) { - const BindPoseNode = fbxTree.Objects.Pose; - for (const nodeID in BindPoseNode) { - if (BindPoseNode[nodeID].attrType === "BindPose" && BindPoseNode[nodeID].NbPoseNodes > 0) { - const poseNodes = BindPoseNode[nodeID].PoseNode; - if (Array.isArray(poseNodes)) { - poseNodes.forEach(function(poseNode) { - bindMatrices[poseNode.Node] = new Matrix4().fromArray(poseNode.Matrix.a); - }); - } else { - bindMatrices[poseNodes.Node] = new Matrix4().fromArray(poseNodes.Matrix.a); - } - } - } - } - return bindMatrices; - } - addGlobalSceneSettings() { - if ("GlobalSettings" in fbxTree) { - if ("AmbientColor" in fbxTree.GlobalSettings) { - const ambientColor = fbxTree.GlobalSettings.AmbientColor.value; - const r = ambientColor[0]; - const g = ambientColor[1]; - const b = ambientColor[2]; - if (r !== 0 || g !== 0 || b !== 0) { - const color = new Color().setRGB(r, g, b, SRGBColorSpace); - sceneGraph.add(new AmbientLight(color, 1)); - } - } - if ("UnitScaleFactor" in fbxTree.GlobalSettings) { - sceneGraph.userData.unitScaleFactor = fbxTree.GlobalSettings.UnitScaleFactor.value; - } - } - } -} -class GeometryParser { - static { - __name(this, "GeometryParser"); - } - constructor() { - this.negativeMaterialIndices = false; - } - // Parse nodes in FBXTree.Objects.Geometry - parse(deformers) { - const geometryMap = /* @__PURE__ */ new Map(); - if ("Geometry" in fbxTree.Objects) { - const geoNodes = fbxTree.Objects.Geometry; - for (const nodeID in geoNodes) { - const relationships = connections.get(parseInt(nodeID)); - const geo = this.parseGeometry(relationships, geoNodes[nodeID], deformers); - geometryMap.set(parseInt(nodeID), geo); - } - } - if (this.negativeMaterialIndices === true) { - console.warn("THREE.FBXLoader: The FBX file contains invalid (negative) material indices. The asset might not render as expected."); - } - return geometryMap; - } - // Parse single node in FBXTree.Objects.Geometry - parseGeometry(relationships, geoNode, deformers) { - switch (geoNode.attrType) { - case "Mesh": - return this.parseMeshGeometry(relationships, geoNode, deformers); - break; - case "NurbsCurve": - return this.parseNurbsGeometry(geoNode); - break; - } - } - // Parse single node mesh geometry in FBXTree.Objects.Geometry - parseMeshGeometry(relationships, geoNode, deformers) { - const skeletons = deformers.skeletons; - const morphTargets = []; - const modelNodes = relationships.parents.map(function(parent) { - return fbxTree.Objects.Model[parent.ID]; - }); - if (modelNodes.length === 0) return; - const skeleton = relationships.children.reduce(function(skeleton2, child) { - if (skeletons[child.ID] !== void 0) skeleton2 = skeletons[child.ID]; - return skeleton2; - }, null); - relationships.children.forEach(function(child) { - if (deformers.morphTargets[child.ID] !== void 0) { - morphTargets.push(deformers.morphTargets[child.ID]); - } - }); - const modelNode = modelNodes[0]; - const transformData = {}; - if ("RotationOrder" in modelNode) transformData.eulerOrder = getEulerOrder(modelNode.RotationOrder.value); - if ("InheritType" in modelNode) transformData.inheritType = parseInt(modelNode.InheritType.value); - if ("GeometricTranslation" in modelNode) transformData.translation = modelNode.GeometricTranslation.value; - if ("GeometricRotation" in modelNode) transformData.rotation = modelNode.GeometricRotation.value; - if ("GeometricScaling" in modelNode) transformData.scale = modelNode.GeometricScaling.value; - const transform = generateTransform(transformData); - return this.genGeometry(geoNode, skeleton, morphTargets, transform); - } - // Generate a BufferGeometry from a node in FBXTree.Objects.Geometry - genGeometry(geoNode, skeleton, morphTargets, preTransform) { - const geo = new BufferGeometry(); - if (geoNode.attrName) geo.name = geoNode.attrName; - const geoInfo = this.parseGeoNode(geoNode, skeleton); - const buffers = this.genBuffers(geoInfo); - const positionAttribute = new Float32BufferAttribute(buffers.vertex, 3); - positionAttribute.applyMatrix4(preTransform); - geo.setAttribute("position", positionAttribute); - if (buffers.colors.length > 0) { - geo.setAttribute("color", new Float32BufferAttribute(buffers.colors, 3)); - } - if (skeleton) { - geo.setAttribute("skinIndex", new Uint16BufferAttribute(buffers.weightsIndices, 4)); - geo.setAttribute("skinWeight", new Float32BufferAttribute(buffers.vertexWeights, 4)); - geo.FBX_Deformer = skeleton; - } - if (buffers.normal.length > 0) { - const normalMatrix = new Matrix3().getNormalMatrix(preTransform); - const normalAttribute = new Float32BufferAttribute(buffers.normal, 3); - normalAttribute.applyNormalMatrix(normalMatrix); - geo.setAttribute("normal", normalAttribute); - } - buffers.uvs.forEach(function(uvBuffer, i) { - const name = i === 0 ? "uv" : `uv${i}`; - geo.setAttribute(name, new Float32BufferAttribute(buffers.uvs[i], 2)); - }); - if (geoInfo.material && geoInfo.material.mappingType !== "AllSame") { - let prevMaterialIndex = buffers.materialIndex[0]; - let startIndex = 0; - buffers.materialIndex.forEach(function(currentIndex, i) { - if (currentIndex !== prevMaterialIndex) { - geo.addGroup(startIndex, i - startIndex, prevMaterialIndex); - prevMaterialIndex = currentIndex; - startIndex = i; - } - }); - if (geo.groups.length > 0) { - const lastGroup = geo.groups[geo.groups.length - 1]; - const lastIndex = lastGroup.start + lastGroup.count; - if (lastIndex !== buffers.materialIndex.length) { - geo.addGroup(lastIndex, buffers.materialIndex.length - lastIndex, prevMaterialIndex); - } - } - if (geo.groups.length === 0) { - geo.addGroup(0, buffers.materialIndex.length, buffers.materialIndex[0]); - } - } - this.addMorphTargets(geo, geoNode, morphTargets, preTransform); - return geo; - } - parseGeoNode(geoNode, skeleton) { - const geoInfo = {}; - geoInfo.vertexPositions = geoNode.Vertices !== void 0 ? geoNode.Vertices.a : []; - geoInfo.vertexIndices = geoNode.PolygonVertexIndex !== void 0 ? geoNode.PolygonVertexIndex.a : []; - if (geoNode.LayerElementColor) { - geoInfo.color = this.parseVertexColors(geoNode.LayerElementColor[0]); - } - if (geoNode.LayerElementMaterial) { - geoInfo.material = this.parseMaterialIndices(geoNode.LayerElementMaterial[0]); - } - if (geoNode.LayerElementNormal) { - geoInfo.normal = this.parseNormals(geoNode.LayerElementNormal[0]); - } - if (geoNode.LayerElementUV) { - geoInfo.uv = []; - let i = 0; - while (geoNode.LayerElementUV[i]) { - if (geoNode.LayerElementUV[i].UV) { - geoInfo.uv.push(this.parseUVs(geoNode.LayerElementUV[i])); - } - i++; - } - } - geoInfo.weightTable = {}; - if (skeleton !== null) { - geoInfo.skeleton = skeleton; - skeleton.rawBones.forEach(function(rawBone, i) { - rawBone.indices.forEach(function(index, j) { - if (geoInfo.weightTable[index] === void 0) geoInfo.weightTable[index] = []; - geoInfo.weightTable[index].push({ - id: i, - weight: rawBone.weights[j] - }); - }); - }); - } - return geoInfo; - } - genBuffers(geoInfo) { - const buffers = { - vertex: [], - normal: [], - colors: [], - uvs: [], - materialIndex: [], - vertexWeights: [], - weightsIndices: [] - }; - let polygonIndex = 0; - let faceLength = 0; - let displayedWeightsWarning = false; - let facePositionIndexes = []; - let faceNormals = []; - let faceColors = []; - let faceUVs = []; - let faceWeights = []; - let faceWeightIndices = []; - const scope = this; - geoInfo.vertexIndices.forEach(function(vertexIndex, polygonVertexIndex) { - let materialIndex; - let endOfFace = false; - if (vertexIndex < 0) { - vertexIndex = vertexIndex ^ -1; - endOfFace = true; - } - let weightIndices = []; - let weights = []; - facePositionIndexes.push(vertexIndex * 3, vertexIndex * 3 + 1, vertexIndex * 3 + 2); - if (geoInfo.color) { - const data = getData(polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.color); - faceColors.push(data[0], data[1], data[2]); - } - if (geoInfo.skeleton) { - if (geoInfo.weightTable[vertexIndex] !== void 0) { - geoInfo.weightTable[vertexIndex].forEach(function(wt) { - weights.push(wt.weight); - weightIndices.push(wt.id); - }); - } - if (weights.length > 4) { - if (!displayedWeightsWarning) { - console.warn("THREE.FBXLoader: Vertex has more than 4 skinning weights assigned to vertex. Deleting additional weights."); - displayedWeightsWarning = true; - } - const wIndex = [0, 0, 0, 0]; - const Weight = [0, 0, 0, 0]; - weights.forEach(function(weight, weightIndex) { - let currentWeight = weight; - let currentIndex = weightIndices[weightIndex]; - Weight.forEach(function(comparedWeight, comparedWeightIndex, comparedWeightArray) { - if (currentWeight > comparedWeight) { - comparedWeightArray[comparedWeightIndex] = currentWeight; - currentWeight = comparedWeight; - const tmp2 = wIndex[comparedWeightIndex]; - wIndex[comparedWeightIndex] = currentIndex; - currentIndex = tmp2; - } - }); - }); - weightIndices = wIndex; - weights = Weight; - } - while (weights.length < 4) { - weights.push(0); - weightIndices.push(0); - } - for (let i = 0; i < 4; ++i) { - faceWeights.push(weights[i]); - faceWeightIndices.push(weightIndices[i]); - } - } - if (geoInfo.normal) { - const data = getData(polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.normal); - faceNormals.push(data[0], data[1], data[2]); - } - if (geoInfo.material && geoInfo.material.mappingType !== "AllSame") { - materialIndex = getData(polygonVertexIndex, polygonIndex, vertexIndex, geoInfo.material)[0]; - if (materialIndex < 0) { - scope.negativeMaterialIndices = true; - materialIndex = 0; - } - } - if (geoInfo.uv) { - geoInfo.uv.forEach(function(uv, i) { - const data = getData(polygonVertexIndex, polygonIndex, vertexIndex, uv); - if (faceUVs[i] === void 0) { - faceUVs[i] = []; - } - faceUVs[i].push(data[0]); - faceUVs[i].push(data[1]); - }); - } - faceLength++; - if (endOfFace) { - scope.genFace(buffers, geoInfo, facePositionIndexes, materialIndex, faceNormals, faceColors, faceUVs, faceWeights, faceWeightIndices, faceLength); - polygonIndex++; - faceLength = 0; - facePositionIndexes = []; - faceNormals = []; - faceColors = []; - faceUVs = []; - faceWeights = []; - faceWeightIndices = []; - } - }); - return buffers; - } - // See https://www.khronos.org/opengl/wiki/Calculating_a_Surface_Normal - getNormalNewell(vertices) { - const normal = new Vector3(0, 0, 0); - for (let i = 0; i < vertices.length; i++) { - const current = vertices[i]; - const next = vertices[(i + 1) % vertices.length]; - normal.x += (current.y - next.y) * (current.z + next.z); - normal.y += (current.z - next.z) * (current.x + next.x); - normal.z += (current.x - next.x) * (current.y + next.y); - } - normal.normalize(); - return normal; - } - getNormalTangentAndBitangent(vertices) { - const normalVector = this.getNormalNewell(vertices); - const up = Math.abs(normalVector.z) > 0.5 ? new Vector3(0, 1, 0) : new Vector3(0, 0, 1); - const tangent = up.cross(normalVector).normalize(); - const bitangent = normalVector.clone().cross(tangent).normalize(); - return { - normal: normalVector, - tangent, - bitangent - }; - } - flattenVertex(vertex2, normalTangent, normalBitangent) { - return new Vector2( - vertex2.dot(normalTangent), - vertex2.dot(normalBitangent) - ); - } - // Generate data for a single face in a geometry. If the face is a quad then split it into 2 tris - genFace(buffers, geoInfo, facePositionIndexes, materialIndex, faceNormals, faceColors, faceUVs, faceWeights, faceWeightIndices, faceLength) { - let triangles; - if (faceLength > 3) { - const vertices = []; - const positions = geoInfo.baseVertexPositions || geoInfo.vertexPositions; - for (let i = 0; i < facePositionIndexes.length; i += 3) { - vertices.push( - new Vector3( - positions[facePositionIndexes[i]], - positions[facePositionIndexes[i + 1]], - positions[facePositionIndexes[i + 2]] - ) - ); - } - const { tangent, bitangent } = this.getNormalTangentAndBitangent(vertices); - const triangulationInput = []; - for (const vertex2 of vertices) { - triangulationInput.push(this.flattenVertex(vertex2, tangent, bitangent)); - } - triangles = ShapeUtils.triangulateShape(triangulationInput, []); - } else { - triangles = [[0, 1, 2]]; - } - for (const [i0, i1, i2] of triangles) { - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i0 * 3]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i0 * 3 + 1]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i0 * 3 + 2]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i1 * 3]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i1 * 3 + 1]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i1 * 3 + 2]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i2 * 3]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i2 * 3 + 1]]); - buffers.vertex.push(geoInfo.vertexPositions[facePositionIndexes[i2 * 3 + 2]]); - if (geoInfo.skeleton) { - buffers.vertexWeights.push(faceWeights[i0 * 4]); - buffers.vertexWeights.push(faceWeights[i0 * 4 + 1]); - buffers.vertexWeights.push(faceWeights[i0 * 4 + 2]); - buffers.vertexWeights.push(faceWeights[i0 * 4 + 3]); - buffers.vertexWeights.push(faceWeights[i1 * 4]); - buffers.vertexWeights.push(faceWeights[i1 * 4 + 1]); - buffers.vertexWeights.push(faceWeights[i1 * 4 + 2]); - buffers.vertexWeights.push(faceWeights[i1 * 4 + 3]); - buffers.vertexWeights.push(faceWeights[i2 * 4]); - buffers.vertexWeights.push(faceWeights[i2 * 4 + 1]); - buffers.vertexWeights.push(faceWeights[i2 * 4 + 2]); - buffers.vertexWeights.push(faceWeights[i2 * 4 + 3]); - buffers.weightsIndices.push(faceWeightIndices[i0 * 4]); - buffers.weightsIndices.push(faceWeightIndices[i0 * 4 + 1]); - buffers.weightsIndices.push(faceWeightIndices[i0 * 4 + 2]); - buffers.weightsIndices.push(faceWeightIndices[i0 * 4 + 3]); - buffers.weightsIndices.push(faceWeightIndices[i1 * 4]); - buffers.weightsIndices.push(faceWeightIndices[i1 * 4 + 1]); - buffers.weightsIndices.push(faceWeightIndices[i1 * 4 + 2]); - buffers.weightsIndices.push(faceWeightIndices[i1 * 4 + 3]); - buffers.weightsIndices.push(faceWeightIndices[i2 * 4]); - buffers.weightsIndices.push(faceWeightIndices[i2 * 4 + 1]); - buffers.weightsIndices.push(faceWeightIndices[i2 * 4 + 2]); - buffers.weightsIndices.push(faceWeightIndices[i2 * 4 + 3]); - } - if (geoInfo.color) { - buffers.colors.push(faceColors[i0 * 3]); - buffers.colors.push(faceColors[i0 * 3 + 1]); - buffers.colors.push(faceColors[i0 * 3 + 2]); - buffers.colors.push(faceColors[i1 * 3]); - buffers.colors.push(faceColors[i1 * 3 + 1]); - buffers.colors.push(faceColors[i1 * 3 + 2]); - buffers.colors.push(faceColors[i2 * 3]); - buffers.colors.push(faceColors[i2 * 3 + 1]); - buffers.colors.push(faceColors[i2 * 3 + 2]); - } - if (geoInfo.material && geoInfo.material.mappingType !== "AllSame") { - buffers.materialIndex.push(materialIndex); - buffers.materialIndex.push(materialIndex); - buffers.materialIndex.push(materialIndex); - } - if (geoInfo.normal) { - buffers.normal.push(faceNormals[i0 * 3]); - buffers.normal.push(faceNormals[i0 * 3 + 1]); - buffers.normal.push(faceNormals[i0 * 3 + 2]); - buffers.normal.push(faceNormals[i1 * 3]); - buffers.normal.push(faceNormals[i1 * 3 + 1]); - buffers.normal.push(faceNormals[i1 * 3 + 2]); - buffers.normal.push(faceNormals[i2 * 3]); - buffers.normal.push(faceNormals[i2 * 3 + 1]); - buffers.normal.push(faceNormals[i2 * 3 + 2]); - } - if (geoInfo.uv) { - geoInfo.uv.forEach(function(uv, j) { - if (buffers.uvs[j] === void 0) buffers.uvs[j] = []; - buffers.uvs[j].push(faceUVs[j][i0 * 2]); - buffers.uvs[j].push(faceUVs[j][i0 * 2 + 1]); - buffers.uvs[j].push(faceUVs[j][i1 * 2]); - buffers.uvs[j].push(faceUVs[j][i1 * 2 + 1]); - buffers.uvs[j].push(faceUVs[j][i2 * 2]); - buffers.uvs[j].push(faceUVs[j][i2 * 2 + 1]); - }); - } - } - } - addMorphTargets(parentGeo, parentGeoNode, morphTargets, preTransform) { - if (morphTargets.length === 0) return; - parentGeo.morphTargetsRelative = true; - parentGeo.morphAttributes.position = []; - const scope = this; - morphTargets.forEach(function(morphTarget) { - morphTarget.rawTargets.forEach(function(rawTarget) { - const morphGeoNode = fbxTree.Objects.Geometry[rawTarget.geoID]; - if (morphGeoNode !== void 0) { - scope.genMorphGeometry(parentGeo, parentGeoNode, morphGeoNode, preTransform, rawTarget.name); - } - }); - }); - } - // a morph geometry node is similar to a standard node, and the node is also contained - // in FBXTree.Objects.Geometry, however it can only have attributes for position, normal - // and a special attribute Index defining which vertices of the original geometry are affected - // Normal and position attributes only have data for the vertices that are affected by the morph - genMorphGeometry(parentGeo, parentGeoNode, morphGeoNode, preTransform, name) { - const basePositions = parentGeoNode.Vertices !== void 0 ? parentGeoNode.Vertices.a : []; - const baseIndices = parentGeoNode.PolygonVertexIndex !== void 0 ? parentGeoNode.PolygonVertexIndex.a : []; - const morphPositionsSparse = morphGeoNode.Vertices !== void 0 ? morphGeoNode.Vertices.a : []; - const morphIndices = morphGeoNode.Indexes !== void 0 ? morphGeoNode.Indexes.a : []; - const length = parentGeo.attributes.position.count * 3; - const morphPositions = new Float32Array(length); - for (let i = 0; i < morphIndices.length; i++) { - const morphIndex = morphIndices[i] * 3; - morphPositions[morphIndex] = morphPositionsSparse[i * 3]; - morphPositions[morphIndex + 1] = morphPositionsSparse[i * 3 + 1]; - morphPositions[morphIndex + 2] = morphPositionsSparse[i * 3 + 2]; - } - const morphGeoInfo = { - vertexIndices: baseIndices, - vertexPositions: morphPositions, - baseVertexPositions: basePositions - }; - const morphBuffers = this.genBuffers(morphGeoInfo); - const positionAttribute = new Float32BufferAttribute(morphBuffers.vertex, 3); - positionAttribute.name = name || morphGeoNode.attrName; - positionAttribute.applyMatrix4(preTransform); - parentGeo.morphAttributes.position.push(positionAttribute); - } - // Parse normal from FBXTree.Objects.Geometry.LayerElementNormal if it exists - parseNormals(NormalNode) { - const mappingType = NormalNode.MappingInformationType; - const referenceType = NormalNode.ReferenceInformationType; - const buffer = NormalNode.Normals.a; - let indexBuffer = []; - if (referenceType === "IndexToDirect") { - if ("NormalIndex" in NormalNode) { - indexBuffer = NormalNode.NormalIndex.a; - } else if ("NormalsIndex" in NormalNode) { - indexBuffer = NormalNode.NormalsIndex.a; - } - } - return { - dataSize: 3, - buffer, - indices: indexBuffer, - mappingType, - referenceType - }; - } - // Parse UVs from FBXTree.Objects.Geometry.LayerElementUV if it exists - parseUVs(UVNode) { - const mappingType = UVNode.MappingInformationType; - const referenceType = UVNode.ReferenceInformationType; - const buffer = UVNode.UV.a; - let indexBuffer = []; - if (referenceType === "IndexToDirect") { - indexBuffer = UVNode.UVIndex.a; - } - return { - dataSize: 2, - buffer, - indices: indexBuffer, - mappingType, - referenceType - }; - } - // Parse Vertex Colors from FBXTree.Objects.Geometry.LayerElementColor if it exists - parseVertexColors(ColorNode) { - const mappingType = ColorNode.MappingInformationType; - const referenceType = ColorNode.ReferenceInformationType; - const buffer = ColorNode.Colors.a; - let indexBuffer = []; - if (referenceType === "IndexToDirect") { - indexBuffer = ColorNode.ColorIndex.a; - } - for (let i = 0, c = new Color(); i < buffer.length; i += 4) { - c.fromArray(buffer, i); - ColorManagement.toWorkingColorSpace(c, SRGBColorSpace); - c.toArray(buffer, i); - } - return { - dataSize: 4, - buffer, - indices: indexBuffer, - mappingType, - referenceType - }; - } - // Parse mapping and material data in FBXTree.Objects.Geometry.LayerElementMaterial if it exists - parseMaterialIndices(MaterialNode) { - const mappingType = MaterialNode.MappingInformationType; - const referenceType = MaterialNode.ReferenceInformationType; - if (mappingType === "NoMappingInformation") { - return { - dataSize: 1, - buffer: [0], - indices: [0], - mappingType: "AllSame", - referenceType - }; - } - const materialIndexBuffer = MaterialNode.Materials.a; - const materialIndices = []; - for (let i = 0; i < materialIndexBuffer.length; ++i) { - materialIndices.push(i); - } - return { - dataSize: 1, - buffer: materialIndexBuffer, - indices: materialIndices, - mappingType, - referenceType - }; - } - // Generate a NurbGeometry from a node in FBXTree.Objects.Geometry - parseNurbsGeometry(geoNode) { - const order = parseInt(geoNode.Order); - if (isNaN(order)) { - console.error("THREE.FBXLoader: Invalid Order %s given for geometry ID: %s", geoNode.Order, geoNode.id); - return new BufferGeometry(); - } - const degree = order - 1; - const knots = geoNode.KnotVector.a; - const controlPoints = []; - const pointsValues = geoNode.Points.a; - for (let i = 0, l = pointsValues.length; i < l; i += 4) { - controlPoints.push(new Vector4().fromArray(pointsValues, i)); - } - let startKnot, endKnot; - if (geoNode.Form === "Closed") { - controlPoints.push(controlPoints[0]); - } else if (geoNode.Form === "Periodic") { - startKnot = degree; - endKnot = knots.length - 1 - startKnot; - for (let i = 0; i < degree; ++i) { - controlPoints.push(controlPoints[i]); - } - } - const curve = new NURBSCurve(degree, knots, controlPoints, startKnot, endKnot); - const points = curve.getPoints(controlPoints.length * 12); - return new BufferGeometry().setFromPoints(points); - } -} -class AnimationParser { - static { - __name(this, "AnimationParser"); - } - // take raw animation clips and turn them into three.js animation clips - parse() { - const animationClips = []; - const rawClips = this.parseClips(); - if (rawClips !== void 0) { - for (const key in rawClips) { - const rawClip = rawClips[key]; - const clip = this.addClip(rawClip); - animationClips.push(clip); - } - } - return animationClips; - } - parseClips() { - if (fbxTree.Objects.AnimationCurve === void 0) return void 0; - const curveNodesMap = this.parseAnimationCurveNodes(); - this.parseAnimationCurves(curveNodesMap); - const layersMap = this.parseAnimationLayers(curveNodesMap); - const rawClips = this.parseAnimStacks(layersMap); - return rawClips; - } - // parse nodes in FBXTree.Objects.AnimationCurveNode - // each AnimationCurveNode holds data for an animation transform for a model (e.g. left arm rotation ) - // and is referenced by an AnimationLayer - parseAnimationCurveNodes() { - const rawCurveNodes = fbxTree.Objects.AnimationCurveNode; - const curveNodesMap = /* @__PURE__ */ new Map(); - for (const nodeID in rawCurveNodes) { - const rawCurveNode = rawCurveNodes[nodeID]; - if (rawCurveNode.attrName.match(/S|R|T|DeformPercent/) !== null) { - const curveNode = { - id: rawCurveNode.id, - attr: rawCurveNode.attrName, - curves: {} - }; - curveNodesMap.set(curveNode.id, curveNode); - } - } - return curveNodesMap; - } - // parse nodes in FBXTree.Objects.AnimationCurve and connect them up to - // previously parsed AnimationCurveNodes. Each AnimationCurve holds data for a single animated - // axis ( e.g. times and values of x rotation) - parseAnimationCurves(curveNodesMap) { - const rawCurves = fbxTree.Objects.AnimationCurve; - for (const nodeID in rawCurves) { - const animationCurve = { - id: rawCurves[nodeID].id, - times: rawCurves[nodeID].KeyTime.a.map(convertFBXTimeToSeconds), - values: rawCurves[nodeID].KeyValueFloat.a - }; - const relationships = connections.get(animationCurve.id); - if (relationships !== void 0) { - const animationCurveID = relationships.parents[0].ID; - const animationCurveRelationship = relationships.parents[0].relationship; - if (animationCurveRelationship.match(/X/)) { - curveNodesMap.get(animationCurveID).curves["x"] = animationCurve; - } else if (animationCurveRelationship.match(/Y/)) { - curveNodesMap.get(animationCurveID).curves["y"] = animationCurve; - } else if (animationCurveRelationship.match(/Z/)) { - curveNodesMap.get(animationCurveID).curves["z"] = animationCurve; - } else if (animationCurveRelationship.match(/DeformPercent/) && curveNodesMap.has(animationCurveID)) { - curveNodesMap.get(animationCurveID).curves["morph"] = animationCurve; - } - } - } - } - // parse nodes in FBXTree.Objects.AnimationLayer. Each layers holds references - // to various AnimationCurveNodes and is referenced by an AnimationStack node - // note: theoretically a stack can have multiple layers, however in practice there always seems to be one per stack - parseAnimationLayers(curveNodesMap) { - const rawLayers = fbxTree.Objects.AnimationLayer; - const layersMap = /* @__PURE__ */ new Map(); - for (const nodeID in rawLayers) { - const layerCurveNodes = []; - const connection = connections.get(parseInt(nodeID)); - if (connection !== void 0) { - const children = connection.children; - children.forEach(function(child, i) { - if (curveNodesMap.has(child.ID)) { - const curveNode = curveNodesMap.get(child.ID); - if (curveNode.curves.x !== void 0 || curveNode.curves.y !== void 0 || curveNode.curves.z !== void 0) { - if (layerCurveNodes[i] === void 0) { - const modelID = connections.get(child.ID).parents.filter(function(parent) { - return parent.relationship !== void 0; - })[0].ID; - if (modelID !== void 0) { - const rawModel = fbxTree.Objects.Model[modelID.toString()]; - if (rawModel === void 0) { - console.warn("THREE.FBXLoader: Encountered a unused curve.", child); - return; - } - const node = { - modelName: rawModel.attrName ? PropertyBinding.sanitizeNodeName(rawModel.attrName) : "", - ID: rawModel.id, - initialPosition: [0, 0, 0], - initialRotation: [0, 0, 0], - initialScale: [1, 1, 1] - }; - sceneGraph.traverse(function(child2) { - if (child2.ID === rawModel.id) { - node.transform = child2.matrix; - if (child2.userData.transformData) node.eulerOrder = child2.userData.transformData.eulerOrder; - } - }); - if (!node.transform) node.transform = new Matrix4(); - if ("PreRotation" in rawModel) node.preRotation = rawModel.PreRotation.value; - if ("PostRotation" in rawModel) node.postRotation = rawModel.PostRotation.value; - layerCurveNodes[i] = node; - } - } - if (layerCurveNodes[i]) layerCurveNodes[i][curveNode.attr] = curveNode; - } else if (curveNode.curves.morph !== void 0) { - if (layerCurveNodes[i] === void 0) { - const deformerID = connections.get(child.ID).parents.filter(function(parent) { - return parent.relationship !== void 0; - })[0].ID; - const morpherID = connections.get(deformerID).parents[0].ID; - const geoID = connections.get(morpherID).parents[0].ID; - const modelID = connections.get(geoID).parents[0].ID; - const rawModel = fbxTree.Objects.Model[modelID]; - const node = { - modelName: rawModel.attrName ? PropertyBinding.sanitizeNodeName(rawModel.attrName) : "", - morphName: fbxTree.Objects.Deformer[deformerID].attrName - }; - layerCurveNodes[i] = node; - } - layerCurveNodes[i][curveNode.attr] = curveNode; - } - } - }); - layersMap.set(parseInt(nodeID), layerCurveNodes); - } - } - return layersMap; - } - // parse nodes in FBXTree.Objects.AnimationStack. These are the top level node in the animation - // hierarchy. Each Stack node will be used to create a AnimationClip - parseAnimStacks(layersMap) { - const rawStacks = fbxTree.Objects.AnimationStack; - const rawClips = {}; - for (const nodeID in rawStacks) { - const children = connections.get(parseInt(nodeID)).children; - if (children.length > 1) { - console.warn("THREE.FBXLoader: Encountered an animation stack with multiple layers, this is currently not supported. Ignoring subsequent layers."); - } - const layer = layersMap.get(children[0].ID); - rawClips[nodeID] = { - name: rawStacks[nodeID].attrName, - layer - }; - } - return rawClips; - } - addClip(rawClip) { - let tracks = []; - const scope = this; - rawClip.layer.forEach(function(rawTracks) { - tracks = tracks.concat(scope.generateTracks(rawTracks)); - }); - return new AnimationClip(rawClip.name, -1, tracks); - } - generateTracks(rawTracks) { - const tracks = []; - let initialPosition = new Vector3(); - let initialScale = new Vector3(); - if (rawTracks.transform) rawTracks.transform.decompose(initialPosition, new Quaternion(), initialScale); - initialPosition = initialPosition.toArray(); - initialScale = initialScale.toArray(); - if (rawTracks.T !== void 0 && Object.keys(rawTracks.T.curves).length > 0) { - const positionTrack = this.generateVectorTrack(rawTracks.modelName, rawTracks.T.curves, initialPosition, "position"); - if (positionTrack !== void 0) tracks.push(positionTrack); - } - if (rawTracks.R !== void 0 && Object.keys(rawTracks.R.curves).length > 0) { - const rotationTrack = this.generateRotationTrack(rawTracks.modelName, rawTracks.R.curves, rawTracks.preRotation, rawTracks.postRotation, rawTracks.eulerOrder); - if (rotationTrack !== void 0) tracks.push(rotationTrack); - } - if (rawTracks.S !== void 0 && Object.keys(rawTracks.S.curves).length > 0) { - const scaleTrack = this.generateVectorTrack(rawTracks.modelName, rawTracks.S.curves, initialScale, "scale"); - if (scaleTrack !== void 0) tracks.push(scaleTrack); - } - if (rawTracks.DeformPercent !== void 0) { - const morphTrack = this.generateMorphTrack(rawTracks); - if (morphTrack !== void 0) tracks.push(morphTrack); - } - return tracks; - } - generateVectorTrack(modelName, curves, initialValue, type) { - const times = this.getTimesForAllAxes(curves); - const values = this.getKeyframeTrackValues(times, curves, initialValue); - return new VectorKeyframeTrack(modelName + "." + type, times, values); - } - generateRotationTrack(modelName, curves, preRotation, postRotation, eulerOrder) { - let times; - let values; - if (curves.x !== void 0 && curves.y !== void 0 && curves.z !== void 0) { - const result = this.interpolateRotations(curves.x, curves.y, curves.z, eulerOrder); - times = result[0]; - values = result[1]; - } - const defaultEulerOrder = getEulerOrder(0); - if (preRotation !== void 0) { - preRotation = preRotation.map(MathUtils.degToRad); - preRotation.push(defaultEulerOrder); - preRotation = new Euler().fromArray(preRotation); - preRotation = new Quaternion().setFromEuler(preRotation); - } - if (postRotation !== void 0) { - postRotation = postRotation.map(MathUtils.degToRad); - postRotation.push(defaultEulerOrder); - postRotation = new Euler().fromArray(postRotation); - postRotation = new Quaternion().setFromEuler(postRotation).invert(); - } - const quaternion = new Quaternion(); - const euler = new Euler(); - const quaternionValues = []; - if (!values || !times) return new QuaternionKeyframeTrack(modelName + ".quaternion", [0], [0]); - for (let i = 0; i < values.length; i += 3) { - euler.set(values[i], values[i + 1], values[i + 2], eulerOrder); - quaternion.setFromEuler(euler); - if (preRotation !== void 0) quaternion.premultiply(preRotation); - if (postRotation !== void 0) quaternion.multiply(postRotation); - if (i > 2) { - const prevQuat = new Quaternion().fromArray( - quaternionValues, - (i - 3) / 3 * 4 - ); - if (prevQuat.dot(quaternion) < 0) { - quaternion.set(-quaternion.x, -quaternion.y, -quaternion.z, -quaternion.w); - } - } - quaternion.toArray(quaternionValues, i / 3 * 4); - } - return new QuaternionKeyframeTrack(modelName + ".quaternion", times, quaternionValues); - } - generateMorphTrack(rawTracks) { - const curves = rawTracks.DeformPercent.curves.morph; - const values = curves.values.map(function(val) { - return val / 100; - }); - const morphNum = sceneGraph.getObjectByName(rawTracks.modelName).morphTargetDictionary[rawTracks.morphName]; - return new NumberKeyframeTrack(rawTracks.modelName + ".morphTargetInfluences[" + morphNum + "]", curves.times, values); - } - // For all animated objects, times are defined separately for each axis - // Here we'll combine the times into one sorted array without duplicates - getTimesForAllAxes(curves) { - let times = []; - if (curves.x !== void 0) times = times.concat(curves.x.times); - if (curves.y !== void 0) times = times.concat(curves.y.times); - if (curves.z !== void 0) times = times.concat(curves.z.times); - times = times.sort(function(a, b) { - return a - b; - }); - if (times.length > 1) { - let targetIndex = 1; - let lastValue = times[0]; - for (let i = 1; i < times.length; i++) { - const currentValue = times[i]; - if (currentValue !== lastValue) { - times[targetIndex] = currentValue; - lastValue = currentValue; - targetIndex++; - } - } - times = times.slice(0, targetIndex); - } - return times; - } - getKeyframeTrackValues(times, curves, initialValue) { - const prevValue = initialValue; - const values = []; - let xIndex = -1; - let yIndex = -1; - let zIndex = -1; - times.forEach(function(time) { - if (curves.x) xIndex = curves.x.times.indexOf(time); - if (curves.y) yIndex = curves.y.times.indexOf(time); - if (curves.z) zIndex = curves.z.times.indexOf(time); - if (xIndex !== -1) { - const xValue = curves.x.values[xIndex]; - values.push(xValue); - prevValue[0] = xValue; - } else { - values.push(prevValue[0]); - } - if (yIndex !== -1) { - const yValue = curves.y.values[yIndex]; - values.push(yValue); - prevValue[1] = yValue; - } else { - values.push(prevValue[1]); - } - if (zIndex !== -1) { - const zValue = curves.z.values[zIndex]; - values.push(zValue); - prevValue[2] = zValue; - } else { - values.push(prevValue[2]); - } - }); - return values; - } - // Rotations are defined as Euler angles which can have values of any size - // These will be converted to quaternions which don't support values greater than - // PI, so we'll interpolate large rotations - interpolateRotations(curvex, curvey, curvez, eulerOrder) { - const times = []; - const values = []; - times.push(curvex.times[0]); - values.push(MathUtils.degToRad(curvex.values[0])); - values.push(MathUtils.degToRad(curvey.values[0])); - values.push(MathUtils.degToRad(curvez.values[0])); - for (let i = 1; i < curvex.values.length; i++) { - const initialValue = [ - curvex.values[i - 1], - curvey.values[i - 1], - curvez.values[i - 1] - ]; - if (isNaN(initialValue[0]) || isNaN(initialValue[1]) || isNaN(initialValue[2])) { - continue; - } - const initialValueRad = initialValue.map(MathUtils.degToRad); - const currentValue = [ - curvex.values[i], - curvey.values[i], - curvez.values[i] - ]; - if (isNaN(currentValue[0]) || isNaN(currentValue[1]) || isNaN(currentValue[2])) { - continue; - } - const currentValueRad = currentValue.map(MathUtils.degToRad); - const valuesSpan = [ - currentValue[0] - initialValue[0], - currentValue[1] - initialValue[1], - currentValue[2] - initialValue[2] - ]; - const absoluteSpan = [ - Math.abs(valuesSpan[0]), - Math.abs(valuesSpan[1]), - Math.abs(valuesSpan[2]) - ]; - if (absoluteSpan[0] >= 180 || absoluteSpan[1] >= 180 || absoluteSpan[2] >= 180) { - const maxAbsSpan = Math.max(...absoluteSpan); - const numSubIntervals = maxAbsSpan / 180; - const E1 = new Euler(...initialValueRad, eulerOrder); - const E2 = new Euler(...currentValueRad, eulerOrder); - const Q1 = new Quaternion().setFromEuler(E1); - const Q2 = new Quaternion().setFromEuler(E2); - if (Q1.dot(Q2)) { - Q2.set(-Q2.x, -Q2.y, -Q2.z, -Q2.w); - } - const initialTime = curvex.times[i - 1]; - const timeSpan = curvex.times[i] - initialTime; - const Q = new Quaternion(); - const E = new Euler(); - for (let t2 = 0; t2 < 1; t2 += 1 / numSubIntervals) { - Q.copy(Q1.clone().slerp(Q2.clone(), t2)); - times.push(initialTime + t2 * timeSpan); - E.setFromQuaternion(Q, eulerOrder); - values.push(E.x); - values.push(E.y); - values.push(E.z); - } - } else { - times.push(curvex.times[i]); - values.push(MathUtils.degToRad(curvex.values[i])); - values.push(MathUtils.degToRad(curvey.values[i])); - values.push(MathUtils.degToRad(curvez.values[i])); - } - } - return [times, values]; - } -} -class TextParser { - static { - __name(this, "TextParser"); - } - getPrevNode() { - return this.nodeStack[this.currentIndent - 2]; - } - getCurrentNode() { - return this.nodeStack[this.currentIndent - 1]; - } - getCurrentProp() { - return this.currentProp; - } - pushStack(node) { - this.nodeStack.push(node); - this.currentIndent += 1; - } - popStack() { - this.nodeStack.pop(); - this.currentIndent -= 1; - } - setCurrentProp(val, name) { - this.currentProp = val; - this.currentPropName = name; - } - parse(text) { - this.currentIndent = 0; - this.allNodes = new FBXTree(); - this.nodeStack = []; - this.currentProp = []; - this.currentPropName = ""; - const scope = this; - const split = text.split(/[\r\n]+/); - split.forEach(function(line, i) { - const matchComment = line.match(/^[\s\t]*;/); - const matchEmpty = line.match(/^[\s\t]*$/); - if (matchComment || matchEmpty) return; - const matchBeginning = line.match("^\\t{" + scope.currentIndent + "}(\\w+):(.*){", ""); - const matchProperty = line.match("^\\t{" + scope.currentIndent + "}(\\w+):[\\s\\t\\r\\n](.*)"); - const matchEnd = line.match("^\\t{" + (scope.currentIndent - 1) + "}}"); - if (matchBeginning) { - scope.parseNodeBegin(line, matchBeginning); - } else if (matchProperty) { - scope.parseNodeProperty(line, matchProperty, split[++i]); - } else if (matchEnd) { - scope.popStack(); - } else if (line.match(/^[^\s\t}]/)) { - scope.parseNodePropertyContinued(line); - } - }); - return this.allNodes; - } - parseNodeBegin(line, property) { - const nodeName = property[1].trim().replace(/^"/, "").replace(/"$/, ""); - const nodeAttrs = property[2].split(",").map(function(attr) { - return attr.trim().replace(/^"/, "").replace(/"$/, ""); - }); - const node = { name: nodeName }; - const attrs = this.parseNodeAttr(nodeAttrs); - const currentNode = this.getCurrentNode(); - if (this.currentIndent === 0) { - this.allNodes.add(nodeName, node); - } else { - if (nodeName in currentNode) { - if (nodeName === "PoseNode") { - currentNode.PoseNode.push(node); - } else if (currentNode[nodeName].id !== void 0) { - currentNode[nodeName] = {}; - currentNode[nodeName][currentNode[nodeName].id] = currentNode[nodeName]; - } - if (attrs.id !== "") currentNode[nodeName][attrs.id] = node; - } else if (typeof attrs.id === "number") { - currentNode[nodeName] = {}; - currentNode[nodeName][attrs.id] = node; - } else if (nodeName !== "Properties70") { - if (nodeName === "PoseNode") currentNode[nodeName] = [node]; - else currentNode[nodeName] = node; - } - } - if (typeof attrs.id === "number") node.id = attrs.id; - if (attrs.name !== "") node.attrName = attrs.name; - if (attrs.type !== "") node.attrType = attrs.type; - this.pushStack(node); - } - parseNodeAttr(attrs) { - let id2 = attrs[0]; - if (attrs[0] !== "") { - id2 = parseInt(attrs[0]); - if (isNaN(id2)) { - id2 = attrs[0]; - } - } - let name = "", type = ""; - if (attrs.length > 1) { - name = attrs[1].replace(/^(\w+)::/, ""); - type = attrs[2]; - } - return { id: id2, name, type }; - } - parseNodeProperty(line, property, contentLine) { - let propName = property[1].replace(/^"/, "").replace(/"$/, "").trim(); - let propValue = property[2].replace(/^"/, "").replace(/"$/, "").trim(); - if (propName === "Content" && propValue === ",") { - propValue = contentLine.replace(/"/g, "").replace(/,$/, "").trim(); - } - const currentNode = this.getCurrentNode(); - const parentName = currentNode.name; - if (parentName === "Properties70") { - this.parseNodeSpecialProperty(line, propName, propValue); - return; - } - if (propName === "C") { - const connProps = propValue.split(",").slice(1); - const from = parseInt(connProps[0]); - const to = parseInt(connProps[1]); - let rest = propValue.split(",").slice(3); - rest = rest.map(function(elem) { - return elem.trim().replace(/^"/, ""); - }); - propName = "connections"; - propValue = [from, to]; - append(propValue, rest); - if (currentNode[propName] === void 0) { - currentNode[propName] = []; - } - } - if (propName === "Node") currentNode.id = propValue; - if (propName in currentNode && Array.isArray(currentNode[propName])) { - currentNode[propName].push(propValue); - } else { - if (propName !== "a") currentNode[propName] = propValue; - else currentNode.a = propValue; - } - this.setCurrentProp(currentNode, propName); - if (propName === "a" && propValue.slice(-1) !== ",") { - currentNode.a = parseNumberArray(propValue); - } - } - parseNodePropertyContinued(line) { - const currentNode = this.getCurrentNode(); - currentNode.a += line; - if (line.slice(-1) !== ",") { - currentNode.a = parseNumberArray(currentNode.a); - } - } - // parse "Property70" - parseNodeSpecialProperty(line, propName, propValue) { - const props = propValue.split('",').map(function(prop) { - return prop.trim().replace(/^\"/, "").replace(/\s/, "_"); - }); - const innerPropName = props[0]; - const innerPropType1 = props[1]; - const innerPropType2 = props[2]; - const innerPropFlag = props[3]; - let innerPropValue = props[4]; - switch (innerPropType1) { - case "int": - case "enum": - case "bool": - case "ULongLong": - case "double": - case "Number": - case "FieldOfView": - innerPropValue = parseFloat(innerPropValue); - break; - case "Color": - case "ColorRGB": - case "Vector3D": - case "Lcl_Translation": - case "Lcl_Rotation": - case "Lcl_Scaling": - innerPropValue = parseNumberArray(innerPropValue); - break; - } - this.getPrevNode()[innerPropName] = { - "type": innerPropType1, - "type2": innerPropType2, - "flag": innerPropFlag, - "value": innerPropValue - }; - this.setCurrentProp(this.getPrevNode(), innerPropName); - } -} -class BinaryParser { - static { - __name(this, "BinaryParser"); - } - parse(buffer) { - const reader = new BinaryReader(buffer); - reader.skip(23); - const version = reader.getUint32(); - if (version < 6400) { - throw new Error("THREE.FBXLoader: FBX version not supported, FileVersion: " + version); - } - const allNodes = new FBXTree(); - while (!this.endOfContent(reader)) { - const node = this.parseNode(reader, version); - if (node !== null) allNodes.add(node.name, node); - } - return allNodes; - } - // Check if reader has reached the end of content. - endOfContent(reader) { - if (reader.size() % 16 === 0) { - return (reader.getOffset() + 160 + 16 & ~15) >= reader.size(); - } else { - return reader.getOffset() + 160 + 16 >= reader.size(); - } - } - // recursively parse nodes until the end of the file is reached - parseNode(reader, version) { - const node = {}; - const endOffset = version >= 7500 ? reader.getUint64() : reader.getUint32(); - const numProperties = version >= 7500 ? reader.getUint64() : reader.getUint32(); - version >= 7500 ? reader.getUint64() : reader.getUint32(); - const nameLen = reader.getUint8(); - const name = reader.getString(nameLen); - if (endOffset === 0) return null; - const propertyList = []; - for (let i = 0; i < numProperties; i++) { - propertyList.push(this.parseProperty(reader)); - } - const id2 = propertyList.length > 0 ? propertyList[0] : ""; - const attrName = propertyList.length > 1 ? propertyList[1] : ""; - const attrType = propertyList.length > 2 ? propertyList[2] : ""; - node.singleProperty = numProperties === 1 && reader.getOffset() === endOffset ? true : false; - while (endOffset > reader.getOffset()) { - const subNode = this.parseNode(reader, version); - if (subNode !== null) this.parseSubNode(name, node, subNode); - } - node.propertyList = propertyList; - if (typeof id2 === "number") node.id = id2; - if (attrName !== "") node.attrName = attrName; - if (attrType !== "") node.attrType = attrType; - if (name !== "") node.name = name; - return node; - } - parseSubNode(name, node, subNode) { - if (subNode.singleProperty === true) { - const value = subNode.propertyList[0]; - if (Array.isArray(value)) { - node[subNode.name] = subNode; - subNode.a = value; - } else { - node[subNode.name] = value; - } - } else if (name === "Connections" && subNode.name === "C") { - const array = []; - subNode.propertyList.forEach(function(property, i) { - if (i !== 0) array.push(property); - }); - if (node.connections === void 0) { - node.connections = []; - } - node.connections.push(array); - } else if (subNode.name === "Properties70") { - const keys = Object.keys(subNode); - keys.forEach(function(key) { - node[key] = subNode[key]; - }); - } else if (name === "Properties70" && subNode.name === "P") { - let innerPropName = subNode.propertyList[0]; - let innerPropType1 = subNode.propertyList[1]; - const innerPropType2 = subNode.propertyList[2]; - const innerPropFlag = subNode.propertyList[3]; - let innerPropValue; - if (innerPropName.indexOf("Lcl ") === 0) innerPropName = innerPropName.replace("Lcl ", "Lcl_"); - if (innerPropType1.indexOf("Lcl ") === 0) innerPropType1 = innerPropType1.replace("Lcl ", "Lcl_"); - if (innerPropType1 === "Color" || innerPropType1 === "ColorRGB" || innerPropType1 === "Vector" || innerPropType1 === "Vector3D" || innerPropType1.indexOf("Lcl_") === 0) { - innerPropValue = [ - subNode.propertyList[4], - subNode.propertyList[5], - subNode.propertyList[6] - ]; - } else { - innerPropValue = subNode.propertyList[4]; - } - node[innerPropName] = { - "type": innerPropType1, - "type2": innerPropType2, - "flag": innerPropFlag, - "value": innerPropValue - }; - } else if (node[subNode.name] === void 0) { - if (typeof subNode.id === "number") { - node[subNode.name] = {}; - node[subNode.name][subNode.id] = subNode; - } else { - node[subNode.name] = subNode; - } - } else { - if (subNode.name === "PoseNode") { - if (!Array.isArray(node[subNode.name])) { - node[subNode.name] = [node[subNode.name]]; - } - node[subNode.name].push(subNode); - } else if (node[subNode.name][subNode.id] === void 0) { - node[subNode.name][subNode.id] = subNode; - } - } - } - parseProperty(reader) { - const type = reader.getString(1); - let length; - switch (type) { - case "C": - return reader.getBoolean(); - case "D": - return reader.getFloat64(); - case "F": - return reader.getFloat32(); - case "I": - return reader.getInt32(); - case "L": - return reader.getInt64(); - case "R": - length = reader.getUint32(); - return reader.getArrayBuffer(length); - case "S": - length = reader.getUint32(); - return reader.getString(length); - case "Y": - return reader.getInt16(); - case "b": - case "c": - case "d": - case "f": - case "i": - case "l": - const arrayLength = reader.getUint32(); - const encoding = reader.getUint32(); - const compressedLength = reader.getUint32(); - if (encoding === 0) { - switch (type) { - case "b": - case "c": - return reader.getBooleanArray(arrayLength); - case "d": - return reader.getFloat64Array(arrayLength); - case "f": - return reader.getFloat32Array(arrayLength); - case "i": - return reader.getInt32Array(arrayLength); - case "l": - return reader.getInt64Array(arrayLength); - } - } - const data = unzlibSync(new Uint8Array(reader.getArrayBuffer(compressedLength))); - const reader2 = new BinaryReader(data.buffer); - switch (type) { - case "b": - case "c": - return reader2.getBooleanArray(arrayLength); - case "d": - return reader2.getFloat64Array(arrayLength); - case "f": - return reader2.getFloat32Array(arrayLength); - case "i": - return reader2.getInt32Array(arrayLength); - case "l": - return reader2.getInt64Array(arrayLength); - } - break; - default: - throw new Error("THREE.FBXLoader: Unknown property type " + type); - } - } -} -class BinaryReader { - static { - __name(this, "BinaryReader"); - } - constructor(buffer, littleEndian) { - this.dv = new DataView(buffer); - this.offset = 0; - this.littleEndian = littleEndian !== void 0 ? littleEndian : true; - this._textDecoder = new TextDecoder(); - } - getOffset() { - return this.offset; - } - size() { - return this.dv.buffer.byteLength; - } - skip(length) { - this.offset += length; - } - // seems like true/false representation depends on exporter. - // true: 1 or 'Y'(=0x59), false: 0 or 'T'(=0x54) - // then sees LSB. - getBoolean() { - return (this.getUint8() & 1) === 1; - } - getBooleanArray(size) { - const a = []; - for (let i = 0; i < size; i++) { - a.push(this.getBoolean()); - } - return a; - } - getUint8() { - const value = this.dv.getUint8(this.offset); - this.offset += 1; - return value; - } - getInt16() { - const value = this.dv.getInt16(this.offset, this.littleEndian); - this.offset += 2; - return value; - } - getInt32() { - const value = this.dv.getInt32(this.offset, this.littleEndian); - this.offset += 4; - return value; - } - getInt32Array(size) { - const a = []; - for (let i = 0; i < size; i++) { - a.push(this.getInt32()); - } - return a; - } - getUint32() { - const value = this.dv.getUint32(this.offset, this.littleEndian); - this.offset += 4; - return value; - } - // JavaScript doesn't support 64-bit integer so calculate this here - // 1 << 32 will return 1 so using multiply operation instead here. - // There's a possibility that this method returns wrong value if the value - // is out of the range between Number.MAX_SAFE_INTEGER and Number.MIN_SAFE_INTEGER. - // TODO: safely handle 64-bit integer - getInt64() { - let low, high; - if (this.littleEndian) { - low = this.getUint32(); - high = this.getUint32(); - } else { - high = this.getUint32(); - low = this.getUint32(); - } - if (high & 2147483648) { - high = ~high & 4294967295; - low = ~low & 4294967295; - if (low === 4294967295) high = high + 1 & 4294967295; - low = low + 1 & 4294967295; - return -(high * 4294967296 + low); - } - return high * 4294967296 + low; - } - getInt64Array(size) { - const a = []; - for (let i = 0; i < size; i++) { - a.push(this.getInt64()); - } - return a; - } - // Note: see getInt64() comment - getUint64() { - let low, high; - if (this.littleEndian) { - low = this.getUint32(); - high = this.getUint32(); - } else { - high = this.getUint32(); - low = this.getUint32(); - } - return high * 4294967296 + low; - } - getFloat32() { - const value = this.dv.getFloat32(this.offset, this.littleEndian); - this.offset += 4; - return value; - } - getFloat32Array(size) { - const a = []; - for (let i = 0; i < size; i++) { - a.push(this.getFloat32()); - } - return a; - } - getFloat64() { - const value = this.dv.getFloat64(this.offset, this.littleEndian); - this.offset += 8; - return value; - } - getFloat64Array(size) { - const a = []; - for (let i = 0; i < size; i++) { - a.push(this.getFloat64()); - } - return a; - } - getArrayBuffer(size) { - const value = this.dv.buffer.slice(this.offset, this.offset + size); - this.offset += size; - return value; - } - getString(size) { - const start = this.offset; - let a = new Uint8Array(this.dv.buffer, start, size); - this.skip(size); - const nullByte = a.indexOf(0); - if (nullByte >= 0) a = new Uint8Array(this.dv.buffer, start, nullByte); - return this._textDecoder.decode(a); - } -} -class FBXTree { - static { - __name(this, "FBXTree"); - } - add(key, val) { - this[key] = val; - } -} -function isFbxFormatBinary(buffer) { - const CORRECT = "Kaydara FBX Binary \0"; - return buffer.byteLength >= CORRECT.length && CORRECT === convertArrayBufferToString(buffer, 0, CORRECT.length); -} -__name(isFbxFormatBinary, "isFbxFormatBinary"); -function isFbxFormatASCII(text) { - const CORRECT = ["K", "a", "y", "d", "a", "r", "a", "\\", "F", "B", "X", "\\", "B", "i", "n", "a", "r", "y", "\\", "\\"]; - let cursor = 0; - function read(offset) { - const result = text[offset - 1]; - text = text.slice(cursor + offset); - cursor++; - return result; - } - __name(read, "read"); - for (let i = 0; i < CORRECT.length; ++i) { - const num = read(1); - if (num === CORRECT[i]) { - return false; - } - } - return true; -} -__name(isFbxFormatASCII, "isFbxFormatASCII"); -function getFbxVersion(text) { - const versionRegExp = /FBXVersion: (\d+)/; - const match = text.match(versionRegExp); - if (match) { - const version = parseInt(match[1]); - return version; - } - throw new Error("THREE.FBXLoader: Cannot find the version number for the file given."); -} -__name(getFbxVersion, "getFbxVersion"); -function convertFBXTimeToSeconds(time) { - return time / 46186158e3; -} -__name(convertFBXTimeToSeconds, "convertFBXTimeToSeconds"); -const dataArray = []; -function getData(polygonVertexIndex, polygonIndex, vertexIndex, infoObject) { - let index; - switch (infoObject.mappingType) { - case "ByPolygonVertex": - index = polygonVertexIndex; - break; - case "ByPolygon": - index = polygonIndex; - break; - case "ByVertice": - index = vertexIndex; - break; - case "AllSame": - index = infoObject.indices[0]; - break; - default: - console.warn("THREE.FBXLoader: unknown attribute mapping type " + infoObject.mappingType); - } - if (infoObject.referenceType === "IndexToDirect") index = infoObject.indices[index]; - const from = index * infoObject.dataSize; - const to = from + infoObject.dataSize; - return slice(dataArray, infoObject.buffer, from, to); -} -__name(getData, "getData"); -const tempEuler = new Euler(); -const tempVec = new Vector3(); -function generateTransform(transformData) { - const lTranslationM = new Matrix4(); - const lPreRotationM = new Matrix4(); - const lRotationM = new Matrix4(); - const lPostRotationM = new Matrix4(); - const lScalingM = new Matrix4(); - const lScalingPivotM = new Matrix4(); - const lScalingOffsetM = new Matrix4(); - const lRotationOffsetM = new Matrix4(); - const lRotationPivotM = new Matrix4(); - const lParentGX = new Matrix4(); - const lParentLX = new Matrix4(); - const lGlobalT = new Matrix4(); - const inheritType = transformData.inheritType ? transformData.inheritType : 0; - if (transformData.translation) lTranslationM.setPosition(tempVec.fromArray(transformData.translation)); - const defaultEulerOrder = getEulerOrder(0); - if (transformData.preRotation) { - const array = transformData.preRotation.map(MathUtils.degToRad); - array.push(defaultEulerOrder); - lPreRotationM.makeRotationFromEuler(tempEuler.fromArray(array)); - } - if (transformData.rotation) { - const array = transformData.rotation.map(MathUtils.degToRad); - array.push(transformData.eulerOrder || defaultEulerOrder); - lRotationM.makeRotationFromEuler(tempEuler.fromArray(array)); - } - if (transformData.postRotation) { - const array = transformData.postRotation.map(MathUtils.degToRad); - array.push(defaultEulerOrder); - lPostRotationM.makeRotationFromEuler(tempEuler.fromArray(array)); - lPostRotationM.invert(); - } - if (transformData.scale) lScalingM.scale(tempVec.fromArray(transformData.scale)); - if (transformData.scalingOffset) lScalingOffsetM.setPosition(tempVec.fromArray(transformData.scalingOffset)); - if (transformData.scalingPivot) lScalingPivotM.setPosition(tempVec.fromArray(transformData.scalingPivot)); - if (transformData.rotationOffset) lRotationOffsetM.setPosition(tempVec.fromArray(transformData.rotationOffset)); - if (transformData.rotationPivot) lRotationPivotM.setPosition(tempVec.fromArray(transformData.rotationPivot)); - if (transformData.parentMatrixWorld) { - lParentLX.copy(transformData.parentMatrix); - lParentGX.copy(transformData.parentMatrixWorld); - } - const lLRM = lPreRotationM.clone().multiply(lRotationM).multiply(lPostRotationM); - const lParentGRM = new Matrix4(); - lParentGRM.extractRotation(lParentGX); - const lParentTM = new Matrix4(); - lParentTM.copyPosition(lParentGX); - const lParentGRSM = lParentTM.clone().invert().multiply(lParentGX); - const lParentGSM = lParentGRM.clone().invert().multiply(lParentGRSM); - const lLSM = lScalingM; - const lGlobalRS = new Matrix4(); - if (inheritType === 0) { - lGlobalRS.copy(lParentGRM).multiply(lLRM).multiply(lParentGSM).multiply(lLSM); - } else if (inheritType === 1) { - lGlobalRS.copy(lParentGRM).multiply(lParentGSM).multiply(lLRM).multiply(lLSM); - } else { - const lParentLSM = new Matrix4().scale(new Vector3().setFromMatrixScale(lParentLX)); - const lParentLSM_inv = lParentLSM.clone().invert(); - const lParentGSM_noLocal = lParentGSM.clone().multiply(lParentLSM_inv); - lGlobalRS.copy(lParentGRM).multiply(lLRM).multiply(lParentGSM_noLocal).multiply(lLSM); - } - const lRotationPivotM_inv = lRotationPivotM.clone().invert(); - const lScalingPivotM_inv = lScalingPivotM.clone().invert(); - let lTransform = lTranslationM.clone().multiply(lRotationOffsetM).multiply(lRotationPivotM).multiply(lPreRotationM).multiply(lRotationM).multiply(lPostRotationM).multiply(lRotationPivotM_inv).multiply(lScalingOffsetM).multiply(lScalingPivotM).multiply(lScalingM).multiply(lScalingPivotM_inv); - const lLocalTWithAllPivotAndOffsetInfo = new Matrix4().copyPosition(lTransform); - const lGlobalTranslation = lParentGX.clone().multiply(lLocalTWithAllPivotAndOffsetInfo); - lGlobalT.copyPosition(lGlobalTranslation); - lTransform = lGlobalT.clone().multiply(lGlobalRS); - lTransform.premultiply(lParentGX.invert()); - return lTransform; -} -__name(generateTransform, "generateTransform"); -function getEulerOrder(order) { - order = order || 0; - const enums = [ - "ZYX", - // -> XYZ extrinsic - "YZX", - // -> XZY extrinsic - "XZY", - // -> YZX extrinsic - "ZXY", - // -> YXZ extrinsic - "YXZ", - // -> ZXY extrinsic - "XYZ" - // -> ZYX extrinsic - //'SphericXYZ', // not possible to support - ]; - if (order === 6) { - console.warn("THREE.FBXLoader: unsupported Euler Order: Spherical XYZ. Animations and rotations may be incorrect."); - return enums[0]; - } - return enums[order]; -} -__name(getEulerOrder, "getEulerOrder"); -function parseNumberArray(value) { - const array = value.split(",").map(function(val) { - return parseFloat(val); - }); - return array; -} -__name(parseNumberArray, "parseNumberArray"); -function convertArrayBufferToString(buffer, from, to) { - if (from === void 0) from = 0; - if (to === void 0) to = buffer.byteLength; - return new TextDecoder().decode(new Uint8Array(buffer, from, to)); -} -__name(convertArrayBufferToString, "convertArrayBufferToString"); -function append(a, b) { - for (let i = 0, j = a.length, l = b.length; i < l; i++, j++) { - a[j] = b[i]; - } -} -__name(append, "append"); -function slice(a, b, from, to) { - for (let i = from, j = 0; i < to; i++, j++) { - a[j] = b[i]; - } - return a; -} -__name(slice, "slice"); -function computeMikkTSpaceTangents(geometry, MikkTSpace, negateSign = true) { - if (!MikkTSpace || !MikkTSpace.isReady) { - throw new Error("BufferGeometryUtils: Initialized MikkTSpace library required."); - } - if (!geometry.hasAttribute("position") || !geometry.hasAttribute("normal") || !geometry.hasAttribute("uv")) { - throw new Error('BufferGeometryUtils: Tangents require "position", "normal", and "uv" attributes.'); - } - function getAttributeArray(attribute) { - if (attribute.normalized || attribute.isInterleavedBufferAttribute) { - const dstArray = new Float32Array(attribute.count * attribute.itemSize); - for (let i = 0, j = 0; i < attribute.count; i++) { - dstArray[j++] = attribute.getX(i); - dstArray[j++] = attribute.getY(i); - if (attribute.itemSize > 2) { - dstArray[j++] = attribute.getZ(i); - } - } - return dstArray; - } - if (attribute.array instanceof Float32Array) { - return attribute.array; - } - return new Float32Array(attribute.array); - } - __name(getAttributeArray, "getAttributeArray"); - const _geometry2 = geometry.index ? geometry.toNonIndexed() : geometry; - const tangents = MikkTSpace.generateTangents( - getAttributeArray(_geometry2.attributes.position), - getAttributeArray(_geometry2.attributes.normal), - getAttributeArray(_geometry2.attributes.uv) - ); - if (negateSign) { - for (let i = 3; i < tangents.length; i += 4) { - tangents[i] *= -1; - } - } - _geometry2.setAttribute("tangent", new BufferAttribute(tangents, 4)); - if (geometry !== _geometry2) { - geometry.copy(_geometry2); - } - return geometry; -} -__name(computeMikkTSpaceTangents, "computeMikkTSpaceTangents"); -function mergeGeometries(geometries, useGroups = false) { - const isIndexed = geometries[0].index !== null; - const attributesUsed = new Set(Object.keys(geometries[0].attributes)); - const morphAttributesUsed = new Set(Object.keys(geometries[0].morphAttributes)); - const attributes = {}; - const morphAttributes = {}; - const morphTargetsRelative = geometries[0].morphTargetsRelative; - const mergedGeometry = new BufferGeometry(); - let offset = 0; - for (let i = 0; i < geometries.length; ++i) { - const geometry = geometries[i]; - let attributesCount = 0; - if (isIndexed !== (geometry.index !== null)) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + ". All geometries must have compatible attributes; make sure index attribute exists among all geometries, or in none of them."); - return null; - } - for (const name in geometry.attributes) { - if (!attributesUsed.has(name)) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + '. All geometries must have compatible attributes; make sure "' + name + '" attribute exists among all geometries, or in none of them.'); - return null; - } - if (attributes[name] === void 0) attributes[name] = []; - attributes[name].push(geometry.attributes[name]); - attributesCount++; - } - if (attributesCount !== attributesUsed.size) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + ". Make sure all geometries have the same number of attributes."); - return null; - } - if (morphTargetsRelative !== geometry.morphTargetsRelative) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + ". .morphTargetsRelative must be consistent throughout all geometries."); - return null; - } - for (const name in geometry.morphAttributes) { - if (!morphAttributesUsed.has(name)) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + ". .morphAttributes must be consistent throughout all geometries."); - return null; - } - if (morphAttributes[name] === void 0) morphAttributes[name] = []; - morphAttributes[name].push(geometry.morphAttributes[name]); - } - if (useGroups) { - let count; - if (isIndexed) { - count = geometry.index.count; - } else if (geometry.attributes.position !== void 0) { - count = geometry.attributes.position.count; - } else { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed with geometry at index " + i + ". The geometry must have either an index or a position attribute"); - return null; - } - mergedGeometry.addGroup(offset, count, i); - offset += count; - } - } - if (isIndexed) { - let indexOffset = 0; - const mergedIndex = []; - for (let i = 0; i < geometries.length; ++i) { - const index = geometries[i].index; - for (let j = 0; j < index.count; ++j) { - mergedIndex.push(index.getX(j) + indexOffset); - } - indexOffset += geometries[i].attributes.position.count; - } - mergedGeometry.setIndex(mergedIndex); - } - for (const name in attributes) { - const mergedAttribute = mergeAttributes(attributes[name]); - if (!mergedAttribute) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed while trying to merge the " + name + " attribute."); - return null; - } - mergedGeometry.setAttribute(name, mergedAttribute); - } - for (const name in morphAttributes) { - const numMorphTargets = morphAttributes[name][0].length; - if (numMorphTargets === 0) break; - mergedGeometry.morphAttributes = mergedGeometry.morphAttributes || {}; - mergedGeometry.morphAttributes[name] = []; - for (let i = 0; i < numMorphTargets; ++i) { - const morphAttributesToMerge = []; - for (let j = 0; j < morphAttributes[name].length; ++j) { - morphAttributesToMerge.push(morphAttributes[name][j][i]); - } - const mergedMorphAttribute = mergeAttributes(morphAttributesToMerge); - if (!mergedMorphAttribute) { - console.error("THREE.BufferGeometryUtils: .mergeGeometries() failed while trying to merge the " + name + " morphAttribute."); - return null; - } - mergedGeometry.morphAttributes[name].push(mergedMorphAttribute); - } - } - return mergedGeometry; -} -__name(mergeGeometries, "mergeGeometries"); -function mergeAttributes(attributes) { - let TypedArray; - let itemSize; - let normalized; - let gpuType = -1; - let arrayLength = 0; - for (let i = 0; i < attributes.length; ++i) { - const attribute = attributes[i]; - if (TypedArray === void 0) TypedArray = attribute.array.constructor; - if (TypedArray !== attribute.array.constructor) { - console.error("THREE.BufferGeometryUtils: .mergeAttributes() failed. BufferAttribute.array must be of consistent array types across matching attributes."); - return null; - } - if (itemSize === void 0) itemSize = attribute.itemSize; - if (itemSize !== attribute.itemSize) { - console.error("THREE.BufferGeometryUtils: .mergeAttributes() failed. BufferAttribute.itemSize must be consistent across matching attributes."); - return null; - } - if (normalized === void 0) normalized = attribute.normalized; - if (normalized !== attribute.normalized) { - console.error("THREE.BufferGeometryUtils: .mergeAttributes() failed. BufferAttribute.normalized must be consistent across matching attributes."); - return null; - } - if (gpuType === -1) gpuType = attribute.gpuType; - if (gpuType !== attribute.gpuType) { - console.error("THREE.BufferGeometryUtils: .mergeAttributes() failed. BufferAttribute.gpuType must be consistent across matching attributes."); - return null; - } - arrayLength += attribute.count * itemSize; - } - const array = new TypedArray(arrayLength); - const result = new BufferAttribute(array, itemSize, normalized); - let offset = 0; - for (let i = 0; i < attributes.length; ++i) { - const attribute = attributes[i]; - if (attribute.isInterleavedBufferAttribute) { - const tupleOffset = offset / itemSize; - for (let j = 0, l = attribute.count; j < l; j++) { - for (let c = 0; c < itemSize; c++) { - const value = attribute.getComponent(j, c); - result.setComponent(j + tupleOffset, c, value); - } - } - } else { - array.set(attribute.array, offset); - } - offset += attribute.count * itemSize; - } - if (gpuType !== void 0) { - result.gpuType = gpuType; - } - return result; -} -__name(mergeAttributes, "mergeAttributes"); -function deepCloneAttribute(attribute) { - if (attribute.isInstancedInterleavedBufferAttribute || attribute.isInterleavedBufferAttribute) { - return deinterleaveAttribute(attribute); - } - if (attribute.isInstancedBufferAttribute) { - return new InstancedBufferAttribute().copy(attribute); - } - return new BufferAttribute().copy(attribute); -} -__name(deepCloneAttribute, "deepCloneAttribute"); -function interleaveAttributes(attributes) { - let TypedArray; - let arrayLength = 0; - let stride = 0; - for (let i = 0, l = attributes.length; i < l; ++i) { - const attribute = attributes[i]; - if (TypedArray === void 0) TypedArray = attribute.array.constructor; - if (TypedArray !== attribute.array.constructor) { - console.error("AttributeBuffers of different types cannot be interleaved"); - return null; - } - arrayLength += attribute.array.length; - stride += attribute.itemSize; - } - const interleavedBuffer = new InterleavedBuffer(new TypedArray(arrayLength), stride); - let offset = 0; - const res = []; - const getters = ["getX", "getY", "getZ", "getW"]; - const setters = ["setX", "setY", "setZ", "setW"]; - for (let j = 0, l = attributes.length; j < l; j++) { - const attribute = attributes[j]; - const itemSize = attribute.itemSize; - const count = attribute.count; - const iba = new InterleavedBufferAttribute(interleavedBuffer, itemSize, offset, attribute.normalized); - res.push(iba); - offset += itemSize; - for (let c = 0; c < count; c++) { - for (let k = 0; k < itemSize; k++) { - iba[setters[k]](c, attribute[getters[k]](c)); - } - } - } - return res; -} -__name(interleaveAttributes, "interleaveAttributes"); -function deinterleaveAttribute(attribute) { - const cons = attribute.data.array.constructor; - const count = attribute.count; - const itemSize = attribute.itemSize; - const normalized = attribute.normalized; - const array = new cons(count * itemSize); - let newAttribute; - if (attribute.isInstancedInterleavedBufferAttribute) { - newAttribute = new InstancedBufferAttribute(array, itemSize, normalized, attribute.meshPerAttribute); - } else { - newAttribute = new BufferAttribute(array, itemSize, normalized); - } - for (let i = 0; i < count; i++) { - newAttribute.setX(i, attribute.getX(i)); - if (itemSize >= 2) { - newAttribute.setY(i, attribute.getY(i)); - } - if (itemSize >= 3) { - newAttribute.setZ(i, attribute.getZ(i)); - } - if (itemSize >= 4) { - newAttribute.setW(i, attribute.getW(i)); - } - } - return newAttribute; -} -__name(deinterleaveAttribute, "deinterleaveAttribute"); -function deinterleaveGeometry(geometry) { - const attributes = geometry.attributes; - const morphTargets = geometry.morphTargets; - const attrMap = /* @__PURE__ */ new Map(); - for (const key in attributes) { - const attr = attributes[key]; - if (attr.isInterleavedBufferAttribute) { - if (!attrMap.has(attr)) { - attrMap.set(attr, deinterleaveAttribute(attr)); - } - attributes[key] = attrMap.get(attr); - } - } - for (const key in morphTargets) { - const attr = morphTargets[key]; - if (attr.isInterleavedBufferAttribute) { - if (!attrMap.has(attr)) { - attrMap.set(attr, deinterleaveAttribute(attr)); - } - morphTargets[key] = attrMap.get(attr); - } - } -} -__name(deinterleaveGeometry, "deinterleaveGeometry"); -function estimateBytesUsed(geometry) { - let mem = 0; - for (const name in geometry.attributes) { - const attr = geometry.getAttribute(name); - mem += attr.count * attr.itemSize * attr.array.BYTES_PER_ELEMENT; - } - const indices = geometry.getIndex(); - mem += indices ? indices.count * indices.itemSize * indices.array.BYTES_PER_ELEMENT : 0; - return mem; -} -__name(estimateBytesUsed, "estimateBytesUsed"); -function mergeVertices(geometry, tolerance = 1e-4) { - tolerance = Math.max(tolerance, Number.EPSILON); - const hashToIndex = {}; - const indices = geometry.getIndex(); - const positions = geometry.getAttribute("position"); - const vertexCount = indices ? indices.count : positions.count; - let nextIndex = 0; - const attributeNames = Object.keys(geometry.attributes); - const tmpAttributes = {}; - const tmpMorphAttributes = {}; - const newIndices = []; - const getters = ["getX", "getY", "getZ", "getW"]; - const setters = ["setX", "setY", "setZ", "setW"]; - for (let i = 0, l = attributeNames.length; i < l; i++) { - const name = attributeNames[i]; - const attr = geometry.attributes[name]; - tmpAttributes[name] = new attr.constructor( - new attr.array.constructor(attr.count * attr.itemSize), - attr.itemSize, - attr.normalized - ); - const morphAttributes = geometry.morphAttributes[name]; - if (morphAttributes) { - if (!tmpMorphAttributes[name]) tmpMorphAttributes[name] = []; - morphAttributes.forEach((morphAttr, i2) => { - const array = new morphAttr.array.constructor(morphAttr.count * morphAttr.itemSize); - tmpMorphAttributes[name][i2] = new morphAttr.constructor(array, morphAttr.itemSize, morphAttr.normalized); - }); - } - } - const halfTolerance = tolerance * 0.5; - const exponent = Math.log10(1 / tolerance); - const hashMultiplier = Math.pow(10, exponent); - const hashAdditive = halfTolerance * hashMultiplier; - for (let i = 0; i < vertexCount; i++) { - const index = indices ? indices.getX(i) : i; - let hash = ""; - for (let j = 0, l = attributeNames.length; j < l; j++) { - const name = attributeNames[j]; - const attribute = geometry.getAttribute(name); - const itemSize = attribute.itemSize; - for (let k = 0; k < itemSize; k++) { - hash += `${~~(attribute[getters[k]](index) * hashMultiplier + hashAdditive)},`; - } - } - if (hash in hashToIndex) { - newIndices.push(hashToIndex[hash]); - } else { - for (let j = 0, l = attributeNames.length; j < l; j++) { - const name = attributeNames[j]; - const attribute = geometry.getAttribute(name); - const morphAttributes = geometry.morphAttributes[name]; - const itemSize = attribute.itemSize; - const newArray = tmpAttributes[name]; - const newMorphArrays = tmpMorphAttributes[name]; - for (let k = 0; k < itemSize; k++) { - const getterFunc = getters[k]; - const setterFunc = setters[k]; - newArray[setterFunc](nextIndex, attribute[getterFunc](index)); - if (morphAttributes) { - for (let m = 0, ml = morphAttributes.length; m < ml; m++) { - newMorphArrays[m][setterFunc](nextIndex, morphAttributes[m][getterFunc](index)); - } - } - } - } - hashToIndex[hash] = nextIndex; - newIndices.push(nextIndex); - nextIndex++; - } - } - const result = geometry.clone(); - for (const name in geometry.attributes) { - const tmpAttribute = tmpAttributes[name]; - result.setAttribute(name, new tmpAttribute.constructor( - tmpAttribute.array.slice(0, nextIndex * tmpAttribute.itemSize), - tmpAttribute.itemSize, - tmpAttribute.normalized - )); - if (!(name in tmpMorphAttributes)) continue; - for (let j = 0; j < tmpMorphAttributes[name].length; j++) { - const tmpMorphAttribute = tmpMorphAttributes[name][j]; - result.morphAttributes[name][j] = new tmpMorphAttribute.constructor( - tmpMorphAttribute.array.slice(0, nextIndex * tmpMorphAttribute.itemSize), - tmpMorphAttribute.itemSize, - tmpMorphAttribute.normalized - ); - } - } - result.setIndex(newIndices); - return result; -} -__name(mergeVertices, "mergeVertices"); -function toTrianglesDrawMode(geometry, drawMode) { - if (drawMode === TrianglesDrawMode) { - console.warn("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Geometry already defined as triangles."); - return geometry; - } - if (drawMode === TriangleFanDrawMode || drawMode === TriangleStripDrawMode) { - let index = geometry.getIndex(); - if (index === null) { - const indices = []; - const position = geometry.getAttribute("position"); - if (position !== void 0) { - for (let i = 0; i < position.count; i++) { - indices.push(i); - } - geometry.setIndex(indices); - index = geometry.getIndex(); - } else { - console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Undefined position attribute. Processing not possible."); - return geometry; - } - } - const numberOfTriangles = index.count - 2; - const newIndices = []; - if (drawMode === TriangleFanDrawMode) { - for (let i = 1; i <= numberOfTriangles; i++) { - newIndices.push(index.getX(0)); - newIndices.push(index.getX(i)); - newIndices.push(index.getX(i + 1)); - } - } else { - for (let i = 0; i < numberOfTriangles; i++) { - if (i % 2 === 0) { - newIndices.push(index.getX(i)); - newIndices.push(index.getX(i + 1)); - newIndices.push(index.getX(i + 2)); - } else { - newIndices.push(index.getX(i + 2)); - newIndices.push(index.getX(i + 1)); - newIndices.push(index.getX(i)); - } - } - } - if (newIndices.length / 3 !== numberOfTriangles) { - console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unable to generate correct amount of triangles."); - } - const newGeometry = geometry.clone(); - newGeometry.setIndex(newIndices); - newGeometry.clearGroups(); - return newGeometry; - } else { - console.error("THREE.BufferGeometryUtils.toTrianglesDrawMode(): Unknown draw mode:", drawMode); - return geometry; - } -} -__name(toTrianglesDrawMode, "toTrianglesDrawMode"); -function computeMorphedAttributes(object) { - const _vA2 = new Vector3(); - const _vB2 = new Vector3(); - const _vC2 = new Vector3(); - const _tempA2 = new Vector3(); - const _tempB = new Vector3(); - const _tempC = new Vector3(); - const _morphA2 = new Vector3(); - const _morphB = new Vector3(); - const _morphC = new Vector3(); - function _calculateMorphedAttributeData(object2, attribute, morphAttribute, morphTargetsRelative2, a2, b3, c2, modifiedAttributeArray) { - _vA2.fromBufferAttribute(attribute, a2); - _vB2.fromBufferAttribute(attribute, b3); - _vC2.fromBufferAttribute(attribute, c2); - const morphInfluences = object2.morphTargetInfluences; - if (morphAttribute && morphInfluences) { - _morphA2.set(0, 0, 0); - _morphB.set(0, 0, 0); - _morphC.set(0, 0, 0); - for (let i2 = 0, il2 = morphAttribute.length; i2 < il2; i2++) { - const influence = morphInfluences[i2]; - const morph = morphAttribute[i2]; - if (influence === 0) continue; - _tempA2.fromBufferAttribute(morph, a2); - _tempB.fromBufferAttribute(morph, b3); - _tempC.fromBufferAttribute(morph, c2); - if (morphTargetsRelative2) { - _morphA2.addScaledVector(_tempA2, influence); - _morphB.addScaledVector(_tempB, influence); - _morphC.addScaledVector(_tempC, influence); - } else { - _morphA2.addScaledVector(_tempA2.sub(_vA2), influence); - _morphB.addScaledVector(_tempB.sub(_vB2), influence); - _morphC.addScaledVector(_tempC.sub(_vC2), influence); - } - } - _vA2.add(_morphA2); - _vB2.add(_morphB); - _vC2.add(_morphC); - } - if (object2.isSkinnedMesh) { - object2.applyBoneTransform(a2, _vA2); - object2.applyBoneTransform(b3, _vB2); - object2.applyBoneTransform(c2, _vC2); - } - modifiedAttributeArray[a2 * 3 + 0] = _vA2.x; - modifiedAttributeArray[a2 * 3 + 1] = _vA2.y; - modifiedAttributeArray[a2 * 3 + 2] = _vA2.z; - modifiedAttributeArray[b3 * 3 + 0] = _vB2.x; - modifiedAttributeArray[b3 * 3 + 1] = _vB2.y; - modifiedAttributeArray[b3 * 3 + 2] = _vB2.z; - modifiedAttributeArray[c2 * 3 + 0] = _vC2.x; - modifiedAttributeArray[c2 * 3 + 1] = _vC2.y; - modifiedAttributeArray[c2 * 3 + 2] = _vC2.z; - } - __name(_calculateMorphedAttributeData, "_calculateMorphedAttributeData"); - const geometry = object.geometry; - const material = object.material; - let a, b, c; - const index = geometry.index; - const positionAttribute = geometry.attributes.position; - const morphPosition = geometry.morphAttributes.position; - const morphTargetsRelative = geometry.morphTargetsRelative; - const normalAttribute = geometry.attributes.normal; - const morphNormal = geometry.morphAttributes.position; - const groups = geometry.groups; - const drawRange = geometry.drawRange; - let i, j, il, jl; - let group; - let start, end; - const modifiedPosition = new Float32Array(positionAttribute.count * positionAttribute.itemSize); - const modifiedNormal = new Float32Array(normalAttribute.count * normalAttribute.itemSize); - if (index !== null) { - if (Array.isArray(material)) { - for (i = 0, il = groups.length; i < il; i++) { - group = groups[i]; - start = Math.max(group.start, drawRange.start); - end = Math.min(group.start + group.count, drawRange.start + drawRange.count); - for (j = start, jl = end; j < jl; j += 3) { - a = index.getX(j); - b = index.getX(j + 1); - c = index.getX(j + 2); - _calculateMorphedAttributeData( - object, - positionAttribute, - morphPosition, - morphTargetsRelative, - a, - b, - c, - modifiedPosition - ); - _calculateMorphedAttributeData( - object, - normalAttribute, - morphNormal, - morphTargetsRelative, - a, - b, - c, - modifiedNormal - ); - } - } - } else { - start = Math.max(0, drawRange.start); - end = Math.min(index.count, drawRange.start + drawRange.count); - for (i = start, il = end; i < il; i += 3) { - a = index.getX(i); - b = index.getX(i + 1); - c = index.getX(i + 2); - _calculateMorphedAttributeData( - object, - positionAttribute, - morphPosition, - morphTargetsRelative, - a, - b, - c, - modifiedPosition - ); - _calculateMorphedAttributeData( - object, - normalAttribute, - morphNormal, - morphTargetsRelative, - a, - b, - c, - modifiedNormal - ); - } - } - } else { - if (Array.isArray(material)) { - for (i = 0, il = groups.length; i < il; i++) { - group = groups[i]; - start = Math.max(group.start, drawRange.start); - end = Math.min(group.start + group.count, drawRange.start + drawRange.count); - for (j = start, jl = end; j < jl; j += 3) { - a = j; - b = j + 1; - c = j + 2; - _calculateMorphedAttributeData( - object, - positionAttribute, - morphPosition, - morphTargetsRelative, - a, - b, - c, - modifiedPosition - ); - _calculateMorphedAttributeData( - object, - normalAttribute, - morphNormal, - morphTargetsRelative, - a, - b, - c, - modifiedNormal - ); - } - } - } else { - start = Math.max(0, drawRange.start); - end = Math.min(positionAttribute.count, drawRange.start + drawRange.count); - for (i = start, il = end; i < il; i += 3) { - a = i; - b = i + 1; - c = i + 2; - _calculateMorphedAttributeData( - object, - positionAttribute, - morphPosition, - morphTargetsRelative, - a, - b, - c, - modifiedPosition - ); - _calculateMorphedAttributeData( - object, - normalAttribute, - morphNormal, - morphTargetsRelative, - a, - b, - c, - modifiedNormal - ); - } - } - } - const morphedPositionAttribute = new Float32BufferAttribute(modifiedPosition, 3); - const morphedNormalAttribute = new Float32BufferAttribute(modifiedNormal, 3); - return { - positionAttribute, - normalAttribute, - morphedPositionAttribute, - morphedNormalAttribute - }; -} -__name(computeMorphedAttributes, "computeMorphedAttributes"); -function mergeGroups(geometry) { - if (geometry.groups.length === 0) { - console.warn("THREE.BufferGeometryUtils.mergeGroups(): No groups are defined. Nothing to merge."); - return geometry; - } - let groups = geometry.groups; - groups = groups.sort((a, b) => { - if (a.materialIndex !== b.materialIndex) return a.materialIndex - b.materialIndex; - return a.start - b.start; - }); - if (geometry.getIndex() === null) { - const positionAttribute = geometry.getAttribute("position"); - const indices = []; - for (let i = 0; i < positionAttribute.count; i += 3) { - indices.push(i, i + 1, i + 2); - } - geometry.setIndex(indices); - } - const index = geometry.getIndex(); - const newIndices = []; - for (let i = 0; i < groups.length; i++) { - const group = groups[i]; - const groupStart = group.start; - const groupLength = groupStart + group.count; - for (let j = groupStart; j < groupLength; j++) { - newIndices.push(index.getX(j)); - } - } - geometry.dispose(); - geometry.setIndex(newIndices); - let start = 0; - for (let i = 0; i < groups.length; i++) { - const group = groups[i]; - group.start = start; - start += group.count; - } - let currentGroup = groups[0]; - geometry.groups = [currentGroup]; - for (let i = 1; i < groups.length; i++) { - const group = groups[i]; - if (currentGroup.materialIndex === group.materialIndex) { - currentGroup.count += group.count; - } else { - currentGroup = group; - geometry.groups.push(currentGroup); - } - } - return geometry; -} -__name(mergeGroups, "mergeGroups"); -function toCreasedNormals(geometry, creaseAngle = Math.PI / 3) { - const creaseDot = Math.cos(creaseAngle); - const hashMultiplier = (1 + 1e-10) * 100; - const verts = [new Vector3(), new Vector3(), new Vector3()]; - const tempVec1 = new Vector3(); - const tempVec2 = new Vector3(); - const tempNorm = new Vector3(); - const tempNorm2 = new Vector3(); - function hashVertex(v) { - const x = ~~(v.x * hashMultiplier); - const y = ~~(v.y * hashMultiplier); - const z = ~~(v.z * hashMultiplier); - return `${x},${y},${z}`; - } - __name(hashVertex, "hashVertex"); - const resultGeometry = geometry.index ? geometry.toNonIndexed() : geometry; - const posAttr = resultGeometry.attributes.position; - const vertexMap = {}; - for (let i = 0, l = posAttr.count / 3; i < l; i++) { - const i3 = 3 * i; - const a = verts[0].fromBufferAttribute(posAttr, i3 + 0); - const b = verts[1].fromBufferAttribute(posAttr, i3 + 1); - const c = verts[2].fromBufferAttribute(posAttr, i3 + 2); - tempVec1.subVectors(c, b); - tempVec2.subVectors(a, b); - const normal = new Vector3().crossVectors(tempVec1, tempVec2).normalize(); - for (let n = 0; n < 3; n++) { - const vert = verts[n]; - const hash = hashVertex(vert); - if (!(hash in vertexMap)) { - vertexMap[hash] = []; - } - vertexMap[hash].push(normal); - } - } - const normalArray = new Float32Array(posAttr.count * 3); - const normAttr = new BufferAttribute(normalArray, 3, false); - for (let i = 0, l = posAttr.count / 3; i < l; i++) { - const i3 = 3 * i; - const a = verts[0].fromBufferAttribute(posAttr, i3 + 0); - const b = verts[1].fromBufferAttribute(posAttr, i3 + 1); - const c = verts[2].fromBufferAttribute(posAttr, i3 + 2); - tempVec1.subVectors(c, b); - tempVec2.subVectors(a, b); - tempNorm.crossVectors(tempVec1, tempVec2).normalize(); - for (let n = 0; n < 3; n++) { - const vert = verts[n]; - const hash = hashVertex(vert); - const otherNormals = vertexMap[hash]; - tempNorm2.set(0, 0, 0); - for (let k = 0, lk = otherNormals.length; k < lk; k++) { - const otherNorm = otherNormals[k]; - if (tempNorm.dot(otherNorm) > creaseDot) { - tempNorm2.add(otherNorm); - } - } - tempNorm2.normalize(); - normAttr.setXYZ(i3 + n, tempNorm2.x, tempNorm2.y, tempNorm2.z); - } - } - resultGeometry.setAttribute("normal", normAttr); - return resultGeometry; -} -__name(toCreasedNormals, "toCreasedNormals"); -class GLTFLoader extends Loader { - static { - __name(this, "GLTFLoader"); - } - constructor(manager) { - super(manager); - this.dracoLoader = null; - this.ktx2Loader = null; - this.meshoptDecoder = null; - this.pluginCallbacks = []; - this.register(function(parser) { - return new GLTFMaterialsClearcoatExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsDispersionExtension(parser); - }); - this.register(function(parser) { - return new GLTFTextureBasisUExtension(parser); - }); - this.register(function(parser) { - return new GLTFTextureWebPExtension(parser); - }); - this.register(function(parser) { - return new GLTFTextureAVIFExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsSheenExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsTransmissionExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsVolumeExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsIorExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsEmissiveStrengthExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsSpecularExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsIridescenceExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsAnisotropyExtension(parser); - }); - this.register(function(parser) { - return new GLTFMaterialsBumpExtension(parser); - }); - this.register(function(parser) { - return new GLTFLightsExtension(parser); - }); - this.register(function(parser) { - return new GLTFMeshoptCompression(parser); - }); - this.register(function(parser) { - return new GLTFMeshGpuInstancing(parser); - }); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - let resourcePath; - if (this.resourcePath !== "") { - resourcePath = this.resourcePath; - } else if (this.path !== "") { - const relativeUrl = LoaderUtils.extractUrlBase(url); - resourcePath = LoaderUtils.resolveURL(relativeUrl, this.path); - } else { - resourcePath = LoaderUtils.extractUrlBase(url); - } - this.manager.itemStart(url); - const _onError = /* @__PURE__ */ __name(function(e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - scope.manager.itemEnd(url); - }, "_onError"); - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setResponseType("arraybuffer"); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(data) { - try { - scope.parse(data, resourcePath, function(gltf) { - onLoad(gltf); - scope.manager.itemEnd(url); - }, _onError); - } catch (e) { - _onError(e); - } - }, onProgress, _onError); - } - setDRACOLoader(dracoLoader) { - this.dracoLoader = dracoLoader; - return this; - } - setKTX2Loader(ktx2Loader) { - this.ktx2Loader = ktx2Loader; - return this; - } - setMeshoptDecoder(meshoptDecoder) { - this.meshoptDecoder = meshoptDecoder; - return this; - } - register(callback) { - if (this.pluginCallbacks.indexOf(callback) === -1) { - this.pluginCallbacks.push(callback); - } - return this; - } - unregister(callback) { - if (this.pluginCallbacks.indexOf(callback) !== -1) { - this.pluginCallbacks.splice(this.pluginCallbacks.indexOf(callback), 1); - } - return this; - } - parse(data, path, onLoad, onError) { - let json; - const extensions = {}; - const plugins = {}; - const textDecoder = new TextDecoder(); - if (typeof data === "string") { - json = JSON.parse(data); - } else if (data instanceof ArrayBuffer) { - const magic = textDecoder.decode(new Uint8Array(data, 0, 4)); - if (magic === BINARY_EXTENSION_HEADER_MAGIC) { - try { - extensions[EXTENSIONS.KHR_BINARY_GLTF] = new GLTFBinaryExtension(data); - } catch (error) { - if (onError) onError(error); - return; - } - json = JSON.parse(extensions[EXTENSIONS.KHR_BINARY_GLTF].content); - } else { - json = JSON.parse(textDecoder.decode(data)); - } - } else { - json = data; - } - if (json.asset === void 0 || json.asset.version[0] < 2) { - if (onError) onError(new Error("THREE.GLTFLoader: Unsupported asset. glTF versions >=2.0 are supported.")); - return; - } - const parser = new GLTFParser(json, { - path: path || this.resourcePath || "", - crossOrigin: this.crossOrigin, - requestHeader: this.requestHeader, - manager: this.manager, - ktx2Loader: this.ktx2Loader, - meshoptDecoder: this.meshoptDecoder - }); - parser.fileLoader.setRequestHeader(this.requestHeader); - for (let i = 0; i < this.pluginCallbacks.length; i++) { - const plugin = this.pluginCallbacks[i](parser); - if (!plugin.name) console.error("THREE.GLTFLoader: Invalid plugin found: missing name"); - plugins[plugin.name] = plugin; - extensions[plugin.name] = true; - } - if (json.extensionsUsed) { - for (let i = 0; i < json.extensionsUsed.length; ++i) { - const extensionName = json.extensionsUsed[i]; - const extensionsRequired = json.extensionsRequired || []; - switch (extensionName) { - case EXTENSIONS.KHR_MATERIALS_UNLIT: - extensions[extensionName] = new GLTFMaterialsUnlitExtension(); - break; - case EXTENSIONS.KHR_DRACO_MESH_COMPRESSION: - extensions[extensionName] = new GLTFDracoMeshCompressionExtension(json, this.dracoLoader); - break; - case EXTENSIONS.KHR_TEXTURE_TRANSFORM: - extensions[extensionName] = new GLTFTextureTransformExtension(); - break; - case EXTENSIONS.KHR_MESH_QUANTIZATION: - extensions[extensionName] = new GLTFMeshQuantizationExtension(); - break; - default: - if (extensionsRequired.indexOf(extensionName) >= 0 && plugins[extensionName] === void 0) { - console.warn('THREE.GLTFLoader: Unknown extension "' + extensionName + '".'); - } - } - } - } - parser.setExtensions(extensions); - parser.setPlugins(plugins); - parser.parse(onLoad, onError); - } - parseAsync(data, path) { - const scope = this; - return new Promise(function(resolve, reject) { - scope.parse(data, path, resolve, reject); - }); - } -} -function GLTFRegistry() { - let objects = {}; - return { - get: /* @__PURE__ */ __name(function(key) { - return objects[key]; - }, "get"), - add: /* @__PURE__ */ __name(function(key, object) { - objects[key] = object; - }, "add"), - remove: /* @__PURE__ */ __name(function(key) { - delete objects[key]; - }, "remove"), - removeAll: /* @__PURE__ */ __name(function() { - objects = {}; - }, "removeAll") - }; -} -__name(GLTFRegistry, "GLTFRegistry"); -const EXTENSIONS = { - KHR_BINARY_GLTF: "KHR_binary_glTF", - KHR_DRACO_MESH_COMPRESSION: "KHR_draco_mesh_compression", - KHR_LIGHTS_PUNCTUAL: "KHR_lights_punctual", - KHR_MATERIALS_CLEARCOAT: "KHR_materials_clearcoat", - KHR_MATERIALS_DISPERSION: "KHR_materials_dispersion", - KHR_MATERIALS_IOR: "KHR_materials_ior", - KHR_MATERIALS_SHEEN: "KHR_materials_sheen", - KHR_MATERIALS_SPECULAR: "KHR_materials_specular", - KHR_MATERIALS_TRANSMISSION: "KHR_materials_transmission", - KHR_MATERIALS_IRIDESCENCE: "KHR_materials_iridescence", - KHR_MATERIALS_ANISOTROPY: "KHR_materials_anisotropy", - KHR_MATERIALS_UNLIT: "KHR_materials_unlit", - KHR_MATERIALS_VOLUME: "KHR_materials_volume", - KHR_TEXTURE_BASISU: "KHR_texture_basisu", - KHR_TEXTURE_TRANSFORM: "KHR_texture_transform", - KHR_MESH_QUANTIZATION: "KHR_mesh_quantization", - KHR_MATERIALS_EMISSIVE_STRENGTH: "KHR_materials_emissive_strength", - EXT_MATERIALS_BUMP: "EXT_materials_bump", - EXT_TEXTURE_WEBP: "EXT_texture_webp", - EXT_TEXTURE_AVIF: "EXT_texture_avif", - EXT_MESHOPT_COMPRESSION: "EXT_meshopt_compression", - EXT_MESH_GPU_INSTANCING: "EXT_mesh_gpu_instancing" -}; -class GLTFLightsExtension { - static { - __name(this, "GLTFLightsExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_LIGHTS_PUNCTUAL; - this.cache = { refs: {}, uses: {} }; - } - _markDefs() { - const parser = this.parser; - const nodeDefs = this.parser.json.nodes || []; - for (let nodeIndex = 0, nodeLength = nodeDefs.length; nodeIndex < nodeLength; nodeIndex++) { - const nodeDef = nodeDefs[nodeIndex]; - if (nodeDef.extensions && nodeDef.extensions[this.name] && nodeDef.extensions[this.name].light !== void 0) { - parser._addNodeRef(this.cache, nodeDef.extensions[this.name].light); - } - } - } - _loadLight(lightIndex) { - const parser = this.parser; - const cacheKey = "light:" + lightIndex; - let dependency = parser.cache.get(cacheKey); - if (dependency) return dependency; - const json = parser.json; - const extensions = json.extensions && json.extensions[this.name] || {}; - const lightDefs = extensions.lights || []; - const lightDef = lightDefs[lightIndex]; - let lightNode; - const color = new Color(16777215); - if (lightDef.color !== void 0) color.setRGB(lightDef.color[0], lightDef.color[1], lightDef.color[2], LinearSRGBColorSpace); - const range = lightDef.range !== void 0 ? lightDef.range : 0; - switch (lightDef.type) { - case "directional": - lightNode = new DirectionalLight(color); - lightNode.target.position.set(0, 0, -1); - lightNode.add(lightNode.target); - break; - case "point": - lightNode = new PointLight(color); - lightNode.distance = range; - break; - case "spot": - lightNode = new SpotLight(color); - lightNode.distance = range; - lightDef.spot = lightDef.spot || {}; - lightDef.spot.innerConeAngle = lightDef.spot.innerConeAngle !== void 0 ? lightDef.spot.innerConeAngle : 0; - lightDef.spot.outerConeAngle = lightDef.spot.outerConeAngle !== void 0 ? lightDef.spot.outerConeAngle : Math.PI / 4; - lightNode.angle = lightDef.spot.outerConeAngle; - lightNode.penumbra = 1 - lightDef.spot.innerConeAngle / lightDef.spot.outerConeAngle; - lightNode.target.position.set(0, 0, -1); - lightNode.add(lightNode.target); - break; - default: - throw new Error("THREE.GLTFLoader: Unexpected light type: " + lightDef.type); - } - lightNode.position.set(0, 0, 0); - lightNode.decay = 2; - assignExtrasToUserData(lightNode, lightDef); - if (lightDef.intensity !== void 0) lightNode.intensity = lightDef.intensity; - lightNode.name = parser.createUniqueName(lightDef.name || "light_" + lightIndex); - dependency = Promise.resolve(lightNode); - parser.cache.add(cacheKey, dependency); - return dependency; - } - getDependency(type, index) { - if (type !== "light") return; - return this._loadLight(index); - } - createNodeAttachment(nodeIndex) { - const self2 = this; - const parser = this.parser; - const json = parser.json; - const nodeDef = json.nodes[nodeIndex]; - const lightDef = nodeDef.extensions && nodeDef.extensions[this.name] || {}; - const lightIndex = lightDef.light; - if (lightIndex === void 0) return null; - return this._loadLight(lightIndex).then(function(light) { - return parser._getNodeRef(self2.cache, lightIndex, light); - }); - } -} -class GLTFMaterialsUnlitExtension { - static { - __name(this, "GLTFMaterialsUnlitExtension"); - } - constructor() { - this.name = EXTENSIONS.KHR_MATERIALS_UNLIT; - } - getMaterialType() { - return MeshBasicMaterial; - } - extendParams(materialParams, materialDef, parser) { - const pending = []; - materialParams.color = new Color(1, 1, 1); - materialParams.opacity = 1; - const metallicRoughness = materialDef.pbrMetallicRoughness; - if (metallicRoughness) { - if (Array.isArray(metallicRoughness.baseColorFactor)) { - const array = metallicRoughness.baseColorFactor; - materialParams.color.setRGB(array[0], array[1], array[2], LinearSRGBColorSpace); - materialParams.opacity = array[3]; - } - if (metallicRoughness.baseColorTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "map", metallicRoughness.baseColorTexture, SRGBColorSpace)); - } - } - return Promise.all(pending); - } -} -class GLTFMaterialsEmissiveStrengthExtension { - static { - __name(this, "GLTFMaterialsEmissiveStrengthExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_EMISSIVE_STRENGTH; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const emissiveStrength = materialDef.extensions[this.name].emissiveStrength; - if (emissiveStrength !== void 0) { - materialParams.emissiveIntensity = emissiveStrength; - } - return Promise.resolve(); - } -} -class GLTFMaterialsClearcoatExtension { - static { - __name(this, "GLTFMaterialsClearcoatExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_CLEARCOAT; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - if (extension.clearcoatFactor !== void 0) { - materialParams.clearcoat = extension.clearcoatFactor; - } - if (extension.clearcoatTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "clearcoatMap", extension.clearcoatTexture)); - } - if (extension.clearcoatRoughnessFactor !== void 0) { - materialParams.clearcoatRoughness = extension.clearcoatRoughnessFactor; - } - if (extension.clearcoatRoughnessTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "clearcoatRoughnessMap", extension.clearcoatRoughnessTexture)); - } - if (extension.clearcoatNormalTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "clearcoatNormalMap", extension.clearcoatNormalTexture)); - if (extension.clearcoatNormalTexture.scale !== void 0) { - const scale = extension.clearcoatNormalTexture.scale; - materialParams.clearcoatNormalScale = new Vector2(scale, scale); - } - } - return Promise.all(pending); - } -} -class GLTFMaterialsDispersionExtension { - static { - __name(this, "GLTFMaterialsDispersionExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_DISPERSION; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const extension = materialDef.extensions[this.name]; - materialParams.dispersion = extension.dispersion !== void 0 ? extension.dispersion : 0; - return Promise.resolve(); - } -} -class GLTFMaterialsIridescenceExtension { - static { - __name(this, "GLTFMaterialsIridescenceExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_IRIDESCENCE; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - if (extension.iridescenceFactor !== void 0) { - materialParams.iridescence = extension.iridescenceFactor; - } - if (extension.iridescenceTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "iridescenceMap", extension.iridescenceTexture)); - } - if (extension.iridescenceIor !== void 0) { - materialParams.iridescenceIOR = extension.iridescenceIor; - } - if (materialParams.iridescenceThicknessRange === void 0) { - materialParams.iridescenceThicknessRange = [100, 400]; - } - if (extension.iridescenceThicknessMinimum !== void 0) { - materialParams.iridescenceThicknessRange[0] = extension.iridescenceThicknessMinimum; - } - if (extension.iridescenceThicknessMaximum !== void 0) { - materialParams.iridescenceThicknessRange[1] = extension.iridescenceThicknessMaximum; - } - if (extension.iridescenceThicknessTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "iridescenceThicknessMap", extension.iridescenceThicknessTexture)); - } - return Promise.all(pending); - } -} -class GLTFMaterialsSheenExtension { - static { - __name(this, "GLTFMaterialsSheenExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_SHEEN; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - materialParams.sheenColor = new Color(0, 0, 0); - materialParams.sheenRoughness = 0; - materialParams.sheen = 1; - const extension = materialDef.extensions[this.name]; - if (extension.sheenColorFactor !== void 0) { - const colorFactor = extension.sheenColorFactor; - materialParams.sheenColor.setRGB(colorFactor[0], colorFactor[1], colorFactor[2], LinearSRGBColorSpace); - } - if (extension.sheenRoughnessFactor !== void 0) { - materialParams.sheenRoughness = extension.sheenRoughnessFactor; - } - if (extension.sheenColorTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "sheenColorMap", extension.sheenColorTexture, SRGBColorSpace)); - } - if (extension.sheenRoughnessTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "sheenRoughnessMap", extension.sheenRoughnessTexture)); - } - return Promise.all(pending); - } -} -class GLTFMaterialsTransmissionExtension { - static { - __name(this, "GLTFMaterialsTransmissionExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_TRANSMISSION; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - if (extension.transmissionFactor !== void 0) { - materialParams.transmission = extension.transmissionFactor; - } - if (extension.transmissionTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "transmissionMap", extension.transmissionTexture)); - } - return Promise.all(pending); - } -} -class GLTFMaterialsVolumeExtension { - static { - __name(this, "GLTFMaterialsVolumeExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_VOLUME; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - materialParams.thickness = extension.thicknessFactor !== void 0 ? extension.thicknessFactor : 0; - if (extension.thicknessTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "thicknessMap", extension.thicknessTexture)); - } - materialParams.attenuationDistance = extension.attenuationDistance || Infinity; - const colorArray = extension.attenuationColor || [1, 1, 1]; - materialParams.attenuationColor = new Color().setRGB(colorArray[0], colorArray[1], colorArray[2], LinearSRGBColorSpace); - return Promise.all(pending); - } -} -class GLTFMaterialsIorExtension { - static { - __name(this, "GLTFMaterialsIorExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_IOR; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const extension = materialDef.extensions[this.name]; - materialParams.ior = extension.ior !== void 0 ? extension.ior : 1.5; - return Promise.resolve(); - } -} -class GLTFMaterialsSpecularExtension { - static { - __name(this, "GLTFMaterialsSpecularExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_SPECULAR; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - materialParams.specularIntensity = extension.specularFactor !== void 0 ? extension.specularFactor : 1; - if (extension.specularTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "specularIntensityMap", extension.specularTexture)); - } - const colorArray = extension.specularColorFactor || [1, 1, 1]; - materialParams.specularColor = new Color().setRGB(colorArray[0], colorArray[1], colorArray[2], LinearSRGBColorSpace); - if (extension.specularColorTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "specularColorMap", extension.specularColorTexture, SRGBColorSpace)); - } - return Promise.all(pending); - } -} -class GLTFMaterialsBumpExtension { - static { - __name(this, "GLTFMaterialsBumpExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.EXT_MATERIALS_BUMP; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - materialParams.bumpScale = extension.bumpFactor !== void 0 ? extension.bumpFactor : 1; - if (extension.bumpTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "bumpMap", extension.bumpTexture)); - } - return Promise.all(pending); - } -} -class GLTFMaterialsAnisotropyExtension { - static { - __name(this, "GLTFMaterialsAnisotropyExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_MATERIALS_ANISOTROPY; - } - getMaterialType(materialIndex) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) return null; - return MeshPhysicalMaterial; - } - extendMaterialParams(materialIndex, materialParams) { - const parser = this.parser; - const materialDef = parser.json.materials[materialIndex]; - if (!materialDef.extensions || !materialDef.extensions[this.name]) { - return Promise.resolve(); - } - const pending = []; - const extension = materialDef.extensions[this.name]; - if (extension.anisotropyStrength !== void 0) { - materialParams.anisotropy = extension.anisotropyStrength; - } - if (extension.anisotropyRotation !== void 0) { - materialParams.anisotropyRotation = extension.anisotropyRotation; - } - if (extension.anisotropyTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "anisotropyMap", extension.anisotropyTexture)); - } - return Promise.all(pending); - } -} -class GLTFTextureBasisUExtension { - static { - __name(this, "GLTFTextureBasisUExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.KHR_TEXTURE_BASISU; - } - loadTexture(textureIndex) { - const parser = this.parser; - const json = parser.json; - const textureDef = json.textures[textureIndex]; - if (!textureDef.extensions || !textureDef.extensions[this.name]) { - return null; - } - const extension = textureDef.extensions[this.name]; - const loader = parser.options.ktx2Loader; - if (!loader) { - if (json.extensionsRequired && json.extensionsRequired.indexOf(this.name) >= 0) { - throw new Error("THREE.GLTFLoader: setKTX2Loader must be called before loading KTX2 textures"); - } else { - return null; - } - } - return parser.loadTextureImage(textureIndex, extension.source, loader); - } -} -class GLTFTextureWebPExtension { - static { - __name(this, "GLTFTextureWebPExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.EXT_TEXTURE_WEBP; - this.isSupported = null; - } - loadTexture(textureIndex) { - const name = this.name; - const parser = this.parser; - const json = parser.json; - const textureDef = json.textures[textureIndex]; - if (!textureDef.extensions || !textureDef.extensions[name]) { - return null; - } - const extension = textureDef.extensions[name]; - const source = json.images[extension.source]; - let loader = parser.textureLoader; - if (source.uri) { - const handler = parser.options.manager.getHandler(source.uri); - if (handler !== null) loader = handler; - } - return this.detectSupport().then(function(isSupported) { - if (isSupported) return parser.loadTextureImage(textureIndex, extension.source, loader); - if (json.extensionsRequired && json.extensionsRequired.indexOf(name) >= 0) { - throw new Error("THREE.GLTFLoader: WebP required by asset but unsupported."); - } - return parser.loadTexture(textureIndex); - }); - } - detectSupport() { - if (!this.isSupported) { - this.isSupported = new Promise(function(resolve) { - const image = new Image(); - image.src = "data:image/webp;base64,UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoBAAEADsD+JaQAA3AAAAAA"; - image.onload = image.onerror = function() { - resolve(image.height === 1); - }; - }); - } - return this.isSupported; - } -} -class GLTFTextureAVIFExtension { - static { - __name(this, "GLTFTextureAVIFExtension"); - } - constructor(parser) { - this.parser = parser; - this.name = EXTENSIONS.EXT_TEXTURE_AVIF; - this.isSupported = null; - } - loadTexture(textureIndex) { - const name = this.name; - const parser = this.parser; - const json = parser.json; - const textureDef = json.textures[textureIndex]; - if (!textureDef.extensions || !textureDef.extensions[name]) { - return null; - } - const extension = textureDef.extensions[name]; - const source = json.images[extension.source]; - let loader = parser.textureLoader; - if (source.uri) { - const handler = parser.options.manager.getHandler(source.uri); - if (handler !== null) loader = handler; - } - return this.detectSupport().then(function(isSupported) { - if (isSupported) return parser.loadTextureImage(textureIndex, extension.source, loader); - if (json.extensionsRequired && json.extensionsRequired.indexOf(name) >= 0) { - throw new Error("THREE.GLTFLoader: AVIF required by asset but unsupported."); - } - return parser.loadTexture(textureIndex); - }); - } - detectSupport() { - if (!this.isSupported) { - this.isSupported = new Promise(function(resolve) { - const image = new Image(); - image.src = "data:image/avif;base64,AAAAIGZ0eXBhdmlmAAAAAGF2aWZtaWYxbWlhZk1BMUIAAADybWV0YQAAAAAAAAAoaGRscgAAAAAAAAAAcGljdAAAAAAAAAAAAAAAAGxpYmF2aWYAAAAADnBpdG0AAAAAAAEAAAAeaWxvYwAAAABEAAABAAEAAAABAAABGgAAABcAAAAoaWluZgAAAAAAAQAAABppbmZlAgAAAAABAABhdjAxQ29sb3IAAAAAamlwcnAAAABLaXBjbwAAABRpc3BlAAAAAAAAAAEAAAABAAAAEHBpeGkAAAAAAwgICAAAAAxhdjFDgQAMAAAAABNjb2xybmNseAACAAIABoAAAAAXaXBtYQAAAAAAAAABAAEEAQKDBAAAAB9tZGF0EgAKCBgABogQEDQgMgkQAAAAB8dSLfI="; - image.onload = image.onerror = function() { - resolve(image.height === 1); - }; - }); - } - return this.isSupported; - } -} -class GLTFMeshoptCompression { - static { - __name(this, "GLTFMeshoptCompression"); - } - constructor(parser) { - this.name = EXTENSIONS.EXT_MESHOPT_COMPRESSION; - this.parser = parser; - } - loadBufferView(index) { - const json = this.parser.json; - const bufferView = json.bufferViews[index]; - if (bufferView.extensions && bufferView.extensions[this.name]) { - const extensionDef = bufferView.extensions[this.name]; - const buffer = this.parser.getDependency("buffer", extensionDef.buffer); - const decoder = this.parser.options.meshoptDecoder; - if (!decoder || !decoder.supported) { - if (json.extensionsRequired && json.extensionsRequired.indexOf(this.name) >= 0) { - throw new Error("THREE.GLTFLoader: setMeshoptDecoder must be called before loading compressed files"); - } else { - return null; - } - } - return buffer.then(function(res) { - const byteOffset = extensionDef.byteOffset || 0; - const byteLength = extensionDef.byteLength || 0; - const count = extensionDef.count; - const stride = extensionDef.byteStride; - const source = new Uint8Array(res, byteOffset, byteLength); - if (decoder.decodeGltfBufferAsync) { - return decoder.decodeGltfBufferAsync(count, stride, source, extensionDef.mode, extensionDef.filter).then(function(res2) { - return res2.buffer; - }); - } else { - return decoder.ready.then(function() { - const result = new ArrayBuffer(count * stride); - decoder.decodeGltfBuffer(new Uint8Array(result), count, stride, source, extensionDef.mode, extensionDef.filter); - return result; - }); - } - }); - } else { - return null; - } - } -} -class GLTFMeshGpuInstancing { - static { - __name(this, "GLTFMeshGpuInstancing"); - } - constructor(parser) { - this.name = EXTENSIONS.EXT_MESH_GPU_INSTANCING; - this.parser = parser; - } - createNodeMesh(nodeIndex) { - const json = this.parser.json; - const nodeDef = json.nodes[nodeIndex]; - if (!nodeDef.extensions || !nodeDef.extensions[this.name] || nodeDef.mesh === void 0) { - return null; - } - const meshDef = json.meshes[nodeDef.mesh]; - for (const primitive of meshDef.primitives) { - if (primitive.mode !== WEBGL_CONSTANTS.TRIANGLES && primitive.mode !== WEBGL_CONSTANTS.TRIANGLE_STRIP && primitive.mode !== WEBGL_CONSTANTS.TRIANGLE_FAN && primitive.mode !== void 0) { - return null; - } - } - const extensionDef = nodeDef.extensions[this.name]; - const attributesDef = extensionDef.attributes; - const pending = []; - const attributes = {}; - for (const key in attributesDef) { - pending.push(this.parser.getDependency("accessor", attributesDef[key]).then((accessor) => { - attributes[key] = accessor; - return attributes[key]; - })); - } - if (pending.length < 1) { - return null; - } - pending.push(this.parser.createNodeMesh(nodeIndex)); - return Promise.all(pending).then((results) => { - const nodeObject = results.pop(); - const meshes = nodeObject.isGroup ? nodeObject.children : [nodeObject]; - const count = results[0].count; - const instancedMeshes = []; - for (const mesh of meshes) { - const m = new Matrix4(); - const p = new Vector3(); - const q = new Quaternion(); - const s = new Vector3(1, 1, 1); - const instancedMesh = new InstancedMesh(mesh.geometry, mesh.material, count); - for (let i = 0; i < count; i++) { - if (attributes.TRANSLATION) { - p.fromBufferAttribute(attributes.TRANSLATION, i); - } - if (attributes.ROTATION) { - q.fromBufferAttribute(attributes.ROTATION, i); - } - if (attributes.SCALE) { - s.fromBufferAttribute(attributes.SCALE, i); - } - instancedMesh.setMatrixAt(i, m.compose(p, q, s)); - } - for (const attributeName in attributes) { - if (attributeName === "_COLOR_0") { - const attr = attributes[attributeName]; - instancedMesh.instanceColor = new InstancedBufferAttribute(attr.array, attr.itemSize, attr.normalized); - } else if (attributeName !== "TRANSLATION" && attributeName !== "ROTATION" && attributeName !== "SCALE") { - mesh.geometry.setAttribute(attributeName, attributes[attributeName]); - } - } - Object3D.prototype.copy.call(instancedMesh, mesh); - this.parser.assignFinalMaterial(instancedMesh); - instancedMeshes.push(instancedMesh); - } - if (nodeObject.isGroup) { - nodeObject.clear(); - nodeObject.add(...instancedMeshes); - return nodeObject; - } - return instancedMeshes[0]; - }); - } -} -const BINARY_EXTENSION_HEADER_MAGIC = "glTF"; -const BINARY_EXTENSION_HEADER_LENGTH = 12; -const BINARY_EXTENSION_CHUNK_TYPES = { JSON: 1313821514, BIN: 5130562 }; -class GLTFBinaryExtension { - static { - __name(this, "GLTFBinaryExtension"); - } - constructor(data) { - this.name = EXTENSIONS.KHR_BINARY_GLTF; - this.content = null; - this.body = null; - const headerView = new DataView(data, 0, BINARY_EXTENSION_HEADER_LENGTH); - const textDecoder = new TextDecoder(); - this.header = { - magic: textDecoder.decode(new Uint8Array(data.slice(0, 4))), - version: headerView.getUint32(4, true), - length: headerView.getUint32(8, true) - }; - if (this.header.magic !== BINARY_EXTENSION_HEADER_MAGIC) { - throw new Error("THREE.GLTFLoader: Unsupported glTF-Binary header."); - } else if (this.header.version < 2) { - throw new Error("THREE.GLTFLoader: Legacy binary file detected."); - } - const chunkContentsLength = this.header.length - BINARY_EXTENSION_HEADER_LENGTH; - const chunkView = new DataView(data, BINARY_EXTENSION_HEADER_LENGTH); - let chunkIndex = 0; - while (chunkIndex < chunkContentsLength) { - const chunkLength = chunkView.getUint32(chunkIndex, true); - chunkIndex += 4; - const chunkType = chunkView.getUint32(chunkIndex, true); - chunkIndex += 4; - if (chunkType === BINARY_EXTENSION_CHUNK_TYPES.JSON) { - const contentArray = new Uint8Array(data, BINARY_EXTENSION_HEADER_LENGTH + chunkIndex, chunkLength); - this.content = textDecoder.decode(contentArray); - } else if (chunkType === BINARY_EXTENSION_CHUNK_TYPES.BIN) { - const byteOffset = BINARY_EXTENSION_HEADER_LENGTH + chunkIndex; - this.body = data.slice(byteOffset, byteOffset + chunkLength); - } - chunkIndex += chunkLength; - } - if (this.content === null) { - throw new Error("THREE.GLTFLoader: JSON content not found."); - } - } -} -class GLTFDracoMeshCompressionExtension { - static { - __name(this, "GLTFDracoMeshCompressionExtension"); - } - constructor(json, dracoLoader) { - if (!dracoLoader) { - throw new Error("THREE.GLTFLoader: No DRACOLoader instance provided."); - } - this.name = EXTENSIONS.KHR_DRACO_MESH_COMPRESSION; - this.json = json; - this.dracoLoader = dracoLoader; - this.dracoLoader.preload(); - } - decodePrimitive(primitive, parser) { - const json = this.json; - const dracoLoader = this.dracoLoader; - const bufferViewIndex = primitive.extensions[this.name].bufferView; - const gltfAttributeMap = primitive.extensions[this.name].attributes; - const threeAttributeMap = {}; - const attributeNormalizedMap = {}; - const attributeTypeMap = {}; - for (const attributeName in gltfAttributeMap) { - const threeAttributeName = ATTRIBUTES[attributeName] || attributeName.toLowerCase(); - threeAttributeMap[threeAttributeName] = gltfAttributeMap[attributeName]; - } - for (const attributeName in primitive.attributes) { - const threeAttributeName = ATTRIBUTES[attributeName] || attributeName.toLowerCase(); - if (gltfAttributeMap[attributeName] !== void 0) { - const accessorDef = json.accessors[primitive.attributes[attributeName]]; - const componentType = WEBGL_COMPONENT_TYPES[accessorDef.componentType]; - attributeTypeMap[threeAttributeName] = componentType.name; - attributeNormalizedMap[threeAttributeName] = accessorDef.normalized === true; - } - } - return parser.getDependency("bufferView", bufferViewIndex).then(function(bufferView) { - return new Promise(function(resolve, reject) { - dracoLoader.decodeDracoFile(bufferView, function(geometry) { - for (const attributeName in geometry.attributes) { - const attribute = geometry.attributes[attributeName]; - const normalized = attributeNormalizedMap[attributeName]; - if (normalized !== void 0) attribute.normalized = normalized; - } - resolve(geometry); - }, threeAttributeMap, attributeTypeMap, LinearSRGBColorSpace, reject); - }); - }); - } -} -class GLTFTextureTransformExtension { - static { - __name(this, "GLTFTextureTransformExtension"); - } - constructor() { - this.name = EXTENSIONS.KHR_TEXTURE_TRANSFORM; - } - extendTexture(texture, transform) { - if ((transform.texCoord === void 0 || transform.texCoord === texture.channel) && transform.offset === void 0 && transform.rotation === void 0 && transform.scale === void 0) { - return texture; - } - texture = texture.clone(); - if (transform.texCoord !== void 0) { - texture.channel = transform.texCoord; - } - if (transform.offset !== void 0) { - texture.offset.fromArray(transform.offset); - } - if (transform.rotation !== void 0) { - texture.rotation = transform.rotation; - } - if (transform.scale !== void 0) { - texture.repeat.fromArray(transform.scale); - } - texture.needsUpdate = true; - return texture; - } -} -class GLTFMeshQuantizationExtension { - static { - __name(this, "GLTFMeshQuantizationExtension"); - } - constructor() { - this.name = EXTENSIONS.KHR_MESH_QUANTIZATION; - } -} -class GLTFCubicSplineInterpolant extends Interpolant { - static { - __name(this, "GLTFCubicSplineInterpolant"); - } - constructor(parameterPositions, sampleValues, sampleSize, resultBuffer) { - super(parameterPositions, sampleValues, sampleSize, resultBuffer); - } - copySampleValue_(index) { - const result = this.resultBuffer, values = this.sampleValues, valueSize = this.valueSize, offset = index * valueSize * 3 + valueSize; - for (let i = 0; i !== valueSize; i++) { - result[i] = values[offset + i]; - } - return result; - } - interpolate_(i1, t0, t2, t1) { - const result = this.resultBuffer; - const values = this.sampleValues; - const stride = this.valueSize; - const stride2 = stride * 2; - const stride3 = stride * 3; - const td2 = t1 - t0; - const p = (t2 - t0) / td2; - const pp = p * p; - const ppp = pp * p; - const offset1 = i1 * stride3; - const offset0 = offset1 - stride3; - const s2 = -2 * ppp + 3 * pp; - const s3 = ppp - pp; - const s0 = 1 - s2; - const s1 = s3 - pp + p; - for (let i = 0; i !== stride; i++) { - const p0 = values[offset0 + i + stride]; - const m0 = values[offset0 + i + stride2] * td2; - const p1 = values[offset1 + i + stride]; - const m1 = values[offset1 + i] * td2; - result[i] = s0 * p0 + s1 * m0 + s2 * p1 + s3 * m1; - } - return result; - } -} -const _q = new Quaternion(); -class GLTFCubicSplineQuaternionInterpolant extends GLTFCubicSplineInterpolant { - static { - __name(this, "GLTFCubicSplineQuaternionInterpolant"); - } - interpolate_(i1, t0, t2, t1) { - const result = super.interpolate_(i1, t0, t2, t1); - _q.fromArray(result).normalize().toArray(result); - return result; - } -} -const WEBGL_CONSTANTS = { - FLOAT: 5126, - //FLOAT_MAT2: 35674, - FLOAT_MAT3: 35675, - FLOAT_MAT4: 35676, - FLOAT_VEC2: 35664, - FLOAT_VEC3: 35665, - FLOAT_VEC4: 35666, - LINEAR: 9729, - REPEAT: 10497, - SAMPLER_2D: 35678, - POINTS: 0, - LINES: 1, - LINE_LOOP: 2, - LINE_STRIP: 3, - TRIANGLES: 4, - TRIANGLE_STRIP: 5, - TRIANGLE_FAN: 6, - UNSIGNED_BYTE: 5121, - UNSIGNED_SHORT: 5123 -}; -const WEBGL_COMPONENT_TYPES = { - 5120: Int8Array, - 5121: Uint8Array, - 5122: Int16Array, - 5123: Uint16Array, - 5125: Uint32Array, - 5126: Float32Array -}; -const WEBGL_FILTERS = { - 9728: NearestFilter, - 9729: LinearFilter, - 9984: NearestMipmapNearestFilter, - 9985: LinearMipmapNearestFilter, - 9986: NearestMipmapLinearFilter, - 9987: LinearMipmapLinearFilter -}; -const WEBGL_WRAPPINGS = { - 33071: ClampToEdgeWrapping, - 33648: MirroredRepeatWrapping, - 10497: RepeatWrapping -}; -const WEBGL_TYPE_SIZES = { - "SCALAR": 1, - "VEC2": 2, - "VEC3": 3, - "VEC4": 4, - "MAT2": 4, - "MAT3": 9, - "MAT4": 16 -}; -const ATTRIBUTES = { - POSITION: "position", - NORMAL: "normal", - TANGENT: "tangent", - TEXCOORD_0: "uv", - TEXCOORD_1: "uv1", - TEXCOORD_2: "uv2", - TEXCOORD_3: "uv3", - COLOR_0: "color", - WEIGHTS_0: "skinWeight", - JOINTS_0: "skinIndex" -}; -const PATH_PROPERTIES = { - scale: "scale", - translation: "position", - rotation: "quaternion", - weights: "morphTargetInfluences" -}; -const INTERPOLATION = { - CUBICSPLINE: void 0, - // We use a custom interpolant (GLTFCubicSplineInterpolation) for CUBICSPLINE tracks. Each - // keyframe track will be initialized with a default interpolation type, then modified. - LINEAR: InterpolateLinear, - STEP: InterpolateDiscrete -}; -const ALPHA_MODES = { - OPAQUE: "OPAQUE", - MASK: "MASK", - BLEND: "BLEND" -}; -function createDefaultMaterial(cache) { - if (cache["DefaultMaterial"] === void 0) { - cache["DefaultMaterial"] = new MeshStandardMaterial({ - color: 16777215, - emissive: 0, - metalness: 1, - roughness: 1, - transparent: false, - depthTest: true, - side: FrontSide - }); - } - return cache["DefaultMaterial"]; -} -__name(createDefaultMaterial, "createDefaultMaterial"); -function addUnknownExtensionsToUserData(knownExtensions, object, objectDef) { - for (const name in objectDef.extensions) { - if (knownExtensions[name] === void 0) { - object.userData.gltfExtensions = object.userData.gltfExtensions || {}; - object.userData.gltfExtensions[name] = objectDef.extensions[name]; - } - } -} -__name(addUnknownExtensionsToUserData, "addUnknownExtensionsToUserData"); -function assignExtrasToUserData(object, gltfDef) { - if (gltfDef.extras !== void 0) { - if (typeof gltfDef.extras === "object") { - Object.assign(object.userData, gltfDef.extras); - } else { - console.warn("THREE.GLTFLoader: Ignoring primitive type .extras, " + gltfDef.extras); - } - } -} -__name(assignExtrasToUserData, "assignExtrasToUserData"); -function addMorphTargets(geometry, targets, parser) { - let hasMorphPosition = false; - let hasMorphNormal = false; - let hasMorphColor = false; - for (let i = 0, il = targets.length; i < il; i++) { - const target = targets[i]; - if (target.POSITION !== void 0) hasMorphPosition = true; - if (target.NORMAL !== void 0) hasMorphNormal = true; - if (target.COLOR_0 !== void 0) hasMorphColor = true; - if (hasMorphPosition && hasMorphNormal && hasMorphColor) break; - } - if (!hasMorphPosition && !hasMorphNormal && !hasMorphColor) return Promise.resolve(geometry); - const pendingPositionAccessors = []; - const pendingNormalAccessors = []; - const pendingColorAccessors = []; - for (let i = 0, il = targets.length; i < il; i++) { - const target = targets[i]; - if (hasMorphPosition) { - const pendingAccessor = target.POSITION !== void 0 ? parser.getDependency("accessor", target.POSITION) : geometry.attributes.position; - pendingPositionAccessors.push(pendingAccessor); - } - if (hasMorphNormal) { - const pendingAccessor = target.NORMAL !== void 0 ? parser.getDependency("accessor", target.NORMAL) : geometry.attributes.normal; - pendingNormalAccessors.push(pendingAccessor); - } - if (hasMorphColor) { - const pendingAccessor = target.COLOR_0 !== void 0 ? parser.getDependency("accessor", target.COLOR_0) : geometry.attributes.color; - pendingColorAccessors.push(pendingAccessor); - } - } - return Promise.all([ - Promise.all(pendingPositionAccessors), - Promise.all(pendingNormalAccessors), - Promise.all(pendingColorAccessors) - ]).then(function(accessors) { - const morphPositions = accessors[0]; - const morphNormals = accessors[1]; - const morphColors = accessors[2]; - if (hasMorphPosition) geometry.morphAttributes.position = morphPositions; - if (hasMorphNormal) geometry.morphAttributes.normal = morphNormals; - if (hasMorphColor) geometry.morphAttributes.color = morphColors; - geometry.morphTargetsRelative = true; - return geometry; - }); -} -__name(addMorphTargets, "addMorphTargets"); -function updateMorphTargets(mesh, meshDef) { - mesh.updateMorphTargets(); - if (meshDef.weights !== void 0) { - for (let i = 0, il = meshDef.weights.length; i < il; i++) { - mesh.morphTargetInfluences[i] = meshDef.weights[i]; - } - } - if (meshDef.extras && Array.isArray(meshDef.extras.targetNames)) { - const targetNames = meshDef.extras.targetNames; - if (mesh.morphTargetInfluences.length === targetNames.length) { - mesh.morphTargetDictionary = {}; - for (let i = 0, il = targetNames.length; i < il; i++) { - mesh.morphTargetDictionary[targetNames[i]] = i; - } - } else { - console.warn("THREE.GLTFLoader: Invalid extras.targetNames length. Ignoring names."); - } - } -} -__name(updateMorphTargets, "updateMorphTargets"); -function createPrimitiveKey(primitiveDef) { - let geometryKey; - const dracoExtension = primitiveDef.extensions && primitiveDef.extensions[EXTENSIONS.KHR_DRACO_MESH_COMPRESSION]; - if (dracoExtension) { - geometryKey = "draco:" + dracoExtension.bufferView + ":" + dracoExtension.indices + ":" + createAttributesKey(dracoExtension.attributes); - } else { - geometryKey = primitiveDef.indices + ":" + createAttributesKey(primitiveDef.attributes) + ":" + primitiveDef.mode; - } - if (primitiveDef.targets !== void 0) { - for (let i = 0, il = primitiveDef.targets.length; i < il; i++) { - geometryKey += ":" + createAttributesKey(primitiveDef.targets[i]); - } - } - return geometryKey; -} -__name(createPrimitiveKey, "createPrimitiveKey"); -function createAttributesKey(attributes) { - let attributesKey = ""; - const keys = Object.keys(attributes).sort(); - for (let i = 0, il = keys.length; i < il; i++) { - attributesKey += keys[i] + ":" + attributes[keys[i]] + ";"; - } - return attributesKey; -} -__name(createAttributesKey, "createAttributesKey"); -function getNormalizedComponentScale(constructor) { - switch (constructor) { - case Int8Array: - return 1 / 127; - case Uint8Array: - return 1 / 255; - case Int16Array: - return 1 / 32767; - case Uint16Array: - return 1 / 65535; - default: - throw new Error("THREE.GLTFLoader: Unsupported normalized accessor component type."); - } -} -__name(getNormalizedComponentScale, "getNormalizedComponentScale"); -function getImageURIMimeType(uri) { - if (uri.search(/\.jpe?g($|\?)/i) > 0 || uri.search(/^data\:image\/jpeg/) === 0) return "image/jpeg"; - if (uri.search(/\.webp($|\?)/i) > 0 || uri.search(/^data\:image\/webp/) === 0) return "image/webp"; - if (uri.search(/\.ktx2($|\?)/i) > 0 || uri.search(/^data\:image\/ktx2/) === 0) return "image/ktx2"; - return "image/png"; -} -__name(getImageURIMimeType, "getImageURIMimeType"); -const _identityMatrix = new Matrix4(); -class GLTFParser { - static { - __name(this, "GLTFParser"); - } - constructor(json = {}, options = {}) { - this.json = json; - this.extensions = {}; - this.plugins = {}; - this.options = options; - this.cache = new GLTFRegistry(); - this.associations = /* @__PURE__ */ new Map(); - this.primitiveCache = {}; - this.nodeCache = {}; - this.meshCache = { refs: {}, uses: {} }; - this.cameraCache = { refs: {}, uses: {} }; - this.lightCache = { refs: {}, uses: {} }; - this.sourceCache = {}; - this.textureCache = {}; - this.nodeNamesUsed = {}; - let isSafari = false; - let safariVersion = -1; - let isFirefox = false; - let firefoxVersion = -1; - if (typeof navigator !== "undefined") { - const userAgent = navigator.userAgent; - isSafari = /^((?!chrome|android).)*safari/i.test(userAgent) === true; - const safariMatch = userAgent.match(/Version\/(\d+)/); - safariVersion = isSafari && safariMatch ? parseInt(safariMatch[1], 10) : -1; - isFirefox = userAgent.indexOf("Firefox") > -1; - firefoxVersion = isFirefox ? userAgent.match(/Firefox\/([0-9]+)\./)[1] : -1; - } - if (typeof createImageBitmap === "undefined" || isSafari && safariVersion < 17 || isFirefox && firefoxVersion < 98) { - this.textureLoader = new TextureLoader(this.options.manager); - } else { - this.textureLoader = new ImageBitmapLoader(this.options.manager); - } - this.textureLoader.setCrossOrigin(this.options.crossOrigin); - this.textureLoader.setRequestHeader(this.options.requestHeader); - this.fileLoader = new FileLoader(this.options.manager); - this.fileLoader.setResponseType("arraybuffer"); - if (this.options.crossOrigin === "use-credentials") { - this.fileLoader.setWithCredentials(true); - } - } - setExtensions(extensions) { - this.extensions = extensions; - } - setPlugins(plugins) { - this.plugins = plugins; - } - parse(onLoad, onError) { - const parser = this; - const json = this.json; - const extensions = this.extensions; - this.cache.removeAll(); - this.nodeCache = {}; - this._invokeAll(function(ext2) { - return ext2._markDefs && ext2._markDefs(); - }); - Promise.all(this._invokeAll(function(ext2) { - return ext2.beforeRoot && ext2.beforeRoot(); - })).then(function() { - return Promise.all([ - parser.getDependencies("scene"), - parser.getDependencies("animation"), - parser.getDependencies("camera") - ]); - }).then(function(dependencies) { - const result = { - scene: dependencies[0][json.scene || 0], - scenes: dependencies[0], - animations: dependencies[1], - cameras: dependencies[2], - asset: json.asset, - parser, - userData: {} - }; - addUnknownExtensionsToUserData(extensions, result, json); - assignExtrasToUserData(result, json); - return Promise.all(parser._invokeAll(function(ext2) { - return ext2.afterRoot && ext2.afterRoot(result); - })).then(function() { - for (const scene of result.scenes) { - scene.updateMatrixWorld(); - } - onLoad(result); - }); - }).catch(onError); - } - /** - * Marks the special nodes/meshes in json for efficient parse. - */ - _markDefs() { - const nodeDefs = this.json.nodes || []; - const skinDefs = this.json.skins || []; - const meshDefs = this.json.meshes || []; - for (let skinIndex = 0, skinLength = skinDefs.length; skinIndex < skinLength; skinIndex++) { - const joints = skinDefs[skinIndex].joints; - for (let i = 0, il = joints.length; i < il; i++) { - nodeDefs[joints[i]].isBone = true; - } - } - for (let nodeIndex = 0, nodeLength = nodeDefs.length; nodeIndex < nodeLength; nodeIndex++) { - const nodeDef = nodeDefs[nodeIndex]; - if (nodeDef.mesh !== void 0) { - this._addNodeRef(this.meshCache, nodeDef.mesh); - if (nodeDef.skin !== void 0) { - meshDefs[nodeDef.mesh].isSkinnedMesh = true; - } - } - if (nodeDef.camera !== void 0) { - this._addNodeRef(this.cameraCache, nodeDef.camera); - } - } - } - /** - * Counts references to shared node / Object3D resources. These resources - * can be reused, or "instantiated", at multiple nodes in the scene - * hierarchy. Mesh, Camera, and Light instances are instantiated and must - * be marked. Non-scenegraph resources (like Materials, Geometries, and - * Textures) can be reused directly and are not marked here. - * - * Example: CesiumMilkTruck sample model reuses "Wheel" meshes. - */ - _addNodeRef(cache, index) { - if (index === void 0) return; - if (cache.refs[index] === void 0) { - cache.refs[index] = cache.uses[index] = 0; - } - cache.refs[index]++; - } - /** Returns a reference to a shared resource, cloning it if necessary. */ - _getNodeRef(cache, index, object) { - if (cache.refs[index] <= 1) return object; - const ref = object.clone(); - const updateMappings = /* @__PURE__ */ __name((original, clone) => { - const mappings = this.associations.get(original); - if (mappings != null) { - this.associations.set(clone, mappings); - } - for (const [i, child] of original.children.entries()) { - updateMappings(child, clone.children[i]); - } - }, "updateMappings"); - updateMappings(object, ref); - ref.name += "_instance_" + cache.uses[index]++; - return ref; - } - _invokeOne(func) { - const extensions = Object.values(this.plugins); - extensions.push(this); - for (let i = 0; i < extensions.length; i++) { - const result = func(extensions[i]); - if (result) return result; - } - return null; - } - _invokeAll(func) { - const extensions = Object.values(this.plugins); - extensions.unshift(this); - const pending = []; - for (let i = 0; i < extensions.length; i++) { - const result = func(extensions[i]); - if (result) pending.push(result); - } - return pending; - } - /** - * Requests the specified dependency asynchronously, with caching. - * @param {string} type - * @param {number} index - * @return {Promise} - */ - getDependency(type, index) { - const cacheKey = type + ":" + index; - let dependency = this.cache.get(cacheKey); - if (!dependency) { - switch (type) { - case "scene": - dependency = this.loadScene(index); - break; - case "node": - dependency = this._invokeOne(function(ext2) { - return ext2.loadNode && ext2.loadNode(index); - }); - break; - case "mesh": - dependency = this._invokeOne(function(ext2) { - return ext2.loadMesh && ext2.loadMesh(index); - }); - break; - case "accessor": - dependency = this.loadAccessor(index); - break; - case "bufferView": - dependency = this._invokeOne(function(ext2) { - return ext2.loadBufferView && ext2.loadBufferView(index); - }); - break; - case "buffer": - dependency = this.loadBuffer(index); - break; - case "material": - dependency = this._invokeOne(function(ext2) { - return ext2.loadMaterial && ext2.loadMaterial(index); - }); - break; - case "texture": - dependency = this._invokeOne(function(ext2) { - return ext2.loadTexture && ext2.loadTexture(index); - }); - break; - case "skin": - dependency = this.loadSkin(index); - break; - case "animation": - dependency = this._invokeOne(function(ext2) { - return ext2.loadAnimation && ext2.loadAnimation(index); - }); - break; - case "camera": - dependency = this.loadCamera(index); - break; - default: - dependency = this._invokeOne(function(ext2) { - return ext2 != this && ext2.getDependency && ext2.getDependency(type, index); - }); - if (!dependency) { - throw new Error("Unknown type: " + type); - } - break; - } - this.cache.add(cacheKey, dependency); - } - return dependency; - } - /** - * Requests all dependencies of the specified type asynchronously, with caching. - * @param {string} type - * @return {Promise>} - */ - getDependencies(type) { - let dependencies = this.cache.get(type); - if (!dependencies) { - const parser = this; - const defs = this.json[type + (type === "mesh" ? "es" : "s")] || []; - dependencies = Promise.all(defs.map(function(def, index) { - return parser.getDependency(type, index); - })); - this.cache.add(type, dependencies); - } - return dependencies; - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#buffers-and-buffer-views - * @param {number} bufferIndex - * @return {Promise} - */ - loadBuffer(bufferIndex) { - const bufferDef = this.json.buffers[bufferIndex]; - const loader = this.fileLoader; - if (bufferDef.type && bufferDef.type !== "arraybuffer") { - throw new Error("THREE.GLTFLoader: " + bufferDef.type + " buffer type is not supported."); - } - if (bufferDef.uri === void 0 && bufferIndex === 0) { - return Promise.resolve(this.extensions[EXTENSIONS.KHR_BINARY_GLTF].body); - } - const options = this.options; - return new Promise(function(resolve, reject) { - loader.load(LoaderUtils.resolveURL(bufferDef.uri, options.path), resolve, void 0, function() { - reject(new Error('THREE.GLTFLoader: Failed to load buffer "' + bufferDef.uri + '".')); - }); - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#buffers-and-buffer-views - * @param {number} bufferViewIndex - * @return {Promise} - */ - loadBufferView(bufferViewIndex) { - const bufferViewDef = this.json.bufferViews[bufferViewIndex]; - return this.getDependency("buffer", bufferViewDef.buffer).then(function(buffer) { - const byteLength = bufferViewDef.byteLength || 0; - const byteOffset = bufferViewDef.byteOffset || 0; - return buffer.slice(byteOffset, byteOffset + byteLength); - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#accessors - * @param {number} accessorIndex - * @return {Promise} - */ - loadAccessor(accessorIndex) { - const parser = this; - const json = this.json; - const accessorDef = this.json.accessors[accessorIndex]; - if (accessorDef.bufferView === void 0 && accessorDef.sparse === void 0) { - const itemSize = WEBGL_TYPE_SIZES[accessorDef.type]; - const TypedArray = WEBGL_COMPONENT_TYPES[accessorDef.componentType]; - const normalized = accessorDef.normalized === true; - const array = new TypedArray(accessorDef.count * itemSize); - return Promise.resolve(new BufferAttribute(array, itemSize, normalized)); - } - const pendingBufferViews = []; - if (accessorDef.bufferView !== void 0) { - pendingBufferViews.push(this.getDependency("bufferView", accessorDef.bufferView)); - } else { - pendingBufferViews.push(null); - } - if (accessorDef.sparse !== void 0) { - pendingBufferViews.push(this.getDependency("bufferView", accessorDef.sparse.indices.bufferView)); - pendingBufferViews.push(this.getDependency("bufferView", accessorDef.sparse.values.bufferView)); - } - return Promise.all(pendingBufferViews).then(function(bufferViews) { - const bufferView = bufferViews[0]; - const itemSize = WEBGL_TYPE_SIZES[accessorDef.type]; - const TypedArray = WEBGL_COMPONENT_TYPES[accessorDef.componentType]; - const elementBytes = TypedArray.BYTES_PER_ELEMENT; - const itemBytes = elementBytes * itemSize; - const byteOffset = accessorDef.byteOffset || 0; - const byteStride = accessorDef.bufferView !== void 0 ? json.bufferViews[accessorDef.bufferView].byteStride : void 0; - const normalized = accessorDef.normalized === true; - let array, bufferAttribute; - if (byteStride && byteStride !== itemBytes) { - const ibSlice = Math.floor(byteOffset / byteStride); - const ibCacheKey = "InterleavedBuffer:" + accessorDef.bufferView + ":" + accessorDef.componentType + ":" + ibSlice + ":" + accessorDef.count; - let ib = parser.cache.get(ibCacheKey); - if (!ib) { - array = new TypedArray(bufferView, ibSlice * byteStride, accessorDef.count * byteStride / elementBytes); - ib = new InterleavedBuffer(array, byteStride / elementBytes); - parser.cache.add(ibCacheKey, ib); - } - bufferAttribute = new InterleavedBufferAttribute(ib, itemSize, byteOffset % byteStride / elementBytes, normalized); - } else { - if (bufferView === null) { - array = new TypedArray(accessorDef.count * itemSize); - } else { - array = new TypedArray(bufferView, byteOffset, accessorDef.count * itemSize); - } - bufferAttribute = new BufferAttribute(array, itemSize, normalized); - } - if (accessorDef.sparse !== void 0) { - const itemSizeIndices = WEBGL_TYPE_SIZES.SCALAR; - const TypedArrayIndices = WEBGL_COMPONENT_TYPES[accessorDef.sparse.indices.componentType]; - const byteOffsetIndices = accessorDef.sparse.indices.byteOffset || 0; - const byteOffsetValues = accessorDef.sparse.values.byteOffset || 0; - const sparseIndices = new TypedArrayIndices(bufferViews[1], byteOffsetIndices, accessorDef.sparse.count * itemSizeIndices); - const sparseValues = new TypedArray(bufferViews[2], byteOffsetValues, accessorDef.sparse.count * itemSize); - if (bufferView !== null) { - bufferAttribute = new BufferAttribute(bufferAttribute.array.slice(), bufferAttribute.itemSize, bufferAttribute.normalized); - } - bufferAttribute.normalized = false; - for (let i = 0, il = sparseIndices.length; i < il; i++) { - const index = sparseIndices[i]; - bufferAttribute.setX(index, sparseValues[i * itemSize]); - if (itemSize >= 2) bufferAttribute.setY(index, sparseValues[i * itemSize + 1]); - if (itemSize >= 3) bufferAttribute.setZ(index, sparseValues[i * itemSize + 2]); - if (itemSize >= 4) bufferAttribute.setW(index, sparseValues[i * itemSize + 3]); - if (itemSize >= 5) throw new Error("THREE.GLTFLoader: Unsupported itemSize in sparse BufferAttribute."); - } - bufferAttribute.normalized = normalized; - } - return bufferAttribute; - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#textures - * @param {number} textureIndex - * @return {Promise} - */ - loadTexture(textureIndex) { - const json = this.json; - const options = this.options; - const textureDef = json.textures[textureIndex]; - const sourceIndex = textureDef.source; - const sourceDef = json.images[sourceIndex]; - let loader = this.textureLoader; - if (sourceDef.uri) { - const handler = options.manager.getHandler(sourceDef.uri); - if (handler !== null) loader = handler; - } - return this.loadTextureImage(textureIndex, sourceIndex, loader); - } - loadTextureImage(textureIndex, sourceIndex, loader) { - const parser = this; - const json = this.json; - const textureDef = json.textures[textureIndex]; - const sourceDef = json.images[sourceIndex]; - const cacheKey = (sourceDef.uri || sourceDef.bufferView) + ":" + textureDef.sampler; - if (this.textureCache[cacheKey]) { - return this.textureCache[cacheKey]; - } - const promise = this.loadImageSource(sourceIndex, loader).then(function(texture) { - texture.flipY = false; - texture.name = textureDef.name || sourceDef.name || ""; - if (texture.name === "" && typeof sourceDef.uri === "string" && sourceDef.uri.startsWith("data:image/") === false) { - texture.name = sourceDef.uri; - } - const samplers = json.samplers || {}; - const sampler = samplers[textureDef.sampler] || {}; - texture.magFilter = WEBGL_FILTERS[sampler.magFilter] || LinearFilter; - texture.minFilter = WEBGL_FILTERS[sampler.minFilter] || LinearMipmapLinearFilter; - texture.wrapS = WEBGL_WRAPPINGS[sampler.wrapS] || RepeatWrapping; - texture.wrapT = WEBGL_WRAPPINGS[sampler.wrapT] || RepeatWrapping; - texture.generateMipmaps = !texture.isCompressedTexture && texture.minFilter !== NearestFilter && texture.minFilter !== LinearFilter; - parser.associations.set(texture, { textures: textureIndex }); - return texture; - }).catch(function() { - return null; - }); - this.textureCache[cacheKey] = promise; - return promise; - } - loadImageSource(sourceIndex, loader) { - const parser = this; - const json = this.json; - const options = this.options; - if (this.sourceCache[sourceIndex] !== void 0) { - return this.sourceCache[sourceIndex].then((texture) => texture.clone()); - } - const sourceDef = json.images[sourceIndex]; - const URL2 = self.URL || self.webkitURL; - let sourceURI = sourceDef.uri || ""; - let isObjectURL = false; - if (sourceDef.bufferView !== void 0) { - sourceURI = parser.getDependency("bufferView", sourceDef.bufferView).then(function(bufferView) { - isObjectURL = true; - const blob = new Blob([bufferView], { type: sourceDef.mimeType }); - sourceURI = URL2.createObjectURL(blob); - return sourceURI; - }); - } else if (sourceDef.uri === void 0) { - throw new Error("THREE.GLTFLoader: Image " + sourceIndex + " is missing URI and bufferView"); - } - const promise = Promise.resolve(sourceURI).then(function(sourceURI2) { - return new Promise(function(resolve, reject) { - let onLoad = resolve; - if (loader.isImageBitmapLoader === true) { - onLoad = /* @__PURE__ */ __name(function(imageBitmap) { - const texture = new Texture(imageBitmap); - texture.needsUpdate = true; - resolve(texture); - }, "onLoad"); - } - loader.load(LoaderUtils.resolveURL(sourceURI2, options.path), onLoad, void 0, reject); - }); - }).then(function(texture) { - if (isObjectURL === true) { - URL2.revokeObjectURL(sourceURI); - } - assignExtrasToUserData(texture, sourceDef); - texture.userData.mimeType = sourceDef.mimeType || getImageURIMimeType(sourceDef.uri); - return texture; - }).catch(function(error) { - console.error("THREE.GLTFLoader: Couldn't load texture", sourceURI); - throw error; - }); - this.sourceCache[sourceIndex] = promise; - return promise; - } - /** - * Asynchronously assigns a texture to the given material parameters. - * @param {Object} materialParams - * @param {string} mapName - * @param {Object} mapDef - * @return {Promise} - */ - assignTexture(materialParams, mapName, mapDef, colorSpace) { - const parser = this; - return this.getDependency("texture", mapDef.index).then(function(texture) { - if (!texture) return null; - if (mapDef.texCoord !== void 0 && mapDef.texCoord > 0) { - texture = texture.clone(); - texture.channel = mapDef.texCoord; - } - if (parser.extensions[EXTENSIONS.KHR_TEXTURE_TRANSFORM]) { - const transform = mapDef.extensions !== void 0 ? mapDef.extensions[EXTENSIONS.KHR_TEXTURE_TRANSFORM] : void 0; - if (transform) { - const gltfReference = parser.associations.get(texture); - texture = parser.extensions[EXTENSIONS.KHR_TEXTURE_TRANSFORM].extendTexture(texture, transform); - parser.associations.set(texture, gltfReference); - } - } - if (colorSpace !== void 0) { - texture.colorSpace = colorSpace; - } - materialParams[mapName] = texture; - return texture; - }); - } - /** - * Assigns final material to a Mesh, Line, or Points instance. The instance - * already has a material (generated from the glTF material options alone) - * but reuse of the same glTF material may require multiple threejs materials - * to accommodate different primitive types, defines, etc. New materials will - * be created if necessary, and reused from a cache. - * @param {Object3D} mesh Mesh, Line, or Points instance. - */ - assignFinalMaterial(mesh) { - const geometry = mesh.geometry; - let material = mesh.material; - const useDerivativeTangents = geometry.attributes.tangent === void 0; - const useVertexColors = geometry.attributes.color !== void 0; - const useFlatShading = geometry.attributes.normal === void 0; - if (mesh.isPoints) { - const cacheKey = "PointsMaterial:" + material.uuid; - let pointsMaterial = this.cache.get(cacheKey); - if (!pointsMaterial) { - pointsMaterial = new PointsMaterial(); - Material.prototype.copy.call(pointsMaterial, material); - pointsMaterial.color.copy(material.color); - pointsMaterial.map = material.map; - pointsMaterial.sizeAttenuation = false; - this.cache.add(cacheKey, pointsMaterial); - } - material = pointsMaterial; - } else if (mesh.isLine) { - const cacheKey = "LineBasicMaterial:" + material.uuid; - let lineMaterial = this.cache.get(cacheKey); - if (!lineMaterial) { - lineMaterial = new LineBasicMaterial(); - Material.prototype.copy.call(lineMaterial, material); - lineMaterial.color.copy(material.color); - lineMaterial.map = material.map; - this.cache.add(cacheKey, lineMaterial); - } - material = lineMaterial; - } - if (useDerivativeTangents || useVertexColors || useFlatShading) { - let cacheKey = "ClonedMaterial:" + material.uuid + ":"; - if (useDerivativeTangents) cacheKey += "derivative-tangents:"; - if (useVertexColors) cacheKey += "vertex-colors:"; - if (useFlatShading) cacheKey += "flat-shading:"; - let cachedMaterial = this.cache.get(cacheKey); - if (!cachedMaterial) { - cachedMaterial = material.clone(); - if (useVertexColors) cachedMaterial.vertexColors = true; - if (useFlatShading) cachedMaterial.flatShading = true; - if (useDerivativeTangents) { - if (cachedMaterial.normalScale) cachedMaterial.normalScale.y *= -1; - if (cachedMaterial.clearcoatNormalScale) cachedMaterial.clearcoatNormalScale.y *= -1; - } - this.cache.add(cacheKey, cachedMaterial); - this.associations.set(cachedMaterial, this.associations.get(material)); - } - material = cachedMaterial; - } - mesh.material = material; - } - getMaterialType() { - return MeshStandardMaterial; - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#materials - * @param {number} materialIndex - * @return {Promise} - */ - loadMaterial(materialIndex) { - const parser = this; - const json = this.json; - const extensions = this.extensions; - const materialDef = json.materials[materialIndex]; - let materialType; - const materialParams = {}; - const materialExtensions = materialDef.extensions || {}; - const pending = []; - if (materialExtensions[EXTENSIONS.KHR_MATERIALS_UNLIT]) { - const kmuExtension = extensions[EXTENSIONS.KHR_MATERIALS_UNLIT]; - materialType = kmuExtension.getMaterialType(); - pending.push(kmuExtension.extendParams(materialParams, materialDef, parser)); - } else { - const metallicRoughness = materialDef.pbrMetallicRoughness || {}; - materialParams.color = new Color(1, 1, 1); - materialParams.opacity = 1; - if (Array.isArray(metallicRoughness.baseColorFactor)) { - const array = metallicRoughness.baseColorFactor; - materialParams.color.setRGB(array[0], array[1], array[2], LinearSRGBColorSpace); - materialParams.opacity = array[3]; - } - if (metallicRoughness.baseColorTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "map", metallicRoughness.baseColorTexture, SRGBColorSpace)); - } - materialParams.metalness = metallicRoughness.metallicFactor !== void 0 ? metallicRoughness.metallicFactor : 1; - materialParams.roughness = metallicRoughness.roughnessFactor !== void 0 ? metallicRoughness.roughnessFactor : 1; - if (metallicRoughness.metallicRoughnessTexture !== void 0) { - pending.push(parser.assignTexture(materialParams, "metalnessMap", metallicRoughness.metallicRoughnessTexture)); - pending.push(parser.assignTexture(materialParams, "roughnessMap", metallicRoughness.metallicRoughnessTexture)); - } - materialType = this._invokeOne(function(ext2) { - return ext2.getMaterialType && ext2.getMaterialType(materialIndex); - }); - pending.push(Promise.all(this._invokeAll(function(ext2) { - return ext2.extendMaterialParams && ext2.extendMaterialParams(materialIndex, materialParams); - }))); - } - if (materialDef.doubleSided === true) { - materialParams.side = DoubleSide; - } - const alphaMode = materialDef.alphaMode || ALPHA_MODES.OPAQUE; - if (alphaMode === ALPHA_MODES.BLEND) { - materialParams.transparent = true; - materialParams.depthWrite = false; - } else { - materialParams.transparent = false; - if (alphaMode === ALPHA_MODES.MASK) { - materialParams.alphaTest = materialDef.alphaCutoff !== void 0 ? materialDef.alphaCutoff : 0.5; - } - } - if (materialDef.normalTexture !== void 0 && materialType !== MeshBasicMaterial) { - pending.push(parser.assignTexture(materialParams, "normalMap", materialDef.normalTexture)); - materialParams.normalScale = new Vector2(1, 1); - if (materialDef.normalTexture.scale !== void 0) { - const scale = materialDef.normalTexture.scale; - materialParams.normalScale.set(scale, scale); - } - } - if (materialDef.occlusionTexture !== void 0 && materialType !== MeshBasicMaterial) { - pending.push(parser.assignTexture(materialParams, "aoMap", materialDef.occlusionTexture)); - if (materialDef.occlusionTexture.strength !== void 0) { - materialParams.aoMapIntensity = materialDef.occlusionTexture.strength; - } - } - if (materialDef.emissiveFactor !== void 0 && materialType !== MeshBasicMaterial) { - const emissiveFactor = materialDef.emissiveFactor; - materialParams.emissive = new Color().setRGB(emissiveFactor[0], emissiveFactor[1], emissiveFactor[2], LinearSRGBColorSpace); - } - if (materialDef.emissiveTexture !== void 0 && materialType !== MeshBasicMaterial) { - pending.push(parser.assignTexture(materialParams, "emissiveMap", materialDef.emissiveTexture, SRGBColorSpace)); - } - return Promise.all(pending).then(function() { - const material = new materialType(materialParams); - if (materialDef.name) material.name = materialDef.name; - assignExtrasToUserData(material, materialDef); - parser.associations.set(material, { materials: materialIndex }); - if (materialDef.extensions) addUnknownExtensionsToUserData(extensions, material, materialDef); - return material; - }); - } - /** When Object3D instances are targeted by animation, they need unique names. */ - createUniqueName(originalName) { - const sanitizedName = PropertyBinding.sanitizeNodeName(originalName || ""); - if (sanitizedName in this.nodeNamesUsed) { - return sanitizedName + "_" + ++this.nodeNamesUsed[sanitizedName]; - } else { - this.nodeNamesUsed[sanitizedName] = 0; - return sanitizedName; - } - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#geometry - * - * Creates BufferGeometries from primitives. - * - * @param {Array} primitives - * @return {Promise>} - */ - loadGeometries(primitives) { - const parser = this; - const extensions = this.extensions; - const cache = this.primitiveCache; - function createDracoPrimitive(primitive) { - return extensions[EXTENSIONS.KHR_DRACO_MESH_COMPRESSION].decodePrimitive(primitive, parser).then(function(geometry) { - return addPrimitiveAttributes(geometry, primitive, parser); - }); - } - __name(createDracoPrimitive, "createDracoPrimitive"); - const pending = []; - for (let i = 0, il = primitives.length; i < il; i++) { - const primitive = primitives[i]; - const cacheKey = createPrimitiveKey(primitive); - const cached = cache[cacheKey]; - if (cached) { - pending.push(cached.promise); - } else { - let geometryPromise; - if (primitive.extensions && primitive.extensions[EXTENSIONS.KHR_DRACO_MESH_COMPRESSION]) { - geometryPromise = createDracoPrimitive(primitive); - } else { - geometryPromise = addPrimitiveAttributes(new BufferGeometry(), primitive, parser); - } - cache[cacheKey] = { primitive, promise: geometryPromise }; - pending.push(geometryPromise); - } - } - return Promise.all(pending); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md#meshes - * @param {number} meshIndex - * @return {Promise} - */ - loadMesh(meshIndex) { - const parser = this; - const json = this.json; - const extensions = this.extensions; - const meshDef = json.meshes[meshIndex]; - const primitives = meshDef.primitives; - const pending = []; - for (let i = 0, il = primitives.length; i < il; i++) { - const material = primitives[i].material === void 0 ? createDefaultMaterial(this.cache) : this.getDependency("material", primitives[i].material); - pending.push(material); - } - pending.push(parser.loadGeometries(primitives)); - return Promise.all(pending).then(function(results) { - const materials = results.slice(0, results.length - 1); - const geometries = results[results.length - 1]; - const meshes = []; - for (let i = 0, il = geometries.length; i < il; i++) { - const geometry = geometries[i]; - const primitive = primitives[i]; - let mesh; - const material = materials[i]; - if (primitive.mode === WEBGL_CONSTANTS.TRIANGLES || primitive.mode === WEBGL_CONSTANTS.TRIANGLE_STRIP || primitive.mode === WEBGL_CONSTANTS.TRIANGLE_FAN || primitive.mode === void 0) { - mesh = meshDef.isSkinnedMesh === true ? new SkinnedMesh(geometry, material) : new Mesh(geometry, material); - if (mesh.isSkinnedMesh === true) { - mesh.normalizeSkinWeights(); - } - if (primitive.mode === WEBGL_CONSTANTS.TRIANGLE_STRIP) { - mesh.geometry = toTrianglesDrawMode(mesh.geometry, TriangleStripDrawMode); - } else if (primitive.mode === WEBGL_CONSTANTS.TRIANGLE_FAN) { - mesh.geometry = toTrianglesDrawMode(mesh.geometry, TriangleFanDrawMode); - } - } else if (primitive.mode === WEBGL_CONSTANTS.LINES) { - mesh = new LineSegments(geometry, material); - } else if (primitive.mode === WEBGL_CONSTANTS.LINE_STRIP) { - mesh = new Line(geometry, material); - } else if (primitive.mode === WEBGL_CONSTANTS.LINE_LOOP) { - mesh = new LineLoop(geometry, material); - } else if (primitive.mode === WEBGL_CONSTANTS.POINTS) { - mesh = new Points(geometry, material); - } else { - throw new Error("THREE.GLTFLoader: Primitive mode unsupported: " + primitive.mode); - } - if (Object.keys(mesh.geometry.morphAttributes).length > 0) { - updateMorphTargets(mesh, meshDef); - } - mesh.name = parser.createUniqueName(meshDef.name || "mesh_" + meshIndex); - assignExtrasToUserData(mesh, meshDef); - if (primitive.extensions) addUnknownExtensionsToUserData(extensions, mesh, primitive); - parser.assignFinalMaterial(mesh); - meshes.push(mesh); - } - for (let i = 0, il = meshes.length; i < il; i++) { - parser.associations.set(meshes[i], { - meshes: meshIndex, - primitives: i - }); - } - if (meshes.length === 1) { - if (meshDef.extensions) addUnknownExtensionsToUserData(extensions, meshes[0], meshDef); - return meshes[0]; - } - const group = new Group(); - if (meshDef.extensions) addUnknownExtensionsToUserData(extensions, group, meshDef); - parser.associations.set(group, { meshes: meshIndex }); - for (let i = 0, il = meshes.length; i < il; i++) { - group.add(meshes[i]); - } - return group; - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#cameras - * @param {number} cameraIndex - * @return {Promise} - */ - loadCamera(cameraIndex) { - let camera; - const cameraDef = this.json.cameras[cameraIndex]; - const params = cameraDef[cameraDef.type]; - if (!params) { - console.warn("THREE.GLTFLoader: Missing camera parameters."); - return; - } - if (cameraDef.type === "perspective") { - camera = new PerspectiveCamera(MathUtils.radToDeg(params.yfov), params.aspectRatio || 1, params.znear || 1, params.zfar || 2e6); - } else if (cameraDef.type === "orthographic") { - camera = new OrthographicCamera(-params.xmag, params.xmag, params.ymag, -params.ymag, params.znear, params.zfar); - } - if (cameraDef.name) camera.name = this.createUniqueName(cameraDef.name); - assignExtrasToUserData(camera, cameraDef); - return Promise.resolve(camera); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#skins - * @param {number} skinIndex - * @return {Promise} - */ - loadSkin(skinIndex) { - const skinDef = this.json.skins[skinIndex]; - const pending = []; - for (let i = 0, il = skinDef.joints.length; i < il; i++) { - pending.push(this._loadNodeShallow(skinDef.joints[i])); - } - if (skinDef.inverseBindMatrices !== void 0) { - pending.push(this.getDependency("accessor", skinDef.inverseBindMatrices)); - } else { - pending.push(null); - } - return Promise.all(pending).then(function(results) { - const inverseBindMatrices = results.pop(); - const jointNodes = results; - const bones = []; - const boneInverses = []; - for (let i = 0, il = jointNodes.length; i < il; i++) { - const jointNode = jointNodes[i]; - if (jointNode) { - bones.push(jointNode); - const mat = new Matrix4(); - if (inverseBindMatrices !== null) { - mat.fromArray(inverseBindMatrices.array, i * 16); - } - boneInverses.push(mat); - } else { - console.warn('THREE.GLTFLoader: Joint "%s" could not be found.', skinDef.joints[i]); - } - } - return new Skeleton(bones, boneInverses); - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#animations - * @param {number} animationIndex - * @return {Promise} - */ - loadAnimation(animationIndex) { - const json = this.json; - const parser = this; - const animationDef = json.animations[animationIndex]; - const animationName = animationDef.name ? animationDef.name : "animation_" + animationIndex; - const pendingNodes = []; - const pendingInputAccessors = []; - const pendingOutputAccessors = []; - const pendingSamplers = []; - const pendingTargets = []; - for (let i = 0, il = animationDef.channels.length; i < il; i++) { - const channel = animationDef.channels[i]; - const sampler = animationDef.samplers[channel.sampler]; - const target = channel.target; - const name = target.node; - const input = animationDef.parameters !== void 0 ? animationDef.parameters[sampler.input] : sampler.input; - const output = animationDef.parameters !== void 0 ? animationDef.parameters[sampler.output] : sampler.output; - if (target.node === void 0) continue; - pendingNodes.push(this.getDependency("node", name)); - pendingInputAccessors.push(this.getDependency("accessor", input)); - pendingOutputAccessors.push(this.getDependency("accessor", output)); - pendingSamplers.push(sampler); - pendingTargets.push(target); - } - return Promise.all([ - Promise.all(pendingNodes), - Promise.all(pendingInputAccessors), - Promise.all(pendingOutputAccessors), - Promise.all(pendingSamplers), - Promise.all(pendingTargets) - ]).then(function(dependencies) { - const nodes = dependencies[0]; - const inputAccessors = dependencies[1]; - const outputAccessors = dependencies[2]; - const samplers = dependencies[3]; - const targets = dependencies[4]; - const tracks = []; - for (let i = 0, il = nodes.length; i < il; i++) { - const node = nodes[i]; - const inputAccessor = inputAccessors[i]; - const outputAccessor = outputAccessors[i]; - const sampler = samplers[i]; - const target = targets[i]; - if (node === void 0) continue; - if (node.updateMatrix) { - node.updateMatrix(); - } - const createdTracks = parser._createAnimationTracks(node, inputAccessor, outputAccessor, sampler, target); - if (createdTracks) { - for (let k = 0; k < createdTracks.length; k++) { - tracks.push(createdTracks[k]); - } - } - } - return new AnimationClip(animationName, void 0, tracks); - }); - } - createNodeMesh(nodeIndex) { - const json = this.json; - const parser = this; - const nodeDef = json.nodes[nodeIndex]; - if (nodeDef.mesh === void 0) return null; - return parser.getDependency("mesh", nodeDef.mesh).then(function(mesh) { - const node = parser._getNodeRef(parser.meshCache, nodeDef.mesh, mesh); - if (nodeDef.weights !== void 0) { - node.traverse(function(o) { - if (!o.isMesh) return; - for (let i = 0, il = nodeDef.weights.length; i < il; i++) { - o.morphTargetInfluences[i] = nodeDef.weights[i]; - } - }); - } - return node; - }); - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#nodes-and-hierarchy - * @param {number} nodeIndex - * @return {Promise} - */ - loadNode(nodeIndex) { - const json = this.json; - const parser = this; - const nodeDef = json.nodes[nodeIndex]; - const nodePending = parser._loadNodeShallow(nodeIndex); - const childPending = []; - const childrenDef = nodeDef.children || []; - for (let i = 0, il = childrenDef.length; i < il; i++) { - childPending.push(parser.getDependency("node", childrenDef[i])); - } - const skeletonPending = nodeDef.skin === void 0 ? Promise.resolve(null) : parser.getDependency("skin", nodeDef.skin); - return Promise.all([ - nodePending, - Promise.all(childPending), - skeletonPending - ]).then(function(results) { - const node = results[0]; - const children = results[1]; - const skeleton = results[2]; - if (skeleton !== null) { - node.traverse(function(mesh) { - if (!mesh.isSkinnedMesh) return; - mesh.bind(skeleton, _identityMatrix); - }); - } - for (let i = 0, il = children.length; i < il; i++) { - node.add(children[i]); - } - return node; - }); - } - // ._loadNodeShallow() parses a single node. - // skin and child nodes are created and added in .loadNode() (no '_' prefix). - _loadNodeShallow(nodeIndex) { - const json = this.json; - const extensions = this.extensions; - const parser = this; - if (this.nodeCache[nodeIndex] !== void 0) { - return this.nodeCache[nodeIndex]; - } - const nodeDef = json.nodes[nodeIndex]; - const nodeName = nodeDef.name ? parser.createUniqueName(nodeDef.name) : ""; - const pending = []; - const meshPromise = parser._invokeOne(function(ext2) { - return ext2.createNodeMesh && ext2.createNodeMesh(nodeIndex); - }); - if (meshPromise) { - pending.push(meshPromise); - } - if (nodeDef.camera !== void 0) { - pending.push(parser.getDependency("camera", nodeDef.camera).then(function(camera) { - return parser._getNodeRef(parser.cameraCache, nodeDef.camera, camera); - })); - } - parser._invokeAll(function(ext2) { - return ext2.createNodeAttachment && ext2.createNodeAttachment(nodeIndex); - }).forEach(function(promise) { - pending.push(promise); - }); - this.nodeCache[nodeIndex] = Promise.all(pending).then(function(objects) { - let node; - if (nodeDef.isBone === true) { - node = new Bone(); - } else if (objects.length > 1) { - node = new Group(); - } else if (objects.length === 1) { - node = objects[0]; - } else { - node = new Object3D(); - } - if (node !== objects[0]) { - for (let i = 0, il = objects.length; i < il; i++) { - node.add(objects[i]); - } - } - if (nodeDef.name) { - node.userData.name = nodeDef.name; - node.name = nodeName; - } - assignExtrasToUserData(node, nodeDef); - if (nodeDef.extensions) addUnknownExtensionsToUserData(extensions, node, nodeDef); - if (nodeDef.matrix !== void 0) { - const matrix = new Matrix4(); - matrix.fromArray(nodeDef.matrix); - node.applyMatrix4(matrix); - } else { - if (nodeDef.translation !== void 0) { - node.position.fromArray(nodeDef.translation); - } - if (nodeDef.rotation !== void 0) { - node.quaternion.fromArray(nodeDef.rotation); - } - if (nodeDef.scale !== void 0) { - node.scale.fromArray(nodeDef.scale); - } - } - if (!parser.associations.has(node)) { - parser.associations.set(node, {}); - } - parser.associations.get(node).nodes = nodeIndex; - return node; - }); - return this.nodeCache[nodeIndex]; - } - /** - * Specification: https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#scenes - * @param {number} sceneIndex - * @return {Promise} - */ - loadScene(sceneIndex) { - const extensions = this.extensions; - const sceneDef = this.json.scenes[sceneIndex]; - const parser = this; - const scene = new Group(); - if (sceneDef.name) scene.name = parser.createUniqueName(sceneDef.name); - assignExtrasToUserData(scene, sceneDef); - if (sceneDef.extensions) addUnknownExtensionsToUserData(extensions, scene, sceneDef); - const nodeIds = sceneDef.nodes || []; - const pending = []; - for (let i = 0, il = nodeIds.length; i < il; i++) { - pending.push(parser.getDependency("node", nodeIds[i])); - } - return Promise.all(pending).then(function(nodes) { - for (let i = 0, il = nodes.length; i < il; i++) { - scene.add(nodes[i]); - } - const reduceAssociations = /* @__PURE__ */ __name((node) => { - const reducedAssociations = /* @__PURE__ */ new Map(); - for (const [key, value] of parser.associations) { - if (key instanceof Material || key instanceof Texture) { - reducedAssociations.set(key, value); - } - } - node.traverse((node2) => { - const mappings = parser.associations.get(node2); - if (mappings != null) { - reducedAssociations.set(node2, mappings); - } - }); - return reducedAssociations; - }, "reduceAssociations"); - parser.associations = reduceAssociations(scene); - return scene; - }); - } - _createAnimationTracks(node, inputAccessor, outputAccessor, sampler, target) { - const tracks = []; - const targetName = node.name ? node.name : node.uuid; - const targetNames = []; - if (PATH_PROPERTIES[target.path] === PATH_PROPERTIES.weights) { - node.traverse(function(object) { - if (object.morphTargetInfluences) { - targetNames.push(object.name ? object.name : object.uuid); - } - }); - } else { - targetNames.push(targetName); - } - let TypedKeyframeTrack; - switch (PATH_PROPERTIES[target.path]) { - case PATH_PROPERTIES.weights: - TypedKeyframeTrack = NumberKeyframeTrack; - break; - case PATH_PROPERTIES.rotation: - TypedKeyframeTrack = QuaternionKeyframeTrack; - break; - case PATH_PROPERTIES.position: - case PATH_PROPERTIES.scale: - TypedKeyframeTrack = VectorKeyframeTrack; - break; - default: - switch (outputAccessor.itemSize) { - case 1: - TypedKeyframeTrack = NumberKeyframeTrack; - break; - case 2: - case 3: - default: - TypedKeyframeTrack = VectorKeyframeTrack; - break; - } - break; - } - const interpolation = sampler.interpolation !== void 0 ? INTERPOLATION[sampler.interpolation] : InterpolateLinear; - const outputArray = this._getArrayFromAccessor(outputAccessor); - for (let j = 0, jl = targetNames.length; j < jl; j++) { - const track = new TypedKeyframeTrack( - targetNames[j] + "." + PATH_PROPERTIES[target.path], - inputAccessor.array, - outputArray, - interpolation - ); - if (sampler.interpolation === "CUBICSPLINE") { - this._createCubicSplineTrackInterpolant(track); - } - tracks.push(track); - } - return tracks; - } - _getArrayFromAccessor(accessor) { - let outputArray = accessor.array; - if (accessor.normalized) { - const scale = getNormalizedComponentScale(outputArray.constructor); - const scaled = new Float32Array(outputArray.length); - for (let j = 0, jl = outputArray.length; j < jl; j++) { - scaled[j] = outputArray[j] * scale; - } - outputArray = scaled; - } - return outputArray; - } - _createCubicSplineTrackInterpolant(track) { - track.createInterpolant = /* @__PURE__ */ __name(function InterpolantFactoryMethodGLTFCubicSpline(result) { - const interpolantType = this instanceof QuaternionKeyframeTrack ? GLTFCubicSplineQuaternionInterpolant : GLTFCubicSplineInterpolant; - return new interpolantType(this.times, this.values, this.getValueSize() / 3, result); - }, "InterpolantFactoryMethodGLTFCubicSpline"); - track.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline = true; - } -} -function computeBounds(geometry, primitiveDef, parser) { - const attributes = primitiveDef.attributes; - const box = new Box3(); - if (attributes.POSITION !== void 0) { - const accessor = parser.json.accessors[attributes.POSITION]; - const min = accessor.min; - const max2 = accessor.max; - if (min !== void 0 && max2 !== void 0) { - box.set( - new Vector3(min[0], min[1], min[2]), - new Vector3(max2[0], max2[1], max2[2]) - ); - if (accessor.normalized) { - const boxScale = getNormalizedComponentScale(WEBGL_COMPONENT_TYPES[accessor.componentType]); - box.min.multiplyScalar(boxScale); - box.max.multiplyScalar(boxScale); - } - } else { - console.warn("THREE.GLTFLoader: Missing min/max properties for accessor POSITION."); - return; - } - } else { - return; - } - const targets = primitiveDef.targets; - if (targets !== void 0) { - const maxDisplacement = new Vector3(); - const vector = new Vector3(); - for (let i = 0, il = targets.length; i < il; i++) { - const target = targets[i]; - if (target.POSITION !== void 0) { - const accessor = parser.json.accessors[target.POSITION]; - const min = accessor.min; - const max2 = accessor.max; - if (min !== void 0 && max2 !== void 0) { - vector.setX(Math.max(Math.abs(min[0]), Math.abs(max2[0]))); - vector.setY(Math.max(Math.abs(min[1]), Math.abs(max2[1]))); - vector.setZ(Math.max(Math.abs(min[2]), Math.abs(max2[2]))); - if (accessor.normalized) { - const boxScale = getNormalizedComponentScale(WEBGL_COMPONENT_TYPES[accessor.componentType]); - vector.multiplyScalar(boxScale); - } - maxDisplacement.max(vector); - } else { - console.warn("THREE.GLTFLoader: Missing min/max properties for accessor POSITION."); - } - } - } - box.expandByVector(maxDisplacement); - } - geometry.boundingBox = box; - const sphere = new Sphere(); - box.getCenter(sphere.center); - sphere.radius = box.min.distanceTo(box.max) / 2; - geometry.boundingSphere = sphere; -} -__name(computeBounds, "computeBounds"); -function addPrimitiveAttributes(geometry, primitiveDef, parser) { - const attributes = primitiveDef.attributes; - const pending = []; - function assignAttributeAccessor(accessorIndex, attributeName) { - return parser.getDependency("accessor", accessorIndex).then(function(accessor) { - geometry.setAttribute(attributeName, accessor); - }); - } - __name(assignAttributeAccessor, "assignAttributeAccessor"); - for (const gltfAttributeName in attributes) { - const threeAttributeName = ATTRIBUTES[gltfAttributeName] || gltfAttributeName.toLowerCase(); - if (threeAttributeName in geometry.attributes) continue; - pending.push(assignAttributeAccessor(attributes[gltfAttributeName], threeAttributeName)); - } - if (primitiveDef.indices !== void 0 && !geometry.index) { - const accessor = parser.getDependency("accessor", primitiveDef.indices).then(function(accessor2) { - geometry.setIndex(accessor2); - }); - pending.push(accessor); - } - if (ColorManagement.workingColorSpace !== LinearSRGBColorSpace && "COLOR_0" in attributes) { - console.warn(`THREE.GLTFLoader: Converting vertex colors from "srgb-linear" to "${ColorManagement.workingColorSpace}" not supported.`); - } - assignExtrasToUserData(geometry, primitiveDef); - computeBounds(geometry, primitiveDef, parser); - return Promise.all(pending).then(function() { - return primitiveDef.targets !== void 0 ? addMorphTargets(geometry, primitiveDef.targets, parser) : geometry; - }); -} -__name(addPrimitiveAttributes, "addPrimitiveAttributes"); -class MTLLoader extends Loader { - static { - __name(this, "MTLLoader"); - } - constructor(manager) { - super(manager); - } - /** - * Loads and parses a MTL asset from a URL. - * - * @param {String} url - URL to the MTL file. - * @param {Function} [onLoad] - Callback invoked with the loaded object. - * @param {Function} [onProgress] - Callback for download progress. - * @param {Function} [onError] - Callback for download errors. - * - * @see setPath setResourcePath - * - * @note In order for relative texture references to resolve correctly - * you must call setResourcePath() explicitly prior to load. - */ - load(url, onLoad, onProgress, onError) { - const scope = this; - const path = this.path === "" ? LoaderUtils.extractUrlBase(url) : this.path; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(text, path)); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - setMaterialOptions(value) { - this.materialOptions = value; - return this; - } - /** - * Parses a MTL file. - * - * @param {String} text - Content of MTL file - * @return {MaterialCreator} - * - * @see setPath setResourcePath - * - * @note In order for relative texture references to resolve correctly - * you must call setResourcePath() explicitly prior to parse. - */ - parse(text, path) { - const lines = text.split("\n"); - let info = {}; - const delimiter_pattern = /\s+/; - const materialsInfo = {}; - for (let i = 0; i < lines.length; i++) { - let line = lines[i]; - line = line.trim(); - if (line.length === 0 || line.charAt(0) === "#") { - continue; - } - const pos = line.indexOf(" "); - let key = pos >= 0 ? line.substring(0, pos) : line; - key = key.toLowerCase(); - let value = pos >= 0 ? line.substring(pos + 1) : ""; - value = value.trim(); - if (key === "newmtl") { - info = { name: value }; - materialsInfo[value] = info; - } else { - if (key === "ka" || key === "kd" || key === "ks" || key === "ke") { - const ss = value.split(delimiter_pattern, 3); - info[key] = [parseFloat(ss[0]), parseFloat(ss[1]), parseFloat(ss[2])]; - } else { - info[key] = value; - } - } - } - const materialCreator = new MaterialCreator(this.resourcePath || path, this.materialOptions); - materialCreator.setCrossOrigin(this.crossOrigin); - materialCreator.setManager(this.manager); - materialCreator.setMaterials(materialsInfo); - return materialCreator; - } -} -class MaterialCreator { - static { - __name(this, "MaterialCreator"); - } - constructor(baseUrl = "", options = {}) { - this.baseUrl = baseUrl; - this.options = options; - this.materialsInfo = {}; - this.materials = {}; - this.materialsArray = []; - this.nameLookup = {}; - this.crossOrigin = "anonymous"; - this.side = this.options.side !== void 0 ? this.options.side : FrontSide; - this.wrap = this.options.wrap !== void 0 ? this.options.wrap : RepeatWrapping; - } - setCrossOrigin(value) { - this.crossOrigin = value; - return this; - } - setManager(value) { - this.manager = value; - } - setMaterials(materialsInfo) { - this.materialsInfo = this.convert(materialsInfo); - this.materials = {}; - this.materialsArray = []; - this.nameLookup = {}; - } - convert(materialsInfo) { - if (!this.options) return materialsInfo; - const converted = {}; - for (const mn in materialsInfo) { - const mat = materialsInfo[mn]; - const covmat = {}; - converted[mn] = covmat; - for (const prop in mat) { - let save = true; - let value = mat[prop]; - const lprop = prop.toLowerCase(); - switch (lprop) { - case "kd": - case "ka": - case "ks": - if (this.options && this.options.normalizeRGB) { - value = [value[0] / 255, value[1] / 255, value[2] / 255]; - } - if (this.options && this.options.ignoreZeroRGBs) { - if (value[0] === 0 && value[1] === 0 && value[2] === 0) { - save = false; - } - } - break; - default: - break; - } - if (save) { - covmat[lprop] = value; - } - } - } - return converted; - } - preload() { - for (const mn in this.materialsInfo) { - this.create(mn); - } - } - getIndex(materialName) { - return this.nameLookup[materialName]; - } - getAsArray() { - let index = 0; - for (const mn in this.materialsInfo) { - this.materialsArray[index] = this.create(mn); - this.nameLookup[mn] = index; - index++; - } - return this.materialsArray; - } - create(materialName) { - if (this.materials[materialName] === void 0) { - this.createMaterial_(materialName); - } - return this.materials[materialName]; - } - createMaterial_(materialName) { - const scope = this; - const mat = this.materialsInfo[materialName]; - const params = { - name: materialName, - side: this.side - }; - function resolveURL(baseUrl, url) { - if (typeof url !== "string" || url === "") - return ""; - if (/^https?:\/\//i.test(url)) return url; - return baseUrl + url; - } - __name(resolveURL, "resolveURL"); - function setMapForType(mapType, value) { - if (params[mapType]) return; - const texParams = scope.getTextureParams(value, params); - const map = scope.loadTexture(resolveURL(scope.baseUrl, texParams.url)); - map.repeat.copy(texParams.scale); - map.offset.copy(texParams.offset); - map.wrapS = scope.wrap; - map.wrapT = scope.wrap; - if (mapType === "map" || mapType === "emissiveMap") { - map.colorSpace = SRGBColorSpace; - } - params[mapType] = map; - } - __name(setMapForType, "setMapForType"); - for (const prop in mat) { - const value = mat[prop]; - let n; - if (value === "") continue; - switch (prop.toLowerCase()) { - case "kd": - params.color = ColorManagement.toWorkingColorSpace(new Color().fromArray(value), SRGBColorSpace); - break; - case "ks": - params.specular = ColorManagement.toWorkingColorSpace(new Color().fromArray(value), SRGBColorSpace); - break; - case "ke": - params.emissive = ColorManagement.toWorkingColorSpace(new Color().fromArray(value), SRGBColorSpace); - break; - case "map_kd": - setMapForType("map", value); - break; - case "map_ks": - setMapForType("specularMap", value); - break; - case "map_ke": - setMapForType("emissiveMap", value); - break; - case "norm": - setMapForType("normalMap", value); - break; - case "map_bump": - case "bump": - setMapForType("bumpMap", value); - break; - case "map_d": - setMapForType("alphaMap", value); - params.transparent = true; - break; - case "ns": - params.shininess = parseFloat(value); - break; - case "d": - n = parseFloat(value); - if (n < 1) { - params.opacity = n; - params.transparent = true; - } - break; - case "tr": - n = parseFloat(value); - if (this.options && this.options.invertTrProperty) n = 1 - n; - if (n > 0) { - params.opacity = 1 - n; - params.transparent = true; - } - break; - default: - break; - } - } - this.materials[materialName] = new MeshPhongMaterial(params); - return this.materials[materialName]; - } - getTextureParams(value, matParams) { - const texParams = { - scale: new Vector2(1, 1), - offset: new Vector2(0, 0) - }; - const items = value.split(/\s+/); - let pos; - pos = items.indexOf("-bm"); - if (pos >= 0) { - matParams.bumpScale = parseFloat(items[pos + 1]); - items.splice(pos, 2); - } - pos = items.indexOf("-s"); - if (pos >= 0) { - texParams.scale.set(parseFloat(items[pos + 1]), parseFloat(items[pos + 2])); - items.splice(pos, 4); - } - pos = items.indexOf("-o"); - if (pos >= 0) { - texParams.offset.set(parseFloat(items[pos + 1]), parseFloat(items[pos + 2])); - items.splice(pos, 4); - } - texParams.url = items.join(" ").trim(); - return texParams; - } - loadTexture(url, mapping, onLoad, onProgress, onError) { - const manager = this.manager !== void 0 ? this.manager : DefaultLoadingManager; - let loader = manager.getHandler(url); - if (loader === null) { - loader = new TextureLoader(manager); - } - if (loader.setCrossOrigin) loader.setCrossOrigin(this.crossOrigin); - const texture = loader.load(url, onLoad, onProgress, onError); - if (mapping !== void 0) texture.mapping = mapping; - return texture; - } -} -const _object_pattern = /^[og]\s*(.+)?/; -const _material_library_pattern = /^mtllib /; -const _material_use_pattern = /^usemtl /; -const _map_use_pattern = /^usemap /; -const _face_vertex_data_separator_pattern = /\s+/; -const _vA = new Vector3(); -const _vB = new Vector3(); -const _vC = new Vector3(); -const _ab = new Vector3(); -const _cb = new Vector3(); -const _color = new Color(); -function ParserState() { - const state = { - objects: [], - object: {}, - vertices: [], - normals: [], - colors: [], - uvs: [], - materials: {}, - materialLibraries: [], - startObject: /* @__PURE__ */ __name(function(name, fromDeclaration) { - if (this.object && this.object.fromDeclaration === false) { - this.object.name = name; - this.object.fromDeclaration = fromDeclaration !== false; - return; - } - const previousMaterial = this.object && typeof this.object.currentMaterial === "function" ? this.object.currentMaterial() : void 0; - if (this.object && typeof this.object._finalize === "function") { - this.object._finalize(true); - } - this.object = { - name: name || "", - fromDeclaration: fromDeclaration !== false, - geometry: { - vertices: [], - normals: [], - colors: [], - uvs: [], - hasUVIndices: false - }, - materials: [], - smooth: true, - startMaterial: /* @__PURE__ */ __name(function(name2, libraries) { - const previous = this._finalize(false); - if (previous && (previous.inherited || previous.groupCount <= 0)) { - this.materials.splice(previous.index, 1); - } - const material = { - index: this.materials.length, - name: name2 || "", - mtllib: Array.isArray(libraries) && libraries.length > 0 ? libraries[libraries.length - 1] : "", - smooth: previous !== void 0 ? previous.smooth : this.smooth, - groupStart: previous !== void 0 ? previous.groupEnd : 0, - groupEnd: -1, - groupCount: -1, - inherited: false, - clone: /* @__PURE__ */ __name(function(index) { - const cloned = { - index: typeof index === "number" ? index : this.index, - name: this.name, - mtllib: this.mtllib, - smooth: this.smooth, - groupStart: 0, - groupEnd: -1, - groupCount: -1, - inherited: false - }; - cloned.clone = this.clone.bind(cloned); - return cloned; - }, "clone") - }; - this.materials.push(material); - return material; - }, "startMaterial"), - currentMaterial: /* @__PURE__ */ __name(function() { - if (this.materials.length > 0) { - return this.materials[this.materials.length - 1]; - } - return void 0; - }, "currentMaterial"), - _finalize: /* @__PURE__ */ __name(function(end) { - const lastMultiMaterial = this.currentMaterial(); - if (lastMultiMaterial && lastMultiMaterial.groupEnd === -1) { - lastMultiMaterial.groupEnd = this.geometry.vertices.length / 3; - lastMultiMaterial.groupCount = lastMultiMaterial.groupEnd - lastMultiMaterial.groupStart; - lastMultiMaterial.inherited = false; - } - if (end && this.materials.length > 1) { - for (let mi = this.materials.length - 1; mi >= 0; mi--) { - if (this.materials[mi].groupCount <= 0) { - this.materials.splice(mi, 1); - } - } - } - if (end && this.materials.length === 0) { - this.materials.push({ - name: "", - smooth: this.smooth - }); - } - return lastMultiMaterial; - }, "_finalize") - }; - if (previousMaterial && previousMaterial.name && typeof previousMaterial.clone === "function") { - const declared = previousMaterial.clone(0); - declared.inherited = true; - this.object.materials.push(declared); - } - this.objects.push(this.object); - }, "startObject"), - finalize: /* @__PURE__ */ __name(function() { - if (this.object && typeof this.object._finalize === "function") { - this.object._finalize(true); - } - }, "finalize"), - parseVertexIndex: /* @__PURE__ */ __name(function(value, len) { - const index = parseInt(value, 10); - return (index >= 0 ? index - 1 : index + len / 3) * 3; - }, "parseVertexIndex"), - parseNormalIndex: /* @__PURE__ */ __name(function(value, len) { - const index = parseInt(value, 10); - return (index >= 0 ? index - 1 : index + len / 3) * 3; - }, "parseNormalIndex"), - parseUVIndex: /* @__PURE__ */ __name(function(value, len) { - const index = parseInt(value, 10); - return (index >= 0 ? index - 1 : index + len / 2) * 2; - }, "parseUVIndex"), - addVertex: /* @__PURE__ */ __name(function(a, b, c) { - const src = this.vertices; - const dst = this.object.geometry.vertices; - dst.push(src[a + 0], src[a + 1], src[a + 2]); - dst.push(src[b + 0], src[b + 1], src[b + 2]); - dst.push(src[c + 0], src[c + 1], src[c + 2]); - }, "addVertex"), - addVertexPoint: /* @__PURE__ */ __name(function(a) { - const src = this.vertices; - const dst = this.object.geometry.vertices; - dst.push(src[a + 0], src[a + 1], src[a + 2]); - }, "addVertexPoint"), - addVertexLine: /* @__PURE__ */ __name(function(a) { - const src = this.vertices; - const dst = this.object.geometry.vertices; - dst.push(src[a + 0], src[a + 1], src[a + 2]); - }, "addVertexLine"), - addNormal: /* @__PURE__ */ __name(function(a, b, c) { - const src = this.normals; - const dst = this.object.geometry.normals; - dst.push(src[a + 0], src[a + 1], src[a + 2]); - dst.push(src[b + 0], src[b + 1], src[b + 2]); - dst.push(src[c + 0], src[c + 1], src[c + 2]); - }, "addNormal"), - addFaceNormal: /* @__PURE__ */ __name(function(a, b, c) { - const src = this.vertices; - const dst = this.object.geometry.normals; - _vA.fromArray(src, a); - _vB.fromArray(src, b); - _vC.fromArray(src, c); - _cb.subVectors(_vC, _vB); - _ab.subVectors(_vA, _vB); - _cb.cross(_ab); - _cb.normalize(); - dst.push(_cb.x, _cb.y, _cb.z); - dst.push(_cb.x, _cb.y, _cb.z); - dst.push(_cb.x, _cb.y, _cb.z); - }, "addFaceNormal"), - addColor: /* @__PURE__ */ __name(function(a, b, c) { - const src = this.colors; - const dst = this.object.geometry.colors; - if (src[a] !== void 0) dst.push(src[a + 0], src[a + 1], src[a + 2]); - if (src[b] !== void 0) dst.push(src[b + 0], src[b + 1], src[b + 2]); - if (src[c] !== void 0) dst.push(src[c + 0], src[c + 1], src[c + 2]); - }, "addColor"), - addUV: /* @__PURE__ */ __name(function(a, b, c) { - const src = this.uvs; - const dst = this.object.geometry.uvs; - dst.push(src[a + 0], src[a + 1]); - dst.push(src[b + 0], src[b + 1]); - dst.push(src[c + 0], src[c + 1]); - }, "addUV"), - addDefaultUV: /* @__PURE__ */ __name(function() { - const dst = this.object.geometry.uvs; - dst.push(0, 0); - dst.push(0, 0); - dst.push(0, 0); - }, "addDefaultUV"), - addUVLine: /* @__PURE__ */ __name(function(a) { - const src = this.uvs; - const dst = this.object.geometry.uvs; - dst.push(src[a + 0], src[a + 1]); - }, "addUVLine"), - addFace: /* @__PURE__ */ __name(function(a, b, c, ua, ub, uc, na, nb, nc) { - const vLen = this.vertices.length; - let ia = this.parseVertexIndex(a, vLen); - let ib = this.parseVertexIndex(b, vLen); - let ic = this.parseVertexIndex(c, vLen); - this.addVertex(ia, ib, ic); - this.addColor(ia, ib, ic); - if (na !== void 0 && na !== "") { - const nLen = this.normals.length; - ia = this.parseNormalIndex(na, nLen); - ib = this.parseNormalIndex(nb, nLen); - ic = this.parseNormalIndex(nc, nLen); - this.addNormal(ia, ib, ic); - } else { - this.addFaceNormal(ia, ib, ic); - } - if (ua !== void 0 && ua !== "") { - const uvLen = this.uvs.length; - ia = this.parseUVIndex(ua, uvLen); - ib = this.parseUVIndex(ub, uvLen); - ic = this.parseUVIndex(uc, uvLen); - this.addUV(ia, ib, ic); - this.object.geometry.hasUVIndices = true; - } else { - this.addDefaultUV(); - } - }, "addFace"), - addPointGeometry: /* @__PURE__ */ __name(function(vertices) { - this.object.geometry.type = "Points"; - const vLen = this.vertices.length; - for (let vi = 0, l = vertices.length; vi < l; vi++) { - const index = this.parseVertexIndex(vertices[vi], vLen); - this.addVertexPoint(index); - this.addColor(index); - } - }, "addPointGeometry"), - addLineGeometry: /* @__PURE__ */ __name(function(vertices, uvs) { - this.object.geometry.type = "Line"; - const vLen = this.vertices.length; - const uvLen = this.uvs.length; - for (let vi = 0, l = vertices.length; vi < l; vi++) { - this.addVertexLine(this.parseVertexIndex(vertices[vi], vLen)); - } - for (let uvi = 0, l = uvs.length; uvi < l; uvi++) { - this.addUVLine(this.parseUVIndex(uvs[uvi], uvLen)); - } - }, "addLineGeometry") - }; - state.startObject("", false); - return state; -} -__name(ParserState, "ParserState"); -class OBJLoader extends Loader { - static { - __name(this, "OBJLoader"); - } - constructor(manager) { - super(manager); - this.materials = null; - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(text)); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - setMaterials(materials) { - this.materials = materials; - return this; - } - parse(text) { - const state = new ParserState(); - if (text.indexOf("\r\n") !== -1) { - text = text.replace(/\r\n/g, "\n"); - } - if (text.indexOf("\\\n") !== -1) { - text = text.replace(/\\\n/g, ""); - } - const lines = text.split("\n"); - let result = []; - for (let i = 0, l = lines.length; i < l; i++) { - const line = lines[i].trimStart(); - if (line.length === 0) continue; - const lineFirstChar = line.charAt(0); - if (lineFirstChar === "#") continue; - if (lineFirstChar === "v") { - const data = line.split(_face_vertex_data_separator_pattern); - switch (data[0]) { - case "v": - state.vertices.push( - parseFloat(data[1]), - parseFloat(data[2]), - parseFloat(data[3]) - ); - if (data.length >= 7) { - _color.setRGB( - parseFloat(data[4]), - parseFloat(data[5]), - parseFloat(data[6]), - SRGBColorSpace - ); - state.colors.push(_color.r, _color.g, _color.b); - } else { - state.colors.push(void 0, void 0, void 0); - } - break; - case "vn": - state.normals.push( - parseFloat(data[1]), - parseFloat(data[2]), - parseFloat(data[3]) - ); - break; - case "vt": - state.uvs.push( - parseFloat(data[1]), - parseFloat(data[2]) - ); - break; - } - } else if (lineFirstChar === "f") { - const lineData = line.slice(1).trim(); - const vertexData = lineData.split(_face_vertex_data_separator_pattern); - const faceVertices = []; - for (let j = 0, jl = vertexData.length; j < jl; j++) { - const vertex2 = vertexData[j]; - if (vertex2.length > 0) { - const vertexParts = vertex2.split("/"); - faceVertices.push(vertexParts); - } - } - const v1 = faceVertices[0]; - for (let j = 1, jl = faceVertices.length - 1; j < jl; j++) { - const v2 = faceVertices[j]; - const v3 = faceVertices[j + 1]; - state.addFace( - v1[0], - v2[0], - v3[0], - v1[1], - v2[1], - v3[1], - v1[2], - v2[2], - v3[2] - ); - } - } else if (lineFirstChar === "l") { - const lineParts = line.substring(1).trim().split(" "); - let lineVertices = []; - const lineUVs = []; - if (line.indexOf("/") === -1) { - lineVertices = lineParts; - } else { - for (let li = 0, llen = lineParts.length; li < llen; li++) { - const parts = lineParts[li].split("/"); - if (parts[0] !== "") lineVertices.push(parts[0]); - if (parts[1] !== "") lineUVs.push(parts[1]); - } - } - state.addLineGeometry(lineVertices, lineUVs); - } else if (lineFirstChar === "p") { - const lineData = line.slice(1).trim(); - const pointData = lineData.split(" "); - state.addPointGeometry(pointData); - } else if ((result = _object_pattern.exec(line)) !== null) { - const name = (" " + result[0].slice(1).trim()).slice(1); - state.startObject(name); - } else if (_material_use_pattern.test(line)) { - state.object.startMaterial(line.substring(7).trim(), state.materialLibraries); - } else if (_material_library_pattern.test(line)) { - state.materialLibraries.push(line.substring(7).trim()); - } else if (_map_use_pattern.test(line)) { - console.warn('THREE.OBJLoader: Rendering identifier "usemap" not supported. Textures must be defined in MTL files.'); - } else if (lineFirstChar === "s") { - result = line.split(" "); - if (result.length > 1) { - const value = result[1].trim().toLowerCase(); - state.object.smooth = value !== "0" && value !== "off"; - } else { - state.object.smooth = true; - } - const material = state.object.currentMaterial(); - if (material) material.smooth = state.object.smooth; - } else { - if (line === "\0") continue; - console.warn('THREE.OBJLoader: Unexpected line: "' + line + '"'); - } - } - state.finalize(); - const container = new Group(); - container.materialLibraries = [].concat(state.materialLibraries); - const hasPrimitives = !(state.objects.length === 1 && state.objects[0].geometry.vertices.length === 0); - if (hasPrimitives === true) { - for (let i = 0, l = state.objects.length; i < l; i++) { - const object = state.objects[i]; - const geometry = object.geometry; - const materials = object.materials; - const isLine = geometry.type === "Line"; - const isPoints = geometry.type === "Points"; - let hasVertexColors = false; - if (geometry.vertices.length === 0) continue; - const buffergeometry = new BufferGeometry(); - buffergeometry.setAttribute("position", new Float32BufferAttribute(geometry.vertices, 3)); - if (geometry.normals.length > 0) { - buffergeometry.setAttribute("normal", new Float32BufferAttribute(geometry.normals, 3)); - } - if (geometry.colors.length > 0) { - hasVertexColors = true; - buffergeometry.setAttribute("color", new Float32BufferAttribute(geometry.colors, 3)); - } - if (geometry.hasUVIndices === true) { - buffergeometry.setAttribute("uv", new Float32BufferAttribute(geometry.uvs, 2)); - } - const createdMaterials = []; - for (let mi = 0, miLen = materials.length; mi < miLen; mi++) { - const sourceMaterial = materials[mi]; - const materialHash = sourceMaterial.name + "_" + sourceMaterial.smooth + "_" + hasVertexColors; - let material = state.materials[materialHash]; - if (this.materials !== null) { - material = this.materials.create(sourceMaterial.name); - if (isLine && material && !(material instanceof LineBasicMaterial)) { - const materialLine = new LineBasicMaterial(); - Material.prototype.copy.call(materialLine, material); - materialLine.color.copy(material.color); - material = materialLine; - } else if (isPoints && material && !(material instanceof PointsMaterial)) { - const materialPoints = new PointsMaterial({ size: 10, sizeAttenuation: false }); - Material.prototype.copy.call(materialPoints, material); - materialPoints.color.copy(material.color); - materialPoints.map = material.map; - material = materialPoints; - } - } - if (material === void 0) { - if (isLine) { - material = new LineBasicMaterial(); - } else if (isPoints) { - material = new PointsMaterial({ size: 1, sizeAttenuation: false }); - } else { - material = new MeshPhongMaterial(); - } - material.name = sourceMaterial.name; - material.flatShading = sourceMaterial.smooth ? false : true; - material.vertexColors = hasVertexColors; - state.materials[materialHash] = material; - } - createdMaterials.push(material); - } - let mesh; - if (createdMaterials.length > 1) { - for (let mi = 0, miLen = materials.length; mi < miLen; mi++) { - const sourceMaterial = materials[mi]; - buffergeometry.addGroup(sourceMaterial.groupStart, sourceMaterial.groupCount, mi); - } - if (isLine) { - mesh = new LineSegments(buffergeometry, createdMaterials); - } else if (isPoints) { - mesh = new Points(buffergeometry, createdMaterials); - } else { - mesh = new Mesh(buffergeometry, createdMaterials); - } - } else { - if (isLine) { - mesh = new LineSegments(buffergeometry, createdMaterials[0]); - } else if (isPoints) { - mesh = new Points(buffergeometry, createdMaterials[0]); - } else { - mesh = new Mesh(buffergeometry, createdMaterials[0]); - } - } - mesh.name = object.name; - container.add(mesh); - } - } else { - if (state.vertices.length > 0) { - const material = new PointsMaterial({ size: 1, sizeAttenuation: false }); - const buffergeometry = new BufferGeometry(); - buffergeometry.setAttribute("position", new Float32BufferAttribute(state.vertices, 3)); - if (state.colors.length > 0 && state.colors[0] !== void 0) { - buffergeometry.setAttribute("color", new Float32BufferAttribute(state.colors, 3)); - material.vertexColors = true; - } - const points = new Points(buffergeometry, material); - container.add(points); - } - } - return container; - } -} -class STLLoader extends Loader { - static { - __name(this, "STLLoader"); - } - constructor(manager) { - super(manager); - } - load(url, onLoad, onProgress, onError) { - const scope = this; - const loader = new FileLoader(this.manager); - loader.setPath(this.path); - loader.setResponseType("arraybuffer"); - loader.setRequestHeader(this.requestHeader); - loader.setWithCredentials(this.withCredentials); - loader.load(url, function(text) { - try { - onLoad(scope.parse(text)); - } catch (e) { - if (onError) { - onError(e); - } else { - console.error(e); - } - scope.manager.itemError(url); - } - }, onProgress, onError); - } - parse(data) { - function isBinary(data2) { - const reader = new DataView(data2); - const face_size = 32 / 8 * 3 + 32 / 8 * 3 * 3 + 16 / 8; - const n_faces = reader.getUint32(80, true); - const expect = 80 + 32 / 8 + n_faces * face_size; - if (expect === reader.byteLength) { - return true; - } - const solid = [115, 111, 108, 105, 100]; - for (let off = 0; off < 5; off++) { - if (matchDataViewAt(solid, reader, off)) return false; - } - return true; - } - __name(isBinary, "isBinary"); - function matchDataViewAt(query, reader, offset) { - for (let i = 0, il = query.length; i < il; i++) { - if (query[i] !== reader.getUint8(offset + i)) return false; - } - return true; - } - __name(matchDataViewAt, "matchDataViewAt"); - function parseBinary(data2) { - const reader = new DataView(data2); - const faces = reader.getUint32(80, true); - let r, g, b, hasColors = false, colors; - let defaultR, defaultG, defaultB, alpha; - for (let index = 0; index < 80 - 10; index++) { - if (reader.getUint32(index, false) == 1129270351 && reader.getUint8(index + 4) == 82 && reader.getUint8(index + 5) == 61) { - hasColors = true; - colors = new Float32Array(faces * 3 * 3); - defaultR = reader.getUint8(index + 6) / 255; - defaultG = reader.getUint8(index + 7) / 255; - defaultB = reader.getUint8(index + 8) / 255; - alpha = reader.getUint8(index + 9) / 255; - } - } - const dataOffset = 84; - const faceLength = 12 * 4 + 2; - const geometry = new BufferGeometry(); - const vertices = new Float32Array(faces * 3 * 3); - const normals = new Float32Array(faces * 3 * 3); - const color = new Color(); - for (let face = 0; face < faces; face++) { - const start = dataOffset + face * faceLength; - const normalX = reader.getFloat32(start, true); - const normalY = reader.getFloat32(start + 4, true); - const normalZ = reader.getFloat32(start + 8, true); - if (hasColors) { - const packedColor = reader.getUint16(start + 48, true); - if ((packedColor & 32768) === 0) { - r = (packedColor & 31) / 31; - g = (packedColor >> 5 & 31) / 31; - b = (packedColor >> 10 & 31) / 31; - } else { - r = defaultR; - g = defaultG; - b = defaultB; - } - } - for (let i = 1; i <= 3; i++) { - const vertexstart = start + i * 12; - const componentIdx = face * 3 * 3 + (i - 1) * 3; - vertices[componentIdx] = reader.getFloat32(vertexstart, true); - vertices[componentIdx + 1] = reader.getFloat32(vertexstart + 4, true); - vertices[componentIdx + 2] = reader.getFloat32(vertexstart + 8, true); - normals[componentIdx] = normalX; - normals[componentIdx + 1] = normalY; - normals[componentIdx + 2] = normalZ; - if (hasColors) { - color.setRGB(r, g, b, SRGBColorSpace); - colors[componentIdx] = color.r; - colors[componentIdx + 1] = color.g; - colors[componentIdx + 2] = color.b; - } - } - } - geometry.setAttribute("position", new BufferAttribute(vertices, 3)); - geometry.setAttribute("normal", new BufferAttribute(normals, 3)); - if (hasColors) { - geometry.setAttribute("color", new BufferAttribute(colors, 3)); - geometry.hasColors = true; - geometry.alpha = alpha; - } - return geometry; - } - __name(parseBinary, "parseBinary"); - function parseASCII(data2) { - const geometry = new BufferGeometry(); - const patternSolid = /solid([\s\S]*?)endsolid/g; - const patternFace = /facet([\s\S]*?)endfacet/g; - const patternName = /solid\s(.+)/; - let faceCounter = 0; - const patternFloat = /[\s]+([+-]?(?:\d*)(?:\.\d*)?(?:[eE][+-]?\d+)?)/.source; - const patternVertex = new RegExp("vertex" + patternFloat + patternFloat + patternFloat, "g"); - const patternNormal = new RegExp("normal" + patternFloat + patternFloat + patternFloat, "g"); - const vertices = []; - const normals = []; - const groupNames = []; - const normal = new Vector3(); - let result; - let groupCount = 0; - let startVertex = 0; - let endVertex = 0; - while ((result = patternSolid.exec(data2)) !== null) { - startVertex = endVertex; - const solid = result[0]; - const name = (result = patternName.exec(solid)) !== null ? result[1] : ""; - groupNames.push(name); - while ((result = patternFace.exec(solid)) !== null) { - let vertexCountPerFace = 0; - let normalCountPerFace = 0; - const text = result[0]; - while ((result = patternNormal.exec(text)) !== null) { - normal.x = parseFloat(result[1]); - normal.y = parseFloat(result[2]); - normal.z = parseFloat(result[3]); - normalCountPerFace++; - } - while ((result = patternVertex.exec(text)) !== null) { - vertices.push(parseFloat(result[1]), parseFloat(result[2]), parseFloat(result[3])); - normals.push(normal.x, normal.y, normal.z); - vertexCountPerFace++; - endVertex++; - } - if (normalCountPerFace !== 1) { - console.error("THREE.STLLoader: Something isn't right with the normal of face number " + faceCounter); - } - if (vertexCountPerFace !== 3) { - console.error("THREE.STLLoader: Something isn't right with the vertices of face number " + faceCounter); - } - faceCounter++; - } - const start = startVertex; - const count = endVertex - startVertex; - geometry.userData.groupNames = groupNames; - geometry.addGroup(start, count, groupCount); - groupCount++; - } - geometry.setAttribute("position", new Float32BufferAttribute(vertices, 3)); - geometry.setAttribute("normal", new Float32BufferAttribute(normals, 3)); - return geometry; - } - __name(parseASCII, "parseASCII"); - function ensureString(buffer) { - if (typeof buffer !== "string") { - return new TextDecoder().decode(buffer); - } - return buffer; - } - __name(ensureString, "ensureString"); - function ensureBinary(buffer) { - if (typeof buffer === "string") { - const array_buffer = new Uint8Array(buffer.length); - for (let i = 0; i < buffer.length; i++) { - array_buffer[i] = buffer.charCodeAt(i) & 255; - } - return array_buffer.buffer || array_buffer; - } else { - return buffer; - } - } - __name(ensureBinary, "ensureBinary"); - const binData = ensureBinary(data); - return isBinary(binData) ? parseBinary(binData) : parseASCII(ensureString(data)); - } -} -async function uploadTempImage(imageData, prefix) { - const blob = await fetch(imageData).then((r) => r.blob()); - const name = `${prefix}_${Date.now()}.png`; - const file2 = new File([blob], name); - const body = new FormData(); - body.append("image", file2); - body.append("subfolder", "threed"); - body.append("type", "temp"); - const resp = await api.fetchApi("/upload/image", { - method: "POST", - body - }); - if (resp.status !== 200) { - const err2 = `Error uploading temp image: ${resp.status} - ${resp.statusText}`; - useToastStore().addAlert(err2); - throw new Error(err2); - } - return await resp.json(); -} -__name(uploadTempImage, "uploadTempImage"); -async function uploadFile$1(load3d, file2, fileInput) { - let uploadPath; - try { - const body = new FormData(); - body.append("image", file2); - body.append("subfolder", "3d"); - const resp = await api.fetchApi("/upload/image", { - method: "POST", - body - }); - if (resp.status === 200) { - const data = await resp.json(); - let path = data.name; - if (data.subfolder) path = data.subfolder + "/" + path; - uploadPath = path; - const modelUrl = api.apiURL( - getResourceURL$1(...splitFilePath$1(path), "input") - ); - await load3d.loadModel(modelUrl, file2.name); - const fileExt = file2.name.split(".").pop()?.toLowerCase(); - if (fileExt === "obj" && fileInput?.files) { - try { - const mtlFile = Array.from(fileInput.files).find( - (f) => f.name.toLowerCase().endsWith(".mtl") - ); - if (mtlFile) { - const mtlFormData = new FormData(); - mtlFormData.append("image", mtlFile); - mtlFormData.append("subfolder", "3d"); - await api.fetchApi("/upload/image", { - method: "POST", - body: mtlFormData - }); - } - } catch (mtlError) { - console.warn("Failed to upload MTL file:", mtlError); - } - } - } else { - useToastStore().addAlert(resp.status + " - " + resp.statusText); - } - } catch (error) { - console.error("Upload error:", error); - useToastStore().addAlert( - error instanceof Error ? error.message : "Upload failed" - ); - } - return uploadPath; -} -__name(uploadFile$1, "uploadFile$1"); -class Load3d { - static { - __name(this, "Load3d"); - } - scene; - perspectiveCamera; - orthographicCamera; - activeCamera; - renderer; - controls; - gltfLoader; - objLoader; - mtlLoader; - fbxLoader; - stlLoader; - currentModel = null; - originalModel = null; - node; - animationFrameId = null; - gridHelper; - lights = []; - clock; - normalMaterial; - standardMaterial; - wireframeMaterial; - depthMaterial; - originalMaterials = /* @__PURE__ */ new WeakMap(); - materialMode = "original"; - currentUpDirection = "original"; - originalRotation = null; - constructor(container) { - this.scene = new Scene(); - this.perspectiveCamera = new PerspectiveCamera(75, 1, 0.1, 1e3); - this.perspectiveCamera.position.set(5, 5, 5); - const frustumSize = 10; - this.orthographicCamera = new OrthographicCamera( - -frustumSize / 2, - frustumSize / 2, - frustumSize / 2, - -frustumSize / 2, - 0.1, - 1e3 - ); - this.orthographicCamera.position.set(5, 5, 5); - this.activeCamera = this.perspectiveCamera; - this.perspectiveCamera.lookAt(0, 0, 0); - this.orthographicCamera.lookAt(0, 0, 0); - this.renderer = new WebGLRenderer({ alpha: true, antialias: true }); - this.renderer.setSize(300, 300); - this.renderer.setClearColor(2631720); - const rendererDomElement = this.renderer.domElement; - container.appendChild(rendererDomElement); - this.controls = new OrbitControls( - this.activeCamera, - this.renderer.domElement - ); - this.controls.enableDamping = true; - this.gltfLoader = new GLTFLoader(); - this.objLoader = new OBJLoader(); - this.mtlLoader = new MTLLoader(); - this.fbxLoader = new FBXLoader(); - this.stlLoader = new STLLoader(); - this.clock = new Clock(); - this.setupLights(); - this.gridHelper = new GridHelper(10, 10); - this.gridHelper.position.set(0, 0, 0); - this.scene.add(this.gridHelper); - this.normalMaterial = new MeshNormalMaterial({ - flatShading: false, - side: DoubleSide, - normalScale: new Vector2(1, 1), - transparent: false, - opacity: 1 - }); - this.wireframeMaterial = new MeshBasicMaterial({ - color: 16777215, - wireframe: true, - transparent: false, - opacity: 1 - }); - this.depthMaterial = new MeshDepthMaterial({ - depthPacking: BasicDepthPacking, - side: DoubleSide - }); - this.standardMaterial = this.createSTLMaterial(); - this.animate(); - this.handleResize(); - this.startAnimation(); - } - setFOV(fov2) { - if (this.activeCamera === this.perspectiveCamera) { - this.perspectiveCamera.fov = fov2; - this.perspectiveCamera.updateProjectionMatrix(); - this.renderer.render(this.scene, this.activeCamera); - } - } - getCameraState() { - const currentType = this.getCurrentCameraType(); - return { - position: this.activeCamera.position.clone(), - target: this.controls.target.clone(), - zoom: this.activeCamera instanceof OrthographicCamera ? this.activeCamera.zoom : this.activeCamera.zoom, - cameraType: currentType - }; - } - setCameraState(state) { - if (this.activeCamera !== (state.cameraType === "perspective" ? this.perspectiveCamera : this.orthographicCamera)) { - this.toggleCamera(state.cameraType); - } - this.activeCamera.position.copy(state.position); - this.controls.target.copy(state.target); - if (this.activeCamera instanceof OrthographicCamera) { - this.activeCamera.zoom = state.zoom; - this.activeCamera.updateProjectionMatrix(); - } else if (this.activeCamera instanceof PerspectiveCamera) { - this.activeCamera.zoom = state.zoom; - this.activeCamera.updateProjectionMatrix(); - } - this.controls.update(); - } - setUpDirection(direction) { - if (!this.currentModel) return; - if (!this.originalRotation && this.currentModel.rotation) { - this.originalRotation = this.currentModel.rotation.clone(); - } - this.currentUpDirection = direction; - if (this.originalRotation) { - this.currentModel.rotation.copy(this.originalRotation); - } - switch (direction) { - case "original": - break; - case "-x": - this.currentModel.rotation.z = Math.PI / 2; - break; - case "+x": - this.currentModel.rotation.z = -Math.PI / 2; - break; - case "-y": - this.currentModel.rotation.x = Math.PI; - break; - case "+y": - break; - case "-z": - this.currentModel.rotation.x = Math.PI / 2; - break; - case "+z": - this.currentModel.rotation.x = -Math.PI / 2; - break; - } - this.renderer.render(this.scene, this.activeCamera); - } - setMaterialMode(mode) { - this.materialMode = mode; - if (this.currentModel) { - if (mode === "depth") { - this.renderer.outputColorSpace = LinearSRGBColorSpace; - } else { - this.renderer.outputColorSpace = SRGBColorSpace; - } - this.currentModel.traverse((child) => { - if (child instanceof Mesh) { - switch (mode) { - case "depth": - if (!this.originalMaterials.has(child)) { - this.originalMaterials.set(child, child.material); - } - const depthMat = new MeshDepthMaterial({ - depthPacking: BasicDepthPacking, - side: DoubleSide - }); - depthMat.onBeforeCompile = (shader) => { - shader.uniforms.cameraType = { - value: this.activeCamera instanceof OrthographicCamera ? 1 : 0 - }; - shader.fragmentShader = ` - uniform float cameraType; - ${shader.fragmentShader} - `; - shader.fragmentShader = shader.fragmentShader.replace( - /gl_FragColor\s*=\s*vec4\(\s*vec3\(\s*1.0\s*-\s*fragCoordZ\s*\)\s*,\s*opacity\s*\)\s*;/, - ` - float depth = 1.0 - fragCoordZ; - if (cameraType > 0.5) { - depth = pow(depth, 400.0); - } else { - depth = pow(depth, 0.6); - } - gl_FragColor = vec4(vec3(depth), opacity); - ` - ); - }; - depthMat.customProgramCacheKey = () => { - return this.activeCamera instanceof OrthographicCamera ? "ortho" : "persp"; - }; - child.material = depthMat; - break; - case "normal": - if (!this.originalMaterials.has(child)) { - this.originalMaterials.set(child, child.material); - } - child.material = new MeshNormalMaterial({ - flatShading: false, - side: DoubleSide, - normalScale: new Vector2(1, 1), - transparent: false, - opacity: 1 - }); - child.geometry.computeVertexNormals(); - break; - case "wireframe": - if (!this.originalMaterials.has(child)) { - this.originalMaterials.set(child, child.material); - } - child.material = new MeshBasicMaterial({ - color: 16777215, - wireframe: true, - transparent: false, - opacity: 1 - }); - break; - case "original": - const originalMaterial = this.originalMaterials.get(child); - if (originalMaterial) { - child.material = originalMaterial; - } else { - child.material = this.standardMaterial; - } - break; - } - } - }); - this.renderer.render(this.scene, this.activeCamera); - } - } - setupLights() { - const ambientLight = new AmbientLight(16777215, 0.5); - this.scene.add(ambientLight); - this.lights.push(ambientLight); - const mainLight = new DirectionalLight(16777215, 0.8); - mainLight.position.set(0, 10, 10); - this.scene.add(mainLight); - this.lights.push(mainLight); - const backLight = new DirectionalLight(16777215, 0.5); - backLight.position.set(0, 10, -10); - this.scene.add(backLight); - this.lights.push(backLight); - const leftFillLight = new DirectionalLight(16777215, 0.3); - leftFillLight.position.set(-10, 0, 0); - this.scene.add(leftFillLight); - this.lights.push(leftFillLight); - const rightFillLight = new DirectionalLight(16777215, 0.3); - rightFillLight.position.set(10, 0, 0); - this.scene.add(rightFillLight); - this.lights.push(rightFillLight); - const bottomLight = new DirectionalLight(16777215, 0.2); - bottomLight.position.set(0, -10, 0); - this.scene.add(bottomLight); - this.lights.push(bottomLight); - } - toggleCamera(cameraType) { - const oldCamera = this.activeCamera; - const position = oldCamera.position.clone(); - const rotation = oldCamera.rotation.clone(); - const target = this.controls.target.clone(); - if (!cameraType) { - this.activeCamera = oldCamera === this.perspectiveCamera ? this.orthographicCamera : this.perspectiveCamera; - } else { - this.activeCamera = cameraType === "perspective" ? this.perspectiveCamera : this.orthographicCamera; - if (oldCamera === this.activeCamera) { - return; - } - } - this.activeCamera.position.copy(position); - this.activeCamera.rotation.copy(rotation); - if (this.materialMode === "depth" && oldCamera !== this.activeCamera) { - this.setMaterialMode("depth"); - } - this.controls.object = this.activeCamera; - this.controls.target.copy(target); - this.controls.update(); - this.handleResize(); - } - getCurrentCameraType() { - return this.activeCamera === this.perspectiveCamera ? "perspective" : "orthographic"; - } - toggleGrid(showGrid) { - if (this.gridHelper) { - this.gridHelper.visible = showGrid; - } - } - setLightIntensity(intensity) { - this.lights.forEach((light) => { - if (light instanceof DirectionalLight) { - if (light === this.lights[1]) { - light.intensity = intensity * 0.8; - } else if (light === this.lights[2]) { - light.intensity = intensity * 0.5; - } else if (light === this.lights[5]) { - light.intensity = intensity * 0.2; - } else { - light.intensity = intensity * 0.3; - } - } else if (light instanceof AmbientLight) { - light.intensity = intensity * 0.5; - } - }); - } - startAnimation() { - const animate = /* @__PURE__ */ __name(() => { - this.animationFrameId = requestAnimationFrame(animate); - this.controls.update(); - this.renderer.render(this.scene, this.activeCamera); - }, "animate"); - animate(); - } - clearModel() { - const objectsToRemove = []; - this.scene.traverse((object) => { - const isEnvironmentObject = object === this.gridHelper || this.lights.includes(object) || object === this.perspectiveCamera || object === this.orthographicCamera; - if (!isEnvironmentObject) { - objectsToRemove.push(object); - } - }); - objectsToRemove.forEach((obj) => { - if (obj.parent && obj.parent !== this.scene) { - obj.parent.remove(obj); - } else { - this.scene.remove(obj); - } - if (obj instanceof Mesh) { - obj.geometry?.dispose(); - if (Array.isArray(obj.material)) { - obj.material.forEach((material) => material.dispose()); - } else { - obj.material?.dispose(); - } - } - }); - this.resetScene(); - } - resetScene() { - this.currentModel = null; - this.originalRotation = null; - const defaultDistance = 10; - this.perspectiveCamera.position.set( - defaultDistance, - defaultDistance, - defaultDistance - ); - this.orthographicCamera.position.set( - defaultDistance, - defaultDistance, - defaultDistance - ); - this.perspectiveCamera.lookAt(0, 0, 0); - this.orthographicCamera.lookAt(0, 0, 0); - const frustumSize = 10; - const aspect2 = this.renderer.domElement.width / this.renderer.domElement.height; - this.orthographicCamera.left = -frustumSize * aspect2 / 2; - this.orthographicCamera.right = frustumSize * aspect2 / 2; - this.orthographicCamera.top = frustumSize / 2; - this.orthographicCamera.bottom = -frustumSize / 2; - this.perspectiveCamera.updateProjectionMatrix(); - this.orthographicCamera.updateProjectionMatrix(); - this.controls.target.set(0, 0, 0); - this.controls.update(); - this.renderer.render(this.scene, this.activeCamera); - this.materialMode = "original"; - this.originalMaterials = /* @__PURE__ */ new WeakMap(); - this.renderer.outputColorSpace = SRGBColorSpace; - } - remove() { - if (this.animationFrameId !== null) { - cancelAnimationFrame(this.animationFrameId); - } - this.controls.dispose(); - this.renderer.dispose(); - this.renderer.domElement.remove(); - this.scene.clear(); - } - async loadModelInternal(url, fileExtension) { - let model = null; - switch (fileExtension) { - case "stl": - const geometry = await this.stlLoader.loadAsync(url); - this.originalModel = geometry; - geometry.computeVertexNormals(); - const mesh = new Mesh(geometry, this.standardMaterial); - const group = new Group(); - group.add(mesh); - model = group; - break; - case "fbx": - const fbxModel = await this.fbxLoader.loadAsync(url); - this.originalModel = fbxModel; - model = fbxModel; - fbxModel.traverse((child) => { - if (child instanceof Mesh) { - this.originalMaterials.set(child, child.material); - } - }); - break; - case "obj": - if (this.materialMode === "original") { - const mtlUrl = url.replace(/\.obj([^.]*$)/, ".mtl$1"); - try { - const materials = await this.mtlLoader.loadAsync(mtlUrl); - materials.preload(); - this.objLoader.setMaterials(materials); - } catch (e) { - console.log( - "No MTL file found or error loading it, continuing without materials" - ); - } - } - model = await this.objLoader.loadAsync(url); - model.traverse((child) => { - if (child instanceof Mesh) { - this.originalMaterials.set(child, child.material); - } - }); - break; - case "gltf": - case "glb": - const gltf = await this.gltfLoader.loadAsync(url); - this.originalModel = gltf; - model = gltf.scene; - gltf.scene.traverse((child) => { - if (child instanceof Mesh) { - child.geometry.computeVertexNormals(); - this.originalMaterials.set(child, child.material); - } - }); - break; - } - return model; - } - async loadModel(url, originalFileName) { - try { - this.clearModel(); - let fileExtension; - if (originalFileName) { - fileExtension = originalFileName.split(".").pop()?.toLowerCase(); - } else { - const filename = new URLSearchParams(url.split("?")[1]).get("filename"); - fileExtension = filename?.split(".").pop()?.toLowerCase(); - } - if (!fileExtension) { - useToastStore().addAlert("Could not determine file type"); - return; - } - let model = await this.loadModelInternal(url, fileExtension); - if (model) { - this.currentModel = model; - await this.setupModel(model); - } - } catch (error) { - console.error("Error loading model:", error); - } - } - async setupModel(model) { - const box = new Box3().setFromObject(model); - const size = box.getSize(new Vector3()); - const center = box.getCenter(new Vector3()); - const maxDim = Math.max(size.x, size.y, size.z); - const targetSize = 5; - const scale = targetSize / maxDim; - model.scale.multiplyScalar(scale); - box.setFromObject(model); - box.getCenter(center); - box.getSize(size); - model.position.set(-center.x, -box.min.y, -center.z); - this.scene.add(model); - if (this.materialMode !== "original") { - this.setMaterialMode(this.materialMode); - } - if (this.currentUpDirection !== "original") { - this.setUpDirection(this.currentUpDirection); - } - await this.setupCamera(size); - } - async setupCamera(size) { - const distance = Math.max(size.x, size.z) * 2; - const height = size.y * 2; - this.perspectiveCamera.position.set(distance, height, distance); - this.orthographicCamera.position.set(distance, height, distance); - if (this.activeCamera === this.perspectiveCamera) { - this.perspectiveCamera.lookAt(0, size.y / 2, 0); - this.perspectiveCamera.updateProjectionMatrix(); - } else { - const frustumSize = Math.max(size.x, size.y, size.z) * 2; - const aspect2 = this.renderer.domElement.width / this.renderer.domElement.height; - this.orthographicCamera.left = -frustumSize * aspect2 / 2; - this.orthographicCamera.right = frustumSize * aspect2 / 2; - this.orthographicCamera.top = frustumSize / 2; - this.orthographicCamera.bottom = -frustumSize / 2; - this.orthographicCamera.lookAt(0, size.y / 2, 0); - this.orthographicCamera.updateProjectionMatrix(); - } - this.controls.target.set(0, size.y / 2, 0); - this.controls.update(); - this.renderer.outputColorSpace = SRGBColorSpace; - this.renderer.toneMapping = ACESFilmicToneMapping; - this.renderer.toneMappingExposure = 1; - this.handleResize(); - } - handleResize() { - const parentElement = this.renderer?.domElement?.parentElement; - if (!parentElement) { - console.warn("Parent element not found"); - return; - } - const width = parentElement?.clientWidth; - const height = parentElement?.clientHeight; - if (this.activeCamera === this.perspectiveCamera) { - this.perspectiveCamera.aspect = width / height; - this.perspectiveCamera.updateProjectionMatrix(); - } else { - const frustumSize = 10; - const aspect2 = width / height; - this.orthographicCamera.left = -frustumSize * aspect2 / 2; - this.orthographicCamera.right = frustumSize * aspect2 / 2; - this.orthographicCamera.top = frustumSize / 2; - this.orthographicCamera.bottom = -frustumSize / 2; - this.orthographicCamera.updateProjectionMatrix(); - } - this.renderer.setSize(width, height); - } - animate = /* @__PURE__ */ __name(() => { - requestAnimationFrame(this.animate); - this.controls.update(); - this.renderer.render(this.scene, this.activeCamera); - }, "animate"); - captureScene(width, height) { - return new Promise(async (resolve, reject) => { - try { - const originalWidth = this.renderer.domElement.width; - const originalHeight = this.renderer.domElement.height; - const originalClearColor = this.renderer.getClearColor( - new Color() - ); - const originalClearAlpha = this.renderer.getClearAlpha(); - this.renderer.setSize(width, height); - if (this.activeCamera === this.perspectiveCamera) { - this.perspectiveCamera.aspect = width / height; - this.perspectiveCamera.updateProjectionMatrix(); - } else { - const frustumSize = 10; - const aspect2 = width / height; - this.orthographicCamera.left = -frustumSize * aspect2 / 2; - this.orthographicCamera.right = frustumSize * aspect2 / 2; - this.orthographicCamera.top = frustumSize / 2; - this.orthographicCamera.bottom = -frustumSize / 2; - this.orthographicCamera.updateProjectionMatrix(); - } - this.renderer.render(this.scene, this.activeCamera); - const sceneData = this.renderer.domElement.toDataURL("image/png"); - this.renderer.setClearColor(0, 0); - this.renderer.render(this.scene, this.activeCamera); - const maskData = this.renderer.domElement.toDataURL("image/png"); - this.renderer.setClearColor(originalClearColor, originalClearAlpha); - this.renderer.setSize(originalWidth, originalHeight); - this.handleResize(); - resolve({ scene: sceneData, mask: maskData }); - } catch (error) { - reject(error); - } - }); - } - createSTLMaterial() { - return new MeshStandardMaterial({ - color: 8421504, - metalness: 0.1, - roughness: 0.8, - flatShading: false, - side: DoubleSide - }); - } - setViewPosition(position) { - if (!this.currentModel) { - return; - } - const box = new Box3(); - let center = new Vector3(); - let size = new Vector3(); - if (this.currentModel) { - box.setFromObject(this.currentModel); - box.getCenter(center); - box.getSize(size); - } - const maxDim = Math.max(size.x, size.y, size.z); - const distance = maxDim * 2; - switch (position) { - case "front": - this.activeCamera.position.set(0, 0, distance); - break; - case "top": - this.activeCamera.position.set(0, distance, 0); - break; - case "right": - this.activeCamera.position.set(distance, 0, 0); - break; - case "isometric": - this.activeCamera.position.set(distance, distance, distance); - break; - } - this.activeCamera.lookAt(center); - this.controls.target.copy(center); - this.controls.update(); - } - setBackgroundColor(color) { - this.renderer.setClearColor(new Color(color)); - this.renderer.render(this.scene, this.activeCamera); - } -} -class Load3dAnimation extends Load3d { - static { - __name(this, "Load3dAnimation"); - } - currentAnimation = null; - animationActions = []; - animationClips = []; - selectedAnimationIndex = 0; - isAnimationPlaying = false; - animationSpeed = 1; - constructor(container) { - super(container); - } - async setupModel(model) { - await super.setupModel(model); - if (this.currentAnimation) { - this.currentAnimation.stopAllAction(); - this.animationActions = []; - } - let animations = []; - if (model.animations?.length > 0) { - animations = model.animations; - } else if (this.originalModel && "animations" in this.originalModel) { - animations = this.originalModel.animations; - } - if (animations.length > 0) { - this.animationClips = animations; - if (model.type === "Scene") { - this.currentAnimation = new AnimationMixer(model); - } else { - this.currentAnimation = new AnimationMixer(this.currentModel); - } - if (this.animationClips.length > 0) { - this.updateSelectedAnimation(0); - } - } - } - setAnimationSpeed(speed) { - this.animationSpeed = speed; - this.animationActions.forEach((action) => { - action.setEffectiveTimeScale(speed); - }); - } - updateSelectedAnimation(index) { - if (!this.currentAnimation || !this.animationClips || index >= this.animationClips.length) { - console.warn("Invalid animation update request"); - return; - } - this.animationActions.forEach((action2) => { - action2.stop(); - }); - this.currentAnimation.stopAllAction(); - this.animationActions = []; - this.selectedAnimationIndex = index; - const clip = this.animationClips[index]; - const action = this.currentAnimation.clipAction(clip); - action.setEffectiveTimeScale(this.animationSpeed); - action.reset(); - action.clampWhenFinished = false; - action.loop = LoopRepeat; - if (this.isAnimationPlaying) { - action.play(); - } else { - action.play(); - action.paused = true; - } - this.animationActions = [action]; - } - clearModel() { - if (this.currentAnimation) { - this.animationActions.forEach((action) => { - action.stop(); - }); - this.currentAnimation = null; - } - this.animationActions = []; - this.animationClips = []; - this.selectedAnimationIndex = 0; - this.isAnimationPlaying = false; - this.animationSpeed = 1; - super.clearModel(); - } - getAnimationNames() { - return this.animationClips.map((clip, index) => { - return clip.name || `Animation ${index + 1}`; - }); - } - toggleAnimation(play) { - if (!this.currentAnimation || this.animationActions.length === 0) { - console.warn("No animation to toggle"); - return; - } - this.isAnimationPlaying = play ?? !this.isAnimationPlaying; - this.animationActions.forEach((action) => { - if (this.isAnimationPlaying) { - action.paused = false; - if (action.time === 0 || action.time === action.getClip().duration) { - action.reset(); - } - } else { - action.paused = true; - } - }); - } - animate = /* @__PURE__ */ __name(() => { - requestAnimationFrame(this.animate); - if (this.currentAnimation && this.isAnimationPlaying) { - const delta = this.clock.getDelta(); - this.currentAnimation.update(delta); - } - this.controls.update(); - this.renderer.render(this.scene, this.activeCamera); - }, "animate"); -} -function splitFilePath$1(path) { - const folder_separator = path.lastIndexOf("/"); - if (folder_separator === -1) { - return ["", path]; - } - return [ - path.substring(0, folder_separator), - path.substring(folder_separator + 1) - ]; -} -__name(splitFilePath$1, "splitFilePath$1"); -function getResourceURL$1(subfolder, filename, type = "input") { - const params = [ - "filename=" + encodeURIComponent(filename), - "type=" + type, - "subfolder=" + subfolder, - app.getRandParam().substring(1) - ].join("&"); - return `/view?${params}`; -} -__name(getResourceURL$1, "getResourceURL$1"); -const load3dCSSCLASS = `display: flex; - flex-direction: column; - background: transparent; - flex: 1; - position: relative; - overflow: hidden;`; -const load3dCanvasCSSCLASS = `display: flex; - width: 100% !important; - height: 100% !important;`; -const containerToLoad3D = /* @__PURE__ */ new Map(); -function configureLoad3D(load3d, loadFolder, modelWidget, showGrid, cameraType, view, material, bgColor, lightIntensity, upDirection, fov2, cameraState, postModelUpdateFunc) { - const createModelUpdateHandler = /* @__PURE__ */ __name(() => { - let isFirstLoad = true; - return async (value) => { - if (!value) return; - const filename = value; - const modelUrl = api.apiURL( - getResourceURL$1(...splitFilePath$1(filename), loadFolder) - ); - await load3d.loadModel(modelUrl, filename); - load3d.setMaterialMode( - material.value - ); - load3d.setUpDirection( - upDirection.value - ); - if (postModelUpdateFunc) { - postModelUpdateFunc(load3d); - } - if (isFirstLoad && cameraState && typeof cameraState === "object") { - try { - load3d.setCameraState(cameraState); - } catch (error) { - console.warn("Failed to restore camera state:", error); - } - isFirstLoad = false; - } - }; - }, "createModelUpdateHandler"); - const onModelWidgetUpdate = createModelUpdateHandler(); - if (modelWidget.value) { - onModelWidgetUpdate(modelWidget.value); - } - modelWidget.callback = onModelWidgetUpdate; - load3d.toggleGrid(showGrid.value); - showGrid.callback = (value) => { - load3d.toggleGrid(value); - }; - load3d.toggleCamera(cameraType.value); - cameraType.callback = (value) => { - load3d.toggleCamera(value); - }; - view.callback = (value) => { - load3d.setViewPosition(value); - }; - material.callback = (value) => { - load3d.setMaterialMode(value); - }; - load3d.setMaterialMode(material.value); - load3d.setBackgroundColor(bgColor.value); - bgColor.callback = (value) => { - load3d.setBackgroundColor(value); - }; - load3d.setLightIntensity(lightIntensity.value); - lightIntensity.callback = (value) => { - load3d.setLightIntensity(value); - }; - upDirection.callback = (value) => { - load3d.setUpDirection(value); - }; - load3d.setUpDirection( - upDirection.value - ); - fov2.callback = (value) => { - load3d.setFOV(value); - }; - load3d.setFOV(fov2.value); -} -__name(configureLoad3D, "configureLoad3D"); -app.registerExtension({ - name: "Comfy.Load3D", - getCustomWidgets(app2) { - return { - LOAD_3D(node, inputName) { - let load3dNode = app2.graph._nodes.filter((wi) => wi.type == "Load3D"); - node.addProperty("Camera Info", ""); - const container = document.createElement("div"); - container.id = `comfy-load-3d-${load3dNode.length}`; - container.classList.add("comfy-load-3d"); - const load3d = new Load3d(container); - containerToLoad3D.set(container.id, load3d); - node.onResize = function() { - if (load3d) { - load3d.handleResize(); - } - }; - const origOnRemoved = node.onRemoved; - node.onRemoved = function() { - if (load3d) { - load3d.remove(); - } - containerToLoad3D.delete(container.id); - origOnRemoved?.apply(this, []); - }; - node.onDrawBackground = function() { - load3d.renderer.domElement.hidden = this.flags.collapsed ?? false; - }; - const fileInput = document.createElement("input"); - fileInput.type = "file"; - fileInput.accept = ".gltf,.glb,.obj,.mtl,.fbx,.stl"; - fileInput.style.display = "none"; - fileInput.onchange = async () => { - if (fileInput.files?.length) { - const modelWidget = node.widgets?.find( - (w) => w.name === "model_file" - ); - const uploadPath = await uploadFile$1( - load3d, - fileInput.files[0], - fileInput - ).catch((error) => { - console.error("File upload failed:", error); - useToastStore().addAlert("File upload failed"); - }); - if (uploadPath && modelWidget) { - if (!modelWidget.options?.values?.includes(uploadPath)) { - modelWidget.options?.values?.push(uploadPath); - } - modelWidget.value = uploadPath; - } - } - }; - node.addWidget("button", "upload 3d model", "upload3dmodel", () => { - fileInput.click(); - }); - node.addWidget("button", "clear", "clear", () => { - load3d.clearModel(); - const modelWidget = node.widgets?.find( - (w) => w.name === "model_file" - ); - if (modelWidget) { - modelWidget.value = ""; - } - }); - return { - widget: node.addDOMWidget(inputName, "LOAD_3D", container) - }; - } - }; - }, - init() { - const style = document.createElement("style"); - style.innerText = ` - .comfy-load-3d { - ${load3dCSSCLASS} - } - - .comfy-load-3d canvas { - ${load3dCanvasCSSCLASS} - } - `; - document.head.appendChild(style); - }, - async nodeCreated(node) { - if (node.constructor.comfyClass !== "Load3D") return; - const [oldWidth, oldHeight] = node.size; - node.setSize([Math.max(oldWidth, 300), Math.max(oldHeight, 600)]); - await nextTick(); - const sceneWidget = node.widgets.find((w2) => w2.name === "image"); - const container = sceneWidget.element; - const load3d = containerToLoad3D.get(container.id); - const modelWidget = node.widgets.find( - (w2) => w2.name === "model_file" - ); - const showGrid = node.widgets.find((w2) => w2.name === "show_grid"); - const cameraType = node.widgets.find( - (w2) => w2.name === "camera_type" - ); - const view = node.widgets.find((w2) => w2.name === "view"); - const material = node.widgets.find((w2) => w2.name === "material"); - const bgColor = node.widgets.find((w2) => w2.name === "bg_color"); - const lightIntensity = node.widgets.find( - (w2) => w2.name === "light_intensity" - ); - const upDirection = node.widgets.find( - (w2) => w2.name === "up_direction" - ); - const fov2 = node.widgets.find((w2) => w2.name === "fov"); - let cameraState; - try { - const cameraInfo = node.properties["Camera Info"]; - if (cameraInfo && typeof cameraInfo === "string" && cameraInfo.trim() !== "") { - cameraState = JSON.parse(cameraInfo); - } - } catch (error) { - console.warn("Failed to parse camera state:", error); - cameraState = void 0; - } - configureLoad3D( - load3d, - "input", - modelWidget, - showGrid, - cameraType, - view, - material, - bgColor, - lightIntensity, - upDirection, - fov2, - cameraState - ); - const w = node.widgets.find((w2) => w2.name === "width"); - const h = node.widgets.find((w2) => w2.name === "height"); - sceneWidget.serializeValue = async () => { - node.properties["Camera Info"] = JSON.stringify(load3d.getCameraState()); - const { scene: imageData, mask: maskData } = await load3d.captureScene( - w.value, - h.value - ); - const [data, dataMask] = await Promise.all([ - uploadTempImage(imageData, "scene"), - uploadTempImage(maskData, "scene_mask") - ]); - return { - image: `threed/${data.name} [temp]`, - mask: `threed/${dataMask.name} [temp]` - }; - }; - } -}); -app.registerExtension({ - name: "Comfy.Load3DAnimation", - getCustomWidgets(app2) { - return { - LOAD_3D_ANIMATION(node, inputName) { - let load3dNode = app2.graph._nodes.filter( - (wi) => wi.type == "Load3DAnimation" - ); - node.addProperty("Camera Info", ""); - const container = document.createElement("div"); - container.id = `comfy-load-3d-animation-${load3dNode.length}`; - container.classList.add("comfy-load-3d-animation"); - const load3d = new Load3dAnimation(container); - containerToLoad3D.set(container.id, load3d); - node.onResize = function() { - if (load3d) { - load3d.handleResize(); - } - }; - const origOnRemoved = node.onRemoved; - node.onRemoved = function() { - if (load3d) { - load3d.remove(); - } - containerToLoad3D.delete(container.id); - origOnRemoved?.apply(this, []); - }; - node.onDrawBackground = function() { - load3d.renderer.domElement.hidden = this.flags.collapsed ?? false; - }; - const fileInput = document.createElement("input"); - fileInput.type = "file"; - fileInput.accept = ".fbx,glb,gltf"; - fileInput.style.display = "none"; - fileInput.onchange = async () => { - if (fileInput.files?.length) { - const modelWidget = node.widgets?.find( - (w) => w.name === "model_file" - ); - const uploadPath = await uploadFile$1( - load3d, - fileInput.files[0], - fileInput - ).catch((error) => { - console.error("File upload failed:", error); - useToastStore().addAlert("File upload failed"); - }); - if (uploadPath && modelWidget) { - if (!modelWidget.options?.values?.includes(uploadPath)) { - modelWidget.options?.values?.push(uploadPath); - } - modelWidget.value = uploadPath; - } - } - }; - node.addWidget("button", "upload 3d model", "upload3dmodel", () => { - fileInput.click(); - }); - node.addWidget("button", "clear", "clear", () => { - load3d.clearModel(); - const modelWidget = node.widgets?.find( - (w) => w.name === "model_file" - ); - if (modelWidget) { - modelWidget.value = ""; - } - const animationSelect2 = node.widgets?.find( - (w) => w.name === "animation" - ); - if (animationSelect2) { - animationSelect2.options.values = []; - animationSelect2.value = ""; - } - const speedSelect = node.widgets?.find( - (w) => w.name === "animation_speed" - ); - if (speedSelect) { - speedSelect.value = "1"; - } - }); - node.addWidget( - "button", - "Play/Pause Animation", - "toggle_animation", - () => { - load3d.toggleAnimation(); - } - ); - const animationSelect = node.addWidget( - "combo", - "animation", - "", - () => "", - { - values: [] - } - ); - animationSelect.callback = (value) => { - const names = load3d.getAnimationNames(); - const index = names.indexOf(value); - if (index !== -1) { - const wasPlaying = load3d.isAnimationPlaying; - if (wasPlaying) { - load3d.toggleAnimation(false); - } - load3d.updateSelectedAnimation(index); - if (wasPlaying) { - load3d.toggleAnimation(true); - } - } - }; - return { - widget: node.addDOMWidget(inputName, "LOAD_3D_ANIMATION", container) - }; - } - }; - }, - init() { - const style = document.createElement("style"); - style.innerText = ` - .comfy-load-3d-animation { - ${load3dCSSCLASS} - } - - .comfy-load-3d-animation canvas { - ${load3dCanvasCSSCLASS} - } - `; - document.head.appendChild(style); - }, - async nodeCreated(node) { - if (node.constructor.comfyClass !== "Load3DAnimation") return; - const [oldWidth, oldHeight] = node.size; - node.setSize([Math.max(oldWidth, 300), Math.max(oldHeight, 700)]); - await nextTick(); - const sceneWidget = node.widgets.find((w2) => w2.name === "image"); - const container = sceneWidget.element; - const load3d = containerToLoad3D.get(container.id); - const modelWidget = node.widgets.find( - (w2) => w2.name === "model_file" - ); - const showGrid = node.widgets.find((w2) => w2.name === "show_grid"); - const cameraType = node.widgets.find( - (w2) => w2.name === "camera_type" - ); - const view = node.widgets.find((w2) => w2.name === "view"); - const material = node.widgets.find((w2) => w2.name === "material"); - const bgColor = node.widgets.find((w2) => w2.name === "bg_color"); - const lightIntensity = node.widgets.find( - (w2) => w2.name === "light_intensity" - ); - const upDirection = node.widgets.find( - (w2) => w2.name === "up_direction" - ); - const speedSelect = node.widgets.find( - (w2) => w2.name === "animation_speed" - ); - speedSelect.callback = (value) => { - const load3d2 = containerToLoad3D.get(container.id); - if (load3d2) { - load3d2.setAnimationSpeed(parseFloat(value)); - } - }; - const fov2 = node.widgets.find((w2) => w2.name === "fov"); - let cameraState; - try { - const cameraInfo = node.properties["Camera Info"]; - if (cameraInfo && typeof cameraInfo === "string" && cameraInfo.trim() !== "") { - cameraState = JSON.parse(cameraInfo); - } - } catch (error) { - console.warn("Failed to parse camera state:", error); - cameraState = void 0; - } - configureLoad3D( - load3d, - "input", - modelWidget, - showGrid, - cameraType, - view, - material, - bgColor, - lightIntensity, - upDirection, - fov2, - cameraState, - (load3d2) => { - const animationLoad3d = load3d2; - const names = animationLoad3d.getAnimationNames(); - const animationSelect = node.widgets.find( - (w2) => w2.name === "animation" - ); - animationSelect.options.values = names; - if (names.length) { - animationSelect.value = names[0]; - } - } - ); - const w = node.widgets.find((w2) => w2.name === "width"); - const h = node.widgets.find((w2) => w2.name === "height"); - sceneWidget.serializeValue = async () => { - node.properties["Camera Info"] = JSON.stringify(load3d.getCameraState()); - const { scene: imageData, mask: maskData } = await load3d.captureScene( - w.value, - h.value - ); - const [data, dataMask] = await Promise.all([ - uploadTempImage(imageData, "scene"), - uploadTempImage(maskData, "scene_mask") - ]); - return { - image: `threed/${data.name} [temp]`, - mask: `threed/${dataMask.name} [temp]` - }; - }; - } -}); -app.registerExtension({ - name: "Comfy.Preview3D", - async beforeRegisterNodeDef(nodeType, nodeData) { - if ( - // @ts-expect-error ComfyNode - ["Preview3D"].includes(nodeType.comfyClass) - ) { - nodeData.input.required.image = ["PREVIEW_3D"]; - } - }, - getCustomWidgets(app2) { - return { - PREVIEW_3D(node, inputName) { - let load3dNode = app2.graph._nodes.filter((wi) => wi.type == "Preview3D"); - const container = document.createElement("div"); - container.id = `comfy-preview-3d-${load3dNode.length}`; - container.classList.add("comfy-preview-3d"); - const load3d = new Load3d(container); - containerToLoad3D.set(container.id, load3d); - node.onResize = function() { - if (load3d) { - load3d.handleResize(); - } - }; - const origOnRemoved = node.onRemoved; - node.onRemoved = function() { - if (load3d) { - load3d.remove(); - } - containerToLoad3D.delete(container.id); - origOnRemoved?.apply(this, []); - }; - node.onDrawBackground = function() { - load3d.renderer.domElement.hidden = this.flags.collapsed ?? false; - }; - return { - widget: node.addDOMWidget(inputName, "PREVIEW_3D", container) - }; - } - }; - }, - init() { - const style = document.createElement("style"); - style.innerText = ` - .comfy-preview-3d { - ${load3dCSSCLASS} - } - - .comfy-preview-3d canvas { - ${load3dCanvasCSSCLASS} - } - `; - document.head.appendChild(style); - }, - async nodeCreated(node) { - if (node.constructor.comfyClass !== "Preview3D") return; - const [oldWidth, oldHeight] = node.size; - node.setSize([Math.max(oldWidth, 300), Math.max(oldHeight, 550)]); - await nextTick(); - const sceneWidget = node.widgets.find((w) => w.name === "image"); - const container = sceneWidget.element; - const load3d = containerToLoad3D.get(container.id); - const modelWidget = node.widgets.find( - (w) => w.name === "model_file" - ); - const showGrid = node.widgets.find((w) => w.name === "show_grid"); - const cameraType = node.widgets.find( - (w) => w.name === "camera_type" - ); - const view = node.widgets.find((w) => w.name === "view"); - const material = node.widgets.find((w) => w.name === "material"); - const bgColor = node.widgets.find((w) => w.name === "bg_color"); - const lightIntensity = node.widgets.find( - (w) => w.name === "light_intensity" - ); - const upDirection = node.widgets.find( - (w) => w.name === "up_direction" - ); - const fov2 = node.widgets.find((w) => w.name === "fov"); - const onExecuted = node.onExecuted; - node.onExecuted = function(message) { - onExecuted?.apply(this, arguments); - let filePath = message.model_file[0]; - if (!filePath) { - const msg = "unable to get model file path."; - console.error(msg); - useToastStore().addAlert(msg); - } - modelWidget.value = filePath.replaceAll("\\", "/"); - configureLoad3D( - load3d, - "output", - modelWidget, - showGrid, - cameraType, - view, - material, - bgColor, - lightIntensity, - upDirection, - fov2 - ); - }; - } -}); -function dataURLToBlob(dataURL) { - const parts = dataURL.split(";base64,"); - const contentType = parts[0].split(":")[1]; - const byteString = atob(parts[1]); - const arrayBuffer = new ArrayBuffer(byteString.length); - const uint8Array = new Uint8Array(arrayBuffer); - for (let i = 0; i < byteString.length; i++) { - uint8Array[i] = byteString.charCodeAt(i); - } - return new Blob([arrayBuffer], { type: contentType }); -} -__name(dataURLToBlob, "dataURLToBlob"); -function loadedImageToBlob(image) { - const canvas = document.createElement("canvas"); - canvas.width = image.width; - canvas.height = image.height; - const ctx = canvas.getContext("2d"); - ctx.drawImage(image, 0, 0); - const dataURL = canvas.toDataURL("image/png", 1); - const blob = dataURLToBlob(dataURL); - return blob; -} -__name(loadedImageToBlob, "loadedImageToBlob"); -function loadImage(imagePath) { - return new Promise((resolve, reject) => { - const image = new Image(); - image.onload = function() { - resolve(image); - }; - image.src = imagePath; - }); -} -__name(loadImage, "loadImage"); -async function uploadMask(filepath, formData) { - await api.fetchApi("/upload/mask", { - method: "POST", - body: formData - }).then((response) => { - }).catch((error) => { - console.error("Error:", error); - }); - ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]] = new Image(); - ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]].src = api.apiURL( - "/view?" + new URLSearchParams(filepath).toString() + app.getPreviewFormatParam() + app.getRandParam() - ); - if (ComfyApp.clipspace.images) - ComfyApp.clipspace.images[ComfyApp.clipspace["selectedIndex"]] = filepath; - ClipspaceDialog.invalidatePreview(); -} -__name(uploadMask, "uploadMask"); -function prepare_mask(image, maskCanvas, maskCtx, maskColor) { - maskCtx.drawImage(image, 0, 0, maskCanvas.width, maskCanvas.height); - const maskData = maskCtx.getImageData( - 0, - 0, - maskCanvas.width, - maskCanvas.height - ); - for (let i = 0; i < maskData.data.length; i += 4) { - if (maskData.data[i + 3] == 255) maskData.data[i + 3] = 0; - else maskData.data[i + 3] = 255; - maskData.data[i] = maskColor.r; - maskData.data[i + 1] = maskColor.g; - maskData.data[i + 2] = maskColor.b; - } - maskCtx.globalCompositeOperation = "source-over"; - maskCtx.putImageData(maskData, 0, 0); -} -__name(prepare_mask, "prepare_mask"); -var PointerType = /* @__PURE__ */ ((PointerType2) => { - PointerType2["Arc"] = "arc"; - PointerType2["Rect"] = "rect"; - return PointerType2; -})(PointerType || {}); -var CompositionOperation$1 = /* @__PURE__ */ ((CompositionOperation2) => { - CompositionOperation2["SourceOver"] = "source-over"; - CompositionOperation2["DestinationOut"] = "destination-out"; - return CompositionOperation2; -})(CompositionOperation$1 || {}); -class MaskEditorDialogOld extends ComfyDialog { - static { - __name(this, "MaskEditorDialogOld"); - } - static instance = null; - static mousedown_x = null; - static mousedown_y = null; - brush; - maskCtx; - maskCanvas; - brush_size_slider; - brush_opacity_slider; - colorButton; - saveButton; - zoom_ratio; - pan_x; - pan_y; - imgCanvas; - last_display_style; - is_visible; - image; - handler_registered; - brush_slider_input; - cursorX; - cursorY; - mousedown_pan_x; - mousedown_pan_y; - last_pressure; - pointer_type; - brush_pointer_type_select; - static getInstance() { - if (!MaskEditorDialogOld.instance) { - MaskEditorDialogOld.instance = new MaskEditorDialogOld(); - } - return MaskEditorDialogOld.instance; - } - is_layout_created = false; - constructor() { - super(); - this.element = $el("div.comfy-modal", { parent: document.body }, [ - $el("div.comfy-modal-content", [...this.createButtons()]) - ]); - } - createButtons() { - return []; - } - createButton(name, callback) { - var button = document.createElement("button"); - button.style.pointerEvents = "auto"; - button.innerText = name; - button.addEventListener("click", callback); - return button; - } - createLeftButton(name, callback) { - var button = this.createButton(name, callback); - button.style.cssFloat = "left"; - button.style.marginRight = "4px"; - return button; - } - createRightButton(name, callback) { - var button = this.createButton(name, callback); - button.style.cssFloat = "right"; - button.style.marginLeft = "4px"; - return button; - } - createLeftSlider(self2, name, callback) { - const divElement = document.createElement("div"); - divElement.id = "maskeditor-slider"; - divElement.style.cssFloat = "left"; - divElement.style.fontFamily = "sans-serif"; - divElement.style.marginRight = "4px"; - divElement.style.color = "var(--input-text)"; - divElement.style.backgroundColor = "var(--comfy-input-bg)"; - divElement.style.borderRadius = "8px"; - divElement.style.borderColor = "var(--border-color)"; - divElement.style.borderStyle = "solid"; - divElement.style.fontSize = "15px"; - divElement.style.height = "25px"; - divElement.style.padding = "1px 6px"; - divElement.style.display = "flex"; - divElement.style.position = "relative"; - divElement.style.top = "2px"; - divElement.style.pointerEvents = "auto"; - self2.brush_slider_input = document.createElement("input"); - self2.brush_slider_input.setAttribute("type", "range"); - self2.brush_slider_input.setAttribute("min", "1"); - self2.brush_slider_input.setAttribute("max", "100"); - self2.brush_slider_input.setAttribute("value", "10"); - const labelElement = document.createElement("label"); - labelElement.textContent = name; - divElement.appendChild(labelElement); - divElement.appendChild(self2.brush_slider_input); - self2.brush_slider_input.addEventListener("change", callback); - return divElement; - } - createOpacitySlider(self2, name, callback) { - const divElement = document.createElement("div"); - divElement.id = "maskeditor-opacity-slider"; - divElement.style.cssFloat = "left"; - divElement.style.fontFamily = "sans-serif"; - divElement.style.marginRight = "4px"; - divElement.style.color = "var(--input-text)"; - divElement.style.backgroundColor = "var(--comfy-input-bg)"; - divElement.style.borderRadius = "8px"; - divElement.style.borderColor = "var(--border-color)"; - divElement.style.borderStyle = "solid"; - divElement.style.fontSize = "15px"; - divElement.style.height = "25px"; - divElement.style.padding = "1px 6px"; - divElement.style.display = "flex"; - divElement.style.position = "relative"; - divElement.style.top = "2px"; - divElement.style.pointerEvents = "auto"; - self2.opacity_slider_input = document.createElement("input"); - self2.opacity_slider_input.setAttribute("type", "range"); - self2.opacity_slider_input.setAttribute("min", "0.1"); - self2.opacity_slider_input.setAttribute("max", "1.0"); - self2.opacity_slider_input.setAttribute("step", "0.01"); - self2.opacity_slider_input.setAttribute("value", "0.7"); - const labelElement = document.createElement("label"); - labelElement.textContent = name; - divElement.appendChild(labelElement); - divElement.appendChild(self2.opacity_slider_input); - self2.opacity_slider_input.addEventListener("input", callback); - return divElement; - } - createPointerTypeSelect(self2) { - const divElement = document.createElement("div"); - divElement.id = "maskeditor-pointer-type"; - divElement.style.cssFloat = "left"; - divElement.style.fontFamily = "sans-serif"; - divElement.style.marginRight = "4px"; - divElement.style.color = "var(--input-text)"; - divElement.style.backgroundColor = "var(--comfy-input-bg)"; - divElement.style.borderRadius = "8px"; - divElement.style.borderColor = "var(--border-color)"; - divElement.style.borderStyle = "solid"; - divElement.style.fontSize = "15px"; - divElement.style.height = "25px"; - divElement.style.padding = "1px 6px"; - divElement.style.display = "flex"; - divElement.style.position = "relative"; - divElement.style.top = "2px"; - divElement.style.pointerEvents = "auto"; - const labelElement = document.createElement("label"); - labelElement.textContent = "Pointer Type:"; - const selectElement = document.createElement("select"); - selectElement.style.borderRadius = "0"; - selectElement.style.borderColor = "transparent"; - selectElement.style.borderStyle = "unset"; - selectElement.style.fontSize = "0.9em"; - const optionArc = document.createElement("option"); - optionArc.value = "arc"; - optionArc.text = "Circle"; - optionArc.selected = true; - const optionRect = document.createElement("option"); - optionRect.value = "rect"; - optionRect.text = "Square"; - selectElement.appendChild(optionArc); - selectElement.appendChild(optionRect); - selectElement.addEventListener("change", (event) => { - const target = event.target; - self2.pointer_type = target.value; - this.setBrushBorderRadius(self2); - }); - divElement.appendChild(labelElement); - divElement.appendChild(selectElement); - return divElement; - } - setBrushBorderRadius(self2) { - if (self2.pointer_type === "rect") { - this.brush.style.borderRadius = "0%"; - this.brush.style.MozBorderRadius = "0%"; - this.brush.style.WebkitBorderRadius = "0%"; - } else { - this.brush.style.borderRadius = "50%"; - this.brush.style.MozBorderRadius = "50%"; - this.brush.style.WebkitBorderRadius = "50%"; - } - } - setlayout(imgCanvas, maskCanvas) { - const self2 = this; - self2.pointer_type = "arc"; - var bottom_panel = document.createElement("div"); - bottom_panel.style.position = "absolute"; - bottom_panel.style.bottom = "0px"; - bottom_panel.style.left = "20px"; - bottom_panel.style.right = "20px"; - bottom_panel.style.height = "50px"; - bottom_panel.style.pointerEvents = "none"; - var brush = document.createElement("div"); - brush.id = "brush"; - brush.style.backgroundColor = "transparent"; - brush.style.outline = "1px dashed black"; - brush.style.boxShadow = "0 0 0 1px white"; - brush.style.position = "absolute"; - brush.style.zIndex = "8889"; - brush.style.pointerEvents = "none"; - this.brush = brush; - this.setBrushBorderRadius(self2); - this.element.appendChild(imgCanvas); - this.element.appendChild(maskCanvas); - this.element.appendChild(bottom_panel); - document.body.appendChild(brush); - var clearButton = this.createLeftButton("Clear", () => { - self2.maskCtx.clearRect( - 0, - 0, - self2.maskCanvas.width, - self2.maskCanvas.height - ); - }); - this.brush_size_slider = this.createLeftSlider( - self2, - "Thickness", - (event) => { - self2.brush_size = event.target.value; - self2.updateBrushPreview(self2); - } - ); - this.brush_opacity_slider = this.createOpacitySlider( - self2, - "Opacity", - (event) => { - self2.brush_opacity = event.target.value; - if (self2.brush_color_mode !== "negative") { - self2.maskCanvas.style.opacity = self2.brush_opacity.toString(); - } - } - ); - this.brush_pointer_type_select = this.createPointerTypeSelect(self2); - this.colorButton = this.createLeftButton(this.getColorButtonText(), () => { - if (self2.brush_color_mode === "black") { - self2.brush_color_mode = "white"; - } else if (self2.brush_color_mode === "white") { - self2.brush_color_mode = "negative"; - } else { - self2.brush_color_mode = "black"; - } - self2.updateWhenBrushColorModeChanged(); - }); - var cancelButton = this.createRightButton("Cancel", () => { - document.removeEventListener("keydown", MaskEditorDialogOld.handleKeyDown); - self2.close(); - }); - this.saveButton = this.createRightButton("Save", () => { - document.removeEventListener("keydown", MaskEditorDialogOld.handleKeyDown); - self2.save(); - }); - this.element.appendChild(imgCanvas); - this.element.appendChild(maskCanvas); - this.element.appendChild(bottom_panel); - bottom_panel.appendChild(clearButton); - bottom_panel.appendChild(this.saveButton); - bottom_panel.appendChild(cancelButton); - bottom_panel.appendChild(this.brush_size_slider); - bottom_panel.appendChild(this.brush_opacity_slider); - bottom_panel.appendChild(this.brush_pointer_type_select); - bottom_panel.appendChild(this.colorButton); - imgCanvas.style.position = "absolute"; - maskCanvas.style.position = "absolute"; - imgCanvas.style.top = "200"; - imgCanvas.style.left = "0"; - maskCanvas.style.top = imgCanvas.style.top; - maskCanvas.style.left = imgCanvas.style.left; - const maskCanvasStyle = this.getMaskCanvasStyle(); - maskCanvas.style.mixBlendMode = maskCanvasStyle.mixBlendMode; - maskCanvas.style.opacity = maskCanvasStyle.opacity.toString(); - } - async show() { - this.zoom_ratio = 1; - this.pan_x = 0; - this.pan_y = 0; - if (!this.is_layout_created) { - const imgCanvas = document.createElement("canvas"); - const maskCanvas = document.createElement("canvas"); - imgCanvas.id = "imageCanvas"; - maskCanvas.id = "maskCanvas"; - this.setlayout(imgCanvas, maskCanvas); - this.imgCanvas = imgCanvas; - this.maskCanvas = maskCanvas; - this.maskCtx = maskCanvas.getContext("2d", { willReadFrequently: true }); - this.setEventHandler(maskCanvas); - this.is_layout_created = true; - const self2 = this; - const observer = new MutationObserver(function(mutations) { - mutations.forEach(function(mutation) { - if (mutation.type === "attributes" && mutation.attributeName === "style") { - if (self2.last_display_style && self2.last_display_style != "none" && self2.element.style.display == "none") { - self2.brush.style.display = "none"; - ComfyApp.onClipspaceEditorClosed(); - } - self2.last_display_style = self2.element.style.display; - } - }); - }); - const config = { attributes: true }; - observer.observe(this.element, config); - } - document.addEventListener("keydown", MaskEditorDialogOld.handleKeyDown); - if (ComfyApp.clipspace_return_node) { - this.saveButton.innerText = "Save to node"; - } else { - this.saveButton.innerText = "Save"; - } - this.saveButton.disabled = false; - this.element.style.display = "block"; - this.element.style.width = "85%"; - this.element.style.margin = "0 7.5%"; - this.element.style.height = "100vh"; - this.element.style.top = "50%"; - this.element.style.left = "42%"; - this.element.style.zIndex = "8888"; - await this.setImages(this.imgCanvas); - this.is_visible = true; - } - isOpened() { - return this.element.style.display == "block"; - } - invalidateCanvas(orig_image, mask_image) { - this.imgCanvas.width = orig_image.width; - this.imgCanvas.height = orig_image.height; - this.maskCanvas.width = orig_image.width; - this.maskCanvas.height = orig_image.height; - let imgCtx = this.imgCanvas.getContext("2d", { willReadFrequently: true }); - let maskCtx = this.maskCanvas.getContext("2d", { - willReadFrequently: true - }); - imgCtx.drawImage(orig_image, 0, 0, orig_image.width, orig_image.height); - prepare_mask(mask_image, this.maskCanvas, maskCtx, this.getMaskColor()); - } - async setImages(imgCanvas) { - let self2 = this; - const imgCtx = imgCanvas.getContext("2d", { willReadFrequently: true }); - const maskCtx = this.maskCtx; - const maskCanvas = this.maskCanvas; - imgCtx.clearRect(0, 0, this.imgCanvas.width, this.imgCanvas.height); - maskCtx.clearRect(0, 0, this.maskCanvas.width, this.maskCanvas.height); - const filepath = ComfyApp.clipspace.images; - const alpha_url = new URL( - ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]].src - ); - alpha_url.searchParams.delete("channel"); - alpha_url.searchParams.delete("preview"); - alpha_url.searchParams.set("channel", "a"); - let mask_image = await loadImage(alpha_url); - const rgb_url = new URL( - ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]].src - ); - rgb_url.searchParams.delete("channel"); - rgb_url.searchParams.set("channel", "rgb"); - this.image = new Image(); - this.image.onload = function() { - maskCanvas.width = self2.image.width; - maskCanvas.height = self2.image.height; - self2.invalidateCanvas(self2.image, mask_image); - self2.initializeCanvasPanZoom(); - }; - this.image.src = rgb_url.toString(); - } - initializeCanvasPanZoom() { - let drawWidth = this.image.width; - let drawHeight = this.image.height; - let width = this.element.clientWidth; - let height = this.element.clientHeight; - if (this.image.width > width) { - drawWidth = width; - drawHeight = drawWidth / this.image.width * this.image.height; - } - if (drawHeight > height) { - drawHeight = height; - drawWidth = drawHeight / this.image.height * this.image.width; - } - this.zoom_ratio = drawWidth / this.image.width; - const canvasX = (width - drawWidth) / 2; - const canvasY = (height - drawHeight) / 2; - this.pan_x = canvasX; - this.pan_y = canvasY; - this.invalidatePanZoom(); - } - invalidatePanZoom() { - let raw_width = this.image.width * this.zoom_ratio; - let raw_height = this.image.height * this.zoom_ratio; - if (this.pan_x + raw_width < 10) { - this.pan_x = 10 - raw_width; - } - if (this.pan_y + raw_height < 10) { - this.pan_y = 10 - raw_height; - } - let width = `${raw_width}px`; - let height = `${raw_height}px`; - let left = `${this.pan_x}px`; - let top = `${this.pan_y}px`; - this.maskCanvas.style.width = width; - this.maskCanvas.style.height = height; - this.maskCanvas.style.left = left; - this.maskCanvas.style.top = top; - this.imgCanvas.style.width = width; - this.imgCanvas.style.height = height; - this.imgCanvas.style.left = left; - this.imgCanvas.style.top = top; - } - setEventHandler(maskCanvas) { - const self2 = this; - if (!this.handler_registered) { - maskCanvas.addEventListener("contextmenu", (event) => { - event.preventDefault(); - }); - this.element.addEventListener( - "wheel", - (event) => this.handleWheelEvent(self2, event) - ); - this.element.addEventListener( - "pointermove", - (event) => this.pointMoveEvent(self2, event) - ); - this.element.addEventListener( - "touchmove", - (event) => this.pointMoveEvent(self2, event) - ); - this.element.addEventListener("dragstart", (event) => { - if (event.ctrlKey) { - event.preventDefault(); - } - }); - maskCanvas.addEventListener( - "pointerdown", - (event) => this.handlePointerDown(self2, event) - ); - maskCanvas.addEventListener( - "pointermove", - (event) => this.draw_move(self2, event) - ); - maskCanvas.addEventListener( - "touchmove", - (event) => this.draw_move(self2, event) - ); - maskCanvas.addEventListener("pointerover", (event) => { - this.brush.style.display = "block"; - }); - maskCanvas.addEventListener("pointerleave", (event) => { - this.brush.style.display = "none"; - }); - document.addEventListener( - "pointerup", - MaskEditorDialogOld.handlePointerUp - ); - this.handler_registered = true; - } - } - getMaskCanvasStyle() { - if (this.brush_color_mode === "negative") { - return { - mixBlendMode: "difference", - opacity: "1" - }; - } else { - return { - mixBlendMode: "initial", - opacity: this.brush_opacity - }; - } - } - getMaskColor() { - if (this.brush_color_mode === "black") { - return { r: 0, g: 0, b: 0 }; - } - if (this.brush_color_mode === "white") { - return { r: 255, g: 255, b: 255 }; - } - if (this.brush_color_mode === "negative") { - return { r: 255, g: 255, b: 255 }; - } - return { r: 0, g: 0, b: 0 }; - } - getMaskFillStyle() { - const maskColor = this.getMaskColor(); - return "rgb(" + maskColor.r + "," + maskColor.g + "," + maskColor.b + ")"; - } - getColorButtonText() { - let colorCaption = "unknown"; - if (this.brush_color_mode === "black") { - colorCaption = "black"; - } else if (this.brush_color_mode === "white") { - colorCaption = "white"; - } else if (this.brush_color_mode === "negative") { - colorCaption = "negative"; - } - return "Color: " + colorCaption; - } - updateWhenBrushColorModeChanged() { - this.colorButton.innerText = this.getColorButtonText(); - const maskCanvasStyle = this.getMaskCanvasStyle(); - this.maskCanvas.style.mixBlendMode = maskCanvasStyle.mixBlendMode; - this.maskCanvas.style.opacity = maskCanvasStyle.opacity.toString(); - const maskColor = this.getMaskColor(); - const maskData = this.maskCtx.getImageData( - 0, - 0, - this.maskCanvas.width, - this.maskCanvas.height - ); - for (let i = 0; i < maskData.data.length; i += 4) { - maskData.data[i] = maskColor.r; - maskData.data[i + 1] = maskColor.g; - maskData.data[i + 2] = maskColor.b; - } - this.maskCtx.putImageData(maskData, 0, 0); - } - brush_opacity = 0.7; - brush_size = 10; - brush_color_mode = "black"; - drawing_mode = false; - lastx = -1; - lasty = -1; - lasttime = 0; - static handleKeyDown(event) { - const self2 = MaskEditorDialogOld.instance; - if (event.key === "]") { - self2.brush_size = Math.min(self2.brush_size + 2, 100); - self2.brush_slider_input.value = self2.brush_size; - } else if (event.key === "[") { - self2.brush_size = Math.max(self2.brush_size - 2, 1); - self2.brush_slider_input.value = self2.brush_size; - } else if (event.key === "Enter") { - self2.save(); - } - self2.updateBrushPreview(self2); - } - static handlePointerUp(event) { - event.preventDefault(); - this.mousedown_x = null; - this.mousedown_y = null; - MaskEditorDialogOld.instance.drawing_mode = false; - } - updateBrushPreview(self2) { - const brush = self2.brush; - var centerX = self2.cursorX; - var centerY = self2.cursorY; - brush.style.width = self2.brush_size * 2 * this.zoom_ratio + "px"; - brush.style.height = self2.brush_size * 2 * this.zoom_ratio + "px"; - brush.style.left = centerX - self2.brush_size * this.zoom_ratio + "px"; - brush.style.top = centerY - self2.brush_size * this.zoom_ratio + "px"; - } - handleWheelEvent(self2, event) { - event.preventDefault(); - if (event.ctrlKey) { - if (event.deltaY < 0) { - this.zoom_ratio = Math.min(10, this.zoom_ratio + 0.2); - } else { - this.zoom_ratio = Math.max(0.2, this.zoom_ratio - 0.2); - } - this.invalidatePanZoom(); - } else { - if (event.deltaY < 0) this.brush_size = Math.min(this.brush_size + 2, 100); - else this.brush_size = Math.max(this.brush_size - 2, 1); - this.brush_slider_input.value = this.brush_size.toString(); - this.updateBrushPreview(this); - } - } - pointMoveEvent(self2, event) { - this.cursorX = event.pageX; - this.cursorY = event.pageY; - self2.updateBrushPreview(self2); - if (event.ctrlKey) { - event.preventDefault(); - self2.pan_move(self2, event); - } - let left_button_down = window.TouchEvent && event instanceof TouchEvent || event.buttons == 1; - if (event.shiftKey && left_button_down) { - self2.drawing_mode = false; - const y = event.clientY; - let delta = (self2.zoom_lasty - y) * 5e-3; - self2.zoom_ratio = Math.max( - Math.min(10, self2.last_zoom_ratio - delta), - 0.2 - ); - this.invalidatePanZoom(); - return; - } - } - pan_move(self2, event) { - if (event.buttons == 1) { - if (MaskEditorDialogOld.mousedown_x) { - let deltaX = MaskEditorDialogOld.mousedown_x - event.clientX; - let deltaY = MaskEditorDialogOld.mousedown_y - event.clientY; - self2.pan_x = this.mousedown_pan_x - deltaX; - self2.pan_y = this.mousedown_pan_y - deltaY; - self2.invalidatePanZoom(); - } - } - } - draw_move(self2, event) { - if (event.ctrlKey || event.shiftKey) { - return; - } - event.preventDefault(); - this.cursorX = event.pageX; - this.cursorY = event.pageY; - self2.updateBrushPreview(self2); - let left_button_down = window.TouchEvent && event instanceof TouchEvent || event.buttons == 1; - let right_button_down = [2, 5, 32].includes(event.buttons); - if (!event.altKey && left_button_down) { - var diff = performance.now() - self2.lasttime; - const maskRect = self2.maskCanvas.getBoundingClientRect(); - var x = event.offsetX; - var y = event.offsetY; - if (event.offsetX == null) { - x = event.targetTouches[0].clientX - maskRect.left; - } - if (event.offsetY == null) { - y = event.targetTouches[0].clientY - maskRect.top; - } - x /= self2.zoom_ratio; - y /= self2.zoom_ratio; - var brush_size = this.brush_size; - if (event instanceof PointerEvent && event.pointerType == "pen") { - brush_size *= event.pressure; - this.last_pressure = event.pressure; - } else if (window.TouchEvent && event instanceof TouchEvent && diff < 20) { - brush_size *= this.last_pressure; - } else { - brush_size = this.brush_size; - } - if (diff > 20 && !this.drawing_mode) - requestAnimationFrame(() => { - self2.init_shape( - self2, - "source-over" - /* SourceOver */ - ); - self2.draw_shape(self2, x, y, brush_size); - self2.lastx = x; - self2.lasty = y; - }); - else - requestAnimationFrame(() => { - self2.init_shape( - self2, - "source-over" - /* SourceOver */ - ); - var dx = x - self2.lastx; - var dy = y - self2.lasty; - var distance = Math.sqrt(dx * dx + dy * dy); - var directionX = dx / distance; - var directionY = dy / distance; - for (var i = 0; i < distance; i += 5) { - var px2 = self2.lastx + directionX * i; - var py2 = self2.lasty + directionY * i; - self2.draw_shape(self2, px2, py2, brush_size); - } - self2.lastx = x; - self2.lasty = y; - }); - self2.lasttime = performance.now(); - } else if (event.altKey && left_button_down || right_button_down) { - const maskRect = self2.maskCanvas.getBoundingClientRect(); - const x2 = (event.offsetX || event.targetTouches[0].clientX - maskRect.left) / self2.zoom_ratio; - const y2 = (event.offsetY || event.targetTouches[0].clientY - maskRect.top) / self2.zoom_ratio; - var brush_size = this.brush_size; - if (event instanceof PointerEvent && event.pointerType == "pen") { - brush_size *= event.pressure; - this.last_pressure = event.pressure; - } else if (window.TouchEvent && event instanceof TouchEvent && diff < 20) { - brush_size *= this.last_pressure; - } else { - brush_size = this.brush_size; - } - if (diff > 20 && !this.drawing_mode) - requestAnimationFrame(() => { - self2.init_shape( - self2, - "destination-out" - /* DestinationOut */ - ); - self2.draw_shape(self2, x2, y2, brush_size); - self2.lastx = x2; - self2.lasty = y2; - }); - else - requestAnimationFrame(() => { - self2.init_shape( - self2, - "destination-out" - /* DestinationOut */ - ); - var dx = x2 - self2.lastx; - var dy = y2 - self2.lasty; - var distance = Math.sqrt(dx * dx + dy * dy); - var directionX = dx / distance; - var directionY = dy / distance; - for (var i = 0; i < distance; i += 5) { - var px2 = self2.lastx + directionX * i; - var py2 = self2.lasty + directionY * i; - self2.draw_shape(self2, px2, py2, brush_size); - } - self2.lastx = x2; - self2.lasty = y2; - }); - self2.lasttime = performance.now(); - } - } - handlePointerDown(self2, event) { - if (event.ctrlKey) { - if (event.buttons == 1) { - MaskEditorDialogOld.mousedown_x = event.clientX; - MaskEditorDialogOld.mousedown_y = event.clientY; - this.mousedown_pan_x = this.pan_x; - this.mousedown_pan_y = this.pan_y; - } - return; - } - var brush_size = this.brush_size; - if (event instanceof PointerEvent && event.pointerType == "pen") { - brush_size *= event.pressure; - this.last_pressure = event.pressure; - } - if ([0, 2, 5].includes(event.button)) { - self2.drawing_mode = true; - event.preventDefault(); - if (event.shiftKey) { - self2.zoom_lasty = event.clientY; - self2.last_zoom_ratio = self2.zoom_ratio; - return; - } - const maskRect = self2.maskCanvas.getBoundingClientRect(); - const x = (event.offsetX || event.targetTouches[0].clientX - maskRect.left) / self2.zoom_ratio; - const y = (event.offsetY || event.targetTouches[0].clientY - maskRect.top) / self2.zoom_ratio; - if (!event.altKey && event.button == 0) { - self2.init_shape( - self2, - "source-over" - /* SourceOver */ - ); - } else { - self2.init_shape( - self2, - "destination-out" - /* DestinationOut */ - ); - } - self2.draw_shape(self2, x, y, brush_size); - self2.lastx = x; - self2.lasty = y; - self2.lasttime = performance.now(); - } - } - init_shape(self2, compositionOperation) { - self2.maskCtx.beginPath(); - if (compositionOperation == "source-over") { - self2.maskCtx.fillStyle = this.getMaskFillStyle(); - self2.maskCtx.globalCompositeOperation = "source-over"; - } else if (compositionOperation == "destination-out") { - self2.maskCtx.globalCompositeOperation = "destination-out"; - } - } - draw_shape(self2, x, y, brush_size) { - if (self2.pointer_type === "rect") { - self2.maskCtx.rect( - x - brush_size, - y - brush_size, - brush_size * 2, - brush_size * 2 - ); - } else { - self2.maskCtx.arc(x, y, brush_size, 0, Math.PI * 2, false); - } - self2.maskCtx.fill(); - } - async save() { - const backupCanvas = document.createElement("canvas"); - const backupCtx = backupCanvas.getContext("2d", { - willReadFrequently: true - }); - backupCanvas.width = this.image.width; - backupCanvas.height = this.image.height; - backupCtx.clearRect(0, 0, backupCanvas.width, backupCanvas.height); - backupCtx.drawImage( - this.maskCanvas, - 0, - 0, - this.maskCanvas.width, - this.maskCanvas.height, - 0, - 0, - backupCanvas.width, - backupCanvas.height - ); - const backupData = backupCtx.getImageData( - 0, - 0, - backupCanvas.width, - backupCanvas.height - ); - for (let i = 0; i < backupData.data.length; i += 4) { - if (backupData.data[i + 3] == 255) backupData.data[i + 3] = 0; - else backupData.data[i + 3] = 255; - backupData.data[i] = 0; - backupData.data[i + 1] = 0; - backupData.data[i + 2] = 0; - } - backupCtx.globalCompositeOperation = "source-over"; - backupCtx.putImageData(backupData, 0, 0); - const formData = new FormData(); - const filename = "clipspace-mask-" + performance.now() + ".png"; - const item = { - filename, - subfolder: "clipspace", - type: "input" - }; - if (ComfyApp.clipspace.images) ComfyApp.clipspace.images[0] = item; - if (ComfyApp.clipspace.widgets) { - const index = ComfyApp.clipspace.widgets.findIndex( - (obj) => obj.name === "image" - ); - if (index >= 0) ComfyApp.clipspace.widgets[index].value = item; - } - const dataURL = backupCanvas.toDataURL(); - const blob = dataURLToBlob(dataURL); - let original_url = new URL(this.image.src); - const original_ref = { - filename: original_url.searchParams.get("filename") - }; - let original_subfolder = original_url.searchParams.get("subfolder"); - if (original_subfolder) original_ref.subfolder = original_subfolder; - let original_type = original_url.searchParams.get("type"); - if (original_type) original_ref.type = original_type; - formData.append("image", blob, filename); - formData.append("original_ref", JSON.stringify(original_ref)); - formData.append("type", "input"); - formData.append("subfolder", "clipspace"); - this.saveButton.innerText = "Saving..."; - this.saveButton.disabled = true; - await uploadMask(item, formData); - ComfyApp.onClipspaceEditorSave(); - this.close(); - } -} -window.comfyAPI = window.comfyAPI || {}; -window.comfyAPI.maskEditorOld = window.comfyAPI.maskEditorOld || {}; -window.comfyAPI.maskEditorOld.MaskEditorDialogOld = MaskEditorDialogOld; -var styles = ` - #maskEditorContainer { - display: fixed; - } - #maskEditor_brush { - position: absolute; - backgroundColor: transparent; - z-index: 8889; - pointer-events: none; - border-radius: 50%; - overflow: visible; - outline: 1px dashed black; - box-shadow: 0 0 0 1px white; - } - #maskEditor_brushPreviewGradient { - position: absolute; - width: 100%; - height: 100%; - border-radius: 50%; - display: none; - } - #maskEditor { - display: block; - width: 100%; - height: 100vh; - left: 0; - z-index: 8888; - position: fixed; - background: rgba(50,50,50,0.75); - backdrop-filter: blur(10px); - overflow: hidden; - user-select: none; - } - #maskEditor_sidePanelContainer { - height: 100%; - width: 220px; - z-index: 8888; - display: flex; - flex-direction: column; - } - #maskEditor_sidePanel { - background: var(--comfy-menu-bg); - height: 100%; - display: flex; - align-items: center; - overflow-y: hidden; - width: 220px; - } - #maskEditor_sidePanelShortcuts { - display: flex; - flex-direction: row; - width: 200px; - margin-top: 10px; - gap: 10px; - justify-content: center; - } - .maskEditor_sidePanelIconButton { - width: 40px; - height: 40px; - pointer-events: auto; - display: flex; - justify-content: center; - align-items: center; - transition: background-color 0.1s; - } - .maskEditor_sidePanelIconButton:hover { - background-color: rgba(0, 0, 0, 0.2); - } - #maskEditor_sidePanelBrushSettings { - display: flex; - flex-direction: column; - gap: 10px; - width: 200px; - padding: 10px; - } - .maskEditor_sidePanelTitle { - text-align: center; - font-size: 15px; - font-family: sans-serif; - color: var(--descrip-text); - margin-top: 10px; - } - #maskEditor_sidePanelBrushShapeContainer { - display: flex; - width: 180px; - height: 50px; - border: 1px solid var(--border-color); - pointer-events: auto; - background: rgba(0, 0, 0, 0.2); - } - #maskEditor_sidePanelBrushShapeCircle { - width: 35px; - height: 35px; - border-radius: 50%; - border: 1px solid var(--border-color); - pointer-events: auto; - transition: background 0.1s; - margin-left: 7.5px; - } - .maskEditor_sidePanelBrushRange { - width: 180px; - -webkit-appearance: none; - appearance: none; - background: transparent; - cursor: pointer; - } - .maskEditor_sidePanelBrushRange::-webkit-slider-thumb { - height: 20px; - width: 20px; - border-radius: 50%; - cursor: grab; - margin-top: -8px; - background: var(--p-surface-700); - border: 1px solid var(--border-color); - } - .maskEditor_sidePanelBrushRange::-moz-range-thumb { - height: 20px; - width: 20px; - border-radius: 50%; - cursor: grab; - background: var(--p-surface-800); - border: 1px solid var(--border-color); - } - .maskEditor_sidePanelBrushRange::-webkit-slider-runnable-track { - background: var(--p-surface-700); - height: 3px; - } - .maskEditor_sidePanelBrushRange::-moz-range-track { - background: var(--p-surface-700); - height: 3px; - } - - #maskEditor_sidePanelBrushShapeSquare { - width: 35px; - height: 35px; - margin: 5px; - border: 1px solid var(--border-color); - pointer-events: auto; - transition: background 0.1s; - } - - .maskEditor_brushShape_dark { - background: transparent; - } - - .maskEditor_brushShape_dark:hover { - background: var(--p-surface-900); - } - - .maskEditor_brushShape_light { - background: transparent; - } - - .maskEditor_brushShape_light:hover { - background: var(--comfy-menu-bg); - } - - #maskEditor_sidePanelImageLayerSettings { - display: flex; - flex-direction: column; - gap: 10px; - width: 200px; - align-items: center; - } - .maskEditor_sidePanelLayer { - display: flex; - width: 200px; - height: 50px; - } - .maskEditor_sidePanelLayerVisibilityContainer { - width: 50px; - height: 50px; - border-radius: 8px; - display: flex; - justify-content: center; - align-items: center; - } - .maskEditor_sidePanelVisibilityToggle { - width: 12px; - height: 12px; - border-radius: 50%; - pointer-events: auto; - } - .maskEditor_sidePanelLayerIconContainer { - width: 60px; - height: 50px; - border-radius: 8px; - display: flex; - justify-content: center; - align-items: center; - fill: var(--input-text); - } - .maskEditor_sidePanelLayerIconContainer svg { - width: 30px; - height: 30px; - } - #maskEditor_sidePanelMaskLayerBlendingContainer { - width: 80px; - height: 50px; - border-radius: 8px; - display: flex; - justify-content: center; - align-items: center; - } - #maskEditor_sidePanelMaskLayerBlendingSelect { - width: 80px; - height: 30px; - border: 1px solid var(--border-color); - background-color: rgba(0, 0, 0, 0.2); - color: var(--input-text); - font-family: sans-serif; - font-size: 15px; - pointer-events: auto; - transition: background-color border 0.1s; - } - #maskEditor_sidePanelClearCanvasButton:hover { - background-color: var(--p-overlaybadge-outline-color); - border: none; - } - #maskEditor_sidePanelClearCanvasButton { - width: 180px; - height: 30px; - border: none; - background: rgba(0, 0, 0, 0.2); - border: 1px solid var(--border-color); - color: var(--input-text); - font-family: sans-serif; - font-size: 15px; - pointer-events: auto; - transition: background-color 0.1s; - } - #maskEditor_sidePanelClearCanvasButton:hover { - background-color: var(--p-overlaybadge-outline-color); - } - #maskEditor_sidePanelHorizontalButtonContainer { - display: flex; - gap: 10px; - height: 40px; - } - .maskEditor_sidePanelBigButton { - width: 85px; - height: 30px; - border: none; - background: rgba(0, 0, 0, 0.2); - border: 1px solid var(--border-color); - color: var(--input-text); - font-family: sans-serif; - font-size: 15px; - pointer-events: auto; - transition: background-color border 0.1s; - } - .maskEditor_sidePanelBigButton:hover { - background-color: var(--p-overlaybadge-outline-color); - border: none; - } - #maskEditor_toolPanel { - height: 100%; - width: var(--sidebar-width); - z-index: 8888; - background: var(--comfy-menu-bg); - display: flex; - flex-direction: column; - } - .maskEditor_toolPanelContainer { - width: var(--sidebar-width); - height: var(--sidebar-width); - display: flex; - justify-content: center; - align-items: center; - position: relative; - transition: background-color 0.2s; - } - .maskEditor_toolPanelContainerSelected svg { - fill: var(--p-button-text-primary-color) !important; - } - .maskEditor_toolPanelContainerSelected .maskEditor_toolPanelIndicator { - display: block; - } - .maskEditor_toolPanelContainer svg { - width: 75%; - aspect-ratio: 1/1; - fill: var(--p-button-text-secondary-color); - } - - .maskEditor_toolPanelContainerDark:hover { - background-color: var(--p-surface-800); - } - - .maskEditor_toolPanelContainerLight:hover { - background-color: var(--p-surface-300); - } - - .maskEditor_toolPanelIndicator { - display: none; - height: 100%; - width: 4px; - position: absolute; - left: 0; - background: var(--p-button-text-primary-color); - } - #maskEditor_sidePanelPaintBucketSettings { - display: flex; - flex-direction: column; - gap: 10px; - width: 200px; - padding: 10px; - } - #canvasBackground { - background: white; - width: 100%; - height: 100%; - } - #maskEditor_sidePanelButtonsContainer { - display: flex; - flex-direction: column; - gap: 10px; - margin-top: 10px; - } - .maskEditor_sidePanelSeparator { - width: 200px; - height: 2px; - background: var(--border-color); - margin-top: 5px; - margin-bottom: 5px; - } - #maskEditor_pointerZone { - width: calc(100% - var(--sidebar-width) - 220px); - height: 100%; - } - #maskEditor_uiContainer { - width: 100%; - height: 100%; - position: absolute; - z-index: 8888; - display: flex; - flex-direction: column; - } - #maskEditorCanvasContainer { - position: absolute; - width: 1000px; - height: 667px; - left: 359px; - top: 280px; - } - #imageCanvas { - width: 100%; - height: 100%; - } - #maskCanvas { - width: 100%; - height: 100%; - } - #maskEditor_uiHorizontalContainer { - width: 100%; - height: 100%; - display: flex; - } - #maskEditor_topBar { - display: flex; - height: 44px; - align-items: center; - background: var(--comfy-menu-bg); - } - #maskEditor_topBarTitle { - margin: 0; - margin-left: 0.5rem; - margin-right: 0.5rem; - font-size: 1.2em; - } - #maskEditor_topBarButtonContainer { - display: flex; - gap: 10px; - margin-right: 0.5rem; - position: absolute; - right: 0; - width: 200px; - } - #maskEditor_topBarShortcutsContainer { - display: flex; - gap: 10px; - margin-left: 5px; - } - - .maskEditor_topPanelIconButton_dark { - width: 50px; - height: 30px; - pointer-events: auto; - display: flex; - justify-content: center; - align-items: center; - transition: background-color 0.1s; - background: var(--p-surface-800); - border: 1px solid var(--p-form-field-border-color); - border-radius: 10px; - } - - .maskEditor_topPanelIconButton_dark:hover { - background-color: var(--p-surface-900); - } - - .maskEditor_topPanelIconButton_dark svg { - width: 25px; - height: 25px; - pointer-events: none; - fill: var(--input-text); - } - - .maskEditor_topPanelIconButton_light { - width: 50px; - height: 30px; - pointer-events: auto; - display: flex; - justify-content: center; - align-items: center; - transition: background-color 0.1s; - background: var(--comfy-menu-bg); - border: 1px solid var(--p-form-field-border-color); - border-radius: 10px; - } - - .maskEditor_topPanelIconButton_light:hover { - background-color: var(--p-surface-300); - } - - .maskEditor_topPanelIconButton_light svg { - width: 25px; - height: 25px; - pointer-events: none; - fill: var(--input-text); - } - - .maskEditor_topPanelButton_dark { - height: 30px; - background: var(--p-surface-800); - border: 1px solid var(--p-form-field-border-color); - border-radius: 10px; - color: var(--input-text); - font-family: sans-serif; - pointer-events: auto; - transition: 0.1s; - width: 60px; - } - - .maskEditor_topPanelButton_dark:hover { - background-color: var(--p-surface-900); - } - - .maskEditor_topPanelButton_light { - height: 30px; - background: var(--comfy-menu-bg); - border: 1px solid var(--p-form-field-border-color); - border-radius: 10px; - color: var(--input-text); - font-family: sans-serif; - pointer-events: auto; - transition: 0.1s; - width: 60px; - } - - .maskEditor_topPanelButton_light:hover { - background-color: var(--p-surface-300); - } - - - #maskEditor_sidePanelColorSelectSettings { - flex-direction: column; - } - - .maskEditor_sidePanel_paintBucket_Container { - width: 180px; - display: flex; - flex-direction: column; - position: relative; - } - - .maskEditor_sidePanel_colorSelect_Container { - display: flex; - width: 180px; - align-items: center; - gap: 5px; - height: 30px; - } - - #maskEditor_sidePanelVisibilityToggle { - position: absolute; - right: 0; - } - - #maskEditor_sidePanelColorSelectMethodSelect { - position: absolute; - right: 0; - height: 30px; - border-radius: 0; - border: 1px solid var(--border-color); - background: rgba(0,0,0,0.2); - } - - #maskEditor_sidePanelVisibilityToggle { - position: absolute; - right: 0; - } - - .maskEditor_sidePanel_colorSelect_tolerance_container { - display: flex; - flex-direction: column; - gap: 10px; - margin-bottom: 10px; - } - - .maskEditor_sidePanelContainerColumn { - display: flex; - flex-direction: column; - gap: 12px; - } - - .maskEditor_sidePanelContainerRow { - display: flex; - flex-direction: row; - gap: 10px; - align-items: center; - min-height: 24px; - position: relative; - } - - .maskEditor_accent_bg_dark { - background: var(--p-surface-800); - } - - .maskEditor_accent_bg_very_dark { - background: var(--p-surface-900); - } - - .maskEditor_accent_bg_light { - background: var(--p-surface-300); - } - - .maskEditor_accent_bg_very_light { - background: var(--comfy-menu-bg); - } - - #maskEditor_paintBucketSettings { - display: none; - } - - #maskEditor_colorSelectSettings { - display: none; - } - - .maskEditor_sidePanelToggleContainer { - cursor: pointer; - display: inline-block; - position: absolute; - right: 0; - } - - .maskEditor_toggle_bg_dark { - background: var(--p-surface-700); - } - - .maskEditor_toggle_bg_light { - background: var(--p-surface-300); - } - - .maskEditor_sidePanelToggleSwitch { - display: inline-block; - border-radius: 16px; - width: 40px; - height: 24px; - position: relative; - vertical-align: middle; - transition: background 0.25s; - } - .maskEditor_sidePanelToggleSwitch:before, .maskEditor_sidePanelToggleSwitch:after { - content: ""; - } - .maskEditor_sidePanelToggleSwitch:before { - display: block; - background: linear-gradient(to bottom, #fff 0%, #eee 100%); - border-radius: 50%; - width: 16px; - height: 16px; - position: absolute; - top: 4px; - left: 4px; - transition: ease 0.2s; - } - .maskEditor_sidePanelToggleContainer:hover .maskEditor_sidePanelToggleSwitch:before { - background: linear-gradient(to bottom, #fff 0%, #fff 100%); - } - .maskEditor_sidePanelToggleCheckbox:checked + .maskEditor_sidePanelToggleSwitch { - background: var(--p-button-text-primary-color); - } - .maskEditor_sidePanelToggleCheckbox:checked + .maskEditor_toggle_bg_dark:before { - background: var(--p-surface-900); - } - .maskEditor_sidePanelToggleCheckbox:checked + .maskEditor_toggle_bg_light:before { - background: var(--comfy-menu-bg); - } - .maskEditor_sidePanelToggleCheckbox:checked + .maskEditor_sidePanelToggleSwitch:before { - left: 20px; - } - - .maskEditor_sidePanelToggleCheckbox { - position: absolute; - visibility: hidden; - } - - .maskEditor_sidePanelDropdown_dark { - border: 1px solid var(--p-form-field-border-color); - background: var(--p-surface-900); - height: 24px; - padding-left: 5px; - padding-right: 5px; - border-radius: 6px; - transition: background 0.1s; - } - - .maskEditor_sidePanelDropdown_dark option { - background: var(--p-surface-900); - } - - .maskEditor_sidePanelDropdown_dark:focus { - outline: 1px solid var(--p-button-text-primary-color); - } - - .maskEditor_sidePanelDropdown_dark option:hover { - background: white; - } - .maskEditor_sidePanelDropdown_dark option:active { - background: var(--p-highlight-background); - } - - .maskEditor_sidePanelDropdown_light { - border: 1px solid var(--p-form-field-border-color); - background: var(--comfy-menu-bg); - height: 24px; - padding-left: 5px; - padding-right: 5px; - border-radius: 6px; - transition: background 0.1s; - } - - .maskEditor_sidePanelDropdown_light option { - background: var(--comfy-menu-bg); - } - - .maskEditor_sidePanelDropdown_light:focus { - outline: 1px solid var(--p-surface-300); - } - - .maskEditor_sidePanelDropdown_light option:hover { - background: white; - } - .maskEditor_sidePanelDropdown_light option:active { - background: var(--p-surface-300); - } - - .maskEditor_layerRow { - height: 50px; - width: 200px; - border-radius: 10px; - } - - .maskEditor_sidePanelLayerPreviewContainer { - width: 40px; - height: 30px; - } - - .maskEditor_sidePanelLayerPreviewContainer > svg{ - width: 100%; - height: 100%; - object-fit: contain; - fill: var(--p-surface-100); - } - - #maskEditor_sidePanelImageLayerImage { - width: 100%; - height: 100%; - object-fit: contain; - } - - .maskEditor_sidePanelSubTitle { - text-align: left; - font-size: 12px; - font-family: sans-serif; - color: var(--descrip-text); - } - - .maskEditor_containerDropdown { - position: absolute; - right: 0; - } - - .maskEditor_sidePanelLayerCheckbox { - margin-left: 15px; - } - - .maskEditor_toolPanelZoomIndicator { - width: var(--sidebar-width); - height: var(--sidebar-width); - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; - gap: 5px; - color: var(--p-button-text-secondary-color); - position: absolute; - bottom: 0; - transition: background-color 0.2s; - } - - #maskEditor_toolPanelDimensionsText { - font-size: 12px; - } - - #maskEditor_topBarSaveButton { - background: var(--p-primary-color) !important; - color: var(--p-button-primary-color) !important; - } - - #maskEditor_topBarSaveButton:hover { - background: var(--p-primary-hover-color) !important; - } - -`; -var styleSheet = document.createElement("style"); -styleSheet.type = "text/css"; -styleSheet.innerText = styles; -document.head.appendChild(styleSheet); -var BrushShape = /* @__PURE__ */ ((BrushShape2) => { - BrushShape2["Arc"] = "arc"; - BrushShape2["Rect"] = "rect"; - return BrushShape2; -})(BrushShape || {}); -var Tools = /* @__PURE__ */ ((Tools2) => { - Tools2["Pen"] = "pen"; - Tools2["Eraser"] = "eraser"; - Tools2["PaintBucket"] = "paintBucket"; - Tools2["ColorSelect"] = "colorSelect"; - return Tools2; -})(Tools || {}); -var CompositionOperation = /* @__PURE__ */ ((CompositionOperation2) => { - CompositionOperation2["SourceOver"] = "source-over"; - CompositionOperation2["DestinationOut"] = "destination-out"; - return CompositionOperation2; -})(CompositionOperation || {}); -var MaskBlendMode = /* @__PURE__ */ ((MaskBlendMode2) => { - MaskBlendMode2["Black"] = "black"; - MaskBlendMode2["White"] = "white"; - MaskBlendMode2["Negative"] = "negative"; - return MaskBlendMode2; -})(MaskBlendMode || {}); -var ColorComparisonMethod = /* @__PURE__ */ ((ColorComparisonMethod2) => { - ColorComparisonMethod2["Simple"] = "simple"; - ColorComparisonMethod2["HSL"] = "hsl"; - ColorComparisonMethod2["LAB"] = "lab"; - return ColorComparisonMethod2; -})(ColorComparisonMethod || {}); -class MaskEditorDialog extends ComfyDialog { - static { - __name(this, "MaskEditorDialog"); - } - static instance = null; - //new - uiManager; - toolManager; - panAndZoomManager; - brushTool; - paintBucketTool; - colorSelectTool; - canvasHistory; - messageBroker; - keyboardManager; - rootElement; - imageURL; - isLayoutCreated = false; - isOpen = false; - //variables needed? - last_display_style = null; - constructor() { - super(); - this.rootElement = $el( - "div.maskEditor_hidden", - { parent: document.body }, - [] - ); - this.element = this.rootElement; - } - static getInstance() { - if (!ComfyApp.clipspace || !ComfyApp.clipspace.imgs) { - throw new Error("No clipspace images found"); - } - const currentSrc = ComfyApp.clipspace.imgs[ComfyApp.clipspace["selectedIndex"]].src; - if (!MaskEditorDialog.instance || currentSrc !== MaskEditorDialog.instance.imageURL) { - MaskEditorDialog.instance = new MaskEditorDialog(); - } - return MaskEditorDialog.instance; - } - async show() { - this.cleanup(); - if (!this.isLayoutCreated) { - this.messageBroker = new MessageBroker(); - this.canvasHistory = new CanvasHistory(this, 20); - this.paintBucketTool = new PaintBucketTool(this); - this.brushTool = new BrushTool(this); - this.panAndZoomManager = new PanAndZoomManager(this); - this.toolManager = new ToolManager(this); - this.keyboardManager = new KeyboardManager(this); - this.uiManager = new UIManager(this.rootElement, this); - this.colorSelectTool = new ColorSelectTool(this); - const self2 = this; - const observer = new MutationObserver(function(mutations) { - mutations.forEach(function(mutation) { - if (mutation.type === "attributes" && mutation.attributeName === "style") { - if (self2.last_display_style && self2.last_display_style != "none" && self2.element.style.display == "none") { - ComfyApp.onClipspaceEditorClosed(); - } - self2.last_display_style = self2.element.style.display; - } - }); - }); - const config = { attributes: true }; - observer.observe(this.rootElement, config); - this.isLayoutCreated = true; - await this.uiManager.setlayout(); - } - this.rootElement.id = "maskEditor"; - this.rootElement.style.display = "flex"; - this.element.style.display = "flex"; - await this.uiManager.initUI(); - this.paintBucketTool.initPaintBucketTool(); - this.colorSelectTool.initColorSelectTool(); - await this.canvasHistory.saveInitialState(); - this.isOpen = true; - if (ComfyApp.clipspace && ComfyApp.clipspace.imgs) { - this.uiManager.setSidebarImage(); - } - this.keyboardManager.addListeners(); - } - cleanup() { - const maskEditors = document.querySelectorAll('[id^="maskEditor"]'); - maskEditors.forEach((element) => element.remove()); - const brushElements = document.querySelectorAll("#maskEditor_brush"); - brushElements.forEach((element) => element.remove()); - } - isOpened() { - return this.isOpen; - } - async save() { - const backupCanvas = document.createElement("canvas"); - const imageCanvas = this.uiManager.getImgCanvas(); - const maskCanvas = this.uiManager.getMaskCanvas(); - const image = this.uiManager.getImage(); - const backupCtx = backupCanvas.getContext("2d", { - willReadFrequently: true - }); - backupCanvas.width = imageCanvas.width; - backupCanvas.height = imageCanvas.height; - if (!backupCtx) { - return; - } - const maskImageLoaded = new Promise((resolve, reject) => { - const maskImage = new Image(); - maskImage.src = maskCanvas.toDataURL(); - maskImage.onload = () => { - resolve(); - }; - maskImage.onerror = (error) => { - reject(error); - }; - }); - try { - await maskImageLoaded; - } catch (error) { - console.error("Error loading mask image:", error); - return; - } - backupCtx.clearRect(0, 0, backupCanvas.width, backupCanvas.height); - backupCtx.drawImage( - maskCanvas, - 0, - 0, - maskCanvas.width, - maskCanvas.height, - 0, - 0, - backupCanvas.width, - backupCanvas.height - ); - let maskHasContent = false; - const maskData = backupCtx.getImageData( - 0, - 0, - backupCanvas.width, - backupCanvas.height - ); - for (let i = 0; i < maskData.data.length; i += 4) { - if (maskData.data[i + 3] !== 0) { - maskHasContent = true; - break; - } - } - const backupData = backupCtx.getImageData( - 0, - 0, - backupCanvas.width, - backupCanvas.height - ); - let backupHasContent = false; - for (let i = 0; i < backupData.data.length; i += 4) { - if (backupData.data[i + 3] !== 0) { - backupHasContent = true; - break; - } - } - if (maskHasContent && !backupHasContent) { - console.error("Mask appears to be empty"); - alert("Cannot save empty mask"); - return; - } - for (let i = 0; i < backupData.data.length; i += 4) { - const alpha = backupData.data[i + 3]; - backupData.data[i] = 0; - backupData.data[i + 1] = 0; - backupData.data[i + 2] = 0; - backupData.data[i + 3] = 255 - alpha; - } - backupCtx.globalCompositeOperation = "source-over"; - backupCtx.putImageData(backupData, 0, 0); - const formData = new FormData(); - const filename = "clipspace-mask-" + performance.now() + ".png"; - const item = { - filename, - subfolder: "clipspace", - type: "input" - }; - if (ComfyApp?.clipspace?.widgets?.length) { - const index = ComfyApp.clipspace.widgets.findIndex( - (obj) => obj?.name === "image" - ); - if (index >= 0 && item !== void 0) { - try { - ComfyApp.clipspace.widgets[index].value = item; - } catch (err2) { - console.warn("Failed to set widget value:", err2); - } - } - } - const dataURL = backupCanvas.toDataURL(); - const blob = this.dataURLToBlob(dataURL); - let original_url = new URL(image.src); - this.uiManager.setBrushOpacity(0); - const filenameRef = original_url.searchParams.get("filename"); - if (!filenameRef) { - throw new Error("filename parameter is required"); - } - const original_ref = { - filename: filenameRef - }; - let original_subfolder = original_url.searchParams.get("subfolder"); - if (original_subfolder) original_ref.subfolder = original_subfolder; - let original_type = original_url.searchParams.get("type"); - if (original_type) original_ref.type = original_type; - formData.append("image", blob, filename); - formData.append("original_ref", JSON.stringify(original_ref)); - formData.append("type", "input"); - formData.append("subfolder", "clipspace"); - this.uiManager.setSaveButtonText("Saving"); - this.uiManager.setSaveButtonEnabled(false); - this.keyboardManager.removeListeners(); - const maxRetries = 3; - let attempt = 0; - let success = false; - while (attempt < maxRetries && !success) { - try { - await this.uploadMask(item, formData); - success = true; - } catch (error) { - console.error(`Upload attempt ${attempt + 1} failed:`, error); - attempt++; - if (attempt < maxRetries) { - console.log("Retrying upload..."); - } else { - console.log("Max retries reached. Upload failed."); - } - } - } - if (success) { - ComfyApp.onClipspaceEditorSave(); - this.close(); - this.isOpen = false; - } else { - this.uiManager.setSaveButtonText("Save"); - this.uiManager.setSaveButtonEnabled(true); - this.keyboardManager.addListeners(); - } - } - getMessageBroker() { - return this.messageBroker; - } - // Helper function to convert a data URL to a Blob object - dataURLToBlob(dataURL) { - const parts = dataURL.split(";base64,"); - const contentType = parts[0].split(":")[1]; - const byteString = atob(parts[1]); - const arrayBuffer = new ArrayBuffer(byteString.length); - const uint8Array = new Uint8Array(arrayBuffer); - for (let i = 0; i < byteString.length; i++) { - uint8Array[i] = byteString.charCodeAt(i); - } - return new Blob([arrayBuffer], { type: contentType }); - } - async uploadMask(filepath, formData, retries = 3) { - if (retries <= 0) { - throw new Error("Max retries reached"); - return; - } - await api.fetchApi("/upload/mask", { - method: "POST", - body: formData - }).then((response) => { - if (!response.ok) { - console.log("Failed to upload mask:", response); - this.uploadMask(filepath, formData, 2); - } - }).catch((error) => { - console.error("Error:", error); - }); - try { - const selectedIndex = ComfyApp.clipspace?.selectedIndex; - if (ComfyApp.clipspace?.imgs && selectedIndex !== void 0) { - const newImage = new Image(); - newImage.src = api.apiURL( - "/view?" + new URLSearchParams(filepath).toString() + app.getPreviewFormatParam() + app.getRandParam() - ); - ComfyApp.clipspace.imgs[selectedIndex] = newImage; - if (ComfyApp.clipspace.images) { - ComfyApp.clipspace.images[selectedIndex] = filepath; - } - } - } catch (err2) { - console.warn("Failed to update clipspace image:", err2); - } - ClipspaceDialog.invalidatePreview(); - } -} -class CanvasHistory { - static { - __name(this, "CanvasHistory"); - } - maskEditor; - messageBroker; - canvas; - ctx; - states = []; - currentStateIndex = -1; - maxStates = 20; - initialized = false; - constructor(maskEditor, maxStates = 20) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.maxStates = maxStates; - this.createListeners(); - } - async pullCanvas() { - this.canvas = await this.messageBroker.pull("maskCanvas"); - this.ctx = await this.messageBroker.pull("maskCtx"); - } - createListeners() { - this.messageBroker.subscribe("saveState", () => this.saveState()); - this.messageBroker.subscribe("undo", () => this.undo()); - this.messageBroker.subscribe("redo", () => this.redo()); - } - clearStates() { - this.states = []; - this.currentStateIndex = -1; - this.initialized = false; - } - async saveInitialState() { - await this.pullCanvas(); - if (!this.canvas.width || !this.canvas.height) { - requestAnimationFrame(() => this.saveInitialState()); - return; - } - this.clearStates(); - const state = this.ctx.getImageData( - 0, - 0, - this.canvas.width, - this.canvas.height - ); - this.states.push(state); - this.currentStateIndex = 0; - this.initialized = true; - } - saveState() { - if (!this.initialized || this.currentStateIndex === -1) { - this.saveInitialState(); - return; - } - this.states = this.states.slice(0, this.currentStateIndex + 1); - const state = this.ctx.getImageData( - 0, - 0, - this.canvas.width, - this.canvas.height - ); - this.states.push(state); - this.currentStateIndex++; - if (this.states.length > this.maxStates) { - this.states.shift(); - this.currentStateIndex--; - } - } - undo() { - if (this.states.length > 1 && this.currentStateIndex > 0) { - this.currentStateIndex--; - this.restoreState(this.states[this.currentStateIndex]); - } else { - alert("No more undo states available"); - } - } - redo() { - if (this.states.length > 1 && this.currentStateIndex < this.states.length - 1) { - this.currentStateIndex++; - this.restoreState(this.states[this.currentStateIndex]); - } else { - alert("No more redo states available"); - } - } - restoreState(state) { - if (state && this.initialized) { - this.ctx.putImageData(state, 0, 0); - } - } -} -class PaintBucketTool { - static { - __name(this, "PaintBucketTool"); - } - maskEditor; - messageBroker; - canvas; - ctx; - width = null; - height = null; - imageData = null; - data = null; - tolerance = 5; - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.createListeners(); - this.addPullTopics(); - } - initPaintBucketTool() { - this.pullCanvas(); - } - async pullCanvas() { - this.canvas = await this.messageBroker.pull("maskCanvas"); - this.ctx = await this.messageBroker.pull("maskCtx"); - } - createListeners() { - this.messageBroker.subscribe( - "setPaintBucketTolerance", - (tolerance) => this.setTolerance(tolerance) - ); - this.messageBroker.subscribe( - "paintBucketFill", - (point) => this.floodFill(point) - ); - this.messageBroker.subscribe("invert", () => this.invertMask()); - } - addPullTopics() { - this.messageBroker.createPullTopic( - "getTolerance", - async () => this.tolerance - ); - } - getPixel(x, y) { - return this.data[(y * this.width + x) * 4 + 3]; - } - setPixel(x, y, alpha, color) { - const index = (y * this.width + x) * 4; - this.data[index] = color.r; - this.data[index + 1] = color.g; - this.data[index + 2] = color.b; - this.data[index + 3] = alpha; - } - shouldProcessPixel(currentAlpha, targetAlpha, tolerance, isFillMode) { - if (currentAlpha === -1) return false; - if (isFillMode) { - return currentAlpha !== 255 && Math.abs(currentAlpha - targetAlpha) <= tolerance; - } else { - return currentAlpha === 255 || Math.abs(currentAlpha - targetAlpha) <= tolerance; - } - } - async floodFill(point) { - let startX = Math.floor(point.x); - let startY = Math.floor(point.y); - this.width = this.canvas.width; - this.height = this.canvas.height; - if (startX < 0 || startX >= this.width || startY < 0 || startY >= this.height) { - return; - } - this.imageData = this.ctx.getImageData(0, 0, this.width, this.height); - this.data = this.imageData.data; - const targetAlpha = this.getPixel(startX, startY); - const isFillMode = targetAlpha !== 255; - if (targetAlpha === -1) return; - const maskColor = await this.messageBroker.pull("getMaskColor"); - const stack = []; - const visited = new Uint8Array(this.width * this.height); - if (this.shouldProcessPixel( - targetAlpha, - targetAlpha, - this.tolerance, - isFillMode - )) { - stack.push([startX, startY]); - } - while (stack.length > 0) { - const [x, y] = stack.pop(); - const visitedIndex = y * this.width + x; - if (visited[visitedIndex]) continue; - const currentAlpha = this.getPixel(x, y); - if (!this.shouldProcessPixel( - currentAlpha, - targetAlpha, - this.tolerance, - isFillMode - )) { - continue; - } - visited[visitedIndex] = 1; - this.setPixel(x, y, isFillMode ? 255 : 0, maskColor); - const checkNeighbor = /* @__PURE__ */ __name((nx, ny) => { - if (nx < 0 || nx >= this.width || ny < 0 || ny >= this.height) return; - if (!visited[ny * this.width + nx]) { - const alpha = this.getPixel(nx, ny); - if (this.shouldProcessPixel( - alpha, - targetAlpha, - this.tolerance, - isFillMode - )) { - stack.push([nx, ny]); - } - } - }, "checkNeighbor"); - checkNeighbor(x - 1, y); - checkNeighbor(x + 1, y); - checkNeighbor(x, y - 1); - checkNeighbor(x, y + 1); - } - this.ctx.putImageData(this.imageData, 0, 0); - this.imageData = null; - this.data = null; - } - setTolerance(tolerance) { - this.tolerance = tolerance; - } - getTolerance() { - return this.tolerance; - } - //invert mask - invertMask() { - const imageData = this.ctx.getImageData( - 0, - 0, - this.canvas.width, - this.canvas.height - ); - const data = imageData.data; - let maskR = 0, maskG = 0, maskB = 0; - for (let i = 0; i < data.length; i += 4) { - if (data[i + 3] > 0) { - maskR = data[i]; - maskG = data[i + 1]; - maskB = data[i + 2]; - break; - } - } - for (let i = 0; i < data.length; i += 4) { - const alpha = data[i + 3]; - data[i + 3] = 255 - alpha; - if (alpha === 0) { - data[i] = maskR; - data[i + 1] = maskG; - data[i + 2] = maskB; - } - } - this.ctx.putImageData(imageData, 0, 0); - this.messageBroker.publish("saveState"); - } -} -class ColorSelectTool { - static { - __name(this, "ColorSelectTool"); - } - maskEditor; - messageBroker; - width = null; - height = null; - canvas; - maskCTX; - imageCTX; - maskData = null; - imageData = null; - tolerance = 20; - livePreview = false; - lastPoint = null; - colorComparisonMethod = "simple"; - applyWholeImage = false; - maskBoundry = false; - maskTolerance = 0; - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.createListeners(); - this.addPullTopics(); - } - async initColorSelectTool() { - await this.pullCanvas(); - } - async pullCanvas() { - this.canvas = await this.messageBroker.pull("imgCanvas"); - this.maskCTX = await this.messageBroker.pull("maskCtx"); - this.imageCTX = await this.messageBroker.pull("imageCtx"); - } - createListeners() { - this.messageBroker.subscribe( - "colorSelectFill", - (point) => this.fillColorSelection(point) - ); - this.messageBroker.subscribe( - "setColorSelectTolerance", - (tolerance) => this.setTolerance(tolerance) - ); - this.messageBroker.subscribe( - "setLivePreview", - (livePreview) => this.setLivePreview(livePreview) - ); - this.messageBroker.subscribe( - "setColorComparisonMethod", - (method) => this.setComparisonMethod(method) - ); - this.messageBroker.subscribe("clearLastPoint", () => this.clearLastPoint()); - this.messageBroker.subscribe( - "setWholeImage", - (applyWholeImage) => this.setApplyWholeImage(applyWholeImage) - ); - this.messageBroker.subscribe( - "setMaskBoundary", - (maskBoundry) => this.setMaskBoundary(maskBoundry) - ); - this.messageBroker.subscribe( - "setMaskTolerance", - (maskTolerance) => this.setMaskTolerance(maskTolerance) - ); - } - async addPullTopics() { - this.messageBroker.createPullTopic( - "getLivePreview", - async () => this.livePreview - ); - } - getPixel(x, y) { - const index = (y * this.width + x) * 4; - return { - r: this.imageData[index], - g: this.imageData[index + 1], - b: this.imageData[index + 2] - }; - } - getMaskAlpha(x, y) { - return this.maskData[(y * this.width + x) * 4 + 3]; - } - isPixelInRange(pixel, target) { - switch (this.colorComparisonMethod) { - case "simple": - return this.isPixelInRangeSimple(pixel, target); - case "hsl": - return this.isPixelInRangeHSL(pixel, target); - case "lab": - return this.isPixelInRangeLab(pixel, target); - default: - return this.isPixelInRangeSimple(pixel, target); - } - } - isPixelInRangeSimple(pixel, target) { - const distance = Math.sqrt( - Math.pow(pixel.r - target.r, 2) + Math.pow(pixel.g - target.g, 2) + Math.pow(pixel.b - target.b, 2) - ); - return distance <= this.tolerance; - } - isPixelInRangeHSL(pixel, target) { - const pixelHSL = this.rgbToHSL(pixel.r, pixel.g, pixel.b); - const targetHSL = this.rgbToHSL(target.r, target.g, target.b); - const hueDiff = Math.abs(pixelHSL.h - targetHSL.h); - const satDiff = Math.abs(pixelHSL.s - targetHSL.s); - const lightDiff = Math.abs(pixelHSL.l - targetHSL.l); - const distance = Math.sqrt( - Math.pow(hueDiff / 360 * 255, 2) + Math.pow(satDiff / 100 * 255, 2) + Math.pow(lightDiff / 100 * 255, 2) - ); - return distance <= this.tolerance; - } - rgbToHSL(r, g, b) { - r /= 255; - g /= 255; - b /= 255; - const max2 = Math.max(r, g, b); - const min = Math.min(r, g, b); - let h = 0, s = 0, l = (max2 + min) / 2; - if (max2 !== min) { - const d = max2 - min; - s = l > 0.5 ? d / (2 - max2 - min) : d / (max2 + min); - switch (max2) { - case r: - h = (g - b) / d + (g < b ? 6 : 0); - break; - case g: - h = (b - r) / d + 2; - break; - case b: - h = (r - g) / d + 4; - break; - } - h /= 6; - } - return { - h: h * 360, - s: s * 100, - l: l * 100 - }; - } - isPixelInRangeLab(pixel, target) { - const pixelLab = this.rgbToLab(pixel); - const targetLab = this.rgbToLab(target); - const deltaE = Math.sqrt( - Math.pow(pixelLab.l - targetLab.l, 2) + Math.pow(pixelLab.a - targetLab.a, 2) + Math.pow(pixelLab.b - targetLab.b, 2) - ); - const normalizedDeltaE = deltaE / 100 * 255; - return normalizedDeltaE <= this.tolerance; - } - rgbToLab(rgb) { - let r = rgb.r / 255; - let g = rgb.g / 255; - let b = rgb.b / 255; - r = r > 0.04045 ? Math.pow((r + 0.055) / 1.055, 2.4) : r / 12.92; - g = g > 0.04045 ? Math.pow((g + 0.055) / 1.055, 2.4) : g / 12.92; - b = b > 0.04045 ? Math.pow((b + 0.055) / 1.055, 2.4) : b / 12.92; - r *= 100; - g *= 100; - b *= 100; - const x = r * 0.4124 + g * 0.3576 + b * 0.1805; - const y = r * 0.2126 + g * 0.7152 + b * 0.0722; - const z = r * 0.0193 + g * 0.1192 + b * 0.9505; - const xn = 95.047; - const yn = 100; - const zn = 108.883; - const xyz = [x / xn, y / yn, z / zn]; - for (let i = 0; i < xyz.length; i++) { - xyz[i] = xyz[i] > 8856e-6 ? Math.pow(xyz[i], 1 / 3) : 7.787 * xyz[i] + 16 / 116; - } - return { - l: 116 * xyz[1] - 16, - a: 500 * (xyz[0] - xyz[1]), - b: 200 * (xyz[1] - xyz[2]) - }; - } - setPixel(x, y, alpha, color) { - const index = (y * this.width + x) * 4; - this.maskData[index] = color.r; - this.maskData[index + 1] = color.g; - this.maskData[index + 2] = color.b; - this.maskData[index + 3] = alpha; - } - async fillColorSelection(point) { - this.width = this.canvas.width; - this.height = this.canvas.height; - this.lastPoint = point; - const maskData = this.maskCTX.getImageData(0, 0, this.width, this.height); - this.maskData = maskData.data; - this.imageData = this.imageCTX.getImageData( - 0, - 0, - this.width, - this.height - ).data; - if (this.applyWholeImage) { - const targetPixel = this.getPixel( - Math.floor(point.x), - Math.floor(point.y) - ); - const maskColor = await this.messageBroker.pull("getMaskColor"); - const width = this.width; - const height = this.height; - const CHUNK_SIZE = 1e4; - for (let i = 0; i < width * height; i += CHUNK_SIZE) { - const endIndex = Math.min(i + CHUNK_SIZE, width * height); - for (let pixelIndex = i; pixelIndex < endIndex; pixelIndex++) { - const x = pixelIndex % width; - const y = Math.floor(pixelIndex / width); - if (this.isPixelInRange(this.getPixel(x, y), targetPixel)) { - this.setPixel(x, y, 255, maskColor); - } - } - await new Promise((resolve) => setTimeout(resolve, 0)); - } - } else { - let startX = Math.floor(point.x); - let startY = Math.floor(point.y); - if (startX < 0 || startX >= this.width || startY < 0 || startY >= this.height) { - return; - } - const pixel = this.getPixel(startX, startY); - const stack = []; - const visited = new Uint8Array(this.width * this.height); - stack.push([startX, startY]); - const maskColor = await this.messageBroker.pull("getMaskColor"); - while (stack.length > 0) { - const [x, y] = stack.pop(); - const visitedIndex = y * this.width + x; - if (visited[visitedIndex] || !this.isPixelInRange(this.getPixel(x, y), pixel)) { - continue; - } - visited[visitedIndex] = 1; - this.setPixel(x, y, 255, maskColor); - if (x > 0 && !visited[y * this.width + (x - 1)] && this.isPixelInRange(this.getPixel(x - 1, y), pixel)) { - if (!this.maskBoundry || 255 - this.getMaskAlpha(x - 1, y) > this.maskTolerance) { - stack.push([x - 1, y]); - } - } - if (x < this.width - 1 && !visited[y * this.width + (x + 1)] && this.isPixelInRange(this.getPixel(x + 1, y), pixel)) { - if (!this.maskBoundry || 255 - this.getMaskAlpha(x + 1, y) > this.maskTolerance) { - stack.push([x + 1, y]); - } - } - if (y > 0 && !visited[(y - 1) * this.width + x] && this.isPixelInRange(this.getPixel(x, y - 1), pixel)) { - if (!this.maskBoundry || 255 - this.getMaskAlpha(x, y - 1) > this.maskTolerance) { - stack.push([x, y - 1]); - } - } - if (y < this.height - 1 && !visited[(y + 1) * this.width + x] && this.isPixelInRange(this.getPixel(x, y + 1), pixel)) { - if (!this.maskBoundry || 255 - this.getMaskAlpha(x, y + 1) > this.maskTolerance) { - stack.push([x, y + 1]); - } - } - } - } - this.maskCTX.putImageData(maskData, 0, 0); - this.messageBroker.publish("saveState"); - this.maskData = null; - this.imageData = null; - } - setTolerance(tolerance) { - this.tolerance = tolerance; - if (this.lastPoint && this.livePreview) { - this.messageBroker.publish("undo"); - this.fillColorSelection(this.lastPoint); - } - } - setLivePreview(livePreview) { - this.livePreview = livePreview; - } - setComparisonMethod(method) { - this.colorComparisonMethod = method; - if (this.lastPoint && this.livePreview) { - this.messageBroker.publish("undo"); - this.fillColorSelection(this.lastPoint); - } - } - clearLastPoint() { - this.lastPoint = null; - } - setApplyWholeImage(applyWholeImage) { - this.applyWholeImage = applyWholeImage; - } - setMaskBoundary(maskBoundry) { - this.maskBoundry = maskBoundry; - } - setMaskTolerance(maskTolerance) { - this.maskTolerance = maskTolerance; - } -} -class BrushTool { - static { - __name(this, "BrushTool"); - } - brushSettings; - //this saves the current brush settings - maskBlendMode; - isDrawing = false; - isDrawingLine = false; - lineStartPoint = null; - smoothingPrecision = 10; - smoothingCordsArray = []; - smoothingLastDrawTime; - maskCtx = null; - initialDraw = true; - brushStrokeCanvas = null; - brushStrokeCtx = null; - //brush adjustment - isBrushAdjusting = false; - brushPreviewGradient = null; - initialPoint = null; - useDominantAxis = false; - brushAdjustmentSpeed = 1; - maskEditor; - messageBroker; - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.createListeners(); - this.addPullTopics(); - this.useDominantAxis = app.extensionManager.setting.get( - "Comfy.MaskEditor.UseDominantAxis" - ); - this.brushAdjustmentSpeed = app.extensionManager.setting.get( - "Comfy.MaskEditor.BrushAdjustmentSpeed" - ); - this.brushSettings = { - size: 10, - opacity: 100, - hardness: 1, - type: "arc" - /* Arc */ - }; - this.maskBlendMode = "black"; - } - createListeners() { - this.messageBroker.subscribe( - "setBrushSize", - (size) => this.setBrushSize(size) - ); - this.messageBroker.subscribe( - "setBrushOpacity", - (opacity) => this.setBrushOpacity(opacity) - ); - this.messageBroker.subscribe( - "setBrushHardness", - (hardness) => this.setBrushHardness(hardness) - ); - this.messageBroker.subscribe( - "setBrushShape", - (type) => this.setBrushType(type) - ); - this.messageBroker.subscribe( - "setBrushSmoothingPrecision", - (precision) => this.setBrushSmoothingPrecision(precision) - ); - this.messageBroker.subscribe( - "brushAdjustmentStart", - (event) => this.startBrushAdjustment(event) - ); - this.messageBroker.subscribe( - "brushAdjustment", - (event) => this.handleBrushAdjustment(event) - ); - this.messageBroker.subscribe( - "drawStart", - (event) => this.startDrawing(event) - ); - this.messageBroker.subscribe( - "draw", - (event) => this.handleDrawing(event) - ); - this.messageBroker.subscribe( - "drawEnd", - (event) => this.drawEnd(event) - ); - } - addPullTopics() { - this.messageBroker.createPullTopic( - "brushSize", - async () => this.brushSettings.size - ); - this.messageBroker.createPullTopic( - "brushOpacity", - async () => this.brushSettings.opacity - ); - this.messageBroker.createPullTopic( - "brushHardness", - async () => this.brushSettings.hardness - ); - this.messageBroker.createPullTopic( - "brushType", - async () => this.brushSettings.type - ); - this.messageBroker.createPullTopic( - "maskBlendMode", - async () => this.maskBlendMode - ); - this.messageBroker.createPullTopic( - "brushSettings", - async () => this.brushSettings - ); - } - async createBrushStrokeCanvas() { - if (this.brushStrokeCanvas !== null) { - return; - } - const maskCanvas = await this.messageBroker.pull("maskCanvas"); - const canvas = document.createElement("canvas"); - canvas.width = maskCanvas.width; - canvas.height = maskCanvas.height; - this.brushStrokeCanvas = canvas; - this.brushStrokeCtx = canvas.getContext("2d"); - } - async startDrawing(event) { - this.isDrawing = true; - let compositionOp; - let currentTool = await this.messageBroker.pull("currentTool"); - let coords = { x: event.offsetX, y: event.offsetY }; - let coords_canvas = await this.messageBroker.pull("screenToCanvas", coords); - await this.createBrushStrokeCanvas(); - if (currentTool === "eraser" || event.buttons == 2) { - compositionOp = "destination-out"; - } else { - compositionOp = "source-over"; - } - if (event.shiftKey && this.lineStartPoint) { - this.isDrawingLine = true; - this.drawLine(this.lineStartPoint, coords_canvas, compositionOp); - } else { - this.isDrawingLine = false; - this.init_shape(compositionOp); - this.draw_shape(coords_canvas); - } - this.lineStartPoint = coords_canvas; - this.smoothingCordsArray = [coords_canvas]; - this.smoothingLastDrawTime = /* @__PURE__ */ new Date(); - } - async handleDrawing(event) { - var diff = performance.now() - this.smoothingLastDrawTime.getTime(); - let coords = { x: event.offsetX, y: event.offsetY }; - let coords_canvas = await this.messageBroker.pull("screenToCanvas", coords); - let currentTool = await this.messageBroker.pull("currentTool"); - if (diff > 20 && !this.isDrawing) - requestAnimationFrame(() => { - this.init_shape( - "source-over" - /* SourceOver */ - ); - this.draw_shape(coords_canvas); - this.smoothingCordsArray.push(coords_canvas); - }); - else - requestAnimationFrame(() => { - if (currentTool === "eraser" || event.buttons == 2) { - this.init_shape( - "destination-out" - /* DestinationOut */ - ); - } else { - this.init_shape( - "source-over" - /* SourceOver */ - ); - } - this.drawWithBetterSmoothing(coords_canvas); - }); - this.smoothingLastDrawTime = /* @__PURE__ */ new Date(); - } - async drawEnd(event) { - const coords = { x: event.offsetX, y: event.offsetY }; - const coords_canvas = await this.messageBroker.pull( - "screenToCanvas", - coords - ); - if (this.isDrawing) { - this.isDrawing = false; - this.messageBroker.publish("saveState"); - this.lineStartPoint = coords_canvas; - this.initialDraw = true; - } - } - drawWithBetterSmoothing(point) { - if (!this.smoothingCordsArray) { - this.smoothingCordsArray = []; - } - const opacityConstant = 1 / (1 + Math.exp(3)); - const interpolatedOpacity = 1 / (1 + Math.exp(-6 * (this.brushSettings.opacity - 0.5))) - opacityConstant; - this.smoothingCordsArray.push(point); - const POINTS_NR = 5; - if (this.smoothingCordsArray.length < POINTS_NR) { - return; - } - let totalLength = 0; - const points = this.smoothingCordsArray; - const len = points.length - 1; - let dx, dy; - for (let i = 0; i < len; i++) { - dx = points[i + 1].x - points[i].x; - dy = points[i + 1].y - points[i].y; - totalLength += Math.sqrt(dx * dx + dy * dy); - } - const distanceBetweenPoints = this.brushSettings.size / this.smoothingPrecision * 6; - const stepNr = Math.ceil(totalLength / distanceBetweenPoints); - let interpolatedPoints = points; - if (stepNr > 0) { - interpolatedPoints = this.generateEquidistantPoints( - this.smoothingCordsArray, - distanceBetweenPoints - // Distance between interpolated points - ); - } - if (!this.initialDraw) { - const spliceIndex = interpolatedPoints.findIndex( - (point2) => point2.x === this.smoothingCordsArray[2].x && point2.y === this.smoothingCordsArray[2].y - ); - if (spliceIndex !== -1) { - interpolatedPoints = interpolatedPoints.slice(spliceIndex + 1); - } - } - for (const point2 of interpolatedPoints) { - this.draw_shape(point2, interpolatedOpacity); - } - if (!this.initialDraw) { - this.smoothingCordsArray = this.smoothingCordsArray.slice(2); - } else { - this.initialDraw = false; - } - } - async drawLine(p1, p2, compositionOp) { - const brush_size = await this.messageBroker.pull("brushSize"); - const distance = Math.sqrt((p2.x - p1.x) ** 2 + (p2.y - p1.y) ** 2); - const steps = Math.ceil( - distance / (brush_size / this.smoothingPrecision * 4) - ); - const interpolatedOpacity = 1 / (1 + Math.exp(-6 * (this.brushSettings.opacity - 0.5))) - 1 / (1 + Math.exp(3)); - this.init_shape(compositionOp); - for (let i = 0; i <= steps; i++) { - const t2 = i / steps; - const x = p1.x + (p2.x - p1.x) * t2; - const y = p1.y + (p2.y - p1.y) * t2; - const point = { x, y }; - this.draw_shape(point, interpolatedOpacity); - } - } - //brush adjustment - async startBrushAdjustment(event) { - event.preventDefault(); - const coords = { x: event.offsetX, y: event.offsetY }; - let coords_canvas = await this.messageBroker.pull("screenToCanvas", coords); - this.messageBroker.publish("setBrushPreviewGradientVisibility", true); - this.initialPoint = coords_canvas; - this.isBrushAdjusting = true; - return; - } - async handleBrushAdjustment(event) { - const coords = { x: event.offsetX, y: event.offsetY }; - const brushDeadZone = 5; - let coords_canvas = await this.messageBroker.pull("screenToCanvas", coords); - const delta_x = coords_canvas.x - this.initialPoint.x; - const delta_y = coords_canvas.y - this.initialPoint.y; - const effectiveDeltaX = Math.abs(delta_x) < brushDeadZone ? 0 : delta_x; - const effectiveDeltaY = Math.abs(delta_y) < brushDeadZone ? 0 : delta_y; - let finalDeltaX = effectiveDeltaX; - let finalDeltaY = effectiveDeltaY; - console.log(this.useDominantAxis); - if (this.useDominantAxis) { - const ratio = Math.abs(effectiveDeltaX) / Math.abs(effectiveDeltaY); - const threshold = 2; - if (ratio > threshold) { - finalDeltaY = 0; - } else if (ratio < 1 / threshold) { - finalDeltaX = 0; - } - } - const cappedDeltaX = Math.max(-100, Math.min(100, finalDeltaX)); - const cappedDeltaY = Math.max(-100, Math.min(100, finalDeltaY)); - const sizeDelta = cappedDeltaX / 40; - const hardnessDelta = cappedDeltaY / 800; - const newSize = Math.max( - 1, - Math.min( - 100, - this.brushSettings.size + cappedDeltaX / 35 * this.brushAdjustmentSpeed - ) - ); - const newHardness = Math.max( - 0, - Math.min( - 1, - this.brushSettings.hardness - cappedDeltaY / 4e3 * this.brushAdjustmentSpeed - ) - ); - this.brushSettings.size = newSize; - this.brushSettings.hardness = newHardness; - this.messageBroker.publish("updateBrushPreview"); - } - //helper functions - async draw_shape(point, overrideOpacity) { - const brushSettings = this.brushSettings; - const maskCtx = this.maskCtx || await this.messageBroker.pull("maskCtx"); - const brushType = await this.messageBroker.pull("brushType"); - const maskColor = await this.messageBroker.pull("getMaskColor"); - const size = brushSettings.size; - const sliderOpacity = brushSettings.opacity; - const opacity = overrideOpacity == void 0 ? sliderOpacity : overrideOpacity; - const hardness = brushSettings.hardness; - const x = point.x; - const y = point.y; - const extendedSize = size * (2 - hardness); - let gradient = maskCtx.createRadialGradient(x, y, 0, x, y, extendedSize); - const isErasing = maskCtx.globalCompositeOperation === "destination-out"; - if (hardness === 1) { - console.log(sliderOpacity, opacity); - gradient.addColorStop( - 0, - isErasing ? `rgba(255, 255, 255, ${opacity})` : `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, ${opacity})` - ); - gradient.addColorStop( - 1, - isErasing ? `rgba(255, 255, 255, ${opacity})` : `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, ${opacity})` - ); - } else { - let softness = 1 - hardness; - let innerStop = Math.max(0, hardness - softness); - let outerStop = size / extendedSize; - if (isErasing) { - gradient.addColorStop(0, `rgba(255, 255, 255, ${opacity})`); - gradient.addColorStop(innerStop, `rgba(255, 255, 255, ${opacity})`); - gradient.addColorStop(outerStop, `rgba(255, 255, 255, ${opacity / 2})`); - gradient.addColorStop(1, `rgba(255, 255, 255, 0)`); - } else { - gradient.addColorStop( - 0, - `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, ${opacity})` - ); - gradient.addColorStop( - innerStop, - `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, ${opacity})` - ); - gradient.addColorStop( - outerStop, - `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, ${opacity / 2})` - ); - gradient.addColorStop( - 1, - `rgba(${maskColor.r}, ${maskColor.g}, ${maskColor.b}, 0)` - ); - } - } - maskCtx.fillStyle = gradient; - maskCtx.beginPath(); - if (brushType === "rect") { - maskCtx.rect( - x - extendedSize, - y - extendedSize, - extendedSize * 2, - extendedSize * 2 - ); - } else { - maskCtx.arc(x, y, extendedSize, 0, Math.PI * 2, false); - } - maskCtx.fill(); - } - async init_shape(compositionOperation) { - const maskBlendMode = await this.messageBroker.pull("maskBlendMode"); - const maskCtx = this.maskCtx || await this.messageBroker.pull("maskCtx"); - maskCtx.beginPath(); - if (compositionOperation == "source-over") { - maskCtx.fillStyle = maskBlendMode; - maskCtx.globalCompositeOperation = "source-over"; - } else if (compositionOperation == "destination-out") { - maskCtx.globalCompositeOperation = "destination-out"; - } - } - calculateCubicSplinePoints(points, numSegments = 10) { - const result = []; - const xCoords = points.map((p) => p.x); - const yCoords = points.map((p) => p.y); - const xDerivatives = this.calculateSplineCoefficients(xCoords); - const yDerivatives = this.calculateSplineCoefficients(yCoords); - for (let i = 0; i < points.length - 1; i++) { - const p0 = points[i]; - const p1 = points[i + 1]; - const d0x = xDerivatives[i]; - const d1x = xDerivatives[i + 1]; - const d0y = yDerivatives[i]; - const d1y = yDerivatives[i + 1]; - for (let t2 = 0; t2 <= numSegments; t2++) { - const t_normalized = t2 / numSegments; - const h00 = 2 * t_normalized ** 3 - 3 * t_normalized ** 2 + 1; - const h10 = t_normalized ** 3 - 2 * t_normalized ** 2 + t_normalized; - const h01 = -2 * t_normalized ** 3 + 3 * t_normalized ** 2; - const h11 = t_normalized ** 3 - t_normalized ** 2; - const x = h00 * p0.x + h10 * d0x + h01 * p1.x + h11 * d1x; - const y = h00 * p0.y + h10 * d0y + h01 * p1.y + h11 * d1y; - result.push({ x, y }); - } - } - return result; - } - generateEvenlyDistributedPoints(splinePoints, numPoints) { - const distances = [0]; - for (let i = 1; i < splinePoints.length; i++) { - const dx = splinePoints[i].x - splinePoints[i - 1].x; - const dy = splinePoints[i].y - splinePoints[i - 1].y; - const dist = Math.hypot(dx, dy); - distances.push(distances[i - 1] + dist); - } - const totalLength = distances[distances.length - 1]; - const interval = totalLength / (numPoints - 1); - const result = []; - let currentIndex = 0; - for (let i = 0; i < numPoints; i++) { - const targetDistance = i * interval; - while (currentIndex < distances.length - 1 && distances[currentIndex + 1] < targetDistance) { - currentIndex++; - } - const t2 = (targetDistance - distances[currentIndex]) / (distances[currentIndex + 1] - distances[currentIndex]); - const x = splinePoints[currentIndex].x + t2 * (splinePoints[currentIndex + 1].x - splinePoints[currentIndex].x); - const y = splinePoints[currentIndex].y + t2 * (splinePoints[currentIndex + 1].y - splinePoints[currentIndex].y); - result.push({ x, y }); - } - return result; - } - generateEquidistantPoints(points, distance) { - const result = []; - const cumulativeDistances = [0]; - for (let i = 1; i < points.length; i++) { - const dx = points[i].x - points[i - 1].x; - const dy = points[i].y - points[i - 1].y; - const dist = Math.hypot(dx, dy); - cumulativeDistances[i] = cumulativeDistances[i - 1] + dist; - } - const totalLength = cumulativeDistances[cumulativeDistances.length - 1]; - const numPoints = Math.floor(totalLength / distance); - for (let i = 0; i <= numPoints; i++) { - const targetDistance = i * distance; - let idx = 0; - while (idx < cumulativeDistances.length - 1 && cumulativeDistances[idx + 1] < targetDistance) { - idx++; - } - if (idx >= points.length - 1) { - result.push(points[points.length - 1]); - continue; - } - const d0 = cumulativeDistances[idx]; - const d1 = cumulativeDistances[idx + 1]; - const t2 = (targetDistance - d0) / (d1 - d0); - const x = points[idx].x + t2 * (points[idx + 1].x - points[idx].x); - const y = points[idx].y + t2 * (points[idx + 1].y - points[idx].y); - result.push({ x, y }); - } - return result; - } - calculateSplineCoefficients(values) { - const n = values.length - 1; - const matrix = new Array(n + 1).fill(0).map(() => new Array(n + 1).fill(0)); - const rhs = new Array(n + 1).fill(0); - for (let i = 1; i < n; i++) { - matrix[i][i - 1] = 1; - matrix[i][i] = 4; - matrix[i][i + 1] = 1; - rhs[i] = 3 * (values[i + 1] - values[i - 1]); - } - matrix[0][0] = 2; - matrix[0][1] = 1; - matrix[n][n - 1] = 1; - matrix[n][n] = 2; - rhs[0] = 3 * (values[1] - values[0]); - rhs[n] = 3 * (values[n] - values[n - 1]); - for (let i = 1; i <= n; i++) { - const m = matrix[i][i - 1] / matrix[i - 1][i - 1]; - matrix[i][i] -= m * matrix[i - 1][i]; - rhs[i] -= m * rhs[i - 1]; - } - const solution = new Array(n + 1); - solution[n] = rhs[n] / matrix[n][n]; - for (let i = n - 1; i >= 0; i--) { - solution[i] = (rhs[i] - matrix[i][i + 1] * solution[i + 1]) / matrix[i][i]; - } - return solution; - } - setBrushSize(size) { - this.brushSettings.size = size; - } - setBrushOpacity(opacity) { - this.brushSettings.opacity = opacity; - } - setBrushHardness(hardness) { - this.brushSettings.hardness = hardness; - } - setBrushType(type) { - this.brushSettings.type = type; - } - setBrushSmoothingPrecision(precision) { - this.smoothingPrecision = precision; - } -} -class UIManager { - static { - __name(this, "UIManager"); - } - rootElement; - brush; - brushPreviewGradient; - maskCtx; - imageCtx; - maskCanvas; - imgCanvas; - brushSettingsHTML; - paintBucketSettingsHTML; - colorSelectSettingsHTML; - maskOpacitySlider; - brushHardnessSlider; - brushSizeSlider; - brushOpacitySlider; - sidebarImage; - saveButton; - toolPanel; - sidePanel; - pointerZone; - canvasBackground; - canvasContainer; - image; - imageURL; - darkMode = true; - maskEditor; - messageBroker; - mask_opacity = 1; - maskBlendMode = "black"; - zoomTextHTML; - dimensionsTextHTML; - constructor(rootElement, maskEditor) { - this.rootElement = rootElement; - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.addListeners(); - this.addPullTopics(); - } - addListeners() { - this.messageBroker.subscribe( - "updateBrushPreview", - async () => this.updateBrushPreview() - ); - this.messageBroker.subscribe( - "paintBucketCursor", - (isPaintBucket) => this.handlePaintBucketCursor(isPaintBucket) - ); - this.messageBroker.subscribe( - "panCursor", - (isPan) => this.handlePanCursor(isPan) - ); - this.messageBroker.subscribe( - "setBrushVisibility", - (isVisible) => this.setBrushVisibility(isVisible) - ); - this.messageBroker.subscribe( - "setBrushPreviewGradientVisibility", - (isVisible) => this.setBrushPreviewGradientVisibility(isVisible) - ); - this.messageBroker.subscribe("updateCursor", () => this.updateCursor()); - this.messageBroker.subscribe( - "setZoomText", - (text) => this.setZoomText(text) - ); - } - addPullTopics() { - this.messageBroker.createPullTopic( - "maskCanvas", - async () => this.maskCanvas - ); - this.messageBroker.createPullTopic("maskCtx", async () => this.maskCtx); - this.messageBroker.createPullTopic("imageCtx", async () => this.imageCtx); - this.messageBroker.createPullTopic("imgCanvas", async () => this.imgCanvas); - this.messageBroker.createPullTopic( - "screenToCanvas", - async (coords) => this.screenToCanvas(coords) - ); - this.messageBroker.createPullTopic( - "getCanvasContainer", - async () => this.canvasContainer - ); - this.messageBroker.createPullTopic( - "getMaskColor", - async () => this.getMaskColor() - ); - } - async setlayout() { - this.detectLightMode(); - var user_ui = await this.createUI(); - var canvasContainer = this.createBackgroundUI(); - var brush = await this.createBrush(); - await this.setBrushBorderRadius(); - this.setBrushOpacity(1); - this.rootElement.appendChild(canvasContainer); - this.rootElement.appendChild(user_ui); - document.body.appendChild(brush); - } - async createUI() { - var ui_container = document.createElement("div"); - ui_container.id = "maskEditor_uiContainer"; - var top_bar = await this.createTopBar(); - var ui_horizontal_container = document.createElement("div"); - ui_horizontal_container.id = "maskEditor_uiHorizontalContainer"; - var side_panel_container = await this.createSidePanel(); - var pointer_zone = this.createPointerZone(); - var tool_panel = this.createToolPanel(); - ui_horizontal_container.appendChild(tool_panel); - ui_horizontal_container.appendChild(pointer_zone); - ui_horizontal_container.appendChild(side_panel_container); - ui_container.appendChild(top_bar); - ui_container.appendChild(ui_horizontal_container); - return ui_container; - } - createBackgroundUI() { - const canvasContainer = document.createElement("div"); - canvasContainer.id = "maskEditorCanvasContainer"; - const imgCanvas = document.createElement("canvas"); - imgCanvas.id = "imageCanvas"; - const maskCanvas = document.createElement("canvas"); - maskCanvas.id = "maskCanvas"; - const canvas_background = document.createElement("div"); - canvas_background.id = "canvasBackground"; - canvasContainer.appendChild(imgCanvas); - canvasContainer.appendChild(maskCanvas); - canvasContainer.appendChild(canvas_background); - this.imgCanvas = imgCanvas; - this.maskCanvas = maskCanvas; - this.canvasContainer = canvasContainer; - this.canvasBackground = canvas_background; - let maskCtx = maskCanvas.getContext("2d", { willReadFrequently: true }); - if (maskCtx) { - this.maskCtx = maskCtx; - } - let imgCtx = imgCanvas.getContext("2d", { willReadFrequently: true }); - if (imgCtx) { - this.imageCtx = imgCtx; - } - this.setEventHandler(); - this.imgCanvas.style.position = "absolute"; - this.maskCanvas.style.position = "absolute"; - this.imgCanvas.style.top = "200"; - this.imgCanvas.style.left = "0"; - this.maskCanvas.style.top = this.imgCanvas.style.top; - this.maskCanvas.style.left = this.imgCanvas.style.left; - const maskCanvasStyle = this.getMaskCanvasStyle(); - this.maskCanvas.style.mixBlendMode = maskCanvasStyle.mixBlendMode; - this.maskCanvas.style.opacity = maskCanvasStyle.opacity.toString(); - return canvasContainer; - } - async setBrushBorderRadius() { - const brushSettings = await this.messageBroker.pull("brushSettings"); - if (brushSettings.type === "rect") { - this.brush.style.borderRadius = "0%"; - this.brush.style.MozBorderRadius = "0%"; - this.brush.style.WebkitBorderRadius = "0%"; - } else { - this.brush.style.borderRadius = "50%"; - this.brush.style.MozBorderRadius = "50%"; - this.brush.style.WebkitBorderRadius = "50%"; - } - } - async initUI() { - this.saveButton.innerText = "Save"; - this.saveButton.disabled = false; - await this.setImages(this.imgCanvas); - } - async createSidePanel() { - const side_panel = this.createContainer(true); - side_panel.id = "maskEditor_sidePanel"; - const brush_settings = await this.createBrushSettings(); - brush_settings.id = "maskEditor_brushSettings"; - this.brushSettingsHTML = brush_settings; - const paint_bucket_settings = await this.createPaintBucketSettings(); - paint_bucket_settings.id = "maskEditor_paintBucketSettings"; - this.paintBucketSettingsHTML = paint_bucket_settings; - const color_select_settings = await this.createColorSelectSettings(); - color_select_settings.id = "maskEditor_colorSelectSettings"; - this.colorSelectSettingsHTML = color_select_settings; - const image_layer_settings = await this.createImageLayerSettings(); - const separator = this.createSeparator(); - side_panel.appendChild(brush_settings); - side_panel.appendChild(paint_bucket_settings); - side_panel.appendChild(color_select_settings); - side_panel.appendChild(separator); - side_panel.appendChild(image_layer_settings); - return side_panel; - } - async createBrushSettings() { - const shapeColor = this.darkMode ? "maskEditor_brushShape_dark" : "maskEditor_brushShape_light"; - const brush_settings_container = this.createContainer(true); - const brush_settings_title = this.createHeadline("Brush Settings"); - const brush_shape_outer_container = this.createContainer(true); - const brush_shape_title = this.createContainerTitle("Brush Shape"); - const brush_shape_container = this.createContainer(false); - const accentColor = this.darkMode ? "maskEditor_accent_bg_dark" : "maskEditor_accent_bg_light"; - brush_shape_container.classList.add(accentColor); - brush_shape_container.classList.add("maskEditor_layerRow"); - const circle_shape = document.createElement("div"); - circle_shape.id = "maskEditor_sidePanelBrushShapeCircle"; - circle_shape.classList.add(shapeColor); - circle_shape.style.background = "var(--p-button-text-primary-color)"; - circle_shape.addEventListener("click", () => { - this.messageBroker.publish( - "setBrushShape", - "arc" - /* Arc */ - ); - this.setBrushBorderRadius(); - circle_shape.style.background = "var(--p-button-text-primary-color)"; - square_shape.style.background = ""; - }); - const square_shape = document.createElement("div"); - square_shape.id = "maskEditor_sidePanelBrushShapeSquare"; - square_shape.classList.add(shapeColor); - square_shape.style.background = ""; - square_shape.addEventListener("click", () => { - this.messageBroker.publish( - "setBrushShape", - "rect" - /* Rect */ - ); - this.setBrushBorderRadius(); - square_shape.style.background = "var(--p-button-text-primary-color)"; - circle_shape.style.background = ""; - }); - brush_shape_container.appendChild(circle_shape); - brush_shape_container.appendChild(square_shape); - brush_shape_outer_container.appendChild(brush_shape_title); - brush_shape_outer_container.appendChild(brush_shape_container); - const thicknesSliderObj = this.createSlider( - "Thickness", - 1, - 100, - 1, - 10, - (event, value) => { - this.messageBroker.publish("setBrushSize", parseInt(value)); - this.updateBrushPreview(); - } - ); - this.brushSizeSlider = thicknesSliderObj.slider; - const opacitySliderObj = this.createSlider( - "Opacity", - 0, - 1, - 0.01, - 0.7, - (event, value) => { - this.messageBroker.publish("setBrushOpacity", parseFloat(value)); - this.updateBrushPreview(); - } - ); - this.brushOpacitySlider = opacitySliderObj.slider; - const hardnessSliderObj = this.createSlider( - "Hardness", - 0, - 1, - 0.01, - 1, - (event, value) => { - this.messageBroker.publish("setBrushHardness", parseFloat(value)); - this.updateBrushPreview(); - } - ); - this.brushHardnessSlider = hardnessSliderObj.slider; - const brushSmoothingPrecisionSliderObj = this.createSlider( - "Smoothing Precision", - 1, - 100, - 1, - 10, - (event, value) => { - this.messageBroker.publish( - "setBrushSmoothingPrecision", - parseInt(value) - ); - } - ); - brush_settings_container.appendChild(brush_settings_title); - brush_settings_container.appendChild(brush_shape_outer_container); - brush_settings_container.appendChild(thicknesSliderObj.container); - brush_settings_container.appendChild(opacitySliderObj.container); - brush_settings_container.appendChild(hardnessSliderObj.container); - brush_settings_container.appendChild( - brushSmoothingPrecisionSliderObj.container - ); - return brush_settings_container; - } - async createPaintBucketSettings() { - const paint_bucket_settings_container = this.createContainer(true); - const paint_bucket_settings_title = this.createHeadline( - "Paint Bucket Settings" - ); - const tolerance = await this.messageBroker.pull("getTolerance"); - const paintBucketToleranceSliderObj = this.createSlider( - "Tolerance", - 0, - 255, - 1, - tolerance, - (event, value) => { - this.messageBroker.publish("setPaintBucketTolerance", parseInt(value)); - } - ); - paint_bucket_settings_container.appendChild(paint_bucket_settings_title); - paint_bucket_settings_container.appendChild( - paintBucketToleranceSliderObj.container - ); - return paint_bucket_settings_container; - } - async createColorSelectSettings() { - const color_select_settings_container = this.createContainer(true); - const color_select_settings_title = this.createHeadline( - "Color Select Settings" - ); - var tolerance = await this.messageBroker.pull("getTolerance"); - const colorSelectToleranceSliderObj = this.createSlider( - "Tolerance", - 0, - 255, - 1, - tolerance, - (event, value) => { - this.messageBroker.publish("setColorSelectTolerance", parseInt(value)); - } - ); - const livePreviewToggle = this.createToggle( - "Live Preview", - (event, value) => { - this.messageBroker.publish("setLivePreview", value); - } - ); - const wholeImageToggle = this.createToggle( - "Apply to Whole Image", - (event, value) => { - this.messageBroker.publish("setWholeImage", value); - } - ); - const methodOptions = Object.values(ColorComparisonMethod); - const methodSelect = this.createDropdown( - "Method", - methodOptions, - (event, value) => { - this.messageBroker.publish("setColorComparisonMethod", value); - } - ); - const maskBoundaryToggle = this.createToggle( - "Stop at mask", - (event, value) => { - this.messageBroker.publish("setMaskBoundary", value); - } - ); - const maskToleranceSliderObj = this.createSlider( - "Mask Tolerance", - 0, - 255, - 1, - 0, - (event, value) => { - this.messageBroker.publish("setMaskTolerance", parseInt(value)); - } - ); - color_select_settings_container.appendChild(color_select_settings_title); - color_select_settings_container.appendChild( - colorSelectToleranceSliderObj.container - ); - color_select_settings_container.appendChild(livePreviewToggle); - color_select_settings_container.appendChild(wholeImageToggle); - color_select_settings_container.appendChild(methodSelect); - color_select_settings_container.appendChild(maskBoundaryToggle); - color_select_settings_container.appendChild( - maskToleranceSliderObj.container - ); - return color_select_settings_container; - } - async createImageLayerSettings() { - const accentColor = this.darkMode ? "maskEditor_accent_bg_dark" : "maskEditor_accent_bg_light"; - const image_layer_settings_container = this.createContainer(true); - const image_layer_settings_title = this.createHeadline("Layers"); - const mask_layer_title = this.createContainerTitle("Mask Layer"); - const mask_layer_container = this.createContainer(false); - mask_layer_container.classList.add(accentColor); - mask_layer_container.classList.add("maskEditor_layerRow"); - const mask_layer_visibility_checkbox = document.createElement("input"); - mask_layer_visibility_checkbox.setAttribute("type", "checkbox"); - mask_layer_visibility_checkbox.checked = true; - mask_layer_visibility_checkbox.classList.add( - "maskEditor_sidePanelLayerCheckbox" - ); - mask_layer_visibility_checkbox.addEventListener("change", (event) => { - if (!event.target.checked) { - this.maskCanvas.style.opacity = "0"; - } else { - this.maskCanvas.style.opacity = String(this.mask_opacity); - } - }); - var mask_layer_image_container = document.createElement("div"); - mask_layer_image_container.classList.add( - "maskEditor_sidePanelLayerPreviewContainer" - ); - mask_layer_image_container.innerHTML = ' '; - var blending_options = ["black", "white", "negative"]; - const sidePanelDropdownAccent = this.darkMode ? "maskEditor_sidePanelDropdown_dark" : "maskEditor_sidePanelDropdown_light"; - var mask_layer_dropdown = document.createElement("select"); - mask_layer_dropdown.classList.add(sidePanelDropdownAccent); - mask_layer_dropdown.classList.add(sidePanelDropdownAccent); - blending_options.forEach((option) => { - var option_element = document.createElement("option"); - option_element.value = option; - option_element.innerText = option; - mask_layer_dropdown.appendChild(option_element); - if (option == this.maskBlendMode) { - option_element.selected = true; - } - }); - mask_layer_dropdown.addEventListener("change", (event) => { - const selectedValue = event.target.value; - this.maskBlendMode = selectedValue; - this.updateMaskColor(); - }); - mask_layer_container.appendChild(mask_layer_visibility_checkbox); - mask_layer_container.appendChild(mask_layer_image_container); - mask_layer_container.appendChild(mask_layer_dropdown); - const mask_layer_opacity_sliderObj = this.createSlider( - "Mask Opacity", - 0, - 1, - 0.01, - this.mask_opacity, - (event, value) => { - this.mask_opacity = parseFloat(value); - this.maskCanvas.style.opacity = String(this.mask_opacity); - if (this.mask_opacity == 0) { - mask_layer_visibility_checkbox.checked = false; - } else { - mask_layer_visibility_checkbox.checked = true; - } - } - ); - this.maskOpacitySlider = mask_layer_opacity_sliderObj.slider; - const image_layer_title = this.createContainerTitle("Image Layer"); - const image_layer_container = this.createContainer(false); - image_layer_container.classList.add(accentColor); - image_layer_container.classList.add("maskEditor_layerRow"); - const image_layer_visibility_checkbox = document.createElement("input"); - image_layer_visibility_checkbox.setAttribute("type", "checkbox"); - image_layer_visibility_checkbox.classList.add( - "maskEditor_sidePanelLayerCheckbox" - ); - image_layer_visibility_checkbox.checked = true; - image_layer_visibility_checkbox.addEventListener("change", (event) => { - if (!event.target.checked) { - this.imgCanvas.style.opacity = "0"; - } else { - this.imgCanvas.style.opacity = "1"; - } - }); - const image_layer_image_container = document.createElement("div"); - image_layer_image_container.classList.add( - "maskEditor_sidePanelLayerPreviewContainer" - ); - const image_layer_image = document.createElement("img"); - image_layer_image.id = "maskEditor_sidePanelImageLayerImage"; - image_layer_image.src = ComfyApp.clipspace?.imgs?.[ComfyApp.clipspace?.selectedIndex ?? 0]?.src ?? ""; - this.sidebarImage = image_layer_image; - image_layer_image_container.appendChild(image_layer_image); - image_layer_container.appendChild(image_layer_visibility_checkbox); - image_layer_container.appendChild(image_layer_image_container); - image_layer_settings_container.appendChild(image_layer_settings_title); - image_layer_settings_container.appendChild(mask_layer_title); - image_layer_settings_container.appendChild(mask_layer_container); - image_layer_settings_container.appendChild( - mask_layer_opacity_sliderObj.container - ); - image_layer_settings_container.appendChild(image_layer_title); - image_layer_settings_container.appendChild(image_layer_container); - return image_layer_settings_container; - } - createHeadline(title) { - var headline = document.createElement("h3"); - headline.classList.add("maskEditor_sidePanelTitle"); - headline.innerText = title; - return headline; - } - createContainer(flexDirection) { - var container = document.createElement("div"); - if (flexDirection) { - container.classList.add("maskEditor_sidePanelContainerColumn"); - } else { - container.classList.add("maskEditor_sidePanelContainerRow"); - } - return container; - } - createContainerTitle(title) { - var container_title = document.createElement("span"); - container_title.classList.add("maskEditor_sidePanelSubTitle"); - container_title.innerText = title; - return container_title; - } - createSlider(title, min, max2, step, value, callback) { - var slider_container = this.createContainer(true); - var slider_title = this.createContainerTitle(title); - var slider = document.createElement("input"); - slider.classList.add("maskEditor_sidePanelBrushRange"); - slider.setAttribute("type", "range"); - slider.setAttribute("min", String(min)); - slider.setAttribute("max", String(max2)); - slider.setAttribute("step", String(step)); - slider.setAttribute("value", String(value)); - slider.addEventListener("input", (event) => { - callback(event, event.target.value); - }); - slider_container.appendChild(slider_title); - slider_container.appendChild(slider); - return { container: slider_container, slider }; - } - createToggle(title, callback) { - var outer_Container = this.createContainer(false); - var toggle_title = this.createContainerTitle(title); - var toggle_container = document.createElement("label"); - toggle_container.classList.add("maskEditor_sidePanelToggleContainer"); - var toggle_checkbox = document.createElement("input"); - toggle_checkbox.setAttribute("type", "checkbox"); - toggle_checkbox.classList.add("maskEditor_sidePanelToggleCheckbox"); - toggle_checkbox.addEventListener("change", (event) => { - callback(event, event.target.checked); - }); - var toggleAccentColor = this.darkMode ? "maskEditor_toggle_bg_dark" : "maskEditor_toggle_bg_light"; - var toggle_switch = document.createElement("div"); - toggle_switch.classList.add("maskEditor_sidePanelToggleSwitch"); - toggle_switch.classList.add(toggleAccentColor); - toggle_container.appendChild(toggle_checkbox); - toggle_container.appendChild(toggle_switch); - outer_Container.appendChild(toggle_title); - outer_Container.appendChild(toggle_container); - return outer_Container; - } - createDropdown(title, options, callback) { - const sidePanelDropdownAccent = this.darkMode ? "maskEditor_sidePanelDropdown_dark" : "maskEditor_sidePanelDropdown_light"; - var dropdown_container = this.createContainer(false); - var dropdown_title = this.createContainerTitle(title); - var dropdown = document.createElement("select"); - dropdown.classList.add(sidePanelDropdownAccent); - dropdown.classList.add("maskEditor_containerDropdown"); - options.forEach((option) => { - var option_element = document.createElement("option"); - option_element.value = option; - option_element.innerText = option; - dropdown.appendChild(option_element); - }); - dropdown.addEventListener("change", (event) => { - callback(event, event.target.value); - }); - dropdown_container.appendChild(dropdown_title); - dropdown_container.appendChild(dropdown); - return dropdown_container; - } - createSeparator() { - var separator = document.createElement("div"); - separator.classList.add("maskEditor_sidePanelSeparator"); - return separator; - } - //---------------- - async createTopBar() { - const buttonAccentColor = this.darkMode ? "maskEditor_topPanelButton_dark" : "maskEditor_topPanelButton_light"; - const iconButtonAccentColor = this.darkMode ? "maskEditor_topPanelIconButton_dark" : "maskEditor_topPanelIconButton_light"; - var top_bar = document.createElement("div"); - top_bar.id = "maskEditor_topBar"; - var top_bar_title_container = document.createElement("div"); - top_bar_title_container.id = "maskEditor_topBarTitleContainer"; - var top_bar_title = document.createElement("h1"); - top_bar_title.id = "maskEditor_topBarTitle"; - top_bar_title.innerText = "ComfyUI"; - top_bar_title_container.appendChild(top_bar_title); - var top_bar_shortcuts_container = document.createElement("div"); - top_bar_shortcuts_container.id = "maskEditor_topBarShortcutsContainer"; - var top_bar_undo_button = document.createElement("div"); - top_bar_undo_button.id = "maskEditor_topBarUndoButton"; - top_bar_undo_button.classList.add(iconButtonAccentColor); - top_bar_undo_button.innerHTML = ' '; - top_bar_undo_button.addEventListener("click", () => { - this.messageBroker.publish("undo"); - }); - var top_bar_redo_button = document.createElement("div"); - top_bar_redo_button.id = "maskEditor_topBarRedoButton"; - top_bar_redo_button.classList.add(iconButtonAccentColor); - top_bar_redo_button.innerHTML = ' '; - top_bar_redo_button.addEventListener("click", () => { - this.messageBroker.publish("redo"); - }); - var top_bar_invert_button = document.createElement("button"); - top_bar_invert_button.id = "maskEditor_topBarInvertButton"; - top_bar_invert_button.classList.add(buttonAccentColor); - top_bar_invert_button.innerText = "Invert"; - top_bar_invert_button.addEventListener("click", () => { - this.messageBroker.publish("invert"); - }); - var top_bar_clear_button = document.createElement("button"); - top_bar_clear_button.id = "maskEditor_topBarClearButton"; - top_bar_clear_button.classList.add(buttonAccentColor); - top_bar_clear_button.innerText = "Clear"; - top_bar_clear_button.addEventListener("click", () => { - this.maskCtx.clearRect( - 0, - 0, - this.maskCanvas.width, - this.maskCanvas.height - ); - this.messageBroker.publish("saveState"); - }); - var top_bar_save_button = document.createElement("button"); - top_bar_save_button.id = "maskEditor_topBarSaveButton"; - top_bar_save_button.classList.add(buttonAccentColor); - top_bar_save_button.innerText = "Save"; - this.saveButton = top_bar_save_button; - top_bar_save_button.addEventListener("click", () => { - this.maskEditor.save(); - }); - var top_bar_cancel_button = document.createElement("button"); - top_bar_cancel_button.id = "maskEditor_topBarCancelButton"; - top_bar_cancel_button.classList.add(buttonAccentColor); - top_bar_cancel_button.innerText = "Cancel"; - top_bar_cancel_button.addEventListener("click", () => { - this.maskEditor.close(); - }); - top_bar_shortcuts_container.appendChild(top_bar_undo_button); - top_bar_shortcuts_container.appendChild(top_bar_redo_button); - top_bar_shortcuts_container.appendChild(top_bar_invert_button); - top_bar_shortcuts_container.appendChild(top_bar_clear_button); - top_bar_shortcuts_container.appendChild(top_bar_save_button); - top_bar_shortcuts_container.appendChild(top_bar_cancel_button); - top_bar.appendChild(top_bar_title_container); - top_bar.appendChild(top_bar_shortcuts_container); - return top_bar; - } - createToolPanel() { - var tool_panel = document.createElement("div"); - tool_panel.id = "maskEditor_toolPanel"; - this.toolPanel = tool_panel; - var toolPanelHoverAccent = this.darkMode ? "maskEditor_toolPanelContainerDark" : "maskEditor_toolPanelContainerLight"; - var toolElements = []; - var toolPanel_brushToolContainer = document.createElement("div"); - toolPanel_brushToolContainer.classList.add("maskEditor_toolPanelContainer"); - toolPanel_brushToolContainer.classList.add( - "maskEditor_toolPanelContainerSelected" - ); - toolPanel_brushToolContainer.classList.add(toolPanelHoverAccent); - toolPanel_brushToolContainer.innerHTML = ` - - - - - `; - toolElements.push(toolPanel_brushToolContainer); - toolPanel_brushToolContainer.addEventListener("click", () => { - this.messageBroker.publish( - "setTool", - "pen" - /* Pen */ - ); - for (let toolElement of toolElements) { - if (toolElement != toolPanel_brushToolContainer) { - toolElement.classList.remove("maskEditor_toolPanelContainerSelected"); - } else { - toolElement.classList.add("maskEditor_toolPanelContainerSelected"); - this.brushSettingsHTML.style.display = "flex"; - this.colorSelectSettingsHTML.style.display = "none"; - this.paintBucketSettingsHTML.style.display = "none"; - } - } - this.messageBroker.publish( - "setTool", - "pen" - /* Pen */ - ); - this.pointerZone.style.cursor = "none"; - }); - var toolPanel_brushToolIndicator = document.createElement("div"); - toolPanel_brushToolIndicator.classList.add("maskEditor_toolPanelIndicator"); - toolPanel_brushToolContainer.appendChild(toolPanel_brushToolIndicator); - var toolPanel_eraserToolContainer = document.createElement("div"); - toolPanel_eraserToolContainer.classList.add("maskEditor_toolPanelContainer"); - toolPanel_eraserToolContainer.classList.add(toolPanelHoverAccent); - toolPanel_eraserToolContainer.innerHTML = ` - - - - - - - - `; - toolElements.push(toolPanel_eraserToolContainer); - toolPanel_eraserToolContainer.addEventListener("click", () => { - this.messageBroker.publish( - "setTool", - "eraser" - /* Eraser */ - ); - for (let toolElement of toolElements) { - if (toolElement != toolPanel_eraserToolContainer) { - toolElement.classList.remove("maskEditor_toolPanelContainerSelected"); - } else { - toolElement.classList.add("maskEditor_toolPanelContainerSelected"); - this.brushSettingsHTML.style.display = "flex"; - this.colorSelectSettingsHTML.style.display = "none"; - this.paintBucketSettingsHTML.style.display = "none"; - } - } - this.messageBroker.publish( - "setTool", - "eraser" - /* Eraser */ - ); - this.pointerZone.style.cursor = "none"; - }); - var toolPanel_eraserToolIndicator = document.createElement("div"); - toolPanel_eraserToolIndicator.classList.add("maskEditor_toolPanelIndicator"); - toolPanel_eraserToolContainer.appendChild(toolPanel_eraserToolIndicator); - var toolPanel_paintBucketToolContainer = document.createElement("div"); - toolPanel_paintBucketToolContainer.classList.add( - "maskEditor_toolPanelContainer" - ); - toolPanel_paintBucketToolContainer.classList.add(toolPanelHoverAccent); - toolPanel_paintBucketToolContainer.innerHTML = ` - - - - - - `; - toolElements.push(toolPanel_paintBucketToolContainer); - toolPanel_paintBucketToolContainer.addEventListener("click", () => { - this.messageBroker.publish( - "setTool", - "paintBucket" - /* PaintBucket */ - ); - for (let toolElement of toolElements) { - if (toolElement != toolPanel_paintBucketToolContainer) { - toolElement.classList.remove("maskEditor_toolPanelContainerSelected"); - } else { - toolElement.classList.add("maskEditor_toolPanelContainerSelected"); - this.brushSettingsHTML.style.display = "none"; - this.colorSelectSettingsHTML.style.display = "none"; - this.paintBucketSettingsHTML.style.display = "flex"; - } - } - this.messageBroker.publish( - "setTool", - "paintBucket" - /* PaintBucket */ - ); - this.pointerZone.style.cursor = "url('/cursor/paintBucket.png') 30 25, auto"; - this.brush.style.opacity = "0"; - }); - var toolPanel_paintBucketToolIndicator = document.createElement("div"); - toolPanel_paintBucketToolIndicator.classList.add( - "maskEditor_toolPanelIndicator" - ); - toolPanel_paintBucketToolContainer.appendChild( - toolPanel_paintBucketToolIndicator - ); - var toolPanel_colorSelectToolContainer = document.createElement("div"); - toolPanel_colorSelectToolContainer.classList.add( - "maskEditor_toolPanelContainer" - ); - toolPanel_colorSelectToolContainer.classList.add(toolPanelHoverAccent); - toolPanel_colorSelectToolContainer.innerHTML = ` - - - - `; - toolElements.push(toolPanel_colorSelectToolContainer); - toolPanel_colorSelectToolContainer.addEventListener("click", () => { - this.messageBroker.publish("setTool", "colorSelect"); - for (let toolElement of toolElements) { - if (toolElement != toolPanel_colorSelectToolContainer) { - toolElement.classList.remove("maskEditor_toolPanelContainerSelected"); - } else { - toolElement.classList.add("maskEditor_toolPanelContainerSelected"); - this.brushSettingsHTML.style.display = "none"; - this.paintBucketSettingsHTML.style.display = "none"; - this.colorSelectSettingsHTML.style.display = "flex"; - } - } - this.messageBroker.publish( - "setTool", - "colorSelect" - /* ColorSelect */ - ); - this.pointerZone.style.cursor = "url('/cursor/colorSelect.png') 15 25, auto"; - this.brush.style.opacity = "0"; - }); - var toolPanel_colorSelectToolIndicator = document.createElement("div"); - toolPanel_colorSelectToolIndicator.classList.add( - "maskEditor_toolPanelIndicator" - ); - toolPanel_colorSelectToolContainer.appendChild( - toolPanel_colorSelectToolIndicator - ); - var toolPanel_zoomIndicator = document.createElement("div"); - toolPanel_zoomIndicator.classList.add("maskEditor_toolPanelZoomIndicator"); - toolPanel_zoomIndicator.classList.add(toolPanelHoverAccent); - var toolPanel_zoomText = document.createElement("span"); - toolPanel_zoomText.id = "maskEditor_toolPanelZoomText"; - toolPanel_zoomText.innerText = "100%"; - this.zoomTextHTML = toolPanel_zoomText; - var toolPanel_DimensionsText = document.createElement("span"); - toolPanel_DimensionsText.id = "maskEditor_toolPanelDimensionsText"; - toolPanel_DimensionsText.innerText = " "; - this.dimensionsTextHTML = toolPanel_DimensionsText; - toolPanel_zoomIndicator.appendChild(toolPanel_zoomText); - toolPanel_zoomIndicator.appendChild(toolPanel_DimensionsText); - toolPanel_zoomIndicator.addEventListener("click", () => { - this.messageBroker.publish("resetZoom"); - }); - tool_panel.appendChild(toolPanel_brushToolContainer); - tool_panel.appendChild(toolPanel_eraserToolContainer); - tool_panel.appendChild(toolPanel_paintBucketToolContainer); - tool_panel.appendChild(toolPanel_colorSelectToolContainer); - tool_panel.appendChild(toolPanel_zoomIndicator); - return tool_panel; - } - createPointerZone() { - const pointer_zone = document.createElement("div"); - pointer_zone.id = "maskEditor_pointerZone"; - this.pointerZone = pointer_zone; - pointer_zone.addEventListener("pointerdown", (event) => { - this.messageBroker.publish("pointerDown", event); - }); - pointer_zone.addEventListener("pointermove", (event) => { - this.messageBroker.publish("pointerMove", event); - }); - pointer_zone.addEventListener("pointerup", (event) => { - this.messageBroker.publish("pointerUp", event); - }); - pointer_zone.addEventListener("pointerleave", (event) => { - this.brush.style.opacity = "0"; - this.pointerZone.style.cursor = ""; - }); - pointer_zone.addEventListener("touchstart", (event) => { - this.messageBroker.publish("handleTouchStart", event); - }); - pointer_zone.addEventListener("touchmove", (event) => { - this.messageBroker.publish("handleTouchMove", event); - }); - pointer_zone.addEventListener("touchend", (event) => { - this.messageBroker.publish("handleTouchEnd", event); - }); - pointer_zone.addEventListener( - "wheel", - (event) => this.messageBroker.publish("wheel", event) - ); - pointer_zone.addEventListener( - "pointerenter", - async (event) => { - this.updateCursor(); - } - ); - return pointer_zone; - } - async screenToCanvas(clientPoint) { - const zoomRatio = await this.messageBroker.pull("zoomRatio"); - const canvasRect = this.maskCanvas.getBoundingClientRect(); - const offsetX = clientPoint.x - canvasRect.left + this.toolPanel.clientWidth; - const offsetY = clientPoint.y - canvasRect.top + 44; - const x = offsetX / zoomRatio; - const y = offsetY / zoomRatio; - return { x, y }; - } - setEventHandler() { - this.maskCanvas.addEventListener("contextmenu", (event) => { - event.preventDefault(); - }); - this.rootElement.addEventListener("contextmenu", (event) => { - event.preventDefault(); - }); - this.rootElement.addEventListener("dragstart", (event) => { - if (event.ctrlKey) { - event.preventDefault(); - } - }); - } - async createBrush() { - var brush = document.createElement("div"); - const brushSettings = await this.messageBroker.pull("brushSettings"); - brush.id = "maskEditor_brush"; - var brush_preview_gradient = document.createElement("div"); - brush_preview_gradient.id = "maskEditor_brushPreviewGradient"; - brush.appendChild(brush_preview_gradient); - this.brush = brush; - this.brushPreviewGradient = brush_preview_gradient; - return brush; - } - async setImages(imgCanvas) { - const imgCtx = imgCanvas.getContext("2d", { willReadFrequently: true }); - const maskCtx = this.maskCtx; - const maskCanvas = this.maskCanvas; - imgCtx.clearRect(0, 0, this.imgCanvas.width, this.imgCanvas.height); - maskCtx.clearRect(0, 0, this.maskCanvas.width, this.maskCanvas.height); - const alpha_url = new URL( - ComfyApp.clipspace?.imgs?.[ComfyApp.clipspace?.selectedIndex ?? 0]?.src ?? "" - ); - alpha_url.searchParams.delete("channel"); - alpha_url.searchParams.delete("preview"); - alpha_url.searchParams.set("channel", "a"); - let mask_image = await this.loadImage(alpha_url); - if (!ComfyApp.clipspace?.imgs?.[ComfyApp.clipspace?.selectedIndex ?? 0]?.src) { - throw new Error( - "Unable to access image source - clipspace or image is null" - ); - } - const rgb_url = new URL( - ComfyApp.clipspace.imgs[ComfyApp.clipspace.selectedIndex].src - ); - this.imageURL = rgb_url; - console.log(rgb_url); - rgb_url.searchParams.delete("channel"); - rgb_url.searchParams.set("channel", "rgb"); - this.image = new Image(); - this.image = await new Promise((resolve, reject) => { - const img = new Image(); - img.onload = () => resolve(img); - img.onerror = reject; - img.src = rgb_url.toString(); - }); - maskCanvas.width = this.image.width; - maskCanvas.height = this.image.height; - this.dimensionsTextHTML.innerText = `${this.image.width}x${this.image.height}`; - await this.invalidateCanvas(this.image, mask_image); - this.messageBroker.publish("initZoomPan", [this.image, this.rootElement]); - } - async invalidateCanvas(orig_image, mask_image) { - this.imgCanvas.width = orig_image.width; - this.imgCanvas.height = orig_image.height; - this.maskCanvas.width = orig_image.width; - this.maskCanvas.height = orig_image.height; - let imgCtx = this.imgCanvas.getContext("2d", { willReadFrequently: true }); - let maskCtx = this.maskCanvas.getContext("2d", { - willReadFrequently: true - }); - imgCtx.drawImage(orig_image, 0, 0, orig_image.width, orig_image.height); - await this.prepare_mask( - mask_image, - this.maskCanvas, - maskCtx, - await this.getMaskColor() - ); - } - async prepare_mask(image, maskCanvas, maskCtx, maskColor) { - maskCtx.drawImage(image, 0, 0, maskCanvas.width, maskCanvas.height); - const maskData = maskCtx.getImageData( - 0, - 0, - maskCanvas.width, - maskCanvas.height - ); - for (let i = 0; i < maskData.data.length; i += 4) { - const alpha = maskData.data[i + 3]; - maskData.data[i] = maskColor.r; - maskData.data[i + 1] = maskColor.g; - maskData.data[i + 2] = maskColor.b; - maskData.data[i + 3] = 255 - alpha; - } - maskCtx.globalCompositeOperation = "source-over"; - maskCtx.putImageData(maskData, 0, 0); - } - async updateMaskColor() { - const maskCanvasStyle = this.getMaskCanvasStyle(); - this.maskCanvas.style.mixBlendMode = maskCanvasStyle.mixBlendMode; - this.maskCanvas.style.opacity = maskCanvasStyle.opacity.toString(); - const maskColor = await this.getMaskColor(); - this.maskCtx.fillStyle = `rgb(${maskColor.r}, ${maskColor.g}, ${maskColor.b})`; - this.setCanvasBackground(); - const maskData = this.maskCtx.getImageData( - 0, - 0, - this.maskCanvas.width, - this.maskCanvas.height - ); - for (let i = 0; i < maskData.data.length; i += 4) { - maskData.data[i] = maskColor.r; - maskData.data[i + 1] = maskColor.g; - maskData.data[i + 2] = maskColor.b; - } - this.maskCtx.putImageData(maskData, 0, 0); - } - getMaskCanvasStyle() { - if (this.maskBlendMode === "negative") { - return { - mixBlendMode: "difference", - opacity: "1" - }; - } else { - return { - mixBlendMode: "initial", - opacity: this.mask_opacity - }; - } - } - detectLightMode() { - this.darkMode = document.body.classList.contains("dark-theme"); - } - loadImage(imagePath) { - return new Promise((resolve, reject) => { - const image = new Image(); - image.onload = function() { - resolve(image); - }; - image.onerror = function(error) { - reject(error); - }; - image.src = imagePath.href; - }); - } - async updateBrushPreview() { - const cursorPoint = await this.messageBroker.pull("cursorPoint"); - const pan_offset = await this.messageBroker.pull("panOffset"); - const brushSettings = await this.messageBroker.pull("brushSettings"); - const zoom_ratio = await this.messageBroker.pull("zoomRatio"); - const centerX = cursorPoint.x + pan_offset.x; - const centerY = cursorPoint.y + pan_offset.y; - const brush = this.brush; - const hardness = brushSettings.hardness; - const extendedSize = brushSettings.size * (2 - hardness) * 2 * zoom_ratio; - this.brushSizeSlider.value = String(brushSettings.size); - this.brushHardnessSlider.value = String(hardness); - brush.style.width = extendedSize + "px"; - brush.style.height = extendedSize + "px"; - brush.style.left = centerX - extendedSize / 2 + "px"; - brush.style.top = centerY - extendedSize / 2 + "px"; - if (hardness === 1) { - this.brushPreviewGradient.style.background = "rgba(255, 0, 0, 0.5)"; - return; - } - const opacityStop = hardness / 4 + 0.25; - this.brushPreviewGradient.style.background = ` - radial-gradient( - circle, - rgba(255, 0, 0, 0.5) 0%, - rgba(255, 0, 0, ${opacityStop}) ${hardness * 100}%, - rgba(255, 0, 0, 0) 100% - ) - `; - } - getMaskBlendMode() { - return this.maskBlendMode; - } - setSidebarImage() { - this.sidebarImage.src = this.imageURL.href; - } - async getMaskColor() { - if (this.maskBlendMode === "black") { - return { r: 0, g: 0, b: 0 }; - } - if (this.maskBlendMode === "white") { - return { r: 255, g: 255, b: 255 }; - } - if (this.maskBlendMode === "negative") { - return { r: 255, g: 255, b: 255 }; - } - return { r: 0, g: 0, b: 0 }; - } - async getMaskFillStyle() { - const maskColor = await this.getMaskColor(); - return "rgb(" + maskColor.r + "," + maskColor.g + "," + maskColor.b + ")"; - } - async setCanvasBackground() { - if (this.maskBlendMode === "white") { - this.canvasBackground.style.background = "black"; - } else { - this.canvasBackground.style.background = "white"; - } - } - getMaskCanvas() { - return this.maskCanvas; - } - getImgCanvas() { - return this.imgCanvas; - } - getImage() { - return this.image; - } - setBrushOpacity(opacity) { - this.brush.style.opacity = String(opacity); - } - setSaveButtonEnabled(enabled) { - this.saveButton.disabled = !enabled; - } - setSaveButtonText(text) { - this.saveButton.innerText = text; - } - handlePaintBucketCursor(isPaintBucket) { - if (isPaintBucket) { - this.pointerZone.style.cursor = "url('/cursor/paintBucket.png') 30 25, auto"; - } else { - this.pointerZone.style.cursor = "none"; - } - } - handlePanCursor(isPanning) { - if (isPanning) { - this.pointerZone.style.cursor = "grabbing"; - } else { - this.pointerZone.style.cursor = "none"; - } - } - setBrushVisibility(visible) { - this.brush.style.opacity = visible ? "1" : "0"; - } - setBrushPreviewGradientVisibility(visible) { - this.brushPreviewGradient.style.display = visible ? "block" : "none"; - } - async updateCursor() { - const currentTool = await this.messageBroker.pull("currentTool"); - if (currentTool === "paintBucket") { - this.pointerZone.style.cursor = "url('/cursor/paintBucket.png') 30 25, auto"; - this.setBrushOpacity(0); - } else if (currentTool === "colorSelect") { - this.pointerZone.style.cursor = "url('/cursor/colorSelect.png') 15 25, auto"; - this.setBrushOpacity(0); - } else { - this.pointerZone.style.cursor = "none"; - this.setBrushOpacity(1); - } - this.updateBrushPreview(); - this.setBrushPreviewGradientVisibility(false); - } - setZoomText(zoomText) { - this.zoomTextHTML.innerText = zoomText; - } - setDimensionsText(dimensionsText) { - this.dimensionsTextHTML.innerText = dimensionsText; - } -} -class ToolManager { - static { - __name(this, "ToolManager"); - } - maskEditor; - messageBroker; - mouseDownPoint = null; - currentTool = "pen"; - isAdjustingBrush = false; - // is user adjusting brush size or hardness with alt + right mouse button - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.addListeners(); - this.addPullTopics(); - } - addListeners() { - this.messageBroker.subscribe("setTool", async (tool) => { - this.setTool(tool); - }); - this.messageBroker.subscribe("pointerDown", async (event) => { - this.handlePointerDown(event); - }); - this.messageBroker.subscribe("pointerMove", async (event) => { - this.handlePointerMove(event); - }); - this.messageBroker.subscribe("pointerUp", async (event) => { - this.handlePointerUp(event); - }); - this.messageBroker.subscribe("wheel", async (event) => { - this.handleWheelEvent(event); - }); - } - async addPullTopics() { - this.messageBroker.createPullTopic( - "currentTool", - async () => this.getCurrentTool() - ); - } - //tools - setTool(tool) { - this.currentTool = tool; - if (tool != "colorSelect") { - this.messageBroker.publish("clearLastPoint"); - } - } - getCurrentTool() { - return this.currentTool; - } - async handlePointerDown(event) { - event.preventDefault(); - if (event.pointerType == "touch") return; - var isSpacePressed = await this.messageBroker.pull("isKeyPressed", " "); - if (event.buttons === 4 || event.buttons === 1 && isSpacePressed) { - this.messageBroker.publish("panStart", event); - this.messageBroker.publish("setBrushVisibility", false); - return; - } - if (this.currentTool === "paintBucket" && event.button === 0) { - const offset = { x: event.offsetX, y: event.offsetY }; - const coords_canvas = await this.messageBroker.pull( - "screenToCanvas", - offset - ); - this.messageBroker.publish("paintBucketFill", coords_canvas); - this.messageBroker.publish("saveState"); - return; - } - if (this.currentTool === "colorSelect" && event.button === 0) { - const offset = { x: event.offsetX, y: event.offsetY }; - const coords_canvas = await this.messageBroker.pull( - "screenToCanvas", - offset - ); - this.messageBroker.publish("colorSelectFill", coords_canvas); - return; - } - if (event.altKey && event.button === 2) { - this.isAdjustingBrush = true; - this.messageBroker.publish("brushAdjustmentStart", event); - return; - } - var isDrawingTool = [ - "pen", - "eraser" - /* Eraser */ - ].includes(this.currentTool); - if ([0, 2].includes(event.button) && isDrawingTool) { - this.messageBroker.publish("drawStart", event); - return; - } - } - async handlePointerMove(event) { - event.preventDefault(); - if (event.pointerType == "touch") return; - const newCursorPoint = { x: event.clientX, y: event.clientY }; - this.messageBroker.publish("cursorPoint", newCursorPoint); - var isSpacePressed = await this.messageBroker.pull("isKeyPressed", " "); - this.messageBroker.publish("updateBrushPreview"); - if (event.buttons === 4 || event.buttons === 1 && isSpacePressed) { - this.messageBroker.publish("panMove", event); - return; - } - var isDrawingTool = [ - "pen", - "eraser" - /* Eraser */ - ].includes(this.currentTool); - if (!isDrawingTool) return; - if (this.isAdjustingBrush && (this.currentTool === "pen" || this.currentTool === "eraser") && event.altKey && event.buttons === 2) { - this.messageBroker.publish("brushAdjustment", event); - return; - } - if (event.buttons == 1 || event.buttons == 2) { - this.messageBroker.publish("draw", event); - return; - } - } - handlePointerUp(event) { - this.messageBroker.publish("panCursor", false); - if (event.pointerType === "touch") return; - this.messageBroker.publish("updateCursor"); - this.isAdjustingBrush = false; - this.messageBroker.publish("drawEnd", event); - this.mouseDownPoint = null; - } - handleWheelEvent(event) { - this.messageBroker.publish("zoom", event); - const newCursorPoint = { x: event.clientX, y: event.clientY }; - this.messageBroker.publish("cursorPoint", newCursorPoint); - } -} -class PanAndZoomManager { - static { - __name(this, "PanAndZoomManager"); - } - maskEditor; - messageBroker; - DOUBLE_TAP_DELAY = 300; - lastTwoFingerTap = 0; - isTouchZooming = false; - lastTouchZoomDistance = 0; - lastTouchMidPoint = { x: 0, y: 0 }; - lastTouchPoint = { x: 0, y: 0 }; - zoom_ratio = 1; - interpolatedZoomRatio = 1; - pan_offset = { x: 0, y: 0 }; - mouseDownPoint = null; - initialPan = { x: 0, y: 0 }; - canvasContainer = null; - maskCanvas = null; - rootElement = null; - image = null; - imageRootWidth = 0; - imageRootHeight = 0; - cursorPoint = { x: 0, y: 0 }; - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.addListeners(); - this.addPullTopics(); - } - addListeners() { - this.messageBroker.subscribe( - "initZoomPan", - async (args) => { - await this.initializeCanvasPanZoom(args[0], args[1]); - } - ); - this.messageBroker.subscribe("panStart", async (event) => { - this.handlePanStart(event); - }); - this.messageBroker.subscribe("panMove", async (event) => { - this.handlePanMove(event); - }); - this.messageBroker.subscribe("zoom", async (event) => { - this.zoom(event); - }); - this.messageBroker.subscribe("cursorPoint", async (point) => { - this.updateCursorPosition(point); - }); - this.messageBroker.subscribe( - "handleTouchStart", - async (event) => { - this.handleTouchStart(event); - } - ); - this.messageBroker.subscribe( - "handleTouchMove", - async (event) => { - this.handleTouchMove(event); - } - ); - this.messageBroker.subscribe( - "handleTouchEnd", - async (event) => { - this.handleTouchEnd(event); - } - ); - this.messageBroker.subscribe("resetZoom", async () => { - if (this.interpolatedZoomRatio === 1) return; - await this.smoothResetView(); - }); - } - addPullTopics() { - this.messageBroker.createPullTopic( - "cursorPoint", - async () => this.cursorPoint - ); - this.messageBroker.createPullTopic("zoomRatio", async () => this.zoom_ratio); - this.messageBroker.createPullTopic("panOffset", async () => this.pan_offset); - } - handleTouchStart(event) { - event.preventDefault(); - if (event.touches[0].touchType === "stylus") return; - this.messageBroker.publish("setBrushVisibility", false); - if (event.touches.length === 2) { - const currentTime = (/* @__PURE__ */ new Date()).getTime(); - const tapTimeDiff = currentTime - this.lastTwoFingerTap; - if (tapTimeDiff < this.DOUBLE_TAP_DELAY) { - this.handleDoubleTap(); - this.lastTwoFingerTap = 0; - } else { - this.lastTwoFingerTap = currentTime; - this.isTouchZooming = true; - this.lastTouchZoomDistance = this.getTouchDistance(event.touches); - const midpoint = this.getTouchMidpoint(event.touches); - this.lastTouchMidPoint = midpoint; - } - } else if (event.touches.length === 1) { - this.lastTouchPoint = { - x: event.touches[0].clientX, - y: event.touches[0].clientY - }; - } - } - async handleTouchMove(event) { - event.preventDefault(); - if (event.touches[0].touchType === "stylus") return; - this.lastTwoFingerTap = 0; - if (this.isTouchZooming && event.touches.length === 2) { - const newDistance = this.getTouchDistance(event.touches); - const zoomFactor = newDistance / this.lastTouchZoomDistance; - const oldZoom = this.zoom_ratio; - this.zoom_ratio = Math.max( - 0.2, - Math.min(10, this.zoom_ratio * zoomFactor) - ); - const newZoom = this.zoom_ratio; - const midpoint = this.getTouchMidpoint(event.touches); - if (this.lastTouchMidPoint) { - const deltaX = midpoint.x - this.lastTouchMidPoint.x; - const deltaY = midpoint.y - this.lastTouchMidPoint.y; - this.pan_offset.x += deltaX; - this.pan_offset.y += deltaY; - } - if (this.maskCanvas === null) { - this.maskCanvas = await this.messageBroker.pull("maskCanvas"); - } - const rect = this.maskCanvas.getBoundingClientRect(); - const touchX = midpoint.x - rect.left; - const touchY = midpoint.y - rect.top; - const scaleFactor = newZoom / oldZoom; - this.pan_offset.x += touchX - touchX * scaleFactor; - this.pan_offset.y += touchY - touchY * scaleFactor; - this.invalidatePanZoom(); - this.lastTouchZoomDistance = newDistance; - this.lastTouchMidPoint = midpoint; - } else if (event.touches.length === 1) { - this.handleSingleTouchPan(event.touches[0]); - } - } - handleTouchEnd(event) { - event.preventDefault(); - if (event.touches.length === 0 && event.touches[0].touchType === "stylus") { - return; - } - this.isTouchZooming = false; - this.lastTouchMidPoint = { x: 0, y: 0 }; - if (event.touches.length === 0) { - this.lastTouchPoint = { x: 0, y: 0 }; - } else if (event.touches.length === 1) { - this.lastTouchPoint = { - x: event.touches[0].clientX, - y: event.touches[0].clientY - }; - } - } - getTouchDistance(touches) { - const dx = touches[0].clientX - touches[1].clientX; - const dy = touches[0].clientY - touches[1].clientY; - return Math.sqrt(dx * dx + dy * dy); - } - getTouchMidpoint(touches) { - return { - x: (touches[0].clientX + touches[1].clientX) / 2, - y: (touches[0].clientY + touches[1].clientY) / 2 - }; - } - async handleSingleTouchPan(touch) { - if (this.lastTouchPoint === null) { - this.lastTouchPoint = { x: touch.clientX, y: touch.clientY }; - return; - } - const deltaX = touch.clientX - this.lastTouchPoint.x; - const deltaY = touch.clientY - this.lastTouchPoint.y; - this.pan_offset.x += deltaX; - this.pan_offset.y += deltaY; - await this.invalidatePanZoom(); - this.lastTouchPoint = { x: touch.clientX, y: touch.clientY }; - } - updateCursorPosition(clientPoint) { - var cursorX = clientPoint.x - this.pan_offset.x; - var cursorY = clientPoint.y - this.pan_offset.y; - this.cursorPoint = { x: cursorX, y: cursorY }; - } - //prob redundant - handleDoubleTap() { - this.messageBroker.publish("undo"); - } - async zoom(event) { - const cursorPoint = { x: event.clientX, y: event.clientY }; - const oldZoom = this.zoom_ratio; - const zoomFactor = event.deltaY < 0 ? 1.1 : 0.9; - this.zoom_ratio = Math.max( - 0.2, - Math.min(10, this.zoom_ratio * zoomFactor) - ); - const newZoom = this.zoom_ratio; - const maskCanvas = await this.messageBroker.pull("maskCanvas"); - const rect = maskCanvas.getBoundingClientRect(); - const mouseX = cursorPoint.x - rect.left; - const mouseY = cursorPoint.y - rect.top; - console.log(oldZoom, newZoom); - const scaleFactor = newZoom / oldZoom; - this.pan_offset.x += mouseX - mouseX * scaleFactor; - this.pan_offset.y += mouseY - mouseY * scaleFactor; - await this.invalidatePanZoom(); - const newImageWidth = maskCanvas.clientWidth; - const zoomRatio = newImageWidth / this.imageRootWidth; - this.interpolatedZoomRatio = zoomRatio; - this.messageBroker.publish("setZoomText", `${Math.round(zoomRatio * 100)}%`); - this.updateCursorPosition(cursorPoint); - requestAnimationFrame(() => { - this.messageBroker.publish("updateBrushPreview"); - }); - } - async smoothResetView(duration = 500) { - const startZoom = this.zoom_ratio; - const startPan = { ...this.pan_offset }; - const sidePanelWidth = 220; - const toolPanelWidth = 64; - const topBarHeight = 44; - const availableWidth = this.rootElement.clientWidth - sidePanelWidth - toolPanelWidth; - const availableHeight = this.rootElement.clientHeight - topBarHeight; - const zoomRatioWidth = availableWidth / this.image.width; - const zoomRatioHeight = availableHeight / this.image.height; - const targetZoom = Math.min(zoomRatioWidth, zoomRatioHeight); - const aspectRatio = this.image.width / this.image.height; - let finalWidth = 0; - let finalHeight = 0; - const targetPan = { x: toolPanelWidth, y: topBarHeight }; - if (zoomRatioHeight > zoomRatioWidth) { - finalWidth = availableWidth; - finalHeight = finalWidth / aspectRatio; - targetPan.y = (availableHeight - finalHeight) / 2 + topBarHeight; - } else { - finalHeight = availableHeight; - finalWidth = finalHeight * aspectRatio; - targetPan.x = (availableWidth - finalWidth) / 2 + toolPanelWidth; - } - const startTime = performance.now(); - const animate = /* @__PURE__ */ __name((currentTime) => { - const elapsed = currentTime - startTime; - const progress = Math.min(elapsed / duration, 1); - const eased = 1 - Math.pow(1 - progress, 3); - const currentZoom = startZoom + (targetZoom - startZoom) * eased; - this.zoom_ratio = currentZoom; - this.pan_offset.x = startPan.x + (targetPan.x - startPan.x) * eased; - this.pan_offset.y = startPan.y + (targetPan.y - startPan.y) * eased; - this.invalidatePanZoom(); - const interpolatedZoomRatio = startZoom + (1 - startZoom) * eased; - this.messageBroker.publish( - "setZoomText", - `${Math.round(interpolatedZoomRatio * 100)}%` - ); - if (progress < 1) { - requestAnimationFrame(animate); - } - }, "animate"); - requestAnimationFrame(animate); - this.interpolatedZoomRatio = 1; - } - async initializeCanvasPanZoom(image, rootElement) { - let sidePanelWidth = 220; - const toolPanelWidth = 64; - let topBarHeight = 44; - this.rootElement = rootElement; - let availableWidth = rootElement.clientWidth - sidePanelWidth - toolPanelWidth; - let availableHeight = rootElement.clientHeight - topBarHeight; - let zoomRatioWidth = availableWidth / image.width; - let zoomRatioHeight = availableHeight / image.height; - let aspectRatio = image.width / image.height; - let finalWidth = 0; - let finalHeight = 0; - let pan_offset = { x: toolPanelWidth, y: topBarHeight }; - if (zoomRatioHeight > zoomRatioWidth) { - finalWidth = availableWidth; - finalHeight = finalWidth / aspectRatio; - pan_offset.y = (availableHeight - finalHeight) / 2 + topBarHeight; - } else { - finalHeight = availableHeight; - finalWidth = finalHeight * aspectRatio; - pan_offset.x = (availableWidth - finalWidth) / 2 + toolPanelWidth; - } - if (this.image === null) { - this.image = image; - } - this.imageRootWidth = finalWidth; - this.imageRootHeight = finalHeight; - this.zoom_ratio = Math.min(zoomRatioWidth, zoomRatioHeight); - this.pan_offset = pan_offset; - await this.invalidatePanZoom(); - } - async invalidatePanZoom() { - if (!this.image?.width || !this.image?.height || !this.pan_offset || !this.zoom_ratio) { - console.warn("Missing required properties for pan/zoom"); - return; - } - const raw_width = this.image.width * this.zoom_ratio; - const raw_height = this.image.height * this.zoom_ratio; - this.canvasContainer ??= await this.messageBroker?.pull("getCanvasContainer"); - if (!this.canvasContainer) return; - Object.assign(this.canvasContainer.style, { - width: `${raw_width}px`, - height: `${raw_height}px`, - left: `${this.pan_offset.x}px`, - top: `${this.pan_offset.y}px` - }); - } - handlePanStart(event) { - let coords_canvas = this.messageBroker.pull("screenToCanvas", { - x: event.offsetX, - y: event.offsetY - }); - this.mouseDownPoint = { x: event.clientX, y: event.clientY }; - this.messageBroker.publish("panCursor", true); - this.initialPan = this.pan_offset; - return; - } - handlePanMove(event) { - if (this.mouseDownPoint === null) throw new Error("mouseDownPoint is null"); - let deltaX = this.mouseDownPoint.x - event.clientX; - let deltaY = this.mouseDownPoint.y - event.clientY; - let pan_x = this.initialPan.x - deltaX; - let pan_y = this.initialPan.y - deltaY; - this.pan_offset = { x: pan_x, y: pan_y }; - this.invalidatePanZoom(); - } -} -class MessageBroker { - static { - __name(this, "MessageBroker"); - } - pushTopics = {}; - pullTopics = {}; - constructor() { - this.registerListeners(); - } - // Push - registerListeners() { - this.createPushTopic("panStart"); - this.createPushTopic("paintBucketFill"); - this.createPushTopic("saveState"); - this.createPushTopic("brushAdjustmentStart"); - this.createPushTopic("drawStart"); - this.createPushTopic("panMove"); - this.createPushTopic("updateBrushPreview"); - this.createPushTopic("brushAdjustment"); - this.createPushTopic("draw"); - this.createPushTopic("paintBucketCursor"); - this.createPushTopic("panCursor"); - this.createPushTopic("drawEnd"); - this.createPushTopic("zoom"); - this.createPushTopic("undo"); - this.createPushTopic("redo"); - this.createPushTopic("cursorPoint"); - this.createPushTopic("panOffset"); - this.createPushTopic("zoomRatio"); - this.createPushTopic("getMaskCanvas"); - this.createPushTopic("getCanvasContainer"); - this.createPushTopic("screenToCanvas"); - this.createPushTopic("isKeyPressed"); - this.createPushTopic("isCombinationPressed"); - this.createPushTopic("setPaintBucketTolerance"); - this.createPushTopic("setBrushSize"); - this.createPushTopic("setBrushHardness"); - this.createPushTopic("setBrushOpacity"); - this.createPushTopic("setBrushShape"); - this.createPushTopic("initZoomPan"); - this.createPushTopic("setTool"); - this.createPushTopic("pointerDown"); - this.createPushTopic("pointerMove"); - this.createPushTopic("pointerUp"); - this.createPushTopic("wheel"); - this.createPushTopic("initPaintBucketTool"); - this.createPushTopic("setBrushVisibility"); - this.createPushTopic("setBrushPreviewGradientVisibility"); - this.createPushTopic("handleTouchStart"); - this.createPushTopic("handleTouchMove"); - this.createPushTopic("handleTouchEnd"); - this.createPushTopic("colorSelectFill"); - this.createPushTopic("setColorSelectTolerance"); - this.createPushTopic("setLivePreview"); - this.createPushTopic("updateCursor"); - this.createPushTopic("setColorComparisonMethod"); - this.createPushTopic("clearLastPoint"); - this.createPushTopic("setWholeImage"); - this.createPushTopic("setMaskBoundary"); - this.createPushTopic("setMaskTolerance"); - this.createPushTopic("setBrushSmoothingPrecision"); - this.createPushTopic("setZoomText"); - this.createPushTopic("resetZoom"); - this.createPushTopic("invert"); - } - /** - * Creates a new push topic (listener is notified) - * - * @param {string} topicName - The name of the topic to create. - * @throws {Error} If the topic already exists. - */ - createPushTopic(topicName) { - if (this.topicExists(this.pushTopics, topicName)) { - throw new Error("Topic already exists"); - } - this.pushTopics[topicName] = []; - } - /** - * Subscribe a callback function to the given topic. - * - * @param {string} topicName - The name of the topic to subscribe to. - * @param {Callback} callback - The callback function to be subscribed. - * @throws {Error} If the topic does not exist. - */ - subscribe(topicName, callback) { - if (!this.topicExists(this.pushTopics, topicName)) { - throw new Error(`Topic "${topicName}" does not exist!`); - } - this.pushTopics[topicName].push(callback); - } - /** - * Removes a callback function from the list of subscribers for a given topic. - * - * @param {string} topicName - The name of the topic to unsubscribe from. - * @param {Callback} callback - The callback function to remove from the subscribers list. - * @throws {Error} If the topic does not exist in the list of topics. - */ - unsubscribe(topicName, callback) { - if (!this.topicExists(this.pushTopics, topicName)) { - throw new Error("Topic does not exist"); - } - const index = this.pushTopics[topicName].indexOf(callback); - if (index > -1) { - this.pushTopics[topicName].splice(index, 1); - } - } - /** - * Publishes data to a specified topic with variable number of arguments. - * @param {string} topicName - The name of the topic to publish to. - * @param {...any[]} args - Variable number of arguments to pass to subscribers - * @throws {Error} If the specified topic does not exist. - */ - publish(topicName, ...args) { - if (!this.topicExists(this.pushTopics, topicName)) { - throw new Error(`Topic "${topicName}" does not exist!`); - } - this.pushTopics[topicName].forEach((callback) => { - callback(...args); - }); - } - // Pull - /** - * Creates a new pull topic (listener must request data) - * - * @param {string} topicName - The name of the topic to create. - * @param {() => Promise} callBack - The callback function to be called when data is requested. - * @throws {Error} If the topic already exists. - */ - createPullTopic(topicName, callBack) { - if (this.topicExists(this.pullTopics, topicName)) { - throw new Error("Topic already exists"); - } - this.pullTopics[topicName] = callBack; - } - /** - * Requests data from a specified pull topic. - * @param {string} topicName - The name of the topic to request data from. - * @returns {Promise} - The data from the pull topic. - * @throws {Error} If the specified topic does not exist. - */ - async pull(topicName, data) { - if (!this.topicExists(this.pullTopics, topicName)) { - throw new Error("Topic does not exist"); - } - const callBack = this.pullTopics[topicName]; - try { - const result = await callBack(data); - return result; - } catch (error) { - console.error(`Error pulling data from topic "${topicName}":`, error); - throw error; - } - } - // Helper Methods - /** - * Checks if a topic exists in the given topics object. - * @param {Record} topics - The topics object to check. - * @param {string} topicName - The name of the topic to check. - * @returns {boolean} - True if the topic exists, false otherwise. - */ - topicExists(topics, topicName) { - return topics.hasOwnProperty(topicName); - } -} -class KeyboardManager { - static { - __name(this, "KeyboardManager"); - } - keysDown = []; - maskEditor; - messageBroker; - constructor(maskEditor) { - this.maskEditor = maskEditor; - this.messageBroker = maskEditor.getMessageBroker(); - this.addPullTopics(); - } - addPullTopics() { - this.messageBroker.createPullTopic( - "isKeyPressed", - (key) => Promise.resolve(this.isKeyDown(key)) - ); - } - addListeners() { - document.addEventListener("keydown", (event) => this.handleKeyDown(event)); - document.addEventListener("keyup", (event) => this.handleKeyUp(event)); - window.addEventListener("blur", () => this.clearKeys()); - } - removeListeners() { - document.removeEventListener( - "keydown", - (event) => this.handleKeyDown(event) - ); - document.removeEventListener("keyup", (event) => this.handleKeyUp(event)); - } - clearKeys() { - this.keysDown = []; - } - handleKeyDown(event) { - if (!this.keysDown.includes(event.key)) { - this.keysDown.push(event.key); - } - } - handleKeyUp(event) { - this.keysDown = this.keysDown.filter((key) => key !== event.key); - } - isKeyDown(key) { - return this.keysDown.includes(key); - } - // combinations - undoCombinationPressed() { - const combination = ["ctrl", "z"]; - const keysDownLower = this.keysDown.map((key) => key.toLowerCase()); - const result = combination.every((key) => keysDownLower.includes(key)); - if (result) this.messageBroker.publish("undo"); - return result; - } - redoCombinationPressed() { - const combination = ["ctrl", "shift", "z"]; - const keysDownLower = this.keysDown.map((key) => key.toLowerCase()); - const result = combination.every((key) => keysDownLower.includes(key)); - if (result) this.messageBroker.publish("redo"); - return result; - } -} -app.registerExtension({ - name: "Comfy.MaskEditor", - settings: [ - { - id: "Comfy.MaskEditor.UseNewEditor", - category: ["Mask Editor", "NewEditor"], - name: "Use new mask editor", - tooltip: "Switch to the new mask editor interface", - type: "boolean", - defaultValue: true, - experimental: true - }, - { - id: "Comfy.MaskEditor.BrushAdjustmentSpeed", - category: ["Mask Editor", "BrushAdjustment", "Sensitivity"], - name: "Brush adjustment speed multiplier", - tooltip: "Controls how quickly the brush size and hardness change when adjusting. Higher values mean faster changes.", - experimental: true, - type: "slider", - attrs: { - min: 0.1, - max: 2, - step: 0.1 - }, - defaultValue: 1, - versionAdded: "1.0.0" - }, - { - id: "Comfy.MaskEditor.UseDominantAxis", - category: ["Mask Editor", "BrushAdjustment", "UseDominantAxis"], - name: "Lock brush adjustment to dominant axis", - tooltip: "When enabled, brush adjustments will only affect size OR hardness based on which direction you move more", - type: "boolean", - defaultValue: true, - experimental: true - } - ], - init(app2) { - function openMaskEditor() { - const useNewEditor = app2.extensionManager.setting.get( - "Comfy.MaskEditor.UseNewEditor" - ); - if (useNewEditor) { - const dlg = MaskEditorDialog.getInstance(); - if (dlg?.isOpened && !dlg.isOpened()) { - dlg.show(); - } - } else { - const dlg = MaskEditorDialogOld.getInstance(); - if (dlg?.isOpened && !dlg.isOpened()) { - dlg.show(); - } - } - } - __name(openMaskEditor, "openMaskEditor"); - ; - ComfyApp.open_maskeditor = openMaskEditor; - const context_predicate = /* @__PURE__ */ __name(() => { - return !!(ComfyApp.clipspace && ComfyApp.clipspace.imgs && ComfyApp.clipspace.imgs.length > 0); - }, "context_predicate"); - ClipspaceDialog.registerButton( - "MaskEditor", - context_predicate, - openMaskEditor - ); - } -}); -const id = "Comfy.NodeTemplates"; -const file = "comfy.templates.json"; -class ManageTemplates extends ComfyDialog { - static { - __name(this, "ManageTemplates"); - } - templates; - draggedEl; - saveVisualCue; - emptyImg; - importInput; - constructor() { - super(); - this.load().then((v) => { - this.templates = v; - }); - this.element.classList.add("comfy-manage-templates"); - this.draggedEl = null; - this.saveVisualCue = null; - this.emptyImg = new Image(); - this.emptyImg.src = "data:image/gif;base64,R0lGODlhAQABAIAAAAUEBAAAACwAAAAAAQABAAACAkQBADs="; - this.importInput = $el("input", { - type: "file", - accept: ".json", - multiple: true, - style: { display: "none" }, - parent: document.body, - onchange: /* @__PURE__ */ __name(() => this.importAll(), "onchange") - }); - } - createButtons() { - const btns = super.createButtons(); - btns[0].textContent = "Close"; - btns[0].onclick = (e) => { - clearTimeout(this.saveVisualCue); - this.close(); - }; - btns.unshift( - $el("button", { - type: "button", - textContent: "Export", - onclick: /* @__PURE__ */ __name(() => this.exportAll(), "onclick") - }) - ); - btns.unshift( - $el("button", { - type: "button", - textContent: "Import", - onclick: /* @__PURE__ */ __name(() => { - this.importInput.click(); - }, "onclick") - }) - ); - return btns; - } - async load() { - let templates = []; - const res = await api.getUserData(file); - if (res.status === 200) { - try { - templates = await res.json(); - } catch (error) { - } - } else if (res.status !== 404) { - console.error(res.status + " " + res.statusText); - } - return templates ?? []; - } - async store() { - const templates = JSON.stringify(this.templates, void 0, 4); - try { - await api.storeUserData(file, templates, { stringify: false }); - } catch (error) { - console.error(error); - useToastStore().addAlert(error.message); - } - } - async importAll() { - for (const file2 of this.importInput.files) { - if (file2.type === "application/json" || file2.name.endsWith(".json")) { - const reader = new FileReader(); - reader.onload = async () => { - const importFile = JSON.parse(reader.result); - if (importFile?.templates) { - for (const template of importFile.templates) { - if (template?.name && template?.data) { - this.templates.push(template); - } - } - await this.store(); - } - }; - await reader.readAsText(file2); - } - } - this.importInput.value = null; - this.close(); - } - exportAll() { - if (this.templates.length == 0) { - useToastStore().addAlert("No templates to export."); - return; - } - const json = JSON.stringify({ templates: this.templates }, null, 2); - const blob = new Blob([json], { type: "application/json" }); - const url = URL.createObjectURL(blob); - const a = $el("a", { - href: url, - download: "node_templates.json", - style: { display: "none" }, - parent: document.body - }); - a.click(); - setTimeout(function() { - a.remove(); - window.URL.revokeObjectURL(url); - }, 0); - } - show() { - super.show( - $el( - "div", - {}, - this.templates.flatMap((t2, i) => { - let nameInput; - return [ - $el( - "div", - { - dataset: { id: i.toString() }, - className: "templateManagerRow", - style: { - display: "grid", - gridTemplateColumns: "1fr auto", - border: "1px dashed transparent", - gap: "5px", - backgroundColor: "var(--comfy-menu-bg)" - }, - ondragstart: /* @__PURE__ */ __name((e) => { - this.draggedEl = e.currentTarget; - e.currentTarget.style.opacity = "0.6"; - e.currentTarget.style.border = "1px dashed yellow"; - e.dataTransfer.effectAllowed = "move"; - e.dataTransfer.setDragImage(this.emptyImg, 0, 0); - }, "ondragstart"), - ondragend: /* @__PURE__ */ __name((e) => { - e.target.style.opacity = "1"; - e.currentTarget.style.border = "1px dashed transparent"; - e.currentTarget.removeAttribute("draggable"); - this.element.querySelectorAll(".templateManagerRow").forEach((el, i2) => { - var prev_i = Number.parseInt(el.dataset.id); - if (el == this.draggedEl && prev_i != i2) { - this.templates.splice( - i2, - 0, - this.templates.splice(prev_i, 1)[0] - ); - } - el.dataset.id = i2.toString(); - }); - this.store(); - }, "ondragend"), - ondragover: /* @__PURE__ */ __name((e) => { - e.preventDefault(); - if (e.currentTarget == this.draggedEl) return; - let rect = e.currentTarget.getBoundingClientRect(); - if (e.clientY > rect.top + rect.height / 2) { - e.currentTarget.parentNode.insertBefore( - this.draggedEl, - e.currentTarget.nextSibling - ); - } else { - e.currentTarget.parentNode.insertBefore( - this.draggedEl, - e.currentTarget - ); - } - }, "ondragover") - }, - [ - $el( - "label", - { - textContent: "Name: ", - style: { - cursor: "grab" - }, - onmousedown: /* @__PURE__ */ __name((e) => { - if (e.target.localName == "label") - e.currentTarget.parentNode.draggable = "true"; - }, "onmousedown") - }, - [ - $el("input", { - value: t2.name, - dataset: { name: t2.name }, - style: { - transitionProperty: "background-color", - transitionDuration: "0s" - }, - onchange: /* @__PURE__ */ __name((e) => { - clearTimeout(this.saveVisualCue); - var el = e.target; - var row = el.parentNode.parentNode; - this.templates[row.dataset.id].name = el.value.trim() || "untitled"; - this.store(); - el.style.backgroundColor = "rgb(40, 95, 40)"; - el.style.transitionDuration = "0s"; - this.saveVisualCue = setTimeout(function() { - el.style.transitionDuration = ".7s"; - el.style.backgroundColor = "var(--comfy-input-bg)"; - }, 15); - }, "onchange"), - onkeypress: /* @__PURE__ */ __name((e) => { - var el = e.target; - clearTimeout(this.saveVisualCue); - el.style.transitionDuration = "0s"; - el.style.backgroundColor = "var(--comfy-input-bg)"; - }, "onkeypress"), - $: /* @__PURE__ */ __name((el) => nameInput = el, "$") - }) - ] - ), - $el("div", {}, [ - $el("button", { - textContent: "Export", - style: { - fontSize: "12px", - fontWeight: "normal" - }, - onclick: /* @__PURE__ */ __name((e) => { - const json = JSON.stringify({ templates: [t2] }, null, 2); - const blob = new Blob([json], { - type: "application/json" - }); - const url = URL.createObjectURL(blob); - const a = $el("a", { - href: url, - download: (nameInput.value || t2.name) + ".json", - style: { display: "none" }, - parent: document.body - }); - a.click(); - setTimeout(function() { - a.remove(); - window.URL.revokeObjectURL(url); - }, 0); - }, "onclick") - }), - $el("button", { - textContent: "Delete", - style: { - fontSize: "12px", - color: "red", - fontWeight: "normal" - }, - onclick: /* @__PURE__ */ __name((e) => { - const item = e.target.parentNode.parentNode; - item.parentNode.removeChild(item); - this.templates.splice(item.dataset.id * 1, 1); - this.store(); - var that = this; - setTimeout(function() { - that.element.querySelectorAll(".templateManagerRow").forEach((el, i2) => { - el.dataset.id = i2.toString(); - }); - }, 0); - }, "onclick") - }) - ]) - ] - ) - ]; - }) - ) - ); - } -} -app.registerExtension({ - name: id, - setup() { - const manage = new ManageTemplates(); - const clipboardAction = /* @__PURE__ */ __name(async (cb) => { - const old = localStorage.getItem("litegrapheditor_clipboard"); - await cb(); - localStorage.setItem("litegrapheditor_clipboard", old); - }, "clipboardAction"); - const orig = LGraphCanvas.prototype.getCanvasMenuOptions; - LGraphCanvas.prototype.getCanvasMenuOptions = function() { - const options = orig.apply(this, arguments); - options.push(null); - options.push({ - content: `Save Selected as Template`, - disabled: !Object.keys(app.canvas.selected_nodes || {}).length, - callback: /* @__PURE__ */ __name(async () => { - const name = await useDialogService().prompt({ - title: t("nodeTemplates.saveAsTemplate"), - message: t("nodeTemplates.enterName"), - defaultValue: "" - }); - if (!name?.trim()) return; - clipboardAction(() => { - app.canvas.copyToClipboard(); - let data = localStorage.getItem("litegrapheditor_clipboard"); - data = JSON.parse(data); - const nodeIds = Object.keys(app.canvas.selected_nodes); - for (let i = 0; i < nodeIds.length; i++) { - const node = app.graph.getNodeById(nodeIds[i]); - const nodeData = node?.constructor.nodeData; - let groupData = GroupNodeHandler.getGroupData(node); - if (groupData) { - groupData = groupData.nodeData; - if (!data.groupNodes) { - data.groupNodes = {}; - } - data.groupNodes[nodeData.name] = groupData; - data.nodes[i].type = nodeData.name; - } - } - manage.templates.push({ - name, - data: JSON.stringify(data) - }); - manage.store(); - }); - }, "callback") - }); - const subItems = manage.templates.map((t2) => { - return { - content: t2.name, - callback: /* @__PURE__ */ __name(() => { - clipboardAction(async () => { - const data = JSON.parse(t2.data); - await GroupNodeConfig.registerFromWorkflow(data.groupNodes, {}); - if (!data.reroutes) { - deserialiseAndCreate(t2.data, app.canvas); - } else { - localStorage.setItem("litegrapheditor_clipboard", t2.data); - app.canvas.pasteFromClipboard(); - } - }); - }, "callback") - }; - }); - subItems.push(null, { - content: "Manage", - callback: /* @__PURE__ */ __name(() => manage.show(), "callback") - }); - options.push({ - content: "Node Templates", - submenu: { - options: subItems - } - }); - return options; - }; - } -}); -app.registerExtension({ - name: "Comfy.NoteNode", - registerCustomNodes() { - class NoteNode extends LGraphNode { - static { - __name(this, "NoteNode"); - } - static category; - color = LGraphCanvas.node_colors.yellow.color; - bgcolor = LGraphCanvas.node_colors.yellow.bgcolor; - groupcolor = LGraphCanvas.node_colors.yellow.groupcolor; - isVirtualNode; - collapsable; - title_mode; - constructor(title) { - super(title); - if (!this.properties) { - this.properties = { text: "" }; - } - ComfyWidgets.STRING( - // Should we extends LGraphNode? Yesss - this, - "", - ["", { default: this.properties.text, multiline: true }], - app - ); - this.serialize_widgets = true; - this.isVirtualNode = true; - } - } - LiteGraph.registerNodeType( - "Note", - Object.assign(NoteNode, { - title_mode: LiteGraph.NORMAL_TITLE, - title: "Note", - collapsable: true - }) - ); - NoteNode.category = "utils"; - class MarkdownNoteNode extends LGraphNode { - static { - __name(this, "MarkdownNoteNode"); - } - static title = "Markdown Note"; - color = LGraphCanvas.node_colors.yellow.color; - bgcolor = LGraphCanvas.node_colors.yellow.bgcolor; - groupcolor = LGraphCanvas.node_colors.yellow.groupcolor; - constructor(title) { - super(title); - if (!this.properties) { - this.properties = { text: "" }; - } - ComfyWidgets.MARKDOWN( - this, - "", - ["", { default: this.properties.text }], - app - ); - this.serialize_widgets = true; - this.isVirtualNode = true; - } - } - LiteGraph.registerNodeType("MarkdownNote", MarkdownNoteNode); - MarkdownNoteNode.category = "utils"; - } -}); -app.registerExtension({ - name: "Comfy.RerouteNode", - registerCustomNodes(app2) { - class RerouteNode extends LGraphNode { - static { - __name(this, "RerouteNode"); - } - static category; - static defaultVisibility = false; - constructor(title) { - super(title); - if (!this.properties) { - this.properties = {}; - } - this.properties.showOutputText = RerouteNode.defaultVisibility; - this.properties.horizontal = false; - this.addInput("", "*"); - this.addOutput(this.properties.showOutputText ? "*" : "", "*"); - this.onAfterGraphConfigured = function() { - requestAnimationFrame(() => { - this.onConnectionsChange(LiteGraph.INPUT, null, true, null); - }); - }; - this.onConnectionsChange = (type, index, connected, link_info) => { - if (app2.configuringGraph) return; - this.applyOrientation(); - if (connected && type === LiteGraph.OUTPUT) { - const types = new Set( - this.outputs[0].links.map((l) => app2.graph.links[l].type).filter((t2) => t2 !== "*") - ); - if (types.size > 1) { - const linksToDisconnect = []; - for (let i = 0; i < this.outputs[0].links.length - 1; i++) { - const linkId = this.outputs[0].links[i]; - const link = app2.graph.links[linkId]; - linksToDisconnect.push(link); - } - for (const link of linksToDisconnect) { - const node = app2.graph.getNodeById(link.target_id); - node.disconnectInput(link.target_slot); - } - } - } - let currentNode = this; - let updateNodes = []; - let inputType = null; - let inputNode = null; - while (currentNode) { - updateNodes.unshift(currentNode); - const linkId = currentNode.inputs[0].link; - if (linkId !== null) { - const link = app2.graph.links[linkId]; - if (!link) return; - const node = app2.graph.getNodeById(link.origin_id); - const type2 = node.constructor.type; - if (type2 === "Reroute") { - if (node === this) { - currentNode.disconnectInput(link.target_slot); - currentNode = null; - } else { - currentNode = node; - } - } else { - inputNode = currentNode; - inputType = node.outputs[link.origin_slot]?.type ?? null; - break; - } - } else { - currentNode = null; - break; - } - } - const nodes = [this]; - let outputType = null; - while (nodes.length) { - currentNode = nodes.pop(); - const outputs = (currentNode.outputs ? currentNode.outputs[0].links : []) || []; - if (outputs.length) { - for (const linkId of outputs) { - const link = app2.graph.links[linkId]; - if (!link) continue; - const node = app2.graph.getNodeById(link.target_id); - const type2 = node.constructor.type; - if (type2 === "Reroute") { - nodes.push(node); - updateNodes.push(node); - } else { - const nodeOutType = node.inputs && node.inputs[link?.target_slot] && node.inputs[link.target_slot].type ? node.inputs[link.target_slot].type : null; - if (inputType && !LiteGraph.isValidConnection(inputType, nodeOutType)) { - node.disconnectInput(link.target_slot); - } else { - outputType = nodeOutType; - } - } - } - } else { - } - } - const displayType = inputType || outputType || "*"; - const color = LGraphCanvas.link_type_colors[displayType]; - let widgetConfig; - let targetWidget; - let widgetType; - for (const node of updateNodes) { - node.outputs[0].type = inputType || "*"; - node.__outputType = displayType; - node.outputs[0].name = node.properties.showOutputText ? displayType : ""; - node.size = node.computeSize(); - node.applyOrientation(); - for (const l of node.outputs[0].links || []) { - const link = app2.graph.links[l]; - if (link) { - link.color = color; - if (app2.configuringGraph) continue; - const targetNode = app2.graph.getNodeById(link.target_id); - const targetInput = targetNode.inputs?.[link.target_slot]; - if (targetInput?.widget) { - const config = getWidgetConfig(targetInput); - if (!widgetConfig) { - widgetConfig = config[1] ?? {}; - widgetType = config[0]; - } - if (!targetWidget) { - targetWidget = targetNode.widgets?.find( - (w) => w.name === targetInput.widget.name - ); - } - const merged = mergeIfValid(targetInput, [ - config[0], - widgetConfig - ]); - if (merged.customConfig) { - widgetConfig = merged.customConfig; - } - } - } - } - } - for (const node of updateNodes) { - if (widgetConfig && outputType) { - node.inputs[0].widget = { name: "value" }; - setWidgetConfig( - node.inputs[0], - [widgetType ?? displayType, widgetConfig], - targetWidget - ); - } else { - setWidgetConfig(node.inputs[0], null); - } - } - if (inputNode) { - const link = app2.graph.links[inputNode.inputs[0].link]; - if (link) { - link.color = color; - } - } - }; - this.clone = function() { - const cloned = RerouteNode.prototype.clone.apply(this); - cloned.removeOutput(0); - cloned.addOutput(this.properties.showOutputText ? "*" : "", "*"); - cloned.size = cloned.computeSize(); - return cloned; - }; - this.isVirtualNode = true; - } - getExtraMenuOptions(_, options) { - options.unshift( - { - content: (this.properties.showOutputText ? "Hide" : "Show") + " Type", - callback: /* @__PURE__ */ __name(() => { - this.properties.showOutputText = !this.properties.showOutputText; - if (this.properties.showOutputText) { - this.outputs[0].name = this.__outputType || this.outputs[0].type; - } else { - this.outputs[0].name = ""; - } - this.size = this.computeSize(); - this.applyOrientation(); - app2.graph.setDirtyCanvas(true, true); - }, "callback") - }, - { - content: (RerouteNode.defaultVisibility ? "Hide" : "Show") + " Type By Default", - callback: /* @__PURE__ */ __name(() => { - RerouteNode.setDefaultTextVisibility( - !RerouteNode.defaultVisibility - ); - }, "callback") - }, - { - // naming is inverted with respect to LiteGraphNode.horizontal - // LiteGraphNode.horizontal == true means that - // each slot in the inputs and outputs are laid out horizontally, - // which is the opposite of the visual orientation of the inputs and outputs as a node - content: "Set " + (this.properties.horizontal ? "Horizontal" : "Vertical"), - callback: /* @__PURE__ */ __name(() => { - this.properties.horizontal = !this.properties.horizontal; - this.applyOrientation(); - }, "callback") - } - ); - return []; - } - applyOrientation() { - this.horizontal = this.properties.horizontal; - if (this.horizontal) { - this.inputs[0].pos = [this.size[0] / 2, 0]; - } else { - delete this.inputs[0].pos; - } - app2.graph.setDirtyCanvas(true, true); - } - computeSize() { - return [ - this.properties.showOutputText && this.outputs && this.outputs.length ? Math.max( - 75, - LiteGraph.NODE_TEXT_SIZE * this.outputs[0].name.length * 0.6 + 40 - ) : 75, - 26 - ]; - } - static setDefaultTextVisibility(visible) { - RerouteNode.defaultVisibility = visible; - if (visible) { - localStorage["Comfy.RerouteNode.DefaultVisibility"] = "true"; - } else { - delete localStorage["Comfy.RerouteNode.DefaultVisibility"]; - } - } - } - RerouteNode.setDefaultTextVisibility( - !!localStorage["Comfy.RerouteNode.DefaultVisibility"] - ); - LiteGraph.registerNodeType( - "Reroute", - Object.assign(RerouteNode, { - title_mode: LiteGraph.NO_TITLE, - title: "Reroute", - collapsable: false - }) - ); - RerouteNode.category = "utils"; - } -}); -app.registerExtension({ - name: "Comfy.SaveImageExtraOutput", - async beforeRegisterNodeDef(nodeType, nodeData, app2) { - if (nodeData.name === "SaveImage" || nodeData.name === "SaveAnimatedWEBP") { - const onNodeCreated = nodeType.prototype.onNodeCreated; - nodeType.prototype.onNodeCreated = function() { - const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0; - const widget = this.widgets.find((w) => w.name === "filename_prefix"); - widget.serializeValue = () => { - return applyTextReplacements(app2, widget.value); - }; - return r; - }; - } else { - const onNodeCreated = nodeType.prototype.onNodeCreated; - nodeType.prototype.onNodeCreated = function() { - const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0; - if (!this.properties || !("Node name for S&R" in this.properties)) { - this.addProperty("Node name for S&R", this.constructor.type, "string"); - } - return r; - }; - } - } -}); -let touchZooming; -let touchCount = 0; -app.registerExtension({ - name: "Comfy.SimpleTouchSupport", - setup() { - let touchDist; - let touchTime; - let lastTouch; - let lastScale; - function getMultiTouchPos(e) { - return Math.hypot( - e.touches[0].clientX - e.touches[1].clientX, - e.touches[0].clientY - e.touches[1].clientY - ); - } - __name(getMultiTouchPos, "getMultiTouchPos"); - function getMultiTouchCenter(e) { - return { - clientX: (e.touches[0].clientX + e.touches[1].clientX) / 2, - clientY: (e.touches[0].clientY + e.touches[1].clientY) / 2 - }; - } - __name(getMultiTouchCenter, "getMultiTouchCenter"); - app.canvasEl.parentElement.addEventListener( - "touchstart", - (e) => { - touchCount++; - lastTouch = null; - lastScale = null; - if (e.touches?.length === 1) { - touchTime = /* @__PURE__ */ new Date(); - lastTouch = e.touches[0]; - } else { - touchTime = null; - if (e.touches?.length === 2) { - lastScale = app.canvas.ds.scale; - lastTouch = getMultiTouchCenter(e); - touchDist = getMultiTouchPos(e); - app.canvas.pointer.isDown = false; - } - } - }, - true - ); - app.canvasEl.parentElement.addEventListener("touchend", (e) => { - touchCount--; - if (e.touches?.length !== 1) touchZooming = false; - if (touchTime && !e.touches?.length) { - if ((/* @__PURE__ */ new Date()).getTime() - touchTime > 600) { - if (e.target === app.canvasEl) { - app.canvasEl.dispatchEvent( - new PointerEvent("pointerdown", { - button: 2, - clientX: e.changedTouches[0].clientX, - clientY: e.changedTouches[0].clientY - }) - ); - e.preventDefault(); - } - } - touchTime = null; - } - }); - app.canvasEl.parentElement.addEventListener( - "touchmove", - (e) => { - touchTime = null; - if (e.touches?.length === 2 && lastTouch && !e.ctrlKey && !e.shiftKey) { - e.preventDefault(); - app.canvas.pointer.isDown = false; - touchZooming = true; - LiteGraph.closeAllContextMenus(window); - app.canvas.search_box?.close(); - const newTouchDist = getMultiTouchPos(e); - const center = getMultiTouchCenter(e); - let scale = lastScale * newTouchDist / touchDist; - const newX = (center.clientX - lastTouch.clientX) / scale; - const newY = (center.clientY - lastTouch.clientY) / scale; - if (scale < app.canvas.ds.min_scale) { - scale = app.canvas.ds.min_scale; - } else if (scale > app.canvas.ds.max_scale) { - scale = app.canvas.ds.max_scale; - } - const oldScale = app.canvas.ds.scale; - app.canvas.ds.scale = scale; - if (Math.abs(app.canvas.ds.scale - 1) < 0.01) { - app.canvas.ds.scale = 1; - } - const newScale = app.canvas.ds.scale; - const convertScaleToOffset = /* @__PURE__ */ __name((scale2) => [ - center.clientX / scale2 - app.canvas.ds.offset[0], - center.clientY / scale2 - app.canvas.ds.offset[1] - ], "convertScaleToOffset"); - var oldCenter = convertScaleToOffset(oldScale); - var newCenter = convertScaleToOffset(newScale); - app.canvas.ds.offset[0] += newX + newCenter[0] - oldCenter[0]; - app.canvas.ds.offset[1] += newY + newCenter[1] - oldCenter[1]; - lastTouch.clientX = center.clientX; - lastTouch.clientY = center.clientY; - app.canvas.setDirty(true, true); - } - }, - true - ); - } -}); -const processMouseDown = LGraphCanvas.prototype.processMouseDown; -LGraphCanvas.prototype.processMouseDown = function(e) { - if (touchZooming || touchCount) { - return; - } - app.canvas.pointer.isDown = false; - return processMouseDown.apply(this, arguments); -}; -const processMouseMove = LGraphCanvas.prototype.processMouseMove; -LGraphCanvas.prototype.processMouseMove = function(e) { - if (touchZooming || touchCount > 1) { - return; - } - return processMouseMove.apply(this, arguments); -}; -app.registerExtension({ - name: "Comfy.SlotDefaults", - suggestionsNumber: null, - init() { - LiteGraph.search_filter_enabled = true; - LiteGraph.middle_click_slot_add_default_node = true; - this.suggestionsNumber = app.ui.settings.addSetting({ - id: "Comfy.NodeSuggestions.number", - category: ["Comfy", "Node Search Box", "NodeSuggestions"], - name: "Number of nodes suggestions", - tooltip: "Only for litegraph searchbox/context menu", - type: "slider", - attrs: { - min: 1, - max: 100, - step: 1 - }, - defaultValue: 5, - onChange: /* @__PURE__ */ __name((newVal, oldVal) => { - this.setDefaults(newVal); - }, "onChange") - }); - }, - slot_types_default_out: {}, - slot_types_default_in: {}, - async beforeRegisterNodeDef(nodeType, nodeData, app2) { - var nodeId = nodeData.name; - const inputs = nodeData["input"]?.["required"]; - for (const inputKey in inputs) { - var input = inputs[inputKey]; - if (typeof input[0] !== "string") continue; - var type = input[0]; - if (type in ComfyWidgets) { - var customProperties = input[1]; - if (!customProperties?.forceInput) continue; - } - if (!(type in this.slot_types_default_out)) { - this.slot_types_default_out[type] = ["Reroute"]; - } - if (this.slot_types_default_out[type].includes(nodeId)) continue; - this.slot_types_default_out[type].push(nodeId); - const lowerType = type.toLocaleLowerCase(); - if (!(lowerType in LiteGraph.registered_slot_in_types)) { - LiteGraph.registered_slot_in_types[lowerType] = { nodes: [] }; - } - LiteGraph.registered_slot_in_types[lowerType].nodes.push( - // @ts-expect-error ComfyNode - nodeType.comfyClass - ); - } - var outputs = nodeData["output"] ?? []; - for (const el of outputs) { - const type2 = el; - if (!(type2 in this.slot_types_default_in)) { - this.slot_types_default_in[type2] = ["Reroute"]; - } - this.slot_types_default_in[type2].push(nodeId); - if (!(type2 in LiteGraph.registered_slot_out_types)) { - LiteGraph.registered_slot_out_types[type2] = { nodes: [] }; - } - LiteGraph.registered_slot_out_types[type2].nodes.push(nodeType.comfyClass); - if (!LiteGraph.slot_types_out.includes(type2)) { - LiteGraph.slot_types_out.push(type2); - } - } - var maxNum = this.suggestionsNumber.value; - this.setDefaults(maxNum); - }, - setDefaults(maxNum) { - LiteGraph.slot_types_default_out = {}; - LiteGraph.slot_types_default_in = {}; - for (const type in this.slot_types_default_out) { - LiteGraph.slot_types_default_out[type] = this.slot_types_default_out[type].slice(0, maxNum); - } - for (const type in this.slot_types_default_in) { - LiteGraph.slot_types_default_in[type] = this.slot_types_default_in[type].slice(0, maxNum); - } - } -}); -function splitFilePath(path) { - const folder_separator = path.lastIndexOf("/"); - if (folder_separator === -1) { - return ["", path]; - } - return [ - path.substring(0, folder_separator), - path.substring(folder_separator + 1) - ]; -} -__name(splitFilePath, "splitFilePath"); -function getResourceURL(subfolder, filename, type = "input") { - const params = [ - "filename=" + encodeURIComponent(filename), - "type=" + type, - "subfolder=" + subfolder, - app.getRandParam().substring(1) - ].join("&"); - return `/view?${params}`; -} -__name(getResourceURL, "getResourceURL"); -async function uploadFile(audioWidget, audioUIWidget, file2, updateNode, pasted = false) { - try { - const body = new FormData(); - body.append("image", file2); - if (pasted) body.append("subfolder", "pasted"); - const resp = await api.fetchApi("/upload/image", { - method: "POST", - body - }); - if (resp.status === 200) { - const data = await resp.json(); - let path = data.name; - if (data.subfolder) path = data.subfolder + "/" + path; - if (!audioWidget.options.values.includes(path)) { - audioWidget.options.values.push(path); - } - if (updateNode) { - audioUIWidget.element.src = api.apiURL( - getResourceURL(...splitFilePath(path)) - ); - audioWidget.value = path; - } - } else { - useToastStore().addAlert(resp.status + " - " + resp.statusText); - } - } catch (error) { - useToastStore().addAlert(error); - } -} -__name(uploadFile, "uploadFile"); -app.registerExtension({ - name: "Comfy.AudioWidget", - async beforeRegisterNodeDef(nodeType, nodeData) { - if ( - // @ts-expect-error ComfyNode - ["LoadAudio", "SaveAudio", "PreviewAudio"].includes(nodeType.comfyClass) - ) { - nodeData.input.required.audioUI = ["AUDIO_UI"]; - } - }, - getCustomWidgets() { - return { - AUDIO_UI(node, inputName) { - const audio = document.createElement("audio"); - audio.controls = true; - audio.classList.add("comfy-audio"); - audio.setAttribute("name", "media"); - const audioUIWidget = node.addDOMWidget( - inputName, - /* name=*/ - "audioUI", - audio, - { - serialize: false - } - ); - const isOutputNode = node.constructor.nodeData.output_node; - if (isOutputNode) { - audioUIWidget.element.classList.add("empty-audio-widget"); - const onExecuted = node.onExecuted; - node.onExecuted = function(message) { - onExecuted?.apply(this, arguments); - const audios = message.audio; - if (!audios) return; - const audio2 = audios[0]; - audioUIWidget.element.src = api.apiURL( - getResourceURL(audio2.subfolder, audio2.filename, audio2.type) - ); - audioUIWidget.element.classList.remove("empty-audio-widget"); - }; - } - return { widget: audioUIWidget }; - } - }; - }, - onNodeOutputsUpdated(nodeOutputs) { - for (const [nodeId, output] of Object.entries(nodeOutputs)) { - const node = app.graph.getNodeById(nodeId); - if ("audio" in output) { - const audioUIWidget = node.widgets.find( - (w) => w.name === "audioUI" - ); - const audio = output.audio[0]; - audioUIWidget.element.src = api.apiURL( - getResourceURL(audio.subfolder, audio.filename, audio.type) - ); - audioUIWidget.element.classList.remove("empty-audio-widget"); - } - } - } -}); -app.registerExtension({ - name: "Comfy.UploadAudio", - async beforeRegisterNodeDef(nodeType, nodeData) { - if (nodeData?.input?.required?.audio?.[1]?.audio_upload === true) { - nodeData.input.required.upload = ["AUDIOUPLOAD"]; - } - }, - getCustomWidgets() { - return { - AUDIOUPLOAD(node, inputName) { - const audioWidget = node.widgets.find( - (w) => w.name === "audio" - ); - const audioUIWidget = node.widgets.find( - (w) => w.name === "audioUI" - ); - const onAudioWidgetUpdate = /* @__PURE__ */ __name(() => { - audioUIWidget.element.src = api.apiURL( - getResourceURL(...splitFilePath(audioWidget.value)) - ); - }, "onAudioWidgetUpdate"); - if (audioWidget.value) { - onAudioWidgetUpdate(); - } - audioWidget.callback = onAudioWidgetUpdate; - const onGraphConfigured = node.onGraphConfigured; - node.onGraphConfigured = function() { - onGraphConfigured?.apply(this, arguments); - if (audioWidget.value) { - onAudioWidgetUpdate(); - } - }; - const fileInput = document.createElement("input"); - fileInput.type = "file"; - fileInput.accept = "audio/*"; - fileInput.style.display = "none"; - fileInput.onchange = () => { - if (fileInput.files.length) { - uploadFile(audioWidget, audioUIWidget, fileInput.files[0], true); - } - }; - const uploadWidget = node.addWidget( - "button", - inputName, - /* value=*/ - "", - () => { - fileInput.click(); - }, - { serialize: false } - ); - uploadWidget.label = "choose file to upload"; - return { widget: uploadWidget }; - } - }; - } -}); -app.registerExtension({ - name: "Comfy.UploadImage", - beforeRegisterNodeDef(nodeType, nodeData) { - if (nodeData?.input?.required?.image?.[1]?.image_upload === true) { - nodeData.input.required.upload = ["IMAGEUPLOAD"]; - } - } -}); -const WEBCAM_READY = Symbol(); -app.registerExtension({ - name: "Comfy.WebcamCapture", - getCustomWidgets(app2) { - return { - WEBCAM(node, inputName) { - let res; - node[WEBCAM_READY] = new Promise((resolve) => res = resolve); - const container = document.createElement("div"); - container.style.background = "rgba(0,0,0,0.25)"; - container.style.textAlign = "center"; - const video = document.createElement("video"); - video.style.height = video.style.width = "100%"; - const loadVideo = /* @__PURE__ */ __name(async () => { - try { - const stream = await navigator.mediaDevices.getUserMedia({ - video: true, - audio: false - }); - container.replaceChildren(video); - setTimeout(() => res(video), 500); - video.addEventListener("loadedmetadata", () => res(video), false); - video.srcObject = stream; - video.play(); - } catch (error) { - const label = document.createElement("div"); - label.style.color = "red"; - label.style.overflow = "auto"; - label.style.maxHeight = "100%"; - label.style.whiteSpace = "pre-wrap"; - if (window.isSecureContext) { - label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message; - } else { - label.textContent = "Unable to load webcam. A secure context is required, if you are not accessing ComfyUI on localhost (127.0.0.1) you will have to enable TLS (https)\n\n" + error.message; - } - container.replaceChildren(label); - } - }, "loadVideo"); - loadVideo(); - return { widget: node.addDOMWidget(inputName, "WEBCAM", container) }; - } - }; - }, - nodeCreated(node) { - if (node.type, node.constructor.comfyClass !== "WebcamCapture") return; - let video; - const camera = node.widgets.find((w2) => w2.name === "image"); - const w = node.widgets.find((w2) => w2.name === "width"); - const h = node.widgets.find((w2) => w2.name === "height"); - const captureOnQueue = node.widgets.find( - (w2) => w2.name === "capture_on_queue" - ); - const canvas = document.createElement("canvas"); - const capture = /* @__PURE__ */ __name(() => { - canvas.width = w.value; - canvas.height = h.value; - const ctx = canvas.getContext("2d"); - ctx.drawImage(video, 0, 0, w.value, h.value); - const data = canvas.toDataURL("image/png"); - const img = new Image(); - img.onload = () => { - node.imgs = [img]; - app.graph.setDirtyCanvas(true); - requestAnimationFrame(() => { - node.setSizeForImage?.(); - }); - }; - img.src = data; - }, "capture"); - const btn = node.addWidget( - "button", - "waiting for camera...", - "capture", - capture - ); - btn.disabled = true; - btn.serializeValue = () => void 0; - camera.serializeValue = async () => { - if (captureOnQueue.value) { - capture(); - } else if (!node.imgs?.length) { - const err2 = `No webcam image captured`; - useToastStore().addAlert(err2); - throw new Error(err2); - } - const blob = await new Promise((r) => canvas.toBlob(r)); - const name = `${+/* @__PURE__ */ new Date()}.png`; - const file2 = new File([blob], name); - const body = new FormData(); - body.append("image", file2); - body.append("subfolder", "webcam"); - body.append("type", "temp"); - const resp = await api.fetchApi("/upload/image", { - method: "POST", - body - }); - if (resp.status !== 200) { - const err2 = `Error uploading camera image: ${resp.status} - ${resp.statusText}`; - useToastStore().addAlert(err2); - throw new Error(err2); - } - return `webcam/${name} [temp]`; - }; - node[WEBCAM_READY].then((v) => { - video = v; - if (!w.value) { - w.value = video.videoWidth || 640; - h.value = video.videoHeight || 480; - } - btn.disabled = false; - btn.label = "capture"; - }); - } -}); -//# sourceMappingURL=index-5Sv744Dr.js.map diff --git a/web/assets/index-B7ycxfFq.js b/web/assets/index-B7ycxfFq.js deleted file mode 100644 index 2cfff2fa4..000000000 --- a/web/assets/index-B7ycxfFq.js +++ /dev/null @@ -1,27 +0,0 @@ -var __defProp = Object.defineProperty; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -import { ct as script$1, H as createBaseVNode, o as openBlock, f as createElementBlock, D as mergeProps } from "./index-C4Fk50Nx.js"; -var script = { - name: "PlusIcon", - "extends": script$1 -}; -var _hoisted_1 = /* @__PURE__ */ createBaseVNode("path", { - d: "M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z", - fill: "currentColor" -}, null, -1); 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- align-items: center; - gap: 10px; - justify-content: space-between; - background: var(--comfy-menu-bg); - padding: 15px 20px; -} -.comfy-group-manage-outer > header select { - background: var(--comfy-input-bg); - border: 1px solid var(--border-color); - color: var(--input-text); - padding: 5px 10px; - border-radius: 5px; -} -.comfy-group-manage h2 { - margin: 0; - font-weight: normal; -} -.comfy-group-manage main { - display: flex; - overflow: hidden; -} -.comfy-group-manage .drag-handle { - font-weight: bold; -} -.comfy-group-manage-list { - border-right: 1px solid var(--comfy-menu-bg); -} -.comfy-group-manage-list ul { - margin: 40px 0 0; - padding: 0; - list-style: none; -} -.comfy-group-manage-list-items { - max-height: calc(100% - 40px); - overflow-y: scroll; - overflow-x: hidden; -} -.comfy-group-manage-list li { - display: flex; - padding: 10px 20px 10px 10px; - cursor: pointer; - align-items: center; - gap: 5px; -} -.comfy-group-manage-list div { - display: flex; - flex-direction: column; -} -.comfy-group-manage-list li:not(.selected):hover div { - text-decoration: underline; -} -.comfy-group-manage-list li.selected { - background: var(--border-color); -} -.comfy-group-manage-list li span { - opacity: 0.7; - font-size: smaller; -} -.comfy-group-manage-node { - flex: auto; - background: var(--border-color); - display: flex; - flex-direction: column; -} -.comfy-group-manage-node > div { - overflow: auto; -} -.comfy-group-manage-node header { - display: flex; - background: var(--bg-color); - height: 40px; -} -.comfy-group-manage-node header a { - text-align: center; - flex: auto; - border-right: 1px solid var(--comfy-menu-bg); - border-bottom: 1px solid var(--comfy-menu-bg); - padding: 10px; - cursor: pointer; - font-size: 15px; -} -.comfy-group-manage-node header a:last-child { - border-right: none; -} -.comfy-group-manage-node header a:not(.active):hover { - text-decoration: underline; -} -.comfy-group-manage-node header a.active { - background: var(--border-color); - border-bottom: none; 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- break; - } - } - } - return index2; -} -__name(findIndexInList, "findIndexInList"); -function findLast$1(arr, callback) { - let item3; - if (isNotEmpty(arr)) { - try { - item3 = arr.findLast(callback); - } catch (e2) { - item3 = [...arr].reverse().find(callback); - } - } - return item3; -} -__name(findLast$1, "findLast$1"); -function findLastIndex(arr, callback) { - let index2 = -1; - if (isNotEmpty(arr)) { - try { - index2 = arr.findLastIndex(callback); - } catch (e2) { - index2 = arr.lastIndexOf([...arr].reverse().find(callback)); - } - } - return index2; -} -__name(findLastIndex, "findLastIndex"); -function isObject$7(value4, empty3 = true) { - return value4 instanceof Object && value4.constructor === Object && (empty3 || Object.keys(value4).length !== 0); -} -__name(isObject$7, "isObject$7"); -function resolve$1(obj, ...params) { - return isFunction$5(obj) ? obj(...params) : obj; -} -__name(resolve$1, "resolve$1"); -function isString$8(value4, empty3 = true) { - return typeof value4 === "string" && (empty3 || value4 !== ""); -} -__name(isString$8, "isString$8"); -function toFlatCase(str) { - return isString$8(str) ? str.replace(/(-|_)/g, "").toLowerCase() : str; -} -__name(toFlatCase, "toFlatCase"); -function getKeyValue(obj, key = "", params = {}) { - const fKeys = toFlatCase(key).split("."); - const fKey = fKeys.shift(); - return fKey ? isObject$7(obj) ? getKeyValue(resolve$1(obj[Object.keys(obj).find((k) => toFlatCase(k) === fKey) || ""], params), fKeys.join("."), params) : void 0 : resolve$1(obj, params); -} -__name(getKeyValue, "getKeyValue"); -function insertIntoOrderedArray(item3, index2, arr, sourceArr) { - if (arr.length > 0) { - let injected = false; - for (let i2 = 0; i2 < arr.length; i2++) { - let currentItemIndex = findIndexInList(arr[i2], sourceArr); - if (currentItemIndex > index2) { - arr.splice(i2, 0, item3); - injected = true; - break; - } - } - if (!injected) { - arr.push(item3); - } - } else { - arr.push(item3); - } -} -__name(insertIntoOrderedArray, "insertIntoOrderedArray"); -function isArray$5(value4, empty3 = true) { - return Array.isArray(value4) && (empty3 || value4.length !== 0); -} -__name(isArray$5, "isArray$5"); -function isDate$3(value4) { - return value4 instanceof Date && value4.constructor === Date; -} -__name(isDate$3, "isDate$3"); -function isNumber$5(value4) { - return isNotEmpty(value4) && !isNaN(value4); -} -__name(isNumber$5, "isNumber$5"); -function isPrintableCharacter(char = "") { - return isNotEmpty(char) && char.length === 1 && !!char.match(/\S| /); -} -__name(isPrintableCharacter, "isPrintableCharacter"); -function localeComparator() { - return new Intl.Collator(void 0, { numeric: true }).compare; -} -__name(localeComparator, "localeComparator"); -function matchRegex(str, regex2) { - if (regex2) { - const match2 = regex2.test(str); - regex2.lastIndex = 0; - return match2; - } - return false; -} -__name(matchRegex, "matchRegex"); -function mergeKeys(...args) { - const _mergeKeys = /* @__PURE__ */ __name((target = {}, source = {}) => { - const mergedObj = __spreadValues$1({}, target); - Object.keys(source).forEach((key) => { - if (isObject$7(source[key]) && key in target && isObject$7(target[key])) { - mergedObj[key] = _mergeKeys(target[key], source[key]); - } else { - mergedObj[key] = source[key]; - } - }); - return mergedObj; - }, "_mergeKeys"); - return args.reduce((acc, obj, i2) => i2 === 0 ? obj : _mergeKeys(acc, obj), {}); -} -__name(mergeKeys, "mergeKeys"); -function minifyCSS(css3) { - return css3 ? css3.replace(/\/\*(?:(?!\*\/)[\s\S])*\*\/|[\r\n\t]+/g, "").replace(/ {2,}/g, " ").replace(/ ([{:}]) /g, "$1").replace(/([;,]) /g, "$1").replace(/ !/g, "!").replace(/: /g, ":") : css3; -} -__name(minifyCSS, "minifyCSS"); -function nestedKeys(obj = {}, parentKey = "") { - return Object.entries(obj).reduce((o, [key, value4]) => { - const currentKey = parentKey ? `${parentKey}.${key}` : key; - isObject$7(value4) ? o = o.concat(nestedKeys(value4, currentKey)) : o.push(currentKey); - return o; - }, []); -} -__name(nestedKeys, "nestedKeys"); -function removeAccents(str) { - if (str && str.search(/[\xC0-\xFF]/g) > -1) { - str = str.replace(/[\xC0-\xC5]/g, "A").replace(/[\xC6]/g, "AE").replace(/[\xC7]/g, "C").replace(/[\xC8-\xCB]/g, "E").replace(/[\xCC-\xCF]/g, "I").replace(/[\xD0]/g, "D").replace(/[\xD1]/g, "N").replace(/[\xD2-\xD6\xD8]/g, "O").replace(/[\xD9-\xDC]/g, "U").replace(/[\xDD]/g, "Y").replace(/[\xDE]/g, "P").replace(/[\xE0-\xE5]/g, "a").replace(/[\xE6]/g, "ae").replace(/[\xE7]/g, "c").replace(/[\xE8-\xEB]/g, "e").replace(/[\xEC-\xEF]/g, "i").replace(/[\xF1]/g, "n").replace(/[\xF2-\xF6\xF8]/g, "o").replace(/[\xF9-\xFC]/g, "u").replace(/[\xFE]/g, "p").replace(/[\xFD\xFF]/g, "y"); - } - return str; -} -__name(removeAccents, "removeAccents"); -function reorderArray(value4, from2, to) { - if (value4 && from2 !== to) { - if (to >= value4.length) { - to %= value4.length; - from2 %= value4.length; - } - value4.splice(to, 0, value4.splice(from2, 1)[0]); - } -} -__name(reorderArray, "reorderArray"); -function sort(value1, value22, order = 1, comparator2, nullSortOrder = 1) { - const result = compare$1(value1, value22, comparator2, order); - let finalSortOrder = order; - if (isEmpty$1(value1) || isEmpty$1(value22)) { - finalSortOrder = nullSortOrder === 1 ? order : nullSortOrder; - } - return finalSortOrder * result; -} -__name(sort, "sort"); -function stringify(value4, indent = 2, currentIndent = 0) { - const currentIndentStr = " ".repeat(currentIndent); - const nextIndentStr = " ".repeat(currentIndent + indent); - if (isArray$5(value4)) { - return "[" + value4.map((v2) => stringify(v2, indent, currentIndent + indent)).join(", ") + "]"; - } else if (isDate$3(value4)) { - return value4.toISOString(); - } else if (isFunction$5(value4)) { - return value4.toString(); - } else if (isObject$7(value4)) { - return "{\n" + Object.entries(value4).map(([k, v2]) => `${nextIndentStr}${k}: ${stringify(v2, indent, currentIndent + indent)}`).join(",\n") + ` -${currentIndentStr}}`; - } else { - return JSON.stringify(value4); - } -} -__name(stringify, "stringify"); -function toCapitalCase(str) { - return isString$8(str, false) ? str[0].toUpperCase() + str.slice(1) : str; -} -__name(toCapitalCase, "toCapitalCase"); -function toKebabCase(str) { - return isString$8(str) ? str.replace(/(_)/g, "-").replace(/[A-Z]/g, (c, i2) => i2 === 0 ? c : "-" + c.toLowerCase()).toLowerCase() : str; -} -__name(toKebabCase, "toKebabCase"); -function toTokenKey$1(str) { - return isString$8(str) ? str.replace(/[A-Z]/g, (c, i2) => i2 === 0 ? c : "." + c.toLowerCase()).toLowerCase() : str; -} -__name(toTokenKey$1, "toTokenKey$1"); -function EventBus() { - const allHandlers = /* @__PURE__ */ new Map(); - return { - on(type, handler6) { - let handlers2 = allHandlers.get(type); - if (!handlers2) handlers2 = [handler6]; - else handlers2.push(handler6); - allHandlers.set(type, handlers2); - return this; - }, - off(type, handler6) { - let handlers2 = allHandlers.get(type); - if (handlers2) { - handlers2.splice(handlers2.indexOf(handler6) >>> 0, 1); - } - return this; - }, - emit(type, evt) { - let handlers2 = allHandlers.get(type); - if (handlers2) { - handlers2.slice().map((handler6) => { - handler6(evt); - }); - } - }, - clear() { - allHandlers.clear(); - } - }; -} -__name(EventBus, "EventBus"); -var __defProp$1 = Object.defineProperty; -var __defProps = Object.defineProperties; -var __getOwnPropDescs = Object.getOwnPropertyDescriptors; -var __getOwnPropSymbols = Object.getOwnPropertySymbols; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __propIsEnum = Object.prototype.propertyIsEnumerable; -var __defNormalProp$1 = /* @__PURE__ */ __name((obj, key, value4) => key in obj ? __defProp$1(obj, key, { enumerable: true, configurable: true, writable: true, value: value4 }) : obj[key] = value4, "__defNormalProp$1"); -var __spreadValues = /* @__PURE__ */ __name((a, b) => { - for (var prop2 in b || (b = {})) - if (__hasOwnProp.call(b, prop2)) - __defNormalProp$1(a, prop2, b[prop2]); - if (__getOwnPropSymbols) - for (var prop2 of __getOwnPropSymbols(b)) { - if (__propIsEnum.call(b, prop2)) - __defNormalProp$1(a, prop2, b[prop2]); - } - return a; -}, "__spreadValues"); -var __spreadProps = /* @__PURE__ */ __name((a, b) => __defProps(a, __getOwnPropDescs(b)), "__spreadProps"); -var __objRest = /* @__PURE__ */ __name((source, exclude) => { - var target = {}; - for (var prop2 in source) - if (__hasOwnProp.call(source, prop2) && exclude.indexOf(prop2) < 0) - target[prop2] = source[prop2]; - if (source != null && __getOwnPropSymbols) - for (var prop2 of __getOwnPropSymbols(source)) { - if (exclude.indexOf(prop2) < 0 && __propIsEnum.call(source, prop2)) - target[prop2] = source[prop2]; - } - return target; -}, "__objRest"); -function definePreset(...presets) { - return mergeKeys(...presets); -} -__name(definePreset, "definePreset"); -var ThemeService = EventBus(); -var service_default = ThemeService; -function toTokenKey(str) { - return isString$8(str) ? str.replace(/[A-Z]/g, (c, i2) => i2 === 0 ? c : "." + c.toLowerCase()).toLowerCase() : str; -} -__name(toTokenKey, "toTokenKey"); -function merge$1(value1, value22) { - if (isArray$5(value1)) { - value1.push(...value22 || []); - } else if (isObject$7(value1)) { - Object.assign(value1, value22); - } -} -__name(merge$1, "merge$1"); -function toValue$2(value4) { - return isObject$7(value4) && value4.hasOwnProperty("value") && value4.hasOwnProperty("type") ? value4.value : value4; -} -__name(toValue$2, "toValue$2"); -function toUnit(value4, variable = "") { - const excludedProperties = ["opacity", "z-index", "line-height", "font-weight", "flex", "flex-grow", "flex-shrink", "order"]; - if (!excludedProperties.some((property) => variable.endsWith(property))) { - const val = `${value4}`.trim(); - const valArr = val.split(" "); - return valArr.map((v2) => isNumber$5(v2) ? `${v2}px` : v2).join(" "); - } - return value4; -} -__name(toUnit, "toUnit"); -function toNormalizePrefix(prefix2) { - return prefix2.replaceAll(/ /g, "").replace(/[^\w]/g, "-"); -} -__name(toNormalizePrefix, "toNormalizePrefix"); -function toNormalizeVariable(prefix2 = "", variable = "") { - return toNormalizePrefix(`${isString$8(prefix2, false) && isString$8(variable, false) ? `${prefix2}-` : prefix2}${variable}`); -} -__name(toNormalizeVariable, "toNormalizeVariable"); -function getVariableName(prefix2 = "", variable = "") { - return `--${toNormalizeVariable(prefix2, variable)}`; -} -__name(getVariableName, "getVariableName"); -function getVariableValue(value4, variable = "", prefix2 = "", excludedKeyRegexes = [], fallback) { - if (isString$8(value4)) { - const regex2 = /{([^}]*)}/g; - const val = value4.trim(); - if (matchRegex(val, regex2)) { - const _val = val.replaceAll(regex2, (v2) => { - const path = v2.replace(/{|}/g, ""); - const keys2 = path.split(".").filter((_v) => !excludedKeyRegexes.some((_r) => matchRegex(_v, _r))); - return `var(${getVariableName(prefix2, toKebabCase(keys2.join("-")))}${isNotEmpty(fallback) ? `, ${fallback}` : ""})`; - }); - const calculationRegex = /(\d+\s+[\+\-\*\/]\s+\d+)/g; - const cleanedVarRegex = /var\([^)]+\)/g; - return matchRegex(_val.replace(cleanedVarRegex, "0"), calculationRegex) ? `calc(${_val})` : _val; - } - return toUnit(val, variable); - } else if (isNumber$5(value4)) { - return toUnit(value4, variable); - } - return void 0; -} -__name(getVariableValue, "getVariableValue"); -function getComputedValue(obj = {}, value4) { - if (isString$8(value4)) { - const regex2 = /{([^}]*)}/g; - const val = value4.trim(); - return matchRegex(val, regex2) ? val.replaceAll(regex2, (v2) => getKeyValue(obj, v2.replace(/{|}/g, ""))) : val; - } else if (isNumber$5(value4)) { - return value4; - } - return void 0; -} -__name(getComputedValue, "getComputedValue"); -function setProperty(properties, key, value4) { - if (isString$8(key, false)) { - properties.push(`${key}:${value4};`); - } -} -__name(setProperty, "setProperty"); -function getRule(selector, properties) { - if (selector) { - return `${selector}{${properties}}`; - } - return ""; -} -__name(getRule, "getRule"); -function normalizeColor(color2) { - if (color2.length === 4) { - return `#${color2[1]}${color2[1]}${color2[2]}${color2[2]}${color2[3]}${color2[3]}`; - } - return color2; -} -__name(normalizeColor, "normalizeColor"); -function hexToRgb$1(hex) { - var bigint = parseInt(hex.substring(1), 16); - var r = bigint >> 16 & 255; - var g2 = bigint >> 8 & 255; - var b = bigint & 255; - return { r, g: g2, b }; -} -__name(hexToRgb$1, "hexToRgb$1"); -function rgbToHex(r, g2, b) { - return `#${r.toString(16).padStart(2, "0")}${g2.toString(16).padStart(2, "0")}${b.toString(16).padStart(2, "0")}`; -} -__name(rgbToHex, "rgbToHex"); -var mix_default = /* @__PURE__ */ __name((color1, color2, weight) => { - color1 = normalizeColor(color1); - color2 = normalizeColor(color2); - var p2 = weight / 100; - var w = p2 * 2 - 1; - var w1 = (w + 1) / 2; - var w2 = 1 - w1; - var rgb1 = hexToRgb$1(color1); - var rgb2 = hexToRgb$1(color2); - var r = Math.round(rgb1.r * w1 + rgb2.r * w2); - var g2 = Math.round(rgb1.g * w1 + rgb2.g * w2); - var b = Math.round(rgb1.b * w1 + rgb2.b * w2); - return rgbToHex(r, g2, b); -}, "mix_default"); -var shade_default = /* @__PURE__ */ __name((color2, percent) => mix_default("#000000", color2, percent), "shade_default"); -var tint_default = /* @__PURE__ */ __name((color2, percent) => mix_default("#ffffff", color2, percent), "tint_default"); -var scales = [50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 950]; -var palette_default = /* @__PURE__ */ __name((color2) => { - if (/{([^}]*)}/g.test(color2)) { - const token = color2.replace(/{|}/g, ""); - return scales.reduce((acc, scale) => (acc[scale] = `{${token}.${scale}}`, acc), {}); - } - return typeof color2 === "string" ? scales.reduce((acc, scale, i2) => (acc[scale] = i2 <= 5 ? tint_default(color2, (5 - i2) * 19) : shade_default(color2, (i2 - 5) * 15), acc), {}) : color2; -}, "palette_default"); -var $dt = /* @__PURE__ */ __name((tokenPath) => { - var _a2; - const theme40 = config_default.getTheme(); - const variable = dtwt(theme40, tokenPath, void 0, "variable"); - const name2 = (_a2 = variable.match(/--[\w-]+/g)) == null ? void 0 : _a2[0]; - const value4 = dtwt(theme40, tokenPath, void 0, "value"); - return { - name: name2, - variable, - value: value4 - }; -}, "$dt"); -var dt = /* @__PURE__ */ __name((...args) => { - return dtwt(config_default.getTheme(), ...args); -}, "dt"); -var dtwt = /* @__PURE__ */ __name((theme40 = {}, tokenPath, fallback, type = "variable") => { - if (tokenPath) { - const { variable: VARIABLE, options: OPTIONS } = config_default.defaults || {}; - const { prefix: prefix2, transform: transform2 } = (theme40 == null ? void 0 : theme40.options) || OPTIONS || {}; - const regex2 = /{([^}]*)}/g; - const token = matchRegex(tokenPath, regex2) ? tokenPath : `{${tokenPath}}`; - const isStrictTransform = type === "value" || transform2 === "strict"; - return isStrictTransform ? config_default.getTokenValue(tokenPath) : getVariableValue(token, void 0, prefix2, [VARIABLE.excludedKeyRegex], fallback); - } - return ""; -}, "dtwt"); -function css$2(style2) { - return resolve$1(style2, { dt }); -} -__name(css$2, "css$2"); -var $t = /* @__PURE__ */ __name((theme40 = {}) => { - let { preset: _preset, options: _options } = theme40; - return { - preset(value4) { - _preset = _preset ? mergeKeys(_preset, value4) : value4; - return this; - }, - options(value4) { - _options = _options ? __spreadValues(__spreadValues({}, _options), value4) : value4; - return this; - }, - // features - primaryPalette(primary) { - const { semantic } = _preset || {}; - _preset = __spreadProps(__spreadValues({}, _preset), { semantic: __spreadProps(__spreadValues({}, semantic), { primary }) }); - return this; - }, - surfacePalette(surface) { - var _a2, _b; - const { semantic } = _preset || {}; - const lightSurface = (surface == null ? void 0 : surface.hasOwnProperty("light")) ? surface == null ? void 0 : surface.light : surface; - const darkSurface = (surface == null ? void 0 : surface.hasOwnProperty("dark")) ? surface == null ? void 0 : surface.dark : surface; - const newColorScheme = { - colorScheme: { - light: __spreadValues(__spreadValues({}, (_a2 = semantic == null ? void 0 : semantic.colorScheme) == null ? void 0 : _a2.light), !!lightSurface && { surface: lightSurface }), - dark: __spreadValues(__spreadValues({}, (_b = semantic == null ? void 0 : semantic.colorScheme) == null ? void 0 : _b.dark), !!darkSurface && { surface: darkSurface }) - } - }; - _preset = __spreadProps(__spreadValues({}, _preset), { semantic: __spreadValues(__spreadValues({}, semantic), newColorScheme) }); - return this; - }, - // actions - define({ useDefaultPreset = false, useDefaultOptions = false } = {}) { - return { - preset: useDefaultPreset ? config_default.getPreset() : _preset, - options: useDefaultOptions ? config_default.getOptions() : _options - }; - }, - update({ mergePresets = true, mergeOptions: mergeOptions2 = true } = {}) { - const newTheme = { - preset: mergePresets ? mergeKeys(config_default.getPreset(), _preset) : _preset, - options: mergeOptions2 ? __spreadValues(__spreadValues({}, config_default.getOptions()), _options) : _options - }; - config_default.setTheme(newTheme); - return newTheme; - }, - use(options4) { - const newTheme = this.define(options4); - config_default.setTheme(newTheme); - return newTheme; - } - }; -}, "$t"); -function toVariables_default(theme40, options4 = {}) { - const VARIABLE = config_default.defaults.variable; - const { prefix: prefix2 = VARIABLE.prefix, selector = VARIABLE.selector, excludedKeyRegex = VARIABLE.excludedKeyRegex } = options4; - const _toVariables = /* @__PURE__ */ __name((_theme, _prefix = "") => { - return Object.entries(_theme).reduce( - (acc, [key, value4]) => { - const px = matchRegex(key, excludedKeyRegex) ? toNormalizeVariable(_prefix) : toNormalizeVariable(_prefix, toKebabCase(key)); - const v2 = toValue$2(value4); - if (isObject$7(v2)) { - const { variables: variables2, tokens: tokens2 } = _toVariables(v2, px); - merge$1(acc["tokens"], tokens2); - merge$1(acc["variables"], variables2); - } else { - acc["tokens"].push((prefix2 ? px.replace(`${prefix2}-`, "") : px).replaceAll("-", ".")); - setProperty(acc["variables"], getVariableName(px), getVariableValue(v2, px, prefix2, [excludedKeyRegex])); - } - return acc; - }, - { variables: [], tokens: [] } - ); - }, "_toVariables"); - const { variables, tokens } = _toVariables(theme40, prefix2); - return { - value: variables, - tokens, - declarations: variables.join(""), - css: getRule(selector, variables.join("")) - }; -} -__name(toVariables_default, "toVariables_default"); -var themeUtils_default = { - regex: { - rules: { - class: { - pattern: /^\.([a-zA-Z][\w-]*)$/, - resolve(value4) { - return { type: "class", selector: value4, matched: this.pattern.test(value4.trim()) }; - } - }, - attr: { - pattern: /^\[(.*)\]$/, - resolve(value4) { - return { type: "attr", selector: `:root${value4}`, matched: this.pattern.test(value4.trim()) }; - } - }, - media: { - pattern: /^@media (.*)$/, - resolve(value4) { - return { type: "media", selector: `${value4}{:root{[CSS]}}`, matched: this.pattern.test(value4.trim()) }; - } - }, - system: { - pattern: /^system$/, - resolve(value4) { - return { type: "system", selector: "@media (prefers-color-scheme: dark){:root{[CSS]}}", matched: this.pattern.test(value4.trim()) }; - } - }, - custom: { - resolve(value4) { - return { type: "custom", selector: value4, matched: true }; - } - } - }, - resolve(value4) { - const rules = Object.keys(this.rules).filter((k) => k !== "custom").map((r) => this.rules[r]); - return [value4].flat().map((v2) => { - var _a2; - return (_a2 = rules.map((r) => r.resolve(v2)).find((rr) => rr.matched)) != null ? _a2 : this.rules.custom.resolve(v2); - }); - } - }, - _toVariables(theme40, options4) { - return toVariables_default(theme40, { prefix: options4 == null ? void 0 : options4.prefix }); - }, - getCommon({ name: name2 = "", theme: theme40 = {}, params, set: set3, defaults: defaults2 }) { - var _c, _d, _e, _f; - const { preset, options: options4 } = theme40; - let primitive_css, primitive_tokens, semantic_css, semantic_tokens; - if (isNotEmpty(preset)) { - const { primitive, semantic } = preset; - const _a2 = semantic || {}, { colorScheme } = _a2, sRest = __objRest(_a2, ["colorScheme"]); - const _b = colorScheme || {}, { dark: dark2 } = _b, csRest = __objRest(_b, ["dark"]); - const prim_var = isNotEmpty(primitive) ? this._toVariables({ primitive }, options4) : {}; - const sRest_var = isNotEmpty(sRest) ? this._toVariables({ semantic: sRest }, options4) : {}; - const csRest_var = isNotEmpty(csRest) ? this._toVariables({ light: csRest }, options4) : {}; - const dark_var = isNotEmpty(dark2) ? this._toVariables({ dark: dark2 }, options4) : {}; - const [prim_css, prim_tokens] = [(_c = prim_var.declarations) != null ? _c : "", prim_var.tokens]; - const [sRest_css, sRest_tokens] = [(_d = sRest_var.declarations) != null ? _d : "", sRest_var.tokens || []]; - const [csRest_css, csRest_tokens] = [(_e = csRest_var.declarations) != null ? _e : "", csRest_var.tokens || []]; - const [dark_css, dark_tokens] = [(_f = dark_var.declarations) != null ? _f : "", dark_var.tokens || []]; - primitive_css = this.transformCSS(name2, prim_css, "light", "variable", options4, set3, defaults2); - primitive_tokens = prim_tokens; - const semantic_light_css = this.transformCSS(name2, `${sRest_css}${csRest_css}color-scheme:light`, "light", "variable", options4, set3, defaults2); - const semantic_dark_css = this.transformCSS(name2, `${dark_css}color-scheme:dark`, "dark", "variable", options4, set3, defaults2); - semantic_css = `${semantic_light_css}${semantic_dark_css}`; - semantic_tokens = [.../* @__PURE__ */ new Set([...sRest_tokens, ...csRest_tokens, ...dark_tokens])]; - } - return { - primitive: { - css: primitive_css, - tokens: primitive_tokens - }, - semantic: { - css: semantic_css, - tokens: semantic_tokens - } - }; - }, - getPreset({ name: name2 = "", preset = {}, options: options4, params, set: set3, defaults: defaults2, selector }) { - var _c, _d, _e; - const _name = name2.replace("-directive", ""); - const _a2 = preset, { colorScheme } = _a2, vRest = __objRest(_a2, ["colorScheme"]); - const _b = colorScheme || {}, { dark: dark2 } = _b, csRest = __objRest(_b, ["dark"]); - const vRest_var = isNotEmpty(vRest) ? this._toVariables({ [_name]: vRest }, options4) : {}; - const csRest_var = isNotEmpty(csRest) ? this._toVariables({ [_name]: csRest }, options4) : {}; - const dark_var = isNotEmpty(dark2) ? this._toVariables({ [_name]: dark2 }, options4) : {}; - const [vRest_css, vRest_tokens] = [(_c = vRest_var.declarations) != null ? _c : "", vRest_var.tokens || []]; - const [csRest_css, csRest_tokens] = [(_d = csRest_var.declarations) != null ? _d : "", csRest_var.tokens || []]; - const [dark_css, dark_tokens] = [(_e = dark_var.declarations) != null ? _e : "", dark_var.tokens || []]; - const tokens = [.../* @__PURE__ */ new Set([...vRest_tokens, ...csRest_tokens, ...dark_tokens])]; - const light_variable_css = this.transformCSS(_name, `${vRest_css}${csRest_css}`, "light", "variable", options4, set3, defaults2, selector); - const dark_variable_css = this.transformCSS(_name, dark_css, "dark", "variable", options4, set3, defaults2, selector); - return { - css: `${light_variable_css}${dark_variable_css}`, - tokens - }; - }, - getPresetC({ name: name2 = "", theme: theme40 = {}, params, set: set3, defaults: defaults2 }) { - var _a2; - const { preset, options: options4 } = theme40; - const cPreset = (_a2 = preset == null ? void 0 : preset.components) == null ? void 0 : _a2[name2]; - return this.getPreset({ name: name2, preset: cPreset, options: options4, params, set: set3, defaults: defaults2 }); - }, - getPresetD({ name: name2 = "", theme: theme40 = {}, params, set: set3, defaults: defaults2 }) { - var _a2; - const dName = name2.replace("-directive", ""); - const { preset, options: options4 } = theme40; - const dPreset = (_a2 = preset == null ? void 0 : preset.directives) == null ? void 0 : _a2[dName]; - return this.getPreset({ name: dName, preset: dPreset, options: options4, params, set: set3, defaults: defaults2 }); - }, - getColorSchemeOption(options4, defaults2) { - var _a2; - return this.regex.resolve((_a2 = options4.darkModeSelector) != null ? _a2 : defaults2.options.darkModeSelector); - }, - getLayerOrder(name2, options4 = {}, params, defaults2) { - const { cssLayer } = options4; - if (cssLayer) { - const order = resolve$1(cssLayer.order || "primeui", params); - return `@layer ${order}`; - } - return ""; - }, - getCommonStyleSheet({ name: name2 = "", theme: theme40 = {}, params, props = {}, set: set3, defaults: defaults2 }) { - const common = this.getCommon({ name: name2, theme: theme40, params, set: set3, defaults: defaults2 }); - const _props = Object.entries(props).reduce((acc, [k, v2]) => acc.push(`${k}="${v2}"`) && acc, []).join(" "); - return Object.entries(common || {}).reduce((acc, [key, value4]) => { - if (value4 == null ? void 0 : value4.css) { - const _css = minifyCSS(value4 == null ? void 0 : value4.css); - const id3 = `${key}-variables`; - acc.push(``); - } - return acc; - }, []).join(""); - }, - getStyleSheet({ name: name2 = "", theme: theme40 = {}, params, props = {}, set: set3, defaults: defaults2 }) { - var _a2; - const options4 = { name: name2, theme: theme40, params, set: set3, defaults: defaults2 }; - const preset_css = (_a2 = name2.includes("-directive") ? this.getPresetD(options4) : this.getPresetC(options4)) == null ? void 0 : _a2.css; - const _props = Object.entries(props).reduce((acc, [k, v2]) => acc.push(`${k}="${v2}"`) && acc, []).join(" "); - return preset_css ? `` : ""; - }, - createTokens(obj = {}, defaults2, parentKey = "", parentPath = "", tokens = {}) { - Object.entries(obj).forEach(([key, value4]) => { - const currentKey = matchRegex(key, defaults2.variable.excludedKeyRegex) ? parentKey : parentKey ? `${parentKey}.${toTokenKey$1(key)}` : toTokenKey$1(key); - const currentPath = parentPath ? `${parentPath}.${key}` : key; - if (isObject$7(value4)) { - this.createTokens(value4, defaults2, currentKey, currentPath, tokens); - } else { - tokens[currentKey] || (tokens[currentKey] = { - paths: [], - computed(colorScheme, tokenPathMap = {}) { - if (colorScheme) { - const path = this.paths.find((p2) => p2.scheme === colorScheme) || this.paths.find((p2) => p2.scheme === "none"); - return path == null ? void 0 : path.computed(colorScheme, tokenPathMap["binding"]); - } - return this.paths.map((p2) => p2.computed(p2.scheme, tokenPathMap[p2.scheme])); - } - }); - tokens[currentKey].paths.push({ - path: currentPath, - value: value4, - scheme: currentPath.includes("colorScheme.light") ? "light" : currentPath.includes("colorScheme.dark") ? "dark" : "none", - computed(colorScheme, tokenPathMap = {}) { - const regex2 = /{([^}]*)}/g; - let computedValue = value4; - tokenPathMap["name"] = this.path; - tokenPathMap["binding"] || (tokenPathMap["binding"] = {}); - if (matchRegex(value4, regex2)) { - const val = value4.trim(); - const _val = val.replaceAll(regex2, (v2) => { - var _a2, _b; - const path = v2.replace(/{|}/g, ""); - return (_b = (_a2 = tokens[path]) == null ? void 0 : _a2.computed(colorScheme, tokenPathMap)) == null ? void 0 : _b.value; - }); - const calculationRegex = /(\d+\w*\s+[\+\-\*\/]\s+\d+\w*)/g; - const cleanedVarRegex = /var\([^)]+\)/g; - computedValue = matchRegex(_val.replace(cleanedVarRegex, "0"), calculationRegex) ? `calc(${_val})` : _val; - } - isEmpty$1(tokenPathMap["binding"]) && delete tokenPathMap["binding"]; - return { - colorScheme, - path: this.path, - paths: tokenPathMap, - value: computedValue.includes("undefined") ? void 0 : computedValue - }; - } - }); - } - }); - return tokens; - }, - getTokenValue(tokens, path, defaults2) { - var _a2; - const normalizePath = /* @__PURE__ */ __name((str) => { - const strArr = str.split("."); - return strArr.filter((s) => !matchRegex(s.toLowerCase(), defaults2.variable.excludedKeyRegex)).join("."); - }, "normalizePath"); - const token = normalizePath(path); - const colorScheme = path.includes("colorScheme.light") ? "light" : path.includes("colorScheme.dark") ? "dark" : void 0; - const computedValues = [(_a2 = tokens[token]) == null ? void 0 : _a2.computed(colorScheme)].flat().filter((computed2) => computed2); - return computedValues.length === 1 ? computedValues[0].value : computedValues.reduce((acc = {}, computed2) => { - const _a22 = computed2, { colorScheme: cs } = _a22, rest = __objRest(_a22, ["colorScheme"]); - acc[cs] = rest; - return acc; - }, void 0); - }, - transformCSS(name2, css22, mode2, type, options4 = {}, set3, defaults2, selector) { - if (isNotEmpty(css22)) { - const { cssLayer } = options4; - if (type !== "style") { - const colorSchemeOption = this.getColorSchemeOption(options4, defaults2); - const _css = selector ? getRule(selector, css22) : css22; - css22 = mode2 === "dark" ? colorSchemeOption.reduce((acc, { selector: _selector }) => { - if (isNotEmpty(_selector)) { - acc += _selector.includes("[CSS]") ? _selector.replace("[CSS]", _css) : getRule(_selector, _css); - } - return acc; - }, "") : getRule(selector != null ? selector : ":root", css22); - } - if (cssLayer) { - const layerOptions = { - name: "primeui", - order: "primeui" - }; - isObject$7(cssLayer) && (layerOptions.name = resolve$1(cssLayer.name, { name: name2, type })); - if (isNotEmpty(layerOptions.name)) { - css22 = getRule(`@layer ${layerOptions.name}`, css22); - set3 == null ? void 0 : set3.layerNames(layerOptions.name); - } - } - return css22; - } - return ""; - } -}; -var config_default = { - defaults: { - variable: { - prefix: "p", - selector: ":root", - excludedKeyRegex: /^(primitive|semantic|components|directives|variables|colorscheme|light|dark|common|root|states)$/gi - }, - options: { - prefix: "p", - darkModeSelector: "system", - cssLayer: false - } - }, - _theme: void 0, - _layerNames: /* @__PURE__ */ new Set(), - _loadedStyleNames: /* @__PURE__ */ new Set(), - _loadingStyles: /* @__PURE__ */ new Set(), - _tokens: {}, - update(newValues = {}) { - const { theme: theme40 } = newValues; - if (theme40) { - this._theme = __spreadProps(__spreadValues({}, theme40), { - options: __spreadValues(__spreadValues({}, this.defaults.options), theme40.options) - }); - this._tokens = themeUtils_default.createTokens(this.preset, this.defaults); - this.clearLoadedStyleNames(); - } - }, - get theme() { - return this._theme; - }, - get preset() { - var _a2; - return ((_a2 = this.theme) == null ? void 0 : _a2.preset) || {}; - }, - get options() { - var _a2; - return ((_a2 = this.theme) == null ? void 0 : _a2.options) || {}; - }, - get tokens() { - return this._tokens; - }, - getTheme() { - return this.theme; - }, - setTheme(newValue2) { - this.update({ theme: newValue2 }); - service_default.emit("theme:change", newValue2); - }, - getPreset() { - return this.preset; - }, - setPreset(newValue2) { - this._theme = __spreadProps(__spreadValues({}, this.theme), { preset: newValue2 }); - this._tokens = themeUtils_default.createTokens(newValue2, this.defaults); - this.clearLoadedStyleNames(); - service_default.emit("preset:change", newValue2); - service_default.emit("theme:change", this.theme); - }, - getOptions() { - return this.options; - }, - setOptions(newValue2) { - this._theme = __spreadProps(__spreadValues({}, this.theme), { options: newValue2 }); - this.clearLoadedStyleNames(); - service_default.emit("options:change", newValue2); - service_default.emit("theme:change", this.theme); - }, - getLayerNames() { - return [...this._layerNames]; - }, - setLayerNames(layerName) { - this._layerNames.add(layerName); - }, - getLoadedStyleNames() { - return this._loadedStyleNames; - }, - isStyleNameLoaded(name2) { - return this._loadedStyleNames.has(name2); - }, - setLoadedStyleName(name2) { - this._loadedStyleNames.add(name2); - }, - deleteLoadedStyleName(name2) { - this._loadedStyleNames.delete(name2); - }, - clearLoadedStyleNames() { - this._loadedStyleNames.clear(); - }, - getTokenValue(tokenPath) { - return themeUtils_default.getTokenValue(this.tokens, tokenPath, this.defaults); - }, - getCommon(name2 = "", params) { - return themeUtils_default.getCommon({ name: name2, theme: this.theme, params, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }); - }, - getComponent(name2 = "", params) { - const options4 = { name: name2, theme: this.theme, params, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }; - return themeUtils_default.getPresetC(options4); - }, - getDirective(name2 = "", params) { - const options4 = { name: name2, theme: this.theme, params, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }; - return themeUtils_default.getPresetD(options4); - }, - getCustomPreset(name2 = "", preset, selector, params) { - const options4 = { name: name2, preset, options: this.options, selector, params, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }; - return themeUtils_default.getPreset(options4); - }, - getLayerOrderCSS(name2 = "") { - return themeUtils_default.getLayerOrder(name2, this.options, { names: this.getLayerNames() }, this.defaults); - }, - transformCSS(name2 = "", css22, type = "style", mode2) { - return themeUtils_default.transformCSS(name2, css22, mode2, type, this.options, { layerNames: this.setLayerNames.bind(this) }, this.defaults); - }, - getCommonStyleSheet(name2 = "", params, props = {}) { - return themeUtils_default.getCommonStyleSheet({ name: name2, theme: this.theme, params, props, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }); - }, - getStyleSheet(name2, params, props = {}) { - return themeUtils_default.getStyleSheet({ name: name2, theme: this.theme, params, props, defaults: this.defaults, set: { layerNames: this.setLayerNames.bind(this) } }); - }, - onStyleMounted(name2) { - this._loadingStyles.add(name2); - }, - onStyleUpdated(name2) { - this._loadingStyles.add(name2); - }, - onStyleLoaded(event, { name: name2 }) { - if (this._loadingStyles.size) { - this._loadingStyles.delete(name2); - service_default.emit(`theme:${name2}:load`, event); - !this._loadingStyles.size && service_default.emit("theme:load"); - } - } -}; -function updatePreset(...presets) { - const newPreset = mergeKeys(config_default.getPreset(), ...presets); - config_default.setPreset(newPreset); - return newPreset; -} -__name(updatePreset, "updatePreset"); -function updatePrimaryPalette(primary) { - return $t().primaryPalette(primary).update().preset; -} -__name(updatePrimaryPalette, "updatePrimaryPalette"); -function updateSurfacePalette(palette) { - return $t().surfacePalette(palette).update().preset; -} -__name(updateSurfacePalette, "updateSurfacePalette"); -function usePreset(...presets) { - const newPreset = mergeKeys(...presets); - config_default.setPreset(newPreset); - return newPreset; -} -__name(usePreset, "usePreset"); -function useTheme(theme40) { - return $t(theme40).update({ mergePresets: false }); -} -__name(useTheme, "useTheme"); -var index$1n = { - root: { - transitionDuration: "{transition.duration}" - }, - panel: { - borderWidth: "0 0 1px 0", - borderColor: "{content.border.color}" - }, - header: { - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{text.color}", - padding: "1.125rem", - fontWeight: "600", - borderRadius: "0", - borderWidth: "0", - borderColor: "{content.border.color}", - background: "{content.background}", - hoverBackground: "{content.background}", - activeBackground: "{content.background}", - activeHoverBackground: "{content.background}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - toggleIcon: { - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{text.color}", - activeHoverColor: "{text.color}" - }, - first: { - topBorderRadius: "{content.border.radius}", - borderWidth: "0" - }, - last: { - bottomBorderRadius: "{content.border.radius}", - activeBottomBorderRadius: "0" - } - }, - content: { - borderWidth: "0", - borderColor: "{content.border.color}", - background: "{content.background}", - color: "{text.color}", - padding: "0 1.125rem 1.125rem 1.125rem" - } -}; -var index$1m = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - list: { - padding: "{list.padding}", - gap: "{list.gap}" - }, - option: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}" - }, - optionGroup: { - background: "{list.option.group.background}", - color: "{list.option.group.color}", - fontWeight: "{list.option.group.font.weight}", - padding: "{list.option.group.padding}" - }, - dropdown: { - width: "2.5rem", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.border.color}", - activeBorderColor: "{form.field.border.color}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - chip: { - borderRadius: "{border.radius.sm}" - }, - emptyMessage: { - padding: "{list.option.padding}" - }, - colorScheme: { - light: { - dropdown: { - background: "{surface.100}", - hoverBackground: "{surface.200}", - activeBackground: "{surface.300}", - color: "{surface.600}", - hoverColor: "{surface.700}", - activeColor: "{surface.800}" - } - }, - dark: { - dropdown: { - background: "{surface.800}", - hoverBackground: "{surface.700}", - activeBackground: "{surface.600}", - color: "{surface.300}", - hoverColor: "{surface.200}", - activeColor: "{surface.100}" - } - } - } -}; -var index$1l = { - root: { - width: "2rem", - height: "2rem", - fontSize: "1rem", - background: "{content.border.color}", - borderRadius: "{content.border.radius}" - }, - group: { - borderColor: "{content.background}", - offset: "-1rem" - }, - lg: { - width: "3rem", - height: "3rem", - fontSize: "1.5rem" - }, - xl: { - width: "4rem", - height: "4rem", - fontSize: "2rem" - } -}; -var index$1k = { - root: { - borderRadius: "{border.radius.md}", - padding: "0 0.5rem", - fontSize: "0.75rem", - fontWeight: "700", - minWidth: "1.5rem", - height: "1.5rem" - }, - dot: { - size: "0.5rem" - }, - sm: { - fontSize: "0.625rem", - minWidth: "1.25rem", - height: "1.25rem" - }, - lg: { - fontSize: "0.875rem", - minWidth: "1.75rem", - height: "1.75rem" - }, - xl: { - fontSize: "1rem", - minWidth: "2rem", - height: "2rem" - }, - colorScheme: { - light: { - primary: { - background: "{primary.color}", - color: "{primary.contrast.color}" - }, - secondary: { - background: "{surface.100}", - color: "{surface.600}" - }, - success: { - background: "{green.500}", - color: "{surface.0}" - }, - info: { - background: "{sky.500}", - color: "{surface.0}" - }, - warn: { - background: "{orange.500}", - color: "{surface.0}" - }, - danger: { - background: "{red.500}", - color: "{surface.0}" - }, - contrast: { - background: "{surface.950}", - color: "{surface.0}" - } - }, - dark: { - primary: { - background: "{primary.color}", - color: "{primary.contrast.color}" - }, - secondary: { - background: "{surface.800}", - color: "{surface.300}" - }, - success: { - background: "{green.400}", - color: "{green.950}" - }, - info: { - background: "{sky.400}", - color: "{sky.950}" - }, - warn: { - background: "{orange.400}", - color: "{orange.950}" - }, - danger: { - background: "{red.400}", - color: "{red.950}" - }, - contrast: { - background: "{surface.0}", - color: "{surface.950}" - } - } - } -}; -var index$1j = { - root: { - borderRadius: "{content.border.radius}" - } -}; -var index$1i = { - root: { - padding: "1rem", - background: "{content.background}", - gap: "0.5rem", - transitionDuration: "{transition.duration}" - }, - item: { - color: "{text.muted.color}", - hoverColor: "{text.color}", - borderRadius: "{content.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - hoverColor: "{navigation.item.icon.focus.color}" - }, - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - separator: { - color: "{navigation.item.icon.color}" - } -}; -var index$1h = { - root: { - borderRadius: "{form.field.border.radius}", - roundedBorderRadius: "2rem", - gap: "0.5rem", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - iconOnlyWidth: "2.5rem", - sm: { - fontSize: "0.875rem", - paddingX: "0.625rem", - paddingY: "0.375rem" - }, - lg: { - fontSize: "1.125rem", - paddingX: "0.875rem", - paddingY: "0.625rem" - }, - label: { - fontWeight: "500" - }, - raisedShadow: "0 3px 1px -2px rgba(0, 0, 0, 0.2), 0 2px 2px 0 rgba(0, 0, 0, 0.14), 0 1px 5px 0 rgba(0, 0, 0, 0.12)", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - offset: "{focus.ring.offset}" - }, - badgeSize: "1rem", - transitionDuration: "{form.field.transition.duration}" - }, - colorScheme: { - light: { - root: { - primary: { - background: "{primary.color}", - hoverBackground: "{primary.hover.color}", - activeBackground: "{primary.active.color}", - borderColor: "{primary.color}", - hoverBorderColor: "{primary.hover.color}", - activeBorderColor: "{primary.active.color}", - color: "{primary.contrast.color}", - hoverColor: "{primary.contrast.color}", - activeColor: "{primary.contrast.color}", - focusRing: { - color: "{primary.color}", - shadow: "none" - } - }, - secondary: { - background: "{surface.100}", - hoverBackground: "{surface.200}", - activeBackground: "{surface.300}", - borderColor: "{surface.100}", - hoverBorderColor: "{surface.200}", - activeBorderColor: "{surface.300}", - color: "{surface.600}", - hoverColor: "{surface.700}", - activeColor: "{surface.800}", - focusRing: { - color: "{surface.600}", - shadow: "none" - } - }, - info: { - background: "{sky.500}", - hoverBackground: "{sky.600}", - activeBackground: "{sky.700}", - borderColor: "{sky.500}", - hoverBorderColor: "{sky.600}", - activeBorderColor: "{sky.700}", - color: "#ffffff", - hoverColor: "#ffffff", - activeColor: "#ffffff", - focusRing: { - color: "{sky.500}", - shadow: "none" - } - }, - success: { - background: "{green.500}", - hoverBackground: "{green.600}", - activeBackground: "{green.700}", - borderColor: "{green.500}", - hoverBorderColor: "{green.600}", - activeBorderColor: "{green.700}", - color: "#ffffff", - hoverColor: "#ffffff", - activeColor: "#ffffff", - focusRing: { - color: "{green.500}", - shadow: "none" - } - }, - warn: { - background: "{orange.500}", - hoverBackground: "{orange.600}", - activeBackground: "{orange.700}", - borderColor: "{orange.500}", - hoverBorderColor: "{orange.600}", - activeBorderColor: "{orange.700}", - color: "#ffffff", - hoverColor: "#ffffff", - activeColor: "#ffffff", - focusRing: { - color: "{orange.500}", - shadow: "none" - } - }, - help: { - background: "{purple.500}", - hoverBackground: "{purple.600}", - activeBackground: "{purple.700}", - borderColor: "{purple.500}", - hoverBorderColor: "{purple.600}", - activeBorderColor: "{purple.700}", - color: "#ffffff", - hoverColor: "#ffffff", - activeColor: "#ffffff", - focusRing: { - color: "{purple.500}", - shadow: "none" - } - }, - danger: { - background: "{red.500}", - hoverBackground: "{red.600}", - activeBackground: "{red.700}", - borderColor: "{red.500}", - hoverBorderColor: "{red.600}", - activeBorderColor: "{red.700}", - color: "#ffffff", - hoverColor: "#ffffff", - activeColor: "#ffffff", - focusRing: { - color: "{red.500}", - shadow: "none" - } - }, - contrast: { - background: "{surface.950}", - hoverBackground: "{surface.900}", - activeBackground: "{surface.800}", - borderColor: "{surface.950}", - hoverBorderColor: "{surface.900}", - activeBorderColor: "{surface.800}", - color: "{surface.0}", - hoverColor: "{surface.0}", - activeColor: "{surface.0}", - focusRing: { - color: "{surface.950}", - shadow: "none" - } - } - }, - outlined: { - primary: { - hoverBackground: "{primary.50}", - activeBackground: "{primary.100}", - borderColor: "{primary.200}", - color: "{primary.color}" - }, - secondary: { - hoverBackground: "{surface.50}", - activeBackground: "{surface.100}", - borderColor: "{surface.200}", - color: "{surface.500}" - }, - success: { - hoverBackground: "{green.50}", - activeBackground: "{green.100}", - borderColor: "{green.200}", - color: "{green.500}" - }, - info: { - hoverBackground: "{sky.50}", - activeBackground: "{sky.100}", - borderColor: "{sky.200}", - color: "{sky.500}" - }, - warn: { - hoverBackground: "{orange.50}", - activeBackground: "{orange.100}", - borderColor: "{orange.200}", - color: "{orange.500}" - }, - help: { - hoverBackground: "{purple.50}", - activeBackground: "{purple.100}", - borderColor: "{purple.200}", - color: "{purple.500}" - }, - danger: { - hoverBackground: "{red.50}", - activeBackground: "{red.100}", - borderColor: "{red.200}", - color: "{red.500}" - }, - contrast: { - hoverBackground: "{surface.50}", - activeBackground: "{surface.100}", - borderColor: "{surface.700}", - color: "{surface.950}" - }, - plain: { - hoverBackground: "{surface.50}", - activeBackground: "{surface.100}", - borderColor: "{surface.200}", - color: "{surface.700}" - } - }, - text: { - primary: { - hoverBackground: "{primary.50}", - activeBackground: "{primary.100}", - color: "{primary.color}" - }, - secondary: { - hoverBackground: "{surface.50}", - activeBackground: "{surface.100}", - color: "{surface.500}" - }, - success: { - hoverBackground: "{green.50}", - activeBackground: "{green.100}", - color: "{green.500}" - }, - info: { - hoverBackground: "{sky.50}", - activeBackground: "{sky.100}", - color: "{sky.500}" - }, - warn: { - hoverBackground: "{orange.50}", - activeBackground: "{orange.100}", - color: "{orange.500}" - }, - help: { - hoverBackground: "{purple.50}", - activeBackground: "{purple.100}", - color: "{purple.500}" - }, - danger: { - hoverBackground: "{red.50}", - activeBackground: "{red.100}", - color: "{red.500}" - }, - plain: { - hoverBackground: "{surface.50}", - activeBackground: "{surface.100}", - color: "{surface.700}" - } - }, - link: { - color: "{primary.color}", - hoverColor: "{primary.color}", - activeColor: "{primary.color}" - } - }, - dark: { - root: { - primary: { - background: "{primary.color}", - hoverBackground: "{primary.hover.color}", - activeBackground: "{primary.active.color}", - borderColor: "{primary.color}", - hoverBorderColor: "{primary.hover.color}", - activeBorderColor: "{primary.active.color}", - color: "{primary.contrast.color}", - hoverColor: "{primary.contrast.color}", - activeColor: "{primary.contrast.color}", - focusRing: { - color: "{primary.color}", - shadow: "none" - } - }, - secondary: { - background: "{surface.800}", - hoverBackground: "{surface.700}", - activeBackground: "{surface.600}", - borderColor: "{surface.800}", - hoverBorderColor: "{surface.700}", - activeBorderColor: "{surface.600}", - color: "{surface.300}", - hoverColor: "{surface.200}", - activeColor: "{surface.100}", - focusRing: { - color: "{surface.300}", - shadow: "none" - } - }, - info: { - background: "{sky.400}", - hoverBackground: "{sky.300}", - activeBackground: "{sky.200}", - borderColor: "{sky.400}", - hoverBorderColor: "{sky.300}", - activeBorderColor: "{sky.200}", - color: "{sky.950}", - hoverColor: "{sky.950}", - activeColor: "{sky.950}", - focusRing: { - color: "{sky.400}", - shadow: "none" - } - }, - success: { - background: "{green.400}", - hoverBackground: "{green.300}", - activeBackground: "{green.200}", - borderColor: "{green.400}", - hoverBorderColor: "{green.300}", - activeBorderColor: "{green.200}", - color: "{green.950}", - hoverColor: "{green.950}", - activeColor: "{green.950}", - focusRing: { - color: "{green.400}", - shadow: "none" - } - }, - warn: { - background: "{orange.400}", - hoverBackground: "{orange.300}", - activeBackground: "{orange.200}", - borderColor: "{orange.400}", - hoverBorderColor: "{orange.300}", - activeBorderColor: "{orange.200}", - color: "{orange.950}", - hoverColor: "{orange.950}", - activeColor: "{orange.950}", - focusRing: { - color: "{orange.400}", - shadow: "none" - } - }, - help: { - background: "{purple.400}", - hoverBackground: "{purple.300}", - activeBackground: "{purple.200}", - borderColor: "{purple.400}", - hoverBorderColor: "{purple.300}", - activeBorderColor: "{purple.200}", - color: "{purple.950}", - hoverColor: "{purple.950}", - activeColor: "{purple.950}", - focusRing: { - color: "{purple.400}", - shadow: "none" - } - }, - danger: { - background: "{red.400}", - hoverBackground: "{red.300}", - activeBackground: "{red.200}", - borderColor: "{red.400}", - hoverBorderColor: "{red.300}", - activeBorderColor: "{red.200}", - color: "{red.950}", - hoverColor: "{red.950}", - activeColor: "{red.950}", - focusRing: { - color: "{red.400}", - shadow: "none" - } - }, - contrast: { - background: "{surface.0}", - hoverBackground: "{surface.100}", - activeBackground: "{surface.200}", - borderColor: "{surface.0}", - hoverBorderColor: "{surface.100}", - activeBorderColor: "{surface.200}", - color: "{surface.950}", - hoverColor: "{surface.950}", - activeColor: "{surface.950}", - focusRing: { - color: "{surface.0}", - shadow: "none" - } - } - }, - outlined: { - primary: { - hoverBackground: "color-mix(in srgb, {primary.color}, transparent 96%)", - activeBackground: "color-mix(in srgb, {primary.color}, transparent 84%)", - borderColor: "{primary.700}", - color: "{primary.color}" - }, - secondary: { - hoverBackground: "rgba(255,255,255,0.04)", - activeBackground: "rgba(255,255,255,0.16)", - borderColor: "{surface.700}", - color: "{surface.400}" - }, - success: { - hoverBackground: "color-mix(in srgb, {green.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {green.400}, transparent 84%)", - borderColor: "{green.700}", - color: "{green.400}" - }, - info: { - hoverBackground: "color-mix(in srgb, {sky.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {sky.400}, transparent 84%)", - borderColor: "{sky.700}", - color: "{sky.400}" - }, - warn: { - hoverBackground: "color-mix(in srgb, {orange.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {orange.400}, transparent 84%)", - borderColor: "{orange.700}", - color: "{orange.400}" - }, - help: { - hoverBackground: "color-mix(in srgb, {purple.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {purple.400}, transparent 84%)", - borderColor: "{purple.700}", - color: "{purple.400}" - }, - danger: { - hoverBackground: "color-mix(in srgb, {red.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {red.400}, transparent 84%)", - borderColor: "{red.700}", - color: "{red.400}" - }, - contrast: { - hoverBackground: "{surface.800}", - activeBackground: "{surface.700}", - borderColor: "{surface.500}", - color: "{surface.0}" - }, - plain: { - hoverBackground: "{surface.800}", - activeBackground: "{surface.700}", - borderColor: "{surface.600}", - color: "{surface.0}" - } - }, - text: { - primary: { - hoverBackground: "color-mix(in srgb, {primary.color}, transparent 96%)", - activeBackground: "color-mix(in srgb, {primary.color}, transparent 84%)", - color: "{primary.color}" - }, - secondary: { - hoverBackground: "{surface.800}", - activeBackground: "{surface.700}", - color: "{surface.400}" - }, - success: { - hoverBackground: "color-mix(in srgb, {green.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {green.400}, transparent 84%)", - color: "{green.400}" - }, - info: { - hoverBackground: "color-mix(in srgb, {sky.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {sky.400}, transparent 84%)", - color: "{sky.400}" - }, - warn: { - hoverBackground: "color-mix(in srgb, {orange.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {orange.400}, transparent 84%)", - color: "{orange.400}" - }, - help: { - hoverBackground: "color-mix(in srgb, {purple.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {purple.400}, transparent 84%)", - color: "{purple.400}" - }, - danger: { - hoverBackground: "color-mix(in srgb, {red.400}, transparent 96%)", - activeBackground: "color-mix(in srgb, {red.400}, transparent 84%)", - color: "{red.400}" - }, - plain: { - hoverBackground: "{surface.800}", - activeBackground: "{surface.700}", - color: "{surface.0}" - } - }, - link: { - color: "{primary.color}", - hoverColor: "{primary.color}", - activeColor: "{primary.color}" - } - } - } -}; -var index$1g = { - root: { - background: "{content.background}", - borderRadius: "{border.radius.xl}", - color: "{content.color}", - shadow: "0 1px 3px 0 rgba(0, 0, 0, 0.1), 0 1px 2px -1px rgba(0, 0, 0, 0.1)" - }, - body: { - padding: "1.25rem", - gap: "0.5rem" - }, - caption: { - gap: "0.5rem" - }, - title: { - fontSize: "1.25rem", - fontWeight: "500" - }, - subtitle: { - color: "{text.muted.color}" - } -}; -var index$1f = { - root: { - transitionDuration: "{transition.duration}" - }, - content: { - gap: "0.25rem" - }, - indicatorList: { - padding: "1rem", - gap: "0.5rem" - }, - indicator: { - width: "2rem", - height: "0.5rem", - borderRadius: "{content.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - colorScheme: { - light: { - indicator: { - background: "{surface.200}", - hoverBackground: "{surface.300}", - activeBackground: "{primary.color}" - } - }, - dark: { - indicator: { - background: "{surface.700}", - hoverBackground: "{surface.600}", - activeBackground: "{primary.color}" - } - } - } -}; -var index$1e = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - dropdown: { - width: "2.5rem", - color: "{form.field.icon.color}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - list: { - padding: "{list.padding}", - gap: "{list.gap}" - }, - option: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}", - icon: { - color: "{list.option.icon.color}", - focusColor: "{list.option.icon.focus.color}", - size: "0.875rem" - } - } -}; -var index$1d = { - root: { - borderRadius: "{border.radius.sm}", - width: "1.25rem", - height: "1.25rem", - background: "{form.field.background}", - checkedBackground: "{primary.color}", - checkedHoverBackground: "{primary.hover.color}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.border.color}", - checkedBorderColor: "{primary.color}", - checkedHoverBorderColor: "{primary.hover.color}", - checkedFocusBorderColor: "{primary.color}", - checkedDisabledBorderColor: "{form.field.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - shadow: "{form.field.shadow}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - icon: { - size: "0.875rem", - color: "{form.field.color}", - checkedColor: "{primary.contrast.color}", - checkedHoverColor: "{primary.contrast.color}", - disabledColor: "{form.field.disabled.color}" - } -}; -var index$1c = { - root: { - borderRadius: "16px", - paddingX: "0.75rem", - paddingY: "0.5rem", - gap: "0.5rem", - transitionDuration: "{transition.duration}" - }, - image: { - width: "2rem", - height: "2rem" - }, - icon: { - size: "1rem" - }, - removeIcon: { - size: "1rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - } - }, - colorScheme: { - light: { - root: { - background: "{surface.100}", - color: "{surface.800}" - }, - icon: { - color: "{surface.800}" - }, - removeIcon: { - color: "{surface.800}" - } - }, - dark: { - root: { - background: "{surface.800}", - color: "{surface.0}" - }, - icon: { - color: "{surface.0}" - }, - removeIcon: { - color: "{surface.0}" - } - } - } -}; -var index$1b = { - root: { - transitionDuration: "{transition.duration}" - }, - preview: { - width: "1.5rem", - height: "1.5rem", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - panel: { - shadow: "{overlay.popover.shadow}", - borderRadius: "{overlay.popover.borderRadius}" - }, - colorScheme: { - light: { - panel: { - background: "{surface.800}", - borderColor: "{surface.900}" - }, - handle: { - color: "{surface.0}" - } - }, - dark: { - panel: { - background: "{surface.900}", - borderColor: "{surface.700}" - }, - handle: { - color: "{surface.0}" - } - } - } -}; -var index$1a = { - icon: { - size: "2rem", - color: "{overlay.modal.color}" - }, - content: { - gap: "1rem" - } -}; -var index$19 = { - root: { - background: "{overlay.popover.background}", - borderColor: "{overlay.popover.border.color}", - color: "{overlay.popover.color}", - borderRadius: "{overlay.popover.border.radius}", - shadow: "{overlay.popover.shadow}", - gutter: "10px", - arrowOffset: "1.25rem" - }, - content: { - padding: "{overlay.popover.padding}", - gap: "1rem" - }, - icon: { - size: "1.5rem", - color: "{overlay.popover.color}" - }, - footer: { - gap: "0.5rem", - padding: "0 {overlay.popover.padding} {overlay.popover.padding} {overlay.popover.padding}" - } -}; -var index$18 = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}", - shadow: "{overlay.navigation.shadow}", - transitionDuration: "{transition.duration}" - }, - list: { - padding: "{navigation.list.padding}", - gap: "{navigation.list.gap}" - }, - item: { - focusBackground: "{navigation.item.focus.background}", - activeBackground: "{navigation.item.active.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - activeColor: "{navigation.item.active.color}", - padding: "{navigation.item.padding}", - borderRadius: "{navigation.item.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}", - activeColor: "{navigation.item.icon.active.color}" - } - }, - submenuIcon: { - size: "{navigation.submenu.icon.size}", - color: "{navigation.submenu.icon.color}", - focusColor: "{navigation.submenu.icon.focus.color}", - activeColor: "{navigation.submenu.icon.active.color}" - }, - separator: { - borderColor: "{content.border.color}" - } -}; -var index$17 = { - root: { - transitionDuration: "{transition.duration}" - }, - header: { - background: "{content.background}", - borderColor: "{datatable.border.color}", - color: "{content.color}", - borderWidth: "0 0 1px 0", - padding: "0.75rem 1rem" - }, - headerCell: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - borderColor: "{datatable.border.color}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - selectedColor: "{highlight.color}", - gap: "0.5rem", - padding: "0.75rem 1rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - columnTitle: { - fontWeight: "600" - }, - row: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - selectedColor: "{highlight.color}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - bodyCell: { - borderColor: "{datatable.border.color}", - padding: "0.75rem 1rem" - }, - footerCell: { - background: "{content.background}", - borderColor: "{datatable.border.color}", - color: "{content.color}", - padding: "0.75rem 1rem" - }, - columnFooter: { - fontWeight: "600" - }, - footer: { - background: "{content.background}", - borderColor: "{datatable.border.color}", - color: "{content.color}", - borderWidth: "0 0 1px 0", - padding: "0.75rem 1rem" - }, - dropPointColor: "{primary.color}", - columnResizerWidth: "0.5rem", - resizeIndicator: { - width: "1px", - color: "{primary.color}" - }, - sortIcon: { - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}" - }, - loadingIcon: { - size: "2rem" - }, - rowToggleButton: { - hoverBackground: "{content.hover.background}", - selectedHoverBackground: "{content.background}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - selectedHoverColor: "{primary.color}", - size: "1.75rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - filter: { - inlineGap: "0.5rem", - overlaySelect: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - overlayPopover: { - background: "{overlay.popover.background}", - borderColor: "{overlay.popover.border.color}", - borderRadius: "{overlay.popover.border.radius}", - color: "{overlay.popover.color}", - shadow: "{overlay.popover.shadow}", - padding: "{overlay.popover.padding}", - gap: "0.5rem" - }, - rule: { - borderColor: "{content.border.color}" - }, - constraintList: { - padding: "{list.padding}", - gap: "{list.gap}" - }, - constraint: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - separator: { - borderColor: "{content.border.color}" - }, - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}" - } - }, - paginatorTop: { - borderColor: "{datatable.border.color}", - borderWidth: "0 0 1px 0" - }, - paginatorBottom: { - borderColor: "{datatable.border.color}", - borderWidth: "0 0 1px 0" - }, - colorScheme: { - light: { - root: { - borderColor: "{content.border.color}" - }, - row: { - stripedBackground: "{surface.50}" - }, - bodyCell: { - selectedBorderColor: "{primary.100}" - } - }, - dark: { - root: { - borderColor: "{surface.800}" - }, - row: { - stripedBackground: "{surface.950}" - }, - bodyCell: { - selectedBorderColor: "{primary.900}" - } - } - } -}; -var index$16 = { - root: { - borderColor: "transparent", - borderWidth: "0", - borderRadius: "0", - padding: "0" - }, - header: { - background: "{content.background}", - color: "{content.color}", - borderColor: "{content.border.color}", - borderWidth: "0 0 1px 0", - padding: "0.75rem 1rem", - borderRadius: "0" - }, - content: { - background: "{content.background}", - color: "{content.color}", - borderColor: "transparent", - borderWidth: "0", - padding: "0", - borderRadius: "0" - }, - footer: { - background: "{content.background}", - color: "{content.color}", - borderColor: "{content.border.color}", - borderWidth: "1px 0 0 0", - padding: "0.75rem 1rem", - borderRadius: "0" - }, - paginatorTop: { - borderColor: "{content.border.color}", - borderWidth: "0 0 1px 0" - }, - paginatorBottom: { - borderColor: "{content.border.color}", - borderWidth: "1px 0 0 0" - } -}; -var index$15 = { - root: { - transitionDuration: "{transition.duration}" - }, - panel: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}", - shadow: "{overlay.popover.shadow}", - padding: "{overlay.popover.padding}" - }, - header: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - padding: "0 0 0.5rem 0", - fontWeight: "500", - gap: "0.5rem" - }, - title: { - gap: "0.5rem", - fontWeight: "500" - }, - dropdown: { - width: "2.5rem", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.border.color}", - activeBorderColor: "{form.field.border.color}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - inputIcon: { - color: "{form.field.icon.color}" - }, - selectMonth: { - hoverBackground: "{content.hover.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - padding: "0.25rem 0.5rem", - borderRadius: "{content.border.radius}" - }, - selectYear: { - hoverBackground: "{content.hover.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - padding: "0.25rem 0.5rem", - borderRadius: "{content.border.radius}" - }, - group: { - borderColor: "{content.border.color}", - gap: "{overlay.popover.padding}" - }, - dayView: { - margin: "0.5rem 0 0 0" - }, - weekDay: { - padding: "0.25rem", - fontWeight: "500", - color: "{content.color}" - }, - date: { - hoverBackground: "{content.hover.background}", - selectedBackground: "{primary.color}", - rangeSelectedBackground: "{highlight.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - selectedColor: "{primary.contrast.color}", - rangeSelectedColor: "{highlight.color}", - width: "2rem", - height: "2rem", - borderRadius: "50%", - padding: "0.25rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - monthView: { - margin: "0.5rem 0 0 0" - }, - month: { - borderRadius: "{content.border.radius}" - }, - yearView: { - margin: "0.5rem 0 0 0" - }, - year: { - borderRadius: "{content.border.radius}" - }, - buttonbar: { - padding: "0.5rem 0 0 0", - borderColor: "{content.border.color}" - }, - timePicker: { - padding: "0.5rem 0 0 0", - borderColor: "{content.border.color}", - gap: "0.5rem", - buttonGap: "0.25rem" - }, - colorScheme: { - light: { - dropdown: { - background: "{surface.100}", - hoverBackground: "{surface.200}", - activeBackground: "{surface.300}", - color: "{surface.600}", - hoverColor: "{surface.700}", - activeColor: "{surface.800}" - }, - today: { - background: "{surface.200}", - color: "{surface.900}" - } - }, - dark: { - dropdown: { - background: "{surface.800}", - hoverBackground: "{surface.700}", - activeBackground: "{surface.600}", - color: "{surface.300}", - hoverColor: "{surface.200}", - activeColor: "{surface.100}" - }, - today: { - background: "{surface.700}", - color: "{surface.0}" - } - } - } -}; -var index$14 = { - root: { - background: "{overlay.modal.background}", - borderColor: "{overlay.modal.border.color}", - color: "{overlay.modal.color}", - borderRadius: "{overlay.modal.border.radius}", - shadow: "{overlay.modal.shadow}" - }, - header: { - padding: "{overlay.modal.padding}", - gap: "0.5rem" - }, - title: { - fontSize: "1.25rem", - fontWeight: "600" - }, - content: { - padding: "0 {overlay.modal.padding} {overlay.modal.padding} {overlay.modal.padding}" - }, - footer: { - padding: "0 {overlay.modal.padding} {overlay.modal.padding} {overlay.modal.padding}", - gap: "0.5rem" - } -}; -var index$13 = { - root: { - borderColor: "{content.border.color}" - }, - content: { - background: "{content.background}", - color: "{text.color}" - }, - horizontal: { - margin: "1rem 0", - padding: "0 1rem", - content: { - padding: "0 0.5rem" - } - }, - vertical: { - margin: "0 1rem", - padding: "0.5rem 0", - content: { - padding: "0.5rem 0" - } - } -}; -var index$12 = { - root: { - background: "rgba(255, 255, 255, 0.1)", - borderColor: "rgba(255, 255, 255, 0.2)", - padding: "0.5rem", - borderRadius: "{border.radius.xl}" - }, - item: { - borderRadius: "{content.border.radius}", - padding: "0.5rem", - size: "3rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - } -}; -var index$11 = { - root: { - background: "{overlay.modal.background}", - borderColor: "{overlay.modal.border.color}", - color: "{overlay.modal.color}", - borderRadius: "{overlay.modal.border.radius}", - shadow: "{overlay.modal.shadow}" - }, - header: { - padding: "{overlay.modal.padding}" - }, - title: { - fontSize: "1.5rem", - fontWeight: "600" - }, - content: { - padding: "0 {overlay.modal.padding} {overlay.modal.padding} {overlay.modal.padding}" - } -}; -var index$10 = { - toolbar: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}" - }, - toolbarItem: { - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{primary.color}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}", - padding: "{list.padding}" - }, - overlayOption: { - focusBackground: "{list.option.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}" - }, - content: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}" - } -}; -var index$$ = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - color: "{content.color}", - padding: "0 1.125rem 1.125rem 1.125rem", - transitionDuration: "{transition.duration}" - }, - legend: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - borderRadius: "{content.border.radius}", - borderWidth: "1px", - borderColor: "transparent", - padding: "0.5rem 0.75rem", - gap: "0.5rem", - fontWeight: "600", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - toggleIcon: { - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}" - }, - content: { - padding: "0" - } -}; -var index$_ = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}", - transitionDuration: "{transition.duration}" - }, - header: { - background: "transparent", - color: "{text.color}", - padding: "1.125rem", - borderWidth: "0", - borderRadius: "0", - gap: "0.5rem" - }, - content: { - highlightBorderColor: "{primary.color}", - padding: "0 1.125rem 1.125rem 1.125rem" - }, - file: { - padding: "1rem", - gap: "1rem", - borderColor: "{content.border.color}", - info: { - gap: "0.5rem" - } - }, - progressbar: { - height: "0.25rem" - }, - basic: { - gap: "0.5rem" - } -}; -var index$Z = { - root: { - color: "{form.field.float.label.color}", - focusColor: "{form.field.float.label.focus.color}", - invalidColor: "{form.field.float.label.invalid.color}", - transitionDuration: "0.2s" - } -}; -var index$Y = { - root: { - borderWidth: "1px", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - transitionDuration: "{transition.duration}" - }, - navButton: { - background: "rgba(255, 255, 255, 0.1)", - hoverBackground: "rgba(255, 255, 255, 0.2)", - color: "{surface.100}", - hoverColor: "{surface.0}", - size: "3rem", - gutter: "0.5rem", - prev: { - borderRadius: "50%" - }, - next: { - borderRadius: "50%" - }, - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - navIcon: { - size: "1.5rem" - }, - thumbnailsContent: { - background: "{content.background}", - padding: "1rem 0.25rem" - }, - thumbnailNavButton: { - size: "2rem", - borderRadius: "{content.border.radius}", - gutter: "0.5rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - thumbnailNavButtonIcon: { - size: "1rem" - }, - caption: { - background: "rgba(0, 0, 0, 0.5)", - color: "{surface.100}", - padding: "1rem" - }, - indicatorList: { - gap: "0.5rem", - padding: "1rem" - }, - indicatorButton: { - width: "1rem", - height: "1rem", - activeBackground: "{primary.color}", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - insetIndicatorList: { - background: "rgba(0, 0, 0, 0.5)" - }, - insetIndicatorButton: { - background: "rgba(255, 255, 255, 0.4)", - hoverBackground: "rgba(255, 255, 255, 0.6)", - activeBackground: "rgba(255, 255, 255, 0.9)" - }, - mask: { - background: "{mask.background}", - color: "{mask.color}" - }, - closeButton: { - size: "3rem", - gutter: "0.5rem", - background: "rgba(255, 255, 255, 0.1)", - hoverBackground: "rgba(255, 255, 255, 0.2)", - color: "{surface.50}", - hoverColor: "{surface.0}", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - closeButtonIcon: { - size: "1.5rem" - }, - colorScheme: { - light: { - thumbnailNavButton: { - hoverBackground: "{surface.100}", - color: "{surface.600}", - hoverColor: "{surface.700}" - }, - indicatorButton: { - background: "{surface.200}", - hoverBackground: "{surface.300}" - } - }, - dark: { - thumbnailNavButton: { - hoverBackground: "{surface.700}", - color: "{surface.400}", - hoverColor: "{surface.0}" - }, - indicatorButton: { - background: "{surface.700}", - hoverBackground: "{surface.600}" - } - } - } -}; -var index$X = { - icon: { - color: "{form.field.icon.color}" - } -}; -var index$W = { - root: { - transitionDuration: "{transition.duration}" - }, - preview: { - icon: { - size: "1.5rem" - }, - mask: { - background: "{mask.background}", - color: "{mask.color}" - } - }, - toolbar: { - position: { - left: "auto", - right: "1rem", - top: "1rem", - bottom: "auto" - }, - blur: "8px", - background: "rgba(255,255,255,0.1)", - borderColor: "rgba(255,255,255,0.2)", - borderWidth: "1px", - borderRadius: "30px", - padding: ".5rem", - gap: "0.5rem" - }, - action: { - hoverBackground: "rgba(255,255,255,0.1)", - color: "{surface.50}", - hoverColor: "{surface.0}", - size: "3rem", - iconSize: "1.5rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - } -}; -var index$V = { - root: { - padding: "{form.field.padding.y} {form.field.padding.x}", - borderRadius: "{content.border.radius}", - gap: "0.5rem" - }, - text: { - fontWeight: "500" - }, - icon: { - size: "1rem" - }, - colorScheme: { - light: { - info: { - background: "color-mix(in srgb, {blue.50}, transparent 5%)", - borderColor: "{blue.200}", - color: "{blue.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)" - }, - success: { - background: "color-mix(in srgb, {green.50}, transparent 5%)", - borderColor: "{green.200}", - color: "{green.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)" - }, - warn: { - background: "color-mix(in srgb,{yellow.50}, transparent 5%)", - borderColor: "{yellow.200}", - color: "{yellow.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)" - }, - error: { - background: "color-mix(in srgb, {red.50}, transparent 5%)", - borderColor: "{red.200}", - color: "{red.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)" - }, - secondary: { - background: "{surface.100}", - borderColor: "{surface.200}", - color: "{surface.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)" - }, - contrast: { - background: "{surface.900}", - borderColor: "{surface.950}", - color: "{surface.50}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)" - } - }, - dark: { - info: { - background: "color-mix(in srgb, {blue.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {blue.700}, transparent 64%)", - color: "{blue.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)" - }, - success: { - background: "color-mix(in srgb, {green.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {green.700}, transparent 64%)", - color: "{green.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)" - }, - warn: { - background: "color-mix(in srgb, {yellow.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {yellow.700}, transparent 64%)", - color: "{yellow.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)" - }, - error: { - background: "color-mix(in srgb, {red.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {red.700}, transparent 64%)", - color: "{red.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)" - }, - secondary: { - background: "{surface.800}", - borderColor: "{surface.700}", - color: "{surface.300}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)" - }, - contrast: { - background: "{surface.0}", - borderColor: "{surface.100}", - color: "{surface.950}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)" - } - } - } -}; -var index$U = { - root: { - padding: "{form.field.padding.y} {form.field.padding.x}", - borderRadius: "{content.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - transitionDuration: "{transition.duration}" - }, - display: { - hoverBackground: "{content.hover.background}", - hoverColor: "{content.hover.color}" - } -}; -var index$T = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - chip: { - borderRadius: "{border.radius.sm}" - }, - colorScheme: { - light: { - chip: { - focusBackground: "{surface.200}", - color: "{surface.800}" - } - }, - dark: { - chip: { - focusBackground: "{surface.700}", - color: "{surface.0}" - } - } - } -}; -var index$S = { - addon: { - background: "{form.field.background}", - borderColor: "{form.field.border.color}", - color: "{form.field.icon.color}", - borderRadius: "{form.field.border.radius}" - } -}; -var index$R = { - root: { - transitionDuration: "{transition.duration}" - }, - button: { - width: "2.5rem", - borderRadius: "{form.field.border.radius}", - verticalPadding: "{form.field.padding.y}" - }, - colorScheme: { - light: { - button: { - background: "transparent", - hoverBackground: "{surface.100}", - activeBackground: "{surface.200}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.border.color}", - activeBorderColor: "{form.field.border.color}", - color: "{surface.400}", - hoverColor: "{surface.500}", - activeColor: "{surface.600}" - } - }, - dark: { - button: { - background: "transparent", - hoverBackground: "{surface.800}", - activeBackground: "{surface.700}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.border.color}", - activeBorderColor: "{form.field.border.color}", - color: "{surface.400}", - hoverColor: "{surface.300}", - activeColor: "{surface.200}" - } - } - } -}; -var index$Q = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}", - sm: { - fontSize: "0.875rem", - paddingX: "0.625rem", - paddingY: "0.375rem" - }, - lg: { - fontSize: "1.125rem", - paddingX: "0.875rem", - paddingY: "0.625rem" - } - } -}; -var index$P = { - root: { - transitionDuration: "{transition.duration}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - value: { - background: "{primary.color}" - }, - range: { - background: "{content.border.color}" - }, - text: { - color: "{text.muted.color}" - } -}; -var index$O = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - shadow: "{form.field.shadow}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - list: { - padding: "{list.padding}", - gap: "{list.gap}", - header: { - padding: "{list.header.padding}" - } - }, - option: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}" - }, - optionGroup: { - background: "{list.option.group.background}", - color: "{list.option.group.color}", - fontWeight: "{list.option.group.font.weight}", - padding: "{list.option.group.padding}" - }, - checkmark: { - color: "{list.option.color}", - gutterStart: "-0.375rem", - gutterEnd: "0.375rem" - }, - emptyMessage: { - padding: "{list.option.padding}" - }, - colorScheme: { - light: { - option: { - stripedBackground: "{surface.50}" - } - }, - dark: { - option: { - stripedBackground: "{surface.900}" - } - } - } -}; -var index$N = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - color: "{content.color}", - gap: "0.5rem", - verticalOrientation: { - padding: "{navigation.list.padding}", - gap: "0" - }, - horizontalOrientation: { - padding: "0.5rem 0.75rem" - }, - transitionDuration: "{transition.duration}" - }, - baseItem: { - borderRadius: "{content.border.radius}", - padding: "{navigation.item.padding}" - }, - item: { - focusBackground: "{navigation.item.focus.background}", - activeBackground: "{navigation.item.active.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - activeColor: "{navigation.item.active.color}", - padding: "{navigation.item.padding}", - borderRadius: "{navigation.item.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}", - activeColor: "{navigation.item.icon.active.color}" - } - }, - overlay: { - padding: "0", - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - color: "{content.color}", - shadow: "{overlay.navigation.shadow}", - gap: "0.5rem" - }, - submenu: { - padding: "{navigation.list.padding}", - gap: "{navigation.list.gap}" - }, - submenuLabel: { - padding: "{navigation.submenu.label.padding}", - fontWeight: "{navigation.submenu.label.font.weight}", - background: "{navigation.submenu.label.background.}", - color: "{navigation.submenu.label.color}" - }, - submenuIcon: { - size: "{navigation.submenu.icon.size}", - color: "{navigation.submenu.icon.color}", - focusColor: "{navigation.submenu.icon.focus.color}", - activeColor: "{navigation.submenu.icon.active.color}" - }, - separator: { - borderColor: "{content.border.color}" - }, - mobileButton: { - borderRadius: "50%", - size: "1.75rem", - color: "{text.muted.color}", - hoverColor: "{text.muted.hover.color}", - hoverBackground: "{content.hover.background}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - } -}; -var index$M = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}", - shadow: "{overlay.navigation.shadow}", - transitionDuration: "{transition.duration}" - }, - list: { - padding: "{navigation.list.padding}", - gap: "{navigation.list.gap}" - }, - item: { - focusBackground: "{navigation.item.focus.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - padding: "{navigation.item.padding}", - borderRadius: "{navigation.item.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}" - } - }, - submenuLabel: { - padding: "{navigation.submenu.label.padding}", - fontWeight: "{navigation.submenu.label.font.weight}", - background: "{navigation.submenu.label.background}", - color: "{navigation.submenu.label.color}" - }, - separator: { - borderColor: "{content.border.color}" - } -}; -var index$L = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - color: "{content.color}", - gap: "0.5rem", - padding: "0.5rem 0.75rem", - transitionDuration: "{transition.duration}" - }, - baseItem: { - borderRadius: "{content.border.radius}", - padding: "{navigation.item.padding}" - }, - item: { - focusBackground: "{navigation.item.focus.background}", - activeBackground: "{navigation.item.active.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - activeColor: "{navigation.item.active.color}", - padding: "{navigation.item.padding}", - borderRadius: "{navigation.item.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}", - activeColor: "{navigation.item.icon.active.color}" - } - }, - submenu: { - padding: "{navigation.list.padding}", - gap: "{navigation.list.gap}", - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - shadow: "{overlay.navigation.shadow}", - mobileIndent: "1rem" - }, - submenuIcon: { - size: "{navigation.submenu.icon.size}", - color: "{navigation.submenu.icon.color}", - focusColor: "{navigation.submenu.icon.focus.color}", - activeColor: "{navigation.submenu.icon.active.color}" - }, - separator: { - borderColor: "{content.border.color}" - }, - mobileButton: { - borderRadius: "50%", - size: "1.75rem", - color: "{text.muted.color}", - hoverColor: "{text.muted.hover.color}", - hoverBackground: "{content.hover.background}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - } -}; -var index$K = { - root: { - borderRadius: "{content.border.radius}", - borderWidth: "1px", - transitionDuration: "{transition.duration}" - }, - content: { - padding: "0.5rem 0.75rem", - gap: "0.5rem" - }, - text: { - fontSize: "1rem", - fontWeight: "500" - }, - icon: { - size: "1.125rem" - }, - closeButton: { - width: "1.75rem", - height: "1.75rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - offset: "{focus.ring.offset}" - } - }, - closeIcon: { - size: "1rem" - }, - colorScheme: { - light: { - info: { - background: "color-mix(in srgb, {blue.50}, transparent 5%)", - borderColor: "{blue.200}", - color: "{blue.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)", - closeButton: { - hoverBackground: "{blue.100}", - focusRing: { - color: "{blue.600}", - shadow: "none" - } - } - }, - success: { - background: "color-mix(in srgb, {green.50}, transparent 5%)", - borderColor: "{green.200}", - color: "{green.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)", - closeButton: { - hoverBackground: "{green.100}", - focusRing: { - color: "{green.600}", - shadow: "none" - } - } - }, - warn: { - background: "color-mix(in srgb,{yellow.50}, transparent 5%)", - borderColor: "{yellow.200}", - color: "{yellow.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)", - closeButton: { - hoverBackground: "{yellow.100}", - focusRing: { - color: "{yellow.600}", - shadow: "none" - } - } - }, - error: { - background: "color-mix(in srgb, {red.50}, transparent 5%)", - borderColor: "{red.200}", - color: "{red.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)", - closeButton: { - hoverBackground: "{red.100}", - focusRing: { - color: "{red.600}", - shadow: "none" - } - } - }, - secondary: { - background: "{surface.100}", - borderColor: "{surface.200}", - color: "{surface.600}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.200}", - focusRing: { - color: "{surface.600}", - shadow: "none" - } - } - }, - contrast: { - background: "{surface.900}", - borderColor: "{surface.950}", - color: "{surface.50}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.800}", - focusRing: { - color: "{surface.50}", - shadow: "none" - } - } - } - }, - dark: { - info: { - background: "color-mix(in srgb, {blue.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {blue.700}, transparent 64%)", - color: "{blue.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{blue.500}", - shadow: "none" - } - } - }, - success: { - background: "color-mix(in srgb, {green.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {green.700}, transparent 64%)", - color: "{green.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{green.500}", - shadow: "none" - } - } - }, - warn: { - background: "color-mix(in srgb, {yellow.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {yellow.700}, transparent 64%)", - color: "{yellow.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{yellow.500}", - shadow: "none" - } - } - }, - error: { - background: "color-mix(in srgb, {red.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {red.700}, transparent 64%)", - color: "{red.500}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{red.500}", - shadow: "none" - } - } - }, - secondary: { - background: "{surface.800}", - borderColor: "{surface.700}", - color: "{surface.300}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.700}", - focusRing: { - color: "{surface.300}", - shadow: "none" - } - } - }, - contrast: { - background: "{surface.0}", - borderColor: "{surface.100}", - color: "{surface.950}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.100}", - focusRing: { - color: "{surface.950}", - shadow: "none" - } - } - } - } - } -}; -var index$J = { - root: { - borderRadius: "{content.border.radius}", - gap: "1rem" - }, - meters: { - background: "{content.border.color}", - size: "0.5rem" - }, - label: { - gap: "0.5rem" - }, - labelMarker: { - size: "0.5rem" - }, - labelIcon: { - size: "1rem" - }, - labelList: { - verticalGap: "0.5rem", - horizontalGap: "1rem" - } -}; -var index$I = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - dropdown: { - width: "2.5rem", - color: "{form.field.icon.color}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - list: { - padding: "{list.padding}", - gap: "{list.gap}", - header: { - padding: "{list.header.padding}" - } - }, - option: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}", - gap: "0.5rem" - }, - optionGroup: { - background: "{list.option.group.background}", - color: "{list.option.group.color}", - fontWeight: "{list.option.group.font.weight}", - padding: "{list.option.group.padding}" - }, - chip: { - borderRadius: "{border.radius.sm}" - }, - emptyMessage: { - padding: "{list.option.padding}" - } -}; -var index$H = { - root: { - gap: "1.125rem" - }, - controls: { - gap: "0.5rem" - } -}; -var index$G = { - root: { - gutter: "0.75rem", - transitionDuration: "{transition.duration}" - }, - node: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - selectedColor: "{highlight.color}", - hoverColor: "{content.hover.color}", - padding: "0.75rem 1rem", - toggleablePadding: "0.75rem 1rem 1.25rem 1rem", - borderRadius: "{content.border.radius}" - }, - nodeToggleButton: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - borderColor: "{content.border.color}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - size: "1.5rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - connector: { - color: "{content.border.color}", - borderRadius: "{content.border.radius}", - height: "24px" - } -}; -var index$F = { - root: { - outline: { - width: "2px", - color: "{content.background}" - } - } -}; -var index$E = { - root: { - padding: "0.5rem 1rem", - gap: "0.25rem", - borderRadius: "{content.border.radius}", - background: "{content.background}", - color: "{content.color}", - transitionDuration: "{transition.duration}" - }, - navButton: { - background: "transparent", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}", - selectedColor: "{highlight.color}", - width: "2.5rem", - height: "2.5rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - currentPageReport: { - color: "{text.muted.color}" - }, - jumpToPageInput: { - maxWidth: "2.5rem" - } -}; -var index$D = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}" - }, - header: { - background: "transparent", - color: "{text.color}", - padding: "1.125rem", - borderColor: "{content.border.color}", - borderWidth: "0", - borderRadius: "0" - }, - toggleableHeader: { - padding: "0.375rem 1.125rem" - }, - title: { - fontWeight: "600" - }, - content: { - padding: "0 1.125rem 1.125rem 1.125rem" - }, - footer: { - padding: "0 1.125rem 1.125rem 1.125rem" - } -}; -var index$C = { - root: { - gap: "0.5rem", - transitionDuration: "{transition.duration}" - }, - panel: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderWidth: "1px", - color: "{content.color}", - padding: "0.25rem 0.25rem", - borderRadius: "{content.border.radius}", - first: { - borderWidth: "1px", - topBorderRadius: "{content.border.radius}" - }, - last: { - borderWidth: "1px", - bottomBorderRadius: "{content.border.radius}" - } - }, - item: { - focusBackground: "{navigation.item.focus.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - gap: "0.5rem", - padding: "{navigation.item.padding}", - borderRadius: "{content.border.radius}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}" - } - }, - submenu: { - indent: "1rem" - }, - submenuIcon: { - color: "{navigation.submenu.icon.color}", - focusColor: "{navigation.submenu.icon.focus.color}" - } -}; -var index$B = { - meter: { - background: "{content.border.color}", - borderRadius: "{content.border.radius}", - height: ".75rem" - }, - icon: { - color: "{form.field.icon.color}" - }, - overlay: { - background: "{overlay.popover.background}", - borderColor: "{overlay.popover.border.color}", - borderRadius: "{overlay.popover.border.radius}", - color: "{overlay.popover.color}", - padding: "{overlay.popover.padding}", - shadow: "{overlay.popover.shadow}" - }, - content: { - gap: "0.5rem" - }, - colorScheme: { - light: { - strength: { - weakBackground: "{red.500}", - mediumBackground: "{amber.500}", - strongBackground: "{green.500}" - } - }, - dark: { - strength: { - weakBackground: "{red.400}", - mediumBackground: "{amber.400}", - strongBackground: "{green.400}" - } - } - } -}; -var index$A = { - root: { - gap: "1.125rem" - }, - controls: { - gap: "0.5rem" - } -}; -var index$z = { - root: { - background: "{overlay.popover.background}", - borderColor: "{overlay.popover.border.color}", - color: "{overlay.popover.color}", - borderRadius: "{overlay.popover.border.radius}", - shadow: "{overlay.popover.shadow}", - gutter: "10px", - arrowOffset: "1.25rem" - }, - content: { - padding: "{overlay.popover.padding}" - } -}; -var index$y = { - root: { - background: "{content.border.color}", - borderRadius: "{content.border.radius}", - height: "1.25rem" - }, - value: { - background: "{primary.color}" - }, - label: { - color: "{primary.contrast.color}", - fontSize: "0.75rem", - fontWeight: "600" - } -}; -var index$x = { - colorScheme: { - light: { - root: { - "color.1": "{red.500}", - "color.2": "{blue.500}", - "color.3": "{green.500}", - "color.4": "{yellow.500}" - } - }, - dark: { - root: { - "color.1": "{red.400}", - "color.2": "{blue.400}", - "color.3": "{green.400}", - "color.4": "{yellow.400}" - } - } - } -}; -var index$w = { - root: { - width: "1.25rem", - height: "1.25rem", - background: "{form.field.background}", - checkedBackground: "{primary.color}", - checkedHoverBackground: "{primary.hover.color}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.border.color}", - checkedBorderColor: "{primary.color}", - checkedHoverBorderColor: "{primary.hover.color}", - checkedFocusBorderColor: "{primary.color}", - checkedDisabledBorderColor: "{form.field.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - shadow: "{form.field.shadow}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - icon: { - size: "0.75rem", - checkedColor: "{primary.contrast.color}", - checkedHoverColor: "{primary.contrast.color}", - disabledColor: "{form.field.disabled.color}" - } -}; -var index$v = { - root: { - gap: "0.25rem", - transitionDuration: "{transition.duration}" - }, - icon: { - size: "1rem", - color: "{text.muted.color}", - hoverColor: "{primary.color}", - activeColor: "{primary.color}" - } -}; -var index$u = { - colorScheme: { - light: { - root: { - background: "rgba(0,0,0,0.1)" - } - }, - dark: { - root: { - background: "rgba(255,255,255,0.3)" - } - } - } -}; -var index$t = { - root: { - transitionDuration: "{transition.duration}" - }, - bar: { - size: "9px", - borderRadius: "{border.radius.sm}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - colorScheme: { - light: { - bar: { - background: "{surface.100}" - } - }, - dark: { - bar: { - background: "{surface.800}" - } - } - } -}; -var index$s = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - dropdown: { - width: "2.5rem", - color: "{form.field.icon.color}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - list: { - padding: "{list.padding}", - gap: "{list.gap}", - header: { - padding: "{list.header.padding}" - } - }, - option: { - focusBackground: "{list.option.focus.background}", - selectedBackground: "{list.option.selected.background}", - selectedFocusBackground: "{list.option.selected.focus.background}", - color: "{list.option.color}", - focusColor: "{list.option.focus.color}", - selectedColor: "{list.option.selected.color}", - selectedFocusColor: "{list.option.selected.focus.color}", - padding: "{list.option.padding}", - borderRadius: "{list.option.border.radius}" - }, - optionGroup: { - background: "{list.option.group.background}", - color: "{list.option.group.color}", - fontWeight: "{list.option.group.font.weight}", - padding: "{list.option.group.padding}" - }, - clearIcon: { - color: "{form.field.icon.color}" - }, - checkmark: { - color: "{list.option.color}", - gutterStart: "-0.375rem", - gutterEnd: "0.375rem" - }, - emptyMessage: { - padding: "{list.option.padding}" - } -}; -var index$r = { - root: { - borderRadius: "{form.field.border.radius}" - }, - colorScheme: { - light: { - root: { - invalidBorderColor: "{form.field.invalid.border.color}" - } - }, - dark: { - root: { - invalidBorderColor: "{form.field.invalid.border.color}" - } - } - } -}; -var index$q = { - root: { - borderRadius: "{content.border.radius}" - }, - colorScheme: { - light: { - root: { - background: "{surface.200}", - animationBackground: "rgba(255,255,255,0.4)" - } - }, - dark: { - root: { - background: "rgba(255, 255, 255, 0.06)", - animationBackground: "rgba(255, 255, 255, 0.04)" - } - } - } -}; -var index$p = { - root: { - transitionDuration: "{transition.duration}" - }, - track: { - background: "{content.border.color}", - borderRadius: "{content.border.radius}", - size: "3px" - }, - range: { - background: "{primary.color}" - }, - handle: { - width: "20px", - height: "20px", - borderRadius: "50%", - background: "{content.border.color}", - hoverBackground: "{content.border.color}", - content: { - borderRadius: "50%", - hoverBackground: "{content.background}", - width: "16px", - height: "16px", - shadow: "0px 0.5px 0px 0px rgba(0, 0, 0, 0.08), 0px 1px 1px 0px rgba(0, 0, 0, 0.14)" - }, - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - colorScheme: { - light: { - handle: { - contentBackground: "{surface.0}" - } - }, - dark: { - handle: { - contentBackground: "{surface.950}" - } - } - } -}; -var index$o = { - root: { - gap: "0.5rem", - transitionDuration: "{transition.duration}" - } -}; -var index$n = { - root: { - borderRadius: "{form.field.border.radius}", - roundedBorderRadius: "2rem", - raisedShadow: "0 3px 1px -2px rgba(0, 0, 0, 0.2), 0 2px 2px 0 rgba(0, 0, 0, 0.14), 0 1px 5px 0 rgba(0, 0, 0, 0.12)" - } -}; -var index$m = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - transitionDuration: "{transition.duration}" - }, - gutter: { - background: "{content.border.color}" - }, - handle: { - size: "24px", - background: "transparent", - borderRadius: "{content.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - } -}; -var index$l = { - root: { - transitionDuration: "{transition.duration}" - }, - separator: { - background: "{content.border.color}", - activeBackground: "{primary.color}", - margin: "0 0 0 1.625rem", - size: "2px" - }, - step: { - padding: "0.5rem", - gap: "1rem" - }, - stepHeader: { - padding: "0", - borderRadius: "{content.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - gap: "0.5rem" - }, - stepTitle: { - color: "{text.muted.color}", - activeColor: "{primary.color}", - fontWeight: "500" - }, - stepNumber: { - background: "{content.background}", - activeBackground: "{content.background}", - borderColor: "{content.border.color}", - activeBorderColor: "{content.border.color}", - color: "{text.muted.color}", - activeColor: "{primary.color}", - size: "2rem", - fontSize: "1.143rem", - fontWeight: "500", - borderRadius: "50%", - shadow: "0px 0.5px 0px 0px rgba(0, 0, 0, 0.06), 0px 1px 1px 0px rgba(0, 0, 0, 0.12)" - }, - steppanels: { - padding: "0.875rem 0.5rem 1.125rem 0.5rem" - }, - steppanel: { - background: "{content.background}", - color: "{content.color}", - padding: "0 0 0 1rem" - } -}; -var index$k = { - root: { - transitionDuration: "{transition.duration}" - }, - separator: { - background: "{content.border.color}" - }, - itemLink: { - borderRadius: "{content.border.radius}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - gap: "0.5rem" - }, - itemLabel: { - color: "{text.muted.color}", - activeColor: "{primary.color}", - fontWeight: "500" - }, - itemNumber: { - background: "{content.background}", - activeBackground: "{content.background}", - borderColor: "{content.border.color}", - activeBorderColor: "{content.border.color}", - color: "{text.muted.color}", - activeColor: "{primary.color}", - size: "2rem", - fontSize: "1.143rem", - fontWeight: "500", - borderRadius: "50%", - shadow: "0px 0.5px 0px 0px rgba(0, 0, 0, 0.06), 0px 1px 1px 0px rgba(0, 0, 0, 0.12)" - } -}; -var index$j = { - root: { - transitionDuration: "{transition.duration}" - }, - tablist: { - borderWidth: "0 0 1px 0", - background: "{content.background}", - borderColor: "{content.border.color}" - }, - item: { - background: "transparent", - hoverBackground: "transparent", - activeBackground: "transparent", - borderWidth: "0 0 1px 0", - borderColor: "{content.border.color}", - hoverBorderColor: "{content.border.color}", - activeBorderColor: "{primary.color}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{primary.color}", - padding: "1rem 1.125rem", - fontWeight: "600", - margin: "0 0 -1px 0", - gap: "0.5rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - itemIcon: { - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{primary.color}" - }, - activeBar: { - height: "1px", - bottom: "-1px", - background: "{primary.color}" - } -}; -var index$i = { - root: { - transitionDuration: "{transition.duration}" - }, - tablist: { - borderWidth: "0 0 1px 0", - background: "{content.background}", - borderColor: "{content.border.color}" - }, - tab: { - background: "transparent", - hoverBackground: "transparent", - activeBackground: "transparent", - borderWidth: "0 0 1px 0", - borderColor: "{content.border.color}", - hoverBorderColor: "{content.border.color}", - activeBorderColor: "{primary.color}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{primary.color}", - padding: "1rem 1.125rem", - fontWeight: "600", - margin: "0 0 -1px 0", - gap: "0.5rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - tabpanel: { - background: "{content.background}", - color: "{content.color}", - padding: "0.875rem 1.125rem 1.125rem 1.125rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "inset {focus.ring.shadow}" - } - }, - navButton: { - background: "{content.background}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - width: "2.5rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - activeBar: { - height: "1px", - bottom: "-1px", - background: "{primary.color}" - }, - colorScheme: { - light: { - navButton: { - shadow: "0px 0px 10px 50px rgba(255, 255, 255, 0.6)" - } - }, - dark: { - navButton: { - shadow: "0px 0px 10px 50px color-mix(in srgb, {content.background}, transparent 50%)" - } - } - } -}; -var index$h = { - root: { - transitionDuration: "{transition.duration}" - }, - tabList: { - background: "{content.background}", - borderColor: "{content.border.color}" - }, - tab: { - borderColor: "{content.border.color}", - activeBorderColor: "{primary.color}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - activeColor: "{primary.color}" - }, - tabPanel: { - background: "{content.background}", - color: "{content.color}" - }, - navButton: { - background: "{content.background}", - color: "{text.muted.color}", - hoverColor: "{text.color}" - }, - colorScheme: { - light: { - navButton: { - shadow: "0px 0px 10px 50px rgba(255, 255, 255, 0.6)" - } - }, - dark: { - navButton: { - shadow: "0px 0px 10px 50px color-mix(in srgb, {content.background}, transparent 50%)" - } - } - } -}; -var index$g = { - root: { - fontSize: "0.875rem", - fontWeight: "700", - padding: "0.25rem 0.5rem", - gap: "0.25rem", - borderRadius: "{content.border.radius}", - roundedBorderRadius: "{border.radius.xl}" - }, - icon: { - size: "0.75rem" - }, - colorScheme: { - light: { - primary: { - background: "{primary.100}", - color: "{primary.700}" - }, - secondary: { - background: "{surface.100}", - color: "{surface.600}" - }, - success: { - background: "{green.100}", - color: "{green.700}" - }, - info: { - background: "{sky.100}", - color: "{sky.700}" - }, - warn: { - background: "{orange.100}", - color: "{orange.700}" - }, - danger: { - background: "{red.100}", - color: "{red.700}" - }, - contrast: { - background: "{surface.950}", - color: "{surface.0}" - } - }, - dark: { - primary: { - background: "color-mix(in srgb, {primary.500}, transparent 84%)", - color: "{primary.300}" - }, - secondary: { - background: "{surface.800}", - color: "{surface.300}" - }, - success: { - background: "color-mix(in srgb, {green.500}, transparent 84%)", - color: "{green.300}" - }, - info: { - background: "color-mix(in srgb, {sky.500}, transparent 84%)", - color: "{sky.300}" - }, - warn: { - background: "color-mix(in srgb, {orange.500}, transparent 84%)", - color: "{orange.300}" - }, - danger: { - background: "color-mix(in srgb, {red.500}, transparent 84%)", - color: "{red.300}" - }, - contrast: { - background: "{surface.0}", - color: "{surface.950}" - } - } - } -}; -var index$f = { - root: { - background: "{form.field.background}", - borderColor: "{form.field.border.color}", - color: "{form.field.color}", - height: "18rem", - padding: "{form.field.padding.y} {form.field.padding.x}", - borderRadius: "{form.field.border.radius}" - }, - prompt: { - gap: "0.25rem" - }, - commandResponse: { - margin: "2px 0" - } -}; -var index$e = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - } -}; -var index$d = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - color: "{content.color}", - borderRadius: "{content.border.radius}", - shadow: "{overlay.navigation.shadow}", - transitionDuration: "{transition.duration}" - }, - list: { - padding: "{navigation.list.padding}", - gap: "{navigation.list.gap}" - }, - item: { - focusBackground: "{navigation.item.focus.background}", - activeBackground: "{navigation.item.active.background}", - color: "{navigation.item.color}", - focusColor: "{navigation.item.focus.color}", - activeColor: "{navigation.item.active.color}", - padding: "{navigation.item.padding}", - borderRadius: "{navigation.item.border.radius}", - gap: "{navigation.item.gap}", - icon: { - color: "{navigation.item.icon.color}", - focusColor: "{navigation.item.icon.focus.color}", - activeColor: "{navigation.item.icon.active.color}" - } - }, - submenuLabel: { - padding: "{navigation.submenu.label.padding}", - fontWeight: "{navigation.submenu.label.font.weight}", - background: "{navigation.submenu.label.background.}", - color: "{navigation.submenu.label.color}" - }, - submenuIcon: { - size: "{navigation.submenu.icon.size}", - color: "{navigation.submenu.icon.color}", - focusColor: "{navigation.submenu.icon.focus.color}", - activeColor: "{navigation.submenu.icon.active.color}" - }, - separator: { - borderColor: "{content.border.color}" - } -}; -var index$c = { - event: { - minHeight: "5rem" - }, - horizontal: { - eventContent: { - padding: "1rem 0" - } - }, - vertical: { - eventContent: { - padding: "0 1rem" - } - }, - eventMarker: { - size: "1.125rem", - borderRadius: "50%", - borderWidth: "2px", - background: "{content.background}", - borderColor: "{content.border.color}", - content: { - borderRadius: "50%", - size: "0.375rem", - background: "{primary.color}", - insetShadow: "0px 0.5px 0px 0px rgba(0, 0, 0, 0.06), 0px 1px 1px 0px rgba(0, 0, 0, 0.12)" - } - }, - eventConnector: { - color: "{content.border.color}", - size: "2px" - } -}; -var index$b = { - root: { - width: "25rem", - borderRadius: "{content.border.radius}", - borderWidth: "1px", - transitionDuration: "{transition.duration}" - }, - icon: { - size: "1.125rem" - }, - content: { - padding: "{overlay.popover.padding}", - gap: "0.5rem" - }, - text: { - gap: "0.5rem" - }, - summary: { - fontWeight: "500", - fontSize: "1rem" - }, - detail: { - fontWeight: "500", - fontSize: "0.875rem" - }, - closeButton: { - width: "1.75rem", - height: "1.75rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - offset: "{focus.ring.offset}" - } - }, - closeIcon: { - size: "1rem" - }, - colorScheme: { - light: { - blur: "1.5px", - info: { - background: "color-mix(in srgb, {blue.50}, transparent 5%)", - borderColor: "{blue.200}", - color: "{blue.600}", - detailColor: "{surface.700}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)", - closeButton: { - hoverBackground: "{blue.100}", - focusRing: { - color: "{blue.600}", - shadow: "none" - } - } - }, - success: { - background: "color-mix(in srgb, {green.50}, transparent 5%)", - borderColor: "{green.200}", - color: "{green.600}", - detailColor: "{surface.700}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)", - closeButton: { - hoverBackground: "{green.100}", - focusRing: { - color: "{green.600}", - shadow: "none" - } - } - }, - warn: { - background: "color-mix(in srgb,{yellow.50}, transparent 5%)", - borderColor: "{yellow.200}", - color: "{yellow.600}", - detailColor: "{surface.700}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)", - closeButton: { - hoverBackground: "{yellow.100}", - focusRing: { - color: "{yellow.600}", - shadow: "none" - } - } - }, - error: { - background: "color-mix(in srgb, {red.50}, transparent 5%)", - borderColor: "{red.200}", - color: "{red.600}", - detailColor: "{surface.700}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)", - closeButton: { - hoverBackground: "{red.100}", - focusRing: { - color: "{red.600}", - shadow: "none" - } - } - }, - secondary: { - background: "{surface.100}", - borderColor: "{surface.200}", - color: "{surface.600}", - detailColor: "{surface.700}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.200}", - focusRing: { - color: "{surface.600}", - shadow: "none" - } - } - }, - contrast: { - background: "{surface.900}", - borderColor: "{surface.950}", - color: "{surface.50}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.800}", - focusRing: { - color: "{surface.50}", - shadow: "none" - } - } - } - }, - dark: { - blur: "10px", - info: { - background: "color-mix(in srgb, {blue.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {blue.700}, transparent 64%)", - color: "{blue.500}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {blue.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{blue.500}", - shadow: "none" - } - } - }, - success: { - background: "color-mix(in srgb, {green.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {green.700}, transparent 64%)", - color: "{green.500}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {green.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{green.500}", - shadow: "none" - } - } - }, - warn: { - background: "color-mix(in srgb, {yellow.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {yellow.700}, transparent 64%)", - color: "{yellow.500}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {yellow.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{yellow.500}", - shadow: "none" - } - } - }, - error: { - background: "color-mix(in srgb, {red.500}, transparent 84%)", - borderColor: "color-mix(in srgb, {red.700}, transparent 64%)", - color: "{red.500}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {red.500}, transparent 96%)", - closeButton: { - hoverBackground: "rgba(255, 255, 255, 0.05)", - focusRing: { - color: "{red.500}", - shadow: "none" - } - } - }, - secondary: { - background: "{surface.800}", - borderColor: "{surface.700}", - color: "{surface.300}", - detailColor: "{surface.0}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.500}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.700}", - focusRing: { - color: "{surface.300}", - shadow: "none" - } - } - }, - contrast: { - background: "{surface.0}", - borderColor: "{surface.100}", - color: "{surface.950}", - detailColor: "{surface.950}", - shadow: "0px 4px 8px 0px color-mix(in srgb, {surface.950}, transparent 96%)", - closeButton: { - hoverBackground: "{surface.100}", - focusRing: { - color: "{surface.950}", - shadow: "none" - } - } - } - } - } -}; -var index$a = { - root: { - padding: "0.5rem 1rem", - borderRadius: "{content.border.radius}", - gap: "0.5rem", - fontWeight: "500", - disabledBackground: "{form.field.disabled.background}", - disabledBorderColor: "{form.field.disabled.background}", - disabledColor: "{form.field.disabled.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - icon: { - disabledColor: "{form.field.disabled.color}" - }, - content: { - left: "0.25rem", - top: "0.25rem", - checkedShadow: "0px 1px 2px 0px rgba(0, 0, 0, 0.02), 0px 1px 2px 0px rgba(0, 0, 0, 0.04)" - }, - colorScheme: { - light: { - root: { - background: "{surface.100}", - checkedBackground: "{surface.100}", - hoverBackground: "{surface.100}", - borderColor: "{surface.100}", - color: "{surface.500}", - hoverColor: "{surface.700}", - checkedColor: "{surface.900}", - checkedBorderColor: "{surface.100}" - }, - content: { - checkedBackground: "{surface.0}" - }, - icon: { - color: "{surface.500}", - hoverColor: "{surface.700}", - checkedColor: "{surface.900}" - } - }, - dark: { - root: { - background: "{surface.950}", - checkedBackground: "{surface.950}", - hoverBackground: "{surface.950}", - borderColor: "{surface.950}", - color: "{surface.400}", - hoverColor: "{surface.300}", - checkedColor: "{surface.0}", - checkedBorderColor: "{surface.950}" - }, - content: { - checkedBackground: "{surface.800}" - }, - icon: { - color: "{surface.400}", - hoverColor: "{surface.300}", - checkedColor: "{surface.0}" - } - } - } -}; -var index$9 = { - root: { - width: "2.5rem", - height: "1.5rem", - borderRadius: "30px", - gap: "0.25rem", - shadow: "{form.field.shadow}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - }, - borderWidth: "1px", - borderColor: "transparent", - hoverBorderColor: "transparent", - checkedBorderColor: "transparent", - checkedHoverBorderColor: "transparent", - invalidBorderColor: "{form.field.invalid.border.color}", - transitionDuration: "{form.field.transition.duration}", - slideDuration: "0.2s", - disabledBackground: "{form.field.disabled.background}" - }, - handle: { - borderRadius: "50%", - size: "1rem", - disabledBackground: "{form.field.disabled.color}" - }, - colorScheme: { - light: { - root: { - background: "{surface.300}", - hoverBackground: "{surface.400}", - checkedBackground: "{primary.color}", - checkedHoverBackground: "{primary.hover.color}" - }, - handle: { - background: "{surface.0}", - hoverBackground: "{surface.0}", - checkedBackground: "{surface.0}", - checkedHoverBackground: "{surface.0}" - } - }, - dark: { - root: { - background: "{surface.700}", - hoverBackground: "{surface.600}", - checkedBackground: "{primary.color}", - checkedHoverBackground: "{primary.hover.color}" - }, - handle: { - background: "{surface.400}", - hoverBackground: "{surface.300}", - checkedBackground: "{surface.900}", - checkedHoverBackground: "{surface.900}" - } - } - } -}; -var index$8 = { - root: { - background: "{content.background}", - borderColor: "{content.border.color}", - borderRadius: "{content.border.radius}", - color: "{content.color}", - gap: "0.5rem", - padding: "0.75rem" - } -}; -var index$7 = { - root: { - maxWidth: "12.5rem", - gutter: "0.25rem", - shadow: "{overlay.popover.shadow}", - padding: "0.5rem 0.75rem", - borderRadius: "{overlay.popover.border.radius}" - }, - colorScheme: { - light: { - root: { - background: "{surface.700}", - color: "{surface.0}" - } - }, - dark: { - root: { - background: "{surface.700}", - color: "{surface.0}" - } - } - } -}; -var index$6 = { - root: { - background: "{content.background}", - color: "{content.color}", - padding: "1rem", - gap: "2px", - indent: "1rem", - transitionDuration: "{transition.duration}" - }, - node: { - padding: "0.25rem 0.5rem", - borderRadius: "{content.border.radius}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - color: "{text.color}", - hoverColor: "{text.hover.color}", - selectedColor: "{highlight.color}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - }, - gap: "0.25rem" - }, - nodeIcon: { - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}", - selectedColor: "{highlight.color}" - }, - nodeToggleButton: { - borderRadius: "50%", - size: "1.75rem", - hoverBackground: "{content.hover.background}", - selectedHoverBackground: "{content.background}", - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}", - selectedHoverColor: "{primary.color}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - loadingIcon: { - size: "2rem" - } -}; -var index$5 = { - root: { - background: "{form.field.background}", - disabledBackground: "{form.field.disabled.background}", - filledBackground: "{form.field.filled.background}", - filledFocusBackground: "{form.field.filled.focus.background}", - borderColor: "{form.field.border.color}", - hoverBorderColor: "{form.field.hover.border.color}", - focusBorderColor: "{form.field.focus.border.color}", - invalidBorderColor: "{form.field.invalid.border.color}", - color: "{form.field.color}", - disabledColor: "{form.field.disabled.color}", - placeholderColor: "{form.field.placeholder.color}", - shadow: "{form.field.shadow}", - paddingX: "{form.field.padding.x}", - paddingY: "{form.field.padding.y}", - borderRadius: "{form.field.border.radius}", - focusRing: { - width: "{form.field.focus.ring.width}", - style: "{form.field.focus.ring.style}", - color: "{form.field.focus.ring.color}", - offset: "{form.field.focus.ring.offset}", - shadow: "{form.field.focus.ring.shadow}" - }, - transitionDuration: "{form.field.transition.duration}" - }, - dropdown: { - width: "2.5rem", - color: "{form.field.icon.color}" - }, - overlay: { - background: "{overlay.select.background}", - borderColor: "{overlay.select.border.color}", - borderRadius: "{overlay.select.border.radius}", - color: "{overlay.select.color}", - shadow: "{overlay.select.shadow}" - }, - tree: { - padding: "{list.padding}" - }, - emptyMessage: { - padding: "{list.option.padding}" - }, - chip: { - borderRadius: "{border.radius.sm}" - } -}; -var index$4 = { - root: { - transitionDuration: "{transition.duration}" - }, - header: { - background: "{content.background}", - borderColor: "{treetable.border.color}", - color: "{content.color}", - borderWidth: "0 0 1px 0", - padding: "0.75rem 1rem" - }, - headerCell: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - borderColor: "{treetable.border.color}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - selectedColor: "{highlight.color}", - gap: "0.5rem", - padding: "0.75rem 1rem", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - columnTitle: { - fontWeight: "600" - }, - row: { - background: "{content.background}", - hoverBackground: "{content.hover.background}", - selectedBackground: "{highlight.background}", - color: "{content.color}", - hoverColor: "{content.hover.color}", - selectedColor: "{highlight.color}", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "-1px", - shadow: "{focus.ring.shadow}" - } - }, - bodyCell: { - borderColor: "{treetable.border.color}", - padding: "0.75rem 1rem", - gap: "0.5rem" - }, - footerCell: { - background: "{content.background}", - borderColor: "{treetable.border.color}", - color: "{content.color}", - padding: "0.75rem 1rem" - }, - columnFooter: { - fontWeight: "600" - }, - footer: { - background: "{content.background}", - borderColor: "{treetable.border.color}", - color: "{content.color}", - borderWidth: "0 0 1px 0", - padding: "0.75rem 1rem" - }, - columnResizerWidth: "0.5rem", - resizeIndicator: { - width: "1px", - color: "{primary.color}" - }, - sortIcon: { - color: "{text.muted.color}", - hoverColor: "{text.hover.muted.color}" - }, - loadingIcon: { - size: "2rem" - }, - nodeToggleButton: { - hoverBackground: "{content.hover.background}", - selectedHoverBackground: "{content.background}", - color: "{text.muted.color}", - hoverColor: "{text.color}", - selectedHoverColor: "{primary.color}", - size: "1.75rem", - borderRadius: "50%", - focusRing: { - width: "{focus.ring.width}", - style: "{focus.ring.style}", - color: "{focus.ring.color}", - offset: "{focus.ring.offset}", - shadow: "{focus.ring.shadow}" - } - }, - paginatorTop: { - borderColor: "{content.border.color}", - borderWidth: "0 0 1px 0" - }, - paginatorBottom: { - borderColor: "{content.border.color}", - borderWidth: "0 0 1px 0" - }, - colorScheme: { - light: { - root: { - borderColor: "{content.border.color}" - }, - bodyCell: { - selectedBorderColor: "{primary.100}" - } - }, - dark: { - root: { - borderColor: "{surface.800}" - }, - bodyCell: { - selectedBorderColor: "{primary.900}" - } - } - } -}; -var index$3 = { - loader: { - mask: { - background: "{content.background}", - color: "{text.muted.color}" - }, - icon: { - size: "2rem" - } - } -}; -var index$2 = { - primitive: { - borderRadius: { - none: "0", - xs: "2px", - sm: "4px", - md: "6px", - lg: "8px", - xl: "12px" - }, - emerald: { - 50: "#ecfdf5", - 100: "#d1fae5", - 200: "#a7f3d0", - 300: "#6ee7b7", - 400: "#34d399", - 500: "#10b981", - 600: "#059669", - 700: "#047857", - 800: "#065f46", - 900: "#064e3b", - 950: "#022c22" - }, - green: { - 50: "#f0fdf4", - 100: "#dcfce7", - 200: "#bbf7d0", - 300: "#86efac", - 400: "#4ade80", - 500: "#22c55e", - 600: "#16a34a", - 700: "#15803d", - 800: "#166534", - 900: "#14532d", - 950: "#052e16" - }, - lime: { - 50: "#f7fee7", - 100: "#ecfccb", - 200: "#d9f99d", - 300: "#bef264", - 400: "#a3e635", - 500: "#84cc16", - 600: "#65a30d", - 700: "#4d7c0f", - 800: "#3f6212", - 900: "#365314", - 950: "#1a2e05" - }, - red: { - 50: "#fef2f2", - 100: "#fee2e2", - 200: "#fecaca", - 300: "#fca5a5", - 400: "#f87171", - 500: "#ef4444", - 600: "#dc2626", - 700: "#b91c1c", - 800: "#991b1b", - 900: "#7f1d1d", - 950: "#450a0a" - }, - orange: { - 50: "#fff7ed", - 100: "#ffedd5", - 200: "#fed7aa", - 300: "#fdba74", - 400: "#fb923c", - 500: "#f97316", - 600: "#ea580c", - 700: "#c2410c", - 800: "#9a3412", - 900: "#7c2d12", - 950: "#431407" - }, - amber: { - 50: "#fffbeb", - 100: "#fef3c7", - 200: "#fde68a", - 300: "#fcd34d", - 400: "#fbbf24", - 500: "#f59e0b", - 600: "#d97706", - 700: "#b45309", - 800: "#92400e", - 900: "#78350f", - 950: "#451a03" - }, - yellow: { - 50: "#fefce8", - 100: "#fef9c3", - 200: "#fef08a", - 300: "#fde047", - 400: "#facc15", - 500: "#eab308", - 600: "#ca8a04", - 700: "#a16207", - 800: "#854d0e", - 900: "#713f12", - 950: "#422006" - }, - teal: { - 50: "#f0fdfa", - 100: "#ccfbf1", - 200: "#99f6e4", - 300: "#5eead4", - 400: "#2dd4bf", - 500: "#14b8a6", - 600: "#0d9488", - 700: "#0f766e", - 800: "#115e59", - 900: "#134e4a", - 950: "#042f2e" - }, - cyan: { - 50: "#ecfeff", - 100: "#cffafe", - 200: "#a5f3fc", - 300: "#67e8f9", - 400: "#22d3ee", - 500: "#06b6d4", - 600: "#0891b2", - 700: "#0e7490", - 800: "#155e75", - 900: "#164e63", - 950: "#083344" - }, - sky: { - 50: "#f0f9ff", - 100: "#e0f2fe", - 200: "#bae6fd", - 300: "#7dd3fc", - 400: "#38bdf8", - 500: "#0ea5e9", - 600: "#0284c7", - 700: "#0369a1", - 800: "#075985", - 900: "#0c4a6e", - 950: "#082f49" - }, - blue: { - 50: "#eff6ff", - 100: "#dbeafe", - 200: "#bfdbfe", - 300: "#93c5fd", - 400: "#60a5fa", - 500: "#3b82f6", - 600: "#2563eb", - 700: "#1d4ed8", - 800: "#1e40af", - 900: "#1e3a8a", - 950: "#172554" - }, - indigo: { - 50: "#eef2ff", - 100: "#e0e7ff", - 200: "#c7d2fe", - 300: "#a5b4fc", - 400: "#818cf8", - 500: "#6366f1", - 600: "#4f46e5", - 700: "#4338ca", - 800: "#3730a3", - 900: "#312e81", - 950: "#1e1b4b" - }, - violet: { - 50: "#f5f3ff", - 100: "#ede9fe", - 200: "#ddd6fe", - 300: "#c4b5fd", - 400: "#a78bfa", - 500: "#8b5cf6", - 600: "#7c3aed", - 700: "#6d28d9", - 800: "#5b21b6", - 900: "#4c1d95", - 950: "#2e1065" - }, - purple: { - 50: "#faf5ff", - 100: "#f3e8ff", - 200: "#e9d5ff", - 300: "#d8b4fe", - 400: "#c084fc", - 500: "#a855f7", - 600: "#9333ea", - 700: "#7e22ce", - 800: "#6b21a8", - 900: "#581c87", - 950: "#3b0764" - }, - fuchsia: { - 50: "#fdf4ff", - 100: "#fae8ff", - 200: "#f5d0fe", - 300: "#f0abfc", - 400: "#e879f9", - 500: "#d946ef", - 600: "#c026d3", - 700: "#a21caf", - 800: "#86198f", - 900: "#701a75", - 950: "#4a044e" - }, - pink: { - 50: "#fdf2f8", - 100: "#fce7f3", - 200: "#fbcfe8", - 300: "#f9a8d4", - 400: "#f472b6", - 500: "#ec4899", - 600: "#db2777", - 700: "#be185d", - 800: "#9d174d", - 900: "#831843", - 950: "#500724" - }, - rose: { - 50: "#fff1f2", - 100: "#ffe4e6", - 200: "#fecdd3", - 300: "#fda4af", - 400: "#fb7185", - 500: "#f43f5e", - 600: "#e11d48", - 700: "#be123c", - 800: "#9f1239", - 900: "#881337", - 950: "#4c0519" - }, - slate: { - 50: "#f8fafc", - 100: "#f1f5f9", - 200: "#e2e8f0", - 300: "#cbd5e1", - 400: "#94a3b8", - 500: "#64748b", - 600: "#475569", - 700: "#334155", - 800: "#1e293b", - 900: "#0f172a", - 950: "#020617" - }, - gray: { - 50: "#f9fafb", - 100: "#f3f4f6", - 200: "#e5e7eb", - 300: "#d1d5db", - 400: "#9ca3af", - 500: "#6b7280", - 600: "#4b5563", - 700: "#374151", - 800: "#1f2937", - 900: "#111827", - 950: "#030712" - }, - zinc: { - 50: "#fafafa", - 100: "#f4f4f5", - 200: "#e4e4e7", - 300: "#d4d4d8", - 400: "#a1a1aa", - 500: "#71717a", - 600: "#52525b", - 700: "#3f3f46", - 800: "#27272a", - 900: "#18181b", - 950: "#09090b" - }, - neutral: { - 50: "#fafafa", - 100: "#f5f5f5", - 200: "#e5e5e5", - 300: "#d4d4d4", - 400: "#a3a3a3", - 500: "#737373", - 600: "#525252", - 700: "#404040", - 800: "#262626", - 900: "#171717", - 950: "#0a0a0a" - }, - stone: { - 50: "#fafaf9", - 100: "#f5f5f4", - 200: "#e7e5e4", - 300: "#d6d3d1", - 400: "#a8a29e", - 500: "#78716c", - 600: "#57534e", - 700: "#44403c", - 800: "#292524", - 900: "#1c1917", - 950: "#0c0a09" - } - }, - semantic: { - transitionDuration: "0.2s", - focusRing: { - width: "1px", - style: "solid", - color: "{primary.color}", - offset: "2px", - shadow: "none" - }, - disabledOpacity: "0.6", - iconSize: "1rem", - anchorGutter: "2px", - primary: { - 50: "{emerald.50}", - 100: "{emerald.100}", - 200: "{emerald.200}", - 300: "{emerald.300}", - 400: "{emerald.400}", - 500: "{emerald.500}", - 600: "{emerald.600}", - 700: "{emerald.700}", - 800: "{emerald.800}", - 900: "{emerald.900}", - 950: "{emerald.950}" - }, - formField: { - paddingX: "0.75rem", - paddingY: "0.5rem", - borderRadius: "{border.radius.md}", - focusRing: { - width: "0", - style: "none", - color: "transparent", - offset: "0", - shadow: "none" - }, - transitionDuration: "{transition.duration}" - }, - list: { - padding: "0.25rem 0.25rem", - gap: "2px", - header: { - padding: "0.5rem 1rem 0.25rem 1rem" - }, - option: { - padding: "0.5rem 0.75rem", - borderRadius: "{border.radius.sm}" - }, - optionGroup: { - padding: "0.5rem 0.75rem", - fontWeight: "600" - } - }, - content: { - borderRadius: "{border.radius.md}" - }, - mask: { - transitionDuration: "0.15s" - }, - navigation: { - list: { - padding: "0.25rem 0.25rem", - gap: "2px" - }, - item: { - padding: "0.5rem 0.75rem", - borderRadius: "{border.radius.sm}", - gap: "0.5rem" - }, - submenuLabel: { - padding: "0.5rem 0.75rem", - fontWeight: "600" - }, - submenuIcon: { - size: "0.875rem" - } - }, - overlay: { - select: { - borderRadius: "{border.radius.md}", - shadow: "0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -2px rgba(0, 0, 0, 0.1)" - }, - popover: { - borderRadius: "{border.radius.md}", - padding: "0.75rem", - shadow: "0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -2px rgba(0, 0, 0, 0.1)" - }, - modal: { - borderRadius: "{border.radius.xl}", - padding: "1.25rem", - shadow: "0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 8px 10px -6px rgba(0, 0, 0, 0.1)" - }, - navigation: { - shadow: "0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -2px rgba(0, 0, 0, 0.1)" - } - }, - colorScheme: { - light: { - surface: { - 0: "#ffffff", - 50: "{slate.50}", - 100: "{slate.100}", - 200: "{slate.200}", - 300: "{slate.300}", - 400: "{slate.400}", - 500: "{slate.500}", - 600: "{slate.600}", - 700: "{slate.700}", - 800: "{slate.800}", - 900: "{slate.900}", - 950: "{slate.950}" - }, - primary: { - color: "{primary.500}", - contrastColor: "#ffffff", - hoverColor: "{primary.600}", - activeColor: "{primary.700}" - }, - highlight: { - background: "{primary.50}", - focusBackground: "{primary.100}", - color: "{primary.700}", - focusColor: "{primary.800}" - }, - mask: { - background: "rgba(0,0,0,0.4)", - color: "{surface.200}" - }, - formField: { - background: "{surface.0}", - disabledBackground: "{surface.200}", - filledBackground: "{surface.50}", - filledFocusBackground: "{surface.50}", - borderColor: "{surface.300}", - hoverBorderColor: "{surface.400}", - focusBorderColor: "{primary.color}", - invalidBorderColor: "{red.400}", - color: "{surface.700}", - disabledColor: "{surface.500}", - placeholderColor: "{surface.500}", - floatLabelColor: "{surface.500}", - floatLabelFocusColor: "{surface.500}", - floatLabelInvalidColor: "{red.400}", - iconColor: "{surface.400}", - shadow: "0 0 #0000, 0 0 #0000, 0 1px 2px 0 rgba(18, 18, 23, 0.05)" - }, - text: { - color: "{surface.700}", - hoverColor: "{surface.800}", - mutedColor: "{surface.500}", - hoverMutedColor: "{surface.600}" - }, - content: { - background: "{surface.0}", - hoverBackground: "{surface.100}", - borderColor: "{surface.200}", - color: "{text.color}", - hoverColor: "{text.hover.color}" - }, - overlay: { - select: { - background: "{surface.0}", - borderColor: "{surface.200}", - color: "{text.color}" - }, - popover: { - background: "{surface.0}", - borderColor: "{surface.200}", - color: "{text.color}" - }, - modal: { - background: "{surface.0}", - borderColor: "{surface.200}", - color: "{text.color}" - } - }, - list: { - option: { - focusBackground: "{surface.100}", - selectedBackground: "{highlight.background}", - selectedFocusBackground: "{highlight.focus.background}", - color: "{text.color}", - focusColor: "{text.hover.color}", - selectedColor: "{highlight.color}", - selectedFocusColor: "{highlight.focus.color}", - icon: { - color: "{surface.400}", - focusColor: "{surface.500}" - } - }, - optionGroup: { - background: "transparent", - color: "{text.muted.color}" - } - }, - navigation: { - item: { - focusBackground: "{surface.100}", - activeBackground: "{surface.100}", - color: "{text.color}", - focusColor: "{text.hover.color}", - activeColor: "{text.hover.color}", - icon: { - color: "{surface.400}", - focusColor: "{surface.500}", - activeColor: "{surface.500}" - } - }, - submenuLabel: { - background: "transparent", - color: "{text.muted.color}" - }, - submenuIcon: { - color: "{surface.400}", - focusColor: "{surface.500}", - activeColor: "{surface.500}" - } - } - }, - dark: { - surface: { - 0: "#ffffff", - 50: "{zinc.50}", - 100: "{zinc.100}", - 200: "{zinc.200}", - 300: "{zinc.300}", - 400: "{zinc.400}", - 500: "{zinc.500}", - 600: "{zinc.600}", - 700: "{zinc.700}", - 800: "{zinc.800}", - 900: "{zinc.900}", - 950: "{zinc.950}" - }, - primary: { - color: "{primary.400}", - contrastColor: "{surface.900}", - hoverColor: "{primary.300}", - activeColor: "{primary.200}" - }, - highlight: { - background: "color-mix(in srgb, {primary.400}, transparent 84%)", - focusBackground: "color-mix(in srgb, {primary.400}, transparent 76%)", - color: "rgba(255,255,255,.87)", - focusColor: "rgba(255,255,255,.87)" - }, - mask: { - background: "rgba(0,0,0,0.6)", - color: "{surface.200}" - }, - formField: { - background: "{surface.950}", - disabledBackground: "{surface.700}", - filledBackground: "{surface.800}", - filledFocusBackground: "{surface.800}", - borderColor: "{surface.700}", - hoverBorderColor: "{surface.600}", - focusBorderColor: "{primary.color}", - invalidBorderColor: "{red.300}", - color: "{surface.0}", - disabledColor: "{surface.400}", - placeholderColor: "{surface.400}", - floatLabelColor: "{surface.400}", - floatLabelFocusColor: "{surface.400}", - floatLabelInvalidColor: "{red.300}", - iconColor: "{surface.400}", - shadow: "0 0 #0000, 0 0 #0000, 0 1px 2px 0 rgba(18, 18, 23, 0.05)" - }, - text: { - color: "{surface.0}", - hoverColor: "{surface.0}", - mutedColor: "{surface.400}", - hoverMutedColor: "{surface.300}" - }, - content: { - background: "{surface.900}", - hoverBackground: "{surface.800}", - borderColor: "{surface.700}", - color: "{text.color}", - hoverColor: "{text.hover.color}" - }, - overlay: { - select: { - background: "{surface.900}", - borderColor: "{surface.700}", - color: "{text.color}" - }, - popover: { - background: "{surface.900}", - borderColor: "{surface.700}", - color: "{text.color}" - }, - modal: { - background: "{surface.900}", - borderColor: "{surface.700}", - color: "{text.color}" - } - }, - list: { - option: { - focusBackground: "{surface.800}", - selectedBackground: "{highlight.background}", - selectedFocusBackground: "{highlight.focus.background}", - color: "{text.color}", - focusColor: "{text.hover.color}", - selectedColor: "{highlight.color}", - selectedFocusColor: "{highlight.focus.color}", - icon: { - color: "{surface.500}", - focusColor: "{surface.400}" - } - }, - optionGroup: { - background: "transparent", - color: "{text.muted.color}" - } - }, - navigation: { - item: { - focusBackground: "{surface.800}", - activeBackground: "{surface.800}", - color: "{text.color}", - focusColor: "{text.hover.color}", - activeColor: "{text.hover.color}", - icon: { - color: "{surface.500}", - focusColor: "{surface.400}", - activeColor: "{surface.400}" - } - }, - submenuLabel: { - background: "transparent", - color: "{text.muted.color}" - }, - submenuIcon: { - color: "{surface.500}", - focusColor: "{surface.400}", - activeColor: "{surface.400}" - } - } - } - } - }, - components: { - accordion: index$1n, - autocomplete: index$1m, - avatar: index$1l, - badge: index$1k, - blockui: index$1j, - breadcrumb: index$1i, - button: index$1h, - datepicker: index$15, - card: index$1g, - carousel: index$1f, - cascadeselect: index$1e, - checkbox: index$1d, - chip: index$1c, - colorpicker: index$1b, - confirmdialog: index$1a, - confirmpopup: index$19, - contextmenu: index$18, - dataview: index$16, - datatable: index$17, - dialog: index$14, - divider: index$13, - dock: index$12, - drawer: index$11, - editor: index$10, - fieldset: index$$, - fileupload: index$_, - floatlabel: index$Z, - galleria: index$Y, - iconfield: index$X, - image: index$W, - inlinemessage: index$V, - inplace: index$U, - inputchips: index$T, - inputgroup: index$S, - inputnumber: index$R, - inputtext: index$Q, - knob: index$P, - listbox: index$O, - megamenu: index$N, - menu: index$M, - menubar: index$L, - message: index$K, - metergroup: index$J, - multiselect: index$I, - orderlist: index$H, - organizationchart: index$G, - overlaybadge: index$F, - popover: index$z, - paginator: index$E, - password: index$B, - panel: index$D, - panelmenu: index$C, - picklist: index$A, - progressbar: index$y, - progressspinner: index$x, - radiobutton: index$w, - rating: index$v, - scrollpanel: index$t, - select: index$s, - selectbutton: index$r, - skeleton: index$q, - slider: index$p, - speeddial: index$o, - splitter: index$m, - splitbutton: index$n, - stepper: index$l, - steps: index$k, - tabmenu: index$j, - tabs: index$i, - tabview: index$h, - textarea: index$e, - tieredmenu: index$d, - tag: index$g, - terminal: index$f, - timeline: index$c, - togglebutton: index$a, - toggleswitch: index$9, - tree: index$6, - treeselect: index$5, - treetable: index$4, - toast: index$b, - toolbar: index$8, - virtualscroller: index$3 - }, - directives: { - tooltip: index$7, - ripple: index$u - } -}; -/** -* @vue/shared v3.4.31 -* (c) 2018-present Yuxi (Evan) You and Vue contributors -* @license MIT -**/ -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function makeMap(str, expectsLowerCase) { - const set3 = new Set(str.split(",")); - return expectsLowerCase ? (val) => set3.has(val.toLowerCase()) : (val) => set3.has(val); -} -__name(makeMap, "makeMap"); -const EMPTY_OBJ = false ? Object.freeze({}) : {}; -const EMPTY_ARR = false ? Object.freeze([]) : []; -const NOOP = /* @__PURE__ */ __name(() => { -}, "NOOP"); -const NO = /* @__PURE__ */ __name(() => false, "NO"); -const isOn = /* @__PURE__ */ __name((key) => key.charCodeAt(0) === 111 && key.charCodeAt(1) === 110 && // uppercase letter -(key.charCodeAt(2) > 122 || key.charCodeAt(2) < 97), "isOn"); -const isModelListener = /* @__PURE__ */ __name((key) => key.startsWith("onUpdate:"), "isModelListener"); -const extend$1 = Object.assign; -const remove$1 = /* @__PURE__ */ __name((arr, el) => { - const i2 = arr.indexOf(el); - if (i2 > -1) { - arr.splice(i2, 1); - } -}, "remove$1"); -const hasOwnProperty$3 = Object.prototype.hasOwnProperty; -const hasOwn$3 = /* @__PURE__ */ __name((val, key) => hasOwnProperty$3.call(val, key), "hasOwn$3"); -const isArray$4 = Array.isArray; -const isMap = /* @__PURE__ */ __name((val) => toTypeString$1(val) === "[object Map]", "isMap"); -const isSet = /* @__PURE__ */ __name((val) => toTypeString$1(val) === "[object Set]", "isSet"); -const isDate$2 = /* @__PURE__ */ __name((val) => toTypeString$1(val) === "[object Date]", "isDate$2"); -const isRegExp$4 = /* @__PURE__ */ __name((val) => toTypeString$1(val) === "[object RegExp]", "isRegExp$4"); -const isFunction$4 = /* @__PURE__ */ __name((val) => typeof val === "function", "isFunction$4"); -const isString$7 = /* @__PURE__ */ __name((val) => typeof val === "string", "isString$7"); -const isSymbol$1 = /* @__PURE__ */ __name((val) => typeof val === "symbol", "isSymbol$1"); -const isObject$6 = /* @__PURE__ */ __name((val) => val !== null && typeof val === "object", "isObject$6"); -const isPromise$1 = /* @__PURE__ */ __name((val) => { - return (isObject$6(val) || isFunction$4(val)) && isFunction$4(val.then) && isFunction$4(val.catch); -}, "isPromise$1"); -const objectToString$1 = Object.prototype.toString; -const toTypeString$1 = /* @__PURE__ */ __name((value4) => objectToString$1.call(value4), "toTypeString$1"); -const toRawType = /* @__PURE__ */ __name((value4) => { - return toTypeString$1(value4).slice(8, -1); -}, "toRawType"); -const isPlainObject$4 = /* @__PURE__ */ __name((val) => toTypeString$1(val) === "[object Object]", "isPlainObject$4"); -const isIntegerKey = /* @__PURE__ */ __name((key) => isString$7(key) && key !== "NaN" && key[0] !== "-" && "" + parseInt(key, 10) === key, "isIntegerKey"); -const isReservedProp = /* @__PURE__ */ makeMap( - // the leading comma is intentional so empty string "" is also included - ",key,ref,ref_for,ref_key,onVnodeBeforeMount,onVnodeMounted,onVnodeBeforeUpdate,onVnodeUpdated,onVnodeBeforeUnmount,onVnodeUnmounted" -); -const isBuiltInDirective = /* @__PURE__ */ makeMap( - "bind,cloak,else-if,else,for,html,if,model,on,once,pre,show,slot,text,memo" -); -const cacheStringFunction$1 = /* @__PURE__ */ __name((fn) => { - const cache2 = /* @__PURE__ */ Object.create(null); - return (str) => { - const hit = cache2[str]; - return hit || (cache2[str] = fn(str)); - }; -}, "cacheStringFunction$1"); -const camelizeRE$1 = /-(\w)/g; -const camelize$1 = cacheStringFunction$1((str) => { - return str.replace(camelizeRE$1, (_2, c) => c ? c.toUpperCase() : ""); -}); -const hyphenateRE$1 = /\B([A-Z])/g; -const hyphenate$1 = cacheStringFunction$1( - (str) => str.replace(hyphenateRE$1, "-$1").toLowerCase() -); -const capitalize$1 = cacheStringFunction$1((str) => { - return str.charAt(0).toUpperCase() + str.slice(1); -}); -const toHandlerKey = cacheStringFunction$1((str) => { - const s = str ? `on${capitalize$1(str)}` : ``; - return s; -}); -const hasChanged = /* @__PURE__ */ __name((value4, oldValue2) => !Object.is(value4, oldValue2), "hasChanged"); -const invokeArrayFns = /* @__PURE__ */ __name((fns, ...arg) => { - for (let i2 = 0; i2 < fns.length; i2++) { - fns[i2](...arg); - } -}, "invokeArrayFns"); -const def = /* @__PURE__ */ __name((obj, key, value4, writable = false) => { - Object.defineProperty(obj, key, { - configurable: true, - enumerable: false, - writable, - value: value4 - }); -}, "def"); -const looseToNumber = /* @__PURE__ */ __name((val) => { - const n = parseFloat(val); - return isNaN(n) ? val : n; -}, "looseToNumber"); -const toNumber = /* @__PURE__ */ __name((val) => { - const n = isString$7(val) ? Number(val) : NaN; - return isNaN(n) ? val : n; -}, "toNumber"); -let _globalThis$1; -const getGlobalThis$1 = /* @__PURE__ */ __name(() => { - return _globalThis$1 || (_globalThis$1 = typeof globalThis !== "undefined" ? globalThis : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : typeof global !== "undefined" ? global : {}); -}, "getGlobalThis$1"); -const identRE = /^[_$a-zA-Z\xA0-\uFFFF][_$a-zA-Z0-9\xA0-\uFFFF]*$/; -function genPropsAccessExp(name2) { - return identRE.test(name2) ? `__props.${name2}` : `__props[${JSON.stringify(name2)}]`; -} -__name(genPropsAccessExp, "genPropsAccessExp"); -const PatchFlags = { - "TEXT": 1, - "1": "TEXT", - "CLASS": 2, - "2": "CLASS", - "STYLE": 4, - "4": "STYLE", - "PROPS": 8, - "8": "PROPS", - "FULL_PROPS": 16, - "16": "FULL_PROPS", - "NEED_HYDRATION": 32, - "32": "NEED_HYDRATION", - "STABLE_FRAGMENT": 64, - "64": "STABLE_FRAGMENT", - "KEYED_FRAGMENT": 128, - "128": "KEYED_FRAGMENT", - "UNKEYED_FRAGMENT": 256, - "256": "UNKEYED_FRAGMENT", - "NEED_PATCH": 512, - "512": "NEED_PATCH", - "DYNAMIC_SLOTS": 1024, - "1024": "DYNAMIC_SLOTS", - "DEV_ROOT_FRAGMENT": 2048, - "2048": "DEV_ROOT_FRAGMENT", - "HOISTED": -1, - "-1": "HOISTED", - "BAIL": -2, - "-2": "BAIL" -}; 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-const SVG_TAGS = "svg,animate,animateMotion,animateTransform,circle,clipPath,color-profile,defs,desc,discard,ellipse,feBlend,feColorMatrix,feComponentTransfer,feComposite,feConvolveMatrix,feDiffuseLighting,feDisplacementMap,feDistantLight,feDropShadow,feFlood,feFuncA,feFuncB,feFuncG,feFuncR,feGaussianBlur,feImage,feMerge,feMergeNode,feMorphology,feOffset,fePointLight,feSpecularLighting,feSpotLight,feTile,feTurbulence,filter,foreignObject,g,hatch,hatchpath,image,line,linearGradient,marker,mask,mesh,meshgradient,meshpatch,meshrow,metadata,mpath,path,pattern,polygon,polyline,radialGradient,rect,set,solidcolor,stop,switch,symbol,text,textPath,title,tspan,unknown,use,view"; -const MATH_TAGS = "annotation,annotation-xml,maction,maligngroup,malignmark,math,menclose,merror,mfenced,mfrac,mfraction,mglyph,mi,mlabeledtr,mlongdiv,mmultiscripts,mn,mo,mover,mpadded,mphantom,mprescripts,mroot,mrow,ms,mscarries,mscarry,msgroup,msline,mspace,msqrt,msrow,mstack,mstyle,msub,msubsup,msup,mtable,mtd,mtext,mtr,munder,munderover,none,semantics"; -const VOID_TAGS = "area,base,br,col,embed,hr,img,input,link,meta,param,source,track,wbr"; -const isHTMLTag = /* @__PURE__ */ makeMap(HTML_TAGS); -const isSVGTag = /* @__PURE__ */ makeMap(SVG_TAGS); -const isMathMLTag = /* @__PURE__ */ makeMap(MATH_TAGS); -const isVoidTag = /* @__PURE__ */ makeMap(VOID_TAGS); -const specialBooleanAttrs = `itemscope,allowfullscreen,formnovalidate,ismap,nomodule,novalidate,readonly`; -const isSpecialBooleanAttr = /* @__PURE__ */ makeMap(specialBooleanAttrs); -const isBooleanAttr = /* @__PURE__ */ makeMap( - specialBooleanAttrs + `,async,autofocus,autoplay,controls,default,defer,disabled,hidden,inert,loop,open,required,reversed,scoped,seamless,checked,muted,multiple,selected` -); -function includeBooleanAttr(value4) { - return !!value4 || value4 === ""; -} -__name(includeBooleanAttr, "includeBooleanAttr"); -const unsafeAttrCharRE = /[>/="'\u0009\u000a\u000c\u0020]/; -const attrValidationCache = {}; -function isSSRSafeAttrName(name2) { - if (attrValidationCache.hasOwnProperty(name2)) { - return attrValidationCache[name2]; 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-const commentStripRE = /^-?>||--!>| looseEqual(item3, val)); -} -__name(looseIndexOf, "looseIndexOf"); -const isRef$1 = /* @__PURE__ */ __name((val) => { - return !!(val && val.__v_isRef === true); -}, "isRef$1"); -const toDisplayString$1 = /* @__PURE__ */ __name((val) => { - return isString$7(val) ? val : val == null ? "" : isArray$4(val) || isObject$6(val) && (val.toString === objectToString$1 || !isFunction$4(val.toString)) ? isRef$1(val) ? toDisplayString$1(val.value) : JSON.stringify(val, replacer, 2) : String(val); -}, "toDisplayString$1"); -const replacer = /* @__PURE__ */ __name((_key, val) => { - if (isRef$1(val)) { - return replacer(_key, val.value); - } else if (isMap(val)) { - return { - [`Map(${val.size})`]: [...val.entries()].reduce( - (entries, [key, val2], i2) => { - entries[stringifySymbol(key, i2) + " =>"] = val2; - return entries; - }, - {} - ) - }; - } else if (isSet(val)) { - return { - [`Set(${val.size})`]: [...val.values()].map((v2) => stringifySymbol(v2)) - }; - } else if (isSymbol$1(val)) { - return stringifySymbol(val); - } else if (isObject$6(val) && !isArray$4(val) && !isPlainObject$4(val)) { - return String(val); - } - return val; -}, "replacer"); -const stringifySymbol = /* @__PURE__ */ __name((v2, i2 = "") => { - var _a2; - return ( - // Symbol.description in es2019+ so we need to cast here to pass - // the lib: es2016 check - isSymbol$1(v2) ? `Symbol(${(_a2 = v2.description) != null ? _a2 : i2})` : v2 - ); -}, "stringifySymbol"); -/** -* @vue/reactivity v3.4.31 -* (c) 2018-present Yuxi (Evan) You and Vue contributors -* @license MIT -**/ -function warn$4(msg, ...args) { - console.warn(`[Vue warn] ${msg}`, ...args); -} -__name(warn$4, "warn$4"); -let activeEffectScope; -class EffectScope { - static { - __name(this, "EffectScope"); - } - constructor(detached = false) { - this.detached = detached; - this._active = true; - this.effects = []; - this.cleanups = []; - this.parent = activeEffectScope; - if (!detached && activeEffectScope) { - this.index = (activeEffectScope.scopes || (activeEffectScope.scopes = [])).push( - this - ) - 1; - } - } - get active() { - return this._active; - } - run(fn) { - if (this._active) { - const currentEffectScope = activeEffectScope; - try { - activeEffectScope = this; - return fn(); - } finally { - activeEffectScope = currentEffectScope; - } - } else if (false) { - warn$4(`cannot run an inactive effect scope.`); - } - } - /** - * This should only be called on non-detached scopes - * @internal - */ - on() { - activeEffectScope = this; - } - /** - * This should only be called on non-detached scopes - * @internal - */ - off() { - activeEffectScope = this.parent; - } - stop(fromParent) { - if (this._active) { - let i2, l; - for (i2 = 0, l = this.effects.length; i2 < l; i2++) { - this.effects[i2].stop(); - } - for (i2 = 0, l = this.cleanups.length; i2 < l; i2++) { - this.cleanups[i2](); - } - if (this.scopes) { - for (i2 = 0, l = this.scopes.length; i2 < l; i2++) { - this.scopes[i2].stop(true); - } - } - if (!this.detached && this.parent && !fromParent) { - const last = this.parent.scopes.pop(); - if (last && last !== this) { - this.parent.scopes[this.index] = last; - last.index = this.index; - } - } - this.parent = void 0; - this._active = false; - } - } -} -function effectScope(detached) { - return new EffectScope(detached); -} -__name(effectScope, "effectScope"); -function recordEffectScope(effect2, scope = activeEffectScope) { - if (scope && scope.active) { - scope.effects.push(effect2); - } -} -__name(recordEffectScope, "recordEffectScope"); -function getCurrentScope() { - return activeEffectScope; -} -__name(getCurrentScope, "getCurrentScope"); -function onScopeDispose(fn) { - if (activeEffectScope) { - activeEffectScope.cleanups.push(fn); - } else if (false) { - warn$4( - `onScopeDispose() is called when there is no active effect scope to be associated with.` - ); - } -} -__name(onScopeDispose, "onScopeDispose"); -let activeEffect; -class ReactiveEffect { - static { - __name(this, "ReactiveEffect"); - } - constructor(fn, trigger2, scheduler, scope) { - this.fn = fn; - this.trigger = trigger2; - this.scheduler = scheduler; - this.active = true; - this.deps = []; - this._dirtyLevel = 4; - this._trackId = 0; - this._runnings = 0; - this._shouldSchedule = false; - this._depsLength = 0; - recordEffectScope(this, scope); - } - get dirty() { - if (this._dirtyLevel === 2 || this._dirtyLevel === 3) { - this._dirtyLevel = 1; - pauseTracking(); - for (let i2 = 0; i2 < this._depsLength; i2++) { - const dep = this.deps[i2]; - if (dep.computed) { - triggerComputed(dep.computed); - if (this._dirtyLevel >= 4) { - break; - } - } - } - if (this._dirtyLevel === 1) { - this._dirtyLevel = 0; - } - resetTracking(); - } - return this._dirtyLevel >= 4; - } - set dirty(v2) { - this._dirtyLevel = v2 ? 4 : 0; - } - run() { - this._dirtyLevel = 0; - if (!this.active) { - return this.fn(); - } - let lastShouldTrack = shouldTrack; - let lastEffect = activeEffect; - try { - shouldTrack = true; - activeEffect = this; - this._runnings++; - preCleanupEffect(this); - return this.fn(); - } finally { - postCleanupEffect(this); - this._runnings--; - activeEffect = lastEffect; - shouldTrack = lastShouldTrack; - } - } - stop() { - if (this.active) { - preCleanupEffect(this); - postCleanupEffect(this); - this.onStop && this.onStop(); - this.active = false; - } - } -} -function triggerComputed(computed2) { - return computed2.value; -} -__name(triggerComputed, "triggerComputed"); -function preCleanupEffect(effect2) { - effect2._trackId++; - effect2._depsLength = 0; -} -__name(preCleanupEffect, "preCleanupEffect"); -function postCleanupEffect(effect2) { - if (effect2.deps.length > effect2._depsLength) { - for (let i2 = effect2._depsLength; i2 < effect2.deps.length; i2++) { - cleanupDepEffect(effect2.deps[i2], effect2); - } - effect2.deps.length = effect2._depsLength; - } -} -__name(postCleanupEffect, "postCleanupEffect"); -function cleanupDepEffect(dep, effect2) { - const trackId = dep.get(effect2); - if (trackId !== void 0 && effect2._trackId !== trackId) { - dep.delete(effect2); - if (dep.size === 0) { - dep.cleanup(); - } - } -} -__name(cleanupDepEffect, "cleanupDepEffect"); -function effect(fn, options4) { - if (fn.effect instanceof ReactiveEffect) { - fn = fn.effect.fn; - } - const _effect = new ReactiveEffect(fn, NOOP, () => { - if (_effect.dirty) { - _effect.run(); - } - }); - if (options4) { - extend$1(_effect, options4); - if (options4.scope) recordEffectScope(_effect, options4.scope); - } - if (!options4 || !options4.lazy) { - _effect.run(); - } - const runner = _effect.run.bind(_effect); - runner.effect = _effect; - return runner; -} -__name(effect, "effect"); -function stop(runner) { - runner.effect.stop(); -} -__name(stop, "stop"); -let shouldTrack = true; -let pauseScheduleStack = 0; -const trackStack = []; -function pauseTracking() { - trackStack.push(shouldTrack); - shouldTrack = false; -} -__name(pauseTracking, "pauseTracking"); -function enableTracking() { - trackStack.push(shouldTrack); - shouldTrack = true; -} -__name(enableTracking, "enableTracking"); -function resetTracking() { - const last = trackStack.pop(); - shouldTrack = last === void 0 ? true : last; -} -__name(resetTracking, "resetTracking"); -function pauseScheduling() { - pauseScheduleStack++; -} -__name(pauseScheduling, "pauseScheduling"); -function resetScheduling() { - pauseScheduleStack--; - while (!pauseScheduleStack && queueEffectSchedulers.length) { - queueEffectSchedulers.shift()(); - } -} -__name(resetScheduling, "resetScheduling"); -function trackEffect(effect2, dep, debuggerEventExtraInfo) { - var _a2; - if (dep.get(effect2) !== effect2._trackId) { - dep.set(effect2, effect2._trackId); - const oldDep = effect2.deps[effect2._depsLength]; - if (oldDep !== dep) { - if (oldDep) { - cleanupDepEffect(oldDep, effect2); - } - effect2.deps[effect2._depsLength++] = dep; - } else { - effect2._depsLength++; - } - if (false) { - (_a2 = effect2.onTrack) == null ? void 0 : _a2.call(effect2, extend$1({ effect: effect2 }, debuggerEventExtraInfo)); - } - } -} -__name(trackEffect, "trackEffect"); -const queueEffectSchedulers = []; -function triggerEffects(dep, dirtyLevel, debuggerEventExtraInfo) { - var _a2; - pauseScheduling(); - for (const effect2 of dep.keys()) { - let tracking; - if (effect2._dirtyLevel < dirtyLevel && (tracking != null ? tracking : tracking = dep.get(effect2) === effect2._trackId)) { - effect2._shouldSchedule || (effect2._shouldSchedule = effect2._dirtyLevel === 0); - effect2._dirtyLevel = dirtyLevel; - } - if (effect2._shouldSchedule && (tracking != null ? tracking : tracking = dep.get(effect2) === effect2._trackId)) { - if (false) { - (_a2 = effect2.onTrigger) == null ? void 0 : _a2.call(effect2, extend$1({ effect: effect2 }, debuggerEventExtraInfo)); - } - effect2.trigger(); - if ((!effect2._runnings || effect2.allowRecurse) && effect2._dirtyLevel !== 2) { - effect2._shouldSchedule = false; - if (effect2.scheduler) { - queueEffectSchedulers.push(effect2.scheduler); - } - } - } - } - resetScheduling(); -} -__name(triggerEffects, "triggerEffects"); -const createDep = /* @__PURE__ */ __name((cleanup, computed2) => { - const dep = /* @__PURE__ */ new Map(); - dep.cleanup = cleanup; - dep.computed = computed2; - return dep; -}, "createDep"); -const targetMap = /* @__PURE__ */ new WeakMap(); -const ITERATE_KEY = Symbol(false ? "iterate" : ""); -const MAP_KEY_ITERATE_KEY = Symbol(false ? "Map key iterate" : ""); -function track(target, type, key) { - if (shouldTrack && activeEffect) { - let depsMap = targetMap.get(target); - if (!depsMap) { - targetMap.set(target, depsMap = /* @__PURE__ */ new Map()); - } - let dep = depsMap.get(key); - if (!dep) { - depsMap.set(key, dep = createDep(() => depsMap.delete(key))); - } - trackEffect( - activeEffect, - dep, - false ? { - target, - type, - key - } : void 0 - ); - } -} -__name(track, "track"); -function trigger(target, type, key, newValue2, oldValue2, oldTarget) { - const depsMap = targetMap.get(target); - if (!depsMap) { - return; - } - let deps = []; - if (type === "clear") { - deps = [...depsMap.values()]; - } else if (key === "length" && isArray$4(target)) { - const newLength = Number(newValue2); - depsMap.forEach((dep, key2) => { - if (key2 === "length" || !isSymbol$1(key2) && key2 >= newLength) { - deps.push(dep); - } - }); - } else { - if (key !== void 0) { - deps.push(depsMap.get(key)); - } - switch (type) { - case "add": - if (!isArray$4(target)) { - deps.push(depsMap.get(ITERATE_KEY)); - if (isMap(target)) { - deps.push(depsMap.get(MAP_KEY_ITERATE_KEY)); - } - } else if (isIntegerKey(key)) { - deps.push(depsMap.get("length")); - } - break; - case "delete": - if (!isArray$4(target)) { - deps.push(depsMap.get(ITERATE_KEY)); - if (isMap(target)) { - deps.push(depsMap.get(MAP_KEY_ITERATE_KEY)); - } - } - break; - case "set": - if (isMap(target)) { - deps.push(depsMap.get(ITERATE_KEY)); - } - break; - } - } - pauseScheduling(); - for (const dep of deps) { - if (dep) { - triggerEffects( - dep, - 4, - false ? { - target, - type, - key, - newValue: newValue2, - oldValue: oldValue2, - oldTarget - } : void 0 - ); - } - } - resetScheduling(); -} -__name(trigger, "trigger"); -function getDepFromReactive(object, key) { - const depsMap = targetMap.get(object); - return depsMap && depsMap.get(key); -} -__name(getDepFromReactive, "getDepFromReactive"); -const isNonTrackableKeys = /* @__PURE__ */ makeMap(`__proto__,__v_isRef,__isVue`); -const builtInSymbols = new Set( - /* @__PURE__ */ Object.getOwnPropertyNames(Symbol).filter((key) => key !== "arguments" && key !== "caller").map((key) => Symbol[key]).filter(isSymbol$1) -); -const arrayInstrumentations = /* @__PURE__ */ createArrayInstrumentations(); -function createArrayInstrumentations() { - const instrumentations = {}; - ["includes", "indexOf", "lastIndexOf"].forEach((key) => { - instrumentations[key] = function(...args) { - const arr = toRaw(this); - for (let i2 = 0, l = this.length; i2 < l; i2++) { - track(arr, "get", i2 + ""); - } - const res = arr[key](...args); - if (res === -1 || res === false) { - return arr[key](...args.map(toRaw)); - } else { - return res; - } - }; - }); - ["push", "pop", "shift", "unshift", "splice"].forEach((key) => { - instrumentations[key] = function(...args) { - pauseTracking(); - pauseScheduling(); - const res = toRaw(this)[key].apply(this, args); - resetScheduling(); - resetTracking(); - return res; - }; - }); - return instrumentations; -} -__name(createArrayInstrumentations, "createArrayInstrumentations"); -function hasOwnProperty$2(key) { - if (!isSymbol$1(key)) key = String(key); - const obj = toRaw(this); - track(obj, "has", key); - return obj.hasOwnProperty(key); -} -__name(hasOwnProperty$2, "hasOwnProperty$2"); -class BaseReactiveHandler { - static { - __name(this, "BaseReactiveHandler"); - } - constructor(_isReadonly = false, _isShallow = false) { - this._isReadonly = _isReadonly; - this._isShallow = _isShallow; - } - get(target, key, receiver) { - const isReadonly2 = this._isReadonly, isShallow2 = this._isShallow; - if (key === "__v_isReactive") { - return !isReadonly2; - } else if (key === "__v_isReadonly") { - return isReadonly2; - } else if (key === "__v_isShallow") { - return isShallow2; - } else if (key === "__v_raw") { - if (receiver === (isReadonly2 ? isShallow2 ? shallowReadonlyMap : readonlyMap : isShallow2 ? shallowReactiveMap : reactiveMap).get(target) || // receiver is not the reactive proxy, but has the same prototype - // this means the reciever is a user proxy of the reactive proxy - Object.getPrototypeOf(target) === Object.getPrototypeOf(receiver)) { - return target; - } - return; - } - const targetIsArray = isArray$4(target); - if (!isReadonly2) { - if (targetIsArray && hasOwn$3(arrayInstrumentations, key)) { - return Reflect.get(arrayInstrumentations, key, receiver); - } - if (key === "hasOwnProperty") { - return hasOwnProperty$2; - } - } - const res = Reflect.get(target, key, receiver); - if (isSymbol$1(key) ? builtInSymbols.has(key) : isNonTrackableKeys(key)) { - return res; - } - if (!isReadonly2) { - track(target, "get", key); - } - if (isShallow2) { - return res; - } - if (isRef(res)) { - return targetIsArray && isIntegerKey(key) ? res : res.value; - } - if (isObject$6(res)) { - return isReadonly2 ? readonly(res) : reactive(res); - } - return res; - } -} -class MutableReactiveHandler extends BaseReactiveHandler { - static { - __name(this, "MutableReactiveHandler"); - } - constructor(isShallow2 = false) { - super(false, isShallow2); - } - set(target, key, value4, receiver) { - let oldValue2 = target[key]; - if (!this._isShallow) { - const isOldValueReadonly = isReadonly(oldValue2); - if (!isShallow(value4) && !isReadonly(value4)) { - oldValue2 = toRaw(oldValue2); - value4 = toRaw(value4); - } - if (!isArray$4(target) && isRef(oldValue2) && !isRef(value4)) { - if (isOldValueReadonly) { - return false; - } else { - oldValue2.value = value4; - return true; - } - } - } - const hadKey = isArray$4(target) && isIntegerKey(key) ? Number(key) < target.length : hasOwn$3(target, key); - const result = Reflect.set(target, key, value4, receiver); - if (target === toRaw(receiver)) { - if (!hadKey) { - trigger(target, "add", key, value4); - } else if (hasChanged(value4, oldValue2)) { - trigger(target, "set", key, value4, oldValue2); - } - } - return result; - } - deleteProperty(target, key) { - const hadKey = hasOwn$3(target, key); - const oldValue2 = target[key]; - const result = Reflect.deleteProperty(target, key); - if (result && hadKey) { - trigger(target, "delete", key, void 0, oldValue2); - } - return result; - } - has(target, key) { - const result = Reflect.has(target, key); - if (!isSymbol$1(key) || !builtInSymbols.has(key)) { - track(target, "has", key); - } - return result; - } - ownKeys(target) { - track( - target, - "iterate", - isArray$4(target) ? "length" : ITERATE_KEY - ); - return Reflect.ownKeys(target); - } -} -class ReadonlyReactiveHandler extends BaseReactiveHandler { - static { - __name(this, "ReadonlyReactiveHandler"); - } - constructor(isShallow2 = false) { - super(true, isShallow2); - } - set(target, key) { - if (false) { - warn$4( - `Set operation on key "${String(key)}" failed: target is readonly.`, - target - ); - } - return true; - } - deleteProperty(target, key) { - if (false) { - warn$4( - `Delete operation on key "${String(key)}" failed: target is readonly.`, - target - ); - } - return true; - } -} -const mutableHandlers = /* @__PURE__ */ new MutableReactiveHandler(); -const readonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(); -const shallowReactiveHandlers = /* @__PURE__ */ new MutableReactiveHandler( - true -); -const shallowReadonlyHandlers = /* @__PURE__ */ new ReadonlyReactiveHandler(true); -const toShallow = /* @__PURE__ */ __name((value4) => value4, "toShallow"); -const getProto = /* @__PURE__ */ __name((v2) => Reflect.getPrototypeOf(v2), "getProto"); -function get$3(target, key, isReadonly2 = false, isShallow2 = false) { - target = target["__v_raw"]; - const rawTarget = toRaw(target); - const rawKey = toRaw(key); - if (!isReadonly2) { - if (hasChanged(key, rawKey)) { - track(rawTarget, "get", key); - } - track(rawTarget, "get", rawKey); - } - const { has: has2 } = getProto(rawTarget); - const wrap2 = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; - if (has2.call(rawTarget, key)) { - return wrap2(target.get(key)); - } else if (has2.call(rawTarget, rawKey)) { - return wrap2(target.get(rawKey)); - } else if (target !== rawTarget) { - target.get(key); - } -} -__name(get$3, "get$3"); -function has$1(key, isReadonly2 = false) { - const target = this["__v_raw"]; - const rawTarget = toRaw(target); - const rawKey = toRaw(key); - if (!isReadonly2) { - if (hasChanged(key, rawKey)) { - track(rawTarget, "has", key); - } - track(rawTarget, "has", rawKey); - } - return key === rawKey ? target.has(key) : target.has(key) || target.has(rawKey); -} -__name(has$1, "has$1"); -function size(target, isReadonly2 = false) { - target = target["__v_raw"]; - !isReadonly2 && track(toRaw(target), "iterate", ITERATE_KEY); - return Reflect.get(target, "size", target); -} -__name(size, "size"); -function add$1(value4) { - value4 = toRaw(value4); - const target = toRaw(this); - const proto = getProto(target); - const hadKey = proto.has.call(target, value4); - if (!hadKey) { - target.add(value4); - trigger(target, "add", value4, value4); - } - return this; -} -__name(add$1, "add$1"); -function set$4(key, value4) { - value4 = toRaw(value4); - const target = toRaw(this); - const { has: has2, get: get22 } = getProto(target); - let hadKey = has2.call(target, key); - if (!hadKey) { - key = toRaw(key); - hadKey = has2.call(target, key); - } else if (false) { - checkIdentityKeys(target, has2, key); - } - const oldValue2 = get22.call(target, key); - target.set(key, value4); - if (!hadKey) { - trigger(target, "add", key, value4); - } else if (hasChanged(value4, oldValue2)) { - trigger(target, "set", key, value4, oldValue2); - } - return this; -} -__name(set$4, "set$4"); -function deleteEntry(key) { - const target = toRaw(this); - const { has: has2, get: get22 } = getProto(target); - let hadKey = has2.call(target, key); - if (!hadKey) { - key = toRaw(key); - hadKey = has2.call(target, key); - } else if (false) { - checkIdentityKeys(target, has2, key); - } - const oldValue2 = get22 ? get22.call(target, key) : void 0; - const result = target.delete(key); - if (hadKey) { - trigger(target, "delete", key, void 0, oldValue2); - } - return result; -} -__name(deleteEntry, "deleteEntry"); -function clear() { - const target = toRaw(this); - const hadItems = target.size !== 0; - const oldTarget = false ? isMap(target) ? new Map(target) : new Set(target) : void 0; - const result = target.clear(); - if (hadItems) { - trigger(target, "clear", void 0, void 0, oldTarget); - } - return result; -} -__name(clear, "clear"); -function createForEach(isReadonly2, isShallow2) { - return /* @__PURE__ */ __name(function forEach3(callback, thisArg) { - const observed = this; - const target = observed["__v_raw"]; - const rawTarget = toRaw(target); - const wrap2 = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; - !isReadonly2 && track(rawTarget, "iterate", ITERATE_KEY); - return target.forEach((value4, key) => { - return callback.call(thisArg, wrap2(value4), wrap2(key), observed); - }); - }, "forEach"); -} -__name(createForEach, "createForEach"); -function createIterableMethod(method, isReadonly2, isShallow2) { - return function(...args) { - const target = this["__v_raw"]; - const rawTarget = toRaw(target); - const targetIsMap = isMap(rawTarget); - const isPair = method === "entries" || method === Symbol.iterator && targetIsMap; - const isKeyOnly = method === "keys" && targetIsMap; - const innerIterator = target[method](...args); - const wrap2 = isShallow2 ? toShallow : isReadonly2 ? toReadonly : toReactive$1; - !isReadonly2 && track( - rawTarget, - "iterate", - isKeyOnly ? MAP_KEY_ITERATE_KEY : ITERATE_KEY - ); - return { - // iterator protocol - next() { - const { value: value4, done } = innerIterator.next(); - return done ? { value: value4, done } : { - value: isPair ? [wrap2(value4[0]), wrap2(value4[1])] : wrap2(value4), - done - }; - }, - // iterable protocol - [Symbol.iterator]() { - return this; - } - }; - }; -} -__name(createIterableMethod, "createIterableMethod"); -function createReadonlyMethod(type) { - return function(...args) { - if (false) { - const key = args[0] ? `on key "${args[0]}" ` : ``; - warn$4( - `${capitalize$1(type)} operation ${key}failed: target is readonly.`, - toRaw(this) - ); - } - return type === "delete" ? false : type === "clear" ? void 0 : this; - }; -} -__name(createReadonlyMethod, "createReadonlyMethod"); -function createInstrumentations() { - const mutableInstrumentations2 = { - get(key) { - return get$3(this, key); - }, - get size() { - return size(this); - }, - has: has$1, - add: add$1, - set: set$4, - delete: deleteEntry, - clear, - forEach: createForEach(false, false) - }; - const shallowInstrumentations2 = { - get(key) { - return get$3(this, key, false, true); - }, - get size() { - return size(this); - }, - has: has$1, - add: add$1, - set: set$4, - delete: deleteEntry, - clear, - forEach: createForEach(false, true) - }; - const readonlyInstrumentations2 = { - get(key) { - return get$3(this, key, true); - }, - get size() { - return size(this, true); - }, - has(key) { - return has$1.call(this, key, true); - }, - add: createReadonlyMethod("add"), - set: createReadonlyMethod("set"), - delete: createReadonlyMethod("delete"), - clear: createReadonlyMethod("clear"), - forEach: createForEach(true, false) - }; - const shallowReadonlyInstrumentations2 = { - get(key) { - return get$3(this, key, true, true); - }, - get size() { - return size(this, true); - }, - has(key) { - return has$1.call(this, key, true); - }, - add: createReadonlyMethod("add"), - set: createReadonlyMethod("set"), - delete: createReadonlyMethod("delete"), - clear: createReadonlyMethod("clear"), - forEach: createForEach(true, true) - }; - const iteratorMethods = [ - "keys", - "values", - "entries", - Symbol.iterator - ]; - iteratorMethods.forEach((method) => { - mutableInstrumentations2[method] = createIterableMethod(method, false, false); - readonlyInstrumentations2[method] = createIterableMethod(method, true, false); - shallowInstrumentations2[method] = createIterableMethod(method, false, true); - shallowReadonlyInstrumentations2[method] = createIterableMethod( - method, - true, - true - ); - }); - return [ - mutableInstrumentations2, - readonlyInstrumentations2, - shallowInstrumentations2, - shallowReadonlyInstrumentations2 - ]; -} -__name(createInstrumentations, "createInstrumentations"); -const [ - mutableInstrumentations, - readonlyInstrumentations, - shallowInstrumentations, - shallowReadonlyInstrumentations -] = /* @__PURE__ */ createInstrumentations(); -function createInstrumentationGetter(isReadonly2, shallow) { - const instrumentations = shallow ? isReadonly2 ? shallowReadonlyInstrumentations : shallowInstrumentations : isReadonly2 ? readonlyInstrumentations : mutableInstrumentations; - return (target, key, receiver) => { - if (key === "__v_isReactive") { - return !isReadonly2; - } else if (key === "__v_isReadonly") { - return isReadonly2; - } else if (key === "__v_raw") { - return target; - } - return Reflect.get( - hasOwn$3(instrumentations, key) && key in target ? instrumentations : target, - key, - receiver - ); - }; -} -__name(createInstrumentationGetter, "createInstrumentationGetter"); -const mutableCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(false, false) -}; -const shallowCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(false, true) -}; -const readonlyCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(true, false) -}; -const shallowReadonlyCollectionHandlers = { - get: /* @__PURE__ */ createInstrumentationGetter(true, true) -}; -function checkIdentityKeys(target, has2, key) { - const rawKey = toRaw(key); - if (rawKey !== key && has2.call(target, rawKey)) { - const type = toRawType(target); - warn$4( - `Reactive ${type} contains both the raw and reactive versions of the same object${type === `Map` ? ` as keys` : ``}, which can lead to inconsistencies. Avoid differentiating between the raw and reactive versions of an object and only use the reactive version if possible.` - ); - } -} -__name(checkIdentityKeys, "checkIdentityKeys"); -const reactiveMap = /* @__PURE__ */ new WeakMap(); -const shallowReactiveMap = /* @__PURE__ */ new WeakMap(); -const readonlyMap = /* @__PURE__ */ new WeakMap(); -const shallowReadonlyMap = /* @__PURE__ */ new WeakMap(); -function targetTypeMap(rawType) { - switch (rawType) { - case "Object": - case "Array": - return 1; - case "Map": - case "Set": - case "WeakMap": - case "WeakSet": - return 2; - default: - return 0; - } -} -__name(targetTypeMap, "targetTypeMap"); -function getTargetType(value4) { - return value4["__v_skip"] || !Object.isExtensible(value4) ? 0 : targetTypeMap(toRawType(value4)); -} -__name(getTargetType, "getTargetType"); -function reactive(target) { - if (isReadonly(target)) { - return target; - } - return createReactiveObject( - target, - false, - mutableHandlers, - mutableCollectionHandlers, - reactiveMap - ); -} -__name(reactive, "reactive"); -function shallowReactive(target) { - return createReactiveObject( - target, - false, - shallowReactiveHandlers, - shallowCollectionHandlers, - shallowReactiveMap - ); -} -__name(shallowReactive, "shallowReactive"); -function readonly(target) { - return createReactiveObject( - target, - true, - readonlyHandlers, - readonlyCollectionHandlers, - readonlyMap - ); -} -__name(readonly, "readonly"); -function shallowReadonly(target) { - return createReactiveObject( - target, - true, - shallowReadonlyHandlers, - shallowReadonlyCollectionHandlers, - shallowReadonlyMap - ); -} -__name(shallowReadonly, "shallowReadonly"); -function createReactiveObject(target, isReadonly2, baseHandlers, collectionHandlers, proxyMap) { - if (!isObject$6(target)) { - if (false) { - warn$4( - `value cannot be made ${isReadonly2 ? "readonly" : "reactive"}: ${String( - target - )}` - ); - } - return target; - } - if (target["__v_raw"] && !(isReadonly2 && target["__v_isReactive"])) { - return target; - } - const existingProxy = proxyMap.get(target); - if (existingProxy) { - return existingProxy; - } - const targetType = getTargetType(target); - if (targetType === 0) { - return target; - } - const proxy = new Proxy( - target, - targetType === 2 ? collectionHandlers : baseHandlers - ); - proxyMap.set(target, proxy); - return proxy; -} -__name(createReactiveObject, "createReactiveObject"); -function isReactive(value4) { - if (isReadonly(value4)) { - return isReactive(value4["__v_raw"]); - } - return !!(value4 && value4["__v_isReactive"]); -} -__name(isReactive, "isReactive"); -function isReadonly(value4) { - return !!(value4 && value4["__v_isReadonly"]); -} -__name(isReadonly, "isReadonly"); -function isShallow(value4) { - return !!(value4 && value4["__v_isShallow"]); -} -__name(isShallow, "isShallow"); -function isProxy(value4) { - return value4 ? !!value4["__v_raw"] : false; -} -__name(isProxy, "isProxy"); -function toRaw(observed) { - const raw = observed && observed["__v_raw"]; - return raw ? toRaw(raw) : observed; -} -__name(toRaw, "toRaw"); -function markRaw(value4) { - if (Object.isExtensible(value4)) { - def(value4, "__v_skip", true); - } - return value4; -} -__name(markRaw, "markRaw"); -const toReactive$1 = /* @__PURE__ */ __name((value4) => isObject$6(value4) ? reactive(value4) : value4, "toReactive$1"); -const toReadonly = /* @__PURE__ */ __name((value4) => isObject$6(value4) ? readonly(value4) : value4, "toReadonly"); -const COMPUTED_SIDE_EFFECT_WARN = `Computed is still dirty after getter evaluation, likely because a computed is mutating its own dependency in its getter. State mutations in computed getters should be avoided. Check the docs for more details: https://vuejs.org/guide/essentials/computed.html#getters-should-be-side-effect-free`; -class ComputedRefImpl { - static { - __name(this, "ComputedRefImpl"); - } - constructor(getter, _setter, isReadonly2, isSSR) { - this.getter = getter; - this._setter = _setter; - this.dep = void 0; - this.__v_isRef = true; - this["__v_isReadonly"] = false; - this.effect = new ReactiveEffect( - () => getter(this._value), - () => triggerRefValue( - this, - this.effect._dirtyLevel === 2 ? 2 : 3 - ) - ); - this.effect.computed = this; - this.effect.active = this._cacheable = !isSSR; - this["__v_isReadonly"] = isReadonly2; - } - get value() { - const self2 = toRaw(this); - if ((!self2._cacheable || self2.effect.dirty) && hasChanged(self2._value, self2._value = self2.effect.run())) { - triggerRefValue(self2, 4); - } - trackRefValue(self2); - if (self2.effect._dirtyLevel >= 2) { - if (false) { - warn$4(COMPUTED_SIDE_EFFECT_WARN, ` - -getter: `, this.getter); - } - triggerRefValue(self2, 2); - } - return self2._value; - } - set value(newValue2) { - this._setter(newValue2); - } - // #region polyfill _dirty for backward compatibility third party code for Vue <= 3.3.x - get _dirty() { - return this.effect.dirty; - } - set _dirty(v2) { - this.effect.dirty = v2; - } - // #endregion -} -function computed$1(getterOrOptions, debugOptions, isSSR = false) { - let getter; - let setter; - const onlyGetter = isFunction$4(getterOrOptions); - if (onlyGetter) { - getter = getterOrOptions; - setter = false ? () => { - warn$4("Write operation failed: computed value is readonly"); - } : NOOP; - } else { - getter = getterOrOptions.get; - setter = getterOrOptions.set; - } - const cRef = new ComputedRefImpl(getter, setter, onlyGetter || !setter, isSSR); - if (false) { - cRef.effect.onTrack = debugOptions.onTrack; - cRef.effect.onTrigger = debugOptions.onTrigger; - } - return cRef; -} -__name(computed$1, "computed$1"); -function trackRefValue(ref2) { - var _a2; - if (shouldTrack && activeEffect) { - ref2 = toRaw(ref2); - trackEffect( - activeEffect, - (_a2 = ref2.dep) != null ? _a2 : ref2.dep = createDep( - () => ref2.dep = void 0, - ref2 instanceof ComputedRefImpl ? ref2 : void 0 - ), - false ? { - target: ref2, - type: "get", - key: "value" - } : void 0 - ); - } -} -__name(trackRefValue, "trackRefValue"); -function triggerRefValue(ref2, dirtyLevel = 4, newVal, oldVal) { - ref2 = toRaw(ref2); - const dep = ref2.dep; - if (dep) { - triggerEffects( - dep, - dirtyLevel, - false ? { - target: ref2, - type: "set", - key: "value", - newValue: newVal, - oldValue: oldVal - } : void 0 - ); - } -} -__name(triggerRefValue, "triggerRefValue"); -function isRef(r) { - return !!(r && r.__v_isRef === true); -} -__name(isRef, "isRef"); -function ref(value4) { - return createRef(value4, false); -} -__name(ref, "ref"); -function shallowRef(value4) { - return createRef(value4, true); -} -__name(shallowRef, "shallowRef"); -function createRef(rawValue, shallow) { - if (isRef(rawValue)) { - return rawValue; - } - return new RefImpl(rawValue, shallow); -} -__name(createRef, "createRef"); -class RefImpl { - static { - __name(this, "RefImpl"); - } - constructor(value4, __v_isShallow) { - this.__v_isShallow = __v_isShallow; - this.dep = void 0; - this.__v_isRef = true; - this._rawValue = __v_isShallow ? value4 : toRaw(value4); - this._value = __v_isShallow ? value4 : toReactive$1(value4); - } - get value() { - trackRefValue(this); - return this._value; - } - set value(newVal) { - const useDirectValue = this.__v_isShallow || isShallow(newVal) || isReadonly(newVal); - newVal = useDirectValue ? newVal : toRaw(newVal); - if (hasChanged(newVal, this._rawValue)) { - const oldVal = this._rawValue; - this._rawValue = newVal; - this._value = useDirectValue ? newVal : toReactive$1(newVal); - triggerRefValue(this, 4, newVal, oldVal); - } - } -} -function triggerRef(ref2) { - triggerRefValue(ref2, 4, false ? ref2.value : void 0); -} -__name(triggerRef, "triggerRef"); -function unref(ref2) { - return isRef(ref2) ? ref2.value : ref2; -} -__name(unref, "unref"); -function toValue$1(source) { - return isFunction$4(source) ? source() : unref(source); -} -__name(toValue$1, "toValue$1"); -const shallowUnwrapHandlers = { - get: /* @__PURE__ */ __name((target, key, receiver) => unref(Reflect.get(target, key, receiver)), "get"), - set: /* @__PURE__ */ __name((target, key, value4, receiver) => { - const oldValue2 = target[key]; - if (isRef(oldValue2) && !isRef(value4)) { - oldValue2.value = value4; - return true; - } else { - return Reflect.set(target, key, value4, receiver); - } - }, "set") -}; -function proxyRefs(objectWithRefs) { - return isReactive(objectWithRefs) ? objectWithRefs : new Proxy(objectWithRefs, shallowUnwrapHandlers); -} -__name(proxyRefs, "proxyRefs"); -class CustomRefImpl { - static { - __name(this, "CustomRefImpl"); - } - constructor(factory) { - this.dep = void 0; - this.__v_isRef = true; - const { get: get22, set: set22 } = factory( - () => trackRefValue(this), - () => triggerRefValue(this) - ); - this._get = get22; - this._set = set22; - } - get value() { - return this._get(); - } - set value(newVal) { - this._set(newVal); - } -} -function customRef(factory) { - return new CustomRefImpl(factory); -} -__name(customRef, "customRef"); -function toRefs$1(object) { - if (false) { - warn$4(`toRefs() expects a reactive object but received a plain one.`); - } - const ret = isArray$4(object) ? new Array(object.length) : {}; - for (const key in object) { - ret[key] = propertyToRef(object, key); - } - return ret; -} -__name(toRefs$1, "toRefs$1"); -class ObjectRefImpl { - static { - __name(this, "ObjectRefImpl"); - } - constructor(_object, _key, _defaultValue) { - this._object = _object; - this._key = _key; - this._defaultValue = _defaultValue; - this.__v_isRef = true; - } - get value() { - const val = this._object[this._key]; - return val === void 0 ? this._defaultValue : val; - } - set value(newVal) { - this._object[this._key] = newVal; - } - get dep() { - return getDepFromReactive(toRaw(this._object), this._key); - } -} -class GetterRefImpl { - static { - __name(this, "GetterRefImpl"); - } - constructor(_getter) { - this._getter = _getter; - this.__v_isRef = true; - this.__v_isReadonly = true; - } - get value() { - return this._getter(); - } -} -function toRef$1(source, key, defaultValue) { - if (isRef(source)) { - return source; - } else if (isFunction$4(source)) { - return new GetterRefImpl(source); - } else if (isObject$6(source) && arguments.length > 1) { - return propertyToRef(source, key, defaultValue); - } else { - return ref(source); - } -} -__name(toRef$1, "toRef$1"); -function propertyToRef(source, key, defaultValue) { - const val = source[key]; - return isRef(val) ? val : new ObjectRefImpl(source, key, defaultValue); -} -__name(propertyToRef, "propertyToRef"); -const deferredComputed = computed$1; -const TrackOpTypes = { - "GET": "get", - "HAS": "has", - "ITERATE": "iterate" -}; -const TriggerOpTypes = { - "SET": "set", - "ADD": "add", - "DELETE": "delete", - "CLEAR": "clear" -}; -const ReactiveFlags = { - "SKIP": "__v_skip", - "IS_REACTIVE": "__v_isReactive", - "IS_READONLY": "__v_isReadonly", - "IS_SHALLOW": "__v_isShallow", - "RAW": "__v_raw" -}; -/** -* @vue/runtime-core v3.4.31 -* (c) 2018-present Yuxi (Evan) You and Vue contributors -* @license MIT -**/ -const stack = []; -function pushWarningContext(vnode) { - stack.push(vnode); -} -__name(pushWarningContext, "pushWarningContext"); -function popWarningContext() { - stack.pop(); -} -__name(popWarningContext, "popWarningContext"); -function warn$1$1(msg, ...args) { - pauseTracking(); - const instance = stack.length ? stack[stack.length - 1].component : null; - const appWarnHandler = instance && instance.appContext.config.warnHandler; - const trace = getComponentTrace(); - if (appWarnHandler) { - callWithErrorHandling( - appWarnHandler, - instance, - 11, - [ - // eslint-disable-next-line no-restricted-syntax - msg + args.map((a) => { - var _a2, _b; - return (_b = (_a2 = a.toString) == null ? void 0 : _a2.call(a)) != null ? _b : JSON.stringify(a); - }).join(""), - instance && instance.proxy, - trace.map( - ({ vnode }) => `at <${formatComponentName(instance, vnode.type)}>` - ).join("\n"), - trace - ] - ); - } else { - const warnArgs = [`[Vue warn]: ${msg}`, ...args]; - if (trace.length && // avoid spamming console during tests - true) { - warnArgs.push(` -`, ...formatTrace(trace)); - } - console.warn(...warnArgs); - } - resetTracking(); -} -__name(warn$1$1, "warn$1$1"); -function getComponentTrace() { - let currentVNode = stack[stack.length - 1]; - if (!currentVNode) { - return []; - } - const normalizedStack = []; - while (currentVNode) { - const last = normalizedStack[0]; - if (last && last.vnode === currentVNode) { - last.recurseCount++; - } else { - normalizedStack.push({ - vnode: currentVNode, - recurseCount: 0 - }); - } - const parentInstance = currentVNode.component && currentVNode.component.parent; - currentVNode = parentInstance && parentInstance.vnode; - } - return normalizedStack; -} -__name(getComponentTrace, "getComponentTrace"); -function formatTrace(trace) { - const logs = []; - trace.forEach((entry, i2) => { - logs.push(...i2 === 0 ? [] : [` -`], ...formatTraceEntry(entry)); - }); - return logs; -} -__name(formatTrace, "formatTrace"); -function formatTraceEntry({ vnode, recurseCount }) { - const postfix = recurseCount > 0 ? `... (${recurseCount} recursive calls)` : ``; - const isRoot = vnode.component ? vnode.component.parent == null : false; - const open2 = ` at <${formatComponentName( - vnode.component, - vnode.type, - isRoot - )}`; - const close5 = `>` + postfix; - return vnode.props ? [open2, ...formatProps(vnode.props), close5] : [open2 + close5]; -} -__name(formatTraceEntry, "formatTraceEntry"); -function formatProps(props) { - const res = []; - const keys2 = Object.keys(props); - keys2.slice(0, 3).forEach((key) => { - res.push(...formatProp(key, props[key])); - }); - if (keys2.length > 3) { - res.push(` ...`); - } - return res; -} -__name(formatProps, "formatProps"); -function formatProp(key, value4, raw) { - if (isString$7(value4)) { - value4 = JSON.stringify(value4); - return raw ? value4 : [`${key}=${value4}`]; - } else if (typeof value4 === "number" || typeof value4 === "boolean" || value4 == null) { - return raw ? value4 : [`${key}=${value4}`]; - } else if (isRef(value4)) { - value4 = formatProp(key, toRaw(value4.value), true); - return raw ? value4 : [`${key}=Ref<`, value4, `>`]; - } else if (isFunction$4(value4)) { - return [`${key}=fn${value4.name ? `<${value4.name}>` : ``}`]; - } else { - value4 = toRaw(value4); - return raw ? value4 : [`${key}=`, value4]; - } -} -__name(formatProp, "formatProp"); -function assertNumber(val, type) { - if (true) return; - if (val === void 0) { - return; - } else if (typeof val !== "number") { - warn$1$1(`${type} is not a valid number - got ${JSON.stringify(val)}.`); - } else if (isNaN(val)) { - warn$1$1(`${type} is NaN - the duration expression might be incorrect.`); - } -} -__name(assertNumber, "assertNumber"); -const ErrorCodes = { - "SETUP_FUNCTION": 0, - "0": "SETUP_FUNCTION", - "RENDER_FUNCTION": 1, - "1": "RENDER_FUNCTION", - "WATCH_GETTER": 2, - "2": "WATCH_GETTER", - "WATCH_CALLBACK": 3, - "3": "WATCH_CALLBACK", - "WATCH_CLEANUP": 4, - "4": "WATCH_CLEANUP", - "NATIVE_EVENT_HANDLER": 5, - "5": "NATIVE_EVENT_HANDLER", - "COMPONENT_EVENT_HANDLER": 6, - "6": "COMPONENT_EVENT_HANDLER", - "VNODE_HOOK": 7, - "7": "VNODE_HOOK", - "DIRECTIVE_HOOK": 8, - "8": "DIRECTIVE_HOOK", - "TRANSITION_HOOK": 9, - "9": "TRANSITION_HOOK", - "APP_ERROR_HANDLER": 10, - "10": "APP_ERROR_HANDLER", - "APP_WARN_HANDLER": 11, - "11": "APP_WARN_HANDLER", - "FUNCTION_REF": 12, - "12": "FUNCTION_REF", - "ASYNC_COMPONENT_LOADER": 13, - "13": "ASYNC_COMPONENT_LOADER", - "SCHEDULER": 14, - "14": "SCHEDULER" -}; -const ErrorTypeStrings$1 = { - ["sp"]: "serverPrefetch hook", - ["bc"]: "beforeCreate hook", - ["c"]: "created hook", - ["bm"]: "beforeMount hook", - ["m"]: "mounted hook", - ["bu"]: "beforeUpdate hook", - ["u"]: "updated", - ["bum"]: "beforeUnmount hook", - ["um"]: "unmounted hook", - ["a"]: "activated hook", - ["da"]: "deactivated hook", - ["ec"]: "errorCaptured hook", - ["rtc"]: "renderTracked hook", - ["rtg"]: "renderTriggered hook", - [0]: "setup function", - [1]: "render function", - [2]: "watcher getter", - [3]: "watcher callback", - [4]: "watcher cleanup function", - [5]: "native event handler", - [6]: "component event handler", - [7]: "vnode hook", - [8]: "directive hook", - [9]: "transition hook", - [10]: "app errorHandler", - [11]: "app warnHandler", - [12]: "ref function", - [13]: "async component loader", - [14]: "scheduler flush. This is likely a Vue internals bug. Please open an issue at https://github.com/vuejs/core ." -}; -function callWithErrorHandling(fn, instance, type, args) { - try { - return args ? fn(...args) : fn(); - } catch (err) { - handleError(err, instance, type); - } -} -__name(callWithErrorHandling, "callWithErrorHandling"); -function callWithAsyncErrorHandling(fn, instance, type, args) { - if (isFunction$4(fn)) { - const res = callWithErrorHandling(fn, instance, type, args); - if (res && isPromise$1(res)) { - res.catch((err) => { - handleError(err, instance, type); - }); - } - return res; - } - if (isArray$4(fn)) { - const values = []; - for (let i2 = 0; i2 < fn.length; i2++) { - values.push(callWithAsyncErrorHandling(fn[i2], instance, type, args)); - } - return values; - } else if (false) { - warn$1$1( - `Invalid value type passed to callWithAsyncErrorHandling(): ${typeof fn}` - ); - } -} -__name(callWithAsyncErrorHandling, "callWithAsyncErrorHandling"); -function handleError(err, instance, type, throwInDev = true) { - const contextVNode = instance ? instance.vnode : null; - if (instance) { - let cur = instance.parent; - const exposedInstance = instance.proxy; - const errorInfo = false ? ErrorTypeStrings$1[type] : `https://vuejs.org/error-reference/#runtime-${type}`; - while (cur) { - const errorCapturedHooks = cur.ec; - if (errorCapturedHooks) { - for (let i2 = 0; i2 < errorCapturedHooks.length; i2++) { - if (errorCapturedHooks[i2](err, exposedInstance, errorInfo) === false) { - return; - } - } - } - cur = cur.parent; - } - const appErrorHandler = instance.appContext.config.errorHandler; - if (appErrorHandler) { - pauseTracking(); - callWithErrorHandling( - appErrorHandler, - null, - 10, - [err, exposedInstance, errorInfo] - ); - resetTracking(); - return; - } - } - logError(err, type, contextVNode, throwInDev); -} -__name(handleError, "handleError"); -function logError(err, type, contextVNode, throwInDev = true) { - if (false) { - const info = ErrorTypeStrings$1[type]; - if (contextVNode) { - pushWarningContext(contextVNode); - } - warn$1$1(`Unhandled error${info ? ` during execution of ${info}` : ``}`); - if (contextVNode) { - popWarningContext(); - } - if (throwInDev) { - throw err; - } else { - console.error(err); - } - } else { - console.error(err); - } -} -__name(logError, "logError"); -let isFlushing = false; -let isFlushPending = false; -const queue = []; -let flushIndex = 0; -const pendingPostFlushCbs = []; -let activePostFlushCbs = null; -let postFlushIndex = 0; -const resolvedPromise = /* @__PURE__ */ Promise.resolve(); -let currentFlushPromise = null; -const RECURSION_LIMIT = 100; -function nextTick(fn) { - const p2 = currentFlushPromise || resolvedPromise; - return fn ? p2.then(this ? fn.bind(this) : fn) : p2; -} -__name(nextTick, "nextTick"); -function findInsertionIndex$1(id3) { - let start2 = flushIndex + 1; - let end = queue.length; - while (start2 < end) { - const middle = start2 + end >>> 1; - const middleJob = queue[middle]; - const middleJobId = getId(middleJob); - if (middleJobId < id3 || middleJobId === id3 && middleJob.pre) { - start2 = middle + 1; - } else { - end = middle; - } - } - return start2; -} -__name(findInsertionIndex$1, "findInsertionIndex$1"); -function queueJob(job) { - if (!queue.length || !queue.includes( - job, - isFlushing && job.allowRecurse ? flushIndex + 1 : flushIndex - )) { - if (job.id == null) { - queue.push(job); - } else { - queue.splice(findInsertionIndex$1(job.id), 0, job); - } - queueFlush(); - } -} -__name(queueJob, "queueJob"); -function queueFlush() { - if (!isFlushing && !isFlushPending) { - isFlushPending = true; - currentFlushPromise = resolvedPromise.then(flushJobs); - } -} -__name(queueFlush, "queueFlush"); -function invalidateJob(job) { - const i2 = queue.indexOf(job); - if (i2 > flushIndex) { - queue.splice(i2, 1); - } -} -__name(invalidateJob, "invalidateJob"); -function queuePostFlushCb(cb) { - if (!isArray$4(cb)) { - if (!activePostFlushCbs || !activePostFlushCbs.includes( - cb, - cb.allowRecurse ? postFlushIndex + 1 : postFlushIndex - )) { - pendingPostFlushCbs.push(cb); - } - } else { - pendingPostFlushCbs.push(...cb); - } - queueFlush(); -} -__name(queuePostFlushCb, "queuePostFlushCb"); -function flushPreFlushCbs(instance, seen2, i2 = isFlushing ? flushIndex + 1 : 0) { - if (false) { - seen2 = seen2 || /* @__PURE__ */ new Map(); - } - for (; i2 < queue.length; i2++) { - const cb = queue[i2]; - if (cb && cb.pre) { - if (instance && cb.id !== instance.uid) { - continue; - } - if (false) { - continue; - } - queue.splice(i2, 1); - i2--; - cb(); - } - } -} -__name(flushPreFlushCbs, "flushPreFlushCbs"); -function flushPostFlushCbs(seen2) { - if (pendingPostFlushCbs.length) { - const deduped = [...new Set(pendingPostFlushCbs)].sort( - (a, b) => getId(a) - getId(b) - ); - pendingPostFlushCbs.length = 0; - if (activePostFlushCbs) { - activePostFlushCbs.push(...deduped); - return; - } - activePostFlushCbs = deduped; - if (false) { - seen2 = seen2 || /* @__PURE__ */ new Map(); - } - for (postFlushIndex = 0; postFlushIndex < activePostFlushCbs.length; postFlushIndex++) { - const cb = activePostFlushCbs[postFlushIndex]; - if (false) { - continue; - } - if (cb.active !== false) cb(); - } - activePostFlushCbs = null; - postFlushIndex = 0; - } -} -__name(flushPostFlushCbs, "flushPostFlushCbs"); -const getId = /* @__PURE__ */ __name((job) => job.id == null ? Infinity : job.id, "getId"); -const comparator = /* @__PURE__ */ __name((a, b) => { - const diff2 = getId(a) - getId(b); - if (diff2 === 0) { - if (a.pre && !b.pre) return -1; - if (b.pre && !a.pre) return 1; - } - return diff2; -}, "comparator"); -function flushJobs(seen2) { - isFlushPending = false; - isFlushing = true; - if (false) { - seen2 = seen2 || /* @__PURE__ */ new Map(); - } - queue.sort(comparator); - const check = false ? (job) => checkRecursiveUpdates(seen2, job) : NOOP; - try { - for (flushIndex = 0; flushIndex < queue.length; flushIndex++) { - const job = queue[flushIndex]; - if (job && job.active !== false) { - if (false) { - continue; - } - callWithErrorHandling(job, null, 14); - } - } - } finally { - flushIndex = 0; - queue.length = 0; - flushPostFlushCbs(seen2); - isFlushing = false; - currentFlushPromise = null; - if (queue.length || pendingPostFlushCbs.length) { - flushJobs(seen2); - } - } -} -__name(flushJobs, "flushJobs"); -function checkRecursiveUpdates(seen2, fn) { - if (!seen2.has(fn)) { - seen2.set(fn, 1); - } else { - const count = seen2.get(fn); - if (count > RECURSION_LIMIT) { - const instance = fn.ownerInstance; - const componentName = instance && getComponentName(instance.type); - handleError( - `Maximum recursive updates exceeded${componentName ? ` in component <${componentName}>` : ``}. This means you have a reactive effect that is mutating its own dependencies and thus recursively triggering itself. Possible sources include component template, render function, updated hook or watcher source function.`, - null, - 10 - ); - return true; - } else { - seen2.set(fn, count + 1); - } - } -} -__name(checkRecursiveUpdates, "checkRecursiveUpdates"); -let isHmrUpdating = false; -const hmrDirtyComponents = /* @__PURE__ */ new Set(); -if (false) { - getGlobalThis$1().__VUE_HMR_RUNTIME__ = { - createRecord: tryWrap(createRecord), - rerender: tryWrap(rerender), - reload: tryWrap(reload) - }; -} -const map$1 = /* @__PURE__ */ new Map(); -function registerHMR(instance) { - const id3 = instance.type.__hmrId; - let record = map$1.get(id3); - if (!record) { - createRecord(id3, instance.type); - record = map$1.get(id3); - } - record.instances.add(instance); -} -__name(registerHMR, "registerHMR"); -function unregisterHMR(instance) { - map$1.get(instance.type.__hmrId).instances.delete(instance); -} -__name(unregisterHMR, "unregisterHMR"); -function createRecord(id3, initialDef) { - if (map$1.has(id3)) { - return false; - } - map$1.set(id3, { - initialDef: normalizeClassComponent(initialDef), - instances: /* @__PURE__ */ new Set() - }); - return true; -} -__name(createRecord, "createRecord"); -function normalizeClassComponent(component) { - return isClassComponent(component) ? component.__vccOpts : component; -} -__name(normalizeClassComponent, "normalizeClassComponent"); -function rerender(id3, newRender) { - const record = map$1.get(id3); - if (!record) { - return; - } - record.initialDef.render = newRender; - [...record.instances].forEach((instance) => { - if (newRender) { - instance.render = newRender; - normalizeClassComponent(instance.type).render = newRender; - } - instance.renderCache = []; - isHmrUpdating = true; - instance.effect.dirty = true; - instance.update(); - isHmrUpdating = false; - }); -} -__name(rerender, "rerender"); -function reload(id3, newComp) { - const record = map$1.get(id3); - if (!record) return; - newComp = normalizeClassComponent(newComp); - updateComponentDef(record.initialDef, newComp); - const instances = [...record.instances]; - for (const instance of instances) { - const oldComp = normalizeClassComponent(instance.type); - if (!hmrDirtyComponents.has(oldComp)) { - if (oldComp !== record.initialDef) { - updateComponentDef(oldComp, newComp); - } - hmrDirtyComponents.add(oldComp); - } - instance.appContext.propsCache.delete(instance.type); - instance.appContext.emitsCache.delete(instance.type); - instance.appContext.optionsCache.delete(instance.type); - if (instance.ceReload) { - hmrDirtyComponents.add(oldComp); - instance.ceReload(newComp.styles); - hmrDirtyComponents.delete(oldComp); - } else if (instance.parent) { - instance.parent.effect.dirty = true; - queueJob(() => { - instance.parent.update(); - hmrDirtyComponents.delete(oldComp); - }); - } else if (instance.appContext.reload) { - instance.appContext.reload(); - } else if (typeof window !== "undefined") { - window.location.reload(); - } else { - console.warn( - "[HMR] Root or manually mounted instance modified. Full reload required." - ); - } - } - queuePostFlushCb(() => { - for (const instance of instances) { - hmrDirtyComponents.delete( - normalizeClassComponent(instance.type) - ); - } - }); -} -__name(reload, "reload"); -function updateComponentDef(oldComp, newComp) { - extend$1(oldComp, newComp); - for (const key in oldComp) { - if (key !== "__file" && !(key in newComp)) { - delete oldComp[key]; - } - } -} -__name(updateComponentDef, "updateComponentDef"); -function tryWrap(fn) { - return (id3, arg) => { - try { - return fn(id3, arg); - } catch (e2) { - console.error(e2); - console.warn( - `[HMR] Something went wrong during Vue component hot-reload. Full reload required.` - ); - } - }; -} -__name(tryWrap, "tryWrap"); -let devtools$1; -let buffer = []; -let devtoolsNotInstalled = false; -function emit$1(event, ...args) { - if (devtools$1) { - devtools$1.emit(event, ...args); - } else if (!devtoolsNotInstalled) { - buffer.push({ event, args }); - } -} -__name(emit$1, "emit$1"); -function setDevtoolsHook$1(hook, target) { - var _a2, _b; - devtools$1 = hook; - if (devtools$1) { - devtools$1.enabled = true; - buffer.forEach(({ event, args }) => devtools$1.emit(event, ...args)); - buffer = []; - } else if ( - // handle late devtools injection - only do this if we are in an actual - // browser environment to avoid the timer handle stalling test runner exit - // (#4815) - typeof window !== "undefined" && // some envs mock window but not fully - window.HTMLElement && // also exclude jsdom - // eslint-disable-next-line no-restricted-syntax - !((_b = (_a2 = window.navigator) == null ? void 0 : _a2.userAgent) == null ? void 0 : _b.includes("jsdom")) - ) { - const replay = target.__VUE_DEVTOOLS_HOOK_REPLAY__ = target.__VUE_DEVTOOLS_HOOK_REPLAY__ || []; - replay.push((newHook) => { - setDevtoolsHook$1(newHook, target); - }); - setTimeout(() => { - if (!devtools$1) { - target.__VUE_DEVTOOLS_HOOK_REPLAY__ = null; - devtoolsNotInstalled = true; - buffer = []; - } - }, 3e3); - } else { - devtoolsNotInstalled = true; - buffer = []; - } -} -__name(setDevtoolsHook$1, "setDevtoolsHook$1"); -function devtoolsInitApp(app2, version2) { - emit$1("app:init", app2, version2, { - Fragment: Fragment$1, - Text: Text$4, - Comment, - Static - }); -} -__name(devtoolsInitApp, "devtoolsInitApp"); -function devtoolsUnmountApp(app2) { - emit$1("app:unmount", app2); -} -__name(devtoolsUnmountApp, "devtoolsUnmountApp"); -const devtoolsComponentAdded = /* @__PURE__ */ createDevtoolsComponentHook( - "component:added" - /* COMPONENT_ADDED */ -); -const devtoolsComponentUpdated = /* @__PURE__ */ createDevtoolsComponentHook( - "component:updated" - /* COMPONENT_UPDATED */ -); -const _devtoolsComponentRemoved = /* @__PURE__ */ createDevtoolsComponentHook( - "component:removed" - /* COMPONENT_REMOVED */ -); -const devtoolsComponentRemoved = /* @__PURE__ */ __name((component) => { - if (devtools$1 && typeof devtools$1.cleanupBuffer === "function" && // remove the component if it wasn't buffered - !devtools$1.cleanupBuffer(component)) { - _devtoolsComponentRemoved(component); - } -}, "devtoolsComponentRemoved"); -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function createDevtoolsComponentHook(hook) { - return (component) => { - emit$1( - hook, - component.appContext.app, - component.uid, - component.parent ? component.parent.uid : void 0, - component - ); - }; -} -__name(createDevtoolsComponentHook, "createDevtoolsComponentHook"); -const devtoolsPerfStart = /* @__PURE__ */ createDevtoolsPerformanceHook( - "perf:start" - /* PERFORMANCE_START */ -); -const devtoolsPerfEnd = /* @__PURE__ */ createDevtoolsPerformanceHook( - "perf:end" - /* PERFORMANCE_END */ -); -function createDevtoolsPerformanceHook(hook) { - return (component, type, time) => { - emit$1(hook, component.appContext.app, component.uid, component, type, time); - }; -} -__name(createDevtoolsPerformanceHook, "createDevtoolsPerformanceHook"); -function devtoolsComponentEmit(component, event, params) { - emit$1( - "component:emit", - component.appContext.app, - component, - event, - params - ); -} -__name(devtoolsComponentEmit, "devtoolsComponentEmit"); -function emit(instance, event, ...rawArgs) { - if (instance.isUnmounted) return; - const props = instance.vnode.props || EMPTY_OBJ; - if (false) { - const { - emitsOptions, - propsOptions: [propsOptions] - } = instance; - if (emitsOptions) { - if (!(event in emitsOptions) && true) { - if (!propsOptions || !(toHandlerKey(event) in propsOptions)) { - warn$1$1( - `Component emitted event "${event}" but it is neither declared in the emits option nor as an "${toHandlerKey(event)}" prop.` - ); - } - } else { - const validator3 = emitsOptions[event]; - if (isFunction$4(validator3)) { - const isValid2 = validator3(...rawArgs); - if (!isValid2) { - warn$1$1( - `Invalid event arguments: event validation failed for event "${event}".` - ); - } - } - } - } - } - let args = rawArgs; - const isModelListener2 = event.startsWith("update:"); - const modelArg = isModelListener2 && event.slice(7); - if (modelArg && modelArg in props) { - const modifiersKey = `${modelArg === "modelValue" ? "model" : modelArg}Modifiers`; - const { number: number2, trim: trim2 } = props[modifiersKey] || EMPTY_OBJ; - if (trim2) { - args = rawArgs.map((a) => isString$7(a) ? a.trim() : a); - } - if (number2) { - args = rawArgs.map(looseToNumber); - } - } - if (false) { - devtoolsComponentEmit(instance, event, args); - } - if (false) { - const lowerCaseEvent = event.toLowerCase(); - if (lowerCaseEvent !== event && props[toHandlerKey(lowerCaseEvent)]) { - warn$1$1( - `Event "${lowerCaseEvent}" is emitted in component ${formatComponentName( - instance, - instance.type - )} but the handler is registered for "${event}". Note that HTML attributes are case-insensitive and you cannot use v-on to listen to camelCase events when using in-DOM templates. You should probably use "${hyphenate$1( - event - )}" instead of "${event}".` - ); - } - } - let handlerName; - let handler6 = props[handlerName = toHandlerKey(event)] || // also try camelCase event handler (#2249) - props[handlerName = toHandlerKey(camelize$1(event))]; - if (!handler6 && isModelListener2) { - handler6 = props[handlerName = toHandlerKey(hyphenate$1(event))]; - } - if (handler6) { - callWithAsyncErrorHandling( - handler6, - instance, - 6, - args - ); - } - const onceHandler = props[handlerName + `Once`]; - if (onceHandler) { - if (!instance.emitted) { - instance.emitted = {}; - } else if (instance.emitted[handlerName]) { - return; - } - instance.emitted[handlerName] = true; - callWithAsyncErrorHandling( - onceHandler, - instance, - 6, - args - ); - } -} -__name(emit, "emit"); -function normalizeEmitsOptions(comp, appContext, asMixin = false) { - const cache2 = appContext.emitsCache; - const cached = cache2.get(comp); - if (cached !== void 0) { - return cached; - } - const raw = comp.emits; - let normalized = {}; - let hasExtends = false; - if (!isFunction$4(comp)) { - const extendEmits = /* @__PURE__ */ __name((raw2) => { - const normalizedFromExtend = normalizeEmitsOptions(raw2, appContext, true); - if (normalizedFromExtend) { - hasExtends = true; - extend$1(normalized, normalizedFromExtend); - } - }, "extendEmits"); - if (!asMixin && appContext.mixins.length) { - appContext.mixins.forEach(extendEmits); - } - if (comp.extends) { - extendEmits(comp.extends); - } - if (comp.mixins) { - comp.mixins.forEach(extendEmits); - } - } - if (!raw && !hasExtends) { - if (isObject$6(comp)) { - cache2.set(comp, null); - } - return null; - } - if (isArray$4(raw)) { - raw.forEach((key) => normalized[key] = null); - } else { - extend$1(normalized, raw); - } - if (isObject$6(comp)) { - cache2.set(comp, normalized); - } - return normalized; -} -__name(normalizeEmitsOptions, "normalizeEmitsOptions"); -function isEmitListener(options4, key) { - if (!options4 || !isOn(key)) { - return false; - } - key = key.slice(2).replace(/Once$/, ""); - return hasOwn$3(options4, key[0].toLowerCase() + key.slice(1)) || hasOwn$3(options4, hyphenate$1(key)) || hasOwn$3(options4, key); -} -__name(isEmitListener, "isEmitListener"); -let currentRenderingInstance = null; -let currentScopeId = null; -function setCurrentRenderingInstance(instance) { - const prev2 = currentRenderingInstance; - currentRenderingInstance = instance; - currentScopeId = instance && instance.type.__scopeId || null; - return prev2; -} -__name(setCurrentRenderingInstance, "setCurrentRenderingInstance"); -function pushScopeId(id3) { - currentScopeId = id3; -} -__name(pushScopeId, "pushScopeId"); -function popScopeId() { - currentScopeId = null; -} -__name(popScopeId, "popScopeId"); -const withScopeId = /* @__PURE__ */ __name((_id2) => withCtx, "withScopeId"); -function withCtx(fn, ctx = currentRenderingInstance, isNonScopedSlot) { - if (!ctx) return fn; - if (fn._n) { - return fn; - } - const renderFnWithContext = /* @__PURE__ */ __name((...args) => { - if (renderFnWithContext._d) { - setBlockTracking(-1); - } - const prevInstance = setCurrentRenderingInstance(ctx); - let res; - try { - res = fn(...args); - } finally { - setCurrentRenderingInstance(prevInstance); - if (renderFnWithContext._d) { - setBlockTracking(1); - } - } - if (false) { - devtoolsComponentUpdated(ctx); - } - return res; - }, "renderFnWithContext"); - renderFnWithContext._n = true; - renderFnWithContext._c = true; - renderFnWithContext._d = true; - return renderFnWithContext; -} -__name(withCtx, "withCtx"); -let accessedAttrs = false; -function markAttrsAccessed() { - accessedAttrs = true; -} -__name(markAttrsAccessed, "markAttrsAccessed"); -function renderComponentRoot(instance) { - const { - type: Component, - vnode, - proxy, - withProxy, - propsOptions: [propsOptions], - slots, - attrs: attrs4, - emit: emit2, - render: render2, - renderCache, - props, - data: data24, - setupState, - ctx, - inheritAttrs - } = instance; - const prev2 = setCurrentRenderingInstance(instance); - let result; - let fallthroughAttrs; - if (false) { - accessedAttrs = false; - } - try { - if (vnode.shapeFlag & 4) { - const proxyToUse = withProxy || proxy; - const thisProxy = false ? new Proxy(proxyToUse, { - get(target, key, receiver) { - warn$1$1( - `Property '${String( - key - )}' was accessed via 'this'. Avoid using 'this' in templates.` - ); - return Reflect.get(target, key, receiver); - } - }) : proxyToUse; - result = normalizeVNode( - render2.call( - thisProxy, - proxyToUse, - renderCache, - false ? shallowReadonly(props) : props, - setupState, - data24, - ctx - ) - ); - fallthroughAttrs = attrs4; - } else { - const render22 = Component; - if (false) { - markAttrsAccessed(); - } - result = normalizeVNode( - render22.length > 1 ? render22( - false ? shallowReadonly(props) : props, - false ? { - get attrs() { - markAttrsAccessed(); - return shallowReadonly(attrs4); - }, - slots, - emit: emit2 - } : { attrs: attrs4, slots, emit: emit2 } - ) : render22( - false ? shallowReadonly(props) : props, - null - ) - ); - fallthroughAttrs = Component.props ? attrs4 : getFunctionalFallthrough(attrs4); - } - } catch (err) { - blockStack.length = 0; - handleError(err, instance, 1); - result = createVNode(Comment); - } - let root24 = result; - let setRoot = void 0; - if (false) { - [root24, setRoot] = getChildRoot(result); - } - if (fallthroughAttrs && inheritAttrs !== false) { - const keys2 = Object.keys(fallthroughAttrs); - const { shapeFlag } = root24; - if (keys2.length) { - if (shapeFlag & (1 | 6)) { - if (propsOptions && keys2.some(isModelListener)) { - fallthroughAttrs = filterModelListeners( - fallthroughAttrs, - propsOptions - ); - } - root24 = cloneVNode(root24, fallthroughAttrs, false, true); - } else if (false) { - const allAttrs = Object.keys(attrs4); - const eventAttrs = []; - const extraAttrs = []; - for (let i2 = 0, l = allAttrs.length; i2 < l; i2++) { - const key = allAttrs[i2]; - if (isOn(key)) { - if (!isModelListener(key)) { - eventAttrs.push(key[2].toLowerCase() + key.slice(3)); - } - } else { - extraAttrs.push(key); - } - } - if (extraAttrs.length) { - warn$1$1( - `Extraneous non-props attributes (${extraAttrs.join(", ")}) were passed to component but could not be automatically inherited because component renders fragment or text root nodes.` - ); - } - if (eventAttrs.length) { - warn$1$1( - `Extraneous non-emits event listeners (${eventAttrs.join(", ")}) were passed to component but could not be automatically inherited because component renders fragment or text root nodes. If the listener is intended to be a component custom event listener only, declare it using the "emits" option.` - ); - } - } - } - } - if (vnode.dirs) { - if (false) { - warn$1$1( - `Runtime directive used on component with non-element root node. The directives will not function as intended.` - ); - } - root24 = cloneVNode(root24, null, false, true); - root24.dirs = root24.dirs ? root24.dirs.concat(vnode.dirs) : vnode.dirs; - } - if (vnode.transition) { - if (false) { - warn$1$1( - `Component inside renders non-element root node that cannot be animated.` - ); - } - root24.transition = vnode.transition; - } - if (false) { - setRoot(root24); - } else { - result = root24; - } - setCurrentRenderingInstance(prev2); - return result; -} -__name(renderComponentRoot, "renderComponentRoot"); -const getChildRoot = /* @__PURE__ */ __name((vnode) => { - const rawChildren = vnode.children; - const dynamicChildren = vnode.dynamicChildren; - const childRoot = filterSingleRoot(rawChildren, false); - if (!childRoot) { - return [vnode, void 0]; - } else if (false) { - return getChildRoot(childRoot); - } - const index2 = rawChildren.indexOf(childRoot); - const dynamicIndex = dynamicChildren ? dynamicChildren.indexOf(childRoot) : -1; - const setRoot = /* @__PURE__ */ __name((updatedRoot) => { - rawChildren[index2] = updatedRoot; - if (dynamicChildren) { - if (dynamicIndex > -1) { - dynamicChildren[dynamicIndex] = updatedRoot; - } else if (updatedRoot.patchFlag > 0) { - vnode.dynamicChildren = [...dynamicChildren, updatedRoot]; - } - } - }, "setRoot"); - return [normalizeVNode(childRoot), setRoot]; -}, "getChildRoot"); -function filterSingleRoot(children, recurse = true) { - let singleRoot; - for (let i2 = 0; i2 < children.length; i2++) { - const child = children[i2]; - if (isVNode$1(child)) { - if (child.type !== Comment || child.children === "v-if") { - if (singleRoot) { - return; - } else { - singleRoot = child; - if (false) { - return filterSingleRoot(singleRoot.children); - } - } - } - } else { - return; - } - } - return singleRoot; -} -__name(filterSingleRoot, "filterSingleRoot"); -const getFunctionalFallthrough = /* @__PURE__ */ __name((attrs4) => { - let res; - for (const key in attrs4) { - if (key === "class" || key === "style" || isOn(key)) { - (res || (res = {}))[key] = attrs4[key]; - } - } - return res; -}, "getFunctionalFallthrough"); -const filterModelListeners = /* @__PURE__ */ __name((attrs4, props) => { - const res = {}; - for (const key in attrs4) { - if (!isModelListener(key) || !(key.slice(9) in props)) { - res[key] = attrs4[key]; - } - } - return res; -}, "filterModelListeners"); -const isElementRoot = /* @__PURE__ */ __name((vnode) => { - return vnode.shapeFlag & (6 | 1) || vnode.type === Comment; -}, "isElementRoot"); -function shouldUpdateComponent(prevVNode, nextVNode, optimized) { - const { props: prevProps, children: prevChildren, component } = prevVNode; - const { props: nextProps, children: nextChildren, patchFlag } = nextVNode; - const emits = component.emitsOptions; - if (false) { - return true; - } - if (nextVNode.dirs || nextVNode.transition) { - return true; - } - if (optimized && patchFlag >= 0) { - if (patchFlag & 1024) { - return true; - } - if (patchFlag & 16) { - if (!prevProps) { - return !!nextProps; - } - return hasPropsChanged(prevProps, nextProps, emits); - } else if (patchFlag & 8) { - const dynamicProps = nextVNode.dynamicProps; - for (let i2 = 0; i2 < dynamicProps.length; i2++) { - const key = dynamicProps[i2]; - if (nextProps[key] !== prevProps[key] && !isEmitListener(emits, key)) { - return true; - } - } - } - } else { - if (prevChildren || nextChildren) { - if (!nextChildren || !nextChildren.$stable) { - return true; - } - } - if (prevProps === nextProps) { - return false; - } - if (!prevProps) { - return !!nextProps; - } - if (!nextProps) { - return true; - } - return hasPropsChanged(prevProps, nextProps, emits); - } - return false; -} -__name(shouldUpdateComponent, "shouldUpdateComponent"); -function hasPropsChanged(prevProps, nextProps, emitsOptions) { - const nextKeys = Object.keys(nextProps); - if (nextKeys.length !== Object.keys(prevProps).length) { - return true; - } - for (let i2 = 0; i2 < nextKeys.length; i2++) { - const key = nextKeys[i2]; - if (nextProps[key] !== prevProps[key] && !isEmitListener(emitsOptions, key)) { - return true; - } - } - return false; -} -__name(hasPropsChanged, "hasPropsChanged"); -function updateHOCHostEl({ vnode, parent }, el) { - while (parent) { - const root24 = parent.subTree; - if (root24.suspense && root24.suspense.activeBranch === vnode) { - root24.el = vnode.el; - } - if (root24 === vnode) { - (vnode = parent.vnode).el = el; - parent = parent.parent; - } else { - break; - } - } -} -__name(updateHOCHostEl, "updateHOCHostEl"); -const COMPONENTS = "components"; -const DIRECTIVES = "directives"; -function resolveComponent(name2, maybeSelfReference) { - return resolveAsset(COMPONENTS, name2, true, maybeSelfReference) || name2; -} -__name(resolveComponent, "resolveComponent"); -const NULL_DYNAMIC_COMPONENT = Symbol.for("v-ndc"); -function resolveDynamicComponent(component) { - if (isString$7(component)) { - return resolveAsset(COMPONENTS, component, false) || component; - } else { - return component || NULL_DYNAMIC_COMPONENT; - } -} -__name(resolveDynamicComponent, "resolveDynamicComponent"); -function resolveDirective(name2) { - return resolveAsset(DIRECTIVES, name2); -} -__name(resolveDirective, "resolveDirective"); -function resolveAsset(type, name2, warnMissing = true, maybeSelfReference = false) { - const instance = currentRenderingInstance || currentInstance; - if (instance) { - const Component = instance.type; - if (type === COMPONENTS) { - const selfName = getComponentName( - Component, - false - ); - if (selfName && (selfName === name2 || selfName === camelize$1(name2) || selfName === capitalize$1(camelize$1(name2)))) { - return Component; - } - } - const res = ( - // local registration - // check instance[type] first which is resolved for options API - resolve(instance[type] || Component[type], name2) || // global registration - resolve(instance.appContext[type], name2) - ); - if (!res && maybeSelfReference) { - return Component; - } - if (false) { - const extra = type === COMPONENTS ? ` -If this is a native custom element, make sure to exclude it from component resolution via compilerOptions.isCustomElement.` : ``; - warn$1$1(`Failed to resolve ${type.slice(0, -1)}: ${name2}${extra}`); - } - return res; - } else if (false) { - warn$1$1( - `resolve${capitalize$1(type.slice(0, -1))} can only be used in render() or setup().` - ); - } -} -__name(resolveAsset, "resolveAsset"); -function resolve(registry, name2) { - return registry && (registry[name2] || registry[camelize$1(name2)] || registry[capitalize$1(camelize$1(name2))]); -} -__name(resolve, "resolve"); -const isSuspense = /* @__PURE__ */ __name((type) => type.__isSuspense, "isSuspense"); -let suspenseId = 0; -const SuspenseImpl = { - name: "Suspense", - // In order to make Suspense tree-shakable, we need to avoid importing it - // directly in the renderer. The renderer checks for the __isSuspense flag - // on a vnode's type and calls the `process` method, passing in renderer - // internals. - __isSuspense: true, - process(n1, n2, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals) { - if (n1 == null) { - mountSuspense( - n2, - container, - anchor, - parentComponent, - parentSuspense, - namespace, - slotScopeIds, - optimized, - rendererInternals - ); - } else { - if (parentSuspense && parentSuspense.deps > 0 && !n1.suspense.isInFallback) { - n2.suspense = n1.suspense; - n2.suspense.vnode = n2; - n2.el = n1.el; - return; - } - patchSuspense( - n1, - n2, - container, - anchor, - parentComponent, - namespace, - slotScopeIds, - optimized, - rendererInternals - ); - } - }, - hydrate: hydrateSuspense, - normalize: normalizeSuspenseChildren -}; -const Suspense = SuspenseImpl; -function triggerEvent(vnode, name2) { - const eventListener = vnode.props && vnode.props[name2]; - if (isFunction$4(eventListener)) { - eventListener(); - } -} -__name(triggerEvent, "triggerEvent"); -function mountSuspense(vnode, container, anchor, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals) { - const { - p: patch2, - o: { createElement: createElement2 } - } = rendererInternals; - const hiddenContainer = createElement2("div"); - const suspense = vnode.suspense = createSuspenseBoundary( - vnode, - parentSuspense, - parentComponent, - container, - hiddenContainer, - anchor, - namespace, - slotScopeIds, - optimized, - rendererInternals - ); - patch2( - null, - suspense.pendingBranch = vnode.ssContent, - hiddenContainer, - null, - parentComponent, - suspense, - namespace, - slotScopeIds - ); - if (suspense.deps > 0) { - triggerEvent(vnode, "onPending"); - triggerEvent(vnode, "onFallback"); - patch2( - null, - vnode.ssFallback, - container, - anchor, - parentComponent, - null, - // fallback tree will not have suspense context - namespace, - slotScopeIds - ); - setActiveBranch(suspense, vnode.ssFallback); - } else { - suspense.resolve(false, true); - } -} -__name(mountSuspense, "mountSuspense"); -function patchSuspense(n1, n2, container, anchor, parentComponent, namespace, slotScopeIds, optimized, { p: patch2, um: unmount, o: { createElement: createElement2 } }) { - const suspense = n2.suspense = n1.suspense; - suspense.vnode = n2; - n2.el = n1.el; - const newBranch = n2.ssContent; - const newFallback = n2.ssFallback; - const { activeBranch, pendingBranch, isInFallback, isHydrating } = suspense; - if (pendingBranch) { - suspense.pendingBranch = newBranch; - if (isSameVNodeType(newBranch, pendingBranch)) { - patch2( - pendingBranch, - newBranch, - suspense.hiddenContainer, - null, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - if (suspense.deps <= 0) { - suspense.resolve(); - } else if (isInFallback) { - if (!isHydrating) { - patch2( - activeBranch, - newFallback, - container, - anchor, - parentComponent, - null, - // fallback tree will not have suspense context - namespace, - slotScopeIds, - optimized - ); - setActiveBranch(suspense, newFallback); - } - } - } else { - suspense.pendingId = suspenseId++; - if (isHydrating) { - suspense.isHydrating = false; - suspense.activeBranch = pendingBranch; - } else { - unmount(pendingBranch, parentComponent, suspense); - } - suspense.deps = 0; - suspense.effects.length = 0; - suspense.hiddenContainer = createElement2("div"); - if (isInFallback) { - patch2( - null, - newBranch, - suspense.hiddenContainer, - null, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - if (suspense.deps <= 0) { - suspense.resolve(); - } else { - patch2( - activeBranch, - newFallback, - container, - anchor, - parentComponent, - null, - // fallback tree will not have suspense context - namespace, - slotScopeIds, - optimized - ); - setActiveBranch(suspense, newFallback); - } - } else if (activeBranch && isSameVNodeType(newBranch, activeBranch)) { - patch2( - activeBranch, - newBranch, - container, - anchor, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - suspense.resolve(true); - } else { - patch2( - null, - newBranch, - suspense.hiddenContainer, - null, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - if (suspense.deps <= 0) { - suspense.resolve(); - } - } - } - } else { - if (activeBranch && isSameVNodeType(newBranch, activeBranch)) { - patch2( - activeBranch, - newBranch, - container, - anchor, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - setActiveBranch(suspense, newBranch); - } else { - triggerEvent(n2, "onPending"); - suspense.pendingBranch = newBranch; - if (newBranch.shapeFlag & 512) { - suspense.pendingId = newBranch.component.suspenseId; - } else { - suspense.pendingId = suspenseId++; - } - patch2( - null, - newBranch, - suspense.hiddenContainer, - null, - parentComponent, - suspense, - namespace, - slotScopeIds, - optimized - ); - if (suspense.deps <= 0) { - suspense.resolve(); - } else { - const { timeout, pendingId } = suspense; - if (timeout > 0) { - setTimeout(() => { - if (suspense.pendingId === pendingId) { - suspense.fallback(newFallback); - } - }, timeout); - } else if (timeout === 0) { - suspense.fallback(newFallback); - } - } - } - } -} -__name(patchSuspense, "patchSuspense"); -let hasWarned$1 = false; -function createSuspenseBoundary(vnode, parentSuspense, parentComponent, container, hiddenContainer, anchor, namespace, slotScopeIds, optimized, rendererInternals, isHydrating = false) { - if (false) { - hasWarned$1 = true; - console[console.info ? "info" : "log"]( - ` is an experimental feature and its API will likely change.` - ); - } - const { - p: patch2, - m: move, - um: unmount, - n: next2, - o: { parentNode: parentNode2, remove: remove22 } - } = rendererInternals; - let parentSuspenseId; - const isSuspensible = isVNodeSuspensible(vnode); - if (isSuspensible) { - if (parentSuspense && parentSuspense.pendingBranch) { - parentSuspenseId = parentSuspense.pendingId; - parentSuspense.deps++; - } - } - const timeout = vnode.props ? toNumber(vnode.props.timeout) : void 0; - if (false) { - assertNumber(timeout, `Suspense timeout`); - } - const initialAnchor = anchor; - const suspense = { - vnode, - parent: parentSuspense, - parentComponent, - namespace, - container, - hiddenContainer, - deps: 0, - pendingId: suspenseId++, - timeout: typeof timeout === "number" ? timeout : -1, - activeBranch: null, - pendingBranch: null, - isInFallback: !isHydrating, - isHydrating, - isUnmounted: false, - effects: [], - resolve(resume = false, sync = false) { - if (false) { - if (!resume && !suspense.pendingBranch) { - throw new Error( - `suspense.resolve() is called without a pending branch.` - ); - } - if (suspense.isUnmounted) { - throw new Error( - `suspense.resolve() is called on an already unmounted suspense boundary.` - ); - } - } - const { - vnode: vnode2, - activeBranch, - pendingBranch, - pendingId, - effects, - parentComponent: parentComponent2, - container: container2 - } = suspense; - let delayEnter = false; - if (suspense.isHydrating) { - suspense.isHydrating = false; - } else if (!resume) { - delayEnter = activeBranch && pendingBranch.transition && pendingBranch.transition.mode === "out-in"; - if (delayEnter) { - activeBranch.transition.afterLeave = () => { - if (pendingId === suspense.pendingId) { - move( - pendingBranch, - container2, - anchor === initialAnchor ? next2(activeBranch) : anchor, - 0 - ); - queuePostFlushCb(effects); - } - }; - } - if (activeBranch) { - if (parentNode2(activeBranch.el) !== suspense.hiddenContainer) { - anchor = next2(activeBranch); - } - unmount(activeBranch, parentComponent2, suspense, true); - } - if (!delayEnter) { - move(pendingBranch, container2, anchor, 0); - } - } - setActiveBranch(suspense, pendingBranch); - suspense.pendingBranch = null; - suspense.isInFallback = false; - let parent = suspense.parent; - let hasUnresolvedAncestor = false; - while (parent) { - if (parent.pendingBranch) { - parent.effects.push(...effects); - hasUnresolvedAncestor = true; - break; - } - parent = parent.parent; - } - if (!hasUnresolvedAncestor && !delayEnter) { - queuePostFlushCb(effects); - } - suspense.effects = []; - if (isSuspensible) { - if (parentSuspense && parentSuspense.pendingBranch && parentSuspenseId === parentSuspense.pendingId) { - parentSuspense.deps--; - if (parentSuspense.deps === 0 && !sync) { - parentSuspense.resolve(); - } - } - } - triggerEvent(vnode2, "onResolve"); - }, - fallback(fallbackVNode) { - if (!suspense.pendingBranch) { - return; - } - const { vnode: vnode2, activeBranch, parentComponent: parentComponent2, container: container2, namespace: namespace2 } = suspense; - triggerEvent(vnode2, "onFallback"); - const anchor2 = next2(activeBranch); - const mountFallback = /* @__PURE__ */ __name(() => { - if (!suspense.isInFallback) { - return; - } - patch2( - null, - fallbackVNode, - container2, - anchor2, - parentComponent2, - null, - // fallback tree will not have suspense context - namespace2, - slotScopeIds, - optimized - ); - setActiveBranch(suspense, fallbackVNode); - }, "mountFallback"); - const delayEnter = fallbackVNode.transition && fallbackVNode.transition.mode === "out-in"; - if (delayEnter) { - activeBranch.transition.afterLeave = mountFallback; - } - suspense.isInFallback = true; - unmount( - activeBranch, - parentComponent2, - null, - // no suspense so unmount hooks fire now - true - // shouldRemove - ); - if (!delayEnter) { - mountFallback(); - } - }, - move(container2, anchor2, type) { - suspense.activeBranch && move(suspense.activeBranch, container2, anchor2, type); - suspense.container = container2; - }, - next() { - return suspense.activeBranch && next2(suspense.activeBranch); - }, - registerDep(instance, setupRenderEffect, optimized2) { - const isInPendingSuspense = !!suspense.pendingBranch; - if (isInPendingSuspense) { - suspense.deps++; - } - const hydratedEl = instance.vnode.el; - instance.asyncDep.catch((err) => { - handleError(err, instance, 0); - }).then((asyncSetupResult) => { - if (instance.isUnmounted || suspense.isUnmounted || suspense.pendingId !== instance.suspenseId) { - return; - } - instance.asyncResolved = true; - const { vnode: vnode2 } = instance; - if (false) { - pushWarningContext(vnode2); - } - handleSetupResult(instance, asyncSetupResult, false); - if (hydratedEl) { - vnode2.el = hydratedEl; - } - const placeholder = !hydratedEl && instance.subTree.el; - setupRenderEffect( - instance, - vnode2, - // component may have been moved before resolve. - // if this is not a hydration, instance.subTree will be the comment - // placeholder. - parentNode2(hydratedEl || instance.subTree.el), - // anchor will not be used if this is hydration, so only need to - // consider the comment placeholder case. - hydratedEl ? null : next2(instance.subTree), - suspense, - namespace, - optimized2 - ); - if (placeholder) { - remove22(placeholder); - } - updateHOCHostEl(instance, vnode2.el); - if (false) { - popWarningContext(); - } - if (isInPendingSuspense && --suspense.deps === 0) { - suspense.resolve(); - } - }); - }, - unmount(parentSuspense2, doRemove) { - suspense.isUnmounted = true; - if (suspense.activeBranch) { - unmount( - suspense.activeBranch, - parentComponent, - parentSuspense2, - doRemove - ); - } - if (suspense.pendingBranch) { - unmount( - suspense.pendingBranch, - parentComponent, - parentSuspense2, - doRemove - ); - } - } - }; - return suspense; -} -__name(createSuspenseBoundary, "createSuspenseBoundary"); -function hydrateSuspense(node3, vnode, parentComponent, parentSuspense, namespace, slotScopeIds, optimized, rendererInternals, hydrateNode) { - const suspense = vnode.suspense = createSuspenseBoundary( - vnode, - parentSuspense, - parentComponent, - node3.parentNode, - // eslint-disable-next-line no-restricted-globals - document.createElement("div"), - null, - namespace, - slotScopeIds, - optimized, - rendererInternals, - true - ); - const result = hydrateNode( - node3, - suspense.pendingBranch = vnode.ssContent, - parentComponent, - suspense, - slotScopeIds, - optimized - ); - if (suspense.deps === 0) { - suspense.resolve(false, true); - } - return result; -} -__name(hydrateSuspense, "hydrateSuspense"); -function normalizeSuspenseChildren(vnode) { - const { shapeFlag, children } = vnode; - const isSlotChildren = shapeFlag & 32; - vnode.ssContent = normalizeSuspenseSlot( - isSlotChildren ? children.default : children - ); - vnode.ssFallback = isSlotChildren ? normalizeSuspenseSlot(children.fallback) : createVNode(Comment); -} -__name(normalizeSuspenseChildren, "normalizeSuspenseChildren"); -function normalizeSuspenseSlot(s) { - let block3; - if (isFunction$4(s)) { - const trackBlock = isBlockTreeEnabled && s._c; - if (trackBlock) { - s._d = false; - openBlock(); - } - s = s(); - if (trackBlock) { - s._d = true; - block3 = currentBlock; - closeBlock(); - } - } - if (isArray$4(s)) { - const singleChild = filterSingleRoot(s); - if (false) { - warn$1$1(` slots expect a single root node.`); - } - s = singleChild; - } - s = normalizeVNode(s); - if (block3 && !s.dynamicChildren) { - s.dynamicChildren = block3.filter((c) => c !== s); - } - return s; -} -__name(normalizeSuspenseSlot, "normalizeSuspenseSlot"); -function queueEffectWithSuspense(fn, suspense) { - if (suspense && suspense.pendingBranch) { - if (isArray$4(fn)) { - suspense.effects.push(...fn); - } else { - suspense.effects.push(fn); - } - } else { - queuePostFlushCb(fn); - } -} -__name(queueEffectWithSuspense, "queueEffectWithSuspense"); -function setActiveBranch(suspense, branch) { - suspense.activeBranch = branch; - const { vnode, parentComponent } = suspense; - let el = branch.el; - while (!el && branch.component) { - branch = branch.component.subTree; - el = branch.el; - } - vnode.el = el; - if (parentComponent && parentComponent.subTree === vnode) { - parentComponent.vnode.el = el; - updateHOCHostEl(parentComponent, el); - } -} -__name(setActiveBranch, "setActiveBranch"); -function isVNodeSuspensible(vnode) { - const suspensible = vnode.props && vnode.props.suspensible; - return suspensible != null && suspensible !== false; -} -__name(isVNodeSuspensible, "isVNodeSuspensible"); -function injectHook(type, hook, target = currentInstance, prepend2 = false) { - if (target) { - const hooks = target[type] || (target[type] = []); - const wrappedHook = hook.__weh || (hook.__weh = (...args) => { - pauseTracking(); - const reset2 = setCurrentInstance(target); - const res = callWithAsyncErrorHandling(hook, target, type, args); - reset2(); - resetTracking(); - return res; - }); - if (prepend2) { - hooks.unshift(wrappedHook); - } else { - hooks.push(wrappedHook); - } - return wrappedHook; - } else if (false) { - const apiName = toHandlerKey(ErrorTypeStrings$1[type].replace(/ hook$/, "")); - warn$1$1( - `${apiName} is called when there is no active component instance to be associated with. Lifecycle injection APIs can only be used during execution of setup(). If you are using async setup(), make sure to register lifecycle hooks before the first await statement.` - ); - } -} -__name(injectHook, "injectHook"); -const createHook = /* @__PURE__ */ __name((lifecycle2) => (hook, target = currentInstance) => { - if (!isInSSRComponentSetup || lifecycle2 === "sp") { - injectHook(lifecycle2, (...args) => hook(...args), target); - } -}, "createHook"); -const onBeforeMount = createHook("bm"); -const onMounted = createHook("m"); -const onBeforeUpdate = createHook("bu"); -const onUpdated = createHook("u"); -const onBeforeUnmount = createHook("bum"); -const onUnmounted = createHook("um"); -const onServerPrefetch = createHook("sp"); -const onRenderTriggered = createHook( - "rtg" -); -const onRenderTracked = createHook( - "rtc" -); -function onErrorCaptured(hook, target = currentInstance) { - injectHook("ec", hook, target); -} -__name(onErrorCaptured, "onErrorCaptured"); -function validateDirectiveName(name2) { - if (isBuiltInDirective(name2)) { - warn$1$1("Do not use built-in directive ids as custom directive id: " + name2); - } -} -__name(validateDirectiveName, "validateDirectiveName"); -function withDirectives(vnode, directives) { - if (currentRenderingInstance === null) { - return vnode; - } - const instance = getComponentPublicInstance(currentRenderingInstance); - const bindings = vnode.dirs || (vnode.dirs = []); - for (let i2 = 0; i2 < directives.length; i2++) { - let [dir, value4, arg, modifiers2 = EMPTY_OBJ] = directives[i2]; - if (dir) { - if (isFunction$4(dir)) { - dir = { - mounted: dir, - updated: dir - }; - } - if (dir.deep) { - traverse(value4); - } - bindings.push({ - dir, - instance, - value: value4, - oldValue: void 0, - arg, - modifiers: modifiers2 - }); - } - } - return vnode; -} -__name(withDirectives, "withDirectives"); -function invokeDirectiveHook(vnode, prevVNode, instance, name2) { - const bindings = vnode.dirs; - const oldBindings = prevVNode && prevVNode.dirs; - for (let i2 = 0; i2 < bindings.length; i2++) { - const binding = bindings[i2]; - if (oldBindings) { - binding.oldValue = oldBindings[i2].value; - } - let hook = binding.dir[name2]; - if (hook) { - pauseTracking(); - callWithAsyncErrorHandling(hook, instance, 8, [ - vnode.el, - binding, - vnode, - prevVNode - ]); - resetTracking(); - } - } -} -__name(invokeDirectiveHook, "invokeDirectiveHook"); -function renderList(source, renderItem, cache2, index2) { - let ret; - const cached = cache2 && cache2[index2]; - if (isArray$4(source) || isString$7(source)) { - ret = new Array(source.length); - for (let i2 = 0, l = source.length; i2 < l; i2++) { - ret[i2] = renderItem(source[i2], i2, void 0, cached && cached[i2]); - } - } else if (typeof source === "number") { - if (false) { - warn$1$1(`The v-for range expect an integer value but got ${source}.`); - } - ret = new Array(source); - for (let i2 = 0; i2 < source; i2++) { - ret[i2] = renderItem(i2 + 1, i2, void 0, cached && cached[i2]); - } - } else if (isObject$6(source)) { - if (source[Symbol.iterator]) { - ret = Array.from( - source, - (item3, i2) => renderItem(item3, i2, void 0, cached && cached[i2]) - ); - } else { - const keys2 = Object.keys(source); - ret = new Array(keys2.length); - for (let i2 = 0, l = keys2.length; i2 < l; i2++) { - const key = keys2[i2]; - ret[i2] = renderItem(source[key], key, i2, cached && cached[i2]); - } - } - } else { - ret = []; - } - if (cache2) { - cache2[index2] = ret; - } - return ret; -} -__name(renderList, "renderList"); -function createSlots(slots, dynamicSlots) { - for (let i2 = 0; i2 < dynamicSlots.length; i2++) { - const slot = dynamicSlots[i2]; - if (isArray$4(slot)) { - for (let j = 0; j < slot.length; j++) { - slots[slot[j].name] = slot[j].fn; - } - } else if (slot) { - slots[slot.name] = slot.key ? (...args) => { - const res = slot.fn(...args); - if (res) res.key = slot.key; - return res; - } : slot.fn; - } - } - return slots; -} -__name(createSlots, "createSlots"); -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function defineComponent(options4, extraOptions) { - return isFunction$4(options4) ? ( - // #8326: extend call and options.name access are considered side-effects - // by Rollup, so we have to wrap it in a pure-annotated IIFE. - /* @__PURE__ */ (() => extend$1({ name: options4.name }, extraOptions, { setup: options4 }))() - ) : options4; -} -__name(defineComponent, "defineComponent"); -const isAsyncWrapper = /* @__PURE__ */ __name((i2) => !!i2.type.__asyncLoader, "isAsyncWrapper"); -/*! #__NO_SIDE_EFFECTS__ */ -// @__NO_SIDE_EFFECTS__ -function defineAsyncComponent(source) { - if (isFunction$4(source)) { - source = { loader: source }; - } - const { - loader, - loadingComponent, - errorComponent, - delay = 200, - timeout, - // undefined = never times out - suspensible = true, - onError: userOnError - } = source; - let pendingRequest = null; - let resolvedComp; - let retries = 0; - const retry = /* @__PURE__ */ __name(() => { - retries++; - pendingRequest = null; - return load2(); - }, "retry"); - const load2 = /* @__PURE__ */ __name(() => { - let thisRequest; - return pendingRequest || (thisRequest = pendingRequest = loader().catch((err) => { - err = err instanceof Error ? err : new Error(String(err)); - if (userOnError) { - return new Promise((resolve2, reject3) => { - const userRetry = /* @__PURE__ */ __name(() => resolve2(retry()), "userRetry"); - const userFail = /* @__PURE__ */ __name(() => reject3(err), "userFail"); - userOnError(err, userRetry, userFail, retries + 1); - }); - } else { - throw err; - } - }).then((comp) => { - if (thisRequest !== pendingRequest && pendingRequest) { - return pendingRequest; - } - if (false) { - warn$1$1( - `Async component loader resolved to undefined. If you are using retry(), make sure to return its return value.` - ); - } - if (comp && (comp.__esModule || comp[Symbol.toStringTag] === "Module")) { - comp = comp.default; - } - if (false) { - throw new Error(`Invalid async component load result: ${comp}`); - } - resolvedComp = comp; - return comp; - })); - }, "load"); - return /* @__PURE__ */ defineComponent({ - name: "AsyncComponentWrapper", - __asyncLoader: load2, - get __asyncResolved() { - return resolvedComp; - }, - setup() { - const instance = currentInstance; - if (resolvedComp) { - return () => createInnerComp(resolvedComp, instance); - } - const onError = /* @__PURE__ */ __name((err) => { - pendingRequest = null; - handleError( - err, - instance, - 13, - !errorComponent - ); - }, "onError"); - if (suspensible && instance.suspense || isInSSRComponentSetup) { - return load2().then((comp) => { - return () => createInnerComp(comp, instance); - }).catch((err) => { - onError(err); - return () => errorComponent ? createVNode(errorComponent, { - error: err - }) : null; - }); - } - const loaded = ref(false); - const error2 = ref(); - const delayed = ref(!!delay); - if (delay) { - setTimeout(() => { - delayed.value = false; - }, delay); - } - if (timeout != null) { - setTimeout(() => { - if (!loaded.value && !error2.value) { - const err = new Error( - `Async component timed out after ${timeout}ms.` - ); - onError(err); - error2.value = err; - } - }, timeout); - } - load2().then(() => { - loaded.value = true; - if (instance.parent && isKeepAlive(instance.parent.vnode)) { - instance.parent.effect.dirty = true; - queueJob(instance.parent.update); - } - }).catch((err) => { - onError(err); - error2.value = err; - }); - return () => { - if (loaded.value && resolvedComp) { - return createInnerComp(resolvedComp, instance); - } else if (error2.value && errorComponent) { - return createVNode(errorComponent, { - error: error2.value - }); - } else if (loadingComponent && !delayed.value) { - return createVNode(loadingComponent); - } - }; - } - }); -} -__name(defineAsyncComponent, "defineAsyncComponent"); -function createInnerComp(comp, parent) { - const { ref: ref22, props, children, ce } = parent.vnode; - const vnode = createVNode(comp, props, children); - vnode.ref = ref22; - vnode.ce = ce; - delete parent.vnode.ce; - return vnode; -} -__name(createInnerComp, "createInnerComp"); -function renderSlot(slots, name2, props = {}, fallback, noSlotted) { - if (currentRenderingInstance.isCE || currentRenderingInstance.parent && isAsyncWrapper(currentRenderingInstance.parent) && currentRenderingInstance.parent.isCE) { - if (name2 !== "default") props.name = name2; - return createVNode("slot", props, fallback && fallback()); - } - let slot = slots[name2]; - if (false) { - warn$1$1( - `SSR-optimized slot function detected in a non-SSR-optimized render function. You need to mark this component with $dynamic-slots in the parent template.` - ); - slot = /* @__PURE__ */ __name(() => [], "slot"); - } - if (slot && slot._c) { - slot._d = false; - } - openBlock(); - const validSlotContent = slot && ensureValidVNode(slot(props)); - const rendered = createBlock( - Fragment$1, - { - key: props.key || // slot content array of a dynamic conditional slot may have a branch - // key attached in the `createSlots` helper, respect that - validSlotContent && validSlotContent.key || `_${name2}` - }, - validSlotContent || (fallback ? fallback() : []), - validSlotContent && slots._ === 1 ? 64 : -2 - ); - if (!noSlotted && rendered.scopeId) { - rendered.slotScopeIds = [rendered.scopeId + "-s"]; - } - if (slot && slot._c) { - slot._d = true; - } - return rendered; -} -__name(renderSlot, "renderSlot"); -function ensureValidVNode(vnodes) { - return vnodes.some((child) => { - if (!isVNode$1(child)) return true; - if (child.type === Comment) return false; - if (child.type === Fragment$1 && !ensureValidVNode(child.children)) - return false; - return true; - }) ? vnodes : null; -} -__name(ensureValidVNode, "ensureValidVNode"); -function toHandlers(obj, preserveCaseIfNecessary) { - const ret = {}; - if (false) { - warn$1$1(`v-on with no argument expects an object value.`); - return ret; - } - for (const key in obj) { - ret[preserveCaseIfNecessary && /[A-Z]/.test(key) ? `on:${key}` : toHandlerKey(key)] = obj[key]; - } - return ret; -} -__name(toHandlers, "toHandlers"); -const getPublicInstance = /* @__PURE__ */ __name((i2) => { - if (!i2) return null; - if (isStatefulComponent(i2)) return getComponentPublicInstance(i2); - return getPublicInstance(i2.parent); -}, "getPublicInstance"); -const publicPropertiesMap = ( - // Move PURE marker to new line to workaround compiler discarding it - // due to type annotation - /* @__PURE__ */ extend$1(/* @__PURE__ */ Object.create(null), { - $: /* @__PURE__ */ __name((i2) => i2, "$"), - $el: /* @__PURE__ */ __name((i2) => i2.vnode.el, "$el"), - $data: /* @__PURE__ */ __name((i2) => i2.data, "$data"), - $props: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.props) : i2.props, "$props"), - $attrs: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.attrs) : i2.attrs, "$attrs"), - $slots: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.slots) : i2.slots, "$slots"), - $refs: /* @__PURE__ */ __name((i2) => false ? shallowReadonly(i2.refs) : i2.refs, "$refs"), - $parent: /* @__PURE__ */ __name((i2) => getPublicInstance(i2.parent), "$parent"), - $root: /* @__PURE__ */ __name((i2) => getPublicInstance(i2.root), "$root"), - $emit: /* @__PURE__ */ __name((i2) => i2.emit, "$emit"), - $options: /* @__PURE__ */ __name((i2) => true ? resolveMergedOptions(i2) : i2.type, "$options"), - $forceUpdate: /* @__PURE__ */ __name((i2) => i2.f || (i2.f = () => { - i2.effect.dirty = true; - queueJob(i2.update); - }), "$forceUpdate"), - $nextTick: /* @__PURE__ */ __name((i2) => i2.n || (i2.n = nextTick.bind(i2.proxy)), "$nextTick"), - $watch: /* @__PURE__ */ __name((i2) => true ? instanceWatch.bind(i2) : NOOP, "$watch") - }) -); -const isReservedPrefix = /* @__PURE__ */ __name((key) => key === "_" || key === "$", "isReservedPrefix"); -const hasSetupBinding = /* @__PURE__ */ __name((state, key) => state !== EMPTY_OBJ && !state.__isScriptSetup && hasOwn$3(state, key), "hasSetupBinding"); -const PublicInstanceProxyHandlers = { - get({ _: instance }, key) { - if (key === "__v_skip") { - return true; - } - const { ctx, setupState, data: data24, props, accessCache, type, appContext } = instance; - if (false) { - return true; - } - let normalizedProps; - if (key[0] !== "$") { - const n = accessCache[key]; - if (n !== void 0) { - switch (n) { - case 1: - return setupState[key]; - case 2: - return data24[key]; - case 4: - return ctx[key]; - case 3: - return props[key]; - } - } else if (hasSetupBinding(setupState, key)) { - accessCache[key] = 1; - return setupState[key]; - } else if (data24 !== EMPTY_OBJ && hasOwn$3(data24, key)) { - accessCache[key] = 2; - return data24[key]; - } else if ( - // only cache other properties when instance has declared (thus stable) - // props - (normalizedProps = instance.propsOptions[0]) && hasOwn$3(normalizedProps, key) - ) { - accessCache[key] = 3; - return props[key]; - } else if (ctx !== EMPTY_OBJ && hasOwn$3(ctx, key)) { - accessCache[key] = 4; - return ctx[key]; - } else if (shouldCacheAccess) { - accessCache[key] = 0; - } - } - const publicGetter = publicPropertiesMap[key]; - let cssModule, globalProperties; - if (publicGetter) { - if (key === "$attrs") { - track(instance.attrs, "get", ""); - } else if (false) { - track(instance, "get", key); - } - return publicGetter(instance); - } else if ( - // css module (injected by vue-loader) - (cssModule = type.__cssModules) && (cssModule = cssModule[key]) - ) { - return cssModule; - } else if (ctx !== EMPTY_OBJ && hasOwn$3(ctx, key)) { - accessCache[key] = 4; - return ctx[key]; - } else if ( - // global properties - globalProperties = appContext.config.globalProperties, hasOwn$3(globalProperties, key) - ) { - { - return globalProperties[key]; - } - } else if (false) { - if (data24 !== EMPTY_OBJ && isReservedPrefix(key[0]) && hasOwn$3(data24, key)) { - warn$1$1( - `Property ${JSON.stringify( - key - )} must be accessed via $data because it starts with a reserved character ("$" or "_") and is not proxied on the render context.` - ); - } else if (instance === currentRenderingInstance) { - warn$1$1( - `Property ${JSON.stringify(key)} was accessed during render but is not defined on instance.` - ); - } - } - }, - set({ _: instance }, key, value4) { - const { data: data24, setupState, ctx } = instance; - if (hasSetupBinding(setupState, key)) { - setupState[key] = value4; - return true; - } else if (false) { - warn$1$1(`Cannot mutate - - - -
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::before{content:"\F02E5"}.mdi-pot-mix::before{content:"\F065B"}.mdi-pot-mix-outline::before{content:"\F0677"}.mdi-pot-outline::before{content:"\F02FF"}.mdi-pot-steam::before{content:"\F065A"}.mdi-pot-steam-outline::before{content:"\F0326"}.mdi-pound::before{content:"\F0423"}.mdi-pound-box::before{content:"\F0424"}.mdi-pound-box-outline::before{content:"\F117F"}.mdi-power::before{content:"\F0425"}.mdi-power-cycle::before{content:"\F0901"}.mdi-power-off::before{content:"\F0902"}.mdi-power-on::before{content:"\F0903"}.mdi-power-plug::before{content:"\F06A5"}.mdi-power-plug-battery::before{content:"\F1C3B"}.mdi-power-plug-battery-outline::before{content:"\F1C3C"}.mdi-power-plug-off::before{content:"\F06A6"}.mdi-power-plug-off-outline::before{content:"\F1424"}.mdi-power-plug-outline::before{content:"\F1425"}.mdi-power-settings::before{content:"\F0426"}.mdi-power-sleep::before{content:"\F0904"}.mdi-power-socket::before{content:"\F0427"}.mdi-power-socket-au::before{content:"\F0905"}.mdi-power-socket-ch::before{content:"\F0FB3"}.mdi-power-socket-de::before{content:"\F1107"}.mdi-power-socket-eu::before{content:"\F07E7"}.mdi-power-socket-fr::before{content:"\F1108"}.mdi-power-socket-it::before{content:"\F14FF"}.mdi-power-socket-jp::before{content:"\F1109"}.mdi-power-socket-uk::before{content:"\F07E8"}.mdi-power-socket-us::before{content:"\F07E9"}.mdi-power-standby::before{content:"\F0906"}.mdi-powershell::before{content:"\F0A0A"}.mdi-prescription::before{content:"\F0706"}.mdi-presentation::before{content:"\F0428"}.mdi-presentation-play::before{content:"\F0429"}.mdi-pretzel::before{content:"\F1562"}.mdi-printer::before{content:"\F042A"}.mdi-printer-3d::before{content:"\F042B"}.mdi-printer-3d-nozzle::before{content:"\F0E5B"}.mdi-printer-3d-nozzle-alert::before{content:"\F11C0"}.mdi-printer-3d-nozzle-alert-outline::before{content:"\F11C1"}.mdi-printer-3d-nozzle-heat::before{content:"\F18B8"}.mdi-printer-3d-nozzle-heat-outline::before{content:"\F18B9"}.mdi-printer-3d-nozzle-off::before{content:"\F1B19"}.mdi-printer-3d-nozzle-off-outline::before{content:"\F1B1A"}.mdi-printer-3d-nozzle-outline::before{content:"\F0E5C"}.mdi-printer-3d-off::before{content:"\F1B0E"}.mdi-printer-alert::before{content:"\F042C"}.mdi-printer-check::before{content:"\F1146"}.mdi-printer-eye::before{content:"\F1458"}.mdi-printer-off::before{content:"\F0E5D"}.mdi-printer-off-outline::before{content:"\F1785"}.mdi-printer-outline::before{content:"\F1786"}.mdi-printer-pos::before{content:"\F1057"}.mdi-printer-pos-alert::before{content:"\F1BBC"}.mdi-printer-pos-alert-outline::before{content:"\F1BBD"}.mdi-printer-pos-cancel::before{content:"\F1BBE"}.mdi-printer-pos-cancel-outline::before{content:"\F1BBF"}.mdi-printer-pos-check::before{content:"\F1BC0"}.mdi-printer-pos-check-outline::before{content:"\F1BC1"}.mdi-printer-pos-cog::before{content:"\F1BC2"}.mdi-printer-pos-cog-outline::before{content:"\F1BC3"}.mdi-printer-pos-edit::before{content:"\F1BC4"}.mdi-printer-pos-edit-outline::before{content:"\F1BC5"}.mdi-printer-pos-minus::before{content:"\F1BC6"}.mdi-printer-pos-minus-outline::before{content:"\F1BC7"}.mdi-printer-pos-network::before{content:"\F1BC8"}.mdi-printer-pos-network-outline::before{content:"\F1BC9"}.mdi-printer-pos-off::before{content:"\F1BCA"}.mdi-printer-pos-off-outline::before{content:"\F1BCB"}.mdi-printer-pos-outline::before{content:"\F1BCC"}.mdi-printer-pos-pause::before{content:"\F1BCD"}.mdi-printer-pos-pause-outline::before{content:"\F1BCE"}.mdi-printer-pos-play::before{content:"\F1BCF"}.mdi-printer-pos-play-outline::before{content:"\F1BD0"}.mdi-printer-pos-plus::before{content:"\F1BD1"}.mdi-printer-pos-plus-outline::before{content:"\F1BD2"}.mdi-printer-pos-refresh::before{content:"\F1BD3"}.mdi-printer-pos-refresh-outline::before{content:"\F1BD4"}.mdi-printer-pos-remove::before{content:"\F1BD5"}.mdi-printer-pos-remove-outline::before{content:"\F1BD6"}.mdi-printer-pos-star::before{content:"\F1BD7"}.mdi-printer-pos-star-outline::before{content:"\F1BD8"}.mdi-printer-pos-stop::before{content:"\F1BD9"}.mdi-printer-pos-stop-outline::before{content:"\F1BDA"}.mdi-printer-pos-sync::before{content:"\F1BDB"}.mdi-printer-pos-sync-outline::before{content:"\F1BDC"}.mdi-printer-pos-wrench::before{content:"\F1BDD"}.mdi-printer-pos-wrench-outline::before{content:"\F1BDE"}.mdi-printer-search::before{content:"\F1457"}.mdi-printer-settings::before{content:"\F0707"}.mdi-printer-wireless::before{content:"\F0A0B"}.mdi-priority-high::before{content:"\F0603"}.mdi-priority-low::before{content:"\F0604"}.mdi-professional-hexagon::before{content:"\F042D"}.mdi-progress-alert::before{content:"\F0CBC"}.mdi-progress-check::before{content:"\F0995"}.mdi-progress-clock::before{content:"\F0996"}.mdi-progress-close::before{content:"\F110A"}.mdi-progress-download::before{content:"\F0997"}.mdi-progress-helper::before{content:"\F1BA2"}.mdi-progress-pencil::before{content:"\F1787"}.mdi-progress-question::before{content:"\F1522"}.mdi-progress-star::before{content:"\F1788"}.md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2s infinite linear;animation:mdi-spin 2s infinite linear}@-webkit-keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}} - -/*# sourceMappingURL=materialdesignicons.css.map */ diff --git a/web/scripts/api.js b/web/scripts/api.js deleted file mode 100644 index e022d4e16..000000000 --- a/web/scripts/api.js +++ /dev/null @@ -1,3 +0,0 @@ -// Shim for scripts/api.ts -export const ComfyApi = window.comfyAPI.api.ComfyApi; -export const api = window.comfyAPI.api.api; diff --git a/web/scripts/app.js b/web/scripts/app.js deleted file mode 100644 index 3ae1a239d..000000000 --- a/web/scripts/app.js +++ /dev/null @@ -1,4 +0,0 @@ -// Shim for scripts/app.ts -export const ANIM_PREVIEW_WIDGET = window.comfyAPI.app.ANIM_PREVIEW_WIDGET; -export const ComfyApp = window.comfyAPI.app.ComfyApp; -export const app = window.comfyAPI.app.app; diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js deleted file mode 100644 index c4c391cf2..000000000 --- a/web/scripts/changeTracker.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/changeTracker.ts -export const ChangeTracker = window.comfyAPI.changeTracker.ChangeTracker; diff --git a/web/scripts/defaultGraph.js b/web/scripts/defaultGraph.js deleted file mode 100644 index eaefc5b0c..000000000 --- a/web/scripts/defaultGraph.js +++ /dev/null @@ -1,4 +0,0 @@ -// Shim for scripts/defaultGraph.ts -export const defaultGraph = window.comfyAPI.defaultGraph.defaultGraph; -export const defaultGraphJSON = window.comfyAPI.defaultGraph.defaultGraphJSON; -export const blankGraph = window.comfyAPI.defaultGraph.blankGraph; diff --git a/web/scripts/metadata/flac.js b/web/scripts/metadata/flac.js deleted file mode 100644 index 3b247b43c..000000000 --- a/web/scripts/metadata/flac.js +++ /dev/null @@ -1,3 +0,0 @@ -// Shim for scripts/metadata/flac.ts -export const getFromFlacBuffer = window.comfyAPI.flac.getFromFlacBuffer; -export const getFromFlacFile = window.comfyAPI.flac.getFromFlacFile; diff --git a/web/scripts/metadata/png.js b/web/scripts/metadata/png.js deleted file mode 100644 index 789ef4f0f..000000000 --- a/web/scripts/metadata/png.js +++ /dev/null @@ -1,3 +0,0 @@ -// Shim for scripts/metadata/png.ts -export const getFromPngBuffer = window.comfyAPI.png.getFromPngBuffer; -export const getFromPngFile = window.comfyAPI.png.getFromPngFile; diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js deleted file mode 100644 index 991d2929c..000000000 --- a/web/scripts/pnginfo.js +++ /dev/null @@ -1,6 +0,0 @@ -// Shim for scripts/pnginfo.ts -export const getPngMetadata = window.comfyAPI.pnginfo.getPngMetadata; -export const getFlacMetadata = window.comfyAPI.pnginfo.getFlacMetadata; -export const getWebpMetadata = window.comfyAPI.pnginfo.getWebpMetadata; -export const getLatentMetadata = window.comfyAPI.pnginfo.getLatentMetadata; -export const importA1111 = window.comfyAPI.pnginfo.importA1111; diff --git a/web/scripts/ui.js b/web/scripts/ui.js deleted file mode 100644 index 948a039b8..000000000 --- a/web/scripts/ui.js +++ /dev/null @@ -1,4 +0,0 @@ -// Shim for scripts/ui.ts -export const ComfyDialog = window.comfyAPI.ui.ComfyDialog; -export const $el = window.comfyAPI.ui.$el; -export const ComfyUI = window.comfyAPI.ui.ComfyUI; diff --git a/web/scripts/ui/components/asyncDialog.js b/web/scripts/ui/components/asyncDialog.js deleted file mode 100644 index 43a8e19e6..000000000 --- a/web/scripts/ui/components/asyncDialog.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/components/asyncDialog.ts -export const ComfyAsyncDialog = window.comfyAPI.asyncDialog.ComfyAsyncDialog; diff --git a/web/scripts/ui/components/button.js b/web/scripts/ui/components/button.js deleted file mode 100644 index 7fe39ee29..000000000 --- a/web/scripts/ui/components/button.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/components/button.ts -export const ComfyButton = window.comfyAPI.button.ComfyButton; diff --git a/web/scripts/ui/components/buttonGroup.js b/web/scripts/ui/components/buttonGroup.js deleted file mode 100644 index 5fa0021ab..000000000 --- a/web/scripts/ui/components/buttonGroup.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/components/buttonGroup.ts -export const ComfyButtonGroup = window.comfyAPI.buttonGroup.ComfyButtonGroup; diff --git a/web/scripts/ui/components/popup.js b/web/scripts/ui/components/popup.js deleted file mode 100644 index 93f9f093d..000000000 --- a/web/scripts/ui/components/popup.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/components/popup.ts -export const ComfyPopup = window.comfyAPI.popup.ComfyPopup; diff --git a/web/scripts/ui/components/splitButton.js b/web/scripts/ui/components/splitButton.js deleted file mode 100644 index 53a21b69b..000000000 --- a/web/scripts/ui/components/splitButton.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/components/splitButton.ts -export const ComfySplitButton = window.comfyAPI.splitButton.ComfySplitButton; diff --git a/web/scripts/ui/dialog.js b/web/scripts/ui/dialog.js deleted file mode 100644 index 7475d42fa..000000000 --- a/web/scripts/ui/dialog.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/dialog.ts -export const ComfyDialog = window.comfyAPI.dialog.ComfyDialog; diff --git a/web/scripts/ui/draggableList.js b/web/scripts/ui/draggableList.js deleted file mode 100644 index 2c9596550..000000000 --- a/web/scripts/ui/draggableList.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/draggableList.ts -export const DraggableList = window.comfyAPI.draggableList.DraggableList; diff --git a/web/scripts/ui/imagePreview.js b/web/scripts/ui/imagePreview.js deleted file mode 100644 index 4153bbf5f..000000000 --- a/web/scripts/ui/imagePreview.js +++ /dev/null @@ -1,3 +0,0 @@ -// Shim for scripts/ui/imagePreview.ts -export const calculateImageGrid = window.comfyAPI.imagePreview.calculateImageGrid; -export const createImageHost = window.comfyAPI.imagePreview.createImageHost; diff --git a/web/scripts/ui/menu/index.js b/web/scripts/ui/menu/index.js deleted file mode 100644 index 777b5b993..000000000 --- a/web/scripts/ui/menu/index.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/menu/index.ts -export const ComfyAppMenu = window.comfyAPI.index.ComfyAppMenu; diff --git a/web/scripts/ui/settings.js b/web/scripts/ui/settings.js deleted file mode 100644 index 3fd01c755..000000000 --- a/web/scripts/ui/settings.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/settings.ts -export const ComfySettingsDialog = window.comfyAPI.settings.ComfySettingsDialog; diff --git a/web/scripts/ui/toggleSwitch.js b/web/scripts/ui/toggleSwitch.js deleted file mode 100644 index 21be93d17..000000000 --- a/web/scripts/ui/toggleSwitch.js +++ /dev/null @@ -1,2 +0,0 @@ -// Shim for scripts/ui/toggleSwitch.ts -export const toggleSwitch = window.comfyAPI.toggleSwitch.toggleSwitch; diff --git a/web/scripts/ui/utils.js b/web/scripts/ui/utils.js deleted file mode 100644 index cd4b49137..000000000 --- a/web/scripts/ui/utils.js +++ /dev/null @@ -1,3 +0,0 @@ -// Shim for scripts/ui/utils.ts -export const applyClasses = window.comfyAPI.utils.applyClasses; -export const toggleElement = window.comfyAPI.utils.toggleElement; diff --git a/web/scripts/utils.js b/web/scripts/utils.js deleted file mode 100644 index 8bff241fa..000000000 --- a/web/scripts/utils.js +++ /dev/null @@ -1,9 +0,0 @@ -// Shim for scripts/utils.ts -export const clone = window.comfyAPI.utils.clone; -export const applyTextReplacements = window.comfyAPI.utils.applyTextReplacements; -export const addStylesheet = window.comfyAPI.utils.addStylesheet; -export const downloadBlob = window.comfyAPI.utils.downloadBlob; -export const uploadFile = window.comfyAPI.utils.uploadFile; -export const prop = window.comfyAPI.utils.prop; -export const getStorageValue = window.comfyAPI.utils.getStorageValue; -export const setStorageValue = window.comfyAPI.utils.setStorageValue; diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js deleted file mode 100644 index dd4dcb4e3..000000000 --- a/web/scripts/widgets.js +++ /dev/null @@ -1,6 +0,0 @@ -// Shim for scripts/widgets.ts -export const updateControlWidgetLabel = window.comfyAPI.widgets.updateControlWidgetLabel; -export const IS_CONTROL_WIDGET = window.comfyAPI.widgets.IS_CONTROL_WIDGET; -export const addValueControlWidget = window.comfyAPI.widgets.addValueControlWidget; -export const addValueControlWidgets = window.comfyAPI.widgets.addValueControlWidgets; -export const ComfyWidgets = window.comfyAPI.widgets.ComfyWidgets; diff --git a/web/templates/default.jpg b/web/templates/default.jpg deleted file mode 100644 index a2b870cef..000000000 Binary files a/web/templates/default.jpg and /dev/null differ diff --git a/web/templates/default.json b/web/templates/default.json deleted file mode 100644 index 657ac107c..000000000 --- a/web/templates/default.json +++ /dev/null @@ -1,356 +0,0 @@ -{ - "last_node_id": 9, - "last_link_id": 9, - "nodes": [ - { - "id": 7, - "type": "CLIPTextEncode", - "pos": [ - 413, - 389 - ], - "size": [ - 425.27801513671875, - 180.6060791015625 - ], - "flags": {}, - "order": 3, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 5 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - "links": [ - 6 - ], - "slot_index": 0 - } - ], - "properties": {}, - "widgets_values": [ - "text, watermark" - ] - }, - { - "id": 6, - "type": "CLIPTextEncode", - "pos": [ - 415, - 186 - ], - "size": [ - 422.84503173828125, - 164.31304931640625 - ], - "flags": {}, - "order": 2, - "mode": 0, - "inputs": [ - { - "name": "clip", - "type": "CLIP", - "link": 3 - } - ], - "outputs": [ - { - "name": "CONDITIONING", - "type": "CONDITIONING", - 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