Merge branch 'master' into add-constant-value-nodes

.ci/update_windows/update.py

@@ -62,12 +62,38 @@ except:
 
 print("checking out master branch")
 branch = repo.lookup_branch('master')
-ref = repo.lookup_reference(branch.name)
-repo.checkout(ref)
+if branch is None:
+    ref = repo.lookup_reference('refs/remotes/origin/master')
+    repo.checkout(ref)
+    branch = repo.lookup_branch('master')
+    if branch is None:
+        repo.create_branch('master', repo.get(ref.target))
+else:
+    ref = repo.lookup_reference(branch.name)
+    repo.checkout(ref)
 
 print("pulling latest changes")
 pull(repo)
 
+if "--stable" in sys.argv:
+    def latest_tag(repo):
+        versions = []
+        for k in repo.references:
+            try:
+                prefix = "refs/tags/v"
+                if k.startswith(prefix):
+                    version = list(map(int, k[len(prefix):].split(".")))
+                    versions.append((version[0] * 10000000000 + version[1] * 100000 + version[2], k))
+            except:
+                pass
+        versions.sort()
+        if len(versions) > 0:
+            return versions[-1][1]
+        return None
+    latest_tag = latest_tag(repo)
+    if latest_tag is not None:
+        repo.checkout(latest_tag)
+
 print("Done!")
 
 self_update = True
@@ -108,3 +134,13 @@ if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
         shutil.copy(repo_req_path, req_path)
     except:
         pass
+
+
+stable_update_script = os.path.join(repo_path, ".ci/update_windows/update_comfyui_stable.bat")
+stable_update_script_to = os.path.join(cur_path, "update_comfyui_stable.bat")
+
+try:
+    if not file_size(stable_update_script_to) > 10:
+        shutil.copy(stable_update_script, stable_update_script_to)
+except:
+    pass

.ci/update_windows/update_comfyui_stable.bat

@@ -0,0 +1,8 @@
+@echo off
+..\python_embeded\python.exe .\update.py ..\ComfyUI\ --stable
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update --stable
+)
+if "%~1"=="" pause

.gitattributes

@@ -0,0 +1,2 @@
+/web/assets/** linguist-generated
+/web/** linguist-vendored

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,45 +1,48 @@
 name: Bug Report
 description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
-labels: [ "Potential Bug" ]
+labels: ["Potential Bug"]
 body:
-    - type: markdown
-      attributes:
-        value: |
-                Before submitting a **Bug Report**, please ensure the following:
+  - type: markdown
+    attributes:
+      value: |
+        Before submitting a **Bug Report**, please ensure the following:
 
-                **1:** You are running the latest version of ComfyUI.
-                **2:** You have looked at the existing bug reports and made sure this isn't already reported.
-                **3:** This is an actual bug in ComfyUI, not just a support question and not caused by an custom node. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
+        - **1:** You are running the latest version of ComfyUI.
+        - **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+        - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
+        `--disable-all-custom-nodes` command line argument.
+        - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
+        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: textarea
-      attributes:
-            label: Expected Behavior
-            description: "What you expected to happen."
-      validations:
-            required: true
-    - type: textarea
-      attributes:
-                label: Actual Behavior
-                description: "What actually happened. Please include a screenshot of the issue if possible."
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Steps to Reproduce
-                description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Debug Logs
-                description: "Please copy the output from your terminal logs here."
-                render: powershell
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Other
-                description: "Any other additional information you think might be helpful."
-      validations:
-                required: false
+        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: textarea
+    attributes:
+      label: Expected Behavior
+      description: "What you expected to happen."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Actual Behavior
+      description: "What actually happened. Please include a screenshot of the issue if possible."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Steps to Reproduce
+      description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Debug Logs
+      description: "Please copy the output from your terminal logs here."
+      render: powershell
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Other
+      description: "Any other additional information you think might be helpful."
+    validations:
+      required: false

.github/conda-environments/linux-environment.yml

@@ -1,28 +0,0 @@
-name: comfyui
-channels:
-  - pytorch
-  - nvidia
-  - defaults
-dependencies:
-  - python>=3.9
-  - pip
-  - pytorch
-  - torchvision
-  - torchaudio
-  - pytorch-cuda=12.1
-  - pip:
-    # comfyui requirements
-    - einops
-    - transformers>=4.25.1
-    - safetensors>=0.4.2
-    - aiohttp
-    - pyyaml
-    - Pillow
-    - scipy
-    - tqdm
-    - psutil
-    # comfy-action requirements
-    - requests
-    - google-cloud-storage
-    - comfy-cli
-    - charset-normalizer

.github/conda-environments/mac-environment.yml

@@ -1,26 +0,0 @@
-name: comfyui
-channels:
-  - pytorch-nightly
-  - defaults
-dependencies:
-  - python>=3.9
-  - pytorch-nightly::pytorch
-  - torchvision
-  - torchaudio
-  - pip:
-    - pip
-    # comfyui requirements
-    - einops
-    - transformers>=4.25.1
-    - safetensors>=0.4.2
-    - aiohttp
-    - pyyaml
-    - Pillow
-    - scipy
-    - tqdm
-    - psutil
-    # comfy-action requirements
-    - requests
-    - google-cloud-storage
-    - comfy-cli
-    - charset-normalizer

.github/workflows/linux-integration-test.yml

@@ -1,50 +0,0 @@
-name: (Linux) ComfyUI Integration Tests
-on:
-  push:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-  workflow_dispatch:
-  pull_request:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-
-jobs:
-  test-workflows:
-    runs-on: [self-hosted, Linux, t4] 
-    steps:
-    - name: Test ComfyUI Workflows
-      uses: comfy-org/comfy-action@main
-      with:
-        os: linux
-        cuda_version: 12.1
-        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
-        workflow_filenames: "default.json"
-        gcs_bucket_name: 'comfy-ci-results'
-        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
-        output_prefix: 'ComfyUI'
-        conda_env_file: '.github/conda-environments/linux-environment.yml'
-        timeout: 50

.github/workflows/mac-integration-test.yml

@@ -1,50 +0,0 @@
-name: (macOS) ComfyUI Integration Tests
-on:
-  push:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-  workflow_dispatch:
-  pull_request:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-
-jobs:
-  test-workflows:
-    runs-on: [self-hosted, m2] 
-    steps:
-    - name: Test ComfyUI Workflows
-      uses: comfy-org/comfy-action@main
-      with:
-        os: mac
-        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
-        workflow_filenames: "default.json"
-        gcs_bucket_name: 'comfy-ci-results'
-        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
-        output_prefix: 'ComfyUI'
-        conda_env_file: '.github/conda-environments/mac-environment.yml'
-        timeout: 50
-

.github/workflows/pullrequest-ci-run.yml

@@ -0,0 +1,53 @@
+# This is the GitHub Workflow that drives full-GPU-enabled tests of pull requests to ComfyUI, when the 'Run-CI-Test' label is added
+# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
+name: Pull Request CI Workflow Runs
+on:
+    pull_request_target:
+        types: [labeled]
+
+jobs:
+  pr-test-stable:
+    if: ${{ github.event.label.name == 'Run-CI-Test' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos, linux, windows]
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["stable"]
+        include:
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+          - os: windows
+            runner_label: [self-hosted, win]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}
+          use_prior_commit: 'true'
+  comment:
+    if: ${{ github.event.label.name == 'Run-CI-Test' }}
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    steps:
+      - uses: actions/github-script@v6
+        with:
+          script: |
+            github.rest.issues.createComment({
+              issue_number: context.issue.number,
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              body: '(Automated Bot Message) CI Tests are running, you can view the results at https://ci.comfy.org/?branch=${{ github.event.pull_request.number }}%2Fmerge'
+            })

.github/workflows/pylint.yml

@@ -0,0 +1,23 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  pylint:
+    name: Run Pylint
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.x
+
+    - name: Install Pylint
+      run: pip install pylint
+
+    - name: Run Pylint
+      run: pylint --rcfile=.pylintrc $(find . -type f -name "*.py")

.github/workflows/stable-release.yml

@@ -0,0 +1,104 @@
+
+name: "Release Stable Version"
+
+on:
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+      cu:
+        description: 'CUDA version'
+        required: true
+        type: string
+        default: "121"
+      python_minor:
+        description: 'Python minor version'
+        required: true
+        type: string
+        default: "11"
+      python_patch:
+        description: 'Python patch version'
+        required: true
+        type: string
+        default: "9"
+
+
+jobs:
+  package_comfy_windows:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    runs-on: windows-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.git_tag }}
+          fetch-depth: 0
+          persist-credentials: false
+      - uses: actions/cache/restore@v4
+        id: cache
+        with:
+          path: |
+            cu${{ inputs.cu }}_python_deps.tar
+            update_comfyui_and_python_dependencies.bat
+          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
+      - shell: bash
+        run: |
+          mv cu${{ inputs.cu }}_python_deps.tar ../
+          mv update_comfyui_and_python_dependencies.bat ../
+          cd ..
+          tar xf cu${{ inputs.cu }}_python_deps.tar
+          pwd
+          ls
+
+      - shell: bash
+        run: |
+          cd ..
+          cp -r ComfyUI ComfyUI_copy
+          curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
+          unzip python_embeded.zip -d python_embeded
+          cd python_embeded
+          echo ${{ env.MINOR_VERSION }}
+          echo 'import site' >> ./python3${{ 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 ..
+
+          git clone --depth 1 https://github.com/comfyanonymous/taesd
+          cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
+
+          mkdir ComfyUI_windows_portable
+          mv python_embeded ComfyUI_windows_portable
+          mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
+
+          cd ComfyUI_windows_portable
+
+          mkdir update
+          cp -r ComfyUI/.ci/update_windows/* ./update/
+          cp -r ComfyUI/.ci/windows_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
+
+          cd ComfyUI_windows_portable
+          python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
+
+          ls
+
+      - name: Upload binaries to release
+        uses: svenstaro/upload-release-action@v2
+        with:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: ComfyUI_windows_portable_nvidia.7z
+          tag: ${{ inputs.git_tag }}
+          overwrite: true
+          prerelease: true
+          make_latest: false

.github/workflows/test-browser.yml

@@ -1,63 +0,0 @@
-# This is a temporary action during frontend TS migration.
-# This file should be removed after TS migration is completed.
-# The browser test is here to ensure TS repo is working the same way as the
-# current JS code.
-# If you are adding UI feature, please sync your changes to the TS repo:
-# huchenlei/ComfyUI_frontend and update test expectation files accordingly.
-name: Playwright Browser Tests CI
-
-on:
-  push:
-    branches: [ main, master ]
-  pull_request:
-    branches: [ main, master ]
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout ComfyUI
-      uses: actions/checkout@v4
-      with:
-        repository: "comfyanonymous/ComfyUI"
-        path: "ComfyUI"
-    - name: Checkout ComfyUI_frontend
-      uses: actions/checkout@v4
-      with:
-        repository: "huchenlei/ComfyUI_frontend"
-        path: "ComfyUI_frontend"
-        ref: "fcc54d803e5b6a9b08a462a1d94899318c96dcbb"
-    - uses: actions/setup-node@v3
-      with:
-        node-version: lts/*
-    - 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
-      working-directory: ComfyUI
-    - name: Start ComfyUI server
-      run: |
-        python main.py --cpu &
-        wait-for-it --service 127.0.0.1:8188 -t 600
-      working-directory: ComfyUI
-    - name: Install ComfyUI_frontend dependencies
-      run: |
-        npm ci
-      working-directory: ComfyUI_frontend
-    - name: Install Playwright Browsers
-      run: npx playwright install --with-deps
-      working-directory: ComfyUI_frontend
-    - name: Run Playwright tests
-      run: npx playwright test
-      working-directory: ComfyUI_frontend
-    - uses: actions/upload-artifact@v4
-      if: always()
-      with:
-        name: playwright-report
-        path: ComfyUI_frontend/playwright-report/
-        retention-days: 30

.github/workflows/test-ci.yml

@@ -0,0 +1,95 @@
+# This is the GitHub Workflow that drives automatic full-GPU-enabled tests of all new commits to the master branch of ComfyUI
+# Results are reported as checkmarks on the commits, as well as onto https://ci.comfy.org/
+name: Full Comfy CI Workflow Runs
+on:
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'notebooks/**'
+      - 'script_examples/**'
+      - '.github/**'
+      - 'web/**'
+  workflow_dispatch:
+
+jobs:
+  test-stable:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos, linux, windows]
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["stable"]
+        include:
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+          - os: windows
+            runner_label: [self-hosted, win]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}
+
+  test-win-nightly:
+    strategy:
+      fail-fast: true
+      matrix:
+        os: [windows]
+        python_version: ["3.9", "3.10", "3.11", "3.12"]
+        cuda_version: ["12.1"]
+        torch_version: ["nightly"]
+        include:
+          - os: windows
+            runner_label: [self-hosted, win]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}
+
+  test-unix-nightly:
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [macos, linux]
+        python_version: ["3.11"]
+        cuda_version: ["12.1"]
+        torch_version: ["nightly"]
+        include:
+          - os: macos
+            runner_label: [self-hosted, macOS]
+            flags: "--use-pytorch-cross-attention"
+          - os: linux
+            runner_label: [self-hosted, Linux]
+            flags: ""
+    runs-on: ${{ matrix.runner_label }}
+    steps:
+      - name: Test Workflows
+        uses: comfy-org/comfy-action@main
+        with:
+          os: ${{ matrix.os }}
+          python_version: ${{ matrix.python_version }}
+          torch_version: ${{ matrix.torch_version }}
+          google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+          comfyui_flags: ${{ matrix.flags }}

.github/workflows/test-launch.yml

@@ -0,0 +1,45 @@
+name: Test server launches without errors
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout ComfyUI
+      uses: actions/checkout@v4
+      with:
+        repository: "comfyanonymous/ComfyUI"
+        path: "ComfyUI"
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.8'
+    - 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
+      working-directory: ComfyUI
+    - name: Start ComfyUI server
+      run: |
+        python main.py --cpu 2>&1 | tee console_output.log &
+        wait-for-it --service 127.0.0.1:8188 -t 600
+      working-directory: ComfyUI
+    - name: Check for unhandled exceptions in server log
+      run: |
+        if grep -qE "Exception|Error" console_output.log; then
+          echo "Unhandled exception/error found in server log."
+          exit 1
+        fi
+      working-directory: ComfyUI
+    - uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: console-output
+        path: ComfyUI/console_output.log
+        retention-days: 30

.github/workflows/test-ui.yaml

@@ -1,26 +0,0 @@
-name: Tests CI
-
-on: [push, pull_request]
-
-jobs:
-  test:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-node@v3
-      with:
-        node-version: 18
-    - 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
-    - name: Run Tests
-      run: | 
-        npm ci
-        npm run test:generate
-        npm test -- --verbose
-      working-directory: ./tests-ui

.github/workflows/windows_release_dependencies.yml

@@ -8,11 +8,16 @@ on:
         required: false
         type: string
         default: ""
+      extra_dependencies:
+        description: 'extra dependencies'
+        required: false
+        type: string
+        default: "\"numpy<2\""
       cu:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -24,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master
@@ -33,8 +38,8 @@ jobs:
   build_dependencies:
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
-        - uses: actions/setup-python@v4
+        - uses: actions/checkout@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
 
@@ -51,14 +56,14 @@ 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 ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
+            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
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir
             mv temp_wheel_dir cu${{ inputs.cu }}_python_deps
             tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps
 
-        - uses: actions/cache/save@v3
+        - uses: actions/cache/save@v4
           with:
             path: |
               cu${{ inputs.cu }}_python_deps.tar

.github/workflows/windows_release_nightly_pytorch.yml

@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "3"
+        default: "4"
 #  push:
 #    branches:
 #      - master
@@ -32,11 +32,11 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
-        - uses: actions/setup-python@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
         - shell: bash
@@ -49,13 +49,13 @@ jobs:
             echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+            python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
             cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable_nightly_pytorch
@@ -73,7 +73,7 @@ jobs:
             pause" > ./update/update_comfyui_and_python_dependencies.bat
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -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=8 -mfb=64 -md=32m -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

.github/workflows/windows_release_package.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master
@@ -32,7 +32,7 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/cache/restore@v3
+        - uses: actions/cache/restore@v4
           id: cache
           with:
             path: |
@@ -48,7 +48,7 @@ jobs:
             pwd
             ls
 
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
@@ -66,7 +66,7 @@ jobs:
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
             cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable
@@ -82,7 +82,7 @@ jobs:
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -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=8 -mfb=64 -md=32m -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

.gitignore

@@ -17,4 +17,6 @@ venv/
 !/web/extensions/core/
 /tests-ui/data/object_info.json
 /user/
-*.log
+*.log
+web_custom_versions/
+.DS_Store

.pylintrc

@@ -0,0 +1,3 @@
+[MESSAGES CONTROL]
+disable=all
+enable=eval-used

CONTRIBUTING.md

@@ -0,0 +1,41 @@
+# Contributing to ComfyUI
+
+Welcome, and thank you for your interest in contributing to ComfyUI!
+
+There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
+
+## Asking Questions
+
+Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
+
+## Providing Feedback
+
+Your comments and feedback are welcome, and the development team is available via a handful of different channels.
+
+See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
+
+## Reporting Issues
+
+Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
+
+
+### Look For an Existing Issue
+
+Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
+
+If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
+
+
+### Creating Pull Requests
+
+* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
+
+
+## Thank You
+
+Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.

README.md

@@ -11,16 +11,17 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) and [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
+- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)
+- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
 - 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.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
-- Loading full workflows (with seeds) from generated PNG files.
+- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
 - Saving/Loading workflows as Json files.
 - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
 - [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
@@ -32,6 +33,8 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
+- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
+- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Starts up very fast.
 - Works fully offline: will never download anything.
@@ -76,7 +79,7 @@ Ctrl can also be replaced with Cmd instead for macOS users
 
 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).
 
-### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu121_or_cpu.7z)
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
 
 Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
 
@@ -162,20 +165,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
 
-### I already have another UI for Stable Diffusion installed do I really have to install all of these dependencies?
-
-You don't. If you have another UI installed and working with its own python venv you can use that venv to run ComfyUI. You can open up your favorite terminal and activate it:
-
-```source path_to_other_sd_gui/venv/bin/activate```
-
-or on Windows:
-
-With Powershell: ```"path_to_other_sd_gui\venv\Scripts\Activate.ps1"```
-
-With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
-
-And then you can use that terminal to run ComfyUI without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
-
 # Running
 
 ```python main.py```
@@ -225,12 +214,11 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 [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/)
+
 # QA
 
-### Why did you make this?
+### Which GPU should I buy for this?
 
-I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions.
+[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
 
-### Who is this for?
-
-This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs.

app/frontend_management.py

@@ -0,0 +1,188 @@
+from __future__ import annotations
+import argparse
+import logging
+import os
+import re
+import tempfile
+import zipfile
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import TypedDict
+
+import requests
+from typing_extensions import NotRequired
+from comfy.cli_args import DEFAULT_VERSION_STRING
+
+
+REQUEST_TIMEOUT = 10  # seconds
+
+
+class Asset(TypedDict):
+    url: str
+
+
+class Release(TypedDict):
+    id: int
+    tag_name: str
+    name: str
+    prerelease: bool
+    created_at: str
+    published_at: str
+    body: str
+    assets: NotRequired[list[Asset]]
+
+
+@dataclass
+class FrontEndProvider:
+    owner: str
+    repo: str
+
+    @property
+    def folder_name(self) -> str:
+        return f"{self.owner}_{self.repo}"
+
+    @property
+    def release_url(self) -> str:
+        return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases"
+
+    @cached_property
+    def all_releases(self) -> list[Release]:
+        releases = []
+        api_url = self.release_url
+        while api_url:
+            response = requests.get(api_url, timeout=REQUEST_TIMEOUT)
+            response.raise_for_status()  # Raises an HTTPError if the response was an error
+            releases.extend(response.json())
+            # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.
+            if "next" in response.links:
+                api_url = response.links["next"]["url"]
+            else:
+                api_url = None
+        return releases
+
+    @cached_property
+    def latest_release(self) -> Release:
+        latest_release_url = f"{self.release_url}/latest"
+        response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)
+        response.raise_for_status()  # Raises an HTTPError if the response was an error
+        return response.json()
+
+    def get_release(self, version: str) -> Release:
+        if version == "latest":
+            return self.latest_release
+        else:
+            for release in self.all_releases:
+                if release["tag_name"] in [version, f"v{version}"]:
+                    return release
+            raise ValueError(f"Version {version} not found in releases")
+
+
+def download_release_asset_zip(release: Release, destination_path: str) -> None:
+    """Download dist.zip from github release."""
+    asset_url = None
+    for asset in release.get("assets", []):
+        if asset["name"] == "dist.zip":
+            asset_url = asset["url"]
+            break
+
+    if not asset_url:
+        raise ValueError("dist.zip not found in the release assets")
+
+    # Use a temporary file to download the zip content
+    with tempfile.TemporaryFile() as tmp_file:
+        headers = {"Accept": "application/octet-stream"}
+        response = requests.get(
+            asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT
+        )
+        response.raise_for_status()  # Ensure we got a successful response
+
+        # Write the content to the temporary file
+        tmp_file.write(response.content)
+
+        # Go back to the beginning of the temporary file
+        tmp_file.seek(0)
+
+        # Extract the zip file content to the destination path
+        with zipfile.ZipFile(tmp_file, "r") as zip_ref:
+            zip_ref.extractall(destination_path)
+
+
+class FrontendManager:
+    DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web")
+    CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
+
+    @classmethod
+    def parse_version_string(cls, value: str) -> tuple[str, str, str]:
+        """
+        Args:
+            value (str): The version string to parse.
+
+        Returns:
+            tuple[str, str]: A tuple containing provider name and version.
+
+        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)$"
+        match_result = re.match(VERSION_PATTERN, value)
+        if match_result is None:
+            raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
+
+        return match_result.group(1), match_result.group(2), match_result.group(3)
+
+    @classmethod
+    def init_frontend_unsafe(cls, version_string: str) -> str:
+        """
+        Initializes the frontend for the specified version.
+
+        Args:
+            version_string (str): The version string.
+
+        Returns:
+            str: The path to the initialized frontend.
+
+        Raises:
+            Exception: If there is an error during the initialization process.
+            main error source might be request timeout or invalid URL.
+        """
+        if version_string == DEFAULT_VERSION_STRING:
+            return cls.DEFAULT_FRONTEND_PATH
+
+        repo_owner, repo_name, version = cls.parse_version_string(version_string)
+        provider = FrontEndProvider(repo_owner, repo_name)
+        release = provider.get_release(version)
+
+        semantic_version = release["tag_name"].lstrip("v")
+        web_root = str(
+            Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
+        )
+        if not os.path.exists(web_root):
+            os.makedirs(web_root, exist_ok=True)
+            logging.info(
+                "Downloading frontend(%s) version(%s) to (%s)",
+                provider.folder_name,
+                semantic_version,
+                web_root,
+            )
+            logging.debug(release)
+            download_release_asset_zip(release, destination_path=web_root)
+        return web_root
+
+    @classmethod
+    def init_frontend(cls, version_string: str) -> str:
+        """
+        Initializes the frontend with the specified version string.
+
+        Args:
+            version_string (str): The version string to initialize the frontend with.
+
+        Returns:
+            str: The path of the initialized frontend.
+        """
+        try:
+            return cls.init_frontend_unsafe(version_string)
+        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

comfy/cldm/cldm.py

@@ -13,7 +13,47 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
+from .control_types import UNION_CONTROLNET_TYPES
+from collections import OrderedDict
 import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+class OptimizedAttention(nn.Module):
+    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = nhead
+        self.c = c
+
+        self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
+        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.in_proj(x)
+        q, k, v = x.split(self.c, dim=2)
+        out = optimized_attention(q, k, v, self.heads)
+        return self.out_proj(out)
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+class ResBlockUnionControlnet(nn.Module):
+    def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
+        self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
+        self.mlp = nn.Sequential(
+            OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
+                         ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
+        self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
+
+    def attention(self, x: torch.Tensor):
+        return self.attn(x)
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -53,6 +93,7 @@ class ControlNet(nn.Module):
         transformer_depth_middle=None,
         transformer_depth_output=None,
         attn_precision=None,
+        union_controlnet_num_control_type=None,
         device=None,
         operations=comfy.ops.disable_weight_init,
         **kwargs,
@@ -280,6 +321,65 @@ class ControlNet(nn.Module):
         self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
         self._feature_size += ch
 
+        if union_controlnet_num_control_type is not None:
+            self.num_control_type = union_controlnet_num_control_type
+            num_trans_channel = 320
+            num_trans_head = 8
+            num_trans_layer = 1
+            num_proj_channel = 320
+            # task_scale_factor = num_trans_channel ** 0.5
+            self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
+
+            self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
+            self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
+            #-----------------------------------------------------------------------------------------------------
+
+            control_add_embed_dim = 256
+            class ControlAddEmbedding(nn.Module):
+                def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
+                    super().__init__()
+                    self.num_control_type = num_control_type
+                    self.in_dim = in_dim
+                    self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
+                    self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
+                def forward(self, control_type, dtype, device):
+                    c_type = torch.zeros((self.num_control_type,), device=device)
+                    c_type[control_type] = 1.0
+                    c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
+                    return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
+
+            self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
+        else:
+            self.task_embedding = None
+            self.control_add_embedding = None
+
+    def union_controlnet_merge(self, hint, control_type, emb, context):
+        # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
+        inputs = []
+        condition_list = []
+
+        for idx in range(min(1, len(control_type))):
+            controlnet_cond = self.input_hint_block(hint[idx], emb, context)
+            feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+            if idx < len(control_type):
+                feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
+
+            inputs.append(feat_seq.unsqueeze(1))
+            condition_list.append(controlnet_cond)
+
+        x = torch.cat(inputs, dim=1)
+        x = self.transformer_layes(x)
+        controlnet_cond_fuser = None
+        for idx in range(len(control_type)):
+            alpha = self.spatial_ch_projs(x[:, idx])
+            alpha = alpha.unsqueeze(-1).unsqueeze(-1)
+            o = condition_list[idx] + alpha
+            if controlnet_cond_fuser is None:
+                controlnet_cond_fuser = o
+            else:
+                controlnet_cond_fuser += o
+        return controlnet_cond_fuser
+
     def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
         return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
 
@@ -287,9 +387,33 @@ class ControlNet(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
-        guided_hint = self.input_hint_block(hint, emb, context)
+        guided_hint = None
+        if self.control_add_embedding is not None: #Union Controlnet
+            control_type = kwargs.get("control_type", [])
 
-        outs = []
+            if any([c >= self.num_control_type for c in control_type]):
+                max_type = max(control_type)
+                max_type_name = {
+                    v: k for k, v in UNION_CONTROLNET_TYPES.items()
+                }[max_type]
+                raise ValueError(
+                    f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
+                    f"({self.num_control_type}) supported.\n" +
+                    "Please consider using the ProMax ControlNet Union model.\n" +
+                    "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
+                )
+
+            emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
+            if len(control_type) > 0:
+                if len(hint.shape) < 5:
+                    hint = hint.unsqueeze(dim=0)
+                guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
+
+        if guided_hint is None:
+            guided_hint = self.input_hint_block(hint, emb, context)
+
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -304,10 +428,10 @@ class ControlNet(nn.Module):
                 guided_hint = None
             else:
                 h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
+            out_output.append(zero_conv(h, emb, context))
 
         h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
+        out_middle.append(self.middle_block_out(h, emb, context))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 

comfy/cldm/control_types.py

@@ -0,0 +1,10 @@
+UNION_CONTROLNET_TYPES = {
+    "openpose": 0,
+    "depth": 1,
+    "hed/pidi/scribble/ted": 2,
+    "canny/lineart/anime_lineart/mlsd": 3,
+    "normal": 4,
+    "segment": 5,
+    "tile": 6,
+    "repaint": 7,
+}

comfy/cldm/mmdit.py

@@ -0,0 +1,77 @@
+import torch
+from typing import Dict, Optional
+import comfy.ldm.modules.diffusionmodules.mmdit
+
+class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
+    def __init__(
+        self,
+        num_blocks = None,
+        dtype = None,
+        device = None,
+        operations = None,
+        **kwargs,
+    ):
+        super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
+        # controlnet_blocks
+        self.controlnet_blocks = torch.nn.ModuleList([])
+        for _ in range(len(self.joint_blocks)):
+            self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
+
+        self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
+            None,
+            self.patch_size,
+            self.in_channels,
+            self.hidden_size,
+            bias=True,
+            strict_img_size=False,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        hint = None,
+    ) -> torch.Tensor:
+
+        #weird sd3 controlnet specific stuff
+        y = torch.zeros_like(y)
+
+        if self.context_processor is not None:
+            context = self.context_processor(context)
+
+        hw = x.shape[-2:]
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        x += self.pos_embed_input(hint)
+
+        c = self.t_embedder(timesteps, dtype=x.dtype)
+        if y is not None and self.y_embedder is not None:
+            y = self.y_embedder(y)
+            c = c + y
+
+        if context is not None:
+            context = self.context_embedder(context)
+
+        output = []
+
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
+                context,
+                x,
+                c=c,
+                use_checkpoint=self.use_checkpoint,
+            )
+
+            out = self.controlnet_blocks[i](x)
+            count = self.depth // blocks
+            if i == blocks - 1:
+                count -= 1
+            for j in range(count):
+                output.append(out)
+
+        return {"output": output}

comfy/cli_args.py

@@ -1,7 +1,10 @@
 import argparse
 import enum
+import os
+from typing import Optional
 import comfy.options
 
+
 class EnumAction(argparse.Action):
     """
     Argparse action for handling Enums
@@ -89,6 +92,10 @@ class LatentPreviewMethod(enum.Enum):
 
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
+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.")
+
 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.")
@@ -109,6 +116,7 @@ vram_group.add_argument("--lowvram", action="store_true", help="Split the unet i
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
+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.")
@@ -118,11 +126,44 @@ parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test
 parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
 
 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("--multi-user", action="store_true", help="Enables per-user storage.")
 
 parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
 
+# The default built-in provider hosted under web/
+DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
+
+parser.add_argument(
+    "--front-end-version",
+    type=str,
+    default=DEFAULT_VERSION_STRING,
+    help="""
+    Specifies the version of the frontend to be used. This command needs internet connectivity to query and
+    download available frontend implementations from GitHub releases.
+
+    The version string should be in the format of:
+    [repoOwner]/[repoName]@[version]
+    where version is one of: "latest" or a valid version number (e.g. "1.0.0")
+    """,
+)
+
+def is_valid_directory(path: Optional[str]) -> Optional[str]:
+    """Validate if the given path is a directory."""
+    if path is None:
+        return None
+
+    if not os.path.isdir(path):
+        raise argparse.ArgumentTypeError(f"{path} is not a valid directory.")
+    return path
+
+parser.add_argument(
+    "--front-end-root",
+    type=is_valid_directory,
+    default=None,
+    help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
+)
 
 if comfy.options.args_parsing:
     args = parser.parse_args()

comfy/clip_config_bigg.json

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1280,
   "initializer_factor": 1.0,

comfy/clip_model.py

@@ -1,5 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -71,13 +72,13 @@ class CLIPEncoder(torch.nn.Module):
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
+    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
         super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
+    def forward(self, input_tokens, dtype=torch.float32):
+        return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
 
 
 class CLIPTextModel_(torch.nn.Module):
@@ -87,14 +88,15 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
+        self.embeddings = CLIPEmbeddings(embed_dim, dtype=dtype, device=device, operations=operations)
         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):
-        x = self.embeddings(input_tokens)
+    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)
         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])
@@ -111,7 +113,7 @@ 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), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
+        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):
@@ -153,11 +155,11 @@ class CLIPVisionEmbeddings(torch.nn.Module):
 
         num_patches = (image_size // patch_size) ** 2
         num_positions = num_patches + 1
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
+        return torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):
@@ -169,7 +171,7 @@ class CLIPVision(torch.nn.Module):
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
 
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations)
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=dtype, device=device, operations=operations)
         self.pre_layrnorm = operations.LayerNorm(embed_dim)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.post_layernorm = operations.LayerNorm(embed_dim)

comfy/clip_vision.py

@@ -34,6 +34,7 @@ class ClipVisionModel():
         with open(json_config) as f:
             config = json.load(f)
 
+        self.image_size = config.get("image_size", 224)
         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)
@@ -50,7 +51,7 @@ class ClipVisionModel():
 
     def encode_image(self, image):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device)).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size).float()
         out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()
@@ -93,7 +94,10 @@ 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:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+        if 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")
+        else:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
     else:
         return None
 

comfy/clip_vision_config_vitl_336.json

@@ -0,0 +1,18 @@
+{
+  "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,
+  "torch_dtype": "float32"
+}

comfy/controlnet.py

@@ -1,4 +1,24 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+
 import torch
+from enum import Enum
 import math
 import os
 import logging
@@ -7,10 +27,14 @@ import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
 import comfy.ops
+import comfy.latent_formats
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
 import comfy.ldm.cascade.controlnet
+import comfy.cldm.mmdit
+import comfy.ldm.hydit.controlnet
+import comfy.ldm.flux.controlnet_xlabs
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
@@ -31,26 +55,37 @@ def broadcast_image_to(tensor, target_batch_size, batched_number):
     else:
         return torch.cat([tensor] * batched_number, dim=0)
 
+class StrengthType(Enum):
+    CONSTANT = 1
+    LINEAR_UP = 2
+
 class ControlBase:
     def __init__(self, device=None):
         self.cond_hint_original = None
         self.cond_hint = None
         self.strength = 1.0
         self.timestep_percent_range = (0.0, 1.0)
+        self.latent_format = None
+        self.vae = None
         self.global_average_pooling = False
         self.timestep_range = None
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
+        self.extra_args = {}
 
         if device is None:
             device = comfy.model_management.get_torch_device()
         self.device = device
         self.previous_controlnet = None
+        self.extra_conds = []
+        self.strength_type = StrengthType.CONSTANT
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
+        if self.latent_format is not None:
+            self.vae = vae
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -83,43 +118,41 @@ class ControlBase:
         c.global_average_pooling = self.global_average_pooling
         c.compression_ratio = self.compression_ratio
         c.upscale_algorithm = self.upscale_algorithm
+        c.latent_format = self.latent_format
+        c.extra_args = self.extra_args.copy()
+        c.vae = self.vae
+        c.extra_conds = self.extra_conds.copy()
+        c.strength_type = self.strength_type
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
             return self.previous_controlnet.inference_memory_requirements(dtype)
         return 0
 
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
+    def control_merge(self, control, control_prev, output_dtype):
         out = {'input':[], 'middle':[], 'output': []}
 
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
+        for key in control:
+            control_output = control[key]
+            applied_to = set()
             for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
                 x = control_output[i]
                 if x is not None:
                     if self.global_average_pooling:
                         x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
 
-                    x *= self.strength
+                    if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
+                        applied_to.add(x)
+                        if self.strength_type == StrengthType.CONSTANT:
+                            x *= self.strength
+                        elif self.strength_type == StrengthType.LINEAR_UP:
+                            x *= (self.strength ** float(len(control_output) - i))
+
                     if x.dtype != output_dtype:
                         x = x.to(output_dtype)
 
                 out[key].append(x)
+
         if control_prev is not None:
             for x in ['input', 'middle', 'output']:
                 o = out[x]
@@ -134,20 +167,28 @@ class ControlBase:
                             if o[i].shape[0] < prev_val.shape[0]:
                                 o[i] = prev_val + o[i]
                             else:
-                                o[i] += prev_val
+                                o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
         return out
 
+    def set_extra_arg(self, argument, value=None):
+        self.extra_args[argument] = value
+
+
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
         if control_model is not None:
             self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
 
+        self.compression_ratio = compression_ratio
         self.global_average_pooling = global_average_pooling
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
+        self.latent_format = latent_format
+        self.extra_conds += extra_conds
+        self.strength_type = strength_type
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -170,19 +211,32 @@ class ControlNet(ControlBase):
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device)
+            compression_ratio = self.compression_ratio
+            if self.vae is not None:
+                compression_ratio *= self.vae.downscale_ratio
+            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")
+            if self.vae is not None:
+                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+                comfy.model_management.load_models_gpu(loaded_models)
+            if self.latent_format is not None:
+                self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
         context = cond.get('crossattn_controlnet', cond['c_crossattn'])
-        y = cond.get('y', None)
-        if y is not None:
-            y = y.to(dtype)
+        extra = self.extra_args.copy()
+        for c in self.extra_conds:
+            temp = cond.get(c, None)
+            if temp is not None:
+                extra[c] = temp.to(dtype)
+
         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.float(), context=context.to(dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
+        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.to(dtype), context=context.to(dtype), **extra)
+        return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
         c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
@@ -270,6 +324,7 @@ class ControlLora(ControlNet):
         ControlBase.__init__(self, device)
         self.control_weights = control_weights
         self.global_average_pooling = global_average_pooling
+        self.extra_conds += ["y"]
 
     def pre_run(self, model, percent_to_timestep_function):
         super().pre_run(model, percent_to_timestep_function)
@@ -322,8 +377,73 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
+def controlnet_config(sd):
+    model_config = comfy.model_detection.model_config_from_unet(sd, "", True)
+
+    supported_inference_dtypes = model_config.supported_inference_dtypes
+
+    controlnet_config = model_config.unet_config
+    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+    load_device = comfy.model_management.get_torch_device()
+    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    if manual_cast_dtype is not None:
+        operations = comfy.ops.manual_cast
+    else:
+        operations = comfy.ops.disable_weight_init
+
+    return model_config, operations, load_device, unet_dtype, manual_cast_dtype
+
+def controlnet_load_state_dict(control_model, sd):
+    missing, unexpected = control_model.load_state_dict(sd, strict=False)
+
+    if len(missing) > 0:
+        logging.warning("missing controlnet keys: {}".format(missing))
+
+    if len(unexpected) > 0:
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
+    return control_model
+
+def load_controlnet_mmdit(sd):
+    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(new_sd)
+    num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
+    for k in sd:
+        new_sd[k] = sd[k]
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = controlnet_load_state_dict(control_model, new_sd)
+
+    latent_format = comfy.latent_formats.SD3()
+    latent_format.shift_factor = 0 #SD3 controlnet weirdness
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    return control
+
+
+def load_controlnet_hunyuandit(controlnet_data):
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(controlnet_data)
+
+    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=load_device, dtype=unet_dtype)
+    control_model = controlnet_load_state_dict(control_model, controlnet_data)
+
+    latent_format = comfy.latent_formats.SDXL()
+    extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img']
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
+    return control
+
+def load_controlnet_flux_xlabs(sd):
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(sd)
+    control_model = comfy.ldm.flux.controlnet_xlabs.ControlNetFlux(operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = controlnet_load_state_dict(control_model, sd)
+    extra_conds = ['y', 'guidance']
+    control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
+    return control
+
+
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
+        return load_controlnet_hunyuandit(controlnet_data)
+
     if "lora_controlnet" in controlnet_data:
         return ControlLora(controlnet_data)
 
@@ -370,10 +490,21 @@ def load_controlnet(ckpt_path, model=None):
             if k in controlnet_data:
                 new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
 
+        if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
+            controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0]
+            for k in list(controlnet_data.keys()):
+                new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
+                new_sd[new_k] = controlnet_data.pop(k)
+
         leftover_keys = controlnet_data.keys()
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
+    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+        if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
+            return load_controlnet_flux_xlabs(controlnet_data)
+        else:
+            return load_controlnet_mmdit(controlnet_data)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
@@ -490,12 +621,11 @@ class T2IAdapter(ControlBase):
             self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
             self.t2i_model.cpu()
 
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
+        control_input = {}
+        for k in self.control_input:
+            control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
+
+        return self.control_merge(control_input, control_prev, x_noisy.dtype)
 
     def copy(self):
         c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)

comfy/diffusers_load.py

@@ -22,7 +22,7 @@ def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_dire
     if text_encoder2_path is not None:
         text_encoder_paths.append(text_encoder2_path)
 
-    unet = comfy.sd.load_unet(unet_path)
+    unet = comfy.sd.load_diffusion_model(unet_path)
 
     clip = None
     if output_clip:

comfy/k_diffusion/sampling.py

@@ -998,7 +998,7 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return x_next
 
 @torch.no_grad()
-def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+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]
@@ -1013,16 +1013,16 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No
     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 - denoised + temp[0], sigma_hat, denoised)
+        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})
         dt = sigmas[i + 1] - sigma_hat
         # Euler method
-        x = x + d * dt
+        x = denoised + d * sigmas[i + 1]
     return x
 
 @torch.no_grad()
-def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+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."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -1041,10 +1041,10 @@ def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None,
         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 - denoised + temp[0], sigmas[i], denoised)
+        d = to_d(x, sigmas[i], temp[0])
         # Euler method
         dt = sigma_down - sigmas[i]
-        x = x + d * dt
+        x = denoised + d * sigma_down
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x

comfy/latent_formats.py

@@ -139,3 +139,32 @@ class SD3(LatentFormat):
 
 class StableAudio1(LatentFormat):
     latent_channels = 64
+
+class Flux(SD3):
+    def __init__(self):
+        self.scale_factor = 0.3611
+        self.shift_factor = 0.1159
+        self.latent_rgb_factors =[
+            [-0.0404,  0.0159,  0.0609],
+            [ 0.0043,  0.0298,  0.0850],
+            [ 0.0328, -0.0749, -0.0503],
+            [-0.0245,  0.0085,  0.0549],
+            [ 0.0966,  0.0894,  0.0530],
+            [ 0.0035,  0.0399,  0.0123],
+            [ 0.0583,  0.1184,  0.1262],
+            [-0.0191, -0.0206, -0.0306],
+            [-0.0324,  0.0055,  0.1001],
+            [ 0.0955,  0.0659, -0.0545],
+            [-0.0504,  0.0231, -0.0013],
+            [ 0.0500, -0.0008, -0.0088],
+            [ 0.0982,  0.0941,  0.0976],
+            [-0.1233, -0.0280, -0.0897],
+            [-0.0005, -0.0530, -0.0020],
+            [-0.1273, -0.0932, -0.0680]
+        ]
+
+    def process_in(self, latent):
+        return (latent - self.shift_factor) * self.scale_factor
+
+    def process_out(self, latent):
+        return (latent / self.scale_factor) + self.shift_factor

comfy/ldm/audio/dit.py

@@ -9,6 +9,7 @@ from einops import rearrange
 from torch import nn
 from torch.nn import functional as F
 import math
+import comfy.ops
 
 class FourierFeatures(nn.Module):
     def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
@@ -18,7 +19,7 @@ class FourierFeatures(nn.Module):
             [out_features // 2, in_features], dtype=dtype, device=device))
 
     def forward(self, input):
-        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
+        f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
         return torch.cat([f.cos(), f.sin()], dim=-1)
 
 # norms
@@ -38,9 +39,9 @@ class LayerNorm(nn.Module):
 
     def forward(self, x):
         beta = self.beta
-        if self.beta is not None:
-            beta = beta.to(dtype=x.dtype, device=x.device)
-        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
+        if beta is not None:
+            beta = comfy.ops.cast_to_input(beta, x)
+        return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
 
 class GLU(nn.Module):
     def __init__(
@@ -123,7 +124,9 @@ class RotaryEmbedding(nn.Module):
         scale_base = 512,
         interpolation_factor = 1.,
         base = 10000,
-        base_rescale_factor = 1.
+        base_rescale_factor = 1.,
+        dtype=None,
+        device=None,
     ):
         super().__init__()
         # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
@@ -131,8 +134,8 @@ class RotaryEmbedding(nn.Module):
         # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
         base *= base_rescale_factor ** (dim / (dim - 2))
 
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
+        # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype))
 
         assert interpolation_factor >= 1.
         self.interpolation_factor = interpolation_factor
@@ -161,14 +164,14 @@ class RotaryEmbedding(nn.Module):
 
         t = t / self.interpolation_factor
 
-        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
+        freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t))
         freqs = torch.cat((freqs, freqs), dim = -1)
 
         if self.scale is None:
             return freqs, 1.
 
         power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
-        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
+        scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')
         scale = torch.cat((scale, scale), dim = -1)
 
         return freqs, scale
@@ -568,7 +571,7 @@ class ContinuousTransformer(nn.Module):
         self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
 
         if rotary_pos_emb:
-            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype)
         else:
             self.rotary_pos_emb = None
 

comfy/ldm/aura/mmdit.py

@@ -0,0 +1,478 @@
+#AuraFlow MMDiT
+#Originally written by the AuraFlow Authors
+
+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.ops
+import comfy.ldm.common_dit
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        if hidden_dim is None:
+            hidden_dim = 4 * dim
+
+        n_hidden = int(2 * hidden_dim / 3)
+        n_hidden = find_multiple(n_hidden, 256)
+
+        self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class MultiHeadLayerNorm(nn.Module):
+    def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
+        # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
+
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        mean = hidden_states.mean(-1, keepdim=True)
+        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+        hidden_states = (hidden_states - mean) * torch.rsqrt(
+            variance + self.variance_epsilon
+        )
+        hidden_states = self.weight.to(torch.float32) * hidden_states
+        return hidden_states.to(input_dtype)
+
+class SingleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+    #@torch.compile()
+    def forward(self, c):
+
+        bsz, seqlen1, _ = c.shape
+
+        q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
+        q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        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)
+        c = self.w1o(output)
+        return c
+
+
+
+class DoubleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # this is for x
+        self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+        self.q_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+
+    #@torch.compile()
+    def forward(self, c, x):
+
+        bsz, seqlen1, _ = c.shape
+        bsz, seqlen2, _ = x.shape
+        seqlen = seqlen1 + seqlen2
+
+        cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
+        cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cq, ck = self.q_norm1(cq), self.k_norm1(ck)
+
+        xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
+        xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xq, xk = self.q_norm2(xq), self.k_norm2(xk)
+
+        # concat all
+        q, k, v = (
+            torch.cat([cq, xq], dim=1),
+            torch.cat([ck, xk], dim=1),
+            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)
+
+        c, x = output.split([seqlen1, seqlen2], dim=1)
+        c = self.w1o(c)
+        x = self.w2o(x)
+
+        return c, x
+
+
+class MMDiTBlock(nn.Module):
+    def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        if not is_last:
+            self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+            )
+        else:
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+            )
+
+        self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+        self.modX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.is_last = is_last
+
+    #@torch.compile()
+    def forward(self, c, x, global_cond, **kwargs):
+
+        cres, xres = c, x
+
+        cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
+            self.modC(global_cond).chunk(6, dim=1)
+        )
+
+        c = modulate(self.normC1(c), cshift_msa, cscale_msa)
+
+        # xpath
+        xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
+            self.modX(global_cond).chunk(6, dim=1)
+        )
+
+        x = modulate(self.normX1(x), xshift_msa, xscale_msa)
+
+        # attention
+        c, x = self.attn(c, x)
+
+
+        c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
+        c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
+        c = cres + c
+
+        x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
+        x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
+        x = xres + x
+
+        return c, x
+
+class DiTBlock(nn.Module):
+    # like MMDiTBlock, but it only has X
+    def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+
+        self.modCX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+
+    #@torch.compile()
+    def forward(self, cx, global_cond, **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.norm2(cxres + gate_msa.unsqueeze(1) * cx)
+        mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
+        cx = gate_mlp.unsqueeze(1) * mlpout
+
+        cx = cxres + cx
+
+        return cx
+
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = 1000 * torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half) / half
+        ).to(t.device)
+        args = t[:, None] * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    #@torch.compile()
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        in_channels=4,
+        out_channels=4,
+        patch_size=2,
+        dim=3072,
+        n_layers=36,
+        n_double_layers=4,
+        n_heads=12,
+        global_conddim=3072,
+        cond_seq_dim=2048,
+        max_seq=32 * 32,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
+
+        self.cond_seq_linear = operations.Linear(
+            cond_seq_dim, dim, bias=False, dtype=dtype, device=device
+        )  # linear for something like text sequence.
+        self.init_x_linear = operations.Linear(
+            patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
+        )  # init linear for patchified image.
+
+        self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
+        self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
+
+        self.double_layers = nn.ModuleList([])
+        self.single_layers = nn.ModuleList([])
+
+
+        for idx in range(n_double_layers):
+            self.double_layers.append(
+                MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
+            )
+
+        for idx in range(n_double_layers, n_layers):
+            self.single_layers.append(
+                DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
+            )
+
+
+        self.final_linear = operations.Linear(
+            dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
+        )
+
+        self.modF = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.out_channels = out_channels
+        self.patch_size = patch_size
+        self.n_double_layers = n_double_layers
+        self.n_layers = n_layers
+
+        self.h_max = round(max_seq**0.5)
+        self.w_max = round(max_seq**0.5)
+
+    @torch.no_grad()
+    def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
+        # extend pe
+        pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
+
+        pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
+
+        # now we need to extend this to target_dim. for this we will use interpolation.
+        # we will use torch.nn.functional.interpolate
+        pe_as_2d = F.interpolate(
+            pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
+        )
+        pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+        self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
+        self.h_max, self.w_max = target_dim
+        print("PE extended to", target_dim)
+
+    def pe_selection_index_based_on_dim(self, h, w):
+        h_p, w_p = h // self.patch_size, w // self.patch_size
+        original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
+        original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
+        starth =  self.h_max // 2 - h_p // 2
+        endh =starth + h_p
+        startw = self.w_max // 2 - w_p // 2
+        endw = startw + w_p
+        original_pe_indexes = original_pe_indexes[
+            starth:endh, startw:endw
+        ]
+        return original_pe_indexes.flatten()
+
+    def unpatchify(self, x, h, w):
+        c = self.out_channels
+        p = self.patch_size
+
+        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
+
+    def patchify(self, x):
+        B, C, H, W = x.size()
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        x = x.view(
+            B,
+            C,
+            (H + 1) // self.patch_size,
+            self.patch_size,
+            (W + 1) // self.patch_size,
+            self.patch_size,
+        )
+        x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
+        return x
+
+    def apply_pos_embeds(self, x, h, w):
+        h = (h + 1) // self.patch_size
+        w = (w + 1) // self.patch_size
+        max_dim = max(h, w)
+
+        cur_dim = self.h_max
+        pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)
+
+        if max_dim > cur_dim:
+            pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
+            cur_dim = max_dim
+
+        from_h = (cur_dim - h) // 2
+        from_w = (cur_dim - w) // 2
+        pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
+        return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
+
+    def forward(self, x, timestep, context, **kwargs):
+        # patchify x, add PE
+        b, c, h, w = x.shape
+
+        # pe_indexes = self.pe_selection_index_based_on_dim(h, w)
+        # print(pe_indexes, pe_indexes.shape)
+
+        x = self.init_x_linear(self.patchify(x))  # B, T_x, D
+        x = self.apply_pos_embeds(x, h, w)
+        # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
+        # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
+
+        # process conditions for MMDiT Blocks
+        c_seq = context  # B, T_c, D_c
+        t = timestep
+
+        c = self.cond_seq_linear(c_seq)  # B, T_c, D
+        c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)
+
+        global_cond = self.t_embedder(t, x.dtype)  # B, D
+
+        if len(self.double_layers) > 0:
+            for layer in self.double_layers:
+                c, x = layer(c, x, global_cond, **kwargs)
+
+        if len(self.single_layers) > 0:
+            c_len = c.size(1)
+            cx = torch.cat([c, x], dim=1)
+            for layer in self.single_layers:
+                cx = layer(cx, global_cond, **kwargs)
+
+            x = cx[:, c_len:]
+
+        fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
+
+        x = modulate(x, fshift, fscale)
+        x = self.final_linear(x)
+        x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+        return x

comfy/ldm/cascade/common.py

@@ -19,14 +19,7 @@
 import torch
 import torch.nn as nn
 from comfy.ldm.modules.attention import optimized_attention
-
-class Linear(torch.nn.Linear):
-    def reset_parameters(self):
-        return None
-
-class Conv2d(torch.nn.Conv2d):
-    def reset_parameters(self):
-        return None
+import comfy.ops
 
 class OptimizedAttention(nn.Module):
     def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
@@ -78,13 +71,13 @@ class GlobalResponseNorm(nn.Module):
     "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
     def __init__(self, dim, dtype=None, device=None):
         super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
-        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
+        self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
+        self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
 
     def forward(self, x):
         Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
         Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
-        return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x
+        return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x
 
 
 class ResBlock(nn.Module):

comfy/ldm/cascade/controlnet.py

@@ -90,4 +90,4 @@ class ControlNet(nn.Module):
         proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
         for i, idx in enumerate(self.proj_blocks):
             proj_outputs[idx] = self.projections[i](x)
-        return proj_outputs
+        return {"input": proj_outputs[::-1]}

comfy/ldm/common_dit.py

@@ -0,0 +1,8 @@
+import torch
+
+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():
+        padding_mode = "reflect"
+    pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0]
+    pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1]
+    return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode)

comfy/ldm/flux/controlnet_xlabs.py

@@ -0,0 +1,104 @@
+#Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+
+import torch
+from torch import Tensor, nn
+from einops import rearrange, repeat
+
+from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
+                                 MLPEmbedder, SingleStreamBlock,
+                                 timestep_embedding)
+
+from .model import Flux
+import comfy.ldm.common_dit
+
+
+class ControlNetFlux(Flux):
+    def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
+
+        # add ControlNet blocks
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(self.params.depth):
+            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+            # controlnet_block = zero_module(controlnet_block)
+            self.controlnet_blocks.append(controlnet_block)
+        self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.gradient_checkpointing = False
+        self.input_hint_block = nn.Sequential(
+            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        controlnet_cond: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        controlnet_cond = self.input_hint_block(controlnet_cond)
+        controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        controlnet_cond = self.pos_embed_input(controlnet_cond)
+        img = img + controlnet_cond
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        block_res_samples = ()
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+            block_res_samples = block_res_samples + (img,)
+
+        controlnet_block_res_samples = ()
+        for block_res_sample, controlnet_block in zip(block_res_samples, self.controlnet_blocks):
+            block_res_sample = controlnet_block(block_res_sample)
+            controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
+
+        return {"output": (controlnet_block_res_samples * 10)[:19]}
+
+    def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
+        hint = hint * 2.0 - 1.0
+
+        bs, c, h, w = x.shape
+        patch_size = 2
+        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)
+        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)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        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)
+        return self.forward_orig(img, img_ids, hint, context, txt_ids, timesteps, y, guidance)

comfy/ldm/flux/layers.py

@@ -0,0 +1,251 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from .math import attention, rope
+import comfy.ops
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> 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(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.silu = nn.SiLU()
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * comfy.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+        self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        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)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = 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_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        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_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        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),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
+        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_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_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)
+
+        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)
+
+        # 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)
+
+        if txt.dtype == torch.float16:
+            txt = txt.clip(-65504, 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")
+        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> 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)
+
+        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)
+        # 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
+        if x.dtype == torch.float16:
+            x = x.clip(-65504, 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, 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, :]
+        x = self.linear(x)
+        return x

comfy/ldm/flux/math.py

@@ -0,0 +1,35 @@
+import torch
+from einops import rearrange
+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) -> Tensor:
+    q, k = apply_rope(q, k, pe)
+
+    heads = q.shape[1]
+    x = optimized_attention(q, k, v, heads, skip_reshape=True)
+    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():
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    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_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)

comfy/ldm/flux/model.py

@@ -0,0 +1,151 @@
+#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 .layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+from einops import rearrange, repeat
+import comfy.ldm.common_dit
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    vec_in_dim: 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
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(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 = FluxParams(**kwargs)
+        self.params = params
+        self.in_channels = params.in_channels * 2 * 2
+        self.out_channels = self.in_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.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.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)
+        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()
+        )
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+
+        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)
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor = None,
+        control=None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        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))
+
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        for i in range(len(self.double_blocks)):
+            img, txt = self.double_blocks[i](img=img, txt=txt, vec=vec, pe=pe)
+
+            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 += add
+
+        img = torch.cat((txt, img), 1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        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, **kwargs):
+        bs, c, h, w = x.shape
+        patch_size = 2
+        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)
+        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)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
+        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, y, guidance, control)
+        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]

comfy/ldm/hydit/attn_layers.py

@@ -0,0 +1,218 @@
+import torch
+import torch.nn as nn
+from typing import Tuple, Union, Optional
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):
+    """
+    Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    Args:
+        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+        head_first (bool): head dimension first (except batch dim) or not.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+
+    Raises:
+        AssertionError: If the frequency tensor doesn't match the expected shape.
+        AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
+    """
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+
+    if isinstance(freqs_cis, tuple):
+        # freqs_cis: (cos, sin) in real space
+        if head_first:
+            assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        else:
+            assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+    else:
+        # freqs_cis: values in complex space
+        if head_first:
+            assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        else:
+            assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+
+def rotate_half(x):
+    x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+    return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+
+def apply_rotary_emb(
+        xq: torch.Tensor,
+        xk: Optional[torch.Tensor],
+        freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+        head_first: bool = False,
+) -> 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 '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:
+        xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]
+        xk (torch.Tensor): Key tensor to apply rotary embeddings.   [B, S, H, D]
+        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.
+        head_first (bool): head dimension first (except batch dim) or not.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+
+    """
+    xk_out = None
+    if isinstance(freqs_cis, tuple):
+        cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)    # [S, D]
+        xq_out = (xq * cos + rotate_half(xq) * sin)
+        if xk is not None:
+            xk_out = (xk * cos + rotate_half(xk) * sin)
+    else:
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))  # [B, S, H, D//2]
+        freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device)   # [S, D//2] --> [1, S, 1, D//2]
+        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+        if xk is not None:
+            xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))  # [B, S, H, D//2]
+            xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+
+    return xq_out, xk_out
+
+
+
+class CrossAttention(nn.Module):
+    """
+    Use QK Normalization.
+    """
+    def __init__(self,
+                 qdim,
+                 kdim,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_norm=False,
+                 attn_drop=0.0,
+                 proj_drop=0.0,
+                 attn_precision=None,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__()
+        self.attn_precision = attn_precision
+        self.qdim = qdim
+        self.kdim = kdim
+        self.num_heads = num_heads
+        assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
+        self.head_dim = self.qdim // num_heads
+        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+        self.scale = self.head_dim ** -0.5
+
+        self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+        self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
+
+        # TODO: eps should be 1 / 65530 if using fp16
+        self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, y, freqs_cis_img=None):
+        """
+        Parameters
+        ----------
+        x: torch.Tensor
+            (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)
+        y: torch.Tensor
+            (batch, seqlen2, hidden_dim2)
+        freqs_cis_img: torch.Tensor
+            (batch, hidden_dim // 2), RoPE for image
+        """
+        b, s1, c = x.shape     # [b, s1, D]
+        _, s2, c = y.shape     # [b, s2, 1024]
+
+        q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim)   # [b, s1, h, d]
+        kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim)    # [b, s2, 2, h, d]
+        k, v = kv.unbind(dim=2) # [b, s, h, d]
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # Apply RoPE if needed
+        if freqs_cis_img is not None:
+            qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
+            assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
+            q = qq
+
+        q = q.transpose(-2, -3).contiguous()        # q ->  B, L1, H, C - B, H, L1, C
+        k = k.transpose(-2, -3).contiguous()      # k ->  B, L2, H, C - B, H, C, L2
+        v = v.transpose(-2, -3).contiguous() 
+
+        context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+
+        out = self.out_proj(context)  # context.reshape - B, L1, -1
+        out = self.proj_drop(out)
+
+        out_tuple = (out,)
+
+        return out_tuple
+
+
+class Attention(nn.Module):
+    """
+    We rename some layer names to align with flash attention
+    """
+    def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn_precision = attn_precision
+        self.dim = dim
+        self.num_heads = num_heads
+        assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.head_dim = self.dim // num_heads
+        # This assertion is aligned with flash attention
+        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+        self.scale = self.head_dim ** -0.5
+
+        # qkv --> Wqkv
+        self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        # TODO: eps should be 1 / 65530 if using fp16
+        self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, freqs_cis_img=None):
+        B, N, C = x.shape
+        qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)   # [3, b, h, s, d]
+        q, k, v = qkv.unbind(0)     # [b, h, s, d]
+        q = self.q_norm(q)          # [b, h, s, d]
+        k = self.k_norm(k)          # [b, h, s, d]
+
+        # Apply RoPE if needed
+        if freqs_cis_img is not None:
+            qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)
+            assert qq.shape == q.shape and kk.shape == k.shape, \
+                f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
+            q, k = qq, kk
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+        x = self.out_proj(x)
+        x = self.proj_drop(x)
+
+        out_tuple = (x,)
+
+        return out_tuple

comfy/ldm/hydit/controlnet.py

@@ -0,0 +1,321 @@
+from typing import Any, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torch.utils import checkpoint
+
+from comfy.ldm.modules.diffusionmodules.mmdit import (
+    Mlp,
+    TimestepEmbedder,
+    PatchEmbed,
+    RMSNorm,
+)
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from .poolers import AttentionPool
+
+import comfy.latent_formats
+from .models import HunYuanDiTBlock, calc_rope
+
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+
+class HunYuanControlNet(nn.Module):
+    """
+    HunYuanDiT: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+    Parameters
+    ----------
+    args: argparse.Namespace
+        The arguments parsed by argparse.
+    input_size: tuple
+        The size of the input image.
+    patch_size: int
+        The size of the patch.
+    in_channels: int
+        The number of input channels.
+    hidden_size: int
+        The hidden size of the transformer backbone.
+    depth: int
+        The number of transformer blocks.
+    num_heads: int
+        The number of attention heads.
+    mlp_ratio: float
+        The ratio of the hidden size of the MLP in the transformer block.
+    log_fn: callable
+        The logging function.
+    """
+
+    def __init__(
+        self,
+        input_size: tuple = 128,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        hidden_size: int = 1408,
+        depth: int = 40,
+        num_heads: int = 16,
+        mlp_ratio: float = 4.3637,
+        text_states_dim=1024,
+        text_states_dim_t5=2048,
+        text_len=77,
+        text_len_t5=256,
+        qk_norm=True,  # See http://arxiv.org/abs/2302.05442 for details.
+        size_cond=False,
+        use_style_cond=False,
+        learn_sigma=True,
+        norm="layer",
+        log_fn: callable = print,
+        attn_precision=None,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.log_fn = log_fn
+        self.depth = depth
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.text_states_dim = text_states_dim
+        self.text_states_dim_t5 = text_states_dim_t5
+        self.text_len = text_len
+        self.text_len_t5 = text_len_t5
+        self.size_cond = size_cond
+        self.use_style_cond = use_style_cond
+        self.norm = norm
+        self.dtype = dtype
+        self.latent_format = comfy.latent_formats.SDXL
+
+        self.mlp_t5 = nn.Sequential(
+            nn.Linear(
+                self.text_states_dim_t5,
+                self.text_states_dim_t5 * 4,
+                bias=True,
+                dtype=dtype,
+                device=device,
+            ),
+            nn.SiLU(),
+            nn.Linear(
+                self.text_states_dim_t5 * 4,
+                self.text_states_dim,
+                bias=True,
+                dtype=dtype,
+                device=device,
+            ),
+        )
+        # learnable replace
+        self.text_embedding_padding = nn.Parameter(
+            torch.randn(
+                self.text_len + self.text_len_t5,
+                self.text_states_dim,
+                dtype=dtype,
+                device=device,
+            )
+        )
+
+        # Attention pooling
+        pooler_out_dim = 1024
+        self.pooler = AttentionPool(
+            self.text_len_t5,
+            self.text_states_dim_t5,
+            num_heads=8,
+            output_dim=pooler_out_dim,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+
+        # Dimension of the extra input vectors
+        self.extra_in_dim = pooler_out_dim
+
+        if self.size_cond:
+            # Image size and crop size conditions
+            self.extra_in_dim += 6 * 256
+
+        if self.use_style_cond:
+            # Here we use a default learned embedder layer for future extension.
+            self.style_embedder = nn.Embedding(
+                1, hidden_size, dtype=dtype, device=device
+            )
+            self.extra_in_dim += hidden_size
+
+        # Text embedding for `add`
+        self.x_embedder = PatchEmbed(
+            input_size,
+            patch_size,
+            in_channels,
+            hidden_size,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations
+        )
+        self.extra_embedder = nn.Sequential(
+            operations.Linear(
+                self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device
+            ),
+            nn.SiLU(),
+            operations.Linear(
+                hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device
+            ),
+        )
+
+        # Image embedding
+        num_patches = self.x_embedder.num_patches
+
+        # HUnYuanDiT Blocks
+        self.blocks = nn.ModuleList(
+            [
+                HunYuanDiTBlock(
+                    hidden_size=hidden_size,
+                    c_emb_size=hidden_size,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    text_states_dim=self.text_states_dim,
+                    qk_norm=qk_norm,
+                    norm_type=self.norm,
+                    skip=False,
+                    attn_precision=attn_precision,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                )
+                for _ in range(19)
+            ]
+        )
+
+        # Input zero linear for the first block
+        self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
+
+        # Output zero linear for the every block
+        self.after_proj_list = nn.ModuleList(
+            [
+
+                    operations.Linear(
+                        self.hidden_size, self.hidden_size, dtype=dtype, device=device
+                    )
+                for _ in range(len(self.blocks))
+            ]
+        )
+
+    def forward(
+        self,
+        x,
+        hint,
+        timesteps,
+        context,#encoder_hidden_states=None,
+        text_embedding_mask=None,
+        encoder_hidden_states_t5=None,
+        text_embedding_mask_t5=None,
+        image_meta_size=None,
+        style=None,
+        return_dict=False,
+        **kwarg,
+    ):
+        """
+        Forward pass of the encoder.
+
+        Parameters
+        ----------
+        x: torch.Tensor
+            (B, D, H, W)
+        t: torch.Tensor
+            (B)
+        encoder_hidden_states: torch.Tensor
+            CLIP text embedding, (B, L_clip, D)
+        text_embedding_mask: torch.Tensor
+            CLIP text embedding mask, (B, L_clip)
+        encoder_hidden_states_t5: torch.Tensor
+            T5 text embedding, (B, L_t5, D)
+        text_embedding_mask_t5: torch.Tensor
+            T5 text embedding mask, (B, L_t5)
+        image_meta_size: torch.Tensor
+            (B, 6)
+        style: torch.Tensor
+            (B)
+        cos_cis_img: torch.Tensor
+        sin_cis_img: torch.Tensor
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+        condition = hint
+        if condition.shape[0] == 1:
+            condition = torch.repeat_interleave(condition, x.shape[0], dim=0)
+
+        text_states = context  # 2,77,1024
+        text_states_t5 = encoder_hidden_states_t5  # 2,256,2048
+        text_states_mask = text_embedding_mask.bool()  # 2,77
+        text_states_t5_mask = text_embedding_mask_t5.bool()  # 2,256
+        b_t5, l_t5, c_t5 = text_states_t5.shape
+        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
+
+        padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
+
+        text_states[:, -self.text_len :] = torch.where(
+            text_states_mask[:, -self.text_len :].unsqueeze(2),
+            text_states[:, -self.text_len :],
+            padding[: self.text_len],
+        )
+        text_states_t5[:, -self.text_len_t5 :] = torch.where(
+            text_states_t5_mask[:, -self.text_len_t5 :].unsqueeze(2),
+            text_states_t5[:, -self.text_len_t5 :],
+            padding[self.text_len :],
+        )
+
+        text_states = torch.cat([text_states, text_states_t5], dim=1)  # 2,205，1024
+
+        # _, _, oh, ow = x.shape
+        # th, tw = oh // self.patch_size, ow // self.patch_size
+
+        # Get image RoPE embedding according to `reso`lution.
+        freqs_cis_img = calc_rope(
+            x, self.patch_size, self.hidden_size // self.num_heads
+        )  # (cos_cis_img, sin_cis_img)
+
+        # ========================= Build time and image embedding =========================
+        t = self.t_embedder(timesteps, dtype=self.dtype)
+        x = self.x_embedder(x)
+
+        # ========================= Concatenate all extra vectors =========================
+        # Build text tokens with pooling
+        extra_vec = self.pooler(encoder_hidden_states_t5)
+
+        # Build image meta size tokens if applicable
+        # if image_meta_size is not None:
+        #     image_meta_size = timestep_embedding(image_meta_size.view(-1), 256)   # [B * 6, 256]
+        #     if image_meta_size.dtype != self.dtype:
+        #         image_meta_size = image_meta_size.half()
+        #     image_meta_size = image_meta_size.view(-1, 6 * 256)
+        #     extra_vec = torch.cat([extra_vec, image_meta_size], dim=1)  # [B, D + 6 * 256]
+
+        # Build style tokens
+        if style is not None:
+            style_embedding = self.style_embedder(style)
+            extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+        # Concatenate all extra vectors
+        c = t + self.extra_embedder(extra_vec)  # [B, D]
+
+        # ========================= Deal with Condition =========================
+        condition = self.x_embedder(condition)
+
+        # ========================= Forward pass through HunYuanDiT blocks =========================
+        controls = []
+        x = x + self.before_proj(condition)  # add condition
+        for layer, block in enumerate(self.blocks):
+            x = block(x, c, text_states, freqs_cis_img)
+            controls.append(self.after_proj_list[layer](x))  # zero linear for output
+
+        return {"output": controls}

comfy/ldm/hydit/models.py

@@ -0,0 +1,410 @@
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import comfy.ops
+from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from torch.utils import checkpoint
+
+from .attn_layers import Attention, CrossAttention
+from .poolers import AttentionPool
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+def calc_rope(x, patch_size, head_size):
+    th = (x.shape[2] + (patch_size // 2)) // patch_size
+    tw = (x.shape[3] + (patch_size // 2)) // patch_size
+    base_size = 512 // 8 // patch_size
+    start, stop = get_fill_resize_and_crop((th, tw), base_size)
+    sub_args = [start, stop, (th, tw)]
+    # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
+    rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+    rope = (rope[0].to(x), rope[1].to(x))
+    return rope
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class HunYuanDiTBlock(nn.Module):
+    """
+    A HunYuanDiT block with `add` conditioning.
+    """
+    def __init__(self,
+                 hidden_size,
+                 c_emb_size,
+                 num_heads,
+                 mlp_ratio=4.0,
+                 text_states_dim=1024,
+                 qk_norm=False,
+                 norm_type="layer",
+                 skip=False,
+                 attn_precision=None,
+                 dtype=None,
+                 device=None,
+                 operations=None,
+                 ):
+        super().__init__()
+        use_ele_affine = True
+
+        if norm_type == "layer":
+            norm_layer = operations.LayerNorm
+        elif norm_type == "rms":
+            norm_layer = RMSNorm
+        else:
+            raise ValueError(f"Unknown norm_type: {norm_type}")
+
+        # ========================= Self-Attention =========================
+        self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+        self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+
+        # ========================= FFN =========================
+        self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations)
+
+        # ========================= Add =========================
+        # Simply use add like SDXL.
+        self.default_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+        # ========================= Cross-Attention =========================
+        self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
+                                        qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+        self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+
+        # ========================= Skip Connection =========================
+        if skip:
+            self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+            self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device)
+        else:
+            self.skip_linear = None
+
+        self.gradient_checkpointing = False
+
+    def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+        # Long Skip Connection
+        if self.skip_linear is not None:
+            cat = torch.cat([x, skip], dim=-1)
+            if cat.dtype != x.dtype:
+                cat = cat.to(x.dtype)
+            cat = self.skip_norm(cat)
+            x = self.skip_linear(cat)
+
+        # Self-Attention
+        shift_msa = self.default_modulation(c).unsqueeze(dim=1)
+        attn_inputs = (
+            self.norm1(x) + shift_msa, freq_cis_img,
+        )
+        x = x + self.attn1(*attn_inputs)[0]
+
+        # Cross-Attention
+        cross_inputs = (
+            self.norm3(x), text_states, freq_cis_img
+        )
+        x = x + self.attn2(*cross_inputs)[0]
+
+        # FFN Layer
+        mlp_inputs = self.norm2(x)
+        x = x + self.mlp(mlp_inputs)
+
+        return x
+
+    def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+        if self.gradient_checkpointing and self.training:
+            return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip)
+        return self._forward(x, c, text_states, freq_cis_img, skip)
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of HunYuanDiT.
+    """
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class HunYuanDiT(nn.Module):
+    """
+    HunYuanDiT: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+    Parameters
+    ----------
+    args: argparse.Namespace
+        The arguments parsed by argparse.
+    input_size: tuple
+        The size of the input image.
+    patch_size: int
+        The size of the patch.
+    in_channels: int
+        The number of input channels.
+    hidden_size: int
+        The hidden size of the transformer backbone.
+    depth: int
+        The number of transformer blocks.
+    num_heads: int
+        The number of attention heads.
+    mlp_ratio: float
+        The ratio of the hidden size of the MLP in the transformer block.
+    log_fn: callable
+        The logging function.
+    """
+    #@register_to_config
+    def __init__(self,
+                 input_size: tuple = 32,
+                 patch_size: int = 2,
+                 in_channels: int = 4,
+                 hidden_size: int = 1152,
+                 depth: int = 28,
+                 num_heads: int = 16,
+                 mlp_ratio: float = 4.0,
+                 text_states_dim = 1024,
+                 text_states_dim_t5 = 2048,
+                 text_len = 77,
+                 text_len_t5 = 256,
+                 qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details.
+                 size_cond = False,
+                 use_style_cond = False,
+                 learn_sigma = True,
+                 norm = "layer",
+                 log_fn: callable = print,
+                 attn_precision=None,
+                 dtype=None,
+                 device=None,
+                 operations=None,
+                 **kwargs,
+    ):
+        super().__init__()
+        self.log_fn = log_fn
+        self.depth = depth
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.text_states_dim = text_states_dim
+        self.text_states_dim_t5 = text_states_dim_t5
+        self.text_len = text_len
+        self.text_len_t5 = text_len_t5
+        self.size_cond = size_cond
+        self.use_style_cond = use_style_cond
+        self.norm = norm
+        self.dtype = dtype
+        #import pdb
+        #pdb.set_trace()
+
+        self.mlp_t5 = nn.Sequential(
+            operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),
+        )
+        # learnable replace
+        self.text_embedding_padding = nn.Parameter(
+            torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))
+
+        # Attention pooling
+        pooler_out_dim = 1024
+        self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations)
+
+        # Dimension of the extra input vectors
+        self.extra_in_dim = pooler_out_dim
+
+        if self.size_cond:
+            # Image size and crop size conditions
+            self.extra_in_dim += 6 * 256
+
+        if self.use_style_cond:
+            # Here we use a default learned embedder layer for future extension.
+            self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device)
+            self.extra_in_dim += hidden_size
+
+        # Text embedding for `add`
+        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations)
+        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
+        self.extra_embedder = nn.Sequential(
+            operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        # Image embedding
+        num_patches = self.x_embedder.num_patches
+
+        # HUnYuanDiT Blocks
+        self.blocks = nn.ModuleList([
+            HunYuanDiTBlock(hidden_size=hidden_size,
+                            c_emb_size=hidden_size,
+                            num_heads=num_heads,
+                            mlp_ratio=mlp_ratio,
+                            text_states_dim=self.text_states_dim,
+                            qk_norm=qk_norm,
+                            norm_type=self.norm,
+                            skip=layer > depth // 2,
+                            attn_precision=attn_precision,
+                            dtype=dtype,
+                            device=device,
+                            operations=operations,
+                            )
+            for layer in range(depth)
+        ])
+
+        self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        self.unpatchify_channels = self.out_channels
+
+
+
+    def forward(self,
+                x,
+                t,
+                context,#encoder_hidden_states=None,
+                text_embedding_mask=None,
+                encoder_hidden_states_t5=None,
+                text_embedding_mask_t5=None,
+                image_meta_size=None,
+                style=None,
+                return_dict=False,
+                control=None,
+                transformer_options=None,
+                ):
+        """
+        Forward pass of the encoder.
+
+        Parameters
+        ----------
+        x: torch.Tensor
+            (B, D, H, W)
+        t: torch.Tensor
+            (B)
+        encoder_hidden_states: torch.Tensor
+            CLIP text embedding, (B, L_clip, D)
+        text_embedding_mask: torch.Tensor
+            CLIP text embedding mask, (B, L_clip)
+        encoder_hidden_states_t5: torch.Tensor
+            T5 text embedding, (B, L_t5, D)
+        text_embedding_mask_t5: torch.Tensor
+            T5 text embedding mask, (B, L_t5)
+        image_meta_size: torch.Tensor
+            (B, 6)
+        style: torch.Tensor
+            (B)
+        cos_cis_img: torch.Tensor
+        sin_cis_img: torch.Tensor
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+        #import pdb
+        #pdb.set_trace()
+        encoder_hidden_states = context
+        text_states = encoder_hidden_states                     # 2,77,1024
+        text_states_t5 = encoder_hidden_states_t5               # 2,256,2048
+        text_states_mask = text_embedding_mask.bool()           # 2,77
+        text_states_t5_mask = text_embedding_mask_t5.bool()     # 2,256
+        b_t5, l_t5, c_t5 = text_states_t5.shape
+        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
+
+        padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
+
+        text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])
+        text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])
+
+        text_states = torch.cat([text_states, text_states_t5], dim=1)  # 2,205，1024
+        # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
+
+        _, _, oh, ow = x.shape
+        th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size
+
+
+        # Get image RoPE embedding according to `reso`lution.
+        freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img)
+
+        # ========================= Build time and image embedding =========================
+        t = self.t_embedder(t, dtype=x.dtype)
+        x = self.x_embedder(x)
+
+        # ========================= Concatenate all extra vectors =========================
+        # Build text tokens with pooling
+        extra_vec = self.pooler(encoder_hidden_states_t5)
+
+        # Build image meta size tokens if applicable
+        if self.size_cond:
+            image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype)   # [B * 6, 256]
+            image_meta_size = image_meta_size.view(-1, 6 * 256)
+            extra_vec = torch.cat([extra_vec, image_meta_size], dim=1)  # [B, D + 6 * 256]
+
+        # Build style tokens
+        if self.use_style_cond:
+            if style is None:
+                style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
+            style_embedding = self.style_embedder(style, out_dtype=x.dtype)
+            extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+        # Concatenate all extra vectors
+        c = t + self.extra_embedder(extra_vec)  # [B, D]
+
+        controls = None
+        if control:
+            controls = control.get("output", None)
+        # ========================= Forward pass through HunYuanDiT blocks =========================
+        skips = []
+        for layer, block in enumerate(self.blocks):
+            if layer > self.depth // 2:
+                if controls is not None:
+                    skip = skips.pop() + controls.pop()
+                else:
+                    skip = skips.pop()
+                x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)
+            else:
+                x = block(x, c, text_states, freqs_cis_img)         # (N, L, D)
+
+            if layer < (self.depth // 2 - 1):
+                skips.append(x)
+        if controls is not None and len(controls) != 0:
+            raise ValueError("The number of controls is not equal to the number of skip connections.")
+
+        # ========================= Final layer =========================
+        x = self.final_layer(x, c)                              # (N, L, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x, th, tw)                          # (N, out_channels, H, W)
+
+        if return_dict:
+            return {'x': x}
+        if self.learn_sigma:
+            return x[:,:self.out_channels // 2,:oh,:ow]
+        return x[:,:,:oh,:ow]
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.unpatchify_channels
+        p = self.x_embedder.patch_size[0]
+        # h = w = int(x.shape[1] ** 0.5)
+        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

comfy/ldm/hydit/poolers.py

@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
+
+class AttentionPool(nn.Module):
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device))
+        self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device)
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+
+    def forward(self, x):
+        x = x[:,:self.positional_embedding.shape[0] - 1]
+        x = x.permute(1, 0, 2)  # NLC -> LNC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
+        x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC
+
+        q = self.q_proj(x[:1])
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+
+        batch_size = q.shape[1]
+        head_dim = self.embed_dim // self.num_heads
+        q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+        k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+        v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+
+        attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1)
+
+        attn_output = self.c_proj(attn_output)
+        return attn_output.squeeze(0)

comfy/ldm/hydit/posemb_layers.py

@@ -0,0 +1,224 @@
+import torch
+import numpy as np
+from typing import Union
+
+
+def _to_tuple(x):
+    if isinstance(x, int):
+        return x, x
+    else:
+        return x
+
+
+def get_fill_resize_and_crop(src, tgt):
+    th, tw = _to_tuple(tgt)
+    h, w = _to_tuple(src)
+
+    tr = th / tw        # base resolution
+    r = h / w           # target resolution
+
+    # resize
+    if r > tr:
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))    # resize the target resolution down based on the base resolution
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+def get_meshgrid(start, *args):
+    if len(args) == 0:
+        # start is grid_size
+        num = _to_tuple(start)
+        start = (0, 0)
+        stop = num
+    elif len(args) == 1:
+        # start is start, args[0] is stop, step is 1
+        start = _to_tuple(start)
+        stop = _to_tuple(args[0])
+        num = (stop[0] - start[0], stop[1] - start[1])
+    elif len(args) == 2:
+        # start is start, args[0] is stop, args[1] is num
+        start = _to_tuple(start)
+        stop = _to_tuple(args[0])
+        num = _to_tuple(args[1])
+    else:
+        raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+    grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32)
+    grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)   # [2, W, H]
+    return grid
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid = get_meshgrid(start, *args)   # [2, H, w]
+    # grid_h = np.arange(grid_size, dtype=np.float32)
+    # grid_w = np.arange(grid_size, dtype=np.float32)
+    # grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    # grid = np.stack(grid, axis=0)   # [2, W, H]
+
+    grid = grid.reshape([2, 1, *grid.shape[1:]])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)    # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (W,H)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)   # (M, D/2)
+    emb_cos = np.cos(out)   # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+#################################################################################
+#                   Rotary Positional Embedding Functions                       #
+#################################################################################
+# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443
+
+def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
+    """
+    This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure.
+
+    Parameters
+    ----------
+    embed_dim: int
+        embedding dimension size
+    start: int or tuple of int
+        If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1;
+        If len(args) == 2, start is start, args[0] is stop, args[1] is num.
+    use_real: bool
+        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+    Returns
+    -------
+    pos_embed: torch.Tensor
+        [HW, D/2]
+    """
+    grid = get_meshgrid(start, *args)   # [2, H, w]
+    grid = grid.reshape([2, 1, *grid.shape[1:]])   # Returns a sampling matrix with the same resolution as the target resolution
+    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+    return pos_embed
+
+
+def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
+    assert embed_dim % 4 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
+    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
+
+    if use_real:
+        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)    # (H*W, D/2)
+        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)    # (H*W, D/2)
+        return cos, sin
+    else:
+        emb = torch.cat([emb_h, emb_w], dim=1)    # (H*W, D/2)
+        return emb
+
+
+def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (bool, optional): If True, return real part and imaginary part separately.
+                                   Otherwise, return complex numbers.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2]
+
+    """
+    if isinstance(pos, int):
+        pos = np.arange(pos)
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))  # [D/2]
+    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
+    freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
+    if use_real:
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+
+def calc_sizes(rope_img, patch_size, th, tw):
+    if rope_img == 'extend':
+        # Expansion mode
+        sub_args = [(th, tw)]
+    elif rope_img.startswith('base'):
+        # Based on the specified dimensions, other dimensions are obtained through interpolation.
+        base_size = int(rope_img[4:]) // 8 // patch_size
+        start, stop = get_fill_resize_and_crop((th, tw), base_size)
+        sub_args = [start, stop, (th, tw)]
+    else:
+        raise ValueError(f"Unknown rope_img: {rope_img}")
+    return sub_args
+
+
+def init_image_posemb(rope_img,
+                      resolutions,
+                      patch_size,
+                      hidden_size,
+                      num_heads,
+                      log_fn,
+                      rope_real=True,
+                      ):
+    freqs_cis_img = {}
+    for reso in resolutions:
+        th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size
+        sub_args = calc_sizes(rope_img, patch_size, th, tw)
+        freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real)
+        log_fn(f"    Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) "
+               f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}")
+    return freqs_cis_img

comfy/ldm/modules/attention.py

@@ -358,7 +358,7 @@ 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)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
 
     if skip_reshape:
          q, k, v = map(

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -7,6 +7,9 @@ import torch
 import torch.nn as nn
 from .. import attention
 from einops import rearrange, repeat
+from .util import timestep_embedding
+import comfy.ops
+import comfy.ldm.common_dit
 
 def default(x, y):
     if x is not None:
@@ -68,12 +71,14 @@ class PatchEmbed(nn.Module):
             bias: bool = True,
             strict_img_size: bool = True,
             dynamic_img_pad: bool = True,
+            padding_mode='circular',
             dtype=None,
             device=None,
             operations=None,
     ):
         super().__init__()
         self.patch_size = (patch_size, patch_size)
+        self.padding_mode = padding_mode
         if img_size is not None:
             self.img_size = (img_size, img_size)
             self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
@@ -107,9 +112,7 @@ class PatchEmbed(nn.Module):
         #             f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
         #         )
         if self.dynamic_img_pad:
-            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
-            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
-            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
         x = self.proj(x)
         if self.flatten:
             x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
@@ -230,34 +233,8 @@ class TimestepEmbedder(nn.Module):
         )
         self.frequency_embedding_size = frequency_embedding_size
 
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
-            / half
-        )
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat(
-                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
-            )
-        if torch.is_floating_point(t):
-            embedding = embedding.to(dtype=t.dtype)
-        return embedding
-
     def forward(self, t, dtype, **kwargs):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)
         t_emb = self.mlp(t_freq)
         return t_emb
 
@@ -745,6 +722,8 @@ class MMDiT(nn.Module):
         qkv_bias: bool = True,
         context_processor_layers = None,
         context_size = 4096,
+        num_blocks = None,
+        final_layer = True,
         dtype = None, #TODO
         device = None,
         operations = None,
@@ -766,7 +745,10 @@ class MMDiT(nn.Module):
         # apply magic --> this defines a head_size of 64
         self.hidden_size = 64 * depth
         num_heads = depth
+        if num_blocks is None:
+            num_blocks = depth
 
+        self.depth = depth
         self.num_heads = num_heads
 
         self.x_embedder = PatchEmbed(
@@ -821,7 +803,7 @@ class MMDiT(nn.Module):
                     mlp_ratio=mlp_ratio,
                     qkv_bias=qkv_bias,
                     attn_mode=attn_mode,
-                    pre_only=i == depth - 1,
+                    pre_only=(i == num_blocks - 1) and final_layer,
                     rmsnorm=rmsnorm,
                     scale_mod_only=scale_mod_only,
                     swiglu=swiglu,
@@ -830,11 +812,12 @@ class MMDiT(nn.Module):
                     device=device,
                     operations=operations
                 )
-                for i in range(depth)
+                for i in range(num_blocks)
             ]
         )
 
-        self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        if final_layer:
+            self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
 
         if compile_core:
             assert False
@@ -893,6 +876,7 @@ class MMDiT(nn.Module):
         x: torch.Tensor,
         c_mod: torch.Tensor,
         context: Optional[torch.Tensor] = None,
+        control = None,
     ) -> torch.Tensor:
         if self.register_length > 0:
             context = torch.cat(
@@ -905,13 +889,20 @@ class MMDiT(nn.Module):
 
         # context is B, L', D
         # x is B, L, D
-        for block in self.joint_blocks:
-            context, x = block(
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
                 context,
                 x,
                 c=c_mod,
                 use_checkpoint=self.use_checkpoint,
             )
+            if control is not None:
+                control_o = control.get("output")
+                if i < len(control_o):
+                    add = control_o[i]
+                    if add is not None:
+                        x += add
 
         x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
         return x
@@ -922,6 +913,7 @@ class MMDiT(nn.Module):
         t: torch.Tensor,
         y: Optional[torch.Tensor] = None,
         context: Optional[torch.Tensor] = None,
+        control = None,
     ) -> torch.Tensor:
         """
         Forward pass of DiT.
@@ -934,7 +926,7 @@ class MMDiT(nn.Module):
             context = self.context_processor(context)
 
         hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
         c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
         if y is not None and self.y_embedder is not None:
             y = self.y_embedder(y)  # (N, D)
@@ -943,7 +935,7 @@ class MMDiT(nn.Module):
         if context is not None:
             context = self.context_embedder(context)
 
-        x = self.forward_core_with_concat(x, c, context)
+        x = self.forward_core_with_concat(x, c, context, control)
 
         x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
         return x[:,:,:hw[-2],:hw[-1]]
@@ -956,7 +948,8 @@ class OpenAISignatureMMDITWrapper(MMDiT):
         timesteps: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         y: Optional[torch.Tensor] = None,
+        control = None,
         **kwargs,
     ) -> torch.Tensor:
-        return super().forward(x, timesteps, context=context, y=y)
+        return super().forward(x, timesteps, context=context, y=y, control=control)
 

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -809,7 +809,7 @@ class UNetModel(nn.Module):
         self.out = nn.Sequential(
             operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             nn.SiLU(),
-            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
+            operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(

comfy/lora.py

@@ -1,3 +1,21 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import comfy.utils
 import logging
 
@@ -218,11 +236,17 @@ def model_lora_keys_clip(model, key_map={}):
                     lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
                     key_map[lora_key] = k
 
-    for k in sdk: #OneTrainer SD3 lora
-        if k.startswith("t5xxl.transformer.") and k.endswith(".weight"):
-            l_key = k[len("t5xxl.transformer."):-len(".weight")]
-            lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
-            key_map[lora_key] = k
+    for k in sdk:
+        if k.endswith(".weight"):
+            if k.startswith("t5xxl.transformer."):#OneTrainer SD3 lora
+                l_key = k[len("t5xxl.transformer."):-len(".weight")]
+                lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
+                key_map[lora_key] = k
+            elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
+                l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
+                lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
+                key_map[lora_key] = k
+
 
     k = "clip_g.transformer.text_projection.weight"
     if k in sdk:
@@ -245,6 +269,7 @@ def model_lora_keys_unet(model, key_map={}):
             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
 
     diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
     for k in diffusers_keys:
@@ -274,4 +299,26 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
                 key_map[key_lora] = to
 
+    if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
+        diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
+                key_map[key_lora] = to
+
+    if isinstance(model, comfy.model_base.HunyuanDiT):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format
+
+    if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux
+        diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers flux lora format
+                key_map[key_lora] = to
+
     return key_map

comfy/model_base.py

@@ -1,3 +1,21 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import torch
 import logging
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
@@ -6,8 +24,12 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.aura.mmdit
+import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
+import comfy.ldm.flux.model
+
 import comfy.model_management
 import comfy.conds
 import comfy.ops
@@ -24,6 +46,7 @@ class ModelType(Enum):
     EDM = 5
     FLOW = 6
     V_PREDICTION_CONTINUOUS = 7
+    FLUX = 8
 
 
 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
@@ -51,6 +74,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
         c = V_PREDICTION
         s = ModelSamplingContinuousV
+    elif model_type == ModelType.FLUX:
+        c = comfy.model_sampling.CONST
+        s = comfy.model_sampling.ModelSamplingFlux
 
     class ModelSampling(s, c):
         pass
@@ -66,16 +92,21 @@ class BaseModel(torch.nn.Module):
         self.latent_format = model_config.latent_format
         self.model_config = model_config
         self.manual_cast_dtype = model_config.manual_cast_dtype
+        self.device = device
 
         if not unet_config.get("disable_unet_model_creation", False):
-            if self.manual_cast_dtype is not None:
-                operations = comfy.ops.manual_cast
+            if model_config.custom_operations is None:
+                if self.manual_cast_dtype is not None:
+                    operations = comfy.ops.manual_cast
+                else:
+                    operations = comfy.ops.disable_weight_init
             else:
-                operations = comfy.ops.disable_weight_init
+                operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
             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")
+            logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
@@ -86,6 +117,7 @@ class BaseModel(torch.nn.Module):
         self.concat_keys = ()
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
+        self.memory_usage_factor = model_config.memory_usage_factor
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         sigma = t
@@ -244,11 +276,11 @@ class BaseModel(torch.nn.Module):
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
+            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:])
-            return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
+            return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
 
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
@@ -346,6 +378,7 @@ class SDXL(BaseModel):
         flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
 
+
 class SVD_img2vid(BaseModel):
     def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
         super().__init__(model_config, model_type, device=device)
@@ -586,17 +619,17 @@ class SD3(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
 
-    def memory_required(self, input_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 probably needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024)
-        else:
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * 0.3) * (1024 * 1024)
+
+class AuraFlow(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
+
+    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)
+        return out
 
 
 class StableAudio1(BaseModel):
@@ -636,3 +669,50 @@ class StableAudio1(BaseModel):
             for l in s:
                 sd["{}{}".format(k, l)] = s[l]
         return sd
+
+class HunyuanDiT(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT)
+
+    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)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask)
+
+        conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None)
+        if conditioning_mt5xl is not None:
+            out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl)
+
+        attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None)
+        if attention_mask_mt5xl is not None:
+            out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl)
+
+        width = kwargs.get("width", 768)
+        height = kwargs.get("height", 768)
+        crop_w = kwargs.get("crop_w", 0)
+        crop_h = kwargs.get("crop_h", 0)
+        target_width = kwargs.get("target_width", width)
+        target_height = kwargs.get("target_height", height)
+
+        out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
+        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 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)
+        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+        return out

comfy/model_detection.py

@@ -41,7 +41,9 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
         patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
         unet_config["patch_size"] = patch_size
-        unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size)
+        final_layer = '{}final_layer.linear.weight'.format(key_prefix)
+        if final_layer in state_dict:
+            unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
 
         unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
         unet_config["input_size"] = None
@@ -103,6 +105,49 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["audio_model"] = "dit1.0"
         return unet_config
 
+    if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
+        unet_config = {}
+        unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
+        unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+        double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
+        single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
+        unet_config["n_double_layers"] = double_layers
+        unet_config["n_layers"] = double_layers + single_layers
+        return unet_config
+
+    if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
+        unet_config = {}
+        unet_config["image_model"] = "hydit"
+        unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+        unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
+        if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
+            unet_config["mlp_ratio"] = 4.3637
+        if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
+            unet_config["size_cond"] = True
+            unet_config["use_style_cond"] = True
+            unet_config["image_model"] = "hydit1"
+        return unet_config
+
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
+        dit_config = {}
+        dit_config["image_model"] = "flux"
+        dit_config["in_channels"] = 16
+        dit_config["vec_in_dim"] = 768
+        dit_config["context_in_dim"] = 4096
+        dit_config["hidden_size"] = 3072
+        dit_config["mlp_ratio"] = 4.0
+        dit_config["num_heads"] = 24
+        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"] = 10000
+        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 '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
+        return None
+
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
@@ -237,6 +282,8 @@ def model_config_from_unet_config(unet_config, state_dict=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)
+    if unet_config is None:
+        return None
     model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
         return comfy.supported_models_base.BASE(unet_config)
@@ -244,11 +291,22 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         return model_config
 
 def unet_prefix_from_state_dict(state_dict):
-    if "model.model.postprocess_conv.weight" in state_dict: #audio models
-        unet_key_prefix = "model.model."
+    candidates = ["model.diffusion_model.", #ldm/sgm models
+                  "model.model.", #audio models
+                  ]
+    counts = {k: 0 for k in candidates}
+    for k in state_dict:
+        for c in candidates:
+            if k.startswith(c):
+                counts[c] += 1
+                break
+
+    top = max(counts, key=counts.get)
+    if counts[top] > 5:
+        return top
     else:
-        unet_key_prefix = "model.diffusion_model."
-    return unet_key_prefix
+        return "model." #aura flow and others
+
 
 def convert_config(unet_config):
     new_config = unet_config.copy()
@@ -435,36 +493,55 @@ def model_config_from_diffusers_unet(state_dict):
     return None
 
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
-    depth = count_blocks(state_dict, 'transformer_blocks.{}.')
-    if depth > 0:
-        out_sd = {}
-        sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix)
-        for k in sd_map:
-            weight = state_dict.get(k, None)
-            if weight is not None:
-                t = sd_map[k]
+    out_sd = {}
 
-                if not isinstance(t, str):
-                    if len(t) > 2:
-                        fun = t[2]
-                    else:
-                        fun = lambda a: a
-                    offset = t[1]
-                    if offset is not None:
-                        old_weight = out_sd.get(t[0], None)
-                        if old_weight is None:
-                            old_weight = torch.empty_like(weight)
-                            old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+    if 'transformer_blocks.0.attn.norm_added_k.weight' in state_dict: #Flux
+        depth = count_blocks(state_dict, 'transformer_blocks.{}.')
+        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
+        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)
+    elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    else:
+        return None
 
-                        w = old_weight.narrow(offset[0], offset[1], offset[2])
-                    else:
-                        old_weight = weight
-                        w = weight
-                    w[:] = fun(weight)
-                    t = t[0]
-                    out_sd[t] = old_weight
+    for k in sd_map:
+        weight = state_dict.get(k, None)
+        if weight is not None:
+            t = sd_map[k]
+
+            if not isinstance(t, str):
+                if len(t) > 2:
+                    fun = t[2]
                 else:
-                    out_sd[t] = weight
-                state_dict.pop(k)
+                    fun = lambda a: a
+                offset = t[1]
+                if offset is not None:
+                    old_weight = out_sd.get(t[0], None)
+                    if old_weight is None:
+                        old_weight = torch.empty_like(weight)
+                    if old_weight.shape[offset[0]] < offset[1] + offset[2]:
+                        exp = list(weight.shape)
+                        exp[offset[0]] = offset[1] + offset[2]
+                        new = torch.empty(exp, device=weight.device, dtype=weight.dtype)
+                        new[:old_weight.shape[0]] = old_weight
+                        old_weight = new
+
+                    w = old_weight.narrow(offset[0], offset[1], offset[2])
+                else:
+                    old_weight = weight
+                    w = weight
+                w[:] = fun(weight)
+                t = t[0]
+                out_sd[t] = old_weight
+            else:
+                out_sd[t] = weight
+            state_dict.pop(k)
 
     return out_sd

comfy/model_management.py

@@ -1,3 +1,21 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import psutil
 import logging
 from enum import Enum
@@ -273,9 +291,12 @@ class LoadedModel:
     def model_memory(self):
         return self.model.model_size()
 
+    def model_offloaded_memory(self):
+        return self.model.model_size() - self.model.loaded_size()
+
     def model_memory_required(self, device):
-        if device == self.model.current_device:
-            return 0
+        if device == self.model.current_loaded_device():
+            return self.model_offloaded_memory()
         else:
             return self.model_memory()
 
@@ -287,15 +308,21 @@ class LoadedModel:
 
         load_weights = not self.weights_loaded
 
-        try:
-            if lowvram_model_memory > 0 and load_weights:
-                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
-            else:
-                self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
-        except Exception as e:
-            self.model.unpatch_model(self.model.offload_device)
-            self.model_unload()
-            raise e
+        if self.model.loaded_size() > 0:
+            use_more_vram = lowvram_model_memory
+            if use_more_vram == 0:
+                use_more_vram = 1e32
+            self.model_use_more_vram(use_more_vram)
+        else:
+            try:
+                if lowvram_model_memory > 0 and load_weights:
+                    self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
+                else:
+                    self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
+            except Exception as e:
+                self.model.unpatch_model(self.model.offload_device)
+                self.model_unload()
+                raise e
 
         if is_intel_xpu() and not args.disable_ipex_optimize:
             self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
@@ -304,21 +331,44 @@ class LoadedModel:
         return self.real_model
 
     def should_reload_model(self, force_patch_weights=False):
-        if force_patch_weights and self.model.lowvram_patch_counter > 0:
+        if force_patch_weights and self.model.lowvram_patch_counter() > 0:
             return True
         return False
 
-    def model_unload(self, unpatch_weights=True):
+    def model_unload(self, memory_to_free=None, unpatch_weights=True):
+        if memory_to_free is not None:
+            if memory_to_free < self.model.loaded_size():
+                freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
+                if freed >= memory_to_free:
+                    return False
         self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
         self.model.model_patches_to(self.model.offload_device)
         self.weights_loaded = self.weights_loaded and not unpatch_weights
         self.real_model = None
+        return True
+
+    def model_use_more_vram(self, extra_memory):
+        return self.model.partially_load(self.device, extra_memory)
 
     def __eq__(self, other):
         return self.model is other.model
 
+def use_more_memory(extra_memory, loaded_models, device):
+    for m in loaded_models:
+        if m.device == device:
+            extra_memory -= m.model_use_more_vram(extra_memory)
+            if extra_memory <= 0:
+                break
+
+def offloaded_memory(loaded_models, device):
+    offloaded_mem = 0
+    for m in loaded_models:
+        if m.device == device:
+            offloaded_mem += m.model_offloaded_memory()
+    return offloaded_mem
+
 def minimum_inference_memory():
-    return (1024 * 1024 * 1024)
+    return (1024 * 1024 * 1024) * 1.2
 
 def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []
@@ -352,6 +402,7 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True):
 def free_memory(memory_required, device, keep_loaded=[]):
     unloaded_model = []
     can_unload = []
+    unloaded_models = []
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
@@ -362,14 +413,18 @@ def free_memory(memory_required, device, keep_loaded=[]):
 
     for x in sorted(can_unload):
         i = x[-1]
+        memory_to_free = None
         if not DISABLE_SMART_MEMORY:
-            if get_free_memory(device) > memory_required:
+            free_mem = get_free_memory(device)
+            if free_mem > memory_required:
                 break
-        current_loaded_models[i].model_unload()
-        unloaded_model.append(i)
+            memory_to_free = memory_required - free_mem
+        logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
+        if current_loaded_models[i].model_unload(memory_to_free):
+            unloaded_model.append(i)
 
     for i in sorted(unloaded_model, reverse=True):
-        current_loaded_models.pop(i)
+        unloaded_models.append(current_loaded_models.pop(i))
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
@@ -378,12 +433,17 @@ def free_memory(memory_required, device, keep_loaded=[]):
             mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
+    return unloaded_models
 
-def load_models_gpu(models, memory_required=0, force_patch_weights=False):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
     global vram_state
 
     inference_memory = minimum_inference_memory()
-    extra_mem = max(inference_memory, memory_required)
+    extra_mem = max(inference_memory, memory_required + 300 * 1024 * 1024)
+    if minimum_memory_required is None:
+        minimum_memory_required = extra_mem
+    else:
+        minimum_memory_required = max(inference_memory, minimum_memory_required + 300 * 1024 * 1024)
 
     models = set(models)
 
@@ -416,25 +476,36 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         devs = set(map(lambda a: a.device, models_already_loaded))
         for d in devs:
             if d != torch.device("cpu"):
-                free_memory(extra_mem, d, models_already_loaded)
-        return
+                free_memory(extra_mem + offloaded_memory(models_already_loaded, d), d, models_already_loaded)
+                free_mem = get_free_memory(d)
+                if free_mem < minimum_memory_required:
+                    logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed.
+                    models_to_load = free_memory(minimum_memory_required, d)
+                    logging.info("{} models unloaded.".format(len(models_to_load)))
+                else:
+                    use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)
+        if len(models_to_load) == 0:
+            return
 
     logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
 
     total_memory_required = {}
     for loaded_model in models_to_load:
-        if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different
-            total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+        unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) #unload clones where the weights are different
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
 
-    for device in total_memory_required:
-        if device != torch.device("cpu"):
-            free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
+    for loaded_model in models_already_loaded:
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
 
     for loaded_model in models_to_load:
         weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
         if weights_unloaded is not None:
             loaded_model.weights_loaded = not weights_unloaded
 
+    for device in total_memory_required:
+        if device != torch.device("cpu"):
+            free_memory(total_memory_required[device] * 1.1 + extra_mem, device, models_already_loaded)
+
     for loaded_model in models_to_load:
         model = loaded_model.model
         torch_dev = model.load_device
@@ -443,11 +514,11 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         else:
             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):
+        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)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
-            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
+            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_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:
@@ -455,6 +526,14 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
 
         cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
         current_loaded_models.insert(0, loaded_model)
+
+
+    devs = set(map(lambda a: a.device, models_already_loaded))
+    for d in devs:
+        if d != torch.device("cpu"):
+            free_mem = get_free_memory(d)
+            if free_mem > minimum_memory_required:
+                use_more_memory(free_mem - minimum_memory_required, models_already_loaded, d)
     return
 
 
@@ -523,6 +602,9 @@ def unet_inital_load_device(parameters, dtype):
     else:
         return cpu_dev
 
+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]):
     if args.bf16_unet:
         return torch.bfloat16
@@ -532,12 +614,37 @@ 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 should_use_fp16(device=device, model_params=model_params, manual_cast=True):
-        if torch.float16 in supported_dtypes:
-            return torch.float16
-    if should_use_bf16(device, model_params=model_params, manual_cast=True):
-        if torch.bfloat16 in supported_dtypes:
-            return torch.bfloat16
+
+    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 fp8_dtype is not None:
+        free_model_memory = maximum_vram_for_weights(device)
+        if model_params * 2 > free_model_memory:
+            return fp8_dtype
+
+    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:
+                return torch.float16
+        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):
+            if torch.bfloat16 in supported_dtypes:
+                return torch.bfloat16
+
+    for dt in supported_dtypes:
+        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):
+            if torch.float16 in supported_dtypes:
+                return torch.float16
+        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):
+            if torch.bfloat16 in supported_dtypes:
+                return torch.bfloat16
+
     return torch.float32
 
 # None means no manual cast
@@ -553,13 +660,13 @@ def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.flo
     if bf16_supported and weight_dtype == torch.bfloat16:
         return None
 
-    if 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
+        if dt == torch.bfloat16 and bf16_supported:
+            return torch.bfloat16
 
-    elif bf16_supported and torch.bfloat16 in supported_dtypes:
-        return torch.bfloat16
-    else:
-        return torch.float32
+    return torch.float32
 
 def text_encoder_offload_device():
     if args.gpu_only:
@@ -578,6 +685,20 @@ def text_encoder_device():
     else:
         return torch.device("cpu")
 
+def text_encoder_initial_device(load_device, offload_device, model_size=0):
+    if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
+        return offload_device
+
+    if is_device_mps(load_device):
+        return offload_device
+
+    mem_l = get_free_memory(load_device)
+    mem_o = get_free_memory(offload_device)
+    if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l:
+        return load_device
+    else:
+        return offload_device
+
 def text_encoder_dtype(device=None):
     if args.fp8_e4m3fn_text_enc:
         return torch.float8_e4m3fn
@@ -649,18 +770,29 @@ def supports_cast(device, dtype): #TODO
         return True
     if dtype == torch.float16:
         return True
-    if is_device_mps(device):
-        return False
     if directml_enabled: #TODO: test this
         return False
     if dtype == torch.bfloat16:
         return True
+    if is_device_mps(device):
+        return False
     if dtype == torch.float8_e4m3fn:
         return True
     if dtype == torch.float8_e5m2:
         return True
     return False
 
+def pick_weight_dtype(dtype, fallback_dtype, device=None):
+    if dtype is None:
+        dtype = fallback_dtype
+    elif dtype_size(dtype) > dtype_size(fallback_dtype):
+        dtype = fallback_dtype
+
+    if not supports_cast(device, dtype):
+        dtype = fallback_dtype
+
+    return dtype
+
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
@@ -856,7 +988,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
             fp16_works = True
 
     if fp16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
+        free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
 
@@ -876,9 +1008,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
             return False
 
-    if device is not None: #TODO not sure about mps bf16 support
+    if device is not None:
         if is_device_mps(device):
-            return False
+            return True
 
     if FORCE_FP32:
         return False
@@ -886,23 +1018,23 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if directml_enabled:
         return False
 
-    if cpu_mode() or mps_mode():
+    if mps_mode():
+        return True
+
+    if cpu_mode():
         return False
 
     if is_intel_xpu():
         return True
 
-    if device is None:
-        device = torch.device("cuda")
-
-    props = torch.cuda.get_device_properties(device)
+    props = torch.cuda.get_device_properties("cuda")
     if props.major >= 8:
         return True
 
     bf16_works = torch.cuda.is_bf16_supported()
 
     if bf16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
+        free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
 

comfy/model_patcher.py

@@ -1,8 +1,27 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import torch
 import copy
 import inspect
 import logging
 import uuid
+import collections
 
 import comfy.utils
 import comfy.model_management
@@ -57,10 +76,37 @@ def set_model_options_post_cfg_function(model_options, post_cfg_function, disabl
         model_options["disable_cfg1_optimization"] = True
     return model_options
 
+def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):
+    model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function]
+    if disable_cfg1_optimization:
+        model_options["disable_cfg1_optimization"] = True
+    return model_options
+
+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
+
+class LowVramPatch:
+    def __init__(self, key, model_patcher):
+        self.key = key
+        self.model_patcher = model_patcher
+    def __call__(self, weight):
+        return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key)
+
+
 class ModelPatcher:
-    def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
+    def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
         self.size = size
         self.model = model
+        if not hasattr(self.model, 'device'):
+            logging.debug("Model doesn't have a device attribute.")
+            self.model.device = offload_device
+        elif self.model.device is None:
+            self.model.device = offload_device
+
         self.patches = {}
         self.backup = {}
         self.object_patches = {}
@@ -69,24 +115,32 @@ class ModelPatcher:
         self.model_size()
         self.load_device = load_device
         self.offload_device = offload_device
-        if current_device is None:
-            self.current_device = self.offload_device
-        else:
-            self.current_device = current_device
-
         self.weight_inplace_update = weight_inplace_update
-        self.model_lowvram = False
-        self.lowvram_patch_counter = 0
         self.patches_uuid = uuid.uuid4()
 
+        if not hasattr(self.model, 'model_loaded_weight_memory'):
+            self.model.model_loaded_weight_memory = 0
+
+        if not hasattr(self.model, 'lowvram_patch_counter'):
+            self.model.lowvram_patch_counter = 0
+
+        if not hasattr(self.model, 'model_lowvram'):
+            self.model.model_lowvram = False
+
     def model_size(self):
         if self.size > 0:
             return self.size
         self.size = comfy.model_management.module_size(self.model)
         return self.size
 
+    def loaded_size(self):
+        return self.model.model_loaded_weight_memory
+
+    def lowvram_patch_counter(self):
+        return self.model.lowvram_patch_counter
+
     def clone(self):
-        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
+        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -130,6 +184,9 @@ class ModelPatcher:
     def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
         self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
 
+    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_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 
@@ -255,16 +312,16 @@ class ModelPatcher:
                     sd.pop(k)
         return sd
 
-    def patch_weight_to_device(self, key, device_to=None):
+    def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
         if key not in self.patches:
             return
 
         weight = comfy.utils.get_attr(self.model, key)
 
-        inplace_update = self.weight_inplace_update
+        inplace_update = self.weight_inplace_update or inplace_update
 
         if key not in self.backup:
-            self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
+            self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
 
         if device_to is not None:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
@@ -293,29 +350,25 @@ class ModelPatcher:
 
             if device_to is not None:
                 self.model.to(device_to)
-                self.current_device = device_to
+                self.model.device = device_to
+                self.model.model_loaded_weight_memory = self.model_size()
 
         return self.model
 
-    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False):
-        self.patch_model(device_to, patch_weights=False)
-
-        logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
-        class LowVramPatch:
-            def __init__(self, key, model_patcher):
-                self.key = key
-                self.model_patcher = model_patcher
-            def __call__(self, weight):
-                return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key)
-
+    def lowvram_load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
         mem_counter = 0
         patch_counter = 0
+        lowvram_counter = 0
         for n, m in self.model.named_modules():
             lowvram_weight = False
-            if hasattr(m, "comfy_cast_weights"):
+
+            if not full_load and hasattr(m, "comfy_cast_weights"):
                 module_mem = comfy.model_management.module_size(m)
                 if mem_counter + module_mem >= lowvram_model_memory:
                     lowvram_weight = True
+                    lowvram_counter += 1
+                    if m.comfy_cast_weights:
+                        continue
 
             weight_key = "{}.weight".format(n)
             bias_key = "{}.bias".format(n)
@@ -337,15 +390,40 @@ class ModelPatcher:
                 m.prev_comfy_cast_weights = m.comfy_cast_weights
                 m.comfy_cast_weights = True
             else:
+                if hasattr(m, "comfy_cast_weights"):
+                    if m.comfy_cast_weights:
+                        wipe_lowvram_weight(m)
+
                 if hasattr(m, "weight"):
-                    self.patch_weight_to_device(weight_key, device_to)
-                    self.patch_weight_to_device(bias_key, device_to)
-                    m.to(device_to)
                     mem_counter += comfy.model_management.module_size(m)
+                    param = list(m.parameters())
+                    if len(param) > 0:
+                        weight = param[0]
+                        if weight.device == device_to:
+                            continue
+
+                    weight_to = None
+                    if full_load:#TODO
+                        weight_to = device_to
+                    self.patch_weight_to_device(weight_key, device_to=weight_to) #TODO: speed this up without OOM
+                    self.patch_weight_to_device(bias_key, device_to=weight_to)
+                    m.to(device_to)
                     logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
 
-        self.model_lowvram = True
-        self.lowvram_patch_counter = patch_counter
+        if lowvram_counter > 0:
+            logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+            self.model.model_lowvram = True
+        else:
+            logging.info("loaded completely {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024)))
+            self.model.model_lowvram = False
+        self.model.lowvram_patch_counter += patch_counter
+        self.model.device = device_to
+        self.model.model_loaded_weight_memory = mem_counter
+
+
+    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False):
+        self.patch_model(device_to, patch_weights=False)
+        self.lowvram_load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
         return self.model
 
     def calculate_weight(self, patches, weight, key):
@@ -518,34 +596,89 @@ class ModelPatcher:
 
     def unpatch_model(self, device_to=None, unpatch_weights=True):
         if unpatch_weights:
-            if self.model_lowvram:
+            if self.model.model_lowvram:
                 for m in self.model.modules():
-                    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
+                    wipe_lowvram_weight(m)
 
-                self.model_lowvram = False
-                self.lowvram_patch_counter = 0
+                self.model.model_lowvram = False
+                self.model.lowvram_patch_counter = 0
 
             keys = list(self.backup.keys())
 
-            if self.weight_inplace_update:
-                for k in keys:
-                    comfy.utils.copy_to_param(self.model, k, self.backup[k])
-            else:
-                for k in keys:
-                    comfy.utils.set_attr_param(self.model, k, self.backup[k])
+            for k in keys:
+                bk = self.backup[k]
+                if bk.inplace_update:
+                    comfy.utils.copy_to_param(self.model, k, bk.weight)
+                else:
+                    comfy.utils.set_attr_param(self.model, k, bk.weight)
 
             self.backup.clear()
 
             if device_to is not None:
                 self.model.to(device_to)
-                self.current_device = device_to
+                self.model.device = device_to
+            self.model.model_loaded_weight_memory = 0
 
         keys = list(self.object_patches_backup.keys())
         for k in keys:
             comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
 
         self.object_patches_backup.clear()
+
+    def partially_unload(self, device_to, memory_to_free=0):
+        memory_freed = 0
+        patch_counter = 0
+
+        for n, m in list(self.model.named_modules())[::-1]:
+            if memory_to_free < memory_freed:
+                break
+
+            shift_lowvram = False
+            if hasattr(m, "comfy_cast_weights"):
+                module_mem = comfy.model_management.module_size(m)
+                weight_key = "{}.weight".format(n)
+                bias_key = "{}.bias".format(n)
+
+
+                if m.weight is not None and m.weight.device != device_to:
+                    for key in [weight_key, bias_key]:
+                        bk = self.backup.get(key, None)
+                        if bk is not None:
+                            if bk.inplace_update:
+                                comfy.utils.copy_to_param(self.model, key, bk.weight)
+                            else:
+                                comfy.utils.set_attr_param(self.model, key, bk.weight)
+                            self.backup.pop(key)
+
+                    m.to(device_to)
+                    if weight_key in self.patches:
+                        m.weight_function = LowVramPatch(weight_key, self)
+                        patch_counter += 1
+                    if bias_key in self.patches:
+                        m.bias_function = LowVramPatch(bias_key, self)
+                        patch_counter += 1
+
+                    m.prev_comfy_cast_weights = m.comfy_cast_weights
+                    m.comfy_cast_weights = True
+                    memory_freed += module_mem
+                    logging.debug("freed {}".format(n))
+
+        self.model.model_lowvram = True
+        self.model.lowvram_patch_counter += patch_counter
+        self.model.model_loaded_weight_memory -= memory_freed
+        return memory_freed
+
+    def partially_load(self, device_to, extra_memory=0):
+        self.unpatch_model(unpatch_weights=False)
+        self.patch_model(patch_weights=False)
+        full_load = False
+        if self.model.model_lowvram == False:
+            return 0
+        if self.model.model_loaded_weight_memory + extra_memory > self.model_size():
+            full_load = True
+        current_used = self.model.model_loaded_weight_memory
+        self.lowvram_load(device_to, lowvram_model_memory=current_used + extra_memory, full_load=full_load)
+        return self.model.model_loaded_weight_memory - current_used
+
+    def current_loaded_device(self):
+        return self.model.device

comfy/model_sampling.py

@@ -59,8 +59,9 @@ class ModelSamplingDiscrete(torch.nn.Module):
         beta_schedule = sampling_settings.get("beta_schedule", "linear")
         linear_start = sampling_settings.get("linear_start", 0.00085)
         linear_end = sampling_settings.get("linear_end", 0.012)
+        timesteps = sampling_settings.get("timesteps", 1000)
 
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
         self.sigma_data = 1.0
 
     def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
@@ -190,11 +191,12 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         else:
             sampling_settings = {}
 
-        self.set_parameters(shift=sampling_settings.get("shift", 1.0))
+        self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000))
 
-    def set_parameters(self, shift=1.0, timesteps=1000):
+    def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
         self.shift = shift
-        ts = self.sigma(torch.arange(1, timesteps + 1, 1))
+        self.multiplier = multiplier
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)
         self.register_buffer('sigmas', ts)
 
     @property
@@ -206,10 +208,10 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         return self.sigmas[-1]
 
     def timestep(self, sigma):
-        return sigma * 1000
+        return sigma * self.multiplier
 
     def sigma(self, timestep):
-        return time_snr_shift(self.shift, timestep / 1000)
+        return time_snr_shift(self.shift, timestep / self.multiplier)
 
     def percent_to_sigma(self, percent):
         if percent <= 0.0:
@@ -270,3 +272,43 @@ class StableCascadeSampling(ModelSamplingDiscrete):
 
         percent = 1.0 - percent
         return self.sigma(torch.tensor(percent))
+
+
+def flux_time_shift(mu: float, sigma: float, t):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+class ModelSamplingFlux(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(shift=sampling_settings.get("shift", 1.15))
+
+    def set_parameters(self, shift=1.15, timesteps=10000):
+        self.shift = shift
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))
+        self.register_buffer('sigmas', ts)
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        return sigma
+
+    def sigma(self, timestep):
+        return flux_time_shift(self.shift, 1.0, timestep)
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 1.0
+        if percent >= 1.0:
+            return 0.0
+        return 1.0 - percent

comfy/ops.py

@@ -19,14 +19,27 @@
 import torch
 import comfy.model_management
 
-def cast_bias_weight(s, input):
+
+def cast_to(weight, dtype=None, device=None, non_blocking=False):
+    return weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
+
+def cast_to_input(weight, input, non_blocking=False):
+    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
+
+def cast_bias_weight(s, input=None, dtype=None, device=None):
+    if input is not None:
+        if dtype is None:
+            dtype = input.dtype
+        if device is None:
+            device = input.device
+
     bias = None
-    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
+    non_blocking = comfy.model_management.device_should_use_non_blocking(device)
     if s.bias is not None:
-        bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+        bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
         if s.bias_function is not None:
             bias = s.bias_function(bias)
-    weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
     if s.weight_function is not None:
         weight = s.weight_function(weight)
     return weight, bias
@@ -168,6 +181,26 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+        def reset_parameters(self):
+            self.bias = None
+            return None
+
+        def forward_comfy_cast_weights(self, input, out_dtype=None):
+            output_dtype = out_dtype
+            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+                out_dtype = None
+            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+            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:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                if "out_dtype" in kwargs:
+                    kwargs.pop("out_dtype")
+                return super().forward(*args, **kwargs)
+
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -202,3 +235,6 @@ class manual_cast(disable_weight_init):
 
     class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
         comfy_cast_weights = True
+
+    class Embedding(disable_weight_init.Embedding):
+        comfy_cast_weights = True

comfy/sampler_helpers.py

@@ -61,7 +61,9 @@ def prepare_sampling(model, noise_shape, conds):
     device = model.load_device
     real_model = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
-    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
+    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)
     real_model = model.model
 
     return real_model, conds, models

comfy/samplers.py

@@ -6,6 +6,8 @@ from comfy import model_management
 import math
 import logging
 import comfy.sampler_helpers
+import scipy
+import numpy
 
 def get_area_and_mult(conds, x_in, timestep_in):
     dims = tuple(x_in.shape[2:])
@@ -169,7 +171,7 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options):
         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) < free_memory:
+            if model.memory_required(input_shape) * 1.5 < free_memory:
                 to_batch = batch_amount
                 break
 
@@ -275,6 +277,12 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
 
     conds = [cond, uncond_]
     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)
+
     return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
 
 
@@ -305,13 +313,18 @@ def simple_scheduler(model_sampling, steps):
 def ddim_scheduler(model_sampling, steps):
     s = model_sampling
     sigs = []
-    ss = max(len(s.sigmas) // steps, 1)
     x = 1
+    if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):
+        steps += 1
+        sigs = []
+    else:
+        sigs = [0.0]
+
+    ss = max(len(s.sigmas) // steps, 1)
     while x < len(s.sigmas):
         sigs += [float(s.sigmas[x])]
         x += ss
     sigs = sigs[::-1]
-    sigs += [0.0]
     return torch.FloatTensor(sigs)
 
 def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
@@ -319,15 +332,34 @@ def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
     start = s.timestep(s.sigma_max)
     end = s.timestep(s.sigma_min)
 
+    append_zero = True
     if sgm:
         timesteps = torch.linspace(start, end, steps + 1)[:-1]
     else:
+        if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):
+            steps += 1
+            append_zero = False
         timesteps = torch.linspace(start, end, steps)
 
     sigs = []
     for x in range(len(timesteps)):
         ts = timesteps[x]
-        sigs.append(s.sigma(ts))
+        sigs.append(float(s.sigma(ts)))
+
+    if append_zero:
+        sigs += [0.0]
+
+    return torch.FloatTensor(sigs)
+
+# Implemented based on: https://arxiv.org/abs/2407.12173
+def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
+    total_timesteps = (len(model_sampling.sigmas) - 1)
+    ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)
+    ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
+
+    sigs = []
+    for t in ts:
+        sigs += [float(model_sampling.sigmas[int(t)])]
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
@@ -537,7 +569,7 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+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_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis"]
@@ -697,7 +729,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 def calculate_sigmas(model_sampling, scheduler_name, steps):
@@ -713,6 +745,8 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
         sigmas = ddim_scheduler(model_sampling, steps)
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model_sampling, steps, sgm=True)
+    elif scheduler_name == "beta":
+        sigmas = beta_scheduler(model_sampling, steps)
     else:
         logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas

comfy/sd.py

@@ -17,10 +17,13 @@ from . import diffusers_convert
 from . import model_detection
 
 from . import sd1_clip
-from . import sd2_clip
 from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
+import comfy.text_encoders.sd2_clip
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
+import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 import comfy.model_patcher
 import comfy.lora
@@ -28,36 +31,6 @@ import comfy.t2i_adapter.adapter
 import comfy.supported_models_base
 import comfy.taesd.taesd
 
-def load_model_weights(model, sd):
-    m, u = model.load_state_dict(sd, strict=False)
-    m = set(m)
-    unexpected_keys = set(u)
-
-    k = list(sd.keys())
-    for x in k:
-        if x not in unexpected_keys:
-            w = sd.pop(x)
-            del w
-    if len(m) > 0:
-        logging.warning("missing {}".format(m))
-    return model
-
-def load_clip_weights(model, sd):
-    k = list(sd.keys())
-    for x in k:
-        if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
-            y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
-            sd[y] = sd.pop(x)
-
-    if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
-        ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
-        if ids.dtype == torch.float32:
-            sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
-
-    sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.")
-    return load_model_weights(model, sd)
-
-
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     key_map = {}
     if model is not None:
@@ -89,7 +62,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0):
         if no_init:
             return
         params = target.params.copy()
@@ -98,20 +71,24 @@ class CLIP:
 
         load_device = model_management.text_encoder_device()
         offload_device = model_management.text_encoder_offload_device()
-        params['device'] = offload_device
         dtype = model_management.text_encoder_dtype(load_device)
         params['dtype'] = dtype
-
+        params['device'] = model_management.text_encoder_initial_device(load_device, offload_device, parameters * model_management.dtype_size(dtype))
         self.cond_stage_model = clip(**(params))
 
         for dt in self.cond_stage_model.dtypes:
             if not model_management.supports_cast(load_device, dt):
                 load_device = offload_device
+                if params['device'] != offload_device:
+                    self.cond_stage_model.to(offload_device)
+                    logging.warning("Had to shift TE back.")
 
-        self.tokenizer = tokenizer(embedding_directory=embedding_directory)
+        self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
+        if params['device'] == load_device:
+            model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
-        logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
+        logging.debug("CLIP model load device: {}, offload device: {}, current: {}".format(load_device, offload_device, params['device']))
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -130,7 +107,7 @@ class CLIP:
     def tokenize(self, text, return_word_ids=False):
         return self.tokenizer.tokenize_with_weights(text, return_word_ids)
 
-    def encode_from_tokens(self, tokens, return_pooled=False):
+    def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):
         self.cond_stage_model.reset_clip_options()
 
         if self.layer_idx is not None:
@@ -140,7 +117,15 @@ class CLIP:
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
-        cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
+        o = self.cond_stage_model.encode_token_weights(tokens)
+        cond, pooled = o[:2]
+        if return_dict:
+            out = {"cond": cond, "pooled_output": pooled}
+            if len(o) > 2:
+                for k in o[2]:
+                    out[k] = o[2][k]
+            return out
+
         if return_pooled:
             return cond, pooled
         return cond
@@ -156,7 +141,11 @@ class CLIP:
             return self.cond_stage_model.load_sd(sd)
 
     def get_sd(self):
-        return self.cond_stage_model.state_dict()
+        sd_clip = self.cond_stage_model.state_dict()
+        sd_tokenizer = self.tokenizer.state_dict()
+        for k in sd_tokenizer:
+            sd_clip[k] = sd_tokenizer[k]
+        return sd_clip
 
     def load_model(self):
         model_management.load_model_gpu(self.patcher)
@@ -402,6 +391,8 @@ class CLIPType(Enum):
     STABLE_CASCADE = 2
     SD3 = 3
     STABLE_AUDIO = 4
+    HUNYUAN_DIT = 5
+    FLUX = 6
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -429,30 +420,51 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                 clip_target.tokenizer = sdxl_clip.SDXLTokenizer
         elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
-            clip_target.clip = sd2_clip.SD2ClipModel
-            clip_target.tokenizer = sd2_clip.SD2Tokenizer
+            clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
+            clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
         elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
-            dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+            dtype_t5 = weight.dtype
+            if weight.shape[-1] == 4096:
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+            elif weight.shape[-1] == 2048:
+                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
-            clip_target.clip = sa_t5.SAT5Model
-            clip_target.tokenizer = sa_t5.SAT5Tokenizer
+            clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
+            clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
         if clip_type == CLIPType.SD3:
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+        elif clip_type == CLIPType.HUNYUAN_DIT:
+            clip_target.clip = comfy.text_encoders.hydit.HyditModel
+            clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
+        elif clip_type == CLIPType.FLUX:
+            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
+            dtype_t5 = None
+            if weight is not None:
+                dtype_t5 = weight.dtype
+
+            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
+            clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
-        clip_target.clip = sd3_clip.SD3ClipModel
-        clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        clip_target.clip = comfy.text_encoders.sd3_clip.SD3ClipModel
+        clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
-    clip = CLIP(clip_target, embedding_directory=embedding_directory)
+    parameters = 0
+    for c in clip_data:
+        parameters += comfy.utils.calculate_parameters(c)
+
+    clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters)
     for c in clip_data:
         m, u = clip.load_sd(c)
         if len(m) > 0:
@@ -494,28 +506,42 @@ 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):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, model_options={}):
     sd = comfy.utils.load_torch_file(ckpt_path)
-    sd_keys = sd.keys()
+    out = load_state_dict_guess_config(sd, output_vae, output_clip, output_clipvision, embedding_directory, output_model, model_options)
+    if out is None:
+        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
+    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={}):
     clip = None
     clipvision = None
     vae = None
     model = None
     model_patcher = None
-    clip_target = None
 
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
+    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)
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    if model_config is None:
+        return None
+
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+    if weight_dtype is not None:
+        unet_weight_dtype.append(weight_dtype)
+
+    model_config.custom_operations = model_options.get("custom_operations", None)
+    unet_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)
+
     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)
 
-    if model_config is None:
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
-
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
@@ -536,7 +562,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         if clip_target is not None:
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
-                clip = CLIP(clip_target, embedding_directory=embedding_directory)
+                parameters = comfy.utils.calculate_parameters(clip_sd)
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters)
                 m, u = clip.load_sd(clip_sd, full_model=True)
                 if len(m) > 0:
                     m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
@@ -555,50 +582,58 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         logging.debug("left over keys: {}".format(left_over))
 
     if output_model:
-        model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
+        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")
-            model_management.load_model_gpu(model_patcher)
+            model_management.load_models_gpu([model_patcher], force_full_load=True)
 
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers format
+def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffusers or regular format
+    dtype = model_options.get("dtype", None)
+
+    #Allow loading unets from checkpoint files
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+    if len(temp_sd) > 0:
+        sd = temp_sd
+
     parameters = comfy.utils.calculate_parameters(sd)
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
+    model_config = model_detection.model_config_from_unet(sd, "")
 
-    if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
-        new_sd = model_detection.convert_diffusers_mmdit(sd, "")
-        if new_sd is None:
-            return None
-        model_config = model_detection.model_config_from_unet(new_sd, "")
-        if model_config is None:
-            return None
-    elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
-        model_config = model_detection.model_config_from_unet(sd, "")
-        if model_config is None:
-            return None
+    if model_config is not None:
         new_sd = sd
+    else:
+        new_sd = model_detection.convert_diffusers_mmdit(sd, "")
+        if new_sd is not None: #diffusers mmdit
+            model_config = model_detection.model_config_from_unet(new_sd, "")
+            if model_config is None:
+                return None
+        else: #diffusers unet
+            model_config = model_detection.model_config_from_diffusers_unet(sd)
+            if model_config is None:
+                return None
 
-    else: #diffusers
-        model_config = model_detection.model_config_from_diffusers_unet(sd)
-        if model_config is None:
-            return None
+            diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
 
-        diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
-
-        new_sd = {}
-        for k in diffusers_keys:
-            if k in sd:
-                new_sd[diffusers_keys[k]] = sd.pop(k)
-            else:
-                logging.warning("{} {}".format(diffusers_keys[k], k))
+            new_sd = {}
+            for k in diffusers_keys:
+                if k in sd:
+                    new_sd[diffusers_keys[k]] = sd.pop(k)
+                else:
+                    logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    else:
+        unet_dtype = 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)
+    model_config.custom_operations = model_options.get("custom_operations", None)
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")
@@ -607,14 +642,23 @@ def load_unet_state_dict(sd): #load unet in diffusers format
         logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
-def load_unet(unet_path):
+
+def load_diffusion_model(unet_path, model_options={}):
     sd = comfy.utils.load_torch_file(unet_path)
-    model = load_unet_state_dict(sd)
+    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))
     return model
 
+def load_unet(unet_path, dtype=None):
+    print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
+    return load_diffusion_model(unet_path, model_options={"dtype": dtype})
+
+def load_unet_state_dict(sd, dtype=None):
+    print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
+    return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype})
+
 def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
     clip_sd = None
     load_models = [model]
@@ -628,4 +672,9 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
     for k in extra_keys:
         sd[k] = extra_keys[k]
 
+    for k in sd:
+        t = sd[k]
+        if not t.is_contiguous():
+            sd[k] = t.contiguous()
+
     comfy.utils.save_torch_file(sd, output_path, metadata=metadata)

comfy/sd1_clip.py

@@ -38,7 +38,9 @@ class ClipTokenWeightEncoder:
         if has_weights or sections == 0:
             to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
 
-        out, pooled = self.encode(to_encode)
+        o = self.encode(to_encode)
+        out, pooled = o[:2]
+
         if pooled is not None:
             first_pooled = pooled[0:1].to(model_management.intermediate_device())
         else:
@@ -57,8 +59,20 @@ class ClipTokenWeightEncoder:
             output.append(z)
 
         if (len(output) == 0):
-            return out[-1:].to(model_management.intermediate_device()), first_pooled
-        return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled
+            r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
+        else:
+            r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
+
+        if len(o) > 2:
+            extra = {}
+            for k in o[2]:
+                v = o[2][k]
+                if k == "attention_mask":
+                    v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
+                extra[k] = v
+
+            r = r + (extra,)
+        return r
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
@@ -70,7 +84,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
                  special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
-                 return_projected_pooled=True):  # clip-vit-base-patch32
+                 return_projected_pooled=True, return_attention_masks=False):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -80,7 +94,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         with open(textmodel_json_config) as f:
             config = json.load(f)
 
-        self.transformer = model_class(config, dtype, device, comfy.ops.manual_cast)
+        self.operations = comfy.ops.manual_cast
+        self.transformer = model_class(config, dtype, device, self.operations)
         self.num_layers = self.transformer.num_layers
 
         self.max_length = max_length
@@ -96,6 +111,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
+        self.return_attention_masks = return_attention_masks
 
         if layer == "hidden":
             assert layer_idx is not None
@@ -125,15 +141,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
-        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
+        next_new_token = token_dict_size = current_embeds.weight.shape[0]
         embedding_weights = []
 
         for x in tokens:
             tokens_temp = []
             for y in x:
                 if isinstance(y, numbers.Integral):
-                    if y == token_dict_size: #EOS token
-                        y = -1
                     tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
@@ -148,12 +162,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         n = token_dict_size
         if len(embedding_weights) > 0:
-            new_embedding = torch.nn.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[:-1]
+            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
-            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding
             self.transformer.set_input_embeddings(new_embedding)
 
         processed_tokens = []
@@ -169,7 +182,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = torch.LongTensor(tokens).to(device)
 
         attention_mask = None
-        if self.enable_attention_masks:
+        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", -1)
             for x in range(attention_mask.shape[0]):
@@ -178,7 +191,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
                     if tokens[x, y] == end_token:
                         break
 
-        outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
+        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 == "last":
@@ -186,7 +203,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         else:
             z = outputs[1].float()
 
-        if self.zero_out_masked and attention_mask is not None:
+        if self.zero_out_masked:
             z *= attention_mask.unsqueeze(-1).float()
 
         pooled_output = None
@@ -196,6 +213,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             elif outputs[2] is not None:
                 pooled_output = outputs[2].float()
 
+        extra = {}
+        if self.return_attention_masks:
+            extra["attention_mask"] = attention_mask
+
+        if len(extra) > 0:
+            return z, pooled_output, extra
+
         return z, pooled_output
 
     def encode(self, tokens):
@@ -289,6 +313,17 @@ def expand_directory_list(directories):
             dirs.add(root)
     return list(dirs)
 
+def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
+    i = 0
+    out_list = []
+    for k in embed:
+        if k.startswith(prefix) and k.endswith(suffix):
+            out_list.append(embed[k])
+    if len(out_list) == 0:
+        return None
+
+    return torch.cat(out_list, dim=0)
+
 def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
     if isinstance(embedding_directory, str):
         embedding_directory = [embedding_directory]
@@ -355,12 +390,16 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
         elif embed_key is not None and embed_key in embed:
             embed_out = embed[embed_key]
         else:
-            values = embed.values()
-            embed_out = next(iter(values))
+            embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')
+            if embed_out is None:
+                embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))
+            if embed_out is None:
+                values = embed.values()
+                embed_out = next(iter(values))
     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, pad_to_max_length=True, min_length=None):
+    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, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}):
         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)
@@ -376,6 +415,14 @@ class SDTokenizer:
             self.tokens_start = 0
             self.start_token = None
             self.end_token = empty[0]
+
+        if pad_token is not None:
+            self.pad_token = pad_token
+        elif pad_with_end:
+            self.pad_token = self.end_token
+        else:
+            self.pad_token = 0
+
         self.pad_with_end = pad_with_end
         self.pad_to_max_length = pad_to_max_length
 
@@ -408,10 +455,6 @@ class SDTokenizer:
         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
         '''
-        if self.pad_with_end:
-            pad_token = self.end_token
-        else:
-            pad_token = 0
 
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
@@ -463,7 +506,7 @@ class SDTokenizer:
                     else:
                         batch.append((self.end_token, 1.0, 0))
                         if self.pad_to_max_length:
-                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
+                            batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
                     #start new batch
                     batch = []
                     if self.start_token is not None:
@@ -476,9 +519,9 @@ class SDTokenizer:
         #fill last batch
         batch.append((self.end_token, 1.0, 0))
         if self.pad_to_max_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+            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([(pad_token, 1.0, 0)] * (self.min_length - len(batch)))
+            batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
@@ -489,12 +532,14 @@ class SDTokenizer:
     def untokenize(self, token_weight_pair):
         return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
 
+    def state_dict(self):
+        return {}
 
 class SD1Tokenizer:
-    def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer):
+    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)
-        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory))
+        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
         out = {}
@@ -504,12 +549,20 @@ class SD1Tokenizer:
     def untokenize(self, token_weight_pair):
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
 
 class SD1ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs):
+    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, name=None, **kwargs):
         super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
+
+        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)
+
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
 
         self.dtypes = set()
@@ -524,8 +577,8 @@ class SD1ClipModel(torch.nn.Module):
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs = token_weight_pairs[self.clip_name]
-        out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
-        return out, pooled
+        out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
+        return out
 
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)

comfy/sd1_clip_config.json

@@ -6,7 +6,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "quick_gelu",
   "hidden_size": 768,
   "initializer_factor": 1.0,

comfy/sdxl_clip.py

@@ -16,12 +16,12 @@ class SDXLClipG(sd1_clip.SDClipModel):
         return super().load_sd(sd)
 
 class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    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')
 
 
 class SDXLTokenizer:
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
@@ -34,6 +34,9 @@ class SDXLTokenizer:
     def untokenize(self, token_weight_pair):
         return self.clip_g.untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
+
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
@@ -68,12 +71,12 @@ class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
 
 
 class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    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')
 
 class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
 
 class StableCascadeClipG(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None):

comfy/supported_models.py

@@ -3,10 +3,13 @@ from . import model_base
 from . import utils
 
 from . import sd1_clip
-from . import sd2_clip
 from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
+import comfy.text_encoders.sd2_clip
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
+import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 from . import supported_models_base
 from . import latent_formats
@@ -28,6 +31,7 @@ class SD15(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SD15
+    memory_usage_factor = 1.0
 
     def process_clip_state_dict(self, state_dict):
         k = list(state_dict.keys())
@@ -74,6 +78,7 @@ class SD20(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SD15
+    memory_usage_factor = 1.0
 
     def model_type(self, state_dict, prefix=""):
         if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
@@ -99,7 +104,7 @@ class SD20(supported_models_base.BASE):
         return state_dict
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel)
 
 class SD21UnclipL(SD20):
     unet_config = {
@@ -137,6 +142,7 @@ class SDXLRefiner(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SDXL
+    memory_usage_factor = 1.0
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.SDXLRefiner(self, device=device)
@@ -175,6 +181,8 @@ class SDXL(supported_models_base.BASE):
 
     latent_format = latent_formats.SDXL
 
+    memory_usage_factor = 0.8
+
     def model_type(self, state_dict, prefix=""):
         if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
             self.latent_format = latent_formats.SDXL_Playground_2_5()
@@ -502,6 +510,9 @@ class SD3(supported_models_base.BASE):
 
     unet_extra_config = {}
     latent_format = latent_formats.SD3
+
+    memory_usage_factor = 1.2
+
     text_encoder_key_prefix = ["text_encoders."]
 
     def get_model(self, state_dict, prefix="", device=None):
@@ -523,7 +534,7 @@ class SD3(supported_models_base.BASE):
             t5 = True
             dtype_t5 = state_dict[t5_key].dtype
 
-        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
 
 class StableAudio(supported_models_base.BASE):
     unet_config = {
@@ -554,9 +565,115 @@ class StableAudio(supported_models_base.BASE):
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
+        return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
+
+class AuraFlow(supported_models_base.BASE):
+    unet_config = {
+        "cond_seq_dim": 2048,
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.73,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SDXL
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.AuraFlow(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
+
+class HunyuanDiT(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "hydit",
+    }
+
+    unet_extra_config = {
+        "attn_precision": torch.float32,
+    }
+
+    sampling_settings = {
+        "linear_start": 0.00085,
+        "linear_end": 0.018,
+    }
+
+    latent_format = latent_formats.SDXL
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanDiT(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
+
+class HunyuanDiT1(HunyuanDiT):
+    unet_config = {
+        "image_model": "hydit1",
+    }
+
+    unet_extra_config = {}
+
+    sampling_settings = {
+        "linear_start" : 0.00085,
+        "linear_end" : 0.03,
+    }
+
+class Flux(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": True,
+    }
+
+    sampling_settings = {
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+
+    memory_usage_factor = 2.8
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        if t5_key in state_dict:
+            dtype_t5 = state_dict[t5_key].dtype
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
+
+class FluxSchnell(Flux):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": False,
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.0,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
+        return out
 
 
-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]
+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, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
 
 models += [SVD_img2vid]

comfy/supported_models_base.py

@@ -1,3 +1,21 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import torch
 from . import model_base
 from . import utils
@@ -27,7 +45,10 @@ class BASE:
     text_encoder_key_prefix = ["cond_stage_model."]
     supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
 
+    memory_usage_factor = 2.0
+
     manual_cast_dtype = None
+    custom_operations = None
 
     @classmethod
     def matches(s, unet_config, state_dict=None):

comfy/t2i_adapter/adapter.py

@@ -153,7 +153,13 @@ class Adapter(nn.Module):
                 features.append(None)
             features.append(x)
 
-        return features
+        features = features[::-1]
+
+        if self.xl:
+            return {"input": features[1:], "middle": features[:1]}
+        else:
+            return {"input": features}
+
 
 
 class LayerNorm(nn.LayerNorm):
@@ -290,4 +296,4 @@ class Adapter_light(nn.Module):
             features.append(None)
             features.append(x)
 
-        return features
+        return {"input": features[::-1]}

comfy/text_encoders/aura_t5.py

@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+class PT5XlModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+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)
+
+class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+
+class AuraT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)

comfy/text_encoders/bert.py

@@ -0,0 +1,140 @@
+import torch
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class BertAttention(torch.nn.Module):
+    def __init__(self, embed_dim, heads, dtype, device, operations):
+        super().__init__()
+
+        self.heads = heads
+        self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+        self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+        self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+
+    def forward(self, x, mask=None, optimized_attention=None):
+        q = self.query(x)
+        k = self.key(x)
+        v = self.value(x)
+
+        out = optimized_attention(q, k, v, self.heads, mask)
+        return out
+
+class BertOutput(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)
+        self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(0.0)
+
+    def forward(self, x, y):
+        x = self.dense(x)
+        # hidden_states = self.dropout(hidden_states)
+        x = self.LayerNorm(x + y)
+        return x
+
+class BertAttentionBlock(torch.nn.Module):
+    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.self = BertAttention(embed_dim, heads, dtype, device, operations)
+        self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+    def forward(self, x, mask, optimized_attention):
+        y = self.self(x, mask, optimized_attention)
+        return self.output(y, x)
+
+class BertIntermediate(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):
+        super().__init__()
+        self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.dense(x)
+        return torch.nn.functional.gelu(x)
+
+
+class BertBlock(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)
+        self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)
+        self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+    def forward(self, x, mask, optimized_attention):
+        x = self.attention(x, mask, optimized_attention)
+        y = self.intermediate(x)
+        return self.output(y, x)
+
+class BertEncoder(torch.nn.Module):
+    def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)])
+
+    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)
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.layer) + intermediate_output
+
+        intermediate = None
+        for i, l in enumerate(self.layer):
+            x = l(x, mask, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        return x, intermediate
+
+class BertEmbeddings(torch.nn.Module):
+    def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+        self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+        self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+
+        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)
+        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)
+        else:
+            x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
+        x = self.LayerNorm(x)
+        return x
+
+
+class BertModel_(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        embed_dim = config_dict["hidden_size"]
+        layer_norm_eps = config_dict["layer_norm_eps"]
+
+        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)
+        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"))
+
+        x, i = self.encoder(x, mask, intermediate_output)
+        return x, i
+
+
+class BertModel(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.bert = BertModel_(config_dict, dtype, device, operations)
+        self.num_layers = config_dict["num_hidden_layers"]
+
+    def get_input_embeddings(self):
+        return self.bert.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embeddings):
+        self.bert.embeddings.word_embeddings = embeddings
+
+    def forward(self, *args, **kwargs):
+        return self.bert(*args, **kwargs)

comfy/text_encoders/flux.py

@@ -0,0 +1,71 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.model_management
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_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)
+
+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, 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)
+
+
+class FluxTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_l.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {}
+
+
+class FluxClipModel(torch.nn.Module):
+    def __init__(self, dtype_t5=None, device="cpu", dtype=None):
+        super().__init__()
+        dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
+        self.dtypes = set([dtype, dtype_t5])
+
+    def set_clip_options(self, options):
+        self.clip_l.set_clip_options(options)
+        self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.clip_l.reset_clip_options()
+        self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+
+        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+        return t5_out, l_pooled
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
+
+def flux_clip(dtype_t5=None):
+    class FluxClipModel_(FluxClipModel):
+        def __init__(self, device="cpu", dtype=None):
+            super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype)
+    return FluxClipModel_

comfy/text_encoders/hydit.py

@@ -0,0 +1,79 @@
+from comfy import sd1_clip
+from transformers import BertTokenizer
+from .spiece_tokenizer import SPieceTokenizer
+from .bert import BertModel
+import comfy.text_encoders.t5
+import os
+import torch
+
+class HyditBertModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
+        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)
+
+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)
+
+
+class MT5XLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.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, return_attention_masks=True)
+
+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)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+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)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        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)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.hydit_clip.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
+
+class HyditModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__()
+        self.hydit_clip = HyditBertModel(dtype=dtype)
+        self.mt5xl = MT5XLModel(dtype=dtype)
+
+        self.dtypes = set()
+        if dtype is not None:
+            self.dtypes.add(dtype)
+
+    def encode_token_weights(self, token_weight_pairs):
+        hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"])
+        mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"])
+        return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]}
+
+    def load_sd(self, sd):
+        if "bert.encoder.layer.0.attention.self.query.weight" in sd:
+            return self.hydit_clip.load_sd(sd)
+        else:
+            return self.mt5xl.load_sd(sd)
+
+    def set_clip_options(self, options):
+        self.hydit_clip.set_clip_options(options)
+        self.mt5xl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.hydit_clip.reset_clip_options()
+        self.mt5xl.reset_clip_options()

comfy/text_encoders/hydit_clip.json

@@ -0,0 +1,35 @@
+{
+  "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 47020
+}
+

comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json

@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "name_or_path": "hfl/chinese-roberta-wwm-ext",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]",
+  "model_max_length": 77
+}

comfy/text_encoders/mt5_config_xl.json

@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "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": "mt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 250112
+}

comfy/text_encoders/sa_t5.py

@@ -1,22 +1,22 @@
 from comfy import sd1_clip
 from transformers import T5TokenizerFast
-import comfy.t5
+import comfy.text_encoders.t5
 import os
 
 class T5BaseModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_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.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+        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)
 
 class T5BaseTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
+    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)
 
 class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer)
 
 class SAT5Model(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)
+        super().__init__(device=device, dtype=dtype, name="t5base", clip_model=T5BaseModel, **kwargs)

comfy/text_encoders/sd2_clip.py

@@ -11,12 +11,12 @@ class SD2ClipHModel(sd1_clip.SDClipModel):
         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})
 
 class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    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)
 
 class SD2Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer)
 
 class SD2ClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):

comfy/text_encoders/sd2_clip_config.json

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1024,
   "initializer_factor": 1.0,

comfy/text_encoders/sd3_clip.py

@@ -1,7 +1,7 @@
 from comfy import sd1_clip
 from comfy import sdxl_clip
 from transformers import T5TokenizerFast
-import comfy.t5
+import comfy.text_encoders.t5
 import torch
 import os
 import comfy.model_management
@@ -10,25 +10,16 @@ import logging
 class T5XXLModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_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.t5.T5)
+        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)
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
+    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=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
 
-class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-
-class SDT5XXLModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
-
-
 
 class SD3Tokenizer:
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
@@ -43,6 +34,9 @@ class SD3Tokenizer:
     def untokenize(self, token_weight_pair):
         return self.clip_g.untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
+
 class SD3ClipModel(torch.nn.Module):
     def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):
         super().__init__()
@@ -60,14 +54,7 @@ class SD3ClipModel(torch.nn.Module):
             self.clip_g = None
 
         if t5:
-            if dtype_t5 is None:
-                dtype_t5 = dtype
-            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
-                dtype_t5 = dtype
-
-            if not comfy.model_management.supports_cast(device, dtype_t5):
-                dtype_t5 = dtype
-
+            dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
             self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
             self.dtypes.add(dtype_t5)
         else:
@@ -94,7 +81,7 @@ class SD3ClipModel(torch.nn.Module):
     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_pars_t5 = token_weight_pairs["t5xxl"]
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
         lg_out = None
         pooled = None
         out = None
@@ -121,7 +108,7 @@ class SD3ClipModel(torch.nn.Module):
             pooled = torch.cat((l_pooled, g_pooled), dim=-1)
 
         if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
             if lg_out is not None:
                 out = torch.cat([lg_out, t5_out], dim=-2)
             else:

comfy/text_encoders/spiece_tokenizer.py

@@ -0,0 +1,32 @@
+import os
+import torch
+
+class SPieceTokenizer:
+    add_eos = True
+
+    @staticmethod
+    def from_pretrained(path):
+        return SPieceTokenizer(path)
+
+    def __init__(self, tokenizer_path):
+        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)
+        else:
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
+
+    def get_vocab(self):
+        out = {}
+        for i in range(self.tokenizer.get_piece_size()):
+            out[self.tokenizer.id_to_piece(i)] = i
+        return out
+
+    def __call__(self, string):
+        out = self.tokenizer.encode(string)
+        return {"input_ids": out}
+
+    def serialize_model(self):
+        return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))

comfy/text_encoders/t5.py

@@ -1,6 +1,7 @@
 import torch
 import math
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class T5LayerNorm(torch.nn.Module):
     def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
@@ -11,31 +12,38 @@ class T5LayerNorm(torch.nn.Module):
     def forward(self, x):
         variance = x.pow(2).mean(-1, keepdim=True)
         x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
+        return comfy.ops.cast_to_input(self.weight, x) * x
+
+activations = {
+    "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+    "relu": torch.nn.functional.relu,
+}
 
 class T5DenseActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
         super().__init__()
         self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
         # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
 
     def forward(self, x):
-        x = torch.nn.functional.relu(self.wi(x))
+        x = self.act(self.wi(x))
         # x = self.dropout(x)
         x = self.wo(x)
         return x
 
 class T5DenseGatedActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
         super().__init__()
         self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
         # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
 
     def forward(self, x):
-        hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
+        hidden_gelu = self.act(self.wi_0(x))
         hidden_linear = self.wi_1(x)
         x = hidden_gelu * hidden_linear
         # x = self.dropout(x)
@@ -43,12 +51,12 @@ class T5DenseGatedActDense(torch.nn.Module):
         return x
 
 class T5LayerFF(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
         super().__init__()
-        if ff_activation == "gelu_pytorch_tanh":
-            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
-        elif ff_activation == "relu":
-            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
+        if gated_act:
+            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+        else:
+            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
 
         self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
@@ -75,7 +83,7 @@ class T5Attention(torch.nn.Module):
         if relative_attention_bias:
             self.relative_attention_num_buckets = 32
             self.relative_attention_max_distance = 128
-            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
+            self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
 
     @staticmethod
     def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
@@ -125,7 +133,7 @@ class T5Attention(torch.nn.Module):
         relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
         return relative_buckets
 
-    def compute_bias(self, query_length, key_length, device):
+    def compute_bias(self, query_length, key_length, device, dtype):
         """Compute binned relative position bias"""
         context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
         memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
@@ -136,7 +144,7 @@ class T5Attention(torch.nn.Module):
             num_buckets=self.relative_attention_num_buckets,
             max_distance=self.relative_attention_max_distance,
         )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        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
 
@@ -145,7 +153,7 @@ class T5Attention(torch.nn.Module):
         k = self.k(x)
         v = self.v(x)
         if self.relative_attention_bias is not None:
-            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
 
         if past_bias is not None:
             if mask is not None:
@@ -171,11 +179,11 @@ class T5LayerSelfAttention(torch.nn.Module):
         return x, past_bias
 
 class T5Block(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
+    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
         super().__init__()
         self.layer = torch.nn.ModuleList()
         self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
-        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
+        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
 
     def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
         x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
@@ -183,16 +191,16 @@ class T5Block(torch.nn.Module):
         return x, past_bias
 
 class T5Stack(torch.nn.Module):
-    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
         super().__init__()
 
         self.block = torch.nn.ModuleList(
-            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
         )
         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):
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         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])
@@ -216,9 +224,9 @@ class T5(torch.nn.Module):
         self.num_layers = config_dict["num_layers"]
         model_dim = config_dict["d_model"]
 
-        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
+        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.dtype = dtype
-        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
+        self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
 
     def get_input_embeddings(self):
         return self.shared
@@ -227,5 +235,7 @@ class T5(torch.nn.Module):
         self.shared = embeddings
 
     def forward(self, input_ids, *args, **kwargs):
-        x = self.shared(input_ids)
+        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)

comfy/text_encoders/t5_config_base.json

@@ -8,6 +8,7 @@
   "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": 12,

comfy/text_encoders/t5_config_xxl.json

@@ -8,6 +8,7 @@
   "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": 24,

comfy/text_encoders/t5_pile_config_xl.json

@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "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": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}

comfy/utils.py

@@ -1,3 +1,22 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+
 import torch
 import math
 import struct
@@ -11,7 +30,7 @@ import itertools
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
-    if ckpt.lower().endswith(".safetensors"):
+    if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         sd = safetensors.torch.load_file(ckpt, device=device.type)
     else:
         if safe_load:
@@ -40,9 +59,22 @@ def calculate_parameters(sd, prefix=""):
     params = 0
     for k in sd.keys():
         if k.startswith(prefix):
-            params += sd[k].nelement()
+            w = sd[k]
+            params += w.nelement()
     return params
 
+def weight_dtype(sd, prefix=""):
+    dtypes = {}
+    for k in sd.keys():
+        if k.startswith(prefix):
+            w = sd[k]
+            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
+
+    if len(dtypes) == 0:
+        return None
+
+    return max(dtypes, key=dtypes.get)
+
 def state_dict_key_replace(state_dict, keys_to_replace):
     for x in keys_to_replace:
         if x in state_dict:
@@ -298,7 +330,8 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
     key_map = {}
 
     depth = mmdit_config.get("depth", 0)
-    for i in range(depth):
+    num_blocks = mmdit_config.get("num_blocks", depth)
+    for i in range(num_blocks):
         block_from = "transformer_blocks.{}".format(i)
         block_to = "{}joint_blocks.{}".format(output_prefix, i)
 
@@ -331,6 +364,180 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+
+def auraflow_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("n_double_layers", 0)
+    n_layers = mmdit_config.get("n_layers", 0)
+
+    key_map = {}
+    for i in range(n_layers):
+        if i < n_double_layers:
+            index = i
+            prefix_from = "joint_transformer_blocks"
+            prefix_to = "{}double_layers".format(output_prefix)
+            block_map = {
+                            "attn.to_q.weight": "attn.w2q.weight",
+                            "attn.to_k.weight": "attn.w2k.weight",
+                            "attn.to_v.weight": "attn.w2v.weight",
+                            "attn.to_out.0.weight": "attn.w2o.weight",
+                            "attn.add_q_proj.weight": "attn.w1q.weight",
+                            "attn.add_k_proj.weight": "attn.w1k.weight",
+                            "attn.add_v_proj.weight": "attn.w1v.weight",
+                            "attn.to_add_out.weight": "attn.w1o.weight",
+                            "ff.linear_1.weight": "mlpX.c_fc1.weight",
+                            "ff.linear_2.weight": "mlpX.c_fc2.weight",
+                            "ff.out_projection.weight": "mlpX.c_proj.weight",
+                            "ff_context.linear_1.weight": "mlpC.c_fc1.weight",
+                            "ff_context.linear_2.weight": "mlpC.c_fc2.weight",
+                            "ff_context.out_projection.weight": "mlpC.c_proj.weight",
+                            "norm1.linear.weight": "modX.1.weight",
+                            "norm1_context.linear.weight": "modC.1.weight",
+                        }
+        else:
+            index = i - n_double_layers
+            prefix_from = "single_transformer_blocks"
+            prefix_to = "{}single_layers".format(output_prefix)
+
+            block_map = {
+                            "attn.to_q.weight": "attn.w1q.weight",
+                            "attn.to_k.weight": "attn.w1k.weight",
+                            "attn.to_v.weight": "attn.w1v.weight",
+                            "attn.to_out.0.weight": "attn.w1o.weight",
+                            "norm1.linear.weight": "modCX.1.weight",
+                            "ff.linear_1.weight": "mlp.c_fc1.weight",
+                            "ff.linear_2.weight": "mlp.c_fc2.weight",
+                            "ff.out_projection.weight": "mlp.c_proj.weight"
+                        }
+
+        for k in block_map:
+            key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k])
+
+    MAP_BASIC = {
+        ("positional_encoding", "pos_embed.pos_embed"),
+        ("register_tokens", "register_tokens"),
+        ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"),
+        ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"),
+        ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"),
+        ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"),
+        ("cond_seq_linear.weight", "context_embedder.weight"),
+        ("init_x_linear.weight", "pos_embed.proj.weight"),
+        ("init_x_linear.bias", "pos_embed.proj.bias"),
+        ("final_linear.weight", "proj_out.weight"),
+        ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+def flux_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("depth", 0)
+    n_single_layers = mmdit_config.get("depth_single_blocks", 0)
+    hidden_size = mmdit_config.get("hidden_size", 0)
+
+    key_map = {}
+    for index in range(n_double_layers):
+        prefix_from = "transformer_blocks.{}".format(index)
+        prefix_to = "{}double_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.img_attn.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end)
+            key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {
+                        "attn.to_out.0.weight": "img_attn.proj.weight",
+                        "attn.to_out.0.bias": "img_attn.proj.bias",
+                        "norm1.linear.weight": "img_mod.lin.weight",
+                        "norm1.linear.bias": "img_mod.lin.bias",
+                        "norm1_context.linear.weight": "txt_mod.lin.weight",
+                        "norm1_context.linear.bias": "txt_mod.lin.bias",
+                        "attn.to_add_out.weight": "txt_attn.proj.weight",
+                        "attn.to_add_out.bias": "txt_attn.proj.bias",
+                        "ff.net.0.proj.weight": "img_mlp.0.weight",
+                        "ff.net.0.proj.bias": "img_mlp.0.bias",
+                        "ff.net.2.weight": "img_mlp.2.weight",
+                        "ff.net.2.bias": "img_mlp.2.bias",
+                        "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+                        "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+                        "ff_context.net.2.weight": "txt_mlp.2.weight",
+                        "ff_context.net.2.bias": "txt_mlp.2.bias",
+                        "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+                        "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+                        "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+                        "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    for index in range(n_single_layers):
+        prefix_from = "single_transformer_blocks.{}".format(index)
+        prefix_to = "{}single_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.linear1.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+            key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4))
+
+        block_map = {
+                        "norm.linear.weight": "modulation.lin.weight",
+                        "norm.linear.bias": "modulation.lin.bias",
+                        "proj_out.weight": "linear2.weight",
+                        "proj_out.bias": "linear2.bias",
+                        "attn.norm_q.weight": "norm.query_norm.scale",
+                        "attn.norm_k.weight": "norm.key_norm.scale",
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    MAP_BASIC = {
+        ("final_layer.linear.bias", "proj_out.bias"),
+        ("final_layer.linear.weight", "proj_out.weight"),
+        ("img_in.bias", "x_embedder.bias"),
+        ("img_in.weight", "x_embedder.weight"),
+        ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+        ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+        ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+        ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+        ("txt_in.bias", "context_embedder.bias"),
+        ("txt_in.weight", "context_embedder.weight"),
+        ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"),
+        ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"),
+        ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"),
+        ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"),
+        ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"),
+        ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"),
+        ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"),
+        ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"),
+        ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+        ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)

comfy_execution/caching.py

@@ -0,0 +1,299 @@
+import itertools
+from typing import Sequence, Mapping
+from comfy_execution.graph import DynamicPrompt
+
+import nodes
+
+from comfy_execution.graph_utils import is_link
+
+class CacheKeySet:
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        self.keys = {}
+        self.subcache_keys = {}
+
+    def add_keys(self, node_ids):
+        raise NotImplementedError()
+
+    def all_node_ids(self):
+        return set(self.keys.keys())
+
+    def get_used_keys(self):
+        return self.keys.values()
+
+    def get_used_subcache_keys(self):
+        return self.subcache_keys.values()
+
+    def get_data_key(self, node_id):
+        return self.keys.get(node_id, None)
+
+    def get_subcache_key(self, node_id):
+        return self.subcache_keys.get(node_id, None)
+
+class Unhashable:
+    def __init__(self):
+        self.value = float("NaN")
+
+def to_hashable(obj):
+    # So that we don't infinitely recurse since frozenset and tuples
+    # are Sequences.
+    if isinstance(obj, (int, float, str, bool, type(None))):
+        return obj
+    elif isinstance(obj, Mapping):
+        return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
+    elif isinstance(obj, Sequence):
+        return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
+    else:
+        # TODO - Support other objects like tensors?
+        return Unhashable()
+
+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):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = (node_id, node["class_type"])
+            self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+class CacheKeySetInputSignature(CacheKeySet):
+    def __init__(self, dynprompt, node_ids, is_changed_cache):
+        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):
+        for node_id in node_ids:
+            if node_id in self.keys:
+                continue
+            node = self.dynprompt.get_node(node_id)
+            self.keys[node_id] = 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):
+        signature = []
+        ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
+        signature.append(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))
+        return to_hashable(signature)
+
+    def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        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)]
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT):
+            signature.append(node_id)
+        inputs = node["inputs"]
+        for key in sorted(inputs.keys()):
+            if is_link(inputs[key]):
+                (ancestor_id, ancestor_socket) = inputs[key]
+                ancestor_index = ancestor_order_mapping[ancestor_id]
+                signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
+            else:
+                signature.append((key, inputs[key]))
+        return signature
+
+    # This function returns a list of all ancestors of the given node. The order of the list is
+    # deterministic based on which specific inputs the ancestor is connected by.
+    def get_ordered_ancestry(self, dynprompt, node_id):
+        ancestors = []
+        order_mapping = {}
+        self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
+        return ancestors, order_mapping
+
+    def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        inputs = dynprompt.get_node(node_id)["inputs"]
+        input_keys = sorted(inputs.keys())
+        for key in input_keys:
+            if is_link(inputs[key]):
+                ancestor_id = inputs[key][0]
+                if ancestor_id not in order_mapping:
+                    ancestors.append(ancestor_id)
+                    order_mapping[ancestor_id] = len(ancestors) - 1
+                    self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
+
+class BasicCache:
+    def __init__(self, key_class):
+        self.key_class = key_class
+        self.initialized = False
+        self.dynprompt: DynamicPrompt
+        self.cache_key_set: CacheKeySet
+        self.cache = {}
+        self.subcaches = {}
+
+    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)
+        self.is_changed_cache = is_changed_cache
+        self.initialized = True
+
+    def all_node_ids(self):
+        assert self.initialized
+        node_ids = self.cache_key_set.all_node_ids()
+        for subcache in self.subcaches.values():
+            node_ids = node_ids.union(subcache.all_node_ids())
+        return node_ids
+
+    def _clean_cache(self):
+        preserve_keys = set(self.cache_key_set.get_used_keys())
+        to_remove = []
+        for key in self.cache:
+            if key not in preserve_keys:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.cache[key]
+
+    def _clean_subcaches(self):
+        preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
+
+        to_remove = []
+        for key in self.subcaches:
+            if key not in preserve_subcaches:
+                to_remove.append(key)
+        for key in to_remove:
+            del self.subcaches[key]
+
+    def clean_unused(self):
+        assert self.initialized
+        self._clean_cache()
+        self._clean_subcaches()
+
+    def _set_immediate(self, node_id, value):
+        assert self.initialized
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        self.cache[cache_key] = value
+
+    def _get_immediate(self, node_id):
+        if not self.initialized:
+            return None
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key in self.cache:
+            return self.cache[cache_key]
+        else:
+            return None
+
+    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)
+        return subcache
+
+    def _get_subcache(self, node_id):
+        assert self.initialized
+        subcache_key = self.cache_key_set.get_subcache_key(node_id)
+        if subcache_key in self.subcaches:
+            return self.subcaches[subcache_key]
+        else:
+            return None
+
+    def recursive_debug_dump(self):
+        result = []
+        for key in self.cache:
+            result.append({"key": key, "value": self.cache[key]})
+        for key in self.subcaches:
+            result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
+        return result
+
+class HierarchicalCache(BasicCache):
+    def __init__(self, key_class):
+        super().__init__(key_class)
+
+    def _get_cache_for(self, node_id):
+        assert self.dynprompt is not None
+        parent_id = self.dynprompt.get_parent_node_id(node_id)
+        if parent_id is None:
+            return self
+
+        hierarchy = []
+        while parent_id is not None:
+            hierarchy.append(parent_id)
+            parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+        cache = self
+        for parent_id in reversed(hierarchy):
+            cache = cache._get_subcache(parent_id)
+            if cache is None:
+                return None
+        return cache
+
+    def get(self, node_id):
+        cache = self._get_cache_for(node_id)
+        if cache is None:
+            return None
+        return cache._get_immediate(node_id)
+
+    def set(self, node_id, value):
+        cache = self._get_cache_for(node_id)
+        assert cache is not None
+        cache._set_immediate(node_id, value)
+
+    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)
+
+class LRUCache(BasicCache):
+    def __init__(self, key_class, max_size=100):
+        super().__init__(key_class)
+        self.max_size = max_size
+        self.min_generation = 0
+        self.generation = 0
+        self.used_generation = {}
+        self.children = {}
+
+    def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+        super().set_prompt(dynprompt, node_ids, is_changed_cache)
+        self.generation += 1
+        for node_id in node_ids:
+            self._mark_used(node_id)
+
+    def clean_unused(self):
+        while len(self.cache) > self.max_size and self.min_generation < self.generation:
+            self.min_generation += 1
+            to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
+            for key in to_remove:
+                del self.cache[key]
+                del self.used_generation[key]
+                if key in self.children:
+                    del self.children[key]
+        self._clean_subcaches()
+
+    def get(self, node_id):
+        self._mark_used(node_id)
+        return self._get_immediate(node_id)
+
+    def _mark_used(self, node_id):
+        cache_key = self.cache_key_set.get_data_key(node_id)
+        if cache_key is not None:
+            self.used_generation[cache_key] = self.generation
+
+    def set(self, node_id, value):
+        self._mark_used(node_id)
+        return self._set_immediate(node_id, value)
+
+    def ensure_subcache_for(self, node_id, children_ids):
+        # Just uses subcaches for tracking 'live' nodes
+        super()._ensure_subcache(node_id, children_ids)
+
+        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] = []
+        for child_id in children_ids:
+            self._mark_used(child_id)
+            self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
+        return self
+

comfy_execution/graph.py

@@ -0,0 +1,237 @@
+import nodes
+
+from comfy_execution.graph_utils import is_link
+
+class DependencyCycleError(Exception):
+    pass
+
+class NodeInputError(Exception):
+    pass
+
+class NodeNotFoundError(Exception):
+    pass
+
+class DynamicPrompt:
+    def __init__(self, original_prompt):
+        # The original prompt provided by the user
+        self.original_prompt = original_prompt
+        # Any extra pieces of the graph created during execution
+        self.ephemeral_prompt = {}
+        self.ephemeral_parents = {}
+        self.ephemeral_display = {}
+
+    def get_node(self, node_id):
+        if node_id in self.ephemeral_prompt:
+            return self.ephemeral_prompt[node_id]
+        if node_id in self.original_prompt:
+            return self.original_prompt[node_id]
+        raise NodeNotFoundError(f"Node {node_id} not found")
+
+    def has_node(self, node_id):
+        return node_id in self.original_prompt or node_id in self.ephemeral_prompt
+
+    def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
+        self.ephemeral_prompt[node_id] = node_info
+        self.ephemeral_parents[node_id] = parent_id
+        self.ephemeral_display[node_id] = display_id
+
+    def get_real_node_id(self, node_id):
+        while node_id in self.ephemeral_parents:
+            node_id = self.ephemeral_parents[node_id]
+        return node_id
+
+    def get_parent_node_id(self, node_id):
+        return self.ephemeral_parents.get(node_id, None)
+
+    def get_display_node_id(self, node_id):
+        while node_id in self.ephemeral_display:
+            node_id = self.ephemeral_display[node_id]
+        return node_id
+
+    def all_node_ids(self):
+        return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
+
+    def get_original_prompt(self):
+        return self.original_prompt
+
+def get_input_info(class_def, input_name):
+    valid_inputs = class_def.INPUT_TYPES()
+    input_info = None
+    input_category = None
+    if "required" in valid_inputs and input_name in valid_inputs["required"]:
+        input_category = "required"
+        input_info = valid_inputs["required"][input_name]
+    elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
+        input_category = "optional"
+        input_info = valid_inputs["optional"][input_name]
+    elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
+        input_category = "hidden"
+        input_info = valid_inputs["hidden"][input_name]
+    if input_info is None:
+        return None, None, None
+    input_type = input_info[0]
+    if len(input_info) > 1:
+        extra_info = input_info[1]
+    else:
+        extra_info = {}
+    return input_type, input_category, extra_info
+
+class TopologicalSort:
+    def __init__(self, dynprompt):
+        self.dynprompt = dynprompt
+        self.pendingNodes = {}
+        self.blockCount = {} # Number of nodes this node is directly blocked by
+        self.blocking = {} # Which nodes are blocked by this node
+
+    def get_input_info(self, unique_id, input_name):
+        class_type = self.dynprompt.get_node(unique_id)["class_type"]
+        class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+        return get_input_info(class_def, input_name)
+
+    def make_input_strong_link(self, to_node_id, to_input):
+        inputs = self.dynprompt.get_node(to_node_id)["inputs"]
+        if to_input not in inputs:
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
+        value = inputs[to_input]
+        if not is_link(value):
+            raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
+        from_node_id, from_socket = value
+        self.add_strong_link(from_node_id, from_socket, to_node_id)
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        self.add_node(from_node_id)
+        if to_node_id not in self.blocking[from_node_id]:
+            self.blocking[from_node_id][to_node_id] = {}
+            self.blockCount[to_node_id] += 1
+        self.blocking[from_node_id][to_node_id][from_socket] = True
+
+    def add_node(self, unique_id, include_lazy=False, subgraph_nodes=None):
+        if unique_id in self.pendingNodes:
+            return
+        self.pendingNodes[unique_id] = True
+        self.blockCount[unique_id] = 0
+        self.blocking[unique_id] = {}
+
+        inputs = self.dynprompt.get_node(unique_id)["inputs"]
+        for input_name in inputs:
+            value = inputs[input_name]
+            if is_link(value):
+                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)
+                is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+                if include_lazy or not is_lazy:
+                    self.add_strong_link(from_node_id, from_socket, unique_id)
+
+    def get_ready_nodes(self):
+        return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
+
+    def pop_node(self, unique_id):
+        del self.pendingNodes[unique_id]
+        for blocked_node_id in self.blocking[unique_id]:
+            self.blockCount[blocked_node_id] -= 1
+        del self.blocking[unique_id]
+
+    def is_empty(self):
+        return len(self.pendingNodes) == 0
+
+class ExecutionList(TopologicalSort):
+    """
+    ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
+    it can still be returned to the graph after having further dependencies added.
+    """
+    def __init__(self, dynprompt, output_cache):
+        super().__init__(dynprompt)
+        self.output_cache = output_cache
+        self.staged_node_id = None
+
+    def add_strong_link(self, from_node_id, from_socket, to_node_id):
+        if self.output_cache.get(from_node_id) is not None:
+            # Nothing to do
+            return
+        super().add_strong_link(from_node_id, from_socket, to_node_id)
+
+    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()
+        if len(available) == 0:
+            cycled_nodes = self.get_nodes_in_cycle()
+            # Because cycles composed entirely of static nodes are caught during initial validation,
+            # we will 'blame' the first node in the cycle that is not a static node.
+            blamed_node = cycled_nodes[0]
+            for node_id in cycled_nodes:
+                display_node_id = self.dynprompt.get_display_node_id(node_id)
+                if display_node_id != node_id:
+                    blamed_node = display_node_id
+                    break
+            ex = DependencyCycleError("Dependency cycle detected")
+            error_details = {
+                "node_id": blamed_node,
+                "exception_message": str(ex),
+                "exception_type": "graph.DependencyCycleError",
+                "traceback": [],
+                "current_inputs": []
+            }
+            return None, error_details, ex
+        next_node = available[0]
+        # If an output node is available, do that first.
+        # Technically this has no effect on the overall length of execution, but it feels better as a user
+        # for a PreviewImage to display a result as soon as it can
+        # Some other heuristics could probably be used here to improve the UX further.
+        for node_id in available:
+            class_type = self.dynprompt.get_node(node_id)["class_type"]
+            class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+            if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+                next_node = node_id
+                break
+        self.staged_node_id = next_node
+        return self.staged_node_id, None, None
+
+    def unstage_node_execution(self):
+        assert self.staged_node_id is not None
+        self.staged_node_id = None
+
+    def complete_node_execution(self):
+        node_id = self.staged_node_id
+        self.pop_node(node_id)
+        self.staged_node_id = None
+
+    def get_nodes_in_cycle(self):
+        # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
+        # We're skipping some of the performance optimizations from the original TopologicalSort to keep
+        # the code simple (and because having a cycle in the first place is a catastrophic error)
+        blocked_by = { node_id: {} for node_id in self.pendingNodes }
+        for from_node_id in self.blocking:
+            for to_node_id in self.blocking[from_node_id]:
+                if True in self.blocking[from_node_id][to_node_id].values():
+                    blocked_by[to_node_id][from_node_id] = True
+        to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+        while len(to_remove) > 0:
+            for node_id in to_remove:
+                for to_node_id in blocked_by:
+                    if node_id in blocked_by[to_node_id]:
+                        del blocked_by[to_node_id][node_id]
+                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
+

comfy_execution/graph_utils.py

@@ -0,0 +1,139 @@
+def is_link(obj):
+    if not isinstance(obj, list):
+        return False
+    if len(obj) != 2:
+        return False
+    if not isinstance(obj[0], str):
+        return False
+    if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+        return False
+    return True
+
+# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
+class GraphBuilder:
+    _default_prefix_root = ""
+    _default_prefix_call_index = 0
+    _default_prefix_graph_index = 0
+
+    def __init__(self, prefix = None):
+        if prefix is None:
+            self.prefix = GraphBuilder.alloc_prefix()
+        else:
+            self.prefix = prefix
+        self.nodes = {}
+        self.id_gen = 1
+
+    @classmethod
+    def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
+        cls._default_prefix_root = prefix_root
+        cls._default_prefix_call_index = call_index
+        cls._default_prefix_graph_index = graph_index
+
+    @classmethod
+    def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
+        if root is None:
+            root = GraphBuilder._default_prefix_root
+        if call_index is None:
+            call_index = GraphBuilder._default_prefix_call_index
+        if graph_index is None:
+            graph_index = GraphBuilder._default_prefix_graph_index
+        result = f"{root}.{call_index}.{graph_index}."
+        GraphBuilder._default_prefix_graph_index += 1
+        return result
+
+    def node(self, class_type, id=None, **kwargs):
+        if id is None:
+            id = str(self.id_gen)
+            self.id_gen += 1
+        id = self.prefix + id
+        if id in self.nodes:
+            return self.nodes[id]
+
+        node = Node(id, class_type, kwargs)
+        self.nodes[id] = node
+        return node
+
+    def lookup_node(self, id):
+        id = self.prefix + id
+        return self.nodes.get(id)
+
+    def finalize(self):
+        output = {}
+        for node_id, node in self.nodes.items():
+            output[node_id] = node.serialize()
+        return output
+
+    def replace_node_output(self, node_id, index, new_value):
+        node_id = self.prefix + node_id
+        to_remove = []
+        for node in self.nodes.values():
+            for key, value in node.inputs.items():
+                if is_link(value) and value[0] == node_id and value[1] == index:
+                    if new_value is None:
+                        to_remove.append((node, key))
+                    else:
+                        node.inputs[key] = new_value
+        for node, key in to_remove:
+            del node.inputs[key]
+
+    def remove_node(self, id):
+        id = self.prefix + id
+        del self.nodes[id]
+
+class Node:
+    def __init__(self, id, class_type, inputs):
+        self.id = id
+        self.class_type = class_type
+        self.inputs = inputs
+        self.override_display_id = None
+
+    def out(self, index):
+        return [self.id, index]
+
+    def set_input(self, key, value):
+        if value is None:
+            if key in self.inputs:
+                del self.inputs[key]
+        else:
+            self.inputs[key] = value
+
+    def get_input(self, key):
+        return self.inputs.get(key)
+
+    def set_override_display_id(self, override_display_id):
+        self.override_display_id = override_display_id
+
+    def serialize(self):
+        serialized = {
+            "class_type": self.class_type,
+            "inputs": self.inputs
+        }
+        if self.override_display_id is not None:
+            serialized["override_display_id"] = self.override_display_id
+        return serialized
+
+def add_graph_prefix(graph, outputs, prefix):
+    # Change the node IDs and any internal links
+    new_graph = {}
+    for node_id, node_info in graph.items():
+        # Make sure the added nodes have unique IDs
+        new_node_id = prefix + node_id
+        new_node = { "class_type": node_info["class_type"], "inputs": {} }
+        for input_name, input_value in node_info.get("inputs", {}).items():
+            if is_link(input_value):
+                new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
+            else:
+                new_node["inputs"][input_name] = input_value
+        new_graph[new_node_id] = new_node
+
+    # Change the node IDs in the outputs
+    new_outputs = []
+    for n in range(len(outputs)):
+        output = outputs[n]
+        if is_link(output):
+            new_outputs.append([prefix + output[0], output[1]])
+        else:
+            new_outputs.append(output)
+
+    return new_graph, tuple(new_outputs)
+

comfy_extras/nodes_advanced_samplers.py

@@ -60,7 +60,7 @@ from comfy.k_diffusion.sampling import to_d
 import comfy.model_patcher
 
 @torch.no_grad()
-def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
     extra_args = {} if extra_args is None else extra_args
 
     temp = [0]
@@ -75,11 +75,11 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable
     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])
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
         dt = sigmas[i + 1] - sigma_hat
-        x = denoised + sigmas[i + 1] * d
+        x = x + d * dt
     return x
 
 
@@ -96,10 +96,10 @@ class SamplerEulerCFGpp:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, version):
-        if version == "regular":
-            sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp)
+        if version == "alternative":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
         else:
-            sampler = comfy.samplers.ksampler("euler_pp")
+            sampler = comfy.samplers.ksampler("euler_cfg_pp")
         return (sampler, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_audio.py

@@ -6,6 +6,8 @@ import os
 import io
 import json
 import struct
+import random
+import hashlib
 from comfy.cli_args import args
 
 class EmptyLatentAudio:
@@ -14,15 +16,16 @@ class EmptyLatentAudio:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {}}
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
-    def generate(self):
+    def generate(self, seconds):
         batch_size = 1
-        latent = torch.zeros([batch_size, 64, 1024], device=self.device)
+        length = round((seconds * 44100 / 2048) / 2) * 2
+        latent = torch.zeros([batch_size, 64, length], device=self.device)
         return ({"samples":latent, "type": "audio"}, )
 
 class VAEEncodeAudio:
@@ -32,7 +35,7 @@ class VAEEncodeAudio:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
     def encode(self, vae, audio):
         sample_rate = audio["sample_rate"]
@@ -51,7 +54,7 @@ class VAEDecodeAudio:
     RETURN_TYPES = ("AUDIO",)
     FUNCTION = "decode"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
     def decode(self, vae, samples):
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
@@ -117,7 +120,6 @@ class SaveAudio:
         self.output_dir = folder_paths.get_output_directory()
         self.type = "output"
         self.prefix_append = ""
-        self.compress_level = 4
 
     @classmethod
     def INPUT_TYPES(s):
@@ -131,7 +133,7 @@ class SaveAudio:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "audio"
 
     def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
@@ -146,7 +148,7 @@ class SaveAudio:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        for (batch_number, waveform) in enumerate(audio["waveform"]):
+        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"
 
@@ -167,6 +169,19 @@ class SaveAudio:
 
         return { "ui": { "audio": results } }
 
+class PreviewAudio(SaveAudio):
+    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))
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"audio": ("AUDIO", ), },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
 class LoadAudio:
     SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
 
@@ -181,7 +196,7 @@ class LoadAudio:
         ]
         return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "audio"
 
     RETURN_TYPES = ("AUDIO", )
     FUNCTION = "load"
@@ -189,7 +204,6 @@ class LoadAudio:
     def load(self, audio):
         audio_path = folder_paths.get_annotated_filepath(audio)
         waveform, sample_rate = torchaudio.load(audio_path)
-        multiplier = 1.0
         audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
         return (audio, )
 
@@ -213,4 +227,5 @@ NODE_CLASS_MAPPINGS = {
     "VAEDecodeAudio": VAEDecodeAudio,
     "SaveAudio": SaveAudio,
     "LoadAudio": LoadAudio,
+    "PreviewAudio": PreviewAudio,
 }

comfy_extras/nodes_controlnet.py

@@ -0,0 +1,27 @@
+from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+
+class SetUnionControlNetType:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"control_net": ("CONTROL_NET", ),
+                             "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),)
+                             }}
+
+    CATEGORY = "conditioning/controlnet"
+    RETURN_TYPES = ("CONTROL_NET",)
+
+    FUNCTION = "set_controlnet_type"
+
+    def set_controlnet_type(self, control_net, type):
+        control_net = control_net.copy()
+        type_number = UNION_CONTROLNET_TYPES.get(type, -1)
+        if type_number >= 0:
+            control_net.set_extra_arg("control_type", [type_number])
+        else:
+            control_net.set_extra_arg("control_type", [])
+
+        return (control_net,)
+
+NODE_CLASS_MAPPINGS = {
+    "SetUnionControlNetType": SetUnionControlNetType,
+}

comfy_extras/nodes_custom_sampler.py

@@ -111,6 +111,25 @@ class SDTurboScheduler:
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 
+class BetaSamplingScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+                     "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model, steps, alpha, beta):
+        sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
+        return (sigmas, )
+
 class VPScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -276,6 +295,23 @@ class SamplerDPMPP_SDE:
         sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+class SamplerDPMPP_2S_Ancestral:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerEulerAncestral:
     @classmethod
     def INPUT_TYPES(s):
@@ -293,6 +329,25 @@ class SamplerEulerAncestral:
         sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
         return (sampler, )
 
+class SamplerEulerAncestralCFGPP:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}),
+                "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}),
+            }}
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler(
+            "euler_ancestral_cfg_pp",
+            {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerLMS:
     @classmethod
     def INPUT_TYPES(s):
@@ -619,13 +674,16 @@ NODE_CLASS_MAPPINGS = {
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
     "VPScheduler": VPScheduler,
+    "BetaSamplingScheduler": BetaSamplingScheduler,
     "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
     "SamplerEulerAncestral": SamplerEulerAncestral,
+    "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP,
     "SamplerLMS": SamplerLMS,
     "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral,
     "SamplerDPMAdaptative": SamplerDPMAdaptative,
     "SplitSigmas": SplitSigmas,
     "SplitSigmasDenoise": SplitSigmasDenoise,
@@ -639,3 +697,7 @@ NODE_CLASS_MAPPINGS = {
     "AddNoise": AddNoise,
     "SamplerCustomAdvanced": SamplerCustomAdvanced,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
+}

comfy_extras/nodes_flux.py

@@ -0,0 +1,47 @@
+import node_helpers
+
+class CLIPTextEncodeFlux:
+    @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"
+
+    CATEGORY = "advanced/conditioning/flux"
+
+    def encode(self, clip, clip_l, t5xxl, guidance):
+        tokens = clip.tokenize(clip_l)
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        output["guidance"] = guidance
+        return ([[cond, output]], )
+
+class FluxGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning": ("CONDITIONING", ),
+            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/flux"
+
+    def append(self, conditioning, guidance):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
+        return (c, )
+
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
+    "FluxGuidance": FluxGuidance,
+}