test

.ci/update_windows/update.py

@@ -1,117 +0,0 @@
-import pygit2
-from datetime import datetime
-import sys
-import os
-import shutil
-import filecmp
-
-def pull(repo, remote_name='origin', branch='master'):
-    for remote in repo.remotes:
-        if remote.name == remote_name:
-            remote.fetch()
-            remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
-            merge_result, _ = repo.merge_analysis(remote_master_id)
-            # Up to date, do nothing
-            if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
-                return
-            # We can just fastforward
-            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
-                repo.checkout_tree(repo.get(remote_master_id))
-                try:
-                    master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
-                    master_ref.set_target(remote_master_id)
-                except KeyError:
-                    repo.create_branch(branch, repo.get(remote_master_id))
-                repo.head.set_target(remote_master_id)
-            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
-                repo.merge(remote_master_id)
-
-                if repo.index.conflicts is not None:
-                    for conflict in repo.index.conflicts:
-                        print('Conflicts found in:', conflict[0].path)
-                    raise AssertionError('Conflicts, ahhhhh!!')
-
-                user = repo.default_signature
-                tree = repo.index.write_tree()
-                commit = repo.create_commit('HEAD',
-                                            user,
-                                            user,
-                                            'Merge!',
-                                            tree,
-                                            [repo.head.target, remote_master_id])
-                # We need to do this or git CLI will think we are still merging.
-                repo.state_cleanup()
-            else:
-                raise AssertionError('Unknown merge analysis result')
-
-pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
-repo_path = str(sys.argv[1])
-repo = pygit2.Repository(repo_path)
-ident = pygit2.Signature('comfyui', 'comfy@ui')
-try:
-    print("stashing current changes")
-    repo.stash(ident)
-except KeyError:
-    print("nothing to stash")
-backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
-print("creating backup branch: {}".format(backup_branch_name))
-try:
-    repo.branches.local.create(backup_branch_name, repo.head.peel())
-except:
-    pass
-
-print("checking out master branch")
-branch = repo.lookup_branch('master')
-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)
-
-print("Done!")
-
-self_update = True
-if len(sys.argv) > 2:
-    self_update = '--skip_self_update' not in sys.argv
-
-update_py_path = os.path.realpath(__file__)
-repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py")
-
-cur_path = os.path.dirname(update_py_path)
-
-
-req_path = os.path.join(cur_path, "current_requirements.txt")
-repo_req_path = os.path.join(repo_path, "requirements.txt")
-
-
-def files_equal(file1, file2):
-    try:
-        return filecmp.cmp(file1, file2, shallow=False)
-    except:
-        return False
-
-def file_size(f):
-    try:
-        return os.path.getsize(f)
-    except:
-        return 0
-
-
-if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10:
-    shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py"))
-    exit()
-
-if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
-    import subprocess
-    try:
-        subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path])
-        shutil.copy(repo_req_path, req_path)
-    except:
-        pass

.ci/update_windows/update_comfyui.bat

@@ -1,8 +0,0 @@
-@echo off
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-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
-)
-if "%~1"=="" pause

.ci/windows_base_files/README_VERY_IMPORTANT.txt

@@ -1,31 +0,0 @@
-HOW TO RUN:
-
-if you have a NVIDIA gpu:
-
-run_nvidia_gpu.bat
-
-
-
-To run it in slow CPU mode:
-
-run_cpu.bat
-
-
-
-IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
-
-You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
-
-
-RECOMMENDED WAY TO UPDATE:
-To update the ComfyUI code: update\update_comfyui.bat
-
-
-
-To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
-update\update_comfyui_and_python_dependencies.bat
-
-
-TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
-In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
-Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.

.ci/windows_base_files/run_cpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
-pause

.ci/windows_base_files/run_nvidia_gpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
-pause

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,48 +0,0 @@
-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"]
-body:
-  - 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:** 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

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: ComfyUI Matrix Space
-    url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
-    about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).
-  - name: Comfy Org Discord
-    url: https://discord.gg/comfyorg
-    about: The Comfy Org Discord is available for support and general discussion related to ComfyUI.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,32 +0,0 @@
-name: Feature Request
-description: "You have an idea for something new you would like to see added to ComfyUI's core."
-labels: [ "Feature" ]
-body:
-    - type: markdown
-      attributes:
-        value: |
-                Before submitting a **Feature Request**, please ensure the following:
-
-                **1:** You are running the latest version of ComfyUI.
-                **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea.
-                **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node.
-
-                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: Feature Idea
-            description: "Describe the feature you want to see."
-      validations:
-            required: true
-    - type: textarea
-      attributes:
-                label: Existing Solutions
-                description: "Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference."
-      validations:
-                required: false
-    - type: textarea
-      attributes:
-                label: Other
-                description: "Any other additional information you think might be helpful."
-      validations:
-                required: false

.github/ISSUE_TEMPLATE/user-support.yml

@@ -1,32 +0,0 @@
-name: User Support
-description: "Use this if you need help with something, or you're experiencing an issue."
-labels: [ "User Support" ]
-body:
-    - type: markdown
-      attributes:
-        value: |
-            Before submitting a **User Report** issue, please ensure the following:
-
-            **1:** You are running the latest version of ComfyUI.
-            **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
-
-                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
-    - type: textarea
-      attributes:
-            label: Your question
-            description: "Post your question here. Please be as detailed as possible."
-      validations:
-            required: true
-    - type: textarea
-      attributes:
-                label: Logs
-                description: "If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here."
-                render: powershell
-      validations:
-                required: false
-    - type: textarea
-      attributes:
-                label: Other
-                description: "Any other additional information you think might be helpful."
-      validations:
-                required: false

.github/workflows/pylint.yml

@@ -1,23 +0,0 @@
-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

@@ -1,104 +0,0 @@
-
-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

@@ -1,76 +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: '3.8'
-    - 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: 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
-    - 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: playwright-report
-        path: ComfyUI_frontend/playwright-report/
-        retention-days: 30
-    - uses: actions/upload-artifact@v4
-      if: always()
-      with:
-        name: console-output
-        path: ComfyUI/console_output.log
-        retention-days: 30

.github/workflows/test-build.yml

@@ -1,31 +0,0 @@
-name: Build package
-
-#
-# This workflow is a test of the python package build.
-# Install Python dependencies across different Python versions.
-#
-
-on:
-  push:
-    paths:
-      - "requirements.txt"
-      - ".github/workflows/test-build.yml"
-
-jobs:
-  build:
-    name: Build Test
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements.txt

.github/workflows/test-ui.yaml

@@ -1,30 +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
-    - name: Run Unit Tests
-      run: |
-        pip install -r tests-unit/requirements.txt
-        python -m pytest tests-unit

.github/workflows/windows_release_dependencies.yml

@@ -1,71 +0,0 @@
-name: "Windows Release dependencies"
-
-on:
-  workflow_dispatch:
-    inputs:
-      xformers:
-        description: 'xformers version'
-        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: "124"
-
-      python_minor:
-        description: 'python minor version'
-        required: true
-        type: string
-        default: "11"
-
-      python_patch:
-        description: 'python patch version'
-        required: true
-        type: string
-        default: "9"
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build_dependencies:
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v4
-        - uses: actions/setup-python@v5
-          with:
-            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
-
-        - shell: bash
-          run: |
-            echo "@echo off
-            call update_comfyui.bat nopause
-            echo -
-            echo This will try to update pytorch and all python dependencies.
-            echo -
-            echo If you just want to update normally, close this and run update_comfyui.bat instead.
-            echo -
-            pause
-            ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
-            pause" > update_comfyui_and_python_dependencies.bat
-
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
-            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${{ inputs.cu }}_python_deps
-
-        - uses: actions/cache/save@v4
-          with:
-            path: |
-              cu${{ inputs.cu }}_python_deps.tar
-              update_comfyui_and_python_dependencies.bat
-            key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -1,90 +0,0 @@
-name: "Windows Release Nightly pytorch"
-
-on:
-  workflow_dispatch:
-    inputs:
-      cu:
-        description: 'cuda version'
-        required: true
-        type: string
-        default: "124"
-
-      python_minor:
-        description: 'python minor version'
-        required: true
-        type: string
-        default: "12"
-
-      python_patch:
-        description: 'python patch version'
-        required: true
-        type: string
-        default: "4"
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build:
-    permissions:
-        contents: "write"
-        packages: "write"
-        pull-requests: "read"
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v4
-          with:
-            fetch-depth: 0
-            persist-credentials: false
-        - uses: actions/setup-python@v5
-          with:
-            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
-        - 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 'import site' >> ./python3${{ inputs.python_minor }}._pth
-            curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
-            ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ 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${{ inputs.python_minor }}._pth
-            cd ..
-
-            git clone --depth 1 https://github.com/comfyanonymous/taesd
-            cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
-
-            mkdir ComfyUI_windows_portable_nightly_pytorch
-            mv python_embeded ComfyUI_windows_portable_nightly_pytorch
-            mv ComfyUI_copy ComfyUI_windows_portable_nightly_pytorch/ComfyUI
-
-            cd ComfyUI_windows_portable_nightly_pytorch
-
-            mkdir update
-            cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
-
-            echo "call update_comfyui.bat nopause
-            ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
-            pause" > ./update/update_comfyui_and_python_dependencies.bat
-            cd ..
-
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
-            mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
-
-            cd ComfyUI_windows_portable_nightly_pytorch
-            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_or_cpu_nightly_pytorch.7z
-                tag: "latest"
-                overwrite: true

.github/workflows/windows_release_package.yml

@@ -1,100 +0,0 @@
-name: "Windows Release packaging"
-
-on:
-  workflow_dispatch:
-    inputs:
-      cu:
-        description: 'cuda version'
-        required: true
-        type: string
-        default: "124"
-
-      python_minor:
-        description: 'python minor version'
-        required: true
-        type: string
-        default: "11"
-
-      python_patch:
-        description: 'python patch version'
-        required: true
-        type: string
-        default: "9"
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  package_comfyui:
-    permissions:
-        contents: "write"
-        packages: "write"
-        pull-requests: "read"
-    runs-on: windows-latest
-    steps:
-        - 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${{ inputs.cu }}_python_deps.tar
-            pwd
-            ls
-
-        - uses: actions/checkout@v4
-          with:
-            fetch-depth: 0
-            persist-credentials: false
-        - shell: bash
-          run: |
-            cd ..
-            cp -r ComfyUI ComfyUI_copy
-            curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
-            unzip python_embeded.zip -d python_embeded
-            cd python_embeded
-            echo 'import site' >> ./python3${{ 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/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
-
-            cd ComfyUI_windows_portable
-            python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
-
-            ls
-
-        - name: Upload binaries to release
-          uses: svenstaro/upload-release-action@v2
-          with:
-                repo_token: ${{ secrets.GITHUB_TOKEN }}
-                file: new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
-                tag: "latest"
-                overwrite: true
-

.gitignore

@@ -1,21 +0,0 @@
-__pycache__/
-*.py[cod]
-/output/
-/input/
-!/input/example.png
-/models/
-/temp/
-/custom_nodes/
-!custom_nodes/example_node.py.example
-extra_model_paths.yaml
-/.vs
-.vscode/
-.idea/
-venv/
-/web/extensions/*
-!/web/extensions/logging.js.example
-!/web/extensions/core/
-/tests-ui/data/object_info.json
-/user/
-*.log
-web_custom_versions/

.pylintrc

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

CODEOWNERS

@@ -1 +0,0 @@
-*       @comfyanonymous

CONTRIBUTING.md

@@ -1,41 +0,0 @@
-# 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.

LICENSE

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-  Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so.  This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software.  The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable.  Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products.  If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
-  Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish to
-avoid the special danger that patents applied to a free program could
-make it effectively proprietary.  To prevent this, the GPL assures that
-patents cannot be used to render the program non-free.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
-  "The Program" refers to any copyrightable work licensed under this
-License.  Each licensee is addressed as "you".  "Licensees" and
-"recipients" may be individuals or organizations.
-
-  To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of an
-exact copy.  The resulting work is called a "modified version" of the
-earlier work or a work "based on" the earlier work.
-
-  A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-  To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
-computer or modifying a private copy.  Propagation includes copying,
-distribution (with or without modification), making available to the
-public, and in some countries other activities as well.
-
-  To "convey" a work means any kind of propagation that enables other
-parties to make or receive copies.  Mere interaction with a user through
-a computer network, with no transfer of a copy, is not conveying.
-
-  An interactive user interface displays "Appropriate Legal Notices"
-to the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License.  If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-  1. Source Code.
-
-  The "source code" for a work means the preferred form of the work
-for making modifications to it.  "Object code" means any non-source
-form of a work.
-
-  A "Standard Interface" means an interface that either is an official
-standard defined by a recognized standards body, or, in the case of
-interfaces specified for a particular programming language, one that
-is widely used among developers working in that language.
-
-  The "System Libraries" of an executable work include anything, other
-than the work as a whole, that (a) is included in the normal form of
-packaging a Major Component, but which is not part of that Major
-Component, and (b) serves only to enable use of the work with that
-Major Component, or to implement a Standard Interface for which an
-implementation is available to the public in source code form.  A
-"Major Component", in this context, means a major essential component
-(kernel, window system, and so on) of the specific operating system
-(if any) on which the executable work runs, or a compiler used to
-produce the work, or an object code interpreter used to run it.
-
-  The "Corresponding Source" for a work in object code form means all
-the source code needed to generate, install, and (for an executable
-work) run the object code and to modify the work, including scripts to
-control those activities.  However, it does not include the work's
-System Libraries, or general-purpose tools or generally available free
-programs which are used unmodified in performing those activities but
-which are not part of the work.  For example, Corresponding Source
-includes interface definition files associated with source files for
-the work, and the source code for shared libraries and dynamically
-linked subprograms that the work is specifically designed to require,
-such as by intimate data communication or control flow between those
-subprograms and other parts of the work.
-
-  The Corresponding Source need not include anything that users
-can regenerate automatically from other parts of the Corresponding
-Source.
-
-  The Corresponding Source for a work in source code form is that
-same work.
-
-  2. Basic Permissions.
-
-  All rights granted under this License are granted for the term of
-copyright on the Program, and are irrevocable provided the stated
-conditions are met.  This License explicitly affirms your unlimited
-permission to run the unmodified Program.  The output from running a
-covered work is covered by this License only if the output, given its
-content, constitutes a covered work.  This License acknowledges your
-rights of fair use or other equivalent, as provided by copyright law.
-
-  You may make, run and propagate covered works that you do not
-convey, without conditions so long as your license otherwise remains
-in force.  You may convey covered works to others for the sole purpose
-of having them make modifications exclusively for you, or provide you
-with facilities for running those works, provided that you comply with
-the terms of this License in conveying all material for which you do
-not control copyright.  Those thus making or running the covered works
-for you must do so exclusively on your behalf, under your direction
-and control, on terms that prohibit them from making any copies of
-your copyrighted material outside their relationship with you.
-
-  Conveying under any other circumstances is permitted solely under
-the conditions stated below.  Sublicensing is not allowed; section 10
-makes it unnecessary.
-
-  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
-
-  No covered work shall be deemed part of an effective technological
-measure under any applicable law fulfilling obligations under article
-11 of the WIPO copyright treaty adopted on 20 December 1996, or
-similar laws prohibiting or restricting circumvention of such
-measures.
-
-  When you convey a covered work, you waive any legal power to forbid
-circumvention of technological measures to the extent such circumvention
-is effected by exercising rights under this License with respect to
-the covered work, and you disclaim any intention to limit operation or
-modification of the work as a means of enforcing, against the work's
-users, your or third parties' legal rights to forbid circumvention of
-technological measures.
-
-  4. Conveying Verbatim Copies.
-
-  You may convey verbatim copies of the Program's source code as you
-receive it, in any medium, provided that you conspicuously and
-appropriately publish on each copy an appropriate copyright notice;
-keep intact all notices stating that this License and any
-non-permissive terms added in accord with section 7 apply to the code;
-keep intact all notices of the absence of any warranty; and give all
-recipients a copy of this License along with the Program.
-
-  You may charge any price or no price for each copy that you convey,
-and you may offer support or warranty protection for a fee.
-
-  5. Conveying Modified Source Versions.
-
-  You may convey a work based on the Program, or the modifications to
-produce it from the Program, in the form of source code under the
-terms of section 4, provided that you also meet all of these conditions:
-
-    a) The work must carry prominent notices stating that you modified
-    it, and giving a relevant date.
-
-    b) The work must carry prominent notices stating that it is
-    released under this License and any conditions added under section
-    7.  This requirement modifies the requirement in section 4 to
-    "keep intact all notices".
-
-    c) You must license the entire work, as a whole, under this
-    License to anyone who comes into possession of a copy.  This
-    License will therefore apply, along with any applicable section 7
-    additional terms, to the whole of the work, and all its parts,
-    regardless of how they are packaged.  This License gives no
-    permission to license the work in any other way, but it does not
-    invalidate such permission if you have separately received it.
-
-    d) If the work has interactive user interfaces, each must display
-    Appropriate Legal Notices; however, if the Program has interactive
-    interfaces that do not display Appropriate Legal Notices, your
-    work need not make them do so.
-
-  A compilation of a covered work with other separate and independent
-works, which are not by their nature extensions of the covered work,
-and which are not combined with it such as to form a larger program,
-in or on a volume of a storage or distribution medium, is called an
-"aggregate" if the compilation and its resulting copyright are not
-used to limit the access or legal rights of the compilation's users
-beyond what the individual works permit.  Inclusion of a covered work
-in an aggregate does not cause this License to apply to the other
-parts of the aggregate.
-
-  6. Conveying Non-Source Forms.
-
-  You may convey a covered work in object code form under the terms
-of sections 4 and 5, provided that you also convey the
-machine-readable Corresponding Source under the terms of this License,
-in one of these ways:
-
-    a) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by the
-    Corresponding Source fixed on a durable physical medium
-    customarily used for software interchange.
-
-    b) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by a
-    written offer, valid for at least three years and valid for as
-    long as you offer spare parts or customer support for that product
-    model, to give anyone who possesses the object code either (1) a
-    copy of the Corresponding Source for all the software in the
-    product that is covered by this License, on a durable physical
-    medium customarily used for software interchange, for a price no
-    more than your reasonable cost of physically performing this
-    conveying of source, or (2) access to copy the
-    Corresponding Source from a network server at no charge.
-
-    c) Convey individual copies of the object code with a copy of the
-    written offer to provide the Corresponding Source.  This
-    alternative is allowed only occasionally and noncommercially, and
-    only if you received the object code with such an offer, in accord
-    with subsection 6b.
-
-    d) Convey the object code by offering access from a designated
-    place (gratis or for a charge), and offer equivalent access to the
-    Corresponding Source in the same way through the same place at no
-    further charge.  You need not require recipients to copy the
-    Corresponding Source along with the object code.  If the place to
-    copy the object code is a network server, the Corresponding Source
-    may be on a different server (operated by you or a third party)
-    that supports equivalent copying facilities, provided you maintain
-    clear directions next to the object code saying where to find the
-    Corresponding Source.  Regardless of what server hosts the
-    Corresponding Source, you remain obligated to ensure that it is
-    available for as long as needed to satisfy these requirements.
-
-    e) Convey the object code using peer-to-peer transmission, provided
-    you inform other peers where the object code and Corresponding
-    Source of the work are being offered to the general public at no
-    charge under subsection 6d.
-
-  A separable portion of the object code, whose source code is excluded
-from the Corresponding Source as a System Library, need not be
-included in conveying the object code work.
-
-  A "User Product" is either (1) a "consumer product", which means any
-tangible personal property which is normally used for personal, family,
-or household purposes, or (2) anything designed or sold for incorporation
-into a dwelling.  In determining whether a product is a consumer product,
-doubtful cases shall be resolved in favor of coverage.  For a particular
-product received by a particular user, "normally used" refers to a
-typical or common use of that class of product, regardless of the status
-of the particular user or of the way in which the particular user
-actually uses, or expects or is expected to use, the product.  A product
-is a consumer product regardless of whether the product has substantial
-commercial, industrial or non-consumer uses, unless such uses represent
-the only significant mode of use of the product.
-
-  "Installation Information" for a User Product means any methods,
-procedures, authorization keys, or other information required to install
-and execute modified versions of a covered work in that User Product from
-a modified version of its Corresponding Source.  The information must
-suffice to ensure that the continued functioning of the modified object
-code is in no case prevented or interfered with solely because
-modification has been made.
-
-  If you convey an object code work under this section in, or with, or
-specifically for use in, a User Product, and the conveying occurs as
-part of a transaction in which the right of possession and use of the
-User Product is transferred to the recipient in perpetuity or for a
-fixed term (regardless of how the transaction is characterized), the
-Corresponding Source conveyed under this section must be accompanied
-by the Installation Information.  But this requirement does not apply
-if neither you nor any third party retains the ability to install
-modified object code on the User Product (for example, the work has
-been installed in ROM).
-
-  The requirement to provide Installation Information does not include a
-requirement to continue to provide support service, warranty, or updates
-for a work that has been modified or installed by the recipient, or for
-the User Product in which it has been modified or installed.  Access to a
-network may be denied when the modification itself materially and
-adversely affects the operation of the network or violates the rules and
-protocols for communication across the network.
-
-  Corresponding Source conveyed, and Installation Information provided,
-in accord with this section must be in a format that is publicly
-documented (and with an implementation available to the public in
-source code form), and must require no special password or key for
-unpacking, reading or copying.
-
-  7. Additional Terms.
-
-  "Additional permissions" are terms that supplement the terms of this
-License by making exceptions from one or more of its conditions.
-Additional permissions that are applicable to the entire Program shall
-be treated as though they were included in this License, to the extent
-that they are valid under applicable law.  If additional permissions
-apply only to part of the Program, that part may be used separately
-under those permissions, but the entire Program remains governed by
-this License without regard to the additional permissions.
-
-  When you convey a copy of a covered work, you may at your option
-remove any additional permissions from that copy, or from any part of
-it.  (Additional permissions may be written to require their own
-removal in certain cases when you modify the work.)  You may place
-additional permissions on material, added by you to a covered work,
-for which you have or can give appropriate copyright permission.
-
-  Notwithstanding any other provision of this License, for material you
-add to a covered work, you may (if authorized by the copyright holders of
-that material) supplement the terms of this License with terms:
-
-    a) Disclaiming warranty or limiting liability differently from the
-    terms of sections 15 and 16 of this License; or
-
-    b) Requiring preservation of specified reasonable legal notices or
-    author attributions in that material or in the Appropriate Legal
-    Notices displayed by works containing it; or
-
-    c) Prohibiting misrepresentation of the origin of that material, or
-    requiring that modified versions of such material be marked in
-    reasonable ways as different from the original version; or
-
-    d) Limiting the use for publicity purposes of names of licensors or
-    authors of the material; or
-
-    e) Declining to grant rights under trademark law for use of some
-    trade names, trademarks, or service marks; or
-
-    f) Requiring indemnification of licensors and authors of that
-    material by anyone who conveys the material (or modified versions of
-    it) with contractual assumptions of liability to the recipient, for
-    any liability that these contractual assumptions directly impose on
-    those licensors and authors.
-
-  All other non-permissive additional terms are considered "further
-restrictions" within the meaning of section 10.  If the Program as you
-received it, or any part of it, contains a notice stating that it is
-governed by this License along with a term that is a further
-restriction, you may remove that term.  If a license document contains
-a further restriction but permits relicensing or conveying under this
-License, you may add to a covered work material governed by the terms
-of that license document, provided that the further restriction does
-not survive such relicensing or conveying.
-
-  If you add terms to a covered work in accord with this section, you
-must place, in the relevant source files, a statement of the
-additional terms that apply to those files, or a notice indicating
-where to find the applicable terms.
-
-  Additional terms, permissive or non-permissive, may be stated in the
-form of a separately written license, or stated as exceptions;
-the above requirements apply either way.
-
-  8. Termination.
-
-  You may not propagate or modify a covered work except as expressly
-provided under this License.  Any attempt otherwise to propagate or
-modify it is void, and will automatically terminate your rights under
-this License (including any patent licenses granted under the third
-paragraph of section 11).
-
-  However, if you cease all violation of this License, then your
-license from a particular copyright holder is reinstated (a)
-provisionally, unless and until the copyright holder explicitly and
-finally terminates your license, and (b) permanently, if the copyright
-holder fails to notify you of the violation by some reasonable means
-prior to 60 days after the cessation.
-
-  Moreover, your license from a particular copyright holder is
-reinstated permanently if the copyright holder notifies you of the
-violation by some reasonable means, this is the first time you have
-received notice of violation of this License (for any work) from that
-copyright holder, and you cure the violation prior to 30 days after
-your receipt of the notice.
-
-  Termination of your rights under this section does not terminate the
-licenses of parties who have received copies or rights from you under
-this License.  If your rights have been terminated and not permanently
-reinstated, you do not qualify to receive new licenses for the same
-material under section 10.
-
-  9. Acceptance Not Required for Having Copies.
-
-  You are not required to accept this License in order to receive or
-run a copy of the Program.  Ancillary propagation of a covered work
-occurring solely as a consequence of using peer-to-peer transmission
-to receive a copy likewise does not require acceptance.  However,
-nothing other than this License grants you permission to propagate or
-modify any covered work.  These actions infringe copyright if you do
-not accept this License.  Therefore, by modifying or propagating a
-covered work, you indicate your acceptance of this License to do so.
-
-  10. Automatic Licensing of Downstream Recipients.
-
-  Each time you convey a covered work, the recipient automatically
-receives a license from the original licensors, to run, modify and
-propagate that work, subject to this License.  You are not responsible
-for enforcing compliance by third parties with this License.
-
-  An "entity transaction" is a transaction transferring control of an
-organization, or substantially all assets of one, or subdividing an
-organization, or merging organizations.  If propagation of a covered
-work results from an entity transaction, each party to that
-transaction who receives a copy of the work also receives whatever
-licenses to the work the party's predecessor in interest had or could
-give under the previous paragraph, plus a right to possession of the
-Corresponding Source of the work from the predecessor in interest, if
-the predecessor has it or can get it with reasonable efforts.
-
-  You may not impose any further restrictions on the exercise of the
-rights granted or affirmed under this License.  For example, you may
-not impose a license fee, royalty, or other charge for exercise of
-rights granted under this License, and you may not initiate litigation
-(including a cross-claim or counterclaim in a lawsuit) alleging that
-any patent claim is infringed by making, using, selling, offering for
-sale, or importing the Program or any portion of it.
-
-  11. Patents.
-
-  A "contributor" is a copyright holder who authorizes use under this
-License of the Program or a work on which the Program is based.  The
-work thus licensed is called the contributor's "contributor version".
-
-  A contributor's "essential patent claims" are all patent claims
-owned or controlled by the contributor, whether already acquired or
-hereafter acquired, that would be infringed by some manner, permitted
-by this License, of making, using, or selling its contributor version,
-but do not include claims that would be infringed only as a
-consequence of further modification of the contributor version.  For
-purposes of this definition, "control" includes the right to grant
-patent sublicenses in a manner consistent with the requirements of
-this License.
-
-  Each contributor grants you a non-exclusive, worldwide, royalty-free
-patent license under the contributor's essential patent claims, to
-make, use, sell, offer for sale, import and otherwise run, modify and
-propagate the contents of its contributor version.
-
-  In the following three paragraphs, a "patent license" is any express
-agreement or commitment, however denominated, not to enforce a patent
-(such as an express permission to practice a patent or covenant not to
-sue for patent infringement).  To "grant" such a patent license to a
-party means to make such an agreement or commitment not to enforce a
-patent against the party.
-
-  If you convey a covered work, knowingly relying on a patent license,
-and the Corresponding Source of the work is not available for anyone
-to copy, free of charge and under the terms of this License, through a
-publicly available network server or other readily accessible means,
-then you must either (1) cause the Corresponding Source to be so
-available, or (2) arrange to deprive yourself of the benefit of the
-patent license for this particular work, or (3) arrange, in a manner
-consistent with the requirements of this License, to extend the patent
-license to downstream recipients.  "Knowingly relying" means you have
-actual knowledge that, but for the patent license, your conveying the
-covered work in a country, or your recipient's use of the covered work
-in a country, would infringe one or more identifiable patents in that
-country that you have reason to believe are valid.
-
-  If, pursuant to or in connection with a single transaction or
-arrangement, you convey, or propagate by procuring conveyance of, a
-covered work, and grant a patent license to some of the parties
-receiving the covered work authorizing them to use, propagate, modify
-or convey a specific copy of the covered work, then the patent license
-you grant is automatically extended to all recipients of the covered
-work and works based on it.
-
-  A patent license is "discriminatory" if it does not include within
-the scope of its coverage, prohibits the exercise of, or is
-conditioned on the non-exercise of one or more of the rights that are
-specifically granted under this License.  You may not convey a covered
-work if you are a party to an arrangement with a third party that is
-in the business of distributing software, under which you make payment
-to the third party based on the extent of your activity of conveying
-the work, and under which the third party grants, to any of the
-parties who would receive the covered work from you, a discriminatory
-patent license (a) in connection with copies of the covered work
-conveyed by you (or copies made from those copies), or (b) primarily
-for and in connection with specific products or compilations that
-contain the covered work, unless you entered into that arrangement,
-or that patent license was granted, prior to 28 March 2007.
-
-  Nothing in this License shall be construed as excluding or limiting
-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
-  12. No Surrender of Others' Freedom.
-
-  If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License.  If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you may
-not convey it at all.  For example, if you agree to terms that obligate you
-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
-License would be to refrain entirely from conveying the Program.
-
-  13. Use with the GNU Affero General Public License.
-
-  Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU Affero General Public License into a single
-combined work, and to convey the resulting work.  The terms of this
-License will continue to apply to the part which is the covered work,
-but the special requirements of the GNU Affero General Public License,
-section 13, concerning interaction through a network will apply to the
-combination as such.
-
-  14. Revised Versions of this License.
-
-  The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
-versions of the GNU General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
-  Later license versions may give you additional or different
-permissions.  However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
-  15. Disclaimer of Warranty.
-
-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    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/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,224 +0,0 @@
-ComfyUI
-=======
-The most powerful and modular stable diffusion GUI and backend.
------------
-![ComfyUI Screenshot](comfyui_screenshot.png)
-
-This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
-### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
-
-### [Installing ComfyUI](#installing)
-
-## 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/), [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.
-- 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, 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/)
-- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
-- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
-- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
-- [unCLIP Models](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)
-- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
-- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
-- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
-- [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.
-- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
-
-Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
-
-## Shortcuts
-
-| Keybind                            | Explanation                                                                                                        |
-|------------------------------------|--------------------------------------------------------------------------------------------------------------------|
-| Ctrl + Enter                       | Queue up current graph for generation                                                                              |
-| Ctrl + Shift + Enter               | Queue up current graph as first for generation                                                                     |
-| Ctrl + Z/Ctrl + Y                  | Undo/Redo                                                                                                          |
-| Ctrl + S                           | Save workflow                                                                                                      |
-| Ctrl + O                           | Load workflow                                                                                                      |
-| Ctrl + A                           | Select all nodes                                                                                                   |
-| Alt + C                            | Collapse/uncollapse selected nodes                                                                                 |
-| Ctrl + M                           | Mute/unmute selected nodes                                                                                         |
-| Ctrl + B                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
-| Delete/Backspace                   | Delete selected nodes                                                                                              |
-| Ctrl + Backspace                   | Delete the current graph                                                                                           |
-| Space                              | Move the canvas around when held and moving the cursor                                                             |
-| Ctrl/Shift + Click                 | Add clicked node to selection                                                                                      |
-| Ctrl + C/Ctrl + V                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
-| Ctrl + C/Ctrl + Shift + V          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
-| Shift + Drag                       | Move multiple selected nodes at the same time                                                                      |
-| Ctrl + D                           | Load default graph                                                                                                 |
-| Alt + `+`                          | Canvas Zoom in                                                                                                     |
-| Alt + `-`                          | Canvas Zoom out                                                                                                    |
-| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
-| Q                                  | Toggle visibility of the queue                                                                                     |
-| H                                  | Toggle visibility of history                                                                                       |
-| R                                  | Refresh graph                                                                                                      |
-| Double-Click LMB                   | Open node quick search palette                                                                                     |
-
-Ctrl can also be replaced with Cmd instead for macOS users
-
-# Installing
-
-## Windows
-
-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/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
-
-If you have trouble extracting it, right click the file -> properties -> unblock
-
-#### How do I share models between another UI and ComfyUI?
-
-See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
-
-## Jupyter Notebook
-
-To run it on services like paperspace, kaggle or colab you can use my [Jupyter Notebook](notebooks/comfyui_colab.ipynb)
-
-## Manual Install (Windows, Linux)
-
-Git clone this repo.
-
-Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
-
-Put your VAE in: models/vae
-
-
-### AMD GPUs (Linux only)
-AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
-
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0```
-
-This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
-
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1```
-
-### NVIDIA
-
-Nvidia users should install stable pytorch using this command:
-
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121```
-
-This is the command to install pytorch nightly instead which might have performance improvements:
-
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
-
-#### Troubleshooting
-
-If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
-
-```pip uninstall torch```
-
-And install it again with the command above.
-
-### Dependencies
-
-Install the dependencies by opening your terminal inside the ComfyUI folder and:
-
-```pip install -r requirements.txt```
-
-After this you should have everything installed and can proceed to running ComfyUI.
-
-### Others:
-
-#### Intel GPUs
-
-Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
-
-1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
-1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
-1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
-1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
-
-Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
-
-#### Apple Mac silicon
-
-You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
-
-1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
-1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
-1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
-1. Launch ComfyUI by running `python main.py`
-
-> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
-
-#### DirectML (AMD Cards on Windows)
-
-```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
-
-# Running
-
-```python main.py```
-
-### For AMD cards not officially supported by ROCm
-
-Try running it with this command if you have issues:
-
-For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
-
-For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
-
-# Notes
-
-Only parts of the graph that have an output with all the correct inputs will be executed.
-
-Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
-
-Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
-
-You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
-
-You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
-
-Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
-
-To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
-
-```embedding:embedding_filename.pt```
-
-
-## How to show high-quality previews?
-
-Use ```--preview-method auto``` to enable previews.
-
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
-
-## How to use TLS/SSL?
-Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
-
-Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
-
-> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. 
-<br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
-
-## Support and dev channel
-
-[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
-
-### Which GPU should I buy for this?
-
-[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
-

app/app_settings.py

@@ -1,54 +0,0 @@
-import os
-import json
-from aiohttp import web
-
-
-class AppSettings():
-    def __init__(self, user_manager):
-        self.user_manager = user_manager
-
-    def get_settings(self, request):
-        file = self.user_manager.get_request_user_filepath(
-            request, "comfy.settings.json")
-        if os.path.isfile(file):
-            with open(file) as f:
-                return json.load(f)
-        else:
-            return {}
-
-    def save_settings(self, request, settings):
-        file = self.user_manager.get_request_user_filepath(
-            request, "comfy.settings.json")
-        with open(file, "w") as f:
-            f.write(json.dumps(settings, indent=4))
-
-    def add_routes(self, routes):
-        @routes.get("/settings")
-        async def get_settings(request):
-            return web.json_response(self.get_settings(request))
-
-        @routes.get("/settings/{id}")
-        async def get_setting(request):
-            value = None
-            settings = self.get_settings(request)
-            setting_id = request.match_info.get("id", None)
-            if setting_id and setting_id in settings:
-                value = settings[setting_id]
-            return web.json_response(value)
-
-        @routes.post("/settings")
-        async def post_settings(request):
-            settings = self.get_settings(request)
-            new_settings = await request.json()
-            self.save_settings(request, {**settings, **new_settings})
-            return web.Response(status=200)
-
-        @routes.post("/settings/{id}")
-        async def post_setting(request):
-            setting_id = request.match_info.get("id", None)
-            if not setting_id:
-                return web.Response(status=400)
-            settings = self.get_settings(request)
-            settings[setting_id] = await request.json()
-            self.save_settings(request, settings)
-            return web.Response(status=200)

app/frontend_management.py

@@ -1,188 +0,0 @@
-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

app/user_manager.py

@@ -1,205 +0,0 @@
-import json
-import os
-import re
-import uuid
-import glob
-import shutil
-from aiohttp import web
-from comfy.cli_args import args
-from folder_paths import user_directory
-from .app_settings import AppSettings
-
-default_user = "default"
-users_file = os.path.join(user_directory, "users.json")
-
-
-class UserManager():
-    def __init__(self):
-        global user_directory
-
-        self.settings = AppSettings(self)
-        if not os.path.exists(user_directory):
-            os.mkdir(user_directory)
-            if not args.multi_user:
-                print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
-                print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
-
-        if args.multi_user:
-            if os.path.isfile(users_file):
-                with open(users_file) as f:
-                    self.users = json.load(f)
-            else:
-                self.users = {}
-        else:
-            self.users = {"default": "default"}
-
-    def get_request_user_id(self, request):
-        user = "default"
-        if args.multi_user and "comfy-user" in request.headers:
-            user = request.headers["comfy-user"]
-
-        if user not in self.users:
-            raise KeyError("Unknown user: " + user)
-
-        return user
-
-    def get_request_user_filepath(self, request, file, type="userdata", create_dir=True):
-        global user_directory
-
-        if type == "userdata":
-            root_dir = user_directory
-        else:
-            raise KeyError("Unknown filepath type:" + type)
-
-        user = self.get_request_user_id(request)
-        path = user_root = os.path.abspath(os.path.join(root_dir, user))
-
-        # prevent leaving /{type}
-        if os.path.commonpath((root_dir, user_root)) != root_dir:
-            return None
-
-        if file is not None:
-            # prevent leaving /{type}/{user}
-            path = os.path.abspath(os.path.join(user_root, file))
-            if os.path.commonpath((user_root, path)) != user_root:
-                return None
-
-        parent = os.path.split(path)[0]
-
-        if create_dir and not os.path.exists(parent):
-            os.makedirs(parent, exist_ok=True)
-
-        return path
-
-    def add_user(self, name):
-        name = name.strip()
-        if not name:
-            raise ValueError("username not provided")
-        user_id = re.sub("[^a-zA-Z0-9-_]+", '-', name)
-        user_id = user_id + "_" + str(uuid.uuid4())
-
-        self.users[user_id] = name
-
-        global users_file
-        with open(users_file, "w") as f:
-            json.dump(self.users, f)
-
-        return user_id
-
-    def add_routes(self, routes):
-        self.settings.add_routes(routes)
-
-        @routes.get("/users")
-        async def get_users(request):
-            if args.multi_user:
-                return web.json_response({"storage": "server", "users": self.users})
-            else:
-                user_dir = self.get_request_user_filepath(request, None, create_dir=False)
-                return web.json_response({
-                    "storage": "server",
-                    "migrated": os.path.exists(user_dir)
-                })
-
-        @routes.post("/users")
-        async def post_users(request):
-            body = await request.json()
-            username = body["username"]
-            if username in self.users.values():
-                return web.json_response({"error": "Duplicate username."}, status=400)
-
-            user_id = self.add_user(username)
-            return web.json_response(user_id)
-
-        @routes.get("/userdata")
-        async def listuserdata(request):
-            directory = request.rel_url.query.get('dir', '')
-            if not directory:
-                return web.Response(status=400)
-                
-            path = self.get_request_user_filepath(request, directory)
-            if not path:
-                return web.Response(status=403)
-            
-            if not os.path.exists(path):
-                return web.Response(status=404)
-            
-            recurse = request.rel_url.query.get('recurse', '').lower() == "true"
-            results = glob.glob(os.path.join(
-                glob.escape(path), '**/*'), recursive=recurse)
-            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
-            
-            split_path = request.rel_url.query.get('split', '').lower() == "true"
-            if split_path:
-                results = [[x] + x.split(os.sep) for x in results]
-
-            return web.json_response(results)
-
-        def get_user_data_path(request, check_exists = False, param = "file"):
-            file = request.match_info.get(param, None)
-            if not file:
-                return web.Response(status=400)
-                
-            path = self.get_request_user_filepath(request, file)
-            if not path:
-                return web.Response(status=403)
-            
-            if check_exists and not os.path.exists(path):
-                return web.Response(status=404)
-            
-            return path
-
-        @routes.get("/userdata/{file}")
-        async def getuserdata(request):
-            path = get_user_data_path(request, check_exists=True)
-            if not isinstance(path, str):
-                return path
-            
-            return web.FileResponse(path)
-
-        @routes.post("/userdata/{file}")
-        async def post_userdata(request):
-            path = get_user_data_path(request)
-            if not isinstance(path, str):
-                return path
-            
-            overwrite = request.query["overwrite"] != "false"
-            if not overwrite and os.path.exists(path):
-                return web.Response(status=409)
-
-            body = await request.read()
-
-            with open(path, "wb") as f:
-                f.write(body)
-                
-            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
-            return web.json_response(resp)
-
-        @routes.delete("/userdata/{file}")
-        async def delete_userdata(request):
-            path = get_user_data_path(request, check_exists=True)
-            if not isinstance(path, str):
-                return path
-
-            os.remove(path)
-                
-            return web.Response(status=204)
-
-        @routes.post("/userdata/{file}/move/{dest}")
-        async def move_userdata(request):
-            source = get_user_data_path(request, check_exists=True)
-            if not isinstance(source, str):
-                return source
-            
-            dest = get_user_data_path(request, check_exists=False, param="dest")
-            if not isinstance(source, str):
-                return dest
-            
-            overwrite = request.query["overwrite"] != "false"
-            if not overwrite and os.path.exists(dest):
-                return web.Response(status=409)
-
-            print(f"moving '{source}' -> '{dest}'")
-            shutil.move(source, dest)
-                
-            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
-            return web.json_response(resp)

comfy/cldm/cldm.py

@@ -15,8 +15,8 @@ from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedS
 from ..ldm.util import exists
 from .control_types import UNION_CONTROLNET_TYPES
 from collections import OrderedDict
-import comfy.ops
+import totoro.ops
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
 
 class OptimizedAttention(nn.Module):
     def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
@@ -95,7 +95,7 @@ class ControlNet(nn.Module):
         attn_precision=None,
         union_controlnet_num_control_type=None,
         device=None,
-        operations=comfy.ops.disable_weight_init,
+        operations=totoro.ops.disable_weight_init,
         **kwargs,
     ):
         super().__init__()

comfy/cldm/mmdit.py

@@ -1,8 +1,8 @@
 import torch
 from typing import Dict, Optional
-import comfy.ldm.modules.diffusionmodules.mmdit
+import totoro.ldm.modules.diffusionmodules.mmdit
 
-class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
+class ControlNet(totoro.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
         self,
         num_blocks = None,
@@ -17,7 +17,7 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
         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(
+        self.pos_embed_input = totoro.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
             None,
             self.patch_size,
             self.in_channels,

comfy/cli_args.py

@@ -2,7 +2,7 @@ import argparse
 import enum
 import os
 from typing import Optional
-import comfy.options
+import totoro.options
 
 
 class EnumAction(argparse.Action):
@@ -44,10 +44,10 @@ parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORI
 parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
 
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
+parser.add_argument("--output-directory", type=str, default=None, help="Set the totoroUI output directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the totoroUI temp directory (default is in the totoroUI directory).")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the totoroUI input directory.")
-parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
+parser.add_argument("--auto-launch", action="store_true", help="Automatically launch totoroUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
 cm_group = parser.add_mutually_exclusive_group()
@@ -114,7 +114,7 @@ vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for e
 
 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("--disable-smart-memory", action="store_true", help="Force totoroUI to agressively offload to regular ram instead of keeping models in vram when it can.")
 parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
 
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
@@ -129,7 +129,7 @@ parser.add_argument("--multi-user", action="store_true", help="Enables per-user
 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"
+DEFAULT_VERSION_STRING = "totoroanonymous/totoroUI@latest"
 
 parser.add_argument(
     "--front-end-version",
@@ -161,7 +161,7 @@ parser.add_argument(
     help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
 )
 
-if comfy.options.args_parsing:
+if totoro.options.args_parsing:
     args = parser.parse_args()
 else:
     args = parser.parse_args([])

comfy/clip_model.py

@@ -1,6 +1,6 @@
 import torch
-from comfy.ldm.modules.attention import optimized_attention_for_device
+from totoro.ldm.modules.attention import optimized_attention_for_device
-import comfy.ops
+import totoro.ops
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -78,7 +78,7 @@ class CLIPEmbeddings(torch.nn.Module):
         self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
     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)
+        return self.token_embedding(input_tokens, out_dtype=dtype) + totoro.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
 
 
 class CLIPTextModel_(torch.nn.Module):
@@ -159,7 +159,7 @@ class CLIPVisionEmbeddings(torch.nn.Module):
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        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)
+        return torch.cat([totoro.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + totoro.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):

comfy/clip_vision.py

@@ -4,11 +4,11 @@ import torch
 import json
 import logging
 
-import comfy.ops
+import totoro.ops
-import comfy.model_patcher
+import totoro.model_patcher
-import comfy.model_management
+import totoro.model_management
-import comfy.utils
+import totoro.utils
-import comfy.clip_model
+import totoro.clip_model
 
 class Output:
     def __getitem__(self, key):
@@ -35,13 +35,13 @@ class ClipVisionModel():
             config = json.load(f)
 
         self.image_size = config.get("image_size", 224)
-        self.load_device = comfy.model_management.text_encoder_device()
+        self.load_device = totoro.model_management.text_encoder_device()
-        offload_device = comfy.model_management.text_encoder_offload_device()
+        offload_device = totoro.model_management.text_encoder_offload_device()
-        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        self.dtype = totoro.model_management.text_encoder_dtype(self.load_device)
-        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model = totoro.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, totoro.ops.manual_cast)
         self.model.eval()
 
-        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+        self.patcher = totoro.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
 
     def load_sd(self, sd):
         return self.model.load_state_dict(sd, strict=False)
@@ -50,14 +50,14 @@ class ClipVisionModel():
         return self.model.state_dict()
 
     def encode_image(self, image):
-        comfy.model_management.load_model_gpu(self.patcher)
+        totoro.model_management.load_model_gpu(self.patcher)
         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()
-        outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
+        outputs["last_hidden_state"] = out[0].to(totoro.model_management.intermediate_device())
-        outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
+        outputs["image_embeds"] = out[2].to(totoro.model_management.intermediate_device())
-        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+        outputs["penultimate_hidden_states"] = out[1].to(totoro.model_management.intermediate_device())
         return outputs
 
 def convert_to_transformers(sd, prefix):

comfy/conds.py

@@ -1,6 +1,6 @@
 import torch
 import math
-import comfy.utils
+import totoro.utils
 
 
 def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
@@ -14,7 +14,7 @@ class CONDRegular:
         return self.__class__(cond)
 
     def process_cond(self, batch_size, device, **kwargs):
-        return self._copy_with(comfy.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
+        return self._copy_with(totoro.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
 
     def can_concat(self, other):
         if self.cond.shape != other.cond.shape:
@@ -35,7 +35,7 @@ class CONDNoiseShape(CONDRegular):
             for i in range(dims):
                 data = data.narrow(i + 2, area[i + dims], area[i])
 
-        return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
+        return self._copy_with(totoro.utils.repeat_to_batch_size(data, batch_size).to(device))
 
 
 class CONDCrossAttn(CONDRegular):

comfy/controlnet.py

@@ -2,17 +2,17 @@ import torch
 import math
 import os
 import logging
-import comfy.utils
+import totoro.utils
-import comfy.model_management
+import totoro.model_management
-import comfy.model_detection
+import totoro.model_detection
-import comfy.model_patcher
+import totoro.model_patcher
-import comfy.ops
+import totoro.ops
-import comfy.latent_formats
+import totoro.latent_formats
 
-import comfy.cldm.cldm
+import totoro.cldm.cldm
-import comfy.t2i_adapter.adapter
+import totoro.t2i_adapter.adapter
-import comfy.ldm.cascade.controlnet
+import totoro.ldm.cascade.controlnet
-import comfy.cldm.mmdit
+import totoro.cldm.mmdit
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
@@ -48,7 +48,7 @@ class ControlBase:
         self.extra_args = {}
 
         if device is None:
-            device = comfy.model_management.get_torch_device()
+            device = totoro.model_management.get_torch_device()
         self.device = device
         self.previous_controlnet = None
 
@@ -147,7 +147,7 @@ class ControlNet(ControlBase):
         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.control_model_wrapped = totoro.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=totoro.model_management.unet_offload_device())
 
         self.compression_ratio = compression_ratio
         self.global_average_pooling = global_average_pooling
@@ -179,11 +179,11 @@ class ControlNet(ControlBase):
             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")
+            self.cond_hint = totoro.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)
+                loaded_models = totoro.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)
+                totoro.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)
@@ -224,7 +224,7 @@ class ControlNet(ControlBase):
         super().cleanup()
 
 class ControlLoraOps:
-    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+    class Linear(torch.nn.Module, totoro.ops.CastWeightBiasOp):
         def __init__(self, in_features: int, out_features: int, bias: bool = True,
                     device=None, dtype=None) -> None:
             factory_kwargs = {'device': device, 'dtype': dtype}
@@ -237,13 +237,13 @@ class ControlLoraOps:
             self.bias = None
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias = totoro.ops.cast_bias_weight(self, input)
             if self.up is not None:
                 return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
             else:
                 return torch.nn.functional.linear(input, weight, bias)
 
-    class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+    class Conv2d(torch.nn.Module, totoro.ops.CastWeightBiasOp):
         def __init__(
             self,
             in_channels,
@@ -277,7 +277,7 @@ class ControlLoraOps:
 
 
         def forward(self, input):
-            weight, bias = comfy.ops.cast_bias_weight(self, input)
+            weight, bias = totoro.ops.cast_bias_weight(self, input)
             if self.up is not None:
                 return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
             else:
@@ -298,17 +298,17 @@ class ControlLora(ControlNet):
         self.manual_cast_dtype = model.manual_cast_dtype
         dtype = model.get_dtype()
         if self.manual_cast_dtype is None:
-            class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init):
+            class control_lora_ops(ControlLoraOps, totoro.ops.disable_weight_init):
                 pass
         else:
-            class control_lora_ops(ControlLoraOps, comfy.ops.manual_cast):
+            class control_lora_ops(ControlLoraOps, totoro.ops.manual_cast):
                 pass
             dtype = self.manual_cast_dtype
 
         controlnet_config["operations"] = control_lora_ops
         controlnet_config["dtype"] = dtype
-        self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+        self.control_model = totoro.cldm.cldm.ControlNet(**controlnet_config)
-        self.control_model.to(comfy.model_management.get_torch_device())
+        self.control_model.to(totoro.model_management.get_torch_device())
         diffusion_model = model.diffusion_model
         sd = diffusion_model.state_dict()
         cm = self.control_model.state_dict()
@@ -316,13 +316,13 @@ class ControlLora(ControlNet):
         for k in sd:
             weight = sd[k]
             try:
-                comfy.utils.set_attr_param(self.control_model, k, weight)
+                totoro.utils.set_attr_param(self.control_model, k, weight)
             except:
                 pass
 
         for k in self.control_weights:
             if k not in {"lora_controlnet"}:
-                comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+                totoro.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(totoro.model_management.get_torch_device()))
 
     def copy(self):
         c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
@@ -339,21 +339,21 @@ class ControlLora(ControlNet):
         return out
 
     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)
+        return totoro.utils.calculate_parameters(self.control_weights) * totoro.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)
+    model_config = totoro.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)
+    unet_dtype = totoro.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
-    load_device = comfy.model_management.get_torch_device()
+    load_device = totoro.model_management.get_torch_device()
-    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    manual_cast_dtype = totoro.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
-        operations = comfy.ops.manual_cast
+        operations = totoro.ops.manual_cast
     else:
-        operations = comfy.ops.disable_weight_init
+        operations = totoro.ops.disable_weight_init
 
     return model_config, operations, load_device, unet_dtype, manual_cast_dtype
 
@@ -368,23 +368,23 @@ def controlnet_load_state_dict(control_model, sd):
     return control_model
 
 def load_controlnet_mmdit(sd):
-    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    new_sd = totoro.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.{}.')
+    num_blocks = totoro.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 = totoro.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 = totoro.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(ckpt_path, model=None):
-    controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    controlnet_data = totoro.utils.load_torch_file(ckpt_path, safe_load=True)
     if "lora_controlnet" in controlnet_data:
         return ControlLora(controlnet_data)
 
@@ -392,8 +392,8 @@ def load_controlnet(ckpt_path, model=None):
     supported_inference_dtypes = None
 
     if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
-        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)
+        controlnet_config = totoro.model_detection.unet_config_from_diffusers_unet(controlnet_data)
-        diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
+        diffusers_keys = totoro.utils.unet_to_diffusers(controlnet_config)
         diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
         diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
 
@@ -460,28 +460,28 @@ def load_controlnet(ckpt_path, model=None):
         return net
 
     if controlnet_config is None:
-        model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
+        model_config = totoro.model_detection.model_config_from_unet(controlnet_data, prefix, True)
         supported_inference_dtypes = model_config.supported_inference_dtypes
         controlnet_config = model_config.unet_config
 
-    load_device = comfy.model_management.get_torch_device()
+    load_device = totoro.model_management.get_torch_device()
     if supported_inference_dtypes is None:
-        unet_dtype = comfy.model_management.unet_dtype()
+        unet_dtype = totoro.model_management.unet_dtype()
     else:
-        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+        unet_dtype = totoro.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
 
-    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    manual_cast_dtype = totoro.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
-        controlnet_config["operations"] = comfy.ops.manual_cast
+        controlnet_config["operations"] = totoro.ops.manual_cast
     controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
-    control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
+    control_model = totoro.cldm.cldm.ControlNet(**controlnet_config)
 
     if pth:
         if 'difference' in controlnet_data:
             if model is not None:
-                comfy.model_management.load_models_gpu([model])
+                totoro.model_management.load_models_gpu([model])
                 model_sd = model.model_state_dict()
                 for x in controlnet_data:
                     c_m = "control_model."
@@ -548,7 +548,7 @@ class T2IAdapter(ControlBase):
             self.control_input = None
             self.cond_hint = None
             width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
+            self.cond_hint = totoro.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
@@ -583,12 +583,12 @@ def load_t2i_adapter(t2i_data):
                 prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j)
             prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2)
         prefix_replace["adapter."] = ""
-        t2i_data = comfy.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
+        t2i_data = totoro.utils.state_dict_prefix_replace(t2i_data, prefix_replace)
     keys = t2i_data.keys()
 
     if "body.0.in_conv.weight" in keys:
         cin = t2i_data['body.0.in_conv.weight'].shape[1]
-        model_ad = comfy.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
+        model_ad = totoro.t2i_adapter.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4)
     elif 'conv_in.weight' in keys:
         cin = t2i_data['conv_in.weight'].shape[1]
         channel = t2i_data['conv_in.weight'].shape[0]
@@ -600,13 +600,13 @@ def load_t2i_adapter(t2i_data):
         xl = False
         if cin == 256 or cin == 768:
             xl = True
-        model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
+        model_ad = totoro.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
     elif "backbone.0.0.weight" in keys:
-        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+        model_ad = totoro.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
         compression_ratio = 32
         upscale_algorithm = 'bilinear'
     elif "backbone.10.blocks.0.weight" in keys:
-        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+        model_ad = totoro.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
         compression_ratio = 1
         upscale_algorithm = 'nearest-exact'
     else:

comfy/diffusers_load.py

@@ -1,6 +1,6 @@
 import os
 
-import comfy.sd
+import totoro.sd
 
 def first_file(path, filenames):
     for f in filenames:
@@ -22,15 +22,15 @@ 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 = totoro.sd.load_unet(unet_path)
 
     clip = None
     if output_clip:
-        clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
+        clip = totoro.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
 
     vae = None
     if output_vae:
-        sd = comfy.utils.load_torch_file(vae_path)
+        sd = totoro.utils.load_torch_file(vae_path)
-        vae = comfy.sd.VAE(sd=sd)
+        vae = totoro.sd.VAE(sd=sd)
 
     return (unet, clip, vae)

comfy/gligen.py

@@ -2,8 +2,8 @@ import torch
 from torch import nn
 from .ldm.modules.attention import CrossAttention
 from inspect import isfunction
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.manual_cast
+ops = totoro.ops.manual_cast
 
 def exists(val):
     return val is not None

comfy/k_diffusion/sampling.py

@@ -8,7 +8,7 @@ from tqdm.auto import trange, tqdm
 
 from . import utils
 from . import deis
-import comfy.model_patcher
+import totoro.model_patcher
 
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])
@@ -789,7 +789,7 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
 
 @torch.no_grad()
 def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-    # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
+
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     s_end = sigmas[-1]
@@ -1007,7 +1007,7 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+    extra_args["model_options"] = totoro.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
@@ -1033,7 +1033,7 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+    extra_args["model_options"] = totoro.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):

comfy/ldm/audio/autoencoder.py

@@ -4,8 +4,8 @@ import torch
 from torch import nn
 from typing import Literal, Dict, Any
 import math
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.disable_weight_init
+ops = totoro.ops.disable_weight_init
 
 def vae_sample(mean, scale):
         stdev = nn.functional.softplus(scale) + 1e-4

comfy/ldm/audio/dit.py

@@ -1,6 +1,6 @@
 # code adapted from: https://github.com/Stability-AI/stable-audio-tools
 
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
 import typing as tp
 
 import torch
@@ -9,7 +9,7 @@ from einops import rearrange
 from torch import nn
 from torch.nn import functional as F
 import math
-import comfy.ops
+import totoro.ops
 
 class FourierFeatures(nn.Module):
     def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
@@ -19,7 +19,7 @@ class FourierFeatures(nn.Module):
             [out_features // 2, in_features], dtype=dtype, device=device))
 
     def forward(self, input):
-        f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
+        f = 2 * math.pi * input @ totoro.ops.cast_to_input(self.weight.T, input)
         return torch.cat([f.cos(), f.sin()], dim=-1)
 
 # norms
@@ -40,8 +40,8 @@ class LayerNorm(nn.Module):
     def forward(self, x):
         beta = self.beta
         if beta is not None:
-            beta = comfy.ops.cast_to_input(beta, x)
+            beta = totoro.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)
+        return F.layer_norm(x, x.shape[-1:], weight=totoro.ops.cast_to_input(self.gamma, x), bias=beta)
 
 class GLU(nn.Module):
     def __init__(
@@ -164,14 +164,14 @@ class RotaryEmbedding(nn.Module):
 
         t = t / self.interpolation_factor
 
-        freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t))
+        freqs = torch.einsum('i , j -> i j', t, totoro.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 = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')
+        scale = totoro.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')
         scale = torch.cat((scale, scale), dim = -1)
 
         return freqs, scale

comfy/ldm/audio/embedders.py

@@ -6,7 +6,7 @@ from torch import Tensor, einsum
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
 from einops import rearrange
 import math
-import comfy.ops
+import totoro.ops
 
 class LearnedPositionalEmbedding(nn.Module):
     """Used for continuous time"""
@@ -27,7 +27,7 @@ class LearnedPositionalEmbedding(nn.Module):
 def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
     return nn.Sequential(
         LearnedPositionalEmbedding(dim),
-        comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
+        totoro.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
     )
 
 

comfy/ldm/aura/mmdit.py

@@ -7,9 +7,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
-import comfy.ops
+import totoro.ops
-import comfy.ldm.common_dit
+import totoro.ldm.common_dit
 
 def modulate(x, shift, scale):
     return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
@@ -408,7 +408,7 @@ class MMDiT(nn.Module):
 
     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 = totoro.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
         x = x.view(
             B,
             C,
@@ -426,7 +426,7 @@ class MMDiT(nn.Module):
         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)
+        pos_encoding = totoro.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)
@@ -454,7 +454,7 @@ class MMDiT(nn.Module):
         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)
+        c = torch.cat([totoro.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
 

comfy/ldm/cascade/common.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify
@@ -18,8 +18,8 @@
 
 import torch
 import torch.nn as nn
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
-import comfy.ops
+import totoro.ops
 
 class OptimizedAttention(nn.Module):
     def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
@@ -77,7 +77,7 @@ class GlobalResponseNorm(nn.Module):
     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 comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x
+        return totoro.ops.cast_to_input(self.gamma, x) * (x * Nx) + totoro.ops.cast_to_input(self.beta, x) + x
 
 
 class ResBlock(nn.Module):

comfy/ldm/cascade/controlnet.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify

comfy/ldm/cascade/stage_a.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify

comfy/ldm/cascade/stage_b.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify

comfy/ldm/cascade/stage_c.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify

comfy/ldm/cascade/stage_c_coder.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify

comfy/ldm/flux/layers.py

@@ -6,7 +6,7 @@ from einops import rearrange
 from torch import Tensor, nn
 
 from .math import attention, rope
-import comfy.ops
+import totoro.ops
 
 class EmbedND(nn.Module):
     def __init__(self, dim: int, theta: int, axes_dim: list):
@@ -69,7 +69,7 @@ class RMSNorm(torch.nn.Module):
         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)
+        return (x * rrms).to(dtype=x_dtype) * totoro.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
 
 
 class QKNorm(torch.nn.Module):

comfy/ldm/flux/math.py

@@ -1,8 +1,8 @@
 import torch
 from einops import rearrange
 from torch import Tensor
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
-import comfy.model_management
+import totoro.model_management
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
     q, k = apply_rope(q, k, pe)
@@ -14,7 +14,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu():
+    if totoro.model_management.is_device_mps(pos.device) or totoro.model_management.is_intel_xpu():
         device = torch.device("cpu")
     else:
         device = pos.device

comfy/ldm/flux/model.py

@@ -15,7 +15,7 @@ from .layers import (
 )
 
 from einops import rearrange, repeat
-import comfy.ldm.common_dit
+import totoro.ldm.common_dit
 
 @dataclass
 class FluxParams:
@@ -126,7 +126,7 @@ class Flux(nn.Module):
     def forward(self, x, timestep, context, y, guidance, **kwargs):
         bs, c, h, w = x.shape
         patch_size = 2
-        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
+        x = totoro.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)
 

comfy/ldm/hydit/attn_layers.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from typing import Tuple, Union, Optional
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
 
 
 def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):

comfy/ldm/hydit/models.py

@@ -4,9 +4,9 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-import comfy.ops
+import totoro.ops
-from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
+from totoro.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
-from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from totoro.ldm.modules.diffusionmodules.util import timestep_embedding
 from torch.utils import checkpoint
 
 from .attn_layers import Attention, CrossAttention
@@ -322,7 +322,7 @@ class HunYuanDiT(nn.Module):
         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)
+        padding = totoro.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:])

comfy/ldm/hydit/poolers.py

@@ -1,8 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.attention import optimized_attention
+from totoro.ldm.modules.attention import optimized_attention
-import comfy.ops
+import totoro.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):
@@ -19,7 +19,7 @@ class AttentionPool(nn.Module):
         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
+        x = x + totoro.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC
 
         q = self.q_proj(x[:1])
         k = self.k_proj(x)

comfy/ldm/models/autoencoder.py

@@ -2,11 +2,11 @@ import torch
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+from totoro.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
 
-from comfy.ldm.util import instantiate_from_config
+from totoro.ldm.util import instantiate_from_config
-from comfy.ldm.modules.ema import LitEma
+from totoro.ldm.modules.ema import LitEma
-import comfy.ops
+import totoro.ops
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
     def __init__(self, sample: bool = True):
@@ -151,21 +151,21 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
         ddconfig = kwargs.pop("ddconfig")
         super().__init__(
             encoder_config={
-                "target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
+                "target": "totoro.ldm.modules.diffusionmodules.model.Encoder",
                 "params": ddconfig,
             },
             decoder_config={
-                "target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
+                "target": "totoro.ldm.modules.diffusionmodules.model.Decoder",
                 "params": ddconfig,
             },
             **kwargs,
         )
-        self.quant_conv = comfy.ops.disable_weight_init.Conv2d(
+        self.quant_conv = totoro.ops.disable_weight_init.Conv2d(
             (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
             (1 + ddconfig["double_z"]) * embed_dim,
             1,
         )
-        self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.post_quant_conv = totoro.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
     def get_autoencoder_params(self) -> list:
@@ -219,7 +219,7 @@ class AutoencoderKL(AutoencodingEngineLegacy):
         super().__init__(
             regularizer_config={
                 "target": (
-                    "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"
+                    "totoro.ldm.models.autoencoder.DiagonalGaussianRegularizer"
                 )
             },
             **kwargs,

comfy/ldm/modules/attention.py

@@ -9,15 +9,15 @@ import logging
 from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
 
-from comfy import model_management
+from totoro import model_management
 
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
-from comfy.cli_args import args
+from totoro.cli_args import args
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.disable_weight_init
+ops = totoro.ops.disable_weight_init
 
 FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
 

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -8,8 +8,8 @@ import torch.nn as nn
 from .. import attention
 from einops import rearrange, repeat
 from .util import timestep_embedding
-import comfy.ops
+import totoro.ops
-import comfy.ldm.common_dit
+import totoro.ldm.common_dit
 
 def default(x, y):
     if x is not None:
@@ -112,7 +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:
-            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
+            x = totoro.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
@@ -926,7 +926,7 @@ class MMDiT(nn.Module):
             context = self.context_processor(context)
 
         hw = x.shape[-2:]
-        x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
+        x = self.x_embedder(x) + totoro.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)

comfy/ldm/modules/diffusionmodules/model.py

@@ -6,9 +6,9 @@ import numpy as np
 from typing import Optional, Any
 import logging
 
-from comfy import model_management
+from totoro import model_management
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.disable_weight_init
+ops = totoro.ops.disable_weight_init
 
 if model_management.xformers_enabled_vae():
     import xformers

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -14,9 +14,9 @@ from .util import (
     AlphaBlender,
 )
 from ..attention import SpatialTransformer, SpatialVideoTransformer, default
-from comfy.ldm.util import exists
+from totoro.ldm.util import exists
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.disable_weight_init
+ops = totoro.ops.disable_weight_init
 
 class TimestepBlock(nn.Module):
     """

comfy/ldm/modules/diffusionmodules/upscaling.py

@@ -4,7 +4,7 @@ import numpy as np
 from functools import partial
 
 from .util import extract_into_tensor, make_beta_schedule
-from comfy.ldm.util import default
+from totoro.ldm.util import default
 
 
 class AbstractLowScaleModel(nn.Module):

comfy/ldm/modules/diffusionmodules/util.py

@@ -15,7 +15,7 @@ import torch.nn as nn
 import numpy as np
 from einops import repeat, rearrange
 
-from comfy.ldm.util import instantiate_from_config
+from totoro.ldm.util import instantiate_from_config
 
 class AlphaBlender(nn.Module):
     strategies = ["learned", "fixed", "learned_with_images"]

comfy/ldm/modules/sub_quadratic_attention.py

@@ -25,7 +25,7 @@ except ImportError:
 from torch import Tensor
 from typing import List
 
-from comfy import model_management
+from totoro import model_management
 
 def dynamic_slice(
     x: Tensor,

comfy/ldm/modules/temporal_ae.py

@@ -4,8 +4,8 @@ from typing import Callable, Iterable, Union
 import torch
 from einops import rearrange, repeat
 
-import comfy.ops
+import totoro.ops
-ops = comfy.ops.disable_weight_init
+ops = totoro.ops.disable_weight_init
 
 from .diffusionmodules.model import (
     AttnBlock,

comfy/lora.py

@@ -1,4 +1,4 @@
-import comfy.utils
+import totoro.utils
 import logging
 
 LORA_CLIP_MAP = {
@@ -247,7 +247,7 @@ def model_lora_keys_unet(model, key_map={}):
             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)
+    diffusers_keys = totoro.utils.unet_to_diffusers(model.model_config.unet_config)
     for k in diffusers_keys:
         if k.endswith(".weight"):
             unet_key = "diffusion_model.{}".format(diffusers_keys[k])
@@ -261,8 +261,8 @@ def model_lora_keys_unet(model, key_map={}):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
 
-    if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
+    if isinstance(model, totoro.model_base.SD3): #Diffusers lora SD3
-        diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        diffusers_keys = totoro.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:
             if k.endswith(".weight"):
                 to = diffusers_keys[k]
@@ -275,22 +275,22 @@ 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
+    if isinstance(model, totoro.model_base.AuraFlow): #Diffusers lora AuraFlow
-        diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        diffusers_keys = totoro.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):
+    if isinstance(model, totoro.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
+    if isinstance(model, totoro.model_base.Flux): #Diffusers lora Flux
-        diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        diffusers_keys = totoro.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]

comfy/model_base.py

@@ -1,23 +1,23 @@
 import torch
 import logging
-from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
+from totoro.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
-from comfy.ldm.cascade.stage_c import StageC
+from totoro.ldm.cascade.stage_c import StageC
-from comfy.ldm.cascade.stage_b import StageB
+from totoro.ldm.cascade.stage_b import StageB
-from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
+from totoro.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
-from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from totoro.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
-from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+from totoro.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
-import comfy.ldm.aura.mmdit
+import totoro.ldm.aura.mmdit
-import comfy.ldm.hydit.models
+import totoro.ldm.hydit.models
-import comfy.ldm.audio.dit
+import totoro.ldm.audio.dit
-import comfy.ldm.audio.embedders
+import totoro.ldm.audio.embedders
-import comfy.ldm.flux.model
+import totoro.ldm.flux.model
 
-import comfy.model_management
+import totoro.model_management
-import comfy.conds
+import totoro.conds
-import comfy.ops
+import totoro.ops
 from enum import Enum
 from . import utils
-import comfy.latent_formats
+import totoro.latent_formats
 import math
 
 class ModelType(Enum):
@@ -31,7 +31,7 @@ class ModelType(Enum):
     FLUX = 8
 
 
-from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
+from totoro.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
 
 
 def model_sampling(model_config, model_type):
@@ -45,8 +45,8 @@ def model_sampling(model_config, model_type):
         c = V_PREDICTION
         s = ModelSamplingContinuousEDM
     elif model_type == ModelType.FLOW:
-        c = comfy.model_sampling.CONST
+        c = totoro.model_sampling.CONST
-        s = comfy.model_sampling.ModelSamplingDiscreteFlow
+        s = totoro.model_sampling.ModelSamplingDiscreteFlow
     elif model_type == ModelType.STABLE_CASCADE:
         c = EPS
         s = StableCascadeSampling
@@ -57,8 +57,8 @@ def model_sampling(model_config, model_type):
         c = V_PREDICTION
         s = ModelSamplingContinuousV
     elif model_type == ModelType.FLUX:
-        c = comfy.model_sampling.CONST
+        c = totoro.model_sampling.CONST
-        s = comfy.model_sampling.ModelSamplingFlux
+        s = totoro.model_sampling.ModelSamplingFlux
 
     class ModelSampling(s, c):
         pass
@@ -78,11 +78,11 @@ class BaseModel(torch.nn.Module):
 
         if not unet_config.get("disable_unet_model_creation", False):
             if self.manual_cast_dtype is not None:
-                operations = comfy.ops.manual_cast
+                operations = totoro.ops.manual_cast
             else:
-                operations = comfy.ops.disable_weight_init
+                operations = totoro.ops.disable_weight_init
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
-            if comfy.model_management.force_channels_last():
+            if totoro.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))
@@ -173,23 +173,23 @@ class BaseModel(torch.nn.Module):
                     elif ck == "masked_image":
                         cond_concat.append(self.blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
-            out['c_concat'] = comfy.conds.CONDNoiseShape(data)
+            out['c_concat'] = totoro.conds.CONDNoiseShape(data)
 
         adm = self.encode_adm(**kwargs)
         if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
+            out['y'] = totoro.conds.CONDRegular(adm)
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+            out['c_crossattn'] = totoro.conds.CONDCrossAttn(cross_attn)
 
         cross_attn_cnet = kwargs.get("cross_attn_controlnet", None)
         if cross_attn_cnet is not None:
-            out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet)
+            out['crossattn_controlnet'] = totoro.conds.CONDCrossAttn(cross_attn_cnet)
 
         c_concat = kwargs.get("noise_concat", None)
         if c_concat is not None:
-            out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat)
+            out['c_concat'] = totoro.conds.CONDNoiseShape(c_concat)
 
         return out
 
@@ -249,13 +249,13 @@ class BaseModel(torch.nn.Module):
         self.blank_inpaint_image_like = blank_inpaint_image_like
 
     def memory_required(self, input_shape):
-        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+        if totoro.model_management.xformers_enabled() or totoro.model_management.pytorch_attention_flash_attention():
             dtype = self.get_dtype()
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024)
+            return (area * totoro.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:])
@@ -380,7 +380,7 @@ class SVD_img2vid(BaseModel):
         out = {}
         adm = self.encode_adm(**kwargs)
         if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
+            out['y'] = totoro.conds.CONDRegular(adm)
 
         latent_image = kwargs.get("concat_latent_image", None)
         noise = kwargs.get("noise", None)
@@ -394,16 +394,16 @@ class SVD_img2vid(BaseModel):
 
         latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
+        out['c_concat'] = totoro.conds.CONDNoiseShape(latent_image)
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+            out['c_crossattn'] = totoro.conds.CONDCrossAttn(cross_attn)
 
         if "time_conditioning" in kwargs:
-            out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])
+            out["time_context"] = totoro.conds.CONDCrossAttn(kwargs["time_conditioning"])
 
-        out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])
+        out['num_video_frames'] = totoro.conds.CONDConstant(noise.shape[0])
         return out
 
 class SV3D_u(SVD_img2vid):
@@ -439,7 +439,7 @@ class SV3D_p(SVD_img2vid):
 class Stable_Zero123(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
         super().__init__(model_config, model_type, device=device)
-        self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
+        self.cc_projection = totoro.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device)
         self.cc_projection.weight.copy_(cc_projection_weight)
         self.cc_projection.bias.copy_(cc_projection_bias)
 
@@ -457,13 +457,13 @@ class Stable_Zero123(BaseModel):
 
         latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0])
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(latent_image)
+        out['c_concat'] = totoro.conds.CONDNoiseShape(latent_image)
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             if cross_attn.shape[-1] != 768:
                 cross_attn = self.cc_projection(cross_attn)
-            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
+            out['c_crossattn'] = totoro.conds.CONDCrossAttn(cross_attn)
         return out
 
 class SD_X4Upscaler(BaseModel):
@@ -494,8 +494,8 @@ class SD_X4Upscaler(BaseModel):
 
         image = utils.resize_to_batch_size(image, noise.shape[0])
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(image)
+        out['c_concat'] = totoro.conds.CONDNoiseShape(image)
-        out['y'] = comfy.conds.CONDRegular(noise_level)
+        out['y'] = totoro.conds.CONDRegular(noise_level)
         return out
 
 class IP2P:
@@ -514,10 +514,10 @@ class IP2P:
 
         image = utils.resize_to_batch_size(image, noise.shape[0])
 
-        out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
+        out['c_concat'] = totoro.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
         adm = self.encode_adm(**kwargs)
         if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
+            out['y'] = totoro.conds.CONDRegular(adm)
         return out
 
 class SD15_instructpix2pix(IP2P, BaseModel):
@@ -529,7 +529,7 @@ class SDXL_instructpix2pix(IP2P, SDXL):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)
         if model_type == ModelType.V_PREDICTION_EDM:
-            self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p
+            self.process_ip2p_image_in = lambda image: totoro.latent_formats.SDXL().process_in(image) #cosxl ip2p
         else:
             self.process_ip2p_image_in = lambda image: image #diffusers ip2p
 
@@ -543,7 +543,7 @@ class StableCascade_C(BaseModel):
         out = {}
         clip_text_pooled = kwargs["pooled_output"]
         if clip_text_pooled is not None:
-            out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
+            out['clip_text_pooled'] = totoro.conds.CONDRegular(clip_text_pooled)
 
         if "unclip_conditioning" in kwargs:
             embeds = []
@@ -553,13 +553,13 @@ class StableCascade_C(BaseModel):
             clip_img = torch.cat(embeds, dim=1)
         else:
             clip_img = torch.zeros((1, 1, 768))
-        out["clip_img"] = comfy.conds.CONDRegular(clip_img)
+        out["clip_img"] = totoro.conds.CONDRegular(clip_img)
-        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+        out["sca"] = totoro.conds.CONDRegular(torch.zeros((1,)))
-        out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+        out["crp"] = totoro.conds.CONDRegular(torch.zeros((1,)))
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
-            out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)
+            out['clip_text'] = totoro.conds.CONDCrossAttn(cross_attn)
         return out
 
 
@@ -574,13 +574,13 @@ class StableCascade_B(BaseModel):
 
         clip_text_pooled = kwargs["pooled_output"]
         if clip_text_pooled is not None:
-            out['clip'] = comfy.conds.CONDRegular(clip_text_pooled)
+            out['clip'] = totoro.conds.CONDRegular(clip_text_pooled)
 
         #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
         prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
 
-        out["effnet"] = comfy.conds.CONDRegular(prior)
+        out["effnet"] = totoro.conds.CONDRegular(prior)
-        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+        out["sca"] = totoro.conds.CONDRegular(torch.zeros((1,)))
         return out
 
 
@@ -595,27 +595,27 @@ class SD3(BaseModel):
         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['c_crossattn'] = totoro.conds.CONDRegular(cross_attn)
         return out
 
 
 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)
+        super().__init__(model_config, model_type, device=device, unet_model=totoro.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)
+            out['c_crossattn'] = totoro.conds.CONDRegular(cross_attn)
         return out
 
 
 class StableAudio1(BaseModel):
     def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
+        super().__init__(model_config, model_type, device=device, unet_model=totoro.ldm.audio.dit.AudioDiffusionTransformer)
-        self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_start_embedder = totoro.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
-        self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_total_embedder = totoro.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
         self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
         self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
 
@@ -632,12 +632,12 @@ class StableAudio1(BaseModel):
         seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
 
         global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
-        out['global_embed'] = comfy.conds.CONDRegular(global_embed)
+        out['global_embed'] = totoro.conds.CONDRegular(global_embed)
 
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
-            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+            out['c_crossattn'] = totoro.conds.CONDRegular(cross_attn)
         return out
 
     def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
@@ -651,25 +651,25 @@ class StableAudio1(BaseModel):
 
 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)
+        super().__init__(model_config, model_type, device=device, unet_model=totoro.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)
+            out['c_crossattn'] = totoro.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)
+            out['text_embedding_mask'] = totoro.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)
+            out['encoder_hidden_states_t5'] = totoro.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)
+            out['text_embedding_mask_t5'] = totoro.conds.CONDRegular(attention_mask_mt5xl)
 
         width = kwargs.get("width", 768)
         height = kwargs.get("height", 768)
@@ -678,12 +678,12 @@ class HunyuanDiT(BaseModel):
         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]]))
+        out['image_meta_size'] = totoro.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)
+        super().__init__(model_config, model_type, device=device, unet_model=totoro.ldm.flux.model.Flux)
 
     def encode_adm(self, **kwargs):
         return kwargs["pooled_output"]
@@ -692,6 +692,6 @@ class Flux(BaseModel):
         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['c_crossattn'] = totoro.conds.CONDRegular(cross_attn)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+        out['guidance'] = totoro.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out

comfy/model_detection.py

@@ -1,6 +1,6 @@
-import comfy.supported_models
+import totoro.supported_models
-import comfy.supported_models_base
+import totoro.supported_models_base
-import comfy.utils
+import totoro.utils
 import math
 import logging
 import torch
@@ -273,7 +273,7 @@ def detect_unet_config(state_dict, key_prefix):
     return unet_config
 
 def model_config_from_unet_config(unet_config, state_dict=None):
-    for model_config in comfy.supported_models.models:
+    for model_config in totoro.supported_models.models:
         if model_config.matches(unet_config, state_dict):
             return model_config(unet_config)
 
@@ -286,7 +286,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         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)
+        return totoro.supported_models_base.BASE(unet_config)
     else:
         return model_config
 
@@ -498,11 +498,11 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
     if '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)
+        sd_map = totoro.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)
+        sd_map = totoro.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
     else:
         return None
 

comfy/model_management.py

@@ -1,7 +1,7 @@
 import psutil
 import logging
 from enum import Enum
-from comfy.cli_args import args
+from totoro.cli_args import args
 import torch
 import sys
 import platform
@@ -975,7 +975,7 @@ def unload_all_models():
 
 
 def resolve_lowvram_weight(weight, model, key): #TODO: remove
-    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
+    print("WARNING: The totoro.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
     return weight
 
 #TODO: might be cleaner to put this somewhere else

comfy/model_patcher.py

@@ -4,13 +4,13 @@ import inspect
 import logging
 import uuid
 
-import comfy.utils
+import totoro.utils
-import comfy.model_management
+import totoro.model_management
-from comfy.types import UnetWrapperFunction
+from totoro.types import UnetWrapperFunction
 
 
 def weight_decompose(dora_scale, weight, lora_diff, alpha, strength):
-    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32)
+    dora_scale = totoro.model_management.cast_to_device(dora_scale, weight.device, torch.float32)
     lora_diff *= alpha
     weight_calc = weight + lora_diff.type(weight.dtype)
     weight_norm = (
@@ -89,7 +89,7 @@ class ModelPatcher:
     def model_size(self):
         if self.size > 0:
             return self.size
-        self.size = comfy.model_management.module_size(self.model)
+        self.size = totoro.model_management.module_size(self.model)
         return self.size
 
     def clone(self):
@@ -192,7 +192,7 @@ class ModelPatcher:
             if name in self.object_patches_backup:
                 return self.object_patches_backup[name]
             else:
-                return comfy.utils.get_attr(self.model, name)
+                return totoro.utils.get_attr(self.model, name)
 
     def model_patches_to(self, device):
         to = self.model_options["transformer_options"]
@@ -243,7 +243,7 @@ class ModelPatcher:
         return list(p)
 
     def get_key_patches(self, filter_prefix=None):
-        comfy.model_management.unload_model_clones(self)
+        totoro.model_management.unload_model_clones(self)
         model_sd = self.model_state_dict()
         p = {}
         for k in model_sd:
@@ -269,7 +269,7 @@ class ModelPatcher:
         if key not in self.patches:
             return
 
-        weight = comfy.utils.get_attr(self.model, key)
+        weight = totoro.utils.get_attr(self.model, key)
 
         inplace_update = self.weight_inplace_update
 
@@ -277,18 +277,18 @@ class ModelPatcher:
             self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
 
         if device_to is not None:
-            temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            temp_weight = totoro.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
         else:
             temp_weight = weight.to(torch.float32, copy=True)
         out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
         if inplace_update:
-            comfy.utils.copy_to_param(self.model, key, out_weight)
+            totoro.utils.copy_to_param(self.model, key, out_weight)
         else:
-            comfy.utils.set_attr_param(self.model, key, out_weight)
+            totoro.utils.set_attr_param(self.model, key, out_weight)
 
     def patch_model(self, device_to=None, patch_weights=True):
         for k in self.object_patches:
-            old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
+            old = totoro.utils.set_attr(self.model, k, self.object_patches[k])
             if k not in self.object_patches_backup:
                 self.object_patches_backup[k] = old
 
@@ -322,8 +322,8 @@ class ModelPatcher:
         patch_counter = 0
         for n, m in self.model.named_modules():
             lowvram_weight = False
-            if hasattr(m, "comfy_cast_weights"):
+            if hasattr(m, "totoro_cast_weights"):
-                module_mem = comfy.model_management.module_size(m)
+                module_mem = totoro.model_management.module_size(m)
                 if mem_counter + module_mem >= lowvram_model_memory:
                     lowvram_weight = True
 
@@ -344,14 +344,14 @@ class ModelPatcher:
                         m.bias_function = LowVramPatch(bias_key, self)
                         patch_counter += 1
 
-                m.prev_comfy_cast_weights = m.comfy_cast_weights
+                m.prev_totoro_cast_weights = m.totoro_cast_weights
-                m.comfy_cast_weights = True
+                m.totoro_cast_weights = True
             else:
                 if hasattr(m, "weight"):
                     self.patch_weight_to_device(weight_key) #TODO: speed this up without causing OOM
                     self.patch_weight_to_device(bias_key)
                     m.to(device_to)
-                    mem_counter += comfy.model_management.module_size(m)
+                    mem_counter += totoro.model_management.module_size(m)
                     logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
 
         self.model_lowvram = True
@@ -392,10 +392,10 @@ class ModelPatcher:
                     if w1.shape != weight.shape:
                         logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
+                        weight += function(strength * totoro.model_management.cast_to_device(w1, weight.device, weight.dtype))
             elif patch_type == "lora": #lora/locon
-                mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
+                mat1 = totoro.model_management.cast_to_device(v[0], weight.device, torch.float32)
-                mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
+                mat2 = totoro.model_management.cast_to_device(v[1], weight.device, torch.float32)
                 dora_scale = v[4]
                 if v[2] is not None:
                     alpha = v[2] / mat2.shape[0]
@@ -404,7 +404,7 @@ class ModelPatcher:
 
                 if v[3] is not None:
                     #locon mid weights, hopefully the math is fine because I didn't properly test it
-                    mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
+                    mat3 = totoro.model_management.cast_to_device(v[3], weight.device, torch.float32)
                     final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
                 try:
@@ -428,23 +428,23 @@ class ModelPatcher:
 
                 if w1 is None:
                     dim = w1_b.shape[0]
-                    w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, torch.float32),
+                    w1 = torch.mm(totoro.model_management.cast_to_device(w1_a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w1_b, weight.device, torch.float32))
+                                  totoro.model_management.cast_to_device(w1_b, weight.device, torch.float32))
                 else:
-                    w1 = comfy.model_management.cast_to_device(w1, weight.device, torch.float32)
+                    w1 = totoro.model_management.cast_to_device(w1, weight.device, torch.float32)
 
                 if w2 is None:
                     dim = w2_b.shape[0]
                     if t2 is None:
-                        w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32),
+                        w2 = torch.mm(totoro.model_management.cast_to_device(w2_a, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32))
+                                      totoro.model_management.cast_to_device(w2_b, weight.device, torch.float32))
                     else:
                         w2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                          comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
+                                          totoro.model_management.cast_to_device(t2, weight.device, torch.float32),
-                                          comfy.model_management.cast_to_device(w2_b, weight.device, torch.float32),
+                                          totoro.model_management.cast_to_device(w2_b, weight.device, torch.float32),
-                                          comfy.model_management.cast_to_device(w2_a, weight.device, torch.float32))
+                                          totoro.model_management.cast_to_device(w2_a, weight.device, torch.float32))
                 else:
-                    w2 = comfy.model_management.cast_to_device(w2, weight.device, torch.float32)
+                    w2 = totoro.model_management.cast_to_device(w2, weight.device, torch.float32)
 
                 if len(w2.shape) == 4:
                     w1 = w1.unsqueeze(2).unsqueeze(2)
@@ -476,19 +476,19 @@ class ModelPatcher:
                     t1 = v[5]
                     t2 = v[6]
                     m1 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t1, weight.device, torch.float32),
+                                      totoro.model_management.cast_to_device(t1, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w1b, weight.device, torch.float32),
+                                      totoro.model_management.cast_to_device(w1b, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w1a, weight.device, torch.float32))
+                                      totoro.model_management.cast_to_device(w1a, weight.device, torch.float32))
 
                     m2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t2, weight.device, torch.float32),
+                                      totoro.model_management.cast_to_device(t2, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2b, weight.device, torch.float32),
+                                      totoro.model_management.cast_to_device(w2b, weight.device, torch.float32),
-                                      comfy.model_management.cast_to_device(w2a, weight.device, torch.float32))
+                                      totoro.model_management.cast_to_device(w2a, weight.device, torch.float32))
                 else:
-                    m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, torch.float32),
+                    m1 = torch.mm(totoro.model_management.cast_to_device(w1a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w1b, weight.device, torch.float32))
+                                  totoro.model_management.cast_to_device(w1b, weight.device, torch.float32))
-                    m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, torch.float32),
+                    m2 = torch.mm(totoro.model_management.cast_to_device(w2a, weight.device, torch.float32),
-                                  comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
+                                  totoro.model_management.cast_to_device(w2b, weight.device, torch.float32))
 
                 try:
                     lora_diff = (m1 * m2).reshape(weight.shape)
@@ -506,10 +506,10 @@ class ModelPatcher:
 
                 dora_scale = v[5]
 
-                a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
+                a1 = totoro.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
-                a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
+                a2 = totoro.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
-                b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
+                b1 = totoro.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
-                b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
+                b2 = totoro.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
 
                 try:
                     lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
@@ -531,9 +531,9 @@ class ModelPatcher:
         if unpatch_weights:
             if self.model_lowvram:
                 for m in self.model.modules():
-                    if hasattr(m, "prev_comfy_cast_weights"):
+                    if hasattr(m, "prev_totoro_cast_weights"):
-                        m.comfy_cast_weights = m.prev_comfy_cast_weights
+                        m.totoro_cast_weights = m.prev_totoro_cast_weights
-                        del m.prev_comfy_cast_weights
+                        del m.prev_totoro_cast_weights
                     m.weight_function = None
                     m.bias_function = None
 
@@ -544,10 +544,10 @@ class ModelPatcher:
 
             if self.weight_inplace_update:
                 for k in keys:
-                    comfy.utils.copy_to_param(self.model, k, self.backup[k])
+                    totoro.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])
+                    totoro.utils.set_attr_param(self.model, k, self.backup[k])
 
             self.backup.clear()
 
@@ -557,7 +557,7 @@ class ModelPatcher:
 
         keys = list(self.object_patches_backup.keys())
         for k in keys:
-            comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
+            totoro.utils.set_attr(self.model, k, self.object_patches_backup[k])
 
         self.object_patches_backup.clear()
 

comfy/model_sampling.py

@@ -1,5 +1,5 @@
 import torch
-from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
+from totoro.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
 class EPS:

comfy/ops.py

@@ -1,5 +1,5 @@
 """
-    This file is part of ComfyUI.
+    This file is part of totoroUI.
     Copyright (C) 2024 Stability AI
 
     This program is free software: you can redistribute it and/or modify
@@ -17,7 +17,7 @@
 """
 
 import torch
-import comfy.model_management
+import totoro.model_management
 
 
 def cast_to(weight, dtype=None, device=None, non_blocking=False):
@@ -34,7 +34,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None):
             device = input.device
 
     bias = None
-    non_blocking = comfy.model_management.device_should_use_non_blocking(device)
+    non_blocking = totoro.model_management.device_should_use_non_blocking(device)
     if s.bias is not None:
         bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
         if s.bias_function is not None:
@@ -45,7 +45,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None):
     return weight, bias
 
 class CastWeightBiasOp:
-    comfy_cast_weights = False
+    totoro_cast_weights = False
     weight_function = None
     bias_function = None
 
@@ -54,13 +54,13 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -68,13 +68,13 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -82,13 +82,13 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -96,13 +96,13 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -110,13 +110,13 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -125,7 +125,7 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input):
+        def forward_totoro_cast_weights(self, input):
             if self.weight is not None:
                 weight, bias = cast_bias_weight(self, input)
             else:
@@ -134,8 +134,8 @@ class disable_weight_init:
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -143,7 +143,7 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input, output_size=None):
+        def forward_totoro_cast_weights(self, input, output_size=None):
             num_spatial_dims = 2
             output_padding = self._output_padding(
                 input, output_size, self.stride, self.padding, self.kernel_size,
@@ -155,8 +155,8 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -164,7 +164,7 @@ class disable_weight_init:
         def reset_parameters(self):
             return None
 
-        def forward_comfy_cast_weights(self, input, output_size=None):
+        def forward_totoro_cast_weights(self, input, output_size=None):
             num_spatial_dims = 1
             output_padding = self._output_padding(
                 input, output_size, self.stride, self.padding, self.kernel_size,
@@ -176,8 +176,8 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
@@ -186,7 +186,7 @@ class disable_weight_init:
             self.bias = None
             return None
 
-        def forward_comfy_cast_weights(self, input, out_dtype=None):
+        def forward_totoro_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
@@ -194,8 +194,8 @@ class disable_weight_init:
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.totoro_cast_weights:
-                return self.forward_comfy_cast_weights(*args, **kwargs)
+                return self.forward_totoro_cast_weights(*args, **kwargs)
             else:
                 if "out_dtype" in kwargs:
                     kwargs.pop("out_dtype")
@@ -213,28 +213,28 @@ class disable_weight_init:
 
 class manual_cast(disable_weight_init):
     class Linear(disable_weight_init.Linear):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class Conv1d(disable_weight_init.Conv1d):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class Conv2d(disable_weight_init.Conv2d):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class Conv3d(disable_weight_init.Conv3d):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class GroupNorm(disable_weight_init.GroupNorm):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class LayerNorm(disable_weight_init.LayerNorm):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
-        comfy_cast_weights = True
+        totoro_cast_weights = True
 
     class Embedding(disable_weight_init.Embedding):
-        comfy_cast_weights = True
+        totoro_cast_weights = True

comfy/sample.py

@@ -1,7 +1,7 @@
 import torch
-import comfy.model_management
+import totoro.model_management
-import comfy.samplers
+import totoro.samplers
-import comfy.utils
+import totoro.utils
 import numpy as np
 import logging
 
@@ -27,24 +27,24 @@ def prepare_noise(latent_image, seed, noise_inds=None):
 def fix_empty_latent_channels(model, latent_image):
     latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
     if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
-        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
+        latent_image = totoro.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
     return latent_image
 
 def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
-    logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
+    logging.warning("Warning: totoro.sample.prepare_sampling isn't used anymore and can be removed")
     return model, positive, negative, noise_mask, []
 
 def cleanup_additional_models(models):
-    logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
+    logging.warning("Warning: totoro.sample.cleanup_additional_models isn't used anymore and can be removed")
 
 def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
-    sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
+    sampler = totoro.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
     samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(comfy.model_management.intermediate_device())
+    samples = samples.to(totoro.model_management.intermediate_device())
     return samples
 
 def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
-    samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+    samples = totoro.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
-    samples = samples.to(comfy.model_management.intermediate_device())
+    samples = samples.to(totoro.model_management.intermediate_device())
     return samples

comfy/sampler_helpers.py

@@ -1,12 +1,12 @@
 import torch
-import comfy.model_management
+import totoro.model_management
-import comfy.conds
+import totoro.conds
 
 def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""
     noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
     noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
+    noise_mask = totoro.utils.repeat_to_batch_size(noise_mask, shape[0])
     noise_mask = noise_mask.to(device)
     return noise_mask
 
@@ -23,7 +23,7 @@ def convert_cond(cond):
         temp = c[1].copy()
         model_conds = temp.get("model_conds", {})
         if c[0] is not None:
-            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
+            model_conds["c_crossattn"] = totoro.conds.CONDCrossAttn(c[0]) #TODO: remove
             temp["cross_attn"] = c[0]
         temp["model_conds"] = model_conds
         out.append(temp)
@@ -63,7 +63,7 @@ def prepare_sampling(model, noise_shape, conds):
     models, inference_memory = get_additional_models(conds, model.model_dtype())
     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)
+    totoro.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

@@ -2,10 +2,10 @@ from .k_diffusion import sampling as k_diffusion_sampling
 from .extra_samplers import uni_pc
 import torch
 import collections
-from comfy import model_management
+from totoro import model_management
 import math
 import logging
-import comfy.sampler_helpers
+import totoro.sampler_helpers
 import scipy
 import numpy
 
@@ -249,7 +249,7 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options):
     return out_conds
 
 def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
-    logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
+    logging.warning("WARNING: The totoro.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
     return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
 
 def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
@@ -434,7 +434,7 @@ def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
             conditions[i] = modified
 
 def resolve_areas_and_cond_masks(conditions, h, w, device):
-    logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
+    logging.warning("WARNING: The totoro.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
     return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
 
 def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
@@ -676,7 +676,7 @@ class CFGGuider:
 
     def inner_set_conds(self, conds):
         for k in conds:
-            self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
+            self.original_conds[k] = totoro.sampler_helpers.convert_cond(conds[k])
 
     def __call__(self, *args, **kwargs):
         return self.predict_noise(*args, **kwargs)
@@ -703,11 +703,11 @@ class CFGGuider:
         for k in self.original_conds:
             self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
 
-        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
+        self.inner_model, self.conds, self.loaded_models = totoro.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
         device = self.model_patcher.load_device
 
         if denoise_mask is not None:
-            denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
+            denoise_mask = totoro.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
 
         noise = noise.to(device)
         latent_image = latent_image.to(device)
@@ -715,7 +715,7 @@ class CFGGuider:
 
         output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
-        comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+        totoro.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
         del self.inner_model
         del self.conds
         del self.loaded_models

comfy/sd.py

@@ -2,14 +2,14 @@ import torch
 from enum import Enum
 import logging
 
-from comfy import model_management
+from totoro import model_management
 from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
 from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
 import yaml
 
-import comfy.utils
+import totoro.utils
 
 from . import clip_vision
 from . import gligen
@@ -18,27 +18,27 @@ from . import model_detection
 
 from . import sd1_clip
 from . import sdxl_clip
-import comfy.text_encoders.sd2_clip
+import totoro.text_encoders.sd2_clip
-import comfy.text_encoders.sd3_clip
+import totoro.text_encoders.sd3_clip
-import comfy.text_encoders.sa_t5
+import totoro.text_encoders.sa_t5
-import comfy.text_encoders.aura_t5
+import totoro.text_encoders.aura_t5
-import comfy.text_encoders.hydit
+import totoro.text_encoders.hydit
-import comfy.text_encoders.flux
+import totoro.text_encoders.flux
 
-import comfy.model_patcher
+import totoro.model_patcher
-import comfy.lora
+import totoro.lora
-import comfy.t2i_adapter.adapter
+import totoro.t2i_adapter.adapter
-import comfy.supported_models_base
+import totoro.supported_models_base
-import comfy.taesd.taesd
+import totoro.taesd.taesd
 
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     key_map = {}
     if model is not None:
-        key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+        key_map = totoro.lora.model_lora_keys_unet(model.model, key_map)
     if clip is not None:
-        key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
+        key_map = totoro.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
 
-    loaded = comfy.lora.load_lora(lora, key_map)
+    loaded = totoro.lora.load_lora(lora, key_map)
     if model is not None:
         new_modelpatcher = model.clone()
         k = new_modelpatcher.add_patches(loaded, strength_model)
@@ -82,7 +82,7 @@ class CLIP:
                 load_device = offload_device
 
         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)
+        self.patcher = totoro.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         self.layer_idx = None
         logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
 
@@ -171,12 +171,12 @@ class VAE:
                 decoder_config = encoder_config.copy()
                 decoder_config["video_kernel_size"] = [3, 1, 1]
                 decoder_config["alpha"] = 0.0
-                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "totoro.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                            encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
+                                                            encoder_config={'target': "totoro.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
-                                                            decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
+                                                            decoder_config={'target': "totoro.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
                 self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
-                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
+                self.first_stage_model = totoro.taesd.taesd.TAESD(latent_channels=self.latent_channels)
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()
                 self.downscale_ratio = 4
@@ -218,9 +218,9 @@ class VAE:
                 if 'quant_conv.weight' in sd:
                     self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
                 else:
-                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "totoro.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
+                                                                encoder_config={'target': "totoro.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
-                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+                                                                decoder_config={'target': "totoro.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
             elif "decoder.layers.1.layers.0.beta" in sd:
                 self.first_stage_model = AudioOobleckVAE()
                 self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
@@ -257,7 +257,7 @@ class VAE:
         self.first_stage_model.to(self.vae_dtype)
         self.output_device = model_management.intermediate_device()
 
-        self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
+        self.patcher = totoro.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
         logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
 
     def vae_encode_crop_pixels(self, pixels):
@@ -270,39 +270,39 @@ class VAE:
         return pixels
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+        steps = samples.shape[0] * totoro.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
-        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += samples.shape[0] * totoro.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        steps += samples.shape[0] * totoro.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = comfy.utils.ProgressBar(steps)
+        pbar = totoro.utils.ProgressBar(steps)
 
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
         output = self.process_output(
-            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+            (totoro.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
-            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+            totoro.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
-             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
+             totoro.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
             / 3.0)
         return output
 
     def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
-        return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
+        return totoro.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
-        steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+        steps = pixel_samples.shape[0] * totoro.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
-        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += pixel_samples.shape[0] * totoro.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
-        steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
+        steps += pixel_samples.shape[0] * totoro.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
-        pbar = comfy.utils.ProgressBar(steps)
+        pbar = totoro.utils.ProgressBar(steps)
 
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
-        samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+        samples = totoro.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+        samples += totoro.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
-        samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
+        samples += totoro.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples /= 3.0
         return samples
 
     def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
-        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
+        return totoro.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
 
     def decode(self, samples_in):
         try:
@@ -373,10 +373,10 @@ class StyleModel:
 
 
 def load_style_model(ckpt_path):
-    model_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    model_data = totoro.utils.load_torch_file(ckpt_path, safe_load=True)
     keys = model_data.keys()
     if "style_embedding" in keys:
-        model = comfy.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
+        model = totoro.t2i_adapter.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8)
     else:
         raise Exception("invalid style model {}".format(ckpt_path))
     model.load_state_dict(model_data)
@@ -393,14 +393,14 @@ class CLIPType(Enum):
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
     for p in ckpt_paths:
-        clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
+        clip_data.append(totoro.utils.load_torch_file(p, safe_load=True))
 
     class EmptyClass:
         pass
 
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
-            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+            clip_data[i] = totoro.utils.clip_text_transformers_convert(clip_data[i], "", "")
         else:
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
@@ -416,30 +416,30 @@ 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 = comfy.text_encoders.sd2_clip.SD2ClipModel
+            clip_target.clip = totoro.text_encoders.sd2_clip.SD2ClipModel
-            clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
+            clip_target.tokenizer = totoro.text_encoders.sd2_clip.SD2Tokenizer
         elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
             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.clip = totoro.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
+                clip_target.tokenizer = totoro.text_encoders.sd3_clip.SD3Tokenizer
             elif weight.shape[-1] == 2048:
-                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+                clip_target.clip = totoro.text_encoders.aura_t5.AuraT5Model
-                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
+                clip_target.tokenizer = totoro.text_encoders.aura_t5.AuraT5Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
-            clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
+            clip_target.clip = totoro.text_encoders.sa_t5.SAT5Model
-            clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
+            clip_target.tokenizer = totoro.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 = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            clip_target.clip = totoro.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
-            clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+            clip_target.tokenizer = totoro.text_encoders.sd3_clip.SD3Tokenizer
         elif clip_type == CLIPType.HUNYUAN_DIT:
-            clip_target.clip = comfy.text_encoders.hydit.HyditModel
+            clip_target.clip = totoro.text_encoders.hydit.HyditModel
-            clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
+            clip_target.tokenizer = totoro.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))
@@ -447,14 +447,14 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             if weight is not None:
                 dtype_t5 = weight.dtype
 
-            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
+            clip_target.clip = totoro.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
-            clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
+            clip_target.tokenizer = totoro.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 = comfy.text_encoders.sd3_clip.SD3ClipModel
+        clip_target.clip = totoro.text_encoders.sd3_clip.SD3ClipModel
-        clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+        clip_target.tokenizer = totoro.text_encoders.sd3_clip.SD3Tokenizer
 
     clip = CLIP(clip_target, embedding_directory=embedding_directory)
     for c in clip_data:
@@ -467,11 +467,11 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
     return clip
 
 def load_gligen(ckpt_path):
-    data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
+    data = totoro.utils.load_torch_file(ckpt_path, safe_load=True)
     model = gligen.load_gligen(data)
     if model_management.should_use_fp16():
         model = model.half()
-    return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
+    return totoro.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
 
 def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
     logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
@@ -487,7 +487,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     if "parameterization" in model_config_params:
         if model_config_params["parameterization"] == "v":
             m = model.clone()
-            class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):
+            class ModelSamplingAdvanced(totoro.model_sampling.ModelSamplingDiscrete, totoro.model_sampling.V_PREDICTION):
                 pass
             m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
             model = m
@@ -499,7 +499,7 @@ 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):
-    sd = comfy.utils.load_torch_file(ckpt_path)
+    sd = totoro.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
@@ -509,8 +509,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     clip_target = None
 
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
-    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
+    parameters = totoro.utils.calculate_parameters(sd, diffusion_model_prefix)
-    weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
+    weight_dtype = totoro.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)
@@ -536,7 +536,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         model.load_model_weights(sd, diffusion_model_prefix)
 
     if output_vae:
-        vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
+        vae_sd = totoro.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
         vae_sd = model_config.process_vae_state_dict(vae_sd)
         vae = VAE(sd=vae_sd)
 
@@ -564,7 +564,7 @@ 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())
+        model_patcher = totoro.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)
@@ -576,11 +576,11 @@ def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular for
 
     #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)
+    temp_sd = totoro.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)
+    parameters = totoro.utils.calculate_parameters(sd)
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -597,7 +597,7 @@ def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular for
             if model_config is None:
                 return None
 
-            diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
+            diffusers_keys = totoro.utils.unet_to_diffusers(model_config.unet_config)
 
             new_sd = {}
             for k in diffusers_keys:
@@ -620,10 +620,10 @@ def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular for
     left_over = sd.keys()
     if len(left_over) > 0:
         logging.info("left over keys in unet: {}".format(left_over))
-    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
+    return totoro.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 def load_unet(unet_path, dtype=None):
-    sd = comfy.utils.load_torch_file(unet_path)
+    sd = totoro.utils.load_torch_file(unet_path)
     model = load_unet_state_dict(sd, dtype=dtype)
     if model is None:
         logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
@@ -648,4 +648,4 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
         if not t.is_contiguous():
             sd[k] = t.contiguous()
 
-    comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
+    totoro.utils.save_torch_file(sd, output_path, metadata=metadata)

comfy/sd1_clip.py

@@ -1,12 +1,12 @@
 import os
 
 from transformers import CLIPTokenizer
-import comfy.ops
+import totoro.ops
 import torch
 import traceback
 import zipfile
 from . import model_management
-import comfy.clip_model
+import totoro.clip_model
 import json
 import logging
 import numbers
@@ -82,7 +82,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         "hidden"
     ]
     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,
+                 freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=totoro.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, return_attention_masks=False):  # clip-vit-base-patch32
         super().__init__()
@@ -94,7 +94,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         with open(textmodel_json_config) as f:
             config = json.load(f)
 
-        self.operations = comfy.ops.manual_cast
+        self.operations = totoro.ops.manual_cast
         self.transformer = model_class(config, dtype, device, self.operations)
         self.num_layers = self.transformer.num_layers
 

comfy/sd1_tokenizer/vocab.json

@@ -9213,7 +9213,7 @@
   "comfortably</w>": 30392,
   "comforting</w>": 33835,
   "comforts</w>": 42243,
-  "comfy</w>": 15736,
+  "totoro</w>": 15736,
   "comi": 40781,
   "comic": 7729,
   "comic</w>": 4962,

comfy/sdxl_clip.py

@@ -1,4 +1,4 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
 import torch
 import os
 

comfy/supported_models.py

@@ -4,12 +4,12 @@ from . import utils
 
 from . import sd1_clip
 from . import sdxl_clip
-import comfy.text_encoders.sd2_clip
+import totoro.text_encoders.sd2_clip
-import comfy.text_encoders.sd3_clip
+import totoro.text_encoders.sd3_clip
-import comfy.text_encoders.sa_t5
+import totoro.text_encoders.sa_t5
-import comfy.text_encoders.aura_t5
+import totoro.text_encoders.aura_t5
-import comfy.text_encoders.hydit
+import totoro.text_encoders.hydit
-import comfy.text_encoders.flux
+import totoro.text_encoders.flux
 
 from . import supported_models_base
 from . import latent_formats
@@ -104,7 +104,7 @@ class SD20(supported_models_base.BASE):
         return state_dict
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel)
+        return supported_models_base.ClipTarget(totoro.text_encoders.sd2_clip.SD2Tokenizer, totoro.text_encoders.sd2_clip.SD2ClipModel)
 
 class SD21UnclipL(SD20):
     unet_config = {
@@ -534,7 +534,7 @@ class SD3(supported_models_base.BASE):
             t5 = True
             dtype_t5 = state_dict[t5_key].dtype
 
-        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))
+        return supported_models_base.ClipTarget(totoro.text_encoders.sd3_clip.SD3Tokenizer, totoro.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 = {
@@ -565,7 +565,7 @@ 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(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
+        return supported_models_base.ClipTarget(totoro.text_encoders.sa_t5.SAT5Tokenizer, totoro.text_encoders.sa_t5.SAT5Model)
 
 class AuraFlow(supported_models_base.BASE):
     unet_config = {
@@ -588,7 +588,7 @@ class AuraFlow(supported_models_base.BASE):
         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)
+        return supported_models_base.ClipTarget(totoro.text_encoders.aura_t5.AuraT5Tokenizer, totoro.text_encoders.aura_t5.AuraT5Model)
 
 class HunyuanDiT(supported_models_base.BASE):
     unet_config = {
@@ -614,7 +614,7 @@ class HunyuanDiT(supported_models_base.BASE):
         return out
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
+        return supported_models_base.ClipTarget(totoro.text_encoders.hydit.HyditTokenizer, totoro.text_encoders.hydit.HyditModel)
 
 class HunyuanDiT1(HunyuanDiT):
     unet_config = {
@@ -656,7 +656,7 @@ class Flux(supported_models_base.BASE):
         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))
+        return supported_models_base.ClipTarget(totoro.text_encoders.flux.FluxTokenizer, totoro.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
 
 class FluxSchnell(Flux):
     unet_config = {

comfy/taesd/taesd.py

@@ -6,11 +6,11 @@ Tiny AutoEncoder for Stable Diffusion
 import torch
 import torch.nn as nn
 
-import comfy.utils
+import totoro.utils
-import comfy.ops
+import totoro.ops
 
 def conv(n_in, n_out, **kwargs):
-    return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+    return totoro.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
 
 class Clamp(nn.Module):
     def forward(self, x):
@@ -20,7 +20,7 @@ class Block(nn.Module):
     def __init__(self, n_in, n_out):
         super().__init__()
         self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
-        self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.skip = totoro.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
         self.fuse = nn.ReLU()
     def forward(self, x):
         return self.fuse(self.conv(x) + self.skip(x))
@@ -56,9 +56,9 @@ class TAESD(nn.Module):
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
         self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
         if encoder_path is not None:
-            self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
+            self.taesd_encoder.load_state_dict(totoro.utils.load_torch_file(encoder_path, safe_load=True))
         if decoder_path is not None:
-            self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
+            self.taesd_decoder.load_state_dict(totoro.utils.load_torch_file(decoder_path, safe_load=True))
 
     @staticmethod
     def scale_latents(x):

comfy/text_encoders/aura_t5.py

@@ -1,12 +1,12 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
 from .spiece_tokenizer import SPieceTokenizer
-import comfy.text_encoders.t5
+import totoro.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)
+        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=totoro.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={}):

comfy/text_encoders/bert.py

@@ -1,6 +1,6 @@
 import torch
-from comfy.ldm.modules.attention import optimized_attention_for_device
+from totoro.ldm.modules.attention import optimized_attention_for_device
-import comfy.ops
+import totoro.ops
 
 class BertAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -95,11 +95,11 @@ class BertEmbeddings(torch.nn.Module):
 
     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)
+        x += totoro.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 += totoro.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
         x = self.LayerNorm(x)
         return x
 

comfy/text_encoders/flux.py

@@ -1,6 +1,6 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
-import comfy.text_encoders.t5
+import totoro.text_encoders.t5
-import comfy.model_management
+import totoro.model_management
 from transformers import T5TokenizerFast
 import torch
 import os
@@ -8,7 +8,7 @@ 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)
+        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=totoro.text_encoders.t5.T5)
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -37,7 +37,7 @@ class FluxTokenizer:
 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)
+        dtype_t5 = totoro.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])

comfy/text_encoders/hydit.py

@@ -1,8 +1,8 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
 from transformers import BertTokenizer
 from .spiece_tokenizer import SPieceTokenizer
 from .bert import BertModel
-import comfy.text_encoders.t5
+import totoro.text_encoders.t5
 import os
 import torch
 
@@ -20,7 +20,7 @@ class HyditBertTokenizer(sd1_clip.SDTokenizer):
 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)
+        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=totoro.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={}):

comfy/text_encoders/sa_t5.py

@@ -1,12 +1,12 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
 from transformers import T5TokenizerFast
-import comfy.text_encoders.t5
+import totoro.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.text_encoders.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=totoro.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 class T5BaseTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):

comfy/text_encoders/sd2_clip.py

@@ -1,4 +1,4 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
 import os
 
 class SD2ClipHModel(sd1_clip.SDClipModel):

comfy/text_encoders/sd3_clip.py

@@ -1,16 +1,16 @@
-from comfy import sd1_clip
+from totoro import sd1_clip
-from comfy import sdxl_clip
+from totoro import sdxl_clip
 from transformers import T5TokenizerFast
-import comfy.text_encoders.t5
+import totoro.text_encoders.t5
 import torch
 import os
-import comfy.model_management
+import totoro.model_management
 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.text_encoders.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=totoro.text_encoders.t5.T5)
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
@@ -54,7 +54,7 @@ class SD3ClipModel(torch.nn.Module):
             self.clip_g = None
 
         if t5:
-            dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+            dtype_t5 = totoro.model_management.pick_weight_dtype(dtype_t5, dtype, device)
             self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
             self.dtypes.add(dtype_t5)
         else:
@@ -90,7 +90,7 @@ class SD3ClipModel(torch.nn.Module):
             if self.clip_l is not None:
                 lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
             else:
-                l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+                l_pooled = torch.zeros((1, 768), device=totoro.model_management.intermediate_device())
 
             if self.clip_g is not None:
                 g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
@@ -100,7 +100,7 @@ class SD3ClipModel(torch.nn.Module):
                     lg_out = torch.nn.functional.pad(g_out, (768, 0))
             else:
                 g_out = None
-                g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+                g_pooled = torch.zeros((1, 1280), device=totoro.model_management.intermediate_device())
 
             if lg_out is not None:
                 lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
@@ -115,10 +115,10 @@ class SD3ClipModel(torch.nn.Module):
                 out = t5_out
 
         if out is None:
-            out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+            out = torch.zeros((1, 77, 4096), device=totoro.model_management.intermediate_device())
 
         if pooled is None:
-            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+            pooled = torch.zeros((1, 768 + 1280), device=totoro.model_management.intermediate_device())
 
         return out, pooled
 

comfy/text_encoders/t5.py

@@ -1,7 +1,7 @@
 import torch
 import math
-from comfy.ldm.modules.attention import optimized_attention_for_device
+from totoro.ldm.modules.attention import optimized_attention_for_device
-import comfy.ops
+import totoro.ops
 
 class T5LayerNorm(torch.nn.Module):
     def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
@@ -12,7 +12,7 @@ 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 comfy.ops.cast_to_input(self.weight, x) * x
+        return totoro.ops.cast_to_input(self.weight, x) * x
 
 activations = {
     "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),

comfy/text_encoders/t5_tokenizer/tokenizer.json

@@ -75683,7 +75683,7 @@
         -12.425559997558594
       ],
       [
-        "▁comfy",
+        "▁totoro",
         -12.425569534301758
       ],
       [

comfy/types.py

@@ -3,7 +3,7 @@ from typing import Callable, Protocol, TypedDict, Optional, List
 
 
 class UnetApplyFunction(Protocol):
-    """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
+    """Function signature protocol on totoro.model_base.BaseModel.apply_model"""
 
     def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
         pass

comfy/utils.py

@@ -1,7 +1,7 @@
 import torch
 import math
 import struct
-import comfy.checkpoint_pickle
+import totoro.checkpoint_pickle
 import safetensors.torch
 import numpy as np
 from PIL import Image
@@ -21,7 +21,7 @@ def load_torch_file(ckpt, safe_load=False, device=None):
         if safe_load:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
         else:
-            pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
+            pl_sd = torch.load(ckpt, map_location=device, pickle_module=totoro.checkpoint_pickle)
         if "global_step" in pl_sd:
             logging.debug(f"Global Step: {pl_sd['global_step']}")
         if "state_dict" in pl_sd:

comfy_extras/chainner_models/model_loading.py

@@ -1,5 +1,5 @@
 from spandrel import ModelLoader
 
 def load_state_dict(state_dict):
-    print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    print("WARNING: totoro_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
     return ModelLoader().load_from_state_dict(state_dict).eval()

comfy_extras/nodes_advanced_samplers.py

@@ -1,5 +1,5 @@
-import comfy.samplers
+import totoro.samplers
-import comfy.utils
+import totoro.utils
 import torch
 import numpy as np
 from tqdm.auto import trange, tqdm
@@ -27,7 +27,7 @@ def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable
 
         x = denoised
         if i < len(upscales):
-            x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
+            x = totoro.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
 
         if sigmas[i + 1] > 0:
             x += sigmas[i + 1] * torch.randn_like(x)
@@ -53,11 +53,11 @@ class SamplerLCMUpscale:
     def get_sampler(self, scale_ratio, scale_steps, upscale_method):
         if scale_steps < 0:
             scale_steps = None
-        sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
+        sampler = totoro.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
         return (sampler, )
 
-from comfy.k_diffusion.sampling import to_d
+from totoro.k_diffusion.sampling import to_d
-import comfy.model_patcher
+import totoro.model_patcher
 
 @torch.no_grad()
 def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
@@ -69,7 +69,7 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No
         return args["denoised"]
 
     model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+    extra_args["model_options"] = totoro.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
@@ -97,9 +97,9 @@ class SamplerEulerCFGpp:
 
     def get_sampler(self, version):
         if version == "alternative":
-            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
+            sampler = totoro.samplers.KSAMPLER(sample_euler_pp)
         else:
-            sampler = comfy.samplers.ksampler("euler_cfg_pp")
+            sampler = totoro.samplers.ksampler("euler_cfg_pp")
         return (sampler, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_audio.py

@@ -1,6 +1,6 @@
 import torchaudio
 import torch
-import comfy.model_management
+import totoro.model_management
 import folder_paths
 import os
 import io
@@ -8,11 +8,11 @@ import json
 import struct
 import random
 import hashlib
-from comfy.cli_args import args
+from totoro.cli_args import args
 
 class EmptyLatentAudio:
     def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
+        self.device = totoro.model_management.intermediate_device()
 
     @classmethod
     def INPUT_TYPES(s):
@@ -62,7 +62,7 @@ class VAEDecodeAudio:
 
 
 def create_vorbis_comment_block(comment_dict, last_block):
-    vendor_string = b'ComfyUI'
+    vendor_string = b'totoroUI'
     vendor_length = len(vendor_string)
 
     comments = []
@@ -124,7 +124,7 @@ class SaveAudio:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "audio": ("AUDIO", ),
-                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                              "filename_prefix": ("STRING", {"default": "audio/totoroUI"})},
                 "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
                 }
 
@@ -135,7 +135,7 @@ class SaveAudio:
 
     CATEGORY = "audio"
 
-    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+    def save_audio(self, audio, filename_prefix="totoroUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
         results = list()

comfy_extras/nodes_canny.py

@@ -1,5 +1,5 @@
 from kornia.filters import canny
-import comfy.model_management
+import totoro.model_management
 
 
 class Canny:
@@ -16,8 +16,8 @@ class Canny:
     CATEGORY = "image/preprocessors"
 
     def detect_edge(self, image, low_threshold, high_threshold):
-        output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
+        output = canny(image.to(totoro.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
-        img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
+        img_out = output[1].to(totoro.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
         return (img_out,)
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_compositing.py

@@ -1,6 +1,6 @@
 import numpy as np
 import torch
-import comfy.utils
+import totoro.utils
 from enum import Enum
 
 def resize_mask(mask, shape):
@@ -135,15 +135,15 @@ class PorterDuffImageComposite:
 
             if dst_alpha.shape[:2] != dst_image.shape[:2]:
                 upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = totoro.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_image.shape != dst_image.shape:
                 upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = totoro.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_alpha.shape != dst_alpha.shape:
                 upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
-                upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
+                upscale_output = totoro.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
                 src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
 
             out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])

comfy_extras/nodes_controlnet.py

@@ -1,4 +1,4 @@
-from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
+from totoro.cldm.control_types import UNION_CONTROLNET_TYPES
 
 class SetUnionControlNetType:
     @classmethod

comfy_extras/nodes_custom_sampler.py

@@ -1,9 +1,9 @@
-import comfy.samplers
+import totoro.samplers
-import comfy.sample
+import totoro.sample
-from comfy.k_diffusion import sampling as k_diffusion_sampling
+from totoro.k_diffusion import sampling as k_diffusion_sampling
 import latent_preview
 import torch
-import comfy.utils
+import totoro.utils
 import node_helpers
 
 
@@ -12,7 +12,7 @@ class BasicScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"model": ("MODEL",),
-                     "scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
+                     "scheduler": (totoro.samplers.SCHEDULER_NAMES, ),
                      "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                      "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                       }
@@ -29,7 +29,7 @@ class BasicScheduler:
                 return (torch.FloatTensor([]),)
             total_steps = int(steps/denoise)
 
-        sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
+        sigmas = totoro.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
         sigmas = sigmas[-(steps + 1):]
         return (sigmas, )
 
@@ -127,7 +127,7 @@ class BetaSamplingScheduler:
     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)
+        sigmas = totoro.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
         return (sigmas, )
 
 class VPScheduler:
@@ -214,7 +214,7 @@ class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
         return {"required":
-                    {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ),
+                    {"sampler_name": (totoro.samplers.SAMPLER_NAMES, ),
                       }
                }
     RETURN_TYPES = ("SAMPLER",)
@@ -223,7 +223,7 @@ class KSamplerSelect:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, sampler_name):
-        sampler = comfy.samplers.sampler_object(sampler_name)
+        sampler = totoro.samplers.sampler_object(sampler_name)
         return (sampler, )
 
 class SamplerDPMPP_3M_SDE:
@@ -245,7 +245,7 @@ class SamplerDPMPP_3M_SDE:
             sampler_name = "dpmpp_3m_sde"
         else:
             sampler_name = "dpmpp_3m_sde_gpu"
-        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
+        sampler = totoro.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
         return (sampler, )
 
 class SamplerDPMPP_2M_SDE:
@@ -268,7 +268,7 @@ class SamplerDPMPP_2M_SDE:
             sampler_name = "dpmpp_2m_sde"
         else:
             sampler_name = "dpmpp_2m_sde_gpu"
-        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
+        sampler = totoro.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
         return (sampler, )
 
 
@@ -292,7 +292,7 @@ class SamplerDPMPP_SDE:
             sampler_name = "dpmpp_sde"
         else:
             sampler_name = "dpmpp_sde_gpu"
-        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
+        sampler = totoro.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
 class SamplerDPMPP_2S_Ancestral:
@@ -309,7 +309,7 @@ class SamplerDPMPP_2S_Ancestral:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, eta, s_noise):
-        sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
+        sampler = totoro.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
         return (sampler, )
 
 class SamplerEulerAncestral:
@@ -326,7 +326,7 @@ class SamplerEulerAncestral:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, eta, s_noise):
-        sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
+        sampler = totoro.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
         return (sampler, )
 
 class SamplerEulerAncestralCFGPP:
@@ -343,7 +343,7 @@ class SamplerEulerAncestralCFGPP:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, eta, s_noise):
-        sampler = comfy.samplers.ksampler(
+        sampler = totoro.samplers.ksampler(
             "euler_ancestral_cfg_pp",
             {"eta": eta, "s_noise": s_noise})
         return (sampler, )
@@ -361,7 +361,7 @@ class SamplerLMS:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, order):
-        sampler = comfy.samplers.ksampler("lms", {"order": order})
+        sampler = totoro.samplers.ksampler("lms", {"order": order})
         return (sampler, )
 
 class SamplerDPMAdaptative:
@@ -386,7 +386,7 @@ class SamplerDPMAdaptative:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise):
-        sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
+        sampler = totoro.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
                                                               "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
                                                               "s_noise":s_noise })
         return (sampler, )
@@ -407,7 +407,7 @@ class Noise_RandomNoise:
     def generate_noise(self, input_latent):
         latent_image = input_latent["samples"]
         batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
-        return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
+        return totoro.sample.prepare_noise(latent_image, self.seed, batch_inds)
 
 class SamplerCustom:
     @classmethod
@@ -436,7 +436,7 @@ class SamplerCustom:
         latent = latent_image
         latent_image = latent["samples"]
         latent = latent.copy()
-        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+        latent_image = totoro.sample.fix_empty_latent_channels(model, latent_image)
         latent["samples"] = latent_image
 
         if not add_noise:
@@ -451,8 +451,8 @@ class SamplerCustom:
         x0_output = {}
         callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
 
-        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        disable_pbar = not totoro.utils.PROGRESS_BAR_ENABLED
-        samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+        samples = totoro.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
 
         out = latent.copy()
         out["samples"] = samples
@@ -463,7 +463,7 @@ class SamplerCustom:
             out_denoised = out
         return (out, out_denoised)
 
-class Guider_Basic(comfy.samplers.CFGGuider):
+class Guider_Basic(totoro.samplers.CFGGuider):
     def set_conds(self, positive):
         self.inner_set_conds({"positive": positive})
 
@@ -503,12 +503,12 @@ class CFGGuider:
     CATEGORY = "sampling/custom_sampling/guiders"
 
     def get_guider(self, model, positive, negative, cfg):
-        guider = comfy.samplers.CFGGuider(model)
+        guider = totoro.samplers.CFGGuider(model)
         guider.set_conds(positive, negative)
         guider.set_cfg(cfg)
         return (guider,)
 
-class Guider_DualCFG(comfy.samplers.CFGGuider):
+class Guider_DualCFG(totoro.samplers.CFGGuider):
     def set_cfg(self, cfg1, cfg2):
         self.cfg1 = cfg1
         self.cfg2 = cfg2
@@ -521,8 +521,8 @@ class Guider_DualCFG(comfy.samplers.CFGGuider):
         negative_cond = self.conds.get("negative", None)
         middle_cond = self.conds.get("middle", None)
 
-        out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options)
+        out = totoro.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options)
-        return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+        return totoro.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
 
 class DualCFGGuider:
     @classmethod
@@ -598,7 +598,7 @@ class SamplerCustomAdvanced:
         latent = latent_image
         latent_image = latent["samples"]
         latent = latent.copy()
-        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+        latent_image = totoro.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
         latent["samples"] = latent_image
 
         noise_mask = None
@@ -608,9 +608,9 @@ class SamplerCustomAdvanced:
         x0_output = {}
         callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
 
-        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        disable_pbar = not totoro.utils.PROGRESS_BAR_ENABLED
         samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
-        samples = samples.to(comfy.model_management.intermediate_device())
+        samples = samples.to(totoro.model_management.intermediate_device())
 
         out = latent.copy()
         out["samples"] = samples

comfy_extras/nodes_hypernetwork.py

@@ -1,10 +1,10 @@
-import comfy.utils
+import totoro.utils
 import folder_paths
 import torch
 import logging
 
 def load_hypernetwork_patch(path, strength):
-    sd = comfy.utils.load_torch_file(path, safe_load=True)
+    sd = totoro.utils.load_torch_file(path, safe_load=True)
     activation_func = sd.get('activation_func', 'linear')
     is_layer_norm = sd.get('is_layer_norm', False)
     use_dropout = sd.get('use_dropout', False)

comfy_extras/nodes_images.py

@@ -1,6 +1,6 @@
 import nodes
 import folder_paths
-from comfy.cli_args import args
+from totoro.cli_args import args
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
@@ -78,7 +78,7 @@ class SaveAnimatedWEBP:
     def INPUT_TYPES(s):
         return {"required":
                     {"images": ("IMAGE", ),
-                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+                     "filename_prefix": ("STRING", {"default": "totoroUI"}),
                      "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
                      "lossless": ("BOOLEAN", {"default": True}),
                      "quality": ("INT", {"default": 80, "min": 0, "max": 100}),
@@ -143,7 +143,7 @@ class SaveAnimatedPNG:
     def INPUT_TYPES(s):
         return {"required":
                     {"images": ("IMAGE", ),
-                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
+                     "filename_prefix": ("STRING", {"default": "totoroUI"}),
                      "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
                      "compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
                      },
@@ -157,7 +157,7 @@ class SaveAnimatedPNG:
 
     CATEGORY = "image/animation"
 
-    def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+    def save_images(self, images, fps, compress_level, filename_prefix="totoroUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         results = list()

comfy_extras/nodes_latent.py

@@ -1,10 +1,10 @@
-import comfy.utils
+import totoro.utils
 import torch
 
 def reshape_latent_to(target_shape, latent):
     if latent.shape[1:] != target_shape[1:]:
-        latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
+        latent = totoro.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
-    return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    return totoro.utils.repeat_to_batch_size(latent, target_shape[0])
 
 
 class LatentAdd:
@@ -116,7 +116,7 @@ class LatentBatch:
         s2 = samples2["samples"]
 
         if s1.shape[1:] != s2.shape[1:]:
-            s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
+            s2 = totoro.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])

comfy_extras/nodes_mask.py

@@ -1,7 +1,7 @@
 import numpy as np
 import scipy.ndimage
 import torch
-import comfy.utils
+import totoro.utils
 
 from nodes import MAX_RESOLUTION
 
@@ -10,7 +10,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     if resize_source:
         source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
 
-    source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
+    source = totoro.utils.repeat_to_batch_size(source, destination.shape[0])
 
     x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
     y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
@@ -23,7 +23,7 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     else:
         mask = mask.to(destination.device, copy=True)
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
-        mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
+        mask = totoro.utils.repeat_to_batch_size(mask, source.shape[0])
 
     # calculate the bounds of the source that will be overlapping the destination
     # this prevents the source trying to overwrite latent pixels that are out of bounds