Jacob Segal
8 months ago
64 changed files with 1453 additions and 884 deletions
@ -1,2 +1,8 @@
|
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@echo off |
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..\python_embeded\python.exe .\update.py ..\ComfyUI\ |
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pause |
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if exist update_new.py ( |
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move /y update_new.py update.py |
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echo Running updater again since it got updated. |
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..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update |
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) |
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if "%~1"=="" pause |
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|
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@ -1,3 +0,0 @@
|
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..\python_embeded\python.exe .\update.py ..\ComfyUI\ |
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..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2 |
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pause |
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@ -1,11 +0,0 @@
|
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@echo off |
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..\python_embeded\python.exe .\update.py ..\ComfyUI\ |
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echo |
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echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff |
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echo You should not be running this anyways unless you really have to |
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echo |
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echo If you just want to update normally, close this and run update_comfyui.bat instead. |
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echo |
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pause |
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..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers -r ../ComfyUI/requirements.txt pygit2 |
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pause |
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@ -1,71 +0,0 @@
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name: "Windows Release cu118 dependencies" |
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|
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on: |
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workflow_dispatch: |
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# push: |
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# branches: |
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# - master |
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|
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jobs: |
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build_dependencies: |
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env: |
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# you need at least cuda 5.0 for some of the stuff compiled here. |
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TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 8.9" |
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FORCE_CUDA: 1 |
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MAX_JOBS: 1 # will crash otherwise |
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DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc |
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XFORMERS_BUILD_TYPE: "Release" |
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runs-on: windows-latest |
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steps: |
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- name: Cache Built Dependencies |
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uses: actions/cache@v3 |
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id: cache-cu118_python_stuff |
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with: |
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path: cu118_python_deps.tar |
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key: ${{ runner.os }}-build-cu118 |
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|
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- if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' |
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uses: actions/checkout@v3 |
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|
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- if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' |
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uses: actions/setup-python@v4 |
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with: |
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python-version: '3.10.9' |
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|
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- if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' |
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uses: comfyanonymous/cuda-toolkit@test |
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id: cuda-toolkit |
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with: |
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cuda: '11.8.0' |
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# copied from xformers github |
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- name: Setup MSVC |
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uses: ilammy/msvc-dev-cmd@v1 |
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- name: Configure Pagefile |
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# windows runners will OOM with many CUDA architectures |
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# we cheat here with a page file |
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uses: al-cheb/configure-pagefile-action@v1.3 |
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with: |
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minimum-size: 2GB |
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# really unfortunate: https://github.com/ilammy/msvc-dev-cmd#name-conflicts-with-shell-bash |
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- name: Remove link.exe |
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shell: bash |
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run: rm /usr/bin/link |
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|
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- if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true' |
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shell: bash |
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run: | |
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python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir |
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python -m pip install --no-cache-dir ./temp_wheel_dir/* |
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echo installed basic |
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git clone --recurse-submodules https://github.com/facebookresearch/xformers.git |
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cd xformers |
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python -m pip install --no-cache-dir wheel setuptools twine |
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echo building xformers |
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python setup.py bdist_wheel -d ../temp_wheel_dir/ |
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cd .. |
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rm -rf xformers |
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ls -lah temp_wheel_dir |
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mv temp_wheel_dir cu118_python_deps |
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tar cf cu118_python_deps.tar cu118_python_deps |
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|
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|
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@ -1,37 +0,0 @@
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name: "Windows Release cu118 dependencies 2" |
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|
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on: |
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workflow_dispatch: |
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inputs: |
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xformers: |
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description: 'xformers version' |
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required: true |
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type: string |
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default: "xformers" |
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|
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# push: |
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# branches: |
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# - master |
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|
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jobs: |
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build_dependencies: |
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runs-on: windows-latest |
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steps: |
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- uses: actions/checkout@v3 |
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- uses: actions/setup-python@v4 |
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with: |
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python-version: '3.10.9' |
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|
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- shell: bash |
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run: | |
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python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir |
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python -m pip install --no-cache-dir ./temp_wheel_dir/* |
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echo installed basic |
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ls -lah temp_wheel_dir |
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mv temp_wheel_dir cu118_python_deps |
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tar cf cu118_python_deps.tar cu118_python_deps |
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|
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- uses: actions/cache/save@v3 |
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with: |
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path: cu118_python_deps.tar |
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key: ${{ runner.os }}-build-cu118 |
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@ -1,79 +0,0 @@
|
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name: "Windows Release cu118 packaging" |
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|
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on: |
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workflow_dispatch: |
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# push: |
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# branches: |
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# - master |
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|
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jobs: |
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package_comfyui: |
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permissions: |
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contents: "write" |
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packages: "write" |
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pull-requests: "read" |
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runs-on: windows-latest |
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steps: |
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- uses: actions/cache/restore@v3 |
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id: cache |
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with: |
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path: cu118_python_deps.tar |
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key: ${{ runner.os }}-build-cu118 |
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- shell: bash |
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run: | |
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mv cu118_python_deps.tar ../ |
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cd .. |
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tar xf cu118_python_deps.tar |
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pwd |
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ls |
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|
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- uses: actions/checkout@v3 |
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with: |
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fetch-depth: 0 |
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persist-credentials: false |
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- shell: bash |
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run: | |
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cd .. |
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cp -r ComfyUI ComfyUI_copy |
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curl https://www.python.org/ftp/python/3.10.9/python-3.10.9-embed-amd64.zip -o python_embeded.zip |
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unzip python_embeded.zip -d python_embeded |
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cd python_embeded |
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echo 'import site' >> ./python310._pth |
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curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py |
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./python.exe get-pip.py |
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./python.exe -s -m pip install ../cu118_python_deps/* |
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sed -i '1i../ComfyUI' ./python310._pth |
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cd .. |
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|
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git clone https://github.com/comfyanonymous/taesd |
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cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/ |
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|
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mkdir ComfyUI_windows_portable |
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mv python_embeded ComfyUI_windows_portable |
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mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI |
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|
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cd ComfyUI_windows_portable |
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|
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mkdir update |
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cp -r ComfyUI/.ci/update_windows/* ./update/ |
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cp -r ComfyUI/.ci/update_windows_cu118/* ./update/ |
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cp -r ComfyUI/.ci/windows_base_files/* ./ |
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|
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cd .. |
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|
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"C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable |
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mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z |
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|
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cd ComfyUI_windows_portable |
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python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu |
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|
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ls |
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|
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- name: Upload binaries to release |
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uses: svenstaro/upload-release-action@v2 |
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with: |
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repo_token: ${{ secrets.GITHUB_TOKEN }} |
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file: new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z |
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tag: "latest" |
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overwrite: true |
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|
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@ -0,0 +1,93 @@
|
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""" |
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This file is part of ComfyUI. |
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Copyright (C) 2024 Stability AI |
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|
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This program is free software: you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation, either version 3 of the License, or |
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(at your option) any later version. |
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|
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This program is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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GNU General Public License for more details. |
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|
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You should have received a copy of the GNU General Public License |
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along with this program. If not, see <https://www.gnu.org/licenses/>. |
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""" |
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|
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import torch |
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import torchvision |
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from torch import nn |
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from .common import LayerNorm2d_op |
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|
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|
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class CNetResBlock(nn.Module): |
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def __init__(self, c, dtype=None, device=None, operations=None): |
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super().__init__() |
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self.blocks = nn.Sequential( |
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LayerNorm2d_op(operations)(c, dtype=dtype, device=device), |
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nn.GELU(), |
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operations.Conv2d(c, c, kernel_size=3, padding=1), |
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LayerNorm2d_op(operations)(c, dtype=dtype, device=device), |
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nn.GELU(), |
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operations.Conv2d(c, c, kernel_size=3, padding=1), |
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) |
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|
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def forward(self, x): |
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return x + self.blocks(x) |
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|
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|
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class ControlNet(nn.Module): |
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def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn): |
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super().__init__() |
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if bottleneck_mode is None: |
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bottleneck_mode = 'effnet' |
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self.proj_blocks = proj_blocks |
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if bottleneck_mode == 'effnet': |
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embd_channels = 1280 |
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self.backbone = torchvision.models.efficientnet_v2_s().features.eval() |
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if c_in != 3: |
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in_weights = self.backbone[0][0].weight.data |
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self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device) |
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if c_in > 3: |
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# nn.init.constant_(self.backbone[0][0].weight, 0) |
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self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone() |
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else: |
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self.backbone[0][0].weight.data = in_weights[:, :c_in].clone() |
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elif bottleneck_mode == 'simple': |
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embd_channels = c_in |
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self.backbone = nn.Sequential( |
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operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device), |
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nn.LeakyReLU(0.2, inplace=True), |
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operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device), |
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) |
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elif bottleneck_mode == 'large': |
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self.backbone = nn.Sequential( |
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operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device), |
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nn.LeakyReLU(0.2, inplace=True), |
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operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device), |
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*[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)], |
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operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device), |
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) |
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embd_channels = 1280 |
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else: |
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raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}') |
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self.projections = nn.ModuleList() |
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for _ in range(len(proj_blocks)): |
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self.projections.append(nn.Sequential( |
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operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device), |
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nn.LeakyReLU(0.2, inplace=True), |
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operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device), |
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)) |
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# nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection |
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self.xl = False |
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self.input_channels = c_in |
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self.unshuffle_amount = 8 |
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|
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def forward(self, x): |
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x = self.backbone(x) |
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proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)] |
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for i, idx in enumerate(self.proj_blocks): |
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proj_outputs[idx] = self.projections[i](x) |
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return proj_outputs |
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@ -0,0 +1,42 @@
|
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# code adapted from https://github.com/exx8/differential-diffusion |
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|
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import torch |
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|
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class DifferentialDiffusion(): |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": {"model": ("MODEL", ), |
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}} |
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RETURN_TYPES = ("MODEL",) |
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FUNCTION = "apply" |
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CATEGORY = "_for_testing" |
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INIT = False |
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|
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def apply(self, model): |
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model = model.clone() |
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model.set_model_denoise_mask_function(self.forward) |
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return (model,) |
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|
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def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict): |
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model = extra_options["model"] |
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step_sigmas = extra_options["sigmas"] |
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sigma_to = model.inner_model.model_sampling.sigma_min |
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if step_sigmas[-1] > sigma_to: |
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sigma_to = step_sigmas[-1] |
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sigma_from = step_sigmas[0] |
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|
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ts_from = model.inner_model.model_sampling.timestep(sigma_from) |
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ts_to = model.inner_model.model_sampling.timestep(sigma_to) |
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current_ts = model.inner_model.model_sampling.timestep(sigma[0]) |
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|
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threshold = (current_ts - ts_to) / (ts_from - ts_to) |
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|
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return (denoise_mask >= threshold).to(denoise_mask.dtype) |
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|
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|
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NODE_CLASS_MAPPINGS = { |
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"DifferentialDiffusion": DifferentialDiffusion, |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"DifferentialDiffusion": "Differential Diffusion", |
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} |
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@ -0,0 +1,49 @@
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import torch |
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import comfy.model_management |
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|
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from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat |
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|
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|
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class Morphology: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": {"image": ("IMAGE",), |
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"operation": (["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],), |
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"kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}), |
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}} |
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|
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "process" |
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|
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CATEGORY = "image/postprocessing" |
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|
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def process(self, image, operation, kernel_size): |
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device = comfy.model_management.get_torch_device() |
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kernel = torch.ones(kernel_size, kernel_size, device=device) |
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image_k = image.to(device).movedim(-1, 1) |
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if operation == "erode": |
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output = erosion(image_k, kernel) |
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elif operation == "dilate": |
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output = dilation(image_k, kernel) |
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elif operation == "open": |
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output = opening(image_k, kernel) |
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elif operation == "close": |
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output = closing(image_k, kernel) |
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elif operation == "gradient": |
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output = gradient(image_k, kernel) |
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elif operation == "top_hat": |
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output = top_hat(image_k, kernel) |
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elif operation == "bottom_hat": |
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output = bottom_hat(image_k, kernel) |
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else: |
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raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'") |
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img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1) |
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return (img_out,) |
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|
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NODE_CLASS_MAPPINGS = { |
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"Morphology": Morphology, |
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} |
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|
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"Morphology": "ImageMorphology", |
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} |
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@ -0,0 +1,35 @@
|
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import os |
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import shutil |
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|
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base_path = os.path.dirname(os.path.realpath(__file__)) |
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|
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|
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def update_windows_updater(): |
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top_path = os.path.dirname(base_path) |
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updater_path = os.path.join(base_path, ".ci/update_windows/update.py") |
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bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat") |
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|
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dest_updater_path = os.path.join(top_path, "update/update.py") |
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dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat") |
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dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat") |
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|
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try: |
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with open(dest_bat_path, 'rb') as f: |
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contents = f.read() |
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except: |
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return |
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|
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if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"): |
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return |
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|
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shutil.copy(updater_path, dest_updater_path) |
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try: |
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with open(dest_bat_deps_path, 'rb') as f: |
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contents = f.read() |
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contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause') |
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with open(dest_bat_deps_path, 'wb') as f: |
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f.write(contents) |
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except: |
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pass |
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shutil.copy(bat_path, dest_bat_path) |
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print("Updated the windows standalone package updater.") |
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@ -0,0 +1,159 @@
|
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#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without |
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#them being saved to disk |
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|
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import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client) |
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import uuid |
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import json |
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import urllib.request |
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import urllib.parse |
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|
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server_address = "127.0.0.1:8188" |
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client_id = str(uuid.uuid4()) |
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|
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def queue_prompt(prompt): |
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p = {"prompt": prompt, "client_id": client_id} |
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data = json.dumps(p).encode('utf-8') |
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req = urllib.request.Request("http://{}/prompt".format(server_address), data=data) |
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return json.loads(urllib.request.urlopen(req).read()) |
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|
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def get_image(filename, subfolder, folder_type): |
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data = {"filename": filename, "subfolder": subfolder, "type": folder_type} |
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url_values = urllib.parse.urlencode(data) |
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with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response: |
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return response.read() |
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|
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def get_history(prompt_id): |
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with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response: |
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return json.loads(response.read()) |
||||
|
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def get_images(ws, prompt): |
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prompt_id = queue_prompt(prompt)['prompt_id'] |
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output_images = {} |
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current_node = "" |
||||
while True: |
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out = ws.recv() |
||||
if isinstance(out, str): |
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message = json.loads(out) |
||||
if message['type'] == 'executing': |
||||
data = message['data'] |
||||
if data['prompt_id'] == prompt_id: |
||||
if data['node'] is None: |
||||
break #Execution is done |
||||
else: |
||||
current_node = data['node'] |
||||
else: |
||||
if current_node == 'save_image_websocket_node': |
||||
images_output = output_images.get(current_node, []) |
||||
images_output.append(out[8:]) |
||||
output_images[current_node] = images_output |
||||
|
||||
return output_images |
||||
|
||||
prompt_text = """ |
||||
{ |
||||
"3": { |
||||
"class_type": "KSampler", |
||||
"inputs": { |
||||
"cfg": 8, |
||||
"denoise": 1, |
||||
"latent_image": [ |
||||
"5", |
||||
0 |
||||
], |
||||
"model": [ |
||||
"4", |
||||
0 |
||||
], |
||||
"negative": [ |
||||
"7", |
||||
0 |
||||
], |
||||
"positive": [ |
||||
"6", |
||||
0 |
||||
], |
||||
"sampler_name": "euler", |
||||
"scheduler": "normal", |
||||
"seed": 8566257, |
||||
"steps": 20 |
||||
} |
||||
}, |
||||
"4": { |
||||
"class_type": "CheckpointLoaderSimple", |
||||
"inputs": { |
||||
"ckpt_name": "v1-5-pruned-emaonly.ckpt" |
||||
} |
||||
}, |
||||
"5": { |
||||
"class_type": "EmptyLatentImage", |
||||
"inputs": { |
||||
"batch_size": 1, |
||||
"height": 512, |
||||
"width": 512 |
||||
} |
||||
}, |
||||
"6": { |
||||
"class_type": "CLIPTextEncode", |
||||
"inputs": { |
||||
"clip": [ |
||||
"4", |
||||
1 |
||||
], |
||||
"text": "masterpiece best quality girl" |
||||
} |
||||
}, |
||||
"7": { |
||||
"class_type": "CLIPTextEncode", |
||||
"inputs": { |
||||
"clip": [ |
||||
"4", |
||||
1 |
||||
], |
||||
"text": "bad hands" |
||||
} |
||||
}, |
||||
"8": { |
||||
"class_type": "VAEDecode", |
||||
"inputs": { |
||||
"samples": [ |
||||
"3", |
||||
0 |
||||
], |
||||
"vae": [ |
||||
"4", |
||||
2 |
||||
] |
||||
} |
||||
}, |
||||
"save_image_websocket_node": { |
||||
"class_type": "SaveImageWebsocket", |
||||
"inputs": { |
||||
"images": [ |
||||
"8", |
||||
0 |
||||
] |
||||
} |
||||
} |
||||
} |
||||
""" |
||||
|
||||
prompt = json.loads(prompt_text) |
||||
#set the text prompt for our positive CLIPTextEncode |
||||
prompt["6"]["inputs"]["text"] = "masterpiece best quality man" |
||||
|
||||
#set the seed for our KSampler node |
||||
prompt["3"]["inputs"]["seed"] = 5 |
||||
|
||||
ws = websocket.WebSocket() |
||||
ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id)) |
||||
images = get_images(ws, prompt) |
||||
|
||||
#Commented out code to display the output images: |
||||
|
||||
# for node_id in images: |
||||
# for image_data in images[node_id]: |
||||
# from PIL import Image |
||||
# import io |
||||
# image = Image.open(io.BytesIO(image_data)) |
||||
# image.show() |
||||
|
||||
Loading…
Reference in new issue