forked from extern/easydiffusion
commit
206f9b97bb
17
CHANGES.md
17
CHANGES.md
@ -1,5 +1,22 @@
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# What's new?
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# What's new?
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## v2.5
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### Major Changes
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- **Nearly twice as fast** - significantly faster speed of image generation. We're now pretty close to automatic1111's speed. Code contributions are welcome to make our project even faster: https://github.com/easydiffusion/sdkit/#is-it-fast
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- **Full support for Stable Diffusion 2.1** - supports loading v1.4 or v2.0 or v2.1 models seamlessly. No need to enable "Test SD2", and no need to add `sd2_` to your SD 2.0 model file names.
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- **Memory optimized Stable Diffusion 2.1** - you can now use 768x768 models for SD 2.1, with the same low VRAM optimizations that we've always had for SD 1.4.
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- **6 new samplers!** - explore the new samplers, some of which can generate great images in less than 10 inference steps!
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- **Model Merging** - You can now merge two models (`.ckpt` or `.safetensors`) and output `.ckpt` or `.safetensors` models, optionally in `fp16` precision. Details: https://github.com/cmdr2/stable-diffusion-ui/wiki/Model-Merging
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- **Fast loading/unloading of VAEs** - No longer needs to reload the entire Stable Diffusion model, each time you change the VAE
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- **Database of known models** - automatically picks the right configuration for known models. E.g. we automatically detect and apply "v" parameterization (required for some SD 2.0 models), and "fp32" attention precision (required for some SD 2.1 models).
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- **Color correction for img2img** - an option to preserve the color profile (histogram) of the initial image. This is especially useful if you're getting red-tinted images after inpainting/masking.
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- **Three GPU Memory Usage Settings** - `High` (fastest, maximum VRAM usage), `Balanced` (default - almost as fast, significantly lower VRAM usage), `Low` (slowest, very low VRAM usage). The `Low` setting is applied automatically for GPUs with less than 4 GB of VRAM.
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- **Save metadata as JSON** - You can now save the metadata files as either text or json files (choose in the Settings tab).
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- **Major rewrite of the code** - Most of the codebase has been reorganized and rewritten, to make it more manageable and easier for new developers to contribute features. We've separated our core engine into a new project called `sdkit`, which allows anyone to easily integrate Stable Diffusion (and related modules like GFPGAN etc) into their programming projects (via a simple `pip install sdkit`): https://github.com/easydiffusion/sdkit/
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- **Name change** - Last, and probably the least, the UI is now called "Easy Diffusion". It indicates the focus of this project - an easy way for people to play with Stable Diffusion.
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Our focus continues to remain on an easy installation experience, and an easy user-interface. While still remaining pretty powerful, in terms of features and speed.
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## v2.4
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## v2.4
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### Major Changes
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### Major Changes
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- **Allow reordering the task queue** (by dragging and dropping tasks). Thanks @madrang
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- **Allow reordering the task queue** (by dragging and dropping tasks). Thanks @madrang
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@ -23,23 +23,20 @@ call conda --version
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echo.
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echo.
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@rem activate the environment
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@rem activate the legacy environment (if present) and set PYTHONPATH
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if exist "installer_files\env" (
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set PYTHONPATH=%cd%\installer_files\env\lib\site-packages
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)
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if exist "stable-diffusion\env" (
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call conda activate .\stable-diffusion\env
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call conda activate .\stable-diffusion\env
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set PYTHONPATH=%cd%\stable-diffusion\env\lib\site-packages
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)
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call where python
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call where python
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call python --version
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call python --version
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@rem set the PYTHONPATH
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cd stable-diffusion
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set SD_DIR=%cd%
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cd env\lib\site-packages
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set PYTHONPATH=%SD_DIR%;%cd%
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cd ..\..\..
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echo PYTHONPATH=%PYTHONPATH%
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echo PYTHONPATH=%PYTHONPATH%
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cd ..
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@rem done
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@rem done
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echo.
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echo.
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@ -24,7 +24,7 @@ if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Librar
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set PACKAGES_TO_INSTALL=
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set PACKAGES_TO_INSTALL=
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if not exist "%LEGACY_INSTALL_ENV_DIR%\etc\profile.d\conda.sh" (
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if not exist "%LEGACY_INSTALL_ENV_DIR%\etc\profile.d\conda.sh" (
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if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
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if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda python=3.8.5
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)
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)
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call git --version >.tmp1 2>.tmp2
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call git --version >.tmp1 2>.tmp2
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@ -39,7 +39,7 @@ if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
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PACKAGES_TO_INSTALL=""
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PACKAGES_TO_INSTALL=""
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if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda"; fi
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if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.8.5"; fi
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if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
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if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
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if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
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if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
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13
scripts/check_modules.py
Normal file
13
scripts/check_modules.py
Normal file
@ -0,0 +1,13 @@
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'''
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This script checks if the given modules exist
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'''
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import sys
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import pkgutil
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modules = sys.argv[1:]
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missing_modules = []
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for m in modules:
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if pkgutil.find_loader(m) is None:
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print('module', m, 'not found')
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exit(1)
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@ -26,21 +26,23 @@ if [ "$0" == "bash" ]; then
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echo ""
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echo ""
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# activate the environment
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# activate the legacy environment (if present) and set PYTHONPATH
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if [ -e "installer_files/env" ]; then
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export PYTHONPATH="$(pwd)/installer_files/env/lib/python3.8/site-packages"
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fi
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if [ -e "stable-diffusion/env" ]; then
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CONDA_BASEPATH=$(conda info --base)
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CONDA_BASEPATH=$(conda info --base)
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source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
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source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
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conda activate ./stable-diffusion/env
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conda activate ./stable-diffusion/env
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export PYTHONPATH="$(pwd)/stable-diffusion/env/lib/python3.8/site-packages"
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fi
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which python
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which python
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python --version
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python --version
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# set the PYTHONPATH
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cd stable-diffusion
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SD_PATH=`pwd`
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export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
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echo "PYTHONPATH=$PYTHONPATH"
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echo "PYTHONPATH=$PYTHONPATH"
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cd ..
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# done
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# done
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@ -53,6 +53,7 @@ if "%update_branch%"=="" (
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@xcopy sd-ui-files\ui ui /s /i /Y /q
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@xcopy sd-ui-files\ui ui /s /i /Y /q
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@copy sd-ui-files\scripts\on_sd_start.bat scripts\ /Y
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@copy sd-ui-files\scripts\on_sd_start.bat scripts\ /Y
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@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
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@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
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@copy sd-ui-files\scripts\check_modules.py scripts\ /Y
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@copy "sd-ui-files\scripts\Start Stable Diffusion UI.cmd" . /Y
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@copy "sd-ui-files\scripts\Start Stable Diffusion UI.cmd" . /Y
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@copy "sd-ui-files\scripts\Developer Console.cmd" . /Y
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@copy "sd-ui-files\scripts\Developer Console.cmd" . /Y
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@ -37,6 +37,7 @@ rm -rf ui
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cp -Rf sd-ui-files/ui .
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cp -Rf sd-ui-files/ui .
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cp sd-ui-files/scripts/on_sd_start.sh scripts/
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cp sd-ui-files/scripts/on_sd_start.sh scripts/
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cp sd-ui-files/scripts/bootstrap.sh scripts/
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cp sd-ui-files/scripts/bootstrap.sh scripts/
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cp sd-ui-files/scripts/check_modules.py scripts/
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cp sd-ui-files/scripts/start.sh .
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cp sd-ui-files/scripts/start.sh .
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cp sd-ui-files/scripts/developer_console.sh .
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cp sd-ui-files/scripts/developer_console.sh .
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@ -5,11 +5,20 @@
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@copy sd-ui-files\scripts\on_env_start.bat scripts\ /Y
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@copy sd-ui-files\scripts\on_env_start.bat scripts\ /Y
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@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
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@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
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@copy sd-ui-files\scripts\check_modules.py scripts\ /Y
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if exist "%cd%\profile" (
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if exist "%cd%\profile" (
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set USERPROFILE=%cd%\profile
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set USERPROFILE=%cd%\profile
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)
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)
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@rem set the correct installer path (current vs legacy)
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if exist "%cd%\installer_files\env" (
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set INSTALL_ENV_DIR=%cd%\installer_files\env
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)
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if exist "%cd%\stable-diffusion\env" (
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set INSTALL_ENV_DIR=%cd%\stable-diffusion\env
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)
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@mkdir tmp
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@mkdir tmp
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@set TMP=%cd%\tmp
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@set TMP=%cd%\tmp
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@set TEMP=%cd%\tmp
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@set TEMP=%cd%\tmp
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@ -27,150 +36,92 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
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|
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@call python -c "import os; import shutil; frm = 'sd-ui-files\\ui\\hotfix\\9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'; dst = os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'transformers', '9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'); shutil.copyfile(frm, dst) if os.path.exists(dst) else print(''); print('Hotfixed broken JSON file from OpenAI');"
|
@call python -c "import os; import shutil; frm = 'sd-ui-files\\ui\\hotfix\\9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'; dst = os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'transformers', '9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'); shutil.copyfile(frm, dst) if os.path.exists(dst) else print(''); print('Hotfixed broken JSON file from OpenAI');"
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if NOT DEFINED test_sd2 set test_sd2=N
|
@rem create the stable-diffusion folder, to work with legacy installations
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|
if not exist "stable-diffusion" mkdir stable-diffusion
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cd stable-diffusion
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|
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@>nul findstr /m "sd_git_cloned" scripts\install_status.txt
|
@rem activate the old stable-diffusion env, if it exists
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@if "%ERRORLEVEL%" EQU "0" (
|
if exist "env" (
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@echo "Stable Diffusion's git repository was already installed. Updating.."
|
call conda activate .\env
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|
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@cd stable-diffusion
|
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|
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@call git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
|
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|
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@call git reset --hard
|
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@call git pull
|
|
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|
|
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if "%test_sd2%" == "N" (
|
|
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@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
|
||||||
)
|
|
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if "%test_sd2%" == "Y" (
|
|
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@call git -c advice.detachedHead=false checkout 733a1f6f9cae9b9a9b83294bf3281b123378cb1f
|
|
||||||
)
|
)
|
||||||
|
|
||||||
@cd ..
|
@rem disable the legacy src and ldm folder (otherwise this prevents installing gfpgan and realesrgan)
|
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|
if exist src rename src src-old
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|
if exist ldm rename ldm ldm-old
|
||||||
|
|
||||||
|
@rem install torch and torchvision
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|
call python ..\scripts\check_modules.py torch torchvision
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|
if "%ERRORLEVEL%" EQU "0" (
|
||||||
|
echo "torch and torchvision have already been installed."
|
||||||
) else (
|
) else (
|
||||||
@echo. & echo "Downloading Stable Diffusion.." & echo.
|
echo "Installing torch and torchvision.."
|
||||||
|
|
||||||
@call git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion && (
|
@REM prevent from using packages from the user's home directory, to avoid conflicts
|
||||||
@echo sd_git_cloned >> scripts\install_status.txt
|
set PYTHONNOUSERSITE=1
|
||||||
) || (
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
@echo "Error downloading Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
|
||||||
pause
|
|
||||||
@exit /b
|
|
||||||
)
|
|
||||||
|
|
||||||
@cd stable-diffusion
|
call pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 || (
|
||||||
@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
echo "Error installing torch. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
||||||
|
|
||||||
@cd ..
|
|
||||||
)
|
|
||||||
|
|
||||||
@cd stable-diffusion
|
|
||||||
|
|
||||||
@>nul findstr /m "conda_sd_env_created" ..\scripts\install_status.txt
|
|
||||||
@if "%ERRORLEVEL%" EQU "0" (
|
|
||||||
@echo "Packages necessary for Stable Diffusion were already installed"
|
|
||||||
|
|
||||||
@call conda activate .\env
|
|
||||||
) else (
|
|
||||||
@echo. & echo "Downloading packages necessary for Stable Diffusion.." & echo. & echo "***** This will take some time (depending on the speed of the Internet connection) and may appear to be stuck, but please be patient ***** .." & echo.
|
|
||||||
|
|
||||||
@rmdir /s /q .\env
|
|
||||||
|
|
||||||
@REM prevent conda from using packages from the user's home directory, to avoid conflicts
|
|
||||||
@set PYTHONNOUSERSITE=1
|
|
||||||
|
|
||||||
set USERPROFILE=%cd%\profile
|
|
||||||
|
|
||||||
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
|
|
||||||
|
|
||||||
@call conda env create --prefix env -f environment.yaml || (
|
|
||||||
@echo. & echo "Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
|
|
||||||
pause
|
pause
|
||||||
exit /b
|
exit /b
|
||||||
)
|
)
|
||||||
|
)
|
||||||
|
|
||||||
@call conda activate .\env
|
@rem install/upgrade sdkit
|
||||||
|
call python ..\scripts\check_modules.py sdkit sdkit.models ldm transformers numpy antlr4 gfpgan realesrgan
|
||||||
|
if "%ERRORLEVEL%" EQU "0" (
|
||||||
|
echo "sdkit is already installed."
|
||||||
|
|
||||||
for /f "tokens=*" %%a in ('python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"') do if "%%a" NEQ "42" (
|
@REM prevent from using packages from the user's home directory, to avoid conflicts
|
||||||
@echo. & echo "Dependency test failed! Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
|
set PYTHONNOUSERSITE=1
|
||||||
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
|
|
||||||
|
call >nul pip install --upgrade sdkit || (
|
||||||
|
echo "Error updating sdkit"
|
||||||
|
)
|
||||||
|
) else (
|
||||||
|
echo "Installing sdkit: https://pypi.org/project/sdkit/"
|
||||||
|
|
||||||
|
@REM prevent from using packages from the user's home directory, to avoid conflicts
|
||||||
|
set PYTHONNOUSERSITE=1
|
||||||
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
|
|
||||||
|
call pip install sdkit || (
|
||||||
|
echo "Error installing sdkit. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
||||||
pause
|
pause
|
||||||
exit /b
|
exit /b
|
||||||
)
|
)
|
||||||
|
|
||||||
@echo conda_sd_env_created >> ..\scripts\install_status.txt
|
|
||||||
)
|
)
|
||||||
|
|
||||||
@rem allow rolling back the sdkit-based changes
|
@rem install rich
|
||||||
if exist "src-old" (
|
call python ..\scripts\check_modules.py rich
|
||||||
if not exist "src" (
|
if "%ERRORLEVEL%" EQU "0" (
|
||||||
rename "src-old" "src"
|
echo "rich has already been installed."
|
||||||
|
) else (
|
||||||
|
echo "Installing rich.."
|
||||||
|
|
||||||
if exist "ldm-old" (
|
set PYTHONNOUSERSITE=1
|
||||||
rd /s /q "ldm-old"
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
)
|
|
||||||
|
|
||||||
call pip uninstall -y sdkit stable-diffusion-sdkit
|
call pip install rich || (
|
||||||
|
echo "Error installing rich. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
||||||
|
pause
|
||||||
|
exit /b
|
||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
|
||||||
set PATH=C:\Windows\System32;%PATH%
|
set PATH=C:\Windows\System32;%PATH%
|
||||||
|
|
||||||
@>nul findstr /m "conda_sd_gfpgan_deps_installed" ..\scripts\install_status.txt
|
call python ..\scripts\check_modules.py uvicorn fastapi
|
||||||
@if "%ERRORLEVEL%" EQU "0" (
|
|
||||||
@echo "Packages necessary for GFPGAN (Face Correction) were already installed"
|
|
||||||
) else (
|
|
||||||
@echo. & echo "Downloading packages necessary for GFPGAN (Face Correction).." & echo.
|
|
||||||
|
|
||||||
@set PYTHONNOUSERSITE=1
|
|
||||||
|
|
||||||
set USERPROFILE=%cd%\profile
|
|
||||||
|
|
||||||
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
|
|
||||||
|
|
||||||
for /f "tokens=*" %%a in ('python -c "from gfpgan import GFPGANer; print(42)"') do if "%%a" NEQ "42" (
|
|
||||||
@echo. & echo "Dependency test failed! Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
|
|
||||||
pause
|
|
||||||
exit /b
|
|
||||||
)
|
|
||||||
|
|
||||||
@echo conda_sd_gfpgan_deps_installed >> ..\scripts\install_status.txt
|
|
||||||
)
|
|
||||||
|
|
||||||
@>nul findstr /m "conda_sd_esrgan_deps_installed" ..\scripts\install_status.txt
|
|
||||||
@if "%ERRORLEVEL%" EQU "0" (
|
|
||||||
@echo "Packages necessary for ESRGAN (Resolution Upscaling) were already installed"
|
|
||||||
) else (
|
|
||||||
@echo. & echo "Downloading packages necessary for ESRGAN (Resolution Upscaling).." & echo.
|
|
||||||
|
|
||||||
@set PYTHONNOUSERSITE=1
|
|
||||||
|
|
||||||
set USERPROFILE=%cd%\profile
|
|
||||||
|
|
||||||
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
|
|
||||||
|
|
||||||
for /f "tokens=*" %%a in ('python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"') do if "%%a" NEQ "42" (
|
|
||||||
@echo. & echo "Dependency test failed! Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
|
|
||||||
pause
|
|
||||||
exit /b
|
|
||||||
)
|
|
||||||
|
|
||||||
@echo conda_sd_esrgan_deps_installed >> ..\scripts\install_status.txt
|
|
||||||
)
|
|
||||||
|
|
||||||
@>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
|
|
||||||
@if "%ERRORLEVEL%" EQU "0" (
|
@if "%ERRORLEVEL%" EQU "0" (
|
||||||
echo "Packages necessary for Stable Diffusion UI were already installed"
|
echo "Packages necessary for Stable Diffusion UI were already installed"
|
||||||
) else (
|
) else (
|
||||||
@echo. & echo "Downloading packages necessary for Stable Diffusion UI.." & echo.
|
@echo. & echo "Downloading packages necessary for Stable Diffusion UI.." & echo.
|
||||||
|
|
||||||
@set PYTHONNOUSERSITE=1
|
set PYTHONNOUSERSITE=1
|
||||||
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
|
|
||||||
set USERPROFILE=%cd%\profile
|
@call conda install -c conda-forge -y uvicorn fastapi || (
|
||||||
|
|
||||||
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
|
|
||||||
|
|
||||||
@call conda install -c conda-forge -y --prefix env uvicorn fastapi || (
|
|
||||||
echo "Error installing the packages necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
echo "Error installing the packages necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
||||||
pause
|
pause
|
||||||
exit /b
|
exit /b
|
||||||
@ -185,26 +136,6 @@ call WHERE uvicorn > .tmp
|
|||||||
exit /b
|
exit /b
|
||||||
)
|
)
|
||||||
|
|
||||||
@>nul 2>nul call python -m picklescan --help
|
|
||||||
@if "%ERRORLEVEL%" NEQ "0" (
|
|
||||||
@echo. & echo Picklescan not found. Installing
|
|
||||||
@call pip install picklescan || (
|
|
||||||
echo "Error installing the picklescan package necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
|
||||||
pause
|
|
||||||
exit /b
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
@>nul 2>nul call python -c "import safetensors"
|
|
||||||
@if "%ERRORLEVEL%" NEQ "0" (
|
|
||||||
@echo. & echo SafeTensors not found. Installing
|
|
||||||
@call pip install safetensors || (
|
|
||||||
echo "Error installing the safetensors package necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
|
|
||||||
pause
|
|
||||||
exit /b
|
|
||||||
)
|
|
||||||
)
|
|
||||||
|
|
||||||
@>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
|
@>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
|
||||||
@if "%ERRORLEVEL%" NEQ "0" (
|
@if "%ERRORLEVEL%" NEQ "0" (
|
||||||
@echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
|
@echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
|
||||||
@ -212,12 +143,7 @@ call WHERE uvicorn > .tmp
|
|||||||
|
|
||||||
|
|
||||||
|
|
||||||
if not exist "..\models\stable-diffusion" mkdir "..\models\stable-diffusion"
|
|
||||||
if not exist "..\models\vae" mkdir "..\models\vae"
|
if not exist "..\models\vae" mkdir "..\models\vae"
|
||||||
if not exist "..\models\hypernetwork" mkdir "..\models\hypernetwork"
|
|
||||||
echo. > "..\models\stable-diffusion\Put your custom ckpt files here.txt"
|
|
||||||
echo. > "..\models\vae\Put your VAE files here.txt"
|
|
||||||
echo. > "..\models\hypernetwork\Put your hypernetwork files here.txt"
|
|
||||||
|
|
||||||
@if exist "sd-v1-4.ckpt" (
|
@if exist "sd-v1-4.ckpt" (
|
||||||
for %%I in ("sd-v1-4.ckpt") do if "%%~zI" EQU "4265380512" (
|
for %%I in ("sd-v1-4.ckpt") do if "%%~zI" EQU "4265380512" (
|
||||||
@ -375,10 +301,6 @@ echo. > "..\models\hypernetwork\Put your hypernetwork files here.txt"
|
|||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
|
||||||
if "%test_sd2%" == "Y" (
|
|
||||||
@call pip install open_clip_torch==2.0.2
|
|
||||||
)
|
|
||||||
|
|
||||||
@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
|
@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
|
||||||
@if "%ERRORLEVEL%" NEQ "0" (
|
@if "%ERRORLEVEL%" NEQ "0" (
|
||||||
@echo sd_weights_downloaded >> ..\scripts\install_status.txt
|
@echo sd_weights_downloaded >> ..\scripts\install_status.txt
|
||||||
@ -389,10 +311,8 @@ if "%test_sd2%" == "Y" (
|
|||||||
|
|
||||||
@set SD_DIR=%cd%
|
@set SD_DIR=%cd%
|
||||||
|
|
||||||
@cd env\lib\site-packages
|
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
|
||||||
@set PYTHONPATH=%SD_DIR%;%cd%
|
echo PYTHONPATH=%PYTHONPATH%
|
||||||
@cd ..\..\..
|
|
||||||
@echo PYTHONPATH=%PYTHONPATH%
|
|
||||||
|
|
||||||
call where python
|
call where python
|
||||||
call python --version
|
call python --version
|
||||||
@ -401,17 +321,9 @@ call python --version
|
|||||||
@set SD_UI_PATH=%cd%\ui
|
@set SD_UI_PATH=%cd%\ui
|
||||||
@cd stable-diffusion
|
@cd stable-diffusion
|
||||||
|
|
||||||
@rem
|
|
||||||
@rem Rewrite easy-install.pth. This fixes the installation if the user has relocated the SDUI installation
|
|
||||||
@rem
|
|
||||||
>env\Lib\site-packages\easy-install.pth echo %cd%\src\taming-transformers
|
|
||||||
>>env\Lib\site-packages\easy-install.pth echo %cd%\src\clip
|
|
||||||
>>env\Lib\site-packages\easy-install.pth echo %cd%\src\gfpgan
|
|
||||||
>>env\Lib\site-packages\easy-install.pth echo %cd%\src\realesrgan
|
|
||||||
|
|
||||||
@if NOT DEFINED SD_UI_BIND_PORT set SD_UI_BIND_PORT=9000
|
@if NOT DEFINED SD_UI_BIND_PORT set SD_UI_BIND_PORT=9000
|
||||||
@if NOT DEFINED SD_UI_BIND_IP set SD_UI_BIND_IP=0.0.0.0
|
@if NOT DEFINED SD_UI_BIND_IP set SD_UI_BIND_IP=0.0.0.0
|
||||||
@uvicorn server:app --app-dir "%SD_UI_PATH%" --port %SD_UI_BIND_PORT% --host %SD_UI_BIND_IP%
|
@uvicorn main:server_api --app-dir "%SD_UI_PATH%" --port %SD_UI_BIND_PORT% --host %SD_UI_BIND_IP% --log-level error
|
||||||
|
|
||||||
|
|
||||||
@pause
|
@pause
|
||||||
|
@ -4,6 +4,7 @@ source ./scripts/functions.sh
|
|||||||
|
|
||||||
cp sd-ui-files/scripts/on_env_start.sh scripts/
|
cp sd-ui-files/scripts/on_env_start.sh scripts/
|
||||||
cp sd-ui-files/scripts/bootstrap.sh scripts/
|
cp sd-ui-files/scripts/bootstrap.sh scripts/
|
||||||
|
cp sd-ui-files/scripts/check_modules.py scripts/
|
||||||
|
|
||||||
# activate the installer env
|
# activate the installer env
|
||||||
CONDA_BASEPATH=$(conda info --base)
|
CONDA_BASEPATH=$(conda info --base)
|
||||||
@ -21,125 +22,89 @@ python -c "import os; import shutil; frm = 'sd-ui-files/ui/hotfix/9c24e6cd9f499d
|
|||||||
# Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
|
# Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
|
||||||
# Note to self: Please rewrite this in Python. For the sake of your own sanity.
|
# Note to self: Please rewrite this in Python. For the sake of your own sanity.
|
||||||
|
|
||||||
if [ "$test_sd2" == "" ]; then
|
# set the correct installer path (current vs legacy)
|
||||||
export test_sd2="N"
|
if [ -e "installer_files/env" ]; then
|
||||||
|
export INSTALL_ENV_DIR="$(pwd)/installer_files/env"
|
||||||
|
fi
|
||||||
|
if [ -e "stable-diffusion/env" ]; then
|
||||||
|
export INSTALL_ENV_DIR="$(pwd)/stable-diffusion/env"
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/install_status.txt` -gt "0" ]; then
|
# create the stable-diffusion folder, to work with legacy installations
|
||||||
echo "Stable Diffusion's git repository was already installed. Updating.."
|
if [ ! -e "stable-diffusion" ]; then mkdir stable-diffusion; fi
|
||||||
|
|
||||||
cd stable-diffusion
|
cd stable-diffusion
|
||||||
|
|
||||||
git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
|
# activate the old stable-diffusion env, if it exists
|
||||||
|
if [ -e "env" ]; then
|
||||||
git reset --hard
|
|
||||||
git pull
|
|
||||||
|
|
||||||
if [ "$test_sd2" == "N" ]; then
|
|
||||||
git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
|
||||||
elif [ "$test_sd2" == "Y" ]; then
|
|
||||||
git -c advice.detachedHead=false checkout 733a1f6f9cae9b9a9b83294bf3281b123378cb1f
|
|
||||||
fi
|
|
||||||
|
|
||||||
cd ..
|
|
||||||
else
|
|
||||||
printf "\n\nDownloading Stable Diffusion..\n\n"
|
|
||||||
|
|
||||||
if git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion ; then
|
|
||||||
echo sd_git_cloned >> scripts/install_status.txt
|
|
||||||
else
|
|
||||||
fail "git clone of basujindal/stable-diffusion.git failed"
|
|
||||||
fi
|
|
||||||
|
|
||||||
cd stable-diffusion
|
|
||||||
git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
|
||||||
|
|
||||||
cd ..
|
|
||||||
fi
|
|
||||||
|
|
||||||
cd stable-diffusion
|
|
||||||
|
|
||||||
if [ `grep -c conda_sd_env_created ../scripts/install_status.txt` -gt "0" ]; then
|
|
||||||
echo "Packages necessary for Stable Diffusion were already installed"
|
|
||||||
|
|
||||||
conda activate ./env || fail "conda activate failed"
|
conda activate ./env || fail "conda activate failed"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# disable the legacy src and ldm folder (otherwise this prevents installing gfpgan and realesrgan)
|
||||||
|
if [ -e "src" ]; then mv src src-old; fi
|
||||||
|
if [ -e "ldm" ]; then mv ldm ldm-old; fi
|
||||||
|
|
||||||
|
# install torch and torchvision
|
||||||
|
if python ../scripts/check_modules.py torch torchvision; then
|
||||||
|
echo "torch and torchvision have already been installed."
|
||||||
else
|
else
|
||||||
printf "\n\nDownloading packages necessary for Stable Diffusion..\n"
|
echo "Installing torch and torchvision.."
|
||||||
printf "\n\n***** This will take some time (depending on the speed of the Internet connection) and may appear to be stuck, but please be patient ***** ..\n\n"
|
|
||||||
|
|
||||||
# prevent conda from using packages from the user's home directory, to avoid conflicts
|
|
||||||
export PYTHONNOUSERSITE=1
|
|
||||||
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
|
|
||||||
|
|
||||||
if conda env create --prefix env --force -f environment.yaml ; then
|
|
||||||
echo "Installed. Testing.."
|
|
||||||
else
|
|
||||||
fail "'conda env create' failed"
|
|
||||||
fi
|
|
||||||
|
|
||||||
conda activate ./env || fail "conda activate failed"
|
|
||||||
|
|
||||||
out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"`
|
|
||||||
if [ "$out_test" != "42" ]; then
|
|
||||||
fail "Dependency test failed"
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo conda_sd_env_created >> ../scripts/install_status.txt
|
|
||||||
fi
|
|
||||||
|
|
||||||
# allow rolling back the sdkit-based changes
|
|
||||||
if [ -e "src-old" ] && [ ! -e "src" ]; then
|
|
||||||
mv src-old src
|
|
||||||
|
|
||||||
if [ -e "ldm-old" ]; then rm -r ldm-old; fi
|
|
||||||
|
|
||||||
pip uninstall -y sdkit stable-diffusion-sdkit
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [ `grep -c conda_sd_gfpgan_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
|
|
||||||
echo "Packages necessary for GFPGAN (Face Correction) were already installed"
|
|
||||||
else
|
|
||||||
printf "\n\nDownloading packages necessary for GFPGAN (Face Correction)..\n"
|
|
||||||
|
|
||||||
export PYTHONNOUSERSITE=1
|
export PYTHONNOUSERSITE=1
|
||||||
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
|
|
||||||
out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
|
if pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 ; then
|
||||||
if [ "$out_test" != "42" ]; then
|
echo "Installed."
|
||||||
echo "EE The dependency check has failed. This usually means that some system libraries are missing."
|
|
||||||
echo "EE On Debian/Ubuntu systems, this are often these packages: libsm6 libxext6 libxrender-dev"
|
|
||||||
echo "EE Other Linux distributions might have different package names for these libraries."
|
|
||||||
fail "GFPGAN dependency test failed"
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo conda_sd_gfpgan_deps_installed >> ../scripts/install_status.txt
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [ `grep -c conda_sd_esrgan_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
|
|
||||||
echo "Packages necessary for ESRGAN (Resolution Upscaling) were already installed"
|
|
||||||
else
|
else
|
||||||
printf "\n\nDownloading packages necessary for ESRGAN (Resolution Upscaling)..\n"
|
fail "torch install failed"
|
||||||
|
fi
|
||||||
|
fi
|
||||||
|
|
||||||
|
# install/upgrade sdkit
|
||||||
|
if python ../scripts/check_modules.py sdkit sdkit.models ldm transformers numpy antlr4 gfpgan realesrgan ; then
|
||||||
|
echo "sdkit is already installed."
|
||||||
|
|
||||||
export PYTHONNOUSERSITE=1
|
export PYTHONNOUSERSITE=1
|
||||||
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
|
|
||||||
out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"`
|
pip install --upgrade sdkit > /dev/null
|
||||||
if [ "$out_test" != "42" ]; then
|
else
|
||||||
fail "ESRGAN dependency test failed"
|
echo "Installing sdkit: https://pypi.org/project/sdkit/"
|
||||||
|
|
||||||
|
export PYTHONNOUSERSITE=1
|
||||||
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
|
|
||||||
|
if pip install sdkit ; then
|
||||||
|
echo "Installed."
|
||||||
|
else
|
||||||
|
fail "sdkit install failed"
|
||||||
|
fi
|
||||||
fi
|
fi
|
||||||
|
|
||||||
echo conda_sd_esrgan_deps_installed >> ../scripts/install_status.txt
|
# install rich
|
||||||
|
if python ../scripts/check_modules.py rich; then
|
||||||
|
echo "rich has already been installed."
|
||||||
|
else
|
||||||
|
echo "Installing rich.."
|
||||||
|
|
||||||
|
export PYTHONNOUSERSITE=1
|
||||||
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
|
|
||||||
|
if pip install rich ; then
|
||||||
|
echo "Installed."
|
||||||
|
else
|
||||||
|
fail "Install failed for rich"
|
||||||
|
fi
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ `grep -c conda_sd_ui_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
|
if python ../scripts/check_modules.py uvicorn fastapi ; then
|
||||||
echo "Packages necessary for Stable Diffusion UI were already installed"
|
echo "Packages necessary for Stable Diffusion UI were already installed"
|
||||||
else
|
else
|
||||||
printf "\n\nDownloading packages necessary for Stable Diffusion UI..\n\n"
|
printf "\n\nDownloading packages necessary for Stable Diffusion UI..\n\n"
|
||||||
|
|
||||||
export PYTHONNOUSERSITE=1
|
export PYTHONNOUSERSITE=1
|
||||||
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
|
|
||||||
if conda install -c conda-forge --prefix ./env -y uvicorn fastapi ; then
|
if conda install -c conda-forge -y uvicorn fastapi ; then
|
||||||
echo "Installed. Testing.."
|
echo "Installed. Testing.."
|
||||||
else
|
else
|
||||||
fail "'conda install uvicorn' failed"
|
fail "'conda install uvicorn' failed"
|
||||||
@ -148,32 +113,9 @@ else
|
|||||||
if ! command -v uvicorn &> /dev/null; then
|
if ! command -v uvicorn &> /dev/null; then
|
||||||
fail "UI packages not found!"
|
fail "UI packages not found!"
|
||||||
fi
|
fi
|
||||||
|
|
||||||
echo conda_sd_ui_deps_installed >> ../scripts/install_status.txt
|
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if python -m picklescan --help >/dev/null 2>&1; then
|
|
||||||
echo "Picklescan is already installed."
|
|
||||||
else
|
|
||||||
echo "Picklescan not found, installing."
|
|
||||||
pip install picklescan || fail "Picklescan installation failed."
|
|
||||||
fi
|
|
||||||
|
|
||||||
if python -c "import safetensors" --help >/dev/null 2>&1; then
|
|
||||||
echo "SafeTensors is already installed."
|
|
||||||
else
|
|
||||||
echo "SafeTensors not found, installing."
|
|
||||||
pip install safetensors || fail "SafeTensors installation failed."
|
|
||||||
fi
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
mkdir -p "../models/stable-diffusion"
|
|
||||||
mkdir -p "../models/vae"
|
mkdir -p "../models/vae"
|
||||||
mkdir -p "../models/hypernetwork"
|
|
||||||
echo "" > "../models/stable-diffusion/Put your custom ckpt files here.txt"
|
|
||||||
echo "" > "../models/vae/Put your VAE files here.txt"
|
|
||||||
echo "" > "../models/hypernetwork/Put your hypernetwork files here.txt"
|
|
||||||
|
|
||||||
if [ -f "sd-v1-4.ckpt" ]; then
|
if [ -f "sd-v1-4.ckpt" ]; then
|
||||||
model_size=`find "sd-v1-4.ckpt" -printf "%s"`
|
model_size=`find "sd-v1-4.ckpt" -printf "%s"`
|
||||||
@ -314,10 +256,6 @@ if [ ! -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
|
|||||||
fi
|
fi
|
||||||
fi
|
fi
|
||||||
|
|
||||||
if [ "$test_sd2" == "Y" ]; then
|
|
||||||
pip install open_clip_torch==2.0.2
|
|
||||||
fi
|
|
||||||
|
|
||||||
if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
|
if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
|
||||||
echo sd_weights_downloaded >> ../scripts/install_status.txt
|
echo sd_weights_downloaded >> ../scripts/install_status.txt
|
||||||
echo sd_install_complete >> ../scripts/install_status.txt
|
echo sd_install_complete >> ../scripts/install_status.txt
|
||||||
@ -326,7 +264,8 @@ fi
|
|||||||
printf "\n\nStable Diffusion is ready!\n\n"
|
printf "\n\nStable Diffusion is ready!\n\n"
|
||||||
|
|
||||||
SD_PATH=`pwd`
|
SD_PATH=`pwd`
|
||||||
export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
|
|
||||||
|
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
|
||||||
echo "PYTHONPATH=$PYTHONPATH"
|
echo "PYTHONPATH=$PYTHONPATH"
|
||||||
|
|
||||||
which python
|
which python
|
||||||
@ -336,6 +275,6 @@ cd ..
|
|||||||
export SD_UI_PATH=`pwd`/ui
|
export SD_UI_PATH=`pwd`/ui
|
||||||
cd stable-diffusion
|
cd stable-diffusion
|
||||||
|
|
||||||
uvicorn server:app --app-dir "$SD_UI_PATH" --port ${SD_UI_BIND_PORT:-9000} --host ${SD_UI_BIND_IP:-0.0.0.0}
|
uvicorn main:server_api --app-dir "$SD_UI_PATH" --port ${SD_UI_BIND_PORT:-9000} --host ${SD_UI_BIND_IP:-0.0.0.0} --log-level error
|
||||||
|
|
||||||
read -p "Press any key to continue"
|
read -p "Press any key to continue"
|
||||||
|
0
ui/easydiffusion/__init__.py
Normal file
0
ui/easydiffusion/__init__.py
Normal file
165
ui/easydiffusion/app.py
Normal file
165
ui/easydiffusion/app.py
Normal file
@ -0,0 +1,165 @@
|
|||||||
|
import os
|
||||||
|
import socket
|
||||||
|
import sys
|
||||||
|
import json
|
||||||
|
import traceback
|
||||||
|
import logging
|
||||||
|
from rich.logging import RichHandler
|
||||||
|
|
||||||
|
from sdkit.utils import log as sdkit_log # hack, so we can overwrite the log config
|
||||||
|
|
||||||
|
from easydiffusion import task_manager
|
||||||
|
from easydiffusion.utils import log
|
||||||
|
|
||||||
|
# Remove all handlers associated with the root logger object.
|
||||||
|
for handler in logging.root.handlers[:]:
|
||||||
|
logging.root.removeHandler(handler)
|
||||||
|
|
||||||
|
LOG_FORMAT = '%(asctime)s.%(msecs)03d %(levelname)s %(threadName)s %(message)s'
|
||||||
|
logging.basicConfig(
|
||||||
|
level=logging.INFO,
|
||||||
|
format=LOG_FORMAT,
|
||||||
|
datefmt="%X",
|
||||||
|
handlers=[RichHandler(markup=True, rich_tracebacks=True, show_time=False, show_level=False)]
|
||||||
|
)
|
||||||
|
|
||||||
|
SD_DIR = os.getcwd()
|
||||||
|
|
||||||
|
SD_UI_DIR = os.getenv('SD_UI_PATH', None)
|
||||||
|
sys.path.append(os.path.dirname(SD_UI_DIR))
|
||||||
|
|
||||||
|
CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
|
||||||
|
MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'models'))
|
||||||
|
|
||||||
|
USER_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'))
|
||||||
|
CORE_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, 'plugins', 'ui'))
|
||||||
|
UI_PLUGINS_SOURCES = ((CORE_UI_PLUGINS_DIR, 'core'), (USER_UI_PLUGINS_DIR, 'user'))
|
||||||
|
|
||||||
|
OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
|
||||||
|
TASK_TTL = 15 * 60 # Discard last session's task timeout
|
||||||
|
APP_CONFIG_DEFAULTS = {
|
||||||
|
# auto: selects the cuda device with the most free memory, cuda: use the currently active cuda device.
|
||||||
|
'render_devices': 'auto', # valid entries: 'auto', 'cpu' or 'cuda:N' (where N is a GPU index)
|
||||||
|
'update_branch': 'main',
|
||||||
|
'ui': {
|
||||||
|
'open_browser_on_start': True,
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
def init():
|
||||||
|
os.makedirs(USER_UI_PLUGINS_DIR, exist_ok=True)
|
||||||
|
|
||||||
|
update_render_threads()
|
||||||
|
|
||||||
|
def getConfig(default_val=APP_CONFIG_DEFAULTS):
|
||||||
|
try:
|
||||||
|
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||||
|
if not os.path.exists(config_json_path):
|
||||||
|
return default_val
|
||||||
|
with open(config_json_path, 'r', encoding='utf-8') as f:
|
||||||
|
config = json.load(f)
|
||||||
|
if 'net' not in config:
|
||||||
|
config['net'] = {}
|
||||||
|
if os.getenv('SD_UI_BIND_PORT') is not None:
|
||||||
|
config['net']['listen_port'] = int(os.getenv('SD_UI_BIND_PORT'))
|
||||||
|
if os.getenv('SD_UI_BIND_IP') is not None:
|
||||||
|
config['net']['listen_to_network'] = (os.getenv('SD_UI_BIND_IP') == '0.0.0.0')
|
||||||
|
return config
|
||||||
|
except Exception as e:
|
||||||
|
log.warn(traceback.format_exc())
|
||||||
|
return default_val
|
||||||
|
|
||||||
|
def setConfig(config):
|
||||||
|
try: # config.json
|
||||||
|
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||||
|
with open(config_json_path, 'w', encoding='utf-8') as f:
|
||||||
|
json.dump(config, f)
|
||||||
|
except:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
|
||||||
|
try: # config.bat
|
||||||
|
config_bat_path = os.path.join(CONFIG_DIR, 'config.bat')
|
||||||
|
config_bat = []
|
||||||
|
|
||||||
|
if 'update_branch' in config:
|
||||||
|
config_bat.append(f"@set update_branch={config['update_branch']}")
|
||||||
|
|
||||||
|
config_bat.append(f"@set SD_UI_BIND_PORT={config['net']['listen_port']}")
|
||||||
|
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
||||||
|
config_bat.append(f"@set SD_UI_BIND_IP={bind_ip}")
|
||||||
|
|
||||||
|
if len(config_bat) > 0:
|
||||||
|
with open(config_bat_path, 'w', encoding='utf-8') as f:
|
||||||
|
f.write('\r\n'.join(config_bat))
|
||||||
|
except:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
|
||||||
|
try: # config.sh
|
||||||
|
config_sh_path = os.path.join(CONFIG_DIR, 'config.sh')
|
||||||
|
config_sh = ['#!/bin/bash']
|
||||||
|
|
||||||
|
if 'update_branch' in config:
|
||||||
|
config_sh.append(f"export update_branch={config['update_branch']}")
|
||||||
|
|
||||||
|
config_sh.append(f"export SD_UI_BIND_PORT={config['net']['listen_port']}")
|
||||||
|
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
||||||
|
config_sh.append(f"export SD_UI_BIND_IP={bind_ip}")
|
||||||
|
|
||||||
|
if len(config_sh) > 1:
|
||||||
|
with open(config_sh_path, 'w', encoding='utf-8') as f:
|
||||||
|
f.write('\n'.join(config_sh))
|
||||||
|
except:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
|
||||||
|
def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, vram_usage_level):
|
||||||
|
config = getConfig()
|
||||||
|
if 'model' not in config:
|
||||||
|
config['model'] = {}
|
||||||
|
|
||||||
|
config['model']['stable-diffusion'] = ckpt_model_name
|
||||||
|
config['model']['vae'] = vae_model_name
|
||||||
|
config['model']['hypernetwork'] = hypernetwork_model_name
|
||||||
|
|
||||||
|
if vae_model_name is None or vae_model_name == "":
|
||||||
|
del config['model']['vae']
|
||||||
|
if hypernetwork_model_name is None or hypernetwork_model_name == "":
|
||||||
|
del config['model']['hypernetwork']
|
||||||
|
|
||||||
|
config['vram_usage_level'] = vram_usage_level
|
||||||
|
|
||||||
|
setConfig(config)
|
||||||
|
|
||||||
|
def update_render_threads():
|
||||||
|
config = getConfig()
|
||||||
|
render_devices = config.get('render_devices', 'auto')
|
||||||
|
active_devices = task_manager.get_devices()['active'].keys()
|
||||||
|
|
||||||
|
log.debug(f'requesting for render_devices: {render_devices}')
|
||||||
|
task_manager.update_render_threads(render_devices, active_devices)
|
||||||
|
|
||||||
|
def getUIPlugins():
|
||||||
|
plugins = []
|
||||||
|
|
||||||
|
for plugins_dir, dir_prefix in UI_PLUGINS_SOURCES:
|
||||||
|
for file in os.listdir(plugins_dir):
|
||||||
|
if file.endswith('.plugin.js'):
|
||||||
|
plugins.append(f'/plugins/{dir_prefix}/{file}')
|
||||||
|
|
||||||
|
return plugins
|
||||||
|
|
||||||
|
def getIPConfig():
|
||||||
|
try:
|
||||||
|
ips = socket.gethostbyname_ex(socket.gethostname())
|
||||||
|
ips[2].append(ips[0])
|
||||||
|
return ips[2]
|
||||||
|
except Exception as e:
|
||||||
|
log.exception(e)
|
||||||
|
return []
|
||||||
|
|
||||||
|
def open_browser():
|
||||||
|
config = getConfig()
|
||||||
|
ui = config.get('ui', {})
|
||||||
|
net = config.get('net', {'listen_port':9000})
|
||||||
|
port = net.get('listen_port', 9000)
|
||||||
|
if ui.get('open_browser_on_start', True):
|
||||||
|
import webbrowser; webbrowser.open(f"http://localhost:{port}")
|
@ -3,6 +3,15 @@ import torch
|
|||||||
import traceback
|
import traceback
|
||||||
import re
|
import re
|
||||||
|
|
||||||
|
from easydiffusion.utils import log
|
||||||
|
|
||||||
|
'''
|
||||||
|
Set `FORCE_FULL_PRECISION` in the environment variables, or in `config.bat`/`config.sh` to set full precision (i.e. float32).
|
||||||
|
Otherwise the models will load at half-precision (i.e. float16).
|
||||||
|
|
||||||
|
Half-precision is fine most of the time. Full precision is only needed for working around GPU bugs (like NVIDIA 16xx GPUs).
|
||||||
|
'''
|
||||||
|
|
||||||
COMPARABLE_GPU_PERCENTILE = 0.65 # if a GPU's free_mem is within this % of the GPU with the most free_mem, it will be picked
|
COMPARABLE_GPU_PERCENTILE = 0.65 # if a GPU's free_mem is within this % of the GPU with the most free_mem, it will be picked
|
||||||
|
|
||||||
mem_free_threshold = 0
|
mem_free_threshold = 0
|
||||||
@ -34,7 +43,7 @@ def get_device_delta(render_devices, active_devices):
|
|||||||
if 'auto' in render_devices:
|
if 'auto' in render_devices:
|
||||||
render_devices = auto_pick_devices(active_devices)
|
render_devices = auto_pick_devices(active_devices)
|
||||||
if 'cpu' in render_devices:
|
if 'cpu' in render_devices:
|
||||||
print('WARNING: Could not find a compatible GPU. Using the CPU, but this will be very slow!')
|
log.warn('WARNING: Could not find a compatible GPU. Using the CPU, but this will be very slow!')
|
||||||
|
|
||||||
active_devices = set(active_devices)
|
active_devices = set(active_devices)
|
||||||
render_devices = set(render_devices)
|
render_devices = set(render_devices)
|
||||||
@ -53,7 +62,7 @@ def auto_pick_devices(currently_active_devices):
|
|||||||
if device_count == 1:
|
if device_count == 1:
|
||||||
return ['cuda:0'] if is_device_compatible('cuda:0') else ['cpu']
|
return ['cuda:0'] if is_device_compatible('cuda:0') else ['cpu']
|
||||||
|
|
||||||
print('Autoselecting GPU. Using most free memory.')
|
log.debug('Autoselecting GPU. Using most free memory.')
|
||||||
devices = []
|
devices = []
|
||||||
for device in range(device_count):
|
for device in range(device_count):
|
||||||
device = f'cuda:{device}'
|
device = f'cuda:{device}'
|
||||||
@ -64,7 +73,7 @@ def auto_pick_devices(currently_active_devices):
|
|||||||
mem_free /= float(10**9)
|
mem_free /= float(10**9)
|
||||||
mem_total /= float(10**9)
|
mem_total /= float(10**9)
|
||||||
device_name = torch.cuda.get_device_name(device)
|
device_name = torch.cuda.get_device_name(device)
|
||||||
print(f'{device} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb')
|
log.debug(f'{device} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb')
|
||||||
devices.append({'device': device, 'device_name': device_name, 'mem_free': mem_free})
|
devices.append({'device': device, 'device_name': device_name, 'mem_free': mem_free})
|
||||||
|
|
||||||
devices.sort(key=lambda x:x['mem_free'], reverse=True)
|
devices.sort(key=lambda x:x['mem_free'], reverse=True)
|
||||||
@ -82,7 +91,7 @@ def auto_pick_devices(currently_active_devices):
|
|||||||
devices = list(map(lambda x: x['device'], devices))
|
devices = list(map(lambda x: x['device'], devices))
|
||||||
return devices
|
return devices
|
||||||
|
|
||||||
def device_init(thread_data, device):
|
def device_init(context, device):
|
||||||
'''
|
'''
|
||||||
This function assumes the 'device' has already been verified to be compatible.
|
This function assumes the 'device' has already been verified to be compatible.
|
||||||
`get_device_delta()` has already filtered out incompatible devices.
|
`get_device_delta()` has already filtered out incompatible devices.
|
||||||
@ -91,27 +100,45 @@ def device_init(thread_data, device):
|
|||||||
validate_device_id(device, log_prefix='device_init')
|
validate_device_id(device, log_prefix='device_init')
|
||||||
|
|
||||||
if device == 'cpu':
|
if device == 'cpu':
|
||||||
thread_data.device = 'cpu'
|
context.device = 'cpu'
|
||||||
thread_data.device_name = get_processor_name()
|
context.device_name = get_processor_name()
|
||||||
print('Render device CPU available as', thread_data.device_name)
|
context.half_precision = False
|
||||||
|
log.debug(f'Render device CPU available as {context.device_name}')
|
||||||
return
|
return
|
||||||
|
|
||||||
thread_data.device_name = torch.cuda.get_device_name(device)
|
context.device_name = torch.cuda.get_device_name(device)
|
||||||
thread_data.device = device
|
context.device = device
|
||||||
|
|
||||||
# Force full precision on 1660 and 1650 NVIDIA cards to avoid creating green images
|
# Force full precision on 1660 and 1650 NVIDIA cards to avoid creating green images
|
||||||
device_name = thread_data.device_name.lower()
|
if needs_to_force_full_precision(context):
|
||||||
thread_data.force_full_precision = (('nvidia' in device_name or 'geforce' in device_name) and (' 1660' in device_name or ' 1650' in device_name)) or ('Quadro T2000' in device_name)
|
log.warn(f'forcing full precision on this GPU, to avoid green images. GPU detected: {context.device_name}')
|
||||||
if thread_data.force_full_precision:
|
|
||||||
print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', thread_data.device_name)
|
|
||||||
# Apply force_full_precision now before models are loaded.
|
# Apply force_full_precision now before models are loaded.
|
||||||
thread_data.precision = 'full'
|
context.half_precision = False
|
||||||
|
|
||||||
print(f'Setting {device} as active')
|
log.info(f'Setting {device} as active, with precision: {"half" if context.half_precision else "full"}')
|
||||||
torch.cuda.device(device)
|
torch.cuda.device(device)
|
||||||
|
|
||||||
return
|
return
|
||||||
|
|
||||||
|
def needs_to_force_full_precision(context):
|
||||||
|
if 'FORCE_FULL_PRECISION' in os.environ:
|
||||||
|
return True
|
||||||
|
|
||||||
|
device_name = context.device_name.lower()
|
||||||
|
return (('nvidia' in device_name or 'geforce' in device_name) and (' 1660' in device_name or ' 1650' in device_name)) or ('Quadro T2000' in device_name)
|
||||||
|
|
||||||
|
def get_max_vram_usage_level(device):
|
||||||
|
if device != 'cpu':
|
||||||
|
_, mem_total = torch.cuda.mem_get_info(device)
|
||||||
|
mem_total /= float(10**9)
|
||||||
|
|
||||||
|
if mem_total < 4.5:
|
||||||
|
return 'low'
|
||||||
|
elif mem_total < 6.5:
|
||||||
|
return 'balanced'
|
||||||
|
|
||||||
|
return 'high'
|
||||||
|
|
||||||
def validate_device_id(device, log_prefix=''):
|
def validate_device_id(device, log_prefix=''):
|
||||||
def is_valid():
|
def is_valid():
|
||||||
if not isinstance(device, str):
|
if not isinstance(device, str):
|
||||||
@ -132,7 +159,7 @@ def is_device_compatible(device):
|
|||||||
try:
|
try:
|
||||||
validate_device_id(device, log_prefix='is_device_compatible')
|
validate_device_id(device, log_prefix='is_device_compatible')
|
||||||
except:
|
except:
|
||||||
print(str(e))
|
log.error(str(e))
|
||||||
return False
|
return False
|
||||||
|
|
||||||
if device == 'cpu': return True
|
if device == 'cpu': return True
|
||||||
@ -141,10 +168,10 @@ def is_device_compatible(device):
|
|||||||
_, mem_total = torch.cuda.mem_get_info(device)
|
_, mem_total = torch.cuda.mem_get_info(device)
|
||||||
mem_total /= float(10**9)
|
mem_total /= float(10**9)
|
||||||
if mem_total < 3.0:
|
if mem_total < 3.0:
|
||||||
print(f'GPU {device} with less than 3 GB of VRAM is not compatible with Stable Diffusion')
|
log.warn(f'GPU {device} with less than 3 GB of VRAM is not compatible with Stable Diffusion')
|
||||||
return False
|
return False
|
||||||
except RuntimeError as e:
|
except RuntimeError as e:
|
||||||
print(str(e))
|
log.error(str(e))
|
||||||
return False
|
return False
|
||||||
return True
|
return True
|
||||||
|
|
||||||
@ -164,5 +191,5 @@ def get_processor_name():
|
|||||||
if "model name" in line:
|
if "model name" in line:
|
||||||
return re.sub(".*model name.*:", "", line, 1).strip()
|
return re.sub(".*model name.*:", "", line, 1).strip()
|
||||||
except:
|
except:
|
||||||
print(traceback.format_exc())
|
log.error(traceback.format_exc())
|
||||||
return "cpu"
|
return "cpu"
|
223
ui/easydiffusion/model_manager.py
Normal file
223
ui/easydiffusion/model_manager.py
Normal file
@ -0,0 +1,223 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
from easydiffusion import app, device_manager
|
||||||
|
from easydiffusion.types import TaskData
|
||||||
|
from easydiffusion.utils import log
|
||||||
|
|
||||||
|
from sdkit import Context
|
||||||
|
from sdkit.models import load_model, unload_model, get_model_info_from_db, scan_model
|
||||||
|
from sdkit.utils import hash_file_quick
|
||||||
|
|
||||||
|
KNOWN_MODEL_TYPES = ['stable-diffusion', 'vae', 'hypernetwork', 'gfpgan', 'realesrgan']
|
||||||
|
MODEL_EXTENSIONS = {
|
||||||
|
'stable-diffusion': ['.ckpt', '.safetensors'],
|
||||||
|
'vae': ['.vae.pt', '.ckpt', '.safetensors'],
|
||||||
|
'hypernetwork': ['.pt', '.safetensors'],
|
||||||
|
'gfpgan': ['.pth'],
|
||||||
|
'realesrgan': ['.pth'],
|
||||||
|
}
|
||||||
|
DEFAULT_MODELS = {
|
||||||
|
'stable-diffusion': [ # needed to support the legacy installations
|
||||||
|
'custom-model', # only one custom model file was supported initially, creatively named 'custom-model'
|
||||||
|
'sd-v1-4', # Default fallback.
|
||||||
|
],
|
||||||
|
'gfpgan': ['GFPGANv1.3'],
|
||||||
|
'realesrgan': ['RealESRGAN_x4plus'],
|
||||||
|
}
|
||||||
|
VRAM_USAGE_LEVEL_TO_OPTIMIZATIONS = {
|
||||||
|
'balanced': {'KEEP_FS_AND_CS_IN_CPU', 'SET_ATTENTION_STEP_TO_4'},
|
||||||
|
'low': {'KEEP_ENTIRE_MODEL_IN_CPU'},
|
||||||
|
'high': {},
|
||||||
|
}
|
||||||
|
MODELS_TO_LOAD_ON_START = ['stable-diffusion', 'vae', 'hypernetwork']
|
||||||
|
|
||||||
|
known_models = {}
|
||||||
|
|
||||||
|
def init():
|
||||||
|
make_model_folders()
|
||||||
|
getModels() # run this once, to cache the picklescan results
|
||||||
|
|
||||||
|
def load_default_models(context: Context):
|
||||||
|
set_vram_optimizations(context)
|
||||||
|
|
||||||
|
# init default model paths
|
||||||
|
for model_type in MODELS_TO_LOAD_ON_START:
|
||||||
|
context.model_paths[model_type] = resolve_model_to_use(model_type=model_type)
|
||||||
|
load_model(context, model_type)
|
||||||
|
|
||||||
|
def unload_all(context: Context):
|
||||||
|
for model_type in KNOWN_MODEL_TYPES:
|
||||||
|
unload_model(context, model_type)
|
||||||
|
|
||||||
|
def resolve_model_to_use(model_name:str=None, model_type:str=None):
|
||||||
|
model_extensions = MODEL_EXTENSIONS.get(model_type, [])
|
||||||
|
default_models = DEFAULT_MODELS.get(model_type, [])
|
||||||
|
config = app.getConfig()
|
||||||
|
|
||||||
|
model_dirs = [os.path.join(app.MODELS_DIR, model_type), app.SD_DIR]
|
||||||
|
if not model_name: # When None try user configured model.
|
||||||
|
# config = getConfig()
|
||||||
|
if 'model' in config and model_type in config['model']:
|
||||||
|
model_name = config['model'][model_type]
|
||||||
|
|
||||||
|
if model_name:
|
||||||
|
# Check models directory
|
||||||
|
models_dir_path = os.path.join(app.MODELS_DIR, model_type, model_name)
|
||||||
|
for model_extension in model_extensions:
|
||||||
|
if os.path.exists(models_dir_path + model_extension):
|
||||||
|
return models_dir_path + model_extension
|
||||||
|
if os.path.exists(model_name + model_extension):
|
||||||
|
return os.path.abspath(model_name + model_extension)
|
||||||
|
|
||||||
|
# Default locations
|
||||||
|
if model_name in default_models:
|
||||||
|
default_model_path = os.path.join(app.SD_DIR, model_name)
|
||||||
|
for model_extension in model_extensions:
|
||||||
|
if os.path.exists(default_model_path + model_extension):
|
||||||
|
return default_model_path + model_extension
|
||||||
|
|
||||||
|
# Can't find requested model, check the default paths.
|
||||||
|
for default_model in default_models:
|
||||||
|
for model_dir in model_dirs:
|
||||||
|
default_model_path = os.path.join(model_dir, default_model)
|
||||||
|
for model_extension in model_extensions:
|
||||||
|
if os.path.exists(default_model_path + model_extension):
|
||||||
|
if model_name is not None:
|
||||||
|
log.warn(f'Could not find the configured custom model {model_name}{model_extension}. Using the default one: {default_model_path}{model_extension}')
|
||||||
|
return default_model_path + model_extension
|
||||||
|
|
||||||
|
return None
|
||||||
|
|
||||||
|
def reload_models_if_necessary(context: Context, task_data: TaskData):
|
||||||
|
model_paths_in_req = {
|
||||||
|
'stable-diffusion': task_data.use_stable_diffusion_model,
|
||||||
|
'vae': task_data.use_vae_model,
|
||||||
|
'hypernetwork': task_data.use_hypernetwork_model,
|
||||||
|
'gfpgan': task_data.use_face_correction,
|
||||||
|
'realesrgan': task_data.use_upscale,
|
||||||
|
}
|
||||||
|
models_to_reload = {model_type: path for model_type, path in model_paths_in_req.items() if context.model_paths.get(model_type) != path}
|
||||||
|
|
||||||
|
if set_vram_optimizations(context): # reload SD
|
||||||
|
models_to_reload['stable-diffusion'] = model_paths_in_req['stable-diffusion']
|
||||||
|
|
||||||
|
if 'stable-diffusion' in models_to_reload:
|
||||||
|
quick_hash = hash_file_quick(models_to_reload['stable-diffusion'])
|
||||||
|
known_model_info = get_model_info_from_db(quick_hash=quick_hash)
|
||||||
|
|
||||||
|
for model_type, model_path_in_req in models_to_reload.items():
|
||||||
|
context.model_paths[model_type] = model_path_in_req
|
||||||
|
|
||||||
|
action_fn = unload_model if context.model_paths[model_type] is None else load_model
|
||||||
|
action_fn(context, model_type, scan_model=False) # we've scanned them already
|
||||||
|
|
||||||
|
def resolve_model_paths(task_data: TaskData):
|
||||||
|
task_data.use_stable_diffusion_model = resolve_model_to_use(task_data.use_stable_diffusion_model, model_type='stable-diffusion')
|
||||||
|
task_data.use_vae_model = resolve_model_to_use(task_data.use_vae_model, model_type='vae')
|
||||||
|
task_data.use_hypernetwork_model = resolve_model_to_use(task_data.use_hypernetwork_model, model_type='hypernetwork')
|
||||||
|
|
||||||
|
if task_data.use_face_correction: task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, 'gfpgan')
|
||||||
|
if task_data.use_upscale: task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, 'realesrgan')
|
||||||
|
|
||||||
|
def set_vram_optimizations(context: Context):
|
||||||
|
config = app.getConfig()
|
||||||
|
|
||||||
|
max_usage_level = device_manager.get_max_vram_usage_level(context.device)
|
||||||
|
vram_usage_level = config.get('vram_usage_level', 'balanced')
|
||||||
|
|
||||||
|
v = {'low': 0, 'balanced': 1, 'high': 2}
|
||||||
|
if v[vram_usage_level] > v[max_usage_level]:
|
||||||
|
log.error(f'Requested GPU Memory Usage level ({vram_usage_level}) is higher than what is ' + \
|
||||||
|
f'possible ({max_usage_level}) on this device ({context.device}). Using "{max_usage_level}" instead')
|
||||||
|
vram_usage_level = max_usage_level
|
||||||
|
|
||||||
|
vram_optimizations = VRAM_USAGE_LEVEL_TO_OPTIMIZATIONS[vram_usage_level]
|
||||||
|
|
||||||
|
if vram_optimizations != context.vram_optimizations:
|
||||||
|
context.vram_optimizations = vram_optimizations
|
||||||
|
return True
|
||||||
|
|
||||||
|
return False
|
||||||
|
|
||||||
|
def make_model_folders():
|
||||||
|
for model_type in KNOWN_MODEL_TYPES:
|
||||||
|
model_dir_path = os.path.join(app.MODELS_DIR, model_type)
|
||||||
|
|
||||||
|
os.makedirs(model_dir_path, exist_ok=True)
|
||||||
|
|
||||||
|
help_file_name = f'Place your {model_type} model files here.txt'
|
||||||
|
help_file_contents = f'Supported extensions: {" or ".join(MODEL_EXTENSIONS.get(model_type))}'
|
||||||
|
|
||||||
|
with open(os.path.join(model_dir_path, help_file_name), 'w', encoding='utf-8') as f:
|
||||||
|
f.write(help_file_contents)
|
||||||
|
|
||||||
|
def is_malicious_model(file_path):
|
||||||
|
try:
|
||||||
|
scan_result = scan_model(file_path)
|
||||||
|
if scan_result.issues_count > 0 or scan_result.infected_files > 0:
|
||||||
|
log.warn(":warning: [bold red]Scan %s: %d scanned, %d issue, %d infected.[/bold red]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
log.debug("Scan %s: [green]%d scanned, %d issue, %d infected.[/green]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
|
||||||
|
return False
|
||||||
|
except Exception as e:
|
||||||
|
log.error(f'error while scanning: {file_path}, error: {e}')
|
||||||
|
return False
|
||||||
|
|
||||||
|
def getModels():
|
||||||
|
models = {
|
||||||
|
'active': {
|
||||||
|
'stable-diffusion': 'sd-v1-4',
|
||||||
|
'vae': '',
|
||||||
|
'hypernetwork': '',
|
||||||
|
},
|
||||||
|
'options': {
|
||||||
|
'stable-diffusion': ['sd-v1-4'],
|
||||||
|
'vae': [],
|
||||||
|
'hypernetwork': [],
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
models_scanned = 0
|
||||||
|
def listModels(model_type):
|
||||||
|
nonlocal models_scanned
|
||||||
|
|
||||||
|
model_extensions = MODEL_EXTENSIONS.get(model_type, [])
|
||||||
|
models_dir = os.path.join(app.MODELS_DIR, model_type)
|
||||||
|
if not os.path.exists(models_dir):
|
||||||
|
os.makedirs(models_dir)
|
||||||
|
|
||||||
|
for file in os.listdir(models_dir):
|
||||||
|
for model_extension in model_extensions:
|
||||||
|
if not file.endswith(model_extension):
|
||||||
|
continue
|
||||||
|
|
||||||
|
model_path = os.path.join(models_dir, file)
|
||||||
|
mtime = os.path.getmtime(model_path)
|
||||||
|
mod_time = known_models[model_path] if model_path in known_models else -1
|
||||||
|
if mod_time != mtime:
|
||||||
|
models_scanned += 1
|
||||||
|
if is_malicious_model(model_path):
|
||||||
|
models['scan-error'] = file
|
||||||
|
return
|
||||||
|
known_models[model_path] = mtime
|
||||||
|
|
||||||
|
model_name = file[:-len(model_extension)]
|
||||||
|
models['options'][model_type].append(model_name)
|
||||||
|
|
||||||
|
models['options'][model_type] = [*set(models['options'][model_type])] # remove duplicates
|
||||||
|
models['options'][model_type].sort()
|
||||||
|
|
||||||
|
# custom models
|
||||||
|
listModels(model_type='stable-diffusion')
|
||||||
|
listModels(model_type='vae')
|
||||||
|
listModels(model_type='hypernetwork')
|
||||||
|
|
||||||
|
if models_scanned > 0: log.info(f'[green]Scanned {models_scanned} models. Nothing infected[/]')
|
||||||
|
|
||||||
|
# legacy
|
||||||
|
custom_weight_path = os.path.join(app.SD_DIR, 'custom-model.ckpt')
|
||||||
|
if os.path.exists(custom_weight_path):
|
||||||
|
models['options']['stable-diffusion'].append('custom-model')
|
||||||
|
|
||||||
|
return models
|
124
ui/easydiffusion/renderer.py
Normal file
124
ui/easydiffusion/renderer.py
Normal file
@ -0,0 +1,124 @@
|
|||||||
|
import queue
|
||||||
|
import time
|
||||||
|
import json
|
||||||
|
|
||||||
|
from easydiffusion import device_manager
|
||||||
|
from easydiffusion.types import TaskData, Response, Image as ResponseImage, UserInitiatedStop, GenerateImageRequest
|
||||||
|
from easydiffusion.utils import get_printable_request, save_images_to_disk, log
|
||||||
|
|
||||||
|
from sdkit import Context
|
||||||
|
from sdkit.generate import generate_images
|
||||||
|
from sdkit.filter import apply_filters
|
||||||
|
from sdkit.utils import img_to_buffer, img_to_base64_str, latent_samples_to_images, gc
|
||||||
|
|
||||||
|
context = Context() # thread-local
|
||||||
|
'''
|
||||||
|
runtime data (bound locally to this thread), for e.g. device, references to loaded models, optimization flags etc
|
||||||
|
'''
|
||||||
|
|
||||||
|
def init(device):
|
||||||
|
'''
|
||||||
|
Initializes the fields that will be bound to this runtime's context, and sets the current torch device
|
||||||
|
'''
|
||||||
|
context.stop_processing = False
|
||||||
|
context.temp_images = {}
|
||||||
|
context.partial_x_samples = None
|
||||||
|
|
||||||
|
device_manager.device_init(context, device)
|
||||||
|
|
||||||
|
def make_images(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback):
|
||||||
|
context.stop_processing = False
|
||||||
|
log.info(f'request: {get_printable_request(req)}')
|
||||||
|
log.info(f'task data: {task_data.dict()}')
|
||||||
|
|
||||||
|
images = make_images_internal(req, task_data, data_queue, task_temp_images, step_callback)
|
||||||
|
|
||||||
|
res = Response(req, task_data, images=construct_response(images, task_data, base_seed=req.seed))
|
||||||
|
res = res.json()
|
||||||
|
data_queue.put(json.dumps(res))
|
||||||
|
log.info('Task completed')
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
|
def make_images_internal(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback):
|
||||||
|
images, user_stopped = generate_images_internal(req, task_data, data_queue, task_temp_images, step_callback, task_data.stream_image_progress)
|
||||||
|
filtered_images = filter_images(task_data, images, user_stopped)
|
||||||
|
|
||||||
|
if task_data.save_to_disk_path is not None:
|
||||||
|
save_images_to_disk(images, filtered_images, req, task_data)
|
||||||
|
|
||||||
|
return filtered_images if task_data.show_only_filtered_image else images + filtered_images
|
||||||
|
|
||||||
|
def generate_images_internal(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback, stream_image_progress: bool):
|
||||||
|
context.temp_images.clear()
|
||||||
|
|
||||||
|
callback = make_step_callback(req, task_data, data_queue, task_temp_images, step_callback, stream_image_progress)
|
||||||
|
|
||||||
|
try:
|
||||||
|
images = generate_images(context, callback=callback, **req.dict())
|
||||||
|
user_stopped = False
|
||||||
|
except UserInitiatedStop:
|
||||||
|
images = []
|
||||||
|
user_stopped = True
|
||||||
|
if context.partial_x_samples is not None:
|
||||||
|
images = latent_samples_to_images(context, context.partial_x_samples)
|
||||||
|
context.partial_x_samples = None
|
||||||
|
finally:
|
||||||
|
gc(context)
|
||||||
|
|
||||||
|
return images, user_stopped
|
||||||
|
|
||||||
|
def filter_images(task_data: TaskData, images: list, user_stopped):
|
||||||
|
if user_stopped or (task_data.use_face_correction is None and task_data.use_upscale is None):
|
||||||
|
return images
|
||||||
|
|
||||||
|
filters_to_apply = []
|
||||||
|
if task_data.use_face_correction and 'gfpgan' in task_data.use_face_correction.lower(): filters_to_apply.append('gfpgan')
|
||||||
|
if task_data.use_upscale and 'realesrgan' in task_data.use_upscale.lower(): filters_to_apply.append('realesrgan')
|
||||||
|
|
||||||
|
return apply_filters(context, filters_to_apply, images)
|
||||||
|
|
||||||
|
def construct_response(images: list, task_data: TaskData, base_seed: int):
|
||||||
|
return [
|
||||||
|
ResponseImage(
|
||||||
|
data=img_to_base64_str(img, task_data.output_format, task_data.output_quality),
|
||||||
|
seed=base_seed + i
|
||||||
|
) for i, img in enumerate(images)
|
||||||
|
]
|
||||||
|
|
||||||
|
def make_step_callback(req: GenerateImageRequest, task_data: TaskData, data_queue: queue.Queue, task_temp_images: list, step_callback, stream_image_progress: bool):
|
||||||
|
n_steps = req.num_inference_steps if req.init_image is None else int(req.num_inference_steps * req.prompt_strength)
|
||||||
|
last_callback_time = -1
|
||||||
|
|
||||||
|
def update_temp_img(x_samples, task_temp_images: list):
|
||||||
|
partial_images = []
|
||||||
|
images = latent_samples_to_images(context, x_samples)
|
||||||
|
for i, img in enumerate(images):
|
||||||
|
buf = img_to_buffer(img, output_format='JPEG')
|
||||||
|
|
||||||
|
context.temp_images[f"{task_data.request_id}/{i}"] = buf
|
||||||
|
task_temp_images[i] = buf
|
||||||
|
partial_images.append({'path': f"/image/tmp/{task_data.request_id}/{i}"})
|
||||||
|
del images
|
||||||
|
return partial_images
|
||||||
|
|
||||||
|
def on_image_step(x_samples, i):
|
||||||
|
nonlocal last_callback_time
|
||||||
|
|
||||||
|
context.partial_x_samples = x_samples
|
||||||
|
step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
|
||||||
|
last_callback_time = time.time()
|
||||||
|
|
||||||
|
progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
|
||||||
|
|
||||||
|
if stream_image_progress and i % 5 == 0:
|
||||||
|
progress['output'] = update_temp_img(x_samples, task_temp_images)
|
||||||
|
|
||||||
|
data_queue.put(json.dumps(progress))
|
||||||
|
|
||||||
|
step_callback()
|
||||||
|
|
||||||
|
if context.stop_processing:
|
||||||
|
raise UserInitiatedStop("User requested that we stop processing")
|
||||||
|
|
||||||
|
return on_image_step
|
219
ui/easydiffusion/server.py
Normal file
219
ui/easydiffusion/server.py
Normal file
@ -0,0 +1,219 @@
|
|||||||
|
"""server.py: FastAPI SD-UI Web Host.
|
||||||
|
Notes:
|
||||||
|
async endpoints always run on the main thread. Without they run on the thread pool.
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import traceback
|
||||||
|
import datetime
|
||||||
|
from typing import List, Union
|
||||||
|
|
||||||
|
from fastapi import FastAPI, HTTPException
|
||||||
|
from fastapi.staticfiles import StaticFiles
|
||||||
|
from starlette.responses import FileResponse, JSONResponse, StreamingResponse
|
||||||
|
from pydantic import BaseModel
|
||||||
|
|
||||||
|
from easydiffusion import app, model_manager, task_manager
|
||||||
|
from easydiffusion.types import TaskData, GenerateImageRequest
|
||||||
|
from easydiffusion.utils import log
|
||||||
|
|
||||||
|
log.info(f'started in {app.SD_DIR}')
|
||||||
|
log.info(f'started at {datetime.datetime.now():%x %X}')
|
||||||
|
|
||||||
|
server_api = FastAPI()
|
||||||
|
|
||||||
|
NOCACHE_HEADERS={"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
|
||||||
|
|
||||||
|
class NoCacheStaticFiles(StaticFiles):
|
||||||
|
def is_not_modified(self, response_headers, request_headers) -> bool:
|
||||||
|
if 'content-type' in response_headers and ('javascript' in response_headers['content-type'] or 'css' in response_headers['content-type']):
|
||||||
|
response_headers.update(NOCACHE_HEADERS)
|
||||||
|
return False
|
||||||
|
|
||||||
|
return super().is_not_modified(response_headers, request_headers)
|
||||||
|
|
||||||
|
class SetAppConfigRequest(BaseModel):
|
||||||
|
update_branch: str = None
|
||||||
|
render_devices: Union[List[str], List[int], str, int] = None
|
||||||
|
model_vae: str = None
|
||||||
|
ui_open_browser_on_start: bool = None
|
||||||
|
listen_to_network: bool = None
|
||||||
|
listen_port: int = None
|
||||||
|
|
||||||
|
def init():
|
||||||
|
server_api.mount('/media', NoCacheStaticFiles(directory=os.path.join(app.SD_UI_DIR, 'media')), name="media")
|
||||||
|
|
||||||
|
for plugins_dir, dir_prefix in app.UI_PLUGINS_SOURCES:
|
||||||
|
server_api.mount(f'/plugins/{dir_prefix}', NoCacheStaticFiles(directory=plugins_dir), name=f"plugins-{dir_prefix}")
|
||||||
|
|
||||||
|
@server_api.post('/app_config')
|
||||||
|
async def set_app_config(req : SetAppConfigRequest):
|
||||||
|
return set_app_config_internal(req)
|
||||||
|
|
||||||
|
@server_api.get('/get/{key:path}')
|
||||||
|
def read_web_data(key:str=None):
|
||||||
|
return read_web_data_internal(key)
|
||||||
|
|
||||||
|
@server_api.get('/ping') # Get server and optionally session status.
|
||||||
|
def ping(session_id:str=None):
|
||||||
|
return ping_internal(session_id)
|
||||||
|
|
||||||
|
@server_api.post('/render')
|
||||||
|
def render(req: dict):
|
||||||
|
return render_internal(req)
|
||||||
|
|
||||||
|
@server_api.get('/image/stream/{task_id:int}')
|
||||||
|
def stream(task_id:int):
|
||||||
|
return stream_internal(task_id)
|
||||||
|
|
||||||
|
@server_api.get('/image/stop')
|
||||||
|
def stop(task: int):
|
||||||
|
return stop_internal(task)
|
||||||
|
|
||||||
|
@server_api.get('/image/tmp/{task_id:int}/{img_id:int}')
|
||||||
|
def get_image(task_id: int, img_id: int):
|
||||||
|
return get_image_internal(task_id, img_id)
|
||||||
|
|
||||||
|
@server_api.get('/')
|
||||||
|
def read_root():
|
||||||
|
return FileResponse(os.path.join(app.SD_UI_DIR, 'index.html'), headers=NOCACHE_HEADERS)
|
||||||
|
|
||||||
|
@server_api.on_event("shutdown")
|
||||||
|
def shutdown_event(): # Signal render thread to close on shutdown
|
||||||
|
task_manager.current_state_error = SystemExit('Application shutting down.')
|
||||||
|
|
||||||
|
# API implementations
|
||||||
|
def set_app_config_internal(req : SetAppConfigRequest):
|
||||||
|
config = app.getConfig()
|
||||||
|
if req.update_branch is not None:
|
||||||
|
config['update_branch'] = req.update_branch
|
||||||
|
if req.render_devices is not None:
|
||||||
|
update_render_devices_in_config(config, req.render_devices)
|
||||||
|
if req.ui_open_browser_on_start is not None:
|
||||||
|
if 'ui' not in config:
|
||||||
|
config['ui'] = {}
|
||||||
|
config['ui']['open_browser_on_start'] = req.ui_open_browser_on_start
|
||||||
|
if req.listen_to_network is not None:
|
||||||
|
if 'net' not in config:
|
||||||
|
config['net'] = {}
|
||||||
|
config['net']['listen_to_network'] = bool(req.listen_to_network)
|
||||||
|
if req.listen_port is not None:
|
||||||
|
if 'net' not in config:
|
||||||
|
config['net'] = {}
|
||||||
|
config['net']['listen_port'] = int(req.listen_port)
|
||||||
|
try:
|
||||||
|
app.setConfig(config)
|
||||||
|
|
||||||
|
if req.render_devices:
|
||||||
|
app.update_render_threads()
|
||||||
|
|
||||||
|
return JSONResponse({'status': 'OK'}, headers=NOCACHE_HEADERS)
|
||||||
|
except Exception as e:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
raise HTTPException(status_code=500, detail=str(e))
|
||||||
|
|
||||||
|
def update_render_devices_in_config(config, render_devices):
|
||||||
|
if render_devices not in ('cpu', 'auto') and not render_devices.startswith('cuda:'):
|
||||||
|
raise HTTPException(status_code=400, detail=f'Invalid render device requested: {render_devices}')
|
||||||
|
|
||||||
|
if render_devices.startswith('cuda:'):
|
||||||
|
render_devices = render_devices.split(',')
|
||||||
|
|
||||||
|
config['render_devices'] = render_devices
|
||||||
|
|
||||||
|
def read_web_data_internal(key:str=None):
|
||||||
|
if not key: # /get without parameters, stable-diffusion easter egg.
|
||||||
|
raise HTTPException(status_code=418, detail="StableDiffusion is drawing a teapot!") # HTTP418 I'm a teapot
|
||||||
|
elif key == 'app_config':
|
||||||
|
return JSONResponse(app.getConfig(), headers=NOCACHE_HEADERS)
|
||||||
|
elif key == 'system_info':
|
||||||
|
config = app.getConfig()
|
||||||
|
system_info = {
|
||||||
|
'devices': task_manager.get_devices(),
|
||||||
|
'hosts': app.getIPConfig(),
|
||||||
|
'default_output_dir': os.path.join(os.path.expanduser("~"), app.OUTPUT_DIRNAME),
|
||||||
|
}
|
||||||
|
system_info['devices']['config'] = config.get('render_devices', "auto")
|
||||||
|
return JSONResponse(system_info, headers=NOCACHE_HEADERS)
|
||||||
|
elif key == 'models':
|
||||||
|
return JSONResponse(model_manager.getModels(), headers=NOCACHE_HEADERS)
|
||||||
|
elif key == 'modifiers': return FileResponse(os.path.join(app.SD_UI_DIR, 'modifiers.json'), headers=NOCACHE_HEADERS)
|
||||||
|
elif key == 'ui_plugins': return JSONResponse(app.getUIPlugins(), headers=NOCACHE_HEADERS)
|
||||||
|
else:
|
||||||
|
raise HTTPException(status_code=404, detail=f'Request for unknown {key}') # HTTP404 Not Found
|
||||||
|
|
||||||
|
def ping_internal(session_id:str=None):
|
||||||
|
if task_manager.is_alive() <= 0: # Check that render threads are alive.
|
||||||
|
if task_manager.current_state_error: raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
|
||||||
|
raise HTTPException(status_code=500, detail='Render thread is dead.')
|
||||||
|
if task_manager.current_state_error and not isinstance(task_manager.current_state_error, StopAsyncIteration): raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
|
||||||
|
# Alive
|
||||||
|
response = {'status': str(task_manager.current_state)}
|
||||||
|
if session_id:
|
||||||
|
session = task_manager.get_cached_session(session_id, update_ttl=True)
|
||||||
|
response['tasks'] = {id(t): t.status for t in session.tasks}
|
||||||
|
response['devices'] = task_manager.get_devices()
|
||||||
|
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||||
|
|
||||||
|
def render_internal(req: dict):
|
||||||
|
try:
|
||||||
|
# separate out the request data into rendering and task-specific data
|
||||||
|
render_req: GenerateImageRequest = GenerateImageRequest.parse_obj(req)
|
||||||
|
task_data: TaskData = TaskData.parse_obj(req)
|
||||||
|
|
||||||
|
render_req.init_image_mask = req.get('mask') # hack: will rename this in the HTTP API in a future revision
|
||||||
|
|
||||||
|
app.save_to_config(task_data.use_stable_diffusion_model, task_data.use_vae_model, task_data.use_hypernetwork_model, task_data.vram_usage_level)
|
||||||
|
|
||||||
|
# enqueue the task
|
||||||
|
new_task = task_manager.render(render_req, task_data)
|
||||||
|
response = {
|
||||||
|
'status': str(task_manager.current_state),
|
||||||
|
'queue': len(task_manager.tasks_queue),
|
||||||
|
'stream': f'/image/stream/{id(new_task)}',
|
||||||
|
'task': id(new_task)
|
||||||
|
}
|
||||||
|
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||||
|
except ChildProcessError as e: # Render thread is dead
|
||||||
|
raise HTTPException(status_code=500, detail=f'Rendering thread has died.') # HTTP500 Internal Server Error
|
||||||
|
except ConnectionRefusedError as e: # Unstarted task pending limit reached, deny queueing too many.
|
||||||
|
raise HTTPException(status_code=503, detail=str(e)) # HTTP503 Service Unavailable
|
||||||
|
except Exception as e:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
raise HTTPException(status_code=500, detail=str(e))
|
||||||
|
|
||||||
|
def stream_internal(task_id:int):
|
||||||
|
#TODO Move to WebSockets ??
|
||||||
|
task = task_manager.get_cached_task(task_id, update_ttl=True)
|
||||||
|
if not task: raise HTTPException(status_code=404, detail=f'Request {task_id} not found.') # HTTP404 NotFound
|
||||||
|
#if (id(task) != task_id): raise HTTPException(status_code=409, detail=f'Wrong task id received. Expected:{id(task)}, Received:{task_id}') # HTTP409 Conflict
|
||||||
|
if task.buffer_queue.empty() and not task.lock.locked():
|
||||||
|
if task.response:
|
||||||
|
#log.info(f'Session {session_id} sending cached response')
|
||||||
|
return JSONResponse(task.response, headers=NOCACHE_HEADERS)
|
||||||
|
raise HTTPException(status_code=425, detail='Too Early, task not started yet.') # HTTP425 Too Early
|
||||||
|
#log.info(f'Session {session_id} opened live render stream {id(task.buffer_queue)}')
|
||||||
|
return StreamingResponse(task.read_buffer_generator(), media_type='application/json')
|
||||||
|
|
||||||
|
def stop_internal(task: int):
|
||||||
|
if not task:
|
||||||
|
if task_manager.current_state == task_manager.ServerStates.Online or task_manager.current_state == task_manager.ServerStates.Unavailable:
|
||||||
|
raise HTTPException(status_code=409, detail='Not currently running any tasks.') # HTTP409 Conflict
|
||||||
|
task_manager.current_state_error = StopAsyncIteration('')
|
||||||
|
return {'OK'}
|
||||||
|
task_id = task
|
||||||
|
task = task_manager.get_cached_task(task_id, update_ttl=False)
|
||||||
|
if not task: raise HTTPException(status_code=404, detail=f'Task {task_id} was not found.') # HTTP404 Not Found
|
||||||
|
if isinstance(task.error, StopAsyncIteration): raise HTTPException(status_code=409, detail=f'Task {task_id} is already stopped.') # HTTP409 Conflict
|
||||||
|
task.error = StopAsyncIteration(f'Task {task_id} stop requested.')
|
||||||
|
return {'OK'}
|
||||||
|
|
||||||
|
def get_image_internal(task_id: int, img_id: int):
|
||||||
|
task = task_manager.get_cached_task(task_id, update_ttl=True)
|
||||||
|
if not task: raise HTTPException(status_code=410, detail=f'Task {task_id} could not be found.') # HTTP404 NotFound
|
||||||
|
if not task.temp_images[img_id]: raise HTTPException(status_code=425, detail='Too Early, task data is not available yet.') # HTTP425 Too Early
|
||||||
|
try:
|
||||||
|
img_data = task.temp_images[img_id]
|
||||||
|
img_data.seek(0)
|
||||||
|
return StreamingResponse(img_data, media_type='image/jpeg')
|
||||||
|
except KeyError as e:
|
||||||
|
raise HTTPException(status_code=500, detail=str(e))
|
@ -11,12 +11,13 @@ TASK_TTL = 15 * 60 # seconds, Discard last session's task timeout
|
|||||||
|
|
||||||
import torch
|
import torch
|
||||||
import queue, threading, time, weakref
|
import queue, threading, time, weakref
|
||||||
from typing import Any, Generator, Hashable, Optional, Union
|
from typing import Any, Hashable
|
||||||
|
|
||||||
from pydantic import BaseModel
|
from easydiffusion import device_manager
|
||||||
from sd_internal import Request, Response, runtime, device_manager
|
from easydiffusion.types import TaskData, GenerateImageRequest
|
||||||
|
from easydiffusion.utils import log
|
||||||
|
|
||||||
THREAD_NAME_PREFIX = 'Runtime-Render/'
|
THREAD_NAME_PREFIX = ''
|
||||||
ERR_LOCK_FAILED = ' failed to acquire lock within timeout.'
|
ERR_LOCK_FAILED = ' failed to acquire lock within timeout.'
|
||||||
LOCK_TIMEOUT = 15 # Maximum locking time in seconds before failing a task.
|
LOCK_TIMEOUT = 15 # Maximum locking time in seconds before failing a task.
|
||||||
# It's better to get an exception than a deadlock... ALWAYS use timeout in critical paths.
|
# It's better to get an exception than a deadlock... ALWAYS use timeout in critical paths.
|
||||||
@ -36,12 +37,13 @@ class ServerStates:
|
|||||||
class Unavailable(Symbol): pass
|
class Unavailable(Symbol): pass
|
||||||
|
|
||||||
class RenderTask(): # Task with output queue and completion lock.
|
class RenderTask(): # Task with output queue and completion lock.
|
||||||
def __init__(self, req: Request):
|
def __init__(self, req: GenerateImageRequest, task_data: TaskData):
|
||||||
req.request_id = id(self)
|
task_data.request_id = id(self)
|
||||||
self.request: Request = req # Initial Request
|
self.render_request: GenerateImageRequest = req # Initial Request
|
||||||
|
self.task_data: TaskData = task_data
|
||||||
self.response: Any = None # Copy of the last reponse
|
self.response: Any = None # Copy of the last reponse
|
||||||
self.render_device = None # Select the task affinity. (Not used to change active devices).
|
self.render_device = None # Select the task affinity. (Not used to change active devices).
|
||||||
self.temp_images:list = [None] * req.num_outputs * (1 if req.show_only_filtered_image else 2)
|
self.temp_images:list = [None] * req.num_outputs * (1 if task_data.show_only_filtered_image else 2)
|
||||||
self.error: Exception = None
|
self.error: Exception = None
|
||||||
self.lock: threading.Lock = threading.Lock() # Locks at task start and unlocks when task is completed
|
self.lock: threading.Lock = threading.Lock() # Locks at task start and unlocks when task is completed
|
||||||
self.buffer_queue: queue.Queue = queue.Queue() # Queue of JSON string segments
|
self.buffer_queue: queue.Queue = queue.Queue() # Queue of JSON string segments
|
||||||
@ -69,54 +71,6 @@ class RenderTask(): # Task with output queue and completion lock.
|
|||||||
def is_pending(self):
|
def is_pending(self):
|
||||||
return bool(not self.response and not self.error)
|
return bool(not self.response and not self.error)
|
||||||
|
|
||||||
# defaults from https://huggingface.co/blog/stable_diffusion
|
|
||||||
class ImageRequest(BaseModel):
|
|
||||||
session_id: str = "session"
|
|
||||||
prompt: str = ""
|
|
||||||
negative_prompt: str = ""
|
|
||||||
init_image: str = None # base64
|
|
||||||
mask: str = None # base64
|
|
||||||
num_outputs: int = 1
|
|
||||||
num_inference_steps: int = 50
|
|
||||||
guidance_scale: float = 7.5
|
|
||||||
width: int = 512
|
|
||||||
height: int = 512
|
|
||||||
seed: int = 42
|
|
||||||
prompt_strength: float = 0.8
|
|
||||||
sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
|
||||||
# allow_nsfw: bool = False
|
|
||||||
save_to_disk_path: str = None
|
|
||||||
turbo: bool = True
|
|
||||||
use_cpu: bool = False ##TODO Remove after UI and plugins transition.
|
|
||||||
render_device: str = None # Select the task affinity. (Not used to change active devices).
|
|
||||||
use_full_precision: bool = False
|
|
||||||
use_face_correction: str = None # or "GFPGANv1.3"
|
|
||||||
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
|
|
||||||
use_stable_diffusion_model: str = "sd-v1-4"
|
|
||||||
use_vae_model: str = None
|
|
||||||
use_hypernetwork_model: str = None
|
|
||||||
hypernetwork_strength: float = None
|
|
||||||
show_only_filtered_image: bool = False
|
|
||||||
output_format: str = "jpeg" # or "png"
|
|
||||||
output_quality: int = 75
|
|
||||||
|
|
||||||
stream_progress_updates: bool = False
|
|
||||||
stream_image_progress: bool = False
|
|
||||||
|
|
||||||
class FilterRequest(BaseModel):
|
|
||||||
session_id: str = "session"
|
|
||||||
model: str = None
|
|
||||||
name: str = ""
|
|
||||||
init_image: str = None # base64
|
|
||||||
width: int = 512
|
|
||||||
height: int = 512
|
|
||||||
save_to_disk_path: str = None
|
|
||||||
turbo: bool = True
|
|
||||||
render_device: str = None
|
|
||||||
use_full_precision: bool = False
|
|
||||||
output_format: str = "jpeg" # or "png"
|
|
||||||
output_quality: int = 75
|
|
||||||
|
|
||||||
# Temporary cache to allow to query tasks results for a short time after they are completed.
|
# Temporary cache to allow to query tasks results for a short time after they are completed.
|
||||||
class DataCache():
|
class DataCache():
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
@ -139,11 +93,11 @@ class DataCache():
|
|||||||
for key in to_delete:
|
for key in to_delete:
|
||||||
(_, val) = self._base[key]
|
(_, val) = self._base[key]
|
||||||
if isinstance(val, RenderTask):
|
if isinstance(val, RenderTask):
|
||||||
print(f'RenderTask {key} expired. Data removed.')
|
log.debug(f'RenderTask {key} expired. Data removed.')
|
||||||
elif isinstance(val, SessionState):
|
elif isinstance(val, SessionState):
|
||||||
print(f'Session {key} expired. Data removed.')
|
log.debug(f'Session {key} expired. Data removed.')
|
||||||
else:
|
else:
|
||||||
print(f'Key {key} expired. Data removed.')
|
log.debug(f'Key {key} expired. Data removed.')
|
||||||
del self._base[key]
|
del self._base[key]
|
||||||
finally:
|
finally:
|
||||||
self._lock.release()
|
self._lock.release()
|
||||||
@ -177,8 +131,7 @@ class DataCache():
|
|||||||
self._get_ttl_time(ttl), value
|
self._get_ttl_time(ttl), value
|
||||||
)
|
)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
print(str(e))
|
log.error(traceback.format_exc())
|
||||||
print(traceback.format_exc())
|
|
||||||
return False
|
return False
|
||||||
else:
|
else:
|
||||||
return True
|
return True
|
||||||
@ -189,7 +142,7 @@ class DataCache():
|
|||||||
try:
|
try:
|
||||||
ttl, value = self._base.get(key, (None, None))
|
ttl, value = self._base.get(key, (None, None))
|
||||||
if ttl is not None and self._is_expired(ttl):
|
if ttl is not None and self._is_expired(ttl):
|
||||||
print(f'Session {key} expired. Discarding data.')
|
log.debug(f'Session {key} expired. Discarding data.')
|
||||||
del self._base[key]
|
del self._base[key]
|
||||||
return None
|
return None
|
||||||
return value
|
return value
|
||||||
@ -200,15 +153,9 @@ manager_lock = threading.RLock()
|
|||||||
render_threads = []
|
render_threads = []
|
||||||
current_state = ServerStates.Init
|
current_state = ServerStates.Init
|
||||||
current_state_error:Exception = None
|
current_state_error:Exception = None
|
||||||
current_model_path = None
|
|
||||||
current_vae_path = None
|
|
||||||
current_hypernetwork_path = None
|
|
||||||
tasks_queue = []
|
tasks_queue = []
|
||||||
session_cache = DataCache()
|
session_cache = DataCache()
|
||||||
task_cache = DataCache()
|
task_cache = DataCache()
|
||||||
default_model_to_load = None
|
|
||||||
default_vae_to_load = None
|
|
||||||
default_hypernetwork_to_load = None
|
|
||||||
weak_thread_data = weakref.WeakKeyDictionary()
|
weak_thread_data = weakref.WeakKeyDictionary()
|
||||||
idle_event: threading.Event = threading.Event()
|
idle_event: threading.Event = threading.Event()
|
||||||
|
|
||||||
@ -236,40 +183,10 @@ class SessionState():
|
|||||||
self._tasks_ids.pop(0)
|
self._tasks_ids.pop(0)
|
||||||
return True
|
return True
|
||||||
|
|
||||||
def preload_model(ckpt_file_path=None, vae_file_path=None, hypernetwork_file_path=None):
|
|
||||||
global current_state, current_state_error, current_model_path, current_vae_path, current_hypernetwork_path
|
|
||||||
if ckpt_file_path == None:
|
|
||||||
ckpt_file_path = default_model_to_load
|
|
||||||
if vae_file_path == None:
|
|
||||||
vae_file_path = default_vae_to_load
|
|
||||||
if hypernetwork_file_path == None:
|
|
||||||
hypernetwork_file_path = default_hypernetwork_to_load
|
|
||||||
if ckpt_file_path == current_model_path and vae_file_path == current_vae_path:
|
|
||||||
return
|
|
||||||
current_state = ServerStates.LoadingModel
|
|
||||||
try:
|
|
||||||
from . import runtime
|
|
||||||
runtime.thread_data.hypernetwork_file = hypernetwork_file_path
|
|
||||||
runtime.thread_data.ckpt_file = ckpt_file_path
|
|
||||||
runtime.thread_data.vae_file = vae_file_path
|
|
||||||
runtime.load_model_ckpt()
|
|
||||||
runtime.load_hypernetwork()
|
|
||||||
current_model_path = ckpt_file_path
|
|
||||||
current_vae_path = vae_file_path
|
|
||||||
current_hypernetwork_path = hypernetwork_file_path
|
|
||||||
current_state_error = None
|
|
||||||
current_state = ServerStates.Online
|
|
||||||
except Exception as e:
|
|
||||||
current_model_path = None
|
|
||||||
current_vae_path = None
|
|
||||||
current_state_error = e
|
|
||||||
current_state = ServerStates.Unavailable
|
|
||||||
print(traceback.format_exc())
|
|
||||||
|
|
||||||
def thread_get_next_task():
|
def thread_get_next_task():
|
||||||
from . import runtime
|
from easydiffusion import renderer
|
||||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
|
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
|
||||||
print('Render thread on device', runtime.thread_data.device, 'failed to acquire manager lock.')
|
log.warn(f'Render thread on device: {renderer.context.device} failed to acquire manager lock.')
|
||||||
return None
|
return None
|
||||||
if len(tasks_queue) <= 0:
|
if len(tasks_queue) <= 0:
|
||||||
manager_lock.release()
|
manager_lock.release()
|
||||||
@ -277,7 +194,7 @@ def thread_get_next_task():
|
|||||||
task = None
|
task = None
|
||||||
try: # Select a render task.
|
try: # Select a render task.
|
||||||
for queued_task in tasks_queue:
|
for queued_task in tasks_queue:
|
||||||
if queued_task.render_device and runtime.thread_data.device != queued_task.render_device:
|
if queued_task.render_device and renderer.context.device != queued_task.render_device:
|
||||||
# Is asking for a specific render device.
|
# Is asking for a specific render device.
|
||||||
if is_alive(queued_task.render_device) > 0:
|
if is_alive(queued_task.render_device) > 0:
|
||||||
continue # requested device alive, skip current one.
|
continue # requested device alive, skip current one.
|
||||||
@ -286,7 +203,7 @@ def thread_get_next_task():
|
|||||||
queued_task.error = Exception(queued_task.render_device + ' is not currently active.')
|
queued_task.error = Exception(queued_task.render_device + ' is not currently active.')
|
||||||
task = queued_task
|
task = queued_task
|
||||||
break
|
break
|
||||||
if not queued_task.render_device and runtime.thread_data.device == 'cpu' and is_alive() > 1:
|
if not queued_task.render_device and renderer.context.device == 'cpu' and is_alive() > 1:
|
||||||
# not asking for any specific devices, cpu want to grab task but other render devices are alive.
|
# not asking for any specific devices, cpu want to grab task but other render devices are alive.
|
||||||
continue # Skip Tasks, don't run on CPU unless there is nothing else or user asked for it.
|
continue # Skip Tasks, don't run on CPU unless there is nothing else or user asked for it.
|
||||||
task = queued_task
|
task = queued_task
|
||||||
@ -298,31 +215,36 @@ def thread_get_next_task():
|
|||||||
manager_lock.release()
|
manager_lock.release()
|
||||||
|
|
||||||
def thread_render(device):
|
def thread_render(device):
|
||||||
global current_state, current_state_error, current_model_path, current_vae_path, current_hypernetwork_path
|
global current_state, current_state_error
|
||||||
from . import runtime
|
|
||||||
|
from easydiffusion import renderer, model_manager
|
||||||
try:
|
try:
|
||||||
runtime.thread_init(device)
|
renderer.init(device)
|
||||||
except Exception as e:
|
|
||||||
print(traceback.format_exc())
|
|
||||||
weak_thread_data[threading.current_thread()] = {
|
weak_thread_data[threading.current_thread()] = {
|
||||||
'error': e
|
'device': renderer.context.device,
|
||||||
}
|
'device_name': renderer.context.device_name,
|
||||||
return
|
|
||||||
weak_thread_data[threading.current_thread()] = {
|
|
||||||
'device': runtime.thread_data.device,
|
|
||||||
'device_name': runtime.thread_data.device_name,
|
|
||||||
'alive': True
|
'alive': True
|
||||||
}
|
}
|
||||||
if runtime.thread_data.device != 'cpu' or is_alive() == 1:
|
|
||||||
preload_model()
|
current_state = ServerStates.LoadingModel
|
||||||
|
model_manager.load_default_models(renderer.context)
|
||||||
|
|
||||||
current_state = ServerStates.Online
|
current_state = ServerStates.Online
|
||||||
|
except Exception as e:
|
||||||
|
log.error(traceback.format_exc())
|
||||||
|
weak_thread_data[threading.current_thread()] = {
|
||||||
|
'error': e,
|
||||||
|
'alive': False
|
||||||
|
}
|
||||||
|
return
|
||||||
|
|
||||||
while True:
|
while True:
|
||||||
session_cache.clean()
|
session_cache.clean()
|
||||||
task_cache.clean()
|
task_cache.clean()
|
||||||
if not weak_thread_data[threading.current_thread()]['alive']:
|
if not weak_thread_data[threading.current_thread()]['alive']:
|
||||||
print(f'Shutting down thread for device {runtime.thread_data.device}')
|
log.info(f'Shutting down thread for device {renderer.context.device}')
|
||||||
runtime.unload_models()
|
model_manager.unload_all(renderer.context)
|
||||||
runtime.unload_filters()
|
|
||||||
return
|
return
|
||||||
if isinstance(current_state_error, SystemExit):
|
if isinstance(current_state_error, SystemExit):
|
||||||
current_state = ServerStates.Unavailable
|
current_state = ServerStates.Unavailable
|
||||||
@ -333,7 +255,7 @@ def thread_render(device):
|
|||||||
idle_event.wait(timeout=1)
|
idle_event.wait(timeout=1)
|
||||||
continue
|
continue
|
||||||
if task.error is not None:
|
if task.error is not None:
|
||||||
print(task.error)
|
log.error(task.error)
|
||||||
task.response = {"status": 'failed', "detail": str(task.error)}
|
task.response = {"status": 'failed', "detail": str(task.error)}
|
||||||
task.buffer_queue.put(json.dumps(task.response))
|
task.buffer_queue.put(json.dumps(task.response))
|
||||||
continue
|
continue
|
||||||
@ -342,51 +264,45 @@ def thread_render(device):
|
|||||||
task.response = {"status": 'failed', "detail": str(task.error)}
|
task.response = {"status": 'failed', "detail": str(task.error)}
|
||||||
task.buffer_queue.put(json.dumps(task.response))
|
task.buffer_queue.put(json.dumps(task.response))
|
||||||
continue
|
continue
|
||||||
print(f'Session {task.request.session_id} starting task {id(task)} on {runtime.thread_data.device_name}')
|
log.info(f'Session {task.task_data.session_id} starting task {id(task)} on {renderer.context.device_name}')
|
||||||
if not task.lock.acquire(blocking=False): raise Exception('Got locked task from queue.')
|
if not task.lock.acquire(blocking=False): raise Exception('Got locked task from queue.')
|
||||||
try:
|
try:
|
||||||
if runtime.is_hypernetwork_reload_necessary(task.request):
|
|
||||||
runtime.reload_hypernetwork()
|
|
||||||
current_hypernetwork_path = task.request.use_hypernetwork_model
|
|
||||||
|
|
||||||
if runtime.is_model_reload_necessary(task.request):
|
|
||||||
current_state = ServerStates.LoadingModel
|
|
||||||
runtime.reload_model()
|
|
||||||
current_model_path = task.request.use_stable_diffusion_model
|
|
||||||
current_vae_path = task.request.use_vae_model
|
|
||||||
|
|
||||||
def step_callback():
|
def step_callback():
|
||||||
global current_state_error
|
global current_state_error
|
||||||
|
|
||||||
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
||||||
runtime.thread_data.stop_processing = True
|
renderer.context.stop_processing = True
|
||||||
if isinstance(current_state_error, StopAsyncIteration):
|
if isinstance(current_state_error, StopAsyncIteration):
|
||||||
task.error = current_state_error
|
task.error = current_state_error
|
||||||
current_state_error = None
|
current_state_error = None
|
||||||
print(f'Session {task.request.session_id} sent cancel signal for task {id(task)}')
|
log.info(f'Session {task.task_data.session_id} sent cancel signal for task {id(task)}')
|
||||||
|
|
||||||
|
current_state = ServerStates.LoadingModel
|
||||||
|
model_manager.resolve_model_paths(task.task_data)
|
||||||
|
model_manager.reload_models_if_necessary(renderer.context, task.task_data)
|
||||||
|
|
||||||
current_state = ServerStates.Rendering
|
current_state = ServerStates.Rendering
|
||||||
task.response = runtime.mk_img(task.request, task.buffer_queue, task.temp_images, step_callback)
|
task.response = renderer.make_images(task.render_request, task.task_data, task.buffer_queue, task.temp_images, step_callback)
|
||||||
# Before looping back to the generator, mark cache as still alive.
|
# Before looping back to the generator, mark cache as still alive.
|
||||||
task_cache.keep(id(task), TASK_TTL)
|
task_cache.keep(id(task), TASK_TTL)
|
||||||
session_cache.keep(task.request.session_id, TASK_TTL)
|
session_cache.keep(task.task_data.session_id, TASK_TTL)
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
task.error = e
|
task.error = e
|
||||||
task.response = {"status": 'failed', "detail": str(task.error)}
|
task.response = {"status": 'failed', "detail": str(task.error)}
|
||||||
task.buffer_queue.put(json.dumps(task.response))
|
task.buffer_queue.put(json.dumps(task.response))
|
||||||
print(traceback.format_exc())
|
log.error(traceback.format_exc())
|
||||||
continue
|
continue
|
||||||
finally:
|
finally:
|
||||||
# Task completed
|
# Task completed
|
||||||
task.lock.release()
|
task.lock.release()
|
||||||
task_cache.keep(id(task), TASK_TTL)
|
task_cache.keep(id(task), TASK_TTL)
|
||||||
session_cache.keep(task.request.session_id, TASK_TTL)
|
session_cache.keep(task.task_data.session_id, TASK_TTL)
|
||||||
if isinstance(task.error, StopAsyncIteration):
|
if isinstance(task.error, StopAsyncIteration):
|
||||||
print(f'Session {task.request.session_id} task {id(task)} cancelled!')
|
log.info(f'Session {task.task_data.session_id} task {id(task)} cancelled!')
|
||||||
elif task.error is not None:
|
elif task.error is not None:
|
||||||
print(f'Session {task.request.session_id} task {id(task)} failed!')
|
log.info(f'Session {task.task_data.session_id} task {id(task)} failed!')
|
||||||
else:
|
else:
|
||||||
print(f'Session {task.request.session_id} task {id(task)} completed by {runtime.thread_data.device_name}.')
|
log.info(f'Session {task.task_data.session_id} task {id(task)} completed by {renderer.context.device_name}.')
|
||||||
current_state = ServerStates.Online
|
current_state = ServerStates.Online
|
||||||
|
|
||||||
def get_cached_task(task_id:str, update_ttl:bool=False):
|
def get_cached_task(task_id:str, update_ttl:bool=False):
|
||||||
@ -423,6 +339,7 @@ def get_devices():
|
|||||||
'name': torch.cuda.get_device_name(device),
|
'name': torch.cuda.get_device_name(device),
|
||||||
'mem_free': mem_free,
|
'mem_free': mem_free,
|
||||||
'mem_total': mem_total,
|
'mem_total': mem_total,
|
||||||
|
'max_vram_usage_level': device_manager.get_max_vram_usage_level(device),
|
||||||
}
|
}
|
||||||
|
|
||||||
# list the compatible devices
|
# list the compatible devices
|
||||||
@ -472,7 +389,7 @@ def is_alive(device=None):
|
|||||||
|
|
||||||
def start_render_thread(device):
|
def start_render_thread(device):
|
||||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('start_render_thread' + ERR_LOCK_FAILED)
|
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('start_render_thread' + ERR_LOCK_FAILED)
|
||||||
print('Start new Rendering Thread on device', device)
|
log.info(f'Start new Rendering Thread on device: {device}')
|
||||||
try:
|
try:
|
||||||
rthread = threading.Thread(target=thread_render, kwargs={'device': device})
|
rthread = threading.Thread(target=thread_render, kwargs={'device': device})
|
||||||
rthread.daemon = True
|
rthread.daemon = True
|
||||||
@ -484,7 +401,7 @@ def start_render_thread(device):
|
|||||||
timeout = DEVICE_START_TIMEOUT
|
timeout = DEVICE_START_TIMEOUT
|
||||||
while not rthread.is_alive() or not rthread in weak_thread_data or not 'device' in weak_thread_data[rthread]:
|
while not rthread.is_alive() or not rthread in weak_thread_data or not 'device' in weak_thread_data[rthread]:
|
||||||
if rthread in weak_thread_data and 'error' in weak_thread_data[rthread]:
|
if rthread in weak_thread_data and 'error' in weak_thread_data[rthread]:
|
||||||
print(rthread, device, 'error:', weak_thread_data[rthread]['error'])
|
log.error(f"{rthread}, {device}, error: {weak_thread_data[rthread]['error']}")
|
||||||
return False
|
return False
|
||||||
if timeout <= 0:
|
if timeout <= 0:
|
||||||
return False
|
return False
|
||||||
@ -496,11 +413,11 @@ def stop_render_thread(device):
|
|||||||
try:
|
try:
|
||||||
device_manager.validate_device_id(device, log_prefix='stop_render_thread')
|
device_manager.validate_device_id(device, log_prefix='stop_render_thread')
|
||||||
except:
|
except:
|
||||||
print(traceback.format_exc())
|
log.error(traceback.format_exc())
|
||||||
return False
|
return False
|
||||||
|
|
||||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('stop_render_thread' + ERR_LOCK_FAILED)
|
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('stop_render_thread' + ERR_LOCK_FAILED)
|
||||||
print('Stopping Rendering Thread on device', device)
|
log.info(f'Stopping Rendering Thread on device: {device}')
|
||||||
|
|
||||||
try:
|
try:
|
||||||
thread_to_remove = None
|
thread_to_remove = None
|
||||||
@ -523,79 +440,44 @@ def stop_render_thread(device):
|
|||||||
|
|
||||||
def update_render_threads(render_devices, active_devices):
|
def update_render_threads(render_devices, active_devices):
|
||||||
devices_to_start, devices_to_stop = device_manager.get_device_delta(render_devices, active_devices)
|
devices_to_start, devices_to_stop = device_manager.get_device_delta(render_devices, active_devices)
|
||||||
print('devices_to_start', devices_to_start)
|
log.debug(f'devices_to_start: {devices_to_start}')
|
||||||
print('devices_to_stop', devices_to_stop)
|
log.debug(f'devices_to_stop: {devices_to_stop}')
|
||||||
|
|
||||||
for device in devices_to_stop:
|
for device in devices_to_stop:
|
||||||
if is_alive(device) <= 0:
|
if is_alive(device) <= 0:
|
||||||
print(device, 'is not alive')
|
log.debug(f'{device} is not alive')
|
||||||
continue
|
continue
|
||||||
if not stop_render_thread(device):
|
if not stop_render_thread(device):
|
||||||
print(device, 'could not stop render thread')
|
log.warn(f'{device} could not stop render thread')
|
||||||
|
|
||||||
for device in devices_to_start:
|
for device in devices_to_start:
|
||||||
if is_alive(device) >= 1:
|
if is_alive(device) >= 1:
|
||||||
print(device, 'already registered.')
|
log.debug(f'{device} already registered.')
|
||||||
continue
|
continue
|
||||||
if not start_render_thread(device):
|
if not start_render_thread(device):
|
||||||
print(device, 'failed to start.')
|
log.warn(f'{device} failed to start.')
|
||||||
|
|
||||||
if is_alive() <= 0: # No running devices, probably invalid user config.
|
if is_alive() <= 0: # No running devices, probably invalid user config.
|
||||||
raise EnvironmentError('ERROR: No active render devices! Please verify the "render_devices" value in config.json')
|
raise EnvironmentError('ERROR: No active render devices! Please verify the "render_devices" value in config.json')
|
||||||
|
|
||||||
print('active devices', get_devices()['active'])
|
log.debug(f"active devices: {get_devices()['active']}")
|
||||||
|
|
||||||
def shutdown_event(): # Signal render thread to close on shutdown
|
def shutdown_event(): # Signal render thread to close on shutdown
|
||||||
global current_state_error
|
global current_state_error
|
||||||
current_state_error = SystemExit('Application shutting down.')
|
current_state_error = SystemExit('Application shutting down.')
|
||||||
|
|
||||||
def render(req : ImageRequest):
|
def render(render_req: GenerateImageRequest, task_data: TaskData):
|
||||||
current_thread_count = is_alive()
|
current_thread_count = is_alive()
|
||||||
if current_thread_count <= 0: # Render thread is dead
|
if current_thread_count <= 0: # Render thread is dead
|
||||||
raise ChildProcessError('Rendering thread has died.')
|
raise ChildProcessError('Rendering thread has died.')
|
||||||
|
|
||||||
# Alive, check if task in cache
|
# Alive, check if task in cache
|
||||||
session = get_cached_session(req.session_id, update_ttl=True)
|
session = get_cached_session(task_data.session_id, update_ttl=True)
|
||||||
pending_tasks = list(filter(lambda t: t.is_pending, session.tasks))
|
pending_tasks = list(filter(lambda t: t.is_pending, session.tasks))
|
||||||
if current_thread_count < len(pending_tasks):
|
if current_thread_count < len(pending_tasks):
|
||||||
raise ConnectionRefusedError(f'Session {req.session_id} already has {len(pending_tasks)} pending tasks out of {current_thread_count}.')
|
raise ConnectionRefusedError(f'Session {task_data.session_id} already has {len(pending_tasks)} pending tasks out of {current_thread_count}.')
|
||||||
|
|
||||||
from . import runtime
|
new_task = RenderTask(render_req, task_data)
|
||||||
r = Request()
|
|
||||||
r.session_id = req.session_id
|
|
||||||
r.prompt = req.prompt
|
|
||||||
r.negative_prompt = req.negative_prompt
|
|
||||||
r.init_image = req.init_image
|
|
||||||
r.mask = req.mask
|
|
||||||
r.num_outputs = req.num_outputs
|
|
||||||
r.num_inference_steps = req.num_inference_steps
|
|
||||||
r.guidance_scale = req.guidance_scale
|
|
||||||
r.width = req.width
|
|
||||||
r.height = req.height
|
|
||||||
r.seed = req.seed
|
|
||||||
r.prompt_strength = req.prompt_strength
|
|
||||||
r.sampler = req.sampler
|
|
||||||
# r.allow_nsfw = req.allow_nsfw
|
|
||||||
r.turbo = req.turbo
|
|
||||||
r.use_full_precision = req.use_full_precision
|
|
||||||
r.save_to_disk_path = req.save_to_disk_path
|
|
||||||
r.use_upscale: str = req.use_upscale
|
|
||||||
r.use_face_correction = req.use_face_correction
|
|
||||||
r.use_stable_diffusion_model = req.use_stable_diffusion_model
|
|
||||||
r.use_vae_model = req.use_vae_model
|
|
||||||
r.use_hypernetwork_model = req.use_hypernetwork_model
|
|
||||||
r.hypernetwork_strength = req.hypernetwork_strength
|
|
||||||
r.show_only_filtered_image = req.show_only_filtered_image
|
|
||||||
r.output_format = req.output_format
|
|
||||||
r.output_quality = req.output_quality
|
|
||||||
|
|
||||||
r.stream_progress_updates = True # the underlying implementation only supports streaming
|
|
||||||
r.stream_image_progress = req.stream_image_progress
|
|
||||||
|
|
||||||
if not req.stream_progress_updates:
|
|
||||||
r.stream_image_progress = False
|
|
||||||
|
|
||||||
new_task = RenderTask(r)
|
|
||||||
if session.put(new_task, TASK_TTL):
|
if session.put(new_task, TASK_TTL):
|
||||||
# Use twice the normal timeout for adding user requests.
|
# Use twice the normal timeout for adding user requests.
|
||||||
# Tries to force session.put to fail before tasks_queue.put would.
|
# Tries to force session.put to fail before tasks_queue.put would.
|
87
ui/easydiffusion/types.py
Normal file
87
ui/easydiffusion/types.py
Normal file
@ -0,0 +1,87 @@
|
|||||||
|
from pydantic import BaseModel
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
class GenerateImageRequest(BaseModel):
|
||||||
|
prompt: str = ""
|
||||||
|
negative_prompt: str = ""
|
||||||
|
|
||||||
|
seed: int = 42
|
||||||
|
width: int = 512
|
||||||
|
height: int = 512
|
||||||
|
|
||||||
|
num_outputs: int = 1
|
||||||
|
num_inference_steps: int = 50
|
||||||
|
guidance_scale: float = 7.5
|
||||||
|
|
||||||
|
init_image: Any = None
|
||||||
|
init_image_mask: Any = None
|
||||||
|
prompt_strength: float = 0.8
|
||||||
|
preserve_init_image_color_profile = False
|
||||||
|
|
||||||
|
sampler_name: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
||||||
|
hypernetwork_strength: float = 0
|
||||||
|
|
||||||
|
class TaskData(BaseModel):
|
||||||
|
request_id: str = None
|
||||||
|
session_id: str = "session"
|
||||||
|
save_to_disk_path: str = None
|
||||||
|
vram_usage_level: str = "balanced" # or "low" or "medium"
|
||||||
|
|
||||||
|
use_face_correction: str = None # or "GFPGANv1.3"
|
||||||
|
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
|
||||||
|
use_stable_diffusion_model: str = "sd-v1-4"
|
||||||
|
use_stable_diffusion_config: str = "v1-inference"
|
||||||
|
use_vae_model: str = None
|
||||||
|
use_hypernetwork_model: str = None
|
||||||
|
|
||||||
|
show_only_filtered_image: bool = False
|
||||||
|
output_format: str = "jpeg" # or "png"
|
||||||
|
output_quality: int = 75
|
||||||
|
metadata_output_format: str = "txt" # or "json"
|
||||||
|
stream_image_progress: bool = False
|
||||||
|
|
||||||
|
class Image:
|
||||||
|
data: str # base64
|
||||||
|
seed: int
|
||||||
|
is_nsfw: bool
|
||||||
|
path_abs: str = None
|
||||||
|
|
||||||
|
def __init__(self, data, seed):
|
||||||
|
self.data = data
|
||||||
|
self.seed = seed
|
||||||
|
|
||||||
|
def json(self):
|
||||||
|
return {
|
||||||
|
"data": self.data,
|
||||||
|
"seed": self.seed,
|
||||||
|
"path_abs": self.path_abs,
|
||||||
|
}
|
||||||
|
|
||||||
|
class Response:
|
||||||
|
render_request: GenerateImageRequest
|
||||||
|
task_data: TaskData
|
||||||
|
images: list
|
||||||
|
|
||||||
|
def __init__(self, render_request: GenerateImageRequest, task_data: TaskData, images: list):
|
||||||
|
self.render_request = render_request
|
||||||
|
self.task_data = task_data
|
||||||
|
self.images = images
|
||||||
|
|
||||||
|
def json(self):
|
||||||
|
del self.render_request.init_image
|
||||||
|
del self.render_request.init_image_mask
|
||||||
|
|
||||||
|
res = {
|
||||||
|
"status": 'succeeded',
|
||||||
|
"render_request": self.render_request.dict(),
|
||||||
|
"task_data": self.task_data.dict(),
|
||||||
|
"output": [],
|
||||||
|
}
|
||||||
|
|
||||||
|
for image in self.images:
|
||||||
|
res["output"].append(image.json())
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
|
class UserInitiatedStop(Exception):
|
||||||
|
pass
|
8
ui/easydiffusion/utils/__init__.py
Normal file
8
ui/easydiffusion/utils/__init__.py
Normal file
@ -0,0 +1,8 @@
|
|||||||
|
import logging
|
||||||
|
|
||||||
|
log = logging.getLogger('easydiffusion')
|
||||||
|
|
||||||
|
from .save_utils import (
|
||||||
|
save_images_to_disk,
|
||||||
|
get_printable_request,
|
||||||
|
)
|
79
ui/easydiffusion/utils/save_utils.py
Normal file
79
ui/easydiffusion/utils/save_utils.py
Normal file
@ -0,0 +1,79 @@
|
|||||||
|
import os
|
||||||
|
import time
|
||||||
|
import base64
|
||||||
|
import re
|
||||||
|
|
||||||
|
from easydiffusion.types import TaskData, GenerateImageRequest
|
||||||
|
|
||||||
|
from sdkit.utils import save_images, save_dicts
|
||||||
|
|
||||||
|
filename_regex = re.compile('[^a-zA-Z0-9]')
|
||||||
|
|
||||||
|
# keep in sync with `ui/media/js/dnd.js`
|
||||||
|
TASK_TEXT_MAPPING = {
|
||||||
|
'prompt': 'Prompt',
|
||||||
|
'width': 'Width',
|
||||||
|
'height': 'Height',
|
||||||
|
'seed': 'Seed',
|
||||||
|
'num_inference_steps': 'Steps',
|
||||||
|
'guidance_scale': 'Guidance Scale',
|
||||||
|
'prompt_strength': 'Prompt Strength',
|
||||||
|
'use_face_correction': 'Use Face Correction',
|
||||||
|
'use_upscale': 'Use Upscaling',
|
||||||
|
'sampler_name': 'Sampler',
|
||||||
|
'negative_prompt': 'Negative Prompt',
|
||||||
|
'use_stable_diffusion_model': 'Stable Diffusion model',
|
||||||
|
'use_hypernetwork_model': 'Hypernetwork model',
|
||||||
|
'hypernetwork_strength': 'Hypernetwork Strength'
|
||||||
|
}
|
||||||
|
|
||||||
|
def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageRequest, task_data: TaskData):
|
||||||
|
save_dir_path = os.path.join(task_data.save_to_disk_path, filename_regex.sub('_', task_data.session_id))
|
||||||
|
metadata_entries = get_metadata_entries_for_request(req, task_data)
|
||||||
|
|
||||||
|
if task_data.show_only_filtered_image or filtered_images == images:
|
||||||
|
save_images(filtered_images, save_dir_path, file_name=make_filename_callback(req), output_format=task_data.output_format, output_quality=task_data.output_quality)
|
||||||
|
save_dicts(metadata_entries, save_dir_path, file_name=make_filename_callback(req), output_format=task_data.metadata_output_format)
|
||||||
|
else:
|
||||||
|
save_images(images, save_dir_path, file_name=make_filename_callback(req), output_format=task_data.output_format, output_quality=task_data.output_quality)
|
||||||
|
save_images(filtered_images, save_dir_path, file_name=make_filename_callback(req, suffix='filtered'), output_format=task_data.output_format, output_quality=task_data.output_quality)
|
||||||
|
save_dicts(metadata_entries, save_dir_path, file_name=make_filename_callback(req, suffix='filtered'), output_format=task_data.metadata_output_format)
|
||||||
|
|
||||||
|
def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskData):
|
||||||
|
metadata = get_printable_request(req)
|
||||||
|
metadata.update({
|
||||||
|
'use_stable_diffusion_model': task_data.use_stable_diffusion_model,
|
||||||
|
'use_vae_model': task_data.use_vae_model,
|
||||||
|
'use_hypernetwork_model': task_data.use_hypernetwork_model,
|
||||||
|
'use_face_correction': task_data.use_face_correction,
|
||||||
|
'use_upscale': task_data.use_upscale,
|
||||||
|
})
|
||||||
|
|
||||||
|
# if text, format it in the text format expected by the UI
|
||||||
|
is_txt_format = (task_data.metadata_output_format.lower() == 'txt')
|
||||||
|
if is_txt_format:
|
||||||
|
metadata = {TASK_TEXT_MAPPING[key]: val for key, val in metadata.items() if key in TASK_TEXT_MAPPING}
|
||||||
|
|
||||||
|
entries = [metadata.copy() for _ in range(req.num_outputs)]
|
||||||
|
for i, entry in enumerate(entries):
|
||||||
|
entry['Seed' if is_txt_format else 'seed'] = req.seed + i
|
||||||
|
|
||||||
|
return entries
|
||||||
|
|
||||||
|
def get_printable_request(req: GenerateImageRequest):
|
||||||
|
metadata = req.dict()
|
||||||
|
del metadata['init_image']
|
||||||
|
del metadata['init_image_mask']
|
||||||
|
return metadata
|
||||||
|
|
||||||
|
def make_filename_callback(req: GenerateImageRequest, suffix=None):
|
||||||
|
def make_filename(i):
|
||||||
|
img_id = base64.b64encode(int(time.time()+i).to_bytes(8, 'big')).decode() # Generate unique ID based on time.
|
||||||
|
img_id = img_id.translate({43:None, 47:None, 61:None})[-8:] # Remove + / = and keep last 8 chars.
|
||||||
|
|
||||||
|
prompt_flattened = filename_regex.sub('_', req.prompt)[:50]
|
||||||
|
name = f"{prompt_flattened}_{img_id}"
|
||||||
|
name = name if suffix is None else f'{name}_{suffix}'
|
||||||
|
return name
|
||||||
|
|
||||||
|
return make_filename
|
@ -24,8 +24,8 @@
|
|||||||
<div id="top-nav">
|
<div id="top-nav">
|
||||||
<div id="logo">
|
<div id="logo">
|
||||||
<h1>
|
<h1>
|
||||||
Stable Diffusion UI
|
Easy Diffusion
|
||||||
<small>v2.4.22 <span id="updateBranchLabel"></span></small>
|
<small>v2.5.0 <span id="updateBranchLabel"></span></small>
|
||||||
</h1>
|
</h1>
|
||||||
</div>
|
</div>
|
||||||
<div id="server-status">
|
<div id="server-status">
|
||||||
@ -129,22 +129,32 @@
|
|||||||
</select>
|
</select>
|
||||||
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Custom-Models" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about custom models</span></i></a>
|
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Custom-Models" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about custom models</span></i></a>
|
||||||
</td></tr>
|
</td></tr>
|
||||||
|
<!-- <tr id="modelConfigSelection" class="pl-5"><td><label for="model_config">Model Config:</i></label></td><td>
|
||||||
|
<select id="model_config" name="model_config">
|
||||||
|
</select>
|
||||||
|
</td></tr> -->
|
||||||
<tr class="pl-5"><td><label for="vae_model">Custom VAE:</i></label></td><td>
|
<tr class="pl-5"><td><label for="vae_model">Custom VAE:</i></label></td><td>
|
||||||
<select id="vae_model" name="vae_model">
|
<select id="vae_model" name="vae_model">
|
||||||
<!-- <option value="" selected>None</option> -->
|
<!-- <option value="" selected>None</option> -->
|
||||||
</select>
|
</select>
|
||||||
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/VAE-Variational-Auto-Encoder" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about VAEs</span></i></a>
|
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/VAE-Variational-Auto-Encoder" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about VAEs</span></i></a>
|
||||||
</td></tr>
|
</td></tr>
|
||||||
<tr id="samplerSelection" class="pl-5"><td><label for="sampler">Sampler:</label></td><td>
|
<tr id="samplerSelection" class="pl-5"><td><label for="sampler_name">Sampler:</label></td><td>
|
||||||
<select id="sampler" name="sampler">
|
<select id="sampler_name" name="sampler_name">
|
||||||
<option value="plms">plms</option>
|
<option value="plms">PLMS</option>
|
||||||
<option value="ddim">ddim</option>
|
<option value="ddim">DDIM</option>
|
||||||
<option value="heun">heun</option>
|
<option value="heun">Heun</option>
|
||||||
<option value="euler">euler</option>
|
<option value="euler">Euler</option>
|
||||||
<option value="euler_a" selected>euler_a</option>
|
<option value="euler_a" selected>Euler Ancestral</option>
|
||||||
<option value="dpm2">dpm2</option>
|
<option value="dpm2">DPM2</option>
|
||||||
<option value="dpm2_a">dpm2_a</option>
|
<option value="dpm2_a">DPM2 Ancestral</option>
|
||||||
<option value="lms">lms</option>
|
<option value="lms">LMS</option>
|
||||||
|
<option value="dpm_solver_stability">DPM Solver (Stability AI)</option>
|
||||||
|
<option value="dpmpp_2s_a" selected>DPM++ 2s Ancestral</option>
|
||||||
|
<option value="dpmpp_2m">DPM++ 2m</option>
|
||||||
|
<option value="dpmpp_sde">DPM++ SDE</option>
|
||||||
|
<option value="dpm_fast">DPM Fast</option>
|
||||||
|
<option value="dpm_adaptive">DPM Adaptive</option>
|
||||||
</select>
|
</select>
|
||||||
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/How-to-Use#samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
|
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/How-to-Use#samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
|
||||||
</td></tr>
|
</td></tr>
|
||||||
@ -220,6 +230,7 @@
|
|||||||
<div><ul>
|
<div><ul>
|
||||||
<li><b class="settings-subheader">Render Settings</b></li>
|
<li><b class="settings-subheader">Render Settings</b></li>
|
||||||
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slower images)</small></label></li>
|
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slower images)</small></label></li>
|
||||||
|
<li id="apply_color_correction_setting" class="pl-5"><input id="apply_color_correction" name="apply_color_correction" type="checkbox"> <label for="apply_color_correction">Preserve color profile <small>(helps during inpainting)</small></label></li>
|
||||||
<li class="pl-5"><input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes <small>(uses GFPGAN)</small></label></li>
|
<li class="pl-5"><input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes <small>(uses GFPGAN)</small></label></li>
|
||||||
<li class="pl-5">
|
<li class="pl-5">
|
||||||
<input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Upscale image by 4x with </label>
|
<input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Upscale image by 4x with </label>
|
||||||
@ -416,7 +427,6 @@
|
|||||||
async function init() {
|
async function init() {
|
||||||
await initSettings()
|
await initSettings()
|
||||||
await getModels()
|
await getModels()
|
||||||
await getDiskPath()
|
|
||||||
await getAppConfig()
|
await getAppConfig()
|
||||||
await loadUIPlugins()
|
await loadUIPlugins()
|
||||||
await loadModifiers()
|
await loadModifiers()
|
||||||
|
10
ui/main.py
Normal file
10
ui/main.py
Normal file
@ -0,0 +1,10 @@
|
|||||||
|
from easydiffusion import model_manager, app, server
|
||||||
|
from easydiffusion.server import server_api # required for uvicorn
|
||||||
|
|
||||||
|
# Init the app
|
||||||
|
model_manager.init()
|
||||||
|
app.init()
|
||||||
|
server.init()
|
||||||
|
|
||||||
|
# start the browser ui
|
||||||
|
app.open_browser()
|
@ -15,7 +15,7 @@ const SETTINGS_IDS_LIST = [
|
|||||||
"stable_diffusion_model",
|
"stable_diffusion_model",
|
||||||
"vae_model",
|
"vae_model",
|
||||||
"hypernetwork_model",
|
"hypernetwork_model",
|
||||||
"sampler",
|
"sampler_name",
|
||||||
"width",
|
"width",
|
||||||
"height",
|
"height",
|
||||||
"num_inference_steps",
|
"num_inference_steps",
|
||||||
@ -36,10 +36,11 @@ const SETTINGS_IDS_LIST = [
|
|||||||
"save_to_disk",
|
"save_to_disk",
|
||||||
"diskPath",
|
"diskPath",
|
||||||
"sound_toggle",
|
"sound_toggle",
|
||||||
"turbo",
|
"vram_usage_level",
|
||||||
"use_full_precision",
|
|
||||||
"confirm_dangerous_actions",
|
"confirm_dangerous_actions",
|
||||||
"auto_save_settings"
|
"metadata_output_format",
|
||||||
|
"auto_save_settings",
|
||||||
|
"apply_color_correction"
|
||||||
]
|
]
|
||||||
|
|
||||||
const IGNORE_BY_DEFAULT = [
|
const IGNORE_BY_DEFAULT = [
|
||||||
@ -277,7 +278,6 @@ function tryLoadOldSettings() {
|
|||||||
"soundEnabled": "sound_toggle",
|
"soundEnabled": "sound_toggle",
|
||||||
"saveToDisk": "save_to_disk",
|
"saveToDisk": "save_to_disk",
|
||||||
"useCPU": "use_cpu",
|
"useCPU": "use_cpu",
|
||||||
"useFullPrecision": "use_full_precision",
|
|
||||||
"useTurboMode": "turbo",
|
"useTurboMode": "turbo",
|
||||||
"diskPath": "diskPath",
|
"diskPath": "diskPath",
|
||||||
"useFaceCorrection": "use_face_correction",
|
"useFaceCorrection": "use_face_correction",
|
||||||
|
@ -25,6 +25,7 @@ function parseBoolean(stringValue) {
|
|||||||
case "no":
|
case "no":
|
||||||
case "off":
|
case "off":
|
||||||
case "0":
|
case "0":
|
||||||
|
case "none":
|
||||||
case null:
|
case null:
|
||||||
case undefined:
|
case undefined:
|
||||||
return false;
|
return false;
|
||||||
@ -160,9 +161,9 @@ const TASK_MAPPING = {
|
|||||||
readUI: () => (useUpscalingField.checked ? upscaleModelField.value : undefined),
|
readUI: () => (useUpscalingField.checked ? upscaleModelField.value : undefined),
|
||||||
parse: (val) => val
|
parse: (val) => val
|
||||||
},
|
},
|
||||||
sampler: { name: 'Sampler',
|
sampler_name: { name: 'Sampler',
|
||||||
setUI: (sampler) => {
|
setUI: (sampler_name) => {
|
||||||
samplerField.value = sampler
|
samplerField.value = sampler_name
|
||||||
},
|
},
|
||||||
readUI: () => samplerField.value,
|
readUI: () => samplerField.value,
|
||||||
parse: (val) => val
|
parse: (val) => val
|
||||||
@ -171,7 +172,7 @@ const TASK_MAPPING = {
|
|||||||
setUI: (use_stable_diffusion_model) => {
|
setUI: (use_stable_diffusion_model) => {
|
||||||
const oldVal = stableDiffusionModelField.value
|
const oldVal = stableDiffusionModelField.value
|
||||||
|
|
||||||
use_stable_diffusion_model = getModelPath(use_stable_diffusion_model, ['.ckpt'])
|
use_stable_diffusion_model = getModelPath(use_stable_diffusion_model, ['.ckpt', '.safetensors'])
|
||||||
stableDiffusionModelField.value = use_stable_diffusion_model
|
stableDiffusionModelField.value = use_stable_diffusion_model
|
||||||
|
|
||||||
if (!stableDiffusionModelField.value) {
|
if (!stableDiffusionModelField.value) {
|
||||||
@ -184,6 +185,7 @@ const TASK_MAPPING = {
|
|||||||
use_vae_model: { name: 'VAE model',
|
use_vae_model: { name: 'VAE model',
|
||||||
setUI: (use_vae_model) => {
|
setUI: (use_vae_model) => {
|
||||||
const oldVal = vaeModelField.value
|
const oldVal = vaeModelField.value
|
||||||
|
use_vae_model = (use_vae_model === undefined || use_vae_model === null || use_vae_model === 'None' ? '' : use_vae_model)
|
||||||
|
|
||||||
if (use_vae_model !== '') {
|
if (use_vae_model !== '') {
|
||||||
use_vae_model = getModelPath(use_vae_model, ['.vae.pt', '.ckpt'])
|
use_vae_model = getModelPath(use_vae_model, ['.vae.pt', '.ckpt'])
|
||||||
@ -197,6 +199,7 @@ const TASK_MAPPING = {
|
|||||||
use_hypernetwork_model: { name: 'Hypernetwork model',
|
use_hypernetwork_model: { name: 'Hypernetwork model',
|
||||||
setUI: (use_hypernetwork_model) => {
|
setUI: (use_hypernetwork_model) => {
|
||||||
const oldVal = hypernetworkModelField.value
|
const oldVal = hypernetworkModelField.value
|
||||||
|
use_hypernetwork_model = (use_hypernetwork_model === undefined || use_hypernetwork_model === null || use_hypernetwork_model === 'None' ? '' : use_hypernetwork_model)
|
||||||
|
|
||||||
if (use_hypernetwork_model !== '') {
|
if (use_hypernetwork_model !== '') {
|
||||||
use_hypernetwork_model = getModelPath(use_hypernetwork_model, ['.pt'])
|
use_hypernetwork_model = getModelPath(use_hypernetwork_model, ['.pt'])
|
||||||
@ -239,13 +242,6 @@ const TASK_MAPPING = {
|
|||||||
readUI: () => turboField.checked,
|
readUI: () => turboField.checked,
|
||||||
parse: (val) => Boolean(val)
|
parse: (val) => Boolean(val)
|
||||||
},
|
},
|
||||||
use_full_precision: { name: 'Use Full Precision',
|
|
||||||
setUI: (use_full_precision) => {
|
|
||||||
useFullPrecisionField.checked = use_full_precision
|
|
||||||
},
|
|
||||||
readUI: () => useFullPrecisionField.checked,
|
|
||||||
parse: (val) => Boolean(val)
|
|
||||||
},
|
|
||||||
|
|
||||||
stream_image_progress: { name: 'Stream Image Progress',
|
stream_image_progress: { name: 'Stream Image Progress',
|
||||||
setUI: (stream_image_progress) => {
|
setUI: (stream_image_progress) => {
|
||||||
@ -350,6 +346,7 @@ function getModelPath(filename, extensions)
|
|||||||
}
|
}
|
||||||
|
|
||||||
const TASK_TEXT_MAPPING = {
|
const TASK_TEXT_MAPPING = {
|
||||||
|
prompt: 'Prompt',
|
||||||
width: 'Width',
|
width: 'Width',
|
||||||
height: 'Height',
|
height: 'Height',
|
||||||
seed: 'Seed',
|
seed: 'Seed',
|
||||||
@ -358,7 +355,7 @@ const TASK_TEXT_MAPPING = {
|
|||||||
prompt_strength: 'Prompt Strength',
|
prompt_strength: 'Prompt Strength',
|
||||||
use_face_correction: 'Use Face Correction',
|
use_face_correction: 'Use Face Correction',
|
||||||
use_upscale: 'Use Upscaling',
|
use_upscale: 'Use Upscaling',
|
||||||
sampler: 'Sampler',
|
sampler_name: 'Sampler',
|
||||||
negative_prompt: 'Negative Prompt',
|
negative_prompt: 'Negative Prompt',
|
||||||
use_stable_diffusion_model: 'Stable Diffusion model',
|
use_stable_diffusion_model: 'Stable Diffusion model',
|
||||||
use_hypernetwork_model: 'Hypernetwork model',
|
use_hypernetwork_model: 'Hypernetwork model',
|
||||||
@ -410,6 +407,9 @@ async function parseContent(text) {
|
|||||||
if (text.startsWith('{') && text.endsWith('}')) {
|
if (text.startsWith('{') && text.endsWith('}')) {
|
||||||
try {
|
try {
|
||||||
const task = JSON.parse(text)
|
const task = JSON.parse(text)
|
||||||
|
if (!('reqBody' in task)) { // support the format saved to the disk, by the UI
|
||||||
|
task.reqBody = Object.assign({}, task)
|
||||||
|
}
|
||||||
restoreTaskToUI(task)
|
restoreTaskToUI(task)
|
||||||
return true
|
return true
|
||||||
} catch (e) {
|
} catch (e) {
|
||||||
@ -477,7 +477,6 @@ document.addEventListener("dragover", dragOverHandler)
|
|||||||
const TASK_REQ_NO_EXPORT = [
|
const TASK_REQ_NO_EXPORT = [
|
||||||
"use_cpu",
|
"use_cpu",
|
||||||
"turbo",
|
"turbo",
|
||||||
"use_full_precision",
|
|
||||||
"save_to_disk_path"
|
"save_to_disk_path"
|
||||||
]
|
]
|
||||||
const resetSettings = document.getElementById('reset-image-settings')
|
const resetSettings = document.getElementById('reset-image-settings')
|
||||||
|
@ -728,7 +728,6 @@
|
|||||||
"stream_image_progress": 'boolean',
|
"stream_image_progress": 'boolean',
|
||||||
"show_only_filtered_image": 'boolean',
|
"show_only_filtered_image": 'boolean',
|
||||||
"turbo": 'boolean',
|
"turbo": 'boolean',
|
||||||
"use_full_precision": 'boolean',
|
|
||||||
"output_format": 'string',
|
"output_format": 'string',
|
||||||
"output_quality": 'number',
|
"output_quality": 'number',
|
||||||
}
|
}
|
||||||
@ -744,7 +743,6 @@
|
|||||||
"stream_image_progress": true,
|
"stream_image_progress": true,
|
||||||
"show_only_filtered_image": true,
|
"show_only_filtered_image": true,
|
||||||
"turbo": false,
|
"turbo": false,
|
||||||
"use_full_precision": false,
|
|
||||||
"output_format": "png",
|
"output_format": "png",
|
||||||
"output_quality": 75,
|
"output_quality": 75,
|
||||||
}
|
}
|
||||||
|
@ -26,9 +26,11 @@ let initImagePreview = document.querySelector("#init_image_preview")
|
|||||||
let initImageSizeBox = document.querySelector("#init_image_size_box")
|
let initImageSizeBox = document.querySelector("#init_image_size_box")
|
||||||
let maskImageSelector = document.querySelector("#mask")
|
let maskImageSelector = document.querySelector("#mask")
|
||||||
let maskImagePreview = document.querySelector("#mask_preview")
|
let maskImagePreview = document.querySelector("#mask_preview")
|
||||||
|
let applyColorCorrectionField = document.querySelector('#apply_color_correction')
|
||||||
|
let colorCorrectionSetting = document.querySelector('#apply_color_correction_setting')
|
||||||
let promptStrengthSlider = document.querySelector('#prompt_strength_slider')
|
let promptStrengthSlider = document.querySelector('#prompt_strength_slider')
|
||||||
let promptStrengthField = document.querySelector('#prompt_strength')
|
let promptStrengthField = document.querySelector('#prompt_strength')
|
||||||
let samplerField = document.querySelector('#sampler')
|
let samplerField = document.querySelector('#sampler_name')
|
||||||
let samplerSelectionContainer = document.querySelector("#samplerSelection")
|
let samplerSelectionContainer = document.querySelector("#samplerSelection")
|
||||||
let useFaceCorrectionField = document.querySelector("#use_face_correction")
|
let useFaceCorrectionField = document.querySelector("#use_face_correction")
|
||||||
let useUpscalingField = document.querySelector("#use_upscale")
|
let useUpscalingField = document.querySelector("#use_upscale")
|
||||||
@ -610,7 +612,7 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
|
|||||||
<b>Suggestions</b>:
|
<b>Suggestions</b>:
|
||||||
<br/>
|
<br/>
|
||||||
1. If you have set an initial image, please try reducing its dimension to ${MAX_INIT_IMAGE_DIMENSION}x${MAX_INIT_IMAGE_DIMENSION} or smaller.<br/>
|
1. If you have set an initial image, please try reducing its dimension to ${MAX_INIT_IMAGE_DIMENSION}x${MAX_INIT_IMAGE_DIMENSION} or smaller.<br/>
|
||||||
2. Try disabling the '<em>Turbo mode</em>' under '<em>Advanced Settings</em>'.<br/>
|
2. Try picking a lower level in the '<em>GPU Memory Usage</em>' setting (in the '<em>Settings</em>' tab).<br/>
|
||||||
3. Try generating a smaller image.<br/>`
|
3. Try generating a smaller image.<br/>`
|
||||||
}
|
}
|
||||||
} else {
|
} else {
|
||||||
@ -789,7 +791,8 @@ function createTask(task) {
|
|||||||
let w = task.reqBody.width * h / task.reqBody.height >>0
|
let w = task.reqBody.width * h / task.reqBody.height >>0
|
||||||
taskConfig += `<div class="task-initimg" style="float:left;"><img style="width:${w}px;height:${h}px;" src="${task.reqBody.init_image}"><div class="task-fs-initimage"></div></div>`
|
taskConfig += `<div class="task-initimg" style="float:left;"><img style="width:${w}px;height:${h}px;" src="${task.reqBody.init_image}"><div class="task-fs-initimage"></div></div>`
|
||||||
}
|
}
|
||||||
taskConfig += `<b>Seed:</b> ${task.seed}, <b>Sampler:</b> ${task.reqBody.sampler}, <b>Inference Steps:</b> ${task.reqBody.num_inference_steps}, <b>Guidance Scale:</b> ${task.reqBody.guidance_scale}, <b>Model:</b> ${task.reqBody.use_stable_diffusion_model}`
|
taskConfig += `<b>Seed:</b> ${task.seed}, <b>Sampler:</b> ${task.reqBody.sampler_name}, <b>Inference Steps:</b> ${task.reqBody.num_inference_steps}, <b>Guidance Scale:</b> ${task.reqBody.guidance_scale}, <b>Model:</b> ${task.reqBody.use_stable_diffusion_model}`
|
||||||
|
|
||||||
if (task.reqBody.use_vae_model.trim() !== '') {
|
if (task.reqBody.use_vae_model.trim() !== '') {
|
||||||
taskConfig += `, <b>VAE:</b> ${task.reqBody.use_vae_model}`
|
taskConfig += `, <b>VAE:</b> ${task.reqBody.use_vae_model}`
|
||||||
}
|
}
|
||||||
@ -809,6 +812,9 @@ function createTask(task) {
|
|||||||
taskConfig += `, <b>Hypernetwork:</b> ${task.reqBody.use_hypernetwork_model}`
|
taskConfig += `, <b>Hypernetwork:</b> ${task.reqBody.use_hypernetwork_model}`
|
||||||
taskConfig += `, <b>Hypernetwork Strength:</b> ${task.reqBody.hypernetwork_strength}`
|
taskConfig += `, <b>Hypernetwork Strength:</b> ${task.reqBody.hypernetwork_strength}`
|
||||||
}
|
}
|
||||||
|
if (task.reqBody.preserve_init_image_color_profile) {
|
||||||
|
taskConfig += `, <b>Preserve Color Profile:</b> true`
|
||||||
|
}
|
||||||
|
|
||||||
let taskEntry = document.createElement('div')
|
let taskEntry = document.createElement('div')
|
||||||
taskEntry.id = `imageTaskContainer-${Date.now()}`
|
taskEntry.id = `imageTaskContainer-${Date.now()}`
|
||||||
@ -914,9 +920,8 @@ function getCurrentUserRequest() {
|
|||||||
width: parseInt(widthField.value),
|
width: parseInt(widthField.value),
|
||||||
height: parseInt(heightField.value),
|
height: parseInt(heightField.value),
|
||||||
// allow_nsfw: allowNSFWField.checked,
|
// allow_nsfw: allowNSFWField.checked,
|
||||||
turbo: turboField.checked,
|
vram_usage_level: vramUsageLevelField.value,
|
||||||
//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
|
//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
|
||||||
use_full_precision: useFullPrecisionField.checked,
|
|
||||||
use_stable_diffusion_model: stableDiffusionModelField.value,
|
use_stable_diffusion_model: stableDiffusionModelField.value,
|
||||||
use_vae_model: vaeModelField.value,
|
use_vae_model: vaeModelField.value,
|
||||||
stream_progress_updates: true,
|
stream_progress_updates: true,
|
||||||
@ -924,6 +929,7 @@ function getCurrentUserRequest() {
|
|||||||
show_only_filtered_image: showOnlyFilteredImageField.checked,
|
show_only_filtered_image: showOnlyFilteredImageField.checked,
|
||||||
output_format: outputFormatField.value,
|
output_format: outputFormatField.value,
|
||||||
output_quality: parseInt(outputQualityField.value),
|
output_quality: parseInt(outputQualityField.value),
|
||||||
|
metadata_output_format: document.querySelector('#metadata_output_format').value,
|
||||||
original_prompt: promptField.value,
|
original_prompt: promptField.value,
|
||||||
active_tags: (activeTags.map(x => x.name))
|
active_tags: (activeTags.map(x => x.name))
|
||||||
}
|
}
|
||||||
@ -938,9 +944,10 @@ function getCurrentUserRequest() {
|
|||||||
if (maskSetting.checked) {
|
if (maskSetting.checked) {
|
||||||
newTask.reqBody.mask = imageInpainter.getImg()
|
newTask.reqBody.mask = imageInpainter.getImg()
|
||||||
}
|
}
|
||||||
newTask.reqBody.sampler = 'ddim'
|
newTask.reqBody.preserve_init_image_color_profile = applyColorCorrectionField.checked
|
||||||
|
newTask.reqBody.sampler_name = 'ddim'
|
||||||
} else {
|
} else {
|
||||||
newTask.reqBody.sampler = samplerField.value
|
newTask.reqBody.sampler_name = samplerField.value
|
||||||
}
|
}
|
||||||
if (saveToDiskField.checked && diskPathField.value.trim() !== '') {
|
if (saveToDiskField.checked && diskPathField.value.trim() !== '') {
|
||||||
newTask.reqBody.save_to_disk_path = diskPathField.value.trim()
|
newTask.reqBody.save_to_disk_path = diskPathField.value.trim()
|
||||||
@ -1349,6 +1356,7 @@ function img2imgLoad() {
|
|||||||
promptStrengthContainer.style.display = 'table-row'
|
promptStrengthContainer.style.display = 'table-row'
|
||||||
samplerSelectionContainer.style.display = "none"
|
samplerSelectionContainer.style.display = "none"
|
||||||
initImagePreviewContainer.classList.add("has-image")
|
initImagePreviewContainer.classList.add("has-image")
|
||||||
|
colorCorrectionSetting.style.display = ''
|
||||||
|
|
||||||
initImageSizeBox.textContent = initImagePreview.naturalWidth + " x " + initImagePreview.naturalHeight
|
initImageSizeBox.textContent = initImagePreview.naturalWidth + " x " + initImagePreview.naturalHeight
|
||||||
imageEditor.setImage(this.src, initImagePreview.naturalWidth, initImagePreview.naturalHeight)
|
imageEditor.setImage(this.src, initImagePreview.naturalWidth, initImagePreview.naturalHeight)
|
||||||
@ -1363,6 +1371,7 @@ function img2imgUnload() {
|
|||||||
promptStrengthContainer.style.display = "none"
|
promptStrengthContainer.style.display = "none"
|
||||||
samplerSelectionContainer.style.display = ""
|
samplerSelectionContainer.style.display = ""
|
||||||
initImagePreviewContainer.classList.remove("has-image")
|
initImagePreviewContainer.classList.remove("has-image")
|
||||||
|
colorCorrectionSetting.style.display = 'none'
|
||||||
imageEditor.setImage(null, parseInt(widthField.value), parseInt(heightField.value))
|
imageEditor.setImage(null, parseInt(widthField.value), parseInt(heightField.value))
|
||||||
|
|
||||||
}
|
}
|
||||||
|
@ -53,6 +53,23 @@ var PARAMETERS = [
|
|||||||
return `<input id="${parameter.id}" name="${parameter.id}" size="30" disabled>`
|
return `<input id="${parameter.id}" name="${parameter.id}" size="30" disabled>`
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
id: "metadata_output_format",
|
||||||
|
type: ParameterType.select,
|
||||||
|
label: "Metadata format",
|
||||||
|
note: "will be saved to disk in this format",
|
||||||
|
default: "txt",
|
||||||
|
options: [
|
||||||
|
{
|
||||||
|
value: "txt",
|
||||||
|
label: "txt"
|
||||||
|
},
|
||||||
|
{
|
||||||
|
value: "json",
|
||||||
|
label: "json"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
},
|
||||||
{
|
{
|
||||||
id: "sound_toggle",
|
id: "sound_toggle",
|
||||||
type: ParameterType.checkbox,
|
type: ParameterType.checkbox,
|
||||||
@ -77,12 +94,20 @@ var PARAMETERS = [
|
|||||||
default: true,
|
default: true,
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
id: "turbo",
|
id: "vram_usage_level",
|
||||||
type: ParameterType.checkbox,
|
type: ParameterType.select,
|
||||||
label: "Turbo Mode",
|
label: "GPU Memory Usage",
|
||||||
note: "generates images faster, but uses an additional 1 GB of GPU memory",
|
note: "Faster performance requires more GPU memory (VRAM)<br/><br/>" +
|
||||||
|
"<b>Balanced:</b> nearly as fast as High, much lower VRAM usage<br/>" +
|
||||||
|
"<b>High:</b> fastest, maximum GPU memory usage</br>" +
|
||||||
|
"<b>Low:</b> slowest, force-used for GPUs with 4 GB (or less) memory",
|
||||||
icon: "fa-forward",
|
icon: "fa-forward",
|
||||||
default: true,
|
default: "balanced",
|
||||||
|
options: [
|
||||||
|
{value: "balanced", label: "Balanced"},
|
||||||
|
{value: "high", label: "High"},
|
||||||
|
{value: "low", label: "Low"}
|
||||||
|
],
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
id: "use_cpu",
|
id: "use_cpu",
|
||||||
@ -105,14 +130,6 @@ var PARAMETERS = [
|
|||||||
note: "to process in parallel",
|
note: "to process in parallel",
|
||||||
default: false,
|
default: false,
|
||||||
},
|
},
|
||||||
{
|
|
||||||
id: "use_full_precision",
|
|
||||||
type: ParameterType.checkbox,
|
|
||||||
label: "Use Full Precision",
|
|
||||||
note: "for GPU-only. warning: this will consume more VRAM",
|
|
||||||
icon: "fa-crosshairs",
|
|
||||||
default: false,
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
id: "auto_save_settings",
|
id: "auto_save_settings",
|
||||||
type: ParameterType.checkbox,
|
type: ParameterType.checkbox,
|
||||||
@ -147,14 +164,6 @@ var PARAMETERS = [
|
|||||||
return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
|
return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
{
|
|
||||||
id: "test_sd2",
|
|
||||||
type: ParameterType.checkbox,
|
|
||||||
label: "Test SD 2.0",
|
|
||||||
note: "Experimental! High memory usage! GPU-only! Not the final version! Please restart the program after changing this.",
|
|
||||||
icon: "fa-fire",
|
|
||||||
default: false,
|
|
||||||
},
|
|
||||||
{
|
{
|
||||||
id: "use_beta_channel",
|
id: "use_beta_channel",
|
||||||
type: ParameterType.checkbox,
|
type: ParameterType.checkbox,
|
||||||
@ -210,16 +219,14 @@ function initParameters() {
|
|||||||
|
|
||||||
initParameters()
|
initParameters()
|
||||||
|
|
||||||
let turboField = document.querySelector('#turbo')
|
let vramUsageLevelField = document.querySelector('#vram_usage_level')
|
||||||
let useCPUField = document.querySelector('#use_cpu')
|
let useCPUField = document.querySelector('#use_cpu')
|
||||||
let autoPickGPUsField = document.querySelector('#auto_pick_gpus')
|
let autoPickGPUsField = document.querySelector('#auto_pick_gpus')
|
||||||
let useGPUsField = document.querySelector('#use_gpus')
|
let useGPUsField = document.querySelector('#use_gpus')
|
||||||
let useFullPrecisionField = document.querySelector('#use_full_precision')
|
|
||||||
let saveToDiskField = document.querySelector('#save_to_disk')
|
let saveToDiskField = document.querySelector('#save_to_disk')
|
||||||
let diskPathField = document.querySelector('#diskPath')
|
let diskPathField = document.querySelector('#diskPath')
|
||||||
let listenToNetworkField = document.querySelector("#listen_to_network")
|
let listenToNetworkField = document.querySelector("#listen_to_network")
|
||||||
let listenPortField = document.querySelector("#listen_port")
|
let listenPortField = document.querySelector("#listen_port")
|
||||||
let testSD2Field = document.querySelector("#test_sd2")
|
|
||||||
let useBetaChannelField = document.querySelector("#use_beta_channel")
|
let useBetaChannelField = document.querySelector("#use_beta_channel")
|
||||||
let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
|
let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
|
||||||
let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
|
let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
|
||||||
@ -256,12 +263,6 @@ async function getAppConfig() {
|
|||||||
if (config.ui && config.ui.open_browser_on_start === false) {
|
if (config.ui && config.ui.open_browser_on_start === false) {
|
||||||
uiOpenBrowserOnStartField.checked = false
|
uiOpenBrowserOnStartField.checked = false
|
||||||
}
|
}
|
||||||
if ('test_sd2' in config) {
|
|
||||||
testSD2Field.checked = config['test_sd2']
|
|
||||||
}
|
|
||||||
|
|
||||||
let testSD2SettingEntry = getParameterSettingsEntry('test_sd2')
|
|
||||||
testSD2SettingEntry.style.display = (config.update_branch === 'beta' ? '' : 'none')
|
|
||||||
if (config.net && config.net.listen_to_network === false) {
|
if (config.net && config.net.listen_to_network === false) {
|
||||||
listenToNetworkField.checked = false
|
listenToNetworkField.checked = false
|
||||||
}
|
}
|
||||||
@ -327,20 +328,10 @@ autoPickGPUsField.addEventListener('click', function() {
|
|||||||
gpuSettingEntry.style.display = (this.checked ? 'none' : '')
|
gpuSettingEntry.style.display = (this.checked ? 'none' : '')
|
||||||
})
|
})
|
||||||
|
|
||||||
async function getDiskPath() {
|
async function setDiskPath(defaultDiskPath) {
|
||||||
try {
|
|
||||||
var diskPath = getSetting("diskPath")
|
var diskPath = getSetting("diskPath")
|
||||||
if (diskPath == '' || diskPath == undefined || diskPath == "undefined") {
|
if (diskPath == '' || diskPath == undefined || diskPath == "undefined") {
|
||||||
let res = await fetch('/get/output_dir')
|
setSetting("diskPath", defaultDiskPath)
|
||||||
if (res.status === 200) {
|
|
||||||
res = await res.json()
|
|
||||||
res = res.output_dir
|
|
||||||
|
|
||||||
setSetting("diskPath", res)
|
|
||||||
}
|
|
||||||
}
|
|
||||||
} catch (e) {
|
|
||||||
console.log('error fetching output dir path', e)
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -415,6 +406,7 @@ async function getSystemInfo() {
|
|||||||
|
|
||||||
setDeviceInfo(devices)
|
setDeviceInfo(devices)
|
||||||
setHostInfo(res['hosts'])
|
setHostInfo(res['hosts'])
|
||||||
|
setDiskPath(res['default_output_dir'])
|
||||||
} catch (e) {
|
} catch (e) {
|
||||||
console.log('error fetching devices', e)
|
console.log('error fetching devices', e)
|
||||||
}
|
}
|
||||||
@ -435,8 +427,7 @@ saveSettingsBtn.addEventListener('click', function() {
|
|||||||
'update_branch': updateBranch,
|
'update_branch': updateBranch,
|
||||||
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
|
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
|
||||||
'listen_to_network': listenToNetworkField.checked,
|
'listen_to_network': listenToNetworkField.checked,
|
||||||
'listen_port': listenPortField.value,
|
'listen_port': listenPortField.value
|
||||||
'test_sd2': testSD2Field.checked
|
|
||||||
})
|
})
|
||||||
saveSettingsBtn.classList.add('active')
|
saveSettingsBtn.classList.add('active')
|
||||||
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
|
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
|
||||||
|
@ -1,119 +0,0 @@
|
|||||||
import json
|
|
||||||
|
|
||||||
class Request:
|
|
||||||
request_id: str = None
|
|
||||||
session_id: str = "session"
|
|
||||||
prompt: str = ""
|
|
||||||
negative_prompt: str = ""
|
|
||||||
init_image: str = None # base64
|
|
||||||
mask: str = None # base64
|
|
||||||
num_outputs: int = 1
|
|
||||||
num_inference_steps: int = 50
|
|
||||||
guidance_scale: float = 7.5
|
|
||||||
width: int = 512
|
|
||||||
height: int = 512
|
|
||||||
seed: int = 42
|
|
||||||
prompt_strength: float = 0.8
|
|
||||||
sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
|
|
||||||
# allow_nsfw: bool = False
|
|
||||||
precision: str = "autocast" # or "full"
|
|
||||||
save_to_disk_path: str = None
|
|
||||||
turbo: bool = True
|
|
||||||
use_full_precision: bool = False
|
|
||||||
use_face_correction: str = None # or "GFPGANv1.3"
|
|
||||||
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
|
|
||||||
use_stable_diffusion_model: str = "sd-v1-4"
|
|
||||||
use_vae_model: str = None
|
|
||||||
use_hypernetwork_model: str = None
|
|
||||||
hypernetwork_strength: float = 1
|
|
||||||
show_only_filtered_image: bool = False
|
|
||||||
output_format: str = "jpeg" # or "png"
|
|
||||||
output_quality: int = 75
|
|
||||||
|
|
||||||
stream_progress_updates: bool = False
|
|
||||||
stream_image_progress: bool = False
|
|
||||||
|
|
||||||
def json(self):
|
|
||||||
return {
|
|
||||||
"session_id": self.session_id,
|
|
||||||
"prompt": self.prompt,
|
|
||||||
"negative_prompt": self.negative_prompt,
|
|
||||||
"num_outputs": self.num_outputs,
|
|
||||||
"num_inference_steps": self.num_inference_steps,
|
|
||||||
"guidance_scale": self.guidance_scale,
|
|
||||||
"hypernetwork_strengtgh": self.guidance_scale,
|
|
||||||
"width": self.width,
|
|
||||||
"height": self.height,
|
|
||||||
"seed": self.seed,
|
|
||||||
"prompt_strength": self.prompt_strength,
|
|
||||||
"sampler": self.sampler,
|
|
||||||
"use_face_correction": self.use_face_correction,
|
|
||||||
"use_upscale": self.use_upscale,
|
|
||||||
"use_stable_diffusion_model": self.use_stable_diffusion_model,
|
|
||||||
"use_vae_model": self.use_vae_model,
|
|
||||||
"use_hypernetwork_model": self.use_hypernetwork_model,
|
|
||||||
"hypernetwork_strength": self.hypernetwork_strength,
|
|
||||||
"output_format": self.output_format,
|
|
||||||
"output_quality": self.output_quality,
|
|
||||||
}
|
|
||||||
|
|
||||||
def __str__(self):
|
|
||||||
return f'''
|
|
||||||
session_id: {self.session_id}
|
|
||||||
prompt: {self.prompt}
|
|
||||||
negative_prompt: {self.negative_prompt}
|
|
||||||
seed: {self.seed}
|
|
||||||
num_inference_steps: {self.num_inference_steps}
|
|
||||||
sampler: {self.sampler}
|
|
||||||
guidance_scale: {self.guidance_scale}
|
|
||||||
w: {self.width}
|
|
||||||
h: {self.height}
|
|
||||||
precision: {self.precision}
|
|
||||||
save_to_disk_path: {self.save_to_disk_path}
|
|
||||||
turbo: {self.turbo}
|
|
||||||
use_full_precision: {self.use_full_precision}
|
|
||||||
use_face_correction: {self.use_face_correction}
|
|
||||||
use_upscale: {self.use_upscale}
|
|
||||||
use_stable_diffusion_model: {self.use_stable_diffusion_model}
|
|
||||||
use_vae_model: {self.use_vae_model}
|
|
||||||
use_hypernetwork_model: {self.use_hypernetwork_model}
|
|
||||||
hypernetwork_strength: {self.hypernetwork_strength}
|
|
||||||
show_only_filtered_image: {self.show_only_filtered_image}
|
|
||||||
output_format: {self.output_format}
|
|
||||||
output_quality: {self.output_quality}
|
|
||||||
|
|
||||||
stream_progress_updates: {self.stream_progress_updates}
|
|
||||||
stream_image_progress: {self.stream_image_progress}'''
|
|
||||||
|
|
||||||
class Image:
|
|
||||||
data: str # base64
|
|
||||||
seed: int
|
|
||||||
is_nsfw: bool
|
|
||||||
path_abs: str = None
|
|
||||||
|
|
||||||
def __init__(self, data, seed):
|
|
||||||
self.data = data
|
|
||||||
self.seed = seed
|
|
||||||
|
|
||||||
def json(self):
|
|
||||||
return {
|
|
||||||
"data": self.data,
|
|
||||||
"seed": self.seed,
|
|
||||||
"path_abs": self.path_abs,
|
|
||||||
}
|
|
||||||
|
|
||||||
class Response:
|
|
||||||
request: Request
|
|
||||||
images: list
|
|
||||||
|
|
||||||
def json(self):
|
|
||||||
res = {
|
|
||||||
"status": 'succeeded',
|
|
||||||
"request": self.request.json(),
|
|
||||||
"output": [],
|
|
||||||
}
|
|
||||||
|
|
||||||
for image in self.images:
|
|
||||||
res["output"].append(image.json())
|
|
||||||
|
|
||||||
return res
|
|
@ -1,162 +0,0 @@
|
|||||||
diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
|
|
||||||
index 79058bc..a473411 100644
|
|
||||||
--- a/optimizedSD/ddpm.py
|
|
||||||
+++ b/optimizedSD/ddpm.py
|
|
||||||
@@ -564,12 +564,12 @@ class UNet(DDPM):
|
|
||||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
||||||
callback=callback, img_callback=img_callback)
|
|
||||||
|
|
||||||
+ yield from samples
|
|
||||||
+
|
|
||||||
if(self.turbo):
|
|
||||||
self.model1.to("cpu")
|
|
||||||
self.model2.to("cpu")
|
|
||||||
|
|
||||||
- return samples
|
|
||||||
-
|
|
||||||
@torch.no_grad()
|
|
||||||
def plms_sampling(self, cond,b, img,
|
|
||||||
ddim_use_original_steps=False,
|
|
||||||
@@ -608,10 +608,10 @@ class UNet(DDPM):
|
|
||||||
old_eps.append(e_t)
|
|
||||||
if len(old_eps) >= 4:
|
|
||||||
old_eps.pop(0)
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(pred_x0, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(pred_x0, i)
|
|
||||||
|
|
||||||
- return img
|
|
||||||
+ yield from img_callback(img, len(iterator)-1)
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
||||||
@@ -740,13 +740,13 @@ class UNet(DDPM):
|
|
||||||
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
||||||
unconditional_conditioning=unconditional_conditioning)
|
|
||||||
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x_dec, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x_dec, i)
|
|
||||||
|
|
||||||
if mask is not None:
|
|
||||||
- return x0 * mask + (1. - mask) * x_dec
|
|
||||||
+ x_dec = x0 * mask + (1. - mask) * x_dec
|
|
||||||
|
|
||||||
- return x_dec
|
|
||||||
+ yield from img_callback(x_dec, len(iterator)-1)
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
@@ -820,12 +820,12 @@ class UNet(DDPM):
|
|
||||||
|
|
||||||
|
|
||||||
d = to_d(x, sigma_hat, denoised)
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
dt = sigmas[i + 1] - sigma_hat
|
|
||||||
# Euler method
|
|
||||||
x = x + d * dt
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, img_callback=None):
|
|
||||||
@@ -852,14 +852,14 @@ class UNet(DDPM):
|
|
||||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
||||||
|
|
||||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
d = to_d(x, sigmas[i], denoised)
|
|
||||||
# Euler method
|
|
||||||
dt = sigma_down - sigmas[i]
|
|
||||||
x = x + d * dt
|
|
||||||
x = x + torch.randn_like(x) * sigma_up
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@@ -892,8 +892,8 @@ class UNet(DDPM):
|
|
||||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
||||||
|
|
||||||
d = to_d(x, sigma_hat, denoised)
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
dt = sigmas[i + 1] - sigma_hat
|
|
||||||
if sigmas[i + 1] == 0:
|
|
||||||
# Euler method
|
|
||||||
@@ -913,7 +913,7 @@ class UNet(DDPM):
|
|
||||||
d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
|
|
||||||
d_prime = (d + d_2) / 2
|
|
||||||
x = x + d_prime * dt
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
@@ -944,8 +944,8 @@ class UNet(DDPM):
|
|
||||||
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
|
|
||||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
||||||
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
|
|
||||||
d = to_d(x, sigma_hat, denoised)
|
|
||||||
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
|
|
||||||
@@ -966,7 +966,7 @@ class UNet(DDPM):
|
|
||||||
|
|
||||||
d_2 = to_d(x_2, sigma_mid, denoised_2)
|
|
||||||
x = x + d_2 * dt_2
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
@@ -994,8 +994,8 @@ class UNet(DDPM):
|
|
||||||
|
|
||||||
|
|
||||||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
d = to_d(x, sigmas[i], denoised)
|
|
||||||
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
|
|
||||||
sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
|
|
||||||
@@ -1016,7 +1016,7 @@ class UNet(DDPM):
|
|
||||||
d_2 = to_d(x_2, sigma_mid, denoised_2)
|
|
||||||
x = x + d_2 * dt_2
|
|
||||||
x = x + torch.randn_like(x) * sigma_up
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
||||||
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
@@ -1042,8 +1042,8 @@ class UNet(DDPM):
|
|
||||||
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
|
|
||||||
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
||||||
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(x, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(x, i)
|
|
||||||
|
|
||||||
d = to_d(x, sigmas[i], denoised)
|
|
||||||
ds.append(d)
|
|
||||||
@@ -1054,4 +1054,4 @@ class UNet(DDPM):
|
|
||||||
cur_order = min(i + 1, order)
|
|
||||||
coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
|
|
||||||
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
|
|
||||||
- return x
|
|
||||||
+ yield from img_callback(x, len(sigmas)-1)
|
|
@ -1,84 +0,0 @@
|
|||||||
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
|
|
||||||
index 27ead0e..6215939 100644
|
|
||||||
--- a/ldm/models/diffusion/ddim.py
|
|
||||||
+++ b/ldm/models/diffusion/ddim.py
|
|
||||||
@@ -100,7 +100,7 @@ class DDIMSampler(object):
|
|
||||||
size = (batch_size, C, H, W)
|
|
||||||
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
|
||||||
|
|
||||||
- samples, intermediates = self.ddim_sampling(conditioning, size,
|
|
||||||
+ samples = self.ddim_sampling(conditioning, size,
|
|
||||||
callback=callback,
|
|
||||||
img_callback=img_callback,
|
|
||||||
quantize_denoised=quantize_x0,
|
|
||||||
@@ -117,7 +117,8 @@ class DDIMSampler(object):
|
|
||||||
dynamic_threshold=dynamic_threshold,
|
|
||||||
ucg_schedule=ucg_schedule
|
|
||||||
)
|
|
||||||
- return samples, intermediates
|
|
||||||
+ # return samples, intermediates
|
|
||||||
+ yield from samples
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def ddim_sampling(self, cond, shape,
|
|
||||||
@@ -168,14 +169,15 @@ class DDIMSampler(object):
|
|
||||||
unconditional_conditioning=unconditional_conditioning,
|
|
||||||
dynamic_threshold=dynamic_threshold)
|
|
||||||
img, pred_x0 = outs
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(pred_x0, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(pred_x0, i)
|
|
||||||
|
|
||||||
if index % log_every_t == 0 or index == total_steps - 1:
|
|
||||||
intermediates['x_inter'].append(img)
|
|
||||||
intermediates['pred_x0'].append(pred_x0)
|
|
||||||
|
|
||||||
- return img, intermediates
|
|
||||||
+ # return img, intermediates
|
|
||||||
+ yield from img_callback(pred_x0, len(iterator)-1)
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
||||||
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
|
|
||||||
index 7002a36..0951f39 100644
|
|
||||||
--- a/ldm/models/diffusion/plms.py
|
|
||||||
+++ b/ldm/models/diffusion/plms.py
|
|
||||||
@@ -96,7 +96,7 @@ class PLMSSampler(object):
|
|
||||||
size = (batch_size, C, H, W)
|
|
||||||
print(f'Data shape for PLMS sampling is {size}')
|
|
||||||
|
|
||||||
- samples, intermediates = self.plms_sampling(conditioning, size,
|
|
||||||
+ samples = self.plms_sampling(conditioning, size,
|
|
||||||
callback=callback,
|
|
||||||
img_callback=img_callback,
|
|
||||||
quantize_denoised=quantize_x0,
|
|
||||||
@@ -112,7 +112,8 @@ class PLMSSampler(object):
|
|
||||||
unconditional_conditioning=unconditional_conditioning,
|
|
||||||
dynamic_threshold=dynamic_threshold,
|
|
||||||
)
|
|
||||||
- return samples, intermediates
|
|
||||||
+ #return samples, intermediates
|
|
||||||
+ yield from samples
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def plms_sampling(self, cond, shape,
|
|
||||||
@@ -165,14 +166,15 @@ class PLMSSampler(object):
|
|
||||||
old_eps.append(e_t)
|
|
||||||
if len(old_eps) >= 4:
|
|
||||||
old_eps.pop(0)
|
|
||||||
- if callback: callback(i)
|
|
||||||
- if img_callback: img_callback(pred_x0, i)
|
|
||||||
+ if callback: yield from callback(i)
|
|
||||||
+ if img_callback: yield from img_callback(pred_x0, i)
|
|
||||||
|
|
||||||
if index % log_every_t == 0 or index == total_steps - 1:
|
|
||||||
intermediates['x_inter'].append(img)
|
|
||||||
intermediates['pred_x0'].append(pred_x0)
|
|
||||||
|
|
||||||
- return img, intermediates
|
|
||||||
+ # return img, intermediates
|
|
||||||
+ yield from img_callback(pred_x0, len(iterator)-1)
|
|
||||||
|
|
||||||
@torch.no_grad()
|
|
||||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
@ -1,198 +0,0 @@
|
|||||||
# this is basically a cut down version of https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/c9a2cfdf2a53d37c2de1908423e4f548088667ef/modules/hypernetworks/hypernetwork.py, mostly for feature parity
|
|
||||||
# I, c0bra5, don't really understand how deep learning works. I just know how to port stuff.
|
|
||||||
|
|
||||||
import inspect
|
|
||||||
import torch
|
|
||||||
import optimizedSD.splitAttention
|
|
||||||
from . import runtime
|
|
||||||
from einops import rearrange
|
|
||||||
|
|
||||||
optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
|
|
||||||
|
|
||||||
loaded_hypernetwork = None
|
|
||||||
|
|
||||||
class HypernetworkModule(torch.nn.Module):
|
|
||||||
multiplier = 0.5
|
|
||||||
activation_dict = {
|
|
||||||
"linear": torch.nn.Identity,
|
|
||||||
"relu": torch.nn.ReLU,
|
|
||||||
"leakyrelu": torch.nn.LeakyReLU,
|
|
||||||
"elu": torch.nn.ELU,
|
|
||||||
"swish": torch.nn.Hardswish,
|
|
||||||
"tanh": torch.nn.Tanh,
|
|
||||||
"sigmoid": torch.nn.Sigmoid,
|
|
||||||
}
|
|
||||||
activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
|
|
||||||
|
|
||||||
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
|
|
||||||
add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=False):
|
|
||||||
super().__init__()
|
|
||||||
|
|
||||||
assert layer_structure is not None, "layer_structure must not be None"
|
|
||||||
assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!"
|
|
||||||
assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!"
|
|
||||||
|
|
||||||
linears = []
|
|
||||||
for i in range(len(layer_structure) - 1):
|
|
||||||
|
|
||||||
# Add a fully-connected layer
|
|
||||||
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
|
|
||||||
|
|
||||||
# Add an activation func except last layer
|
|
||||||
if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
|
|
||||||
pass
|
|
||||||
elif activation_func in self.activation_dict:
|
|
||||||
linears.append(self.activation_dict[activation_func]())
|
|
||||||
else:
|
|
||||||
raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
|
|
||||||
|
|
||||||
# Add layer normalization
|
|
||||||
if add_layer_norm:
|
|
||||||
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
|
|
||||||
|
|
||||||
# Add dropout except last layer
|
|
||||||
if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
|
|
||||||
linears.append(torch.nn.Dropout(p=0.3))
|
|
||||||
|
|
||||||
self.linear = torch.nn.Sequential(*linears)
|
|
||||||
|
|
||||||
self.fix_old_state_dict(state_dict)
|
|
||||||
self.load_state_dict(state_dict)
|
|
||||||
|
|
||||||
self.to(runtime.thread_data.device)
|
|
||||||
|
|
||||||
def fix_old_state_dict(self, state_dict):
|
|
||||||
changes = {
|
|
||||||
'linear1.bias': 'linear.0.bias',
|
|
||||||
'linear1.weight': 'linear.0.weight',
|
|
||||||
'linear2.bias': 'linear.1.bias',
|
|
||||||
'linear2.weight': 'linear.1.weight',
|
|
||||||
}
|
|
||||||
|
|
||||||
for fr, to in changes.items():
|
|
||||||
x = state_dict.get(fr, None)
|
|
||||||
if x is None:
|
|
||||||
continue
|
|
||||||
|
|
||||||
del state_dict[fr]
|
|
||||||
state_dict[to] = x
|
|
||||||
|
|
||||||
def forward(self, x: torch.Tensor):
|
|
||||||
return x + self.linear(x) * runtime.thread_data.hypernetwork_strength
|
|
||||||
|
|
||||||
def apply_hypernetwork(hypernetwork, context, layer=None):
|
|
||||||
hypernetwork_layers = hypernetwork.get(context.shape[2], None)
|
|
||||||
|
|
||||||
if hypernetwork_layers is None:
|
|
||||||
return context, context
|
|
||||||
|
|
||||||
if layer is not None:
|
|
||||||
layer.hyper_k = hypernetwork_layers[0]
|
|
||||||
layer.hyper_v = hypernetwork_layers[1]
|
|
||||||
|
|
||||||
context_k = hypernetwork_layers[0](context)
|
|
||||||
context_v = hypernetwork_layers[1](context)
|
|
||||||
return context_k, context_v
|
|
||||||
|
|
||||||
def get_kv(context, hypernetwork):
|
|
||||||
if hypernetwork is None:
|
|
||||||
return context, context
|
|
||||||
else:
|
|
||||||
return apply_hypernetwork(runtime.thread_data.hypernetwork, context)
|
|
||||||
|
|
||||||
# This might need updating as the optimisedSD code changes
|
|
||||||
# I think yall have a system for this (patch files in sd_internal) but idk how it works and no amount of searching gave me any clue
|
|
||||||
# just in case for attribution https://github.com/easydiffusion/diffusion-kit/blob/e8ea0cadd543056059cd951e76d4744de76327d2/optimizedSD/splitAttention.py#L171
|
|
||||||
def new_cross_attention_forward(self, x, context=None, mask=None):
|
|
||||||
h = self.heads
|
|
||||||
|
|
||||||
q = self.to_q(x)
|
|
||||||
# default context
|
|
||||||
context = context if context is not None else x() if inspect.isfunction(x) else x
|
|
||||||
# hypernetwork!
|
|
||||||
context_k, context_v = get_kv(context, runtime.thread_data.hypernetwork)
|
|
||||||
k = self.to_k(context_k)
|
|
||||||
v = self.to_v(context_v)
|
|
||||||
del context, x
|
|
||||||
|
|
||||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
||||||
|
|
||||||
|
|
||||||
limit = k.shape[0]
|
|
||||||
att_step = self.att_step
|
|
||||||
q_chunks = list(torch.tensor_split(q, limit//att_step, dim=0))
|
|
||||||
k_chunks = list(torch.tensor_split(k, limit//att_step, dim=0))
|
|
||||||
v_chunks = list(torch.tensor_split(v, limit//att_step, dim=0))
|
|
||||||
|
|
||||||
q_chunks.reverse()
|
|
||||||
k_chunks.reverse()
|
|
||||||
v_chunks.reverse()
|
|
||||||
sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
|
||||||
del k, q, v
|
|
||||||
for i in range (0, limit, att_step):
|
|
||||||
|
|
||||||
q_buffer = q_chunks.pop()
|
|
||||||
k_buffer = k_chunks.pop()
|
|
||||||
v_buffer = v_chunks.pop()
|
|
||||||
sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
|
|
||||||
|
|
||||||
del k_buffer, q_buffer
|
|
||||||
# attention, what we cannot get enough of, by chunks
|
|
||||||
|
|
||||||
sim_buffer = sim_buffer.softmax(dim=-1)
|
|
||||||
|
|
||||||
sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
|
|
||||||
del v_buffer
|
|
||||||
sim[i:i+att_step,:,:] = sim_buffer
|
|
||||||
|
|
||||||
del sim_buffer
|
|
||||||
sim = rearrange(sim, '(b h) n d -> b n (h d)', h=h)
|
|
||||||
return self.to_out(sim)
|
|
||||||
|
|
||||||
|
|
||||||
def load_hypernetwork(path: str):
|
|
||||||
|
|
||||||
state_dict = torch.load(path, map_location='cpu')
|
|
||||||
|
|
||||||
layer_structure = state_dict.get('layer_structure', [1, 2, 1])
|
|
||||||
activation_func = state_dict.get('activation_func', None)
|
|
||||||
weight_init = state_dict.get('weight_initialization', 'Normal')
|
|
||||||
add_layer_norm = state_dict.get('is_layer_norm', False)
|
|
||||||
use_dropout = state_dict.get('use_dropout', False)
|
|
||||||
activate_output = state_dict.get('activate_output', True)
|
|
||||||
last_layer_dropout = state_dict.get('last_layer_dropout', False)
|
|
||||||
# this is a bit verbose so leaving it commented out for the poor soul who ever has to debug this
|
|
||||||
# print(f"layer_structure: {layer_structure}")
|
|
||||||
# print(f"activation_func: {activation_func}")
|
|
||||||
# print(f"weight_init: {weight_init}")
|
|
||||||
# print(f"add_layer_norm: {add_layer_norm}")
|
|
||||||
# print(f"use_dropout: {use_dropout}")
|
|
||||||
# print(f"activate_output: {activate_output}")
|
|
||||||
# print(f"last_layer_dropout: {last_layer_dropout}")
|
|
||||||
|
|
||||||
layers = {}
|
|
||||||
for size, sd in state_dict.items():
|
|
||||||
if type(size) == int:
|
|
||||||
layers[size] = (
|
|
||||||
HypernetworkModule(size, sd[0], layer_structure, activation_func, weight_init, add_layer_norm,
|
|
||||||
use_dropout, activate_output, last_layer_dropout=last_layer_dropout),
|
|
||||||
HypernetworkModule(size, sd[1], layer_structure, activation_func, weight_init, add_layer_norm,
|
|
||||||
use_dropout, activate_output, last_layer_dropout=last_layer_dropout),
|
|
||||||
)
|
|
||||||
print(f"hypernetwork loaded")
|
|
||||||
return layers
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
# overriding of original function
|
|
||||||
old_cross_attention_forward = optimizedSD.splitAttention.CrossAttention.forward
|
|
||||||
# hijacks the cross attention forward function to add hyper network support
|
|
||||||
def hijack_cross_attention():
|
|
||||||
print("hypernetwork functionality added to cross attention")
|
|
||||||
optimizedSD.splitAttention.CrossAttention.forward = new_cross_attention_forward
|
|
||||||
# there was a cop on board
|
|
||||||
def unhijack_cross_attention_forward():
|
|
||||||
print("hypernetwork functionality removed from cross attention")
|
|
||||||
optimizedSD.splitAttention.CrossAttention.forward = old_cross_attention_forward
|
|
||||||
|
|
||||||
hijack_cross_attention()
|
|
File diff suppressed because it is too large
Load Diff
500
ui/server.py
500
ui/server.py
@ -1,500 +0,0 @@
|
|||||||
"""server.py: FastAPI SD-UI Web Host.
|
|
||||||
Notes:
|
|
||||||
async endpoints always run on the main thread. Without they run on the thread pool.
|
|
||||||
"""
|
|
||||||
import json
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
import sys
|
|
||||||
import os
|
|
||||||
import socket
|
|
||||||
import picklescan.scanner
|
|
||||||
import rich
|
|
||||||
|
|
||||||
SD_DIR = os.getcwd()
|
|
||||||
print('started in ', SD_DIR)
|
|
||||||
|
|
||||||
SD_UI_DIR = os.getenv('SD_UI_PATH', None)
|
|
||||||
sys.path.append(os.path.dirname(SD_UI_DIR))
|
|
||||||
|
|
||||||
CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
|
|
||||||
MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'models'))
|
|
||||||
|
|
||||||
USER_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'))
|
|
||||||
CORE_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, 'plugins', 'ui'))
|
|
||||||
UI_PLUGINS_SOURCES = ((CORE_UI_PLUGINS_DIR, 'core'), (USER_UI_PLUGINS_DIR, 'user'))
|
|
||||||
|
|
||||||
STABLE_DIFFUSION_MODEL_EXTENSIONS = ['.ckpt', '.safetensors']
|
|
||||||
VAE_MODEL_EXTENSIONS = ['.vae.pt', '.ckpt']
|
|
||||||
HYPERNETWORK_MODEL_EXTENSIONS = ['.pt']
|
|
||||||
|
|
||||||
OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
|
|
||||||
TASK_TTL = 15 * 60 # Discard last session's task timeout
|
|
||||||
APP_CONFIG_DEFAULTS = {
|
|
||||||
# auto: selects the cuda device with the most free memory, cuda: use the currently active cuda device.
|
|
||||||
'render_devices': 'auto', # valid entries: 'auto', 'cpu' or 'cuda:N' (where N is a GPU index)
|
|
||||||
'update_branch': 'main',
|
|
||||||
'ui': {
|
|
||||||
'open_browser_on_start': True,
|
|
||||||
},
|
|
||||||
}
|
|
||||||
APP_CONFIG_DEFAULT_MODELS = [
|
|
||||||
# needed to support the legacy installations
|
|
||||||
'custom-model', # Check if user has a custom model, use it first.
|
|
||||||
'sd-v1-4', # Default fallback.
|
|
||||||
]
|
|
||||||
|
|
||||||
from fastapi import FastAPI, HTTPException
|
|
||||||
from fastapi.staticfiles import StaticFiles
|
|
||||||
from starlette.responses import FileResponse, JSONResponse, StreamingResponse
|
|
||||||
from pydantic import BaseModel
|
|
||||||
import logging
|
|
||||||
from typing import Any, Generator, Hashable, List, Optional, Union
|
|
||||||
|
|
||||||
from sd_internal import Request, Response, task_manager
|
|
||||||
|
|
||||||
app = FastAPI()
|
|
||||||
|
|
||||||
outpath = os.path.join(os.path.expanduser("~"), OUTPUT_DIRNAME)
|
|
||||||
|
|
||||||
os.makedirs(USER_UI_PLUGINS_DIR, exist_ok=True)
|
|
||||||
|
|
||||||
# don't show access log entries for URLs that start with the given prefix
|
|
||||||
ACCESS_LOG_SUPPRESS_PATH_PREFIXES = ['/ping', '/image', '/modifier-thumbnails']
|
|
||||||
|
|
||||||
NOCACHE_HEADERS={"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
|
|
||||||
|
|
||||||
class NoCacheStaticFiles(StaticFiles):
|
|
||||||
def is_not_modified(self, response_headers, request_headers) -> bool:
|
|
||||||
if 'content-type' in response_headers and ('javascript' in response_headers['content-type'] or 'css' in response_headers['content-type']):
|
|
||||||
response_headers.update(NOCACHE_HEADERS)
|
|
||||||
return False
|
|
||||||
|
|
||||||
return super().is_not_modified(response_headers, request_headers)
|
|
||||||
|
|
||||||
app.mount('/media', NoCacheStaticFiles(directory=os.path.join(SD_UI_DIR, 'media')), name="media")
|
|
||||||
|
|
||||||
for plugins_dir, dir_prefix in UI_PLUGINS_SOURCES:
|
|
||||||
app.mount(f'/plugins/{dir_prefix}', NoCacheStaticFiles(directory=plugins_dir), name=f"plugins-{dir_prefix}")
|
|
||||||
|
|
||||||
def getConfig(default_val=APP_CONFIG_DEFAULTS):
|
|
||||||
try:
|
|
||||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
|
||||||
if not os.path.exists(config_json_path):
|
|
||||||
return default_val
|
|
||||||
with open(config_json_path, 'r', encoding='utf-8') as f:
|
|
||||||
config = json.load(f)
|
|
||||||
if 'net' not in config:
|
|
||||||
config['net'] = {}
|
|
||||||
if os.getenv('SD_UI_BIND_PORT') is not None:
|
|
||||||
config['net']['listen_port'] = int(os.getenv('SD_UI_BIND_PORT'))
|
|
||||||
if os.getenv('SD_UI_BIND_IP') is not None:
|
|
||||||
config['net']['listen_to_network'] = ( os.getenv('SD_UI_BIND_IP') == '0.0.0.0' )
|
|
||||||
return config
|
|
||||||
except Exception as e:
|
|
||||||
print(str(e))
|
|
||||||
print(traceback.format_exc())
|
|
||||||
return default_val
|
|
||||||
|
|
||||||
def setConfig(config):
|
|
||||||
print( json.dumps(config) )
|
|
||||||
try: # config.json
|
|
||||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
|
||||||
with open(config_json_path, 'w', encoding='utf-8') as f:
|
|
||||||
json.dump(config, f)
|
|
||||||
except:
|
|
||||||
print(traceback.format_exc())
|
|
||||||
|
|
||||||
try: # config.bat
|
|
||||||
config_bat_path = os.path.join(CONFIG_DIR, 'config.bat')
|
|
||||||
config_bat = []
|
|
||||||
|
|
||||||
if 'update_branch' in config:
|
|
||||||
config_bat.append(f"@set update_branch={config['update_branch']}")
|
|
||||||
|
|
||||||
config_bat.append(f"@set SD_UI_BIND_PORT={config['net']['listen_port']}")
|
|
||||||
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
|
||||||
config_bat.append(f"@set SD_UI_BIND_IP={bind_ip}")
|
|
||||||
|
|
||||||
config_bat.append(f"@set test_sd2={'Y' if config.get('test_sd2', False) else 'N'}")
|
|
||||||
|
|
||||||
if len(config_bat) > 0:
|
|
||||||
with open(config_bat_path, 'w', encoding='utf-8') as f:
|
|
||||||
f.write('\r\n'.join(config_bat))
|
|
||||||
except:
|
|
||||||
print(traceback.format_exc())
|
|
||||||
|
|
||||||
try: # config.sh
|
|
||||||
config_sh_path = os.path.join(CONFIG_DIR, 'config.sh')
|
|
||||||
config_sh = ['#!/bin/bash']
|
|
||||||
|
|
||||||
if 'update_branch' in config:
|
|
||||||
config_sh.append(f"export update_branch={config['update_branch']}")
|
|
||||||
|
|
||||||
config_sh.append(f"export SD_UI_BIND_PORT={config['net']['listen_port']}")
|
|
||||||
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
|
||||||
config_sh.append(f"export SD_UI_BIND_IP={bind_ip}")
|
|
||||||
|
|
||||||
config_sh.append(f"export test_sd2=\"{'Y' if config.get('test_sd2', False) else 'N'}\"")
|
|
||||||
|
|
||||||
if len(config_sh) > 1:
|
|
||||||
with open(config_sh_path, 'w', encoding='utf-8') as f:
|
|
||||||
f.write('\n'.join(config_sh))
|
|
||||||
except:
|
|
||||||
print(traceback.format_exc())
|
|
||||||
|
|
||||||
def resolve_model_to_use(model_name:str, model_type:str, model_dir:str, model_extensions:list, default_models=[]):
|
|
||||||
config = getConfig()
|
|
||||||
|
|
||||||
model_dirs = [os.path.join(MODELS_DIR, model_dir), SD_DIR]
|
|
||||||
if not model_name: # When None try user configured model.
|
|
||||||
# config = getConfig()
|
|
||||||
if 'model' in config and model_type in config['model']:
|
|
||||||
model_name = config['model'][model_type]
|
|
||||||
if model_name:
|
|
||||||
is_sd2 = config.get('test_sd2', False)
|
|
||||||
if model_name.startswith('sd2_') and not is_sd2: # temp hack, until SD2 is unified with 1.4
|
|
||||||
print('ERROR: Cannot use SD 2.0 models with SD 1.0 code. Using the sd-v1-4 model instead!')
|
|
||||||
model_name = 'sd-v1-4'
|
|
||||||
|
|
||||||
# Check models directory
|
|
||||||
models_dir_path = os.path.join(MODELS_DIR, model_dir, model_name)
|
|
||||||
for model_extension in model_extensions:
|
|
||||||
if os.path.exists(models_dir_path + model_extension):
|
|
||||||
return models_dir_path
|
|
||||||
if os.path.exists(model_name + model_extension):
|
|
||||||
# Direct Path to file
|
|
||||||
model_name = os.path.abspath(model_name)
|
|
||||||
return model_name
|
|
||||||
# Default locations
|
|
||||||
if model_name in default_models:
|
|
||||||
default_model_path = os.path.join(SD_DIR, model_name)
|
|
||||||
for model_extension in model_extensions:
|
|
||||||
if os.path.exists(default_model_path + model_extension):
|
|
||||||
return default_model_path
|
|
||||||
# Can't find requested model, check the default paths.
|
|
||||||
for default_model in default_models:
|
|
||||||
for model_dir in model_dirs:
|
|
||||||
default_model_path = os.path.join(model_dir, default_model)
|
|
||||||
for model_extension in model_extensions:
|
|
||||||
if os.path.exists(default_model_path + model_extension):
|
|
||||||
if model_name is not None:
|
|
||||||
print(f'Could not find the configured custom model {model_name}{model_extension}. Using the default one: {default_model_path}{model_extension}')
|
|
||||||
return default_model_path
|
|
||||||
raise Exception('No valid models found.')
|
|
||||||
|
|
||||||
def resolve_ckpt_to_use(model_name:str=None):
|
|
||||||
return resolve_model_to_use(model_name, model_type='stable-diffusion', model_dir='stable-diffusion', model_extensions=STABLE_DIFFUSION_MODEL_EXTENSIONS, default_models=APP_CONFIG_DEFAULT_MODELS)
|
|
||||||
|
|
||||||
def resolve_vae_to_use(model_name:str=None):
|
|
||||||
try:
|
|
||||||
return resolve_model_to_use(model_name, model_type='vae', model_dir='vae', model_extensions=VAE_MODEL_EXTENSIONS, default_models=[])
|
|
||||||
except:
|
|
||||||
return None
|
|
||||||
|
|
||||||
def resolve_hypernetwork_to_use(model_name:str=None):
|
|
||||||
try:
|
|
||||||
return resolve_model_to_use(model_name, model_type='hypernetwork', model_dir='hypernetwork', model_extensions=HYPERNETWORK_MODEL_EXTENSIONS, default_models=[])
|
|
||||||
except:
|
|
||||||
return None
|
|
||||||
|
|
||||||
class SetAppConfigRequest(BaseModel):
|
|
||||||
update_branch: str = None
|
|
||||||
render_devices: Union[List[str], List[int], str, int] = None
|
|
||||||
model_vae: str = None
|
|
||||||
ui_open_browser_on_start: bool = None
|
|
||||||
listen_to_network: bool = None
|
|
||||||
listen_port: int = None
|
|
||||||
test_sd2: bool = None
|
|
||||||
|
|
||||||
@app.post('/app_config')
|
|
||||||
async def setAppConfig(req : SetAppConfigRequest):
|
|
||||||
config = getConfig()
|
|
||||||
if req.update_branch is not None:
|
|
||||||
config['update_branch'] = req.update_branch
|
|
||||||
if req.render_devices is not None:
|
|
||||||
update_render_devices_in_config(config, req.render_devices)
|
|
||||||
if req.ui_open_browser_on_start is not None:
|
|
||||||
if 'ui' not in config:
|
|
||||||
config['ui'] = {}
|
|
||||||
config['ui']['open_browser_on_start'] = req.ui_open_browser_on_start
|
|
||||||
if req.listen_to_network is not None:
|
|
||||||
if 'net' not in config:
|
|
||||||
config['net'] = {}
|
|
||||||
config['net']['listen_to_network'] = bool(req.listen_to_network)
|
|
||||||
if req.listen_port is not None:
|
|
||||||
if 'net' not in config:
|
|
||||||
config['net'] = {}
|
|
||||||
config['net']['listen_port'] = int(req.listen_port)
|
|
||||||
if req.test_sd2 is not None:
|
|
||||||
config['test_sd2'] = req.test_sd2
|
|
||||||
try:
|
|
||||||
setConfig(config)
|
|
||||||
|
|
||||||
if req.render_devices:
|
|
||||||
update_render_threads()
|
|
||||||
|
|
||||||
return JSONResponse({'status': 'OK'}, headers=NOCACHE_HEADERS)
|
|
||||||
except Exception as e:
|
|
||||||
print(traceback.format_exc())
|
|
||||||
raise HTTPException(status_code=500, detail=str(e))
|
|
||||||
|
|
||||||
def is_malicious_model(file_path):
|
|
||||||
try:
|
|
||||||
scan_result = picklescan.scanner.scan_file_path(file_path)
|
|
||||||
if scan_result.issues_count > 0 or scan_result.infected_files > 0:
|
|
||||||
rich.print(":warning: [bold red]Scan %s: %d scanned, %d issue, %d infected.[/bold red]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
|
|
||||||
return True
|
|
||||||
else:
|
|
||||||
rich.print("Scan %s: [green]%d scanned, %d issue, %d infected.[/green]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
|
|
||||||
return False
|
|
||||||
except Exception as e:
|
|
||||||
print('error while scanning', file_path, 'error:', e)
|
|
||||||
return False
|
|
||||||
|
|
||||||
known_models = {}
|
|
||||||
def getModels():
|
|
||||||
models = {
|
|
||||||
'active': {
|
|
||||||
'stable-diffusion': 'sd-v1-4',
|
|
||||||
'vae': '',
|
|
||||||
'hypernetwork': '',
|
|
||||||
},
|
|
||||||
'options': {
|
|
||||||
'stable-diffusion': ['sd-v1-4'],
|
|
||||||
'vae': [],
|
|
||||||
'hypernetwork': [],
|
|
||||||
},
|
|
||||||
}
|
|
||||||
|
|
||||||
def listModels(models_dirname, model_type, model_extensions):
|
|
||||||
models_dir = os.path.join(MODELS_DIR, models_dirname)
|
|
||||||
if not os.path.exists(models_dir):
|
|
||||||
os.makedirs(models_dir)
|
|
||||||
|
|
||||||
for file in os.listdir(models_dir):
|
|
||||||
for model_extension in model_extensions:
|
|
||||||
if not file.endswith(model_extension):
|
|
||||||
continue
|
|
||||||
|
|
||||||
model_path = os.path.join(models_dir, file)
|
|
||||||
mtime = os.path.getmtime(model_path)
|
|
||||||
mod_time = known_models[model_path] if model_path in known_models else -1
|
|
||||||
if mod_time != mtime:
|
|
||||||
if is_malicious_model(model_path):
|
|
||||||
models['scan-error'] = file
|
|
||||||
return
|
|
||||||
known_models[model_path] = mtime
|
|
||||||
|
|
||||||
model_name = file[:-len(model_extension)]
|
|
||||||
models['options'][model_type].append(model_name)
|
|
||||||
|
|
||||||
models['options'][model_type] = [*set(models['options'][model_type])] # remove duplicates
|
|
||||||
models['options'][model_type].sort()
|
|
||||||
|
|
||||||
# custom models
|
|
||||||
listModels(models_dirname='stable-diffusion', model_type='stable-diffusion', model_extensions=STABLE_DIFFUSION_MODEL_EXTENSIONS)
|
|
||||||
listModels(models_dirname='vae', model_type='vae', model_extensions=VAE_MODEL_EXTENSIONS)
|
|
||||||
listModels(models_dirname='hypernetwork', model_type='hypernetwork', model_extensions=HYPERNETWORK_MODEL_EXTENSIONS)
|
|
||||||
# legacy
|
|
||||||
custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt')
|
|
||||||
if os.path.exists(custom_weight_path):
|
|
||||||
models['options']['stable-diffusion'].append('custom-model')
|
|
||||||
|
|
||||||
return models
|
|
||||||
|
|
||||||
def getUIPlugins():
|
|
||||||
plugins = []
|
|
||||||
|
|
||||||
for plugins_dir, dir_prefix in UI_PLUGINS_SOURCES:
|
|
||||||
for file in os.listdir(plugins_dir):
|
|
||||||
if file.endswith('.plugin.js'):
|
|
||||||
plugins.append(f'/plugins/{dir_prefix}/{file}')
|
|
||||||
|
|
||||||
return plugins
|
|
||||||
|
|
||||||
def getIPConfig():
|
|
||||||
try:
|
|
||||||
ips = socket.gethostbyname_ex(socket.gethostname())
|
|
||||||
ips[2].append(ips[0])
|
|
||||||
return ips[2]
|
|
||||||
except Exception as e:
|
|
||||||
print(e)
|
|
||||||
print(traceback.format_exc())
|
|
||||||
return []
|
|
||||||
|
|
||||||
|
|
||||||
@app.get('/get/{key:path}')
|
|
||||||
def read_web_data(key:str=None):
|
|
||||||
if not key: # /get without parameters, stable-diffusion easter egg.
|
|
||||||
raise HTTPException(status_code=418, detail="StableDiffusion is drawing a teapot!") # HTTP418 I'm a teapot
|
|
||||||
elif key == 'app_config':
|
|
||||||
config = getConfig(default_val=None)
|
|
||||||
if config is None:
|
|
||||||
config = APP_CONFIG_DEFAULTS
|
|
||||||
return JSONResponse(config, headers=NOCACHE_HEADERS)
|
|
||||||
elif key == 'system_info':
|
|
||||||
config = getConfig()
|
|
||||||
system_info = {
|
|
||||||
'devices': task_manager.get_devices(),
|
|
||||||
'hosts': getIPConfig(),
|
|
||||||
}
|
|
||||||
system_info['devices']['config'] = config.get('render_devices', "auto")
|
|
||||||
return JSONResponse(system_info, headers=NOCACHE_HEADERS)
|
|
||||||
elif key == 'models':
|
|
||||||
return JSONResponse(getModels(), headers=NOCACHE_HEADERS)
|
|
||||||
elif key == 'modifiers': return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=NOCACHE_HEADERS)
|
|
||||||
elif key == 'output_dir': return JSONResponse({ 'output_dir': outpath }, headers=NOCACHE_HEADERS)
|
|
||||||
elif key == 'ui_plugins': return JSONResponse(getUIPlugins(), headers=NOCACHE_HEADERS)
|
|
||||||
else:
|
|
||||||
raise HTTPException(status_code=404, detail=f'Request for unknown {key}') # HTTP404 Not Found
|
|
||||||
|
|
||||||
@app.get('/ping') # Get server and optionally session status.
|
|
||||||
def ping(session_id:str=None):
|
|
||||||
if task_manager.is_alive() <= 0: # Check that render threads are alive.
|
|
||||||
if task_manager.current_state_error: raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
|
|
||||||
raise HTTPException(status_code=500, detail='Render thread is dead.')
|
|
||||||
if task_manager.current_state_error and not isinstance(task_manager.current_state_error, StopAsyncIteration): raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
|
|
||||||
# Alive
|
|
||||||
response = {'status': str(task_manager.current_state)}
|
|
||||||
if session_id:
|
|
||||||
session = task_manager.get_cached_session(session_id, update_ttl=True)
|
|
||||||
response['tasks'] = {id(t): t.status for t in session.tasks}
|
|
||||||
response['devices'] = task_manager.get_devices()
|
|
||||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
|
||||||
|
|
||||||
def save_model_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name):
|
|
||||||
config = getConfig()
|
|
||||||
if 'model' not in config:
|
|
||||||
config['model'] = {}
|
|
||||||
|
|
||||||
config['model']['stable-diffusion'] = ckpt_model_name
|
|
||||||
config['model']['vae'] = vae_model_name
|
|
||||||
config['model']['hypernetwork'] = hypernetwork_model_name
|
|
||||||
|
|
||||||
if vae_model_name is None or vae_model_name == "":
|
|
||||||
del config['model']['vae']
|
|
||||||
if hypernetwork_model_name is None or hypernetwork_model_name == "":
|
|
||||||
del config['model']['hypernetwork']
|
|
||||||
|
|
||||||
setConfig(config)
|
|
||||||
|
|
||||||
def update_render_devices_in_config(config, render_devices):
|
|
||||||
if render_devices not in ('cpu', 'auto') and not render_devices.startswith('cuda:'):
|
|
||||||
raise HTTPException(status_code=400, detail=f'Invalid render device requested: {render_devices}')
|
|
||||||
|
|
||||||
if render_devices.startswith('cuda:'):
|
|
||||||
render_devices = render_devices.split(',')
|
|
||||||
|
|
||||||
config['render_devices'] = render_devices
|
|
||||||
|
|
||||||
@app.post('/render')
|
|
||||||
def render(req : task_manager.ImageRequest):
|
|
||||||
try:
|
|
||||||
save_model_to_config(req.use_stable_diffusion_model, req.use_vae_model, req.use_hypernetwork_model)
|
|
||||||
req.use_stable_diffusion_model = resolve_ckpt_to_use(req.use_stable_diffusion_model)
|
|
||||||
req.use_vae_model = resolve_vae_to_use(req.use_vae_model)
|
|
||||||
req.use_hypernetwork_model = resolve_hypernetwork_to_use(req.use_hypernetwork_model)
|
|
||||||
new_task = task_manager.render(req)
|
|
||||||
response = {
|
|
||||||
'status': str(task_manager.current_state),
|
|
||||||
'queue': len(task_manager.tasks_queue),
|
|
||||||
'stream': f'/image/stream/{id(new_task)}',
|
|
||||||
'task': id(new_task)
|
|
||||||
}
|
|
||||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
|
||||||
except ChildProcessError as e: # Render thread is dead
|
|
||||||
raise HTTPException(status_code=500, detail=f'Rendering thread has died.') # HTTP500 Internal Server Error
|
|
||||||
except ConnectionRefusedError as e: # Unstarted task pending limit reached, deny queueing too many.
|
|
||||||
raise HTTPException(status_code=503, detail=str(e)) # HTTP503 Service Unavailable
|
|
||||||
except Exception as e:
|
|
||||||
print(e)
|
|
||||||
print(traceback.format_exc())
|
|
||||||
raise HTTPException(status_code=500, detail=str(e))
|
|
||||||
|
|
||||||
@app.get('/image/stream/{task_id:int}')
|
|
||||||
def stream(task_id:int):
|
|
||||||
#TODO Move to WebSockets ??
|
|
||||||
task = task_manager.get_cached_task(task_id, update_ttl=True)
|
|
||||||
if not task: raise HTTPException(status_code=404, detail=f'Request {task_id} not found.') # HTTP404 NotFound
|
|
||||||
#if (id(task) != task_id): raise HTTPException(status_code=409, detail=f'Wrong task id received. Expected:{id(task)}, Received:{task_id}') # HTTP409 Conflict
|
|
||||||
if task.buffer_queue.empty() and not task.lock.locked():
|
|
||||||
if task.response:
|
|
||||||
#print(f'Session {session_id} sending cached response')
|
|
||||||
return JSONResponse(task.response, headers=NOCACHE_HEADERS)
|
|
||||||
raise HTTPException(status_code=425, detail='Too Early, task not started yet.') # HTTP425 Too Early
|
|
||||||
#print(f'Session {session_id} opened live render stream {id(task.buffer_queue)}')
|
|
||||||
return StreamingResponse(task.read_buffer_generator(), media_type='application/json')
|
|
||||||
|
|
||||||
@app.get('/image/stop')
|
|
||||||
def stop(task: int):
|
|
||||||
if not task:
|
|
||||||
if task_manager.current_state == task_manager.ServerStates.Online or task_manager.current_state == task_manager.ServerStates.Unavailable:
|
|
||||||
raise HTTPException(status_code=409, detail='Not currently running any tasks.') # HTTP409 Conflict
|
|
||||||
task_manager.current_state_error = StopAsyncIteration('')
|
|
||||||
return {'OK'}
|
|
||||||
task_id = task
|
|
||||||
task = task_manager.get_cached_task(task_id, update_ttl=False)
|
|
||||||
if not task: raise HTTPException(status_code=404, detail=f'Task {task_id} was not found.') # HTTP404 Not Found
|
|
||||||
if isinstance(task.error, StopAsyncIteration): raise HTTPException(status_code=409, detail=f'Task {task_id} is already stopped.') # HTTP409 Conflict
|
|
||||||
task.error = StopAsyncIteration(f'Task {task_id} stop requested.')
|
|
||||||
return {'OK'}
|
|
||||||
|
|
||||||
@app.get('/image/tmp/{task_id:int}/{img_id:int}')
|
|
||||||
def get_image(task_id: int, img_id: int):
|
|
||||||
task = task_manager.get_cached_task(task_id, update_ttl=True)
|
|
||||||
if not task: raise HTTPException(status_code=410, detail=f'Task {task_id} could not be found.') # HTTP404 NotFound
|
|
||||||
if not task.temp_images[img_id]: raise HTTPException(status_code=425, detail='Too Early, task data is not available yet.') # HTTP425 Too Early
|
|
||||||
try:
|
|
||||||
img_data = task.temp_images[img_id]
|
|
||||||
img_data.seek(0)
|
|
||||||
return StreamingResponse(img_data, media_type='image/jpeg')
|
|
||||||
except KeyError as e:
|
|
||||||
raise HTTPException(status_code=500, detail=str(e))
|
|
||||||
|
|
||||||
@app.get('/')
|
|
||||||
def read_root():
|
|
||||||
return FileResponse(os.path.join(SD_UI_DIR, 'index.html'), headers=NOCACHE_HEADERS)
|
|
||||||
|
|
||||||
@app.on_event("shutdown")
|
|
||||||
def shutdown_event(): # Signal render thread to close on shutdown
|
|
||||||
task_manager.current_state_error = SystemExit('Application shutting down.')
|
|
||||||
|
|
||||||
# don't log certain requests
|
|
||||||
class LogSuppressFilter(logging.Filter):
|
|
||||||
def filter(self, record: logging.LogRecord) -> bool:
|
|
||||||
path = record.getMessage()
|
|
||||||
for prefix in ACCESS_LOG_SUPPRESS_PATH_PREFIXES:
|
|
||||||
if path.find(prefix) != -1:
|
|
||||||
return False
|
|
||||||
return True
|
|
||||||
logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter())
|
|
||||||
|
|
||||||
# Check models and prepare cache for UI open
|
|
||||||
getModels()
|
|
||||||
|
|
||||||
# Start the task_manager
|
|
||||||
task_manager.default_model_to_load = resolve_ckpt_to_use()
|
|
||||||
task_manager.default_vae_to_load = resolve_vae_to_use()
|
|
||||||
task_manager.default_hypernetwork_to_load = resolve_hypernetwork_to_use()
|
|
||||||
|
|
||||||
def update_render_threads():
|
|
||||||
config = getConfig()
|
|
||||||
render_devices = config.get('render_devices', 'auto')
|
|
||||||
active_devices = task_manager.get_devices()['active'].keys()
|
|
||||||
|
|
||||||
print('requesting for render_devices', render_devices)
|
|
||||||
task_manager.update_render_threads(render_devices, active_devices)
|
|
||||||
|
|
||||||
update_render_threads()
|
|
||||||
|
|
||||||
# start the browser ui
|
|
||||||
def open_browser():
|
|
||||||
config = getConfig()
|
|
||||||
ui = config.get('ui', {})
|
|
||||||
net = config.get('net', {'listen_port':9000})
|
|
||||||
port = net.get('listen_port', 9000)
|
|
||||||
if ui.get('open_browser_on_start', True):
|
|
||||||
import webbrowser; webbrowser.open(f"http://localhost:{port}")
|
|
||||||
|
|
||||||
open_browser()
|
|
Loading…
Reference in New Issue
Block a user