Merge pull request #695 from cmdr2/refactor

v2.5 - move to sdkit
2022-12-24 23:28:10 +05:30 · 2022-12-24 23:28:10 +05:30 · 206f9b97bb
commit 206f9b97bb
parent 6743ec14f1 13721f160e
33 changed files with 1306 additions and 2734 deletions
--- a/CHANGES.md
+++ b/CHANGES.md
@ -1,5 +1,22 @@
 # What's new?
 ## v2.5
 ### Major Changes
 - **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
 - **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.
 - **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.
 - **6 new samplers!** - explore the new samplers, some of which can generate great images in less than 10 inference steps!
 - **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
 - **Fast loading/unloading of VAEs** - No longer needs to reload the entire Stable Diffusion model, each time you change the VAE
 - **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).
 - **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.
 - **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.
 - **Save metadata as JSON** - You can now save the metadata files as either text or json files (choose in the Settings tab).
 - **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/
 - **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.
 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.
 ## v2.4
 ### Major Changes
 - **Allow reordering the task queue** (by dragging and dropping tasks). Thanks @madrang
--- a/scripts/Developer
+++ b/scripts/Developer
@ -23,23 +23,20 @@ call conda --version
 echo.
-@rem activate the environment
+@rem activate the legacy environment (if present) and set PYTHONPATH
-call conda activate .\stable-diffusion\env
+if exist "installer_files\env" (
    set PYTHONPATH=%cd%\installer_files\env\lib\site-packages
 )
 if exist "stable-diffusion\env" (
    call conda activate .\stable-diffusion\env
    set PYTHONPATH=%cd%\stable-diffusion\env\lib\site-packages
 )
 call where python
 call python --version
@rem set the PYTHONPATH
 cd stable-diffusion
 set SD_DIR=%cd%
 cd env\lib\site-packages
 set PYTHONPATH=%SD_DIR%;%cd%
 cd ..\..\..
 echo PYTHONPATH=%PYTHONPATH%
 cd ..
@rem done
 echo.
--- a/scripts/bootstrap.bat
+++ b/scripts/bootstrap.bat
@ -24,7 +24,7 @@ if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Librar
 set PACKAGES_TO_INSTALL=
 if not exist "%LEGACY_INSTALL_ENV_DIR%\etc\profile.d\conda.sh" (
-    if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
+    if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda python=3.8.5
 )
 call git --version >.tmp1 2>.tmp2
--- a/scripts/bootstrap.sh
+++ b/scripts/bootstrap.sh
@ -39,7 +39,7 @@ if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
 PACKAGES_TO_INSTALL=""
-if [ ! -e "$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
+if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.8.5"; fi
 if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
 if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi
--- a/scripts/check_modules.py
+++ b/scripts/check_modules.py
@ -0,0 +1,13 @@
 '''
 This script checks if the given modules exist
 '''
 import sys
 import pkgutil
 modules = sys.argv[1:]
 missing_modules = []
 for m in modules:
    if pkgutil.find_loader(m) is None:
        print('module', m, 'not found')
        exit(1)
--- a/scripts/developer_console.sh
+++ b/scripts/developer_console.sh
@ -26,21 +26,23 @@ if [ "$0" == "bash" ]; then
  echo ""
-  # activate the environment
+  # activate the legacy environment (if present) and set PYTHONPATH
-  CONDA_BASEPATH=$(conda info --base)
+  if [ -e "installer_files/env" ]; then
-  source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
+    export PYTHONPATH="$(pwd)/installer_files/env/lib/python3.8/site-packages"
  fi
  if [ -e "stable-diffusion/env" ]; then
    CONDA_BASEPATH=$(conda info --base)
    source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
-  conda activate ./stable-diffusion/env
+    conda activate ./stable-diffusion/env
    export PYTHONPATH="$(pwd)/stable-diffusion/env/lib/python3.8/site-packages"
  fi
  which python
  python --version
  # set the PYTHONPATH
  cd stable-diffusion
  SD_PATH=`pwd`
  export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
  echo "PYTHONPATH=$PYTHONPATH"
  cd ..
  # done
--- a/scripts/on_env_start.bat
+++ b/scripts/on_env_start.bat
@ -53,6 +53,7 @@ if "%update_branch%"=="" (
@xcopy sd-ui-files\ui ui /s /i /Y /q
@copy sd-ui-files\scripts\on_sd_start.bat scripts\ /Y
@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
@copy sd-ui-files\scripts\check_modules.py scripts\ /Y
@copy "sd-ui-files\scripts\Start Stable Diffusion UI.cmd" . /Y
@copy "sd-ui-files\scripts\Developer Console.cmd" . /Y
--- a/scripts/on_env_start.sh
+++ b/scripts/on_env_start.sh
@ -37,6 +37,7 @@ rm -rf ui
 cp -Rf sd-ui-files/ui .
 cp sd-ui-files/scripts/on_sd_start.sh scripts/
 cp sd-ui-files/scripts/bootstrap.sh scripts/
 cp sd-ui-files/scripts/check_modules.py scripts/
 cp sd-ui-files/scripts/start.sh .
 cp sd-ui-files/scripts/developer_console.sh .
--- a/scripts/on_sd_start.bat
+++ b/scripts/on_sd_start.bat
@ -5,11 +5,20 @@
@copy sd-ui-files\scripts\on_env_start.bat scripts\ /Y
@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
@copy sd-ui-files\scripts\check_modules.py scripts\ /Y
 if exist "%cd%\profile" (
    set USERPROFILE=%cd%\profile
 )
@rem set the correct installer path (current vs legacy)
 if exist "%cd%\installer_files\env" (
    set INSTALL_ENV_DIR=%cd%\installer_files\env
 )
 if exist "%cd%\stable-diffusion\env" (
    set INSTALL_ENV_DIR=%cd%\stable-diffusion\env
 )
@mkdir tmp
@set TMP=%cd%\tmp
@set TEMP=%cd%\tmp
@ -27,150 +36,92 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
@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');"
-if NOT DEFINED test_sd2 set test_sd2=N
+@rem create the stable-diffusion folder, to work with legacy installations
 if not exist "stable-diffusion" mkdir stable-diffusion
 cd stable-diffusion
-@>nul findstr /m "sd_git_cloned" scripts\install_status.txt
+@rem activate the old stable-diffusion env, if it exists
-@if "%ERRORLEVEL%" EQU "0" (
+if exist "env" (
-    @echo "Stable Diffusion's git repository was already installed. Updating.."
+    call conda activate .\env
    @cd stable-diffusion
    @call git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
    @call git reset --hard
    @call git pull
    if "%test_sd2%" == "N" (
        @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
    )
    if "%test_sd2%" == "Y" (
        @call git -c advice.detachedHead=false checkout 733a1f6f9cae9b9a9b83294bf3281b123378cb1f
    )
    @cd ..
 ) else (
    @echo. & echo "Downloading Stable Diffusion.." & echo.
    @call git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion && (
        @echo sd_git_cloned >> scripts\install_status.txt
    ) || (
        @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 git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
    @cd ..
 )
-@cd stable-diffusion
+@rem disable the legacy src and ldm folder (otherwise this prevents installing gfpgan and realesrgan)
 if exist src rename src src-old
 if exist ldm rename ldm ldm-old
-@>nul findstr /m "conda_sd_env_created" ..\scripts\install_status.txt
+@rem install torch and torchvision
-@if "%ERRORLEVEL%" EQU "0" (
+call python ..\scripts\check_modules.py torch torchvision
-    @echo "Packages necessary for Stable Diffusion were already installed"
+if "%ERRORLEVEL%" EQU "0" (
-
+    echo "torch and torchvision have already been 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.
+    echo "Installing torch and torchvision.."
-    @rmdir /s /q .\env
+    @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
-    @REM prevent conda from using packages from the user's home directory, to avoid conflicts
+    call pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 || (
-    @set PYTHONNOUSERSITE=1
+        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!"
    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
        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
        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%
-@>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" (
    echo "Packages necessary for Stable Diffusion UI were already installed"
 ) else (
    @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!"
        pause
        exit /b
@ -185,26 +136,6 @@ call WHERE uvicorn > .tmp
    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
@if "%ERRORLEVEL%" NEQ "0" (
    @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\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" (
    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
@if "%ERRORLEVEL%" NEQ "0" (
    @echo sd_weights_downloaded >> ..\scripts\install_status.txt
@ -389,10 +311,8 @@ if "%test_sd2%" == "Y" (
@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 python --version
@ -401,17 +321,9 @@ call python --version
@set SD_UI_PATH=%cd%\ui
@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_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
--- a/scripts/on_sd_start.sh
+++ b/scripts/on_sd_start.sh
@ -4,6 +4,7 @@ source ./scripts/functions.sh
 cp sd-ui-files/scripts/on_env_start.sh scripts/
 cp sd-ui-files/scripts/bootstrap.sh scripts/
 cp sd-ui-files/scripts/check_modules.py scripts/
 # activate the installer env
 CONDA_BASEPATH=$(conda info --base)
@ -21,125 +22,89 @@ python -c "import os; import shutil; frm = 'sd-ui-files/ui/hotfix/9c24e6cd9f499d
 # Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
 # Note to self: Please rewrite this in Python. For the sake of your own sanity.
-if [ "$test_sd2" == "" ]; then
+# set the correct installer path (current vs legacy)
-    export test_sd2="N"
+if [ -e "installer_files/env" ]; then
-fi
+    export INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-
+fi
-if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/install_status.txt` -gt "0" ]; then
+if [ -e "stable-diffusion/env" ]; then
-    echo "Stable Diffusion's git repository was already installed. Updating.."
+    export INSTALL_ENV_DIR="$(pwd)/stable-diffusion/env"
    cd stable-diffusion
    git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
    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
 # create the stable-diffusion folder, to work with legacy installations
 if [ ! -e "stable-diffusion" ]; then mkdir stable-diffusion; fi
 cd stable-diffusion
-if [ `grep -c conda_sd_env_created ../scripts/install_status.txt` -gt "0" ]; then
+# activate the old stable-diffusion env, if it exists
-    echo "Packages necessary for Stable Diffusion were already installed"
+if [ -e "env" ]; then
    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
-    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"
+    export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
-    if conda env create --prefix env --force -f environment.yaml ; then
+    if pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 ; then
-        echo "Installed. Testing.."
+        echo "Installed."
    else
-        fail "'conda env create' failed"
+        fail "torch install failed" 
    fi
    conda activate ./env || fail "conda activate failed"
    out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"`
    if [ "$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
+# install/upgrade sdkit
-if [ -e "src-old" ] && [ ! -e "src" ]; then
+if python ../scripts/check_modules.py sdkit sdkit.models ldm transformers numpy antlr4 gfpgan realesrgan ; then
-    mv src-old src
+    echo "sdkit is already installed."
    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 PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
+    export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
-    out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
+    pip install --upgrade sdkit > /dev/null
    if [ "$out_test" != "42" ]; then
        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
-    printf "\n\nDownloading packages necessary for ESRGAN (Resolution Upscaling)..\n"
+    echo "Installing sdkit: https://pypi.org/project/sdkit/"
    export PYTHONNOUSERSITE=1
-    export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
+    export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
-    out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"`
+    if pip install sdkit ; then
-    if [ "$out_test" != "42" ]; then
+        echo "Installed."
-        fail "ESRGAN dependency test failed"
+    else
        fail "sdkit install failed"
    fi
    echo conda_sd_esrgan_deps_installed >> ../scripts/install_status.txt
 fi
-if [ `grep -c conda_sd_ui_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
+# 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="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
    if pip install rich ; then
        echo "Installed."
    else
        fail "Install failed for rich"
    fi
 fi
 if python ../scripts/check_modules.py uvicorn fastapi ; then
    echo "Packages necessary for Stable Diffusion UI were already installed"
 else
    printf "\n\nDownloading packages necessary for Stable Diffusion UI..\n\n"
    export PYTHONNOUSERSITE=1
-    export PYTHONPATH="$(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.."
    else
        fail "'conda install uvicorn' failed" 
@ -148,32 +113,9 @@ else
    if ! command -v uvicorn &> /dev/null; then
        fail "UI packages not found!"
    fi
    echo conda_sd_ui_deps_installed >> ../scripts/install_status.txt
 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/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
    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
 if [ "$test_sd2" == "Y" ]; then
    pip install open_clip_torch==2.0.2
 fi
 if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
    echo sd_weights_downloaded >> ../scripts/install_status.txt
    echo sd_install_complete >> ../scripts/install_status.txt
@ -326,7 +264,8 @@ fi
 printf "\n\nStable Diffusion is ready!\n\n"
 SD_PATH=`pwd`
-export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
+
 export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
 echo "PYTHONPATH=$PYTHONPATH"
 which python
@ -336,6 +275,6 @@ cd ..
 export SD_UI_PATH=`pwd`/ui
 cd stable-diffusion
-uvicorn server:app --app-dir "$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"
--- a/ui/easydiffusion/init.py
+++ b/ui/easydiffusion/init.py
--- a/ui/easydiffusion/app.py
+++ b/ui/easydiffusion/app.py
@ -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}")
--- a/ui/easydiffusion/device_manager.py
+++ b/ui/easydiffusion/device_manager.py
@ -3,6 +3,15 @@ import torch
 import traceback
 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
 mem_free_threshold = 0
@ -34,7 +43,7 @@ def get_device_delta(render_devices, active_devices):
    if 'auto' in render_devices:
        render_devices = auto_pick_devices(active_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)
    render_devices = set(render_devices)
@ -53,7 +62,7 @@ def auto_pick_devices(currently_active_devices):
    if device_count == 1:
        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 = []
    for device in range(device_count):
        device = f'cuda:{device}'
@ -64,7 +73,7 @@ def auto_pick_devices(currently_active_devices):
        mem_free /= float(10**9)
        mem_total /= float(10**9)
        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.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))
    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.
    `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')
    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
-    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
-    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.
-        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)
    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 is_valid():
        if not isinstance(device, str):
@ -132,7 +159,7 @@ def is_device_compatible(device):
    try:
        validate_device_id(device, log_prefix='is_device_compatible')
    except:
-        print(str(e))
+        log.error(str(e))
        return False
    if device == 'cpu': return True
@ -141,10 +168,10 @@ def is_device_compatible(device):
        _, mem_total = torch.cuda.mem_get_info(device)
        mem_total /= float(10**9)
        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
    except RuntimeError as e:
-        print(str(e))
+        log.error(str(e))
        return False
    return True
@ -164,5 +191,5 @@ def get_processor_name():
                if "model name" in line:
                    return re.sub(".*model name.*:", "", line, 1).strip()
    except:
-        print(traceback.format_exc())
+        log.error(traceback.format_exc())
        return "cpu"
--- a/ui/easydiffusion/model_manager.py
+++ b/ui/easydiffusion/model_manager.py
@ -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
--- a/ui/easydiffusion/renderer.py
+++ b/ui/easydiffusion/renderer.py
@ -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
--- a/ui/easydiffusion/server.py
+++ b/ui/easydiffusion/server.py
@ -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))
--- a/ui/easydiffusion/task_manager.py
+++ b/ui/easydiffusion/task_manager.py
@ -11,12 +11,13 @@ TASK_TTL = 15 * 60 # seconds, Discard last session's task timeout
 import torch
 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.'
 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.
@ -36,12 +37,13 @@ class ServerStates:
    class Unavailable(Symbol): pass
 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.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.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
@ -69,54 +71,6 @@ class RenderTask(): # Task with output queue and completion lock.
    def is_pending(self):
        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.
 class DataCache():
    def __init__(self):
@ -139,11 +93,11 @@ class DataCache():
            for key in to_delete:
                (_, val) = self._base[key]
                if isinstance(val, RenderTask):
-                    print(f'RenderTask {key} expired. Data removed.')
+                    log.debug(f'RenderTask {key} expired. Data removed.')
                elif isinstance(val, SessionState):
-                    print(f'Session {key} expired. Data removed.')
+                    log.debug(f'Session {key} expired. Data removed.')
                else:
-                    print(f'Key {key} expired. Data removed.')
+                    log.debug(f'Key {key} expired. Data removed.')
                del self._base[key]
        finally:
            self._lock.release()
@ -177,8 +131,7 @@ class DataCache():
                self._get_ttl_time(ttl), value
            )
        except Exception as e:
-            print(str(e))
+            log.error(traceback.format_exc())
            print(traceback.format_exc())
            return False
        else:
            return True
@ -189,7 +142,7 @@ class DataCache():
        try:
            ttl, value = self._base.get(key, (None, None))
            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]
                return None
            return value
@ -200,15 +153,9 @@ manager_lock = threading.RLock()
 render_threads = []
 current_state = ServerStates.Init
 current_state_error:Exception = None
 current_model_path = None
 current_vae_path = None
 current_hypernetwork_path = None
 tasks_queue = []
 session_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()
 idle_event: threading.Event = threading.Event()
@ -236,40 +183,10 @@ class SessionState():
            self._tasks_ids.pop(0)
        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():
-    from . import runtime
+    from easydiffusion import renderer
    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
    if len(tasks_queue) <= 0:
        manager_lock.release()
@ -277,7 +194,7 @@ def thread_get_next_task():
    task = None
    try:  # Select a render task.
        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.
                if is_alive(queued_task.render_device) > 0:
                    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.')
                    task = queued_task
                    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.
                continue  # Skip Tasks, don't run on CPU unless there is nothing else or user asked for it.
            task = queued_task
@ -298,31 +215,36 @@ def thread_get_next_task():
        manager_lock.release()
 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:
-        runtime.thread_init(device)
+        renderer.init(device)
-    except Exception as e:
+
        print(traceback.format_exc())
        weak_thread_data[threading.current_thread()] = {
-            'error': e
+            'device': renderer.context.device,
            'device_name': renderer.context.device_name,
            'alive': True
        }
        current_state = ServerStates.LoadingModel
        model_manager.load_default_models(renderer.context)
        current_state = ServerStates.Online
    except Exception as e:
        log.error(traceback.format_exc())
        weak_thread_data[threading.current_thread()] = {
            'error': e,
            'alive': False
        }
        return
-    weak_thread_data[threading.current_thread()] = {
+
        'device': runtime.thread_data.device,
        'device_name': runtime.thread_data.device_name,
        'alive': True
    }
    if runtime.thread_data.device != 'cpu' or is_alive() == 1:
        preload_model()
        current_state = ServerStates.Online
    while True:
        session_cache.clean()
        task_cache.clean()
        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
        if isinstance(current_state_error, SystemExit):
            current_state = ServerStates.Unavailable
@ -333,7 +255,7 @@ def thread_render(device):
            idle_event.wait(timeout=1)
            continue
        if task.error is not None:
-            print(task.error)
+            log.error(task.error)
            task.response = {"status": 'failed', "detail": str(task.error)}
            task.buffer_queue.put(json.dumps(task.response))
            continue
@ -342,51 +264,45 @@ def thread_render(device):
            task.response = {"status": 'failed', "detail": str(task.error)}
            task.buffer_queue.put(json.dumps(task.response))
            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.')
        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():
                global current_state_error
                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):
                        task.error = current_state_error
                        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
-            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.
            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:
            task.error = e
            task.response = {"status": 'failed', "detail": str(task.error)}
            task.buffer_queue.put(json.dumps(task.response))
-            print(traceback.format_exc())
+            log.error(traceback.format_exc())
            continue
        finally:
            # Task completed
            task.lock.release()
        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):
-            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:
-            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:
-            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
 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),
            'mem_free': mem_free,
            'mem_total': mem_total,
            'max_vram_usage_level': device_manager.get_max_vram_usage_level(device),
        }
    # list the compatible devices
@ -472,7 +389,7 @@ def is_alive(device=None):
 def start_render_thread(device):
    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:
        rthread = threading.Thread(target=thread_render, kwargs={'device': device})
        rthread.daemon = True
@ -484,7 +401,7 @@ def start_render_thread(device):
    timeout = DEVICE_START_TIMEOUT
    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]:
-            print(rthread, device, 'error:', weak_thread_data[rthread]['error'])
+            log.error(f"{rthread}, {device}, error: {weak_thread_data[rthread]['error']}")
            return False
        if timeout <= 0:
            return False
@ -496,11 +413,11 @@ def stop_render_thread(device):
    try:
        device_manager.validate_device_id(device, log_prefix='stop_render_thread')
    except:
-        print(traceback.format_exc())
+        log.error(traceback.format_exc())
        return False
    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:
        thread_to_remove = None
@ -523,79 +440,44 @@ def stop_render_thread(device):
 def update_render_threads(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:
        if is_alive(device) <= 0:
-            print(device, 'is not alive')
+            log.debug(f'{device} is not alive')
            continue
        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:
        if is_alive(device) >= 1:
-            print(device, 'already registered.')
+            log.debug(f'{device} already registered.')
            continue
        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.
        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
    global current_state_error
    current_state_error = SystemExit('Application shutting down.')
-def render(req : ImageRequest):
+def render(render_req: GenerateImageRequest, task_data: TaskData):
    current_thread_count = is_alive()
    if current_thread_count <= 0:  # Render thread is dead
        raise ChildProcessError('Rendering thread has died.')
    # 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))
    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):
        # Use twice the normal timeout for adding user requests.
        # Tries to force session.put to fail before tasks_queue.put would.
--- a/ui/easydiffusion/types.py
+++ b/ui/easydiffusion/types.py
@ -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
--- a/ui/easydiffusion/utils/init.py
+++ b/ui/easydiffusion/utils/init.py
@ -0,0 +1,8 @@
 import logging
 log = logging.getLogger('easydiffusion')
 from .save_utils import (
    save_images_to_disk,
    get_printable_request,
 )
--- a/ui/easydiffusion/utils/save_utils.py
+++ b/ui/easydiffusion/utils/save_utils.py
@ -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
--- a/ui/index.html
+++ b/ui/index.html
@ -24,8 +24,8 @@
    <div id="top-nav">
        <div id="logo">
            <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>
        </div>
        <div id="server-status">
@ -129,22 +129,32 @@
                        </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>
                    </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>
                        <select id="vae_model" name="vae_model">
                            <!-- <option value="" selected>None</option> -->
                        </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>
                    </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>
                        <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>
@ -220,6 +230,7 @@
                    <div><ul>
                    <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 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_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Upscale image by 4x with </label>
@ -416,7 +427,6 @@
 async function init() {
    await initSettings()
    await getModels()
    await getDiskPath()
    await getAppConfig()
    await loadUIPlugins()
    await loadModifiers()
--- a/ui/main.py
+++ b/ui/main.py
@ -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()
--- a/ui/media/js/auto-save.js
+++ b/ui/media/js/auto-save.js
@ -15,7 +15,7 @@ const SETTINGS_IDS_LIST = [
    "stable_diffusion_model",
    "vae_model",
    "hypernetwork_model",
-    "sampler",
+    "sampler_name",
    "width",
    "height",
    "num_inference_steps",
@ -36,10 +36,11 @@ const SETTINGS_IDS_LIST = [
    "save_to_disk",
    "diskPath",
    "sound_toggle",
-    "turbo",
+    "vram_usage_level",
    "use_full_precision",
    "confirm_dangerous_actions",
-    "auto_save_settings"
+    "metadata_output_format",
    "auto_save_settings",
    "apply_color_correction"
 ]
 const IGNORE_BY_DEFAULT = [
@ -277,7 +278,6 @@ function tryLoadOldSettings() {
        "soundEnabled": "sound_toggle",
        "saveToDisk": "save_to_disk",
        "useCPU": "use_cpu",
        "useFullPrecision": "use_full_precision",
        "useTurboMode": "turbo",
        "diskPath": "diskPath",
        "useFaceCorrection": "use_face_correction",
--- a/ui/media/js/dnd.js
+++ b/ui/media/js/dnd.js
@ -25,6 +25,7 @@ function parseBoolean(stringValue) {
        case "no":
        case "off":
        case "0":
        case "none":
        case null:
        case undefined:
          return false;
@ -160,9 +161,9 @@ const TASK_MAPPING = {
        readUI: () => (useUpscalingField.checked ? upscaleModelField.value : undefined),
        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,
        parse: (val) => val
@ -171,7 +172,7 @@ const TASK_MAPPING = {
        setUI: (use_stable_diffusion_model) => {
            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
            if (!stableDiffusionModelField.value) {
@ -184,6 +185,7 @@ const TASK_MAPPING = {
    use_vae_model: { name: 'VAE model',
        setUI: (use_vae_model) => {
            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 !== '') {
                use_vae_model = getModelPath(use_vae_model, ['.vae.pt', '.ckpt'])
@ -197,6 +199,7 @@ const TASK_MAPPING = {
    use_hypernetwork_model: { name: 'Hypernetwork model',
        setUI: (use_hypernetwork_model) => {
            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 !== '') {
                use_hypernetwork_model = getModelPath(use_hypernetwork_model, ['.pt'])
@ -239,13 +242,6 @@ const TASK_MAPPING = {
        readUI: () => turboField.checked,
        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',
        setUI: (stream_image_progress) => {
@ -350,6 +346,7 @@ function getModelPath(filename, extensions)
 }
 const TASK_TEXT_MAPPING = {
    prompt: 'Prompt',
    width: 'Width',
    height: 'Height',
    seed: 'Seed',
@ -358,7 +355,7 @@ const TASK_TEXT_MAPPING = {
    prompt_strength: 'Prompt Strength',
    use_face_correction: 'Use Face Correction',
    use_upscale: 'Use Upscaling',
-    sampler: 'Sampler',
+    sampler_name: 'Sampler',
    negative_prompt: 'Negative Prompt',
    use_stable_diffusion_model: 'Stable Diffusion model',
    use_hypernetwork_model: 'Hypernetwork model',
@ -410,6 +407,9 @@ async function parseContent(text) {
    if (text.startsWith('{') && text.endsWith('}')) {
        try {
            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)
            return true
        } catch (e) {
@ -477,7 +477,6 @@ document.addEventListener("dragover", dragOverHandler)
 const TASK_REQ_NO_EXPORT = [
    "use_cpu",
    "turbo",
    "use_full_precision",
    "save_to_disk_path"
 ]
 const resetSettings = document.getElementById('reset-image-settings')
--- a/ui/media/js/engine.js
+++ b/ui/media/js/engine.js
@ -728,7 +728,6 @@
        "stream_image_progress": 'boolean',
        "show_only_filtered_image": 'boolean',
        "turbo": 'boolean',
        "use_full_precision": 'boolean',
        "output_format": 'string',
        "output_quality": 'number',
    }
@ -744,7 +743,6 @@
        "stream_image_progress": true,
        "show_only_filtered_image": true,
        "turbo": false,
        "use_full_precision": false,
        "output_format": "png",
        "output_quality": 75,
    }
--- a/ui/media/js/main.js
+++ b/ui/media/js/main.js
@ -26,9 +26,11 @@ let initImagePreview = document.querySelector("#init_image_preview")
 let initImageSizeBox = document.querySelector("#init_image_size_box")
 let maskImageSelector = document.querySelector("#mask")
 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 promptStrengthField = document.querySelector('#prompt_strength')
-let samplerField = document.querySelector('#sampler')
+let samplerField = document.querySelector('#sampler_name')
 let samplerSelectionContainer = document.querySelector("#samplerSelection")
 let useFaceCorrectionField = document.querySelector("#use_face_correction")
 let useUpscalingField = document.querySelector("#use_upscale")
@ -610,7 +612,7 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
                            <b>Suggestions</b>:
                            <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/>`
                }
            } else {
@ -786,10 +788,11 @@ function createTask(task) {
    if (task.reqBody.init_image !== undefined) {
        let h = 80
-	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 += `<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() !== '') {
        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 Strength:</b> ${task.reqBody.hypernetwork_strength}`
    }
    if (task.reqBody.preserve_init_image_color_profile) {
        taskConfig += `, <b>Preserve Color Profile:</b> true`
    }
    let taskEntry = document.createElement('div')
    taskEntry.id = `imageTaskContainer-${Date.now()}`
@ -914,9 +920,8 @@ function getCurrentUserRequest() {
            width: parseInt(widthField.value),
            height: parseInt(heightField.value),
            // allow_nsfw: allowNSFWField.checked,
-            turbo: turboField.checked,
+            vram_usage_level: vramUsageLevelField.value,
            //render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
            use_full_precision: useFullPrecisionField.checked,
            use_stable_diffusion_model: stableDiffusionModelField.value,
            use_vae_model: vaeModelField.value,
            stream_progress_updates: true,
@ -924,6 +929,7 @@ function getCurrentUserRequest() {
            show_only_filtered_image: showOnlyFilteredImageField.checked,
            output_format: outputFormatField.value,
            output_quality: parseInt(outputQualityField.value),
            metadata_output_format: document.querySelector('#metadata_output_format').value,
            original_prompt: promptField.value,
            active_tags: (activeTags.map(x => x.name))
        }
@ -938,9 +944,10 @@ function getCurrentUserRequest() {
        if (maskSetting.checked) {
            newTask.reqBody.mask = imageInpainter.getImg()
        }
-        newTask.reqBody.sampler = 'ddim'
+        newTask.reqBody.preserve_init_image_color_profile = applyColorCorrectionField.checked
        newTask.reqBody.sampler_name = 'ddim'
    } else {
-        newTask.reqBody.sampler = samplerField.value
+        newTask.reqBody.sampler_name = samplerField.value
    }
    if (saveToDiskField.checked && diskPathField.value.trim() !== '') {
        newTask.reqBody.save_to_disk_path = diskPathField.value.trim()
@ -1349,6 +1356,7 @@ function img2imgLoad() {
    promptStrengthContainer.style.display = 'table-row'
    samplerSelectionContainer.style.display = "none"
    initImagePreviewContainer.classList.add("has-image")
    colorCorrectionSetting.style.display = ''
    initImageSizeBox.textContent = initImagePreview.naturalWidth + " x " + initImagePreview.naturalHeight
    imageEditor.setImage(this.src, initImagePreview.naturalWidth, initImagePreview.naturalHeight)
@ -1363,6 +1371,7 @@ function img2imgUnload() {
    promptStrengthContainer.style.display = "none"
    samplerSelectionContainer.style.display = ""
    initImagePreviewContainer.classList.remove("has-image")
    colorCorrectionSetting.style.display = 'none'
    imageEditor.setImage(null, parseInt(widthField.value), parseInt(heightField.value))
 }
--- a/ui/media/js/parameters.js
+++ b/ui/media/js/parameters.js
@ -53,6 +53,23 @@ var PARAMETERS = [
            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",
        type: ParameterType.checkbox,
@ -77,12 +94,20 @@ var PARAMETERS = [
        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",
-        default: true,
+        default: "balanced",
        options: [
            {value: "balanced", label: "Balanced"},
            {value: "high", label: "High"},
            {value: "low", label: "Low"}
        ],
    },
    {
        id: "use_cpu",
@ -105,14 +130,6 @@ var PARAMETERS = [
        note: "to process in parallel",
        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",
        type: ParameterType.checkbox,
@ -147,14 +164,6 @@ var PARAMETERS = [
            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",
        type: ParameterType.checkbox,
@ -210,16 +219,14 @@ function initParameters() {
 initParameters()
-let turboField = document.querySelector('#turbo')
+let vramUsageLevelField = document.querySelector('#vram_usage_level')
 let useCPUField = document.querySelector('#use_cpu')
 let autoPickGPUsField = document.querySelector('#auto_pick_gpus')
 let useGPUsField = document.querySelector('#use_gpus')
 let useFullPrecisionField = document.querySelector('#use_full_precision')
 let saveToDiskField = document.querySelector('#save_to_disk')
 let diskPathField = document.querySelector('#diskPath')
 let listenToNetworkField = document.querySelector("#listen_to_network")
 let listenPortField = document.querySelector("#listen_port")
 let testSD2Field = document.querySelector("#test_sd2")
 let useBetaChannelField = document.querySelector("#use_beta_channel")
 let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
 let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
@ -256,12 +263,6 @@ async function getAppConfig() {
        if (config.ui && config.ui.open_browser_on_start === 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) {
            listenToNetworkField.checked = false
        }
@ -327,20 +328,10 @@ autoPickGPUsField.addEventListener('click', function() {
    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") {
+        setSetting("diskPath", defaultDiskPath)
            let res = await fetch('/get/output_dir')
            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)
        setHostInfo(res['hosts'])
        setDiskPath(res['default_output_dir'])
    } catch (e) {
        console.log('error fetching devices', e)
    }
@ -435,8 +427,7 @@ saveSettingsBtn.addEventListener('click', function() {
        'update_branch': updateBranch,
        'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
        'listen_to_network': listenToNetworkField.checked,
-        'listen_port': listenPortField.value,
+        'listen_port': listenPortField.value
        'test_sd2': testSD2Field.checked
    })
    saveSettingsBtn.classList.add('active')
    asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
--- a/ui/sd_internal/init.py
+++ b/ui/sd_internal/init.py
@ -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
--- a/ui/sd_internal/ddim_callback.patch
+++ b/ui/sd_internal/ddim_callback.patch
@ -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)
--- a/ui/sd_internal/ddim_callback_sd2.patch
+++ b/ui/sd_internal/ddim_callback_sd2.patch
@ -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,
--- a/ui/sd_internal/hypernetwork.py
+++ b/ui/sd_internal/hypernetwork.py
@ -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()
--- a/ui/sd_internal/runtime.py
+++ b/ui/sd_internal/runtime.py
--- a/ui/server.py
+++ b/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()