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48
CHANGES.md
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@ -1,5 +1,53 @@
# 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 (including CPU)** - 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. Works on CPU as well.
- **Memory optimized Stable Diffusion 2.1** - you can now use Stable Diffusion 2.1 models, with the same low VRAM optimizations that we've always had for SD 1.4. Please note, the SD 2.0 and 2.1 models require more GPU and System RAM, as compared to the SD 1.4 and 1.5 models.
- **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.
- **Find models in sub-folders** - This allows you to organize your models into sub-folders inside `models/stable-diffusion`, instead of keeping them all in a single folder.
- **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.
### Detailed changelog
* 2.5.15 - 8 Feb 2023 - Allow using 'balanced' VRAM usage mode on GPUs with 4 GB or less of VRAM. This mode used to be called 'Turbo' in the previous version.
* 2.5.14 - 8 Feb 2023 - Fix broken auto-save settings. We renamed `sampler` to `sampler_name`, which caused old settings to fail.
* 2.5.14 - 6 Feb 2023 - Simplify the UI for merging models, and some other minor UI tweaks. Better error reporting if a model failed to load.
* 2.5.14 - 3 Feb 2023 - Fix the 'Make Similar Images' button, which was producing incorrect images (weren't very similar).
* 2.5.13 - 1 Feb 2023 - Fix the remaining GPU memory leaks, including a better fix (more comprehensive) for the change in 2.5.12 (27 Jan).
* 2.5.12 - 27 Jan 2023 - Fix a memory leak, which made the UI unresponsive after an out-of-memory error. The allocated memory is now freed-up after an error.
* 2.5.11 - 25 Jan 2023 - UI for Merging Models. Thanks @JeLuf. More info: https://github.com/cmdr2/stable-diffusion-ui/wiki/Model-Merging
* 2.5.10 - 24 Jan 2023 - Reduce the VRAM usage for img2img in 'balanced' mode (without reducing the rendering speed), to make it similar to v2.4 of this UI.
* 2.5.9 - 23 Jan 2023 - Fix a bug where img2img would produce poorer-quality images for the same settings, as compared to version 2.4 of this UI.
* 2.5.9 - 23 Jan 2023 - Reduce the VRAM usage for 'balanced' mode (without reducing the rendering speed), to make it similar to v2.4 of the UI.
* 2.5.8 - 17 Jan 2023 - Fix a bug where 'Low' VRAM usage would consume a LOT of VRAM (on higher-end GPUs). Also fixed a bug that caused out-of-memory errors on SD 2.1-768 models, on 'high' VRAM usage setting.
* 2.5.7 - 16 Jan 2023 - Fix a bug where VAE files ending with .vae.pt weren't getting displayed. Thanks Madrang, rbertus2000 and JeLuf.
* 2.5.6 - 10 Jan 2023 - `Fill` tool for the Image Editor, to allow filling areas with color (or the entire image). And some bug fixes to the Image Editor. Thanks @mdiller.
* 2.5.6 - 10 Jan 2023 - Find Stable Diffusion models in sub-folders inside `models/stable-diffusion`. This allows you to organize your models into sub-folders, instead of keeping them all in a single folder. Thanks @JeLuf.
* 2.5.5 - 9 Jan 2023 - Lots of bug fixes. Thanks @patriceac and @JeLuf.
* 2.5.4 - 29 Dec 2022 - Press Esc key on the keyboard to close the Image Editor. Thanks @patriceac.
* 2.5.4 - 29 Dec 2022 - Lots of bug fixes in the UI. Thanks @patriceac.
* 2.5.4 - 28 Dec 2022 - Full support for running tasks in parallel on multiple GPUs. Warning: 'Euler Ancestral', 'DPM2 Ancestral' and 'DPM++ 2s Ancestral' may produce slight variations in the image (if run in parallel), so we recommend using the other samplers.
* 2.5.3 - 27 Dec 2022 - Fix broken drag-and-drop for text metadata files (as well as paste in clipboard).
* 2.5.3 - 27 Dec 2022 - Allow upscaling by 2x as well as 4x.
* 2.5.3 - 27 Dec 2022 - Fix broken renders on a second GPU.
* 2.5.3 - 26 Dec 2022 - Add a `Remove` button on each image. Thanks @JeLuf.
* 2.5.2 - 26 Dec 2022 - Fix broken inpainting if using non-square target images.
* 2.5.2 - 26 Dec 2022 - Fix a bug where an incorrect model config would get used for some SD 2.1 models.
* 2.5.2 - 26 Dec 2022 - Slight performance and memory improvement while rendering using SD 2.1 models.
* 2.5.1 - 25 Dec 2022 - Allow custom config yaml files for models. You can put a config file (`.yaml`) next to the model file, with the same name as the model. For e.g. if you put `robo-diffusion-v2-base.yaml` next to `robo-diffusion-v2-base.ckpt`, it'll automatically use that config file.
* 2.5.1 - 25 Dec 2022 - Fix broken rendering for SD 2.1-768 models. Fix broken rendering SD 2.0 safetensor models.
* 2.5.0 - 25 Dec 2022 - Major new release! Nearly twice as fast, Full support for SD 2.1 (including low GPU RAM optimizations), 6 new samplers, Model Merging, Fast loading/unloading of VAEs, Database of known models, Color correction for img2img, Three GPU Memory Usage Settings, Save metadata as JSON, Major rewrite of the code, Name change.
## v2.4
### Major Changes
- **Allow reordering the task queue** (by dragging and dropping tasks). Thanks @madrang

47
README.md
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@ -1,14 +1,13 @@
# Stable Diffusion UI
### The easiest way to install and use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on your own computer. Does not require technical knowledge, does not require pre-installed software. 1-click install, powerful features, friendly community.
# Easy Diffusion 2.5
### The easiest way to install and use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on your own computer.
[![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB) (for support, and development discussion) | [Troubleshooting guide for common problems](https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting)
Does not require technical knowledge, does not require pre-installed software. 1-click install, powerful features, friendly community.
### New:
Experimental support for Stable Diffusion 2.0 is available in beta!
[Installation guide](#step-1-download-and-extract-the-installer) | [Troubleshooting guide](https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting) | <sub>[![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB)</sub> <sup>(for support queries, and development discussions)</sup>
----
![t2i](https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/assets/stable-samples/txt2img/768/merged-0006.png)
# Step 1: Download and prepare the installer
# Step 1: Download and extract the installer
Click the download button for your operating system:
<p float="left">
@ -44,9 +43,18 @@ The installer will take care of whatever is needed. If you face any problems, yo
### User experience
- **Hassle-free installation**: Does not require technical knowledge, does not require pre-installed software. Just download and run!
- **Clutter-free UI**: A friendly and simple UI, while providing a lot of powerful features.
- **Task Queue**: Queue up all your ideas, without waiting for the current task to finish.
- **Intelligent Model Detection**: Automatically figures out the YAML config file to use for the chosen model (via a models database).
- **Live Preview**: See the image as the AI is drawing it.
- **Image Modifiers**: A library of *modifier tags* like *"Realistic"*, *"Pencil Sketch"*, *"ArtStation"* etc. Experiment with various styles quickly.
- **Multiple Prompts File**: Queue multiple prompts by entering one prompt per line, or by running a text file.
- **Save generated images to disk**: Save your images to your PC!
- **UI Themes**: Customize the program to your liking.
- **Organize your models into sub-folders**
### Image generation
- **Supports**: "*Text to Image*" and "*Image to Image*".
- **14 Samplers**: `ddim`, `plms`, `heun`, `euler`, `euler_a`, `dpm2`, `dpm2_a`, `lms`, `dpm_solver_stability`, `dpmpp_2s_a`, `dpmpp_2m`, `dpmpp_sde`, `dpm_fast`, `dpm_adaptive`
- **In-Painting**: Specify areas of your image to paint into.
- **Simple Drawing Tool**: Draw basic images to guide the AI, without needing an external drawing program.
- **Face Correction (GFPGAN)**
@ -56,7 +64,6 @@ The installer will take care of whatever is needed. If you face any problems, yo
- **Attention/Emphasis**: () in the prompt increases the model's attention to enclosed words, and [] decreases it.
- **Weighted Prompts**: Use weights for specific words in your prompt to change their importance, e.g. `red:2.4 dragon:1.2`.
- **Prompt Matrix**: Quickly create multiple variations of your prompt, e.g. `a photograph of an astronaut riding a horse | illustration | cinematic lighting`.
- **Lots of Samplers**: ddim, plms, heun, euler, euler_a, dpm2, dpm2_a, lms.
- **1-click Upscale/Face Correction**: Upscale or correct an image after it has been generated.
- **Make Similar Images**: Click to generate multiple variations of a generated image.
- **NSFW Setting**: A setting in the UI to control *NSFW content*.
@ -64,13 +71,15 @@ The installer will take care of whatever is needed. If you face any problems, yo
### Advanced features
- **Custom Models**: Use your own `.ckpt` or `.safetensors` file, by placing it inside the `models/stable-diffusion` folder!
- **Stable Diffusion 2.0 support (experimental)**: available in beta channel.
- **Stable Diffusion 2.1 support**
- **Merge Models**
- **Use custom VAE models**
- **Use pre-trained Hypernetworks**
- **UI Plugins**: Choose from a growing list of [community-generated UI plugins](https://github.com/cmdr2/stable-diffusion-ui/wiki/UI-Plugins), or write your own plugin to add features to the project!
### Performance and security
- **Low Memory Usage**: Creates 512x512 images with less than 4GB of GPU RAM!
- **Fast**: Creates a 512x512 image with euler_a in 5 seconds, on an NVIDIA 3060 12GB.
- **Low Memory Usage**: Create 512x512 images with less than 3 GB of GPU RAM, and 768x768 images with less than 4 GB of GPU RAM!
- **Use CPU setting**: If you don't have a compatible graphics card, but still want to run it on your CPU.
- **Multi-GPU support**: Automatically spreads your tasks across multiple GPUs (if available), for faster performance!
- **Auto scan for malicious models**: Uses picklescan to prevent malicious models.
@ -78,23 +87,17 @@ The installer will take care of whatever is needed. If you face any problems, yo
- **Auto-updater**: Gets you the latest improvements and bug-fixes to a rapidly evolving project.
- **Developer Console**: A developer-mode for those who want to modify their Stable Diffusion code, and edit the conda environment.
### Usability:
- **Live Preview**: See the image as the AI is drawing it.
- **Task Queue**: Queue up all your ideas, without waiting for the current task to finish.
- **Image Modifiers**: A library of *modifier tags* like *"Realistic"*, *"Pencil Sketch"*, *"ArtStation"* etc. Experiment with various styles quickly.
- **Multiple Prompts File**: Queue multiple prompts by entering one prompt per line, or by running a text file.
- **Save generated images to disk**: Save your images to your PC!
- **UI Themes**: Customize the program to your liking.
**(and a lot more)**
----
## Easy for new users:
![Screenshot of the initial UI](media/shot-v10-simple.jpg?raw=true)
![Screenshot of the initial UI](https://user-images.githubusercontent.com/844287/217043152-29454d15-0387-4228-b70d-9a4b84aeb8ba.png)
## Powerful features for advanced users:
![Screenshot of advanced settings](media/shot-v10.jpg?raw=true)
![Screenshot of advanced settings](https://user-images.githubusercontent.com/844287/217042588-fc53c975-bacd-4a9c-af88-37408734ade3.png)
## Live Preview
Useful for judging (and stopping) an image quickly, without waiting for it to finish rendering.
@ -102,7 +105,9 @@ Useful for judging (and stopping) an image quickly, without waiting for it to fi
![live-512](https://user-images.githubusercontent.com/844287/192097249-729a0a1e-a677-485e-9ccc-16a9e848fabe.gif)
## Task Queue
![Screenshot of task queue](media/task-queue-v1.jpg?raw=true)
![Screenshot of task queue](https://user-images.githubusercontent.com/844287/217043984-0b35f73b-1318-47cb-9eed-a2a91b430490.png)
# System Requirements
1. Windows 10/11, or Linux. Experimental support for Mac is coming soon.

17
scripts/Developer Console.cmd
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@ -23,23 +23,20 @@ call conda --version
echo.
@rem activate the environment
@rem activate the legacy environment (if present) and set PYTHONPATH
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.

2
scripts/bootstrap.bat
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@ -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

3
scripts/bootstrap.sh
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@ -21,6 +21,7 @@ OS_ARCH=$(uname -m)
case "${OS_ARCH}" in
x86_64*) OS_ARCH="64";;
arm64*) OS_ARCH="arm64";;
aarch64*) OS_ARCH="arm64";;
*) echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
esac
@ -39,7 +40,7 @@ if [ -e "$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

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scripts/check_modules.py Normal file
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@ -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)

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scripts/developer_console.sh
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@ -26,21 +26,23 @@ if [ "$0" == "bash" ]; then
echo ""
# activate the environment
# activate the legacy environment (if present) and set PYTHONPATH
if [ -e "installer_files/env" ]; then
export PYTHONPATH="$(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
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

1
scripts/on_env_start.bat
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@ -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

1
scripts/on_env_start.sh
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@ -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 .

316
scripts/on_sd_start.bat
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@ -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 +36,117 @@ 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
@if "%ERRORLEVEL%" EQU "0" (
@echo "Stable Diffusion's git repository was already installed. Updating.."
@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
@rem activate the old stable-diffusion env, if it exists
if exist "env" (
call conda activate .\env
)
@cd ..
@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
if not exist "..\models\stable-diffusion" mkdir "..\models\stable-diffusion"
if not exist "..\models\gfpgan" mkdir "..\models\gfpgan"
if not exist "..\models\realesrgan" mkdir "..\models\realesrgan"
if not exist "..\models\vae" mkdir "..\models\vae"
@rem migrate the legacy models to the correct path (if already downloaded)
if exist "sd-v1-4.ckpt" move sd-v1-4.ckpt ..\models\stable-diffusion\
if exist "custom-model.ckpt" move custom-model.ckpt ..\models\stable-diffusion\
if exist "GFPGANv1.3.pth" move GFPGANv1.3.pth ..\models\gfpgan\
if exist "RealESRGAN_x4plus.pth" move RealESRGAN_x4plus.pth ..\models\realesrgan\
if exist "RealESRGAN_x4plus_anime_6B.pth" move RealESRGAN_x4plus_anime_6B.pth ..\models\realesrgan\
@rem install torch and torchvision
call python ..\scripts\check_modules.py torch torchvision
if "%ERRORLEVEL%" EQU "0" (
echo "torch and torchvision have already been installed."
) else (
@echo. & echo "Downloading Stable Diffusion.." & echo.
echo "Installing torch and torchvision.."
@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
)
@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
@cd stable-diffusion
@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
@cd ..
)
@cd stable-diffusion
@>nul findstr /m "conda_sd_env_created" ..\scripts\install_status.txt
@if "%ERRORLEVEL%" EQU "0" (
@echo "Packages necessary for Stable Diffusion were already installed"
@call conda activate .\env
) else (
@echo. & echo "Downloading packages necessary for Stable Diffusion.." & echo. & echo "***** This will take some time (depending on the speed of the Internet connection) and may appear to be stuck, but please be patient ***** .." & echo.
@rmdir /s /q .\env
@REM prevent conda from using packages from the user's home directory, to avoid conflicts
@set PYTHONNOUSERSITE=1
set USERPROFILE=%cd%\profile
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
@call conda env create --prefix env -f environment.yaml || (
@echo. & echo "Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo " 1. Run this installer again." & echo " 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo " 3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo " 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
call python -m pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 || (
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!"
pause
exit /b
)
@call conda activate .\env
for /f "tokens=*" %%a in ('python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"') do if "%%a" NEQ "42" (
@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.
pause
exit /b
)
@echo conda_sd_env_created >> ..\scripts\install_status.txt
)
@rem allow rolling back the sdkit-based changes
if exist "src-old" (
if not exist "src" (
rename "src-old" "src"
if exist "ldm-old" (
rd /s /q "ldm-old"
)
call pip uninstall -y sdkit stable-diffusion-sdkit
)
)
set PATH=C:\Windows\System32;%PATH%
@>nul findstr /m "conda_sd_gfpgan_deps_installed" ..\scripts\install_status.txt
@if "%ERRORLEVEL%" EQU "0" (
@echo "Packages necessary for GFPGAN (Face Correction) were already installed"
@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."
@rem skip sdkit upgrade if in developer-mode
if not exist "..\src\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 python -m pip install --upgrade sdkit -q || (
echo "Error updating sdkit"
)
)
) else (
@echo. & echo "Downloading packages necessary for GFPGAN (Face Correction).." & echo.
echo "Installing sdkit: https://pypi.org/project/sdkit/"
@set PYTHONNOUSERSITE=1
@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
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.
call python -m 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_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"
call python -c "from importlib.metadata import version; print('sdkit version:', version('sdkit'))"
@rem upgrade stable-diffusion-sdkit
call python -m pip install --upgrade stable-diffusion-sdkit -q || (
echo "Error updating stable-diffusion-sdkit"
)
call python -c "from importlib.metadata import version; print('stable-diffusion version:', version('stable-diffusion-sdkit'))"
@rem install rich
call python ..\scripts\check_modules.py rich
if "%ERRORLEVEL%" EQU "0" (
echo "rich has already been installed."
) else (
@echo. & echo "Downloading packages necessary for ESRGAN (Resolution Upscaling).." & echo.
echo "Installing rich.."
@set PYTHONNOUSERSITE=1
set PYTHONNOUSERSITE=1
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
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.
call python -m 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
)
@echo conda_sd_esrgan_deps_installed >> ..\scripts\install_status.txt
)
@>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
set PATH=C:\Windows\System32;%PATH%
call python ..\scripts\check_modules.py uvicorn fastapi
@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
set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
@call conda install -c conda-forge -y --prefix env uvicorn fastapi || (
@call conda install -c conda-forge -y 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
@ -185,70 +161,35 @@ 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
)
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"
@rem if downgrading from v2.5, migrate the models to the legacy path (if already downloaded)
if exist "..\models\stable-diffusion\sd-v1-4.ckpt" if not exist "sd-v1-4.ckpt" move ..\models\stable-diffusion\sd-v1-4.ckpt .
if exist "..\models\gfpgan\GFPGANv1.3.pth" if not exist "GFPGANv1.3.pth" move ..\models\gfpgan\GFPGANv1.3.pth .
if exist "..\models\realesrgan\RealESRGAN_x4plus.pth" if not exist "RealESRGAN_x4plus.pth" move ..\models\realesrgan\RealESRGAN_x4plus.pth .
if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" if not exist "RealESRGAN_x4plus_anime_6B.pth" move ..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth .
@if exist "sd-v1-4.ckpt" (
for %%I in ("sd-v1-4.ckpt") do if "%%~zI" EQU "4265380512" (
@if exist "..\models\stable-diffusion\sd-v1-4.ckpt" (
for %%I in ("..\models\stable-diffusion\sd-v1-4.ckpt") do if "%%~zI" EQU "4265380512" (
echo "Data files (weights) necessary for Stable Diffusion were already downloaded. Using the HuggingFace 4 GB Model."
) else (
for %%J in ("sd-v1-4.ckpt") do if "%%~zJ" EQU "7703807346" (
for %%J in ("..\models\stable-diffusion\sd-v1-4.ckpt") do if "%%~zJ" EQU "7703807346" (
echo "Data files (weights) necessary for Stable Diffusion were already downloaded. Using the HuggingFace 7 GB Model."
) else (
for %%K in ("sd-v1-4.ckpt") do if "%%~zK" EQU "7703810927" (
for %%K in ("..\models\stable-diffusion\sd-v1-4.ckpt") do if "%%~zK" EQU "7703810927" (
echo "Data files (weights) necessary for Stable Diffusion were already downloaded. Using the Waifu Model."
) else (
echo. & echo "The model file present at %cd%\sd-v1-4.ckpt is invalid. It is only %%~zK bytes in size. Re-downloading.." & echo.
del "sd-v1-4.ckpt"
echo. & echo "The model file present at models\stable-diffusion\sd-v1-4.ckpt is invalid. It is only %%~zK bytes in size. Re-downloading.." & echo.
del "..\models\stable-diffusion\sd-v1-4.ckpt"
)
)
)
)
@if not exist "sd-v1-4.ckpt" (
@if not exist "..\models\stable-diffusion\sd-v1-4.ckpt" (
@echo. & echo "Downloading data files (weights) for Stable Diffusion.." & echo.
@call curl -L -k https://me.cmdr2.org/stable-diffusion-ui/sd-v1-4.ckpt > sd-v1-4.ckpt
@call curl -L -k https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt > ..\models\stable-diffusion\sd-v1-4.ckpt
@if exist "sd-v1-4.ckpt" (
for %%I in ("sd-v1-4.ckpt") do if "%%~zI" NEQ "4265380512" (
@if exist "..\models\stable-diffusion\sd-v1-4.ckpt" (
for %%I in ("..\models\stable-diffusion\sd-v1-4.ckpt") do if "%%~zI" NEQ "4265380512" (
echo. & echo "Error: The downloaded model file was invalid! Bytes downloaded: %%~zI" & echo.
echo. & echo "Error downloading the data files (weights) 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
@ -263,22 +204,22 @@ if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" if not exist "Rea
@if exist "GFPGANv1.3.pth" (
for %%I in ("GFPGANv1.3.pth") do if "%%~zI" EQU "348632874" (
@if exist "..\models\gfpgan\GFPGANv1.3.pth" (
for %%I in ("..\models\gfpgan\GFPGANv1.3.pth") do if "%%~zI" EQU "348632874" (
echo "Data files (weights) necessary for GFPGAN (Face Correction) were already downloaded"
) else (
echo. & echo "The GFPGAN model file present at %cd%\GFPGANv1.3.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "GFPGANv1.3.pth"
echo. & echo "The GFPGAN model file present at models\gfpgan\GFPGANv1.3.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "..\models\gfpgan\GFPGANv1.3.pth"
)
)
@if not exist "GFPGANv1.3.pth" (
@if not exist "..\models\gfpgan\GFPGANv1.3.pth" (
@echo. & echo "Downloading data files (weights) for GFPGAN (Face Correction).." & echo.
@call curl -L -k https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth > GFPGANv1.3.pth
@call curl -L -k https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth > ..\models\gfpgan\GFPGANv1.3.pth
@if exist "GFPGANv1.3.pth" (
for %%I in ("GFPGANv1.3.pth") do if "%%~zI" NEQ "348632874" (
@if exist "..\models\gfpgan\GFPGANv1.3.pth" (
for %%I in ("..\models\gfpgan\GFPGANv1.3.pth") do if "%%~zI" NEQ "348632874" (
echo. & echo "Error: The downloaded GFPGAN model file was invalid! Bytes downloaded: %%~zI" & echo.
echo. & echo "Error downloading the data files (weights) 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
@ -293,22 +234,22 @@ if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" if not exist "Rea
@if exist "RealESRGAN_x4plus.pth" (
for %%I in ("RealESRGAN_x4plus.pth") do if "%%~zI" EQU "67040989" (
@if exist "..\models\realesrgan\RealESRGAN_x4plus.pth" (
for %%I in ("..\models\realesrgan\RealESRGAN_x4plus.pth") do if "%%~zI" EQU "67040989" (
echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus were already downloaded"
) else (
echo. & echo "The RealESRGAN model file present at %cd%\RealESRGAN_x4plus.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "RealESRGAN_x4plus.pth"
echo. & echo "The RealESRGAN model file present at models\realesrgan\RealESRGAN_x4plus.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "..\models\realesrgan\RealESRGAN_x4plus.pth"
)
)
@if not exist "RealESRGAN_x4plus.pth" (
@if not exist "..\models\realesrgan\RealESRGAN_x4plus.pth" (
@echo. & echo "Downloading data files (weights) for ESRGAN (Resolution Upscaling) x4plus.." & echo.
@call curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth > RealESRGAN_x4plus.pth
@call curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth > ..\models\realesrgan\RealESRGAN_x4plus.pth
@if exist "RealESRGAN_x4plus.pth" (
for %%I in ("RealESRGAN_x4plus.pth") do if "%%~zI" NEQ "67040989" (
@if exist "..\models\realesrgan\RealESRGAN_x4plus.pth" (
for %%I in ("..\models\realesrgan\RealESRGAN_x4plus.pth") do if "%%~zI" NEQ "67040989" (
echo. & echo "Error: The downloaded ESRGAN x4plus model file was invalid! Bytes downloaded: %%~zI" & echo.
echo. & echo "Error downloading the data files (weights) for ESRGAN (Resolution Upscaling) x4plus. 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
@ -323,21 +264,21 @@ if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" if not exist "Rea
@if exist "RealESRGAN_x4plus_anime_6B.pth" (
for %%I in ("RealESRGAN_x4plus_anime_6B.pth") do if "%%~zI" EQU "17938799" (
@if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" (
for %%I in ("..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth") do if "%%~zI" EQU "17938799" (
echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus_anime were already downloaded"
) else (
echo. & echo "The RealESRGAN model file present at %cd%\RealESRGAN_x4plus_anime_6B.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "RealESRGAN_x4plus_anime_6B.pth"
echo. & echo "The RealESRGAN model file present at models\realesrgan\RealESRGAN_x4plus_anime_6B.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
del "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth"
)
)
@if not exist "RealESRGAN_x4plus_anime_6B.pth" (
@if not exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" (
@echo. & echo "Downloading data files (weights) for ESRGAN (Resolution Upscaling) x4plus_anime.." & echo.
@call curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth > RealESRGAN_x4plus_anime_6B.pth
@call curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth > ..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth
@if exist "RealESRGAN_x4plus_anime_6B.pth" (
@if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" (
for %%I in ("RealESRGAN_x4plus_anime_6B.pth") do if "%%~zI" NEQ "17938799" (
echo. & echo "Error: The downloaded ESRGAN x4plus_anime model file was invalid! Bytes downloaded: %%~zI" & echo.
echo. & echo "Error downloading the data files (weights) for ESRGAN (Resolution Upscaling) x4plus_anime. 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.
@ -381,10 +322,6 @@ if exist "..\models\realesrgan\RealESRGAN_x4plus_anime_6B.pth" if not exist "Rea
)
)
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
@ -395,10 +332,8 @@ if "%test_sd2%" == "Y" (
@set SD_DIR=%cd%
@cd env\lib\site-packages
@set PYTHONPATH=%SD_DIR%;%cd%
@cd ..\..\..
@echo PYTHONPATH=%PYTHONPATH%
set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
echo PYTHONPATH=%PYTHONPATH%
call where python
call python --version
@ -407,17 +342,12 @@ 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
@rem set any overrides
set HF_HUB_DISABLE_SYMLINKS_WARNING=true
@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

282
scripts/on_sd_start.sh
View File

@ -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=$(conda info --base)
@ -21,125 +22,110 @@ python -c "import os; import shutil; frm = 'sd-ui-files/ui/hotfix/9c24e6cd9f499d
# Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
# Note to self: Please rewrite this in Python. For the sake of your own sanity.
if [ "$test_sd2" == "" ]; then
export test_sd2="N"
# set the correct installer path (current vs legacy)
if [ -e "installer_files/env" ]; then
export INSTALL_ENV_DIR="$(pwd)/installer_files/env"
fi
if [ -e "stable-diffusion/env" ]; then
export INSTALL_ENV_DIR="$(pwd)/stable-diffusion/env"
fi
if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/install_status.txt` -gt "0" ]; then
echo "Stable Diffusion's git repository was already installed. Updating.."
# create the stable-diffusion folder, to work with legacy installations
if [ ! -e "stable-diffusion" ]; then mkdir stable-diffusion; fi
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
cd stable-diffusion
if [ `grep -c conda_sd_env_created ../scripts/install_status.txt` -gt "0" ]; then
echo "Packages necessary for Stable Diffusion were already installed"
# activate the old stable-diffusion env, if it exists
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
mkdir -p "../models/stable-diffusion"
mkdir -p "../models/gfpgan"
mkdir -p "../models/realesrgan"
mkdir -p "../models/vae"
# migrate the legacy models to the correct path (if already downloaded)
if [ -e "sd-v1-4.ckpt" ]; then mv sd-v1-4.ckpt ../models/stable-diffusion/; fi
if [ -e "custom-model.ckpt" ]; then mv custom-model.ckpt ../models/stable-diffusion/; fi
if [ -e "GFPGANv1.3.pth" ]; then mv GFPGANv1.3.pth ../models/gfpgan/; fi
if [ -e "RealESRGAN_x4plus.pth" ]; then mv RealESRGAN_x4plus.pth ../models/realesrgan/; fi
if [ -e "RealESRGAN_x4plus_anime_6B.pth" ]; then mv RealESRGAN_x4plus_anime_6B.pth ../models/realesrgan/; 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"
printf "\n\n***** This will take some time (depending on the speed of the Internet connection) and may appear to be stuck, but please be patient ***** ..\n\n"
# prevent conda from using packages from the user's home directory, to avoid conflicts
export PYTHONNOUSERSITE=1
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
if conda env create --prefix env --force -f environment.yaml ; then
echo "Installed. Testing.."
else
fail "'conda env create' failed"
fi
conda activate ./env || fail "conda activate failed"
out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"`
if [ "$out_test" != "42" ]; then
fail "Dependency test failed"
fi
echo conda_sd_env_created >> ../scripts/install_status.txt
fi
# allow rolling back the sdkit-based changes
if [ -e "src-old" ] && [ ! -e "src" ]; then
mv src-old src
if [ -e "ldm-old" ]; then rm -r ldm-old; fi
pip uninstall -y sdkit stable-diffusion-sdkit
fi
if [ `grep -c conda_sd_gfpgan_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
echo "Packages necessary for GFPGAN (Face Correction) were already installed"
else
printf "\n\nDownloading packages necessary for GFPGAN (Face Correction)..\n"
echo "Installing torch and torchvision.."
export PYTHONNOUSERSITE=1
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
if [ "$out_test" != "42" ]; then
echo "EE The dependency check has failed. This usually means that some system libraries are missing."
echo "EE On Debian/Ubuntu systems, this are often these packages: libsm6 libxext6 libxrender-dev"
echo "EE Other Linux distributions might have different package names for these libraries."
fail "GFPGAN dependency test failed"
fi
echo conda_sd_gfpgan_deps_installed >> ../scripts/install_status.txt
fi
if [ `grep -c conda_sd_esrgan_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
echo "Packages necessary for ESRGAN (Resolution Upscaling) were already installed"
if python -m pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 ; then
echo "Installed."
else
printf "\n\nDownloading packages necessary for ESRGAN (Resolution Upscaling)..\n"
fail "torch install failed"
fi
fi
# install/upgrade sdkit
if python ../scripts/check_modules.py sdkit sdkit.models ldm transformers numpy antlr4 gfpgan realesrgan ; then
echo "sdkit is already installed."
# skip sdkit upgrade if in developer-mode
if [ ! -e "../src/sdkit" ]; then
export PYTHONNOUSERSITE=1
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
python -m pip install --upgrade sdkit -q
fi
else
echo "Installing sdkit: https://pypi.org/project/sdkit/"
export PYTHONNOUSERSITE=1
export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"`
if [ "$out_test" != "42" ]; then
fail "ESRGAN dependency test failed"
if python -m pip install sdkit ; then
echo "Installed."
else
fail "sdkit install failed"
fi
fi
echo conda_sd_esrgan_deps_installed >> ../scripts/install_status.txt
python -c "from importlib.metadata import version; print('sdkit version:', version('sdkit'))"
# upgrade stable-diffusion-sdkit
python -m pip install --upgrade stable-diffusion-sdkit -q
python -c "from importlib.metadata import version; print('stable-diffusion version:', version('stable-diffusion-sdkit'))"
# 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="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
if python -m pip install rich ; then
echo "Installed."
else
fail "Install failed for rich"
fi
fi
if [ `grep -c conda_sd_ui_deps_installed ../scripts/install_status.txt` -gt "0" ]; then
if python ../scripts/check_modules.py uvicorn fastapi ; then
echo "Packages necessary for Stable Diffusion UI were already installed"
else
printf "\n\nDownloading packages necessary for Stable Diffusion UI..\n\n"
export PYTHONNOUSERSITE=1
export PYTHONPATH="$(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"
@ -148,57 +134,26 @@ 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 downgrading from v2.5, migrate the models to the legacy path (if already downloaded)
if [ -e "../models/stable-diffusion/sd-v1-4.ckpt" ] && [ ! -e "sd-v1-4.ckpt" ]; then mv ../models/stable-diffusion/sd-v1-4.ckpt . ; fi
if [ -e "../models/gfpgan/GFPGANv1.3.pth" ] && [ ! -e "GFPGANv1.3.pth" ]; then mv ../models/gfpgan/GFPGANv1.3.pth . ; fi
if [ -e "../models/realesrgan/RealESRGAN_x4plus.pth" ] && [ ! -e "RealESRGAN_x4plus.pth" ]; then mv ../models/realesrgan/RealESRGAN_x4plus.pth . ; fi
if [ -e "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" ] && [ ! -e "RealESRGAN_x4plus_anime_6B.pth" ]; then mv ../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth . ; fi
if [ -f "sd-v1-4.ckpt" ]; then
model_size=`find "sd-v1-4.ckpt" -printf "%s"`
if [ -f "../models/stable-diffusion/sd-v1-4.ckpt" ]; then
model_size=`find "../models/stable-diffusion/sd-v1-4.ckpt" -printf "%s"`
if [ "$model_size" -eq "4265380512" ] || [ "$model_size" -eq "7703807346" ] || [ "$model_size" -eq "7703810927" ]; then
echo "Data files (weights) necessary for Stable Diffusion were already downloaded"
else
printf "\n\nThe model file present at $PWD/sd-v1-4.ckpt is invalid. It is only $model_size bytes in size. Re-downloading.."
rm sd-v1-4.ckpt
printf "\n\nThe model file present at models/stable-diffusion/sd-v1-4.ckpt is invalid. It is only $model_size bytes in size. Re-downloading.."
rm ../models/stable-diffusion/sd-v1-4.ckpt
fi
fi
if [ ! -f "sd-v1-4.ckpt" ]; then
if [ ! -f "../models/stable-diffusion/sd-v1-4.ckpt" ]; then
echo "Downloading data files (weights) for Stable Diffusion.."
curl -L -k https://me.cmdr2.org/stable-diffusion-ui/sd-v1-4.ckpt > sd-v1-4.ckpt
curl -L -k https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt > ../models/stable-diffusion/sd-v1-4.ckpt
if [ -f "sd-v1-4.ckpt" ]; then
model_size=`find "sd-v1-4.ckpt" -printf "%s"`
if [ -f "../models/stable-diffusion/sd-v1-4.ckpt" ]; then
model_size=`find "../models/stable-diffusion/sd-v1-4.ckpt" -printf "%s"`
if [ ! "$model_size" == "4265380512" ]; then
fail "The downloaded model file was invalid! Bytes downloaded: $model_size"
fi
@ -208,24 +163,24 @@ if [ ! -f "sd-v1-4.ckpt" ]; then
fi
if [ -f "GFPGANv1.3.pth" ]; then
model_size=`find "GFPGANv1.3.pth" -printf "%s"`
if [ -f "../models/gfpgan/GFPGANv1.3.pth" ]; then
model_size=`find "../models/gfpgan/GFPGANv1.3.pth" -printf "%s"`
if [ "$model_size" -eq "348632874" ]; then
echo "Data files (weights) necessary for GFPGAN (Face Correction) were already downloaded"
else
printf "\n\nThe model file present at $PWD/GFPGANv1.3.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm GFPGANv1.3.pth
printf "\n\nThe model file present at models/gfpgan/GFPGANv1.3.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm ../models/gfpgan/GFPGANv1.3.pth
fi
fi
if [ ! -f "GFPGANv1.3.pth" ]; then
if [ ! -f "../models/gfpgan/GFPGANv1.3.pth" ]; then
echo "Downloading data files (weights) for GFPGAN (Face Correction).."
curl -L -k https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth > GFPGANv1.3.pth
curl -L -k https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth > ../models/gfpgan/GFPGANv1.3.pth
if [ -f "GFPGANv1.3.pth" ]; then
model_size=`find "GFPGANv1.3.pth" -printf "%s"`
if [ -f "../models/gfpgan/GFPGANv1.3.pth" ]; then
model_size=`find "../models/gfpgan/GFPGANv1.3.pth" -printf "%s"`
if [ ! "$model_size" -eq "348632874" ]; then
fail "The downloaded GFPGAN model file was invalid! Bytes downloaded: $model_size"
fi
@ -235,24 +190,24 @@ if [ ! -f "GFPGANv1.3.pth" ]; then
fi
if [ -f "RealESRGAN_x4plus.pth" ]; then
model_size=`find "RealESRGAN_x4plus.pth" -printf "%s"`
if [ -f "../models/realesrgan/RealESRGAN_x4plus.pth" ]; then
model_size=`find "../models/realesrgan/RealESRGAN_x4plus.pth" -printf "%s"`
if [ "$model_size" -eq "67040989" ]; then
echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus were already downloaded"
else
printf "\n\nThe model file present at $PWD/RealESRGAN_x4plus.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm RealESRGAN_x4plus.pth
printf "\n\nThe model file present at models/realesrgan/RealESRGAN_x4plus.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm ../models/realesrgan/RealESRGAN_x4plus.pth
fi
fi
if [ ! -f "RealESRGAN_x4plus.pth" ]; then
if [ ! -f "../models/realesrgan/RealESRGAN_x4plus.pth" ]; then
echo "Downloading data files (weights) for ESRGAN (Resolution Upscaling) x4plus.."
curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth > RealESRGAN_x4plus.pth
curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth > ../models/realesrgan/RealESRGAN_x4plus.pth
if [ -f "RealESRGAN_x4plus.pth" ]; then
model_size=`find "RealESRGAN_x4plus.pth" -printf "%s"`
if [ -f "../models/realesrgan/RealESRGAN_x4plus.pth" ]; then
model_size=`find "../models/realesrgan/RealESRGAN_x4plus.pth" -printf "%s"`
if [ ! "$model_size" -eq "67040989" ]; then
fail "The downloaded ESRGAN x4plus model file was invalid! Bytes downloaded: $model_size"
fi
@ -262,24 +217,24 @@ if [ ! -f "RealESRGAN_x4plus.pth" ]; then
fi
if [ -f "RealESRGAN_x4plus_anime_6B.pth" ]; then
model_size=`find "RealESRGAN_x4plus_anime_6B.pth" -printf "%s"`
if [ -f "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" ]; then
model_size=`find "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" -printf "%s"`
if [ "$model_size" -eq "17938799" ]; then
echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus_anime were already downloaded"
else
printf "\n\nThe model file present at $PWD/RealESRGAN_x4plus_anime_6B.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm RealESRGAN_x4plus_anime_6B.pth
printf "\n\nThe model file present at models/realesrgan/RealESRGAN_x4plus_anime_6B.pth is invalid. It is only $model_size bytes in size. Re-downloading.."
rm ../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth
fi
fi
if [ ! -f "RealESRGAN_x4plus_anime_6B.pth" ]; then
if [ ! -f "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" ]; then
echo "Downloading data files (weights) for ESRGAN (Resolution Upscaling) x4plus_anime.."
curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth > RealESRGAN_x4plus_anime_6B.pth
curl -L -k https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth > ../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth
if [ -f "RealESRGAN_x4plus_anime_6B.pth" ]; then
model_size=`find "RealESRGAN_x4plus_anime_6B.pth" -printf "%s"`
if [ -f "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" ]; then
model_size=`find "../models/realesrgan/RealESRGAN_x4plus_anime_6B.pth" -printf "%s"`
if [ ! "$model_size" -eq "17938799" ]; then
fail "The downloaded ESRGAN x4plus_anime model file was invalid! Bytes downloaded: $model_size"
fi
@ -320,10 +275,6 @@ if [ ! -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
fi
fi
if [ "$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
@ -332,7 +283,8 @@ 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
@ -342,6 +294,6 @@ 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"

1
scripts/start.sh
View File

@ -19,4 +19,5 @@ which conda
conda --version || exit 1
# Download the rest of the installer and UI
chmod +x scripts/*.sh
scripts/on_env_start.sh

0
ui/easydiffusion/__init__.py Normal file
View File

165
ui/easydiffusion/app.py Normal file
View File

@ -0,0 +1,165 @@
import os
import socket
import sys
import json
import traceback
import logging
from rich.logging import RichHandler
from sdkit.utils import log as sdkit_log # hack, so we can overwrite the log config
from easydiffusion import task_manager
from easydiffusion.utils import log
# Remove all handlers associated with the root logger object.
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
LOG_FORMAT = '%(asctime)s.%(msecs)03d %(levelname)s %(threadName)s %(message)s'
logging.basicConfig(
level=logging.INFO,
format=LOG_FORMAT,
datefmt="%X",
handlers=[RichHandler(markup=True, rich_tracebacks=False, 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}")

69
ui/sd_internal/device_manager.py → ui/easydiffusion/device_manager.py
View File

@ -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'
thread_data.device_name = get_processor_name()
print('Render device CPU available as', thread_data.device_name)
context.device = 'cpu'
context.device_name = get_processor_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)
thread_data.device = device
context.device_name = torch.cuda.get_device_name(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()
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)
if thread_data.force_full_precision:
print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', thread_data.device_name)
if needs_to_force_full_precision(context):
log.warn(f'forcing full precision on this GPU, to avoid green images. GPU detected: {context.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 or 'quadro' in device_name) and (' 1660' in device_name or ' 1650' in device_name or ' t400' in device_name or ' t500' in device_name or ' t550' in device_name or ' t600' in device_name or ' t1000' in device_name or ' t1200' in device_name or ' 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):
@ -129,10 +156,12 @@ def is_device_compatible(device):
'''
Returns True/False, and prints any compatibility errors
'''
# static variable "history".
is_device_compatible.history = getattr(is_device_compatible, 'history', {})
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 +170,12 @@ 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')
if is_device_compatible.history.get(device) == None:
log.warn(f'GPU {device} with less than 3 GB of VRAM is not compatible with Stable Diffusion')
is_device_compatible.history[device] = 1
return False
except RuntimeError as e:
print(str(e))
log.error(str(e))
return False
return True
@ -164,5 +195,5 @@ 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"

222
ui/easydiffusion/model_manager.py Normal file
View File

@ -0,0 +1,222 @@
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'],
}
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)
try:
load_model(context, model_type)
except Exception as e:
log.error(f'[red]Error while loading {model_type} model: {context.model_paths[model_type]}[/red]')
log.error(f'[red]Error: {e}[/red]')
log.error(f'[red]Consider removing the model from the model folder.[red]')
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']
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()
vram_usage_level = config.get('vram_usage_level', 'balanced')
if vram_usage_level != context.vram_usage_level:
context.vram_usage_level = vram_usage_level
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
class MaliciousModelException(Exception):
"Raised when picklescan reports a problem with a model"
pass
def scan_directory(directory, suffixes):
nonlocal models_scanned
tree = []
for entry in os.scandir(directory):
if entry.is_file():
matching_suffix = list(filter(lambda s: entry.name.endswith(s), suffixes))
if len(matching_suffix) == 0: continue
matching_suffix = matching_suffix[0]
mtime = entry.stat().st_mtime
mod_time = known_models[entry.path] if entry.path in known_models else -1
if mod_time != mtime:
models_scanned += 1
if is_malicious_model(entry.path):
raise MaliciousModelException(entry.path)
known_models[entry.path] = mtime
tree.append(entry.name[:-len(matching_suffix)])
elif entry.is_dir():
scan=scan_directory(entry.path, suffixes)
if len(scan) != 0:
tree.append( (entry.name, scan ) )
return tree
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)
try:
models['options'][model_type] = scan_directory(models_dir, model_extensions)
except MaliciousModelException as e:
models['scan-error'] = e
# 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

137
ui/easydiffusion/renderer.py Normal file
View File

@ -0,0 +1,137 @@
import queue
import time
import json
import pprint
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
print_task_info(req, task_data)
images, seeds = make_images_internal(req, task_data, data_queue, task_temp_images, step_callback)
res = Response(req, task_data, images=construct_response(images, seeds, task_data, base_seed=req.seed))
res = res.json()
data_queue.put(json.dumps(res))
log.info('Task completed')
return res
def print_task_info(req: GenerateImageRequest, task_data: TaskData):
req_str = pprint.pformat(get_printable_request(req)).replace("[","\[")
task_str = pprint.pformat(task_data.dict()).replace("[","\[")
log.info(f'request: {req_str}')
log.info(f'task data: {task_str}')
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)
seeds = [*range(req.seed, req.seed + len(images))]
if task_data.show_only_filtered_image or filtered_images is images:
return filtered_images, seeds
else:
return images + filtered_images, seeds + seeds
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:
if req.init_image is not None: req.sampler_name = 'ddim'
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)
finally:
if hasattr(context, 'partial_x_samples') and context.partial_x_samples is not None:
del context.partial_x_samples
context.partial_x_samples = None
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, scale=task_data.upscale_amount)
def construct_response(images: list, seeds: list, task_data: TaskData, base_seed: int):
return [
ResponseImage(
data=img_to_base64_str(img, task_data.output_format, task_data.output_quality),
seed=seed,
) for img, seed in zip(images, seeds)
]
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

241
ui/easydiffusion/server.py Normal file
View File

@ -0,0 +1,241 @@
"""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, MergeRequest
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.post('/model/merge')
def model_merge(req: dict):
print(req)
return model_merge_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 model_merge_internal(req: dict):
try:
from sdkit.train import merge_models
from easydiffusion.utils.save_utils import filename_regex
mergeReq: MergeRequest = MergeRequest.parse_obj(req)
merge_models(model_manager.resolve_model_to_use(mergeReq.model0,'stable-diffusion'),
model_manager.resolve_model_to_use(mergeReq.model1,'stable-diffusion'),
mergeReq.ratio,
os.path.join(app.MODELS_DIR, 'stable-diffusion', filename_regex.sub('_', mergeReq.out_path)),
mergeReq.use_fp16
)
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 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))

267
ui/sd_internal/task_manager.py → ui/easydiffusion/task_manager.py
View File

@ -11,12 +11,15 @@ 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 sd_internal import Request, Response, runtime, device_manager
from easydiffusion import device_manager
from easydiffusion.types import TaskData, GenerateImageRequest
from easydiffusion.utils import log
THREAD_NAME_PREFIX = 'Runtime-Render/'
from sdkit.utils import gc
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 +39,13 @@ class ServerStates:
class Unavailable(Symbol): pass
class RenderTask(): # Task with output queue and completion lock.
def __init__(self, req: Request):
req.request_id = id(self)
self.request: Request = req # Initial Request
def __init__(self, req: GenerateImageRequest, task_data: TaskData):
task_data.request_id = id(self)
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 +73,6 @@ 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 +95,11 @@ 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 +133,7 @@ class DataCache():
self._get_ttl_time(ttl), value
)
except Exception as e:
print(str(e))
print(traceback.format_exc())
log.error(traceback.format_exc())
return False
else:
return True
@ -189,7 +144,7 @@ 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 +155,9 @@ 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 +185,10 @@ 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 +196,7 @@ 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 +205,7 @@ 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 +217,36 @@ 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
from . import runtime
global current_state, current_state_error
from easydiffusion import renderer, model_manager
try:
runtime.thread_init(device)
except Exception as e:
print(traceback.format_exc())
renderer.init(device)
weak_thread_data[threading.current_thread()] = {
'error': e
}
return
weak_thread_data[threading.current_thread()] = {
'device': runtime.thread_data.device,
'device_name': runtime.thread_data.device_name,
'device': renderer.context.device,
'device_name': renderer.context.device_name,
'alive': True
}
if runtime.thread_data.device != 'cpu' or is_alive() == 1:
preload_model()
current_state = ServerStates.LoadingModel
model_manager.load_default_models(renderer.context)
current_state = ServerStates.Online
except Exception as e:
log.error(traceback.format_exc())
weak_thread_data[threading.current_thread()] = {
'error': e,
'alive': False
}
return
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}')
runtime.unload_models()
runtime.unload_filters()
log.info(f'Shutting down thread for device {renderer.context.device}')
model_manager.unload_all(renderer.context)
return
if isinstance(current_state_error, SystemExit):
current_state = ServerStates.Unavailable
@ -333,7 +257,7 @@ 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 +266,44 @@ 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.error = str(e)
task.response = {"status": 'failed', "detail": str(task.error)}
task.buffer_queue.put(json.dumps(task.response))
print(traceback.format_exc())
continue
log.error(traceback.format_exc())
finally:
# Task completed
gc(renderer.context)
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 +340,7 @@ 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 +390,7 @@ 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 +402,7 @@ 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 +414,11 @@ 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 +441,44 @@ 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)
print('devices_to_stop', devices_to_stop)
log.debug(f'devices_to_start: {devices_to_start}')
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
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)
new_task = RenderTask(render_req, task_data)
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.

95
ui/easydiffusion/types.py Normal file
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@ -0,0 +1,95 @@
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"
upscale_amount: int = 4 # or 2
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 MergeRequest(BaseModel):
model0: str = None
model1: str = None
ratio: float = None
out_path: str = "mix"
use_fp16 = True
class Image:
data: str # base64
seed: int
is_nsfw: bool
path_abs: str = None
def __init__(self, data, seed):
self.data = data
self.seed = seed
def json(self):
return {
"data": self.data,
"seed": self.seed,
"path_abs": self.path_abs,
}
class Response:
render_request: GenerateImageRequest
task_data: TaskData
images: list
def __init__(self, render_request: GenerateImageRequest, task_data: TaskData, images: list):
self.render_request = render_request
self.task_data = task_data
self.images = images
def json(self):
del self.render_request.init_image
del self.render_request.init_image_mask
res = {
"status": 'succeeded',
"render_request": self.render_request.dict(),
"task_data": self.task_data.dict(),
"output": [],
}
for image in self.images:
res["output"].append(image.json())
return res
class UserInitiatedStop(Exception):
pass

8
ui/easydiffusion/utils/__init__.py Normal file
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@ -0,0 +1,8 @@
import logging
log = logging.getLogger('easydiffusion')
from .save_utils import (
save_images_to_disk,
get_printable_request,
)

88
ui/easydiffusion/utils/save_utils.py Normal file
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@ -0,0 +1,88 @@
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',
'upscale_amount': 'Upscale By',
'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):
now = time.time()
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)
make_filename = make_filename_callback(req, now=now)
if task_data.show_only_filtered_image or filtered_images is images:
save_images(filtered_images, save_dir_path, file_name=make_filename, output_format=task_data.output_format, output_quality=task_data.output_quality)
save_dicts(metadata_entries, save_dir_path, file_name=make_filename, output_format=task_data.metadata_output_format)
else:
make_filter_filename = make_filename_callback(req, now=now, suffix='filtered')
save_images(images, save_dir_path, file_name=make_filename, output_format=task_data.output_format, output_quality=task_data.output_quality)
save_images(filtered_images, save_dir_path, file_name=make_filter_filename, output_format=task_data.output_format, output_quality=task_data.output_quality)
save_dicts(metadata_entries, save_dir_path, file_name=make_filter_filename, 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 metadata['use_upscale'] is not None:
metadata['upscale_amount'] = task_data.upscale_amount
# 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, now=None):
if now is None:
now = time.time()
def make_filename(i):
img_id = base64.b64encode(int(now+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

54
ui/index.html
View File

@ -1,11 +1,12 @@
<!DOCTYPE html>
<html>
<head>
<title>Stable Diffusion UI</title>
<title>Easy Diffusion</title>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="theme-color" content="#673AB6">
<link rel="icon" type="image/png" href="/media/images/favicon-16x16.png" sizes="16x16">
<link rel="icon" type="image/png" href="/media/images/favicon-32x32.png" sizes="32x32">
<link rel="stylesheet" href="/media/css/jquery-confirm.min.css">
<link rel="stylesheet" href="/media/css/fonts.css">
<link rel="stylesheet" href="/media/css/themes.css">
<link rel="stylesheet" href="/media/css/main.css">
@ -13,7 +14,6 @@
<link rel="stylesheet" href="/media/css/modifier-thumbnails.css">
<link rel="stylesheet" href="/media/css/fontawesome-all.min.css">
<link rel="stylesheet" href="/media/css/image-editor.css">
<link rel="stylesheet" href="/media/css/jquery-confirm.min.css">
<link rel="manifest" href="/media/manifest.webmanifest">
<script src="/media/js/jquery-3.6.1.min.js"></script>
<script src="/media/js/jquery-confirm.min.js"></script>
@ -24,15 +24,15 @@
<div id="top-nav">
<div id="logo">
<h1>
Stable Diffusion UI
<small>v2.4.24 <span id="updateBranchLabel"></span></small>
Easy Diffusion
<small>v2.5.15 <span id="updateBranchLabel"></span></small>
</h1>
</div>
<div id="server-status">
<div id="server-status-color">●</div>
<span id="server-status-msg">Stable Diffusion is starting..</span>
</div>
<div id="tab-container">
<div id="tab-container" class="tab-container">
<span id="tab-main" class="tab active">
<span><i class="fa fa-image icon"></i> Generate</span>
</span>
@ -55,7 +55,7 @@
<input id="prompt_from_file" name="prompt_from_file" type="file" /> <!-- hidden -->
<label for="negative_prompt" class="collapsible" id="negative_prompt_handle">
Negative Prompt
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Writing-prompts#negative-prompts" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about Negative Prompts</span></i></a>
<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Writing-prompts#negative-prompts" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top">Click to learn more about Negative Prompts</span></i></a>
<small>(optional)</small>
</label>
<div class="collapsible-content">
@ -92,10 +92,12 @@
</div>
</div>
<div 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></div>
</div>
<div id="editor-inputs-tags-container" class="row">
<label>Image Modifiers <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">click an Image Modifier to remove it, use Ctrl+Mouse Wheel to adjust its weight</span></i></label>
<label>Image Modifiers <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">click an Image Modifier to remove it, right-click to temporarily disable it, use Ctrl+Mouse Wheel to adjust its weight</span></i></label>
<div id="editor-inputs-tags-list"></div>
</div>
@ -129,22 +131,32 @@
</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>
<select id="sampler" name="sampler">
<option value="plms">plms</option>
<option value="ddim">ddim</option>
<option value="heun">heun</option>
<option value="euler">euler</option>
<option value="euler_a" selected>euler_a</option>
<option value="dpm2">dpm2</option>
<option value="dpm2_a">dpm2_a</option>
<option value="lms">lms</option>
<tr id="samplerSelection" class="pl-5"><td><label for="sampler_name">Sampler:</label></td><td>
<select id="sampler_name" name="sampler_name">
<option value="plms">PLMS</option>
<option value="ddim">DDIM</option>
<option value="heun">Heun</option>
<option value="euler">Euler</option>
<option value="euler_a" selected>Euler Ancestral</option>
<option value="dpm2">DPM2</option>
<option value="dpm2_a">DPM2 Ancestral</option>
<option value="lms">LMS</option>
<option value="dpm_solver_stability">DPM Solver (Stability AI)</option>
<option value="dpmpp_2s_a">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>
@ -222,7 +234,12 @@
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slower images)</small></label></li>
<li class="pl-5"><input id="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>
<input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Scale up by</label>
<select id="upscale_amount" name="upscale_amount">
<option value="2">2x</option>
<option value="4" selected>4x</option>
</select>
with
<select id="upscale_model" name="upscale_model">
<option value="RealESRGAN_x4plus" selected>RealESRGAN_x4plus</option>
<option value="RealESRGAN_x4plus_anime_6B">RealESRGAN_x4plus_anime_6B</option>
@ -416,7 +433,6 @@
async function init() {
await initSettings()
await getModels()
await getDiskPath()
await getAppConfig()
await loadUIPlugins()
await loadModifiers()

10
ui/main.py Normal file
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@ -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()

2
ui/media/css/image-editor.css
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@ -2,12 +2,12 @@
padding-left: 32px;
text-align: left;
padding-bottom: 20px;
max-width: min-content;
}
.editor-options-container {
display: flex;
row-gap: 10px;
max-width: 210px;
}
.editor-options-container > * {

39
ui/media/css/main.css
View File

@ -107,6 +107,7 @@ code {
.imgContainer {
display: flex;
justify-content: flex-end;
position: relative;
}
.imgItemInfo {
padding-bottom: 0.5em;
@ -114,16 +115,29 @@ code {
align-items: flex-end;
flex-direction: column;
position: absolute;
padding: 5px;
padding-right: 5pt;
padding-top: 6pt;
opacity: 0;
transition: 0.1s all;
}
.imgPreviewItemClearBtn {
opacity: 0;
}
.imgPreviewItemClearBtn:hover {
background: rgb(177, 27, 0);
}
.imgContainer:hover > .imgItemInfo {
opacity: 1;
}
.imgContainer:hover > .imgPreviewItemClearBtn {
opacity: 1;
}
.imgItemInfo * {
margin-bottom: 7px;
}
.imgItem .image_clear_btn {
transform: translate(40%, -50%);
}
#container {
min-height: 100vh;
width: 100%;
@ -251,6 +265,11 @@ button#resume {
img {
box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.15), 0 6px 20px 0 rgba(0, 0, 0, 0.15);
}
div.img-preview img {
width:100%;
height: 100%;
max-height: 70vh;
}
.line-separator {
background: var(--background-color3);
height: 1pt;
@ -879,7 +898,8 @@ input::file-selector-button {
visibility: hidden;
opacity: 0;
position: absolute;
white-space: nowrap;
width: max-content;
max-width: 300px;
padding: 8px 12px;
transition: 0.3s all;
@ -895,7 +915,7 @@ input::file-selector-button {
.simple-tooltip.right {
right: 0px;
top: 50%;
transform: translate(calc(100% - 15%), -50%);
transform: translate(100%, -50%);
}
:hover > .simple-tooltip.right {
transform: translate(100%, -50%);
@ -1023,7 +1043,7 @@ input::file-selector-button {
}
/* TABS */
#tab-container {
.tab-container {
display: flex;
align-items: flex-end;
}
@ -1103,7 +1123,7 @@ div.task-initimg > img {
}
div.task-fs-initimage {
display: none;
# position: absolute;
position: absolute;
}
div.task-initimg:hover div.task-fs-initimage {
display: block;
@ -1111,6 +1131,8 @@ div.task-initimg:hover div.task-fs-initimage {
z-index: 9999;
box-shadow: 0 0 30px #000;
margin-top:-64px;
max-width: 75vw;
max-height: 75vh;
}
div.top-right {
position: absolute;
@ -1174,3 +1196,10 @@ body.wait-pause {
50% { border: solid 12px var(--background-color1); }
100% { border: solid 12px var(--accent-color); }
}
.jconfirm.jconfirm-modern .jconfirm-box div.jconfirm-title-c {
color: var(--button-text-color);
}
.jconfirm.jconfirm-modern .jconfirm-box {
background-color: var(--background-color1);
}

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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 576" width="24" height="24">
<!--! Font Awesome Pro 6.2.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2022 Fonticons, Inc.-->
<path style="filter: drop-shadow(0px 0px 20px white)" d="M290.7 57.4 57.4 290.7c-25 25-25 65.5 0 90.5l80 80c12 12 28.3 18.7 45.3 18.7H512c17.7 0 32-14.3 32-32s-14.3-32-32-32H387.9l130.7-130.6c25-25 25-65.5 0-90.5L381.3 57.4c-25-25-65.5-25-90.5 0zm6.7 358.6H182.6l-80-80 124.7-124.7 137.4 137.4-67.3 67.3z"/>
</svg>
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@ -0,0 +1,4 @@
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" width="24" height="24">
<!--! Font Awesome Pro 6.2.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2022 Fonticons, Inc.-->
<path style="filter: drop-shadow(0px 0px 20px white)" d="M341.6 29.2 240.1 130.8l-9.4-9.4c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3l160 160c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3l-9.4-9.4 101.5-101.6c39-39 39-102.2 0-141.1s-102.2-39-141.1 0zM55.4 323.3c-15 15-23.4 35.4-23.4 56.6v42.4L5.4 462.2c-8.5 12.7-6.8 29.6 4 40.4s27.7 12.5 40.4 4L89.7 480h42.4c21.2 0 41.6-8.4 56.6-23.4l120.7-120.7-45.3-45.3-120.7 120.7c-3 3-7.1 4.7-11.3 4.7H96v-36.1c0-4.2 1.7-8.3 4.7-11.3l120.7-120.7-45.3-45.3L55.4 323.3z"/>
</svg>
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@ -0,0 +1,4 @@
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 576" width="24" height="24">
<!--! Font Awesome Pro 6.2.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2022 Fonticons, Inc.-->
<path style="filter: drop-shadow(0px 0px 20px white)" d="M118.6 9.4c-12.5-12.5-32.7-12.5-45.2 0s-12.5 32.8 0 45.3l81.3 81.3-92.1 92.1c-37.5 37.5-37.5 98.3 0 135.8l117.5 117.5c37.5 37.5 98.3 37.5 135.8 0l190.4-190.5c28.1-28.1 28.1-73.7 0-101.8L354.9 37.7c-28.1-28.1-73.7-28.1-101.8 0l-53.1 53-81.4-81.3zM200 181.3l49.4 49.4c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L245.3 136l53.1-53.1c3.1-3.1 8.2-3.1 11.3 0l151.4 151.4c3.1 3.1 3.1 8.2 0 11.3L418.7 288H99.5c1.4-5.4 4.2-10.4 8.4-14.6l92.1-92.1z"/>
</svg>
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@ -0,0 +1,4 @@
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<!--! Font Awesome Pro 6.2.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) Copyright 2022 Fonticons, Inc.-->
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15
ui/media/js/auto-save.js
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@ -15,7 +15,7 @@ const SETTINGS_IDS_LIST = [
"stable_diffusion_model",
"vae_model",
"hypernetwork_model",
"sampler",
"sampler_name",
"width",
"height",
"num_inference_steps",
@ -28,6 +28,7 @@ const SETTINGS_IDS_LIST = [
"stream_image_progress",
"use_face_correction",
"use_upscale",
"upscale_amount",
"show_only_filtered_image",
"upscale_model",
"preview-image",
@ -36,10 +37,12 @@ const SETTINGS_IDS_LIST = [
"save_to_disk",
"diskPath",
"sound_toggle",
"turbo",
"use_full_precision",
"vram_usage_level",
"confirm_dangerous_actions",
"auto_save_settings"
"metadata_output_format",
"auto_save_settings",
"apply_color_correction",
"process_order_toggle"
]
const IGNORE_BY_DEFAULT = [
@ -259,10 +262,12 @@ function tryLoadOldSettings() {
var saved_settings = JSON.parse(saved_settings_text)
Object.keys(saved_settings.should_save).forEach(key => {
key = key in old_map ? old_map[key] : key
if (!(key in SETTINGS)) return
SETTINGS[key].ignore = !saved_settings.should_save[key]
});
Object.keys(saved_settings.values).forEach(key => {
key = key in old_map ? old_map[key] : key
if (!(key in SETTINGS)) return
var setting = SETTINGS[key]
if (!setting.ignore) {
setting.value = saved_settings.values[key]
@ -277,8 +282,6 @@ function tryLoadOldSettings() {
"soundEnabled": "sound_toggle",
"saveToDisk": "save_to_disk",
"useCPU": "use_cpu",
"useFullPrecision": "use_full_precision",
"useTurboMode": "turbo",
"diskPath": "diskPath",
"useFaceCorrection": "use_face_correction",
"useUpscaling": "use_upscale",

124
ui/media/js/dnd.js
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@ -25,6 +25,7 @@ function parseBoolean(stringValue) {
case "no":
case "off":
case "0":
case "none":
case null:
case undefined:
return false;
@ -58,6 +59,13 @@ const TASK_MAPPING = {
readUI: () => activeTags.map(x => x.name),
parse: (val) => val
},
inactive_tags: { name: "Inactive Image Modifiers",
setUI: (inactive_tags) => {
refreshInactiveTags(inactive_tags)
},
readUI: () => activeTags.filter(tag => tag.inactive === true).map(x => x.name),
parse: (val) => val
},
width: { name: 'Width',
setUI: (width) => {
const oldVal = widthField.value
@ -136,6 +144,13 @@ const TASK_MAPPING = {
readUI: () => (maskSetting.checked ? imageInpainter.getImg() : undefined),
parse: (val) => val
},
preserve_init_image_color_profile: { name: 'Preserve Color Profile',
setUI: (preserve_init_image_color_profile) => {
applyColorCorrectionField.checked = parseBoolean(preserve_init_image_color_profile)
},
readUI: () => applyColorCorrectionField.checked,
parse: (val) => parseBoolean(val)
},
use_face_correction: { name: 'Use Face Correction',
setUI: (use_face_correction) => {
@ -147,12 +162,14 @@ const TASK_MAPPING = {
use_upscale: { name: 'Use Upscaling',
setUI: (use_upscale) => {
const oldVal = upscaleModelField.value
upscaleModelField.value = use_upscale
upscaleModelField.value = getModelPath(use_upscale, ['.pth'])
if (upscaleModelField.value) { // Is a valid value for the field.
useUpscalingField.checked = true
upscaleModelField.disabled = false
upscaleAmountField.disabled = false
} else { // Not a valid value, restore the old value and disable the filter.
upscaleModelField.disabled = true
upscaleAmountField.disabled = true
upscaleModelField.value = oldVal
useUpscalingField.checked = false
}
@ -160,9 +177,16 @@ const TASK_MAPPING = {
readUI: () => (useUpscalingField.checked ? upscaleModelField.value : undefined),
parse: (val) => val
},
sampler: { name: 'Sampler',
setUI: (sampler) => {
samplerField.value = sampler
upscale_amount: { name: 'Upscale By',
setUI: (upscale_amount) => {
upscaleAmountField.value = upscale_amount
},
readUI: () => upscaleAmountField.value,
parse: (val) => val
},
sampler_name: { name: 'Sampler',
setUI: (sampler_name) => {
samplerField.value = sampler_name
},
readUI: () => samplerField.value,
parse: (val) => val
@ -171,7 +195,7 @@ 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 +208,7 @@ 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 +222,7 @@ 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'])
@ -232,20 +258,6 @@ const TASK_MAPPING = {
readUI: () => useCPUField.checked,
parse: (val) => val
},
turbo: { name: 'Turbo',
setUI: (turbo) => {
turboField.checked = turbo
},
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) => {
@ -277,6 +289,7 @@ const TASK_MAPPING = {
parse: (val) => val
}
}
function restoreTaskToUI(task, fieldsToSkip) {
fieldsToSkip = fieldsToSkip || []
@ -296,8 +309,17 @@ function restoreTaskToUI(task, fieldsToSkip) {
}
}
// restore the original tag
promptField.value = task.reqBody.original_prompt || task.reqBody.prompt
// properly reset fields not present in the task
if (!('use_hypernetwork_model' in task.reqBody)) {
hypernetworkModelField.value = ""
hypernetworkModelField.dispatchEvent(new Event("change"))
}
// restore the original prompt if provided (e.g. use settings), fallback to prompt as needed (e.g. copy/paste or d&d)
promptField.value = task.reqBody.original_prompt
if (!('original_prompt' in task.reqBody)) {
promptField.value = task.reqBody.prompt
}
// properly reset checkboxes
if (!('use_face_correction' in task.reqBody)) {
@ -306,19 +328,26 @@ function restoreTaskToUI(task, fieldsToSkip) {
if (!('use_upscale' in task.reqBody)) {
useUpscalingField.checked = false
}
if (!('mask' in task.reqBody)) {
if (!('mask' in task.reqBody) && maskSetting.checked) {
maskSetting.checked = false
maskSetting.dispatchEvent(new Event("click"))
}
upscaleModelField.disabled = !useUpscalingField.checked
upscaleAmountField.disabled = !useUpscalingField.checked
// Show the source picture if present
initImagePreview.src = (task.reqBody.init_image == undefined ? '' : task.reqBody.init_image)
if (IMAGE_REGEX.test(initImagePreview.src)) {
if (Boolean(task.reqBody.mask)) {
setTimeout(() => { // add a delay to insure this happens AFTER the main image loads (which reloads the inpainter)
imageInpainter.setImg(task.reqBody.mask)
}, 250)
// hide/show source picture as needed
if (IMAGE_REGEX.test(initImagePreview.src) && task.reqBody.init_image == undefined) {
// hide source image
initImageClearBtn.dispatchEvent(new Event("click"))
}
else if (task.reqBody.init_image !== undefined) {
// listen for inpainter loading event, which happens AFTER the main image loads (which reloads the inpainter)
initImagePreview.addEventListener('load', function() {
if (Boolean(task.reqBody.mask)) {
imageInpainter.setImg(task.reqBody.mask)
}
}, { once: true })
initImagePreview.src = task.reqBody.init_image
}
}
function readUI() {
@ -350,6 +379,7 @@ function getModelPath(filename, extensions)
}
const TASK_TEXT_MAPPING = {
prompt: 'Prompt',
width: 'Width',
height: 'Height',
seed: 'Seed',
@ -358,26 +388,39 @@ const TASK_TEXT_MAPPING = {
prompt_strength: 'Prompt Strength',
use_face_correction: 'Use Face Correction',
use_upscale: 'Use Upscaling',
sampler: 'Sampler',
upscale_amount: 'Upscale By',
sampler_name: 'Sampler',
negative_prompt: 'Negative Prompt',
use_stable_diffusion_model: 'Stable Diffusion model',
use_hypernetwork_model: 'Hypernetwork model',
hypernetwork_strength: 'Hypernetwork Strength'
}
const afterPromptRe = /^\s*Width\s*:\s*\d+\s*(?:\r\n|\r|\n)+\s*Height\s*:\s*\d+\s*(\r\n|\r|\n)+Seed\s*:\s*\d+\s*$/igm
function parseTaskFromText(str) {
const taskReqBody = {}
const lines = str.split('\n')
if (lines.length === 0) {
return
}
// Prompt
afterPromptRe.lastIndex = 0
const match = afterPromptRe.exec(str)
if (match) {
let prompt = str.slice(0, match.index)
str = str.slice(prompt.length)
taskReqBody.prompt = prompt.trim()
let knownKeyOnFirstLine = false
for (let key in TASK_TEXT_MAPPING) {
if (lines[0].startsWith(TASK_TEXT_MAPPING[key] + ':')) {
knownKeyOnFirstLine = true
break
}
}
if (!knownKeyOnFirstLine) {
taskReqBody.prompt = lines[0]
console.log('Prompt:', taskReqBody.prompt)
}
for (const key in TASK_TEXT_MAPPING) {
if (key in taskReqBody) {
continue
}
const name = TASK_TEXT_MAPPING[key];
let val = undefined
@ -410,6 +453,9 @@ 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) {
@ -419,7 +465,7 @@ async function parseContent(text) {
}
// Normal txt file.
const task = parseTaskFromText(text)
if (task) {
if (text.toLowerCase().includes('seed:') && task) { // only parse valid task content
restoreTaskToUI(task)
return true
} else {
@ -476,8 +522,6 @@ 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')

8
ui/media/js/engine.js
View File

@ -718,7 +718,7 @@
"height": 'number',
"seed": 'number',
"sampler": 'string',
"sampler_name": 'string',
"use_stable_diffusion_model": 'string',
"num_inference_steps": 'number',
"guidance_scale": 'number',
@ -727,13 +727,11 @@
"stream_progress_updates": 'boolean',
"stream_image_progress": 'boolean',
"show_only_filtered_image": 'boolean',
"turbo": 'boolean',
"use_full_precision": 'boolean',
"output_format": 'string',
"output_quality": 'number',
}
const TASK_DEFAULTS = {
"sampler": "plms",
"sampler_name": "plms",
"use_stable_diffusion_model": "sd-v1-4",
"num_inference_steps": 50,
"guidance_scale": 7.5,
@ -743,8 +741,6 @@
"stream_progress_updates": true,
"stream_image_progress": true,
"show_only_filtered_image": true,
"turbo": false,
"use_full_precision": false,
"output_format": "png",
"output_quality": 75,
}

169
ui/media/js/image-editor.js
View File

@ -36,13 +36,14 @@ const defaultToolEnd = (editor, ctx, x, y, is_overlay = false) => {
ctx.clearRect(0, 0, editor.width, editor.height)
}
}
const toolDoNothing = (editor, ctx, x, y, is_overlay = false) => {}
const IMAGE_EDITOR_TOOLS = [
{
id: "draw",
name: "Draw",
icon: "fa-solid fa-pencil",
cursor: "url(/media/images/fa-pencil.png) 0 24, pointer",
cursor: "url(/media/images/fa-pencil.svg) 0 24, pointer",
begin: defaultToolBegin,
move: defaultToolMove,
end: defaultToolEnd
@ -51,7 +52,7 @@ const IMAGE_EDITOR_TOOLS = [
id: "erase",
name: "Erase",
icon: "fa-solid fa-eraser",
cursor: "url(/media/images/fa-eraser.png) 0 18, pointer",
cursor: "url(/media/images/fa-eraser.svg) 0 14, pointer",
begin: defaultToolBegin,
move: (editor, ctx, x, y, is_overlay = false) => {
ctx.lineTo(x, y)
@ -78,11 +79,27 @@ const IMAGE_EDITOR_TOOLS = [
}
},
{
id: "colorpicker",
name: "Color Picker",
icon: "fa-solid fa-eye-dropper",
cursor: "url(/media/images/fa-eye-dropper.png) 0 24, pointer",
id: "fill",
name: "Fill",
icon: "fa-solid fa-fill",
cursor: "url(/media/images/fa-fill.svg) 20 6, pointer",
begin: (editor, ctx, x, y, is_overlay = false) => {
if (!is_overlay) {
var color = hexToRgb(ctx.fillStyle)
color.a = parseInt(ctx.globalAlpha * 255) // layer.ctx.globalAlpha
flood_fill(editor, ctx, parseInt(x), parseInt(y), color)
}
},
move: toolDoNothing,
end: toolDoNothing
},
{
id: "colorpicker",
name: "Picker",
icon: "fa-solid fa-eye-dropper",
cursor: "url(/media/images/fa-eye-dropper.svg) 0 24, pointer",
begin: (editor, ctx, x, y, is_overlay = false) => {
if (!is_overlay) {
var img_rgb = editor.layers.background.ctx.getImageData(x, y, 1, 1).data
var drawn_rgb = editor.ctx_current.getImageData(x, y, 1, 1).data
var drawn_opacity = drawn_rgb[3] / 255
@ -92,13 +109,26 @@ const IMAGE_EDITOR_TOOLS = [
b: (drawn_rgb[2] * drawn_opacity) + (img_rgb[2] * (1 - drawn_opacity)),
})
editor.custom_color_input.dispatchEvent(new Event("change"))
}
},
move: (editor, ctx, x, y, is_overlay = false) => {},
end: (editor, ctx, x, y, is_overlay = false) => {}
move: toolDoNothing,
end: toolDoNothing
}
]
const IMAGE_EDITOR_ACTIONS = [
{
id: "fill_all",
name: "Fill all",
icon: "fa-solid fa-paint-roller",
handler: (editor) => {
editor.ctx_current.globalCompositeOperation = "source-over"
editor.ctx_current.rect(0, 0, editor.width, editor.height)
editor.ctx_current.fill()
editor.setBrush()
},
trackHistory: true
},
{
id: "clear",
name: "Clear",
@ -467,8 +497,8 @@ class ImageEditor {
width = (multiplier * width).toFixed()
height = (multiplier * height).toFixed()
}
this.width = width
this.height = height
this.width = parseInt(width)
this.height = parseInt(height)
this.container.style.width = width + "px"
this.container.style.height = height + "px"
@ -494,8 +524,10 @@ class ImageEditor {
}
setImage(url, width, height) {
this.setSize(width, height)
this.layers.drawing.ctx.clearRect(0, 0, this.width, this.height)
this.layers.background.ctx.clearRect(0, 0, this.width, this.height)
if (!(url && this.inpainter)) {
this.layers.drawing.ctx.clearRect(0, 0, this.width, this.height)
}
if (url) {
var image = new Image()
image.onload = () => {
@ -604,6 +636,9 @@ class ImageEditor {
if (event.key == "y" && event.ctrlKey) {
this.history.redo()
}
if (event.key === "Escape") {
this.hide()
}
}
// dropper ctrl holding handler stuff
@ -682,14 +717,6 @@ class ImageEditor {
}
}
function rgbToHex(rgb) {
function componentToHex(c) {
var hex = parseInt(c).toString(16)
return hex.length == 1 ? "0" + hex : hex
}
return "#" + componentToHex(rgb.r) + componentToHex(rgb.g) + componentToHex(rgb.b)
}
const imageEditor = new ImageEditor(document.getElementById("image-editor"))
const imageInpainter = new ImageEditor(document.getElementById("image-inpainter"), true)
@ -704,3 +731,107 @@ document.getElementById("init_image_button_inpaint").addEventListener("click", (
})
img2imgUnload() // no init image when the app starts
function rgbToHex(rgb) {
function componentToHex(c) {
var hex = parseInt(c).toString(16)
return hex.length == 1 ? "0" + hex : hex
}
return "#" + componentToHex(rgb.r) + componentToHex(rgb.g) + componentToHex(rgb.b)
}
function hexToRgb(hex) {
var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex);
return result ? {
r: parseInt(result[1], 16),
g: parseInt(result[2], 16),
b: parseInt(result[3], 16)
} : null;
}
function pixelCompare(int1, int2) {
return Math.abs(int1 - int2) < 4
}
// adapted from https://ben.akrin.com/canvas_fill/fill_04.html
function flood_fill(editor, the_canvas_context, x, y, color) {
pixel_stack = [{x:x, y:y}] ;
pixels = the_canvas_context.getImageData( 0, 0, editor.width, editor.height ) ;
var linear_cords = ( y * editor.width + x ) * 4 ;
var original_color = {r:pixels.data[linear_cords],
g:pixels.data[linear_cords+1],
b:pixels.data[linear_cords+2],
a:pixels.data[linear_cords+3]} ;
var opacity = color.a / 255;
var new_color = {
r: parseInt((color.r * opacity) + (original_color.r * (1 - opacity))),
g: parseInt((color.g * opacity) + (original_color.g * (1 - opacity))),
b: parseInt((color.b * opacity) + (original_color.b * (1 - opacity)))
}
if ((pixelCompare(new_color.r, original_color.r) &&
pixelCompare(new_color.g, original_color.g) &&
pixelCompare(new_color.b, original_color.b)))
{
return; // This color is already the color we want, so do nothing
}
var max_stack_size = editor.width * editor.height;
while( pixel_stack.length > 0 && pixel_stack.length < max_stack_size ) {
new_pixel = pixel_stack.shift() ;
x = new_pixel.x ;
y = new_pixel.y ;
linear_cords = ( y * editor.width + x ) * 4 ;
while( y-->=0 &&
(pixelCompare(pixels.data[linear_cords], original_color.r) &&
pixelCompare(pixels.data[linear_cords+1], original_color.g) &&
pixelCompare(pixels.data[linear_cords+2], original_color.b))) {
linear_cords -= editor.width * 4 ;
}
linear_cords += editor.width * 4 ;
y++ ;
var reached_left = false ;
var reached_right = false ;
while( y++<editor.height &&
(pixelCompare(pixels.data[linear_cords], original_color.r) &&
pixelCompare(pixels.data[linear_cords+1], original_color.g) &&
pixelCompare(pixels.data[linear_cords+2], original_color.b))) {
pixels.data[linear_cords] = new_color.r ;
pixels.data[linear_cords+1] = new_color.g ;
pixels.data[linear_cords+2] = new_color.b ;
pixels.data[linear_cords+3] = 255 ;
if( x>0 ) {
if( pixelCompare(pixels.data[linear_cords-4], original_color.r) &&
pixelCompare(pixels.data[linear_cords-4+1], original_color.g) &&
pixelCompare(pixels.data[linear_cords-4+2], original_color.b)) {
if( !reached_left ) {
pixel_stack.push( {x:x-1, y:y} ) ;
reached_left = true ;
}
} else if( reached_left ) {
reached_left = false ;
}
}
if( x<editor.width-1 ) {
if( pixelCompare(pixels.data[linear_cords+4], original_color.r) &&
pixelCompare(pixels.data[linear_cords+4+1], original_color.g) &&
pixelCompare(pixels.data[linear_cords+4+2], original_color.b)) {
if( !reached_right ) {
pixel_stack.push( {x:x+1,y:y} ) ;
reached_right = true ;
}
} else if( reached_right ) {
reached_right = false ;
}
}
linear_cords += editor.width * 4 ;
}
}
the_canvas_context.putImageData( pixels, 0, 0 ) ;
}

23
ui/media/js/image-modifiers.js
View File

@ -104,6 +104,7 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
}
refreshTagsList()
document.dispatchEvent(new Event('refreshImageModifiers'))
})
}
})
@ -146,6 +147,7 @@ async function loadModifiers() {
}
loadCustomModifiers()
document.dispatchEvent(new Event('loadImageModifiers'))
}
function refreshModifiersState(newTags) {
@ -202,6 +204,26 @@ function refreshModifiersState(newTags) {
refreshTagsList()
}
function refreshInactiveTags(inactiveTags) {
// update inactive tags
if (inactiveTags !== undefined && inactiveTags.length > 0) {
activeTags.forEach (tag => {
if (inactiveTags.find(element => element === tag.name) !== undefined) {
tag.inactive = true
}
})
}
// update cards
let overlays = document.querySelector('#editor-inputs-tags-list').querySelectorAll('.modifier-card-overlay')
overlays.forEach (i => {
let modifierName = i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText
if (inactiveTags.find(element => element === modifierName) !== undefined) {
i.parentElement.classList.add('modifier-toggle-inactive')
}
})
}
function refreshTagsList() {
editorModifierTagsList.innerHTML = ''
@ -227,6 +249,7 @@ function refreshTagsList() {
activeTags.splice(idx, 1)
refreshTagsList()
}
document.dispatchEvent(new Event('refreshImageModifiers'))
})
})

75
ui/media/js/main.js
View File

@ -26,13 +26,16 @@ 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")
let upscaleModelField = document.querySelector("#upscale_model")
let upscaleAmountField = document.querySelector("#upscale_amount")
let stableDiffusionModelField = document.querySelector('#stable_diffusion_model')
let vaeModelField = document.querySelector('#vae_model')
let hypernetworkModelField = document.querySelector('#hypernetwork_model')
@ -260,6 +263,7 @@ function showImages(reqBody, res, outputContainer, livePreview) {
<div class="imgItemInfo">
<span class="imgSeedLabel"></span>
</div>
<button class="imgPreviewItemClearBtn image_clear_btn"><i class="fa-solid fa-xmark"></i></button>
</div>
`
outputContainer.appendChild(imageItemElem)
@ -272,6 +276,11 @@ function showImages(reqBody, res, outputContainer, livePreview) {
imageElem.setAttribute('data-steps', imageInferenceSteps)
imageElem.setAttribute('data-guidance', imageGuidanceScale)
const imageRemoveBtn = imageItemElem.querySelector('.imgPreviewItemClearBtn')
imageRemoveBtn.addEventListener('click', (e) => {
console.log(e)
shiftOrConfirm(e, "Remove the image from the results?", () => { imageItemElem.style.display = 'none' })
})
const imageInfo = imageItemElem.querySelector('.imgItemInfo')
imageInfo.style.visibility = (livePreview ? 'hidden' : 'visible')
@ -302,9 +311,12 @@ function showImages(reqBody, res, outputContainer, livePreview) {
const newButton = document.createElement('button')
newButton.classList.add('tasksBtns')
newButton.innerText = btnInfo.text
newButton.addEventListener('click', function() {
btnInfo.on_click(req, img)
newButton.addEventListener('click', function(event) {
btnInfo.on_click(req, img, event)
})
if (btnInfo.class !== undefined) {
newButton.classList.add(btnInfo.class)
}
imgItemInfo.appendChild(newButton)
}
buttons.forEach(btn => {
@ -613,7 +625,7 @@ 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 {
@ -647,7 +659,7 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
task.progressBar.classList.remove("active")
setStatus('request', 'done', 'success')
} else {
task.outputMsg.innerText += `Task ended after ${time}`
task.outputMsg.innerText += `. Task ended after ${time}`
}
if (randomSeedField.checked) {
@ -662,6 +674,9 @@ function onTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
return
}
if (pauseClient) {
resumeBtn.click()
}
renderButtons.style.display = 'none'
renameMakeImageButton()
@ -792,7 +807,8 @@ function createTask(task) {
let w = task.reqBody.width * h / task.reqBody.height >>0
taskConfig += `<div class="task-initimg" style="float:left;"><img style="width:${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}`
}
@ -806,12 +822,15 @@ function createTask(task) {
taskConfig += `, <b>Fix Faces:</b> ${task.reqBody.use_face_correction}`
}
if (task.reqBody.use_upscale) {
taskConfig += `, <b>Upscale:</b> ${task.reqBody.use_upscale}`
taskConfig += `, <b>Upscale:</b> ${task.reqBody.use_upscale} (${task.reqBody.upscale_amount || 4}x)`
}
if (task.reqBody.use_hypernetwork_model) {
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()}`
@ -894,7 +913,7 @@ function createTask(task) {
if (task.previewPrompt.innerText.trim() === '') {
task.previewPrompt.innerHTML = '&nbsp;' // allows the results to be collapsed
}
return taskEntry.id
}
function getCurrentUserRequest() {
@ -917,9 +936,8 @@ 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,
@ -927,8 +945,10 @@ 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))
active_tags: (activeTags.map(x => x.name)),
inactive_tags: (activeTags.filter(tag => tag.inactive === true).map(x => x.name))
}
}
if (IMAGE_REGEX.test(initImagePreview.src)) {
@ -941,9 +961,10 @@ 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()
@ -953,6 +974,7 @@ function getCurrentUserRequest() {
}
if (useUpscalingField.checked) {
newTask.reqBody.use_upscale = upscaleModelField.value
newTask.reqBody.upscale_amount = upscaleAmountField.value
}
if (hypernetworkModelField.value) {
newTask.reqBody.use_hypernetwork_model = hypernetworkModelField.value
@ -1148,8 +1170,10 @@ function onDimensionChange() {
diskPathField.disabled = !saveToDiskField.checked
upscaleModelField.disabled = !useUpscalingField.checked
upscaleAmountField.disabled = !useUpscalingField.checked
useUpscalingField.addEventListener('change', function(e) {
upscaleModelField.disabled = !this.checked
upscaleAmountField.disabled = !this.checked
})
makeImageBtn.addEventListener('click', makeImage)
@ -1286,17 +1310,23 @@ async function getModels() {
vaeOptions.unshift('') // add a None option
hypernetworkOptions.unshift('') // add a None option
function createModelOptions(modelField, selectedModel) {
return function(modelName) {
function createModelOptions(modelField, selectedModel, path="") {
return function fn(modelName) {
if (typeof(modelName) == 'string') {
const modelOption = document.createElement('option')
modelOption.value = modelName
modelOption.innerText = modelName !== '' ? modelName : 'None'
modelOption.value = path + modelName
modelOption.innerHTML = modelName !== '' ? (path != "" ? "&nbsp;&nbsp;"+modelName : modelName) : 'None'
if (modelName === selectedModel) {
if (path + modelName === selectedModel) {
modelOption.selected = true
}
modelField.appendChild(modelOption)
} else {
const modelGroup = document.createElement('optgroup')
modelGroup.label = path + modelName[0]
modelField.appendChild(modelGroup)
modelName[1].forEach( createModelOptions(modelField, selectedModel, path + modelName[0] + "/" ) )
}
}
}
@ -1352,6 +1382,7 @@ 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)
@ -1366,6 +1397,7 @@ 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))
}
@ -1417,7 +1449,7 @@ function selectTab(tab_id) {
let tabInfo = tabElements.find(t => t.tab.id == tab_id)
if (!tabInfo.tab.classList.contains("active")) {
tabElements.forEach(info => {
if (info.tab.classList.contains("active")) {
if (info.tab.classList.contains("active") && info.tab.parentNode === tabInfo.tab.parentNode) {
info.tab.classList.toggle("active")
info.content.classList.toggle("active")
}
@ -1438,6 +1470,9 @@ function linkTabContents(tab) {
tab.addEventListener("click", event => selectTab(tab.id))
}
function isTabActive(tab) {
return tab.classList.contains("active")
}
let pauseClient = false

80
ui/media/js/parameters.js
View File

@ -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,
@ -66,6 +83,7 @@ var PARAMETERS = [
type: ParameterType.checkbox,
label: "Process newest jobs first",
note: "reverse the normal processing order",
icon: "fa-arrow-down-short-wide",
default: false,
},
{
@ -77,12 +95,20 @@ var PARAMETERS = [
default: true,
},
{
id: "turbo",
type: ParameterType.checkbox,
label: "Turbo Mode",
note: "generates images faster, but uses an additional 1 GB of GPU memory",
id: "vram_usage_level",
type: ParameterType.select,
label: "GPU Memory Usage",
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, recommended for GPUs with 3 to 4 GB 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 +131,6 @@ 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 +165,6 @@ 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 +220,14 @@ 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 +264,6 @@ 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 +329,10 @@ autoPickGPUsField.addEventListener('click', function() {
gpuSettingEntry.style.display = (this.checked ? 'none' : '')
})
async function getDiskPath() {
try {
async function setDiskPath(defaultDiskPath) {
var diskPath = getSetting("diskPath")
if (diskPath == '' || diskPath == undefined || diskPath == "undefined") {
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)
setSetting("diskPath", defaultDiskPath)
}
}
@ -415,6 +407,7 @@ async function getSystemInfo() {
setDeviceInfo(devices)
setHostInfo(res['hosts'])
setDiskPath(res['default_output_dir'])
} catch (e) {
console.log('error fetching devices', e)
}
@ -435,8 +428,7 @@ saveSettingsBtn.addEventListener('click', function() {
'update_branch': updateBranch,
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
'listen_to_network': listenToNetworkField.checked,
'listen_port': listenPortField.value,
'test_sd2': testSD2Field.checked
'listen_port': listenPortField.value
})
saveSettingsBtn.classList.add('active')
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))

15
ui/media/js/themes.js
View File

@ -13,8 +13,15 @@ function initTheme() {
.filter(sheet => sheet.href?.startsWith(window.location.origin))
.flatMap(sheet => Array.from(sheet.cssRules))
.forEach(rule => {
var selector = rule.selectorText; // TODO: also do selector == ":root", re-run un-set props
var selector = rule.selectorText;
if (selector && selector.startsWith(".theme-") && !selector.includes(" ")) {
if (DEFAULT_THEME) { // re-add props that dont change (css needs this so they update correctly)
Array.from(DEFAULT_THEME.rule.style)
.filter(cssVariable => !Array.from(rule.style).includes(cssVariable))
.forEach(cssVariable => {
rule.style.setProperty(cssVariable, DEFAULT_THEME.rule.style.getPropertyValue(cssVariable));
});
}
var theme_key = selector.substring(1);
THEMES.push({
key: theme_key,
@ -62,12 +69,6 @@ function themeFieldChanged() {
var theme = THEMES.find(t => t.key == theme_key);
let borderColor = undefined
if (theme) {
// refresh variables incase they are back referencing
Array.from(DEFAULT_THEME.rule.style)
.filter(cssVariable => !Array.from(theme.rule.style).includes(cssVariable))
.forEach(cssVariable => {
body.style.setProperty(cssVariable, DEFAULT_THEME.rule.style.getPropertyValue(cssVariable));
});
borderColor = theme.rule.style.getPropertyValue('--input-border-color').trim()
if (!borderColor.startsWith('#')) {
borderColor = theme.rule.style.getPropertyValue('--theme-color-fallback')

13
ui/media/js/utils.js
View File

@ -20,6 +20,19 @@ function getNextSibling(elem, selector) {
}
}
function findClosestAncestor(element, selector) {
if (!element || !element.parentNode) {
// reached the top of the DOM tree, return null
return null;
} else if (element.parentNode.matches(selector)) {
// found an ancestor that matches the selector, return it
return element.parentNode;
} else {
// continue searching upwards
return findClosestAncestor(element.parentNode, selector);
}
}
/* Panel Stuff */

1
ui/plugins/ui/Modifiers-dnd.plugin.js
View File

@ -74,6 +74,7 @@
// update activeTags
const tag = activeTags.splice(currentPos, 1)
activeTags.splice(droppedPos, 0, tag[0])
document.dispatchEvent(new Event('refreshImageModifiers'))
}
}
};

1
ui/plugins/ui/Modifiers-wheel.plugin.js
View File

@ -58,6 +58,7 @@
break
}
}
document.dispatchEvent(new Event('refreshImageModifiers'))
}
}
})

471
ui/plugins/ui/merge.plugin.js Normal file
View File

@ -0,0 +1,471 @@
(function() {
"use strict"
///////////////////// Function section
function smoothstep(x) {
return x * x * (3 - 2 * x)
}
function smootherstep(x) {
return x * x * x * (x * (x * 6 - 15) + 10)
}
function smootheststep(x) {
let y = -20 * Math.pow(x, 7)
y += 70 * Math.pow(x, 6)
y -= 84 * Math.pow(x, 5)
y += 35 * Math.pow(x, 4)
return y
}
function getCurrentTime() {
const now = new Date();
let hours = now.getHours();
let minutes = now.getMinutes();
let seconds = now.getSeconds();
hours = hours < 10 ? `0${hours}` : hours;
minutes = minutes < 10 ? `0${minutes}` : minutes;
seconds = seconds < 10 ? `0${seconds}` : seconds;
return `${hours}:${minutes}:${seconds}`;
}
function addLogMessage(message) {
const logContainer = document.getElementById('merge-log');
logContainer.innerHTML += `<i>${getCurrentTime()}</i> ${message}<br>`;
// Scroll to the bottom of the log
logContainer.scrollTop = logContainer.scrollHeight;
document.querySelector('#merge-log-container').style.display = 'block'
}
function addLogSeparator() {
const logContainer = document.getElementById('merge-log');
logContainer.innerHTML += '<hr>'
logContainer.scrollTop = logContainer.scrollHeight;
}
function drawDiagram(fn) {
const SIZE = 300
const canvas = document.getElementById('merge-canvas');
canvas.height = canvas.width = SIZE
const ctx = canvas.getContext('2d');
// Draw coordinate system
ctx.scale(1, -1);
ctx.translate(0, -canvas.height);
ctx.lineWidth = 1;
ctx.beginPath();
ctx.strokeStyle = 'white'
ctx.moveTo(0,0); ctx.lineTo(0,SIZE); ctx.lineTo(SIZE,SIZE); ctx.lineTo(SIZE,0); ctx.lineTo(0,0); ctx.lineTo(SIZE,SIZE);
ctx.stroke()
ctx.beginPath()
ctx.setLineDash([1,2])
const n = SIZE / 10
for (let i=n; i<SIZE; i+=n) {
ctx.moveTo(0,i)
ctx.lineTo(SIZE,i)
ctx.moveTo(i,0)
ctx.lineTo(i,SIZE)
}
ctx.stroke()
ctx.beginPath()
ctx.setLineDash([])
ctx.beginPath();
ctx.strokeStyle = 'black'
ctx.lineWidth = 3;
// Plot function
const numSamples = 20;
for (let i = 0; i <= numSamples; i++) {
const x = i / numSamples;
const y = fn(x);
const canvasX = x * SIZE;
const canvasY = y * SIZE;
if (i === 0) {
ctx.moveTo(canvasX, canvasY);
} else {
ctx.lineTo(canvasX, canvasY);
}
}
ctx.stroke()
// Plot alpha values (yellow boxes)
let start = parseFloat( document.querySelector('#merge-start').value )
let step = parseFloat( document.querySelector('#merge-step').value )
let iterations = document.querySelector('#merge-count').value>>0
ctx.beginPath()
ctx.fillStyle = "yellow"
for (let i=0; i< iterations; i++) {
const alpha = ( start + i * step ) / 100
const x = alpha*SIZE
const y = fn(alpha) * SIZE
if (x <= SIZE) {
ctx.rect(x-3,y-3,6,6)
ctx.fill()
} else {
ctx.strokeStyle = 'red'
ctx.moveTo(0,0); ctx.lineTo(0,SIZE); ctx.lineTo(SIZE,SIZE); ctx.lineTo(SIZE,0); ctx.lineTo(0,0); ctx.lineTo(SIZE,SIZE);
ctx.stroke()
addLogMessage('<i>Warning: maximum ratio is &#8805; 100%</i>')
}
}
}
function updateChart() {
let fn = (x) => x
switch (document.querySelector('#merge-interpolation').value) {
case 'SmoothStep':
fn = smoothstep
break
case 'SmootherStep':
fn = smootherstep
break
case 'SmoothestStep':
fn = smootheststep
break
}
drawDiagram(fn)
}
/////////////////////// Tab implementation
document.querySelector('.tab-container')?.insertAdjacentHTML('beforeend', `
<span id="tab-merge" class="tab">
<span><i class="fa fa-code-merge icon"></i> Merge models <small>(beta)</small></span>
</span>
`)
document.querySelector('#tab-content-wrapper')?.insertAdjacentHTML('beforeend', `
<div id="tab-content-merge" class="tab-content">
<div id="merge" class="tab-content-inner">
Loading..
</div>
</div>
`)
const tabMerge = document.querySelector('#tab-merge')
if (tabMerge) {
linkTabContents(tabMerge)
}
const merge = document.querySelector('#merge')
if (!merge) {
// merge tab not found, dont exec plugin code.
return
}
document.querySelector('body').insertAdjacentHTML('beforeend', `
<style>
#tab-content-merge .tab-content-inner {
max-width: 100%;
padding: 10pt;
}
.merge-container {
margin-left: 15%;
margin-right: 15%;
text-align: left;
display: inline-grid;
grid-template-columns: 1fr 1fr;
grid-template-rows: auto auto auto;
gap: 0px 0px;
grid-auto-flow: row;
grid-template-areas:
"merge-input merge-config"
"merge-buttons merge-buttons";
}
.merge-container p {
margin-top: 3pt;
margin-bottom: 3pt;
}
.merge-config .tab-content {
background: var(--background-color1);
border-radius: 3pt;
}
.merge-config .tab-content-inner {
text-align: left;
}
.merge-input {
grid-area: merge-input;
padding-left:1em;
}
.merge-config {
grid-area: merge-config;
padding:1em;
}
.merge-config input {
margin-bottom: 3px;
}
.merge-config select {
margin-bottom: 3px;
}
.merge-buttons {
grid-area: merge-buttons;
padding:1em;
text-align: center;
}
#merge-button {
padding: 8px;
width:20em;
}
div#merge-log {
height:150px;
overflow-x:hidden;
overflow-y:scroll;
background:var(--background-color1);
border-radius: 3pt;
}
div#merge-log i {
color: hsl(var(--accent-hue), 100%, calc(2*var(--accent-lightness)));
font-family: monospace;
}
.disabled {
background: var(--background-color4);
color: var(--text-color);
}
#merge-type-tabs {
border-bottom: 1px solid black;
}
#merge-log-container {
display: none;
}
.merge-container #merge-warning {
color: rgb(153, 153, 153);
}
</style>
`)
merge.innerHTML = `
<div class="merge-container panel-box">
<div class="merge-input">
<p><label for="#mergeModelA">Select Model A:</label></p>
<select id="mergeModelA">
<option>A</option>
</select>
<p><label for="#mergeModelB">Select Model B:</label></p>
<select id="mergeModelB">
<option>A</option>
</select>
<br/><br/>
<p id="merge-warning"><small><b>Important:</b> Please merge models of similar type.<br/>For e.g. <code>SD 1.4</code> models with only <code>SD 1.4/1.5</code> models,<br/><code>SD 2.0</code> with <code>SD 2.0</code>-type, and <code>SD 2.1</code> with <code>SD 2.1</code>-type models.</small></p>
<br/>
<table>
<tr>
<td><label for="#merge-filename">Output file name:</label></td>
<td><input id="merge-filename" size=24> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Base name of the output file.<br>Mix ratio and file suffix will be appended to this.</span></i></td>
</tr>
<tr>
<td><label for="#merge-fp">Output precision:</label></td>
<td><select id="merge-fp">
<option value="fp16">fp16 (smaller file size)</option>
<option value="fp32">fp32 (larger file size)</option>
</select>
<i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Image generation uses fp16, so it's a good choice.<br>Use fp32 if you want to use the result models for more mixes</span></i>
</td>
</tr>
<tr>
<td><label for="#merge-format">Output file format:</label></td>
<td><select id="merge-format">
<option value="safetensors">Safetensors (recommended)</option>
<option value="ckpt">CKPT/Pickle (legacy format)</option>
</select>
</td>
</tr>
</table>
<br/>
<div id="merge-log-container">
<p><label for="#merge-log">Log messages:</label></p>
<div id="merge-log"></div>
</div>
</div>
<div class="merge-config">
<div class="tab-container">
<span id="tab-merge-opts-single" class="tab active">
<span>Make a single file</small></span>
</span>
<span id="tab-merge-opts-batch" class="tab">
<span>Make multiple variations</small></span>
</span>
</div>
<div>
<div id="tab-content-merge-opts-single" class="tab-content active">
<div class="tab-content-inner">
<small>Saves a single merged model file, at the specified merge ratio.</small><br/><br/>
<label for="#single-merge-ratio-slider">Merge ratio:</label>
<input id="single-merge-ratio-slider" name="single-merge-ratio-slider" class="editor-slider" value="50" type="range" min="1" max="1000">
<input id="single-merge-ratio" size=2 value="5">%
<i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Model A's contribution to the mix. The rest will be from Model B.</span></i>
</div>
</div>
<div id="tab-content-merge-opts-batch" class="tab-content">
<div class="tab-content-inner">
<small>Saves multiple variations of the model, at different merge ratios.<br/>Each variation will be saved as a separate file.</small><br/><br/>
<table>
<tr><td><label for="#merge-count">Number of variations:</label></td>
<td> <input id="merge-count" size=2 value="5"></td>
<td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Number of models to create</span></i></td></tr>
<tr><td><label for="#merge-start">Starting merge ratio:</label></td>
<td> <input id="merge-start" size=2 value="5">%</td>
<td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Smallest share of model A in the mix</span></i></td></tr>
<tr><td><label for="#merge-step">Increment each step:</label></td>
<td> <input id="merge-step" size=2 value="10">%</td>
<td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Share of model A added into the mix per step</span></i></td></tr>
<tr><td><label for="#merge-interpolation">Interpolation model:</label></td>
<td> <select id="merge-interpolation">
<option>Exact</option>
<option>SmoothStep</option>
<option>SmootherStep</option>
<option>SmoothestStep</option>
</select></td>
<td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Sigmoid function to be applied to the model share before mixing</span></i></td></tr>
</table>
<br/>
<small>Preview of variation ratios:</small><br/>
<canvas id="merge-canvas" width="400" height="400"></canvas>
</div>
</div>
</div>
</div>
<div class="merge-buttons">
<button id="merge-button" class="primaryButton">Merge models</button>
</div>
</div>`
const tabSettingsSingle = document.querySelector('#tab-merge-opts-single')
const tabSettingsBatch = document.querySelector('#tab-merge-opts-batch')
linkTabContents(tabSettingsSingle)
linkTabContents(tabSettingsBatch)
/////////////////////// Event Listener
document.addEventListener('tabClick', (e) => {
if (e.detail.name == 'merge') {
console.log('Activate')
let modelList = stableDiffusionModelField.cloneNode(true)
modelList.id = "mergeModelA"
document.querySelector("#mergeModelA").replaceWith(modelList)
modelList = stableDiffusionModelField.cloneNode(true)
modelList.id = "mergeModelB"
document.querySelector("#mergeModelB").replaceWith(modelList)
updateChart()
}
})
// slider
const singleMergeRatioField = document.querySelector('#single-merge-ratio')
const singleMergeRatioSlider = document.querySelector('#single-merge-ratio-slider')
function updateSingleMergeRatio() {
singleMergeRatioField.value = singleMergeRatioSlider.value / 10
singleMergeRatioField.dispatchEvent(new Event("change"))
}
function updateSingleMergeRatioSlider() {
if (singleMergeRatioField.value < 0) {
singleMergeRatioField.value = 0
} else if (singleMergeRatioField.value > 100) {
singleMergeRatioField.value = 100
}
singleMergeRatioSlider.value = singleMergeRatioField.value * 10
singleMergeRatioSlider.dispatchEvent(new Event("change"))
}
singleMergeRatioSlider.addEventListener('input', updateSingleMergeRatio)
singleMergeRatioField.addEventListener('input', updateSingleMergeRatioSlider)
updateSingleMergeRatio()
document.querySelector('.merge-config').addEventListener('change', updateChart)
document.querySelector('#merge-button').addEventListener('click', async function(e) {
// Build request template
let model0 = document.querySelector('#mergeModelA').value
let model1 = document.querySelector('#mergeModelB').value
let request = { model0: model0, model1: model1 }
request['use_fp16'] = document.querySelector('#merge-fp').value == 'fp16'
let iterations = document.querySelector('#merge-count').value>>0
let start = parseFloat( document.querySelector('#merge-start').value )
let step = parseFloat( document.querySelector('#merge-step').value )
if (isTabActive(tabSettingsSingle)) {
start = parseFloat(singleMergeRatioField.value)
step = 0
iterations = 1
addLogMessage(`merge ratio = ${start}%`)
} else {
addLogMessage(`start = ${start}%`)
addLogMessage(`step = ${step}%`)
}
if (start + (iterations-1) * step >= 100) {
addLogMessage('<i>Aborting: maximum ratio is &#8805; 100%</i>')
addLogMessage('Reduce the number of variations or the step size')
addLogSeparator()
document.querySelector('#merge-count').focus()
return
}
if (document.querySelector('#merge-filename').value == "") {
addLogMessage('<i>Aborting: No output file name specified</i>')
addLogSeparator()
document.querySelector('#merge-filename').focus()
return
}
// Disable merge button
e.target.disabled=true
e.target.classList.add('disabled')
let cursor = $("body").css("cursor");
let label = document.querySelector('#merge-button').innerHTML
$("body").css("cursor", "progress");
document.querySelector('#merge-button').innerHTML = 'Merging models ...'
addLogMessage("Merging models")
addLogMessage("Model A: "+model0)
addLogMessage("Model B: "+model1)
// Batch main loop
for (let i=0; i<iterations; i++) {
let alpha = ( start + i * step ) / 100
switch (document.querySelector('#merge-interpolation').value) {
case 'SmoothStep':
alpha = smoothstep(alpha)
break
case 'SmootherStep':
alpha = smootherstep(alpha)
break
case 'SmoothestStep':
alpha = smootheststep(alpha)
break
}
addLogMessage(`merging batch job ${i+1}/${iterations}, alpha = ${alpha.toFixed(5)}...`)
request['out_path'] = document.querySelector('#merge-filename').value
request['out_path'] += '-' + alpha.toFixed(5) + '.' + document.querySelector('#merge-format').value
addLogMessage(`&nbsp;&nbsp;filename: ${request['out_path']}`)
request['ratio'] = alpha
let res = await fetch('/model/merge', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request) })
const data = await res.json();
addLogMessage(JSON.stringify(data))
}
addLogMessage("<b>Done.</b> The models have been saved to your <tt>models/stable-diffusion</tt> folder.")
addLogSeparator()
// Re-enable merge button
$("body").css("cursor", cursor);
document.querySelector('#merge-button').innerHTML = label
e.target.disabled=false
e.target.classList.remove('disabled')
// Update model list
stableDiffusionModelField.innerHTML = ''
vaeModelField.innerHTML = ''
hypernetworkModelField.innerHTML = ''
await getModels()
})
})()

2
ui/plugins/ui/modifiers-toggle.plugin.js
View File

@ -46,7 +46,7 @@
return obj;
});
console.log(activeTags)
document.dispatchEvent(new Event('refreshImageModifiers'))
}
})
}

2
ui/plugins/ui/release-notes.plugin.js
View File

@ -9,7 +9,7 @@
}
}
document.querySelector('#tab-container')?.insertAdjacentHTML('beforeend', `
document.querySelector('.tab-container')?.insertAdjacentHTML('beforeend', `
<span id="tab-news" class="tab">
<span><i class="fa fa-bolt icon"></i> What's new?</span>
</span>

119
ui/sd_internal/__init__.py
View File

@ -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

162
ui/sd_internal/ddim_callback.patch
View File

@ -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)

84
ui/sd_internal/ddim_callback_sd2.patch
View File

@ -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,

198
ui/sd_internal/hypernetwork.py
View File

@ -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()

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ui/sd_internal/runtime.py
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ui/server.py
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"""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()