mirror of
https://github.com/easydiffusion/easydiffusion.git
synced 2024-11-21 15:53:17 +01:00
Keep v2 files in the repo, for the updater
This commit is contained in:
cmdr2
parent
cc14ac0bac
commit
472b8d0e51
1
scripts/Start Stable Diffusion UI.cmd
Normal file
1
scripts/Start Stable Diffusion UI.cmd
Normal file
@ -0,0 +1 @@
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installer\Scripts\activate.bat
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30
scripts/on_env_start.bat
Normal file
30
scripts/on_env_start.bat
Normal file
@ -0,0 +1,30 @@
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@echo. & echo "Stable Diffusion UI" & echo.
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@cd ..
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@>nul grep -c "sd_ui_git_cloned" scripts\install_status.txt
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@if "%ERRORLEVEL%" EQU "0" (
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@echo "Stable Diffusion UI's git repository was already installed. Updating.."
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@cd sd-ui-files
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@call git reset --hard
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@call git pull
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@cd ..
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) else (
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@echo. & echo "Downloading Stable Diffusion UI.." & echo.
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@call git clone https://github.com/cmdr2/stable-diffusion-ui.git sd-ui-files && (
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@echo sd_ui_git_cloned >> scripts\install_status.txt
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) || (
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@echo "Error downloading Stable Diffusion UI. Please try re-running this installer. If it doesn't work, please copy the messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB or file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues"
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pause
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@exit /b
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)
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)
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@xcopy sd-ui-files\ui ui /s /i
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@xcopy sd-ui-files\scripts scripts /s /i
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@call scripts\on_sd_start.bat
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82
scripts/on_sd_start.bat
Normal file
82
scripts/on_sd_start.bat
Normal file
@ -0,0 +1,82 @@
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@>nul grep -c "sd_git_cloned" scripts\install_status.txt
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@if "%ERRORLEVEL%" EQU "0" (
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@echo "Stable Diffusion's git repository was already installed. Updating.."
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@cd stable-diffusion
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@call git reset --hard
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@call git pull
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@cd ..
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) else (
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@echo. & echo "Downloading Stable Diffusion.." & echo.
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@call git clone https://github.com/basujindal/stable-diffusion.git && (
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@echo sd_git_cloned >> scripts\install_status.txt
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) || (
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@echo "Error downloading Stable Diffusion. Please try re-running this installer. If it doesn't work, please copy the messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB or file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues"
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pause
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@exit /b
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)
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)
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@cd stable-diffusion
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@>nul grep -c "conda_sd_env_created" ..\scripts\install_status.txt
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@if "%ERRORLEVEL%" EQU "0" (
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@echo "Packages necessary for Stable Diffusion were already installed"
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) else (
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@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.
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@rmdir /s /q .\env
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@call conda env create --prefix env -f environment.yaml && (
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@echo conda_sd_env_created >> ..\scripts\install_status.txt
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) || (
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echo "Error installing the packages necessary for Stable Diffusion. Please try re-running this installer. If it doesn't work, please copy the messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB or file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues"
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pause
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exit /b
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)
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)
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@call conda activate .\env
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@>nul grep -c "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
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@if "%ERRORLEVEL%" EQU "0" (
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echo "Packages necessary for Stable Diffusion UI were already installed"
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) else (
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@echo. & echo "Downloading packages necessary for Stable Diffusion UI.." & echo.
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@call conda install -c conda-forge -y --prefix env uvicorn fastapi && (
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@echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
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) || (
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echo "Error installing the packages necessary for Stable Diffusion UI. Please try re-running this installer. If it doesn't work, please copy the messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB or file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues"
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pause
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exit /b
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)
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)
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@if exist "sd-v1-4.ckpt" (
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echo "Data files (weights) necessary for Stable Diffusion were already downloaded"
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) else (
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@echo. & echo "Downloading data files (weights) for Stable Diffusion.." & echo.
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@call curl https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media > sd-v1-4.ckpt
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@if not exist "sd-v1-4.ckpt" (
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echo "Error downloading the data files (weights) for Stable Diffusion. Please try re-running this installer. If it doesn't work, please copy the messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB or file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues"
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pause
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exit /b
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)
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)
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@echo sd_weights_downloaded >> ..\scripts\install_status.txt
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@echo sd_install_complete >> ..\scripts\install_status.txt
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@echo. & echo "Stable Diffusion is ready!" & echo.
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@set SD_UI_PATH=%cd%\..\ui
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@uvicorn server:app --app-dir "%SD_UI_PATH%" --port 9000 --host 0.0.0.0
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@pause
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6
scripts/post_activate.bat
Normal file
6
scripts/post_activate.bat
Normal file
@ -0,0 +1,6 @@
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@call conda --version
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@call git --version
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cd %CONDA_PREFIX%\..\scripts
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on_env_start.bat
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2
scripts/win_enable_long_filepaths.ps1
Normal file
2
scripts/win_enable_long_filepaths.ps1
Normal file
@ -0,0 +1,2 @@
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Set-ItemProperty -Path 'HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem' -Name LongPathsEnabled -Type DWord -Value 1
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pause
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921
ui/index.html
Normal file
921
ui/index.html
Normal file
@ -0,0 +1,921 @@
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<!DOCTYPE html>
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<html>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<style>
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body {
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font-family: Arial, Helvetica, sans-serif;
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font-size: 11pt;
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background-color: rgb(32, 33, 36);
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color: #eee;
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}
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a {
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color: rgb(0, 102, 204);
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}
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a:visited {
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color: rgb(0, 102, 204);
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}
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label {
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font-size: 10pt;
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}
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#prompt {
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width: 100%;
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height: 50pt;
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}
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@media screen and (max-width: 600px) {
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#prompt {
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width: 95%;
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}
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}
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.image_preview_container {
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display: none;
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margin-top: 10pt;
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}
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.image_clear_btn {
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position: absolute;
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transform: translateX(-50%) translateY(-35%);
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background: black;
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color: white;
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border: 2pt solid #ccc;
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padding: 0;
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cursor: pointer;
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outline: inherit;
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border-radius: 8pt;
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width: 16pt;
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height: 16pt;
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font-family: Verdana;
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font-size: 8pt;
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}
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#editor-settings-entries {
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font-size: 9pt;
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margin-bottom: 5px;
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padding-left: 10px;
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list-style-type: none;
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}
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#editor-settings-entries li {
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padding-bottom: 3pt;
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}
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#guidance_scale {
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transform: translateY(30%);
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}
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#outputMsg {
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font-size: small;
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}
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#footer {
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font-size: small;
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padding-left: 10pt;
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background: none;
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}
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#footer-legal {
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font-size: 8pt;
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}
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.imgSeedLabel {
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position: absolute;
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transform: translateX(-100%);
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margin-top: 5pt;
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margin-left: -5pt;
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font-size: 10pt;
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background-color: #333;
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opacity: 0.8;
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color: #ddd;
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border-radius: 3pt;
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padding: 1pt 3pt;
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}
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.imgUseBtn {
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position: absolute;
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transform: translateX(-100%);
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margin-top: 30pt;
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margin-left: -5pt;
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}
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.imgSaveBtn {
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position: absolute;
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transform: translateX(-100%);
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margin-top: 55pt;
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margin-left: -5pt;
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}
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.imgItem {
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display: inline;
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padding-right: 10px;
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}
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.imgItemInfo {
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opacity: 0.5;
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}
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#container {
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width: 75%;
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margin-left: auto;
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margin-right: auto;
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}
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@media screen and (max-width: 1400px) {
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#container {
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width: 100%;
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}
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}
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#meta small {
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font-size: 11pt;
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}
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#editor {
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padding: 5px;
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}
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#editor label {
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font-weight: bold;
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}
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#preview {
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padding: 5px;
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}
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#editor-inputs {
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margin-bottom: 20px;
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}
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#editor-inputs-prompt {
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flex: 1;
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}
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#editor-inputs .row {
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padding-bottom: 10px;
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}
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#makeImage {
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border-radius: 6px;
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}
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#editor-modifiers h5 {
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padding: 5pt 0;
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margin: 0;
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}
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#makeImage {
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flex: 0 0 70px;
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background: rgb(80, 0, 185);
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border: 2px solid rgb(40, 0, 78);
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color: rgb(255, 221, 255);
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width: 100%;
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height: 30pt;
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}
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#makeImage:hover {
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background: rgb(93, 0, 214);
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}
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.flex-container {
|
||||
display: flex;
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||||
}
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||||
.col-50 {
|
||||
flex: 50%;
|
||||
}
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||||
.col-free {
|
||||
flex: 1;
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}
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.collapsible {
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||||
cursor: pointer;
|
||||
}
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||||
.collapsible-content {
|
||||
display: none;
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||||
padding-left: 15px;
|
||||
}
|
||||
.collapsible-content h5 {
|
||||
padding: 5pt 0pt;
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||||
margin: 0;
|
||||
font-size: 10pt;
|
||||
}
|
||||
.collapsible-handle {
|
||||
color: white;
|
||||
padding-right: 5px;
|
||||
}
|
||||
.panel-box {
|
||||
background: rgb(44, 45, 48);
|
||||
border: 1px solid rgb(47, 49, 53);
|
||||
border-radius: 7px;
|
||||
padding: 5px;
|
||||
margin-bottom: 15px;
|
||||
box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.15), 0 6px 20px 0 rgba(0, 0, 0, 0.15);
|
||||
}
|
||||
.panel-box h4 {
|
||||
margin: 0;
|
||||
padding: 2px 0;
|
||||
}
|
||||
.prompt-modifier-tag {
|
||||
border: 1px solid rgb(10, 0, 24);
|
||||
border-radius: 4px;
|
||||
padding: 0pt 3pt;
|
||||
margin-right: 2pt;
|
||||
cursor: pointer;
|
||||
display: inline;
|
||||
background: rgb(163, 163, 163);
|
||||
color: black;
|
||||
line-height: 25pt;
|
||||
float: left;
|
||||
font-size: 9pt;
|
||||
}
|
||||
.prompt-modifier-tag:hover {
|
||||
background: black;
|
||||
color: white;
|
||||
}
|
||||
#editor-modifiers-entries .prompt-modifier-tag {
|
||||
background: #110f0f;
|
||||
color: rgb(212, 212, 212);
|
||||
margin-bottom: 4pt;
|
||||
font-size: 10pt;
|
||||
}
|
||||
#editor-modifiers-entries .prompt-modifier-tag:hover {
|
||||
background: rgb(163, 163, 163);
|
||||
color: black;
|
||||
}
|
||||
#editor-modifiers .editor-modifiers-leaf {
|
||||
padding-top: 10pt;
|
||||
padding-bottom: 10pt;
|
||||
}
|
||||
#preview {
|
||||
margin-left: 20pt;
|
||||
}
|
||||
img {
|
||||
box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.15), 0 6px 20px 0 rgba(0, 0, 0, 0.15);
|
||||
}
|
||||
.line-separator {
|
||||
background: rgb(56, 56, 56);
|
||||
height: 1pt;
|
||||
margin: 15pt 0;
|
||||
}
|
||||
#editor-inputs-tags-container {
|
||||
margin-top: 5pt;
|
||||
display: none;
|
||||
}
|
||||
#server-status {
|
||||
float: right;
|
||||
}
|
||||
#server-status-color {
|
||||
width: 8pt;
|
||||
height: 8pt;
|
||||
border-radius: 4pt;
|
||||
background-color: rgb(128, 87, 0);
|
||||
/* background-color: rgb(197, 1, 1); */
|
||||
float: left;
|
||||
transform: translateY(15%);
|
||||
}
|
||||
#server-status-msg {
|
||||
color: rgb(128, 87, 0);
|
||||
padding-left: 2pt;
|
||||
font-size: 10pt;
|
||||
}
|
||||
#preview-prompt {
|
||||
font-size: 16pt;
|
||||
margin-bottom: 10pt;
|
||||
}
|
||||
</style>
|
||||
</html>
|
||||
<body>
|
||||
<div id="container">
|
||||
<div class="flex-container">
|
||||
<div id="editor" class="col-50">
|
||||
<div id="meta">
|
||||
<div id="server-status">
|
||||
<div id="server-status-color"> </div>
|
||||
<span id="server-status-msg">Stable Diffusion is starting..</span>
|
||||
</div>
|
||||
<h1>Stable Diffusion UI <small>v2 (beta)</small></h1>
|
||||
</div>
|
||||
<div id="editor-inputs">
|
||||
<div id="editor-inputs-prompt" class="row">
|
||||
<label for="prompt">Prompt</label>
|
||||
<textarea id="prompt" class="col-free">a photograph of an astronaut riding a horse</textarea>
|
||||
</div>
|
||||
|
||||
<div id="editor-inputs-init-image" class="row">
|
||||
<label for="init_image"><b>Initial Image:</b> (optional) </label> <input id="init_image" name="init_image" type="file" /> </button><br/>
|
||||
<div id="init_image_preview_container" class="image_preview_container">
|
||||
<img id="init_image_preview" src="" width="100" height="100" />
|
||||
<button id="init_image_clear" class="image_clear_btn">X</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="editor-inputs-tags-container" class="row">
|
||||
<label>Tags: <small>(click a tag to remove it)</small></label>
|
||||
<div id="editor-inputs-tags-list">
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<button id="makeImage">Make Image</button>
|
||||
</div>
|
||||
|
||||
<div class="line-separator"> </div>
|
||||
|
||||
<div id="editor-settings" class="panel-box">
|
||||
<h4 class="collapsible">Advanced Settings</h4>
|
||||
<ul id="editor-settings-entries" class="collapsible-content">
|
||||
<li><label for="seed">Seed:</label> <input id="seed" name="seed" size="10" value="30000"> <input id="random_seed" name="random_seed" type="checkbox" checked> <label for="random_seed">Random Image</label></li>
|
||||
<li><label for="num_outputs_total">Number of images to make:</label> <input id="num_outputs_total" name="num_outputs_total" value="1" size="4"> <label for="num_outputs_parallel">Generate in parallel:</label> <input id="num_outputs_parallel" name="num_outputs_parallel" value="1" size="4"> (images at once)</li>
|
||||
<li><label for="width">Width:</label> <select id="width" name="width" value="512"><option value="128">128</option><option value="256">256</option><option value="512" selected>512</option><option value="768">768</option><option value="1024">1024</option></select></li>
|
||||
<li><label for="height">Height:</label> <select id="height" name="height" value="512"><option value="128">128</option><option value="256">256</option><option value="512" selected>512</option><option value="768">768</option></select></li>
|
||||
<li><label for="num_inference_steps">Number of inference steps:</label> <input id="num_inference_steps" name="num_inference_steps" size="4" value="50"></li>
|
||||
<li><label for="guidance_scale">Guidance Scale:</label> <input id="guidance_scale" name="guidance_scale" value="75" type="range" min="10" max="200"> <span id="guidance_scale_value"></span></li>
|
||||
<li><span id="prompt_strength_container"><label for="prompt_strength">Prompt Strength:</label> <input id="prompt_strength" name="prompt_strength" value="8" type="range" min="0" max="10"> <span id="prompt_strength_value"></span><br/></span></li>
|
||||
<li> </li>
|
||||
<li><input id="save_to_disk" name="save_to_disk" type="checkbox"> <label for="save_to_disk">Automatically save to disk <span id="diskPath"></span></label></li>
|
||||
<li><input id="sound_toggle" name="sound_toggle" type="checkbox" checked> <label for="sound_toggle">Play sound on task completion</label></li>
|
||||
<li><input id="turbo" name="turbo" type="checkbox" checked> <label for="turbo">Turbo mode (generates images faster, but uses an additional 1 GB of GPU memory)</label></li>
|
||||
<li><input id="use_cpu" name="use_cpu" type="checkbox"> <label for="use_cpu">Use CPU instead of GPU (warning: this will be *very* slow)</label></li>
|
||||
<li><input id="use_full_precision" name="use_full_precision" type="checkbox"> <label for="use_full_precision">Use full precision (for GPU-only. warning: this will consume more VRAM)</label></li>
|
||||
<!-- <li><input id="allow_nsfw" name="allow_nsfw" type="checkbox"> <label for="allow_nsfw">Allow NSFW Content (You confirm you are above 18 years of age)</label></li> -->
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div id="editor-modifiers" class="panel-box">
|
||||
<h4 class="collapsible">Image Modifiers (art styles, tags etc)</h4>
|
||||
<div id="editor-modifiers-entries" class="collapsible-content">
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="preview" class="col-50">
|
||||
<div id="preview-prompt">Type a prompt and press the "Make Image" button.<br/><br/>You can also add modifiers like "Realistic", "Pencil Sketch", "ArtStation" etc by browsing through the "Image Modifiers" section and selecting the desired modifiers.<br/><br/>Click "Advanced Settings" for additional settings like seed, image size, number of images to generate etc.<br/><br/>Enjoy! :)</div>
|
||||
|
||||
<div id="outputMsg"></div>
|
||||
<div id="current-images" class="img-preview">
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="line-separator"> </div>
|
||||
|
||||
<div id="footer" class="panel-box">
|
||||
<p>Please feel free to <a href="https://github.com/cmdr2/stable-diffusion-ui/issues" target="_blank">file an issue</a> if you have any problems or suggestions in using this interface.</p>
|
||||
<div id="footer-legal">
|
||||
<p><b>Disclaimer:</b> The authors of this project are not responsible for any content generated using this interface.</p>
|
||||
<p>This license of this software forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, <br/>spread misinformation and target vulnerable groups. For the full list of restrictions please read <a href="https://github.com/cmdr2/stable-diffusion-ui/blob/main/LICENSE" target="_blank">the license</a>.</p>
|
||||
<p>By using this software, you consent to the terms and conditions of the license.</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
|
||||
<script>
|
||||
const SOUND_ENABLED_KEY = "soundEnabled"
|
||||
const HEALTH_PING_INTERVAL = 5 // seconds
|
||||
|
||||
let promptField = document.querySelector('#prompt')
|
||||
let numOutputsTotalField = document.querySelector('#num_outputs_total')
|
||||
let numOutputsParallelField = document.querySelector('#num_outputs_parallel')
|
||||
let numInferenceStepsField = document.querySelector('#num_inference_steps')
|
||||
let guidanceScaleField = document.querySelector('#guidance_scale')
|
||||
let guidanceScaleValueLabel = document.querySelector('#guidance_scale_value')
|
||||
let randomSeedField = document.querySelector("#random_seed")
|
||||
let seedField = document.querySelector('#seed')
|
||||
let widthField = document.querySelector('#width')
|
||||
let heightField = document.querySelector('#height')
|
||||
let initImageSelector = document.querySelector("#init_image")
|
||||
let initImagePreview = document.querySelector("#init_image_preview")
|
||||
// let maskImageSelector = document.querySelector("#mask")
|
||||
// let maskImagePreview = document.querySelector("#mask_preview")
|
||||
let turboField = document.querySelector('#turbo')
|
||||
let useCPUField = document.querySelector('#use_cpu')
|
||||
let useFullPrecisionField = document.querySelector('#use_full_precision')
|
||||
let saveToDiskField = document.querySelector('#save_to_disk')
|
||||
// let allowNSFWField = document.querySelector("#allow_nsfw")
|
||||
let promptStrengthField = document.querySelector('#prompt_strength')
|
||||
let promptStrengthValueLabel = document.querySelector('#prompt_strength_value')
|
||||
|
||||
let makeImageBtn = document.querySelector('#makeImage')
|
||||
|
||||
let imagesContainer = document.querySelector('#current-images')
|
||||
let initImagePreviewContainer = document.querySelector('#init_image_preview_container')
|
||||
let initImageClearBtn = document.querySelector('#init_image_clear')
|
||||
let promptStrengthContainer = document.querySelector('#prompt_strength_container')
|
||||
|
||||
// let maskSetting = document.querySelector('#mask_setting')
|
||||
// let maskImagePreviewContainer = document.querySelector('#mask_preview_container')
|
||||
// let maskImageClearBtn = document.querySelector('#mask_clear')
|
||||
|
||||
let editorModifierEntries = document.querySelector('#editor-modifiers-entries')
|
||||
let editorModifierTagsList = document.querySelector('#editor-inputs-tags-list')
|
||||
let editorTagsContainer = document.querySelector('#editor-inputs-tags-container')
|
||||
|
||||
let previewPrompt = document.querySelector('#preview-prompt')
|
||||
|
||||
let showConfigToggle = document.querySelector('#configToggleBtn')
|
||||
// let configBox = document.querySelector('#config')
|
||||
let outputMsg = document.querySelector('#outputMsg')
|
||||
|
||||
let soundToggle = document.querySelector('#sound_toggle')
|
||||
|
||||
let serverStatusColor = document.querySelector('#server-status-color')
|
||||
let serverStatusMsg = document.querySelector('#server-status-msg')
|
||||
|
||||
let serverStatus = 'offline'
|
||||
let activeTags = []
|
||||
|
||||
function isSoundEnabled() {
|
||||
if (localStorage.getItem(SOUND_ENABLED_KEY) === 'false') {
|
||||
return false
|
||||
}
|
||||
return true
|
||||
}
|
||||
|
||||
function setStatus(statusType, msg, msgType) {
|
||||
if (statusType !== 'server') {
|
||||
return;
|
||||
}
|
||||
|
||||
if (msgType == 'error') {
|
||||
// msg = '<span style="color: red">' + msg + '<span>'
|
||||
serverStatusColor.style.backgroundColor = 'red'
|
||||
serverStatusMsg.style.color = 'red'
|
||||
serverStatusMsg.innerHTML = 'Stable Diffusion has stopped'
|
||||
} else if (msgType == 'success') {
|
||||
// msg = '<span style="color: green">' + msg + '<span>'
|
||||
serverStatusColor.style.backgroundColor = 'green'
|
||||
serverStatusMsg.style.color = 'green'
|
||||
serverStatusMsg.innerHTML = 'Stable Diffusion is ready'
|
||||
serverStatus = 'online'
|
||||
}
|
||||
}
|
||||
|
||||
function logError(msg, res) {
|
||||
outputMsg.innerHTML = '<span style="color: red">Error: ' + msg + '</span>'
|
||||
console.log('request error', res)
|
||||
setStatus('request', 'error', 'error')
|
||||
}
|
||||
|
||||
function playSound() {
|
||||
const audio = new Audio('/media/ding.mp3')
|
||||
audio.volume = 0.2
|
||||
audio.play()
|
||||
}
|
||||
|
||||
async function healthCheck() {
|
||||
try {
|
||||
let res = await fetch('/ping')
|
||||
res = await res.json()
|
||||
|
||||
if (res[0] == 'OK') {
|
||||
setStatus('server', 'online', 'success')
|
||||
} else {
|
||||
setStatus('server', 'offline', 'error')
|
||||
}
|
||||
} catch (e) {
|
||||
setStatus('server', 'offline', 'error')
|
||||
}
|
||||
}
|
||||
|
||||
// makes a single image. don't call this directly, use makeImage() instead
|
||||
async function doMakeImage(reqBody) {
|
||||
let res = ''
|
||||
let seed = reqBody['seed']
|
||||
|
||||
try {
|
||||
res = await fetch('/image', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify(reqBody)
|
||||
})
|
||||
|
||||
if (res.status != 200) {
|
||||
if (serverStatus === 'online') {
|
||||
logError('Stable Diffusion had an error: ' + await res.text() + '. This happens sometimes. Maybe modify the prompt or seed a little bit?', res)
|
||||
} else {
|
||||
logError("Stable Diffusion is still starting up, please wait. If this goes on beyond a few minutes, Stable Diffusion has probably crashed.", res)
|
||||
}
|
||||
res = undefined
|
||||
} else {
|
||||
res = await res.json()
|
||||
|
||||
if (res.status !== 'succeeded') {
|
||||
let msg = ''
|
||||
if (res.detail !== undefined) {
|
||||
msg = res.detail
|
||||
} else {
|
||||
msg = res
|
||||
}
|
||||
logError(msg, res)
|
||||
res = undefined
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('request error', e)
|
||||
setStatus('request', 'error', 'error')
|
||||
}
|
||||
|
||||
if (!res) {
|
||||
return false
|
||||
}
|
||||
|
||||
for (let idx in res.output) {
|
||||
let imgBody = ''
|
||||
|
||||
try {
|
||||
let imgData = res.output[idx]
|
||||
imgBody = imgData.data
|
||||
} catch (e) {
|
||||
console.log(imgBody)
|
||||
setStatus('request', 'invalid image', 'error')
|
||||
continue
|
||||
}
|
||||
|
||||
let imgItem = document.createElement('div')
|
||||
imgItem.className = 'imgItem'
|
||||
|
||||
let img = document.createElement('img')
|
||||
img.width = parseInt(reqBody.width)
|
||||
img.height = parseInt(reqBody.height)
|
||||
img.src = imgBody
|
||||
|
||||
let imgItemInfo = document.createElement('span')
|
||||
imgItemInfo.className = 'imgItemInfo'
|
||||
|
||||
let imgSeedLabel = document.createElement('span')
|
||||
imgSeedLabel.className = 'imgSeedLabel'
|
||||
imgSeedLabel.innerHTML = 'Seed: ' + seed
|
||||
|
||||
let imgUseBtn = document.createElement('button')
|
||||
imgUseBtn.className = 'imgUseBtn'
|
||||
imgUseBtn.innerHTML = 'Use as Input'
|
||||
|
||||
let imgSaveBtn = document.createElement('button')
|
||||
imgSaveBtn.className = 'imgSaveBtn'
|
||||
imgSaveBtn.innerHTML = 'Download'
|
||||
|
||||
imgItem.appendChild(img)
|
||||
imgItem.appendChild(imgItemInfo)
|
||||
imgItemInfo.appendChild(imgSeedLabel)
|
||||
imgItemInfo.appendChild(imgUseBtn)
|
||||
imgItemInfo.appendChild(imgSaveBtn)
|
||||
imagesContainer.appendChild(imgItem)
|
||||
|
||||
imgUseBtn.addEventListener('click', function() {
|
||||
initImageSelector.value = null
|
||||
initImagePreview.src = imgBody
|
||||
|
||||
initImagePreviewContainer.style.display = 'block'
|
||||
promptStrengthContainer.style.display = 'block'
|
||||
|
||||
// maskSetting.style.display = 'block'
|
||||
|
||||
randomSeedField.checked = false
|
||||
seedField.value = seed
|
||||
seedField.disabled = false
|
||||
})
|
||||
|
||||
imgSaveBtn.addEventListener('click', function() {
|
||||
let imgDownload = document.createElement('a')
|
||||
imgDownload.download = generateUUID() + '.png'
|
||||
imgDownload.href = imgBody
|
||||
imgDownload.click()
|
||||
})
|
||||
|
||||
imgItem.addEventListener('mouseenter', function() {
|
||||
imgItemInfo.style.opacity = 1
|
||||
})
|
||||
|
||||
imgItem.addEventListener('mouseleave', function() {
|
||||
imgItemInfo.style.opacity = 0.5
|
||||
})
|
||||
}
|
||||
|
||||
return true
|
||||
}
|
||||
|
||||
async function makeImage() {
|
||||
if (serverStatus !== 'online') {
|
||||
logError('The server is still starting up..')
|
||||
return
|
||||
}
|
||||
|
||||
setStatus('request', 'fetching..')
|
||||
|
||||
makeImageBtn.innerHTML = 'Processing..'
|
||||
makeImageBtn.disabled = true
|
||||
|
||||
outputMsg.innerHTML = 'Fetching..'
|
||||
|
||||
const imageRegex = new RegExp('data:image/[A-Za-z]+;base64')
|
||||
let seed = (randomSeedField.checked ? Math.floor(Math.random() * 10000) : parseInt(seedField.value))
|
||||
let numOutputsTotal = parseInt(numOutputsTotalField.value)
|
||||
let numOutputsParallel = parseInt(numOutputsParallelField.value)
|
||||
let batchCount = Math.ceil(numOutputsTotal / numOutputsParallel)
|
||||
let batchSize = numOutputsParallel
|
||||
|
||||
let prompt = promptField.value
|
||||
if (activeTags.length > 0) {
|
||||
let promptTags = activeTags.join(", ")
|
||||
prompt += ", " + promptTags
|
||||
}
|
||||
|
||||
previewPrompt.innerHTML = prompt
|
||||
|
||||
let reqBody = {
|
||||
prompt: prompt,
|
||||
num_outputs: batchSize,
|
||||
num_inference_steps: numInferenceStepsField.value,
|
||||
guidance_scale: parseInt(guidanceScaleField.value) / 10,
|
||||
width: widthField.value,
|
||||
height: heightField.value,
|
||||
// allow_nsfw: allowNSFWField.checked,
|
||||
save_to_disk: saveToDiskField.checked,
|
||||
turbo: turboField.checked,
|
||||
use_cpu: useCPUField.checked,
|
||||
use_full_precision: useFullPrecisionField.checked
|
||||
}
|
||||
|
||||
if (imageRegex.test(initImagePreview.src)) {
|
||||
reqBody['init_image'] = initImagePreview.src
|
||||
reqBody['prompt_strength'] = parseInt(promptStrengthField.value) / 10
|
||||
|
||||
// if (imageRegex.test(maskImagePreview.src)) {
|
||||
// reqBody['mask'] = maskImagePreview.src
|
||||
// }
|
||||
}
|
||||
|
||||
let time = new Date().getTime()
|
||||
imagesContainer.innerHTML = ''
|
||||
|
||||
let successCount = 0
|
||||
|
||||
for (let i = 0; i < batchCount; i++) {
|
||||
reqBody['seed'] = seed + i
|
||||
|
||||
let success = await doMakeImage(reqBody)
|
||||
|
||||
if (success) {
|
||||
outputMsg.innerHTML = 'Processed batch ' + (i+1) + '/' + batchCount
|
||||
successCount++
|
||||
}
|
||||
}
|
||||
|
||||
makeImageBtn.innerHTML = 'Make Image'
|
||||
makeImageBtn.disabled = false
|
||||
|
||||
if (isSoundEnabled()) {
|
||||
playSound()
|
||||
}
|
||||
|
||||
time = new Date().getTime() - time
|
||||
time /= 1000
|
||||
|
||||
if (successCount === batchCount) {
|
||||
outputMsg.innerHTML = 'Processed ' + numOutputsTotal + ' images in ' + time + ' seconds'
|
||||
|
||||
setStatus('request', 'done', 'success')
|
||||
}
|
||||
|
||||
if (randomSeedField.checked) {
|
||||
seedField.value = seed
|
||||
}
|
||||
}
|
||||
|
||||
function generateUUID() { // Public Domain/MIT
|
||||
var d = new Date().getTime();//Timestamp
|
||||
var d2 = ((typeof performance !== 'undefined') && performance.now && (performance.now()*1000)) || 0;//Time in microseconds since page-load or 0 if unsupported
|
||||
return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {
|
||||
var r = Math.random() * 16;//random number between 0 and 16
|
||||
if(d > 0){//Use timestamp until depleted
|
||||
r = (d + r)%16 | 0;
|
||||
d = Math.floor(d/16);
|
||||
} else {//Use microseconds since page-load if supported
|
||||
r = (d2 + r)%16 | 0;
|
||||
d2 = Math.floor(d2/16);
|
||||
}
|
||||
return (c === 'x' ? r : (r & 0x3 | 0x8)).toString(16);
|
||||
});
|
||||
}
|
||||
|
||||
function handleAudioEnabledChange(e) {
|
||||
localStorage.setItem(SOUND_ENABLED_KEY, e.target.checked.toString())
|
||||
}
|
||||
|
||||
soundToggle.addEventListener('click', handleAudioEnabledChange)
|
||||
soundToggle.checked = isSoundEnabled();
|
||||
|
||||
makeImageBtn.addEventListener('click', makeImage)
|
||||
|
||||
// configBox.style.display = 'none'
|
||||
|
||||
// showConfigToggle.addEventListener('click', function() {
|
||||
// configBox.style.display = (configBox.style.display === 'none' ? 'block' : 'none')
|
||||
// showConfigToggle.innerHTML = (configBox.style.display === 'none' ? 'show' : 'hide')
|
||||
// return false
|
||||
// })
|
||||
|
||||
function updateGuidanceScale() {
|
||||
guidanceScaleValueLabel.innerHTML = guidanceScaleField.value / 10
|
||||
}
|
||||
|
||||
guidanceScaleField.addEventListener('input', updateGuidanceScale)
|
||||
updateGuidanceScale()
|
||||
|
||||
function updatePromptStrength() {
|
||||
promptStrengthValueLabel.innerHTML = promptStrengthField.value / 10
|
||||
}
|
||||
|
||||
promptStrengthField.addEventListener('input', updatePromptStrength)
|
||||
updatePromptStrength()
|
||||
|
||||
function checkRandomSeed() {
|
||||
if (randomSeedField.checked) {
|
||||
seedField.disabled = true
|
||||
seedField.value = "random"
|
||||
} else {
|
||||
seedField.disabled = false
|
||||
}
|
||||
}
|
||||
randomSeedField.addEventListener('input', checkRandomSeed)
|
||||
checkRandomSeed()
|
||||
|
||||
function showInitImagePreview() {
|
||||
if (initImageSelector.files.length === 0) {
|
||||
initImagePreviewContainer.style.display = 'none'
|
||||
promptStrengthContainer.style.display = 'none'
|
||||
// maskSetting.style.display = 'none'
|
||||
return
|
||||
}
|
||||
|
||||
let reader = new FileReader()
|
||||
let file = initImageSelector.files[0]
|
||||
|
||||
reader.addEventListener('load', function() {
|
||||
// console.log(file.name, reader.result)
|
||||
initImagePreview.src = reader.result
|
||||
initImagePreviewContainer.style.display = 'block'
|
||||
promptStrengthContainer.style.display = 'block'
|
||||
|
||||
// maskSetting.style.display = 'block'
|
||||
})
|
||||
|
||||
if (file) {
|
||||
reader.readAsDataURL(file)
|
||||
}
|
||||
}
|
||||
initImageSelector.addEventListener('change', showInitImagePreview)
|
||||
showInitImagePreview()
|
||||
|
||||
initImageClearBtn.addEventListener('click', function() {
|
||||
initImageSelector.value = null
|
||||
// maskImageSelector.value = null
|
||||
|
||||
initImagePreview.src = ''
|
||||
// maskImagePreview.src = ''
|
||||
|
||||
initImagePreviewContainer.style.display = 'none'
|
||||
// maskImagePreviewContainer.style.display = 'none'
|
||||
|
||||
// maskSetting.style.display = 'none'
|
||||
|
||||
promptStrengthContainer.style.display = 'none'
|
||||
})
|
||||
|
||||
// function showMaskImagePreview() {
|
||||
// if (maskImageSelector.files.length === 0) {
|
||||
// maskImagePreviewContainer.style.display = 'none'
|
||||
// return
|
||||
// }
|
||||
|
||||
// let reader = new FileReader()
|
||||
// let file = maskImageSelector.files[0]
|
||||
|
||||
// reader.addEventListener('load', function() {
|
||||
// maskImagePreview.src = reader.result
|
||||
// maskImagePreviewContainer.style.display = 'block'
|
||||
// })
|
||||
|
||||
// if (file) {
|
||||
// reader.readAsDataURL(file)
|
||||
// }
|
||||
// }
|
||||
// maskImageSelector.addEventListener('change', showMaskImagePreview)
|
||||
// showMaskImagePreview()
|
||||
|
||||
// maskImageClearBtn.addEventListener('click', function() {
|
||||
// maskImageSelector.value = null
|
||||
// maskImagePreview.src = ''
|
||||
// maskImagePreviewContainer.style.display = 'none'
|
||||
// })
|
||||
</script>
|
||||
<script>
|
||||
function createCollapsibles(node) {
|
||||
if (!node) {
|
||||
node = document
|
||||
}
|
||||
|
||||
let collapsibles = node.querySelectorAll(".collapsible")
|
||||
collapsibles.forEach(function(c) {
|
||||
let handle = document.createElement('span')
|
||||
handle.className = 'collapsible-handle'
|
||||
handle.innerHTML = '➕'
|
||||
c.insertBefore(handle, c.firstChild)
|
||||
|
||||
c.addEventListener('click', function() {
|
||||
this.classList.toggle("active")
|
||||
let content = this.nextElementSibling
|
||||
if (content.style.display === "block") {
|
||||
content.style.display = "none"
|
||||
handle.innerHTML = '➕' // plus
|
||||
} else {
|
||||
content.style.display = "block"
|
||||
handle.innerHTML = '➖' // minus
|
||||
}
|
||||
})
|
||||
})
|
||||
}
|
||||
createCollapsibles()
|
||||
|
||||
function refreshTagsList() {
|
||||
editorModifierTagsList.innerHTML = ''
|
||||
|
||||
if (activeTags.length == 0) {
|
||||
editorTagsContainer.style.display = 'none'
|
||||
return
|
||||
} else {
|
||||
editorTagsContainer.style.display = 'block'
|
||||
}
|
||||
|
||||
activeTags.forEach(function(tag) {
|
||||
let el = document.createElement('div')
|
||||
el.className = 'prompt-modifier-tag'
|
||||
el.innerHTML = tag
|
||||
|
||||
editorModifierTagsList.appendChild(el)
|
||||
|
||||
el.addEventListener('click', function() {
|
||||
let idx = activeTags.indexOf(tag)
|
||||
if (idx !== -1) {
|
||||
activeTags.splice(idx, 1)
|
||||
refreshTagsList()
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
let brk = document.createElement('br')
|
||||
brk.style.clear = 'both'
|
||||
editorModifierTagsList.appendChild(brk)
|
||||
}
|
||||
|
||||
async function getDiskPath() {
|
||||
try {
|
||||
let res = await fetch('/output_dir')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
res = res[0]
|
||||
|
||||
document.querySelector('#diskPath').innerHTML = '(to ' + res + ')'
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('error fetching output dir path', e)
|
||||
}
|
||||
}
|
||||
|
||||
async function loadModifiers() {
|
||||
try {
|
||||
let res = await fetch('/modifiers.json')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
|
||||
res.forEach(function(m) {
|
||||
let title = m[0]
|
||||
let modifiers = m[1]
|
||||
|
||||
let titleEl = document.createElement('h5')
|
||||
titleEl.className = 'collapsible'
|
||||
titleEl.innerHTML = title
|
||||
|
||||
let modifiersEl = document.createElement('div')
|
||||
modifiersEl.classList.add('collapsible-content', 'editor-modifiers-leaf')
|
||||
|
||||
modifiers.forEach(function(modifier) {
|
||||
let tagEl = document.createElement('div')
|
||||
tagEl.className = 'prompt-modifier-tag'
|
||||
tagEl.innerHTML = modifier
|
||||
|
||||
modifiersEl.appendChild(tagEl)
|
||||
|
||||
tagEl.addEventListener('click', function() {
|
||||
if (activeTags.includes(modifier)) {
|
||||
return
|
||||
}
|
||||
|
||||
activeTags.push(modifier)
|
||||
refreshTagsList()
|
||||
})
|
||||
})
|
||||
let brk = document.createElement('br')
|
||||
brk.style.clear = 'both'
|
||||
modifiersEl.appendChild(brk)
|
||||
|
||||
let e = document.createElement('div')
|
||||
e.appendChild(titleEl)
|
||||
e.appendChild(modifiersEl)
|
||||
|
||||
editorModifierEntries.appendChild(e)
|
||||
})
|
||||
|
||||
createCollapsibles(editorModifierEntries)
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('error fetching modifiers', e)
|
||||
}
|
||||
}
|
||||
|
||||
async function init() {
|
||||
await loadModifiers()
|
||||
await getDiskPath()
|
||||
|
||||
setInterval(healthCheck, HEALTH_PING_INTERVAL * 1000)
|
||||
healthCheck()
|
||||
}
|
||||
|
||||
init()
|
||||
</script>
|
||||
|
||||
</html>
|
||||
BIN
ui/media/ding.mp3
Normal file
BIN
ui/media/ding.mp3
Normal file
Binary file not shown.
92
ui/modifiers.json
Normal file
92
ui/modifiers.json
Normal file
@ -0,0 +1,92 @@
|
||||
[
|
||||
["Drawing Style", [
|
||||
"Sketch",
|
||||
"Doodle",
|
||||
"Children's Drawing",
|
||||
"Line Art",
|
||||
"Dot Art",
|
||||
"Crosshatch",
|
||||
"Detailed and Intricate",
|
||||
"Cel Shading"
|
||||
]],
|
||||
["Visual Style", [
|
||||
"2D",
|
||||
"Cartoon",
|
||||
"8-bit",
|
||||
"16-bit",
|
||||
"Graphic Novel",
|
||||
"Visual Novel",
|
||||
"Street Art",
|
||||
"Fantasy",
|
||||
"Realistic",
|
||||
"Photo",
|
||||
"Hard Edge Painting",
|
||||
"Mural",
|
||||
"Mosaic",
|
||||
"Hydrodipped",
|
||||
"Modern Art",
|
||||
"Concept Art",
|
||||
"Digital Art",
|
||||
"CGI",
|
||||
"Anaglyph",
|
||||
"Comic Book",
|
||||
"Lithography"
|
||||
]],
|
||||
["Pen", [
|
||||
"Graphite",
|
||||
"Colored Pencil",
|
||||
"Ink",
|
||||
"Chalk",
|
||||
"Pastel Art",
|
||||
"Oil Paint"
|
||||
]],
|
||||
["Carving and Etching", [
|
||||
"Etching",
|
||||
"Wood-Carving",
|
||||
"Papercutting",
|
||||
"Paper-Mache",
|
||||
"Paper Model",
|
||||
"Linocut",
|
||||
"Pyrography"
|
||||
]],
|
||||
["Camera", [
|
||||
"HD",
|
||||
"Color Grading",
|
||||
"Film Grain",
|
||||
"White Balance",
|
||||
"Golden Hour",
|
||||
"Glamor Shot",
|
||||
"War Photography",
|
||||
"Lens Flare",
|
||||
"Polaroid",
|
||||
"Vintage"
|
||||
]],
|
||||
["Color", [
|
||||
"Colorful",
|
||||
"Electric Colors",
|
||||
"Warm Color Palette",
|
||||
"Infrared",
|
||||
"Beautiful Lighting"
|
||||
]],
|
||||
["Emotions", [
|
||||
"Happy",
|
||||
"Excited",
|
||||
"Sad",
|
||||
"Lonely",
|
||||
"Angry",
|
||||
"Good",
|
||||
"Evil"
|
||||
]],
|
||||
["Style of an artist or community", [
|
||||
"by Andy Warhol",
|
||||
"Artstation",
|
||||
"by Asaf Hanuka",
|
||||
"by Aubrey Beardsley",
|
||||
"by H.R. Giger",
|
||||
"by Hayao Mizaki",
|
||||
"by Salvador Dali",
|
||||
"by Tivadar Csontváry Kosztka",
|
||||
"by Lisa Frank",
|
||||
"by Pablo Piccaso"
|
||||
]]
|
||||
]
|
||||
62
ui/sd_internal/__init__.py
Normal file
62
ui/sd_internal/__init__.py
Normal file
@ -0,0 +1,62 @@
|
||||
import json
|
||||
|
||||
class Request:
|
||||
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
|
||||
# allow_nsfw: bool = False
|
||||
precision: str = "autocast" # or "full"
|
||||
save_to_disk_path: str = None
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
use_full_precision: bool = False
|
||||
|
||||
def to_string(self):
|
||||
return f'''
|
||||
prompt: {self.prompt}
|
||||
seed: {self.seed}
|
||||
num_inference_steps: {self.num_inference_steps}
|
||||
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_cpu: {self.use_cpu}
|
||||
use_full_precision: {self.use_full_precision}'''
|
||||
|
||||
class Image:
|
||||
data: str # base64
|
||||
seed: int
|
||||
is_nsfw: bool
|
||||
|
||||
def __init__(self, data, seed):
|
||||
self.data = data
|
||||
self.seed = seed
|
||||
|
||||
def json(self):
|
||||
return {
|
||||
"data": self.data,
|
||||
"seed": self.seed,
|
||||
}
|
||||
|
||||
class Response:
|
||||
images: list
|
||||
|
||||
def json(self):
|
||||
res = {
|
||||
"status": 'succeeded',
|
||||
"output": [],
|
||||
}
|
||||
|
||||
for image in self.images:
|
||||
res["output"].append(image.json())
|
||||
|
||||
return res
|
||||
342
ui/sd_internal/runtime.py
Normal file
342
ui/sd_internal/runtime.py
Normal file
@ -0,0 +1,342 @@
|
||||
import os, re
|
||||
import torch
|
||||
import numpy as np
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image
|
||||
from tqdm import tqdm, trange
|
||||
from itertools import islice
|
||||
from einops import rearrange
|
||||
import time
|
||||
from pytorch_lightning import seed_everything
|
||||
from torch import autocast
|
||||
from contextlib import nullcontext
|
||||
from einops import rearrange, repeat
|
||||
from ldm.util import instantiate_from_config
|
||||
from optimizedSD.optimUtils import split_weighted_subprompts
|
||||
from transformers import logging
|
||||
|
||||
logging.set_verbosity_error()
|
||||
|
||||
# consts
|
||||
config_yaml = "optimizedSD/v1-inference.yaml"
|
||||
|
||||
# api stuff
|
||||
from . import Request, Response, Image as ResponseImage
|
||||
import base64
|
||||
from io import BytesIO
|
||||
|
||||
# local
|
||||
ckpt = None
|
||||
model = None
|
||||
modelCS = None
|
||||
modelFS = None
|
||||
model_is_half = False
|
||||
model_fs_is_half = False
|
||||
device = None
|
||||
unet_bs = 1
|
||||
precision = 'autocast'
|
||||
sampler_plms = None
|
||||
sampler_ddim = None
|
||||
|
||||
# api
|
||||
def load_model(ckpt_to_use, device_to_use='cuda', turbo=False, unet_bs_to_use=1, precision_to_use='autocast', half_model_fs=False):
|
||||
global ckpt, model, modelCS, modelFS, model_is_half, device, unet_bs, precision, model_fs_is_half
|
||||
|
||||
ckpt = ckpt_to_use
|
||||
device = device_to_use
|
||||
precision = precision_to_use
|
||||
unet_bs = unet_bs_to_use
|
||||
|
||||
sd = load_model_from_config(f"{ckpt}")
|
||||
li, lo = [], []
|
||||
for key, value in sd.items():
|
||||
sp = key.split(".")
|
||||
if (sp[0]) == "model":
|
||||
if "input_blocks" in sp:
|
||||
li.append(key)
|
||||
elif "middle_block" in sp:
|
||||
li.append(key)
|
||||
elif "time_embed" in sp:
|
||||
li.append(key)
|
||||
else:
|
||||
lo.append(key)
|
||||
for key in li:
|
||||
sd["model1." + key[6:]] = sd.pop(key)
|
||||
for key in lo:
|
||||
sd["model2." + key[6:]] = sd.pop(key)
|
||||
|
||||
config = OmegaConf.load(f"{config_yaml}")
|
||||
|
||||
model = instantiate_from_config(config.modelUNet)
|
||||
_, _ = model.load_state_dict(sd, strict=False)
|
||||
model.eval()
|
||||
model.cdevice = device
|
||||
model.unet_bs = unet_bs
|
||||
model.turbo = turbo
|
||||
|
||||
modelCS = instantiate_from_config(config.modelCondStage)
|
||||
_, _ = modelCS.load_state_dict(sd, strict=False)
|
||||
modelCS.eval()
|
||||
modelCS.cond_stage_model.device = device
|
||||
|
||||
modelFS = instantiate_from_config(config.modelFirstStage)
|
||||
_, _ = modelFS.load_state_dict(sd, strict=False)
|
||||
modelFS.eval()
|
||||
del sd
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
model.half()
|
||||
modelCS.half()
|
||||
model_is_half = True
|
||||
else:
|
||||
model_is_half = False
|
||||
|
||||
if half_model_fs:
|
||||
modelFS.half()
|
||||
model_fs_is_half = True
|
||||
else:
|
||||
model_fs_is_half = False
|
||||
|
||||
def mk_img(req: Request):
|
||||
global modelFS, device
|
||||
|
||||
res = Response()
|
||||
res.images = []
|
||||
|
||||
model.turbo = req.turbo
|
||||
if req.use_cpu:
|
||||
device = 'cpu'
|
||||
|
||||
if model_is_half:
|
||||
print('reloading model for cpu')
|
||||
load_model(ckpt, device)
|
||||
else:
|
||||
device = 'cuda'
|
||||
|
||||
if (precision == 'autocast' and (req.use_full_precision or not model_is_half)) or \
|
||||
(precision == 'full' and not req.use_full_precision) or \
|
||||
(req.init_image is None and model_fs_is_half) or \
|
||||
(req.init_image is not None and not model_fs_is_half):
|
||||
|
||||
print('reloading model for cuda')
|
||||
load_model(ckpt, device, model.turbo, unet_bs, ('full' if req.use_full_precision else 'autocast'), half_model_fs=(req.init_image is not None and not req.use_full_precision))
|
||||
|
||||
model.cdevice = device
|
||||
modelCS.cond_stage_model.device = device
|
||||
|
||||
opt_prompt = req.prompt
|
||||
opt_seed = req.seed
|
||||
opt_n_samples = req.num_outputs
|
||||
opt_n_iter = 1
|
||||
opt_scale = req.guidance_scale
|
||||
opt_C = 4
|
||||
opt_H = req.height
|
||||
opt_W = req.width
|
||||
opt_f = 8
|
||||
opt_ddim_steps = req.num_inference_steps
|
||||
opt_ddim_eta = 0.0
|
||||
opt_strength = req.prompt_strength
|
||||
opt_save_to_disk_path = req.save_to_disk_path
|
||||
opt_init_img = req.init_image
|
||||
opt_format = 'png'
|
||||
|
||||
print(req.to_string(), 'device', device)
|
||||
|
||||
seed_everything(opt_seed)
|
||||
|
||||
batch_size = opt_n_samples
|
||||
prompt = opt_prompt
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
|
||||
if precision == "autocast" and device != "cpu":
|
||||
precision_scope = autocast
|
||||
else:
|
||||
precision_scope = nullcontext
|
||||
|
||||
if req.init_image is None:
|
||||
handler = _txt2img
|
||||
|
||||
init_latent = None
|
||||
t_enc = None
|
||||
else:
|
||||
handler = _img2img
|
||||
|
||||
init_image = load_img(req.init_image)
|
||||
init_image = init_image.to(device)
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
init_image = init_image.half()
|
||||
|
||||
modelFS.to(device)
|
||||
|
||||
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
||||
init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space
|
||||
|
||||
if device != "cpu":
|
||||
mem = torch.cuda.memory_allocated() / 1e6
|
||||
modelFS.to("cpu")
|
||||
while torch.cuda.memory_allocated() / 1e6 >= mem:
|
||||
time.sleep(1)
|
||||
|
||||
assert 0. <= opt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(opt_strength * opt_ddim_steps)
|
||||
print(f"target t_enc is {t_enc} steps")
|
||||
|
||||
seeds = ""
|
||||
with torch.no_grad():
|
||||
for n in trange(opt_n_iter, desc="Sampling"):
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
sample_path = os.path.join(opt_save_to_disk_path, "_".join(re.split(":| ", prompts[0])))[:150]
|
||||
os.makedirs(sample_path, exist_ok=True)
|
||||
base_count = len(os.listdir(sample_path))
|
||||
|
||||
with precision_scope("cuda"):
|
||||
modelCS.to(device)
|
||||
uc = None
|
||||
if opt_scale != 1.0:
|
||||
uc = modelCS.get_learned_conditioning(batch_size * [""])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
|
||||
subprompts, weights = split_weighted_subprompts(prompts[0])
|
||||
if len(subprompts) > 1:
|
||||
c = torch.zeros_like(uc)
|
||||
totalWeight = sum(weights)
|
||||
# normalize each "sub prompt" and add it
|
||||
for i in range(len(subprompts)):
|
||||
weight = weights[i]
|
||||
# if not skip_normalize:
|
||||
weight = weight / totalWeight
|
||||
c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
else:
|
||||
c = modelCS.get_learned_conditioning(prompts)
|
||||
|
||||
# run the handler
|
||||
if handler == _txt2img:
|
||||
x_samples = _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed)
|
||||
else:
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed)
|
||||
|
||||
modelFS.to(device)
|
||||
|
||||
print("saving images")
|
||||
for i in range(batch_size):
|
||||
|
||||
x_samples_ddim = modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
|
||||
img = Image.fromarray(x_sample.astype(np.uint8))
|
||||
|
||||
img_data = img_to_base64_str(img)
|
||||
res.images.append(ResponseImage(data=img_data, seed=opt_seed))
|
||||
|
||||
if opt_save_to_disk_path is not None:
|
||||
img.save(
|
||||
os.path.join(sample_path, "seed_" + str(opt_seed) + "_" + f"{base_count:05}.{opt_format}")
|
||||
)
|
||||
base_count += 1
|
||||
|
||||
seeds += str(opt_seed) + ","
|
||||
opt_seed += 1
|
||||
|
||||
if device != "cpu":
|
||||
mem = torch.cuda.memory_allocated() / 1e6
|
||||
modelFS.to("cpu")
|
||||
while torch.cuda.memory_allocated() / 1e6 >= mem:
|
||||
time.sleep(1)
|
||||
del x_samples
|
||||
print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
|
||||
|
||||
return res
|
||||
|
||||
def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed):
|
||||
shape = [opt_n_samples, opt_C, opt_H // opt_f, opt_W // opt_f]
|
||||
|
||||
if device != "cpu":
|
||||
mem = torch.cuda.memory_allocated() / 1e6
|
||||
modelCS.to("cpu")
|
||||
while torch.cuda.memory_allocated() / 1e6 >= mem:
|
||||
time.sleep(1)
|
||||
|
||||
samples_ddim = model.sample(
|
||||
S=opt_ddim_steps,
|
||||
conditioning=c,
|
||||
seed=opt_seed,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt_ddim_eta,
|
||||
x_T=start_code,
|
||||
sampler = 'plms',
|
||||
)
|
||||
|
||||
return samples_ddim
|
||||
|
||||
def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed):
|
||||
# encode (scaled latent)
|
||||
z_enc = model.stochastic_encode(
|
||||
init_latent,
|
||||
torch.tensor([t_enc] * batch_size).to(device),
|
||||
opt_seed,
|
||||
opt_ddim_eta,
|
||||
opt_ddim_steps,
|
||||
)
|
||||
# decode it
|
||||
samples_ddim = model.sample(
|
||||
t_enc,
|
||||
c,
|
||||
z_enc,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
sampler = 'ddim'
|
||||
)
|
||||
|
||||
return samples_ddim
|
||||
|
||||
# internal
|
||||
|
||||
def chunk(it, size):
|
||||
it = iter(it)
|
||||
return iter(lambda: tuple(islice(it, size)), ())
|
||||
|
||||
|
||||
def load_model_from_config(ckpt, verbose=False):
|
||||
print(f"Loading model from {ckpt}")
|
||||
pl_sd = torch.load(ckpt, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
return sd
|
||||
|
||||
# utils
|
||||
|
||||
def load_img(img_str):
|
||||
image = base64_str_to_img(img_str).convert("RGB")
|
||||
w, h = image.size
|
||||
print(f"loaded input image of size ({w}, {h}) from base64")
|
||||
w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64
|
||||
image = image.resize((w, h), resample=Image.LANCZOS)
|
||||
image = np.array(image).astype(np.float32) / 255.0
|
||||
image = image[None].transpose(0, 3, 1, 2)
|
||||
image = torch.from_numpy(image)
|
||||
return 2.*image - 1.
|
||||
|
||||
# https://stackoverflow.com/a/61114178
|
||||
def img_to_base64_str(img):
|
||||
buffered = BytesIO()
|
||||
img.save(buffered, format="PNG")
|
||||
buffered.seek(0)
|
||||
img_byte = buffered.getvalue()
|
||||
img_str = "data:image/png;base64," + base64.b64encode(img_byte).decode()
|
||||
return img_str
|
||||
|
||||
def base64_str_to_img(img_str):
|
||||
img_str = img_str[len("data:image/png;base64,"):]
|
||||
data = base64.b64decode(img_str)
|
||||
buffered = BytesIO(data)
|
||||
img = Image.open(buffered)
|
||||
return img
|
||||
118
ui/server.py
Normal file
118
ui/server.py
Normal file
@ -0,0 +1,118 @@
|
||||
import traceback
|
||||
|
||||
import sys
|
||||
import os
|
||||
|
||||
SCRIPT_DIR = os.getcwd()
|
||||
print('started in ', SCRIPT_DIR)
|
||||
|
||||
SD_UI_DIR = os.getenv('SD_UI_PATH', None)
|
||||
sys.path.append(os.path.dirname(SD_UI_DIR))
|
||||
|
||||
OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
|
||||
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from starlette.responses import FileResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
from sd_internal import Request, Response
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
model_loaded = False
|
||||
model_is_loading = False
|
||||
|
||||
modifiers_cache = None
|
||||
outpath = os.path.join(os.path.expanduser("~"), OUTPUT_DIRNAME)
|
||||
|
||||
# defaults from https://huggingface.co/blog/stable_diffusion
|
||||
class ImageRequest(BaseModel):
|
||||
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
|
||||
# allow_nsfw: bool = False
|
||||
save_to_disk: bool = False
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
use_full_precision: bool = False
|
||||
|
||||
@app.get('/')
|
||||
def read_root():
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'index.html'))
|
||||
|
||||
@app.get('/ping')
|
||||
async def ping():
|
||||
global model_loaded, model_is_loading
|
||||
|
||||
try:
|
||||
if model_loaded:
|
||||
return {'OK'}
|
||||
|
||||
if model_is_loading:
|
||||
return {'ERROR'}
|
||||
|
||||
model_is_loading = True
|
||||
|
||||
from sd_internal import runtime
|
||||
runtime.load_model(ckpt_to_use="sd-v1-4.ckpt")
|
||||
|
||||
model_loaded = True
|
||||
model_is_loading = False
|
||||
|
||||
return {'OK'}
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.post('/image')
|
||||
async def image(req : ImageRequest):
|
||||
from sd_internal import runtime
|
||||
|
||||
r = Request()
|
||||
r.prompt = req.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.allow_nsfw = req.allow_nsfw
|
||||
r.turbo = req.turbo
|
||||
r.use_cpu = req.use_cpu
|
||||
r.use_full_precision = req.use_full_precision
|
||||
|
||||
if req.save_to_disk:
|
||||
r.save_to_disk_path = outpath
|
||||
|
||||
try:
|
||||
res: Response = runtime.mk_img(r)
|
||||
|
||||
return res.json()
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
return HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get('/media/ding.mp3')
|
||||
def read_ding():
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'media/ding.mp3'))
|
||||
|
||||
@app.get('/modifiers.json')
|
||||
def read_modifiers():
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'))
|
||||
|
||||
@app.get('/output_dir')
|
||||
def read_home_dir():
|
||||
return {outpath}
|
||||
|
||||
# start the browser ui
|
||||
import webbrowser; webbrowser.open('http://localhost:9000')
|
||||
Loading…
Reference in New Issue
Block a user