forked from extern/easydiffusion
cmdr2
GitHub
commit
5e0f525932
@ -50,7 +50,7 @@ if "%update_branch%"=="" (
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)
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)
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@xcopy sd-ui-files\ui ui /s /i /Y
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@xcopy sd-ui-files\ui ui /s /i /Y /q
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@copy sd-ui-files\scripts\on_sd_start.bat scripts\ /Y
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@copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
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@copy "sd-ui-files\scripts\Start Stable Diffusion UI.cmd" . /Y
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@ -199,7 +199,9 @@ call WHERE uvicorn > .tmp
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if not exist "..\models\stable-diffusion" mkdir "..\models\stable-diffusion"
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if not exist "..\models\vae" mkdir "..\models\vae"
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echo. > "..\models\stable-diffusion\Put your custom ckpt files here.txt"
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echo. > "..\models\vae\Put your VAE files here.txt"
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@if exist "sd-v1-4.ckpt" (
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for %%I in ("sd-v1-4.ckpt") do if "%%~zI" EQU "4265380512" (
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@ -329,6 +331,36 @@ echo. > "..\models\stable-diffusion\Put your custom ckpt files here.txt"
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@if exist "..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt" (
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for %%I in ("..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt") do if "%%~zI" EQU "334695179" (
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echo "Data files (weights) necessary for the default VAE (sd-vae-ft-mse-original) were already downloaded"
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) else (
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echo. & echo "The default VAE (sd-vae-ft-mse-original) file present at models\vae\vae-ft-mse-840000-ema-pruned.ckpt is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
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del "..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt"
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)
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)
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@if not exist "..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt" (
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@echo. & echo "Downloading data files (weights) for the default VAE (sd-vae-ft-mse-original).." & echo.
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@call curl -L -k https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt > ..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt
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@if exist "..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt" (
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for %%I in ("..\models\vae\vae-ft-mse-840000-ema-pruned.ckpt") do if "%%~zI" NEQ "334695179" (
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echo. & echo "Error: The downloaded default VAE (sd-vae-ft-mse-original) file was invalid! Bytes downloaded: %%~zI" & echo.
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echo. & echo "Error downloading the data files (weights) for the default VAE (sd-vae-ft-mse-original). 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.
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pause
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exit /b
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)
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) else (
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@echo. & echo "Error downloading the data files (weights) for the default VAE (sd-vae-ft-mse-original). 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.
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pause
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exit /b
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)
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)
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@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
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@if "%ERRORLEVEL%" NEQ "0" (
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@echo sd_weights_downloaded >> ..\scripts\install_status.txt
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@ -159,7 +159,9 @@ fi
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mkdir -p "../models/stable-diffusion"
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mkdir -p "../models/vae"
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echo "" > "../models/stable-diffusion/Put your custom ckpt files here.txt"
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echo "" > "../models/vae/Put your VAE files here.txt"
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if [ -f "sd-v1-4.ckpt" ]; then
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model_size=`find "sd-v1-4.ckpt" -printf "%s"`
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@ -269,6 +271,38 @@ if [ ! -f "RealESRGAN_x4plus_anime_6B.pth" ]; then
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fi
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if [ -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
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model_size=`find ../models/vae/vae-ft-mse-840000-ema-pruned.ckpt -printf "%s"`
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if [ "$model_size" -eq "334695179" ]; then
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echo "Data files (weights) necessary for the default VAE (sd-vae-ft-mse-original) were already downloaded"
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else
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printf "\n\nThe model file present at models/vae/vae-ft-mse-840000-ema-pruned.ckpt is invalid. It is only $model_size bytes in size. Re-downloading.."
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rm ../models/vae/vae-ft-mse-840000-ema-pruned.ckpt
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fi
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fi
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if [ ! -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
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echo "Downloading data files (weights) for the default VAE (sd-vae-ft-mse-original).."
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curl -L -k https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt > ../models/vae/vae-ft-mse-840000-ema-pruned.ckpt
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if [ -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
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model_size=`find ../models/vae/vae-ft-mse-840000-ema-pruned.ckpt -printf "%s"`
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if [ ! "$model_size" -eq "334695179" ]; then
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printf "\n\nError: The downloaded default VAE (sd-vae-ft-mse-original) file was invalid! Bytes downloaded: $model_size\n\n"
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printf "\n\nError downloading the data files (weights) for the default VAE (sd-vae-ft-mse-original). Sorry about that, please try to:\n 1. Run this installer again.\n 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting\n 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\n 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues\nThanks!\n\n"
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read -p "Press any key to continue"
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exit
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fi
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else
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printf "\n\nError downloading the data files (weights) for the default VAE (sd-vae-ft-mse-original). Sorry about that, please try to:\n 1. Run this installer again.\n 2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting\n 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\n 4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues\nThanks!\n\n"
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read -p "Press any key to continue"
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exit
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fi
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fi
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if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
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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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208
ui/index.html
208
ui/index.html
@ -1,14 +1,15 @@
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<!DOCTYPE html>
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<html>
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<head>
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<title>Stable Diffusion UI</title>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<link rel="icon" type="image/png" href="/media/images/favicon-16x16.png" sizes="16x16">
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<link rel="icon" type="image/png" href="/media/images/favicon-32x32.png" sizes="32x32">
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<link rel="stylesheet" href="/media/css/fonts.css?v=1">
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<link rel="stylesheet" href="/media/css/themes.css?v=1">
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<link rel="stylesheet" href="/media/css/main.css?v=3">
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<link rel="stylesheet" href="/media/css/auto-save.css?v=2">
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<link rel="stylesheet" href="/media/css/modifier-thumbnails.css?v=2">
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<link rel="stylesheet" href="/media/css/themes.css?v=3">
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<link rel="stylesheet" href="/media/css/main.css?v=17">
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<link rel="stylesheet" href="/media/css/auto-save.css?v=5">
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<link rel="stylesheet" href="/media/css/modifier-thumbnails.css?v=4">
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<link rel="stylesheet" href="/media/css/fontawesome-all.min.css?v=1">
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<link rel="stylesheet" href="/media/css/drawingboard.min.css">
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<script src="/media/js/jquery-3.6.1.min.js"></script>
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@ -18,59 +19,39 @@
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<div id="container">
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<div id="top-nav">
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<div id="logo">
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<h1>Stable Diffusion UI <small>v2.3.5 <span id="updateBranchLabel"></span></small></h1>
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<h1>Stable Diffusion UI <small>v2.4.4 <span id="updateBranchLabel"></span></small></h1>
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</div>
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<ul id="top-nav-items">
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<li class="dropdown">
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<span><i class="fa fa-comments icon"></i> Help & Community</span>
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<ul id="community-links" class="dropdown-content">
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<li><a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/" target="_blank"><i class="fa-solid fa-book fa-fw"></i> User guide</a></li>
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<li><a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" target="_blank"><i class="fa-solid fa-circle-question fa-fw"></i> Usual problems and solutions</a></li>
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<li><a href="https://discord.com/invite/u9yhsFmEkB" target="_blank"><i class="fa-brands fa-discord fa-fw"></i> Discord user community</a></li>
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<li><a href="https://www.reddit.com/r/StableDiffusionUI/" target="_blank"><i class="fa-brands fa-reddit fa-fw"></i> Reddit community</a></li>
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<li><a href="https://github.com/cmdr2/stable-diffusion-ui" target="_blank"><i class="fa-brands fa-github fa-fw"></i> Source code on GitHub</a></li>
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</ul>
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</li>
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<li class="dropdown">
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<div id="server-status">
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<div id="server-status-color">●</div>
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<span id="server-status-msg">Stable Diffusion is starting..</span>
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</div>
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<div id="tab-container">
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<span id="tab-main" class="tab active">
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<span><i class="fa fa-image icon"></i> Generate</span>
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</span>
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<span id="tab-settings" class="tab">
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<span><i class="fa fa-gear icon"></i> Settings</span>
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<div id="system-settings" class="panel-box settings-box dropdown-content">
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<ul id="system-settings-entries">
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<li><b class="settings-subheader">System Settings</b></li>
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<br/>
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<li><label for="theme">Theme: </label><select id="theme" name="theme"><option value="theme-default">Default</option></select></li>
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<li><input id="save_to_disk" name="save_to_disk" type="checkbox"> <label for="save_to_disk">Automatically save to <input id="diskPath" name="diskPath" size="40" disabled></label></li>
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<li><input id="sound_toggle" name="sound_toggle" type="checkbox" checked> <label for="sound_toggle">Play sound on task completion</label></li>
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<li><input id="turbo" name="turbo" type="checkbox" checked> <label for="turbo">Turbo mode <small>(generates images faster, but uses an additional 1 GB of GPU memory)</small></label></li>
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<li><input id="use_cpu" name="use_cpu" type="checkbox"> <label for="use_cpu">Use CPU instead of GPU <small>(warning: this will be *very* slow)</small></label></li>
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<li><input id="use_full_precision" name="use_full_precision" type="checkbox"> <label for="use_full_precision">Use full precision <small>(for GPU-only. warning: this will consume more VRAM)</small></label></li>
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<li>
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<input id="auto_save_settings" name="auto_save_settings" checked type="checkbox">
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<label for="auto_save_settings">Automatically save settings <small>(settings restored on browser load)</small></label>
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<br/>
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<button id="configureSettingsSaveBtn">Configure</button>
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</li>
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<!-- <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> -->
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<br/>
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<li><input id="use_beta_channel" name="use_beta_channel" type="checkbox"> <label for="use_beta_channel">🔥Beta channel. Get the latest features immediately (but could be less stable). Please restart the program after changing this.</label></li>
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</ul>
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</div>
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</li>
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</ul>
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</span>
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<span id="tab-about" class="tab">
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<span><i class="fa fa-comments icon"></i> Help & Community</span>
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</span>
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</div>
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</div>
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<div class="flex-container">
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<div id="editor" class="col-fixed-10">
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<div id="server-status">
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<div id="server-status-color">●</div>
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<span id="server-status-msg">Stable Diffusion is starting..</span>
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</div>
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<div id="tab-content-wrapper">
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<div id="tab-content-main" class="tab-content active flex-container">
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<div id="editor">
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<div id="editor-inputs">
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<div id="editor-inputs-prompt" class="row">
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<label for="prompt"><b>Enter Prompt</b></label> <small>or</small> <button id="promptsFromFileBtn">Load from a file</button>
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<textarea id="prompt" class="col-free">a photograph of an astronaut riding a horse</textarea>
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<input id="prompt_from_file" name="prompt_from_file" type="file" /> <!-- hidden -->
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<label for="negative_prompt" class="collapsible" id="negative_prompt_handle">Negative Prompt <small>(optional)</small></label>
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<label for="negative_prompt" class="collapsible" id="negative_prompt_handle">
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Negative Prompt
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<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 right">Click to learn more about Negative Prompts</span></i></a>
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<small>(optional)</small>
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</label>
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<div class="collapsible-content">
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<input id="negative_prompt" name="negative_prompt" placeholder="list the things to remove from the image (e.g. fog, green)">
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</div>
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@ -87,7 +68,12 @@
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</div>
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<br/>
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<input id="enable_mask" name="enable_mask" type="checkbox"> <label for="enable_mask">In-Painting (beta) <small>(select the area which the AI will paint into)</small></label>
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<input id="enable_mask" name="enable_mask" type="checkbox">
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<label for="enable_mask">
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In-Painting (beta)
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<a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Inpainting" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">Click to learn more about InPainting</span></i></a>
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<small>(select the area which the AI will paint into)</small>
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</label>
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<div id="inpaintingEditor"></div>
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</div>
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</div>
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@ -101,19 +87,19 @@
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<button id="stopImage" class="secondaryButton">Stop All</button>
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</div>
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<div class="line-separator"> </div>
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<span class="line-separator"></span>
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<div id="editor-settings" class="panel-box settings-box">
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<div id="editor-settings" class="settings-box panel-box">
|
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<h4 class="collapsible">
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Image Settings
|
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<i id="reset-image-settings" class="fa-solid fa-arrow-rotate-left">
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<span class="simple-tooltip right">
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<i id="reset-image-settings" class="fa-solid fa-arrow-rotate-left section-button">
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<span class="simple-tooltip left">
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Reset Image Settings
|
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</span>
|
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</i>
|
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</h4>
|
||||
<ul id="editor-settings-entries" class="collapsible-content">
|
||||
<li><table>
|
||||
<div id="editor-settings-entries" class="collapsible-content">
|
||||
<div><table>
|
||||
<tr><b class="settings-subheader">Image Settings</b></tr>
|
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<tr class="pl-5"><td><label for="seed">Seed:</label></td><td><input id="seed" name="seed" size="10" value="30000" onkeypress="preventNonNumericalInput(event)"> <input id="random_seed" name="random_seed" type="checkbox" checked><label for="random_seed">Random</label></td></tr>
|
||||
<tr class="pl-5"><td><label for="num_outputs_total">Number of Images:</label></td><td><input id="num_outputs_total" name="num_outputs_total" value="1" size="1" onkeypress="preventNonNumericalInput(event)"> <label><small>(total)</small></label> <input id="num_outputs_parallel" name="num_outputs_parallel" value="1" size="1" onkeypress="preventNonNumericalInput(event)"> <label for="num_outputs_parallel"><small>(in parallel)</small></label></td></tr>
|
||||
@ -121,6 +107,13 @@
|
||||
<select id="stable_diffusion_model" name="stable_diffusion_model">
|
||||
<!-- <option value="sd-v1-4" selected>sd-v1-4</option> -->
|
||||
</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 right">Click to learn more about custom models</span></i></a>
|
||||
</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 right">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">
|
||||
@ -133,6 +126,7 @@
|
||||
<option value="dpm2_a">dpm2_a</option>
|
||||
<option value="lms">lms</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 right">Click to learn more about samplers</span></i></a>
|
||||
</td></tr>
|
||||
<tr class="pl-5"><td><label>Image Size: </label></td><td>
|
||||
<select id="width" name="width" value="512">
|
||||
@ -189,12 +183,11 @@
|
||||
<option value="png">png</option>
|
||||
</select>
|
||||
</td></tr>
|
||||
</li></table>
|
||||
|
||||
<br/>
|
||||
</table></div>
|
||||
|
||||
<div><ul>
|
||||
<li><b class="settings-subheader">Render Settings</b></li>
|
||||
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, slightly slower image creation)</small></label></li>
|
||||
<li class="pl-5"><input id="stream_image_progress" name="stream_image_progress" type="checkbox"> <label for="stream_image_progress">Show a live preview <small>(uses more VRAM, and slower image creation)</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>
|
||||
@ -204,12 +197,19 @@
|
||||
</select>
|
||||
</li>
|
||||
<li class="pl-5"><input id="show_only_filtered_image" name="show_only_filtered_image" type="checkbox" checked> <label for="show_only_filtered_image">Show only the corrected/upscaled image</label></li>
|
||||
</ul>
|
||||
</ul></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="editor-modifiers" class="panel-box">
|
||||
<button id="modifier-settings-btn" title="Add custom modifiers"><i class="fa fa-gear"></i></button>
|
||||
<h4 class="collapsible">Image Modifiers (art styles, tags etc)</h4>
|
||||
<h4 class="collapsible">
|
||||
Image Modifiers (art styles, tags etc)
|
||||
<i id="modifier-settings-btn" class="fa-solid fa-gear section-button">
|
||||
<span class="simple-tooltip left">
|
||||
Add Custom Modifiers
|
||||
</span>
|
||||
</i>
|
||||
</h4>
|
||||
<div id="editor-modifiers-entries" class="collapsible-content">
|
||||
<div id="editor-modifiers-entries-toolbar">
|
||||
<label for="preview-image">Image Style:</label>
|
||||
@ -236,21 +236,76 @@
|
||||
<button id="clear-all-previews" class="secondaryButton"><i class="fa-solid fa-trash-can"></i> Clear All</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="save-settings-config" style="display:none">
|
||||
<div id="tab-content-settings" class="tab-content">
|
||||
<div id="system-settings" class="tab-content-inner">
|
||||
<h1>System Settings</h1>
|
||||
<table class="form-table"></table>
|
||||
<br/>
|
||||
<button id="save-system-settings-btn" class="primaryButton">Save</button>
|
||||
<br/><br/>
|
||||
<div>
|
||||
<h3><i class="fa fa-microchip icon"></i> System Info</h3>
|
||||
<div id="system-info"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="tab-content-about" class="tab-content">
|
||||
<div class="tab-content-inner">
|
||||
<div class="float-container">
|
||||
<div class="float-child">
|
||||
<h1>Help</h1>
|
||||
<ul id="help-links">
|
||||
<li><span class="help-section">Using the software</span>
|
||||
<ul>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/How-To-Use" target="_blank"><i class="fa-solid fa-book fa-fw"></i> How to use</a>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/UI-Overview" target="_blank"><i class="fa-solid fa-list fa-fw"></i> UI Overview</a>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Writing-Prompts" target="_blank"><i class="fa-solid fa-pen-to-square fa-fw"></i> Writing prompts</a>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Inpainting" target="_blank"><i class="fa-solid fa-paintbrush fa-fw"></i> Inpainting</a>
|
||||
</ul>
|
||||
|
||||
<li><span class="help-section">Installation</span>
|
||||
<ul>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" target="_blank"><i class="fa-solid fa-circle-question fa-fw"></i> Troubleshooting</a>
|
||||
</ul>
|
||||
|
||||
<li><span class="help-section">Downloadable Content</span>
|
||||
<ul>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Custom-Models" target="_blank"><i class="fa-solid fa-images fa-fw"></i> Custom Models</a>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/UI-Plugins" target="_blank"><i class="fa-solid fa-puzzle-piece fa-fw"></i> UI Plugins</a>
|
||||
<li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/VAE-Variational-Auto-Encoder" target="_blank"><i class="fa-solid fa-hand-sparkles fa-fw"></i> VAE Variational Auto Encoder</a>
|
||||
</ul>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="float-child">
|
||||
<h1>Community</h1>
|
||||
<ul id="community-links">
|
||||
<li><a href="https://discord.com/invite/u9yhsFmEkB" target="_blank"><i class="fa-brands fa-discord fa-fw"></i> Discord user community</a></li>
|
||||
<li><a href="https://www.reddit.com/r/StableDiffusionUI/" target="_blank"><i class="fa-brands fa-reddit fa-fw"></i> Reddit community</a></li>
|
||||
<li><a href="https://github.com/cmdr2/stable-diffusion-ui" target="_blank"><i class="fa-brands fa-github fa-fw"></i> Source code on GitHub</a></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div id="save-settings-config" class="popup">
|
||||
<div>
|
||||
<span id="save-settings-config-close-btn">X</span>
|
||||
<i class="close-button fa-solid fa-xmark"></i>
|
||||
<h1>Save Settings Configuration</h1>
|
||||
<p>Select which settings should be remembered when restarting the browser</p>
|
||||
<table id="save-settings-config-table">
|
||||
<table id="save-settings-config-table" class="form-table">
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="modifier-settings-config" style="display:none">
|
||||
<div id="modifier-settings-config" class="popup">
|
||||
<div>
|
||||
<span id="modifier-settings-config-close-btn">X</span>
|
||||
<i class="close-button fa-solid fa-xmark"></i>
|
||||
<h1>Modifier Settings</h1>
|
||||
<p>Set your custom modifiers (one per line)</p>
|
||||
<textarea id="custom-modifiers-input" placeholder="Enter your custom modifiers, one-per-line"></textarea>
|
||||
@ -258,9 +313,9 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="line-separator"> </div>
|
||||
|
||||
<div id="footer" class="panel-box">
|
||||
<div id="footer-spacer"></div>
|
||||
<div id="footer">
|
||||
<div class="line-separator"> </div>
|
||||
<p>If you found this project useful and want to help keep it alive, please <a href="https://ko-fi.com/cmdr2_stablediffusion_ui" target="_blank"><img src="/media/images/kofi.png" id="coffeeButton"></a> to help cover the cost of development and maintenance! Thank you for your support!</p>
|
||||
<p>Please feel free to join the <a href="https://discord.com/invite/u9yhsFmEkB" target="_blank">discord community</a> or <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">
|
||||
@ -272,13 +327,15 @@
|
||||
</div>
|
||||
</body>
|
||||
|
||||
<script src="media/js/parameters.js?v=8"></script>
|
||||
<script src="media/js/plugins.js?v=1"></script>
|
||||
<script src="media/js/utils.js?v=5"></script>
|
||||
<script src="media/js/utils.js?v=6"></script>
|
||||
<script src="media/js/inpainting-editor.js?v=1"></script>
|
||||
<script src="media/js/image-modifiers.js?v=3"></script>
|
||||
<script src="media/js/auto-save.js?v=2.3"></script>
|
||||
<script src="media/js/main.js?v=5"></script>
|
||||
<script src="media/js/themes.js?v=2"></script>
|
||||
<script src="media/js/image-modifiers.js?v=6"></script>
|
||||
<script src="media/js/auto-save.js?v=8"></script>
|
||||
<script src="media/js/main.js?v=22.1"></script>
|
||||
<script src="media/js/themes.js?v=4"></script>
|
||||
<script src="media/js/dnd.js?v=9"></script>
|
||||
<script>
|
||||
async function init() {
|
||||
await initSettings()
|
||||
@ -287,6 +344,7 @@ async function init() {
|
||||
await getAppConfig()
|
||||
await loadModifiers()
|
||||
await loadUIPlugins()
|
||||
await getDevices()
|
||||
|
||||
setInterval(healthCheck, HEALTH_PING_INTERVAL * 1000)
|
||||
healthCheck()
|
||||
|
||||
@ -6,69 +6,43 @@
|
||||
display: none;
|
||||
}
|
||||
|
||||
#save-settings-config {
|
||||
position: absolute;
|
||||
background: rgba(32, 33, 36, 50%);
|
||||
top: 0px;
|
||||
left: 0px;
|
||||
right: 0px;
|
||||
bottom: 0px;
|
||||
z-index: 1000;
|
||||
}
|
||||
|
||||
#save-settings-config > div {
|
||||
background: var(--background-color3);
|
||||
max-width: 600px;
|
||||
margin: auto;
|
||||
margin-top: 50px;
|
||||
border-radius: 6px;
|
||||
padding: 30px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
#save-settings-config-table {
|
||||
.form-table {
|
||||
margin: auto;
|
||||
}
|
||||
|
||||
#save-settings-config-table th {
|
||||
.form-table th {
|
||||
padding-top: 15px;
|
||||
padding-bottom: 5px;
|
||||
}
|
||||
|
||||
#save-settings-config-table td:first-child,
|
||||
#save-settings-config-table th:first-child {
|
||||
.form-table td:first-child > *,
|
||||
.form-table th:first-child > * {
|
||||
float: right;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
#save-settings-config-table td:last-child,
|
||||
#save-settings-config-table th:last-child {
|
||||
.form-table td:last-child > *,
|
||||
.form-table th:last-child > * {
|
||||
float: left;
|
||||
}
|
||||
|
||||
#save-settings-config-table td small {
|
||||
.form-table small {
|
||||
color: rgb(153, 153, 153);
|
||||
}
|
||||
|
||||
#save-settings-config-close-btn {
|
||||
float: right;
|
||||
cursor: pointer;
|
||||
padding: 10px;
|
||||
transform: translate(50%, -50%) scaleX(130%);
|
||||
#system-settings .form-table td {
|
||||
height: 24px;
|
||||
}
|
||||
|
||||
#reset-image-settings {
|
||||
cursor: pointer;
|
||||
float: right;
|
||||
padding: 8px;
|
||||
opacity: 1;
|
||||
transition: opacity 0.5;
|
||||
#system-settings .form-table td:last-child div {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
#system-settings .form-table td:last-child div > :not([type="checkbox"]):first-child {
|
||||
margin-left: 3px;
|
||||
}
|
||||
|
||||
.collapsible:not(.active) #reset-image-settings {
|
||||
display: none;
|
||||
}
|
||||
|
||||
#reset-image-settings.hidden {
|
||||
opacity: 0;
|
||||
pointer-events: none;
|
||||
}
|
||||
#system-settings .form-table td:last-child div small {
|
||||
padding-left: 5px;
|
||||
text-align: left;
|
||||
}
|
||||
@ -8,6 +8,7 @@ html {
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
font-size: 11pt;
|
||||
background-color: var(--background-color1);
|
||||
color: var(--text-color);
|
||||
@ -26,9 +27,10 @@ label {
|
||||
height: 65pt;
|
||||
font-size: 13px;
|
||||
margin-bottom: 6px;
|
||||
margin-top: 5px;
|
||||
display: block;
|
||||
}
|
||||
.image_preview_container {
|
||||
/* display: none; */
|
||||
margin-top: 10pt;
|
||||
}
|
||||
.image_clear_btn {
|
||||
@ -64,17 +66,17 @@ label {
|
||||
font-size: small;
|
||||
padding-bottom: 3pt;
|
||||
}
|
||||
#progressBar {
|
||||
font-size: small;
|
||||
}
|
||||
#footer {
|
||||
font-size: small;
|
||||
padding-left: 10pt;
|
||||
padding: 10pt;
|
||||
background: none;
|
||||
}
|
||||
#footer-legal {
|
||||
font-size: 8pt;
|
||||
}
|
||||
#footer-spacer {
|
||||
flex: 0.7
|
||||
}
|
||||
.imgSeedLabel {
|
||||
font-size: 0.8em;
|
||||
background-color: var(--background-color2);
|
||||
@ -107,33 +109,42 @@ label {
|
||||
margin-bottom: 7px;
|
||||
}
|
||||
#container {
|
||||
width: 95%;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
@media screen and (max-width: 1800px) {
|
||||
#container {
|
||||
width: 100%;
|
||||
}
|
||||
min-height: 100vh;
|
||||
width: 100%;
|
||||
margin: 0px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
#logo small {
|
||||
font-size: 11pt;
|
||||
}
|
||||
#editor {
|
||||
padding: 5px;
|
||||
background: var(--background-color1);
|
||||
padding: 16px;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
flex: 0 0 370pt;
|
||||
}
|
||||
#editor label {
|
||||
font-weight: normal;
|
||||
}
|
||||
#editor h4 {
|
||||
margin: 0px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
#editor .collapsible-content {
|
||||
width: 100%;
|
||||
}
|
||||
.settings-box label small {
|
||||
color: rgb(153, 153, 153);
|
||||
margin-right: 10px;
|
||||
}
|
||||
#preview {
|
||||
padding: 5px;
|
||||
padding: 8px;
|
||||
background: var(--background-color1);
|
||||
}
|
||||
#editor-inputs {
|
||||
margin-bottom: 20px;
|
||||
#preview .collapsible-content {
|
||||
padding: 0px 15px;
|
||||
}
|
||||
#editor-inputs-prompt {
|
||||
flex: 1;
|
||||
@ -151,7 +162,7 @@ label {
|
||||
#makeImage {
|
||||
flex: 0 0 70px;
|
||||
background: var(--accent-color);
|
||||
border: var(--make-image-border);
|
||||
border: var(--primary-button-border);
|
||||
color: rgb(255, 221, 255);
|
||||
width: 100%;
|
||||
height: 30pt;
|
||||
@ -168,6 +179,7 @@ label {
|
||||
height: 30pt;
|
||||
border-radius: 6px;
|
||||
display: none;
|
||||
margin-top: 2pt;
|
||||
}
|
||||
#stopImage:hover {
|
||||
background: rgb(177, 27, 0);
|
||||
@ -176,12 +188,6 @@ label {
|
||||
display: flex;
|
||||
width: 100%;
|
||||
}
|
||||
.col-50 {
|
||||
flex: 50%;
|
||||
}
|
||||
.col-fixed-10 {
|
||||
flex: 0 0 350pt;
|
||||
}
|
||||
.col-free {
|
||||
flex: 1;
|
||||
}
|
||||
@ -189,7 +195,7 @@ label {
|
||||
cursor: pointer;
|
||||
}
|
||||
.collapsible-content {
|
||||
display: none;
|
||||
display: block;
|
||||
padding-left: 15px;
|
||||
}
|
||||
.collapsible-content h5 {
|
||||
@ -201,50 +207,40 @@ label {
|
||||
color: white;
|
||||
padding-right: 5px;
|
||||
}
|
||||
.panel-box {
|
||||
background: var(--background-color2);
|
||||
border: 1px solid var(--background-color3);
|
||||
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);
|
||||
.collapsible:not(.active) ~ .collapsible-content {
|
||||
display: none !important;
|
||||
}
|
||||
.panel-box h4 {
|
||||
margin: 0;
|
||||
padding: 2px 0;
|
||||
#editor-modifiers {
|
||||
max-width: 600px;
|
||||
overflow-y: auto;
|
||||
overflow-x: hidden;
|
||||
}
|
||||
#editor-modifiers .editor-modifiers-leaf {
|
||||
padding-top: 10pt;
|
||||
padding-bottom: 10pt;
|
||||
}
|
||||
#preview {
|
||||
margin-left: 10pt;
|
||||
}
|
||||
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);
|
||||
background: var(--background-color3);
|
||||
height: 1pt;
|
||||
margin: 15pt 0;
|
||||
margin: 16px 0px;
|
||||
}
|
||||
#editor-inputs-tags-container {
|
||||
margin-top: 5pt;
|
||||
display: none;
|
||||
}
|
||||
#server-status {
|
||||
display: inline;
|
||||
float: right;
|
||||
transform: translateY(-5pt);
|
||||
position: absolute;
|
||||
right: 16px;
|
||||
top: 50%;
|
||||
transform: translateY(-50%);
|
||||
text-align: right;
|
||||
}
|
||||
#server-status-color {
|
||||
/* width: 8pt;
|
||||
height: 8pt;
|
||||
border-radius: 4pt; */
|
||||
font-size: 14pt;
|
||||
color: rgb(200, 139, 0);
|
||||
/* background-color: rgb(197, 1, 1); */
|
||||
/* transform: translateY(15%); */
|
||||
display: inline;
|
||||
}
|
||||
#server-status-msg {
|
||||
@ -288,16 +284,19 @@ img {
|
||||
}
|
||||
|
||||
#top-nav {
|
||||
padding-top: 3pt;
|
||||
padding-bottom: 15pt;
|
||||
position: relative;
|
||||
background: var(--background-color4);
|
||||
display: flex;
|
||||
}
|
||||
#top-nav .icon {
|
||||
.tab .icon {
|
||||
padding-right: 4pt;
|
||||
font-size: 14pt;
|
||||
transform: translateY(1pt);
|
||||
}
|
||||
#logo {
|
||||
display: inline;
|
||||
padding: 12px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
#logo h1 {
|
||||
display: inline;
|
||||
@ -311,6 +310,8 @@ img {
|
||||
float: left;
|
||||
display: inline;
|
||||
padding-left: 20pt;
|
||||
}
|
||||
#top-nav-items > li:first-child {
|
||||
cursor: default;
|
||||
}
|
||||
#initial-text {
|
||||
@ -324,26 +325,12 @@ img {
|
||||
.pl-5 {
|
||||
padding-left: 5pt;
|
||||
}
|
||||
#system-settings {
|
||||
width: 360pt;
|
||||
transform: translateX(-100%) translateX(70pt);
|
||||
|
||||
padding-top: 10pt;
|
||||
padding-bottom: 10pt;
|
||||
}
|
||||
#system-settings ul {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
#system-settings li {
|
||||
padding-left: 5pt;
|
||||
}
|
||||
#community-links {
|
||||
display: inline-block;
|
||||
list-style-type: none;
|
||||
margin: 0;
|
||||
padding: 12pt;
|
||||
padding-bottom: 0pt;
|
||||
transform: translateX(-15%);
|
||||
text-align: left;
|
||||
margin: auto;
|
||||
padding: 0px;
|
||||
}
|
||||
#community-links li {
|
||||
padding-bottom: 12pt;
|
||||
@ -357,6 +344,35 @@ img {
|
||||
color: var(--text-color);
|
||||
text-decoration: none;
|
||||
}
|
||||
.float-child h1 {
|
||||
border-bottom: var(--button-border);
|
||||
}
|
||||
#help-links {
|
||||
display: inline-block;
|
||||
list-style-type: none;
|
||||
text-align: left;
|
||||
margin: auto;
|
||||
padding: 0px;
|
||||
}
|
||||
#help-links li {
|
||||
padding-bottom: 12pt;
|
||||
display: block;
|
||||
font-size: 10pt;
|
||||
}
|
||||
#help-links li .fa-fw {
|
||||
padding-right: 2pt;
|
||||
}
|
||||
#help-links li a {
|
||||
color: var(--text-color);
|
||||
text-decoration: none;
|
||||
}
|
||||
#help-links li ul {
|
||||
padding-inline-start: 10px;
|
||||
margin-top: 8px;
|
||||
}
|
||||
.help-section {
|
||||
font-size: 130%;
|
||||
}
|
||||
.dropdown {
|
||||
overflow: hidden;
|
||||
}
|
||||
@ -383,6 +399,9 @@ img {
|
||||
border-radius: 5pt;
|
||||
box-shadow: 0 20px 28px 0 rgba(0, 0, 0, 0.15), 0 6px 20px 0 rgba(0, 0, 0, 0.15);
|
||||
}
|
||||
.imageTaskContainer > div > .collapsible-handle {
|
||||
display: none;
|
||||
}
|
||||
.taskStatusLabel {
|
||||
float: left;
|
||||
font-size: 8pt;
|
||||
@ -402,6 +421,12 @@ img {
|
||||
border: 1px solid rgb(107, 75, 0);
|
||||
color:rgb(255, 242, 211)
|
||||
}
|
||||
.primaryButton {
|
||||
flex: 0 0 70px;
|
||||
background: var(--accent-color);
|
||||
border: var(--primary-button-border);
|
||||
color: rgb(255, 221, 255);
|
||||
}
|
||||
.secondaryButton {
|
||||
background: rgb(132, 8, 0);
|
||||
border: 1px solid rgb(122, 29, 0);
|
||||
@ -433,6 +458,8 @@ img {
|
||||
#init_image_preview {
|
||||
max-width: 150px;
|
||||
max-height: 150px;
|
||||
height: 100%;
|
||||
width: 100%;
|
||||
object-fit: contain;
|
||||
border-radius: 6px;
|
||||
transition: all 1s ease-in-out;
|
||||
@ -441,6 +468,7 @@ img {
|
||||
#init_image_preview:hover {
|
||||
max-width: 500px;
|
||||
max-height: 1000px;
|
||||
|
||||
transition: all 1s 0.5s ease-in-out;
|
||||
}
|
||||
|
||||
@ -462,6 +490,20 @@ img {
|
||||
border-radius: 6px 0px;
|
||||
}
|
||||
|
||||
#editor-settings-entries {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
#editor-settings-entries > div {
|
||||
margin-top: 15px;
|
||||
}
|
||||
|
||||
#editor-settings-entries ul {
|
||||
margin: 0px;
|
||||
padding: 0px;
|
||||
}
|
||||
|
||||
#editor-settings-entries table td {
|
||||
padding: 0px;
|
||||
line-height: 28px;
|
||||
@ -477,6 +519,7 @@ img {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
/* INPUTS STYLING */
|
||||
button,
|
||||
input[type="file"],
|
||||
input[type="checkbox"],
|
||||
@ -536,13 +579,16 @@ input::file-selector-button {
|
||||
height: 19px;
|
||||
}
|
||||
|
||||
/* MOBILE SUPPORT */
|
||||
@media screen and (max-width: 700px) {
|
||||
#top-nav {
|
||||
flex-direction: column;
|
||||
}
|
||||
body {
|
||||
margin: 0px;
|
||||
}
|
||||
#container {
|
||||
margin: 0px;
|
||||
padding: 10px
|
||||
}
|
||||
.flex-container {
|
||||
flex-direction: column;
|
||||
@ -571,21 +617,98 @@ input::file-selector-button {
|
||||
left: 0px;
|
||||
right: 0px;
|
||||
}
|
||||
#editor {
|
||||
padding: 16px 8px;
|
||||
}
|
||||
.tab-content-inner {
|
||||
margin: 0px;
|
||||
}
|
||||
.tab {
|
||||
font-size: 0;
|
||||
}
|
||||
.tab .icon {
|
||||
padding-right: 0px;
|
||||
}
|
||||
#server-status {
|
||||
display: none;
|
||||
}
|
||||
.popup > div {
|
||||
padding-left: 5px !important;
|
||||
padding-right: 5px !important;
|
||||
}
|
||||
.popup > div input, .popup > div select {
|
||||
max-width: 40vw;
|
||||
}
|
||||
.popup .close-button {
|
||||
padding: 0px !important;
|
||||
margin: 24px !important;
|
||||
}
|
||||
.simple-tooltip.right {
|
||||
right: initial;
|
||||
left: 0px;
|
||||
top: 50%;
|
||||
transform: translate(calc(-100% + 15%), -50%);
|
||||
}
|
||||
:hover > .simple-tooltip.right {
|
||||
transform: translate(100%, -50%);
|
||||
}
|
||||
}
|
||||
|
||||
@media (min-width: 700px) {
|
||||
/* #editor {
|
||||
max-width: 480px;
|
||||
} */
|
||||
.float-container {
|
||||
padding: 20px;
|
||||
}
|
||||
.float-child {
|
||||
width: 50%;
|
||||
float: left;
|
||||
padding: 20px;
|
||||
}
|
||||
}
|
||||
|
||||
.help-btn {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
#promptsFromFileBtn {
|
||||
font-size: 9pt;
|
||||
}
|
||||
|
||||
#reset-image-settings {
|
||||
.section-button {
|
||||
position: relative;
|
||||
transform: translateY(-13%);
|
||||
}
|
||||
.collapsible:not(.active) #copy-image-settings {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.section-button {
|
||||
cursor: pointer;
|
||||
float: right;
|
||||
padding: 8px;
|
||||
opacity: 1;
|
||||
transition: opacity 0.5;
|
||||
}
|
||||
|
||||
.section-button {
|
||||
cursor: pointer;
|
||||
float: right;
|
||||
padding: 8px;
|
||||
opacity: 1;
|
||||
transition: opacity 0.5;
|
||||
}
|
||||
|
||||
.collapsible:not(.active) .section-button {
|
||||
display: none;
|
||||
}
|
||||
|
||||
/* SIMPLE TOOTIP */
|
||||
.simple-tooltip {
|
||||
border-radius: 3px;
|
||||
font-weight: bold;
|
||||
font-size: 16px;
|
||||
font-size: 12px;
|
||||
background-color: var(--background-color3);
|
||||
|
||||
visibility: hidden;
|
||||
@ -604,8 +727,6 @@ input::file-selector-button {
|
||||
visibility: visible;
|
||||
}
|
||||
}
|
||||
|
||||
/* position specific */
|
||||
.simple-tooltip.right {
|
||||
right: 0px;
|
||||
top: 50%;
|
||||
@ -641,3 +762,154 @@ input::file-selector-button {
|
||||
:hover > .simple-tooltip.bottom {
|
||||
transform: translate(-50%, 100%);
|
||||
}
|
||||
|
||||
/* PROGRESS BAR */
|
||||
.progress-bar {
|
||||
background: var(--background-color3);
|
||||
border-radius: 4px;
|
||||
border: 2px solid var(--background-color3);
|
||||
height: 16px;
|
||||
position: relative;
|
||||
transition: 0.25s 1s border, 0.25s 1s height;
|
||||
}
|
||||
.progress-bar > div {
|
||||
background: var(--accent-color);
|
||||
border-radius: 4px;
|
||||
position: absolute;
|
||||
left: 0;
|
||||
top: 0;
|
||||
bottom: 0;
|
||||
width: 0%;
|
||||
transition: width 1s ease-in-out;
|
||||
}
|
||||
.progress-bar.active {
|
||||
background: repeating-linear-gradient(-65deg,
|
||||
var(--background-color2),
|
||||
var(--background-color2) 4px,
|
||||
var(--background-color3) 5px,
|
||||
var(--background-color3) 9px,
|
||||
var(--background-color2) 10px);
|
||||
background-size: 200% auto;
|
||||
background-position: 0 100%;
|
||||
animation: progress-anim 2s infinite;
|
||||
animation-fill-mode: forwards;
|
||||
animation-timing-function: linear;
|
||||
}
|
||||
|
||||
@keyframes progress-anim {
|
||||
0% { background-position: -55px 0; }
|
||||
100% { background-position: 0 0; }
|
||||
}
|
||||
|
||||
/* POPUPS */
|
||||
.popup:not(.active) {
|
||||
visibility: hidden;
|
||||
opacity: 0;
|
||||
}
|
||||
|
||||
.popup {
|
||||
position: absolute;
|
||||
background: rgba(32, 33, 36, 50%);
|
||||
top: 0px;
|
||||
left: 0px;
|
||||
right: 0px;
|
||||
bottom: 0px;
|
||||
z-index: 1000;
|
||||
opacity: 1;
|
||||
transition: 0s visibility, 0.3s opacity;
|
||||
}
|
||||
|
||||
@media only screen and (min-height: 1050px) {
|
||||
.popup {
|
||||
position: fixed;
|
||||
}
|
||||
}
|
||||
|
||||
.popup > div {
|
||||
position: relative;
|
||||
background: var(--background-color2);
|
||||
border: solid 1px var(--background-color3);
|
||||
max-width: 700px;
|
||||
margin: auto;
|
||||
margin-top: 50px;
|
||||
border-radius: 6px;
|
||||
padding: 30px;
|
||||
text-align: center;
|
||||
box-shadow: 0px 0px 30px black;
|
||||
}
|
||||
|
||||
.popup .close-button {
|
||||
position: absolute;
|
||||
right: 0px;
|
||||
top: 0px;
|
||||
transform: scale(150%);
|
||||
cursor: pointer;
|
||||
padding: 24px;
|
||||
}
|
||||
|
||||
/* TABS */
|
||||
#tab-container {
|
||||
display: flex;
|
||||
align-items: flex-end;
|
||||
}
|
||||
|
||||
.tab {
|
||||
padding: 8px 16px;
|
||||
border-radius: 4px 4px 0px 0px;
|
||||
margin-left: 8px;
|
||||
cursor: pointer;
|
||||
background: var(--background-color1);
|
||||
opacity: 50%;
|
||||
transition: opacity 0.25s;
|
||||
}
|
||||
|
||||
.tab:hover {
|
||||
opacity: 75%;
|
||||
}
|
||||
|
||||
.tab.active {
|
||||
opacity: 100%;
|
||||
}
|
||||
|
||||
.tab-content:not(.active) {
|
||||
display: none;
|
||||
}
|
||||
|
||||
#tab-content-wrapper {
|
||||
border-top: 8px solid var(--background-color1);
|
||||
}
|
||||
|
||||
.tab-content-inner {
|
||||
margin: auto;
|
||||
max-width: 600px;
|
||||
text-align: center;
|
||||
padding: 20px 10px;
|
||||
}
|
||||
|
||||
.panel-box {
|
||||
background: var(--background-color2);
|
||||
border: 1px solid var(--background-color3);
|
||||
border-radius: 7px;
|
||||
padding: 7px;
|
||||
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);
|
||||
}
|
||||
|
||||
i.active {
|
||||
background: var(--accent-color);
|
||||
}
|
||||
#system-info {
|
||||
max-width: 800px;
|
||||
font-size: 10pt;
|
||||
}
|
||||
#system-info .value {
|
||||
text-align: left;
|
||||
padding-left: 10pt;
|
||||
}
|
||||
#system-info label {
|
||||
float: right;
|
||||
font-weight: bold;
|
||||
}
|
||||
#save-system-settings-btn {
|
||||
padding: 4pt 8pt;
|
||||
}
|
||||
|
||||
@ -217,32 +217,6 @@
|
||||
#modifier-settings-btn {
|
||||
float: right;
|
||||
}
|
||||
|
||||
#modifier-settings-config {
|
||||
position: fixed;
|
||||
background: rgba(32, 33, 36, 50%);
|
||||
top: 0px;
|
||||
left: 0px;
|
||||
width: 100vw;
|
||||
height: 100vh;
|
||||
z-index: 1000;
|
||||
}
|
||||
|
||||
#modifier-settings-config > div {
|
||||
background: var(--background-color2);
|
||||
max-width: 600px;
|
||||
margin: auto;
|
||||
margin-top: 100px;
|
||||
border-radius: 6px;
|
||||
padding: 30px;
|
||||
text-align: center;
|
||||
}
|
||||
#modifier-settings-config-close-btn {
|
||||
float: right;
|
||||
cursor: pointer;
|
||||
padding: 10px;
|
||||
transform: translate(50%, -50%) scaleX(130%);
|
||||
}
|
||||
#modifier-settings-config textarea {
|
||||
width: 90%;
|
||||
height: 150px;
|
||||
|
||||
@ -23,12 +23,12 @@
|
||||
--input-border-size: 1px;
|
||||
--accent-color: hsl(var(--accent-hue), 100%, var(--accent-lightness));
|
||||
--accent-color-hover: hsl(var(--accent-hue), 100%, var(--accent-lightness-hover));
|
||||
--make-image-border: 2px solid hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) - 21%));
|
||||
--primary-button-border: none;
|
||||
}
|
||||
|
||||
.theme-light {
|
||||
--background-color1: white;
|
||||
--background-color2: #dddddd;
|
||||
--background-color2: #ececec;
|
||||
--background-color3: #e7e9eb;
|
||||
--background-color4: #cccccc;
|
||||
|
||||
@ -47,7 +47,7 @@
|
||||
|
||||
--accent-hue: 235;
|
||||
--accent-lightness: 65%;
|
||||
--make-image-border: none;
|
||||
--primary-button-border: none;
|
||||
|
||||
--button-color: var(--accent-color);
|
||||
--button-border: none;
|
||||
@ -61,7 +61,7 @@
|
||||
.theme-cool-blue {
|
||||
--main-hue: 222;
|
||||
--main-saturation: 18%;
|
||||
--value-base: 19%;
|
||||
--value-base: 18%;
|
||||
--value-step: 3%;
|
||||
--background-color1: hsl(var(--main-hue), var(--main-saturation), var(--value-base));
|
||||
--background-color2: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (1 * var(--value-step))));
|
||||
@ -69,7 +69,7 @@
|
||||
--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
|
||||
|
||||
--accent-hue: 212;
|
||||
--make-image-border: none;
|
||||
--primary-button-border: none;
|
||||
|
||||
--button-color: var(--accent-color);
|
||||
--button-border: none;
|
||||
@ -91,7 +91,7 @@
|
||||
--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (2 * var(--value-step))));
|
||||
--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
|
||||
|
||||
--make-image-border: none;
|
||||
--primary-button-border: none;
|
||||
|
||||
--button-color: var(--accent-color);
|
||||
--button-border: none;
|
||||
@ -110,9 +110,9 @@
|
||||
--background-color1: hsl(var(--main-hue), var(--main-saturation), var(--value-base));
|
||||
--background-color2: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (1 * var(--value-step))));
|
||||
--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (2 * var(--value-step))));
|
||||
--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (3 * var(--value-step))));
|
||||
--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (1.4 * var(--value-step))));
|
||||
|
||||
--make-image-border: none;
|
||||
--primary-button-border: none;
|
||||
|
||||
--button-color: var(--accent-color);
|
||||
--button-border: none;
|
||||
@ -134,7 +134,7 @@
|
||||
--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
|
||||
|
||||
--accent-hue: 212;
|
||||
--make-image-border: none;
|
||||
--primary-button-border: none;
|
||||
|
||||
--button-color: var(--accent-color);
|
||||
--button-border: none;
|
||||
|
||||
@ -13,6 +13,7 @@ const SETTINGS_IDS_LIST = [
|
||||
"num_outputs_total",
|
||||
"num_outputs_parallel",
|
||||
"stable_diffusion_model",
|
||||
"vae_model",
|
||||
"sampler",
|
||||
"width",
|
||||
"height",
|
||||
@ -33,7 +34,6 @@ const SETTINGS_IDS_LIST = [
|
||||
"diskPath",
|
||||
"sound_toggle",
|
||||
"turbo",
|
||||
"use_cpu",
|
||||
"use_full_precision",
|
||||
"auto_save_settings"
|
||||
]
|
||||
@ -52,7 +52,9 @@ const SETTINGS_SECTIONS = [ // gets the "keys" property filled in with an ordere
|
||||
async function initSettings() {
|
||||
SETTINGS_IDS_LIST.forEach(id => {
|
||||
var element = document.getElementById(id)
|
||||
var label = document.querySelector(`label[for='${element.id}']`)
|
||||
if (!element) {
|
||||
console.error(`Missing settings element ${id}`)
|
||||
}
|
||||
SETTINGS[id] = {
|
||||
key: id,
|
||||
element: element,
|
||||
@ -68,7 +70,8 @@ async function initSettings() {
|
||||
SETTINGS_SECTIONS.forEach(section => {
|
||||
var name = section.name
|
||||
var element = document.getElementById(section.id)
|
||||
var children = Array.from(element.querySelectorAll(unsorted_settings_ids.map(id => `#${id}`).join(",")))
|
||||
var unsorted_ids = unsorted_settings_ids.map(id => `#${id}`).join(",")
|
||||
var children = unsorted_ids == "" ? [] : Array.from(element.querySelectorAll(unsorted_ids));
|
||||
section.keys = []
|
||||
children.forEach(e => {
|
||||
section.keys.push(e.id)
|
||||
@ -126,10 +129,11 @@ function loadSettings() {
|
||||
return
|
||||
}
|
||||
CURRENTLY_LOADING_SETTINGS = true
|
||||
saved_settings.map(saved_setting => {
|
||||
saved_settings.forEach(saved_setting => {
|
||||
var setting = SETTINGS[saved_setting.key]
|
||||
if (setting === undefined) {
|
||||
return
|
||||
if (!setting) {
|
||||
console.warn(`Attempted to load setting ${saved_setting.key}, but no setting found`);
|
||||
return null;
|
||||
}
|
||||
setting.ignore = saved_setting.ignore
|
||||
if (!setting.ignore) {
|
||||
@ -211,20 +215,22 @@ function fillSaveSettingsConfigTable() {
|
||||
})
|
||||
}
|
||||
|
||||
document.getElementById("save-settings-config-close-btn").addEventListener('click', () => {
|
||||
saveSettingsConfigOverlay.style.display = 'none'
|
||||
// configureSettingsSaveBtn
|
||||
|
||||
|
||||
|
||||
|
||||
var autoSaveSettings = document.getElementById("auto_save_settings")
|
||||
var configSettingsButton = document.createElement("button")
|
||||
configSettingsButton.textContent = "Configure"
|
||||
configSettingsButton.style.margin = "0px 5px"
|
||||
autoSaveSettings.insertAdjacentElement("afterend", configSettingsButton)
|
||||
autoSaveSettings.addEventListener("change", () => {
|
||||
configSettingsButton.style.display = autoSaveSettings.checked ? "block" : "none"
|
||||
})
|
||||
document.getElementById("configureSettingsSaveBtn").addEventListener('click', () => {
|
||||
configSettingsButton.addEventListener('click', () => {
|
||||
fillSaveSettingsConfigTable()
|
||||
saveSettingsConfigOverlay.style.display = 'block'
|
||||
})
|
||||
saveSettingsConfigOverlay.addEventListener('click', (event) => {
|
||||
if (event.target.id == saveSettingsConfigOverlay.id) {
|
||||
saveSettingsConfigOverlay.style.display = 'none'
|
||||
}
|
||||
})
|
||||
document.getElementById("save-settings-config-close-btn").addEventListener('click', () => {
|
||||
saveSettingsConfigOverlay.style.display = 'none'
|
||||
saveSettingsConfigOverlay.classList.add("active")
|
||||
})
|
||||
resetImageSettingsButton.addEventListener('click', event => {
|
||||
loadDefaultSettingsSection("editor-settings");
|
||||
@ -276,9 +282,11 @@ function tryLoadOldSettings() {
|
||||
Object.keys(individual_settings_map).forEach(localStorageKey => {
|
||||
var localStorageValue = localStorage.getItem(localStorageKey);
|
||||
if (localStorageValue !== null) {
|
||||
var setting = SETTINGS[individual_settings_map[localStorageKey]]
|
||||
if (setting == null || setting == undefined) {
|
||||
return
|
||||
let key = individual_settings_map[localStorageKey]
|
||||
var setting = SETTINGS[key]
|
||||
if (!setting) {
|
||||
console.warn(`Attempted to map old setting ${key}, but no setting found`);
|
||||
return null;
|
||||
}
|
||||
if (setting.element.type == "checkbox" && (typeof localStorageValue === "string" || localStorageValue instanceof String)) {
|
||||
localStorageValue = localStorageValue == "true"
|
||||
|
||||
469
ui/media/js/dnd.js
Normal file
469
ui/media/js/dnd.js
Normal file
@ -0,0 +1,469 @@
|
||||
"use strict" // Opt in to a restricted variant of JavaScript
|
||||
|
||||
const EXT_REGEX = /(?:\.([^.]+))?$/
|
||||
const TEXT_EXTENSIONS = ['txt', 'json']
|
||||
const IMAGE_EXTENSIONS = ['jpg', 'jpeg', 'png', 'bmp', 'tiff', 'tif', 'tga']
|
||||
|
||||
function parseBoolean(stringValue) {
|
||||
if (typeof stringValue === 'boolean') {
|
||||
return stringValue
|
||||
}
|
||||
if (typeof stringValue === 'number') {
|
||||
return stringValue !== 0
|
||||
}
|
||||
if (typeof stringValue !== 'string') {
|
||||
return false
|
||||
}
|
||||
switch(stringValue?.toLowerCase()?.trim()) {
|
||||
case "true":
|
||||
case "yes":
|
||||
case "on":
|
||||
case "1":
|
||||
return true;
|
||||
|
||||
case "false":
|
||||
case "no":
|
||||
case "off":
|
||||
case "0":
|
||||
case null:
|
||||
case undefined:
|
||||
return false;
|
||||
}
|
||||
try {
|
||||
return Boolean(JSON.parse(stringValue));
|
||||
} catch {
|
||||
return Boolean(stringValue)
|
||||
}
|
||||
}
|
||||
|
||||
const TASK_MAPPING = {
|
||||
prompt: { name: 'Prompt',
|
||||
setUI: (prompt) => {
|
||||
promptField.value = prompt
|
||||
},
|
||||
readUI: () => promptField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
negative_prompt: { name: 'Negative Prompt',
|
||||
setUI: (negative_prompt) => {
|
||||
negativePromptField.value = negative_prompt
|
||||
},
|
||||
readUI: () => negativePromptField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
width: { name: 'Width',
|
||||
setUI: (width) => {
|
||||
const oldVal = widthField.value
|
||||
widthField.value = width
|
||||
if (!widthField.value) {
|
||||
widthField.value = oldVal
|
||||
}
|
||||
},
|
||||
readUI: () => parseInt(widthField.value),
|
||||
parse: (val) => parseInt(val)
|
||||
},
|
||||
height: { name: 'Height',
|
||||
setUI: (height) => {
|
||||
const oldVal = heightField.value
|
||||
heightField.value = height
|
||||
if (!heightField.value) {
|
||||
heightField.value = oldVal
|
||||
}
|
||||
},
|
||||
readUI: () => parseInt(heightField.value),
|
||||
parse: (val) => parseInt(val)
|
||||
},
|
||||
seed: { name: 'Seed',
|
||||
setUI: (seed) => {
|
||||
if (!seed) {
|
||||
randomSeedField.checked = true
|
||||
seedField.disabled = true
|
||||
return
|
||||
}
|
||||
randomSeedField.checked = false
|
||||
seedField.disabled = false
|
||||
seedField.value = seed
|
||||
},
|
||||
readUI: () => (randomSeedField.checked ? Math.floor(Math.random() * 10000000) : parseInt(seedField.value)),
|
||||
parse: (val) => parseInt(val)
|
||||
},
|
||||
num_inference_steps: { name: 'Steps',
|
||||
setUI: (num_inference_steps) => {
|
||||
numInferenceStepsField.value = num_inference_steps
|
||||
},
|
||||
readUI: () => parseInt(numInferenceStepsField.value),
|
||||
parse: (val) => parseInt(val)
|
||||
},
|
||||
guidance_scale: { name: 'Guidance Scale',
|
||||
setUI: (guidance_scale) => {
|
||||
guidanceScaleField.value = guidance_scale
|
||||
updateGuidanceScaleSlider()
|
||||
},
|
||||
readUI: () => parseFloat(guidanceScaleField.value),
|
||||
parse: (val) => parseFloat(val)
|
||||
},
|
||||
prompt_strength: { name: 'Prompt Strength',
|
||||
setUI: (prompt_strength) => {
|
||||
promptStrengthField.value = prompt_strength
|
||||
updatePromptStrengthSlider()
|
||||
},
|
||||
readUI: () => parseFloat(promptStrengthField.value),
|
||||
parse: (val) => parseFloat(val)
|
||||
},
|
||||
|
||||
init_image: { name: 'Initial Image',
|
||||
setUI: (init_image) => {
|
||||
initImagePreview.src = init_image
|
||||
},
|
||||
readUI: () => initImagePreview.src,
|
||||
parse: (val) => val
|
||||
},
|
||||
mask: { name: 'Mask',
|
||||
setUI: (mask) => {
|
||||
inpaintingEditor.setImg(mask)
|
||||
maskSetting.checked = Boolean(mask)
|
||||
},
|
||||
readUI: () => (maskSetting.checked ? inpaintingEditor.getImg() : undefined),
|
||||
parse: (val) => val
|
||||
},
|
||||
|
||||
use_face_correction: { name: 'Use Face Correction',
|
||||
setUI: (use_face_correction) => {
|
||||
useFaceCorrectionField.checked = parseBoolean(use_face_correction)
|
||||
},
|
||||
readUI: () => useFaceCorrectionField.checked,
|
||||
parse: (val) => parseBoolean(val)
|
||||
},
|
||||
use_upscale: { name: 'Use Upscaling',
|
||||
setUI: (use_upscale) => {
|
||||
const oldVal = upscaleModelField.value
|
||||
upscaleModelField.value = use_upscale
|
||||
if (upscaleModelField.value) { // Is a valid value for the field.
|
||||
useUpscalingField.checked = true
|
||||
upscaleModelField.disabled = false
|
||||
} else { // Not a valid value, restore the old value and disable the filter.
|
||||
upscaleModelField.disabled = true
|
||||
upscaleModelField.value = oldVal
|
||||
useUpscalingField.checked = false
|
||||
}
|
||||
},
|
||||
readUI: () => (useUpscalingField.checked ? upscaleModelField.value : undefined),
|
||||
parse: (val) => val
|
||||
},
|
||||
sampler: { name: 'Sampler',
|
||||
setUI: (sampler) => {
|
||||
samplerField.value = sampler
|
||||
},
|
||||
readUI: () => samplerField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
use_stable_diffusion_model: { name: 'Stable Diffusion model',
|
||||
setUI: (use_stable_diffusion_model) => {
|
||||
const oldVal = stableDiffusionModelField.value
|
||||
|
||||
let pathIdx = use_stable_diffusion_model.lastIndexOf('/') // Linux, Mac paths
|
||||
if (pathIdx < 0) {
|
||||
pathIdx = use_stable_diffusion_model.lastIndexOf('\\') // Windows paths.
|
||||
}
|
||||
if (pathIdx >= 0) {
|
||||
use_stable_diffusion_model = use_stable_diffusion_model.slice(pathIdx + 1)
|
||||
}
|
||||
const modelExt = '.ckpt'
|
||||
if (use_stable_diffusion_model.endsWith(modelExt)) {
|
||||
use_stable_diffusion_model = use_stable_diffusion_model.slice(0, use_stable_diffusion_model.length - modelExt.length)
|
||||
}
|
||||
|
||||
stableDiffusionModelField.value = use_stable_diffusion_model
|
||||
|
||||
if (!stableDiffusionModelField.value) {
|
||||
stableDiffusionModelField.value = oldVal
|
||||
}
|
||||
},
|
||||
readUI: () => stableDiffusionModelField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
|
||||
numOutputsParallel: { name: 'Parallel Images',
|
||||
setUI: (numOutputsParallel) => {
|
||||
numOutputsParallelField.value = numOutputsParallel
|
||||
},
|
||||
readUI: () => parseInt(numOutputsParallelField.value),
|
||||
parse: (val) => val
|
||||
},
|
||||
|
||||
use_cpu: { name: 'Use CPU',
|
||||
setUI: (use_cpu) => {
|
||||
useCPUField.checked = use_cpu
|
||||
},
|
||||
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) => {
|
||||
streamImageProgressField.checked = (parseInt(numOutputsTotalField.value) > 50 ? false : stream_image_progress)
|
||||
},
|
||||
readUI: () => streamImageProgressField.checked,
|
||||
parse: (val) => Boolean(val)
|
||||
},
|
||||
show_only_filtered_image: { name: 'Show only the corrected/upscaled image',
|
||||
setUI: (show_only_filtered_image) => {
|
||||
showOnlyFilteredImageField.checked = show_only_filtered_image
|
||||
},
|
||||
readUI: () => showOnlyFilteredImageField.checked,
|
||||
parse: (val) => Boolean(val)
|
||||
},
|
||||
output_format: { name: 'Output Format',
|
||||
setUI: (output_format) => {
|
||||
outputFormatField.value = output_format
|
||||
},
|
||||
readUI: () => outputFormatField.value,
|
||||
parse: (val) => val
|
||||
},
|
||||
save_to_disk_path: { name: 'Save to disk path',
|
||||
setUI: (save_to_disk_path) => {
|
||||
saveToDiskField.checked = Boolean(save_to_disk_path)
|
||||
diskPathField.value = save_to_disk_path
|
||||
},
|
||||
readUI: () => diskPathField.value,
|
||||
parse: (val) => val
|
||||
}
|
||||
}
|
||||
function restoreTaskToUI(task) {
|
||||
if ('numOutputsTotal' in task) {
|
||||
numOutputsTotalField.value = task.numOutputsTotal
|
||||
}
|
||||
if ('seed' in task) {
|
||||
randomSeedField.checked = false
|
||||
seedField.value = task.seed
|
||||
}
|
||||
if (!('reqBody' in task)) {
|
||||
return
|
||||
}
|
||||
for (const key in TASK_MAPPING) {
|
||||
if (key in task.reqBody) {
|
||||
TASK_MAPPING[key].setUI(task.reqBody[key])
|
||||
}
|
||||
}
|
||||
}
|
||||
function readUI() {
|
||||
const reqBody = {}
|
||||
for (const key in TASK_MAPPING) {
|
||||
reqBody[key] = TASK_MAPPING[key].readUI()
|
||||
}
|
||||
return {
|
||||
'numOutputsTotal': parseInt(numOutputsTotalField.value),
|
||||
'seed': TASK_MAPPING['seed'].readUI(),
|
||||
'reqBody': reqBody
|
||||
}
|
||||
}
|
||||
|
||||
const TASK_TEXT_MAPPING = {
|
||||
width: 'Width',
|
||||
height: 'Height',
|
||||
seed: 'Seed',
|
||||
num_inference_steps: 'Steps',
|
||||
guidance_scale: 'Guidance Scale',
|
||||
prompt_strength: 'Prompt Strength',
|
||||
use_face_correction: 'Use Face Correction',
|
||||
use_upscale: 'Use Upscaling',
|
||||
sampler: 'Sampler',
|
||||
negative_prompt: 'Negative Prompt',
|
||||
use_stable_diffusion_model: 'Stable Diffusion model'
|
||||
}
|
||||
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*$/igm
|
||||
function parseTaskFromText(str) {
|
||||
const taskReqBody = {}
|
||||
|
||||
// Prompt
|
||||
afterPromptRe.lastIndex = 0
|
||||
const match = afterPromptRe.exec(str)
|
||||
if (match) {
|
||||
let prompt = str.slice(0, match.index)
|
||||
str = str.slice(prompt.length)
|
||||
taskReqBody.prompt = prompt.trim()
|
||||
console.log('Prompt:', taskReqBody.prompt)
|
||||
}
|
||||
for (const key in TASK_TEXT_MAPPING) {
|
||||
const name = TASK_TEXT_MAPPING[key];
|
||||
let val = undefined
|
||||
|
||||
const reName = new RegExp(`${name}\\ *:\\ *(.*)(?:\\r\\n|\\r|\\n)*`, 'igm')
|
||||
const match = reName.exec(str);
|
||||
if (match) {
|
||||
str = str.slice(0, match.index) + str.slice(match.index + match[0].length)
|
||||
val = match[1]
|
||||
}
|
||||
if (val !== undefined) {
|
||||
taskReqBody[key] = TASK_MAPPING[key].parse(val.trim())
|
||||
console.log(TASK_MAPPING[key].name + ':', taskReqBody[key])
|
||||
if (!str) {
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
if (Object.keys(taskReqBody).length <= 0) {
|
||||
return undefined
|
||||
}
|
||||
const task = { reqBody: taskReqBody }
|
||||
if ('seed' in taskReqBody) {
|
||||
task.seed = taskReqBody.seed
|
||||
}
|
||||
return task
|
||||
}
|
||||
|
||||
async function readFile(file, i) {
|
||||
const fileContent = (await file.text()).trim()
|
||||
|
||||
// JSON File.
|
||||
if (fileContent.startsWith('{') && fileContent.endsWith('}')) {
|
||||
try {
|
||||
const task = JSON.parse(fileContent)
|
||||
restoreTaskToUI(task)
|
||||
} catch (e) {
|
||||
console.warn(`file[${i}]:${file.name} - File couldn't be parsed.`, e)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// Normal txt file.
|
||||
const task = parseTaskFromText(fileContent)
|
||||
if (task) {
|
||||
restoreTaskToUI(task)
|
||||
} else {
|
||||
console.warn(`file[${i}]:${file.name} - File couldn't be parsed.`)
|
||||
}
|
||||
}
|
||||
|
||||
function dropHandler(ev) {
|
||||
console.log('Content dropped...')
|
||||
let items = []
|
||||
|
||||
if (ev?.dataTransfer?.items) { // Use DataTransferItemList interface
|
||||
items = Array.from(ev.dataTransfer.items)
|
||||
items = items.filter(item => item.kind === 'file')
|
||||
items = items.map(item => item.getAsFile())
|
||||
} else if (ev?.dataTransfer?.files) { // Use DataTransfer interface
|
||||
items = Array.from(ev.dataTransfer.files)
|
||||
}
|
||||
|
||||
items.forEach(item => {item.file_ext = EXT_REGEX.exec(item.name.toLowerCase())[1]})
|
||||
|
||||
let text_items = items.filter(item => TEXT_EXTENSIONS.includes(item.file_ext))
|
||||
let image_items = items.filter(item => IMAGE_EXTENSIONS.includes(item.file_ext))
|
||||
|
||||
if (image_items.length > 0 && ev.target == initImageSelector) {
|
||||
return // let the event bubble up, so that the Init Image filepicker can receive this
|
||||
}
|
||||
|
||||
ev.preventDefault() // Prevent default behavior (Prevent file/content from being opened)
|
||||
text_items.forEach(readFile)
|
||||
}
|
||||
function dragOverHandler(ev) {
|
||||
console.log('Content in drop zone')
|
||||
|
||||
// Prevent default behavior (Prevent file/content from being opened)
|
||||
ev.preventDefault()
|
||||
|
||||
ev.dataTransfer.dropEffect = "copy"
|
||||
|
||||
let img = new Image()
|
||||
img.src = location.host + '/media/images/favicon-32x32.png'
|
||||
ev.dataTransfer.setDragImage(img, 16, 16)
|
||||
}
|
||||
|
||||
document.addEventListener("drop", dropHandler)
|
||||
document.addEventListener("dragover", dragOverHandler)
|
||||
|
||||
const TASK_REQ_NO_EXPORT = [
|
||||
"use_cpu",
|
||||
"turbo",
|
||||
"use_full_precision",
|
||||
"save_to_disk_path"
|
||||
]
|
||||
|
||||
// Retrieve clipboard content and try to parse it
|
||||
async function pasteFromClipboard() {
|
||||
//const text = await navigator.clipboard.readText()
|
||||
let text = await navigator.clipboard.readText();
|
||||
text=text.trim();
|
||||
if (text.startsWith('{') && text.endsWith('}')) {
|
||||
try {
|
||||
const task = JSON.parse(text)
|
||||
restoreTaskToUI(task)
|
||||
} catch (e) {
|
||||
console.warn(`Clipboard JSON couldn't be parsed.`, e)
|
||||
}
|
||||
return
|
||||
}
|
||||
// Normal txt file.
|
||||
const task = parseTaskFromText(text)
|
||||
if (task) {
|
||||
restoreTaskToUI(task)
|
||||
} else {
|
||||
console.warn(`Clipboard content - File couldn't be parsed.`)
|
||||
}
|
||||
}
|
||||
|
||||
// Adds a copy and a paste icon if the browser grants permission to write to clipboard.
|
||||
function checkWriteToClipboardPermission (result) {
|
||||
if (result.state == "granted" || result.state == "prompt") {
|
||||
const resetSettings = document.getElementById('reset-image-settings')
|
||||
|
||||
// COPY ICON
|
||||
const copyIcon = document.createElement('i')
|
||||
copyIcon.className = 'fa-solid fa-clipboard section-button'
|
||||
copyIcon.innerHTML = `<span class="simple-tooltip right">Copy Image Settings</span>`
|
||||
copyIcon.addEventListener('click', (event) => {
|
||||
event.stopPropagation()
|
||||
// Add css class 'active'
|
||||
copyIcon.classList.add('active')
|
||||
// In 1000 ms remove the 'active' class
|
||||
asyncDelay(1000).then(() => copyIcon.classList.remove('active'))
|
||||
const uiState = readUI()
|
||||
TASK_REQ_NO_EXPORT.forEach((key) => delete uiState.reqBody[key])
|
||||
if (uiState.reqBody.init_image && !IMAGE_REGEX.test(uiState.reqBody.init_image)) {
|
||||
delete uiState.reqBody.init_image
|
||||
delete uiState.reqBody.prompt_strength
|
||||
}
|
||||
navigator.clipboard.writeText(JSON.stringify(uiState, undefined, 4))
|
||||
})
|
||||
resetSettings.parentNode.insertBefore(copyIcon, resetSettings)
|
||||
|
||||
// PASTE ICON
|
||||
const pasteIcon = document.createElement('i')
|
||||
pasteIcon.className = 'fa-solid fa-paste section-button'
|
||||
pasteIcon.innerHTML = `<span class="simple-tooltip right">Paste Image Settings</span>`
|
||||
pasteIcon.addEventListener('click', (event) => {
|
||||
event.stopPropagation()
|
||||
// Add css class 'active'
|
||||
pasteIcon.classList.add('active')
|
||||
// In 1000 ms remove the 'active' class
|
||||
asyncDelay(1000).then(() => pasteIcon.classList.remove('active'))
|
||||
pasteFromClipboard()
|
||||
})
|
||||
resetSettings.parentNode.insertBefore(pasteIcon, resetSettings)
|
||||
}
|
||||
}
|
||||
|
||||
// Determine which access we have to the clipboard. Clipboard access is only available on localhost or via TLS.
|
||||
navigator.permissions.query({ name: "clipboard-write" }).then(checkWriteToClipboardPermission, (e) => {
|
||||
if (e instanceof TypeError && typeof navigator?.clipboard?.writeText === 'function') {
|
||||
// Fix for firefox https://bugzilla.mozilla.org/show_bug.cgi?id=1560373
|
||||
checkWriteToClipboardPermission({state:"granted"})
|
||||
}
|
||||
})
|
||||
@ -75,7 +75,6 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
|
||||
|
||||
if (initiallyExpanded === true) {
|
||||
titleEl.className += ' active'
|
||||
modifiersEl.style.display = 'block'
|
||||
}
|
||||
|
||||
modifiers.forEach(modObj => {
|
||||
@ -245,16 +244,9 @@ function resizeModifierCards(val) {
|
||||
modifierCardSizeSlider.onchange = () => resizeModifierCards(modifierCardSizeSlider.value)
|
||||
previewImageField.onchange = () => changePreviewImages(previewImageField.value)
|
||||
|
||||
modifierSettingsBtn.addEventListener('click', function() {
|
||||
modifierSettingsOverlay.style.display = 'block'
|
||||
})
|
||||
document.getElementById("modifier-settings-config-close-btn").addEventListener('click', () => {
|
||||
modifierSettingsOverlay.style.display = 'none'
|
||||
})
|
||||
modifierSettingsOverlay.addEventListener('click', (event) => {
|
||||
if (event.target.id == modifierSettingsOverlay.id) {
|
||||
modifierSettingsOverlay.style.display = 'none'
|
||||
}
|
||||
modifierSettingsBtn.addEventListener('click', function(e) {
|
||||
modifierSettingsOverlay.classList.add("active")
|
||||
e.stopPropagation()
|
||||
})
|
||||
|
||||
function saveCustomModifiers() {
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
"use strict" // Opt in to a restricted variant of JavaScript
|
||||
const HEALTH_PING_INTERVAL = 5 // seconds
|
||||
const MAX_INIT_IMAGE_DIMENSION = 768
|
||||
const MIN_GPUS_TO_SHOW_SELECTION = 2
|
||||
|
||||
const IMAGE_REGEX = new RegExp('data:image/[A-Za-z]+;base64')
|
||||
|
||||
@ -24,13 +25,6 @@ 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 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 diskPathField = document.querySelector('#diskPath')
|
||||
// let allowNSFWField = document.querySelector("#allow_nsfw")
|
||||
let useBetaChannelField = document.querySelector("#use_beta_channel")
|
||||
let promptStrengthSlider = document.querySelector('#prompt_strength_slider')
|
||||
let promptStrengthField = document.querySelector('#prompt_strength')
|
||||
let samplerField = document.querySelector('#sampler')
|
||||
@ -39,6 +33,7 @@ let useFaceCorrectionField = document.querySelector("#use_face_correction")
|
||||
let useUpscalingField = document.querySelector("#use_upscale")
|
||||
let upscaleModelField = document.querySelector("#upscale_model")
|
||||
let stableDiffusionModelField = document.querySelector('#stable_diffusion_model')
|
||||
let vaeModelField = document.querySelector('#vae_model')
|
||||
let outputFormatField = document.querySelector('#output_format')
|
||||
let showOnlyFilteredImageField = document.querySelector("#show_only_filtered_image")
|
||||
let updateBranchLabel = document.querySelector("#updateBranchLabel")
|
||||
@ -56,22 +51,10 @@ let initialText = document.querySelector("#initial-text")
|
||||
let previewTools = document.querySelector("#preview-tools")
|
||||
let clearAllPreviewsBtn = document.querySelector("#clear-all-previews")
|
||||
|
||||
// let maskSetting = document.querySelector('#editor-inputs-mask_setting')
|
||||
// let maskImagePreviewContainer = document.querySelector('#mask_preview_container')
|
||||
// let maskImageClearBtn = document.querySelector('#mask_clear')
|
||||
let maskSetting = document.querySelector('#enable_mask')
|
||||
|
||||
let imagePreview = document.querySelector("#preview")
|
||||
|
||||
// let previewPrompt = document.querySelector('#preview-prompt')
|
||||
|
||||
let showConfigToggle = document.querySelector('#configToggleBtn')
|
||||
// let configBox = document.querySelector('#config')
|
||||
// let outputMsg = document.querySelector('#outputMsg')
|
||||
// let progressBar = document.querySelector("#progressBar")
|
||||
|
||||
let soundToggle = document.querySelector('#sound_toggle')
|
||||
|
||||
let serverStatusColor = document.querySelector('#server-status-color')
|
||||
let serverStatusMsg = document.querySelector('#server-status-msg')
|
||||
|
||||
@ -85,7 +68,6 @@ maskResetButton.style.fontWeight = 'normal'
|
||||
maskResetButton.style.fontSize = '10pt'
|
||||
|
||||
let serverState = {'status': 'Offline', 'time': Date.now()}
|
||||
let lastPromptUsed = ''
|
||||
let bellPending = false
|
||||
|
||||
let taskQueue = []
|
||||
@ -187,6 +169,34 @@ function playSound() {
|
||||
})
|
||||
}
|
||||
}
|
||||
function setSystemInfo(devices) {
|
||||
let cpu = devices.all.cpu.name
|
||||
let allGPUs = Object.keys(devices.all).filter(d => d != 'cpu')
|
||||
let activeGPUs = Object.keys(devices.active)
|
||||
|
||||
function ID_TO_TEXT(d) {
|
||||
let info = devices.all[d]
|
||||
if ("mem_free" in info && "mem_total" in info) {
|
||||
return `${info.name} <small>(${d}) (${info.mem_free.toFixed(1)}Gb free / ${info.mem_total.toFixed(1)} Gb total)</small>`
|
||||
} else {
|
||||
return `${info.name} <small>(${d}) (no memory info)</small>`
|
||||
}
|
||||
}
|
||||
|
||||
allGPUs = allGPUs.map(ID_TO_TEXT)
|
||||
activeGPUs = activeGPUs.map(ID_TO_TEXT)
|
||||
|
||||
let systemInfo = `
|
||||
<table>
|
||||
<tr><td><label>Processor:</label></td><td class="value">${cpu}</td></tr>
|
||||
<tr><td><label>Compatible Graphics Cards (all):</label></td><td class="value">${allGPUs.join('</br>')}</td></tr>
|
||||
<tr><td></td><td> </td></tr>
|
||||
<tr><td><label>Used for rendering 🔥:</label></td><td class="value">${activeGPUs.join('</br>')}</td></tr>
|
||||
</table>`
|
||||
|
||||
let systemInfoEl = document.querySelector('#system-info')
|
||||
systemInfoEl.innerHTML = systemInfo
|
||||
}
|
||||
|
||||
async function healthCheck() {
|
||||
try {
|
||||
@ -220,8 +230,12 @@ async function healthCheck() {
|
||||
setServerStatus('error', serverState.status.toLowerCase())
|
||||
break
|
||||
}
|
||||
if (serverState.devices) {
|
||||
setSystemInfo(serverState.devices)
|
||||
}
|
||||
serverState.time = Date.now()
|
||||
} catch (e) {
|
||||
console.log(e)
|
||||
serverState = {'status': 'Offline', 'time': Date.now()}
|
||||
setServerStatus('error', 'offline')
|
||||
}
|
||||
@ -340,10 +354,10 @@ function onDownloadImageClick(req, img) {
|
||||
imgDownload.click()
|
||||
}
|
||||
|
||||
function modifyCurrentRequest(req, ...reqDiff) {
|
||||
function modifyCurrentRequest(...reqDiff) {
|
||||
const newTaskRequest = getCurrentUserRequest()
|
||||
|
||||
newTaskRequest.reqBody = Object.assign({}, req, ...reqDiff, {
|
||||
newTaskRequest.reqBody = Object.assign(newTaskRequest.reqBody, ...reqDiff, {
|
||||
use_cpu: useCPUField.checked
|
||||
})
|
||||
newTaskRequest.seed = newTaskRequest.reqBody.seed
|
||||
@ -410,7 +424,7 @@ async function doMakeImage(task) {
|
||||
const RETRY_DELAY_IF_BUFFER_IS_EMPTY = 1000 // ms
|
||||
const RETRY_DELAY_IF_SERVER_IS_BUSY = 30 * 1000 // ms, status_code 503, already a task running
|
||||
const TASK_START_DELAY_ON_SERVER = 1500 // ms
|
||||
const SERVER_STATE_VALIDITY_DURATION = 10 * 1000 // ms
|
||||
const SERVER_STATE_VALIDITY_DURATION = 90 * 1000 // ms
|
||||
|
||||
const reqBody = task.reqBody
|
||||
const batchCount = task.batchCount
|
||||
@ -422,10 +436,10 @@ async function doMakeImage(task) {
|
||||
const outputMsg = task['outputMsg']
|
||||
const previewPrompt = task['previewPrompt']
|
||||
const progressBar = task['progressBar']
|
||||
const progressBarInner = progressBar.querySelector("div")
|
||||
|
||||
let res = undefined
|
||||
try {
|
||||
const lastTask = serverState.task
|
||||
let renderRequest = undefined
|
||||
do {
|
||||
res = await fetch('/render', {
|
||||
@ -561,6 +575,13 @@ async function doMakeImage(task) {
|
||||
outputMsg.innerHTML += `. Time remaining (approx): ${timeRemaining}`
|
||||
outputMsg.style.display = 'block'
|
||||
|
||||
progressBarInner.style.width = `${percent}%`
|
||||
if (percent == 100) {
|
||||
task.progressBar.style.height = "0px"
|
||||
task.progressBar.style.border = "0px solid var(--background-color3)"
|
||||
task.progressBar.classList.remove("active")
|
||||
}
|
||||
|
||||
if (stepUpdate.output !== undefined) {
|
||||
showImages(reqBody, stepUpdate, outputContainer, true)
|
||||
}
|
||||
@ -620,17 +641,14 @@ async function doMakeImage(task) {
|
||||
let msg = `Unexpected Read Error:<br/><pre>Response: ${res}<br/>StepUpdate: ${typeof stepUpdate === 'object' ? JSON.stringify(stepUpdate, undefined, 4) : stepUpdate}</pre>`
|
||||
logError(msg, res, outputMsg)
|
||||
}
|
||||
progressBar.style.display = 'none'
|
||||
return false
|
||||
}
|
||||
|
||||
lastPromptUsed = reqBody['prompt']
|
||||
showImages(reqBody, stepUpdate, outputContainer, false)
|
||||
} catch (e) {
|
||||
console.log('request error', e)
|
||||
logError('Stable Diffusion had an error. Please check the logs in the command-line window. <br/><br/>' + e + '<br/><pre>' + e.stack + '</pre>', res, outputMsg)
|
||||
setStatus('request', 'error', 'error')
|
||||
progressBar.style.display = 'none'
|
||||
return false
|
||||
}
|
||||
return true
|
||||
@ -713,6 +731,9 @@ async function checkTasks() {
|
||||
|
||||
if (successCount === task.batchCount) {
|
||||
task.outputMsg.innerText = 'Processed ' + task.numOutputsTotal + ' images in ' + time + ' seconds'
|
||||
task.progressBar.style.height = "0px"
|
||||
task.progressBar.style.border = "0px solid var(--background-color3)"
|
||||
task.progressBar.classList.remove("active")
|
||||
// setStatus('request', 'done', 'success')
|
||||
} else {
|
||||
if (task.outputMsg.innerText.toLowerCase().indexOf('error') === -1) {
|
||||
@ -762,9 +783,9 @@ function getCurrentUserRequest() {
|
||||
height: heightField.value,
|
||||
// allow_nsfw: allowNSFWField.checked,
|
||||
turbo: turboField.checked,
|
||||
use_cpu: useCPUField.checked,
|
||||
use_full_precision: useFullPrecisionField.checked,
|
||||
use_stable_diffusion_model: stableDiffusionModelField.value,
|
||||
use_vae_model: vaeModelField.value,
|
||||
stream_progress_updates: true,
|
||||
stream_image_progress: (numOutputsTotal > 50 ? false : streamImageProgressField.checked),
|
||||
show_only_filtered_image: showOnlyFilteredImageField.checked,
|
||||
@ -813,29 +834,34 @@ function makeImage() {
|
||||
}
|
||||
|
||||
function createTask(task) {
|
||||
let taskConfig = `Seed: ${task.seed}, Sampler: ${task.reqBody.sampler}, Inference Steps: ${task.reqBody.num_inference_steps}, Guidance Scale: ${task.reqBody.guidance_scale}, Model: ${task.reqBody.use_stable_diffusion_model}`
|
||||
if (negativePromptField.value.trim() !== '') {
|
||||
taskConfig += `, Negative Prompt: ${task.reqBody.negative_prompt}`
|
||||
let 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}`
|
||||
if (task.reqBody.use_vae_model.trim() !== '') {
|
||||
taskConfig += `, <b>VAE:</b> ${task.reqBody.use_vae_model}`
|
||||
}
|
||||
if (task.reqBody.negative_prompt.trim() !== '') {
|
||||
taskConfig += `, <b>Negative Prompt:</b> ${task.reqBody.negative_prompt}`
|
||||
}
|
||||
if (task.reqBody.init_image !== undefined) {
|
||||
taskConfig += `, Prompt Strength: ${task.reqBody.prompt_strength}`
|
||||
taskConfig += `, <b>Prompt Strength:</b> ${task.reqBody.prompt_strength}`
|
||||
}
|
||||
if (task.reqBody.use_face_correction) {
|
||||
taskConfig += `, Fix Faces: ${task.reqBody.use_face_correction}`
|
||||
taskConfig += `, <b>Fix Faces:</b> ${task.reqBody.use_face_correction}`
|
||||
}
|
||||
if (task.reqBody.use_upscale) {
|
||||
taskConfig += `, Upscale: ${task.reqBody.use_upscale}`
|
||||
taskConfig += `, <b>Upscale:</b> ${task.reqBody.use_upscale}`
|
||||
}
|
||||
|
||||
let taskEntry = document.createElement('div')
|
||||
taskEntry.className = 'imageTaskContainer'
|
||||
taskEntry.innerHTML = ` <div class="taskStatusLabel">Enqueued</div>
|
||||
<button class="secondaryButton stopTask"><i class="fa-solid fa-trash-can"></i> Remove</button>
|
||||
<div class="preview-prompt collapsible active"></div>
|
||||
<div class="taskConfig">${taskConfig}</div>
|
||||
<div class="collapsible-content" style="display: block">
|
||||
taskEntry.innerHTML = ` <div class="header-content panel collapsible active">
|
||||
<div class="taskStatusLabel">Enqueued</div>
|
||||
<button class="secondaryButton stopTask"><i class="fa-solid fa-trash-can"></i> Remove</button>
|
||||
<div class="preview-prompt collapsible active"></div>
|
||||
<div class="taskConfig">${taskConfig}</div>
|
||||
<div class="outputMsg"></div>
|
||||
<div class="progressBar"></div>
|
||||
<div class="progress-bar active"><div></div></div>
|
||||
</div>
|
||||
<div class="collapsible-content">
|
||||
<div class="img-preview">
|
||||
</div>`
|
||||
|
||||
@ -845,12 +871,14 @@ function createTask(task) {
|
||||
task['outputContainer'] = taskEntry.querySelector('.img-preview')
|
||||
task['outputMsg'] = taskEntry.querySelector('.outputMsg')
|
||||
task['previewPrompt'] = taskEntry.querySelector('.preview-prompt')
|
||||
task['progressBar'] = taskEntry.querySelector('.progressBar')
|
||||
task['progressBar'] = taskEntry.querySelector('.progress-bar')
|
||||
task['stopTask'] = taskEntry.querySelector('.stopTask')
|
||||
|
||||
task['stopTask'].addEventListener('click', async function() {
|
||||
task['stopTask'].addEventListener('click', async function(e) {
|
||||
e.stopPropagation()
|
||||
if (task['isProcessing']) {
|
||||
task.isProcessing = false
|
||||
task.progressBar.classList.remove("active")
|
||||
try {
|
||||
let res = await fetch('/image/stop?session_id=' + sessionId)
|
||||
} catch (e) {
|
||||
@ -1044,16 +1072,25 @@ function onDimensionChange() {
|
||||
resizeInpaintingEditor(widthValue, heightValue)
|
||||
}
|
||||
|
||||
saveToDiskField.addEventListener('click', function(e) {
|
||||
diskPathField.disabled = !this.checked
|
||||
})
|
||||
diskPathField.disabled = !saveToDiskField.checked
|
||||
|
||||
useUpscalingField.addEventListener('click', function(e) {
|
||||
upscaleModelField.disabled = !useUpscalingField.checked
|
||||
useUpscalingField.addEventListener('change', function(e) {
|
||||
upscaleModelField.disabled = !this.checked
|
||||
})
|
||||
|
||||
if (useBetaChannelField.checked) {
|
||||
updateBranchLabel.innerText = "(beta)"
|
||||
}
|
||||
|
||||
makeImageBtn.addEventListener('click', makeImage)
|
||||
|
||||
document.onkeydown = function(e) {
|
||||
if (e.ctrlKey && e.code === 'Enter') {
|
||||
makeImage()
|
||||
e.preventDefault()
|
||||
}
|
||||
}
|
||||
|
||||
function updateGuidanceScale() {
|
||||
guidanceScaleField.value = guidanceScaleSlider.value / 10
|
||||
@ -1095,80 +1132,44 @@ promptStrengthSlider.addEventListener('input', updatePromptStrength)
|
||||
promptStrengthField.addEventListener('input', updatePromptStrengthSlider)
|
||||
updatePromptStrength()
|
||||
|
||||
useBetaChannelField.addEventListener('click', async function(e) {
|
||||
if (!isServerAvailable()) {
|
||||
// logError('The server is still starting up..')
|
||||
alert('The server is still starting up..')
|
||||
e.preventDefault()
|
||||
return false
|
||||
}
|
||||
|
||||
let updateBranch = (this.checked ? 'beta' : 'main')
|
||||
|
||||
try {
|
||||
let res = await fetch('/app_config', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify({
|
||||
'update_branch': updateBranch
|
||||
})
|
||||
})
|
||||
res = await res.json()
|
||||
|
||||
console.log('set config status response', res)
|
||||
} catch (e) {
|
||||
console.log('set config status error', e)
|
||||
}
|
||||
})
|
||||
|
||||
async function getAppConfig() {
|
||||
try {
|
||||
let res = await fetch('/get/app_config')
|
||||
const config = await res.json()
|
||||
|
||||
if (config.update_branch === 'beta') {
|
||||
useBetaChannelField.checked = true
|
||||
updateBranchLabel.innerText = "(beta)"
|
||||
}
|
||||
|
||||
console.log('get config status response', config)
|
||||
} catch (e) {
|
||||
console.log('get config status error', e)
|
||||
}
|
||||
}
|
||||
|
||||
async function getModels() {
|
||||
try {
|
||||
var model_setting_key = "stable_diffusion_model"
|
||||
var selectedModel = SETTINGS[model_setting_key].value
|
||||
var sd_model_setting_key = "stable_diffusion_model"
|
||||
var vae_model_setting_key = "vae_model"
|
||||
var selectedSDModel = SETTINGS[sd_model_setting_key].value
|
||||
var selectedVaeModel = SETTINGS[vae_model_setting_key].value
|
||||
let res = await fetch('/get/models')
|
||||
const models = await res.json()
|
||||
|
||||
// let activeModel = models['active']
|
||||
console.log('get models response', models)
|
||||
|
||||
let modelOptions = models['options']
|
||||
let stableDiffusionOptions = modelOptions['stable-diffusion']
|
||||
let vaeOptions = modelOptions['vae']
|
||||
vaeOptions.unshift('') // add a None option
|
||||
|
||||
stableDiffusionOptions.forEach(modelName => {
|
||||
let modelOption = document.createElement('option')
|
||||
modelOption.value = modelName
|
||||
modelOption.innerText = modelName
|
||||
function createModelOptions(modelField, selectedModel) {
|
||||
return function(modelName) {
|
||||
let modelOption = document.createElement('option')
|
||||
modelOption.value = modelName
|
||||
modelOption.innerText = modelName !== '' ? modelName : 'None'
|
||||
|
||||
if (modelName === selectedModel) {
|
||||
modelOption.selected = true
|
||||
if (modelName === selectedModel) {
|
||||
modelOption.selected = true
|
||||
}
|
||||
|
||||
modelField.appendChild(modelOption)
|
||||
}
|
||||
|
||||
stableDiffusionModelField.appendChild(modelOption)
|
||||
})
|
||||
|
||||
// TODO: set default for model here too
|
||||
SETTINGS[model_setting_key].default = stableDiffusionOptions[0]
|
||||
if (getSetting(model_setting_key) == '' || SETTINGS[model_setting_key].value == '') {
|
||||
setSetting(model_setting_key, stableDiffusionOptions[0])
|
||||
}
|
||||
|
||||
console.log('get models response', models)
|
||||
stableDiffusionOptions.forEach(createModelOptions(stableDiffusionModelField, selectedSDModel))
|
||||
vaeOptions.forEach(createModelOptions(vaeModelField, selectedVaeModel))
|
||||
|
||||
// TODO: set default for model here too
|
||||
SETTINGS[sd_model_setting_key].default = stableDiffusionOptions[0]
|
||||
if (getSetting(sd_model_setting_key) == '' || SETTINGS[sd_model_setting_key].value == '') {
|
||||
setSetting(sd_model_setting_key, stableDiffusionOptions[0])
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('get models error', e)
|
||||
}
|
||||
@ -1267,21 +1268,56 @@ promptsFromFileSelector.addEventListener('change', function() {
|
||||
}
|
||||
})
|
||||
|
||||
async function getDiskPath() {
|
||||
try {
|
||||
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)
|
||||
}
|
||||
/* setup popup handlers */
|
||||
document.querySelectorAll('.popup').forEach(popup => {
|
||||
popup.addEventListener('click', event => {
|
||||
if (event.target == popup) {
|
||||
popup.classList.remove("active")
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('error fetching output dir path', e)
|
||||
})
|
||||
var closeButton = popup.querySelector(".close-button")
|
||||
if (closeButton) {
|
||||
closeButton.addEventListener('click', () => {
|
||||
popup.classList.remove("active")
|
||||
})
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
var tabElements = [];
|
||||
document.querySelectorAll(".tab").forEach(tab => {
|
||||
var name = tab.id.replace("tab-", "");
|
||||
var content = document.getElementById(`tab-content-${name}`)
|
||||
tabElements.push({
|
||||
name: name,
|
||||
tab: tab,
|
||||
content: content
|
||||
})
|
||||
|
||||
tab.addEventListener("click", event => {
|
||||
if (!tab.classList.contains("active")) {
|
||||
tabElements.forEach(tabInfo => {
|
||||
if (tabInfo.tab.classList.contains("active")) {
|
||||
tabInfo.tab.classList.toggle("active")
|
||||
tabInfo.content.classList.toggle("active")
|
||||
}
|
||||
})
|
||||
tab.classList.toggle("active")
|
||||
content.classList.toggle("active")
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
window.addEventListener("beforeunload", function(e) {
|
||||
const msg = "Unsaved pictures will be lost!";
|
||||
|
||||
let elementList = document.getElementsByClassName("imageTaskContainer");
|
||||
if (elementList.length != 0) {
|
||||
e.preventDefault();
|
||||
(e || window.event).returnValue = msg;
|
||||
return msg;
|
||||
} else {
|
||||
return true;
|
||||
}
|
||||
});
|
||||
|
||||
createCollapsibles()
|
||||
|
||||
318
ui/media/js/parameters.js
Normal file
318
ui/media/js/parameters.js
Normal file
@ -0,0 +1,318 @@
|
||||
/**
|
||||
* Enum of parameter types
|
||||
* @readonly
|
||||
* @enum {string}
|
||||
*/
|
||||
var ParameterType = {
|
||||
checkbox: "checkbox",
|
||||
select: "select",
|
||||
select_multiple: "select_multiple",
|
||||
custom: "custom",
|
||||
};
|
||||
|
||||
/**
|
||||
* JSDoc style
|
||||
* @typedef {object} Parameter
|
||||
* @property {string} id
|
||||
* @property {ParameterType} type
|
||||
* @property {string} label
|
||||
* @property {?string} note
|
||||
* @property {number|boolean|string} default
|
||||
*/
|
||||
|
||||
|
||||
/** @type {Array.<Parameter>} */
|
||||
var PARAMETERS = [
|
||||
{
|
||||
id: "theme",
|
||||
type: ParameterType.select,
|
||||
label: "Theme",
|
||||
default: "theme-default",
|
||||
options: [ // Note: options expanded dynamically
|
||||
{
|
||||
value: "theme-default",
|
||||
label: "Default"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
id: "save_to_disk",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Images",
|
||||
note: "automatically saves images to the specified location",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "diskPath",
|
||||
type: ParameterType.custom,
|
||||
label: "Save Location",
|
||||
render: (parameter) => {
|
||||
return `<input id="${parameter.id}" name="${parameter.id}" size="30" disabled>`
|
||||
}
|
||||
},
|
||||
{
|
||||
id: "sound_toggle",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Enable Sound",
|
||||
note: "plays a sound on task completion",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "turbo",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Turbo Mode",
|
||||
default: true,
|
||||
note: "generates images faster, but uses an additional 1 GB of GPU memory",
|
||||
},
|
||||
{
|
||||
id: "use_cpu",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Use CPU (not GPU)",
|
||||
note: "warning: this will be *very* slow",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_pick_gpus",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Automatically pick the GPUs (experimental)",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_gpus",
|
||||
type: ParameterType.select_multiple,
|
||||
label: "GPUs to use (experimental)",
|
||||
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",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_save_settings",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Settings",
|
||||
note: "restores settings on browser load",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "use_beta_channel",
|
||||
type: ParameterType.checkbox,
|
||||
label: "🔥Beta channel",
|
||||
note: "Get the latest features immediately (but could be less stable). Please restart the program after changing this.",
|
||||
default: false,
|
||||
},
|
||||
];
|
||||
|
||||
function getParameterSettingsEntry(id) {
|
||||
let parameter = PARAMETERS.filter(p => p.id === id)
|
||||
if (parameter.length === 0) {
|
||||
return
|
||||
}
|
||||
return parameter[0].settingsEntry
|
||||
}
|
||||
|
||||
function getParameterElement(parameter) {
|
||||
switch (parameter.type) {
|
||||
case ParameterType.checkbox:
|
||||
var is_checked = parameter.default ? " checked" : "";
|
||||
return `<input id="${parameter.id}" name="${parameter.id}"${is_checked} type="checkbox">`
|
||||
case ParameterType.select:
|
||||
case ParameterType.select_multiple:
|
||||
var options = (parameter.options || []).map(option => `<option value="${option.value}">${option.label}</option>`).join("")
|
||||
var multiple = (parameter.type == ParameterType.select_multiple ? 'multiple' : '')
|
||||
return `<select id="${parameter.id}" name="${parameter.id}" ${multiple}>${options}</select>`
|
||||
case ParameterType.custom:
|
||||
return parameter.render(parameter)
|
||||
default:
|
||||
console.error(`Invalid type for parameter ${parameter.id}`);
|
||||
return "ERROR: Invalid Type"
|
||||
}
|
||||
}
|
||||
|
||||
let parametersTable = document.querySelector("#system-settings table")
|
||||
/* fill in the system settings popup table */
|
||||
function initParameters() {
|
||||
PARAMETERS.forEach(parameter => {
|
||||
var element = getParameterElement(parameter)
|
||||
var note = parameter.note ? `<small>${parameter.note}</small>` : "";
|
||||
var newrow = document.createElement('tr')
|
||||
newrow.innerHTML = `
|
||||
<td><label for="${parameter.id}">${parameter.label}</label></td>
|
||||
<td><div>${element}${note}<div></td>`
|
||||
parametersTable.appendChild(newrow)
|
||||
parameter.settingsEntry = newrow
|
||||
})
|
||||
}
|
||||
|
||||
initParameters()
|
||||
|
||||
let turboField = document.querySelector('#turbo')
|
||||
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 useBetaChannelField = document.querySelector("#use_beta_channel")
|
||||
|
||||
let saveSettingsBtn = document.querySelector('#save-system-settings-btn')
|
||||
|
||||
async function changeAppConfig(configDelta) {
|
||||
try {
|
||||
let res = await fetch('/app_config', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
},
|
||||
body: JSON.stringify(configDelta)
|
||||
})
|
||||
res = await res.json()
|
||||
|
||||
console.log('set config status response', res)
|
||||
} catch (e) {
|
||||
console.log('set config status error', e)
|
||||
}
|
||||
}
|
||||
|
||||
async function getAppConfig() {
|
||||
try {
|
||||
let res = await fetch('/get/app_config')
|
||||
const config = await res.json()
|
||||
|
||||
if (config.update_branch === 'beta') {
|
||||
useBetaChannelField.checked = true
|
||||
}
|
||||
|
||||
console.log('get config status response', config)
|
||||
} catch (e) {
|
||||
console.log('get config status error', e)
|
||||
}
|
||||
}
|
||||
|
||||
saveToDiskField.addEventListener('change', function(e) {
|
||||
diskPathField.disabled = !this.checked
|
||||
})
|
||||
|
||||
function getCurrentRenderDeviceSelection() {
|
||||
let selectedGPUs = $('#use_gpus').val()
|
||||
|
||||
if (useCPUField.checked && !autoPickGPUsField.checked) {
|
||||
return 'cpu'
|
||||
}
|
||||
if (autoPickGPUsField.checked || selectedGPUs.length == 0) {
|
||||
return 'auto'
|
||||
}
|
||||
|
||||
return selectedGPUs.join(',')
|
||||
}
|
||||
|
||||
useCPUField.addEventListener('click', function() {
|
||||
let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
|
||||
let autoPickGPUSettingEntry = getParameterSettingsEntry('auto_pick_gpus')
|
||||
if (this.checked) {
|
||||
gpuSettingEntry.style.display = 'none'
|
||||
autoPickGPUSettingEntry.style.display = 'none'
|
||||
autoPickGPUsField.setAttribute('data-old-value', autoPickGPUsField.checked)
|
||||
autoPickGPUsField.checked = false
|
||||
} else if (useGPUsField.options.length >= MIN_GPUS_TO_SHOW_SELECTION) {
|
||||
gpuSettingEntry.style.display = ''
|
||||
autoPickGPUSettingEntry.style.display = ''
|
||||
let oldVal = autoPickGPUsField.getAttribute('data-old-value')
|
||||
if (oldVal === null || oldVal === undefined) { // the UI started with CPU selected by default
|
||||
autoPickGPUsField.checked = true
|
||||
} else {
|
||||
autoPickGPUsField.checked = (oldVal === 'true')
|
||||
}
|
||||
gpuSettingEntry.style.display = (autoPickGPUsField.checked ? 'none' : '')
|
||||
}
|
||||
})
|
||||
|
||||
useGPUsField.addEventListener('click', function() {
|
||||
let selectedGPUs = $('#use_gpus').val()
|
||||
autoPickGPUsField.checked = (selectedGPUs.length === 0)
|
||||
})
|
||||
|
||||
autoPickGPUsField.addEventListener('click', function() {
|
||||
if (this.checked) {
|
||||
$('#use_gpus').val([])
|
||||
}
|
||||
|
||||
let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
|
||||
gpuSettingEntry.style.display = (this.checked ? 'none' : '')
|
||||
})
|
||||
|
||||
async function getDiskPath() {
|
||||
try {
|
||||
var diskPath = getSetting("diskPath")
|
||||
if (diskPath == '' || diskPath == undefined || diskPath == "undefined") {
|
||||
let res = await fetch('/get/output_dir')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
res = res.output_dir
|
||||
|
||||
setSetting("diskPath", res)
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('error fetching output dir path', e)
|
||||
}
|
||||
}
|
||||
|
||||
async function getDevices() {
|
||||
try {
|
||||
let res = await fetch('/get/devices')
|
||||
if (res.status === 200) {
|
||||
res = await res.json()
|
||||
|
||||
let allDeviceIds = Object.keys(res['all']).filter(d => d !== 'cpu')
|
||||
let activeDeviceIds = Object.keys(res['active']).filter(d => d !== 'cpu')
|
||||
|
||||
if (activeDeviceIds.length === 0) {
|
||||
useCPUField.checked = true
|
||||
}
|
||||
|
||||
if (allDeviceIds.length < MIN_GPUS_TO_SHOW_SELECTION || useCPUField.checked) {
|
||||
let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
|
||||
gpuSettingEntry.style.display = 'none'
|
||||
let autoPickGPUSettingEntry = getParameterSettingsEntry('auto_pick_gpus')
|
||||
autoPickGPUSettingEntry.style.display = 'none'
|
||||
}
|
||||
|
||||
if (allDeviceIds.length === 0) {
|
||||
useCPUField.checked = true
|
||||
useCPUField.disabled = true // no compatible GPUs, so make the CPU mandatory
|
||||
}
|
||||
|
||||
autoPickGPUsField.checked = (res['config'] === 'auto')
|
||||
|
||||
useGPUsField.innerHTML = ''
|
||||
allDeviceIds.forEach(device => {
|
||||
let deviceName = res['all'][device]['name']
|
||||
let deviceOption = `<option value="${device}">${deviceName} (${device})</option>`
|
||||
useGPUsField.insertAdjacentHTML('beforeend', deviceOption)
|
||||
})
|
||||
|
||||
if (autoPickGPUsField.checked) {
|
||||
let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
|
||||
gpuSettingEntry.style.display = 'none'
|
||||
} else {
|
||||
$('#use_gpus').val(activeDeviceIds)
|
||||
}
|
||||
}
|
||||
} catch (e) {
|
||||
console.log('error fetching devices', e)
|
||||
}
|
||||
}
|
||||
|
||||
saveSettingsBtn.addEventListener('click', function() {
|
||||
let updateBranch = (useBetaChannelField.checked ? 'beta' : 'main')
|
||||
|
||||
changeAppConfig({
|
||||
'render_devices': getCurrentRenderDeviceSelection(),
|
||||
'update_branch': updateBranch
|
||||
})
|
||||
})
|
||||
@ -29,12 +29,16 @@ function toggleCollapsible(element) {
|
||||
var handle = element.querySelector(".collapsible-handle");
|
||||
collapsibleHeader.classList.toggle("active")
|
||||
let content = getNextSibling(collapsibleHeader, '.collapsible-content')
|
||||
if (content.style.display === "block") {
|
||||
if (!collapsibleHeader.classList.contains("active")) {
|
||||
content.style.display = "none"
|
||||
handle.innerHTML = '➕' // plus
|
||||
if (handle != null) { // render results don't have a handle
|
||||
handle.innerHTML = '➕' // plus
|
||||
}
|
||||
} else {
|
||||
content.style.display = "block"
|
||||
handle.innerHTML = '➖' // minus
|
||||
if (handle != null) { // render results don't have a handle
|
||||
handle.innerHTML = '➖' // minus
|
||||
}
|
||||
}
|
||||
|
||||
if (COLLAPSIBLES_INITIALIZED && COLLAPSIBLE_PANELS.includes(element)) {
|
||||
@ -65,7 +69,7 @@ function createCollapsibles(node) {
|
||||
let handle = document.createElement('span')
|
||||
handle.className = 'collapsible-handle'
|
||||
|
||||
if (c.className.indexOf('active') !== -1) {
|
||||
if (c.classList.contains("active")) {
|
||||
handle.innerHTML = '➖' // minus
|
||||
} else {
|
||||
handle.innerHTML = '➕' // plus
|
||||
|
||||
@ -18,11 +18,11 @@ class Request:
|
||||
precision: str = "autocast" # or "full"
|
||||
save_to_disk_path: str = None
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
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
|
||||
show_only_filtered_image: bool = False
|
||||
output_format: str = "jpeg" # or "png"
|
||||
|
||||
@ -45,10 +45,11 @@ class Request:
|
||||
"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,
|
||||
"output_format": self.output_format,
|
||||
}
|
||||
|
||||
def to_string(self):
|
||||
def __str__(self):
|
||||
return f'''
|
||||
session_id: {self.session_id}
|
||||
prompt: {self.prompt}
|
||||
@ -62,11 +63,11 @@ class Request:
|
||||
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}
|
||||
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}
|
||||
show_only_filtered_image: {self.show_only_filtered_image}
|
||||
output_format: {self.output_format}
|
||||
|
||||
|
||||
168
ui/sd_internal/device_manager.py
Normal file
168
ui/sd_internal/device_manager.py
Normal file
@ -0,0 +1,168 @@
|
||||
import os
|
||||
import torch
|
||||
import traceback
|
||||
import re
|
||||
|
||||
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
|
||||
|
||||
def get_device_delta(render_devices, active_devices):
|
||||
'''
|
||||
render_devices: 'cpu', or 'auto' or ['cuda:N'...]
|
||||
active_devices: ['cpu', 'cuda:N'...]
|
||||
'''
|
||||
|
||||
if render_devices in ('cpu', 'auto'):
|
||||
render_devices = [render_devices]
|
||||
elif render_devices is not None:
|
||||
if isinstance(render_devices, str):
|
||||
render_devices = [render_devices]
|
||||
if isinstance(render_devices, list) and len(render_devices) > 0:
|
||||
render_devices = list(filter(lambda x: x.startswith('cuda:'), render_devices))
|
||||
if len(render_devices) == 0:
|
||||
raise Exception('Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "auto"}')
|
||||
|
||||
render_devices = list(filter(lambda x: is_device_compatible(x), render_devices))
|
||||
if len(render_devices) == 0:
|
||||
raise Exception('Sorry, none of the render_devices configured in config.json are compatible with Stable Diffusion')
|
||||
else:
|
||||
raise Exception('Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "auto"}')
|
||||
else:
|
||||
render_devices = ['auto']
|
||||
|
||||
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!')
|
||||
|
||||
active_devices = set(active_devices)
|
||||
render_devices = set(render_devices)
|
||||
|
||||
devices_to_start = render_devices - active_devices
|
||||
devices_to_stop = active_devices - render_devices
|
||||
|
||||
return devices_to_start, devices_to_stop
|
||||
|
||||
def auto_pick_devices(currently_active_devices):
|
||||
global mem_free_threshold
|
||||
|
||||
if not torch.cuda.is_available(): return ['cpu']
|
||||
|
||||
device_count = torch.cuda.device_count()
|
||||
if device_count == 1:
|
||||
return ['cuda:0'] if is_device_compatible('cuda:0') else ['cpu']
|
||||
|
||||
print('Autoselecting GPU. Using most free memory.')
|
||||
devices = []
|
||||
for device in range(device_count):
|
||||
device = f'cuda:{device}'
|
||||
if not is_device_compatible(device):
|
||||
continue
|
||||
|
||||
mem_free, mem_total = torch.cuda.mem_get_info(device)
|
||||
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')
|
||||
devices.append({'device': device, 'device_name': device_name, 'mem_free': mem_free})
|
||||
|
||||
devices.sort(key=lambda x:x['mem_free'], reverse=True)
|
||||
max_mem_free = devices[0]['mem_free']
|
||||
curr_mem_free_threshold = COMPARABLE_GPU_PERCENTILE * max_mem_free
|
||||
mem_free_threshold = max(curr_mem_free_threshold, mem_free_threshold)
|
||||
|
||||
# Auto-pick algorithm:
|
||||
# 1. Pick the top 75 percentile of the GPUs, sorted by free_mem.
|
||||
# 2. Also include already-running devices (GPU-only), otherwise their free_mem will
|
||||
# always be very low (since their VRAM contains the model).
|
||||
# These already-running devices probably aren't terrible, since they were picked in the past.
|
||||
# Worst case, the user can restart the program and that'll get rid of them.
|
||||
devices = list(filter((lambda x: x['mem_free'] > mem_free_threshold or x['device'] in currently_active_devices), devices))
|
||||
devices = list(map(lambda x: x['device'], devices))
|
||||
return devices
|
||||
|
||||
def device_init(thread_data, device):
|
||||
'''
|
||||
This function assumes the 'device' has already been verified to be compatible.
|
||||
`get_device_delta()` has already filtered out incompatible devices.
|
||||
'''
|
||||
|
||||
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)
|
||||
return
|
||||
|
||||
thread_data.device_name = torch.cuda.get_device_name(device)
|
||||
thread_data.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)
|
||||
if thread_data.force_full_precision:
|
||||
print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', thread_data.device_name)
|
||||
# Apply force_full_precision now before models are loaded.
|
||||
thread_data.precision = 'full'
|
||||
|
||||
print(f'Setting {device} as active')
|
||||
torch.cuda.device(device)
|
||||
|
||||
return
|
||||
|
||||
def validate_device_id(device, log_prefix=''):
|
||||
def is_valid():
|
||||
if not isinstance(device, str):
|
||||
return False
|
||||
if device == 'cpu':
|
||||
return True
|
||||
if not device.startswith('cuda:') or not device[5:].isnumeric():
|
||||
return False
|
||||
return True
|
||||
|
||||
if not is_valid():
|
||||
raise EnvironmentError(f"{log_prefix}: device id should be 'cpu', or 'cuda:N' (where N is an integer index for the GPU). Got: {device}")
|
||||
|
||||
def is_device_compatible(device):
|
||||
'''
|
||||
Returns True/False, and prints any compatibility errors
|
||||
'''
|
||||
try:
|
||||
validate_device_id(device, log_prefix='is_device_compatible')
|
||||
except:
|
||||
print(str(e))
|
||||
return False
|
||||
|
||||
if device == 'cpu': return True
|
||||
# Memory check
|
||||
try:
|
||||
_, 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')
|
||||
return False
|
||||
except RuntimeError as e:
|
||||
print(str(e))
|
||||
return False
|
||||
return True
|
||||
|
||||
def get_processor_name():
|
||||
try:
|
||||
import platform, subprocess
|
||||
if platform.system() == "Windows":
|
||||
return platform.processor()
|
||||
elif platform.system() == "Darwin":
|
||||
os.environ['PATH'] = os.environ['PATH'] + os.pathsep + '/usr/sbin'
|
||||
command = "sysctl -n machdep.cpu.brand_string"
|
||||
return subprocess.check_output(command).strip()
|
||||
elif platform.system() == "Linux":
|
||||
command = "cat /proc/cpuinfo"
|
||||
all_info = subprocess.check_output(command, shell=True).decode().strip()
|
||||
for line in all_info.split("\n"):
|
||||
if "model name" in line:
|
||||
return re.sub(".*model name.*:", "", line, 1).strip()
|
||||
except:
|
||||
print(traceback.format_exc())
|
||||
return "cpu"
|
||||
@ -1,8 +1,15 @@
|
||||
"""runtime.py: torch device owned by a thread.
|
||||
Notes:
|
||||
Avoid device switching, transfering all models will get too complex.
|
||||
To use a diffrent device signal the current render device to exit
|
||||
And then start a new clean thread for the new device.
|
||||
"""
|
||||
import json
|
||||
import os, re
|
||||
import traceback
|
||||
import torch
|
||||
import numpy as np
|
||||
from gc import collect as gc_collect
|
||||
from omegaconf import OmegaConf
|
||||
from PIL import Image, ImageOps
|
||||
from tqdm import tqdm, trange
|
||||
@ -28,70 +35,64 @@ logging.set_verbosity_error()
|
||||
# consts
|
||||
config_yaml = "optimizedSD/v1-inference.yaml"
|
||||
filename_regex = re.compile('[^a-zA-Z0-9]')
|
||||
force_gfpgan_to_cuda0 = True # workaround: gfpgan currently works only on cuda:0
|
||||
|
||||
# api stuff
|
||||
from sd_internal import device_manager
|
||||
from . import Request, Response, Image as ResponseImage
|
||||
import base64
|
||||
from io import BytesIO
|
||||
#from colorama import Fore
|
||||
|
||||
# local
|
||||
stop_processing = False
|
||||
temp_images = {}
|
||||
from threading import local as LocalThreadVars
|
||||
thread_data = LocalThreadVars()
|
||||
|
||||
ckpt_file = None
|
||||
gfpgan_file = None
|
||||
real_esrgan_file = None
|
||||
def thread_init(device):
|
||||
# Thread bound properties
|
||||
thread_data.stop_processing = False
|
||||
thread_data.temp_images = {}
|
||||
|
||||
model = None
|
||||
modelCS = None
|
||||
modelFS = None
|
||||
model_gfpgan = None
|
||||
model_real_esrgan = None
|
||||
thread_data.ckpt_file = None
|
||||
thread_data.vae_file = None
|
||||
thread_data.gfpgan_file = None
|
||||
thread_data.real_esrgan_file = None
|
||||
|
||||
model_is_half = False
|
||||
model_fs_is_half = False
|
||||
device = None
|
||||
unet_bs = 1
|
||||
precision = 'autocast'
|
||||
sampler_plms = None
|
||||
sampler_ddim = None
|
||||
thread_data.model = None
|
||||
thread_data.modelCS = None
|
||||
thread_data.modelFS = None
|
||||
thread_data.model_gfpgan = None
|
||||
thread_data.model_real_esrgan = None
|
||||
|
||||
has_valid_gpu = False
|
||||
force_full_precision = False
|
||||
try:
|
||||
gpu = torch.cuda.current_device()
|
||||
gpu_name = torch.cuda.get_device_name(gpu)
|
||||
print('GPU detected: ', gpu_name)
|
||||
thread_data.model_is_half = False
|
||||
thread_data.model_fs_is_half = False
|
||||
thread_data.device = None
|
||||
thread_data.device_name = None
|
||||
thread_data.unet_bs = 1
|
||||
thread_data.precision = 'autocast'
|
||||
thread_data.sampler_plms = None
|
||||
thread_data.sampler_ddim = None
|
||||
|
||||
force_full_precision = ('nvidia' in gpu_name.lower() or 'geforce' in gpu_name.lower()) and (' 1660' in gpu_name or ' 1650' in gpu_name) # otherwise these NVIDIA cards create green images
|
||||
if force_full_precision:
|
||||
print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', gpu_name)
|
||||
thread_data.turbo = False
|
||||
thread_data.force_full_precision = False
|
||||
thread_data.reduced_memory = True
|
||||
|
||||
mem_free, mem_total = torch.cuda.mem_get_info(gpu)
|
||||
mem_total /= float(10**9)
|
||||
if mem_total < 3.0:
|
||||
print("GPUs with less than 3 GB of VRAM are not compatible with Stable Diffusion")
|
||||
raise Exception()
|
||||
device_manager.device_init(thread_data, device)
|
||||
|
||||
has_valid_gpu = True
|
||||
except:
|
||||
print('WARNING: No compatible GPU found. Using the CPU, but this will be very slow!')
|
||||
pass
|
||||
def load_model_ckpt():
|
||||
if not thread_data.ckpt_file: raise ValueError(f'Thread ckpt_file is undefined.')
|
||||
if not os.path.exists(thread_data.ckpt_file + '.ckpt'): raise FileNotFoundError(f'Cannot find {thread_data.ckpt_file}.ckpt')
|
||||
|
||||
def load_model_ckpt(ckpt_to_use, device_to_use='cuda', turbo=False, unet_bs_to_use=1, precision_to_use='autocast'):
|
||||
global ckpt_file, model, modelCS, modelFS, model_is_half, device, unet_bs, precision, model_fs_is_half
|
||||
if not thread_data.precision:
|
||||
thread_data.precision = 'full' if thread_data.force_full_precision else 'autocast'
|
||||
|
||||
device = device_to_use if has_valid_gpu else 'cpu'
|
||||
precision = precision_to_use if not force_full_precision else 'full'
|
||||
unet_bs = unet_bs_to_use
|
||||
if not thread_data.unet_bs:
|
||||
thread_data.unet_bs = 1
|
||||
|
||||
unload_model()
|
||||
if thread_data.device == 'cpu':
|
||||
thread_data.precision = 'full'
|
||||
|
||||
if device == 'cpu':
|
||||
precision = 'full'
|
||||
|
||||
sd = load_model_from_config(f"{ckpt_to_use}.ckpt")
|
||||
print('loading', thread_data.ckpt_file + '.ckpt', 'to device', thread_data.device, 'using precision', thread_data.precision)
|
||||
sd = load_model_from_config(thread_data.ckpt_file + '.ckpt')
|
||||
li, lo = [], []
|
||||
for key, value in sd.items():
|
||||
sp = key.split(".")
|
||||
@ -114,114 +115,205 @@ def load_model_ckpt(ckpt_to_use, device_to_use='cuda', turbo=False, unet_bs_to_u
|
||||
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
|
||||
model.cdevice = torch.device(thread_data.device)
|
||||
model.unet_bs = thread_data.unet_bs
|
||||
model.turbo = thread_data.turbo
|
||||
if thread_data.device != 'cpu':
|
||||
model.to(thread_data.device)
|
||||
#if thread_data.reduced_memory:
|
||||
#model.model1.to("cpu")
|
||||
#model.model2.to("cpu")
|
||||
thread_data.model = model
|
||||
|
||||
modelCS = instantiate_from_config(config.modelCondStage)
|
||||
_, _ = modelCS.load_state_dict(sd, strict=False)
|
||||
modelCS.eval()
|
||||
modelCS.cond_stage_model.device = device
|
||||
modelCS.cond_stage_model.device = torch.device(thread_data.device)
|
||||
if thread_data.device != 'cpu':
|
||||
if thread_data.reduced_memory:
|
||||
modelCS.to('cpu')
|
||||
else:
|
||||
modelCS.to(thread_data.device) # Preload on device if not already there.
|
||||
thread_data.modelCS = modelCS
|
||||
|
||||
modelFS = instantiate_from_config(config.modelFirstStage)
|
||||
_, _ = modelFS.load_state_dict(sd, strict=False)
|
||||
|
||||
if thread_data.vae_file is not None:
|
||||
for model_extension in ['.ckpt', '.vae.pt']:
|
||||
if os.path.exists(thread_data.vae_file + model_extension):
|
||||
print(f"Loading VAE weights from: {thread_data.vae_file}{model_extension}")
|
||||
vae_ckpt = torch.load(thread_data.vae_file + model_extension, map_location="cpu")
|
||||
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
|
||||
modelFS.first_stage_model.load_state_dict(vae_dict, strict=False)
|
||||
break
|
||||
else:
|
||||
print(f'Cannot find VAE file: {thread_data.vae_file}{model_extension}')
|
||||
|
||||
modelFS.eval()
|
||||
if thread_data.device != 'cpu':
|
||||
if thread_data.reduced_memory:
|
||||
modelFS.to('cpu')
|
||||
else:
|
||||
modelFS.to(thread_data.device) # Preload on device if not already there.
|
||||
thread_data.modelFS = modelFS
|
||||
del sd
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
model.half()
|
||||
modelCS.half()
|
||||
modelFS.half()
|
||||
model_is_half = True
|
||||
model_fs_is_half = True
|
||||
if thread_data.device != "cpu" and thread_data.precision == "autocast":
|
||||
thread_data.model.half()
|
||||
thread_data.modelCS.half()
|
||||
thread_data.modelFS.half()
|
||||
thread_data.model_is_half = True
|
||||
thread_data.model_fs_is_half = True
|
||||
else:
|
||||
model_is_half = False
|
||||
model_fs_is_half = False
|
||||
thread_data.model_is_half = False
|
||||
thread_data.model_fs_is_half = False
|
||||
|
||||
ckpt_file = ckpt_to_use
|
||||
print(f'''loaded model
|
||||
model file: {thread_data.ckpt_file}.ckpt
|
||||
model.device: {model.device}
|
||||
modelCS.device: {modelCS.cond_stage_model.device}
|
||||
modelFS.device: {thread_data.modelFS.device}
|
||||
using precision: {thread_data.precision}''')
|
||||
|
||||
print('loaded ', ckpt_file, 'to', device, 'precision', precision)
|
||||
def unload_filters():
|
||||
if thread_data.model_gfpgan is not None:
|
||||
if thread_data.device != 'cpu': thread_data.model_gfpgan.gfpgan.to('cpu')
|
||||
|
||||
def unload_model():
|
||||
global model, modelCS, modelFS
|
||||
del thread_data.model_gfpgan
|
||||
thread_data.model_gfpgan = None
|
||||
|
||||
if model is not None:
|
||||
del model
|
||||
del modelCS
|
||||
del modelFS
|
||||
if thread_data.model_real_esrgan is not None:
|
||||
if thread_data.device != 'cpu': thread_data.model_real_esrgan.model.to('cpu')
|
||||
|
||||
model = None
|
||||
modelCS = None
|
||||
modelFS = None
|
||||
del thread_data.model_real_esrgan
|
||||
thread_data.model_real_esrgan = None
|
||||
|
||||
def load_model_gfpgan(gfpgan_to_use):
|
||||
global gfpgan_file, model_gfpgan
|
||||
gc()
|
||||
|
||||
if gfpgan_to_use is None:
|
||||
return
|
||||
def unload_models():
|
||||
if thread_data.model is not None:
|
||||
print('Unloading models...')
|
||||
if thread_data.device != 'cpu':
|
||||
thread_data.modelFS.to('cpu')
|
||||
thread_data.modelCS.to('cpu')
|
||||
thread_data.model.model1.to("cpu")
|
||||
thread_data.model.model2.to("cpu")
|
||||
|
||||
gfpgan_file = gfpgan_to_use
|
||||
model_path = gfpgan_to_use + ".pth"
|
||||
del thread_data.model
|
||||
del thread_data.modelCS
|
||||
del thread_data.modelFS
|
||||
|
||||
if device == 'cpu':
|
||||
model_gfpgan = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cpu'))
|
||||
else:
|
||||
model_gfpgan = GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=torch.device('cuda'))
|
||||
thread_data.model = None
|
||||
thread_data.modelCS = None
|
||||
thread_data.modelFS = None
|
||||
|
||||
print('loaded ', gfpgan_to_use, 'to', device, 'precision', precision)
|
||||
gc()
|
||||
|
||||
def load_model_real_esrgan(real_esrgan_to_use):
|
||||
global real_esrgan_file, model_real_esrgan
|
||||
def wait_model_move_to(model, target_device): # Send to target_device and wait until complete.
|
||||
if thread_data.device == target_device: return
|
||||
start_mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
|
||||
if start_mem <= 0: return
|
||||
model_name = model.__class__.__name__
|
||||
print(f'Device {thread_data.device} - Sending model {model_name} to {target_device} | Memory transfer starting. Memory Used: {round(start_mem)}Mb')
|
||||
start_time = time.time()
|
||||
model.to(target_device)
|
||||
time_step = start_time
|
||||
WARNING_TIMEOUT = 1.5 # seconds - Show activity in console after timeout.
|
||||
last_mem = start_mem
|
||||
is_transfering = True
|
||||
while is_transfering:
|
||||
time.sleep(0.5) # 500ms
|
||||
mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
|
||||
is_transfering = bool(mem > 0 and mem < last_mem) # still stuff loaded, but less than last time.
|
||||
last_mem = mem
|
||||
if not is_transfering:
|
||||
break;
|
||||
if time.time() - time_step > WARNING_TIMEOUT: # Long delay, print to console to show activity.
|
||||
print(f'Device {thread_data.device} - Waiting for Memory transfer. Memory Used: {round(mem)}Mb, Transfered: {round(start_mem - mem)}Mb')
|
||||
time_step = time.time()
|
||||
print(f'Device {thread_data.device} - {model_name} Moved: {round(start_mem - last_mem)}Mb in {round(time.time() - start_time, 3)} seconds to {target_device}')
|
||||
|
||||
if real_esrgan_to_use is None:
|
||||
return
|
||||
def load_model_gfpgan():
|
||||
if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
|
||||
model_path = thread_data.gfpgan_file + ".pth"
|
||||
device = 'cuda:0' if force_gfpgan_to_cuda0 else thread_data.device
|
||||
thread_data.model_gfpgan = GFPGANer(device=torch.device(device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
|
||||
print('loaded', thread_data.gfpgan_file, 'to', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
|
||||
|
||||
real_esrgan_file = real_esrgan_to_use
|
||||
model_path = real_esrgan_to_use + ".pth"
|
||||
def load_model_real_esrgan():
|
||||
if thread_data.real_esrgan_file is None: raise ValueError(f'Thread real_esrgan_file is undefined.')
|
||||
model_path = thread_data.real_esrgan_file + ".pth"
|
||||
|
||||
RealESRGAN_models = {
|
||||
'RealESRGAN_x4plus': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4),
|
||||
'RealESRGAN_x4plus_anime_6B': RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
|
||||
}
|
||||
|
||||
model_to_use = RealESRGAN_models[real_esrgan_to_use]
|
||||
model_to_use = RealESRGAN_models[thread_data.real_esrgan_file]
|
||||
|
||||
if device == 'cpu':
|
||||
model_real_esrgan = RealESRGANer(scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=False) # cpu does not support half
|
||||
model_real_esrgan.device = torch.device('cpu')
|
||||
model_real_esrgan.model.to('cpu')
|
||||
if thread_data.device == 'cpu':
|
||||
thread_data.model_real_esrgan = RealESRGANer(device=torch.device(thread_data.device), scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=False) # cpu does not support half
|
||||
#thread_data.model_real_esrgan.device = torch.device(thread_data.device)
|
||||
thread_data.model_real_esrgan.model.to('cpu')
|
||||
else:
|
||||
model_real_esrgan = RealESRGANer(scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=model_is_half)
|
||||
thread_data.model_real_esrgan = RealESRGANer(device=torch.device(thread_data.device), scale=2, model_path=model_path, model=model_to_use, pre_pad=0, half=thread_data.model_is_half)
|
||||
|
||||
model_real_esrgan.model.name = real_esrgan_to_use
|
||||
thread_data.model_real_esrgan.model.name = thread_data.real_esrgan_file
|
||||
print('loaded ', thread_data.real_esrgan_file, 'to', thread_data.model_real_esrgan.device, 'precision', thread_data.precision)
|
||||
|
||||
print('loaded ', real_esrgan_to_use, 'to', device, 'precision', precision)
|
||||
|
||||
def get_session_out_path(disk_path, session_id):
|
||||
if disk_path is None: return None
|
||||
if session_id is None: return None
|
||||
|
||||
session_out_path = os.path.join(disk_path, filename_regex.sub('_',session_id))
|
||||
os.makedirs(session_out_path, exist_ok=True)
|
||||
return session_out_path
|
||||
|
||||
def get_base_path(disk_path, session_id, prompt, img_id, ext, suffix=None):
|
||||
if disk_path is None: return None
|
||||
if session_id is None: return None
|
||||
if ext is None: raise Exception('Missing ext')
|
||||
|
||||
session_out_path = os.path.join(disk_path, session_id)
|
||||
os.makedirs(session_out_path, exist_ok=True)
|
||||
session_out_path = get_session_out_path(disk_path, session_id)
|
||||
|
||||
prompt_flattened = filename_regex.sub('_', prompt)[:50]
|
||||
|
||||
|
||||
if suffix is not None:
|
||||
return os.path.join(session_out_path, f"{prompt_flattened}_{img_id}_{suffix}.{ext}")
|
||||
return os.path.join(session_out_path, f"{prompt_flattened}_{img_id}.{ext}")
|
||||
|
||||
def apply_filters(filter_name, image_data):
|
||||
def apply_filters(filter_name, image_data, model_path=None):
|
||||
print(f'Applying filter {filter_name}...')
|
||||
gc()
|
||||
gc() # Free space before loading new data.
|
||||
|
||||
if filter_name == 'gfpgan':
|
||||
_, _, output = model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
if isinstance(image_data, torch.Tensor):
|
||||
image_data.to('cuda:0' if force_gfpgan_to_cuda0 else thread_data.device)
|
||||
|
||||
if model_path is not None and model_path != thread_data.gfpgan_file:
|
||||
thread_data.gfpgan_file = model_path
|
||||
load_model_gfpgan()
|
||||
elif not thread_data.model_gfpgan:
|
||||
load_model_gfpgan()
|
||||
if thread_data.model_gfpgan is None: raise Exception('Model "gfpgan" not loaded.')
|
||||
print('enhance with', thread_data.gfpgan_file, 'on', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
|
||||
_, _, output = thread_data.model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
image_data = output[:,:,::-1]
|
||||
|
||||
if filter_name == 'real_esrgan':
|
||||
output, _ = model_real_esrgan.enhance(image_data[:,:,::-1])
|
||||
if isinstance(image_data, torch.Tensor):
|
||||
image_data.to(thread_data.device)
|
||||
|
||||
if model_path is not None and model_path != thread_data.real_esrgan_file:
|
||||
thread_data.real_esrgan_file = model_path
|
||||
load_model_real_esrgan()
|
||||
elif not thread_data.model_real_esrgan:
|
||||
load_model_real_esrgan()
|
||||
if thread_data.model_real_esrgan is None: raise Exception('Model "gfpgan" not loaded.')
|
||||
print('enhance with', thread_data.real_esrgan_file, 'on', thread_data.model_real_esrgan.device, 'precision', thread_data.precision)
|
||||
output, _ = thread_data.model_real_esrgan.enhance(image_data[:,:,::-1])
|
||||
image_data = output[:,:,::-1]
|
||||
|
||||
return image_data
|
||||
@ -232,83 +324,102 @@ def mk_img(req: Request):
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
|
||||
gc()
|
||||
|
||||
if device != "cpu":
|
||||
modelFS.to("cpu")
|
||||
modelCS.to("cpu")
|
||||
|
||||
model.model1.to("cpu")
|
||||
model.model2.to("cpu")
|
||||
|
||||
gc()
|
||||
if thread_data.device != 'cpu':
|
||||
thread_data.modelFS.to('cpu')
|
||||
thread_data.modelCS.to('cpu')
|
||||
thread_data.model.model1.to("cpu")
|
||||
thread_data.model.model2.to("cpu")
|
||||
|
||||
gc() # Release from memory.
|
||||
yield json.dumps({
|
||||
"status": 'failed',
|
||||
"detail": str(e)
|
||||
})
|
||||
|
||||
def do_mk_img(req: Request):
|
||||
global ckpt_file
|
||||
global model, modelCS, modelFS, device
|
||||
global model_gfpgan, model_real_esrgan
|
||||
global stop_processing
|
||||
def update_temp_img(req, x_samples):
|
||||
partial_images = []
|
||||
for i in range(req.num_outputs):
|
||||
x_sample_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
x_sample = torch.clamp((x_sample_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")
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
img = Image.fromarray(x_sample)
|
||||
buf = BytesIO()
|
||||
img.save(buf, format='JPEG')
|
||||
buf.seek(0)
|
||||
|
||||
stop_processing = False
|
||||
del img, x_sample, x_sample_ddim
|
||||
# don't delete x_samples, it is used in the code that called this callback
|
||||
|
||||
thread_data.temp_images[str(req.session_id) + '/' + str(i)] = buf
|
||||
partial_images.append({'path': f'/image/tmp/{req.session_id}/{i}'})
|
||||
return partial_images
|
||||
|
||||
# Build and return the apropriate generator for do_mk_img
|
||||
def get_image_progress_generator(req, extra_props=None):
|
||||
if not req.stream_progress_updates:
|
||||
def empty_callback(x_samples, i): return x_samples
|
||||
return empty_callback
|
||||
|
||||
thread_data.partial_x_samples = None
|
||||
last_callback_time = -1
|
||||
def img_callback(x_samples, i):
|
||||
nonlocal last_callback_time
|
||||
|
||||
thread_data.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}
|
||||
if extra_props is not None:
|
||||
progress.update(extra_props)
|
||||
|
||||
if req.stream_image_progress and i % 5 == 0:
|
||||
progress['output'] = update_temp_img(req, x_samples)
|
||||
|
||||
yield json.dumps(progress)
|
||||
|
||||
if thread_data.stop_processing:
|
||||
raise UserInitiatedStop("User requested that we stop processing")
|
||||
return img_callback
|
||||
|
||||
def do_mk_img(req: Request):
|
||||
thread_data.stop_processing = False
|
||||
|
||||
res = Response()
|
||||
res.request = req
|
||||
res.images = []
|
||||
|
||||
temp_images.clear()
|
||||
thread_data.temp_images.clear()
|
||||
|
||||
# custom model support:
|
||||
# the req.use_stable_diffusion_model needs to be a valid path
|
||||
# to the ckpt file (without the extension).
|
||||
if not os.path.exists(req.use_stable_diffusion_model + '.ckpt'): raise FileNotFoundError(f'Cannot find {req.use_stable_diffusion_model}.ckpt')
|
||||
|
||||
needs_model_reload = False
|
||||
ckpt_to_use = ckpt_file
|
||||
if ckpt_to_use != req.use_stable_diffusion_model:
|
||||
ckpt_to_use = req.use_stable_diffusion_model
|
||||
if not thread_data.model or thread_data.ckpt_file != req.use_stable_diffusion_model or thread_data.vae_file != req.use_vae_model:
|
||||
thread_data.ckpt_file = req.use_stable_diffusion_model
|
||||
thread_data.vae_file = req.use_vae_model
|
||||
needs_model_reload = True
|
||||
|
||||
model.turbo = req.turbo
|
||||
if req.use_cpu:
|
||||
if device != 'cpu':
|
||||
device = 'cpu'
|
||||
|
||||
if model_is_half:
|
||||
load_model_ckpt(ckpt_to_use, device)
|
||||
needs_model_reload = False
|
||||
|
||||
load_model_gfpgan(gfpgan_file)
|
||||
load_model_real_esrgan(real_esrgan_file)
|
||||
else:
|
||||
if has_valid_gpu:
|
||||
prev_device = device
|
||||
device = 'cuda'
|
||||
|
||||
if (precision == 'autocast' and (req.use_full_precision or not model_is_half)) or \
|
||||
(precision == 'full' and not req.use_full_precision and not force_full_precision):
|
||||
|
||||
load_model_ckpt(ckpt_to_use, device, req.turbo, unet_bs, ('full' if req.use_full_precision else 'autocast'))
|
||||
needs_model_reload = False
|
||||
|
||||
if prev_device != device:
|
||||
load_model_gfpgan(gfpgan_file)
|
||||
load_model_real_esrgan(real_esrgan_file)
|
||||
if thread_data.device != 'cpu':
|
||||
if (thread_data.precision == 'autocast' and (req.use_full_precision or not thread_data.model_is_half)) or \
|
||||
(thread_data.precision == 'full' and not req.use_full_precision and not thread_data.force_full_precision):
|
||||
thread_data.precision = 'full' if req.use_full_precision else 'autocast'
|
||||
needs_model_reload = True
|
||||
|
||||
if needs_model_reload:
|
||||
load_model_ckpt(ckpt_to_use, device, req.turbo, unet_bs, precision)
|
||||
unload_models()
|
||||
unload_filters()
|
||||
load_model_ckpt()
|
||||
|
||||
if req.use_face_correction != gfpgan_file:
|
||||
load_model_gfpgan(req.use_face_correction)
|
||||
if thread_data.turbo != req.turbo:
|
||||
thread_data.turbo = req.turbo
|
||||
thread_data.model.turbo = req.turbo
|
||||
|
||||
if req.use_upscale != real_esrgan_file:
|
||||
load_model_real_esrgan(req.use_upscale)
|
||||
|
||||
model.cdevice = device
|
||||
modelCS.cond_stage_model.device = device
|
||||
# Start by cleaning memory, loading and unloading things can leave memory allocated.
|
||||
gc()
|
||||
|
||||
opt_prompt = req.prompt
|
||||
opt_seed = req.seed
|
||||
@ -316,11 +427,9 @@ def do_mk_img(req: Request):
|
||||
opt_C = 4
|
||||
opt_f = 8
|
||||
opt_ddim_eta = 0.0
|
||||
opt_init_img = req.init_image
|
||||
|
||||
print(req.to_string(), '\n device', device)
|
||||
|
||||
print('\n\n Using precision:', precision)
|
||||
print(req, '\n device', torch.device(thread_data.device), "as", thread_data.device_name)
|
||||
print('\n\n Using precision:', thread_data.precision)
|
||||
|
||||
seed_everything(opt_seed)
|
||||
|
||||
@ -329,7 +438,7 @@ def do_mk_img(req: Request):
|
||||
assert prompt is not None
|
||||
data = [batch_size * [prompt]]
|
||||
|
||||
if precision == "autocast" and device != "cpu":
|
||||
if thread_data.precision == "autocast" and thread_data.device != "cpu":
|
||||
precision_scope = autocast
|
||||
else:
|
||||
precision_scope = nullcontext
|
||||
@ -345,46 +454,46 @@ def do_mk_img(req: Request):
|
||||
handler = _img2img
|
||||
|
||||
init_image = load_img(req.init_image, req.width, req.height)
|
||||
init_image = init_image.to(device)
|
||||
init_image = init_image.to(thread_data.device)
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
if thread_data.device != "cpu" and thread_data.precision == "autocast":
|
||||
init_image = init_image.half()
|
||||
|
||||
modelFS.to(device)
|
||||
thread_data.modelFS.to(thread_data.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
|
||||
init_latent = thread_data.modelFS.get_first_stage_encoding(thread_data.modelFS.encode_first_stage(init_image)) # move to latent space
|
||||
|
||||
if req.mask is not None:
|
||||
mask = load_mask(req.mask, req.width, req.height, init_latent.shape[2], init_latent.shape[3], True).to(device)
|
||||
mask = load_mask(req.mask, req.width, req.height, init_latent.shape[2], init_latent.shape[3], True).to(thread_data.device)
|
||||
mask = mask[0][0].unsqueeze(0).repeat(4, 1, 1).unsqueeze(0)
|
||||
mask = repeat(mask, '1 ... -> b ...', b=batch_size)
|
||||
|
||||
if device != "cpu" and precision == "autocast":
|
||||
if thread_data.device != "cpu" and thread_data.precision == "autocast":
|
||||
mask = mask.half()
|
||||
|
||||
move_fs_to_cpu()
|
||||
# Send to CPU and wait until complete.
|
||||
wait_model_move_to(thread_data.modelFS, 'cpu')
|
||||
|
||||
assert 0. <= req.prompt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(req.prompt_strength * req.num_inference_steps)
|
||||
print(f"target t_enc is {t_enc} steps")
|
||||
|
||||
if req.save_to_disk_path is not None:
|
||||
session_out_path = os.path.join(req.save_to_disk_path, req.session_id)
|
||||
os.makedirs(session_out_path, exist_ok=True)
|
||||
session_out_path = get_session_out_path(req.save_to_disk_path, req.session_id)
|
||||
else:
|
||||
session_out_path = None
|
||||
|
||||
seeds = ""
|
||||
with torch.no_grad():
|
||||
for n in trange(opt_n_iter, desc="Sampling"):
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
|
||||
with precision_scope("cuda"):
|
||||
modelCS.to(device)
|
||||
if thread_data.reduced_memory:
|
||||
thread_data.modelCS.to(thread_data.device)
|
||||
uc = None
|
||||
if req.guidance_scale != 1.0:
|
||||
uc = modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
uc = thread_data.modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
|
||||
@ -397,85 +506,65 @@ def do_mk_img(req: Request):
|
||||
weight = weights[i]
|
||||
# if not skip_normalize:
|
||||
weight = weight / totalWeight
|
||||
c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
c = torch.add(c, thread_data.modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
else:
|
||||
c = modelCS.get_learned_conditioning(prompts)
|
||||
c = thread_data.modelCS.get_learned_conditioning(prompts)
|
||||
|
||||
modelFS.to(device)
|
||||
if thread_data.reduced_memory:
|
||||
thread_data.modelFS.to(thread_data.device)
|
||||
|
||||
partial_x_samples = None
|
||||
last_callback_time = -1
|
||||
def img_callback(x_samples, i):
|
||||
nonlocal partial_x_samples, last_callback_time
|
||||
|
||||
partial_x_samples = x_samples
|
||||
|
||||
if req.stream_progress_updates:
|
||||
n_steps = req.num_inference_steps if req.init_image is None else t_enc
|
||||
step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
|
||||
last_callback_time = time.time()
|
||||
|
||||
progress = {"step": i, "total_steps": n_steps, "step_time": step_time}
|
||||
|
||||
if req.stream_image_progress and i % 5 == 0:
|
||||
partial_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")
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
img = Image.fromarray(x_sample)
|
||||
buf = BytesIO()
|
||||
img.save(buf, format='JPEG')
|
||||
buf.seek(0)
|
||||
|
||||
del img, x_sample, x_samples_ddim
|
||||
# don't delete x_samples, it is used in the code that called this callback
|
||||
|
||||
temp_images[str(req.session_id) + '/' + str(i)] = buf
|
||||
partial_images.append({'path': f'/image/tmp/{req.session_id}/{i}'})
|
||||
|
||||
progress['output'] = partial_images
|
||||
|
||||
yield json.dumps(progress)
|
||||
|
||||
if stop_processing:
|
||||
raise UserInitiatedStop("User requested that we stop processing")
|
||||
n_steps = req.num_inference_steps if req.init_image is None else t_enc
|
||||
img_callback = get_image_progress_generator(req, {"total_steps": n_steps})
|
||||
|
||||
# run the handler
|
||||
try:
|
||||
print('Running handler...')
|
||||
if handler == _txt2img:
|
||||
x_samples = _txt2img(req.width, req.height, req.num_outputs, req.num_inference_steps, req.guidance_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, req.sampler)
|
||||
else:
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, req.guidance_scale, c, uc, req.num_inference_steps, opt_ddim_eta, opt_seed, img_callback, mask)
|
||||
|
||||
yield from x_samples
|
||||
|
||||
x_samples = partial_x_samples
|
||||
if req.stream_progress_updates:
|
||||
yield from x_samples
|
||||
if hasattr(thread_data, 'partial_x_samples'):
|
||||
if thread_data.partial_x_samples is not None:
|
||||
x_samples = thread_data.partial_x_samples
|
||||
del thread_data.partial_x_samples
|
||||
except UserInitiatedStop:
|
||||
if partial_x_samples is None:
|
||||
if not hasattr(thread_data, 'partial_x_samples'):
|
||||
continue
|
||||
if thread_data.partial_x_samples is None:
|
||||
del thread_data.partial_x_samples
|
||||
continue
|
||||
x_samples = thread_data.partial_x_samples
|
||||
del thread_data.partial_x_samples
|
||||
|
||||
x_samples = partial_x_samples
|
||||
|
||||
print("saving images")
|
||||
print("decoding images")
|
||||
img_data = [None] * batch_size
|
||||
for i in range(batch_size):
|
||||
img_id = base64.b64encode(int(time.time()+i).to_bytes(8, 'big')).decode() # Generate unique ID based on time.
|
||||
img_id = img_id.translate({43:None, 47:None, 61:None})[-8:] # Remove + / = and keep last 8 chars.
|
||||
|
||||
x_samples_ddim = modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
x_samples_ddim = thread_data.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")
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
img = Image.fromarray(x_sample)
|
||||
img_data[i] = x_sample
|
||||
del x_samples, x_samples_ddim, x_sample
|
||||
|
||||
if thread_data.reduced_memory:
|
||||
# Send to CPU and wait until complete.
|
||||
wait_model_move_to(thread_data.modelFS, 'cpu')
|
||||
|
||||
print("saving images")
|
||||
for i in range(batch_size):
|
||||
img = Image.fromarray(img_data[i])
|
||||
img_id = base64.b64encode(int(time.time()+i).to_bytes(8, 'big')).decode() # Generate unique ID based on time.
|
||||
img_id = img_id.translate({43:None, 47:None, 61:None})[-8:] # Remove + / = and keep last 8 chars.
|
||||
|
||||
has_filters = (req.use_face_correction is not None and req.use_face_correction.startswith('GFPGAN')) or \
|
||||
(req.use_upscale is not None and req.use_upscale.startswith('RealESRGAN'))
|
||||
|
||||
return_orig_img = not has_filters or not req.show_only_filtered_image
|
||||
|
||||
if stop_processing:
|
||||
if thread_data.stop_processing:
|
||||
return_orig_img = True
|
||||
|
||||
if req.save_to_disk_path is not None:
|
||||
@ -486,25 +575,24 @@ def do_mk_img(req: Request):
|
||||
save_metadata(meta_out_path, req, prompts[0], opt_seed)
|
||||
|
||||
if return_orig_img:
|
||||
img_data = img_to_base64_str(img, req.output_format)
|
||||
res_image_orig = ResponseImage(data=img_data, seed=opt_seed)
|
||||
img_str = img_to_base64_str(img, req.output_format)
|
||||
res_image_orig = ResponseImage(data=img_str, seed=opt_seed)
|
||||
res.images.append(res_image_orig)
|
||||
|
||||
if req.save_to_disk_path is not None:
|
||||
res_image_orig.path_abs = img_out_path
|
||||
|
||||
del img
|
||||
|
||||
if has_filters and not stop_processing:
|
||||
if has_filters and not thread_data.stop_processing:
|
||||
filters_applied = []
|
||||
if req.use_face_correction:
|
||||
x_sample = apply_filters('gfpgan', x_sample)
|
||||
img_data[i] = apply_filters('gfpgan', img_data[i], req.use_face_correction)
|
||||
filters_applied.append(req.use_face_correction)
|
||||
if req.use_upscale:
|
||||
x_sample = apply_filters('real_esrgan', x_sample)
|
||||
img_data[i] = apply_filters('real_esrgan', img_data[i], req.use_upscale)
|
||||
filters_applied.append(req.use_upscale)
|
||||
if (len(filters_applied) > 0):
|
||||
filtered_image = Image.fromarray(x_sample)
|
||||
filtered_image = Image.fromarray(img_data[i])
|
||||
filtered_img_data = img_to_base64_str(filtered_image, req.output_format)
|
||||
response_image = ResponseImage(data=filtered_img_data, seed=opt_seed)
|
||||
res.images.append(response_image)
|
||||
@ -513,17 +601,17 @@ def do_mk_img(req: Request):
|
||||
save_image(filtered_image, filtered_img_out_path)
|
||||
response_image.path_abs = filtered_img_out_path
|
||||
del filtered_image
|
||||
|
||||
seeds += str(opt_seed) + ","
|
||||
# Filter Applied, move to next seed
|
||||
opt_seed += 1
|
||||
|
||||
move_fs_to_cpu()
|
||||
# if thread_data.reduced_memory:
|
||||
# unload_filters()
|
||||
del img_data
|
||||
gc()
|
||||
del x_samples, x_samples_ddim, x_sample
|
||||
print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
|
||||
if thread_data.device != 'cpu':
|
||||
print(f'memory_final = {round(torch.cuda.memory_allocated(thread_data.device) / 1e6, 2)}Mb')
|
||||
|
||||
print('Task completed')
|
||||
|
||||
yield json.dumps(res.json())
|
||||
|
||||
def save_image(img, img_out_path):
|
||||
@ -533,7 +621,7 @@ def save_image(img, img_out_path):
|
||||
print('could not save the file', traceback.format_exc())
|
||||
|
||||
def save_metadata(meta_out_path, req, prompt, opt_seed):
|
||||
metadata = f"""{prompt}
|
||||
metadata = f'''{prompt}
|
||||
Width: {req.width}
|
||||
Height: {req.height}
|
||||
Seed: {opt_seed}
|
||||
@ -544,8 +632,9 @@ Use Face Correction: {req.use_face_correction}
|
||||
Use Upscaling: {req.use_upscale}
|
||||
Sampler: {req.sampler}
|
||||
Negative Prompt: {req.negative_prompt}
|
||||
Stable Diffusion Model: {req.use_stable_diffusion_model + '.ckpt'}
|
||||
"""
|
||||
Stable Diffusion model: {req.use_stable_diffusion_model + '.ckpt'}
|
||||
VAE model: {req.use_vae_model}
|
||||
'''
|
||||
try:
|
||||
with open(meta_out_path, 'w', encoding='utf-8') as f:
|
||||
f.write(metadata)
|
||||
@ -555,16 +644,13 @@ Stable Diffusion Model: {req.use_stable_diffusion_model + '.ckpt'}
|
||||
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, img_callback, mask, sampler_name):
|
||||
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)
|
||||
# Send to CPU and wait until complete.
|
||||
wait_model_move_to(thread_data.modelCS, 'cpu')
|
||||
|
||||
if sampler_name == 'ddim':
|
||||
model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
thread_data.model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
|
||||
samples_ddim = model.sample(
|
||||
samples_ddim = thread_data.model.sample(
|
||||
S=opt_ddim_steps,
|
||||
conditioning=c,
|
||||
seed=opt_seed,
|
||||
@ -578,14 +664,13 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
|
||||
mask=mask,
|
||||
sampler = sampler_name,
|
||||
)
|
||||
|
||||
yield from samples_ddim
|
||||
|
||||
def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask):
|
||||
# encode (scaled latent)
|
||||
z_enc = model.stochastic_encode(
|
||||
z_enc = thread_data.model.stochastic_encode(
|
||||
init_latent,
|
||||
torch.tensor([t_enc] * batch_size).to(device),
|
||||
torch.tensor([t_enc] * batch_size).to(thread_data.device),
|
||||
opt_seed,
|
||||
opt_ddim_eta,
|
||||
opt_ddim_steps,
|
||||
@ -593,7 +678,7 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
|
||||
x_T = None if mask is None else init_latent
|
||||
|
||||
# decode it
|
||||
samples_ddim = model.sample(
|
||||
samples_ddim = thread_data.model.sample(
|
||||
t_enc,
|
||||
c,
|
||||
z_enc,
|
||||
@ -604,20 +689,12 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
|
||||
x_T=x_T,
|
||||
sampler = 'ddim'
|
||||
)
|
||||
|
||||
yield from samples_ddim
|
||||
|
||||
def move_fs_to_cpu():
|
||||
if device != "cpu":
|
||||
mem = torch.cuda.memory_allocated() / 1e6
|
||||
modelFS.to("cpu")
|
||||
while torch.cuda.memory_allocated() / 1e6 >= mem:
|
||||
time.sleep(1)
|
||||
|
||||
def gc():
|
||||
if device == 'cpu':
|
||||
gc_collect()
|
||||
if thread_data.device == 'cpu':
|
||||
return
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
|
||||
@ -627,7 +704,6 @@ 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")
|
||||
@ -689,10 +765,14 @@ def img_to_base64_str(img, output_format="PNG"):
|
||||
img_str = f"data:{mime_type};base64," + base64.b64encode(img_byte).decode()
|
||||
return img_str
|
||||
|
||||
def base64_str_to_img(img_str):
|
||||
def base64_str_to_buffer(img_str):
|
||||
mime_type = "image/png" if img_str.startswith("data:image/png;") else "image/jpeg"
|
||||
img_str = img_str[len(f"data:{mime_type};base64,"):]
|
||||
data = base64.b64decode(img_str)
|
||||
buffered = BytesIO(data)
|
||||
return buffered
|
||||
|
||||
def base64_str_to_img(img_str):
|
||||
buffered = base64_str_to_buffer(img_str)
|
||||
img = Image.open(buffered)
|
||||
return img
|
||||
|
||||
@ -1,13 +1,27 @@
|
||||
"""task_manager.py: manage tasks dispatching and render threads.
|
||||
Notes:
|
||||
render_threads should be the only hard reference held by the manager to the threads.
|
||||
Use weak_thread_data to store all other data using weak keys.
|
||||
This will allow for garbage collection after the thread dies.
|
||||
"""
|
||||
import json
|
||||
import traceback
|
||||
|
||||
TASK_TTL = 15 * 60 # seconds, Discard last session's task timeout
|
||||
|
||||
import queue, threading, time
|
||||
import torch
|
||||
import queue, threading, time, weakref
|
||||
from typing import Any, Generator, Hashable, Optional, Union
|
||||
|
||||
from pydantic import BaseModel
|
||||
from sd_internal import Request, Response
|
||||
from sd_internal import Request, Response, runtime, device_manager
|
||||
|
||||
THREAD_NAME_PREFIX = 'Runtime-Render/'
|
||||
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.
|
||||
|
||||
DEVICE_START_TIMEOUT = 60 # seconds - Maximum time to wait for a render device to init.
|
||||
|
||||
class SymbolClass(type): # Print nicely formatted Symbol names.
|
||||
def __repr__(self): return self.__qualname__
|
||||
@ -25,7 +39,8 @@ class RenderTask(): # Task with output queue and completion lock.
|
||||
def __init__(self, req: Request):
|
||||
self.request: Request = req # Initial Request
|
||||
self.response: Any = None # Copy of the last reponse
|
||||
self.temp_images:[] = [None] * req.num_outputs * (1 if req.show_only_filtered_image else 2)
|
||||
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.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
|
||||
@ -55,28 +70,43 @@ class ImageRequest(BaseModel):
|
||||
# allow_nsfw: bool = False
|
||||
save_to_disk_path: str = None
|
||||
turbo: bool = True
|
||||
use_cpu: bool = False
|
||||
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
|
||||
show_only_filtered_image: bool = False
|
||||
output_format: str = "jpeg" # or "png"
|
||||
|
||||
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"
|
||||
|
||||
# Temporary cache to allow to query tasks results for a short time after they are completed.
|
||||
class TaskCache():
|
||||
def __init__(self):
|
||||
self._base = dict()
|
||||
self._lock: threading.Lock = threading.RLock()
|
||||
self._lock: threading.Lock = threading.Lock()
|
||||
def _get_ttl_time(self, ttl: int) -> int:
|
||||
return int(time.time()) + ttl
|
||||
def _is_expired(self, timestamp: int) -> bool:
|
||||
return int(time.time()) >= timestamp
|
||||
def clean(self) -> None:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.clean failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.clean' + ERR_LOCK_FAILED)
|
||||
try:
|
||||
# Create a list of expired keys to delete
|
||||
to_delete = []
|
||||
@ -91,11 +121,11 @@ class TaskCache():
|
||||
finally:
|
||||
self._lock.release()
|
||||
def clear(self) -> None:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.clear failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.clear' + ERR_LOCK_FAILED)
|
||||
try: self._base.clear()
|
||||
finally: self._lock.release()
|
||||
def delete(self, key: Hashable) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.delete failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.delete' + ERR_LOCK_FAILED)
|
||||
try:
|
||||
if key not in self._base:
|
||||
return False
|
||||
@ -104,7 +134,7 @@ class TaskCache():
|
||||
finally:
|
||||
self._lock.release()
|
||||
def keep(self, key: Hashable, ttl: int) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.keep failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.keep' + ERR_LOCK_FAILED)
|
||||
try:
|
||||
if key in self._base:
|
||||
_, value = self._base.get(key)
|
||||
@ -114,7 +144,7 @@ class TaskCache():
|
||||
finally:
|
||||
self._lock.release()
|
||||
def put(self, key: Hashable, value: Any, ttl: int) -> bool:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.put failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.put' + ERR_LOCK_FAILED)
|
||||
try:
|
||||
self._base[key] = (
|
||||
self._get_ttl_time(ttl), value
|
||||
@ -128,131 +158,343 @@ class TaskCache():
|
||||
finally:
|
||||
self._lock.release()
|
||||
def tryGet(self, key: Hashable) -> Any:
|
||||
if not self._lock.acquire(blocking=True, timeout=10): raise Exception('TaskCache.tryGet failed to acquire lock within timeout.')
|
||||
if not self._lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('TaskCache.tryGet' + ERR_LOCK_FAILED)
|
||||
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.')
|
||||
self.delete(key)
|
||||
del self._base[key]
|
||||
return None
|
||||
return value
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
manager_lock = threading.RLock()
|
||||
render_threads = []
|
||||
current_state = ServerStates.Init
|
||||
current_state_error:Exception = None
|
||||
current_model_path = None
|
||||
tasks_queue = queue.Queue()
|
||||
current_vae_path = None
|
||||
tasks_queue = []
|
||||
task_cache = TaskCache()
|
||||
default_model_to_load = None
|
||||
default_vae_to_load = None
|
||||
weak_thread_data = weakref.WeakKeyDictionary()
|
||||
|
||||
def preload_model(file_path=None):
|
||||
global current_state, current_state_error, current_model_path
|
||||
if file_path == None:
|
||||
file_path = default_model_to_load
|
||||
if file_path == current_model_path:
|
||||
def preload_model(ckpt_file_path=None, vae_file_path=None):
|
||||
global current_state, current_state_error, current_model_path, current_vae_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 ckpt_file_path == current_model_path and vae_file_path == current_vae_path:
|
||||
return
|
||||
current_state = ServerStates.LoadingModel
|
||||
try:
|
||||
from . import runtime
|
||||
runtime.load_model_ckpt(ckpt_to_use=file_path)
|
||||
current_model_path = file_path
|
||||
runtime.thread_data.ckpt_file = ckpt_file_path
|
||||
runtime.thread_data.vae_file = vae_file_path
|
||||
runtime.load_model_ckpt()
|
||||
current_model_path = ckpt_file_path
|
||||
current_vae_path = vae_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_render():
|
||||
global current_state, current_state_error, current_model_path
|
||||
def thread_get_next_task():
|
||||
from . import runtime
|
||||
current_state = ServerStates.Online
|
||||
preload_model()
|
||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
|
||||
print('Render thread on device', runtime.thread_data.device, 'failed to acquire manager lock.')
|
||||
return None
|
||||
if len(tasks_queue) <= 0:
|
||||
manager_lock.release()
|
||||
return None
|
||||
task = None
|
||||
try: # Select a render task.
|
||||
for queued_task in tasks_queue:
|
||||
if queued_task.request.use_face_correction and runtime.thread_data.device == 'cpu' and is_alive() == 1:
|
||||
queued_task.error = Exception('The CPU cannot be used to run this task currently. Please remove "Fix incorrect faces" from Image Settings and try again.')
|
||||
task = queued_task
|
||||
break
|
||||
if queued_task.render_device and runtime.thread_data.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.
|
||||
else:
|
||||
# Requested device is not active, return error to UI.
|
||||
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:
|
||||
# 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
|
||||
break
|
||||
if task is not None:
|
||||
del tasks_queue[tasks_queue.index(task)]
|
||||
return task
|
||||
finally:
|
||||
manager_lock.release()
|
||||
|
||||
def thread_render(device):
|
||||
global current_state, current_state_error, current_model_path, current_vae_path
|
||||
from . import runtime
|
||||
try:
|
||||
runtime.thread_init(device)
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
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,
|
||||
'alive': True
|
||||
}
|
||||
if runtime.thread_data.device != 'cpu' or is_alive() == 1:
|
||||
preload_model()
|
||||
current_state = ServerStates.Online
|
||||
while True:
|
||||
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()
|
||||
return
|
||||
if isinstance(current_state_error, SystemExit):
|
||||
current_state = ServerStates.Unavailable
|
||||
return
|
||||
task = None
|
||||
try:
|
||||
task = tasks_queue.get(timeout=1)
|
||||
except queue.Empty as e:
|
||||
if isinstance(current_state_error, SystemExit):
|
||||
current_state = ServerStates.Unavailable
|
||||
return
|
||||
else: continue
|
||||
#if current_model_path != task.request.use_stable_diffusion_model:
|
||||
# preload_model(task.request.use_stable_diffusion_model)
|
||||
task = thread_get_next_task()
|
||||
if task is None:
|
||||
time.sleep(1)
|
||||
continue
|
||||
if task.error is not None:
|
||||
print(task.error)
|
||||
task.response = {"status": 'failed', "detail": str(task.error)}
|
||||
task.buffer_queue.put(json.dumps(task.response))
|
||||
continue
|
||||
if current_state_error:
|
||||
task.error = current_state_error
|
||||
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)}')
|
||||
print(f'Session {task.request.session_id} starting task {id(task)} on {runtime.thread_data.device_name}')
|
||||
if not task.lock.acquire(blocking=False): raise Exception('Got locked task from queue.')
|
||||
try:
|
||||
task.lock.acquire(blocking=False)
|
||||
if runtime.thread_data.device == 'cpu' and is_alive() > 1:
|
||||
# CPU is not the only device. Keep track of active time to unload resources later.
|
||||
runtime.thread_data.lastActive = time.time()
|
||||
# Open data generator.
|
||||
res = runtime.mk_img(task.request)
|
||||
if current_model_path == task.request.use_stable_diffusion_model:
|
||||
current_state = ServerStates.Rendering
|
||||
else:
|
||||
current_state = ServerStates.LoadingModel
|
||||
# Start reading from generator.
|
||||
dataQueue = None
|
||||
if task.request.stream_progress_updates:
|
||||
dataQueue = task.buffer_queue
|
||||
for result in res:
|
||||
if current_state == ServerStates.LoadingModel:
|
||||
current_state = ServerStates.Rendering
|
||||
current_model_path = task.request.use_stable_diffusion_model
|
||||
current_vae_path = task.request.use_vae_model
|
||||
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
||||
runtime.thread_data.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)}')
|
||||
if dataQueue:
|
||||
dataQueue.put(result)
|
||||
if isinstance(result, str):
|
||||
result = json.loads(result)
|
||||
task.response = result
|
||||
if 'output' in result:
|
||||
for out_obj in result['output']:
|
||||
if 'path' in out_obj:
|
||||
img_id = out_obj['path'][out_obj['path'].rindex('/') + 1:]
|
||||
task.temp_images[int(img_id)] = runtime.thread_data.temp_images[out_obj['path'][11:]]
|
||||
elif 'data' in out_obj:
|
||||
buf = runtime.base64_str_to_buffer(out_obj['data'])
|
||||
task.temp_images[result['output'].index(out_obj)] = buf
|
||||
# Before looping back to the generator, mark cache as still alive.
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
except Exception as e:
|
||||
task.error = e
|
||||
task.lock.release()
|
||||
tasks_queue.task_done()
|
||||
print(traceback.format_exc())
|
||||
continue
|
||||
dataQueue = None
|
||||
if task.request.stream_progress_updates:
|
||||
dataQueue = task.buffer_queue
|
||||
for result in res:
|
||||
if current_state == ServerStates.LoadingModel:
|
||||
current_state = ServerStates.Rendering
|
||||
current_model_path = task.request.use_stable_diffusion_model
|
||||
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
||||
runtime.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)}')
|
||||
if dataQueue:
|
||||
dataQueue.put(result)
|
||||
if isinstance(result, str):
|
||||
result = json.loads(result)
|
||||
task.response = result
|
||||
if 'output' in result:
|
||||
for out_obj in result['output']:
|
||||
if 'path' in out_obj:
|
||||
img_id = out_obj['path'][out_obj['path'].rindex('/') + 1:]
|
||||
task.temp_images[int(img_id)] = runtime.temp_images[out_obj['path'][11:]]
|
||||
elif 'data' in out_obj:
|
||||
task.temp_images[result['output'].index(out_obj)] = out_obj['data']
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
# Task completed
|
||||
task.lock.release()
|
||||
tasks_queue.task_done()
|
||||
finally:
|
||||
# Task completed
|
||||
task.lock.release()
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
if isinstance(task.error, StopAsyncIteration):
|
||||
print(f'Session {task.request.session_id} task {id(task)} cancelled!')
|
||||
elif task.error is not None:
|
||||
print(f'Session {task.request.session_id} task {id(task)} failed!')
|
||||
else:
|
||||
print(f'Session {task.request.session_id} task {id(task)} completed.')
|
||||
print(f'Session {task.request.session_id} task {id(task)} completed by {runtime.thread_data.device_name}.')
|
||||
current_state = ServerStates.Online
|
||||
|
||||
render_thread = threading.Thread(target=thread_render)
|
||||
def get_cached_task(session_id:str, update_ttl:bool=False):
|
||||
# By calling keep before tryGet, wont discard if was expired.
|
||||
if update_ttl and not task_cache.keep(session_id, TASK_TTL):
|
||||
# Failed to keep task, already gone.
|
||||
return None
|
||||
return task_cache.tryGet(session_id)
|
||||
|
||||
def start_render_thread():
|
||||
# Start Rendering Thread
|
||||
render_thread.daemon = True
|
||||
render_thread.start()
|
||||
def get_devices():
|
||||
devices = {
|
||||
'all': {},
|
||||
'active': {},
|
||||
}
|
||||
|
||||
def get_device_info(device):
|
||||
if device == 'cpu':
|
||||
return {'name': device_manager.get_processor_name()}
|
||||
|
||||
mem_free, mem_total = torch.cuda.mem_get_info(device)
|
||||
mem_free /= float(10**9)
|
||||
mem_total /= float(10**9)
|
||||
|
||||
return {
|
||||
'name': torch.cuda.get_device_name(device),
|
||||
'mem_free': mem_free,
|
||||
'mem_total': mem_total,
|
||||
}
|
||||
|
||||
# list the compatible devices
|
||||
gpu_count = torch.cuda.device_count()
|
||||
for device in range(gpu_count):
|
||||
device = f'cuda:{device}'
|
||||
if not device_manager.is_device_compatible(device):
|
||||
continue
|
||||
|
||||
devices['all'].update({device: get_device_info(device)})
|
||||
|
||||
devices['all'].update({'cpu': get_device_info('cpu')})
|
||||
|
||||
# list the activated devices
|
||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('get_devices' + ERR_LOCK_FAILED)
|
||||
try:
|
||||
for rthread in render_threads:
|
||||
if not rthread.is_alive():
|
||||
continue
|
||||
weak_data = weak_thread_data.get(rthread)
|
||||
if not weak_data or not 'device' in weak_data or not 'device_name' in weak_data:
|
||||
continue
|
||||
device = weak_data['device']
|
||||
devices['active'].update({device: get_device_info(device)})
|
||||
finally:
|
||||
manager_lock.release()
|
||||
|
||||
return devices
|
||||
|
||||
def is_alive(device=None):
|
||||
if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('is_alive' + ERR_LOCK_FAILED)
|
||||
nbr_alive = 0
|
||||
try:
|
||||
for rthread in render_threads:
|
||||
if device is not None:
|
||||
weak_data = weak_thread_data.get(rthread)
|
||||
if weak_data is None or not 'device' in weak_data or weak_data['device'] is None:
|
||||
continue
|
||||
thread_device = weak_data['device']
|
||||
if thread_device != device:
|
||||
continue
|
||||
if rthread.is_alive():
|
||||
nbr_alive += 1
|
||||
return nbr_alive
|
||||
finally:
|
||||
manager_lock.release()
|
||||
|
||||
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)
|
||||
try:
|
||||
rthread = threading.Thread(target=thread_render, kwargs={'device': device})
|
||||
rthread.daemon = True
|
||||
rthread.name = THREAD_NAME_PREFIX + device
|
||||
rthread.start()
|
||||
render_threads.append(rthread)
|
||||
finally:
|
||||
manager_lock.release()
|
||||
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'])
|
||||
return False
|
||||
if timeout <= 0:
|
||||
return False
|
||||
timeout -= 1
|
||||
time.sleep(1)
|
||||
return True
|
||||
|
||||
def stop_render_thread(device):
|
||||
try:
|
||||
device_manager.validate_device_id(device, log_prefix='stop_render_thread')
|
||||
except:
|
||||
print(traceback.format_exec())
|
||||
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)
|
||||
|
||||
try:
|
||||
thread_to_remove = None
|
||||
for rthread in render_threads:
|
||||
weak_data = weak_thread_data.get(rthread)
|
||||
if weak_data is None or not 'device' in weak_data or weak_data['device'] is None:
|
||||
continue
|
||||
thread_device = weak_data['device']
|
||||
if thread_device == device:
|
||||
weak_data['alive'] = False
|
||||
thread_to_remove = rthread
|
||||
break
|
||||
if thread_to_remove is not None:
|
||||
render_threads.remove(rthread)
|
||||
return True
|
||||
finally:
|
||||
manager_lock.release()
|
||||
|
||||
return False
|
||||
|
||||
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)
|
||||
|
||||
for device in devices_to_stop:
|
||||
if is_alive(device) <= 0:
|
||||
print(device, 'is not alive')
|
||||
continue
|
||||
if not stop_render_thread(device):
|
||||
print(device, 'could not stop render thread')
|
||||
|
||||
for device in devices_to_start:
|
||||
if is_alive(device) >= 1:
|
||||
print(device, 'already registered.')
|
||||
continue
|
||||
if not start_render_thread(device):
|
||||
print(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'])
|
||||
|
||||
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):
|
||||
if not render_thread.is_alive(): # Render thread is dead
|
||||
if is_alive() <= 0: # Render thread is dead
|
||||
raise ChildProcessError('Rendering thread has died.')
|
||||
# Alive, check if task in cache
|
||||
task = task_cache.tryGet(req.session_id)
|
||||
@ -277,12 +519,12 @@ def render(req : ImageRequest):
|
||||
r.sampler = req.sampler
|
||||
# r.allow_nsfw = req.allow_nsfw
|
||||
r.turbo = req.turbo
|
||||
r.use_cpu = req.use_cpu
|
||||
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.show_only_filtered_image = req.show_only_filtered_image
|
||||
r.output_format = req.output_format
|
||||
|
||||
@ -293,7 +535,14 @@ def render(req : ImageRequest):
|
||||
r.stream_image_progress = False
|
||||
|
||||
new_task = RenderTask(r)
|
||||
|
||||
if task_cache.put(r.session_id, new_task, TASK_TTL):
|
||||
tasks_queue.put(new_task, block=True, timeout=30)
|
||||
return new_task
|
||||
# Use twice the normal timeout for adding user requests.
|
||||
# Tries to force task_cache.put to fail before tasks_queue.put would.
|
||||
if manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT * 2):
|
||||
try:
|
||||
tasks_queue.append(new_task)
|
||||
return new_task
|
||||
finally:
|
||||
manager_lock.release()
|
||||
raise RuntimeError('Failed to add task to cache.')
|
||||
|
||||
463
ui/server.py
463
ui/server.py
@ -1,3 +1,7 @@
|
||||
"""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
|
||||
|
||||
@ -16,14 +20,24 @@ UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'))
|
||||
|
||||
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',
|
||||
}
|
||||
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
|
||||
import queue, threading, time
|
||||
from typing import Any, Generator, Hashable, Optional, Union
|
||||
#import queue, threading, time
|
||||
from typing import Any, Generator, Hashable, List, Optional, Union
|
||||
|
||||
from sd_internal import Request, Response, task_manager
|
||||
|
||||
@ -42,171 +56,7 @@ NOCACHE_HEADERS={"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma
|
||||
app.mount('/media', StaticFiles(directory=os.path.join(SD_UI_DIR, 'media')), name="media")
|
||||
app.mount('/plugins', StaticFiles(directory=UI_PLUGINS_DIR), name="plugins")
|
||||
|
||||
class SetAppConfigRequest(BaseModel):
|
||||
update_branch: str = "main"
|
||||
|
||||
# needs to support the legacy installations
|
||||
def get_initial_model_to_load():
|
||||
custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt')
|
||||
ckpt_to_use = "sd-v1-4" if not os.path.exists(custom_weight_path) else "custom-model"
|
||||
|
||||
ckpt_to_use = os.path.join(SD_DIR, ckpt_to_use)
|
||||
|
||||
config = getConfig()
|
||||
if 'model' in config and 'stable-diffusion' in config['model']:
|
||||
model_name = config['model']['stable-diffusion']
|
||||
model_path = resolve_model_to_use(model_name)
|
||||
|
||||
if os.path.exists(model_path + '.ckpt'):
|
||||
ckpt_to_use = model_path
|
||||
else:
|
||||
print('Could not find the configured custom model at:', model_path + '.ckpt', '. Using the default one:', ckpt_to_use + '.ckpt')
|
||||
return ckpt_to_use
|
||||
|
||||
def resolve_model_to_use(model_name):
|
||||
if model_name in ('sd-v1-4', 'custom-model'):
|
||||
model_path = os.path.join(MODELS_DIR, 'stable-diffusion', model_name)
|
||||
|
||||
legacy_model_path = os.path.join(SD_DIR, model_name)
|
||||
if not os.path.exists(model_path + '.ckpt') and os.path.exists(legacy_model_path + '.ckpt'):
|
||||
model_path = legacy_model_path
|
||||
else:
|
||||
model_path = os.path.join(MODELS_DIR, 'stable-diffusion', model_name)
|
||||
return model_path
|
||||
|
||||
@app.on_event("shutdown")
|
||||
def shutdown_event(): # Signal render thread to close on shutdown
|
||||
task_manager.current_state_error = SystemExit('Application shutting down.')
|
||||
|
||||
@app.get('/')
|
||||
def read_root():
|
||||
return FileResponse(os.path.join(SD_UI_DIR, 'index.html'), headers=NOCACHE_HEADERS)
|
||||
|
||||
@app.get('/ping') # Get server and optionally session status.
|
||||
def ping(session_id:str=None):
|
||||
if not task_manager.render_thread.is_alive(): # Render thread is dead.
|
||||
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:
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if task:
|
||||
response['task'] = id(task)
|
||||
if task.lock.locked():
|
||||
response['session'] = 'running'
|
||||
elif isinstance(task.error, StopAsyncIteration):
|
||||
response['session'] = 'stopped'
|
||||
elif task.error:
|
||||
response['session'] = 'error'
|
||||
elif not task.buffer_queue.empty():
|
||||
response['session'] = 'buffer'
|
||||
elif task.response:
|
||||
response['session'] = 'completed'
|
||||
else:
|
||||
response['session'] = 'pending'
|
||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||
|
||||
def save_model_to_config(model_name):
|
||||
config = getConfig()
|
||||
if 'model' not in config:
|
||||
config['model'] = {}
|
||||
|
||||
config['model']['stable-diffusion'] = model_name
|
||||
setConfig(config)
|
||||
|
||||
@app.post('/render')
|
||||
def render(req : task_manager.ImageRequest):
|
||||
try:
|
||||
save_model_to_config(req.use_stable_diffusion_model)
|
||||
req.use_stable_diffusion_model = resolve_model_to_use(req.use_stable_diffusion_model)
|
||||
new_task = task_manager.render(req)
|
||||
response = {
|
||||
'status': str(task_manager.current_state),
|
||||
'queue': task_manager.tasks_queue.qsize(),
|
||||
'stream': f'/image/stream/{req.session_id}/{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, deny queueing more than one.
|
||||
raise HTTPException(status_code=503, detail=f'Session {req.session_id} has an already pending task.') # HTTP503 Service Unavailable
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get('/image/stream/{session_id:str}/{task_id:int}')
|
||||
def stream(session_id:str, task_id:int):
|
||||
#TODO Move to WebSockets ??
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: raise HTTPException(status_code=410, detail='No request received.') # HTTP410 Gone
|
||||
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(session_id:str=None):
|
||||
if not session_id:
|
||||
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 = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: raise HTTPException(status_code=404, detail=f'Session {session_id} has no active task.') # HTTP404 Not Found
|
||||
if isinstance(task.error, StopAsyncIteration): raise HTTPException(status_code=409, detail=f'Session {session_id} task is already stopped.') # HTTP409 Conflict
|
||||
task.error = StopAsyncIteration('')
|
||||
return {'OK'}
|
||||
|
||||
@app.get('/image/tmp/{session_id}/{img_id:int}')
|
||||
def get_image(session_id, img_id):
|
||||
task = task_manager.task_cache.tryGet(session_id)
|
||||
if not task: raise HTTPException(status_code=410, detail=f'Session {session_id} has not submitted a task.') # HTTP410 Gone
|
||||
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]
|
||||
if isinstance(img_data, str):
|
||||
return img_data
|
||||
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.post('/app_config')
|
||||
async def setAppConfig(req : SetAppConfigRequest):
|
||||
try:
|
||||
config = {
|
||||
'update_branch': req.update_branch
|
||||
}
|
||||
|
||||
config_json_str = json.dumps(config)
|
||||
config_bat_str = f'@set update_branch={req.update_branch}'
|
||||
config_sh_str = f'export update_branch={req.update_branch}'
|
||||
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
config_bat_path = os.path.join(CONFIG_DIR, 'config.bat')
|
||||
config_sh_path = os.path.join(CONFIG_DIR, 'config.sh')
|
||||
|
||||
with open(config_json_path, 'w', encoding='utf-8') as f:
|
||||
f.write(config_json_str)
|
||||
|
||||
with open(config_bat_path, 'w', encoding='utf-8') as f:
|
||||
f.write(config_bat_str)
|
||||
|
||||
with open(config_sh_path, 'w', encoding='utf-8') as f:
|
||||
f.write(config_sh_str)
|
||||
|
||||
return {'OK'}
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
def getConfig(default_val={}):
|
||||
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):
|
||||
@ -219,41 +69,147 @@ def getConfig(default_val={}):
|
||||
return default_val
|
||||
|
||||
def setConfig(config):
|
||||
try:
|
||||
try: # config.json
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
with open(config_json_path, 'w', encoding='utf-8') as f:
|
||||
return json.dump(config, f)
|
||||
except Exception as e:
|
||||
print(str(e))
|
||||
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']}")
|
||||
if os.getenv('SD_UI_BIND_PORT') is not None:
|
||||
config_bat.append(f"@set SD_UI_BIND_PORT={os.getenv('SD_UI_BIND_PORT')}")
|
||||
if os.getenv('SD_UI_BIND_IP') is not None:
|
||||
config_bat.append(f"@set SD_UI_BIND_IP={os.getenv('SD_UI_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:
|
||||
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']}")
|
||||
if os.getenv('SD_UI_BIND_PORT') is not None:
|
||||
config_sh.append(f"export SD_UI_BIND_PORT={os.getenv('SD_UI_BIND_PORT')}")
|
||||
if os.getenv('SD_UI_BIND_IP') is not None:
|
||||
config_sh.append(f"export SD_UI_BIND_IP={os.getenv('SD_UI_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:
|
||||
print(traceback.format_exc())
|
||||
|
||||
def resolve_model_to_use(model_name:str, model_type:str, model_dir:str, model_extensions:list, default_models=[]):
|
||||
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:
|
||||
# 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=['.ckpt'], 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.pt', '.ckpt'], 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
|
||||
|
||||
@app.post('/app_config')
|
||||
async def setAppConfig(req : SetAppConfigRequest):
|
||||
config = getConfig()
|
||||
if req.update_branch:
|
||||
config['update_branch'] = req.update_branch
|
||||
if req.render_devices:
|
||||
update_render_devices_in_config(config, req.render_devices)
|
||||
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 getModels():
|
||||
models = {
|
||||
'active': {
|
||||
'stable-diffusion': 'sd-v1-4',
|
||||
'vae': '',
|
||||
},
|
||||
'options': {
|
||||
'stable-diffusion': ['sd-v1-4'],
|
||||
'vae': [],
|
||||
},
|
||||
}
|
||||
|
||||
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 file.endswith(model_extension):
|
||||
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
|
||||
sd_models_dir = os.path.join(MODELS_DIR, 'stable-diffusion')
|
||||
for file in os.listdir(sd_models_dir):
|
||||
if file.endswith('.ckpt'):
|
||||
model_name = os.path.splitext(file)[0]
|
||||
models['options']['stable-diffusion'].append(model_name)
|
||||
listModels(models_dirname='stable-diffusion', model_type='stable-diffusion', model_extensions=['.ckpt'])
|
||||
listModels(models_dirname='vae', model_type='vae', model_extensions=['.vae.pt', '.ckpt'])
|
||||
|
||||
# legacy
|
||||
custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt')
|
||||
if os.path.exists(custom_weight_path):
|
||||
models['active']['stable-diffusion'] = 'custom-model'
|
||||
models['options']['stable-diffusion'].append('custom-model')
|
||||
|
||||
config = getConfig()
|
||||
if 'model' in config and 'stable-diffusion' in config['model']:
|
||||
models['active']['stable-diffusion'] = config['model']['stable-diffusion']
|
||||
|
||||
return models
|
||||
|
||||
def getUIPlugins():
|
||||
@ -274,6 +230,11 @@ def read_web_data(key:str=None):
|
||||
if config is None:
|
||||
raise HTTPException(status_code=500, detail="Config file is missing or unreadable")
|
||||
return JSONResponse(config, headers=NOCACHE_HEADERS)
|
||||
elif key == 'devices':
|
||||
config = getConfig()
|
||||
devices = task_manager.get_devices()
|
||||
devices['config'] = config.get('render_devices', "auto")
|
||||
return JSONResponse(devices, 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)
|
||||
@ -282,6 +243,123 @@ def read_web_data(key:str=None):
|
||||
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:
|
||||
task = task_manager.get_cached_task(session_id, update_ttl=True)
|
||||
if task:
|
||||
response['task'] = id(task)
|
||||
if task.lock.locked():
|
||||
response['session'] = 'running'
|
||||
elif isinstance(task.error, StopAsyncIteration):
|
||||
response['session'] = 'stopped'
|
||||
elif task.error:
|
||||
response['session'] = 'error'
|
||||
elif not task.buffer_queue.empty():
|
||||
response['session'] = 'buffer'
|
||||
elif task.response:
|
||||
response['session'] = 'completed'
|
||||
else:
|
||||
response['session'] = 'pending'
|
||||
response['devices'] = task_manager.get_devices()
|
||||
return JSONResponse(response, headers=NOCACHE_HEADERS)
|
||||
|
||||
def save_model_to_config(ckpt_model_name, vae_model_name):
|
||||
config = getConfig()
|
||||
if 'model' not in config:
|
||||
config['model'] = {}
|
||||
|
||||
config['model']['stable-diffusion'] = ckpt_model_name
|
||||
config['model']['vae'] = vae_model_name
|
||||
|
||||
if vae_model_name is None or vae_model_name == "":
|
||||
del config['model']['vae']
|
||||
|
||||
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_stable_diffusion_model = resolve_ckpt_to_use(req.use_stable_diffusion_model)
|
||||
req.use_vae_model = resolve_vae_to_use(req.use_vae_model)
|
||||
new_task = task_manager.render(req)
|
||||
response = {
|
||||
'status': str(task_manager.current_state),
|
||||
'queue': len(task_manager.tasks_queue),
|
||||
'stream': f'/image/stream/{req.session_id}/{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, deny queueing more than one.
|
||||
raise HTTPException(status_code=503, detail=f'Session {req.session_id} has an already pending task.') # HTTP503 Service Unavailable
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@app.get('/image/stream/{session_id:str}/{task_id:int}')
|
||||
def stream(session_id:str, task_id:int):
|
||||
#TODO Move to WebSockets ??
|
||||
task = task_manager.get_cached_task(session_id, update_ttl=True)
|
||||
if not task: raise HTTPException(status_code=410, detail='No request received.') # HTTP410 Gone
|
||||
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(session_id:str=None):
|
||||
if not session_id:
|
||||
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 = task_manager.get_cached_task(session_id, update_ttl=False)
|
||||
if not task: raise HTTPException(status_code=404, detail=f'Session {session_id} has no active task.') # HTTP404 Not Found
|
||||
if isinstance(task.error, StopAsyncIteration): raise HTTPException(status_code=409, detail=f'Session {session_id} task is already stopped.') # HTTP409 Conflict
|
||||
task.error = StopAsyncIteration('')
|
||||
return {'OK'}
|
||||
|
||||
@app.get('/image/tmp/{session_id}/{img_id:int}')
|
||||
def get_image(session_id, img_id):
|
||||
task = task_manager.get_cached_task(session_id, update_ttl=True)
|
||||
if not task: raise HTTPException(status_code=410, detail=f'Session {session_id} has not submitted a task.') # HTTP410 Gone
|
||||
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:
|
||||
@ -292,8 +370,25 @@ class LogSuppressFilter(logging.Filter):
|
||||
return True
|
||||
logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter())
|
||||
|
||||
task_manager.default_model_to_load = get_initial_model_to_load()
|
||||
task_manager.start_render_thread()
|
||||
# Start the task_manager
|
||||
task_manager.default_model_to_load = resolve_ckpt_to_use()
|
||||
task_manager.default_vae_to_load = resolve_vae_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
|
||||
import webbrowser; webbrowser.open('http://localhost:9000')
|
||||
def open_browser():
|
||||
config = getConfig()
|
||||
ui = config.get('ui', {})
|
||||
if ui.get('open_browser_on_start', True):
|
||||
import webbrowser; webbrowser.open('http://localhost:9000')
|
||||
|
||||
open_browser()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user