Merge pull request #614 from cmdr2/beta

Beta

3rd-PARTY-LICENSES

@@ -0,0 +1,27 @@
+jquery-confirm
+==============
+https://craftpip.github.io/jquery-confirm/
+
+jquery-confirm is licensed under the MIT license:
+
+  The MIT License (MIT)
+
+  Copyright (c) 2019 Boniface Pereira
+
+  Permission is hereby granted, free of charge, to any person obtaining a copy
+  of this software and associated documentation files (the "Software"), to deal
+  in the Software without restriction, including without limitation the rights
+  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+  copies of the Software, and to permit persons to whom the Software is
+  furnished to do so, subject to the following conditions:
+
+  The above copyright notice and this permission notice shall be included in all
+  copies or substantial portions of the Software.
+
+  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+  SOFTWARE.

CHANGES.md

@@ -2,9 +2,10 @@
 
 ## v2.4
 ### Major Changes
-- **Automatic scanning for malicious model files** - using `picklescan`. Thanks @JeLuf
+- **Automatic scanning for malicious model files** - using `picklescan`, and support for `safetensor` model format. Thanks @JeLuf
 - **Support for custom VAE models**. You can place your VAE files in the `models/vae` folder, and refresh the browser page to use them. More info: https://github.com/cmdr2/stable-diffusion-ui/wiki/VAE-Variational-Auto-Encoder
 - **Experimental support for multiple GPUs!** It should work automatically. Just open one browser tab per GPU, and spread your tasks across your GPUs. For e.g. open our UI in two browser tabs if you have two GPUs. You can customize which GPUs it should use in the "Settings" tab, otherwise let it automatically pick the best GPUs. Thanks @madrang . More info: https://github.com/cmdr2/stable-diffusion-ui/wiki/Run-on-Multiple-GPUs
+- **Image Editor** - for drawing simple images for guiding the AI. Thanks @mdiller
 - **Cleaner UI design** - Show settings and help in new tabs, instead of dropdown popups (which were buggy). Thanks @mdiller
 - **Progress bar.** Thanks @mdiller
 - **Custom Image Modifiers** - You can now save your custom image modifiers! Your saved modifiers can include special characters like `{}, (), [], |`
@@ -19,8 +20,22 @@
 - Configuration to prevent the browser from opening on startup
 - Lots of minor bug fixes
 - A `What's New?` tab in the UI
+- Ask for a confimation before clearing the results pane or stopping a render task. The dialog can be skipped by holding down the shift key while clicking on the button.
+- Show the network addresses of the server in the systems setting dialog
+- Support loading models in the safetensor format, for improved safety
 
 ### Detailed changelog
+* 2.4.18 - 5 Dec 2022 - Make JPEG Output quality user controllable. Thanks @JeLuf
+* 2.4.18 - 5 Dec 2022 - Support loading models in the safetensor format, for improved safety. Thanks @JeLuf
+* 2.4.18 - 1 Dec 2022 - Image Editor, for drawing simple images for guiding the AI. Thanks @mdiller
+* 2.4.18 - 1 Dec 2022 - Disable an image modifier temporarily by right-clicking it. Thanks @patriceac
+* 2.4.17 - 30 Nov 2022 - Scroll to generated image. Thanks @patriceac
+* 2.4.17 - 30 Nov 2022 - Show the network addresses of the server in the systems setting dialog. Thanks @JeLuf
+* 2.4.17 - 30 Nov 2022 - Fix a bug where GFPGAN wouldn't work properly when multiple GPUs tried to run it at the same time. Thanks @madrang
+* 2.4.17 - 30 Nov 2022 - Confirm before stopping or clearing all the tasks. Thanks @JeLuf
+* 2.4.16 - 29 Nov 2022 - Bug fixes for SD 2.0 - remove the need for patching, default to SD 1.4 model if trying to load an SD2 model in SD1.4.
+* 2.4.15 - 25 Nov 2022 - Experimental support for SD 2.0. Uses lots of memory, not optimized, probably GPU-only.
+* 2.4.14 - 22 Nov 2022 - Change the backend to a custom fork of Stable Diffusion
 * 2.4.13 - 21 Nov 2022 - Change the modifier weight via mouse wheel, drag to reorder selected modifiers, and some more modifier-related fixes. Thanks @patriceac
 * 2.4.12 - 21 Nov 2022 - Another fix for improving how long images take to generate. Reduces the time taken for an enqueued task to start processing.
 * 2.4.11 - 21 Nov 2022 - Installer improvements: avoid crashing if the username contains a space or special characters, allow moving/renaming the folder after installation on Windows, whitespace fix on git apply

README.md

@@ -3,6 +3,8 @@
 
 [![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB) (for support, and development discussion) | [Troubleshooting guide for common problems](Troubleshooting.md)
 
+New! Experimental support for Stable Diffusion 2.0 is available in beta!
+
 ----
 
 ## Step 1: Download the installer
@@ -28,7 +30,9 @@ The installer will take care of whatever is needed. A friendly [Discord communit
 - **No Dependencies or Technical Knowledge Required**: 1-click install for Windows 10/11 and Linux. *No dependencies*, no need for WSL or Docker or Conda or technical setup. Just download and run!
 - **Clutter-free UI**: a friendly and simple UI, while providing a lot of powerful features
 - Supports "*Text to Image*" and "*Image to Image*"
+- **Stable Diffusion 2.0 support (experimental)** - available in beta channel
 - **Custom Models**: Use your own `.ckpt` file, by placing it inside the `models/stable-diffusion` folder!
+- **Auto scan for malicious models** - uses picklescan to prevent malicious models
 - **Live Preview**: See the image as the AI is drawing it
 - **Task Queue**: Queue up all your ideas, without waiting for the current task to finish
 - **In-Painting**: Specify areas of your image to paint into
@@ -71,7 +75,7 @@ Useful for judging (and stopping) an image quickly, without waiting for it to fi
 You don't need to install or struggle with Python, Anaconda, Docker etc. The installer will take care of whatever is needed.
 
 # Installation
-1. **Download** [for Windows](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.3.5/stable-diffusion-ui-windows.zip) or [for Linux](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.3.5/stable-diffusion-ui-linux.zip).
+1. **Download** [for Windows](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.4.13/stable-diffusion-ui-windows.zip) or [for Linux](https://github.com/cmdr2/stable-diffusion-ui/releases/download/v2.4.13/stable-diffusion-ui-linux.zip).
 
 2. **Extract**:
   - For Windows: After unzipping the file, please move the `stable-diffusion-ui` folder to your `C:` (or any drive like D:, at the top root level), e.g. `C:\stable-diffusion-ui`. This will avoid a common problem with Windows (file path length limits).

scripts/Developer Console.cmd

@@ -29,6 +29,18 @@ call conda activate .\stable-diffusion\env
 call where python
 call python --version
 
+@rem set the PYTHONPATH
+cd stable-diffusion
+set SD_DIR=%cd%
+
+cd env\lib\site-packages
+set PYTHONPATH=%SD_DIR%;%cd%
+cd ..\..\..
+echo PYTHONPATH=%PYTHONPATH%
+
+cd ..
+
+@rem done
 echo.
 
 cmd /k

scripts/bootstrap.bat

@@ -42,11 +42,11 @@ if "%PACKAGES_TO_INSTALL%" NEQ "" (
         mkdir "%MAMBA_ROOT_PREFIX%"
         call curl -Lk "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"
 
-        @REM if "%ERRORLEVEL%" NEQ "0" (
-        @REM     echo "There was a problem downloading micromamba. Cannot continue."
-        @REM     pause
-        @REM     exit /b
-        @REM )
+        if "%ERRORLEVEL%" NEQ "0" (
+            echo "There was a problem downloading micromamba. Cannot continue."
+            pause
+            exit /b
+        )
 
         mkdir "%APPDATA%"
         mkdir "%USERPROFILE%"

scripts/developer_console.sh

@@ -35,6 +35,15 @@ if [ "$0" == "bash" ]; then
   which python
   python --version
 
+  # set the PYTHONPATH
+  cd stable-diffusion
+  SD_PATH=`pwd`
+  export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
+  echo "PYTHONPATH=$PYTHONPATH"
+  cd ..
+
+  # done
+
   echo ""
 else
   file_name=$(basename "${BASH_SOURCE[0]}")

scripts/on_sd_start.bat

@@ -16,35 +16,42 @@ if exist "%cd%\profile" (
 
 @rem activate the installer env
 call conda activate
-@rem @if "%ERRORLEVEL%" NEQ "0" (
-@rem        @echo. & echo "Error activating conda for Stable Diffusion. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
-@rem        pause
-@rem        exit /b
-@rem )
+@if "%ERRORLEVEL%" NEQ "0" (
+       @echo. & echo "Error activating conda for Stable Diffusion. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
+       pause
+       exit /b
+)
 
 @REM remove the old version of the dev console script, if it's still present
 if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
 
 @call python -c "import os; import shutil; frm = 'sd-ui-files\\ui\\hotfix\\9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'; dst = os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'transformers', '9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'); shutil.copyfile(frm, dst) if os.path.exists(dst) else print(''); print('Hotfixed broken JSON file from OpenAI');"
 
+if NOT DEFINED test_sd2 set test_sd2=N
+
 @>nul findstr /m "sd_git_cloned" scripts\install_status.txt
 @if "%ERRORLEVEL%" EQU "0" (
     @echo "Stable Diffusion's git repository was already installed. Updating.."
 
     @cd stable-diffusion
 
+    @call git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
+
     @call git reset --hard
     @call git pull
-    @call git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c
 
-    @call git apply --whitespace=nowarn ..\ui\sd_internal\ddim_callback.patch
-    @call git apply --whitespace=nowarn ..\ui\sd_internal\env_yaml.patch
+    if "%test_sd2%" == "N" (
+        @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
+    )
+    if "%test_sd2%" == "Y" (
+        @call git -c advice.detachedHead=false checkout b1a80dfc75388914252ce363f923103185eaf48f
+    )
 
     @cd ..
 ) else (
     @echo. & echo "Downloading Stable Diffusion.." & echo.
 
-    @call git clone https://github.com/basujindal/stable-diffusion.git && (
+    @call git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion && (
         @echo sd_git_cloned >> scripts\install_status.txt
     ) || (
         @echo "Error downloading Stable Diffusion. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
@@ -53,10 +60,7 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
     )
 
     @cd stable-diffusion
-    @call git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c
-
-    @call git apply --whitespace=nowarn ..\ui\sd_internal\ddim_callback.patch
-    @call git apply --whitespace=nowarn ..\ui\sd_internal\env_yaml.patch
+    @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
 
     @cd ..
 )
@@ -88,12 +92,6 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
 
     @call conda activate .\env
 
-    @call conda install -c conda-forge -y --prefix env antlr4-python3-runtime=4.8 || (
-        @echo. & echo "Error installing antlr4-python3-runtime for Stable Diffusion. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
-        pause
-        exit /b
-    )
-
     for /f "tokens=*" %%a in ('python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"') do if "%%a" NEQ "42" (
         @echo. & echo "Dependency test failed! Error installing the packages necessary for Stable Diffusion. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
         pause
@@ -117,18 +115,6 @@ set PATH=C:\Windows\System32;%PATH%
 
     set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
 
-    @call pip install -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN || (
-        @echo. & echo "Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
-        pause
-        exit /b
-    )
-
-    @call pip install basicsr==1.4.2 || (
-        @echo. & echo "Error installing the basicsr package necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
-        pause
-        exit /b
-    )
-
     for /f "tokens=*" %%a in ('python -c "from gfpgan import GFPGANer; print(42)"') do if "%%a" NEQ "42" (
         @echo. & echo "Dependency test failed! Error installing the packages necessary for GFPGAN (Face Correction). Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
         pause
@@ -150,12 +136,6 @@ set PATH=C:\Windows\System32;%PATH%
 
     set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
 
-    @call pip install -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan || (
-        @echo. & echo "Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
-        pause
-        exit /b
-    )
-
     for /f "tokens=*" %%a in ('python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"') do if "%%a" NEQ "42" (
         @echo. & echo "Dependency test failed! Error installing the packages necessary for ESRGAN (Resolution Upscaling). Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!" & echo.
         pause
@@ -202,6 +182,16 @@ call WHERE uvicorn > .tmp
     )
 )
 
+@>nul 2>nul call python -c "import safetensors"
+@if "%ERRORLEVEL%" NEQ "0" (
+    @echo. & echo SafeTensors not found. Installing
+    @call pip install safetensors || (
+        echo "Error installing the safetensors package necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
+        pause
+        exit /b
+    )
+)
+
 @>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
 @if "%ERRORLEVEL%" NEQ "0" (
     @echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
@@ -370,7 +360,9 @@ echo. > "..\models\vae\Put your VAE files here.txt"
     )
 )
 
-
+if "%test_sd2%" == "Y" (
+    @call pip install open_clip_torch==2.0.2
+)
 
 @>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
 @if "%ERRORLEVEL%" NEQ "0" (

scripts/on_sd_start.sh

@@ -21,33 +21,38 @@ python -c "import os; import shutil; frm = 'sd-ui-files/ui/hotfix/9c24e6cd9f499d
 # Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
 # Note to self: Please rewrite this in Python. For the sake of your own sanity.
 
+if [ "$test_sd2" == "" ]; then
+    export test_sd2="N"
+fi
+
 if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/install_status.txt` -gt "0" ]; then
     echo "Stable Diffusion's git repository was already installed. Updating.."
 
     cd stable-diffusion
 
+    git remote set-url origin https://github.com/easydiffusion/diffusion-kit.git
+
     git reset --hard
     git pull
-    git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c
 
-    git apply --whitespace=nowarn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
-    git apply --whitespace=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
+    if [ "$test_sd2" == "N" ]; then
+        git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
+    elif [ "$test_sd2" == "Y" ]; then
+        git -c advice.detachedHead=false checkout b1a80dfc75388914252ce363f923103185eaf48f
+    fi
 
     cd ..
 else
     printf "\n\nDownloading Stable Diffusion..\n\n"
 
-    if git clone https://github.com/basujindal/stable-diffusion.git ; then
+    if git clone https://github.com/easydiffusion/diffusion-kit.git stable-diffusion ; then
         echo sd_git_cloned >> scripts/install_status.txt
     else
         fail "git clone of basujindal/stable-diffusion.git failed"
     fi
 
     cd stable-diffusion
-    git -c advice.detachedHead=false checkout f6cfebffa752ee11a7b07497b8529d5971de916c
-
-    git apply --whitespace=nowarn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
-    git apply --whitespace=nowarn ../ui/sd_internal/env_yaml.patch || fail "yaml patch failed"
+    git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
 
     cd ..
 fi
@@ -74,12 +79,6 @@ else
 
     conda activate ./env || fail "conda activate failed"
 
-    if conda install -c conda-forge --prefix ./env -y antlr4-python3-runtime=4.8 ; then
-        echo "Installed. Testing.."
-    else
-        fail "Error installing antlr4-python3-runtime"
-    fi
-
     out_test=`python -c "import torch; import ldm; import transformers; import numpy; import antlr4; print(42)"`
     if [ "$out_test" != "42" ]; then
         fail "Dependency test failed"
@@ -96,12 +95,6 @@ else
     export PYTHONNOUSERSITE=1
     export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
 
-    if pip install -e git+https://github.com/TencentARC/GFPGAN#egg=GFPGAN ; then
-        echo "Installed. Testing.."
-    else
-        fail "Error installing the packages necessary for GFPGAN (Face Correction)."
-    fi
-
     out_test=`python -c "from gfpgan import GFPGANer; print(42)"`
     if [ "$out_test" != "42" ]; then
         echo "EE The dependency check has failed. This usually means that some system libraries are missing."
@@ -121,12 +114,6 @@ else
     export PYTHONNOUSERSITE=1
     export PYTHONPATH="$(pwd):$(pwd)/env/lib/site-packages"
 
-    if pip install -e git+https://github.com/xinntao/Real-ESRGAN#egg=realesrgan ; then
-        echo "Installed. Testing.."
-    else
-        fail "Error installing the packages necessary for ESRGAN"
-    fi
-
     out_test=`python -c "from basicsr.archs.rrdbnet_arch import RRDBNet; from realesrgan import RealESRGANer; print(42)"`
     if [ "$out_test" != "42" ]; then
         fail "ESRGAN dependency test failed"
@@ -163,6 +150,13 @@ else
     pip install picklescan || fail "Picklescan installation failed."
 fi
 
+if python -c "import safetensors" --help >/dev/null 2>&1; then
+    echo "SafeTensors is already installed."
+else
+    echo "SafeTensors not found, installing."
+    pip install safetensors || fail "SafeTensors installation failed."
+fi
+
 
 
 mkdir -p "../models/stable-diffusion"
@@ -309,6 +303,9 @@ if [ ! -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
     fi
 fi
 
+if [ "$test_sd2" == "Y" ]; then
+    pip install open_clip_torch==2.0.2
+fi
 
 if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
     echo sd_weights_downloaded >> ../scripts/install_status.txt

scripts/post_activate.bat

@@ -1,6 +0,0 @@
-@call conda --version
-@call git --version
-
-cd %CONDA_PREFIX%\..\scripts
-
-on_env_start.bat

scripts/post_activate.sh

@@ -1,12 +0,0 @@
-#!/bin/bash
-
-conda-unpack
-
-source $CONDA_PREFIX/etc/profile.d/conda.sh
-
-conda --version
-git --version
-
-cd $CONDA_PREFIX/../scripts
-
-./on_env_start.sh

ui/index.html

@@ -3,6 +3,7 @@
 <head>
     <title>Stable Diffusion UI</title>
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <meta name="theme-color" content="#673AB6">
     <link rel="icon" type="image/png" href="/media/images/favicon-16x16.png" sizes="16x16">
     <link rel="icon" type="image/png" href="/media/images/favicon-32x32.png" sizes="32x32">
     <link rel="stylesheet" href="/media/css/fonts.css">
@@ -11,16 +12,21 @@
     <link rel="stylesheet" href="/media/css/auto-save.css">
     <link rel="stylesheet" href="/media/css/modifier-thumbnails.css">
     <link rel="stylesheet" href="/media/css/fontawesome-all.min.css">
-    <link rel="stylesheet" href="/media/css/drawingboard.min.css">
+    <link rel="stylesheet" href="/media/css/image-editor.css">
+    <link rel="stylesheet" href="/media/css/jquery-confirm.min.css">
+    <link rel="manifest" href="/media/manifest.webmanifest">
     <script src="/media/js/jquery-3.6.1.min.js"></script>
-    <script src="/media/js/drawingboard.min.js"></script>
+    <script src="/media/js/jquery-confirm.min.js"></script>
     <script src="/media/js/marked.min.js"></script>
 </head>
 <body>
 <div id="container">
     <div id="top-nav">
         <div id="logo">
-            <h1>Stable Diffusion UI <small>v2.4.13 <span id="updateBranchLabel"></span></small></h1>
+            <h1>
+                Stable Diffusion UI
+                <small>v2.4.18 <span id="updateBranchLabel"></span></small>
+            </h1>
         </div>
         <div id="server-status">
             <div id="server-status-color">●</div>
@@ -58,28 +64,38 @@
                 </div>
 
                 <div id="editor-inputs-init-image" class="row">
-                    <label for="init_image">Initial Image (img2img) <small>(optional)</small> </label> <input id="init_image" name="init_image" type="file" /><br/>
+                    <label for="init_image">Initial Image (img2img) <small>(optional)</small> </label>
 
                     <div id="init_image_preview_container" class="image_preview_container">
                         <div id="init_image_wrapper">
                             <img id="init_image_preview" src="" />
                             <span id="init_image_size_box"></span>
-                            <button class="init_image_clear image_clear_btn">X</button>
+                            <button class="init_image_clear image_clear_btn"><i class="fa-solid fa-xmark"></i></button>
+                        </div>
+                        <div id="init_image_buttons">
+                            <div class="button">
+                                <i class="fa-regular fa-folder-open"></i>
+                                Browse
+                                <input id="init_image" name="init_image" type="file" />
+                            </div>
+                            <div id="init_image_button_draw" class="button">
+                                <i class="fa-solid fa-pencil"></i>
+                                Draw
+                            </div>
+                            <div id="inpaint_button_container">
+                                <div id="init_image_button_inpaint" class="button">
+                                    <i class="fa-solid fa-paintbrush"></i>
+                                    Inpaint
+                                </div>
+                                <input id="enable_mask" name="enable_mask" type="checkbox">
+                            </div>
                         </div>
-
-                        <br/>
-                        <input id="enable_mask" name="enable_mask" type="checkbox">
-                        <label for="enable_mask">
-                            In-Painting (beta)
-                            <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>
-                            <small>(select the area which the AI will paint into)</small>
-                        </label>
-                        <div id="inpaintingEditor"></div>
                     </div>
+
                 </div>
 
                 <div id="editor-inputs-tags-container" class="row">
-                    <label>Image Modifiers: <small>(click an Image Modifier to remove it)</small></label>
+                    <label>Image Modifiers <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">click an Image Modifier to remove it, use Ctrl+Mouse Wheel to adjust its weight</span></i>:</label>
                     <div id="editor-inputs-tags-list"></div>
                 </div>
 
@@ -101,7 +117,7 @@
                 <div id="editor-settings-entries" class="collapsible-content">
                     <div><table>
                     <tr><b class="settings-subheader">Image Settings</b></tr>
-                    <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="seed">Seed:</label></td><td><input id="seed" name="seed" size="10" value="0" 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>
                     <tr class="pl-5"><td><label for="stable_diffusion_model">Model:</label></td><td>
                         <select id="stable_diffusion_model" name="stable_diffusion_model">
@@ -183,11 +199,14 @@
                             <option value="png">png</option>
                         </select>
                     </td></tr>
+                    <tr class="pl-5" id="output_quality_row"><td><label for="output_quality">JPEG Quality:</label></td><td>
+		        <input id="output_quality_slider" name="output_quality" class="editor-slider" value="75" type="range" min="10" max="95"> <input id="output_quality" name="output_quality" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)">
+	            </td></tr>
                     </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, and 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, slower images)</small></label></li>
                     <li class="pl-5"><input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes <small>(uses GFPGAN)</small></label></li>
                     <li class="pl-5">
                         <input id="use_upscale" name="use_upscale" type="checkbox"> <label for="use_upscale">Upscale image by 4x with </label>
@@ -247,8 +266,17 @@
                 <br/><br/>
                 <div>
                     <h3><i class="fa fa-microchip icon"></i> System Info</h3>
-                    <div id="system-info"></div>
+                    <div id="system-info">
+                        <table>
+                            <tr><td><label>Processor:</label></td><td id="system-info-cpu" class="value"></td></tr>
+                            <tr><td><label>Compatible Graphics Cards (all):</label></td><td id="system-info-gpus-all" class="value"></td></tr>
+                            <tr><td></td><td>&nbsp;</td></tr>
+                            <tr><td><label>Used for rendering 🔥:</label></td><td id="system-info-rendering-devices" class="value"></td></tr>
+                            <tr><td><label>Server Addresses <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">You can access Stable Diffusion UI from other devices using these addresses</span></i> :</label></td><td id="system-info-server-hosts" class="value"></td></tr>
+                        </table>
+                    </div>
                 </div>
+
             </div>
         </div>
         <div id="tab-content-about" class="tab-content">
@@ -314,6 +342,38 @@
         </div>
     </div>
 
+    <div id="image-editor" class="popup image-editor-popup">
+        <div>
+            <i class="close-button fa-solid fa-xmark"></i>
+            <h1>Image Editor</h1>
+            <div class="flex-container">
+                <div class="editor-controls-left"></div>
+                <div class="editor-controls-center">
+                    <div></div>
+                </div>
+                <div class="editor-controls-right">
+                    <div></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div id="image-inpainter" class="popup image-editor-popup">
+        <div>
+            <i class="close-button fa-solid fa-xmark"></i>
+            <h1>Inpainter</h1>
+            <div class="flex-container">
+                <div class="editor-controls-left"></div>
+                <div class="editor-controls-center">
+                    <div></div>
+                </div>
+                <div class="editor-controls-right">
+                    <div></div>
+                </div>
+            </div>
+        </div>
+    </div>
+
     <div id="footer-spacer"></div>
     <div id="footer">
         <div class="line-separator">&nbsp;</div>
@@ -327,25 +387,24 @@
     </div>
 </div>
 </body>
-
 <script src="media/js/utils.js"></script>
 <script src="media/js/parameters.js"></script>
 <script src="media/js/plugins.js"></script>
-<script src="media/js/inpainting-editor.js"></script>
 <script src="media/js/image-modifiers.js"></script>
 <script src="media/js/auto-save.js"></script>
 <script src="media/js/main.js"></script>
 <script src="media/js/themes.js"></script>
 <script src="media/js/dnd.js"></script>
+<script src="media/js/image-editor.js"></script>
 <script>
 async function init() {
     await initSettings()
     await getModels()
     await getDiskPath()
     await getAppConfig()
-    await loadModifiers()
     await loadUIPlugins()
-    await getDevices()
+    await loadModifiers()
+    await getSystemInfo()
 
     setInterval(healthCheck, HEALTH_PING_INTERVAL * 1000)
     healthCheck()

ui/media/css/image-editor.css

@@ -0,0 +1,211 @@
+.editor-controls-left {
+	padding-left: 32px;
+	text-align: left;
+	padding-bottom: 20px;
+}
+
+.editor-options-container {
+	display: flex;
+	row-gap: 10px;
+	max-width: 210px;
+}
+
+.editor-options-container > * {
+	flex: 1;
+	display: flex;
+	justify-content: center;
+	align-items: center;
+}
+
+.editor-options-container > * > * {
+	position: inherit;
+	width: 32px;
+	height: 32px;
+	border-radius: 16px;
+	background: var(--background-color3);
+	cursor: pointer;
+	transition: opacity 0.25s;
+}
+.editor-options-container > * > *:hover {
+	opacity: 0.75;
+}
+
+.editor-options-container > * > *.active {
+	border: 2px solid #3584e4;
+}
+
+.image_editor_opacity .editor-options-container > * > *:not(.active) {
+	border: 1px solid var(--background-color3);
+}
+
+.image_editor_color .editor-options-container {
+	flex-wrap: wrap;
+}
+.image_editor_color .editor-options-container > * {
+	flex: 20%;
+}
+.image_editor_color .editor-options-container > * > * {
+	position: relative;
+}
+.image_editor_color .editor-options-container > * > *.active::before {
+	content: "\f00c";
+	display: var(--fa-display,inline-block);
+	font-style: normal;
+	font-variant: normal;
+	line-height: 1;
+	text-rendering: auto;
+	font-family: var(--fa-style-family, "Font Awesome 6 Free");
+	font-weight: var(--fa-style, 900);
+	position: absolute;
+	left: 50%;
+	top: 50%;
+	transform: translate(-50%, -50%) scale(125%);
+	color: black;
+}
+.image_editor_color .editor-options-container > *:first-child {
+	flex: 100%;
+}
+.image_editor_color .editor-options-container > *:first-child > * {
+	width: 100%;
+}
+.image_editor_color .editor-options-container > *:first-child > * > input {
+	width: 100%;
+	height: 100%;
+	opacity: 0;
+	cursor: pointer;
+}
+.image_editor_color .editor-options-container > *:first-child > * > span {
+	position: absolute;
+	left: 50%;
+	top: 50%;
+	transform: translate(-50%, -50%);
+	opacity: 0.5;
+}
+.image_editor_color .editor-options-container > *:first-child > *.active > span {
+	opacity: 0;
+}
+
+.image_editor_tool .editor-options-container {
+	flex-wrap: wrap;
+}
+
+.image_editor_tool .editor-options-container > * {
+	padding: 2px;
+	flex: 50%;
+}
+
+.editor-controls-center {
+	/* background: var(--background-color2); */
+	flex: 1;
+	display: flex;
+	justify-content: center;
+	align-items: center;
+}
+
+.editor-controls-center > div {
+	position: relative;
+	background: black;
+}
+
+.editor-controls-center canvas {
+	position: absolute;
+	left: 0;
+	top: 0;
+}
+
+.editor-controls-right {
+	padding: 32px;
+	display: flex;
+	flex-direction: column;
+}
+
+
+.editor-controls-right > div:last-child {
+	flex: 1;
+	display: flex;
+	flex-direction: column;
+	min-width: 200px;
+	gap: 5px;
+	justify-content: end;
+}
+
+.image-editor-button {
+	width: 100%;
+	height: 32px;
+	border-radius: 16px;
+	background: var(--background-color3);
+}
+
+#init_image_button_inpaint .input-toggle {
+	position: absolute;
+	left: 16px;
+}
+
+#init_image_button_inpaint .input-toggle input:not(:checked) ~ label {
+	pointer-events: none;
+}
+
+
+.image-editor-popup {
+	--popup-margin: 16px;
+	--popup-padding: 24px;
+}
+
+.image-editor-popup > div {
+	margin: var(--popup-margin);
+	padding: var(--popup-padding);
+	min-height: calc(100vh - (2 * var(--popup-margin)));
+	max-width: none;
+}
+
+.image-editor-popup h1 {
+	position: absolute;
+	top: 32px;
+	left: 50%;
+	transform: translateX(-50%);
+}
+
+
+@media screen and (max-width: 700px) {
+	.image-editor-popup > div {
+		margin: 0px;
+		padding: 0px;
+	}
+
+	.image-editor-popup h1 {
+		position: relative;
+		transform: none;
+		left: auto;
+	}
+}
+
+
+.image-editor-popup > div > div {
+	min-height: calc(100vh - (2 * var(--popup-margin)) - (2 * var(--popup-padding)));
+}
+
+.inpainter .image_editor_color {
+	display: none;
+}
+
+.inpainter .editor-canvas-background {
+	opacity: 0.75;
+}
+
+#init_image_preview_container .button {
+	display: flex;
+	padding: 6px;
+	height: 24px;
+	box-shadow: 2px 2px 1px 1px #00000088;
+}
+
+#init_image_preview_container .button:hover {
+  background: var(--background-color4)
+}
+
+.image-editor-popup .button {
+	display: flex;
+}
+.image-editor-popup h4 {
+	text-align: left;
+}

ui/media/css/main.css

@@ -44,9 +44,6 @@ code {
     margin-top: 5px;
     display: block;
 }
-.image_preview_container {
-    margin-top: 10pt;
-}
 .image_clear_btn {
     position: absolute;
     transform: translate(30%, -30%);
@@ -64,6 +61,11 @@ code {
     top: 0px;
     right: 0px;
 }
+.image_clear_btn:active {
+    position: absolute;
+    top: 0px;
+    left: auto;
+}
 .settings-box ul {
     font-size: 9pt;
     margin-bottom: 5px;
@@ -210,7 +212,7 @@ code {
 }
 .collapsible-content {
     display: block;
-    padding-left: 15px;
+    padding-left: 10px;
 }
 .collapsible-content h5 {
     padding: 5pt 0pt;
@@ -270,32 +272,6 @@ img {
     transform: translateY(25%);
 }
 
-#inpaintingEditor {
-    width: 300pt;
-    height: 300pt;
-    margin-top: 5pt;
-}
-.drawing-board-canvas-wrapper {
-    background-size: 100% 100%;
-}
-.drawing-board-controls {
-    min-width: 273px;
-}
-.drawing-board-control > button {
-    background-color: #eee;
-    border-radius: 3pt;
-}
-.drawing-board-control-inner {
-    background-color: #eee;
-    border-radius: 3pt;
-}
-#inpaintingEditor canvas {
-    opacity: 0.6;
-}
-#enable_mask {
-    margin-top: 8pt;
-}
-
 #top-nav {
     position: relative;
     background: var(--background-color4);
@@ -479,8 +455,58 @@ img {
 #prompt_from_file {
     display: none;
 }
+
+#init_image_preview_container {
+    display: flex;
+    margin-top: 6px;
+    margin-bottom: 8px;
+}
+
+#init_image_preview_container:not(.has-image) #init_image_wrapper,
+#init_image_preview_container:not(.has-image) #inpaint_button_container  {
+    display: none;
+}
+
+
+#init_image_buttons {
+    display: flex;
+    gap: 8px;
+}
+
+#init_image_preview_container.has-image #init_image_buttons {
+    flex-direction: column;
+    padding-left: 8px;
+}
+
+#init_image_buttons .button {
+    position: relative;
+    height: 32px;
+    width: 150px;
+}
+
+#init_image_buttons .button > input {
+    position: absolute;
+    left: 0;
+    top: 0;
+    right: 0;
+    bottom: 0;
+    opacity: 0;
+}
+
+#inpaint_button_container {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+#init_image_wrapper {
+    grid-row: span 3;
+    position: relative;
+    width: fit-content;
+    max-height: 150px;
+}
+
 #init_image_preview {
-    max-width: 150px;
     max-height: 150px;
     height: 100%;
     width: 100%;
@@ -488,23 +514,18 @@ img {
     border-radius: 6px;
     transition: all 1s ease-in-out;
 }
-
+/* 
 #init_image_preview:hover {
     max-width: 500px;
     max-height: 1000px;
 
     transition: all 1s 0.5s ease-in-out;
-}
-
-#init_image_wrapper {
-    position: relative;
-    width: fit-content;
-}
+} */
 
 #init_image_size_box {
     position: absolute;
     right: 0px;
-    bottom: 3px;
+    bottom: 0px;
     padding: 3px;
     background: black;
     color: white;
@@ -556,6 +577,10 @@ option {
     cursor: pointer;
 }
 
+input[type="file"] * {
+    cursor: pointer;
+}
+
 input,
 select,
 textarea {
@@ -594,12 +619,26 @@ input[type="file"] {
 }
 
 button,
-input::file-selector-button { 
+input::file-selector-button,
+.button { 
     padding: 2px 4px;
-    border-radius: 4px;
+    border-radius: var(--input-border-radius);
     background: var(--button-color);
     color: var(--button-text-color);
     border: var(--button-border);
+    align-items: center;
+    justify-content: center;
+    cursor: pointer;
+}
+
+.button i {
+    margin-right: 8px;
+}
+
+button:hover,
+.button:hover {
+  transition-duration: 0.1s;
+  background: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 6%));
 }
 
 input::file-selector-button {
@@ -658,11 +697,15 @@ input::file-selector-button {
     opacity: 1;
 }
 
-/* MOBILE SUPPORT */
-@media screen and (max-width: 700px) {
+/* Small screens */
+@media screen and (max-width: 1265px) {
     #top-nav {
         flex-direction: column;
     }
+}
+
+/* MOBILE SUPPORT */
+@media screen and (max-width: 700px) {
     body {
         margin: 0px;
     }
@@ -712,7 +755,7 @@ input::file-selector-button {
         padding-right: 0px;
     }
     #server-status {
-        display: none;
+        top: 75%;
     }
     .popup > div {
         padding-left: 5px !important;
@@ -730,6 +773,15 @@ input::file-selector-button {
     }
 }
 
+@media screen and (max-width: 500px) {
+    #server-status #server-status-msg {
+        display: none;
+    }
+    #server-status:hover #server-status-msg {
+        display: inline;
+    }
+}
+
 @media (min-width: 700px) {
     /* #editor {
         max-width: 480px;
@@ -750,6 +802,8 @@ input::file-selector-button {
 
 #promptsFromFileBtn {
     font-size: 9pt;
+    display: inline;
+    background-color: var(--accent-color);
 }
 
 .section-button {
@@ -951,8 +1005,8 @@ input::file-selector-button {
     display: none;
 }
 
-#tab-content-wrapper {
-    border-top: 8px solid var(--background-color1);
+#tab-content-wrapper > * {
+    padding-top: 8px;
 }
 
 .tab-content-inner {
@@ -989,16 +1043,21 @@ i.active {
     float: right;
     font-weight: bold;
 }
-button:hover {
-  transition-duration: 0.1s;
-  background: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 6%));
-}
 
 button:active {
   transition-duration: 0.1s;
   background-color: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 24%));
+  position: relative;
+  top: 1px;
+  left: 1px;
 }
 
 button#save-system-settings-btn {
     padding: 4pt 8pt;
 }
+#ip-info a {
+    color:var(--text-color)
+}
+#ip-info div {
+    line-height: 200%;
+}

ui/media/css/themes.css

@@ -19,7 +19,7 @@
 	--input-border-color: var(--background-color4);
 
     --button-text-color: var(--input-text-color);
-    --button-color: var(--accent-color);
+    --button-color: var(--input-background-color);
     --button-border: none;
 
     /* other */
@@ -30,6 +30,9 @@
     --primary-button-border: none;
     --input-switch-padding: 1px;
     --input-height: 18px;
+
+    /* Main theme color, hex color fallback. */
+    --theme-color-fallback: #673AB6;
 }
 
 .theme-light {
@@ -39,11 +42,12 @@
     --background-color4: #cccccc;
 
     --text-color: black;
-    --button-text-color: white;
 
     --input-text-color: black;
     --input-background-color: #f8f9fa; 
     --input-border-color: grey; 
+
+    --theme-color-fallback: #aaaaaa;
 }
 
 .theme-discord {
@@ -58,6 +62,8 @@
     --input-border-size: 2px; 
     --input-background-color: #202225; 
     --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #202225;
 }
 
 .theme-cool-blue {
@@ -71,8 +77,10 @@
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
 
     --input-background-color: var(--background-color3);
-    
+
     --accent-hue: 212;
+
+    --theme-color-fallback: #0056b8;
 }
 
 
@@ -87,6 +95,8 @@
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
 
     --input-background-color: var(--background-color3);
+
+    --theme-color-fallback: #5300b8;
 }
 
 .theme-super-dark {
@@ -101,6 +111,8 @@
 
     --input-background-color: var(--background-color3);
     --input-border-size: 0px;
+
+    --theme-color-fallback: #000000;
 }
 
 .theme-wild {
@@ -117,10 +129,11 @@
     
     --input-border-size: 1px; 
     --input-background-color: hsl(222, var(--main-saturation), calc(var(--value-base) - (2 * var(--value-step))));
-	--input-text-color: red;
-	--input-border-color: green;
+    --input-text-color: #FF0000;
+    --input-border-color: #005E05;
 }
 
+
 .theme-gnomie {
     --background-color1: #242424;
     --background-color2: #353535;
@@ -136,11 +149,12 @@
     --input-background-color: #2a2a2a; 
     --input-border-size: 0px; 
     --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #2168bf;
 }
 
 .theme-gnomie .panel-box {
     border: none;
     box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.25);
     border-radius: 10px;
-}
-
+}

ui/media/js/auto-save.js

@@ -21,6 +21,7 @@ const SETTINGS_IDS_LIST = [
     "guidance_scale",
     "prompt_strength",
     "output_format",
+    "output_quality",
     "negative_prompt",
     "stream_image_progress",
     "use_face_correction",
@@ -35,6 +36,7 @@ const SETTINGS_IDS_LIST = [
     "sound_toggle",
     "turbo",
     "use_full_precision",
+    "confirm_dangerous_actions",
     "auto_save_settings"
 ]
 
@@ -55,6 +57,9 @@ async function initSettings() {
         if (!element) {
             console.error(`Missing settings element ${id}`)
         }
+        if (id in SETTINGS) { // don't create it again
+            return
+        }
         SETTINGS[id] = {
             key: id,
             element: element,

ui/media/js/dnd.js

@@ -51,6 +51,13 @@ const TASK_MAPPING = {
         readUI: () => negativePromptField.value,
         parse: (val) => val
     },
+    active_tags: { name: "Image Modifiers",
+        setUI: (active_tags) => {
+            refreshModifiersState(active_tags)
+        },
+        readUI: () => activeTags.map(x => x.name),
+        parse: (val) => val
+    },
     width: { name: 'Width',
         setUI: (width) => {
             const oldVal = widthField.value
@@ -78,13 +85,14 @@ const TASK_MAPPING = {
             if (!seed) {
                 randomSeedField.checked = true
                 seedField.disabled = true
+                seedField.value = 0
                 return
             }
             randomSeedField.checked = false
             seedField.disabled = false
             seedField.value = seed
         },
-        readUI: () => (randomSeedField.checked ? Math.floor(Math.random() * 10000000) : parseInt(seedField.value)),
+        readUI: () => parseInt(seedField.value), // just return the value the user is seeing in the UI
         parse: (val) => parseInt(val)
     },
     num_inference_steps: { name: 'Steps',
@@ -120,10 +128,12 @@ const TASK_MAPPING = {
     },
     mask:  { name: 'Mask',
         setUI: (mask) => {
-            inpaintingEditor.setImg(mask)
+            setTimeout(() => { // add a delay to insure this happens AFTER the main image loads (which reloads the inpainter)
+                imageInpainter.setImg(mask)
+            }, 250)
             maskSetting.checked = Boolean(mask)
         },
-        readUI: () => (maskSetting.checked ? inpaintingEditor.getImg() : undefined),
+        readUI: () => (maskSetting.checked ? imageInpainter.getImg() : undefined),
         parse: (val) => val
     },
 
@@ -185,9 +195,9 @@ const TASK_MAPPING = {
         parse: (val) => val
     },
 
-    numOutputsParallel: { name: 'Parallel Images',
-        setUI: (numOutputsParallel) => {
-            numOutputsParallelField.value = numOutputsParallel
+    num_outputs: { name: 'Parallel Images',
+        setUI: (num_outputs) => {
+            numOutputsParallelField.value = num_outputs
         },
         readUI: () => parseInt(numOutputsParallelField.value),
         parse: (val) => val
@@ -267,11 +277,6 @@ function restoreTaskToUI(task, fieldsToSkip) {
     // restore the original tag
     promptField.value = task.reqBody.original_prompt || task.reqBody.prompt
 
-    // Restore modifiers
-    if (task.reqBody.active_tags) {
-        refreshModifiersState(task.reqBody.active_tags)
-    }
-
     // properly reset checkboxes
     if (!('use_face_correction' in task.reqBody)) {
         useFaceCorrectionField.checked = false
@@ -287,18 +292,11 @@ function restoreTaskToUI(task, fieldsToSkip) {
     // Show the source picture if present
     initImagePreview.src = (task.reqBody.init_image == undefined ? '' : task.reqBody.init_image)
     if (IMAGE_REGEX.test(initImagePreview.src)) {
-        Boolean(task.reqBody.mask) ? inpaintingEditor.setImg(task.reqBody.mask) : inpaintingEditor.resetBackground()
-        initImagePreviewContainer.style.display = 'block'
-        inpaintingEditorContainer.style.display = 'none'
-        promptStrengthContainer.style.display = 'table-row'
-        //samplerSelectionContainer.style.display = 'none'
-        // maskSetting.checked = false
-        inpaintingEditorContainer.style.display = maskSetting.checked ? 'block' : 'none'
-    } else {
-        initImagePreviewContainer.style.display = 'none'
-        // inpaintingEditorContainer.style.display = 'none'
-        promptStrengthContainer.style.display = 'none'
-        // maskSetting.style.display = 'none'
+        if (Boolean(task.reqBody.mask)) {
+            setTimeout(() => { // add a delay to insure this happens AFTER the main image loads (which reloads the inpainter)
+                imageInpainter.setImg(task.reqBody.mask)
+            }, 250)
+        }
     }
 }
 function readUI() {
@@ -326,6 +324,7 @@ function getModelPath(filename, extensions)
             filename = filename.slice(0, filename.length - ext.length)
         }
     })
+    return filename
 }
 
 const TASK_TEXT_MAPPING = {
@@ -406,7 +405,7 @@ async function parseContent(text) {
 }
 
 async function readFile(file, i) {
-    console.log(`Event %o reading file[${i}]:${file.name}...`, e)
+    console.log(`Event %o reading file[${i}]:${file.name}...`)
     const fileContent = (await file.text()).trim()
     return await parseContent(fileContent)
 }

ui/media/js/image-editor.js

@@ -0,0 +1,680 @@
+var editorControlsLeft = document.getElementById("image-editor-controls-left")
+
+const IMAGE_EDITOR_MAX_SIZE = 800
+
+const IMAGE_EDITOR_BUTTONS = [
+	{
+		name: "Cancel",
+		icon: "fa-regular fa-circle-xmark",
+		handler: editor => {
+			editor.hide()
+		}
+	},
+	{
+		name: "Save",
+		icon: "fa-solid fa-floppy-disk",
+		handler: editor => {
+			editor.saveImage()
+		}
+	}
+]
+
+const defaultToolBegin = (editor, ctx, x, y, is_overlay = false) => {
+	ctx.beginPath()
+	ctx.moveTo(x, y)
+}
+const defaultToolMove = (editor, ctx, x, y, is_overlay = false) => {
+	ctx.lineTo(x, y)
+	if (is_overlay) {
+		ctx.clearRect(0, 0, editor.width, editor.height)
+		ctx.stroke()
+	}
+}
+const defaultToolEnd = (editor, ctx, x, y, is_overlay = false) => {
+	ctx.stroke()
+	if (is_overlay) {
+		ctx.clearRect(0, 0, editor.width, editor.height)
+	}
+}
+
+const IMAGE_EDITOR_TOOLS = [
+	{
+		id: "draw",
+		name: "Draw",
+		icon: "fa-solid fa-pencil",
+		cursor: "url(/media/images/fa-pencil.png) 0 24, pointer",
+		begin: defaultToolBegin,
+		move: defaultToolMove,
+		end: defaultToolEnd
+	},
+	{
+		id: "erase",
+		name: "Erase",
+		icon: "fa-solid fa-eraser",
+		cursor: "url(/media/images/fa-eraser.png) 0 18, pointer",
+		begin: defaultToolBegin,
+		move: (editor, ctx, x, y, is_overlay = false) => {
+			ctx.lineTo(x, y)
+			if (is_overlay) {
+				ctx.clearRect(0, 0, editor.width, editor.height)
+				ctx.globalCompositeOperation = "source-over"
+				ctx.globalAlpha = 1
+				ctx.filter = "none"
+				ctx.drawImage(editor.canvas_current, 0, 0)
+				editor.setBrush(editor.layers.overlay)
+				ctx.stroke()
+				editor.canvas_current.style.opacity = 0
+			}
+		},
+		end: (editor, ctx, x, y, is_overlay = false) => {
+			ctx.stroke()
+			if (is_overlay) {
+				ctx.clearRect(0, 0, editor.width, editor.height)
+				editor.canvas_current.style.opacity = ""
+			}
+		},
+		setBrush: (editor, layer) => {
+			layer.ctx.globalCompositeOperation = "destination-out"
+		}
+	},
+	{
+		id: "colorpicker",
+		name: "Color Picker",
+		icon: "fa-solid fa-eye-dropper",
+		cursor: "url(/media/images/fa-eye-dropper.png) 0 24, pointer",
+		begin: (editor, ctx, x, y, is_overlay = false) => {
+			var img_rgb = editor.layers.background.ctx.getImageData(x, y, 1, 1).data
+			var drawn_rgb = editor.ctx_current.getImageData(x, y, 1, 1).data
+			var drawn_opacity = drawn_rgb[3] / 255
+			editor.custom_color_input.value = rgbToHex({ 
+				r: (drawn_rgb[0] * drawn_opacity) + (img_rgb[0] * (1 - drawn_opacity)),
+				g: (drawn_rgb[1] * drawn_opacity) + (img_rgb[1] * (1 - drawn_opacity)),
+				b: (drawn_rgb[2] * drawn_opacity) + (img_rgb[2] * (1 - drawn_opacity)),
+			})
+			editor.custom_color_input.dispatchEvent(new Event("change"))
+		},
+		move: (editor, ctx, x, y, is_overlay = false) => {},
+		end: (editor, ctx, x, y, is_overlay = false) => {}
+	}
+]
+
+const IMAGE_EDITOR_ACTIONS = [
+	{
+		id: "clear",
+		name: "Clear",
+		icon: "fa-solid fa-xmark",
+		handler: (editor) => {
+			editor.ctx_current.clearRect(0, 0, editor.width, editor.height)
+		}
+	}
+]
+
+var IMAGE_EDITOR_SECTIONS = [
+	{
+		name: "tool",
+		title: "Tool",
+		default: "draw",
+		options: Array.from(IMAGE_EDITOR_TOOLS.map(t => t.id)),
+		initElement: (element, option) => {
+			var tool_info = IMAGE_EDITOR_TOOLS.find(t => t.id == option)
+			element.className = "image-editor-button button"
+			var sub_element = document.createElement("div")
+			var icon = document.createElement("i")
+			tool_info.icon.split(" ").forEach(c => icon.classList.add(c))
+			sub_element.appendChild(icon)
+			sub_element.append(tool_info.name)
+			element.appendChild(sub_element)
+		}
+	},
+	{
+		name: "color",
+		title: "Color",
+		default: "#f1c232",
+		options: [
+			"custom",
+			"#ea9999", "#e06666", "#cc0000", "#990000", "#660000",
+			"#f9cb9c", "#f6b26b", "#e69138", "#b45f06", "#783f04",
+			"#ffe599", "#ffd966", "#f1c232", "#bf9000", "#7f6000",
+			"#b6d7a8", "#93c47d", "#6aa84f", "#38761d", "#274e13",
+			"#a4c2f4", "#6d9eeb", "#3c78d8", "#1155cc", "#1c4587",
+			"#b4a7d6", "#8e7cc3", "#674ea7", "#351c75", "#20124d",
+			"#d5a6bd", "#c27ba0", "#a64d79", "#741b47", "#4c1130",
+			"#ffffff", "#c0c0c0", "#838383", "#525252", "#000000",
+		],
+		initElement: (element, option) => {
+			if (option == "custom") {
+				var input = document.createElement("input")
+				input.type = "color"
+				element.appendChild(input)
+				var span = document.createElement("span")
+				span.textContent = "Custom"
+				span.onclick = function(e) {
+					input.click()
+				}
+				element.appendChild(span)
+			}
+			else {
+				element.style.background = option
+			}
+		},
+		getCustom: editor => {
+			var input = editor.popup.querySelector(".image_editor_color input")
+			return input.value
+		}
+	},
+	{
+		name: "brush_size",
+		title: "Brush Size",
+		default: 48,
+		options: [ 16, 24, 32, 48, 64 ],
+		initElement: (element, option) => {
+			element.parentElement.style.flex = option
+			element.style.width = option + "px"
+			element.style.height = option + "px"
+			element.style["border-radius"] = (option / 2).toFixed() + "px"
+		}
+	},
+	{
+		name: "opacity",
+		title: "Opacity",
+		default: 0,
+		options: [ 0, 0.2, 0.4, 0.6, 0.8 ],
+		initElement: (element, option) => {
+			element.style.background = `repeating-conic-gradient(rgba(0, 0, 0, ${option}) 0% 25%, rgba(255, 255, 255, ${option}) 0% 50%) 50% / 10px 10px`
+		}
+	},
+	{
+		name: "sharpness",
+		title: "Sharpness",
+		default: 0,
+		options: [ 0, 0.05, 0.1, 0.2, 0.3 ],
+		initElement: (element, option) => {
+			var size = 32
+			var blur_amount = parseInt(option * size)
+			var sub_element = document.createElement("div")
+			sub_element.style.background = `var(--background-color3)`
+			sub_element.style.filter = `blur(${blur_amount}px)`
+			sub_element.style.width = `${size - 4}px`
+			sub_element.style.height = `${size - 4}px`
+			sub_element.style['border-radius'] = `${size}px`
+			element.style.background = "none"
+			element.appendChild(sub_element)
+		}
+	}
+]
+
+class EditorHistory {
+	constructor(editor) {
+		this.editor = editor
+		this.events = [] // stack of all events (actions/edits)
+		this.current_edit = null
+		this.rewind_index = 0 // how many events back into the history we've rewound to. (current state is just after event at index 'length - this.rewind_index - 1')
+	}
+	push(event) {
+		// probably add something here eventually to save state every x events
+		if (this.rewind_index != 0) {
+			this.events = this.events.slice(0, 0 - this.rewind_index)
+			this.rewind_index = 0
+		}
+		var snapshot_frequency = 20 // (every x edits, take a snapshot of the current drawing state, for faster rewinding)
+		if (this.events.length > 0 && this.events.length % snapshot_frequency == 0) {
+			event.snapshot = this.editor.layers.drawing.ctx.getImageData(0, 0, this.editor.width, this.editor.height)
+		}
+		this.events.push(event)
+	}
+	pushAction(action) {
+		this.push({
+			type: "action",
+			id: action
+		});
+	}
+	editBegin(x, y) {
+		this.current_edit = {
+			type: "edit",
+			id: this.editor.getOptionValue("tool"),
+			options: Object.assign({}, this.editor.options),
+			points: [ { x: x, y: y } ]
+		}
+	}
+	editMove(x, y) {
+		if (this.current_edit) {
+			this.current_edit.points.push({ x: x, y: y })
+		}
+	}
+	editEnd(x, y) {
+		if (this.current_edit) {
+			this.push(this.current_edit)
+			this.current_edit = null
+		}
+	}
+	clear() {
+		this.events = []
+	}
+	undo() {
+		this.rewindTo(this.rewind_index + 1)
+	}
+	redo() {
+		this.rewindTo(this.rewind_index - 1)
+	}
+	rewindTo(new_rewind_index) {
+		if (new_rewind_index < 0 || new_rewind_index > this.events.length) {
+			return; // do nothing if target index is out of bounds
+		}
+
+		var ctx = this.editor.layers.drawing.ctx
+		ctx.clearRect(0, 0, this.editor.width, this.editor.height)
+
+		var target_index = this.events.length - 1 - new_rewind_index
+		var snapshot_index = target_index
+		while (snapshot_index > -1) {
+			if (this.events[snapshot_index].snapshot) {
+				break
+			}
+			snapshot_index--
+		}
+
+		if (snapshot_index != -1) {
+			ctx.putImageData(this.events[snapshot_index].snapshot, 0, 0);
+		}
+
+		for (var i = (snapshot_index + 1); i <= target_index; i++) {
+			var event = this.events[i]
+			if (event.type == "action") {
+				var action = IMAGE_EDITOR_ACTIONS.find(a => a.id == event.id)
+				action.handler(this.editor)
+			}
+			else if (event.type == "edit") {
+				var tool = IMAGE_EDITOR_TOOLS.find(t => t.id == event.id)
+				this.editor.setBrush(this.editor.layers.drawing, event.options)
+
+				var first_point = event.points[0]
+				tool.begin(this.editor, ctx, first_point.x, first_point.y)
+				for (var point_i = 1; point_i < event.points.length; point_i++) {
+					tool.move(this.editor, ctx, event.points[point_i].x, event.points[point_i].y)
+				}
+				var last_point = event.points[event.points.length - 1]
+				tool.end(this.editor, ctx, last_point.x, last_point.y)
+			}
+		}
+
+		// re-set brush to current settings
+		this.editor.setBrush(this.editor.layers.drawing)
+
+		this.rewind_index = new_rewind_index
+	}
+}
+
+class ImageEditor {
+	constructor(popup, inpainter = false) {
+		this.inpainter = inpainter
+		this.popup = popup
+		this.history = new EditorHistory(this)
+		if (inpainter) {
+			this.popup.classList.add("inpainter")
+		}
+		this.drawing = false
+		this.temp_previous_tool = null // used for the ctrl-colorpicker functionality
+		this.container = popup.querySelector(".editor-controls-center > div")
+		this.layers = {}
+		var layer_names = [
+			"background",
+			"drawing",
+			"overlay"
+		]
+		layer_names.forEach(name => {
+			let canvas = document.createElement("canvas")
+			canvas.className = `editor-canvas-${name}`
+			this.container.appendChild(canvas)
+			this.layers[name] = {
+				name: name,
+				canvas: canvas,
+				ctx: canvas.getContext("2d")
+			}
+		})
+
+		// add mouse handlers
+		this.container.addEventListener("mousedown", this.mouseHandler.bind(this))
+		this.container.addEventListener("mouseup", this.mouseHandler.bind(this))
+		this.container.addEventListener("mousemove", this.mouseHandler.bind(this))
+		this.container.addEventListener("mouseout", this.mouseHandler.bind(this))
+		this.container.addEventListener("mouseenter", this.mouseHandler.bind(this))
+
+		this.container.addEventListener("touchstart", this.mouseHandler.bind(this))
+		this.container.addEventListener("touchmove", this.mouseHandler.bind(this))
+		this.container.addEventListener("touchcancel", this.mouseHandler.bind(this))
+		this.container.addEventListener("touchend", this.mouseHandler.bind(this))
+
+		// initialize editor controls
+		this.options = {}
+		this.optionElements = {}
+		IMAGE_EDITOR_SECTIONS.forEach(section => {
+			section.id = `image_editor_${section.name}`
+			var sectionElement = document.createElement("div")
+			sectionElement.className = section.id
+	
+			var title = document.createElement("h4")
+			title.innerText = section.title
+			sectionElement.appendChild(title)
+	
+			var optionsContainer = document.createElement("div")
+			optionsContainer.classList.add("editor-options-container")
+	
+			this.optionElements[section.name] = []
+			section.options.forEach((option, index) => {
+				var optionHolder = document.createElement("div")
+				var optionElement = document.createElement("div")
+				optionHolder.appendChild(optionElement)
+				section.initElement(optionElement, option)
+				optionElement.addEventListener("click", target => this.selectOption(section.name, index))
+				optionsContainer.appendChild(optionHolder)
+				this.optionElements[section.name].push(optionElement)
+			})
+			this.selectOption(section.name, section.options.indexOf(section.default))
+	
+			sectionElement.appendChild(optionsContainer)
+	
+			this.popup.querySelector(".editor-controls-left").appendChild(sectionElement)
+		})
+
+		this.custom_color_input = this.popup.querySelector(`input[type="color"]`)
+		this.custom_color_input.addEventListener("change", () => {
+			this.custom_color_input.parentElement.style.background = this.custom_color_input.value
+			this.selectOption("color", 0)
+		})
+
+		if (this.inpainter) {
+			this.selectOption("color", IMAGE_EDITOR_SECTIONS.find(s => s.name == "color").options.indexOf("#ffffff"))
+		}
+
+		// initialize the right-side controls
+		var buttonContainer = document.createElement("div")
+		IMAGE_EDITOR_BUTTONS.forEach(button => {
+			var element = document.createElement("div")
+			var icon = document.createElement("i")
+			element.className = "image-editor-button button"
+			icon.className = button.icon
+			element.appendChild(icon)
+			element.append(button.name)
+			buttonContainer.appendChild(element)
+			element.addEventListener("click", event => button.handler(this))
+		})
+		var actionsContainer = document.createElement("div")
+		var actionsTitle = document.createElement("h4")
+		actionsTitle.textContent = "Actions"
+		actionsContainer.appendChild(actionsTitle);
+		IMAGE_EDITOR_ACTIONS.forEach(action => {
+			var element = document.createElement("div")
+			var icon = document.createElement("i")
+			element.className = "image-editor-button button"
+			icon.className = action.icon
+			element.appendChild(icon)
+			element.append(action.name)
+			actionsContainer.appendChild(element)
+			element.addEventListener("click", event => this.runAction(action.id))
+		})
+		this.popup.querySelector(".editor-controls-right").appendChild(actionsContainer)
+		this.popup.querySelector(".editor-controls-right").appendChild(buttonContainer)
+
+		this.keyHandlerBound = this.keyHandler.bind(this)
+
+		this.setSize(512, 512)
+	}
+	show() {
+		this.popup.classList.add("active")
+		document.addEventListener("keydown", this.keyHandlerBound)
+		document.addEventListener("keyup", this.keyHandlerBound)
+	}
+	hide() {
+		this.popup.classList.remove("active")
+		document.removeEventListener("keydown", this.keyHandlerBound)
+		document.removeEventListener("keyup", this.keyHandlerBound)
+	}
+	setSize(width, height) {
+		if (width == this.width && height == this.height) {
+			return
+		}
+
+		var max_size = Math.min(parseInt(window.innerWidth * 0.9), width, 768)
+		if (width > height) {
+			var multiplier = max_size / width
+			width = (multiplier * width).toFixed()
+			height = (multiplier * height).toFixed()
+		}
+		else {
+			var multiplier = max_size / height
+			width = (multiplier * width).toFixed()
+			height = (multiplier * height).toFixed()
+		}
+		this.width = width
+		this.height = height
+	
+		this.container.style.width = width + "px"
+		this.container.style.height = height + "px"
+		
+		Object.values(this.layers).forEach(layer => {
+			layer.canvas.width = width
+			layer.canvas.height = height
+		})
+
+		if (this.inpainter) {
+			this.saveImage() // We've reset the size of the image so inpainting is different
+		}
+		this.setBrush()
+		this.history.clear()
+	}
+	get tool() {
+		var tool_id = this.getOptionValue("tool")
+		return IMAGE_EDITOR_TOOLS.find(t => t.id == tool_id);
+	}
+	loadTool() {
+		this.drawing = false
+		this.container.style.cursor = this.tool.cursor;
+	}
+	setImage(url, width, height) {
+		this.setSize(width, height)
+		this.layers.drawing.ctx.clearRect(0, 0, this.width, this.height)
+		this.layers.background.ctx.clearRect(0, 0, this.width, this.height)
+		if (url) {
+			var image = new Image()
+			image.onload = () => { 
+				this.layers.background.ctx.drawImage(image, 0, 0, this.width, this.height)
+			}
+			image.src = url
+		}
+		else {
+			this.layers.background.ctx.fillStyle = "#ffffff"
+			this.layers.background.ctx.beginPath()
+			this.layers.background.ctx.rect(0, 0, this.width, this.height)
+			this.layers.background.ctx.fill()
+		}
+		this.history.clear()
+	}
+	saveImage() {
+		if (!this.inpainter) {
+			// This is not an inpainter, so save the image as the new img2img input
+			this.layers.background.ctx.drawImage(this.layers.drawing.canvas, 0, 0, this.width, this.height)
+			var base64 = this.layers.background.canvas.toDataURL()
+			initImagePreview.src = base64 // this will trigger the rest of the app to use it
+		}
+		else {
+			// This is an inpainter, so make sure the toggle is set accordingly
+			var is_blank = !this.layers.drawing.ctx
+				.getImageData(0, 0, this.width, this.height).data
+				.some(channel => channel !== 0)
+			maskSetting.checked = !is_blank
+		}
+		this.hide()
+	}
+	getImg() { // a drop-in replacement of the drawingboard version
+		return this.layers.drawing.canvas.toDataURL()
+	}
+	setImg(dataUrl) { // a drop-in replacement of the drawingboard version
+		var image = new Image()
+		image.onload = () => {
+			var ctx = this.layers.drawing.ctx;
+			ctx.clearRect(0, 0, this.width, this.height)
+			ctx.globalCompositeOperation = "source-over"
+			ctx.globalAlpha = 1
+			ctx.filter = "none"
+			ctx.drawImage(image, 0, 0, this.width, this.height)
+			this.setBrush(this.layers.drawing)
+		}
+		image.src = dataUrl
+	}
+	runAction(action_id) {
+		var action = IMAGE_EDITOR_ACTIONS.find(a => a.id == action_id)
+		this.history.pushAction(action_id)
+		action.handler(this)
+	}
+	setBrush(layer = null, options = null) {
+		if (options == null) {
+			options = this.options
+		}
+		if (layer) {
+			layer.ctx.lineCap = "round"
+			layer.ctx.lineJoin = "round"
+			layer.ctx.lineWidth = options.brush_size
+			layer.ctx.fillStyle = options.color
+			layer.ctx.strokeStyle = options.color
+			var sharpness = parseInt(options.sharpness * options.brush_size)
+			layer.ctx.filter = sharpness == 0 ? `none` : `blur(${sharpness}px)`
+			layer.ctx.globalAlpha = (1 - options.opacity)
+			layer.ctx.globalCompositeOperation = "source-over"
+			var tool = IMAGE_EDITOR_TOOLS.find(t => t.id == options.tool)
+			if (tool && tool.setBrush) {
+				tool.setBrush(editor, layer)
+			}
+		}
+		else {
+			Object.values([ "drawing", "overlay" ]).map(name => this.layers[name]).forEach(l => {
+				this.setBrush(l)
+			})
+		}
+	}
+	get ctx_overlay() {
+		return this.layers.overlay.ctx
+	}
+	get ctx_current() { // the idea is this will help support having custom layers and editing each one
+		return this.layers.drawing.ctx
+	}
+	get canvas_current() {
+		return this.layers.drawing.canvas
+	}
+	keyHandler(event) { // handles keybinds like ctrl+z, ctrl+y
+		if (!this.popup.classList.contains("active")) {
+			document.removeEventListener("keydown", this.keyHandlerBound)
+			document.removeEventListener("keyup", this.keyHandlerBound)
+			return // this catches if something else closes the window but doesnt properly unbind the key handler
+		}
+
+		// keybindings
+		if (event.type == "keydown") {
+			if ((event.key == "z" || event.key == "Z") && event.ctrlKey) {
+				if (!event.shiftKey) {
+					this.history.undo()
+				}
+				else {
+					this.history.redo()
+				}
+			}
+			if (event.key == "y" && event.ctrlKey) {
+				this.history.redo()
+			}
+		}
+		
+		// dropper ctrl holding handler stuff
+		var dropper_active = this.temp_previous_tool != null;
+		if (dropper_active && !event.ctrlKey) {
+			this.selectOption("tool", IMAGE_EDITOR_TOOLS.findIndex(t => t.id == this.temp_previous_tool))
+			this.temp_previous_tool = null
+		}
+		else if (!dropper_active && event.ctrlKey) {
+			this.temp_previous_tool = this.getOptionValue("tool")
+			this.selectOption("tool", IMAGE_EDITOR_TOOLS.findIndex(t => t.id == "colorpicker"))
+		}
+	}
+	mouseHandler(event) {
+		var bbox = this.layers.overlay.canvas.getBoundingClientRect()
+		var x = (event.clientX || 0) - bbox.left
+		var y = (event.clientY || 0) - bbox.top
+		var type = event.type;
+		var touchmap = {
+			touchstart: "mousedown",
+			touchmove: "mousemove",
+			touchend: "mouseup",
+			touchcancel: "mouseup"
+		}
+		if (type in touchmap) {
+			type = touchmap[type]
+			if (event.touches && event.touches[0]) {
+				var touch = event.touches[0]				
+				var x = (touch.clientX || 0) - bbox.left
+				var y = (touch.clientY || 0) - bbox.top
+			}
+		}
+		event.preventDefault()	
+		// do drawing-related stuff
+		if (type == "mousedown" || (type == "mouseenter" && event.buttons == 1)) {
+			this.drawing = true
+			this.tool.begin(this, this.ctx_current, x, y)
+			this.tool.begin(this, this.ctx_overlay, x, y, true)
+			this.history.editBegin(x, y)
+		}
+		if (type == "mouseup" || type == "mousemove") {
+			if (this.drawing) {
+				if (x > 0 && y > 0) {
+					this.tool.move(this, this.ctx_current, x, y)
+					this.tool.move(this, this.ctx_overlay, x, y, true)
+					this.history.editMove(x, y)
+				}
+			}
+		}
+		if (type == "mouseup" || type == "mouseout") {
+			if (this.drawing) {
+				this.drawing = false
+				this.tool.end(this, this.ctx_current, x, y)
+				this.tool.end(this, this.ctx_overlay, x, y, true)
+				this.history.editEnd(x, y)
+			}
+		}
+	}
+	getOptionValue(section_name) {
+		var section = IMAGE_EDITOR_SECTIONS.find(s => s.name == section_name)
+		return this.options && section_name in this.options ? this.options[section_name] : section.default
+	}
+	selectOption(section_name, option_index) {
+		var section = IMAGE_EDITOR_SECTIONS.find(s => s.name == section_name)
+		var value = section.options[option_index]
+		this.options[section_name] = value == "custom" ? section.getCustom(this) : value
+		
+		this.optionElements[section_name].forEach(element => element.classList.remove("active"))
+		this.optionElements[section_name][option_index].classList.add("active")
+	
+		// change the editor
+		this.setBrush()
+		if (section.name == "tool") {
+			this.loadTool()
+		}
+	}
+}
+
+function rgbToHex(rgb) {
+	function componentToHex(c) {
+		var hex = parseInt(c).toString(16)
+		return hex.length == 1 ? "0" + hex : hex
+	}
+	return "#" + componentToHex(rgb.r) + componentToHex(rgb.g) + componentToHex(rgb.b)
+}
+
+const imageEditor = new ImageEditor(document.getElementById("image-editor"))
+const imageInpainter = new ImageEditor(document.getElementById("image-inpainter"), true)
+
+imageEditor.setImage(null, 512, 512)
+imageInpainter.setImage(null, 512, 512)
+
+document.getElementById("init_image_button_draw").addEventListener("click", () => {
+	imageEditor.show()
+})
+document.getElementById("init_image_button_inpaint").addEventListener("click", () => {
+	imageInpainter.show()
+})

ui/media/js/image-modifiers.js

@@ -85,14 +85,13 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
 
         if(typeof modifierCard == 'object') {
             modifiersEl.appendChild(modifierCard)
+            const trimmedName = trimModifiers(modifierName)
 
             modifierCard.addEventListener('click', () => {
-                if (activeTags.map(x => x.name).includes(modifierName)) {
+                if (activeTags.map(x => trimModifiers(x.name)).includes(trimmedName)) {
                     // remove modifier from active array
-                    activeTags = activeTags.filter(x => x.name != modifierName)
-                    modifierCard.classList.remove(activeCardClass)
-
-                    modifierCard.querySelector('.modifier-card-image-overlay').innerText = '+'
+                    activeTags = activeTags.filter(x => trimModifiers(x.name) != trimmedName)
+                    toggleCardState(trimmedName, false)
                 } else {
                     // add modifier to active array
                     activeTags.push({
@@ -101,10 +100,7 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
                         'originElement': modifierCard,
                         'previews': modifierPreviews
                     })
-
-                    modifierCard.classList.add(activeCardClass)
-
-                    modifierCard.querySelector('.modifier-card-image-overlay').innerText = '-'
+                    toggleCardState(trimmedName, true)
                 }
 
                 refreshTagsList()
@@ -125,6 +121,10 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
     return e
 }
 
+function trimModifiers(tag) {
+    return tag.replace(/^\(+|\)+$/g, '').replace(/^\[+|\]+$/g, '')
+}
+
 async function loadModifiers() {
     try {
         let res = await fetch('/get/modifiers')
@@ -219,11 +219,10 @@ function refreshTagsList() {
         editorModifierTagsList.appendChild(tag.element)
 
         tag.element.addEventListener('click', () => {
-            let idx = activeTags.indexOf(tag)
+            let idx = activeTags.findIndex(o => { return o.name === tag.name })            
 
-            if (idx !== -1 && activeTags[idx].originElement !== undefined) {
-                activeTags[idx].originElement.classList.remove(activeCardClass)
-                activeTags[idx].originElement.querySelector('.modifier-card-image-overlay').innerText = '+'
+            if (idx !== -1) {
+                toggleCardState(activeTags[idx].name, false)
 
                 activeTags.splice(idx, 1)
                 refreshTagsList()
@@ -236,6 +235,23 @@ function refreshTagsList() {
     editorModifierTagsList.appendChild(brk)
 }
 
+function toggleCardState(modifierName, makeActive) {
+    document.querySelector('#editor-modifiers').querySelectorAll('.modifier-card').forEach(card => {
+        const name = card.querySelector('.modifier-card-label').innerText
+        if (   trimModifiers(modifierName) == trimModifiers(name)
+            || trimModifiers(modifierName) == 'by ' + trimModifiers(name)) {
+            if(makeActive) {
+                card.classList.add(activeCardClass)
+                card.querySelector('.modifier-card-image-overlay').innerText = '-'
+            }
+            else{
+                card.classList.remove(activeCardClass)
+                card.querySelector('.modifier-card-image-overlay').innerText = '+'
+            }
+        }
+    })
+}
+
 function changePreviewImages(val) {
     const previewImages = document.querySelectorAll('.modifier-card-image-container img')
 
@@ -310,31 +326,7 @@ function saveCustomModifiers() {
 }
 
 function loadCustomModifiers() {
-    let customModifiers = localStorage.getItem(CUSTOM_MODIFIERS_KEY, '')
-    customModifiersTextBox.value = customModifiers
-
-    if (customModifiersGroupElement !== undefined) {
-        customModifiersGroupElement.remove()
-    }
-
-    if (customModifiers && customModifiers.trim() !== '') {
-        customModifiers = customModifiers.split('\n')
-        customModifiers = customModifiers.filter(m => m.trim() !== '')
-        customModifiers = customModifiers.map(function(m) {
-            return {
-                "modifier": m
-            }
-        })
-
-        let customGroup = {
-            'category': 'Custom Modifiers',
-            'modifiers': customModifiers
-        }
-
-        customModifiersGroupElement = createModifierGroup(customGroup, true)
-
-        createCollapsibles(customModifiersGroupElement)
-    }
+    PLUGINS['MODIFIERS_LOAD'].forEach(fn=>fn.loader.call())
 }
 
 customModifiersTextBox.addEventListener('change', saveCustomModifiers)

ui/media/js/inpainting-editor.js

@@ -1,41 +0,0 @@
-const INPAINTING_EDITOR_SIZE = 450
-
-let inpaintingEditorContainer = document.querySelector('#inpaintingEditor')
-let inpaintingEditor = new DrawingBoard.Board('inpaintingEditor', {
-    color: "#ffffff",
-    background: false,
-    size: 30,
-    webStorage: false,
-    controls: [{'DrawingMode': {'filler': false}}, 'Size', 'Navigation']
-})
-let inpaintingEditorCanvasBackground = document.querySelector('.drawing-board-canvas-wrapper')
-
-function resizeInpaintingEditor(widthValue, heightValue) {
-    if (widthValue === heightValue) {
-        widthValue = INPAINTING_EDITOR_SIZE
-        heightValue = INPAINTING_EDITOR_SIZE
-    } else if (widthValue > heightValue) {
-        heightValue = (heightValue / widthValue) * INPAINTING_EDITOR_SIZE
-        widthValue = INPAINTING_EDITOR_SIZE
-    } else {
-        widthValue = (widthValue / heightValue) * INPAINTING_EDITOR_SIZE
-        heightValue = INPAINTING_EDITOR_SIZE
-    }
-    if (inpaintingEditor.opts.aspectRatio === (widthValue / heightValue).toFixed(3)) {
-        // Same ratio, don't reset the canvas.
-        return
-    }
-    inpaintingEditor.opts.aspectRatio = (widthValue / heightValue).toFixed(3)
-
-    inpaintingEditorContainer.style.width = widthValue + 'px'
-    inpaintingEditorContainer.style.height = heightValue + 'px'
-    inpaintingEditor.opts.enlargeYourContainer = true
-
-    inpaintingEditor.opts.size = inpaintingEditor.ctx.lineWidth
-    inpaintingEditor.resize()
-
-    inpaintingEditor.ctx.lineCap = "round"
-    inpaintingEditor.ctx.lineJoin = "round"
-    inpaintingEditor.ctx.lineWidth = inpaintingEditor.opts.size
-    inpaintingEditor.setColor(inpaintingEditor.opts.color)
-}

ui/media/js/main.js

@@ -16,6 +16,9 @@ let numOutputsParallelField = document.querySelector('#num_outputs_parallel')
 let numInferenceStepsField = document.querySelector('#num_inference_steps')
 let guidanceScaleSlider = document.querySelector('#guidance_scale_slider')
 let guidanceScaleField = document.querySelector('#guidance_scale')
+let outputQualitySlider = document.querySelector('#output_quality_slider')
+let outputQualityField = document.querySelector('#output_quality')
+let outputQualityRow = document.querySelector('#output_quality_row')
 let randomSeedField = document.querySelector("#random_seed")
 let seedField = document.querySelector('#seed')
 let widthField = document.querySelector('#width')
@@ -59,14 +62,6 @@ let serverStatusColor = document.querySelector('#server-status-color')
 let serverStatusMsg = document.querySelector('#server-status-msg')
 
 
-document.querySelector('.drawing-board-control-navigation-back').innerHTML = '<i class="fa-solid fa-rotate-left"></i>'
-document.querySelector('.drawing-board-control-navigation-forward').innerHTML = '<i class="fa-solid fa-rotate-right"></i>'
-
-let maskResetButton = document.querySelector('.drawing-board-control-navigation-reset')
-maskResetButton.innerHTML = 'Clear'
-maskResetButton.style.fontWeight = 'normal'
-maskResetButton.style.fontSize = '10pt'
-
 let serverState = {'status': 'Offline', 'time': Date.now()}
 let bellPending = false
 
@@ -138,6 +133,35 @@ function isServerAvailable() {
     }
 }
 
+// shiftOrConfirm(e, prompt, fn)
+//   e      : MouseEvent
+//   prompt : Text to be shown as prompt. Should be a question to which "yes" is a good answer.
+//   fn     : function to be called if the user confirms the dialog or has the shift key pressed
+//
+// If the user had the shift key pressed while clicking, the function fn will be executed.
+// If the setting "confirm_dangerous_actions" in the system settings is disabled, the function 
+// fn will be executed.
+// Otherwise, a confirmation dialog is shown. If the user confirms, the function fn will also
+// be executed.
+function shiftOrConfirm(e, prompt, fn) {
+    e.stopPropagation()
+    if (e.shiftKey || !confirmDangerousActionsField.checked) {
+         fn(e)
+    } else {
+        $.confirm({
+            theme: 'modern',
+            title: prompt,
+            useBootstrap: false,
+            animateFromElement: false,
+            content: '<small>Tip: To skip this dialog, use shift-click or disable the "Confirm dangerous actions" setting in the Settings tab.</small>',
+            buttons: {
+                yes: () => { fn(e) },
+                cancel: () => {}
+            }
+        }); 
+    }
+}
+
 function logMsg(msg, level, outputMsg) {
     if (outputMsg.hasChildNodes()) {
         outputMsg.appendChild(document.createElement('br'))
@@ -169,34 +193,6 @@ function playSound() {
         })
     }
 }
-function setSystemInfo(devices) {
-    let cpu = devices.all.cpu.name
-    let allGPUs = Object.keys(devices.all).filter(d => d != 'cpu')
-    let activeGPUs = Object.keys(devices.active)
-
-    function ID_TO_TEXT(d) {
-        let info = devices.all[d]
-        if ("mem_free" in info && "mem_total" in info) {
-            return `${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">${allGPUs.join('</br>')}</td></tr>
-        <tr><td></td><td>&nbsp;</td></tr>
-        <tr><td><label>Used for rendering 🔥:</label></td><td class="value">${activeGPUs.join('</br>')}</td></tr>
-    </table>`
-
-    let systemInfoEl = document.querySelector('#system-info')
-    systemInfoEl.innerHTML = systemInfo
-}
 
 async function healthCheck() {
     try {
@@ -231,7 +227,7 @@ async function healthCheck() {
                 break
         }
         if (serverState.devices) {
-            setSystemInfo(serverState.devices)
+            setDeviceInfo(serverState.devices)
         }
         serverState.time = Date.now()
     } catch (e) {
@@ -334,11 +330,7 @@ function onUseAsInputClick(req, img) {
     initImageSelector.value = null
     initImagePreview.src = imgData
 
-    initImagePreviewContainer.style.display = 'block'
-    inpaintingEditorContainer.style.display = 'none'
-    promptStrengthContainer.style.display = 'table-row'
     maskSetting.checked = false
-    samplerSelectionContainer.style.display = 'none'
 }
 
 function onDownloadImageClick(req, img) {
@@ -386,11 +378,21 @@ function onMakeSimilarClick(req, img) {
 function enqueueImageVariationTask(req, img, reqDiff) {
     const imageSeed = img.getAttribute('data-seed')
 
-    const newTaskRequest = modifyCurrentRequest(req, reqDiff, {
+    const newRequestBody = {
         num_outputs: 1, // this can be user-configurable in the future
         seed: imageSeed
-    })
+    }
 
+    // If the user is editing pictures, stop modifyCurrentRequest from importing
+    // new values by setting the missing properties to undefined
+    if (!('init_image' in req) && !('init_image' in reqDiff)) {
+        newRequestBody.init_image = undefined
+        newRequestBody.mask = undefined
+    } else if (!('mask' in req) && !('mask' in reqDiff)) {
+        newRequestBody.mask = undefined
+    }
+
+    const newTaskRequest = modifyCurrentRequest(req, reqDiff, newRequestBody)
     newTaskRequest.numOutputsTotal = 1 // this can be user-configurable in the future
     newTaskRequest.batchCount = 1
 
@@ -695,6 +697,12 @@ async function checkTasks() {
 
     const genSeeds = Boolean(typeof task.reqBody.seed !== 'number' || (task.reqBody.seed === task.seed && task.numOutputsTotal > 1))
     const startSeed = task.reqBody.seed || task.seed
+    
+    // Update the seed *before* starting the processing so it's retained if user stops the task
+    if (randomSeedField.checked) {
+        seedField.value = task.seed
+    }
+    
     for (let i = 0; i < task.batchCount; i++) {
         let newTask = task
         if (task.batchCount > 1) {
@@ -741,10 +749,6 @@ async function checkTasks() {
         }
     }
 
-    if (randomSeedField.checked) {
-        seedField.value = task.seed
-    }
-
     currentTask = null
 
     if (typeof requestIdleCallback === 'function') {
@@ -790,6 +794,7 @@ function getCurrentUserRequest() {
             stream_image_progress: (numOutputsTotal > 50 ? false : streamImageProgressField.checked),
             show_only_filtered_image: showOnlyFilteredImageField.checked,
             output_format: outputFormatField.value,
+            output_quality: outputQualityField.value,
             original_prompt: promptField.value,
             active_tags: (activeTags.map(x => x.name))
         }
@@ -802,7 +807,7 @@ function getCurrentUserRequest() {
         //     newTask.reqBody.mask = maskImagePreview.src
         // }
         if (maskSetting.checked) {
-            newTask.reqBody.mask = inpaintingEditor.getImg()
+            newTask.reqBody.mask = imageInpainter.getImg()
         }
         newTask.reqBody.sampler = 'ddim'
     } else {
@@ -887,24 +892,26 @@ function createTask(task) {
     task['progressBar'] = taskEntry.querySelector('.progress-bar')
     task['stopTask'] = taskEntry.querySelector('.stopTask')
 
-    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) {
-                console.log(e)
-            }
-        } else {
-            let idx = taskQueue.indexOf(task)
-            if (idx >= 0) {
-                taskQueue.splice(idx, 1)
-            }
+    task['stopTask'].addEventListener('click', (e) => {
+        let question = (task['isProcessing'] ? "Stop this task?" : "Remove this task?")
+        shiftOrConfirm(e, question, async function(e) {
+            if (task['isProcessing']) {
+                task.isProcessing = false
+                task.progressBar.classList.remove("active")
+                try {
+                    let res = await fetch('/image/stop?session_id=' + sessionId)
+                } catch (e) {
+                    console.log(e)
+                }
+            } else {
+                let idx = taskQueue.indexOf(task)
+                if (idx >= 0) {
+                    taskQueue.splice(idx, 1)
+                }
 
-            taskEntry.remove()
-        }
+                removeTask(taskEntry)
+            }
+        })
     })
 
     task['useSettings'] = taskEntry.querySelector('.useSettings')
@@ -933,11 +940,12 @@ function getPrompts() {
     prompts = prompts.map(prompt => prompt.trim())
     prompts = prompts.filter(prompt => prompt !== '')
 
-    if (activeTags.length > 0) {
-	const promptTags = activeTags.map(x => x.name).join(", ")
-	prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
+    const newTags = activeTags.filter(tag => tag.inactive === undefined || tag.inactive === false)
+    if (newTags.length > 0) {
+        const promptTags = newTags.map(x => x.name).join(", ")
+        prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
     }
-	
+
     let promptsToMake = applySetOperator(prompts)
     promptsToMake = applyPermuteOperator(promptsToMake)
 
@@ -1047,21 +1055,25 @@ async function stopAllTasks() {
     }
 }
 
-clearAllPreviewsBtn.addEventListener('click', async function() {
+function removeTask(taskToRemove) {
+    taskToRemove.remove()
+
+    if (document.querySelector('.imageTaskContainer') === null) {
+        previewTools.style.display = 'none'
+        initialText.style.display = 'block'
+    }
+}
+
+clearAllPreviewsBtn.addEventListener('click', (e) => { shiftOrConfirm(e, "Clear all the results and tasks in this window?", async function() {
     await stopAllTasks()
 
     let taskEntries = document.querySelectorAll('.imageTaskContainer')
-    taskEntries.forEach(task => {
-        task.remove()
-    })
+    taskEntries.forEach(removeTask)
+})})
 
-    previewTools.style.display = 'none'
-    initialText.style.display = 'block'
-})
-
-stopImageBtn.addEventListener('click', async function() {
+stopImageBtn.addEventListener('click', (e) => { shiftOrConfirm(e, "Stop all the tasks?", async function(e) {
     await stopAllTasks()
-})
+})})
 
 widthField.addEventListener('change', onDimensionChange)
 heightField.addEventListener('change', onDimensionChange)
@@ -1082,13 +1094,14 @@ numOutputsTotalField.addEventListener('change', renameMakeImageButton)
 numOutputsParallelField.addEventListener('change', renameMakeImageButton)
 
 function onDimensionChange() {
-    if (!maskSetting.checked) {
-        return
-    }
     let widthValue = parseInt(widthField.value)
     let heightValue = parseInt(heightField.value)
-
-    resizeInpaintingEditor(widthValue, heightValue)
+    if (!initImagePreviewContainer.classList.contains("has-image")) {
+        imageEditor.setImage(null, widthValue, heightValue)
+    }
+    else {
+        imageInpainter.setImage(initImagePreview.src, widthValue, heightValue)
+    }
 }
 
 diskPathField.disabled = !saveToDiskField.checked
@@ -1107,6 +1120,7 @@ document.onkeydown = function(e) {
     }
 }
 
+/********************* Guidance **************************/
 function updateGuidanceScale() {
     guidanceScaleField.value = guidanceScaleSlider.value / 10
     guidanceScaleField.dispatchEvent(new Event("change"))
@@ -1127,6 +1141,7 @@ guidanceScaleSlider.addEventListener('input', updateGuidanceScale)
 guidanceScaleField.addEventListener('input', updateGuidanceScaleSlider)
 updateGuidanceScale()
 
+/********************* Prompt Strength *******************/
 function updatePromptStrength() {
     promptStrengthField.value = promptStrengthSlider.value / 100
     promptStrengthField.dispatchEvent(new Event("change"))
@@ -1147,6 +1162,36 @@ promptStrengthSlider.addEventListener('input', updatePromptStrength)
 promptStrengthField.addEventListener('input', updatePromptStrengthSlider)
 updatePromptStrength()
 
+/********************* JPEG Quality **********************/
+function updateOutputQuality() {
+    outputQualityField.value =  0 | outputQualitySlider.value
+    outputQualityField.dispatchEvent(new Event("change"))
+}
+
+function updateOutputQualitySlider() {
+    if (outputQualityField.value < 10) {
+        outputQualityField.value = 10
+    } else if (outputQualityField.value > 95) {
+        outputQualityField.value = 95
+    }
+
+    outputQualitySlider.value =  0 | outputQualityField.value
+    outputQualitySlider.dispatchEvent(new Event("change"))
+}
+
+outputQualitySlider.addEventListener('input', updateOutputQuality)
+outputQualityField.addEventListener('input', debounce(updateOutputQualitySlider))
+updateOutputQuality()
+
+outputFormatField.addEventListener('change', e => {
+    if (outputFormatField.value == 'jpeg') {
+        outputQualityRow.style.display='table-row'
+    } else {
+        outputQualityRow.style.display='none'
+    }
+})
+
+
 async function getModels() {
     try {
         var sd_model_setting_key = "stable_diffusion_model"
@@ -1201,7 +1246,7 @@ async function getModels() {
 function checkRandomSeed() {
     if (randomSeedField.checked) {
         seedField.disabled = true
-        seedField.value = "0"
+        //seedField.value = "0" // This causes the seed to be lost if the user changes their mind after toggling the checkbox
     } else {
         seedField.disabled = false
     }
@@ -1209,12 +1254,8 @@ function checkRandomSeed() {
 randomSeedField.addEventListener('input', checkRandomSeed)
 checkRandomSeed()
 
-function showInitImagePreview() {
+function loadImg2ImgFromFile() {
     if (initImageSelector.files.length === 0) {
-        initImagePreviewContainer.style.display = 'none'
-        // inpaintingEditorContainer.style.display = 'none'
-        promptStrengthContainer.style.display = 'none'
-        // maskSetting.style.display = 'none'
         return
     }
 
@@ -1222,51 +1263,41 @@ function showInitImagePreview() {
     let file = initImageSelector.files[0]
 
     reader.addEventListener('load', function(event) {
-        // console.log(file.name, reader.result)
         initImagePreview.src = reader.result
-        initImagePreviewContainer.style.display = 'block'
-        inpaintingEditorContainer.style.display = 'none'
-        promptStrengthContainer.style.display = 'table-row'
-        samplerSelectionContainer.style.display = 'none'
-        // maskSetting.checked = false
     })
 
     if (file) {
         reader.readAsDataURL(file)
     }
 }
-initImageSelector.addEventListener('change', showInitImagePreview)
-showInitImagePreview()
+initImageSelector.addEventListener('change', loadImg2ImgFromFile)
+loadImg2ImgFromFile()
+
+function img2imgLoad() {
+    promptStrengthContainer.style.display = 'table-row'
+    samplerSelectionContainer.style.display = "none"
+    initImagePreviewContainer.classList.add("has-image")
 
-initImagePreview.addEventListener('load', function() {
-    inpaintingEditorCanvasBackground.style.backgroundImage = "url('" + this.src + "')"
-    // maskSetting.style.display = 'block'
-    // inpaintingEditorContainer.style.display = 'block'
     initImageSizeBox.textContent = initImagePreview.naturalWidth + " x " + initImagePreview.naturalHeight
-    initImageSizeBox.style.display = 'block'
-})
+    imageEditor.setImage(this.src, initImagePreview.naturalWidth, initImagePreview.naturalHeight)
+    imageInpainter.setImage(this.src, parseInt(widthField.value), parseInt(heightField.value))
+}
 
-initImageClearBtn.addEventListener('click', function() {
+function img2imgUnload() {
     initImageSelector.value = null
-    // maskImageSelector.value = null
-
     initImagePreview.src = ''
-    // maskImagePreview.src = ''
     maskSetting.checked = false
 
-    initImagePreviewContainer.style.display = 'none'
-    // inpaintingEditorContainer.style.display = 'none'
-    // maskImagePreviewContainer.style.display = 'none'
+    promptStrengthContainer.style.display = "none"
+    samplerSelectionContainer.style.display = ""
+    initImagePreviewContainer.classList.remove("has-image")
+    imageEditor.setImage(null, parseInt(widthField.value), parseInt(heightField.value))
 
-    // maskSetting.style.display = 'none'
-
-    promptStrengthContainer.style.display = 'none'
-    samplerSelectionContainer.style.display = 'table-row'
-    initImageSizeBox.style.display = 'none'
-})
+}
+initImagePreview.addEventListener('load', img2imgLoad)
+initImageClearBtn.addEventListener('click', img2imgUnload)
 
 maskSetting.addEventListener('click', function() {
-    inpaintingEditorContainer.style.display = (this.checked ? 'block' : 'none')
     onDimensionChange()
 })
 
@@ -1306,9 +1337,22 @@ document.querySelectorAll('.popup').forEach(popup => {
     }
 })
 
-var tabElements = [];
+var tabElements = []
+function selectTab(tab_id) {
+    let tabInfo = tabElements.find(t => t.tab.id == tab_id)
+    if (!tabInfo.tab.classList.contains("active")) {
+        tabElements.forEach(info => {
+            if (info.tab.classList.contains("active")) {
+                info.tab.classList.toggle("active")
+                info.content.classList.toggle("active")
+            }
+        })
+        tabInfo.tab.classList.toggle("active")
+        tabInfo.content.classList.toggle("active")
+    }
+}
 function linkTabContents(tab) {
-    var name = tab.id.replace("tab-", "");
+    var name = tab.id.replace("tab-", "")
     var content = document.getElementById(`tab-content-${name}`)
     tabElements.push({
         name: name,
@@ -1316,18 +1360,7 @@ function linkTabContents(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")
-        }
-    })
+    tab.addEventListener("click", event => selectTab(tab.id))
 }
 
 document.querySelectorAll(".tab").forEach(linkTabContents)

ui/media/js/parameters.js

@@ -5,9 +5,9 @@
  */
  var ParameterType = {
     checkbox: "checkbox",
-	select: "select",
-	select_multiple: "select_multiple",
-	custom: "custom",
+    select: "select",
+    select_multiple: "select_multiple",
+    custom: "custom",
 };
 
 /**
@@ -23,166 +23,182 @@
 
 /** @type {Array.<Parameter>} */
 var PARAMETERS = [
-	{
-		id: "theme",
-		type: ParameterType.select,
-		label: "Theme",
-		default: "theme-default",
-		note: "customize the look and feel of the ui",
-		options: [ // Note: options expanded dynamically
-			{
-				value: "theme-default",
-				label: "Default"
-			}
-		],
-		icon: "fa-palette"
-	},
-	{
-		id: "save_to_disk",
-		type: ParameterType.checkbox,
-		label: "Auto-Save Images",
-		note: "automatically saves images to the specified location",
-		icon: "fa-download",
-		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",
-		icon: "fa-volume-low",
-		default: true,
-	},
-	{
-		id: "ui_open_browser_on_start",
-		type: ParameterType.checkbox,
-		label: "Open browser on startup",
-		note: "starts the default browser on startup",
-		icon: "fa-window-restore",
-		default: true,
-	},
-	{
-		id: "turbo",
-		type: ParameterType.checkbox,
-		label: "Turbo Mode",
-		note: "generates images faster, but uses an additional 1 GB of GPU memory",
-		icon: "fa-forward",
-		default: true,
-	},
-	{
-		id: "use_cpu",
-		type: ParameterType.checkbox,
-		label: "Use CPU (not GPU)",
-		note: "warning: this will be *very* slow",
-		icon: "fa-microchip",
-		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",
-		icon: "fa-crosshairs",
-		default: false,
-	},
-	{
-		id: "auto_save_settings",
-		type: ParameterType.checkbox,
-		label: "Auto-Save Settings",
-		note: "restores settings on browser load",
-		icon: "fa-gear",
-		default: true,
-	},
-	{
-		id: "listen_to_network",
-		type: ParameterType.checkbox,
-		label: "Make Stable Diffusion available on your network",
-		note: "Other devices on your network can access this web page",
-		icon: "fa-network-wired",
-		default: true,
-	},
-	{
-		id: "listen_port",
-		type: ParameterType.custom,
-		label: "Network port",
-		note: "Port that this server listens to. The '9000' part in 'http://localhost:9000'",
-		icon: "fa-anchor",
-		render: (parameter) => {
-			return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
-		}
-	},
-	{
-		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.",
-		icon: "fa-fire",
-		default: false,
-	},
+    {
+        id: "theme",
+        type: ParameterType.select,
+        label: "Theme",
+        default: "theme-default",
+        note: "customize the look and feel of the ui",
+        options: [ // Note: options expanded dynamically
+            {
+                value: "theme-default",
+                label: "Default"
+            }
+        ],
+        icon: "fa-palette"
+    },
+    {
+        id: "save_to_disk",
+        type: ParameterType.checkbox,
+        label: "Auto-Save Images",
+        note: "automatically saves images to the specified location",
+        icon: "fa-download",
+        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",
+        icon: "fa-volume-low",
+        default: true,
+    },
+    {
+        id: "ui_open_browser_on_start",
+        type: ParameterType.checkbox,
+        label: "Open browser on startup",
+        note: "starts the default browser on startup",
+        icon: "fa-window-restore",
+        default: true,
+    },
+    {
+        id: "turbo",
+        type: ParameterType.checkbox,
+        label: "Turbo Mode",
+        note: "generates images faster, but uses an additional 1 GB of GPU memory",
+        icon: "fa-forward",
+        default: true,
+    },
+    {
+        id: "use_cpu",
+        type: ParameterType.checkbox,
+        label: "Use CPU (not GPU)",
+        note: "warning: this will be *very* slow",
+        icon: "fa-microchip",
+        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",
+        icon: "fa-crosshairs",
+        default: false,
+    },
+    {
+        id: "auto_save_settings",
+        type: ParameterType.checkbox,
+        label: "Auto-Save Settings",
+        note: "restores settings on browser load",
+        icon: "fa-gear",
+        default: true,
+    },
+    {
+        id: "confirm_dangerous_actions",
+        type: ParameterType.checkbox,
+        label: "Confirm dangerous actions",
+        note: "Actions that might lead to data loss must either be clicked with the shift key pressed, or confirmed in an 'Are you sure?' dialog",
+        icon: "fa-check-double",
+        default: true,
+    },
+    {
+        id: "listen_to_network",
+        type: ParameterType.checkbox,
+        label: "Make Stable Diffusion available on your network",
+        note: "Other devices on your network can access this web page",
+        icon: "fa-network-wired",
+        default: true,
+    },
+    {
+        id: "listen_port",
+        type: ParameterType.custom,
+        label: "Network port",
+        note: "Port that this server listens to. The '9000' part in 'http://localhost:9000'",
+        icon: "fa-anchor",
+        render: (parameter) => {
+            return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
+        }
+    },
+    {
+        id: "test_sd2",
+        type: ParameterType.checkbox,
+        label: "Test SD 2.0",
+        note: "Experimental! High memory usage! GPU-only! Not the final version! Please restart the program after changing this.",
+        icon: "fa-fire",
+        default: false,
+    },
+    {
+        id: "use_beta_channel",
+        type: ParameterType.checkbox,
+        label: "Beta channel",
+        note: "Get the latest features immediately (but could be less stable). Please restart the program after changing this.",
+        icon: "fa-fire",
+        default: false,
+    },
 ];
 
 function getParameterSettingsEntry(id) {
-	let parameter = PARAMETERS.filter(p => p.id === id)
-	if (parameter.length === 0) {
-		return
-	}
-	return parameter[0].settingsEntry
+    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 ${parameter.id}`);
-			return "ERROR: Invalid Type"
-	}
+    switch (parameter.type) {
+        case ParameterType.checkbox:
+            var is_checked = parameter.default ? " checked" : "";
+            return `<input id="${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 .parameters-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 icon = parameter.icon ? `<i class="fa ${parameter.icon}"></i>` : "";
-		var newrow = document.createElement('div')
-		newrow.innerHTML = `
-			<div>${icon}</div>
-			<div><label for="${parameter.id}">${parameter.label}</label>${note}</div>
-			<div>${element}</div>`
-		parametersTable.appendChild(newrow)
-		parameter.settingsEntry = newrow
-	})
+    PARAMETERS.forEach(parameter => {
+        var element = getParameterElement(parameter)
+        var note = parameter.note ? `<small>${parameter.note}</small>` : "";
+        var icon = parameter.icon ? `<i class="fa ${parameter.icon}"></i>` : "";
+        var newrow = document.createElement('div')
+        newrow.innerHTML = `
+            <div>${icon}</div>
+            <div><label for="${parameter.id}">${parameter.label}</label>${note}</div>
+            <div>${element}</div>`
+        parametersTable.appendChild(newrow)
+        parameter.settingsEntry = newrow
+    })
 }
 
 initParameters()
@@ -196,11 +212,14 @@ let saveToDiskField = document.querySelector('#save_to_disk')
 let diskPathField = document.querySelector('#diskPath')
 let listenToNetworkField = document.querySelector("#listen_to_network")
 let listenPortField = document.querySelector("#listen_port")
+let testSD2Field = document.querySelector("#test_sd2")
 let useBetaChannelField = document.querySelector("#use_beta_channel")
 let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
+let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
 
 let saveSettingsBtn = document.querySelector('#save-system-settings-btn')
 
+
 async function changeAppConfig(configDelta) {
     try {
         let res = await fetch('/app_config', {
@@ -230,12 +249,18 @@ async function getAppConfig() {
         if (config.ui && config.ui.open_browser_on_start === false) {
             uiOpenBrowserOnStartField.checked = false
         }
-	if (config.net && config.net.listen_to_network === false) {
-	    listenToNetworkField.checked = false
-	}
-	if (config.net && config.net.listen_port !== undefined) {
-	    listenPortField.value = config.net.listen_port
-	}
+        if ('test_sd2' in config) {
+            testSD2Field.checked = config['test_sd2']
+        }
+
+        let testSD2SettingEntry = getParameterSettingsEntry('test_sd2')
+        testSD2SettingEntry.style.display = (config.update_branch === 'beta' ? '' : 'none')
+        if (config.net && config.net.listen_to_network === false) {
+            listenToNetworkField.checked = false
+        }
+        if (config.net && config.net.listen_port !== undefined) {
+            listenPortField.value = config.net.listen_port
+        }
 
         console.log('get config status response', config)
     } catch (e) {
@@ -263,7 +288,6 @@ function getCurrentRenderDeviceSelection() {
 useCPUField.addEventListener('click', function() {
     let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
     let autoPickGPUSettingEntry = getParameterSettingsEntry('auto_pick_gpus')
-	console.log("hello", this.checked);
     if (this.checked) {
         gpuSettingEntry.style.display = 'none'
         autoPickGPUSettingEntry.style.display = 'none'
@@ -313,14 +337,45 @@ async function getDiskPath() {
     }
 }
 
-async function getDevices() {
+function setDeviceInfo(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>(${d}) (no memory info)</small>`
+        }
+    }
+
+    allGPUs = allGPUs.map(ID_TO_TEXT)
+    activeGPUs = activeGPUs.map(ID_TO_TEXT)
+
+    let systemInfoEl = document.querySelector('#system-info')
+    systemInfoEl.querySelector('#system-info-cpu').innerText = cpu
+    systemInfoEl.querySelector('#system-info-gpus-all').innerHTML = allGPUs.join('</br>')
+    systemInfoEl.querySelector('#system-info-rendering-devices').innerHTML = activeGPUs.join('</br>')
+}
+
+function setHostInfo(hosts) {
+    let port = listenPortField.value
+    hosts = hosts.map(addr => `http://${addr}:${port}/`).map(url => `<div><a href="${url}">${url}</a></div>`)
+    document.querySelector('#system-info-server-hosts').innerHTML = hosts.join('')
+}
+
+async function getSystemInfo() {
     try {
-        let res = await fetch('/get/devices')
+        let res = await fetch('/get/system_info')
         if (res.status === 200) {
             res = await res.json()
+            let devices = res['devices']
+            let hosts = res['hosts']
 
-            let allDeviceIds = Object.keys(res['all']).filter(d => d !== 'cpu')
-            let activeDeviceIds = Object.keys(res['active']).filter(d => d !== 'cpu')
+            let allDeviceIds = Object.keys(devices['all']).filter(d => d !== 'cpu')
+            let activeDeviceIds = Object.keys(devices['active']).filter(d => d !== 'cpu')
 
             if (activeDeviceIds.length === 0) {
                 useCPUField.checked = true
@@ -338,11 +393,11 @@ async function getDevices() {
                 useCPUField.disabled = true // no compatible GPUs, so make the CPU mandatory
             }
 
-            autoPickGPUsField.checked = (res['config'] === 'auto')
+            autoPickGPUsField.checked = (devices['config'] === 'auto')
 
             useGPUsField.innerHTML = ''
             allDeviceIds.forEach(device => {
-                let deviceName = res['all'][device]['name']
+                let deviceName = devices['all'][device]['name']
                 let deviceOption = `<option value="${device}">${deviceName} (${device})</option>`
                 useGPUsField.insertAdjacentHTML('beforeend', deviceOption)
             })
@@ -353,6 +408,9 @@ async function getDevices() {
             } else {
                 $('#use_gpus').val(activeDeviceIds)
             }
+
+            setDeviceInfo(devices)
+            setHostInfo(hosts)
         }
     } catch (e) {
         console.log('error fetching devices', e)
@@ -360,22 +418,23 @@ async function getDevices() {
 }
 
 saveSettingsBtn.addEventListener('click', function() {
-	let updateBranch = (useBetaChannelField.checked ? 'beta' : 'main')
+    let updateBranch = (useBetaChannelField.checked ? 'beta' : 'main')
 
-        if (listenPortField.value == '') {
-            alert('The network port field must not be empty.')
-        } else if (listenPortField.value<1 || listenPortField.value>65535) {
-            alert('The network port must be a number from 1 to 65535')
-        } else {
-	    changeAppConfig({
-              'render_devices': getCurrentRenderDeviceSelection(),
-              'update_branch': updateBranch,
-              'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
-              'listen_to_network': listenToNetworkField.checked,
-              'listen_port': listenPortField.value
-            })
-        }
+    if (listenPortField.value == '') {
+        alert('The network port field must not be empty.')
+    } else if (listenPortField.value<1 || listenPortField.value>65535) {
+        alert('The network port must be a number from 1 to 65535')
+    } else {
+        changeAppConfig({
+            'render_devices': getCurrentRenderDeviceSelection(),
+            'update_branch': updateBranch,
+            'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
+            'listen_to_network': listenToNetworkField.checked,
+            'listen_port': listenPortField.value,
+            'test_sd2': testSD2Field.checked
+        })
+    }
 
-        saveSettingsBtn.classList.add('active')
-        asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
+    saveSettingsBtn.classList.add('active')
+    asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
 })

ui/media/js/plugins.js

@@ -24,7 +24,8 @@ const PLUGINS = {
      *   }
      * })
      */
-    IMAGE_INFO_BUTTONS: []
+    IMAGE_INFO_BUTTONS: [],
+    MODIFIERS_LOAD: []
 }
 
 async function loadUIPlugins() {

ui/media/js/themes.js

@@ -60,6 +60,7 @@ function themeFieldChanged() {
 
     body.style = "";
     var theme = THEMES.find(t => t.key == theme_key);
+    let borderColor = undefined
     if (theme) {
         // refresh variables incase they are back referencing
         Array.from(DEFAULT_THEME.rule.style)
@@ -67,7 +68,14 @@ function themeFieldChanged() {
             .forEach(cssVariable => {
             body.style.setProperty(cssVariable, DEFAULT_THEME.rule.style.getPropertyValue(cssVariable));
         });
+        borderColor = theme.rule.style.getPropertyValue('--input-border-color').trim()
+        if (!borderColor.startsWith('#')) {
+            borderColor = theme.rule.style.getPropertyValue('--theme-color-fallback')
+        }
+    } else {
+        borderColor = DEFAULT_THEME.rule.style.getPropertyValue('--theme-color-fallback')
     }
+    document.querySelector('meta[name="theme-color"]').setAttribute("content", borderColor)
 }
 
 themeField.addEventListener('change', themeFieldChanged);

ui/media/js/utils.js

@@ -1,17 +1,17 @@
 // https://gomakethings.com/finding-the-next-and-previous-sibling-elements-that-match-a-selector-with-vanilla-js/
 function getNextSibling(elem, selector) {
-	// Get the next sibling element
-	var sibling = elem.nextElementSibling
+    // Get the next sibling element
+    var sibling = elem.nextElementSibling
 
-	// If there's no selector, return the first sibling
-	if (!selector) return sibling
+    // If there's no selector, return the first sibling
+    if (!selector) return sibling
 
-	// If the sibling matches our selector, use it
-	// If not, jump to the next sibling and continue the loop
-	while (sibling) {
-		if (sibling.matches(selector)) return sibling
-		sibling = sibling.nextElementSibling
-	}
+    // If the sibling matches our selector, use it
+    // If not, jump to the next sibling and continue the loop
+    while (sibling) {
+        if (sibling.matches(selector)) return sibling
+        sibling = sibling.nextElementSibling
+    }
 }
 
 
@@ -347,6 +347,16 @@ function asyncDelay(timeout) {
     })
 }
 
+/* Simple debounce function, placeholder for the one in engine.js for simple use cases */
+function debounce(func, timeout = 300){
+  let timer;
+  return (...args) => {
+    clearTimeout(timer);
+    timer = setTimeout(() => { func.apply(this, args); }, timeout);
+  };
+}
+
+
 function preventNonNumericalInput(e) {
     e = e || window.event;
     let charCode = (typeof e.which == "undefined") ? e.keyCode : e.which;

ui/media/manifest.webmanifest

@@ -0,0 +1,8 @@
+{
+  "name": "Stable Diffusion UI",
+  "display": "standalone",
+  "display_override": [
+    "window-controls-overlay"
+  ],
+  "theme_color": "#000000"
+}

ui/plugins/ui/Autoscroll.plugin.js

@@ -0,0 +1,51 @@
+(function () {
+    "use strict"
+
+    var styleSheet = document.createElement("style");
+    styleSheet.textContent = `
+        .auto-scroll {
+            float: right;
+        }
+    `;
+    document.head.appendChild(styleSheet);
+    
+    const autoScrollControl = document.createElement('div');
+    autoScrollControl.innerHTML = `<input id="auto_scroll" name="auto_scroll" type="checkbox">
+                            <label for="auto_scroll">Scroll to generated image</label>`
+    autoScrollControl.className = "auto-scroll"
+    clearAllPreviewsBtn.parentNode.insertBefore(autoScrollControl, clearAllPreviewsBtn.nextSibling)
+    prettifyInputs(document);
+    let autoScroll = document.querySelector("#auto_scroll")
+
+    /**
+     * the use of initSettings() in the autoscroll plugin seems to be breaking the models dropdown and the save-to-disk folder field
+     * in the settings tab. They're both blank, because they're being re-initialized. Their earlier values came from the API call,
+     * but those values aren't stored in localStorage, since they aren't user-specified.
+     * So when initSettings() is called a second time, it overwrites the values with an empty string.
+     *
+     * We could either rework how new components can register themselves to be auto-saved, without having to call initSettings() again.
+     * Or we could move the autoscroll code into the main code, and include it in the list of fields in auto-save.js
+     */
+    // SETTINGS_IDS_LIST.push("auto_scroll")
+    // initSettings()
+
+    // observe for changes in the preview pane
+    var observer = new MutationObserver(function (mutations) {
+        mutations.forEach(function (mutation) {
+            if (mutation.target.className == 'img-batch') {
+                Autoscroll(mutation.target)
+            }
+        })
+    })
+    
+    observer.observe(document.getElementById('preview'), {
+            childList: true,
+            subtree: true
+    })
+
+    function Autoscroll(target) {
+        if (autoScroll.checked && target !== null) {
+            target.parentElement.parentElement.parentElement.scrollIntoView();
+        }
+    }
+})()

ui/plugins/ui/Modifiers-wheel.plugin.js

@@ -18,40 +18,42 @@
         let overlays = document.querySelector('#editor-inputs-tags-list').querySelectorAll('.modifier-card-overlay')
         overlays.forEach (i => {
             i.onwheel = (e) => {
-                e.preventDefault()
-
-                const delta = Math.sign(event.deltaY)
-                let s = i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText
-                if (delta < 0) {
-                    // wheel scrolling up
-                    if (s.substring(0, 1) == '[' && s.substring(s.length-1) == ']') {
-                        s = s.substring(1, s.length - 1)
-                    }
-                    else
-                    {
-                        if (s.substring(0, 10) !== '('.repeat(10) && s.substring(s.length-10) !== ')'.repeat(10)) {
-                            s = '(' + s + ')'
+                if (e.ctrlKey == true) {
+                    e.preventDefault()
+    
+                    const delta = Math.sign(event.deltaY)
+                    let s = i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText
+                    if (delta < 0) {
+                        // wheel scrolling up
+                        if (s.substring(0, 1) == '[' && s.substring(s.length-1) == ']') {
+                            s = s.substring(1, s.length - 1)
+                        }
+                        else
+                        {
+                            if (s.substring(0, 10) !== '('.repeat(10) && s.substring(s.length-10) !== ')'.repeat(10)) {
+                                s = '(' + s + ')'
+                            }
                         }
                     }
-                }
-                else{
-                    // wheel scrolling down
-                    if (s.substring(0, 1) == '(' && s.substring(s.length-1) == ')') {
-                        s = s.substring(1, s.length - 1)
-                    }
-                    else
-                    {
-                        if (s.substring(0, 10) !== '['.repeat(10) && s.substring(s.length-10) !== ']'.repeat(10)) {
-                            s = '[' + s + ']'
+                    else{
+                        // wheel scrolling down
+                        if (s.substring(0, 1) == '(' && s.substring(s.length-1) == ')') {
+                            s = s.substring(1, s.length - 1)
+                        }
+                        else
+                        {
+                            if (s.substring(0, 10) !== '['.repeat(10) && s.substring(s.length-10) !== ']'.repeat(10)) {
+                                s = '[' + s + ']'
+                            }
                         }
                     }
-                }
-                i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText = s
-                // update activeTags
-                for (let it = 0; it < overlays.length; it++) {
-                    if (i == overlays[it]) {
-                        activeTags[it].name = s
-                        break
+                    i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText = s
+                    // update activeTags
+                    for (let it = 0; it < overlays.length; it++) {
+                        if (i == overlays[it]) {
+                            activeTags[it].name = s
+                            break
+                        }
                     }
                 }
             }

ui/plugins/ui/custom-modifiers.plugin.js

@@ -0,0 +1,31 @@
+(function() {
+    PLUGINS['MODIFIERS_LOAD'].push({
+        loader: function() {
+            let customModifiers = localStorage.getItem(CUSTOM_MODIFIERS_KEY, '')
+            customModifiersTextBox.value = customModifiers
+
+            if (customModifiersGroupElement !== undefined) {
+                customModifiersGroupElement.remove()
+            }
+
+            if (customModifiers && customModifiers.trim() !== '') {
+                customModifiers = customModifiers.split('\n')
+                customModifiers = customModifiers.filter(m => m.trim() !== '')
+                customModifiers = customModifiers.map(function(m) {
+                    return {
+                        "modifier": m
+                    }
+                })
+
+                let customGroup = {
+                    'category': 'Custom Modifiers',
+                    'modifiers': customModifiers
+                }
+
+                customModifiersGroupElement = createModifierGroup(customGroup, true)
+
+                createCollapsibles(customModifiersGroupElement)
+            }
+        }
+    })
+})()

ui/plugins/ui/modifiers-toggle.plugin.js

@@ -0,0 +1,53 @@
+(function () {
+    "use strict"
+
+    var styleSheet = document.createElement("style");
+    styleSheet.textContent = `
+        .modifier-card-tiny.modifier-toggle-inactive {
+            background: transparent;
+            border: 2px dashed red;
+            opacity:0.2;
+        }
+    `;
+    document.head.appendChild(styleSheet);
+
+    // observe for changes in tag list
+    var observer = new MutationObserver(function (mutations) {
+    //    mutations.forEach(function (mutation) {
+            if (editorModifierTagsList.childNodes.length > 0) {
+                ModifierToggle()
+            }
+    //    })
+    })
+
+    observer.observe(editorModifierTagsList, {
+            childList: true
+    })
+
+    function ModifierToggle() {
+        let overlays = document.querySelector('#editor-inputs-tags-list').querySelectorAll('.modifier-card-overlay')
+        overlays.forEach (i => {
+            i.oncontextmenu = (e) => {
+                e.preventDefault()
+
+                if (i.parentElement.classList.contains('modifier-toggle-inactive')) {
+                    i.parentElement.classList.remove('modifier-toggle-inactive')
+                }
+                else
+                {
+                    i.parentElement.classList.add('modifier-toggle-inactive')
+                }
+                // refresh activeTags
+                let modifierName = i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText
+                activeTags = activeTags.map(obj => {
+                    if (obj.name === modifierName) {
+                        return {...obj, inactive: (obj.element.classList.contains('modifier-toggle-inactive'))};
+                    }
+                    
+                    return obj;
+                });
+                console.log(activeTags)
+            }
+        })
+    }
+})()

ui/sd_internal/__init__.py

@@ -25,6 +25,7 @@ class Request:
     use_vae_model: str = None
     show_only_filtered_image: bool = False
     output_format: str = "jpeg" # or "png"
+    output_quality: int = 75
 
     stream_progress_updates: bool = False
     stream_image_progress: bool = False
@@ -47,6 +48,7 @@ class Request:
             "use_stable_diffusion_model": self.use_stable_diffusion_model,
             "use_vae_model": self.use_vae_model,
             "output_format": self.output_format,
+            "output_quality": self.output_quality,
         }
 
     def __str__(self):
@@ -70,6 +72,7 @@ class Request:
     use_vae_model: {self.use_vae_model}
     show_only_filtered_image: {self.show_only_filtered_image}
     output_format: {self.output_format}
+    output_quality: {self.output_quality}
 
     stream_progress_updates: {self.stream_progress_updates}
     stream_image_progress: {self.stream_image_progress}'''

ui/sd_internal/ddim_callback.patch

@@ -1,72 +1,13 @@
 diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
-index b967b55..35ef520 100644
+index 79058bc..a473411 100644
 --- a/optimizedSD/ddpm.py
 +++ b/optimizedSD/ddpm.py
-@@ -22,7 +22,7 @@ from ldm.util import exists, default, instantiate_from_config
- from ldm.modules.diffusionmodules.util import make_beta_schedule
- from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
- from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
--from samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
-+from .samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
+@@ -564,12 +564,12 @@ class UNet(DDPM):
+                                         unconditional_guidance_scale=unconditional_guidance_scale,
+                                         callback=callback, img_callback=img_callback)
  
- def disabled_train(self):
-     """Overwrite model.train with this function to make sure train/eval mode
-@@ -506,6 +506,8 @@ class UNet(DDPM):
- 
-         x_latent = noise if x0 is None else x0
-         # sampling
-+        if sampler in ('ddim', 'dpm2', 'heun', 'dpm2_a', 'lms') and not hasattr(self, 'ddim_timesteps'):
-+            self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
-         
-         if sampler == "plms":
-             self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
-@@ -528,39 +530,46 @@ class UNet(DDPM):
-         elif sampler == "ddim":
-             samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
-                                          unconditional_conditioning=unconditional_conditioning,
--                                         mask = mask,init_latent=x_T,use_original_steps=False)
-+                                         mask = mask,init_latent=x_T,use_original_steps=False,
-+                                         callback=callback, img_callback=img_callback)
- 
-         elif sampler == "euler":
-             self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
-             samples = self.euler_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
-         elif sampler == "euler_a":
-             self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
-             samples = self.euler_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
- 
-         elif sampler == "dpm2":
-             samples = self.dpm_2_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
-         elif sampler == "heun":
-             samples = self.heun_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
- 
-         elif sampler == "dpm2_a":
-             samples = self.dpm_2_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
- 
- 
-         elif sampler == "lms":
-             samples = self.lms_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
--                                        unconditional_guidance_scale=unconditional_guidance_scale)
-+                                        unconditional_guidance_scale=unconditional_guidance_scale,
-+                                        img_callback=img_callback)
-+
 +        yield from samples
- 
++
          if(self.turbo):
              self.model1.to("cpu")
              self.model2.to("cpu")
@@ -76,7 +17,7 @@ index b967b55..35ef520 100644
      @torch.no_grad()
      def plms_sampling(self, cond,b, img,
                        ddim_use_original_steps=False,
-@@ -599,10 +608,10 @@ class UNet(DDPM):
+@@ -608,10 +608,10 @@ class UNet(DDPM):
              old_eps.append(e_t)
              if len(old_eps) >= 4:
                  old_eps.pop(0)
@@ -90,23 +31,15 @@ index b967b55..35ef520 100644
  
      @torch.no_grad()
      def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-@@ -706,7 +715,8 @@ class UNet(DDPM):
- 
-     @torch.no_grad()
-     def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
--               mask = None,init_latent=None,use_original_steps=False):
-+               mask = None,init_latent=None,use_original_steps=False,
-+               callback=None, img_callback=None):
- 
-         timesteps = self.ddim_timesteps
-         timesteps = timesteps[:t_start]
-@@ -730,10 +740,13 @@ class UNet(DDPM):
+@@ -740,13 +740,13 @@ class UNet(DDPM):
                                            unconditional_guidance_scale=unconditional_guidance_scale,
                                            unconditional_conditioning=unconditional_conditioning)
-         
+ 
+-            if callback: callback(i)
+-            if img_callback: img_callback(x_dec, i)
 +            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x_dec, i)
-+
+         
          if mask is not None:
 -            return x0 * mask + (1. - mask) * x_dec
 +            x_dec = x0 * mask + (1. - mask) * x_dec
@@ -116,217 +49,114 @@ index b967b55..35ef520 100644
  
  
      @torch.no_grad()
-@@ -779,13 +792,16 @@ class UNet(DDPM):
+@@ -820,12 +820,12 @@ class UNet(DDPM):
  
  
-     @torch.no_grad()
--    def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-+    def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
-+                        img_callback=None):
-         """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
-         extra_args = {} if extra_args is None else extra_args
-         cvd = CompVisDenoiser(ac)
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
- 
-+        print(f"Running Euler Sampling with {len(sigmas) - 1} timesteps")
-+
-         s_in = x.new_ones([x.shape[0]]).half()
-         for i in trange(len(sigmas) - 1, disable=disable):
-             gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-@@ -807,13 +823,18 @@ class UNet(DDPM):
              d = to_d(x, sigma_hat, denoised)
-             if callback is not None:
-                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-+
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
-+
              dt = sigmas[i + 1] - sigma_hat
              # Euler method
              x = x + d * dt
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
  
      @torch.no_grad()
--    def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None):
-+    def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None,
-+                        img_callback=None):
-         """Ancestral sampling with Euler method steps."""
-         extra_args = {} if extra_args is None else extra_args
+     def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, img_callback=None):
+@@ -852,14 +852,14 @@ class UNet(DDPM):
+             denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
  
-@@ -822,6 +843,8 @@ class UNet(DDPM):
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
- 
-+        print(f"Running Euler Ancestral Sampling with {len(sigmas) - 1} timesteps")
-+
-         s_in = x.new_ones([x.shape[0]]).half()
-         for i in trange(len(sigmas) - 1, disable=disable):
- 
-@@ -837,17 +860,22 @@ class UNet(DDPM):
              sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
-             if callback is not None:
-                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-+
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
-+
              d = to_d(x, sigmas[i], denoised)
              # Euler method
              dt = sigma_down - sigmas[i]
              x = x + d * dt
              x = x + torch.randn_like(x) * sigma_up
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
  
  
  
-     @torch.no_grad()
--    def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-+    def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
-+                        img_callback=None):
-         """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
-         extra_args = {} if extra_args is None else extra_args
+@@ -892,8 +892,8 @@ class UNet(DDPM):
+             denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
  
-@@ -855,6 +883,8 @@ class UNet(DDPM):
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
- 
-+        print(f"Running Heun Sampling with {len(sigmas) - 1} timesteps")
-+
- 
-         s_in = x.new_ones([x.shape[0]]).half()
-         for i in trange(len(sigmas) - 1, disable=disable):
-@@ -876,6 +906,9 @@ class UNet(DDPM):
              d = to_d(x, sigma_hat, denoised)
-             if callback is not None:
-                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-+
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
-+
              dt = sigmas[i + 1] - sigma_hat
              if sigmas[i + 1] == 0:
                  # Euler method
-@@ -895,11 +928,13 @@ class UNet(DDPM):
+@@ -913,7 +913,7 @@ class UNet(DDPM):
                  d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
                  d_prime = (d + d_2) / 2
                  x = x + d_prime * dt
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
  
  
      @torch.no_grad()
--    def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
-+    def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
-+                        img_callback=None):
-         """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
-         extra_args = {} if extra_args is None else extra_args
- 
-@@ -907,6 +942,8 @@ class UNet(DDPM):
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
- 
-+        print(f"Running DPM2 Sampling with {len(sigmas) - 1} timesteps")
-+
-         s_in = x.new_ones([x.shape[0]]).half()
-         for i in trange(len(sigmas) - 1, disable=disable):
-             gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-@@ -924,7 +961,7 @@ class UNet(DDPM):
+@@ -944,8 +944,8 @@ class UNet(DDPM):
              e_t_uncond, e_t = (x_in  + eps * c_out).chunk(2)
              denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
  
--
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
              
              d = to_d(x, sigma_hat, denoised)
              # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
-@@ -945,11 +982,13 @@ class UNet(DDPM):
+@@ -966,7 +966,7 @@ class UNet(DDPM):
  
              d_2 = to_d(x_2, sigma_mid, denoised_2)
              x = x + d_2 * dt_2
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
  
  
      @torch.no_grad()
--    def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None):
-+    def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None,
-+                        img_callback=None):
-         """Ancestral sampling with DPM-Solver inspired second-order steps."""
-         extra_args = {} if extra_args is None else extra_args
+@@ -994,8 +994,8 @@ class UNet(DDPM):
  
-@@ -957,6 +996,8 @@ class UNet(DDPM):
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
  
-+        print(f"Running DPM2 Ancestral Sampling with {len(sigmas) - 1} timesteps")
-+
-         s_in = x.new_ones([x.shape[0]]).half()
-         for i in trange(len(sigmas) - 1, disable=disable):
- 
-@@ -973,6 +1014,9 @@ class UNet(DDPM):
              sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
-             if callback is not None:
-                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-+
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
-+
              d = to_d(x, sigmas[i], denoised)
              # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
              sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
-@@ -993,11 +1037,13 @@ class UNet(DDPM):
+@@ -1016,7 +1016,7 @@ class UNet(DDPM):
              d_2 = to_d(x_2, sigma_mid, denoised_2)
              x = x + d_2 * dt_2
              x = x + torch.randn_like(x) * sigma_up
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
  
  
      @torch.no_grad()
--    def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4):
-+    def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4,
-+                        img_callback=None):
-         extra_args = {} if extra_args is None else extra_args
-         s_in = x.new_ones([x.shape[0]])
- 
-@@ -1005,6 +1051,8 @@ class UNet(DDPM):
-         sigmas = cvd.get_sigmas(S)
-         x = x*sigmas[0]
- 
-+        print(f"Running LMS Sampling with {len(sigmas) - 1} timesteps")
-+
-         ds = []
-         for i in trange(len(sigmas) - 1, disable=disable):
- 
-@@ -1017,6 +1065,7 @@ class UNet(DDPM):
+@@ -1042,8 +1042,8 @@ class UNet(DDPM):
              e_t_uncond, e_t = (x_in  + eps * c_out).chunk(2)
              denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
  
+-            if callback: callback(i)
+-            if img_callback: img_callback(x, i)
++            if callback: yield from callback(i)
 +            if img_callback: yield from img_callback(x, i)
  
              d = to_d(x, sigmas[i], denoised)
              ds.append(d)
-@@ -1027,4 +1076,5 @@ class UNet(DDPM):
+@@ -1054,4 +1054,4 @@ class UNet(DDPM):
              cur_order = min(i + 1, order)
              coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
              x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
 -        return x
-+
 +        yield from img_callback(x, len(sigmas)-1)
-diff --git a/optimizedSD/openaimodelSplit.py b/optimizedSD/openaimodelSplit.py
-index abc3098..7a32ffe 100644
---- a/optimizedSD/openaimodelSplit.py
-+++ b/optimizedSD/openaimodelSplit.py
-@@ -13,7 +13,7 @@ from ldm.modules.diffusionmodules.util import (
-     normalization,
-     timestep_embedding,
- )
--from splitAttention import SpatialTransformer
-+from .splitAttention import SpatialTransformer
- 
- 
- class AttentionPool2d(nn.Module):

ui/sd_internal/ddim_callback_sd2.patch

@@ -0,0 +1,84 @@
+diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
+index 27ead0e..6215939 100644
+--- a/ldm/models/diffusion/ddim.py
++++ b/ldm/models/diffusion/ddim.py
+@@ -100,7 +100,7 @@ class DDIMSampler(object):
+         size = (batch_size, C, H, W)
+         print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+ 
+-        samples, intermediates = self.ddim_sampling(conditioning, size,
++        samples = self.ddim_sampling(conditioning, size,
+                                                     callback=callback,
+                                                     img_callback=img_callback,
+                                                     quantize_denoised=quantize_x0,
+@@ -117,7 +117,8 @@ class DDIMSampler(object):
+                                                     dynamic_threshold=dynamic_threshold,
+                                                     ucg_schedule=ucg_schedule
+                                                     )
+-        return samples, intermediates
++        # return samples, intermediates
++        yield from samples
+ 
+     @torch.no_grad()
+     def ddim_sampling(self, cond, shape,
+@@ -168,14 +169,15 @@ class DDIMSampler(object):
+                                       unconditional_conditioning=unconditional_conditioning,
+                                       dynamic_threshold=dynamic_threshold)
+             img, pred_x0 = outs
+-            if callback: callback(i)
+-            if img_callback: img_callback(pred_x0, i)
++            if callback: yield from callback(i)
++            if img_callback: yield from img_callback(pred_x0, i)
+ 
+             if index % log_every_t == 0 or index == total_steps - 1:
+                 intermediates['x_inter'].append(img)
+                 intermediates['pred_x0'].append(pred_x0)
+ 
+-        return img, intermediates
++        # return img, intermediates
++        yield from img_callback(pred_x0, len(iterator)-1)
+ 
+     @torch.no_grad()
+     def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
+index 7002a36..0951f39 100644
+--- a/ldm/models/diffusion/plms.py
++++ b/ldm/models/diffusion/plms.py
+@@ -96,7 +96,7 @@ class PLMSSampler(object):
+         size = (batch_size, C, H, W)
+         print(f'Data shape for PLMS sampling is {size}')
+ 
+-        samples, intermediates = self.plms_sampling(conditioning, size,
++        samples = self.plms_sampling(conditioning, size,
+                                                     callback=callback,
+                                                     img_callback=img_callback,
+                                                     quantize_denoised=quantize_x0,
+@@ -112,7 +112,8 @@ class PLMSSampler(object):
+                                                     unconditional_conditioning=unconditional_conditioning,
+                                                     dynamic_threshold=dynamic_threshold,
+                                                     )
+-        return samples, intermediates
++        #return samples, intermediates
++        yield from samples
+ 
+     @torch.no_grad()
+     def plms_sampling(self, cond, shape,
+@@ -165,14 +166,15 @@ class PLMSSampler(object):
+             old_eps.append(e_t)
+             if len(old_eps) >= 4:
+                 old_eps.pop(0)
+-            if callback: callback(i)
+-            if img_callback: img_callback(pred_x0, i)
++            if callback: yield from callback(i)
++            if img_callback: yield from img_callback(pred_x0, i)
+ 
+             if index % log_every_t == 0 or index == total_steps - 1:
+                 intermediates['x_inter'].append(img)
+                 intermediates['pred_x0'].append(pred_x0)
+ 
+-        return img, intermediates
++        # return img, intermediates
++        yield from img_callback(pred_x0, len(iterator)-1)
+ 
+     @torch.no_grad()
+     def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,

ui/sd_internal/device_manager.py

@@ -101,7 +101,7 @@ def device_init(thread_data, 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)
+    thread_data.force_full_precision = (('nvidia' in device_name or 'geforce' in device_name) and (' 1660' in device_name or ' 1650' in device_name)) or ('Quadro T2000' in device_name)
     if thread_data.force_full_precision:
         print('forcing full precision on NVIDIA 16xx cards, to avoid green images. GPU detected: ', thread_data.device_name)
         # Apply force_full_precision now before models are loaded.

ui/sd_internal/env_yaml.patch

@@ -1,13 +0,0 @@
-diff --git a/environment.yaml b/environment.yaml
-index 7f25da8..306750f 100644
---- a/environment.yaml
-+++ b/environment.yaml
-@@ -23,6 +23,8 @@ dependencies:
-     - torch-fidelity==0.3.0
-     - transformers==4.19.2
-     - torchmetrics==0.6.0
-+    - pywavelets==1.3.0
-+    - pandas==1.4.4
-     - kornia==0.6
-     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
-     - -e git+https://github.com/openai/CLIP.git@main#egg=clip

ui/sd_internal/runtime.py

@@ -7,6 +7,7 @@ Notes:
 import json
 import os, re
 import traceback
+import queue
 import torch
 import numpy as np
 from gc import collect as gc_collect
@@ -21,13 +22,15 @@ from torch import autocast
 from contextlib import nullcontext
 from einops import rearrange, repeat
 from ldm.util import instantiate_from_config
-from optimizedSD.optimUtils import split_weighted_subprompts
 from transformers import logging
 
 from gfpgan import GFPGANer
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from realesrgan import RealESRGANer
 
+from threading import Lock
+from safetensors.torch import load_file
+
 import uuid
 
 logging.set_verbosity_error()
@@ -35,7 +38,7 @@ 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
+gfpgan_temp_device_lock = Lock() # workaround: gfpgan currently can only start on one device at a time.
 
 # api stuff
 from sd_internal import device_manager
@@ -69,18 +72,37 @@ def thread_init(device):
     thread_data.device_name = None
     thread_data.unet_bs = 1
     thread_data.precision = 'autocast'
-    thread_data.sampler_plms = None
-    thread_data.sampler_ddim = None
 
     thread_data.turbo = False
     thread_data.force_full_precision = False
     thread_data.reduced_memory = True
 
+    thread_data.test_sd2 = isSD2()
+
     device_manager.device_init(thread_data, device)
 
+# temp hack, will remove soon
+def isSD2():
+    try:
+        SD_UI_DIR = os.getenv('SD_UI_PATH', None)
+        CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
+        config_json_path = os.path.join(CONFIG_DIR, 'config.json')
+        if not os.path.exists(config_json_path):
+            return False
+        with open(config_json_path, 'r', encoding='utf-8') as f:
+            config = json.load(f)
+            return config.get('test_sd2', False)
+    except Exception as e:
+        return False
+
 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')
+    if os.path.exists(thread_data.ckpt_file + '.ckpt'):
+        thread_data.ckpt_file += '.ckpt'
+    elif os.path.exists(thread_data.ckpt_file + '.safetensors'):
+        thread_data.ckpt_file += '.safetensors'
+    elif not os.path.exists(thread_data.ckpt_file):
+        raise FileNotFoundError(f'Cannot find {thread_data.ckpt_file}.ckpt or .safetensors')
 
     if not thread_data.precision:
         thread_data.precision = 'full' if thread_data.force_full_precision else 'autocast'
@@ -91,8 +113,15 @@ def load_model_ckpt():
     if thread_data.device == 'cpu':
         thread_data.precision = 'full'
 
-    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')
+    print('loading', thread_data.ckpt_file, 'to device', thread_data.device, 'using precision', thread_data.precision)
+
+    if thread_data.test_sd2:
+        load_model_ckpt_sd2()
+    else:
+        load_model_ckpt_sd1()
+
+def load_model_ckpt_sd1():
+    sd = load_model_from_config(thread_data.ckpt_file)
     li, lo = [], []
     for key, value in sd.items():
         sp = key.split(".")
@@ -179,12 +208,46 @@ def load_model_ckpt():
         thread_data.model_fs_is_half = False
 
     print(f'''loaded model
- model file: {thread_data.ckpt_file}.ckpt
+ model file: {thread_data.ckpt_file}
  model.device: {model.device}
  modelCS.device: {modelCS.cond_stage_model.device}
  modelFS.device: {thread_data.modelFS.device}
  using precision: {thread_data.precision}''')
 
+def load_model_ckpt_sd2():
+    config_file = 'configs/stable-diffusion/v2-inference-v.yaml' if 'sd2_' in thread_data.ckpt_file else "configs/stable-diffusion/v1-inference.yaml"
+    config = OmegaConf.load(config_file)
+    verbose = False
+
+    sd = load_model_from_config(thread_data.ckpt_file)
+
+    thread_data.model = instantiate_from_config(config.model)
+    m, u = thread_data.model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    thread_data.model.to(thread_data.device)
+    thread_data.model.eval()
+    del sd
+
+    thread_data.model.cond_stage_model.device = torch.device(thread_data.device)
+
+    if thread_data.device != "cpu" and thread_data.precision == "autocast":
+        thread_data.model.half()
+        thread_data.model_is_half = True
+        thread_data.model_fs_is_half = True
+    else:
+        thread_data.model_is_half = False
+        thread_data.model_fs_is_half = False
+
+    print(f'''loaded model
+ model file: {thread_data.ckpt_file}
+ using precision: {thread_data.precision}''')
+
 def unload_filters():
     if thread_data.model_gfpgan is not None:
         if thread_data.device != 'cpu': thread_data.model_gfpgan.gfpgan.to('cpu')
@@ -204,10 +267,11 @@ 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")
+            if not thread_data.test_sd2:
+                thread_data.modelFS.to('cpu')
+                thread_data.modelCS.to('cpu')
+                thread_data.model.model1.to("cpu")
+                thread_data.model.model2.to("cpu")
 
         del thread_data.model
         del thread_data.modelCS
@@ -253,12 +317,6 @@ def move_to_cpu(model):
 
 def load_model_gfpgan():
     if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
-
-    # hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
-    from facexlib.detection import retinaface
-    retinaface.device = torch.device(thread_data.device)
-    print('forced retinaface.device to', thread_data.device)
-
     model_path = thread_data.gfpgan_file + ".pth"
     thread_data.model_gfpgan = GFPGANer(device=torch.device(thread_data.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)
@@ -314,15 +372,23 @@ def apply_filters(filter_name, image_data, model_path=None):
         image_data.to(thread_data.device)
 
     if filter_name == 'gfpgan':
-        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]
+        # This lock is only ever used here. No need to use timeout for the request. Should never deadlock.
+        with gfpgan_temp_device_lock: # Wait for any other devices to complete before starting.
+            # hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
+            from facexlib.detection import retinaface
+            retinaface.device = torch.device(thread_data.device)
+            print('forced retinaface.device to', 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':
         if model_path is not None and model_path != thread_data.real_esrgan_file:
@@ -337,45 +403,78 @@ def apply_filters(filter_name, image_data, model_path=None):
 
     return image_data
 
-def mk_img(req: Request):
+def is_model_reload_necessary(req: Request):
+    # custom model support:
+    #  the req.use_stable_diffusion_model needs to be a valid path
+    #  to the ckpt file (without the extension).
+    if os.path.exists(req.use_stable_diffusion_model + '.ckpt'):
+        req.use_stable_diffusion_model += '.ckpt'
+    elif os.path.exists(req.use_stable_diffusion_model + '.safetensors'):
+        req.use_stable_diffusion_model += '.safetensors'
+    elif not os.path.exists(req.use_stable_diffusion_model): 
+        raise FileNotFoundError(f'Cannot find {req.use_stable_diffusion_model}.ckpt or .safetensors')
+
+    needs_model_reload = False
+    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
+
+    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
+
+    return needs_model_reload
+
+def reload_model():
+    unload_models()
+    unload_filters()
+    load_model_ckpt()
+
+def mk_img(req: Request, data_queue: queue.Queue, task_temp_images: list, step_callback):
     try:
-        yield from do_mk_img(req)
+        return do_mk_img(req, data_queue, task_temp_images, step_callback)
     except Exception as e:
         print(traceback.format_exc())
 
-        if thread_data.device != 'cpu':
+        if thread_data.device != 'cpu' and not thread_data.test_sd2:
             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({
+        data_queue.put(json.dumps({
             "status": 'failed',
             "detail": str(e)
-        })
+        }))
+        raise e
 
-def update_temp_img(req, x_samples):
+def update_temp_img(req, x_samples, task_temp_images: list):
     partial_images = []
     for i in range(req.num_outputs):
-        x_sample_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
+        if thread_data.test_sd2:
+            x_sample_ddim = thread_data.model.decode_first_stage(x_samples[i].unsqueeze(0))
+        else:
+            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)
+        buf = img_to_buffer(img, output_format='JPEG')
 
         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
+        task_temp_images[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):
+def get_image_progress_generator(req, data_queue: queue.Queue, task_temp_images: list, step_callback, extra_props=None):
     if not req.stream_progress_updates:
         def empty_callback(x_samples, i): return x_samples
         return empty_callback
@@ -394,15 +493,17 @@ def get_image_progress_generator(req, extra_props=None):
             progress.update(extra_props)
 
         if req.stream_image_progress and i % 5 == 0:
-            progress['output'] = update_temp_img(req, x_samples)
+            progress['output'] = update_temp_img(req, x_samples, task_temp_images)
 
-        yield json.dumps(progress)
+        data_queue.put(json.dumps(progress))
+
+        step_callback()
 
         if thread_data.stop_processing:
             raise UserInitiatedStop("User requested that we stop processing")
     return img_callback
 
-def do_mk_img(req: Request):
+def do_mk_img(req: Request, data_queue: queue.Queue, task_temp_images: list, step_callback):
     thread_data.stop_processing = False
 
     res = Response()
@@ -411,29 +512,7 @@ def do_mk_img(req: Request):
 
     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
-    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
-
-    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:
-        unload_models()
-        unload_filters()
-        load_model_ckpt()
-
-    if thread_data.turbo != req.turbo:
+    if thread_data.turbo != req.turbo and not thread_data.test_sd2:
         thread_data.turbo = req.turbo
         thread_data.model.turbo = req.turbo
 
@@ -478,10 +557,14 @@ def do_mk_img(req: Request):
         if thread_data.device != "cpu" and thread_data.precision == "autocast":
             init_image = init_image.half()
 
-        thread_data.modelFS.to(thread_data.device)
+        if not thread_data.test_sd2:
+            thread_data.modelFS.to(thread_data.device)
 
         init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
-        init_latent = thread_data.modelFS.get_first_stage_encoding(thread_data.modelFS.encode_first_stage(init_image))  # move to latent space
+        if thread_data.test_sd2:
+            init_latent = thread_data.model.get_first_stage_encoding(thread_data.model.encode_first_stage(init_image))  # move to latent space
+        else:
+            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(thread_data.device)
@@ -493,7 +576,8 @@ def do_mk_img(req: Request):
 
         # Send to CPU and wait until complete.
         # wait_model_move_to(thread_data.modelFS, 'cpu')
-        move_to_cpu(thread_data.modelFS)
+        if not thread_data.test_sd2:
+            move_to_cpu(thread_data.modelFS)
 
         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)
@@ -509,11 +593,14 @@ def do_mk_img(req: Request):
             for prompts in tqdm(data, desc="data"):
 
                 with precision_scope("cuda"):
-                    if thread_data.reduced_memory:
+                    if thread_data.reduced_memory and not thread_data.test_sd2:
                         thread_data.modelCS.to(thread_data.device)
                     uc = None
                     if req.guidance_scale != 1.0:
-                        uc = thread_data.modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
+                        if thread_data.test_sd2:
+                            uc = thread_data.model.get_learned_conditioning(batch_size * [req.negative_prompt])
+                        else:
+                            uc = thread_data.modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
                     if isinstance(prompts, tuple):
                         prompts = list(prompts)
 
@@ -526,15 +613,21 @@ def do_mk_img(req: Request):
                             weight = weights[i]
                             # if not skip_normalize:
                             weight = weight / totalWeight
-                            c = torch.add(c, thread_data.modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
+                            if thread_data.test_sd2:
+                                c = torch.add(c, thread_data.model.get_learned_conditioning(subprompts[i]), alpha=weight)
+                            else:
+                                c = torch.add(c, thread_data.modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
                     else:
-                        c = thread_data.modelCS.get_learned_conditioning(prompts)
+                        if thread_data.test_sd2:
+                            c = thread_data.model.get_learned_conditioning(prompts)
+                        else:
+                            c = thread_data.modelCS.get_learned_conditioning(prompts)
 
-                    if thread_data.reduced_memory:
+                    if thread_data.reduced_memory and not thread_data.test_sd2:
                         thread_data.modelFS.to(thread_data.device)
 
                     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})
+                    img_callback = get_image_progress_generator(req, data_queue, task_temp_images, step_callback, {"total_steps": n_steps})
 
                     # run the handler
                     try:
@@ -542,14 +635,7 @@ def do_mk_img(req: Request):
                         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)
-
-                        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
+                            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, opt_C, req.height, req.width, opt_f)
                     except UserInitiatedStop:
                         if not hasattr(thread_data, 'partial_x_samples'):
                             continue
@@ -562,7 +648,10 @@ def do_mk_img(req: Request):
                     print("decoding images")
                     img_data = [None] * batch_size
                     for i in range(batch_size):
-                        x_samples_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
+                        if thread_data.test_sd2:
+                            x_samples_ddim = thread_data.model.decode_first_stage(x_samples[i].unsqueeze(0))
+                        else:
+                            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)
@@ -586,14 +675,16 @@ def do_mk_img(req: Request):
                         if req.save_to_disk_path is not None:
                             if return_orig_img:
                                 img_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], img_id, req.output_format)
-                                save_image(img, img_out_path)
+                                save_image(img, img_out_path, req.output_format, req.output_quality)
                             meta_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], img_id, 'txt')
                             save_metadata(meta_out_path, req, prompts[0], opt_seed)
 
                         if return_orig_img:
-                            img_str = img_to_base64_str(img, req.output_format)
+                            img_buffer = img_to_buffer(img, req.output_format, req.output_quality)
+                            img_str = buffer_to_base64_str(img_buffer, req.output_format)
                             res_image_orig = ResponseImage(data=img_str, seed=opt_seed)
                             res.images.append(res_image_orig)
+                            task_temp_images[i] = img_buffer
 
                             if req.save_to_disk_path is not None:
                                 res_image_orig.path_abs = img_out_path
@@ -609,12 +700,14 @@ def do_mk_img(req: Request):
                                 filters_applied.append(req.use_upscale)
                             if (len(filters_applied) > 0):
                                 filtered_image = Image.fromarray(img_data[i])
-                                filtered_img_data = img_to_base64_str(filtered_image, req.output_format)
+                                filtered_buffer = img_to_buffer(filtered_image, req.output_format, req.output_quality)
+                                filtered_img_data = buffer_to_base64_str(filtered_buffer, req.output_format)
                                 response_image = ResponseImage(data=filtered_img_data, seed=opt_seed)
                                 res.images.append(response_image)
+                                task_temp_images[i] = filtered_buffer
                                 if req.save_to_disk_path is not None:
                                     filtered_img_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], img_id, req.output_format, "_".join(filters_applied))
-                                    save_image(filtered_image, filtered_img_out_path)
+                                    save_image(filtered_image, filtered_img_out_path, req.output_format, req.output_quality)
                                     response_image.path_abs = filtered_img_out_path
                                 del filtered_image
                         # Filter Applied, move to next seed
@@ -622,18 +715,25 @@ def do_mk_img(req: Request):
 
                     # if thread_data.reduced_memory:
                     #     unload_filters()
-                    move_to_cpu(thread_data.modelFS)
+                    if not thread_data.test_sd2:
+                        move_to_cpu(thread_data.modelFS)
                     del img_data
                     gc()
                     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())
+    res = res.json()
+    data_queue.put(json.dumps(res))
 
-def save_image(img, img_out_path):
+    return res
+
+def save_image(img, img_out_path, output_format="", output_quality=75):
     try:
-        img.save(img_out_path)
+        if output_format.upper() == "JPEG":
+            img.save(img_out_path, quality=output_quality)
+        else:
+            img.save(img_out_path)
     except:
         print('could not save the file', traceback.format_exc())
 
@@ -664,51 +764,109 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
     # Send to CPU and wait until complete.
     # wait_model_move_to(thread_data.modelCS, 'cpu')
 
-    move_to_cpu(thread_data.modelCS)
+    if not thread_data.test_sd2:
+        move_to_cpu(thread_data.modelCS)
 
-    if sampler_name == 'ddim':
-        thread_data.model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
+    if thread_data.test_sd2 and sampler_name not in ('plms', 'ddim', 'dpm2'):
+        raise Exception('Only plms and ddim samplers are supported right now, in SD 2.0')
 
-    samples_ddim = thread_data.model.sample(
-        S=opt_ddim_steps,
-        conditioning=c,
-        seed=opt_seed,
-        shape=shape,
-        verbose=False,
-        unconditional_guidance_scale=opt_scale,
-        unconditional_conditioning=uc,
-        eta=opt_ddim_eta,
-        x_T=start_code,
-        img_callback=img_callback,
-        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):
+    # samples, _ = sampler.sample(S=opt.steps,
+    #                                                  conditioning=c,
+    #                                                  batch_size=opt.n_samples,
+    #                                                  shape=shape,
+    #                                                  verbose=False,
+    #                                                  unconditional_guidance_scale=opt.scale,
+    #                                                  unconditional_conditioning=uc,
+    #                                                  eta=opt.ddim_eta,
+    #                                                  x_T=start_code)
+
+    if thread_data.test_sd2:
+        if sampler_name == 'plms':
+            from ldm.models.diffusion.plms import PLMSSampler
+            sampler = PLMSSampler(thread_data.model)
+        elif sampler_name == 'ddim':
+            from ldm.models.diffusion.ddim import DDIMSampler
+            sampler = DDIMSampler(thread_data.model)
+            sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
+        elif sampler_name == 'dpm2':
+            from ldm.models.diffusion.dpm_solver import DPMSolverSampler
+            sampler = DPMSolverSampler(thread_data.model)
+
+        shape = [opt_C, opt_H // opt_f, opt_W // opt_f]
+
+        samples_ddim, intermediates = sampler.sample(
+            S=opt_ddim_steps,
+            conditioning=c,
+            batch_size=opt_n_samples,
+            seed=opt_seed,
+            shape=shape,
+            verbose=False,
+            unconditional_guidance_scale=opt_scale,
+            unconditional_conditioning=uc,
+            eta=opt_ddim_eta,
+            x_T=start_code,
+            img_callback=img_callback,
+            mask=mask,
+            sampler = sampler_name,
+        )
+    else:
+        if sampler_name == 'ddim':
+            thread_data.model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
+
+        samples_ddim = thread_data.model.sample(
+            S=opt_ddim_steps,
+            conditioning=c,
+            seed=opt_seed,
+            shape=shape,
+            verbose=False,
+            unconditional_guidance_scale=opt_scale,
+            unconditional_conditioning=uc,
+            eta=opt_ddim_eta,
+            x_T=start_code,
+            img_callback=img_callback,
+            mask=mask,
+            sampler = sampler_name,
+        )
+    return 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, opt_C=1, opt_H=1, opt_W=1, opt_f=1):
     # encode (scaled latent)
-    z_enc = thread_data.model.stochastic_encode(
-        init_latent,
-        torch.tensor([t_enc] * batch_size).to(thread_data.device),
-        opt_seed,
-        opt_ddim_eta,
-        opt_ddim_steps,
-    )
     x_T = None if mask is None else init_latent
 
-    # decode it
-    samples_ddim = thread_data.model.sample(
-        t_enc,
-        c,
-        z_enc,
-        unconditional_guidance_scale=opt_scale,
-        unconditional_conditioning=uc,
-        img_callback=img_callback,
-        mask=mask,
-        x_T=x_T,
-        sampler = 'ddim'
-    )
-    yield from samples_ddim
+    if thread_data.test_sd2:
+        from ldm.models.diffusion.ddim import DDIMSampler
+
+        sampler = DDIMSampler(thread_data.model)
+
+        sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
+
+        z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(thread_data.device))
+
+        samples_ddim = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt_scale,unconditional_conditioning=uc, img_callback=img_callback)
+
+    else:
+        z_enc = thread_data.model.stochastic_encode(
+            init_latent,
+            torch.tensor([t_enc] * batch_size).to(thread_data.device),
+            opt_seed,
+            opt_ddim_eta,
+            opt_ddim_steps,
+        )
+
+        # decode it
+        samples_ddim = thread_data.model.sample(
+            t_enc,
+            c,
+            z_enc,
+            unconditional_guidance_scale=opt_scale,
+            unconditional_conditioning=uc,
+            img_callback=img_callback,
+            mask=mask,
+            x_T=x_T,
+            sampler = 'ddim'
+        )
+    return samples_ddim
 
 def gc():
     gc_collect()
@@ -725,13 +883,21 @@ def chunk(it, size):
 
 def load_model_from_config(ckpt, verbose=False):
     print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
+
+    if ckpt.endswith(".safetensors"):
+        print("Loading from safetensors")
+        pl_sd = load_file(ckpt, device="cpu")
+    else:
+        pl_sd = torch.load(ckpt, map_location="cpu")
+
     if "global_step" in pl_sd:
         print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-    return sd
 
-# utils
+    if "state_dict" in pl_sd:
+        return pl_sd["state_dict"]
+    else:
+        return pl_sd
+
 class UserInitiatedStop(Exception):
     pass
 
@@ -775,9 +941,20 @@ def load_mask(mask_str, h0, w0, newH, newW, invert=False):
     return image
 
 # https://stackoverflow.com/a/61114178
-def img_to_base64_str(img, output_format="PNG"):
+def img_to_base64_str(img, output_format="PNG", output_quality=75):
+    buffered = img_to_buffer(img, output_format, quality=output_quality)
+    return buffer_to_base64_str(buffered, output_format)
+
+def img_to_buffer(img, output_format="PNG", output_quality=75):
     buffered = BytesIO()
-    img.save(buffered, format=output_format)
+    if ( output_format.upper() == "JPEG" ):
+        img.save(buffered, format=output_format, quality=output_quality)
+    else:
+        img.save(buffered, format=output_format)
+    buffered.seek(0)
+    return buffered
+
+def buffer_to_base64_str(buffered, output_format="PNG"):
     buffered.seek(0)
     img_byte = buffered.getvalue()
     mime_type = "image/png" if output_format.lower() == "png" else "image/jpeg"
@@ -795,3 +972,48 @@ def base64_str_to_img(img_str):
     buffered = base64_str_to_buffer(img_str)
     img = Image.open(buffered)
     return img
+
+def split_weighted_subprompts(text):
+    """
+    grabs all text up to the first occurrence of ':' 
+    uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
+    if ':' has no value defined, defaults to 1.0
+    repeats until no text remaining
+    """
+    remaining = len(text)
+    prompts = []
+    weights = []
+    while remaining > 0:
+        if ":" in text:
+            idx = text.index(":") # first occurrence from start
+            # grab up to index as sub-prompt
+            prompt = text[:idx]
+            remaining -= idx
+            # remove from main text
+            text = text[idx+1:]
+            # find value for weight 
+            if " " in text:
+                idx = text.index(" ") # first occurence
+            else: # no space, read to end
+                idx = len(text)
+            if idx != 0:
+                try:
+                    weight = float(text[:idx])
+                except: # couldn't treat as float
+                    print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
+                    weight = 1.0
+            else: # no value found
+                weight = 1.0
+            # remove from main text
+            remaining -= idx
+            text = text[idx+1:]
+            # append the sub-prompt and its weight
+            prompts.append(prompt)
+            weights.append(weight)
+        else: # no : found
+            if len(text) > 0: # there is still text though
+                # take remainder as weight 1
+                prompts.append(text)
+                weights.append(1.0)
+            remaining = 0
+    return prompts, weights

ui/sd_internal/task_manager.py

@@ -79,6 +79,7 @@ class ImageRequest(BaseModel):
     use_vae_model: str = None
     show_only_filtered_image: bool = False
     output_format: str = "jpeg" # or "png"
+    output_quality: int = 75
 
     stream_progress_updates: bool = False
     stream_image_progress: bool = False
@@ -95,6 +96,7 @@ class FilterRequest(BaseModel):
     render_device: str = None
     use_full_precision: bool = False
     output_format: str = "jpeg" # or "png"
+    output_quality: int = 75
 
 # Temporary cache to allow to query tasks results for a short time after they are completed.
 class TaskCache():
@@ -283,45 +285,26 @@ def thread_render(device):
         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:
-            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:
+            if runtime.is_model_reload_necessary(task.request):
                 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
+                runtime.reload_model()
+                current_model_path = task.request.use_stable_diffusion_model
+                current_vae_path = task.request.use_vae_model
+
+            def step_callback():
+                global current_state_error
+
                 if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
                     runtime.thread_data.stop_processing = True
                     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)
+
+                    task_cache.keep(task.request.session_id, TASK_TTL)
+
+            current_state = ServerStates.Rendering
+            task.response = runtime.mk_img(task.request, task.buffer_queue, task.temp_images, step_callback)
         except Exception as e:
             task.error = e
             print(traceback.format_exc())
@@ -523,6 +506,7 @@ def render(req : ImageRequest):
     r.use_vae_model = req.use_vae_model
     r.show_only_filtered_image = req.show_only_filtered_image
     r.output_format = req.output_format
+    r.output_quality = req.output_quality
 
     r.stream_progress_updates = True # the underlying implementation only supports streaming
     r.stream_image_progress = req.stream_image_progress

ui/server.py

@@ -7,6 +7,7 @@ import traceback
 
 import sys
 import os
+import socket
 import picklescan.scanner
 import rich
 
@@ -23,7 +24,7 @@ USER_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'
 CORE_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, 'plugins', 'ui'))
 UI_PLUGINS_SOURCES = ((CORE_UI_PLUGINS_DIR, 'core'), (USER_UI_PLUGINS_DIR, 'user'))
 
-STABLE_DIFFUSION_MODEL_EXTENSIONS = ['.ckpt']
+STABLE_DIFFUSION_MODEL_EXTENSIONS = ['.ckpt', '.safetensors']
 VAE_MODEL_EXTENSIONS = ['.vae.pt', '.ckpt']
 
 OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
@@ -116,6 +117,8 @@ def setConfig(config):
         bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
         config_bat.append(f"@set SD_UI_BIND_IP={bind_ip}")
 
+        config_bat.append(f"@set test_sd2={'Y' if config.get('test_sd2', False) else 'N'}")
+
         if len(config_bat) > 0:
             with open(config_bat_path, 'w', encoding='utf-8') as f:
                 f.write('\r\n'.join(config_bat))
@@ -133,6 +136,8 @@ def setConfig(config):
         bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
         config_sh.append(f"export SD_UI_BIND_IP={bind_ip}")
 
+        config_sh.append(f"export test_sd2=\"{'Y' if config.get('test_sd2', False) else 'N'}\"")
+
         if len(config_sh) > 1:
             with open(config_sh_path, 'w', encoding='utf-8') as f:
                 f.write('\n'.join(config_sh))
@@ -140,12 +145,19 @@ def setConfig(config):
         print(traceback.format_exc())
 
 def resolve_model_to_use(model_name:str, model_type:str, model_dir:str, model_extensions:list, default_models=[]):
+    config = getConfig()
+
     model_dirs = [os.path.join(MODELS_DIR, model_dir), SD_DIR]
     if not model_name: # When None try user configured model.
-        config = getConfig()
+        # config = getConfig()
         if 'model' in config and model_type in config['model']:
             model_name = config['model'][model_type]
     if model_name:
+        is_sd2 = config.get('test_sd2', False)
+        if model_name.startswith('sd2_') and not is_sd2: # temp hack, until SD2 is unified with 1.4
+            print('ERROR: Cannot use SD 2.0 models with SD 1.0 code. Using the sd-v1-4 model instead!')
+            model_name = 'sd-v1-4'
+
         # Check models directory
         models_dir_path = os.path.join(MODELS_DIR, model_dir, model_name)
         for model_extension in model_extensions:
@@ -188,6 +200,7 @@ class SetAppConfigRequest(BaseModel):
     ui_open_browser_on_start: bool = None
     listen_to_network: bool = None
     listen_port: int = None
+    test_sd2: bool = None
 
 @app.post('/app_config')
 async def setAppConfig(req : SetAppConfigRequest):
@@ -208,6 +221,8 @@ async def setAppConfig(req : SetAppConfigRequest):
        if 'net' not in config:
            config['net'] = {}
        config['net']['listen_port'] = int(req.listen_port)
+    if req.test_sd2 is not None:
+        config['test_sd2'] = req.test_sd2
     try:
         setConfig(config)
 
@@ -230,9 +245,9 @@ def is_malicious_model(file_path):
             return False
     except Exception as e:
         print('error while scanning', file_path, 'error:', e)
-
     return False
 
+known_models = {}
 def getModels():
     models = {
         'active': {
@@ -255,9 +270,14 @@ def getModels():
                 if not file.endswith(model_extension):
                     continue
 
-                if is_malicious_model(os.path.join(models_dir, file)):
-                    models['scan-error'] = file
-                    return
+                model_path = os.path.join(models_dir, file)
+                mtime = os.path.getmtime(model_path)
+                mod_time = known_models[model_path] if model_path in known_models else -1
+                if mod_time != mtime:
+                    if is_malicious_model(model_path):
+                        models['scan-error'] = file
+                        return
+                known_models[model_path] = mtime
 
                 model_name = file[:-len(model_extension)]
                 models['options'][model_type].append(model_name)
@@ -286,6 +306,11 @@ def getUIPlugins():
 
     return plugins
 
+def getIPConfig():
+    ips = socket.gethostbyname_ex(socket.gethostname())
+    ips[2].append(ips[0])
+    return ips[2]
+
 @app.get('/get/{key:path}')
 def read_web_data(key:str=None):
     if not key: # /get without parameters, stable-diffusion easter egg.
@@ -295,11 +320,14 @@ def read_web_data(key:str=None):
         if config is None:
             config = APP_CONFIG_DEFAULTS
         return JSONResponse(config, headers=NOCACHE_HEADERS)
-    elif key == 'devices':
+    elif key == 'system_info':
         config = getConfig()
-        devices = task_manager.get_devices()
-        devices['config'] = config.get('render_devices', "auto")
-        return JSONResponse(devices, headers=NOCACHE_HEADERS)
+        system_info = {
+            'devices': task_manager.get_devices(),
+            'hosts': getIPConfig(),
+        }
+        system_info['devices']['config'] = config.get('render_devices', "auto")
+        return JSONResponse(system_info, headers=NOCACHE_HEADERS)
     elif key == 'models':
         return JSONResponse(getModels(), headers=NOCACHE_HEADERS)
     elif key == 'modifiers': return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=NOCACHE_HEADERS)
@@ -435,6 +463,9 @@ class LogSuppressFilter(logging.Filter):
         return True
 logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter())
 
+# Check models and prepare cache for UI open
+getModels()
+
 # Start the task_manager
 task_manager.default_model_to_load = resolve_ckpt_to_use()
 task_manager.default_vae_to_load = resolve_vae_to_use()