Merge pull request #582 from cmdr2/beta

Beta
Typo in css path
2025-08-13 17:57:20 +02:00 · 2022-12-01 13:59:13 +05:30 · 2022-12-01 13:57:38 +05:30 · 2022-12-01 13:54:49 +05:30 · 2022-12-01 10:35:32 +05:30 · 2022-12-01 00:59:12 +01:00
30 changed files with 1130 additions and 768 deletions
--- a/27
+++ b/27
@ -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.
--- a/CHANGES.md
+++ b/CHANGES.md
@ -19,8 +19,17 @@
 - 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

 ### Detailed changelog
+* 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
--- a/README.md
+++ b/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).
--- a/scripts/Developer
+++ b/scripts/Developer
@ -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
--- a/scripts/bootstrap.bat
+++ b/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%"
--- a/scripts/developer_console.sh
+++ b/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]}")
--- a/scripts/on_sd_start.bat
+++ b/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 5d647c5459f4cd790672512222bc41903c01bb71
+    )

    @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
@ -370,7 +350,9 @@ 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" (
--- a/scripts/on_sd_start.sh
+++ b/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 5d647c5459f4cd790672512222bc41903c01bb71
+    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"
@ -309,6 +296,9 @@ 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
--- a/ui/index.html
+++ b/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">
@ -12,7 +13,10 @@
    <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/jquery-confirm.min.css">
+    <link rel="manifest" href="/media/manifest.webmanifest">
    <script src="/media/js/jquery-3.6.1.min.js"></script>
+    <script src="/media/js/jquery-confirm.min.js"></script>
    <script src="/media/js/drawingboard.min.js"></script>
    <script src="/media/js/marked.min.js"></script>
 </head>
@ -20,7 +24,10 @@
 <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>v2.4.17 <span id="updateBranchLabel"></span></small>
+            </h1>
        </div>
        <div id="server-status">
            <div id="server-status-color">●</div>
@ -64,7 +71,7 @@
                        <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>

                        <br/>
@ -79,7 +86,7 @@
                </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>

@ -247,8 +254,17 @@
                <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">
@ -345,7 +361,7 @@ async function init() {
    await getAppConfig()
    await loadModifiers()
    await loadUIPlugins()
-    await getDevices()
+    await getSystemInfo()

    setInterval(healthCheck, HEALTH_PING_INTERVAL * 1000)
    healthCheck()
--- a/ui/media/css/jquery-confirm.min.css
+++ b/ui/media/css/jquery-confirm.min.css
--- a/ui/media/css/main.css
+++ b/ui/media/css/main.css
@ -64,6 +64,11 @@ 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 +215,7 @@ code {
 }
 .collapsible-content {
    display: block;
-    padding-left: 15px;
+    padding-left: 10px;
 }
 .collapsible-content h5 {
    padding: 5pt 0pt;
@ -658,11 +663,15 @@ 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 +721,7 @@ input::file-selector-button {
        padding-right: 0px;
    }
    #server-status {
-        display: none;
+        top: 75%;
    }
    .popup > div {
        padding-left: 5px !important;
@ -730,6 +739,15 @@ 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;
@ -997,8 +1015,17 @@ button:hover {
 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%;
+}
--- a/ui/media/css/themes.css
+++ b/ui/media/css/themes.css
@ -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 {
@ -44,6 +47,8 @@
    --input-text-color: black;
    --input-background-color: #f8f9fa; 
    --input-border-color: grey; 
+
+    --theme-color-fallback: #aaaaaa;
 }

 .theme-discord {
@ -58,6 +63,8 @@
    --input-border-size: 2px; 
    --input-background-color: #202225; 
    --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #202225;
 }

 .theme-cool-blue {
@ -71,8 +78,10 @@
 	--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 +96,8 @@
 	--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 +112,8 @@

    --input-background-color: var(--background-color3);
    --input-border-size: 0px;
+
+    --theme-color-fallback: #000000;
 }

 .theme-wild {
@ -117,8 +130,8 @@
    
    --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 {
@ -136,6 +149,8 @@
    --input-background-color: #2a2a2a; 
    --input-border-size: 0px; 
    --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #2168bf;
 }

 .theme-gnomie .panel-box {
--- a/ui/media/js/auto-save.js
+++ b/ui/media/js/auto-save.js
@ -35,6 +35,7 @@ const SETTINGS_IDS_LIST = [
    "sound_toggle",
    "turbo",
    "use_full_precision",
+    "confirm_dangerous_actions",
    "auto_save_settings"
 ]

@ -55,6 +56,9 @@ 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,
--- a/ui/media/js/dnd.js
+++ b/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
@ -185,9 +192,9 @@ 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 +274,6 @@ 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
@ -326,6 +328,7 @@ function getModelPath(filename, extensions)
            filename = filename.slice(0, filename.length - ext.length)
        }
    })
+    return filename
 }

 const TASK_TEXT_MAPPING = {
@ -406,7 +409,7 @@ 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)
 }
--- a/ui/media/js/image-modifiers.js
+++ b/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(modifierCard, 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(modifierCard, 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')
@ -222,8 +222,7 @@ function refreshTagsList() {
            let idx = activeTags.indexOf(tag)

            if (idx !== -1 && activeTags[idx].originElement !== undefined) {
-                activeTags[idx].originElement.classList.remove(activeCardClass)
-                activeTags[idx].originElement.querySelector('.modifier-card-image-overlay').innerText = '+'
+                toggleCardState(activeTags[idx].originElement, false)

                activeTags.splice(idx, 1)
                refreshTagsList()
@ -236,6 +235,16 @@ function refreshTagsList() {
    editorModifierTagsList.appendChild(brk)
 }

+function toggleCardState(card, makeActive) {
+    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')

--- a/ui/media/js/jquery-confirm.min.js
+++ b/ui/media/js/jquery-confirm.min.js
--- a/ui/media/js/main.js
+++ b/ui/media/js/main.js
@ -138,6 +138,35 @@ 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 {
+        $.confirm({
+            theme: 'modern',
+            title: prompt,
+            useBootstrap: false,
+            animateFromElement: false,
+            content: '<small>Tip: To skip this dialog, use shift-click or disable the "Confirm dangerous actions" setting in the Settings tab.</small>',
+            buttons: {
+                yes: () => { fn(e) },
+                cancel: () => {}
+            }
+        }); 
+    }
+}
+
 function logMsg(msg, level, outputMsg) {
    if (outputMsg.hasChildNodes()) {
        outputMsg.appendChild(document.createElement('br'))
@ -169,34 +198,6 @@ function playSound() {
        })
    }
 }
-function setSystemInfo(devices) {
-    let cpu = devices.all.cpu.name
-    let allGPUs = Object.keys(devices.all).filter(d => d != 'cpu')
-    let activeGPUs = Object.keys(devices.active)
-
-    function ID_TO_TEXT(d) {
-        let info = devices.all[d]
-        if ("mem_free" in info && "mem_total" in info) {
-            return `${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 +232,7 @@ async function healthCheck() {
                break
        }
        if (serverState.devices) {
-            setSystemInfo(serverState.devices)
+            setDeviceInfo(serverState.devices)
        }
        serverState.time = Date.now()
    } catch (e) {
@ -887,24 +888,26 @@ 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')
@ -934,10 +937,10 @@ function getPrompts() {
    prompts = prompts.filter(prompt => prompt !== '')

    if (activeTags.length > 0) {
-	const promptTags = activeTags.map(x => x.name).join(", ")
-	prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
+        const promptTags = activeTags.map(x => x.name).join(", ")
+        prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
    }
-	
+
    let promptsToMake = applySetOperator(prompts)
    promptsToMake = applyPermuteOperator(promptsToMake)

@ -1047,21 +1050,25 @@ 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)
--- a/ui/media/js/parameters.js
+++ b/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'))
 })
--- a/ui/media/js/themes.js
+++ b/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);
--- a/ui/media/js/utils.js
+++ b/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
+    }
 }


--- a/ui/media/manifest.webmanifest
+++ b/ui/media/manifest.webmanifest
@ -0,0 +1,8 @@
+{
+  "name": "Stable Diffusion UI",
+  "display": "standalone",
+  "display_override": [
+    "window-controls-overlay"
+  ],
+  "theme_color": "#000000"
+}
--- a/ui/plugins/ui/Autoscroll.plugin.js
+++ b/ui/plugins/ui/Autoscroll.plugin.js
@ -0,0 +1,42 @@
+(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")
+
+    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();
+        }
+    }
+})()
--- a/ui/plugins/ui/Modifiers-wheel.plugin.js
+++ b/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
+                        }
                    }
                }
            }
--- a/ui/sd_internal/ddim_callback.patch
+++ b/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):
--- a/ui/sd_internal/ddim_callback_sd2.patch
+++ b/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,
--- a/ui/sd_internal/device_manager.py
+++ b/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.
--- a/ui/sd_internal/env_yaml.patch
+++ b/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
--- a/ui/sd_internal/runtime.py
+++ b/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 +22,14 @@ 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
+
 import uuid

 logging.set_verbosity_error()
@ -35,7 +37,7 @@ 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
@ -76,8 +78,24 @@ def thread_init(device):
    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')
@ -92,6 +110,13 @@ def load_model_ckpt():
        thread_data.precision = 'full'

    print('loading', thread_data.ckpt_file + '.ckpt', '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 + '.ckpt')
    li, lo = [], []
    for key, value in sd.items():
@ -185,6 +210,38 @@ def load_model_ckpt():
 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 + '.ckpt')
+
+    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
+
+    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}.ckpt
+ 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 +261,11 @@ 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 +311,6 @@ 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 +366,23 @@ 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 +397,73 @@ 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 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
+
+    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 +482,17 @@ 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 +501,7 @@ 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 +546,14 @@ 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 +565,8 @@ 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 +582,14 @@ 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 +602,21 @@ 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 +624,7 @@ 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 +637,10 @@ 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)
@ -591,9 +669,11 @@ def do_mk_img(req: Request):
                            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)
+                            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
@ -609,9 +689,11 @@ 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)
+                                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)
@ -622,14 +704,18 @@ 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))
+
+    return res

 def save_image(img, img_out_path):
    try:
@ -664,51 +750,109 @@ 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'):
+        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:
+        from ldm.models.diffusion.ddim import DDIMSampler
+        from ldm.models.diffusion.plms import PLMSSampler
+
+        shape = [opt_C, opt_H // opt_f, opt_W // opt_f]
+
+        if sampler_name == 'plms':
+            sampler = PLMSSampler(thread_data.model)
+        elif sampler_name == 'ddim':
+            sampler = DDIMSampler(thread_data.model)
+
+            sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
+
+
+        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()
@ -776,8 +920,16 @@ def load_mask(mask_str, h0, w0, newH, newW, invert=False):

 # https://stackoverflow.com/a/61114178
 def img_to_base64_str(img, output_format="PNG"):
+    buffered = img_to_buffer(img, output_format)
+    return buffer_to_base64_str(buffered, output_format)
+
+def img_to_buffer(img, output_format="PNG"):
    buffered = BytesIO()
    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 +947,48 @@ 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
--- a/ui/sd_internal/task_manager.py
+++ b/ui/sd_internal/task_manager.py
@ -283,45 +283,26 @@ 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())
--- a/ui/server.py
+++ b/ui/server.py
@ -7,6 +7,7 @@ import traceback

 import sys
 import os
+import socket
 import picklescan.scanner
 import rich

@ -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()
Author	SHA1	Message	Date
cmdr2	5b47da67f6	Merge pull request #582 from cmdr2/beta Beta	2022-12-01 13:59:13 +05:30
cmdr2	292f68ff97	Typo in css path	2022-12-01 13:57:38 +05:30
cmdr2	3b554d881a	Styling changes for the confirm dialog	2022-12-01 13:54:49 +05:30
cmdr2	4bc6e51862	Merge pull request #580 from JeLuF/patch-5 Add Quadro T2000 to force_full_precision list.	2022-12-01 10:35:32 +05:30
JeLuF	427861cf13	Add Quadro T2000 to force_full_precision list.	2022-12-01 00:59:12 +01:00
cmdr2	da3e7a2eb8	Fix the broken image close button	2022-11-30 21:14:18 +05:30
cmdr2	2979f04c82	Use socket.gethostname() instead of socket.getfqdn()	2022-11-30 20:17:18 +05:30
cmdr2	1949d8a50c	Tweak modifiers help msg	2022-11-30 16:32:43 +05:30
cmdr2	ee66c799e0	Merge pull request #563 from patriceac/Mouse-wheel-behavior-fixes Improved Mouse Wheel UX with Image Modifiers	2022-11-30 16:24:05 +05:30
cmdr2	7c50b8bf94	Merge branch 'beta' into Mouse-wheel-behavior-fixes	2022-11-30 16:22:45 +05:30
cmdr2	141ff74ece	Merge pull request #557 from madrang/webmanifest Added web manifest to allow installing the Url as a web app.	2022-11-30 16:19:04 +05:30
cmdr2	321e5f1ed6	Merge pull request #564 from patriceac/Fix-UI-display-when-removing-the-last-task Fix UI display when removing the last task	2022-11-30 16:14:59 +05:30
cmdr2	6d131d9d8e	Merge branch 'beta' into Fix-UI-display-when-removing-the-last-task	2022-11-30 16:14:28 +05:30
cmdr2	7e69b8eb31	Merge branch 'beta' of github.com:cmdr2/stable-diffusion-ui into beta	2022-11-30 16:11:22 +05:30
cmdr2	4e0b33e6a4	Merge pull request #566 from patriceac/Visual-feedback-on-buttons Visual feedback on button click	2022-11-30 16:11:08 +05:30
cmdr2	54f7e6fcb8	SD2 fix - register buffer on the correct device	2022-11-30 16:05:06 +05:30
cmdr2	529169c4da	Merge pull request #541 from patriceac/patch-2 Fix restoration of parallel output setting	2022-11-30 15:54:04 +05:30
cmdr2	a2c8c99215	Merge pull request #541 from patriceac/patch-2 Fix restoration of parallel output setting	2022-11-30 15:53:30 +05:30
cmdr2	e8bf3fd009	Merge pull request #542 from patriceac/patch-1 Fix restoration of model and VAE	2022-11-30 15:52:26 +05:30
cmdr2	465676e9ea	Merge pull request #542 from patriceac/patch-1 Fix restoration of model and VAE	2022-11-30 15:51:31 +05:30
cmdr2	af53b57047	Changelog	2022-11-30 15:49:47 +05:30
cmdr2	54b5f75905	Rename auto-scroll to reflect its purpose	2022-11-30 15:47:24 +05:30
cmdr2	4348333497	Don't register listeners for an autosave setting, if they've already been registered	2022-11-30 15:45:30 +05:30
cmdr2	cc31110bcf	Merge pull request #537 from patriceac/Generate-screen-layout Auto-scroll plugin	2022-11-30 15:44:31 +05:30
cmdr2	f7c04bf7a6	bump version	2022-11-30 14:34:42 +05:30
cmdr2	029509ebad	Unify IP info with devices, into a system_info table	2022-11-30 14:34:24 +05:30
cmdr2	65102bb64d	Merge pull request #536 from JeLuF/serverip Show network addresses in system settings	2022-11-30 14:00:18 +05:30
cmdr2	b96b55c5ce	Merge branch 'beta' into serverip	2022-11-30 14:00:12 +05:30
Marc-Andre Ferland	1f5aba010e	Merge branch 'beta' of https://github.com/cmdr2/stable-diffusion-ui.git into webmanifest # Conflicts: # ui/index.html	2022-11-30 03:29:46 -05:00
cmdr2	f0b3bea4e3	Also confirm before the 'Stop All' button acts; Tweak wording of confirm dialog	2022-11-30 13:54:42 +05:30
cmdr2	84fae2d9e0	Merge pull request #531 from JeLuF/confirm Confirm 'Clear All' and 'Stop Task'	2022-11-30 13:48:14 +05:30
cmdr2	0b96fa112d	Merge branch 'beta' into confirm	2022-11-30 13:47:08 +05:30
cmdr2	c64bcd23d3	Picklescanner is mandatory	2022-11-30 13:38:22 +05:30
cmdr2	efd9a22bb5	Merge pull request #530 from madrang/list-models Scan model once as start, then only if changed.	2022-11-30 13:37:27 +05:30
cmdr2	159c3edfe3	Simplify the logic for toggling modifier cards, no need to loop through the cards, since we already have the card object in hand	2022-11-30 13:33:20 +05:30
cmdr2	f74fa8657b	Merge pull request #518 from patriceac/patch-6 Fix duplicate custom modifiers activation states	2022-11-30 13:27:14 +05:30
cmdr2	648b142a4b	Merge pull request #571 from madrang/tabs-css Add a new css rule for screens smaller than 500px.	2022-11-30 13:24:38 +05:30
cmdr2	426f92595e	Merge pull request #520 from madrang/fix-gfpgan Fix the gfpgan fix for multi-gpu	2022-11-30 13:08:10 +05:30
cmdr2	82a8d9b644	Merge pull request #577 from cmdr2/beta Beta	2022-11-30 12:19:44 +05:30
cmdr2	ff9430b8a2	Tabs to 4 spaces	2022-11-30 12:18:34 +05:30
cmdr2	2e69ffcb5e	Merge pull request #576 from cmdr2/beta v2.4.16 - Remove the use of git-apply	2022-11-30 12:12:17 +05:30
cmdr2	0ea38db7ef	Show the SD 2.0 setting only to beta users	2022-11-30 12:05:46 +05:30
cmdr2	2706149399	Tweak left padding of editor panel	2022-11-29 15:27:13 +05:30
cmdr2	3d0cdc1cb6	Bump version	2022-11-29 13:32:29 +05:30
cmdr2	ac605e9352	Typos and minor fixes for sd 2	2022-11-29 13:30:08 +05:30
cmdr2	5432297691	Default to sd-v1-4 when trying to use a SD2 model with SD 1.4, and warn the user. This will eventually be unnecessary	2022-11-29 13:14:58 +05:30
cmdr2	e37be0f954	Remove the need to use yield in the core loop for streaming results. This removes the need to patch the Stable Diffusion code, which can be fragile	2022-11-29 13:03:57 +05:30
Marc-Andre Ferland	a99209b674	Add a new css rule for screens smaller than 500px.	2022-11-28 20:23:17 -05:00
cmdr2	cb02b5ba18	Merge pull request #567 from madrang/tabs-css Improved tabs flow on small screens.	2022-11-28 18:27:29 +05:30
patriceac	14714b950d	Slight improvement of detection logic	2022-11-28 00:14:12 -08:00
cmdr2	13654cb8c0	Make on_sd_start.sh executable	2022-11-28 13:00:02 +05:30
cmdr2	00276228cf	Make on_sd_start.sh executable	2022-11-28 12:59:33 +05:30
Marc-Andre Ferland	8583bb8d7b	Improved tabs flow on small screens.	2022-11-27 20:37:20 -05:00
patriceac	d48951fe00	Visual feedback on button click When there are too many tasks and the top of the list is not visible, there is no visual feedback that a task has been successfully added to the queue. Adding a subtle visual feedback on buttons upon click to reflect that the mouse event was taken into account.	2022-11-27 16:26:01 -08:00
Marc-Andre Ferland	99bdcfa0a5	Set theme-color from the current selected theme.	2022-11-27 15:49:54 -05:00
patriceac	e64e1a92e6	Fix UI display when removing the last task Clear All button properly shows the "welcome message", but Remove the last task would just result in a blank Preview pane.	2022-11-27 12:42:51 -08:00
patriceac	e278e639a3	Fix removal of image modifiers with non-zero weights Properly handles removal of image modifiers that had (((modifiers))) or [[[modifiers]]] updated at runtime.	2022-11-27 03:00:19 -08:00
patriceac	c4bad5c454	Conciseness Shortening the sentence.	2022-11-27 01:42:39 -08:00
patriceac	da41a74efc	Require Ctrl+Mouse Wheel for modifier weight adjustment The current behavior is just too annoying, and scrolling the page is a much more frequent activity than tweaking the weights.	2022-11-27 01:35:47 -08:00
Marc-Andre Ferland	9c91f57b19	Added web manifest to allow installing the Url as a web app.	2022-11-26 15:51:26 -05:00
cmdr2	f14afcd129	Update README.md	2022-11-26 12:44:19 +05:30
cmdr2	5c1a3d82d7	Update README.md	2022-11-26 12:23:03 +05:30
patriceac	e02a917569	Improved logic for auto-scroll toggle insertion Updating the insertion logic to prepare for future UI improvements.	2022-11-25 22:51:49 -08:00
cmdr2	347fa0fda1	Update on_sd_start.bat	2022-11-26 01:50:30 +05:30
cmdr2	6510d4cb02	Merge pull request #553 from cmdr2/revert-552-beta Revert "Patching patch again"	2022-11-26 01:45:57 +05:30
cmdr2	91e4ccf6f8	Update on_sd_start.bat	2022-11-26 01:43:41 +05:30
cmdr2	36249874bc	Revert "Patching patch again"	2022-11-26 01:42:16 +05:30
cmdr2	d2b5d6cce9	Merge pull request #552 from jsuelwald/beta Patching patch again	2022-11-26 01:40:02 +05:30
Jens Sülwald	b2922741c9	Patching patch again	2022-11-25 21:06:19 +01:00
cmdr2	300f3e27db	Merge pull request #551 from cmdr2/revert-549-patch-2 Revert "Update ddim_callback_sd2.patch"	2022-11-26 01:25:17 +05:30
cmdr2	d7330b80a9	Revert "Update ddim_callback_sd2.patch"	2022-11-26 01:22:35 +05:30
cmdr2	acdd7667b7	Merge pull request #549 from jsuelwald/patch-2 Update ddim_callback_sd2.patch	2022-11-26 01:19:08 +05:30
jsuelwald	8114fa3f5d	Update ddim_callback_sd2.patch	2022-11-25 20:46:24 +01:00
cmdr2	4bc5508f38	Rollback	2022-11-26 01:07:55 +05:30
cmdr2	e503c6092e	Ddim decode for img2img	2022-11-26 00:55:39 +05:30
cmdr2	6a8985d8dd	Update ddim_callback_sd2.patch	2022-11-26 00:49:15 +05:30
cmdr2	bee67fd883	Shape	2022-11-25 23:54:08 +05:30
cmdr2	a1d75d40aa	Update runtime.py	2022-11-25 23:36:43 +05:30
cmdr2	29484867ca	Typo	2022-11-25 23:32:56 +05:30
cmdr2	7fa983b971	Img2img sd2 attempt 2	2022-11-25 23:28:31 +05:30
cmdr2	617a8b2814	Fix for make_schedule error in sd2	2022-11-25 23:15:22 +05:30
cmdr2	b924d323d4	img2img attempt for sd2	2022-11-25 22:36:02 +05:30
patriceac	a2efda41d3	Cleaning up the code	2022-11-25 03:50:47 -08:00
cmdr2	642c114501	Working txt2img	2022-11-25 14:29:24 +05:30
cmdr2	02dd3e457d	Tweaks to load sd1 models in sd2 code, typos	2022-11-25 13:57:15 +05:30
cmdr2	ea7b28c9d5	Placeholder changes for SD 2.0 support, haven't tested yet	2022-11-25 12:17:44 +05:30
patriceac	472ab4a9ce	Fix restoration of parallel output setting	2022-11-24 14:15:27 -08:00
patriceac	fca84e3edf	Fix restoration of model and VAE 😅	2022-11-24 13:47:35 -08:00
cmdr2	b70235ff92	Set the PYTHONPATH in the developer console, before the prompt shows up	2022-11-24 11:48:27 +05:30
JeLuF	6eff591df7	System settings to disable the 'Are you sure?'-dialogs	2022-11-23 23:05:30 +01:00
patriceac	d0b2bf736e	Auto-scroll off by default	2022-11-23 03:23:51 -08:00
patriceac	6b6443406d	Create Autoscroll.plugin.js	2022-11-23 02:57:07 -08:00
JeLuF	3452d7852a	Merge branch 'beta' into serverip	2022-11-23 11:28:05 +01:00
JeLuF	f1fa10badd	Show network addresses in system settings Users sometimes struggle to get the IP address of their PC. This PR adds a button to the system settings pane that will list the server's IP addresses.	2022-11-23 11:25:36 +01:00
cmdr2	1267621424	Merge pull request #535 from cmdr2/beta Switch to new custom backend	2022-11-23 15:09:09 +05:30
cmdr2	8a0ec95fe1	Merge branch 'main' into beta	2022-11-23 15:08:34 +05:30
Marc-Andre Ferland	2de96d4dc9	Scan model once as start, then only if changed.	2022-11-22 20:41:08 -05:00
JeLuF	a486f20892	Merge branch 'beta' into confirm	2022-11-22 21:33:18 +01:00
JeLuF	49535deb2e	Confirm 'Clear All' and 'Stop Task' 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.	2022-11-22 21:27:36 +01:00
cmdr2	7cbf62cf12	Revert whitespace fix	2022-11-22 23:30:03 +05:30
cmdr2	3b0ace3410	Revert whitespace fix	2022-11-22 23:27:46 +05:30
cmdr2	5a9c8e1d87	Warn but don't fix whitespaces in a patch	2022-11-22 23:21:11 +05:30
cmdr2	daaa65dc0a	Warn but don't fix whitespaces in a patch	2022-11-22 23:20:24 +05:30
cmdr2	ab4e371524	Fix whitespace during git apply	2022-11-22 22:25:36 +05:30
cmdr2	927fd304b0	Merge branch 'beta' of github.com:cmdr2/stable-diffusion-ui into beta	2022-11-22 22:22:07 +05:30
cmdr2	5af84b8e90	Fix whitespace during git apply	2022-11-22 22:21:54 +05:30
cmdr2	d425dac499	Merge pull request #529 from madrang/dragNdrop Fixing file drag and drop.	2022-11-22 21:57:34 +05:30
cmdr2	d056459e76	Merge pull request #529 from madrang/dragNdrop Fixing file drag and drop.	2022-11-22 21:56:07 +05:30
Marc-Andre Ferland	3169485f33	Fixing file drag and drop.	2022-11-22 11:11:06 -05:00
cmdr2	d9b9f80a93	diffusion-kit upgrade	2022-11-22 17:39:51 +05:30
cmdr2	d429505b71	Update version of diffusion-kit	2022-11-22 17:14:20 +05:30
cmdr2	72ee708917	Remove the need to install realesrgan, gfpgan and certain specific package versions, since the new backend should install them directly	2022-11-22 16:50:10 +05:30
cmdr2	93bbfac29a	Change the backend to a custom fork of SD, since basujindal's fork is no longer under development. This fork is intended to include the common models/tools used like RealESRGAN, GFPGAN, Codeformer etc, and is meant to be a community-developed project	2022-11-22 16:38:39 +05:30
cmdr2	040d7a6563	Merge pull request #528 from patriceac/patch-1 Add support for custom modifiers to d&d and clipboard	2022-11-22 16:06:26 +05:30
patriceac	e8dd930a50	Add support for custom modifiers to d&d and clipboard Add support for custom modifiers to d&d and clipboard and remove now-redundant code in restoreTaskToUI.	2022-11-22 00:06:43 -08:00
cmdr2	31c049ebfe	Version css	2022-11-22 11:09:01 +05:30
cmdr2	d343a37fb2	Merge branch 'beta' of github.com:cmdr2/stable-diffusion-ui into beta	2022-11-22 11:08:07 +05:30
cmdr2	7097175c6f	CSS tweak for logo and version	2022-11-22 11:07:50 +05:30
cmdr2	8e57c49043	Merge pull request #527 from cmdr2/beta Beta	2022-11-22 11:00:25 +05:30
cmdr2	9f036ceefd	Merge branch 'main' into beta	2022-11-22 10:59:51 +05:30
cmdr2	ff3ca8b36b	link to new downloads	2022-11-22 10:48:43 +05:30
cmdr2	87a7b70a27	Shell error code check	2022-11-22 10:40:20 +05:30
cmdr2	9c71c966ca	Shell error code check	2022-11-22 10:39:47 +05:30
cmdr2	6dc99e676e	Reduce the width of the editor sidebar, regression	2022-11-21 18:45:37 +05:30
cmdr2	3bf5e11f94	Nowarn for fresh installation (git apply whitespace)	2022-11-21 17:19:55 +05:30
cmdr2	eef9af2266	Typo	2022-11-21 17:14:54 +05:30
cmdr2	8316a002da	Don't warn about whitespace in the git patch application	2022-11-21 17:11:38 +05:30
cmdr2	c3bf767024	Merge pull request #525 from cmdr2/beta Beta	2022-11-21 14:08:47 +05:30
Marc-Andre Ferland	0a21a69a9f	Updated facexlib fix for usage on multi-gpu.	2022-11-20 13:04:22 -05:00
patriceac	cbc48e31e1	Fix duplicate custom modifiers activation states Fixing activation state for custom modifier cards sharing the same tag where only one of the cards gets (de)activated.	2022-11-19 19:25:28 -08:00