Merge pull request #582 from cmdr2/beta

Beta

3rd-PARTY-LICENSES

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

CHANGES.md

@@ -0,0 +1,48 @@
+# What's new?
+
+## v2.4
+### Major Changes
+- **Automatic scanning for malicious model files** - using `picklescan`. Thanks @JeLuf
+- **Support for custom VAE models**. You can place your VAE files in the `models/vae` folder, and refresh the browser page to use them. More info: https://github.com/cmdr2/stable-diffusion-ui/wiki/VAE-Variational-Auto-Encoder
+- **Experimental support for multiple GPUs!** It should work automatically. Just open one browser tab per GPU, and spread your tasks across your GPUs. For e.g. open our UI in two browser tabs if you have two GPUs. You can customize which GPUs it should use in the "Settings" tab, otherwise let it automatically pick the best GPUs. Thanks @madrang . More info: https://github.com/cmdr2/stable-diffusion-ui/wiki/Run-on-Multiple-GPUs
+- **Cleaner UI design** - Show settings and help in new tabs, instead of dropdown popups (which were buggy). Thanks @mdiller
+- **Progress bar.** Thanks @mdiller
+- **Custom Image Modifiers** - You can now save your custom image modifiers! Your saved modifiers can include special characters like `{}, (), [], |`
+- Drag and Drop **text files generated from previously saved images**, and copy settings to clipboard. Thanks @madrang
+- Paste settings from clipboard. Thanks @JeLuf
+- Bug fixes to reduce the chances of tasks crashing during long multi-hour runs (chrome can put long-running background tabs to sleep). Thanks @JeLuf and @madrang
+- **Improved documentation.** Thanks @JeLuf and @jsuelwald
+- Improved the codebase for dealing with system settings and UI settings. Thanks @mdiller
+- Help instructions next to some setttings, and in the tab
+- Show system info in the settings tab
+- Keyboard shortcut: Ctrl+Enter to start a task
+- 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
+* 2.4.11 - 21 Nov 2022 - Validate inputs before submitting the Image request
+* 2.4.11 - 19 Nov 2022 - New system settings to manage the network config (port number and whether to only listen on localhost)
+* 2.4.11 - 19 Nov 2022 - Address a regression in how long images take to generate. Use the previous code for moving a model to CPU. This improves things by a second or two per image, but we still have a regression (investigating).
+* 2.4.10 - 18 Nov 2022 - Textarea for negative prompts. Thanks @JeLuf
+* 2.4.10 - 18 Nov 2022 - Improved design for Settings, and rounded toggle buttons instead of checkboxes for a more modern look. Thanks @mdiller
+* 2.4.9 - 18 Nov 2022 - Add Picklescan - a scanner for malicious model files. If it finds a malicious file, it will halt the web application and alert the user. Thanks @JeLuf
+* 2.4.8 - 18 Nov 2022 - A `Use these settings` button to use the settings from a previously generated image task. Thanks @patriceac
+* 2.4.7 - 18 Nov 2022 - Don't crash if a VAE file fails to load
+* 2.4.7 - 17 Nov 2022 - Fix a bug where Face Correction (GFPGAN) would fail on cuda:N (i.e. GPUs other than cuda:0), as well as fail on CPU if the system had an incompatible GPU.
+* 2.4.6 - 16 Nov 2022 - Fix a regression in VRAM usage during startup, which caused 'Out of Memory' errors when starting on GPUs with 4gb (or less) VRAM
+* 2.4.5 - 16 Nov 2022 - Add checkbox for "Open browser on startup".
+* 2.4.5 - 16 Nov 2022 - Add a directory for core plugins that ship with Stable Diffusion UI by default.
+* 2.4.5 - 16 Nov 2022 - Add a "What's New?" tab as a core plugin, which fetches the contents of CHANGES.md from the app's release branch.

CONTRIBUTING.md

@@ -40,6 +40,7 @@ or for Windows
 `mklink /J \projects\stable-diffusion-ui-archive\ui \projects\stable-diffusion-ui-repo\ui` (link name first, source repo dir second)
 9) Run the project again (like in step 2) and ensure you can still use the UI.
 10) Congrats, now any changes you make in your repo `ui` folder are linked to this running archive of the app and can be previewed in the browser.
+11) Please update CHANGES.md in your pull requests.
 
 Check the `ui/frontend/build/README.md` for instructions on running and building the React code.
 

NSIS/README.md

@@ -0,0 +1 @@
+Scripts to be used with the Nullsoft Scriptable Installation System

NSIS/sdui.nsi

@@ -0,0 +1,265 @@
+; Script generated by the HM NIS Edit Script Wizard.
+
+Target x86-unicode
+Unicode True
+!AddPluginDir /x86-unicode "."
+; HM NIS Edit Wizard helper defines
+!define PRODUCT_NAME "Stable Diffusion UI"
+!define PRODUCT_VERSION "Installer 2.35"
+!define PRODUCT_PUBLISHER "cmdr2 and contributors"
+!define PRODUCT_WEB_SITE "https://stable-diffusion-ui.github.io"
+!define PRODUCT_DIR_REGKEY "Software\Microsoft\Cmdr2\App Paths\installer.exe"
+
+; MUI 1.67 compatible ------
+!include "MUI.nsh"
+!include "LogicLib.nsh"
+!include "nsDialogs.nsh"
+
+Var Dialog
+Var Label
+Var Button
+
+Var InstDirLen
+Var LongPathsEnabled
+Var AccountType
+
+;---------------------------------------------------------------------------------------------------------
+; This function returns the number of spaces in a string.
+; The string is passed on the stack (using Push $STRING)
+; The result is also returned on the stack and can be consumed with Pop $var
+; https://nsis.sourceforge.io/Check_for_spaces_in_a_directory_path
+Function CheckForSpaces
+  Exch $R0
+  Push $R1
+  Push $R2
+  Push $R3
+  StrCpy $R1 -1
+  StrCpy $R3 $R0
+  StrCpy $R0 0
+  loop:
+    StrCpy $R2 $R3 1 $R1
+    IntOp $R1 $R1 - 1
+    StrCmp $R2 "" done
+    StrCmp $R2 " " 0 loop
+    IntOp $R0 $R0 + 1
+  Goto loop
+  done:
+  Pop $R3
+  Pop $R2
+  Pop $R1
+  Exch $R0
+FunctionEnd
+
+;---------------------------------------------------------------------------------------------------------
+; The function DirectoryLeave is called after the user chose the installation directory.
+; If it calls "abort", the user is sent back to choose a different directory.
+Function DirectoryLeave
+   ; check whether the installation directory path is longer than 30 characters.
+   ; If yes, we suggest to the user to enable long filename support
+   ;----------------------------------------------------------------------------
+   StrLen $InstDirLen "$INSTDIR"
+
+   ; Check whether the registry key that allows for >260 characters in a path name is set
+   ReadRegStr $LongPathsEnabled HKLM "SYSTEM\CurrentControlSet\Control\FileSystem" "LongPathsEnabled"
+
+   ${If} $InstDirLen > 30
+   ${AndIf} $LongPathsEnabled == "0"
+      ; Check whether we're in the Admin group
+      UserInfo::GetAccountType
+      Pop $AccountType
+
+      ${If} $AccountType == "Admin"
+      ${AndIf} ${Cmd} `MessageBox MB_YESNO|MB_ICONQUESTION 'The path name is too long. $\n$\nYou can either enable long file name support in Windows,$\nor you can go back and choose a different path.$\n$\nFor details see: shorturl.at/auBD1$\n$\nEnable long path name support in Windows?' IDYES`
+          ; Enable long path names
+          WriteRegDWORD HKLM "SYSTEM\CurrentControlSet\Control\FileSystem" "LongPathsEnabled" 1
+      ${Else}
+          MessageBox MB_OK|MB_ICONEXCLAMATION "Installation path name too long. The installation path must not have more than 30 characters."
+          abort
+      ${EndIf}
+   ${EndIf}
+   
+   ; Check for spaces in the installation directory path.
+   ; ----------------------------------------------------
+
+   ; $R0 = CheckForSpaces( $INSTDIR )
+   Push $INSTDIR # Input string (install path).
+     Call CheckForSpaces
+   Pop $R0 # The function returns the number of spaces found in the input string.
+
+   ; Check if any spaces exist in $INSTDIR.
+   ${If} $R0 != 0
+     ; Plural if more than 1 space in $INSTDIR.
+     ; If $R0 == 1: $R1 = ""; else: $R1 = "s"
+     StrCmp $R0 1 0 +3
+       StrCpy $R1 ""
+     Goto +2
+       StrCpy $R1 "s"
+
+     ; Show message box then take the user back to the Directory page.
+     MessageBox MB_OK|MB_ICONEXCLAMATION "Error: The Installaton directory \
+     has $R0 space character$R1.$\nPlease choose an installation directory without space characters."
+     Abort
+   ${EndIf}
+   
+   ; Check for NTFS filesystem. Installations on FAT fail.
+   ; -----------------------------------------------------
+   StrCpy $5 $INSTDIR 3
+   System::Call 'Kernel32::GetVolumeInformation(t "$5",t,i ${NSIS_MAX_STRLEN},*i,*i,*i,t.r1,i ${NSIS_MAX_STRLEN})i.r0'
+   ${If} $0 <> 0
+   ${AndIf} $1 == "NTFS"
+       MessageBox mb_ok "$5 has filesystem type '$1'.$\nOnly NTFS filesystems are supported.$\nPlease choose a different drive."
+       Abort
+   ${EndIf}
+
+FunctionEnd
+
+
+;---------------------------------------------------------------------------------------------------------
+; Open the MS download page in a browser and enable the [Next] button
+Function MSMediaFeaturepack
+    ExecShell "open" "https://www.microsoft.com/en-us/software-download/mediafeaturepack"
+
+    GetDlgItem $0 $HWNDPARENT 1
+    EnableWindow $0 1
+FunctionEnd
+
+;---------------------------------------------------------------------------------------------------------
+; Install the MS Media Feature Pack, if it is missing (e.g. on Windows 10 N)
+Function MediaPackDialog
+    !insertmacro MUI_HEADER_TEXT "Windows Media Feature Pack" "Required software module is missing"
+
+    ; Skip this dialog if mf.dll is installed
+    ${If} ${FileExists} "$WINDIR\system32\mf.dll"
+        Abort
+    ${EndIf}
+    
+    nsDialogs::Create 1018
+    Pop $Dialog
+
+    ${If} $Dialog == error
+	Abort
+    ${EndIf}
+
+    ${NSD_CreateLabel} 0 0 100% 48u "The Windows Media Feature Pack is missing on this computer. It is required for the Stable Diffusion UI.$\nYou can continue the installation after installing the Windows Media Feature Pack."
+    Pop $Label
+ 	
+    ${NSD_CreateButton} 10% 49u 80% 12u "Download Meda Feature Pack from Microsoft"
+    Pop $Button
+
+    GetFunctionAddress $0 MSMediaFeaturePack
+    nsDialogs::OnClick $Button $0
+    GetDlgItem $0 $HWNDPARENT 1
+    EnableWindow $0 0
+    nsDialogs::Show
+FunctionEnd
+
+;---------------------------------------------------------------------------------------------------------
+; MUI Settings
+;---------------------------------------------------------------------------------------------------------
+!define MUI_ABORTWARNING
+!define MUI_ICON "sd.ico"
+
+!define MUI_WELCOMEFINISHPAGE_BITMAP "astro.bmp"
+
+; Welcome page
+!define MUI_WELCOMEPAGE_TEXT "This installer will guide you through the installation of Stable Diffusion UI.$\n$\n\
+Click Next to continue."
+!insertmacro MUI_PAGE_WELCOME
+Page custom MediaPackDialog
+
+; License page
+!insertmacro MUI_PAGE_LICENSE "..\LICENSE"
+!insertmacro MUI_PAGE_LICENSE "..\CreativeML Open RAIL-M License"
+; Directory page
+!define MUI_PAGE_CUSTOMFUNCTION_LEAVE "DirectoryLeave"
+!insertmacro MUI_PAGE_DIRECTORY
+
+; Instfiles page
+!insertmacro MUI_PAGE_INSTFILES 
+
+; Finish page
+!define MUI_FINISHPAGE_RUN "$INSTDIR\Start Stable Diffusion UI.cmd"
+!insertmacro MUI_PAGE_FINISH
+
+; Language files
+!insertmacro MUI_LANGUAGE "English"
+;---------------------------------------------------------------------------------------------------------
+; MUI end
+;---------------------------------------------------------------------------------------------------------
+
+Name "${PRODUCT_NAME} ${PRODUCT_VERSION}"
+OutFile "Install Stable Diffusion UI.exe"
+InstallDir "C:\Stable-Diffusion-UI\"
+InstallDirRegKey HKLM "${PRODUCT_DIR_REGKEY}" ""
+ShowInstDetails show
+
+;---------------------------------------------------------------------------------------------------------
+; List of files to be installed
+Section "MainSection" SEC01
+  SetOutPath "$INSTDIR"
+  File "..\CreativeML Open RAIL-M License"
+  File "..\How to install and run.txt"
+  File "..\LICENSE"
+  File "..\Start Stable Diffusion UI.cmd"
+  SetOutPath "$INSTDIR\scripts"
+  File "..\scripts\bootstrap.bat"
+  File "..\scripts\install_status.txt"
+  File "..\scripts\on_env_start.bat"
+  File "C:\windows\system32\curl.exe"
+  CreateDirectory "$INSTDIR\profile"
+  CreateDirectory "$SMPROGRAMS\Stable Diffusion UI"
+  CreateShortCut "$SMPROGRAMS\Stable Diffusion UI\Start Stable Diffusion UI.lnk" "$INSTDIR\Start Stable Diffusion UI.cmd"
+SectionEnd
+
+;---------------------------------------------------------------------------------------------------------
+; Our installer only needs 25 KB, but once it has run, we need 25 GB
+; So we need to overwrite the automatically detected space requirements.
+; https://nsis.sourceforge.io/Docs/Chapter4.html#4.9.13.7
+; The example in section 4.9.13.7 seems to be wrong: the number
+; needs to be provided in Kilobytes.
+Function .onInit
+   ; Set required size of section 'SEC01' to 25 Gigabytes
+   SectionSetSize ${SEC01} 26214400
+  
+  
+   ; Check system meory size. We need at least 8GB
+   ; ----------------------------------------------------
+
+   ; allocate a few bytes of memory
+   System::Alloc 64
+   Pop $1
+
+   ; Retrieve HW info from the Windows Kernel
+   System::Call "*$1(i64)"
+   System::Call "Kernel32::GlobalMemoryStatusEx(i r1)"
+   ; unpack the data into $R2 - $R10
+   System::Call "*$1(i.r2, i.r3, l.r4, l.r5, l.r6, l.r7, l.r8, l.r9, l.r10)"
+
+   # free up the memory
+   System::Free $1
+
+   ; Result mapping:
+   ; "Structure size: $2 bytes"
+   ; "Memory load: $3%"
+   ; "Total physical memory: $4 bytes"
+   ; "Free physical memory: $5 bytes"
+   ; "Total page file: $6 bytes"
+   ; "Free page file: $7 bytes"
+   ; "Total virtual: $8 bytes"
+   ; "Free virtual: $9 bytes"
+
+   ; Mem size in MB
+   System::Int64Op $4 / 1048576
+   Pop $4
+
+   ${If} $4 < "8000"
+      MessageBox MB_OK|MB_ICONEXCLAMATION "Warning!$\n$\nYour system has less than 8GB of memory (RAM).$\n$\n\
+You can still try to install Stable Diffusion UI,$\nbut it might have problems to start, or run$\nvery slowly."
+   ${EndIf}
+  
+FunctionEnd
+
+
+;Section -Post
+;  WriteRegStr HKLM "${PRODUCT_DIR_REGKEY}" "" "$INSTDIR\installer.exe"
+;SectionEnd

README.md

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

scripts/Developer Console.cmd

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

scripts/bootstrap.bat

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

scripts/developer_console.sh

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

scripts/on_sd_start.bat

@@ -1,5 +1,8 @@
 @echo off
 
+@REM Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
+@REM Note to self: Please rewrite this in Python. For the sake of your own sanity.
+
 @copy sd-ui-files\scripts\on_env_start.bat scripts\ /Y
 @copy sd-ui-files\scripts\bootstrap.bat scripts\ /Y
 
@@ -7,35 +10,48 @@ if exist "%cd%\profile" (
     set USERPROFILE=%cd%\profile
 )
 
+@mkdir tmp
+@set TMP=%cd%\tmp
+@set TEMP=%cd%\tmp
+
 @rem activate the installer env
 call conda activate
-
-@REM Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
-@REM Note to self: Please rewrite this in Python. For the sake of your own sanity.
+@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 ..\ui\sd_internal\ddim_callback.patch
-    @call git apply ..\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!"
@@ -44,10 +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 ..\ui\sd_internal\ddim_callback.patch
-    @call git apply ..\ui\sd_internal\env_yaml.patch
+    @call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
 
     @cd ..
 )
@@ -68,8 +81,6 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
     @set PYTHONNOUSERSITE=1
 
     set USERPROFILE=%cd%\profile
-    set TMP=%cd%\tmp
-    set TEMP=%cd%\tmp
 
     set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
 
@@ -81,12 +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
@@ -107,23 +112,9 @@ set PATH=C:\Windows\System32;%PATH%
     @set PYTHONNOUSERSITE=1
 
     set USERPROFILE=%cd%\profile
-    set TMP=%cd%\tmp
-    set TEMP=%cd%\tmp
 
     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
@@ -142,17 +133,9 @@ set PATH=C:\Windows\System32;%PATH%
     @set PYTHONNOUSERSITE=1
 
     set USERPROFILE=%cd%\profile
-    set TMP=%cd%\tmp
-    set TEMP=%cd%\tmp
 
     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
@@ -171,8 +154,6 @@ set PATH=C:\Windows\System32;%PATH%
     @set PYTHONNOUSERSITE=1
 
     set USERPROFILE=%cd%\profile
-    set TMP=%cd%\tmp
-    set TEMP=%cd%\tmp
 
     set PYTHONPATH=%cd%;%cd%\env\lib\site-packages
 
@@ -191,6 +172,16 @@ call WHERE uvicorn > .tmp
     exit /b
 )
 
+@>nul 2>nul call python -m picklescan --help
+@if "%ERRORLEVEL%" NEQ "0" (
+    @echo. & echo Picklescan not found. Installing
+    @call pip install picklescan || (
+        echo "Error installing the picklescan package necessary for Stable Diffusion UI. Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/cmdr2/stable-diffusion-ui/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/cmdr2/stable-diffusion-ui/issues" & echo "Thanks!"
+        pause
+        exit /b
+    )
+)
+
 @>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
 @if "%ERRORLEVEL%" NEQ "0" (
     @echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
@@ -275,7 +266,7 @@ echo. > "..\models\vae\Put your VAE files here.txt"
     for %%I in ("RealESRGAN_x4plus.pth") do if "%%~zI" EQU "67040989" (
         echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus were already downloaded"
     ) else (
-        echo. & echo "The GFPGAN model file present at %cd%\RealESRGAN_x4plus.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
+        echo. & echo "The RealESRGAN model file present at %cd%\RealESRGAN_x4plus.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
         del "RealESRGAN_x4plus.pth"
     )
 )
@@ -305,7 +296,7 @@ echo. > "..\models\vae\Put your VAE files here.txt"
     for %%I in ("RealESRGAN_x4plus_anime_6B.pth") do if "%%~zI" EQU "17938799" (
         echo "Data files (weights) necessary for ESRGAN (Resolution Upscaling) x4plus_anime were already downloaded"
     ) else (
-        echo. & echo "The GFPGAN model file present at %cd%\RealESRGAN_x4plus_anime_6B.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
+        echo. & echo "The RealESRGAN model file present at %cd%\RealESRGAN_x4plus_anime_6B.pth is invalid. It is only %%~zI bytes in size. Re-downloading.." & echo.
         del "RealESRGAN_x4plus_anime_6B.pth"
     )
 )
@@ -359,7 +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" (
@@ -383,6 +376,14 @@ call python --version
 @set SD_UI_PATH=%cd%\ui
 @cd stable-diffusion
 
+@rem
+@rem Rewrite easy-install.pth. This fixes the installation if the user has relocated the SDUI installation
+@rem
+>env\Lib\site-packages\easy-install.pth echo %cd%\src\taming-transformers
+>>env\Lib\site-packages\easy-install.pth echo %cd%\src\clip
+>>env\Lib\site-packages\easy-install.pth echo %cd%\src\gfpgan
+>>env\Lib\site-packages\easy-install.pth echo %cd%\src\realesrgan
+
 @if NOT DEFINED SD_UI_BIND_PORT set SD_UI_BIND_PORT=9000
 @if NOT DEFINED SD_UI_BIND_IP set SD_UI_BIND_IP=0.0.0.0
 @uvicorn server:app --app-dir "%SD_UI_PATH%" --port %SD_UI_BIND_PORT% --host %SD_UI_BIND_IP%

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 ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
-    git apply ../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 ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
-    git apply ../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"
@@ -156,6 +143,13 @@ else
     echo conda_sd_ui_deps_installed >> ../scripts/install_status.txt
 fi
 
+if python -m picklescan --help >/dev/null 2>&1; then
+    echo "Picklescan is already installed."
+else
+    echo "Picklescan not found, installing."
+    pip install picklescan || fail "Picklescan installation failed."
+fi
+
 
 
 mkdir -p "../models/stable-diffusion"
@@ -302,6 +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

ui/index.html

@@ -3,23 +3,31 @@
 <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?v=1">
-    <link rel="stylesheet" href="/media/css/themes.css?v=3">
-    <link rel="stylesheet" href="/media/css/main.css?v=17">
-    <link rel="stylesheet" href="/media/css/auto-save.css?v=5">
-    <link rel="stylesheet" href="/media/css/modifier-thumbnails.css?v=4">
-    <link rel="stylesheet" href="/media/css/fontawesome-all.min.css?v=1">
+    <link rel="stylesheet" href="/media/css/fonts.css">
+    <link rel="stylesheet" href="/media/css/themes.css">
+    <link rel="stylesheet" href="/media/css/main.css">
+    <link rel="stylesheet" href="/media/css/auto-save.css">
+    <link rel="stylesheet" href="/media/css/modifier-thumbnails.css">
+    <link rel="stylesheet" href="/media/css/fontawesome-all.min.css">
     <link rel="stylesheet" href="/media/css/drawingboard.min.css">
+    <link rel="stylesheet" href="/media/css/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>
 <body>
 <div id="container">
     <div id="top-nav">
         <div id="logo">
-            <h1>Stable Diffusion UI <small>v2.4.6 <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>
@@ -46,14 +54,13 @@
                     <label for="prompt"><b>Enter Prompt</b></label> <small>or</small> <button id="promptsFromFileBtn">Load from a file</button>
                     <textarea id="prompt" class="col-free">a photograph of an astronaut riding a horse</textarea>
                     <input id="prompt_from_file" name="prompt_from_file" type="file" /> <!-- hidden -->
-
                     <label for="negative_prompt" class="collapsible" id="negative_prompt_handle">
                         Negative Prompt
                         <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Writing-prompts#negative-prompts" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">Click to learn more about Negative Prompts</span></i></a>
                         <small>(optional)</small>
                     </label>
                     <div class="collapsible-content">
-                        <input id="negative_prompt" name="negative_prompt" placeholder="list the things to remove from the image (e.g. fog, green)">
+                        <textarea id="negative_prompt" name="negative_prompt" placeholder="list the things to remove from the image (e.g. fog, green)"></textarea>
                     </div>
                 </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/>
@@ -79,11 +86,11 @@
                 </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>
 
-                <button id="makeImage">Make Image</button>
+                <button id="makeImage" class="primaryButton">Make Image</button>
                 <button id="stopImage" class="secondaryButton">Stop All</button>
             </div>
 
@@ -93,7 +100,7 @@
                 <h4 class="collapsible">
                     Image Settings
                     <i id="reset-image-settings" class="fa-solid fa-arrow-rotate-left section-button">
-                        <span class="simple-tooltip left">
+                        <span class="simple-tooltip right">
                             Reset Image Settings
                         </span>
                     </i>
@@ -241,15 +248,24 @@
         <div id="tab-content-settings" class="tab-content">
             <div id="system-settings" class="tab-content-inner">
                 <h1>System Settings</h1>
-                <table class="form-table"></table>
+                <div class="parameters-table"></div>
                 <br/>
                 <button id="save-system-settings-btn" class="primaryButton">Save</button>
                 <br/><br/>
                 <div>
                     <h3><i class="fa fa-microchip icon"></i> System Info</h3>
-                    <div id="system-info"></div>
+                    <div 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">
             <div class="tab-content-inner">
@@ -263,6 +279,7 @@
                                 <li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/UI-Overview" target="_blank"><i class="fa-solid fa-list fa-fw"></i> UI Overview</a>
                                 <li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Writing-Prompts" target="_blank"><i class="fa-solid fa-pen-to-square fa-fw"></i> Writing prompts</a>
                                 <li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Inpainting" target="_blank"><i class="fa-solid fa-paintbrush fa-fw"></i> Inpainting</a>
+                                <li> <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Run-on-Multiple-GPUs" target="_blank"><i class="fa-solid fa-paintbrush fa-fw"></i> Run on Multiple GPUs</a>
                             </ul>
   
                             <li><span class="help-section">Installation</span>
@@ -327,15 +344,15 @@
 </div>
 </body>
 
-<script src="media/js/parameters.js?v=9"></script>
-<script src="media/js/plugins.js?v=1"></script>
-<script src="media/js/utils.js?v=6"></script>
-<script src="media/js/inpainting-editor.js?v=1"></script>
-<script src="media/js/image-modifiers.js?v=6"></script>
-<script src="media/js/auto-save.js?v=8"></script>
-<script src="media/js/main.js?v=22.1"></script>
-<script src="media/js/themes.js?v=4"></script>
-<script src="media/js/dnd.js?v=9"></script>
+<script src="media/js/utils.js"></script>
+<script src="media/js/parameters.js"></script>
+<script src="media/js/plugins.js"></script>
+<script src="media/js/inpainting-editor.js"></script>
+<script src="media/js/image-modifiers.js"></script>
+<script src="media/js/auto-save.js"></script>
+<script src="media/js/main.js"></script>
+<script src="media/js/themes.js"></script>
+<script src="media/js/dnd.js"></script>
 <script>
 async function init() {
     await initSettings()
@@ -344,7 +361,7 @@ async function init() {
     await getAppConfig()
     await loadModifiers()
     await loadUIPlugins()
-    await getDevices()
+    await getSystemInfo()
 
     setInterval(healthCheck, HEALTH_PING_INTERVAL * 1000)
     healthCheck()

ui/media/css/auto-save.css

@@ -26,23 +26,56 @@
     float: left;
 }
 
-.form-table small {
+
+.parameters-table {
+    display: flex;
+    flex-direction: column;
+    gap: 1px;
+}
+
+.parameters-table > div {
+    background: var(--background-color2);
+    display: flex;
+    padding: 0px 4px;
+}
+
+.parameters-table > div > div {
+    padding: 10px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.parameters-table small {
     color: rgb(153, 153, 153);
 }
 
-#system-settings .form-table td {
-    height: 24px;
+.parameters-table > div > div:nth-child(1) {
+    font-size: 20px;
+    width: 45px;
 }
 
-#system-settings .form-table td:last-child div {
-    display: flex;
-    align-items: center;
-}
-#system-settings .form-table td:last-child div > :not([type="checkbox"]):first-child {
-    margin-left: 3px;
-}
-
-#system-settings .form-table td:last-child div small {
-    padding-left: 5px;
+.parameters-table > div > div:nth-child(2) {
+    flex: 1;
+    flex-direction: column;
     text-align: left;
+    justify-content: center;
+    align-items: start;
+    gap: 4px;
+}
+
+.parameters-table > div > div:nth-child(3) {
+    text-align: right;
+}
+
+.parameters-table > div:first-child {
+    border-radius: 12px 12px 0px 0px;
+}
+
+.parameters-table > div:last-child {
+    border-radius: 0px 0px 12px 12px;
+}
+
+.parameters-table .fa-fire {
+    color: #F7630C;
 }

ui/media/css/main.css

@@ -22,13 +22,27 @@ a:visited {
 label {
     font-size: 10pt;
 }
+code {
+    background: var(--background-color4);
+    padding: 2px 4px;
+    border-radius: 4px;
+}
 #prompt {
     width: 100%;
     height: 65pt;
-    font-size: 13px;
+    font-size: 14px;
     margin-bottom: 6px;
     margin-top: 5px;
     display: block;
+    border: 2px solid var(--background-color2);
+}
+#negative_prompt {
+    width: 100%;
+    height: 50pt;
+    font-size: 13px;
+    margin-bottom: 5px;
+    margin-top: 5px;
+    display: block;
 }
 .image_preview_container {
     margin-top: 10pt;
@@ -50,6 +64,11 @@ label {
     top: 0px;
     right: 0px;
 }
+.image_clear_btn:active {
+    position: absolute;
+    top: 0px;
+    left: auto;
+}
 .settings-box ul {
     font-size: 9pt;
     margin-bottom: 5px;
@@ -196,7 +215,7 @@ label {
 }
 .collapsible-content {
     display: block;
-    padding-left: 15px;
+    padding-left: 10px;
 }
 .collapsible-content h5 {
     padding: 5pt 0pt;
@@ -211,7 +230,6 @@ label {
     display: none !important;
 }
 #editor-modifiers {
-    max-width: 600px;
     overflow-y: auto;
     overflow-x: hidden;
 }
@@ -437,6 +455,17 @@ img {
 .secondaryButton:hover {
     background: rgb(177, 27, 0);
 }
+.useSettings {
+    background: var(--accent-color);
+    border: 1px solid var(--accent-color);
+    color: rgb(255, 221, 255);
+    padding: 3pt 6pt;
+    margin-right: 6pt;
+    float: right;
+}
+.useSettings:hover {
+    background: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 6%));
+}
 .stopTask {
     float: right;
 }
@@ -490,6 +519,10 @@ img {
     border-radius: 6px 0px;
 }
 
+#editor-settings {
+    min-width: 350px;
+}
+
 #editor-settings-entries {
     display: flex;
     flex-direction: column;
@@ -579,11 +612,66 @@ input::file-selector-button {
     height: 19px;
 }
 
-/* MOBILE SUPPORT */
-@media screen and (max-width: 700px) {
+
+.input-toggle {
+    display: inline-block;
+    position: relative;
+    vertical-align: middle;
+    width: calc(var(--input-height) * 2);
+    user-select: none;
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    margin-right: 4px;
+}
+.input-toggle > input {
+    position: absolute;
+    opacity: 0;
+    pointer-events: none;
+}
+.input-toggle > label {
+    display: block;
+    overflow: hidden;
+    cursor: pointer;
+    height: var(--input-height);
+    padding: 0;
+    line-height: var(--input-height);
+    border: var(--input-border-size) solid var(--input-border-color);
+    border-radius: var(--input-height);
+    background: var(--input-background-color);
+    transition: background 0.2s ease-in;
+}
+.input-toggle > label:before {
+    content: "";
+    display: block;
+    width: calc(var(--input-height) - ((var(--input-border-size) + var(--input-switch-padding)) * 2));
+    margin: 0px;
+    background: var(--input-text-color);
+    position: absolute;
+    top: calc(var(--input-border-size) + var(--input-switch-padding));
+    bottom: calc(var(--input-border-size) + var(--input-switch-padding));
+    right: calc(var(--input-border-size) + var(--input-switch-padding) + var(--input-height));
+    border-radius: calc(var(--input-height) - ((var(--input-border-size) + var(--input-switch-padding)) * 2));
+    transition: all 0.2s ease-in 0s; 
+    opacity: 0.8;
+}
+.input-toggle > input:checked + label {
+    background: var(--accent-color);
+}
+.input-toggle > input:checked + label:before {
+    right: calc(var(--input-border-size) + var(--input-switch-padding));
+    opacity: 1;
+}
+
+/* Small screens */
+@media screen and (max-width: 1265px) {
     #top-nav {
         flex-direction: column;
     }
+}
+
+/* MOBILE SUPPORT */
+@media screen and (max-width: 700px) {
     body {
         margin: 0px;
     }
@@ -620,6 +708,9 @@ input::file-selector-button {
     #editor {
         padding: 16px 8px;
     }
+    #editor-settings {
+        min-width: 0px;
+    }
     .tab-content-inner {
         margin: 0px;
     }
@@ -630,7 +721,7 @@ input::file-selector-button {
         padding-right: 0px;
     }
     #server-status {
-        display: none;
+        top: 75%;
     }
     .popup > div {
         padding-left: 5px !important;
@@ -643,21 +734,24 @@ input::file-selector-button {
         padding: 0px !important;
         margin: 24px !important;
     }
-    .simple-tooltip.right {
-        right: initial;
-        left: 0px;
-        top: 50%;
-        transform: translate(calc(-100% + 15%), -50%);
+    .simple-tooltip {
+        display: none;
     }
-    :hover > .simple-tooltip.right {
-        transform: translate(100%, -50%);
+}
+
+@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;
-    } */
+    }*/
     .float-container {
         padding: 20px;
     }
@@ -898,6 +992,9 @@ input::file-selector-button {
 i.active {
     background: var(--accent-color);
 }
+.primaryButton.active {
+    background: hsl(var(--accent-hue), 100%, 50%);
+}
 #system-info {
     max-width: 800px;
     font-size: 10pt;
@@ -910,6 +1007,25 @@ i.active {
     float: right;
     font-weight: bold;
 }
-#save-system-settings-btn {
+button:hover {
+  transition-duration: 0.1s;
+  background: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 6%));
+}
+
+button:active {
+  transition-duration: 0.1s;
+  background-color: hsl(var(--accent-hue), 100%, calc(var(--accent-lightness) + 24%));
+  position: relative;
+  top: 1px;
+  left: 1px;
+}
+
+button#save-system-settings-btn {
     padding: 4pt 8pt;
 }
+#ip-info a {
+    color:var(--text-color)
+}
+#ip-info div {
+    line-height: 200%;
+}

ui/media/css/themes.css

@@ -1,22 +1,26 @@
 :root {
-    --background-color1: rgb(32, 33, 36); /* main parts of the page */
-    --background-color2: rgb(44, 45, 48); /* main panels */
-    --background-color3: rgb(47, 49, 53);
-    --background-color4: rgb(18, 18, 19); /* settings dropdowns */
+    --main-hue: 222;
+	--main-saturation: 4%;
+	--value-base: 13%;
+	--value-step: 5%;
+	--background-color1: hsl(var(--main-hue), var(--main-saturation), var(--value-base));
+	--background-color2: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (1 * var(--value-step))));
+	--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (0.5 * var(--value-step))));
+	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (1.5 * var(--value-step))));
 
-    --accent-hue: 266;
+    --accent-hue: 267;
     --accent-lightness: 36%;
     --accent-lightness-hover: 40%;
 
     --text-color: #eee;
 
-    --input-text-color: black;
-    --input-background-color: #e9e9ed; 
-    --input-border-color: #8f8f9d; 
+	--input-text-color: #eee;
+    --input-background-color: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (0.7 * var(--value-step))));
+	--input-border-color: var(--background-color4);
 
     --button-text-color: var(--input-text-color);
-    --button-color: #e9e9ed;
-    --button-border: 1px solid #8f8f9d;
+    --button-color: var(--accent-color);
+    --button-border: none;
 
     /* other */
     --input-border-radius: 4px;
@@ -24,6 +28,11 @@
     --accent-color: hsl(var(--accent-hue), 100%, var(--accent-lightness));
     --accent-color-hover: hsl(var(--accent-hue), 100%, var(--accent-lightness-hover));
     --primary-button-border: none;
+    --input-switch-padding: 1px;
+    --input-height: 18px;
+
+    /* Main theme color, hex color fallback. */
+    --theme-color-fallback: #673AB6;
 }
 
 .theme-light {
@@ -33,10 +42,13 @@
     --background-color4: #cccccc;
 
     --text-color: black;
+    --button-text-color: white;
 
     --input-text-color: black;
     --input-background-color: #f8f9fa; 
     --input-border-color: grey; 
+
+    --theme-color-fallback: #aaaaaa;
 }
 
 .theme-discord {
@@ -47,15 +59,12 @@
     
     --accent-hue: 235;
     --accent-lightness: 65%;
-    --primary-button-border: none;
 
-    --button-color: var(--accent-color);
-    --button-border: none;
-
-    --input-text-color: #ccc;
     --input-border-size: 2px; 
     --input-background-color: #202225; 
     --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #202225;
 }
 
 .theme-cool-blue {
@@ -68,16 +77,11 @@
 	--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (2 * var(--value-step))));
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
 
-    --accent-hue: 212;
-    --primary-button-border: none;
-
-    --button-color: var(--accent-color);
-    --button-border: none;
-    
-    --input-border-size: 1px; 
     --input-background-color: var(--background-color3);
-	--input-text-color: #ccc;
-	--input-border-color: var(--background-color4);
+
+    --accent-hue: 212;
+
+    --theme-color-fallback: #0056b8;
 }
 
 
@@ -91,15 +95,9 @@
 	--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (2 * var(--value-step))));
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
 
-    --primary-button-border: none;
-
-    --button-color: var(--accent-color);
-    --button-border: none;
-    
-    --input-border-size: 1px; 
     --input-background-color: var(--background-color3);
-	--input-text-color: #ccc;
-	--input-border-color: var(--background-color4);
+
+    --theme-color-fallback: #5300b8;
 }
 
 .theme-super-dark {
@@ -112,15 +110,10 @@
 	--background-color3: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (2 * var(--value-step))));
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) + (1.4 * var(--value-step))));
 
-    --primary-button-border: none;
-
-    --button-color: var(--accent-color);
-    --button-border: none;
-    
-    --input-border-size: 0px; 
     --input-background-color: var(--background-color3);
-	--input-text-color: #ccc;
-	--input-border-color: var(--background-color4);
+    --input-border-size: 0px;
+
+    --theme-color-fallback: #000000;
 }
 
 .theme-wild {
@@ -134,13 +127,35 @@
 	--background-color4: hsl(var(--main-hue), var(--main-saturation), calc(var(--value-base) - (3 * var(--value-step))));
     
     --accent-hue: 212;
-    --primary-button-border: none;
-
-    --button-color: var(--accent-color);
-    --button-border: none;
     
     --input-border-size: 1px; 
     --input-background-color: hsl(222, var(--main-saturation), calc(var(--value-base) - (2 * var(--value-step))));
-	--input-text-color: red;
-	--input-border-color: green;
+    --input-text-color: #FF0000;
+    --input-border-color: #005E05;
 }
+
+.theme-gnomie {
+    --background-color1: #242424;
+    --background-color2: #353535;
+    --background-color3: #494949;
+    --background-color4: #000000;
+    
+    --accent-hue: 213;
+    --accent-lightness: 55%;
+    --accent-color: #2168bf;
+
+    --input-border-radius: 6px;
+    --input-text-color: #ffffff;
+    --input-background-color: #2a2a2a; 
+    --input-border-size: 0px; 
+    --input-border-color: var(--input-background-color); 
+
+    --theme-color-fallback: #2168bf;
+}
+
+.theme-gnomie .panel-box {
+    border: none;
+    box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.25);
+    border-radius: 10px;
+}
+

ui/media/js/auto-save.js

@@ -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,
@@ -213,6 +217,7 @@ function fillSaveSettingsConfigTable() {
             })
         })
     })
+    prettifyInputs(saveSettingsConfigTable)
 }
 
 // configureSettingsSaveBtn
@@ -224,7 +229,7 @@ var autoSaveSettings = document.getElementById("auto_save_settings")
 var configSettingsButton = document.createElement("button")
 configSettingsButton.textContent = "Configure"
 configSettingsButton.style.margin = "0px 5px"
-autoSaveSettings.insertAdjacentElement("afterend", configSettingsButton)
+autoSaveSettings.insertAdjacentElement("beforebegin", configSettingsButton)
 autoSaveSettings.addEventListener("change", () => {
     configSettingsButton.style.display = autoSaveSettings.checked ? "block" : "none"
 })

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
@@ -161,18 +168,7 @@ const TASK_MAPPING = {
         setUI: (use_stable_diffusion_model) => {
             const oldVal = stableDiffusionModelField.value
 
-            let pathIdx = use_stable_diffusion_model.lastIndexOf('/') // Linux, Mac paths
-            if (pathIdx < 0) {
-                pathIdx = use_stable_diffusion_model.lastIndexOf('\\') // Windows paths.
-            }
-            if (pathIdx >= 0) {
-                use_stable_diffusion_model = use_stable_diffusion_model.slice(pathIdx + 1)
-            }
-            const modelExt = '.ckpt'
-            if (use_stable_diffusion_model.endsWith(modelExt)) {
-                use_stable_diffusion_model = use_stable_diffusion_model.slice(0, use_stable_diffusion_model.length - modelExt.length)
-            }
-
+            use_stable_diffusion_model = getModelPath(use_stable_diffusion_model, ['.ckpt'])
             stableDiffusionModelField.value = use_stable_diffusion_model
 
             if (!stableDiffusionModelField.value) {
@@ -182,10 +178,23 @@ const TASK_MAPPING = {
         readUI: () => stableDiffusionModelField.value,
         parse: (val) => val
     },
+    use_vae_model: { name: 'VAE model',
+        setUI: (use_vae_model) => {
+            const oldVal = vaeModelField.value
 
-    numOutputsParallel: { name: 'Parallel Images',
-        setUI: (numOutputsParallel) => {
-            numOutputsParallelField.value = numOutputsParallel
+            if (use_vae_model !== '') {
+                use_vae_model = getModelPath(use_vae_model, ['.vae.pt', '.ckpt'])
+                use_vae_model = use_vae_model !== '' ? use_vae_model : oldVal
+            }
+            vaeModelField.value = use_vae_model
+        },
+        readUI: () => vaeModelField.value,
+        parse: (val) => val
+    },
+
+    num_outputs: { name: 'Parallel Images',
+        setUI: (num_outputs) => {
+            numOutputsParallelField.value = num_outputs
         },
         readUI: () => parseInt(numOutputsParallelField.value),
         parse: (val) => val
@@ -243,7 +252,9 @@ const TASK_MAPPING = {
         parse: (val) => val
     }
 }
-function restoreTaskToUI(task) {
+function restoreTaskToUI(task, fieldsToSkip) {
+    fieldsToSkip = fieldsToSkip || []
+
     if ('numOutputsTotal' in task) {
         numOutputsTotalField.value = task.numOutputsTotal
     }
@@ -255,10 +266,42 @@ function restoreTaskToUI(task) {
         return
     }
     for (const key in TASK_MAPPING) {
-        if (key in task.reqBody) {
+        if (key in task.reqBody && !fieldsToSkip.includes(key)) {
             TASK_MAPPING[key].setUI(task.reqBody[key])
         }
     }
+
+    // restore the original tag
+    promptField.value = task.reqBody.original_prompt || task.reqBody.prompt
+
+    // properly reset checkboxes
+    if (!('use_face_correction' in task.reqBody)) {
+        useFaceCorrectionField.checked = false
+    }
+    if (!('use_upscale' in task.reqBody)) {
+        useUpscalingField.checked = false
+    }
+    if (!('mask' in task.reqBody)) {
+        maskSetting.checked = false
+    }
+    upscaleModelField.disabled = !useUpscalingField.checked
+
+    // Show the source picture if present
+    initImagePreview.src = (task.reqBody.init_image == undefined ? '' : task.reqBody.init_image)
+    if (IMAGE_REGEX.test(initImagePreview.src)) {
+        Boolean(task.reqBody.mask) ? inpaintingEditor.setImg(task.reqBody.mask) : inpaintingEditor.resetBackground()
+        initImagePreviewContainer.style.display = 'block'
+        inpaintingEditorContainer.style.display = 'none'
+        promptStrengthContainer.style.display = 'table-row'
+        //samplerSelectionContainer.style.display = 'none'
+        // maskSetting.checked = false
+        inpaintingEditorContainer.style.display = maskSetting.checked ? 'block' : 'none'
+    } else {
+        initImagePreviewContainer.style.display = 'none'
+        // inpaintingEditorContainer.style.display = 'none'
+        promptStrengthContainer.style.display = 'none'
+        // maskSetting.style.display = 'none'
+    }
 }
 function readUI() {
     const reqBody = {}
@@ -271,6 +314,22 @@ function readUI() {
         'reqBody': reqBody
     }
 }
+function getModelPath(filename, extensions)
+{
+    let pathIdx = filename.lastIndexOf('/') // Linux, Mac paths
+    if (pathIdx < 0) {
+        pathIdx = filename.lastIndexOf('\\') // Windows paths.
+    }
+    if (pathIdx >= 0) {
+        filename = filename.slice(pathIdx + 1)
+    }
+    extensions.forEach(ext => {
+        if (filename.endsWith(ext)) {
+            filename = filename.slice(0, filename.length - ext.length)
+        }
+    })
+    return filename
+}
 
 const TASK_TEXT_MAPPING = {
     width: 'Width',
@@ -326,29 +385,35 @@ function parseTaskFromText(str) {
     return task
 }
 
-async function readFile(file, i) {
-    const fileContent = (await file.text()).trim()
-
-    // JSON File.
-    if (fileContent.startsWith('{') && fileContent.endsWith('}')) {
+async function parseContent(text) {
+    text = text.trim();
+    if (text.startsWith('{') && text.endsWith('}')) {
         try {
-            const task = JSON.parse(fileContent)
+            const task = JSON.parse(text)
             restoreTaskToUI(task)
+            return true
         } catch (e) {
-            console.warn(`file[${i}]:${file.name} - File couldn't be parsed.`, e)
+            console.warn(`JSON text content couldn't be parsed.`, e)
         }
-        return
+        return false
     }
-
     // Normal txt file.
-    const task = parseTaskFromText(fileContent)
+    const task = parseTaskFromText(text)
     if (task) {
         restoreTaskToUI(task)
+        return true
     } else {
-        console.warn(`file[${i}]:${file.name} - File couldn't be parsed.`)
+        console.warn(`Raw text content couldn't be parsed.`)
+        return false
     }
 }
 
+async function readFile(file, i) {
+    console.log(`Event %o reading file[${i}]:${file.name}...`)
+    const fileContent = (await file.text()).trim()
+    return await parseContent(fileContent)
+}
+
 function dropHandler(ev) {
     console.log('Content dropped...')
     let items = []
@@ -395,35 +460,52 @@ const TASK_REQ_NO_EXPORT = [
     "use_full_precision",
     "save_to_disk_path"
 ]
+const resetSettings = document.getElementById('reset-image-settings')
 
-// Retrieve clipboard content and try to parse it 
-async function pasteFromClipboard() {
-    //const text = await navigator.clipboard.readText()
-    let text = await navigator.clipboard.readText();
-    text=text.trim();
-    if (text.startsWith('{') && text.endsWith('}')) {
-        try {
-            const task = JSON.parse(text)
-            restoreTaskToUI(task)
-        } catch (e) {
-            console.warn(`Clipboard JSON couldn't be parsed.`, e)
-        }
+function checkReadTextClipboardPermission (result) {
+    if (result.state != "granted" && result.state != "prompt") {
         return
     }
-    // Normal txt file.
-    const task = parseTaskFromText(text)
-    if (task) {
-        restoreTaskToUI(task)
-    } else {
-        console.warn(`Clipboard content - File couldn't be parsed.`)
-    }
+    // PASTE ICON
+    const pasteIcon = document.createElement('i')
+    pasteIcon.className = 'fa-solid fa-paste section-button'
+    pasteIcon.innerHTML = `<span class="simple-tooltip right">Paste Image Settings</span>`
+    pasteIcon.addEventListener('click', async (event) => {
+        event.stopPropagation()
+        // Add css class 'active'
+        pasteIcon.classList.add('active')
+        // In 350 ms remove the 'active' class
+        asyncDelay(350).then(() => pasteIcon.classList.remove('active'))
+
+        // Retrieve clipboard content and try to parse it
+        const text = await navigator.clipboard.readText();
+        await parseContent(text)
+    })
+    resetSettings.parentNode.insertBefore(pasteIcon, resetSettings)
 }
+navigator.permissions.query({ name: "clipboard-read" }).then(checkReadTextClipboardPermission, (reason) => console.log('clipboard-read is not available. %o', reason))
+
+document.addEventListener('paste', async (event) => {
+    if (event.target) {
+        const targetTag = event.target.tagName.toLowerCase()
+        // Disable when targeting input elements.
+        if (targetTag === 'input' || targetTag === 'textarea') {
+            return
+        }
+    }
+    const paste = (event.clipboardData || window.clipboardData).getData('text')
+    const selection = window.getSelection()
+    if (selection.toString().trim().length <= 0 && await parseContent(paste)) {
+        event.preventDefault()
+        return
+    }
+})
 
 // Adds a copy and a paste icon if the browser grants permission to write to clipboard.
 function checkWriteToClipboardPermission (result) {
-    if (result.state == "granted" || result.state == "prompt") {
-        const resetSettings = document.getElementById('reset-image-settings')
-
+    if (result.state != "granted" && result.state != "prompt") {
+        return
+    }
     // COPY ICON
     const copyIcon = document.createElement('i')
     copyIcon.className = 'fa-solid fa-clipboard section-button'
@@ -432,8 +514,8 @@ function checkWriteToClipboardPermission (result) {
         event.stopPropagation()
         // Add css class 'active'
         copyIcon.classList.add('active')
-	    // In 1000 ms remove the 'active' class
-	    asyncDelay(1000).then(() => copyIcon.classList.remove('active'))
+        // In 350 ms remove the 'active' class
+        asyncDelay(350).then(() => copyIcon.classList.remove('active'))
         const uiState = readUI()
         TASK_REQ_NO_EXPORT.forEach((key) => delete uiState.reqBody[key])
         if (uiState.reqBody.init_image && !IMAGE_REGEX.test(uiState.reqBody.init_image)) {
@@ -443,23 +525,7 @@ function checkWriteToClipboardPermission (result) {
         navigator.clipboard.writeText(JSON.stringify(uiState, undefined, 4))
     })
     resetSettings.parentNode.insertBefore(copyIcon, resetSettings)
-
-        // PASTE ICON
-        const pasteIcon = document.createElement('i')
-        pasteIcon.className = 'fa-solid fa-paste section-button'
-        pasteIcon.innerHTML = `<span class="simple-tooltip right">Paste Image Settings</span>`
-        pasteIcon.addEventListener('click', (event) => {
-            event.stopPropagation()
-	    // Add css class 'active'
-	    pasteIcon.classList.add('active')
-	    // In 1000 ms remove the 'active' class
-	    asyncDelay(1000).then(() => pasteIcon.classList.remove('active'))
-            pasteFromClipboard()
-        })
-        resetSettings.parentNode.insertBefore(pasteIcon, resetSettings)
-    }
 }
-
 // Determine which access we have to the clipboard. Clipboard access is only available on localhost or via TLS.
 navigator.permissions.query({ name: "clipboard-write" }).then(checkWriteToClipboardPermission, (e) => {
     if (e instanceof TypeError && typeof navigator?.clipboard?.writeText === 'function') {

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')
@@ -148,6 +148,60 @@ async function loadModifiers() {
     loadCustomModifiers()
 }
 
+function refreshModifiersState(newTags) {
+    // clear existing modifiers
+    document.querySelector('#editor-modifiers').querySelectorAll('.modifier-card').forEach(modifierCard => {
+        const modifierName = modifierCard.querySelector('.modifier-card-label').innerText
+        if (activeTags.map(x => x.name).includes(modifierName)) {
+            modifierCard.classList.remove(activeCardClass)
+            modifierCard.querySelector('.modifier-card-image-overlay').innerText = '+'
+        }
+    })
+    activeTags = []
+
+    // set new modifiers
+    newTags.forEach(tag => {
+        let found = false
+        document.querySelector('#editor-modifiers').querySelectorAll('.modifier-card').forEach(modifierCard => {
+            const modifierName = modifierCard.querySelector('.modifier-card-label').innerText
+            if (tag == modifierName) {
+                // add modifier to active array
+                if (!activeTags.map(x => x.name).includes(tag)) { // only add each tag once even if several custom modifier cards share the same tag
+                    activeTags.push({
+                        'name': modifierName,
+                        'element': modifierCard.cloneNode(true),
+                        'originElement': modifierCard
+                    })
+                }
+                modifierCard.classList.add(activeCardClass)
+                modifierCard.querySelector('.modifier-card-image-overlay').innerText = '-'
+                found = true
+            }
+        })
+        if (found == false) { // custom tag went missing, create one here
+            let modifierCard = createModifierCard(tag, undefined) // create a modifier card for the missing tag, no image
+            
+            modifierCard.addEventListener('click', () => {
+                if (activeTags.map(x => x.name).includes(tag)) {
+                    // remove modifier from active array
+                    activeTags = activeTags.filter(x => x.name != tag)
+                    modifierCard.classList.remove(activeCardClass)
+
+                    modifierCard.querySelector('.modifier-card-image-overlay').innerText = '+'
+                }
+                refreshTagsList()
+            })
+
+            activeTags.push({
+                'name': tag,
+                'element': modifierCard,
+                'originElement': undefined  // no origin element for missing tags
+            })
+        }
+    })
+    refreshTagsList()
+}
+
 function refreshTagsList() {
     editorModifierTagsList.innerHTML = ''
 
@@ -167,9 +221,8 @@ function refreshTagsList() {
         tag.element.addEventListener('click', () => {
             let idx = activeTags.indexOf(tag)
 
-            if (idx !== -1) {
-                activeTags[idx].originElement.classList.remove(activeCardClass)
-                activeTags[idx].originElement.querySelector('.modifier-card-image-overlay').innerText = '+'
+            if (idx !== -1 && activeTags[idx].originElement !== undefined) {
+                toggleCardState(activeTags[idx].originElement, false)
 
                 activeTags.splice(idx, 1)
                 refreshTagsList()
@@ -182,6 +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')
 

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) {
@@ -789,7 +790,9 @@ function getCurrentUserRequest() {
             stream_progress_updates: true,
             stream_image_progress: (numOutputsTotal > 50 ? false : streamImageProgressField.checked),
             show_only_filtered_image: showOnlyFilteredImageField.checked,
-            output_format: outputFormatField.value
+            output_format: outputFormatField.value,
+            original_prompt: promptField.value,
+            active_tags: (activeTags.map(x => x.name))
         }
     }
     if (IMAGE_REGEX.test(initImagePreview.src)) {
@@ -821,8 +824,17 @@ function getCurrentUserRequest() {
 function makeImage() {
     if (!isServerAvailable()) {
         alert('The server is not available.')
-        return
-    }
+    } else if (!randomSeedField.checked && seedField.value == '') {
+        alert('The "Seed" field must not be empty.')
+    } else if (numOutputsTotalField.value == '') {
+        alert('The "Number of Images" field must not be empty.')
+    } else if (numOutputsParallelField.value == '') {
+        alert('The "Number of parallel Images" field must not be empty.')
+    } else if (numInferenceStepsField.value == '') {
+        alert('The "Inference Steps" field must not be empty.')
+    } else if (guidanceScaleField.value == '') {
+        alert('The Guidance Scale field must not be empty.')
+    } else {
         const taskTemplate = getCurrentUserRequest()
         const newTaskRequests = []
         getPrompts().forEach((prompt) => newTaskRequests.push(Object.assign({}, taskTemplate, {
@@ -831,6 +843,7 @@ function makeImage() {
         newTaskRequests.forEach(createTask)
 
         initialText.style.display = 'none'
+    }
 }
 
 function createTask(task) {
@@ -856,6 +869,7 @@ function createTask(task) {
     taskEntry.innerHTML = ` <div class="header-content panel collapsible active">
                                 <div class="taskStatusLabel">Enqueued</div>
                                 <button class="secondaryButton stopTask"><i class="fa-solid fa-trash-can"></i> Remove</button>
+                                <button class="secondaryButton useSettings"><i class="fa-solid fa-redo"></i> Use these settings</button>
                                 <div class="preview-prompt collapsible active"></div>
                                 <div class="taskConfig">${taskConfig}</div>
                                 <div class="outputMsg"></div>
@@ -874,8 +888,9 @@ function createTask(task) {
     task['progressBar'] = taskEntry.querySelector('.progress-bar')
     task['stopTask'] = taskEntry.querySelector('.stopTask')
 
-    task['stopTask'].addEventListener('click', async function(e) {
-        e.stopPropagation()
+    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")
@@ -890,9 +905,16 @@ function createTask(task) {
                     taskQueue.splice(idx, 1)
                 }
 
-            taskEntry.remove()
+                removeTask(taskEntry)
             }
         })
+    })
+
+    task['useSettings'] = taskEntry.querySelector('.useSettings')
+    task['useSettings'].addEventListener('click', function(e) {
+        e.stopPropagation()
+        restoreTaskToUI(task, TASK_REQ_NO_EXPORT)
+    })
 
     imagePreview.insertBefore(taskEntry, previewTools.nextSibling)
 
@@ -1028,21 +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)
@@ -1079,10 +1105,6 @@ useUpscalingField.addEventListener('change', function(e) {
     upscaleModelField.disabled = !this.checked
 })
 
-if (useBetaChannelField.checked) {
-    updateBranchLabel.innerText = "(beta)"
-}
-
 makeImageBtn.addEventListener('click', makeImage)
 
 document.onkeydown = function(e) {
@@ -1141,8 +1163,16 @@ async function getModels() {
         let res = await fetch('/get/models')
         const models = await res.json()
 
-        console.log('get models response', models)
+        console.log('got models response', models)
 
+        if ( "scan-error" in models ) {
+            // let previewPane = document.getElementById('tab-content-wrapper')
+            let previewPane = document.getElementById('preview')
+            previewPane.style.background="red"
+            previewPane.style.textAlign="center"
+            previewPane.innerHTML = '<H1>🔥Malware alert!🔥</H1><h2>The file <i>' + models['scan-error'] + '</i> in your <tt>models/stable-diffusion</tt> folder is probably malware infected.</h2><h2>Please delete this file from the folder before proceeding!</h2>After deleting the file, reload this page.<br><br><button onClick="window.location.reload();">Reload Page</button>'
+            makeImageBtn.disabled = true
+        }
         let modelOptions = models['options']
         let stableDiffusionOptions = modelOptions['stable-diffusion']
         let vaeOptions = modelOptions['vae']
@@ -1284,7 +1314,7 @@ document.querySelectorAll('.popup').forEach(popup => {
 })
 
 var tabElements = [];
-document.querySelectorAll(".tab").forEach(tab => {
+function linkTabContents(tab) {
     var name = tab.id.replace("tab-", "");
     var content = document.getElementById(`tab-content-${name}`)
     tabElements.push({
@@ -1305,7 +1335,9 @@ document.querySelectorAll(".tab").forEach(tab => {
             content.classList.toggle("active")
         }
     })
-})
+}
+
+document.querySelectorAll(".tab").forEach(linkTabContents)
 
 window.addEventListener("beforeunload", function(e) {
     const msg = "Unsaved pictures will be lost!";
@@ -1321,3 +1353,4 @@ window.addEventListener("beforeunload", function(e) {
 });
 
 createCollapsibles()
+prettifyInputs(document);

ui/media/js/parameters.js

@@ -28,18 +28,21 @@ var PARAMETERS = [
         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,
     },
     {
@@ -55,6 +58,7 @@ var PARAMETERS = [
         type: ParameterType.checkbox,
         label: "Enable Sound",
         note: "plays a sound on task completion",
+        icon: "fa-volume-low",
         default: true,
     },
     {
@@ -62,20 +66,23 @@ var PARAMETERS = [
         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",
-		default: true,
         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,
     },
     {
@@ -96,6 +103,7 @@ var PARAMETERS = [
         type: ParameterType.checkbox,
         label: "Use Full Precision",
         note: "for GPU-only. warning: this will consume more VRAM",
+        icon: "fa-crosshairs",
         default: false,
     },
     {
@@ -103,13 +111,49 @@ var PARAMETERS = [
         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",
+        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,
     },
 ];
@@ -140,16 +184,18 @@ function getParameterElement(parameter) {
     }
 }
 
-let parametersTable = document.querySelector("#system-settings table")
+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 newrow = document.createElement('tr')
+        var icon = parameter.icon ? `<i class="fa ${parameter.icon}"></i>` : "";
+        var newrow = document.createElement('div')
         newrow.innerHTML = `
-			<td><label for="${parameter.id}">${parameter.label}</label></td>
-			<td><div>${element}${note}<div></td>`
+            <div>${icon}</div>
+            <div><label for="${parameter.id}">${parameter.label}</label>${note}</div>
+            <div>${element}</div>`
         parametersTable.appendChild(newrow)
         parameter.settingsEntry = newrow
     })
@@ -164,11 +210,16 @@ let useGPUsField = document.querySelector('#use_gpus')
 let useFullPrecisionField = document.querySelector('#use_full_precision')
 let saveToDiskField = document.querySelector('#save_to_disk')
 let diskPathField = document.querySelector('#diskPath')
+let listenToNetworkField = document.querySelector("#listen_to_network")
+let listenPortField = document.querySelector("#listen_port")
+let testSD2Field = document.querySelector("#test_sd2")
 let useBetaChannelField = document.querySelector("#use_beta_channel")
 let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
+let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
 
 let saveSettingsBtn = document.querySelector('#save-system-settings-btn')
 
+
 async function changeAppConfig(configDelta) {
     try {
         let res = await fetch('/app_config', {
@@ -193,10 +244,23 @@ async function getAppConfig() {
 
         if (config.update_branch === 'beta') {
             useBetaChannelField.checked = true
+            document.querySelector("#updateBranchLabel").innerText = "(beta)"
         }
         if (config.ui && config.ui.open_browser_on_start === false) {
             uiOpenBrowserOnStartField.checked = false
         }
+        if ('test_sd2' in config) {
+            testSD2Field.checked = config['test_sd2']
+        }
+
+        let testSD2SettingEntry = getParameterSettingsEntry('test_sd2')
+        testSD2SettingEntry.style.display = (config.update_branch === 'beta' ? '' : 'none')
+        if (config.net && config.net.listen_to_network === false) {
+            listenToNetworkField.checked = false
+        }
+        if (config.net && config.net.listen_port !== undefined) {
+            listenPortField.value = config.net.listen_port
+        }
 
         console.log('get config status response', config)
     } catch (e) {
@@ -273,14 +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
@@ -298,11 +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)
             })
@@ -313,6 +408,9 @@ async function getDevices() {
             } else {
                 $('#use_gpus').val(activeDeviceIds)
             }
+
+            setDeviceInfo(devices)
+            setHostInfo(hosts)
         }
     } catch (e) {
         console.log('error fetching devices', e)
@@ -322,9 +420,21 @@ async function getDevices() {
 saveSettingsBtn.addEventListener('click', function() {
     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
+            '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'))
 })

ui/media/js/themes.js

@@ -14,7 +14,7 @@ function initTheme() {
         .flatMap(sheet => Array.from(sheet.cssRules))
         .forEach(rule => {
             var selector = rule.selectorText; // TODO: also do selector == ":root", re-run un-set props
-            if (selector && selector.startsWith(".theme-")) {
+            if (selector && selector.startsWith(".theme-") && !selector.includes(" ")) {
                 var theme_key = selector.substring(1);
                 THEMES.push({
                     key: theme_key,
@@ -60,6 +60,7 @@ function themeFieldChanged() {
 
     body.style = "";
     var theme = THEMES.find(t => t.key == theme_key);
+    let borderColor = undefined
     if (theme) {
         // refresh variables incase they are back referencing
         Array.from(DEFAULT_THEME.rule.style)
@@ -67,7 +68,14 @@ function themeFieldChanged() {
             .forEach(cssVariable => {
             body.style.setProperty(cssVariable, DEFAULT_THEME.rule.style.getPropertyValue(cssVariable));
         });
+        borderColor = theme.rule.style.getPropertyValue('--input-border-color').trim()
+        if (!borderColor.startsWith('#')) {
+            borderColor = theme.rule.style.getPropertyValue('--theme-color-fallback')
         }
+    } else {
+        borderColor = DEFAULT_THEME.rule.style.getPropertyValue('--theme-color-fallback')
+    }
+    document.querySelector('meta[name="theme-color"]').setAttribute("content", borderColor)
 }
 
 themeField.addEventListener('change', themeFieldChanged);

ui/media/js/utils.js

@@ -358,3 +358,19 @@ function preventNonNumericalInput(e) {
         e.preventDefault();
     }
 }
+
+/* inserts custom html to allow prettifying of inputs */
+function prettifyInputs(root_element) {
+    root_element.querySelectorAll(`input[type="checkbox"]`).forEach(element => {
+        var parent = element.parentNode;
+        if (!parent.classList.contains("input-toggle")) {
+            var wrapper = document.createElement("div");
+            wrapper.classList.add("input-toggle");
+            parent.replaceChild(wrapper, element);
+            wrapper.appendChild(element);
+            var label = document.createElement("label");
+            label.htmlFor = element.id;
+            wrapper.appendChild(label);
+        }
+    })
+}

ui/media/manifest.webmanifest

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

ui/plugins/ui/Autoscroll.plugin.js

@@ -0,0 +1,42 @@
+(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();
+        }
+    }
+})()

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

@@ -0,0 +1,91 @@
+(function () {
+    "use strict"
+
+    var styleSheet = document.createElement("style");
+    styleSheet.textContent = `
+        .modifier-card-tiny.drag-sort-active {
+            background: transparent;
+            border: 2px dashed white;
+            opacity:0.2;
+        }
+    `;
+    document.head.appendChild(styleSheet);
+
+    // observe for changes in tag list
+    var observer = new MutationObserver(function (mutations) {
+    //    mutations.forEach(function (mutation) {
+            if (editorModifierTagsList.childNodes.length > 0) {
+                ModifierDragAndDrop(editorModifierTagsList)
+            }
+    //    })
+    })
+    
+    observer.observe(editorModifierTagsList, {
+            childList: true
+    })
+
+    let current
+    function ModifierDragAndDrop(target) {
+        let overlays = document.querySelector('#editor-inputs-tags-list').querySelectorAll('.modifier-card-overlay')
+        overlays.forEach (i => {
+            i.parentElement.draggable = true;
+            
+            i.parentElement.ondragstart = (e) => {
+                current = i
+                i.parentElement.getElementsByClassName('modifier-card-image-overlay')[0].innerText = ''
+                i.parentElement.draggable = true
+                i.parentElement.classList.add('drag-sort-active')
+                for(let item of document.querySelector('#editor-inputs-tags-list').getElementsByClassName('modifier-card-image-overlay')) {
+                    if (item.parentElement.parentElement.getElementsByClassName('modifier-card-overlay')[0] != current) {
+                        item.parentElement.parentElement.getElementsByClassName('modifier-card-image-overlay')[0].style.opacity = 0
+                        if(item.parentElement.getElementsByClassName('modifier-card-image').length > 0) {
+                            item.parentElement.getElementsByClassName('modifier-card-image')[0].style.filter = 'none'
+                        }
+                        item.parentElement.parentElement.style.transform = 'none'
+                        item.parentElement.parentElement.style.boxShadow = 'none'
+                    }
+                    item.innerText = ''
+                }
+            }
+            
+            i.ondragenter = (e) => {
+                e.preventDefault()
+                if (i != current) {
+                    let currentPos = 0, droppedPos = 0;
+                    for (let it = 0; it < overlays.length; it++) {
+                        if (current == overlays[it]) { currentPos = it; }
+                        if (i == overlays[it]) { droppedPos = it; }
+                    }
+
+                    if (i.parentElement != current.parentElement) {
+                        let currentPos = 0, droppedPos = 0
+                        for (let it = 0; it < overlays.length; it++) {
+                            if (current == overlays[it]) { currentPos = it }
+                            if (i == overlays[it]) { droppedPos = it }
+                        }
+                        if (currentPos < droppedPos) {
+                            current = i.parentElement.parentNode.insertBefore(current.parentElement, i.parentElement.nextSibling).getElementsByClassName('modifier-card-overlay')[0]
+                        } else {
+                            current = i.parentElement.parentNode.insertBefore(current.parentElement, i.parentElement).getElementsByClassName('modifier-card-overlay')[0]
+                        }
+                        // update activeTags
+                        const tag = activeTags.splice(currentPos, 1)
+                        activeTags.splice(droppedPos, 0, tag[0])
+                    }
+                }
+            };
+
+            i.ondragover = (e) => {
+                e.preventDefault()
+            }
+            
+            i.parentElement.ondragend = (e) => {
+                i.parentElement.classList.remove('drag-sort-active')
+                for(let item of document.querySelector('#editor-inputs-tags-list').getElementsByClassName('modifier-card-image-overlay')) {
+                    item.style.opacity = ''
+                    item.innerText = '-'
+                }
+            }
+        })
+    }
+})()

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

@@ -0,0 +1,62 @@
+(function () {
+    "use strict"
+
+    // observe for changes in tag list
+    var observer = new MutationObserver(function (mutations) {
+    //    mutations.forEach(function (mutation) {
+            if (editorModifierTagsList.childNodes.length > 0) {
+                ModifierMouseWheel(editorModifierTagsList)
+            }
+    //    })
+    })
+    
+    observer.observe(editorModifierTagsList, {
+            childList: true
+    })
+
+    function ModifierMouseWheel(target) {
+        let overlays = document.querySelector('#editor-inputs-tags-list').querySelectorAll('.modifier-card-overlay')
+        overlays.forEach (i => {
+            i.onwheel = (e) => {
+                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 + ']'
+                            }
+                        }
+                    }
+                    i.parentElement.getElementsByClassName('modifier-card-label')[0].getElementsByTagName("p")[0].innerText = s
+                    // update activeTags
+                    for (let it = 0; it < overlays.length; it++) {
+                        if (i == overlays[it]) {
+                            activeTags[it].name = s
+                            break
+                        }
+                    }
+                }
+            }
+        })
+    }
+})()

ui/plugins/ui/Put UI plugins here (developer team only).txt

@@ -0,0 +1,3 @@
+Custom plugins in this folder will be shipped to all the users by default.
+
+This allows UI features to be built as plugins (testing our Plugins API, and keeping our core lean and modular).

ui/plugins/ui/release-notes.plugin.js

@@ -0,0 +1,47 @@
+(function() {
+    document.querySelector('#tab-container').insertAdjacentHTML('beforeend', `
+        <span id="tab-news" class="tab">
+            <span><i class="fa fa-bolt icon"></i> What's new?</span>
+        </span>
+    `)
+
+    document.querySelector('#tab-content-wrapper').insertAdjacentHTML('beforeend', `
+        <div id="tab-content-news" class="tab-content">
+            <div id="news" class="tab-content-inner">
+                Loading..
+            </div>
+        </div>
+    `)
+
+    document.querySelector('body').insertAdjacentHTML('beforeend', `
+        <style>
+        #tab-content-news .tab-content-inner {
+            max-width: 100%;
+            text-align: left;
+            padding: 10pt;
+        }
+        </style>
+    `)
+
+    linkTabContents(document.querySelector('#tab-news'))
+
+    let markedScript = document.createElement('script')
+    markedScript.src = '/media/js/marked.min.js'
+
+    markedScript.onload = async function() {
+        let appConfig = await fetch('/get/app_config')
+        appConfig = await appConfig.json()
+
+        let updateBranch = appConfig.update_branch || 'main'
+
+        let news = document.querySelector('#news')
+        let releaseNotes = await fetch(`https://raw.githubusercontent.com/cmdr2/stable-diffusion-ui/${updateBranch}/CHANGES.md`)
+        if (releaseNotes.status != 200) {
+            return
+        }
+        releaseNotes = await releaseNotes.text()
+        news.innerHTML = marked.parse(releaseNotes)
+    }
+
+    document.querySelector('body').appendChild(markedScript)
+})()

ui/sd_internal/ddim_callback.patch

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

ui/sd_internal/ddim_callback_sd2.patch

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

ui/sd_internal/device_manager.py

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

ui/sd_internal/env_yaml.patch

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

ui/sd_internal/runtime.py

@@ -7,6 +7,7 @@ Notes:
 import json
 import os, re
 import traceback
+import queue
 import torch
 import numpy as np
 from gc import collect as gc_collect
@@ -21,13 +22,14 @@ 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():
@@ -140,15 +165,24 @@ def load_model_ckpt():
     _, _ = modelFS.load_state_dict(sd, strict=False)
 
     if thread_data.vae_file is not None:
+        try:
+            loaded = False
             for model_extension in ['.ckpt', '.vae.pt']:
                 if os.path.exists(thread_data.vae_file + model_extension):
                     print(f"Loading VAE weights from: {thread_data.vae_file}{model_extension}")
                     vae_ckpt = torch.load(thread_data.vae_file + model_extension, map_location="cpu")
                     vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
                     modelFS.first_stage_model.load_state_dict(vae_dict, strict=False)
+                    loaded = True
                     break
-            else:
-                print(f'Cannot find VAE file: {thread_data.vae_file}{model_extension}')
+
+            if not loaded:
+                print(f'Cannot find VAE: {thread_data.vae_file}')
+                thread_data.vae_file = None
+        except:
+            print(traceback.format_exc())
+            print(f'Could not load VAE: {thread_data.vae_file}')
+            thread_data.vae_file = None
 
     modelFS.eval()
     # if thread_data.device != 'cpu':
@@ -176,6 +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')
@@ -195,6 +261,7 @@ def unload_models():
     if thread_data.model is not None:
         print('Unloading models...')
         if thread_data.device != 'cpu':
+            if not thread_data.test_sd2:
                 thread_data.modelFS.to('cpu')
                 thread_data.modelCS.to('cpu')
                 thread_data.model.model1.to("cpu")
@@ -210,35 +277,42 @@ def unload_models():
 
     gc()
 
-def wait_model_move_to(model, target_device): # Send to target_device and wait until complete.
-    if thread_data.device == target_device: return
-    start_mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
-    if start_mem <= 0: return
-    model_name = model.__class__.__name__
-    print(f'Device {thread_data.device} - Sending model {model_name} to {target_device} | Memory transfer starting. Memory Used: {round(start_mem)}Mb')
-    start_time = time.time()
-    model.to(target_device)
-    time_step = start_time
-    WARNING_TIMEOUT = 1.5 # seconds - Show activity in console after timeout.
-    last_mem = start_mem
-    is_transfering = True
-    while is_transfering:
-        time.sleep(0.5) # 500ms
-        mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
-        is_transfering = bool(mem > 0 and mem < last_mem) # still stuff loaded, but less than last time.
-        last_mem = mem
-        if not is_transfering:
-            break;
-        if time.time() - time_step > WARNING_TIMEOUT: # Long delay, print to console to show activity.
-            print(f'Device {thread_data.device} - Waiting for Memory transfer. Memory Used: {round(mem)}Mb, Transfered: {round(start_mem - mem)}Mb')
-            time_step = time.time()
-    print(f'Device {thread_data.device} - {model_name} Moved: {round(start_mem - last_mem)}Mb in {round(time.time() - start_time, 3)} seconds to {target_device}')
+# def wait_model_move_to(model, target_device): # Send to target_device and wait until complete.
+#     if thread_data.device == target_device: return
+#     start_mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
+#     if start_mem <= 0: return
+#     model_name = model.__class__.__name__
+#     print(f'Device {thread_data.device} - Sending model {model_name} to {target_device} | Memory transfer starting. Memory Used: {round(start_mem)}Mb')
+#     start_time = time.time()
+#     model.to(target_device)
+#     time_step = start_time
+#     WARNING_TIMEOUT = 1.5 # seconds - Show activity in console after timeout.
+#     last_mem = start_mem
+#     is_transfering = True
+#     while is_transfering:
+#         time.sleep(0.5) # 500ms
+#         mem = torch.cuda.memory_allocated(thread_data.device) / 1e6
+#         is_transfering = bool(mem > 0 and mem < last_mem) # still stuff loaded, but less than last time.
+#         last_mem = mem
+#         if not is_transfering:
+#             break;
+#         if time.time() - time_step > WARNING_TIMEOUT: # Long delay, print to console to show activity.
+#             print(f'Device {thread_data.device} - Waiting for Memory transfer. Memory Used: {round(mem)}Mb, Transfered: {round(start_mem - mem)}Mb')
+#             time_step = time.time()
+#     print(f'Device {thread_data.device} - {model_name} Moved: {round(start_mem - last_mem)}Mb in {round(time.time() - start_time, 3)} seconds to {target_device}')
+
+def move_to_cpu(model):
+    if thread_data.device != "cpu":
+        d = torch.device(thread_data.device)
+        mem = torch.cuda.memory_allocated(d) / 1e6
+        model.to("cpu")
+        while torch.cuda.memory_allocated(d) / 1e6 >= mem:
+            time.sleep(1)
 
 def load_model_gfpgan():
     if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
     model_path = thread_data.gfpgan_file + ".pth"
-    device = 'cuda:0' if force_gfpgan_to_cuda0 else thread_data.device
-    thread_data.model_gfpgan = GFPGANer(device=torch.device(device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    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)
 
 def load_model_real_esrgan():
@@ -288,9 +362,16 @@ def apply_filters(filter_name, image_data, model_path=None):
     print(f'Applying filter {filter_name}...')
     gc() # Free space before loading new data.
 
-    if filter_name == 'gfpgan':
     if isinstance(image_data, torch.Tensor):
-            image_data.to('cuda:0' if force_gfpgan_to_cuda0 else thread_data.device)
+        image_data.to(thread_data.device)
+
+    if filter_name == 'gfpgan':
+        # 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
@@ -298,14 +379,12 @@ def apply_filters(filter_name, image_data, model_path=None):
             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 isinstance(image_data, torch.Tensor):
-            image_data.to(thread_data.device)
-
         if model_path is not None and model_path != thread_data.real_esrgan_file:
             thread_data.real_esrgan_file = model_path
             load_model_real_esrgan()
@@ -318,45 +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):
+        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
@@ -375,15 +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()
@@ -392,29 +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
 
@@ -459,9 +546,13 @@ def do_mk_img(req: Request):
         if thread_data.device != "cpu" and thread_data.precision == "autocast":
             init_image = init_image.half()
 
+        if not thread_data.test_sd2:
             thread_data.modelFS.to(thread_data.device)
 
         init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
+        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:
@@ -473,7 +564,9 @@ def do_mk_img(req: Request):
                 mask = mask.half()
 
         # Send to CPU and wait until complete.
-        wait_model_move_to(thread_data.modelFS, 'cpu')
+        # wait_model_move_to(thread_data.modelFS, 'cpu')
+        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)
@@ -489,10 +582,13 @@ 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:
+                        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)
@@ -506,15 +602,21 @@ def do_mk_img(req: Request):
                             weight = weights[i]
                             # if not skip_normalize:
                             weight = weight / totalWeight
+                            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:
+                        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:
@@ -522,14 +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
@@ -542,6 +637,9 @@ def do_mk_img(req: Request):
                     print("decoding images")
                     img_data = [None] * batch_size
                     for i in range(batch_size):
+                        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")
@@ -549,10 +647,6 @@ def do_mk_img(req: Request):
                         img_data[i] = x_sample
                     del x_samples, x_samples_ddim, x_sample
 
-                    if thread_data.reduced_memory:
-                        # Send to CPU and wait until complete.
-                        wait_model_move_to(thread_data.modelFS, 'cpu')
-
                     print("saving images")
                     for i in range(batch_size):
                         img = Image.fromarray(img_data[i])
@@ -575,9 +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
@@ -593,9 +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)
@@ -606,13 +704,18 @@ def do_mk_img(req: Request):
 
                     # if thread_data.reduced_memory:
                     #     unload_filters()
+                    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:
@@ -645,8 +748,55 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
     shape = [opt_n_samples, opt_C, opt_H // opt_f, opt_W // opt_f]
 
     # Send to CPU and wait until complete.
-    wait_model_move_to(thread_data.modelCS, 'cpu')
+    # wait_model_move_to(thread_data.modelCS, 'cpu')
 
+    if not thread_data.test_sd2:
+        move_to_cpu(thread_data.modelCS)
+
+    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, _ = 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)
 
@@ -664,10 +814,24 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
             mask=mask,
             sampler = sampler_name,
         )
-    yield from samples_ddim
+    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):
+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)
+    x_T = None if mask is None else init_latent
+
+    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),
@@ -675,7 +839,6 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
             opt_ddim_eta,
             opt_ddim_steps,
         )
-    x_T = None if mask is None else init_latent
 
         # decode it
         samples_ddim = thread_data.model.sample(
@@ -689,7 +852,7 @@ def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, o
             x_T=x_T,
             sampler = 'ddim'
         )
-    yield from samples_ddim
+    return samples_ddim
 
 def gc():
     gc_collect()
@@ -757,8 +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"
@@ -776,3 +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

ui/sd_internal/task_manager.py

@@ -217,10 +217,6 @@ def thread_get_next_task():
     task = None
     try:  # Select a render task.
         for queued_task in tasks_queue:
-            if queued_task.request.use_face_correction and runtime.thread_data.device == 'cpu' and is_alive() == 1:
-                queued_task.error = Exception('The CPU cannot be used to run this task currently. Please remove "Fix incorrect faces" from Image Settings and try again.')
-                task = queued_task
-                break
             if queued_task.render_device and runtime.thread_data.device != queued_task.render_device:
                 # Is asking for a specific render device.
                 if is_alive(queued_task.render_device) > 0:
@@ -272,7 +268,7 @@ def thread_render(device):
             return
         task = thread_get_next_task()
         if task is None:
-            time.sleep(1)
+            time.sleep(0.05)
             continue
         if task.error is not None:
             print(task.error)
@@ -287,45 +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
+                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)
+
+            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())
@@ -440,7 +417,7 @@ def stop_render_thread(device):
     try:
         device_manager.validate_device_id(device, log_prefix='stop_render_thread')
     except:
-        print(traceback.format_exec())
+        print(traceback.format_exc())
         return False
 
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT): raise Exception('stop_render_thread' + ERR_LOCK_FAILED)

ui/server.py

@@ -7,6 +7,9 @@ import traceback
 
 import sys
 import os
+import socket
+import picklescan.scanner
+import rich
 
 SD_DIR = os.getcwd()
 print('started in ', SD_DIR)
@@ -16,7 +19,13 @@ sys.path.append(os.path.dirname(SD_UI_DIR))
 
 CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
 MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'models'))
-UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'))
+
+USER_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, '..', 'plugins', 'ui'))
+CORE_UI_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, 'plugins', 'ui'))
+UI_PLUGINS_SOURCES = ((CORE_UI_PLUGINS_DIR, 'core'), (USER_UI_PLUGINS_DIR, 'user'))
+
+STABLE_DIFFUSION_MODEL_EXTENSIONS = ['.ckpt']
+VAE_MODEL_EXTENSIONS = ['.vae.pt', '.ckpt']
 
 OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder
 TASK_TTL = 15 * 60 # Discard last session's task timeout
@@ -49,15 +58,25 @@ app = FastAPI()
 modifiers_cache = None
 outpath = os.path.join(os.path.expanduser("~"), OUTPUT_DIRNAME)
 
-os.makedirs(UI_PLUGINS_DIR, exist_ok=True)
+os.makedirs(USER_UI_PLUGINS_DIR, exist_ok=True)
 
 # don't show access log entries for URLs that start with the given prefix
 ACCESS_LOG_SUPPRESS_PATH_PREFIXES = ['/ping', '/image', '/modifier-thumbnails']
 
 NOCACHE_HEADERS={"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"}
 
-app.mount('/media', StaticFiles(directory=os.path.join(SD_UI_DIR, 'media')), name="media")
-app.mount('/plugins', StaticFiles(directory=UI_PLUGINS_DIR), name="plugins")
+class NoCacheStaticFiles(StaticFiles):
+    def is_not_modified(self, response_headers, request_headers) -> bool:
+        if 'content-type' in response_headers and ('javascript' in response_headers['content-type'] or 'css' in response_headers['content-type']):
+            response_headers.update(NOCACHE_HEADERS)
+            return False
+
+        return super().is_not_modified(response_headers, request_headers)
+
+app.mount('/media', NoCacheStaticFiles(directory=os.path.join(SD_UI_DIR, 'media')), name="media")
+
+for plugins_dir, dir_prefix in UI_PLUGINS_SOURCES:
+    app.mount(f'/plugins/{dir_prefix}', NoCacheStaticFiles(directory=plugins_dir), name=f"plugins-{dir_prefix}")
 
 def getConfig(default_val=APP_CONFIG_DEFAULTS):
     try:
@@ -65,13 +84,21 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
         if not os.path.exists(config_json_path):
             return default_val
         with open(config_json_path, 'r', encoding='utf-8') as f:
-            return json.load(f)
+            config = json.load(f)
+            if 'net' not in config:
+                config['net'] = {}
+            if os.getenv('SD_UI_BIND_PORT') is not None:
+                config['net']['listen_port'] = int(os.getenv('SD_UI_BIND_PORT'))
+            if os.getenv('SD_UI_BIND_IP') is not None:
+                config['net']['listen_to_network'] = ( os.getenv('SD_UI_BIND_IP') == '0.0.0.0' )
+            return config
     except Exception as e:
         print(str(e))
         print(traceback.format_exc())
         return default_val
 
 def setConfig(config):
+    print( json.dumps(config) )
     try: # config.json
         config_json_path = os.path.join(CONFIG_DIR, 'config.json')
         with open(config_json_path, 'w', encoding='utf-8') as f:
@@ -85,10 +112,12 @@ def setConfig(config):
 
         if 'update_branch' in config:
             config_bat.append(f"@set update_branch={config['update_branch']}")
-        if os.getenv('SD_UI_BIND_PORT') is not None:
-            config_bat.append(f"@set SD_UI_BIND_PORT={os.getenv('SD_UI_BIND_PORT')}")
-        if os.getenv('SD_UI_BIND_IP') is not None:
-            config_bat.append(f"@set SD_UI_BIND_IP={os.getenv('SD_UI_BIND_IP')}")
+
+        config_bat.append(f"@set SD_UI_BIND_PORT={config['net']['listen_port']}")
+        bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
+        config_bat.append(f"@set SD_UI_BIND_IP={bind_ip}")
+
+        config_bat.append(f"@set test_sd2={'Y' if config.get('test_sd2', False) else 'N'}")
 
         if len(config_bat) > 0:
             with open(config_bat_path, 'w', encoding='utf-8') as f:
@@ -102,10 +131,12 @@ def setConfig(config):
 
         if 'update_branch' in config:
             config_sh.append(f"export update_branch={config['update_branch']}")
-        if os.getenv('SD_UI_BIND_PORT') is not None:
-            config_sh.append(f"export SD_UI_BIND_PORT={os.getenv('SD_UI_BIND_PORT')}")
-        if os.getenv('SD_UI_BIND_IP') is not None:
-            config_sh.append(f"export SD_UI_BIND_IP={os.getenv('SD_UI_BIND_IP')}")
+
+        config_sh.append(f"export SD_UI_BIND_PORT={config['net']['listen_port']}")
+        bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
+        config_sh.append(f"export SD_UI_BIND_IP={bind_ip}")
+
+        config_sh.append(f"export test_sd2=\"{'Y' if config.get('test_sd2', False) else 'N'}\"")
 
         if len(config_sh) > 1:
             with open(config_sh_path, 'w', encoding='utf-8') as f:
@@ -114,12 +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:
@@ -147,11 +185,11 @@ def resolve_model_to_use(model_name:str, model_type:str, model_dir:str, model_ex
     raise Exception('No valid models found.')
 
 def resolve_ckpt_to_use(model_name:str=None):
-    return resolve_model_to_use(model_name, model_type='stable-diffusion', model_dir='stable-diffusion', model_extensions=['.ckpt'], default_models=APP_CONFIG_DEFAULT_MODELS)
+    return resolve_model_to_use(model_name, model_type='stable-diffusion', model_dir='stable-diffusion', model_extensions=STABLE_DIFFUSION_MODEL_EXTENSIONS, default_models=APP_CONFIG_DEFAULT_MODELS)
 
 def resolve_vae_to_use(model_name:str=None):
     try:
-        return resolve_model_to_use(model_name, model_type='vae', model_dir='vae', model_extensions=['.vae.pt', '.ckpt'], default_models=[])
+        return resolve_model_to_use(model_name, model_type='vae', model_dir='vae', model_extensions=VAE_MODEL_EXTENSIONS, default_models=[])
     except:
         return None
 
@@ -160,6 +198,9 @@ class SetAppConfigRequest(BaseModel):
     render_devices: Union[List[str], List[int], str, int] = None
     model_vae: str = None
     ui_open_browser_on_start: bool = None
+    listen_to_network: bool = None
+    listen_port: int = None
+    test_sd2: bool = None
 
 @app.post('/app_config')
 async def setAppConfig(req : SetAppConfigRequest):
@@ -172,6 +213,16 @@ async def setAppConfig(req : SetAppConfigRequest):
         if 'ui' not in config:
             config['ui'] = {}
         config['ui']['open_browser_on_start'] = req.ui_open_browser_on_start
+    if req.listen_to_network is not None:
+       if 'net' not in config:
+           config['net'] = {}
+       config['net']['listen_to_network'] = bool(req.listen_to_network)
+    if req.listen_port is not None:
+       if 'net' not in config:
+           config['net'] = {}
+       config['net']['listen_port'] = int(req.listen_port)
+    if req.test_sd2 is not None:
+        config['test_sd2'] = req.test_sd2
     try:
         setConfig(config)
 
@@ -183,6 +234,20 @@ async def setAppConfig(req : SetAppConfigRequest):
         print(traceback.format_exc())
         raise HTTPException(status_code=500, detail=str(e))
 
+def is_malicious_model(file_path):
+    try:
+        scan_result = picklescan.scanner.scan_file_path(file_path)
+        if scan_result.issues_count > 0 or scan_result.infected_files > 0:
+            rich.print(":warning: [bold red]Scan %s: %d scanned, %d issue, %d infected.[/bold red]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
+            return True
+        else:
+            rich.print("Scan %s: [green]%d scanned, %d issue, %d infected.[/green]" % (file_path, scan_result.scanned_files, scan_result.issues_count, scan_result.infected_files))
+            return False
+    except Exception as e:
+        print('error while scanning', file_path, 'error:', e)
+    return False
+
+known_models = {}
 def getModels():
     models = {
         'active': {
@@ -202,7 +267,18 @@ def getModels():
 
         for file in os.listdir(models_dir):
             for model_extension in model_extensions:
-                if file.endswith(model_extension):
+                if not file.endswith(model_extension):
+                    continue
+
+                model_path = os.path.join(models_dir, file)
+                mtime = os.path.getmtime(model_path)
+                mod_time = known_models[model_path] if model_path in known_models else -1
+                if mod_time != mtime:
+                    if is_malicious_model(model_path):
+                        models['scan-error'] = file
+                        return
+                known_models[model_path] = mtime
+
                 model_name = file[:-len(model_extension)]
                 models['options'][model_type].append(model_name)
 
@@ -210,8 +286,8 @@ def getModels():
         models['options'][model_type].sort()
 
     # custom models
-    listModels(models_dirname='stable-diffusion', model_type='stable-diffusion', model_extensions=['.ckpt'])
-    listModels(models_dirname='vae', model_type='vae', model_extensions=['.vae.pt', '.ckpt'])
+    listModels(models_dirname='stable-diffusion', model_type='stable-diffusion', model_extensions=STABLE_DIFFUSION_MODEL_EXTENSIONS)
+    listModels(models_dirname='vae', model_type='vae', model_extensions=VAE_MODEL_EXTENSIONS)
 
     # legacy
     custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt')
@@ -223,12 +299,18 @@ def getModels():
 def getUIPlugins():
     plugins = []
 
-    for file in os.listdir(UI_PLUGINS_DIR):
+    for plugins_dir, dir_prefix in UI_PLUGINS_SOURCES:
+        for file in os.listdir(plugins_dir):
             if file.endswith('.plugin.js'):
-            plugins.append(f'/plugins/{file}')
+                plugins.append(f'/plugins/{dir_prefix}/{file}')
 
     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.
@@ -238,11 +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)
@@ -378,6 +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()
@@ -396,7 +484,9 @@ update_render_threads()
 def open_browser():
     config = getConfig()
     ui = config.get('ui', {})
+    net = config.get('net', {'listen_port':9000})
+    port = net.get('listen_port', 9000)
     if ui.get('open_browser_on_start', True):
-        import webbrowser; webbrowser.open('http://localhost:9000')
+        import webbrowser; webbrowser.open(f"http://localhost:{port}")
 
 open_browser()