Merge branch 'dev' into text-drag-fix

.eslintignore

@@ -0,0 +1,4 @@
+extensions
+extensions-disabled
+repositories
+venv

.eslintrc.js

@@ -0,0 +1,88 @@
+/* global module */
+module.exports = {
+    env: {
+        browser: true,
+        es2021: true,
+    },
+    extends: "eslint:recommended",
+    parserOptions: {
+        ecmaVersion: "latest",
+    },
+    rules: {
+        "arrow-spacing": "error",
+        "block-spacing": "error",
+        "brace-style": "error",
+        "comma-dangle": ["error", "only-multiline"],
+        "comma-spacing": "error",
+        "comma-style": ["error", "last"],
+        "curly": ["error", "multi-line", "consistent"],
+        "eol-last": "error",
+        "func-call-spacing": "error",
+        "function-call-argument-newline": ["error", "consistent"],
+        "function-paren-newline": ["error", "consistent"],
+        "indent": ["error", 4],
+        "key-spacing": "error",
+        "keyword-spacing": "error",
+        "linebreak-style": ["error", "unix"],
+        "no-extra-semi": "error",
+        "no-mixed-spaces-and-tabs": "error",
+        "no-multi-spaces": "error",
+        "no-redeclare": ["error", {builtinGlobals: false}],
+        "no-trailing-spaces": "error",
+        "no-unused-vars": "off",
+        "no-whitespace-before-property": "error",
+        "object-curly-newline": ["error", {consistent: true, multiline: true}],
+        "object-curly-spacing": ["error", "never"],
+        "operator-linebreak": ["error", "after"],
+        "quote-props": ["error", "consistent-as-needed"],
+        "semi": ["error", "always"],
+        "semi-spacing": "error",
+        "semi-style": ["error", "last"],
+        "space-before-blocks": "error",
+        "space-before-function-paren": ["error", "never"],
+        "space-in-parens": ["error", "never"],
+        "space-infix-ops": "error",
+        "space-unary-ops": "error",
+        "switch-colon-spacing": "error",
+        "template-curly-spacing": ["error", "never"],
+        "unicode-bom": "error",
+    },
+    globals: {
+        //script.js
+        gradioApp: "readonly",
+        onUiLoaded: "readonly",
+        onUiUpdate: "readonly",
+        onOptionsChanged: "readonly",
+        uiCurrentTab: "writable",
+        uiElementIsVisible: "readonly",
+        uiElementInSight: "readonly",
+        executeCallbacks: "readonly",
+        //ui.js
+        opts: "writable",
+        all_gallery_buttons: "readonly",
+        selected_gallery_button: "readonly",
+        selected_gallery_index: "readonly",
+        switch_to_txt2img: "readonly",
+        switch_to_img2img_tab: "readonly",
+        switch_to_img2img: "readonly",
+        switch_to_sketch: "readonly",
+        switch_to_inpaint: "readonly",
+        switch_to_inpaint_sketch: "readonly",
+        switch_to_extras: "readonly",
+        get_tab_index: "readonly",
+        create_submit_args: "readonly",
+        restart_reload: "readonly",
+        updateInput: "readonly",
+        //extraNetworks.js
+        requestGet: "readonly",
+        popup: "readonly",
+        // from python
+        localization: "readonly",
+        // progrssbar.js
+        randomId: "readonly",
+        requestProgress: "readonly",
+        // imageviewer.js
+        modalPrevImage: "readonly",
+        modalNextImage: "readonly",
+    }
+};

.git-blame-ignore-revs

@@ -0,0 +1,2 @@
+# Apply ESlint
+9c54b78d9dde5601e916f308d9a9d6953ec39430

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -47,6 +47,15 @@ body:
       description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
     validations:
       required: true
+  - type: dropdown
+    id: py-version
+    attributes:
+      label: What Python version are you running on ?
+      multiple: false
+      options:
+        - Python 3.10.x
+        - Python 3.11.x (above, no supported yet)
+        - Python 3.9.x (below, no recommended)
   - type: dropdown
     id: platforms
     attributes:
@@ -59,6 +68,18 @@ body:
         - iOS
         - Android
         - Other/Cloud
+  - type: dropdown
+    id: device
+    attributes:
+        label: What device are you running WebUI on?
+        multiple: true
+        options:
+        - Nvidia GPUs (RTX 20 above)
+        - Nvidia GPUs (GTX 16 below)
+        - AMD GPUs (RX 6000 above)
+        - AMD GPUs (RX 5000 below)
+        - CPU
+        - Other GPUs
   - type: dropdown
     id: browsers
     attributes:

.github/pull_request_template.md

@@ -1,28 +1,15 @@
-# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
+## Description
 
-If you have a large change, pay special attention to this paragraph:
+* a simple description of what you're trying to accomplish
+* a summary of changes in code
+* which issues it fixes, if any
 
-> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
+## Screenshots/videos:
 
-Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
 
-**Describe what this pull request is trying to achieve.**
+## Checklist:
 
-A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
+- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
-
+- [ ] I have performed a self-review of my own code
-**Additional notes and description of your changes**
+- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
-
+- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)
-More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
-
-**Environment this was tested in**
-
-List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
- - OS: [e.g. Windows, Linux]
- - Browser: [e.g. chrome, safari]
- - Graphics card: [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
-
-**Screenshots or videos of your changes**
-
-If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
-
-This is **required** for anything that touches the user interface.

.github/workflows/on_pull_request.yaml

@@ -1,39 +1,34 @@
-# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
 name: Run Linting/Formatting on Pull Requests
 
 on:
   - push
   - pull_request
-  # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
-  # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
-  # pull_request:  
-  #  branches:
-  #    - master
-  #  branches-ignore:
-  #    - development
 
 jobs:
-  lint:
+  lint-python:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout Code
         uses: actions/checkout@v3
-      - name: Set up Python 3.10
+      - uses: actions/setup-python@v4
-        uses: actions/setup-python@v4
         with:
-          python-version: 3.10.6
+          python-version: 3.11
-          cache: pip
+          # NB: there's no cache: pip here since we're not installing anything
-          cache-dependency-path: |
+          #     from the requirements.txt file(s) in the repository; it's faster
-            **/requirements*txt
+          #     not to have GHA download an (at the time of writing) 4 GB cache
-      - name: Install PyLint
+          #     of PyTorch and other dependencies.
-        run: | 
+      - name: Install Ruff
-          python -m pip install --upgrade pip
+        run: pip install ruff==0.0.265
-          pip install pylint
+      - name: Run Ruff
-      # This lets PyLint check to see if it can resolve imports
+        run: ruff .
-      - name: Install dependencies
+  lint-js:
-        run: |
+    runs-on: ubuntu-latest
-          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
+    steps:
-          python launch.py
+      - name: Checkout Code
-      - name: Analysing the code with pylint
+        uses: actions/checkout@v3
-        run: |
+      - name: Install Node.js
-          pylint $(git ls-files '*.py')
+        uses: actions/setup-node@v3
+        with:
+          node-version: 18
+      - run: npm i --ci
+      - run: npm run lint

.github/workflows/run_tests.yaml

@@ -17,13 +17,54 @@ jobs:
           cache: pip
           cache-dependency-path: |
             **/requirements*txt
+            launch.py
+      - name: Install test dependencies
+        run: pip install wait-for-it -r requirements-test.txt
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+      - name: Setup environment
+        run: python launch.py --skip-torch-cuda-test --exit
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+          TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
+          WEBUI_LAUNCH_LIVE_OUTPUT: "1"
+          PYTHONUNBUFFERED: "1"
+      - name: Start test server
+        run: >
+          python -m coverage run
+          --data-file=.coverage.server
+          launch.py
+          --skip-prepare-environment
+          --skip-torch-cuda-test
+          --test-server
+          --no-half
+          --disable-opt-split-attention
+          --use-cpu all
+          --add-stop-route
+          2>&1 | tee output.txt &
       - name: Run tests
-        run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+        run: |
-      - name: Upload main app stdout-stderr
+          wait-for-it --service 127.0.0.1:7860 -t 600
+          python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
+      - name: Kill test server
+        if: always()
+        run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
+      - name: Show coverage
+        run: |
+          python -m coverage combine .coverage*
+          python -m coverage report -i
+          python -m coverage html -i
+      - name: Upload main app output
         uses: actions/upload-artifact@v3
         if: always()
         with:
-          name: stdout-stderr
+          name: output
-          path: |
+          path: output.txt
-            test/stdout.txt
+      - name: Upload coverage HTML
-            test/stderr.txt
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: htmlcov
+          path: htmlcov

.gitignore

@@ -34,3 +34,6 @@ notification.mp3
 /test/stderr.txt
 /cache.json*
 /config_states/
+/node_modules
+/package-lock.json
+/.coverage*

CHANGELOG.md

@@ -1,56 +1,116 @@
+## Upcoming 1.3.0
+
+### Features:
+ * add UI to edit defaults
+ * token merging (via dbolya/tomesd)
+ * settings tab rework: add a lot of additional explanations and links
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
+ * TAESD - another option for cheap live previews
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
+ * calculate hashes for Lora
+ * add lora hashes to infotext
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
+ * select cross attention optimization from UI
+
+### Minor:
+ * bump Gradio to 3.31.0
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
+ * allow setting defaults for elements in extensions' tabs
+ * allow selecting file type for live previews
+ * show "Loading..." for extra networks when displaying for the first time
+ * suppress ENSD infotext for samplers that don't use it
+ * clientside optimizations
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
+ * allow whitespace in styles.csv
+ * add option to reorder tabs
+ * move some functionality (swap resolution and set seed to -1) to client
+ * option to specify editor height for img2img
+ * button to copy image resolution into img2img width/height sliders
+ * switch from pyngrok to ngrok-py
+ * lazy-load images in extra networks UI
+ * set "Navigate image viewer with gamepad" option to false by default, by request
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
+ * allow hiding buttons in ui-config.json
+
+### Extensions:
+ * add /sdapi/v1/script-info api
+ * use Ruff to lint Python code
+ * use ESlint to lint Javascript code
+ * add/modify CFG callbacks for Self-Attention Guidance extension
+ * add command and endpoint for graceful server stopping
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
+ * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
+ * add /sdapi/v1/refresh-loras api checkpoint post request
+ * tests overhaul
+
+### Bug Fixes:
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
+ * fix broken prompts from file script
+ * fix symlink scanning for extra networks
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
+ * allow web UI to be ran fully offline
+ * fix inability to run with --freeze-settings
+ * fix inability to merge checkpoint without adding metadata
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
+ * remove blinking effect from text in hires fix and scale resolution preview
+ * make links to `http://<...>.git` extensions work in the extension tab
+ * fix bug with webui hanging at startup due to hanging git process
+
+
 ## 1.2.1
 
 ### Features:
- * add an option to always refer to lora by filenames
+ * add an option to always refer to LoRA by filenames
 
 ### Bug Fixes:
- * never refer to lora by an alias if multiple loras have same alias or the alias is called none
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
  * fix upscalers disappearing after the user reloads UI
- * allow bf16 in safe unpickler (resolves problems with loading some loras)
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
  * allow web UI to be ran fully offline
  * fix localizations not working
- * fix error for loras: 'LatentDiffusion' object has no attribute 'lora_layer_mapping'
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
 
 ## 1.2.0
 
 ### Features:
- * do not wait for stable diffusion model to load at startup
+ * do not wait for Stable Diffusion model to load at startup
- * add filename patterns: [denoising]
+ * add filename patterns: `[denoising]`
- * directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
- * Lora: for the `<...>` text in prompt, use name of Lora that is in the metdata of the file, if present, instead of filename (both can be used to activate lora)
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
- * Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
- * Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
- * Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
  * add version to infotext, footer and console output when starting
  * add links to wiki for filename pattern settings
  * add extended info for quicksettings setting and use multiselect input instead of a text field
 
 ### Minor:
- * gradio bumped to 3.29.0
+ * bump Gradio to 3.29.0
- * torch bumped to 2.0.1
+ * bump PyTorch to 2.0.1
- * --subpath option for gradio for use with reverse proxy
+ * `--subpath` option for gradio for use with reverse proxy
- * linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
- * possible frontend optimization: do not apply localizations if there are none
+ * do not apply localizations if there are none (possible frontend optimization)
- * Add extra `None` option for VAE in XYZ plot
+ * add extra `None` option for VAE in XYZ plot
  * print error to console when batch processing in img2img fails
  * create HTML for extra network pages only on demand
- * allow directories starting with . to still list their models for lora, checkpoints, etc
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
  * put infotext options into their own category in settings tab
  * do not show licenses page when user selects Show all pages in settings
 
 ### Extensions:
- * Tooltip localization support
+ * tooltip localization support
- * Add api method to get LoRA models with prompt
+ * add API method to get LoRA models with prompt
 
 ### Bug Fixes:
- * re-add /docs endpoint
+ * re-add `/docs` endpoint
  * fix gamepad navigation
  * make the lightbox fullscreen image function properly
  * fix squished thumbnails in extras tab
  * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
  * fix webui showing the same image if you configure the generation to always save results into same file
  * fix bug with upscalers not working properly
- * Fix MPS on PyTorch 2.0.1, Intel Macs
+ * fix MPS on PyTorch 2.0.1, Intel Macs
  * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
  * prevent Reload UI button/link from reloading the page when it's not yet ready
  * fix prompts from file script failing to read contents from a drag/drop file
@@ -58,20 +118,20 @@
 
 ## 1.1.1
 ### Bug Fixes:
- * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
 
 ## 1.1.0
 ### Features:
- * switch to torch 2.0.0 (except for AMD GPUs)
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
  * visual improvements to custom code scripts
- * add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
  * add support for saving init images in img2img, and record their hashes in infotext for reproducability
  * automatically select current word when adjusting weight with ctrl+up/down
  * add dropdowns for X/Y/Z plot
- * setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
  * support Gradio's theme API
  * use TCMalloc on Linux by default; possible fix for memory leaks
- * (optimization) option to remove negative conditioning at low sigma values #9177
+ * add optimization option to remove negative conditioning at low sigma values #9177
  * embed model merge metadata in .safetensors file
  * extension settings backup/restore feature #9169
  * add "resize by" and "resize to" tabs to img2img
@@ -80,22 +140,22 @@
  * button to restore the progress from session lost / tab reload
 
 ### Minor:
- * gradio bumped to 3.28.1
+ * bump Gradio to 3.28.1
- * in extra tab, change extras "scale to" to sliders
+ * change "scale to" to sliders in Extras tab
  * add labels to tool buttons to make it possible to hide them
  * add tiled inference support for ScuNET
  * add branch support for extension installation
- * change linux installation script to insall into current directory rather than /home/username
+ * change Linux installation script to install into current directory rather than `/home/username`
- * sort textual inversion embeddings by name (case insensitive)
+ * sort textual inversion embeddings by name (case-insensitive)
  * allow styles.csv to be symlinked or mounted in docker
  * remove the "do not add watermark to images" option
  * make selected tab configurable with UI config
- * extra networks UI in now fixed height and scrollable
+ * make the extra networks UI fixed height and scrollable
- * add disable_tls_verify arg for use with self-signed certs
+ * add `disable_tls_verify` arg for use with self-signed certs
 
 ### Extensions:
- * Add reload callback
+ * add reload callback
- * add is_hr_pass field for processing
+ * add `is_hr_pass` field for processing
 
 ### Bug Fixes:
  * fix broken batch image processing on 'Extras/Batch Process' tab
@@ -111,10 +171,10 @@
  * one broken image in img2img batch won't stop all processing
  * fix image orientation bug in train/preprocess
  * fix Ngrok recreating tunnels every reload
- * fix --realesrgan-models-path and --ldsr-models-path not working
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
- * fix --skip-install not working
+ * fix `--skip-install` not working
- * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
- * do not fail all Loras if some have failed to load when making a picture
+ * do not fail all LoRAs if some have failed to load when making a picture
 
 ## 1.0.0
   * everything

README.md

@@ -15,7 +15,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Attention, specify parts of text that the model should pay more attention to
     - a man in a `((tuxedo))` - will pay more attention to tuxedo
     - a man in a `(tuxedo:1.21)` - alternative syntax
-    - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
+    - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
 - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
 - Textual Inversion
@@ -99,6 +99,12 @@ Alternatively, use online services (like Google Colab):
 
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
+### Installation on Windows 10/11 with NVidia-GPUs using release package
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+2. Run `update.bat`.
+3. Run `run.bat`.
+> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
+
 ### Automatic Installation on Windows
 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
 2. Install [git](https://git-scm.com/download/win).
@@ -158,5 +164,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
 - Security advice - RyotaK
 - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
+- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -88,7 +88,7 @@ class LDSR:
 
         x_t = None
         logs = None
-        for n in range(n_runs):
+        for _ in range(n_runs):
             if custom_shape is not None:
                 x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
                 x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
@@ -110,7 +110,6 @@ class LDSR:
         diffusion_steps = int(steps)
         eta = 1.0
 
-        down_sample_method = 'Lanczos'
 
         gc.collect()
         if torch.cuda.is_available:
@@ -131,11 +130,11 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        
+
         # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
         pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
         im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
-        
+
         logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
@@ -158,7 +157,7 @@ class LDSR:
 
 
 def get_cond(selected_path):
-    example = dict()
+    example = {}
     up_f = 4
     c = selected_path.convert('RGB')
     c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
@@ -196,7 +195,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s
 @torch.no_grad()
 def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
                               corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
-    log = dict()
+    log = {}
 
     z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
                                         return_first_stage_outputs=True,
@@ -244,7 +243,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
         x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
         log["sample_noquant"] = x_sample_noquant
         log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
+    except Exception:
         pass
 
     log["sample"] = x_sample

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -7,7 +7,8 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks
-import sd_hijack_autoencoder, sd_hijack_ddpm_v1
+import sd_hijack_autoencoder  # noqa: F401
+import sd_hijack_ddpm_v1  # noqa: F401
 
 
 class UpscalerLDSR(Upscaler):
@@ -44,9 +45,9 @@ class UpscalerLDSR(Upscaler):
         if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
             model = local_safetensors_path
         else:
-            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
+            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
 
-        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
+        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
 
         try:
             return LDSR(model, yaml)

extensions-builtin/LDSR/sd_hijack_autoencoder.py

@@ -1,16 +1,21 @@
 # The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
 # The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
 # As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
-
+import numpy as np
 import torch
 import pytorch_lightning as pl
 import torch.nn.functional as F
 from contextlib import contextmanager
+
+from torch.optim.lr_scheduler import LambdaLR
+
+from ldm.modules.ema import LitEma
 from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
 from ldm.modules.diffusionmodules.model import Encoder, Decoder
 from ldm.util import instantiate_from_config
 
 import ldm.models.autoencoder
+from packaging import version
 
 class VQModel(pl.LightningModule):
     def __init__(self,
@@ -19,7 +24,7 @@ class VQModel(pl.LightningModule):
                  n_embed,
                  embed_dim,
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  image_key="image",
                  colorize_nlabels=None,
                  monitor=None,
@@ -57,7 +62,7 @@ class VQModel(pl.LightningModule):
             print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
         self.scheduler_config = scheduler_config
         self.lr_g_factor = lr_g_factor
 
@@ -76,11 +81,11 @@ class VQModel(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list()):
+    def init_from_ckpt(self, path, ignore_keys=None):
         sd = torch.load(path, map_location="cpu")["state_dict"]
         keys = list(sd.keys())
         for k in keys:
-            for ik in ignore_keys:
+            for ik in ignore_keys or []:
                 if k.startswith(ik):
                     print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
@@ -165,7 +170,7 @@ class VQModel(pl.LightningModule):
     def validation_step(self, batch, batch_idx):
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+            self._validation_step(batch, batch_idx, suffix="_ema")
         return log_dict
 
     def _validation_step(self, batch, batch_idx, suffix=""):
@@ -232,7 +237,7 @@ class VQModel(pl.LightningModule):
         return self.decoder.conv_out.weight
 
     def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.image_key)
         x = x.to(self.device)
         if only_inputs:
@@ -249,7 +254,8 @@ class VQModel(pl.LightningModule):
         if plot_ema:
             with self.ema_scope():
                 xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                if x.shape[1] > 3:
+                    xrec_ema = self.to_rgb(xrec_ema)
                 log["reconstructions_ema"] = xrec_ema
         return log
 
@@ -264,7 +270,7 @@ class VQModel(pl.LightningModule):
 
 class VQModelInterface(VQModel):
     def __init__(self, embed_dim, *args, **kwargs):
-        super().__init__(embed_dim=embed_dim, *args, **kwargs)
+        super().__init__(*args, embed_dim=embed_dim, **kwargs)
         self.embed_dim = embed_dim
 
     def encode(self, x):
@@ -282,5 +288,5 @@ class VQModelInterface(VQModel):
         dec = self.decoder(quant)
         return dec
 
-setattr(ldm.models.autoencoder, "VQModel", VQModel)
+ldm.models.autoencoder.VQModel = VQModel
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
+ldm.models.autoencoder.VQModelInterface = VQModelInterface

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
                  beta_schedule="linear",
                  loss_type="l2",
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  load_only_unet=False,
                  monitor="val/loss",
                  use_ema=True,
@@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
         if monitor is not None:
             self.monitor = monitor
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
 
         self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
                                linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
@@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+    def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
         keys = list(sd.keys())
         for k in keys:
-            for ik in ignore_keys:
+            for ik in ignore_keys or []:
                 if k.startswith(ik):
                     print("Deleting key {} from state_dict.".format(k))
                     del sd[k]
@@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):
 
     @torch.no_grad()
     def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.first_stage_key)
         N = min(x.shape[0], N)
         n_row = min(x.shape[0], n_row)
@@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
         log["inputs"] = x
 
         # get diffusion row
-        diffusion_row = list()
+        diffusion_row = []
         x_start = x[:n_row]
 
         for t in range(self.num_timesteps):
@@ -444,13 +444,13 @@ class LatentDiffusionV1(DDPMV1):
             conditioning_key = None
         ckpt_path = kwargs.pop("ckpt_path", None)
         ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+        super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
         self.concat_mode = concat_mode
         self.cond_stage_trainable = cond_stage_trainable
         self.cond_stage_key = cond_stage_key
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -460,7 +460,7 @@ class LatentDiffusionV1(DDPMV1):
         self.instantiate_cond_stage(cond_stage_config)
         self.cond_stage_forward = cond_stage_forward
         self.clip_denoised = False
-        self.bbox_tokenizer = None  
+        self.bbox_tokenizer = None
 
         self.restarted_from_ckpt = False
         if ckpt_path is not None:
@@ -792,7 +792,7 @@ class LatentDiffusionV1(DDPMV1):
                 z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
 
                 # 2. apply model loop over last dim
-                if isinstance(self.first_stage_model, VQModelInterface):  
+                if isinstance(self.first_stage_model, VQModelInterface):
                     output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
                                                                  force_not_quantize=predict_cids or force_not_quantize)
                                    for i in range(z.shape[-1])]
@@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
         return self.p_losses(x, c, t, *args, **kwargs)
 
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
@@ -900,7 +890,7 @@ class LatentDiffusionV1(DDPMV1):
 
         if hasattr(self, "split_input_params"):
             assert len(cond) == 1  # todo can only deal with one conditioning atm
-            assert not return_ids  
+            assert not return_ids
             ks = self.split_input_params["ks"]  # eg. (128, 128)
             stride = self.split_input_params["stride"]  # eg. (64, 64)
 
@@ -1126,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
 
@@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1221,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
         return self.p_sample_loop(cond,
@@ -1253,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):
 
         use_ddim = ddim_steps is not None
 
-        log = dict()
+        log = {}
         z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
                                            return_first_stage_outputs=True,
                                            force_c_encode=True,
@@ -1280,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):
 
         if plot_diffusion_rows:
             # get diffusion row
-            diffusion_row = list()
+            diffusion_row = []
             z_start = z[:n_row]
             for t in range(self.num_timesteps):
                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
@@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):
 
             if inpaint:
                 # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                h, w = z.shape[2], z.shape[3]
                 mask = torch.ones(N, h, w).to(self.device)
                 # zeros will be filled in
                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
@@ -1424,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
     # TODO: move all layout-specific hacks to this class
     def __init__(self, cond_stage_key, *args, **kwargs):
         assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+        super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
 
     def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+        logs = super().log_images(*args, batch=batch, N=N, **kwargs)
 
         key = 'train' if self.training else 'validation'
         dset = self.trainer.datamodule.datasets[key]
@@ -1443,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
         logs['bbox_image'] = cond_img
         return logs
 
-setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1

extensions-builtin/Lora/extra_networks_lora.py

@@ -23,5 +23,23 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
 
         lora.load_loras(names, multipliers)
 
+        if shared.opts.lora_add_hashes_to_infotext:
+            lora_hashes = []
+            for item in lora.loaded_loras:
+                shorthash = item.lora_on_disk.shorthash
+                if not shorthash:
+                    continue
+
+                alias = item.mentioned_name
+                if not alias:
+                    continue
+
+                alias = alias.replace(":", "").replace(",", "")
+
+                lora_hashes.append(f"{alias}: {shorthash}")
+
+            if lora_hashes:
+                p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes)
+
     def deactivate(self, p):
         pass

extensions-builtin/Lora/lora.py

@@ -1,10 +1,9 @@
-import glob
 import os
 import re
 import torch
 from typing import Union
 
-from modules import shared, devices, sd_models, errors, scripts
+from modules import shared, devices, sd_models, errors, scripts, sd_hijack, hashes
 
 metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
 
@@ -77,9 +76,9 @@ class LoraOnDisk:
         self.name = name
         self.filename = filename
         self.metadata = {}
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
 
-        _, ext = os.path.splitext(filename)
+        if self.is_safetensors:
-        if ext.lower() == ".safetensors":
             try:
                 self.metadata = sd_models.read_metadata_from_safetensors(filename)
             except Exception as e:
@@ -95,14 +94,43 @@ class LoraOnDisk:
         self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text
         self.alias = self.metadata.get('ss_output_name', self.name)
 
+        self.hash = None
+        self.shorthash = None
+        self.set_hash(
+            self.metadata.get('sshs_model_hash') or
+            hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
+            ''
+        )
+
+    def set_hash(self, v):
+        self.hash = v
+        self.shorthash = self.hash[0:12]
+
+        if self.shorthash:
+            available_lora_hash_lookup[self.shorthash] = self
+
+    def read_hash(self):
+        if not self.hash:
+            self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
+
+    def get_alias(self):
+        if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in forbidden_lora_aliases:
+            return self.name
+        else:
+            return self.alias
+
 
 class LoraModule:
-    def __init__(self, name):
+    def __init__(self, name, lora_on_disk: LoraOnDisk):
         self.name = name
+        self.lora_on_disk = lora_on_disk
         self.multiplier = 1.0
         self.modules = {}
         self.mtime = None
 
+        self.mentioned_name = None
+        """the text that was used to add lora to prompt - can be either name or an alias"""
+
 
 class LoraUpDownModule:
     def __init__(self):
@@ -127,11 +155,11 @@ def assign_lora_names_to_compvis_modules(sd_model):
     sd_model.lora_layer_mapping = lora_layer_mapping
 
 
-def load_lora(name, filename):
+def load_lora(name, lora_on_disk):
-    lora = LoraModule(name)
+    lora = LoraModule(name, lora_on_disk)
-    lora.mtime = os.path.getmtime(filename)
+    lora.mtime = os.path.getmtime(lora_on_disk.filename)
 
-    sd = sd_models.read_state_dict(filename)
+    sd = sd_models.read_state_dict(lora_on_disk.filename)
 
     # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
     if not hasattr(shared.sd_model, 'lora_layer_mapping'):
@@ -177,7 +205,7 @@ def load_lora(name, filename):
         else:
             print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
             continue
-            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
+            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
 
         with torch.no_grad():
             module.weight.copy_(weight)
@@ -189,10 +217,10 @@ def load_lora(name, filename):
         elif lora_key == "lora_down.weight":
             lora_module.down = module
         else:
-            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
+            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
 
     if len(keys_failed_to_match) > 0:
-        print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
+        print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")
 
     return lora
 
@@ -207,30 +235,41 @@ def load_loras(names, multipliers=None):
     loaded_loras.clear()
 
     loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
-    if any([x is None for x in loras_on_disk]):
+    if any(x is None for x in loras_on_disk):
         list_available_loras()
 
         loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
 
+    failed_to_load_loras = []
+
     for i, name in enumerate(names):
         lora = already_loaded.get(name, None)
 
         lora_on_disk = loras_on_disk[i]
+
         if lora_on_disk is not None:
             if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
                 try:
-                    lora = load_lora(name, lora_on_disk.filename)
+                    lora = load_lora(name, lora_on_disk)
                 except Exception as e:
                     errors.display(e, f"loading Lora {lora_on_disk.filename}")
                     continue
 
+            lora.mentioned_name = name
+
+            lora_on_disk.read_hash()
+
         if lora is None:
+            failed_to_load_loras.append(name)
             print(f"Couldn't find Lora with name {name}")
             continue
 
         lora.multiplier = multipliers[i] if multipliers else 1.0
         loaded_loras.append(lora)
 
+    if len(failed_to_load_loras) > 0:
+        sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras))
+
 
 def lora_calc_updown(lora, module, target):
     with torch.no_grad():
@@ -314,7 +353,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
 
             print(f'failed to calculate lora weights for layer {lora_layer_name}')
 
-        setattr(self, "lora_current_names", wanted_names)
+        self.lora_current_names = wanted_names
 
 
 def lora_forward(module, input, original_forward):
@@ -348,8 +387,8 @@ def lora_forward(module, input, original_forward):
 
 
 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
-    setattr(self, "lora_current_names", ())
+    self.lora_current_names = ()
-    setattr(self, "lora_weights_backup", None)
+    self.lora_weights_backup = None
 
 
 def lora_Linear_forward(self, input):
@@ -398,7 +437,8 @@ def list_available_loras():
     available_loras.clear()
     available_lora_aliases.clear()
     forbidden_lora_aliases.clear()
-    forbidden_lora_aliases.update({"none": 1})
+    available_lora_hash_lookup.clear()
+    forbidden_lora_aliases.update({"none": 1, "Addams": 1})
 
     os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
 
@@ -428,7 +468,7 @@ def infotext_pasted(infotext, params):
 
     added = []
 
-    for k, v in params.items():
+    for k in params:
         if not k.startswith("AddNet Model "):
             continue
 
@@ -452,8 +492,10 @@ def infotext_pasted(infotext, params):
     if added:
         params["Prompt"] += "\n" + "".join(added)
 
+
 available_loras = {}
 available_lora_aliases = {}
+available_lora_hash_lookup = {}
 forbidden_lora_aliases = {}
 loaded_loras = []
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -1,3 +1,5 @@
+import re
+
 import torch
 import gradio as gr
 from fastapi import FastAPI
@@ -53,8 +55,9 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
-    "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
+    "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
+    "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
 }))
 
 
@@ -77,6 +80,37 @@ def api_loras(_: gr.Blocks, app: FastAPI):
     async def get_loras():
         return [create_lora_json(obj) for obj in lora.available_loras.values()]
 
+    @app.post("/sdapi/v1/refresh-loras")
+    async def refresh_loras():
+        return lora.list_available_loras()
+
 
 script_callbacks.on_app_started(api_loras)
 
+re_lora = re.compile("<lora:([^:]+):")
+
+
+def infotext_pasted(infotext, d):
+    hashes = d.get("Lora hashes")
+    if not hashes:
+        return
+
+    hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
+    hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
+
+    def lora_replacement(m):
+        alias = m.group(1)
+        shorthash = hashes.get(alias)
+        if shorthash is None:
+            return m.group(0)
+
+        lora_on_disk = lora.available_lora_hash_lookup.get(shorthash)
+        if lora_on_disk is None:
+            return m.group(0)
+
+        return f'<lora:{lora_on_disk.get_alias()}:'
+
+    d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"])
+
+
+script_callbacks.on_infotext_pasted(infotext_pasted)

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -16,10 +16,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         for name, lora_on_disk in lora.available_loras.items():
             path, ext = os.path.splitext(lora_on_disk.filename)
 
-            if shared.opts.lora_preferred_name == "Filename" or lora_on_disk.alias.lower() in lora.forbidden_lora_aliases:
+            alias = lora_on_disk.get_alias()
-                alias = name
-            else:
-                alias = lora_on_disk.alias
 
             yield {
                 "name": name,

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -10,10 +10,9 @@ from tqdm import tqdm
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
-from modules import devices, modelloader
+from modules import devices, modelloader, script_callbacks
 from scunet_model_arch import SCUNet as net
 from modules.shared import opts
-from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -122,8 +121,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
         if "http" in path:
-            filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
+            filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
-                                          progress=True)
         else:
             filename = path
         if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
@@ -133,8 +131,19 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-        for k, v in model.named_parameters():
+        for _, v in model.named_parameters():
             v.requires_grad = False
         model = model.to(device)
 
         return model
+
+
+def on_ui_settings():
+    import gradio as gr
+    from modules import shared
+
+    shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
+    shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/ScuNET/scunet_model_arch.py

@@ -61,7 +61,9 @@ class WMSA(nn.Module):
         Returns:
             output: tensor shape [b h w c]
         """
-        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        if self.type != 'W':
+            x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+
         x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
         h_windows = x.size(1)
         w_windows = x.size(2)
@@ -85,8 +87,9 @@ class WMSA(nn.Module):
         output = self.linear(output)
         output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
 
-        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
+        if self.type != 'W':
-                                                 dims=(1, 2))
+            output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
         return output
 
     def relative_embedding(self):
@@ -262,4 +265,4 @@ class SCUNet(nn.Module):
                 nn.init.constant_(m.bias, 0)
         elif isinstance(m, nn.LayerNorm):
             nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
+            nn.init.constant_(m.weight, 1.0)

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -1,4 +1,3 @@
-import contextlib
 import os
 
 import numpy as np
@@ -8,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
-from modules.shared import cmd_opts, opts, state
+from modules.shared import opts, state
 from swinir_model_arch import SwinIR as net
 from swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -45,14 +44,14 @@ class UpscalerSwinIR(Upscaler):
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
-        except:
+        except Exception:
             pass
         return img
 
     def load_model(self, path, scale=4):
         if "http" in path:
             dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
-            filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
+            filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
         else:
             filename = path
         if filename is None or not os.path.exists(filename):
@@ -151,7 +150,7 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
             for w_idx in w_idx_list:
                 if state.interrupted or state.skipped:
                     break
-                
+
                 in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
                 out_patch = model(in_patch)
                 out_patch_mask = torch.ones_like(out_patch)

extensions-builtin/SwinIR/swinir_model_arch.py

@@ -644,7 +644,7 @@ class SwinIR(nn.Module):
     """
 
     def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
                  window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
@@ -805,7 +805,7 @@ class SwinIR(nn.Module):
     def forward(self, x):
         H, W = x.shape[2:]
         x = self.check_image_size(x)
-        
+
         self.mean = self.mean.type_as(x)
         x = (x - self.mean) * self.img_range
 
@@ -844,7 +844,7 @@ class SwinIR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

extensions-builtin/SwinIR/swinir_model_arch_v2.py

@@ -74,7 +74,7 @@ class WindowAttention(nn.Module):
     """
 
     def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=[0, 0]):
+                 pretrained_window_size=(0, 0)):
 
         super().__init__()
         self.dim = dim
@@ -241,7 +241,7 @@ class SwinTransformerBlock(nn.Module):
             attn_mask = None
 
         self.register_buffer("attn_mask", attn_mask)
-        
+
     def calculate_mask(self, x_size):
         # calculate attention mask for SW-MSA
         H, W = x_size
@@ -263,7 +263,7 @@ class SwinTransformerBlock(nn.Module):
         attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
         attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
 
-        return attn_mask        
+        return attn_mask
 
     def forward(self, x, x_size):
         H, W = x_size
@@ -288,7 +288,7 @@ class SwinTransformerBlock(nn.Module):
             attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
         else:
             attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-            
+
         # merge windows
         attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
         shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
@@ -369,7 +369,7 @@ class PatchMerging(nn.Module):
         H, W = self.input_resolution
         flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
         flops += H * W * self.dim // 2
-        return flops    
+        return flops
 
 class BasicLayer(nn.Module):
     """ A basic Swin Transformer layer for one stage.
@@ -447,7 +447,7 @@ class BasicLayer(nn.Module):
             nn.init.constant_(blk.norm1.weight, 0)
             nn.init.constant_(blk.norm2.bias, 0)
             nn.init.constant_(blk.norm2.weight, 0)
-            
+
 class PatchEmbed(nn.Module):
     r""" Image to Patch Embedding
     Args:
@@ -492,7 +492,7 @@ class PatchEmbed(nn.Module):
         flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
         if self.norm is not None:
             flops += Ho * Wo * self.embed_dim
-        return flops           
+        return flops
 
 class RSTB(nn.Module):
     """Residual Swin Transformer Block (RSTB).
@@ -531,7 +531,7 @@ class RSTB(nn.Module):
                                          num_heads=num_heads,
                                          window_size=window_size,
                                          mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, 
+                                         qkv_bias=qkv_bias,
                                          drop=drop, attn_drop=attn_drop,
                                          drop_path=drop_path,
                                          norm_layer=norm_layer,
@@ -622,7 +622,7 @@ class Upsample(nn.Sequential):
         else:
             raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
         super(Upsample, self).__init__(*m)
-        
+
 class Upsample_hf(nn.Sequential):
     """Upsample module.
 
@@ -642,7 +642,7 @@ class Upsample_hf(nn.Sequential):
             m.append(nn.PixelShuffle(3))
         else:
             raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample_hf, self).__init__(*m)        
+        super(Upsample_hf, self).__init__(*m)
 
 
 class UpsampleOneStep(nn.Sequential):
@@ -667,8 +667,8 @@ class UpsampleOneStep(nn.Sequential):
         H, W = self.input_resolution
         flops = H * W * self.num_feat * 3 * 9
         return flops
-    
+
-    
+
 
 class Swin2SR(nn.Module):
     r""" Swin2SR
@@ -698,8 +698,8 @@ class Swin2SR(nn.Module):
     """
 
     def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
-                 window_size=7, mlp_ratio=4., qkv_bias=True, 
+                 window_size=7, mlp_ratio=4., qkv_bias=True,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
                  use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
@@ -764,7 +764,7 @@ class Swin2SR(nn.Module):
                          num_heads=num_heads[i_layer],
                          window_size=window_size,
                          mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, 
+                         qkv_bias=qkv_bias,
                          drop=drop_rate, attn_drop=attn_drop_rate,
                          drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
                          norm_layer=norm_layer,
@@ -776,7 +776,7 @@ class Swin2SR(nn.Module):
 
                          )
             self.layers.append(layer)
-            
+
         if self.upsampler == 'pixelshuffle_hf':
             self.layers_hf = nn.ModuleList()
             for i_layer in range(self.num_layers):
@@ -787,7 +787,7 @@ class Swin2SR(nn.Module):
                              num_heads=num_heads[i_layer],
                              window_size=window_size,
                              mlp_ratio=self.mlp_ratio,
-                             qkv_bias=qkv_bias, 
+                             qkv_bias=qkv_bias,
                              drop=drop_rate, attn_drop=attn_drop_rate,
                              drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
                              norm_layer=norm_layer,
@@ -799,7 +799,7 @@ class Swin2SR(nn.Module):
 
                              )
                 self.layers_hf.append(layer)
-                        
+
         self.norm = norm_layer(self.num_features)
 
         # build the last conv layer in deep feature extraction
@@ -829,10 +829,10 @@ class Swin2SR(nn.Module):
             self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
             self.conv_after_aux = nn.Sequential(
                 nn.Conv2d(3, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))            
+                nn.LeakyReLU(inplace=True))
             self.upsample = Upsample(upscale, num_feat)
             self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
+
         elif self.upsampler == 'pixelshuffle_hf':
             self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                       nn.LeakyReLU(inplace=True))
@@ -846,7 +846,7 @@ class Swin2SR(nn.Module):
                 nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                 nn.LeakyReLU(inplace=True))
             self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            
+
         elif self.upsampler == 'pixelshuffledirect':
             # for lightweight SR (to save parameters)
             self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
@@ -905,7 +905,7 @@ class Swin2SR(nn.Module):
         x = self.patch_unembed(x, x_size)
 
         return x
-    
+
     def forward_features_hf(self, x):
         x_size = (x.shape[2], x.shape[3])
         x = self.patch_embed(x)
@@ -919,7 +919,7 @@ class Swin2SR(nn.Module):
         x = self.norm(x)  # B L C
         x = self.patch_unembed(x, x_size)
 
-        return x    
+        return x
 
     def forward(self, x):
         H, W = x.shape[2:]
@@ -951,7 +951,7 @@ class Swin2SR(nn.Module):
             x = self.conv_after_body(self.forward_features(x)) + x
             x_before = self.conv_before_upsample(x)
             x_out = self.conv_last(self.upsample(x_before))
-            
+
             x_hf = self.conv_first_hf(x_before)
             x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
             x_hf = self.conv_before_upsample_hf(x_hf)
@@ -977,15 +977,15 @@ class Swin2SR(nn.Module):
             x_first = self.conv_first(x)
             res = self.conv_after_body(self.forward_features(x_first)) + x_first
             x = x + self.conv_last(res)
-        
+
         x = x / self.img_range + self.mean
         if self.upsampler == "pixelshuffle_aux":
             return x[:, :, :H*self.upscale, :W*self.upscale], aux
-        
+
         elif self.upsampler == "pixelshuffle_hf":
             x_out = x_out / self.img_range + self.mean
             return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-        
+
         else:
             return x[:, :, :H*self.upscale, :W*self.upscale]
 
@@ -994,7 +994,7 @@ class Swin2SR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()
@@ -1014,4 +1014,4 @@ if __name__ == '__main__':
 
     x = torch.randn((1, 3, height, width))
     x = model(x)
-    print(x.shape)
+    print(x.shape)

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

@@ -4,39 +4,39 @@
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
 
 function checkBrackets(textArea, counterElt) {
-  var counts = {};
+    var counts = {};
-  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => {
-    counts[bracket] = (counts[bracket] || 0) + 1;
+        counts[bracket] = (counts[bracket] || 0) + 1;
-  });
+    });
-  var errors = [];
+    var errors = [];
 
-  function checkPair(open, close, kind) {
+    function checkPair(open, close, kind) {
-    if (counts[open] !== counts[close]) {
+        if (counts[open] !== counts[close]) {
-      errors.push(
+            errors.push(
-        `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+                `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
-      );
+            );
+        }
     }
-  }
 
-  checkPair('(', ')', 'round brackets');
+    checkPair('(', ')', 'round brackets');
-  checkPair('[', ']', 'square brackets');
+    checkPair('[', ']', 'square brackets');
-  checkPair('{', '}', 'curly brackets');
+    checkPair('{', '}', 'curly brackets');
-  counterElt.title = errors.join('\n');
+    counterElt.title = errors.join('\n');
-  counterElt.classList.toggle('error', errors.length !== 0);
+    counterElt.classList.toggle('error', errors.length !== 0);
 }
 
 function setupBracketChecking(id_prompt, id_counter) {
-  var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+    var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
-  var counter = gradioApp().getElementById(id_counter)
+    var counter = gradioApp().getElementById(id_counter);
 
-  if (textarea && counter) {
+    if (textarea && counter) {
-    textarea.addEventListener("input", () => checkBrackets(textarea, counter));
+        textarea.addEventListener("input", () => checkBrackets(textarea, counter));
-  }
+    }
 }
 
-onUiLoaded(function () {
+onUiLoaded(function() {
-  setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+    setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
-  setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+    setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
-  setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+    setupBracketChecking('img2img_prompt', 'img2img_token_counter');
-  setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+    setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
 });

html/extra-networks-card.html

@@ -1,15 +1,14 @@
 <div class='card' style={style} onclick={card_clicked}>
+	{background_image}
 	{metadata_button}
-
 	<div class='actions'>
 		<div class='additional'>
 			<ul>
 				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
-			<span style="display:none" class='search_term{serach_only}'>{search_term}</span>
+			<span style="display:none" class='search_term{search_only}'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
 		<span class='description'>{description}</span>
 	</div>
 </div>
-

html/licenses.html

@@ -661,4 +661,30 @@ 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.
+</pre>
+
+<h2><a href="https://github.com/madebyollin/taesd/blob/main/LICENSE">TAESD</a></h2>
+<small>Tiny AutoEncoder for Stable Diffusion option for live previews</small>
+<pre>
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+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.
 </pre>

javascript/aspectRatioOverlay.js

@@ -1,111 +1,113 @@
-
+
-let currentWidth = null;
+let currentWidth = null;
-let currentHeight = null;
+let currentHeight = null;
-let arFrameTimeout = setTimeout(function(){},0);
+let arFrameTimeout = setTimeout(function() {}, 0);
-
+
-function dimensionChange(e, is_width, is_height){
+function dimensionChange(e, is_width, is_height) {
-
+
-	if(is_width){
+    if (is_width) {
-		currentWidth = e.target.value*1.0
+        currentWidth = e.target.value * 1.0;
-	}
+    }
-	if(is_height){
+    if (is_height) {
-		currentHeight = e.target.value*1.0
+        currentHeight = e.target.value * 1.0;
-	}
+    }
-
+
-	var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
+    var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
-
+
-	if(!inImg2img){
+    if (!inImg2img) {
-		return;
+        return;
-	}
+    }
-
+
-	var targetElement = null;
+    var targetElement = null;
-
+
-    var tabIndex = get_tab_index('mode_img2img')
+    var tabIndex = get_tab_index('mode_img2img');
-	if(tabIndex == 0){ // img2img
+    if (tabIndex == 0) { // img2img
-		targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
+        targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
-	} else if(tabIndex == 1){ //Sketch
+    } else if (tabIndex == 1) { //Sketch
-		targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
+        targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
-	} else if(tabIndex == 2){ // Inpaint
+    } else if (tabIndex == 2) { // Inpaint
-		targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
+        targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
-	} else if(tabIndex == 3){ // Inpaint sketch
+    } else if (tabIndex == 3) { // Inpaint sketch
-		targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
+        targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
-	}
+    }
-
+
-
+
-	if(targetElement){
+    if (targetElement) {
-
+
-		var arPreviewRect = gradioApp().querySelector('#imageARPreview');
+        var arPreviewRect = gradioApp().querySelector('#imageARPreview');
-		if(!arPreviewRect){
+        if (!arPreviewRect) {
-		    arPreviewRect = document.createElement('div')
+            arPreviewRect = document.createElement('div');
-		    arPreviewRect.id = "imageARPreview";
+            arPreviewRect.id = "imageARPreview";
-		    gradioApp().appendChild(arPreviewRect)
+            gradioApp().appendChild(arPreviewRect);
-		}
+        }
-
+
-
+
-
+
-		var viewportOffset = targetElement.getBoundingClientRect();
+        var viewportOffset = targetElement.getBoundingClientRect();
-
+
-		var viewportscale = Math.min(  targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
+        var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight);
-
+
-		var scaledx = targetElement.naturalWidth*viewportscale
+        var scaledx = targetElement.naturalWidth * viewportscale;
-		var scaledy = targetElement.naturalHeight*viewportscale
+        var scaledy = targetElement.naturalHeight * viewportscale;
-
+
-		var cleintRectTop    = (viewportOffset.top+window.scrollY)
+        var cleintRectTop = (viewportOffset.top + window.scrollY);
-		var cleintRectLeft   = (viewportOffset.left+window.scrollX)
+        var cleintRectLeft = (viewportOffset.left + window.scrollX);
-		var cleintRectCentreY = cleintRectTop  + (targetElement.clientHeight/2)
+        var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
-		var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
+        var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth / 2);
-
+
-		var arscale = Math.min(  scaledx/currentWidth, scaledy/currentHeight )
+        var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight);
-		var arscaledx = currentWidth*arscale
+        var arscaledx = currentWidth * arscale;
-		var arscaledy = currentHeight*arscale
+        var arscaledy = currentHeight * arscale;
-
+
-		var arRectTop    = cleintRectCentreY-(arscaledy/2)
+        var arRectTop = cleintRectCentreY - (arscaledy / 2);
-		var arRectLeft   = cleintRectCentreX-(arscaledx/2)
+        var arRectLeft = cleintRectCentreX - (arscaledx / 2);
-		var arRectWidth  = arscaledx
+        var arRectWidth = arscaledx;
-		var arRectHeight = arscaledy
+        var arRectHeight = arscaledy;
-
+
-	    arPreviewRect.style.top  = arRectTop+'px';
+        arPreviewRect.style.top = arRectTop + 'px';
-	    arPreviewRect.style.left = arRectLeft+'px';
+        arPreviewRect.style.left = arRectLeft + 'px';
-	    arPreviewRect.style.width = arRectWidth+'px';
+        arPreviewRect.style.width = arRectWidth + 'px';
-	    arPreviewRect.style.height = arRectHeight+'px';
+        arPreviewRect.style.height = arRectHeight + 'px';
-
+
-	    clearTimeout(arFrameTimeout);
+        clearTimeout(arFrameTimeout);
-	    arFrameTimeout = setTimeout(function(){
+        arFrameTimeout = setTimeout(function() {
-	    	arPreviewRect.style.display = 'none';
+            arPreviewRect.style.display = 'none';
-	    },2000);
+        }, 2000);
-
+
-	    arPreviewRect.style.display = 'block';
+        arPreviewRect.style.display = 'block';
-
+
-	}
+    }
-
+
-}
+}
-
+
-
+
-onUiUpdate(function(){
+onUiUpdate(function() {
-	var arPreviewRect = gradioApp().querySelector('#imageARPreview');
+    var arPreviewRect = gradioApp().querySelector('#imageARPreview');
-	if(arPreviewRect){
+    if (arPreviewRect) {
-		arPreviewRect.style.display = 'none';
+        arPreviewRect.style.display = 'none';
-	}
+    }
-    var tabImg2img = gradioApp().querySelector("#tab_img2img");
+    var tabImg2img = gradioApp().querySelector("#tab_img2img");
-    if (tabImg2img) {
+    if (tabImg2img) {
-        var inImg2img = tabImg2img.style.display == "block";
+        var inImg2img = tabImg2img.style.display == "block";
-        if(inImg2img){
+        if (inImg2img) {
-            let inputs = gradioApp().querySelectorAll('input');
+            let inputs = gradioApp().querySelectorAll('input');
-            inputs.forEach(function(e){
+            inputs.forEach(function(e) {
-                var is_width = e.parentElement.id == "img2img_width"
+                var is_width = e.parentElement.id == "img2img_width";
-                var is_height = e.parentElement.id == "img2img_height"
+                var is_height = e.parentElement.id == "img2img_height";
-
+
-                if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+                if ((is_width || is_height) && !e.classList.contains('scrollwatch')) {
-                    e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+                    e.addEventListener('input', function(e) {
-                    e.classList.add('scrollwatch')
+                        dimensionChange(e, is_width, is_height);
-                }
+                    });
-                if(is_width){
+                    e.classList.add('scrollwatch');
-                    currentWidth = e.value*1.0
+                }
-                }
+                if (is_width) {
-                if(is_height){
+                    currentWidth = e.value * 1.0;
-                    currentHeight = e.value*1.0
+                }
-                }
+                if (is_height) {
-            })
+                    currentHeight = e.value * 1.0;
-        }
+                }
-    }
+            });
-});
+        }
+    }
+});

javascript/contextMenus.js

@@ -1,166 +1,172 @@
-
+
-contextMenuInit = function(){
+var contextMenuInit = function() {
-  let eventListenerApplied=false;
+    let eventListenerApplied = false;
-  let menuSpecs = new Map();
+    let menuSpecs = new Map();
-
+
-  const uid = function(){
+    const uid = function() {
-    return Date.now().toString(36) + Math.random().toString(36).substring(2);
+        return Date.now().toString(36) + Math.random().toString(36).substring(2);
-  }
+    };
-
+
-  function showContextMenu(event,element,menuEntries){
+    function showContextMenu(event, element, menuEntries) {
-    let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
+        let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
-    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
+        let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
-
+
-    let oldMenu = gradioApp().querySelector('#context-menu')
+        let oldMenu = gradioApp().querySelector('#context-menu');
-    if(oldMenu){
+        if (oldMenu) {
-      oldMenu.remove()
+            oldMenu.remove();
-    }
+        }
-
+
-    let baseStyle = window.getComputedStyle(uiCurrentTab)
+        let baseStyle = window.getComputedStyle(uiCurrentTab);
-
+
-    const contextMenu = document.createElement('nav')
+        const contextMenu = document.createElement('nav');
-    contextMenu.id = "context-menu"
+        contextMenu.id = "context-menu";
-    contextMenu.style.background = baseStyle.background
+        contextMenu.style.background = baseStyle.background;
-    contextMenu.style.color = baseStyle.color
+        contextMenu.style.color = baseStyle.color;
-    contextMenu.style.fontFamily = baseStyle.fontFamily
+        contextMenu.style.fontFamily = baseStyle.fontFamily;
-    contextMenu.style.top = posy+'px'
+        contextMenu.style.top = posy + 'px';
-    contextMenu.style.left = posx+'px'
+        contextMenu.style.left = posx + 'px';
-
+
-
+
-
+
-    const contextMenuList = document.createElement('ul')
+        const contextMenuList = document.createElement('ul');
-    contextMenuList.className = 'context-menu-items';
+        contextMenuList.className = 'context-menu-items';
-    contextMenu.append(contextMenuList);
+        contextMenu.append(contextMenuList);
-
+
-    menuEntries.forEach(function(entry){
+        menuEntries.forEach(function(entry) {
-      let contextMenuEntry = document.createElement('a')
+            let contextMenuEntry = document.createElement('a');
-      contextMenuEntry.innerHTML = entry['name']
+            contextMenuEntry.innerHTML = entry['name'];
-      contextMenuEntry.addEventListener("click", function() {
+            contextMenuEntry.addEventListener("click", function() {
-        entry['func']();
+                entry['func']();
-      })
+            });
-      contextMenuList.append(contextMenuEntry);
+            contextMenuList.append(contextMenuEntry);
-
+
-    })
+        });
-
+
-    gradioApp().appendChild(contextMenu)
+        gradioApp().appendChild(contextMenu);
-
+
-    let menuWidth = contextMenu.offsetWidth + 4;
+        let menuWidth = contextMenu.offsetWidth + 4;
-    let menuHeight = contextMenu.offsetHeight + 4;
+        let menuHeight = contextMenu.offsetHeight + 4;
-
+
-    let windowWidth = window.innerWidth;
+        let windowWidth = window.innerWidth;
-    let windowHeight = window.innerHeight;
+        let windowHeight = window.innerHeight;
-
+
-    if ( (windowWidth - posx) < menuWidth ) {
+        if ((windowWidth - posx) < menuWidth) {
-      contextMenu.style.left = windowWidth - menuWidth + "px";
+            contextMenu.style.left = windowWidth - menuWidth + "px";
-    }
+        }
-
+
-    if ( (windowHeight - posy) < menuHeight ) {
+        if ((windowHeight - posy) < menuHeight) {
-      contextMenu.style.top = windowHeight - menuHeight + "px";
+            contextMenu.style.top = windowHeight - menuHeight + "px";
-    }
+        }
-
+
-  }
+    }
-
+
-  function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
+    function appendContextMenuOption(targetElementSelector, entryName, entryFunction) {
-
+
-    var currentItems = menuSpecs.get(targetElementSelector)
+        var currentItems = menuSpecs.get(targetElementSelector);
-
+
-    if(!currentItems){
+        if (!currentItems) {
-      currentItems = []
+            currentItems = [];
-      menuSpecs.set(targetElementSelector,currentItems);
+            menuSpecs.set(targetElementSelector, currentItems);
-    }
+        }
-    let newItem = {'id':targetElementSelector+'_'+uid(),
+        let newItem = {
-                   'name':entryName,
+            id: targetElementSelector + '_' + uid(),
-                   'func':entryFunction,
+            name: entryName,
-                   'isNew':true}
+            func: entryFunction,
-
+            isNew: true
-    currentItems.push(newItem)
+        };
-    return newItem['id']
+
-  }
+        currentItems.push(newItem);
-
+        return newItem['id'];
-  function removeContextMenuOption(uid){
+    }
-    menuSpecs.forEach(function(v) {
+
-      let index = -1
+    function removeContextMenuOption(uid) {
-      v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
+        menuSpecs.forEach(function(v) {
-      if(index>=0){
+            let index = -1;
-        v.splice(index, 1);
+            v.forEach(function(e, ei) {
-      }
+                if (e['id'] == uid) {
-    })
+                    index = ei;
-  }
+                }
-
+            });
-  function addContextMenuEventListener(){
+            if (index >= 0) {
-    if(eventListenerApplied){
+                v.splice(index, 1);
-      return;
+            }
-    }
+        });
-    gradioApp().addEventListener("click", function(e) {
+    }
-      if(! e.isTrusted){
+
-        return
+    function addContextMenuEventListener() {
-      }
+        if (eventListenerApplied) {
-
+            return;
-      let oldMenu = gradioApp().querySelector('#context-menu')
+        }
-      if(oldMenu){
+        gradioApp().addEventListener("click", function(e) {
-        oldMenu.remove()
+            if (!e.isTrusted) {
-      }
+                return;
-    });
+            }
-    gradioApp().addEventListener("contextmenu", function(e) {
+
-      let oldMenu = gradioApp().querySelector('#context-menu')
+            let oldMenu = gradioApp().querySelector('#context-menu');
-      if(oldMenu){
+            if (oldMenu) {
-        oldMenu.remove()
+                oldMenu.remove();
-      }
+            }
-      menuSpecs.forEach(function(v,k) {
+        });
-        if(e.composedPath()[0].matches(k)){
+        gradioApp().addEventListener("contextmenu", function(e) {
-          showContextMenu(e,e.composedPath()[0],v)
+            let oldMenu = gradioApp().querySelector('#context-menu');
-          e.preventDefault()
+            if (oldMenu) {
-        }
+                oldMenu.remove();
-      })
+            }
-    });
+            menuSpecs.forEach(function(v, k) {
-    eventListenerApplied=true
+                if (e.composedPath()[0].matches(k)) {
-
+                    showContextMenu(e, e.composedPath()[0], v);
-  }
+                    e.preventDefault();
-
+                }
-  return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
+            });
-}
+        });
-
+        eventListenerApplied = true;
-initResponse = contextMenuInit();
+
-appendContextMenuOption     = initResponse[0];
+    }
-removeContextMenuOption     = initResponse[1];
+
-addContextMenuEventListener = initResponse[2];
+    return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener];
-
+};
-(function(){
+
-  //Start example Context Menu Items
+var initResponse = contextMenuInit();
-  let generateOnRepeat = function(genbuttonid,interruptbuttonid){
+var appendContextMenuOption = initResponse[0];
-    let genbutton = gradioApp().querySelector(genbuttonid);
+var removeContextMenuOption = initResponse[1];
-    let interruptbutton = gradioApp().querySelector(interruptbuttonid);
+var addContextMenuEventListener = initResponse[2];
-    if(!interruptbutton.offsetParent){
+
-      genbutton.click();
+(function() {
-    }
+    //Start example Context Menu Items
-    clearInterval(window.generateOnRepeatInterval)
+    let generateOnRepeat = function(genbuttonid, interruptbuttonid) {
-    window.generateOnRepeatInterval = setInterval(function(){
+        let genbutton = gradioApp().querySelector(genbuttonid);
-      if(!interruptbutton.offsetParent){
+        let interruptbutton = gradioApp().querySelector(interruptbuttonid);
-        genbutton.click();
+        if (!interruptbutton.offsetParent) {
-      }
+            genbutton.click();
-    },
+        }
-    500)
+        clearInterval(window.generateOnRepeatInterval);
-  }
+        window.generateOnRepeatInterval = setInterval(function() {
-
+            if (!interruptbutton.offsetParent) {
-  appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+                genbutton.click();
-    generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
+            }
-  })
+        },
-  appendContextMenuOption('#img2img_generate','Generate forever',function(){
+        500);
-    generateOnRepeat('#img2img_generate','#img2img_interrupt');
+    };
-  })
+
-
+    appendContextMenuOption('#txt2img_generate', 'Generate forever', function() {
-  let cancelGenerateForever = function(){
+        generateOnRepeat('#txt2img_generate', '#txt2img_interrupt');
-    clearInterval(window.generateOnRepeatInterval)
+    });
-  }
+    appendContextMenuOption('#img2img_generate', 'Generate forever', function() {
-
+        generateOnRepeat('#img2img_generate', '#img2img_interrupt');
-  appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+    });
-  appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
-  appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
+    let cancelGenerateForever = function() {
-  appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
+        clearInterval(window.generateOnRepeatInterval);
-
+    };
-})();
+
-//End example Context Menu Items
+    appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
-
+    appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever);
-onUiUpdate(function(){
+    appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever);
-  addContextMenuEventListener()
+    appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever);
-});
+
+})();
+//End example Context Menu Items
+
+onUiUpdate(function() {
+    addContextMenuEventListener();
+});

javascript/dragdrop.js

@@ -1,11 +1,11 @@
 // allows drag-dropping files into gradio image elements, and also pasting images from clipboard
 
-function isValidImageList( files ) {
+function isValidImageList(files) {
     return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
 }
 
-function dropReplaceImage( imgWrap, files ) {
+function dropReplaceImage(imgWrap, files) {
-    if ( ! isValidImageList( files ) ) {
+    if (!isValidImageList(files)) {
         return;
     }
 
@@ -14,37 +14,37 @@ function dropReplaceImage( imgWrap, files ) {
     imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
     const callback = () => {
         const fileInput = imgWrap.querySelector('input[type="file"]');
-        if ( fileInput ) {
+        if (fileInput) {
-            if ( files.length === 0 ) {
+            if (files.length === 0) {
                 files = new DataTransfer();
                 files.items.add(tmpFile);
                 fileInput.files = files.files;
             } else {
                 fileInput.files = files;
             }
-            fileInput.dispatchEvent(new Event('change'));   
+            fileInput.dispatchEvent(new Event('change'));
         }
     };
-    
+
-    if ( imgWrap.closest('#pnginfo_image') ) {
+    if (imgWrap.closest('#pnginfo_image')) {
         // special treatment for PNG Info tab, wait for fetch request to finish
         const oldFetch = window.fetch;
-        window.fetch = async (input, options) => {
+        window.fetch = async(input, options) => {
             const response = await oldFetch(input, options);
-            if ( 'api/predict/' === input ) {
+            if ('api/predict/' === input) {
                 const content = await response.text();
                 window.fetch = oldFetch;
-                window.requestAnimationFrame( () => callback() );
+                window.requestAnimationFrame(() => callback());
                 return new Response(content, {
                     status: response.status,
                     statusText: response.statusText,
                     headers: response.headers
-                })
+                });
             }
             return response;
-        };        
+        };
     } else {
-        window.requestAnimationFrame( () => callback() );
+        window.requestAnimationFrame(() => callback());
     }
 }
 
@@ -65,33 +65,37 @@ window.document.addEventListener('drop', e => {
         return;
     }
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap ) {
+    if (!imgWrap) {
         return;
     }
     e.stopPropagation();
     e.preventDefault();
     const files = e.dataTransfer.files;
-    dropReplaceImage( imgWrap, files );
+    dropReplaceImage(imgWrap, files);
 });
 
 window.addEventListener('paste', e => {
     const files = e.clipboardData.files;
-    if ( ! isValidImageList( files ) ) {
+    if (!isValidImageList(files)) {
         return;
     }
 
     const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
-        .filter(el => uiElementIsVisible(el));
+        .filter(el => uiElementIsVisible(el))
-    if ( ! visibleImageFields.length ) {
+        .sort((a, b) => uiElementInSight(b) - uiElementInSight(a));
+
+
+    if (!visibleImageFields.length) {
         return;
     }
-    
+
     const firstFreeImageField = visibleImageFields
         .filter(el => el.querySelector('input[type=file]'))?.[0];
 
     dropReplaceImage(
         firstFreeImageField ?
-        firstFreeImageField :
+            firstFreeImageField :
-        visibleImageFields[visibleImageFields.length - 1]
+            visibleImageFields[visibleImageFields.length - 1]
-    , files );
+        , files
+    );
 });

javascript/edit-attention.js

@@ -1,120 +1,120 @@
-function keyupEditAttention(event){
+function keyupEditAttention(event) {
-	let target = event.originalTarget || event.composedPath()[0];
+    let target = event.originalTarget || event.composedPath()[0];
-	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
+    if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return;
-	if (! (event.metaKey || event.ctrlKey)) return;
+    if (!(event.metaKey || event.ctrlKey)) return;
-
+
-	let isPlus = event.key == "ArrowUp"
+    let isPlus = event.key == "ArrowUp";
-	let isMinus = event.key == "ArrowDown"
+    let isMinus = event.key == "ArrowDown";
-	if (!isPlus && !isMinus) return;
+    if (!isPlus && !isMinus) return;
-
+
-	let selectionStart = target.selectionStart;
+    let selectionStart = target.selectionStart;
-	let selectionEnd = target.selectionEnd;
+    let selectionEnd = target.selectionEnd;
-	let text = target.value;
+    let text = target.value;
-
+
-    function selectCurrentParenthesisBlock(OPEN, CLOSE){
+    function selectCurrentParenthesisBlock(OPEN, CLOSE) {
-        if (selectionStart !== selectionEnd) return false;
+        if (selectionStart !== selectionEnd) return false;
-
+
-		// Find opening parenthesis around current cursor
+        // Find opening parenthesis around current cursor
-		const before = text.substring(0, selectionStart);
+        const before = text.substring(0, selectionStart);
-		let beforeParen = before.lastIndexOf(OPEN);
+        let beforeParen = before.lastIndexOf(OPEN);
-		if (beforeParen == -1) return false;
+        if (beforeParen == -1) return false;
-		let beforeParenClose = before.lastIndexOf(CLOSE);
+        let beforeParenClose = before.lastIndexOf(CLOSE);
-		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
-			beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
+            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
-			beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
+            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
-		}
+        }
-
+
-		// Find closing parenthesis around current cursor
+        // Find closing parenthesis around current cursor
-		const after = text.substring(selectionStart);
+        const after = text.substring(selectionStart);
-		let afterParen = after.indexOf(CLOSE);
+        let afterParen = after.indexOf(CLOSE);
-		if (afterParen == -1) return false;
+        if (afterParen == -1) return false;
-		let afterParenOpen = after.indexOf(OPEN);
+        let afterParenOpen = after.indexOf(OPEN);
-		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
-			afterParen = after.indexOf(CLOSE, afterParen + 1);
+            afterParen = after.indexOf(CLOSE, afterParen + 1);
-			afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
+            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
-		}
+        }
-		if (beforeParen === -1 || afterParen === -1) return false;
+        if (beforeParen === -1 || afterParen === -1) return false;
-
+
-		// Set the selection to the text between the parenthesis
+        // Set the selection to the text between the parenthesis
-		const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
+        const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
-		const lastColon = parenContent.lastIndexOf(":");
+        const lastColon = parenContent.lastIndexOf(":");
-		selectionStart = beforeParen + 1;
+        selectionStart = beforeParen + 1;
-		selectionEnd = selectionStart + lastColon;
+        selectionEnd = selectionStart + lastColon;
-		target.setSelectionRange(selectionStart, selectionEnd);
+        target.setSelectionRange(selectionStart, selectionEnd);
-		return true;
+        return true;
-    }
+    }
-    
+
-    function selectCurrentWord(){
+    function selectCurrentWord() {
-        if (selectionStart !== selectionEnd) return false;
+        if (selectionStart !== selectionEnd) return false;
-        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
-        
+
-        // seek backward until to find beggining
+        // seek backward until to find beggining
-        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
-            selectionStart--;
+            selectionStart--;
-        }
+        }
-        
+
-        // seek forward to find end
+        // seek forward to find end
-        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
-            selectionEnd++;
+            selectionEnd++;
-        }
+        }
-
+
-        target.setSelectionRange(selectionStart, selectionEnd);
+        target.setSelectionRange(selectionStart, selectionEnd);
-        return true;
+        return true;
-    }
+    }
-
+
-	// If the user hasn't selected anything, let's select their current parenthesis block or word
+    // If the user hasn't selected anything, let's select their current parenthesis block or word
-    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
-        selectCurrentWord();
+        selectCurrentWord();
-    }
+    }
-
+
-	event.preventDefault();
+    event.preventDefault();
-
+
-    var closeCharacter = ')'
+    var closeCharacter = ')';
-    var delta = opts.keyedit_precision_attention
+    var delta = opts.keyedit_precision_attention;
-
+
-    if (selectionStart > 0 && text[selectionStart - 1] == '<'){
+    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
-        closeCharacter = '>'
+        closeCharacter = '>';
-        delta = opts.keyedit_precision_extra
+        delta = opts.keyedit_precision_extra;
-    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
+    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
-
+
-        // do not include spaces at the end
+        // do not include spaces at the end
-        while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
+        while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
-            selectionEnd -= 1;
+            selectionEnd -= 1;
-        }
+        }
-        if(selectionStart == selectionEnd){
+        if (selectionStart == selectionEnd) {
-            return
+            return;
-        }
+        }
-
+
-        text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
+        text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
-
+
-        selectionStart += 1;
+        selectionStart += 1;
-        selectionEnd += 1;
+        selectionEnd += 1;
-    }
+    }
-
+
-	var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-	var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
-	if (isNaN(weight)) return;
+    if (isNaN(weight)) return;
-
+
-	weight += isPlus ? delta : -delta;
+    weight += isPlus ? delta : -delta;
-	weight = parseFloat(weight.toPrecision(12));
+    weight = parseFloat(weight.toPrecision(12));
-	if(String(weight).length == 1) weight += ".0"
+    if (String(weight).length == 1) weight += ".0";
-
+
-    if (closeCharacter == ')' && weight == 1) {
+    if (closeCharacter == ')' && weight == 1) {
-        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
-        selectionStart--;
+        selectionStart--;
-        selectionEnd--;
+        selectionEnd--;
-    } else {
+    } else {
-        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
-    }
+    }
-
+
-	target.focus();
+    target.focus();
-	target.value = text;
+    target.value = text;
-	target.selectionStart = selectionStart;
+    target.selectionStart = selectionStart;
-	target.selectionEnd = selectionEnd;
+    target.selectionEnd = selectionEnd;
-
+
-	updateInput(target)
+    updateInput(target);
-}
+}
-
+
-addEventListener('keydown', (event) => {
+addEventListener('keydown', (event) => {
-    keyupEditAttention(event);
+    keyupEditAttention(event);
-});
+});

javascript/extensions.js

@@ -1,71 +1,74 @@
-
+
-function extensions_apply(_disabled_list, _update_list, disable_all){
+function extensions_apply(_disabled_list, _update_list, disable_all) {
-    var disable = []
+    var disable = [];
-    var update = []
+    var update = [];
-
+
-    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
-        if(x.name.startsWith("enable_") && ! x.checked)
+        if (x.name.startsWith("enable_") && !x.checked) {
-            disable.push(x.name.substring(7))
+            disable.push(x.name.substring(7));
-
+        }
-        if(x.name.startsWith("update_") && x.checked)
+
-            update.push(x.name.substring(7))
+        if (x.name.startsWith("update_") && x.checked) {
-    })
+            update.push(x.name.substring(7));
-
+        }
-    restart_reload()
+    });
-
+
-    return [JSON.stringify(disable), JSON.stringify(update), disable_all]
+    restart_reload();
-}
+
-
+    return [JSON.stringify(disable), JSON.stringify(update), disable_all];
-function extensions_check(){
+}
-    var disable = []
+
-
+function extensions_check() {
-    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+    var disable = [];
-        if(x.name.startsWith("enable_") && ! x.checked)
+
-            disable.push(x.name.substring(7))
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) {
-    })
+        if (x.name.startsWith("enable_") && !x.checked) {
-
+            disable.push(x.name.substring(7));
-    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+        }
-        x.innerHTML = "Loading..."
+    });
-    })
+
-
+    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
-
+        x.innerHTML = "Loading...";
-    var id = randomId()
+    });
-    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
+
-
+
-    })
+    var id = randomId();
-
+    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
-    return [id, JSON.stringify(disable)]
+
-}
+    });
-
+
-function install_extension_from_index(button, url){
+    return [id, JSON.stringify(disable)];
-    button.disabled = "disabled"
+}
-    button.value = "Installing..."
+
-
+function install_extension_from_index(button, url) {
-    var textarea = gradioApp().querySelector('#extension_to_install textarea')
+    button.disabled = "disabled";
-    textarea.value = url
+    button.value = "Installing...";
-    updateInput(textarea)
+
-
+    var textarea = gradioApp().querySelector('#extension_to_install textarea');
-    gradioApp().querySelector('#install_extension_button').click()
+    textarea.value = url;
-}
+    updateInput(textarea);
-
+
-function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+    gradioApp().querySelector('#install_extension_button').click();
-    if (config_state_name == "Current") {
+}
-        return [false, config_state_name, config_restore_type];
+
-    }
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
-    let restored = "";
+    if (config_state_name == "Current") {
-    if (config_restore_type == "extensions") {
+        return [false, config_state_name, config_restore_type];
-        restored = "all saved extension versions";
+    }
-    } else if (config_restore_type == "webui") {
+    let restored = "";
-        restored = "the webui version";
+    if (config_restore_type == "extensions") {
-    } else {
+        restored = "all saved extension versions";
-        restored = "the webui version and all saved extension versions";
+    } else if (config_restore_type == "webui") {
-    }
+        restored = "the webui version";
-    let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+    } else {
-    if (confirmed) {
+        restored = "the webui version and all saved extension versions";
-        restart_reload();
+    }
-        gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+    let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
-            x.innerHTML = "Loading..."
+    if (confirmed) {
-        })
+        restart_reload();
-    }
+        gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) {
-    return [confirmed, config_state_name, config_restore_type];
+            x.innerHTML = "Loading...";
-}
+        });
+    }
+    return [confirmed, config_state_name, config_restore_type];
+}

javascript/extraNetworks.js

@@ -1,196 +1,215 @@
-function setupExtraNetworksForTab(tabname){
+function setupExtraNetworksForTab(tabname) {
-    gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
+    gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks');
-
+
-    var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
+    var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div');
-    var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
+    var search = gradioApp().querySelector('#' + tabname + '_extra_search textarea');
-    var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
+    var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
-
+
-    search.classList.add('search')
+    search.classList.add('search');
-    tabs.appendChild(search)
+    tabs.appendChild(search);
-    tabs.appendChild(refresh)
+    tabs.appendChild(refresh);
-
+
-    var applyFilter = function(){
+    var applyFilter = function() {
-        var searchTerm = search.value.toLowerCase()
+        var searchTerm = search.value.toLowerCase();
-
+
-        gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
+        gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) {
-            var searchOnly = elem.querySelector('.search_only')
+            var searchOnly = elem.querySelector('.search_only');
-            var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
+            var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase();
-
+
-            var visible = text.indexOf(searchTerm) != -1
+            var visible = text.indexOf(searchTerm) != -1;
-
+
-            if(searchOnly && searchTerm.length < 4){
+            if (searchOnly && searchTerm.length < 4) {
-                visible = false
+                visible = false;
-            }
+            }
-
+
-            elem.style.display = visible ? "" : "none"
+            elem.style.display = visible ? "" : "none";
-        })
+        });
-    }
+    };
-
+
-    search.addEventListener("input", applyFilter);
+    search.addEventListener("input", applyFilter);
-    applyFilter();
+    applyFilter();
-
+
-    extraNetworksApplyFilter[tabname] = applyFilter;
+    extraNetworksApplyFilter[tabname] = applyFilter;
-}
+}
-
+
-function applyExtraNetworkFilter(tabname){
+function applyExtraNetworkFilter(tabname) {
-    setTimeout(extraNetworksApplyFilter[tabname], 1);
+    setTimeout(extraNetworksApplyFilter[tabname], 1);
-}
+}
-
+
-var extraNetworksApplyFilter = {}
+var extraNetworksApplyFilter = {};
-var activePromptTextarea = {};
+var activePromptTextarea = {};
-
+
-function setupExtraNetworks(){
+function setupExtraNetworks() {
-    setupExtraNetworksForTab('txt2img')
+    setupExtraNetworksForTab('txt2img');
-    setupExtraNetworksForTab('img2img')
+    setupExtraNetworksForTab('img2img');
-
+
-    function registerPrompt(tabname, id){
+    function registerPrompt(tabname, id) {
-        var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
+        var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
-
+
-        if (! activePromptTextarea[tabname]){
+        if (!activePromptTextarea[tabname]) {
-            activePromptTextarea[tabname] = textarea
+            activePromptTextarea[tabname] = textarea;
-        }
+        }
-
+
-		textarea.addEventListener("focus", function(){
+        textarea.addEventListener("focus", function() {
-            activePromptTextarea[tabname] = textarea;
+            activePromptTextarea[tabname] = textarea;
-		});
+        });
-    }
+    }
-
+
-    registerPrompt('txt2img', 'txt2img_prompt')
+    registerPrompt('txt2img', 'txt2img_prompt');
-    registerPrompt('txt2img', 'txt2img_neg_prompt')
+    registerPrompt('txt2img', 'txt2img_neg_prompt');
-    registerPrompt('img2img', 'img2img_prompt')
+    registerPrompt('img2img', 'img2img_prompt');
-    registerPrompt('img2img', 'img2img_neg_prompt')
+    registerPrompt('img2img', 'img2img_neg_prompt');
-}
+}
-
+
-onUiLoaded(setupExtraNetworks)
+onUiLoaded(setupExtraNetworks);
-
+
-var re_extranet   =    /<([^:]+:[^:]+):[\d\.]+>/;
+var re_extranet = /<([^:]+:[^:]+):[\d.]+>/;
-var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
+var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
-
+
-function tryToRemoveExtraNetworkFromPrompt(textarea, text){
+function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
-    var m = text.match(re_extranet)
+    var m = text.match(re_extranet);
-    if(! m) return false
+    var replaced = false;
-
+    var newTextareaText;
-    var partToSearch = m[1]
+    if (m) {
-    var replaced = false
+        var partToSearch = m[1];
-    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
+        newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found) {
-        m = found.match(re_extranet);
+            m = found.match(re_extranet);
-        if(m[1] == partToSearch){
+            if (m[1] == partToSearch) {
-            replaced = true;
+                replaced = true;
-            return ""
+                return "";
-        }
+            }
-        return found;
+            return found;
-    })
+        });
-
+    } else {
-    if(replaced){
+        newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
-        textarea.value = newTextareaText
+            if (found == text) {
-        return true;
+                replaced = true;
-    }
+                return "";
-
+            }
-    return false
+            return found;
-}
+        });
-
+    }
-function cardClicked(tabname, textToAdd, allowNegativePrompt){
+
-    var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
+    if (replaced) {
-
+        textarea.value = newTextareaText;
-    if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
+        return true;
-        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
+    }
-    }
+
-
+    return false;
-    updateInput(textarea)
+}
-}
+
-
+function cardClicked(tabname, textToAdd, allowNegativePrompt) {
-function saveCardPreview(event, tabname, filename){
+    var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
-    var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename  > label > textarea')
+
-    var button = gradioApp().getElementById(tabname + '_save_preview')
+    if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
-
+        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
-    textarea.value = filename
+    }
-    updateInput(textarea)
+
-
+    updateInput(textarea);
-    button.click()
+}
-
+
-    event.stopPropagation()
+function saveCardPreview(event, tabname, filename) {
-    event.preventDefault()
+    var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename  > label > textarea');
-}
+    var button = gradioApp().getElementById(tabname + '_save_preview');
-
+
-function extraNetworksSearchButton(tabs_id, event){
+    textarea.value = filename;
-    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
+    updateInput(textarea);
-    var button = event.target
+
-    var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
+    button.click();
-
+
-    searchTextarea.value = text
+    event.stopPropagation();
-    updateInput(searchTextarea)
+    event.preventDefault();
-}
+}
-
+
-var globalPopup = null;
+function extraNetworksSearchButton(tabs_id, event) {
-var globalPopupInner = null;
+    var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea');
-function popup(contents){
+    var button = event.target;
-    if(! globalPopup){
+    var text = button.classList.contains("search-all") ? "" : button.textContent.trim();
-        globalPopup = document.createElement('div')
+
-        globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
+    searchTextarea.value = text;
-        globalPopup.classList.add('global-popup');
+    updateInput(searchTextarea);
-
+}
-        var close = document.createElement('div')
+
-        close.classList.add('global-popup-close');
+var globalPopup = null;
-        close.onclick = function(){ globalPopup.style.display = "none"; };
+var globalPopupInner = null;
-        close.title = "Close";
+function popup(contents) {
-        globalPopup.appendChild(close)
+    if (!globalPopup) {
-
+        globalPopup = document.createElement('div');
-        globalPopupInner = document.createElement('div')
+        globalPopup.onclick = function() {
-        globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
+            globalPopup.style.display = "none";
-        globalPopupInner.classList.add('global-popup-inner');
+        };
-        globalPopup.appendChild(globalPopupInner)
+        globalPopup.classList.add('global-popup');
-
+
-        gradioApp().appendChild(globalPopup);
+        var close = document.createElement('div');
-    }
+        close.classList.add('global-popup-close');
-
+        close.onclick = function() {
-    globalPopupInner.innerHTML = '';
+            globalPopup.style.display = "none";
-    globalPopupInner.appendChild(contents);
+        };
-
+        close.title = "Close";
-    globalPopup.style.display = "flex";
+        globalPopup.appendChild(close);
-}
+
-
+        globalPopupInner = document.createElement('div');
-function extraNetworksShowMetadata(text){
+        globalPopupInner.onclick = function(event) {
-    var elem = document.createElement('pre')
+            event.stopPropagation(); return false;
-    elem.classList.add('popup-metadata');
+        };
-    elem.textContent = text;
+        globalPopupInner.classList.add('global-popup-inner');
-
+        globalPopup.appendChild(globalPopupInner);
-    popup(elem);
+
-}
+        gradioApp().appendChild(globalPopup);
-
+    }
-function requestGet(url, data, handler, errorHandler){
+
-    var xhr = new XMLHttpRequest();
+    globalPopupInner.innerHTML = '';
-    var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+    globalPopupInner.appendChild(contents);
-    xhr.open("GET", url + "?" + args, true);
+
-
+    globalPopup.style.display = "flex";
-    xhr.onreadystatechange = function () {
+}
-        if (xhr.readyState === 4) {
+
-            if (xhr.status === 200) {
+function extraNetworksShowMetadata(text) {
-                try {
+    var elem = document.createElement('pre');
-                    var js = JSON.parse(xhr.responseText);
+    elem.classList.add('popup-metadata');
-                    handler(js)
+    elem.textContent = text;
-                } catch (error) {
+
-                    console.error(error);
+    popup(elem);
-                    errorHandler()
+}
-                }
+
-            } else{
+function requestGet(url, data, handler, errorHandler) {
-                errorHandler()
+    var xhr = new XMLHttpRequest();
-            }
+    var args = Object.keys(data).map(function(k) {
-        }
+        return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]);
-    };
+    }).join('&');
-    var js = JSON.stringify(data);
+    xhr.open("GET", url + "?" + args, true);
-    xhr.send(js);
+
-}
+    xhr.onreadystatechange = function() {
-
+        if (xhr.readyState === 4) {
-function extraNetworksRequestMetadata(event, extraPage, cardName){
+            if (xhr.status === 200) {
-    var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+                try {
-
+                    var js = JSON.parse(xhr.responseText);
-    requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+                    handler(js);
-        if(data && data.metadata){
+                } catch (error) {
-            extraNetworksShowMetadata(data.metadata)
+                    console.error(error);
-        } else{
+                    errorHandler();
-            showError()
+                }
-        }
+            } else {
-    }, showError)
+                errorHandler();
-
+            }
-    event.stopPropagation()
+        }
-}
+    };
+    var js = JSON.stringify(data);
+    xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(event, extraPage, cardName) {
+    var showError = function() {
+        extraNetworksShowMetadata("there was an error getting metadata");
+    };
+
+    requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) {
+        if (data && data.metadata) {
+            extraNetworksShowMetadata(data.metadata);
+        } else {
+            showError();
+        }
+    }, showError);
+
+    event.stopPropagation();
+}

javascript/generationParams.js

@@ -1,33 +1,35 @@
 // attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
 
 let txt2img_gallery, img2img_gallery, modal = undefined;
-onUiUpdate(function(){
+onUiUpdate(function() {
-	if (!txt2img_gallery) {
+    if (!txt2img_gallery) {
-		txt2img_gallery = attachGalleryListeners("txt2img")
+        txt2img_gallery = attachGalleryListeners("txt2img");
-	}
+    }
-	if (!img2img_gallery) {
+    if (!img2img_gallery) {
-		img2img_gallery = attachGalleryListeners("img2img")
+        img2img_gallery = attachGalleryListeners("img2img");
-	}
+    }
-	if (!modal) {
+    if (!modal) {
-		modal = gradioApp().getElementById('lightboxModal')
+        modal = gradioApp().getElementById('lightboxModal');
-		modalObserver.observe(modal,  { attributes : true, attributeFilter : ['style'] });
+        modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']});
-	}
+    }
 });
 
 let modalObserver = new MutationObserver(function(mutations) {
-	mutations.forEach(function(mutationRecord) {
+    mutations.forEach(function(mutationRecord) {
-		let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
+        let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText;
-		if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
+        if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) {
-			gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
+            gradioApp().getElementById(selectedTab + "_generation_info_button")?.click();
-	});
+        }
+    });
 });
 
 function attachGalleryListeners(tab_name) {
-	var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+    var gallery = gradioApp().querySelector('#' + tab_name + '_gallery');
-	gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
+    gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click());
-	gallery?.addEventListener('keydown', (e) => {
+    gallery?.addEventListener('keydown', (e) => {
-		if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
+        if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow
-			gradioApp().getElementById(tab_name+"_generation_info_button").click()
+            gradioApp().getElementById(tab_name + "_generation_info_button").click();
-	});
+        }
-	return gallery;
+    });
+    return gallery;
 }

javascript/hints.js

@@ -1,16 +1,17 @@
 // mouseover tooltips for various UI elements
 
-titles = {
+var titles = {
     "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
     "Sampling method": "Which algorithm to use to produce the image",
-	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
+    "GFPGAN": "Restore low quality faces using GFPGAN neural network",
-	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
+    "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
-	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
+    "DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
-	"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
+    "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
-	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 
+    "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution",
 
-	"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
+    "\u{1F4D0}": "Auto detect size from img2img",
-	"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
+    "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
+    "Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
     "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
     "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
     "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
@@ -40,7 +41,7 @@ titles = {
     "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",
 
     "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-    
+
     "Skip": "Stop processing current image and continue processing.",
     "Interrupt": "Stop processing images and return any results accumulated so far.",
     "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
@@ -66,8 +67,8 @@ titles = {
 
     "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",
 
-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
+    "Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
+    "Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.",
     "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
     "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
@@ -96,7 +97,7 @@ titles = {
     "Add difference": "Result = A + (B - C) * M",
     "No interpolation": "Result = A",
 
-	"Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
+    "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
     "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
 
     "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
@@ -113,38 +114,55 @@ titles = {
     "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
     "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
     "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
+};
+
+function updateTooltipForSpan(span) {
+    if (span.title) return; // already has a title
+
+    let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
+
+    if (!tooltip) {
+        tooltip = localization[titles[span.value]] || titles[span.value];
+    }
+
+    if (!tooltip) {
+        for (const c of span.classList) {
+            if (c in titles) {
+                tooltip = localization[titles[c]] || titles[c];
+                break;
+            }
+        }
+    }
+
+    if (tooltip) {
+        span.title = tooltip;
+    }
 }
 
+function updateTooltipForSelect(select) {
+    if (select.onchange != null) return;
 
-onUiUpdate(function(){
+    select.onchange = function() {
-	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
+        select.title = localization[titles[select.value]] || titles[select.value] || "";
-		if (span.title) return;  // already has a title
+    };
+}
 
-		let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
+var observedTooltipElements = {SPAN: 1, BUTTON: 1, SELECT: 1, P: 1};
 
-    if(!tooltip){
+onUiUpdate(function(m) {
-		    tooltip = localization[titles[span.value]] || titles[span.value];
+    m.forEach(function(record) {
-		}
+        record.addedNodes.forEach(function(node) {
+            if (observedTooltipElements[node.tagName]) {
+                updateTooltipForSpan(node);
+            }
+            if (node.tagName == "SELECT") {
+                updateTooltipForSelect(node);
+            }
 
-		if(!tooltip){
+            if (node.querySelectorAll) {
-			for (const c of span.classList) {
+                node.querySelectorAll('span, button, select, p').forEach(updateTooltipForSpan);
-				if (c in titles) {
+                node.querySelectorAll('select').forEach(updateTooltipForSelect);
-					tooltip = localization[titles[c]] || titles[c];
+            }
-					break;
+        });
-				}
+    });
-			}
+});
-		}
-
-		if(tooltip){
-			span.title = tooltip;
-		}
-	})
-
-	gradioApp().querySelectorAll('select').forEach(function(select){
-	    if (select.onchange != null) return;
-
-	    select.onchange = function(){
-            select.title = localization[titles[select.value]] || titles[select.value] || "";
-	    }
-	})
-})

javascript/hires_fix.js

@@ -1,18 +1,18 @@
-
+
-function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
+function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) {
-    function setInactive(elem, inactive){
+    function setInactive(elem, inactive) {
-        elem.classList.toggle('inactive', !!inactive)
+        elem.classList.toggle('inactive', !!inactive);
-    }
+    }
-
+
-    var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
+    var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale');
-    var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
+    var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x');
-    var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
+    var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y');
-
+
-    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
+    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : "";
-
+
-    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
+    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0);
-    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
+    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0);
-    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
+    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0);
-
+
-    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
+    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y];
-}
+}

javascript/imageMaskFix.js

@@ -4,17 +4,16 @@
  */
 function imageMaskResize() {
     const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
-    if ( ! canvases.length ) {
+    if (!canvases.length) {
-    canvases_fixed = false; // TODO: this is unused..?
+        window.removeEventListener('resize', imageMaskResize);
-    window.removeEventListener( 'resize', imageMaskResize );
+        return;
-    return;
     }
 
     const wrapper = canvases[0].closest('.touch-none');
     const previewImage = wrapper.previousElementSibling;
 
-    if ( ! previewImage.complete ) {
+    if (!previewImage.complete) {
-        previewImage.addEventListener( 'load', imageMaskResize);
+        previewImage.addEventListener('load', imageMaskResize);
         return;
     }
 
@@ -24,15 +23,15 @@ function imageMaskResize() {
     const nh = previewImage.naturalHeight;
     const portrait = nh > nw;
 
-    const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
+    const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw);
-    const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
+    const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh);
 
     wrapper.style.width = `${wW}px`;
     wrapper.style.height = `${wH}px`;
     wrapper.style.left = `0px`;
     wrapper.style.top = `0px`;
 
-    canvases.forEach( c => {
+    canvases.forEach(c => {
         c.style.width = c.style.height = '';
         c.style.maxWidth = '100%';
         c.style.maxHeight = '100%';
@@ -41,4 +40,4 @@ function imageMaskResize() {
 }
 
 onUiUpdate(imageMaskResize);
-window.addEventListener( 'resize', imageMaskResize);
+window.addEventListener('resize', imageMaskResize);

javascript/imageParams.js

@@ -1,4 +1,4 @@
-window.onload = (function(){
+window.onload = (function() {
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
         if (e.dataTransfer.files.length == 0 || target.placeholder.indexOf("Prompt") == -1) return;
@@ -10,7 +10,7 @@ window.onload = (function(){
         const imgParent = gradioApp().getElementById(prompt_target);
         const files = e.dataTransfer.files;
         const fileInput = imgParent.querySelector('input[type="file"]');
-        if ( fileInput ) {
+        if (fileInput) {
             fileInput.files = files;
             fileInput.dispatchEvent(new Event('change'));
         }

javascript/imageviewer.js

@@ -5,24 +5,24 @@ function closeModal() {
 
 function showModal(event) {
     const source = event.target || event.srcElement;
-    const modalImage = gradioApp().getElementById("modalImage")
+    const modalImage = gradioApp().getElementById("modalImage");
-    const lb = gradioApp().getElementById("lightboxModal")
+    const lb = gradioApp().getElementById("lightboxModal");
-    modalImage.src = source.src
+    modalImage.src = source.src;
     if (modalImage.style.display === 'none') {
         lb.style.setProperty('background-image', 'url(' + source.src + ')');
     }
     lb.style.display = "flex";
-    lb.focus()
+    lb.focus();
 
-    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
-    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img");
     // show the save button in modal only on txt2img or img2img tabs
     if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") {
-        gradioApp().getElementById("modal_save").style.display = "inline"
+        gradioApp().getElementById("modal_save").style.display = "inline";
     } else {
-        gradioApp().getElementById("modal_save").style.display = "none"
+        gradioApp().getElementById("modal_save").style.display = "none";
     }
-    event.stopPropagation()
+    event.stopPropagation();
 }
 
 function negmod(n, m) {
@@ -30,14 +30,15 @@ function negmod(n, m) {
 }
 
 function updateOnBackgroundChange() {
-    const modalImage = gradioApp().getElementById("modalImage")
+    const modalImage = gradioApp().getElementById("modalImage");
     if (modalImage && modalImage.offsetParent) {
         let currentButton = selected_gallery_button();
 
         if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
             if (modalImage.style.display === 'none') {
-                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+                const modal = gradioApp().getElementById("lightboxModal");
+                modal.style.setProperty('background-image', `url(${modalImage.src})`);
             }
         }
     }
@@ -49,108 +50,109 @@ function modalImageSwitch(offset) {
     if (galleryButtons.length > 1) {
         var currentButton = selected_gallery_button();
 
-        var result = -1
+        var result = -1;
         galleryButtons.forEach(function(v, i) {
             if (v == currentButton) {
-                result = i
+                result = i;
             }
-        })
+        });
 
         if (result != -1) {
-            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
+            var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
-            nextButton.click()
+            nextButton.click();
             const modalImage = gradioApp().getElementById("modalImage");
             const modal = gradioApp().getElementById("lightboxModal");
             modalImage.src = nextButton.children[0].src;
             if (modalImage.style.display === 'none') {
-                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+                modal.style.setProperty('background-image', `url(${modalImage.src})`);
             }
             setTimeout(function() {
-                modal.focus()
+                modal.focus();
-            }, 10)
+            }, 10);
         }
     }
 }
 
-function saveImage(){
+function saveImage() {
-    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
+    const tabTxt2Img = gradioApp().getElementById("tab_txt2img");
-    const tabImg2Img = gradioApp().getElementById("tab_img2img")
+    const tabImg2Img = gradioApp().getElementById("tab_img2img");
-    const saveTxt2Img = "save_txt2img"
+    const saveTxt2Img = "save_txt2img";
-    const saveImg2Img = "save_img2img"
+    const saveImg2Img = "save_img2img";
     if (tabTxt2Img.style.display != "none") {
-        gradioApp().getElementById(saveTxt2Img).click()
+        gradioApp().getElementById(saveTxt2Img).click();
     } else if (tabImg2Img.style.display != "none") {
-        gradioApp().getElementById(saveImg2Img).click()
+        gradioApp().getElementById(saveImg2Img).click();
     } else {
-        console.error("missing implementation for saving modal of this type")
+        console.error("missing implementation for saving modal of this type");
     }
 }
 
 function modalSaveImage(event) {
-    saveImage()
+    saveImage();
-    event.stopPropagation()
+    event.stopPropagation();
 }
 
 function modalNextImage(event) {
-    modalImageSwitch(1)
+    modalImageSwitch(1);
-    event.stopPropagation()
+    event.stopPropagation();
 }
 
 function modalPrevImage(event) {
-    modalImageSwitch(-1)
+    modalImageSwitch(-1);
-    event.stopPropagation()
+    event.stopPropagation();
 }
 
 function modalKeyHandler(event) {
     switch (event.key) {
-        case "s":
+    case "s":
-            saveImage()
+        saveImage();
-            break;
+        break;
-        case "ArrowLeft":
+    case "ArrowLeft":
-            modalPrevImage(event)
+        modalPrevImage(event);
-            break;
+        break;
-        case "ArrowRight":
+    case "ArrowRight":
-            modalNextImage(event)
+        modalNextImage(event);
-            break;
+        break;
-        case "Escape":
+    case "Escape":
-            closeModal();
+        closeModal();
-            break;
+        break;
     }
 }
 
 function setupImageForLightbox(e) {
-	if (e.dataset.modded)
+    if (e.dataset.modded) {
-		return;
+        return;
+    }
 
-	e.dataset.modded = true;
+    e.dataset.modded = true;
-	e.style.cursor='pointer'
+    e.style.cursor = 'pointer';
-	e.style.userSelect='none'
+    e.style.userSelect = 'none';
 
-	var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
+    var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
 
-	// For Firefox, listening on click first switched to next image then shows the lightbox.
+    // For Firefox, listening on click first switched to next image then shows the lightbox.
-	// If you know how to fix this without switching to mousedown event, please.
+    // If you know how to fix this without switching to mousedown event, please.
-	// For other browsers the event is click to make it possiblr to drag picture.
+    // For other browsers the event is click to make it possiblr to drag picture.
-	var event = isFirefox ? 'mousedown' : 'click'
+    var event = isFirefox ? 'mousedown' : 'click';
 
-	e.addEventListener(event, function (evt) {
+    e.addEventListener(event, function(evt) {
-		if(!opts.js_modal_lightbox || evt.button != 0) return;
+        if (!opts.js_modal_lightbox || evt.button != 0) return;
 
-		modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
+        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);
-		evt.preventDefault()
+        evt.preventDefault();
-		showModal(evt)
+        showModal(evt);
-	}, true);
+    }, true);
 
 }
 
 function modalZoomSet(modalImage, enable) {
-    if(modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
+    if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
 }
 
 function modalZoomToggle(event) {
     var modalImage = gradioApp().getElementById("modalImage");
-    modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
+    modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'));
-    event.stopPropagation()
+    event.stopPropagation();
 }
 
 function modalTileImageToggle(event) {
@@ -159,99 +161,93 @@ function modalTileImageToggle(event) {
     const isTiling = modalImage.style.display === 'none';
     if (isTiling) {
         modalImage.style.display = 'block';
-        modal.style.setProperty('background-image', 'none')
+        modal.style.setProperty('background-image', 'none');
     } else {
         modalImage.style.display = 'none';
-        modal.style.setProperty('background-image', `url(${modalImage.src})`)
+        modal.style.setProperty('background-image', `url(${modalImage.src})`);
     }
 
-    event.stopPropagation()
+    event.stopPropagation();
-}
-
-function galleryImageHandler(e) {
-    //if (e && e.parentElement.tagName == 'BUTTON') {
-        e.onclick = showGalleryImage;
-    //}
 }
 
 onUiUpdate(function() {
-    var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
+    var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img');
     if (fullImg_preview != null) {
         fullImg_preview.forEach(setupImageForLightbox);
     }
     updateOnBackgroundChange();
-})
+});
 
 document.addEventListener("DOMContentLoaded", function() {
     //const modalFragment = document.createDocumentFragment();
-    const modal = document.createElement('div')
+    const modal = document.createElement('div');
     modal.onclick = closeModal;
     modal.id = "lightboxModal";
-    modal.tabIndex = 0
+    modal.tabIndex = 0;
-    modal.addEventListener('keydown', modalKeyHandler, true)
+    modal.addEventListener('keydown', modalKeyHandler, true);
 
-    const modalControls = document.createElement('div')
+    const modalControls = document.createElement('div');
     modalControls.className = 'modalControls gradio-container';
     modal.append(modalControls);
 
-    const modalZoom = document.createElement('span')
+    const modalZoom = document.createElement('span');
     modalZoom.className = 'modalZoom cursor';
-    modalZoom.innerHTML = '⤡'
+    modalZoom.innerHTML = '⤡';
-    modalZoom.addEventListener('click', modalZoomToggle, true)
+    modalZoom.addEventListener('click', modalZoomToggle, true);
     modalZoom.title = "Toggle zoomed view";
-    modalControls.appendChild(modalZoom)
+    modalControls.appendChild(modalZoom);
 
-    const modalTileImage = document.createElement('span')
+    const modalTileImage = document.createElement('span');
     modalTileImage.className = 'modalTileImage cursor';
-    modalTileImage.innerHTML = '⊞'
+    modalTileImage.innerHTML = '⊞';
-    modalTileImage.addEventListener('click', modalTileImageToggle, true)
+    modalTileImage.addEventListener('click', modalTileImageToggle, true);
     modalTileImage.title = "Preview tiling";
-    modalControls.appendChild(modalTileImage)
+    modalControls.appendChild(modalTileImage);
 
-    const modalSave = document.createElement("span")
+    const modalSave = document.createElement("span");
-    modalSave.className = "modalSave cursor"
+    modalSave.className = "modalSave cursor";
-    modalSave.id = "modal_save"
+    modalSave.id = "modal_save";
-    modalSave.innerHTML = "🖫"
+    modalSave.innerHTML = "🖫";
-    modalSave.addEventListener("click", modalSaveImage, true)
+    modalSave.addEventListener("click", modalSaveImage, true);
-    modalSave.title = "Save Image(s)"
+    modalSave.title = "Save Image(s)";
-    modalControls.appendChild(modalSave)
+    modalControls.appendChild(modalSave);
 
-    const modalClose = document.createElement('span')
+    const modalClose = document.createElement('span');
     modalClose.className = 'modalClose cursor';
-    modalClose.innerHTML = '×'
+    modalClose.innerHTML = '×';
     modalClose.onclick = closeModal;
     modalClose.title = "Close image viewer";
-    modalControls.appendChild(modalClose)
+    modalControls.appendChild(modalClose);
 
-    const modalImage = document.createElement('img')
+    const modalImage = document.createElement('img');
     modalImage.id = 'modalImage';
     modalImage.onclick = closeModal;
-    modalImage.tabIndex = 0
+    modalImage.tabIndex = 0;
-    modalImage.addEventListener('keydown', modalKeyHandler, true)
+    modalImage.addEventListener('keydown', modalKeyHandler, true);
-    modal.appendChild(modalImage)
+    modal.appendChild(modalImage);
 
-    const modalPrev = document.createElement('a')
+    const modalPrev = document.createElement('a');
     modalPrev.className = 'modalPrev';
-    modalPrev.innerHTML = '❮'
+    modalPrev.innerHTML = '❮';
-    modalPrev.tabIndex = 0
+    modalPrev.tabIndex = 0;
     modalPrev.addEventListener('click', modalPrevImage, true);
-    modalPrev.addEventListener('keydown', modalKeyHandler, true)
+    modalPrev.addEventListener('keydown', modalKeyHandler, true);
-    modal.appendChild(modalPrev)
+    modal.appendChild(modalPrev);
 
-    const modalNext = document.createElement('a')
+    const modalNext = document.createElement('a');
     modalNext.className = 'modalNext';
-    modalNext.innerHTML = '❯'
+    modalNext.innerHTML = '❯';
-    modalNext.tabIndex = 0
+    modalNext.tabIndex = 0;
     modalNext.addEventListener('click', modalNextImage, true);
-    modalNext.addEventListener('keydown', modalKeyHandler, true)
+    modalNext.addEventListener('keydown', modalKeyHandler, true);
 
-    modal.appendChild(modalNext)
+    modal.appendChild(modalNext);
 
     try {
-		gradioApp().appendChild(modal);
+        gradioApp().appendChild(modal);
-	} catch (e) {
+    } catch (e) {
-		gradioApp().body.appendChild(modal);
+        gradioApp().body.appendChild(modal);
-	}
+    }
 
     document.body.appendChild(modal);
 

javascript/imageviewerGamepad.js

@@ -1,7 +1,7 @@
 window.addEventListener('gamepadconnected', (e) => {
     const index = e.gamepad.index;
     let isWaiting = false;
-    setInterval(async () => {
+    setInterval(async() => {
         if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
         const gamepad = navigator.getGamepads()[index];
         const xValue = gamepad.axes[0];
@@ -14,7 +14,7 @@ window.addEventListener('gamepadconnected', (e) => {
         }
         if (isWaiting) {
             await sleepUntil(() => {
-                const xValue = navigator.getGamepads()[index].axes[0]
+                const xValue = navigator.getGamepads()[index].axes[0];
                 if (xValue < 0.3 && xValue > -0.3) {
                     return true;
                 }

javascript/localization.js

@@ -1,177 +1,176 @@
-
+
-// localization = {} -- the dict with translations is created by the backend
+// localization = {} -- the dict with translations is created by the backend
-
+
-ignore_ids_for_localization={
+var ignore_ids_for_localization = {
-    setting_sd_hypernetwork: 'OPTION',
+    setting_sd_hypernetwork: 'OPTION',
-    setting_sd_model_checkpoint: 'OPTION',
+    setting_sd_model_checkpoint: 'OPTION',
-    setting_realesrgan_enabled_models: 'OPTION',
+    modelmerger_primary_model_name: 'OPTION',
-    modelmerger_primary_model_name: 'OPTION',
+    modelmerger_secondary_model_name: 'OPTION',
-    modelmerger_secondary_model_name: 'OPTION',
+    modelmerger_tertiary_model_name: 'OPTION',
-    modelmerger_tertiary_model_name: 'OPTION',
+    train_embedding: 'OPTION',
-    train_embedding: 'OPTION',
+    train_hypernetwork: 'OPTION',
-    train_hypernetwork: 'OPTION',
+    txt2img_styles: 'OPTION',
-    txt2img_styles: 'OPTION',
+    img2img_styles: 'OPTION',
-    img2img_styles: 'OPTION',
+    setting_random_artist_categories: 'SPAN',
-    setting_random_artist_categories: 'SPAN',
+    setting_face_restoration_model: 'SPAN',
-    setting_face_restoration_model: 'SPAN',
+    setting_realesrgan_enabled_models: 'SPAN',
-    setting_realesrgan_enabled_models: 'SPAN',
+    extras_upscaler_1: 'SPAN',
-    extras_upscaler_1: 'SPAN',
+    extras_upscaler_2: 'SPAN',
-    extras_upscaler_2: 'SPAN',
+};
-}
+
-
+var re_num = /^[.\d]+$/;
-re_num = /^[\.\d]+$/
+var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u;
-re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
+
-
+var original_lines = {};
-original_lines = {}
+var translated_lines = {};
-translated_lines = {}
+
-
+function hasLocalization() {
-function hasLocalization() {
+    return window.localization && Object.keys(window.localization).length > 0;
-    return window.localization && Object.keys(window.localization).length > 0;
+}
-}
+
-
+function textNodesUnder(el) {
-function textNodesUnder(el){
+    var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false);
-    var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
+    while ((n = walk.nextNode())) a.push(n);
-    while(n=walk.nextNode()) a.push(n);
+    return a;
-    return a;
+}
-}
+
-
+function canBeTranslated(node, text) {
-function canBeTranslated(node, text){
+    if (!text) return false;
-    if(! text) return false;
+    if (!node.parentElement) return false;
-    if(! node.parentElement) return false;
+
-
+    var parentType = node.parentElement.nodeName;
-    var parentType = node.parentElement.nodeName
+    if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false;
-    if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
+
-
+    if (parentType == 'OPTION' || parentType == 'SPAN') {
-    if (parentType=='OPTION' || parentType=='SPAN'){
+        var pnode = node;
-        var pnode = node
+        for (var level = 0; level < 4; level++) {
-        for(var level=0; level<4; level++){
+            pnode = pnode.parentElement;
-            pnode = pnode.parentElement
+            if (!pnode) break;
-            if(! pnode) break;
+
-
+            if (ignore_ids_for_localization[pnode.id] == parentType) return false;
-            if(ignore_ids_for_localization[pnode.id] == parentType) return false;
+        }
-        }
+    }
-    }
+
-
+    if (re_num.test(text)) return false;
-    if(re_num.test(text)) return false;
+    if (re_emoji.test(text)) return false;
-    if(re_emoji.test(text)) return false;
+    return true;
-    return true
+}
-}
+
-
+function getTranslation(text) {
-function getTranslation(text){
+    if (!text) return undefined;
-    if(! text) return undefined
+
-
+    if (translated_lines[text] === undefined) {
-    if(translated_lines[text] === undefined){
+        original_lines[text] = 1;
-        original_lines[text] = 1
+    }
-    }
+
-
+    var tl = localization[text];
-    tl = localization[text]
+    if (tl !== undefined) {
-    if(tl !== undefined){
+        translated_lines[tl] = 1;
-        translated_lines[tl] = 1
+    }
-    }
+
-
+    return tl;
-    return tl
+}
-}
+
-
+function processTextNode(node) {
-function processTextNode(node){
+    var text = node.textContent.trim();
-    var text = node.textContent.trim()
+
-
+    if (!canBeTranslated(node, text)) return;
-    if(! canBeTranslated(node, text)) return
+
-
+    var tl = getTranslation(text);
-    tl = getTranslation(text)
+    if (tl !== undefined) {
-    if(tl !== undefined){
+        node.textContent = tl;
-        node.textContent = tl
+    }
-    }
+}
-}
+
-
+function processNode(node) {
-function processNode(node){
+    if (node.nodeType == 3) {
-    if(node.nodeType == 3){
+        processTextNode(node);
-        processTextNode(node)
+        return;
-        return
+    }
-    }
+
-
+    if (node.title) {
-    if(node.title){
+        let tl = getTranslation(node.title);
-        tl = getTranslation(node.title)
+        if (tl !== undefined) {
-        if(tl !== undefined){
+            node.title = tl;
-            node.title = tl
+        }
-        }
+    }
-    }
+
-
+    if (node.placeholder) {
-    if(node.placeholder){
+        let tl = getTranslation(node.placeholder);
-        tl = getTranslation(node.placeholder)
+        if (tl !== undefined) {
-        if(tl !== undefined){
+            node.placeholder = tl;
-            node.placeholder = tl
+        }
-        }
+    }
-    }
+
-
+    textNodesUnder(node).forEach(function(node) {
-    textNodesUnder(node).forEach(function(node){
+        processTextNode(node);
-        processTextNode(node)
+    });
-    })
+}
-}
+
-
+function dumpTranslations() {
-function dumpTranslations(){
+    if (!hasLocalization()) {
-    if(!hasLocalization()) {
+        // If we don't have any localization,
-        // If we don't have any localization,
+        // we will not have traversed the app to find
-        // we will not have traversed the app to find
+        // original_lines, so do that now.
-        // original_lines, so do that now.
+        processNode(gradioApp());
-        processNode(gradioApp());
+    }
-    }
+    var dumped = {};
-    var dumped = {}
+    if (localization.rtl) {
-    if (localization.rtl) {
+        dumped.rtl = true;
-        dumped.rtl = true;
+    }
-    }
+
-
+    for (const text in original_lines) {
-    for (const text in original_lines) {
+        if (dumped[text] !== undefined) continue;
-        if(dumped[text] !== undefined) continue;
+        dumped[text] = localization[text] || text;
-        dumped[text] = localization[text] || text;
+    }
-    }
+
-
+    return dumped;
-    return dumped;
+}
-}
+
-
+function download_localization() {
-function download_localization() {
+    var text = JSON.stringify(dumpTranslations(), null, 4);
-    var text = JSON.stringify(dumpTranslations(), null, 4)
+
-
+    var element = document.createElement('a');
-    var element = document.createElement('a');
+    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-    element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
+    element.setAttribute('download', "localization.json");
-    element.setAttribute('download', "localization.json");
+    element.style.display = 'none';
-    element.style.display = 'none';
+    document.body.appendChild(element);
-    document.body.appendChild(element);
+
-
+    element.click();
-    element.click();
+
-
+    document.body.removeChild(element);
-    document.body.removeChild(element);
+}
-}
+
-
+document.addEventListener("DOMContentLoaded", function() {
-document.addEventListener("DOMContentLoaded", function () {
+    if (!hasLocalization()) {
-    if (!hasLocalization()) {
+        return;
-        return;
+    }
-    }
+
-
+    onUiUpdate(function(m) {
-    onUiUpdate(function (m) {
+        m.forEach(function(mutation) {
-        m.forEach(function (mutation) {
+            mutation.addedNodes.forEach(function(node) {
-            mutation.addedNodes.forEach(function (node) {
+                processNode(node);
-                processNode(node)
+            });
-            })
+        });
-        });
+    });
-    })
+
-
+    processNode(gradioApp());
-    processNode(gradioApp())
+
-
+    if (localization.rtl) { // if the language is from right to left,
-    if (localization.rtl) {  // if the language is from right to left,
+        (new MutationObserver((mutations, observer) => { // wait for the style to load
-        (new MutationObserver((mutations, observer) => { // wait for the style to load
+            mutations.forEach(mutation => {
-            mutations.forEach(mutation => {
+                mutation.addedNodes.forEach(node => {
-                mutation.addedNodes.forEach(node => {
+                    if (node.tagName === 'STYLE') {
-                    if (node.tagName === 'STYLE') {
+                        observer.disconnect();
-                        observer.disconnect();
+
-
+                        for (const x of node.sheet.rules) { // find all rtl media rules
-                        for (const x of node.sheet.rules) {  // find all rtl media rules
+                            if (Array.from(x.media || []).includes('rtl')) {
-                            if (Array.from(x.media || []).includes('rtl')) {
+                                x.media.appendMedium('all'); // enable them
-                                x.media.appendMedium('all');  // enable them
+                            }
-                            }
+                        }
-                        }
+                    }
-                    }
+                });
-                })
+            });
-            });
+        })).observe(gradioApp(), {childList: true});
-        })).observe(gradioApp(), { childList: true });
+    }
-    }
+});
-})

javascript/notification.js

@@ -4,14 +4,14 @@ let lastHeadImg = null;
 
 let notificationButton = null;
 
-onUiUpdate(function(){
+onUiUpdate(function() {
-    if(notificationButton == null){
+    if (notificationButton == null) {
-        notificationButton = gradioApp().getElementById('request_notifications')
+        notificationButton = gradioApp().getElementById('request_notifications');
 
-        if(notificationButton != null){
+        if (notificationButton != null) {
             notificationButton.addEventListener('click', () => {
                 void Notification.requestPermission();
-            },true);
+            }, true);
         }
     }
 
@@ -42,7 +42,7 @@ onUiUpdate(function(){
         }
     );
 
-    notification.onclick = function(_){
+    notification.onclick = function(_) {
         parent.focus();
         this.close();
     };

javascript/progressbar.js

@@ -1,29 +1,29 @@
 // code related to showing and updating progressbar shown as the image is being made
 
-function rememberGallerySelection(){
+function rememberGallerySelection() {
 
 }
 
-function getGallerySelectedIndex(){
+function getGallerySelectedIndex() {
 
 }
 
-function request(url, data, handler, errorHandler){
+function request(url, data, handler, errorHandler) {
     var xhr = new XMLHttpRequest();
     xhr.open("POST", url, true);
     xhr.setRequestHeader("Content-Type", "application/json");
-    xhr.onreadystatechange = function () {
+    xhr.onreadystatechange = function() {
         if (xhr.readyState === 4) {
             if (xhr.status === 200) {
                 try {
                     var js = JSON.parse(xhr.responseText);
-                    handler(js)
+                    handler(js);
                 } catch (error) {
                     console.error(error);
-                    errorHandler()
+                    errorHandler();
                 }
-            } else{
+            } else {
-                errorHandler()
+                errorHandler();
             }
         }
     };
@@ -31,147 +31,147 @@ function request(url, data, handler, errorHandler){
     xhr.send(js);
 }
 
-function pad2(x){
+function pad2(x) {
-    return x<10 ? '0'+x : x
+    return x < 10 ? '0' + x : x;
 }
 
-function formatTime(secs){
+function formatTime(secs) {
-    if(secs > 3600){
+    if (secs > 3600) {
-        return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60)
+        return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60);
-    } else if(secs > 60){
+    } else if (secs > 60) {
-        return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60)
+        return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60);
-    } else{
+    } else {
-        return Math.floor(secs) + "s"
+        return Math.floor(secs) + "s";
     }
 }
 
-function setTitle(progress){
+function setTitle(progress) {
-    var title = 'Stable Diffusion'
+    var title = 'Stable Diffusion';
 
-    if(opts.show_progress_in_title && progress){
+    if (opts.show_progress_in_title && progress) {
         title = '[' + progress.trim() + '] ' + title;
     }
 
-    if(document.title != title){
+    if (document.title != title) {
-        document.title =  title;
+        document.title = title;
     }
 }
 
 
-function randomId(){
+function randomId() {
-    return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")"
+    return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")";
 }
 
 // starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
 // preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
 // calls onProgress every time there is a progress update
-function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout=40){
+function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) {
-    var dateStart = new Date()
+    var dateStart = new Date();
-    var wasEverActive = false
+    var wasEverActive = false;
-    var parentProgressbar = progressbarContainer.parentNode
+    var parentProgressbar = progressbarContainer.parentNode;
-    var parentGallery = gallery ? gallery.parentNode : null
+    var parentGallery = gallery ? gallery.parentNode : null;
 
-    var divProgress = document.createElement('div')
+    var divProgress = document.createElement('div');
-    divProgress.className='progressDiv'
+    divProgress.className = 'progressDiv';
-    divProgress.style.display = opts.show_progressbar ? "block" : "none"
+    divProgress.style.display = opts.show_progressbar ? "block" : "none";
-    var divInner = document.createElement('div')
+    var divInner = document.createElement('div');
-    divInner.className='progress'
+    divInner.className = 'progress';
 
-    divProgress.appendChild(divInner)
+    divProgress.appendChild(divInner);
-    parentProgressbar.insertBefore(divProgress, progressbarContainer)
+    parentProgressbar.insertBefore(divProgress, progressbarContainer);
 
-    if(parentGallery){
+    if (parentGallery) {
-        var livePreview = document.createElement('div')
+        var livePreview = document.createElement('div');
-        livePreview.className='livePreview'
+        livePreview.className = 'livePreview';
-        parentGallery.insertBefore(livePreview, gallery)
+        parentGallery.insertBefore(livePreview, gallery);
     }
 
-    var removeProgressBar = function(){
+    var removeProgressBar = function() {
-        setTitle("")
+        setTitle("");
-        parentProgressbar.removeChild(divProgress)
+        parentProgressbar.removeChild(divProgress);
-        if(parentGallery) parentGallery.removeChild(livePreview)
+        if (parentGallery) parentGallery.removeChild(livePreview);
-        atEnd()
+        atEnd();
-    }
+    };
 
-    var fun = function(id_task, id_live_preview){
+    var fun = function(id_task, id_live_preview) {
-        request("./internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){
+        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
-            if(res.completed){
+            if (res.completed) {
-                removeProgressBar()
+                removeProgressBar();
-                return
+                return;
             }
 
-            var rect = progressbarContainer.getBoundingClientRect()
+            var rect = progressbarContainer.getBoundingClientRect();
 
-            if(rect.width){
+            if (rect.width) {
                 divProgress.style.width = rect.width + "px";
             }
 
-            let progressText = ""
+            let progressText = "";
 
-            divInner.style.width = ((res.progress || 0) * 100.0) + '%'
+            divInner.style.width = ((res.progress || 0) * 100.0) + '%';
-            divInner.style.background = res.progress ? "" : "transparent"
+            divInner.style.background = res.progress ? "" : "transparent";
 
-            if(res.progress > 0){
+            if (res.progress > 0) {
-                progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'
+                progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%';
             }
 
-            if(res.eta){
+            if (res.eta) {
-                progressText += " ETA: " + formatTime(res.eta)
+                progressText += " ETA: " + formatTime(res.eta);
             }
 
 
-            setTitle(progressText)
+            setTitle(progressText);
 
-            if(res.textinfo && res.textinfo.indexOf("\n") == -1){
+            if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
-                progressText = res.textinfo + " " + progressText
+                progressText = res.textinfo + " " + progressText;
             }
 
-            divInner.textContent = progressText
+            divInner.textContent = progressText;
 
-            var elapsedFromStart = (new Date() - dateStart) / 1000
+            var elapsedFromStart = (new Date() - dateStart) / 1000;
 
-            if(res.active) wasEverActive = true;
+            if (res.active) wasEverActive = true;
 
-            if(! res.active && wasEverActive){
+            if (!res.active && wasEverActive) {
-                removeProgressBar()
+                removeProgressBar();
-                return
+                return;
             }
 
-            if(elapsedFromStart > inactivityTimeout && !res.queued && !res.active){
+            if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) {
-                removeProgressBar()
+                removeProgressBar();
-                return
+                return;
             }
 
 
-            if(res.live_preview && gallery){
+            if (res.live_preview && gallery) {
-                var rect = gallery.getBoundingClientRect()
+                rect = gallery.getBoundingClientRect();
-                if(rect.width){
+                if (rect.width) {
-                    livePreview.style.width = rect.width + "px"
+                    livePreview.style.width = rect.width + "px";
-                    livePreview.style.height = rect.height + "px"
+                    livePreview.style.height = rect.height + "px";
                 }
 
                 var img = new Image();
                 img.onload = function() {
-                    livePreview.appendChild(img)
+                    livePreview.appendChild(img);
-                    if(livePreview.childElementCount > 2){
+                    if (livePreview.childElementCount > 2) {
-                        livePreview.removeChild(livePreview.firstElementChild)
+                        livePreview.removeChild(livePreview.firstElementChild);
                     }
-                }
+                };
                 img.src = res.live_preview;
             }
 
 
-            if(onProgress){
+            if (onProgress) {
-                onProgress(res)
+                onProgress(res);
             }
 
             setTimeout(() => {
                 fun(id_task, res.id_live_preview);
-            }, opts.live_preview_refresh_period || 500)
+            }, opts.live_preview_refresh_period || 500);
-        }, function(){
+        }, function() {
-            removeProgressBar()
+            removeProgressBar();
-        })
+        });
-    }
+    };
 
-    fun(id_task, 0)
+    fun(id_task, 0);
 }

javascript/textualInversion.js

@@ -1,17 +1,17 @@
-
+
-
+
-
+
-function start_training_textual_inversion(){
+function start_training_textual_inversion() {
-    gradioApp().querySelector('#ti_error').innerHTML=''
+    gradioApp().querySelector('#ti_error').innerHTML = '';
-
+
-    var id = randomId()
+    var id = randomId();
-    requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function(){}, function(progress){
+    requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) {
-        gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo
+        gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo;
-    })
+    });
-
+
-    var res = args_to_array(arguments)
+    var res = Array.from(arguments);
-
+
-    res[0] = id
+    res[0] = id;
-
+
-    return res
+    return res;
-}
+}

javascript/ui.js

@@ -1,9 +1,9 @@
 // various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
-function set_theme(theme){
+function set_theme(theme) {
-    var gradioURL = window.location.href
+    var gradioURL = window.location.href;
     if (!gradioURL.includes('?__theme=')) {
-      window.location.replace(gradioURL + '?__theme=' + theme);
+        window.location.replace(gradioURL + '?__theme=' + theme);
     }
 }
 
@@ -14,7 +14,7 @@ function all_gallery_buttons() {
         if (elem.parentElement.offsetParent) {
             visibleGalleryButtons.push(elem);
         }
-    })
+    });
     return visibleGalleryButtons;
 }
 
@@ -25,31 +25,35 @@ function selected_gallery_button() {
         if (elem.parentElement.offsetParent) {
             visibleCurrentButton = elem;
         }
-    })
+    });
     return visibleCurrentButton;
 }
 
-function selected_gallery_index(){
+function selected_gallery_index() {
     var buttons = all_gallery_buttons();
     var button = selected_gallery_button();
 
-    var result = -1
+    var result = -1;
-    buttons.forEach(function(v, i){ if(v==button) { result = i } })
+    buttons.forEach(function(v, i) {
+        if (v == button) {
+            result = i;
+        }
+    });
 
-    return result
+    return result;
 }
 
-function extract_image_from_gallery(gallery){
+function extract_image_from_gallery(gallery) {
-    if (gallery.length == 0){
+    if (gallery.length == 0) {
         return [null];
     }
-    if (gallery.length == 1){
+    if (gallery.length == 1) {
         return [gallery[0]];
     }
 
-    var index = selected_gallery_index()
+    var index = selected_gallery_index();
 
-    if (index < 0 || index >= gallery.length){
+    if (index < 0 || index >= gallery.length) {
         // Use the first image in the gallery as the default
         index = 0;
     }
@@ -57,249 +61,226 @@ function extract_image_from_gallery(gallery){
     return [gallery[index]];
 }
 
-function args_to_array(args){
+window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around
-    var res = []
-    for(var i=0;i<args.length;i++){
-        res.push(args[i])
-    }
-    return res
-}
 
-function switch_to_txt2img(){
+function switch_to_txt2img() {
     gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
 
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function switch_to_img2img_tab(no){
+function switch_to_img2img_tab(no) {
     gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
     gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click();
 }
-function switch_to_img2img(){
+function switch_to_img2img() {
     switch_to_img2img_tab(0);
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function switch_to_sketch(){
+function switch_to_sketch() {
     switch_to_img2img_tab(1);
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function switch_to_inpaint(){
+function switch_to_inpaint() {
     switch_to_img2img_tab(2);
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function switch_to_inpaint_sketch(){
+function switch_to_inpaint_sketch() {
     switch_to_img2img_tab(3);
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function switch_to_inpaint(){
+function switch_to_extras() {
-    gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
-    gradioApp().getElementById('mode_img2img').querySelectorAll('button')[2].click();
-
-    return args_to_array(arguments);
-}
-
-function switch_to_extras(){
     gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
 
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function get_tab_index(tabId){
+function get_tab_index(tabId) {
-    var res = 0
+    let buttons = gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button');
-
+    for (let i = 0; i < buttons.length; i++) {
-    gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
+        if (buttons[i].classList.contains('selected')) {
-        if(button.className.indexOf('selected') != -1)
+            return i;
-            res = i
+        }
-    })
-
-    return res
-}
-
-function create_tab_index_args(tabId, args){
-    var res = []
-    for(var i=0; i<args.length; i++){
-        res.push(args[i])
     }
+    return 0;
+}
 
-    res[0] = get_tab_index(tabId)
+function create_tab_index_args(tabId, args) {
-
+    var res = Array.from(args);
-    return res
+    res[0] = get_tab_index(tabId);
+    return res;
 }
 
 function get_img2img_tab_index() {
-    let res = args_to_array(arguments)
+    let res = Array.from(arguments);
-    res.splice(-2)
+    res.splice(-2);
-    res[0] = get_tab_index('mode_img2img')
+    res[0] = get_tab_index('mode_img2img');
-    return res
+    return res;
 }
 
-function create_submit_args(args){
+function create_submit_args(args) {
-    var res = []
+    var res = Array.from(args);
-    for(var i=0;i<args.length;i++){
-        res.push(args[i])
-    }
 
     // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
     // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
     // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
     // If gradio at some point stops sending outputs, this may break something
-    if(Array.isArray(res[res.length - 3])){
+    if (Array.isArray(res[res.length - 3])) {
-        res[res.length - 3] = null
+        res[res.length - 3] = null;
     }
 
-    return res
+    return res;
 }
 
-function showSubmitButtons(tabname, show){
+function showSubmitButtons(tabname, show) {
-    gradioApp().getElementById(tabname+'_interrupt').style.display = show ? "none" : "block"
+    gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block";
-    gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block"
+    gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block";
 }
 
-function showRestoreProgressButton(tabname, show){
+function showRestoreProgressButton(tabname, show) {
-    var button = gradioApp().getElementById(tabname + "_restore_progress")
+    var button = gradioApp().getElementById(tabname + "_restore_progress");
-    if(! button) return
+    if (!button) return;
 
-    button.style.display = show ? "flex" : "none"
+    button.style.display = show ? "flex" : "none";
 }
 
-function submit(){
+function submit() {
-    rememberGallerySelection('txt2img_gallery')
+    showSubmitButtons('txt2img', false);
-    showSubmitButtons('txt2img', false)
 
-    var id = randomId()
+    var id = randomId();
     localStorage.setItem("txt2img_task_id", id);
 
-    requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
+    requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
-        showSubmitButtons('txt2img', true)
+        showSubmitButtons('txt2img', true);
-        localStorage.removeItem("txt2img_task_id")
+        localStorage.removeItem("txt2img_task_id");
-        showRestoreProgressButton('txt2img', false)
+        showRestoreProgressButton('txt2img', false);
-    })
+    });
 
-    var res = create_submit_args(arguments)
+    var res = create_submit_args(arguments);
 
-    res[0] = id
+    res[0] = id;
 
-    return res
+    return res;
 }
 
-function submit_img2img(){
+function submit_img2img() {
-    rememberGallerySelection('img2img_gallery')
+    showSubmitButtons('img2img', false);
-    showSubmitButtons('img2img', false)
 
-    var id = randomId()
+    var id = randomId();
     localStorage.setItem("img2img_task_id", id);
 
-    requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
+    requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
-        showSubmitButtons('img2img', true)
+        showSubmitButtons('img2img', true);
-        localStorage.removeItem("img2img_task_id")
+        localStorage.removeItem("img2img_task_id");
-        showRestoreProgressButton('img2img', false)
+        showRestoreProgressButton('img2img', false);
-    })
+    });
 
-    var res = create_submit_args(arguments)
+    var res = create_submit_args(arguments);
 
-    res[0] = id
+    res[0] = id;
-    res[1] = get_tab_index('mode_img2img')
+    res[1] = get_tab_index('mode_img2img');
 
-    return res
+    return res;
 }
 
-function restoreProgressTxt2img(){
+function restoreProgressTxt2img() {
-    showRestoreProgressButton("txt2img", false)
+    showRestoreProgressButton("txt2img", false);
-    var id = localStorage.getItem("txt2img_task_id")
+    var id = localStorage.getItem("txt2img_task_id");
 
-    id = localStorage.getItem("txt2img_task_id")
+    id = localStorage.getItem("txt2img_task_id");
 
-    if(id) {
+    if (id) {
-        requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
+        requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function() {
-            showSubmitButtons('txt2img', true)
+            showSubmitButtons('txt2img', true);
-        }, null, 0)
+        }, null, 0);
     }
 
-    return id
+    return id;
 }
 
-function restoreProgressImg2img(){
+function restoreProgressImg2img() {
-    showRestoreProgressButton("img2img", false)
+    showRestoreProgressButton("img2img", false);
-    
-    var id = localStorage.getItem("img2img_task_id")
 
-    if(id) {
+    var id = localStorage.getItem("img2img_task_id");
-        requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
+
-            showSubmitButtons('img2img', true)
+    if (id) {
-        }, null, 0)
+        requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function() {
+            showSubmitButtons('img2img', true);
+        }, null, 0);
     }
 
-    return id
+    return id;
 }
 
 
-onUiLoaded(function () {
+onUiLoaded(function() {
-    showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"))
+    showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"));
-    showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"))
+    showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"));
 });
 
 
-function modelmerger(){
+function modelmerger() {
-    var id = randomId()
+    var id = randomId();
-    requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
+    requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function() {});
 
-    var res = create_submit_args(arguments)
+    var res = create_submit_args(arguments);
-    res[0] = id
+    res[0] = id;
-    return res
+    return res;
 }
 
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
-    var name_ = prompt('Style name:')
+    var name_ = prompt('Style name:');
-    return [name_, prompt_text, negative_prompt_text]
+    return [name_, prompt_text, negative_prompt_text];
 }
 
 function confirm_clear_prompt(prompt, negative_prompt) {
-    if(confirm("Delete prompt?")) {
+    if (confirm("Delete prompt?")) {
-        prompt = ""
+        prompt = "";
-        negative_prompt = ""
+        negative_prompt = "";
     }
 
-    return [prompt, negative_prompt]
+    return [prompt, negative_prompt];
 }
 
 
-promptTokecountUpdateFuncs = {}
+var promptTokecountUpdateFuncs = {};
 
-function recalculatePromptTokens(name){
+function recalculatePromptTokens(name) {
-    if(promptTokecountUpdateFuncs[name]){
+    if (promptTokecountUpdateFuncs[name]) {
-        promptTokecountUpdateFuncs[name]()
+        promptTokecountUpdateFuncs[name]();
     }
 }
 
-function recalculate_prompts_txt2img(){
+function recalculate_prompts_txt2img() {
-    recalculatePromptTokens('txt2img_prompt')
+    recalculatePromptTokens('txt2img_prompt');
-    recalculatePromptTokens('txt2img_neg_prompt')
+    recalculatePromptTokens('txt2img_neg_prompt');
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
-function recalculate_prompts_img2img(){
+function recalculate_prompts_img2img() {
-    recalculatePromptTokens('img2img_prompt')
+    recalculatePromptTokens('img2img_prompt');
-    recalculatePromptTokens('img2img_neg_prompt')
+    recalculatePromptTokens('img2img_neg_prompt');
-    return args_to_array(arguments);
+    return Array.from(arguments);
 }
 
 
-var opts = {}
+var opts = {};
-onUiUpdate(function(){
+onUiUpdate(function() {
-	if(Object.keys(opts).length != 0) return;
+    if (Object.keys(opts).length != 0) return;
 
-	var json_elem = gradioApp().getElementById('settings_json')
+    var json_elem = gradioApp().getElementById('settings_json');
-	if(json_elem == null) return;
+    if (json_elem == null) return;
 
-    var textarea = json_elem.querySelector('textarea')
+    var textarea = json_elem.querySelector('textarea');
-    var jsdata = textarea.value
+    var jsdata = textarea.value;
-    opts = JSON.parse(jsdata)
+    opts = JSON.parse(jsdata);
-    executeCallbacks(optionsChangedCallbacks);
+
+    executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/
 
     Object.defineProperty(textarea, 'value', {
         set: function(newValue) {
@@ -308,7 +289,7 @@ onUiUpdate(function(){
             valueProp.set.call(textarea, newValue);
 
             if (oldValue != newValue) {
-                opts = JSON.parse(textarea.value)
+                opts = JSON.parse(textarea.value);
             }
 
             executeCallbacks(optionsChangedCallbacks);
@@ -319,123 +300,157 @@ onUiUpdate(function(){
         }
     });
 
-    json_elem.parentElement.style.display="none"
+    json_elem.parentElement.style.display = "none";
 
-    function registerTextarea(id, id_counter, id_button){
+    function registerTextarea(id, id_counter, id_button) {
-        var prompt = gradioApp().getElementById(id)
+        var prompt = gradioApp().getElementById(id);
-        var counter = gradioApp().getElementById(id_counter)
+        var counter = gradioApp().getElementById(id_counter);
         var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
 
-        if(counter.parentElement == prompt.parentElement){
+        if (counter.parentElement == prompt.parentElement) {
-            return
+            return;
         }
 
-        prompt.parentElement.insertBefore(counter, prompt)
+        prompt.parentElement.insertBefore(counter, prompt);
-        prompt.parentElement.style.position = "relative"
+        prompt.parentElement.style.position = "relative";
 
-		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
+        promptTokecountUpdateFuncs[id] = function() {
-		textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
+            update_token_counter(id_button);
+        };
+        textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
     }
 
-    registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
+    registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button');
-    registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button')
+    registerTextarea('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button');
-    registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
+    registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button');
-    registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
+    registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button');
 
-    var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
-    var settings_tabs = gradioApp().querySelector('#settings div')
+    var settings_tabs = gradioApp().querySelector('#settings div');
-    if(show_all_pages && settings_tabs){
+    if (show_all_pages && settings_tabs) {
-        settings_tabs.appendChild(show_all_pages)
+        settings_tabs.appendChild(show_all_pages);
-        show_all_pages.onclick = function(){
+        show_all_pages.onclick = function() {
-            gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+            gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
-                if(elem.id == "settings_tab_licenses")
+                if (elem.id == "settings_tab_licenses") {
                     return;
+                }
 
                 elem.style.display = "block";
-            })
+            });
-        }
+        };
     }
-})
+});
 
-onOptionsChanged(function(){
+onOptionsChanged(function() {
-    var elem = gradioApp().getElementById('sd_checkpoint_hash')
+    var elem = gradioApp().getElementById('sd_checkpoint_hash');
-    var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
+    var sd_checkpoint_hash = opts.sd_checkpoint_hash || "";
-    var shorthash = sd_checkpoint_hash.substring(0,10)
+    var shorthash = sd_checkpoint_hash.substring(0, 10);
 
-	if(elem && elem.textContent != shorthash){
+    if (elem && elem.textContent != shorthash) {
-	    elem.textContent = shorthash
+        elem.textContent = shorthash;
-	    elem.title = sd_checkpoint_hash
+        elem.title = sd_checkpoint_hash;
-	    elem.href = "https://google.com/search?q=" + sd_checkpoint_hash
+        elem.href = "https://google.com/search?q=" + sd_checkpoint_hash;
-	}
+    }
-})
+});
 
 let txt2img_textarea, img2img_textarea = undefined;
-let wait_time = 800
+let wait_time = 800;
 let token_timeouts = {};
 
 function update_txt2img_tokens(...args) {
-	update_token_counter("txt2img_token_button")
+    update_token_counter("txt2img_token_button");
-	if (args.length == 2)
+    if (args.length == 2) {
-		return args[0]
+        return args[0];
-	return args;
+    }
+    return args;
 }
 
 function update_img2img_tokens(...args) {
-	update_token_counter("img2img_token_button")
+    update_token_counter(
-	if (args.length == 2)
+        "img2img_token_button"
-		return args[0]
+    );
-	return args;
+    if (args.length == 2) {
+        return args[0];
+    }
+    return args;
 }
 
 function update_token_counter(button_id) {
-	if (token_timeouts[button_id])
+    if (token_timeouts[button_id]) {
-		clearTimeout(token_timeouts[button_id]);
+        clearTimeout(token_timeouts[button_id]);
-	token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
+    }
+    token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
-function restart_reload(){
+function restart_reload() {
-    document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
+    document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
 
-    var requestPing = function(){
+    var requestPing = function() {
-        requestGet("./internal/ping", {}, function(data){
+        requestGet("./internal/ping", {}, function(data) {
             location.reload();
-        }, function(){
+        }, function() {
             setTimeout(requestPing, 500);
-        })
+        });
-    }
+    };
 
     setTimeout(requestPing, 2000);
 
-    return []
+    return [];
 }
 
 // Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits
 // will only visible on web page and not sent to python.
-function updateInput(target){
+function updateInput(target) {
-	let e = new Event("input", { bubbles: true })
+    let e = new Event("input", {bubbles: true});
-	Object.defineProperty(e, "target", {value: target})
+    Object.defineProperty(e, "target", {value: target});
-	target.dispatchEvent(e);
+    target.dispatchEvent(e);
 }
 
 
 var desiredCheckpointName = null;
-function selectCheckpoint(name){
+function selectCheckpoint(name) {
     desiredCheckpointName = name;
-    gradioApp().getElementById('change_checkpoint').click()
+    gradioApp().getElementById('change_checkpoint').click();
 }
 
-function currentImg2imgSourceResolution(_, _, scaleBy){
+function currentImg2imgSourceResolution(w, h, scaleBy) {
-    var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img')
+    var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img');
-    return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]
+    return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy];
 }
 
-function updateImg2imgResizeToTextAfterChangingImage(){
+function updateImg2imgResizeToTextAfterChangingImage() {
     // At the time this is called from gradio, the image has no yet been replaced.
     // There may be a better solution, but this is simple and straightforward so I'm going with it.
 
     setTimeout(function() {
-        gradioApp().getElementById('img2img_update_resize_to').click()
+        gradioApp().getElementById('img2img_update_resize_to').click();
     }, 500);
 
-    return []
+    return [];
+
+}
+
+
+
+function setRandomSeed(elem_id) {
+    var input = gradioApp().querySelector("#" + elem_id + " input");
+    if (!input) return [];
+
+    input.value = "-1";
+    updateInput(input);
+    return [];
+}
+
+function switchWidthHeight(tabname) {
+    var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]");
+    var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]");
+    if (!width || !height) return [];
+
+    var tmp = width.value;
+    width.value = height.value;
+    height.value = tmp;
+
+    updateInput(width);
+    updateInput(height);
+    return [];
 }

javascript/ui_settings_hints.js

@@ -1,41 +1,62 @@
-// various hints and extra info for the settings tab
+// various hints and extra info for the settings tab
-
+
-onUiLoaded(function(){
+var settingsHintsSetup = false;
-    createLink = function(elem_id, text, href){
+
-        var a = document.createElement('A')
+onOptionsChanged(function() {
-        a.textContent = text
+    if (settingsHintsSetup) return;
-        a.target = '_blank';
+    settingsHintsSetup = true;
-
+
-        elem = gradioApp().querySelector('#'+elem_id)
+    gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) {
-        elem.insertBefore(a, elem.querySelector('label'))
+        var name = div.id.substr(8);
-
+        var commentBefore = opts._comments_before[name];
-        return a
+        var commentAfter = opts._comments_after[name];
-    }
+
-
+        if (!commentBefore && !commentAfter) return;
-    createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
+
-    createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
+        var span = null;
-
+        if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span');
-    createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
+        else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild;
-        requestGet("./internal/quicksettings-hint", {}, function(data){
+        else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild;
-            var table = document.createElement('table')
+        else span = div.querySelector('label span').firstChild;
-            table.className = 'settings-value-table'
+
-
+        if (!span) return;
-            data.forEach(function(obj){
+
-                var tr = document.createElement('tr')
+        if (commentBefore) {
-                var td = document.createElement('td')
+            var comment = document.createElement('DIV');
-                td.textContent = obj.name
+            comment.className = 'settings-comment';
-                tr.appendChild(td)
+            comment.innerHTML = commentBefore;
-
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
-                var td = document.createElement('td')
+            span.parentElement.insertBefore(comment, span);
-                td.textContent = obj.label
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span);
-                tr.appendChild(td)
+        }
-
+        if (commentAfter) {
-                table.appendChild(tr)
+            comment = document.createElement('DIV');
-            })
+            comment.className = 'settings-comment';
-
+            comment.innerHTML = commentAfter;
-            popup(table);
+            span.parentElement.insertBefore(comment, span.nextSibling);
-        })
+            span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling);
-    });
+        }
-})
+    });
-
+});
-
+
+function settingsHintsShowQuicksettings() {
+    requestGet("./internal/quicksettings-hint", {}, function(data) {
+        var table = document.createElement('table');
+        table.className = 'settings-value-table';
+
+        data.forEach(function(obj) {
+            var tr = document.createElement('tr');
+            var td = document.createElement('td');
+            td.textContent = obj.name;
+            tr.appendChild(td);
+
+            td = document.createElement('td');
+            td.textContent = obj.label;
+            tr.appendChild(td);
+
+            table.appendChild(tr);
+        });
+
+        popup(table);
+    });
+}

launch.py

@@ -1,370 +1,38 @@
-# this scripts installs necessary requirements and launches main program in webui.py
+from modules import launch_utils
-import subprocess
-import os
-import sys
-import importlib.util
-import shlex
-import platform
-import json
 
-from modules import cmd_args
-from modules.paths_internal import script_path, extensions_dir
 
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+args = launch_utils.args
-sys.argv += shlex.split(commandline_args)
+python = launch_utils.python
+git = launch_utils.git
+index_url = launch_utils.index_url
+dir_repos = launch_utils.dir_repos
 
-args, _ = cmd_args.parser.parse_known_args()
+commit_hash = launch_utils.commit_hash
+git_tag = launch_utils.git_tag
 
-python = sys.executable
+run = launch_utils.run
-git = os.environ.get('GIT', "git")
+is_installed = launch_utils.is_installed
-index_url = os.environ.get('INDEX_URL', "")
+repo_dir = launch_utils.repo_dir
-stored_commit_hash = None
-stored_git_tag = None
-dir_repos = "repositories"
 
-if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+run_pip = launch_utils.run_pip
-    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+check_run_python = launch_utils.check_run_python
+git_clone = launch_utils.git_clone
+git_pull_recursive = launch_utils.git_pull_recursive
+run_extension_installer = launch_utils.run_extension_installer
+prepare_environment = launch_utils.prepare_environment
+configure_for_tests = launch_utils.configure_for_tests
+start = launch_utils.start
 
 
-def check_python_version():
+def main():
-    is_windows = platform.system() == "Windows"
+    if not args.skip_prepare_environment:
-    major = sys.version_info.major
+        prepare_environment()
-    minor = sys.version_info.minor
-    micro = sys.version_info.micro
 
-    if is_windows:
+    if args.test_server:
-        supported_minors = [10]
+        configure_for_tests()
-    else:
-        supported_minors = [7, 8, 9, 10, 11]
 
-    if not (major == 3 and minor in supported_minors):
+    start()
-        import modules.errors
-
-        modules.errors.print_error_explanation(f"""
-INCOMPATIBLE PYTHON VERSION
-
-This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
-If you encounter an error with "RuntimeError: Couldn't install torch." message,
-or any other error regarding unsuccessful package (library) installation,
-please downgrade (or upgrade) to the latest version of 3.10 Python
-and delete current Python and "venv" folder in WebUI's directory.
-
-You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
-
-{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
-
-Use --skip-python-version-check to suppress this warning.
-""")
-
-
-def commit_hash():
-    global stored_commit_hash
-
-    if stored_commit_hash is not None:
-        return stored_commit_hash
-
-    try:
-        stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
-    except Exception:
-        stored_commit_hash = "<none>"
-
-    return stored_commit_hash
-
-
-def git_tag():
-    global stored_git_tag
-
-    if stored_git_tag is not None:
-        return stored_git_tag
-
-    try:
-        stored_git_tag = run(f"{git} describe --tags").strip()
-    except Exception:
-        stored_git_tag = "<none>"
-
-    return stored_git_tag
-
-
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
-    if desc is not None:
-        print(desc)
-
-    if live:
-        result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
-        if result.returncode != 0:
-            raise RuntimeError(f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}""")
-
-        return ""
-
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
-
-    if result.returncode != 0:
-
-        message = f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}
-stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
-stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
-"""
-        raise RuntimeError(message)
-
-    return result.stdout.decode(encoding="utf8", errors="ignore")
-
-
-def check_run(command):
-    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
-    return result.returncode == 0
-
-
-def is_installed(package):
-    try:
-        spec = importlib.util.find_spec(package)
-    except ModuleNotFoundError:
-        return False
-
-    return spec is not None
-
-
-def repo_dir(name):
-    return os.path.join(script_path, dir_repos, name)
-
-
-def run_python(code, desc=None, errdesc=None):
-    return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(command, desc=None, live=False):
-    if args.skip_install:
-        return
-
-    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
-    return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
-
-
-def check_run_python(code):
-    return check_run(f'"{python}" -c "{code}"')
-
-
-def git_clone(url, dir, name, commithash=None):
-    # TODO clone into temporary dir and move if successful
-
-    if os.path.exists(dir):
-        if commithash is None:
-            return
-
-        current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
-        if current_hash == commithash:
-            return
-
-        run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
-        return
-
-    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
-
-    if commithash is not None:
-        run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
-
-
-def git_pull_recursive(dir):
-    for subdir, _, _ in os.walk(dir):
-        if os.path.exists(os.path.join(subdir, '.git')):
-            try:
-                output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
-                print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
-            except subprocess.CalledProcessError as e:
-                print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
-
-
-def version_check(commit):
-    try:
-        import requests
-        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
-        if commit != "<none>" and commits['commit']['sha'] != commit:
-            print("--------------------------------------------------------")
-            print("| You are not up to date with the most recent release. |")
-            print("| Consider running `git pull` to update.               |")
-            print("--------------------------------------------------------")
-        elif commits['commit']['sha'] == commit:
-            print("You are up to date with the most recent release.")
-        else:
-            print("Not a git clone, can't perform version check.")
-    except Exception as e:
-        print("version check failed", e)
-
-
-def run_extension_installer(extension_dir):
-    path_installer = os.path.join(extension_dir, "install.py")
-    if not os.path.isfile(path_installer):
-        return
-
-    try:
-        env = os.environ.copy()
-        env['PYTHONPATH'] = os.path.abspath(".")
-
-        print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
-    except Exception as e:
-        print(e, file=sys.stderr)
-
-
-def list_extensions(settings_file):
-    settings = {}
-
-    try:
-        if os.path.isfile(settings_file):
-            with open(settings_file, "r", encoding="utf8") as file:
-                settings = json.load(file)
-    except Exception as e:
-        print(e, file=sys.stderr)
-
-    disabled_extensions = set(settings.get('disabled_extensions', []))
-    disable_all_extensions = settings.get('disable_all_extensions', 'none')
-
-    if disable_all_extensions != 'none':
-        return []
-
-    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
-
-
-def run_extensions_installers(settings_file):
-    if not os.path.isdir(extensions_dir):
-        return
-
-    for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
-
-
-def prepare_environment():
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url https://download.pytorch.org/whl/cu118")
-    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
-    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
-    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
-
-    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
-    taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
-    k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
-    codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
-    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
-
-    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
-    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
-    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
-    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-
-    if not args.skip_python_version_check:
-        check_python_version()
-
-    commit = commit_hash()
-    tag = git_tag()
-
-    print(f"Python {sys.version}")
-    print(f"Version: {tag}")
-    print(f"Commit hash: {commit}")
-
-    if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
-        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
-
-    if not args.skip_torch_cuda_test:
-        run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
-
-    if not is_installed("gfpgan"):
-        run_pip(f"install {gfpgan_package}", "gfpgan")
-
-    if not is_installed("clip"):
-        run_pip(f"install {clip_package}", "clip")
-
-    if not is_installed("open_clip"):
-        run_pip(f"install {openclip_package}", "open_clip")
-
-    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
-        if platform.system() == "Windows":
-            if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
-            else:
-                print("Installation of xformers is not supported in this version of Python.")
-                print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
-                if not is_installed("xformers"):
-                    exit(0)
-        elif platform.system() == "Linux":
-            run_pip(f"install {xformers_package}", "xformers")
-
-    if not is_installed("pyngrok") and args.ngrok:
-        run_pip("install pyngrok", "ngrok")
-
-    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
-
-    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
-
-    if not is_installed("lpips"):
-        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
-
-    if not os.path.isfile(requirements_file):
-        requirements_file = os.path.join(script_path, requirements_file)
-    run_pip(f"install -r \"{requirements_file}\"", "requirements")
-
-    run_extensions_installers(settings_file=args.ui_settings_file)
-
-    if args.update_check:
-        version_check(commit)
-
-    if args.update_all_extensions:
-        git_pull_recursive(extensions_dir)
-    
-    if "--exit" in sys.argv:
-        print("Exiting because of --exit argument")
-        exit(0)
-
-    if args.tests and not args.no_tests:
-        exitcode = tests(args.tests)
-        exit(exitcode)
-
-
-def tests(test_dir):
-    if "--api" not in sys.argv:
-        sys.argv.append("--api")
-    if "--ckpt" not in sys.argv:
-        sys.argv.append("--ckpt")
-        sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
-    if "--skip-torch-cuda-test" not in sys.argv:
-        sys.argv.append("--skip-torch-cuda-test")
-    if "--disable-nan-check" not in sys.argv:
-        sys.argv.append("--disable-nan-check")
-    if "--no-tests" not in sys.argv:
-        sys.argv.append("--no-tests")
-
-    print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
-
-    os.environ['COMMANDLINE_ARGS'] = ""
-    with open(os.path.join(script_path, 'test/stdout.txt'), "w", encoding="utf8") as stdout, open(os.path.join(script_path, 'test/stderr.txt'), "w", encoding="utf8") as stderr:
-        proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
-
-    import test.server_poll
-    exitcode = test.server_poll.run_tests(proc, test_dir)
-
-    print(f"Stopping Web UI process with id {proc.pid}")
-    proc.kill()
-    return exitcode
-
-
-def start():
-    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
-    import webui
-    if '--nowebui' in sys.argv:
-        webui.api_only()
-    else:
-        webui.webui()
 
 
 if __name__ == "__main__":
-    prepare_environment()
+    main()
-    start()

modules/api/api.py

@@ -15,7 +15,8 @@ from secrets import compare_digest
 
 import modules.shared as shared
 from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
-from modules.api.models import *
+from modules.api import models
+from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.textual_inversion.preprocess import preprocess
@@ -25,21 +26,24 @@ from modules.sd_models import checkpoints_list, unload_model_weights, reload_mod
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
-from typing import List
+from typing import Dict, List, Any
 import piexif
 import piexif.helper
 
+
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
-    except:
+    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e
+
 
 def script_name_to_index(name, scripts):
     try:
         return [script.title().lower() for script in scripts].index(name.lower())
-    except:
+    except Exception as e:
-        raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
+        raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e
+
 
 def validate_sampler_name(name):
     config = sd_samplers.all_samplers_map.get(name, None)
@@ -48,20 +52,23 @@ def validate_sampler_name(name):
 
     return name
 
+
 def setUpscalers(req: dict):
     reqDict = vars(req)
     reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
     reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
     return reqDict
 
+
 def decode_base64_to_image(encoding):
     if encoding.startswith("data:image/"):
         encoding = encoding.split(";")[1].split(",")[1]
     try:
         image = Image.open(BytesIO(base64.b64decode(encoding)))
         return image
-    except Exception as err:
+    except Exception as e:
-        raise HTTPException(status_code=500, detail="Invalid encoded image")
+        raise HTTPException(status_code=500, detail="Invalid encoded image") from e
+
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
@@ -92,6 +99,7 @@ def encode_pil_to_base64(image):
 
     return base64.b64encode(bytes_data)
 
+
 def api_middleware(app: FastAPI):
     rich_available = True
     try:
@@ -99,7 +107,7 @@ def api_middleware(app: FastAPI):
         import starlette # importing just so it can be placed on silent list
         from rich.console import Console
         console = Console()
-    except:
+    except Exception:
         import traceback
         rich_available = False
 
@@ -157,7 +165,7 @@ def api_middleware(app: FastAPI):
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
         if shared.cmd_opts.api_auth:
-            self.credentials = dict()
+            self.credentials = {}
             for auth in shared.cmd_opts.api_auth.split(","):
                 user, password = auth.split(":")
                 self.credentials[user] = password
@@ -166,36 +174,37 @@ class Api:
         self.app = app
         self.queue_lock = queue_lock
         api_middleware(self.app)
-        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
-        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
-        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
-        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse)
-        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse)
-        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse)
         self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
-        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
+        self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
         self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
-        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
-        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
-        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
-        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
-        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
-        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
-        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
+        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
-        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse)
-        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
+        self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse)
-        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
+        self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=models.PreprocessResponse)
-        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse)
-        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
+        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse)
-        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
+        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse)
         self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
-        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
+        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList)
+        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
 
         self.default_script_arg_txt2img = []
         self.default_script_arg_img2img = []
@@ -219,17 +228,25 @@ class Api:
         script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
         script = script_runner.selectable_scripts[script_idx]
         return script, script_idx
-    
-    def get_scripts_list(self):
-        t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
-        i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
 
-        return ScriptsList(txt2img = t2ilist, img2img = i2ilist)  
+    def get_scripts_list(self):
+        t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None]
+        i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None]
+
+        return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist)
+
+    def get_script_info(self):
+        res = []
+
+        for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]:
+            res += [script.api_info for script in script_list if script.api_info is not None]
+
+        return res
 
     def get_script(self, script_name, script_runner):
         if script_name is None or script_name == "":
             return None, None
-        
+
         script_idx = script_name_to_index(script_name, script_runner.scripts)
         return script_runner.scripts[script_idx]
 
@@ -264,11 +281,11 @@ class Api:
         if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
             for alwayson_script_name in request.alwayson_scripts.keys():
                 alwayson_script = self.get_script(alwayson_script_name, script_runner)
-                if alwayson_script == None:
+                if alwayson_script is None:
                     raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
                 # Selectable script in always on script param check
-                if alwayson_script.alwayson == False:
+                if alwayson_script.alwayson is False:
-                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
+                    raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
                 # always on script with no arg should always run so you don't really need to add them to the requests
                 if "args" in request.alwayson_scripts[alwayson_script_name]:
                     # min between arg length in scriptrunner and arg length in the request
@@ -276,7 +293,7 @@ class Api:
                         script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
         return script_args
 
-    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
+    def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
         script_runner = scripts.scripts_txt2img
         if not script_runner.scripts:
             script_runner.initialize_scripts(False)
@@ -310,7 +327,7 @@ class Api:
             p.outpath_samples = opts.outdir_txt2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -320,9 +337,9 @@ class Api:
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
-        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
+        return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
-    def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
+    def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
         init_images = img2imgreq.init_images
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
@@ -367,7 +384,7 @@ class Api:
             p.outpath_samples = opts.outdir_img2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -381,9 +398,9 @@ class Api:
             img2imgreq.init_images = None
             img2imgreq.mask = None
 
-        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
+        return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+    def extras_single_image_api(self, req: models.ExtrasSingleImageRequest):
         reqDict = setUpscalers(req)
 
         reqDict['image'] = decode_base64_to_image(reqDict['image'])
@@ -391,9 +408,9 @@ class Api:
         with self.queue_lock:
             result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
 
-        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
+        return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
 
-    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+    def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
         image_list = reqDict.pop('imageList', [])
@@ -402,15 +419,15 @@ class Api:
         with self.queue_lock:
             result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
-        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
+        return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
-    def pnginfoapi(self, req: PNGInfoRequest):
+    def pnginfoapi(self, req: models.PNGInfoRequest):
         if(not req.image.strip()):
-            return PNGInfoResponse(info="")
+            return models.PNGInfoResponse(info="")
 
         image = decode_base64_to_image(req.image.strip())
         if image is None:
-            return PNGInfoResponse(info="")
+            return models.PNGInfoResponse(info="")
 
         geninfo, items = images.read_info_from_image(image)
         if geninfo is None:
@@ -418,13 +435,13 @@ class Api:
 
         items = {**{'parameters': geninfo}, **items}
 
-        return PNGInfoResponse(info=geninfo, items=items)
+        return models.PNGInfoResponse(info=geninfo, items=items)
 
-    def progressapi(self, req: ProgressRequest = Depends()):
+    def progressapi(self, req: models.ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
-            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
+            return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
 
         # avoid dividing zero
         progress = 0.01
@@ -446,9 +463,9 @@ class Api:
         if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
+        return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
 
-    def interrogateapi(self, interrogatereq: InterrogateRequest):
+    def interrogateapi(self, interrogatereq: models.InterrogateRequest):
         image_b64 = interrogatereq.image
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found")
@@ -465,7 +482,7 @@ class Api:
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
 
-        return InterrogateResponse(caption=processed)
+        return models.InterrogateResponse(caption=processed)
 
     def interruptapi(self):
         shared.state.interrupt()
@@ -570,36 +587,36 @@ class Api:
             filename = create_embedding(**args) # create empty embedding
             sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
             shared.state.end()
-            return CreateResponse(info=f"create embedding filename: {filename}")
+            return models.CreateResponse(info=f"create embedding filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info=f"create embedding error: {e}")
+            return models.TrainResponse(info=f"create embedding error: {e}")
 
     def create_hypernetwork(self, args: dict):
         try:
             shared.state.begin()
             filename = create_hypernetwork(**args) # create empty embedding
             shared.state.end()
-            return CreateResponse(info=f"create hypernetwork filename: {filename}")
+            return models.CreateResponse(info=f"create hypernetwork filename: {filename}")
         except AssertionError as e:
             shared.state.end()
-            return TrainResponse(info=f"create hypernetwork error: {e}")
+            return models.TrainResponse(info=f"create hypernetwork error: {e}")
 
     def preprocess(self, args: dict):
         try:
             shared.state.begin()
             preprocess(**args) # quick operation unless blip/booru interrogation is enabled
             shared.state.end()
-            return PreprocessResponse(info = 'preprocess complete')
+            return models.PreprocessResponse(info = 'preprocess complete')
         except KeyError as e:
             shared.state.end()
-            return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
+            return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
         except AssertionError as e:
             shared.state.end()
-            return PreprocessResponse(info=f"preprocess error: {e}")
+            return models.PreprocessResponse(info=f"preprocess error: {e}")
         except FileNotFoundError as e:
             shared.state.end()
-            return PreprocessResponse(info=f'preprocess error: {e}')
+            return models.PreprocessResponse(info=f'preprocess error: {e}')
 
     def train_embedding(self, args: dict):
         try:
@@ -617,10 +634,10 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
+            return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
         except AssertionError as msg:
             shared.state.end()
-            return TrainResponse(info=f"train embedding error: {msg}")
+            return models.TrainResponse(info=f"train embedding error: {msg}")
 
     def train_hypernetwork(self, args: dict):
         try:
@@ -641,14 +658,15 @@ class Api:
                 if not apply_optimizations:
                     sd_hijack.apply_optimizations()
                 shared.state.end()
-            return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
+            return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
-        except AssertionError as msg:
+        except AssertionError:
             shared.state.end()
-            return TrainResponse(info=f"train embedding error: {error}")
+            return models.TrainResponse(info=f"train embedding error: {error}")
 
     def get_memory(self):
         try:
-            import os, psutil
+            import os
+            import psutil
             process = psutil.Process(os.getpid())
             res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values
             ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe
@@ -675,10 +693,10 @@ class Api:
                     'events': warnings,
                 }
             else:
-                cuda = { 'error': 'unavailable' }
+                cuda = {'error': 'unavailable'}
         except Exception as err:
-            cuda = { 'error': f'{err}' }
+            cuda = {'error': f'{err}'}
-        return MemoryResponse(ram = ram, cuda = cuda)
+        return models.MemoryResponse(ram=ram, cuda=cuda)
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)

modules/api/models.py

@@ -223,8 +223,9 @@ for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
         _type = str
-        if _options[key].default is not None: _type = type(_options[key].default)
+        if _options[key].default is not None:
-        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+            _type = type(_options[key].default)
+        flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
@@ -286,6 +287,23 @@ class MemoryResponse(BaseModel):
     ram: dict = Field(title="RAM", description="System memory stats")
     cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
 
+
 class ScriptsList(BaseModel):
-    txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
+    txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
-    img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
+    img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
+
+
+class ScriptArg(BaseModel):
+    label: str = Field(default=None, title="Label", description="Name of the argument in UI")
+    value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
+    minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
+    maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
+    step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
+    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+
+
+class ScriptInfo(BaseModel):
+    name: str = Field(default=None, title="Name", description="Script name")
+    is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
+    is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
+    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")

modules/cmd_args.py

@@ -1,6 +1,7 @@
 import argparse
+import json
 import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file  # noqa: F401
 
 parser = argparse.ArgumentParser()
 
@@ -11,8 +12,8 @@ parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.
 parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
 parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
 parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
-parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
+parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
-parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
+parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
 parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
 parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
@@ -39,7 +40,8 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
+parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
 parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
@@ -51,16 +53,16 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
 parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization")
 parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
 parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
 parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization")
-parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
-parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="does not do anything")
 parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
@@ -75,6 +77,7 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l
 parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
 parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
 parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
@@ -102,4 +105,5 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra
 parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
 parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
 parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
-parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
+parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
+parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')

modules/codeformer/codeformer_arch.py

@@ -1,14 +1,12 @@
 # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
 
 import math
-import numpy as np
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
-from typing import Optional, List
+from typing import Optional
 
-from modules.codeformer.vqgan_arch import *
+from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
-from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
 def calc_mean_std(feat, eps=1e-5):
@@ -121,7 +119,7 @@ class TransformerSALayer(nn.Module):
                 tgt_mask: Optional[Tensor] = None,
                 tgt_key_padding_mask: Optional[Tensor] = None,
                 query_pos: Optional[Tensor] = None):
-        
+
         # self attention
         tgt2 = self.norm1(tgt)
         q = k = self.with_pos_embed(tgt2, query_pos)
@@ -161,10 +159,10 @@ class Fuse_sft_block(nn.Module):
 
 @ARCH_REGISTRY.register()
 class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
+    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
                 codebook_size=1024, latent_size=256,
-                connect_list=['32', '64', '128', '256'],
+                connect_list=('32', '64', '128', '256'),
-                fix_modules=['quantize','generator']):
+                fix_modules=('quantize', 'generator')):
         super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
 
         if fix_modules is not None:
@@ -181,14 +179,14 @@ class CodeFormer(VQAutoEncoder):
         self.feat_emb = nn.Linear(256, self.dim_embd)
 
         # transformer
-        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) 
+        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
                                     for _ in range(self.n_layers)])
 
         # logits_predict head
         self.idx_pred_layer = nn.Sequential(
             nn.LayerNorm(dim_embd),
             nn.Linear(dim_embd, codebook_size, bias=False))
-        
+
         self.channels = {
             '16': 512,
             '32': 256,
@@ -223,7 +221,7 @@ class CodeFormer(VQAutoEncoder):
         enc_feat_dict = {}
         out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
         for i, block in enumerate(self.encoder.blocks):
-            x = block(x) 
+            x = block(x)
             if i in out_list:
                 enc_feat_dict[str(x.shape[-1])] = x.clone()
 
@@ -268,11 +266,11 @@ class CodeFormer(VQAutoEncoder):
         fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
 
         for i, block in enumerate(self.generator.blocks):
-            x = block(x) 
+            x = block(x)
             if i in fuse_list: # fuse after i-th block
                 f_size = str(x.shape[-1])
                 if w>0:
                     x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
         out = x
         # logits doesn't need softmax before cross_entropy loss
-        return out, logits, lq_feat
+        return out, logits, lq_feat

modules/codeformer/vqgan_arch.py

@@ -5,17 +5,15 @@ VQGAN code, adapted from the original created by the Unleashing Transformers aut
 https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
 
 '''
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import copy
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 
 def normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
+
 
 @torch.jit.script
 def swish(x):
@@ -212,15 +210,15 @@ class AttnBlock(nn.Module):
         # compute attention
         b, c, h, w = q.shape
         q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
+        q = q.permute(0, 2, 1)
         k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
+        w_ = torch.bmm(q, k)
         w_ = w_ * (int(c)**(-0.5))
         w_ = F.softmax(w_, dim=2)
 
         # attend to values
         v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
+        w_ = w_.permute(0, 2, 1)
         h_ = torch.bmm(v, w_)
         h_ = h_.reshape(b, c, h, w)
 
@@ -272,18 +270,18 @@ class Encoder(nn.Module):
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
 
 class Generator(nn.Module):
     def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
         super().__init__()
-        self.nf = nf 
+        self.nf = nf
-        self.ch_mult = ch_mult 
+        self.ch_mult = ch_mult
         self.num_resolutions = len(self.ch_mult)
         self.num_res_blocks = res_blocks
-        self.resolution = img_size 
+        self.resolution = img_size
         self.attn_resolutions = attn_resolutions
         self.in_channels = emb_dim
         self.out_channels = 3
@@ -317,29 +315,29 @@ class Generator(nn.Module):
         blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
 
         self.blocks = nn.ModuleList(blocks)
-   
+
 
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
-  
+
 @ARCH_REGISTRY.register()
 class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
+    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
                 beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
         super().__init__()
         logger = get_root_logger()
-        self.in_channels = 3 
+        self.in_channels = 3
-        self.nf = nf 
+        self.nf = nf
-        self.n_blocks = res_blocks 
+        self.n_blocks = res_blocks
         self.codebook_size = codebook_size
         self.embed_dim = emb_dim
         self.ch_mult = ch_mult
         self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
+        self.attn_resolutions = attn_resolutions or [16]
         self.quantizer_type = quantizer
         self.encoder = Encoder(
             self.in_channels,
@@ -365,11 +363,11 @@ class VQAutoEncoder(nn.Module):
                 self.kl_weight
             )
         self.generator = Generator(
-            self.nf, 
+            self.nf,
             self.embed_dim,
-            self.ch_mult, 
+            self.ch_mult,
-            self.n_blocks, 
+            self.n_blocks,
-            self.resolution, 
+            self.resolution,
             self.attn_resolutions
         )
 
@@ -434,4 +432,4 @@ class VQGANDiscriminator(nn.Module):
                 raise ValueError('Wrong params!')
 
     def forward(self, x):
-        return self.main(x)
+        return self.main(x)

modules/codeformer_model.py

@@ -33,11 +33,9 @@ def setup_model(dirname):
     try:
         from torchvision.transforms.functional import normalize
         from modules.codeformer.codeformer_arch import CodeFormer
-        from basicsr.utils.download_util import load_file_from_url
+        from basicsr.utils import img2tensor, tensor2img
-        from basicsr.utils import imwrite, img2tensor, tensor2img
         from facelib.utils.face_restoration_helper import FaceRestoreHelper
         from facelib.detection.retinaface import retinaface
-        from modules.shared import cmd_opts
 
         net_class = CodeFormer
 
@@ -96,7 +94,7 @@ def setup_model(dirname):
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                 self.face_helper.align_warp_face()
 
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+                for cropped_face in self.face_helper.cropped_faces:
                     cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
                     normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
                     cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)

modules/config_states.py

@@ -14,7 +14,7 @@ from collections import OrderedDict
 import git
 
 from modules import shared, extensions
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir
+from modules.paths_internal import script_path, config_states_dir
 
 
 all_config_states = OrderedDict()
@@ -35,7 +35,7 @@ def list_config_states():
                 j["filepath"] = path
                 config_states.append(j)
 
-    config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
+    config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
 
     for cs in config_states:
         timestamp = time.asctime(time.gmtime(cs["created_at"]))
@@ -83,6 +83,8 @@ def get_extension_config():
     ext_config = {}
 
     for ext in extensions.extensions:
+        ext.read_info_from_repo()
+
         entry = {
             "name": ext.name,
             "path": ext.path,

modules/deepbooru.py

@@ -2,7 +2,6 @@ import os
 import re
 
 import torch
-from PIL import Image
 import numpy as np
 
 from modules import modelloader, paths, deepbooru_model, devices, images, shared
@@ -79,7 +78,7 @@ class DeepDanbooru:
 
         res = []
 
-        filtertags = set([x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")])
+        filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")}
 
         for tag in [x for x in tags if x not in filtertags]:
             probability = probability_dict[tag]

modules/devices.py

@@ -1,5 +1,7 @@
 import sys
 import contextlib
+from functools import lru_cache
+
 import torch
 from modules import errors
 
@@ -65,7 +67,7 @@ def enable_tf32():
 
         # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
         # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
-        if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
+        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
             torch.backends.cudnn.benchmark = True
 
         torch.backends.cuda.matmul.allow_tf32 = True
@@ -154,3 +156,19 @@ def test_for_nans(x, where):
     message += " Use --disable-nan-check commandline argument to disable this check."
 
     raise NansException(message)
+
+
+@lru_cache
+def first_time_calculation():
+    """
+    just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
+    spends about 2.7 seconds doing that, at least wih NVidia.
+    """
+
+    x = torch.zeros((1, 1)).to(device, dtype)
+    linear = torch.nn.Linear(1, 1).to(device, dtype)
+    linear(x)
+
+    x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
+    conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
+    conv2d(x)

modules/esrgan_model.py

@@ -6,7 +6,7 @@ from PIL import Image
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.esrgan_model_arch as arch
-from modules import shared, modelloader, images, devices
+from modules import modelloader, images, devices
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
 
@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer
     if 'conv_first.weight' in state_dict:
         crt_net = {}
-        items = []
+        items = list(state_dict)
-        for k, v in state_dict.items():
-            items.append(k)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
         re8x = 0
         crt_net = {}
-        items = []
+        items = list(state_dict)
-        for k, v in state_dict.items():
-            items.append(k)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -158,7 +154,7 @@ class UpscalerESRGAN(Upscaler):
         if "http" in path:
             filename = load_file_from_url(
                 url=self.model_url,
-                model_dir=self.model_path,
+                model_dir=self.model_download_path,
                 file_name=f"{self.model_name}.pth",
                 progress=True,
             )

modules/esrgan_model_arch.py

@@ -2,7 +2,6 @@
 
 from collections import OrderedDict
 import math
-import functools
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -106,7 +105,7 @@ class ResidualDenseBlock_5C(nn.Module):
     Modified options that can be used:
         - "Partial Convolution based Padding" arXiv:1811.11718
         - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
             {Rakotonirina} and A. {Rasoanaivo}
     """
 
@@ -171,7 +170,7 @@ class GaussianNoise(nn.Module):
             scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
             sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
             x = x + sampled_noise
-        return x 
+        return x
 
 def conv1x1(in_planes, out_planes, stride=1):
     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
@@ -438,9 +437,11 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
     padding = padding if pad_type == 'zero' else 0
 
     if convtype=='PartialConv2D':
+        from torchvision.ops import PartialConv2d  # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
         c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='DeformConv2D':
+        from torchvision.ops import DeformConv2d  # not tested
         c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='Conv3D':

modules/extensions.py

@@ -1,13 +1,12 @@
 import os
 import sys
+import threading
 import traceback
 
-import time
-from datetime import datetime
 import git
 
 from modules import shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
 extensions = []
 
@@ -25,6 +24,8 @@ def active():
 
 
 class Extension:
+    lock = threading.Lock()
+
     def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
@@ -43,8 +44,13 @@ class Extension:
         if self.is_builtin or self.have_info_from_repo:
             return
 
-        self.have_info_from_repo = True
+        with self.lock:
+            if self.have_info_from_repo:
+                return
 
+            self.do_read_info_from_repo()
+
+    def do_read_info_from_repo(self):
         repo = None
         try:
             if os.path.exists(os.path.join(self.path, ".git")):
@@ -59,18 +65,19 @@ class Extension:
             try:
                 self.status = 'unknown'
                 self.remote = next(repo.remote().urls, None)
-                head = repo.head.commit
+                commit = repo.head.commit
-                self.commit_date = repo.head.commit.committed_date
+                self.commit_date = commit.committed_date
-                ts = time.asctime(time.gmtime(self.commit_date))
                 if repo.active_branch:
                     self.branch = repo.active_branch.name
-                self.commit_hash = head.hexsha
+                self.commit_hash = commit.hexsha
-                self.version = f'{self.commit_hash[:8]} ({ts})'
+                self.version = self.commit_hash[:8]
 
             except Exception as ex:
                 print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
                 self.remote = None
 
+        self.have_info_from_repo = True
+
     def list_files(self, subdir, extension):
         from modules import scripts
 

modules/extra_networks.py

@@ -14,9 +14,23 @@ def register_extra_network(extra_network):
     extra_network_registry[extra_network.name] = extra_network
 
 
+def register_default_extra_networks():
+    from modules.extra_networks_hypernet import ExtraNetworkHypernet
+    register_extra_network(ExtraNetworkHypernet())
+
+
 class ExtraNetworkParams:
     def __init__(self, items=None):
         self.items = items or []
+        self.positional = []
+        self.named = {}
+
+        for item in self.items:
+            parts = item.split('=', 2)
+            if len(parts) == 2:
+                self.named[parts[0]] = parts[1]
+            else:
+                self.positional.append(item)
 
 
 class ExtraNetwork:
@@ -91,7 +105,7 @@ def deactivate(p, extra_network_data):
     """call deactivate for extra networks in extra_network_data in specified order, then call
     deactivate for all remaining registered networks"""
 
-    for extra_network_name, extra_network_args in extra_network_data.items():
+    for extra_network_name in extra_network_data:
         extra_network = extra_network_registry.get(extra_network_name, None)
         if extra_network is None:
             continue

modules/extra_networks_hypernet.py

@@ -1,4 +1,4 @@
-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 

modules/extras.py

@@ -136,14 +136,14 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     result_is_instruct_pix2pix_model = False
 
     if theta_func2:
-        shared.state.textinfo = f"Loading B"
+        shared.state.textinfo = "Loading B"
         print(f"Loading {secondary_model_info.filename}...")
         theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
     else:
         theta_1 = None
 
     if theta_func1:
-        shared.state.textinfo = f"Loading C"
+        shared.state.textinfo = "Loading C"
         print(f"Loading {tertiary_model_info.filename}...")
         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
 
@@ -199,7 +199,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
                     result_is_inpainting_model = True
             else:
                 theta_0[key] = theta_func2(a, b, multiplier)
-            
+
             theta_0[key] = to_half(theta_0[key], save_as_half)
 
         shared.state.sampling_step += 1
@@ -242,9 +242,11 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
     shared.state.textinfo = "Saving"
     print(f"Saving to {output_modelname}...")
 
-    metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+    metadata = None
 
     if save_metadata:
+        metadata = {"format": "pt"}
+
         merge_recipe = {
             "type": "webui", # indicate this model was merged with webui's built-in merger
             "primary_model_hash": primary_model_info.sha256,
@@ -262,15 +264,17 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         }
         metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
 
+        sd_merge_models = {}
+
         def add_model_metadata(checkpoint_info):
             checkpoint_info.calculate_shorthash()
-            metadata["sd_merge_models"][checkpoint_info.sha256] = {
+            sd_merge_models[checkpoint_info.sha256] = {
                 "name": checkpoint_info.name,
                 "legacy_hash": checkpoint_info.hash,
                 "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
             }
 
-            metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+            sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {}))
 
         add_model_metadata(primary_model_info)
         if secondary_model_info:
@@ -278,7 +282,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
         if tertiary_model_info:
             add_model_metadata(tertiary_model_info)
 
-        metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+        metadata["sd_merge_models"] = json.dumps(sd_merge_models)
 
     _, extension = os.path.splitext(output_modelname)
     if extension.lower() == ".safetensors":

modules/generation_parameters_copypaste.py

@@ -1,15 +1,12 @@
 import base64
-import html
 import io
-import math
+import json
 import os
 import re
-from pathlib import Path
 
 import gradio as gr
 from modules.paths import data_path
 from modules import shared, ui_tempdir, script_callbacks
-import tempfile
 from PIL import Image
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
@@ -23,14 +20,14 @@ registered_param_bindings = []
 
 
 class ParamBinding:
-    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
+    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
         self.paste_button = paste_button
         self.tabname = tabname
         self.source_text_component = source_text_component
         self.source_image_component = source_image_component
         self.source_tabname = source_tabname
         self.override_settings_component = override_settings_component
-        self.paste_field_names = paste_field_names
+        self.paste_field_names = paste_field_names or []
 
 
 def reset():
@@ -38,13 +35,20 @@ def reset():
 
 
 def quote(text):
-    if ',' not in str(text):
+    if ',' not in str(text) and '\n' not in str(text):
         return text
 
-    text = str(text)
+    return json.dumps(text, ensure_ascii=False)
-    text = text.replace('\\', '\\\\')
+
-    text = text.replace('"', '\\"')
+
-    return f'"{text}"'
+def unquote(text):
+    if len(text) == 0 or text[0] != '"' or text[-1] != '"':
+        return text
+
+    try:
+        return json.loads(text)
+    except Exception:
+        return text
 
 
 def image_from_url_text(filedata):
@@ -251,12 +255,11 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         lines.append(lastline)
         lastline = ''
 
-    for i, line in enumerate(lines):
+    for line in lines:
         line = line.strip()
         if line.startswith("Negative prompt:"):
             done_with_prompt = True
             line = line[16:].strip()
-
         if done_with_prompt:
             negative_prompt += ("" if negative_prompt == "" else "\n") + line
         else:
@@ -266,7 +269,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     res["Negative prompt"] = negative_prompt
 
     for k, v in re_param.findall(lastline):
-        v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
+        if v[0] == '"' and v[-1] == '"':
+            v = unquote(v)
+
         m = re_imagesize.match(v)
         if m is not None:
             res[f"{k}-1"] = m.group(1)
@@ -286,6 +291,15 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         res["Hires resize-1"] = 0
         res["Hires resize-2"] = 0
 
+    if "Hires sampler" not in res:
+        res["Hires sampler"] = "Use same sampler"
+
+    if "Hires prompt" not in res:
+        res["Hires prompt"] = ""
+
+    if "Hires negative prompt" not in res:
+        res["Hires negative prompt"] = ""
+
     restore_old_hires_fix_params(res)
 
     # Missing RNG means the default was set, which is GPU RNG
@@ -312,6 +326,8 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('Token merging ratio', 'token_merging_ratio'),
+    ('Token merging ratio hr', 'token_merging_ratio_hr'),
     ('RNG', 'randn_source'),
     ('NGMS', 's_min_uncond'),
 ]

modules/gfpgan_model.py

@@ -78,7 +78,7 @@ def setup_model(dirname):
 
     try:
         from gfpgan import GFPGANer
-        from facexlib import detection, parsing
+        from facexlib import detection, parsing  # noqa: F401
         global user_path
         global have_gfpgan
         global gfpgan_constructor

modules/hashes.py

@@ -46,8 +46,8 @@ def calculate_sha256(filename):
     return hash_sha256.hexdigest()
 
 
-def sha256_from_cache(filename, title):
+def sha256_from_cache(filename, title, use_addnet_hash=False):
-    hashes = cache("hashes")
+    hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
     ondisk_mtime = os.path.getmtime(filename)
 
     if title not in hashes:
@@ -62,10 +62,10 @@ def sha256_from_cache(filename, title):
     return cached_sha256
 
 
-def sha256(filename, title):
+def sha256(filename, title, use_addnet_hash=False):
-    hashes = cache("hashes")
+    hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
 
-    sha256_value = sha256_from_cache(filename, title)
+    sha256_value = sha256_from_cache(filename, title, use_addnet_hash)
     if sha256_value is not None:
         return sha256_value
 
@@ -73,7 +73,11 @@ def sha256(filename, title):
         return None
 
     print(f"Calculating sha256 for {filename}: ", end='')
-    sha256_value = calculate_sha256(filename)
+    if use_addnet_hash:
+        with open(filename, "rb") as file:
+            sha256_value = addnet_hash_safetensors(file)
+    else:
+        sha256_value = calculate_sha256(filename)
     print(f"{sha256_value}")
 
     hashes[title] = {
@@ -86,6 +90,19 @@ def sha256(filename, title):
     return sha256_value
 
 
+def addnet_hash_safetensors(b):
+    """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
 
+    b.seek(0)
+    header = b.read(8)
+    n = int.from_bytes(header, "little")
 
+    offset = n + 8
+    b.seek(offset)
+    for chunk in iter(lambda: b.read(blksize), b""):
+        hash_sha256.update(chunk)
+
+    return hash_sha256.hexdigest()
 

modules/hypernetworks/hypernetwork.py

@@ -1,4 +1,3 @@
-import csv
 import datetime
 import glob
 import html
@@ -18,7 +17,7 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
 
-from collections import defaultdict, deque
+from collections import deque
 from statistics import stdev, mean
 
 
@@ -178,34 +177,34 @@ class Hypernetwork:
 
     def weights(self):
         res = []
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 res += layer.parameters()
         return res
 
     def train(self, mode=True):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.train(mode=mode)
                 for param in layer.parameters():
                     param.requires_grad = mode
 
     def to(self, device):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.to(device)
 
         return self
 
     def set_multiplier(self, multiplier):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.multiplier = multiplier
 
         return self
 
     def eval(self):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.eval()
                 for param in layer.parameters():
@@ -404,7 +403,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     k = self.to_k(context_k)
     v = self.to_v(context_v)
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
 
     sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
@@ -541,7 +540,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
-    
+
     clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
     if clip_grad:
         clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
@@ -594,7 +593,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
             print(e)
 
     scaler = torch.cuda.amp.GradScaler()
-    
+
     batch_size = ds.batch_size
     gradient_step = ds.gradient_step
     # n steps = batch_size * gradient_step * n image processed
@@ -620,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     try:
         sd_hijack_checkpoint.add()
 
-        for i in range((steps-initial_step) * gradient_step):
+        for _ in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
             if shared.state.interrupted:
@@ -637,7 +636,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                 if clip_grad:
                     clip_grad_sched.step(hypernetwork.step)
-                
+
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if use_weight:
@@ -658,14 +657,14 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                     _loss_step += loss.item()
                 scaler.scale(loss).backward()
-                
+
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
                 loss_logging.append(_loss_step)
                 if clip_grad:
                     clip_grad(weights, clip_grad_sched.learn_rate)
-                
+
                 scaler.step(optimizer)
                 scaler.update()
                 hypernetwork.step += 1
@@ -675,7 +674,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 _loss_step = 0
 
                 steps_done = hypernetwork.step + 1
-                
+
                 epoch_num = hypernetwork.step // steps_per_epoch
                 epoch_step = hypernetwork.step % steps_per_epoch
 

modules/hypernetworks/ui.py

@@ -1,19 +1,17 @@
 import html
-import os
-import re
 
 import gradio as gr
 import modules.hypernetworks.hypernetwork
 from modules import devices, sd_hijack, shared
 
 not_available = ["hardswish", "multiheadattention"]
-keys = list(x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available]
 
 
 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None):
     filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure)
 
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {filename}", ""
+    return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", ""
 
 
 def train_hypernetwork(*args):

modules/images.py

@@ -13,17 +13,24 @@ import numpy as np
 import piexif
 import piexif.helper
 from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
-from fonts.ttf import Roboto
 import string
 import json
 import hashlib
 
 from modules import sd_samplers, shared, script_callbacks, errors
-from modules.shared import opts, cmd_opts
+from modules.paths_internal import roboto_ttf_file
+from modules.shared import opts
 
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
 
+def get_font(fontsize: int):
+    try:
+        return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize)
+    except Exception:
+        return ImageFont.truetype(roboto_ttf_file, fontsize)
+
+
 def image_grid(imgs, batch_size=1, rows=None):
     if rows is None:
         if opts.n_rows > 0:
@@ -142,14 +149,8 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
                 lines.append(word)
         return lines
 
-    def get_font(fontsize):
-        try:
-            return ImageFont.truetype(opts.font or Roboto, fontsize)
-        except Exception:
-            return ImageFont.truetype(Roboto, fontsize)
-
     def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
-        for i, line in enumerate(lines):
+        for line in lines:
             fnt = initial_fnt
             fontsize = initial_fontsize
             while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:
@@ -366,7 +367,7 @@ class FilenameGenerator:
         self.seed = seed
         self.prompt = prompt
         self.image = image
-        
+
     def hasprompt(self, *args):
         lower = self.prompt.lower()
         if self.p is None or self.prompt is None:
@@ -409,13 +410,13 @@ class FilenameGenerator:
         time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
-        except pytz.exceptions.UnknownTimeZoneError as _:
+        except pytz.exceptions.UnknownTimeZoneError:
             time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
-        except (ValueError, TypeError) as _:
+        except (ValueError, TypeError):
             formatted_time = time_zone_time.strftime(self.default_time_format)
 
         return sanitize_filename_part(formatted_time, replace_spaces=False)
@@ -472,15 +473,52 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            parts = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
-                result = max(int(l[0]), result)
+                result = max(int(parts[0]), result)
             except ValueError:
                 pass
 
     return result + 1
 
 
+def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_pnginfo=None):
+    if extension is None:
+        extension = os.path.splitext(filename)[1]
+
+    image_format = Image.registered_extensions()[extension]
+
+    existing_pnginfo = existing_pnginfo or {}
+    if opts.enable_pnginfo:
+        existing_pnginfo['parameters'] = geninfo
+
+    if extension.lower() == '.png':
+        pnginfo_data = PngImagePlugin.PngInfo()
+        for k, v in (existing_pnginfo or {}).items():
+            pnginfo_data.add_text(k, str(v))
+
+        image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
+
+    elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+        if image.mode == 'RGBA':
+            image = image.convert("RGB")
+        elif image.mode == 'I;16':
+            image = image.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
+
+        image.save(filename, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
+
+        if opts.enable_pnginfo and geninfo is not None:
+            exif_bytes = piexif.dump({
+                "Exif": {
+                    piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
+                },
+            })
+
+            piexif.insert(exif_bytes, filename)
+    else:
+        image.save(filename, format=image_format, quality=opts.jpeg_quality)
+
+
 def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
     """Save an image.
 
@@ -565,38 +603,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     info = params.pnginfo.get(pnginfo_section_name, None)
 
     def _atomically_save_image(image_to_save, filename_without_extension, extension):
-        # save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        """
+        save image with .tmp extension to avoid race condition when another process detects new image in the directory
+        """
         temp_file_path = f"{filename_without_extension}.tmp"
-        image_format = Image.registered_extensions()[extension]
 
-        if extension.lower() == '.png':
+        save_image_with_geninfo(image_to_save, info, temp_file_path, extension, params.pnginfo)
-            pnginfo_data = PngImagePlugin.PngInfo()
-            if opts.enable_pnginfo:
-                for k, v in params.pnginfo.items():
-                    pnginfo_data.add_text(k, str(v))
 
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
-
-        elif extension.lower() in (".jpg", ".jpeg", ".webp"):
-            if image_to_save.mode == 'RGBA':
-                image_to_save = image_to_save.convert("RGB")
-            elif image_to_save.mode == 'I;16':
-                image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
-
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
-
-            if opts.enable_pnginfo and info is not None:
-                exif_bytes = piexif.dump({
-                    "Exif": {
-                        piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(info or "", encoding="unicode")
-                    },
-                })
-
-                piexif.insert(exif_bytes, temp_file_path)
-        else:
-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
-
-        # atomically rename the file with correct extension
         os.replace(temp_file_path, filename_without_extension + extension)
 
     fullfn_without_extension, extension = os.path.splitext(params.filename)

modules/img2img.py

@@ -1,19 +1,15 @@
-import math
 import os
-import sys
-import traceback
 
 import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
 
-from modules import devices, sd_samplers
+from modules import sd_samplers
 from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
-import modules.images as images
 import modules.scripts
 
 
@@ -59,7 +55,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             # try to find corresponding mask for an image using simple filename matching
             mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
             # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
+            if mask_image_path not in inpaint_masks:
                 mask_image_path = inpaint_masks[0]
             mask_image = Image.open(mask_image_path)
             p.image_mask = mask_image

modules/interrogate.py

@@ -11,7 +11,6 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
-import modules.shared as shared
 from modules import devices, paths, shared, lowvram, modelloader, errors
 
 blip_image_eval_size = 384
@@ -160,7 +159,7 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
+        text_tokens = clip.tokenize(list(text_array), truncate=True).to(devices.device_interrogate)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
@@ -208,8 +207,8 @@ class InterrogateModels:
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
-                for name, topn, items in self.categories():
+                for cat in self.categories():
-                    matches = self.rank(image_features, items, top_count=topn)
+                    matches = self.rank(image_features, cat.items, top_count=cat.topn)
                     for match, score in matches:
                         if shared.opts.interrogate_return_ranks:
                             res += f", ({match}:{score/100:.3f})"

modules/launch_utils.py

@@ -0,0 +1,334 @@
+# this scripts installs necessary requirements and launches main program in webui.py
+import subprocess
+import os
+import sys
+import importlib.util
+import platform
+import json
+from functools import lru_cache
+
+from modules import cmd_args
+from modules.paths_internal import script_path, extensions_dir
+
+args, _ = cmd_args.parser.parse_known_args()
+
+python = sys.executable
+git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL', "")
+dir_repos = "repositories"
+
+# Whether to default to printing command output
+default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+
+
+def check_python_version():
+    is_windows = platform.system() == "Windows"
+    major = sys.version_info.major
+    minor = sys.version_info.minor
+    micro = sys.version_info.micro
+
+    if is_windows:
+        supported_minors = [10]
+    else:
+        supported_minors = [7, 8, 9, 10, 11]
+
+    if not (major == 3 and minor in supported_minors):
+        import modules.errors
+
+        modules.errors.print_error_explanation(f"""
+INCOMPATIBLE PYTHON VERSION
+
+This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
+If you encounter an error with "RuntimeError: Couldn't install torch." message,
+or any other error regarding unsuccessful package (library) installation,
+please downgrade (or upgrade) to the latest version of 3.10 Python
+and delete current Python and "venv" folder in WebUI's directory.
+
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
+
+{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
+
+Use --skip-python-version-check to suppress this warning.
+""")
+
+
+@lru_cache()
+def commit_hash():
+    try:
+        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+    except Exception:
+        return "<none>"
+
+
+@lru_cache()
+def git_tag():
+    try:
+        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
+    except Exception:
+        return "<none>"
+
+
+def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str:
+    if desc is not None:
+        print(desc)
+
+    run_kwargs = {
+        "args": command,
+        "shell": True,
+        "env": os.environ if custom_env is None else custom_env,
+        "encoding": 'utf8',
+        "errors": 'ignore',
+    }
+
+    if not live:
+        run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE
+
+    result = subprocess.run(**run_kwargs)
+
+    if result.returncode != 0:
+        error_bits = [
+            f"{errdesc or 'Error running command'}.",
+            f"Command: {command}",
+            f"Error code: {result.returncode}",
+        ]
+        if result.stdout:
+            error_bits.append(f"stdout: {result.stdout}")
+        if result.stderr:
+            error_bits.append(f"stderr: {result.stderr}")
+        raise RuntimeError("\n".join(error_bits))
+
+    return (result.stdout or "")
+
+
+def is_installed(package):
+    try:
+        spec = importlib.util.find_spec(package)
+    except ModuleNotFoundError:
+        return False
+
+    return spec is not None
+
+
+def repo_dir(name):
+    return os.path.join(script_path, dir_repos, name)
+
+
+def run_pip(command, desc=None, live=default_command_live):
+    if args.skip_install:
+        return
+
+    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+    return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
+
+
+def check_run_python(code: str) -> bool:
+    result = subprocess.run([python, "-c", code], capture_output=True, shell=False)
+    return result.returncode == 0
+
+
+def git_clone(url, dir, name, commithash=None):
+    # TODO clone into temporary dir and move if successful
+
+    if os.path.exists(dir):
+        if commithash is None:
+            return
+
+        current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
+        if current_hash == commithash:
+            return
+
+        run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        return
+
+    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
+
+    if commithash is not None:
+        run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
+
+
+def git_pull_recursive(dir):
+    for subdir, _, _ in os.walk(dir):
+        if os.path.exists(os.path.join(subdir, '.git')):
+            try:
+                output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
+                print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
+            except subprocess.CalledProcessError as e:
+                print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
+
+
+def version_check(commit):
+    try:
+        import requests
+        commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
+        if commit != "<none>" and commits['commit']['sha'] != commit:
+            print("--------------------------------------------------------")
+            print("| You are not up to date with the most recent release. |")
+            print("| Consider running `git pull` to update.               |")
+            print("--------------------------------------------------------")
+        elif commits['commit']['sha'] == commit:
+            print("You are up to date with the most recent release.")
+        else:
+            print("Not a git clone, can't perform version check.")
+    except Exception as e:
+        print("version check failed", e)
+
+
+def run_extension_installer(extension_dir):
+    path_installer = os.path.join(extension_dir, "install.py")
+    if not os.path.isfile(path_installer):
+        return
+
+    try:
+        env = os.environ.copy()
+        env['PYTHONPATH'] = os.path.abspath(".")
+
+        print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
+    except Exception as e:
+        print(e, file=sys.stderr)
+
+
+def list_extensions(settings_file):
+    settings = {}
+
+    try:
+        if os.path.isfile(settings_file):
+            with open(settings_file, "r", encoding="utf8") as file:
+                settings = json.load(file)
+    except Exception as e:
+        print(e, file=sys.stderr)
+
+    disabled_extensions = set(settings.get('disabled_extensions', []))
+    disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+    if disable_all_extensions != 'none':
+        return []
+
+    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
+
+
+def run_extensions_installers(settings_file):
+    if not os.path.isdir(extensions_dir):
+        return
+
+    for dirname_extension in list_extensions(settings_file):
+        run_extension_installer(os.path.join(extensions_dir, dirname_extension))
+
+
+def prepare_environment():
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
+    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
+    clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
+    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
+
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
+    taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
+    k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+    codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
+    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
+
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
+    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
+    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
+
+    if not args.skip_python_version_check:
+        check_python_version()
+
+    commit = commit_hash()
+    tag = git_tag()
+
+    print(f"Python {sys.version}")
+    print(f"Version: {tag}")
+    print(f"Commit hash: {commit}")
+
+    if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
+
+    if not args.skip_torch_cuda_test and not check_run_python("import torch; assert torch.cuda.is_available()"):
+        raise RuntimeError(
+            'Torch is not able to use GPU; '
+            'add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'
+        )
+
+    if not is_installed("gfpgan"):
+        run_pip(f"install {gfpgan_package}", "gfpgan")
+
+    if not is_installed("clip"):
+        run_pip(f"install {clip_package}", "clip")
+
+    if not is_installed("open_clip"):
+        run_pip(f"install {openclip_package}", "open_clip")
+
+    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
+        if platform.system() == "Windows":
+            if platform.python_version().startswith("3.10"):
+                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
+            else:
+                print("Installation of xformers is not supported in this version of Python.")
+                print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
+                if not is_installed("xformers"):
+                    exit(0)
+        elif platform.system() == "Linux":
+            run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+
+    if not is_installed("ngrok") and args.ngrok:
+        run_pip("install ngrok", "ngrok")
+
+    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
+
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
+
+    if not is_installed("lpips"):
+        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
+
+    if not os.path.isfile(requirements_file):
+        requirements_file = os.path.join(script_path, requirements_file)
+    run_pip(f"install -r \"{requirements_file}\"", "requirements")
+
+    run_extensions_installers(settings_file=args.ui_settings_file)
+
+    if args.update_check:
+        version_check(commit)
+
+    if args.update_all_extensions:
+        git_pull_recursive(extensions_dir)
+
+    if "--exit" in sys.argv:
+        print("Exiting because of --exit argument")
+        exit(0)
+
+
+def configure_for_tests():
+    if "--api" not in sys.argv:
+        sys.argv.append("--api")
+    if "--ckpt" not in sys.argv:
+        sys.argv.append("--ckpt")
+        sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
+    if "--skip-torch-cuda-test" not in sys.argv:
+        sys.argv.append("--skip-torch-cuda-test")
+    if "--disable-nan-check" not in sys.argv:
+        sys.argv.append("--disable-nan-check")
+
+    os.environ['COMMANDLINE_ARGS'] = ""
+
+
+def start():
+    print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
+    import webui
+    if '--nowebui' in sys.argv:
+        webui.api_only()
+    else:
+        webui.webui()

modules/mac_specific.py

@@ -1,6 +1,5 @@
 import torch
 import platform
-from modules import paths
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
 
@@ -43,7 +42,7 @@ if has_mps:
         # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
         CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
                                                           lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
-        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
         CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
                                                                                         lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
         # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
@@ -61,4 +60,4 @@ if has_mps:
         # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
         if platform.processor() == 'i386':
             for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
-                CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
+                CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')

modules/masking.py

@@ -4,7 +4,7 @@ from PIL import Image, ImageFilter, ImageOps
 def get_crop_region(mask, pad=0):
     """finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
     For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
-    
+
     h, w = mask.shape
 
     crop_left = 0

modules/modelloader.py

@@ -1,4 +1,3 @@
-import glob
 import os
 import shutil
 import importlib
@@ -40,7 +39,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                 if os.path.islink(full_path) and not os.path.exists(full_path):
                     print(f"Skipping broken symlink: {full_path}")
                     continue
-                if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                if ext_blacklist is not None and any(full_path.endswith(x) for x in ext_blacklist):
                     continue
                 if full_path not in output:
                     output.append(full_path)
@@ -48,7 +47,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
         if model_url is not None and len(output) == 0:
             if download_name is not None:
                 from basicsr.utils.download_util import load_file_from_url
-                dl = load_file_from_url(model_url, model_path, True, download_name)
+                dl = load_file_from_url(model_url, places[0], True, download_name)
                 output.append(dl)
             else:
                 output.append(model_url)
@@ -108,12 +107,12 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
                     print(f"Moving {file} from {src_path} to {dest_path}.")
                     try:
                         shutil.move(fullpath, dest_path)
-                    except:
+                    except Exception:
                         pass
             if len(os.listdir(src_path)) == 0:
                 print(f"Removing empty folder: {src_path}")
                 shutil.rmtree(src_path, True)
-    except:
+    except Exception:
         pass
 
 
@@ -127,7 +126,7 @@ def load_upscalers():
             full_model = f"modules.{model_name}_model"
             try:
                 importlib.import_module(full_model)
-            except:
+            except Exception:
                 pass
 
     datas = []
@@ -145,7 +144,10 @@ def load_upscalers():
     for cls in reversed(used_classes.values()):
         name = cls.__name__
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
-        scaler = cls(commandline_options.get(cmd_name, None))
+        commandline_model_path = commandline_options.get(cmd_name, None)
+        scaler = cls(commandline_model_path)
+        scaler.user_path = commandline_model_path
+        scaler.model_download_path = commandline_model_path or scaler.model_path
         datas += scaler.scalers
 
     shared.sd_upscalers = sorted(

modules/models/diffusion/ddpm_edit.py

@@ -52,7 +52,7 @@ class DDPM(pl.LightningModule):
                  beta_schedule="linear",
                  loss_type="l2",
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  load_only_unet=False,
                  monitor="val/loss",
                  use_ema=True,
@@ -107,7 +107,7 @@ class DDPM(pl.LightningModule):
             print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
 
             # If initialing from EMA-only checkpoint, create EMA model after loading.
             if self.use_ema and not load_ema:
@@ -194,7 +194,9 @@ class DDPM(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+    def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
+        ignore_keys = ignore_keys or []
+
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
@@ -403,7 +405,7 @@ class DDPM(pl.LightningModule):
 
     @torch.no_grad()
     def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.first_stage_key)
         N = min(x.shape[0], N)
         n_row = min(x.shape[0], n_row)
@@ -411,7 +413,7 @@ class DDPM(pl.LightningModule):
         log["inputs"] = x
 
         # get diffusion row
-        diffusion_row = list()
+        diffusion_row = []
         x_start = x[:n_row]
 
         for t in range(self.num_timesteps):
@@ -473,13 +475,13 @@ class LatentDiffusion(DDPM):
             conditioning_key = None
         ckpt_path = kwargs.pop("ckpt_path", None)
         ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
+        super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs)
         self.concat_mode = concat_mode
         self.cond_stage_trainable = cond_stage_trainable
         self.cond_stage_key = cond_stage_key
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -891,16 +893,6 @@ class LatentDiffusion(DDPM):
                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
         return self.p_losses(x, c, t, *args, **kwargs)
 
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
@@ -1140,7 +1132,7 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
 
@@ -1171,8 +1163,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1219,8 +1213,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
+            if callback:
-            if img_callback: img_callback(img, i)
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1235,7 +1231,7 @@ class LatentDiffusion(DDPM):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
         return self.p_sample_loop(cond,
@@ -1267,7 +1263,7 @@ class LatentDiffusion(DDPM):
 
         use_ddim = False
 
-        log = dict()
+        log = {}
         z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
                                            return_first_stage_outputs=True,
                                            force_c_encode=True,
@@ -1295,7 +1291,7 @@ class LatentDiffusion(DDPM):
 
         if plot_diffusion_rows:
             # get diffusion row
-            diffusion_row = list()
+            diffusion_row = []
             z_start = z[:n_row]
             for t in range(self.num_timesteps):
                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
@@ -1337,7 +1333,7 @@ class LatentDiffusion(DDPM):
 
             if inpaint:
                 # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                h, w = z.shape[2], z.shape[3]
                 mask = torch.ones(N, h, w).to(self.device)
                 # zeros will be filled in
                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
@@ -1439,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion):
     # TODO: move all layout-specific hacks to this class
     def __init__(self, cond_stage_key, *args, **kwargs):
         assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+        super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
 
     def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+        logs = super().log_images(*args, batch=batch, N=N, **kwargs)
 
         key = 'train' if self.training else 'validation'
         dset = self.trainer.datamodule.datasets[key]

modules/models/diffusion/uni_pc/__init__.py

@@ -1 +1 @@
-from .sampler import UniPCSampler
+from .sampler import UniPCSampler  # noqa: F401

modules/models/diffusion/uni_pc/sampler.py

@@ -54,7 +54,8 @@ class UniPCSampler(object):
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
+                while isinstance(ctmp, list):
+                    ctmp = ctmp[0]
                 cbs = ctmp.shape[0]
                 if cbs != batch_size:
                     print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")

modules/models/diffusion/uni_pc/uni_pc.py

@@ -1,7 +1,6 @@
 import torch
-import torch.nn.functional as F
 import math
-from tqdm.auto import trange
+import tqdm
 
 
 class NoiseScheduleVP:
@@ -179,13 +178,13 @@ def model_wrapper(
     model,
     noise_schedule,
     model_type="noise",
-    model_kwargs={},
+    model_kwargs=None,
     guidance_type="uncond",
     #condition=None,
     #unconditional_condition=None,
     guidance_scale=1.,
     classifier_fn=None,
-    classifier_kwargs={},
+    classifier_kwargs=None,
 ):
     """Create a wrapper function for the noise prediction model.
 
@@ -276,6 +275,9 @@ def model_wrapper(
         A noise prediction model that accepts the noised data and the continuous time as the inputs.
     """
 
+    model_kwargs = model_kwargs or {}
+    classifier_kwargs = classifier_kwargs or {}
+
     def get_model_input_time(t_continuous):
         """
         Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
@@ -342,7 +344,7 @@ def model_wrapper(
                 t_in = torch.cat([t_continuous] * 2)
                 if isinstance(condition, dict):
                     assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
+                    c_in = {}
                     for k in condition:
                         if isinstance(condition[k], list):
                             c_in[k] = [torch.cat([
@@ -353,7 +355,7 @@ def model_wrapper(
                                 unconditional_condition[k],
                                 condition[k]])
                 elif isinstance(condition, list):
-                    c_in = list()
+                    c_in = []
                     assert isinstance(unconditional_condition, list)
                     for i in range(len(condition)):
                         c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
@@ -757,40 +759,44 @@ class UniPC:
                 vec_t = timesteps[0].expand((x.shape[0]))
                 model_prev_list = [self.model_fn(x, vec_t)]
                 t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
+                with tqdm.tqdm(total=steps) as pbar:
-                for init_order in range(1, order):
+                    # Init the first `order` values by lower order multistep DPM-Solver.
-                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    for init_order in range(1, order):
-                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+                        vec_t = timesteps[init_order].expand(x.shape[0])
-                    if model_x is None:
+                        x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
-                        model_x = self.model_fn(x, vec_t)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    model_prev_list.append(model_x)
-                    t_prev_list.append(vec_t)
-                for step in trange(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    if lower_order_final:
-                        step_order = min(order, steps + 1 - step)
-                    else:
-                        step_order = order
-                    #print('this step order:', step_order)
-                    if step == steps:
-                        #print('do not run corrector at the last step')
-                        use_corrector = False
-                    else:
-                        use_corrector = True
-                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
-                    if self.after_update is not None:
-                        self.after_update(x, model_x)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
                         if model_x is None:
                             model_x = self.model_fn(x, vec_t)
-                        model_prev_list[-1] = model_x
+                        if self.after_update is not None:
+                            self.after_update(x, model_x)
+                        model_prev_list.append(model_x)
+                        t_prev_list.append(vec_t)
+                        pbar.update()
+
+                    for step in range(order, steps + 1):
+                        vec_t = timesteps[step].expand(x.shape[0])
+                        if lower_order_final:
+                            step_order = min(order, steps + 1 - step)
+                        else:
+                            step_order = order
+                        #print('this step order:', step_order)
+                        if step == steps:
+                            #print('do not run corrector at the last step')
+                            use_corrector = False
+                        else:
+                            use_corrector = True
+                        x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+                        if self.after_update is not None:
+                            self.after_update(x, model_x)
+                        for i in range(order - 1):
+                            t_prev_list[i] = t_prev_list[i + 1]
+                            model_prev_list[i] = model_prev_list[i + 1]
+                        t_prev_list[-1] = vec_t
+                        # We do not need to evaluate the final model value.
+                        if step < steps:
+                            if model_x is None:
+                                model_x = self.model_fn(x, vec_t)
+                            model_prev_list[-1] = model_x
+                        pbar.update()
         else:
             raise NotImplementedError()
         if denoise_to_zero:

modules/ngrok.py

@@ -1,6 +1,7 @@
-from pyngrok import ngrok, conf, exception
+import ngrok
 
-def connect(token, port, region):
+# Connect to ngrok for ingress
+def connect(token, port, options):
     account = None
     if token is None:
         token = 'None'
@@ -10,28 +11,19 @@ def connect(token, port, region):
             token, username, password = token.split(':', 2)
             account = f"{username}:{password}"
 
-    config = conf.PyngrokConfig(
+    # For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
-        auth_token=token, region=region
+    if not options.get('authtoken_from_env'):
-    )
+        options['authtoken'] = token
-    
+    if account:
-    # Guard for existing tunnels
+        options['basic_auth'] = account
-    existing = ngrok.get_tunnels(pyngrok_config=config)
+    if not options.get('session_metadata'):
-    if existing:
+        options['session_metadata'] = 'stable-diffusion-webui'
-        for established in existing:
+
-            # Extra configuration in the case that the user is also using ngrok for other tunnels
+
-            if established.config['addr'][-4:] == str(port):
-                public_url = existing[0].public_url
-                print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
-                    'You can use this link after the launch is complete.')
-                return
-    
     try:
-        if account is None:
+        public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url()
-            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
+    except Exception as e:
-        else:
+        print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n'
-            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).public_url
-    except exception.PyngrokNgrokError:
-        print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
     else:
         print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'

modules/paths.py

@@ -1,8 +1,8 @@
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir  # noqa: F401
 
-import modules.safe
+import modules.safe  # noqa: F401
 
 
 # data_path = cmd_opts_pre.data

modules/paths_internal.py

@@ -2,8 +2,14 @@
 
 import argparse
 import os
+import sys
+import shlex
 
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+modules_path = os.path.dirname(os.path.realpath(__file__))
+script_path = os.path.dirname(modules_path)
 
 sd_configs_path = os.path.join(script_path, "configs")
 sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
@@ -12,7 +18,7 @@ default_sd_model_file = sd_model_file
 
 # Parse the --data-dir flag first so we can use it as a base for our other argument default values
 parser_pre = argparse.ArgumentParser(add_help=False)
-parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
 cmd_opts_pre = parser_pre.parse_known_args()[0]
 
 data_path = cmd_opts_pre.data_dir
@@ -21,3 +27,5 @@ models_path = os.path.join(data_path, "models")
 extensions_dir = os.path.join(data_path, "extensions")
 extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
 config_states_dir = os.path.join(script_path, "config_states")
+
+roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')

modules/processing.py

@@ -2,7 +2,6 @@ import json
 import math
 import os
 import sys
-import warnings
 import hashlib
 
 import torch
@@ -11,10 +10,10 @@ from PIL import Image, ImageFilter, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -31,6 +30,7 @@ from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
 from einops import repeat, rearrange
 from blendmodes.blend import blendLayers, BlendType
 
+
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
 opt_f = 8
@@ -150,6 +150,8 @@ class StableDiffusionProcessing:
         self.override_settings_restore_afterwards = override_settings_restore_afterwards
         self.is_using_inpainting_conditioning = False
         self.disable_extra_networks = False
+        self.token_merging_ratio = 0
+        self.token_merging_ratio_hr = 0
 
         if not seed_enable_extras:
             self.subseed = -1
@@ -165,7 +167,18 @@ class StableDiffusionProcessing:
         self.all_subseeds = None
         self.iteration = 0
         self.is_hr_pass = False
-        
+        self.sampler = None
+
+        self.prompts = None
+        self.negative_prompts = None
+        self.seeds = None
+        self.subseeds = None
+
+        self.step_multiplier = 1
+        self.cached_uc = [None, None]
+        self.cached_c = [None, None]
+        self.uc = None
+        self.c = None
 
     @property
     def sd_model(self):
@@ -273,6 +286,62 @@ class StableDiffusionProcessing:
 
     def close(self):
         self.sampler = None
+        self.c = None
+        self.uc = None
+        self.cached_c = [None, None]
+        self.cached_uc = [None, None]
+
+    def get_token_merging_ratio(self, for_hr=False):
+        if for_hr:
+            return self.token_merging_ratio_hr or opts.token_merging_ratio_hr or self.token_merging_ratio or opts.token_merging_ratio
+
+        return self.token_merging_ratio or opts.token_merging_ratio
+
+    def setup_prompts(self):
+        if type(self.prompt) == list:
+            self.all_prompts = self.prompt
+        else:
+            self.all_prompts = self.batch_size * self.n_iter * [self.prompt]
+
+        if type(self.negative_prompt) == list:
+            self.all_negative_prompts = self.negative_prompt
+        else:
+            self.all_negative_prompts = self.batch_size * self.n_iter * [self.negative_prompt]
+
+        self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
+        self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]
+
+    def get_conds_with_caching(self, function, required_prompts, steps, cache):
+        """
+        Returns the result of calling function(shared.sd_model, required_prompts, steps)
+        using a cache to store the result if the same arguments have been used before.
+
+        cache is an array containing two elements. The first element is a tuple
+        representing the previously used arguments, or None if no arguments
+        have been used before. The second element is where the previously
+        computed result is stored.
+        """
+
+        if cache[0] is not None and (required_prompts, steps) == cache[0]:
+            return cache[1]
+
+        with devices.autocast():
+            cache[1] = function(shared.sd_model, required_prompts, steps)
+
+        cache[0] = (required_prompts, steps)
+        return cache[1]
+
+    def setup_conds(self):
+        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
+        self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
+
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c)
+
+    def parse_extra_network_prompts(self):
+        self.prompts, extra_network_data = extra_networks.parse_prompts(self.prompts)
+
+        return extra_network_data
 
 
 class Processed:
@@ -303,6 +372,8 @@ class Processed:
         self.styles = p.styles
         self.job_timestamp = state.job_timestamp
         self.clip_skip = opts.CLIP_stop_at_last_layers
+        self.token_merging_ratio = p.token_merging_ratio
+        self.token_merging_ratio_hr = p.token_merging_ratio_hr
 
         self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
@@ -310,6 +381,7 @@ class Processed:
         self.s_tmin = p.s_tmin
         self.s_tmax = p.s_tmax
         self.s_noise = p.s_noise
+        self.s_min_uncond = p.s_min_uncond
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
@@ -360,6 +432,9 @@ class Processed:
     def infotext(self, p: StableDiffusionProcessing, index):
         return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
 
+    def get_token_merging_ratio(self, for_hr=False):
+        return self.token_merging_ratio_hr if for_hr else self.token_merging_ratio
+
 
 # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
 def slerp(val, low, high):
@@ -472,6 +547,13 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
     index = position_in_batch + iteration * p.batch_size
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+    enable_hr = getattr(p, 'enable_hr', False)
+    token_merging_ratio = p.get_token_merging_ratio()
+    token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
+
+    uses_ensd = opts.eta_noise_seed_delta != 0
+    if uses_ensd:
+        uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
 
     generation_params = {
         "Steps": p.steps,
@@ -489,15 +571,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Denoising strength": getattr(p, 'denoising_strength', None),
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
-        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
+        "Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
+        "Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
         "Init image hash": getattr(p, 'init_img_hash', None),
         "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
         "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
+        **p.extra_generation_params,
         "Version": program_version() if opts.add_version_to_infotext else None,
     }
 
-    generation_params.update(p.extra_generation_params)
-
     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
@@ -523,9 +606,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if k == 'sd_vae':
                 sd_vae.reload_vae_weights()
 
+        sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
+
         res = process_images_inner(p)
 
     finally:
+        sd_models.apply_token_merging(p.sd_model, 0)
+
         # restore opts to original state
         if p.override_settings_restore_afterwards:
             for k, v in stored_opts.items():
@@ -555,15 +642,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     comments = {}
 
-    if type(p.prompt) == list:
+    p.setup_prompts()
-        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
-    else:
-        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
-
-    if type(p.negative_prompt) == list:
-        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
-    else:
-        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
 
     if type(seed) == list:
         p.all_seeds = seed
@@ -587,29 +666,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     infotexts = []
     output_images = []
 
-    cached_uc = [None, None]
-    cached_c = [None, None]
-
-    def get_conds_with_caching(function, required_prompts, steps, cache):
-        """
-        Returns the result of calling function(shared.sd_model, required_prompts, steps)
-        using a cache to store the result if the same arguments have been used before.
-
-        cache is an array containing two elements. The first element is a tuple
-        representing the previously used arguments, or None if no arguments
-        have been used before. The second element is where the previously
-        computed result is stored.
-        """
-
-        if cache[0] is not None and (required_prompts, steps) == cache[0]:
-            return cache[1]
-
-        with devices.autocast():
-            cache[1] = function(shared.sd_model, required_prompts, steps)
-
-        cache[0] = (required_prompts, steps)
-        return cache[1]
-
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
             p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -631,25 +687,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
-            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            p.negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
             if p.scripts is not None:
-                p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+                p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
 
-            if len(prompts) == 0:
+            if len(p.prompts) == 0:
                 break
 
-            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+            extra_network_data = p.parse_extra_network_prompts()
 
             if not p.disable_extra_networks:
                 with devices.autocast():
                     extra_networks.activate(p, extra_network_data)
 
             if p.scripts is not None:
-                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+                p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
 
             # params.txt should be saved after scripts.process_batch, since the
             # infotext could be modified by that callback
@@ -660,14 +716,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     processed = Processed(p, [], p.seed, "")
                     file.write(processed.infotext(p, 0))
 
-            step_multiplier = 1
+            p.setup_conds()
-            if not shared.opts.dont_fix_second_order_samplers_schedule:
-                try:
-                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
-                except:
-                    pass
-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
 
             if len(model_hijack.comments) > 0:
                 for comment in model_hijack.comments:
@@ -677,7 +726,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"
 
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
-                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
+                samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
             x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
             for x in x_samples_ddim:
@@ -704,7 +753,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -721,13 +770,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                         image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
-                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
                 image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
-                    images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)
 
                 text = infotext(n, i)
                 infotexts.append(text)
@@ -740,10 +789,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
 
                     if opts.save_mask:
-                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
 
                     if opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
 
                     if opts.return_mask:
                         output_images.append(image_mask)
@@ -785,7 +834,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         images_list=output_images,
         seed=p.all_seeds[0],
         info=infotext(),
-        comments="".join(f"\n\n{comment}" for comment in comments),
+        comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
         infotexts=infotexts,
@@ -812,7 +861,7 @@ def old_hires_fix_first_pass_dimensions(width, height):
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
@@ -823,6 +872,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_resize_y = hr_resize_y
         self.hr_upscale_to_x = hr_resize_x
         self.hr_upscale_to_y = hr_resize_y
+        self.hr_sampler_name = hr_sampler_name
+        self.hr_prompt = hr_prompt
+        self.hr_negative_prompt = hr_negative_prompt
+        self.all_hr_prompts = None
+        self.all_hr_negative_prompts = None
 
         if firstphase_width != 0 or firstphase_height != 0:
             self.hr_upscale_to_x = self.width
@@ -834,8 +888,24 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.truncate_y = 0
         self.applied_old_hires_behavior_to = None
 
+        self.hr_prompts = None
+        self.hr_negative_prompts = None
+        self.hr_extra_network_data = None
+
+        self.hr_c = None
+        self.hr_uc = None
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
+            if self.hr_sampler_name is not None and self.hr_sampler_name != self.sampler_name:
+                self.extra_generation_params["Hires sampler"] = self.hr_sampler_name
+
+            if tuple(self.hr_prompt) != tuple(self.prompt):
+                self.extra_generation_params["Hires prompt"] = self.hr_prompt
+
+            if tuple(self.hr_negative_prompt) != tuple(self.negative_prompt):
+                self.extra_generation_params["Hires negative prompt"] = self.hr_negative_prompt
+
             if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
                 self.hr_resize_x = self.width
                 self.hr_resize_y = self.height
@@ -965,9 +1035,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         shared.state.nextjob()
 
-        img2img_sampler_name = self.sampler_name
+        img2img_sampler_name = self.hr_sampler_name or self.sampler_name
+
         if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
             img2img_sampler_name = 'DDIM'
+
         self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@@ -978,12 +1050,67 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+        if not self.disable_extra_networks:
+            with devices.autocast():
+                extra_networks.activate(self, self.hr_extra_network_data)
+
+        sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio(for_hr=True))
+
+        samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+
+        sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
         self.is_hr_pass = False
 
         return samples
 
+    def close(self):
+        self.hr_c = None
+        self.hr_uc = None
+
+    def setup_prompts(self):
+        super().setup_prompts()
+
+        if not self.enable_hr:
+            return
+
+        if self.hr_prompt == '':
+            self.hr_prompt = self.prompt
+
+        if self.hr_negative_prompt == '':
+            self.hr_negative_prompt = self.negative_prompt
+
+        if type(self.hr_prompt) == list:
+            self.all_hr_prompts = self.hr_prompt
+        else:
+            self.all_hr_prompts = self.batch_size * self.n_iter * [self.hr_prompt]
+
+        if type(self.hr_negative_prompt) == list:
+            self.all_hr_negative_prompts = self.hr_negative_prompt
+        else:
+            self.all_hr_negative_prompts = self.batch_size * self.n_iter * [self.hr_negative_prompt]
+
+        self.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_hr_prompts]
+        self.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_hr_negative_prompts]
+
+    def setup_conds(self):
+        super().setup_conds()
+
+        if self.enable_hr:
+            self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_uc)
+            self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_c)
+
+    def parse_extra_network_prompts(self):
+        res = super().parse_extra_network_prompts()
+
+        if self.enable_hr:
+            self.hr_prompts = self.all_hr_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+            self.hr_negative_prompts = self.all_hr_negative_prompts[self.iteration * self.batch_size:(self.iteration + 1) * self.batch_size]
+
+            self.hr_prompts, self.hr_extra_network_data = extra_networks.parse_prompts(self.hr_prompts)
+
+        return res
+
 
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     sampler = None
@@ -1141,3 +1268,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         devices.torch_gc()
 
         return samples
+
+    def get_token_merging_ratio(self, for_hr=False):
+        return self.token_merging_ratio or ("token_merging_ratio" in self.override_settings and opts.token_merging_ratio) or opts.token_merging_ratio_img2img or opts.token_merging_ratio

modules/progress.py

@@ -95,9 +95,20 @@ def progressapi(req: ProgressRequest):
         image = shared.state.current_image
         if image is not None:
             buffered = io.BytesIO()
-            image.save(buffered, format="png")
+
+            if opts.live_previews_image_format == "png":
+                # using optimize for large images takes an enormous amount of time
+                if max(*image.size) <= 256:
+                    save_kwargs = {"optimize": True}
+                else:
+                    save_kwargs = {"optimize": False, "compress_level": 1}
+
+            else:
+                save_kwargs = {}
+
+            image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
             base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
-            live_preview = f"data:image/png;base64,{base64_image}"
+            live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
             id_live_preview = shared.state.id_live_preview
         else:
             live_preview = None

modules/prompt_parser.py

@@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     """
 
     def collect_steps(steps, tree):
-        l = [steps]
+        res = [steps]
+
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
                 tree.children[-1] = float(tree.children[-1])
                 if tree.children[-1] < 1:
                     tree.children[-1] *= steps
                 tree.children[-1] = min(steps, int(tree.children[-1]))
-                l.append(tree.children[-1])
+                res.append(tree.children[-1])
+
             def alternate(self, tree):
-                l.extend(range(1, steps+1))
+                res.extend(range(1, steps+1))
+
         CollectSteps().visit(tree)
-        return sorted(set(l))
+        return sorted(set(res))
 
     def at_step(step, tree):
         class AtStep(lark.Transformer):
@@ -92,7 +95,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     def get_schedule(prompt):
         try:
             tree = schedule_parser.parse(prompt)
-        except lark.exceptions.LarkError as e:
+        except lark.exceptions.LarkError:
             if 0:
                 import traceback
                 traceback.print_exc()
@@ -140,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
         conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
-        for i, (end_at_step, text) in enumerate(prompt_schedule):
+        for i, (end_at_step, _) in enumerate(prompt_schedule):
             cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
 
         cache[prompt] = cond_schedule
@@ -216,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
         target_index = 0
-        for current, (end_at, cond) in enumerate(cond_schedule):
+        for current, entry in enumerate(cond_schedule):
-            if current_step <= end_at:
+            if current_step <= entry.end_at_step:
                 target_index = current
                 break
         res[i] = cond_schedule[target_index].cond
@@ -231,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     tensors = []
     conds_list = []
 
-    for batch_no, composable_prompts in enumerate(c.batch):
+    for composable_prompts in c.batch:
         conds_for_batch = []
 
-        for cond_index, composable_prompt in enumerate(composable_prompts):
+        for composable_prompt in composable_prompts:
             target_index = 0
-            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
+            for current, entry in enumerate(composable_prompt.schedules):
-                if current_step <= end_at:
+                if current_step <= entry.end_at_step:
                     target_index = current
                     break
 

modules/realesrgan_model.py

@@ -17,9 +17,9 @@ class UpscalerRealESRGAN(Upscaler):
         self.user_path = path
         super().__init__()
         try:
-            from basicsr.archs.rrdbnet_arch import RRDBNet
+            from basicsr.archs.rrdbnet_arch import RRDBNet  # noqa: F401
-            from realesrgan import RealESRGANer
+            from realesrgan import RealESRGANer  # noqa: F401
-            from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+            from realesrgan.archs.srvgg_arch import SRVGGNetCompact  # noqa: F401
             self.enable = True
             self.scalers = []
             scalers = self.load_models(path)
@@ -73,7 +73,7 @@ class UpscalerRealESRGAN(Upscaler):
                 return None
 
             if info.local_data_path.startswith("http"):
-                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
 
             return info
         except Exception as e:
@@ -134,6 +134,6 @@ def get_realesrgan_models(scaler):
             ),
         ]
         return models
-    except Exception as e:
+    except Exception:
         print("Error making Real-ESRGAN models list:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)

modules/safe.py

@@ -95,16 +95,16 @@ def check_pt(filename, extra_handler):
 
     except zipfile.BadZipfile:
 
-        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+        # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
         with open(filename, "rb") as file:
             unpickler = RestrictedUnpickler(file)
             unpickler.extra_handler = extra_handler
-            for i in range(5):
+            for _ in range(5):
                 unpickler.load()
 
 
 def load(filename, *args, **kwargs):
-    return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs)
+    return load_with_extra(filename, *args, extra_handler=global_extra_handler, **kwargs)
 
 
 def load_with_extra(filename, extra_handler=None, *args, **kwargs):

modules/script_callbacks.py

@@ -32,27 +32,42 @@ class CFGDenoiserParams:
     def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
         self.x = x
         """Latent image representation in the process of being denoised"""
-        
+
         self.image_cond = image_cond
         """Conditioning image"""
-        
+
         self.sigma = sigma
         """Current sigma noise step value"""
-        
+
         self.sampling_step = sampling_step
         """Current Sampling step number"""
-        
+
         self.total_sampling_steps = total_sampling_steps
         """Total number of sampling steps planned"""
-        
+
         self.text_cond = text_cond
         """ Encoder hidden states of text conditioning from prompt"""
-        
+
         self.text_uncond = text_uncond
         """ Encoder hidden states of text conditioning from negative prompt"""
 
 
 class CFGDenoisedParams:
+    def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""
+
+        self.inner_model = inner_model
+        """Inner model reference used for denoising"""
+
+
+class AfterCFGCallbackParams:
     def __init__(self, x, sampling_step, total_sampling_steps):
         self.x = x
         """Latent image representation in the process of being denoised"""
@@ -87,6 +102,7 @@ callback_map = dict(
     callbacks_image_saved=[],
     callbacks_cfg_denoiser=[],
     callbacks_cfg_denoised=[],
+    callbacks_cfg_after_cfg=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
     callbacks_image_grid=[],
@@ -94,6 +110,7 @@ callback_map = dict(
     callbacks_script_unloaded=[],
     callbacks_before_ui=[],
     callbacks_on_reload=[],
+    callbacks_list_optimizers=[],
 )
 
 
@@ -186,6 +203,14 @@ def cfg_denoised_callback(params: CFGDenoisedParams):
             report_exception(c, 'cfg_denoised_callback')
 
 
+def cfg_after_cfg_callback(params: AfterCFGCallbackParams):
+    for c in callback_map['callbacks_cfg_after_cfg']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_after_cfg_callback')
+
+
 def before_component_callback(component, **kwargs):
     for c in callback_map['callbacks_before_component']:
         try:
@@ -234,13 +259,25 @@ def before_ui_callback():
             report_exception(c, 'before_ui')
 
 
+def list_optimizers_callback():
+    res = []
+
+    for c in callback_map['callbacks_list_optimizers']:
+        try:
+            c.callback(res)
+        except Exception:
+            report_exception(c, 'list_optimizers')
+
+    return res
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
 
     callbacks.append(ScriptCallback(filename, fun))
 
-    
+
 def remove_current_script_callbacks():
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -332,6 +369,14 @@ def on_cfg_denoised(callback):
     add_callback(callback_map['callbacks_cfg_denoised'], callback)
 
 
+def on_cfg_after_cfg(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after cfg calculations are completed.
+    The callback is called with one argument:
+        - params: AfterCFGCallbackParams - parameters to be passed to the script for post-processing after cfg calculation.
+    """
+    add_callback(callback_map['callbacks_cfg_after_cfg'], callback)
+
+
 def on_before_component(callback):
     """register a function to be called before a component is created.
     The callback is called with arguments:
@@ -377,3 +422,11 @@ def on_before_ui(callback):
     """register a function to be called before the UI is created."""
 
     add_callback(callback_map['callbacks_before_ui'], callback)
+
+
+def on_list_optimizers(callback):
+    """register a function to be called when UI is making a list of cross attention optimization options.
+    The function will be called with one argument, a list, and shall add objects of type modules.sd_hijack_optimizations.SdOptimization
+    to it."""
+
+    add_callback(callback_map['callbacks_list_optimizers'], callback)

modules/script_loading.py

@@ -2,7 +2,6 @@ import os
 import sys
 import traceback
 import importlib.util
-from types import ModuleType
 
 
 def load_module(path):

modules/scripts.py

@@ -17,6 +17,9 @@ class PostprocessImageArgs:
 
 
 class Script:
+    name = None
+    """script's internal name derived from title"""
+
     filename = None
     args_from = None
     args_to = None
@@ -25,8 +28,8 @@ class Script:
     is_txt2img = False
     is_img2img = False
 
-    """A gr.Group component that has all script's UI inside it"""
     group = None
+    """A gr.Group component that has all script's UI inside it"""
 
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -38,6 +41,9 @@ class Script:
     various "Send to <X>" buttons when clicked
     """
 
+    api_info = None
+    """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
+
     def title(self):
         """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
 
@@ -231,7 +237,7 @@ def load_scripts():
     syspath = sys.path
 
     def register_scripts_from_module(module):
-        for key, script_class in module.__dict__.items():
+        for script_class in module.__dict__.values():
             if type(script_class) != type:
                 continue
 
@@ -265,6 +271,12 @@ def load_scripts():
             sys.path = syspath
             current_basedir = paths.script_path
 
+    global scripts_txt2img, scripts_img2img, scripts_postproc
+
+    scripts_txt2img = ScriptRunner()
+    scripts_img2img = ScriptRunner()
+    scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+
 
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
     try:
@@ -295,9 +307,9 @@ class ScriptRunner:
 
         auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
 
-        for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
+        for script_data in auto_processing_scripts + scripts_data:
-            script = script_class()
+            script = script_data.script_class()
-            script.filename = path
+            script.filename = script_data.path
             script.is_txt2img = not is_img2img
             script.is_img2img = is_img2img
 
@@ -313,6 +325,8 @@ class ScriptRunner:
                 self.selectable_scripts.append(script)
 
     def setup_ui(self):
+        import modules.api.models as api_models
+
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
         inputs = [None]
@@ -327,9 +341,28 @@ class ScriptRunner:
             if controls is None:
                 return
 
+            script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
+            api_args = []
+
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
 
+                arg_info = api_models.ScriptArg(label=control.label or "")
+
+                for field in ("value", "minimum", "maximum", "step", "choices"):
+                    v = getattr(control, field, None)
+                    if v is not None:
+                        setattr(arg_info, field, v)
+
+                api_args.append(arg_info)
+
+            script.api_info = api_models.ScriptInfo(
+                name=script.name,
+                is_img2img=script.is_img2img,
+                is_alwayson=script.alwayson,
+                args=api_args,
+            )
+
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
 
@@ -492,7 +525,7 @@ class ScriptRunner:
                 module = script_loading.load_module(script.filename)
                 cache[filename] = module
 
-            for key, script_class in module.__dict__.items():
+            for script_class in module.__dict__.values():
                 if type(script_class) == type and issubclass(script_class, Script):
                     self.scripts[si] = script_class()
                     self.scripts[si].filename = filename
@@ -500,9 +533,9 @@ class ScriptRunner:
                     self.scripts[si].args_to = args_to
 
 
-scripts_txt2img = ScriptRunner()
+scripts_txt2img: ScriptRunner = None
-scripts_img2img = ScriptRunner()
+scripts_img2img: ScriptRunner = None
-scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+scripts_postproc: scripts_postprocessing.ScriptPostprocessingRunner = None
 scripts_current: ScriptRunner = None
 
 
@@ -512,14 +545,7 @@ def reload_script_body_only():
     scripts_img2img.reload_sources(cache)
 
 
-def reload_scripts():
+reload_scripts = load_scripts  # compatibility alias
-    global scripts_txt2img, scripts_img2img, scripts_postproc
-
-    load_scripts()
-
-    scripts_txt2img = ScriptRunner()
-    scripts_img2img = ScriptRunner()
-    scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
 
 
 def add_classes_to_gradio_component(comp):

modules/scripts_auto_postprocessing.py

@@ -17,7 +17,7 @@ class ScriptPostprocessingForMainUI(scripts.Script):
         return self.postprocessing_controls.values()
 
     def postprocess_image(self, p, script_pp, *args):
-        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
+        args_dict = dict(zip(self.postprocessing_controls, args))
 
         pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
         pp.info = {}

modules/scripts_postprocessing.py

@@ -66,9 +66,9 @@ class ScriptPostprocessingRunner:
     def initialize_scripts(self, scripts_data):
         self.scripts = []
 
-        for script_class, path, basedir, script_module in scripts_data:
+        for script_data in scripts_data:
-            script: ScriptPostprocessing = script_class()
+            script: ScriptPostprocessing = script_data.script_class()
-            script.filename = path
+            script.filename = script_data.path
 
             if script.name == "Simple Upscale":
                 continue
@@ -124,7 +124,7 @@ class ScriptPostprocessingRunner:
             script_args = args[script.args_from:script.args_to]
 
             process_args = {}
-            for (name, component), value in zip(script.controls.items(), script_args):
+            for (name, _component), value in zip(script.controls.items(), script_args):
                 process_args[name] = value
 
             script.process(pp, **process_args)

modules/sd_disable_initialization.py

@@ -61,7 +61,7 @@ class DisableInitialization:
                 if res is None:
                     res = original(url, *args, local_files_only=False, **kwargs)
                 return res
-            except Exception as e:
+            except Exception:
                 return original(url, *args, local_files_only=False, **kwargs)
 
         def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

modules/sd_hijack.py

@@ -3,7 +3,7 @@ from torch.nn.functional import silu
 from types import MethodType
 
 import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -28,57 +28,56 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"]
 ldm.modules.attention.print = lambda *args: None
 ldm.modules.diffusionmodules.model.print = lambda *args: None
 
+optimizers = []
+current_optimizer: sd_hijack_optimizations.SdOptimization = None
+
+
+def list_optimizers():
+    new_optimizers = script_callbacks.list_optimizers_callback()
+
+    new_optimizers = [x for x in new_optimizers if x.is_available()]
+
+    new_optimizers = sorted(new_optimizers, key=lambda x: x.priority, reverse=True)
+
+    optimizers.clear()
+    optimizers.extend(new_optimizers)
+
 
 def apply_optimizations():
+    global current_optimizer
+
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-    
-    optimization_method = None
 
-    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
+    if current_optimizer is not None:
+        current_optimizer.undo()
+        current_optimizer = None
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
+    selection = shared.opts.cross_attention_optimization
-        print("Applying xformers cross attention optimization.")
+    if selection == "Automatic" and len(optimizers) > 0:
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+        matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+    else:
-        optimization_method = 'xformers'
+        matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), None)
-    elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
-        print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
-        optimization_method = 'sdp-no-mem'
-    elif cmd_opts.opt_sdp_attention and can_use_sdp:
-        print("Applying scaled dot product cross attention optimization.")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
-        optimization_method = 'sdp'
-    elif cmd_opts.opt_sub_quad_attention:
-        print("Applying sub-quadratic cross attention optimization.")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward
-        optimization_method = 'sub-quadratic'
-    elif cmd_opts.opt_split_attention_v1:
-        print("Applying v1 cross attention optimization.")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-        optimization_method = 'V1'
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not cmd_opts.opt_split_attention and not torch.cuda.is_available()):
-        print("Applying cross attention optimization (InvokeAI).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
-        optimization_method = 'InvokeAI'
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        print("Applying cross attention optimization (Doggettx).")
-        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
-        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
-        optimization_method = 'Doggettx'
 
-    return optimization_method
+    if selection == "None":
+        matching_optimizer = None
+    elif matching_optimizer is None:
+        matching_optimizer = optimizers[0]
+
+    if matching_optimizer is not None:
+        print(f"Applying optimization: {matching_optimizer.name}")
+        matching_optimizer.apply()
+        current_optimizer = matching_optimizer
+        return current_optimizer.name
+    else:
+        return ''
 
 
 def undo_optimizations():
-    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
+    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
 
 
@@ -92,12 +91,12 @@ def fix_checkpoint():
 def weighted_loss(sd_model, pred, target, mean=True):
     #Calculate the weight normally, but ignore the mean
     loss = sd_model._old_get_loss(pred, target, mean=False)
-    
+
     #Check if we have weights available
     weight = getattr(sd_model, '_custom_loss_weight', None)
     if weight is not None:
         loss *= weight
-    
+
     #Return the loss, as mean if specified
     return loss.mean() if mean else loss
 
@@ -105,7 +104,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
     try:
         #Temporarily append weights to a place accessible during loss calc
         sd_model._custom_loss_weight = w
-        
+
         #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
         #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
         if not hasattr(sd_model, '_old_get_loss'):
@@ -118,9 +117,9 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
         try:
             #Delete temporary weights if appended
             del sd_model._custom_loss_weight
-        except AttributeError as e:
+        except AttributeError:
             pass
-            
+
         #If we have an old loss function, reset the loss function to the original one
         if hasattr(sd_model, '_old_get_loss'):
             sd_model.get_loss = sd_model._old_get_loss
@@ -133,7 +132,7 @@ def apply_weighted_forward(sd_model):
 def undo_weighted_forward(sd_model):
     try:
         del sd_model.weighted_forward
-    except AttributeError as e:
+    except AttributeError:
         pass
 
 
@@ -169,7 +168,11 @@ class StableDiffusionModelHijack:
         if m.cond_stage_key == "edit":
             sd_hijack_unet.hijack_ddpm_edit()
 
-        self.optimization_method = apply_optimizations()
+        try:
+            self.optimization_method = apply_optimizations()
+        except Exception as e:
+            errors.display(e, "applying cross attention optimization")
+            undo_optimizations()
 
         self.clip = m.cond_stage_model
 
@@ -184,7 +187,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
-            m.cond_stage_model = m.cond_stage_model.wrapped 
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
@@ -216,10 +219,17 @@ class StableDiffusionModelHijack:
         self.comments = []
 
     def get_prompt_lengths(self, text):
+        if self.clip is None:
+            return "-", "-"
+
         _, token_count = self.clip.process_texts([text])
 
         return token_count, self.clip.get_target_prompt_token_count(token_count)
 
+    def redo_hijack(self, m):
+        self.undo_hijack(m)
+        self.hijack(m)
+
 
 class EmbeddingsWithFixes(torch.nn.Module):
     def __init__(self, wrapped, embeddings):

modules/sd_hijack_clip.py

@@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
             self.hijack.fixes = [x.fixes for x in batch_chunk]
 
             for fixes in self.hijack.fixes:
-                for position, embedding in fixes:
+                for _position, embedding in fixes:
                     used_embeddings[embedding.name] = embedding
 
             z = self.process_tokens(tokens, multipliers)

modules/sd_hijack_inpainting.py

@@ -1,16 +1,10 @@
-import os
 import torch
 
-from einops import repeat
-from omegaconf import ListConfig
-
 import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 
-from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.models.diffusion.ddim import noise_like
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 from ldm.models.diffusion.sampling_util import norm_thresholding
 
 
@@ -29,7 +23,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
             if isinstance(c, dict):
                 assert isinstance(unconditional_conditioning, dict)
-                c_in = dict()
+                c_in = {}
                 for k in c:
                     if isinstance(c[k], list):
                         c_in[k] = [

modules/sd_hijack_ip2p.py

@@ -1,8 +1,5 @@
-import collections
 import os.path
-import sys
+
-import gc
-import time
 
 def should_hijack_ip2p(checkpoint_info):
     from modules import sd_models_config
@@ -10,4 +7,4 @@ def should_hijack_ip2p(checkpoint_info):
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
     cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
 
-    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
+    return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename

modules/sd_hijack_optimizations.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import math
 import sys
 import traceback
@@ -9,10 +10,129 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange
 
-from modules import shared, errors, devices
+from modules import shared, errors, devices, sub_quadratic_attention
 from modules.hypernetworks import hypernetwork
 
-from .sub_quadratic_attention import efficient_dot_product_attention
+import ldm.modules.attention
+import ldm.modules.diffusionmodules.model
+
+diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
+
+
+class SdOptimization:
+    name: str = None
+    label: str | None = None
+    cmd_opt: str | None = None
+    priority: int = 0
+
+    def title(self):
+        if self.label is None:
+            return self.name
+
+        return f"{self.name} - {self.label}"
+
+    def is_available(self):
+        return True
+
+    def apply(self):
+        pass
+
+    def undo(self):
+        ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+
+
+class SdOptimizationXformers(SdOptimization):
+    name = "xformers"
+    cmd_opt = "xformers"
+    priority = 100
+
+    def is_available(self):
+        return shared.cmd_opts.force_enable_xformers or (shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0))
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = xformers_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = xformers_attnblock_forward
+
+
+class SdOptimizationSdpNoMem(SdOptimization):
+    name = "sdp-no-mem"
+    label = "scaled dot product without memory efficient attention"
+    cmd_opt = "opt_sdp_no_mem_attention"
+    priority = 90
+
+    def is_available(self):
+        return hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_no_mem_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_no_mem_attnblock_forward
+
+
+class SdOptimizationSdp(SdOptimizationSdpNoMem):
+    name = "sdp"
+    label = "scaled dot product"
+    cmd_opt = "opt_sdp_attention"
+    priority = 80
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = scaled_dot_product_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sdp_attnblock_forward
+
+
+class SdOptimizationSubQuad(SdOptimization):
+    name = "sub-quadratic"
+    cmd_opt = "opt_sub_quad_attention"
+    priority = 10
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sub_quad_attnblock_forward
+
+
+class SdOptimizationV1(SdOptimization):
+    name = "V1"
+    label = "original v1"
+    cmd_opt = "opt_split_attention_v1"
+    priority = 10
+
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_v1
+
+
+class SdOptimizationInvokeAI(SdOptimization):
+    name = "InvokeAI"
+    cmd_opt = "opt_split_attention_invokeai"
+
+    @property
+    def priority(self):
+        return 1000 if not torch.cuda.is_available() else 10
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
+
+
+class SdOptimizationDoggettx(SdOptimization):
+    name = "Doggettx"
+    cmd_opt = "opt_split_attention"
+    priority = 20
+
+    def apply(self):
+        ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = cross_attention_attnblock_forward
+
+
+def list_optimizers(res):
+    res.extend([
+        SdOptimizationXformers(),
+        SdOptimizationSdpNoMem(),
+        SdOptimizationSdp(),
+        SdOptimizationSubQuad(),
+        SdOptimizationV1(),
+        SdOptimizationInvokeAI(),
+        SdOptimizationDoggettx(),
+    ])
 
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
@@ -49,7 +169,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     v_in = self.to_v(context_v)
     del context, context_k, context_v, x
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -62,10 +182,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
             end = i + 2
             s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
             s1 *= self.scale
-    
+
             s2 = s1.softmax(dim=-1)
             del s1
-    
+
             r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
             del s2
         del q, k, v
@@ -95,43 +215,43 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k_in = k_in * self.scale
-    
+
         del context, x
-    
+
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
         del q_in, k_in, v_in
-    
+
         r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-    
+
         mem_free_total = get_available_vram()
-    
+
         gb = 1024 ** 3
         tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
         modifier = 3 if q.element_size() == 2 else 2.5
         mem_required = tensor_size * modifier
         steps = 1
-    
+
         if mem_required > mem_free_total:
             steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
             # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
             #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-    
+
         if steps > 64:
             max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
             raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                                f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-    
+
         slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
         for i in range(0, q.shape[1], slice_size):
             end = i + slice_size
             s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-    
+
             s2 = s1.softmax(dim=-1, dtype=q.dtype)
             del s1
-    
+
             r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
             del s2
-    
+
         del q, k, v
 
     r1 = r1.to(dtype)
@@ -228,8 +348,8 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
 
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k = k * self.scale
-    
+
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
         r = einsum_op(q, k, v)
     r = r.to(dtype)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
@@ -296,11 +416,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
         # the big matmul fits into our memory limit; do everything in 1 chunk,
         # i.e. send it down the unchunked fast-path
-        query_chunk_size = q_tokens
         kv_chunk_size = k_tokens
 
     with devices.without_autocast(disable=q.dtype == v.dtype):
-        return efficient_dot_product_attention(
+        return sub_quadratic_attention.efficient_dot_product_attention(
             q,
             k,
             v,
@@ -335,7 +454,7 @@ def xformers_attention_forward(self, x, context=None, mask=None):
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -370,7 +489,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
     q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
     k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
     v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
-    
+
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -452,7 +571,7 @@ def cross_attention_attnblock_forward(self, x):
         h3 += x
 
         return h3
-    
+
 def xformers_attnblock_forward(self, x):
     try:
         h_ = x
@@ -461,7 +580,7 @@ def xformers_attnblock_forward(self, x):
         k = self.k(h_)
         v = self.v(h_)
         b, c, h, w = q.shape
-        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
         dtype = q.dtype
         if shared.opts.upcast_attn:
             q, k = q.float(), k.float()
@@ -483,7 +602,7 @@ def sdp_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     dtype = q.dtype
     if shared.opts.upcast_attn:
         q, k = q.float(), k.float()
@@ -507,7 +626,7 @@ def sub_quad_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     q = q.contiguous()
     k = k.contiguous()
     v = v.contiguous()

modules/sd_hijack_xlmr.py

@@ -1,8 +1,6 @@
-import open_clip.tokenizer
 import torch
 
 from modules import sd_hijack_clip, devices
-from modules.shared import opts
 
 
 class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):

modules/sd_models.py

@@ -15,9 +15,9 @@ import ldm.modules.midas as midas
 from ldm.util import instantiate_from_config
 
 from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
-from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack
 from modules.timer import Timer
+import tomesd
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -87,8 +87,7 @@ class CheckpointInfo:
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
-
+    from transformers import logging, CLIPModel  # noqa: F401
-    from transformers import logging, CLIPModel
 
     logging.set_verbosity_error()
 except Exception:
@@ -99,7 +98,6 @@ def setup_model():
     if not os.path.exists(model_path):
         os.makedirs(model_path)
 
-    list_models()
     enable_midas_autodownload()
 
 
@@ -167,7 +165,7 @@ def model_hash(filename):
 
 def select_checkpoint():
     model_checkpoint = shared.opts.sd_model_checkpoint
-        
+
     checkpoint_info = checkpoint_alisases.get(model_checkpoint, None)
     if checkpoint_info is not None:
         return checkpoint_info
@@ -239,7 +237,7 @@ def read_metadata_from_safetensors(filename):
             if isinstance(v, str) and v[0:1] == '{':
                 try:
                     res[k] = json.loads(v)
-                except Exception as e:
+                except Exception:
                     pass
 
         return res
@@ -374,7 +372,7 @@ def enable_midas_autodownload():
         if not os.path.exists(path):
             if not os.path.exists(midas_path):
                 mkdir(midas_path)
-    
+
             print(f"Downloading midas model weights for {model_type} to {path}")
             request.urlretrieve(midas_urls[model_type], path)
             print(f"{model_type} downloaded")
@@ -410,11 +408,18 @@ sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_w
 class SdModelData:
     def __init__(self):
         self.sd_model = None
+        self.was_loaded_at_least_once = False
         self.lock = threading.Lock()
 
     def get_sd_model(self):
+        if self.was_loaded_at_least_once:
+            return self.sd_model
+
         if self.sd_model is None:
             with self.lock:
+                if self.sd_model is not None or self.was_loaded_at_least_once:
+                    return self.sd_model
+
                 try:
                     load_model()
                 except Exception as e:
@@ -467,7 +472,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     try:
         with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
             sd_model = instantiate_from_config(sd_config.model)
-    except Exception as e:
+    except Exception:
         pass
 
     if sd_model is None:
@@ -493,6 +498,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     sd_model.eval()
     model_data.sd_model = sd_model
+    model_data.was_loaded_at_least_once = True
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
 
@@ -502,6 +508,11 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     timer.record("scripts callbacks")
 
+    with devices.autocast(), torch.no_grad():
+        sd_model.cond_stage_model_empty_prompt = sd_model.cond_stage_model([""])
+
+    timer.record("calculate empty prompt")
+
     print(f"Model loaded in {timer.summary()}.")
 
     return sd_model
@@ -538,13 +549,12 @@ def reload_model_weights(sd_model=None, info=None):
 
     if sd_model is None or checkpoint_config != sd_model.used_config:
         del sd_model
-        checkpoints_loaded.clear()
         load_model(checkpoint_info, already_loaded_state_dict=state_dict)
         return model_data.sd_model
 
     try:
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-    except Exception as e:
+    except Exception:
         print("Failed to load checkpoint, restoring previous")
         load_model_weights(sd_model, current_checkpoint_info, None, timer)
         raise
@@ -565,7 +575,7 @@ def reload_model_weights(sd_model=None, info=None):
 
 
 def unload_model_weights(sd_model=None, info=None):
-    from modules import lowvram, devices, sd_hijack
+    from modules import devices, sd_hijack
     timer = Timer()
 
     if model_data.sd_model:
@@ -580,3 +590,29 @@ def unload_model_weights(sd_model=None, info=None):
     print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model
+
+
+def apply_token_merging(sd_model, token_merging_ratio):
+    """
+    Applies speed and memory optimizations from tomesd.
+    """
+
+    current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
+
+    if current_token_merging_ratio == token_merging_ratio:
+        return
+
+    if current_token_merging_ratio > 0:
+        tomesd.remove_patch(sd_model)
+
+    if token_merging_ratio > 0:
+        tomesd.apply_patch(
+            sd_model,
+            ratio=token_merging_ratio,
+            use_rand=False,  # can cause issues with some samplers
+            merge_attn=True,
+            merge_crossattn=False,
+            merge_mlp=False
+        )
+
+    sd_model.applied_token_merged_ratio = token_merging_ratio

modules/sd_models_config.py

@@ -1,4 +1,3 @@
-import re
 import os
 
 import torch