remove space after comma and colon

add exception in case of the infotext is damaged
compact Soft Inpainting infotext
2024-01-23 03:44:21 +09:00 · 2024-01-23 03:34:49 +09:00 · 2024-01-23 02:44:21 +09:00 · 2024-01-23 02:17:21 +09:00 · 2024-01-21 16:01:44 +03:00 · 2024-01-21 09:02:18 +09:00
163 changed files with 6828 additions and 6012 deletions
@@ -1,25 +1,45 @@
 name: Bug Report
-description: You think somethings is broken in the UI
+description: You think something is broken in the UI
 title: "[Bug]: "
 labels: ["bug-report"]
 body:
  - type: checkboxes
    attributes:
      label: Is there an existing issue for this?
      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
      options:
        - label: I have searched the existing issues and checked the recent builds/commits
          required: true
  - type: markdown
    attributes:
      value: |
-        *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
+        > The title of the bug report should be short and descriptive.
        > Use relevant keywords for searchability.
        > Do not leave it blank, but also do not put an entire error log in it.
  - type: checkboxes
    attributes:
      label: Checklist
      description: |
        Please perform basic debugging to see if extensions or configuration is the cause of the issue.
        Basic debug procedure
        　1. Disable all third-party extensions - check if extension is the cause
        　2. Update extensions and webui - sometimes things just need to be updated
        　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration
        　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed
        　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue
        Before making a issue report please, check that the issue hasn't been reported recently.
      options:
        - label: The issue exists after disabling all extensions
        - label: The issue exists on a clean installation of webui
        - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui
        - label: The issue exists in the current version of the webui
        - label: The issue has not been reported before recently
        - label: The issue has been reported before but has not been fixed yet
  - type: markdown
    attributes:
      value: |
        > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible
  - type: textarea
    id: what-did
    attributes:
      label: What happened?
      description: Tell us what happened in a very clear and simple way
      placeholder: |
        txt2img is not working as intended.
    validations:
      required: true
  - type: textarea
@@ -27,9 +47,9 @@ body:
    attributes:
      label: Steps to reproduce the problem
      description: Please provide us with precise step by step instructions on how to reproduce the bug
-      value: |
+      placeholder: |
-        1. Go to .... 
+        1. Go to ...
-        2. Press ....
+        2. Press ...
        3. ...
    validations:
      required: true
@@ -38,13 +58,8 @@ body:
    attributes:
      label: What should have happened?
      description: Tell us what you think the normal behavior should be
-    validations:
+      placeholder: |
-      required: true
+        WebUI should ...
  - type: textarea
    id: sysinfo
    attributes:
      label: Sysinfo
      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
    validations:
      required: true
  - type: dropdown
@@ -58,12 +73,25 @@ body:
        - Brave
        - Apple Safari
        - Microsoft Edge
        - Android
        - iOS
        - Other
  - type: textarea
    id: sysinfo
    attributes:
      label: Sysinfo
      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
      placeholder: |
        1. Go to WebUI Settings -> Sysinfo -> Download system info.
            If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file
        2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text.
    validations:
      required: true
  - type: textarea
    id: logs
    attributes:
      label: Console logs
-      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
+      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
      render: Shell
    validations:
      required: true
@@ -71,4 +99,7 @@ body:
    id: misc
    attributes:
      label: Additional information
-      description: Please provide us with any relevant additional info or context.
+      description: | 
        Please provide us with any relevant additional info or context.
        Examples:
        　I have updated my GPU driver recently.
@@ -20,7 +20,7 @@ jobs:
          #     not to have GHA download an (at the time of writing) 4 GB cache
          #     of PyTorch and other dependencies.
      - name: Install Ruff
-        run: pip install ruff==0.0.272
+        run: pip install ruff==0.1.6
      - name: Run Ruff
        run: ruff .
  lint-js:
@@ -20,6 +20,12 @@ jobs:
          cache-dependency-path: |
            **/requirements*txt
            launch.py
      - name: Cache models
        id: cache-models
        uses: actions/cache@v3
        with:
          path: models
          key: "2023-12-30"
      - name: Install test dependencies
        run: pip install wait-for-it -r requirements-test.txt
        env:
@@ -33,6 +39,8 @@ jobs:
          TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
          WEBUI_LAUNCH_LIVE_OUTPUT: "1"
          PYTHONUNBUFFERED: "1"
      - name: Print installed packages
        run: pip freeze
      - name: Start test server
        run: >
          python -m coverage run
@@ -49,7 +57,7 @@ jobs:
          2>&1 | tee output.txt &
      - name: Run tests
        run: |
-          wait-for-it --service 127.0.0.1:7860 -t 600
+          wait-for-it --service 127.0.0.1:7860 -t 20
          python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
      - name: Kill test server
        if: always()
@@ -37,3 +37,4 @@ notification.mp3
 /node_modules
 /package-lock.json
 /.coverage*
 /test/test_outputs
@@ -1,3 +1,170 @@
 ## 1.7.0
 ### Features:
 * settings tab rework: add search field, add categories, split UI settings page into many
 * add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364))
 * support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610))
 * add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568))
 * option to move prompt from top row into generation parameters
 * add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865))
 * support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692))
 * script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944))
 * support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948))
 * add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170))
 * remove Train->Preprocessing tab and put all its functionality into Extras tab
 * initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171))
 ### Minor:
 * allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767))
 * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
 * extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838))
 * add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909))
 * add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975))
 * add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253))
 * write infotext to gif images
 * read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068))
 * allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189))
 * allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444))
 * prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480))
 * added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631))
 * show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459))
 * support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638))
 * add an option to not print stack traces on ctrl+c
 * start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644))
 * update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733))
 * added a visible checkbox to input accordion
 * added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826))
 * added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968))
 * enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931))
 * option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009))
 * infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page
 * add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046))
 * support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126))
 * allow use of mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
 ### Extensions and API:
 * update gradio to 3.41.2
 * support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774))
 * update pnginfo API to return dict with parsed values
 * add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856))
 * show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281))
 * add an option to choose how to combine hires fix and refiner
 * include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135))
 * sd_unet support for SDXL
 * patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276))
 * xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266))
 * allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077))
 * add onEdit function for js and rework token-counter.js to use it
 * fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567))
 * ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463))
 * call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762))
 * add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884))
 * update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059))
 * add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119))
 * catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120))
 * use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063))
 * update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192))
 ### Bug Fixes:
 * fix pix2pix producing bad results
 * fix defaults settings page breaking when any of main UI tabs are hidden
 * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
 * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
 * prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795))
 * small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797))
 * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792))
 * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780))
 * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
 * hide --gradio-auth and --api-auth values from /internal/sysinfo report
 * add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819))
 * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
 * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
 * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
 * get progressbar to display correctly in extensions tab
 * keep order in list of checkpoints when loading model that doesn't have a checksum
 * fix inpainting models in txt2img creating black pictures
 * fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876))
 * fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926))
 * fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084))
 * fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995))
 * more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924))
 * don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118))
 * fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412))
 * update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411))
 * fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395))
 * fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302))
 * better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231))
 * fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210))
 * fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178))
 * account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877))
 * re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170))
 * fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139))
 * consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121))
 * fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469))
 * repair unload sd checkpoint button
 * edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533))
 * fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718))
 * properly apply sort order for extra network cards when selected from dropdown
 * fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962))
 * thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014))
 * fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156))
 * fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121))
 * make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131))
 * extras tab batch: actually use original filename
 * make webui not crash when running with --disable-all-extensions option
 ### Other:
 * non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814))
 * fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827))
 * remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842))
 * style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837))
 * revert SGM noise multiplier change for img2img because it breaks hires fix
 * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
 * [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839))
 * chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851))
 * WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929))
 * add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028))
 * update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986))
 * fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213))
 * restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976))
 * use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880))
 * put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119))
 * fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846))
 * bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418))
 * update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372))
 * use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313))
 * XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282))
 * initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229))
 * fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458))
 * change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466))
 * fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475))
 * fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630))
 * fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535))
 * update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991))
 * requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
 * fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829))
 * fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797))
 * corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855))
 * feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004))
 * adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996))
 * hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977))
 * fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929))
 * save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035))
 * move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084))
 * compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936))
 * json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108))
 * dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957))
 * alternate implementation for unet forward replacement that does not depend on hijack being applied
 * re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178))
 * fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177))
 * slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181))
 * add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186))
 ## 1.6.1
 ### Bug Fixes:
 * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
 ## 1.6.0
 ### Features:
@@ -1,5 +1,5 @@
 # Stable Diffusion web UI
-A browser interface based on Gradio library for Stable Diffusion.
+A web interface for Stable Diffusion, implemented using Gradio library.
 ![](screenshot.png)
@@ -91,6 +91,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
 - [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
@@ -120,7 +121,9 @@ Alternatively, use online services (like Google Colab):
 # Debian-based:
 sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
 # Red Hat-based:
-sudo dnf install wget git python3
+sudo dnf install wget git python3 gperftools-libs libglvnd-glx 
 # openSUSE-based:
 sudo zypper install wget git python3 libtcmalloc4 libglvnd
 # Arch-based:
 sudo pacman -S wget git python3
 ```
@@ -146,13 +149,14 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
 ## Credits
 Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
+- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
+- Spandrel - https://github.com/chaiNNer-org/spandrel implementing
- CodeFormer - https://github.com/sczhou/CodeFormer
+  - GFPGAN - https://github.com/TencentARC/GFPGAN.git
- ESRGAN - https://github.com/xinntao/ESRGAN
+  - CodeFormer - https://github.com/sczhou/CodeFormer
- SwinIR - https://github.com/JingyunLiang/SwinIR
+  - ESRGAN - https://github.com/xinntao/ESRGAN
- Swin2SR - https://github.com/mv-lab/swin2sr
+  - SwinIR - https://github.com/JingyunLiang/SwinIR
  - Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - MiDaS - https://github.com/isl-org/MiDaS
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
@@ -173,5 +177,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
 - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
 - Hypertile - tfernd - https://github.com/tfernd/HyperTile
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)
@@ -0,0 +1,73 @@
 model:
  base_learning_rate: 1.0e-04
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False
    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 10000 ]
        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
        f_start: [ 1.e-6 ]
        f_max: [ 1. ]
        f_min: [ 1. ]
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        use_checkpoint: True
        legacy: False
    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config:
      target: modules.xlmr_m18.BertSeriesModelWithTransformation
      params:
        name: "XLMR-Large"
@@ -0,0 +1,98 @@
 model:
  target: sgm.models.diffusion.DiffusionEngine
  params:
    scale_factor: 0.13025
    disable_first_stage_autocast: True
    denoiser_config:
      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
      params:
        num_idx: 1000
        weighting_config:
          target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
        scaling_config:
          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
        discretization_config:
          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
    network_config:
      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        adm_in_channels: 2816
        num_classes: sequential
        use_checkpoint: True
        in_channels: 9
        out_channels: 4
        model_channels: 320
        attention_resolutions: [4, 2]
        num_res_blocks: 2
        channel_mult: [1, 2, 4]
        num_head_channels: 64
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: [1, 2, 10]  # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
        context_dim: 2048
        spatial_transformer_attn_type: softmax-xformers
        legacy: False
    conditioner_config:
      target: sgm.modules.GeneralConditioner
      params:
        emb_models:
          # crossattn cond
          - is_trainable: False
            input_key: txt
            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
            params:
              layer: hidden
              layer_idx: 11
          # crossattn and vector cond
          - is_trainable: False
            input_key: txt
            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
            params:
              arch: ViT-bigG-14
              version: laion2b_s39b_b160k
              freeze: True
              layer: penultimate
              always_return_pooled: True
              legacy: False
          # vector cond
          - is_trainable: False
            input_key: original_size_as_tuple
            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
            params:
              outdim: 256  # multiplied by two
          # vector cond
          - is_trainable: False
            input_key: crop_coords_top_left
            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
            params:
              outdim: 256  # multiplied by two
          # vector cond
          - is_trainable: False
            input_key: target_size_as_tuple
            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
            params:
              outdim: 256  # multiplied by two
    first_stage_config:
      target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          attn_type: vanilla-xformers
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult: [1, 2, 4, 4]
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
@@ -19,3 +19,50 @@ def rebuild_cp_decomposition(up, down, mid):
    up = up.reshape(up.size(0), -1)
    down = down.reshape(down.size(0), -1)
    return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
 # copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
 def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
    '''
    return a tuple of two value of input dimension decomposed by the number closest to factor
    second value is higher or equal than first value.
    In LoRA with Kroneckor Product, first value is a value for weight scale.
    secon value is a value for weight.
    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
    examples)
    factor
        -1               2                4               8               16               ...
    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
    '''
    if factor > 0 and (dimension % factor) == 0:
        m = factor
        n = dimension // factor
        if m > n:
            n, m = m, n
        return m, n
    if factor < 0:
        factor = dimension
    m, n = 1, dimension
    length = m + n
    while m<n:
        new_m = m + 1
        while dimension%new_m != 0:
            new_m += 1
        new_n = dimension // new_m
        if new_m + new_n > length or new_m>factor:
            break
        else:
            m, n = new_m, new_n
    if m > n:
        n, m = m, n
    return m, n
@@ -3,6 +3,9 @@ import os
 from collections import namedtuple
 import enum
 import torch.nn as nn
 import torch.nn.functional as F
 from modules import sd_models, cache, errors, hashes, shared
 NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
@@ -115,6 +118,29 @@ class NetworkModule:
        if hasattr(self.sd_module, 'weight'):
            self.shape = self.sd_module.weight.shape
        self.ops = None
        self.extra_kwargs = {}
        if isinstance(self.sd_module, nn.Conv2d):
            self.ops = F.conv2d
            self.extra_kwargs = {
                'stride': self.sd_module.stride,
                'padding': self.sd_module.padding
            }
        elif isinstance(self.sd_module, nn.Linear):
            self.ops = F.linear
        elif isinstance(self.sd_module, nn.LayerNorm):
            self.ops = F.layer_norm
            self.extra_kwargs = {
                'normalized_shape': self.sd_module.normalized_shape,
                'eps': self.sd_module.eps
            }
        elif isinstance(self.sd_module, nn.GroupNorm):
            self.ops = F.group_norm
            self.extra_kwargs = {
                'num_groups': self.sd_module.num_groups,
                'eps': self.sd_module.eps
            }
        self.dim = None
        self.bias = weights.w.get("bias")
        self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
@@ -137,7 +163,7 @@ class NetworkModule:
    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
        if self.bias is not None:
            updown = updown.reshape(self.bias.shape)
-            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
            updown = updown.reshape(output_shape)
        if len(output_shape) == 4:
@@ -155,5 +181,10 @@ class NetworkModule:
        raise NotImplementedError()
    def forward(self, x, y):
-        raise NotImplementedError()
+        """A general forward implementation for all modules"""
        if self.ops is None:
            raise NotImplementedError()
        else:
            updown, ex_bias = self.calc_updown(self.sd_module.weight)
            return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)
@@ -18,9 +18,9 @@ class NetworkModuleFull(network.NetworkModule):
    def calc_updown(self, orig_weight):
        output_shape = self.weight.shape
-        updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        updown = self.weight.to(orig_weight.device)
        if self.ex_bias is not None:
-            ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            ex_bias = self.ex_bias.to(orig_weight.device)
        else:
            ex_bias = None
@@ -0,0 +1,33 @@
 import network
 class ModuleTypeGLora(network.ModuleType):
    def create_module(self, net: network.Network, weights: network.NetworkWeights):
        if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
            return NetworkModuleGLora(net, weights)
        return None
 # adapted from https://github.com/KohakuBlueleaf/LyCORIS
 class NetworkModuleGLora(network.NetworkModule):
    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
        super().__init__(net, weights)
        if hasattr(self.sd_module, 'weight'):
            self.shape = self.sd_module.weight.shape
        self.w1a = weights.w["a1.weight"]
        self.w1b = weights.w["b1.weight"]
        self.w2a = weights.w["a2.weight"]
        self.w2b = weights.w["b2.weight"]
    def calc_updown(self, orig_weight):
        w1a = self.w1a.to(orig_weight.device)
        w1b = self.w1b.to(orig_weight.device)
        w2a = self.w2a.to(orig_weight.device)
        w2b = self.w2b.to(orig_weight.device)
        output_shape = [w1a.size(0), w1b.size(1)]
        updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
        return self.finalize_updown(updown, orig_weight, output_shape)
@@ -27,16 +27,16 @@ class NetworkModuleHada(network.NetworkModule):
        self.t2 = weights.w.get("hada_t2")
    def calc_updown(self, orig_weight):
-        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+        w1a = self.w1a.to(orig_weight.device)
-        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+        w1b = self.w1b.to(orig_weight.device)
-        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+        w2a = self.w2a.to(orig_weight.device)
-        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+        w2b = self.w2b.to(orig_weight.device)
        output_shape = [w1a.size(0), w1b.size(1)]
        if self.t1 is not None:
            output_shape = [w1a.size(1), w1b.size(1)]
-            t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+            t1 = self.t1.to(orig_weight.device)
            updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
            output_shape += t1.shape[2:]
        else:
@@ -45,7 +45,7 @@ class NetworkModuleHada(network.NetworkModule):
            updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
        if self.t2 is not None:
-            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+            t2 = self.t2.to(orig_weight.device)
            updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
        else:
            updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
@@ -17,7 +17,7 @@ class NetworkModuleIa3(network.NetworkModule):
        self.on_input = weights.w["on_input"].item()
    def calc_updown(self, orig_weight):
-        w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+        w = self.w.to(orig_weight.device)
        output_shape = [w.size(0), orig_weight.size(1)]
        if self.on_input:
@@ -37,22 +37,22 @@ class NetworkModuleLokr(network.NetworkModule):
    def calc_updown(self, orig_weight):
        if self.w1 is not None:
-            w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1 = self.w1.to(orig_weight.device)
        else:
-            w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1a = self.w1a.to(orig_weight.device)
-            w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1b = self.w1b.to(orig_weight.device)
            w1 = w1a @ w1b
        if self.w2 is not None:
-            w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2 = self.w2.to(orig_weight.device)
        elif self.t2 is None:
-            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2a = self.w2a.to(orig_weight.device)
-            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2b = self.w2b.to(orig_weight.device)
            w2 = w2a @ w2b
        else:
-            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+            t2 = self.t2.to(orig_weight.device)
-            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2a = self.w2a.to(orig_weight.device)
-            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2b = self.w2b.to(orig_weight.device)
            w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
        output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
@@ -61,13 +61,13 @@ class NetworkModuleLora(network.NetworkModule):
        return module
    def calc_updown(self, orig_weight):
-        up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        up = self.up_model.weight.to(orig_weight.device)
-        down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        down = self.down_model.weight.to(orig_weight.device)
        output_shape = [up.size(0), down.size(1)]
        if self.mid_model is not None:
            # cp-decomposition
-            mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+            mid = self.mid_model.weight.to(orig_weight.device)
            updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
            output_shape += mid.shape[2:]
        else:
@@ -18,10 +18,10 @@ class NetworkModuleNorm(network.NetworkModule):
    def calc_updown(self, orig_weight):
        output_shape = self.w_norm.shape
-        updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+        updown = self.w_norm.to(orig_weight.device)
        if self.b_norm is not None:
-            ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+            ex_bias = self.b_norm.to(orig_weight.device)
        else:
            ex_bias = None
@@ -0,0 +1,82 @@
 import torch
 import network
 from lyco_helpers import factorization
 from einops import rearrange
 class ModuleTypeOFT(network.ModuleType):
    def create_module(self, net: network.Network, weights: network.NetworkWeights):
        if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
            return NetworkModuleOFT(net, weights)
        return None
 # Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
 # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
 class NetworkModuleOFT(network.NetworkModule):
    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
        super().__init__(net, weights)
        self.lin_module = None
        self.org_module: list[torch.Module] = [self.sd_module]
        self.scale = 1.0
        # kohya-ss
        if "oft_blocks" in weights.w.keys():
            self.is_kohya = True
            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
            self.alpha = weights.w["alpha"] # alpha is constraint
            self.dim = self.oft_blocks.shape[0] # lora dim
        # LyCORIS
        elif "oft_diag" in weights.w.keys():
            self.is_kohya = False
            self.oft_blocks = weights.w["oft_diag"]
            # self.alpha is unused
            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
        if is_linear:
            self.out_dim = self.sd_module.out_features
        elif is_conv:
            self.out_dim = self.sd_module.out_channels
        elif is_other_linear:
            self.out_dim = self.sd_module.embed_dim
        if self.is_kohya:
            self.constraint = self.alpha * self.out_dim
            self.num_blocks = self.dim
            self.block_size = self.out_dim // self.dim
        else:
            self.constraint = None
            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
    def calc_updown(self, orig_weight):
        oft_blocks = self.oft_blocks.to(orig_weight.device)
        eye = torch.eye(self.block_size, device=self.oft_blocks.device)
        if self.is_kohya:
            block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
            norm_Q = torch.norm(block_Q.flatten())
            new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
            block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
            oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
        R = oft_blocks.to(orig_weight.device)
        # This errors out for MultiheadAttention, might need to be handled up-stream
        merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
        merged_weight = torch.einsum(
            'k n m, k n ... -> k m ...',
            R,
            merged_weight
        )
        merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
        updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
        output_shape = orig_weight.shape
        return self.finalize_updown(updown, orig_weight, output_shape)
@@ -1,3 +1,4 @@
 import gradio as gr
 import logging
 import os
 import re
@@ -5,17 +6,19 @@ import re
 import lora_patches
 import network
 import network_lora
 import network_glora
 import network_hada
 import network_ia3
 import network_lokr
 import network_full
 import network_norm
 import network_oft
 import torch
 from typing import Union
 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
-from modules.textual_inversion.textual_inversion import Embedding
+import modules.textual_inversion.textual_inversion as textual_inversion
 from lora_logger import logger
@@ -26,6 +29,8 @@ module_types = [
    network_lokr.ModuleTypeLokr(),
    network_full.ModuleTypeFull(),
    network_norm.ModuleTypeNorm(),
    network_glora.ModuleTypeGLora(),
    network_oft.ModuleTypeOFT(),
 ]
@@ -155,7 +160,8 @@ def load_network(name, network_on_disk):
    bundle_embeddings = {}
    for key_network, weight in sd.items():
-        key_network_without_network_parts, network_part = key_network.split(".", 1)
+        key_network_without_network_parts, _, network_part = key_network.partition(".")
        if key_network_without_network_parts == "bundle_emb":
            emb_name, vec_name = network_part.split(".", 1)
            emb_dict = bundle_embeddings.get(emb_name, {})
@@ -187,6 +193,17 @@ def load_network(name, network_on_disk):
                key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
                sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        # kohya_ss OFT module
        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        # KohakuBlueLeaf OFT module
        if sd_module is None and "oft_diag" in key:
            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
        if sd_module is None:
            keys_failed_to_match[key_network] = key
            continue
@@ -210,34 +227,7 @@ def load_network(name, network_on_disk):
    embeddings = {}
    for emb_name, data in bundle_embeddings.items():
-        # textual inversion embeddings
+        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
        if 'string_to_param' in data:
            param_dict = data['string_to_param']
            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
            emb = next(iter(param_dict.items()))[1]
            vec = emb.detach().to(devices.device, dtype=torch.float32)
            shape = vec.shape[-1]
            vectors = vec.shape[0]
        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
            vectors = data['clip_g'].shape[0]
        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
            emb = next(iter(data.values()))
            if len(emb.shape) == 1:
                emb = emb.unsqueeze(0)
            vec = emb.detach().to(devices.device, dtype=torch.float32)
            shape = vec.shape[-1]
            vectors = vec.shape[0]
        else:
            raise Exception(f"Couldn't identify {emb_name} in lora: {name} as neither textual inversion embedding nor diffuser concept.")
        embedding = Embedding(vec, emb_name)
        embedding.vectors = vectors
        embedding.shape = shape
        embedding.loaded = None
        embeddings[emb_name] = embedding
@@ -270,11 +260,11 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
    loaded_networks.clear()
-    networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+    networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
    if any(x is None for x in networks_on_disk):
        list_available_networks()
-        networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+        networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
    failed_to_load_networks = []
@@ -325,7 +315,12 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
                emb_db.skipped_embeddings[name] = embedding
    if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+        lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
        sd_hijack.model_hijack.comments.append(lora_not_found_message)
        if shared.opts.lora_not_found_warning_console:
            print(f'\n{lora_not_found_message}\n')
        if shared.opts.lora_not_found_gradio_warning:
            gr.Warning(lora_not_found_message)
    purge_networks_from_memory()
@@ -400,18 +395,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
            if module is not None and hasattr(self, 'weight'):
                try:
                    with torch.no_grad():
-                        updown, ex_bias = module.calc_updown(self.weight)
+                        if getattr(self, 'fp16_weight', None) is None:
                            weight = self.weight
                            bias = self.bias
                        else:
                            weight = self.fp16_weight.clone().to(self.weight.device)
                            bias = getattr(self, 'fp16_bias', None)
                            if bias is not None:
                                bias = bias.clone().to(self.bias.device)
                        updown, ex_bias = module.calc_updown(weight)
-                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                        if len(weight.shape) == 4 and weight.shape[1] == 9:
                            # inpainting model. zero pad updown to make channel[1]  4 to 9
                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
-                        self.weight += updown
+                        self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
                        if ex_bias is not None and hasattr(self, 'bias'):
                            if self.bias is None:
-                                self.bias = torch.nn.Parameter(ex_bias)
+                                self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
                            else:
-                                self.bias += ex_bias
+                                self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
                except RuntimeError as e:
                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
@@ -455,23 +458,23 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
        self.network_current_names = wanted_names
-def network_forward(module, input, original_forward):
+def network_forward(org_module, input, original_forward):
    """
    Old way of applying Lora by executing operations during layer's forward.
    Stacking many loras this way results in big performance degradation.
    """
    if len(loaded_networks) == 0:
-        return original_forward(module, input)
+        return original_forward(org_module, input)
    input = devices.cond_cast_unet(input)
-    network_restore_weights_from_backup(module)
+    network_restore_weights_from_backup(org_module)
-    network_reset_cached_weight(module)
+    network_reset_cached_weight(org_module)
-    y = original_forward(module, input)
+    y = original_forward(org_module, input)
-    network_layer_name = getattr(module, 'network_layer_name', None)
+    network_layer_name = getattr(org_module, 'network_layer_name', None)
    for lora in loaded_networks:
        module = lora.modules.get(network_layer_name, None)
        if module is None:
@@ -39,6 +39,8 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
    "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
    "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
    "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
    "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
 }))
@@ -54,12 +54,13 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
        self.slider_preferred_weight = None
        self.edit_notes = None
-    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
+    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
        user_metadata = self.get_user_metadata(name)
        user_metadata["description"] = desc
        user_metadata["sd version"] = sd_version
        user_metadata["activation text"] = activation_text
        user_metadata["preferred weight"] = preferred_weight
        user_metadata["negative text"] = negative_text
        user_metadata["notes"] = notes
        self.write_user_metadata(name, user_metadata)
@@ -127,6 +128,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
            gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
            user_metadata.get('activation text', ''),
            float(user_metadata.get('preferred weight', 0.0)),
            user_metadata.get('negative text', ''),
            gr.update(visible=True if tags else False),
            gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
        ]
@@ -162,7 +164,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
        self.taginfo = gr.HighlightedText(label="Training dataset tags")
        self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
        self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
-
+        self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
        with gr.Row() as row_random_prompt:
            with gr.Column(scale=8):
                random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
@@ -198,6 +200,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
            self.taginfo,
            self.edit_activation_text,
            self.slider_preferred_weight,
            self.edit_negative_text,
            row_random_prompt,
            random_prompt,
        ]
@@ -211,7 +214,9 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
            self.select_sd_version,
            self.edit_activation_text,
            self.slider_preferred_weight,
            self.edit_negative_text,
            self.edit_notes,
        ]
        self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
@@ -17,6 +17,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
    def create_item(self, name, index=None, enable_filter=True):
        lora_on_disk = networks.available_networks.get(name)
        if lora_on_disk is None:
            return
        path, ext = os.path.splitext(lora_on_disk.filename)
@@ -43,6 +45,11 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
        if activation_text:
            item["prompt"] += " + " + quote_js(" " + activation_text)
        negative_prompt = item["user_metadata"].get("negative text")
        item["negative_prompt"] = quote_js("")
        if negative_prompt:
            item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)')
        sd_version = item["user_metadata"].get("sd version")
        if sd_version in network.SdVersion.__members__:
            item["sd_version"] = sd_version
@@ -66,9 +73,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
        return item
    def list_items(self):
-        for index, name in enumerate(networks.available_networks):
+        # instantiate a list to protect against concurrent modification
        names = list(networks.available_networks)
        for index, name in enumerate(names):
            item = self.create_item(name, index)
            if item is not None:
                yield item
@@ -1,16 +1,9 @@
 import sys
 import PIL.Image
 import numpy as np
 import torch
 from tqdm import tqdm
 import modules.upscaler
-from modules import devices, modelloader, script_callbacks, errors
+from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils
 from scunet_model_arch import SCUNet
 from modules.modelloader import load_file_from_url
 from modules.shared import opts
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,100 +35,37 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
            scalers.append(scaler_data2)
        self.scalers = scalers
    @staticmethod
    @torch.no_grad()
    def tiled_inference(img, model):
        # test the image tile by tile
        h, w = img.shape[2:]
        tile = opts.SCUNET_tile
        tile_overlap = opts.SCUNET_tile_overlap
        if tile == 0:
            return model(img)
        device = devices.get_device_for('scunet')
        assert tile % 8 == 0, "tile size should be a multiple of window_size"
        sf = 1
        stride = tile - tile_overlap
        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
            for h_idx in h_idx_list:
                for w_idx in w_idx_list:
                    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)
                    E[
                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
                    ].add_(out_patch)
                    W[
                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
                    ].add_(out_patch_mask)
                    pbar.update(1)
        output = E.div_(W)
        return output
    def do_upscale(self, img: PIL.Image.Image, selected_file):
        devices.torch_gc()
        try:
            model = self.load_model(selected_file)
        except Exception as e:
            print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
            return img
-        device = devices.get_device_for('scunet')
+        img = upscaler_utils.upscale_2(
-        tile = opts.SCUNET_tile
+            img,
-        h, w = img.height, img.width
+            model,
-        np_img = np.array(img)
+            tile_size=shared.opts.SCUNET_tile,
-        np_img = np_img[:, :, ::-1]  # RGB to BGR
+            tile_overlap=shared.opts.SCUNET_tile_overlap,
-        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+            scale=1,  # ScuNET is a denoising model, not an upscaler
-        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
+            desc='ScuNET',
-
+        )
        if tile > h or tile > w:
            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
            _img[:, :, :h, :w] = torch_img # pad image
            torch_img = _img
        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
        del torch_img, torch_output
        devices.torch_gc()
-
+        return img
        output = np_output.transpose((1, 2, 0))  # CHW to HWC
        output = output[:, :, ::-1]  # BGR to RGB
        return PIL.Image.fromarray((output * 255).astype(np.uint8))
    def load_model(self, path: str):
        device = devices.get_device_for('scunet')
        if path.startswith("http"):
            # TODO: this doesn't use `path` at all?
-            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
+            filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
        else:
            filename = path
-        model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
+        return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet')
        model.load_state_dict(torch.load(filename), strict=True)
        model.eval()
        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"))
@@ -1,268 +0,0 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 import torch
 import torch.nn as nn
 from einops import rearrange
 from einops.layers.torch import Rearrange
 from timm.models.layers import trunc_normal_, DropPath
 class WMSA(nn.Module):
    """ Self-attention module in Swin Transformer
    """
    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
        super(WMSA, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.head_dim = head_dim
        self.scale = self.head_dim ** -0.5
        self.n_heads = input_dim // head_dim
        self.window_size = window_size
        self.type = type
        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
        self.relative_position_params = nn.Parameter(
            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
        self.linear = nn.Linear(self.input_dim, self.output_dim)
        trunc_normal_(self.relative_position_params, std=.02)
        self.relative_position_params = torch.nn.Parameter(
            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
                                                                                                                 2).transpose(
                0, 1))
    def generate_mask(self, h, w, p, shift):
        """ generating the mask of SW-MSA
        Args:
            shift: shift parameters in CyclicShift.
        Returns:
            attn_mask: should be (1 1 w p p),
        """
        # supporting square.
        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
        if self.type == 'W':
            return attn_mask
        s = p - shift
        attn_mask[-1, :, :s, :, s:, :] = True
        attn_mask[-1, :, s:, :, :s, :] = True
        attn_mask[:, -1, :, :s, :, s:] = True
        attn_mask[:, -1, :, s:, :, :s] = True
        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
        return attn_mask
    def forward(self, x):
        """ Forward pass of Window Multi-head Self-attention module.
        Args:
            x: input tensor with shape of [b h w c];
            attn_mask: attention mask, fill -inf where the value is True;
        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))
        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)
        # square validation
        # assert h_windows == w_windows
        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
        qkv = self.embedding_layer(x)
        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
        # Adding learnable relative embedding
        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
        # Using Attn Mask to distinguish different subwindows.
        if self.type != 'W':
            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
            sim = sim.masked_fill_(attn_mask, float("-inf"))
        probs = nn.functional.softmax(sim, dim=-1)
        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
        output = rearrange(output, 'h b w p c -> b w p (h c)')
        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), dims=(1, 2))
        return output
    def relative_embedding(self):
        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
        # negative is allowed
        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
 class Block(nn.Module):
    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
        """ SwinTransformer Block
        """
        super(Block, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        assert type in ['W', 'SW']
        self.type = type
        if input_resolution <= window_size:
            self.type = 'W'
        self.ln1 = nn.LayerNorm(input_dim)
        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.ln2 = nn.LayerNorm(input_dim)
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, 4 * input_dim),
            nn.GELU(),
            nn.Linear(4 * input_dim, output_dim),
        )
    def forward(self, x):
        x = x + self.drop_path(self.msa(self.ln1(x)))
        x = x + self.drop_path(self.mlp(self.ln2(x)))
        return x
 class ConvTransBlock(nn.Module):
    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
        """ SwinTransformer and Conv Block
        """
        super(ConvTransBlock, self).__init__()
        self.conv_dim = conv_dim
        self.trans_dim = trans_dim
        self.head_dim = head_dim
        self.window_size = window_size
        self.drop_path = drop_path
        self.type = type
        self.input_resolution = input_resolution
        assert self.type in ['W', 'SW']
        if self.input_resolution <= self.window_size:
            self.type = 'W'
        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
                                 self.type, self.input_resolution)
        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
        self.conv_block = nn.Sequential(
            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
            nn.ReLU(True),
            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
        )
    def forward(self, x):
        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
        conv_x = self.conv_block(conv_x) + conv_x
        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
        trans_x = self.trans_block(trans_x)
        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
        x = x + res
        return x
 class SCUNet(nn.Module):
    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
        super(SCUNet, self).__init__()
        if config is None:
            config = [2, 2, 2, 2, 2, 2, 2]
        self.config = config
        self.dim = dim
        self.head_dim = 32
        self.window_size = 8
        # drop path rate for each layer
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
        begin = 0
        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
                                       'W' if not i % 2 else 'SW', input_resolution)
                        for i in range(config[0])] + \
                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
        begin += config[0]
        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
                                       'W' if not i % 2 else 'SW', input_resolution // 2)
                        for i in range(config[1])] + \
                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
        begin += config[1]
        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
                                       'W' if not i % 2 else 'SW', input_resolution // 4)
                        for i in range(config[2])] + \
                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
        begin += config[2]
        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
                                      'W' if not i % 2 else 'SW', input_resolution // 8)
                       for i in range(config[3])]
        begin += config[3]
        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
                                     'W' if not i % 2 else 'SW', input_resolution // 4)
                      for i in range(config[4])]
        begin += config[4]
        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
                                     'W' if not i % 2 else 'SW', input_resolution // 2)
                      for i in range(config[5])]
        begin += config[5]
        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
                                     'W' if not i % 2 else 'SW', input_resolution)
                      for i in range(config[6])]
        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
        self.m_head = nn.Sequential(*self.m_head)
        self.m_down1 = nn.Sequential(*self.m_down1)
        self.m_down2 = nn.Sequential(*self.m_down2)
        self.m_down3 = nn.Sequential(*self.m_down3)
        self.m_body = nn.Sequential(*self.m_body)
        self.m_up3 = nn.Sequential(*self.m_up3)
        self.m_up2 = nn.Sequential(*self.m_up2)
        self.m_up1 = nn.Sequential(*self.m_up1)
        self.m_tail = nn.Sequential(*self.m_tail)
        # self.apply(self._init_weights)
    def forward(self, x0):
        h, w = x0.size()[-2:]
        paddingBottom = int(np.ceil(h / 64) * 64 - h)
        paddingRight = int(np.ceil(w / 64) * 64 - w)
        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
        x1 = self.m_head(x0)
        x2 = self.m_down1(x1)
        x3 = self.m_down2(x2)
        x4 = self.m_down3(x3)
        x = self.m_body(x4)
        x = self.m_up3(x + x4)
        x = self.m_up2(x + x3)
        x = self.m_up1(x + x2)
        x = self.m_tail(x + x1)
        x = x[..., :h, :w]
        return x
    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
@@ -1,20 +1,15 @@
 import logging
 import sys
 import platform
 import numpy as np
 import torch
 from PIL import Image
 from tqdm import tqdm
-from modules import modelloader, devices, script_callbacks, shared
+from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
 from modules.shared import opts, state
 from swinir_model_arch import SwinIR
 from swinir_model_arch_v2 import Swin2SR
 from modules.upscaler import Upscaler, UpscalerData
 SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
-device_swinir = devices.get_device_for('swinir')
+logger = logging.getLogger(__name__)
 class UpscalerSwinIR(Upscaler):
@@ -37,26 +32,28 @@ class UpscalerSwinIR(Upscaler):
            scalers.append(model_data)
        self.scalers = scalers
-    def do_upscale(self, img, model_file):
+    def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
-        use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
+        current_config = (model_file, shared.opts.SWIN_tile)
            and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
        current_config = (model_file, opts.SWIN_tile)
-        if use_compile and self._cached_model_config == current_config:
+        if self._cached_model_config == current_config:
            model = self._cached_model
        else:
            self._cached_model = None
            try:
                model = self.load_model(model_file)
            except Exception as e:
                print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
                return img
-            model = model.to(device_swinir, dtype=devices.dtype)
+            self._cached_model = model
-            if use_compile:
+            self._cached_model_config = current_config
-                model = torch.compile(model)
+
-                self._cached_model = model
+        img = upscaler_utils.upscale_2(
-                self._cached_model_config = current_config
+            img,
-        img = upscale(img, model)
+            model,
            tile_size=shared.opts.SWIN_tile,
            tile_overlap=shared.opts.SWIN_tile_overlap,
            scale=model.scale,
            desc="SwinIR",
        )
        devices.torch_gc()
        return img
@@ -69,115 +66,22 @@ class UpscalerSwinIR(Upscaler):
            )
        else:
            filename = path
        if filename.endswith(".v2.pth"):
            model = Swin2SR(
                upscale=scale,
                in_chans=3,
                img_size=64,
                window_size=8,
                img_range=1.0,
                depths=[6, 6, 6, 6, 6, 6],
                embed_dim=180,
                num_heads=[6, 6, 6, 6, 6, 6],
                mlp_ratio=2,
                upsampler="nearest+conv",
                resi_connection="1conv",
            )
            params = None
        else:
            model = SwinIR(
                upscale=scale,
                in_chans=3,
                img_size=64,
                window_size=8,
                img_range=1.0,
                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
                embed_dim=240,
                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
                mlp_ratio=2,
                upsampler="nearest+conv",
                resi_connection="3conv",
            )
            params = "params_ema"
-        pretrained_model = torch.load(filename)
+        model_descriptor = modelloader.load_spandrel_model(
-        if params is not None:
+            filename,
-            model.load_state_dict(pretrained_model[params], strict=True)
+            device=self._get_device(),
-        else:
+            prefer_half=(devices.dtype == torch.float16),
-            model.load_state_dict(pretrained_model, strict=True)
+            expected_architecture="SwinIR",
-        return model
+        )
        if getattr(shared.opts, 'SWIN_torch_compile', False):
            try:
                model_descriptor.model.compile()
            except Exception:
                logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
        return model_descriptor
-
+    def _get_device(self):
-def upscale(
+        return devices.get_device_for('swinir')
        img,
        model,
        tile=None,
        tile_overlap=None,
        window_size=8,
        scale=4,
 ):
    tile = tile or opts.SWIN_tile
    tile_overlap = tile_overlap or opts.SWIN_tile_overlap
    img = np.array(img)
    img = img[:, :, ::-1]
    img = np.moveaxis(img, 2, 0) / 255
    img = torch.from_numpy(img).float()
    img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
    with torch.no_grad(), devices.autocast():
        _, _, h_old, w_old = img.size()
        h_pad = (h_old // window_size + 1) * window_size - h_old
        w_pad = (w_old // window_size + 1) * window_size - w_old
        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
        output = inference(img, model, tile, tile_overlap, window_size, scale)
        output = output[..., : h_old * scale, : w_old * scale]
        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
        if output.ndim == 3:
            output = np.transpose(
                output[[2, 1, 0], :, :], (1, 2, 0)
            )  # CHW-RGB to HCW-BGR
        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
        return Image.fromarray(output, "RGB")
 def inference(img, model, tile, tile_overlap, window_size, scale):
    # test the image tile by tile
    b, c, h, w = img.size()
    tile = min(tile, h, w)
    assert tile % window_size == 0, "tile size should be a multiple of window_size"
    sf = scale
    stride = tile - tile_overlap
    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
    W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
        for h_idx in h_idx_list:
            if state.interrupted or state.skipped:
                break
            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)
                E[
                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
                ].add_(out_patch)
                W[
                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
                ].add_(out_patch_mask)
                pbar.update(1)
    output = E.div_(W)
    return output
 def on_ui_settings():
@@ -185,8 +89,7 @@ def on_ui_settings():
    shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
    shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
-    if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows":    # torch.compile() require pytorch 2.0 or above, and not on Windows
+    shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
        shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
 script_callbacks.on_ui_settings(on_ui_settings)
@@ -1,867 +0,0 @@
 # -----------------------------------------------------------------------------------
 # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
 # Originally Written by Ze Liu, Modified by Jingyun Liang.
 # -----------------------------------------------------------------------------------
 import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.checkpoint as checkpoint
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
 class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x
 def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows
 def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x
 class WindowAttention(nn.Module):
    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """
    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5
        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        trunc_normal_(self.relative_position_bias_table, std=.02)
        self.softmax = nn.Softmax(dim=-1)
    def forward(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
        q = q * self.scale
        attn = (q @ k.transpose(-2, -1))
        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)
        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)
        attn = self.attn_drop(attn)
        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
    def extra_repr(self) -> str:
        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
    def flops(self, N):
        # calculate flops for 1 window with token length of N
        flops = 0
        # qkv = self.qkv(x)
        flops += N * self.dim * 3 * self.dim
        # attn = (q @ k.transpose(-2, -1))
        flops += self.num_heads * N * (self.dim // self.num_heads) * N
        #  x = (attn @ v)
        flops += self.num_heads * N * N * (self.dim // self.num_heads)
        # x = self.proj(x)
        flops += N * self.dim * self.dim
        return flops
 class SwinTransformerBlock(nn.Module):
    r""" Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """
    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
        if self.shift_size > 0:
            attn_mask = self.calculate_mask(self.input_resolution)
        else:
            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
        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
        h_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        w_slices = (slice(0, -self.window_size),
                    slice(-self.window_size, -self.shift_size),
                    slice(-self.shift_size, None))
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1
        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        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
    def forward(self, x, x_size):
        H, W = x_size
        B, L, C = x.shape
        # assert L == H * W, "input feature has wrong size"
        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)
        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
        else:
            shifted_x = x
        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
        if self.input_resolution == x_size:
            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
        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x
        x = x.view(B, H * W, C)
        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x
    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
    def flops(self):
        flops = 0
        H, W = self.input_resolution
        # norm1
        flops += self.dim * H * W
        # W-MSA/SW-MSA
        nW = H * W / self.window_size / self.window_size
        flops += nW * self.attn.flops(self.window_size * self.window_size)
        # mlp
        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
        # norm2
        flops += self.dim * H * W
        return flops
 class PatchMerging(nn.Module):
    r""" Patch Merging Layer.
    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """
    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)
    def forward(self, x):
        """
        x: B, H*W, C
        """
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
        x = x.view(B, H, W, C)
        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
        x = self.norm(x)
        x = self.reduction(x)
        return x
    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"
    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.dim
        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
        return flops
 class BasicLayer(nn.Module):
    """ A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """
    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint
        # build blocks
        self.blocks = nn.ModuleList([
            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
                                 num_heads=num_heads, window_size=window_size,
                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
                                 mlp_ratio=mlp_ratio,
                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
                                 drop=drop, attn_drop=attn_drop,
                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                                 norm_layer=norm_layer)
            for i in range(depth)])
        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None
    def forward(self, x, x_size):
        for blk in self.blocks:
            if self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, x_size)
            else:
                x = blk(x, x_size)
        if self.downsample is not None:
            x = self.downsample(x)
        return x
    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
    def flops(self):
        flops = 0
        for blk in self.blocks:
            flops += blk.flops()
        if self.downsample is not None:
            flops += self.downsample.flops()
        return flops
 class RSTB(nn.Module):
    """Residual Swin Transformer Block (RSTB).
    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
        img_size: Input image size.
        patch_size: Patch size.
        resi_connection: The convolutional block before residual connection.
    """
    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
                 img_size=224, patch_size=4, resi_connection='1conv'):
        super(RSTB, self).__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.residual_group = BasicLayer(dim=dim,
                                         input_resolution=input_resolution,
                                         depth=depth,
                                         num_heads=num_heads,
                                         window_size=window_size,
                                         mlp_ratio=mlp_ratio,
                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
                                         drop=drop, attn_drop=attn_drop,
                                         drop_path=drop_path,
                                         norm_layer=norm_layer,
                                         downsample=downsample,
                                         use_checkpoint=use_checkpoint)
        if resi_connection == '1conv':
            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
            norm_layer=None)
        self.patch_unembed = PatchUnEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
            norm_layer=None)
    def forward(self, x, x_size):
        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
    def flops(self):
        flops = 0
        flops += self.residual_group.flops()
        H, W = self.input_resolution
        flops += H * W * self.dim * self.dim * 9
        flops += self.patch_embed.flops()
        flops += self.patch_unembed.flops()
        return flops
 class PatchEmbed(nn.Module):
    r""" Image to Patch Embedding
    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """
    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None
    def forward(self, x):
        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
        if self.norm is not None:
            x = self.norm(x)
        return x
    def flops(self):
        flops = 0
        H, W = self.img_size
        if self.norm is not None:
            flops += H * W * self.embed_dim
        return flops
 class PatchUnEmbed(nn.Module):
    r""" Image to Patch Unembedding
    Args:
        img_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """
    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
        self.img_size = img_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]
        self.in_chans = in_chans
        self.embed_dim = embed_dim
    def forward(self, x, x_size):
        B, HW, C = x.shape
        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
        return x
    def flops(self):
        flops = 0
        return flops
 class Upsample(nn.Sequential):
    """Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """
    def __init__(self, scale, num_feat):
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
        super(Upsample, self).__init__(*m)
 class UpsampleOneStep(nn.Sequential):
    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
       Used in lightweight SR to save parameters.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """
    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
        self.num_feat = num_feat
        self.input_resolution = input_resolution
        m = []
        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
        m.append(nn.PixelShuffle(scale))
        super(UpsampleOneStep, self).__init__(*m)
    def flops(self):
        H, W = self.input_resolution
        flops = H * W * self.num_feat * 3 * 9
        return flops
 class SwinIR(nn.Module):
    r""" SwinIR
        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
    Args:
        img_size (int | tuple(int)): Input image size. Default 64
        patch_size (int | tuple(int)): Patch size. Default: 1
        in_chans (int): Number of input image channels. Default: 3
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
        img_range: Image range. 1. or 255.
        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
    """
    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),
                 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,
                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
                 **kwargs):
        super(SwinIR, self).__init__()
        num_in_ch = in_chans
        num_out_ch = in_chans
        num_feat = 64
        self.img_range = img_range
        if in_chans == 3:
            rgb_mean = (0.4488, 0.4371, 0.4040)
            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
        else:
            self.mean = torch.zeros(1, 1, 1, 1)
        self.upscale = upscale
        self.upsampler = upsampler
        self.window_size = window_size
        #####################################################################################################
        ################################### 1, shallow feature extraction ###################################
        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
        #####################################################################################################
        ################################### 2, deep feature extraction ######################################
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = embed_dim
        self.mlp_ratio = mlp_ratio
        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution
        # merge non-overlapping patches into image
        self.patch_unembed = PatchUnEmbed(
            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None)
        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.absolute_pos_embed, std=.02)
        self.pos_drop = nn.Dropout(p=drop_rate)
        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        # build Residual Swin Transformer blocks (RSTB)
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = RSTB(dim=embed_dim,
                         input_resolution=(patches_resolution[0],
                                           patches_resolution[1]),
                         depth=depths[i_layer],
                         num_heads=num_heads[i_layer],
                         window_size=window_size,
                         mlp_ratio=self.mlp_ratio,
                         qkv_bias=qkv_bias, qk_scale=qk_scale,
                         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,
                         downsample=None,
                         use_checkpoint=use_checkpoint,
                         img_size=img_size,
                         patch_size=patch_size,
                         resi_connection=resi_connection
                         )
            self.layers.append(layer)
        self.norm = norm_layer(self.num_features)
        # build the last conv layer in deep feature extraction
        if resi_connection == '1conv':
            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
        elif resi_connection == '3conv':
            # to save parameters and memory
            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
        #####################################################################################################
        ################################ 3, high quality image reconstruction ################################
        if self.upsampler == 'pixelshuffle':
            # for classical SR
            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                      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 == 'pixelshuffledirect':
            # for lightweight SR (to save parameters)
            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
                                            (patches_resolution[0], patches_resolution[1]))
        elif self.upsampler == 'nearest+conv':
            # for real-world SR (less artifacts)
            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
                                                      nn.LeakyReLU(inplace=True))
            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            if self.upscale == 4:
                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        else:
            # for image denoising and JPEG compression artifact reduction
            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
        self.apply(self._init_weights)
    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
    @torch.jit.ignore
    def no_weight_decay(self):
        return {'absolute_pos_embed'}
    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {'relative_position_bias_table'}
    def check_image_size(self, x):
        _, _, h, w = x.size()
        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
        return x
    def forward_features(self, x):
        x_size = (x.shape[2], x.shape[3])
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        for layer in self.layers:
            x = layer(x, x_size)
        x = self.norm(x)  # B L C
        x = self.patch_unembed(x, x_size)
        return x
    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
        if self.upsampler == 'pixelshuffle':
            # for classical SR
            x = self.conv_first(x)
            x = self.conv_after_body(self.forward_features(x)) + x
            x = self.conv_before_upsample(x)
            x = self.conv_last(self.upsample(x))
        elif self.upsampler == 'pixelshuffledirect':
            # for lightweight SR
            x = self.conv_first(x)
            x = self.conv_after_body(self.forward_features(x)) + x
            x = self.upsample(x)
        elif self.upsampler == 'nearest+conv':
            # for real-world SR
            x = self.conv_first(x)
            x = self.conv_after_body(self.forward_features(x)) + x
            x = self.conv_before_upsample(x)
            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
            if self.upscale == 4:
                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
            x = self.conv_last(self.lrelu(self.conv_hr(x)))
        else:
            # for image denoising and JPEG compression artifact reduction
            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
        return x[:, :, :H*self.upscale, :W*self.upscale]
    def flops(self):
        flops = 0
        H, W = self.patches_resolution
        flops += H * W * 3 * self.embed_dim * 9
        flops += self.patch_embed.flops()
        for layer in self.layers:
            flops += layer.flops()
        flops += H * W * 3 * self.embed_dim * self.embed_dim
        flops += self.upsample.flops()
        return flops
 if __name__ == '__main__':
    upscale = 4
    window_size = 8
    height = (1024 // upscale // window_size + 1) * window_size
    width = (720 // upscale // window_size + 1) * window_size
    model = SwinIR(upscale=2, img_size=(height, width),
                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
    print(model)
    print(height, width, model.flops() / 1e9)
    x = torch.randn((1, 3, height, width))
    x = model(x)
    print(x.shape)
@@ -218,6 +218,8 @@ onUiLoaded(async() => {
        canvas_hotkey_fullscreen: "KeyS",
        canvas_hotkey_move: "KeyF",
        canvas_hotkey_overlap: "KeyO",
        canvas_hotkey_shrink_brush: "KeyQ",
        canvas_hotkey_grow_brush: "KeyW",
        canvas_disabled_functions: [],
        canvas_show_tooltip: true,
        canvas_auto_expand: true,
@@ -227,6 +229,8 @@ onUiLoaded(async() => {
    const functionMap = {
        "Zoom": "canvas_hotkey_zoom",
        "Adjust brush size": "canvas_hotkey_adjust",
        "Hotkey shrink brush": "canvas_hotkey_shrink_brush",
        "Hotkey enlarge brush": "canvas_hotkey_grow_brush",
        "Moving canvas": "canvas_hotkey_move",
        "Fullscreen": "canvas_hotkey_fullscreen",
        "Reset Zoom": "canvas_hotkey_reset",
@@ -686,7 +690,9 @@ onUiLoaded(async() => {
            const hotkeyActions = {
                [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
                [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
-                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
+                [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen,
                [hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10),
                [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10)
            };
            const action = hotkeyActions[event.code];
@@ -4,6 +4,8 @@ from modules import shared
 shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
    "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
    "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
    "canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"),
    "canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"),
    "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
    "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
    "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
@@ -11,5 +13,5 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
    "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
    "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
    "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
-    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
+    "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
 }))
@@ -1,7 +1,7 @@
 import math
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules import scripts, shared, ui_components, ui_settings, infotext_utils
 from modules.ui_components import FormColumn
@@ -23,11 +23,12 @@ class ExtraOptionsSection(scripts.Script):
        self.setting_names = []
        self.infotext_fields = []
        extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
        elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
-        mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
+        mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping}
        with gr.Blocks() as interface:
-            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+            with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
                row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
@@ -64,11 +65,14 @@ class ExtraOptionsSection(scripts.Script):
                p.override_settings[name] = value
-shared.options_templates.update(shared.options_section(('ui', "User interface"), {
+shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), {
-    "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
+    "settings_in_ui": shared.OptionHTML("""
-    "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
+This page allows you to add some settings to the main interface of txt2img and img2img tabs.
-    "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
+"""),
-    "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
+    "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
    "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(),
    "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
 }))
@@ -0,0 +1,351 @@
 """
 Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
 Warn: The patch works well only if the input image has a width and height that are multiples of 128
 Original author: @tfernd Github: https://github.com/tfernd/HyperTile
 """
 from __future__ import annotations
 from dataclasses import dataclass
 from typing import Callable
 from functools import wraps, cache
 import math
 import torch.nn as nn
 import random
 from einops import rearrange
@dataclass
 class HypertileParams:
    depth = 0
    layer_name = ""
    tile_size: int = 0
    swap_size: int = 0
    aspect_ratio: float = 1.0
    forward = None
    enabled = False
 # TODO add SD-XL layers
 DEPTH_LAYERS = {
    0: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.1.1.transformer_blocks.0.attn1",
        "input_blocks.2.1.transformer_blocks.0.attn1",
        "output_blocks.9.1.transformer_blocks.0.attn1",
        "output_blocks.10.1.transformer_blocks.0.attn1",
        "output_blocks.11.1.transformer_blocks.0.attn1",
        # SD 1.5 VAE
        "decoder.mid_block.attentions.0",
        "decoder.mid.attn_1",
    ],
    1: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.1.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.1.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.2.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.0.attn1",
        "input_blocks.5.1.transformer_blocks.0.attn1",
        "output_blocks.6.1.transformer_blocks.0.attn1",
        "output_blocks.7.1.transformer_blocks.0.attn1",
        "output_blocks.8.1.transformer_blocks.0.attn1",
    ],
    2: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.2.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.2.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.1.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.7.1.transformer_blocks.0.attn1",
        "input_blocks.8.1.transformer_blocks.0.attn1",
        "output_blocks.3.1.transformer_blocks.0.attn1",
        "output_blocks.4.1.transformer_blocks.0.attn1",
        "output_blocks.5.1.transformer_blocks.0.attn1",
    ],
    3: [
        # SD 1.5 U-Net (diffusers)
        "mid_block.attentions.0.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "middle_block.1.transformer_blocks.0.attn1",
    ],
 }
 # XL layers, thanks for GitHub@gel-crabs for the help
 DEPTH_LAYERS_XL = {
    0: [
        # SD 1.5 U-Net (diffusers)
        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.0.attn1",
        "input_blocks.5.1.transformer_blocks.0.attn1",
        "output_blocks.3.1.transformer_blocks.0.attn1",
        "output_blocks.4.1.transformer_blocks.0.attn1",
        "output_blocks.5.1.transformer_blocks.0.attn1",
        # SD 1.5 VAE
        "decoder.mid_block.attentions.0",
        "decoder.mid.attn_1",
    ],
    1: [
        # SD 1.5 U-Net (diffusers)
        #"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
        #"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
        #"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "input_blocks.4.1.transformer_blocks.1.attn1",
        "input_blocks.5.1.transformer_blocks.1.attn1",
        "output_blocks.3.1.transformer_blocks.1.attn1",
        "output_blocks.4.1.transformer_blocks.1.attn1",
        "output_blocks.5.1.transformer_blocks.1.attn1",
        "input_blocks.7.1.transformer_blocks.0.attn1",
        "input_blocks.8.1.transformer_blocks.0.attn1",
        "output_blocks.0.1.transformer_blocks.0.attn1",
        "output_blocks.1.1.transformer_blocks.0.attn1",
        "output_blocks.2.1.transformer_blocks.0.attn1",
        "input_blocks.7.1.transformer_blocks.1.attn1",
        "input_blocks.8.1.transformer_blocks.1.attn1",
        "output_blocks.0.1.transformer_blocks.1.attn1",
        "output_blocks.1.1.transformer_blocks.1.attn1",
        "output_blocks.2.1.transformer_blocks.1.attn1",
        "input_blocks.7.1.transformer_blocks.2.attn1",
        "input_blocks.8.1.transformer_blocks.2.attn1",
        "output_blocks.0.1.transformer_blocks.2.attn1",
        "output_blocks.1.1.transformer_blocks.2.attn1",
        "output_blocks.2.1.transformer_blocks.2.attn1",
        "input_blocks.7.1.transformer_blocks.3.attn1",
        "input_blocks.8.1.transformer_blocks.3.attn1",
        "output_blocks.0.1.transformer_blocks.3.attn1",
        "output_blocks.1.1.transformer_blocks.3.attn1",
        "output_blocks.2.1.transformer_blocks.3.attn1",
        "input_blocks.7.1.transformer_blocks.4.attn1",
        "input_blocks.8.1.transformer_blocks.4.attn1",
        "output_blocks.0.1.transformer_blocks.4.attn1",
        "output_blocks.1.1.transformer_blocks.4.attn1",
        "output_blocks.2.1.transformer_blocks.4.attn1",
        "input_blocks.7.1.transformer_blocks.5.attn1",
        "input_blocks.8.1.transformer_blocks.5.attn1",
        "output_blocks.0.1.transformer_blocks.5.attn1",
        "output_blocks.1.1.transformer_blocks.5.attn1",
        "output_blocks.2.1.transformer_blocks.5.attn1",
        "input_blocks.7.1.transformer_blocks.6.attn1",
        "input_blocks.8.1.transformer_blocks.6.attn1",
        "output_blocks.0.1.transformer_blocks.6.attn1",
        "output_blocks.1.1.transformer_blocks.6.attn1",
        "output_blocks.2.1.transformer_blocks.6.attn1",
        "input_blocks.7.1.transformer_blocks.7.attn1",
        "input_blocks.8.1.transformer_blocks.7.attn1",
        "output_blocks.0.1.transformer_blocks.7.attn1",
        "output_blocks.1.1.transformer_blocks.7.attn1",
        "output_blocks.2.1.transformer_blocks.7.attn1",
        "input_blocks.7.1.transformer_blocks.8.attn1",
        "input_blocks.8.1.transformer_blocks.8.attn1",
        "output_blocks.0.1.transformer_blocks.8.attn1",
        "output_blocks.1.1.transformer_blocks.8.attn1",
        "output_blocks.2.1.transformer_blocks.8.attn1",
        "input_blocks.7.1.transformer_blocks.9.attn1",
        "input_blocks.8.1.transformer_blocks.9.attn1",
        "output_blocks.0.1.transformer_blocks.9.attn1",
        "output_blocks.1.1.transformer_blocks.9.attn1",
        "output_blocks.2.1.transformer_blocks.9.attn1",
    ],
    2: [
        # SD 1.5 U-Net (diffusers)
        "mid_block.attentions.0.transformer_blocks.0.attn1",
        # SD 1.5 U-Net (ldm)
        "middle_block.1.transformer_blocks.0.attn1",
        "middle_block.1.transformer_blocks.1.attn1",
        "middle_block.1.transformer_blocks.2.attn1",
        "middle_block.1.transformer_blocks.3.attn1",
        "middle_block.1.transformer_blocks.4.attn1",
        "middle_block.1.transformer_blocks.5.attn1",
        "middle_block.1.transformer_blocks.6.attn1",
        "middle_block.1.transformer_blocks.7.attn1",
        "middle_block.1.transformer_blocks.8.attn1",
        "middle_block.1.transformer_blocks.9.attn1",
    ],
    3 : [] # TODO - separate layers for SD-XL
 }
 RNG_INSTANCE = random.Random()
@cache
 def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]:
    """
    Returns divisors of value that
        x * min_value <= value
    in big -> small order, amount of divisors is limited by max_options
    """
    max_options = max(1, max_options) # at least 1 option should be returned
    min_value = min(min_value, value)
    divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element # big -> small order
    return ns
 def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
    """
    Returns a random divisor of value that
        x * min_value <= value
    if max_options is 1, the behavior is deterministic
    """
    ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors
    idx = RNG_INSTANCE.randint(0, len(ns) - 1)
    return ns[idx]
 def set_hypertile_seed(seed: int) -> None:
    RNG_INSTANCE.seed(seed)
@cache
 def largest_tile_size_available(width: int, height: int) -> int:
    """
    Calculates the largest tile size available for a given width and height
    Tile size is always a power of 2
    """
    gcd = math.gcd(width, height)
    largest_tile_size_available = 1
    while gcd % (largest_tile_size_available * 2) == 0:
        largest_tile_size_available *= 2
    return largest_tile_size_available
 def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
    """
    Finds h and w such that h*w = hw and h/w = aspect_ratio
    We check all possible divisors of hw and return the closest to the aspect ratio
    """
    divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
    pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
    ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
    closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
    closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
    return closest_pair
@cache
 def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
    """
    Finds h and w such that h*w = hw and h/w = aspect_ratio
    """
    h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
    # find h and w such that h*w = hw and h/w = aspect_ratio
    if h * w != hw:
        w_candidate = hw / h
        # check if w is an integer
        if not w_candidate.is_integer():
            h_candidate = hw / w
            # check if h is an integer
            if not h_candidate.is_integer():
                return iterative_closest_divisors(hw, aspect_ratio)
            else:
                h = int(h_candidate)
        else:
            w = int(w_candidate)
    return h, w
 def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
    @wraps(params.forward)
    def wrapper(*args, **kwargs):
        if not params.enabled:
            return params.forward(*args, **kwargs)
        latent_tile_size = max(128, params.tile_size) // 8
        x = args[0]
        # VAE
        if x.ndim == 4:
            b, c, h, w = x.shape
            nh = random_divisor(h, latent_tile_size, params.swap_size)
            nw = random_divisor(w, latent_tile_size, params.swap_size)
            if nh * nw > 1:
                x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw)  # split into nh * nw tiles
            out = params.forward(x, *args[1:], **kwargs)
            if nh * nw > 1:
                out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
        # U-Net
        else:
            hw: int = x.size(1)
            h, w = find_hw_candidates(hw, params.aspect_ratio)
            assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
            factor = 2 ** params.depth if scale_depth else 1
            nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
            nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
            if nh * nw > 1:
                x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
            out = params.forward(x, *args[1:], **kwargs)
            if nh * nw > 1:
                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
        return out
    return wrapper
 def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
    hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
    if hypertile_layers is None:
        if not enable:
            return
        hypertile_layers = {}
        layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
        for depth in range(4):
            for layer_name, module in model.named_modules():
                if any(layer_name.endswith(try_name) for try_name in layers[depth]):
                    params = HypertileParams()
                    module.__webui_hypertile_params = params
                    params.forward = module.forward
                    params.depth = depth
                    params.layer_name = layer_name
                    module.forward = self_attn_forward(params)
                    hypertile_layers[layer_name] = 1
        model.__webui_hypertile_layers = hypertile_layers
    aspect_ratio = width / height
    tile_size = min(largest_tile_size_available(width, height), tile_size_max)
    for layer_name, module in model.named_modules():
        if layer_name in hypertile_layers:
            params = module.__webui_hypertile_params
            params.tile_size = tile_size
            params.swap_size = swap_size
            params.aspect_ratio = aspect_ratio
            params.enabled = enable and params.depth <= max_depth
@@ -0,0 +1,109 @@
 import hypertile
 from modules import scripts, script_callbacks, shared
 from scripts.hypertile_xyz import add_axis_options
 class ScriptHypertile(scripts.Script):
    name = "Hypertile"
    def title(self):
        return self.name
    def show(self, is_img2img):
        return scripts.AlwaysVisible
    def process(self, p, *args):
        hypertile.set_hypertile_seed(p.all_seeds[0])
        configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
        self.add_infotext(p)
    def before_hr(self, p, *args):
        enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet
        # exclusive hypertile seed for the second pass
        if enable:
            hypertile.set_hypertile_seed(p.all_seeds[0])
        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable)
        if enable and not shared.opts.hypertile_enable_unet:
            p.extra_generation_params["Hypertile U-Net second pass"] = True
            self.add_infotext(p, add_unet_params=True)
    def add_infotext(self, p, add_unet_params=False):
        def option(name):
            value = getattr(shared.opts, name)
            default_value = shared.opts.get_default(name)
            return None if value == default_value else value
        if shared.opts.hypertile_enable_unet:
            p.extra_generation_params["Hypertile U-Net"] = True
        if shared.opts.hypertile_enable_unet or add_unet_params:
            p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet')
            p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet')
            p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet')
        if shared.opts.hypertile_enable_vae:
            p.extra_generation_params["Hypertile VAE"] = True
            p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae')
            p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae')
            p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae')
 def configure_hypertile(width, height, enable_unet=True):
    hypertile.hypertile_hook_model(
        shared.sd_model.first_stage_model,
        width,
        height,
        swap_size=shared.opts.hypertile_swap_size_vae,
        max_depth=shared.opts.hypertile_max_depth_vae,
        tile_size_max=shared.opts.hypertile_max_tile_vae,
        enable=shared.opts.hypertile_enable_vae,
    )
    hypertile.hypertile_hook_model(
        shared.sd_model.model,
        width,
        height,
        swap_size=shared.opts.hypertile_swap_size_unet,
        max_depth=shared.opts.hypertile_max_depth_unet,
        tile_size_max=shared.opts.hypertile_max_tile_unet,
        enable=enable_unet,
        is_sdxl=shared.sd_model.is_sdxl
    )
 def on_ui_settings():
    import gradio as gr
    options = {
        "hypertile_explanation": shared.OptionHTML("""
    <a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
    resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
    benefit.
    """),
        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"),
        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"),
        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"),
    }
    for name, opt in options.items():
        opt.section = ('hypertile', "Hypertile")
        shared.opts.add_option(name, opt)
 script_callbacks.on_ui_settings(on_ui_settings)
 script_callbacks.on_before_ui(add_axis_options)
@@ -0,0 +1,51 @@
 from modules import scripts
 from modules.shared import opts
 xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
 def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
    """
    Returns a function that applies the given value to the given value_name in opts.data.
    """
    def validate(value_name:str, value:str):
        value = int(value)
        # validate value
        if not min_range == -1:
            assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
        if not max_range == -1:
            assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
    def apply_int(p, x, xs):
        validate(value_name, x)
        opts.data[value_name] = int(x)
    return apply_int
 def bool_applier(value_name:str):
    """
    Returns a function that applies the given value to the given value_name in opts.data.
    """
    def validate(value_name:str, value:str):
        assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
    def apply_bool(p, x, xs):
        validate(value_name, x)
        value_boolean = x.lower() == "true"
        opts.data[value_name] = value_boolean
    return apply_bool
 def add_axis_options():
    extra_axis_options = [
        xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
        xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
        xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
        xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
        xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
        xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
        xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
        xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
        xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
    ]
    set_a = {opt.label for opt in xyz_grid.axis_options}
    set_b = {opt.label for opt in extra_axis_options}
    if set_a.intersection(set_b):
        return
    xyz_grid.axis_options.extend(extra_axis_options)
@@ -12,6 +12,8 @@ function isMobile() {
 }
 function reportWindowSize() {
    if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout
    var currentlyMobile = isMobile();
    if (currentlyMobile == isSetupForMobile) return;
    isSetupForMobile = currentlyMobile;
@@ -0,0 +1,759 @@
 import numpy as np
 import gradio as gr
 import math
 from modules.ui_components import InputAccordion
 import modules.scripts as scripts
 from modules import infotext_utils
 infotext_utils.register_info_json('Soft Inpainting')
 class SoftInpaintingSettings:
    def __init__(self,
                 mask_blend_power,
                 mask_blend_scale,
                 inpaint_detail_preservation,
                 composite_mask_influence,
                 composite_difference_threshold,
                 composite_difference_contrast):
        self.mask_blend_power = mask_blend_power
        self.mask_blend_scale = mask_blend_scale
        self.inpaint_detail_preservation = inpaint_detail_preservation
        self.composite_mask_influence = composite_mask_influence
        self.composite_difference_threshold = composite_difference_threshold
        self.composite_difference_contrast = composite_difference_contrast
    def add_generation_params(self, dest):
        dest['Soft Inpainting'] = {
            'sb': self.mask_blend_power,
            'ps': self.mask_blend_scale,
            'tcb': self.inpaint_detail_preservation,
            'mi': self.composite_mask_influence,
            'dt': self.composite_difference_threshold,
            'dc': self.composite_difference_contrast,
        }
 # ------------------- Methods -------------------
 def processing_uses_inpainting(p):
    # TODO: Figure out a better way to determine if inpainting is being used by p
    if getattr(p, "image_mask", None) is not None:
        return True
    if getattr(p, "mask", None) is not None:
        return True
    if getattr(p, "nmask", None) is not None:
        return True
    return False
 def latent_blend(settings, a, b, t):
    """
    Interpolates two latent image representations according to the parameter t,
    where the interpolated vectors' magnitudes are also interpolated separately.
    The "detail_preservation" factor biases the magnitude interpolation towards
    the larger of the two magnitudes.
    """
    import torch
    # NOTE: We use inplace operations wherever possible.
    # [4][w][h] to [1][4][w][h]
    t2 = t.unsqueeze(0)
    # [4][w][h] to [1][1][w][h] - the [4] seem redundant.
    t3 = t[0].unsqueeze(0).unsqueeze(0)
    one_minus_t2 = 1 - t2
    one_minus_t3 = 1 - t3
    # Linearly interpolate the image vectors.
    a_scaled = a * one_minus_t2
    b_scaled = b * t2
    image_interp = a_scaled
    image_interp.add_(b_scaled)
    result_type = image_interp.dtype
    del a_scaled, b_scaled, t2, one_minus_t2
    # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
    # 64-bit operations are used here to allow large exponents.
    current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
    # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
    a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
        settings.inpaint_detail_preservation) * one_minus_t3
    b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
        settings.inpaint_detail_preservation) * t3
    desired_magnitude = a_magnitude
    desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
    del a_magnitude, b_magnitude, t3, one_minus_t3
    # Change the linearly interpolated image vectors' magnitudes to the value we want.
    # This is the last 64-bit operation.
    image_interp_scaling_factor = desired_magnitude
    image_interp_scaling_factor.div_(current_magnitude)
    image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
    image_interp_scaled = image_interp
    image_interp_scaled.mul_(image_interp_scaling_factor)
    del current_magnitude
    del desired_magnitude
    del image_interp
    del image_interp_scaling_factor
    del result_type
    return image_interp_scaled
 def get_modified_nmask(settings, nmask, sigma):
    """
    Converts a negative mask representing the transparency of the original latent vectors being overlayed
    to a mask that is scaled according to the denoising strength for this step.
    Where:
        0 = fully opaque, infinite density, fully masked
        1 = fully transparent, zero density, fully unmasked
    We bring this transparency to a power, as this allows one to simulate N number of blending operations
    where N can be any positive real value. Using this one can control the balance of influence between
    the denoiser and the original latents according to the sigma value.
    NOTE: "mask" is not used
    """
    import torch
    return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
 def apply_adaptive_masks(
        settings: SoftInpaintingSettings,
        nmask,
        latent_orig,
        latent_processed,
        overlay_images,
        width, height,
        paste_to):
    import torch
    import modules.processing as proc
    import modules.images as images
    from PIL import Image, ImageOps, ImageFilter
    # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
    latent_mask = nmask[0].float()
    # convert the original mask into a form we use to scale distances for thresholding
    mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
    mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
                   + mask_scalar * settings.composite_mask_influence)
    mask_scalar = mask_scalar / (1.00001 - mask_scalar)
    mask_scalar = mask_scalar.cpu().numpy()
    latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
    kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
    masks_for_overlay = []
    for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
        converted_mask = distance_map.float().cpu().numpy()
        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
                                                   percentile_min=0.9, percentile_max=1, min_width=1)
        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
                                                   percentile_min=0.25, percentile_max=0.75, min_width=1)
        # The distance at which opacity of original decreases to 50%
        half_weighted_distance = settings.composite_difference_threshold * mask_scalar
        converted_mask = converted_mask / half_weighted_distance
        converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast)
        converted_mask = smootherstep(converted_mask)
        converted_mask = 1 - converted_mask
        converted_mask = 255. * converted_mask
        converted_mask = converted_mask.astype(np.uint8)
        converted_mask = Image.fromarray(converted_mask)
        converted_mask = images.resize_image(2, converted_mask, width, height)
        converted_mask = proc.create_binary_mask(converted_mask, round=False)
        # Remove aliasing artifacts using a gaussian blur.
        converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
        # Expand the mask to fit the whole image if needed.
        if paste_to is not None:
            converted_mask = proc.uncrop(converted_mask,
                                         (overlay_image.width, overlay_image.height),
                                         paste_to)
        masks_for_overlay.append(converted_mask)
        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
                           mask=ImageOps.invert(converted_mask.convert('L')))
        overlay_images[i] = image_masked.convert('RGBA')
    return masks_for_overlay
 def apply_masks(
        settings,
        nmask,
        overlay_images,
        width, height,
        paste_to):
    import torch
    import modules.processing as proc
    import modules.images as images
    from PIL import Image, ImageOps, ImageFilter
    converted_mask = nmask[0].float()
    converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2)
    converted_mask = 255. * converted_mask
    converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
    converted_mask = Image.fromarray(converted_mask)
    converted_mask = images.resize_image(2, converted_mask, width, height)
    converted_mask = proc.create_binary_mask(converted_mask, round=False)
    # Remove aliasing artifacts using a gaussian blur.
    converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
    # Expand the mask to fit the whole image if needed.
    if paste_to is not None:
        converted_mask = proc.uncrop(converted_mask,
                                     (width, height),
                                     paste_to)
    masks_for_overlay = []
    for i, overlay_image in enumerate(overlay_images):
        masks_for_overlay[i] = converted_mask
        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
                           mask=ImageOps.invert(converted_mask.convert('L')))
        overlay_images[i] = image_masked.convert('RGBA')
    return masks_for_overlay
 def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
    """
    Generalization convolution filter capable of applying
    weighted mean, median, maximum, and minimum filters
    parametrically using an arbitrary kernel.
    Args:
        img (nparray):
            The image, a 2-D array of floats, to which the filter is being applied.
        kernel (nparray):
            The kernel, a 2-D array of floats.
        kernel_center (nparray):
            The kernel center coordinate, a 1-D array with two elements.
        percentile_min (float):
            The lower bound of the histogram window used by the filter,
            from 0 to 1.
        percentile_max (float):
            The upper bound of the histogram window used by the filter,
            from 0 to 1.
        min_width (float):
            The minimum size of the histogram window bounds, in weight units.
            Must be greater than 0.
    Returns:
        (nparray): A filtered copy of the input image "img", a 2-D array of floats.
    """
    # Converts an index tuple into a vector.
    def vec(x):
        return np.array(x)
    kernel_min = -kernel_center
    kernel_max = vec(kernel.shape) - kernel_center
    def weighted_histogram_filter_single(idx):
        idx = vec(idx)
        min_index = np.maximum(0, idx + kernel_min)
        max_index = np.minimum(vec(img.shape), idx + kernel_max)
        window_shape = max_index - min_index
        class WeightedElement:
            """
            An element of the histogram, its weight
            and bounds.
            """
            def __init__(self, value, weight):
                self.value: float = value
                self.weight: float = weight
                self.window_min: float = 0.0
                self.window_max: float = 1.0
        # Collect the values in the image as WeightedElements,
        # weighted by their corresponding kernel values.
        values = []
        for window_tup in np.ndindex(tuple(window_shape)):
            window_index = vec(window_tup)
            image_index = window_index + min_index
            centered_kernel_index = image_index - idx
            kernel_index = centered_kernel_index + kernel_center
            element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
            values.append(element)
        def sort_key(x: WeightedElement):
            return x.value
        values.sort(key=sort_key)
        # Calculate the height of the stack (sum)
        # and each sample's range they occupy in the stack
        sum = 0
        for i in range(len(values)):
            values[i].window_min = sum
            sum += values[i].weight
            values[i].window_max = sum
        # Calculate what range of this stack ("window")
        # we want to get the weighted average across.
        window_min = sum * percentile_min
        window_max = sum * percentile_max
        window_width = window_max - window_min
        # Ensure the window is within the stack and at least a certain size.
        if window_width < min_width:
            window_center = (window_min + window_max) / 2
            window_min = window_center - min_width / 2
            window_max = window_center + min_width / 2
            if window_max > sum:
                window_max = sum
                window_min = sum - min_width
            if window_min < 0:
                window_min = 0
                window_max = min_width
        value = 0
        value_weight = 0
        # Get the weighted average of all the samples
        # that overlap with the window, weighted
        # by the size of their overlap.
        for i in range(len(values)):
            if window_min >= values[i].window_max:
                continue
            if window_max <= values[i].window_min:
                break
            s = max(window_min, values[i].window_min)
            e = min(window_max, values[i].window_max)
            w = e - s
            value += values[i].value * w
            value_weight += w
        return value / value_weight if value_weight != 0 else 0
    img_out = img.copy()
    # Apply the kernel operation over each pixel.
    for index in np.ndindex(img.shape):
        img_out[index] = weighted_histogram_filter_single(index)
    return img_out
 def smoothstep(x):
    """
    The smoothstep function, input should be clamped to 0-1 range.
    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
    """
    return x * x * (3 - 2 * x)
 def smootherstep(x):
    """
    The smootherstep function, input should be clamped to 0-1 range.
    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
    """
    return x * x * x * (x * (6 * x - 15) + 10)
 def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
    """
    Creates a Gaussian kernel with thresholded edges.
    Args:
        stddev_radius (float):
            Standard deviation of the gaussian kernel, in pixels.
        max_radius (int):
            The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
            The kernel is thresholded so that any values one pixel beyond this radius
            is weighted at 0.
    Returns:
        (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
    """
    # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
    def gaussian(sqr_mag):
        return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
    # Helper function for converting a tuple to an array.
    def vec(x):
        return np.array(x)
    """
    Since a gaussian is unbounded, we need to limit ourselves
    to a finite range.
    We taper the ends off at the end of that range so they equal zero
    while preserving the maximum value of 1 at the mean.
    """
    zero_radius = max_radius + 1.0
    gauss_zero = gaussian(zero_radius * zero_radius)
    gauss_kernel_scale = 1 / (1 - gauss_zero)
    def gaussian_kernel_func(coordinate):
        x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
        x = gaussian(x)
        x -= gauss_zero
        x *= gauss_kernel_scale
        x = max(0.0, x)
        return x
    size = max_radius * 2 + 1
    kernel_center = max_radius
    kernel = np.zeros((size, size))
    for index in np.ndindex(kernel.shape):
        kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
    return kernel, kernel_center
 # ------------------- Constants -------------------
 default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2)
 enabled_ui_label = "Soft inpainting"
 enabled_gen_param_label = "Soft inpainting enabled"
 enabled_el_id = "soft_inpainting_enabled"
 ui_labels = SoftInpaintingSettings(
    "Schedule bias",
    "Preservation strength",
    "Transition contrast boost",
    "Mask influence",
    "Difference threshold",
    "Difference contrast")
 ui_info = SoftInpaintingSettings(
    "Shifts when preservation of original content occurs during denoising.",
    "How strongly partially masked content should be preserved.",
    "Amplifies the contrast that may be lost in partially masked regions.",
    "How strongly the original mask should bias the difference threshold.",
    "How much an image region can change before the original pixels are not blended in anymore.",
    "How sharp the transition should be between blended and not blended.")
 gen_param_labels = SoftInpaintingSettings(
    "Soft inpainting schedule bias",
    "Soft inpainting preservation strength",
    "Soft inpainting transition contrast boost",
    "Soft inpainting mask influence",
    "Soft inpainting difference threshold",
    "Soft inpainting difference contrast")
 el_ids = SoftInpaintingSettings(
    "mask_blend_power",
    "mask_blend_scale",
    "inpaint_detail_preservation",
    "composite_mask_influence",
    "composite_difference_threshold",
    "composite_difference_contrast")
 # ------------------- Script -------------------
 class Script(scripts.Script):
    def __init__(self):
        self.section = "inpaint"
        self.masks_for_overlay = None
        self.overlay_images = None
    def title(self):
        return "Soft Inpainting"
    def show(self, is_img2img):
        return scripts.AlwaysVisible if is_img2img else False
    def ui(self, is_img2img):
        if not is_img2img:
            return
        with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
            with gr.Group():
                gr.Markdown(
                    """
                    Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
                    **High _Mask blur_** values are recommended!
                    """)
                power = \
                    gr.Slider(label=ui_labels.mask_blend_power,
                              info=ui_info.mask_blend_power,
                              minimum=0,
                              maximum=8,
                              step=0.1,
                              value=default.mask_blend_power,
                              elem_id=el_ids.mask_blend_power)
                scale = \
                    gr.Slider(label=ui_labels.mask_blend_scale,
                              info=ui_info.mask_blend_scale,
                              minimum=0,
                              maximum=8,
                              step=0.05,
                              value=default.mask_blend_scale,
                              elem_id=el_ids.mask_blend_scale)
                detail = \
                    gr.Slider(label=ui_labels.inpaint_detail_preservation,
                              info=ui_info.inpaint_detail_preservation,
                              minimum=1,
                              maximum=32,
                              step=0.5,
                              value=default.inpaint_detail_preservation,
                              elem_id=el_ids.inpaint_detail_preservation)
                gr.Markdown(
                    """
                    ### Pixel Composite Settings
                    """)
                mask_inf = \
                    gr.Slider(label=ui_labels.composite_mask_influence,
                              info=ui_info.composite_mask_influence,
                              minimum=0,
                              maximum=1,
                              step=0.05,
                              value=default.composite_mask_influence,
                              elem_id=el_ids.composite_mask_influence)
                dif_thresh = \
                    gr.Slider(label=ui_labels.composite_difference_threshold,
                              info=ui_info.composite_difference_threshold,
                              minimum=0,
                              maximum=8,
                              step=0.25,
                              value=default.composite_difference_threshold,
                              elem_id=el_ids.composite_difference_threshold)
                dif_contr = \
                    gr.Slider(label=ui_labels.composite_difference_contrast,
                              info=ui_info.composite_difference_contrast,
                              minimum=0,
                              maximum=8,
                              step=0.25,
                              value=default.composite_difference_contrast,
                              elem_id=el_ids.composite_difference_contrast)
                with gr.Accordion("Help", open=False):
                    gr.Markdown(
                        f"""
                        ### {ui_labels.mask_blend_power}
                        The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
                        This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
                        This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
                        - **Below 1**: Stronger preservation near the end (with low sigma)
                        - **1**: Balanced (proportional to sigma)
                        - **Above 1**: Stronger preservation in the beginning (with high sigma)
                        """)
                    gr.Markdown(
                        f"""
                        ### {ui_labels.mask_blend_scale}
                        Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
                        This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
                        - **Low values**: Favors generated content.
                        - **High values**: Favors original content.
                        """)
                    gr.Markdown(
                        f"""
                        ### {ui_labels.inpaint_detail_preservation}
                        This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
                        With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
                        This can prevent the loss of contrast that occurs with linear interpolation.
                        - **Low values**: Softer blending, details may fade.
                        - **High values**: Stronger contrast, may over-saturate colors.
                        """)
                    gr.Markdown(
                        """
                        ## Pixel Composite Settings
                        Masks are generated based on how much a part of the image changed after denoising.
                        These masks are used to blend the original and final images together.
                        If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process.
                        """)
                    gr.Markdown(
                        f"""
                        ### {ui_labels.composite_mask_influence}
                        This parameter controls how much the mask should bias this sensitivity to difference.
                        - **0**: Ignore the mask, only consider differences in image content.
                        - **1**: Follow the mask closely despite image content changes.
                        """)
                    gr.Markdown(
                        f"""
                        ### {ui_labels.composite_difference_threshold}
                        This value represents the difference at which the original pixels will have less than 50% opacity.
                        - **Low values**: Two images patches must be almost the same in order to retain original pixels.
                        - **High values**: Two images patches can be very different and still retain original pixels.
                        """)
                    gr.Markdown(
                        f"""
                        ### {ui_labels.composite_difference_contrast}
                        This value represents the contrast between the opacity of the original and inpainted content.
                        - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting.
                        - **High values**: Ghosting will be less common, but transitions may be very sudden.
                        """)
        def get_element_value(generation_params: dict, old_key, new_key):
            if 'Soft Inpainting' in generation_params:
                return generation_params['Soft Inpainting'].get(new_key, True)
            else:
                return generation_params.get(old_key)
        self.infotext_fields = [
            (soft_inpainting_enabled, lambda d: get_element_value(d, enabled_gen_param_label, None)),
            (power, lambda d: get_element_value(d, gen_param_labels.mask_blend_power, 'sb')),
            (scale, lambda d: get_element_value(d, gen_param_labels.mask_blend_scale, 'ps')),
            (detail, lambda d: get_element_value(d, gen_param_labels.inpaint_detail_preservation, 'tcb')),
            (mask_inf, lambda d: get_element_value(d, gen_param_labels.composite_mask_influence, 'mi')),
            (dif_thresh, lambda d: get_element_value(d, gen_param_labels.composite_difference_threshold, 'dt')),
            (dif_contr, lambda d: get_element_value(d, gen_param_labels.composite_difference_contrast, 'dc'))
        ]
        self.paste_field_names = []
        for _, field_name in self.infotext_fields:
            self.paste_field_names.append(field_name)
        return [soft_inpainting_enabled,
                power,
                scale,
                detail,
                mask_inf,
                dif_thresh,
                dif_contr]
    def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
        if not enabled:
            return
        if not processing_uses_inpainting(p):
            return
        # Shut off the rounding it normally does.
        p.mask_round = False
        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
        # p.extra_generation_params["Mask rounding"] = False
        settings.add_generation_params(p.extra_generation_params)
    def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
                      dif_thresh, dif_contr):
        if not enabled:
            return
        if not processing_uses_inpainting(p):
            return
        if mba.is_final_blend:
            mba.blended_latent = mba.current_latent
            return
        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
        # todo: Why is sigma 2D? Both values are the same.
        mba.blended_latent = latent_blend(settings,
                                          mba.init_latent,
                                          mba.current_latent,
                                          get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
    def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
                    dif_thresh, dif_contr):
        if not enabled:
            return
        if not processing_uses_inpainting(p):
            return
        nmask = getattr(p, "nmask", None)
        if nmask is None:
            return
        from modules import images
        from modules.shared import opts
        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
        # since the original code puts holes in the existing overlay images,
        # we have to rebuild them.
        self.overlay_images = []
        for img in p.init_images:
            image = images.flatten(img, opts.img2img_background_color)
            if p.paste_to is None and p.resize_mode != 3:
                image = images.resize_image(p.resize_mode, image, p.width, p.height)
            self.overlay_images.append(image.convert('RGBA'))
        if len(p.init_images) == 1:
            self.overlay_images = self.overlay_images * p.batch_size
        if getattr(ps.samples, 'already_decoded', False):
            self.masks_for_overlay = apply_masks(settings=settings,
                                                 nmask=nmask,
                                                 overlay_images=self.overlay_images,
                                                 width=p.width,
                                                 height=p.height,
                                                 paste_to=p.paste_to)
        else:
            self.masks_for_overlay = apply_adaptive_masks(settings=settings,
                                                          nmask=nmask,
                                                          latent_orig=p.init_latent,
                                                          latent_processed=ps.samples,
                                                          overlay_images=self.overlay_images,
                                                          width=p.width,
                                                          height=p.height,
                                                          paste_to=p.paste_to)
    def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
                                detail_preservation, mask_inf, dif_thresh, dif_contr):
        if not enabled:
            return
        if not processing_uses_inpainting(p):
            return
        if self.masks_for_overlay is None:
            return
        if self.overlay_images is None:
            return
        ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
        ppmo.overlay_image = self.overlay_images[ppmo.index]
@@ -4,107 +4,6 @@
    #licenses pre { margin: 1em 0 2em 0;}
 </style>
 <h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
 <small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
 <pre>
 S-Lab License 1.0
 Copyright 2022 S-Lab
 Redistribution and use for non-commercial purpose in source and
 binary forms, with or without modification, are permitted provided
 that the following conditions are met:
 1. Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.
 2. Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in
   the documentation and/or other materials provided with the
   distribution.
 3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived
   from this software without specific prior written permission.
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 In the event that redistribution and/or use for commercial purpose in
 source or binary forms, with or without modification is required,
 please contact the contributor(s) of the work.
 </pre>
 <h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
 <small>Code for architecture and reading models copied.</small>
 <pre>
 MIT License
 Copyright (c) 2021 victorca25
 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>
 <h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
 <small>Some code is copied to support ESRGAN models.</small>
 <pre>
 BSD 3-Clause License
 Copyright (c) 2021, Xintao Wang
 All rights reserved.
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
 1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.
 2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.
 3. Neither the name of the copyright holder nor the names of its
   contributors may be used to endorse or promote products derived from
   this software without specific prior written permission.
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 </pre>
 <h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
 <small>Some code for compatibility with OSX is taken from lstein's repository.</small>
 <pre>
@@ -183,213 +82,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 </pre>
 <h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
 <small>Code added by contributors, most likely copied from this repository.</small>
 <pre>
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   Copyright [2021] [SwinIR Authors]
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
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 </pre>
 <h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
 <small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
 <pre>
@@ -19,16 +19,28 @@ function keyupEditAttention(event) {
        let beforeParen = before.lastIndexOf(OPEN);
        if (beforeParen == -1) return false;
        let beforeClosingParen = before.lastIndexOf(CLOSE);
        if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false;
        // Find closing parenthesis around current cursor
        const after = text.substring(selectionStart);
        let afterParen = after.indexOf(CLOSE);
        if (afterParen == -1) return false;
        let afterOpeningParen = after.indexOf(OPEN);
        if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false;
        // Set the selection to the text between the parenthesis
        const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
-        const lastColon = parenContent.lastIndexOf(":");
+        if (/.*:-?[\d.]+/s.test(parenContent)) {
-        selectionStart = beforeParen + 1;
+            const lastColon = parenContent.lastIndexOf(":");
-        selectionEnd = selectionStart + lastColon;
+            selectionStart = beforeParen + 1;
            selectionEnd = selectionStart + lastColon;
        } else {
            selectionStart = beforeParen + 1;
            selectionEnd = selectionStart + parenContent.length;
        }
        target.setSelectionRange(selectionStart, selectionEnd);
        return true;
    }
@@ -57,7 +69,7 @@ function keyupEditAttention(event) {
    }
    // If the user hasn't selected anything, let's select their current parenthesis block or word
-    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) {
        selectCurrentWord();
    }
@@ -65,33 +77,54 @@ function keyupEditAttention(event) {
    var closeCharacter = ')';
    var delta = opts.keyedit_precision_attention;
    var start = selectionStart > 0 ? text[selectionStart - 1] : "";
    var end = text[selectionEnd];
-    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
+    if (start == '<') {
        closeCharacter = '>';
        delta = opts.keyedit_precision_extra;
-    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
+    } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis)))
        let numParen = 0;
        while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) {
            numParen++;
        }
        if (start == "[") {
            weight = (1 / 1.1) ** numParen;
        } else {
            weight = 1.1 ** numParen;
        }
        weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention;
        text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen);
        selectionStart -= numParen - 1;
        selectionEnd -= numParen - 1;
    } else if (start != '(') {
        // do not include spaces at the end
        while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
-            selectionEnd -= 1;
+            selectionEnd--;
        }
        if (selectionStart == selectionEnd) {
            return;
        }
        text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
-        selectionStart += 1;
+        selectionStart++;
-        selectionEnd += 1;
+        selectionEnd++;
    }
-    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    if (text[selectionEnd] != ':') return;
-    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + end));
+    var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength));
    if (isNaN(weight)) return;
    weight += isPlus ? delta : -delta;
    weight = parseFloat(weight.toPrecision(12));
-    if (String(weight).length == 1) weight += ".0";
+    if (Number.isInteger(weight)) weight += ".0";
    if (closeCharacter == ')' && weight == 1) {
        var endParenPos = text.substring(selectionEnd).indexOf(')');
@@ -99,7 +132,7 @@ function keyupEditAttention(event) {
        selectionStart--;
        selectionEnd--;
    } else {
-        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength);
    }
    target.focus();
@@ -26,8 +26,9 @@ function setupExtraNetworksForTab(tabname) {
    var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
    var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
    var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
    var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container');
    var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt');
    sort.dataset.sortkey = 'sortDefault';
    tabs.appendChild(searchDiv);
    tabs.appendChild(sort);
    tabs.appendChild(sortOrder);
@@ -49,20 +50,23 @@ function setupExtraNetworksForTab(tabname) {
            elem.style.display = visible ? "" : "none";
        });
        applySort();
    };
    var applySort = function() {
        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
        var reverse = sortOrder.classList.contains("sortReverse");
-        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
+        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name";
-        sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
+        sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
-        var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
+        var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
-        if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
+
        if (sortKeyStore == sort.dataset.sortkey) {
            return;
        }
        sort.dataset.sortkey = sortKeyStore;
        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
        cards.forEach(function(card) {
            card.originalParentElement = card.parentElement;
        });
@@ -88,15 +92,13 @@ function setupExtraNetworksForTab(tabname) {
    };
    search.addEventListener("input", applyFilter);
    applyFilter();
    ["change", "blur", "click"].forEach(function(evt) {
        sort.querySelector("input").addEventListener(evt, applySort);
    });
    sortOrder.addEventListener("click", function() {
        sortOrder.classList.toggle("sortReverse");
        applySort();
    });
    applyFilter();
    extraNetworksApplySort[tabname] = applySort;
    extraNetworksApplyFilter[tabname] = applyFilter;
    var showDirsUpdate = function() {
@@ -109,11 +111,51 @@ function setupExtraNetworksForTab(tabname) {
    showDirsUpdate();
 }
 function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) {
    if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout
    var promptContainer = gradioApp().getElementById(tabname + '_prompt_container');
    var prompt = gradioApp().getElementById(tabname + '_prompt_row');
    var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row');
    var elem = id ? gradioApp().getElementById(id) : null;
    if (showNegativePrompt && elem) {
        elem.insertBefore(negPrompt, elem.firstChild);
    } else {
        promptContainer.insertBefore(negPrompt, promptContainer.firstChild);
    }
    if (showPrompt && elem) {
        elem.insertBefore(prompt, elem.firstChild);
    } else {
        promptContainer.insertBefore(prompt, promptContainer.firstChild);
    }
    if (elem) {
        elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
    }
 }
 function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
    extraNetworksMovePromptToTab(tabname, '', false, false);
 }
 function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
    extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
 }
 function applyExtraNetworkFilter(tabname) {
    setTimeout(extraNetworksApplyFilter[tabname], 1);
 }
 function applyExtraNetworkSort(tabname) {
    setTimeout(extraNetworksApplySort[tabname], 1);
 }
 var extraNetworksApplyFilter = {};
 var extraNetworksApplySort = {};
 var activePromptTextarea = {};
 function setupExtraNetworks() {
@@ -143,8 +185,10 @@ onUiLoaded(setupExtraNetworks);
 var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
 var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
-function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
+var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/;
-    var m = text.match(re_extranet);
+var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g;
 function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) {
    var m = text.match(isNeg ? re_extranet_neg : re_extranet);
    var replaced = false;
    var newTextareaText;
    if (m) {
@@ -152,8 +196,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
        var extraTextAfterNet = m[2];
        var partToSearch = m[1];
        var foundAtPosition = -1;
-        newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) {
+        newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) {
-            m = found.match(re_extranet);
+            m = found.match(isNeg ? re_extranet_neg : re_extranet);
            if (m[1] == partToSearch) {
                replaced = true;
                foundAtPosition = pos;
@@ -163,7 +207,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
        });
        if (foundAtPosition >= 0) {
-            if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+            if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
                newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
            }
            if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
@@ -188,14 +232,23 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
    return false;
 }
-function cardClicked(tabname, textToAdd, allowNegativePrompt) {
+function updatePromptArea(text, textArea, isNeg) {
    var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
-    if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
+    if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) {
-        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
+        textArea.value = textArea.value + opts.extra_networks_add_text_separator + text;
    }
-    updateInput(textarea);
+    updateInput(textArea);
 }
 function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) {
    if (textToAddNegative.length > 0) {
        updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"));
        updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true);
    } else {
        var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
        updatePromptArea(textToAdd, textarea);
    }
 }
 function saveCardPreview(event, tabname, filename) {
@@ -350,3 +403,9 @@ function extraNetworksRefreshSingleCard(page, tabname, name) {
        }
    });
 }
 window.addEventListener("keydown", function(event) {
    if (event.key == "Escape") {
        closePopup();
    }
 });
@@ -33,8 +33,11 @@ function updateOnBackgroundChange() {
    const modalImage = gradioApp().getElementById("modalImage");
    if (modalImage && modalImage.offsetParent) {
        let currentButton = selected_gallery_button();
-
+        let preview = gradioApp().querySelectorAll('.livePreview > img');
-        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+        if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
            // show preview image if available
            modalImage.src = preview[preview.length - 1].src;
        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
            modalImage.src = currentButton.children[0].src;
            if (modalImage.style.display === 'none') {
                const modal = gradioApp().getElementById("lightboxModal");
@@ -1,37 +1,68 @@
 var observerAccordionOpen = new MutationObserver(function(mutations) {
    mutations.forEach(function(mutationRecord) {
        var elem = mutationRecord.target;
        var open = elem.classList.contains('open');
        var accordion = elem.parentNode;
        accordion.classList.toggle('input-accordion-open', open);
        var checkbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
        checkbox.checked = open;
        updateInput(checkbox);
        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
        if (extra) {
            extra.style.display = open ? "" : "none";
        }
    });
 });
 function inputAccordionChecked(id, checked) {
-    var label = gradioApp().querySelector('#' + id + " .label-wrap");
+    var accordion = gradioApp().getElementById(id);
-    if (label.classList.contains('open') != checked) {
+    accordion.visibleCheckbox.checked = checked;
-        label.click();
+    accordion.onVisibleCheckboxChange();
 }
 function setupAccordion(accordion) {
    var labelWrap = accordion.querySelector('.label-wrap');
    var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
    var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
    var span = labelWrap.querySelector('span');
    var linked = true;
    var isOpen = function() {
        return labelWrap.classList.contains('open');
    };
    var observerAccordionOpen = new MutationObserver(function(mutations) {
        mutations.forEach(function(mutationRecord) {
            accordion.classList.toggle('input-accordion-open', isOpen());
            if (linked) {
                accordion.visibleCheckbox.checked = isOpen();
                accordion.onVisibleCheckboxChange();
            }
        });
    });
    observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
    if (extra) {
        labelWrap.insertBefore(extra, labelWrap.lastElementChild);
    }
    accordion.onChecked = function(checked) {
        if (isOpen() != checked) {
            labelWrap.click();
        }
    };
    var visibleCheckbox = document.createElement('INPUT');
    visibleCheckbox.type = 'checkbox';
    visibleCheckbox.checked = isOpen();
    visibleCheckbox.id = accordion.id + "-visible-checkbox";
    visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
    span.insertBefore(visibleCheckbox, span.firstChild);
    accordion.visibleCheckbox = visibleCheckbox;
    accordion.onVisibleCheckboxChange = function() {
        if (linked && isOpen() != visibleCheckbox.checked) {
            labelWrap.click();
        }
        gradioCheckbox.checked = visibleCheckbox.checked;
        updateInput(gradioCheckbox);
    };
    visibleCheckbox.addEventListener('click', function(event) {
        linked = false;
        event.stopPropagation();
    });
    visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange);
 }
 onUiLoaded(function() {
    for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
-        var labelWrap = accordion.querySelector('.label-wrap');
+        setupAccordion(accordion);
        observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
        var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
        if (extra) {
            labelWrap.insertBefore(extra, labelWrap.lastElementChild);
        }
    }
 });
@@ -26,7 +26,11 @@ onAfterUiUpdate(function() {
    lastHeadImg = headImg;
    // play notification sound if available
-    gradioApp().querySelector('#audio_notification audio')?.play();
+    const notificationAudio = gradioApp().querySelector('#audio_notification audio');
    if (notificationAudio) {
        notificationAudio.volume = opts.notification_volume / 100.0 || 1.0;
        notificationAudio.play();
    }
    if (document.hasFocus()) return;
@@ -44,3 +44,28 @@ onUiLoaded(function() {
    buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
 });
 onOptionsChanged(function() {
    if (gradioApp().querySelector('#settings .settings-category')) return;
    var sectionMap = {};
    gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
        sectionMap[x.textContent.trim()] = x;
    });
    opts._categories.forEach(function(x) {
        var section = x[0];
        var category = x[1];
        var span = document.createElement('SPAN');
        span.textContent = category;
        span.className = 'settings-category';
        var sectionElem = sectionMap[section];
        if (!sectionElem) return;
        sectionElem.parentElement.insertBefore(span, sectionElem);
    });
 });
@@ -150,6 +150,14 @@ function submit() {
    return res;
 }
 function submit_txt2img_upscale() {
    var res = submit(...arguments);
    res[2] = selected_gallery_index();
    return res;
 }
 function submit_img2img() {
    showSubmitButtons('img2img', false);
@@ -170,6 +178,23 @@ function submit_img2img() {
    return res;
 }
 function submit_extras() {
    showSubmitButtons('extras', false);
    var id = randomId();
    requestProgress(id, gradioApp().getElementById('extras_gallery_container'), gradioApp().getElementById('extras_gallery'), function() {
        showSubmitButtons('extras', true);
    });
    var res = create_submit_args(arguments);
    res[0] = id;
    console.log(res);
    return res;
 }
 function restoreProgressTxt2img() {
    showRestoreProgressButton("txt2img", false);
    var id = localGet("txt2img_task_id");
@@ -198,9 +223,33 @@ function restoreProgressImg2img() {
 }
 /**
 * Configure the width and height elements on `tabname` to accept
 * pasting of resolutions in the form of "width x height".
 */
 function setupResolutionPasting(tabname) {
    var width = gradioApp().querySelector(`#${tabname}_width input[type=number]`);
    var height = gradioApp().querySelector(`#${tabname}_height input[type=number]`);
    for (const el of [width, height]) {
        el.addEventListener('paste', function(event) {
            var pasteData = event.clipboardData.getData('text/plain');
            var parsed = pasteData.match(/^\s*(\d+)\D+(\d+)\s*$/);
            if (parsed) {
                width.value = parsed[1];
                height.value = parsed[2];
                updateInput(width);
                updateInput(height);
                event.preventDefault();
            }
        });
    }
 }
 onUiLoaded(function() {
    showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
    showRestoreProgressButton('img2img', localGet("img2img_task_id"));
    setupResolutionPasting('txt2img');
    setupResolutionPasting('img2img');
 });
@@ -17,15 +17,13 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models
 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
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
-from PIL import PngImagePlugin,Image
+from PIL import PngImagePlugin, Image
 from modules.sd_models import unload_model_weights, reload_model_weights, checkpoint_aliases
 from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
@@ -33,7 +31,7 @@ from typing import Any
 import piexif
 import piexif.helper
 from contextlib import closing
-
+from modules.progress import create_task_id, add_task_to_queue, start_task, finish_task, current_task
 def script_name_to_index(name, scripts):
    try:
@@ -103,7 +101,8 @@ def decode_base64_to_image(encoding):
 def encode_pil_to_base64(image):
    with io.BytesIO() as output_bytes:
-
+        if isinstance(image, str):
            return image
        if opts.samples_format.lower() == 'png':
            use_metadata = False
            metadata = PngImagePlugin.PngInfo()
@@ -235,7 +234,6 @@ class Api:
        self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
        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=models.CreateResponse)
        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=models.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=models.MemoryResponse)
@@ -253,6 +251,24 @@ class Api:
        self.default_script_arg_txt2img = []
        self.default_script_arg_img2img = []
        txt2img_script_runner = scripts.scripts_txt2img
        img2img_script_runner = scripts.scripts_img2img
        if not txt2img_script_runner.scripts or not img2img_script_runner.scripts:
            ui.create_ui()
        if not txt2img_script_runner.scripts:
            txt2img_script_runner.initialize_scripts(False)
        if not self.default_script_arg_txt2img:
            self.default_script_arg_txt2img = self.init_default_script_args(txt2img_script_runner)
        if not img2img_script_runner.scripts:
            img2img_script_runner.initialize_scripts(True)
        if not self.default_script_arg_img2img:
            self.default_script_arg_img2img = self.init_default_script_args(img2img_script_runner)
    def add_api_route(self, path: str, endpoint, **kwargs):
        if shared.cmd_opts.api_auth:
            return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
@@ -314,8 +330,13 @@ class Api:
                    script_args[script.args_from:script.args_to] = ui_default_values
        return script_args
-    def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
+    def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner, *, input_script_args=None):
        script_args = default_script_args.copy()
        if input_script_args is not None:
            for index, value in input_script_args.items():
                script_args[index] = value
        # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
        if selectable_scripts:
            script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
@@ -337,13 +358,83 @@ class Api:
                        script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
        return script_args
    def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None):
        """Processes `infotext` field from the `request`, and sets other fields of the `request` accoring to what's in infotext.
        If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored.
        Additionally, fills `mentioned_script_args` dict with index: value pairs for script arguments read from infotext.
        """
        if not request.infotext:
            return {}
        possible_fields = infotext_utils.paste_fields[tabname]["fields"]
        set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True)  # pydantic v1/v2 have differenrt names for this
        params = infotext_utils.parse_generation_parameters(request.infotext)
        def get_field_value(field, params):
            value = field.function(params) if field.function else params.get(field.label)
            if value is None:
                return None
            if field.api in request.__fields__:
                target_type = request.__fields__[field.api].type_
            else:
                target_type = type(field.component.value)
            if target_type == type(None):
                return None
            if isinstance(value, dict) and value.get('__type__') == 'generic_update':  # this is a gradio.update rather than a value
                value = value.get('value')
            if value is not None and not isinstance(value, target_type):
                value = target_type(value)
            return value
        for field in possible_fields:
            if not field.api:
                continue
            if field.api in set_fields:
                continue
            value = get_field_value(field, params)
            if value is not None:
                setattr(request, field.api, value)
        if request.override_settings is None:
            request.override_settings = {}
        overriden_settings = infotext_utils.get_override_settings(params)
        for _, setting_name, value in overriden_settings:
            if setting_name not in request.override_settings:
                request.override_settings[setting_name] = value
        if script_runner is not None and mentioned_script_args is not None:
            indexes = {v: i for i, v in enumerate(script_runner.inputs)}
            script_fields = ((field, indexes[field.component]) for field in possible_fields if field.component in indexes)
            for field, index in script_fields:
                value = get_field_value(field, params)
                if value is None:
                    continue
                mentioned_script_args[index] = value
        return params
    def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI):
        task_id = txt2imgreq.force_task_id or create_task_id("txt2img")
        script_runner = scripts.scripts_txt2img
-        if not script_runner.scripts:
+
-            script_runner.initialize_scripts(False)
+        infotext_script_args = {}
-            ui.create_ui()
+        self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
-        if not self.default_script_arg_txt2img:
+
            self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
        selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
        populate = txt2imgreq.copy(update={  # Override __init__ params
@@ -358,12 +449,15 @@ class Api:
        args.pop('script_name', None)
        args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
        args.pop('alwayson_scripts', None)
        args.pop('infotext', None)
-        script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
+        script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args)
        send_images = args.pop('send_images', True)
        args.pop('save_images', None)
        add_task_to_queue(task_id)
        with self.queue_lock:
            with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
                p.is_api = True
@@ -373,12 +467,14 @@ class Api:
                try:
                    shared.state.begin(job="scripts_txt2img")
                    start_task(task_id)
                    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:
                        p.script_args = tuple(script_args) # Need to pass args as tuple here
                        processed = process_images(p)
                    finish_task(task_id)
                finally:
                    shared.state.end()
                    shared.total_tqdm.clear()
@@ -388,6 +484,8 @@ class Api:
        return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
    def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI):
        task_id = img2imgreq.force_task_id or create_task_id("img2img")
        init_images = img2imgreq.init_images
        if init_images is None:
            raise HTTPException(status_code=404, detail="Init image not found")
@@ -397,11 +495,10 @@ class Api:
            mask = decode_base64_to_image(mask)
        script_runner = scripts.scripts_img2img
-        if not script_runner.scripts:
+
-            script_runner.initialize_scripts(True)
+        infotext_script_args = {}
-            ui.create_ui()
+        self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
-        if not self.default_script_arg_img2img:
+
            self.default_script_arg_img2img = self.init_default_script_args(script_runner)
        selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
        populate = img2imgreq.copy(update={  # Override __init__ params
@@ -418,12 +515,15 @@ class Api:
        args.pop('script_name', None)
        args.pop('script_args', None)  # will refeed them to the pipeline directly after initializing them
        args.pop('alwayson_scripts', None)
        args.pop('infotext', None)
-        script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
+        script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args)
        send_images = args.pop('send_images', True)
        args.pop('save_images', None)
        add_task_to_queue(task_id)
        with self.queue_lock:
            with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
                p.init_images = [decode_base64_to_image(x) for x in init_images]
@@ -434,12 +534,14 @@ class Api:
                try:
                    shared.state.begin(job="scripts_img2img")
                    start_task(task_id)
                    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:
                        p.script_args = tuple(script_args) # Need to pass args as tuple here
                        processed = process_images(p)
                    finish_task(task_id)
                finally:
                    shared.state.end()
                    shared.total_tqdm.clear()
@@ -482,7 +584,7 @@ class Api:
        if geninfo is None:
            geninfo = ""
-        params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
+        params = infotext_utils.parse_generation_parameters(geninfo)
        script_callbacks.infotext_pasted_callback(geninfo, params)
        return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
@@ -513,7 +615,7 @@ class Api:
        if shared.state.current_image and not req.skip_current_image:
            current_image = encode_pil_to_base64(shared.state.current_image)
-        return models.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, current_task=current_task)
    def interrogateapi(self, interrogatereq: models.InterrogateRequest):
        image_b64 = interrogatereq.image
@@ -540,12 +642,12 @@ class Api:
        return {}
    def unloadapi(self):
-        unload_model_weights()
+        sd_models.unload_model_weights()
        return {}
    def reloadapi(self):
-        reload_model_weights()
+        sd_models.send_model_to_device(shared.sd_model)
        return {}
@@ -565,7 +667,7 @@ class Api:
    def set_config(self, req: dict[str, Any]):
        checkpoint_name = req.get("sd_model_checkpoint", None)
-        if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
+        if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
            raise RuntimeError(f"model {checkpoint_name!r} not found")
        for k, v in req.items():
@@ -675,19 +777,6 @@ class Api:
        finally:
            shared.state.end()
    def preprocess(self, args: dict):
        try:
            shared.state.begin(job="preprocess")
            preprocess(**args) # quick operation unless blip/booru interrogation is enabled
            shared.state.end()
            return models.PreprocessResponse(info='preprocess complete')
        except KeyError as e:
            return models.PreprocessResponse(info=f"preprocess error: invalid token: {e}")
        except Exception as e:
            return models.PreprocessResponse(info=f"preprocess error: {e}")
        finally:
            shared.state.end()
    def train_embedding(self, args: dict):
        try:
            shared.state.begin(job="train_embedding")
@@ -790,7 +879,15 @@ class Api:
    def launch(self, server_name, port, root_path):
        self.app.include_router(self.router)
-        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
+        uvicorn.run(
            self.app,
            host=server_name,
            port=port,
            timeout_keep_alive=shared.cmd_opts.timeout_keep_alive,
            root_path=root_path,
            ssl_keyfile=shared.cmd_opts.tls_keyfile,
            ssl_certfile=shared.cmd_opts.tls_certfile
        )
    def kill_webui(self):
        restart.stop_program()
@@ -107,6 +107,8 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
        {"key": "send_images", "type": bool, "default": True},
        {"key": "save_images", "type": bool, "default": False},
        {"key": "alwayson_scripts", "type": dict, "default": {}},
        {"key": "force_task_id", "type": str, "default": None},
        {"key": "infotext", "type": str, "default": None},
    ]
 ).generate_model()
@@ -124,6 +126,8 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
        {"key": "send_images", "type": bool, "default": True},
        {"key": "save_images", "type": bool, "default": False},
        {"key": "alwayson_scripts", "type": dict, "default": {}},
        {"key": "force_task_id", "type": str, "default": None},
        {"key": "infotext", "type": str, "default": None},
    ]
 ).generate_model()
@@ -202,9 +206,6 @@ class TrainResponse(BaseModel):
 class CreateResponse(BaseModel):
    info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.")
 class PreprocessResponse(BaseModel):
    info: str = Field(title="Preprocess info", description="Response string from preprocessing task.")
 fields = {}
 for key, metadata in opts.data_labels.items():
    value = opts.data.get(key)
@@ -32,7 +32,7 @@ def dump_cache():
        with cache_lock:
            cache_filename_tmp = cache_filename + "-"
            with open(cache_filename_tmp, "w", encoding="utf8") as file:
-                json.dump(cache_data, file, indent=4)
+                json.dump(cache_data, file, indent=4, ensure_ascii=False)
            os.replace(cache_filename_tmp, cache_filename)
@@ -62,16 +62,15 @@ def cache(subsection):
    if cache_data is None:
        with cache_lock:
            if cache_data is None:
-                if not os.path.isfile(cache_filename):
+                try:
                    with open(cache_filename, "r", encoding="utf8") as file:
                        cache_data = json.load(file)
                except FileNotFoundError:
                    cache_data = {}
                except Exception:
                    os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
                    print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
                    cache_data = {}
                else:
                    try:
                        with open(cache_filename, "r", encoding="utf8") as file:
                            cache_data = json.load(file)
                    except Exception:
                        os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
                        print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
                        cache_data = {}
    s = cache_data.get(subsection, {})
    cache_data[subsection] = s
@@ -78,6 +78,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
        shared.state.skipped = False
        shared.state.interrupted = False
        shared.state.stopping_generation = False
        shared.state.job_count = 0
        if not add_stats:
@@ -70,13 +70,16 @@ parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="pre
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
 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("--use-ipex", action="store_true", help="use Intel XPU as torch device")
 parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
 parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
 parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing of all settings globally", default=False)
 parser.add_argument("--freeze-settings-in-sections", type=str, help='disable editing settings in specific sections of the settings page by specifying a comma-delimited list such like "saving-images,upscaling". The list of setting names can be found in the modules/shared_options.py file', default=None)
 parser.add_argument("--freeze-specific-settings", type=str, help='disable editing of individual settings by specifying a comma-delimited list like "samples_save,samples_format". The list of setting names can be found in the config.json file', default=None)
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
@@ -107,7 +110,7 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
 parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
 parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
 parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
-parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions")
 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)
@@ -1,276 +0,0 @@
 # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
 import math
 import torch
 from torch import nn, Tensor
 import torch.nn.functional as F
 from typing import Optional
 from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
 from basicsr.utils.registry import ARCH_REGISTRY
 def calc_mean_std(feat, eps=1e-5):
    """Calculate mean and std for adaptive_instance_normalization.
    Args:
        feat (Tensor): 4D tensor.
        eps (float): A small value added to the variance to avoid
            divide-by-zero. Default: 1e-5.
    """
    size = feat.size()
    assert len(size) == 4, 'The input feature should be 4D tensor.'
    b, c = size[:2]
    feat_var = feat.view(b, c, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(b, c, 1, 1)
    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
    return feat_mean, feat_std
 def adaptive_instance_normalization(content_feat, style_feat):
    """Adaptive instance normalization.
    Adjust the reference features to have the similar color and illuminations
    as those in the degradate features.
    Args:
        content_feat (Tensor): The reference feature.
        style_feat (Tensor): The degradate features.
    """
    size = content_feat.size()
    style_mean, style_std = calc_mean_std(style_feat)
    content_mean, content_std = calc_mean_std(content_feat)
    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
 class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """
    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale
    def forward(self, x, mask=None):
        if mask is None:
            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
        not_mask = ~mask
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos
 def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
 class TransformerSALayer(nn.Module):
    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
        # Implementation of Feedforward model - MLP
        self.linear1 = nn.Linear(embed_dim, dim_mlp)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_mlp, embed_dim)
        self.norm1 = nn.LayerNorm(embed_dim)
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.activation = _get_activation_fn(activation)
    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos
    def forward(self, tgt,
                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)
        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
                              key_padding_mask=tgt_key_padding_mask)[0]
        tgt = tgt + self.dropout1(tgt2)
        # ffn
        tgt2 = self.norm2(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout2(tgt2)
        return tgt
 class Fuse_sft_block(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.encode_enc = ResBlock(2*in_ch, out_ch)
        self.scale = nn.Sequential(
                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
        self.shift = nn.Sequential(
                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
    def forward(self, enc_feat, dec_feat, w=1):
        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
        scale = self.scale(enc_feat)
        shift = self.shift(enc_feat)
        residual = w * (dec_feat * scale + shift)
        out = dec_feat + residual
        return out
@ARCH_REGISTRY.register()
 class CodeFormer(VQAutoEncoder):
    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
                codebook_size=1024, latent_size=256,
                connect_list=('32', '64', '128', '256'),
                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:
            for module in fix_modules:
                for param in getattr(self, module).parameters():
                    param.requires_grad = False
        self.connect_list = connect_list
        self.n_layers = n_layers
        self.dim_embd = dim_embd
        self.dim_mlp = dim_embd*2
        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
        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)
                                    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,
            '64': 256,
            '128': 128,
            '256': 128,
            '512': 64,
        }
        # after second residual block for > 16, before attn layer for ==16
        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
        # after first residual block for > 16, before attn layer for ==16
        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
        # fuse_convs_dict
        self.fuse_convs_dict = nn.ModuleDict()
        for f_size in self.connect_list:
            in_ch = self.channels[f_size]
            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Embedding)):
            module.weight.data.normal_(mean=0.0, std=0.02)
            if isinstance(module, nn.Linear) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
        # ################### Encoder #####################
        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)
            if i in out_list:
                enc_feat_dict[str(x.shape[-1])] = x.clone()
        lq_feat = x
        # ################# Transformer ###################
        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
        # BCHW -> BC(HW) -> (HW)BC
        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
        query_emb = feat_emb
        # Transformer encoder
        for layer in self.ft_layers:
            query_emb = layer(query_emb, query_pos=pos_emb)
        # output logits
        logits = self.idx_pred_layer(query_emb) # (hw)bn
        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
        if code_only: # for training stage II
          # logits doesn't need softmax before cross_entropy loss
            return logits, lq_feat
        # ################# Quantization ###################
        # if self.training:
        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
        #     # b(hw)c -> bc(hw) -> bchw
        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
        # ------------
        soft_one_hot = F.softmax(logits, dim=2)
        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
        # preserve gradients
        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
        if detach_16:
            quant_feat = quant_feat.detach() # for training stage III
        if adain:
            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
        # ################## Generator ####################
        x = quant_feat
        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)
            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
@@ -1,435 +0,0 @@
 # this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
 '''
 VQGAN code, adapted from the original created by the Unleashing Transformers authors:
 https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
 '''
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 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):
    return x*torch.sigmoid(x)
 #  Define VQVAE classes
 class VectorQuantizer(nn.Module):
    def __init__(self, codebook_size, emb_dim, beta):
        super(VectorQuantizer, self).__init__()
        self.codebook_size = codebook_size  # number of embeddings
        self.emb_dim = emb_dim  # dimension of embedding
        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
    def forward(self, z):
        # reshape z -> (batch, height, width, channel) and flatten
        z = z.permute(0, 2, 3, 1).contiguous()
        z_flattened = z.view(-1, self.emb_dim)
        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
            2 * torch.matmul(z_flattened, self.embedding.weight.t())
        mean_distance = torch.mean(d)
        # find closest encodings
        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
        # [0-1], higher score, higher confidence
        min_encoding_scores = torch.exp(-min_encoding_scores/10)
        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
        min_encodings.scatter_(1, min_encoding_indices, 1)
        # get quantized latent vectors
        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
        # compute loss for embedding
        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
        # preserve gradients
        z_q = z + (z_q - z).detach()
        # perplexity
        e_mean = torch.mean(min_encodings, dim=0)
        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
        # reshape back to match original input shape
        z_q = z_q.permute(0, 3, 1, 2).contiguous()
        return z_q, loss, {
            "perplexity": perplexity,
            "min_encodings": min_encodings,
            "min_encoding_indices": min_encoding_indices,
            "min_encoding_scores": min_encoding_scores,
            "mean_distance": mean_distance
            }
    def get_codebook_feat(self, indices, shape):
        # input indices: batch*token_num -> (batch*token_num)*1
        # shape: batch, height, width, channel
        indices = indices.view(-1,1)
        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
        min_encodings.scatter_(1, indices, 1)
        # get quantized latent vectors
        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
        if shape is not None:  # reshape back to match original input shape
            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
        return z_q
 class GumbelQuantizer(nn.Module):
    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
        super().__init__()
        self.codebook_size = codebook_size  # number of embeddings
        self.emb_dim = emb_dim  # dimension of embedding
        self.straight_through = straight_through
        self.temperature = temp_init
        self.kl_weight = kl_weight
        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
        self.embed = nn.Embedding(codebook_size, emb_dim)
    def forward(self, z):
        hard = self.straight_through if self.training else True
        logits = self.proj(z)
        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
        # + kl divergence to the prior loss
        qy = F.softmax(logits, dim=1)
        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
        min_encoding_indices = soft_one_hot.argmax(dim=1)
        return z_q, diff, {
            "min_encoding_indices": min_encoding_indices
        }
 class Downsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
    def forward(self, x):
        pad = (0, 1, 0, 1)
        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
        x = self.conv(x)
        return x
 class Upsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
    def forward(self, x):
        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
        x = self.conv(x)
        return x
 class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels=None):
        super(ResBlock, self).__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.norm1 = normalize(in_channels)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.norm2 = normalize(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        if self.in_channels != self.out_channels:
            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
    def forward(self, x_in):
        x = x_in
        x = self.norm1(x)
        x = swish(x)
        x = self.conv1(x)
        x = self.norm2(x)
        x = swish(x)
        x = self.conv2(x)
        if self.in_channels != self.out_channels:
            x_in = self.conv_out(x_in)
        return x + x_in
 class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels
        self.norm = normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels,
            in_channels,
            kernel_size=1,
            stride=1,
            padding=0
        )
    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)
        # compute attention
        b, c, h, w = q.shape
        q = q.reshape(b, c, h*w)
        q = q.permute(0, 2, 1)
        k = k.reshape(b, c, h*w)
        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)
        h_ = torch.bmm(v, w_)
        h_ = h_.reshape(b, c, h, w)
        h_ = self.proj_out(h_)
        return x+h_
 class Encoder(nn.Module):
    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
        super().__init__()
        self.nf = nf
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.attn_resolutions = attn_resolutions
        curr_res = self.resolution
        in_ch_mult = (1,)+tuple(ch_mult)
        blocks = []
        # initial convultion
        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
        # residual and downsampling blocks, with attention on smaller res (16x16)
        for i in range(self.num_resolutions):
            block_in_ch = nf * in_ch_mult[i]
            block_out_ch = nf * ch_mult[i]
            for _ in range(self.num_res_blocks):
                blocks.append(ResBlock(block_in_ch, block_out_ch))
                block_in_ch = block_out_ch
                if curr_res in attn_resolutions:
                    blocks.append(AttnBlock(block_in_ch))
            if i != self.num_resolutions - 1:
                blocks.append(Downsample(block_in_ch))
                curr_res = curr_res // 2
        # non-local attention block
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        blocks.append(AttnBlock(block_in_ch))
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        # normalise and convert to latent size
        blocks.append(normalize(block_in_ch))
        blocks.append(nn.Conv2d(block_in_ch, emb_dim, 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
 class Generator(nn.Module):
    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
        super().__init__()
        self.nf = nf
        self.ch_mult = ch_mult
        self.num_resolutions = len(self.ch_mult)
        self.num_res_blocks = res_blocks
        self.resolution = img_size
        self.attn_resolutions = attn_resolutions
        self.in_channels = emb_dim
        self.out_channels = 3
        block_in_ch = self.nf * self.ch_mult[-1]
        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
        blocks = []
        # initial conv
        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
        # non-local attention block
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        blocks.append(AttnBlock(block_in_ch))
        blocks.append(ResBlock(block_in_ch, block_in_ch))
        for i in reversed(range(self.num_resolutions)):
            block_out_ch = self.nf * self.ch_mult[i]
            for _ in range(self.num_res_blocks):
                blocks.append(ResBlock(block_in_ch, block_out_ch))
                block_in_ch = block_out_ch
                if curr_res in self.attn_resolutions:
                    blocks.append(AttnBlock(block_in_ch))
            if i != 0:
                blocks.append(Upsample(block_in_ch))
                curr_res = curr_res * 2
        blocks.append(normalize(block_in_ch))
        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=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.nf = nf
        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 or [16]
        self.quantizer_type = quantizer
        self.encoder = Encoder(
            self.in_channels,
            self.nf,
            self.embed_dim,
            self.ch_mult,
            self.n_blocks,
            self.resolution,
            self.attn_resolutions
        )
        if self.quantizer_type == "nearest":
            self.beta = beta #0.25
            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
        elif self.quantizer_type == "gumbel":
            self.gumbel_num_hiddens = emb_dim
            self.straight_through = gumbel_straight_through
            self.kl_weight = gumbel_kl_weight
            self.quantize = GumbelQuantizer(
                self.codebook_size,
                self.embed_dim,
                self.gumbel_num_hiddens,
                self.straight_through,
                self.kl_weight
            )
        self.generator = Generator(
            self.nf,
            self.embed_dim,
            self.ch_mult,
            self.n_blocks,
            self.resolution,
            self.attn_resolutions
        )
        if model_path is not None:
            chkpt = torch.load(model_path, map_location='cpu')
            if 'params_ema' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
            elif 'params' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
                logger.info(f'vqgan is loaded from: {model_path} [params]')
            else:
                raise ValueError('Wrong params!')
    def forward(self, x):
        x = self.encoder(x)
        quant, codebook_loss, quant_stats = self.quantize(x)
        x = self.generator(quant)
        return x, codebook_loss, quant_stats
 # patch based discriminator
@ARCH_REGISTRY.register()
 class VQGANDiscriminator(nn.Module):
    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
        super().__init__()
        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
        ndf_mult = 1
        ndf_mult_prev = 1
        for n in range(1, n_layers):  # gradually increase the number of filters
            ndf_mult_prev = ndf_mult
            ndf_mult = min(2 ** n, 8)
            layers += [
                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
                nn.BatchNorm2d(ndf * ndf_mult),
                nn.LeakyReLU(0.2, True)
            ]
        ndf_mult_prev = ndf_mult
        ndf_mult = min(2 ** n_layers, 8)
        layers += [
            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(ndf * ndf_mult),
            nn.LeakyReLU(0.2, True)
        ]
        layers += [
            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
        self.main = nn.Sequential(*layers)
        if model_path is not None:
            chkpt = torch.load(model_path, map_location='cpu')
            if 'params_d' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
            elif 'params' in chkpt:
                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
            else:
                raise ValueError('Wrong params!')
    def forward(self, x):
        return self.main(x)
@@ -1,132 +1,64 @@
-import os
+from __future__ import annotations
 import logging
 import cv2
 import torch
-import modules.face_restoration
+from modules import (
-import modules.shared
+    devices,
-from modules import shared, devices, modelloader, errors
+    errors,
-from modules.paths import models_path
+    face_restoration,
    face_restoration_utils,
    modelloader,
    shared,
 )
 logger = logging.getLogger(__name__)
 # codeformer people made a choice to include modified basicsr library to their project which makes
 # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
 # I am making a choice to include some files from codeformer to work around this issue.
 model_dir = "Codeformer"
 model_path = os.path.join(models_path, model_dir)
 model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
 model_download_name = 'codeformer-v0.1.0.pth'
-codeformer = None
+# used by e.g. postprocessing_codeformer.py
 codeformer: face_restoration.FaceRestoration | None = None
-def setup_model(dirname):
+class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
-    os.makedirs(model_path, exist_ok=True)
+    def name(self):
        return "CodeFormer"
-    path = modules.paths.paths.get("CodeFormer", None)
+    def load_net(self) -> torch.Module:
-    if path is None:
+        for model_path in modelloader.load_models(
-        return
+            model_path=self.model_path,
            model_url=model_url,
            command_path=self.model_path,
            download_name=model_download_name,
            ext_filter=['.pth'],
        ):
            return modelloader.load_spandrel_model(
                model_path,
                device=devices.device_codeformer,
                expected_architecture='CodeFormer',
            ).model
        raise ValueError("No codeformer model found")
    def get_device(self):
        return devices.device_codeformer
    def restore(self, np_image, w: float | None = None):
        if w is None:
            w = getattr(shared.opts, "code_former_weight", 0.5)
        def restore_face(cropped_face_t):
            assert self.net is not None
            return self.net(cropped_face_t, w=w, adain=True)[0]
        return self.restore_with_helper(np_image, restore_face)
 def setup_model(dirname: str) -> None:
    global codeformer
    try:
        from torchvision.transforms.functional import normalize
        from modules.codeformer.codeformer_arch import CodeFormer
        from basicsr.utils import img2tensor, tensor2img
        from facelib.utils.face_restoration_helper import FaceRestoreHelper
        from facelib.detection.retinaface import retinaface
        net_class = CodeFormer
        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
            def name(self):
                return "CodeFormer"
            def __init__(self, dirname):
                self.net = None
                self.face_helper = None
                self.cmd_dir = dirname
            def create_models(self):
                if self.net is not None and self.face_helper is not None:
                    self.net.to(devices.device_codeformer)
                    return self.net, self.face_helper
                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
                if len(model_paths) != 0:
                    ckpt_path = model_paths[0]
                else:
                    print("Unable to load codeformer model.")
                    return None, None
                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
                checkpoint = torch.load(ckpt_path)['params_ema']
                net.load_state_dict(checkpoint)
                net.eval()
                if hasattr(retinaface, 'device'):
                    retinaface.device = devices.device_codeformer
                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
                self.net = net
                self.face_helper = face_helper
                return net, face_helper
            def send_model_to(self, device):
                self.net.to(device)
                self.face_helper.face_det.to(device)
                self.face_helper.face_parse.to(device)
            def restore(self, np_image, w=None):
                np_image = np_image[:, :, ::-1]
                original_resolution = np_image.shape[0:2]
                self.create_models()
                if self.net is None or self.face_helper is None:
                    return np_image
                self.send_model_to(devices.device_codeformer)
                self.face_helper.clean_all()
                self.face_helper.read_image(np_image)
                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                self.face_helper.align_warp_face()
                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)
                    try:
                        with torch.no_grad():
                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
                        del output
                        devices.torch_gc()
                    except Exception:
                        errors.report('Failed inference for CodeFormer', exc_info=True)
                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
                    restored_face = restored_face.astype('uint8')
                    self.face_helper.add_restored_face(restored_face)
                self.face_helper.get_inverse_affine(None)
                restored_img = self.face_helper.paste_faces_to_input_image()
                restored_img = restored_img[:, :, ::-1]
                if original_resolution != restored_img.shape[0:2]:
                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
                self.face_helper.clean_all()
                if shared.opts.face_restoration_unload:
                    self.send_model_to(devices.cpu)
                return restored_img
        global codeformer
        codeformer = FaceRestorerCodeFormer(dirname)
        shared.face_restorers.append(codeformer)
    except Exception:
        errors.report("Error setting up CodeFormer", exc_info=True)
   # sys.path = stored_sys_path
@@ -0,0 +1,79 @@
 import os
 from modules import modelloader, errors
 from modules.shared import cmd_opts, opts
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 class UpscalerDAT(Upscaler):
    def __init__(self, user_path):
        self.name = "DAT"
        self.user_path = user_path
        self.scalers = []
        super().__init__()
        for file in self.find_models(ext_filter=[".pt", ".pth"]):
            name = modelloader.friendly_name(file)
            scaler_data = UpscalerData(name, file, upscaler=self, scale=None)
            self.scalers.append(scaler_data)
        for model in get_dat_models(self):
            if model.name in opts.dat_enabled_models:
                self.scalers.append(model)
    def do_upscale(self, img, path):
        try:
            info = self.load_model(path)
        except Exception:
            errors.report(f"Unable to load DAT model {path}", exc_info=True)
            return img
        model_descriptor = modelloader.load_spandrel_model(
            info.local_data_path,
            device=self.device,
            prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
            expected_architecture="DAT",
        )
        return upscale_with_model(
            model_descriptor,
            img,
            tile_size=opts.DAT_tile,
            tile_overlap=opts.DAT_tile_overlap,
        )
    def load_model(self, path):
        for scaler in self.scalers:
            if scaler.data_path == path:
                if scaler.local_data_path.startswith("http"):
                    scaler.local_data_path = modelloader.load_file_from_url(
                        scaler.data_path,
                        model_dir=self.model_download_path,
                    )
                if not os.path.exists(scaler.local_data_path):
                    raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
                return scaler
        raise ValueError(f"Unable to find model info: {path}")
 def get_dat_models(scaler):
    return [
        UpscalerData(
            name="DAT x2",
            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
            scale=2,
            upscaler=scaler,
        ),
        UpscalerData(
            name="DAT x3",
            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
            scale=3,
            upscaler=scaler,
        ),
        UpscalerData(
            name="DAT x4",
            path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
            scale=4,
            upscaler=scaler,
        ),
    ]
@@ -4,10 +4,18 @@ from functools import lru_cache
 import torch
 from modules import errors, shared
 from modules import torch_utils
 if sys.platform == "darwin":
    from modules import mac_specific
 if shared.cmd_opts.use_ipex:
    from modules import xpu_specific
 def has_xpu() -> bool:
    return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
 def has_mps() -> bool:
    if sys.platform != "darwin":
@@ -16,6 +24,23 @@ def has_mps() -> bool:
        return mac_specific.has_mps
 def cuda_no_autocast(device_id=None) -> bool:
    if device_id is None:
        device_id = get_cuda_device_id()
    return (
        torch.cuda.get_device_capability(device_id) == (7, 5)
        and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16")
    )
 def get_cuda_device_id():
    return (
        int(shared.cmd_opts.device_id)
        if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit()
        else 0
    ) or torch.cuda.current_device()
 def get_cuda_device_string():
    if shared.cmd_opts.device_id is not None:
        return f"cuda:{shared.cmd_opts.device_id}"
@@ -30,6 +55,9 @@ def get_optimal_device_name():
    if has_mps():
        return "mps"
    if has_xpu():
        return xpu_specific.get_xpu_device_string()
    return "cpu"
@@ -38,7 +66,7 @@ def get_optimal_device():
 def get_device_for(task):
-    if task in shared.cmd_opts.use_cpu:
+    if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
        return cpu
    return get_optimal_device()
@@ -54,14 +82,16 @@ def torch_gc():
    if has_mps():
        mac_specific.torch_mps_gc()
    if has_xpu():
        xpu_specific.torch_xpu_gc()
 def enable_tf32():
    if torch.cuda.is_available():
        # 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
-        device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
+        if cuda_no_autocast():
        if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
            torch.backends.cudnn.benchmark = True
        torch.backends.cuda.matmul.allow_tf32 = True
@@ -71,6 +101,7 @@ def enable_tf32():
 errors.run(enable_tf32, "Enabling TF32")
 cpu: torch.device = torch.device("cpu")
 fp8: bool = False
 device: torch.device = None
 device_interrogate: torch.device = None
 device_gfpgan: torch.device = None
@@ -79,6 +110,7 @@ device_codeformer: torch.device = None
 dtype: torch.dtype = torch.float16
 dtype_vae: torch.dtype = torch.float16
 dtype_unet: torch.dtype = torch.float16
 dtype_inference: torch.dtype = torch.float16
 unet_needs_upcast = False
@@ -91,15 +123,84 @@ def cond_cast_float(input):
 nv_rng = None
 patch_module_list = [
    torch.nn.Linear,
    torch.nn.Conv2d,
    torch.nn.MultiheadAttention,
    torch.nn.GroupNorm,
    torch.nn.LayerNorm,
 ]
 def manual_cast_forward(target_dtype):
    def forward_wrapper(self, *args, **kwargs):
        if any(
            isinstance(arg, torch.Tensor) and arg.dtype != target_dtype
            for arg in args
        ):
            args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
            kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
        org_dtype = torch_utils.get_param(self).dtype
        if org_dtype != target_dtype:
            self.to(target_dtype)
        result = self.org_forward(*args, **kwargs)
        if org_dtype != target_dtype:
            self.to(org_dtype)
        if target_dtype != dtype_inference:
            if isinstance(result, tuple):
                result = tuple(
                    i.to(dtype_inference)
                    if isinstance(i, torch.Tensor)
                    else i
                    for i in result
                )
            elif isinstance(result, torch.Tensor):
                result = result.to(dtype_inference)
        return result
    return forward_wrapper
@contextlib.contextmanager
 def manual_cast(target_dtype):
    applied = False
    for module_type in patch_module_list:
        if hasattr(module_type, "org_forward"):
            continue
        applied = True
        org_forward = module_type.forward
        if module_type == torch.nn.MultiheadAttention and has_xpu():
            module_type.forward = manual_cast_forward(torch.float32)
        else:
            module_type.forward = manual_cast_forward(target_dtype)
        module_type.org_forward = org_forward
    try:
        yield None
    finally:
        if applied:
            for module_type in patch_module_list:
                if hasattr(module_type, "org_forward"):
                    module_type.forward = module_type.org_forward
                    delattr(module_type, "org_forward")
 def autocast(disable=False):
    if disable:
        return contextlib.nullcontext()
-    if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+    if fp8 and device==cpu:
        return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
    if fp8 and dtype_inference == torch.float32:
        return manual_cast(dtype)
    if dtype == torch.float32 or dtype_inference == torch.float32:
        return contextlib.nullcontext()
    if has_xpu() or has_mps() or cuda_no_autocast():
        return manual_cast(dtype)
    return torch.autocast("cuda")
@@ -6,6 +6,21 @@ import traceback
 exception_records = []
 def format_traceback(tb):
    return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
 def format_exception(e, tb):
    return {"exception": str(e), "traceback": format_traceback(tb)}
 def get_exceptions():
    try:
        return list(reversed(exception_records))
    except Exception as e:
        return str(e)
 def record_exception():
    _, e, tb = sys.exc_info()
    if e is None:
@@ -14,8 +29,7 @@ def record_exception():
    if exception_records and exception_records[-1] == e:
        return
-    from modules import sysinfo
+    exception_records.append(format_exception(e, tb))
    exception_records.append(sysinfo.format_exception(e, tb))
    if len(exception_records) > 5:
        exception_records.pop(0)
@@ -93,8 +107,8 @@ def check_versions():
    import torch
    import gradio
-    expected_torch_version = "2.0.0"
+    expected_torch_version = "2.1.2"
-    expected_xformers_version = "0.0.20"
+    expected_xformers_version = "0.0.23.post1"
    expected_gradio_version = "3.41.2"
    if version.parse(torch.__version__) < version.parse(expected_torch_version):
@@ -1,121 +1,7 @@
-import sys
+from modules import modelloader, devices, errors
 import numpy as np
 import torch
 from PIL import Image
 import modules.esrgan_model_arch as arch
 from modules import modelloader, images, devices
 from modules.shared import opts
 from modules.upscaler import Upscaler, UpscalerData
-
+from modules.upscaler_utils import upscale_with_model
 def mod2normal(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    if 'conv_first.weight' in state_dict:
        crt_net = {}
        items = list(state_dict)
        crt_net['model.0.weight'] = state_dict['conv_first.weight']
        crt_net['model.0.bias'] = state_dict['conv_first.bias']
        for k in items.copy():
            if 'RDB' in k:
                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
                if '.weight' in k:
                    ori_k = ori_k.replace('.weight', '.0.weight')
                elif '.bias' in k:
                    ori_k = ori_k.replace('.bias', '.0.bias')
                crt_net[ori_k] = state_dict[k]
                items.remove(k)
        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
        crt_net['model.3.weight'] = state_dict['upconv1.weight']
        crt_net['model.3.bias'] = state_dict['upconv1.bias']
        crt_net['model.6.weight'] = state_dict['upconv2.weight']
        crt_net['model.6.bias'] = state_dict['upconv2.bias']
        crt_net['model.8.weight'] = state_dict['HRconv.weight']
        crt_net['model.8.bias'] = state_dict['HRconv.bias']
        crt_net['model.10.weight'] = state_dict['conv_last.weight']
        crt_net['model.10.bias'] = state_dict['conv_last.bias']
        state_dict = crt_net
    return state_dict
 def resrgan2normal(state_dict, nb=23):
    # this code is copied from https://github.com/victorca25/iNNfer
    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
        re8x = 0
        crt_net = {}
        items = list(state_dict)
        crt_net['model.0.weight'] = state_dict['conv_first.weight']
        crt_net['model.0.bias'] = state_dict['conv_first.bias']
        for k in items.copy():
            if "rdb" in k:
                ori_k = k.replace('body.', 'model.1.sub.')
                ori_k = ori_k.replace('.rdb', '.RDB')
                if '.weight' in k:
                    ori_k = ori_k.replace('.weight', '.0.weight')
                elif '.bias' in k:
                    ori_k = ori_k.replace('.bias', '.0.bias')
                crt_net[ori_k] = state_dict[k]
                items.remove(k)
        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
        if 'conv_up3.weight' in state_dict:
            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
            re8x = 3
            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
        state_dict = crt_net
    return state_dict
 def infer_params(state_dict):
    # this code is copied from https://github.com/victorca25/iNNfer
    scale2x = 0
    scalemin = 6
    n_uplayer = 0
    plus = False
    for block in list(state_dict):
        parts = block.split(".")
        n_parts = len(parts)
        if n_parts == 5 and parts[2] == "sub":
            nb = int(parts[3])
        elif n_parts == 3:
            part_num = int(parts[1])
            if (part_num > scalemin
                and parts[0] == "model"
                and parts[2] == "weight"):
                scale2x += 1
            if part_num > n_uplayer:
                n_uplayer = part_num
                out_nc = state_dict[block].shape[0]
        if not plus and "conv1x1" in block:
            plus = True
    nf = state_dict["model.0.weight"].shape[0]
    in_nc = state_dict["model.0.weight"].shape[1]
    out_nc = out_nc
    scale = 2 ** scale2x
    return in_nc, out_nc, nf, nb, plus, scale
 class UpscalerESRGAN(Upscaler):
@@ -143,12 +29,11 @@ class UpscalerESRGAN(Upscaler):
    def do_upscale(self, img, selected_model):
        try:
            model = self.load_model(selected_model)
-        except Exception as e:
+        except Exception:
-            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
+            errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True)
            return img
        model.to(devices.device_esrgan)
-        img = esrgan_upscale(model, img)
+        return esrgan_upscale(model, img)
        return img
    def load_model(self, path: str):
        if path.startswith("http"):
@@ -161,69 +46,17 @@ class UpscalerESRGAN(Upscaler):
        else:
            filename = path
-        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
+        return modelloader.load_spandrel_model(
-
+            filename,
-        if "params_ema" in state_dict:
+            device=('cpu' if devices.device_esrgan.type == 'mps' else None),
-            state_dict = state_dict["params_ema"]
+            expected_architecture='ESRGAN',
-        elif "params" in state_dict:
+        )
            state_dict = state_dict["params"]
            num_conv = 16 if "realesr-animevideov3" in filename else 32
            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
            model.load_state_dict(state_dict)
            model.eval()
            return model
        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
            state_dict = resrgan2normal(state_dict, nb)
        elif "conv_first.weight" in state_dict:
            state_dict = mod2normal(state_dict)
        elif "model.0.weight" not in state_dict:
            raise Exception("The file is not a recognized ESRGAN model.")
        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
        model.load_state_dict(state_dict)
        model.eval()
        return model
 def upscale_without_tiling(model, img):
    img = np.array(img)
    img = img[:, :, ::-1]
    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
    img = torch.from_numpy(img).float()
    img = img.unsqueeze(0).to(devices.device_esrgan)
    with torch.no_grad():
        output = model(img)
    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = 255. * np.moveaxis(output, 0, 2)
    output = output.astype(np.uint8)
    output = output[:, :, ::-1]
    return Image.fromarray(output, 'RGB')
 def esrgan_upscale(model, img):
-    if opts.ESRGAN_tile == 0:
+    return upscale_with_model(
-        return upscale_without_tiling(model, img)
+        model,
-
+        img,
-    grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
+        tile_size=opts.ESRGAN_tile,
-    newtiles = []
+        tile_overlap=opts.ESRGAN_tile_overlap,
-    scale_factor = 1
+    )
    for y, h, row in grid.tiles:
        newrow = []
        for tiledata in row:
            x, w, tile = tiledata
            output = upscale_without_tiling(model, tile)
            scale_factor = output.width // tile.width
            newrow.append([x * scale_factor, w * scale_factor, output])
        newtiles.append([y * scale_factor, h * scale_factor, newrow])
    newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
    output = images.combine_grid(newgrid)
    return output
@@ -1,465 +0,0 @@
 # this file is adapted from https://github.com/victorca25/iNNfer
 from collections import OrderedDict
 import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 ####################
 # RRDBNet Generator
 ####################
 class RRDBNet(nn.Module):
    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
            finalact=None, gaussian_noise=False, plus=False):
        super(RRDBNet, self).__init__()
        n_upscale = int(math.log(upscale, 2))
        if upscale == 3:
            n_upscale = 1
        self.resrgan_scale = 0
        if in_nc % 16 == 0:
            self.resrgan_scale = 1
        elif in_nc != 4 and in_nc % 4 == 0:
            self.resrgan_scale = 2
        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
        if upsample_mode == 'upconv':
            upsample_block = upconv_block
        elif upsample_mode == 'pixelshuffle':
            upsample_block = pixelshuffle_block
        else:
            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
        if upscale == 3:
            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
        else:
            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
        outact = act(finalact) if finalact else None
        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
            *upsampler, HR_conv0, HR_conv1, outact)
    def forward(self, x, outm=None):
        if self.resrgan_scale == 1:
            feat = pixel_unshuffle(x, scale=4)
        elif self.resrgan_scale == 2:
            feat = pixel_unshuffle(x, scale=2)
        else:
            feat = x
        return self.model(feat)
 class RRDB(nn.Module):
    """
    Residual in Residual Dense Block
    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
    """
    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
            spectral_norm=False, gaussian_noise=False, plus=False):
        super(RRDB, self).__init__()
        # This is for backwards compatibility with existing models
        if nr == 3:
            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
                    gaussian_noise=gaussian_noise, plus=plus)
            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
                    gaussian_noise=gaussian_noise, plus=plus)
            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
                    gaussian_noise=gaussian_noise, plus=plus)
        else:
            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
            self.RDBs = nn.Sequential(*RDB_list)
    def forward(self, x):
        if hasattr(self, 'RDB1'):
            out = self.RDB1(x)
            out = self.RDB2(out)
            out = self.RDB3(out)
        else:
            out = self.RDBs(x)
        return out * 0.2 + x
 class ResidualDenseBlock_5C(nn.Module):
    """
    Residual Dense Block
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    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.
            {Rakotonirina} and A. {Rasoanaivo}
    """
    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
            spectral_norm=False, gaussian_noise=False, plus=False):
        super(ResidualDenseBlock_5C, self).__init__()
        self.noise = GaussianNoise() if gaussian_noise else None
        self.conv1x1 = conv1x1(nf, gc) if plus else None
        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
            spectral_norm=spectral_norm)
        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
            spectral_norm=spectral_norm)
        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
            spectral_norm=spectral_norm)
        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
            spectral_norm=spectral_norm)
        if mode == 'CNA':
            last_act = None
        else:
            last_act = act_type
        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
            spectral_norm=spectral_norm)
    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        if self.conv1x1:
            x2 = x2 + self.conv1x1(x)
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        if self.conv1x1:
            x4 = x4 + x2
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        if self.noise:
            return self.noise(x5.mul(0.2) + x)
        else:
            return x5 * 0.2 + x
 ####################
 # ESRGANplus
 ####################
 class GaussianNoise(nn.Module):
    def __init__(self, sigma=0.1, is_relative_detach=False):
        super().__init__()
        self.sigma = sigma
        self.is_relative_detach = is_relative_detach
        self.noise = torch.tensor(0, dtype=torch.float)
    def forward(self, x):
        if self.training and self.sigma != 0:
            self.noise = self.noise.to(x.device)
            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
 def conv1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
 ####################
 # SRVGGNetCompact
 ####################
 class SRVGGNetCompact(nn.Module):
    """A compact VGG-style network structure for super-resolution.
    This class is copied from https://github.com/xinntao/Real-ESRGAN
    """
    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
        super(SRVGGNetCompact, self).__init__()
        self.num_in_ch = num_in_ch
        self.num_out_ch = num_out_ch
        self.num_feat = num_feat
        self.num_conv = num_conv
        self.upscale = upscale
        self.act_type = act_type
        self.body = nn.ModuleList()
        # the first conv
        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
        # the first activation
        if act_type == 'relu':
            activation = nn.ReLU(inplace=True)
        elif act_type == 'prelu':
            activation = nn.PReLU(num_parameters=num_feat)
        elif act_type == 'leakyrelu':
            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.body.append(activation)
        # the body structure
        for _ in range(num_conv):
            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
            # activation
            if act_type == 'relu':
                activation = nn.ReLU(inplace=True)
            elif act_type == 'prelu':
                activation = nn.PReLU(num_parameters=num_feat)
            elif act_type == 'leakyrelu':
                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
            self.body.append(activation)
        # the last conv
        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
        # upsample
        self.upsampler = nn.PixelShuffle(upscale)
    def forward(self, x):
        out = x
        for i in range(0, len(self.body)):
            out = self.body[i](out)
        out = self.upsampler(out)
        # add the nearest upsampled image, so that the network learns the residual
        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
        out += base
        return out
 ####################
 # Upsampler
 ####################
 class Upsample(nn.Module):
    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
    The input data is assumed to be of the form
    `minibatch x channels x [optional depth] x [optional height] x width`.
    """
    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
        super(Upsample, self).__init__()
        if isinstance(scale_factor, tuple):
            self.scale_factor = tuple(float(factor) for factor in scale_factor)
        else:
            self.scale_factor = float(scale_factor) if scale_factor else None
        self.mode = mode
        self.size = size
        self.align_corners = align_corners
    def forward(self, x):
        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
    def extra_repr(self):
        if self.scale_factor is not None:
            info = f'scale_factor={self.scale_factor}'
        else:
            info = f'size={self.size}'
        info += f', mode={self.mode}'
        return info
 def pixel_unshuffle(x, scale):
    """ Pixel unshuffle.
    Args:
        x (Tensor): Input feature with shape (b, c, hh, hw).
        scale (int): Downsample ratio.
    Returns:
        Tensor: the pixel unshuffled feature.
    """
    b, c, hh, hw = x.size()
    out_channel = c * (scale**2)
    assert hh % scale == 0 and hw % scale == 0
    h = hh // scale
    w = hw // scale
    x_view = x.view(b, c, h, scale, w, scale)
    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
 def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
    """
    Pixel shuffle layer
    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
    Neural Network, CVPR17)
    """
    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
    pixel_shuffle = nn.PixelShuffle(upscale_factor)
    n = norm(norm_type, out_nc) if norm_type else None
    a = act(act_type) if act_type else None
    return sequential(conv, pixel_shuffle, n, a)
 def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
    """ Upconv layer """
    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
    upsample = Upsample(scale_factor=upscale_factor, mode=mode)
    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
    return sequential(upsample, conv)
 ####################
 # Basic blocks
 ####################
 def make_layer(basic_block, num_basic_block, **kwarg):
    """Make layers by stacking the same blocks.
    Args:
        basic_block (nn.module): nn.module class for basic block. (block)
        num_basic_block (int): number of blocks. (n_layers)
    Returns:
        nn.Sequential: Stacked blocks in nn.Sequential.
    """
    layers = []
    for _ in range(num_basic_block):
        layers.append(basic_block(**kwarg))
    return nn.Sequential(*layers)
 def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
    """ activation helper """
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU(inplace)
    elif act_type in ('leakyrelu', 'lrelu'):
        layer = nn.LeakyReLU(neg_slope, inplace)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    elif act_type == 'tanh':  # [-1, 1] range output
        layer = nn.Tanh()
    elif act_type == 'sigmoid':  # [0, 1] range output
        layer = nn.Sigmoid()
    else:
        raise NotImplementedError(f'activation layer [{act_type}] is not found')
    return layer
 class Identity(nn.Module):
    def __init__(self, *kwargs):
        super(Identity, self).__init__()
    def forward(self, x, *kwargs):
        return x
 def norm(norm_type, nc):
    """ Return a normalization layer """
    norm_type = norm_type.lower()
    if norm_type == 'batch':
        layer = nn.BatchNorm2d(nc, affine=True)
    elif norm_type == 'instance':
        layer = nn.InstanceNorm2d(nc, affine=False)
    elif norm_type == 'none':
        def norm_layer(x): return Identity()
    else:
        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
    return layer
 def pad(pad_type, padding):
    """ padding layer helper """
    pad_type = pad_type.lower()
    if padding == 0:
        return None
    if pad_type == 'reflect':
        layer = nn.ReflectionPad2d(padding)
    elif pad_type == 'replicate':
        layer = nn.ReplicationPad2d(padding)
    elif pad_type == 'zero':
        layer = nn.ZeroPad2d(padding)
    else:
        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
    return layer
 def get_valid_padding(kernel_size, dilation):
    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
    padding = (kernel_size - 1) // 2
    return padding
 class ShortcutBlock(nn.Module):
    """ Elementwise sum the output of a submodule to its input """
    def __init__(self, submodule):
        super(ShortcutBlock, self).__init__()
        self.sub = submodule
    def forward(self, x):
        output = x + self.sub(x)
        return output
    def __repr__(self):
        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
 def sequential(*args):
    """ Flatten Sequential. It unwraps nn.Sequential. """
    if len(args) == 1:
        if isinstance(args[0], OrderedDict):
            raise NotImplementedError('sequential does not support OrderedDict input.')
        return args[0]  # No sequential is needed.
    modules = []
    for module in args:
        if isinstance(module, nn.Sequential):
            for submodule in module.children():
                modules.append(submodule)
        elif isinstance(module, nn.Module):
            modules.append(module)
    return nn.Sequential(*modules)
 def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
               spectral_norm=False):
    """ Conv layer with padding, normalization, activation """
    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
    padding = get_valid_padding(kernel_size, dilation)
    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
    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':
        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
    else:
        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
    if spectral_norm:
        c = nn.utils.spectral_norm(c)
    a = act(act_type) if act_type else None
    if 'CNA' in mode:
        n = norm(norm_type, out_nc) if norm_type else None
        return sequential(p, c, n, a)
    elif mode == 'NAC':
        if norm_type is None and act_type is not None:
            a = act(act_type, inplace=False)
        n = norm(norm_type, in_nc) if norm_type else None
        return sequential(n, a, p, c)
@@ -1,11 +1,14 @@
 from __future__ import annotations
 import configparser
 import os
 import threading
 import re
 from modules import shared, errors, cache, scripts
 from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 extensions = []
 os.makedirs(extensions_dir, exist_ok=True)
@@ -19,11 +22,56 @@ def active():
        return [x for x in extensions if x.enabled]
 class ExtensionMetadata:
    filename = "metadata.ini"
    config: configparser.ConfigParser
    canonical_name: str
    requires: list
    def __init__(self, path, canonical_name):
        self.config = configparser.ConfigParser()
        filepath = os.path.join(path, self.filename)
        # `self.config.read()` will quietly swallow OSErrors (which FileNotFoundError is),
        # so no need to check whether the file exists beforehand.
        try:
            self.config.read(filepath)
        except Exception:
            errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
        self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
        self.canonical_name = canonical_name.lower().strip()
        self.requires = self.get_script_requirements("Requires", "Extension")
    def get_script_requirements(self, field, section, extra_section=None):
        """reads a list of requirements from the config; field is the name of the field in the ini file,
        like Requires or Before, and section is the name of the [section] in the ini file; additionally,
        reads more requirements from [extra_section] if specified."""
        x = self.config.get(section, field, fallback='')
        if extra_section:
            x = x + ', ' + self.config.get(extra_section, field, fallback='')
        return self.parse_list(x.lower())
    def parse_list(self, text):
        """converts a line from config ("ext1 ext2, ext3  ") into a python list (["ext1", "ext2", "ext3"])"""
        if not text:
            return []
        # both "," and " " are accepted as separator
        return [x for x in re.split(r"[,\s]+", text.strip()) if x]
 class Extension:
    lock = threading.Lock()
    cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
    metadata: ExtensionMetadata
-    def __init__(self, name, path, enabled=True, is_builtin=False):
+    def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None):
        self.name = name
        self.path = path
        self.enabled = enabled
@@ -36,6 +84,8 @@ class Extension:
        self.branch = None
        self.remote = None
        self.have_info_from_repo = False
        self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower())
        self.canonical_name = metadata.canonical_name
    def to_dict(self):
        return {x: getattr(self, x) for x in self.cached_fields}
@@ -56,6 +106,7 @@ class Extension:
                self.do_read_info_from_repo()
                return self.to_dict()
        try:
            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
            self.from_dict(d)
@@ -136,9 +187,6 @@ class Extension:
 def list_extensions():
    extensions.clear()
    if not os.path.isdir(extensions_dir):
        return
    if shared.cmd_opts.disable_all_extensions:
        print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
    elif shared.opts.disable_all_extensions == "all":
@@ -148,18 +196,46 @@ def list_extensions():
    elif shared.opts.disable_all_extensions == "extra":
        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
-    extension_paths = []
+    loaded_extensions = {}
-    for dirname in [extensions_dir, extensions_builtin_dir]:
+
    # scan through extensions directory and load metadata
    for dirname in [extensions_builtin_dir, extensions_dir]:
        if not os.path.isdir(dirname):
-            return
+            continue
        for extension_dirname in sorted(os.listdir(dirname)):
            path = os.path.join(dirname, extension_dirname)
            if not os.path.isdir(path):
                continue
-            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+            canonical_name = extension_dirname
            metadata = ExtensionMetadata(path, canonical_name)
-    for dirname, path, is_builtin in extension_paths:
+            # check for duplicated canonical names
-        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
+            already_loaded_extension = loaded_extensions.get(metadata.canonical_name)
-        extensions.append(extension)
+            if already_loaded_extension is not None:
                errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False)
                continue
            is_builtin = dirname == extensions_builtin_dir
            extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata)
            extensions.append(extension)
            loaded_extensions[canonical_name] = extension
    # check for requirements
    for extension in extensions:
        if not extension.enabled:
            continue
        for req in extension.metadata.requires:
            required_extension = loaded_extensions.get(req)
            if required_extension is None:
                errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
                continue
            if not required_extension.enabled:
                errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
                continue
 extensions: list[Extension] = []
@@ -206,7 +206,7 @@ def parse_prompts(prompts):
    return res, extra_data
-def get_user_metadata(filename):
+def get_user_metadata(filename, lister=None):
    if filename is None:
        return {}
@@ -215,7 +215,8 @@ def get_user_metadata(filename):
    metadata = {}
    try:
-        if os.path.isfile(metadata_filename):
+        exists = lister.exists(metadata_filename) if lister else os.path.exists(metadata_filename)
        if exists:
            with open(metadata_filename, "r", encoding="utf8") as file:
                metadata = json.load(file)
    except Exception as e:
@@ -0,0 +1,180 @@
 from __future__ import annotations
 import logging
 import os
 from functools import cached_property
 from typing import TYPE_CHECKING, Callable
 import cv2
 import numpy as np
 import torch
 from modules import devices, errors, face_restoration, shared
 if TYPE_CHECKING:
    from facexlib.utils.face_restoration_helper import FaceRestoreHelper
 logger = logging.getLogger(__name__)
 def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor:
    """Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor."""
    assert img.shape[2] == 3, "image must be RGB"
    if img.dtype == "float64":
        img = img.astype("float32")
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return torch.from_numpy(img.transpose(2, 0, 1)).float()
 def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray:
    """
    Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range.
    """
    tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])
    assert tensor.dim() == 3, "tensor must be RGB"
    img_np = tensor.numpy().transpose(1, 2, 0)
    if img_np.shape[2] == 1:  # gray image, no RGB/BGR required
        return np.squeeze(img_np, axis=2)
    return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
 def create_face_helper(device) -> FaceRestoreHelper:
    from facexlib.detection import retinaface
    from facexlib.utils.face_restoration_helper import FaceRestoreHelper
    if hasattr(retinaface, 'device'):
        retinaface.device = device
    return FaceRestoreHelper(
        upscale_factor=1,
        face_size=512,
        crop_ratio=(1, 1),
        det_model='retinaface_resnet50',
        save_ext='png',
        use_parse=True,
        device=device,
    )
 def restore_with_face_helper(
    np_image: np.ndarray,
    face_helper: FaceRestoreHelper,
    restore_face: Callable[[torch.Tensor], torch.Tensor],
 ) -> np.ndarray:
    """
    Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image.
    `restore_face` should take a cropped face image and return a restored face image.
    """
    from torchvision.transforms.functional import normalize
    np_image = np_image[:, :, ::-1]
    original_resolution = np_image.shape[0:2]
    try:
        logger.debug("Detecting faces...")
        face_helper.clean_all()
        face_helper.read_image(np_image)
        face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
        face_helper.align_warp_face()
        logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces))
        for cropped_face in face_helper.cropped_faces:
            cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0)
            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)
            try:
                with torch.no_grad():
                    cropped_face_t = restore_face(cropped_face_t)
                devices.torch_gc()
            except Exception:
                errors.report('Failed face-restoration inference', exc_info=True)
            restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1))
            restored_face = (restored_face * 255.0).astype('uint8')
            face_helper.add_restored_face(restored_face)
        logger.debug("Merging restored faces into image")
        face_helper.get_inverse_affine(None)
        img = face_helper.paste_faces_to_input_image()
        img = img[:, :, ::-1]
        if original_resolution != img.shape[0:2]:
            img = cv2.resize(
                img,
                (0, 0),
                fx=original_resolution[1] / img.shape[1],
                fy=original_resolution[0] / img.shape[0],
                interpolation=cv2.INTER_LINEAR,
            )
        logger.debug("Face restoration complete")
    finally:
        face_helper.clean_all()
    return img
 class CommonFaceRestoration(face_restoration.FaceRestoration):
    net: torch.Module | None
    model_url: str
    model_download_name: str
    def __init__(self, model_path: str):
        super().__init__()
        self.net = None
        self.model_path = model_path
        os.makedirs(model_path, exist_ok=True)
    @cached_property
    def face_helper(self) -> FaceRestoreHelper:
        return create_face_helper(self.get_device())
    def send_model_to(self, device):
        if self.net:
            logger.debug("Sending %s to %s", self.net, device)
            self.net.to(device)
        if self.face_helper:
            logger.debug("Sending face helper to %s", device)
            self.face_helper.face_det.to(device)
            self.face_helper.face_parse.to(device)
    def get_device(self):
        raise NotImplementedError("get_device must be implemented by subclasses")
    def load_net(self) -> torch.Module:
        raise NotImplementedError("load_net must be implemented by subclasses")
    def restore_with_helper(
        self,
        np_image: np.ndarray,
        restore_face: Callable[[torch.Tensor], torch.Tensor],
    ) -> np.ndarray:
        try:
            if self.net is None:
                self.net = self.load_net()
        except Exception:
            logger.warning("Unable to load face-restoration model", exc_info=True)
            return np_image
        try:
            self.send_model_to(self.get_device())
            return restore_with_face_helper(np_image, self.face_helper, restore_face)
        finally:
            if shared.opts.face_restoration_unload:
                self.send_model_to(devices.cpu)
 def patch_facexlib(dirname: str) -> None:
    import facexlib.detection
    import facexlib.parsing
    det_facex_load_file_from_url = facexlib.detection.load_file_from_url
    par_facex_load_file_from_url = facexlib.parsing.load_file_from_url
    def update_kwargs(kwargs):
        return dict(kwargs, save_dir=dirname, model_dir=None)
    def facex_load_file_from_url(**kwargs):
        return det_facex_load_file_from_url(**update_kwargs(kwargs))
    def facex_load_file_from_url2(**kwargs):
        return par_facex_load_file_from_url(**update_kwargs(kwargs))
    facexlib.detection.load_file_from_url = facex_load_file_from_url
    facexlib.parsing.load_file_from_url = facex_load_file_from_url2
@@ -1,110 +1,71 @@
 from __future__ import annotations
 import logging
 import os
-import facexlib
+import torch
 import gfpgan
-import modules.face_restoration
+from modules import (
-from modules import paths, shared, devices, modelloader, errors
+    devices,
    errors,
    face_restoration,
    face_restoration_utils,
    modelloader,
    shared,
 )
-model_dir = "GFPGAN"
+logger = logging.getLogger(__name__)
 user_path = None
 model_path = os.path.join(paths.models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
-have_gfpgan = False
+model_download_name = "GFPGANv1.4.pth"
-loaded_gfpgan_model = None
+gfpgan_face_restorer: face_restoration.FaceRestoration | None = None
-def gfpgann():
+class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
-    global loaded_gfpgan_model
+    def name(self):
-    global model_path
+        return "GFPGAN"
    if loaded_gfpgan_model is not None:
        loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
        return loaded_gfpgan_model
-    if gfpgan_constructor is None:
+    def get_device(self):
-        return None
+        return devices.device_gfpgan
-    models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
+    def load_net(self) -> torch.Module:
-    if len(models) == 1 and models[0].startswith("http"):
+        for model_path in modelloader.load_models(
-        model_file = models[0]
+            model_path=self.model_path,
-    elif len(models) != 0:
+            model_url=model_url,
-        latest_file = max(models, key=os.path.getctime)
+            command_path=self.model_path,
-        model_file = latest_file
+            download_name=model_download_name,
-    else:
+            ext_filter=['.pth'],
-        print("Unable to load gfpgan model!")
+        ):
-        return None
+            if 'GFPGAN' in os.path.basename(model_path):
-    if hasattr(facexlib.detection.retinaface, 'device'):
+                model = modelloader.load_spandrel_model(
-        facexlib.detection.retinaface.device = devices.device_gfpgan
+                    model_path,
-    model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
+                    device=self.get_device(),
-    loaded_gfpgan_model = model
+                    expected_architecture='GFPGAN',
                ).model
                model.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
                return model
        raise ValueError("No GFPGAN model found")
-    return model
+    def restore(self, np_image):
        def restore_face(cropped_face_t):
            assert self.net is not None
            return self.net(cropped_face_t, return_rgb=False)[0]
-
+        return self.restore_with_helper(np_image, restore_face)
 def send_model_to(model, device):
    model.gfpgan.to(device)
    model.face_helper.face_det.to(device)
    model.face_helper.face_parse.to(device)
 def gfpgan_fix_faces(np_image):
-    model = gfpgann()
+    if gfpgan_face_restorer:
-    if model is None:
+        return gfpgan_face_restorer.restore(np_image)
-        return np_image
+    logger.warning("GFPGAN face restorer not set up")
    send_model_to(model, devices.device_gfpgan)
    np_image_bgr = np_image[:, :, ::-1]
    cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
    np_image = gfpgan_output_bgr[:, :, ::-1]
    model.face_helper.clean_all()
    if shared.opts.face_restoration_unload:
        send_model_to(model, devices.cpu)
    return np_image
-gfpgan_constructor = None
+def setup_model(dirname: str) -> None:
    global gfpgan_face_restorer
 def setup_model(dirname):
    try:
-        os.makedirs(model_path, exist_ok=True)
+        face_restoration_utils.patch_facexlib(dirname)
-        from gfpgan import GFPGANer
+        gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname)
-        from facexlib import detection, parsing  # noqa: F401
+        shared.face_restorers.append(gfpgan_face_restorer)
        global user_path
        global have_gfpgan
        global gfpgan_constructor
        load_file_from_url_orig = gfpgan.utils.load_file_from_url
        facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
        facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
        def my_load_file_from_url(**kwargs):
            return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
        def facex_load_file_from_url(**kwargs):
            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
        def facex_load_file_from_url2(**kwargs):
            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
        gfpgan.utils.load_file_from_url = my_load_file_from_url
        facexlib.detection.load_file_from_url = facex_load_file_from_url
        facexlib.parsing.load_file_from_url = facex_load_file_from_url2
        user_path = dirname
        have_gfpgan = True
        gfpgan_constructor = GFPGANer
        class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
            def name(self):
                return "GFPGAN"
            def restore(self, np_image):
                return gfpgan_fix_faces(np_image)
        shared.face_restorers.append(FaceRestorerGFPGAN())
    except Exception:
        errors.report("Error setting up GFPGAN", exc_info=True)
@@ -47,10 +47,20 @@ def Block_get_config(self):
 def BlockContext_init(self, *args, **kwargs):
    if scripts.scripts_current is not None:
        scripts.scripts_current.before_component(self, **kwargs)
    scripts.script_callbacks.before_component_callback(self, **kwargs)
    res = original_BlockContext_init(self, *args, **kwargs)
    add_classes_to_gradio_component(self)
    scripts.script_callbacks.after_component_callback(self, **kwargs)
    if scripts.scripts_current is not None:
        scripts.scripts_current.after_component(self, **kwargs)
    return res
@@ -0,0 +1,43 @@
 import os
 import sys
 from modules import modelloader, devices
 from modules.shared import opts
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 class UpscalerHAT(Upscaler):
    def __init__(self, dirname):
        self.name = "HAT"
        self.scalers = []
        self.user_path = dirname
        super().__init__()
        for file in self.find_models(ext_filter=[".pt", ".pth"]):
            name = modelloader.friendly_name(file)
            scale = 4  # TODO: scale might not be 4, but we can't know without loading the model
            scaler_data = UpscalerData(name, file, upscaler=self, scale=scale)
            self.scalers.append(scaler_data)
    def do_upscale(self, img, selected_model):
        try:
            model = self.load_model(selected_model)
        except Exception as e:
            print(f"Unable to load HAT model {selected_model}: {e}", file=sys.stderr)
            return img
        model.to(devices.device_esrgan)  # TODO: should probably be device_hat
        return upscale_with_model(
            model,
            img,
            tile_size=opts.ESRGAN_tile,  # TODO: should probably be HAT_tile
            tile_overlap=opts.ESRGAN_tile_overlap,  # TODO: should probably be HAT_tile_overlap
        )
    def load_model(self, path: str):
        if not os.path.isfile(path):
            raise FileNotFoundError(f"Model file {path} not found")
        return modelloader.load_spandrel_model(
            path,
            device=devices.device_esrgan,  # TODO: should probably be device_hat
            expected_architecture='HAT',
        )
@@ -61,12 +61,17 @@ def image_grid(imgs, batch_size=1, rows=None):
    return grid
-Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
+class Grid(namedtuple("_Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])):
    @property
    def tile_count(self) -> int:
        """
        The total number of tiles in the grid.
        """
        return sum(len(row[2]) for row in self.tiles)
-def split_grid(image, tile_w=512, tile_h=512, overlap=64):
+def split_grid(image: Image.Image, tile_w: int = 512, tile_h: int = 512, overlap: int = 64) -> Grid:
-    w = image.width
+    w, h = image.size
    h = image.height
    non_overlap_width = tile_w - overlap
    non_overlap_height = tile_h - overlap
@@ -316,7 +321,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):
    return res
-invalid_filename_chars = '<>:"/\\|?*\n\r\t'
+invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
 invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@@ -791,3 +796,4 @@ def flatten(img, bgcolor):
        img = background
    return img.convert('RGB')
@@ -7,7 +7,7 @@ from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageErr
 import gradio as gr
 from modules import images as imgutil
-from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
+from modules.infotext_utils import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 from modules.sd_models import get_closet_checkpoint_match
@@ -44,12 +44,14 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
    steps = p.steps
    override_settings = p.override_settings
    sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
    batch_results = None
    discard_further_results = False
    for i, image in enumerate(images):
        state.job = f"{i+1} out of {len(images)}"
        if state.skipped:
            state.skipped = False
-        if state.interrupted:
+        if state.interrupted or state.stopping_generation:
            break
        try:
@@ -127,7 +129,21 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
        if proc is None:
            p.override_settings.pop('save_images_replace_action', None)
-            process_images(p)
+            proc = process_images(p)
        if not discard_further_results and proc:
            if batch_results:
                batch_results.images.extend(proc.images)
                batch_results.infotexts.extend(proc.infotexts)
            else:
                batch_results = proc
            if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
                discard_further_results = True
                batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
                batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
    return batch_results
 def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
@@ -206,16 +222,13 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
    if shared.opts.enable_console_prompts:
        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
    if mask:
        p.extra_generation_params["Mask blur"] = mask_blur
    with closing(p):
        if is_batch:
            assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
            processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
-            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+            if processed is None:
-
+                processed = Processed(p, [], p.seed, "")
            processed = Processed(p, [], p.seed, "")
        else:
            processed = modules.scripts.scripts_img2img.run(p, *args)
            if processed is None:
@@ -3,3 +3,14 @@ import sys
 # this will break any attempt to import xformers which will prevent stability diffusion repo from trying to use it
 if "--xformers" not in "".join(sys.argv):
    sys.modules["xformers"] = None
 # Hack to fix a changed import in torchvision 0.17+, which otherwise breaks
 # basicsr; see https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13985
 try:
    import torchvision.transforms.functional_tensor  # noqa: F401
 except ImportError:
    try:
        import torchvision.transforms.functional as functional
        sys.modules["torchvision.transforms.functional_tensor"] = functional
    except ImportError:
        pass  # shrug...
@@ -1,22 +1,51 @@
 from __future__ import annotations
 import base64
 import io
 import json
 import os
 import re
 import sys
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
 from PIL import Image
 sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
 re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
 quote_swap = str.maketrans('\'"', '"\'')
 info_json_keys = set()
-paste_fields = {}
+
-registered_param_bindings = []
+def info_json_dumps(data):
    """encode data into json string, but swap single and double quotes to reduce escaping issues"""
    return json.dumps(data, ensure_ascii=False, separators=(',', ':')).translate(quote_swap)
 def info_json_loads(info_json):
    """decode json string into info data, but swap single and double quotes to reduce escaping issues"""
    return json.loads(info_json.translate(quote_swap))
 def build_infotext(info: dict):
    for info_json_key in info_json_keys:
        if info_json_key in info:
            info[info_json_key] = info_json_dumps(info[info_json_key])
    return ", ".join([k if k == v else f'{k}: {quote(v)}' for k, v in info.items() if v is not None])
 def register_info_json(key):
    """register an infotext key as infojson
    after a key is registered, a json compatible data structure like dict or list can be used as a value in
    generation_parameters and extra_generation_parameters
    """
    global info_json_keys
    info_json_keys.add(key)
 class ParamBinding:
@@ -30,6 +59,23 @@ class ParamBinding:
        self.paste_field_names = paste_field_names or []
 class PasteField(tuple):
    def __new__(cls, component, target, *, api=None):
        return super().__new__(cls, (component, target))
    def __init__(self, component, target, *, api=None):
        super().__init__()
        self.api = api
        self.component = component
        self.label = target if isinstance(target, str) else None
        self.function = target if callable(target) else None
 paste_fields: dict[str, dict] = {}
 registered_param_bindings: list[ParamBinding] = []
 def reset():
    paste_fields.clear()
    registered_param_bindings.clear()
@@ -82,6 +128,12 @@ def image_from_url_text(filedata):
 def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
    if fields:
        for i in range(len(fields)):
            if not isinstance(fields[i], PasteField):
                fields[i] = PasteField(*fields[i])
    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
    # backwards compatibility for existing extensions
@@ -113,7 +165,6 @@ def register_paste_params_button(binding: ParamBinding):
 def connect_paste_params_buttons():
    binding: ParamBinding
    for binding in registered_param_bindings:
        destination_image_component = paste_fields[binding.tabname]["init_img"]
        fields = paste_fields[binding.tabname]["fields"]
@@ -207,7 +258,7 @@ def restore_old_hires_fix_params(res):
    res['Hires resize-2'] = height
-def parse_generation_parameters(x: str):
+def parse_generation_parameters(x: str, skip_fields: list[str] | None = None):
    """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
 girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
@@ -217,6 +268,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    returns a dict with field values
    """
    if skip_fields is None:
        skip_fields = shared.opts.infotext_skip_pasting
    res = {}
@@ -289,6 +342,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    if "Hires negative prompt" not in res:
        res["Hires negative prompt"] = ""
    if "Mask mode" not in res:
        res["Mask mode"] = "Inpaint masked"
    if "Masked content" not in res:
        res["Masked content"] = 'original'
    if "Inpaint area" not in res:
        res["Inpaint area"] = "Whole picture"
    if "Masked area padding" not in res:
        res["Masked area padding"] = 32
    restore_old_hires_fix_params(res)
    # Missing RNG means the default was set, which is GPU RNG
@@ -313,6 +378,24 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    if "VAE Decoder" not in res:
        res["VAE Decoder"] = "Full"
    if "FP8 weight" not in res:
        res["FP8 weight"] = "Disable"
    if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
        res["Cache FP16 weight for LoRA"] = False
    for key in info_json_keys:
        if key in res:
            try:
                res[key] = info_json_loads(res[key])
            except Exception:
                print(f'Error parsing "{key}: {res[key]}"')
    infotext_versions.backcompat(res)
    for key in skip_fields:
        res.pop(key, None)
    return res
@@ -361,13 +444,57 @@ def create_override_settings_dict(text_pairs):
    return res
 def get_override_settings(params, *, skip_fields=None):
    """Returns a list of settings overrides from the infotext parameters dictionary.
    This function checks the `params` dictionary for any keys that correspond to settings in `shared.opts` and returns
    a list of tuples containing the parameter name, setting name, and new value cast to correct type.
    It checks for conditions before adding an override:
    - ignores settings that match the current value
    - ignores parameter keys present in skip_fields argument.
    Example input:
        {"Clip skip": "2"}
    Example output:
        [("Clip skip", "CLIP_stop_at_last_layers", 2)]
    """
    res = []
    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
        if param_name in (skip_fields or {}):
            continue
        v = params.get(param_name, None)
        if v is None:
            continue
        if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
            continue
        v = shared.opts.cast_value(setting_name, v)
        current_value = getattr(shared.opts, setting_name, None)
        if v == current_value:
            continue
        res.append((param_name, setting_name, v))
    return res
 def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
    def paste_func(prompt):
        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
            filename = os.path.join(data_path, "params.txt")
-            if os.path.exists(filename):
+            try:
                with open(filename, "r", encoding="utf8") as file:
                    prompt = file.read()
            except OSError:
                pass
        params = parse_generation_parameters(prompt)
        script_callbacks.infotext_pasted_callback(prompt, params)
@@ -389,6 +516,8 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
                    if valtype == bool and v == "False":
                        val = False
                    elif valtype == int:
                        val = float(v)
                    else:
                        val = valtype(v)
@@ -402,29 +531,9 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
        already_handled_fields = {key: 1 for _, key in paste_fields}
        def paste_settings(params):
-            vals = {}
+            vals = get_override_settings(params, skip_fields=already_handled_fields)
-            mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+            vals_pairs = [f"{infotext_text}: {value}" for infotext_text, setting_name, value in vals]
            for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
                if param_name in already_handled_fields:
                    continue
                v = params.get(param_name, None)
                if v is None:
                    continue
                if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
                    continue
                v = shared.opts.cast_value(setting_name, v)
                current_value = getattr(shared.opts, setting_name, None)
                if v == current_value:
                    continue
                vals[param_name] = v
            vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
@@ -443,3 +552,4 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
        outputs=[],
        show_progress=False,
    )
@@ -0,0 +1,39 @@
 from modules import shared
 from packaging import version
 import re
 v160 = version.parse("1.6.0")
 v170_tsnr = version.parse("v1.7.0-225")
 def parse_version(text):
    if text is None:
        return None
    m = re.match(r'([^-]+-[^-]+)-.*', text)
    if m:
        text = m.group(1)
    try:
        return version.parse(text)
    except Exception:
        return None
 def backcompat(d):
    """Checks infotext Version field, and enables backwards compatibility options according to it."""
    if not shared.opts.auto_backcompat:
        return
    ver = parse_version(d.get("Version"))
    if ver is None:
        return
    if ver < v160:
        d["Old prompt editing timelines"] = True
    if ver < v170_tsnr:
        d["Downcast alphas_cumprod"] = True
@@ -1,5 +1,6 @@
 import importlib
 import logging
 import os
 import sys
 import warnings
 from threading import Thread
@@ -18,6 +19,7 @@ def imports():
    warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
    os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
    import gradio  # noqa: F401
    startup_timer.record("import gradio")
@@ -54,9 +56,6 @@ def initialize():
    initialize_util.configure_sigint_handler()
    initialize_util.configure_opts_onchange()
    from modules import modelloader
    modelloader.cleanup_models()
    from modules import sd_models
    sd_models.setup_model()
    startup_timer.record("setup SD model")
@@ -150,10 +150,14 @@ def dumpstacks():
 def configure_sigint_handler():
    # make the program just exit at ctrl+c without waiting for anything
    from modules import shared
    def sigint_handler(sig, frame):
        print(f'Interrupted with signal {sig} in {frame}')
-        dumpstacks()
+        if shared.opts.dump_stacks_on_signal:
            dumpstacks()
        os._exit(0)
@@ -173,6 +177,8 @@ def configure_opts_onchange():
    shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
    shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
    shared.opts.onchange("fp8_storage", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
    shared.opts.onchange("cache_fp16_weight", wrap_queued_call(lambda: sd_models.reload_model_weights(forced_reload=True)), call=False)
    startup_timer.record("opts onchange")
@@ -10,7 +10,7 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
-from modules import devices, paths, shared, lowvram, modelloader, errors
+from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
@@ -131,7 +131,7 @@ class InterrogateModels:
        self.clip_model = self.clip_model.to(devices.device_interrogate)
-        self.dtype = next(self.clip_model.parameters()).dtype
+        self.dtype = torch_utils.get_param(self.clip_model).dtype
    def send_clip_to_ram(self):
        if not shared.opts.interrogate_keep_models_in_memory:
@@ -6,6 +6,7 @@ import os
 import shutil
 import sys
 import importlib.util
 import importlib.metadata
 import platform
 import json
 from functools import lru_cache
@@ -26,8 +27,7 @@ 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.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
 def check_python_version():
@@ -119,11 +119,16 @@ def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_
 def is_installed(package):
    try:
-        spec = importlib.util.find_spec(package)
+        dist = importlib.metadata.distribution(package)
-    except ModuleNotFoundError:
+    except importlib.metadata.PackageNotFoundError:
-        return False
+        try:
            spec = importlib.util.find_spec(package)
        except ModuleNotFoundError:
            return False
-    return spec is not None
+        return spec is not None
    return dist is not None
 def repo_dir(name):
@@ -239,11 +244,14 @@ def list_extensions(settings_file):
    settings = {}
    try:
-        if os.path.isfile(settings_file):
+        with open(settings_file, "r", encoding="utf8") as file:
-            with open(settings_file, "r", encoding="utf8") as file:
+            settings = json.load(file)
-                settings = json.load(file)
+    except FileNotFoundError:
        pass
    except Exception:
-        errors.report("Could not load settings", exc_info=True)
+        errors.report(f'\nCould not load settings\nThe config file "{settings_file}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
        os.replace(settings_file, os.path.join(script_path, "tmp", "config.json"))
        settings = {}
    disabled_extensions = set(settings.get('disabled_extensions', []))
    disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -308,24 +316,44 @@ def requirements_met(requirements_file):
 def prepare_environment():
-    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
-    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url {torch_index_url}")
    if args.use_ipex:
        if platform.system() == "Windows":
            # The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
            # This is NOT an Intel official release so please use it at your own risk!!
            # See https://github.com/Nuullll/intel-extension-for-pytorch/releases/tag/v2.0.110%2Bxpu-master%2Bdll-bundle for details.
            #
            # Strengths (over official IPEX 2.0.110 windows release):
            #   - AOT build (for Arc GPU only) to eliminate JIT compilation overhead: https://github.com/intel/intel-extension-for-pytorch/issues/399
            #   - Bundles minimal oneAPI 2023.2 dependencies into the python wheels, so users don't need to install oneAPI for the whole system.
            #   - Provides a compatible torchvision wheel: https://github.com/intel/intel-extension-for-pytorch/issues/465
            # Limitation:
            #   - Only works for python 3.10
            url_prefix = "https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%2Bxpu-master%2Bdll-bundle"
            torch_command = os.environ.get('TORCH_COMMAND', f"pip install {url_prefix}/torch-2.0.0a0+gite9ebda2-cp310-cp310-win_amd64.whl {url_prefix}/torchvision-0.15.2a0+fa99a53-cp310-cp310-win_amd64.whl {url_prefix}/intel_extension_for_pytorch-2.0.110+gitc6ea20b-cp310-cp310-win_amd64.whl")
        else:
            # Using official IPEX release for linux since it's already an AOT build.
            # However, users still have to install oneAPI toolkit and activate oneAPI environment manually.
            # See https://intel.github.io/intel-extension-for-pytorch/index.html#installation for details.
            torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
            torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
    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")
    assets_repo = os.environ.get('ASSETS_REPO', "https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git")
    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
    stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.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')
    assets_commit_hash = os.environ.get('ASSETS_COMMIT_HASH', "6f7db241d2f8ba7457bac5ca9753331f0c266917")
    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
    stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
    try:
@@ -352,6 +380,8 @@ def prepare_environment():
        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
        startup_timer.record("install torch")
    if args.use_ipex:
        args.skip_torch_cuda_test = 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; '
@@ -377,18 +407,14 @@ def prepare_environment():
    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
    git_clone(assets_repo, repo_dir('stable-diffusion-webui-assets'), "assets", assets_commit_hash)
    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
    git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_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)
    startup_timer.record("clone repositores")
    if not is_installed("lpips"):
        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
        startup_timer.record("install CodeFormer requirements")
    if not os.path.isfile(requirements_file):
        requirements_file = os.path.join(script_path, requirements_file)
@@ -441,7 +467,7 @@ def dump_sysinfo():
    import datetime
    text = sysinfo.get()
-    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.txt"
+    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
    with open(filename, "w", encoding="utf8") as file:
        file.write(text)
@@ -1,16 +1,58 @@
 import os
 import logging
 import os
 try:
    from tqdm import tqdm
    class TqdmLoggingHandler(logging.Handler):
        def __init__(self, fallback_handler: logging.Handler):
            super().__init__()
            self.fallback_handler = fallback_handler
        def emit(self, record):
            try:
                # If there are active tqdm progress bars,
                # attempt to not interfere with them.
                if tqdm._instances:
                    tqdm.write(self.format(record))
                else:
                    self.fallback_handler.emit(record)
            except Exception:
                self.fallback_handler.emit(record)
 except ImportError:
    TqdmLoggingHandler = None
 def setup_logging(loglevel):
    if loglevel is None:
        loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
-    if loglevel:
+    if not loglevel:
-        log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
+        return
        logging.basicConfig(
            level=log_level,
            format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
        )
    if logging.root.handlers:
        # Already configured, do not interfere
        return
    formatter = logging.Formatter(
        '%(asctime)s %(levelname)s [%(name)s] %(message)s',
        '%Y-%m-%d %H:%M:%S',
    )
    if os.environ.get("SD_WEBUI_RICH_LOG"):
        from rich.logging import RichHandler
        handler = RichHandler()
    else:
        handler = logging.StreamHandler()
        handler.setFormatter(formatter)
    if TqdmLoggingHandler:
        handler = TqdmLoggingHandler(handler)
    handler.setFormatter(formatter)
    log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
    logging.root.setLevel(log_level)
    logging.root.addHandler(handler)
@@ -1,6 +1,7 @@
 import logging
 import torch
 from torch import Tensor
 import platform
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
@@ -51,6 +52,17 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
    return cumsum_func(input, *args, **kwargs)
 # MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
 def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor:
    try:
        return orig_func(*args, **kwargs)
    except RuntimeError as e:
        if "not implemented for" in str(e) and "Half" in str(e):
            input_tensor = args[0]
            return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype)
        else:
            print(f"An unexpected RuntimeError occurred: {str(e)}")
 if has_mps:
    if platform.mac_ver()[0].startswith("13.2."):
        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
@@ -77,6 +89,9 @@ if has_mps:
        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
        # MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046
        CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None)
        # MPS workaround for https://github.com/pytorch/pytorch/issues/92311
        if platform.processor() == 'i386':
            for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
@@ -3,40 +3,15 @@ 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)"""
+    For example, if a user has painted the top-right part of a 512x512 image, the result may be (256, 0, 512, 256)"""
-
+    mask_img = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
-    h, w = mask.shape
+    box = mask_img.getbbox()
-
+    if box:
-    crop_left = 0
+        x1, y1, x2, y2 = box
-    for i in range(w):
+    else:  # when no box is found
-        if not (mask[:, i] == 0).all():
+        x1, y1 = mask_img.size
-            break
+        x2 = y2 = 0
-        crop_left += 1
+    return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask_img.size[0]), min(y2 + pad, mask_img.size[1])
    crop_right = 0
    for i in reversed(range(w)):
        if not (mask[:, i] == 0).all():
            break
        crop_right += 1
    crop_top = 0
    for i in range(h):
        if not (mask[i] == 0).all():
            break
        crop_top += 1
    crop_bottom = 0
    for i in reversed(range(h)):
        if not (mask[i] == 0).all():
            break
        crop_bottom += 1
    return (
        int(max(crop_left-pad, 0)),
        int(max(crop_top-pad, 0)),
        int(min(w - crop_right + pad, w)),
        int(min(h - crop_bottom + pad, h))
    )
 def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
@@ -1,13 +1,20 @@
 from __future__ import annotations
 import os
 import shutil
 import importlib
 import logging
 import os
 from typing import TYPE_CHECKING
 from urllib.parse import urlparse
 import torch
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
-from modules.paths import script_path, models_path
+
 if TYPE_CHECKING:
    import spandrel
 logger = logging.getLogger(__name__)
 def load_file_from_url(
@@ -90,54 +97,6 @@ def friendly_name(file: str):
    return model_name
 def cleanup_models():
    # This code could probably be more efficient if we used a tuple list or something to store the src/destinations
    # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler
    # somehow auto-register and just do these things...
    root_path = script_path
    src_path = models_path
    dest_path = os.path.join(models_path, "Stable-diffusion")
    move_files(src_path, dest_path, ".ckpt")
    move_files(src_path, dest_path, ".safetensors")
    src_path = os.path.join(root_path, "ESRGAN")
    dest_path = os.path.join(models_path, "ESRGAN")
    move_files(src_path, dest_path)
    src_path = os.path.join(models_path, "BSRGAN")
    dest_path = os.path.join(models_path, "ESRGAN")
    move_files(src_path, dest_path, ".pth")
    src_path = os.path.join(root_path, "gfpgan")
    dest_path = os.path.join(models_path, "GFPGAN")
    move_files(src_path, dest_path)
    src_path = os.path.join(root_path, "SwinIR")
    dest_path = os.path.join(models_path, "SwinIR")
    move_files(src_path, dest_path)
    src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/")
    dest_path = os.path.join(models_path, "LDSR")
    move_files(src_path, dest_path)
 def move_files(src_path: str, dest_path: str, ext_filter: str = None):
    try:
        os.makedirs(dest_path, exist_ok=True)
        if os.path.exists(src_path):
            for file in os.listdir(src_path):
                fullpath = os.path.join(src_path, file)
                if os.path.isfile(fullpath):
                    if ext_filter is not None:
                        if ext_filter not in file:
                            continue
                    print(f"Moving {file} from {src_path} to {dest_path}.")
                    try:
                        shutil.move(fullpath, dest_path)
                    except Exception:
                        pass
            if len(os.listdir(src_path)) == 0:
                print(f"Removing empty folder: {src_path}")
                shutil.rmtree(src_path, True)
    except Exception:
        pass
 def load_upscalers():
    # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
    # so we'll try to import any _model.py files before looking in __subclasses__
@@ -177,3 +136,34 @@ def load_upscalers():
        # Special case for UpscalerNone keeps it at the beginning of the list.
        key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
    )
 def load_spandrel_model(
    path: str | os.PathLike,
    *,
    device: str | torch.device | None,
    prefer_half: bool = False,
    dtype: str | torch.dtype | None = None,
    expected_architecture: str | None = None,
 ) -> spandrel.ModelDescriptor:
    import spandrel
    model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path))
    if expected_architecture and model_descriptor.architecture != expected_architecture:
        logger.warning(
            f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})",
        )
    half = False
    if prefer_half:
        if model_descriptor.supports_half:
            model_descriptor.model.half()
            half = True
        else:
            logger.info("Model %s does not support half precision, ignoring --half", path)
    if dtype:
        model_descriptor.model.to(dtype=dtype)
    model_descriptor.model.eval()
    logger.debug(
        "Loaded %s from %s (device=%s, half=%s, dtype=%s)",
        model_descriptor, path, device, half, dtype,
    )
    return model_descriptor
@@ -24,10 +24,15 @@ from pytorch_lightning.utilities.distributed import rank_zero_only
 from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
 from ldm.modules.ema import LitEma
 from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
-from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
 from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
 from ldm.models.diffusion.ddim import DDIMSampler
 try:
    from ldm.models.autoencoder import VQModelInterface
 except Exception:
    class VQModelInterface:
        pass
 __conditioning_keys__ = {'concat': 'c_concat',
                         'crossattn': 'c_crossattn',
@@ -1,20 +1,24 @@
 import os
 import json
 import sys
 from dataclasses import dataclass
 import gradio as gr
 from modules import errors
 from modules.shared_cmd_options import cmd_opts
 from modules.paths_internal import script_path
 class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False):
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
        self.default = default
        self.label = label
        self.component = component
        self.component_args = component_args
        self.onchange = onchange
        self.section = section
        self.category_id = category_id
        self.refresh = refresh
        self.do_not_save = False
@@ -63,7 +67,11 @@ class OptionHTML(OptionInfo):
 def options_section(section_identifier, options_dict):
    for v in options_dict.values():
-        v.section = section_identifier
+        if len(section_identifier) == 2:
            v.section = section_identifier
        elif len(section_identifier) == 3:
            v.section = section_identifier[0:2]
            v.category_id = section_identifier[2]
    return options_dict
@@ -76,7 +84,7 @@ class Options:
    def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
        self.data_labels = data_labels
-        self.data = {k: v.default for k, v in self.data_labels.items()}
+        self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
        self.restricted_opts = restricted_opts
    def __setattr__(self, key, value):
@@ -85,18 +93,35 @@ class Options:
        if self.data is not None:
            if key in self.data or key in self.data_labels:
                # Check that settings aren't globally frozen
                assert not cmd_opts.freeze_settings, "changing settings is disabled"
                # Get the info related to the setting being changed
                info = self.data_labels.get(key, None)
                if info.do_not_save:
                    return
                # Restrict component arguments
                comp_args = info.component_args if info else None
                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+                    raise RuntimeError(f"not possible to set '{key}' because it is restricted")
                # Check that this section isn't frozen
                if cmd_opts.freeze_settings_in_sections is not None:
                    frozen_sections = list(map(str.strip, cmd_opts.freeze_settings_in_sections.split(','))) # Trim whitespace from section names
                    section_key = info.section[0]
                    section_name = info.section[1]
                    assert section_key not in frozen_sections, f"not possible to set '{key}' because settings in section '{section_name}' ({section_key}) are frozen with --freeze-settings-in-sections"
                # Check that this section of the settings isn't frozen
                if cmd_opts.freeze_specific_settings is not None:
                    frozen_keys = list(map(str.strip, cmd_opts.freeze_specific_settings.split(','))) # Trim whitespace from setting keys
                    assert key not in frozen_keys, f"not possible to set '{key}' because this setting is frozen with --freeze-specific-settings"
                # Check shorthand option which disables editing options in "saving-paths"
                if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+                    raise RuntimeError(f"not possible to set '{key}' because it is restricted with --hide_ui_dir_config")
                self.data[key] = value
                return
@@ -158,7 +183,7 @@ class Options:
        assert not cmd_opts.freeze_settings, "saving settings is disabled"
        with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4)
+            json.dump(self.data, file, indent=4, ensure_ascii=False)
    def same_type(self, x, y):
        if x is None or y is None:
@@ -170,9 +195,13 @@ class Options:
        return type_x == type_y
    def load(self, filename):
-        with open(filename, "r", encoding="utf8") as file:
+        try:
-            self.data = json.load(file)
+            with open(filename, "r", encoding="utf8") as file:
-
+                self.data = json.load(file)
        except Exception:
            errors.report(f'\nCould not load settings\nThe config file "{filename}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
            os.replace(filename, os.path.join(script_path, "tmp", "config.json"))
            self.data = {}
        # 1.6.0 VAE defaults
        if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
            self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
@@ -206,23 +235,59 @@ class Options:
        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
        item_categories = {}
        for item in self.data_labels.values():
            category = categories.mapping.get(item.category_id)
            category = "Uncategorized" if category is None else category.label
            if category not in item_categories:
                item_categories[category] = item.section[1]
        # _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
        d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
        return json.dumps(d)
    def add_option(self, key, info):
        self.data_labels[key] = info
-        if key not in self.data:
+        if key not in self.data and not info.do_not_save:
            self.data[key] = info.default
    def reorder(self):
-        """reorder settings so that all items related to section always go together"""
+        """Reorder settings so that:
            - all items related to section always go together
            - all sections belonging to a category go together
            - sections inside a category are ordered alphabetically
            - categories are ordered by creation order
        Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
        This function also changes items' category_id so that all items belonging to a section have the same category_id.
        """
        category_ids = {}
        section_categories = {}
        section_ids = {}
        settings_items = self.data_labels.items()
        for _, item in settings_items:
-            if item.section not in section_ids:
+            if item.section not in section_categories:
-                section_ids[item.section] = len(section_ids)
+                section_categories[item.section] = item.category_id
-        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
+        for _, item in settings_items:
            item.category_id = section_categories.get(item.section)
        for category_id in categories.mapping:
            if category_id not in category_ids:
                category_ids[category_id] = len(category_ids)
        def sort_key(x):
            item: OptionInfo = x[1]
            category_order = category_ids.get(item.category_id, len(category_ids))
            section_order = item.section[1]
            return category_order, section_order
        self.data_labels = dict(sorted(settings_items, key=sort_key))
    def cast_value(self, key, value):
        """casts an arbitrary to the same type as this setting's value with key
@@ -245,3 +310,22 @@ class Options:
            value = expected_type(value)
        return value
@dataclass
 class OptionsCategory:
    id: str
    label: str
 class OptionsCategories:
    def __init__(self):
        self.mapping = {}
    def register_category(self, category_id, label):
        if category_id in self.mapping:
            return category_id
        self.mapping[category_id] = OptionsCategory(category_id, label)
 categories = OptionsCategories()
@@ -38,7 +38,6 @@ mute_sdxl_imports()
 path_dirs = [
    (sd_path, 'ldm', 'Stable Diffusion', []),
    (os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
    (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
    (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
    (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
 ]
@@ -28,5 +28,6 @@ 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")
 default_output_dir = os.path.join(data_path, "output")
 roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')
@@ -2,7 +2,7 @@ import os
 from PIL import Image
-from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
 from modules.shared import opts
@@ -29,11 +29,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
            image_list = shared.listfiles(input_dir)
            for filename in image_list:
-                try:
+                yield filename, filename
                    image = Image.open(filename)
                except Exception:
                    continue
                yield image, filename
        else:
            assert image, 'image not selected'
            yield image, None
@@ -45,43 +41,98 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
    infotext = ''
-    for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+    data_to_process = list(get_images(extras_mode, image, image_folder, input_dir))
    shared.state.job_count = len(data_to_process)
    for image_placeholder, name in data_to_process:
        image_data: Image.Image
        shared.state.nextjob()
        shared.state.textinfo = name
        shared.state.skipped = False
        if shared.state.interrupted:
            break
        if isinstance(image_placeholder, str):
            try:
                image_data = Image.open(image_placeholder)
            except Exception:
                continue
        else:
            image_data = image_placeholder
        parameters, existing_pnginfo = images.read_info_from_image(image_data)
        if parameters:
            existing_pnginfo["parameters"] = parameters
-        pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
+        initial_pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
-        scripts.scripts_postproc.run(pp, args)
+        scripts.scripts_postproc.run(initial_pp, args)
-        if opts.use_original_name_batch and name is not None:
+        if shared.state.skipped:
-            basename = os.path.splitext(os.path.basename(name))[0]
+            continue
        else:
            basename = ''
-        infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+        used_suffixes = {}
        for pp in [initial_pp, *initial_pp.extra_images]:
            suffix = pp.get_suffix(used_suffixes)
-        if opts.enable_pnginfo:
+            if opts.use_original_name_batch and name is not None:
-            pp.image.info = existing_pnginfo
+                basename = os.path.splitext(os.path.basename(name))[0]
-            pp.image.info["postprocessing"] = infotext
+                forced_filename = basename + suffix
            else:
                basename = ''
                forced_filename = None
-        if save_output:
+            infotext = infotext_utils.build_infotext(pp.info)
            images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
-        if extras_mode != 2 or show_extras_results:
+            if opts.enable_pnginfo:
-            outputs.append(pp.image)
+                pp.image.info = existing_pnginfo
                pp.image.info["postprocessing"] = infotext
            shared.state.assign_current_image(pp.image)
            if save_output:
                fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
                if pp.caption:
                    caption_filename = os.path.splitext(fullfn)[0] + ".txt"
                    existing_caption = ""
                    try:
                        with open(caption_filename, encoding="utf8") as file:
                            existing_caption = file.read().strip()
                    except FileNotFoundError:
                        pass
                    action = shared.opts.postprocessing_existing_caption_action
                    if action == 'Prepend' and existing_caption:
                        caption = f"{existing_caption} {pp.caption}"
                    elif action == 'Append' and existing_caption:
                        caption = f"{pp.caption} {existing_caption}"
                    elif action == 'Keep' and existing_caption:
                        caption = existing_caption
                    else:
                        caption = pp.caption
                    caption = caption.strip()
                    if caption:
                        with open(caption_filename, "w", encoding="utf8") as file:
                            file.write(caption)
            if extras_mode != 2 or show_extras_results:
                outputs.append(pp.image)
        image_data.close()
    devices.torch_gc()
-
+    shared.state.end()
    return outputs, ui_common.plaintext_to_html(infotext), ''
 def run_postprocessing_webui(id_task, *args, **kwargs):
    return run_postprocessing(*args, **kwargs)
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
    """old handler for API"""
@@ -97,9 +148,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
            "upscaler_2_visibility": extras_upscaler_2_visibility,
        },
        "GFPGAN": {
            "enable": True,
            "gfpgan_visibility": gfpgan_visibility,
        },
        "CodeFormer": {
            "enable": True,
            "codeformer_visibility": codeformer_visibility,
            "codeformer_weight": codeformer_weight,
        },
@@ -16,7 +16,7 @@ from skimage import exposure
 from typing import Any
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
 from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -62,18 +62,22 @@ def apply_color_correction(correction, original_image):
    return image.convert('RGB')
-def apply_overlay(image, paste_loc, index, overlays):
+def uncrop(image, dest_size, paste_loc):
-    if overlays is None or index >= len(overlays):
+    x, y, w, h = paste_loc
    base_image = Image.new('RGBA', dest_size)
    image = images.resize_image(1, image, w, h)
    base_image.paste(image, (x, y))
    image = base_image
    return image
 def apply_overlay(image, paste_loc, overlay):
    if overlay is None:
        return image
    overlay = overlays[index]
    if paste_loc is not None:
-        x, y, w, h = paste_loc
+        image = uncrop(image, (overlay.width, overlay.height), paste_loc)
        base_image = Image.new('RGBA', (overlay.width, overlay.height))
        image = images.resize_image(1, image, w, h)
        base_image.paste(image, (x, y))
        image = base_image
    image = image.convert('RGBA')
    image.alpha_composite(overlay)
@@ -81,9 +85,12 @@ def apply_overlay(image, paste_loc, index, overlays):
    return image
-def create_binary_mask(image):
+def create_binary_mask(image, round=True):
    if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
-        image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+        if round:
            image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
        else:
            image = image.split()[-1].convert("L")
    else:
        image = image.convert('L')
    return image
@@ -106,6 +113,21 @@ def txt2img_image_conditioning(sd_model, x, width, height):
        return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
    else:
        sd = sd_model.model.state_dict()
        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
        if diffusion_model_input is not None:
            if diffusion_model_input.shape[1] == 9:
                # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
                image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
                image_conditioning = images_tensor_to_samples(image_conditioning,
                                                              approximation_indexes.get(opts.sd_vae_encode_method))
                # Add the fake full 1s mask to the first dimension.
                image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
                image_conditioning = image_conditioning.to(x.dtype)
                return image_conditioning
        # Dummy zero conditioning if we're not using inpainting or unclip models.
        # Still takes up a bit of memory, but no encoder call.
        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
@@ -157,6 +179,7 @@ class StableDiffusionProcessing:
    token_merging_ratio = 0
    token_merging_ratio_hr = 0
    disable_extra_networks: bool = False
    firstpass_image: Image = None
    scripts_value: scripts.ScriptRunner = field(default=None, init=False)
    script_args_value: list = field(default=None, init=False)
@@ -296,7 +319,7 @@ class StableDiffusionProcessing:
        return conditioning
    def edit_image_conditioning(self, source_image):
-        conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
+        conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
        return conditioning_image
@@ -308,7 +331,7 @@ class StableDiffusionProcessing:
            c_adm = torch.cat((c_adm, noise_level_emb), 1)
        return c_adm
-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
        self.is_using_inpainting_conditioning = True
        # Handle the different mask inputs
@@ -320,8 +343,10 @@ class StableDiffusionProcessing:
                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
+                if round_image_mask:
-                conditioning_mask = torch.round(conditioning_mask)
+                    # Caller is requesting a discretized mask as input, so we round to either 1.0 or 0.0
                    conditioning_mask = torch.round(conditioning_mask)
        else:
            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
@@ -345,7 +370,7 @@ class StableDiffusionProcessing:
        return image_conditioning
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
        source_image = devices.cond_cast_float(source_image)
        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
@@ -357,11 +382,17 @@ class StableDiffusionProcessing:
            return self.edit_image_conditioning(source_image)
        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
        if self.sampler.conditioning_key == "crossattn-adm":
            return self.unclip_image_conditioning(source_image)
        sd = self.sampler.model_wrap.inner_model.model.state_dict()
        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
        if diffusion_model_input is not None:
            if diffusion_model_input.shape[1] == 9:
                return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
        # Dummy zero conditioning if we're not using inpainting or depth model.
        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@@ -422,6 +453,8 @@ class StableDiffusionProcessing:
            opts.sdxl_crop_top,
            self.width,
            self.height,
            opts.fp8_storage,
            opts.cache_fp16_weight,
        )
    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
@@ -596,20 +629,33 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
        sample = decode_first_stage(model, batch[i:i + 1])[0]
        if check_for_nans:
            try:
                devices.test_for_nans(sample, "vae")
            except devices.NansException as e:
-                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+                if shared.opts.auto_vae_precision_bfloat16:
                    autofix_dtype = torch.bfloat16
                    autofix_dtype_text = "bfloat16"
                    autofix_dtype_setting = "Automatically convert VAE to bfloat16"
                    autofix_dtype_comment = ""
                elif shared.opts.auto_vae_precision:
                    autofix_dtype = torch.float32
                    autofix_dtype_text = "32-bit float"
                    autofix_dtype_setting = "Automatically revert VAE to 32-bit floats"
                    autofix_dtype_comment = "\nTo always start with 32-bit VAE, use --no-half-vae commandline flag."
                else:
                    raise e
                if devices.dtype_vae == autofix_dtype:
                    raise e
                errors.print_error_explanation(
                    "A tensor with all NaNs was produced in VAE.\n"
-                    "Web UI will now convert VAE into 32-bit float and retry.\n"
+                    f"Web UI will now convert VAE into {autofix_dtype_text} and retry.\n"
-                    "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
+                    f"To disable this behavior, disable the '{autofix_dtype_setting}' setting.{autofix_dtype_comment}"
                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."
                )
-                devices.dtype_vae = torch.float32
+                devices.dtype_vae = autofix_dtype
                model.first_stage_model.to(devices.dtype_vae)
                batch = batch.to(devices.dtype_vae)
@@ -679,12 +725,14 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
        "Size": f"{p.width}x{p.height}",
        "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
        "Model": p.sd_model_name if opts.add_model_name_to_info else None,
-        "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+        "FP8 weight": opts.fp8_storage if devices.fp8 else None,
-        "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
+        "Cache FP16 weight for LoRA": opts.cache_fp16_weight if devices.fp8 else None,
        "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
        "VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
        "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
-        "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Denoising strength": p.extra_generation_params.get("Denoising strength"),
        "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": opts.eta_noise_seed_delta if uses_ensd else None,
@@ -699,7 +747,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
        "User": p.user if opts.add_user_name_to_info else None,
    }
-    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])
+    generation_params_text = infotext_utils.build_infotext(generation_params)
    prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
    negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
@@ -711,7 +759,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
    if p.scripts is not None:
        p.scripts.before_process(p)
-    stored_opts = {k: opts.data[k] for k in p.override_settings.keys() if k in opts.data}
+    stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
    try:
        # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@@ -799,7 +847,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
    infotexts = []
    output_images = []
    with torch.no_grad(), p.sd_model.ema_scope():
        with devices.autocast():
            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@@ -819,7 +866,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            if state.skipped:
                state.skipped = False
-            if state.interrupted:
+            if state.interrupted or state.stopping_generation:
                break
            sd_models.reload_model_weights()  # model can be changed for example by refiner
@@ -865,15 +912,47 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            if p.n_iter > 1:
                shared.state.job = f"Batch {n+1} out of {p.n_iter}"
            def rescale_zero_terminal_snr_abar(alphas_cumprod):
                alphas_bar_sqrt = alphas_cumprod.sqrt()
                # Store old values.
                alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
                alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
                # Shift so the last timestep is zero.
                alphas_bar_sqrt -= (alphas_bar_sqrt_T)
                # Scale so the first timestep is back to the old value.
                alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
                # Convert alphas_bar_sqrt to betas
                alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
                alphas_bar[-1] = 4.8973451890853435e-08
                return alphas_bar
            if hasattr(p.sd_model, 'alphas_cumprod') and hasattr(p.sd_model, 'alphas_cumprod_original'):
                p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod_original.to(shared.device)
                if opts.use_downcasted_alpha_bar:
                    p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
                    p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod.half().to(shared.device)
                if opts.sd_noise_schedule == "Zero Terminal SNR":
                    p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
                    p.sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(p.sd_model.alphas_cumprod).to(shared.device)
            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                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)
            if p.scripts is not None:
                ps = scripts.PostSampleArgs(samples_ddim)
                p.scripts.post_sample(p, ps)
                samples_ddim = ps.samples
            if getattr(samples_ddim, 'already_decoded', False):
                x_samples_ddim = samples_ddim
            else:
                if opts.sd_vae_decode_method != 'Full':
                    p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
                x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
            x_samples_ddim = torch.stack(x_samples_ddim).float()
@@ -886,6 +965,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            devices.torch_gc()
            state.nextjob()
            if p.scripts is not None:
                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@@ -922,13 +1003,36 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                    pp = scripts.PostprocessImageArgs(image)
                    p.scripts.postprocess_image(p, pp)
                    image = pp.image
                mask_for_overlay = getattr(p, "mask_for_overlay", None)
                overlay_image = p.overlay_images[i] if getattr(p, "overlay_images", None) is not None and i < len(p.overlay_images) else None
                if p.scripts is not None:
                    ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)
                    p.scripts.postprocess_maskoverlay(p, ppmo)
                    mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image
                if p.color_corrections is not None and i < len(p.color_corrections):
                    if save_samples and opts.save_images_before_color_correction:
-                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+                        image_without_cc = apply_overlay(image, p.paste_to, overlay_image)
                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(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 the intention is to show the output from the model
                # that is being composited over the original image,
                # we need to keep the original image around
                # and use it in the composite step.
                original_denoised_image = image.copy()
                if p.paste_to is not None:
                    original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)
                image = apply_overlay(image, p.paste_to, overlay_image)
                if p.scripts is not None:
                    pp = scripts.PostprocessImageArgs(image)
                    p.scripts.postprocess_image_after_composite(p, pp)
                    image = pp.image
                if save_samples:
                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
@@ -938,27 +1042,28 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                if opts.enable_pnginfo:
                    image.info["parameters"] = text
                output_images.append(image)
                if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
                    image_mask = p.mask_for_overlay.convert('RGB')
                    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:
+                if mask_for_overlay is not None:
-                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
+                    if opts.return_mask or opts.save_mask:
                        image_mask = mask_for_overlay.convert('RGB')
                        if save_samples and opts.save_mask:
                            images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
                        if opts.return_mask:
                            output_images.append(image_mask)
-                    if opts.save_mask_composite:
+                    if opts.return_mask_composite or opts.save_mask_composite:
-                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
+                        image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
-
+                        if save_samples and opts.save_mask_composite:
-                    if opts.return_mask:
+                            images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
-                        output_images.append(image_mask)
+                        if opts.return_mask_composite:
-
+                            output_images.append(image_mask_composite)
                    if opts.return_mask_composite:
                        output_images.append(image_mask_composite)
            del x_samples_ddim
            devices.torch_gc()
-            state.nextjob()
+        if not infotexts:
            infotexts.append(Processed(p, []).infotext(p, 0))
        p.color_corrections = None
@@ -1025,6 +1130,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    hr_sampler_name: str = None
    hr_prompt: str = ''
    hr_negative_prompt: str = ''
    force_task_id: str = None
    cached_hr_uc = [None, None]
    cached_hr_c = [None, None]
@@ -1097,7 +1203,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    def init(self, all_prompts, all_seeds, all_subseeds):
        if self.enable_hr:
-            if self.hr_checkpoint_name:
+            self.extra_generation_params["Denoising strength"] = self.denoising_strength
            if self.hr_checkpoint_name and self.hr_checkpoint_name != 'Use same checkpoint':
                self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
                if self.hr_checkpoint_info is None:
@@ -1124,8 +1232,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
            if not state.processing_has_refined_job_count:
                if state.job_count == -1:
                    state.job_count = self.n_iter
-
+                if getattr(self, 'txt2img_upscale', False):
-                shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
+                    total_steps = (self.hr_second_pass_steps or self.steps) * state.job_count
                else:
                    total_steps = (self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count
                shared.total_tqdm.updateTotal(total_steps)
                state.job_count = state.job_count * 2
                state.processing_has_refined_job_count = True
@@ -1138,23 +1249,49 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
-        x = self.rng.next()
+        if self.firstpass_image is not None and self.enable_hr:
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+            # here we don't need to generate image, we just take self.firstpass_image and prepare it for hires fix
        del x
-        if not self.enable_hr:
+            if self.latent_scale_mode is None:
-            return samples
+                image = np.array(self.firstpass_image).astype(np.float32) / 255.0 * 2.0 - 1.0
                image = np.moveaxis(image, 2, 0)
                samples = None
                decoded_samples = torch.asarray(np.expand_dims(image, 0))
            else:
                image = np.array(self.firstpass_image).astype(np.float32) / 255.0
                image = np.moveaxis(image, 2, 0)
                image = torch.from_numpy(np.expand_dims(image, axis=0))
                image = image.to(shared.device, dtype=devices.dtype_vae)
                if opts.sd_vae_encode_method != 'Full':
                    self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
                samples = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
                decoded_samples = None
                devices.torch_gc()
        if self.latent_scale_mode is None:
            decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
        else:
-            decoded_samples = None
+            # here we generate an image normally
            x = self.rng.next()
            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
            del x
            if not self.enable_hr:
                return samples
            devices.torch_gc()
            if self.latent_scale_mode is None:
                decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
            else:
                decoded_samples = None
        with sd_models.SkipWritingToConfig():
            sd_models.reload_model_weights(info=self.hr_checkpoint_info)
        devices.torch_gc()
        return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
    def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@@ -1162,7 +1299,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
            return samples
        self.is_hr_pass = True
        target_width = self.hr_upscale_to_x
        target_height = self.hr_upscale_to_y
@@ -1251,7 +1387,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
        self.is_hr_pass = False
        return decoded_samples
    def close(self):
@@ -1354,12 +1489,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
    mask_blur_x: int = 4
    mask_blur_y: int = 4
    mask_blur: int = None
    mask_round: bool = True
    inpainting_fill: int = 0
    inpaint_full_res: bool = True
    inpaint_full_res_padding: int = 0
    inpainting_mask_invert: int = 0
    initial_noise_multiplier: float = None
    latent_mask: Image = None
    force_task_id: str = None
    image_mask: Any = field(default=None, init=False)
@@ -1389,6 +1526,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            self.mask_blur_y = value
    def init(self, all_prompts, all_seeds, all_subseeds):
        self.extra_generation_params["Denoising strength"] = self.denoising_strength
        self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
@@ -1399,10 +1538,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
        if image_mask is not None:
            # image_mask is passed in as RGBA by Gradio to support alpha masks,
            # but we still want to support binary masks.
-            image_mask = create_binary_mask(image_mask)
+            image_mask = create_binary_mask(image_mask, round=self.mask_round)
            if self.inpainting_mask_invert:
                image_mask = ImageOps.invert(image_mask)
                self.extra_generation_params["Mask mode"] = "Inpaint not masked"
            if self.mask_blur_x > 0:
                np_mask = np.array(image_mask)
@@ -1416,16 +1556,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
                image_mask = Image.fromarray(np_mask)
            if self.mask_blur_x > 0 or self.mask_blur_y > 0:
                self.extra_generation_params["Mask blur"] = self.mask_blur
            if self.inpaint_full_res:
                self.mask_for_overlay = image_mask
                mask = image_mask.convert('L')
-                crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
+                crop_region = masking.get_crop_region(mask, self.inpaint_full_res_padding)
                crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
                x1, y1, x2, y2 = crop_region
                mask = mask.crop(crop_region)
                image_mask = images.resize_image(2, mask, self.width, self.height)
                self.paste_to = (x1, y1, x2-x1, y2-y1)
                self.extra_generation_params["Inpaint area"] = "Only masked"
                self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
            else:
                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
                np_mask = np.array(image_mask)
@@ -1445,7 +1591,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            # Save init image
            if opts.save_init_img:
                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
-                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False, existing_info=img.info)
            image = images.flatten(img, opts.img2img_background_color)
@@ -1467,6 +1613,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                if self.inpainting_fill != 1:
                    image = masking.fill(image, latent_mask)
                    if self.inpainting_fill == 0:
                        self.extra_generation_params["Masked content"] = 'fill'
            if add_color_corrections:
                self.color_corrections.append(setup_color_correction(image))
@@ -1506,7 +1655,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
            latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
            latmask = latmask[0]
-            latmask = np.around(latmask)
+            if self.mask_round:
                latmask = np.around(latmask)
            latmask = np.tile(latmask[None], (4, 1, 1))
            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
@@ -1515,10 +1665,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            # this needs to be fixed to be done in sample() using actual seeds for batches
            if self.inpainting_fill == 2:
                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
                self.extra_generation_params["Masked content"] = 'latent noise'
            elif self.inpainting_fill == 3:
                self.init_latent = self.init_latent * self.mask
                self.extra_generation_params["Masked content"] = 'latent nothing'
-        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.mask_round)
    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
        x = self.rng.next()
@@ -1530,7 +1683,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
        samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
        if self.mask is not None:
-            samples = samples * self.nmask + self.init_latent * self.mask
+            blended_samples = samples * self.nmask + self.init_latent * self.mask
            if self.scripts is not None:
                mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)
                self.scripts.on_mask_blend(self, mba)
                blended_samples = mba.blended_latent
            samples = blended_samples
        del x
        devices.torch_gc()
@@ -1,6 +1,7 @@
 import gradio as gr
 from modules import scripts, sd_models
 from modules.infotext_utils import PasteField
 from modules.ui_common import create_refresh_button
 from modules.ui_components import InputAccordion
@@ -31,9 +32,9 @@ class ScriptRefiner(scripts.ScriptBuiltinUI):
            return None if info is None else info.title
        self.infotext_fields = [
-            (enable_refiner, lambda d: 'Refiner' in d),
+            PasteField(enable_refiner, lambda d: 'Refiner' in d),
-            (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
+            PasteField(refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner')), api="refiner_checkpoint"),
-            (refiner_switch_at, 'Refiner switch at'),
+            PasteField(refiner_switch_at, 'Refiner switch at', api="refiner_switch_at"),
        ]
        return enable_refiner, refiner_checkpoint, refiner_switch_at
@@ -3,8 +3,10 @@ import json
 import gradio as gr
 from modules import scripts, ui, errors
 from modules.infotext_utils import PasteField
 from modules.shared import cmd_opts
 from modules.ui_components import ToolButton
 from modules import infotext_utils
 class ScriptSeed(scripts.ScriptBuiltinUI):
@@ -51,12 +53,12 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
        seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
        self.infotext_fields = [
-            (self.seed, "Seed"),
+            PasteField(self.seed, "Seed", api="seed"),
-            (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+            PasteField(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
-            (subseed, "Variation seed"),
+            PasteField(subseed, "Variation seed", api="subseed"),
-            (subseed_strength, "Variation seed strength"),
+            PasteField(subseed_strength, "Variation seed strength", api="subseed_strength"),
-            (seed_resize_from_w, "Seed resize from-1"),
+            PasteField(seed_resize_from_w, "Seed resize from-1", api="seed_resize_from_h"),
-            (seed_resize_from_h, "Seed resize from-2"),
+            PasteField(seed_resize_from_h, "Seed resize from-2", api="seed_resize_from_w"),
        ]
        self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
@@ -76,7 +78,6 @@ class ScriptSeed(scripts.ScriptBuiltinUI):
            p.seed_resize_from_h = seed_resize_from_h
 def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
@@ -84,21 +85,14 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
    def copy_seed(gen_info_string: str, index):
        res = -1
        try:
            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
+            infotext = gen_info.get('infotexts')[index]
-
+            gen_parameters = infotext_utils.parse_generation_parameters(infotext, [])
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+            res = int(gen_parameters.get('Variation seed' if is_subseed else 'Seed', -1))
-                all_subseeds = gen_info.get('all_subseeds', [-1])
+        except Exception:
                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
            else:
                all_seeds = gen_info.get('all_seeds', [-1])
                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
        except json.decoder.JSONDecodeError:
            if gen_info_string:
-                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+                errors.report(f"Error retrieving seed from generation info: {gen_info_string}", exc_info=True)
        return [res, gr.update()]
@@ -8,10 +8,13 @@ from pydantic import BaseModel, Field
 from modules.shared import opts
 import modules.shared as shared
-
+from collections import OrderedDict
 import string
 import random
 from typing import List
 current_task = None
-pending_tasks = {}
+pending_tasks = OrderedDict()
 finished_tasks = []
 recorded_results = []
 recorded_results_limit = 2
@@ -34,6 +37,11 @@ def finish_task(id_task):
    if len(finished_tasks) > 16:
        finished_tasks.pop(0)
 def create_task_id(task_type):
    N = 7
    res = ''.join(random.choices(string.ascii_uppercase +
    string.digits, k=N))
    return f"task({task_type}-{res})"
 def record_results(id_task, res):
    recorded_results.append((id_task, res))
@@ -44,6 +52,9 @@ def record_results(id_task, res):
 def add_task_to_queue(id_job):
    pending_tasks[id_job] = time.time()
 class PendingTasksResponse(BaseModel):
    size: int = Field(title="Pending task size")
    tasks: List[str] = Field(title="Pending task ids")
 class ProgressRequest(BaseModel):
    id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
@@ -63,9 +74,16 @@ class ProgressResponse(BaseModel):
 def setup_progress_api(app):
    app.add_api_route("/internal/pending-tasks", get_pending_tasks, methods=["GET"])
    return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=ProgressResponse)
 def get_pending_tasks():
    pending_tasks_ids = list(pending_tasks)
    pending_len = len(pending_tasks_ids)
    return PendingTasksResponse(size=pending_len, tasks=pending_tasks_ids)
 def progressapi(req: ProgressRequest):
    active = req.id_task == current_task
    queued = req.id_task in pending_tasks
@@ -4,7 +4,7 @@ import re
 from collections import namedtuple
 import lark
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
 # [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@@ -1,12 +1,9 @@
 import os
 import numpy as np
 from PIL import Image
 from realesrgan import RealESRGANer
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
 from modules import modelloader, errors
 from modules.shared import cmd_opts, opts
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 class UpscalerRealESRGAN(Upscaler):
@@ -14,29 +11,20 @@ class UpscalerRealESRGAN(Upscaler):
        self.name = "RealESRGAN"
        self.user_path = path
        super().__init__()
-        try:
+        self.enable = True
-            from basicsr.archs.rrdbnet_arch import RRDBNet  # noqa: F401
+        self.scalers = []
-            from realesrgan import RealESRGANer  # noqa: F401
+        scalers = get_realesrgan_models(self)
            from realesrgan.archs.srvgg_arch import SRVGGNetCompact  # noqa: F401
            self.enable = True
            self.scalers = []
            scalers = self.load_models(path)
-            local_model_paths = self.find_models(ext_filter=[".pth"])
+        local_model_paths = self.find_models(ext_filter=[".pth"])
-            for scaler in scalers:
+        for scaler in scalers:
-                if scaler.local_data_path.startswith("http"):
+            if scaler.local_data_path.startswith("http"):
-                    filename = modelloader.friendly_name(scaler.local_data_path)
+                filename = modelloader.friendly_name(scaler.local_data_path)
-                    local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+                local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
-                    if local_model_candidates:
+                if local_model_candidates:
-                        scaler.local_data_path = local_model_candidates[0]
+                    scaler.local_data_path = local_model_candidates[0]
-                if scaler.name in opts.realesrgan_enabled_models:
+            if scaler.name in opts.realesrgan_enabled_models:
-                    self.scalers.append(scaler)
+                self.scalers.append(scaler)
        except Exception:
            errors.report("Error importing Real-ESRGAN", exc_info=True)
            self.enable = False
            self.scalers = []
    def do_upscale(self, img, path):
        if not self.enable:
@@ -48,20 +36,19 @@ class UpscalerRealESRGAN(Upscaler):
            errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
            return img
-        upsampler = RealESRGANer(
+        model_descriptor = modelloader.load_spandrel_model(
-            scale=info.scale,
+            info.local_data_path,
            model_path=info.local_data_path,
            model=info.model(),
            half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
            tile=opts.ESRGAN_tile,
            tile_pad=opts.ESRGAN_tile_overlap,
            device=self.device,
            prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
            expected_architecture="ESRGAN",  # "RealESRGAN" isn't a specific thing for Spandrel
        )
        return upscale_with_model(
            model_descriptor,
            img,
            tile_size=opts.ESRGAN_tile,
            tile_overlap=opts.ESRGAN_tile_overlap,
            # TODO: `outscale`?
        )
        upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
        image = Image.fromarray(upsampled)
        return image
    def load_model(self, path):
        for scaler in self.scalers:
@@ -76,58 +63,43 @@ class UpscalerRealESRGAN(Upscaler):
                return scaler
        raise ValueError(f"Unable to find model info: {path}")
    def load_models(self, _):
        return get_realesrgan_models(self)
-
+def get_realesrgan_models(scaler: UpscalerRealESRGAN):
-def get_realesrgan_models(scaler):
+    return [
-    try:
+        UpscalerData(
-        from basicsr.archs.rrdbnet_arch import RRDBNet
+            name="R-ESRGAN General 4xV3",
-        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-        models = [
+            scale=4,
-            UpscalerData(
+            upscaler=scaler,
-                name="R-ESRGAN General 4xV3",
+        ),
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+        UpscalerData(
-                scale=4,
+            name="R-ESRGAN General WDN 4xV3",
-                upscaler=scaler,
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+            scale=4,
-            ),
+            upscaler=scaler,
-            UpscalerData(
+        ),
-                name="R-ESRGAN General WDN 4xV3",
+        UpscalerData(
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+            name="R-ESRGAN AnimeVideo",
-                scale=4,
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
-                upscaler=scaler,
+            scale=4,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+            upscaler=scaler,
-            ),
+        ),
-            UpscalerData(
+        UpscalerData(
-                name="R-ESRGAN AnimeVideo",
+            name="R-ESRGAN 4x+",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-                scale=4,
+            scale=4,
-                upscaler=scaler,
+            upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        ),
-            ),
+        UpscalerData(
-            UpscalerData(
+            name="R-ESRGAN 4x+ Anime6B",
-                name="R-ESRGAN 4x+",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+            scale=4,
-                scale=4,
+            upscaler=scaler,
-                upscaler=scaler,
+        ),
-                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        UpscalerData(
-            ),
+            name="R-ESRGAN 2x+",
-            UpscalerData(
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-                name="R-ESRGAN 4x+ Anime6B",
+            scale=2,
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+            upscaler=scaler,
-                scale=4,
+        ),
-                upscaler=scaler,
+    ]
                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
            ),
            UpscalerData(
                name="R-ESRGAN 2x+",
                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
                scale=2,
                upscaler=scaler,
                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
            ),
        ]
        return models
    except Exception:
        errors.report("Error making Real-ESRGAN models list", exc_info=True)
@@ -110,7 +110,7 @@ class ImageRNG:
        self.is_first = True
    def first(self):
-        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], self.seed_resize_from_h // 8, self.seed_resize_from_w // 8)
+        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
        xs = []
@@ -41,7 +41,7 @@ class ExtraNoiseParams:
 class CFGDenoiserParams:
-    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond, denoiser=None):
        self.x = x
        """Latent image representation in the process of being denoised"""
@@ -63,6 +63,9 @@ class CFGDenoiserParams:
        self.text_uncond = text_uncond
        """ Encoder hidden states of text conditioning from negative prompt"""
        self.denoiser = denoiser
        """Current CFGDenoiser object with processing parameters"""
 class CFGDenoisedParams:
    def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
@@ -11,11 +11,31 @@ from modules import shared, paths, script_callbacks, extensions, script_loading,
 AlwaysVisible = object()
 class MaskBlendArgs:
    def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):
        self.current_latent = current_latent
        self.nmask = nmask
        self.init_latent = init_latent
        self.mask = mask
        self.blended_latent = blended_latent
        self.denoiser = denoiser
        self.is_final_blend = denoiser is None
        self.sigma = sigma
 class PostSampleArgs:
    def __init__(self, samples):
        self.samples = samples
 class PostprocessImageArgs:
    def __init__(self, image):
        self.image = image
 class PostProcessMaskOverlayArgs:
    def __init__(self, index, mask_for_overlay, overlay_image):
        self.index = index
        self.mask_for_overlay = mask_for_overlay
        self.overlay_image = overlay_image
 class PostprocessBatchListArgs:
    def __init__(self, images):
@@ -71,6 +91,9 @@ class Script:
    setup_for_ui_only = False
    """If true, the script setup will only be run in Gradio UI, not in API"""
    controls = None
    """A list of controls retured by the ui()."""
    def title(self):
        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -206,6 +229,25 @@ class Script:
        pass
    def on_mask_blend(self, p, mba: MaskBlendArgs, *args):
        """
        Called in inpainting mode when the original content is blended with the inpainted content.
        This is called at every step in the denoising process and once at the end.
        If is_final_blend is true, this is called for the final blending stage.
        Otherwise, denoiser and sigma are defined and may be used to inform the procedure.
        """
        pass
    def post_sample(self, p, ps: PostSampleArgs, *args):
        """
        Called after the samples have been generated,
        but before they have been decoded by the VAE, if applicable.
        Check getattr(samples, 'already_decoded', False) to test if the images are decoded.
        """
        pass
    def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
        """
        Called for every image after it has been generated.
@@ -213,6 +255,22 @@ class Script:
        pass
    def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs, *args):
        """
        Called for every image after it has been generated.
        """
        pass
    def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs, *args):
        """
        Called for every image after it has been generated.
        Same as postprocess_image but after inpaint_full_res composite
        So that it operates on the full image instead of the inpaint_full_res crop region.
        """
        pass
    def postprocess(self, p, processed, *args):
        """
        This function is called after processing ends for AlwaysVisible scripts.
@@ -311,20 +369,113 @@ scripts_data = []
 postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 def topological_sort(dependencies):
    """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
    Ignores errors relating to missing dependeencies or circular dependencies
    """
    visited = {}
    result = []
    def inner(name):
        visited[name] = True
        for dep in dependencies.get(name, []):
            if dep in dependencies and dep not in visited:
                inner(dep)
        result.append(name)
    for depname in dependencies:
        if depname not in visited:
            inner(depname)
    return result
@dataclass
 class ScriptWithDependencies:
    script_canonical_name: str
    file: ScriptFile
    requires: list
    load_before: list
    load_after: list
 def list_scripts(scriptdirname, extension, *, include_extensions=True):
-    scripts_list = []
+    scripts = {}
-    basedir = os.path.join(paths.script_path, scriptdirname)
+    loaded_extensions = {ext.canonical_name: ext for ext in extensions.active()}
-    if os.path.exists(basedir):
+    loaded_extensions_scripts = {ext.canonical_name: [] for ext in extensions.active()}
-        for filename in sorted(os.listdir(basedir)):
+
-            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
+    # build script dependency map
    root_script_basedir = os.path.join(paths.script_path, scriptdirname)
    if os.path.exists(root_script_basedir):
        for filename in sorted(os.listdir(root_script_basedir)):
            if not os.path.isfile(os.path.join(root_script_basedir, filename)):
                continue
            if os.path.splitext(filename)[1].lower() != extension:
                continue
            script_file = ScriptFile(paths.script_path, filename, os.path.join(root_script_basedir, filename))
            scripts[filename] = ScriptWithDependencies(filename, script_file, [], [], [])
    if include_extensions:
        for ext in extensions.active():
-            scripts_list += ext.list_files(scriptdirname, extension)
+            extension_scripts_list = ext.list_files(scriptdirname, extension)
            for extension_script in extension_scripts_list:
                if not os.path.isfile(extension_script.path):
                    continue
-    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
+                script_canonical_name = ("builtin/" if ext.is_builtin else "") + ext.canonical_name + "/" + extension_script.filename
                relative_path = scriptdirname + "/" + extension_script.filename
                script = ScriptWithDependencies(
                    script_canonical_name=script_canonical_name,
                    file=extension_script,
                    requires=ext.metadata.get_script_requirements("Requires", relative_path, scriptdirname),
                    load_before=ext.metadata.get_script_requirements("Before", relative_path, scriptdirname),
                    load_after=ext.metadata.get_script_requirements("After", relative_path, scriptdirname),
                )
                scripts[script_canonical_name] = script
                loaded_extensions_scripts[ext.canonical_name].append(script)
    for script_canonical_name, script in scripts.items():
        # load before requires inverse dependency
        # in this case, append the script name into the load_after list of the specified script
        for load_before in script.load_before:
            # if this requires an individual script to be loaded before
            other_script = scripts.get(load_before)
            if other_script:
                other_script.load_after.append(script_canonical_name)
            # if this requires an extension
            other_extension_scripts = loaded_extensions_scripts.get(load_before)
            if other_extension_scripts:
                for other_script in other_extension_scripts:
                    other_script.load_after.append(script_canonical_name)
        # if After mentions an extension, remove it and instead add all of its scripts
        for load_after in list(script.load_after):
            if load_after not in scripts and load_after in loaded_extensions_scripts:
                script.load_after.remove(load_after)
                for other_script in loaded_extensions_scripts.get(load_after, []):
                    script.load_after.append(other_script.script_canonical_name)
    dependencies = {}
    for script_canonical_name, script in scripts.items():
        for required_script in script.requires:
            if required_script not in scripts and required_script not in loaded_extensions:
                errors.report(f'Script "{script_canonical_name}" requires "{required_script}" to be loaded, but it is not.', exc_info=False)
        dependencies[script_canonical_name] = script.load_after
    ordered_scripts = topological_sort(dependencies)
    scripts_list = [scripts[script_canonical_name].file for script_canonical_name in ordered_scripts]
    return scripts_list
@@ -365,15 +516,9 @@ def load_scripts():
            elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
-    def orderby(basedir):
+    # here the scripts_list is already ordered
-        # 1st webui, 2nd extensions-builtin, 3rd extensions
+    # processing_script is not considered though
-        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+    for scriptfile in scripts_list:
        for key in priority:
            if basedir.startswith(key):
                return priority[key]
        return 9999
    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
        try:
            if scriptfile.basedir != paths.script_path:
                sys.path = [scriptfile.basedir] + sys.path
@@ -433,7 +578,12 @@ class ScriptRunner:
        auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
        for script_data in auto_processing_scripts + scripts_data:
-            script = script_data.script_class()
+            try:
                script = script_data.script_class()
            except Exception:
                errors.report(f"Error # failed to initialize Script {script_data.module}: ", exc_info=True)
                continue
            script.filename = script_data.path
            script.is_txt2img = not is_img2img
            script.is_img2img = is_img2img
@@ -473,17 +623,26 @@ class ScriptRunner:
            on_after.clear()
    def create_script_ui(self, script):
        import modules.api.models as api_models
        script.args_from = len(self.inputs)
        script.args_to = len(self.inputs)
        try:
            self.create_script_ui_inner(script)
        except Exception:
            errors.report(f"Error creating UI for {script.name}: ", exc_info=True)
    def create_script_ui_inner(self, script):
        import modules.api.models as api_models
        controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
        script.controls = controls
        if controls is None:
            return
        script.name = wrap_call(script.title, script.filename, "title", default=script.filename).lower()
        api_args = []
        for control in controls:
@@ -550,6 +709,8 @@ class ScriptRunner:
        self.setup_ui_for_section(None, self.selectable_scripts)
        def select_script(script_index):
            if script_index is None:
                script_index = 0
            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
@@ -593,7 +754,7 @@ class ScriptRunner:
    def run(self, p, *args):
        script_index = args[0]
-        if script_index == 0:
+        if script_index == 0 or script_index is None:
            return None
        script = self.selectable_scripts[script_index-1]
@@ -672,6 +833,22 @@ class ScriptRunner:
            except Exception:
                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
    def post_sample(self, p, ps: PostSampleArgs):
        for script in self.alwayson_scripts:
            try:
                script_args = p.script_args[script.args_from:script.args_to]
                script.post_sample(p, ps, *script_args)
            except Exception:
                errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
    def on_mask_blend(self, p, mba: MaskBlendArgs):
        for script in self.alwayson_scripts:
            try:
                script_args = p.script_args[script.args_from:script.args_to]
                script.on_mask_blend(p, mba, *script_args)
            except Exception:
                errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
    def postprocess_image(self, p, pp: PostprocessImageArgs):
        for script in self.alwayson_scripts:
            try:
@@ -680,6 +857,22 @@ class ScriptRunner:
            except Exception:
                errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
    def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs):
        for script in self.alwayson_scripts:
            try:
                script_args = p.script_args[script.args_from:script.args_to]
                script.postprocess_maskoverlay(p, ppmo, *script_args)
            except Exception:
                errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
    def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs):
        for script in self.alwayson_scripts:
            try:
                script_args = p.script_args[script.args_from:script.args_to]
                script.postprocess_image_after_composite(p, pp, *script_args)
            except Exception:
                errors.report(f"Error running postprocess_image_after_composite: {script.filename}", exc_info=True)
    def before_component(self, component, **kwargs):
        for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
            try:
@@ -746,6 +939,23 @@ class ScriptRunner:
            except Exception:
                errors.report(f"Error running setup: {script.filename}", exc_info=True)
    def set_named_arg(self, args, script_type, arg_elem_id, value):
        script = next((x for x in self.scripts if type(x).__name__ == script_type), None)
        if script is None:
            return
        for i, control in enumerate(script.controls):
            if arg_elem_id in control.elem_id:
                index = script.args_from + i
                if isinstance(args, list):
                    args[index] = value
                    return args
                elif isinstance(args, tuple):
                    return args[:index] + (value,) + args[index+1:]
                else:
                    return None
 scripts_txt2img: ScriptRunner = None
 scripts_img2img: ScriptRunner = None
@@ -1,13 +1,56 @@
 import dataclasses
 import os
 import gradio as gr
 from modules import errors, shared
@dataclasses.dataclass
 class PostprocessedImageSharedInfo:
    target_width: int = None
    target_height: int = None
 class PostprocessedImage:
    def __init__(self, image):
        self.image = image
        self.info = {}
        self.shared = PostprocessedImageSharedInfo()
        self.extra_images = []
        self.nametags = []
        self.disable_processing = False
        self.caption = None
    def get_suffix(self, used_suffixes=None):
        used_suffixes = {} if used_suffixes is None else used_suffixes
        suffix = "-".join(self.nametags)
        if suffix:
            suffix = "-" + suffix
        if suffix not in used_suffixes:
            used_suffixes[suffix] = 1
            return suffix
        for i in range(1, 100):
            proposed_suffix = suffix + "-" + str(i)
            if proposed_suffix not in used_suffixes:
                used_suffixes[proposed_suffix] = 1
                return proposed_suffix
        return suffix
    def create_copy(self, new_image, *, nametags=None, disable_processing=False):
        pp = PostprocessedImage(new_image)
        pp.shared = self.shared
        pp.nametags = self.nametags.copy()
        pp.info = self.info.copy()
        pp.disable_processing = disable_processing
        if nametags is not None:
            pp.nametags += nametags
        return pp
 class ScriptPostprocessing:
@@ -42,10 +85,17 @@ class ScriptPostprocessing:
        pass
-    def image_changed(self):
+    def process_firstpass(self, pp: PostprocessedImage, **args):
        """
        Called for all scripts before calling process(). Scripts can examine the image here and set fields
        of the pp object to communicate things to other scripts.
        args contains a dictionary with all values returned by components from ui()
        """
        pass
-
+    def image_changed(self):
        pass
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
@@ -118,16 +168,42 @@ class ScriptPostprocessingRunner:
        return inputs
    def run(self, pp: PostprocessedImage, args):
-        for script in self.scripts_in_preferred_order():
+        scripts = []
            shared.state.job = script.name
        for script in self.scripts_in_preferred_order():
            script_args = args[script.args_from:script.args_to]
            process_args = {}
            for (name, _component), value in zip(script.controls.items(), script_args):
                process_args[name] = value
-            script.process(pp, **process_args)
+            scripts.append((script, process_args))
        for script, process_args in scripts:
            script.process_firstpass(pp, **process_args)
        all_images = [pp]
        for script, process_args in scripts:
            if shared.state.skipped:
                break
            shared.state.job = script.name
            for single_image in all_images.copy():
                if not single_image.disable_processing:
                    script.process(single_image, **process_args)
                for extra_image in single_image.extra_images:
                    if not isinstance(extra_image, PostprocessedImage):
                        extra_image = single_image.create_copy(extra_image)
                    all_images.append(extra_image)
                single_image.extra_images.clear()
        pp.extra_images = all_images[1:]
    def create_args_for_run(self, scripts_args):
        if not self.ui_created:
@@ -215,7 +215,7 @@ class LoadStateDictOnMeta(ReplaceHelper):
            would be on the meta device.
            """
-            if state_dict == sd:
+            if state_dict is sd:
                state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
            original(module, state_dict, strict=strict)
@@ -5,7 +5,7 @@ from types import MethodType
 from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
 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
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -38,8 +38,12 @@ ldm.models.diffusion.ddpm.print = shared.ldm_print
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
-ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+ldm_patched_forward = sd_unet.create_unet_forward(ldm.modules.diffusionmodules.openaimodel.UNetModel.forward)
-sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
+ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", ldm_patched_forward)
 sgm_patched_forward = sd_unet.create_unet_forward(sgm.modules.diffusionmodules.openaimodel.UNetModel.forward)
 sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sgm_patched_forward)
 def list_optimizers():
    new_optimizers = script_callbacks.list_optimizers_callback()
@@ -184,6 +188,20 @@ class StableDiffusionModelHijack:
            errors.display(e, "applying cross attention optimization")
            undo_optimizations()
    def convert_sdxl_to_ssd(self, m):
        """Converts an SDXL model to a Segmind Stable Diffusion model (see https://huggingface.co/segmind/SSD-1B)"""
        delattr(m.model.diffusion_model.middle_block, '1')
        delattr(m.model.diffusion_model.middle_block, '2')
        for i in ['9', '8', '7', '6', '5', '4']:
            delattr(m.model.diffusion_model.input_blocks[7][1].transformer_blocks, i)
            delattr(m.model.diffusion_model.input_blocks[8][1].transformer_blocks, i)
            delattr(m.model.diffusion_model.output_blocks[0][1].transformer_blocks, i)
            delattr(m.model.diffusion_model.output_blocks[1][1].transformer_blocks, i)
        delattr(m.model.diffusion_model.output_blocks[4][1].transformer_blocks, '1')
        delattr(m.model.diffusion_model.output_blocks[5][1].transformer_blocks, '1')
        devices.torch_gc()
    def hijack(self, m):
        conditioner = getattr(m, 'conditioner', None)
        if conditioner:
@@ -211,7 +229,7 @@ class StableDiffusionModelHijack:
            else:
                m.cond_stage_model = conditioner
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
            model_embeddings = m.cond_stage_model.roberta.embeddings
            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
            m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
@@ -242,8 +260,12 @@ class StableDiffusionModelHijack:
        self.layers = flatten(m)
        import modules.models.diffusion.ddpm_edit
        if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
            sd_unet.original_forward = ldm_original_forward
        elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
            sd_unet.original_forward = ldm_original_forward
        elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
            sd_unet.original_forward = sgm_original_forward
        else:
@@ -285,8 +307,6 @@ class StableDiffusionModelHijack:
        self.layers = None
        self.clip = None
        sd_unet.original_forward = None
    def apply_circular(self, enable):
        if self.circular_enabled == enable:
@@ -11,10 +11,14 @@ class CondFunc:
                    break
                except ImportError:
                    pass
-            for attr_name in func_path[i:-1]:
+            try:
-                resolved_obj = getattr(resolved_obj, attr_name)
+                for attr_name in func_path[i:-1]:
-            orig_func = getattr(resolved_obj, func_path[-1])
+                    resolved_obj = getattr(resolved_obj, attr_name)
-            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+                orig_func = getattr(resolved_obj, func_path[-1])
                setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
            except AttributeError:
                print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
                pass
        self.__init__(orig_func, sub_func, cond_func)
        return lambda *args, **kwargs: self(*args, **kwargs)
    def __init__(self, orig_func, sub_func, cond_func):
@@ -1,7 +1,6 @@
 import collections
 import os.path
 import sys
 import gc
 import threading
 import torch
@@ -231,15 +230,19 @@ def select_checkpoint():
    return checkpoint_info
-checkpoint_dict_replacements = {
+checkpoint_dict_replacements_sd1 = {
    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
 }
 checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
    'conditioner.embedders.0.': 'cond_stage_model.',
 }
-def transform_checkpoint_dict_key(k):
+
-    for text, replacement in checkpoint_dict_replacements.items():
+def transform_checkpoint_dict_key(k, replacements):
    for text, replacement in replacements.items():
        if k.startswith(text):
            k = replacement + k[len(text):]
@@ -250,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
    pl_sd = pl_sd.pop("state_dict", pl_sd)
    pl_sd.pop("state_dict", None)
    is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
    sd = {}
    for k, v in pl_sd.items():
-        new_key = transform_checkpoint_dict_key(k)
+        if is_sd2_turbo:
            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
        else:
            new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
        if new_key is not None:
            sd[new_key] = v
@@ -340,10 +348,28 @@ class SkipWritingToConfig:
        SkipWritingToConfig.skip = self.previous
 def check_fp8(model):
    if model is None:
        return None
    if devices.get_optimal_device_name() == "mps":
        enable_fp8 = False
    elif shared.opts.fp8_storage == "Enable":
        enable_fp8 = True
    elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
        enable_fp8 = True
    else:
        enable_fp8 = False
    return enable_fp8
 def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
    sd_model_hash = checkpoint_info.calculate_shorthash()
    timer.record("calculate hash")
    if devices.fp8:
        # prevent model to load state dict in fp8
        model.half()
    if not SkipWritingToConfig.skip:
        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
@@ -353,16 +379,19 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
    model.is_sdxl = hasattr(model, 'conditioner')
    model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
    model.is_sd1 = not model.is_sdxl and not model.is_sd2
-
+    model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
    if model.is_sdxl:
        sd_models_xl.extend_sdxl(model)
-    model.load_state_dict(state_dict, strict=False)
+    if model.is_ssd:
-    timer.record("apply weights to model")
+        sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
    if shared.opts.sd_checkpoint_cache > 0:
        # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = state_dict
+        checkpoints_loaded[checkpoint_info] = state_dict.copy()
    model.load_state_dict(state_dict, strict=False)
    timer.record("apply weights to model")
    del state_dict
@@ -372,6 +401,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
    if shared.cmd_opts.no_half:
        model.float()
        model.alphas_cumprod_original = model.alphas_cumprod
        devices.dtype_unet = torch.float32
        timer.record("apply float()")
    else:
@@ -385,7 +415,11 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
        if shared.cmd_opts.upcast_sampling and depth_model:
            model.depth_model = None
        alphas_cumprod = model.alphas_cumprod
        model.alphas_cumprod = None
        model.half()
        model.alphas_cumprod = alphas_cumprod
        model.alphas_cumprod_original = alphas_cumprod
        model.first_stage_model = vae
        if depth_model:
            model.depth_model = depth_model
@@ -393,6 +427,28 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
        devices.dtype_unet = torch.float16
        timer.record("apply half()")
    for module in model.modules():
        if hasattr(module, 'fp16_weight'):
            del module.fp16_weight
        if hasattr(module, 'fp16_bias'):
            del module.fp16_bias
    if check_fp8(model):
        devices.fp8 = True
        first_stage = model.first_stage_model
        model.first_stage_model = None
        for module in model.modules():
            if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
                if shared.opts.cache_fp16_weight:
                    module.fp16_weight = module.weight.data.clone().cpu().half()
                    if module.bias is not None:
                        module.fp16_bias = module.bias.data.clone().cpu().half()
                module.to(torch.float8_e4m3fn)
        model.first_stage_model = first_stage
        timer.record("apply fp8")
    else:
        devices.fp8 = False
    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
    model.first_stage_model.to(devices.dtype_vae)
@@ -640,6 +696,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
    else:
        weight_dtype_conversion = {
            'first_stage_model': None,
            'alphas_cumprod': None,
            '': torch.float16,
        }
@@ -735,7 +792,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
        return None
-def reload_model_weights(sd_model=None, info=None):
+def reload_model_weights(sd_model=None, info=None, forced_reload=False):
    checkpoint_info = info or select_checkpoint()
    timer = Timer()
@@ -747,11 +804,14 @@ def reload_model_weights(sd_model=None, info=None):
        current_checkpoint_info = None
    else:
        current_checkpoint_info = sd_model.sd_checkpoint_info
-        if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+        if check_fp8(sd_model) != devices.fp8:
            # load from state dict again to prevent extra numerical errors
            forced_reload = True
        elif sd_model.sd_model_checkpoint == checkpoint_info.filename and not forced_reload:
            return sd_model
    sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
-    if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+    if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
        return sd_model
    if sd_model is not None:
@@ -782,13 +842,13 @@ def reload_model_weights(sd_model=None, info=None):
        sd_hijack.model_hijack.hijack(sd_model)
        timer.record("hijack")
        script_callbacks.model_loaded_callback(sd_model)
        timer.record("script callbacks")
        if not sd_model.lowvram:
            sd_model.to(devices.device)
            timer.record("move model to device")
        script_callbacks.model_loaded_callback(sd_model)
        timer.record("script callbacks")
    print(f"Weights loaded in {timer.summary()}.")
    model_data.set_sd_model(sd_model)
@@ -798,17 +858,7 @@ def reload_model_weights(sd_model=None, info=None):
 def unload_model_weights(sd_model=None, info=None):
-    timer = Timer()
+    send_model_to_cpu(sd_model or shared.sd_model)
    if model_data.sd_model:
        model_data.sd_model.to(devices.cpu)
        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
        model_data.sd_model = None
        sd_model = None
        gc.collect()
        devices.torch_gc()
    print(f"Unloaded weights {timer.summary()}.")
    return sd_model
@@ -15,13 +15,14 @@ config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
 config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
 config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
 config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
 config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
 config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
 config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
 config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
 config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
 config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
 config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
-
+config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
 def is_using_v_parameterization_for_sd2(state_dict):
    """
@@ -71,7 +72,10 @@ def guess_model_config_from_state_dict(sd, filename):
    sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
    if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
-        return config_sdxl
+        if diffusion_model_input.shape[1] == 9:
            return config_sdxl_inpainting
        else:
            return config_sdxl
    if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
        return config_sdxl_refiner
    elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
@@ -95,7 +99,10 @@ def guess_model_config_from_state_dict(sd, filename):
        if diffusion_model_input.shape[1] == 8:
            return config_instruct_pix2pix
    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
        if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
            return config_alt_diffusion_m18
        return config_alt_diffusion
    return config_default
@@ -22,7 +22,10 @@ class WebuiSdModel(LatentDiffusion):
    """structure with additional information about the file with model's weights"""
    is_sdxl: bool
-    """True if the model's architecture is SDXL"""
+    """True if the model's architecture is SDXL or SSD"""
    is_ssd: bool
    """True if the model is SSD"""
    is_sd2: bool
    """True if the model's architecture is SD 2.x"""
@@ -6,6 +6,7 @@ import sgm.models.diffusion
 import sgm.modules.diffusionmodules.denoiser_scaling
 import sgm.modules.diffusionmodules.discretizer
 from modules import devices, shared, prompt_parser
 from modules import torch_utils
 def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@@ -34,6 +35,12 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
 def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
    sd = self.model.state_dict()
    diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
    if diffusion_model_input is not None:
        if diffusion_model_input.shape[1] == 9:
            x = torch.cat([x] + cond['c_concat'], dim=1)
    return self.model(x, t, cond)
@@ -84,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt
 def extend_sdxl(model):
    """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
-    dtype = next(model.model.diffusion_model.parameters()).dtype
+    dtype = torch_utils.get_param(model.model.diffusion_model).dtype
    model.model.diffusion_model.dtype = dtype
    model.model.conditioning_key = 'crossattn'
    model.cond_stage_key = 'txt'
@@ -93,7 +100,7 @@ def extend_sdxl(model):
    model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
    discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
-    model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+    model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32)
    model.conditioner.wrapped = torch.nn.Module()
@@ -1,4 +1,4 @@
-from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared
 # imports for functions that previously were here and are used by other modules
 from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  # noqa: F401
@@ -6,6 +6,7 @@ from modules.sd_samplers_common import samples_to_image_grid, sample_to_image  #
 all_samplers = [
    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
    *sd_samplers_timesteps.samplers_data_timesteps,
    *sd_samplers_lcm.samplers_data_lcm,
 ]
 all_samplers_map = {x.name: x for x in all_samplers}
@@ -56,6 +56,9 @@ class CFGDenoiser(torch.nn.Module):
        self.sampler = sampler
        self.model_wrap = None
        self.p = None
        # NOTE: masking before denoising can cause the original latents to be oversmoothed
        # as the original latents do not have noise
        self.mask_before_denoising = False
    @property
@@ -105,8 +108,21 @@ class CFGDenoiser(torch.nn.Module):
        assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
        # If we use masks, blending between the denoised and original latent images occurs here.
        def apply_blend(current_latent):
            blended_latent = current_latent * self.nmask + self.init_latent * self.mask
            if self.p.scripts is not None:
                from modules import scripts
                mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
                self.p.scripts.on_mask_blend(self.p, mba)
                blended_latent = mba.blended_latent
            return blended_latent
        # Blend in the original latents (before)
        if self.mask_before_denoising and self.mask is not None:
-            x = self.init_latent * self.mask + self.nmask * x
+            x = apply_blend(x)
        batch_size = len(conds_list)
        repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -130,7 +146,7 @@ class CFGDenoiser(torch.nn.Module):
            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond, self)
        cfg_denoiser_callback(denoiser_params)
        x_in = denoiser_params.x
        image_cond_in = denoiser_params.image_cond
@@ -207,8 +223,9 @@ class CFGDenoiser(torch.nn.Module):
        else:
            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
        # Blend in the original latents (after)
        if not self.mask_before_denoising and self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
+            denoised = apply_blend(denoised)
        self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
@@ -60,7 +60,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
                while restart_times > 0:
                    restart_times -= 1
-                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
+                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
    last_sigma = None
    for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):
--- a/Show More
+++ b/Show More