Fix SD upscale Batch count (#2950)

Co-authored-by: LilithDragoness <secret@email.com>

.eslintignore

@@ -1,4 +1,5 @@
 extensions
 extensions-disabled
+extensions-builtin/sd_forge_controlnet
 repositories
 venv

.eslintrc.js

@@ -78,6 +78,8 @@ module.exports = {
         //extraNetworks.js
         requestGet: "readonly",
         popup: "readonly",
+        // profilerVisualization.js
+        createVisualizationTable: "readonly",
         // from python
         localization: "readonly",
         // progrssbar.js
@@ -86,8 +88,6 @@ module.exports = {
         // imageviewer.js
         modalPrevImage: "readonly",
         modalNextImage: "readonly",
-        // token-counters.js
-        setupTokenCounters: "readonly",
         // localStorage.js
         localSet: "readonly",
         localGet: "readonly",

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,105 +0,0 @@
-name: Bug Report
-description: You think something is broken in the UI
-title: "[Bug]: "
-labels: ["bug-report"]
-
-body:
-  - type: markdown
-    attributes:
-      value: |
-        > 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
-    id: steps
-    attributes:
-      label: Steps to reproduce the problem
-      description: Please provide us with precise step by step instructions on how to reproduce the bug
-      placeholder: |
-        1. Go to ...
-        2. Press ...
-        3. ...
-    validations:
-      required: true
-  - type: textarea
-    id: what-should
-    attributes:
-      label: What should have happened?
-      description: Tell us what you think the normal behavior should be
-      placeholder: |
-        WebUI should ...
-    validations:
-      required: true
-  - type: dropdown
-    id: browsers
-    attributes:
-      label: What browsers do you use to access the UI ?
-      multiple: true
-      options:
-        - Mozilla Firefox
-        - Google Chrome
-        - 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 the bug occured. If it's very long, provide a link to pastebin or similar service.
-      render: Shell
-    validations:
-      required: true
-  - type: textarea
-    id: misc
-    attributes:
-      label: Additional information
-      description: | 
-        Please provide us with any relevant additional info or context.
-        Examples:
-        　I have updated my GPU driver recently.

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: WebUI Community Support
-    url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
-    about: Please ask and answer questions here.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,40 +0,0 @@
-name: Feature request
-description: Suggest an idea for this project
-title: "[Feature Request]: "
-labels: ["enhancement"]
-
-body:
-  - type: checkboxes
-    attributes:
-      label: Is there an existing issue for this?
-      description: Please search to see if an issue already exists for the feature you want, and that it's not implemented 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, provide screenshots and/or illustrations of the feature if possible*
-  - type: textarea
-    id: feature
-    attributes:
-      label: What would your feature do ?
-      description: Tell us about your feature in a very clear and simple way, and what problem it would solve
-    validations:
-      required: true
-  - type: textarea
-    id: workflow
-    attributes:
-      label: Proposed workflow
-      description: Please provide us with step by step information on how you'd like the feature to be accessed and used
-      value: |
-        1. Go to .... 
-        2. Press ....
-        3. ...
-    validations:
-      required: true
-  - type: textarea
-    id: misc
-    attributes:
-      label: Additional information
-      description: Add any other context or screenshots about the feature request here.

.github/pull_request_template.md

@@ -1,15 +0,0 @@
-## Description
-
-* a simple description of what you're trying to accomplish
-* a summary of changes in code
-* which issues it fixes, if any
-
-## Screenshots/videos:
-
-
-## Checklist:
-
-- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
-- [ ] I have performed a self-review of my own code
-- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
-- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)

.github/workflows/on_pull_request.yaml

@@ -1,38 +0,0 @@
-name: Linter
-
-on:
-  - push
-  - pull_request
-
-jobs:
-  lint-python:
-    name: ruff
-    runs-on: ubuntu-latest
-    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
-        with:
-          python-version: 3.11
-          # NB: there's no cache: pip here since we're not installing anything
-          #     from the requirements.txt file(s) in the repository; it's faster
-          #     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==0.1.6
-      - name: Run Ruff
-        run: ruff .
-  lint-js:
-    name: eslint
-    runs-on: ubuntu-latest
-    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v3
-      - name: Install Node.js
-        uses: actions/setup-node@v3
-        with:
-          node-version: 18
-      - run: npm i --ci
-      - run: npm run lint

.github/workflows/run_tests.yaml

@@ -1,81 +0,0 @@
-name: Tests
-
-on:
-  - push
-  - pull_request
-
-jobs:
-  test:
-    name: tests on CPU with empty model
-    runs-on: ubuntu-latest
-    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v3
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
-        with:
-          python-version: 3.10.6
-          cache: pip
-          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:
-          PIP_DISABLE_PIP_VERSION_CHECK: "1"
-          PIP_PROGRESS_BAR: "off"
-      - name: Setup environment
-        run: python launch.py --skip-torch-cuda-test --exit
-        env:
-          PIP_DISABLE_PIP_VERSION_CHECK: "1"
-          PIP_PROGRESS_BAR: "off"
-          TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
-          WEBUI_LAUNCH_LIVE_OUTPUT: "1"
-          PYTHONUNBUFFERED: "1"
-      - name: Print installed packages
-        run: pip freeze
-      - name: Start test server
-        run: >
-          python -m coverage run
-          --data-file=.coverage.server
-          launch.py
-          --skip-prepare-environment
-          --skip-torch-cuda-test
-          --test-server
-          --do-not-download-clip
-          --no-half
-          --disable-opt-split-attention
-          --use-cpu all
-          --api-server-stop
-          2>&1 | tee output.txt &
-      - name: Run tests
-        run: |
-          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()
-        run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
-      - name: Show coverage
-        run: |
-          python -m coverage combine .coverage*
-          python -m coverage report -i
-          python -m coverage html -i
-      - name: Upload main app output
-        uses: actions/upload-artifact@v3
-        if: always()
-        with:
-          name: output
-          path: output.txt
-      - name: Upload coverage HTML
-        uses: actions/upload-artifact@v3
-        if: always()
-        with:
-          name: htmlcov
-          path: htmlcov

.github/workflows/warns_merge_master.yml

@@ -1,19 +0,0 @@
-name: Pull requests can't target master branch
-
-"on":
-  pull_request:
-    types:
-      - opened
-      - synchronize
-      - reopened
-    branches:
-      - master
-
-jobs:
-  check:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Warning marge into master
-        run: |
-          echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch."
-          exit 1

.gitignore

@@ -1,7 +1,17 @@
+huggingface_space_mirror/
+random_test.py
 __pycache__
 *.ckpt
 *.safetensors
 *.pth
+*.dev.js
+*_s.py
+*_u.py
+*_m.py
+*_i.py
+.DS_Store
+/output/
+/outputs/
 /ESRGAN/*
 /SwinIR/*
 /repositories
@@ -39,3 +49,9 @@ notification.mp3
 /package-lock.json
 /.coverage*
 /test/test_outputs
+/cache
+trace.json
+/sysinfo-????-??-??-??-??.json
+/test/results.xml
+coverage.xml
+**/tests/**/expectations

CHANGELOG.md

@@ -1,3 +1,413 @@
+## 1.10.1
+
+### Bug Fixes:
+* fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275))
+
+
+## 1.10.0
+
+### Features:
+* A lot of performance improvements (see below in Performance section)
+* Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212))
+  * Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
+  * T5 text model is disabled by default, enable it in settings
+* New schedulers:
+  * Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751))
+  * KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608))
+  * Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
+  * DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
+  * Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142))
+  * Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235))
+* New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035))
+
+### Minor:
+* Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607))
+* Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742))
+* Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682))
+* Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679))
+* Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602))
+* More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627))
+* When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610))
+* Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978))
+* Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992))
+* Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001))
+* ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065))
+* Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
+* option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242))
+
+### Extensions and API:
+* Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984))
+* Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140))
+
+### Performance:
+* [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803))
+* [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804))
+* [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806))
+* [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816))
+* [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820))
+* [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824))
+* [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823))
+* Check for nans in unet only once, after all steps have been completed
+* Added pption to run torch profiler for image generation
+
+### Bug Fixes:
+* Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958))
+* feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943))
+* Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893))
+* Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600))
+* Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656))
+* more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681))
+* Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831))
+* XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832))
+* fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815))
+* fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797))
+* update pickle safe filenames
+* use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757))
+* When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750))
+* Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738))
+* use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705))
+* fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664))
+* use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680))
+* Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657))
+* Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654))
+* QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632))
+* replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968))
+* Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976))
+* multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988))
+* fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118))
+* Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151))
+* Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116))
+* Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092))
+* stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085))
+* Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079))
+* fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062))
+* Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054))
+* fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
+* fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239))
+* [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202))
+* fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194))
+
+### Other:
+* fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907))
+* ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864))
+* Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851))
+* XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836))
+* scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830))
+* img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817))
+* chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783))
+* Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739))
+* Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730))
+* no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641))
+* .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980))
+* Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144))
+* Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119))
+* docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102))
+* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192))
+* Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059))
+* remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061))
+* chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060))
+* shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170))
+* activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231))
+* add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218))
+* update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178))
+* Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173))
+* do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180))
+* Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166))
+
+
+## 1.9.4
+
+### Bug Fixes:
+*  pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882)) 
+
+## 1.9.3
+
+### Bug Fixes:
+*  fix get_crop_region_v2 ([#15594](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15594)) 
+
+## 1.9.2
+
+### Extensions and API:
+* restore 1.8.0-style naming of scripts
+
+## 1.9.1
+
+### Minor:
+* Add avif support ([#15582](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15582))
+* Add filename patterns: `[sampler_scheduler]` and `[scheduler]` ([#15581](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15581))
+
+### Extensions and API:
+* undo adding scripts to sys.modules
+* Add schedulers API endpoint ([#15577](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15577))
+* Remove API upscaling factor limits ([#15560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15560))
+
+### Bug Fixes:
+* Fix images do not match / Coordinate 'right' is less than 'left' ([#15534](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15534))
+* fix: remove_callbacks_for_function should also remove from the ordered map ([#15533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15533))
+* fix x1 upscalers ([#15555](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15555))
+* Fix cls.__module__ value in extension script ([#15532](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15532))
+* fix typo in function call (eror -> error) ([#15531](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15531))
+
+### Other:
+* Hide 'No Image data blocks found.' message ([#15567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15567))
+* Allow webui.sh to be runnable from arbitrary directories containing a .git file ([#15561](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15561))
+* Compatibility with Debian 11, Fedora 34+ and openSUSE 15.4+ ([#15544](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15544))
+* numpy DeprecationWarning product -> prod ([#15547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15547))
+* get_crop_region_v2 ([#15583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15583), [#15587](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15587))
+
+
+## 1.9.0
+
+### Features:
+* Make refiner switchover based on model timesteps instead of sampling steps ([#14978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14978))
+* add an option to have old-style directory view instead of tree view; stylistic changes for extra network sorting/search controls
+* add UI for reordering callbacks, support for specifying callback order in extension metadata ([#15205](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15205))
+* Sgm uniform scheduler for SDXL-Lightning models ([#15325](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15325))
+* Scheduler selection in main UI ([#15333](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15333), [#15361](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15361), [#15394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15394))
+
+### Minor:
+* "open images directory" button now opens the actual dir ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
+* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
+* make extra network card description plaintext by default, with an option to re-enable HTML as it was
+* resize handle for extra networks ([#15041](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15041))
+* cmd args: `--unix-filenames-sanitization` and `--filenames-max-length` ([#15031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15031))
+* show extra networks parameters in HTML table rather than raw JSON ([#15131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15131))
+* Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr ([#15160](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15160), [#15283](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15283))
+* Add '--no-prompt-history' cmd args for disable last generation prompt history ([#15189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15189))
+* update preview on Replace Preview ([#15201](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15201))
+* only fetch updates for extensions' active git branches ([#15233](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15233))
+* put upscale postprocessing UI into an accordion ([#15223](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15223))
+* Support dragdrop for URLs to read infotext ([#15262](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15262))
+* use diskcache library for caching ([#15287](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15287), [#15299](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15299))
+* Allow PNG-RGBA for Extras Tab ([#15334](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15334))
+* Support cover images embedded in safetensors metadata ([#15319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15319))
+* faster interrupt when using NN upscale ([#15380](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15380))
+* Extras upscaler: an input field to limit maximul side length for the output image ([#15293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15293), [#15415](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15415), [#15417](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15417), [#15425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15425))
+* add an option to hide postprocessing options in Extras tab
+
+### Extensions and API:
+* ResizeHandleRow - allow overriden column scale parametr ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
+* call script_callbacks.ui_settings_callback earlier; fix extra-options-section built-in extension killing the ui if using a setting that doesn't exist
+* make it possible to use zoom.js outside webui context ([#15286](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15286), [#15288](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15288))
+* allow variants for extension name in metadata.ini ([#15290](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15290))
+* make reloading UI scripts optional when doing Reload UI, and off by default
+* put request: gr.Request at start of img2img function similar to txt2img
+* open_folder as util ([#15442](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15442))
+* make it possible to import extensions' script files as `import scripts.<filename>` ([#15423](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15423))
+
+### Performance:
+* performance optimization for extra networks HTML pages
+* optimization for extra networks filtering
+* optimization for extra networks sorting
+
+### Bug Fixes:
+* prevent escape button causing an interrupt when no generation has been made yet
+* [bug] avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
+* possible fix for reload button not appearing in some cases for extra networks.
+* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
+* Fix resize-handle visability for vertical layout (mobile) ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010))
+* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
+* Protect alphas_cumprod during refiner switchover ([#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
+* Fix EXIF orientation in API image loading ([#15062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15062))
+* Only override emphasis if actually used in prompt ([#15141](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15141))
+* Fix emphasis infotext missing from `params.txt` ([#15142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15142))
+* fix extract_style_text_from_prompt #15132 ([#15135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15135))
+* Fix Soft Inpaint for AnimateDiff ([#15148](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15148))
+* edit-attention: deselect surrounding whitespace ([#15178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15178))
+* chore: fix font not loaded ([#15183](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15183))
+* use natural sort in extra networks when ordering by path
+* Fix built-in lora system bugs caused by torch.nn.MultiheadAttention ([#15190](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15190))
+* Avoid error from None in get_learned_conditioning ([#15191](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15191))
+* Add entry to MassFileLister after writing metadata ([#15199](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15199))
+* fix issue with Styles when Hires prompt is used ([#15269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15269), [#15276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15276))
+* Strip comments from hires fix prompt ([#15263](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15263))
+* Make imageviewer event listeners browser consistent ([#15261](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15261))
+* Fix AttributeError in OFT when trying to get MultiheadAttention weight ([#15260](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15260))
+* Add missing .mean() back ([#15239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15239))
+* fix "Restore progress" button ([#15221](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15221))
+* fix ui-config for InputAccordion [custom_script_source] ([#15231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15231))
+* handle 0 wheel deltaY ([#15268](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15268))
+* prevent alt menu for firefox ([#15267](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15267))
+* fix: fix syntax errors ([#15179](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15179))
+* restore outputs path ([#15307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15307))
+* Escape btn_copy_path filename ([#15316](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15316))
+* Fix extra networks buttons when filename contains an apostrophe ([#15331](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15331))
+* escape brackets in lora random prompt generator ([#15343](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15343))
+* fix: Python version check for PyTorch installation compatibility ([#15390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15390))
+* fix typo in call_queue.py ([#15386](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15386))
+* fix: when find already_loaded model, remove loaded by array index ([#15382](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15382))
+* minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350))
+* Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414))
+* Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428))
+* fix limited file write (thanks, Sylwia)
+* Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465))
+* error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470))
+
+### Hardware:
+* Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981))
+* Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820))
+* Better workaround for Navi1, removing --pre for Navi3 ([#15224](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15224))
+* Ascend NPU wiki page ([#15228](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15228))
+
+### Other:
+* Update comment for Pad prompt/negative prompt v0 to add a warning about truncation, make it override the v1 implementation
+* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
+* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
+* Use `absolute` path for normalized filepath ([#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
+* resizeHandle handle double tap ([#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
+* --dat-models-path cmd flag ([#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
+* Add a direct link to the binary release ([#15059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15059))
+* upscaler_utils: Reduce logging ([#15084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15084))
+* Fix various typos with crate-ci/typos ([#15116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15116))
+* fix_jpeg_live_preview ([#15102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15102))
+* [alternative fix] can't load webui if selected wrong extra option in ui ([#15121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15121))
+* Error handling for unsupported transparency ([#14958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14958))
+* Add model description to searched terms ([#15198](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15198))
+* bump action version ([#15272](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15272))
+* PEP 604 annotations ([#15259](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15259))
+* Automatically Set the Scale by value when user selects an Upscale Model ([#15244](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15244))
+* move postprocessing-for-training into builtin extensions ([#15222](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15222))
+* type hinting in shared.py ([#15211](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15211))
+* update ruff to 0.3.3
+* Update pytorch lightning utilities ([#15310](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15310))
+* Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354))
+* Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443))
+* re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446))
+* create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460))
+* update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492))
+
+
+## 1.8.0
+
+### Features:
+* Update torch to version 2.1.2
+* Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208))
+* FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327))
+* Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390))
+* Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809))
+* Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings)
+* Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
+* Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728))
+* [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers
+* Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583))
+* Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
+* Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900))
+* NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801))
+* Prompt comments support
+
+### Minor:
+* Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296))
+* add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307))
+* Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269))
+* Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910))
+* Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464))
+* Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475))
+* xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180))
+* option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295))
+* Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597))
+* Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659))
+* Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291))
+* Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789))
+* Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589))
+* Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638))
+* Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663))
+* Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884))
+* Add an option to not overlay original image for inpainting for #14727
+* Add Pad conds v0 option to support same generation with DDIM as before 1.6.0
+* Add "Interrupting..." placeholder.
+* Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857))
+* Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874))
+* When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior)
+* Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
+* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
+* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
+
+### Extensions and API:
+* Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve
+* Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314))
+* add override_settings support for infotext API
+* rename generation_parameters_copypaste module to infotext_utils
+* prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407))
+* Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471))
+* Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478))
+* Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547))
+* Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563))
+* Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573))
+* Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593))
+* New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657))
+* modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715))
+* set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773))
+* add before_token_counter callback and use it for prompt comments
+* ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
+
+### Performance:
+* Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
+* Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512))
+* Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527))
+
+### Bug Fixes:
+* fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707))
+* Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726))
+* Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266))
+* fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330))
+* include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452))
+* xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394))
+* Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354))
+* fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421))
+* handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487))
+* handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767))
+* paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523))
+* Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537))
+* Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538))
+* Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560))
+* Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699))
+* Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655))
+* Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640))
+* Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639))
+* Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637))
+* Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689))
+* Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702))
+* XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754))
+* fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775))
+* Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791))
+* catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814))
+* fix error when editing extra networks card
+* fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time.
+* util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879))
+* if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873))
+* call the right function for token counter in img2img
+* Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939))
+* Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933))
+* Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932))
+* Disable prompt token counters option actually disables token counting rather than just hiding results.
+* avoid double upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
+* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
+* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
+* Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
+
+### Other:
+* Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270))
+* change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216))
+* Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293))
+* Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446))
+* reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367))
+* Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472))
+* only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352))
+* Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495))
+* Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548))
+* hires button, fix seeds ()
+* Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618))
+* Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645))
+* improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709))
+* Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782))
+* add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803))
+* extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885))
+* Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890))
+* Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930))
+* Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
+* Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916))
+* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
+
 ## 1.7.0
 
 ### Features:
@@ -40,7 +450,8 @@
 * 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))
+* allow use of multiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
+* make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241))
 
 ### Extensions and API:
 * update gradio to 3.41.2
@@ -176,7 +587,7 @@
  * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
  * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
    * makes all of them work with img2img
-   * makes prompt composition posssible (AND)
+   * makes prompt composition possible (AND)
    * makes them available for SDXL
  * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
  * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
@@ -352,7 +763,7 @@
  * user metadata system for custom networks
  * extended Lora metadata editor: set activation text, default weight, view tags, training info
  * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
- * show github stars for extenstions
+ * show github stars for extensions
  * img2img batch mode can read extra stuff from png info
  * img2img batch works with subdirectories
  * hotkeys to move prompt elements: alt+left/right
@@ -571,7 +982,7 @@
  * do not wait for Stable Diffusion model to load at startup
  * add filename patterns: `[denoising]`
  * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
- * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metadata of the file, if present, instead of filename (both can be used to activate LoRA)
  * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
  * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
  * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
@@ -601,7 +1012,7 @@
  * fix gamepad navigation
  * make the lightbox fullscreen image function properly
  * fix squished thumbnails in extras tab
- * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everything after you refreshed)
  * fix webui showing the same image if you configure the generation to always save results into same file
  * fix bug with upscalers not working properly
  * fix MPS on PyTorch 2.0.1, Intel Macs
@@ -619,7 +1030,7 @@
  * switch to PyTorch 2.0.0 (except for AMD GPUs)
  * visual improvements to custom code scripts
  * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
- * add support for saving init images in img2img, and record their hashes in infotext for reproducability
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducibility
  * automatically select current word when adjusting weight with ctrl+up/down
  * add dropdowns for X/Y/Z plot
  * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs

LICENSE.txt

@@ -661,3 +661,28 @@ specific requirements.
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU AGPL, see
 <https://www.gnu.org/licenses/>.
+
+
+---------------------------------Facebook BNB-------------------------------
+
+MIT License
+
+Copyright (c) Facebook, Inc. and its affiliates.
+
+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.

NEWS.md

@@ -0,0 +1,7 @@
+About Gradio 5: will try to upgrade to Gradio 5 at about 2025 March. If failed, then will try again on about 2025 June. relatively positive that we can have Gradio5 before next summer.
+
+2024 Oct 28: A new branch `sd35` is contributed by [#2183](https://github.com/lllyasviel/stable-diffusion-webui-forge/pull/2183) . I will take a look at quants and sampling and transformer's clip-g vs that clip-g rewrite before merging to main ... (Oct 29: okay maybe medium also need to take a look later)
+
+About Flux ControlNet (sync [here](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/932)): The rewrite of ControlNet Intergrated will ~start at about Sep 29~ (delayed) ~start at about Oct 15~  (delayed) ~start at about Oct 30~ (delayed) start at about Nov 20. (When this note is announced, the main targets include some diffusers formatted Flux ControlNets and some community implementation of Union ControlNets. However, this may be extended if stronger models come out after this note.)
+
+2024 Sep 7: New sampler `Flux Realistic` is available now! Recommended scheduler is "simple".

README.md

@@ -1,171 +1,166 @@
-# Stable Diffusion Web UI Forge
+# Stable Diffusion WebUI Forge
 
-Stable Diffusion Web UI Forge is a platform on top of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to make development easier, optimize resource management, and speed up inference.
+Stable Diffusion WebUI Forge is a platform on top of [Stable Diffusion WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (based on [Gradio](https://www.gradio.app/) <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>) to make development easier, optimize resource management, speed up inference, and study experimental features.
 
 The name "Forge" is inspired from "Minecraft Forge". This project is aimed at becoming SD WebUI's Forge.
 
-Compared to original WebUI (for SDXL inference at 1024px), you can expect the below speed-ups:
+Forge is currently based on SD-WebUI 1.10.1 at [this commit](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/82a973c04367123ae98bd9abdf80d9eda9b910e2). (Because original SD-WebUI is almost static now, Forge will sync with original WebUI every 90 days, or when important fixes.)
 
-1. If you use common GPU like 8GB vram, you are expected to get about **30~45% speed up** in inference speed (it/s), the GPU memory peak (in task manager) will drop about 700MB to 1.3GB, the maximum diffusion resolution (that will not OOM) will increase about 2x to 3x, and the maximum diffusion batch size (that will not OOM) will increase about 4x to 6x.
+News are moved to this link: [Click here to see the News section](https://github.com/lllyasviel/stable-diffusion-webui-forge/blob/main/NEWS.md)
 
-2. If you use less powerful GPU like 6GB vram, you are expected to get about **60~75% speed up** in inference speed (it/s), the GPU memory peak (in task manager) will drop about 800MB to 1.5GB, the maximum diffusion resolution (that will not OOM) will increase about 3x, and the maximum diffusion batch size (that will not OOM) will increase about 4x.
+# Quick List
 
-3. If you use powerful GPU like 4090 with 24GB vram, you are expected to get about **3~6% speed up** in inference speed (it/s), the GPU memory peak (in task manager) will drop about 1GB to 1.4GB, the maximum diffusion resolution (that will not OOM) will increase about 1.6x, and the maximum diffusion batch size (that will not OOM) will increase about 2x.
+[Gradio 4 UI Must Read (TLDR: You need to use RIGHT MOUSE BUTTON to move canvas!)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/853)
 
-4. If you use ControlNet for SDXL, the maximum ControlNet count (that will not OOM) will increase about 2x, the speed with SDXL+ControlNet will **speed up about 30~45%**.
+[Flux Tutorial (BitsandBytes Models, NF4, "GPU Weight", "Offload Location", "Offload Method", etc)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/981)
 
-Another very important change that Forge brings is **Unet Patcher**. Using Unet Patcher, methods like Self-Attention Guidance, Kohya High Res Fix, FreeU, StyleAlign, Hypertile can all be implemented in about 100 lines of codes. 
+[Flux Tutorial 2 (Seperated Full Models, GGUF, Technically Correct Comparison between GGUF and NF4, etc)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1050)
 
-Thanks to Unet Patcher, many new things are possible now and supported in Forge, including SVD, Z123, masked Ip-adapter, masked controlnet, photomaker, etc.
+[Forge Extension List and Extension Replacement List (Temporary)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1754)
 
-**No need to monkey patch UNet and conflict other extensions anymore!**
+[How to make LoRAs more precise on low-bit models; How to Skip" Patching LoRAs"; How to only load LoRA one time rather than each generation; How to report LoRAs that do not work](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1038)
+
+[Report Flux Performance Problems (TLDR: DO NOT set "GPU Weight" too high! Lower "GPU Weight" solves 99% problems!)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1181)
+
+[How to solve "Connection errored out" / "Press anykey to continue ..." / etc](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1474)
+
+[(Save Flux BitsandBytes UNet/Checkpoint)](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1224#discussioncomment-10384104)
+
+[LayerDiffuse Transparent Image Editing](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/854)
+
+[Tell us what is missing in ControlNet Integrated](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/932)
+
+[(Policy) Soft Advertisement Removal Policy](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/1286)
+
+(Flux BNB NF4 / GGUF Q8_0/Q5_0/Q5_1/Q4_0/Q4_1 are all natively supported with GPU weight slider and Quene/Async Swap toggle and swap location toggle. All Flux BNB NF4 / GGUF Q8_0/Q5_0/Q4_0 have LoRA support.)
 
 # Installing Forge
 
-You can install Forge using same method as SD-WebUI. (Install Git, Python, Git Clone this repo and then run webui-user.bat).
+**Just use this one-click installation package (with git and python included).**
 
-**Or you can just use this one-click installation package (with git and python included).**
+[>>> Click Here to Download One-Click Package (CUDA 12.1 + Pytorch 2.3.1) <<<](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch231.7z)
 
-[>>> Click Here to Download One-Click Package<<<]()
+Some other CUDA/Torch Versions:
 
-After you download, you can use `update.bat` to update and use `run.bat` to run.
+[Forge with CUDA 12.1 + Pytorch 2.3.1](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch231.7z) <- **Recommended**
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/1251ee5c-33dc-4adc-8dd3-2b43af5ad425)
+[Forge with CUDA 12.4 + Pytorch 2.4](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu124_torch24.7z) <- **Fastest**, but MSVC may be broken, xformers may not work
 
+[Forge with CUDA 12.1 + Pytorch 2.1](https://github.com/lllyasviel/stable-diffusion-webui-forge/releases/download/latest/webui_forge_cu121_torch21.7z) <- the previously used old environments
 
-# Screenshots of Comparison
+After you download, you uncompress, use `update.bat` to update, and use `run.bat` to run.
 
-I tested with several devices, and this is a typical result from 8GB VRAM (3070ti laptop) with SDXL.
+Note that running `update.bat` is important, otherwise you may be using a previous version with potential bugs unfixed.
 
-**This is original WebUI:**
+![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/c49bd60d-82bd-4086-9859-88d472582b94)
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/c32baedd-500b-408f-8cfb-ed4570c883bd)
+### Advanced Install
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/cb6098de-f2d4-4b25-9566-df4302dda396)
+If you are proficient in Git and you want to install Forge as another branch of SD-WebUI, please see [here](https://github.com/continue-revolution/sd-webui-animatediff/blob/forge/master/docs/how-to-use.md#you-have-a1111-and-you-know-git). In this way, you can reuse all SD checkpoints and all extensions you installed previously in your OG SD-WebUI, but you should know what you are doing.
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/5447e8b7-f3ca-4003-9961-02027c8181e8)
+If you know what you are doing, you can also install Forge using same method as SD-WebUI. (Install Git, Python, Git Clone the forge repo `https://github.com/lllyasviel/stable-diffusion-webui-forge.git` and then run webui-user.bat).
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/f3cb57d9-ac7a-4667-8b3f-303139e38afa)
+### Previous Versions
 
-(average about 7.4GB/8GB, peak at about 7.9GB/8GB)
+You can download previous versions [here](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/849).
 
-**This is WebUI Forge:**
+# Forge Status
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/0c45cd98-0b14-42c3-9556-28e48d4d5fa0)
+Based on manual test one-by-one:
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/3a71f5d4-39e5-4ab1-81cf-8eaa790a2dc8)
+| Component                                           | Status                                      | Last Test    |
+|-----------------------------------------------------|---------------------------------------------|--------------|
+| Basic Diffusion                                     | Normal                                      | 2024 Aug 26  |
+| GPU Memory Management System                        | Normal                                      | 2024 Aug 26  |
+| LoRAs                                               | Normal                                      | 2024 Aug 26  |
+| All Preprocessors                                   | Normal                                      | 2024 Aug 26  |
+| All ControlNets                                     | Normal                                      | 2024 Aug 26  |
+| All IP-Adapters                                     | Normal                                      | 2024 Aug 26  |
+| All Instant-IDs                                     | Normal                                      | 2024 July 27 |
+| All Reference-only Methods                          | Normal                                      | 2024 July 27 |
+| All Integrated Extensions                           | Normal                                      | 2024 July 27 |
+| Popular Extensions (Adetailer, etc)                 | Normal                                      | 2024 July 27 |
+| Gradio 4 UIs                                        | Normal                                      | 2024 July 27 |
+| Gradio 4 Forge Canvas                               | Normal                                      | 2024 Aug 26  |
+| LoRA/Checkpoint Selection UI for Gradio 4           | Normal                                      | 2024 July 27 |
+| Photopea/OpenposeEditor/etc for ControlNet          | Normal                                      | 2024 July 27 |
+| Wacom 128 level touch pressure support for Canvas   | Normal                                      | 2024 July 15 |
+| Microsoft Surface touch pressure support for Canvas | Broken, pending fix                         | 2024 July 29 |
+| ControlNets (Union)                                 | Not implemented yet, pending implementation | 2024 Aug 26  |
+| ControlNets (Flux)                                  | Not implemented yet, pending implementation | 2024 Aug 26  |
+| API endpoints (txt2img, img2img, etc)               | Normal, but pending improved Flux support   | 2024 Aug 29  |
+| OFT LoRAs                                           | Broken, pending fix                         | 2024 Sep 9   |
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/65fbb4a5-ee73-4bb9-9c5f-8a958cd9674d)
+Feel free to open issue if anything is broken and I will take a look every several days. If I do not update this "Forge Status" then it means I cannot reproduce any problem. In that case, fresh re-install should help most.
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/76f181a1-c5fb-4323-a6cc-b6308a45587e)
+# UnetPatcher
 
-(average and peak are all 6.3GB/8GB)
+Below are self-supported **single file** of all codes to implement FreeU V2.
 
-You can see that Forge does not change WebUI results. Installing Forge is not a seed breaking change. 
-
-Forge can perfectly keep WebUI unchanged even for most complicated prompts like `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]`.
-
-All your previous works still work in Forge!
-
-Also, Forge promise that we will only do our jobs. We will not add unnecessary opinioned changes to UI. You are still using 100% Automatic1111 WebUI.
-
-# Forge Backend
-
-Forge backend removes all WebUI's codes related to resource management and reworked everything. All previous CMD flags like `medvram, lowvram, medvram-sdxl, precision full, no half, no half vae, attention_xxx, upcast unet`, ... are all **REMOVED**. Adding these flags will not cause error but they will not do anything now. **We highly encourage Forge users to remove all cmd flags and let Forge to decide how to load models.**
-
-Without any cmd flag, Forge can run SDXL with 4GB vram and SD1.5 with 2GB vram.
-
-**The only one flag that you may still need** is `--always-offload-from-vram` (This flag will make things **slower**). This option will let Forge always unload models from VRAM. This can be useful if you use multiple software together and want Forge to use less VRAM and give some vram to other software, or when you are using some old extensions that will compete vram with Forge, or (very rarely) when you get OOM.
-
-If you really want to play with cmd flags, you can additionally control the GPU with:
-
-(extreme VRAM cases)
-
-    --always-gpu
-    --always-cpu
-
-(rare attention cases)
-
-    --attention-split
-    --attention-quad
-    --attention-pytorch
-    --disable-xformers
-    --disable-attention-upcast
-
-(float point type)
-
-    --all-in-fp32
-    --all-in-fp16
-    --unet-in-bf16
-    --unet-in-fp16
-    --unet-in-fp8-e4m3fn
-    --unet-in-fp8-e5m2
-    --vae-in-fp16
-    --vae-in-fp32
-    --vae-in-bf16
-    --clip-in-fp8-e4m3fn
-    --clip-in-fp8-e5m2
-    --clip-in-fp16
-    --clip-in-fp32
-
-(rare platforms)
-
-    --directml
-    --disable-ipex-hijack
-    --pytorch-deterministic
-
-Again, Forge do not recommend users to use any cmd flags unless you are very sure that you really need these.
-
-# UNet Patcher
-
-Now developing an extension is super simple. We finally have a patchable UNet.
-
-Below is using one single file with 80 lines of codes to support FreeU:
-
-`extensions-builtin/sd_forge_freeu/scripts/forge_freeu.py`
+See also `extension-builtin/sd_forge_freeu/scripts/forge_freeu.py`:
 
 ```python
 import torch
 import gradio as gr
+
 from modules import scripts
 
 
 def Fourier_filter(x, threshold, scale):
+    # FFT
     x_freq = torch.fft.fftn(x.float(), dim=(-2, -1))
     x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1))
+
     B, C, H, W = x_freq.shape
     mask = torch.ones((B, C, H, W), device=x.device)
-    crow, ccol = H // 2, W //2
+
+    crow, ccol = H // 2, W // 2
     mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
     x_freq = x_freq * mask
+
+    # IFFT
     x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1))
     x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real
+
     return x_filtered.to(x.dtype)
 
 
-def set_freeu_v2_patch(model, b1, b2, s1, s2):
-    model_channels = model.model.model_config.unet_config["model_channels"]
+def patch_freeu_v2(unet_patcher, b1, b2, s1, s2):
+    model_channels = unet_patcher.model.diffusion_model.config["model_channels"]
     scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
+    on_cpu_devices = {}
 
-    def output_block_patch(h, hsp, *args, **kwargs):
+    def output_block_patch(h, hsp, transformer_options):
         scale = scale_dict.get(h.shape[1], None)
         if scale is not None:
             hidden_mean = h.mean(1).unsqueeze(1)
             B = hidden_mean.shape[0]
             hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True)
             hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True)
-            hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / \
-                          (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
+            hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3)
+
             h[:, :h.shape[1] // 2] = h[:, :h.shape[1] // 2] * ((scale[0] - 1) * hidden_mean + 1)
-            hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
+
+            if hsp.device not in on_cpu_devices:
+                try:
+                    hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
+                except:
+                    print("Device", hsp.device, "does not support the torch.fft.")
+                    on_cpu_devices[hsp.device] = True
+                    hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+            else:
+                hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
+
         return h, hsp
 
-    m = model.clone()
+    m = unet_patcher.clone()
     m.set_model_output_block_patch(output_block_patch)
     return m
 
 
 class FreeUForForge(scripts.Script):
+    sorting_priority = 12  # It will be the 12th item on UI.
+
     def title(self):
         return "FreeU Integrated"
 
@@ -186,7 +181,7 @@ class FreeUForForge(scripts.Script):
     def process_before_every_sampling(self, p, *script_args, **kwargs):
         # This will be called before every sampling.
         # If you use highres fix, this will be called twice.
-        
+
         freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2 = script_args
 
         if not freeu_enabled:
@@ -194,7 +189,7 @@ class FreeUForForge(scripts.Script):
 
         unet = p.sd_model.forge_objects.unet
 
-        unet = set_freeu_v2_patch(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)
+        unet = patch_freeu_v2(unet, freeu_b1, freeu_b2, freeu_s1, freeu_s2)
 
         p.sd_model.forge_objects.unet = unet
 
@@ -211,461 +206,8 @@ class FreeUForForge(scripts.Script):
         return
 ```
 
-It looks like this:
+See also [Forge's Unet Implementation](https://github.com/lllyasviel/stable-diffusion-webui-forge/blob/main/backend/nn/unet.py).
 
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/a7798cf2-057c-43e0-883a-5f8643af8529)
+# Under Construction
 
-Similar components like HyperTile, KohyaHighResFix, SAG, can all be implemented within 100 lines of codes (see also the codes).
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/e2fc1b73-e6ee-405e-864c-c67afd92a1db)
-
-ControlNets can finally be called by different extensions.
-
-Implementing Stable Video Diffusion and Zero123 are also super simple now (see also the codes). 
-
-*Stable Video Diffusion:*
-
-`extensions-builtin/sd_forge_svd/scripts/forge_svd.py`
-
-```python
-import torch
-import gradio as gr
-import os
-import pathlib
-
-from modules import script_callbacks
-from modules.paths import models_path
-from modules.ui_common import ToolButton, refresh_symbol
-from modules import shared
-
-from modules_forge.forge_util import numpy_to_pytorch, pytorch_to_numpy
-from ldm_patched.modules.sd import load_checkpoint_guess_config
-from ldm_patched.contrib.external_video_model import VideoLinearCFGGuidance, SVD_img2vid_Conditioning
-from ldm_patched.contrib.external import KSampler, VAEDecode
-
-
-opVideoLinearCFGGuidance = VideoLinearCFGGuidance()
-opSVD_img2vid_Conditioning = SVD_img2vid_Conditioning()
-opKSampler = KSampler()
-opVAEDecode = VAEDecode()
-
-svd_root = os.path.join(models_path, 'svd')
-os.makedirs(svd_root, exist_ok=True)
-svd_filenames = []
-
-
-def update_svd_filenames():
-    global svd_filenames
-    svd_filenames = [
-        pathlib.Path(x).name for x in
-        shared.walk_files(svd_root, allowed_extensions=[".pt", ".ckpt", ".safetensors"])
-    ]
-    return svd_filenames
-
-
-@torch.inference_mode()
-@torch.no_grad()
-def predict(filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
-            sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
-            sampling_denoise, guidance_min_cfg, input_image):
-    filename = os.path.join(svd_root, filename)
-    model_raw, _, vae, clip_vision = \
-        load_checkpoint_guess_config(filename, output_vae=True, output_clip=False, output_clipvision=True)
-    model = opVideoLinearCFGGuidance.patch(model_raw, guidance_min_cfg)[0]
-    init_image = numpy_to_pytorch(input_image)
-    positive, negative, latent_image = opSVD_img2vid_Conditioning.encode(
-        clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level)
-    output_latent = opKSampler.sample(model, sampling_seed, sampling_steps, sampling_cfg,
-                                      sampling_sampler_name, sampling_scheduler, positive,
-                                      negative, latent_image, sampling_denoise)[0]
-    output_pixels = opVAEDecode.decode(vae, output_latent)[0]
-    outputs = pytorch_to_numpy(output_pixels)
-    return outputs
-
-
-def on_ui_tabs():
-    with gr.Blocks() as svd_block:
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(label='Input Image', source='upload', type='numpy', height=400)
-
-                with gr.Row():
-                    filename = gr.Dropdown(label="SVD Checkpoint Filename",
-                                           choices=svd_filenames,
-                                           value=svd_filenames[0] if len(svd_filenames) > 0 else None)
-                    refresh_button = ToolButton(value=refresh_symbol, tooltip="Refresh")
-                    refresh_button.click(
-                        fn=lambda: gr.update(choices=update_svd_filenames),
-                        inputs=[], outputs=filename)
-
-                width = gr.Slider(label='Width', minimum=16, maximum=8192, step=8, value=1024)
-                height = gr.Slider(label='Height', minimum=16, maximum=8192, step=8, value=576)
-                video_frames = gr.Slider(label='Video Frames', minimum=1, maximum=4096, step=1, value=14)
-                motion_bucket_id = gr.Slider(label='Motion Bucket Id', minimum=1, maximum=1023, step=1, value=127)
-                fps = gr.Slider(label='Fps', minimum=1, maximum=1024, step=1, value=6)
-                augmentation_level = gr.Slider(label='Augmentation Level', minimum=0.0, maximum=10.0, step=0.01,
-                                               value=0.0)
-                sampling_steps = gr.Slider(label='Sampling Steps', minimum=1, maximum=200, step=1, value=20)
-                sampling_cfg = gr.Slider(label='CFG Scale', minimum=0.0, maximum=50.0, step=0.1, value=2.5)
-                sampling_denoise = gr.Slider(label='Sampling Denoise', minimum=0.0, maximum=1.0, step=0.01, value=1.0)
-                guidance_min_cfg = gr.Slider(label='Guidance Min Cfg', minimum=0.0, maximum=100.0, step=0.5, value=1.0)
-                sampling_sampler_name = gr.Radio(label='Sampler Name',
-                                                 choices=['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2',
-                                                          'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive',
-                                                          'dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu',
-                                                          'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu',
-                                                          'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim',
-                                                          'uni_pc', 'uni_pc_bh2'], value='euler')
-                sampling_scheduler = gr.Radio(label='Scheduler',
-                                              choices=['normal', 'karras', 'exponential', 'sgm_uniform', 'simple',
-                                                       'ddim_uniform'], value='karras')
-                sampling_seed = gr.Number(label='Seed', value=12345, precision=0)
-
-                generate_button = gr.Button(value="Generate")
-
-                ctrls = [filename, width, height, video_frames, motion_bucket_id, fps, augmentation_level,
-                         sampling_seed, sampling_steps, sampling_cfg, sampling_sampler_name, sampling_scheduler,
-                         sampling_denoise, guidance_min_cfg, input_image]
-
-            with gr.Column():
-                output_gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain',
-                                            visible=True, height=1024, columns=4)
-
-        generate_button.click(predict, inputs=ctrls, outputs=[output_gallery])
-    return [(svd_block, "SVD", "svd")]
-
-
-update_svd_filenames()
-script_callbacks.on_ui_tabs(on_ui_tabs)
-```
-
-Note that although the above codes look like independent codes, they actually will automatically offload/unload any other models. For example, below is me opening webui, load SDXL, generated an image, then go to SVD, then generated image frames. You can see that the GPU memory is perfectly managed and the SDXL is moved to RAM then SVD is moved to GPU. 
-
-Note that this management is fully automatic. This makes writing extensions super simple.
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/ac7ed152-cd33-4645-94af-4c43bb8c3d88)
-
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/cdcb23ad-02dc-4e39-be74-98e927550ef6)
-
-Similarly, Zero123:
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/d1a4a17d-f382-442d-91f2-fc5b6c10737f)
-
-### Write a simple ControlNet:
-
-Below is a simple extension to have a completely independent pass of ControlNet that never conflicts any other extensions:
-
-`extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py`
-
-Note that this extension is hidden because it is only for developers. To see it in UI, use `--show-controlnet-example`.
-
-The memory optimization in this example is fully automatic. You do not need to care about memory and inference speed, but you may want to cache objects if you wish.
-
-```python
-# Use --show-controlnet-example to see this extension.
-
-import cv2
-import gradio as gr
-import torch
-
-from modules import scripts
-from modules.shared_cmd_options import cmd_opts
-from modules_forge.shared import supported_preprocessors
-from modules.modelloader import load_file_from_url
-from ldm_patched.modules.controlnet import load_controlnet
-from modules_forge.controlnet import apply_controlnet_advanced
-from modules_forge.forge_util import numpy_to_pytorch
-from modules_forge.shared import controlnet_dir
-
-
-class ControlNetExampleForge(scripts.Script):
-    model = None
-
-    def title(self):
-        return "ControlNet Example for Developers"
-
-    def show(self, is_img2img):
-        # make this extension visible in both txt2img and img2img tab.
-        return scripts.AlwaysVisible
-
-    def ui(self, *args, **kwargs):
-        with gr.Accordion(open=False, label=self.title()):
-            gr.HTML('This is an example controlnet extension for developers.')
-            gr.HTML('You see this extension because you used --show-controlnet-example')
-            input_image = gr.Image(source='upload', type='numpy')
-            funny_slider = gr.Slider(label='This slider does nothing. It just shows you how to transfer parameters.',
-                                     minimum=0.0, maximum=1.0, value=0.5)
-
-        return input_image, funny_slider
-
-    def process(self, p, *script_args, **kwargs):
-        input_image, funny_slider = script_args
-
-        # This slider does nothing. It just shows you how to transfer parameters.
-        del funny_slider
-
-        if input_image is None:
-            return
-
-        # controlnet_canny_path = load_file_from_url(
-        #     url='https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/sai_xl_canny_256lora.safetensors',
-        #     model_dir=model_dir,
-        #     file_name='sai_xl_canny_256lora.safetensors'
-        # )
-        controlnet_canny_path = load_file_from_url(
-            url='https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/control_v11p_sd15_canny_fp16.safetensors',
-            model_dir=controlnet_dir,
-            file_name='control_v11p_sd15_canny_fp16.safetensors'
-        )
-        print('The model [control_v11p_sd15_canny_fp16.safetensors] download finished.')
-
-        self.model = load_controlnet(controlnet_canny_path)
-        print('Controlnet loaded.')
-
-        return
-
-    def process_before_every_sampling(self, p, *script_args, **kwargs):
-        # This will be called before every sampling.
-        # If you use highres fix, this will be called twice.
-
-        input_image, funny_slider = script_args
-
-        if input_image is None or self.model is None:
-            return
-
-        B, C, H, W = kwargs['noise'].shape  # latent_shape
-        height = H * 8
-        width = W * 8
-        batch_size = p.batch_size
-
-        preprocessor = supported_preprocessors['canny']
-
-        # detect control at certain resolution
-        control_image = preprocessor(
-            input_image, resolution=512, slider_1=100, slider_2=200, slider_3=None)
-
-        # here we just use nearest neighbour to align input shape.
-        # You may want crop and resize, or crop and fill, or others.
-        control_image = cv2.resize(
-            control_image, (width, height), interpolation=cv2.INTER_NEAREST)
-
-        # Output preprocessor result. Now called every sampling. Cache in your own way.
-        p.extra_result_images.append(control_image)
-
-        print('Preprocessor Canny finished.')
-
-        control_image_bchw = numpy_to_pytorch(control_image).movedim(-1, 1)
-
-        unet = p.sd_model.forge_objects.unet
-
-        # Unet has input, middle, output blocks, and we can give different weights
-        # to each layers in all blocks.
-        # Below is an example for stronger control in middle block.
-        # This is helpful for some high-res fix passes. (p.is_hr_pass)
-        positive_advanced_weighting = {
-            'input': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2],
-            'middle': [1.0],
-            'output': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2]
-        }
-        negative_advanced_weighting = {
-            'input': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25],
-            'middle': [1.05],
-            'output': [0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25]
-        }
-
-        # The advanced_frame_weighting is a weight applied to each image in a batch.
-        # The length of this list must be same with batch size
-        # For example, if batch size is 5, the below list is [0.2, 0.4, 0.6, 0.8, 1.0]
-        # If you view the 5 images as 5 frames in a video, this will lead to
-        # progressively stronger control over time.
-        advanced_frame_weighting = [float(i + 1) / float(batch_size) for i in range(batch_size)]
-
-        # The advanced_sigma_weighting allows you to dynamically compute control
-        # weights given diffusion timestep (sigma).
-        # For example below code can softly make beginning steps stronger than ending steps.
-        sigma_max = unet.model.model_sampling.sigma_max
-        sigma_min = unet.model.model_sampling.sigma_min
-        advanced_sigma_weighting = lambda s: (s - sigma_min) / (sigma_max - sigma_min)
-
-        # You can even input a tensor to mask all control injections
-        # The mask will be automatically resized during inference in UNet.
-        # The size should be B 1 H W and the H and W are not important
-        # because they will be resized automatically
-        advanced_mask_weighting = torch.ones(size=(1, 1, 512, 512))
-
-        # But in this simple example we do not use them
-        positive_advanced_weighting = None
-        negative_advanced_weighting = None
-        advanced_frame_weighting = None
-        advanced_sigma_weighting = None
-        advanced_mask_weighting = None
-
-        unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
-                                         strength=0.6, start_percent=0.0, end_percent=0.8,
-                                         positive_advanced_weighting=positive_advanced_weighting,
-                                         negative_advanced_weighting=negative_advanced_weighting,
-                                         advanced_frame_weighting=advanced_frame_weighting,
-                                         advanced_sigma_weighting=advanced_sigma_weighting,
-                                         advanced_mask_weighting=advanced_mask_weighting)
-
-        p.sd_model.forge_objects.unet = unet
-
-        # Below codes will add some logs to the texts below the image outputs on UI.
-        # The extra_generation_params does not influence results.
-        p.extra_generation_params.update(dict(
-            controlnet_info='You should see these texts below output images!',
-        ))
-
-        return
-
-
-# Use --show-controlnet-example to see this extension.
-if not cmd_opts.show_controlnet_example:
-    del ControlNetExampleForge
-
-```
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/0a703d8b-27df-4608-8b12-aff750f20ffa)
-
-
-### Add a preprocessor
-
-Below is the full codes to add a normalbae preprocessor with perfect memory managements.
-
-You can use arbitrary independent extensions to add a preprocessor.
-
-Your preprocessor will be read by all other extensions using `modules_forge.shared.preprocessors`
-
-Below codes are in `extensions-builtin\forge_preprocessor_normalbae\scripts\preprocessor_normalbae.py`
-
-```python
-from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
-from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
-from modules_forge.forge_util import resize_image_with_pad
-from modules.modelloader import load_file_from_url
-
-import types
-import torch
-import numpy as np
-
-from einops import rearrange
-from annotator.normalbae.models.NNET import NNET
-from annotator.normalbae import load_checkpoint
-from torchvision import transforms
-
-
-class PreprocessorNormalBae(Preprocessor):
-    def __init__(self):
-        super().__init__()
-        self.name = 'normalbae'
-        self.tags = ['NormalMap']
-        self.model_filename_filters = ['normal']
-        self.slider_resolution = PreprocessorParameter(
-            label='Resolution', minimum=128, maximum=2048, value=512, step=8, visible=True)
-        self.slider_1 = PreprocessorParameter(visible=False)
-        self.slider_2 = PreprocessorParameter(visible=False)
-        self.slider_3 = PreprocessorParameter(visible=False)
-        self.show_control_mode = True
-        self.do_not_need_model = False
-        self.sorting_priority = 100  # higher goes to top in the list
-
-    def load_model(self):
-        if self.model_patcher is not None:
-            return
-
-        model_path = load_file_from_url(
-            "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt",
-            model_dir=preprocessor_dir)
-
-        args = types.SimpleNamespace()
-        args.mode = 'client'
-        args.architecture = 'BN'
-        args.pretrained = 'scannet'
-        args.sampling_ratio = 0.4
-        args.importance_ratio = 0.7
-        model = NNET(args)
-        model = load_checkpoint(model_path, model)
-        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-        self.model_patcher = self.setup_model_patcher(model)
-
-    def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, **kwargs):
-        input_image, remove_pad = resize_image_with_pad(input_image, resolution)
-
-        self.load_model()
-
-        self.move_all_model_patchers_to_gpu()
-
-        assert input_image.ndim == 3
-        image_normal = input_image
-
-        with torch.no_grad():
-            image_normal = self.send_tensor_to_model_device(torch.from_numpy(image_normal))
-            image_normal = image_normal / 255.0
-            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
-            image_normal = self.norm(image_normal)
-
-            normal = self.model_patcher.model(image_normal)
-            normal = normal[0][-1][:, :3]
-            normal = ((normal + 1) * 0.5).clip(0, 1)
-
-            normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
-            normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
-
-        return remove_pad(normal_image)
-
-
-add_supported_preprocessor(PreprocessorNormalBae())
-
-```
-
-# New features (that are not available in original WebUI)
-
-Thanks to Unet Patcher, many new things are possible now and supported in Forge, including SVD, Z123, masked Ip-adapter, masked controlnet, photomaker, etc.
-
-Masked Ip-Adapter
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/75432c4b-4f2a-4027-b6ad-fcf48fffad2c)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/c8dad060-9f93-4781-a14d-255180787592)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/2680504e-6c7a-4531-b477-06e4c7b3ef3f)
-
-
-Masked ControlNet
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/1d74e970-fbe3-40df-9c87-70c8426ec6e1)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/0bc7bc7f-1a33-41c6-89ea-f5b5e60f6c1e)
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/10b07d97-a2b8-463c-928a-5ad9b78da25e)
-
-PhotoMaker
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/377699d3-e6b5-4ace-9d0f-054ac1749fc8)
-
-
-Marigold Depth
-
-![image](https://github.com/lllyasviel/stable-diffusion-webui-forge/assets/19834515/95787c05-3fe7-43cc-ba12-baf378284007)
-
-# About Extensions
-
-ControlNet and TiledVAE are integrated, and you should uninstall these two extensions:
-
-    sd-webui-controlnet
-    multidiffusion-upscaler-for-automatic1111
-
-Other extensions should work without problems, like:
-
-    canvas-zoom
-    translations/localizations
-    Dynamic Prompts
-    Adetailer
-    Ultimate SD Upscale
-    Reactor
-
-However, if newer extensions use Forge, their codes can be much shorter. 
-
-Usually if an old extension can rework using Forge's unet patcher, 80% codes can be removed, especially when they need to call controlnet.
+WebUI Forge is now under some constructions, and docs / UI / functionality may change with updates.

_typos.toml

@@ -0,0 +1,5 @@
+[default.extend-words]
+# Part of "RGBa" (Pillow's pre-multiplied alpha RGB mode)
+Ba = "Ba"
+# HSA is something AMD uses for their GPUs
+HSA = "HSA"

backend/README.md

@@ -0,0 +1 @@
+# WIP Backend for Forge

backend/args.py

@@ -0,0 +1,67 @@
+import argparse
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("--gpu-device-id", type=int, default=None, metavar="DEVICE_ID")
+
+fp_group = parser.add_mutually_exclusive_group()
+fp_group.add_argument("--all-in-fp32", action="store_true")
+fp_group.add_argument("--all-in-fp16", action="store_true")
+
+fpunet_group = parser.add_mutually_exclusive_group()
+fpunet_group.add_argument("--unet-in-bf16", action="store_true")
+fpunet_group.add_argument("--unet-in-fp16", action="store_true")
+fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true")
+fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true")
+
+fpvae_group = parser.add_mutually_exclusive_group()
+fpvae_group.add_argument("--vae-in-fp16", action="store_true")
+fpvae_group.add_argument("--vae-in-fp32", action="store_true")
+fpvae_group.add_argument("--vae-in-bf16", action="store_true")
+
+parser.add_argument("--vae-in-cpu", action="store_true")
+
+fpte_group = parser.add_mutually_exclusive_group()
+fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true")
+fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true")
+fpte_group.add_argument("--clip-in-fp16", action="store_true")
+fpte_group.add_argument("--clip-in-fp32", action="store_true")
+
+attn_group = parser.add_mutually_exclusive_group()
+attn_group.add_argument("--attention-split", action="store_true")
+attn_group.add_argument("--attention-quad", action="store_true")
+attn_group.add_argument("--attention-pytorch", action="store_true")
+
+upcast = parser.add_mutually_exclusive_group()
+upcast.add_argument("--force-upcast-attention", action="store_true")
+upcast.add_argument("--disable-attention-upcast", action="store_true")
+
+parser.add_argument("--disable-xformers", action="store_true")
+
+parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1)
+parser.add_argument("--disable-ipex-hijack", action="store_true")
+
+vram_group = parser.add_mutually_exclusive_group()
+vram_group.add_argument("--always-gpu", action="store_true")
+vram_group.add_argument("--always-high-vram", action="store_true")
+vram_group.add_argument("--always-normal-vram", action="store_true")
+vram_group.add_argument("--always-low-vram", action="store_true")
+vram_group.add_argument("--always-no-vram", action="store_true")
+vram_group.add_argument("--always-cpu", action="store_true")
+
+parser.add_argument("--always-offload-from-vram", action="store_true")
+parser.add_argument("--pytorch-deterministic", action="store_true")
+
+parser.add_argument("--cuda-malloc", action="store_true")
+parser.add_argument("--cuda-stream", action="store_true")
+parser.add_argument("--pin-shared-memory", action="store_true")
+
+parser.add_argument("--disable-gpu-warning", action="store_true")
+
+args = parser.parse_known_args()[0]
+
+# Some dynamic args that may be changed by webui rather than cmd flags.
+dynamic_args = dict(
+    embedding_dir='./embeddings',
+    emphasis_name='original'
+)

backend/attention.py

@@ -0,0 +1,501 @@
+import math
+import torch
+import einops
+
+from backend.args import args
+from backend import memory_management
+from backend.misc.sub_quadratic_attention import efficient_dot_product_attention
+
+
+BROKEN_XFORMERS = False
+if memory_management.xformers_enabled():
+    import xformers
+    import xformers.ops
+
+    try:
+        x_vers = xformers.__version__
+        BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
+    except:
+        pass
+
+
+FORCE_UPCAST_ATTENTION_DTYPE = memory_management.force_upcast_attention_dtype()
+
+
+def get_attn_precision(attn_precision=torch.float32):
+    if args.disable_attention_upcast:
+        return None
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
+        return FORCE_UPCAST_ATTENTION_DTYPE
+    return attn_precision
+
+
+def exists(val):
+    return val is not None
+
+
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head ** -0.5
+
+    h = heads
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+    if attn_precision == torch.float32:
+        sim = torch.einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+    else:
+        sim = torch.einsum('b i d, b j d -> b i j', q, k) * scale
+
+    del q, k
+
+    if exists(mask):
+        if mask.dtype == torch.bool:
+            mask = einops.rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = einops.repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+        else:
+            if len(mask.shape) == 2:
+                bs = 1
+            else:
+                bs = mask.shape[0]
+            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+            sim.add_(mask)
+
+    sim = sim.softmax(dim=-1)
+    out = torch.einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+    out = (
+        out.unsqueeze(0)
+        .reshape(b, heads, -1, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b, -1, heads * dim_head)
+    )
+    return out
+
+
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = query.shape
+    else:
+        b, _, dim_head = query.shape
+        dim_head //= heads
+
+    scale = dim_head ** -0.5
+
+    if skip_reshape:
+        query = query.reshape(b * heads, -1, dim_head)
+        value = value.reshape(b * heads, -1, dim_head)
+        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+    else:
+        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
+    dtype = query.dtype
+    upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
+    if upcast_attention:
+        bytes_per_token = torch.finfo(torch.float32).bits // 8
+    else:
+        bytes_per_token = torch.finfo(query.dtype).bits // 8
+    batch_x_heads, q_tokens, _ = query.shape
+    _, _, k_tokens = key.shape
+    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
+
+    mem_free_total, mem_free_torch = memory_management.get_free_memory(query.device, True)
+
+    kv_chunk_size_min = None
+    kv_chunk_size = None
+    query_chunk_size = None
+
+    for x in [4096, 2048, 1024, 512, 256]:
+        count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)
+        if count >= k_tokens:
+            kv_chunk_size = k_tokens
+            query_chunk_size = x
+            break
+
+    if query_chunk_size is None:
+        query_chunk_size = 512
+
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+    hidden_states = efficient_dot_product_attention(
+        query,
+        key,
+        value,
+        query_chunk_size=query_chunk_size,
+        kv_chunk_size=kv_chunk_size,
+        kv_chunk_size_min=kv_chunk_size_min,
+        use_checkpoint=False,
+        upcast_attention=upcast_attention,
+        mask=mask,
+    )
+
+    hidden_states = hidden_states.to(dtype)
+
+    hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1, 2).flatten(start_dim=2)
+    return hidden_states
+
+
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head ** -0.5
+
+    h = heads
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    mem_free_total = memory_management.get_free_memory(q.device)
+
+    if attn_precision == torch.float32:
+        element_size = 4
+        upcast = True
+    else:
+        element_size = q.element_size()
+        upcast = False
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
+    modifier = 3
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+        #      f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+    # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
+    first_op_done = False
+    cleared_cache = False
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                if upcast:
+                    with torch.autocast(enabled=False, device_type='cuda'):
+                        s1 = torch.einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
+                else:
+                    s1 = torch.einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
+
+                if mask is not None:
+                    if len(mask.shape) == 2:
+                        s1 += mask[i:end]
+                    else:
+                        s1 += mask[:, i:end]
+
+                s2 = s1.softmax(dim=-1).to(v.dtype)
+                del s1
+                first_op_done = True
+
+                r1[:, i:end] = torch.einsum('b i j, b j d -> b i d', s2, v)
+                del s2
+            break
+        except memory_management.OOM_EXCEPTION as e:
+            if first_op_done == False:
+                memory_management.soft_empty_cache(True)
+                if cleared_cache == False:
+                    cleared_cache = True
+                    print("out of memory error, emptying cache and trying again")
+                    continue
+                steps *= 2
+                if steps > 64:
+                    raise e
+                print("out of memory error, increasing steps and trying again {}".format(steps))
+            else:
+                raise e
+
+    del q, k, v
+
+    r1 = (
+        r1.unsqueeze(0)
+        .reshape(b, heads, -1, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b, -1, heads * dim_head)
+    )
+    return r1
+
+
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    if BROKEN_XFORMERS and b * heads > 65535:
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
+
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        pad = 8 - q.shape[1] % 8
+        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
+        mask_out[:, :, :mask.shape[-1]] = mask
+        mask = mask_out[:, :, :mask.shape[-1]]
+
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+
+    if skip_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
+
+    return out
+
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    out = (
+        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+    )
+    return out
+
+
+def slice_attention_single_head_spatial(q, k, v):
+    r1 = torch.zeros_like(k, device=q.device)
+    scale = (int(q.shape[-1]) ** (-0.5))
+
+    mem_free_total = memory_management.get_free_memory(q.device)
+
+    gb = 1024 ** 3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                s1 = torch.bmm(q[:, i:end], k) * scale
+
+                s2 = torch.nn.functional.softmax(s1, dim=2).permute(0, 2, 1)
+                del s1
+
+                r1[:, :, i:end] = torch.bmm(v, s2)
+                del s2
+            break
+        except memory_management.OOM_EXCEPTION as e:
+            memory_management.soft_empty_cache(True)
+            steps *= 2
+            if steps > 128:
+                raise e
+            print("out of memory error, increasing steps and trying again {}".format(steps))
+
+    return r1
+
+
+def normal_attention_single_head_spatial(q, k, v):
+    # compute attention
+    b, c, h, w = q.shape
+
+    q = q.reshape(b, c, h * w)
+    q = q.permute(0, 2, 1)  # b,hw,c
+    k = k.reshape(b, c, h * w)  # b,c,hw
+    v = v.reshape(b, c, h * w)
+
+    r1 = slice_attention_single_head_spatial(q, k, v)
+    h_ = r1.reshape(b, c, h, w)
+    del r1
+    return h_
+
+
+def xformers_attention_single_head_spatial(q, k, v):
+    # compute attention
+    B, C, H, W = q.shape
+    q, k, v = map(
+        lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
+        out = out.transpose(1, 2).reshape(B, C, H, W)
+    except NotImplementedError as e:
+        out = slice_attention_single_head_spatial(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2),
+                                                  v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+    return out
+
+
+def pytorch_attention_single_head_spatial(q, k, v):
+    # compute attention
+    B, C, H, W = q.shape
+    q, k, v = map(
+        lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+        out = out.transpose(2, 3).reshape(B, C, H, W)
+    except memory_management.OOM_EXCEPTION as e:
+        print("scaled_dot_product_attention OOMed: switched to slice attention")
+        out = slice_attention_single_head_spatial(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2),
+                                                  v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
+    return out
+
+
+if memory_management.xformers_enabled():
+    print("Using xformers cross attention")
+    attention_function = attention_xformers
+elif memory_management.pytorch_attention_enabled():
+    print("Using pytorch cross attention")
+    attention_function = attention_pytorch
+elif args.attention_split:
+    print("Using split optimization for cross attention")
+    attention_function = attention_split
+else:
+    print("Using sub quadratic optimization for cross attention")
+    attention_function = attention_sub_quad
+
+if memory_management.xformers_enabled_vae():
+    print("Using xformers attention for VAE")
+    attention_function_single_head_spatial = xformers_attention_single_head_spatial
+elif memory_management.pytorch_attention_enabled():
+    print("Using pytorch attention for VAE")
+    attention_function_single_head_spatial = pytorch_attention_single_head_spatial
+else:
+    print("Using split attention for VAE")
+    attention_function_single_head_spatial = normal_attention_single_head_spatial
+
+
+class AttentionProcessorForge:
+    def __call__(self, attn, hidden_states, encoder_hidden_states, attention_mask=None, temb=None, *args, **kwargs):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        hidden_states = attention_function(query, key, value, heads=attn.heads, mask=attention_mask)
+
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states

backend/diffusion_engine/base.py

@@ -0,0 +1,87 @@
+import torch
+import safetensors.torch as sf
+
+from backend import utils
+
+
+class ForgeObjects:
+    def __init__(self, unet, clip, vae, clipvision):
+        self.unet = unet
+        self.clip = clip
+        self.vae = vae
+        self.clipvision = clipvision
+
+    def shallow_copy(self):
+        return ForgeObjects(
+            self.unet,
+            self.clip,
+            self.vae,
+            self.clipvision
+        )
+
+
+class ForgeDiffusionEngine:
+    matched_guesses = []
+
+    def __init__(self, estimated_config, huggingface_components):
+        self.model_config = estimated_config
+        self.is_inpaint = estimated_config.inpaint_model()
+
+        self.forge_objects = None
+        self.forge_objects_original = None
+        self.forge_objects_after_applying_lora = None
+
+        self.current_lora_hash = str([])
+
+        self.fix_for_webui_backward_compatibility()
+
+    def set_clip_skip(self, clip_skip):
+        pass
+
+    def get_first_stage_encoding(self, x):
+        return x  # legacy code, do not change
+
+    def get_learned_conditioning(self, prompt: list[str]):
+        pass
+
+    def encode_first_stage(self, x):
+        pass
+
+    def decode_first_stage(self, x):
+        pass
+
+    def get_prompt_lengths_on_ui(self, prompt):
+        return 0, 75
+
+    def is_webui_legacy_model(self):
+        return self.is_sd1 or self.is_sd2 or self.is_sdxl or self.is_sd3
+
+    def fix_for_webui_backward_compatibility(self):
+        self.tiling_enabled = False
+        self.first_stage_model = None
+        self.cond_stage_model = None
+        self.use_distilled_cfg_scale = False
+        self.is_sd1 = False
+        self.is_sd2 = False
+        self.is_sdxl = False
+        self.is_sd3 = False
+        return
+
+    def save_unet(self, filename):
+        sd = utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model)
+        sf.save_file(sd, filename)
+        return filename
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix='model.diffusion_model.')
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix='text_encoders.')
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix='vae.')
+        )
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/chroma.py

@@ -0,0 +1,72 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.t5_engine import T5TextProcessingEngine
+from backend.args import dynamic_args
+from backend.modules.k_prediction import PredictionFlux
+from backend import memory_management
+
+class Chroma(ForgeDiffusionEngine):
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(
+            model_dict={
+                't5xxl': huggingface_components['text_encoder']
+            },
+            tokenizer_dict={
+                't5xxl': huggingface_components['tokenizer']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+        k_predictor = PredictionFlux(
+            mu=1.0
+        )
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['transformer'],
+            diffusers_scheduler=None,
+            k_predictor=k_predictor,
+            config=estimated_config
+        )
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+            emphasis_name=dynamic_args['emphasis_name'],
+            min_length=1
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+    def set_clip_skip(self, clip_skip):
+        pass
+        
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        return self.text_processing_engine_t5(prompt)
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)        

backend/diffusion_engine/flux.py

@@ -0,0 +1,114 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.text_processing.t5_engine import T5TextProcessingEngine
+from backend.args import dynamic_args
+from backend.modules.k_prediction import PredictionFlux
+from backend import memory_management
+
+
+class Flux(ForgeDiffusionEngine):
+    matched_guesses = [model_list.Flux, model_list.FluxSchnell]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(
+            model_dict={
+                'clip_l': huggingface_components['text_encoder'],
+                't5xxl': huggingface_components['text_encoder_2']
+            },
+            tokenizer_dict={
+                'clip_l': huggingface_components['tokenizer'],
+                't5xxl': huggingface_components['tokenizer_2']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        if 'schnell' in estimated_config.huggingface_repo.lower():
+            k_predictor = PredictionFlux(
+                mu=1.0
+            )
+        else:
+            k_predictor = PredictionFlux(
+                seq_len=4096,
+                base_seq_len=256,
+                max_seq_len=4096,
+                base_shift=0.5,
+                max_shift=1.15,
+            )
+            self.use_distilled_cfg_scale = True
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['transformer'],
+            diffusers_scheduler=None,
+            k_predictor=k_predictor,
+            config=estimated_config
+        )
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_l',
+            embedding_expected_shape=768,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=True,
+        )
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+            emphasis_name=dynamic_args['emphasis_name'],
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond_l, pooled_l = self.text_processing_engine_l(prompt)
+        cond_t5 = self.text_processing_engine_t5(prompt)
+        cond = dict(crossattn=cond_t5, vector=pooled_l)
+
+        if self.use_distilled_cfg_scale:
+            distilled_cfg_scale = getattr(prompt, 'distilled_cfg_scale', 3.5) or 3.5
+            cond['guidance'] = torch.FloatTensor([distilled_cfg_scale] * len(prompt))
+            print(f'Distilled CFG Scale: {distilled_cfg_scale}')
+        else:
+            print('Distilled CFG Scale will be ignored for Schnell')
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/sd15.py

@@ -0,0 +1,100 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+
+import safetensors.torch as sf
+from backend import utils
+
+
+class StableDiffusion(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD15]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_l': huggingface_components['text_encoder']
+            },
+            tokenizer_dict={
+                'clip_l': huggingface_components['tokenizer']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['unet'],
+            diffusers_scheduler=huggingface_components['scheduler'],
+            config=estimated_config
+        )
+
+        self.text_processing_engine = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_l',
+            embedding_expected_shape=768,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=False,
+            final_layer_norm=True,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sd1 = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond = self.text_processing_engine(prompt)
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine.process_texts([prompt])
+        return token_count, self.text_processing_engine.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix='model.diffusion_model.')
+        )
+        sd.update(
+            model_list.SD15.process_clip_state_dict_for_saving(self, 
+                utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix='')
+            )
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix='first_stage_model.')
+        )
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/sd20.py

@@ -0,0 +1,100 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+
+import safetensors.torch as sf
+from backend import utils
+
+
+class StableDiffusion2(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD20]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_h': huggingface_components['text_encoder']
+            },
+            tokenizer_dict={
+                'clip_h': huggingface_components['tokenizer']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['unet'],
+            diffusers_scheduler=huggingface_components['scheduler'],
+            config=estimated_config
+        )
+
+        self.text_processing_engine = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_h,
+            tokenizer=clip.tokenizer.clip_h,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_h',
+            embedding_expected_shape=1024,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=False,
+            final_layer_norm=True,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sd2 = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond = self.text_processing_engine(prompt)
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine.process_texts([prompt])
+        return token_count, self.text_processing_engine.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix='model.diffusion_model.')
+        )
+        sd.update(
+            model_list.SD20.process_clip_state_dict_for_saving(self, 
+                utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix='')
+            )
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix='first_stage_model.')
+        )
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/sd35.py

@@ -0,0 +1,149 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.text_processing.t5_engine import T5TextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+from backend.modules.k_prediction import PredictionDiscreteFlow
+
+from modules.shared import opts
+
+
+##  patch SD3 Class in huggingface_guess.model_list
+def SD3_clip_target(self, state_dict={}):
+        return {'clip_l': 'text_encoder', 'clip_g': 'text_encoder_2', 't5xxl': 'text_encoder_3'}
+
+model_list.SD3.unet_target = 'transformer'
+model_list.SD3.clip_target = SD3_clip_target
+##  end patch
+
+class StableDiffusion3(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD3]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(
+            model_dict={
+                'clip_l': huggingface_components['text_encoder'],
+                'clip_g': huggingface_components['text_encoder_2'],
+                't5xxl' : huggingface_components['text_encoder_3']
+            },
+            tokenizer_dict={
+                'clip_l': huggingface_components['tokenizer'],
+                'clip_g': huggingface_components['tokenizer_2'],
+                't5xxl' : huggingface_components['tokenizer_3']
+            }
+        )
+
+        k_predictor = PredictionDiscreteFlow(shift=3.0)
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['transformer'],
+            diffusers_scheduler= None,
+            k_predictor=k_predictor,
+            config=estimated_config
+        )
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_l',
+            embedding_expected_shape=768,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_g',
+            embedding_expected_shape=1280,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+            emphasis_name=dynamic_args['emphasis_name'],
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sd3 = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+        self.text_processing_engine_g.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+
+        cond_g, g_pooled = self.text_processing_engine_g(prompt)
+        cond_l, l_pooled = self.text_processing_engine_l(prompt)
+        if opts.sd3_enable_t5:
+            cond_t5 = self.text_processing_engine_t5(prompt)
+        else:
+            cond_t5 = torch.zeros([len(prompt), 256, 4096]).to(cond_l.device)
+
+        is_negative_prompt = getattr(prompt, 'is_negative_prompt', False)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in prompt)
+
+        if force_zero_negative_prompt:
+            l_pooled = torch.zeros_like(l_pooled)
+            g_pooled = torch.zeros_like(g_pooled)
+            cond_l = torch.zeros_like(cond_l)
+            cond_g = torch.zeros_like(cond_g)
+            cond_t5 = torch.zeros_like(cond_t5)
+
+        cond_lg = torch.cat([cond_l, cond_g], dim=-1)
+        cond_lg = torch.nn.functional.pad(cond_lg, (0, 4096 - cond_lg.shape[-1]))
+
+        cond = dict(
+            crossattn=torch.cat([cond_lg, cond_t5], dim=-2),
+            vector=torch.cat([l_pooled, g_pooled], dim=-1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+
+        return sample.to(x)

backend/diffusion_engine/sdxl.py

@@ -0,0 +1,272 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+from backend.nn.unet import Timestep
+
+import safetensors.torch as sf
+from backend import utils
+
+from modules.shared import opts
+
+
+class StableDiffusionXL(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SDXL]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_l': huggingface_components['text_encoder'],
+                'clip_g': huggingface_components['text_encoder_2']
+            },
+            tokenizer_dict={
+                'clip_l': huggingface_components['tokenizer'],
+                'clip_g': huggingface_components['tokenizer_2']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['unet'],
+            diffusers_scheduler=huggingface_components['scheduler'],
+            config=estimated_config
+        )
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_l',
+            embedding_expected_shape=2048,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=False,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=False,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_g',
+            embedding_expected_shape=2048,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.embedder = Timestep(256)
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sdxl = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+        self.text_processing_engine_g.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+
+        cond_l = self.text_processing_engine_l(prompt)
+        cond_g, clip_pooled = self.text_processing_engine_g(prompt)
+
+        width = getattr(prompt, 'width', 1024) or 1024
+        height = getattr(prompt, 'height', 1024) or 1024
+        is_negative_prompt = getattr(prompt, 'is_negative_prompt', False)
+
+        crop_w = opts.sdxl_crop_left
+        crop_h = opts.sdxl_crop_top
+        target_width = width
+        target_height = height
+
+        out = [
+            self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])),
+            self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])),
+            self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))
+        ]
+
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in prompt)
+
+        if force_zero_negative_prompt:
+            clip_pooled = torch.zeros_like(clip_pooled)
+            cond_l = torch.zeros_like(cond_l)
+            cond_g = torch.zeros_like(cond_g)
+
+        cond = dict(
+            crossattn=torch.cat([cond_l, cond_g], dim=2),
+            vector=torch.cat([clip_pooled, flat], dim=1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine_l.process_texts([prompt])
+        return token_count, self.text_processing_engine_l.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix='model.diffusion_model.')
+        )
+        sd.update(
+            model_list.SDXL.process_clip_state_dict_for_saving(self,
+                utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix='')
+            )
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix='first_stage_model.')
+        )
+        sf.save_file(sd, filename)
+        return filename
+
+
+class StableDiffusionXLRefiner(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SDXLRefiner]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_g': huggingface_components['text_encoder']
+            },
+            tokenizer_dict={
+                'clip_g': huggingface_components['tokenizer'],
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['unet'],
+            diffusers_scheduler=huggingface_components['scheduler'],
+            config=estimated_config
+        )
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_g',
+            embedding_expected_shape=2048,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.embedder = Timestep(256)
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sdxl = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_g.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+
+        cond_g, clip_pooled = self.text_processing_engine_g(prompt)
+
+        width = getattr(prompt, 'width', 1024) or 1024
+        height = getattr(prompt, 'height', 1024) or 1024
+        is_negative_prompt = getattr(prompt, 'is_negative_prompt', False)
+
+        crop_w = opts.sdxl_crop_left
+        crop_h = opts.sdxl_crop_top
+        aesthetic = opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else opts.sdxl_refiner_high_aesthetic_score
+
+        out = [
+            self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])),
+            self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])),
+            self.embedder(torch.Tensor([aesthetic]))
+        ]
+
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in prompt)
+
+        if force_zero_negative_prompt:
+            clip_pooled = torch.zeros_like(clip_pooled)
+            cond_g = torch.zeros_like(cond_g)
+
+        cond = dict(
+            crossattn=cond_g,
+            vector=torch.cat([clip_pooled, flat], dim=1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine_g.process_texts([prompt])
+        return token_count, self.text_processing_engine_g.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix='model.diffusion_model.')
+        )
+        sd.update(
+            model_list.SDXLRefiner.process_clip_state_dict_for_saving(self,
+                utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix='')
+            )
+        )
+        sd.update(
+            utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix='first_stage_model.')
+        )
+        sf.save_file(sd, filename)
+        return filename

backend/huggingface/Chroma/model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": "FluxPipeline",
+  "_diffusers_version": "0.30.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "ChromaTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

backend/huggingface/Chroma/scheduler/scheduler_config.json

@@ -0,0 +1,11 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.30.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "use_dynamic_shifting": false
+}

backend/huggingface/Chroma/text_encoder/config.json

@@ -0,0 +1,22 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}

backend/huggingface/Chroma/text_encoder/model.safetensors.index.json

@@ -0,0 +1,226 @@
+{
+  "metadata": {
+    "total_size": 9524621312
+  },
+  "weight_map": {
+    "encoder.block.0.layer.0.SelfAttention.k.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.0.SelfAttention.o.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.0.SelfAttention.q.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.0.SelfAttention.v.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.0.layer_norm.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.1.DenseReluDense.wi_0.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.0.layer.1.DenseReluDense.wi_1.weight": "model-00001-of-00002.safetensors",
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+    "encoder.block.9.layer.1.DenseReluDense.wo.weight": "model-00001-of-00002.safetensors",
+    "encoder.block.9.layer.1.layer_norm.weight": "model-00001-of-00002.safetensors",
+    "encoder.final_layer_norm.weight": "model-00002-of-00002.safetensors",
+    "shared.weight": "model-00001-of-00002.safetensors"
+  }
+}

backend/huggingface/Chroma/tokenizer/special_tokens_map.json

@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/Chroma/tokenizer/tokenizer_config.json

@@ -0,0 +1,939 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<extra_id_95>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<extra_id_94>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<extra_id_93>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<extra_id_92>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<extra_id_91>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<extra_id_90>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<extra_id_89>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32011": {
+      "content": "<extra_id_88>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32012": {
+      "content": "<extra_id_87>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32013": {
+      "content": "<extra_id_86>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32014": {
+      "content": "<extra_id_85>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32015": {
+      "content": "<extra_id_84>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32016": {
+      "content": "<extra_id_83>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32017": {
+      "content": "<extra_id_82>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32018": {
+      "content": "<extra_id_81>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32019": {
+      "content": "<extra_id_80>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32020": {
+      "content": "<extra_id_79>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32021": {
+      "content": "<extra_id_78>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32022": {
+      "content": "<extra_id_77>",
+      "lstrip": false,
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+    },
+    "32023": {
+      "content": "<extra_id_76>",
+      "lstrip": false,
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+    },
+    "32024": {
+      "content": "<extra_id_75>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32025": {
+      "content": "<extra_id_74>",
+      "lstrip": false,
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+    },
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+      "lstrip": false,
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+    },
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+      "lstrip": false,
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+    },
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+      "lstrip": false,
+      "normalized": false,
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+    },
+    "32029": {
+      "content": "<extra_id_70>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    },
+    "32030": {
+      "content": "<extra_id_69>",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32031": {
+      "content": "<extra_id_68>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32032": {
+      "content": "<extra_id_67>",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
+    },
+    "32033": {
+      "content": "<extra_id_66>",
+      "lstrip": false,
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+    },
+    "32034": {
+      "content": "<extra_id_65>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32035": {
+      "content": "<extra_id_64>",
+      "lstrip": false,
+      "normalized": false,
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+    },
+    "32036": {
+      "content": "<extra_id_63>",
+      "lstrip": false,
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+    },
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+    },
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+    },
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+    },
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+    },
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+      "lstrip": false,
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+    },
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+      "lstrip": false,
+      "normalized": false,
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+    },
+    "32046": {
+      "content": "<extra_id_53>",
+      "lstrip": false,
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+    },
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+      "lstrip": false,
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+    },
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+      "lstrip": false,
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+    },
+    "32049": {
+      "content": "<extra_id_50>",
+      "lstrip": false,
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+    },
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+      "lstrip": false,
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+    },
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+    },
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+      "lstrip": false,
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+    "transformer.rotary_pos_emb.inv_freq": "pytorch_model-00001-of-00007.bin"
+  }
+}

backend/huggingface/Kwai-Kolors/Kolors/text_encoder/tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "name_or_path": "THUDM/chatglm3-6b-base",
+  "remove_space": false,
+  "do_lower_case": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+      ]
+  }
+}

backend/huggingface/Kwai-Kolors/Kolors/tokenizer/tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "name_or_path": "THUDM/chatglm3-6b-base",
+  "remove_space": false,
+  "do_lower_case": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+      ]
+  }
+}

backend/huggingface/Kwai-Kolors/Kolors/unet/config.json

@@ -0,0 +1,72 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.27.0.dev0",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
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+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": 4096,
+  "encoder_hid_dim_type": "text_proj",
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": 5632,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": true
+}

backend/huggingface/Kwai-Kolors/Kolors/vae/config.json

@@ -0,0 +1,31 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.18.0.dev0",
+  "_name_or_path": "./vae",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/model_index.json

@@ -0,0 +1,41 @@
+{
+  "_class_name": "HunyuanDiTPipeline",
+  "_diffusers_version": "0.29.0.dev0",
+  "feature_extractor": [
+    null,
+    null
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "BertModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "BertTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "T5Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "HunyuanDiT2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/scheduler/scheduler_config.json

@@ -0,0 +1,21 @@
+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.29.0.dev0",
+  "beta_end": 0.03,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}

backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/text_encoder/config.json

@@ -0,0 +1,33 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 47020
+}

backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/text_encoder_2/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/tokenizer/special_tokens_map.json

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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/tokenizer/tokenizer_config.json

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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/tokenizer_2/special_tokens_map.json

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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/tokenizer_2/tokenizer_config.json

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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/transformer/config.json

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backend/huggingface/Tencent-Hunyuan/HunyuanDiT-Diffusers/vae/config.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/model_index.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/scheduler/scheduler_config.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/text_encoder/config.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/text_encoder_2/config.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/tokenizer/special_tokens_map.json

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backend/huggingface/black-forest-labs/FLUX.1-dev/tokenizer_2/special_tokens_map.json

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+}

backend/huggingface/black-forest-labs/FLUX.1-dev/transformer/config.json

@@ -0,0 +1,14 @@
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+  "_diffusers_version": "0.30.0.dev0",
+  "_name_or_path": "../checkpoints/flux-dev/transformer",
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+  "num_layers": 19,
+  "num_single_layers": 38,
+  "patch_size": 1,
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+}

backend/huggingface/black-forest-labs/FLUX.1-dev/vae/config.json

@@ -0,0 +1,38 @@
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+  "_diffusers_version": "0.30.0.dev0",
+  "_name_or_path": "../checkpoints/flux-dev",
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+  "use_post_quant_conv": false,
+  "use_quant_conv": false
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backend/huggingface/black-forest-labs/FLUX.1-schnell/model_index.json

@@ -0,0 +1,32 @@
+{
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+  "_diffusers_version": "0.30.0.dev0",
+  "scheduler": [
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+}

backend/huggingface/black-forest-labs/FLUX.1-schnell/scheduler/scheduler_config.json

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backend/huggingface/black-forest-labs/FLUX.1-schnell/text_encoder/config.json

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+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
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+}

backend/huggingface/black-forest-labs/FLUX.1-schnell/text_encoder_2/config.json

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+  "tie_word_embeddings": false,
+  "vocab_size": 32128
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backend/huggingface/black-forest-labs/FLUX.1-schnell/tokenizer/special_tokens_map.json

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backend/huggingface/black-forest-labs/FLUX.1-schnell/tokenizer/tokenizer_config.json

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backend/huggingface/black-forest-labs/FLUX.1-schnell/tokenizer_2/special_tokens_map.json

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+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
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+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

backend/huggingface/black-forest-labs/FLUX.1-schnell/transformer/config.json

@@ -0,0 +1,13 @@
+{
+  "_class_name": "FluxTransformer2DModel",
+  "_diffusers_version": "0.30.0.dev0",
+  "attention_head_dim": 128,
+  "guidance_embeds": false,
+  "in_channels": 64,
+  "joint_attention_dim": 4096,
+  "num_attention_heads": 24,
+  "num_layers": 19,
+  "num_single_layers": 38,
+  "patch_size": 1,
+  "pooled_projection_dim": 768
+}

backend/huggingface/black-forest-labs/FLUX.1-schnell/vae/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.30.0.dev0",
+  "_name_or_path": "../checkpoints/flux-dev",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/model_index.json

@@ -0,0 +1,35 @@
+{
+  "_class_name": "StableDiffusionXLInpaintPipeline",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0",
+  "force_zeros_for_empty_prompt": true,
+  "requires_aesthetics_score": false,
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.21.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/text_encoder/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "/home/suraj_huggingface_co/.cache/huggingface/hub/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots/bf714989e22c57ddc1c453bf74dab4521acb81d8/text_encoder",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "vocab_size": 49408
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/text_encoder_2/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "/home/suraj_huggingface_co/.cache/huggingface/hub/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots/bf714989e22c57ddc1c453bf74dab4521acb81d8/text_encoder_2",
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "vocab_size": 49408
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/tokenizer/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/tokenizer_2/special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "!",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/tokenizer_2/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/unet/config.json

@@ -0,0 +1,78 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "valhalla/sdxl-inpaint-ema",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "decay": 0.9999,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 9,
+  "inv_gamma": 1.0,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "min_decay": 0.0,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "optimization_step": 37000,
+  "out_channels": 4,
+  "power": 0.6666666666666666,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": null,
+  "update_after_step": 0,
+  "use_ema_warmup": false,
+  "use_linear_projection": true
+}

backend/huggingface/diffusers/stable-diffusion-xl-1.0-inpainting-0.1/vae/config.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "madebyollin/sdxl-vae-fp16-fix",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

backend/huggingface/lllyasviel/control_v11p_sd15_canny/config.json

@@ -0,0 +1,42 @@
+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.16.0.dev0",
+  "_name_or_path": "/home/patrick/controlnet_v1_1/control_v11p_sd15_canny",
+  "act_fn": "silu",
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 768,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_time_scale_shift": "default",
+  "upcast_attention": false,
+  "use_linear_projection": false
+}