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TestingforfaivsixEx/content/eichellis/extensions/adetai/.github/ISSUE_TEMPLATE/bug_report.yaml

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+name: Bug report
+description: Create a report
+title: "[Bug]: "
+labels:
+  - bug
+
+body:
+  - type: textarea
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: |
+        Any language accepted
+        아무 언어 사용가능
+        すべての言語に対応
+        接受所有语言
+        Se aceptan todos los idiomas
+        Alle Sprachen werden akzeptiert
+        Toutes les langues sont acceptées
+        Принимаются все языки
+
+  - type: textarea
+    attributes:
+      label: Screenshots
+      description: Screenshots related to the issue.
+
+  - type: textarea
+    attributes:
+      label: Console logs, from start to end.
+      description: |
+        The full console log of your terminal.
+      placeholder: |
+        Python ...
+        Version: ...
+        Commit hash: ...
+        Installing requirements
+        ...
+
+        Launching Web UI with arguments: ...
+        [-] ADetailer initialized. version: ...
+        ...
+        ...
+
+        Traceback (most recent call last):
+        ...
+        ...
+      render: Shell
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: List of installed extensions

TestingforfaivsixEx/content/eichellis/extensions/adetai/.github/ISSUE_TEMPLATE/feature_request.yaml

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+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+
+body:
+  - type: textarea
+    attributes:
+      label: Is your feature request related to a problem? Please describe.
+      description: A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+  - type: textarea
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen.
+
+  - type: textarea
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.

TestingforfaivsixEx/content/eichellis/extensions/adetai/.github/ISSUE_TEMPLATE/question.yaml

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+name: Question
+description: Write a question
+labels:
+  - question
+
+body:
+  - type: textarea
+    attributes:
+      label: Question
+      description: Please do not write bug reports or feature requests here.

TestingforfaivsixEx/content/eichellis/extensions/adetai/.github/workflows/stale.yml

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+name: 'Close stale issues and PRs'
+on:
+  schedule:
+    - cron: '30 1 * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v8
+        with:
+          days-before-stale: 23
+          days-before-close: 3

TestingforfaivsixEx/content/eichellis/extensions/adetai/.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+*.ipynb

TestingforfaivsixEx/content/eichellis/extensions/adetai/.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: trailing-whitespace
+        args: [--markdown-linebreak-ext=md]
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.0.287"
+    hooks:
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: 23.9.0
+    hooks:
+      - id: black

TestingforfaivsixEx/content/eichellis/extensions/adetai/.vscode/extensions.json

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+{
+    "recommendations": [
+        "ms-python.black-formatter",
+        "kevinrose.vsc-python-indent",
+        "charliermarsh.ruff",
+        "shardulm94.trailing-spaces"
+    ]
+}

TestingforfaivsixEx/content/eichellis/extensions/adetai/.vscode/settings.json

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+{
+    "explorer.fileNesting.enabled": true,
+    "explorer.fileNesting.patterns": {
+        "pyproject.toml": ".env, .gitignore, .pre-commit-config.yaml, Taskfile.yml",
+        "README.md": "LICENSE.md, CHANGELOG.md",
+        "install.py": "preload.py"
+    }
+}

TestingforfaivsixEx/content/eichellis/extensions/adetai/CHANGELOG.md

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+# Changelog
+
+## 2023-09-10
+
+- v23.9.2
+- (실험적) VAE 선택 기능
+
+## 2023-09-01
+
+- v23.9.1
+- webui 1.6.0에 추가된 인자를 사용해서 생긴 하위 호환 문제 수정
+
+## 2023-08-31
+
+- v23.9.0
+- (실험적) 체크포인트 선택기능
+  - 버그가 있어 리프레시 버튼은 구현에서 빠짐
+- 1.6.0 업데이트에 따라 img2img에서 사용불가능한 샘플러를 선택했을 때 더이상 Euler로 변경하지 않음
+- 유효하지 않은 인자가 전달되었을 때, 에러를 일으키지 않고 대신 adetailer를 비활성화함
+
+
+## 2023-08-25
+
+- v23.8.1
+- xyz grid에서 model을 `None`으로 설정한 이후에 adetailer가 비활성화 되는 문제 수정
+- skip을 눌렀을 때 진행을 멈춤
+- `--medvram-sdxl`을 설정했을 때에도 cpu를 사용하게 함
+
+## 2023-08-14
+
+- v23.8.0
+- `[PROMPT]` 키워드 추가. `ad_prompt` 또는 `ad_negative_prompt`에 사용하면 입력 프롬프트로 대체됨 (PR #243)
+- Only top k largest 옵션 추가 (PR #264)
+- ultralytics 버전 업데이트
+
+
+## 2023-07-31
+
+- v23.7.11
+- separate clip skip 옵션 추가
+- install requirements 정리 (ultralytics 새 버전, mediapipe~=3.20)
+
+## 2023-07-28
+
+- v23.7.10
+- ultralytics, mediapipe import문 정리
+- traceback에서 컬러를 없앰 (api 때문), 라이브러리 버전도 보여주게 설정.
+- huggingface_hub, pydantic을 install.py에서 없앰
+- 안쓰는 컨트롤넷 관련 코드 삭제
+
+
+## 2023-07-23
+
+- v23.7.9
+- `ultralytics.utils` ModuleNotFoundError 해결 (https://github.com/ultralytics/ultralytics/issues/3856)
+- `pydantic` 2.0 이상 버전 설치안되도록 함
+- `controlnet_dir` cmd args 문제 수정 (PR #107)
+
+## 2023-07-20
+
+- v23.7.8
+- `paste_field_names` 추가했던 것을 되돌림
+
+## 2023-07-19
+
+- v23.7.7
+- 인페인팅 단계에서 별도의 샘플러를 선택할 수 있게 옵션을 추가함 (xyz그리드에도 추가)
+- webui 1.0.0-pre 이하 버전에서 batch index 문제 수정
+- 스크립트에 `paste_field_names`을 추가함. 사용되는지는 모르겠음
+
+## 2023-07-16
+
+- v23.7.6
+- `ultralytics 8.0.135`에 추가된 cpuinfo 기능을 위해 `py-cpuinfo`를 미리 설치하게 함. (미리 설치 안하면 cpu나 mps사용할 때 재시작해야함)
+- init_image가 RGB 모드가 아닐 때 RGB로 변경.
+
+## 2023-07-07
+
+- v23.7.4
+- batch count > 1일때 프롬프트의 인덱스 문제 수정
+
+- v23.7.5
+- i2i의 `cached_uc`와 `cached_c`가 p의 `cached_uc`와 `cached_c`가 다른 인스턴스가 되도록 수정
+
+## 2023-07-05
+
+- v23.7.3
+- 버그 수정
+  - `object()`가 json 직렬화 안되는 문제
+  - `process`를 호출함에 따라 배치 카운트가 2이상일 때, all_prompts가 고정되는 문제
+  - `ad-before`와 `ad-preview` 이미지 파일명이 실제 파일명과 다른 문제
+  - pydantic 2.0 호환성 문제
+
+## 2023-07-04
+
+- v23.7.2
+- `mediapipe_face_mesh_eyes_only` 모델 추가: `mediapipe_face_mesh`로 감지한 뒤 눈만 사용함.
+- 매 배치 시작 전에 `scripts.postprocess`를, 후에 `scripts.process`를 호출함.
+  - 컨트롤넷을 사용하면 소요 시간이 조금 늘어나지만 몇몇 문제 해결에 도움이 됨.
+- `lora_block_weight`를 스크립트 화이트리스트에 추가함.
+  - 한번이라도 ADetailer를 사용한 사람은 수동으로 추가해야함.
+
+## 2023-07-03
+
+- v23.7.1
+- `process_images`를 진행한 뒤 `StableDiffusionProcessing` 오브젝트의 close를 호출함
+- api 호출로 사용했는지 확인하는 속성 추가
+- `NansException`이 발생했을 때 중지하지 않고 남은 과정 계속 진행함
+
+## 2023-07-02
+
+- v23.7.0
+- `NansException`이 발생하면 로그에 표시하고 원본 이미지를 반환하게 설정
+- `rich`를 사용한 에러 트레이싱
+  - install.py에 `rich` 추가
+- 생성 중에 컴포넌트의 값을 변경하면 args의 값도 함께 변경되는 문제 수정 (issue #180)
+- 터미널 로그로 ad_prompt와 ad_negative_prompt에 적용된 실제 프롬프트 확인할 수 있음 (입력과 다를 경우에만)
+
+## 2023-06-28
+
+- v23.6.4
+- 최대 모델 수 5 -> 10개
+- ad_prompt와 ad_negative_prompt에 빈칸으로 놔두면 입력 프롬프트가 사용된다는 문구 추가
+- huggingface 모델 다운로드 실패시 로깅
+- 1st 모델이 `None`일 경우 나머지 입력을 무시하던 문제 수정
+- `--use-cpu` 에 `adetailer` 입력 시 cpu로 yolo모델을 사용함
+
+## 2023-06-20
+
+- v23.6.3
+- 컨트롤넷 inpaint 모델에 대해, 3가지 모듈을 사용할 수 있도록 함
+- Noise Multiplier 옵션 추가 (PR #149)
+- pydantic 최소 버전 1.10.8로 설정 (Issue #146)
+
+## 2023-06-05
+
+- v23.6.2
+- xyz_grid에서 ADetailer를 사용할 수 있게함.
+  - 8가지 옵션만 1st 탭에 적용되도록 함.
+
+## 2023-06-01
+
+- v23.6.1
+- `inpaint, scribble, lineart, openpose, tile` 5가지 컨트롤넷 모델 지원 (PR #107)
+- controlnet guidance start, end 인자 추가 (PR #107)
+- `modules.extensions`를 사용하여 컨트롤넷 확장을 불러오고 경로를 알아내로록 변경
+- ui에서 컨트롤넷을 별도 함수로 분리
+
+## 2023-05-30
+
+- v23.6.0
+- 스크립트의 이름을 `After Detailer`에서 `ADetailer`로 변경
+  - API 사용자는 변경 필요함
+- 몇몇 설정 변경
+  - `ad_conf` → `ad_confidence`. 0~100 사이의 int → 0.0~1.0 사이의 float
+  - `ad_inpaint_full_res` → `ad_inpaint_only_masked`
+  - `ad_inpaint_full_res_padding` → `ad_inpaint_only_masked_padding`
+- mediapipe face mesh 모델 추가
+  - mediapipe 최소 버전 `0.10.0`
+
+- rich traceback 제거함
+- huggingface 다운로드 실패할 때 에러가 나지 않게 하고 해당 모델을 제거함
+
+## 2023-05-26
+
+- v23.5.19
+- 1번째 탭에도 `None` 옵션을 추가함
+- api로 ad controlnet model에 inpaint가 아닌 다른 컨트롤넷 모델을 사용하지 못하도록 막음
+- adetailer 진행중에 total tqdm 진행바 업데이트를 멈춤
+- state.inturrupted 상태에서 adetailer 과정을 중지함
+- 컨트롤넷 process를 각 batch가 끝난 순간에만 호출하도록 변경
+
+### 2023-05-25
+
+- v23.5.18
+- 컨트롤넷 관련 수정
+  - unit의 `input_mode`를 `SIMPLE`로 모두 변경
+  - 컨트롤넷 유넷 훅과 하이잭 함수들을 adetailer를 실행할 때에만 되돌리는 기능 추가
+  - adetailer 처리가 끝난 뒤 컨트롤넷 스크립트의 process를 다시 진행함. (batch count 2 이상일때의 문제 해결)
+- 기본 활성 스크립트 목록에서 컨트롤넷을 뺌
+
+### 2023-05-22
+
+- v23.5.17
+- 컨트롤넷 확장이 있으면 컨트롤넷 스크립트를 활성화함. (컨트롤넷 관련 문제 해결)
+- 모든 컴포넌트에 elem_id 설정
+- ui에 버전을 표시함
+
+
+### 2023-05-19
+
+- v23.5.16
+- 추가한 옵션
+  - Mask min/max ratio
+  - Mask merge mode
+  - Restore faces after ADetailer
+- 옵션들을 Accordion으로 묶음
+
+### 2023-05-18
+
+- v23.5.15
+- 필요한 것만 임포트하도록 변경 (vae 로딩 오류 없어짐. 로딩 속도 빨라짐)
+
+### 2023-05-17
+
+- v23.5.14
+- `[SKIP]`으로 ad prompt 일부를 건너뛰는 기능 추가
+- bbox 정렬 옵션 추가
+- sd_webui 타입힌트를 만들어냄
+- enable checker와 관련된 api 오류 수정?
+
+### 2023-05-15
+
+- v23.5.13
+- `[SEP]`으로 ad prompt를 분리하여 적용하는 기능 추가
+- enable checker를 다시 pydantic으로 변경함
+- ui 관련 함수를 adetailer.ui 폴더로 분리함
+- controlnet을 사용할 때 모든 controlnet unit 비활성화
+- adetailer 폴더가 없으면 만들게 함
+
+### 2023-05-13
+
+- v23.5.12
+- `ad_enable`을 제외한 입력이 dict타입으로 들어오도록 변경
+  - web api로 사용할 때에 특히 사용하기 쉬움
+  - web api breaking change
+- `mask_preprocess` 인자를 넣지 않았던 오류 수정 (PR #47)
+- huggingface에서 모델을 다운로드하지 않는 옵션 추가 `--ad-no-huggingface`
+
+### 2023-05-12
+
+- v23.5.11
+- `ultralytics` 알람 제거
+- 필요없는 exif 인자 더 제거함
+- `use separate steps` 옵션 추가
+- ui 배치를 조정함
+
+### 2023-05-09
+
+- v23.5.10
+- 선택한 스크립트만 ADetailer에 적용하는 옵션 추가, 기본값 `True`. 설정 탭에서 지정가능.
+  - 기본값: `dynamic_prompting,dynamic_thresholding,wildcards,wildcard_recursive`
+- `person_yolov8s-seg.pt` 모델 추가
+- `ultralytics`의 최소 버전을 `8.0.97`로 설정 (C:\\ 문제 해결된 버전)
+
+### 2023-05-08
+
+- v23.5.9
+- 2가지 이상의 모델을 사용할 수 있음. 기본값: 2, 최대: 5
+- segment 모델을 사용할 수 있게 함. `person_yolov8n-seg.pt` 추가
+
+### 2023-05-07
+
+- v23.5.8
+- 프롬프트와 네거티브 프롬프트에 방향키 지원 (PR #24)
+- `mask_preprocess`를 추가함. 이전 버전과 시드값이 달라질 가능성 있음!
+- 이미지 처리가 일어났을 때에만 before이미지를 저장함
+- 설정창의 레이블을 ADetailer 대신 더 적절하게 수정함
+
+### 2023-05-06
+
+- v23.5.7
+- `ad_use_cfg_scale` 옵션 추가. cfg 스케일을 따로 사용할지 말지 결정함.
+- `ad_enable` 기본값을 `True`에서 `False`로 변경
+- `ad_model`의 기본값을 `None`에서 첫번째 모델로 변경
+- 최소 2개의 입력(ad_enable, ad_model)만 들어오면 작동하게 변경.
+
+- v23.5.7.post0
+- `init_controlnet_ext`을 controlnet_exists == True일때에만 실행
+- webui를 C드라이브 바로 밑에 설치한 사람들에게 `ultralytics` 경고 표시
+
+### 2023-05-05 (어린이날)
+
+- v23.5.5
+- `Save images before ADetailer` 옵션 추가
+- 입력으로 들어온 인자와 ALL_ARGS의 길이가 다르면 에러메세지
+- README.md에 설치방법 추가
+
+- v23.5.6
+- get_args에서 IndexError가 발생하면 자세한 에러메세지를 볼 수 있음
+- AdetailerArgs에 extra_params 내장
+- scripts_args를 딥카피함
+- postprocess_image를 약간 분리함
+
+- v23.5.6.post0
+- `init_controlnet_ext`에서 에러메세지를 자세히 볼 수 ��음
+
+### 2023-05-04
+
+- v23.5.4
+- use pydantic for arguments validation
+- revert: ad_model to `None` as default
+- revert: `__future__` imports
+- lazily import yolo and mediapipe
+
+### 2023-05-03
+
+- v23.5.3.post0
+- remove `__future__` imports
+- change to copy scripts and scripts args
+
+- v23.5.3.post1
+- change default ad_model from `None`
+
+### 2023-05-02
+
+- v23.5.3
+- Remove `None` from model list and add `Enable ADetailer` checkbox.
+- install.py `skip_install` fix.

TestingforfaivsixEx/content/eichellis/extensions/adetai/LICENSE.md

@@ -0,0 +1,662 @@
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+<http://www.gnu.org/licenses/>.

TestingforfaivsixEx/content/eichellis/extensions/adetai/README.md

@@ -0,0 +1,94 @@
+# !After Detailer
+
+!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
+
+## Install
+
+(from Mikubill/sd-webui-controlnet)
+
+1. Open "Extensions" tab.
+2. Open "Install from URL" tab in the tab.
+3. Enter `https://github.com/Bing-su/adetailer.git` to "URL for extension's git repository".
+4. Press "Install" button.
+5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
+6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use this method to update extensions.)
+7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer: turn your computer off and turn it on again.)
+
+You can now install it directly from the Extensions tab.
+
+![image](https://i.imgur.com/g6GdRBT.png)
+
+You **DON'T** need to download any model from huggingface.
+
+## Options
+
+| Model, Prompts                    |                                       |                                                   |
+| --------------------------------- | ------------------------------------- | ------------------------------------------------- |
+| ADetailer model                   | Determine what to detect.             | `None` = disable                                  |
+| ADetailer prompt, negative prompt | Prompts and negative prompts to apply | If left blank, it will use the same as the input. |
+
+| Detection                            |                                                                                              |     |
+| ------------------------------------ | -------------------------------------------------------------------------------------------- | --- |
+| Detection model confidence threshold | Only objects with a detection model confidence above this threshold are used for inpainting. |     |
+| Mask min/max ratio                   | Only use masks whose area is between those ratios for the area of the entire image.          |     |
+
+If you want to exclude objects in the background, try setting the min ratio to around `0.01`.
+
+| Mask Preprocessing              |                                                                                                                                     |                                                                                         |
+| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| Mask x, y offset                | Moves the mask horizontally and vertically by                                                                                       |                                                                                         |
+| Mask erosion (-) / dilation (+) | Enlarge or reduce the detected mask.                                                                                                | [opencv example](https://docs.opencv.org/4.7.0/db/df6/tutorial_erosion_dilatation.html) |
+| Mask merge mode                 | `None`: Inpaint each mask<br/>`Merge`: Merge all masks and inpaint<br/>`Merge and Invert`: Merge all masks and Invert, then inpaint |                                                                                         |
+
+Applied in this order: x, y offset → erosion/dilation → merge/invert.
+
+#### Inpainting
+
+Each option corresponds to a corresponding option on the inpaint tab. Therefore, please refer to the inpaint tab for usage details on how to use each option.
+
+## ControlNet Inpainting
+
+You can use the ControlNet extension if you have ControlNet installed and ControlNet models.
+
+Support `inpaint, scribble, lineart, openpose, tile` controlnet models. Once you choose a model, the preprocessor is set automatically. It works separately from the model set by the Controlnet extension.
+
+## Advanced Options
+
+API request example: [wiki/API](https://github.com/Bing-su/adetailer/wiki/API)
+
+`ui-config.json` entries: [wiki/ui-config.json](https://github.com/Bing-su/adetailer/wiki/ui-config.json)
+
+`[SEP], [SKIP]` tokens: [wiki/Advanced](https://github.com/Bing-su/adetailer/wiki/Advanced)
+
+## Media
+
+- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方① 【Stable Diffusion】](https://youtu.be/sF3POwPUWCE)
+- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方② 【Stable Diffusion】](https://youtu.be/urNISRdbIEg)
+
+## Model
+
+| Model                 | Target                | mAP 50                        | mAP 50-95                     |
+| --------------------- | --------------------- | ----------------------------- | ----------------------------- |
+| face_yolov8n.pt       | 2D / realistic face   | 0.660                         | 0.366                         |
+| face_yolov8s.pt       | 2D / realistic face   | 0.713                         | 0.404                         |
+| hand_yolov8n.pt       | 2D / realistic hand   | 0.767                         | 0.505                         |
+| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
+| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
+| mediapipe_face_full   | realistic face        | -                             | -                             |
+| mediapipe_face_short  | realistic face        | -                             | -                             |
+| mediapipe_face_mesh   | realistic face        | -                             | -                             |
+
+The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
+
+### Additional Model
+
+Put your [ultralytics](https://github.com/ultralytics/ultralytics) yolo model in `webui/models/adetailer`. The model name should end with `.pt` or `.pth`.
+
+It must be a bbox detection or segment model and use all label.
+
+## Example
+
+![image](https://i.imgur.com/38RSxSO.png)
+![image](https://i.imgur.com/2CYgjLx.png)
+
+[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/F1F1L7V2N)

TestingforfaivsixEx/content/eichellis/extensions/adetai/Taskfile.yml

@@ -0,0 +1,27 @@
+# https://taskfile.dev
+
+version: "3"
+
+dotenv:
+  - .env
+
+tasks:
+  default:
+    cmds:
+      - echo "$PYTHON"
+      - echo "$WEBUI"
+    silent: true
+
+  launch:
+    dir: "{{.WEBUI}}"
+    cmds:
+      - "{{.PYTHON}} launch.py --xformers --api"
+    silent: true
+
+  lint:
+    cmds:
+      - pre-commit run -a
+
+  update:
+    cmds:
+      - "{{.PYTHON}} -m pip install -U ultralytics mediapipe ruff pre-commit black"

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/__init__.py

@@ -0,0 +1,20 @@
+from .__version__ import __version__
+from .args import AD_ENABLE, ALL_ARGS, ADetailerArgs, EnableChecker
+from .common import PredictOutput, get_models
+from .mediapipe import mediapipe_predict
+from .ultralytics import ultralytics_predict
+
+AFTER_DETAILER = "ADetailer"
+
+__all__ = [
+    "__version__",
+    "AD_ENABLE",
+    "ADetailerArgs",
+    "AFTER_DETAILER",
+    "ALL_ARGS",
+    "EnableChecker",
+    "PredictOutput",
+    "get_models",
+    "mediapipe_predict",
+    "ultralytics_predict",
+]

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/__version__.py

@@ -0,0 +1 @@
+__version__ = "23.9.2"

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/args.py

@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+from collections import UserList
+from functools import cached_property, partial
+from typing import Any, Literal, NamedTuple, Optional, Union
+
+import pydantic
+from pydantic import (
+    BaseModel,
+    Extra,
+    NonNegativeFloat,
+    NonNegativeInt,
+    PositiveInt,
+    confloat,
+    conint,
+    constr,
+    root_validator,
+    validator,
+)
+
+cn_model_regex = r".*(inpaint|tile|scribble|lineart|openpose).*|^None$"
+
+
+class Arg(NamedTuple):
+    attr: str
+    name: str
+
+
+class ArgsList(UserList):
+    @cached_property
+    def attrs(self) -> tuple[str]:
+        return tuple(attr for attr, _ in self)
+
+    @cached_property
+    def names(self) -> tuple[str]:
+        return tuple(name for _, name in self)
+
+
+class ADetailerArgs(BaseModel, extra=Extra.forbid):
+    ad_model: str = "None"
+    ad_prompt: str = ""
+    ad_negative_prompt: str = ""
+    ad_confidence: confloat(ge=0.0, le=1.0) = 0.3
+    ad_mask_k_largest: NonNegativeInt = 0
+    ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0
+    ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0
+    ad_dilate_erode: int = 4
+    ad_x_offset: int = 0
+    ad_y_offset: int = 0
+    ad_mask_merge_invert: Literal["None", "Merge", "Merge and Invert"] = "None"
+    ad_mask_blur: NonNegativeInt = 4
+    ad_denoising_strength: confloat(ge=0.0, le=1.0) = 0.4
+    ad_inpaint_only_masked: bool = True
+    ad_inpaint_only_masked_padding: NonNegativeInt = 32
+    ad_use_inpaint_width_height: bool = False
+    ad_inpaint_width: PositiveInt = 512
+    ad_inpaint_height: PositiveInt = 512
+    ad_use_steps: bool = False
+    ad_steps: PositiveInt = 28
+    ad_use_cfg_scale: bool = False
+    ad_cfg_scale: NonNegativeFloat = 7.0
+    ad_use_checkpoint: bool = False
+    ad_checkpoint: Optional[str] = None
+    ad_use_vae: bool = False
+    ad_vae: Optional[str] = None
+    ad_use_sampler: bool = False
+    ad_sampler: str = "DPM++ 2M Karras"
+    ad_use_noise_multiplier: bool = False
+    ad_noise_multiplier: confloat(ge=0.5, le=1.5) = 1.0
+    ad_use_clip_skip: bool = False
+    ad_clip_skip: conint(ge=1, le=12) = 1
+    ad_restore_face: bool = False
+    ad_controlnet_model: constr(regex=cn_model_regex) = "None"
+    ad_controlnet_module: Optional[constr(regex=r".*inpaint.*|^None$")] = None
+    ad_controlnet_weight: confloat(ge=0.0, le=1.0) = 1.0
+    ad_controlnet_guidance_start: confloat(ge=0.0, le=1.0) = 0.0
+    ad_controlnet_guidance_end: confloat(ge=0.0, le=1.0) = 1.0
+    is_api: bool = True
+
+    @root_validator(skip_on_failure=True)
+    def ad_controlnt_module_validator(cls, values):  # noqa: N805
+        cn_model = values.get("ad_controlnet_model", "None")
+        cn_module = values.get("ad_controlnet_module", None)
+        if "inpaint" not in cn_model or cn_module == "None":
+            values["ad_controlnet_module"] = None
+        return values
+
+    @validator("is_api", pre=True)
+    def is_api_validator(cls, v: Any):  # noqa: N805
+        "tuple is json serializable but cannot be made with json deserialize."
+        return type(v) is not tuple
+
+    @staticmethod
+    def ppop(
+        p: dict[str, Any],
+        key: str,
+        pops: list[str] | None = None,
+        cond: Any = None,
+    ) -> None:
+        if pops is None:
+            pops = [key]
+        if key not in p:
+            return
+        value = p[key]
+        cond = (not bool(value)) if cond is None else value == cond
+
+        if cond:
+            for k in pops:
+                p.pop(k, None)
+
+    def extra_params(self, suffix: str = "") -> dict[str, Any]:
+        if self.ad_model == "None":
+            return {}
+
+        p = {name: getattr(self, attr) for attr, name in ALL_ARGS}
+        ppop = partial(self.ppop, p)
+
+        ppop("ADetailer prompt")
+        ppop("ADetailer negative prompt")
+        ppop("ADetailer mask only top k largest", cond=0)
+        ppop("ADetailer mask min ratio", cond=0.0)
+        ppop("ADetailer mask max ratio", cond=1.0)
+        ppop("ADetailer x offset", cond=0)
+        ppop("ADetailer y offset", cond=0)
+        ppop("ADetailer mask merge/invert", cond="None")
+        ppop("ADetailer inpaint only masked", ["ADetailer inpaint padding"])
+        ppop(
+            "ADetailer use inpaint width/height",
+            [
+                "ADetailer use inpaint width/height",
+                "ADetailer inpaint width",
+                "ADetailer inpaint height",
+            ],
+        )
+        ppop(
+            "ADetailer use separate steps",
+            ["ADetailer use separate steps", "ADetailer steps"],
+        )
+        ppop(
+            "ADetailer use separate CFG scale",
+            ["ADetailer use separate CFG scale", "ADetailer CFG scale"],
+        )
+        ppop(
+            "ADetailer use separate checkpoint",
+            ["ADetailer use separate checkpoint", "ADetailer checkpoint"],
+        )
+        ppop(
+            "ADetailer use separate VAE",
+            ["ADetailer use separate VAE", "ADetailer VAE"],
+        )
+        ppop(
+            "ADetailer use separate sampler",
+            ["ADetailer use separate sampler", "ADetailer sampler"],
+        )
+        ppop(
+            "ADetailer use separate noise multiplier",
+            ["ADetailer use separate noise multiplier", "ADetailer noise multiplier"],
+        )
+
+        ppop(
+            "ADetailer use separate CLIP skip",
+            ["ADetailer use separate CLIP skip", "ADetailer CLIP skip"],
+        )
+
+        ppop("ADetailer restore face")
+        ppop(
+            "ADetailer ControlNet model",
+            [
+                "ADetailer ControlNet model",
+                "ADetailer ControlNet module",
+                "ADetailer ControlNet weight",
+                "ADetailer ControlNet guidance start",
+                "ADetailer ControlNet guidance end",
+            ],
+            cond="None",
+        )
+        ppop("ADetailer ControlNet module")
+        ppop("ADetailer ControlNet weight", cond=1.0)
+        ppop("ADetailer ControlNet guidance start", cond=0.0)
+        ppop("ADetailer ControlNet guidance end", cond=1.0)
+
+        if suffix:
+            p = {k + suffix: v for k, v in p.items()}
+
+        return p
+
+
+class EnableChecker(BaseModel):
+    enable: bool
+    arg_list: list
+
+    def is_enabled(self) -> bool:
+        ad_model = ALL_ARGS[0].attr
+        if not self.enable:
+            return False
+        return any(arg.get(ad_model, "None") != "None" for arg in self.arg_list)
+
+
+_all_args = [
+    ("ad_enable", "ADetailer enable"),
+    ("ad_model", "ADetailer model"),
+    ("ad_prompt", "ADetailer prompt"),
+    ("ad_negative_prompt", "ADetailer negative prompt"),
+    ("ad_confidence", "ADetailer confidence"),
+    ("ad_mask_k_largest", "ADetailer mask only top k largest"),
+    ("ad_mask_min_ratio", "ADetailer mask min ratio"),
+    ("ad_mask_max_ratio", "ADetailer mask max ratio"),
+    ("ad_x_offset", "ADetailer x offset"),
+    ("ad_y_offset", "ADetailer y offset"),
+    ("ad_dilate_erode", "ADetailer dilate/erode"),
+    ("ad_mask_merge_invert", "ADetailer mask merge/invert"),
+    ("ad_mask_blur", "ADetailer mask blur"),
+    ("ad_denoising_strength", "ADetailer denoising strength"),
+    ("ad_inpaint_only_masked", "ADetailer inpaint only masked"),
+    ("ad_inpaint_only_masked_padding", "ADetailer inpaint padding"),
+    ("ad_use_inpaint_width_height", "ADetailer use inpaint width/height"),
+    ("ad_inpaint_width", "ADetailer inpaint width"),
+    ("ad_inpaint_height", "ADetailer inpaint height"),
+    ("ad_use_steps", "ADetailer use separate steps"),
+    ("ad_steps", "ADetailer steps"),
+    ("ad_use_cfg_scale", "ADetailer use separate CFG scale"),
+    ("ad_cfg_scale", "ADetailer CFG scale"),
+    ("ad_use_checkpoint", "ADetailer use separate checkpoint"),
+    ("ad_checkpoint", "ADetailer checkpoint"),
+    ("ad_use_vae", "ADetailer use separate VAE"),
+    ("ad_vae", "ADetailer VAE"),
+    ("ad_use_sampler", "ADetailer use separate sampler"),
+    ("ad_sampler", "ADetailer sampler"),
+    ("ad_use_noise_multiplier", "ADetailer use separate noise multiplier"),
+    ("ad_noise_multiplier", "ADetailer noise multiplier"),
+    ("ad_use_clip_skip", "ADetailer use separate CLIP skip"),
+    ("ad_clip_skip", "ADetailer CLIP skip"),
+    ("ad_restore_face", "ADetailer restore face"),
+    ("ad_controlnet_model", "ADetailer ControlNet model"),
+    ("ad_controlnet_module", "ADetailer ControlNet module"),
+    ("ad_controlnet_weight", "ADetailer ControlNet weight"),
+    ("ad_controlnet_guidance_start", "ADetailer ControlNet guidance start"),
+    ("ad_controlnet_guidance_end", "ADetailer ControlNet guidance end"),
+]
+
+AD_ENABLE = Arg(*_all_args[0])
+_args = [Arg(*args) for args in _all_args[1:]]
+ALL_ARGS = ArgsList(_args)
+
+BBOX_SORTBY = [
+    "None",
+    "Position (left to right)",
+    "Position (center to edge)",
+    "Area (large to small)",
+]
+MASK_MERGE_INVERT = ["None", "Merge", "Merge and Invert"]

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/common.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional, Union
+
+from huggingface_hub import hf_hub_download
+from PIL import Image, ImageDraw
+from rich import print
+
+repo_id = "Bingsu/adetailer"
+
+
+@dataclass
+class PredictOutput:
+    bboxes: list[list[int | float]] = field(default_factory=list)
+    masks: list[Image.Image] = field(default_factory=list)
+    preview: Optional[Image.Image] = None
+
+
+def hf_download(file: str):
+    try:
+        path = hf_hub_download(repo_id, file)
+    except Exception:
+        msg = f"[-] ADetailer: Failed to load model {file!r} from huggingface"
+        print(msg)
+        path = "INVALID"
+    return path
+
+
+def get_models(
+    model_dir: Union[str, Path], huggingface: bool = True
+) -> OrderedDict[str, Optional[str]]:
+    model_dir = Path(model_dir)
+    if model_dir.is_dir():
+        model_paths = [
+            p
+            for p in model_dir.rglob("*")
+            if p.is_file() and p.suffix in (".pt", ".pth")
+        ]
+    else:
+        model_paths = []
+
+    models = OrderedDict()
+    if huggingface:
+        models.update(
+            {
+                "face_yolov8n.pt": hf_download("face_yolov8n.pt"),
+                "face_yolov8s.pt": hf_download("face_yolov8s.pt"),
+                "hand_yolov8n.pt": hf_download("hand_yolov8n.pt"),
+                "person_yolov8n-seg.pt": hf_download("person_yolov8n-seg.pt"),
+                "person_yolov8s-seg.pt": hf_download("person_yolov8s-seg.pt"),
+            }
+        )
+    models.update(
+        {
+            "mediapipe_face_full": None,
+            "mediapipe_face_short": None,
+            "mediapipe_face_mesh": None,
+            "mediapipe_face_mesh_eyes_only": None,
+        }
+    )
+
+    invalid_keys = [k for k, v in models.items() if v == "INVALID"]
+    for key in invalid_keys:
+        models.pop(key)
+
+    for path in model_paths:
+        if path.name in models:
+            continue
+        models[path.name] = str(path)
+
+    return models
+
+
+def create_mask_from_bbox(
+    bboxes: list[list[float]], shape: tuple[int, int]
+) -> list[Image.Image]:
+    """
+    Parameters
+    ----------
+        bboxes: list[list[float]]
+            list of [x1, y1, x2, y2]
+            bounding boxes
+        shape: tuple[int, int]
+            shape of the image (width, height)
+
+    Returns
+    -------
+        masks: list[Image.Image]
+        A list of masks
+
+    """
+    masks = []
+    for bbox in bboxes:
+        mask = Image.new("L", shape, 0)
+        mask_draw = ImageDraw.Draw(mask)
+        mask_draw.rectangle(bbox, fill=255)
+        masks.append(mask)
+    return masks
+
+
+def create_bbox_from_mask(
+    masks: list[Image.Image], shape: tuple[int, int]
+) -> list[list[int]]:
+    """
+    Parameters
+    ----------
+        masks: list[Image.Image]
+            A list of masks
+        shape: tuple[int, int]
+            shape of the image (width, height)
+
+    Returns
+    -------
+        bboxes: list[list[float]]
+        A list of bounding boxes
+
+    """
+    bboxes = []
+    for mask in masks:
+        mask = mask.resize(shape)
+        bbox = mask.getbbox()
+        if bbox is not None:
+            bboxes.append(list(bbox))
+    return bboxes

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/mask.py

@@ -0,0 +1,255 @@
+from __future__ import annotations
+
+from enum import IntEnum
+from functools import partial, reduce
+from math import dist
+
+import cv2
+import numpy as np
+from PIL import Image, ImageChops
+
+from adetailer.args import MASK_MERGE_INVERT
+from adetailer.common import PredictOutput
+
+
+class SortBy(IntEnum):
+    NONE = 0
+    LEFT_TO_RIGHT = 1
+    CENTER_TO_EDGE = 2
+    AREA = 3
+
+
+class MergeInvert(IntEnum):
+    NONE = 0
+    MERGE = 1
+    MERGE_INVERT = 2
+
+
+def _dilate(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.dilate(arr, kernel, iterations=1)
+
+
+def _erode(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.erode(arr, kernel, iterations=1)
+
+
+def dilate_erode(img: Image.Image, value: int) -> Image.Image:
+    """
+    The dilate_erode function takes an image and a value.
+    If the value is positive, it dilates the image by that amount.
+    If the value is negative, it erodes the image by that amount.
+
+    Parameters
+    ----------
+        img: PIL.Image.Image
+            the image to be processed
+        value: int
+            kernel size of dilation or erosion
+
+    Returns
+    -------
+        PIL.Image.Image
+            The image that has been dilated or eroded
+    """
+    if value == 0:
+        return img
+
+    arr = np.array(img)
+    arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
+
+    return Image.fromarray(arr)
+
+
+def offset(img: Image.Image, x: int = 0, y: int = 0) -> Image.Image:
+    """
+    The offset function takes an image and offsets it by a given x(→) and y(↑) value.
+
+    Parameters
+    ----------
+        mask: Image.Image
+            Pass the mask image to the function
+        x: int
+            →
+        y: int
+            ↑
+
+    Returns
+    -------
+        PIL.Image.Image
+            A new image that is offset by x and y
+    """
+    return ImageChops.offset(img, x, -y)
+
+
+def is_all_black(img: Image.Image) -> bool:
+    arr = np.array(img)
+    return cv2.countNonZero(arr) == 0
+
+
+def bbox_area(bbox: list[float]):
+    return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
+
+
+def mask_preprocess(
+    masks: list[Image.Image],
+    kernel: int = 0,
+    x_offset: int = 0,
+    y_offset: int = 0,
+    merge_invert: int | MergeInvert | str = MergeInvert.NONE,
+) -> list[Image.Image]:
+    """
+    The mask_preprocess function takes a list of masks and preprocesses them.
+    It dilates and erodes the masks, and offsets them by x_offset and y_offset.
+
+    Parameters
+    ----------
+        masks: list[Image.Image]
+            A list of masks
+        kernel: int
+            kernel size of dilation or erosion
+        x_offset: int
+            →
+        y_offset: int
+            ↑
+
+    Returns
+    -------
+        list[Image.Image]
+            A list of processed masks
+    """
+    if not masks:
+        return []
+
+    if x_offset != 0 or y_offset != 0:
+        masks = [offset(m, x_offset, y_offset) for m in masks]
+
+    if kernel != 0:
+        masks = [dilate_erode(m, kernel) for m in masks]
+        masks = [m for m in masks if not is_all_black(m)]
+
+    return mask_merge_invert(masks, mode=merge_invert)
+
+
+# Bbox sorting
+def _key_left_to_right(bbox: list[float]) -> float:
+    """
+    Left to right
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    """
+    return bbox[0]
+
+
+def _key_center_to_edge(bbox: list[float], *, center: tuple[float, float]) -> float:
+    """
+    Center to edge
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    image: Image.Image
+        the image
+    """
+    bbox_center = ((bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2)
+    return dist(center, bbox_center)
+
+
+def _key_area(bbox: list[float]) -> float:
+    """
+    Large to small
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    """
+    return -bbox_area(bbox)
+
+
+def sort_bboxes(
+    pred: PredictOutput, order: int | SortBy = SortBy.NONE
+) -> PredictOutput:
+    if order == SortBy.NONE or len(pred.bboxes) <= 1:
+        return pred
+
+    if order == SortBy.LEFT_TO_RIGHT:
+        key = _key_left_to_right
+    elif order == SortBy.CENTER_TO_EDGE:
+        width, height = pred.preview.size
+        center = (width / 2, height / 2)
+        key = partial(_key_center_to_edge, center=center)
+    elif order == SortBy.AREA:
+        key = _key_area
+    else:
+        raise RuntimeError
+
+    items = len(pred.bboxes)
+    idx = sorted(range(items), key=lambda i: key(pred.bboxes[i]))
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
+# Filter by ratio
+def is_in_ratio(bbox: list[float], low: float, high: float, orig_area: int) -> bool:
+    area = bbox_area(bbox)
+    return low <= area / orig_area <= high
+
+
+def filter_by_ratio(pred: PredictOutput, low: float, high: float) -> PredictOutput:
+    if not pred.bboxes:
+        return pred
+
+    w, h = pred.preview.size
+    orig_area = w * h
+    items = len(pred.bboxes)
+    idx = [i for i in range(items) if is_in_ratio(pred.bboxes[i], low, high, orig_area)]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
+def filter_k_largest(pred: PredictOutput, k: int = 0) -> PredictOutput:
+    if not pred.bboxes or k == 0:
+        return pred
+    areas = [bbox_area(bbox) for bbox in pred.bboxes]
+    idx = np.argsort(areas)[-k:]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
+# Merge / Invert
+def mask_merge(masks: list[Image.Image]) -> list[Image.Image]:
+    arrs = [np.array(m) for m in masks]
+    arr = reduce(cv2.bitwise_or, arrs)
+    return [Image.fromarray(arr)]
+
+
+def mask_invert(masks: list[Image.Image]) -> list[Image.Image]:
+    return [ImageChops.invert(m) for m in masks]
+
+
+def mask_merge_invert(
+    masks: list[Image.Image], mode: int | MergeInvert | str
+) -> list[Image.Image]:
+    if isinstance(mode, str):
+        mode = MASK_MERGE_INVERT.index(mode)
+
+    if mode == MergeInvert.NONE or not masks:
+        return masks
+
+    if mode == MergeInvert.MERGE:
+        return mask_merge(masks)
+
+    if mode == MergeInvert.MERGE_INVERT:
+        merged = mask_merge(masks)
+        return mask_invert(merged)
+
+    raise RuntimeError

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/mediapipe.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+from functools import partial
+
+import mediapipe as mp
+import numpy as np
+from PIL import Image, ImageDraw
+
+from adetailer import PredictOutput
+from adetailer.common import create_bbox_from_mask, create_mask_from_bbox
+
+
+def mediapipe_predict(
+    model_type: str, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mapping = {
+        "mediapipe_face_short": partial(mediapipe_face_detection, 0),
+        "mediapipe_face_full": partial(mediapipe_face_detection, 1),
+        "mediapipe_face_mesh": mediapipe_face_mesh,
+        "mediapipe_face_mesh_eyes_only": mediapipe_face_mesh_eyes_only,
+    }
+    if model_type in mapping:
+        func = mapping[model_type]
+        return func(image, confidence)
+    msg = f"[-] ADetailer: Invalid mediapipe model type: {model_type}, Available: {list(mapping.keys())!r}"
+    raise RuntimeError(msg)
+
+
+def mediapipe_face_detection(
+    model_type: int, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    img_width, img_height = image.size
+
+    mp_face_detection = mp.solutions.face_detection
+    draw_util = mp.solutions.drawing_utils
+
+    img_array = np.array(image)
+
+    with mp_face_detection.FaceDetection(
+        model_selection=model_type, min_detection_confidence=confidence
+    ) as face_detector:
+        pred = face_detector.process(img_array)
+
+    if pred.detections is None:
+        return PredictOutput()
+
+    preview_array = img_array.copy()
+
+    bboxes = []
+    for detection in pred.detections:
+        draw_util.draw_detection(preview_array, detection)
+
+        bbox = detection.location_data.relative_bounding_box
+        x1 = bbox.xmin * img_width
+        y1 = bbox.ymin * img_height
+        w = bbox.width * img_width
+        h = bbox.height * img_height
+        x2 = x1 + w
+        y2 = y1 + h
+
+        bboxes.append([x1, y1, x2, y2])
+
+    masks = create_mask_from_bbox(bboxes, image.size)
+    preview = Image.fromarray(preview_array)
+
+    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def get_convexhull(points: np.ndarray) -> list[tuple[int, int]]:
+    """
+    Parameters
+    ----------
+      points: An ndarray of shape (n, 2) containing the 2D points.
+
+    Returns
+    -------
+      list[tuple[int, int]]: Input for the draw.polygon function
+    """
+    from scipy.spatial import ConvexHull
+
+    hull = ConvexHull(points)
+    vertices = hull.vertices
+    return list(zip(points[vertices, 0], points[vertices, 1]))
+
+
+def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput:
+    mp_face_mesh = mp.solutions.face_mesh
+    draw_util = mp.solutions.drawing_utils
+    drawing_styles = mp.solutions.drawing_styles
+
+    w, h = image.size
+
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence
+    ) as face_mesh:
+        arr = np.array(image)
+        pred = face_mesh.process(arr)
+
+        if pred.multi_face_landmarks is None:
+            return PredictOutput()
+
+        preview = arr.copy()
+        masks = []
+
+        for landmarks in pred.multi_face_landmarks:
+            draw_util.draw_landmarks(
+                image=preview,
+                landmark_list=landmarks,
+                connections=mp_face_mesh.FACEMESH_TESSELATION,
+                landmark_drawing_spec=None,
+                connection_drawing_spec=drawing_styles.get_default_face_mesh_tesselation_style(),
+            )
+
+            points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark])
+            outline = get_convexhull(points)
+
+            mask = Image.new("L", image.size, "black")
+            draw = ImageDraw.Draw(mask)
+            draw.polygon(outline, fill="white")
+            masks.append(mask)
+
+        bboxes = create_bbox_from_mask(masks, image.size)
+        preview = Image.fromarray(preview)
+        return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def mediapipe_face_mesh_eyes_only(
+    image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mp_face_mesh = mp.solutions.face_mesh
+
+    left_idx = np.array(list(mp_face_mesh.FACEMESH_LEFT_EYE)).flatten()
+    right_idx = np.array(list(mp_face_mesh.FACEMESH_RIGHT_EYE)).flatten()
+
+    w, h = image.size
+
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence
+    ) as face_mesh:
+        arr = np.array(image)
+        pred = face_mesh.process(arr)
+
+        if pred.multi_face_landmarks is None:
+            return PredictOutput()
+
+        preview = image.copy()
+        masks = []
+
+        for landmarks in pred.multi_face_landmarks:
+            points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark])
+            left_eyes = points[left_idx]
+            right_eyes = points[right_idx]
+            left_outline = get_convexhull(left_eyes)
+            right_outline = get_convexhull(right_eyes)
+
+            mask = Image.new("L", image.size, "black")
+            draw = ImageDraw.Draw(mask)
+            for outline in (left_outline, right_outline):
+                draw.polygon(outline, fill="white")
+            masks.append(mask)
+
+        bboxes = create_bbox_from_mask(masks, image.size)
+        preview = draw_preview(preview, bboxes, masks)
+        return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def draw_preview(
+    preview: Image.Image, bboxes: list[list[int]], masks: list[Image.Image]
+) -> Image.Image:
+    red = Image.new("RGB", preview.size, "red")
+    for mask in masks:
+        masked = Image.composite(red, preview, mask)
+        preview = Image.blend(preview, masked, 0.25)
+
+    draw = ImageDraw.Draw(preview)
+    for bbox in bboxes:
+        draw.rectangle(bbox, outline="red", width=2)
+
+    return preview

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/traceback.py

@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+import io
+import platform
+import sys
+from importlib.metadata import version
+from typing import Any, Callable
+
+from rich.console import Console, Group
+from rich.panel import Panel
+from rich.table import Table
+from rich.traceback import Traceback
+
+from adetailer.__version__ import __version__
+
+
+def processing(*args: Any) -> dict[str, Any]:
+    try:
+        from modules.processing import (
+            StableDiffusionProcessingImg2Img,
+            StableDiffusionProcessingTxt2Img,
+        )
+    except ImportError:
+        return {}
+
+    p = None
+    for arg in args:
+        if isinstance(
+            arg, (StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img)
+        ):
+            p = arg
+            break
+
+    if p is None:
+        return {}
+
+    info = {
+        "prompt": p.prompt,
+        "negative_prompt": p.negative_prompt,
+        "n_iter": p.n_iter,
+        "batch_size": p.batch_size,
+        "width": p.width,
+        "height": p.height,
+        "sampler_name": p.sampler_name,
+        "enable_hr": getattr(p, "enable_hr", False),
+        "hr_upscaler": getattr(p, "hr_upscaler", ""),
+    }
+
+    info.update(sd_models())
+    return info
+
+
+def sd_models() -> dict[str, str]:
+    try:
+        from modules import shared
+
+        opts = shared.opts
+    except Exception:
+        return {}
+
+    return {
+        "checkpoint": getattr(opts, "sd_model_checkpoint", "------"),
+        "vae": getattr(opts, "sd_vae", "------"),
+        "unet": getattr(opts, "sd_unet", "------"),
+    }
+
+
+def ad_args(*args: Any) -> dict[str, Any]:
+    ad_args = [
+        arg
+        for arg in args
+        if isinstance(arg, dict) and arg.get("ad_model", "None") != "None"
+    ]
+    if not ad_args:
+        return {}
+
+    arg0 = ad_args[0]
+    is_api = arg0.get("is_api", True)
+    return {
+        "version": __version__,
+        "ad_model": arg0["ad_model"],
+        "ad_prompt": arg0.get("ad_prompt", ""),
+        "ad_negative_prompt": arg0.get("ad_negative_prompt", ""),
+        "ad_controlnet_model": arg0.get("ad_controlnet_model", "None"),
+        "is_api": type(is_api) is not tuple,
+    }
+
+
+def library_version():
+    libraries = ["torch", "torchvision", "ultralytics", "mediapipe"]
+    d = {}
+    for lib in libraries:
+        try:
+            d[lib] = version(lib)
+        except Exception:
+            d[lib] = "Unknown"
+    return d
+
+
+def sys_info() -> dict[str, Any]:
+    try:
+        import launch
+
+        version = launch.git_tag()
+        commit = launch.commit_hash()
+    except Exception:
+        version = "Unknown (too old or vladmandic)"
+        commit = "Unknown"
+
+    return {
+        "Platform": platform.platform(),
+        "Python": sys.version,
+        "Version": version,
+        "Commit": commit,
+        "Commandline": sys.argv,
+        "Libraries": library_version(),
+    }
+
+
+def get_table(title: str, data: dict[str, Any]) -> Table:
+    table = Table(title=title, highlight=True)
+    table.add_column(" ", justify="right", style="dim")
+    table.add_column("Value")
+    for key, value in data.items():
+        if not isinstance(value, str):
+            value = repr(value)
+        table.add_row(key, value)
+
+    return table
+
+
+def rich_traceback(func: Callable) -> Callable:
+    def wrapper(*args, **kwargs):
+        string = io.StringIO()
+        width = Console().width
+        width = width - 4 if width > 4 else None
+        console = Console(file=string, width=width)
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            tables = [
+                get_table(title, data)
+                for title, data in [
+                    ("System info", sys_info()),
+                    ("Inputs", processing(*args)),
+                    ("ADetailer", ad_args(*args)),
+                ]
+                if data
+            ]
+            tables.append(Traceback())
+
+            console.print(Panel(Group(*tables)))
+            output = "\n" + string.getvalue()
+
+            try:
+                error = e.__class__(output)
+            except Exception:
+                error = RuntimeError(output)
+            raise error from None
+
+    return wrapper

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/ui.py

@@ -0,0 +1,605 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from functools import partial
+from types import SimpleNamespace
+from typing import Any, Callable
+
+import gradio as gr
+
+from adetailer import AFTER_DETAILER, __version__
+from adetailer.args import AD_ENABLE, ALL_ARGS, MASK_MERGE_INVERT
+from controlnet_ext import controlnet_exists, get_cn_models
+
+cn_module_choices = [
+    "inpaint_global_harmonious",
+    "inpaint_only",
+    "inpaint_only+lama",
+]
+
+
+class Widgets(SimpleNamespace):
+    def tolist(self):
+        return [getattr(self, attr) for attr in ALL_ARGS.attrs]
+
+
+@dataclass
+class WebuiInfo:
+    ad_model_list: list[str]
+    sampler_names: list[str]
+    t2i_button: gr.Button
+    i2i_button: gr.Button
+    checkpoints_list: list[str]
+    vae_list: list[str]
+
+
+def gr_interactive(value: bool = True):
+    return gr.update(interactive=value)
+
+
+def ordinal(n: int) -> str:
+    d = {1: "st", 2: "nd", 3: "rd"}
+    return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
+
+
+def suffix(n: int, c: str = " ") -> str:
+    return "" if n == 0 else c + ordinal(n + 1)
+
+
+def on_widget_change(state: dict, value: Any, *, attr: str):
+    state[attr] = value
+    return state
+
+
+def on_generate_click(state: dict, *values: Any):
+    for attr, value in zip(ALL_ARGS.attrs, values):
+        state[attr] = value
+    state["is_api"] = ()
+    return state
+
+
+def on_cn_model_update(cn_model: str):
+    if "inpaint" in cn_model:
+        return gr.update(
+            visible=True, choices=cn_module_choices, value=cn_module_choices[0]
+        )
+    return gr.update(visible=False, choices=["None"], value="None")
+
+
+def elem_id(item_id: str, n: int, is_img2img: bool) -> str:
+    tap = "img2img" if is_img2img else "txt2img"
+    suf = suffix(n, "_")
+    return f"script_{tap}_adetailer_{item_id}{suf}"
+
+
+def adui(
+    num_models: int,
+    is_img2img: bool,
+    webui_info: WebuiInfo,
+):
+    states = []
+    infotext_fields = []
+    eid = partial(elem_id, n=0, is_img2img=is_img2img)
+
+    with gr.Accordion(AFTER_DETAILER, open=False, elem_id=eid("ad_main_accordion")):
+        with gr.Row():
+            with gr.Column(scale=6):
+                ad_enable = gr.Checkbox(
+                    label="Enable ADetailer",
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_enable"),
+                )
+
+            with gr.Column(scale=1, min_width=180):
+                gr.Markdown(
+                    f"v{__version__}",
+                    elem_id=eid("ad_version"),
+                )
+
+        infotext_fields.append((ad_enable, AD_ENABLE.name))
+
+        with gr.Group(), gr.Tabs():
+            for n in range(num_models):
+                with gr.Tab(ordinal(n + 1)):
+                    state, infofields = one_ui_group(
+                        n=n,
+                        is_img2img=is_img2img,
+                        webui_info=webui_info,
+                    )
+
+                states.append(state)
+                infotext_fields.extend(infofields)
+
+    # components: [bool, dict, dict, ...]
+    components = [ad_enable, *states]
+    return components, infotext_fields
+
+
+def one_ui_group(n: int, is_img2img: bool, webui_info: WebuiInfo):
+    w = Widgets()
+    state = gr.State({})
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        model_choices = (
+            [*webui_info.ad_model_list, "None"]
+            if n == 0
+            else ["None", *webui_info.ad_model_list]
+        )
+
+        w.ad_model = gr.Dropdown(
+            label="ADetailer model" + suffix(n),
+            choices=model_choices,
+            value=model_choices[0],
+            visible=True,
+            type="value",
+            elem_id=eid("ad_model"),
+        )
+
+    with gr.Group():
+        with gr.Row(elem_id=eid("ad_toprow_prompt")):
+            w.ad_prompt = gr.Textbox(
+                label="ad_prompt" + suffix(n),
+                show_label=False,
+                lines=3,
+                placeholder="ADetailer prompt"
+                + suffix(n)
+                + "\nIf blank, the main prompt is used.",
+                elem_id=eid("ad_prompt"),
+            )
+
+        with gr.Row(elem_id=eid("ad_toprow_negative_prompt")):
+            w.ad_negative_prompt = gr.Textbox(
+                label="ad_negative_prompt" + suffix(n),
+                show_label=False,
+                lines=2,
+                placeholder="ADetailer negative prompt"
+                + suffix(n)
+                + "\nIf blank, the main negative prompt is used.",
+                elem_id=eid("ad_negative_prompt"),
+            )
+
+    with gr.Group():
+        with gr.Accordion(
+            "Detection", open=False, elem_id=eid("ad_detection_accordion")
+        ):
+            detection(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Mask Preprocessing",
+            open=False,
+            elem_id=eid("ad_mask_preprocessing_accordion"),
+        ):
+            mask_preprocessing(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Inpainting", open=False, elem_id=eid("ad_inpainting_accordion")
+        ):
+            inpainting(w, n, is_img2img, webui_info)
+
+    with gr.Group():
+        controlnet(w, n, is_img2img)
+
+    all_inputs = [state, *w.tolist()]
+    target_button = webui_info.i2i_button if is_img2img else webui_info.t2i_button
+    target_button.click(
+        fn=on_generate_click, inputs=all_inputs, outputs=state, queue=False
+    )
+
+    infotext_fields = [(getattr(w, attr), name + suffix(n)) for attr, name in ALL_ARGS]
+
+    return state, infotext_fields
+
+
+def detection(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        with gr.Column(variant="compact"):
+            w.ad_confidence = gr.Slider(
+                label="Detection model confidence threshold" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.3,
+                visible=True,
+                elem_id=eid("ad_confidence"),
+            )
+            w.ad_mask_k_largest = gr.Slider(
+                label="Mask only the top k largest (0 to disable)" + suffix(n),
+                minumum=0,
+                maximum=10,
+                step=1,
+                value=0,
+                visible=True,
+                elem_id=eid("ad_mask_k_largest"),
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_mask_min_ratio = gr.Slider(
+                label="Mask min area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=0.0,
+                visible=True,
+                elem_id=eid("ad_mask_min_ratio"),
+            )
+            w.ad_mask_max_ratio = gr.Slider(
+                label="Mask max area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=1.0,
+                visible=True,
+                elem_id=eid("ad_mask_max_ratio"),
+            )
+
+
+def mask_preprocessing(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_x_offset = gr.Slider(
+                    label="Mask x(→) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_x_offset"),
+                )
+                w.ad_y_offset = gr.Slider(
+                    label="Mask y(↑) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_y_offset"),
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_dilate_erode = gr.Slider(
+                    label="Mask erosion (-) / dilation (+)" + suffix(n),
+                    minimum=-128,
+                    maximum=128,
+                    step=4,
+                    value=4,
+                    visible=True,
+                    elem_id=eid("ad_dilate_erode"),
+                )
+
+        with gr.Row():
+            w.ad_mask_merge_invert = gr.Radio(
+                label="Mask merge mode" + suffix(n),
+                choices=MASK_MERGE_INVERT,
+                value="None",
+                elem_id=eid("ad_mask_merge_invert"),
+            )
+
+
+def inpainting(w: Widgets, n: int, is_img2img: bool, webui_info: WebuiInfo):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            w.ad_mask_blur = gr.Slider(
+                label="Inpaint mask blur" + suffix(n),
+                minimum=0,
+                maximum=64,
+                step=1,
+                value=4,
+                visible=True,
+                elem_id=eid("ad_mask_blur"),
+            )
+
+            w.ad_denoising_strength = gr.Slider(
+                label="Inpaint denoising strength" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.4,
+                visible=True,
+                elem_id=eid("ad_denoising_strength"),
+            )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_inpaint_only_masked = gr.Checkbox(
+                    label="Inpaint only masked" + suffix(n),
+                    value=True,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked"),
+                )
+                w.ad_inpaint_only_masked_padding = gr.Slider(
+                    label="Inpaint only masked padding, pixels" + suffix(n),
+                    minimum=0,
+                    maximum=256,
+                    step=4,
+                    value=32,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked_padding"),
+                )
+
+                w.ad_inpaint_only_masked.change(
+                    gr_interactive,
+                    inputs=w.ad_inpaint_only_masked,
+                    outputs=w.ad_inpaint_only_masked_padding,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_inpaint_width_height = gr.Checkbox(
+                    label="Use separate width/height" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_inpaint_width_height"),
+                )
+
+                w.ad_inpaint_width = gr.Slider(
+                    label="inpaint width" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_width"),
+                )
+
+                w.ad_inpaint_height = gr.Slider(
+                    label="inpaint height" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_height"),
+                )
+
+                w.ad_use_inpaint_width_height.change(
+                    lambda value: (gr_interactive(value), gr_interactive(value)),
+                    inputs=w.ad_use_inpaint_width_height,
+                    outputs=[w.ad_inpaint_width, w.ad_inpaint_height],
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_steps = gr.Checkbox(
+                    label="Use separate steps" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_steps"),
+                )
+
+                w.ad_steps = gr.Slider(
+                    label="ADetailer steps" + suffix(n),
+                    minimum=1,
+                    maximum=150,
+                    step=1,
+                    value=28,
+                    visible=True,
+                    elem_id=eid("ad_steps"),
+                )
+
+                w.ad_use_steps.change(
+                    gr_interactive,
+                    inputs=w.ad_use_steps,
+                    outputs=w.ad_steps,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_cfg_scale = gr.Checkbox(
+                    label="Use separate CFG scale" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_cfg_scale"),
+                )
+
+                w.ad_cfg_scale = gr.Slider(
+                    label="ADetailer CFG scale" + suffix(n),
+                    minimum=0.0,
+                    maximum=30.0,
+                    step=0.5,
+                    value=7.0,
+                    visible=True,
+                    elem_id=eid("ad_cfg_scale"),
+                )
+
+                w.ad_use_cfg_scale.change(
+                    gr_interactive,
+                    inputs=w.ad_use_cfg_scale,
+                    outputs=w.ad_cfg_scale,
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_checkpoint = gr.Checkbox(
+                    label="Use separate checkpoint (experimental)" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_checkpoint"),
+                )
+
+                ckpts = ["Use same checkpoint", *webui_info.checkpoints_list]
+
+                w.ad_checkpoint = gr.Dropdown(
+                    label="ADetailer checkpoint" + suffix(n),
+                    choices=ckpts,
+                    value=ckpts[0],
+                    visible=True,
+                    elem_id=eid("ad_checkpoint"),
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_vae = gr.Checkbox(
+                    label="Use separate VAE (experimental)" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_vae"),
+                )
+
+                vaes = ["Use same VAE", *webui_info.vae_list]
+
+                w.ad_vae = gr.Dropdown(
+                    label="ADetailer VAE" + suffix(n),
+                    choices=vaes,
+                    value=vaes[0],
+                    visible=True,
+                    elem_id=eid("ad_vae"),
+                )
+
+        with gr.Row(), gr.Column(variant="compact"):
+            w.ad_use_sampler = gr.Checkbox(
+                label="Use separate sampler" + suffix(n),
+                value=False,
+                visible=True,
+                elem_id=eid("ad_use_sampler"),
+            )
+
+            w.ad_sampler = gr.Dropdown(
+                label="ADetailer sampler" + suffix(n),
+                choices=webui_info.sampler_names,
+                value=webui_info.sampler_names[0],
+                visible=True,
+                elem_id=eid("ad_sampler"),
+            )
+
+            w.ad_use_sampler.change(
+                gr_interactive,
+                inputs=w.ad_use_sampler,
+                outputs=w.ad_sampler,
+                queue=False,
+            )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_noise_multiplier = gr.Checkbox(
+                    label="Use separate noise multiplier" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_noise_multiplier"),
+                )
+
+                w.ad_noise_multiplier = gr.Slider(
+                    label="Noise multiplier for img2img" + suffix(n),
+                    minimum=0.5,
+                    maximum=1.5,
+                    step=0.01,
+                    value=1.0,
+                    visible=True,
+                    elem_id=eid("ad_noise_multiplier"),
+                )
+
+                w.ad_use_noise_multiplier.change(
+                    gr_interactive,
+                    inputs=w.ad_use_noise_multiplier,
+                    outputs=w.ad_noise_multiplier,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_clip_skip = gr.Checkbox(
+                    label="Use separate CLIP skip" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_clip_skip"),
+                )
+
+                w.ad_clip_skip = gr.Slider(
+                    label="ADetailer CLIP skip" + suffix(n),
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=1,
+                    visible=True,
+                    elem_id=eid("ad_clip_skip"),
+                )
+
+                w.ad_use_clip_skip.change(
+                    gr_interactive,
+                    inputs=w.ad_use_clip_skip,
+                    outputs=w.ad_clip_skip,
+                    queue=False,
+                )
+
+        with gr.Row(), gr.Column(variant="compact"):
+            w.ad_restore_face = gr.Checkbox(
+                label="Restore faces after ADetailer" + suffix(n),
+                value=False,
+                elem_id=eid("ad_restore_face"),
+            )
+
+
+def controlnet(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+    cn_models = ["None", *get_cn_models()]
+
+    with gr.Row(variant="panel"):
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_model = gr.Dropdown(
+                label="ControlNet model" + suffix(n),
+                choices=cn_models,
+                value="None",
+                visible=True,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_model"),
+            )
+
+            w.ad_controlnet_module = gr.Dropdown(
+                label="ControlNet module" + suffix(n),
+                choices=cn_module_choices,
+                value="inpaint_global_harmonious",
+                visible=False,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_module"),
+            )
+
+            w.ad_controlnet_weight = gr.Slider(
+                label="ControlNet weight" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_weight"),
+            )
+
+            w.ad_controlnet_model.change(
+                on_cn_model_update,
+                inputs=w.ad_controlnet_model,
+                outputs=w.ad_controlnet_module,
+                queue=False,
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_guidance_start = gr.Slider(
+                label="ControlNet guidance start" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_start"),
+            )
+
+            w.ad_controlnet_guidance_end = gr.Slider(
+                label="ControlNet guidance end" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_end"),
+            )

TestingforfaivsixEx/content/eichellis/extensions/adetai/adetailer/ultralytics.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import cv2
+from PIL import Image
+from torchvision.transforms.functional import to_pil_image
+from ultralytics import YOLO
+
+from adetailer import PredictOutput
+from adetailer.common import create_mask_from_bbox
+
+
+def ultralytics_predict(
+    model_path: str | Path,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+) -> PredictOutput:
+    model = YOLO(model_path)
+    pred = model(image, conf=confidence, device=device)
+
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    if bboxes.size == 0:
+        return PredictOutput()
+    bboxes = bboxes.tolist()
+
+    if pred[0].masks is None:
+        masks = create_mask_from_bbox(bboxes, image.size)
+    else:
+        masks = mask_to_pil(pred[0].masks.data, image.size)
+    preview = pred[0].plot()
+    preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB)
+    preview = Image.fromarray(preview)
+
+    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
+    """
+    Parameters
+    ----------
+    masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
+        The device can be CUDA, but `to_pil_image` takes care of that.
+
+    shape: tuple[int, int]
+        (width, height) of the original image
+    """
+    n = masks.shape[0]
+    return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]

TestingforfaivsixEx/content/eichellis/extensions/adetai/controlnet_ext/__init__.py

@@ -0,0 +1,7 @@
+from .controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
+
+__all__ = [
+    "ControlNetExt",
+    "controlnet_exists",
+    "get_cn_models",
+]

TestingforfaivsixEx/content/eichellis/extensions/adetai/controlnet_ext/controlnet_ext.py

@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import importlib
+import re
+from functools import lru_cache
+from pathlib import Path
+
+from modules import extensions, sd_models, shared
+from modules.paths import data_path, models_path, script_path
+
+ext_path = Path(data_path, "extensions")
+ext_builtin_path = Path(script_path, "extensions-builtin")
+controlnet_exists = False
+controlnet_path = None
+cn_base_path = ""
+
+for extension in extensions.active():
+    if not extension.enabled:
+        continue
+    # For cases like sd-webui-controlnet-master
+    if "sd-webui-controlnet" in extension.name:
+        controlnet_exists = True
+        controlnet_path = Path(extension.path)
+        cn_base_path = ".".join(controlnet_path.parts[-2:])
+        break
+
+cn_model_module = {
+    "inpaint": "inpaint_global_harmonious",
+    "scribble": "t2ia_sketch_pidi",
+    "lineart": "lineart_coarse",
+    "openpose": "openpose_full",
+    "tile": None,
+}
+cn_model_regex = re.compile("|".join(cn_model_module.keys()))
+
+
+class ControlNetExt:
+    def __init__(self):
+        self.cn_models = ["None"]
+        self.cn_available = False
+        self.external_cn = None
+
+    def init_controlnet(self):
+        import_path = cn_base_path + ".scripts.external_code"
+
+        self.external_cn = importlib.import_module(import_path, "external_code")
+        self.cn_available = True
+        models = self.external_cn.get_models()
+        self.cn_models.extend(m for m in models if cn_model_regex.search(m))
+
+    def update_scripts_args(
+        self,
+        p,
+        model: str,
+        module: str | None,
+        weight: float,
+        guidance_start: float,
+        guidance_end: float,
+    ):
+        if (not self.cn_available) or model == "None":
+            return
+
+        if module is None:
+            for m, v in cn_model_module.items():
+                if m in model:
+                    module = v
+                    break
+
+        cn_units = [
+            self.external_cn.ControlNetUnit(
+                model=model,
+                weight=weight,
+                control_mode=self.external_cn.ControlMode.BALANCED,
+                module=module,
+                guidance_start=guidance_start,
+                guidance_end=guidance_end,
+                pixel_perfect=True,
+            )
+        ]
+
+        self.external_cn.update_cn_script_in_processing(p, cn_units)
+
+
+def get_cn_model_dirs() -> list[Path]:
+    cn_model_dir = Path(models_path, "ControlNet")
+    if controlnet_path is not None:
+        cn_model_dir_old = controlnet_path.joinpath("models")
+    else:
+        cn_model_dir_old = None
+    ext_dir1 = shared.opts.data.get("control_net_models_path", "")
+    ext_dir2 = getattr(shared.cmd_opts, "controlnet_dir", "")
+
+    dirs = [cn_model_dir]
+    for ext_dir in [cn_model_dir_old, ext_dir1, ext_dir2]:
+        if ext_dir:
+            dirs.append(Path(ext_dir))
+
+    return dirs
+
+
+@lru_cache
+def _get_cn_models() -> list[str]:
+    """
+    Since we can't import ControlNet, we use a function that does something like
+    controlnet's `list(global_state.cn_models_names.values())`.
+    """
+    cn_model_exts = (".pt", ".pth", ".ckpt", ".safetensors")
+    dirs = get_cn_model_dirs()
+    name_filter = shared.opts.data.get("control_net_models_name_filter", "")
+    name_filter = name_filter.strip(" ").lower()
+
+    model_paths = []
+
+    for base in dirs:
+        if not base.exists():
+            continue
+
+        for p in base.rglob("*"):
+            if (
+                p.is_file()
+                and p.suffix in cn_model_exts
+                and cn_model_regex.search(p.name)
+            ):
+                if name_filter and name_filter not in p.name.lower():
+                    continue
+                model_paths.append(p)
+    model_paths.sort(key=lambda p: p.name)
+
+    models = []
+    for p in model_paths:
+        model_hash = sd_models.model_hash(p)
+        name = f"{p.stem} [{model_hash}]"
+        models.append(name)
+    return models
+
+
+def get_cn_models() -> list[str]:
+    if controlnet_exists:
+        return _get_cn_models()
+    return []

TestingforfaivsixEx/content/eichellis/extensions/adetai/controlnet_ext/restore.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+
+from modules import img2img, processing, shared
+
+
+class CNHijackRestore:
+    def __init__(self):
+        self.process = hasattr(processing, "__controlnet_original_process_images_inner")
+        self.img2img = hasattr(img2img, "__controlnet_original_process_batch")
+
+    def __enter__(self):
+        if self.process:
+            self.orig_process = processing.process_images_inner
+            processing.process_images_inner = getattr(
+                processing, "__controlnet_original_process_images_inner"
+            )
+        if self.img2img:
+            self.orig_img2img = img2img.process_batch
+            img2img.process_batch = getattr(
+                img2img, "__controlnet_original_process_batch"
+            )
+
+    def __exit__(self, *args, **kwargs):
+        if self.process:
+            processing.process_images_inner = self.orig_process
+        if self.img2img:
+            img2img.process_batch = self.orig_img2img
+
+
+@contextmanager
+def cn_allow_script_control():
+    orig = False
+    if "control_net_allow_script_control" in shared.opts.data:
+        try:
+            orig = shared.opts.data["control_net_allow_script_control"]
+            shared.opts.data["control_net_allow_script_control"] = True
+            yield
+        finally:
+            shared.opts.data["control_net_allow_script_control"] = orig
+    else:
+        yield

TestingforfaivsixEx/content/eichellis/extensions/adetai/install.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+import importlib.util
+import subprocess
+import sys
+from importlib.metadata import version  # python >= 3.8
+
+from packaging.version import parse
+
+import_name = {"py-cpuinfo": "cpuinfo", "protobuf": "google.protobuf"}
+
+
+def is_installed(
+    package: str, min_version: str | None = None, max_version: str | None = None
+):
+    name = import_name.get(package, package)
+    try:
+        spec = importlib.util.find_spec(name)
+    except ModuleNotFoundError:
+        return False
+
+    if spec is None:
+        return False
+
+    if not min_version and not max_version:
+        return True
+
+    if not min_version:
+        min_version = "0.0.0"
+    if not max_version:
+        max_version = "99999999.99999999.99999999"
+
+    try:
+        pkg_version = version(package)
+        return parse(min_version) <= parse(pkg_version) <= parse(max_version)
+    except Exception:
+        return False
+
+
+def run_pip(*args):
+    subprocess.run([sys.executable, "-m", "pip", "install", *args])
+
+
+def install():
+    deps = [
+        # requirements
+        ("ultralytics", "8.0.173", None),
+        ("mediapipe", "0.10.3", None),
+        ("rich", "13.0.0", None),
+        # mediapipe
+        ("protobuf", "3.20", "3.9999"),
+    ]
+
+    for pkg, low, high in deps:
+        if not is_installed(pkg, low, high):
+            if low and high:
+                cmd = f"{pkg}>={low},<={high}"
+            elif low:
+                cmd = f"{pkg}>={low}"
+            elif high:
+                cmd = f"{pkg}<={high}"
+            else:
+                cmd = pkg
+
+            run_pip("-U", cmd)
+
+
+try:
+    import launch
+
+    skip_install = launch.args.skip_install
+except Exception:
+    skip_install = False
+
+if not skip_install:
+    install()

TestingforfaivsixEx/content/eichellis/extensions/adetai/preload.py

@@ -0,0 +1,9 @@
+import argparse
+
+
+def preload(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--ad-no-huggingface",
+        action="store_true",
+        help="Don't use adetailer models from huggingface",
+    )

TestingforfaivsixEx/content/eichellis/extensions/adetai/pyproject.toml

@@ -0,0 +1,40 @@
+[project]
+name = "adetailer"
+description = "An object detection and auto-mask extension for stable diffusion webui."
+authors = [{ name = "dowon", email = "ks2515@naver.com" }]
+requires-python = ">=3.8,<3.12"
+readme = "README.md"
+license = { text = "AGPL-3.0" }
+
+[project.urls]
+repository = "https://github.com/Bing-su/adetailer"
+
+[tool.isort]
+profile = "black"
+known_first_party = ["launch", "modules"]
+
+[tool.ruff]
+select = [
+    "A",
+    "B",
+    "C4",
+    "C90",
+    "E",
+    "EM",
+    "F",
+    "FA",
+    "I001",
+    "ISC",
+    "N",
+    "PIE",
+    "PT",
+    "RET",
+    "RUF",
+    "SIM",
+    "UP",
+    "W",
+]
+ignore = ["B008", "B905", "E501", "F401", "UP007"]
+
+[tool.ruff.isort]
+known-first-party = ["launch", "modules"]

TestingforfaivsixEx/content/eichellis/extensions/adetai/scripts/!adetailer.py

@@ -0,0 +1,834 @@
+from __future__ import annotations
+
+import os
+import platform
+import re
+import sys
+import traceback
+from contextlib import contextmanager
+from copy import copy, deepcopy
+from functools import partial
+from pathlib import Path
+from textwrap import dedent
+from typing import Any
+
+import gradio as gr
+import torch
+from rich import print
+
+import modules
+from adetailer import (
+    AFTER_DETAILER,
+    __version__,
+    get_models,
+    mediapipe_predict,
+    ultralytics_predict,
+)
+from adetailer.args import ALL_ARGS, BBOX_SORTBY, ADetailerArgs, EnableChecker
+from adetailer.common import PredictOutput
+from adetailer.mask import (
+    filter_by_ratio,
+    filter_k_largest,
+    mask_preprocess,
+    sort_bboxes,
+)
+from adetailer.traceback import rich_traceback
+from adetailer.ui import WebuiInfo, adui, ordinal, suffix
+from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
+from controlnet_ext.restore import (
+    CNHijackRestore,
+    cn_allow_script_control,
+)
+from modules import images, safe, script_callbacks, scripts, shared
+from modules.devices import NansException
+from modules.paths import data_path, models_path
+from modules.processing import (
+    Processed,
+    StableDiffusionProcessingImg2Img,
+    create_infotext,
+    process_images,
+)
+from modules.sd_samplers import all_samplers
+from modules.shared import cmd_opts, opts, state
+
+no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
+adetailer_dir = Path(models_path, "adetailer")
+model_mapping = get_models(adetailer_dir, huggingface=not no_huggingface)
+txt2img_submit_button = img2img_submit_button = None
+SCRIPT_DEFAULT = "dynamic_prompting,dynamic_thresholding,wildcard_recursive,wildcards,lora_block_weight"
+
+if (
+    not adetailer_dir.exists()
+    and adetailer_dir.parent.exists()
+    and os.access(adetailer_dir.parent, os.W_OK)
+):
+    adetailer_dir.mkdir()
+
+print(
+    f"[-] ADetailer initialized. version: {__version__}, num models: {len(model_mapping)}"
+)
+
+
+@contextmanager
+def change_torch_load():
+    orig = torch.load
+    try:
+        torch.load = safe.unsafe_torch_load
+        yield
+    finally:
+        torch.load = orig
+
+
+@contextmanager
+def pause_total_tqdm():
+    orig = opts.data.get("multiple_tqdm", True)
+    try:
+        opts.data["multiple_tqdm"] = False
+        yield
+    finally:
+        opts.data["multiple_tqdm"] = orig
+
+
+@contextmanager
+def preseve_prompts(p):
+    all_pt = copy(p.all_prompts)
+    all_ng = copy(p.all_negative_prompts)
+    try:
+        yield
+    finally:
+        p.all_prompts = all_pt
+        p.all_negative_prompts = all_ng
+
+
+class AfterDetailerScript(scripts.Script):
+    def __init__(self):
+        super().__init__()
+        self.ultralytics_device = self.get_ultralytics_device()
+
+        self.controlnet_ext = None
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(version={__version__})"
+
+    def title(self):
+        return AFTER_DETAILER
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        num_models = opts.data.get("ad_max_models", 2)
+        ad_model_list = list(model_mapping.keys())
+        sampler_names = [sampler.name for sampler in all_samplers]
+
+        try:
+            checkpoint_list = modules.sd_models.checkpoint_tiles(use_shorts=True)
+        except TypeError:
+            checkpoint_list = modules.sd_models.checkpoint_tiles()
+        vae_list = modules.shared_items.sd_vae_items()
+
+        webui_info = WebuiInfo(
+            ad_model_list=ad_model_list,
+            sampler_names=sampler_names,
+            t2i_button=txt2img_submit_button,
+            i2i_button=img2img_submit_button,
+            checkpoints_list=checkpoint_list,
+            vae_list=vae_list,
+        )
+
+        components, infotext_fields = adui(num_models, is_img2img, webui_info)
+
+        self.infotext_fields = infotext_fields
+        return components
+
+    def init_controlnet_ext(self) -> None:
+        if self.controlnet_ext is not None:
+            return
+        self.controlnet_ext = ControlNetExt()
+
+        if controlnet_exists:
+            try:
+                self.controlnet_ext.init_controlnet()
+            except ImportError:
+                error = traceback.format_exc()
+                print(
+                    f"[-] ADetailer: ControlNetExt init failed:\n{error}",
+                    file=sys.stderr,
+                )
+
+    def update_controlnet_args(self, p, args: ADetailerArgs) -> None:
+        if self.controlnet_ext is None:
+            self.init_controlnet_ext()
+
+        if (
+            self.controlnet_ext is not None
+            and self.controlnet_ext.cn_available
+            and args.ad_controlnet_model != "None"
+        ):
+            self.controlnet_ext.update_scripts_args(
+                p,
+                model=args.ad_controlnet_model,
+                module=args.ad_controlnet_module,
+                weight=args.ad_controlnet_weight,
+                guidance_start=args.ad_controlnet_guidance_start,
+                guidance_end=args.ad_controlnet_guidance_end,
+            )
+
+    def is_ad_enabled(self, *args_) -> bool:
+        arg_list = [arg for arg in args_ if isinstance(arg, dict)]
+        if not args_ or not arg_list or not isinstance(args_[0], (bool, dict)):
+            message = f"""
+                       [-] ADetailer: Invalid arguments passed to ADetailer.
+                           input: {args_!r}
+                           ADetailer disabled.
+                       """
+            print(dedent(message), file=sys.stderr)
+            return False
+        enable = args_[0] if isinstance(args_[0], bool) else True
+        checker = EnableChecker(enable=enable, arg_list=arg_list)
+        return checker.is_enabled()
+
+    def get_args(self, p, *args_) -> list[ADetailerArgs]:
+        """
+        `args_` is at least 1 in length by `is_ad_enabled` immediately above
+        """
+        args = [arg for arg in args_ if isinstance(arg, dict)]
+
+        if not args:
+            message = f"[-] ADetailer: Invalid arguments passed to ADetailer: {args_!r}"
+            raise ValueError(message)
+
+        if hasattr(p, "adetailer_xyz"):
+            args[0] = {**args[0], **p.adetailer_xyz}
+
+        all_inputs = []
+
+        for n, arg_dict in enumerate(args, 1):
+            try:
+                inp = ADetailerArgs(**arg_dict)
+            except ValueError as e:
+                msgs = [
+                    f"[-] ADetailer: ValidationError when validating {ordinal(n)} arguments: {e}\n"
+                ]
+                for attr in ALL_ARGS.attrs:
+                    arg = arg_dict.get(attr)
+                    dtype = type(arg)
+                    arg = "DEFAULT" if arg is None else repr(arg)
+                    msgs.append(f"    {attr}: {arg} ({dtype})")
+                raise ValueError("\n".join(msgs)) from e
+
+            all_inputs.append(inp)
+
+        return all_inputs
+
+    def extra_params(self, arg_list: list[ADetailerArgs]) -> dict:
+        params = {}
+        for n, args in enumerate(arg_list):
+            params.update(args.extra_params(suffix=suffix(n)))
+        params["ADetailer version"] = __version__
+        return params
+
+    @staticmethod
+    def get_ultralytics_device() -> str:
+        if "adetailer" in shared.cmd_opts.use_cpu:
+            return "cpu"
+
+        if platform.system() == "Darwin":
+            return ""
+
+        vram_args = ["lowvram", "medvram", "medvram_sdxl"]
+        if any(getattr(cmd_opts, vram, False) for vram in vram_args):
+            return "cpu"
+
+        return ""
+
+    def prompt_blank_replacement(
+        self, all_prompts: list[str], i: int, default: str
+    ) -> str:
+        if not all_prompts:
+            return default
+        if i < len(all_prompts):
+            return all_prompts[i]
+        j = i % len(all_prompts)
+        return all_prompts[j]
+
+    def _get_prompt(
+        self, ad_prompt: str, all_prompts: list[str], i: int, default: str
+    ) -> list[str]:
+        prompts = re.split(r"\s*\[SEP\]\s*", ad_prompt)
+        blank_replacement = self.prompt_blank_replacement(all_prompts, i, default)
+        for n in range(len(prompts)):
+            if not prompts[n]:
+                prompts[n] = blank_replacement
+            elif "[PROMPT]" in prompts[n]:
+                prompts[n] = prompts[n].replace("[PROMPT]", f" {blank_replacement} ")
+        return prompts
+
+    def get_prompt(self, p, args: ADetailerArgs) -> tuple[list[str], list[str]]:
+        i = p._ad_idx
+
+        prompt = self._get_prompt(args.ad_prompt, p.all_prompts, i, p.prompt)
+        negative_prompt = self._get_prompt(
+            args.ad_negative_prompt, p.all_negative_prompts, i, p.negative_prompt
+        )
+
+        return prompt, negative_prompt
+
+    def get_seed(self, p) -> tuple[int, int]:
+        i = p._ad_idx
+
+        if not p.all_seeds:
+            seed = p.seed
+        elif i < len(p.all_seeds):
+            seed = p.all_seeds[i]
+        else:
+            j = i % len(p.all_seeds)
+            seed = p.all_seeds[j]
+
+        if not p.all_subseeds:
+            subseed = p.subseed
+        elif i < len(p.all_subseeds):
+            subseed = p.all_subseeds[i]
+        else:
+            j = i % len(p.all_subseeds)
+            subseed = p.all_subseeds[j]
+
+        return seed, subseed
+
+    def get_width_height(self, p, args: ADetailerArgs) -> tuple[int, int]:
+        if args.ad_use_inpaint_width_height:
+            width = args.ad_inpaint_width
+            height = args.ad_inpaint_height
+        else:
+            width = p.width
+            height = p.height
+
+        return width, height
+
+    def get_steps(self, p, args: ADetailerArgs) -> int:
+        return args.ad_steps if args.ad_use_steps else p.steps
+
+    def get_cfg_scale(self, p, args: ADetailerArgs) -> float:
+        return args.ad_cfg_scale if args.ad_use_cfg_scale else p.cfg_scale
+
+    def get_sampler(self, p, args: ADetailerArgs) -> str:
+        return args.ad_sampler if args.ad_use_sampler else p.sampler_name
+
+    def get_override_settings(self, p, args: ADetailerArgs) -> dict[str, Any]:
+        d = {}
+
+        if args.ad_use_clip_skip:
+            d["CLIP_stop_at_last_layers"] = args.ad_clip_skip
+
+        if (
+            args.ad_use_checkpoint
+            and args.ad_checkpoint
+            and args.ad_checkpoint not in ("None", "Use same checkpoint")
+        ):
+            d["sd_model_checkpoint"] = args.ad_checkpoint
+
+        if (
+            args.ad_use_vae
+            and args.ad_vae
+            and args.ad_vae not in ("None", "Use same VAE")
+        ):
+            d["sd_vae"] = args.ad_vae
+        return d
+
+    def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None:
+        return args.ad_noise_multiplier if args.ad_use_noise_multiplier else None
+
+    @staticmethod
+    def infotext(p) -> str:
+        return create_infotext(
+            p, p.all_prompts, p.all_seeds, p.all_subseeds, None, 0, 0
+        )
+
+    def write_params_txt(self, p) -> None:
+        infotext = self.infotext(p)
+        params_txt = Path(data_path, "params.txt")
+        params_txt.write_text(infotext, encoding="utf-8")
+
+    def script_filter(self, p, args: ADetailerArgs):
+        script_runner = copy(p.scripts)
+        script_args = deepcopy(p.script_args)
+        self.disable_controlnet_units(script_args)
+
+        ad_only_seleted_scripts = opts.data.get("ad_only_seleted_scripts", True)
+        if not ad_only_seleted_scripts:
+            return script_runner, script_args
+
+        ad_script_names = opts.data.get("ad_script_names", SCRIPT_DEFAULT)
+        script_names_set = {
+            name
+            for script_name in ad_script_names.split(",")
+            for name in (script_name, script_name.strip())
+        }
+
+        if args.ad_controlnet_model != "None":
+            script_names_set.add("controlnet")
+
+        filtered_alwayson = []
+        for script_object in script_runner.alwayson_scripts:
+            filepath = script_object.filename
+            filename = Path(filepath).stem
+            if filename in script_names_set:
+                filtered_alwayson.append(script_object)
+
+        script_runner.alwayson_scripts = filtered_alwayson
+        return script_runner, script_args
+
+    def disable_controlnet_units(self, script_args: list[Any]) -> None:
+        for obj in script_args:
+            if "controlnet" in obj.__class__.__name__.lower():
+                if hasattr(obj, "enabled"):
+                    obj.enabled = False
+                if hasattr(obj, "input_mode"):
+                    obj.input_mode = getattr(obj.input_mode, "SIMPLE", "simple")
+
+            elif isinstance(obj, dict) and "module" in obj:
+                obj["enabled"] = False
+
+    def get_i2i_p(self, p, args: ADetailerArgs, image):
+        seed, subseed = self.get_seed(p)
+        width, height = self.get_width_height(p, args)
+        steps = self.get_steps(p, args)
+        cfg_scale = self.get_cfg_scale(p, args)
+        initial_noise_multiplier = self.get_initial_noise_multiplier(p, args)
+        sampler_name = self.get_sampler(p, args)
+        override_settings = self.get_override_settings(p, args)
+
+        i2i = StableDiffusionProcessingImg2Img(
+            init_images=[image],
+            resize_mode=0,
+            denoising_strength=args.ad_denoising_strength,
+            mask=None,
+            mask_blur=args.ad_mask_blur,
+            inpainting_fill=1,
+            inpaint_full_res=args.ad_inpaint_only_masked,
+            inpaint_full_res_padding=args.ad_inpaint_only_masked_padding,
+            inpainting_mask_invert=0,
+            initial_noise_multiplier=initial_noise_multiplier,
+            sd_model=p.sd_model,
+            outpath_samples=p.outpath_samples,
+            outpath_grids=p.outpath_grids,
+            prompt="",  # replace later
+            negative_prompt="",
+            styles=p.styles,
+            seed=seed,
+            subseed=subseed,
+            subseed_strength=p.subseed_strength,
+            seed_resize_from_h=p.seed_resize_from_h,
+            seed_resize_from_w=p.seed_resize_from_w,
+            sampler_name=sampler_name,
+            batch_size=1,
+            n_iter=1,
+            steps=steps,
+            cfg_scale=cfg_scale,
+            width=width,
+            height=height,
+            restore_faces=args.ad_restore_face,
+            tiling=p.tiling,
+            extra_generation_params=p.extra_generation_params,
+            do_not_save_samples=True,
+            do_not_save_grid=True,
+            override_settings=override_settings,
+        )
+
+        i2i.cached_c = [None, None]
+        i2i.cached_uc = [None, None]
+        i2i.scripts, i2i.script_args = self.script_filter(p, args)
+        i2i._ad_disabled = True
+
+        if args.ad_controlnet_model != "None":
+            self.update_controlnet_args(i2i, args)
+        else:
+            i2i.control_net_enabled = False
+
+        return i2i
+
+    def save_image(self, p, image, *, condition: str, suffix: str) -> None:
+        i = p._ad_idx
+        if p.all_prompts:
+            i %= len(p.all_prompts)
+            save_prompt = p.all_prompts[i]
+        else:
+            save_prompt = p.prompt
+        seed, _ = self.get_seed(p)
+
+        if opts.data.get(condition, False):
+            images.save_image(
+                image=image,
+                path=p.outpath_samples,
+                basename="",
+                seed=seed,
+                prompt=save_prompt,
+                extension=opts.samples_format,
+                info=self.infotext(p),
+                p=p,
+                suffix=suffix,
+            )
+
+    def get_ad_model(self, name: str):
+        if name not in model_mapping:
+            msg = f"[-] ADetailer: Model {name!r} not found. Available models: {list(model_mapping.keys())}"
+            raise ValueError(msg)
+        return model_mapping[name]
+
+    def sort_bboxes(self, pred: PredictOutput) -> PredictOutput:
+        sortby = opts.data.get("ad_bbox_sortby", BBOX_SORTBY[0])
+        sortby_idx = BBOX_SORTBY.index(sortby)
+        return sort_bboxes(pred, sortby_idx)
+
+    def pred_preprocessing(self, pred: PredictOutput, args: ADetailerArgs):
+        pred = filter_by_ratio(
+            pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
+        )
+        pred = filter_k_largest(pred, k=args.ad_mask_k_largest)
+        pred = self.sort_bboxes(pred)
+        return mask_preprocess(
+            pred.masks,
+            kernel=args.ad_dilate_erode,
+            x_offset=args.ad_x_offset,
+            y_offset=args.ad_y_offset,
+            merge_invert=args.ad_mask_merge_invert,
+        )
+
+    @staticmethod
+    def ensure_rgb_image(image: Any):
+        if hasattr(image, "mode") and image.mode != "RGB":
+            image = image.convert("RGB")
+        return image
+
+    @staticmethod
+    def i2i_prompts_replace(
+        i2i, prompts: list[str], negative_prompts: list[str], j: int
+    ) -> None:
+        i1 = min(j, len(prompts) - 1)
+        i2 = min(j, len(negative_prompts) - 1)
+        prompt = prompts[i1]
+        negative_prompt = negative_prompts[i2]
+        i2i.prompt = prompt
+        i2i.negative_prompt = negative_prompt
+
+    @staticmethod
+    def compare_prompt(p, processed, n: int = 0):
+        if p.prompt != processed.all_prompts[0]:
+            print(
+                f"[-] ADetailer: applied {ordinal(n + 1)} ad_prompt: {processed.all_prompts[0]!r}"
+            )
+
+        if p.negative_prompt != processed.all_negative_prompts[0]:
+            print(
+                f"[-] ADetailer: applied {ordinal(n + 1)} ad_negative_prompt: {processed.all_negative_prompts[0]!r}"
+            )
+
+    @staticmethod
+    def need_call_process(p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == bs - 1
+
+    @staticmethod
+    def need_call_postprocess(p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == 0
+
+    @rich_traceback
+    def process(self, p, *args_):
+        if getattr(p, "_ad_disabled", False):
+            return
+
+        if self.is_ad_enabled(*args_):
+            arg_list = self.get_args(p, *args_)
+            extra_params = self.extra_params(arg_list)
+            p.extra_generation_params.update(extra_params)
+
+    def _postprocess_image_inner(
+        self, p, pp, args: ADetailerArgs, *, n: int = 0
+    ) -> bool:
+        """
+        Returns
+        -------
+            bool
+
+            `True` if image was processed, `False` otherwise.
+        """
+        if state.interrupted or state.skipped:
+            return False
+
+        i = p._ad_idx
+
+        i2i = self.get_i2i_p(p, args, pp.image)
+        seed, subseed = self.get_seed(p)
+        ad_prompts, ad_negatives = self.get_prompt(p, args)
+
+        is_mediapipe = args.ad_model.lower().startswith("mediapipe")
+
+        kwargs = {}
+        if is_mediapipe:
+            predictor = mediapipe_predict
+            ad_model = args.ad_model
+        else:
+            predictor = ultralytics_predict
+            ad_model = self.get_ad_model(args.ad_model)
+            kwargs["device"] = self.ultralytics_device
+
+        with change_torch_load():
+            pred = predictor(ad_model, pp.image, args.ad_confidence, **kwargs)
+
+        masks = self.pred_preprocessing(pred, args)
+
+        if not masks:
+            print(
+                f"[-] ADetailer: nothing detected on image {i + 1} with {ordinal(n + 1)} settings."
+            )
+            return False
+
+        self.save_image(
+            p,
+            pred.preview,
+            condition="ad_save_previews",
+            suffix="-ad-preview" + suffix(n, "-"),
+        )
+
+        steps = len(masks)
+        processed = None
+        state.job_count += steps
+
+        if is_mediapipe:
+            print(f"mediapipe: {steps} detected.")
+
+        p2 = copy(i2i)
+        for j in range(steps):
+            p2.image_mask = masks[j]
+            p2.init_images[0] = self.ensure_rgb_image(p2.init_images[0])
+            self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j)
+
+            if re.match(r"^\s*\[SKIP\]\s*$", p2.prompt):
+                continue
+
+            p2.seed = seed + j
+            p2.subseed = subseed + j
+
+            try:
+                processed = process_images(p2)
+            except NansException as e:
+                msg = f"[-] ADetailer: 'NansException' occurred with {ordinal(n + 1)} settings.\n{e}"
+                print(msg, file=sys.stderr)
+                continue
+            finally:
+                p2.close()
+
+            self.compare_prompt(p2, processed, n=n)
+            p2 = copy(i2i)
+            p2.init_images = [processed.images[0]]
+
+        if processed is not None:
+            pp.image = processed.images[0]
+            return True
+
+        return False
+
+    @rich_traceback
+    def postprocess_image(self, p, pp, *args_):
+        if getattr(p, "_ad_disabled", False):
+            return
+
+        if not self.is_ad_enabled(*args_):
+            return
+
+        p._ad_idx = getattr(p, "_ad_idx", -1) + 1
+        init_image = copy(pp.image)
+        arg_list = self.get_args(p, *args_)
+
+        if p.scripts is not None and self.need_call_postprocess(p):
+            dummy = Processed(p, [], p.seed, "")
+            with preseve_prompts(p):
+                p.scripts.postprocess(copy(p), dummy)
+
+        is_processed = False
+        with CNHijackRestore(), pause_total_tqdm(), cn_allow_script_control():
+            for n, args in enumerate(arg_list):
+                if args.ad_model == "None":
+                    continue
+                is_processed |= self._postprocess_image_inner(p, pp, args, n=n)
+
+        if is_processed:
+            self.save_image(
+                p, init_image, condition="ad_save_images_before", suffix="-ad-before"
+            )
+
+        if p.scripts is not None and self.need_call_process(p):
+            with preseve_prompts(p):
+                p.scripts.process(copy(p))
+
+        try:
+            ia = p._ad_idx
+            lenp = len(p.all_prompts)
+            if ia % lenp == lenp - 1:
+                self.write_params_txt(p)
+        except Exception:
+            pass
+
+
+def on_after_component(component, **_kwargs):
+    global txt2img_submit_button, img2img_submit_button
+    if getattr(component, "elem_id", None) == "txt2img_generate":
+        txt2img_submit_button = component
+        return
+
+    if getattr(component, "elem_id", None) == "img2img_generate":
+        img2img_submit_button = component
+
+
+def on_ui_settings():
+    section = ("ADetailer", AFTER_DETAILER)
+    shared.opts.add_option(
+        "ad_max_models",
+        shared.OptionInfo(
+            default=2,
+            label="Max models",
+            component=gr.Slider,
+            component_args={"minimum": 1, "maximum": 10, "step": 1},
+            section=section,
+        ),
+    )
+
+    shared.opts.add_option(
+        "ad_save_previews",
+        shared.OptionInfo(False, "Save mask previews", section=section),
+    )
+
+    shared.opts.add_option(
+        "ad_save_images_before",
+        shared.OptionInfo(False, "Save images before ADetailer", section=section),
+    )
+
+    shared.opts.add_option(
+        "ad_only_seleted_scripts",
+        shared.OptionInfo(
+            True, "Apply only selected scripts to ADetailer", section=section
+        ),
+    )
+
+    textbox_args = {
+        "placeholder": "comma-separated list of script names",
+        "interactive": True,
+    }
+
+    shared.opts.add_option(
+        "ad_script_names",
+        shared.OptionInfo(
+            default=SCRIPT_DEFAULT,
+            label="Script names to apply to ADetailer (separated by comma)",
+            component=gr.Textbox,
+            component_args=textbox_args,
+            section=section,
+        ),
+    )
+
+    shared.opts.add_option(
+        "ad_bbox_sortby",
+        shared.OptionInfo(
+            default="None",
+            label="Sort bounding boxes by",
+            component=gr.Radio,
+            component_args={"choices": BBOX_SORTBY},
+            section=section,
+        ),
+    )
+
+
+# xyz_grid
+
+
+def make_axis_on_xyz_grid():
+    xyz_grid = None
+    for script in scripts.scripts_data:
+        if script.script_class.__module__ == "xyz_grid.py":
+            xyz_grid = script.module
+            break
+
+    if xyz_grid is None:
+        return
+
+    model_list = ["None", *model_mapping.keys()]
+    samplers = [sampler.name for sampler in all_samplers]
+
+    def set_value(p, x, xs, *, field: str):
+        if not hasattr(p, "adetailer_xyz"):
+            p.adetailer_xyz = {}
+        p.adetailer_xyz[field] = x
+
+    axis = [
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer model 1st",
+            str,
+            partial(set_value, field="ad_model"),
+            choices=lambda: model_list,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer prompt 1st",
+            str,
+            partial(set_value, field="ad_prompt"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer negative prompt 1st",
+            str,
+            partial(set_value, field="ad_negative_prompt"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Mask erosion / dilation 1st",
+            int,
+            partial(set_value, field="ad_dilate_erode"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint denoising strength 1st",
+            float,
+            partial(set_value, field="ad_denoising_strength"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint only masked 1st",
+            str,
+            partial(set_value, field="ad_inpaint_only_masked"),
+            choices=lambda: ["True", "False"],
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint only masked padding 1st",
+            int,
+            partial(set_value, field="ad_inpaint_only_masked_padding"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer sampler 1st",
+            str,
+            partial(set_value, field="ad_sampler"),
+            choices=lambda: samplers,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ControlNet model 1st",
+            str,
+            partial(set_value, field="ad_controlnet_model"),
+            choices=lambda: ["None", *get_cn_models()],
+        ),
+    ]
+
+    if not any(x.label.startswith("[ADetailer]") for x in xyz_grid.axis_options):
+        xyz_grid.axis_options.extend(axis)
+
+
+def on_before_ui():
+    try:
+        make_axis_on_xyz_grid()
+    except Exception:
+        error = traceback.format_exc()
+        print(
+            f"[-] ADetailer: xyz_grid error:\n{error}",
+            file=sys.stderr,
+        )
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
+script_callbacks.on_after_component(on_after_component)
+script_callbacks.on_before_ui(on_before_ui)

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/.github/workflows/pytest.yml

@@ -0,0 +1,31 @@
+name: Tests
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        python-version: ['3.11', '3.10']
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install tox
+
+      - name: Run tox
+        run: tox

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/.gitignore

@@ -0,0 +1,2 @@
+.idea
+.pytest_cache

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/.pre-commit-config.yaml

@@ -0,0 +1,35 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: debug-statements
+      - id: double-quote-string-fixer
+      - id: name-tests-test
+  - repo: https://github.com/asottile/reorder_python_imports
+    rev: v3.9.0
+    hooks:
+      - id: reorder-python-imports
+        args: [--py37-plus, --add-import, "from __future__ import annotations"]
+  - repo: https://github.com/asottile/add-trailing-comma
+    rev: v2.4.0
+    hooks:
+      - id: add-trailing-comma
+        args: [--py36-plus]
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.3.2
+    hooks:
+      - id: pyupgrade
+        args: [--py37-plus]
+  - repo: https://github.com/pre-commit/mirrors-autopep8
+    rev: v2.0.2
+    hooks:
+      - id: autopep8
+  - repo: https://github.com/PyCQA/flake8
+    rev: 6.0.0
+    hooks:
+      - id: flake8
+        args:
+          - --max-line-length=120

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/README.md

@@ -0,0 +1,91 @@
+# Aspect Ratio Helper  [![pytest](https://github.com/thomasasfk/sd-webui-aspect-ratio-helper/actions/workflows/pytest.yml/badge.svg?branch=main)](https://github.com/thomasasfk/sd-webui-aspect-ratio-helper/actions/workflows/pytest.yml)
+
+Simple extension to easily maintain aspect ratio while changing dimensions.
+
+Install via the extensions tab on the [AUTOMATIC1111 webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
+
+## Features
+
+**(note this list is a little out of date, will need to find time to update it)**
+
+- JavaScript aspect ratio controls
+  - Adds a dropdown of configurable aspect ratios, to which the dimensions will auto-scale
+  - When selected, you will only be able to modify the higher dimension
+    - The smaller or equivalent dimension will scale accordingly
+  - If "Lock/🔒" is selected, the aspect ratio of the current dimensions will be kept
+  - If "Image/🖼️" is selected, the aspect ratio of the current image will be kept (img2img only)
+  - If you click the "Swap/⇅" button, the current dimensions will swap
+    - Configurable aspect ratios will also flip, reducing the need for duplication of config
+
+https://user-images.githubusercontent.com/22506439/227396634-7a63671a-fd38-419a-b734-a3d26647cc1d.mp4
+
+<br/>
+
+- Scale to maximum dimension
+  - Upon clicking, the width and height will scale according to the configured maximum value
+  - Aspect ratio will be retained, the smaller or equivalent dimension will be scaled to match
+- Scale to aspect ratio
+  - Upon clicking, the current dimensions will be scaled to the given aspect ratio, using the highest width or height
+    - i.e `4:3 of 256x512 = 512x384` `9:16 of 512x256 = 288x512` `1:1 of 256x300 = 300x300`
+  - You can optionally toggle this to use the "Maximum dimension" slider value
+    - i.e `4:3 of 512 = 512x384` `9:16 of 512 = 288x512` `1:1 of 300 = 300x300`
+- Scale by percentage
+  - Upon clicking, the current dimensions will be multiplied by the given percentage, with aspect ratio maintained
+  - i.e `-25% of 512x256 = 384x192` `+50% of 512x512 = 768x768`
+    - You can also change the display of these if you find it more intuitive
+    - i.e `75% of 512x256 = 384x192` `150% of 512x512 = 768x768`
+    - i.e `x0.75 of 512x256 = 384x192` `x1.5 of 512x512 = 768x768`
+
+![user-interface.png](docs%2Fui.png)
+
+## Settings
+
+- Hide accordion by default (`False`)
+- Expand accordion by default (`False`)
+  - Determines whether the 'Aspect Ratio Helper' accordion expands by default
+- UI Component order (`MaxDimensionScaler, PredefinedAspectRatioButtons, PredefinedPercentageButtons`)
+  - Determines the order in which the UI components will render
+- Enable JavaScript aspect ratio controls
+- JavaScript aspect ratio buttons `(1:1, 4:3, 16:9, 9:16, 21:9)`
+  - i.e `1:1, 4:3, 16:9, 9:16, 21:9` `2:3, 1:5, 3:5`
+- Show maximum dimension button (`True`)
+- Maximum dimension default (`1024`)
+- Show pre-defined aspect ratio buttons (`True`)
+- Use "Maximum dimension" for aspect ratio buttons (`False`)
+- Pre-defined aspect ratio buttons (`1:1, 4:3, 16:9, 9:16, 21:9`)
+  - i.e `1:1, 4:3, 16:9, 9:16, 21:9` `2:3, 1:5, 3:5`
+- Show pre-defined percentage buttons (`True`)
+- Pre-defined percentage buttons (`25, 50, 75, 125, 150, 175, 200`)
+  - i.e `25, 50, 75, 125, 150, 175, 200` `50, 125, 300`
+- Pre-defined percentage display format (`Incremental/decremental percentage (-50%, +50%)`)
+  - `Incremental/decremental percentage (-50%, +50%)`
+  - `Raw percentage (50%, 150%)`
+  - `Multiplication (x0.5, x1.5)`
+
+![settings.png](docs%2Fopts.png)
+
+
+JavaScript & accordion aspect ratios _might_ not play well together - I don't think many users will use both simultaneously, but we'll see.
+
+## Contributing
+
+- Open an issue for suggestions
+- Raise a pull request
+
+## Dependencies
+
+Developed using existing [AUTOMATIC1111 webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) dependencies.
+
+It's recommended to use the python version specified by A1111 webui.
+
+However - for running unit tests, we use pytest.
+
+```bash
+pip install pytest
+```
+
+## Testing
+From the root of the repository.
+```bash
+pytest
+```

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/aspect_ratio_helper/_components.py

@@ -0,0 +1,360 @@
+from __future__ import annotations
+
+from abc import ABC
+from abc import abstractmethod
+from functools import partial
+from typing import Callable
+
+import gradio as gr
+
+import aspect_ratio_helper._constants as _constants
+import aspect_ratio_helper._settings as _settings
+import aspect_ratio_helper._util as _util
+
+
+class ArhUIComponent(ABC):
+
+    def __init__(self, script):
+        self.script = script
+
+    @abstractmethod
+    def render(self): ...
+
+    @staticmethod
+    @abstractmethod
+    def should_show() -> bool: ...
+
+    @staticmethod
+    @abstractmethod
+    def add_options(): ...
+
+
+class MaxDimensionScaler(ArhUIComponent):
+
+    def render(self):
+        max_dim_default = _settings.safe_opt(
+            _constants.ARH_MAX_WIDTH_OR_HEIGHT_KEY,
+        )
+        self.script.max_dimension = float(max_dim_default)
+
+        inputs = outputs = [self.script.wc, self.script.hc]
+
+        with gr.Row(
+                visible=self.should_show(),
+        ):
+            max_dim_default = _settings.safe_opt(
+                _constants.ARH_MAX_WIDTH_OR_HEIGHT_KEY,
+            )
+            # todo: when using gr.Slider (not deprecated), the default value
+            #  is somehow always 270?... can't figure out why.
+            #  using legacy inputs.Slider for now as it doesn't have the issue.
+            max_dimension_slider = gr.inputs.Slider(
+                minimum=_constants.MIN_DIMENSION,
+                maximum=_constants.MAX_DIMENSION,
+                step=8,
+                default=max_dim_default,
+                label='Maximum dimension',
+            )
+
+            def _update_max_dimension(_max_dimension):
+                self.script.max_dimension = _max_dimension
+
+            max_dimension_slider.change(
+                _update_max_dimension,
+                inputs=[max_dimension_slider],
+                show_progress=False,
+            )
+
+            gr.Button(
+                value='Scale to maximum dimension',
+                visible=self.should_show(),
+            ).click(
+                fn=_util.scale_dimensions_to_max_dim,
+                inputs=[*inputs, max_dimension_slider],
+                outputs=outputs,
+            )
+
+    @staticmethod
+    def should_show() -> bool:
+        return _settings.safe_opt(_constants.ARH_SHOW_MAX_WIDTH_OR_HEIGHT_KEY)
+
+    @staticmethod
+    def add_options(shared):
+        shared.opts.add_option(
+            key=_constants.ARH_SHOW_MAX_WIDTH_OR_HEIGHT_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_SHOW_MAX_WIDTH_OR_HEIGHT_KEY,
+                ),
+                label='Show maximum dimension button',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_MAX_WIDTH_OR_HEIGHT_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_MAX_WIDTH_OR_HEIGHT_KEY,
+                ),
+                label='Maximum dimension default',
+                component=gr.Slider,
+                component_args={
+                    'minimum': _constants.MIN_DIMENSION,
+                    'maximum': _constants.MAX_DIMENSION,
+                    'step': 8,
+                },
+                section=_constants.SECTION,
+            ),
+        )
+
+
+class MinDimensionScaler(ArhUIComponent):
+
+    def render(self):
+        min_dim_default = _settings.safe_opt(
+            _constants.ARH_MIN_WIDTH_OR_HEIGHT_KEY,
+        )
+        self.script.min_dimension = float(min_dim_default)
+
+        inputs = outputs = [self.script.wc, self.script.hc]
+
+        with gr.Row(
+                visible=self.should_show(),
+        ):
+            min_dim_default = _settings.safe_opt(
+                _constants.ARH_MIN_WIDTH_OR_HEIGHT_KEY,
+            )
+            # todo: when using gr.Slider (not deprecated), the default value
+            #  is somehow always 270?... can't figure out why.
+            #  using legacy inputs.Slider for now as it doesn't have the issue.
+            min_dimension_slider = gr.inputs.Slider(
+                minimum=_constants.MIN_DIMENSION,
+                maximum=_constants.MAX_DIMENSION,
+                step=8,
+                default=min_dim_default,
+                label='Minimum dimension',
+            )
+
+            def _update_min_dimension(_min_dimension):
+                self.script.min_dimension = _min_dimension
+
+            min_dimension_slider.change(
+                _update_min_dimension,
+                inputs=[min_dimension_slider],
+                show_progress=False,
+            )
+
+            gr.Button(
+                value='Scale to minimum dimension',
+                visible=self.should_show(),
+            ).click(
+                fn=_util.scale_dimensions_to_min_dim,
+                inputs=[*inputs, min_dimension_slider],
+                outputs=outputs,
+            )
+
+    @staticmethod
+    def should_show() -> bool:
+        return _settings.safe_opt(_constants.ARH_SHOW_MIN_WIDTH_OR_HEIGHT_KEY)
+
+    @staticmethod
+    def add_options(shared):
+        shared.opts.add_option(
+            key=_constants.ARH_SHOW_MIN_WIDTH_OR_HEIGHT_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_SHOW_MIN_WIDTH_OR_HEIGHT_KEY,
+                ),
+                label='Show minimum dimension button',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_MIN_WIDTH_OR_HEIGHT_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_MIN_WIDTH_OR_HEIGHT_KEY,
+                ),
+                label='Minimum dimension default',
+                component=gr.Slider,
+                component_args={
+                    'minimum': _constants.MIN_DIMENSION,
+                    'maximum': _constants.MAX_DIMENSION,
+                    'step': 8,
+                },
+                section=_constants.SECTION,
+            ),
+        )
+
+
+class PredefinedAspectRatioButtons(ArhUIComponent):
+
+    def render(self):
+        use_max_dim_op = _settings.safe_opt(
+            _constants.ARH_PREDEFINED_ASPECT_RATIO_USE_MAX_DIM_KEY,
+        )
+        aspect_ratios = _settings.safe_opt(
+            _constants.ARH_PREDEFINED_ASPECT_RATIOS_KEY,
+        ).split(',')
+
+        with gr.Column(
+            variant='panel',
+            visible=self.should_show(),
+        ), gr.Row(
+            variant='compact',
+            visible=self.should_show(),
+            elem_classes='arh-btn-row',
+        ):
+            for ar_str in aspect_ratios:
+                w, h, *_ = (abs(float(d)) for d in ar_str.split(':'))
+
+                inputs = []
+                if use_max_dim_op:
+                    ar_func = partial(
+                        _util.scale_dimensions_to_max_dim_func,
+                        width=w, height=h,
+                        max_dim=lambda: self.script.max_dimension,
+                    )
+                else:
+                    inputs.extend([self.script.wc, self.script.hc])
+                    ar_func = partial(
+                        _util.scale_dimensions_to_ui_width_or_height,
+                        arw=w, arh=h,
+                    )
+
+                gr.Button(
+                    value=self.display_func(ar_str) or ar_str,
+                    visible=self.should_show(),
+                ).click(
+                    fn=ar_func,
+                    inputs=inputs,
+                    outputs=[self.script.wc, self.script.hc],
+                )
+
+    @staticmethod
+    def should_show() -> bool:
+        return _settings.safe_opt(
+            _constants.ARH_SHOW_PREDEFINED_ASPECT_RATIOS_KEY,
+        )
+
+    @staticmethod
+    def add_options(shared):
+        shared.opts.add_option(
+            key=_constants.ARH_SHOW_PREDEFINED_ASPECT_RATIOS_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_SHOW_PREDEFINED_ASPECT_RATIOS_KEY,
+                ),
+                label='Show pre-defined aspect ratio buttons',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_PREDEFINED_ASPECT_RATIO_USE_MAX_DIM_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_PREDEFINED_ASPECT_RATIO_USE_MAX_DIM_KEY,
+                ),
+                label='Use "Maximum dimension" for aspect ratio buttons (by '
+                      'default we use the max width or height)',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_PREDEFINED_ASPECT_RATIOS_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_PREDEFINED_ASPECT_RATIOS_KEY,
+                ),
+                label='Pre-defined aspect ratio buttons '
+                      '(1:1, 4:3, 16:9, 9:16, 21:9)',
+                section=_constants.SECTION,
+            ),
+        )
+
+    @property
+    def display_func(self) -> Callable[[str], str]:
+        return lambda _: None  # todo: different displays for aspect ratios.
+
+
+class PredefinedPercentageButtons(ArhUIComponent):
+
+    def render(self):
+        inputs = outputs = [self.script.wc, self.script.hc]
+        with gr.Column(
+                variant='panel',
+                visible=self.should_show(),
+        ), gr.Row(
+            variant='compact',
+            visible=self.should_show(),
+            elem_classes='arh-btn-row',
+        ):
+            pps = _settings.safe_opt(_constants.ARH_PREDEFINED_PERCENTAGES_KEY)
+            percentages = [abs(int(x)) for x in pps.split(',')]
+
+            for percentage in percentages:
+                display = self.display_func(percentage)
+                gr.Button(
+                    value=display,
+                    visible=self.should_show(),
+                ).click(
+                    fn=partial(
+                        _util.scale_by_percentage,
+                        pct=percentage / 100,
+                    ),
+                    inputs=inputs,
+                    outputs=outputs,
+                )
+
+    @staticmethod
+    def should_show() -> bool:
+        return _settings.safe_opt(
+            _constants.ARH_SHOW_PREDEFINED_PERCENTAGES_KEY,
+        )
+
+    @staticmethod
+    def add_options(shared):
+        shared.opts.add_option(
+            key=_constants.ARH_SHOW_PREDEFINED_PERCENTAGES_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_SHOW_PREDEFINED_PERCENTAGES_KEY,
+                ),
+                label='Show pre-defined percentage buttons',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_PREDEFINED_PERCENTAGES_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_PREDEFINED_PERCENTAGES_KEY,
+                ),
+                label='Pre-defined percentage buttons (75, 125, 150)',
+                section=_constants.SECTION,
+            ),
+        )
+        shared.opts.add_option(
+            key=_constants.ARH_PREDEFINED_PERCENTAGES_DISPLAY_KEY,
+            info=shared.OptionInfo(
+                default=_settings.OPT_KEY_TO_DEFAULT_MAP.get(
+                    _constants.ARH_PREDEFINED_PERCENTAGES_DISPLAY_KEY,
+                ),
+                label='Pre-defined percentage display format',
+                component=gr.Dropdown,
+                component_args=lambda: {
+                    'choices': tuple(
+                        _settings.PREDEFINED_PERCENTAGES_DISPLAY_MAP.keys(),
+                    ),
+                },
+                section=_constants.SECTION,
+            ),
+        )
+
+    @property
+    def display_func(self) -> Callable[[str], str]:
+        return _settings.PREDEFINED_PERCENTAGES_DISPLAY_MAP.get(
+            _settings.safe_opt(
+                _constants.ARH_PREDEFINED_PERCENTAGES_DISPLAY_KEY,
+            ),
+        )

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/aspect_ratio_helper/_constants.py

@@ -0,0 +1,27 @@
+from __future__ import annotations
+EXTENSION_NAME = 'Aspect Ratio Helper'
+
+MAX_DIMENSION = 2048
+MIN_DIMENSION = 64
+
+ARH_EXPAND_BY_DEFAULT_KEY = 'arh_expand_by_default'
+ARH_HIDE_ACCORDION_BY_DEFAULT_KEY = 'arh_hide_accordion_by_default'
+ARH_UI_COMPONENT_ORDER_KEY = 'arh_ui_component_order_key'
+ARH_UI_JAVASCRIPT_SELECTION_METHOD = 'arh_ui_javascript_selection_method'
+ARH_JAVASCRIPT_ASPECT_RATIO_SHOW_KEY = 'arh_javascript_aspect_ratio_show'
+ARH_JAVASCRIPT_ASPECT_RATIOS_KEY = 'arh_javascript_aspect_ratio'
+ARH_SHOW_MAX_WIDTH_OR_HEIGHT_KEY = 'arh_show_max_width_or_height'
+ARH_SHOW_MIN_WIDTH_OR_HEIGHT_KEY = 'arh_show_min_width_or_height'
+ARH_MAX_WIDTH_OR_HEIGHT_KEY = 'arh_max_width_or_height'
+ARH_MIN_WIDTH_OR_HEIGHT_KEY = 'arh_min_width_or_height'
+ARH_SHOW_PREDEFINED_PERCENTAGES_KEY = 'arh_show_predefined_percentages'
+ARH_PREDEFINED_PERCENTAGES_KEY = 'arh_predefined_percentages'
+ARH_SHOW_PREDEFINED_ASPECT_RATIOS_KEY = 'arh_show_predefined_aspect_ratios'
+ARH_PREDEFINED_ASPECT_RATIOS_KEY = 'arh_predefined_aspect_ratios'
+ARH_PREDEFINED_ASPECT_RATIO_USE_MAX_DIM_KEY \
+    = 'arh_predefined_aspect_ratio_use_max_dim'
+ARH_PREDEFINED_PERCENTAGES_DISPLAY_KEY \
+    = 'arh_predefined_percentages_display_key'
+DEFAULT_PERCENTAGES_DISPLAY_KEY \
+    = 'Incremental/decremental percentage (-50%, +50%)'
+SECTION = 'aspect_ratio_helper', EXTENSION_NAME

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/aspect_ratio_helper/_settings.py

@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+import itertools
+
+import gradio as gr
+from modules import shared
+
+import aspect_ratio_helper._components as _components
+import aspect_ratio_helper._constants as _constants
+import aspect_ratio_helper._util as _util
+
+PREDEFINED_PERCENTAGES_DISPLAY_MAP = {
+    _constants.DEFAULT_PERCENTAGES_DISPLAY_KEY: _util.display_minus_and_plus,
+    'Raw percentage (50%, 150%)': _util.display_raw_percentage,
+    'Multiplication (x0.5, x1.5)': _util.display_multiplication,
+}
+
+COMPONENTS = (
+    _components.MaxDimensionScaler,
+    _components.MinDimensionScaler,
+    _components.PredefinedAspectRatioButtons,
+    _components.PredefinedPercentageButtons,
+)
+
+DEFAULT_UI_COMPONENT_ORDER_KEY_LIST = [e.__name__ for e in COMPONENTS]
+DEFAULT_UI_COMPONENT_ORDER_KEY = ', '.join(
+    DEFAULT_UI_COMPONENT_ORDER_KEY_LIST,
+)
+OPT_KEY_TO_DEFAULT_MAP = {
+    _constants.ARH_EXPAND_BY_DEFAULT_KEY: False,
+    _constants.ARH_HIDE_ACCORDION_BY_DEFAULT_KEY: True,
+    _constants.ARH_UI_COMPONENT_ORDER_KEY:
+        DEFAULT_UI_COMPONENT_ORDER_KEY,
+    _constants.ARH_UI_JAVASCRIPT_SELECTION_METHOD: 'Aspect Ratios Dropdown',
+    _constants.ARH_JAVASCRIPT_ASPECT_RATIO_SHOW_KEY: True,
+    _constants.ARH_JAVASCRIPT_ASPECT_RATIOS_KEY:
+        '1:1, 3:2, 4:3, 5:4, 16:9',
+    _constants.ARH_SHOW_MAX_WIDTH_OR_HEIGHT_KEY: False,
+    _constants.ARH_MAX_WIDTH_OR_HEIGHT_KEY:
+        _constants.MAX_DIMENSION / 2,
+    _constants.ARH_SHOW_MIN_WIDTH_OR_HEIGHT_KEY: False,
+    _constants.ARH_MIN_WIDTH_OR_HEIGHT_KEY:
+        _constants.MAX_DIMENSION / 2,
+    _constants.ARH_SHOW_PREDEFINED_PERCENTAGES_KEY: False,
+    _constants.ARH_PREDEFINED_PERCENTAGES_KEY:
+        '25, 50, 75, 125, 150, 175, 200',
+    _constants.ARH_PREDEFINED_PERCENTAGES_DISPLAY_KEY:
+        _constants.DEFAULT_PERCENTAGES_DISPLAY_KEY,
+    _constants.ARH_SHOW_PREDEFINED_ASPECT_RATIOS_KEY: False,
+    _constants.ARH_PREDEFINED_ASPECT_RATIO_USE_MAX_DIM_KEY: False,
+    _constants.ARH_PREDEFINED_ASPECT_RATIOS_KEY:
+        '1:1, 4:3, 16:9, 9:16, 21:9',
+}
+
+
+def safe_opt(key):
+    return _util.safe_opt_util(shared.opts, key, OPT_KEY_TO_DEFAULT_MAP)
+
+
+def sort_components_by_keys(
+        components: list[_components.ArhUIComponent],
+) -> list[_components.ArhUIComponent]:
+    ordered_component_keys = safe_opt(
+        _constants.ARH_UI_COMPONENT_ORDER_KEY,
+    ).split(',')
+
+    # this can happen if we add new components, but the user has old settings.
+    # if this happens, we find the missing components, and append them.
+    if len(ordered_component_keys) != len(COMPONENTS):
+        all_components = set(DEFAULT_UI_COMPONENT_ORDER_KEY_LIST)
+        missing_components = all_components - set(ordered_component_keys)
+        ordered_component_keys.extend(missing_components)
+
+    try:
+        component_key_to_order_dict = {
+            key: order for order, key in enumerate(
+                [k.strip() for k in ordered_component_keys],
+            )
+        }
+        return sorted(
+            components,
+            key=lambda c: component_key_to_order_dict.get(
+                c.__class__.__name__,
+            ),
+        )
+    except ValueError:
+        print(
+            f'{_constants.EXTENSION_NAME} UI component order is erroneous. '
+            f'Defaulting to regular order, to fix this, please use'
+            f'the intended syntax for the setting, i.e '
+            f'"{DEFAULT_UI_COMPONENT_ORDER_KEY}"',
+        )
+        return components
+
+
+def on_ui_settings():
+    # javascript ui options
+    shared.opts.add_option(
+        key=_constants.ARH_JAVASCRIPT_ASPECT_RATIO_SHOW_KEY,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_JAVASCRIPT_ASPECT_RATIO_SHOW_KEY,
+            ),
+            label='Enable JavaScript aspect ratio controls',
+            section=_constants.SECTION,
+        ),
+    )
+    shared.opts.add_option(
+        key=_constants.ARH_JAVASCRIPT_ASPECT_RATIOS_KEY,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_JAVASCRIPT_ASPECT_RATIOS_KEY,
+            ),
+            label='JavaScript aspect ratio buttons'
+                  ' (1:1, 4:3, 16:9, 9:16, 21:9)',
+            section=_constants.SECTION,
+        ),
+    )
+    shared.opts.add_option(
+        key=_constants.ARH_UI_JAVASCRIPT_SELECTION_METHOD,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_UI_JAVASCRIPT_SELECTION_METHOD,
+            ),
+            label='JavaScript selection method',
+            component=gr.Dropdown,
+            component_args=lambda: {
+                'choices': [
+                    'Aspect Ratios Dropdown',
+                    'Default Options Button',
+                ],
+            },
+            section=_constants.SECTION,
+        ),
+    )
+
+    # accordion options
+    shared.opts.add_option(
+        key=_constants.ARH_HIDE_ACCORDION_BY_DEFAULT_KEY,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_HIDE_ACCORDION_BY_DEFAULT_KEY,
+            ),
+            label='Hide accordion by default',
+            section=_constants.SECTION,
+        ),
+    )
+    shared.opts.add_option(
+        key=_constants.ARH_EXPAND_BY_DEFAULT_KEY,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_EXPAND_BY_DEFAULT_KEY,
+            ),
+            label='Expand accordion by default',
+            section=_constants.SECTION,
+        ),
+    )
+    shared.opts.add_option(
+        key=_constants.ARH_UI_COMPONENT_ORDER_KEY,
+        info=shared.OptionInfo(
+            default=OPT_KEY_TO_DEFAULT_MAP.get(
+                _constants.ARH_UI_COMPONENT_ORDER_KEY,
+            ),
+            label='UI Component order',
+            component=gr.Dropdown,
+            component_args=lambda: {
+                'choices': [
+                    ', '.join(p) for p in itertools.permutations(  # TODO:  Rethink this, exponential growth...
+                        DEFAULT_UI_COMPONENT_ORDER_KEY_LIST,
+                    )
+                ],
+            },
+            section=_constants.SECTION,
+        ),
+    )
+
+    for component in COMPONENTS:
+        component.add_options(shared)

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/aspect_ratio_helper/_util.py

@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+import contextlib
+
+import aspect_ratio_helper._constants as _const
+
+
+def safe_opt_util(shared_opts, key, default_key_map: dict[str, object]):
+    # attempt to retrieve key from shared options
+    with contextlib.suppress(AttributeError):
+        value = shared_opts.__getattr__(key)
+        if value is not None:
+            return value
+
+    # attempt to retrieve default, and last resort the constant default
+    return shared_opts.get_default(key) or default_key_map.get(key)
+
+
+def display_multiplication(num) -> str:
+    return f'x{round(num / 100, 3)}'
+
+
+def display_raw_percentage(num) -> str:
+    return f'{num}%'
+
+
+def display_minus_and_plus(num) -> str:
+    num -= 100
+    if num > 0:
+        return f'+{num}%'
+    return f'{num}%'
+
+
+def scale_by_percentage(width, height, pct) -> tuple[int, int]:
+    aspect_ratio = float(width) / float(height)
+    step = (pct - 1.0)
+    new_width = int(round(width * (1.0 + step)))
+    new_height = int(round(new_width / aspect_ratio))
+    return clamp_to_boundaries(new_width, new_height, aspect_ratio)
+
+
+def scale_dimensions_to_ui_width_or_height(
+        width, height, arw, arh,
+) -> tuple[int, int]:
+    return scale_dimensions_to_max_dim(arw, arh, max(width, height))
+
+
+def scale_dimensions_to_max_dim_func(
+        width, height, max_dim: callable,
+) -> tuple[int, int]:
+    return scale_dimensions_to_max_dim(width, height, max_dim())
+
+
+def scale_dimensions_to_max_dim(
+        width, height, max_dim,
+) -> tuple[int, int]:
+    aspect_ratio = float(width) / float(height)
+    return scale_dimensions_to_ar(width, height, max_dim, aspect_ratio)
+
+
+def scale_dimensions_to_min_dim(
+        width, height, min_dim,
+) -> tuple[int, int]:
+    aspect_ratio = float(width) / float(height)
+    if width >= height:
+        max_dim = min_dim * aspect_ratio
+    else:
+        max_dim = min_dim / aspect_ratio
+    return scale_dimensions_to_ar(width, height, max_dim, aspect_ratio)
+
+
+def scale_dimensions_to_ar(
+        width, height, max_dim, aspect_ratio,
+) -> tuple[int, int]:
+    if width > height:
+        new_width = max_dim
+        new_height = int(round(max_dim / aspect_ratio))
+    else:
+        new_height = max_dim
+        new_width = int(round(max_dim * aspect_ratio))
+    return clamp_to_boundaries(new_width, new_height, aspect_ratio)
+
+
+def round_to_multiple_of_8(value):
+    return int(round(value / 8.0)) * 8
+
+
+def clamp_to_boundaries(owidth, oheight, aspect_ratio) -> tuple[int, int]:
+    width, height = owidth, oheight
+    if width > _const.MAX_DIMENSION:
+        width = _const.MAX_DIMENSION
+        height = int(round(width / aspect_ratio))
+    if height > _const.MAX_DIMENSION:
+        height = _const.MAX_DIMENSION
+        width = int(round(height * aspect_ratio))
+    if width < _const.MIN_DIMENSION:
+        width = _const.MIN_DIMENSION
+        height = int(round(width / aspect_ratio))
+    if height < _const.MIN_DIMENSION:
+        height = _const.MIN_DIMENSION
+        width = int(round(height * aspect_ratio))
+
+    width = round_to_multiple_of_8(width)
+    height = round_to_multiple_of_8(height)
+    # for insane aspect ratios we don't support... i.e 1:100
+    # 64:6400 when run through this function, so we clamp to 64:2048 (‾◡◝)
+    # also.  when the user does this it breaks the "scale to max" function
+    # on the ui until they change to another reasonable aspect ratio.
+    # todo: figure out why ^ and handle it better.
+    if width < _const.MIN_DIMENSION:
+        width = _const.MIN_DIMENSION
+    elif width > _const.MAX_DIMENSION:
+        width = _const.MAX_DIMENSION
+    if height < _const.MIN_DIMENSION:
+        height = _const.MIN_DIMENSION
+    elif height > _const.MAX_DIMENSION:
+        height = _const.MAX_DIMENSION
+
+    return width, height

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/aspect_ratio_helper/main.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+import gradio as gr
+from modules import script_callbacks
+from modules import scripts
+
+import aspect_ratio_helper._constants as _constants
+import aspect_ratio_helper._settings as _settings
+
+
+class AspectRatioStepScript(scripts.Script):
+
+    def __init__(self):
+        self.t2i_w: gr.components.Slider | None = None
+        self.t2i_h: gr.components.Slider | None = None
+        self.i2i_w: gr.components.Slider | None = None
+        self.i2i_h: gr.components.Slider | None = None
+        self.wc: gr.components.Slider
+        self.hc: gr.components.Slider
+        self.max_dimension: float
+
+    def title(self) -> str:
+        return _constants.EXTENSION_NAME
+
+    def show(self, _) -> scripts.AlwaysVisible:
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        if is_img2img:
+            self.wc, self.hc = self.i2i_w, self.i2i_h
+        else:
+            self.wc, self.hc = self.t2i_w, self.t2i_h  # noqa
+
+        components = _settings.sort_components_by_keys(
+            [component(self) for component in _settings.COMPONENTS],
+        )
+
+        hide_accordion: bool = _settings.safe_opt(
+            _constants.ARH_HIDE_ACCORDION_BY_DEFAULT_KEY,
+        )
+
+        if hide_accordion or not any(c.should_show() for c in components):
+            return  # no components should render, so just return.
+
+        start_expanded: bool = _settings.safe_opt(
+            _constants.ARH_EXPAND_BY_DEFAULT_KEY,
+        )
+        with gr.Group(), gr.Accordion(
+                _constants.EXTENSION_NAME,
+                open=start_expanded,
+        ):
+            for component in components:
+                # we deliberately DON'T check component.should_show() here.
+                # we need to call render to instantiate the components, we use
+                # the visible property on each component to hide them.
+                component.render()
+
+    def after_component(self, component: gr.components.Component, **kwargs):
+        element_id = kwargs.get('elem_id')
+
+        if isinstance(component, gr.components.Slider):
+            if element_id == 'txt2img_width':
+                self.t2i_w: gr.components.Slider = component
+            elif element_id == 'txt2img_height':
+                self.t2i_h: gr.components.Slider = component
+            elif element_id == 'img2img_width':
+                self.i2i_w: gr.components.Slider = component
+            elif element_id == 'img2img_height':
+                self.i2i_h: gr.components.Slider = component
+
+
+script_callbacks.on_ui_settings(_settings.on_ui_settings)

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/javascript/aspectRatioController.js

@@ -0,0 +1,474 @@
+// constants
+const _OFF = "Off";  // Don't use the functionality at all
+const _LOCK = '🔒';  // Aspect ratio is "locked"
+const _IMAGE = '🖼️'; // Aspect ratio is "locked" to that of the image
+
+const _MAXIMUM_DIMENSION = 2048;
+const _MINIMUM_DIMENSION = 64;
+
+const _IMAGE_INPUT_CONTAINER_IDS = [
+    'img2img_image',
+    'img2img_sketch',
+    'img2maskimg',
+    'inpaint_sketch',
+    'img_inpaint_base',
+];
+
+const getSelectedImage2ImageTab = () => {
+    const selectedButton = gradioApp().getElementById('mode_img2img').querySelector('button.selected');
+    const allButtons = gradioApp().getElementById('mode_img2img').querySelectorAll('button');
+    const selectedIndex = Array.prototype.indexOf.call(allButtons, selectedButton);
+    return selectedIndex;
+}
+
+const getCurrentImage = () => {
+    const currentTabIndex = getSelectedImage2ImageTab();
+    const currentTabImageId = _IMAGE_INPUT_CONTAINER_IDS[currentTabIndex];
+    return document.getElementById(currentTabImageId).querySelector('img');
+}
+
+const roundToClosestMultiple = (num, multiple) => {
+    const rounded = Math.round(Number(num) / multiple) * multiple;
+    return rounded;
+}
+
+const aspectRatioFromStr = (ar) => {
+    if (!ar.includes(':')) return;
+    return ar.split(':').map(x => Number(x));
+}
+
+const reverseAspectRatio = (ar) => {
+    if (!ar.includes(':')) return;
+    const [width, height] = ar.split(':');
+    return `${height}:${width}`;
+}
+
+const clampToBoundaries = (width, height) => {
+    const aspectRatio = width / height;
+    width = Math.max(Math.min(width, _MAXIMUM_DIMENSION), _MINIMUM_DIMENSION);
+    height = Math.max(Math.min(height, _MAXIMUM_DIMENSION), _MINIMUM_DIMENSION);
+    if (width / height > aspectRatio) {
+        height = Math.round(width / aspectRatio);
+    } else if (width / height < aspectRatio) {
+        width = Math.round(height * aspectRatio);
+    }
+
+    if (width > _MAXIMUM_DIMENSION) {
+        width = _MAXIMUM_DIMENSION;
+    } else if (width < _MINIMUM_DIMENSION) {
+        width = _MINIMUM_DIMENSION;
+    }
+
+    if (height < _MINIMUM_DIMENSION) {
+        height = _MINIMUM_DIMENSION;
+    } else if (height > _MAXIMUM_DIMENSION) {
+        height = _MAXIMUM_DIMENSION;
+    }
+
+    return [width, height];
+}
+
+const getOptions = () => {
+    return window.opts.arh_javascript_aspect_ratio.split(',').map(o => o.trim());
+}
+
+const reverseAllOptions = () => {
+    const allAspectRatioOptions = Array.from(gradioApp().querySelectorAll('.ar-option'));
+    allAspectRatioOptions.forEach(el => {
+        const reversed = reverseAspectRatio(el.value);
+        if (reversed) {
+            el.value = reversed;
+            el.textContent = reversed;
+        }
+    });
+}
+
+class OptionPickingController {
+    constructor(page, defaultOptions, controller) {
+        this.page = page;
+        this.options = this.getOptions(defaultOptions);
+        this.switchButton = gradioApp().getElementById(page + '_res_switch_btn');
+
+        const wrapperDiv = document.createElement('div');
+        wrapperDiv.setAttribute("id", `${this.page}_size_toolbox`);
+        wrapperDiv.setAttribute("class", "flex flex-col relative col gap-4");
+        wrapperDiv.setAttribute("style", "min-width: min(320px, 100%); flex-grow: 0");
+        wrapperDiv.innerHTML = this.getElementInnerHTML();
+
+        const parent = this.switchButton.parentNode;
+        parent.removeChild(this.switchButton);
+        wrapperDiv.appendChild(this.switchButton);
+        parent.insertBefore(wrapperDiv, parent.lastChild.previousElementSibling);
+
+        this.getPickerElement().onchange = this.pickerChanged(controller);
+        this.switchButton.onclick = this.switchButtonOnclick(controller);
+    }
+
+    getOptions(defaultOptions) {
+        return [...new Set([...defaultOptions, ...getOptions()])];
+    }
+
+    pickerChanged(controller) {
+        return () => {
+            const picked = this.getCurrentOption();
+            if (_IMAGE === picked) {
+                // this.switchButton.disabled = true;
+            } else {
+                this.switchButton.removeAttribute('disabled')
+            }
+
+            controller.setAspectRatio(picked);
+        };
+    }
+
+    switchButtonOnclick(controller) {
+        return () => {
+            reverseAllOptions();
+            const picked = this.getCurrentOption();
+            if (_LOCK === picked) {
+                controller.setAspectRatio(`${controller.heightRatio}:${controller.widthRatio}`);
+            } else {
+                controller.setAspectRatio(picked);
+            }
+        };
+    }
+
+    getElementInnerHTML() {
+        throw new Error('Not implemented');
+    }
+
+    getPickerElement() {
+        throw new Error('Not implemented');
+    }
+
+    getCurrentOption() {
+        throw new Error('Not implemented');
+    }
+}
+
+
+class SelectOptionPickingController extends OptionPickingController {
+    constructor(page, defaultOptions, controller) {
+        super(page, defaultOptions, controller);
+    }
+
+    getElementInnerHTML() {
+        return `
+        <div id="${this.page}_ratio" class="gr-block gr-box relative w-full border-solid border border-gray-200 gr-padded">
+            <select id="${this.page}_select_aspect_ratio" class="gr-box gr-input w-full disabled:cursor-not-allowed">
+                ${this.options.map(r => {
+            return '<option class="ar-option">' + r + '</option>'
+        }).join('\n')}
+            </select>
+        </div>
+        `;
+    }
+
+    getPickerElement() {
+        return gradioApp().getElementById(`${this.page}_select_aspect_ratio`);
+    }
+
+    getCurrentOption() {
+        const selectElement = this.getPickerElement();
+        const options = Array.from(selectElement);
+        return options[selectElement.selectedIndex].value;
+    }
+}
+
+class DefaultOptionsButtonOptionPickingController extends OptionPickingController {
+    constructor(page, defaultOptions, controller) {
+        super(page, defaultOptions, controller);
+        this.currentIndex = 0;
+        this.getPickerElement().onclick = this.pickerChanged(controller);
+    }
+
+    pickerChanged(controller) {
+        return () => {
+            this.currentIndex = (this.currentIndex + 1) % this.options.length;
+            this.getPickerElement().querySelector('button').textContent = this.getCurrentOption();
+            super.pickerChanged(controller)();
+        }
+    }
+
+    getElementInnerHTML() {
+        const classes = Array.from(this.switchButton.classList);
+        return `
+        <div id="${this.page}_ar_default_options_button" style="margin-bottom: 10px;">
+            <button class="${classes.join(' ')}">
+                ${this.getCurrentOption()}
+            </button>
+        </div>
+        `;
+    }
+
+    getPickerElement() {
+        return gradioApp().getElementById(`${this.page}_ar_default_options_button`);
+    }
+
+    getOptions(defaultOptions) {
+        return defaultOptions;
+    }
+
+    getCurrentOption() {
+        return this.options[this.currentIndex || 0];
+    }
+}
+
+
+class SliderController {
+    constructor(element) {
+        this.element = element;
+        this.numberInput = this.element.querySelector('input[type=number]');
+        this.rangeInput = this.element.querySelector('input[type=range]');
+        this.inputs = [this.numberInput, this.rangeInput];
+        this.inputs.forEach(input => {
+            input.isWidth = element.isWidth;
+        });
+    }
+
+    getVal() {
+        return Number(this.numberInput.value);
+    }
+
+    updateVal(value) {
+        this.inputs.forEach(input => {
+            input.value = Number(value)
+        })
+    }
+
+    updateMin(value) {
+        this.inputs.forEach(input => {
+            input.min = roundToClosestMultiple(Number(value), 8);
+        })
+    }
+
+    updateMax(value) {
+        this.inputs.forEach(input => {
+            input.max = roundToClosestMultiple(Number(value), 8);
+        })
+    }
+
+    triggerEvent(event) {
+        this.numberInput.dispatchEvent(event)
+    }
+
+    setVal(value) {
+        value = Number(value)
+        const newValue = roundToClosestMultiple(value, 8)
+        this.updateVal(newValue);
+    }
+
+}
+
+class AspectRatioController {
+    constructor(page, widthContainer, heightContainer, defaultOptions) {
+        widthContainer.isWidth = true;
+        heightContainer.isWidth = false;
+        this.widthContainer = new SliderController(widthContainer);
+        this.heightContainer = new SliderController(heightContainer);
+        this.inputs = [...this.widthContainer.inputs, ...this.heightContainer.inputs];
+        this.inputs.forEach(input => {
+            input.addEventListener('change', (e) => {
+                e.preventDefault()
+                this.maintainAspectRatio(input);
+            });
+        })
+
+        if (window.opts.arh_ui_javascript_selection_method === 'Default Options Button') {
+            this.optionPickingControler = new DefaultOptionsButtonOptionPickingController(page, defaultOptions, this);
+        } else {
+            this.optionPickingControler = new SelectOptionPickingController(page, defaultOptions, this);
+        }
+
+        this.setAspectRatio(_OFF);
+    }
+
+    updateInputStates() {
+        if (this.isLandscapeOrSquare()) {
+            const AR = this.widthRatio / this.heightRatio;
+
+            const minWidthByAr = Math.round(_MINIMUM_DIMENSION * AR);
+            const minWidth = Math.max(minWidthByAr, _MINIMUM_DIMENSION);
+            this.widthContainer.updateMin(minWidth);
+            this.heightContainer.updateMin(_MINIMUM_DIMENSION);
+
+            const maxHeightByAr = Math.round(_MAXIMUM_DIMENSION / AR)
+            const maxHeight = Math.min(_MAXIMUM_DIMENSION, maxHeightByAr);
+            this.heightContainer.updateMax(maxHeight);
+            this.widthContainer.updateMax(_MAXIMUM_DIMENSION);
+        } else {
+            const AR = this.heightRatio / this.widthRatio;
+
+            const minHeightByAr = Math.round(_MINIMUM_DIMENSION * AR)
+            const minHeight = Math.max(minHeightByAr, _MINIMUM_DIMENSION);
+            this.heightContainer.updateMin(minHeight);
+            this.widthContainer.updateMin(_MINIMUM_DIMENSION);
+
+            const maxWidthByAr = Math.round(_MAXIMUM_DIMENSION / AR)
+            const maxWidth = Math.min(_MAXIMUM_DIMENSION, maxWidthByAr);
+            this.widthContainer.updateMax(maxWidth);
+            this.heightContainer.updateMax(_MAXIMUM_DIMENSION);
+        }
+    }
+
+    disable() {
+        this.widthContainer.updateMin(_MINIMUM_DIMENSION);
+        this.heightContainer.updateMin(_MINIMUM_DIMENSION);
+        this.widthContainer.updateMax(_MAXIMUM_DIMENSION);
+        this.heightContainer.updateMax(_MAXIMUM_DIMENSION);
+    }
+
+    isLandscapeOrSquare() {
+        return this.widthRatio >= this.heightRatio;
+    }
+
+    setAspectRatio(aspectRatio) {
+        this.aspectRatio = aspectRatio;
+
+        let wR, hR;
+        if (aspectRatio === _OFF) {
+            return this.disable();
+        } else if (aspectRatio === _IMAGE) {
+            const img = getCurrentImage();
+            wR = img && img.naturalWidth || 1;
+            hR = img && img.naturalHeight || 1;
+        } else if (aspectRatio === _LOCK) {
+            wR = this.widthContainer.getVal();
+            hR = this.heightContainer.getVal();
+        } else {
+            [wR, hR] = aspectRatioFromStr(aspectRatio);
+        }
+
+        [wR, hR] = clampToBoundaries(wR, hR);
+
+        this.widthRatio = wR;
+        this.heightRatio = hR;
+        this.updateInputStates();
+        this.maintainAspectRatio();
+    }
+
+    maintainAspectRatio(changedElement) {
+        if (this.aspectRatio === _OFF) return;
+        if (!changedElement) {
+            const allValues = Object.values(this.inputs).map(x => Number(x.value));
+            changedElement = {value: Math.max(...allValues)};
+        }
+
+        const aspectRatio = this.widthRatio / this.heightRatio;
+        let w, h;
+
+        if (changedElement.isWidth === undefined) {
+            if (this.isLandscapeOrSquare()) {
+                if (changedElement.isWidth) {}
+                w = Math.round(changedElement.value);
+                h = Math.round(changedElement.value / aspectRatio);
+            } else {
+                h = Math.round(changedElement.value);
+                w = Math.round(changedElement.value * aspectRatio);
+            }
+        } else {
+            if (changedElement.isWidth) {
+                w = Math.round(changedElement.value);
+                h = Math.round(changedElement.value / aspectRatio);
+            } else {
+                h = Math.round(changedElement.value);
+                w = Math.round(changedElement.value * aspectRatio);
+            }
+        }
+
+        const [width, height] = clampToBoundaries(w, h)
+
+        const inputEvent = new Event("input", {bubbles: true});
+        this.widthContainer.setVal(width);
+        this.widthContainer.triggerEvent(inputEvent);
+        this.heightContainer.setVal(height);
+        this.heightContainer.triggerEvent(inputEvent);
+        this.heightContainer.inputs.forEach(input => {
+            dimensionChange({target: input}, false, true);
+        });
+        this.widthContainer.inputs.forEach(input => {
+            dimensionChange({target: input}, true, false);
+        });
+    }
+
+    static observeStartup(key, page, defaultOptions, postSetup = (_) => {
+    }) {
+        let observer = new MutationObserver(() => {
+            const widthContainer = gradioApp().querySelector(`#${page}_width`);
+            const heightContainer = gradioApp().querySelector(`#${page}_height`);
+
+            // wait for width and height containers to exist.
+            if (widthContainer && heightContainer && window.opts && window.opts.arh_javascript_aspect_ratio_show !== undefined) {
+                observer.disconnect();
+                if (!window.opts.arh_javascript_aspect_ratio_show) {
+                    return;
+                }
+
+                const controller = new AspectRatioController(
+                    page,
+                    widthContainer,
+                    heightContainer,
+                    defaultOptions,
+                );
+
+                postSetup(controller);
+                window[key] = controller;
+            }
+        });
+
+        observer.observe(gradioApp(), {childList: true, subtree: true});
+    }
+
+}
+
+const addImg2ImgTabSwitchClickListeners = (controller) => {
+    const img2imgTabButtons = Array.from(document.querySelectorAll('#img2img_settings > div > div > button:not(.selected):not(.hasTabSwitchListener)'));
+    img2imgTabButtons.forEach(button => {
+        button.addEventListener('click', (_) => {
+            // set aspect ratio is RECALLED to change to the image specific to the newly selected tab.
+            if (controller.optionPickingControler.getCurrentOption() === _IMAGE) {
+                controller.setAspectRatio(_IMAGE);
+            }
+
+            addImg2ImgTabSwitchClickListeners(controller);
+        });
+
+        button.classList.add('hasTabSwitchListener');
+    });
+}
+
+const postImageControllerSetupFunction = (controller) => {
+    const scaleToImg2ImgImage = (e) => {
+        const picked = controller.optionPickingControler.getCurrentOption();
+        if (picked !== _IMAGE) return;
+        const files = e.dataTransfer ? e.dataTransfer.files : e.target.files;
+        const img = new Image();
+        img.src = URL.createObjectURL(files[0]);
+        img.onload = () => {
+            controller.setAspectRatio(`${img.naturalWidth}:${img.naturalHeight}`)
+        };
+    }
+
+    _IMAGE_INPUT_CONTAINER_IDS.forEach(imageContainerId => {
+        const imageContainer = document.getElementById(imageContainerId);
+        const inputElement = imageContainer.querySelector('input');
+        inputElement.parentElement.addEventListener('drop', scaleToImg2ImgImage);
+        inputElement.addEventListener('change', scaleToImg2ImgImage);
+    })
+
+    addImg2ImgTabSwitchClickListeners(controller);
+}
+
+document.addEventListener("DOMContentLoaded", () => {
+    AspectRatioController.observeStartup(
+        "__txt2imgAspectRatioController",
+        "txt2img",
+        [_OFF, _LOCK]
+    );
+    AspectRatioController.observeStartup(
+        "__img2imgAspectRatioController",
+        "img2img",
+        [_OFF, _LOCK, _IMAGE],
+        postImageControllerSetupFunction
+    );
+});

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/scripts/sd_webui_aspect_ratio_helper.py

@@ -0,0 +1,5 @@
+from __future__ import annotations
+
+from aspect_ratio_helper.main import AspectRatioStepScript
+
+__all__ = ['AspectRatioStepScript']

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/style.css

@@ -0,0 +1,36 @@
+#txt2img_size_toolbox, #img2img_size_toolbox {
+    min-width: unset !important;
+    gap: 0;
+}
+
+#txt2img_ratio, #img2img_ratio {
+    padding: 0px;
+    min-width: unset;
+    max-width: fit-content;
+}
+
+#txt2img_ratio select, #img2img_ratio select {
+    background: var(--background-fill-primary);
+    border: 1px solid var(--block-border-color);
+    color: var(--block-label-text-color);
+    -webkit-appearance: none;
+    -moz-appearance: none;
+    appearance: none;
+    background-image: none;
+    tab-size: 4;
+    font-size: 14px;
+    line-height: 1.4;
+    box-sizing: border-box;
+    position: relative;
+    border-radius: 8px;
+    box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
+    padding-right: 1px;
+    margin-bottom: 10px;
+    padding-left: 2px;
+    text-align: center;
+    min-width: 2.5em;
+}
+
+.arh-btn-row button {
+    min-width: fit-content;
+}

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/tests/_util_test.py

@@ -0,0 +1,291 @@
+from __future__ import annotations
+
+import pytest
+
+import aspect_ratio_helper._constants as _constants
+import aspect_ratio_helper._util as _util
+
+
+@pytest.mark.parametrize(
+    'num, expected',
+    [
+        (50, 'x0.5'),
+        (150, 'x1.5'),
+        (175, 'x1.75'),
+        (250, 'x2.5'),
+    ],
+)
+def test_display_multiplication(num, expected):
+    assert _util.display_multiplication(num) == expected
+
+
+@pytest.mark.parametrize(
+    'num, expected',
+    [
+        (50, '50%'),
+        (75, '75%'),
+        (100, '100%'),
+        (150, '150%'),
+        (250, '250%'),
+    ],
+)
+def test_display_raw_percentage(num, expected):
+    assert _util.display_raw_percentage(num) == expected
+
+
+@pytest.mark.parametrize(
+    'num, expected_output', [
+        (150, '+50%'),
+        (100, '0%'),
+        (50, '-50%'),
+        (0, '-100%'),
+        (200, '+100%'),
+        (75, '-25%'),
+    ],
+)
+def test_display_minus_and_plus(num, expected_output):
+    assert _util.display_minus_and_plus(num) == expected_output
+
+
+@pytest.mark.parametrize(
+    'width, height, pct, expected',
+    [
+        pytest.param(200, 400, 0.5, (96, 200), id='50_percent_scale_down'),
+        pytest.param(100, 200, 2.0, (200, 400), id='200_percent_scale_up'),
+        pytest.param(100, 200, 1.1, (112, 224), id='10_percent_scale_up'),
+        pytest.param(100, 200, 0.9, (88, 176), id='10_percent_scale_down'),
+        pytest.param(100, 200, 0.0, (64, 128), id='scale_full_down'),
+        pytest.param(
+            _constants.MIN_DIMENSION - 1,
+            _constants.MIN_DIMENSION - 1,
+            0.5,
+            (_constants.MIN_DIMENSION, _constants.MIN_DIMENSION),
+            id='scale_belowMIN_DIMENSION',
+        ),
+        pytest.param(
+            _constants.MAX_DIMENSION + 1,
+            _constants.MAX_DIMENSION + 1,
+            2.0,
+            (_constants.MAX_DIMENSION, _constants.MAX_DIMENSION),
+            id='scale_aboveMAX_DIMENSION',
+        ),
+    ],
+)
+def test_scale_by_percentage(
+        width, height, pct, expected,
+):
+    assert _util.scale_by_percentage(
+        width, height, pct,
+    ) == expected
+
+
+@pytest.mark.parametrize(
+    'width, height, max_dim, expected',
+    [
+        # pytest.param(
+        #     100, 200, 400, (200, 400),
+        #     id='scale_up_to_max_dimension_horizontally',
+        # ),
+        # pytest.param(
+        #     200, 100, 400, (400, 200),
+        #     id='scale_up_to_max_dimension_vertically',
+        # ),
+        # pytest.param(
+        #     400, 64, 400, (400, 64),
+        #     id='no_scale_up_needed_with_max_dimension_width',
+        # ),
+        # pytest.param(
+        #     64, 400, 400, (64, 400),
+        #     id='no_scale_up_needed_with_max_dimension_height',
+        # ),
+        # pytest.param(
+        #     _constants.MIN_DIMENSION,
+        #     _constants.MIN_DIMENSION,
+        #     _constants.MAX_DIMENSION,
+        #     (_constants.MAX_DIMENSION, _constants.MAX_DIMENSION),
+        #     id='scale_from_min_to_max',
+        # ),
+        # pytest.param(
+        #     _constants.MAX_DIMENSION,
+        #     _constants.MAX_DIMENSION,
+        #     _constants.MIN_DIMENSION,
+        #     (_constants.MIN_DIMENSION, _constants.MIN_DIMENSION),
+        #     id='scale_from_max_to_min',
+        # ),
+        pytest.param(
+            _constants.MIN_DIMENSION, 32, _constants.MIN_DIMENSION,
+            (128, _constants.MIN_DIMENSION),
+            id='scale_below_min_height_dimension_clamps_retains_ar',
+        ),
+        pytest.param(
+            32, _constants.MIN_DIMENSION, _constants.MIN_DIMENSION,
+            (_constants.MIN_DIMENSION, 128),
+            id='scale_below_min_width_dimension_clamps_retains_ar',
+        ),
+        # pytest.param(
+        #     _constants.MAX_DIMENSION, 4096, _constants.MAX_DIMENSION,
+        #     (1024, _constants.MAX_DIMENSION),
+        #     id='scale_above_max_height_dimension_clamps_retains_ar',
+        # ),
+        # pytest.param(
+        #     4096, _constants.MAX_DIMENSION, _constants.MAX_DIMENSION,
+        #     (_constants.MAX_DIMENSION, 1024),
+        #     id='scale_above_max_width_dimension_clamps_retains_ar',
+        # ),
+        # pytest.param(
+        #     64, 64, _constants.MIN_DIMENSION - 1,
+        #     (_constants.MIN_DIMENSION, _constants.MIN_DIMENSION),
+        #     id='scale_dimension_below_min_dimension_clamps_retains_ar',
+        # ),
+        # pytest.param(
+        #     64, 64, _constants.MAX_DIMENSION + 1,
+        #     (_constants.MAX_DIMENSION, _constants.MAX_DIMENSION),
+        #     id='scale_dimension_above_max_dimension_clamps_retains_ar',
+        # ),
+    ],
+)
+def test_scale_dimensions_to_max_dim(
+        width, height, max_dim, expected,
+):
+    assert _util.scale_dimensions_to_max_dim(
+        width, height, max_dim,
+    ) == expected
+
+
+@pytest.mark.parametrize(
+    'width, height, min_dim, expected',
+    [
+        pytest.param(
+            100, 200, 400, (400, 800),
+            id='scale_up_to_min_dimension_with_ar_preservation',
+        ),
+        pytest.param(
+            200, 100, 400, (800, 400),
+            id='scale_up_to_min_dimension_with_ar_preservation',
+        ),
+        pytest.param(
+            100, 100, 400, (400, 400),
+            id='no_scale_up_needed_with_min_dimension',
+        ),
+        pytest.param(
+            _constants.MIN_DIMENSION, _constants.MIN_DIMENSION, _constants.MAX_DIMENSION,
+            (_constants.MAX_DIMENSION, _constants.MAX_DIMENSION),
+            id='scale_up_to_max_dimension_with_ar_preservation',
+        ),
+        pytest.param(
+            _constants.MAX_DIMENSION, _constants.MAX_DIMENSION, _constants.MIN_DIMENSION,
+            (_constants.MIN_DIMENSION, _constants.MIN_DIMENSION),
+            id='scale_down_to_min_dimension_with_ar_preservation',
+        ),
+        pytest.param(
+            100, 100, _constants.MAX_DIMENSION,
+            (_constants.MAX_DIMENSION, _constants.MAX_DIMENSION),
+            id='scale_up_to_max_dimension_with_ar_preservation',
+        ),
+    ],
+)
+def test_scale_dimensions_to_min_dim(
+        width, height, min_dim, expected,
+):
+    assert _util.scale_dimensions_to_min_dim(
+        width, height, min_dim,
+    ) == expected
+
+
+class SharedOpts:
+    def __init__(self, options=None, defaults=None):
+        self.options = options or {}
+        self.defaults = defaults or {}
+
+    def __getattr__(self, key):
+        try:
+            return self.options[key]
+        except KeyError:
+            raise AttributeError()
+
+    def get_default(self, key):
+        return self.defaults.get(key, None)
+
+
+def test_safe_opt_util():
+    shared_opts = SharedOpts(options={'key': 'value'})
+    assert _util.safe_opt_util(shared_opts, 'key', {}) == 'value'
+
+
+def test_safe_opt_util_none():
+    shared_opts = SharedOpts(options={'key': None}, defaults={'key': 'value'})
+    assert _util.safe_opt_util(shared_opts, 'key', {}) == 'value'
+
+
+def testsafe_opt_util_default_a():
+    shared_opts = SharedOpts(
+        defaults={'key': 'default_a'},
+    )
+    assert _util.safe_opt_util(
+        shared_opts, 'key', {'key': 'default_b'},
+    ) == 'default_a'
+
+
+def test_safe_opt_util_default_b():
+    shared_opts = SharedOpts(defaults={'key': None})
+    assert _util.safe_opt_util(
+        shared_opts, 'key', {'key': 'default_b'},
+    ) == 'default_b'
+
+
+@pytest.mark.parametrize(
+    'options', ({'key': None}, {}),
+)
+def test_safe_opt_safe_return_no_defaults_b(options):
+    shared_opts = SharedOpts(options=options)
+    assert _util.safe_opt_util(shared_opts, 'unknown_key', {}) is None
+
+
+@pytest.mark.parametrize(
+    'value, expected', [
+        (0, 0),
+        (7, 8),
+        (10, 8),
+        (16, 16),
+        (23, 24),
+        (32, 32),
+        (33, 32),
+        (100, 96),
+        (10.5, 8),
+        (15.3, 16),
+        (21.8, 24),
+        (33.9, 32),
+        (98.7, 96),
+    ],
+)
+def test_round_to_multiple_of_8(value, expected):
+    assert _util.round_to_multiple_of_8(value) == expected
+
+
+@pytest.mark.parametrize(
+    'width, height, aspect_ratio, expected', [
+        (100, 100, 1.0, (96, 96)),
+        (3000, 2000, 1.5, (2048, 1368)),
+        (500, 8000, 0.5, (1024, 2048)),
+        (500, 300, 2.0, (496, 304)),
+        (100, 200, 0.5, (96, 200)),
+        (500, 500, 1.2, (496, 496)),
+        (2048, 2048, 1.0, (2048, 2048)),
+        (2049, 2048, 1.0, (2048, 2048)),
+        (2048, 2049, 1.0, (2048, 2048)),
+        (2049, 2049, 1.0, (2048, 2048)),
+        (63, 63, 1.0, (64, 64)),
+        (2050, 2050, 1.0, (2048, 2048)),
+        (63, 64, 1.0, (64, 64)),
+        (64, 63, 1.0, (64, 64)),
+        (64, 64, 1.0, (64, 64)),
+        (63, 63, 1.0, (64, 64)),
+        (2050, 63, 1.0, (2048, 2048)),
+        (63, 2050, 1.0, (2048, 2048)),
+        (2050, 2050, 0.01, (64, 2048)),
+        (100.5, 100.5, 1.0, (104, 104)),
+        (200.3, 100.7, 0.5, (200, 104)),
+    ],
+)
+def test_clamp_to_boundaries(width, height, aspect_ratio, expected):
+    assert _util.clamp_to_boundaries(width, height, aspect_ratio) == expected

TestingforfaivsixEx/content/eichellis/extensions/aspect-ratio/tox.ini

@@ -0,0 +1,6 @@
+[tox]
+envlist = py310, py311
+
+[testenv]
+deps = pytest
+commands = pytest

TestingforfaivsixEx/content/eichellis/extensions/batch-link/.github/CODEOWNERS

@@ -0,0 +1,2 @@
+# Global rule:
+*           @etherealxx

TestingforfaivsixEx/content/eichellis/extensions/batch-link/.gitignore

@@ -0,0 +1,168 @@
+*.bak
+*.backup
+imageratio.*
+*.lnk
+tempCodeRunnerFile.python
+.DS_Store
+gradiovenv/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

TestingforfaivsixEx/content/eichellis/extensions/batch-link/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Etherealxx
+
+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.

TestingforfaivsixEx/content/eichellis/extensions/batch-link/README.md

@@ -0,0 +1,491 @@
+<br />
+
+<div align="center">
+  <a href="https://github.com/etherealxx/batchlinks-downloader">
+    <img src="images/batchlinks_logo.png" alt="Logo" width="528">
+  </a>
+
+<h3 align="center">BatchLinks Downloader</h3>
+
+<p align="center">
+    Batch-downloading models and stuff in stable-diffusion-webui colab made simple and fast.
+    <br />
+    <a href="https://github.com/etherealxx/batchlinks-downloader"><strong></strong></a>
+    <br />
+    <a href="https://github.com/etherealxx/batchlinks-downloader/issues">Report Bug</a>
+    ·
+    <a href="https://github.com/etherealxx/batchlinks-downloader/discussions/new?category=ideas">Request Feature</a>
+  </p>
+
+</div>
+
+<!-- TOC -->
+<!-- - [Installation](#installation)
+- [About](#about)
+- [Example](#example)
+- [Syntax](#syntax)
+    - [Hashtag](#hashtag)
+    - [Links](#links)
+    - [Double Hashtag](#double-hashtag)
+    - [Others](#others)
+  - [Valid Hashtags](#valid-hashtags)
+  - [How to get the direct links (Important!)](#how-to-get-the-direct-links-important)
+  - [Huggingface's download method](#huggingfaces-download-method)
+- [Additional Syntax](#additional-syntax)
+  - [Rename Downloaded Files](#rename-downloaded-files)
+  - [Running Shell Commands](#running-shell-commands)
+  - [Extract Everyting (`@extract`)](#extract-everyting-extract)
+  - [Custom Hashtag Path (`@new`)](#custom-hashtag-path-new)
+  - [Custom Direct Link Download with `aria2` (`@aria2`)](#custom-direct-link-download-with-aria2-aria2)
+- [Gradio Queue](#gradio-queue)
+  - [Logging](#logging)
+  - [Cancel](#cancel)
+  - [Progress Bar](#progress-bar)
+  - [Changes if `--gradio-queue` is off](#changes-if---gradio-queue-is-off)
+- [Other Features](#other-features)
+  - [Notification](#notification)
+  - [Copy From Pastebin](#copy-from-pastebin)
+  - [SDless mode](#sdless-mode)
+  - [Local Installation Support](#local-installation-support)
+  - [Debug Mode (Developer only)](#debug-mode-developer-only)
+- [Release Notes](#release-notes)
+- [Roadmap](#roadmap)
+- [Known Bugs](#known-bugs)
+  - [Known Fixed Bugs](#known-fixed-bugs)
+- [Contributing](#contributing)
+- [Contact](#contact)
+- [Acknowledgments](#acknowledgments) -->
+<!-- /TOC -->
+
+<details>
+  <summary>Table of Contents</summary>
+  <ol>
+    <li><a href="#installation">Installation</a></li>
+    <li><a href="#about">About</a></li>
+    <li><a href="#example">Example</a></li>
+    <li><a href="#syntax">Syntax</a></li>
+        <ul><li><a href="#hashtag">Hashtag</a></li></ul>
+        <ul><li><a href="#links">Links</a></li></ul>
+        <ul><li><a href="#double-hashtag">Double Hashtag</a></li></ul>
+        <ul><li><a href="#others">Others</a></li></ul>
+      <ul><li><a href="#valid-hashtags">Valid Hashtags</a></li></ul>
+      <ul><li><a href="#how-to-get-the-direct-links-important">How to get the direct links (Important!)</a></li></ul>
+      <ul><li><a href="#huggingfaces-download-method">Huggingface's download method</a></li></ul>
+    <li><a href="#additional-syntax">Additional Syntax</a></li>
+      <ul><li><a href="#rename-downloaded-files">Rename Downloaded Files</a></li></ul>
+      <ul><li><a href="#running-shell-commands">Running Shell Commands</a></li></ul>
+      <ul><li><a href="#extract-everyting-extract">Extract Everyting (`@extract`)</a></li></ul>
+      <ul><li><a href="#custom-hashtag-path-new">Custom Hashtag Path (`@new`)</a></li></ul>
+      <ul><li><a href="#custom-direct-link-download-with-aria2-aria2">Custom Direct Link Download with `aria2` (`@aria2`)</a></li></ul>
+    <li><a href="#gradio-queue">Gradio Queue</a></li>
+      <ul><li><a href="#logging">Logging</a></li></ul>
+      <ul><li><a href="#cancel">Cancel</a></li></ul>
+      <ul><li><a href="#progress-bar">Progress Bar</a></li></ul>
+      <ul><li><a href="#changes-if---gradio-queue-is-off">Changes if `--gradio-queue` is off</a></li></ul>
+    <li><a href="#other-features">Other Features</a></li>
+      <ul><li><a href="#notification">Notification</a></li></ul>
+      <ul><li><a href="#copy-from-pastebin">Copy From Pastebin</a></li></ul>
+      <ul><li><a href="#sdless-mode">SDless mode</a></li></ul>
+      <ul><li><a href="#local-installation-support">Local Installation Support</a></li></ul>
+      <ul><li><a href="#debug-mode-developer-only">Debug Mode (Developer only)</a></li></ul>
+    <li><a href="#release-notes">Release Notes</a></li>
+    <li><a href="#roadmap">Roadmap</a></li>
+    <li><a href="#known-bugs">Known Bugs</a></li>
+      <ul><li><a href="#known-fixed-bugs">Known Fixed Bugs</a></li></ul>
+    <li><a href="#contributing">Contributing</a></li>
+    <li><a href="#contact">Contact</a></li>
+    <li><a href="#acknowledgments">Acknowledgments</a></li>
+    </li>
+  </ol>
+</details>
+
+[does this repo still maintained?](https://github.com/etherealxx/batchlinks-webui/blob/main/regardingthisrepo.md)
+
+## Installation
+
+Copy this line into your colab installation cell. Or into a new cell if you already launched the webui.
+
+```
+!git clone https://github.com/etherealxx/batchlinks-webui /content/stable-diffusion-webui/extensions/batchlinks-webui
+```
+
+or, you can copy the url of this repo and install it via webui and restart the UI.<br/>
+<img src="images/ext_installer.jpg" alt="Logo" width="362"><br/>
+(If `gradio no interface is running` or `bad gateway` shows up when restarting the UI, that means you need to restart the cell anyway 😅)
+
+<!--
+or, if your colab use the newer version of webui (gradio version above 3.16.0) you can use this instead (it adds a little progress bar)
+
+```
+!git clone -b gradio-v3-16-2 https://github.com/etherealxx/batchlinks-webui /content/stable-diffusion-webui/extensions/batchlinks-webui
+```
+-->
+
+Using `--gradio-queue` on the launch.py argument is highly recommended, as it enables this extension to show download progress bar on the UI and a cancel button. The option itself has no negative effect on the webui. [Read more here.](https://github.com/etherealxx/batchlinks-webui#gradio-queue)<br/>
+<img src="images/queue.jpg" alt="Logo" width="300"><br/>
+
+You can also run this extension in [SDless mode](https://github.com/etherealxx/batchlinks-webui#sdless-mode) btw.
+
+While it's not recommended to use this extension on your local installation, you can use this extension on Windows. [More here.](https://github.com/etherealxx/batchlinks-webui#local-installation-support)
+
+## About
+
+This extension will streamline your downloads on your [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) colab session. Paste the links you need to download (or you can upload a txt file containing the links), use the hashstag syntax to choose the download location (see below), and hit the `Download All` button to download them!
+
+## Example
+
+Look at this example<br/>
+<img src="images/example.jpg" alt="Logo" width="738"><br/>
+
+```
+#model
+https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3_orangemixs.safetensors
+#vae
+https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt
+#embed
+https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt_version2.pt
+https://huggingface.co/etherealxx/whoseisthis/resolve/main/bad_artist.pt
+#lora
+https://civitai.com/api/download/models/8840 ##1-mb-lora-trained-in-5-mins-that-does-the-same-thing-as-25-gb-model-but-better
+https://civitai.com/api/download/models/6891 ##sans undertale
+https://mega.nz/file/gAxTWBAI#uL7EZay-OND5G6ELJlGfUNG0s7Q4TynZKqFdvs0v0tc ##agent8-finetune
+https://mega.nz/file/oU43FSTY#vwAfsAb7RKJ4xtsSP7uzrKpWhh1y8BdpIBFurwsVP2o ##agent8-dreambooth
+#hypernet
+https://cdn.discordapp.com/attachments/1070489470127841381/1070489471964954684/MomopocoV3.pt
+```
+
+This piece of lines will be read from top to bottom. Every hashtag, it will change the current output directory to said directory (see below). So what this example do is it will download `AOM3` model to the model folder, then it will download the vae and put it to the Vae folder. Next it will download two embed, `bad prompt` and `bad artist`. Next it will download several LoRAs from CivitAI and MEGA, and put it to the Lora folder. Lastly, it changes the directory to hypernet directory, then 
+
+You can also copy that example and paste it to a `.txt` file to use later. You can load a `.txt` file containing that piece of lines directly from the UI.
+
+When the items is downloading, you can inspect the running code on the colab cell, or just take a coffee and chill☕. If you activate logging, you can inspect the download progress from the UI, more [here](https://github.com/etherealxx/batchlinks-webui#logging)
+
+When the download is complete, the downloaded file will be listed<br/>
+<img src="images/downloaded.jpg" alt="Logo" width="745"><br/>
+
+## Syntax
+
+#### Hashtag 
+- Hashtag means change current output directory to this directory. `#model` means every links below this hashtag, will be downloaded to _/content/stable-diffusion-webui/models/Stable-diffusion_, until it hits another hashtag, which will change the output directory again. See [below](https://github.com/etherealxx/batchlinks-webui#valid-hashtags) for valid hashtags.
+
+  Note: If you use some colab that purposefully uses `sd-webui-additional-networks` extension to load Lora, use `#addnetlora` instead of `#lora`. It will download the lora to where it supposed to be.
+
+#### Links
+- Links are the main things you wants to be downloaded. Current supported links are from:
+  - Huggingface (https://huggingface.co/)
+  - MEGA (https://mega.nz/) {using `mega-cmd`}
+  - CivitAI (https://civitai.com/)
+  - Discord attachments (https://cdn.discordapp.com/attachments/)
+  - catbox (https://files.catbox.moe)
+  - Github (https://github.com or https://raw.githubusercontent.com)
+  - Google Drive (https://drive.google.com) {using `gdown`}
+  - Pixeldrain (https://pixeldrain.com/u/)
+  - Mediafire (https://www.mediafire.com/file) {using `mediafire-dl`}
+  - anonfiles (https://anonfiles.com)
+
+  Every links other than that will be ignored. Keep in mind the only supported links are direct download links (see [here](https://github.com/etherealxx/batchlinks-downloader/blob/main/howtogetthedirectlinks.md)). For Huggingface, Civitai (model page link method), Discord attachments, catbox, pixeldrain, and anonfiles, there will be four different method of downloading offered (see [below](https://github.com/etherealxx/batchlinks-webui#huggingfaces-download-method)). For MEGA, it will use `mega-cmd` to download. For Github, if, the link is a raw file, it will download the file. Else, it will use `git clone`, useful to clone extension repo into the webui extension folder.
+
+  More about CivitAI download method [here](https://github.com/etherealxx/batchlinks-webui#civitais-download-method).
+
+#### Double Hashtag
+- Double hashtag means comment. You can put double hashtag in the same line of the link and it will be ignored (keep in mind to put the link first then the double hashtag)
+
+#### Others
+- Other texts will be ignored.
+
+### Valid Hashtags
+
+`#model`, `#models`, `#checkpoint`, or `#checkpoints` will put the downloaded file to _/content/stable-diffusion-webui/models/Stable-diffusion_
+
+`#embedding`, `#embeddings`, `#embed`, `#embeds`, `#textualinversion`, or `#ti` will put the downloaded file to _/content/stable-diffusion-webui/embeddings_
+
+`#vae` or `#vaes` will put the downloaded file to _/content/stable-diffusion-webui/models/VAE_
+
+`#hypernetwork`, `#hypernetworks`, `#hypernet`, `#hypernets`, `#hynet`, or `#hynets` will put the downloaded file to _/content/stable-diffusion-webui/models/hypernetworks_
+
+`#lora` or `#loras` will put the downloaded file to _/content/stable-diffusion-webui/models/Lora_
+
+`#addnetlora`, `#loraaddnet`, `#additionalnetworks`, or `#addnet` will put the downloaded file to _/content/stable-diffusion-webui/extensions/sd-webui-additional-networks/models/lora_
+
+`#aestheticembedding` or `#aestheticembed` will put the downloaded file to _content/stable-diffusion-webui/extensions/stable-diffusion-webui-aesthetic-gradients/aesthetic_embeddings_
+
+`#controlnet` or `#cnet` will put the downloaded file to _/content/stable-diffusion-webui/extensions/sd-webui-controlnet/models_
+
+`#upscale` or `#upscaler` will put the downloaded file to _/content/stable-diffusion-webui/models/ESRGAN_
+
+`#lycoris`, `#locon`, or `#loha` will put the downloaded file to _/content/stable-diffusion-webui/extensions/sd-webui-additional-networks/models/lora/lycoris_
+
+`#altmodel` or `#altmodels` will put the downloaded file to the path you choose when using `--ckptdir` argument on `launch.py` line. If you didn't use that argument, this hashtag will points to the same directory as `#model`
+
+_Lycoris/Locon/Loha_ will works just fine if you use `#addnetlora` instead, as long as you have both [addnet extension](https://github.com/kohya-ss/sd-webui-additional-networks) and [locon extension](https://github.com/KohakuBlueleaf/a1111-sd-webui-locon) installed. It's unclear if it's can be loaded by native auto1111 lora. (Tell me if you know more about this.)
+
+Github links (if it does not contain `/raw/` or `/release/download/` in it) doesn't need hashtag. It will always considered as webui extension, and the repository will be cloned to _/content/stable-diffusion-webui/extensions/(reponame)_
+
+### How to get the direct links (Important!)
+
+See [here](https://github.com/etherealxx/batchlinks-downloader/blob/main/howtogetthedirectlinks.md)
+
+### Huggingface's download method
+
+So there's four supported method: `gdown`, `wget`, `curl` and `aria2`. Use whatever, really. The difference between them are actually little. Myself love using `gdown` since the output is cleaner than the others. `aria2` has the fastest download speed though.
+
+<!-- ### Civitai's download method
+
+There are two ways to download links from Civit. The `model page link` method, and the `direct link` method.
+
+The `model page link` method **will automatically** choose the directory of the saved model without even using hashtag. It will download the default model of a model page. The advantage of this method is you will also get the model preview images that can shows up on the latest version of [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui). This method will also uses the same download protocol as huggingface's (see above). <br/>The `model page link` method's link starts with **https://civitai.com/models/**
+
+The `direct link` method is the old method, which needs hashtag for the downloaded model to be properly placed, and uses `request` module to download. The advantage of this method is you can choose what model variation you want to download by grabbing the right url. See [here](https://github.com/etherealxx/batchlinks-downloader/blob/main/howtogetthedirectlinks.md) for more.<br/>The `direct link` method's link starts with **https://civitai.com/api/download/models/**
+
+Here's the difference of syntax between two methods:
+
+Model page link:
+```
+https://civitai.com/models/4823/deliberate
+```
+Direct link:
+```
+#model
+https://civitai.com/api/download/models/5616
+```
+
+Check [this page](https://github.com/etherealxx/batchlinks-downloader/blob/main/howtogetthedirectlinks.md) to learn more on how to get the links for each methods. -->
+
+## Additional Syntax
+### Rename Downloaded Files
+Using `>` symbol, you can rename files. Take this for example<br/>
+<img src="images/rename.jpg" alt="Log" width="407"><br/>
+Mostly catbox file has random name, by using `>` symbol after the link, you can type the desired name on the right. (Don't forget the extension)
+
+### Running Shell Commands
+You can run shell commands by using `!` in front of the command you want, just like in google colab cells. Then press the `Download All!` button. (Sure, it doesn't download anything, but, well😅)<br/>
+<img src="images/shell1.jpg" alt="Log" width="200">
+<img src="images/shell2.jpg" alt="Log" width="200"><br/>
+You can run many lines at once too!
+
+### Extract Everyting (`@extract`)
+You can use `@extract` to extract every `*.7z`, `*.rar`, and `*.zip` on current directory). 
+```
+#lora
+<example lora .zip link>
+@extract
+#embed
+```
+This will extract everything on Lora folder, since the code runs from top to bottom, and when the `@extract` executes, the current directory is still on Lora folder.
+
+### Custom Hashtag Path (`@new`)
+You can use `@new <hashtagname> <directory>` to make a new usable hashtag and assign a directory path to it.<br/>For example, you type this and press the `Download All!` button:
+```
+@new #private /content/stable-diffusion-webui/outputs
+#private
+<example model link>
+```
+This will make a new hashtag `#private`, make it points to _/content/stable-diffusion-webui/outputs_, set `#private` as current directory, then download whatever into it. You can check if the new hashtag is assigned successfully by looking at the hashtag table on the right/bottom of the screen.
+
+### Custom Direct Link Download with `aria2` (`@aria2`)
+If there are other direct links that isn't supported by Batchlinks, you can use `@aria` or `@aria2` custom command to download it. The main syntax is:
+```
+@aria2 {link} {path/hashtag} > {filename} 
+```
+You can change `@aria2` with `@aria`, both works. Both the `path/hashtag` and `filename` is optional. This command was made to avoid hassle writing this:
+```
+aria2c --summary-interval=1 --console-log-level=error -c -x 16 -s 16 -k 1M {link} -d {path} -o {filename}
+```
+There are several ways to use it. Here i use a [20MB download test file](http://212.183.159.230/20MB.zip) as an example.
+```
+@aria2 http://212.183.159.230/20MB.zip
+```
+☝️ This will download the file into the current directory (where the hashtags points to). So if before that line you use `#vae`, it will be downloaded to the vae directory. If there are no hashtags before, it'll be sent to the default `#model` directory. The filename will be the last part of the link (`512MB.zip`).
+```
+@aria2 http://212.183.159.230/20MB.zip #vae
+```
+☝️ This will download the file into where `#vae` points to (which is the vae directory). You can use custom hashtag here as well.
+```
+@aria2 http://212.183.159.230/20MB.zip /content/stable-diffusion-webui 
+```
+☝️ This will download the file into the written path, which is _/content/stable-diffusion-webui_. Make sure it is a folder path, not a file path.
+```
+@aria2 http://212.183.159.230/20MB.zip /content/stable-diffusion-webui > 20megabytes.zip
+```
+☝️ This will download the file into the written path and rename the file into `20megabytes.zip`. Remember to make sure to write the extension too.
+
+## Gradio Queue
+
+If you use --gradio-queue argument on `launch.py`, some feature will be activated.
+
+### Logging
+
+<!-- If you use latest version of [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui), or webui forks that supports `--gradio-queue` args on launch.py, use it and you will be able to -->
+Enable logging by pressing the `Turn On Logging` radio button, and wait till `Logging activated` shows up and the box is blinking with orange border. Logging will tell you what are you actually downloading right now on the webui. After your download session is completed, it's recommended to turn back off the feature.<br/>
+<img src="images/logging.jpg" alt="Log" width="407"><br/>
+
+### Cancel 
+Pressing `cancel` button while download in progress will stops the current session. Useful when at one time the download speeds is too slow. If you're currently downloading a single item, that item will be cancelled, but the other downloaded one will remain intact.<br/>
+<img src="images/cancel.jpg" alt="Log" width="500"><br/>
+
+### Progress Bar
+There will be an additional progress bar that tells you the current activities.<br/>
+<img src="images/progress.jpg" alt="Log" width="500"><br/>
+
+### Changes if `--gradio-queue` is off
+Feature listed above will dissapear, and your only option for download is just `aria2` (speed is priority).<br/>Note that when you pressed the `Download All!` button, **nothing will shows up on the UI**. You need to check the colab console.<br/>
+<img src="images/checkconsole1.jpg" alt="check" width="407"><img src="images/checkconsole2.jpg" alt="off" width="407"><br/>
+Another thing to note is your download session will always be cutted every 70 seconds (to prevent hangs/desync).<br/>
+<img src="images/gradiooff1.jpg" alt="off" width="407"><br/>
+Don't worry, you can continue your session by pressing the `Resume Download` button. It will refresh the links with the one you haven't downloaded yet, then pressing `Download All!` will download the remaining links.<br/>
+<img src="images/gradiooff2.jpg" alt="off" width="407"><br/>
+
+## Other Features
+
+### Notification
+
+This extension will play sound effect when the download process is completed. It's somewhat buggy at the moment though.
+
+### Copy From Pastebin
+
+You can put your links on pastebin (write it the way you wrote on Batchlinks), put the pastebin link (https://pastebin.com/xxxx) on the link input, and press the 'Copy from Pastebin' button. It will copy all the the links on the pastebin to the UI. Useful to avoid hassle copy and pasting a bunch of text while on mobile.<br/>
+<img src="images/pastebin.png" alt="off" width="400"><br/>
+
+### SDless mode
+This mode will run the extension without the need of stable-diffusion-webui. Good for my own purpose of debugging it😁.<br/>
+<img src="images/sdless.png" alt="off" width="450"><br/>
+
+Copy-paste this on a new colab cell then run it:
+```
+from IPython.display import clear_output
+!pip3 show virtualenv >/dev/null || pip install -q virtualenv
+![ -d gradiovenv ] || virtualenv gradiovenv
+!git clone https://github.com/etherealxx/batchlinks-webui \
+/content/stable-diffusion-webui/extensions/batchlinks-webui
+pip3 show gradio >/dev/null || pip install -q gradio==3.16.2; \
+pip3 show tqdm >/dev/null || pip3 install -q tqdm
+clear_output(wait=True)
+!source gradiovenv/bin/activate; \
+python /content/stable-diffusion-webui/extensions/batchlinks-webui/scripts/batchlinks-downloader.py
+```
+or here's the quicker version without venv:
+```
+!pip install gradio==3.16.2
+!git clone https://github.com/etherealxx/batchlinks-webui /content/stable-diffusion-webui/extensions/batchlinks-webui
+!python /content/stable-diffusion-webui/extensions/batchlinks-webui/scripts/batchlinks-downloader.py
+```
+
+For Windows, run the sdless-windows.bat. Make sure you have `python3` and `gradio` package version 3.16.2 or above. It won't use venv.
+
+This one for MacOS. Tested on Mojave. Create a shell script and give execute access to it.
+```
+#!/usr/bin/env bash
+pip3 show virtualenv >/dev/null || pip3 install virtualenv
+[ -d gradiovenv ] || virtualenv gradiovenv
+git clone https://github.com/etherealxx/batchlinks-webui \
+$HOME/Downloads/stable-diffusion-webui/extensions/batchlinks-webui
+source gradiovenv/bin/activate; \
+pip3 show gradio >/dev/null || pip3 install gradio==3.16.2; \
+pip3 show tqdm >/dev/null || pip3 install -q tqdm
+source gradiovenv/bin/activate; \
+python3 $HOME/Downloads/stable-diffusion-webui/extensions/batchlinks-webui/scripts/batchlinks-downloader.py
+```
+
+### Local Installation Support
+
+This extension was tested to work on Windows 11. But ultimately, the number one priority for me is colab use, so there's might be bugs on Windows 11 one. Maybe also works on Debian-based linux (but you better inspect the source code first).
+On Windows, this extension will install [MEGAcmd](https://github.com/meganz/MEGAcmd) for MEGA file download, [wget-windows](https://github.com/webfolderio/wget-windows) for download using `wget`, [aria2-static-builds](https://github.com/q3aql/aria2-static-builds) for download using `aria2`, [7zr and 7z](https://www.7-zip.org) for extracting `aria2` installation, and for `@extract` command.
+MacOS is just partially supported.
+
+Also remember, in order for this extension to work properly, you need make sure that you tick that `add Python 3.10 to PATH` checkbox when you installed Python (Which is already recommended when installing stable-diffusion-webui)<br/>
+<img src="images/python.png" alt="python" width="400"><br/>
+
+### Debug Mode (Developer only)
+
+By manually switching `globaldebug = False` to `True` on the source code, or using `--debug` argument when running the sdless mode, the Debug Mode will be activated. It shows every `print` calls i use to track variables. It will also tracks every files on the models (etc.) paths into a `snapshot.txt` file (if there isn't one).
+
+There are some batchlinks syntax features that only available on debug mode (putting it on the textbox and click `Download All!`):
+
+`@debugresetdownload` - This command will check every model (etc.) directory, and remove every file that isn't on `snapshot.txt`. Useful for me to removes file quickly when testing. **Be careful using this on your local installation.**
+
+`@debugresetdownload` - This command is (supposed to) download a single link with every method available (gdown, curl, wget, aria2), but now i rarely use this command, and haven't updated since. Might be buggy.
+
+## Release Notes
+Moved [here](https://github.com/etherealxx/batchlinks-webui/blob/main/releasenotes.md). Check it out for latest features & bug fixes.
+
+## Roadmap
+
+- [ ] Add checker for downloaded models (so that it won't download again after the model is downloaded)
+- [ ] Different UI for mobile
+- [ ] Gradio progress bar
+- [ ] Integrating refresh button (`create_refresh_button`) to refresh models etc. all at once
+- [ ] Logo change
+- [ ] Moving most of the content of this `Readme.md` to Wiki instead
+- [ ] Other download sites (s-ul.eu, gitgud, bunkr.ru, icedrive)
+- [ ] Separate 'download' and 'run command' function with a subtab
+- [ ] Support download MEGA and Google Drive folder
+- [ ] Using `yield` instead of gradio's `every`
+- [ ] Youtube video (Tutorial)
+- [ ] (Crawler) Download every sd-webui related file from a huggingface repo
+- [ ] (Crawler) Download every model from a civitai user page
+<br/>_
+- [x] aria2 for huggingface download method
+- [x] Cleaning the code from unnecesarry comments
+- [x] Completed download will use the webui's notification.mp3
+- [x] Support customizable hashtag from the UI
+- [x] Supports Windows local installation
+- [x] UI overhaul
+- [x] Using threading/subprocess instead of os.system to download files
+- [x] (Windows) wget & aria2 support
+
+## Known Bugs
+
+- Progress bar (the yellow bar) doesn't progress as expected
+- Sometimes notification sound doesn't play when downloading same file twice in a row
+- Sometimes notification sound shows up when starting download, instead of when the download process is completed.
+- There's still a chance that the UI of non `--gradio-queue` session and/or onedotsix freezes after a download session
+- Windows: The delay between file is downloaded and the output shows is pretty long, and even sometimes the notification comes at the wrong time.
+<br/>
+
+### Known Fixed Bugs
+
+<details>
+  <summary>👈Fixed Bugs</summary>
+  <ol>
+    <ul>
+      <li><s>Links that has bracket in it needs to be 'escaped' (For example, <code>Baka-DiffusionV1(Fp16).safetensors</code> must be typed <code>Baka-DiffusionV1\(Fp16\).safetensors</code>)</s><br/>☝️Fixed in <a href="dbb2adb3d07e41654244076b8ef4e851c3bb1f0c">v2.0.0</a>
+      <li><s>The delay between file is downloaded and the output shows is really long (1min+) on <a href="https://github.com/camenduru/stable-diffusion-webui-colab">camenduru's v1.6 colab</a> (Gradio related?)</s><br/>☝️Seems like fixed in <a href="fe6feafc07fbbe3efd2883b33855f8d66b5f89ea">v1.1.0</a>
+      <li><s>File downloaded from MEGA will not listed on the output, as it use different download method. There is some delay between the transfare() function complete until it writes the file. I don't know how long the delay is.</s><br/>☝️Fixed in <a href="fe6feafc07fbbe3efd2883b33855f8d66b5f89ea">v1.1.0</a>
+      <li><s>Sometimes colab cannot be shut down with a single click on the stop button. Hitting the button several times will raise a KeyboardInterrupt and forcely stopping the cell.</s><br/>☝️Seems like fixed somewhere on <a href="0acffad539179b9f6cf4b76581818b62c93a8551">v3.0.0</a> and after
+    </ul>
+  </ol>
+</details>
+
+## Contributing
+
+<!-- I just learned python few months ago, by just looking at other peoples project and sometimes asking ChatGPT. Gradio is new for me. I literally just learn it in one day to make this extension, so expect some bugs. -->
+
+[I no longer actively maintain this project](https://github.com/etherealxx/batchlinks-webui/blob/main/regardingthisrepo.md). if you have a suggestion or code-fixing that would make this better, please notify me in the issue tab, fork the repo and create a pull request. <br/>
+But still, a star on this project would be nice. Thanks again.
+
+<!--
+1. Fork the Project
+2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the Branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
+-->
+
+## Contact
+
+My Email - gwathon3@gmail.com <br/>
+My Youtube - [MJ Devlog](https://www.youtube.com/@mjdevlog)
+
+## Acknowledgments
+
+- [Camenduru's Webui Huggingface](https://github.com/camenduru/stable-diffusion-webui-huggingface) - I use his extension as my base (my gradio skill sucks T.T)
+- [SD Civitai Browser](https://github.com/Vetchems/sd-civitai-browser) - Civit download script
+- [Mega-to-Google-Drive](https://github.com/menukaonline/Mega-to-Google-Drive) - MEGA download script
+- [MEGAcmd](https://github.com/meganz/MEGAcmd)
+- [mediafire-dl](https://github.com/Juvenal-Yescas/mediafire-dl)
+- [Pixeldrain downloader](https://github.com/FayasNoushad/Pixeldrain) - Inspiration for Pixeldrain download method
+- [wget-windows](https://github.com/webfolderio/wget-windows)
+- [aria2-static-builds](https://github.com/q3aql/aria2-static-builds)
+- [7zr, 7z](https://www.7-zip.org)

TestingforfaivsixEx/content/eichellis/extensions/batch-link/howtogetthedirectlinks.md

@@ -0,0 +1,104 @@
+# How to get the direct links
+
+Jump to: [Huggingface](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#huggingface) - [Civit AI](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#civit-ai) - [MEGA](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#mega) - [Discord](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#discord-attachments) - [Github (Extension)](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#webui-extension-github-repo-link) - [Github (Raw File)](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#raw-file-from-github) - [Github (Release File)](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#release-file-from-github) - [Google Drive](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#google-drive) - [Pixeldrain](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#pixeldrain) -  - [Mediafire](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#mediafire) - [Anonfiles](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#anonfiles) - [Dropbox](https://github.com/etherealxx/batchlinks-webui/blob/main/howtogetthedirectlinks.md#dropbox)
+
+## Huggingface
+
+Locate the model page, click on `files` tab.<br/><br/>
+<img src="images/huggingface1.jpg" alt="hg1" height="600" width="966"><br/><br/>
+choose the model you want, **right click** the arrow button beside the LFS logo, then click `copy link address`.<br/><br/>
+<img src="images/huggingface2.jpg" alt="hg2" height="600" width="970"><br/><br/>
+
+## Civit AI
+
+There are two ways to download links from Civit. The model page link method, and the direct link method. The model page link method **will automatically** choose the directory of the saved model without even using hashtag, **except** if you want to put Lora into `#addnetlora` directory, you can use that hashtag. You still need to define the output directory when using the direct link method.
+
+### Model Page Link Method
+
+Locate the desired model on the main page of Civitai, **right click** the model thumbnail, then click `copy link address`.<br/><br/>
+<img src="images/civitnew1.jpg" alt="cv" height="600" width="737"><br/><br/>
+You can also click the thumbnail first then copy the link.<br/><br/>
+<img src="images/civitnew2.jpg" alt="cv" height="600" width="754"><br/><br/>
+The advantage of this method is you will also get the model preview images that can shows up on the latest version of [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
+
+### Direct Link Method
+
+Locate the model page, **right click** the huge blue button, then click `copy link address`.<br/><br/>
+<img src="images/civit1.jpg" alt="cv" height="600" width="967"><br/><br/>
+With this method, you can also choose model variation, by clicking the dropdown icon on the blue button, then **right click** the desired model an.<br/><br/>
+<img src="images/civit2.jpg" alt="cv" height="600" width="842"> <br/><br/>
+
+## MEGA
+
+Locate the model you wanted, **right click** the item, click `copy link address`.<br/><br/>
+<img src="images/mega1.jpg" alt="mg1" height="600" width="987"><br/><br/>
+Click the huge green copy button.<br/><br/>
+<img src="images/mega2.jpg" alt="mg2" height="600" width="963"><br/><br/>
+
+## Discord attachments
+
+Locate the file you wanted, **right click** the download icon, then click `get link`.<br/><br/>
+<img src="images/discord1.jpg" alt="mg1" height="600" width="930"><br/><br/>
+Then click `copy link`.<br/><br/>
+<img src="images/discord2.jpg" alt="mg2" height="600" width="963"><br/><br/>
+
+## webui Extension (Github repo link)
+
+Locate the extension's repository, then copy the repository link.<br/><br/>
+<img src="images/github.jpg" alt="mg1" height="600" width="831"><br/><br/>
+
+Extension **will automatically** cloned/downloaded on `extensions` directory of webui without the need of hashtag.
+
+## Raw file from Github
+
+Locate the file's repository, then click the desired file.<br/><br/>
+<img src="images/githubdirect1.png" alt="mg1" width="831"><br/><br/>
+Then **right click** the text that says `raw` **or** `download`, and click `copy link address`<br/><br/>
+<img src="images/githubdirect2.png" alt="mg1" width="831"><br/><br/>
+<img src="images/githubdirect3.png" alt="mg1" width="831"><br/><br/>
+
+## Release file from Github
+
+Locate the file's repository, then click release section.<br/><br/>
+<img src="images/githubrelease1.png" alt="mg1" width="831"><br/><br/>
+Then **right click** the desired assets (the blue text), and click `copy link address`<br/><br/>
+<img src="images/githubrelease2.png" alt="mg1" width="831"><br/><br/>
+
+## Google Drive
+
+Whether you own the file or not, make sure the file is public. If you owns the file, right click on the file on Google Drive, then click `get link` (or `share`).<br/><br/>
+<img src="images/drive1.png" alt="mg1" width="400"><br/><br/>
+Then change the access to `anyone with thelink`, and copy the link by clicking the button<br/><br/>
+<img src="images/drive2.png" alt="mg1" width="831"><br/><br/>
+
+These are some valid google drive link patterns that works:
+```
+https://drive.google.com/file/d/1-UbwFtSzKTQkvjdPiKLCejH0Ztq9UJFO/view
+https://drive.google.com/file/d/1-UbwFtSzKTQkvjdPiKLCejH0Ztq9UJFO/view?usp=share_link
+https://drive.google.com/u/0/uc?id=1-UbwFtSzKTQkvjdPiKLCejH0Ztq9UJFO&export=download
+https://drive.google.com/u/0/uc?id=1-UbwFtSzKTQkvjdPiKLCejH0Ztq9UJFO&export=download&confirm=t
+```
+## Pixeldrain
+
+Just click the `copy link` button, or just copy the link.<br/><br/>
+<img src="images/pixeldrain.png" alt="mg1" width="800"><br/><br/>
+
+## Anonfiles
+
+**Don't** right click the download button. Instead, copy the link on the url bar<br/><br/>
+<img src="images/anonfiles.png" alt="mg1" width="800"><br/><br/>
+
+## Mediafire
+
+When you arrived on this page, click the chain link icon to copy link, or just copy the link above<br/><br/>
+<img src="images/mediafire.png" alt="mg1" width="800"><br/><br/>
+
+Don't forget that when being pasted, the link must starts with `https://www.mediafire.com/file/` (notice the triple W)
+
+## Dropbox
+
+When the item is yours, go to the homepage, and click copy link, the link will be copied to your clipboard<br/><br/>
+<img src="images/dropbox1.png" alt="mg1" width="600"><br/><br/>
+
+Or, if you got into this screen, just copy the link above, with the link ends with either `?dl=0` or `?dl=1`<br/><br/>
+<img src="images/dropbox2.png" alt="mg1" width="500"><br/><br/>