init

adetailer/CHANGELOG.md

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+# Changelog
+
+## 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 있음
+
+### 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.

adetailer/LICENSE.md

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+
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adetailer/README.md

@@ -0,0 +1,105 @@
+# !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
+
+![image](https://i.imgur.com/wyWlT1n.png)
+
+Each option corresponds to a corresponding option on the inpaint tab.
+
+## 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.
+
+## 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).
+
+### User Model
+
+Put your [ultralytics](https://github.com/ultralytics/ultralytics) 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.
+
+### Dataset
+
+Datasets used for training the yolo models are:
+
+#### Face
+
+- [Anime Face CreateML](https://universe.roboflow.com/my-workspace-mph8o/anime-face-createml)
+- [xml2txt](https://universe.roboflow.com/0oooooo0/xml2txt-njqx1)
+- [AN](https://universe.roboflow.com/sed-b8vkf/an-lfg5i)
+- [wider face](http://shuoyang1213.me/WIDERFACE/index.html)
+
+#### Hand
+
+- [AnHDet](https://universe.roboflow.com/1-yshhi/anhdet)
+- [hand-detection-fuao9](https://universe.roboflow.com/catwithawand/hand-detection-fuao9)
+
+#### Person
+
+- [coco2017](https://cocodataset.org/#home) (only person)
+- [AniSeg](https://github.com/jerryli27/AniSeg)
+- [skytnt/anime-segmentation](https://huggingface.co/datasets/skytnt/anime-segmentation)
+
+## 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)

adetailer/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",
+]

adetailer/adetailer/__version__.py

@@ -0,0 +1 @@
+__version__ = "23.7.5"

adetailer/adetailer/args.py

@@ -0,0 +1,214 @@
+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,
+    constr,
+    root_validator,
+    validator,
+)
+
+cn_model_regex = r".*(inpaint|tile|scribble|lineart|openpose).*|^None$"
+
+
+class Arg(NamedTuple):
+    attr: str
+    name: str
+
+
+class ArgsList(UserList):
+    @cached_property
+    def attrs(self) -> tuple[str]:
+        return tuple(attr for attr, _ in self)
+
+    @cached_property
+    def names(self) -> tuple[str]:
+        return tuple(name for _, name in self)
+
+
+class ADetailerArgs(BaseModel, extra=Extra.forbid):
+    ad_model: str = "None"
+    ad_prompt: str = ""
+    ad_negative_prompt: str = ""
+    ad_confidence: confloat(ge=0.0, le=1.0) = 0.3
+    ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0
+    ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0
+    ad_dilate_erode: int = 4
+    ad_x_offset: int = 0
+    ad_y_offset: int = 0
+    ad_mask_merge_invert: Literal["None", "Merge", "Merge and Invert"] = "None"
+    ad_mask_blur: NonNegativeInt = 4
+    ad_denoising_strength: confloat(ge=0.0, le=1.0) = 0.4
+    ad_inpaint_only_masked: bool = True
+    ad_inpaint_only_masked_padding: NonNegativeInt = 32
+    ad_use_inpaint_width_height: bool = False
+    ad_inpaint_width: PositiveInt = 512
+    ad_inpaint_height: PositiveInt = 512
+    ad_use_steps: bool = False
+    ad_steps: PositiveInt = 28
+    ad_use_cfg_scale: bool = False
+    ad_cfg_scale: NonNegativeFloat = 7.0
+    ad_use_noise_multiplier: bool = False
+    ad_noise_multiplier: confloat(ge=0.5, le=1.5) = 1.0
+    ad_restore_face: bool = False
+    ad_controlnet_model: constr(regex=cn_model_regex) = "None"
+    ad_controlnet_module: Optional[constr(regex=r".*inpaint.*|^None$")] = 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 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 noise multiplier",
+            ["ADetailer use separate noise multiplier", "ADetailer noise multiplier"],
+        )
+
+        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_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_noise_multiplier", "ADetailer use separate noise multiplier"),
+    ("ad_noise_multiplier", "ADetailer noise multiplier"),
+    ("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"]

adetailer/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

adetailer/adetailer/mask.py

@@ -0,0 +1,245 @@
+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
+
+
+# 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

adetailer/adetailer/mediapipe.py

@@ -0,0 +1,184 @@
+from __future__ import annotations
+
+from functools import partial
+
+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:
+    import mediapipe as mp
+
+    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:
+    import mediapipe as mp
+
+    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:
+    import mediapipe as mp
+
+    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

adetailer/adetailer/traceback.py

@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+import io
+import platform
+import sys
+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 sys_info() -> dict[str, Any]:
+    try:
+        import launch
+
+        version = launch.git_tag()
+        commit = launch.commit_hash()
+    except Exception:
+        version = commit = "------"
+
+    return {
+        "Platform": platform.platform(),
+        "Python": sys.version,
+        "Version": version,
+        "Commit": commit,
+        "Commandline": sys.argv,
+    }
+
+
+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 force_terminal_value():
+    try:
+        from modules.shared import cmd_opts
+
+        return True if hasattr(cmd_opts, "skip_torch_cuda_test") else None
+    except Exception:
+        return None
+
+
+def rich_traceback(func: Callable) -> Callable:
+    force_terminal = force_terminal_value()
+
+    def wrapper(*args, **kwargs):
+        string = io.StringIO()
+        width = Console().width
+        width = width - 4 if width > 4 else None
+        console = Console(file=string, force_terminal=force_terminal, 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

adetailer/adetailer/ui.py

@@ -0,0 +1,505 @@
+from __future__ import annotations
+
+from functools import partial
+from types import SimpleNamespace
+from typing import Any
+
+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]
+
+
+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,
+    model_list: list[str],
+    t2i_button: gr.Button,
+    i2i_button: gr.Button,
+):
+    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,
+                        model_list=model_list,
+                        t2i_button=t2i_button,
+                        i2i_button=i2i_button,
+                    )
+
+                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,
+    model_list: list[str],
+    t2i_button: gr.Button,
+    i2i_button: gr.Button,
+):
+    w = Widgets()
+    state = gr.State({})
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        model_choices = [*model_list, "None"] if n == 0 else ["None", *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)
+
+    with gr.Group():
+        controlnet(w, n, is_img2img)
+
+    all_inputs = [state, *w.tolist()]
+    target_button = i2i_button if is_img2img else 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():
+            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"),
+            )
+
+        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):
+    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_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,
+                )
+
+            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"),
+            )

adetailer/adetailer/ultralytics.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import cv2
+from PIL import Image
+
+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:
+    from ultralytics import YOLO
+
+    model_path = str(model_path)
+
+    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
+    """
+    from torchvision.transforms.functional import to_pil_image
+
+    n = masks.shape[0]
+    return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]

adetailer/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",
+]

adetailer/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 = shared.opts.data.get("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 []

adetailer/controlnet_ext/restore.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+
+from modules import img2img, processing, shared
+
+
+def cn_restore_unet_hook(p, cn_latest_network):
+    if cn_latest_network is not None:
+        unet = p.sd_model.model.diffusion_model
+        cn_latest_network.restore(unet)
+
+
+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

adetailer/install.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import importlib.util
+import subprocess
+import sys
+from importlib.metadata import version  # python >= 3.8
+
+from packaging.version import parse
+
+
+def is_installed(
+    package: str, min_version: str | None = None, max_version: str | None = None
+):
+    try:
+        spec = importlib.util.find_spec(package)
+    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"
+
+    if package == "google.protobuf":
+        package = "protobuf"
+
+    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", "8.0.97", None),
+        ("mediapipe", "0.10.0", None),
+        ("huggingface_hub", None, None),
+        ("pydantic", "1.10.8", None),
+        ("rich", "13.4.2", None),
+        # mediapipe
+        ("protobuf", "3.20.0", "3.20.9999"),
+    ]
+
+    for pkg, low, high in deps:
+        # https://github.com/protocolbuffers/protobuf/tree/main/python
+        name = "google.protobuf" if pkg == "protobuf" else pkg
+
+        if not is_installed(name, 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()

adetailer/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",
+    )

adetailer/pyproject.toml

@@ -0,0 +1,26 @@
+[project]
+name = "adetailer"
+description = "An object detection and auto-mask extension for stable diffusion webui."
+authors = [
+    {name = "dowon", email = "[email protected]"},
+]
+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"]
+
+[tool.ruff.per-file-ignores]
+"sd_webui/*.py" = ["B027", "F403"]

adetailer/scripts/!adetailer.py

@@ -0,0 +1,784 @@
+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, mask_preprocess, sort_bboxes
+from adetailer.traceback import rich_traceback
+from adetailer.ui import adui, ordinal, suffix
+from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
+from controlnet_ext.restore import (
+    CNHijackRestore,
+    cn_allow_script_control,
+    cn_restore_unet_hook,
+)
+from sd_webui import images, safe, script_callbacks, scripts, shared
+from sd_webui.devices import NansException
+from sd_webui.paths import data_path, models_path
+from sd_webui.processing import (
+    Processed,
+    StableDiffusionProcessingImg2Img,
+    create_infotext,
+    process_images,
+)
+from sd_webui.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
+        self.cn_script = None
+        self.cn_latest_network = 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)
+        model_list = list(model_mapping.keys())
+
+        components, infotext_fields = adui(
+            num_models,
+            is_img2img,
+            model_list,
+            txt2img_submit_button,
+            img2img_submit_button,
+        )
+
+        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}
+                       """
+            raise ValueError(dedent(message))
+        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].update(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 ""
+
+        if any(getattr(cmd_opts, vram, False) for vram in ["lowvram", "medvram"]):
+            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
+        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:
+        if args.ad_use_steps:
+            return args.ad_steps
+        return p.steps
+
+    def get_cfg_scale(self, p, args: ADetailerArgs) -> float:
+        if args.ad_use_cfg_scale:
+            return args.ad_cfg_scale
+        return p.cfg_scale
+
+    def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None:
+        if args.ad_use_noise_multiplier:
+            return args.ad_noise_multiplier
+        return None
+
+    def infotext(self, 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)
+            if filename == "controlnet":
+                self.cn_script = script_object
+                self.cn_latest_network = script_object.latest_network
+
+        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 = p.sampler_name
+        if sampler_name in ["PLMS", "UniPC"]:
+            sampler_name = "Euler"
+
+        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,
+        )
+
+        i2i.cached_c = [None, None]
+        i2i.cached_uc = [None, None]
+        i2i.scripts, i2i.script_args = self.script_filter(p, args)
+        i2i._disable_adetailer = 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 = 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,
+        )
+
+    def i2i_prompts_replace(
+        self, 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}"
+            )
+
+    def need_call_process(self, p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == bs - 1
+
+    def need_call_postprocess(self, p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == 0
+
+    @rich_traceback
+    def process(self, p, *args_):
+        if getattr(p, "_disable_adetailer", 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(self, p, pp, args: ADetailerArgs, *, n: int = 0) -> bool:
+        """
+        Returns
+        -------
+            bool
+
+            `True` if image was processed, `False` otherwise.
+        """
+        if state.interrupted:
+            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.")
+
+        _user_pt = p.prompt
+        _user_ng = p.negative_prompt
+
+        p2 = copy(i2i)
+        for j in range(steps):
+            p2.image_mask = masks[j]
+            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, "_disable_adetailer", 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(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()]
+
+    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] 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)

adetailer/sd_webui/devices.py

@@ -0,0 +1,11 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+
+    class NansException(Exception):  # noqa: N818
+        pass
+
+else:
+    from modules.devices import NansException

adetailer/sd_webui/images.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from PIL import Image, PngImagePlugin
+
+    from sd_webui.processing import StableDiffusionProcessing
+
+    def save_image(
+        image: Image.Image,
+        path: str,
+        basename: str,
+        seed: int | None = None,
+        prompt: str = "",
+        extension: str = "png",
+        info: str | PngImagePlugin.iTXt = "",
+        short_filename: bool = False,
+        no_prompt: bool = False,
+        grid: bool = False,
+        pnginfo_section_name: str = "parameters",
+        p: StableDiffusionProcessing | None = None,
+        existing_info: dict | None = None,
+        forced_filename: str | None = None,
+        suffix: str = "",
+        save_to_dirs: bool = False,
+    ) -> tuple[str, str | None]:
+        """Save an image.
+
+        Args:
+            image (`PIL.Image`):
+                The image to be saved.
+            path (`str`):
+                The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+            basename (`str`):
+                The base filename which will be applied to `filename pattern`.
+            seed, prompt, short_filename,
+            extension (`str`):
+                Image file extension, default is `png`.
+            pngsectionname (`str`):
+                Specify the name of the section which `info` will be saved in.
+            info (`str` or `PngImagePlugin.iTXt`):
+                PNG info chunks.
+            existing_info (`dict`):
+                Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+            no_prompt:
+                TODO I don't know its meaning.
+            p (`StableDiffusionProcessing`)
+            forced_filename (`str`):
+                If specified, `basename` and filename pattern will be ignored.
+            save_to_dirs (bool):
+                If true, the image will be saved into a subdirectory of `path`.
+
+        Returns: (fullfn, txt_fullfn)
+            fullfn (`str`):
+                The full path of the saved imaged.
+            txt_fullfn (`str` or None):
+                If a text file is saved for this image, this will be its full path. Otherwise None.
+        """
+
+else:
+    from modules.images import save_image

adetailer/sd_webui/paths.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import os
+
+    models_path = os.path.join(os.path.dirname(__file__), "1")
+    script_path = os.path.join(os.path.dirname(__file__), "2")
+    data_path = os.path.join(os.path.dirname(__file__), "3")
+    extensions_dir = os.path.join(os.path.dirname(__file__), "4")
+    extensions_builtin_dir = os.path.join(os.path.dirname(__file__), "5")
+else:
+    from modules.paths import data_path, models_path, script_path

adetailer/sd_webui/processing.py

@@ -0,0 +1,176 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from dataclasses import dataclass, field
+    from typing import Any, Callable
+
+    import numpy as np
+    import torch
+    from PIL import Image
+
+    def _image():
+        return Image.new("L", (512, 512))
+
+    @dataclass
+    class StableDiffusionProcessing:
+        sd_model: torch.nn.Module = field(default_factory=lambda: torch.nn.Linear(1, 1))
+        outpath_samples: str = ""
+        outpath_grids: str = ""
+        prompt: str = ""
+        prompt_for_display: str = ""
+        negative_prompt: str = ""
+        styles: list[str] = field(default_factory=list)
+        seed: int = -1
+        subseed: int = -1
+        subseed_strength: float = 0.0
+        seed_resize_from_h: int = -1
+        seed_resize_from_w: int = -1
+        sampler_name: str | None = None
+        batch_size: int = 1
+        n_iter: int = 1
+        steps: int = 50
+        cfg_scale: float = 7.0
+        width: int = 512
+        height: int = 512
+        restore_faces: bool = False
+        tiling: bool = False
+        do_not_save_samples: bool = False
+        do_not_save_grid: bool = False
+        extra_generation_params: dict[str, Any] = field(default_factory=dict)
+        overlay_images: list[Image.Image] = field(default_factory=list)
+        eta: float = 0.0
+        do_not_reload_embeddings: bool = False
+        paste_to: tuple[int | float, ...] = (0, 0, 0, 0)
+        color_corrections: list[np.ndarray] = field(default_factory=list)
+        denoising_strength: float = 0.0
+        sampler_noise_scheduler_override: Callable | None = None
+        ddim_discretize: str = ""
+        s_min_uncond: float = 0.0
+        s_churn: float = 0.0
+        s_tmin: float = 0.0
+        s_tmax: float = 0.0
+        s_noise: float = 0.0
+        override_settings: dict[str, Any] = field(default_factory=dict)
+        override_settings_restore_afterwards: bool = False
+        is_using_inpainting_conditioning: bool = False
+        disable_extra_networks: bool = False
+        scripts: Any = None
+        script_args: list[Any] = field(default_factory=list)
+        all_prompts: list[str] = field(default_factory=list)
+        all_negative_prompts: list[str] = field(default_factory=list)
+        all_seeds: list[int] = field(default_factory=list)
+        all_subseeds: list[int] = field(default_factory=list)
+        iteration: int = 1
+        is_hr_pass: bool = False
+
+        def close(self) -> None:
+            pass
+
+    @dataclass
+    class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+        sampler: Callable | None = None
+        enable_hr: bool = False
+        denoising_strength: float = 0.75
+        hr_scale: float = 2.0
+        hr_upscaler: str = ""
+        hr_second_pass_steps: int = 0
+        hr_resize_x: int = 0
+        hr_resize_y: int = 0
+        hr_upscale_to_x: int = 0
+        hr_upscale_to_y: int = 0
+        width: int = 512
+        height: int = 512
+        truncate_x: int = 512
+        truncate_y: int = 512
+        applied_old_hires_behavior_to: tuple[int, int] = (512, 512)
+
+    @dataclass
+    class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+        sampler: Callable | None = None
+        init_images: list[Image.Image] = field(default_factory=list)
+        resize_mode: int = 0
+        denoising_strength: float = 0.75
+        image_cfg_scale: float | None = None
+        init_latent: torch.Tensor | None = None
+        image_mask: Image.Image = field(default_factory=_image)
+        latent_mask: Image.Image = field(default_factory=_image)
+        mask_for_overlay: Image.Image = field(default_factory=_image)
+        mask_blur: int = 4
+        inpainting_fill: int = 0
+        inpaint_full_res: bool = True
+        inpaint_full_res_padding: int = 0
+        inpainting_mask_invert: int | bool = 0
+        initial_noise_multiplier: float = 1.0
+        mask: torch.Tensor | None = None
+        nmask: torch.Tensor | None = None
+        image_conditioning: torch.Tensor | None = None
+
+    @dataclass
+    class Processed:
+        images: list[Image.Image] = field(default_factory=list)
+        prompt: list[str] = field(default_factory=list)
+        negative_prompt: list[str] = field(default_factory=list)
+        seed: list[int] = field(default_factory=list)
+        subseed: list[int] = field(default_factory=list)
+        subseed_strength: float = 0.0
+        info: str = ""
+        comments: str = ""
+        width: int = 512
+        height: int = 512
+        sampler_name: str = ""
+        cfg_scale: float = 7.0
+        image_cfg_scale: float | None = None
+        steps: int = 50
+        batch_size: int = 1
+        restore_faces: bool = False
+        face_restoration_model: str | None = None
+        sd_model_hash: str = ""
+        seed_resize_from_w: int = -1
+        seed_resize_from_h: int = -1
+        denoising_strength: float = 0.0
+        extra_generation_params: dict[str, Any] = field(default_factory=dict)
+        index_of_first_image: int = 0
+        styles: list[str] = field(default_factory=list)
+        job_timestamp: str = ""
+        clip_skip: int = 1
+        eta: float = 0.0
+        ddim_discretize: str = ""
+        s_churn: float = 0.0
+        s_tmin: float = 0.0
+        s_tmax: float = 0.0
+        s_noise: float = 0.0
+        sampler_noise_scheduler_override: Callable | None = None
+        is_using_inpainting_conditioning: bool = False
+        all_prompts: list[str] = field(default_factory=list)
+        all_negative_prompts: list[str] = field(default_factory=list)
+        all_seeds: list[int] = field(default_factory=list)
+        all_subseeds: list[int] = field(default_factory=list)
+        infotexts: list[str] = field(default_factory=list)
+
+    def create_infotext(
+        p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
+        all_prompts: list[str],
+        all_seeds: list[int],
+        all_subseeds: list[int],
+        comments: Any,
+        iteration: int = 0,
+        position_in_batch: int = 0,
+    ) -> str:
+        pass
+
+    def process_images(
+        p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
+    ) -> Processed:
+        pass
+
+else:
+    from modules.processing import (
+        Processed,
+        StableDiffusionProcessing,
+        StableDiffusionProcessingImg2Img,
+        StableDiffusionProcessingTxt2Img,
+        create_infotext,
+        process_images,
+    )

adetailer/sd_webui/safe.py

@@ -0,0 +1,10 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import torch
+
+    unsafe_torch_load = torch.load
+else:
+    from modules.safe import unsafe_torch_load

adetailer/sd_webui/script_callbacks.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Callable
+
+    def on_app_started(callback: Callable):
+        pass
+
+    def on_ui_settings(callback: Callable):
+        pass
+
+    def on_after_component(callback: Callable):
+        pass
+
+    def on_before_ui(callback: Callable):
+        pass
+
+else:
+    from modules.script_callbacks import (
+        on_after_component,
+        on_app_started,
+        on_before_ui,
+        on_ui_settings,
+    )

adetailer/sd_webui/scripts.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from abc import ABC, abstractmethod
+    from collections import namedtuple
+    from dataclasses import dataclass
+    from typing import Any
+
+    import gradio as gr
+    from PIL import Image
+
+    from sd_webui.processing import (
+        Processed,
+        StableDiffusionProcessingImg2Img,
+        StableDiffusionProcessingTxt2Img,
+    )
+
+    SDPType = StableDiffusionProcessingImg2Img | StableDiffusionProcessingTxt2Img
+    AlwaysVisible = object()
+
+    @dataclass
+    class PostprocessImageArgs:
+        image: Image.Image
+
+    class Script(ABC):
+        filename: str
+        args_from: int
+        args_to: int
+        alwayson: bool
+
+        is_txt2img: bool
+        is_img2img: bool
+
+        group: gr.Group
+        infotext_fields: list[tuple[str, str]]
+        paste_field_names: list[str]
+
+        @abstractmethod
+        def title(self):
+            raise NotImplementedError
+
+        def ui(self, is_img2img: bool):
+            pass
+
+        def show(self, is_img2img: bool):
+            return True
+
+        def run(self, p: SDPType, *args):
+            pass
+
+        def process(self, p: SDPType, *args):
+            pass
+
+        def before_process_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def process_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def postprocess_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def postprocess_image(self, p: SDPType, pp: PostprocessImageArgs, *args):
+            pass
+
+        def postprocess(self, p: SDPType, processed: Processed, *args):
+            pass
+
+        def before_component(self, component, **kwargs):
+            pass
+
+        def after_component(self, component, **kwargs):
+            pass
+
+        def describe(self):
+            return ""
+
+        def elem_id(self, item_id: Any) -> str:
+            pass
+
+    ScriptClassData = namedtuple(
+        "ScriptClassData", ["script_class", "path", "basedir", "module"]
+    )
+    scripts_data: list[ScriptClassData] = []
+
+else:
+    from modules.scripts import (
+        AlwaysVisible,
+        PostprocessImageArgs,
+        Script,
+        scripts_data,
+    )

adetailer/sd_webui/shared.py

@@ -0,0 +1,66 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import argparse
+    from dataclasses import dataclass
+    from typing import Any, Callable
+
+    import torch
+    from PIL import Image
+
+    @dataclass
+    class State:
+        skipped: bool = False
+        interrupted: bool = False
+        job: str = ""
+        job_no: int = 0
+        job_count: int = 0
+        processing_has_refined_job_count: bool = False
+        job_timestamp: str = "0"
+        sampling_step: int = 0
+        sampling_steps: int = 0
+        current_latent: torch.Tensor | None = None
+        current_image: Image.Image | None = None
+        current_image_sampling_step: int = 0
+        id_live_preview: int = 0
+        textinfo: str | None = None
+        time_start: float | None = None
+        need_restart: bool = False
+        server_start: float | None = None
+
+    @dataclass
+    class OptionInfo:
+        default: Any = None
+        label: str = ""
+        component: Any = None
+        component_args: Callable[[], dict] | dict[str, Any] | None = None
+        onchange: Callable[[], None] | None = None
+        section: tuple[str, str] | None = None
+        refresh: Callable[[], None] | None = None
+
+    class Option:
+        data_labels: dict[str, OptionInfo]
+
+        def __init__(self):
+            self.data: dict[str, Any] = {}
+
+        def add_option(self, key: str, info: OptionInfo):
+            pass
+
+        def __getattr__(self, item: str):
+            if self.data is not None and item in self.data:
+                return self.data[item]
+
+            if item in self.data_labels:
+                return self.data_labels[item].default
+
+            return super().__getattribute__(item)
+
+    opts = Option()
+    cmd_opts = argparse.Namespace()
+    state = State()
+
+else:
+    from modules.shared import OptionInfo, cmd_opts, opts, state

openOutpaint-webUI-extension/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 tim h
+
+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.

openOutpaint-webUI-extension/README.MD

@@ -0,0 +1,22 @@
+In this repo lives a mighty handy little wrapper for adding [openOutpaint](https://github.com/zero01101/openOutpaint) to [AUTOMATIC1111 webUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) directly as a native extension.
+
+Please see the respective READMEs and wikis for each of the above projects for a more comprehensive understanding of their feature sets.
+
+This extension also adds buttons to send output from webUI txt2img and img2img tools directly to openOutpaint which will also include the prompts used for convenience.
+
+**_2023-01-23: new `--lock-oo-submodule` commandline argument if you want to roll back to a previous version of openOutpaint and keep it there - be sure to install/run openOutpaint extension at least once before enabling this flag_**
+
+**Note: Requires `--api` flag enabled in your webui-user launch script!**
+
+**_FURTHER NOTE: the commandline flag `--gradio-debug` disables custom API routes and completely breaks openOutpaint. please remove it from your COMMANDLINE_ARGS before running openOutpaint._**
+
+**_EVEN FURTHER NOTE: [PLEASE SEE DOCUMENTATION REGARDING NEW HRfix FEATURES](https://github.com/zero01101/openOutpaint/wiki/Manual#hrfix) IMPLEMENTED AS OF webUI COMMIT [ef27a18](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/ef27a18b6b7cb1a8eebdc9b2e88d25baf2c2414d)_**
+
+![image](https://user-images.githubusercontent.com/1649724/209033089-fb908d92-0c52-4165-a6a3-e6e9f28b032d.png)
+
+### surprising incompatibilities
+
+**_COLAB USERS: you may experience issues installing openOutpaint (and other webUI extensions) - there is a workaround that has been discovered and tested against [TheLastBen's fast-stable-diffusion](https://github.com/TheLastBen/fast-stable-diffusion). Please see [this discussion](https://github.com/TheLastBen/fast-stable-diffusion/discussions/1161) containing the workaround, which requires adding a command into the final cell of the colab, as well as setting `Enable_API` to `True`._**
+
+- [microsoft editor extension for chrome/edge seems to disable the overmask slider](https://github.com/zero01101/openOutpaint/discussions/88#discussioncomment-4498341)
+- ~~[duckduckgo privacy extension for firefox breaks outpainting, resulting in pure black output](https://github.com/zero01101/openOutpaint-webUI-extension/issues/3#issuecomment-1367694000) - add an exception for your openOutpaint host (likely localhost or 127.0.0.1)~~ should be fixed as of [b128943](https://github.com/zero01101/openOutpaint/commit/b128943f0c94970600fdc1c98bfec22de619866f)

openOutpaint-webUI-extension/app/.devtools/README.md

@@ -0,0 +1,12 @@
+# openOutpaint DevTools
+
+This is a folder containing some handy scripts to help developers to automate workflows and write code in openOutpaint's standards.
+All scripts must be run from the root of the project.
+
+## `sethooks.sh` and `sethooks.ps1` scripts
+
+These scripts will setup git hooks for this project. Hooks are mainly for cache busting purposes for now. It is recommended to run this script and setup hooks before any commits are made.
+
+## `lint.sh` script
+
+Uses `npm` to install prettier and lint javascript, html and css files according to `.prettierrc.json`. This script will install node modules locally, so editors with prettier support are recommended over this script.

openOutpaint-webUI-extension/app/.devtools/lint.sh

@@ -0,0 +1,21 @@
+#!/usr/bin/bash
+
+if ! which npx 2>&1 > /dev/null; then
+    echo "[lint] npm/npx is not installed"
+    exit 1
+fi
+
+npx prettier > /dev/null || npm install prettier && echo "[lint] We have 'prettier'"
+npx eslint > /dev/null || npm install eslint && echo "[lint] We have 'eslint'"
+npx prettier-eslint > /dev/null || npm install prettier-eslint-cli && echo "[lint] We have 'prettier-eslint'"
+
+echo "[lint] Linting JavaScript files..."
+npx prettier-eslint --write "**/*.js"
+echo "[lint] Linting HTML files..."
+npx prettier-eslint --write "**/*.html"
+echo "[lint] Linting CSS files..."
+npx prettier-eslint --write "**/*.css"
+echo "[lint] Linting MarkDown files"
+npx prettier-eslint --write "**/*.md"
+
+echo "[lint] Finished Linting."

openOutpaint-webUI-extension/app/.devtools/sethooks.ps1

@@ -0,0 +1,4 @@
+git config core.eol lf
+git config core.autocrlf input
+
+git config core.hooksPath .githooks/windows

openOutpaint-webUI-extension/app/.devtools/sethooks.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/sh
+
+git config core.hooksPath .githooks/linux

openOutpaint-webUI-extension/app/.devtools/updatehashes.ps1

@@ -0,0 +1,27 @@
+# Updates html files with cache busting urls including file hashes.
+
+# Actual file processing
+$htmlfiles = Get-ChildItem -Path . -Recurse -Filter "*.html" | Where {$_.FullName -notlike "*\node_modules\*"} | Resolve-path -relative
+foreach ($htmlfile in $htmlfiles) {
+    Write-Host "[info] Processing '${htmlfile}' for cache busting..." -ForegroundColor Blue
+
+    $resfiles = (@(Get-ChildItem -Path . -Recurse -Filter "*.css") + (Get-ChildItem -Path . -Recurse -Filter "*.js")) | Resolve-Path -relative
+
+    if ($args[0] -eq "gitadd") {
+        $resfiles = (git status -s | Select-String -Pattern "[A-Z]  .+") | ForEach-Object { -split $_.Line | Select-Object -Last 1 }
+    }
+
+    foreach ($resfile in $resfiles) {
+        $resfile = $resfile -replace '\\', '/' -replace '\./', ''
+        # Check if resource is used in html file
+        if ($null -ne (Select-String -Path $htmlfile -Pattern $resfile)) {
+            $hash = (Get-FileHash $resfile -Algorithm SHA1).Hash
+            
+            # This is just for cache busting...
+            # If 7 first characters of SHA1 is okay for git, it should be more than enough for us
+            $hash = $hash.Substring(0, 7).ToLower()
+
+            (Get-Content -Raw -Path $htmlfile).replace('\r\n', "\n") -replace "$resfile(\?v=[a-z0-9]+)?", "${resfile}?v=$hash" | Set-Content $htmlfile
+        }
+    }
+}

openOutpaint-webUI-extension/app/.devtools/updatehashes.sh

@@ -0,0 +1,128 @@
+#!/usr/bin/bash
+#
+# Updates html files with cache busting urls including file hashes.
+
+# Setup colors
+# Reset
+Color_Off='\033[0m'       # Text Reset
+
+# Regular Colors
+Black='\033[0;30m'        # Black
+Red='\033[0;31m'          # Red
+Green='\033[0;32m'        # Green
+Yellow='\033[0;33m'       # Yellow
+Blue='\033[0;34m'         # Blue
+Purple='\033[0;35m'       # Purple
+Cyan='\033[0;36m'         # Cyan
+White='\033[0;37m'        # White
+
+# Bold
+BBlack='\033[1;30m'       # Black
+BRed='\033[1;31m'         # Red
+BGreen='\033[1;32m'       # Green
+BYellow='\033[1;33m'      # Yellow
+BBlue='\033[1;34m'        # Blue
+BPurple='\033[1;35m'      # Purple
+BCyan='\033[1;36m'        # Cyan
+BWhite='\033[1;37m'       # White
+
+# Underline
+UBlack='\033[4;30m'       # Black
+URed='\033[4;31m'         # Red
+UGreen='\033[4;32m'       # Green
+UYellow='\033[4;33m'      # Yellow
+UBlue='\033[4;34m'        # Blue
+UPurple='\033[4;35m'      # Purple
+UCyan='\033[4;36m'        # Cyan
+UWhite='\033[4;37m'       # White
+
+# Background
+On_Black='\033[40m'       # Black
+On_Red='\033[41m'         # Red
+On_Green='\033[42m'       # Green
+On_Yellow='\033[43m'      # Yellow
+On_Blue='\033[44m'        # Blue
+On_Purple='\033[45m'      # Purple
+On_Cyan='\033[46m'        # Cyan
+On_White='\033[47m'       # White
+
+# High Intensity
+IBlack='\033[0;90m'       # Black
+IRed='\033[0;91m'         # Red
+IGreen='\033[0;92m'       # Green
+IYellow='\033[0;93m'      # Yellow
+IBlue='\033[0;94m'        # Blue
+IPurple='\033[0;95m'      # Purple
+ICyan='\033[0;96m'        # Cyan
+IWhite='\033[0;97m'       # White
+
+# Bold High Intensity
+BIBlack='\033[1;90m'      # Black
+BIRed='\033[1;91m'        # Red
+BIGreen='\033[1;92m'      # Green
+BIYellow='\033[1;93m'     # Yellow
+BIBlue='\033[1;94m'       # Blue
+BIPurple='\033[1;95m'     # Purple
+BICyan='\033[1;96m'       # Cyan
+BIWhite='\033[1;97m'      # White
+
+# High Intensity backgrounds
+On_IBlack='\033[0;100m'   # Black
+On_IRed='\033[0;101m'     # Red
+On_IGreen='\033[0;102m'   # Green
+On_IYellow='\033[0;103m'  # Yellow
+On_IBlue='\033[0;104m'    # Blue
+On_IPurple='\033[0;105m'  # Purple
+On_ICyan='\033[0;106m'    # Cyan
+On_IWhite='\033[0;107m'   # White
+
+# Check requirements
+if ! which echo > /dev/null
+then
+    exit -1
+fi
+
+required_programs=(find grep cut sed sha1sum)
+
+for program in $required_programs
+do
+    if ! which $program > /dev/null
+    then
+        echo -e "${Red}[error] Requires '$program' command to be installed${Color_Off}"
+        exit -1
+    fi
+done
+
+# Actual file processing
+for htmlfile in $(find -type f -name \*.html -not -path "./node_modules/*")
+do
+    echo -e "${BIBlue}[info] Processing '${htmlfile}' for cache busting...${Color_Off}"
+    
+    LIST=$(find -type f -regex '.*\.css\|.*\.js' -not -path "./node_modules/*" | sed 's/\.\///g')
+
+    if [ "$1" = "gitadd" ]
+    then
+        LIST=$(git status -s | grep -oE "[A-Z]  .+" | cut -d" " -f3)
+    fi
+
+    for resourcefile in $LIST
+    do
+        # Check if resource is used in html file
+        resourceusage=$(grep -i "$resourcefile" "$htmlfile")
+        if [ $? -eq 0 ]
+        then
+            # This is just for cache busting...
+            # If 7 first characters of SHA1 is okay for git, it should be more than enough for us
+            hash="$(sha1sum $resourcefile | cut -d' ' -f1 | head -c 7)"
+            
+            # Check if resource hash is already correct
+            if ! echo "$resourceusage" | grep -i "=$hash\"" > /dev/null
+            then
+                escaped=$(echo $resourcefile | sed 's/\//\\\//g' | sed 's/\./\\./g')
+                sed -Ei "s/${escaped}(\?v=[a-z0-9]+)?/${escaped}?v=${hash}/g" "$htmlfile"
+                
+                echo -e "${BIBlue}[info]${Color_Off} Updated resource ${resourcefile} to hash ${hash}"
+            fi
+        fi
+    done
+done

openOutpaint-webUI-extension/app/.gitattributes

@@ -0,0 +1 @@
+* text eol=lf

openOutpaint-webUI-extension/app/.githooks/linux/pre-commit

@@ -0,0 +1,9 @@
+#!/bin/sh
+#
+# Script to perform some basic operations to the code before committing.
+
+# Adds file hashes to html script imports for cache busting purposes
+sh .devtools/updatehashes.sh gitadd
+
+# Adds file to current commit
+git add "**.html"

openOutpaint-webUI-extension/app/.githooks/windows/pre-commit

@@ -0,0 +1,9 @@
+#!/bin/sh
+#
+# Script to perform some basic operations to the code before committing.
+
+# Adds file hashes to html script imports for cache busting purposes
+powershell .devtools/updatehashes.ps1 gitadd
+
+# Adds file to current commit
+git add "**.html"

openOutpaint-webUI-extension/app/.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,91 @@
+name: Bug Report
+description: You think somethings is broken in the UI
+title: "[Bug]: "
+labels: ["bug"]
+assignees: zero01101, seijihariki
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        *Please complete this form with as much detailed information as possible.*
+  - type: textarea
+    id: what-did
+    attributes:
+      label: What happened?
+      description: What happened that you weren't expecting, or what happened incorrectly?
+    validations:
+      required: true
+  - type: textarea
+    id: steps
+    attributes:
+      label: Steps to reproduce the problem
+      description: Please provide us with precise step-by-step information on how to reproduce the issue
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ... [etc]
+    validations:
+      required: true
+  - type: textarea
+    id: what-should
+    attributes:
+      label: What should have happened?
+      description: Describe what you believe should have ocurred instead of what actually happened.
+    validations:
+      required: true
+  - type: input
+    id: commit
+    attributes:
+      label: Commit where the problem happens
+      description: Which commit are you running? (i.e. https://github.com/zero01101/openOutpaint/commit/bf21c19ae352800d9e1b37bb490e817b6848e533, bf21c19)
+    validations:
+      required: true
+  - type: dropdown
+    id: platforms
+    attributes:
+      label: What platforms do you use to access openOutpaint?
+      multiple: true
+      options:
+        - Windows
+        - Linux
+        - MacOS
+        - iOS
+        - Android
+        - Other/Cloud
+    validations:
+      required: true
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers do you use to access the UI ?
+      multiple: true
+      options:
+        - Mozilla Firefox
+        - Google Chrome
+        - Brave
+        - Apple Safari
+        - Microsoft Edge
+        - Opera
+        - Other (please list in additional information)
+    validations:
+      required: true
+  - type: textarea
+    id: browser-extensions
+    attributes:
+      label: Browser Extensions/Addons
+      description: Please list all browser extensions/addons you have running.  Some have been known to cause issues with openOutpaint.
+    validations:
+      required: true
+  - type: textarea
+    id: webui-commandline
+    attributes:
+      label: AUTOMATIC1111 webUI Commandline Arguments
+      description: Please list all used commandline arguments passed to A1111 webUI (i.e. `--api`).
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additional information
+      description: Please provide us with any relevant additional information, context, screenshots, etc.

openOutpaint-webUI-extension/app/.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,48 @@
+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+labels: ["enhancement"]
+assignees: zero01101, seijihariki
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        *Please complete this form with as much detailed information as possible.*
+  - type: textarea
+    id: related
+    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 [...]
+    validations:
+      required: true
+  - type: textarea
+    id: feature
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen, preferably with example use-case scenario.
+    validations:
+      required: true
+  - type: textarea
+    id: workflow
+    attributes:
+      label: Proposed workflow
+      description: Please provide us with step by step information on how you'd like the feature to be accessed and used
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+    validations:
+      required: true
+  - type: textarea
+    id: misc
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.

openOutpaint-webUI-extension/app/.github/workflows/autoformat.yml

@@ -0,0 +1,22 @@
+name: Prettier Autoformatting
+on:
+  push:
+    branches:
+      - "main"
+      - "testing"
+  pull_request:
+    branches: [main, testing]
+    types: [opened, synchronize, closed]
+
+jobs:
+  prettier:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.event.pull_request.head.sha }}
+      - name: Prettify
+        uses: creyD/[email protected]
+        with:
+          prettier_options: --write **/*.{js,html,css,md}

openOutpaint-webUI-extension/app/.github/workflows/cachebusting.yml

@@ -0,0 +1,26 @@
+name: Cache Busting
+on:
+  push:
+    branches:
+      - "main"
+      - "testing"
+  pull_request:
+    branches: [main, testing]
+    types: [opened, synchronize, closed]
+
+jobs:
+  update_hashes:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          ref: ${{ github.event.pull_request.head.ref }}
+      - name: Update hashes in html files
+        run: bash .devtools/updatehashes.sh
+      - name: Commit
+        uses: EndBug/[email protected]
+        with:
+          committer_name: Github Actions
+          committer_email: [email protected]
+          message: "Fixed resource hashes"

openOutpaint-webUI-extension/app/.gitignore

@@ -0,0 +1,12 @@
+.vscode/*
+
+# Key for embedding
+key.json
+
+# NPM things
+package.json
+package-lock.json
+node_modules/
+
+# Yarn things
+yarn.lock

openOutpaint-webUI-extension/app/.prettierrc.json

@@ -0,0 +1,19 @@
+{
+	"arrowParens": "always",
+	"bracketSameLine": true,
+	"bracketSpacing": false,
+	"embeddedLanguageFormatting": "auto",
+	"htmlWhitespaceSensitivity": "ignore",
+	"insertPragma": false,
+	"jsxSingleQuote": false,
+	"printWidth": 80,
+	"proseWrap": "preserve",
+	"quoteProps": "as-needed",
+	"requirePragma": false,
+	"semi": true,
+	"singleQuote": false,
+	"tabWidth": 2,
+	"trailingComma": "es5",
+	"useTabs": true,
+	"vueIndentScriptAndStyle": false
+}

openOutpaint-webUI-extension/app/CONTRIBUTING.md

@@ -0,0 +1,63 @@
+# Contributing to openOutpaint
+
+We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing new features
+- Becoming a maintainer
+
+## We Develop with Github
+
+We use github to host code, to track issues and feature requests, as well as accept pull requests.
+
+## We Use [Github Flow](https://guides.github.com/introduction/flow/index.html), So All Code Changes Happen Through Pull Requests
+
+Pull requests are the best way to propose changes to the codebase (we use [Github Flow](https://guides.github.com/introduction/flow/index.html)). We actively welcome your pull requests:
+
+1. Fork the repo and create your branch from `main` or `testing`.
+2. Setup [commit hooks](https://github.com/zero01101/openOutpaint/tree/main/.devtools) for automatic calculation of cache busting hashes for resources.
+3. Please add comments when reasonable, and when possible, use [JSDoc](https://jsdoc.app/) for documenting types. Lack of this will not prevent your pull being merged, but it would be nice to have.
+4. If you've added code that should be tested please pull into `testing`. For documentation and smaller fixes, a pull request directly to `main` should be okay, unless it pertains to changes only present in `testing`.
+5. Create a pull request with a short description of what you did. Thanks for your contribution!
+
+## Any contributions you make will be under the MIT Software License
+
+In short, when you submit code changes, your submissions are understood to be under the same [MIT License](http://choosealicense.com/licenses/mit/) that covers the project. Feel free to contact the maintainers if that's a concern.
+
+## Report bugs using Github's [issues](https://github.com/zero01101/openOutpaint/issues)
+
+We use GitHub issues to track public bugs. Report a bug by [opening a new issue](https://github.com/zero01101/openOutpaint/issues); it's that easy!
+
+## Write bug reports with detail, background, and sample code
+
+If possible, bug reports should have the most detail it is reasonable to have for the bug in question. If you are more versed in javascript, pointing out the issue in code, or even creating a pull request is also appreciated!
+
+**Great Bug Reports** tend to have:
+
+- A quick summary and/or background
+- Steps to reproduce
+  - Be specific!
+  - Give sample code or screenshots if you can!
+- What you expected would happen
+- What actually happens
+- Notes (possibly including why you think this might be happening, or stuff you tried that didn't work)
+
+We have some issue templates that are, admittedly, basically github's default templates. You can and should use that as a guide. Sometimes some fields may not be applicable to your particular report. In this case, things such as _alternative solutions_ don't need to be included.
+
+People _love_ thorough bug reports. I'm not even kidding.
+
+## Use a Consistent Coding Style
+
+For styling, we are currently using prettier for linting. And that's basically it. We are currently using tabs and some other defaults defined in the [.prettierrc.json](https://github.com/zero01101/openOutpaint/blob/main/.prettierrc.json) file. We don't use npm on our project, so you would have to install prettier and prettier-eslint locally. We have a handy [lint.sh](https://github.com/zero01101/openOutpaint/blob/main/lint.sh) script you can run, but it is recommended to use an IDE with prettier support for more practical use.
+
+Any suggestions regarding change of styles or style guides (Airbnb, Google, or whatnot) are welcome, as the current file is quite simplistic.
+
+## License
+
+By contributing, you agree that your contributions will be licensed under its MIT License.
+
+## References
+
+This document was adapted from the open-source contribution guidelines for [Facebook's Draft](https://github.com/facebook/draft-js/blob/a9316a723f9e918afde44dea68b5f9f39b7d9b00/CONTRIBUTING.md) and based on [this template from briandk](https://gist.github.com/briandk/3d2e8b3ec8daf5a27a62).

openOutpaint-webUI-extension/app/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 tim h
+
+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.

openOutpaint-webUI-extension/app/README.md

@@ -0,0 +1,92 @@
+# hello there 🐠
+
+[openOutpaint creating some undersea... well, stuff](https://user-images.githubusercontent.com/1649724/205455599-7817812e-5b50-4c96-807e-268b40fa2fd7.mp4)
+
+_silly demo example current as of [9b174d6](https://github.com/zero01101/openOutpaint/commit/9b174d66c9b9d83ce8657128c97f917b473b13a9) / v0.0.8 / 2022-12-03_ //TODO UPDATE SRSLY
+
+this is a completely vanilla javascript and html canvas outpainting convenience doodad built for the API optionally exposed by [AUTOMATIC1111's stable diffusion webUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), operating similarly to a few others that are probably more well-known. this simply offers an alternative for my following vain desires:
+
+- avoiding the overhead of an additional virtual python environment or impacting a pre-existing one
+- operates against the API exposed by A1111's webUI
+- no external dependencies, extremely boring vanilla
+- no external connectivity, self-hosted and offline
+- unobfuscated (cough cough)
+- <a name="terrible"></a>i am terrible at javascript and should probably correct that
+- i have never used html canvas for anything before and should try it out
+
+## features
+
+- SDXL "support"! (please check outpaint/inpaint fill types in the context menus and fiddle with denoising a LOT for img2img, it's touchy)
+- [now available as an extension for webUI!](https://github.com/zero01101/openOutpaint-webUI-extension) you can find it under the default "available" section in the webUI _extensions_ tab
+  - **_NOTE: extension still requires `--api` flag in webui-user launch script_**
+- intuitive, convenient outpainting - that's like the whole point right
+- queueable, cancelable dreams - just start a'clickin' all over the place
+- arbitrary dream reticle size - draw the rectangle of your dreams
+- an [effectively infinite](https://github.com/zero01101/openOutpaint/pull/108), resizable, scalable canvas for you to paint all over
+  - **_NOTE: v0.0.10 introduces a new "camera control" modifier key - hold [`CTRL`] and use the scrollwheel to zoom (scroll the wheel or use the two-finger vertical gesture on, uh, modern touchpads) and pan (hold the scrollwheel button, or if you don't have one, left-click button) around the canvas_**
+- extremely limited, janky support for a shockingly restrictive list of A1111 extensions including controlnet inpainting for legitimately magic promptless inpainting and outpainting ([a](https://github.com/Mikubill/sd-webui-controlnet/discussions/1464), [b](https://github.com/Mikubill/sd-webui-controlnet/discussions/1143), [c](https://github.com/Mikubill/sd-webui-controlnet/discussions/1597)) and in-line reference preprocessors for keeping existing style while replacing things (reference requires at least 2 controlnet units enabled in A1111 settings), as well as a very very basic dynamic-prompts-on-or-off toggle
+  - **_NOTE: this is_ JANKY, _pull requests greatly welcomed lol_**
+- a very nicely functional and familiar layer system
+- save, load, import, and export workspaces - includes all your layers, history, canvas size, you name it!
+- inpainting/touchup mask brush
+- webUI script support (but you gotta [_find it_](https://github.com/zero01101/openOutpaint/wiki/Manual))
+- prompt history panel
+- optional (visibly) inverted mask mode - red masks get mutated, blue masks stay the same, but you can't take both pills at once
+- inpainting color brush to bring out your inner vincent van bob ross
+- dedicated img2img tool with optional border masking for enhanced output coherence with existing subject matter
+- marquee select tool to select regions and arbitrarily scale, rotate, create stamps, move chunks, peek at lower layers, do all sorts of damage
+- optionally decoupled cursor size and output resolution
+- interrogate tool
+- floating control panel to easily change models/samplers/steps/prompts/CFG/etc options for each dream summoned from the latent void _(NOTE: model switching requires A1111 webUI to be on commit [5a6387e](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/5a6387e189dc365c47a7979b9040d5b6fdd7ba43) or more recent)_
+- floating toolbox with handy keyboard shortcuts
+- optional grid snapping for precision
+- optional hi-res fix for blank/txt2img dreams
+  - **_NOTE: as of v0.0.12.5/webUI commit [ef27a18](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/ef27a18b6b7cb1a8eebdc9b2e88d25baf2c2414d), HRfix has been COMPLETELY reworked and no longer works remotely the same, thus openOutpaint's implementation is no longer compatible with versions of A1111 predating that. You will be alerted to the outdated webUI and the HRfix option will become limited to simply using [reticle dimensions / 2] in this event. Please see the [manual entry](https://github.com/zero01101/openOutpaint/wiki/Manual#hrfix) regarding HRfix and its available options._**
+- optional overmasking for potentially better seams between outpaints - set overmask px value to 0 to disable the feature
+- import arbitrary images and rotate/scale/stamp on the canvas whenever, wherever you'd like
+- upscaler support for final output images
+- saves your preferences/imported images to browser localstorage for maximum convenience
+- reset to defaults button to unsave your preferences if things go squirrely
+- floating navigable undo/redo palette with ctrl+z/y keyboard shortcuts for additional maximum convenience and desquirreliness
+- optional generate-ahead function to keep crankin' out the dreams while you look through the ones that already exist
+- _all this and much more for the low, low price of simply already owning an expensive GPU!_
+
+## operation
+
+**_NOTE: [PLEASE SEE DOCUMENTATION REGARDING NEW HRfix FEATURES](https://github.com/zero01101/openOutpaint/wiki/Manual#hrfix) IMPLEMENTED AS OF webUI COMMIT [ef27a18](https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/ef27a18b6b7cb1a8eebdc9b2e88d25baf2c2414d)_**
+
+### prerequisities
+
+you'll obviously need A1111's webUI installed before you can use this, thus you're presumed to have an operational python install up and running to boot.
+
+A1111 webUI must be launched with the `--api` flag enabled, and the `--cors-allow-origins=` flag set with the host where openOutpaint will be running.
+
+**_NOTE: the commandline flag `--gradio-debug` disables custom API routes and completely breaks openOutpaint. please remove it from your COMMANDLINE_ARGS before running openOutpaint._**
+
+### surprising incompatibilities
+
+**_COLAB USERS: you may experience issues installing openOutpaint (and other webUI extensions) - there is a workaround that has been discovered and tested against [TheLastBen's fast-stable-diffusion](https://github.com/TheLastBen/fast-stable-diffusion). Please see [this discussion](https://github.com/TheLastBen/fast-stable-diffusion/discussions/1161) containing the workaround, which requires adding a command into the final cell of the colab, as well as setting `Enable_API` to `True`._**
+
+If anything goes wrong with openOutpaint, try running it on another browser and disable all extensions and try again. If a new incompatible extension is found, please open an issue so we can notify other users of extension incompatibilities.
+
+- [microsoft editor extension for chrome/edge seems to disable the overmask slider](https://github.com/zero01101/openOutpaint/discussions/88#discussioncomment-4498341)
+- ~~[duckduckgo privacy extension for firefox breaks outpainting, resulting in pure black output](https://github.com/zero01101/openOutpaint-webUI-extension/issues/3#issuecomment-1367694000) - add an exception for your openOutpaint host (likely localhost or 127.0.0.1)~~ should be fixed as of [b128943](https://github.com/zero01101/openOutpaint/commit/b128943f0c94970600fdc1c98bfec22de619866f)
+- ~~[same for dark reader](https://github.com/zero01101/openOutpaint-webUI-extension/issues/3#issuecomment-1367838766)~~ same for dark reader
+
+### quickstart speedrun
+
+1. edit your `cors-allow-origins` to include https://zero01101.github.io and run webUI
+2. go to https://zero01101.github.io/openOutpaint/ and fill in the host value with your webUI API address
+3. click things and do stuff
+
+### step-by-step actually useful instructions
+
+please see the [quickstart wiki article](https://github.com/zero01101/openOutpaint/wiki/SBS-Guided-Example) and comprehensive [manual](https://github.com/zero01101/openOutpaint/wiki/Manual).
+
+## pull requests/bug reports
+
+please do! see [contributing](https://github.com/zero01101/openOutpaint/blob/main/CONTRIBUTING.md) for details!
+
+## warranty
+
+[lmao](https://github.com/moyix/fauxpilot#support-and-warranty)