Merge branch 'dev' into main

.github/ISSUE_TEMPLATE/bug_report.yaml

@@ -0,0 +1,23 @@
+name: Bug report
+description: Create a report
+title: "[Bug]: "
+
+body:
+  - type: textarea
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+
+  - type: textarea
+    attributes:
+      label: Full console logs
+      description: |
+        The full console log of your terminal.
+        From `Python 3.10.*, Version: v1.*, Commit hash: *` to the end.
+      render: Shell
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: List of installed extensions

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/stale.yml

@@ -0,0 +1,13 @@
+name: 'Close stale issues and PRs'
+on:
+  schedule:
+    - cron: '30 1 * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v8
+        with:
+          days-before-stale: 30
+          days-before-close: 5

.pre-commit-config.yaml

@@ -5,6 +5,7 @@ repos:
       - id: trailing-whitespace
         args: [--markdown-linebreak-ext=md]
       - id: end-of-file-fixer
+      - id: mixed-line-ending
 
   - repo: https://github.com/pycqa/isort
     rev: 5.12.0
@@ -12,7 +13,7 @@ repos:
       - id: isort
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.0.267"
+    rev: "v0.0.270"
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

CHANGELOG.md

@@ -1,5 +1,20 @@
 # Changelog
 
+## 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

README.md

@@ -40,9 +40,11 @@ If you want to exclude objects in the background, try setting the min ratio to a
 | 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/KbAeWar.png)
+![image](https://i.imgur.com/wyWlT1n.png)
 
 Each option corresponds to a corresponding option on the inpaint tab.
 
@@ -58,11 +60,12 @@ On the ControlNet tab, select a ControlNet inpaint model and set the model weigh
 | --------------------- | --------------------- | ----------------------------- | ----------------------------- |
 | face_yolov8n.pt       | 2D / realistic face   | 0.660                         | 0.366                         |
 | face_yolov8s.pt       | 2D / realistic face   | 0.713                         | 0.404                         |
-| mediapipe_face_full   | realistic face        | -                             | -                             |
-| mediapipe_face_short  | realistic face        | -                             | -                             |
 | 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).
 

adetailer/__init__.py

@@ -4,7 +4,7 @@ from .common import PredictOutput, get_models
 from .mediapipe import mediapipe_predict
 from .ultralytics import ultralytics_predict
 
-AFTER_DETAILER = "After Detailer"
+AFTER_DETAILER = "ADetailer"
 
 __all__ = [
     "__version__",

adetailer/__version__.py

@@ -1 +1 @@
-__version__ = "23.5.19"
+__version__ = "23.6.0"

adetailer/args.py

@@ -13,7 +13,6 @@ from pydantic import (
     PositiveInt,
     confloat,
     constr,
-    validator,
 )
 
 
@@ -36,7 +35,7 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
     ad_model: str = "None"
     ad_prompt: str = ""
     ad_negative_prompt: str = ""
-    ad_conf: confloat(ge=0.0, le=1.0) = 0.3
+    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 = 32
@@ -45,8 +44,8 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
     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_full_res: bool = True
+    ad_inpaint_only_masked: bool = True
-    ad_inpaint_full_res_padding: NonNegativeInt = 0
+    ad_inpaint_only_masked_padding: NonNegativeInt = 0
     ad_use_inpaint_width_height: bool = False
     ad_inpaint_width: PositiveInt = 512
     ad_inpaint_height: PositiveInt = 512
@@ -58,17 +57,6 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
     ad_controlnet_model: constr(regex=r".*inpaint.*|^None$") = "None"
     ad_controlnet_weight: confloat(ge=0.0, le=1.0) = 1.0
 
-    @validator("ad_conf", pre=True)
-    def check_ad_conf(cls, v: Any):  # noqa: N805
-        if not isinstance(v, (int, float)):
-            try:
-                v = int(v)
-            except ValueError:
-                v = float(v)
-        if isinstance(v, int):
-            v /= 100.0
-        return v
-
     @staticmethod
     def ppop(
         p: dict[str, Any],
@@ -90,7 +78,6 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
             return {}
 
         p = {name: getattr(self, attr) for attr, name in ALL_ARGS}
-        p["ADetailer conf"] = int(p["ADetailer conf"] * 100)
         ppop = partial(self.ppop, p)
 
         ppop("ADetailer prompt")
@@ -100,7 +87,7 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
         ppop("ADetailer x offset", cond=0)
         ppop("ADetailer y offset", cond=0)
         ppop("ADetailer mask merge/invert", cond="None")
-        ppop("ADetailer inpaint full", ["ADetailer inpaint padding"])
+        ppop("ADetailer inpaint only masked", ["ADetailer inpaint padding"])
         ppop(
             "ADetailer use inpaint width/height",
             [
@@ -148,7 +135,7 @@ _all_args = [
     ("ad_model", "ADetailer model"),
     ("ad_prompt", "ADetailer prompt"),
     ("ad_negative_prompt", "ADetailer negative prompt"),
-    ("ad_conf", "ADetailer conf"),
+    ("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"),
@@ -157,8 +144,8 @@ _all_args = [
     ("ad_mask_merge_invert", "ADetailer mask merge/invert"),
     ("ad_mask_blur", "ADetailer mask blur"),
     ("ad_denoising_strength", "ADetailer denoising strength"),
-    ("ad_inpaint_full_res", "ADetailer inpaint full"),
+    ("ad_inpaint_only_masked", "ADetailer inpaint only masked"),
-    ("ad_inpaint_full_res_padding", "ADetailer inpaint padding"),
+    ("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"),

adetailer/common.py

@@ -13,11 +13,19 @@ repo_id = "Bingsu/adetailer"
 
 @dataclass
 class PredictOutput:
-    bboxes: list[list[float]] = field(default_factory=list)
+    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:
+        path = "INVALID"
+    return path
+
+
 def get_models(
     model_dir: Union[str, Path], huggingface: bool = True
 ) -> OrderedDict[str, Optional[str]]:
@@ -31,29 +39,28 @@ def get_models(
     else:
         model_paths = []
 
+    models = OrderedDict()
     if huggingface:
-        models = OrderedDict(
+        models.update(
             {
-                "face_yolov8n.pt": hf_hub_download(repo_id, "face_yolov8n.pt"),
+                "face_yolov8n.pt": hf_download("face_yolov8n.pt"),
-                "face_yolov8s.pt": hf_hub_download(repo_id, "face_yolov8s.pt"),
+                "face_yolov8s.pt": hf_download("face_yolov8s.pt"),
-                "mediapipe_face_full": None,
+                "hand_yolov8n.pt": hf_download("hand_yolov8n.pt"),
-                "mediapipe_face_short": None,
+                "person_yolov8n-seg.pt": hf_download("person_yolov8n-seg.pt"),
-                "hand_yolov8n.pt": hf_hub_download(repo_id, "hand_yolov8n.pt"),
+                "person_yolov8s-seg.pt": hf_download("person_yolov8s-seg.pt"),
-                "person_yolov8n-seg.pt": hf_hub_download(
-                    repo_id, "person_yolov8n-seg.pt"
-                ),
-                "person_yolov8s-seg.pt": hf_hub_download(
-                    repo_id, "person_yolov8s-seg.pt"
-                ),
-            }
-        )
-    else:
-        models = OrderedDict(
-            {
-                "mediapipe_face_full": None,
-                "mediapipe_face_short": None,
             }
         )
+    models.update(
+        {
+            "mediapipe_face_full": None,
+            "mediapipe_face_short": None,
+            "mediapipe_face_mesh": 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:
@@ -88,3 +95,29 @@ def create_mask_from_bbox(
         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/mediapipe.py

@@ -1,19 +1,33 @@
 from __future__ import annotations
 
+from functools import partial
+
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageDraw
 
 from adetailer import PredictOutput
-from adetailer.common import create_mask_from_bbox
+from adetailer.common import create_bbox_from_mask, create_mask_from_bbox
 
 
 def mediapipe_predict(
-    model_type: int | str, image: Image.Image, confidence: float = 0.3
+    model_type: str, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mapping = {
+        "mediapipe_face_short": partial(mediapipe_face_detection, model_type=0),
+        "mediapipe_face_full": partial(mediapipe_face_detection, model_type=1),
+        "mediapipe_face_mesh": mediapipe_face_mesh,
+    }
+    if model_type in mapping:
+        func = mapping[model_type]
+        return func(image, confidence)
+    raise RuntimeError(f"[-] ADetailer: Invalid mediapipe model type: {model_type}")
+
+
+def mediapipe_face_detection(
+    model_type: int, image: Image.Image, confidence: float = 0.3
 ) -> PredictOutput:
     import mediapipe as mp
 
-    if isinstance(model_type, str):
-        model_type = mediapipe_model_name_to_type(model_type)
     img_width, img_height = image.size
 
     mp_face_detection = mp.solutions.face_detection
@@ -51,12 +65,47 @@ def mediapipe_predict(
     return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
 
 
-def mediapipe_model_name_to_type(name: str) -> int:
+def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput:
-    name = name.lower()
+    import mediapipe as mp
-    mapping = {
+    from scipy.spatial import ConvexHull
-        "mediapipe_face_short": 0,
+
-        "mediapipe_face_full": 1,
+    mp_face_mesh = mp.solutions.face_mesh
-    }
+    draw_util = mp.solutions.drawing_utils
-    if name not in mapping:
+    drawing_styles = mp.solutions.drawing_styles
-        raise ValueError(f"[-] ADetailer: Invalid model name: {name}")
+
-    return mapping[name]
+    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])
+            hull = ConvexHull(points)
+            vertices = hull.vertices
+            outline = list(zip(points[vertices, 0], points[vertices, 1]))
+
+            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)

adetailer/ui.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from functools import partial
+from types import SimpleNamespace
 from typing import Any
 
 import gradio as gr
@@ -10,7 +11,7 @@ from adetailer.args import AD_ENABLE, ALL_ARGS, MASK_MERGE_INVERT
 from controlnet_ext import controlnet_exists, get_cn_inpaint_models
 
 
-class Widgets:
+class Widgets(SimpleNamespace):
     def tolist(self):
         return [getattr(self, attr) for attr in ALL_ARGS.attrs]
 
@@ -200,14 +201,14 @@ def detection(w: Widgets, n: int, is_img2img: bool):
 
     with gr.Row():
         with gr.Column():
-            w.ad_conf = gr.Slider(
+            w.ad_confidence = gr.Slider(
-                label="Detection model confidence threshold %" + suffix(n),
+                label="Detection model confidence threshold" + suffix(n),
-                minimum=0,
+                minimum=0.0,
-                maximum=100,
+                maximum=1.0,
-                step=1,
+                step=0.01,
-                value=30,
+                value=0.3,
                 visible=True,
-                elem_id=eid("ad_conf"),
+                elem_id=eid("ad_confidence"),
             )
 
         with gr.Column(variant="compact"):
@@ -262,7 +263,7 @@ def mask_preprocessing(w: Widgets, n: int, is_img2img: bool):
                     minimum=-128,
                     maximum=128,
                     step=4,
-                    value=32,
+                    value=4,
                     visible=True,
                     elem_id=eid("ad_dilate_erode"),
                 )
@@ -303,26 +304,26 @@ def inpainting(w: Widgets, n: int, is_img2img: bool):
 
         with gr.Row():
             with gr.Column(variant="compact"):
-                w.ad_inpaint_full_res = gr.Checkbox(
+                w.ad_inpaint_only_masked = gr.Checkbox(
-                    label="Inpaint at full resolution " + suffix(n),
+                    label="Inpaint only masked" + suffix(n),
                     value=True,
                     visible=True,
                     elem_id=eid("ad_inpaint_full_res"),
                 )
-                w.ad_inpaint_full_res_padding = gr.Slider(
+                w.ad_inpaint_only_masked_padding = gr.Slider(
-                    label="Inpaint at full resolution padding, pixels " + suffix(n),
+                    label="Inpaint only masked padding, pixels" + suffix(n),
                     minimum=0,
                     maximum=256,
                     step=4,
-                    value=0,
+                    value=32,
                     visible=True,
                     elem_id=eid("ad_inpaint_full_res_padding"),
                 )
 
-                w.ad_inpaint_full_res.change(
+                w.ad_inpaint_only_masked.change(
                     gr_interactive,
-                    inputs=w.ad_inpaint_full_res,
+                    inputs=w.ad_inpaint_only_masked,
-                    outputs=w.ad_inpaint_full_res_padding,
+                    outputs=w.ad_inpaint_only_masked_padding,
                     queue=False,
                 )
 

install.py

@@ -1,14 +1,15 @@
+from __future__ import annotations
+
 import importlib.util
 import subprocess
 import sys
 from importlib.metadata import version  # python >= 3.8
-from typing import Optional
 
 from packaging.version import parse
 
 
 def is_installed(
-    package: str, min_version: Optional[str] = None, max_version: Optional[str] = None
+    package: str, min_version: str | None = None, max_version: str | None = None
 ):
     try:
         spec = importlib.util.find_spec(package)
@@ -44,7 +45,7 @@ def install():
     deps = [
         # requirements
         ("ultralytics", "8.0.97", None),
-        ("mediapipe", "0.9.3.0", None),
+        ("mediapipe", "0.10.0", None),
         ("huggingface_hub", None, None),
         ("pydantic", None, None),
         # mediapipe

scripts/!adetailer.py

@@ -5,7 +5,7 @@ import platform
 import re
 import sys
 import traceback
-from contextlib import contextmanager
+from contextlib import contextmanager, suppress
 from copy import copy, deepcopy
 from pathlib import Path
 from textwrap import dedent
@@ -41,13 +41,9 @@ from sd_webui.processing import (
 )
 from sd_webui.shared import cmd_opts, opts, state
 
-try:
+with suppress(ImportError):
     from rich import print
-    from rich.traceback import install
 
-    install(show_locals=True)
-except Exception:
-    pass
 
 no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
 adetailer_dir = Path(models_path, "adetailer")
@@ -347,8 +343,8 @@ class AfterDetailerScript(scripts.Script):
             mask=None,
             mask_blur=args.ad_mask_blur,
             inpainting_fill=1,
-            inpaint_full_res=args.ad_inpaint_full_res,
+            inpaint_full_res=args.ad_inpaint_only_masked,
-            inpaint_full_res_padding=args.ad_inpaint_full_res_padding,
+            inpaint_full_res_padding=args.ad_inpaint_only_masked_padding,
             inpainting_mask_invert=0,
             sd_model=p.sd_model,
             outpath_samples=p.outpath_samples,
@@ -429,7 +425,7 @@ class AfterDetailerScript(scripts.Script):
 
     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]
@@ -437,7 +433,7 @@ class AfterDetailerScript(scripts.Script):
         i2i.prompt = prompt
         i2i.negative_prompt = negative_prompt
 
-    def is_need_call_process(self, p):
+    def is_need_call_process(self, p) -> bool:
         i = p._idx
         n_iter = p.iteration
         bs = p.batch_size
@@ -483,7 +479,7 @@ class AfterDetailerScript(scripts.Script):
             kwargs["device"] = self.ultralytics_device
 
         with change_torch_load():
-            pred = predictor(ad_model, pp.image, args.ad_conf, **kwargs)
+            pred = predictor(ad_model, pp.image, args.ad_confidence, **kwargs)
 
         masks = self.pred_preprocessing(pred, args)