Merge branch 'dev'

2026-03-13 01:10:01 +00:00 · 2024-11-10 18:24:16 +09:00
parent 03ec9d004a 3ab17ae174
commit 8ddf919e1e
14 changed files with 155 additions and 89 deletions
--- a/.github/workflows/pypi.yml
+++ b/.github/workflows/pypi.yml
@@ -7,30 +7,7 @@ on:
 jobs:
  test:
    name: test
-    runs-on: macos-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.10"
-          - "3.11"
-          - "3.12"
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - uses: yezz123/setup-uv@v4
-
-      - name: Install dependencies
-        run: |
-          uv pip install --system ".[test]"
-
-      - name: Run tests
-        run: pytest -v
+    uses: ./.github/workflows/test.yml

  build:
    name: build
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,9 +1,12 @@
-name: Test on PR
+name: Test

 on:
  pull_request:
    paths:
      - "adetailer/**.py"
+  workflow_call:
+  schedule:
+    - cron: "0 0 * * 0"

 jobs:
  test:
@@ -24,7 +27,7 @@ jobs:
        with:
          python-version: ${{ matrix.python-version }}

-      - uses: yezz123/setup-uv@v4
+      - uses: astral-sh/setup-uv@v3

      - name: Install dependencies
        run: |
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -5,7 +5,7 @@ exclude: ^modules/

 repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
    hooks:
      - id: check-added-large-files
        args: [--maxkb=100]
@@ -24,7 +24,7 @@ repos:
      - id: prettier

  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.7
+    rev: v0.7.2
    hooks:
      - id: ruff
        args: [--fix, --exit-non-zero-on-fix]
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,15 @@
 # Changelog

+## 2024-11-10
+
+- v24.11.0
+- `disable_controlnet_units` 함수가 `script_args`의 상태를 변경된 상태로 저장하는 문제 수정
+- XYZ Grid에 CFG Scale, scheduler, noise multiplier 추가
+- Area 또는 Confidence를 기준으로 마스크 최대 갯수를 지정할 수 있도록 함 (PR #720)
+
+- `ADetailer detector classes`의 element id를 `ad_classes`에서 `ad_model_classes`로 변경
+- `mediapipe` 최대 버전을 0.10.15로 제한
+
 ## 2024-09-02

 - v24.9.0
--- a/Taskfile.yml
+++ b/Taskfile.yml
@@ -25,8 +25,8 @@ tasks:

  update:
    cmds:
-      - "{{.PYTHON}} -m uv pip install -U ultralytics mediapipe ruff pre-commit black devtools pytest hypothesis"
+      - "{{.PYTHON}} -m uv pip install -U ultralytics mediapipe ruff pre-commit-uv black devtools pytest hypothesis"

  update-torch:
    cmds:
-      - "{{.PYTHON}} -m uv pip install -U torch torchvision torchaudio -f https://download.pytorch.org/whl/torch_stable.html"
+      - "{{.PYTHON}} -m uv pip install -U torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124"
--- a/aaaaaa/ui.py
+++ b/aaaaaa/ui.py
@@ -204,7 +204,7 @@ def one_ui_group(n: int, is_img2img: bool, webui_info: WebuiInfo):
                label="ADetailer detector classes" + suffix(n),
                value="",
                visible=False,
-                elem_id=eid("ad_classes"),
+                elem_id=eid("ad_model_classes"),
            )

            w.ad_model.change(
@@ -294,14 +294,22 @@ def detection(w: Widgets, n: int, is_img2img: bool):
                visible=True,
                elem_id=eid("ad_confidence"),
            )
-            w.ad_mask_k_largest = gr.Slider(
-                label="Mask only the top k largest (0 to disable)" + suffix(n),
+            w.ad_mask_filter_method = gr.Radio(
+                choices=["Area", "Confidence"],
+                value="Area",
+                label="Method to filter top k masks by (confidence or area)"
+                + suffix(n),
+                visible=True,
+                elem_id=eid("ad_mask_filter_method"),
+            )
+            w.ad_mask_k = gr.Slider(
+                label="Mask only the top k (0 to disable)" + suffix(n),
                minimum=0,
                maximum=10,
                step=1,
                value=0,
                visible=True,
-                elem_id=eid("ad_mask_k_largest"),
+                elem_id=eid("ad_mask_k"),
            )

        with gr.Column(variant="compact"):
--- a/adetailer/version.py
+++ b/adetailer/version.py
@@ -1 +1 @@
-__version__ = "24.9.0"
+__version__ = "24.11.0"
--- a/adetailer/args.py
+++ b/adetailer/args.py
@@ -60,7 +60,8 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
    ad_prompt: str = ""
    ad_negative_prompt: str = ""
    ad_confidence: confloat(ge=0.0, le=1.0) = 0.3
-    ad_mask_k_largest: NonNegativeInt = 0
+    ad_mask_filter_method: Literal["Area", "Confidence"] = "Area"
+    ad_mask_k: NonNegativeInt = 0
    ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0
    ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0
    ad_dilate_erode: int = 4
@@ -131,7 +132,11 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
        ppop("ADetailer prompt")
        ppop("ADetailer negative prompt")
        p.pop("ADetailer tab enable", None)  # always pop
-        ppop("ADetailer mask only top k largest", cond=0)
+        ppop(
+            "ADetailer mask only top k",
+            ["ADetailer mask only top k", "ADetailer method to decide top k masks"],
+            cond=0,
+        )
        ppop("ADetailer mask min ratio", cond=0.0)
        ppop("ADetailer mask max ratio", cond=1.0)
        ppop("ADetailer x offset", cond=0)
@@ -217,7 +222,8 @@ _all_args = [
    ("ad_prompt", "ADetailer prompt"),
    ("ad_negative_prompt", "ADetailer negative prompt"),
    ("ad_confidence", "ADetailer confidence"),
-    ("ad_mask_k_largest", "ADetailer mask only top k largest"),
+    ("ad_mask_filter_method", "ADetailer method to decide top k masks"),
+    ("ad_mask_k", "ADetailer mask only top k"),
    ("ad_mask_min_ratio", "ADetailer mask min ratio"),
    ("ad_mask_max_ratio", "ADetailer mask max ratio"),
    ("ad_x_offset", "ADetailer x offset"),
--- a/adetailer/common.py
+++ b/adetailer/common.py
@@ -22,6 +22,7 @@ T = TypeVar("T", int, float)
 class PredictOutput(Generic[T]):
    bboxes: list[list[T]] = field(default_factory=list)
    masks: list[Image.Image] = field(default_factory=list)
+    confidences: list[float] = field(default_factory=list)
    preview: Optional[Image.Image] = None


--- a/adetailer/mask.py
+++ b/adetailer/mask.py
@@ -225,6 +225,7 @@ def filter_by_ratio(
    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]
+    pred.confidences = [pred.confidences[i] for i in idx]
    return pred


@@ -236,9 +237,31 @@ def filter_k_largest(pred: PredictOutput[T], k: int = 0) -> PredictOutput[T]:
    idx = idx[::-1]
    pred.bboxes = [pred.bboxes[i] for i in idx]
    pred.masks = [pred.masks[i] for i in idx]
+    pred.confidences = [pred.confidences[i] for i in idx]
    return pred


+def filter_k_most_confident(pred: PredictOutput[T], k: int = 0) -> PredictOutput[T]:
+    if not pred.bboxes or not pred.confidences or k == 0:
+        return pred
+    idx = np.argsort(pred.confidences)[-k:]
+    idx = idx[::-1]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    pred.confidences = [pred.confidences[i] for i in idx]
+    return pred
+
+
+def filter_k_by(
+    pred: PredictOutput[T], k: int = 0, by: str = "Area"
+) -> PredictOutput[T]:
+    if by == "Area":
+        return filter_k_largest(pred, k)
+    if by == "Confidence":
+        return filter_k_most_confident(pred, k)
+    raise RuntimeError
+
+
 # Merge / Invert
 def mask_merge(masks: list[Image.Image]) -> list[Image.Image]:
    arrs = [np.array(m) for m in masks]
--- a/adetailer/mediapipe.py
+++ b/adetailer/mediapipe.py
@@ -52,6 +52,7 @@ def mediapipe_face_detection(
    preview_array = img_array.copy()

    bboxes = []
+    confidences = []
    for detection in pred.detections:
        draw_util.draw_detection(preview_array, detection)

@@ -63,12 +64,15 @@ def mediapipe_face_detection(
        x2 = x1 + w
        y2 = y1 + h

+        confidences.append(detection.score)
        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)
+    return PredictOutput(
+        bboxes=bboxes, masks=masks, confidences=confidences, preview=preview
+    )


 def mediapipe_face_mesh(
@@ -141,7 +145,6 @@ def mediapipe_face_mesh_eyes_only(

        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], dtype=int
--- a/adetailer/ultralytics.py
+++ b/adetailer/ultralytics.py
@@ -37,11 +37,16 @@ def ultralytics_predict(
        masks = create_mask_from_bbox(bboxes, image.size)
    else:
        masks = mask_to_pil(pred[0].masks.data, image.size)
+
+    confidences = pred[0].boxes.conf.cpu().numpy().tolist()
+
    preview = pred[0].plot()
    preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB)
    preview = Image.fromarray(preview)

-    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+    return PredictOutput(
+        bboxes=bboxes, masks=masks, confidences=confidences, preview=preview
+    )


 def apply_classes(model: YOLO | YOLOWorld, model_path: str | Path, classes: str):
--- a/install.py
+++ b/install.py
@@ -45,7 +45,7 @@ def install():
    deps = [
        # requirements
        ("ultralytics", "8.2.0", None),
-        ("mediapipe", "0.10.13", None),
+        ("mediapipe", "0.10.13", "0.10.15"),
        ("rich", "13.0.0", None),
    ]

--- a/scripts/!adetailer.py
+++ b/scripts/!adetailer.py
@@ -52,7 +52,7 @@ from adetailer.args import (
 from adetailer.common import PredictOutput, ensure_pil_image, safe_mkdir
 from adetailer.mask import (
    filter_by_ratio,
-    filter_k_largest,
+    filter_k_by,
    has_intersection,
    is_all_black,
    mask_preprocess,
@@ -392,7 +392,7 @@ class AfterDetailerScript(scripts.Script):
            value = args.ad_scheduler
        return {"scheduler": value}

-    def get_override_settings(self, p, args: ADetailerArgs) -> dict[str, Any]:
+    def get_override_settings(self, _p, args: ADetailerArgs) -> dict[str, Any]:
        d = {}

        if args.ad_use_clip_skip:
@@ -413,7 +413,7 @@ class AfterDetailerScript(scripts.Script):
            d["sd_vae"] = args.ad_vae
        return d

-    def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None:
+    def get_initial_noise_multiplier(self, _p, args: ADetailerArgs) -> float | None:
        return args.ad_noise_multiplier if args.ad_use_noise_multiplier else None

    @staticmethod
@@ -474,20 +474,30 @@ class AfterDetailerScript(scripts.Script):
        script_runner.alwayson_scripts = filtered_alwayson
        return script_runner, script_args

-    def disable_controlnet_units(
-        self, script_args: list[Any] | tuple[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")
+    def disable_controlnet_units(self, script_args: Sequence[Any]) -> list[Any]:
+        new_args = []
+        for arg in script_args:
+            if "controlnet" in arg.__class__.__name__.lower():
+                new = copy(arg)
+                if hasattr(new, "enabled"):
+                    new.enabled = False
+                if hasattr(new, "input_mode"):
+                    new.input_mode = getattr(new.input_mode, "SIMPLE", "simple")
+                new_args.append(new)

-            elif isinstance(obj, dict) and "module" in obj:
-                obj["enabled"] = False
+            elif isinstance(arg, dict) and "module" in arg:
+                new = copy(arg)
+                new["enabled"] = False
+                new_args.append(new)

-    def get_i2i_p(self, p, args: ADetailerArgs, image):
+            else:
+                new_args.append(arg)
+
+        return new_args
+
+    def get_i2i_p(
+        self, p, args: ADetailerArgs, image: Image.Image
+    ) -> StableDiffusionProcessingImg2Img:
        seed, subseed = self.get_seed(p)
        width, height = self.get_width_height(p, args)
        steps = self.get_steps(p, args)
@@ -545,7 +555,7 @@ class AfterDetailerScript(scripts.Script):
        i2i._ad_inner = True

        if args.ad_controlnet_model != "Passthrough" and controlnet_type != "forge":
-            self.disable_controlnet_units(i2i.script_args)
+            i2i.script_args = self.disable_controlnet_units(i2i.script_args)

        if args.ad_controlnet_model not in ["None", "Passthrough"]:
            self.update_controlnet_args(i2i, args)
@@ -555,6 +565,9 @@ class AfterDetailerScript(scripts.Script):
        return i2i

    def save_image(self, p, image, *, condition: str, suffix: str) -> None:
+        if not opts.data.get(condition, False):
+            return
+
        i = get_i(p)
        if p.all_prompts:
            i %= len(p.all_prompts)
@@ -563,23 +576,22 @@ class AfterDetailerScript(scripts.Script):
            save_prompt = p.prompt
        seed, _ = self.get_seed(p)

-        if opts.data.get(condition, False):
-            ad_save_images_dir: str = opts.data.get("ad_save_images_dir", "")
+        ad_save_images_dir: str = opts.data.get("ad_save_images_dir", "")

-            if not ad_save_images_dir.strip():
-                ad_save_images_dir = p.outpath_samples
+        if not ad_save_images_dir.strip():
+            ad_save_images_dir = p.outpath_samples

-            images.save_image(
-                image=image,
-                path=ad_save_images_dir,
-                basename="",
-                seed=seed,
-                prompt=save_prompt,
-                extension=opts.samples_format,
-                info=self.infotext(p),
-                p=p,
-                suffix=suffix,
-            )
+        images.save_image(
+            image=image,
+            path=ad_save_images_dir,
+            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:
@@ -596,7 +608,7 @@ class AfterDetailerScript(scripts.Script):
        pred = filter_by_ratio(
            pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
        )
-        pred = filter_k_largest(pred, k=args.ad_mask_k_largest)
+        pred = filter_k_by(pred, k=args.ad_mask_k, by=args.ad_mask_filter_method)
        pred = self.sort_bboxes(pred)
        masks = mask_preprocess(
            pred.masks,
@@ -652,7 +664,7 @@ class AfterDetailerScript(scripts.Script):
        img2img_mask: Image.Image, ad_mask: list[Image.Image]
    ) -> list[Image.Image]:
        if ad_mask and img2img_mask.size != ad_mask[0].size:
-            img2img_mask = img2img_mask.resize(ad_mask[0].size, resample=images.LANCZOS)
+            img2img_mask = img2img_mask.resize(ad_mask[0].size, resample=Image.LANCZOS)
        return [mask for mask in ad_mask if has_intersection(img2img_mask, mask)]

    @staticmethod
@@ -663,14 +675,9 @@ class AfterDetailerScript(scripts.Script):
            mask = ImageChops.invert(mask)
        mask = create_binary_mask(mask)

-        if is_skip_img2img(p):
-            if hasattr(p, "init_images") and p.init_images:
-                width, height = p.init_images[0].size
-            else:
-                msg = "[-] ADetailer: no init_images."
-                raise RuntimeError(msg)
-        else:
-            width, height = p.width, p.height
+        width, height = p.width, p.height
+        if is_skip_img2img(p) and hasattr(p, "init_images") and p.init_images:
+            width, height = p.init_images[0].size
        return images.resize_image(p.resize_mode, mask, width, height)

    @staticmethod
@@ -969,18 +976,22 @@ def on_ui_settings():

    shared.opts.add_option(
        "ad_save_previews",
-        shared.OptionInfo(False, "Save mask previews", section=section),
+        shared.OptionInfo(default=False, label="Save mask previews", section=section),
    )

    shared.opts.add_option(
        "ad_save_images_before",
-        shared.OptionInfo(False, "Save images before ADetailer", section=section),
+        shared.OptionInfo(
+            default=False, label="Save images before ADetailer", section=section
+        ),
    )

    shared.opts.add_option(
        "ad_only_selected_scripts",
        shared.OptionInfo(
-            True, "Apply only selected scripts to ADetailer", section=section
+            default=True,
+            label="Apply only selected scripts to ADetailer",
+            section=section,
        ),
    )

@@ -1014,7 +1025,9 @@ def on_ui_settings():
    shared.opts.add_option(
        "ad_same_seed_for_each_tab",
        shared.OptionInfo(
-            False, "Use same seed for each tab in adetailer", section=section
+            default=False,
+            label="Use same seed for each tab in adetailer",
+            section=section,
        ),
    )

@@ -1080,7 +1093,8 @@ def make_axis_on_xyz_grid():
        return

    model_list = ["None", *model_mapping.keys()]
-    samplers = [sampler.name for sampler in all_samplers]
+    xyz_samplers = [sampler.name for sampler in all_samplers]
+    xyz_schedulers = [scheduler.label for scheduler in schedulers]

    axis = [
        xyz_grid.AxisOption(
@@ -1119,6 +1133,11 @@ def make_axis_on_xyz_grid():
            float,
            partial(set_value, field="ad_denoising_strength"),
        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] CFG scale 1st",
+            float,
+            partial(set_value, field="ad_cfg_scale"),
+        ),
        xyz_grid.AxisOption(
            "[ADetailer] Inpaint only masked 1st",
            str,
@@ -1134,7 +1153,18 @@ def make_axis_on_xyz_grid():
            "[ADetailer] ADetailer sampler 1st",
            str,
            partial(set_value, field="ad_sampler"),
-            choices=lambda: samplers,
+            choices=lambda: xyz_samplers,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer scheduler 1st",
+            str,
+            partial(set_value, field="ad_scheduler"),
+            choices=lambda: xyz_schedulers,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] noise multiplier 1st",
+            float,
+            partial(set_value, field="ad_noise_multiplier"),
        ),
        xyz_grid.AxisOption(
            "[ADetailer] ControlNet model 1st",