diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5b03999..5ec9dd8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,11 @@
# Changelog
+### 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
diff --git a/README.md b/README.md
index cb77f64..0861d66 100644
--- a/README.md
+++ b/README.md
@@ -16,31 +16,9 @@
You **DON'T** need to download any model from huggingface.
-## Model
+## Usage
-| 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 |
-| mediapipe_face_full | realistic | - | - |
-| mediapipe_face_short | realistic | - | - |
-
-The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
-
-### Dataset
-
-Datasets used for training the yolo face detection models are:
-
-- [roboflow AN](https://universe.roboflow.com/sed-b8vkf/an-lfg5i)
-- [roboflow Anime Face CreateML](https://universe.roboflow.com/my-workspace-mph8o/anime-face-createml)
-- [roboflow xml2txt](https://universe.roboflow.com/0oooooo0/xml2txt-njqx1)
-- [wider face](http://shuoyang1213.me/WIDERFACE/index.html)
-
-### 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 model and use only label 0.
+TO DO
## ControlNet Inpainting
@@ -48,6 +26,47 @@ You can use the ControlNet inpaint extension if you have ControlNet installed an
On the ControlNet tab, select a ControlNet inpaint model and set the model weights.
+## 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 |
+| 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)
0.761 (mask) | 0.555 (bbox)
0.460 (mask) |
+
+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
diff --git a/adetailer/__init__.py b/adetailer/__init__.py
index fab3898..92bb26b 100644
--- a/adetailer/__init__.py
+++ b/adetailer/__init__.py
@@ -1,5 +1,5 @@
from .__version__ import __version__
-from .args import ALL_ARGS, ADetailerArgs, EnableChecker, get_args
+from .args import ALL_ARGS, ADetailerArgs, EnableChecker, get_one_args
from .common import PredictOutput, get_models
from .mediapipe import mediapipe_predict
from .ultralytics import ultralytics_predict
@@ -10,7 +10,7 @@ __all__ = [
"ALL_ARGS",
"EnableChecker",
"PredictOutput",
- "get_args",
+ "get_one_args",
"get_models",
"mediapipe_predict",
"ultralytics_predict",
diff --git a/adetailer/__version__.py b/adetailer/__version__.py
index 5f321e3..8d20f28 100644
--- a/adetailer/__version__.py
+++ b/adetailer/__version__.py
@@ -1 +1 @@
-__version__ = "23.5.8"
+__version__ = "23.5.9"
diff --git a/adetailer/args.py b/adetailer/args.py
index 2c435d0..348290d 100644
--- a/adetailer/args.py
+++ b/adetailer/args.py
@@ -75,7 +75,10 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
v /= 100.0
return v
- def extra_params(self):
+ def extra_params(self, suffix: str = ""):
+ if self.ad_model == "None":
+ return {}
+
params = {name: getattr(self, attr) for attr, name in ALL_ARGS[1:]}
params["ADetailer conf"] = int(params["ADetailer conf"] * 100)
@@ -95,6 +98,9 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
params.pop("ADetailer ControlNet model")
params.pop("ADetailer ControlNet weight")
+ if suffix:
+ params = {k + suffix: v for k, v in params.items()}
+
return params
@@ -106,6 +112,6 @@ class EnableChecker(BaseModel):
return self.ad_enable and self.ad_model != "None"
-def get_args(*args: Any) -> ADetailerArgs:
+def get_one_args(*args: Any) -> ADetailerArgs:
arg_dict = {attr: arg for arg, (attr, *_) in zip(args, ALL_ARGS)}
return ADetailerArgs(**arg_dict)
diff --git a/adetailer/common.py b/adetailer/common.py
index 731b081..f12b682 100644
--- a/adetailer/common.py
+++ b/adetailer/common.py
@@ -38,6 +38,7 @@ def get_models(model_dir: Union[str, Path]) -> OrderedDict[str, Optional[str]]:
"mediapipe_face_full": None,
"mediapipe_face_short": None,
"hand_yolov8n.pt": hf_hub_download(repo_id, "hand_yolov8n.pt"),
+ "person_yolov8n-seg.pt": hf_hub_download(repo_id, "person_yolov8n-seg.pt"),
}
)
diff --git a/adetailer/ultralytics.py b/adetailer/ultralytics.py
index b7d2916..90bc68f 100644
--- a/adetailer/ultralytics.py
+++ b/adetailer/ultralytics.py
@@ -32,7 +32,10 @@ def ultralytics_predict(
return PredictOutput()
bboxes = bboxes.tolist()
- masks = create_mask_from_bbox(image, bboxes)
+ if pred[0].masks is None:
+ masks = create_mask_from_bbox(image, bboxes)
+ 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)
@@ -51,3 +54,19 @@ def ultralytics_check():
if p == "C:\\":
message = "[-] ADetailer: if you get stuck here, try moving the stable-diffusion-webui to a different directory, or try running as administrator."
print(message)
+
+
+def mask_to_pil(masks, orig_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.
+
+ orig_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(orig_shape) for i in range(n)]
diff --git a/scripts/!adetailer.py b/scripts/!adetailer.py
index f09443c..fbacb8b 100644
--- a/scripts/!adetailer.py
+++ b/scripts/!adetailer.py
@@ -17,8 +17,8 @@ from adetailer import (
ADetailerArgs,
EnableChecker,
__version__,
- get_args,
get_models,
+ get_one_args,
mediapipe_predict,
ultralytics_predict,
)
@@ -52,7 +52,7 @@ print(
class Widgets:
def tolist(self):
- return [getattr(self, attr) for attr, *_ in ALL_ARGS]
+ return [getattr(self, attr) for attr, *_ in ALL_ARGS[1:]]
class ChangeTorchLoad:
@@ -68,6 +68,15 @@ def gr_show(visible=True):
return {"visible": visible, "__type__": "update"}
+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)
+
+
class AfterDetailerScript(scripts.Script):
def __init__(self):
super().__init__()
@@ -83,183 +92,205 @@ class AfterDetailerScript(scripts.Script):
def ui(self, is_img2img):
model_list = list(model_mapping.keys())
- w = Widgets()
+ num_models = opts.data.get("ad_max_models", 2)
+ w = [Widgets() for _ in range(num_models)]
with gr.Accordion(AFTER_DETAILER, open=False, elem_id="AD_main_acc"):
with gr.Row():
- w.ad_enable = gr.Checkbox(
+ ad_enable = gr.Checkbox(
label="Enable ADetailer",
value=False,
visible=True,
)
- with gr.Group():
- with gr.Row():
- w.ad_model = gr.Dropdown(
- label="ADetailer model",
- choices=model_list,
- value=model_list[0],
- visible=True,
- type="value",
- )
+ with gr.Group(), gr.Tabs():
+ for n in range(num_models):
+ with gr.Tab(ordinal(n + 1)):
+ with gr.Row():
+ model_choices = (
+ model_list if n == 0 else ["None"] + model_list
+ )
- with gr.Row(elem_id="AD_toprow_prompt"):
- w.ad_prompt = gr.Textbox(
- label="ad_prompt",
- show_label=False,
- lines=3,
- placeholder="ADetailer prompt",
- elem_id="AD_prompt",
- )
+ w[n].ad_model = gr.Dropdown(
+ label="ADetailer model" + suffix(n),
+ choices=model_choices,
+ value=model_choices[0],
+ visible=True,
+ type="value",
+ )
- with gr.Row(elem_id="AD_toprow_negative_prompt"):
- w.ad_negative_prompt = gr.Textbox(
- label="ad_negative_prompt",
- show_label=False,
- lines=2,
- placeholder="ADetailer negative prompt",
- elem_id="AD_negative_prompt",
- )
+ with gr.Row(elem_id="AD_toprow_prompt" + suffix(n, "_")):
+ w[n].ad_prompt = gr.Textbox(
+ label="ad_prompt" + suffix(n),
+ show_label=False,
+ lines=3,
+ placeholder="ADetailer prompt" + suffix(n),
+ elem_id="AD_prompt" + suffix(n, "_"),
+ )
- with gr.Group():
- with gr.Row():
- w.ad_conf = gr.Slider(
- label="Detection model confidence threshold %",
- minimum=0,
- maximum=100,
- step=1,
- value=30,
- visible=True,
- )
- w.ad_dilate_erode = gr.Slider(
- label="Mask erosion (-) / dilation (+)",
- minimum=-128,
- maximum=128,
- step=4,
- value=32,
- visible=True,
- )
+ with gr.Row(
+ elem_id="AD_toprow_negative_prompt" + suffix(n, "_")
+ ):
+ w[n].ad_negative_prompt = gr.Textbox(
+ label="ad_negative_prompt" + suffix(n),
+ show_label=False,
+ lines=2,
+ placeholder="ADetailer negative prompt" + suffix(n),
+ elem_id="AD_negative_prompt" + suffix(n, "_"),
+ )
- with gr.Row():
- w.ad_x_offset = gr.Slider(
- label="Mask x(→) offset",
- minimum=-200,
- maximum=200,
- step=1,
- value=0,
- visible=True,
- )
- w.ad_y_offset = gr.Slider(
- label="Mask y(↑) offset",
- minimum=-200,
- maximum=200,
- step=1,
- value=0,
- visible=True,
- )
+ with gr.Group():
+ with gr.Row():
+ w[n].ad_conf = gr.Slider(
+ label="Detection model confidence threshold %"
+ + suffix(n),
+ minimum=0,
+ maximum=100,
+ step=1,
+ value=30,
+ visible=True,
+ )
+ w[n].ad_dilate_erode = gr.Slider(
+ label="Mask erosion (-) / dilation (+)" + suffix(n),
+ minimum=-128,
+ maximum=128,
+ step=4,
+ value=32,
+ visible=True,
+ )
- with gr.Row():
- w.ad_mask_blur = gr.Slider(
- label="Inpaint mask blur",
- minimum=0,
- maximum=64,
- step=1,
- value=4,
- visible=True,
- )
+ with gr.Row():
+ w[n].ad_x_offset = gr.Slider(
+ label="Mask x(→) offset" + suffix(n),
+ minimum=-200,
+ maximum=200,
+ step=1,
+ value=0,
+ visible=True,
+ )
+ w[n].ad_y_offset = gr.Slider(
+ label="Mask y(↑) offset" + suffix(n),
+ minimum=-200,
+ maximum=200,
+ step=1,
+ value=0,
+ visible=True,
+ )
- w.ad_denoising_strength = gr.Slider(
- label="Inpaint denoising strength",
- minimum=0.0,
- maximum=1.0,
- step=0.01,
- value=0.4,
- visible=True,
- )
+ with gr.Row():
+ w[n].ad_mask_blur = gr.Slider(
+ label="Inpaint mask blur" + suffix(n),
+ minimum=0,
+ maximum=64,
+ step=1,
+ value=4,
+ visible=True,
+ )
- with gr.Row():
- w.ad_inpaint_full_res = gr.Checkbox(
- label="Inpaint at full resolution ",
- value=True,
- visible=True,
- )
- w.ad_inpaint_full_res_padding = gr.Slider(
- label="Inpaint at full resolution padding, pixels ",
- minimum=0,
- maximum=256,
- step=4,
- value=0,
- visible=True,
- )
+ w[n].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,
+ )
- with gr.Row():
- w.ad_use_inpaint_width_height = gr.Checkbox(
- label="Use separate width/height",
- value=False,
- visible=True,
- )
+ with gr.Row():
+ w[n].ad_inpaint_full_res = gr.Checkbox(
+ label="Inpaint at full resolution " + suffix(n),
+ value=True,
+ visible=True,
+ )
+ w[n].ad_inpaint_full_res_padding = gr.Slider(
+ label="Inpaint at full resolution padding, pixels "
+ + suffix(n),
+ minimum=0,
+ maximum=256,
+ step=4,
+ value=0,
+ visible=True,
+ )
- w.ad_inpaint_width = gr.Slider(
- label="inpaint width",
- minimum=4,
- maximum=1024,
- step=4,
- value=512,
- visible=True,
- )
+ with gr.Row():
+ w[n].ad_use_inpaint_width_height = gr.Checkbox(
+ label="Use separate width/height" + suffix(n),
+ value=False,
+ visible=True,
+ )
- w.ad_inpaint_height = gr.Slider(
- label="inpaint height",
- minimum=4,
- maximum=1024,
- step=4,
- value=512,
- visible=True,
- )
+ w[n].ad_inpaint_width = gr.Slider(
+ label="inpaint width" + suffix(n),
+ minimum=4,
+ maximum=1024,
+ step=4,
+ value=512,
+ visible=True,
+ )
- with gr.Row():
- w.ad_use_cfg_scale = gr.Checkbox(
- label="Use separate CFG scale",
- value=False,
- visible=True,
- )
+ w[n].ad_inpaint_height = gr.Slider(
+ label="inpaint height" + suffix(n),
+ minimum=4,
+ maximum=1024,
+ step=4,
+ value=512,
+ visible=True,
+ )
- w.ad_cfg_scale = gr.Slider(
- label="ADetailer CFG scale",
- minimum=0.0,
- maximum=30.0,
- step=0.5,
- value=7.0,
- visible=True,
- )
+ with gr.Row():
+ w[n].ad_use_cfg_scale = gr.Checkbox(
+ label="Use separate CFG scale" + suffix(n),
+ value=False,
+ visible=True,
+ )
- cn_inpaint_models = ["None"] + get_cn_inpaint_models()
+ w[n].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,
+ )
- with gr.Group():
- with gr.Row():
- w.ad_controlnet_model = gr.Dropdown(
- label="ControlNet model",
- choices=cn_inpaint_models,
- value="None",
- visible=True,
- type="value",
- interactive=controlnet_exists,
- )
+ cn_inpaint_models = ["None"] + get_cn_inpaint_models()
- with gr.Row():
- w.ad_controlnet_weight = gr.Slider(
- label="ControlNet weight",
- minimum=0.0,
- maximum=1.0,
- step=0.05,
- value=1.0,
- visible=True,
- interactive=controlnet_exists,
- )
+ with gr.Group():
+ with gr.Row():
+ w[n].ad_controlnet_model = gr.Dropdown(
+ label="ControlNet model" + suffix(n),
+ choices=cn_inpaint_models,
+ value="None",
+ visible=True,
+ type="value",
+ interactive=controlnet_exists,
+ )
- self.infotext_fields = [(getattr(w, attr), name) for attr, name, *_ in ALL_ARGS]
+ with gr.Row():
+ w[n].ad_controlnet_weight = gr.Slider(
+ label="ControlNet weight" + suffix(n),
+ minimum=0.0,
+ maximum=1.0,
+ step=0.05,
+ value=1.0,
+ visible=True,
+ interactive=controlnet_exists,
+ )
- return w.tolist()
+ # Accordion end
+
+ self.infotext_fields = [(ad_enable, ALL_ARGS[0].name)]
+ self.infotext_fields += [
+ (getattr(w[n], attr), name + suffix(n))
+ for n in range(num_models)
+ for attr, name, *_ in ALL_ARGS[1:]
+ ]
+
+ ret = [ad_enable]
+ for n in range(num_models):
+ ret.extend(w[n].tolist())
+
+ return ret
def init_controlnet_ext(self) -> None:
if self.controlnet_ext is not None:
@@ -299,23 +330,38 @@ class AfterDetailerScript(scripts.Script):
checker = EnableChecker(ad_enable=args_[0], ad_model=args_[1])
return checker.is_enabled()
- def get_args(self, *args_) -> ADetailerArgs:
- try:
- args = get_args(*args_)
- except ValueError as e:
- message = [
- f"[-] ADetailer: ValidationError when validating arguments: {e}\n"
- ]
- for arg, (attr, *_) in zip_longest(args_, ALL_ARGS):
- dtype = type(arg)
- arg = "MISSING" if arg is None else repr(arg)
- message.append(f" {attr}: {arg} ({dtype})")
- raise ValueError("\n".join(message)) from e
+ def get_args(self, *args_) -> list[ADetailerArgs]:
+ """
+ `args_` is at least 2 in length by `is_ad_enabled` immediately above
+ """
+ enabled = args_[0]
+ rem = args_[1:]
+ length = len(ALL_ARGS) - 1
- return args
+ all_inputs = []
+ iter_args = (rem[i : i + length] for i in range(0, len(rem), length))
- def extra_params(self, args: ADetailerArgs) -> dict:
- params = args.extra_params()
+ for n, args in enumerate(iter_args, 1):
+ try:
+ inp = get_one_args(enabled, *args)
+ except ValueError as e:
+ message = [
+ f"[-] ADetailer: ValidationError when validating {ordinal(n)} arguments: {e}\n"
+ ]
+ for arg, (attr, *_) in zip_longest(args, ALL_ARGS[1:]):
+ dtype = type(arg)
+ arg = "MISSING" if arg is None else repr(arg)
+ message.append(f" {attr}: {arg} ({dtype})")
+ raise ValueError("\n".join(message)) 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
@@ -482,11 +528,11 @@ class AfterDetailerScript(scripts.Script):
return
if self.is_ad_enabled(*args_):
- args = self.get_args(*args_)
- extra_params = self.extra_params(args)
+ arg_list = self.get_args(*args_)
+ extra_params = self.extra_params(arg_list)
p.extra_generation_params.update(extra_params)
- def _postprocess_image(self, p, pp, args: ADetailerArgs) -> bool:
+ def _postprocess_image(self, p, pp, args: ADetailerArgs, *, n: int = 0) -> bool:
"""
Returns
-------
@@ -494,7 +540,6 @@ class AfterDetailerScript(scripts.Script):
`True` if image was processed, `False` otherwise.
"""
- p._idx = getattr(p, "_idx", -1) + 1
i = p._idx
i2i = self.get_i2i_p(p, args, pp.image)
@@ -518,12 +563,15 @@ class AfterDetailerScript(scripts.Script):
if not masks:
print(
- f"[-] ADetailer: nothing detected on image {i + 1} with current settings."
+ 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"
+ p,
+ pred.preview,
+ condition="ad_save_previews",
+ suffix="-ad-preview" + suffix(n, "-"),
)
steps = len(masks)
@@ -556,10 +604,15 @@ class AfterDetailerScript(scripts.Script):
if not self.is_ad_enabled(*args_):
return
+ p._idx = getattr(p, "_idx", -1) + 1
init_image = copy(pp.image)
+ arg_list = self.get_args(*args_)
- args = self.get_args(*args_)
- is_processed = self._postprocess_image(p, pp, args)
+ is_processed = False
+ 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(
@@ -575,6 +628,17 @@ class AfterDetailerScript(scripts.Script):
def on_ui_settings():
section = ("ADetailer", AFTER_DETAILER)
+ shared.opts.add_option(
+ "ad_max_models",
+ shared.OptionInfo(
+ 2,
+ "Max models",
+ gr.Slider,
+ {"minimum": 1, "maximum": 5, "step": 1},
+ section=section,
+ ),
+ )
+
shared.opts.add_option(
"ad_save_previews",
shared.OptionInfo(False, "Save mask previews", section=section),