`Merge`: Merge all masks and inpaint
`Merge and Invert`: Merge all masks and Invert, then inpaint | | + +Applied in this order: x, y offset → erosion/dilation → merge/invert. + +#### Inpainting + +Each option corresponds to a corresponding option on the inpaint tab. Therefore, please refer to the inpaint tab for usage details on how to use each option. + +## ControlNet Inpainting + +You can use the ControlNet extension if you have ControlNet installed and ControlNet models. + +Support `inpaint, scribble, lineart, openpose, tile` controlnet models. Once you choose a model, the preprocessor is set automatically. It works separately from the model set by the Controlnet extension. + +## Advanced Options + +API request example: [wiki/API](https://github.com/Bing-su/adetailer/wiki/API) + +`ui-config.json` entries: [wiki/ui-config.json](https://github.com/Bing-su/adetailer/wiki/ui-config.json) + +`[SEP], [SKIP]` tokens: [wiki/Advanced](https://github.com/Bing-su/adetailer/wiki/Advanced) + +## Media + +- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方① 【Stable Diffusion】](https://youtu.be/sF3POwPUWCE) +- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方② 【Stable Diffusion】](https://youtu.be/urNISRdbIEg) + +## Model + +| Model | Target | mAP 50 | mAP 50-95 | +| --------------------- | --------------------- | ----------------------------- | ----------------------------- | +| face_yolov8n.pt | 2D / realistic face | 0.660 | 0.366 | +| face_yolov8s.pt | 2D / realistic face | 0.713 | 0.404 | +| hand_yolov8n.pt | 2D / realistic hand | 0.767 | 0.505 | +| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)
0.761 (mask) | 0.555 (bbox)
0.460 (mask) | +| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)
0.809 (mask) | 0.605 (bbox)
0.508 (mask) | +| mediapipe_face_full | realistic face | - | - | +| mediapipe_face_short | realistic face | - | - | +| mediapipe_face_mesh | realistic face | - | - | + +The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer). + +### Additional Model + +Put your [ultralytics](https://github.com/ultralytics/ultralytics) yolo model in `webui/models/adetailer`. The model name should end with `.pt` or `.pth`. + +It must be a bbox detection or segment model and use all label. + +## Example + + + + +[](https://ko-fi.com/F1F1L7V2N) diff --git a/adetailer/Taskfile.yml b/adetailer/Taskfile.yml new file mode 100644 index 0000000000000000000000000000000000000000..9c9f45ed9431f14002b8be9d85e1ce41f4313b26 --- /dev/null +++ b/adetailer/Taskfile.yml @@ -0,0 +1,25 @@ +# https://taskfile.dev + +version: "3" + +dotenv: + - .env + +vars: + SHELL: '{{if eq .OS "Windows_NT"}}powershell{{end}}' + +tasks: + default: + cmds: + - echo "$PYTHON" + - echo "$WEBUI" + silent: true + + launch: + dir: "{{.WEBUI}}" + cmds: + - "{{.PYTHON}} launch.py --xformers --api --autolaunch" + + lint: + cmds: + - pre-commit run -a diff --git a/adetailer/adetailer/__init__.py b/adetailer/adetailer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dae6181e4042e320e7854837ed8e96b1da583c83 --- /dev/null +++ b/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", +] diff --git a/adetailer/adetailer/__version__.py b/adetailer/adetailer/__version__.py new file mode 100644 index 0000000000000000000000000000000000000000..3810b2140fc76ab8656d62fb7cf6ed85f0574587 --- /dev/null +++ b/adetailer/adetailer/__version__.py @@ -0,0 +1 @@ +__version__ = "23.7.11" diff --git a/adetailer/adetailer/args.py b/adetailer/adetailer/args.py new file mode 100644 index 0000000000000000000000000000000000000000..f12e4b2c647c11f32c1d8860eec93418962c3da3 --- /dev/null +++ b/adetailer/adetailer/args.py @@ -0,0 +1,232 @@ +from __future__ import annotations + +from collections import UserList +from functools import cached_property, partial +from typing import Any, Literal, NamedTuple, Optional, Union + +import pydantic +from pydantic import ( + BaseModel, + Extra, + NonNegativeFloat, + NonNegativeInt, + PositiveInt, + confloat, + conint, + constr, + root_validator, + validator, +) + +cn_model_regex = r".*(inpaint|tile|scribble|lineart|openpose).*|^None$" + + +class Arg(NamedTuple): + attr: str + name: str + + +class ArgsList(UserList): + @cached_property + def attrs(self) -> tuple[str]: + return tuple(attr for attr, _ in self) + + @cached_property + def names(self) -> tuple[str]: + return tuple(name for _, name in self) + + +class ADetailerArgs(BaseModel, extra=Extra.forbid): + ad_model: str = "None" + ad_prompt: str = "" + ad_negative_prompt: str = "" + ad_confidence: confloat(ge=0.0, le=1.0) = 0.3 + ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0 + ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0 + ad_dilate_erode: int = 4 + ad_x_offset: int = 0 + ad_y_offset: int = 0 + ad_mask_merge_invert: Literal["None", "Merge", "Merge and Invert"] = "None" + ad_mask_blur: NonNegativeInt = 4 + ad_denoising_strength: confloat(ge=0.0, le=1.0) = 0.4 + ad_inpaint_only_masked: bool = True + ad_inpaint_only_masked_padding: NonNegativeInt = 32 + ad_use_inpaint_width_height: bool = False + ad_inpaint_width: PositiveInt = 512 + ad_inpaint_height: PositiveInt = 512 + ad_use_steps: bool = False + ad_steps: PositiveInt = 28 + ad_use_cfg_scale: bool = False + ad_cfg_scale: NonNegativeFloat = 7.0 + ad_use_sampler: bool = False + ad_sampler: str = "DPM++ 2M Karras" + ad_use_noise_multiplier: bool = False + ad_noise_multiplier: confloat(ge=0.5, le=1.5) = 1.0 + ad_use_clip_skip: bool = False + ad_clip_skip: conint(ge=1, le=12) = 1 + ad_restore_face: bool = False + ad_controlnet_model: constr(regex=cn_model_regex) = "None" + ad_controlnet_module: Optional[constr(regex=r".*inpaint.*|^None$")] = None + ad_controlnet_weight: confloat(ge=0.0, le=1.0) = 1.0 + ad_controlnet_guidance_start: confloat(ge=0.0, le=1.0) = 0.0 + ad_controlnet_guidance_end: confloat(ge=0.0, le=1.0) = 1.0 + is_api: bool = True + + @root_validator(skip_on_failure=True) + def ad_controlnt_module_validator(cls, values): # noqa: N805 + cn_model = values.get("ad_controlnet_model", "None") + cn_module = values.get("ad_controlnet_module", None) + if "inpaint" not in cn_model or cn_module == "None": + values["ad_controlnet_module"] = None + return values + + @validator("is_api", pre=True) + def is_api_validator(cls, v: Any): # noqa: N805 + "tuple is json serializable but cannot be made with json deserialize." + return type(v) is not tuple + + @staticmethod + def ppop( + p: dict[str, Any], + key: str, + pops: list[str] | None = None, + cond: Any = None, + ) -> None: + if pops is None: + pops = [key] + if key not in p: + return + value = p[key] + cond = (not bool(value)) if cond is None else value == cond + + if cond: + for k in pops: + p.pop(k, None) + + def extra_params(self, suffix: str = "") -> dict[str, Any]: + if self.ad_model == "None": + return {} + + p = {name: getattr(self, attr) for attr, name in ALL_ARGS} + ppop = partial(self.ppop, p) + + ppop("ADetailer prompt") + ppop("ADetailer negative prompt") + ppop("ADetailer mask 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 sampler", + ["ADetailer use separate sampler", "ADetailer sampler"], + ) + ppop( + "ADetailer use separate noise multiplier", + ["ADetailer use separate noise multiplier", "ADetailer noise multiplier"], + ) + + ppop( + "ADetailer use separate CLIP skip", + ["ADetailer use separate CLIP skip", "ADetailer CLIP skip"], + ) + + ppop("ADetailer restore face") + ppop( + "ADetailer ControlNet model", + [ + "ADetailer ControlNet model", + "ADetailer ControlNet module", + "ADetailer ControlNet weight", + "ADetailer ControlNet guidance start", + "ADetailer ControlNet guidance end", + ], + cond="None", + ) + ppop("ADetailer ControlNet module") + ppop("ADetailer ControlNet weight", cond=1.0) + ppop("ADetailer ControlNet guidance start", cond=0.0) + ppop("ADetailer ControlNet guidance end", cond=1.0) + + if suffix: + p = {k + suffix: v for k, v in p.items()} + + return p + + +class EnableChecker(BaseModel): + enable: bool + arg_list: list + + def is_enabled(self) -> bool: + ad_model = ALL_ARGS[0].attr + if not self.enable: + return False + return any(arg.get(ad_model, "None") != "None" for arg in self.arg_list) + + +_all_args = [ + ("ad_enable", "ADetailer enable"), + ("ad_model", "ADetailer model"), + ("ad_prompt", "ADetailer prompt"), + ("ad_negative_prompt", "ADetailer negative prompt"), + ("ad_confidence", "ADetailer confidence"), + ("ad_mask_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_sampler", "ADetailer use separate sampler"), + ("ad_sampler", "ADetailer sampler"), + ("ad_use_noise_multiplier", "ADetailer use separate noise multiplier"), + ("ad_noise_multiplier", "ADetailer noise multiplier"), + ("ad_use_clip_skip", "ADetailer use separate CLIP skip"), + ("ad_clip_skip", "ADetailer CLIP skip"), + ("ad_restore_face", "ADetailer restore face"), + ("ad_controlnet_model", "ADetailer ControlNet model"), + ("ad_controlnet_module", "ADetailer ControlNet module"), + ("ad_controlnet_weight", "ADetailer ControlNet weight"), + ("ad_controlnet_guidance_start", "ADetailer ControlNet guidance start"), + ("ad_controlnet_guidance_end", "ADetailer ControlNet guidance end"), +] + +AD_ENABLE = Arg(*_all_args[0]) +_args = [Arg(*args) for args in _all_args[1:]] +ALL_ARGS = ArgsList(_args) + +BBOX_SORTBY = [ + "None", + "Position (left to right)", + "Position (center to edge)", + "Area (large to small)", +] +MASK_MERGE_INVERT = ["None", "Merge", "Merge and Invert"] diff --git a/adetailer/adetailer/common.py b/adetailer/adetailer/common.py new file mode 100644 index 0000000000000000000000000000000000000000..a29bdd5beb2ac3e1f027398dc176e70fb77d082d --- /dev/null +++ b/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 diff --git a/adetailer/adetailer/mask.py b/adetailer/adetailer/mask.py new file mode 100644 index 0000000000000000000000000000000000000000..9209b456fac2d7fda987c0b076874e3bb2cd7ee8 --- /dev/null +++ b/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 diff --git a/adetailer/adetailer/mediapipe.py b/adetailer/adetailer/mediapipe.py new file mode 100644 index 0000000000000000000000000000000000000000..e52b620883b6e7360a3ad5e332cdd03f24d6daab --- /dev/null +++ b/adetailer/adetailer/mediapipe.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +from functools import partial + +import mediapipe as mp +import numpy as np +from PIL import Image, ImageDraw + +from adetailer import PredictOutput +from adetailer.common import create_bbox_from_mask, create_mask_from_bbox + + +def mediapipe_predict( + model_type: str, image: Image.Image, confidence: float = 0.3 +) -> PredictOutput: + mapping = { + "mediapipe_face_short": partial(mediapipe_face_detection, 0), + "mediapipe_face_full": partial(mediapipe_face_detection, 1), + "mediapipe_face_mesh": mediapipe_face_mesh, + "mediapipe_face_mesh_eyes_only": mediapipe_face_mesh_eyes_only, + } + if model_type in mapping: + func = mapping[model_type] + return func(image, confidence) + msg = f"[-] ADetailer: Invalid mediapipe model type: {model_type}, Available: {list(mapping.keys())!r}" + raise RuntimeError(msg) + + +def mediapipe_face_detection( + model_type: int, image: Image.Image, confidence: float = 0.3 +) -> PredictOutput: + img_width, img_height = image.size + + mp_face_detection = mp.solutions.face_detection + draw_util = mp.solutions.drawing_utils + + img_array = np.array(image) + + with mp_face_detection.FaceDetection( + model_selection=model_type, min_detection_confidence=confidence + ) as face_detector: + pred = face_detector.process(img_array) + + if pred.detections is None: + return PredictOutput() + + preview_array = img_array.copy() + + bboxes = [] + for detection in pred.detections: + draw_util.draw_detection(preview_array, detection) + + bbox = detection.location_data.relative_bounding_box + x1 = bbox.xmin * img_width + y1 = bbox.ymin * img_height + w = bbox.width * img_width + h = bbox.height * img_height + x2 = x1 + w + y2 = y1 + h + + bboxes.append([x1, y1, x2, y2]) + + masks = create_mask_from_bbox(bboxes, image.size) + preview = Image.fromarray(preview_array) + + return PredictOutput(bboxes=bboxes, masks=masks, preview=preview) + + +def get_convexhull(points: np.ndarray) -> list[tuple[int, int]]: + """ + Parameters + ---------- + points: An ndarray of shape (n, 2) containing the 2D points. + + Returns + ------- + list[tuple[int, int]]: Input for the draw.polygon function + """ + from scipy.spatial import ConvexHull + + hull = ConvexHull(points) + vertices = hull.vertices + return list(zip(points[vertices, 0], points[vertices, 1])) + + +def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput: + mp_face_mesh = mp.solutions.face_mesh + draw_util = mp.solutions.drawing_utils + drawing_styles = mp.solutions.drawing_styles + + w, h = image.size + + with mp_face_mesh.FaceMesh( + static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence + ) as face_mesh: + arr = np.array(image) + pred = face_mesh.process(arr) + + if pred.multi_face_landmarks is None: + return PredictOutput() + + preview = arr.copy() + masks = [] + + for landmarks in pred.multi_face_landmarks: + draw_util.draw_landmarks( + image=preview, + landmark_list=landmarks, + connections=mp_face_mesh.FACEMESH_TESSELATION, + landmark_drawing_spec=None, + connection_drawing_spec=drawing_styles.get_default_face_mesh_tesselation_style(), + ) + + points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark]) + outline = get_convexhull(points) + + mask = Image.new("L", image.size, "black") + draw = ImageDraw.Draw(mask) + draw.polygon(outline, fill="white") + masks.append(mask) + + bboxes = create_bbox_from_mask(masks, image.size) + preview = Image.fromarray(preview) + return PredictOutput(bboxes=bboxes, masks=masks, preview=preview) + + +def mediapipe_face_mesh_eyes_only( + image: Image.Image, confidence: float = 0.3 +) -> PredictOutput: + mp_face_mesh = mp.solutions.face_mesh + + left_idx = np.array(list(mp_face_mesh.FACEMESH_LEFT_EYE)).flatten() + right_idx = np.array(list(mp_face_mesh.FACEMESH_RIGHT_EYE)).flatten() + + w, h = image.size + + with mp_face_mesh.FaceMesh( + static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence + ) as face_mesh: + arr = np.array(image) + pred = face_mesh.process(arr) + + if pred.multi_face_landmarks is None: + return PredictOutput() + + preview = image.copy() + masks = [] + + for landmarks in pred.multi_face_landmarks: + points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark]) + left_eyes = points[left_idx] + right_eyes = points[right_idx] + left_outline = get_convexhull(left_eyes) + right_outline = get_convexhull(right_eyes) + + mask = Image.new("L", image.size, "black") + draw = ImageDraw.Draw(mask) + for outline in (left_outline, right_outline): + draw.polygon(outline, fill="white") + masks.append(mask) + + bboxes = create_bbox_from_mask(masks, image.size) + preview = draw_preview(preview, bboxes, masks) + return PredictOutput(bboxes=bboxes, masks=masks, preview=preview) + + +def draw_preview( + preview: Image.Image, bboxes: list[list[int]], masks: list[Image.Image] +) -> Image.Image: + red = Image.new("RGB", preview.size, "red") + for mask in masks: + masked = Image.composite(red, preview, mask) + preview = Image.blend(preview, masked, 0.25) + + draw = ImageDraw.Draw(preview) + for bbox in bboxes: + draw.rectangle(bbox, outline="red", width=2) + + return preview diff --git a/adetailer/adetailer/traceback.py b/adetailer/adetailer/traceback.py new file mode 100644 index 0000000000000000000000000000000000000000..e3fcd9e8cd8a56281fbc02c83406f636c1c590c0 --- /dev/null +++ b/adetailer/adetailer/traceback.py @@ -0,0 +1,161 @@ +from __future__ import annotations + +import io +import platform +import sys +from importlib.metadata import version +from typing import Any, Callable + +from rich.console import Console, Group +from rich.panel import Panel +from rich.table import Table +from rich.traceback import Traceback + +from adetailer.__version__ import __version__ + + +def processing(*args: Any) -> dict[str, Any]: + try: + from modules.processing import ( + StableDiffusionProcessingImg2Img, + StableDiffusionProcessingTxt2Img, + ) + except ImportError: + return {} + + p = None + for arg in args: + if isinstance( + arg, (StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img) + ): + p = arg + break + + if p is None: + return {} + + info = { + "prompt": p.prompt, + "negative_prompt": p.negative_prompt, + "n_iter": p.n_iter, + "batch_size": p.batch_size, + "width": p.width, + "height": p.height, + "sampler_name": p.sampler_name, + "enable_hr": getattr(p, "enable_hr", False), + "hr_upscaler": getattr(p, "hr_upscaler", ""), + } + + info.update(sd_models()) + return info + + +def sd_models() -> dict[str, str]: + try: + from modules import shared + + opts = shared.opts + except Exception: + return {} + + return { + "checkpoint": getattr(opts, "sd_model_checkpoint", "------"), + "vae": getattr(opts, "sd_vae", "------"), + "unet": getattr(opts, "sd_unet", "------"), + } + + +def ad_args(*args: Any) -> dict[str, Any]: + ad_args = [ + arg + for arg in args + if isinstance(arg, dict) and arg.get("ad_model", "None") != "None" + ] + if not ad_args: + return {} + + arg0 = ad_args[0] + is_api = arg0.get("is_api", True) + return { + "version": __version__, + "ad_model": arg0["ad_model"], + "ad_prompt": arg0.get("ad_prompt", ""), + "ad_negative_prompt": arg0.get("ad_negative_prompt", ""), + "ad_controlnet_model": arg0.get("ad_controlnet_model", "None"), + "is_api": type(is_api) is not tuple, + } + + +def library_version(): + libraries = ["torch", "torchvision", "ultralytics", "mediapipe"] + d = {} + for lib in libraries: + try: + d[lib] = version(lib) + except Exception: + d[lib] = "Unknown" + return d + + +def sys_info() -> dict[str, Any]: + try: + import launch + + version = launch.git_tag() + commit = launch.commit_hash() + except Exception: + version = "Unknown (too old or vladmandic)" + commit = "Unknown" + + return { + "Platform": platform.platform(), + "Python": sys.version, + "Version": version, + "Commit": commit, + "Commandline": sys.argv, + "Libraries": library_version(), + } + + +def get_table(title: str, data: dict[str, Any]) -> Table: + table = Table(title=title, highlight=True) + table.add_column(" ", justify="right", style="dim") + table.add_column("Value") + for key, value in data.items(): + if not isinstance(value, str): + value = repr(value) + table.add_row(key, value) + + return table + + +def rich_traceback(func: Callable) -> Callable: + def wrapper(*args, **kwargs): + string = io.StringIO() + width = Console().width + width = width - 4 if width > 4 else None + console = Console(file=string, width=width) + try: + return func(*args, **kwargs) + except Exception as e: + tables = [ + get_table(title, data) + for title, data in [ + ("System info", sys_info()), + ("Inputs", processing(*args)), + ("ADetailer", ad_args(*args)), + ] + if data + ] + tables.append(Traceback()) + + console.print(Panel(Group(*tables))) + output = "\n" + string.getvalue() + + try: + error = e.__class__(output) + except Exception: + error = RuntimeError(output) + raise error from None + + return wrapper diff --git a/adetailer/adetailer/ui.py b/adetailer/adetailer/ui.py new file mode 100644 index 0000000000000000000000000000000000000000..b4e585eb751f9a0ca3e2cd220c2f8a2f9c35a291 --- /dev/null +++ b/adetailer/adetailer/ui.py @@ -0,0 +1,558 @@ +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], + samplers: 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, + samplers=samplers, + 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], + samplers: 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, samplers) + + 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, samplers: list[str]): + 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_sampler = gr.Checkbox( + label="Use separate sampler" + suffix(n), + value=False, + visible=True, + elem_id=eid("ad_use_sampler"), + ) + + w.ad_sampler = gr.Dropdown( + label="ADetailer sampler" + suffix(n), + choices=samplers, + value=samplers[0], + visible=True, + elem_id=eid("ad_sampler"), + ) + + w.ad_use_sampler.change( + gr_interactive, + inputs=w.ad_use_sampler, + outputs=w.ad_sampler, + queue=False, + ) + + with gr.Column(variant="compact"): + w.ad_use_noise_multiplier = gr.Checkbox( + label="Use separate noise multiplier" + suffix(n), + value=False, + visible=True, + elem_id=eid("ad_use_noise_multiplier"), + ) + + w.ad_noise_multiplier = gr.Slider( + label="Noise multiplier for img2img" + suffix(n), + minimum=0.5, + maximum=1.5, + step=0.01, + value=1.0, + visible=True, + elem_id=eid("ad_noise_multiplier"), + ) + + w.ad_use_noise_multiplier.change( + gr_interactive, + inputs=w.ad_use_noise_multiplier, + outputs=w.ad_noise_multiplier, + queue=False, + ) + + with gr.Row(): + with gr.Column(variant="compact"): + w.ad_use_clip_skip = gr.Checkbox( + label="Use separate CLIP skip" + suffix(n), + value=False, + visible=True, + elem_id=eid("ad_use_clip_skip"), + ) + + w.ad_clip_skip = gr.Slider( + label="ADetailer CLIP skip" + suffix(n), + minimum=1, + maximum=12, + step=1, + value=1, + visible=True, + elem_id=eid("ad_clip_skip"), + ) + + w.ad_use_clip_skip.change( + gr_interactive, + inputs=w.ad_use_clip_skip, + outputs=w.ad_clip_skip, + queue=False, + ) + + with gr.Column(variant="compact"): + w.ad_restore_face = gr.Checkbox( + label="Restore faces after ADetailer" + suffix(n), + value=False, + elem_id=eid("ad_restore_face"), + ) + + +def controlnet(w: Widgets, n: int, is_img2img: bool): + eid = partial(elem_id, n=n, is_img2img=is_img2img) + cn_models = ["None", *get_cn_models()] + + with gr.Row(variant="panel"): + with gr.Column(variant="compact"): + w.ad_controlnet_model = gr.Dropdown( + label="ControlNet model" + suffix(n), + choices=cn_models, + value="None", + visible=True, + type="value", + interactive=controlnet_exists, + elem_id=eid("ad_controlnet_model"), + ) + + w.ad_controlnet_module = gr.Dropdown( + label="ControlNet module" + suffix(n), + choices=cn_module_choices, + value="inpaint_global_harmonious", + visible=False, + type="value", + interactive=controlnet_exists, + elem_id=eid("ad_controlnet_module"), + ) + + w.ad_controlnet_weight = gr.Slider( + label="ControlNet weight" + suffix(n), + minimum=0.0, + maximum=1.0, + step=0.01, + value=1.0, + visible=True, + interactive=controlnet_exists, + elem_id=eid("ad_controlnet_weight"), + ) + + w.ad_controlnet_model.change( + on_cn_model_update, + inputs=w.ad_controlnet_model, + outputs=w.ad_controlnet_module, + queue=False, + ) + + with gr.Column(variant="compact"): + w.ad_controlnet_guidance_start = gr.Slider( + label="ControlNet guidance start" + suffix(n), + minimum=0.0, + maximum=1.0, + step=0.01, + value=0.0, + visible=True, + interactive=controlnet_exists, + elem_id=eid("ad_controlnet_guidance_start"), + ) + + w.ad_controlnet_guidance_end = gr.Slider( + label="ControlNet guidance end" + suffix(n), + minimum=0.0, + maximum=1.0, + step=0.01, + value=1.0, + visible=True, + interactive=controlnet_exists, + elem_id=eid("ad_controlnet_guidance_end"), + ) diff --git a/adetailer/adetailer/ultralytics.py b/adetailer/adetailer/ultralytics.py new file mode 100644 index 0000000000000000000000000000000000000000..addd0f5c01435a03d899a562d5e5e8b113fabcbc --- /dev/null +++ b/adetailer/adetailer/ultralytics.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +from pathlib import Path + +import cv2 +from PIL import Image +from torchvision.transforms.functional import to_pil_image +from ultralytics import YOLO + +from adetailer import PredictOutput +from adetailer.common import create_mask_from_bbox + + +def ultralytics_predict( + model_path: str | Path, + image: Image.Image, + confidence: float = 0.3, + device: str = "", +) -> PredictOutput: + model = YOLO(model_path) + pred = model(image, conf=confidence, device=device) + + bboxes = pred[0].boxes.xyxy.cpu().numpy() + if bboxes.size == 0: + return PredictOutput() + bboxes = bboxes.tolist() + + if pred[0].masks is None: + masks = create_mask_from_bbox(bboxes, image.size) + else: + masks = mask_to_pil(pred[0].masks.data, image.size) + preview = pred[0].plot() + preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB) + preview = Image.fromarray(preview) + + return PredictOutput(bboxes=bboxes, masks=masks, preview=preview) + + +def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]: + """ + Parameters + ---------- + masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W). + The device can be CUDA, but `to_pil_image` takes care of that. + + shape: tuple[int, int] + (width, height) of the original image + """ + n = masks.shape[0] + return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)] diff --git a/adetailer/controlnet_ext/__init__.py b/adetailer/controlnet_ext/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ab666835157561426d684d798735e724a5a4dbe --- /dev/null +++ b/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", +] diff --git a/adetailer/controlnet_ext/controlnet_ext.py b/adetailer/controlnet_ext/controlnet_ext.py new file mode 100644 index 0000000000000000000000000000000000000000..ec86d46b713c2ee39d9bdd615411494cfe05aba3 --- /dev/null +++ b/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 = getattr(shared.cmd_opts, "controlnet_dir", "") + + dirs = [cn_model_dir] + for ext_dir in [cn_model_dir_old, ext_dir1, ext_dir2]: + if ext_dir: + dirs.append(Path(ext_dir)) + + return dirs + + +@lru_cache +def _get_cn_models() -> list[str]: + """ + Since we can't import ControlNet, we use a function that does something like + controlnet's `list(global_state.cn_models_names.values())`. + """ + cn_model_exts = (".pt", ".pth", ".ckpt", ".safetensors") + dirs = get_cn_model_dirs() + name_filter = shared.opts.data.get("control_net_models_name_filter", "") + name_filter = name_filter.strip(" ").lower() + + model_paths = [] + + for base in dirs: + if not base.exists(): + continue + + for p in base.rglob("*"): + if ( + p.is_file() + and p.suffix in cn_model_exts + and cn_model_regex.search(p.name) + ): + if name_filter and name_filter not in p.name.lower(): + continue + model_paths.append(p) + model_paths.sort(key=lambda p: p.name) + + models = [] + for p in model_paths: + model_hash = sd_models.model_hash(p) + name = f"{p.stem} [{model_hash}]" + models.append(name) + return models + + +def get_cn_models() -> list[str]: + if controlnet_exists: + return _get_cn_models() + return [] diff --git a/adetailer/controlnet_ext/restore.py b/adetailer/controlnet_ext/restore.py new file mode 100644 index 0000000000000000000000000000000000000000..152ffd0a9732b5512e4440b795a1ad89bcb27cab --- /dev/null +++ b/adetailer/controlnet_ext/restore.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from contextlib import contextmanager + +from modules import img2img, processing, shared + + +class CNHijackRestore: + def __init__(self): + self.process = hasattr(processing, "__controlnet_original_process_images_inner") + self.img2img = hasattr(img2img, "__controlnet_original_process_batch") + + def __enter__(self): + if self.process: + self.orig_process = processing.process_images_inner + processing.process_images_inner = getattr( + processing, "__controlnet_original_process_images_inner" + ) + if self.img2img: + self.orig_img2img = img2img.process_batch + img2img.process_batch = getattr( + img2img, "__controlnet_original_process_batch" + ) + + def __exit__(self, *args, **kwargs): + if self.process: + processing.process_images_inner = self.orig_process + if self.img2img: + img2img.process_batch = self.orig_img2img + + +@contextmanager +def cn_allow_script_control(): + orig = False + if "control_net_allow_script_control" in shared.opts.data: + try: + orig = shared.opts.data["control_net_allow_script_control"] + shared.opts.data["control_net_allow_script_control"] = True + yield + finally: + shared.opts.data["control_net_allow_script_control"] = orig + else: + yield diff --git a/adetailer/install.py b/adetailer/install.py new file mode 100644 index 0000000000000000000000000000000000000000..fbbf26ba9b91232fb6297e264571e2c4d105b72a --- /dev/null +++ b/adetailer/install.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +import importlib.util +import subprocess +import sys +from importlib.metadata import version # python >= 3.8 + +from packaging.version import parse + +import_name = {"py-cpuinfo": "cpuinfo", "protobuf": "google.protobuf"} + + +def is_installed( + package: str, min_version: str | None = None, max_version: str | None = None +): + name = import_name.get(package, package) + try: + spec = importlib.util.find_spec(name) + except ModuleNotFoundError: + return False + + if spec is None: + return False + + if not min_version and not max_version: + return True + + if not min_version: + min_version = "0.0.0" + if not max_version: + max_version = "99999999.99999999.99999999" + + try: + pkg_version = version(package) + return parse(min_version) <= parse(pkg_version) <= parse(max_version) + except Exception: + return False + + +def run_pip(*args): + subprocess.run([sys.executable, "-m", "pip", "install", *args]) + + +def install(): + deps = [ + # requirements + ("ultralytics", "8.0.145", None), + ("mediapipe", "0.10.2", None), + ("rich", "13.4.2", None), + # ultralytics + ("py-cpuinfo", None, None), + # mediapipe + ("protobuf", "3.20", "3.9999"), + ] + + for pkg, low, high in deps: + if not is_installed(pkg, low, high): + if low and high: + cmd = f"{pkg}>={low},<={high}" + elif low: + cmd = f"{pkg}>={low}" + elif high: + cmd = f"{pkg}<={high}" + else: + cmd = pkg + + run_pip("-U", cmd) + + +try: + import launch + + skip_install = launch.args.skip_install +except Exception: + skip_install = False + +if not skip_install: + install() diff --git a/adetailer/preload.py b/adetailer/preload.py new file mode 100644 index 0000000000000000000000000000000000000000..10be161f22b0a5ef7083609829a21b547eae9aea --- /dev/null +++ b/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", + ) diff --git a/adetailer/pyproject.toml b/adetailer/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..be1fc0c9bea946d9d1dabd2514f573b5117ae683 --- /dev/null +++ b/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 = "ks2515@naver.com"}, +] +requires-python = ">=3.8,<3.12" +readme = "README.md" +license = {text = "AGPL-3.0"} + +[project.urls] +repository = "https://github.com/Bing-su/adetailer" + +[tool.isort] +profile = "black" +known_first_party = ["launch", "modules"] + +[tool.ruff] +select = ["A", "B", "C4", "C90", "E", "EM", "F", "FA", "I001", "ISC", "N", "PIE", "PT", "RET", "RUF", "SIM", "UP", "W"] +ignore = ["B008", "B905", "E501", "F401", "UP007"] + +[tool.ruff.isort] +known-first-party = ["launch", "modules"] + +[tool.ruff.per-file-ignores] +"sd_webui/*.py" = ["B027", "F403"] diff --git a/adetailer/scripts/!adetailer.py b/adetailer/scripts/!adetailer.py new file mode 100644 index 0000000000000000000000000000000000000000..228b984d2d6587714dbfdb45411a1b6dd2871295 --- /dev/null +++ b/adetailer/scripts/!adetailer.py @@ -0,0 +1,808 @@ +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, +) +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.sd_samplers import all_samplers +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 + + 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()) + samplers = [sampler.name for sampler in all_samplers] + + components, infotext_fields = adui( + num_models, + is_img2img, + model_list, + samplers, + 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_sampler(self, p, args: ADetailerArgs) -> str: + sampler_name = args.ad_sampler if args.ad_use_sampler else p.sampler_name + + if sampler_name in ["PLMS", "UniPC"]: + sampler_name = "Euler" + return sampler_name + + def get_override_settings(self, p, args: ADetailerArgs) -> dict[str, Any]: + d = {} + if args.ad_use_clip_skip: + d["CLIP_stop_at_last_layers"] = args.ad_clip_skip + return d + + def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None: + if args.ad_use_noise_multiplier: + return args.ad_noise_multiplier + return None + + @staticmethod + def infotext(p) -> str: + return create_infotext( + p, p.all_prompts, p.all_seeds, p.all_subseeds, None, 0, 0 + ) + + def write_params_txt(self, p) -> None: + infotext = self.infotext(p) + params_txt = Path(data_path, "params.txt") + params_txt.write_text(infotext, encoding="utf-8") + + def script_filter(self, p, args: ADetailerArgs): + script_runner = copy(p.scripts) + script_args = deepcopy(p.script_args) + self.disable_controlnet_units(script_args) + + ad_only_seleted_scripts = opts.data.get("ad_only_seleted_scripts", True) + if not ad_only_seleted_scripts: + return script_runner, script_args + + ad_script_names = opts.data.get("ad_script_names", SCRIPT_DEFAULT) + script_names_set = { + name + for script_name in ad_script_names.split(",") + for name in (script_name, script_name.strip()) + } + + if args.ad_controlnet_model != "None": + script_names_set.add("controlnet") + + filtered_alwayson = [] + for script_object in script_runner.alwayson_scripts: + filepath = script_object.filename + filename = Path(filepath).stem + if filename in script_names_set: + filtered_alwayson.append(script_object) + + script_runner.alwayson_scripts = filtered_alwayson + return script_runner, script_args + + def disable_controlnet_units(self, script_args: list[Any]) -> None: + for obj in script_args: + if "controlnet" in obj.__class__.__name__.lower(): + if hasattr(obj, "enabled"): + obj.enabled = False + if hasattr(obj, "input_mode"): + obj.input_mode = getattr(obj.input_mode, "SIMPLE", "simple") + + elif isinstance(obj, dict) and "module" in obj: + obj["enabled"] = False + + def get_i2i_p(self, p, args: ADetailerArgs, image): + seed, subseed = self.get_seed(p) + width, height = self.get_width_height(p, args) + steps = self.get_steps(p, args) + cfg_scale = self.get_cfg_scale(p, args) + initial_noise_multiplier = self.get_initial_noise_multiplier(p, args) + sampler_name = self.get_sampler(p, args) + override_settings = self.get_override_settings(p, args) + + i2i = StableDiffusionProcessingImg2Img( + init_images=[image], + resize_mode=0, + denoising_strength=args.ad_denoising_strength, + mask=None, + mask_blur=args.ad_mask_blur, + inpainting_fill=1, + inpaint_full_res=args.ad_inpaint_only_masked, + inpaint_full_res_padding=args.ad_inpaint_only_masked_padding, + inpainting_mask_invert=0, + initial_noise_multiplier=initial_noise_multiplier, + sd_model=p.sd_model, + outpath_samples=p.outpath_samples, + outpath_grids=p.outpath_grids, + prompt="", # replace later + negative_prompt="", + styles=p.styles, + seed=seed, + subseed=subseed, + subseed_strength=p.subseed_strength, + seed_resize_from_h=p.seed_resize_from_h, + seed_resize_from_w=p.seed_resize_from_w, + sampler_name=sampler_name, + batch_size=1, + n_iter=1, + steps=steps, + cfg_scale=cfg_scale, + width=width, + height=height, + restore_faces=args.ad_restore_face, + tiling=p.tiling, + extra_generation_params=p.extra_generation_params, + do_not_save_samples=True, + do_not_save_grid=True, + override_settings=override_settings, + ) + + i2i.cached_c = [None, None] + i2i.cached_uc = [None, None] + i2i.scripts, i2i.script_args = self.script_filter(p, args) + i2i._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, + ) + + @staticmethod + def ensure_rgb_image(image: Any): + if hasattr(image, "mode") and image.mode != "RGB": + image = image.convert("RGB") + return image + + @staticmethod + def i2i_prompts_replace( + i2i, prompts: list[str], negative_prompts: list[str], j: int + ) -> None: + i1 = min(j, len(prompts) - 1) + i2 = min(j, len(negative_prompts) - 1) + prompt = prompts[i1] + negative_prompt = negative_prompts[i2] + i2i.prompt = prompt + i2i.negative_prompt = negative_prompt + + @staticmethod + def compare_prompt(p, processed, n: int = 0): + if p.prompt != processed.all_prompts[0]: + print( + f"[-] ADetailer: applied {ordinal(n + 1)} ad_prompt: {processed.all_prompts[0]!r}" + ) + + if p.negative_prompt != processed.all_negative_prompts[0]: + print( + f"[-] ADetailer: applied {ordinal(n + 1)} ad_negative_prompt: {processed.all_negative_prompts[0]!r}" + ) + + @staticmethod + def need_call_process(p) -> bool: + i = p._ad_idx + bs = p.batch_size + return i % bs == bs - 1 + + @staticmethod + def need_call_postprocess(p) -> bool: + i = p._ad_idx + bs = p.batch_size + return i % bs == 0 + + @rich_traceback + def process(self, p, *args_): + if getattr(p, "_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.") + + p2 = copy(i2i) + for j in range(steps): + p2.image_mask = masks[j] + p2.init_images[0] = self.ensure_rgb_image(p2.init_images[0]) + self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j) + + if re.match(r"^\s*\[SKIP\]\s*$", p2.prompt): + continue + + p2.seed = seed + j + p2.subseed = subseed + j + + try: + processed = process_images(p2) + except NansException as e: + msg = f"[-] ADetailer: 'NansException' occurred with {ordinal(n + 1)} settings.\n{e}" + print(msg, file=sys.stderr) + continue + finally: + p2.close() + + self.compare_prompt(p2, processed, n=n) + p2 = copy(i2i) + p2.init_images = [processed.images[0]] + + if processed is not None: + pp.image = processed.images[0] + return True + + return False + + @rich_traceback + def postprocess_image(self, p, pp, *args_): + if getattr(p, "_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()] + samplers = [sampler.name for sampler in all_samplers] + + def set_value(p, x, xs, *, field: str): + if not hasattr(p, "adetailer_xyz"): + p.adetailer_xyz = {} + p.adetailer_xyz[field] = x + + axis = [ + xyz_grid.AxisOption( + "[ADetailer] ADetailer model 1st", + str, + partial(set_value, field="ad_model"), + choices=lambda: model_list, + ), + xyz_grid.AxisOption( + "[ADetailer] ADetailer prompt 1st", + str, + partial(set_value, field="ad_prompt"), + ), + xyz_grid.AxisOption( + "[ADetailer] ADetailer negative prompt 1st", + str, + partial(set_value, field="ad_negative_prompt"), + ), + xyz_grid.AxisOption( + "[ADetailer] Mask erosion / dilation 1st", + int, + partial(set_value, field="ad_dilate_erode"), + ), + xyz_grid.AxisOption( + "[ADetailer] Inpaint denoising strength 1st", + float, + partial(set_value, field="ad_denoising_strength"), + ), + xyz_grid.AxisOption( + "[ADetailer] Inpaint only masked 1st", + str, + partial(set_value, field="ad_inpaint_only_masked"), + choices=lambda: ["True", "False"], + ), + xyz_grid.AxisOption( + "[ADetailer] Inpaint only masked padding 1st", + int, + partial(set_value, field="ad_inpaint_only_masked_padding"), + ), + xyz_grid.AxisOption( + "[ADetailer] ADetailer sampler 1st", + str, + partial(set_value, field="ad_sampler"), + choices=lambda: samplers, + ), + xyz_grid.AxisOption( + "[ADetailer] ControlNet model 1st", + str, + partial(set_value, field="ad_controlnet_model"), + choices=lambda: ["None", *get_cn_models()], + ), + ] + + if not any(x.label.startswith("[ADetailer]") for x in xyz_grid.axis_options): + xyz_grid.axis_options.extend(axis) + + +def on_before_ui(): + try: + make_axis_on_xyz_grid() + except Exception: + error = traceback.format_exc() + print( + f"[-] ADetailer: xyz_grid error:\n{error}", + file=sys.stderr, + ) + + +script_callbacks.on_ui_settings(on_ui_settings) +script_callbacks.on_after_component(on_after_component) +script_callbacks.on_before_ui(on_before_ui) diff --git a/adetailer/sd_webui/__init__.py b/adetailer/sd_webui/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/adetailer/sd_webui/devices.py b/adetailer/sd_webui/devices.py new file mode 100644 index 0000000000000000000000000000000000000000..51d0569a8ea9dda869ac43ac338835e2fbb27782 --- /dev/null +++ b/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 diff --git a/adetailer/sd_webui/images.py b/adetailer/sd_webui/images.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a2dbce09ad3583f3aac4624c969657103c145e --- /dev/null +++ b/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 diff --git a/adetailer/sd_webui/paths.py b/adetailer/sd_webui/paths.py new file mode 100644 index 0000000000000000000000000000000000000000..8050ba080788f291a3c717016798f3c6531e655e --- /dev/null +++ b/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 diff --git a/adetailer/sd_webui/processing.py b/adetailer/sd_webui/processing.py new file mode 100644 index 0000000000000000000000000000000000000000..9787d02c0759ca8eb1d0dcc74455c0682a402cd5 --- /dev/null +++ b/adetailer/sd_webui/processing.py @@ -0,0 +1,179 @@ +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, + use_main_prompt: bool = False, + index: int | None = None, + all_negative_prompts: list[str] | None = None, + ) -> str: + pass + + def process_images( + p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img, + ) -> Processed: + pass + +else: + from modules.processing import ( + Processed, + StableDiffusionProcessing, + StableDiffusionProcessingImg2Img, + StableDiffusionProcessingTxt2Img, + create_infotext, + process_images, + ) diff --git a/adetailer/sd_webui/safe.py b/adetailer/sd_webui/safe.py new file mode 100644 index 0000000000000000000000000000000000000000..c17a1b97d3ce1590d0ecfa49373a826a300404d1 --- /dev/null +++ b/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 diff --git a/adetailer/sd_webui/script_callbacks.py b/adetailer/sd_webui/script_callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..ebb3ac0507bd32a8b6f962e9796829f539cd31a1 --- /dev/null +++ b/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, + ) diff --git a/adetailer/sd_webui/scripts.py b/adetailer/sd_webui/scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..e515bbb70e5e6f880892b80a9af62f96efe87dea --- /dev/null +++ b/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, + ) diff --git a/adetailer/sd_webui/sd_samplers.py b/adetailer/sd_webui/sd_samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..168198f25ad7b4e88bd0077aba61a38c66897058 --- /dev/null +++ b/adetailer/sd_webui/sd_samplers.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from typing import Any, Callable, NamedTuple + + class SamplerData(NamedTuple): + name: str + constructor: Callable + aliases: list[str] + options: dict[str, Any] + + all_samplers: list[SamplerData] = [] + +else: + from modules.sd_samplers import all_samplers diff --git a/adetailer/sd_webui/shared.py b/adetailer/sd_webui/shared.py new file mode 100644 index 0000000000000000000000000000000000000000..18b0cd0801e652340c86dc6e7074fb0c92b99cc4 --- /dev/null +++ b/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 diff --git a/artists-to-study/.github/FUNDING.yml b/artists-to-study/.github/FUNDING.yml new file mode 100644 index 0000000000000000000000000000000000000000..2cba5d94b1f7872a97dc2293bf4c0f5dbfd55ac0 --- /dev/null +++ b/artists-to-study/.github/FUNDING.yml @@ -0,0 +1,13 @@ +# These are supported funding model platforms + +github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] +patreon: camenduru +open_collective: # Replace with a single Open Collective username +ko_fi: camenduru +tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel +community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry +liberapay: # Replace with a single Liberapay username +issuehunt: # Replace with a single IssueHunt username +otechie: # Replace with a single Otechie username +lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry +custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] diff --git a/artists-to-study/LICENSE b/artists-to-study/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..fdddb29aa445bf3d6a5d843d6dd77e10a9f99657 --- /dev/null +++ b/artists-to-study/LICENSE @@ -0,0 +1,24 @@ +This is free and unencumbered software released into the public domain. + +Anyone is free to copy, modify, publish, use, compile, sell, or +distribute this software, either in source code form or as a compiled +binary, for any purpose, commercial or non-commercial, and by any +means. + +In jurisdictions that recognize copyright laws, the author or authors +of this software dedicate any and all copyright interest in the +software to the public domain. We make this dedication for the benefit +of the public at large and to the detriment of our heirs and +successors. We intend this dedication to be an overt act of +relinquishment in perpetuity of all present and future rights to this +software under copyright law. + +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 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. + +For more information, please refer to
+🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
+🥳 Please join my patreon community https://patreon.com/camenduru
+ +# Artists To Study + + + +https://artiststostudy.pages.dev addapted to an extension for [web ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui). + +To install it, clone the repo into the `extensions` directory and restart the web ui: + +`git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study` + +You can add the artist name to the clipboard by clicking on it. (thanks for the idea @gmaciocci) diff --git a/artists-to-study/artists.csv b/artists-to-study/artists.csv new file mode 100644 index 0000000000000000000000000000000000000000..97cc5f6f7d1dae481b0ca6e3eb70542185fffe64 --- /dev/null +++ b/artists-to-study/artists.csv @@ -0,0 +1,3041 @@ +artist,score,category +Peter Max,0.99715996,weird +Roy Lichtenstein,0.98272276,cartoon +Romero Britto,0.9498342,scribbles +Keith Haring,0.9431302,weird +Hiroshige,0.93995106,ukioe +Joan Miró,0.9169429,scribbles +Jean-Michel Basquiat,0.90080947,scribbles +Katsushika Hokusai,0.8887236,ukioe +Paul Klee,0.8868682,scribbles +Marc Chagall,0.8868168,scribbles +Karl Schmidt-Rottluff,0.88444495,scribbles +Howard Hodgkin,0.8808578,scribbles +Jean Metzinger,0.88056004,scribbles +Alma Thomas,0.87658304,weird +Rufino Tamayo,0.8749848,scribbles +Utagawa Hiroshige,0.8728796,ukioe +Chagall,0.8718535,scribbles +Harumi Hironaka,0.86914605,scribbles +Hans Hofmann,0.8686159,scribbles +Kawanabe Kyōsai,0.86612236,ukioe +Andy Warhol,0.8654825,scribbles +Barbara Takenaga,0.86223894,scribbles +Tatsuro Kiuchi,0.8597267,cartoon +Vincent Van Gogh,0.85538065,scribbles +Wassily Kandinsky,0.85490596,scribbles +Georges Seurat,0.8534801,scribbles +Karel Appel,0.8529153,scribbles +Sonia Delaunay,0.8506156,scribbles +Hokusai,0.85046995,ukioe +Eduardo Kobra,0.85036755,weird +Fra Angelico,0.84984255,fineart +Milton Avery,0.849746,scribbles +David Hockney,0.8496144,scribbles +Hiroshi Nagai,0.847129,cartoon +Aristarkh Lentulov,0.846537,scribbles +Lyonel Feininger,0.84573764,scribbles +Mary Blair,0.845709,scribbles +Ellsworth Kelly,0.8455428,scribbles +Jun Kaneko,0.8448367,scribbles +Roz Chast,0.8432013,weird +Ida Rentoul Outhwaite,0.84275174,scribbles +Robert Motherwell,0.8409468,scribbles +Garry Winogrand,0.83994275,black-white +Andrei Rublev,0.83950496,fineart +Alexander Calder,0.83832693,scribbles +Tomokazu Matsuyama,0.8376121,scribbles +August Macke,0.8362022,scribbles +Kazimir Malevich,0.8356527,scribbles +Richard Scarry,0.83554685,scribbles +Victor Vasarely,0.8335438,scribbles +Kitagawa Utamaro,0.83333457,ukioe +Matt Bors,0.83252287,scribbles +Emil Nolde,0.8323225,scribbles +Patrick Caulfield,0.8322225,scribbles +Charles Blackman,0.83200824,scribbles +Peter Doig,0.83111644,scribbles +Alexej von Jawlensky,0.8308932,scribbles +Rumiko Takahashi,0.8301817,anime +Eileen Agar,0.82945526,scribbles +Ernst Ludwig Kirchner,0.82756275,scribbles +Nicolas Delort,0.8261329,scribbles +Marsden Hartley,0.8250993,scribbles +Keith Negley,0.8212553,scribbles +Jamini Roy,0.8212199,scribbles +Quentin Blake,0.82115215,scribbles +Andy Kehoe,0.82063186,cartoon +George barbier,0.82046914,fineart +Frans Masereel,0.81997275,scribbles +Umberto Boccioni,0.81921184,scribbles +Conrad Roset,0.8190752,cartoon +Paul Ranson,0.81903255,scribbles +Yayoi Kusama,0.81886625,weird +Tomi Ungerer,0.81848705,scribbles +Saul Steinberg,0.81778854,scribbles +Jon Klassen,0.81773067,scribbles +W.W. Denslow,0.81708044,fineart +Helen Frankenthaler,0.81704986,scribbles +Jean Jullien,0.816437,scribbles +Brett Whiteley,0.81601924,scribbles +Giotto Di Bondone,0.81427747,fineart +Takashi Murakami,0.81338763,weird +Howard Finster,0.81333554,scribbles +Eduardo Paolozzi,0.81312317,scribbles +Charles Rennie Mackintosh,0.81297064,scribbles +Brandon Mably,0.8128239,weird +Rebecca Louise Law,0.81214285,weird +Victo Ngai,0.81195843,cartoon +Hanabusa Itchō II,0.81187993,ukioe +Edmund Dulac,0.81104875,scribbles +Ben Shahn,0.8104582,scribbles +Howard Arkley,0.8103746,scribbles +Wilfredo Lam,0.8096211,scribbles +Michael Deforge,0.8095954,scribbles +John Hoyland,0.8094592,fineart +Francesco Clemente,0.8090387,scribbles +Leonetto Cappiello,0.8087691,scribbles +Norman Ackroyd,0.80788493,scribbles +Bhupen Khakhar,0.8077607,scribbles +Jeremiah Ketner,0.8075384,cartoon +Chris Ofili,0.8073793,scribbles +Banksy,0.80695426,scribbles +Tom Whalen,0.805867,scribbles +Ernst Wilhelm Nay,0.805295,scribbles +Henri Rousseau,0.8049866,scribbles +Kunisada,0.80493814,ukioe +Naoko Takeuchi,0.80482674,anime +Kaethe Butcher,0.80406916,scribbles +Hasui Kawase,0.8040483,ukioe +Alvin Langdon Coburn,0.8035004,black-white +Stanley Donwood,0.8033054,scribbles +Agnes Martin,0.8028028,scribbles +Osamu Tezuka,0.8005524,cartoon +Frank Stella,0.80049455,scribbles +Dale Chihuly,0.79982775,digipa-high-impact +Evgeni Gordiets,0.79967916,scribbles +Janek Sedlar,0.7993992,fineart +Alasdair Gray,0.7992301,scribbles +Yasuo Kuniyoshi,0.79870003,ukioe +Edward Gorey,0.7984938,scribbles +Johannes Itten,0.798481,scribbles +Cuno Amiet,0.7979497,scribbles +M.C. Escher,0.7976657,scribbles +Albert Irvin,0.79688835,scribbles +Jack Gaughan,0.79443675,scribbles +Ravi Zupa,0.7939542,scribbles +Kay Nielsen,0.79385525,scribbles +Agnolo Gaddi,0.79369193,fineart +Alessandro Gottardo,0.79321593,scribbles +Paul Laffoley,0.79196846,scribbles +Giovanni Battista Piranesi,0.79111177,fineart +Adrian Tomine,0.79109013,scribbles +Adolph Gottlieb,0.79061794,scribbles +Milton Caniff,0.7905358,cartoon +Philip Guston,0.78994095,scribbles +Debbie Criswell,0.7895031,cartoon +Alice Pasquini,0.78949904,cartoon +Johannes Vermeer,0.78931487,fineart +Lisa Frank,0.7892591,cartoon +Patrick Heron,0.78889126,scribbles +Mikhail Nesterov,0.78814346,fineart +Cézanne,0.7879481,scribbles +Tristan Eaton,0.787513,scribbles +Jillian Tamaki,0.7868066,scribbles +Takato Yamamoto,0.78460765,ukioe +Martiros Saryan,0.7844924,scribbles +Emil Orlik,0.7842625,scribbles +Armand Guillaumin,0.7840431,scribbles +Jane Newland,0.7837676,scribbles +Paul Cézanne,0.78368753,scribbles +Tove Jansson,0.78356475,scribbles +Guido Crepax,0.7835321,cartoon +OSGEMEOS,0.7829088,weird +Albert Watson,0.48901254,digipa-med-impact +Emory Douglas,0.78179604,scribbles +Chris Van Allsburg,0.66413003,fineart +Ohara Koson,0.78132576,ukioe +Nicolas de Stael,0.7802779,scribbles +Aubrey Beardsley,0.77970016,scribbles +Hishikawa Moronobu,0.7794119,ukioe +Alfred Wallis,0.77926695,scribbles +Friedensreich Hundertwasser,0.7791805,scribbles +Eyvind Earle,0.7788089,scribbles +Giotto,0.7785216,fineart +Simone Martini,0.77843,fineart +Ivan Bilibin,0.77720606,fineart +Karl Blossfeldt,0.77652574,black-white +Duy Huynh,0.77634746,scribbles +Giovanni da Udina,0.7763063,fineart +Henri-Edmond Cross,0.7762994,fineart +Barry McGee,0.77618384,scribbles +William Kentridge,0.77615225,scribbles +Alexander Archipenko,0.7759824,scribbles +Jaume Plensa,0.7756799,weird +Bill Jacklin,0.77504414,fineart +Alberto Vargas,0.7747376,cartoon +Jean Dubuffet,0.7744374,scribbles +Eugène Grasset,0.7741958,fineart +Arthur Rackham,0.77418125,fineart +Yves Tanguy,0.77380997,scribbles +Elsa Beskow,0.7736908,fineart +Georgia O’Keeffe,0.77368987,scribbles +Georgia O'Keeffe,0.77368987,scribbles +Henri Cartier-Bresson,0.7735415,black-white +Andrea del Verrocchio,0.77307427,fineart +Mark Rothko,0.77294236,scribbles +Bruce Gilden,0.7256681,black-white +Gino Severini,0.77247965,scribbles +Delphin Enjolras,0.5594248,fineart +Alena Aenami,0.77210015,cartoon +Ed Freeman,0.42526615,digipa-low-impact +Apollonia Saintclair,0.7718383,anime +László Moholy-Nagy,0.771497,scribbles +Louis Glackens,0.7713224,fineart +Fang Lijun,0.77097225,fineart +Alfred Kubin,0.74409986,fineart +David Wojnarowicz,0.7705802,scribbles +Tara McPherson,0.77023256,scribbles +Gustav Doré,0.7367536,fineart +Patricia Polacco,0.7696109,scribbles +Norman Bluhm,0.7692634,fineart +Elizabeth Gadd,0.7691194,digipa-high-impact +Gabriele Münter,0.7690926,scribbles +David Inshaw,0.76905304,scribbles +Maurice Sendak,0.7690118,cartoon +Harry Clarke,0.7688428,cartoon +Howardena Pindell,0.7686921,n +Jamie Hewlett,0.7680373,scribbles +Steve Ditko,0.76725733,scribbles +Annie Soudain,0.7671485,scribbles +Albert Gleizes,0.76658314,scribbles +Henry Fuseli,0.69147265,fineart +Alain Laboile,0.67634284,c +Albrecht Altdorfer,0.7663378,fineart +Jack Butler Yeats,0.7661406,fineart +Yue Minjun,0.76583517,scribbles +Art Spiegelman,0.7656343,scribbles +Grete Stern,0.7656276,fineart +Mordecai Ardon,0.7648692,scribbles +Joel Sternfeld,0.76456416,digipa-high-impact +Milton Glaser,0.7641823,scribbles +Eishōsai Chōki,0.7639659,scribbles +Domenico Ghirlandaio,0.76372653,fineart +Alex Timmermans,0.64443207,digipa-high-impact +Andreas Vesalius,0.763446,fineart +Bruce McLean,0.76335883,scribbles +Jacob Lawrence,0.76330304,scribbles +Alex Katz,0.76317835,scribbles +Henri de Toulouse-Lautrec,0.76268333,scribbles +Franz Sedlacek,0.762062,scribbles +Paul Lehr,0.70854837,cartoon +Nicholas Roerich,0.76117516,scribbles +Henri Matisse,0.76110923,scribbles +Colin McCahon,0.76086944,scribbles +Max Dupain,0.6661642,black-white +Stephen Gammell,0.74001735,weird +Alberto Giacometti,0.7596302,scribbles +Goyō Hashiguchi,0.7595048,ukioe +Gustave Doré,0.7018832,fineart +Butcher Billy,0.7593378,cartoon +Pieter de Hooch,0.75916564,fineart +Gaetano Pesce,0.75906265,scribbles +Winsor McCay,0.7589382,scribbles +Claude Cahun,0.7588153,weird +Roger Ballen,0.64683115,black-white +Ellen Gallagher,0.758621,scribbles +Anton Corbijn,0.5550669,digipa-high-impact +Margaret Macdonald Mackintosh,0.75781375,fineart +Franz Kline,0.7576461,scribbles +Cimabue,0.75720495,fineart +André Kertész,0.7319392,black-white +Hans Hartung,0.75718236,scribbles +J. 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Kusama_0.81886625_0072.jpg b/artists-to-study/ats/thumbnail/spaceship/weird/weird_Yayoi Kusama_0.81886625_0072.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ebc48a28efc122907b728b38abb1b8bbc0c887fd --- /dev/null +++ b/artists-to-study/ats/thumbnail/spaceship/weird/weird_Yayoi Kusama_0.81886625_0072.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea3518199c69b5e0c924fa8961a2d232e22c6a4a23223b5f3c7351394d207c15 +size 31050 diff --git a/artists-to-study/javascript/artists_to_study.js b/artists-to-study/javascript/artists_to_study.js new file mode 100644 index 0000000000000000000000000000000000000000..da22d0cd26c6d7e2c35882fe188c4482cd702355 --- /dev/null +++ b/artists-to-study/javascript/artists_to_study.js @@ -0,0 +1,22 @@ +document.addEventListener("DOMContentLoaded", function() { + var mutationObserver = new MutationObserver(function(m){ + var galleries = gradioApp().querySelectorAll(`div[id^="ats-gallery"]`); + if (galleries) { + galleries.forEach(gallery => { + gallery.querySelectorAll('button').forEach(el => el.addEventListener( + "click", + event => { + const div = el.querySelector('div'); + if (div){ + const inside_div = div.querySelector('div').textContent + const after_ = inside_div.substring(inside_div.indexOf('_') + 1); + const before_ = after_.substring(0, after_.indexOf('_')); + navigator.clipboard.writeText(decodeURI(before_)); + } + } + )); + }); + } + }); + mutationObserver.observe( gradioApp(), { childList:true, subtree:true }); +}); diff --git a/artists-to-study/scripts/artists_to_study.py b/artists-to-study/scripts/artists_to_study.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3d738804d2b0e15d9856c80f09e7629ced2568 --- /dev/null +++ b/artists-to-study/scripts/artists_to_study.py @@ -0,0 +1,40 @@ +import os +import gradio as gr +from modules import scripts, script_callbacks +root_path = os.path.join(scripts.basedir(), "ats\\thumbnail") + +jpg_paths = ['anime', 'cartoon', 'digipa-high-impact', 'digipa-med-impact', 'digipa-low-impact', 'fareast', 'fineart', 'scribbles', 'special', 'ukioe', 'weird', 'black-white', 'nudity', 'c', 'n'] +prompt_paths = ['dog', 'house', 'portrait', 'spaceship'] + +def get_images(jpg_path): + images = [] + for jpg_image in os.listdir(f"{jpg_path}"): + final_path = f"{jpg_path}\\{jpg_image}" + final_path = final_path.replace('\\', os.sep).replace('/', os.sep) + try: + images.append((final_path, f"{jpg_image}")) + except Exception as e: + print(final_path, e) + return images + +def on_ui_tabs(): + with gr.Blocks() as artists_to_study: + for prompt_path in prompt_paths: + with gr.Tab(prompt_path): + for jpg_path in jpg_paths: + with gr.Tab(jpg_path): + input_path = f"{root_path}\\{prompt_path}\\{jpg_path}" + input_path = input_path.replace('\\', os.sep).replace('/', os.sep) + gallery_label = f"{prompt_path}-{jpg_path}" + txt = gr.Textbox(value=input_path, interactive=False, show_label=False, visible=True) + btn = gr.Button(value="Get Images", elem_id=f"ats-button-{prompt_path}-{jpg_path}") + gallery = gr.Gallery(label=gallery_label, show_label=True, elem_id=f"ats-gallery-{prompt_path}-{jpg_path}").style(grid=[5], height="auto") + btn.click(get_images, txt, gallery) + gr.HTML( + """ +
artists to study extension by camenduru | github | twitter | youtube | hi-res images
All images generated with CompVis/stable-diffusion-v1-4 + artists.csv | License: Attribution 4.0 International (CC BY 4.0)
+ 
+ 
+ Catppuccin for Stable Diffusion Web UI
+
+
+
+
+ 
+ 
+
+
+
+🌻 Latte
+
+
+
+
+🪴 Frappé
+
+
+
+
+🌺 Macchiato
+
+
+
+
+
+## Usage
+
+### Extension (Recommended)
+
+1. Open WebUI
+2. Click on the "Extensions" tab
+3. Click on the "Install from URL" subsection
+4. Paste `https://github.com/catppuccin/stable-diffusion-webui` into the URL spot, set the local directory name to `Catppuccin Theme`, and install
+5. Go to "Installed" subsection and click "Apply and restart UI"
+6. Configure the theme from the "Settings" tab under "Catppuccin Theme", afterwards you must "Apply Settings" and "Reload UI"
+
+### Extension (Extension index)
+
+NOTE: This method will install the theme under the name "stable-diffusion-webui". You can read more about this issue in the next section.
+
+1. Open WebUI
+2. Click on the "Extensions" tab and navigate to "Available"
+3. Click "Load From:" and find and install "Catppuccin Theme" from the list
+4. Go to "Installed" subsection and click "Apply and restart UI"
+5. Configure the theme from the "Settings" tab under "Catppuccin Theme", afterwards you must "Apply Settings" and "Reload UI"
+
+### Add directly
+
+1. Clone this repository locally
+2. Create the file `user.css` in your stable-diffusion-webui directory
+3. Copy the contents of the desired flavor into `user.css`
+4. Restart the Web UI or click "Reload UI" at the bottom of the screen
+
+## NOTE
+
+Due to catppuccin naming conventions and limitations in webui's extension names, this extension will come up as "stable-diffusion-webui" in the extension update section. I have proposed a solution to WebUI [here](https://github.com/catppuccin/stable-diffusion-webui/issues/5#issuecomment-1420599298), feel free to upvote it increase the chances of this issue being resolved.
+
+## 🙋 FAQ
+
+- Q: **_"I decided not to use the extennsion, how do I change the accent color?"_**\
+ A: Open `user.css` and set the variavble `--accent: var(--{preferred color});`
+- Q: **_"The theme isn't being applied???"_**\
+ A: Sometimes Web UI doesn't automatically figure out your preferred theme. Try adding `/?__theme=dark` to the url and see if the theme is applied. Below are the steps to make this change persistent on different systems:
+
+ - **Linux / MacOS**: Edit `webui-user.sh` and uncomment and set this variable as `export COMMANDLINE_ARGS="--theme=dark $COMMANDLINE_ARGS"`
+ - **Windows**: Edit `webui-user.bat` and set this variable by `set COMMANDLINE_ARGS=--theme=dark`.
+
+ If you are still having trouble then consult the relevant part of the [webui wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#usercss).
+
+- Q: **_"Why doesn't the theme look like the previews?"_**\
+ A: The main UI is constantly being changed upstream, so our previews may not match what the theme will look like for you. Though the previews may be behind, we are frequently updating the themes whenever there is a change in the Web UI repo. Though if you notice a style issue with a recent version of webui, feel free to open an issue on this repo.
+- Q: **_"Why isn't {extension} themed?"_**\
+ A: Some extensions use their own style sheets that don't play nice with `user.css`.
+
+## 💝 Thanks to
+
+- [Kettukaa](https://github.com/Kettukaa)
+- [Ryan Cao](https://github.com/ryanccn)
+
+
+
+🌿 Mocha
+
+
+ 
+
+ Copyright © 2021-present Catppuccin Org +
+ +
+ 
+
+
+Information
+
+
+
diff --git a/matic-promptgen/javascript/promptgen.js b/matic-promptgen/javascript/promptgen.js
new file mode 100644
index 0000000000000000000000000000000000000000..e24478acb1f3a851b52e7ff99b47ad08cd0d2ee1
--- /dev/null
+++ b/matic-promptgen/javascript/promptgen.js
@@ -0,0 +1,22 @@
+
+function promptgen_send_to(where, text){
+ textarea = gradioApp().querySelector('#promptgen_selected_text textarea')
+ textarea.value = text
+ updateInput(textarea)
+
+ gradioApp().querySelector('#promptgen_send_to_'+where).click()
+
+ where == 'txt2img' ? switch_to_txt2img() : switch_to_img2img()
+}
+
+function promptgen_send_to_txt2img(text){ promptgen_send_to('txt2img', text) }
+function promptgen_send_to_img2img(text){ promptgen_send_to('img2img', text) }
+
+function submit_promptgen(){
+ var id = randomId()
+ requestProgress(id, gradioApp().getElementById('promptgen_results_column'), null, function(){})
+
+ var res = create_submit_args(arguments)
+ res[0] = id
+ return res
+}
diff --git a/matic-promptgen/screenshot.png b/matic-promptgen/screenshot.png
new file mode 100644
index 0000000000000000000000000000000000000000..514f5dddbfbe55ae8042e3e0b7b78c09e7221e71
--- /dev/null
+++ b/matic-promptgen/screenshot.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e542302b04091517f64e69a07321aa952666f942d3d039ea818d1f8aa28b4e5c
+size 231634
diff --git a/matic-promptgen/scripts/promptgen.py b/matic-promptgen/scripts/promptgen.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a9489617261c5c5757acf0e3ab112e193e1dae0
--- /dev/null
+++ b/matic-promptgen/scripts/promptgen.py
@@ -0,0 +1,243 @@
+import html
+import os
+import time
+
+import torch
+import transformers
+
+from modules import shared, generation_parameters_copypaste
+
+from modules import scripts, script_callbacks, devices, ui
+import gradio as gr
+
+from modules.ui_components import FormRow
+
+
+class Model:
+ name = None
+ model = None
+ tokenizer = None
+
+
+available_models = []
+current = Model()
+
+base_dir = scripts.basedir()
+models_dir = os.path.join(base_dir, "models")
+
+
+def device():
+ return devices.cpu if shared.opts.promptgen_device == 'cpu' else devices.device
+
+
+def list_available_models():
+ available_models.clear()
+
+ os.makedirs(models_dir, exist_ok=True)
+
+ for dirname in os.listdir(models_dir):
+ if os.path.isdir(os.path.join(models_dir, dirname)):
+ available_models.append(dirname)
+
+ for name in [x.strip() for x in shared.opts.promptgen_names.split(",")]:
+ if not name:
+ continue
+
+ available_models.append(name)
+
+
+def get_model_path(name):
+ dirname = os.path.join(models_dir, name)
+ if not os.path.isdir(dirname):
+ return name
+
+ return dirname
+
+
+def generate_batch(input_ids, min_length, max_length, num_beams, temperature, repetition_penalty, length_penalty, sampling_mode, top_k, top_p):
+ top_p = float(top_p) if sampling_mode == 'Top P' else None
+ top_k = int(top_k) if sampling_mode == 'Top K' else None
+
+ outputs = current.model.generate(
+ input_ids,
+ do_sample=True,
+ temperature=max(float(temperature), 1e-6),
+ repetition_penalty=repetition_penalty,
+ length_penalty=length_penalty,
+ top_p=top_p,
+ top_k=top_k,
+ num_beams=int(num_beams),
+ min_length=min_length,
+ max_length=max_length,
+ pad_token_id=current.tokenizer.pad_token_id or current.tokenizer.eos_token_id
+ )
+ texts = current.tokenizer.batch_decode(outputs, skip_special_tokens=True)
+ return texts
+
+
+def model_selection_changed(model_name):
+ if model_name == "None":
+ current.tokenizer = None
+ current.model = None
+ current.name = None
+
+ devices.torch_gc()
+
+
+def generate(id_task, model_name, batch_count, batch_size, text, *args):
+ shared.state.textinfo = "Loading model..."
+ shared.state.job_count = batch_count
+
+ if current.name != model_name:
+ current.tokenizer = None
+ current.model = None
+ current.name = None
+
+ if model_name != 'None':
+ path = get_model_path(model_name)
+ current.tokenizer = transformers.AutoTokenizer.from_pretrained(path)
+ current.model = transformers.AutoModelForCausalLM.from_pretrained(path)
+ current.name = model_name
+
+ assert current.model, 'No model available'
+ assert current.tokenizer, 'No tokenizer available'
+
+ current.model.to(device())
+
+ shared.state.textinfo = ""
+
+ input_ids = current.tokenizer(text, return_tensors="pt").input_ids
+ if input_ids.shape[1] == 0:
+ input_ids = torch.asarray([[current.tokenizer.bos_token_id]], dtype=torch.long)
+ input_ids = input_ids.to(device())
+ input_ids = input_ids.repeat((batch_size, 1))
+
+ markup = '|
+
+
+{html.escape(generated_text)} + |
++ to txt2img + to img2img + | +
+ 
+
+ 
+
+ 
+
+ 
+
+
+
+ 
+
+
+
+
+
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the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98.\\n\\nUse it with the stablediffusion repository: download the v2-1_768-ema-pruned.ckpt here.\\nUse it with \\uD83E\\uDDE8 diffusers\\nModel Details\\nDeveloped by: Robin Rombach, Patrick Esser\\n\\nModel type: Diffusion-based text-to-image generation model\\n\\nLanguage(s): English\\n\\nLicense: CreativeML Open RAIL++-M License\\n\\nModel Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).\\n\\nResources for more information: GitHub Repository.
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For more information about how Stable Diffusion functions, please have a look at \uD83E\uDD17's Stable Diffusion blog.\n\nThe Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on \"laion-aesthetics v2 5+\" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.\n\nYou can use this both with the \uD83E\uDDE8Diffusers library and the RunwayML GitHub repository.
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"https://msdn.miaoshouai.com/msai/kt/ez/9900922.png" + } + ] + } + ] + } +] \ No newline at end of file diff --git a/miaoshouai-assistant-main/configs/settings.json b/miaoshouai-assistant-main/configs/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..d578c6553d11674796cd2567a7e469431dd9a444 --- /dev/null +++ b/miaoshouai-assistant-main/configs/settings.json @@ -0,0 +1,20 @@ +{ + "boot_settings": { + "drp_args_vram": "CPU Only", + "drp_args_theme": "Dark Mode", + "txt_args_listen_port": "7860", + "Enable xFormers": "--xformers", + "No Half": "--no-half", + "No Half VAE": "False", + "Enable API": "--api", + "Auto Launch": "--autolaunch", + "Allow Local Network Access": "False", + "drp_choose_version": "Official Release", + "txt_args_more": "", + "auto_vram": true, + "disable_log_console_output": true, + "model_source": "liandange.com", + "my_model_source": "civitai.com", + "openai_api": "" + } +} \ No newline at end of file diff --git a/miaoshouai-assistant-main/configs/webui-macos-env.sh b/miaoshouai-assistant-main/configs/webui-macos-env.sh new file mode 100644 index 0000000000000000000000000000000000000000..37cac4fb02c5ea53fa270336729b90ac2ef3ac74 --- /dev/null +++ b/miaoshouai-assistant-main/configs/webui-macos-env.sh @@ -0,0 +1,19 @@ +#!/bin/bash +#################################################################### +# macOS defaults # +# Please modify webui-user.sh to change these instead of this file # +#################################################################### + +if [[ -x "$(command -v python3.10)" ]] +then + python_cmd="python3.10" +fi + +export install_dir="$HOME" +export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate" +export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1" +export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git" +export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" +export PYTORCH_ENABLE_MPS_FALLBACK=1 + +#################################################################### diff --git a/miaoshouai-assistant-main/configs/webui-user-launch.bat b/miaoshouai-assistant-main/configs/webui-user-launch.bat new file mode 100644 index 0000000000000000000000000000000000000000..ef0552f3ca69a7ab8a6191ee62118de737f55865 --- /dev/null +++ b/miaoshouai-assistant-main/configs/webui-user-launch.bat @@ -0,0 +1,10 @@ +echo off +set GIT_PYTHON_GIT_EXECUTABLE=git\\bin\\git.exe + +echo AIԴվ +echo http://resource.miaoshouai.com ȡԴ +set GIT_PYTHON_REFRESH=quiet +call update.bat + +python\python.exe webui.py --autolaunch --api --xformers --medvram --deepdanbooru +pause \ No newline at end of file diff --git a/miaoshouai-assistant-main/configs/webui-user.sh b/miaoshouai-assistant-main/configs/webui-user.sh new file mode 100644 index 0000000000000000000000000000000000000000..5f2a7dd57b11fd77c641a0023a1d9b82ab50281b --- /dev/null +++ b/miaoshouai-assistant-main/configs/webui-user.sh @@ -0,0 +1,47 @@ +#!/bin/bash +######################################################### +# Uncomment and change the variables below to your need:# +######################################################### + +# Install directory without trailing slash +#install_dir="/home/$(whoami)" + +# Name of the subdirectory +#clone_dir="stable-diffusion-webui" + +# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention" +# uncomment the line below based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon +export COMMANDLINE_ARGS="--skip-torch-cuda-test --no-half --use-cpu all" + +# python3 executable +#python_cmd="python3" + +# git executable +#export GIT="git" + +# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv) +#venv_dir="venv" + +# script to launch to start the app +#export LAUNCH_SCRIPT="launch.py" + +# install command for torch +#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113" + +# Requirements file to use for stable-diffusion-webui +#export REQS_FILE="requirements_versions.txt" + +# Fixed git repos +#export K_DIFFUSION_PACKAGE="" +#export GFPGAN_PACKAGE="" + +# Fixed git commits +#export STABLE_DIFFUSION_COMMIT_HASH="" +#export TAMING_TRANSFORMERS_COMMIT_HASH="" +#export CODEFORMER_COMMIT_HASH="" +#export BLIP_COMMIT_HASH="" + +# Uncomment to enable accelerated launch +#export ACCELERATE="True" + +########################################### diff --git a/miaoshouai-assistant-main/install.py b/miaoshouai-assistant-main/install.py new file mode 100644 index 0000000000000000000000000000000000000000..589a47197bc0ee8169c28a3ece26ab585a1e3feb --- /dev/null +++ b/miaoshouai-assistant-main/install.py @@ -0,0 +1,31 @@ +import launch +import os +import gzip +import io +import git +import shutil + +def install_preset_models_if_needed(): + assets_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "assets") + configs_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs") + + for model_filename in ["civitai_models.json", "liandange_models.json", "gpt_index.json"]: + gzip_file = os.path.join(assets_folder, f"{model_filename}.gz") + target_file = os.path.join(configs_folder, f"{model_filename}") + if not os.path.exists(target_file): + with gzip.open(gzip_file, "rb") as compressed_file: + with io.TextIOWrapper(compressed_file, encoding="utf-8") as decoder: + content = decoder.read() + with open(target_file, "w") as model_file: + model_file.write(content) + +req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt") + +with open(req_file) as file: + for lib in file: + lib = lib.strip() + if not launch.is_installed(lib): + launch.run_pip(f"install {lib}", f"Miaoshou assistant requirement: {lib}") + +install_preset_models_if_needed() + diff --git a/miaoshouai-assistant-main/requirements.txt b/miaoshouai-assistant-main/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..75ceccc3324c0bdbd52e07f19aae6d9cc214a41a --- /dev/null +++ b/miaoshouai-assistant-main/requirements.txt @@ -0,0 +1,8 @@ +psutil +rehash +tqdm +openai +numba +gpt_index==0.4.24 +langchain==0.0.132 +gradio_client==0.2.7 \ No newline at end of file diff --git a/miaoshouai-assistant-main/scripts/assistant/__init__.py b/miaoshouai-assistant-main/scripts/assistant/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bf393efc7ee6015f8076f3ce7739143f58642654 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/assistant/__init__.py @@ -0,0 +1 @@ +__all__ = ["miaoshou"] diff --git a/miaoshouai-assistant-main/scripts/assistant/miaoshou.py b/miaoshouai-assistant-main/scripts/assistant/miaoshou.py new file mode 100644 index 0000000000000000000000000000000000000000..def205d250e4ebf706dea01d676913af08dd9867 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/assistant/miaoshou.py @@ -0,0 +1,462 @@ +import os +import sys +import typing as t + +import gradio as gr +from modules.sd_models import CheckpointInfo +from modules.call_queue import wrap_queued_call +import launch +import modules +from modules import shared +from scripts.msai_logging.msai_logger import Logger +from scripts.runtime.msai_prelude import MiaoshouPrelude +from scripts.runtime.msai_runtime import MiaoshouRuntime +import modules.generation_parameters_copypaste as parameters_copypaste +from modules.ui_components import ToolButton + + +class MiaoShouAssistant(object): + # default css definition + default_css = '#my_model_cover{width: 100px;} #my_model_trigger_words{width: 200px;}' + + def __init__(self) -> None: + self.logger = Logger() + self.prelude = MiaoshouPrelude() + self.runtime = MiaoshouRuntime() + self.refresh_symbol = '\U0001f504' + self.coffee_symbol = '\U0001f9cb' # 🧋 + self.folder_symbol = '\U0001f4c2' # 📂 + + def on_event_ui_tabs_opened(self) -> t.List[t.Optional[t.Tuple[t.Any, str, str]]]: + with gr.Blocks(analytics_enabled=False, css=MiaoShouAssistant.default_css) as miaoshou_assistant: + self.create_subtab_boot_assistant() + self.create_subtab_model_management() + self.create_subtab_model_download() + self.create_subtab_update() + + return [(miaoshou_assistant.queue(), "Miaoshou Assistant", "miaoshou_assistant")] + + def create_subtab_boot_assistant(self) -> None: + with gr.TabItem('Boot Assistant', elem_id="boot_assistant_tab") as boot_assistant: + with gr.Row(): + with gr.Column(elem_id="col_model_list"): + gpu, theme, port, chk_args, txt_args, webui_ver = self.runtime.get_default_args() + gr.Markdown(value="Argument settings") + with gr.Row(): + self.drp_gpu = gr.Dropdown(label="VRAM Size", elem_id="drp_args_vram", + choices=list(self.prelude.gpu_setting.keys()), + value=gpu, interactive=True) + self.drp_theme = gr.Dropdown(label="UI Theme", choices=list(self.prelude.theme_setting.keys()), + value=theme, + elem_id="drp_args_theme", interactive=True) + self.txt_listen_port = gr.Text(label='Listen Port', value=port, elem_id="txt_args_listen_port", + interactive=True) + + with gr.Row(): + + self.chk_group_args = gr.CheckboxGroup(choices=list(self.prelude.checkboxes.keys()), value=chk_args, + show_label=False) + self.additional_args = gr.Text(label='COMMANDLINE_ARGS (Divide by space)', value=txt_args, + elem_id="txt_args_more", interactive=True) + + with gr.Row(): + with gr.Column(): + txt_save_status = gr.Markdown(visible=False, interactive=False, show_label=False) + drp_choose_version = gr.Dropdown(label="WebUI Version", + choices=['Official Release', 'Python Integrated'], + value=webui_ver, elem_id="drp_args_version", + interactive=True) + gr.HTML( + '*Save your settings to webui-user.bat file. Use Python Integrated only if your' + ' WebUI is extracted from a zip file and does not need python installation
No downloading tasks ongoing
')
+ downloading_status = gr.Button(value=f"{self.refresh_symbol} Refresh Downloading Status",
+ elem_id="ms_dwn_status")
+ with gr.Row():
+ model_dropdown = gr.Dropdown(choices=['Select Model'], label="Models", show_label=False,
+ value='Select Model', elem_id='ms_dwn_button',
+ interactive=True)
+
+ is_civitai_model_source_active = self.runtime.model_source == "civitai.com"
+ with gr.Row(variant="panel"):
+ dwn_button = gr.Button(value='Download',
+ visible=is_civitai_model_source_active, elem_id='ms_dwn_button')
+ open_url_in_browser_newtab_button = gr.HTML(
+ value='',
+ visible=not is_civitai_model_source_active)
+ with gr.Row():
+ model_info = gr.HTML(visible=True)
+
+ nsfw_checker.change(self.runtime.set_nsfw, inputs=[search_text, nsfw_checker, model_type],
+ outputs=self.runtime.ds_models)
+
+ model_type.change(self.runtime.search_model, inputs=[search_text, nsfw_checker, model_type], outputs=self.runtime.ds_models)
+
+ #btn_fetch.click(self.runtime.refresh_all_models, inputs=[], outputs=self.runtime.ds_models)
+
+ btn_search.click(self.runtime.search_model, inputs=[search_text, nsfw_checker, model_type], outputs=self.runtime.ds_models)
+
+ self.runtime.ds_models.click(self.runtime.get_model_info,
+ inputs=[self.runtime.ds_models],
+ outputs=[
+ self.runtime.ds_cover_gallery,
+ model_dropdown,
+ model_info,
+ open_url_in_browser_newtab_button
+ ])
+
+ dwn_button.click(self.runtime.download_model, inputs=[model_dropdown], outputs=[download_summary])
+ downloading_status.click(self.runtime.get_downloading_status, inputs=[], outputs=[download_summary])
+
+ model_source_dropdown.change(self.switch_model_source,
+ inputs=[model_source_dropdown],
+ outputs=[self.runtime.ds_models, dwn_button, open_url_in_browser_newtab_button])
+
+ def tab_downloads_select():
+ self.runtime.active_model_set = 'model_set'
+
+ tab_downloads.select(tab_downloads_select, inputs=[], outputs=[])
+
+ def create_subtab_update(self) -> None:
+ with gr.TabItem('Setting & Update', elem_id="about_update") as tab_update:
+ with gr.Row():
+ md_api_res = gr.Markdown(visible=False)
+ with gr.Row():
+ if self.prelude.boot_settings['openai_api'] == '':
+ display_text = 'Enter you OpenAI API Key here, you can get it from https://platform.openai.com/account/api-keys'
+ else:
+ display_text = self.prelude.boot_settings['openai_api']
+ txt_gptapi = gr.Textbox(label='OpenAI API Key', value=display_text)
+ with gr.Row():
+ btn_update_gptapi = gr.Button(value="Update API Key")
+ with gr.Row():
+ txt_update_result = gr.Markdown(visible=False)
+ with gr.Row():
+ btn_check_update = gr.Button(value="Check for Update")
+ with gr.Row():
+ chk_dont_update_ms = gr.Checkbox(visible=False, label="Do not update model source", value=False)
+ btn_update = gr.Button(visible=False, value="Update Miaoshouai Assistant")
+ with gr.Row():
+ gr.Markdown(value="About")
+ with gr.Row():
+ gr.HTML(
+ f"""
+ + This extension is created to improve some of the use experience for automatic1111 webui. + It is free of charge, use it as you wish, please DO NOT sell this extension. + Follow us on github, discord and give us suggestions, report bugs. support us with love or coffee~ + Cheers~
+
+ 
"
+ self._mutex.acquire(blocking=True)
+ for name, entry in self._downloading_entries.items():
+ if entry.estimated_size is None:
+ continue
+
+ total_tasks_num += 1
+
+ if entry.total_size > 0.:
+ description += f"
" + finally: + self._mutex.release() + pass + + description += "
"
+ overall = f"""
+ {entry.local_file} ({toolkit.get_readable_size(entry.total_size)}) : " + else: + description += f"
{entry.local_file} ({toolkit.get_readable_size(entry.estimated_size)}) : " + + if entry.is_downloading(): + ongoing_tasks_num += 1 + finished_percent = entry.downloaded_size/entry.estimated_size * 100 + description += f'{round(finished_percent, 2)} %' + elif entry.is_failure(): + failed_tasks_num += 1 + description += 'failed!' + else: + description += 'finished' + description += "
" + finally: + self._mutex.release() + pass + + description += "
+ {ongoing_tasks_num} ongoing, + {total_tasks_num - ongoing_tasks_num - failed_tasks_num} finished, + {failed_tasks_num} failed. +
++
+ """ + + return ongoing_tasks_num, total_tasks_num, overall + description + + diff --git a/miaoshouai-assistant-main/scripts/download/msai_file_downloader.py b/miaoshouai-assistant-main/scripts/download/msai_file_downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..fea928c650a8f361ccece3babea60ce890087243 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/download/msai_file_downloader.py @@ -0,0 +1,222 @@ +import os +import pickle +import queue +import time +import typing as t +from pathlib import Path +from urllib.parse import urlparse + +import rehash +import requests +from requests.adapters import HTTPAdapter +from tqdm import tqdm +from urllib3.util import Retry + +import scripts.msai_utils.msai_toolkit as toolkit +from scripts.msai_logging.msai_logger import Logger + + +class MiaoshouFileDownloader(object): + CHUNK_SIZE = 1024 * 1024 + + def __init__(self, target_url: str = None, + local_file: str = None, local_directory: str = None, estimated_total_length: float = 0., + expected_checksum: str = None, + channel: queue.Queue = None, + max_retries=5) -> None: + self.logger = Logger() + + self.target_url: str = target_url + self.local_file: str = local_file + self.local_directory = local_directory + self.expected_checksum = expected_checksum + self.max_retries = max_retries + + self.accept_ranges: bool = False + self.estimated_content_length = estimated_total_length + self.content_length: int = -1 + self.finished_chunk_size: int = 0 + + self.channel = channel # for communication + + # Support 3 retries and backoff + retry_strategy = Retry( + total=3, + backoff_factor=1, + status_forcelist=[429, 500, 502, 503, 504], + method_whitelist=["HEAD", "GET", "OPTIONS"] + ) + adapter = HTTPAdapter(max_retries=retry_strategy) + self.session = requests.Session() + self.session.mount("https://", adapter) + self.session.mount("http://", adapter) + + # inform message receiver at once + if self.channel: + self.channel.put_nowait(( + self.target_url, + self.finished_chunk_size, + self.estimated_content_length, + )) + + # Head request to get file-length and check whether it supports ranges. + def get_file_info_from_server(self, target_url: str) -> t.Tuple[bool, float]: + try: + headers = {"Accept-Encoding": "identity"} # Avoid dealing with gzip + response = requests.head(target_url, headers=headers, allow_redirects=True) + response.raise_for_status() + content_length = None + if "Content-Length" in response.headers: + content_length = int(response.headers['Content-Length']) + accept_ranges = (response.headers.get("Accept-Ranges") == "bytes") + return accept_ranges, float(content_length) + except Exception as ex: + self.logger.info(f"HEAD Request Error: {ex}") + return False, self.estimated_content_length + + def download_file_full(self, target_url: str, local_filepath: str) -> t.Optional[str]: + try: + checksum = rehash.sha256() + headers = {"Accept-Encoding": "identity"} # Avoid dealing with gzip + + with tqdm(total=self.content_length, unit="byte", unit_scale=1, colour="GREEN", + desc=os.path.basename(self.local_file)) as progressbar, \ + self.session.get(target_url, headers=headers, stream=True, timeout=5) as response, \ + open(local_filepath, 'wb') as file_out: + response.raise_for_status() + + for chunk in response.iter_content(MiaoshouFileDownloader.CHUNK_SIZE): + file_out.write(chunk) + checksum.update(chunk) + progressbar.update(len(chunk)) + self.update_progress(len(chunk)) + + except Exception as ex: + self.logger.info(f"Download error: {ex}") + return None + + return checksum.hexdigest() + + def download_file_resumable(self, target_url: str, local_filepath: str) -> t.Optional[str]: + # Always go off the checkpoint as the file was flushed before writing. + download_checkpoint = local_filepath + ".downloading" + try: + resume_point, checksum = pickle.load(open(download_checkpoint, "rb")) + assert os.path.exists(local_filepath) # catch checkpoint without file + self.logger.info("File already exists, resuming download.") + except Exception as e: + self.logger.error(f"failed to load downloading checkpoint - {download_checkpoint} due to {e}") + resume_point = 0 + checksum = rehash.sha256() + if os.path.exists(local_filepath): + os.remove(local_filepath) + Path(local_filepath).touch() + + assert (resume_point < self.content_length) + + self.finished_chunk_size = resume_point + + # Support resuming + headers = {"Range": f"bytes={resume_point}-", "Accept-Encoding": "identity"} + try: + with tqdm(total=self.content_length, unit="byte", unit_scale=1, colour="GREEN", + desc=os.path.basename(self.local_file)) as progressbar, \ + self.session.get(target_url, headers=headers, stream=True, timeout=5) as response, \ + open(local_filepath, 'r+b') as file_out: + response.raise_for_status() + self.update_progress(resume_point) + file_out.seek(resume_point) + + for chunk in response.iter_content(MiaoshouFileDownloader.CHUNK_SIZE): + file_out.write(chunk) + file_out.flush() + resume_point += len(chunk) + checksum.update(chunk) + pickle.dump((resume_point, checksum), open(download_checkpoint, "wb")) + progressbar.update(len(chunk)) + self.update_progress(len(chunk)) + + # Only remove checkpoint at full size in case connection cut + if os.path.getsize(local_filepath) == self.content_length: + os.remove(download_checkpoint) + else: + return None + + except Exception as ex: + self.logger.error(f"Download error: {ex}") + return None + + return checksum.hexdigest() + + def update_progress(self, finished_chunk_size: int) -> None: + self.finished_chunk_size += finished_chunk_size + + if self.channel: + self.channel.put_nowait(( + self.target_url, + self.finished_chunk_size, + self.content_length, + )) + + # In order to avoid leaving extra garbage meta files behind this + # will overwrite any existing files found at local_file. If you don't want this + # behaviour you can handle this externally. + # local_file and local_directory could write to unexpected places if the source + # is untrusted, be careful! + def download_file(self) -> bool: + success = False + try: + # Need to rebuild local_file_final each time in case of different urls + if not self.local_file: + specific_local_file = os.path.basename(urlparse(self.target_url).path) + else: + specific_local_file = self.local_file + + download_temp_dir = toolkit.get_user_temp_dir() + toolkit.assert_user_temp_dir() + + if self.local_directory: + os.makedirs(self.local_directory, exist_ok=True) + + specific_local_file = os.path.join(download_temp_dir, specific_local_file) + + self.accept_ranges, self.content_length = self.get_file_info_from_server(self.target_url) + self.logger.info(f"Accept-Ranges: {self.accept_ranges}. content length: {self.content_length}") + if self.accept_ranges and self.content_length: + download_method = self.download_file_resumable + self.logger.info("Server supports resume") + else: + download_method = self.download_file_full + self.logger.info(f"Server doesn't support resume.") + + for i in range(self.max_retries): + self.logger.info(f"Download Attempt {i + 1}") + checksum = download_method(self.target_url, specific_local_file) + if checksum: + match = "" + if self.expected_checksum: + match = ", Checksum Match" + + if self.expected_checksum and self.expected_checksum != checksum: + self.logger.info(f"Checksum doesn't match. Calculated {checksum} " + f"Expecting: {self.expected_checksum}") + else: + self.logger.info(f"Download successful{match}. Checksum {checksum}") + success = True + break + time.sleep(1) + + if success: + self.logger.info(f"{self.target_url} [DOWNLOADED COMPLETELY]") + if self.local_directory: + target_local_file = os.path.join(self.local_directory, self.local_file) + else: + target_local_file = self.local_file + toolkit.move_file(specific_local_file, target_local_file) + else: + self.logger.info(f"{self.target_url} [ FAILED ]") + + except Exception as ex: + self.logger.info(f"Unexpected Error: {ex}") # Only from block above + + return success diff --git a/miaoshouai-assistant-main/scripts/main.py b/miaoshouai-assistant-main/scripts/main.py new file mode 100644 index 0000000000000000000000000000000000000000..73d60979c881827b582e1eceb229f6f019e26cbb --- /dev/null +++ b/miaoshouai-assistant-main/scripts/main.py @@ -0,0 +1,27 @@ +import modules +import modules.scripts as scripts + +from scripts.assistant.miaoshou import MiaoShouAssistant +from scripts.runtime.msai_runtime import MiaoshouRuntime + + +class MiaoshouScript(scripts.Script): + def __init__(self) -> None: + super().__init__() + + def title(self): + return "Miaoshou Assistant" + + def show(self, is_img2img): + return scripts.AlwaysVisible + + def ui(self, is_img2img): + return () + + def postprocess(self, p, processed): + self.runtime = MiaoshouRuntime() + self.runtime.mem_release() + return None + +assistant = MiaoShouAssistant() +modules.script_callbacks.on_ui_tabs(assistant.on_event_ui_tabs_opened) diff --git a/miaoshouai-assistant-main/scripts/msai_logging/__init__.py b/miaoshouai-assistant-main/scripts/msai_logging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0e75e22f3d8c5e49b942c7030ba5360d7d8ce583 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/msai_logging/__init__.py @@ -0,0 +1 @@ +__all__ = ["msai_logger"] diff --git a/miaoshouai-assistant-main/scripts/msai_logging/msai_logger.py b/miaoshouai-assistant-main/scripts/msai_logging/msai_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..95dd71ff26b0c9f57958f06d9aef7ad4adf0500d --- /dev/null +++ b/miaoshouai-assistant-main/scripts/msai_logging/msai_logger.py @@ -0,0 +1,100 @@ +import datetime +import logging +import logging.handlers +import os +import typing as t + +from scripts.msai_utils.msai_singleton import MiaoshouSingleton + + +class Logger(metaclass=MiaoshouSingleton): + _dataset = None + + KEY_TRACE_PATH = "trace_path" + KEY_INFO = "info" + KEY_ERROR = "error" + KEY_JOB = "job" + + def _do_init(self, log_folder: str, disable_console_output: bool = False) -> None: + # Setup trace_path with empty string by default, it will be assigned with valid content if needed + self._dataset = {Logger.KEY_TRACE_PATH: ""} + + print(f"logs_location: {log_folder}") + os.makedirs(log_folder, exist_ok=True) + + # Setup basic logging configuration + logging.basicConfig(level=logging.INFO, + filemode='w', + format='%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s') + + # Setup info logging + self._dataset[Logger.KEY_INFO] = logging.getLogger(Logger.KEY_INFO) + msg_handler = logging.FileHandler(os.path.join(log_folder, "info.log"), + "a", + encoding="UTF-8") + msg_handler.setLevel(logging.INFO) + msg_handler.setFormatter( + logging.Formatter(fmt='%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s')) + self._dataset[Logger.KEY_INFO].addHandler(msg_handler) + + # Setup error logging + self._dataset[Logger.KEY_ERROR] = logging.getLogger(Logger.KEY_ERROR) + error_handler = logging.FileHandler( + os.path.join(log_folder, f'error_{datetime.date.today().strftime("%Y%m%d")}.log'), + mode="a", + encoding='UTF-8') + error_handler.setLevel(logging.ERROR) + error_handler.setFormatter( + logging.Formatter( + fmt=f"{self._dataset.get('trace_path')}:\n " + f"%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s")) + self._dataset[Logger.KEY_ERROR].addHandler(error_handler) + + # Setup job logging + self._dataset[Logger.KEY_JOB] = logging.getLogger(Logger.KEY_JOB) + job_handler = logging.FileHandler(os.path.join(log_folder, "jobs.log"), + mode="a", + encoding="UTF-8") + self._dataset[Logger.KEY_JOB].addHandler(job_handler) + + if disable_console_output: + for k in [Logger.KEY_INFO, Logger.KEY_JOB, Logger.KEY_ERROR]: + l: logging.Logger = self._dataset[k] + l.propagate = not disable_console_output + + def __init__(self, log_folder: str = None, disable_console_output: bool = False) -> None: + if self._dataset is None: + try: + self._do_init(log_folder=log_folder, disable_console_output=disable_console_output) + except Exception as e: + print(e) + + def update_path_info(self, current_path: str) -> None: + self._dataset[Logger.KEY_TRACE_PATH] = current_path + + def callback_func(self, exc_type: t.Any, exc_value: t.Any, exc_tracback: t.Any) -> None: + self._dataset[Logger.KEY_JOB].error(f"job failed for {self._dataset[Logger.KEY_TRACE_PATH]}") + self._dataset[Logger.KEY_INFO].error(f"{self._dataset[Logger.KEY_TRACE_PATH]}\n, callback_func: ", + exc_info=(exc_type, exc_value, exc_tracback)) + + def debug(self, fmt, *args, **kwargs) -> None: + l: logging.Logger = self._dataset[Logger.KEY_INFO] + l.debug(fmt, *args, **kwargs, stacklevel=2) + + def info(self, fmt, *args, **kwargs) -> None: + l: logging.Logger = self._dataset[Logger.KEY_INFO] + l.info(fmt, *args, **kwargs, stacklevel=2) + + def warn(self, fmt, *args, **kwargs) -> None: + l: logging.Logger = self._dataset[Logger.KEY_INFO] + l.warn(fmt, *args, **kwargs, stacklevel=2) + + def error(self, fmt, *args, **kwargs) -> None: + l: logging.Logger = self._dataset[Logger.KEY_ERROR] + l.error(fmt, *args, **kwargs, stacklevel=2) + + def job(self, fmt, *args, **kwargs) -> None: + l: logging.Logger = self._dataset[Logger.KEY_JOB] + l.info(fmt, *args, **kwargs, stacklevel=2) + + diff --git a/miaoshouai-assistant-main/scripts/msai_utils/__init__.py b/miaoshouai-assistant-main/scripts/msai_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..474a79a1ac184fcf60ea98f2a6933cac98294e84 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/msai_utils/__init__.py @@ -0,0 +1 @@ +__all__ = ["msai_singleton", "msai_toolkit"] diff --git a/miaoshouai-assistant-main/scripts/msai_utils/msai_singleton.py b/miaoshouai-assistant-main/scripts/msai_utils/msai_singleton.py new file mode 100644 index 0000000000000000000000000000000000000000..c2f831caa60e24706881859eda0034a8ff89fef9 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/msai_utils/msai_singleton.py @@ -0,0 +1,8 @@ +class MiaoshouSingleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(MiaoshouSingleton, cls).__call__(*args, **kwargs) + cls._instances[cls].__init__(*args, **kwargs) + return cls._instances[cls] diff --git a/miaoshouai-assistant-main/scripts/msai_utils/msai_toolkit.py b/miaoshouai-assistant-main/scripts/msai_utils/msai_toolkit.py new file mode 100644 index 0000000000000000000000000000000000000000..cda6706475783efcd1902f00cc32d7cdb33fa110 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/msai_utils/msai_toolkit.py @@ -0,0 +1,88 @@ +import json +import os +import platform +import shutil +import typing as t +from datetime import datetime +from pathlib import Path + + +def read_json(file) -> t.Any: + try: + with open(file, "r", encoding="utf-8-sig") as f: + return json.load(f) + except Exception as e: + print(e) + return None + + +def write_json(file, content) -> None: + try: + with open(file, 'w') as f: + json.dump(content, f, indent=4) + except Exception as e: + print(e) + + +def get_args(args) -> t.List[str]: + parameters = [] + idx = 0 + for arg in args: + if idx == 0 and '--' not in arg: + pass + elif '--' in arg: + parameters.append(rf'{arg}') + idx += 1 + else: + parameters[idx - 1] = parameters[idx - 1] + ' ' + rf'{arg}' + + return parameters + + +def get_readable_size(size: int, precision=2) -> str: + if size is None: + return "" + + suffixes = ['B', 'KB', 'MB', 'GB', 'TB'] + suffixIndex = 0 + while size >= 1024 and suffixIndex < len(suffixes): + suffixIndex += 1 # increment the index of the suffix + size = size / 1024.0 # apply the division + return "%.*f%s" % (precision, size, suffixes[suffixIndex]) + + +def get_file_last_modified_time(path_to_file: str) -> datetime: + if path_to_file is None: + return datetime.now() + + if platform.system() == "Windows": + return datetime.fromtimestamp(os.path.getmtime(path_to_file)) + else: + stat = os.stat(path_to_file) + return datetime.fromtimestamp(stat.st_mtime) + + +def get_not_found_image_url() -> str: + return "https://msdn.miaoshouai.com/msdn/userimage/not-found.svg" + + +def get_user_temp_dir() -> str: + return os.path.join(Path.home().absolute(), ".miaoshou_assistant_download") + + +def assert_user_temp_dir() -> None: + os.makedirs(get_user_temp_dir(), exist_ok=True) + + +def move_file(src: str, dst: str) -> None: + if not src or not dst: + return + + if not os.path.exists(src): + return + + if os.path.exists(dst): + os.remove(dst) + + os.makedirs(os.path.dirname(dst), exist_ok=True) + shutil.move(src, dst) diff --git a/miaoshouai-assistant-main/scripts/runtime/__init__.py b/miaoshouai-assistant-main/scripts/runtime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fe306c75d656d4dfd07a934e1e0bb426fc56de23 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/runtime/__init__.py @@ -0,0 +1,5 @@ +__all__ = ["msai_prelude", "msai_runtime"] + +from . import msai_prelude as prelude + +prelude.MiaoshouPrelude().load() diff --git a/miaoshouai-assistant-main/scripts/runtime/msai_prelude.py b/miaoshouai-assistant-main/scripts/runtime/msai_prelude.py new file mode 100644 index 0000000000000000000000000000000000000000..95ebced9bd352ddd3c428ae8636d4120c2db1253 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/runtime/msai_prelude.py @@ -0,0 +1,209 @@ +import os +import platform +import sys +import typing as t + +import psutil +import torch + +import launch +from modules import shared +from scripts.msai_logging.msai_logger import Logger +from scripts.msai_utils import msai_toolkit as toolkit +from scripts.msai_utils.msai_singleton import MiaoshouSingleton +import modules + + +class MiaoshouPrelude(metaclass=MiaoshouSingleton): + _dataset = None + + def __init__(self) -> None: + # Potential race condition, not call in multithread environment + if MiaoshouPrelude._dataset is None: + self._init_constants() + + MiaoshouPrelude._dataset = { + "log_folder": os.path.join(self.ext_folder, "logs") + } + + disable_log_console_output: bool = False + if self.all_settings.get("boot_settings"): + if self.all_settings["boot_settings"].get("disable_log_console_output") is not None: + disable_log_console_output = self.all_settings["boot_settings"].get("disable_log_console_output") + + self._logger = Logger(self._dataset["log_folder"], disable_console_output=disable_log_console_output) + + def _init_constants(self) -> None: + self._api_url = { + "civitai.com": "https://model-share.com/v1/models", + "liandange.com": "https://model-api.liandange.com/model/api/models", + } + self._ext_folder = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..")) + self._setting_file = os.path.join(self.ext_folder, "configs", "settings.json") + self._model_hash_file = os.path.join(self.ext_folder, "configs", "model_hash.json") + self._gpt_index = os.path.join(self.ext_folder, "configs", "gpt_index.json") + self._assets_folder = os.path.join(self.ext_folder, "assets") + self._cache_folder = os.path.join(self.ext_folder, "cache") + self._cover_folder = os.path.join(self.ext_folder, "covers") + self._no_preview_img = os.path.join(modules.paths.script_path, "html", "card-no-preview.png") + self._model_json = { + 'civitai.com': os.path.join(self.ext_folder, 'configs', 'civitai_models.json'), + 'liandange.com': os.path.join(self.ext_folder, 'configs', 'liandange_models.json'), + 'official_models': os.path.join(self.ext_folder, 'configs', 'official_models.json'), + 'hugging_face': os.path.join(self.ext_folder, 'configs', 'hugging_face.json'), + 'controlnet': os.path.join(self.ext_folder, 'configs', 'controlnet.json') + } + self._checkboxes = { + 'Enable xFormers': '--xformers', + 'No Half': '--no-half', + 'No Half VAE': '--no-half-vae', + 'Enable API': '--api', + 'Auto Launch': '--autolaunch', + 'Allow Local Network Access': '--listen', + } + + self._gpu_setting = { + 'CPU Only': '--precision full --no-half --use-cpu SD GFPGAN BSRGAN ESRGAN SCUNet CodeFormer --all', + 'GTX 16xx': '--lowvram --xformers --precision full --no-half', + 'Low: 4-6G VRAM': '--xformers --lowvram', + 'Med: 6-8G VRAM': '--xformers --medvram', + 'Normal: 8+G VRAM': '', + } + + self._theme_setting = { + 'Auto': '', + 'Light Mode': '--theme light', + 'Dark Mode': '--theme dark', + } + self._ENV_EXCLUSION = ['COLAB_GPU', 'RUNPOD_POD_ID'] + self._model_type = {'Checkpoint': f'{os.path.join(shared.models_path,"Stable-diffusion")}', + 'LORA': f'{os.path.join(shared.models_path,"Lora")}', + 'LoCon': f'{os.path.join(shared.models_path, "LyCORIS")}', + "TextualInversion": f'{os.path.join(shared.script_path,"embeddings")}', + "Hypernetwork": f'{os.path.join(shared.models_path,"hypernetworks")}' + } + + self._gpt_type = ['gpt-3.5-turbo', 'text-davinci-003'] + + @property + def ext_folder(self) -> str: + return self._ext_folder + + @property + def log_folder(self) -> str: + return self._dataset.get("log_folder") + + @property + def all_settings(self) -> t.Any: + return toolkit.read_json(self._setting_file) + + @property + def boot_settings(self) -> t.Any: + all_setting = self.all_settings + if all_setting: + return all_setting['boot_settings'] + else: + return None + + def api_url(self, model_source: str) -> t.Optional[str]: + return self._api_url.get(model_source) + + @property + def setting_file(self) -> str: + return self._setting_file + + @property + def ENV_EXCLUSION(self) -> list[str]: + return self._ENV_EXCLUSION + + @property + def model_hash_file(self) -> str: + return self._model_hash_file + + @property + def gpt_index(self) -> str: + return self._gpt_index + + @property + def cache_folder(self) -> str: + return self._cache_folder + + @property + def assets_folder(self) -> str: + return self._assets_folder + + @property + def cover_folder(self) -> str: + return self._cover_folder + + @property + def no_preview_img(self) -> str: + return self._no_preview_img + + @property + def checkboxes(self) -> t.Dict[str, str]: + return self._checkboxes + + @property + def gpu_setting(self) -> t.Dict[str, str]: + return self._gpu_setting + + @property + def theme_setting(self) -> t.Dict[str, str]: + return self._theme_setting + + @property + def model_type(self) -> t.Dict[str, str]: + return self._model_type + + @property + def gpt_type(self) -> t.Dict[str, str]: + return self._gpt_type + + @property + def model_json(self) -> t.Dict[str, t.Any]: + return self._model_json + + def update_model_json(self, site: str, models: t.Dict[str, t.Any]) -> None: + if self._model_json.get(site) is None: + self._logger.error(f"cannot save model info for {site}") + return + + self._logger.info(f"{self._model_json[site]} updated") + toolkit.write_json(self._model_json[site], models) + + def load(self) -> None: + self._logger.info("start to do prelude") + self._logger.info(f"cmdline args: {' '.join(sys.argv[1:])}") + + @classmethod + def get_sys_info(cls) -> str: + sys_info = 'System Information\n\n' + + sys_info += r'OS Name: {0} {1}'.format(platform.system(), platform.release()) + '\n' + sys_info += r'OS Version: {0}'.format(platform.version()) + '\n' + sys_info += r'WebUI Version: {0}'.format( + f'https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{launch.commit_hash()}') + '\n' + sys_info += r'Torch Version: {0}'.format(getattr(torch, '__long_version__', torch.__version__)) + '\n' + sys_info += r'Python Version: {0}'.format(sys.version) + '\n\n' + sys_info += r'CPU: {0}'.format(platform.processor()) + '\n' + sys_info += r'CPU Cores: {0}/{1}'.format(psutil.cpu_count(logical=False), psutil.cpu_count(logical=True)) + '\n' + + try: + sys_info += r'CPU Frequency: {0} GHz'.format(round(psutil.cpu_freq().max/1000,2)) + '\n' + except Exception as e: + sys_info += r'CPU Frequency: N/A GHz' + '\n' + + sys_info += r'CPU Usage: {0}%'.format(psutil.cpu_percent()) + '\n\n' + sys_info += r'RAM: {0}'.format(toolkit.get_readable_size(psutil.virtual_memory().total)) + '\n' + sys_info += r'Memory Usage: {0}%'.format(psutil.virtual_memory().percent) + '\n\n' + for i in range(torch.cuda.device_count()): + sys_info += r'Graphics Card{0}: {1} ({2})'.format(i, torch.cuda.get_device_properties(i).name, + toolkit.get_readable_size( + torch.cuda.get_device_properties( + i).total_memory)) + '\n' + sys_info += r'Available VRAM: {0}'.format(toolkit.get_readable_size(torch.cuda.mem_get_info(i)[0])) + '\n' + + return sys_info + + diff --git a/miaoshouai-assistant-main/scripts/runtime/msai_runtime.py b/miaoshouai-assistant-main/scripts/runtime/msai_runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..f29c6abb3f51c94480d08be5415cd0da414f5746 --- /dev/null +++ b/miaoshouai-assistant-main/scripts/runtime/msai_runtime.py @@ -0,0 +1,1232 @@ +import datetime +import fileinput +import os +import platform +import re +import shutil +import sys +import io +import time +import typing as t +import gzip +import git +import gradio as gr +import requests +from bs4 import BeautifulSoup +import subprocess +import modules +import random +from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper +import openai +import gc +import json +#import tkinter as tk +#from tkinter import filedialog, ttk +import modules.devices as devices +import torch +from numba import cuda +from modules import shared, sd_hijack, sd_samplers, processing +from modules.sd_models import CheckpointInfo +from scripts.download.msai_downloader_manager import MiaoshouDownloaderManager +from scripts.msai_logging.msai_logger import Logger +from scripts.msai_utils import msai_toolkit as toolkit +from scripts.runtime.msai_prelude import MiaoshouPrelude + + +class MiaoshouRuntime(object): + def __init__(self): + self.cmdline_args: t.List[str] = None + self.logger = Logger() + self.prelude = MiaoshouPrelude() + self._old_additional: str = None + self._model_set: t.List[t.Dict] = None + self._my_model_set: t.List[t.Dict] = None + self._active_model_set: str = None + self._model_set_last_access_time: datetime.datetime = None + self._my_model_set_last_access_time: datetime.datetime = None + self._ds_models: gr.Dataset = None + self._ds_cover_gallery: gr.Dataset = None + self._ds_my_models: gr.Dataset = None + self._ds_my_model_covers: gr.Dataset = None + self._allow_nsfw: bool = False + self._model_source: str = "civitai.com" # civitai is the default model source + self._my_model_source: str = "civitai.com" + self._git_address: str = "https://github.com/miaoshouai/miaoshouai-assistant.git" + + # TODO: may be owned by downloader class + self.model_files = [] + + self.downloader_manager = MiaoshouDownloaderManager() + + + def get_default_args(self, commandline_args: t.List[str] = None): + if commandline_args is None: + commandline_args: t.List[str] = toolkit.get_args(sys.argv[1:]) + commandline_args = list(map(lambda x: x.replace('theme=', 'theme '), commandline_args)) + self.cmdline_args = list(dict.fromkeys(commandline_args)) + + self.logger.info(f"default commandline args: {commandline_args}") + + checkbox_values = [] + additional_args = "" + saved_setting = self.prelude.boot_settings + + gpu = saved_setting.get('drp_args_vram') + theme = saved_setting.get('drp_args_theme') + port = saved_setting.get('txt_args_listen_port') + + for arg in commandline_args: + if 'theme' in arg: + theme = [k for k, v in self.prelude.theme_setting.items() if v == arg][0] + if 'port' in arg: + port = arg.split(' ')[-1] + + for chk in self.prelude.checkboxes: + for arg in commandline_args: + if self.prelude.checkboxes[chk] == arg and chk not in checkbox_values: + checkbox_values.append(chk) + + gpu_arg_list = [f'--{i.strip()}' for i in ' '.join(list(self.prelude.gpu_setting.values())).split('--')] + for arg in commandline_args: + if 'port' not in arg \ + and arg not in list(self.prelude.theme_setting.values()) \ + and arg not in list(self.prelude.checkboxes.values()) \ + and arg not in gpu_arg_list: + additional_args += (' ' + rf'{arg}') + + self._old_additional = additional_args + webui_ver = saved_setting['drp_choose_version'] + + return gpu, theme, port, checkbox_values, additional_args.replace('\\', '\\\\').strip(), webui_ver + + def add_arg(self, args: str = "") -> None: + for arg in args.split('--'): + if f"--{arg.strip()}" not in self.cmdline_args and arg.strip() != '': + self.logger.info(f'add arg: {arg.strip()}') + self.cmdline_args.append(f'--{arg.strip()}') + + #remove duplicates + self.cmdline_args = list(dict.fromkeys(self.cmdline_args)) + #print('added dup',self.cmdline_args) + + def remove_arg(self, args: str = "") -> None: + arg_keywords = ['port', 'theme'] + + for arg in args.split('--'): + if arg in arg_keywords: + for cmdl in self.cmdline_args: + if arg in cmdl: + self.cmdline_args.remove(cmdl) + break + elif f'--{arg.strip()}' in self.cmdline_args and arg.strip() != '': + self.cmdline_args.remove(f'--{arg.strip()}') + + # remove duplicates + self.cmdline_args = list(dict.fromkeys(self.cmdline_args)) + #print('removed dup',self.cmdline_args) + + def get_final_args(self, gpu, theme, port, checkgroup, more_args) -> None: + # remove duplicates + self.cmdline_args = list(dict.fromkeys(self.cmdline_args)) + # gpu settings + for s1 in self.prelude.gpu_setting: + if s1 in gpu: + for s2 in self.prelude.gpu_setting: + if s2 != s1: + self.remove_arg(self.prelude.gpu_setting[s2]) + self.add_arg(self.prelude.gpu_setting[s1]) + + if port != '7860': + self.add_arg(f'--port {port}') + else: + self.remove_arg('--port') + + # theme settings + self.remove_arg('--theme') + for t in self.prelude.theme_setting: + if t == theme: + self.add_arg(self.prelude.theme_setting[t]) + break + + # check box settings + for chked in checkgroup: + self.logger.info(f'checked:{self.prelude.checkboxes[chked]}') + self.add_arg(self.prelude.checkboxes[chked]) + + for unchk in list(set(list(self.prelude.checkboxes.keys())) - set(checkgroup)): + print(f'unchecked:{unchk}') + self.remove_arg(self.prelude.checkboxes[unchk]) + + # additional commandline settings + self.remove_arg(self._old_additional) + self.add_arg(more_args.replace('\\\\', '\\')) + self._old_additional = more_args.replace('\\\\', '\\') + + def refresh_all_models(self) -> None: + self.install_preset_models_if_needed(True) + if self.ds_models: + self.ds_models.samples = self.model_set + self.ds_models.update(samples=self.model_set) + else: + self.logger.error(f"ds models is null") + + def get_images_html(self, search: str = '', chk_nsfw: bool = False, model_type: str = 'All') -> t.List[str]: + self.logger.info(f"get_image_html: model_type = {model_type}, and search pattern = '{search}'") + + model_cover_thumbnails = [] + model_format = [] + + if self.model_set is None: + self.logger.error("model_set is null") + return [] + + self.logger.info(f"{len(self.model_set)} items inside '{self.model_source}'") + + search = search.lower() + for model in self.model_set: + try: + if model.get('type') is not None \ + and model.get('type') not in model_format: + model_format.append(model['type']) + + if search == '' or \ + (model.get('name') is not None and search.lower() in model.get('name').lower()) \ + or (model.get('description') is not None and search.lower() in model.get('description').lower()): + + self._allow_nsfw = chk_nsfw + if (model_type == 'All' or model_type in model.get('type')) \ + and (self.allow_nsfw or (not self.allow_nsfw and not model.get('nsfw'))): + model_cover_thumbnails.append([ + [f""" +
+
+ """],
+ model['id']])
+ except Exception:
+ continue
+
+ return model_cover_thumbnails
+
+ # TODO: add typing hint
+ def update_boot_settings(self, version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args, auto_vram):
+ boot_settings = self.prelude.boot_settings
+ boot_settings['drp_args_vram'] = drp_gpu
+ boot_settings["drp_args_theme"] = drp_theme
+ boot_settings['txt_args_listen_port'] = txt_listen_port
+ for chk in chk_group_args:
+ self.logger.debug(chk)
+ boot_settings[chk] = self.prelude.checkboxes[chk]
+ boot_settings['txt_args_more'] = additional_args
+ boot_settings['drp_choose_version'] = version
+ boot_settings['auto_vram'] = auto_vram
+
+ all_settings = self.prelude.all_settings
+ all_settings['boot_settings'] = boot_settings
+
+ toolkit.write_json(self.prelude.setting_file, all_settings)
+
+ def update_boot_setting(self, setting, value):
+ boot_settings = self.prelude.boot_settings
+ boot_settings[setting] = value
+
+ all_settings = self.prelude.all_settings
+ all_settings['boot_settings'] = boot_settings
+ toolkit.write_json(self.prelude.setting_file, all_settings)
+
+ def change_auto_vram(self, auto_vram):
+ self.update_boot_setting('auto_vram', auto_vram)
+
+ def mem_release(self):
+ try:
+ gc.collect()
+ devices.torch_gc()
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ print('Miaoshouai boot assistant: Memory Released!')
+ except:
+ print('Miaoshouai boot assistant: Memory Release Failed...!')
+
+ def force_mem_release(self):
+ try:
+ if hasattr(sd_samplers, "create_sampler_original_md"):
+ sd_samplers.create_sampler = sd_samplers.create_sampler_original_md
+ del sd_samplers.create_sampler_original_md
+ if hasattr(processing, "create_random_tensors_original_md"):
+ processing.create_random_tensors = processing.create_random_tensors_original_md
+ del processing.create_random_tensors_original_md
+
+ cuda.select_device(0)
+ cuda.close()
+ cuda.select_device(0)
+ self.mem_release()
+ msg = 'Memory Released! (May not work if you already got CUDA out of memory error)'
+ except Exception as e:
+ msg = f'Memory Release Failed! ({str(e)})'
+
+ return gr.Markdown.update(visible=True, value=msg)
+
+ return gr.Markdown.update(visible=True, value=msg)
+ def get_all_models(self, site: str) -> t.Any:
+ return toolkit.read_json(self.prelude.model_json[site])
+
+ def update_model_json(self, site: str, models: t.Any) -> None:
+ toolkit.write_json(self.prelude.model_json[site], models)
+
+ def get_hash_from_json(self, chk_point: CheckpointInfo) -> CheckpointInfo:
+ model_hashes = toolkit.read_json(self.prelude.model_hash_file)
+
+ if len(model_hashes) == 0 or chk_point.title not in model_hashes.keys():
+ chk_point.shorthash = self.calculate_shorthash(chk_point)
+ model_hashes[chk_point.title] = chk_point.shorthash
+ toolkit.write_json(self.prelude.model_hash_file, model_hashes)
+ else:
+ chk_point.shorthash = model_hashes[chk_point.title]
+
+ return chk_point
+
+ def calculate_shorthash(self, chk_point: CheckpointInfo):
+ if chk_point.sha256 is None:
+ return
+ else:
+ return chk_point.sha256[0:10]
+
+
+ def update_my_model_type(self, search_txt, model_type) -> t.Dict:
+ my_models = self.get_local_models(search_txt, model_type)
+ self.ds_my_models.samples = my_models
+
+ return gr.Dataset.update(samples=my_models)
+
+ def get_local_models(self, search_txt='', model_type='Checkpoint') -> t.List[t.Any]:
+ models = []
+
+ for root, dirs, files in os.walk(self.prelude.model_type[model_type]):
+ for file in files:
+ mpath = os.path.join(root, file)
+
+ fname, ext = os.path.splitext(file)
+ if ext in ['.ckpt', '.safetensors', '.pt'] and file != 'scaler.pt' and (search_txt in fname or search_txt == ''):
+ chkpt_info = modules.sd_models.get_closet_checkpoint_match(file)
+ if chkpt_info is None:
+ chkpt_info = CheckpointInfo(os.path.join(root, file))
+
+ if chkpt_info.sha256 is None and chkpt_info.shorthash is None:
+ chkpt_info = self.get_hash_from_json(chkpt_info)
+
+ model_info = self.search_model_info(chkpt_info, mpath, model_type)
+ fname = re.sub(r'\[.*?\]', "", chkpt_info.title)
+
+ if model_info is not None:
+ models.append(model_info)
+ else:
+ self.logger.info(
+ f"{chkpt_info.title}, {chkpt_info.hash}, {chkpt_info.shorthash}, {chkpt_info.sha256}")
+ models.append([
+ self.prelude.no_preview_img,
+ 0,
+ [os.path.basename(fname)],
+ [mpath.replace(self.prelude.model_type[model_type]+'\\', '')]])
+
+ return models
+
+
+ def refresh_local_models(self, search_txt, model_type) -> t.Dict:
+ my_models = self.get_local_models(search_txt, model_type)
+ self.ds_my_models.samples = my_models
+
+ return gr.Dataset.update(samples=my_models)
+
+ def delete_model(self, model, search_txt, model_type):
+ fname = model[3][0]
+ mfolder = self.prelude.model_type[model_type]
+ mpapth = os.path.join(mfolder, fname)
+
+ os.remove(mpapth)
+ my_models = self.get_local_models(search_txt, model_type)
+ self.ds_my_models.samples = my_models
+
+ return gr.Dataset.update(samples=my_models)
+
+ def set_all_covers(self, search_txt, model_type):
+ for model in self.ds_my_models.samples:
+ try:
+ if model[0] == self.prelude.no_preview_img and model[1] != 0:
+ img_list, l1, htmlDetail, h2 = self.get_model_info(model)
+ soup = BeautifulSoup(img_list[0][0])
+ cover_url = soup.findAll('img')[0]['src'].replace('width=150', 'width=450')
+
+ fname = model[3][0]
+ mname, ext = os.path.splitext(fname)
+ mfolder = self.prelude.model_type[model_type]
+ dst = os.path.join(mfolder, f'{mname}.jpg')
+
+ if fname is not None and not os.path.exists(dst):
+ if self.my_model_source == 'liandange.com':
+ cover_url = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450')
+ r = requests.get(cover_url, timeout=30, stream=True)
+ r.raw.decode_content = True
+ with open(dst, 'wb') as f:
+ shutil.copyfileobj(r.raw, f)
+ except Exception as e:
+ print(model[1], cover_url, dst, str(e))
+ continue
+
+ my_models = self.get_local_models(search_txt, model_type)
+ self.ds_my_models.samples = my_models
+
+ return gr.Dataset.update(samples=my_models)
+
+ def set_cover(self, model, cover, search_txt, model_type):
+ fname = model[3][0]
+ mname, ext = os.path.splitext(fname)
+ mfolder = self.prelude.model_type[model_type]
+
+ dst = os.path.join(mfolder, f'{mname}.jpg')
+ cover.save(dst)
+
+ my_models = self.get_local_models(search_txt, model_type)
+ self.ds_my_models.samples = my_models
+
+ return gr.Dataset.update(samples=my_models)
+
+
+
+ def search_model_info(self, chkpt_info: CheckpointInfo, mpath: str, model_type: str) -> t.Optional[t.List[t.Any]]:
+
+ lookup_sha256 = chkpt_info.sha256
+ lookup_shash = chkpt_info.shorthash
+ fname = re.sub(r'\[.*?\]', "", chkpt_info.title)
+ if '\\' in fname:
+ fname = fname.split('\\')[-1]
+
+ self.logger.info(f"lookup_sha256: {lookup_sha256}, lookup_shash: {lookup_shash}, fname: {fname}")
+
+ res = None
+ if lookup_sha256 is None and lookup_shash is None and fname is None:
+ return None
+
+ prefix, ext = os.path.splitext(mpath)
+
+ if os.path.exists(f'{prefix}.jpg'):
+ cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.jpg')
+ elif os.path.exists(f'{prefix}.png'):
+ cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.png')
+ elif os.path.exists(f'{prefix}.webp'):
+ cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.webp')
+ else:
+ cover_img = self.prelude.no_preview_img
+
+ if not os.path.exists(self.prelude.cover_folder):
+ os.mkdir(self.prelude.cover_folder)
+
+ dst = os.path.join(self.prelude.cover_folder, os.path.basename(cover_img))
+ try:
+ if cover_img != self.prelude.no_preview_img and os.path.exists(cover_img) and os.path.exists(dst):
+ dst_size = os.stat(dst).st_size
+ cover_size = os.stat(cover_img).st_size
+ if dst_size != cover_size:
+ print('update to new cover')
+ shutil.copyfile(cover_img, dst)
+ elif cover_img != self.prelude.no_preview_img and os.path.exists(cover_img) and not os.path.exists(dst):
+ shutil.copyfile(cover_img, dst)
+ elif cover_img == self.prelude.no_preview_img:
+ dst = cover_img
+ except Exception as e:
+ dst = self.prelude.no_preview_img
+
+ for model in self.my_model_set:
+ match = False
+
+ for ver in model['modelVersions']:
+ for file in ver['files']:
+ if fname == file['name']:
+ match = True
+ elif lookup_sha256 is not None and 'SHA256' in file['hashes'].keys():
+ match = (lookup_sha256.upper() == file['hashes']['SHA256'].upper())
+ elif lookup_shash is not None:
+ match = (lookup_shash[:10].upper() in [h.upper() for h in file['hashes'].values()])
+
+ if match:
+ mid = model['id']
+
+ res = [
+ dst,
+ mid,
+ [f"{model['name']}/{ver['name']}"],
+ [mpath.replace(self.prelude.model_type[model_type]+'\\', '')]
+ ]
+
+ if match:
+ break
+
+ return res
+
+ def update_xformers(self, gpu, checkgroup):
+ if '--xformers' in self.prelude.gpu_setting[gpu]:
+ if 'Enable xFormers' not in checkgroup:
+ checkgroup.append('Enable xFormers')
+
+ return checkgroup
+
+ def set_nsfw(self, search='', nsfw_checker=False, model_type='All') -> t.Dict:
+ self._allow_nsfw = nsfw_checker
+ new_list = self.get_images_html(search, model_type)
+ if self._ds_models is None:
+ self.logger.error(f"_ds_models is not initialized")
+ return {}
+
+ self._ds_models.samples = new_list
+ return self._ds_models.update(samples=new_list)
+
+ def search_model(self, search='', chk_nsfw=False, model_type='All') -> t.Dict:
+ if self._ds_models is None:
+ self.logger.error(f"_ds_models is not initialized")
+ return {}
+
+ new_list = self.get_images_html(search, chk_nsfw, model_type)
+
+ self._ds_models.samples = new_list
+ return self._ds_models.update(samples=new_list)
+
+ def search_my_model(self, search_txt='', model_type='Checkpoint') -> t.Dict:
+ if self._ds_models is None:
+ self.logger.error(f"_ds_models is not initialized")
+ return {}
+
+ new_list = self.get_local_models(search_txt, model_type)
+
+ self._ds_my_models.samples = new_list
+ return self._ds_my_models.update(samples=new_list)
+
+ def get_model_byid(self, mid, model_source) -> t.List:
+ response = requests.get(self.prelude.api_url(model_source) + f'/{mid}')
+ payload = response.json()
+ if payload.get("success") is not None and not payload.get("success"):
+ return []
+
+ return [payload]
+
+ def get_model_info(self, models) -> t.Tuple[t.List[t.List[str]], t.Dict, str, t.Dict]:
+ drop_list = []
+ cover_imgs = []
+ htmlDetail = "
+ })
+
+
+
+ {model.get('name')}
+Type: {model.get('type')}
+Rating: {model.get('stats')['rating']}
+No info found
'
+ if m.get('name'):
+ htmlDetail += f"
"
+ if latest_version.get('name'):
+ htmlDetail += f"{m['name']}
" + if m.get('stats') and m.get('stats').get('downloadCount'): + htmlDetail += f"Downloads: {m['stats']['downloadCount']}
" + if m.get('stats') and m.get('stats').get('rating'): + htmlDetail += f"Rating: {m['stats']['rating']}
" + if m.get('creator') and m.get('creator').get('username'): + htmlDetail += f"Author: {m['creator']['username']}
| Version: | {latest_version['name']} |
| Updated Time: | {latest_version['updatedAt']} |
| Type: | {m['type']} |
| Base Model: | {latest_version['baseModel']} |
| NFSW: | {m.get('nsfw') if m.get('nsfw') is not None else 'false'} |
| Tags: | " + for t in m['tags']: + htmlDetail += f' |
| Trigger Words: | " + for t in latest_version['trainedWords']: + htmlDetail += f'{t}' + htmlDetail += " |
{m['description'] if m.get('description') else 'N/A'}
"
+
+ self._ds_cover_gallery.samples = cover_imgs
+
+ return (
+ cover_imgs,
+ gr.Dropdown.update(choices=drop_list, value=drop_list[0] if len(drop_list) > 0 else []),
+ htmlDetail,
+ gr.HTML.update(value=f'' + f'Download
') + ) + + def get_my_model_covers(self, model, model_type): + img_list, l1, htmlDetail, h2 = self.get_model_info(model) + if self._ds_my_model_covers is None: + self.logger.error(f"_ds_my_model_covers is not initialized") + return {} + + new_html = '' + if htmlDetail is not None: + new_html = htmlDetail.split('\n'
+ cover_html += f'
'
+ cover_list.append([cover_html])
+
+ if model_type == 'TextualInversion':
+ mname, ext = os.path.splitext(model[3][0])
+ button_html = ''
+
+ elif model_type == 'LORA':
+ mname, ext = os.path.splitext(model[3][0])
+ button_html = ''
+ elif model_type.upper() == 'LoCon'.upper():
+ mname, ext = os.path.splitext(model[3][0])
+ button_html = ''
+ else:
+ mpath = os.path.join(self.prelude.model_type[model_type], model[3][0])
+ checkpoint_info = CheckpointInfo(mpath)
+ button_html = f''
+
+ self._ds_my_model_covers.samples = cover_list
+ return self._ds_my_model_covers.update(samples=cover_list), gr.HTML.update(visible=True, value=new_html), gr.HTML.update(visible=True, value=button_html)
+
+
+ def update_cover_info(self, model, covers):
+
+ soup = BeautifulSoup(covers[0])
+ cover_url = soup.findAll('img')[0]['src'].replace('width=150', 'width=450')
+
+ if self.my_model_set is None:
+ self.logger.error("model_set is null")
+ return []
+
+ mid = model[1]
+ m_list = self.get_model_byid(mid, self.my_model_source)
+ if m_list is not None or m_list != []:
+ m = m_list[0]
+ else:
+ return {}, {}
+
+ generation_info = ''
+ fname = None
+ for mv in m['modelVersions']:
+ for img in mv['images']:
+ if img['url'] == cover_url:
+ if img['meta'] is not None and img['meta'] != '':
+ try:
+ meta = img['meta']
+ generation_info += f"{meta['prompt']}\n"
+ if meta['negativePrompt'] is not None:
+ generation_info += f"Negative prompt: {meta['negativePrompt']}\n"
+ generation_info += f"Steps: {meta['steps']}, Sampler: {meta['sampler']}, "
+ generation_info += f"CFG scale: {meta['cfgScale']}, Seed: {meta['seed']}, Size: {meta['Size']},"
+ if meta['Model hash'] is not None:
+ generation_info += f"Model hash: {meta['Model hash']}"
+
+ except Exception as e:
+ self.logger.info(f"generation_info error:{str(e)}")
+ pass
+
+ if not os.path.exists(self.prelude.cache_folder):
+ os.mkdir(self.prelude.cache_folder)
+
+ if self.my_model_source == 'civitai.com':
+ fname = os.path.join(self.prelude.cache_folder, f"{cover_url.split('/')[-1]}.jpg")
+ elif self.my_model_source == 'liandange.com':
+ fname = os.path.join(self.prelude.cache_folder, cover_url.split('?')[0].split('/')[-1])
+
+ break
+
+ if fname is not None and not os.path.exists(fname):
+ if self.my_model_source == 'liandange.com':
+ cover_url = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450')
+ r = requests.get(cover_url, timeout=30, stream=True)
+ r.raw.decode_content = True
+ with open(fname, 'wb') as f:
+ shutil.copyfileobj(r.raw, f)
+
+ return gr.Button.update(visible=True), gr.Text.update(value=generation_info), gr.Image.update(value=fname)
+
+ def get_downloading_status(self):
+ (_, _, desc) = self.downloader_manager.tasks_summary()
+ return gr.HTML.update(value=desc)
+
+ def download_model(self, filename: str):
+ model_path = modules.paths.models_path
+ script_path = modules.paths.script_path
+
+ urls = []
+ for _, f in enumerate(self.model_files):
+ if not f.get('name'):
+ continue
+ model_fname = re.sub(r"\s*\(\d+(?:\.\d*)?.B\)\s*$", "", f['name'])
+
+ if model_fname in filename:
+ m_pre, m_ext = os.path.splitext(model_fname)
+ cover_fname = f"{m_pre}.jpg"
+ soup = BeautifulSoup(f['cover'])
+ cover_link = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450').replace('width=150', 'width=450')
+
+ if f['type'] == 'LORA':
+ cover_fname = os.path.join(model_path, 'Lora', cover_fname)
+ model_fname = os.path.join(model_path, 'Lora', model_fname)
+ elif f['type'].upper() == 'LoCon'.upper():
+ cover_fname = os.path.join(model_path, 'LyCORIS', cover_fname)
+ model_fname = os.path.join(model_path, 'LyCORIS', model_fname)
+ elif f['type'] == 'VAE':
+ cover_fname = os.path.join(model_path, 'VAE', cover_fname)
+ model_fname = os.path.join(model_path, 'VAE', model_fname)
+ elif f['type'] == 'TextualInversion':
+ cover_fname = os.path.join(script_path, 'embeddings', cover_fname)
+ model_fname = os.path.join(script_path, 'embeddings', model_fname)
+ elif f['type'] == 'Hypernetwork':
+ cover_fname = os.path.join(model_path, 'hypernetworks', cover_fname)
+ model_fname = os.path.join(model_path, 'hypernetworks', model_fname)
+ elif f['type'] == 'Controlnet':
+ cover_fname = os.path.join(shared.script_path, 'extensions', 'sd-webui-controlnet', 'models', cover_fname)
+ model_fname = os.path.join(shared.script_path, 'extensions', 'sd-webui-controlnet', 'models', model_fname)
+ else:
+ cover_fname = os.path.join(model_path, 'Stable-diffusion', cover_fname)
+ model_fname = os.path.join(model_path, 'Stable-diffusion', model_fname)
+
+ urls.append((cover_link, f['url'], f['size'], cover_fname, model_fname))
+ break
+
+ for (cover_url, model_url, total_size, local_cover_name, local_model_name) in urls:
+ self.downloader_manager.download(
+ source_url=cover_url,
+ target_file=local_cover_name,
+ estimated_total_size=None,
+ )
+ self.downloader_manager.download(
+ source_url=model_url,
+ target_file=local_model_name,
+ estimated_total_size=total_size,
+ )
+
+ #
+ # currently, web-ui is without queue enabled.
+ #
+ # webui_queue_enabled = False
+ # if webui_queue_enabled:
+ # start = time.time()
+ # downloading_tasks_iter = self.downloader_manager.iterator()
+ # for i in progressbar.tqdm(range(100), unit="byte", desc="Models Downloading"):
+ # while True:
+ # try:
+ # finished_bytes, total_bytes = next(downloading_tasks_iter)
+ # v = finished_bytes / total_bytes
+ # print(f"\n v = {v}")
+ # if isinstance(v, float) and int(v * 100) < i:
+ # print(f"\nv({v}) < {i}")
+ # continue
+ # else:
+ # break
+ # except StopIteration:
+ # break
+ #
+ # time.sleep(0.5)
+ #
+ # self.logger.info(f"[downloading] finished after {time.time() - start} secs")
+
+ time.sleep(2)
+
+ #self.model_files.clear()
+ return gr.HTML.update(value=f"\n'
+ if len(img_link) > 0:
+ cover_html += f'{img_link[0]}\n'
+
+ cover_html += '
\n{len(urls)} downloading tasks added into task list
") + + def install_preset_models_if_needed(self, update_ds: bool): + assets_folder = os.path.join(self.prelude.ext_folder, "assets") + configs_folder = os.path.join(self.prelude.ext_folder, "configs") + + for model_filename in ["civitai_models.json", "liandange_models.json", "gpt_index.json"]: + gzip_file = os.path.join(assets_folder, f"{model_filename}.gz") + target_file = os.path.join(configs_folder, f"{model_filename}") + + if not os.path.exists(gzip_file): + self.relocate_assets_if_needed() + sub_repo = git.Repo(self.prelude.assets_folder) + sub_repo.git.fetch(all=True) + sub_repo.git.reset('origin', hard=True) + + if update_ds or not os.path.exists(target_file): + with gzip.open(gzip_file, "rb") as compressed_file: + with io.TextIOWrapper(compressed_file, encoding="utf-8") as decoder: + content = decoder.read() + with open(target_file, "w") as model_file: + model_file.write(content) + + print('Data source unpacked successfully') + + def relocate_assets_if_needed(self): + repo = git.Repo(self.prelude.ext_folder) + print('Updating asset repo...') + try: + old_repo = True + if os.path.exists(self.prelude.assets_folder): + for filename in os.listdir(self.prelude.assets_folder): + if '.git' in filename: + old_repo = False + break + + if old_repo: + shutil.rmtree(self.prelude.assets_folder) + + for submodule in repo.submodules: + submodule.update(init=True) + except Exception as e: + print('error', str(e)) + + def get_dir_and_file(self, file_path): + dir_path, file_name = os.path.split(file_path) + return (dir_path, file_name) + + def open_folder(self, folder_path=''): + if not any(var in os.environ for var in self.prelude.ENV_EXCLUSION) and sys.platform != 'darwin': + current_folder_path = folder_path + + initial_dir, initial_file = self.get_dir_and_file(folder_path) + + #root = tk.Tk() + #root.wm_attributes('-topmost', 1) + #root.withdraw() + #folder_path = filedialog.askdirectory(initialdir=initial_dir) + #root.destroy() + + if folder_path == '': + folder_path = current_folder_path + + return folder_path + + def change_model_folder(self, folder_path=''): + + res = 'Model folder is linked successfully' + if folder_path == '': + return gr.Markdown.update(value='No directory is set', visible=True) + + try: + src = shared.models_path + # Destination file path + dst = folder_path + + # Create a symbolic link + # pointing to src named dst + # using os.symlink() method + subprocess.check_call('mklink /J "%s" "%s"' % (src, dst), shell=True) + except Exception as e: + res = str(e) + + return gr.Markdown.update(value=res, visible=True) + + def change_boot_setting(self, version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args): + self.get_final_args(drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args) + self.logger.info(f'saved_cmd: {self.cmdline_args}') + + if version == 'Official Release': + target_webui_user_file = "webui-user.bat" + else: + target_webui_user_file = "webui-user-launch.bat" + script_export_keyword = "export" + if platform.system() == "Linux": + target_webui_user_file = "webui-user.sh" + elif platform.system() == "Darwin": + target_webui_user_file = "webui-macos-env.sh" + else: + script_export_keyword = "set" + + filepath = os.path.join(modules.shared.script_path, target_webui_user_file) + self.logger.info(f"to update: {filepath}") + + msg = 'Result: Setting Saved.' + if version == 'Official Release': + try: + if not os.path.exists(filepath): + shutil.copyfile(os.path.join(self.prelude.ext_folder, 'configs', target_webui_user_file), filepath) + + with fileinput.FileInput(filepath, inplace=True, backup='.bak') as file: + for line in file: + if 'COMMANDLINE_ARGS' in line: + rep_txt = ' '.join(self.cmdline_args).replace('\\', '\\\\') + line = f'{script_export_keyword} COMMANDLINE_ARGS={rep_txt}\n' + sys.stdout.write(line) + + except Exception as e: + msg = f'Error: {str(e)}' + else: + try: + if not os.path.exists(filepath): + shutil.copyfile(os.path.join(self.prelude.ext_folder, 'configs', target_webui_user_file), filepath) + + new_data = '' + with open(filepath, 'r+') as file: + data = file.readlines() + for line in data: + if 'webui.py' in line: + rep_txt = ' '.join(self.cmdline_args).replace('\\', '\\\\') + line = f"python\python.exe webui.py {rep_txt}\n" + new_data += line + file.seek(0) + file.write(new_data) + file.truncate() + + except Exception as e: + msg = f'Error: {str(e)}' + + self.update_boot_settings(version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args) + return gr.update(value=msg, visible=True) + + def check_update(self): + update_status = 'latest' + show_update = False + repo = git.Repo(self.prelude.ext_folder) + print('Checking updates for miaoshouai-assistant...') + for fetch in repo.remote().fetch(dry_run=True): + if fetch.flags != fetch.HEAD_UPTODATE: + show_update = True + update_status = "behind" + break + + print('Checking updates for data source...') + if os.path.exists(self.prelude.assets_folder): + fcount = len([entry for entry in os.listdir(self.prelude.assets_folder) if os.path.isfile(os.path.join(self.prelude.assets_folder, entry))]) + + if not os.path.exists(self.prelude.assets_folder) or fcount <= 0: + self.relocate_assets_if_needed() + show_update = True + update_status = "behind" + else: + try: + asset_repo = git.Repo(self.prelude.asset_folder) + for fetch in asset_repo.remote().fetch(dry_run=True): + if fetch.flags != fetch.HEAD_UPTODATE: + show_update = True + update_status = "behind" + break + except Exception as e: + self.logger.info(f"Error during checking asset, try to relocate.\n{str(e)}") + self.relocate_assets_if_needed() + show_update = True + update_status = "behind" + + return gr.Markdown.update(visible=True, value=update_status), gr.Checkbox.update(visible=show_update), gr.Button.update(visible=show_update) + + def process_prompt(self, model, model_type, prompt: str): + text_replace = {'/': '|', 'a girl': '1girl', 'a boy': '1boy', 'a women': '1women', 'a man': '1man'} + for rep in text_replace.keys(): + prompt = prompt.strip().lower().replace(rep, text_replace[rep]) + + try: + mid = model[1] + m_list = self.get_model_byid(mid, self.my_model_source) + if m_list is not None or m_list != []: + m = m_list[0] + else: + return prompt + except Exception as e: + self.logger.info(f"generation_info error:{str(e)}") + return prompt + + generation_info = '' + for mv in m['modelVersions']: + img_cnt = len(mv['images']) + img = mv['images'][random.randint(0, img_cnt-1)] + if img['meta'] is not None and img['meta'] != '': + try: + meta = img['meta'] + + lora = '' + if model_type == 'LORA': + mname, ext = os.path.splitext(model[3][0]) + lora = f',
+
+
+E1
+ Original image + +  + + 2k upscaled. **Tile size**: 512, **Padding**: 32, **Mask blur**: 16, **Denoise**: 0.4 +  +
+
+
+E2
+ Original image + +  + + 2k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35 +  + + 4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35 +  +
+
+
+# API Usage
+
+```javascript
+{
+"script_name" : "ultimate sd upscale",
+"script_args" : [
+ null, // _ (not used)
+ 512, // tile_width
+ 512, // tile_height
+ 8, // mask_blur
+ 32, // padding
+ 64, // seams_fix_width
+ 0.35, // seams_fix_denoise
+ 32, // seams_fix_padding
+ 0, // upscaler_index
+ true, // save_upscaled_image a.k.a Upscaled
+ 0, // redraw_mode
+ false, // save_seams_fix_image a.k.a Seams fix
+ 8, // seams_fix_mask_blur
+ 0, // seams_fix_type
+ 0, // target_size_type
+ 2048, // custom_width
+ 2048, // custom_height
+ 2 // custom_scale
+]
+}
+```
+upscaler_index
+| Value | |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | Lanczos |
+| 2 | Nearest |
+| 3 | ESRGAN_4x |
+| 4 | LDSR |
+| 5 | R-ESRGAN_4x+ |
+| 6 | R-ESRGAN 4x+ Anime6B |
+| 7 | ScuNET GAN |
+| 8 | ScuNET PSNR |
+| 9 | SwinIR 4x |
+
+redraw_mode
+| Value | |
+|:-------------:| -----:|
+| 0 | Linear |
+| 1 | Chess |
+| 2 | None |
+
+seams_fix_mask_blur
+| Value | |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | BAND_PASS |
+| 2 | HALF_TILE |
+| 3 | HALF_TILE_PLUS_INTERSECTIONS |
+
+seams_fix_type
+| Value | |
+|:-------------:| -----:|
+| 0 | None |
+| 1 | Band pass |
+| 2 | Half tile offset pass |
+| 3 | Half tile offset pass + intersections |
+
+seams_fix_type
+| Value | |
+|:-------------:| -----:|
+| 0 | From img2img2 settings |
+| 1 | Custom size |
+| 2 | Scale from image size |
+
diff --git a/ultimate-upscale/scripts/ultimate-upscale.py b/ultimate-upscale/scripts/ultimate-upscale.py
new file mode 100644
index 0000000000000000000000000000000000000000..0093cbe4a9e8b95ca1746aa19901c9d18a80215f
--- /dev/null
+++ b/ultimate-upscale/scripts/ultimate-upscale.py
@@ -0,0 +1,557 @@
+import math
+import gradio as gr
+from PIL import Image, ImageDraw, ImageOps
+from modules import processing, shared, images, devices, scripts
+from modules.processing import StableDiffusionProcessing
+from modules.processing import Processed
+from modules.shared import opts, state
+from enum import Enum
+
+class USDUMode(Enum):
+ LINEAR = 0
+ CHESS = 1
+ NONE = 2
+
+class USDUSFMode(Enum):
+ NONE = 0
+ BAND_PASS = 1
+ HALF_TILE = 2
+ HALF_TILE_PLUS_INTERSECTIONS = 3
+
+class USDUpscaler():
+
+ def __init__(self, p, image, upscaler_index:int, save_redraw, save_seams_fix, tile_width, tile_height) -> None:
+ self.p:StableDiffusionProcessing = p
+ self.image:Image = image
+ self.scale_factor = math.ceil(max(p.width, p.height) / max(image.width, image.height))
+ self.upscaler = shared.sd_upscalers[upscaler_index]
+ self.redraw = USDURedraw()
+ self.redraw.save = save_redraw
+ self.redraw.tile_width = tile_width if tile_width > 0 else tile_height
+ self.redraw.tile_height = tile_height if tile_height > 0 else tile_width
+ self.seams_fix = USDUSeamsFix()
+ self.seams_fix.save = save_seams_fix
+ self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height
+ self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width
+ self.initial_info = None
+ self.rows = math.ceil(self.p.height / self.redraw.tile_height)
+ self.cols = math.ceil(self.p.width / self.redraw.tile_width)
+
+ def get_factor(self, num):
+ # Its just return, don't need elif
+ if num == 1:
+ return 2
+ if num % 4 == 0:
+ return 4
+ if num % 3 == 0:
+ return 3
+ if num % 2 == 0:
+ return 2
+ return 0
+
+ def get_factors(self):
+ scales = []
+ current_scale = 1
+ current_scale_factor = self.get_factor(self.scale_factor)
+ while current_scale_factor == 0:
+ self.scale_factor += 1
+ current_scale_factor = self.get_factor(self.scale_factor)
+ while current_scale < self.scale_factor:
+ current_scale_factor = self.get_factor(self.scale_factor // current_scale)
+ scales.append(current_scale_factor)
+ current_scale = current_scale * current_scale_factor
+ if current_scale_factor == 0:
+ break
+ self.scales = enumerate(scales)
+
+ def upscale(self):
+ # Log info
+ print(f"Canva size: {self.p.width}x{self.p.height}")
+ print(f"Image size: {self.image.width}x{self.image.height}")
+ print(f"Scale factor: {self.scale_factor}")
+ # Check upscaler is not empty
+ if self.upscaler.name == "None":
+ self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
+ return
+ # Get list with scale factors
+ self.get_factors()
+ # Upscaling image over all factors
+ for index, value in self.scales:
+ print(f"Upscaling iteration {index+1} with scale factor {value}")
+ self.image = self.upscaler.scaler.upscale(self.image, value, self.upscaler.data_path)
+ # Resize image to set values
+ self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
+
+ def setup_redraw(self, redraw_mode, padding, mask_blur):
+ self.redraw.mode = USDUMode(redraw_mode)
+ self.redraw.enabled = self.redraw.mode != USDUMode.NONE
+ self.redraw.padding = padding
+ self.p.mask_blur = mask_blur
+
+ def setup_seams_fix(self, padding, denoise, mask_blur, width, mode):
+ self.seams_fix.padding = padding
+ self.seams_fix.denoise = denoise
+ self.seams_fix.mask_blur = mask_blur
+ self.seams_fix.width = width
+ self.seams_fix.mode = USDUSFMode(mode)
+ self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE
+
+ def save_image(self):
+ if type(self.p.prompt) != list:
+ images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt, opts.samples_format, info=self.initial_info, p=self.p)
+ else:
+ images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt[0], opts.samples_format, info=self.initial_info, p=self.p)
+
+ def calc_jobs_count(self):
+ redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0
+ seams_job_count = 0
+ if self.seams_fix.mode == USDUSFMode.BAND_PASS:
+ seams_job_count = self.rows + self.cols - 2
+ elif self.seams_fix.mode == USDUSFMode.HALF_TILE:
+ seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols
+ elif self.seams_fix.mode == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
+ seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + (self.rows - 1) * (self.cols - 1)
+
+ state.job_count = redraw_job_count + seams_job_count
+
+ def print_info(self):
+ print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}")
+ print(f"Tiles amount: {self.rows * self.cols}")
+ print(f"Grid: {self.rows}x{self.cols}")
+ print(f"Redraw enabled: {self.redraw.enabled}")
+ print(f"Seams fix mode: {self.seams_fix.mode.name}")
+
+ def add_extra_info(self):
+ self.p.extra_generation_params["Ultimate SD upscale upscaler"] = self.upscaler.name
+ self.p.extra_generation_params["Ultimate SD upscale tile_width"] = self.redraw.tile_width
+ self.p.extra_generation_params["Ultimate SD upscale tile_height"] = self.redraw.tile_height
+ self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = self.p.mask_blur
+ self.p.extra_generation_params["Ultimate SD upscale padding"] = self.redraw.padding
+
+ def process(self):
+ state.begin()
+ self.calc_jobs_count()
+ self.result_images = []
+ if self.redraw.enabled:
+ self.image = self.redraw.start(self.p, self.image, self.rows, self.cols)
+ self.initial_info = self.redraw.initial_info
+ self.result_images.append(self.image)
+ if self.redraw.save:
+ self.save_image()
+
+ if self.seams_fix.enabled:
+ self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols)
+ self.initial_info = self.seams_fix.initial_info
+ self.result_images.append(self.image)
+ if self.seams_fix.save:
+ self.save_image()
+ state.end()
+
+class USDURedraw():
+
+ def init_draw(self, p, width, height):
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = self.padding
+ p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
+ p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
+ mask = Image.new("L", (width, height), "black")
+ draw = ImageDraw.Draw(mask)
+ return mask, draw
+
+ def calc_rectangle(self, xi, yi):
+ x1 = xi * self.tile_width
+ y1 = yi * self.tile_height
+ x2 = xi * self.tile_width + self.tile_width
+ y2 = yi * self.tile_height + self.tile_height
+
+ return x1, y1, x2, y2
+
+ def linear_process(self, p, image, rows, cols):
+ mask, draw = self.init_draw(p, image.width, image.height)
+ for yi in range(rows):
+ for xi in range(cols):
+ if state.interrupted:
+ break
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ p.width = image.width
+ p.height = image.height
+ self.initial_info = processed.infotext(p, 0)
+
+ return image
+
+ def chess_process(self, p, image, rows, cols):
+ mask, draw = self.init_draw(p, image.width, image.height)
+ tiles = []
+ # calc tiles colors
+ for yi in range(rows):
+ for xi in range(cols):
+ if state.interrupted:
+ break
+ if xi == 0:
+ tiles.append([])
+ color = xi % 2 == 0
+ if yi > 0 and yi % 2 != 0:
+ color = not color
+ tiles[yi].append(color)
+
+ for yi in range(len(tiles)):
+ for xi in range(len(tiles[yi])):
+ if state.interrupted:
+ break
+ if not tiles[yi][xi]:
+ tiles[yi][xi] = not tiles[yi][xi]
+ continue
+ tiles[yi][xi] = not tiles[yi][xi]
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ for yi in range(len(tiles)):
+ for xi in range(len(tiles[yi])):
+ if state.interrupted:
+ break
+ if not tiles[yi][xi]:
+ continue
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ p.width = image.width
+ p.height = image.height
+ self.initial_info = processed.infotext(p, 0)
+
+ return image
+
+ def start(self, p, image, rows, cols):
+ self.initial_info = None
+ if self.mode == USDUMode.LINEAR:
+ return self.linear_process(p, image, rows, cols)
+ if self.mode == USDUMode.CHESS:
+ return self.chess_process(p, image, rows, cols)
+
+class USDUSeamsFix():
+
+ def init_draw(self, p):
+ self.initial_info = None
+ p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
+ p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
+
+ def half_tile_process(self, p, image, rows, cols):
+
+ self.init_draw(p)
+ processed = None
+
+ gradient = Image.linear_gradient("L")
+ row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
+ row_gradient.paste(gradient.resize(
+ (self.tile_width, self.tile_height//2), resample=Image.BICUBIC), (0, 0))
+ row_gradient.paste(gradient.rotate(180).resize(
+ (self.tile_width, self.tile_height//2), resample=Image.BICUBIC),
+ (0, self.tile_height//2))
+ col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
+ col_gradient.paste(gradient.rotate(90).resize(
+ (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (0, 0))
+ col_gradient.paste(gradient.rotate(270).resize(
+ (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (self.tile_width//2, 0))
+
+ p.denoising_strength = self.denoise
+ p.mask_blur = self.mask_blur
+
+ for yi in range(rows-1):
+ for xi in range(cols):
+ if state.interrupted:
+ break
+ p.width = self.tile_width
+ p.height = self.tile_height
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = self.padding
+ mask = Image.new("L", (image.width, image.height), "black")
+ mask.paste(row_gradient, (xi*self.tile_width, yi*self.tile_height + self.tile_height//2))
+
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ for yi in range(rows):
+ for xi in range(cols-1):
+ if state.interrupted:
+ break
+ p.width = self.tile_width
+ p.height = self.tile_height
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = self.padding
+ mask = Image.new("L", (image.width, image.height), "black")
+ mask.paste(col_gradient, (xi*self.tile_width+self.tile_width//2, yi*self.tile_height))
+
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ p.width = image.width
+ p.height = image.height
+ if processed is not None:
+ self.initial_info = processed.infotext(p, 0)
+
+ return image
+
+ def half_tile_process_corners(self, p, image, rows, cols):
+ fixed_image = self.half_tile_process(p, image, rows, cols)
+ processed = None
+ self.init_draw(p)
+ gradient = Image.radial_gradient("L").resize(
+ (self.tile_width, self.tile_height), resample=Image.BICUBIC)
+ gradient = ImageOps.invert(gradient)
+ p.denoising_strength = self.denoise
+ #p.mask_blur = 0
+ p.mask_blur = self.mask_blur
+
+ for yi in range(rows-1):
+ for xi in range(cols-1):
+ if state.interrupted:
+ break
+ p.width = self.tile_width
+ p.height = self.tile_height
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = 0
+ mask = Image.new("L", (fixed_image.width, fixed_image.height), "black")
+ mask.paste(gradient, (xi*self.tile_width + self.tile_width//2,
+ yi*self.tile_height + self.tile_height//2))
+
+ p.init_images = [fixed_image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ if (len(processed.images) > 0):
+ fixed_image = processed.images[0]
+
+ p.width = fixed_image.width
+ p.height = fixed_image.height
+ if processed is not None:
+ self.initial_info = processed.infotext(p, 0)
+
+ return fixed_image
+
+ def band_pass_process(self, p, image, cols, rows):
+
+ self.init_draw(p)
+ processed = None
+
+ p.denoising_strength = self.denoise
+ p.mask_blur = 0
+
+ gradient = Image.linear_gradient("L")
+ mirror_gradient = Image.new("L", (256, 256), "black")
+ mirror_gradient.paste(gradient.resize((256, 128), resample=Image.BICUBIC), (0, 0))
+ mirror_gradient.paste(gradient.rotate(180).resize((256, 128), resample=Image.BICUBIC), (0, 128))
+
+ row_gradient = mirror_gradient.resize((image.width, self.width), resample=Image.BICUBIC)
+ col_gradient = mirror_gradient.rotate(90).resize((self.width, image.height), resample=Image.BICUBIC)
+
+ for xi in range(1, rows):
+ if state.interrupted:
+ break
+ p.width = self.width + self.padding * 2
+ p.height = image.height
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = self.padding
+ mask = Image.new("L", (image.width, image.height), "black")
+ mask.paste(col_gradient, (xi * self.tile_width - self.width // 2, 0))
+
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+ for yi in range(1, cols):
+ if state.interrupted:
+ break
+ p.width = image.width
+ p.height = self.width + self.padding * 2
+ p.inpaint_full_res = True
+ p.inpaint_full_res_padding = self.padding
+ mask = Image.new("L", (image.width, image.height), "black")
+ mask.paste(row_gradient, (0, yi * self.tile_height - self.width // 2))
+
+ p.init_images = [image]
+ p.image_mask = mask
+ processed = processing.process_images(p)
+ if (len(processed.images) > 0):
+ image = processed.images[0]
+
+ p.width = image.width
+ p.height = image.height
+ if processed is not None:
+ self.initial_info = processed.infotext(p, 0)
+
+ return image
+
+ def start(self, p, image, rows, cols):
+ if USDUSFMode(self.mode) == USDUSFMode.BAND_PASS:
+ return self.band_pass_process(p, image, rows, cols)
+ elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE:
+ return self.half_tile_process(p, image, rows, cols)
+ elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
+ return self.half_tile_process_corners(p, image, rows, cols)
+ else:
+ return image
+
+class Script(scripts.Script):
+ def title(self):
+ return "Ultimate SD upscale"
+
+ def show(self, is_img2img):
+ return is_img2img
+
+ def ui(self, is_img2img):
+
+ target_size_types = [
+ "From img2img2 settings",
+ "Custom size",
+ "Scale from image size"
+ ]
+
+ seams_fix_types = [
+ "None",
+ "Band pass",
+ "Half tile offset pass",
+ "Half tile offset pass + intersections"
+ ]
+
+ redrow_modes = [
+ "Linear",
+ "Chess",
+ "None"
+ ]
+
+ info = gr.HTML(
+ "E3
+ Original image + +  + + 4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.4 +  +Will upscale the image depending on the selected target size type
") + + with gr.Row(): + target_size_type = gr.Dropdown(label="Target size type", choices=[k for k in target_size_types], type="index", + value=next(iter(target_size_types))) + + custom_width = gr.Slider(label='Custom width', minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True) + custom_height = gr.Slider(label='Custom height', minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True) + custom_scale = gr.Slider(label='Scale', minimum=1, maximum=16, step=0.01, value=2, visible=False, interactive=True) + + gr.HTML("Redraw options:
") + with gr.Row(): + upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], + value=shared.sd_upscalers[0].name, type="index") + with gr.Row(): + redraw_mode = gr.Dropdown(label="Type", choices=[k for k in redrow_modes], type="index", value=next(iter(redrow_modes))) + tile_width = gr.Slider(minimum=0, maximum=2048, step=64, label='Tile width', value=512) + tile_height = gr.Slider(minimum=0, maximum=2048, step=64, label='Tile height', value=0) + mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8) + padding = gr.Slider(label='Padding', minimum=0, maximum=512, step=1, value=32) + gr.HTML("Seams fix:
") + with gr.Row(): + seams_fix_type = gr.Dropdown(label="Type", choices=[k for k in seams_fix_types], type="index", value=next(iter(seams_fix_types))) + seams_fix_denoise = gr.Slider(label='Denoise', minimum=0, maximum=1, step=0.01, value=0.35, visible=False, interactive=True) + seams_fix_width = gr.Slider(label='Width', minimum=0, maximum=128, step=1, value=64, visible=False, interactive=True) + seams_fix_mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False, interactive=True) + seams_fix_padding = gr.Slider(label='Padding', minimum=0, maximum=128, step=1, value=16, visible=False, interactive=True) + gr.HTML("Save options:
") + with gr.Row(): + save_upscaled_image = gr.Checkbox(label="Upscaled", value=True) + save_seams_fix_image = gr.Checkbox(label="Seams fix", value=False) + + def select_fix_type(fix_index): + all_visible = fix_index != 0 + mask_blur_visible = fix_index == 2 or fix_index == 3 + width_visible = fix_index == 1 + + return [gr.update(visible=all_visible), + gr.update(visible=width_visible), + gr.update(visible=mask_blur_visible), + gr.update(visible=all_visible)] + + seams_fix_type.change( + fn=select_fix_type, + inputs=seams_fix_type, + outputs=[seams_fix_denoise, seams_fix_width, seams_fix_mask_blur, seams_fix_padding] + ) + + def select_scale_type(scale_index): + is_custom_size = scale_index == 1 + is_custom_scale = scale_index == 2 + + return [gr.update(visible=is_custom_size), + gr.update(visible=is_custom_size), + gr.update(visible=is_custom_scale), + ] + + target_size_type.change( + fn=select_scale_type, + inputs=target_size_type, + outputs=[custom_width, custom_height, custom_scale] + ) + + return [info, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding, + upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur, + seams_fix_type, target_size_type, custom_width, custom_height, custom_scale] + + def run(self, p, _, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding, + upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur, + seams_fix_type, target_size_type, custom_width, custom_height, custom_scale): + + # Init + processing.fix_seed(p) + devices.torch_gc() + + p.do_not_save_grid = True + p.do_not_save_samples = True + p.inpaint_full_res = False + + p.inpainting_fill = 1 + p.n_iter = 1 + p.batch_size = 1 + + seed = p.seed + + # Init image + init_img = p.init_images[0] + if init_img == None: + return Processed(p, [], seed, "Empty image") + init_img = images.flatten(init_img, opts.img2img_background_color) + + #override size + if target_size_type == 1: + p.width = custom_width + p.height = custom_height + if target_size_type == 2: + p.width = math.ceil((init_img.width * custom_scale) / 64) * 64 + p.height = math.ceil((init_img.height * custom_scale) / 64) * 64 + + # Upscaling + upscaler = USDUpscaler(p, init_img, upscaler_index, save_upscaled_image, save_seams_fix_image, tile_width, tile_height) + upscaler.upscale() + + # Drawing + upscaler.setup_redraw(redraw_mode, padding, mask_blur) + upscaler.setup_seams_fix(seams_fix_padding, seams_fix_denoise, seams_fix_mask_blur, seams_fix_width, seams_fix_type) + upscaler.print_info() + upscaler.add_extra_info() + upscaler.process() + result_images = upscaler.result_images + + return Processed(p, result_images, seed, upscaler.initial_info if upscaler.initial_info is not None else "") +