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, filter_k_largest, mask_preprocess, sort_bboxes, ) from adetailer.traceback import rich_traceback from adetailer.ui import WebuiInfo, adui, ordinal, suffix from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models from controlnet_ext.restore import ( CNHijackRestore, cn_allow_script_control, ) from modules import images, safe, script_callbacks, scripts, shared from modules.devices import NansException from modules.paths import data_path, models_path from modules.processing import ( Processed, StableDiffusionProcessingImg2Img, create_infotext, process_images, ) from modules.sd_samplers import all_samplers from modules.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) ad_model_list = list(model_mapping.keys()) sampler_names = [sampler.name for sampler in all_samplers] try: checkpoint_list = modules.sd_models.checkpoint_tiles(use_shorts=True) except TypeError: checkpoint_list = modules.sd_models.checkpoint_tiles() vae_list = modules.shared_items.sd_vae_items() webui_info = WebuiInfo( ad_model_list=ad_model_list, sampler_names=sampler_names, t2i_button=txt2img_submit_button, i2i_button=img2img_submit_button, checkpoints_list=checkpoint_list, vae_list=vae_list, ) components, infotext_fields = adui(num_models, is_img2img, webui_info) 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} ADetailer disabled. """ print(dedent(message), file=sys.stderr) return False 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] = {**args[0], **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 "" vram_args = ["lowvram", "medvram", "medvram_sdxl"] if any(getattr(cmd_opts, vram, False) for vram in vram_args): 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 elif "[PROMPT]" in prompts[n]: prompts[n] = prompts[n].replace("[PROMPT]", f" {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: return args.ad_steps if args.ad_use_steps else p.steps def get_cfg_scale(self, p, args: ADetailerArgs) -> float: return args.ad_cfg_scale if args.ad_use_cfg_scale else p.cfg_scale def get_sampler(self, p, args: ADetailerArgs) -> str: return args.ad_sampler if args.ad_use_sampler else p.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 if ( args.ad_use_checkpoint and args.ad_checkpoint and args.ad_checkpoint not in ("None", "Use same checkpoint") ): d["sd_model_checkpoint"] = args.ad_checkpoint if ( args.ad_use_vae and args.ad_vae and args.ad_vae not in ("None", "Use same VAE") ): d["sd_vae"] = args.ad_vae return d def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None: return args.ad_noise_multiplier if args.ad_use_noise_multiplier else None @staticmethod 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._ad_disabled = 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 = filter_k_largest(pred, k=args.ad_mask_k_largest) 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, "_ad_disabled", 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_inner( self, p, pp, args: ADetailerArgs, *, n: int = 0 ) -> bool: """ Returns ------- bool `True` if image was processed, `False` otherwise. """ if state.interrupted or state.skipped: 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, "_ad_disabled", 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_inner(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)