Upload !adetailer.py

!adetailer.py

@@ -0,0 +1,663 @@
+from __future__ import annotations
+
+import os
+import platform
+import re
+import sys
+import traceback
+from contextlib import contextmanager
+from copy import copy, deepcopy
+from pathlib import Path
+from textwrap import dedent
+from typing import Any
+
+import gradio as gr
+import torch
+
+import modules  # noqa: F401
+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.ui import adui, ordinal, suffix
+from controlnet_ext import ControlNetExt, controlnet_exists
+from controlnet_ext.restore import (
+    CNHijackRestore,
+    cn_allow_script_control,
+    cn_restore_unet_hook,
+)
+from sd_webui import images, safe, script_callbacks, scripts, shared
+from sd_webui.paths import data_path, models_path
+from sd_webui.processing import (
+    StableDiffusionProcessingImg2Img,
+    create_infotext,
+    process_images,
+)
+from sd_webui.shared import cmd_opts, opts, state
+
+try:
+    from rich import print
+    from rich.traceback import install
+
+    install(show_locals=True)
+except Exception:
+    pass
+
+no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
+adetailer_dir = Path(models_path, "adetailer")
+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"
+
+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
+
+
+class AfterDetailerScript(scripts.Script):
+    def __init__(self):
+        super().__init__()
+        self.ultralytics_device = self.get_ultralytics_device()
+
+        self.controlnet_ext = None
+        self.cn_script = None
+        self.cn_latest_network = None
+
+    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())
+
+        components, infotext_fields = adui(
+            num_models,
+            is_img2img,
+            model_list,
+            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, args.ad_controlnet_model, args.ad_controlnet_weight
+            )
+
+    def is_ad_enabled(self, *args_) -> bool:
+        if len(args_) == 0 or (len(args_) == 1 and isinstance(args_[0], bool)):
+            message = f"""
+                       [-] ADetailer: Not enough arguments passed to ADetailer.
+                           input: {args_!r}
+                       """
+            raise ValueError(dedent(message))
+        a0 = args_[0]
+        a1 = args_[1] if len(args_) > 1 else None
+        checker = EnableChecker(a0=a0, a1=a1)
+        return checker.is_enabled()
+
+    def get_args(self, *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)
+
+        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:
+        '`device = ""` means autodetect'
+        device = ""
+        if platform.system() == "Darwin":
+            return device
+
+        if any(getattr(cmd_opts, vram, False) for vram in ["lowvram", "medvram"]):
+            device = "cpu"
+
+        return device
+
+    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._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._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 infotext(self, 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)
+            if filename == "controlnet":
+                self.cn_script = script_object
+                self.cn_latest_network = script_object.latest_network
+
+        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)
+
+        sampler_name = p.sampler_name
+        if sampler_name in ["PLMS", "UniPC"]:
+            sampler_name = "Euler"
+
+        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_full_res,
+            inpaint_full_res_padding=args.ad_inpaint_full_res_padding,
+            inpainting_mask_invert=0,
+            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,
+        )
+
+        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._idx
+        seed, _ = self.get_seed(p)
+
+        if opts.data.get(condition, False):
+            images.save_image(
+                image=image,
+                path=p.outpath_samples,
+                basename="",
+                seed=seed,
+                prompt=p.all_prompts[i] if i < len(p.all_prompts) else p.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)
+        pred = sort_bboxes(pred, sortby_idx)
+        return pred
+
+    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,
+        )
+
+    def i2i_prompts_replace(
+        self, i2i, prompts: list[str], negative_prompts: list[str], j: int
+    ):
+        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
+
+    def is_need_call_process(self, p):
+        i = p._idx
+        n_iter = p.iteration
+        bs = p.batch_size
+        return (i == (n_iter + 1) * bs - 1) and (i != len(p.all_prompts) - 1)
+
+    def process(self, p, *args_):
+        if getattr(p, "_disable_adetailer", False):
+            return
+
+        if self.is_ad_enabled(*args_):
+            arg_list = self.get_args(*args_)
+            extra_params = self.extra_params(arg_list)
+            p.extra_generation_params.update(extra_params)
+
+            p._idx = -1
+
+    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._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_conf, **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]
+            self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j)
+
+            if not re.match(r"^\s*\[SKIP\]\s*$", p2.prompt):
+                if args.ad_controlnet_model == "None":
+                    cn_restore_unet_hook(p2, self.cn_latest_network)
+                processed = process_images(p2)
+
+                p2 = copy(i2i)
+                p2.init_images = [processed.images[0]]
+
+            p2.seed = seed + j + 1
+            p2.subseed = subseed + j + 1
+
+        if processed is not None:
+            pp.image = processed.images[0]
+            return True
+
+        return False
+
+    def postprocess_image(self, p, pp, *args_):
+        if getattr(p, "_disable_adetailer", False):
+            return
+
+        if not self.is_ad_enabled(*args_):
+            return
+
+        p._idx = getattr(p, "_idx", -1) + 1
+        init_image = copy(pp.image)
+        arg_list = self.get_args(*args_)
+
+        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 self.cn_script is not None and self.is_need_call_process(p):
+            self.cn_script.process(p)
+
+        try:
+            if p._idx == len(p.all_prompts) - 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": 5, "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,
+        ),
+    )
+
+########################################################
+
+def make_axis_options():
+    xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
+    ad_denoising_strength = getattr(p, 'inp denoising strength', args.ad_denoising_strength)
+    p.extra_generation_params["ad_denoising_strength"] = args.ad_denoising_strength
+    extra_axis_options = [
+        xyz_grid.AxisOption("[ade] inpaint denoising strength", float, xyz_grid.apply_field("inp denoising strength"))
+        
+    ]
+    
+def callbackBeforeUi():
+    try:
+        make_axis_options()
+    except Exception as e:
+        traceback.print_exc()
+        print(f"Failed to add support for X/Y/Z Plot Script because: {e}")
+
+script_callbacks.on_before_ui(callbackBeforeUi)
+
+
+
+
+
+
+
+
+
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
+script_callbacks.on_after_component(on_after_component)