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from __future__ import annotations
import platform
import re
import sys
import traceback
from collections.abc import Sequence
from copy import copy
from functools import partial
from pathlib import Path
from typing import TYPE_CHECKING, Any, NamedTuple, cast
import gradio as gr
from PIL import Image, ImageChops
from rich import print # noqa: A004 Shadowing built-in 'print'
import modules
from aaaaaa.conditional import create_binary_mask, schedulers
from aaaaaa.helper import (
PPImage,
change_torch_load,
copy_extra_params,
pause_total_tqdm,
preserve_prompts,
)
from aaaaaa.p_method import (
get_i,
is_img2img_inpaint,
is_inpaint_only_masked,
is_skip_img2img,
need_call_postprocess,
need_call_process,
)
from aaaaaa.traceback import rich_traceback
from aaaaaa.ui import WebuiInfo, adui, ordinal, suffix
from adetailer import (
ADETAILER,
__version__,
get_models,
mediapipe_predict,
ultralytics_predict,
)
from adetailer.args import (
BBOX_SORTBY,
BUILTIN_SCRIPT,
INPAINT_BBOX_MATCH_MODES,
SCRIPT_DEFAULT,
ADetailerArgs,
InpaintBBoxMatchMode,
SkipImg2ImgOrig,
)
from adetailer.common import PredictOutput, ensure_pil_image, safe_mkdir
from adetailer.mask import (
filter_by_ratio,
filter_k_by,
has_intersection,
is_all_black,
mask_preprocess,
sort_bboxes,
)
from adetailer.opts import dynamic_denoise_strength, optimal_crop_size
from controlnet_ext import (
CNHijackRestore,
ControlNetExt,
cn_allow_script_control,
controlnet_exists,
controlnet_type,
get_cn_models,
)
from modules import images, paths, script_callbacks, scripts, shared
from modules.devices import NansException
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
if TYPE_CHECKING:
from fastapi import FastAPI
PARAMS_TXT = "params.txt"
no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
adetailer_dir = Path(paths.models_path, "adetailer")
safe_mkdir(adetailer_dir)
extra_models_dirs = shared.opts.data.get("ad_extra_models_dir", "")
model_mapping = get_models(
adetailer_dir,
*extra_models_dirs.split("|"),
huggingface=not no_huggingface,
)
txt2img_submit_button = img2img_submit_button = None
txt2img_submit_button = cast(gr.Button, txt2img_submit_button)
img2img_submit_button = cast(gr.Button, img2img_submit_button)
print(
f"[-] ADetailer initialized. version: {__version__}, num models: {len(model_mapping)}"
)
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 ADETAILER
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]
scheduler_names = [x.label for x in schedulers]
checkpoint_list = modules.sd_models.checkpoint_tiles(use_short=True)
vae_list = modules.shared_items.sd_vae_items()
webui_info = WebuiInfo(
ad_model_list=ad_model_list,
sampler_names=sampler_names,
scheduler_names=scheduler_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 arg_list:
return False
ad_enabled = args[0] if isinstance(args[0], bool) else True
not_none = False
for arg in arg_list:
try:
adarg = ADetailerArgs(**arg)
except ValueError: # noqa: PERF203
continue
else:
if not adarg.need_skip():
not_none = True
break
return ad_enabled and not_none
def set_skip_img2img(self, p, *args_) -> None:
if (
hasattr(p, "_ad_skip_img2img")
or not hasattr(p, "init_images")
or not p.init_images
):
return
if len(args_) >= 2 and isinstance(args_[1], bool):
p._ad_skip_img2img = args_[1]
else:
p._ad_skip_img2img = False
if not p._ad_skip_img2img:
return
if is_img2img_inpaint(p):
p._ad_disabled = True
msg = "[-] ADetailer: img2img inpainting with skip img2img is not supported. (because it's buggy)"
print(msg)
return
p._ad_orig = SkipImg2ImgOrig(
steps=p.steps,
sampler_name=p.sampler_name,
width=p.width,
height=p.height,
)
p.steps = 1
p.sampler_name = "Euler"
p.width = 128
p.height = 128
def get_args(self, p, *args_) -> list[ADetailerArgs]:
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, "_ad_xyz"):
args[0] = {**args[0], **p._ad_xyz}
all_inputs: list[ADetailerArgs] = []
for n, arg_dict in enumerate(args, 1):
try:
inp = ADetailerArgs(**arg_dict)
except ValueError:
msg = f"[-] ADetailer: ValidationError when validating {ordinal(n)} arguments:"
print(msg, arg_dict, file=sys.stderr)
continue
all_inputs.append(inp)
if not all_inputs:
msg = "[-] ADetailer: No valid arguments found."
raise ValueError(msg)
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,
replacements: list[PromptSR],
) -> 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]", blank_replacement)
for pair in replacements:
prompts[n] = prompts[n].replace(pair.s, pair.r)
return prompts
def get_prompt(self, p, args: ADetailerArgs) -> tuple[list[str], list[str]]:
i = get_i(p)
prompt_sr = p._ad_xyz_prompt_sr if hasattr(p, "_ad_xyz_prompt_sr") else []
prompt = self._get_prompt(
ad_prompt=args.ad_prompt,
all_prompts=p.all_prompts,
i=i,
default=p.prompt,
replacements=prompt_sr,
)
negative_prompt = self._get_prompt(
ad_prompt=args.ad_negative_prompt,
all_prompts=p.all_negative_prompts,
i=i,
default=p.negative_prompt,
replacements=prompt_sr,
)
return prompt, negative_prompt
def get_seed(self, p) -> tuple[int, int]:
i = get_i(p)
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
elif hasattr(p, "_ad_orig"):
width = p._ad_orig.width
height = p._ad_orig.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
if hasattr(p, "_ad_orig"):
return p._ad_orig.steps
return 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:
if args.ad_use_sampler:
if args.ad_sampler == "Use same sampler":
return p.sampler_name
return args.ad_sampler
if hasattr(p, "_ad_orig"):
return p._ad_orig.sampler_name
return p.sampler_name
def get_scheduler(self, p, args: ADetailerArgs) -> dict[str, str]:
"webui >= 1.9.0"
if not args.ad_use_sampler:
return {"scheduler": getattr(p, "scheduler", "Automatic")}
if args.ad_scheduler == "Use same scheduler":
value = getattr(p, "scheduler", "Automatic")
else:
value = args.ad_scheduler
return {"scheduler": value}
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 read_params_txt(self) -> str:
params_txt = Path(paths.data_path, PARAMS_TXT)
if params_txt.exists():
return params_txt.read_text(encoding="utf-8")
return ""
def write_params_txt(self, content: str) -> None:
params_txt = Path(paths.data_path, PARAMS_TXT)
if params_txt.exists() and content:
params_txt.write_text(content, encoding="utf-8")
@staticmethod
def script_args_copy(script_args):
type_: type[list] | type[tuple] = type(script_args)
result = []
for arg in script_args:
try:
a = copy(arg)
except TypeError:
a = arg
result.append(a)
return type_(result)
def script_filter(self, p, args: ADetailerArgs):
script_runner = copy(p.scripts)
script_args = self.script_args_copy(p.script_args)
ad_only_selected_scripts = opts.data.get("ad_only_selected_scripts", True)
if not ad_only_selected_scripts:
return script_runner, script_args
ad_script_names_string: str = opts.data.get("ad_script_names", SCRIPT_DEFAULT)
ad_script_names = ad_script_names_string.split(",") + BUILTIN_SCRIPT.split(",")
script_names_set = {
name
for script_name in ad_script_names
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: Sequence[Any]) -> list[Any]:
new_args = []
for arg in script_args:
if "controlnet" in arg.__class__.__name__.lower():
new = copy(arg)
if hasattr(new, "enabled"):
new.enabled = False
if hasattr(new, "input_mode"):
new.input_mode = getattr(new.input_mode, "SIMPLE", "simple")
new_args.append(new)
elif isinstance(arg, dict) and "module" in arg:
new = copy(arg)
new["enabled"] = False
new_args.append(new)
else:
new_args.append(arg)
return new_args
def get_i2i_p(
self, p, args: ADetailerArgs, image: Image.Image
) -> StableDiffusionProcessingImg2Img:
seed, subseed = self.get_seed(p)
width, height = self.get_width_height(p, args)
steps = self.get_steps(p, args)
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)
version_args = {}
if schedulers:
version_args.update(self.get_scheduler(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=copy_extra_params(p.extra_generation_params),
do_not_save_samples=True,
do_not_save_grid=True,
override_settings=override_settings,
**version_args,
)
i2i.cached_c = [None, None]
i2i.cached_uc = [None, None]
i2i.scripts, i2i.script_args = self.script_filter(p, args)
i2i._ad_disabled = True
i2i._ad_inner = True
if args.ad_controlnet_model != "Passthrough" and controlnet_type != "forge":
i2i.script_args = self.disable_controlnet_units(i2i.script_args)
if args.ad_controlnet_model not in ["None", "Passthrough"]:
self.update_controlnet_args(i2i, args)
elif args.ad_controlnet_model == "None":
i2i.control_net_enabled = False
return i2i
def save_image(self, p, image, *, condition: str, suffix: str) -> None:
if not opts.data.get(condition, False):
return
i = get_i(p)
if p.all_prompts:
i %= len(p.all_prompts)
save_prompt = p.all_prompts[i]
else:
save_prompt = p.prompt
seed, _ = self.get_seed(p)
ad_save_images_dir: str = opts.data.get("ad_save_images_dir", "")
if not ad_save_images_dir.strip():
ad_save_images_dir = p.outpath_samples
images.save_image(
image=image,
path=ad_save_images_dir,
basename="",
seed=seed,
prompt=save_prompt,
extension=opts.samples_format,
info=self.infotext(p),
p=p,
suffix=suffix,
)
def get_ad_model(self, name: str):
if name not in model_mapping:
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, p, pred: PredictOutput, args: ADetailerArgs):
pred = filter_by_ratio(
pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
)
pred = filter_k_by(pred, k=args.ad_mask_k, by=args.ad_mask_filter_method)
pred = self.sort_bboxes(pred)
masks = mask_preprocess(
pred.masks,
kernel=args.ad_dilate_erode,
x_offset=args.ad_x_offset,
y_offset=args.ad_y_offset,
merge_invert=args.ad_mask_merge_invert,
)
if is_img2img_inpaint(p) and not is_inpaint_only_masked(p):
image_mask = self.get_image_mask(p)
masks = self.inpaint_mask_filter(image_mask, masks)
return masks
@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(extra_params: dict[str, Any], processed, n: int = 0):
pt = "ADetailer prompt" + suffix(n)
if pt in extra_params and extra_params[pt] != processed.all_prompts[0]:
print(
f"[-] ADetailer: applied {ordinal(n + 1)} ad_prompt: {processed.all_prompts[0]!r}"
)
ng = "ADetailer negative prompt" + suffix(n)
if ng in extra_params and extra_params[ng] != processed.all_negative_prompts[0]:
print(
f"[-] ADetailer: applied {ordinal(n + 1)} ad_negative_prompt: {processed.all_negative_prompts[0]!r}"
)
@staticmethod
def get_i2i_init_image(p, pp: PPImage):
if is_skip_img2img(p):
return p.init_images[0]
return pp.image
@staticmethod
def get_each_tab_seed(seed: int, i: int):
use_same_seed = shared.opts.data.get("ad_same_seed_for_each_tab", False)
return seed if use_same_seed else seed + i
@staticmethod
def inpaint_mask_filter(
img2img_mask: Image.Image, ad_mask: list[Image.Image]
) -> list[Image.Image]:
if ad_mask and img2img_mask.size != ad_mask[0].size:
img2img_mask = img2img_mask.resize(ad_mask[0].size, resample=Image.LANCZOS)
return [mask for mask in ad_mask if has_intersection(img2img_mask, mask)]
@staticmethod
def get_image_mask(p) -> Image.Image:
mask = p.image_mask
mask = ensure_pil_image(mask, "L")
if getattr(p, "inpainting_mask_invert", False):
mask = ImageChops.invert(mask)
mask = create_binary_mask(mask)
width, height = p.width, p.height
if is_skip_img2img(p) and hasattr(p, "init_images") and p.init_images:
width, height = p.init_images[0].size
return images.resize_image(p.resize_mode, mask, width, height)
@staticmethod
def get_dynamic_denoise_strength(
denoise_strength: float, bbox: Sequence[Any], image_size: tuple[int, int]
):
denoise_power = opts.data.get("ad_dynamic_denoise_power", 0)
if denoise_power == 0:
return denoise_strength
modified_strength = dynamic_denoise_strength(
denoise_power=denoise_power,
denoise_strength=denoise_strength,
bbox=bbox,
image_size=image_size,
)
print(
f"[-] ADetailer: dynamic denoising -- {denoise_strength:.2f} -> {modified_strength:.2f}"
)
return modified_strength
@staticmethod
def get_optimal_crop_image_size(
inpaint_width: int, inpaint_height: int, bbox: Sequence[Any]
) -> tuple[int, int]:
calculate_optimal_crop = opts.data.get(
"ad_match_inpaint_bbox_size", InpaintBBoxMatchMode.OFF.value
)
optimal_resolution: tuple[int, int] | None = None
# Off
if calculate_optimal_crop == InpaintBBoxMatchMode.OFF.value:
return (inpaint_width, inpaint_height)
# Strict (SDXL only)
if calculate_optimal_crop == InpaintBBoxMatchMode.STRICT.value:
if not shared.sd_model.is_sdxl:
msg = "[-] ADetailer: strict inpaint bounding box size matching is only available for SDXL. Use Free mode instead."
print(msg)
return (inpaint_width, inpaint_height)
optimal_resolution = optimal_crop_size.sdxl(
inpaint_width, inpaint_height, bbox
)
# Free
elif calculate_optimal_crop == InpaintBBoxMatchMode.FREE.value:
optimal_resolution = optimal_crop_size.free(
inpaint_width, inpaint_height, bbox
)
if optimal_resolution is None:
msg = "[-] ADetailer: unsupported inpaint bounding box match mode. Original inpainting dimensions will be used."
print(msg)
return (inpaint_width, inpaint_height)
# Only use optimal dimensions if they're different enough to current inpaint dimensions.
if (
abs(optimal_resolution[0] - inpaint_width) > inpaint_width * 0.1
or abs(optimal_resolution[1] - inpaint_height) > inpaint_height * 0.1
):
print(
f"[-] ADetailer: inpaint dimensions optimized -- {inpaint_width}x{inpaint_height} -> {optimal_resolution[0]}x{optimal_resolution[1]}"
)
return optimal_resolution
def fix_p2( # noqa: PLR0913
self, p, p2, pp: PPImage, args: ADetailerArgs, pred: PredictOutput, j: int
):
seed, subseed = self.get_seed(p)
p2.seed = self.get_each_tab_seed(seed, j)
p2.subseed = self.get_each_tab_seed(subseed, j)
p2.denoising_strength = self.get_dynamic_denoise_strength(
p2.denoising_strength, pred.bboxes[j], pp.image.size
)
p2.cached_c = [None, None]
p2.cached_uc = [None, None]
# Don't override user-defined dimensions.
if not args.ad_use_inpaint_width_height:
p2.width, p2.height = self.get_optimal_crop_image_size(
p2.width, p2.height, pred.bboxes[j]
)
@rich_traceback
def process(self, p, *args_):
if getattr(p, "_ad_disabled", False):
return
if is_img2img_inpaint(p) and is_all_black(self.get_image_mask(p)):
p._ad_disabled = True
msg = (
"[-] ADetailer: img2img inpainting with no mask -- adetailer disabled."
)
print(msg)
return
if not self.is_ad_enabled(*args_):
p._ad_disabled = True
return
self.set_skip_img2img(p, *args_)
if getattr(p, "_ad_disabled", False):
# case when img2img inpainting with skip img2img
return
arg_list = self.get_args(p, *args_)
if hasattr(p, "_ad_xyz_prompt_sr"):
replaced_positive_prompt, replaced_negative_prompt = self.get_prompt(
p, arg_list[0]
)
arg_list[0].ad_prompt = replaced_positive_prompt[0]
arg_list[0].ad_negative_prompt = replaced_negative_prompt[0]
extra_params = self.extra_params(arg_list)
p.extra_generation_params.update(extra_params)
def _postprocess_image_inner(
self, p, pp: PPImage, args: ADetailerArgs, *, n: int = 0
) -> bool:
"""
Returns
-------
bool
`True` if image was processed, `False` otherwise.
"""
if state.interrupted or state.skipped:
return False
i = get_i(p)
i2i = self.get_i2i_p(p, args, pp.image)
ad_prompts, ad_negatives = self.get_prompt(p, args)
is_mediapipe = args.is_mediapipe()
if is_mediapipe:
pred = mediapipe_predict(args.ad_model, pp.image, args.ad_confidence)
else:
with change_torch_load():
ad_model = self.get_ad_model(args.ad_model)
pred = ultralytics_predict(
ad_model,
image=pp.image,
confidence=args.ad_confidence,
device=self.ultralytics_device,
classes=args.ad_model_classes,
)
if pred.preview is None:
print(
f"[-] ADetailer: nothing detected on image {i + 1} with {ordinal(n + 1)} settings."
)
return False
masks = self.pred_preprocessing(p, pred, args)
shared.state.assign_current_image(pred.preview)
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] = ensure_pil_image(p2.init_images[0], "RGB")
self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j)
if re.match(r"^\s*\[SKIP\]\s*$", p2.prompt):
continue
self.fix_p2(p, p2, pp, args, pred, 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()
if not processed.images:
processed = None
break
self.compare_prompt(p.extra_generation_params, 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: PPImage, *args_):
if getattr(p, "_ad_disabled", False) or not self.is_ad_enabled(*args_):
return
pp.image = self.get_i2i_init_image(p, pp)
pp.image = ensure_pil_image(pp.image, "RGB")
init_image = copy(pp.image)
arg_list = self.get_args(p, *args_)
params_txt_content = self.read_params_txt()
if need_call_postprocess(p):
dummy = Processed(p, [], p.seed, "")
with preserve_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.need_skip():
continue
is_processed |= self._postprocess_image_inner(p, pp, args, n=n)
if is_processed and not is_skip_img2img(p):
self.save_image(
p, init_image, condition="ad_save_images_before", suffix="-ad-before"
)
if need_call_process(p):
with preserve_prompts(p):
copy_p = copy(p)
p.scripts.before_process(copy_p)
p.scripts.process(copy_p)
self.write_params_txt(params_txt_content)
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", ADETAILER)
shared.opts.add_option(
"ad_max_models",
shared.OptionInfo(
default=4,
label="Max tabs",
component=gr.Slider,
component_args={"minimum": 1, "maximum": 15, "step": 1},
section=section,
).needs_reload_ui(),
)
shared.opts.add_option(
"ad_extra_models_dir",
shared.OptionInfo(
default="",
label="Extra paths to scan adetailer models separated by vertical bars(|)",
component=gr.Textbox,
section=section,
)
.info("eg. path\\to\\models|C:\\path\\to\\models|another/path/to/models")
.needs_reload_ui(),
)
shared.opts.add_option(
"ad_save_images_dir",
shared.OptionInfo(
default="",
label="Output directory for adetailer images",
component=gr.Textbox,
section=section,
),
)
shared.opts.add_option(
"ad_save_previews",
shared.OptionInfo(default=False, label="Save mask previews", section=section),
)
shared.opts.add_option(
"ad_save_images_before",
shared.OptionInfo(
default=False, label="Save images before ADetailer", section=section
),
)
shared.opts.add_option(
"ad_only_selected_scripts",
shared.OptionInfo(
default=True,
label="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,
),
)
shared.opts.add_option(
"ad_same_seed_for_each_tab",
shared.OptionInfo(
default=False,
label="Use same seed for each tab in adetailer",
section=section,
),
)
shared.opts.add_option(
"ad_dynamic_denoise_power",
shared.OptionInfo(
default=0,
label="Power scaling for dynamic denoise strength based on bounding box size",
component=gr.Slider,
component_args={"minimum": -10, "maximum": 10, "step": 0.01},
section=section,
).info(
"Smaller areas get higher denoising, larger areas less. Maximum denoise strength is set by 'Inpaint denoising strength'. 0 = disabled; 1 = linear; 2-4 = recommended"
),
)
shared.opts.add_option(
"ad_match_inpaint_bbox_size",
shared.OptionInfo(
default=InpaintBBoxMatchMode.OFF.value, # Off
component=gr.Radio,
component_args={"choices": INPAINT_BBOX_MATCH_MODES},
label="Try to match inpainting size to bounding box size, if 'Use separate width/height' is not set",
section=section,
).info(
"Strict is for SDXL only, and matches exactly to trained SDXL resolutions. Free works with any model, but will use potentially unsupported dimensions."
),
)
# xyz_grid
class PromptSR(NamedTuple):
s: str
r: str
def set_value(p, x: Any, xs: Any, *, field: str):
if not hasattr(p, "_ad_xyz"):
p._ad_xyz = {}
p._ad_xyz[field] = x
def search_and_replace_prompt(p, x: Any, xs: Any, replace_in_main_prompt: bool):
if replace_in_main_prompt:
p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
if not hasattr(p, "_ad_xyz_prompt_sr"):
p._ad_xyz_prompt_sr = []
p._ad_xyz_prompt_sr.append(PromptSR(s=xs[0], r=x))
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()]
xyz_samplers = [sampler.name for sampler in all_samplers]
xyz_schedulers = [scheduler.label for scheduler in schedulers]
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] Prompt S/R (AD 1st)",
str,
partial(search_and_replace_prompt, replace_in_main_prompt=False),
),
xyz_grid.AxisOption(
"[ADetailer] Prompt S/R (AD 1st and main prompt)",
str,
partial(search_and_replace_prompt, replace_in_main_prompt=True),
),
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] CFG scale 1st",
float,
partial(set_value, field="ad_cfg_scale"),
),
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: xyz_samplers,
),
xyz_grid.AxisOption(
"[ADetailer] ADetailer scheduler 1st",
str,
partial(set_value, field="ad_scheduler"),
choices=lambda: xyz_schedulers,
),
xyz_grid.AxisOption(
"[ADetailer] noise multiplier 1st",
float,
partial(set_value, field="ad_noise_multiplier"),
),
xyz_grid.AxisOption(
"[ADetailer] ControlNet model 1st",
str,
partial(set_value, field="ad_controlnet_model"),
choices=lambda: ["None", "Passthrough", *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,
)
# api
def add_api_endpoints(_: gr.Blocks, app: FastAPI):
@app.get("/adetailer/v1/version")
async def version():
return {"version": __version__}
@app.get("/adetailer/v1/schema")
async def schema():
if hasattr(ADetailerArgs, "model_json_schema"):
return ADetailerArgs.model_json_schema()
return ADetailerArgs.schema()
@app.get("/adetailer/v1/ad_model")
async def ad_model():
return {"ad_model": list(model_mapping)}
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_after_component(on_after_component)
script_callbacks.on_app_started(add_api_endpoints)
script_callbacks.on_before_ui(on_before_ui)
|