Upload !adetailer.py
Browse files- !adetailer.py +1000 -0
!adetailer.py
ADDED
@@ -0,0 +1,1000 @@
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1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import os
|
4 |
+
import platform
|
5 |
+
import re
|
6 |
+
import sys
|
7 |
+
import traceback
|
8 |
+
from contextlib import contextmanager, suppress
|
9 |
+
from copy import copy
|
10 |
+
from functools import partial
|
11 |
+
from pathlib import Path
|
12 |
+
from textwrap import dedent
|
13 |
+
from typing import TYPE_CHECKING, Any, NamedTuple
|
14 |
+
|
15 |
+
import gradio as gr
|
16 |
+
import torch
|
17 |
+
from PIL import Image
|
18 |
+
from rich import print
|
19 |
+
from torchvision.transforms.functional import to_pil_image
|
20 |
+
|
21 |
+
import modules
|
22 |
+
from adetailer import (
|
23 |
+
AFTER_DETAILER,
|
24 |
+
__version__,
|
25 |
+
get_models,
|
26 |
+
mediapipe_predict,
|
27 |
+
ultralytics_predict,
|
28 |
+
)
|
29 |
+
from adetailer.args import ALL_ARGS, BBOX_SORTBY, ADetailerArgs, SkipImg2ImgOrig
|
30 |
+
from adetailer.common import PredictOutput
|
31 |
+
from adetailer.mask import (
|
32 |
+
filter_by_ratio,
|
33 |
+
filter_k_largest,
|
34 |
+
mask_preprocess,
|
35 |
+
sort_bboxes,
|
36 |
+
)
|
37 |
+
from adetailer.traceback import rich_traceback
|
38 |
+
from adetailer.ui import WebuiInfo, adui, ordinal, suffix
|
39 |
+
from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
|
40 |
+
from controlnet_ext.restore import (
|
41 |
+
CNHijackRestore,
|
42 |
+
cn_allow_script_control,
|
43 |
+
)
|
44 |
+
from modules import images, paths, safe, script_callbacks, scripts, shared
|
45 |
+
from modules.devices import NansException
|
46 |
+
from modules.processing import (
|
47 |
+
Processed,
|
48 |
+
StableDiffusionProcessingImg2Img,
|
49 |
+
create_infotext,
|
50 |
+
process_images,
|
51 |
+
)
|
52 |
+
from modules.sd_samplers import all_samplers
|
53 |
+
from modules.shared import cmd_opts, opts, state
|
54 |
+
|
55 |
+
if TYPE_CHECKING:
|
56 |
+
from fastapi import FastAPI
|
57 |
+
|
58 |
+
no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
|
59 |
+
adetailer_dir = Path(paths.models_path, "adetailer")
|
60 |
+
extra_models_dir = shared.opts.data.get("ad_extra_models_dir", "")
|
61 |
+
model_mapping = get_models(
|
62 |
+
adetailer_dir, extra_dir=extra_models_dir, huggingface=not no_huggingface
|
63 |
+
)
|
64 |
+
txt2img_submit_button = img2img_submit_button = None
|
65 |
+
SCRIPT_DEFAULT = "dynamic_prompting,dynamic_thresholding,wildcard_recursive,wildcards,lora_block_weight,negpip"
|
66 |
+
|
67 |
+
if (
|
68 |
+
not adetailer_dir.exists()
|
69 |
+
and adetailer_dir.parent.exists()
|
70 |
+
and os.access(adetailer_dir.parent, os.W_OK)
|
71 |
+
):
|
72 |
+
adetailer_dir.mkdir()
|
73 |
+
|
74 |
+
print(
|
75 |
+
f"[-] ADetailer initialized. version: {__version__}, num models: {len(model_mapping)}"
|
76 |
+
)
|
77 |
+
|
78 |
+
|
79 |
+
@contextmanager
|
80 |
+
def change_torch_load():
|
81 |
+
orig = torch.load
|
82 |
+
try:
|
83 |
+
torch.load = safe.unsafe_torch_load
|
84 |
+
yield
|
85 |
+
finally:
|
86 |
+
torch.load = orig
|
87 |
+
|
88 |
+
|
89 |
+
@contextmanager
|
90 |
+
def pause_total_tqdm():
|
91 |
+
orig = opts.data.get("multiple_tqdm", True)
|
92 |
+
try:
|
93 |
+
opts.data["multiple_tqdm"] = False
|
94 |
+
yield
|
95 |
+
finally:
|
96 |
+
opts.data["multiple_tqdm"] = orig
|
97 |
+
|
98 |
+
|
99 |
+
@contextmanager
|
100 |
+
def preseve_prompts(p):
|
101 |
+
all_pt = copy(p.all_prompts)
|
102 |
+
all_ng = copy(p.all_negative_prompts)
|
103 |
+
try:
|
104 |
+
yield
|
105 |
+
finally:
|
106 |
+
p.all_prompts = all_pt
|
107 |
+
p.all_negative_prompts = all_ng
|
108 |
+
|
109 |
+
|
110 |
+
class AfterDetailerScript(scripts.Script):
|
111 |
+
def __init__(self):
|
112 |
+
super().__init__()
|
113 |
+
self.ultralytics_device = self.get_ultralytics_device()
|
114 |
+
|
115 |
+
self.controlnet_ext = None
|
116 |
+
|
117 |
+
def __repr__(self):
|
118 |
+
return f"{self.__class__.__name__}(version={__version__})"
|
119 |
+
|
120 |
+
def title(self):
|
121 |
+
return AFTER_DETAILER
|
122 |
+
|
123 |
+
def show(self, is_img2img):
|
124 |
+
return scripts.AlwaysVisible
|
125 |
+
|
126 |
+
def ui(self, is_img2img):
|
127 |
+
num_models = opts.data.get("ad_max_models", 2)
|
128 |
+
ad_model_list = list(model_mapping.keys())
|
129 |
+
sampler_names = [sampler.name for sampler in all_samplers]
|
130 |
+
|
131 |
+
try:
|
132 |
+
checkpoint_list = modules.sd_models.checkpoint_tiles(use_shorts=True)
|
133 |
+
except TypeError:
|
134 |
+
checkpoint_list = modules.sd_models.checkpoint_tiles()
|
135 |
+
vae_list = modules.shared_items.sd_vae_items()
|
136 |
+
|
137 |
+
webui_info = WebuiInfo(
|
138 |
+
ad_model_list=ad_model_list,
|
139 |
+
sampler_names=sampler_names,
|
140 |
+
t2i_button=txt2img_submit_button,
|
141 |
+
i2i_button=img2img_submit_button,
|
142 |
+
checkpoints_list=checkpoint_list,
|
143 |
+
vae_list=vae_list,
|
144 |
+
)
|
145 |
+
|
146 |
+
components, infotext_fields = adui(num_models, is_img2img, webui_info)
|
147 |
+
|
148 |
+
self.infotext_fields = infotext_fields
|
149 |
+
return components
|
150 |
+
|
151 |
+
def init_controlnet_ext(self) -> None:
|
152 |
+
if self.controlnet_ext is not None:
|
153 |
+
return
|
154 |
+
self.controlnet_ext = ControlNetExt()
|
155 |
+
|
156 |
+
if controlnet_exists:
|
157 |
+
try:
|
158 |
+
self.controlnet_ext.init_controlnet()
|
159 |
+
except ImportError:
|
160 |
+
error = traceback.format_exc()
|
161 |
+
print(
|
162 |
+
f"[-] ADetailer: ControlNetExt init failed:\n{error}",
|
163 |
+
file=sys.stderr,
|
164 |
+
)
|
165 |
+
|
166 |
+
def update_controlnet_args(self, p, args: ADetailerArgs) -> None:
|
167 |
+
if self.controlnet_ext is None:
|
168 |
+
self.init_controlnet_ext()
|
169 |
+
|
170 |
+
if (
|
171 |
+
self.controlnet_ext is not None
|
172 |
+
and self.controlnet_ext.cn_available
|
173 |
+
and args.ad_controlnet_model != "None"
|
174 |
+
):
|
175 |
+
self.controlnet_ext.update_scripts_args(
|
176 |
+
p,
|
177 |
+
model=args.ad_controlnet_model,
|
178 |
+
module=args.ad_controlnet_module,
|
179 |
+
weight=args.ad_controlnet_weight,
|
180 |
+
guidance_start=args.ad_controlnet_guidance_start,
|
181 |
+
guidance_end=args.ad_controlnet_guidance_end,
|
182 |
+
)
|
183 |
+
|
184 |
+
def is_ad_enabled(self, *args_) -> bool:
|
185 |
+
arg_list = [arg for arg in args_ if isinstance(arg, dict)]
|
186 |
+
if not args_ or not arg_list:
|
187 |
+
message = f"""
|
188 |
+
[-] ADetailer: Invalid arguments passed to ADetailer.
|
189 |
+
input: {args_!r}
|
190 |
+
ADetailer disabled.
|
191 |
+
"""
|
192 |
+
print(dedent(message), file=sys.stderr)
|
193 |
+
return False
|
194 |
+
|
195 |
+
ad_enabled = args_[0] if isinstance(args_[0], bool) else True
|
196 |
+
not_none = any(arg.get("ad_model", "None") != "None" for arg in arg_list)
|
197 |
+
return ad_enabled and not_none
|
198 |
+
|
199 |
+
def check_skip_img2img(self, p, *args_) -> None:
|
200 |
+
if (
|
201 |
+
hasattr(p, "_ad_skip_img2img")
|
202 |
+
or not hasattr(p, "init_images")
|
203 |
+
or not p.init_images
|
204 |
+
):
|
205 |
+
return
|
206 |
+
|
207 |
+
if len(args_) >= 2 and isinstance(args_[1], bool):
|
208 |
+
p._ad_skip_img2img = args_[1]
|
209 |
+
if args_[1]:
|
210 |
+
p._ad_orig = SkipImg2ImgOrig(
|
211 |
+
steps=p.steps,
|
212 |
+
sampler_name=p.sampler_name,
|
213 |
+
width=p.width,
|
214 |
+
height=p.height,
|
215 |
+
)
|
216 |
+
p.steps = 1
|
217 |
+
p.sampler_name = "Euler"
|
218 |
+
p.width = 128
|
219 |
+
p.height = 128
|
220 |
+
else:
|
221 |
+
p._ad_skip_img2img = False
|
222 |
+
|
223 |
+
@staticmethod
|
224 |
+
def get_i(p) -> int:
|
225 |
+
it = p.iteration
|
226 |
+
bs = p.batch_size
|
227 |
+
i = p.batch_index
|
228 |
+
return it * bs + i
|
229 |
+
|
230 |
+
def get_args(self, p, *args_) -> list[ADetailerArgs]:
|
231 |
+
"""
|
232 |
+
`args_` is at least 1 in length by `is_ad_enabled` immediately above
|
233 |
+
"""
|
234 |
+
args = [arg for arg in args_ if isinstance(arg, dict)]
|
235 |
+
|
236 |
+
if not args:
|
237 |
+
message = f"[-] ADetailer: Invalid arguments passed to ADetailer: {args_!r}"
|
238 |
+
raise ValueError(message)
|
239 |
+
|
240 |
+
if hasattr(p, "_ad_xyz"):
|
241 |
+
args[0] = {**args[0], **p._ad_xyz}
|
242 |
+
|
243 |
+
all_inputs = []
|
244 |
+
|
245 |
+
for n, arg_dict in enumerate(args, 1):
|
246 |
+
try:
|
247 |
+
inp = ADetailerArgs(**arg_dict)
|
248 |
+
except ValueError as e:
|
249 |
+
msgs = [
|
250 |
+
f"[-] ADetailer: ValidationError when validating {ordinal(n)} arguments: {e}\n"
|
251 |
+
]
|
252 |
+
for attr in ALL_ARGS.attrs:
|
253 |
+
arg = arg_dict.get(attr)
|
254 |
+
dtype = type(arg)
|
255 |
+
arg = "DEFAULT" if arg is None else repr(arg)
|
256 |
+
msgs.append(f" {attr}: {arg} ({dtype})")
|
257 |
+
raise ValueError("\n".join(msgs)) from e
|
258 |
+
|
259 |
+
all_inputs.append(inp)
|
260 |
+
|
261 |
+
return all_inputs
|
262 |
+
|
263 |
+
def extra_params(self, arg_list: list[ADetailerArgs]) -> dict:
|
264 |
+
params = {}
|
265 |
+
for n, args in enumerate(arg_list):
|
266 |
+
params.update(args.extra_params(suffix=suffix(n)))
|
267 |
+
params["ADetailer version"] = __version__
|
268 |
+
return params
|
269 |
+
|
270 |
+
@staticmethod
|
271 |
+
def get_ultralytics_device() -> str:
|
272 |
+
if "adetailer" in shared.cmd_opts.use_cpu:
|
273 |
+
return "cpu"
|
274 |
+
|
275 |
+
if platform.system() == "Darwin":
|
276 |
+
return ""
|
277 |
+
|
278 |
+
vram_args = ["lowvram", "medvram", "medvram_sdxl"]
|
279 |
+
if any(getattr(cmd_opts, vram, False) for vram in vram_args):
|
280 |
+
return "cpu"
|
281 |
+
|
282 |
+
return ""
|
283 |
+
|
284 |
+
def prompt_blank_replacement(
|
285 |
+
self, all_prompts: list[str], i: int, default: str
|
286 |
+
) -> str:
|
287 |
+
if not all_prompts:
|
288 |
+
return default
|
289 |
+
if i < len(all_prompts):
|
290 |
+
return all_prompts[i]
|
291 |
+
j = i % len(all_prompts)
|
292 |
+
return all_prompts[j]
|
293 |
+
|
294 |
+
def _get_prompt(
|
295 |
+
self,
|
296 |
+
ad_prompt: str,
|
297 |
+
all_prompts: list[str],
|
298 |
+
i: int,
|
299 |
+
default: str,
|
300 |
+
replacements: list[PromptSR],
|
301 |
+
) -> list[str]:
|
302 |
+
prompts = re.split(r"\s*\[SEP\]\s*", ad_prompt)
|
303 |
+
blank_replacement = self.prompt_blank_replacement(all_prompts, i, default)
|
304 |
+
for n in range(len(prompts)):
|
305 |
+
if not prompts[n]:
|
306 |
+
prompts[n] = blank_replacement
|
307 |
+
elif "[PROMPT]" in prompts[n]:
|
308 |
+
prompts[n] = prompts[n].replace("[PROMPT]", f" {blank_replacement} ")
|
309 |
+
|
310 |
+
for pair in replacements:
|
311 |
+
prompts[n] = prompts[n].replace(pair.s, pair.r)
|
312 |
+
return prompts
|
313 |
+
|
314 |
+
def get_prompt(self, p, args: ADetailerArgs) -> tuple[list[str], list[str]]:
|
315 |
+
i = self.get_i(p)
|
316 |
+
prompt_sr = p._ad_xyz_prompt_sr if hasattr(p, "_ad_xyz_prompt_sr") else []
|
317 |
+
|
318 |
+
prompt = self._get_prompt(args.ad_prompt, p.all_prompts, i, p.prompt, prompt_sr)
|
319 |
+
negative_prompt = self._get_prompt(
|
320 |
+
args.ad_negative_prompt,
|
321 |
+
p.all_negative_prompts,
|
322 |
+
i,
|
323 |
+
p.negative_prompt,
|
324 |
+
prompt_sr,
|
325 |
+
)
|
326 |
+
|
327 |
+
return prompt, negative_prompt
|
328 |
+
|
329 |
+
def get_seed(self, p) -> tuple[int, int]:
|
330 |
+
i = self.get_i(p)
|
331 |
+
|
332 |
+
if not p.all_seeds:
|
333 |
+
seed = p.seed
|
334 |
+
elif i < len(p.all_seeds):
|
335 |
+
seed = p.all_seeds[i]
|
336 |
+
else:
|
337 |
+
j = i % len(p.all_seeds)
|
338 |
+
seed = p.all_seeds[j]
|
339 |
+
|
340 |
+
if not p.all_subseeds:
|
341 |
+
subseed = p.subseed
|
342 |
+
elif i < len(p.all_subseeds):
|
343 |
+
subseed = p.all_subseeds[i]
|
344 |
+
else:
|
345 |
+
j = i % len(p.all_subseeds)
|
346 |
+
subseed = p.all_subseeds[j]
|
347 |
+
|
348 |
+
return seed, subseed
|
349 |
+
|
350 |
+
def get_width_height(self, p, args: ADetailerArgs) -> tuple[int, int]:
|
351 |
+
if args.ad_use_inpaint_width_height:
|
352 |
+
width = args.ad_inpaint_width
|
353 |
+
height = args.ad_inpaint_height
|
354 |
+
elif hasattr(p, "_ad_orig"):
|
355 |
+
width = p._ad_orig.width
|
356 |
+
height = p._ad_orig.height
|
357 |
+
else:
|
358 |
+
width = p.width
|
359 |
+
height = p.height
|
360 |
+
|
361 |
+
return width, height
|
362 |
+
|
363 |
+
def get_steps(self, p, args: ADetailerArgs) -> int:
|
364 |
+
if args.ad_use_steps:
|
365 |
+
return args.ad_steps
|
366 |
+
if hasattr(p, "_ad_orig"):
|
367 |
+
return p._ad_orig.steps
|
368 |
+
return p.steps
|
369 |
+
|
370 |
+
def get_cfg_scale(self, p, args: ADetailerArgs) -> float:
|
371 |
+
return args.ad_cfg_scale if args.ad_use_cfg_scale else p.cfg_scale
|
372 |
+
|
373 |
+
def get_sampler(self, p, args: ADetailerArgs) -> str:
|
374 |
+
if args.ad_use_sampler:
|
375 |
+
return args.ad_sampler
|
376 |
+
if hasattr(p, "_ad_orig"):
|
377 |
+
return p._ad_orig.sampler_name
|
378 |
+
return p.sampler_name
|
379 |
+
|
380 |
+
def get_override_settings(self, p, args: ADetailerArgs) -> dict[str, Any]:
|
381 |
+
d = {}
|
382 |
+
|
383 |
+
if args.ad_use_clip_skip:
|
384 |
+
d["CLIP_stop_at_last_layers"] = args.ad_clip_skip
|
385 |
+
|
386 |
+
if (
|
387 |
+
args.ad_use_checkpoint
|
388 |
+
and args.ad_checkpoint
|
389 |
+
and args.ad_checkpoint not in ("None", "Use same checkpoint")
|
390 |
+
):
|
391 |
+
d["sd_model_checkpoint"] = args.ad_checkpoint
|
392 |
+
|
393 |
+
if (
|
394 |
+
args.ad_use_vae
|
395 |
+
and args.ad_vae
|
396 |
+
and args.ad_vae not in ("None", "Use same VAE")
|
397 |
+
):
|
398 |
+
d["sd_vae"] = args.ad_vae
|
399 |
+
return d
|
400 |
+
|
401 |
+
def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None:
|
402 |
+
return args.ad_noise_multiplier if args.ad_use_noise_multiplier else None
|
403 |
+
|
404 |
+
@staticmethod
|
405 |
+
def infotext(p) -> str:
|
406 |
+
return create_infotext(
|
407 |
+
p, p.all_prompts, p.all_seeds, p.all_subseeds, None, 0, 0
|
408 |
+
)
|
409 |
+
|
410 |
+
def write_params_txt(self, content: str) -> None:
|
411 |
+
params_txt = Path(paths.data_path, "params.txt")
|
412 |
+
with suppress(Exception):
|
413 |
+
params_txt.write_text(content, encoding="utf-8")
|
414 |
+
|
415 |
+
@staticmethod
|
416 |
+
def script_args_copy(script_args):
|
417 |
+
type_: type[list] | type[tuple] = type(script_args)
|
418 |
+
result = []
|
419 |
+
for arg in script_args:
|
420 |
+
try:
|
421 |
+
a = copy(arg)
|
422 |
+
except TypeError:
|
423 |
+
a = arg
|
424 |
+
result.append(a)
|
425 |
+
return type_(result)
|
426 |
+
|
427 |
+
def script_filter(self, p, args: ADetailerArgs):
|
428 |
+
script_runner = copy(p.scripts)
|
429 |
+
script_args = self.script_args_copy(p.script_args)
|
430 |
+
|
431 |
+
ad_only_seleted_scripts = opts.data.get("ad_only_seleted_scripts", True)
|
432 |
+
if not ad_only_seleted_scripts:
|
433 |
+
return script_runner, script_args
|
434 |
+
|
435 |
+
ad_script_names = opts.data.get("ad_script_names", SCRIPT_DEFAULT)
|
436 |
+
script_names_set = {
|
437 |
+
name
|
438 |
+
for script_name in ad_script_names.split(",")
|
439 |
+
for name in (script_name, script_name.strip())
|
440 |
+
}
|
441 |
+
|
442 |
+
if args.ad_controlnet_model != "None":
|
443 |
+
script_names_set.add("controlnet")
|
444 |
+
|
445 |
+
filtered_alwayson = []
|
446 |
+
for script_object in script_runner.alwayson_scripts:
|
447 |
+
filepath = script_object.filename
|
448 |
+
filename = Path(filepath).stem
|
449 |
+
if filename in script_names_set:
|
450 |
+
filtered_alwayson.append(script_object)
|
451 |
+
|
452 |
+
script_runner.alwayson_scripts = filtered_alwayson
|
453 |
+
return script_runner, script_args
|
454 |
+
|
455 |
+
def disable_controlnet_units(
|
456 |
+
self, script_args: list[Any] | tuple[Any, ...]
|
457 |
+
) -> None:
|
458 |
+
for obj in script_args:
|
459 |
+
if "controlnet" in obj.__class__.__name__.lower():
|
460 |
+
if hasattr(obj, "enabled"):
|
461 |
+
obj.enabled = False
|
462 |
+
if hasattr(obj, "input_mode"):
|
463 |
+
obj.input_mode = getattr(obj.input_mode, "SIMPLE", "simple")
|
464 |
+
|
465 |
+
elif isinstance(obj, dict) and "module" in obj:
|
466 |
+
obj["enabled"] = False
|
467 |
+
|
468 |
+
def get_i2i_p(self, p, args: ADetailerArgs, image):
|
469 |
+
seed, subseed = self.get_seed(p)
|
470 |
+
width, height = self.get_width_height(p, args)
|
471 |
+
steps = self.get_steps(p, args)
|
472 |
+
cfg_scale = self.get_cfg_scale(p, args)
|
473 |
+
initial_noise_multiplier = self.get_initial_noise_multiplier(p, args)
|
474 |
+
sampler_name = self.get_sampler(p, args)
|
475 |
+
override_settings = self.get_override_settings(p, args)
|
476 |
+
|
477 |
+
i2i = StableDiffusionProcessingImg2Img(
|
478 |
+
init_images=[image],
|
479 |
+
resize_mode=0,
|
480 |
+
denoising_strength=args.ad_denoising_strength,
|
481 |
+
mask=None,
|
482 |
+
mask_blur=args.ad_mask_blur,
|
483 |
+
inpainting_fill=1,
|
484 |
+
inpaint_full_res=args.ad_inpaint_only_masked,
|
485 |
+
inpaint_full_res_padding=args.ad_inpaint_only_masked_padding,
|
486 |
+
inpainting_mask_invert=0,
|
487 |
+
initial_noise_multiplier=initial_noise_multiplier,
|
488 |
+
sd_model=p.sd_model,
|
489 |
+
outpath_samples=p.outpath_samples,
|
490 |
+
outpath_grids=p.outpath_grids,
|
491 |
+
prompt="", # replace later
|
492 |
+
negative_prompt="",
|
493 |
+
styles=p.styles,
|
494 |
+
seed=seed,
|
495 |
+
subseed=subseed,
|
496 |
+
subseed_strength=p.subseed_strength,
|
497 |
+
seed_resize_from_h=p.seed_resize_from_h,
|
498 |
+
seed_resize_from_w=p.seed_resize_from_w,
|
499 |
+
sampler_name=sampler_name,
|
500 |
+
batch_size=1,
|
501 |
+
n_iter=1,
|
502 |
+
steps=steps,
|
503 |
+
cfg_scale=cfg_scale,
|
504 |
+
width=width,
|
505 |
+
height=height,
|
506 |
+
restore_faces=args.ad_restore_face,
|
507 |
+
tiling=p.tiling,
|
508 |
+
extra_generation_params=p.extra_generation_params,
|
509 |
+
do_not_save_samples=True,
|
510 |
+
do_not_save_grid=True,
|
511 |
+
override_settings=override_settings,
|
512 |
+
)
|
513 |
+
|
514 |
+
i2i.cached_c = [None, None]
|
515 |
+
i2i.cached_uc = [None, None]
|
516 |
+
i2i.scripts, i2i.script_args = self.script_filter(p, args)
|
517 |
+
i2i._ad_disabled = True
|
518 |
+
i2i._ad_inner = True
|
519 |
+
|
520 |
+
if args.ad_controlnet_model != "Passthrough":
|
521 |
+
self.disable_controlnet_units(i2i.script_args)
|
522 |
+
|
523 |
+
if args.ad_controlnet_model not in ["None", "Passthrough"]:
|
524 |
+
self.update_controlnet_args(i2i, args)
|
525 |
+
elif args.ad_controlnet_model == "None":
|
526 |
+
i2i.control_net_enabled = False
|
527 |
+
|
528 |
+
return i2i
|
529 |
+
|
530 |
+
def save_image(self, p, image, *, condition: str, suffix: str) -> None:
|
531 |
+
i = self.get_i(p)
|
532 |
+
if p.all_prompts:
|
533 |
+
i %= len(p.all_prompts)
|
534 |
+
save_prompt = p.all_prompts[i]
|
535 |
+
else:
|
536 |
+
save_prompt = p.prompt
|
537 |
+
seed, _ = self.get_seed(p)
|
538 |
+
|
539 |
+
if opts.data.get(condition, False):
|
540 |
+
images.save_image(
|
541 |
+
image=image,
|
542 |
+
path=p.outpath_samples,
|
543 |
+
basename="",
|
544 |
+
seed=seed,
|
545 |
+
prompt=save_prompt,
|
546 |
+
extension=opts.samples_format,
|
547 |
+
info=self.infotext(p),
|
548 |
+
p=p,
|
549 |
+
suffix=suffix,
|
550 |
+
)
|
551 |
+
|
552 |
+
def get_ad_model(self, name: str):
|
553 |
+
if name not in model_mapping:
|
554 |
+
msg = f"[-] ADetailer: Model {name!r} not found. Available models: {list(model_mapping.keys())}"
|
555 |
+
raise ValueError(msg)
|
556 |
+
return model_mapping[name]
|
557 |
+
|
558 |
+
def sort_bboxes(self, pred: PredictOutput) -> PredictOutput:
|
559 |
+
sortby = opts.data.get("ad_bbox_sortby", BBOX_SORTBY[0])
|
560 |
+
sortby_idx = BBOX_SORTBY.index(sortby)
|
561 |
+
return sort_bboxes(pred, sortby_idx)
|
562 |
+
|
563 |
+
def pred_preprocessing(self, pred: PredictOutput, args: ADetailerArgs):
|
564 |
+
pred = filter_by_ratio(
|
565 |
+
pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
|
566 |
+
)
|
567 |
+
pred = filter_k_largest(pred, k=args.ad_mask_k_largest)
|
568 |
+
pred = self.sort_bboxes(pred)
|
569 |
+
return mask_preprocess(
|
570 |
+
pred.masks,
|
571 |
+
kernel=args.ad_dilate_erode,
|
572 |
+
x_offset=args.ad_x_offset,
|
573 |
+
y_offset=args.ad_y_offset,
|
574 |
+
merge_invert=args.ad_mask_merge_invert,
|
575 |
+
)
|
576 |
+
|
577 |
+
@staticmethod
|
578 |
+
def ensure_rgb_image(image: Any):
|
579 |
+
if not isinstance(image, Image.Image):
|
580 |
+
image = to_pil_image(image)
|
581 |
+
if image.mode != "RGB":
|
582 |
+
image = image.convert("RGB")
|
583 |
+
return image
|
584 |
+
|
585 |
+
@staticmethod
|
586 |
+
def i2i_prompts_replace(
|
587 |
+
i2i, prompts: list[str], negative_prompts: list[str], j: int
|
588 |
+
) -> None:
|
589 |
+
i1 = min(j, len(prompts) - 1)
|
590 |
+
i2 = min(j, len(negative_prompts) - 1)
|
591 |
+
prompt = prompts[i1]
|
592 |
+
negative_prompt = negative_prompts[i2]
|
593 |
+
i2i.prompt = prompt
|
594 |
+
i2i.negative_prompt = negative_prompt
|
595 |
+
|
596 |
+
@staticmethod
|
597 |
+
def compare_prompt(p, processed, n: int = 0):
|
598 |
+
if p.prompt != processed.all_prompts[0]:
|
599 |
+
print(
|
600 |
+
f"[-] ADetailer: applied {ordinal(n + 1)} ad_prompt: {processed.all_prompts[0]!r}"
|
601 |
+
)
|
602 |
+
|
603 |
+
if p.negative_prompt != processed.all_negative_prompts[0]:
|
604 |
+
print(
|
605 |
+
f"[-] ADetailer: applied {ordinal(n + 1)} ad_negative_prompt: {processed.all_negative_prompts[0]!r}"
|
606 |
+
)
|
607 |
+
|
608 |
+
@staticmethod
|
609 |
+
def need_call_process(p) -> bool:
|
610 |
+
if p.scripts is None:
|
611 |
+
return False
|
612 |
+
i = p.batch_index
|
613 |
+
bs = p.batch_size
|
614 |
+
return i == bs - 1
|
615 |
+
|
616 |
+
@staticmethod
|
617 |
+
def need_call_postprocess(p) -> bool:
|
618 |
+
if p.scripts is None:
|
619 |
+
return False
|
620 |
+
return p.batch_index == 0
|
621 |
+
|
622 |
+
@staticmethod
|
623 |
+
def get_i2i_init_image(p, pp):
|
624 |
+
if getattr(p, "_ad_skip_img2img", False):
|
625 |
+
return p.init_images[0]
|
626 |
+
return pp.image
|
627 |
+
|
628 |
+
@staticmethod
|
629 |
+
def get_each_tap_seed(seed: int, i: int):
|
630 |
+
use_same_seed = shared.opts.data.get("ad_same_seed_for_each_tap", False)
|
631 |
+
return seed if use_same_seed else seed + i
|
632 |
+
|
633 |
+
@staticmethod
|
634 |
+
def is_img2img_inpaint(p) -> bool:
|
635 |
+
return hasattr(p, "image_mask") and bool(p.image_mask)
|
636 |
+
|
637 |
+
@rich_traceback
|
638 |
+
def process(self, p, *args_):
|
639 |
+
if getattr(p, "_ad_disabled", False):
|
640 |
+
return
|
641 |
+
|
642 |
+
# if self.is_img2img_inpaint(p):
|
643 |
+
# p._ad_disabled = True
|
644 |
+
# msg = "[-] ADetailer: img2img inpainting detected. adetailer disabled."
|
645 |
+
# print(msg)
|
646 |
+
# return
|
647 |
+
|
648 |
+
if self.is_ad_enabled(*args_):
|
649 |
+
arg_list = self.get_args(p, *args_)
|
650 |
+
self.check_skip_img2img(p, *args_)
|
651 |
+
extra_params = self.extra_params(arg_list)
|
652 |
+
p.extra_generation_params.update(extra_params)
|
653 |
+
else:
|
654 |
+
p._ad_disabled = True
|
655 |
+
|
656 |
+
def _postprocess_image_inner(
|
657 |
+
self, p, pp, args: ADetailerArgs, *, n: int = 0
|
658 |
+
) -> bool:
|
659 |
+
"""
|
660 |
+
Returns
|
661 |
+
-------
|
662 |
+
bool
|
663 |
+
|
664 |
+
`True` if image was processed, `False` otherwise.
|
665 |
+
"""
|
666 |
+
if state.interrupted or state.skipped:
|
667 |
+
return False
|
668 |
+
|
669 |
+
i = self.get_i(p)
|
670 |
+
|
671 |
+
i2i = self.get_i2i_p(p, args, pp.image)
|
672 |
+
seed, subseed = self.get_seed(p)
|
673 |
+
ad_prompts, ad_negatives = self.get_prompt(p, args)
|
674 |
+
|
675 |
+
is_mediapipe = args.ad_model.lower().startswith("mediapipe")
|
676 |
+
|
677 |
+
kwargs = {}
|
678 |
+
if is_mediapipe:
|
679 |
+
predictor = mediapipe_predict
|
680 |
+
ad_model = args.ad_model
|
681 |
+
else:
|
682 |
+
predictor = ultralytics_predict
|
683 |
+
ad_model = self.get_ad_model(args.ad_model)
|
684 |
+
kwargs["device"] = self.ultralytics_device
|
685 |
+
|
686 |
+
with change_torch_load():
|
687 |
+
pred = predictor(ad_model, pp.image, args.ad_confidence, **kwargs)
|
688 |
+
|
689 |
+
masks = self.pred_preprocessing(pred, args)
|
690 |
+
shared.state.assign_current_image(pred.preview)
|
691 |
+
|
692 |
+
if not masks:
|
693 |
+
print(
|
694 |
+
f"[-] ADetailer: nothing detected on image {i + 1} with {ordinal(n + 1)} settings."
|
695 |
+
)
|
696 |
+
return False
|
697 |
+
|
698 |
+
self.save_image(
|
699 |
+
p,
|
700 |
+
pred.preview,
|
701 |
+
condition="ad_save_previews",
|
702 |
+
suffix="-ad-preview" + suffix(n, "-"),
|
703 |
+
)
|
704 |
+
|
705 |
+
steps = len(masks)
|
706 |
+
processed = None
|
707 |
+
state.job_count += steps
|
708 |
+
|
709 |
+
if is_mediapipe:
|
710 |
+
print(f"mediapipe: {steps} detected.")
|
711 |
+
|
712 |
+
p2 = copy(i2i)
|
713 |
+
for j in range(steps):
|
714 |
+
p2.image_mask = masks[j]
|
715 |
+
p2.init_images[0] = self.ensure_rgb_image(p2.init_images[0])
|
716 |
+
self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j)
|
717 |
+
|
718 |
+
if re.match(r"^\s*\[SKIP\]\s*$", p2.prompt):
|
719 |
+
continue
|
720 |
+
|
721 |
+
p2.seed = self.get_each_tap_seed(seed, j)
|
722 |
+
p2.subseed = self.get_each_tap_seed(subseed, j)
|
723 |
+
|
724 |
+
try:
|
725 |
+
processed = process_images(p2)
|
726 |
+
except NansException as e:
|
727 |
+
msg = f"[-] ADetailer: 'NansException' occurred with {ordinal(n + 1)} settings.\n{e}"
|
728 |
+
print(msg, file=sys.stderr)
|
729 |
+
continue
|
730 |
+
finally:
|
731 |
+
p2.close()
|
732 |
+
|
733 |
+
self.compare_prompt(p2, processed, n=n)
|
734 |
+
p2 = copy(i2i)
|
735 |
+
p2.init_images = [processed.images[0]]
|
736 |
+
|
737 |
+
if processed is not None:
|
738 |
+
pp.image = processed.images[0]
|
739 |
+
return True
|
740 |
+
|
741 |
+
return False
|
742 |
+
|
743 |
+
@rich_traceback
|
744 |
+
def postprocess_image(self, p, pp, *args_):
|
745 |
+
if getattr(p, "_ad_disabled", False) or not self.is_ad_enabled(*args_):
|
746 |
+
return
|
747 |
+
|
748 |
+
pp.image = self.get_i2i_init_image(p, pp)
|
749 |
+
pp.image = self.ensure_rgb_image(pp.image)
|
750 |
+
init_image = copy(pp.image)
|
751 |
+
arg_list = self.get_args(p, *args_)
|
752 |
+
params_txt_content = Path(paths.data_path, "params.txt").read_text("utf-8")
|
753 |
+
|
754 |
+
if self.need_call_postprocess(p):
|
755 |
+
dummy = Processed(p, [], p.seed, "")
|
756 |
+
with preseve_prompts(p):
|
757 |
+
p.scripts.postprocess(copy(p), dummy)
|
758 |
+
|
759 |
+
is_processed = False
|
760 |
+
with CNHijackRestore(), pause_total_tqdm(), cn_allow_script_control():
|
761 |
+
for n, args in enumerate(arg_list):
|
762 |
+
if args.ad_model == "None":
|
763 |
+
continue
|
764 |
+
is_processed |= self._postprocess_image_inner(p, pp, args, n=n)
|
765 |
+
|
766 |
+
if is_processed and not getattr(p, "_ad_skip_img2img", False):
|
767 |
+
self.save_image(
|
768 |
+
p, init_image, condition="ad_save_images_before", suffix="-ad-before"
|
769 |
+
)
|
770 |
+
|
771 |
+
if self.need_call_process(p):
|
772 |
+
with preseve_prompts(p):
|
773 |
+
copy_p = copy(p)
|
774 |
+
if hasattr(p.scripts, "before_process"):
|
775 |
+
p.scripts.before_process(copy_p)
|
776 |
+
p.scripts.process(copy_p)
|
777 |
+
|
778 |
+
self.write_params_txt(params_txt_content)
|
779 |
+
|
780 |
+
|
781 |
+
def on_after_component(component, **_kwargs):
|
782 |
+
global txt2img_submit_button, img2img_submit_button
|
783 |
+
if getattr(component, "elem_id", None) == "txt2img_generate":
|
784 |
+
txt2img_submit_button = component
|
785 |
+
return
|
786 |
+
|
787 |
+
if getattr(component, "elem_id", None) == "img2img_generate":
|
788 |
+
img2img_submit_button = component
|
789 |
+
|
790 |
+
|
791 |
+
def on_ui_settings():
|
792 |
+
section = ("ADetailer", AFTER_DETAILER)
|
793 |
+
shared.opts.add_option(
|
794 |
+
"ad_max_models",
|
795 |
+
shared.OptionInfo(
|
796 |
+
default=2,
|
797 |
+
label="Max models",
|
798 |
+
component=gr.Slider,
|
799 |
+
component_args={"minimum": 1, "maximum": 10, "step": 1},
|
800 |
+
section=section,
|
801 |
+
),
|
802 |
+
)
|
803 |
+
|
804 |
+
shared.opts.add_option(
|
805 |
+
"ad_extra_models_dir",
|
806 |
+
shared.OptionInfo(
|
807 |
+
default="",
|
808 |
+
label="Extra path to scan adetailer models",
|
809 |
+
component=gr.Textbox,
|
810 |
+
section=section,
|
811 |
+
),
|
812 |
+
)
|
813 |
+
|
814 |
+
shared.opts.add_option(
|
815 |
+
"ad_save_previews",
|
816 |
+
shared.OptionInfo(False, "Save mask previews", section=section),
|
817 |
+
)
|
818 |
+
|
819 |
+
shared.opts.add_option(
|
820 |
+
"ad_save_images_before",
|
821 |
+
shared.OptionInfo(False, "Save images before ADetailer", section=section),
|
822 |
+
)
|
823 |
+
|
824 |
+
shared.opts.add_option(
|
825 |
+
"ad_only_seleted_scripts",
|
826 |
+
shared.OptionInfo(
|
827 |
+
True, "Apply only selected scripts to ADetailer", section=section
|
828 |
+
),
|
829 |
+
)
|
830 |
+
|
831 |
+
textbox_args = {
|
832 |
+
"placeholder": "comma-separated list of script names",
|
833 |
+
"interactive": True,
|
834 |
+
}
|
835 |
+
|
836 |
+
shared.opts.add_option(
|
837 |
+
"ad_script_names",
|
838 |
+
shared.OptionInfo(
|
839 |
+
default=SCRIPT_DEFAULT,
|
840 |
+
label="Script names to apply to ADetailer (separated by comma)",
|
841 |
+
component=gr.Textbox,
|
842 |
+
component_args=textbox_args,
|
843 |
+
section=section,
|
844 |
+
),
|
845 |
+
)
|
846 |
+
|
847 |
+
shared.opts.add_option(
|
848 |
+
"ad_bbox_sortby",
|
849 |
+
shared.OptionInfo(
|
850 |
+
default="None",
|
851 |
+
label="Sort bounding boxes by",
|
852 |
+
component=gr.Radio,
|
853 |
+
component_args={"choices": BBOX_SORTBY},
|
854 |
+
section=section,
|
855 |
+
),
|
856 |
+
)
|
857 |
+
|
858 |
+
shared.opts.add_option(
|
859 |
+
"ad_same_seed_for_each_tap",
|
860 |
+
shared.OptionInfo(
|
861 |
+
False, "Use same seed for each tab in adetailer", section=section
|
862 |
+
),
|
863 |
+
)
|
864 |
+
|
865 |
+
|
866 |
+
# xyz_grid
|
867 |
+
|
868 |
+
|
869 |
+
class PromptSR(NamedTuple):
|
870 |
+
s: str
|
871 |
+
r: str
|
872 |
+
|
873 |
+
|
874 |
+
def set_value(p, x: Any, xs: Any, *, field: str):
|
875 |
+
if not hasattr(p, "_ad_xyz"):
|
876 |
+
p._ad_xyz = {}
|
877 |
+
p._ad_xyz[field] = x
|
878 |
+
|
879 |
+
|
880 |
+
def search_and_replace_prompt(p, x: Any, xs: Any, replace_in_main_prompt: bool):
|
881 |
+
if replace_in_main_prompt:
|
882 |
+
p.prompt = p.prompt.replace(xs[0], x)
|
883 |
+
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
884 |
+
|
885 |
+
if not hasattr(p, "_ad_xyz_prompt_sr"):
|
886 |
+
p._ad_xyz_prompt_sr = []
|
887 |
+
p._ad_xyz_prompt_sr.append(PromptSR(s=xs[0], r=x))
|
888 |
+
|
889 |
+
|
890 |
+
def make_axis_on_xyz_grid():
|
891 |
+
xyz_grid = None
|
892 |
+
for script in scripts.scripts_data:
|
893 |
+
if script.script_class.__module__ == "xyz_grid.py":
|
894 |
+
xyz_grid = script.module
|
895 |
+
break
|
896 |
+
|
897 |
+
if xyz_grid is None:
|
898 |
+
return
|
899 |
+
|
900 |
+
model_list = ["None", *model_mapping.keys()]
|
901 |
+
samplers = [sampler.name for sampler in all_samplers]
|
902 |
+
|
903 |
+
axis = [
|
904 |
+
xyz_grid.AxisOption(
|
905 |
+
"[ADetailer] ADetailer model 1st",
|
906 |
+
str,
|
907 |
+
partial(set_value, field="ad_model"),
|
908 |
+
choices=lambda: model_list,
|
909 |
+
),
|
910 |
+
xyz_grid.AxisOption(
|
911 |
+
"[ADetailer] ADetailer prompt 1st",
|
912 |
+
str,
|
913 |
+
partial(set_value, field="ad_prompt"),
|
914 |
+
),
|
915 |
+
xyz_grid.AxisOption(
|
916 |
+
"[ADetailer] ADetailer negative prompt 1st",
|
917 |
+
str,
|
918 |
+
partial(set_value, field="ad_negative_prompt"),
|
919 |
+
),
|
920 |
+
xyz_grid.AxisOption(
|
921 |
+
"[ADetailer] Prompt S/R (AD 1st)",
|
922 |
+
str,
|
923 |
+
partial(search_and_replace_prompt, replace_in_main_prompt=False),
|
924 |
+
),
|
925 |
+
xyz_grid.AxisOption(
|
926 |
+
"[ADetailer] Prompt S/R (AD 1st and main prompt)",
|
927 |
+
str,
|
928 |
+
partial(search_and_replace_prompt, replace_in_main_prompt=True),
|
929 |
+
),
|
930 |
+
xyz_grid.AxisOption(
|
931 |
+
"[ADetailer] Mask erosion / dilation 1st",
|
932 |
+
int,
|
933 |
+
partial(set_value, field="ad_dilate_erode"),
|
934 |
+
),
|
935 |
+
xyz_grid.AxisOption(
|
936 |
+
"[ADetailer] Inpaint denoising strength 1st",
|
937 |
+
float,
|
938 |
+
partial(set_value, field="ad_denoising_strength"),
|
939 |
+
),
|
940 |
+
xyz_grid.AxisOption(
|
941 |
+
"[ADetailer] Inpaint only masked 1st",
|
942 |
+
str,
|
943 |
+
partial(set_value, field="ad_inpaint_only_masked"),
|
944 |
+
choices=lambda: ["True", "False"],
|
945 |
+
),
|
946 |
+
xyz_grid.AxisOption(
|
947 |
+
"[ADetailer] Inpaint only masked padding 1st",
|
948 |
+
int,
|
949 |
+
partial(set_value, field="ad_inpaint_only_masked_padding"),
|
950 |
+
),
|
951 |
+
xyz_grid.AxisOption(
|
952 |
+
"[ADetailer] ADetailer sampler 1st",
|
953 |
+
str,
|
954 |
+
partial(set_value, field="ad_sampler"),
|
955 |
+
choices=lambda: samplers,
|
956 |
+
),
|
957 |
+
xyz_grid.AxisOption(
|
958 |
+
"[ADetailer] ControlNet model 1st",
|
959 |
+
str,
|
960 |
+
partial(set_value, field="ad_controlnet_model"),
|
961 |
+
choices=lambda: ["None", *get_cn_models()],
|
962 |
+
),
|
963 |
+
]
|
964 |
+
|
965 |
+
if not any(x.label.startswith("[ADetailer]") for x in xyz_grid.axis_options):
|
966 |
+
xyz_grid.axis_options.extend(axis)
|
967 |
+
|
968 |
+
|
969 |
+
def on_before_ui():
|
970 |
+
try:
|
971 |
+
make_axis_on_xyz_grid()
|
972 |
+
except Exception:
|
973 |
+
error = traceback.format_exc()
|
974 |
+
print(
|
975 |
+
f"[-] ADetailer: xyz_grid error:\n{error}",
|
976 |
+
file=sys.stderr,
|
977 |
+
)
|
978 |
+
|
979 |
+
|
980 |
+
# api
|
981 |
+
|
982 |
+
|
983 |
+
def add_api_endpoints(_: gr.Blocks, app: FastAPI):
|
984 |
+
@app.get("/adetailer/v1/version")
|
985 |
+
def version():
|
986 |
+
return {"version": __version__}
|
987 |
+
|
988 |
+
@app.get("/adetailer/v1/schema")
|
989 |
+
def schema():
|
990 |
+
return ADetailerArgs.schema()
|
991 |
+
|
992 |
+
@app.get("/adetailer/v1/ad_model")
|
993 |
+
def ad_model():
|
994 |
+
return {"ad_model": list(model_mapping)}
|
995 |
+
|
996 |
+
|
997 |
+
script_callbacks.on_ui_settings(on_ui_settings)
|
998 |
+
script_callbacks.on_after_component(on_after_component)
|
999 |
+
script_callbacks.on_app_started(add_api_endpoints)
|
1000 |
+
script_callbacks.on_before_ui(on_before_ui)
|