File size: 38,369 Bytes
da29f0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 | # Deep Shrink Hires.fix (RU++ v2.1 UI Toggles, fixed)
# Совместимость: Python 3.10+, PyTorch >= 2.0, AUTOMATIC1111 WebUI >= 1.9
from dataclasses import dataclass
from typing import List, Optional, Dict, Any
import json, os
import gradio as gr
import torch
import torch.nn.functional as F
import modules.devices as devices
import modules.scripts as scripts
import modules.script_callbacks as script_callbacks
import modules.sd_unet as sd_unet
import modules.shared as shared
from ldm.modules.attention import SpatialTransformer # noqa: F401
from ldm.modules.diffusionmodules.openaimodel import Upsample, Downsample, ResBlock
from ldm.modules.diffusionmodules.util import timestep_embedding
# -------------------------- Утилиты --------------------------
def _to_scalar(x) -> float:
if isinstance(x, torch.Tensor): return float(x.item())
return float(x)
def _clamp(v: float, lo: float, hi: float) -> float:
return max(lo, min(hi, v))
def _safe_size(h: torch.Tensor, scale_factor: float) -> tuple[int, int]:
h_in, w_in = h.shape[-2], h.shape[-1]
h_out = max(2, int(round(h_in * scale_factor)))
w_out = max(2, int(round(w_in * scale_factor)))
if h_out < DSHF.min_feature_size or w_out < DSHF.min_feature_size:
return h_in, w_in
return h_out, w_out
def _interpolate(img: torch.Tensor, size: tuple[int, int]) -> torch.Tensor:
if size == img.shape[-2:]: return img
dtype = img.dtype
mode = DSHF.interp_method
antialias = bool(DSHF.interp_antialias)
try:
out = F.interpolate(
img.float(), size=size, mode=mode,
align_corners=False if mode in ("bilinear","bicubic") else None,
antialias=antialias if mode in ("bilinear","bicubic") else False
)
except TypeError:
out = F.interpolate(
img.float(), size=size, mode=mode,
align_corners=False if mode in ("bilinear","bicubic") else None
)
return out.to(dtype)
def _resize(h: torch.Tensor, scale_factor: float) -> torch.Tensor:
if scale_factor == 1.0: return h
return _interpolate(h, _safe_size(h, scale_factor))
def _parse_number_list(text: str, as_int: bool = False) -> List[float]:
if text is None: raise ValueError("Пустая строка параметров.")
values: List[float] = []
for raw in str(text).replace("\n"," ").split(";"):
s = raw.strip()
if not s: continue
if "/" in s:
a,b = s.split("/",1); val = float(a.strip())/float(b.strip())
else:
val = float(s)
values.append(val)
if not values: raise ValueError("Не найдено ни одного валидного значения.")
return [int(round(v)) for v in values] if as_int else values
def _get_or_last(seq: List[float], index: int, default: float) -> float:
if not seq: return default
return seq[index] if index < len(seq) else seq[-1]
def _preset_path() -> str:
return os.path.join(os.path.dirname(__file__), "dshf_presets.json")
def _load_all_presets() -> Dict[str, Any]:
p = _preset_path()
if not os.path.exists(p): return {"version": 2, "presets": {}}
try:
with open(p, "r", encoding="utf-8") as f: data = json.load(f)
return data if "presets" in data else {"version": 2, "presets": {}}
except Exception:
return {"version": 2, "presets": {}}
def _save_all_presets(data: Dict[str, Any]) -> None:
p = _preset_path()
try:
with open(p, "w", encoding="utf-8") as f: json.dump(data, f, ensure_ascii=False, indent=2)
except Exception as e:
print(f"[DSHF] Не удалось сохранить пресеты: {e}")
def _build_profile_dict() -> Dict[str, Any]:
return {
"version": 2,
"actions": [{"enable": a.enable,"timestep":a.timestep,"depth":a.depth,"scale":a.scale} for a in DSHF.dshf_actions],
"experimental_enable": DSHF.enableExperimental,
"experimental": [{
"enable": e.enable,"timestep":e.timestep,"scales":e.scales,
"in_multipliers":e.in_multipliers,"out_multipliers":e.out_multipliers,
"dilations":e.dilations,"cfg_scale_scale":e.cfg_scale_scale
} for e in DSHF.dshf_experimental_actions],
"curve": {
"enable": DSHF.enable_curve,"type": DSHF.curve_type,
"t_start": DSHF.curve_t_start,"t_end": DSHF.curve_t_end,
"scale_start": DSHF.curve_scale_start,"scale_end": DSHF.curve_scale_end,
"alpha": DSHF.curve_alpha,"min_feature": DSHF.min_feature_size,
"auto_end_enable": DSHF.auto_end_enable,"auto_end_strength": DSHF.auto_end_strength
},
"runtime": {
"timestep_policy": DSHF.timestep_policy,"interp_method": DSHF.interp_method,
"interp_antialias": DSHF.interp_antialias,"channels_last": DSHF.channels_last,
"enable_soft_clamp": DSHF.enable_soft_clamp,"soft_clamp_beta": DSHF.soft_clamp_beta,
"min_depth": DSHF.min_depth,"max_depth": DSHF.max_depth
}
}
def _apply_profile_dict(data: Dict[str, Any]) -> None:
try:
DSHF.dshf_actions.clear()
for a in data.get("actions", []):
DSHF.dshf_actions.append(DSHFAction(bool(a.get("enable",True)),float(a.get("timestep",0)),
int(a.get("depth",0)),float(a.get("scale",1.0))))
DSHF.enableExperimental = bool(data.get("experimental_enable", False))
DSHF.dshf_experimental_actions.clear()
for e in data.get("experimental", []):
DSHF.dshf_experimental_actions.append(DSHFExperimentalAction(
bool(e.get("enable",False)), float(e.get("timestep",0)),
list(map(float, e.get("scales",[]))),
list(map(float, e.get("in_multipliers",[]))),
list(map(float, e.get("out_multipliers",[]))),
list(map(int, e.get("dilations",[]))),
list(map(float, e.get("cfg_scale_scale",[]))),
))
c = data.get("curve", {})
DSHF.enable_curve = bool(c.get("enable", False))
DSHF.curve_type = str(c.get("type","linear"))
DSHF.curve_t_start = float(c.get("t_start",800))
DSHF.curve_t_end = float(c.get("t_end",200))
DSHF.curve_scale_start = float(c.get("scale_start",1.0))
DSHF.curve_scale_end = float(c.get("scale_end",1.0))
DSHF.curve_alpha = float(_clamp(float(c.get("alpha",0.5)),0.0,1.0))
DSHF.min_feature_size = int(_clamp(float(c.get("min_feature",8)),2,256))
DSHF.auto_end_enable = bool(c.get("auto_end_enable", False))
DSHF.auto_end_strength = float(_clamp(float(c.get("auto_end_strength",0.35)),0.0,1.0))
r = data.get("runtime", {})
DSHF.timestep_policy = str(r.get("timestep_policy", DSHF.timestep_policy))
DSHF.interp_method = str(r.get("interp_method", DSHF.interp_method))
DSHF.interp_antialias = bool(r.get("interp_antialias", DSHF.interp_antialias))
DSHF.channels_last = bool(r.get("channels_last", DSHF.channels_last))
DSHF.enable_soft_clamp = bool(r.get("enable_soft_clamp", DSHF.enable_soft_clamp))
DSHF.soft_clamp_beta = float(_clamp(float(r.get("soft_clamp_beta", DSHF.soft_clamp_beta)),0.0,5.0))
DSHF.min_depth = int(_clamp(int(r.get("min_depth", DSHF.min_depth)),0,99))
DSHF.max_depth = int(_clamp(int(r.get("max_depth", DSHF.max_depth)),0,99))
except Exception as e:
print(f"[DSHF] Ошибка применения профиля: {e}")
# -------------------------- Структуры данных --------------------------
@dataclass
class DSHFAction:
enable: bool; timestep: float; depth: int; scale: float
@dataclass
class DSHFExperimentalAction:
enable: bool; timestep: float
scales: List[float]; in_multipliers: List[float]; out_multipliers: List[float]
dilations: List[int]; cfg_scale_scale: List[float]
# -------------------------- Основной скрипт --------------------------
class DSHF(scripts.Script):
dshf_actions: List[DSHFAction] = []
enableExperimental: bool = False
dshf_experimental_actions: List[DSHFExperimentalAction] = []
currentBlock: int = 0
currentConv: int = 0
currentTimestep: float = 1000.0
enable_curve: bool = False
curve_type: str = "linear"
curve_t_start: float = 800.0
curve_t_end: float = 200.0
curve_scale_start: float = 1.0
curve_scale_end: float = 1.0
curve_alpha: float = 0.5
min_feature_size: int = 8
auto_end_enable: bool = False
auto_end_strength: float = 0.35
timestep_policy: str = "min"
interp_method: str = "bicubic"
interp_antialias: bool = True
channels_last: bool = False
enable_soft_clamp: bool = False
soft_clamp_beta: float = 1.5
min_depth: int = 0
max_depth: int = 999
def title(self): return "Deep Shrink Hires.fix (RU++ v2.1)"
def show(self, is_img2img): return scripts.AlwaysVisible
@staticmethod
def _active_experimental() -> Optional[DSHFExperimentalAction]:
if not DSHF.enableExperimental: return None
ts = DSHF.currentTimestep
for a in DSHF.dshf_experimental_actions:
if a.enable and a.timestep <= ts: return a
return None
@staticmethod
def _curve_weight(ts: float) -> Optional[float]:
if not DSHF.enable_curve: return None
t0, t1 = DSHF.curve_t_start, DSHF.curve_t_end
if t0 == t1: return DSHF.curve_scale_end
x = _clamp((ts - t1) / (t0 - t1), 0.0, 1.0)
if DSHF.curve_type == "linear": w = x
elif DSHF.curve_type == "cosine": w = 0.5 - 0.5 * torch.cos(torch.tensor(x) * torch.pi).item()
else:
w = 1.0 / (1.0 + torch.exp(torch.tensor(-10.0 * (x - 0.5)))).item()
s0, s1 = float(DSHF.curve_scale_start), float(DSHF.curve_scale_end)
return _clamp(s0 + (s1 - s0) * w, 0.25, 4.0)
@staticmethod
def _block_scale(depth: int) -> Optional[float]:
if depth < DSHF.min_depth or depth > DSHF.max_depth: return None
ts = DSHF.currentTimestep
rule_scale = None
for a in DSHF.dshf_actions:
if a.enable and a.depth == depth and a.timestep <= ts:
rule_scale = a.scale; break
curve_scale = DSHF._curve_weight(ts)
if rule_scale is None and curve_scale is None: return None
if rule_scale is None: return curve_scale
if curve_scale is None: return rule_scale
return _clamp(rule_scale * curve_scale, 0.25, 4.0)
@staticmethod
def _auto_scale_end(p) -> Optional[float]:
if not DSHF.auto_end_enable or not DSHF.enable_curve: return None
try:
bw,bh = int(getattr(p,"width",0)), int(getattr(p,"height",0))
if bw<=0 or bh<=0: return None
tw,th = bw,bh
if getattr(p,"enable_hr",False):
hrx,hry = int(getattr(p,"hr_resize_x",0)), int(getattr(p,"hr_resize_y",0))
hrs = float(getattr(p,"hr_scale",0.0) or 0.0)
if hrx>0 and hry>0: tw,th = hrx,hry
elif hrs>0.0: tw,th = int(round(bw*hrs)), int(round(bh*hrs))
r = ((max(1,tw*th))/max(1,bw*bh))**0.5
if r<=1.0: return None
return _clamp(1.0 + float(_clamp(DSHF.auto_end_strength,0.0,1.0))*(r-1.0), 1.0, 1.7)
except Exception: return None
@staticmethod
def _pick_timestep_scalar(timesteps: torch.Tensor) -> float:
pol = DSHF.timestep_policy
vals = timesteps.detach().float()
if pol=="first": return _to_scalar(vals[0])
if pol=="max": return float(vals.max().item())
if pol=="mean": return float(vals.mean().item())
return float(vals.min().item())
@staticmethod
def _soft_clamp(h: torch.Tensor) -> torch.Tensor:
if not DSHF.enable_soft_clamp: return h
beta = float(DSHF.soft_clamp_beta)
if beta<=0: return h
mean = h.mean(dim=(2,3), keepdim=True); std = h.std(dim=(2,3), keepdim=True)+1e-6
limit = mean + std*beta
return torch.minimum(torch.maximum(h, -limit), limit)
# --- UI ---
def ui(self, is_img2img):
presets = _load_all_presets().get("presets", {})
preset_names = sorted(list(presets.keys()))
def toggle(v): return gr.update(visible=bool(v))
with gr.Tabs():
# ===== Настройки =====
with gr.TabItem("Настройки"):
Enable_Ext = gr.Checkbox(value=True, label="Включить расширение")
# Основные пороги
with gr.Accordion(label="Основные пороги (1–2)", open=False):
En_Main = gr.Checkbox(value=True, label="Включить секцию")
with gr.Group(visible=True) as MainGrp:
with gr.Row():
Enable_1 = gr.Checkbox(value=True, label="Включить правило 1")
Timestep_1 = gr.Number(value=625, label="Timestep 1")
Depth_1 = gr.Number(value=3, label="Глубина блока 1", precision=0)
Scale_1 = gr.Number(value=2.0, label="Коэффициент масштаба 1")
with gr.Row():
Enable_2 = gr.Checkbox(value=True, label="Включить правило 2")
Timestep_2 = gr.Number(value=0, label="Timestep 2")
Depth_2 = gr.Number(value=3, label="Глубина блока 2", precision=0)
Scale_2 = gr.Number(value=2.0, label="Коэффициент масштаба 2")
En_Main.change(toggle, En_Main, MainGrp)
# Расширенные пороги
with gr.Accordion(label="Расширенные пороги (3–8)", open=False):
En_Adv = gr.Checkbox(value=False, label="Включить секцию")
with gr.Group(visible=False) as AdvGrp:
rows = []
defaults = [(False,900,3,2.0),(False,650,3,2.0),(False,900,3,2.0),
(False,650,3,2.0),(False,900,3,2.0),(False,650,3,2.0)]
for idx,(en,ts,dp,sc) in enumerate(defaults, start=3):
with gr.Row():
rows.append((
gr.Checkbox(value=en, label=f"Включить правило {idx}"),
gr.Number(value=ts, label=f"Timestep {idx}"),
gr.Number(value=dp, label=f"Глубина блока {idx}", precision=0),
gr.Number(value=sc, label=f"Коэффициент масштаба {idx}")
))
En_Adv.change(toggle, En_Adv, AdvGrp)
# Экспериментальные
with gr.Accordion(label="Экспериментальные (масштабы/дилатации/множители)", open=False):
Enable_Experimental = gr.Checkbox(value=False, label="Включить секцию")
with gr.Group(visible=False) as ExpGrp:
def block(prefix, ts_default):
with gr.Row():
en = gr.Checkbox(value=True, label=f"{prefix}: включить набор")
ts = gr.Number(value=ts_default, label=f"{prefix}: timestep")
with gr.Row():
sc = gr.Textbox(value="1; " * 52 + "1", label=f"{prefix}: масштабы (по свёрткам)", lines=2)
with gr.Row():
cfg = gr.Textbox(value="1;1;1; 1;1;1; 1;1;1; 1;1;1; 1; 1;1;1; 1;1;1; 1;1;1; 1;1;1",
label=f"{prefix}: множители CFG-scale")
dil = gr.Textbox(value="1; " * 52 + "1", label=f"{prefix}: дилатации (по свёрткам)", lines=2)
with gr.Row():
pre = gr.Textbox(value="1; " * 24 + "1", label=f"{prefix}: входные умножители (по блокам)")
post = gr.Textbox(value="1; " * 24 + "1", label=f"{prefix}: выходные умножители (по блокам)")
return en, ts, sc, pre, post, dil, cfg
(Enable_Experimental_1, Timestep_Experimental_1, Scale_Experimental_1,
Premultiplier_Experimental_1, Postmultiplier_Experimental_1,
Dilation_Experimental_1, CFG_Scale_Scale_Experimental_1) = block("Набор 1", 625)
(Enable_Experimental_2, Timestep_Experimental_2, Scale_Experimental_2,
Premultiplier_Experimental_2, Postmultiplier_Experimental_2,
Dilation_Experimental_2, CFG_Scale_Scale_Experimental_2) = block("Набор 2", 0)
(Enable_Experimental_3, Timestep_Experimental_3, Scale_Experimental_3,
Premultiplier_Experimental_3, Postmultiplier_Experimental_3,
Dilation_Experimental_3, CFG_Scale_Scale_Experimental_3) = block("Набор 3", 750)
(Enable_Experimental_4, Timestep_Experimental_4, Scale_Experimental_4,
Premultiplier_Experimental_4, Postmultiplier_Experimental_4,
Dilation_Experimental_4, CFG_Scale_Scale_Experimental_4) = block("Набор 4", 750)
Enable_Experimental.change(toggle, Enable_Experimental, ExpGrp)
# Кривая
with gr.Accordion(label="Глобальная кривая масштаба", open=False):
Enable_Curve = gr.Checkbox(value=False, label="Включить секцию")
with gr.Group(visible=False) as CurveGrp:
Curve_Type = gr.Dropdown(choices=["linear","cosine","sigmoid"], value="linear", label="Тип кривой")
with gr.Row():
Curve_t_start = gr.Number(value=800, label="t_start")
Curve_t_end = gr.Number(value=200, label="t_end")
with gr.Row():
Curve_scale_start = gr.Number(value=1.0, label="scale_start")
Curve_scale_end = gr.Number(value=1.0, label="scale_end")
Curve_alpha = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="alpha (компенсация)")
Min_feature = gr.Slider(2, 64, value=8, step=1, label="Минимальный размер фичей")
with gr.Row():
Auto_end_enable = gr.Checkbox(value=False, label="Автоподбор scale_end")
Auto_end_strength = gr.Slider(0.0, 1.0, value=0.35, step=0.05, label="Сила автоподбора")
Enable_Curve.change(toggle, Enable_Curve, CurveGrp)
# Импорт
with gr.Accordion(label="Импорт профиля (JSON)", open=False):
En_Import = gr.Checkbox(value=False, label="Включить секцию")
with gr.Group(visible=False) as ImportGrp:
Use_Import = gr.Checkbox(value=False, label="Применить JSON ниже")
Json_Profile = gr.Textbox(value="", lines=6, label="JSON: actions/experimental/curve/runtime")
En_Import.change(toggle, En_Import, ImportGrp)
# Пресеты
with gr.Accordion(label="Пресеты", open=False):
En_Presets = gr.Checkbox(value=False, label="Включить секцию")
with gr.Group(visible=False) as PresetGrp:
Preset_Apply = gr.Checkbox(value=False, label="Выполнить действие при генерации")
Preset_Action = gr.Dropdown(choices=["Сохранить","Загрузить"], value="Загрузить", label="Действие")
with gr.Row():
Preset_Name = gr.Textbox(value="", label="Имя пресета")
Preset_Existing = gr.Dropdown(choices=preset_names or [""],
value=(preset_names[0] if preset_names else ""),
label="Выбрать существующий")
gr.Markdown("Подсказка: при «Загрузить» используется поле «Имя пресета», если оно заполнено.")
En_Presets.change(toggle, En_Presets, PresetGrp)
# ===== Выполнение =====
with gr.TabItem("Выполнение"):
En_Runtime = gr.Checkbox(value=True, label="Включить секцию")
with gr.Group(visible=True) as RuntimeGrp:
with gr.Row():
Timestep_Policy = gr.Dropdown(choices=["first","min","max","mean"], value="min", label="Политика timestep")
Interp_Method = gr.Dropdown(choices=["nearest","bilinear","bicubic","area"], value="bicubic", label="Интерполяция")
with gr.Row():
Interp_Antialias = gr.Checkbox(value=True, label="Антиалиасинг (bilinear/bicubic)")
Channels_Last = gr.Checkbox(value=False, label="Оптимизация channels_last")
with gr.Row():
Enable_Soft_Clamp = gr.Checkbox(value=False, label="Мягкий клип амплитуды")
Soft_Clamp_Beta = gr.Slider(0.0, 5.0, value=1.5, step=0.1, label="beta (mean±beta·std)")
with gr.Row():
Min_Depth = gr.Number(value=0, label="Мин. глубина", precision=0)
Max_Depth = gr.Number(value=999, label="Макс. глубина", precision=0)
En_Runtime.change(toggle, En_Runtime, RuntimeGrp)
# ===== Справка =====
with gr.TabItem("Справка"):
gr.Markdown("""
**Глобальные тумблеры** у каждой секции управляют и логикой, и показом.
Если секция выключена — её параметры игнорируются в `process()`.
""")
flat = [Enable_Ext,
En_Main, Enable_1, Timestep_1, Depth_1, Scale_1, Enable_2, Timestep_2, Depth_2, Scale_2,
En_Adv]
for en, ts, dp, sc in rows: flat += [en, ts, dp, sc]
flat += [
Enable_Experimental,
Enable_Experimental_1, Timestep_Experimental_1, Scale_Experimental_1,
Premultiplier_Experimental_1, Postmultiplier_Experimental_1,
Dilation_Experimental_1, CFG_Scale_Scale_Experimental_1,
Enable_Experimental_2, Timestep_Experimental_2, Scale_Experimental_2,
Premultiplier_Experimental_2, Postmultiplier_Experimental_2,
Dilation_Experimental_2, CFG_Scale_Scale_Experimental_2,
Enable_Experimental_3, Timestep_Experimental_3, Scale_Experimental_3,
Premultiplier_Experimental_3, Postmultiplier_Experimental_3,
Dilation_Experimental_3, CFG_Scale_Scale_Experimental_3,
Enable_Experimental_4, Timestep_Experimental_4, Scale_Experimental_4,
Premultiplier_Experimental_4, Postmultiplier_Experimental_4,
Dilation_Experimental_4, CFG_Scale_Scale_Experimental_4,
Enable_Curve, Curve_Type, Curve_t_start, Curve_t_end,
Curve_scale_start, Curve_scale_end, Curve_alpha, Min_feature,
Auto_end_enable, Auto_end_strength,
En_Import, Use_Import, Json_Profile,
En_Presets, Preset_Apply, Preset_Action, Preset_Name, Preset_Existing,
En_Runtime, Timestep_Policy, Interp_Method, Interp_Antialias, Channels_Last,
Enable_Soft_Clamp, Soft_Clamp_Beta, Min_Depth, Max_Depth
]
return flat
def process(self, p, *args):
if not isinstance(sd_unet.current_unet, DSHF.DeepShrinkHiresFixUNet): return
it = iter(args)
def nxt(): return next(it)
enable_ext = bool(nxt())
if not enable_ext: return
# Основные пороги
en_main = bool(nxt())
base_rules = []
for _ in range(2):
base_rules.append((
bool(nxt()), _to_scalar(nxt()), int(_to_scalar(nxt())), float(_to_scalar(nxt()))
))
# Расширенные пороги
en_adv = bool(nxt())
adv_rules = []
for _ in range(6):
adv_rules.append((
bool(nxt()), _to_scalar(nxt()), int(_to_scalar(nxt())), float(_to_scalar(nxt()))
))
DSHF.dshf_actions.clear()
rules = (base_rules if en_main else [(False,0,0,1.0)]*2) + (adv_rules if en_adv else [(False,0,0,1.0)]*6)
for (en,ts,dp,sc) in rules:
DSHF.dshf_actions.append(DSHFAction(bool(en), float(ts), int(dp), float(sc)))
# Экспериментальные
DSHF.enableExperimental = bool(nxt())
exp_sets = []
for _ in range(4):
en = bool(nxt()); ts = _to_scalar(nxt())
sc = _parse_number_list(str(nxt()), as_int=False)
pre = _parse_number_list(str(nxt()), as_int=False)
post = _parse_number_list(str(nxt()), as_int=False)
dil = _parse_number_list(str(nxt()), as_int=True)
cfg = _parse_number_list(str(nxt()), as_int=False)
exp_sets.append(DSHFExperimentalAction(en, ts, sc, pre, post, dil, cfg))
DSHF.dshf_experimental_actions = exp_sets if DSHF.enableExperimental else []
# Кривая
DSHF.enable_curve = bool(nxt())
curve_type = str(nxt()); t0 = _to_scalar(nxt()); t1 = _to_scalar(nxt())
s0 = float(_to_scalar(nxt())); s1 = float(_to_scalar(nxt()))
alpha = float(_clamp(_to_scalar(nxt()),0.0,1.0))
minfeat = int(_clamp(_to_scalar(nxt()),2,256))
auto_en = bool(nxt()); auto_k = float(_clamp(_to_scalar(nxt()),0.0,1.0))
if DSHF.enable_curve:
DSHF.curve_type, DSHF.curve_t_start, DSHF.curve_t_end = curve_type, t0, t1
DSHF.curve_scale_start, DSHF.curve_scale_end = s0, s1
DSHF.curve_alpha, DSHF.min_feature_size = alpha, minfeat
DSHF.auto_end_enable, DSHF.auto_end_strength = auto_en, auto_k
else:
DSHF.auto_end_enable = False
# Импорт
en_import = bool(nxt())
use_import = bool(nxt()); json_text = str(nxt() or "").strip()
if en_import and use_import and json_text:
try: _apply_profile_dict(json.loads(json_text))
except Exception as e: print(f"[DSHF] Ошибка JSON-профиля: {e}")
# Пресеты
en_preset = bool(nxt())
if en_preset:
preset_apply = bool(nxt()); action = str(nxt() or ""); name = str(nxt() or "").strip(); existing = str(nxt() or "").strip()
if preset_apply:
store = _load_all_presets(); bag = store.get("presets", {})
if action == "Сохранить":
key = name or existing
if key:
bag[key] = _build_profile_dict(); store["presets"] = bag; _save_all_presets(store)
print(f"[DSHF] Пресет сохранён: '{key}'")
else:
key = name or existing
prof = bag.get(key)
if prof: _apply_profile_dict(prof); print(f"[DSHF] Пресет загружен: '{key}'")
else:
_ = nxt(); _ = nxt(); _ = nxt(); _ = nxt()
# Выполнение
en_run = bool(nxt())
pol = str(nxt()); im = str(nxt()); aa = bool(nxt()); chlast = bool(nxt())
sclamp = bool(nxt()); beta = float(_clamp(_to_scalar(nxt()),0.0,5.0))
mind = int(_clamp(_to_scalar(nxt()),0,99)); maxd = int(_clamp(_to_scalar(nxt()),0,99))
if en_run:
DSHF.timestep_policy, DSHF.interp_method, DSHF.interp_antialias = pol, im, aa
DSHF.channels_last, DSHF.enable_soft_clamp, DSHF.soft_clamp_beta = chlast, sclamp, beta
DSHF.min_depth, DSHF.max_depth = mind, maxd
auto = self._auto_scale_end(p)
if auto is not None: DSHF.curve_scale_end = float(auto)
# ---------------- Обёртки Conv2d ----------------
class DSHF_Scale(torch.nn.Module):
def __init__(self, conv2d_ref: List[torch.nn.Conv2d]): super().__init__(); self.conv2d_ref = conv2d_ref
def forward(self, h: torch.Tensor):
exp = DSHF._active_experimental()
if exp is not None:
idx = DSHF.currentConv
h = _resize(h, 1.0/_get_or_last(exp.scales, idx, 1.0))
conv = self.conv2d_ref[0]
k = conv.kernel_size if isinstance(conv.kernel_size, tuple) else (conv.kernel_size, conv.kernel_size)
if max(k)>1:
dil = int(_get_or_last(exp.dilations, idx, 1)); conv.dilation = (dil,dil); conv.padding = (dil,dil)
else:
conv.dilation = (1,1); conv.padding = (0,0)
return h
class DSHF_Unscale(torch.nn.Module):
def forward(self, h: torch.Tensor):
exp = DSHF._active_experimental()
if exp is not None:
idx = DSHF.currentConv
s = _get_or_last(exp.scales, idx, 1.0)
if s != 1.0:
h = _resize(h, s)
if DSHF.curve_alpha != 0.0: h = h * (s ** DSHF.curve_alpha)
h = DSHF._soft_clamp(h); DSHF.currentConv += 1; return h
class DSHF_InMul(torch.nn.Module):
def forward(self, h: torch.Tensor):
exp = DSHF._active_experimental()
if exp is not None:
mul = _get_or_last(exp.in_multipliers, DSHF.currentBlock, 1.0)
if mul != 1.0: return h * mul
return h
class DSHF_OutMul(torch.nn.Module):
def forward(self, h: torch.Tensor):
exp = DSHF._active_experimental()
if exp is not None:
mul = _get_or_last(exp.out_multipliers, DSHF.currentBlock, 1.0)
if mul != 1.0: h = h * mul
return DSHF._soft_clamp(h)
# --------------- Подменённый U-Net ---------------
class DeepShrinkHiresFixUNet(sd_unet.SdUnet):
def __init__(self, _model):
super().__init__(); self.model = _model.to(devices.device)
for i, ib in enumerate(self.model.input_blocks):
for j, layer in enumerate(ib):
if isinstance(layer, ResBlock):
for k, il in enumerate(layer.in_layers):
if isinstance(il, torch.nn.Conv2d):
self.model.input_blocks[i][j].in_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([il]), il, DSHF.DSHF_Unscale(), DSHF.DSHF_InMul())
for k, ol in enumerate(layer.out_layers):
if isinstance(ol, torch.nn.Conv2d):
self.model.input_blocks[i][j].out_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([ol]), ol, DSHF.DSHF_Unscale(), DSHF.DSHF_OutMul())
else:
if isinstance(layer, torch.nn.Conv2d):
self.model.input_blocks[i][j] = torch.nn.Sequential(DSHF.DSHF_Scale([layer]), layer, DSHF.DSHF_Unscale())
if isinstance(layer, Downsample):
layer.op = torch.nn.Sequential(DSHF.DSHF_Scale([layer.op]), layer.op, DSHF.DSHF_Unscale())
if isinstance(layer, Upsample):
layer.conv = torch.nn.Sequential(DSHF.DSHF_Scale([layer.conv]), layer.conv, DSHF.DSHF_Unscale())
for j, layer in enumerate(self.model.middle_block):
if isinstance(layer, ResBlock):
for k, il in enumerate(layer.in_layers):
if isinstance(il, torch.nn.Conv2d):
self.model.middle_block[j].in_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([il]), il, DSHF.DSHF_Unscale(), DSHF.DSHF_InMul())
for k, ol in enumerate(layer.out_layers):
if isinstance(ol, torch.nn.Conv2d):
self.model.middle_block[j].out_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([ol]), ol, DSHF.DSHF_Unscale(), DSHF.DSHF_OutMul())
else:
if isinstance(layer, torch.nn.Conv2d):
self.model.middle_block[j] = torch.nn.Sequential(DSHF.DSHF_Scale([layer]), layer, DSHF.DSHF_Unscale())
if isinstance(layer, Downsample):
layer.op = torch.nn.Sequential(DSHF.DSHF_Scale([layer.op]), layer.op, DSHF.DSHF_Unscale())
if isinstance(layer, Upsample):
layer.conv = torch.nn.Sequential(DSHF.DSHF_Scale([layer.conv]), layer.conv, DSHF.DSHF_Unscale())
for i, ob in enumerate(self.model.output_blocks):
for j, layer in enumerate(ob):
if isinstance(layer, ResBlock):
for k, il in enumerate(layer.in_layers):
if isinstance(il, torch.nn.Conv2d):
self.model.output_blocks[i][j].in_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([il]), il, DSHF.DSHF_Unscale(), DSHF.DSHF_InMul())
for k, ol in enumerate(layer.out_layers):
if isinstance(ol, torch.nn.Conv2d):
self.model.output_blocks[i][j].out_layers[k] = torch.nn.Sequential(DSHF.DSHF_Scale([ol]), ol, DSHF.DSHF_Unscale(), DSHF.DSHF_OutMul())
else:
if isinstance(layer, torch.nn.Conv2d):
self.model.output_blocks[i][j] = torch.nn.Sequential(DSHF.DSHF_Scale([layer]), layer, DSHF.DSHF_Unscale())
if isinstance(layer, Downsample):
layer.op = torch.nn.Sequential(DSHF.DSHF_Scale([layer.op]), layer.op, DSHF.DSHF_Unscale())
if isinstance(layer, Upsample):
layer.conv = torch.nn.Sequential(DSHF.DSHF_Scale([layer.conv]), layer.conv, DSHF.DSHF_Unscale())
for i, m in enumerate(self.model.out):
if isinstance(m, torch.nn.Conv2d):
self.model.out[i] = torch.nn.Sequential(DSHF.DSHF_Scale([m]), m, DSHF.DSHF_Unscale())
def forward(self, x, timesteps, context, y=None, **kwargs):
assert (y is not None) == (self.model.num_classes is not None), "must specify y iff class-conditional"
if DSHF.channels_last: x = x.contiguous(memory_format=torch.channels_last)
hs = []
emb = self.model.time_embed(timestep_embedding(timesteps, self.model.model_channels, repeat_only=False))
if self.model.num_classes is not None:
assert y.shape[0]==x.shape[0]; emb = emb + self.model.label_emb(y)
h = x.type(self.model.dtype); depth = 0
DSHF.currentBlock = 0; DSHF.currentConv = 0; DSHF.currentTimestep = DSHF._pick_timestep_scalar(timesteps)
for module in self.model.input_blocks:
context_tmp = context
scale = DSHF._block_scale(depth)
if scale is not None: h = _resize(h, 1.0/float(scale))
exp = DSHF._active_experimental()
if exp is not None:
cfg_mul = _get_or_last(exp.cfg_scale_scale, DSHF.currentBlock, 1.0)
context_tmp = context * float(cfg_mul)
h = module(h, emb, context_tmp); hs.append(h); depth += 1; DSHF.currentBlock += 1
context_tmp = context; scale = DSHF._block_scale(depth)
if scale is not None: h = _resize(h, 1.0/float(scale))
exp = DSHF._active_experimental()
if exp is not None:
cfg_mul = _get_or_last(exp.cfg_scale_scale, DSHF.currentBlock, 1.0)
context_tmp = context * float(cfg_mul)
h = self.model.middle_block(h, emb, context_tmp)
scale = DSHF._block_scale(depth)
if scale is not None: h = _resize(h, float(scale))
DSHF.currentBlock += 1
for module in self.model.output_blocks:
context_tmp = context
exp = DSHF._active_experimental()
if exp is not None:
cfg_mul = _get_or_last(exp.cfg_scale_scale, DSHF.currentBlock, 1.0)
context_tmp = context * float(cfg_mul)
depth -= 1; h = torch.cat([h, hs.pop()], dim=1); h = module(h, emb, context_tmp)
scale = DSHF._block_scale(depth)
if scale is not None: h = _resize(h, float(scale))
DSHF.currentBlock += 1
h = h.type(x.dtype)
return self.model.id_predictor(h) if self.model.predict_codebook_ids else self.model.out(h)
# Регистрация U-Net
DeepShrinkHiresFixUNetOption = sd_unet.SdUnetOption()
DeepShrinkHiresFixUNetOption.label = "Deep Shrink Hires.fix"
DeepShrinkHiresFixUNetOption.create_unet = lambda: DSHF.DeepShrinkHiresFixUNet(shared.sd_model.model.diffusion_model)
script_callbacks.on_list_unets(lambda unets: unets.append(DeepShrinkHiresFixUNetOption))
|