File size: 28,554 Bytes
d5e1b1b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 |
import collections
import os.path
import sys
import gc
import threading
import torch
import re
import safetensors.torch
from omegaconf import OmegaConf
from os import mkdir
from urllib import request
import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
from modules.timer import Timer
import tomesd
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
checkpoints_list = {}
checkpoint_aliases = {}
checkpoint_alisases = checkpoint_aliases # for compatibility with old name
checkpoints_loaded = collections.OrderedDict()
def replace_key(d, key, new_key, value):
keys = list(d.keys())
d[new_key] = value
if key not in keys:
return d
index = keys.index(key)
keys[index] = new_key
new_d = {k: d[k] for k in keys}
d.clear()
d.update(new_d)
return d
class CheckpointInfo:
def __init__(self, filename):
self.filename = filename
abspath = os.path.abspath(filename)
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
name = os.path.basename(filename)
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
def read_metadata():
metadata = read_metadata_from_safetensors(filename)
self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
return metadata
self.metadata = {}
if self.is_safetensors:
try:
self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
except Exception as e:
errors.display(e, f"reading metadata for {filename}")
self.name = name
self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
self.hash = model_hash(filename)
self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
self.shorthash = self.sha256[0:10] if self.sha256 else None
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
if self.shorthash:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
def register(self):
checkpoints_list[self.title] = self
for id in self.ids:
checkpoint_aliases[id] = self
def calculate_shorthash(self):
self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
if self.sha256 is None:
return
shorthash = self.sha256[0:10]
if self.shorthash == self.sha256[0:10]:
return self.shorthash
self.shorthash = shorthash
if self.shorthash not in self.ids:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
old_title = self.title
self.title = f'{self.name} [{self.shorthash}]'
self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
replace_key(checkpoints_list, old_title, self.title, self)
self.register()
return self.shorthash
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging, CLIPModel # noqa: F401
logging.set_verbosity_error()
except Exception:
pass
def setup_model():
os.makedirs(model_path, exist_ok=True)
enable_midas_autodownload()
def checkpoint_tiles(use_short=False):
return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
def list_models():
checkpoints_list.clear()
checkpoint_aliases.clear()
cmd_ckpt = shared.cmd_opts.ckpt
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
model_url = None
else:
model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
if os.path.exists(cmd_ckpt):
checkpoint_info = CheckpointInfo(cmd_ckpt)
checkpoint_info.register()
shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
for filename in model_list:
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
def get_closet_checkpoint_match(search_string):
if not search_string:
return None
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
return checkpoint_info
found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
if found:
return found[0]
search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
if found:
return found[0]
return None
def model_hash(filename):
"""old hash that only looks at a small part of the file and is prone to collisions"""
try:
with open(filename, "rb") as file:
import hashlib
m = hashlib.sha256()
file.seek(0x100000)
m.update(file.read(0x10000))
return m.hexdigest()[0:8]
except FileNotFoundError:
return 'NOFILE'
def select_checkpoint():
"""Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
if len(checkpoints_list) == 0:
error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
return checkpoint_info
checkpoint_dict_replacements = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}
def transform_checkpoint_dict_key(k):
for text, replacement in checkpoint_dict_replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
return k
def get_state_dict_from_checkpoint(pl_sd):
pl_sd = pl_sd.pop("state_dict", pl_sd)
pl_sd.pop("state_dict", None)
sd = {}
for k, v in pl_sd.items():
new_key = transform_checkpoint_dict_key(k)
if new_key is not None:
sd[new_key] = v
pl_sd.clear()
pl_sd.update(sd)
return pl_sd
def read_metadata_from_safetensors(filename):
import json
with open(filename, mode="rb") as file:
metadata_len = file.read(8)
metadata_len = int.from_bytes(metadata_len, "little")
json_start = file.read(2)
assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
json_data = json_start + file.read(metadata_len-2)
json_obj = json.loads(json_data)
res = {}
for k, v in json_obj.get("__metadata__", {}).items():
res[k] = v
if isinstance(v, str) and v[0:1] == '{':
try:
res[k] = json.loads(v)
except Exception:
pass
return res
def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
if not shared.opts.disable_mmap_load_safetensors:
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
else:
pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
if print_global_state and "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = get_state_dict_from_checkpoint(pl_sd)
return sd
def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
if checkpoint_info in checkpoints_loaded:
# use checkpoint cache
print(f"Loading weights [{sd_model_hash}] from cache")
return checkpoints_loaded[checkpoint_info]
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
res = read_state_dict(checkpoint_info.filename)
timer.record("load weights from disk")
return res
class SkipWritingToConfig:
"""This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
skip = False
previous = None
def __enter__(self):
self.previous = SkipWritingToConfig.skip
SkipWritingToConfig.skip = True
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
SkipWritingToConfig.skip = self.previous
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
model.is_sdxl = hasattr(model, 'conditioner')
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2
if model.is_sdxl:
sd_models_xl.extend_sdxl(model)
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
if shared.opts.sd_checkpoint_cache > 0:
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = state_dict
del state_dict
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
timer.record("apply channels_last")
if shared.cmd_opts.no_half:
model.float()
devices.dtype_unet = torch.float32
timer.record("apply float()")
else:
vae = model.first_stage_model
depth_model = getattr(model, 'depth_model', None)
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
if shared.cmd_opts.no_half_vae:
model.first_stage_model = None
# with --upcast-sampling, don't convert the depth model weights to float16
if shared.cmd_opts.upcast_sampling and depth_model:
model.depth_model = None
model.half()
model.first_stage_model = vae
if depth_model:
model.depth_model = depth_model
devices.dtype_unet = torch.float16
timer.record("apply half()")
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
timer.record("apply dtype to VAE")
# clean up cache if limit is reached
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
checkpoints_loaded.popitem(last=False)
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_info.filename
model.sd_checkpoint_info = checkpoint_info
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
if hasattr(model, 'logvar'):
model.logvar = model.logvar.to(devices.device) # fix for training
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
sd_vae.load_vae(model, vae_file, vae_source)
timer.record("load VAE")
def enable_midas_autodownload():
"""
Gives the ldm.modules.midas.api.load_model function automatic downloading.
When the 512-depth-ema model, and other future models like it, is loaded,
it calls midas.api.load_model to load the associated midas depth model.
This function applies a wrapper to download the model to the correct
location automatically.
"""
midas_path = os.path.join(paths.models_path, 'midas')
# stable-diffusion-stability-ai hard-codes the midas model path to
# a location that differs from where other scripts using this model look.
# HACK: Overriding the path here.
for k, v in midas.api.ISL_PATHS.items():
file_name = os.path.basename(v)
midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
midas_urls = {
"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
}
midas.api.load_model_inner = midas.api.load_model
def load_model_wrapper(model_type):
path = midas.api.ISL_PATHS[model_type]
if not os.path.exists(path):
if not os.path.exists(midas_path):
mkdir(midas_path)
print(f"Downloading midas model weights for {model_type} to {path}")
request.urlretrieve(midas_urls[model_type], path)
print(f"{model_type} downloaded")
return midas.api.load_model_inner(model_type)
midas.api.load_model = load_model_wrapper
def repair_config(sd_config):
if not hasattr(sd_config.model.params, "use_ema"):
sd_config.model.params.use_ema = False
if hasattr(sd_config.model.params, 'unet_config'):
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
# For UnCLIP-L, override the hardcoded karlo directory
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
karlo_path = os.path.join(paths.models_path, 'karlo')
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
class SdModelData:
def __init__(self):
self.sd_model = None
self.loaded_sd_models = []
self.was_loaded_at_least_once = False
self.lock = threading.Lock()
def get_sd_model(self):
if self.was_loaded_at_least_once:
return self.sd_model
if self.sd_model is None:
with self.lock:
if self.sd_model is not None or self.was_loaded_at_least_once:
return self.sd_model
try:
load_model()
except Exception as e:
errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
print("Stable diffusion model failed to load", file=sys.stderr)
self.sd_model = None
return self.sd_model
def set_sd_model(self, v, already_loaded=False):
self.sd_model = v
if already_loaded:
sd_vae.base_vae = getattr(v, "base_vae", None)
sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
sd_vae.checkpoint_info = v.sd_checkpoint_info
try:
self.loaded_sd_models.remove(v)
except ValueError:
pass
if v is not None:
self.loaded_sd_models.insert(0, v)
model_data = SdModelData()
def get_empty_cond(sd_model):
p = processing.StableDiffusionProcessingTxt2Img()
extra_networks.activate(p, {})
if hasattr(sd_model, 'conditioner'):
d = sd_model.get_learned_conditioning([""])
return d['crossattn']
else:
return sd_model.cond_stage_model([""])
def send_model_to_cpu(m):
if m.lowvram:
lowvram.send_everything_to_cpu()
else:
m.to(devices.cpu)
devices.torch_gc()
def model_target_device(m):
if lowvram.is_needed(m):
return devices.cpu
else:
return devices.device
def send_model_to_device(m):
lowvram.apply(m)
if not m.lowvram:
m.to(shared.device)
def send_model_to_trash(m):
m.to(device="meta")
devices.torch_gc()
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
from modules import sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
timer = Timer()
if model_data.sd_model:
send_model_to_trash(model_data.sd_model)
model_data.sd_model = None
devices.torch_gc()
timer.record("unload existing model")
if already_loaded_state_dict is not None:
state_dict = already_loaded_state_dict
else:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
timer.record("find config")
sd_config = OmegaConf.load(checkpoint_config)
repair_config(sd_config)
timer.record("load config")
print(f"Creating model from config: {checkpoint_config}")
sd_model = None
try:
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
except Exception as e:
errors.display(e, "creating model quickly", full_traceback=True)
if sd_model is None:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
sd_model.used_config = checkpoint_config
timer.record("create model")
if shared.cmd_opts.no_half:
weight_dtype_conversion = None
else:
weight_dtype_conversion = {
'first_stage_model': None,
'': torch.float16,
}
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
timer.record("load weights from state dict")
send_model_to_device(sd_model)
timer.record("move model to device")
sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
sd_model.eval()
model_data.set_sd_model(sd_model)
model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
timer.record("load textual inversion embeddings")
script_callbacks.model_loaded_callback(sd_model)
timer.record("scripts callbacks")
with devices.autocast(), torch.no_grad():
sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
timer.record("calculate empty prompt")
print(f"Model loaded in {timer.summary()}.")
return sd_model
def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
"""
Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
If not, returns the model that can be used to load weights from checkpoint_info's file.
If no such model exists, returns None.
Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
"""
already_loaded = None
for i in reversed(range(len(model_data.loaded_sd_models))):
loaded_model = model_data.loaded_sd_models[i]
if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
already_loaded = loaded_model
continue
if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
model_data.loaded_sd_models.pop()
send_model_to_trash(loaded_model)
timer.record("send model to trash")
if shared.opts.sd_checkpoints_keep_in_cpu:
send_model_to_cpu(sd_model)
timer.record("send model to cpu")
if already_loaded is not None:
send_model_to_device(already_loaded)
timer.record("send model to device")
model_data.set_sd_model(already_loaded, already_loaded=True)
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
sd_vae.reload_vae_weights(already_loaded)
return model_data.sd_model
elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
model_data.sd_model = None
load_model(checkpoint_info)
return model_data.sd_model
elif len(model_data.loaded_sd_models) > 0:
sd_model = model_data.loaded_sd_models.pop()
model_data.sd_model = sd_model
sd_vae.base_vae = getattr(sd_model, "base_vae", None)
sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
return sd_model
else:
return None
def reload_model_weights(sd_model=None, info=None):
checkpoint_info = info or select_checkpoint()
timer = Timer()
if not sd_model:
sd_model = model_data.sd_model
if sd_model is None: # previous model load failed
current_checkpoint_info = None
else:
current_checkpoint_info = sd_model.sd_checkpoint_info
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return sd_model
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
return sd_model
if sd_model is not None:
sd_unet.apply_unet("None")
send_model_to_cpu(sd_model)
sd_hijack.model_hijack.undo_hijack(sd_model)
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
timer.record("find config")
if sd_model is None or checkpoint_config != sd_model.used_config:
if sd_model is not None:
send_model_to_trash(sd_model)
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return model_data.sd_model
try:
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
except Exception:
print("Failed to load checkpoint, restoring previous")
load_model_weights(sd_model, current_checkpoint_info, None, timer)
raise
finally:
sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
script_callbacks.model_loaded_callback(sd_model)
timer.record("script callbacks")
if not sd_model.lowvram:
sd_model.to(devices.device)
timer.record("move model to device")
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)
sd_unet.apply_unet()
return sd_model
def unload_model_weights(sd_model=None, info=None):
timer = Timer()
if model_data.sd_model:
model_data.sd_model.to(devices.cpu)
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
model_data.sd_model = None
sd_model = None
gc.collect()
devices.torch_gc()
print(f"Unloaded weights {timer.summary()}.")
return sd_model
def apply_token_merging(sd_model, token_merging_ratio):
"""
Applies speed and memory optimizations from tomesd.
"""
current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
if current_token_merging_ratio == token_merging_ratio:
return
if current_token_merging_ratio > 0:
tomesd.remove_patch(sd_model)
if token_merging_ratio > 0:
tomesd.apply_patch(
sd_model,
ratio=token_merging_ratio,
use_rand=False, # can cause issues with some samplers
merge_attn=True,
merge_crossattn=False,
merge_mlp=False
)
sd_model.applied_token_merged_ratio = token_merging_ratio
|