Spaces:
Running
on
L40S
Running
on
L40S
File size: 58,247 Bytes
2252f3d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 |
import logging
import warnings
from typing import Callable, List, Optional, Union, Dict, Any
import PIL
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from packaging import version
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPFeatureExtractor, CLIPTokenizer, CLIPTextModel
from diffusers.utils.import_utils import is_accelerate_available
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.embeddings import get_timestep_embedding
from diffusers.schedulers import KarrasDiffusionSchedulers, PNDMScheduler, DDIMScheduler, DDPMScheduler
from diffusers.utils import deprecate
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from accelerate.utils import ProjectConfiguration, set_seed
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, deprecate, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
import transformers
import diffusers
import accelerate
from accelerate import Accelerator
from torchvision.transforms import InterpolationMode
import argparse
from omegaconf import OmegaConf
from mvdiffusion.models_unclip.unet_mv2d_condition import UNetMV2DConditionModel
# from mvdiffusion.data.objaverse_dataset_unclip_xxdata import ObjaverseDataset as MVDiffusionDataset
from mvdiffusion.data.dreamdata import ObjaverseDataset as MVDiffusionDataset
from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
from accelerate.logging import get_logger
import os
import numpy as np
from PIL import Image
import math
from tqdm import tqdm
from einops import rearrange, repeat
from torchvision.transforms import InterpolationMode
from einops import rearrange, repeat
from diffusers.schedulers import PNDMScheduler
from collections import defaultdict
from torchvision.utils import make_grid, save_image
from mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline
from dataclasses import dataclass
import json
import shutil
from mvdiffusion.models_unclip.face_networks import prepare_face_proj_model
logger = get_logger(__name__, log_level="INFO")
@dataclass
class TrainingConfig:
pretrained_model_name_or_path: str
pretrained_unet_path: Optional[str]
clip_path: str
revision: Optional[str]
data_common: Optional[dict]
train_dataset: Dict
validation_dataset: Dict
validation_train_dataset: Dict
output_dir: str
checkpoint_prefix: str
seed: Optional[int]
train_batch_size: int
validation_batch_size: int
validation_train_batch_size: int
max_train_steps: int
gradient_accumulation_steps: int
gradient_checkpointing: bool
learning_rate: float
scale_lr: bool
lr_scheduler: str
step_rules: Optional[str]
lr_warmup_steps: int
snr_gamma: Optional[float]
use_8bit_adam: bool
allow_tf32: bool
use_ema: bool
dataloader_num_workers: int
adam_beta1: float
adam_beta2: float
adam_weight_decay: float
adam_epsilon: float
max_grad_norm: Optional[float]
prediction_type: Optional[str]
logging_dir: str
vis_dir: str
mixed_precision: Optional[str]
report_to: Optional[str]
local_rank: int
checkpointing_steps: int
checkpoints_total_limit: Optional[int]
resume_from_checkpoint: Optional[str]
enable_xformers_memory_efficient_attention: bool
validation_steps: int
validation_sanity_check: bool
tracker_project_name: str
trainable_modules: Optional[list]
use_classifier_free_guidance: bool
condition_drop_rate: float
scale_input_latents: bool
regress_elevation: bool
regress_focal_length: bool
elevation_loss_weight: float
focal_loss_weight: float
pipe_kwargs: Dict
pipe_validation_kwargs: Dict
unet_from_pretrained_kwargs: Dict
validation_guidance_scales: List[float]
validation_grid_nrow: int
camera_embedding_lr_mult: float
plot_pose_acc: bool
num_views: int
data_view_num: Optional[int]
pred_type: str
drop_type: str
with_smpl: Optional[bool]
@torch.no_grad()
def convert_image(
tensor,
fp,
format: Optional[str] = None,
**kwargs,
) -> None:
"""
Save a given Tensor into an image file.
Args:
tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
saves the tensor as a grid of images by calling ``make_grid``.
fp (string or file object): A filename or a file object
format(Optional): If omitted, the format to use is determined from the filename extension.
If a file object was used instead of a filename, this parameter should always be used.
**kwargs: Other arguments are documented in ``make_grid``.
"""
grid = make_grid(tensor, **kwargs)
# Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
im = Image.fromarray(ndarr)
im.save(fp, format=format)
def log_validation_joint(dataloader, vae, feature_extractor, image_encoder, image_normlizer, image_noising_scheduler, tokenizer, text_encoder,
unet, face_proj_model, cfg:TrainingConfig, accelerator, weight_dtype, global_step, name, save_dir):
pipeline = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=accelerator.unwrap_model(unet),
scheduler=DDIMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
pipeline.set_progress_bar_config(disable=True)
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=unet.device).manual_seed(cfg.seed)
images_cond, pred_cat = [], defaultdict(list)
for i, batch in tqdm(enumerate(dataloader)):
images_cond.append(batch['imgs_in'][:, 0])
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(device=accelerator.device, dtype=weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
# if dino_encoder:
# dino_input = TF.resize(batch['imgs_in'][:, 0], (224, 224)).float().to(accelerator.device)
# dino_feature = dino_encoder(dino_input)
# dino_feature = repeat(dino_feature, "B N C -> (B V) N C", V=cfg.num_views*2)
# else:
# dino_feature = None
imgs_in = torch.cat([batch['imgs_in']]*2, dim=0)
num_views = imgs_in.shape[1]
imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")# (B*Nv, 3, H, W)
if cfg.with_smpl:
smpl_in = torch.cat([batch['smpl_imgs_in']]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W")
else:
smpl_in = None
normal_prompt_embeddings, clr_prompt_embeddings = batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']
prompt_embeddings = torch.cat([normal_prompt_embeddings, clr_prompt_embeddings], dim=0)
prompt_embeddings = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, None, prompt_embeds=prompt_embeddings,
dino_feature=face_embeds, smpl_in=smpl_in,
generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
# print(normals_pred.shape, images_pred.shape)
pred_cat[f"cfg{guidance_scale:.1f}"].append(torch.cat([normals_pred, images_pred], dim=-1)) # b, 3, h, w
# from icecream import ic
images_cond_all = torch.cat(images_cond, dim=0)
images_pred_all = {}
for k, v in pred_cat.items():
images_pred_all[k] = torch.cat(v, dim=0).cpu()
# print(images_pred_all[k].shape)
# import pdb;pdb.set_trace()
nrow = cfg.validation_grid_nrow
# ncol = images_cond_all.shape[0] // nrow
images_cond_grid = make_grid(images_cond_all, nrow=1, padding=0, value_range=(0, 1))
edge_pad = torch.zeros(list(images_cond_grid.shape[:2]) + [3], dtype=torch.float32)
images_vis = torch.cat([images_cond_grid, edge_pad], -1)
for k, v in images_pred_all.items():
images_vis = torch.cat([images_vis, make_grid(v, nrow=nrow, padding=0, value_range=(0, 1)), edge_pad], -1)
save_image(images_vis, os.path.join(save_dir, f"{name}-{global_step}.jpg"))
torch.cuda.empty_cache()
def log_validation(dataloader, vae, feature_extractor, image_encoder, image_normlizer, image_noising_scheduler, tokenizer, text_encoder,
unet, face_proj_model, cfg:TrainingConfig, accelerator, weight_dtype, global_step, name, save_dir):
logger.info(f"Running {name} ... ")
pipeline = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=accelerator.unwrap_model(unet),
scheduler=DDIMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
pipeline.set_progress_bar_config(disable=True)
if cfg.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(cfg.seed)
images_cond, images_gt, images_pred = [], [], defaultdict(list)
for i, batch in enumerate(dataloader):
# (B, Nv, 3, H, W)
imgs_in, colors_out, normals_out = batch['imgs_in'], batch['imgs_out'], batch['normals_out']
images_cond.append(imgs_in[:, 0, :, :, :])
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([imgs_in]*2, dim=0)
imgs_out = torch.cat([normals_out, colors_out], dim=0)
imgs_in, imgs_out = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W"), rearrange(imgs_out, "B Nv C H W -> (B Nv) C H W")
images_gt.append(imgs_out)
if cfg.with_smpl:
smpl_in = torch.cat([batch['smpl_imgs_in']]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W")
else:
smpl_in = None
prompt_embeddings = torch.cat([batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']], dim=0)
# (B*Nv, N, C)
prompt_embeds = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
prompt_embeds = prompt_embeds.to(weight_dtype)
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(device=accelerator.device, dtype=weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, None, prompt_embeds=prompt_embeds, smpl_in=smpl_in, dino_feature=face_embeds, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
shape = out.shape
out0, out1 = out[:shape[0]//2], out[shape[0]//2:]
out = []
for ii in range(shape[0]//2):
out.append(out0[ii])
out.append(out1[ii])
out = torch.stack(out, dim=0)
images_pred[f"{name}-sample_cfg{guidance_scale:.1f}"].append(out)
images_cond_all = torch.cat(images_cond, dim=0)
images_gt_all = torch.cat(images_gt, dim=0)
images_pred_all = {}
for k, v in images_pred.items():
images_pred_all[k] = torch.cat(v, dim=0).cpu()
nrow = cfg.validation_grid_nrow * 2
images_cond_grid = make_grid(images_cond_all, nrow=1, padding=0, value_range=(0, 1))
images_gt_grid = make_grid(images_gt_all, nrow=nrow, padding=0, value_range=(0, 1))
edge_pad = torch.zeros(list(images_cond_grid.shape[:2]) + [3], dtype=torch.float32)
images_vis = torch.cat([images_cond_grid.cpu(), edge_pad], -1)
for k, v in images_pred_all.items():
images_vis = torch.cat([images_vis, make_grid(v, nrow=nrow, padding=0, value_range=(0, 1)), edge_pad], -1)
# images_pred_grid = {}
# for k, v in images_pred_all.items():
# images_pred_grid[k] = make_grid(v, nrow=nrow, padding=0, value_range=(0, 1))
save_image(images_vis, os.path.join(save_dir, f"{global_step}-{name}-cond.jpg"))
save_image(images_gt_grid, os.path.join(save_dir, f"{global_step}-{name}-gt.jpg"))
torch.cuda.empty_cache()
def noise_image_embeddings(
image_embeds: torch.Tensor,
noise_level: int,
noise: Optional[torch.FloatTensor] = None,
generator: Optional[torch.Generator] = None,
image_normalizer: Optional[StableUnCLIPImageNormalizer] = None,
image_noising_scheduler: Optional[DDPMScheduler] = None,
):
"""
Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
`noise_level` increases the variance in the final un-noised images.
The noise is applied in two ways
1. A noise schedule is applied directly to the embeddings
2. A vector of sinusoidal time embeddings are appended to the output.
In both cases, the amount of noise is controlled by the same `noise_level`.
The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
"""
if noise is None:
noise = randn_tensor(
image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
)
noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
image_embeds = image_normalizer.scale(image_embeds)
image_embeds = image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
image_embeds = image_normalizer.unscale(image_embeds)
noise_level = get_timestep_embedding(
timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
)
# `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
# but we might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
noise_level = noise_level.to(image_embeds.dtype)
image_embeds = torch.cat((image_embeds, noise_level), 1)
return image_embeds
def main(cfg: TrainingConfig):
# -------------------------------------------prepare custom log and accelaeator --------------------------------
# override local_rank with envvar
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank not in [-1, cfg.local_rank]:
cfg.local_rank = env_local_rank
logging_dir = os.path.join(cfg.output_dir, cfg.logging_dir)
model_dir = os.path.join(cfg.checkpoint_prefix, cfg.output_dir)
vis_dir = os.path.join(cfg.output_dir, cfg.vis_dir)
accelerator_project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=logging_dir)
# print(os.getenv("SLURM_PROCID"), os.getenv("SLURM_LOCALID"), os.getenv("SLURM_NODEID"), os.getenv('GLOBAL_RANK'), os.getenv('LOCAL_RANK'), os.getenv('RNAK'), os.getenv('MASTER_ADDR'))
# exit()
accelerator = Accelerator(
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
mixed_precision=cfg.mixed_precision,
log_with=cfg.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if cfg.seed is not None:
set_seed(cfg.seed)
# Handle the repository creation
if accelerator.is_main_process:
os.makedirs(model_dir, exist_ok=True)
os.makedirs(cfg.output_dir, exist_ok=True)
os.makedirs(vis_dir, exist_ok=True)
OmegaConf.save(cfg, os.path.join(cfg.output_dir, 'config.yaml'))
## -------------------------------------- load models --------------------------------
image_encoder = CLIPVisionModelWithProjection.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_encoder", revision=cfg.revision)
feature_extractor = CLIPImageProcessor.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="feature_extractor", revision=cfg.revision)
image_noising_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_noising_scheduler")
image_normlizer = StableUnCLIPImageNormalizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_normalizer")
tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="tokenizer", revision=cfg.revision)
text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder='text_encoder', revision=cfg.revision)
# note: official code use PNDMScheduler
noise_scheduler = DDPMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler")
vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision)
if cfg.pretrained_unet_path is None:
unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
else:
logger.info(f'laod pretrained model from {cfg.pretrained_unet_path}')
unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_unet_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
# unet = UNet2DConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision)
if cfg.unet_from_pretrained_kwargs.use_dino:
from models.dinov2_wrapper import Dinov2Wrapper
dino_encoder = Dinov2Wrapper(model_name='dinov2_vitb14', freeze=True)
else:
dino_encoder = None
# TODO: extract face projection model weights
if cfg.unet_from_pretrained_kwargs.use_face_adapter:
face_proj_model = prepare_face_proj_model('models/image_proj_model.pth', cross_attention_dim=1024, pretrain=False)
else:
face_proj_model = None
if cfg.use_ema:
ema_unet = EMAModel(unet.parameters(), model_cls=UNetMV2DConditionModel, model_config=unet.config)
# ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, model_config=unet.config)
def compute_snr(timesteps):
"""
Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
"""
alphas_cumprod = noise_scheduler.alphas_cumprod
sqrt_alphas_cumprod = alphas_cumprod**0.5
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
# Expand the tensors.
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
# Compute SNR.
snr = (alpha / sigma) ** 2
return snr
# Freeze vae, image_encoder, text_encoder
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
image_normlizer.requires_grad_(False)
text_encoder.requires_grad_(False)
if face_proj_model is not None: face_proj_model.requires_grad_(True)
if cfg.trainable_modules is None:
unet.requires_grad_(True)
else:
unet.requires_grad_(False)
for name, module in unet.named_modules():
if name.endswith(tuple(cfg.trainable_modules)):
for params in module.parameters():
params.requires_grad = True
if cfg.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
print("use xformers to speed up")
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if cfg.use_ema:
ema_unet.save_pretrained(os.path.join(cfg.checkpoint_prefix, output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(cfg.checkpoint_prefix, output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if cfg.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(cfg.checkpoint_prefix, input_dir, "unet_ema"), UNetMV2DConditionModel)
ema_unet.load_state_dict(load_model.state_dict())
ema_unet.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNetMV2DConditionModel.from_pretrained(os.path.join(cfg.checkpoint_prefix, input_dir), subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if cfg.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if cfg.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
# -------------------------------------- optimizer and lr --------------------------------
if cfg.scale_lr:
cfg.learning_rate = (
cfg.learning_rate * cfg.gradient_accumulation_steps * cfg.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if cfg.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
params, params_class_embedding, params_rowwise_layers = [], [], []
for name, param in unet.named_parameters():
if ('class_embedding' in name) or ('camera_embedding' in name):
params_class_embedding.append(param)
elif ('attn_mv' in name) or ('norm_mv' in name):
# print('Find mv attn block')
params_rowwise_layers.append(param)
else:
params.append(param)
opti_params = [{"params": params, "lr": cfg.learning_rate}]
if len(params_class_embedding) > 0:
opti_params.append({"params": params_class_embedding, "lr": cfg.learning_rate * cfg.camera_embedding_lr_mult})
if len(params_rowwise_layers) > 0:
opti_params.append({"params": params_rowwise_layers, "lr": cfg.learning_rate * cfg.camera_embedding_lr_mult})
optimizer = optimizer_cls(
opti_params,
betas=(cfg.adam_beta1, cfg.adam_beta2),
weight_decay=cfg.adam_weight_decay,
eps=cfg.adam_epsilon,
)
lr_scheduler = get_scheduler(
cfg.lr_scheduler,
step_rules=cfg.step_rules,
optimizer=optimizer,
num_warmup_steps=cfg.lr_warmup_steps * accelerator.num_processes,
num_training_steps=cfg.max_train_steps * accelerator.num_processes,
)
# -------------------------------------- load dataset --------------------------------
# Get the training dataset
train_dataset = MVDiffusionDataset(
**cfg.train_dataset
)
if cfg.with_smpl:
from mvdiffusion.data.testdata_with_smpl import SingleImageDataset
else:
from mvdiffusion.data.single_image_dataset import SingleImageDataset
validation_dataset = SingleImageDataset(
**cfg.validation_dataset
)
validation_train_dataset = MVDiffusionDataset(
**cfg.validation_train_dataset
)
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=cfg.train_batch_size, shuffle=True, num_workers=cfg.dataloader_num_workers,
)
validation_dataloader = torch.utils.data.DataLoader(
validation_dataset, batch_size=cfg.validation_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
validation_train_dataloader = torch.utils.data.DataLoader(
validation_train_dataset, batch_size=cfg.validation_train_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
if cfg.use_ema:
ema_unet.to(accelerator.device)
# -------------------------------------- cast dtype and device --------------------------------
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
cfg.mixed_precision = accelerator.mixed_precision
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
cfg.mixed_precision = accelerator.mixed_precision
# Move text_encode and vae to gpu and cast to weight_dtype
image_encoder.to(accelerator.device, dtype=weight_dtype)
image_normlizer.to(accelerator.device, weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
if face_proj_model: face_proj_model.to(accelerator.device, dtype=weight_dtype)
if dino_encoder: dino_encoder.to(accelerator.device)
clip_image_mean = torch.as_tensor(feature_extractor.image_mean)[:,None,None].to(accelerator.device, dtype=torch.float32)
clip_image_std = torch.as_tensor(feature_extractor.image_std)[:,None,None].to(accelerator.device, dtype=torch.float32)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps)
num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
# tracker_config = dict(vars(cfg))
tracker_config = {}
accelerator.init_trackers(
project_name= cfg.tracker_project_name,
config= tracker_config,
init_kwargs={"wandb":
{"entity": "lpstarry",
"notes": cfg.output_dir.split('/')[-1],
# "tags": [cfg.output_dir.split('/')[-1]],
}},)
# -------------------------------------- load pipeline --------------------------------
# pipe = StableUnCLIPImg2ImgPipeline(feature_extractor=feature_extractor,
# image_encoder=image_encoder,
# image_normalizer=image_normlizer,
# image_noising_scheduler= image_noising_scheduler,
# tokenizer=tokenizer,
# text_encoder=text_encoder,
# unet=unet,
# scheduler=noise_scheduler,
# vae=vae).to('cuda')
# -------------------------------------- train --------------------------------
total_batch_size = cfg.train_batch_size * accelerator.num_processes * cfg.gradient_accumulation_steps
generator = torch.Generator(device=accelerator.device).manual_seed(cfg.seed)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {cfg.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {cfg.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {cfg.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if cfg.resume_from_checkpoint:
if cfg.resume_from_checkpoint != "latest":
path = os.path.basename(cfg.resume_from_checkpoint)
else:
# Get the most recent checkpoint
if os.path.exists(os.path.join(model_dir, "checkpoint")):
path = "checkpoint"
else:
dirs = os.listdir(model_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{cfg.resume_from_checkpoint}' does not exist. Starting a new training run."
)
cfg.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(model_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
if False:
# log_validation_joint(
# validation_dataloader,
# vae,
# feature_extractor,
# image_encoder,
# image_normlizer,
# image_noising_scheduler,
# tokenizer,
# text_encoder,
# unet,
# dino_encoder,
# cfg,
# accelerator,
# weight_dtype,
# global_step,
# 'validation',
# vis_dir
# )
log_validation(
validation_train_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
cfg,
accelerator,
weight_dtype,
global_step,
'validation-train',
vis_dir
)
exit()
progress_bar = tqdm(
range(0, cfg.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
new_layer_norm = {}
# Main training loop
for epoch in range(first_epoch, num_train_epochs):
unet.train()
train_mse_loss, train_ele_loss, train_focal_loss = 0.0, 0.0, 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
# if cfg.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
# if step % cfg.gradient_accumulation_steps == 0:
# progress_bar.update(1)
# continue
with accelerator.accumulate(unet):
# (B, Nv, 3, H, W)
imgs_in, colors_out, normals_out = batch['imgs_in'], batch['imgs_out'], batch['normals_out']
ids = batch['id']
bnm, Nv = imgs_in.shape[:2]
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([imgs_in]*2, dim=0)
imgs_out = torch.cat([normals_out, colors_out], dim=0)
# (B*Nv, 3, H, W)
imgs_in, imgs_out = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W"), rearrange(imgs_out, "B Nv C H W -> (B Nv) C H W")
imgs_in, imgs_out = imgs_in.to(weight_dtype), imgs_out.to(weight_dtype)
if cfg.with_smpl:
smpl_in = batch['smpl_imgs_in']
smpl_in = torch.cat([smpl_in]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W").to(weight_dtype)
else:
smpl_in = None
prompt_embeddings = torch.cat([batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']], dim=0)
# (B*Nv, N, C)
prompt_embeds = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
prompt_embeds = prompt_embeds.to(weight_dtype) # BV, L, C
# ------------------------------------project face embed --------------------------------
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
# ------------------------------------Encoder input image --------------------------------
imgs_in_proc = TF.resize(imgs_in, (feature_extractor.crop_size['height'], feature_extractor.crop_size['width']), interpolation=InterpolationMode.BICUBIC)
# do the normalization in float32 to preserve precision
imgs_in_proc = ((imgs_in_proc.float() - clip_image_mean) / clip_image_std).to(weight_dtype)
# (B*Nv, 1024)
image_embeddings = image_encoder(imgs_in_proc).image_embeds
noise_level = torch.tensor([0], device=accelerator.device)
# (B*Nv, 2048)
image_embeddings = noise_image_embeddings(image_embeddings, noise_level, generator=generator, image_normalizer=image_normlizer,
image_noising_scheduler= image_noising_scheduler).to(weight_dtype)
#--------------------------------------vae input and output latents ---------------------------------------
cond_vae_embeddings = vae.encode(imgs_in * 2.0 - 1.0).latent_dist.mode() #
if cfg.scale_input_latents:
cond_vae_embeddings *= vae.config.scaling_factor
if cfg.with_smpl:
cond_smpl_embeddings = vae.encode(smpl_in * 2.0 - 1.0).latent_dist.mode()
if cfg.scale_input_latents:
cond_smpl_embeddings *= vae.config.scaling_factor
cond_vae_embeddings = torch.cat([cond_vae_embeddings, cond_smpl_embeddings], dim=1)
# sample outputs latent
latents = vae.encode(imgs_out * 2.0 - 1.0).latent_dist.sample() * vae.config.scaling_factor
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# same noise for different views of the same object
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz // cfg.num_views,), device=latents.device)
timesteps = repeat(timesteps, "b -> (b v)", v=cfg.num_views)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Conditioning dropout to support classifier-free guidance during inference. For more details
# check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.
if cfg.use_classifier_free_guidance and cfg.condition_drop_rate > 0.:
if cfg.drop_type == 'drop_as_a_whole':
# drop a group of normals and colors as a whole
random_p = torch.rand(bnm, device=latents.device, generator=generator)
# Sample masks for the conditioning images.
image_mask_dtype = cond_vae_embeddings.dtype
image_mask = 1 - (
(random_p >= cfg.condition_drop_rate).to(image_mask_dtype)
* (random_p < 3 * cfg.condition_drop_rate).to(image_mask_dtype)
)
image_mask = image_mask.reshape(bnm, 1, 1, 1, 1).repeat(1, Nv, 1, 1, 1)
image_mask = rearrange(image_mask, "B Nv C H W -> (B Nv) C H W")
image_mask = torch.cat([image_mask]*2, dim=0)
# Final image conditioning.
cond_vae_embeddings = image_mask * cond_vae_embeddings
# Sample masks for the conditioning images.
clip_mask_dtype = image_embeddings.dtype
clip_mask = 1 - (
(random_p < 2 * cfg.condition_drop_rate).to(clip_mask_dtype)
)
clip_mask = clip_mask.reshape(bnm, 1, 1).repeat(1, Nv, 1)
clip_mask = rearrange(clip_mask, "B Nv C -> (B Nv) C")
clip_mask = torch.cat([clip_mask]*2, dim=0)
# Final image conditioning.
image_embeddings = clip_mask * image_embeddings
elif cfg.drop_type == 'drop_independent':
random_p = torch.rand(bsz, device=latents.device, generator=generator)
# Sample masks for the conditioning images.
image_mask_dtype = cond_vae_embeddings.dtype
image_mask = 1 - (
(random_p >= cfg.condition_drop_rate).to(image_mask_dtype)
* (random_p < 3 * cfg.condition_drop_rate).to(image_mask_dtype)
)
image_mask = image_mask.reshape(bsz, 1, 1, 1)
# Final image conditioning.
cond_vae_embeddings = image_mask * cond_vae_embeddings
# Sample masks for the conditioning images.
clip_mask_dtype = image_embeddings.dtype
clip_mask = 1 - (
(random_p < 2 * cfg.condition_drop_rate).to(clip_mask_dtype)
)
clip_mask = clip_mask.reshape(bsz, 1, 1)
# Final image conditioning.
image_embeddings = clip_mask * image_embeddings
# (B*Nv, 8, Hl, Wl)
latent_model_input = torch.cat([noisy_latents, cond_vae_embeddings], dim=1)
model_out = unet(
latent_model_input,
timesteps,
encoder_hidden_states=prompt_embeds,
class_labels=image_embeddings,
dino_feature=face_embeds,
vis_max_min=False
)
if cfg.regress_elevation or cfg.regress_focal_length:
model_pred = model_out[0].sample
pose_pred = model_out[1]
else:
model_pred = model_out[0].sample
pose_pred = None
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
# target = noise_scheduler._get_prev_sample(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if cfg.snr_gamma is None:
loss_mse = F.mse_loss(model_pred.float(), target.float(), reduction="mean").to(weight_dtype)
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(timesteps)
mse_loss_weights = (
torch.stack([snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
# We first calculate the original loss. Then we mean over the non-batch dimensions and
# rebalance the sample-wise losses with their respective loss weights.
# Finally, we take the mean of the rebalanced loss.
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss_mse = loss.mean().to(weight_dtype)
# Gather the losses across all processes for logging (if we use distributed training).
avg_mse_loss = accelerator.gather(loss_mse.repeat(cfg.train_batch_size)).mean()
train_mse_loss += avg_mse_loss.item() / cfg.gradient_accumulation_steps
if cfg.regress_elevation:
loss_ele = F.mse_loss(pose_pred[:, 0:1], batch['elevations_cond'].to(accelerator.device).float(), reduction="mean").to(weight_dtype)
avg_ele_loss = accelerator.gather(loss_ele.repeat(cfg.train_batch_size)).mean()
train_ele_loss += avg_ele_loss.item() / cfg.gradient_accumulation_steps
if cfg.plot_pose_acc:
ele_acc = torch.sum(torch.abs(pose_pred[:, 0:1] - torch.cat([batch['elevations_cond']]*2)) < 0.01) / pose_pred.shape[0]
else:
loss_ele = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
train_ele_loss += torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.plot_pose_acc:
ele_acc = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.regress_focal_length:
loss_focal = F.mse_loss(pose_pred[:, 1:], batch['focal_cond'].to(accelerator.device).float(), reduction="mean").to(weight_dtype)
avg_focal_loss = accelerator.gather(loss_focal.repeat(cfg.train_batch_size)).mean()
train_focal_loss += avg_focal_loss.item() / cfg.gradient_accumulation_steps
if cfg.plot_pose_acc:
focal_acc = torch.sum(torch.abs(pose_pred[:, 1:] - torch.cat([batch['focal_cond']]*2)) < 0.01) / pose_pred.shape[0]
else:
loss_focal = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
train_focal_loss += torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.plot_pose_acc:
focal_acc = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
# Backpropagate
loss = loss_mse + cfg.elevation_loss_weight * loss_ele + cfg.focal_loss_weight * loss_focal
accelerator.backward(loss)
if accelerator.sync_gradients and cfg.max_grad_norm is not None:
accelerator.clip_grad_norm_(unet.parameters(), cfg.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if cfg.use_ema:
ema_unet.step(unet)
progress_bar.update(1)
global_step += 1
# accelerator.log({"train_loss": train_loss}, step=global_step)
accelerator.log({"train_mse_loss": train_mse_loss}, step=global_step)
accelerator.log({"train_ele_loss": train_ele_loss}, step=global_step)
if cfg.plot_pose_acc:
accelerator.log({"ele_acc": ele_acc}, step=global_step)
accelerator.log({"focal_acc": focal_acc}, step=global_step)
accelerator.log({"train_focal_loss": train_focal_loss}, step=global_step)
train_ele_loss, train_mse_loss, train_focal_loss = 0.0, 0.0, 0.0
if global_step % cfg.checkpointing_steps == 0:
if accelerator.is_main_process:
if cfg.checkpoints_total_limit is not None:
checkpoints = os.listdir(model_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= cfg.checkpoints_total_limit:
num_to_remove = len(checkpoints) - cfg.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(model_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(model_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if global_step % cfg.validation_steps == 0 or (cfg.validation_sanity_check and global_step == 1):
if accelerator.is_main_process:
if cfg.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
torch.cuda.empty_cache()
log_validation_joint(
validation_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
face_proj_model,
cfg,
accelerator,
weight_dtype,
global_step,
'validation',
vis_dir
)
log_validation(
validation_train_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
face_proj_model,
cfg,
accelerator,
weight_dtype,
global_step,
'validation_train',
vis_dir
)
if cfg.use_ema:
# Switch back to the original UNet parameters.
ema_unet.restore(unet.parameters())
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= cfg.max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if cfg.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=unet,
scheduler=DDIMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
os.makedirs(os.path.join(model_dir, "ckpts"), exist_ok=True)
pipeline.save_pretrained(os.path.join(model_dir, "ckpts"))
accelerator.end_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)
args = parser.parse_args()
schema = OmegaConf.structured(TrainingConfig)
cfg = OmegaConf.load(args.config)
cfg = OmegaConf.merge(schema, cfg)
main(cfg)
# device = 'cuda'
# ## -------------------------------------- load models --------------------------------
# image_encoder = CLIPVisionModelWithProjection.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_encoder", revision=cfg.revision)
# feature_extractor = CLIPImageProcessor.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="feature_extractor", revision=cfg.revision)
# image_noising_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_noising_scheduler")
# image_normlizer = StableUnCLIPImageNormalizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_normalizer")
# tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="tokenizer", revision=cfg.revision)
# text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder='text_encoder', revision=cfg.revision)
# noise_scheduler = PNDMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler")
# vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision)
# unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
# # unet = UNetMV2DConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision,
# # **cfg.unet_from_pretrained_kwargs
# # )
# if cfg.enable_xformers_memory_efficient_attention:
# if is_xformers_available():
# import xformers
# xformers_version = version.parse(xformers.__version__)
# if xformers_version == version.parse("0.0.16"):
# print(
# "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
# )
# unet.enable_xformers_memory_efficient_attention()
# print("use xformers.")
# # from diffusers import StableUnCLIPImg2ImgPipeline
# # -------------------------------------- load pipeline --------------------------------
# pipe = StableUnCLIPImg2ImgPipeline(feature_extractor=feature_extractor,
# image_encoder=image_encoder,
# image_normalizer=image_normlizer,
# image_noising_scheduler= image_noising_scheduler,
# tokenizer=tokenizer,
# text_encoder=text_encoder,
# unet=unet,
# scheduler=noise_scheduler,
# vae=vae).to('cuda')
# # -------------------------------------- input --------------------------------
# # image = Image.open('test/woman.jpg')
# # w, h = image.size
# # image = np.asarray(image)[:w, :w, :]
# # image_in = Image.fromarray(image).resize((768, 768))
# im_path = '/mnt/pfs/users/longxiaoxiao/data/test_images/syncdreamer_testset/box.png'
# rgba = np.array(Image.open(im_path)) / 255.0
# rgb = rgba[:,:,:3]
# alpha = rgba[:,:,3:4]
# bg_color = np.array([1., 1., 1.])
# image_in = rgb * alpha + (1 - alpha) * bg_color[None,None,:]
# image_in = Image.fromarray((image_in * 255).astype(np.uint8)).resize((768, 768))
# res = pipe(image_in, 'a rendering image of 3D models, left view, normal map.').images[0]
# res.save("unclip.png") |