Spaces:
Configuration error
Configuration error
File size: 36,056 Bytes
fd5f698 |
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 |
import argparse
import datetime
import logging
import inspect
import math
import os
import random
import gc
import copy
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
import cv2
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms as T
import diffusers
import transformers
from torchvision import transforms
from tqdm.auto import tqdm
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from models.unet_3d_condition import UNet3DConditionModel
from diffusers.models import AutoencoderKL
from diffusers import DPMSolverMultistepScheduler, DDPMScheduler, TextToVideoSDPipeline
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, export_to_video
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention_processor import AttnProcessor2_0, Attention
from diffusers.models.attention import BasicTransformerBlock
from transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
ImageDataset, VideoFolderDataset, CachedDataset
from einops import rearrange, repeat
from utils.lora import (
extract_lora_ups_down,
inject_trainable_lora,
inject_trainable_lora_extended,
save_lora_weight,
train_patch_pipe,
monkeypatch_or_replace_lora,
monkeypatch_or_replace_lora_extended
)
already_printed_trainables = False
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def create_logging(logging, logger, accelerator):
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)
def accelerate_set_verbose(accelerator):
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()
def get_train_dataset(dataset_types, train_data, tokenizer):
train_datasets = []
# Loop through all available datasets, get the name, then add to list of data to process.
for DataSet in [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset]:
for dataset in dataset_types:
if dataset == DataSet.__getname__():
train_datasets.append(DataSet(**train_data, tokenizer=tokenizer))
if len(train_datasets) > 0:
return train_datasets
else:
raise ValueError("Dataset type not found: 'json', 'single_video', 'folder', 'image'")
def extend_datasets(datasets, dataset_items, extend=False):
biggest_data_len = max(x.__len__() for x in datasets)
extended = []
for dataset in datasets:
if dataset.__len__() == 0:
del dataset
continue
if dataset.__len__() < biggest_data_len:
for item in dataset_items:
if extend and item not in extended and hasattr(dataset, item):
print(f"Extending {item}")
value = getattr(dataset, item)
value *= biggest_data_len
value = value[:biggest_data_len]
setattr(dataset, item, value)
print(f"New {item} dataset length: {dataset.__len__()}")
extended.append(item)
def export_to_video(video_frames, output_video_path, fps):
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
h, w, _ = video_frames[0].shape
video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=fps, frameSize=(w, h))
for i in range(len(video_frames)):
img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
video_writer.write(img)
def create_output_folders(output_dir, config):
now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
out_dir = os.path.join(output_dir, f"train_{now}")
os.makedirs(out_dir, exist_ok=True)
os.makedirs(f"{out_dir}/samples", exist_ok=True)
OmegaConf.save(config, os.path.join(out_dir, 'config.yaml'))
return out_dir
def load_primary_models(pretrained_model_path):
noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
unet = UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet")
return noise_scheduler, tokenizer, text_encoder, vae, unet
def unet_and_text_g_c(unet, text_encoder, unet_enable, text_enable):
unet._set_gradient_checkpointing(value=unet_enable)
text_encoder._set_gradient_checkpointing(CLIPEncoder, value=text_enable)
def freeze_models(models_to_freeze):
for model in models_to_freeze:
if model is not None: model.requires_grad_(False)
def is_attn(name):
return ('attn1' or 'attn2' == name.split('.')[-1])
def set_processors(attentions):
for attn in attentions: attn.set_processor(AttnProcessor2_0())
def set_torch_2_attn(unet):
optim_count = 0
for name, module in unet.named_modules():
if is_attn(name):
if isinstance(module, torch.nn.ModuleList):
for m in module:
if isinstance(m, BasicTransformerBlock):
set_processors([m.attn1, m.attn2])
optim_count += 1
if optim_count > 0:
print(f"{optim_count} Attention layers using Scaled Dot Product Attention.")
def handle_memory_attention(enable_xformers_memory_efficient_attention, enable_torch_2_attn, unet):
try:
is_torch_2 = hasattr(F, 'scaled_dot_product_attention')
if enable_xformers_memory_efficient_attention and not is_torch_2:
if is_xformers_available():
from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
unet.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if enable_torch_2_attn and is_torch_2:
set_torch_2_attn(unet)
except:
print("Could not enable memory efficient attention for xformers or Torch 2.0.")
def inject_lora(use_lora, model, replace_modules, is_extended=False, dropout=0.0, lora_path='', r=16):
injector = (
inject_trainable_lora if not is_extended
else
inject_trainable_lora_extended
)
params = None
negation = None
if os.path.exists(lora_path):
try:
for f in os.listdir(lora_path):
if f.endswith('.pt'):
lora_file = os.path.join(lora_path, f)
if 'text_encoder' in f and isinstance(model, CLIPTextModel):
monkeypatch_or_replace_lora(
model,
torch.load(lora_file),
target_replace_module=replace_modules,
r=r
)
print("Successfully loaded Text Encoder LoRa.")
if 'unet' in f and isinstance(model, UNet3DConditionModel):
monkeypatch_or_replace_lora_extended(
model,
torch.load(lora_file),
target_replace_module=replace_modules,
r=r
)
print("Successfully loaded UNET LoRa.")
except Exception as e:
print(e)
print("Could not load LoRAs. Injecting new ones instead...")
if use_lora:
REPLACE_MODULES = replace_modules
injector_args = {
"model": model,
"target_replace_module": REPLACE_MODULES,
"r": r
}
if not is_extended: injector_args['dropout_p'] = dropout
params, negation = injector(**injector_args)
for _up, _down in extract_lora_ups_down(
model,
target_replace_module=REPLACE_MODULES):
if all(x is not None for x in [_up, _down]):
print(f"Lora successfully injected into {model.__class__.__name__}.")
break
return params, negation
def save_lora(model, name, condition, replace_modules, step, save_path):
if condition and replace_modules is not None:
save_path = f"{save_path}/{step}_{name}.pt"
save_lora_weight(model, save_path, replace_modules)
def handle_lora_save(
use_unet_lora,
use_text_lora,
model,
save_path,
checkpoint_step,
unet_target_modules,
text_encoder_target_modules
):
save_path = f"{save_path}/lora"
os.makedirs(save_path, exist_ok=True)
save_lora(
model.unet,
'unet',
use_unet_lora,
unet_target_modules,
checkpoint_step,
save_path,
)
save_lora(
model.text_encoder,
'text_encoder',
use_text_lora,
text_encoder_target_modules,
checkpoint_step,
save_path
)
train_patch_pipe(model, use_unet_lora, use_text_lora)
def param_optim(model, condition, extra_params=None, is_lora=False, negation=None):
return {
"model": model,
"condition": condition,
'extra_params': extra_params,
'is_lora': is_lora,
"negation": negation
}
def create_optim_params(name='param', params=None, lr=5e-6, extra_params=None):
params = {
"name": name,
"params": params,
"lr": lr
}
if extra_params is not None:
for k, v in extra_params.items():
params[k] = v
return params
def negate_params(name, negation):
# We have to do this if we are co-training with LoRA.
# This ensures that parameter groups aren't duplicated.
if negation is None: return False
for n in negation:
if n in name and 'temp' not in name:
return True
return False
def create_optimizer_params(model_list, lr):
import itertools
optimizer_params = []
for optim in model_list:
model, condition, extra_params, is_lora, negation = optim.values()
# Check if we are doing LoRA training.
if is_lora and condition:
params = create_optim_params(
params=itertools.chain(*model),
extra_params=extra_params
)
optimizer_params.append(params)
continue
# If this is true, we can train it.
if condition:
for n, p in model.named_parameters():
should_negate = 'lora' in n
if should_negate: continue
params = create_optim_params(n, p, lr, extra_params)
optimizer_params.append(params)
return optimizer_params
def get_optimizer(use_8bit_adam):
if 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`"
)
return bnb.optim.AdamW8bit
else:
return torch.optim.AdamW
def is_mixed_precision(accelerator):
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
return weight_dtype
def cast_to_gpu_and_type(model_list, accelerator, weight_dtype):
for model in model_list:
if model is not None: model.to(accelerator.device, dtype=weight_dtype)
def handle_cache_latents(
should_cache,
output_dir,
train_dataloader,
train_batch_size,
vae,
cached_latent_dir=None
):
# Cache latents by storing them in VRAM.
# Speeds up training and saves memory by not encoding during the train loop.
if not should_cache: return None
vae.to('cuda', dtype=torch.float16)
vae.enable_slicing()
cached_latent_dir = (
os.path.abspath(cached_latent_dir) if cached_latent_dir is not None else None
)
if cached_latent_dir is None:
cache_save_dir = f"{output_dir}/cached_latents"
os.makedirs(cache_save_dir, exist_ok=True)
for i, batch in enumerate(tqdm(train_dataloader, desc="Caching Latents.")):
save_name = f"cached_{i}"
full_out_path = f"{cache_save_dir}/{save_name}.pt"
pixel_values = batch['pixel_values'].to('cuda', dtype=torch.float16)
batch['pixel_values'] = tensor_to_vae_latent(pixel_values, vae)
for k, v in batch.items(): batch[k] = v[0]
torch.save(batch, full_out_path)
del pixel_values
del batch
# We do this to avoid fragmentation from casting latents between devices.
torch.cuda.empty_cache()
else:
cache_save_dir = cached_latent_dir
return torch.utils.data.DataLoader(
CachedDataset(cache_dir=cache_save_dir),
batch_size=train_batch_size,
shuffle=True,
num_workers=0
)
def handle_trainable_modules(model, trainable_modules=None, is_enabled=True, negation=None):
global already_printed_trainables
# This can most definitely be refactored :-)
unfrozen_params = 0
if trainable_modules is not None:
for name, module in model.named_modules():
for tm in tuple(trainable_modules):
if tm == 'all':
model.requires_grad_(is_enabled)
unfrozen_params =len(list(model.parameters()))
break
if tm in name and 'lora' not in name:
for m in module.parameters():
m.requires_grad_(is_enabled)
if is_enabled: unfrozen_params +=1
if unfrozen_params > 0 and not already_printed_trainables:
already_printed_trainables = True
print(f"{unfrozen_params} params have been unfrozen for training.")
def tensor_to_vae_latent(t, vae):
video_length = t.shape[1]
t = rearrange(t, "b f c h w -> (b f) c h w")
latents = vae.encode(t).latent_dist.sample()
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
latents = latents * 0.18215
return latents
def sample_noise(latents, noise_strength, use_offset_noise):
b ,c, f, *_ = latents.shape
noise_latents = torch.randn_like(latents, device=latents.device)
offset_noise = None
if use_offset_noise:
offset_noise = torch.randn(b, c, f, 1, 1, device=latents.device)
noise_latents = noise_latents + noise_strength * offset_noise
return noise_latents
def should_sample(global_step, validation_steps, validation_data):
return (global_step % validation_steps == 0 or global_step == 1) \
and validation_data.sample_preview
def save_pipe(
path,
global_step,
accelerator,
unet,
text_encoder,
vae,
output_dir,
use_unet_lora,
use_text_lora,
unet_target_replace_module=None,
text_target_replace_module=None,
is_checkpoint=False,
):
if is_checkpoint:
save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
os.makedirs(save_path, exist_ok=True)
else:
save_path = output_dir
# Save the dtypes so we can continue training at the same precision.
u_dtype, t_dtype, v_dtype = unet.dtype, text_encoder.dtype, vae.dtype
# Copy the model without creating a reference to it. This allows keeping the state of our lora training if enabled.
unet_out = copy.deepcopy(accelerator.unwrap_model(unet, keep_fp32_wrapper=False))
text_encoder_out = copy.deepcopy(accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False))
pipeline = TextToVideoSDPipeline.from_pretrained(
path,
unet=unet_out,
text_encoder=text_encoder_out,
vae=vae,
).to(torch_dtype=torch.float16)
handle_lora_save(
use_unet_lora,
use_text_lora,
pipeline,
output_dir,
global_step,
unet_target_replace_module,
text_target_replace_module
)
pipeline.save_pretrained(save_path)
if is_checkpoint:
unet, text_encoder = accelerator.prepare(unet, text_encoder)
models_to_cast_back = [(unet, u_dtype), (text_encoder, t_dtype), (vae, v_dtype)]
[x[0].to(accelerator.device, dtype=x[1]) for x in models_to_cast_back]
logger.info(f"Saved model at {save_path} on step {global_step}")
del pipeline
del unet_out
del text_encoder_out
torch.cuda.empty_cache()
gc.collect()
def replace_prompt(prompt, token, wlist):
for w in wlist:
if w in prompt: return prompt.replace(w, token)
return prompt
def main(
pretrained_model_path: str,
output_dir: str,
train_data: Dict,
validation_data: Dict,
dataset_types: Tuple[str] = ('json'),
validation_steps: int = 100,
trainable_modules: Tuple[str] = ("attn1", "attn2"),
trainable_text_modules: Tuple[str] = ("all"),
extra_unet_params = None,
extra_text_encoder_params = None,
train_batch_size: int = 1,
max_train_steps: int = 500,
learning_rate: float = 5e-5,
scale_lr: bool = False,
lr_scheduler: str = "constant",
lr_warmup_steps: int = 0,
adam_beta1: float = 0.9,
adam_beta2: float = 0.999,
adam_weight_decay: float = 1e-2,
adam_epsilon: float = 1e-08,
max_grad_norm: float = 1.0,
gradient_accumulation_steps: int = 1,
gradient_checkpointing: bool = False,
text_encoder_gradient_checkpointing: bool = False,
checkpointing_steps: int = 500,
resume_from_checkpoint: Optional[str] = None,
mixed_precision: Optional[str] = "fp16",
use_8bit_adam: bool = False,
enable_xformers_memory_efficient_attention: bool = True,
enable_torch_2_attn: bool = False,
seed: Optional[int] = None,
train_text_encoder: bool = False,
use_offset_noise: bool = False,
offset_noise_strength: float = 0.1,
extend_dataset: bool = False,
cache_latents: bool = False,
cached_latent_dir = None,
use_unet_lora: bool = False,
use_text_lora: bool = False,
unet_lora_modules: Tuple[str] = ["ResnetBlock2D"],
text_encoder_lora_modules: Tuple[str] = ["CLIPEncoderLayer"],
lora_rank: int = 16,
lora_path: str = '',
**kwargs
):
*_, config = inspect.getargvalues(inspect.currentframe())
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
log_with="tensorboard",
logging_dir=output_dir
)
# Make one log on every process with the configuration for debugging.
create_logging(logging, logger, accelerator)
# Initialize accelerate, transformers, and diffusers warnings
accelerate_set_verbose(accelerator)
# If passed along, set the training seed now.
if seed is not None:
set_seed(seed)
# Handle the output folder creation
if accelerator.is_main_process:
output_dir = create_output_folders(output_dir, config)
# Load scheduler, tokenizer and models.
noise_scheduler, tokenizer, text_encoder, vae, unet = load_primary_models(pretrained_model_path)
# Freeze any necessary models
freeze_models([vae, text_encoder, unet])
# Enable xformers if available
handle_memory_attention(enable_xformers_memory_efficient_attention, enable_torch_2_attn, unet)
if scale_lr:
learning_rate = (
learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
optimizer_cls = get_optimizer(use_8bit_adam)
# Use LoRA if enabled.
unet_lora_params, unet_negation = inject_lora(
use_unet_lora, unet, unet_lora_modules, is_extended=True,
r=lora_rank, lora_path=lora_path
)
text_encoder_lora_params, text_encoder_negation = inject_lora(
use_text_lora, text_encoder, text_encoder_lora_modules,
r=lora_rank, lora_path=lora_path
)
# Create parameters to optimize over with a condition (if "condition" is true, optimize it)
optim_params = [
param_optim(unet, trainable_modules is not None, extra_params=extra_unet_params, negation=unet_negation),
param_optim(text_encoder, train_text_encoder and not use_text_lora, extra_params=extra_text_encoder_params,
negation=text_encoder_negation
),
param_optim(text_encoder_lora_params, use_text_lora, is_lora=True, extra_params={"lr": 1e-5}),
param_optim(unet_lora_params, use_unet_lora, is_lora=True, extra_params={"lr": 1e-5})
]
params = create_optimizer_params(optim_params, learning_rate)
# Create Optimizer
optimizer = optimizer_cls(
params,
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
weight_decay=adam_weight_decay,
eps=adam_epsilon,
)
# Scheduler
lr_scheduler = get_scheduler(
lr_scheduler,
optimizer=optimizer,
num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
num_training_steps=max_train_steps * gradient_accumulation_steps,
)
# Get the training dataset based on types (json, single_video, image)
train_datasets = get_train_dataset(dataset_types, train_data, tokenizer)
# Extend datasets that are less than the greatest one. This allows for more balanced training.
attrs = ['train_data', 'frames', 'image_dir', 'video_files']
extend_datasets(train_datasets, attrs, extend=extend_dataset)
# Process one dataset
if len(train_datasets) == 1:
train_dataset = train_datasets[0]
# Process many datasets
else:
train_dataset = torch.utils.data.ConcatDataset(train_datasets)
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True
)
# Latents caching
cached_data_loader = handle_cache_latents(
cache_latents,
output_dir,
train_dataloader,
train_batch_size,
vae,
cached_latent_dir
)
if cached_data_loader is not None:
train_dataloader = cached_data_loader
# Prepare everything with our `accelerator`.
unet, optimizer,train_dataloader, lr_scheduler, text_encoder = accelerator.prepare(
unet,
optimizer,
train_dataloader,
lr_scheduler,
text_encoder
)
# Use Gradient Checkpointing if enabled.
unet_and_text_g_c(
unet,
text_encoder,
gradient_checkpointing,
text_encoder_gradient_checkpointing
)
# Enable VAE slicing to save memory.
vae.enable_slicing()
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = is_mixed_precision(accelerator)
# Move text encoders, and VAE to GPU
models_to_cast = [text_encoder, vae]
cast_to_gpu_and_type(models_to_cast, accelerator, weight_dtype)
# 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) / gradient_accumulation_steps)
# Afterwards we recalculate our number of training epochs
num_train_epochs = math.ceil(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:
accelerator.init_trackers("text2video-fine-tune")
# Train!
total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
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 = {train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {max_train_steps}")
global_step = 0
first_epoch = 0
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
def finetune_unet(batch, train_encoder=False):
# Check if we are training the text encoder
text_trainable = (train_text_encoder or use_text_lora)
# Unfreeze UNET Layers
if global_step == 0:
already_printed_trainables = False
unet.train()
handle_trainable_modules(
unet,
trainable_modules,
is_enabled=True,
negation=unet_negation
)
# Convert videos to latent space
pixel_values = batch["pixel_values"]
if not cache_latents:
latents = tensor_to_vae_latent(pixel_values, vae)
else:
latents = pixel_values
# Get video length
video_length = latents.shape[2]
# Sample noise that we'll add to the latents
noise = sample_noise(latents, offset_noise_strength, use_offset_noise)
bsz = latents.shape[0]
# Sample a random timestep for each video
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Enable text encoder training
if text_trainable:
text_encoder.train()
if use_text_lora:
text_encoder.text_model.embeddings.requires_grad_(True)
if global_step == 0 and train_text_encoder:
handle_trainable_modules(
text_encoder,
trainable_modules=trainable_text_modules,
negation=text_encoder_negation
)
cast_to_gpu_and_type([text_encoder], accelerator, torch.float32)
# Fixes gradient checkpointing training.
# See: https://github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb
if gradient_checkpointing or text_encoder_gradient_checkpointing:
unet.eval()
text_encoder.eval()
# Encode text embeddings
token_ids = batch['prompt_ids']
encoder_hidden_states = text_encoder(token_ids)[0]
# Get the target for loss depending on the prediction type
if noise_scheduler.prediction_type == "epsilon":
target = noise
elif noise_scheduler.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}")
# Here we do two passes for video and text training.
# If we are on the second iteration of the loop, get one frame.
# This allows us to train text information only on the spatial layers.
losses = []
should_truncate_video = (video_length > 1 and text_trainable)
# We detach the encoder hidden states for the first pass (video frames > 1)
# Then we make a clone of the initial state to ensure we can train it in the loop.
detached_encoder_state = encoder_hidden_states.clone().detach()
trainable_encoder_state = encoder_hidden_states.clone()
for i in range(2):
should_detach = noisy_latents.shape[2] > 1 and i == 0
if should_truncate_video and i == 1:
noisy_latents = noisy_latents[:,:,1,:,:].unsqueeze(2)
target = target[:,:,1,:,:].unsqueeze(2)
encoder_hidden_states = (
detached_encoder_state if should_detach else trainable_encoder_state
)
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
losses.append(loss)
# This was most likely single frame training or a single image.
if video_length == 1 and i == 0: break
loss = losses[0] if len(losses) == 1 else losses[0] + losses[1]
return loss, latents
for epoch in range(first_epoch, num_train_epochs):
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet) ,accelerator.accumulate(text_encoder):
text_prompt = batch['text_prompt'][0]
with accelerator.autocast():
loss, latents = finetune_unet(batch, train_encoder=train_text_encoder)
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
train_loss += avg_loss.item() / gradient_accumulation_steps
# Backpropagate
try:
accelerator.backward(loss)
params_to_clip = (
unet.parameters() if not train_text_encoder
else
list(unet.parameters()) + list(text_encoder.parameters())
)
accelerator.clip_grad_norm_(params_to_clip, max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad(set_to_none=True)
except Exception as e:
print(f"An error has occured during backpropogation! {e}")
continue
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % checkpointing_steps == 0:
save_pipe(
pretrained_model_path,
global_step,
accelerator,
unet,
text_encoder,
vae,
output_dir,
use_unet_lora,
use_text_lora,
unet_lora_modules,
text_encoder_lora_modules,
is_checkpoint=True
)
if should_sample(global_step, validation_steps, validation_data):
if global_step == 1: print("Performing validation prompt.")
if accelerator.is_main_process:
with accelerator.autocast():
unet.eval()
text_encoder.eval()
unet_and_text_g_c(unet, text_encoder, False, False)
pipeline = TextToVideoSDPipeline.from_pretrained(
pretrained_model_path,
text_encoder=text_encoder,
vae=vae,
unet=unet
)
diffusion_scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline.scheduler = diffusion_scheduler
prompt = text_prompt if len(validation_data.prompt) <= 0 else validation_data.prompt
curr_dataset_name = batch['dataset']
save_filename = f"{global_step}_dataset-{curr_dataset_name}_{prompt}"
out_file = f"{output_dir}/samples/{save_filename}.mp4"
with torch.no_grad():
video_frames = pipeline(
prompt,
width=validation_data.width,
height=validation_data.height,
num_frames=validation_data.num_frames,
num_inference_steps=validation_data.num_inference_steps,
guidance_scale=validation_data.guidance_scale
).frames
export_to_video(video_frames, out_file, train_data.get('fps', 8))
del pipeline
torch.cuda.empty_cache()
logger.info(f"Saved a new sample to {out_file}")
unet_and_text_g_c(
unet,
text_encoder,
gradient_checkpointing,
text_encoder_gradient_checkpointing
)
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
accelerator.log({"training_loss": loss.detach().item()}, step=step)
progress_bar.set_postfix(**logs)
if global_step >= max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
save_pipe(
pretrained_model_path,
global_step,
accelerator,
unet,
text_encoder,
vae,
output_dir,
use_unet_lora,
use_text_lora,
unet_lora_modules,
text_encoder_lora_modules,
is_checkpoint=False
)
accelerator.end_training()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/my_config.yaml")
args = parser.parse_args()
main(**OmegaConf.load(args.config))
|