250 lines
9.8 KiB
Python
250 lines
9.8 KiB
Python
import argparse
|
|
import math
|
|
import os
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
|
|
from accelerate import Accelerator
|
|
from accelerate.logging import get_logger
|
|
from datasets import load_dataset
|
|
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
|
|
from diffusers.hub_utils import init_git_repo, push_to_hub
|
|
from diffusers.optimization import get_scheduler
|
|
from diffusers.training_utils import EMAModel
|
|
from torchvision.transforms import (
|
|
CenterCrop,
|
|
Compose,
|
|
InterpolationMode,
|
|
Normalize,
|
|
RandomHorizontalFlip,
|
|
Resize,
|
|
ToTensor,
|
|
)
|
|
from tqdm.auto import tqdm
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
def main(args):
|
|
logging_dir = os.path.join(args.output_dir, args.logging_dir)
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with="tensorboard",
|
|
logging_dir=logging_dir,
|
|
)
|
|
|
|
model = UNet2DModel(
|
|
sample_size=args.resolution,
|
|
in_channels=3,
|
|
out_channels=3,
|
|
layers_per_block=2,
|
|
block_out_channels=(128, 128, 256, 256, 512, 512),
|
|
down_block_types=(
|
|
"DownBlock2D",
|
|
"DownBlock2D",
|
|
"DownBlock2D",
|
|
"DownBlock2D",
|
|
"AttnDownBlock2D",
|
|
"DownBlock2D",
|
|
),
|
|
up_block_types=(
|
|
"UpBlock2D",
|
|
"AttnUpBlock2D",
|
|
"UpBlock2D",
|
|
"UpBlock2D",
|
|
"UpBlock2D",
|
|
"UpBlock2D",
|
|
),
|
|
)
|
|
noise_scheduler = DDPMScheduler(num_train_timesteps=1000, tensor_format="pt")
|
|
optimizer = torch.optim.AdamW(
|
|
model.parameters(),
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
augmentations = Compose(
|
|
[
|
|
Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
|
|
CenterCrop(args.resolution),
|
|
RandomHorizontalFlip(),
|
|
ToTensor(),
|
|
Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
if args.dataset_name is not None:
|
|
dataset = load_dataset(
|
|
args.dataset_name,
|
|
args.dataset_config_name,
|
|
cache_dir=args.cache_dir,
|
|
use_auth_token=True if args.use_auth_token else None,
|
|
split="train",
|
|
)
|
|
else:
|
|
dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
|
|
|
|
def transforms(examples):
|
|
images = [augmentations(image.convert("RGB")) for image in examples["image"]]
|
|
return {"input": images}
|
|
|
|
dataset.set_transform(transforms)
|
|
train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.train_batch_size, shuffle=True)
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps,
|
|
num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
|
|
)
|
|
|
|
model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
model, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
|
|
ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
|
|
|
|
if args.push_to_hub:
|
|
repo = init_git_repo(args, at_init=True)
|
|
|
|
if accelerator.is_main_process:
|
|
run = os.path.split(__file__)[-1].split(".")[0]
|
|
accelerator.init_trackers(run)
|
|
|
|
global_step = 0
|
|
for epoch in range(args.num_epochs):
|
|
model.train()
|
|
progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
|
|
progress_bar.set_description(f"Epoch {epoch}")
|
|
for step, batch in enumerate(train_dataloader):
|
|
clean_images = batch["input"]
|
|
# Sample noise that we'll add to the images
|
|
noise = torch.randn(clean_images.shape).to(clean_images.device)
|
|
bsz = clean_images.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(
|
|
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
|
|
).long()
|
|
|
|
# Add noise to the clean images according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
|
|
|
|
with accelerator.accumulate(model):
|
|
# Predict the noise residual
|
|
noise_pred = model(noisy_images, timesteps).sample
|
|
loss = F.mse_loss(noise_pred, noise)
|
|
accelerator.backward(loss)
|
|
|
|
accelerator.clip_grad_norm_(model.parameters(), 1.0)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
if args.use_ema:
|
|
ema_model.step(model)
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
|
|
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
|
|
if args.use_ema:
|
|
logs["ema_decay"] = ema_model.decay
|
|
progress_bar.set_postfix(**logs)
|
|
accelerator.log(logs, step=global_step)
|
|
progress_bar.close()
|
|
|
|
accelerator.wait_for_everyone()
|
|
|
|
# Generate sample images for visual inspection
|
|
if accelerator.is_main_process:
|
|
if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
|
|
pipeline = DDPMPipeline(
|
|
unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
|
|
scheduler=noise_scheduler,
|
|
)
|
|
|
|
generator = torch.manual_seed(0)
|
|
# run pipeline in inference (sample random noise and denoise)
|
|
images = pipeline(generator=generator, batch_size=args.eval_batch_size, output_type="numpy").images
|
|
|
|
# denormalize the images and save to tensorboard
|
|
images_processed = (images * 255).round().astype("uint8")
|
|
accelerator.trackers[0].writer.add_images(
|
|
"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
|
|
)
|
|
|
|
if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
|
|
# save the model
|
|
if args.push_to_hub:
|
|
push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
|
|
else:
|
|
pipeline.save_pretrained(args.output_dir)
|
|
accelerator.wait_for_everyone()
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
|
parser.add_argument("--local_rank", type=int, default=-1)
|
|
parser.add_argument("--dataset_name", type=str, default=None)
|
|
parser.add_argument("--dataset_config_name", type=str, default=None)
|
|
parser.add_argument("--train_data_dir", type=str, default=None, help="A folder containing the training data.")
|
|
parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
|
|
parser.add_argument("--overwrite_output_dir", action="store_true")
|
|
parser.add_argument("--cache_dir", type=str, default=None)
|
|
parser.add_argument("--resolution", type=int, default=64)
|
|
parser.add_argument("--train_batch_size", type=int, default=16)
|
|
parser.add_argument("--eval_batch_size", type=int, default=16)
|
|
parser.add_argument("--num_epochs", type=int, default=100)
|
|
parser.add_argument("--save_images_epochs", type=int, default=10)
|
|
parser.add_argument("--save_model_epochs", type=int, default=10)
|
|
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
|
|
parser.add_argument("--learning_rate", type=float, default=1e-4)
|
|
parser.add_argument("--lr_scheduler", type=str, default="cosine")
|
|
parser.add_argument("--lr_warmup_steps", type=int, default=500)
|
|
parser.add_argument("--adam_beta1", type=float, default=0.95)
|
|
parser.add_argument("--adam_beta2", type=float, default=0.999)
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
|
|
parser.add_argument("--adam_epsilon", type=float, default=1e-08)
|
|
parser.add_argument("--use_ema", action="store_true", default=True)
|
|
parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
|
|
parser.add_argument("--ema_power", type=float, default=3 / 4)
|
|
parser.add_argument("--ema_max_decay", type=float, default=0.9999)
|
|
parser.add_argument("--push_to_hub", action="store_true")
|
|
parser.add_argument("--use_auth_token", action="store_true")
|
|
parser.add_argument("--hub_token", type=str, default=None)
|
|
parser.add_argument("--hub_model_id", type=str, default=None)
|
|
parser.add_argument("--hub_private_repo", action="store_true")
|
|
parser.add_argument("--logging_dir", type=str, default="logs")
|
|
parser.add_argument(
|
|
"--mixed_precision",
|
|
type=str,
|
|
default="no",
|
|
choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose"
|
|
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
|
|
"and an Nvidia Ampere GPU."
|
|
),
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
|
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
|
args.local_rank = env_local_rank
|
|
|
|
if args.dataset_name is None and args.train_data_dir is None:
|
|
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
|
|
|
|
main(args)
|