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train_unconditional.py
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199 lines (171 loc) · 8.08 KB
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import argparse
import os
import torch
import torch.nn.functional as F
import PIL.Image
from accelerate import Accelerator
from datasets import load_dataset
from diffusers import DDPMPipeline, DDPMScheduler, UNetModel
from diffusers.hub_utils import init_git_repo, push_to_hub
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import logging
from torchvision.transforms import (
CenterCrop,
Compose,
InterpolationMode,
Normalize,
RandomHorizontalFlip,
Resize,
ToTensor,
)
from tqdm.auto import tqdm
logger = logging.get_logger(__name__)
def main(args):
accelerator = Accelerator(mixed_precision=args.mixed_precision)
model = UNetModel(
attn_resolutions=(16,),
ch=128,
ch_mult=(1, 2, 4, 8),
dropout=0.0,
num_res_blocks=2,
resamp_with_conv=True,
resolution=args.resolution,
)
noise_scheduler = DDPMScheduler(timesteps=1000, tensor_format="pt")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
augmentations = Compose(
[
Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
CenterCrop(args.resolution),
RandomHorizontalFlip(),
ToTensor(),
Normalize([0.5], [0.5]),
]
)
dataset = load_dataset(args.dataset, 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.batch_size, shuffle=True)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=args.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
)
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)
# Train!
is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
world_size = torch.distributed.get_world_size() if is_distributed else 1
total_train_batch_size = args.batch_size * args.gradient_accumulation_steps * world_size
max_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_epochs
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataloader.dataset)}")
logger.info(f" Num Epochs = {args.num_epochs}")
logger.info(f" Instantaneous batch size per device = {args.batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {max_steps}")
global_step = 0
for epoch in range(args.num_epochs):
model.train()
with tqdm(total=len(train_dataloader), unit="ba") as pbar:
pbar.set_description(f"Epoch {epoch}")
for step, batch in enumerate(train_dataloader):
clean_images = batch["input"]
noise_samples = torch.randn(clean_images.shape).to(clean_images.device)
bsz = clean_images.shape[0]
timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
# add noise onto the clean images according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_images = noise_scheduler.training_step(clean_images, noise_samples, timesteps)
if step % args.gradient_accumulation_steps != 0:
with accelerator.no_sync(model):
output = model(noisy_images, timesteps)
# predict the noise residual
loss = F.mse_loss(output, noise_samples)
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
else:
output = model(noisy_images, timesteps)
# predict the noise residual
loss = F.mse_loss(output, noise_samples)
loss = loss / args.gradient_accumulation_steps
accelerator.backward(loss)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
ema_model.step(model, global_step)
optimizer.zero_grad()
pbar.update(1)
pbar.set_postfix(
loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"], ema_decay=ema_model.decay
)
global_step += 1
accelerator.wait_for_everyone()
# Generate a sample image for visual inspection
if accelerator.is_main_process:
with torch.no_grad():
pipeline = DDPMPipeline(
unet=accelerator.unwrap_model(ema_model.averaged_model), noise_scheduler=noise_scheduler
)
generator = torch.manual_seed(0)
# run pipeline in inference (sample random noise and denoise)
image = pipeline(generator=generator)
# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.type(torch.uint8).numpy()
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
test_dir = os.path.join(args.output_dir, "test_samples")
os.makedirs(test_dir, exist_ok=True)
image_pil.save(f"{test_dir}/{epoch:04d}.png")
# 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()
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", type=str, default="huggan/flowers-102-categories")
parser.add_argument("--output_dir", type=str, default="ddpm-model")
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument("--resolution", type=int, default=64)
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--warmup_steps", type=int, default=500)
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=0.999)
parser.add_argument("--push_to_hub", 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(
"--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
main(args)