import argparse 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( 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 ) 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=len(train_dataloader), 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.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() progress_bar.update(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) global_step += 1 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")["sample"] # 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)