import argparse import os import torch import torch.nn.functional as F import bitsandbytes as bnb import PIL.Image from accelerate import Accelerator from datasets import load_dataset from diffusers import DDPMScheduler, LatentDiffusion, UNetLDMModel from diffusers.hub_utils import init_git_repo, push_to_hub from diffusers.optimization import get_scheduler 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) pipeline = LatentDiffusion.from_pretrained("fusing/latent-diffusion-text2im-large") pipeline.unet = None # this model will be trained from scratch now model = UNetLDMModel( attention_resolutions=[4, 2, 1], channel_mult=[1, 2, 4, 4], context_dim=1280, conv_resample=True, dims=2, dropout=0, image_size=8, in_channels=4, model_channels=320, num_heads=8, num_res_blocks=2, out_channels=4, resblock_updown=False, transformer_depth=1, use_new_attention_order=False, use_scale_shift_norm=False, use_spatial_transformer=True, legacy=False, ) noise_scheduler = DDPMScheduler(timesteps=1000, tensor_format="pt") optimizer = bnb.optim.Adam8bit(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") text_encoder = pipeline.bert.eval() vqvae = pipeline.vqvae.eval() def transforms(examples): images = [augmentations(image.convert("RGB")) for image in examples["image"]] text_inputs = pipeline.tokenizer(examples["caption"], padding="max_length", max_length=77, return_tensors="pt") with torch.no_grad(): text_embeddings = accelerator.unwrap_model(text_encoder)(text_inputs.input_ids.cpu()).last_hidden_state images = 1 / 0.18215 * torch.stack(images, dim=0) latents = accelerator.unwrap_model(vqvae).encode(images.cpu()).mode() return {"images": images, "text_embeddings": text_embeddings, "latents": latents} 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, text_encoder, vqvae, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( model, text_encoder, vqvae, optimizer, train_dataloader, lr_scheduler ) text_encoder = text_encoder.cpu() vqvae = vqvae.cpu() 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_latents = batch["latents"] noise_samples = torch.randn(clean_latents.shape).to(clean_latents.device) bsz = clean_latents.shape[0] timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_latents.device).long() # add noise onto the clean latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.training_step(clean_latents, noise_samples, timesteps) if step % args.gradient_accumulation_steps != 0: with accelerator.no_sync(model): output = model(noisy_latents, timesteps, context=batch["text_embeddings"]) # predict the noise residual loss = F.mse_loss(output, noise_samples) loss = loss / args.gradient_accumulation_steps accelerator.backward(loss) optimizer.step() else: output = model(noisy_latents, timesteps, context=batch["text_embeddings"]) # 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() optimizer.zero_grad() pbar.update(1) pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"]) global_step += 1 accelerator.wait_for_everyone() # Generate a sample image for visual inspection if accelerator.is_main_process: model.eval() with torch.no_grad(): pipeline.unet = accelerator.unwrap_model(model) generator = torch.manual_seed(0) # run pipeline in inference (sample random noise and denoise) image = pipeline( ["a clip art of a corgi"], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50 ) # process image to PIL image_processed = image.cpu().permute(0, 2, 3, 1) image_processed = image_processed * 255.0 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="fusing/dog_captions") parser.add_argument("--output_dir", type=str, default="ldm-text2image") parser.add_argument("--overwrite_output_dir", action="store_true") parser.add_argument("--resolution", type=int, default=128) parser.add_argument("--batch_size", type=int, default=1) parser.add_argument("--num_epochs", type=int, default=100) parser.add_argument("--gradient_accumulation_steps", type=int, default=16) parser.add_argument("--lr", type=float, default=1e-4) parser.add_argument("--warmup_steps", type=int, default=500) 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)