diff --git a/examples/train_latent_text_to_image.py b/examples/train_latent_text_to_image.py new file mode 100644 index 0000000000..fd823fdad9 --- /dev/null +++ b/examples/train_latent_text_to_image.py @@ -0,0 +1,202 @@ +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 DDPM, DDPMScheduler, UNetLDMModel +from diffusers.hub_utils import init_git_repo, push_to_hub +from diffusers.modeling_utils import unwrap_model +from diffusers.optimization import get_scheduler +from diffusers.utils import logging +from torchvision.transforms import ( + CenterCrop, + Compose, + InterpolationMode, + Lambda, + RandomHorizontalFlip, + Resize, + ToTensor, +) +from tqdm.auto import tqdm + + +logger = logging.get_logger(__name__) + + +def main(args): + accelerator = Accelerator(mixed_precision=args.mixed_precision) + + 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=32, + 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 = torch.optim.Adam(model.parameters(), lr=args.lr) + + augmentations = Compose( + [ + Resize(args.resolution, interpolation=InterpolationMode.BILINEAR), + CenterCrop(args.resolution), + RandomHorizontalFlip(), + ToTensor(), + Lambda(lambda x: x * 2 - 1), + ] + ) + 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 + ) + + 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}") + + 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() + optimizer.zero_grad() + pbar.update(1) + pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"]) + + optimizer.step() + if is_distributed: + torch.distributed.barrier() + + # Generate a sample image for visual inspection + if args.local_rank in [-1, 0]: + model.eval() + with torch.no_grad(): + pipeline = DDPM(unet=unwrap_model(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) + if is_distributed: + torch.distributed.barrier() + + +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("--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) diff --git a/src/diffusers/pipelines/pipeline_glide.py b/src/diffusers/pipelines/pipeline_glide.py index 07603e153e..d3706b74e5 100644 --- a/src/diffusers/pipelines/pipeline_glide.py +++ b/src/diffusers/pipelines/pipeline_glide.py @@ -695,22 +695,6 @@ class CLIPTextModel(CLIPPreTrainedModel): ##################### -def _extract_into_tensor(arr, timesteps, broadcast_shape): - """ - Extract values from a 1-D numpy array for a batch of indices. - - :param arr: the 1-D numpy array. - :param timesteps: a tensor of indices into the array to extract. - :param broadcast_shape: a larger shape of K dimensions with the batch - dimension equal to the length of timesteps. - :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. - """ - res = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float() - while len(res.shape) < len(broadcast_shape): - res = res[..., None] - return res + torch.zeros(broadcast_shape, device=timesteps.device) - - class GLIDE(DiffusionPipeline): def __init__( self,