203 lines
7.9 KiB
Python
203 lines
7.9 KiB
Python
import argparse
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import os
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import torch
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import torch.nn.functional as F
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import PIL.Image
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from accelerate import Accelerator
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from datasets import load_dataset
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from diffusers import DDPM, DDPMScheduler, UNetLDMModel
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from diffusers.hub_utils import init_git_repo, push_to_hub
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from diffusers.modeling_utils import unwrap_model
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from diffusers.optimization import get_scheduler
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from diffusers.utils import logging
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from torchvision.transforms import (
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CenterCrop,
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Compose,
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InterpolationMode,
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Lambda,
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RandomHorizontalFlip,
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Resize,
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ToTensor,
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)
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from tqdm.auto import tqdm
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logger = logging.get_logger(__name__)
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def main(args):
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accelerator = Accelerator(mixed_precision=args.mixed_precision)
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model = UNetLDMModel(
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attention_resolutions=[4, 2, 1],
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channel_mult=[1, 2, 4, 4],
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context_dim=1280,
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conv_resample=True,
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dims=2,
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dropout=0,
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image_size=32,
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in_channels=4,
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model_channels=320,
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num_heads=8,
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num_res_blocks=2,
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out_channels=4,
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resblock_updown=False,
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transformer_depth=1,
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use_new_attention_order=False,
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use_scale_shift_norm=False,
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use_spatial_transformer=True,
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legacy=False,
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)
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noise_scheduler = DDPMScheduler(timesteps=1000, tensor_format="pt")
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optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
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augmentations = Compose(
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[
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Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
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CenterCrop(args.resolution),
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RandomHorizontalFlip(),
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ToTensor(),
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Lambda(lambda x: x * 2 - 1),
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]
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)
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dataset = load_dataset(args.dataset, split="train")
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def transforms(examples):
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images = [augmentations(image.convert("RGB")) for image in examples["image"]]
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return {"input": images}
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dataset.set_transform(transforms)
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train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
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lr_scheduler = get_scheduler(
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"linear",
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optimizer=optimizer,
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num_warmup_steps=args.warmup_steps,
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num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
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)
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model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, lr_scheduler
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)
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if args.push_to_hub:
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repo = init_git_repo(args, at_init=True)
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# Train!
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is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
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world_size = torch.distributed.get_world_size() if is_distributed else 1
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total_train_batch_size = args.batch_size * args.gradient_accumulation_steps * world_size
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max_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_epochs
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logger.info("***** Running training *****")
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logger.info(f" Num examples = {len(train_dataloader.dataset)}")
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logger.info(f" Num Epochs = {args.num_epochs}")
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logger.info(f" Instantaneous batch size per device = {args.batch_size}")
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logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
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logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
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logger.info(f" Total optimization steps = {max_steps}")
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for epoch in range(args.num_epochs):
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model.train()
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with tqdm(total=len(train_dataloader), unit="ba") as pbar:
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pbar.set_description(f"Epoch {epoch}")
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for step, batch in enumerate(train_dataloader):
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clean_images = batch["input"]
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noise_samples = torch.randn(clean_images.shape).to(clean_images.device)
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bsz = clean_images.shape[0]
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timesteps = torch.randint(0, noise_scheduler.timesteps, (bsz,), device=clean_images.device).long()
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# add noise onto the clean images according to the noise magnitude at each timestep
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# (this is the forward diffusion process)
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noisy_images = noise_scheduler.training_step(clean_images, noise_samples, timesteps)
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if step % args.gradient_accumulation_steps != 0:
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with accelerator.no_sync(model):
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output = model(noisy_images, timesteps)
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# predict the noise residual
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loss = F.mse_loss(output, noise_samples)
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loss = loss / args.gradient_accumulation_steps
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accelerator.backward(loss)
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else:
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output = model(noisy_images, timesteps)
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# predict the noise residual
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loss = F.mse_loss(output, noise_samples)
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loss = loss / args.gradient_accumulation_steps
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accelerator.backward(loss)
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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lr_scheduler.step()
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optimizer.zero_grad()
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pbar.update(1)
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pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
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optimizer.step()
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if is_distributed:
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torch.distributed.barrier()
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# Generate a sample image for visual inspection
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if args.local_rank in [-1, 0]:
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model.eval()
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with torch.no_grad():
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pipeline = DDPM(unet=unwrap_model(model), noise_scheduler=noise_scheduler)
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generator = torch.manual_seed(0)
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# run pipeline in inference (sample random noise and denoise)
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image = pipeline(generator=generator)
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# process image to PIL
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image_processed = image.cpu().permute(0, 2, 3, 1)
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image_processed = (image_processed + 1.0) * 127.5
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image_processed = image_processed.type(torch.uint8).numpy()
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image_pil = PIL.Image.fromarray(image_processed[0])
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# save image
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test_dir = os.path.join(args.output_dir, "test_samples")
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os.makedirs(test_dir, exist_ok=True)
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image_pil.save(f"{test_dir}/{epoch:04d}.png")
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# save the model
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if args.push_to_hub:
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push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
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else:
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pipeline.save_pretrained(args.output_dir)
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if is_distributed:
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torch.distributed.barrier()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument("--local_rank", type=int, default=-1)
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parser.add_argument("--dataset", type=str, default="huggan/flowers-102-categories")
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parser.add_argument("--output_dir", type=str, default="ddpm-model")
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parser.add_argument("--overwrite_output_dir", action="store_true")
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parser.add_argument("--resolution", type=int, default=64)
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parser.add_argument("--batch_size", type=int, default=16)
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parser.add_argument("--num_epochs", type=int, default=100)
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parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
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parser.add_argument("--lr", type=float, default=1e-4)
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parser.add_argument("--warmup_steps", type=int, default=500)
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parser.add_argument("--push_to_hub", action="store_true")
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parser.add_argument("--hub_token", type=str, default=None)
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parser.add_argument("--hub_model_id", type=str, default=None)
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parser.add_argument("--hub_private_repo", action="store_true")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default="no",
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU."
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),
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)
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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main(args)
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