import torch torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True import torch.distributed as dist from torch.utils.data import Dataset, DataLoader from torch.utils.data.distributed import DistributedSampler from torchvision.datasets import ImageFolder from torchvision import transforms from tqdm import tqdm import os import itertools from PIL import Image import numpy as np import argparse import random from skimage.metrics import peak_signal_noise_ratio as psnr_loss from skimage.metrics import structural_similarity as ssim_loss from diffusers.models import AutoencoderKL class SingleFolderDataset(Dataset): def __init__(self, directory, transform=None): super().__init__() self.directory = directory self.transform = transform self.image_paths = [os.path.join(directory, file_name) for file_name in os.listdir(directory) if os.path.isfile(os.path.join(directory, file_name))] def __len__(self): return len(self.image_paths) def __getitem__(self, idx): image_path = self.image_paths[idx] image = Image.open(image_path).convert('RGB') if self.transform: image = self.transform(image) return image, torch.tensor(0) def create_npz_from_sample_folder(sample_dir, num=50_000): """ Builds a single .npz file from a folder of .png samples. """ samples = [] for i in tqdm(range(num), desc="Building .npz file from samples"): sample_pil = Image.open(f"{sample_dir}/{i:06d}.png") sample_np = np.asarray(sample_pil).astype(np.uint8) samples.append(sample_np) random.shuffle(samples) # This is very important for IS(Inception Score) !!! samples = np.stack(samples) assert samples.shape == (num, samples.shape[1], samples.shape[2], 3) npz_path = f"{sample_dir}.npz" np.savez(npz_path, arr_0=samples) print(f"Saved .npz file to {npz_path} [shape={samples.shape}].") return npz_path def center_crop_arr(pil_image, image_size): """ Center cropping implementation from ADM. https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126 """ while min(*pil_image.size) >= 2 * image_size: pil_image = pil_image.resize( tuple(x // 2 for x in pil_image.size), resample=Image.BOX ) scale = image_size / min(*pil_image.size) pil_image = pil_image.resize( tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC ) arr = np.array(pil_image) crop_y = (arr.shape[0] - image_size) // 2 crop_x = (arr.shape[1] - image_size) // 2 return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size]) def main(args): # Setup PyTorch: assert torch.cuda.is_available(), "Sampling with DDP requires at least one GPU. sample.py supports CPU-only usage" torch.set_grad_enabled(False) # Setup DDP: dist.init_process_group("nccl") rank = dist.get_rank() device = rank % torch.cuda.device_count() seed = args.global_seed * dist.get_world_size() + rank torch.manual_seed(seed) torch.cuda.set_device(device) print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.") # load vae vae = AutoencoderKL.from_pretrained(f"stabilityai/{args.vae}").to(device) # Create folder to save samples: folder_name = f"stabilityai-{args.vae}-{args.dataset}-size-{args.image_size}-seed-{args.global_seed}" sample_folder_dir = f"{args.sample_dir}/{folder_name}" if rank == 0: os.makedirs(sample_folder_dir, exist_ok=True) print(f"Saving .png samples at {sample_folder_dir}") dist.barrier() # Setup data: transform = transforms.Compose([ transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) ]) if args.dataset == 'imagenet': dataset = ImageFolder(args.data_path, transform=transform) num_fid_samples = 50000 elif args.dataset == 'coco': dataset = SingleFolderDataset(args.data_path, transform=transform) num_fid_samples = 5000 else: raise Exception("please check dataset") sampler = DistributedSampler( dataset, num_replicas=dist.get_world_size(), rank=rank, shuffle=False, seed=args.global_seed ) loader = DataLoader( dataset, batch_size=args.per_proc_batch_size, shuffle=False, sampler=sampler, num_workers=args.num_workers, pin_memory=True, drop_last=False ) # Figure out how many samples we need to generate on each GPU and how many iterations we need to run: n = args.per_proc_batch_size global_batch_size = n * dist.get_world_size() psnr_val_rgb = [] ssim_val_rgb = [] loader = tqdm(loader) if rank == 0 else loader total = 0 for x, _ in loader: rgb_gts = x rgb_gts = (rgb_gts.permute(0, 2, 3, 1).to("cpu").numpy() + 1.0) / 2.0 # rgb_gt value is between [0, 1] x = x.to(device) with torch.no_grad(): # Map input images to latent space + normalize latents: latent = vae.encode(x).latent_dist.sample().mul_(0.18215) # reconstruct: samples = vae.decode(latent / 0.18215).sample # output value is between [-1, 1] samples = torch.clamp(127.5 * samples + 128.0, 0, 255).permute(0, 2, 3, 1).to("cpu", dtype=torch.uint8).numpy() # Save samples to disk as individual .png files for i, (sample, rgb_gt) in enumerate(zip(samples, rgb_gts)): index = i * dist.get_world_size() + rank + total Image.fromarray(sample).save(f"{sample_folder_dir}/{index:06d}.png") # metric rgb_restored = sample.astype(np.float32) / 255. # rgb_restored value is between [0, 1] psnr = psnr_loss(rgb_restored, rgb_gt) ssim = ssim_loss(rgb_restored, rgb_gt, multichannel=True, data_range=2.0, channel_axis=-1) psnr_val_rgb.append(psnr) ssim_val_rgb.append(ssim) total += global_batch_size # ------------------------------------ # Summary # ------------------------------------ # Make sure all processes have finished saving their samples dist.barrier() world_size = dist.get_world_size() gather_psnr_val = [None for _ in range(world_size)] gather_ssim_val = [None for _ in range(world_size)] dist.all_gather_object(gather_psnr_val, psnr_val_rgb) dist.all_gather_object(gather_ssim_val, ssim_val_rgb) if rank == 0: gather_psnr_val = list(itertools.chain(*gather_psnr_val)) gather_ssim_val = list(itertools.chain(*gather_ssim_val)) psnr_val_rgb = sum(gather_psnr_val) / len(gather_psnr_val) ssim_val_rgb = sum(gather_ssim_val) / len(gather_ssim_val) print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb)) result_file = f"{sample_folder_dir}_results.txt" print("writing results to {}".format(result_file)) with open(result_file, 'w') as f: print("PSNR: %f, SSIM: %f " % (psnr_val_rgb, ssim_val_rgb), file=f) create_npz_from_sample_folder(sample_folder_dir, num_fid_samples) print("Done.") dist.barrier() dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--data-path", type=str, required=True) parser.add_argument("--dataset", type=str, choices=['imagenet', 'coco'], default='imagenet') parser.add_argument("--vae", type=str, choices=["sdxl-vae", "sd-vae-ft-mse"], default="sd-vae-ft-mse") parser.add_argument("--image-size", type=int, choices=[256, 512], default=256) parser.add_argument("--sample-dir", type=str, default="reconstructions") parser.add_argument("--per-proc-batch-size", type=int, default=32) parser.add_argument("--global-seed", type=int, default=0) parser.add_argument("--num-workers", type=int, default=4) args = parser.parse_args() main(args)