# All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """ Samples a large number of images from a pre-trained Latte model using DDP. Subsequently saves a .npz file that can be used to compute FVD and other evaluation metrics via the ADM repo: https://github.com/openai/guided-diffusion/tree/main/evaluations For a simple single-GPU/CPU sampling script, see sample.py. """ import io import os import sys import torch sys.path.append(os.path.split(sys.path[0])[0]) import torch.distributed as dist from utils import find_model from diffusion import create_diffusion from diffusers.models import AutoencoderKL from tqdm import tqdm import os from PIL import Image import numpy as np import math import argparse import imageio from omegaconf import OmegaConf from models import get_models from einops import rearrange 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) 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 main(args): """ Run sampling. """ torch.backends.cuda.matmul.allow_tf32 = True # True: fast but may lead to some small numerical differences 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() if args.seed: seed = args.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()}.") if args.ckpt is None: assert args.model == "Latte-XL/2", "Only Latte-XL/2 models are available for auto-download." assert args.image_size in [256, 512] assert args.num_classes == 1000 # Load model: latent_size = args.image_size // 8 args.latent_size = latent_size model = get_models(args).to(device) if args.use_compile: model = torch.compile(model) # a pre-trained model or load a custom Latte checkpoint from train.py: ckpt_path = args.ckpt state_dict = find_model(ckpt_path) model.load_state_dict(state_dict) model.eval() # important! diffusion = create_diffusion(str(args.num_sampling_steps)) # vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device) # vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae").to(device) vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="sd-vae-ft-ema").to(device) if args.use_fp16: print('WARNING: using half percision for inferencing!') vae.to(dtype=torch.float16) model.to(dtype=torch.float16) # text_encoder.to(dtype=torch.float16) assert args.cfg_scale >= 1.0, "In almost all cases, cfg_scale be >= 1.0" using_cfg = args.cfg_scale > 1.0 # Create folder to save samples: # model_string_name = args.model.replace("/", "-") # ckpt_string_name = os.path.basename(args.ckpt).replace(".pt", "") if args.ckpt else "pretrained" # folder_name = f"{model_string_name}-{ckpt_string_name}-size-{args.image_size}-vae-{args.vae}-" \ # f"cfg-{args.cfg_scale}-seed-{args.seed}" # sample_folder_dir = f"{args.sample_dir}/{folder_name}" sample_folder_dir = args.save_video_path if args.seed: sample_folder_dir = args.save_video_path + '-seed-' + str(args.seed) if rank == 0: os.makedirs(sample_folder_dir, exist_ok=True) print(f"Saving .mp4 samples at {sample_folder_dir}") dist.barrier() # 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() # To make things evenly-divisible, we'll sample a bit more than we need and then discard the extra samples: total_samples = int(math.ceil(args.num_fvd_samples / global_batch_size) * global_batch_size) if rank == 0: print(f"Total number of images that will be sampled: {total_samples}") assert total_samples % dist.get_world_size() == 0, "total_samples must be divisible by world_size" samples_needed_this_gpu = int(total_samples // dist.get_world_size()) assert samples_needed_this_gpu % n == 0, "samples_needed_this_gpu must be divisible by the per-GPU batch size" iterations = int(samples_needed_this_gpu // n) pbar = range(iterations) pbar = tqdm(pbar) if rank == 0 else pbar total = 0 for _ in pbar: # Sample inputs: if args.use_fp16: z = torch.randn(n, args.num_frames, 4, latent_size, latent_size, dtype=torch.float16, device=device) else: z = torch.randn(n, args.num_frames, 4, latent_size, latent_size, device=device) # Setup classifier-free guidance: if using_cfg: z = torch.cat([z, z], 0) y = torch.randint(0, args.num_classes, (n,), device=device) y_null = torch.tensor([101] * n, device=device) y = torch.cat([y, y_null], dim=0) model_kwargs = dict(y=y, cfg_scale=args.cfg_scale, use_fp16=args.use_fp16) sample_fn = model.forward_with_cfg else: model_kwargs = dict(y=None, use_fp16=args.use_fp16) sample_fn = model.forward # Sample images: if args.sample_method == 'ddim': samples = diffusion.ddim_sample_loop( sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=False, device=device ) elif args.sample_method == 'ddpm': samples = diffusion.p_sample_loop( sample_fn, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=False, device=device ) if using_cfg: samples, _ = samples.chunk(2, dim=0) # Remove null class samples if args.use_fp16: samples = samples.to(dtype=torch.float16) b, f, c, h, w = samples.shape samples = rearrange(samples, 'b f c h w -> (b f) c h w') samples = vae.decode(samples / 0.18215).sample samples = rearrange(samples, '(b f) c h w -> b f c h w', b=b) # Save samples to disk as individual .png files for i, sample in enumerate(samples): sample = ((sample * 0.5 + 0.5) * 255).add_(0.5).clamp_(0, 255).to(dtype=torch.uint8).cpu().permute(0, 2, 3, 1).contiguous() index = i * dist.get_world_size() + rank + total # Image.fromarray(sample).save(f"{sample_folder_dir}/{index:04d}.png") sample_save_path = f"{sample_folder_dir}/{index:04d}.mp4" imageio.mimwrite(sample_save_path, sample, fps=8, quality=9) total += global_batch_size # Make sure all processes have finished saving their samples before attempting to convert to .npz dist.barrier() # if rank == 0: # create_npz_from_sample_folder(sample_folder_dir, args.num_fvd_samples) # print("Done.") # dist.barrier() dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml") parser.add_argument("--ckpt", type=str, default="") parser.add_argument("--save_video_path", type=str, default="./sample_videos/") parser.add_argument("--save_ceph", default=False, action='store_true') args = parser.parse_args() omega_conf = OmegaConf.load(args.config) omega_conf.ckpt = args.ckpt omega_conf.save_video_path = args.save_video_path omega_conf.save_ceph = args.save_ceph main(omega_conf)