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import argparse |
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import json |
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import os |
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import torch |
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import torch.distributed as dist |
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from accelerate.logging import get_logger |
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from diffusers.utils import export_to_video |
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from diffusers.video_processor import VideoProcessor |
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from torch.utils.data import DataLoader, Dataset |
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from torch.utils.data.distributed import DistributedSampler |
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from tqdm import tqdm |
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from fastvideo.utils.load import load_text_encoder, load_vae |
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logger = get_logger(__name__) |
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class T5dataset(Dataset): |
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def __init__( |
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self, |
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json_path, |
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vae_debug, |
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): |
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self.json_path = json_path |
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self.vae_debug = vae_debug |
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with open(self.json_path, "r") as f: |
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train_dataset = json.load(f) |
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self.train_dataset = sorted(train_dataset, |
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key=lambda x: x["latent_path"]) |
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def __getitem__(self, idx): |
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caption = self.train_dataset[idx]["caption"] |
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filename = self.train_dataset[idx]["latent_path"].split(".")[0] |
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length = self.train_dataset[idx]["length"] |
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if self.vae_debug: |
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latents = torch.load( |
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os.path.join(args.output_dir, "latent", |
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self.train_dataset[idx]["latent_path"]), |
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map_location="cpu", |
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) |
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else: |
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latents = [] |
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return dict(caption=caption, |
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latents=latents, |
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filename=filename, |
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length=length) |
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def __len__(self): |
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return len(self.train_dataset) |
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def main(args): |
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local_rank = int(os.getenv("RANK", 0)) |
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world_size = int(os.getenv("WORLD_SIZE", 1)) |
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print("world_size", world_size, "local rank", local_rank) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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torch.cuda.set_device(local_rank) |
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if not dist.is_initialized(): |
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dist.init_process_group(backend="nccl", |
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init_method="env://", |
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world_size=world_size, |
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rank=local_rank) |
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videoprocessor = VideoProcessor(vae_scale_factor=8) |
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os.makedirs(args.output_dir, exist_ok=True) |
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os.makedirs(os.path.join(args.output_dir, "video"), exist_ok=True) |
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os.makedirs(os.path.join(args.output_dir, "latent"), exist_ok=True) |
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os.makedirs(os.path.join(args.output_dir, "prompt_embed"), exist_ok=True) |
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os.makedirs(os.path.join(args.output_dir, "prompt_attention_mask"), |
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exist_ok=True) |
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latents_json_path = os.path.join(args.output_dir, |
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"videos2caption_temp.json") |
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train_dataset = T5dataset(latents_json_path, args.vae_debug) |
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text_encoder = load_text_encoder(args.model_type, |
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args.model_path, |
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device=device) |
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vae, autocast_type, fps = load_vae(args.model_type, args.model_path) |
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vae.enable_tiling() |
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sampler = DistributedSampler(train_dataset, |
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rank=local_rank, |
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num_replicas=world_size, |
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shuffle=True) |
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train_dataloader = DataLoader( |
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train_dataset, |
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sampler=sampler, |
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batch_size=args.train_batch_size, |
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num_workers=args.dataloader_num_workers, |
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) |
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json_data = [] |
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for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0): |
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with torch.inference_mode(): |
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with torch.autocast("cuda", dtype=autocast_type): |
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prompt_embeds, prompt_attention_mask = text_encoder.encode_prompt( |
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prompt=data["caption"], ) |
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if args.vae_debug: |
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latents = data["latents"] |
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video = vae.decode(latents.to(device), |
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return_dict=False)[0] |
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video = videoprocessor.postprocess_video(video) |
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for idx, video_name in enumerate(data["filename"]): |
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prompt_embed_path = os.path.join(args.output_dir, |
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"prompt_embed", |
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video_name + ".pt") |
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video_path = os.path.join(args.output_dir, "video", |
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video_name + ".mp4") |
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prompt_attention_mask_path = os.path.join( |
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args.output_dir, "prompt_attention_mask", |
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video_name + ".pt") |
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torch.save(prompt_embeds[idx], prompt_embed_path) |
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torch.save(prompt_attention_mask[idx], |
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prompt_attention_mask_path) |
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print(f"sample {video_name} saved") |
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if args.vae_debug: |
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export_to_video(video[idx], video_path, fps=fps) |
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item = {} |
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item["length"] = int(data["length"][idx]) |
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item["latent_path"] = video_name + ".pt" |
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item["prompt_embed_path"] = video_name + ".pt" |
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item["prompt_attention_mask"] = video_name + ".pt" |
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item["caption"] = data["caption"][idx] |
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json_data.append(item) |
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dist.barrier() |
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local_data = json_data |
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gathered_data = [None] * world_size |
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dist.all_gather_object(gathered_data, local_data) |
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if local_rank == 0: |
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all_json_data = [item for sublist in gathered_data for item in sublist] |
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with open(os.path.join(args.output_dir, "videos2caption.json"), |
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"w") as f: |
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json.dump(all_json_data, f, indent=4) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_path", type=str, default="data/mochi") |
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parser.add_argument("--model_type", type=str, default="mochi") |
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parser.add_argument( |
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"--dataloader_num_workers", |
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type=int, |
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default=1, |
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help= |
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", |
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) |
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parser.add_argument( |
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"--train_batch_size", |
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type=int, |
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default=1, |
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help="Batch size (per device) for the training dataloader.", |
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) |
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parser.add_argument("--text_encoder_name", |
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type=str, |
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default="google/t5-v1_1-xxl") |
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parser.add_argument("--cache_dir", type=str, default="./cache_dir") |
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parser.add_argument( |
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"--output_dir", |
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type=str, |
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default=None, |
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help= |
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"The output directory where the model predictions and checkpoints will be written.", |
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) |
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parser.add_argument("--vae_debug", action="store_true") |
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args = parser.parse_args() |
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main(args) |
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