118 lines
5.6 KiB
Python
118 lines
5.6 KiB
Python
import argparse
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import torch
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from accelerate.logging import get_logger
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from fastvideo.model.pipeline_mochi import MochiPipeline
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from diffusers.utils import export_to_video
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import json
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import os
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import torch.distributed as dist
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logger = get_logger(__name__)
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from torch.utils.data import Dataset
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from torch.utils.data.distributed import DistributedSampler
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from torch.utils.data import DataLoader
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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, 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(os.path.join(args.output_dir, 'latent', self.train_dataset[idx]['latent_path']), map_location="cpu")
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else:
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latents = []
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return dict(caption=caption, latents=latents, filename=filename, 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', init_method='env://', world_size=world_size, rank=local_rank)
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pipe = MochiPipeline.from_pretrained(args.model_path).to(device)
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pipe.vae.enable_tiling()
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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"), exist_ok=True)
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latents_json_path = os.path.join(args.output_dir, "videos2caption_temp.json")
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train_dataset = T5dataset(latents_json_path, args.vae_debug)
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sampler = DistributedSampler(train_dataset, rank=local_rank, num_replicas=world_size, 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 enumerate(train_dataloader):
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with torch.inference_mode():
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with torch.autocast("cuda", dtype=torch.bfloat16):
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prompt_embeds, prompt_attention_mask, _, _ = pipe.encode_prompt(
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prompt=data['caption'],
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)
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if args.vae_debug:
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latents = data['latents']
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video = pipe.vae.decode(latents.to(device), return_dict=False)[0]
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video = pipe.video_processor.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, "prompt_embed", video_name + ".pt")
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video_path = os.path.join(args.output_dir, "video", video_name + ".mp4")
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prompt_attention_mask_path = os.path.join(args.output_dir, "prompt_attention_mask", video_name + ".pt")
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# save latent
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torch.save(prompt_embeds[idx], prompt_embed_path)
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torch.save(prompt_attention_mask[idx], 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=30)
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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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# os.remove(latents_json_path)
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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"), '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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# dataset & dataloader
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parser.add_argument("--model_path", type=str, default="data/mochi")
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# text encoder & vae & diffusion model
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parser.add_argument("--dataloader_num_workers", type=int, default=1, help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.")
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parser.add_argument("--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.")
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parser.add_argument("--text_encoder_name", type=str, 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("--output_dir", type=str, default=None, help="The output directory where the model predictions and checkpoints will be written.")
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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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