130 lines
5.2 KiB
Python
130 lines
5.2 KiB
Python
from fastvideo.dataset import getdataset
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from torch.utils.data import DataLoader
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from fastvideo.utils.dataset_utils import Collate
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import argparse
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import torch
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import ProjectConfiguration
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import json
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import os
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from diffusers import AutoencoderKLMochi
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import torch.distributed as dist
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from torch.utils.data.distributed import DistributedSampler
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from fastvideo.utils.load import load_vae
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from tqdm import tqdm
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logger = get_logger(__name__)
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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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train_dataset = getdataset(args)
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sampler = DistributedSampler(
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train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True
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)
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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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encoder_device = torch.device(f"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(
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backend="nccl", init_method="env://", world_size=world_size, rank=local_rank
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)
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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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os.makedirs(args.output_dir, exist_ok=True)
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os.makedirs(os.path.join(args.output_dir, "latent"), exist_ok=True)
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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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latents = vae.encode(data["pixel_values"].to(encoder_device))[
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"latent_dist"
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].sample()
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for idx, video_path in enumerate(data["path"]):
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video_name = os.path.basename(video_path).split(".")[0]
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latent_path = os.path.join(
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args.output_dir, "latent", video_name + ".pt"
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)
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torch.save(latents[idx].to(torch.bfloat16), latent_path)
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item = {}
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item["length"] = latents[idx].shape[1]
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item["latent_path"] = video_name + ".pt"
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item["caption"] = data["text"][idx]
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json_data.append(item)
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print(f"{video_name} processed")
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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_temp.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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parser.add_argument("--model_type", type=str, default="mochi")
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parser.add_argument("--data_merge_path", type=str, required=True)
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parser.add_argument("--num_frames", type=int, default=163)
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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="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=16,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--num_latent_t", type=int, default=28, help="Number of latent timesteps."
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)
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parser.add_argument("--max_height", type=int, default=480)
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parser.add_argument("--max_width", type=int, default=848)
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parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
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parser.add_argument("--group_frame", action="store_true") # TODO
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parser.add_argument("--group_resolution", action="store_true") # TODO
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parser.add_argument("--dataset", default="t2v")
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parser.add_argument("--train_fps", type=int, default=30)
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parser.add_argument("--use_image_num", type=int, default=0)
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parser.add_argument("--text_max_length", type=int, default=256)
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parser.add_argument("--speed_factor", type=float, default=1.0)
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parser.add_argument("--drop_short_ratio", type=float, default=1.0)
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# text encoder & vae & diffusion model
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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("--cfg", type=float, default=0.0)
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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="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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args = parser.parse_args()
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main(args)
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