111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
import argparse
|
|
import json
|
|
import os
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from accelerate.logging import get_logger
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
from tqdm import tqdm
|
|
|
|
from fastvideo.dataset import getdataset
|
|
from fastvideo.utils.load import load_vae
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
def main(args):
|
|
local_rank = int(os.getenv("RANK", 0))
|
|
world_size = int(os.getenv("WORLD_SIZE", 1))
|
|
print("world_size", world_size, "local rank", local_rank)
|
|
train_dataset = getdataset(args)
|
|
sampler = DistributedSampler(train_dataset, rank=local_rank, num_replicas=world_size, shuffle=True)
|
|
train_dataloader = DataLoader(
|
|
train_dataset,
|
|
sampler=sampler,
|
|
batch_size=args.train_batch_size,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
encoder_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
torch.cuda.set_device(local_rank)
|
|
if not dist.is_initialized():
|
|
dist.init_process_group(backend="nccl", init_method="env://", world_size=world_size, rank=local_rank)
|
|
vae, autocast_type, fps = load_vae(args.model_type, args.model_path)
|
|
if args.model_type != "wan":
|
|
vae.enable_tiling()
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
os.makedirs(os.path.join(args.output_dir, "latent"), exist_ok=True)
|
|
|
|
json_data = []
|
|
for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0):
|
|
with torch.inference_mode():
|
|
with torch.autocast("cuda", dtype=autocast_type):
|
|
latents = vae.encode(data["pixel_values"].to(encoder_device))["latent_dist"].sample()
|
|
for idx, video_path in enumerate(data["path"]):
|
|
video_name = os.path.basename(video_path).split(".")[0]
|
|
latent_path = os.path.join(args.output_dir, "latent", video_name + ".pt")
|
|
torch.save(latents[idx].to(torch.bfloat16), latent_path)
|
|
item = {}
|
|
item["length"] = latents[idx].shape[1]
|
|
item["latent_path"] = video_name + ".pt"
|
|
item["caption"] = data["text"][idx]
|
|
json_data.append(item)
|
|
print(f"{video_name} processed")
|
|
dist.barrier()
|
|
local_data = json_data
|
|
gathered_data = [None] * world_size
|
|
dist.all_gather_object(gathered_data, local_data)
|
|
if local_rank == 0:
|
|
all_json_data = [item for sublist in gathered_data for item in sublist]
|
|
with open(os.path.join(args.output_dir, "videos2caption_temp.json"), "w") as f:
|
|
json.dump(all_json_data, f, indent=4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
# dataset & dataloader
|
|
parser.add_argument("--model_path", type=str, default="data/mochi")
|
|
parser.add_argument("--model_type", type=str, default="mochi")
|
|
parser.add_argument("--data_merge_path", type=str, required=True)
|
|
parser.add_argument("--num_frames", type=int, default=163)
|
|
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.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_batch_size",
|
|
type=int,
|
|
default=16,
|
|
help="Batch size (per device) for the training dataloader.",
|
|
)
|
|
parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.")
|
|
parser.add_argument("--max_height", type=int, default=480)
|
|
parser.add_argument("--max_width", type=int, default=848)
|
|
parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
|
|
parser.add_argument("--group_frame", action="store_true") # TODO
|
|
parser.add_argument("--group_resolution", action="store_true") # TODO
|
|
parser.add_argument("--dataset", default="t2v")
|
|
parser.add_argument("--train_fps", type=int, default=30)
|
|
parser.add_argument("--use_image_num", type=int, default=0)
|
|
parser.add_argument("--text_max_length", type=int, default=256)
|
|
parser.add_argument("--speed_factor", type=float, default=1.0)
|
|
parser.add_argument("--drop_short_ratio", type=float, default=1.0)
|
|
# text encoder & vae & diffusion model
|
|
parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
|
|
parser.add_argument("--cache_dir", type=str, default="./cache_dir")
|
|
parser.add_argument("--cfg", type=float, default=0.0)
|
|
parser.add_argument(
|
|
"--output_dir",
|
|
type=str,
|
|
default=None,
|
|
help="The output directory where the model predictions and checkpoints will be written.",
|
|
)
|
|
|
|
|
|
args = parser.parse_args()
|
|
main(args)
|