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#This code file is from [https://github.com/hao-ai-lab/FastVideo], which is licensed under Apache License 2.0.
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)
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.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=
("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
)
args = parser.parse_args()
main(args)
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