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# Copyright (c) [2025] [FastVideo Team]
# Copyright (c) [2025] [ByteDance Ltd. and/or its affiliates.]
# SPDX-License-Identifier: [Apache License 2.0]
#
# This file has been modified by [ByteDance Ltd. and/or its affiliates.] in 2025.
#
# Original file was released under [Apache License 2.0], with the full license text
# available at [https://github.com/hao-ai-lab/FastVideo/blob/main/LICENSE].
#
# This modified file is released under the same license.
import argparse
import torch
from accelerate.logging import get_logger
import cv2
import json
import os
import torch.distributed as dist
import pandas as pd
from torch.utils.data.dataset import ConcatDataset, Dataset
import io
import torchvision.transforms as transforms
logger = get_logger(__name__)
from torch.utils.data import Dataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from tqdm import tqdm
import re
from diffusers import FluxPipeline
from PIL import Image
from diffusers.image_processor import VaeImageProcessor
def contains_chinese(text):
return bool(re.search(r'[\u4e00-\u9fff]', text))
class RFPTdataset(Dataset):
def __init__(
self, file_path,
):
self.file_path = file_path
file_names = os.listdir(self.file_path) # each file contains 5,000 images
self.file_names = [os.path.join(self.file_path, file_name) for file_name in file_names]
self.train_dataset = self.read_data()
self.transform = transforms.ToTensor()
def read_data(self):
df_list = [pd.read_parquet(file_name) for file_name in self.file_names]
combined_df = pd.concat(df_list, axis=0, ignore_index=True)
return combined_df
def __len__(self):
return len(self.train_dataset)
def __getitem__(self, index):
image = self.train_dataset.iloc[index]['image']['bytes']
image = self.transform(Image.open(io.BytesIO(image)).convert('RGB'))
# print(image.shape)
caption = self.train_dataset.iloc[index]['caption_composition']
# print(caption)
filename = str(index)
if caption == None or image == None:
return self.__getitem__(index+1)
return dict(caption=caption, image=image, filename=filename)
class T5dataset(Dataset):
def __init__(
self, txt_path, vae_debug,
):
self.txt_path = txt_path
self.vae_debug = vae_debug
with open(self.txt_path, "r", encoding="utf-8") as f:
self.train_dataset = [
line for line in f.read().splitlines() if not contains_chinese(line)
][:50000]
def __getitem__(self, idx):
#import pdb;pdb.set_trace()
caption = self.train_dataset[idx]
filename = str(idx)
#length = self.train_dataset[idx]["length"]
if self.vae_debug:
latents = torch.load(
os.path.join(
args.output_dir, "latent", self.train_dataset[idx]["latent_path"]
),
map_location="cpu",
)
else:
latents = []
return dict(caption=caption, latents=latents, filename=filename)
def __len__(self):
return len(self.train_dataset)
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)
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
)
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(os.path.join(args.output_dir, "prompt_embed"), exist_ok=True)
os.makedirs(os.path.join(args.output_dir, "text_ids"), exist_ok=True)
os.makedirs(os.path.join(args.output_dir, "pooled_prompt_embeds"), exist_ok=True)
os.makedirs(os.path.join(args.output_dir, "images"), exist_ok=True)
# latents_txt_path = args.prompt_dir
# train_dataset = T5dataset(latents_txt_path, args.vae_debug)
train_dataset = RFPTdataset(args.prompt_dir)
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,
)
flux_path = args.model_path
pipe = FluxPipeline.from_pretrained(flux_path, torch_dtype=torch.bfloat16).to(device)
image_processor = VaeImageProcessor(16)
json_data = []
for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0):
try:
with torch.inference_mode():
if args.vae_debug:
latents = data["latents"]
for idx, video_name in enumerate(data["filename"]):
# prompt_embeds, pooled_prompt_embeds, text_ids = pipe.encode_prompt(
# prompt=data["caption"], prompt_2=data["caption"]
# )
# image_latents = pipe.vae.encode(data["image"].to(torch.bfloat16).to(device)).latent_dist.sample()
# output_image = pipe.vae.decode(image_latents, return_dict=False)[0]
# output_image = image_processor.postprocess(output_image)
# output_image[0].save('output.png')
# print(image_latents.latent_dist.sample())
# print(image_latents.latent_dist.sample().shape)
prompt_embed_path = os.path.join(
args.output_dir, "prompt_embed", video_name + ".pt"
)
pooled_prompt_embeds_path = os.path.join(
args.output_dir, "pooled_prompt_embeds", video_name + ".pt"
)
text_ids_path = os.path.join(
args.output_dir, "text_ids", video_name + ".pt"
)
image_latents_path = os.path.join(
args.output_dir, "images", video_name + ".pt"
)
# save latent
# torch.save(prompt_embeds[idx], prompt_embed_path)
# torch.save(pooled_prompt_embeds[idx], pooled_prompt_embeds_path)
# torch.save(text_ids[idx], text_ids_path)
torch.save(data["image"].to(torch.bfloat16), image_latents_path)
item = {}
item["prompt_embed_path"] = video_name + ".pt"
item["text_ids"] = video_name + ".pt"
item["pooled_prompt_embeds_path"] = video_name + ".pt"
item["caption"] = data["caption"][idx]
json_data.append(item)
except Exception as e:
print(f"Rank {local_rank} Error: {repr(e)}")
dist.barrier()
raise
dist.barrier()
local_data = json_data
gathered_data = [None] * world_size
dist.all_gather_object(gathered_data, local_data)
if local_rank == 0:
# os.remove(latents_json_path)
all_json_data = [item for sublist in gathered_data for item in sublist]
with open(os.path.join(args.output_dir, "videos2caption.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")
# text encoder & vae & diffusion model
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=1,
help="Batch size (per device) for the training dataloader.",
)
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(
"--output_dir",
type=str,
default=None,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--vae_debug", action="store_true")
parser.add_argument("--prompt_dir", type=str, default="./empty.txt")
args = parser.parse_args()
main(args) |