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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
# from fastvideo.models.mochi_hf.pipeline_mochi import MochiPipeline
from diffusers.utils import export_to_video
from fastvideo.models.qwenimage.pipeline_qwenimage import QwenImagePipeline
import json
import os
import torch.distributed as dist
logger = get_logger(__name__)
from torch.utils.data import Dataset
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader
from fastvideo.utils.load import load_text_encoder, load_vae
from diffusers.video_processor import VideoProcessor
from tqdm import tqdm
import re
from diffusers import DiffusionPipeline
import torch.nn.functional as F
def contains_chinese(text):
"""检查字符串是否包含中文字符"""
return bool(re.search(r'[\u4e00-\u9fff]', text))
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)
]
#self.train_dataset = sorted(train_dataset)
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
)
#videoprocessor = VideoProcessor(vae_scale_factor=8)
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, "prompt_attention_mask"), exist_ok=True)
latents_txt_path = args.prompt_dir
train_dataset = T5dataset(latents_txt_path, args.vae_debug)
#text_encoder = load_text_encoder(args.model_type, args.model_path, device=device)
#vae, autocast_type, fps = load_vae(args.model_type, args.model_path)
#vae.enable_tiling()
sampler = DistributedSampler(
train_dataset, rank=local_rank, num_replicas=world_size, shuffle=False
)
train_dataloader = DataLoader(
train_dataset,
sampler=sampler,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
)
# Load pipeline but don't move everything to GPU yet
pipe = QwenImagePipeline.from_pretrained(args.model_path, torch_dtype=torch.bfloat16)
# Only move text_encoder to GPU for embedding generation
pipe.text_encoder = pipe.text_encoder.to(device)
# Delete unused components to free up RAM/VRAM
if not args.vae_debug:
# Remove from attributes
if hasattr(pipe, "transformer"):
del pipe.transformer
if hasattr(pipe, "vae"):
del pipe.vae
# Remove from components dictionary to ensure garbage collection
if "transformer" in pipe.components:
del pipe.components["transformer"]
if "vae" in pipe.components:
del pipe.components["vae"]
import gc
gc.collect()
torch.cuda.empty_cache()
# pipe._execution_device = device # This causes AttributeError, removing it.
json_data = []
for _, data in tqdm(enumerate(train_dataloader), disable=local_rank != 0):
with torch.inference_mode():
with torch.autocast("cuda"):
prompt_embeds, prompt_attention_mask = pipe.encode_prompt(
prompt=data["caption"],
device=device # Explicitly pass device
)
# ==================== 代码修改开始 ====================
# 1. 记录原始的序列长度 (第二个维度的大小)
original_length = prompt_embeds.shape[1]
target_length = 1024
# 2. 计算需要填充的长度
# 假设 original_length 不会超过 target_length
pad_len = target_length - original_length
# 3. 填充 prompt_embeds
# prompt_embeds 是一个3D张量 (B, L, D)，我们需要填充第二个维度 L
# F.pad 的填充参数顺序是从最后一个维度开始的 (pad_dim_D_left, pad_dim_D_right, pad_dim_L_left, pad_dim_L_right, ...)
# 我们在维度1（序列长度L）的右侧进行填充
prompt_embeds = F.pad(prompt_embeds, (0, 0, 0, pad_len), "constant", 0)
# 4. 填充 prompt_attention_mask
# prompt_attention_mask 是一个2D张量 (B, L)，我们同样填充第二个维度 L
# 我们在维度1（序列长度L）的右侧进行填充
prompt_attention_mask = F.pad(prompt_attention_mask, (0, pad_len), "constant", 0)
# ==================== 代码修改结束 ====================
if args.vae_debug:
latents = data["latents"]
for idx, video_name in enumerate(data["filename"]):
prompt_embed_path = os.path.join(
args.output_dir, "prompt_embed", video_name + ".pt"
)
prompt_attention_mask_path = os.path.join(
args.output_dir, "prompt_attention_mask", video_name + ".pt"
)
# 保存 latent (注意这里保存的是填充后的张量)
torch.save(prompt_embeds[idx], prompt_embed_path)
torch.save(prompt_attention_mask[idx], prompt_attention_mask_path)
item = {}
item["prompt_embed_path"] = video_name + ".pt"
item["prompt_attention_mask"] = video_name + ".pt"
item["caption"] = data["caption"][idx]
# [新增] 将原始长度记录到 item 字典中
item["original_length"] = original_length
json_data.append(item)
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)
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