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import os
import glob
import torch
import torch.multiprocessing as mp
from diffusers import AutoencoderKLHunyuanVideo
from diffusers.video_processor import VideoProcessor
from diffusers.utils import export_to_video
from concurrent.futures import ProcessPoolExecutor
import time
os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes"
def process_files_on_gpu(gpu_id, file_list, pretrained_model_path, output_folder):
"""在指定GPU上处理文件列表"""
device = f"cuda:{gpu_id}"
# 初始化VAE模型
vae = AutoencoderKLHunyuanVideo.from_pretrained(
pretrained_model_path,
subfolder="vae",
torch_dtype=torch.float32,
).to(device)
vae.eval()
vae.requires_grad_(False)
vae.enable_tiling()
vae_scale_factor_spatial = vae.spatial_compression_ratio
video_processor = VideoProcessor(vae_scale_factor=vae_scale_factor_spatial)
for i, pt_file in enumerate(file_list):
try:
print(f"GPU {gpu_id} - 正在处理 ({i+1}/{len(file_list)}): {os.path.basename(pt_file)}")
# 加载latents
latents = torch.load(pt_file, map_location='cpu', weights_only=False)
vae_latents = latents['vae_latent'] / vae.config.scaling_factor
vae_latents = vae_latents.to(device=device, dtype=vae.dtype)
# 解码视频
video = vae.decode(vae_latents.unsqueeze(0), return_dict=False)[0]
video = video_processor.postprocess_video(video, output_type="pil")
# 生成输出文件名
base_name = os.path.splitext(os.path.basename(pt_file))[0]
output_path = os.path.join(output_folder, f"{base_name}.mp4")
# 导出视频
export_to_video(video[0], output_path, fps=30)
print(f"GPU {gpu_id} - 成功保存: {output_path}")
# 清理GPU内存
del latents, vae_latents, video
torch.cuda.empty_cache()
except Exception as e:
print(f"GPU {gpu_id} - 处理文件 {pt_file} 时出错: {str(e)}")
continue
print(f"GPU {gpu_id} - 完成所有分配的文件处理！")
def main():
# 设置路径
pretrained_model_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/hunyuanvideo-community/HunyuanVideo"
input_folder = "/mnt/bn/yufan-dev-my/ysh/Datasets/dummy_fp_offload_latents"
output_folder = "/mnt/bn/yufan-dev-my/ysh/Datasets/dummy_fp_offload_latents/decoded_videos"
# 创建输出文件夹
os.makedirs(output_folder, exist_ok=True)
# 获取所有.pt文件
pt_files = glob.glob(os.path.join(input_folder, "*.pt"))
print(f"找到 {len(pt_files)} 个.pt文件")
if len(pt_files) == 0:
print("没有找到.pt文件！")
return
# 检查可用GPU数量
num_gpus = min(8, torch.cuda.device_count())
print(f"使用 {num_gpus} 个GPU进行并行处理")
# 将文件分配到不同的GPU
files_per_gpu = len(pt_files) // num_gpus
file_chunks = []
for i in range(num_gpus):
start_idx = i * files_per_gpu
if i == num_gpus - 1: # 最后一个GPU处理剩余的所有文件
end_idx = len(pt_files)
else:
end_idx = (i + 1) * files_per_gpu
file_chunks.append(pt_files[start_idx:end_idx])
print(f"GPU {i} 将处理 {len(file_chunks[i])} 个文件")
# 使用多进程并行处理
start_time = time.time()
processes = []
for gpu_id in range(num_gpus):
if len(file_chunks[gpu_id]) > 0: # 只为有文件的GPU创建进程
p = mp.Process(
target=process_files_on_gpu,
args=(gpu_id, file_chunks[gpu_id], pretrained_model_path, output_folder)
)
p.start()
processes.append(p)
# 等待所有进程完成
for p in processes:
p.join()
end_time = time.time()
print(f"\n所有文件处理完成！总耗时: {end_time - start_time:.2f} 秒")
if __name__ == "__main__":
mp.set_start_method('spawn', force=True) # 确保多进程兼容性
main()
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