dataset_code/vae_decode_hv_batch.py

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()