vivid.py

import argparse
from datetime import datetime
from pathlib import Path
import sys 
import torch
import os 
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection

from src.models.pose_guider import PoseGuider
from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d import UNet3DConditionModel
from src.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
from src.utils.util import get_fps, read_frames, save_videos_grid

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--config",type=str,default="/mnt/lpai-dione/ssai/cvg/team/wjj/ViViD/configs/prompts/valid.yaml")
    parser.add_argument("-W", type=int, default=384)
    parser.add_argument("-H", type=int, default=512)
    parser.add_argument("-L", type=int, default=24)

    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--cfg", type=float, default=3.5)
    parser.add_argument("--steps", type=int, default=20)
    parser.add_argument("--fps", type=int)
    args = parser.parse_args()

    return args


def main():
    args = parse_args()

    config = OmegaConf.load(args.config)

    if config.weight_dtype == "fp16":
        weight_dtype = torch.float16
    else:
        weight_dtype = torch.float32

    vae = AutoencoderKL.from_pretrained(
        config.pretrained_vae_path,
    ).to("cuda", dtype=weight_dtype)

    reference_unet = UNet2DConditionModel.from_pretrained_2d(
        config.pretrained_base_model_path,
        subfolder="unet",
        unet_additional_kwargs={
            "in_channels": 5,
        }
    ).to(dtype=weight_dtype, device="cuda")

    inference_config_path = config.inference_config #'/mnt/lpai-dione/ssai/cvg/team/wjj/ViViD/configs/inference/inference.yaml'
    infer_config = OmegaConf.load(inference_config_path)
    denoising_unet = UNet3DConditionModel.from_pretrained_2d(
        config.pretrained_base_model_path,
        config.motion_module_path,
        subfolder="unet",
        unet_additional_kwargs=infer_config.unet_additional_kwargs,
    ).to(dtype=weight_dtype, device="cuda")

    pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(
        dtype=weight_dtype, device="cuda"
    )


    image_enc = CLIPVisionModelWithProjection.from_pretrained(
        config.image_encoder_path
    ).to(dtype=weight_dtype, device="cuda")

    sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
    scheduler = DDIMScheduler(**sched_kwargs)

    seed = config.get("seed",args.seed)
    generator = torch.manual_seed(seed)

    width, height = args.W, args.H
    clip_length = config.get("L",args.L)  
    steps = args.steps
    guidance_scale = args.cfg

    # load pretrained weights
    denoising_unet.load_state_dict(
        torch.load(config.denoising_unet_path, map_location="cpu"),
        strict=False,
    )

    reference_unet.load_state_dict(
        torch.load(config.reference_unet_path, map_location="cpu"),
    )
   

    pose_guider.load_state_dict(
        torch.load(config.pose_guider_path, map_location="cpu"),
    )
   

   
    pipe = Pose2VideoPipeline(
        vae=vae,
        image_encoder=image_enc,
        reference_unet=reference_unet,
        denoising_unet=denoising_unet,
        pose_guider=pose_guider,
        scheduler=scheduler,
    )
    #  设置日志文件路径  
    # log_file_path = "model_structures.log"  
    # with open(log_file_path, 'w') as log_file:  
    #     # 重定向标准输出到日志文件  
    #     orig_stdout = sys.stdout  # 保存原始的标准输出  
    #     sys.stdout = log_file     # 将标准输出重定向到日志文件  

    #     # 打印模型结构  
    #     print("Denoising UNet structure:")  
    #     print(denoising_unet)  # 打印 denoising_unet 的结构  

    #     print("Reference UNet structure:")  
    #     print(reference_unet)  # 打印 reference_unet 的结构  

    #     print("Pose Guider structure:")  
    #     print(pose_guider)  # 打印 pose_guider 的结构  

    #     print("image_enc:")  
    #     print(image_enc)

    #     print("Pose Guider structure:")  
    #     print(pose_guider)

    #     print("pipe:")  
    #     print(pipe)
    #     # 恢复标准输出  
    #     sys.stdout = orig_stdout  # 还原标准输出  
    # print(f"The model structures have been saved to {log_file_path}.") 
    pipe = pipe.to("cuda", dtype=weight_dtype)
    
    date_str = datetime.now().strftime("%Y%m%d")
    time_str = datetime.now().strftime("%H%M")
    save_dir_name = f"{time_str}--seed_{seed}-{args.W}x{args.H}"

    save_dir = Path(f"output/{date_str}/{save_dir_name}")
    save_dir.mkdir(exist_ok=True, parents=True)

    model_video_paths = config.model_video_paths
    cloth_image_paths = config.cloth_image_paths

    transform = transforms.Compose(
        [transforms.Resize((height, width)), transforms.ToTensor()]
    )


    for model_image_path in model_video_paths:
        # print("model_image_path", model_image_path)
        src_fps = get_fps(model_image_path)

        model_name = Path(model_image_path).stem
        agnostic_path=model_image_path.replace("videos","agnostic") #data/videos/upper1.mp4——>data/agnostic/upper1.mp4
        agn_mask_path=model_image_path.replace("videos","agnostic_mask")
        densepose_path=model_image_path.replace("videos","densepose")

        video_tensor_list=[]
        video_images=read_frames(model_image_path)
        
        clip_length = len(video_images)  # 设置 clip_length 为输入视频的总帧数
        # clip_length=48
        for vid_image_pil in video_images[:clip_length]: #clip_length=24 
            video_tensor_list.append(transform(vid_image_pil))

        video_tensor = torch.stack(video_tensor_list, dim=0)  # (f, c, h, w)
        video_tensor = video_tensor.transpose(0, 1)
        

        agnostic_list=[]
        agnostic_images=read_frames(agnostic_path)
        for agnostic_image_pil in agnostic_images[:clip_length]:
            agnostic_list.append(agnostic_image_pil)

        agn_mask_list=[]
        agn_mask_images=read_frames(agn_mask_path)
        # print(" agn_mask_images",  agn_mask_images)
        for agn_mask_image_pil in agn_mask_images[:clip_length]:
            agn_mask_list.append(agn_mask_image_pil)

        pose_list=[]
        pose_images=read_frames(densepose_path)
        for pose_image_pil in pose_images[:clip_length]:
            pose_list.append(pose_image_pil)

        video_tensor = video_tensor.unsqueeze(0)


        for cloth_image_path in cloth_image_paths:
            cloth_name =  Path(cloth_image_path).stem
            cloth_image_pil = Image.open(cloth_image_path).convert("RGB")

            cloth_mask_path=cloth_image_path.replace("cloth","cloth_mask")
            cloth_mask_pil = Image.open(cloth_mask_path).convert("RGB")

            pipeline_output = pipe(
                agnostic_list,
                agn_mask_list,
                cloth_image_pil,
                cloth_mask_pil,
                pose_list,
                width,
                height,
                clip_length,
                steps,
                guidance_scale,
                generator=generator,
            )
            # print("pipeline_output", pipeline_output)
            video = pipeline_output.videos

            video = torch.cat([video_tensor,video], dim=0)
            save_videos_grid(
                video,
                f"{save_dir}/{model_name}_{cloth_name}_{args.H}x{args.W}_{int(guidance_scale)}_{time_str}.mp4",
                n_rows=2,
                fps=src_fps if args.fps is None else args.fps,
            )


if __name__ == "__main__":
    main()