infer.py

import torch
from diffsynth import ModelManager, WanVideoPipeline
from PIL import Image
import argparse
from transformers import Wav2Vec2Processor, Wav2Vec2Model
import librosa
import os
import subprocess
import cv2
from model import FantasyTalkingAudioConditionModel
from utils import save_video, get_audio_features, resize_image_by_longest_edge
from pathlib import Path
from datetime import datetime
# from modelscope import snapshot_download
from huggingface_hub import snapshot_download

def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    
    parser.add_argument(
        "--wan_model_dir",
        type=str,
        default="./models/Wan2.1-I2V-14B-720P",
        required=False,
        help="The dir of the Wan I2V 14B model.",
    )
    parser.add_argument(
        "--fantasytalking_model_path",
        type=str,
        default="./models/fantasytalking_model.ckpt",
        required=False,
        help="The .ckpt path of fantasytalking model.",
    )
    parser.add_argument(
        "--wav2vec_model_dir",
        type=str,
        default="./models/wav2vec2-base-960h",
        required=False,
        help="The dir of wav2vec model.",
    )

    parser.add_argument(
        "--image_path",
        type=str,
        default="./assets/images/woman.png",
        required=False,
        help="The path of the image.",
    )

    parser.add_argument(
        "--audio_path",
        type=str,
        default="./assets/audios/woman.wav",
        required=False,
        help="The path of the audio.",
    )
    parser.add_argument(
        "--prompt",
        type=str,
        default="A woman is talking.",
        required=False,
        help="prompt.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="./output",
        help="Dir to save the model.",
    )
    parser.add_argument(
        "--image_size",
        type=int,
        default=512,
        help="The image will be resized proportionally to this size.",
    )
    parser.add_argument(
        "--audio_scale",
        type=float,
        default=1.0,
        help="Audio condition injection weight",
    )
    parser.add_argument(
        "--prompt_cfg_scale",
        type=float,
        default=5.0,
        required=False,
        help="Prompt cfg scale",
    )
    parser.add_argument(
        "--audio_cfg_scale",
        type=float,
        default=5.0,
        required=False,
        help="Audio cfg scale",
    )
    parser.add_argument(
        "--max_num_frames",
        type=int,
        default=81,
        required=False,
        help="The maximum frames for generating videos, the audio part exceeding max_num_frames/fps will be truncated."
    )
    parser.add_argument(
        "--fps",
        type=int,
        default=23,
        required=False,
    )
    parser.add_argument(
        "--num_persistent_param_in_dit",
        type=int,
        default=None,
        required=False,
        help="Maximum parameter quantity retained in video memory, small number to reduce VRAM required"
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=1111,
        required=False,
    )
    args = parser.parse_args()
    return args

def load_models(args):
    # Load Wan I2V models

    snapshot_download("Wan-AI/Wan2.1-I2V-14B-720P", local_dir="./models/Wan2.1-I2V-14B-720P")
    snapshot_download("facebook/wav2vec2-base-960h", local_dir="./models/wav2vec2-base-960h")
    snapshot_download("acvlab/FantasyTalking", local_dir="./models")


    model_manager = ModelManager(device="cpu")
    
    model_manager.load_models(
        [
            [
                f"{args.wan_model_dir}/diffusion_pytorch_model-00001-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00002-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00003-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00004-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00005-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00006-of-00007.safetensors",
                f"{args.wan_model_dir}/diffusion_pytorch_model-00007-of-00007.safetensors",
            ],
            f"{args.wan_model_dir}/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
            f"{args.wan_model_dir}/models_t5_umt5-xxl-enc-bf16.pth",
            f"{args.wan_model_dir}/Wan2.1_VAE.pth",
        ],
        # torch_dtype=torch.float8_e4m3fn, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
        torch_dtype=torch.bfloat16, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
    )
    pipe = WanVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda")

    # Load FantasyTalking weights
    fantasytalking = FantasyTalkingAudioConditionModel(pipe.dit, 768, 2048).to("cuda")
    fantasytalking.load_audio_processor(args.fantasytalking_model_path, pipe.dit)
    
    # You can set `num_persistent_param_in_dit` to a small number to reduce VRAM required.
    pipe.enable_vram_management(num_persistent_param_in_dit=args.num_persistent_param_in_dit)

    # Load wav2vec models
    wav2vec_processor = Wav2Vec2Processor.from_pretrained(args.wav2vec_model_dir)
    wav2vec = Wav2Vec2Model.from_pretrained(args.wav2vec_model_dir).to("cuda")

    return pipe,fantasytalking,wav2vec_processor,wav2vec



def main(args,pipe,fantasytalking,wav2vec_processor,wav2vec):
    os.makedirs(args.output_dir,exist_ok=True)

    duration = librosa.get_duration(filename=args.audio_path)
    num_frames = min(int(args.fps*duration//4)*4+5,args.max_num_frames)

    audio_wav2vec_fea = get_audio_features(wav2vec,wav2vec_processor,args.audio_path,args.fps,num_frames)
    image = resize_image_by_longest_edge(args.image_path,args.image_size)
    width, height = image.size

    audio_proj_fea = fantasytalking.get_proj_fea(audio_wav2vec_fea)
    pos_idx_ranges = fantasytalking.split_audio_sequence(audio_proj_fea.size(1),num_frames=num_frames)
    audio_proj_split,audio_context_lens = fantasytalking.split_tensor_with_padding(audio_proj_fea,pos_idx_ranges,expand_length=4) # [b,21,9+8,768]

    # Image-to-video
    video_audio = pipe(
        prompt=args.prompt,
        negative_prompt="人物静止不动，静止，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
        input_image=image,
        width=width,
        height=height,
        num_frames=num_frames,
        num_inference_steps=30,
        seed=args.seed, tiled=True,
        audio_scale=args.audio_scale,
        cfg_scale = args.prompt_cfg_scale,
        audio_cfg_scale=args.audio_cfg_scale,
        audio_proj=audio_proj_split,
        audio_context_lens=audio_context_lens,
        latents_num_frames=(num_frames-1)//4+1
    )
    current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
    save_path_tmp = f"{args.output_dir}/tmp_{Path(args.image_path).stem}_{Path(args.audio_path).stem}_{current_time}.mp4"
    save_video(video_audio, save_path_tmp, fps=args.fps, quality=5)
    
    save_path = f"{args.output_dir}/{Path(args.image_path).stem}_{Path(args.audio_path).stem}_{current_time}.mp4"
    final_command = [
        "ffmpeg", "-y",
        "-i", save_path_tmp,       
        "-i", args.audio_path, 
        "-c:v", "libx264",        
        "-c:a", "aac",           
        "-shortest",             
        save_path              
    ]
    subprocess.run(final_command, check=True)
    os.remove(save_path_tmp)
    return save_path

if __name__ == "__main__":
    args = parse_args()
    pipe,fantasytalking,wav2vec_processor,wav2vec = load_models(args)

    main(args,pipe,fantasytalking,wav2vec_processor,wav2vec)