infer_audio2vid.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
@Project ：EchoMimic
@File    ：audio2vid.py
@Author  ：juzhen.czy
@Date    ：2024/3/4 17:43 
'''
import argparse
import os

import random
import platform
import subprocess
from datetime import datetime
from pathlib import Path

import cv2
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
from PIL import Image

from src.models.unet_2d_condition import UNet2DConditionModel
from src.models.unet_3d_echo import EchoUNet3DConditionModel
from src.models.whisper.audio2feature import load_audio_model
from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline
from src.utils.util import save_videos_grid, crop_and_pad
from src.models.face_locator import FaceLocator
from moviepy.editor import VideoFileClip, AudioFileClip
from facenet_pytorch import MTCNN

ffmpeg_path = os.getenv('FFMPEG_PATH')
if ffmpeg_path is None and platform.system() in ['Linux', 'Darwin']:
    try:
        result = subprocess.run(['which', 'ffmpeg'], capture_output=True, text=True)
        if result.returncode == 0:
            ffmpeg_path = result.stdout.strip()
            print(f"FFmpeg is installed at: {ffmpeg_path}")
        else:
            print("FFmpeg is not installed. Please download ffmpeg-static and export to FFMPEG_PATH.")
            print("For example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static")
    except Exception as e:
        pass

if ffmpeg_path is not None and ffmpeg_path not in os.getenv('PATH'):
    print("Adding FFMPEG_PATH to PATH")
    os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}"

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", type=str, default="./configs/prompts/animation.yaml")
    parser.add_argument("-W", type=int, default=512)
    parser.add_argument("-H", type=int, default=512)
    parser.add_argument("-L", type=int, default=1200)
    parser.add_argument("--seed", type=int, default=420)
    parser.add_argument("--facemusk_dilation_ratio", type=float, default=0.1)
    parser.add_argument("--facecrop_dilation_ratio", type=float, default=0.5)

    parser.add_argument("--context_frames", type=int, default=12)
    parser.add_argument("--context_overlap", type=int, default=3)

    parser.add_argument("--cfg", type=float, default=2.5)
    parser.add_argument("--steps", type=int, default=30)
    parser.add_argument("--sample_rate", type=int, default=16000)
    parser.add_argument("--fps", type=int, default=24)
    parser.add_argument("--device", type=str, default="cuda")

    args = parser.parse_args()

    return args

def select_face(det_bboxes, probs):
    ## max face from faces that the prob is above 0.8
    ## box: xyxy
    if det_bboxes is None or probs is None:
        return None
    filtered_bboxes = []
    for bbox_i in range(len(det_bboxes)):
        if probs[bbox_i] > 0.8:
            filtered_bboxes.append(det_bboxes[bbox_i])
    if len(filtered_bboxes) == 0:
        return None

    sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True)
    return sorted_bboxes[0]



def main():
    args = parse_args()

    config = OmegaConf.load(args.config)
    if config.weight_dtype == "fp16":
        weight_dtype = torch.float16
    else:
        weight_dtype = torch.float32

    device = args.device
    if device.__contains__("cuda") and not torch.cuda.is_available():
        device = "cpu"

    inference_config_path = config.inference_config
    infer_config = OmegaConf.load(inference_config_path)


    ############# model_init started #############

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

    ## reference net init
    reference_unet = UNet2DConditionModel.from_pretrained(
        config.pretrained_base_model_path,
        subfolder="unet",
    ).to(dtype=weight_dtype, device=device)
    reference_unet.load_state_dict(
        torch.load(config.reference_unet_path, map_location="cpu"),
    )

    ## denoising net init
    if os.path.exists(config.motion_module_path):
        ### stage1 + stage2
        denoising_unet = EchoUNet3DConditionModel.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=device)
    else:
        ### only stage1
        denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
            config.pretrained_base_model_path,
            "",
            subfolder="unet",
            unet_additional_kwargs={
                "use_motion_module": False,
                "unet_use_temporal_attention": False,
                "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim
            }
        ).to(dtype=weight_dtype, device=device)
    denoising_unet.load_state_dict(
        torch.load(config.denoising_unet_path, map_location="cpu"),
        strict=False
    )

    ## face locator init
    face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to(
        dtype=weight_dtype, device="cuda"
    )
    face_locator.load_state_dict(torch.load(config.face_locator_path))

    ### load audio processor params
    audio_processor = load_audio_model(model_path=config.audio_model_path, device=device)

    ### load face detector params
    face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=device)

    ############# model_init finished #############

    width, height = args.W, args.H
    sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
    scheduler = DDIMScheduler(**sched_kwargs)

    pipe = Audio2VideoPipeline(
        vae=vae,
        reference_unet=reference_unet,
        denoising_unet=denoising_unet,
        audio_guider=audio_processor,
        face_locator=face_locator,
        scheduler=scheduler,
    )
    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_{args.seed}-{args.W}x{args.H}"
    save_dir = Path(f"output/{date_str}/{save_dir_name}")
    save_dir.mkdir(exist_ok=True, parents=True)

    for ref_image_path in config["test_cases"].keys():
        for audio_path in config["test_cases"][ref_image_path]:

            if args.seed is not None and args.seed > -1:
                generator = torch.manual_seed(args.seed)
            else:
                generator = torch.manual_seed(random.randint(100, 1000000))

            ref_name = Path(ref_image_path).stem
            audio_name = Path(audio_path).stem
            final_fps = args.fps

            #### face musk prepare
            face_img = cv2.imread(ref_image_path)
            face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8')

            det_bboxes, probs = face_detector.detect(face_img)
            select_bbox = select_face(det_bboxes, probs)
            if select_bbox is None:
                face_mask[:, :] = 255
            else:
                xyxy = select_bbox[:4]
                xyxy = np.round(xyxy).astype('int')
                rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2]
                r_pad = int((re - rb) * args.facemusk_dilation_ratio)
                c_pad = int((ce - cb) * args.facemusk_dilation_ratio)
                face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255

                #### face crop
                r_pad_crop = int((re - rb) * args.facecrop_dilation_ratio)
                c_pad_crop = int((ce - cb) * args.facecrop_dilation_ratio)
                crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + c_pad_crop, face_img.shape[0])]
                print(crop_rect)
                face_img, _ = crop_and_pad(face_img, crop_rect)
                face_mask, _ = crop_and_pad(face_mask, crop_rect)
                face_img = cv2.resize(face_img, (args.W, args.H))
                face_mask = cv2.resize(face_mask, (args.W, args.H))

            ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]])
            face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0

            video = pipe(
                ref_image_pil,
                audio_path,
                face_mask_tensor,
                width,
                height,
                args.L,
                args.steps,
                args.cfg,
                generator=generator,
                audio_sample_rate=args.sample_rate,
                context_frames=args.context_frames,
                fps=final_fps,
                context_overlap=args.context_overlap
            ).videos

            video = video
            save_videos_grid(
                video,
                f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4",
                n_rows=1,
                fps=final_fps,
            )

            video_clip = VideoFileClip(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4")
            audio_clip = AudioFileClip(audio_path)
            video_clip = video_clip.set_audio(audio_clip)
            video_clip.write_videofile(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4", codec="libx264", audio_codec="aac")
            print(f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}_withaudio.mp4")


if __name__ == "__main__":
    main()