#!/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()