import argparse import os import random from datetime import datetime from pathlib import Path from typing import List import av import cv2 import numpy as np import torch import torchvision from diffusers import AutoencoderKL, DDIMScheduler from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline from einops import repeat from omegaconf import OmegaConf from PIL import Image from torchvision import transforms from transformers import CLIPVisionModelWithProjection 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_pose import AudioPose2VideoPipeline from src.utils.util import get_fps, read_frames, save_videos_grid, crop_and_pad import sys from src.models.face_locator import FaceLocator from moviepy.editor import VideoFileClip, AudioFileClip from facenet_pytorch import MTCNN from src.utils.draw_utils import FaceMeshVisualizer import pickle def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="./configs/prompts/animation_pose.yaml") parser.add_argument("-W", type=int, default=512) parser.add_argument("-H", type=int, default=512) parser.add_argument("-L", type=int, default=240) parser.add_argument("--seed", type=int, default=420) parser.add_argument("--crop_face", type=int, default=1) 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 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=3, block_out_channels=(16, 32, 96, 256)).to( dtype=weight_dtype, device="cuda" ) face_locator.load_state_dict(torch.load(config.face_locator_path)) visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=False) ### 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 = AudioPose2VideoPipeline( 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 file_path in config["test_cases"][ref_image_path]: if ".wav" in file_path: audio_path = file_path else: pose_dir = file_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 #ref_image_pil = Image.open(ref_image_path).convert("RGB") ### face crop face_img = cv2.imread(ref_image_path) if args.crop_face: det_bboxes, probs = face_detector.detect(face_img) select_bbox = select_face(det_bboxes, probs) if select_bbox is not None: xyxy = select_bbox[:4] xyxy = np.round(xyxy).astype('int') rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] 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_img = cv2.resize(face_img, (args.W, args.H)) ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) # ==================== face_locator ===================== pose_list = [] for index in range(len(os.listdir(pose_dir))): tgt_musk_path = os.path.join(pose_dir, f"{index}.pkl") with open(tgt_musk_path, "rb") as f: tgt_kpts = pickle.load(f) tgt_musk = visualizer.draw_landmarks((args.W, args.H), tgt_kpts) tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB') pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2,0,1) / 255.0) face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(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=12, fps=final_fps, context_overlap=3 ).videos final_length = min(video.shape[2], face_mask_tensor.shape[2], args.L) video = torch.cat([video[:, :, :final_length, :, :], face_mask_tensor[:, :, :final_length, :, :].detach().cpu()], dim=-1) save_videos_grid( video, f"{save_dir}/{ref_name}_{audio_name}_{args.H}x{args.W}_{int(args.cfg)}_{time_str}.mp4", n_rows=2, fps=final_fps, ) from moviepy.editor import VideoFileClip, AudioFileClip 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()