import os import shutil import ffmpeg from datetime import datetime from pathlib import Path import numpy as np import cv2 import torch import spaces # from diffusers import AutoencoderKL, DDIMScheduler # from einops import repeat # 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 # from src.utils.mp_utils import LMKExtractor # from src.utils.draw_util import FaceMeshVisualizer from src.utils.pose_util import project_points_with_trans, matrix_to_euler_and_translation, euler_and_translation_to_matrix from src.audio2vid import smooth_pose_seq from src.utils.crop_face_single import crop_face from src.create_modules import lmk_extractor, vis, pipe @spaces.GPU def video2video(ref_img, source_video, size=512, steps=25, length=150, seed=42): cfg = 3.5 # config = OmegaConf.load('./configs/prompts/animation_facereenac.yaml') # 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( # config.pretrained_base_model_path, # subfolder="unet", # ).to(dtype=weight_dtype, device="cuda") # inference_config_path = config.inference_config # 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(noise_latent_channels=320, use_ca=True).to(device="cuda", dtype=weight_dtype) # not use cross attention # 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) generator = torch.manual_seed(seed) width, height = size, size # # 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, # ) # 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}-{size}x{size}" save_dir = Path(f"output/{date_str}/{save_dir_name}") save_dir.mkdir(exist_ok=True, parents=True) # lmk_extractor = LMKExtractor() # vis = FaceMeshVisualizer(forehead_edge=False) ref_image_np = cv2.cvtColor(ref_img, cv2.COLOR_RGB2BGR) ref_image_np = crop_face(ref_image_np, lmk_extractor) if ref_image_np is None: return None, Image.fromarray(ref_img) ref_image_np = cv2.resize(ref_image_np, (size, size)) ref_image_pil = Image.fromarray(cv2.cvtColor(ref_image_np, cv2.COLOR_BGR2RGB)) face_result = lmk_extractor(ref_image_np) if face_result is None: return None, ref_image_pil lmks = face_result['lmks'].astype(np.float32) ref_pose = vis.draw_landmarks((ref_image_np.shape[1], ref_image_np.shape[0]), lmks, normed=True) source_images = read_frames(source_video) src_fps = get_fps(source_video) pose_transform = transforms.Compose( [transforms.Resize((height, width)), transforms.ToTensor()] ) step = 1 if src_fps == 60: src_fps = 30 step = 2 pose_trans_list = [] verts_list = [] bs_list = [] src_tensor_list = [] args_L = len(source_images) if length==0 or length*step > len(source_images) else length*step args_L = min(args_L, 300*step) for src_image_pil in source_images[: args_L: step]: src_tensor_list.append(pose_transform(src_image_pil)) src_img_np = cv2.cvtColor(np.array(src_image_pil), cv2.COLOR_RGB2BGR) frame_height, frame_width, _ = src_img_np.shape src_img_result = lmk_extractor(src_img_np) if src_img_result is None: break pose_trans_list.append(src_img_result['trans_mat']) verts_list.append(src_img_result['lmks3d']) bs_list.append(src_img_result['bs']) # pose_arr = np.array(pose_trans_list) trans_mat_arr = np.array(pose_trans_list) verts_arr = np.array(verts_list) bs_arr = np.array(bs_list) min_bs_idx = np.argmin(bs_arr.sum(1)) # compute delta pose pose_arr = np.zeros([trans_mat_arr.shape[0], 6]) for i in range(pose_arr.shape[0]): euler_angles, translation_vector = matrix_to_euler_and_translation(trans_mat_arr[i]) # real pose of source pose_arr[i, :3] = euler_angles pose_arr[i, 3:6] = translation_vector init_tran_vec = face_result['trans_mat'][:3, 3] # init translation of tgt pose_arr[:, 3:6] = pose_arr[:, 3:6] - pose_arr[0, 3:6] + init_tran_vec # (relative translation of source) + (init translation of tgt) pose_arr_smooth = smooth_pose_seq(pose_arr, window_size=3) pose_mat_smooth = [euler_and_translation_to_matrix(pose_arr_smooth[i][:3], pose_arr_smooth[i][3:6]) for i in range(pose_arr_smooth.shape[0])] pose_mat_smooth = np.array(pose_mat_smooth) # face retarget verts_arr = verts_arr - verts_arr[min_bs_idx] + face_result['lmks3d'] # project 3D mesh to 2D landmark projected_vertices = project_points_with_trans(verts_arr, pose_mat_smooth, [frame_height, frame_width]) pose_list = [] for i, verts in enumerate(projected_vertices): lmk_img = vis.draw_landmarks((frame_width, frame_height), verts, normed=False) pose_image_np = cv2.resize(lmk_img, (width, height)) pose_list.append(pose_image_np) pose_list = np.array(pose_list) video_length = len(pose_list) video = pipe( ref_image_pil, pose_list, ref_pose, width, height, video_length, steps, cfg, generator=generator, ).videos save_path = f"{save_dir}/{size}x{size}_{time_str}_noaudio.mp4" save_videos_grid( video, save_path, n_rows=1, fps=src_fps, ) audio_output = f'{save_dir}/audio_from_video.aac' # extract audio try: ffmpeg.input(source_video).output(audio_output, acodec='copy').run() # merge audio and video stream = ffmpeg.input(save_path) audio = ffmpeg.input(audio_output) ffmpeg.output(stream.video, audio.audio, save_path.replace('_noaudio.mp4', '.mp4'), vcodec='copy', acodec='aac', shortest=None).run() os.remove(save_path) os.remove(audio_output) except: shutil.move( save_path, save_path.replace('_noaudio.mp4', '.mp4') ) return save_path.replace('_noaudio.mp4', '.mp4'), ref_image_pil