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import argparse
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
from src.audio2vid import smooth_pose_seq
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default='./configs/prompts/animation_facereenac.yaml')
parser.add_argument("-W", type=int, default=512)
parser.add_argument("-H", type=int, default=512)
parser.add_argument("-L", type=int)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--cfg", type=float, default=3.5)
parser.add_argument("--steps", type=int, default=25)
parser.add_argument("--fps", type=int)
args = parser.parse_args()
return args
# @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)
# TODO: 人脸检测+裁剪
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
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
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
trans_mat_inv_frame_0 = np.linalg.inv(trans_mat_arr[0])
pose_arr = np.zeros([trans_mat_arr.shape[0], 6])
for i in range(pose_arr.shape[0]):
pose_mat = trans_mat_inv_frame_0 @ trans_mat_arr[i]
euler_angles, translation_vector = matrix_to_euler_and_translation(pose_mat)
pose_arr[i, :3] = euler_angles
pose_arr[i, 3:6] = translation_vector
pose_arr = smooth_pose_seq(pose_arr)
# 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_arr, [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').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')