AniPortrait_official / src /create_modules.py
zejunyang
debug
18f04c7
import os
import ffmpeg
from datetime import datetime
from pathlib import Path
import numpy as np
import cv2
import spaces
import shutil
import torch
from omegaconf import OmegaConf
from PIL import Image
from scipy.spatial.transform import Rotation as R
from scipy.interpolate import interp1d
from torchvision import transforms
from diffusers import AutoencoderKL, DDIMScheduler
from omegaconf import OmegaConf
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.audio_models.model import Audio2MeshModel
from src.utils.mp_utils import LMKExtractor
from src.utils.draw_util import FaceMeshVisualizer
from src.utils.util import get_fps, read_frames, save_videos_grid
from src.utils.audio_util import prepare_audio_feature
from src.utils.pose_util import project_points_with_trans, matrix_to_euler_and_translation, euler_and_translation_to_matrix, project_points
from src.utils.crop_face_single import crop_face
class Processer():
def __init__(self):
self.a2m_model, self.pipe = self.create_models()
# @spaces.GPU
def create_models(self):
config = OmegaConf.load('./configs/prompts/animation_audio.yaml')
if config.weight_dtype == "fp16":
weight_dtype = torch.float16
else:
weight_dtype = torch.float32
audio_infer_config = OmegaConf.load(config.audio_inference_config)
# prepare model
a2m_model = Audio2MeshModel(audio_infer_config['a2m_model'])
a2m_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2m_ckpt'], map_location="cpu"), strict=False)
a2m_model.to("cuda").eval()
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)
# 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)
return a2m_model, pipe
@spaces.GPU
def audio2video(self, input_audio, ref_img, headpose_video=None, size=512, steps=25, length=150, seed=42):
fps = 30
cfg = 3.5
lmk_extractor = LMKExtractor()
vis = FaceMeshVisualizer()
config = OmegaConf.load('./configs/prompts/animation_audio.yaml')
audio_infer_config = OmegaConf.load(config.audio_inference_config)
generator = torch.manual_seed(seed)
width, height = size, size
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)
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)
sample = prepare_audio_feature(input_audio, wav2vec_model_path=audio_infer_config['a2m_model']['model_path'])
sample['audio_feature'] = torch.from_numpy(sample['audio_feature']).float().cuda()
sample['audio_feature'] = sample['audio_feature'].unsqueeze(0)
# inference
pred = self.a2m_model.infer(sample['audio_feature'], sample['seq_len'])
pred = pred.squeeze().detach().cpu().numpy()
pred = pred.reshape(pred.shape[0], -1, 3)
pred = pred + face_result['lmks3d']
if headpose_video is not None:
pose_seq = get_headpose_temp(headpose_video, lmk_extractor)
else:
pose_seq = np.load(config['pose_temp'])
mirrored_pose_seq = np.concatenate((pose_seq, pose_seq[-2:0:-1]), axis=0)
cycled_pose_seq = np.tile(mirrored_pose_seq, (sample['seq_len'] // len(mirrored_pose_seq) + 1, 1))[:sample['seq_len']]
# project 3D mesh to 2D landmark
projected_vertices = project_points(pred, face_result['trans_mat'], cycled_pose_seq, [height, width])
pose_images = []
for i, verts in enumerate(projected_vertices):
lmk_img = vis.draw_landmarks((width, height), verts, normed=False)
pose_images.append(lmk_img)
pose_list = []
pose_tensor_list = []
pose_transform = transforms.Compose(
[transforms.Resize((height, width)), transforms.ToTensor()]
)
args_L = len(pose_images) if length==0 or length > len(pose_images) else length
args_L = min(args_L, 300)
for pose_image_np in pose_images[: args_L]:
pose_image_pil = Image.fromarray(cv2.cvtColor(pose_image_np, cv2.COLOR_BGR2RGB))
pose_tensor_list.append(pose_transform(pose_image_pil))
pose_image_np = cv2.resize(pose_image_np, (width, height))
pose_list.append(pose_image_np)
pose_list = np.array(pose_list)
video_length = len(pose_tensor_list)
video = self.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=fps,
)
stream = ffmpeg.input(save_path)
audio = ffmpeg.input(input_audio)
ffmpeg.output(stream.video, audio.audio, save_path.replace('_noaudio.mp4', '.mp4'), vcodec='copy', acodec='aac', shortest=None).run()
os.remove(save_path)
return save_path.replace('_noaudio.mp4', '.mp4'), ref_image_pil
@spaces.GPU
def video2video(self, ref_img, source_video, size=512, steps=25, length=150, seed=42):
cfg = 3.5
lmk_extractor = LMKExtractor()
vis = FaceMeshVisualizer()
generator = torch.manual_seed(seed)
width, height = size, size
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)
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'])
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 = self.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
def matrix_to_euler_and_translation(matrix):
rotation_matrix = matrix[:3, :3]
translation_vector = matrix[:3, 3]
rotation = R.from_matrix(rotation_matrix)
euler_angles = rotation.as_euler('xyz', degrees=True)
return euler_angles, translation_vector
def smooth_pose_seq(pose_seq, window_size=5):
smoothed_pose_seq = np.zeros_like(pose_seq)
for i in range(len(pose_seq)):
start = max(0, i - window_size // 2)
end = min(len(pose_seq), i + window_size // 2 + 1)
smoothed_pose_seq[i] = np.mean(pose_seq[start:end], axis=0)
return smoothed_pose_seq
def get_headpose_temp(input_video, lmk_extractor):
cap = cv2.VideoCapture(input_video)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
trans_mat_list = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
result = lmk_extractor(frame)
trans_mat_list.append(result['trans_mat'].astype(np.float32))
cap.release()
trans_mat_arr = np.array(trans_mat_list)
# 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
# interpolate to 30 fps
new_fps = 30
old_time = np.linspace(0, total_frames / fps, total_frames)
new_time = np.linspace(0, total_frames / fps, int(total_frames * new_fps / fps))
pose_arr_interp = np.zeros((len(new_time), 6))
for i in range(6):
interp_func = interp1d(old_time, pose_arr[:, i])
pose_arr_interp[:, i] = interp_func(new_time)
pose_arr_smooth = smooth_pose_seq(pose_arr_interp)
return pose_arr_smooth