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import gradio as gr | |
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, save_pil_imgs | |
from src.audio_models.model import Audio2MeshModel | |
from src.utils.audio_util import prepare_audio_feature | |
from src.utils.mp_utils import LMKExtractor | |
from src.utils.draw_util import FaceMeshVisualizer | |
from src.utils.pose_util import project_points, project_points_with_trans, matrix_to_euler_and_translation, euler_and_translation_to_matrix | |
from src.utils.crop_face_single import crop_face | |
from src.audio2vid import get_headpose_temp, smooth_pose_seq | |
from src.utils.frame_interpolation import init_frame_interpolation_model, batch_images_interpolation_tool | |
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.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) | |
lmk_extractor = LMKExtractor() | |
vis = FaceMeshVisualizer() | |
frame_inter_model = init_frame_interpolation_model() | |
def audio2video(input_audio, ref_img, headpose_video=None, size=512, steps=25, length=150, seed=42): | |
fps = 30 | |
cfg = 3.5 | |
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 = 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) | |
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, 180) | |
for pose_image_np in pose_images[: args_L : 2]: | |
# 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_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=fps, | |
# ) | |
save_path = f"{save_dir}/{size}x{size}_{time_str}_noaudio" | |
save_pil_imgs(video, save_path) | |
save_path = batch_images_interpolation_tool(save_path, frame_inter_model, int(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 | |
def video2video(ref_img, source_video, size=512, steps=25, length=150, seed=42): | |
cfg = 3.5 | |
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, 180*step) | |
for src_image_pil in source_images[: args_L : step*2]: | |
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 = 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, | |
# ) | |
save_path = f"{save_dir}/{size}x{size}_{time_str}_noaudio" | |
save_pil_imgs(video, save_path) | |
save_path = batch_images_interpolation_tool(save_path, frame_inter_model, int(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 | |
################# GUI ################ | |
title = r""" | |
<h1>AniPortrait</h1> | |
""" | |
description = r""" | |
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/Zejun-Yang/AniPortrait' target='_blank'><b>AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animations</b></a>.<br> | |
""" | |
tips = r""" | |
When the video cannot be displayed, you can download the result video. | |
""" | |
with gr.Blocks() as demo: | |
gr.Markdown(title) | |
gr.Markdown(description) | |
gr.Markdown(tips) | |
with gr.Tab("Audio2video"): | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Row(): | |
a2v_input_audio = gr.Audio(sources=["upload", "microphone"], type="filepath", editable=True, label="Input audio", interactive=True) | |
a2v_ref_img = gr.Image(label="Upload reference image", sources="upload") | |
a2v_headpose_video = gr.Video(label="Option: upload head pose reference video", sources="upload") | |
with gr.Row(): | |
a2v_size_slider = gr.Slider(minimum=256, maximum=1024, step=8, value=512, label="Video size (-W & -H)") | |
a2v_step_slider = gr.Slider(minimum=5, maximum=50, step=1, value=20, label="Steps (--steps)") | |
with gr.Row(): | |
a2v_length = gr.Slider(minimum=0, maximum=180, step=1, value=60, label="Length (-L) (Set 0 to automatically calculate video length.)") | |
a2v_seed = gr.Number(value=42, label="Seed (--seed)") | |
a2v_botton = gr.Button("Generate", variant="primary") | |
a2v_output_video = gr.PlayableVideo(label="Result", interactive=False) | |
gr.Examples( | |
examples=[ | |
["configs/inference/audio/lyl.wav", "configs/inference/ref_images/Aragaki.png", None], | |
["configs/inference/audio/lyl.wav", "configs/inference/ref_images/solo.png", None], | |
["configs/inference/audio/lyl.wav", "configs/inference/ref_images/lyl.png", "configs/inference/head_pose_temp/pose_ref_video.mp4"], | |
], | |
inputs=[a2v_input_audio, a2v_ref_img, a2v_headpose_video], | |
) | |
with gr.Tab("Video2video"): | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Row(): | |
v2v_ref_img = gr.Image(label="Upload reference image", sources="upload") | |
v2v_source_video = gr.Video(label="Upload source video", sources="upload") | |
with gr.Row(): | |
v2v_size_slider = gr.Slider(minimum=256, maximum=1024, step=8, value=512, label="Video size (-W & -H)") | |
v2v_step_slider = gr.Slider(minimum=5, maximum=50, step=1, value=20, label="Steps (--steps)") | |
with gr.Row(): | |
v2v_length = gr.Slider(minimum=0, maximum=180, step=1, value=60, label="Length (-L) (Set 0 to automatically calculate video length.)") | |
v2v_seed = gr.Number(value=42, label="Seed (--seed)") | |
v2v_botton = gr.Button("Generate", variant="primary") | |
v2v_output_video = gr.PlayableVideo(label="Result", interactive=False) | |
gr.Examples( | |
examples=[ | |
["configs/inference/ref_images/Aragaki.png", "configs/inference/video/Aragaki_song.mp4"], | |
["configs/inference/ref_images/solo.png", "configs/inference/video/Aragaki_song.mp4"], | |
["configs/inference/ref_images/lyl.png", "configs/inference/head_pose_temp/pose_ref_video.mp4"], | |
], | |
inputs=[v2v_ref_img, v2v_source_video, a2v_headpose_video], | |
) | |
a2v_botton.click( | |
fn=audio2video, | |
inputs=[a2v_input_audio, a2v_ref_img, a2v_headpose_video, | |
a2v_size_slider, a2v_step_slider, a2v_length, a2v_seed], | |
outputs=[a2v_output_video, a2v_ref_img] | |
) | |
v2v_botton.click( | |
fn=video2video, | |
inputs=[v2v_ref_img, v2v_source_video, | |
v2v_size_slider, v2v_step_slider, v2v_length, v2v_seed], | |
outputs=[v2v_output_video, v2v_ref_img] | |
) | |
demo.launch() | |