Update app.py

app.py

@@ -0,0 +1,494 @@
+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 random
+import torch
+
+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 scipy.interpolate import interp1d
+
+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.audio_models.model import Audio2MeshModel
+from src.audio_models.pose_model import Audio2PoseModel
+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, smooth_pose_seq
+from src.utils.util import crop_face
+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()
+
+a2p_model = Audio2PoseModel(audio_infer_config['a2p_model'])
+a2p_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2p_ckpt']), strict=False)
+a2p_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)
+
+frame_inter_model = init_frame_interpolation_model()
+
+def get_headpose_temp(input_video):
+    lmk_extractor = LMKExtractor()
+    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
+
+def audio2video(input_audio, ref_img, headpose_video=None, size=512, steps=25, length=60, seed=42, acc_flag=True):   
+    fps = 30
+    cfg = 3.5
+    fi_step = 3 if acc_flag else 1
+
+    generator = torch.manual_seed(seed)
+    
+    lmk_extractor = LMKExtractor()
+    vis = FaceMeshVisualizer()
+
+    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}")
+    while os.path.exists(save_dir):
+        save_dir = Path(f"output/{date_str}/{save_dir_name}_{np.random.randint(10000):04d}")
+    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)
+        mirrored_pose_seq = np.concatenate((pose_seq, pose_seq[-2:0:-1]), axis=0)
+        pose_seq = np.tile(mirrored_pose_seq, (sample['seq_len'] // len(mirrored_pose_seq) + 1, 1))[:sample['seq_len']]
+    else:
+        id_seed = random.randint(0, 99)
+        id_seed = torch.LongTensor([id_seed]).cuda()
+
+        # Currently, only inference up to a maximum length of 10 seconds is supported.
+        chunk_duration = 5 # 5 seconds
+        sr = 16000
+        fps = 30
+        chunk_size = sr * chunk_duration 
+
+        audio_chunks = list(sample['audio_feature'].split(chunk_size, dim=1))
+        seq_len_list = [chunk_duration*fps] * (len(audio_chunks) - 1) + [sample['seq_len'] % (chunk_duration*fps)] # 30 fps 
+
+        audio_chunks[-2] = torch.cat((audio_chunks[-2], audio_chunks[-1]), dim=1)
+        seq_len_list[-2] = seq_len_list[-2] + seq_len_list[-1]
+        del audio_chunks[-1]
+        del seq_len_list[-1]
+
+        pose_seq = []
+        for audio, seq_len in zip(audio_chunks, seq_len_list):
+            pose_seq_chunk = a2p_model.infer(audio, seq_len, id_seed)
+            pose_seq_chunk = pose_seq_chunk.squeeze().detach().cpu().numpy()
+            pose_seq_chunk[:, :3] *= 0.5
+            pose_seq.append(pose_seq_chunk)
+        
+        pose_seq = np.concatenate(pose_seq, 0)
+        pose_seq = smooth_pose_seq(pose_seq, 7)
+    
+    # project 3D mesh to 2D landmark
+    projected_vertices = project_points(pred, face_result['trans_mat'], 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 = []
+    args_L = len(pose_images) if length==0 or length > len(pose_images) else length
+    for pose_image_np in pose_images[: args_L : fi_step]:
+        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
+    
+    if acc_flag:
+        video = batch_images_interpolation_tool(video, frame_inter_model, inter_frames=fi_step-1)
+
+    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
+
+def video2video(ref_img, source_video, size=512, steps=25, length=60, seed=42, acc_flag=True):
+    cfg = 3.5
+    fi_step = 3 if acc_flag else 1
+    
+    generator = torch.manual_seed(seed)
+    
+    lmk_extractor = LMKExtractor()
+    vis = FaceMeshVisualizer()
+
+    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}")
+    while os.path.exists(save_dir):
+        save_dir = Path(f"output/{date_str}/{save_dir_name}_{np.random.randint(10000):04d}")
+    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)
+    
+    step = 1
+    if src_fps == 60:
+        src_fps = 30
+        step = 2
+    
+    pose_trans_list = []
+    verts_list = []
+    bs_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*fi_step]:
+        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
+    
+    if acc_flag:
+        video = batch_images_interpolation_tool(video, frame_inter_model, inter_frames=fi_step-1)
+
+    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
+
+
+################# 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>
+"""
+
+with gr.Blocks() as demo:
+    
+    gr.Markdown(title)
+    gr.Markdown(description)
+    
+    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=768, step=8, value=512, label="Video size (-W & -H)")
+                    a2v_step_slider = gr.Slider(minimum=5, maximum=30, step=1, value=25, label="Steps (--steps)")
+                
+                with gr.Row():
+                    a2v_length = gr.Slider(minimum=0, maximum=9999, step=1, value=60, label="Length (-L) (Set to 0 to automatically calculate length)")
+                    a2v_seed = gr.Number(value=42, label="Seed (--seed)")
+                
+                with gr.Row():
+                    a2v_acc_flag = gr.Checkbox(value=True, label="Accelerate")
+                    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=768, step=8, value=512, label="Video size (-W & -H)")
+                    v2v_step_slider = gr.Slider(minimum=5, maximum=30, step=1, value=25, label="Steps (--steps)")
+                
+                with gr.Row():
+                    v2v_length = gr.Slider(minimum=0, maximum=9999, step=1, value=60, label="Length (-L) (Set to 0 to automatically calculate length)")
+                    v2v_seed = gr.Number(value=42, label="Seed (--seed)")
+                
+                with gr.Row():
+                    v2v_acc_flag = gr.Checkbox(value=True, label="Accelerate")
+                    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, a2v_acc_flag], 
+        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, v2v_acc_flag], 
+        outputs=[v2v_output_video, v2v_ref_img]
+    )
+    
+demo.launch()