#!/usr/bin/env python # -*- coding: UTF-8 -*- ''' webui ''' import spaces import os os.system('pip install scikit-image') os.system('pip install IPython') import random from datetime import datetime from pathlib import Path import cv2 import numpy as np import torch from diffusers import AutoencoderKL, DDIMScheduler from omegaconf import OmegaConf from PIL import Image from src.models.unet_2d_condition import UNet2DConditionModel from src.models.unet_3d_echo import EchoUNet3DConditionModel from src.models.whisper.audio2feature import load_audio_model from src.pipelines.pipeline_echo_mimic import Audio2VideoPipeline from src.utils.util import save_videos_grid, crop_and_pad from src.models.face_locator import FaceLocator from moviepy.editor import VideoFileClip, AudioFileClip from facenet_pytorch import MTCNN import argparse import gradio as gr import huggingface_hub import pickle from src.utils.draw_utils import FaceMeshVisualizer from src.utils.motion_utils import motion_sync from src.utils.mp_utils import LMKExtractor huggingface_hub.snapshot_download( repo_id='BadToBest/EchoMimic', local_dir='./pretrained_weights', local_dir_use_symlinks=False, ) is_shared_ui = True if "fffiloni/EchoMimic" in os.environ['SPACE_ID'] else False available_property = False if is_shared_ui else True advanced_settings_label = "Advanced Configuration (only for duplicated spaces)" if is_shared_ui else "Advanced Configuration" default_values = { "width": 512, "height": 512, "length": 1200, "seed": 420, "facemask_dilation_ratio": 0.1, "facecrop_dilation_ratio": 0.5, "context_frames": 12, "context_overlap": 3, "cfg": 2.5, "steps": 30, "sample_rate": 16000, "fps": 24, "device": "cuda" } ffmpeg_path = os.getenv('FFMPEG_PATH') if ffmpeg_path is None: print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static") elif ffmpeg_path not in os.getenv('PATH'): print("add ffmpeg to path") os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}" config_path = "./configs/prompts/animation.yaml" config = OmegaConf.load(config_path) if config.weight_dtype == "fp16": weight_dtype = torch.float16 else: weight_dtype = torch.float32 device = "cuda" if not torch.cuda.is_available(): device = "cpu" inference_config_path = config.inference_config infer_config = OmegaConf.load(inference_config_path) ############# model_init started ############# ## vae init vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path).to("cuda", dtype=weight_dtype) ## reference net init reference_unet = UNet2DConditionModel.from_pretrained( config.pretrained_base_model_path, subfolder="unet", ).to(dtype=weight_dtype, device=device) reference_unet.load_state_dict(torch.load(config.reference_unet_path, map_location="cpu")) ## denoising net init if os.path.exists(config.motion_module_path): ### stage1 + stage2 denoising_unet = EchoUNet3DConditionModel.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=device) else: ### only stage1 denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d( config.pretrained_base_model_path, "", subfolder="unet", unet_additional_kwargs={ "use_motion_module": False, "unet_use_temporal_attention": False, "cross_attention_dim": infer_config.unet_additional_kwargs.cross_attention_dim } ).to(dtype=weight_dtype, device=device) denoising_unet.load_state_dict(torch.load(config.denoising_unet_path, map_location="cpu"), strict=False) ## face locator init face_locator = FaceLocator(320, conditioning_channels=1, block_out_channels=(16, 32, 96, 256)).to("cuda", dtype=weight_dtype) face_locator.load_state_dict(torch.load(config.face_locator_path, map_location='cpu')) ## load audio processor params audio_processor = load_audio_model(model_path=config.audio_model_path, device=device) ## load face detector params face_detector = MTCNN(image_size=320, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device="cpu") ############# model_init finished ############# sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs) scheduler = DDIMScheduler(**sched_kwargs) pipe = Audio2VideoPipeline( vae=vae, reference_unet=reference_unet, denoising_unet=denoising_unet, audio_guider=audio_processor, face_locator=face_locator, scheduler=scheduler, ).to("cuda", dtype=weight_dtype) def select_face(det_bboxes, probs): ## max face from faces that the prob is above 0.8 ## box: xyxy if det_bboxes is None or probs is None: return None filtered_bboxes = [] for bbox_i in range(len(det_bboxes)): if probs[bbox_i] > 0.8: filtered_bboxes.append(det_bboxes[bbox_i]) if len(filtered_bboxes) == 0: return None sorted_bboxes = sorted(filtered_bboxes, key=lambda x:(x[3]-x[1]) * (x[2] - x[0]), reverse=True) return sorted_bboxes[0] lmk_extractor = LMKExtractor() def face_detection(uploaded_img, facemask_dilation_ratio, facecrop_dilation_ratio, width, height): face_img = cv2.imread(uploaded_img) if face_img is None: raise gr.Error("input image should be uploaded or selected.") face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8') det_bboxes, probs = face_detector.detect(face_img) select_bbox = select_face(det_bboxes, probs) if select_bbox is None: face_mask[:, :] = 255 else: xyxy = select_bbox[:4] xyxy = np.round(xyxy).astype('int') rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] r_pad = int((re - rb) * facemask_dilation_ratio) c_pad = int((ce - cb) * facemask_dilation_ratio) face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255 r_pad_crop = int((re - rb) * facecrop_dilation_ratio) c_pad_crop = int((ce - cb) * facecrop_dilation_ratio) crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + r_pad_crop, face_img.shape[0])] face_img = crop_and_pad(face_img, crop_rect) face_mask = crop_and_pad(face_mask, crop_rect) face_img = cv2.resize(face_img, (width, height)) face_mask = cv2.resize(face_mask, (width, height)) print('face detect done.') return face_img, face_mask @spaces.GPU(duration=200) def video_pipe(face_img, face_mask, uploaded_audio, width, height, length, context_frames, context_overlap, cfg, steps, sample_rate, fps, device): face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0 ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) video = pipe( ref_image_pil, uploaded_audio, face_mask_tensor, width, height, length, steps, cfg, audio_sample_rate=sample_rate, context_frames=context_frames, fps=fps, context_overlap=context_overlap ).videos print('video pipe done.') save_dir = Path("output/tmp") save_dir.mkdir(exist_ok=True, parents=True) output_video_path = save_dir / "output_video.mp4" save_videos_grid(video, str(output_video_path), n_rows=1, fps=fps) video_clip = VideoFileClip(str(output_video_path)) audio_clip = AudioFileClip(uploaded_audio) final_output_path = save_dir / "output_video_with_audio.mp4" video_clip = video_clip.set_audio(audio_clip) video_clip.write_videofile(str(final_output_path), codec="libx264", audio_codec="aac") return final_output_path def process_video(uploaded_img, uploaded_audio, width, height, length, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device): face_img, face_mask = face_detection(uploaded_img, facemask_dilation_ratio, facecrop_dilation_ratio, width, height) final_output_path = video_pipe(face_img, face_mask, uploaded_audio, width, height, length, context_frames, context_overlap, cfg, steps, sample_rate, fps, device) return final_output_path # @spaces.GPU # def process_video(uploaded_img, uploaded_audio, width, height, length, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device): # #### face musk prepare # face_img = cv2.imread(uploaded_img) # if face_img is None: # raise gr.Error("input image should be uploaded or selected.") # face_mask = np.zeros((face_img.shape[0], face_img.shape[1])).astype('uint8') # det_bboxes, probs = face_detector.detect(face_img) # select_bbox = select_face(det_bboxes, probs) # if select_bbox is None: # face_mask[:, :] = 255 # else: # xyxy = select_bbox[:4] # xyxy = np.round(xyxy).astype('int') # rb, re, cb, ce = xyxy[1], xyxy[3], xyxy[0], xyxy[2] # r_pad = int((re - rb) * facemask_dilation_ratio) # c_pad = int((ce - cb) * facemask_dilation_ratio) # face_mask[rb - r_pad : re + r_pad, cb - c_pad : ce + c_pad] = 255 # #### face crop # r_pad_crop = int((re - rb) * facecrop_dilation_ratio) # c_pad_crop = int((ce - cb) * facecrop_dilation_ratio) # crop_rect = [max(0, cb - c_pad_crop), max(0, rb - r_pad_crop), min(ce + c_pad_crop, face_img.shape[1]), min(re + r_pad_crop, face_img.shape[0])] # face_img = crop_and_pad(face_img, crop_rect) # face_mask = crop_and_pad(face_mask, crop_rect) # face_img = cv2.resize(face_img, (width, height)) # face_mask = cv2.resize(face_mask, (width, height)) # print('face detect done.') # # ==================== face_locator ===================== # ''' # driver_video = "./assets/driven_videos/c.mp4" # input_frames_cv2 = [cv2.resize(center_crop_cv2(pil_to_cv2(i)), (512, 512)) for i in pils_from_video(driver_video)] # ref_det = lmk_extractor(face_img) # visualizer = FaceMeshVisualizer(draw_iris=False, draw_mouse=False) # pose_list = [] # sequence_driver_det = [] # try: # for frame in input_frames_cv2: # result = lmk_extractor(frame) # assert result is not None, "{}, bad video, face not detected".format(driver_video) # sequence_driver_det.append(result) # except: # print("face detection failed") # exit() # sequence_det_ms = motion_sync(sequence_driver_det, ref_det) # for p in sequence_det_ms: # tgt_musk = visualizer.draw_landmarks((width, height), p) # tgt_musk_pil = Image.fromarray(np.array(tgt_musk).astype(np.uint8)).convert('RGB') # pose_list.append(torch.Tensor(np.array(tgt_musk_pil)).to(dtype=weight_dtype, device="cuda").permute(2,0,1) / 255.0) # ''' # # face_mask_tensor = torch.stack(pose_list, dim=1).unsqueeze(0) # face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0 # ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) # #del pose_list, sequence_det_ms, sequence_driver_det, input_frames_cv2 # video = pipe( # ref_image_pil, # uploaded_audio, # face_mask_tensor, # width, # height, # length, # steps, # cfg, # #generator=generator, # audio_sample_rate=sample_rate, # context_frames=context_frames, # fps=fps, # context_overlap=context_overlap # ).videos # print('video pipe done.') # save_dir = Path("output/tmp") # save_dir.mkdir(exist_ok=True, parents=True) # output_video_path = save_dir / "output_video.mp4" # save_videos_grid(video, str(output_video_path), n_rows=1, fps=fps) # video_clip = VideoFileClip(str(output_video_path)) # audio_clip = AudioFileClip(uploaded_audio) # final_output_path = save_dir / "output_video_with_audio.mp4" # video_clip = video_clip.set_audio(audio_clip) # video_clip.write_videofile(str(final_output_path), codec="libx264", audio_codec="aac") # return final_output_path with gr.Blocks() as demo: gr.Markdown('# EchoMimic') gr.Markdown('## Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning') gr.Markdown('Inference time: from ~7mins/240frames to ~50s/240frames on V100 GPU') gr.HTML("""
""") with gr.Row(): with gr.Column(min_width=250): uploaded_img = gr.Image(type="filepath", label="Reference Image") with gr.Column(min_width=250): uploaded_audio = gr.Audio(type="filepath", label="Input Audio") with gr.Accordion(label=advanced_settings_label, open=False): with gr.Row(): width = gr.Slider(label="Width", minimum=128, maximum=1024, value=default_values["width"], interactive=available_property) height = gr.Slider(label="Height", minimum=128, maximum=1024, value=default_values["height"], interactive=available_property) with gr.Row(): length = gr.Slider(label="Length", minimum=100, maximum=5000, value=default_values["length"], interactive=available_property) seed = gr.Slider(label="Seed", minimum=0, maximum=10000, value=default_values["seed"], interactive=available_property) with gr.Row(): facemask_dilation_ratio = gr.Slider(label="Facemask Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facemask_dilation_ratio"], interactive=available_property) facecrop_dilation_ratio = gr.Slider(label="Facecrop Dilation Ratio", minimum=0.0, maximum=1.0, step=0.01, value=default_values["facecrop_dilation_ratio"], interactive=available_property) with gr.Row(): context_frames = gr.Slider(label="Context Frames", minimum=0, maximum=50, step=1, value=default_values["context_frames"], interactive=available_property) context_overlap = gr.Slider(label="Context Overlap", minimum=0, maximum=10, step=1, value=default_values["context_overlap"], interactive=available_property) with gr.Row(): cfg = gr.Slider(label="CFG", minimum=0.0, maximum=10.0, step=0.1, value=default_values["cfg"], interactive=available_property) steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=default_values["steps"], interactive=available_property) with gr.Row(): sample_rate = gr.Slider(label="Sample Rate", minimum=8000, maximum=48000, step=1000, value=default_values["sample_rate"], interactive=available_property) fps = gr.Slider(label="FPS", minimum=1, maximum=60, step=1, value=default_values["fps"], interactive=available_property) device = gr.Radio(label="Device", choices=["cuda", "cpu"], value=default_values["device"], interactive=available_property) with gr.Column(min_width=250): generate_button = gr.Button("Generate Video") output_video = gr.Video() with gr.Row(): gr.Examples( label = "Portrait examples", examples = [ ['assets/test_imgs/a.png'], ['assets/test_imgs/b.png'], ['assets/test_imgs/c.png'], ['assets/test_imgs/d.png'], ['assets/test_imgs/e.png'] ], inputs = [uploaded_img] ) gr.Examples( label = "Audio examples", examples = [ ['assets/test_audios/chunnuanhuakai.wav'], ['assets/test_audios/chunwang.wav'], ['assets/test_audios/echomimic_en_girl.wav'], ['assets/test_audios/echomimic_en.wav'], ['assets/test_audios/echomimic_girl.wav'], ['assets/test_audios/echomimic.wav'], ['assets/test_audios/jane.wav'], ['assets/test_audios/mei.wav'], ['assets/test_audios/walden.wav'], ['assets/test_audios/yun.wav'], ], inputs = [uploaded_audio] ) # gr.HTML(""" #
# # Duplicate this Space # # # Follow me on HF # #
# """) # def generate_video(uploaded_img, uploaded_audio, facemask_dilation_ratio=default_values["facemask_dilation_ratio"], # facecrop_dilation_ratio=default_values["facecrop_dilation_ratio"], # context_frames=default_values["context_frames"], # context_overlap=default_values["context_overlap"], # cfg=default_values["cfg"], # steps=default_values["steps"], # sample_rate=default_values["sample_rate"], # fps=default_values["fps"], # device=default_values["device"], # width=default_values["width"], # height=default_values["height"], # length=default_values["length"] ): # final_output_path = process_video( # uploaded_img, uploaded_audio, width, height, length, seed, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device # ) # output_video= final_output_path # return final_output_path # generate_button.click( # generate_video, # inputs=[ # uploaded_img, # uploaded_audio, # # width, # # height, # # length, # # seed, # # facemask_dilation_ratio, # # facecrop_dilation_ratio, # # context_frames, # # context_overlap, # # cfg, # # steps, # # sample_rate, # # fps, # # device # ], # outputs=output_video, # show_api=False # ) def generate_video(uploaded_img, uploaded_audio, facemask_dilation_ratio=default_values["facemask_dilation_ratio"], facecrop_dilation_ratio=default_values["facecrop_dilation_ratio"], context_frames=default_values["context_frames"], context_overlap=default_values["context_overlap"], cfg=default_values["cfg"], steps=default_values["steps"], sample_rate=default_values["sample_rate"], fps=default_values["fps"], device=default_values["device"], width=default_values["width"], height=default_values["height"], length=default_values["length"] ): final_output_path = process_video( uploaded_img, uploaded_audio, width, height, length, facemask_dilation_ratio, facecrop_dilation_ratio, context_frames, context_overlap, cfg, steps, sample_rate, fps, device ) output_video = final_output_path return final_output_path generate_button.click( generate_video, inputs=[ uploaded_img, uploaded_audio ], outputs=output_video, show_progress=True ) parser = argparse.ArgumentParser(description='EchoMimic') parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name') parser.add_argument('--server_port', type=int, default=7680, help='Server port') args = parser.parse_args() # demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True) if __name__ == '__main__': demo.queue(max_size=3).launch(show_api=False, show_error=True) #demo.launch(server_name=args.server_name, server_port=args.server_port, inbrowser=True)