#!/usr/bin/env python # -*- coding: UTF-8 -*- ''' webui ''' import os 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 from moviepy.editor import VideoFileClip, AudioFileClip, ImageClip huggingface_hub.snapshot_download( repo_id='BadToBest/EchoMimic', local_dir='./pretrained_weights', local_dir_use_symlinks=False, ) # 환경 변수 대신 코드 내에서 직접 설정 is_shared_ui = False # 또는 True, 필요에 따라 설정 # is_shared_ui의 값에 따라 available_property 설정 available_property = not is_shared_ui # 이제 is_shared_ui와 available_property 변수는 코드 내에서 직접 관리됩니다. advanced_settings_label = "Advanced Settings" default_values = { "width": 512, "height": 512, "length": 1200, "seed": 420, "facemask_dilation_ratio": 0.1, "facecrop_dilation_ratio": 1.0, "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(dtype=weight_dtype, device="cuda") face_locator.load_state_dict(torch.load(config.face_locator_path)) ## 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=device) ############# 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] def 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): if seed is not None and seed > -1: generator = torch.manual_seed(seed) else: generator = torch.manual_seed(random.randint(100, 1000000)) #### face musk prepare face_img = cv2.imread(uploaded_img) 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)) ref_image_pil = Image.fromarray(face_img[:, :, [2, 1, 0]]) face_mask_tensor = torch.Tensor(face_mask).to(dtype=weight_dtype, device="cuda").unsqueeze(0).unsqueeze(0).unsqueeze(0) / 255.0 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 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) # 워터마크 이미지 로드 및 크기 조정 watermark = (ImageClip("watermark.png") # 워터마크 이미지 경로 .set_duration(video_clip.duration) .resize(height=50) # 워터마크 크기 조정 .margin(right=8, bottom=8, opacity=0) # 마진 및 투명도 설정 .set_pos(("right", "bottom"))) # 위치 설정 final_clip = video_clip.set_audio(audio_clip).set_position(("center", "center")).fx(vfx.composite, watermark) # APP.PY와 동일한 경로에 위치시키기 final_output_path = Path(__file__).parent / "output_video_with_audio.mp4" final_clip.write_videofile(str(final_output_path), codec="libx264", audio_codec="aac") return final_output_path with gr.Blocks() as demo: gr.Markdown('# Mimic FACE') with gr.Row(): with gr.Column(): uploaded_img = gr.Image(type="filepath", label="Reference Image") 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) generate_button = gr.Button("Generate Video") with gr.Column(): output_video = gr.Video() gr.Examples( label = "Portrait examples", examples = [ ['assets/test_imgs/a.png'], ], inputs = [uploaded_img] ) gr.Examples( label = "Audio examples", examples = [ ['assets/test_audios/chunnuanhuakai.wav'], ], inputs = [uploaded_audio] ) def generate_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): 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, api_name="generate_video_api" # Expose API endpoint ) parser = argparse.ArgumentParser(description='Mimic FACE') parser.add_argument('--server_name', type=str, default='0.0.0.0', help='Server name') parser.add_argument('--server_port', type=int, default=7860, help='Server port') args = parser.parse_args() if __name__ == '__main__': # demo.launch( demo.queue(max_size=4).launch( server_name=args.server_name, server_port=args.server_port, show_api=True # Enable API documentation )