🎭 Sonic: Advanced Portrait Animation
Transform still images into dynamic videos synchronized with audio
import spaces import gradio as gr import os import numpy as np from pydub import AudioSegment import hashlib from sonic import Sonic from PIL import Image import torch # 모델 초기화 cmd = ( 'python3 -m pip install "huggingface_hub[cli]"; ' 'huggingface-cli download LeonJoe13/Sonic --local-dir checkpoints; ' 'huggingface-cli download stabilityai/stable-video-diffusion-img2vid-xt --local-dir checkpoints/stable-video-diffusion-img2vid-xt; ' 'huggingface-cli download openai/whisper-tiny --local-dir checkpoints/whisper-tiny;' ) os.system(cmd) pipe = Sonic() def get_md5(content): md5hash = hashlib.md5(content) return md5hash.hexdigest() @spaces.GPU(duration=300) # 긴 비디오 처리를 위해 duration 300초로 설정 def get_video_res(img_path, audio_path, res_video_path, dynamic_scale=1.0): expand_ratio = 0.5 min_resolution = 512 inference_steps = 25 # 2초 분량의 비디오(25 프레임)로 고정 # 오디오 길이(참고용) 출력 audio = AudioSegment.from_file(audio_path) duration = len(audio) / 1000.0 # 초 단위 print(f"Audio duration: {duration} seconds, using inference_steps: {inference_steps}") face_info = pipe.preprocess(img_path, expand_ratio=expand_ratio) print(f"Face detection info: {face_info}") if face_info['face_num'] > 0: crop_image_path = img_path + '.crop.png' pipe.crop_image(img_path, crop_image_path, face_info['crop_bbox']) img_path = crop_image_path os.makedirs(os.path.dirname(res_video_path), exist_ok=True) # 고정된 inference_steps(25)로 비디오 생성 pipe.process( img_path, audio_path, res_video_path, min_resolution=min_resolution, inference_steps=inference_steps, dynamic_scale=dynamic_scale ) return res_video_path else: return -1 tmp_path = './tmp_path/' res_path = './res_path/' os.makedirs(tmp_path, exist_ok=True) os.makedirs(res_path, exist_ok=True) def process_sonic(image, audio, dynamic_scale): # 입력 검증 if image is None: raise gr.Error("Please upload an image") if audio is None: raise gr.Error("Please upload an audio file") img_md5 = get_md5(np.array(image)) audio_md5 = get_md5(audio[1]) print(f"Processing with image hash: {img_md5}, audio hash: {audio_md5}") sampling_rate, arr = audio[:2] if len(arr.shape) == 1: arr = arr[:, None] # numpy array로부터 AudioSegment 생성 audio_segment = AudioSegment( arr.tobytes(), frame_rate=sampling_rate, sample_width=arr.dtype.itemsize, channels=arr.shape[1] ) audio_segment = audio_segment.set_frame_rate(sampling_rate) # 파일 경로 생성 image_path = os.path.abspath(os.path.join(tmp_path, f'{img_md5}.png')) audio_path = os.path.abspath(os.path.join(tmp_path, f'{audio_md5}.wav')) res_video_path = os.path.abspath(os.path.join(res_path, f'{img_md5}_{audio_md5}_{dynamic_scale}.mp4')) # 입력 파일이 없으면 저장 if not os.path.exists(image_path): image.save(image_path) if not os.path.exists(audio_path): audio_segment.export(audio_path, format="wav") # 캐시된 결과가 있으면 반환, 없으면 새로 생성 if os.path.exists(res_video_path): print(f"Using cached result: {res_video_path}") return res_video_path else: print(f"Generating new video with dynamic scale: {dynamic_scale}") return get_video_res(image_path, audio_path, res_video_path, dynamic_scale) # 예시 데이터를 위한 dummy 함수 (필요시 실제 예시 데이터를 추가하세요) def get_example(): return [] css = """ .gradio-container { font-family: 'Arial', sans-serif; } .main-header { text-align: center; color: #2a2a2a; margin-bottom: 2em; } .parameter-section { background-color: #f5f5f5; padding: 1em; border-radius: 8px; margin: 1em 0; } .example-section { margin-top: 2em; } """ with gr.Blocks(css=css) as demo: gr.HTML("""
Transform still images into dynamic videos synchronized with audio