🎭 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 # Initialize the model 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) # Increased duration to handle longer videos 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 # Get audio duration (정보 출력용) audio = AudioSegment.from_file(audio_path) duration = len(audio) / 1000.0 # 초 단위 변환 face_info = pipe.preprocess(img_path, expand_ratio=expand_ratio) print(f"Face detection info: {face_info}") print(f"Audio duration: {duration} seconds") 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) # NOTE: Sonic.process()는 더 이상 duration 인자를 받지 않으므로 제거합니다. pipe.process( img_path, audio_path, res_video_path, min_resolution=min_resolution, inference_steps=inference_steps, dynamic_scale=dynamic_scale ) 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] # 오디오 세그먼트 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 (필요에 따라 실제 예시 데이터를 넣으세요) 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