app.py

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,theme="apriel") as demo:
    gr.HTML("""
        <div class="main-header">
            <h1>🎭 Sonic: Advanced Portrait Animation</h1>
            <p>Transform still images into dynamic videos synchronized with audio</p>
        </div>
    """)
    
    with gr.Row():
        with gr.Column():
            image_input = gr.Image(
                type='pil',
                label="Portrait Image",
                elem_id="image_input"
            )
            
            audio_input = gr.Audio(
                label="Voice/Audio Input",
                elem_id="audio_input",
                type="numpy"
            )
            
            with gr.Column():
                dynamic_scale = gr.Slider(
                    minimum=0.5,
                    maximum=2.0,
                    value=1.0,
                    step=0.1,
                    label="Animation Intensity",
                    info="Adjust to control movement intensity (0.5: subtle, 2.0: dramatic)"
                )
            
            process_btn = gr.Button(
                "Generate Animation",
                variant="primary",
                elem_id="process_btn"
            )
        
        with gr.Column():
            video_output = gr.Video(
                label="Generated Animation",
                elem_id="video_output"
            )
    
    process_btn.click(
        fn=process_sonic,
        inputs=[image_input, audio_input, dynamic_scale],
        outputs=video_output,
        api_name="animate"
    )
    
    gr.Examples(
        examples=get_example(),
        fn=process_sonic,
        inputs=[image_input, audio_input, dynamic_scale],
        outputs=video_output,
        cache_examples=False
    )
    


# 공개 링크 생성: share=True
demo.launch(share=True)