Create app-backup.py

app-backup.py

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+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)