Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -27,26 +27,26 @@ def get_video_res(img_path, audio_path, res_video_path, dynamic_scale=1.0):
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expand_ratio = 0.5
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min_resolution = 512
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inference_steps = 25
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-
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# Get audio duration
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audio = AudioSegment.from_file(audio_path)
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duration = len(audio) / 1000.0 # Convert to seconds
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-
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face_info = pipe.preprocess(img_path, expand_ratio=expand_ratio)
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print(f"Face detection info: {face_info}")
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print(f"Audio duration: {duration} seconds")
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-
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if face_info['face_num'] > 0:
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crop_image_path = img_path + '.crop.png'
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pipe.crop_image(img_path, crop_image_path, face_info['crop_bbox'])
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img_path = crop_image_path
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os.makedirs(os.path.dirname(res_video_path), exist_ok=True)
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-
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# Process with full audio duration
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pipe.process(
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img_path,
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audio_path,
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res_video_path,
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min_resolution=min_resolution,
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inference_steps=inference_steps,
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dynamic_scale=dynamic_scale,
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@@ -57,8 +57,8 @@ def get_video_res(img_path, audio_path, res_video_path, dynamic_scale=1.0):
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tmp_path = './tmp_path/'
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res_path = './res_path/'
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os.makedirs(tmp_path, exist_ok=
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os.makedirs(res_path, exist_ok=
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def process_sonic(image, audio, dynamic_scale):
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# Input validation
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@@ -66,15 +66,15 @@ def process_sonic(image, audio, dynamic_scale):
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raise gr.Error("Please upload an image")
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if audio is None:
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raise gr.Error("Please upload an audio file")
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-
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img_md5 = get_md5(np.array(image))
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audio_md5 = get_md5(audio[1])
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print(f"Processing with image hash: {img_md5}, audio hash: {audio_md5}")
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-
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sampling_rate, arr = audio[:2]
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if len(arr.shape) == 1:
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arr = arr[:, None]
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-
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# Create audio segment
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audio_segment = AudioSegment(
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arr.tobytes(),
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@@ -83,18 +83,18 @@ def process_sonic(image, audio, dynamic_scale):
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channels=arr.shape[1]
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)
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audio_segment = audio_segment.set_frame_rate(sampling_rate)
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-
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# Generate paths
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image_path = os.path.abspath(os.path.join(tmp_path, f'{img_md5}.png'))
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audio_path = os.path.abspath(os.path.join(tmp_path, f'{audio_md5}.wav'))
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res_video_path = os.path.abspath(os.path.join(res_path, f'{img_md5}_{audio_md5}_{dynamic_scale}.mp4'))
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-
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# Save inputs if they don't exist
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if not os.path.exists(image_path):
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image.save(image_path)
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if not os.path.exists(audio_path):
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audio_segment.export(audio_path, format="wav")
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# Process or return cached result
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if os.path.exists(res_video_path):
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print(f"Using cached result: {res_video_path}")
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@@ -103,6 +103,11 @@ def process_sonic(image, audio, dynamic_scale):
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print(f"Generating new video with dynamic scale: {dynamic_scale}")
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return get_video_res(image_path, audio_path, res_video_path, dynamic_scale)
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# Enhanced UI
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css = """
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.gradio-container {
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@@ -131,22 +136,21 @@ with gr.Blocks(css=css) as demo:
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<p>Transform still images into dynamic videos synchronized with audio</p>
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</div>
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""")
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-
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(
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type='pil',
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label="Portrait Image",
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elem_id="image_input"
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tool="select"
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)
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audio_input = gr.Audio(
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label="Voice/Audio Input",
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elem_id="audio_input",
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type="numpy"
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)
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-
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with gr.Box(elem_classes="parameter-section"):
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dynamic_scale = gr.Slider(
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minimum=0.5,
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@@ -156,19 +160,19 @@ with gr.Blocks(css=css) as demo:
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label="Animation Intensity",
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info="Adjust to control movement intensity (0.5: subtle, 2.0: dramatic)"
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)
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-
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process_btn = gr.Button(
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"Generate Animation",
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variant="primary",
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elem_id="process_btn"
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)
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with gr.Column():
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video_output = gr.Video(
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label="Generated Animation",
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elem_id="video_output"
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)
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-
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# Process button click
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process_btn.click(
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fn=process_sonic,
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@@ -176,7 +180,7 @@ with gr.Blocks(css=css) as demo:
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outputs=video_output,
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api_name="animate"
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)
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-
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# Examples section
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gr.Examples(
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examples=get_example(),
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@@ -186,7 +190,7 @@ with gr.Blocks(css=css) as demo:
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cache_examples=False,
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elem_classes="example-section"
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)
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-
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# Footer with attribution and links
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gr.HTML("""
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<div style="text-align: center; margin-top: 2em;">
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@@ -202,4 +206,4 @@ with gr.Blocks(css=css) as demo:
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</div>
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""")
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demo.launch()
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expand_ratio = 0.5
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min_resolution = 512
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inference_steps = 25
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+
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# Get audio duration
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audio = AudioSegment.from_file(audio_path)
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duration = len(audio) / 1000.0 # Convert to seconds
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+
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face_info = pipe.preprocess(img_path, expand_ratio=expand_ratio)
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print(f"Face detection info: {face_info}")
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print(f"Audio duration: {duration} seconds")
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+
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if face_info['face_num'] > 0:
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crop_image_path = img_path + '.crop.png'
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pipe.crop_image(img_path, crop_image_path, face_info['crop_bbox'])
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img_path = crop_image_path
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os.makedirs(os.path.dirname(res_video_path), exist_ok=True)
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+
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# Process with full audio duration
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pipe.process(
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img_path,
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audio_path,
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res_video_path,
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min_resolution=min_resolution,
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inference_steps=inference_steps,
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dynamic_scale=dynamic_scale,
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tmp_path = './tmp_path/'
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res_path = './res_path/'
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os.makedirs(tmp_path, exist_ok=True)
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os.makedirs(res_path, exist_ok=True)
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def process_sonic(image, audio, dynamic_scale):
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# Input validation
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raise gr.Error("Please upload an image")
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if audio is None:
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raise gr.Error("Please upload an audio file")
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+
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img_md5 = get_md5(np.array(image))
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audio_md5 = get_md5(audio[1])
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print(f"Processing with image hash: {img_md5}, audio hash: {audio_md5}")
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sampling_rate, arr = audio[:2]
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if len(arr.shape) == 1:
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arr = arr[:, None]
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# Create audio segment
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audio_segment = AudioSegment(
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arr.tobytes(),
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channels=arr.shape[1]
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)
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audio_segment = audio_segment.set_frame_rate(sampling_rate)
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+
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# Generate paths
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image_path = os.path.abspath(os.path.join(tmp_path, f'{img_md5}.png'))
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audio_path = os.path.abspath(os.path.join(tmp_path, f'{audio_md5}.wav'))
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res_video_path = os.path.abspath(os.path.join(res_path, f'{img_md5}_{audio_md5}_{dynamic_scale}.mp4'))
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+
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# Save inputs if they don't exist
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if not os.path.exists(image_path):
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image.save(image_path)
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if not os.path.exists(audio_path):
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audio_segment.export(audio_path, format="wav")
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+
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# Process or return cached result
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if os.path.exists(res_video_path):
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print(f"Using cached result: {res_video_path}")
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print(f"Generating new video with dynamic scale: {dynamic_scale}")
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return get_video_res(image_path, audio_path, res_video_path, dynamic_scale)
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+
# Dummy get_example function to prevent errors if examples are not defined
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def get_example():
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# 예시가 없다면 빈 리스트를 반환하거나 실제 예시 데이터를 입력할 수 있습니다.
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return []
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+
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# Enhanced UI
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css = """
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.gradio-container {
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<p>Transform still images into dynamic videos synchronized with audio</p>
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</div>
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""")
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+
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with gr.Row():
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with gr.Column():
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image_input = gr.Image(
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type='pil',
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label="Portrait Image",
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elem_id="image_input"
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)
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+
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audio_input = gr.Audio(
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label="Voice/Audio Input",
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elem_id="audio_input",
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type="numpy"
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)
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+
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with gr.Box(elem_classes="parameter-section"):
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dynamic_scale = gr.Slider(
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minimum=0.5,
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label="Animation Intensity",
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info="Adjust to control movement intensity (0.5: subtle, 2.0: dramatic)"
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)
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+
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process_btn = gr.Button(
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"Generate Animation",
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variant="primary",
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elem_id="process_btn"
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)
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+
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with gr.Column():
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video_output = gr.Video(
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label="Generated Animation",
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elem_id="video_output"
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)
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+
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# Process button click
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process_btn.click(
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fn=process_sonic,
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outputs=video_output,
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api_name="animate"
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)
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+
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# Examples section
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gr.Examples(
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examples=get_example(),
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cache_examples=False,
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elem_classes="example-section"
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)
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+
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# Footer with attribution and links
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gr.HTML("""
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<div style="text-align: center; margin-top: 2em;">
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</div>
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""")
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+
demo.launch()
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