Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -27,15 +27,17 @@ def get_md5(content):
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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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-
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#
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audio = AudioSegment.from_file(audio_path)
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duration = len(audio) / 1000.0 #
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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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if face_info['face_num'] > 0:
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crop_image_path = img_path + '.crop.png'
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@@ -43,7 +45,7 @@ def get_video_res(img_path, audio_path, res_video_path, dynamic_scale=1.0):
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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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pipe.process(
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img_path,
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audio_path,
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@@ -52,7 +54,7 @@ def get_video_res(img_path, audio_path, res_video_path, dynamic_scale=1.0):
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inference_steps=inference_steps,
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dynamic_scale=dynamic_scale
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)
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#
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return res_video_path
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else:
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return -1
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@@ -63,7 +65,7 @@ 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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#
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if image is None:
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raise gr.Error("Please upload an image")
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if audio is None:
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@@ -77,7 +79,7 @@ def process_sonic(image, audio, dynamic_scale):
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if len(arr.shape) == 1:
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arr = arr[:, None]
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#
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audio_segment = AudioSegment(
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arr.tobytes(),
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frame_rate=sampling_rate,
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@@ -86,18 +88,18 @@ def process_sonic(image, audio, dynamic_scale):
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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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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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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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if os.path.exists(res_video_path):
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print(f"Using cached result: {res_video_path}")
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return res_video_path
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@@ -105,7 +107,7 @@ 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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#
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def get_example():
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return []
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@@ -173,7 +175,6 @@ with gr.Blocks(css=css) as demo:
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elem_id="video_output"
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)
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# Process button click: when clicked, process_sonic() is called and its return value is sent to video_output.
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process_btn.click(
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fn=process_sonic,
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inputs=[image_input, audio_input, dynamic_scale],
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@@ -181,7 +182,6 @@ with gr.Blocks(css=css) as demo:
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api_name="animate"
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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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fn=process_sonic,
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@@ -190,7 +190,6 @@ with gr.Blocks(css=css) as demo:
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cache_examples=False
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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 style="margin-bottom: 1em;">
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@@ -205,5 +204,5 @@ with gr.Blocks(css=css) as demo:
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</div>
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""")
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#
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demo.launch(share=True)
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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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fps = 25 # 원하는 프레임 레이트 설정 (예: 25 fps)
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# 오디오 파일로부터 실제 오디오 길이를 구하고, 그에 맞춰 추론 단계를 계산합니다.
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audio = AudioSegment.from_file(audio_path)
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duration = len(audio) / 1000.0 # 초 단위
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# 오디오 길이에 따른 프레임 수 계산 (예: 5초 -> 5*25 = 125 단계)
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inference_steps = int(duration * fps)
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print(f"Audio duration: {duration} seconds, using inference_steps: {inference_steps}")
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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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if face_info['face_num'] > 0:
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crop_image_path = img_path + '.crop.png'
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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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# Sonic.process() 호출 시, 동적으로 계산된 inference_steps를 전달합니다.
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pipe.process(
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img_path,
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audio_path,
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inference_steps=inference_steps,
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dynamic_scale=dynamic_scale
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)
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# 생성된 비디오 파일 경로 반환
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return res_video_path
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else:
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return -1
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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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# 입력 검증
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if image is None:
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raise gr.Error("Please upload an image")
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if audio is None:
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if len(arr.shape) == 1:
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arr = arr[:, None]
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# numpy array로부터 AudioSegment 생성
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audio_segment = AudioSegment(
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arr.tobytes(),
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frame_rate=sampling_rate,
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)
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audio_segment = audio_segment.set_frame_rate(sampling_rate)
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# 파일 경로 생성
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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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# 입력 파일이 없으면 저장
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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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# 캐시된 결과가 있으면 반환, 없으면 새로 생성
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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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return 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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# 예시 데이터를 위한 dummy 함수 (필요시 실제 예시 데이터로 수정)
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def get_example():
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return []
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elem_id="video_output"
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)
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process_btn.click(
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fn=process_sonic,
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inputs=[image_input, audio_input, dynamic_scale],
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api_name="animate"
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)
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gr.Examples(
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examples=get_example(),
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fn=process_sonic,
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cache_examples=False
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)
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gr.HTML("""
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<div style="text-align: center; margin-top: 2em;">
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<div style="margin-bottom: 1em;">
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</div>
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""")
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# 공개 링크를 생성하려면 share=True 옵션 사용
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demo.launch(share=True)
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