Update app.py
Browse files
app.py
CHANGED
@@ -206,8 +206,8 @@ def interpolate_video_frames(
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output_fps=hardcoded_fps,
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desired_duration=hardcoded_duration_sec,
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original_duration=hardcoded_duration_sec,
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-
output_width=
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output_height=
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use_cuda=False, # this requires FFmpeg to have been compiled with CUDA support (to try - I'm not sure the Hugging Face image has that by default)
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verbose=False):
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@@ -216,6 +216,10 @@ def interpolate_video_frames(
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filters = []
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# Scaling if dimensions are provided
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if output_width and output_height:
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filters.append(f'scale={output_width}:{output_height}')
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@@ -244,6 +248,7 @@ def interpolate_video_frames(
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'-i', input_file_path,
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]
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if use_cuda:
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cmd.extend(['-hwaccel', 'cuda', '-hwaccel_output_format', 'cuda'])
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@@ -387,7 +392,7 @@ with gr.Blocks() as demo:
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minimum=128,
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maximum=2048,
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step=32,
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value=
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)
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select_motion = gr.Dropdown(
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label='Motion',
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output_fps=hardcoded_fps,
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desired_duration=hardcoded_duration_sec,
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original_duration=hardcoded_duration_sec,
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output_width=None,
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output_height=None,
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use_cuda=False, # this requires FFmpeg to have been compiled with CUDA support (to try - I'm not sure the Hugging Face image has that by default)
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verbose=False):
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filters = []
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# Scaling if dimensions are provided
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# note: upscaling produces disastrous results,
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# it will double the compute time
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# I think that's either because we are not hardware-accelerated,
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# or because of the interpolation done after it, which thus become more computationally intensive
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if output_width and output_height:
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filters.append(f'scale={output_width}:{output_height}')
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'-i', input_file_path,
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]
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# not supported by the current image, we will have to build it
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if use_cuda:
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cmd.extend(['-hwaccel', 'cuda', '-hwaccel_output_format', 'cuda'])
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minimum=128,
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maximum=2048,
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step=32,
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value=288,
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)
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select_motion = gr.Dropdown(
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label='Motion',
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