TulipAIs commited on
Commit
e3b3edf
1 Parent(s): 3b44621

going back to the stable version? (morning of 08/22/23)

Browse files
Files changed (1) hide show
  1. app.py +2 -2
app.py CHANGED
@@ -1179,7 +1179,7 @@ def ui_full(launch_kwargs):
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  refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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  """
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  )
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- '''with gr.Tab("MusicGen"):
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  gr.Markdown(
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  """
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  ### MusicGen
@@ -1521,7 +1521,7 @@ def ui_full(launch_kwargs):
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  image_a.change(get_size, image_a, outputs=[height_a, width_a])
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  s.change(variable_outputs, s, textboxes)
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  s_a.change(variable_outputs, s_a, textboxes_a)
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- interface.queue().launch(**launch_kwargs)'''
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  def ui_batched(launch_kwargs):
 
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  refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
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  """
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  )
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+ with gr.Tab("MusicGen"):
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  gr.Markdown(
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  """
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  ### MusicGen
 
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  image_a.change(get_size, image_a, outputs=[height_a, width_a])
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  s.change(variable_outputs, s, textboxes)
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  s_a.change(variable_outputs, s_a, textboxes_a)
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+ interface.queue().launch(**launch_kwargs)
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  def ui_batched(launch_kwargs):