Music ControlNet: Multiple Time-varying Controls for Music Generation
Text-to-music generation models are now capable of generating high-quality
music audio in broad styles. However, text control is primarily suitable for
the manipulation of global musical attributes like genre, mood, and tempo, and
is less suitable for precise control over time-varying attributes such as the
positions of beats in time or the changing dynamics of the music. We propose
Music ControlNet, a diffusion-based music generation model that offers multiple
precise, time-varying controls over generated audio. To imbue text-to-music
models with time-varying control, we propose an approach analogous to
pixel-wise control of the image-domain ControlNet method. Specifically, we
extract controls from training audio yielding paired data, and fine-tune a
diffusion-based conditional generative model over audio spectrograms given
melody, dynamics, and rhythm controls. While the image-domain Uni-ControlNet
method already allows generation with any subset of controls, we devise a new
strategy to allow creators to input controls that are only partially specified
in time. We evaluate both on controls extracted from audio and controls we
expect creators to provide, demonstrating that we can generate realistic music
that corresponds to control inputs in both settings. While few comparable music
generation models exist, we benchmark against MusicGen, a recent model that
accepts text and melody input, and show that our model generates music that is
49% more faithful to input melodies despite having 35x fewer parameters,
training on 11x less data, and enabling two additional forms of time-varying
control. Sound examples can be found at https://MusicControlNet.github.io/web/.
6552f1a32fdd261ad61f94b4
Shih-Lun Wu
admin_assigned
2023-11-14T09:59:56.915Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1678031945983-6311001864939fabc00bb4dc.jpeg
Shih-Lun Wu
user
slseanwu
6552f1a32fdd261ad61f94b5
Chris Donahue
admin_assigned
2023-11-14T10:00:04.289Z
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Chris Donahue
user
chrisdonahue
6552f1a32fdd261ad61f94b6
Shinji Watanabe
admin_assigned
2023-11-14T10:00:24.871Z
5feb9dbc29bcc7d2abb1cb76
/avatars/908d392634377e674ba9e5a4318b101e.svg
Shinji Watanabe
user
sw005320
6552f1a32fdd261ad61f94b7
Nicholas J. Bryan
admin_assigned
2025-04-22T21:22:56.813Z
648119ee7a741c7f33f49f25
/avatars/183d2143ba20e1cc8712c63c055aadd7.svg
Nicholas J. Bryan
user
Njb
Music ControlNet: Multiple Time-varying Controls for Music Generation
Text-to-music generation models are now capable of generating high-quality
music audio in broad styles. However, text control is primarily suitable for
the manipulation of global musical attributes like genre, mood, and tempo, and
is less suitable for precise control over time-varying attributes such as the
positions of beats in time or the changing dynamics of the music. We propose
Music ControlNet, a diffusion-based music generation model that offers multiple
precise, time-varying controls over generated audio. To imbue text-to-music
models with time-varying control, we propose an approach analogous to
pixel-wise control of the image-domain ControlNet method. Specifically, we
extract controls from training audio yielding paired data, and fine-tune a
diffusion-based conditional generative model over audio spectrograms given
melody, dynamics, and rhythm controls. While the image-domain Uni-ControlNet
method already allows generation with any subset of controls, we devise a new
strategy to allow creators to input controls that are only partially specified
in time. We evaluate both on controls extracted from audio and controls we
expect creators to provide, demonstrating that we can generate realistic music
that corresponds to control inputs in both settings. While few comparable music
generation models exist, we benchmark against MusicGen, a recent model that
accepts text and melody input, and show that our model generates music that is
49% more faithful to input melodies despite having 35x fewer parameters,
training on 11x less data, and enabling two additional forms of time-varying
control. Sound examples can be found at https://MusicControlNet.github.io/web/.
Music ControlNet, a diffusion-based model, introduces precise, time-varying control over audio generation, offering enhanced realism and efficiency compared to existing models.
diffusion-based music generation model controlnet pixel-wise control audio spectrograms melody dynamics rhythm uni-controlnet time-varying controls musicgen