title: Music ControlNet: Multiple Time-varying Controls for Music Generation
summary: 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/.
paper_authors._id: 6552f1a32fdd261ad61f94b4
paper_authors.name: Shih-Lun Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: Shih-Lun Wu
paper_authors.type: user
paper_authors.user: slseanwu
paper_authors._id: 6552f1a32fdd261ad61f94b5
paper_authors.name: Chris Donahue
paper_authors.status: admin_assigned
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paper_authors._id: 63b1b5f034b91b8272aac6c8
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paper_authors.fullname: Chris Donahue
paper_authors.type: user
paper_authors.user: chrisdonahue
paper_authors._id: 6552f1a32fdd261ad61f94b6
paper_authors.name: Shinji Watanabe
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-14T10:00:24.871Z
paper_authors._id: 5feb9dbc29bcc7d2abb1cb76
paper_authors.avatarUrl: /avatars/908d392634377e674ba9e5a4318b101e.svg
paper_authors.fullname: Shinji Watanabe
paper_authors.type: user
paper_authors.user: sw005320
paper_authors._id: 6552f1a32fdd261ad61f94b7
paper_authors.name: Nicholas J. Bryan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T21:22:56.813Z
paper_authors._id: 648119ee7a741c7f33f49f25
paper_authors.avatarUrl: /avatars/183d2143ba20e1cc8712c63c055aadd7.svg
paper_authors.fullname: Nicholas J. Bryan
paper_authors.type: user
paper_authors.user: Njb
paper_title: Music ControlNet: Multiple Time-varying Controls for Music Generation
paper_summary: 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/.
paper_ai_summary: Music ControlNet, a diffusion-based model, introduces precise, time-varying control over audio generation, offering enhanced realism and efficiency compared to existing models.
paper_ai_keywords: diffusion-based music generation model controlnet pixel-wise control audio spectrograms melody dynamics rhythm uni-controlnet time-varying controls musicgen