title: Music Consistency Models
summary: Consistency models have exhibited remarkable capabilities in facilitating
  efficient image/video generation, enabling synthesis with minimal sampling
  steps. It has proven to be advantageous in mitigating the computational burdens
  associated with diffusion models. Nevertheless, the application of consistency
  models in music generation remains largely unexplored. To address this gap, we
  present Music Consistency Models (MusicCM), which leverages the
  concept of consistency models to efficiently synthesize mel-spectrogram for
  music clips, maintaining high quality while minimizing the number of sampling
  steps. Building upon existing text-to-music diffusion models, the
  MusicCM model incorporates consistency distillation and adversarial
  discriminator training. Moreover, we find it beneficial to generate extended
  coherent music by incorporating multiple diffusion processes with shared
  constraints. Experimental results reveal the effectiveness of our model in
  terms of computational efficiency, fidelity, and naturalness. Notable,
  MusicCM achieves seamless music synthesis with a mere four sampling
  steps, e.g., only one second per minute of the music clip, showcasing the
  potential for real-time application.
paper_authors._id: 66271377b24310dd5f0c3a8f
paper_authors.name: Zhengcong Fei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-23T08:19:48.419Z
paper_authors._id: 617ba1820e4237bd1731b867
paper_authors.avatarUrl: /avatars/f9de06363e64bddd7dc977e96e85df8a.svg
paper_authors.fullname: zhengcong fei
paper_authors.type: user
paper_authors.user: onion
paper_authors._id: 66271377b24310dd5f0c3a90
paper_authors.name: Mingyuan Fan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-23T08:19:58.669Z
paper_authors._id: 634672bfb7b4e71c7f45360f
paper_authors.avatarUrl: /avatars/4b646fc3e271be90b9ec619d42ce3e99.svg
paper_authors.fullname: Fan Mingyuan
paper_authors.type: user
paper_authors.user: MichaelFan
paper_authors._id: 66271377b24310dd5f0c3a91
paper_authors.name: Junshi Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-23T08:20:11.771Z
paper_authors._id: 642e8321be01b88c9447b25e
paper_authors.avatarUrl: /avatars/cc760d9b1bd943da1e6d21f14cc6d495.svg
paper_authors.fullname: HuangJunshi
paper_authors.type: user
paper_authors.user: huangjunshi
paper_title: Music Consistency Models
paper_summary: Consistency models have exhibited remarkable capabilities in facilitating
  efficient image/video generation, enabling synthesis with minimal sampling
  steps. It has proven to be advantageous in mitigating the computational burdens
  associated with diffusion models. Nevertheless, the application of consistency
  models in music generation remains largely unexplored. To address this gap, we
  present Music Consistency Models (MusicCM), which leverages the
  concept of consistency models to efficiently synthesize mel-spectrogram for
  music clips, maintaining high quality while minimizing the number of sampling
  steps. Building upon existing text-to-music diffusion models, the
  MusicCM model incorporates consistency distillation and adversarial
  discriminator training. Moreover, we find it beneficial to generate extended
  coherent music by incorporating multiple diffusion processes with shared
  constraints. Experimental results reveal the effectiveness of our model in
  terms of computational efficiency, fidelity, and naturalness. Notable,
  MusicCM achieves seamless music synthesis with a mere four sampling
  steps, e.g., only one second per minute of the music clip, showcasing the
  potential for real-time application.
paper_ai_summary: Music Consistency Models (MusicCM) efficiently synthesize high-quality music by leveraging consistency models, reducing computational requirements and enabling real-time application.
paper_ai_keywords: consistency models Music Consistency Models mel-spectrogram diffusion models consistency distillation adversarial discriminator real-time application