mlsanityData to 7 Oct 2026

MusicFlow: Cascaded Flow Matching for Text Guided Music Generation

arXiv 2410.20478 ↗First posted 27 Oct 2024cs.SD cs.AI eess.AScited by 14

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Abstract (arXiv)

We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music audios, we construct two flow matching networks to model the conditional distribution of semantic and acoustic features. Additionally, we leverage masked prediction as the training objective, enabling the model to generalize to other tasks such as music infilling and continuation in a zero-shot manner. Experiments on MusicCaps reveal that the music generated by MusicFlow exhibits superior quality and text coherence despite being over 2~5 times smaller and requiring 5 times fewer iterative steps. Simultaneously, the model can perform other music generation tasks and achieves competitive performance in music infilling and continuation. Our code and model will be publicly available.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 38 of its 46 references that are in the map. First posted on arXiv 27 Oct 2024.

Builds on

38 of 46 references.

References · 38

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Citing papers per year: 2025: 8; 2026: 6.

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  1. 2025Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
  2. 2026Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
  3. 2024FLUX that Plays Music
  4. 2023Mousai: Text-to-Music Generation with Long-Context Latent Diffusion
  5. 2024Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
  6. 2025JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment
  7. 2023JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation
  8. 2024High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching