WanSong v1.0 Technical Report

Binghui Chen, Pandeng Li, Yu Liu, Jingren Zhou
7/16/2026
eess.AScs.CV

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Abstract

Music generation foundation models have recently attracted significant industry attention. However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging. To address these needs, we present \textbf{WanSong}, a simple yet powerful approach for long-form, commercial-grade song generation. Unlike autoregressive (AR) and cascaded multi-stage pipelines (\eg, AR followed by diffusion), \textbf{WanSong} is a pure diffusion-based model that directly generates high-fidelity, multilingual songs up to 5 minutes and outputs dual stems (vocals and background music) in a single run. In addition, our diffusion framework enables faster inference through step-distillation, and offers an efficient pathway for fine-tuning and customization to support downstream editing tasks.

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@article{chen2026wansong,
  title  = {WanSong v1.0 Technical Report},
  author = {Binghui Chen and Pandeng Li and Yu Liu and Jingren Zhou},
  year   = {2026},
  eprint = {2607.14749},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.14749v1}
}

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