| title | WanSong v1.0 Technical Report |
|---|---|
| authors | Binghui Chen, Pandeng Li, Yu Liu, Jingren Zhou |
| date | 2026-07-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.14749 |
| affiliation | Wan-AI |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 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), 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. |
cite
@misc{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={https://arxiv.org/abs/2607.14749}
}