WanSong v1.0 Technical Report
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| Authors | Binghui Chen et al. |
| Year | 2026 |
| HF Upvotes | 13 |
| arXiv | 2607.14749 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
The Problem
However, achieving efficient generation and high-fidelity long-form audio while supporting controllability remains challenging.
The Approach
To address these needs, we present WanSong, a simple yet powerful approach for long-form, commercial-grade song generation.
Key Results
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.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Technical
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