RedbeardNZ / ACE-Step-v1-3.5B

ACE-Step: A Step Towards Music Generation Foundation Model

Model Description

ACE-Step is a novel open-source foundation model for music generation that overcomes key limitations of existing approaches through a holistic architectural design. It integrates diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer, achieving state-of-the-art performance in generation speed, musical coherence, and controllability.

Key Features:

• 15× faster than LLM-based baselines (20s for 4-minute music on A100)
• Superior musical coherence across melody, harmony, and rhythm
• full-song generation, duration control and accepts natural language descriptions

Uses

Direct Use

ACE-Step can be used for:

• Generating original music from text descriptions
• Music remixing and style transfer
• edit song lyrics

Downstream Use

The model serves as a foundation for:

• Voice cloning applications
• Specialized music generation (rap, jazz, etc.)
• Music production tools
• Creative AI assistants

Out-of-Scope Use

The model should not be used for:

• Generating copyrighted content without permission
• Creating harmful or offensive content
• Misrepresenting AI-generated music as human-created

How to Get Started

see: https://github.com/ace-step/ACE-Step

Hardware Performance

RTF (Real-Time Factor) shown - higher values indicate faster generation

Limitations

• Performance varies by language (top 10 languages perform best)
• Longer generations (>5 minutes) may lose structural coherence
• Rare instruments may not render perfectly
• Output Inconsistency: Highly sensitive to random seeds and input duration, leading to varied "gacha-style" results.
• Style-specific Weaknesses: Underperforms on certain genres (e.g. Chinese rap/zh_rap) Limited style adherence and musicality ceiling
• Continuity Artifacts: Unnatural transitions in repainting/extend operations
• Vocal Quality: Coarse vocal synthesis lacking nuance
• Control Granularity: Needs finer-grained musical parameter control

Ethical Considerations

Users should:

• Verify originality of generated works
• Disclose AI involvement
• Respect cultural elements and copyrights
• Avoid harmful content generation

Model Details

Developed by: ACE Studio and StepFun
Model type: Diffusion-based music generation with transformer conditioning
License: Apache 2.0
Resources:

• Project Page
• Demo Space
• GitHub Repository

Citation

@misc{gong2025acestep,
  title={ACE-Step: A Step Towards Music Generation Foundation Model},
  author={Junmin Gong, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
  howpublished={\url{https://github.com/ace-step/ACE-Step}},
  year={2025},
  note={GitHub repository}
}

Acknowledgements

This project is co-led by ACE Studio and StepFun.