Update README.md

README.md

@@ -22,4 +22,102 @@ language:
 - hu
 - ko
 - hi
----
+library_name: diffusers
+---
+
+# ACE-Step: A Step Towards Music Generation Foundation Model
+
+![ACE-Step Framework](https://github.com/ACE-Step/ACE-Step/raw/main/fig/ACE-Step_framework.png)
+
+## 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
+
+| Device        | 27 Steps | 60 Steps |
+|---------------|----------|----------|
+| NVIDIA A100   | 27.27x   | 12.27x   |
+| RTX 4090      | 34.48x   | 15.63x   |
+| RTX 3090      | 12.76x   | 6.48x    |
+| M2 Max        | 2.27x    | 1.03x    |
+
+*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](https://ace-step.github.io/)
+- [Demo Space](https://huggingface.co/spaces/ACE-Step/ACE-Step)
+- [GitHub Repository](https://github.com/ACE-Step/ACE-Step)
+
+
+## Citation
+
+```bibtex
+@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.
+