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ACE-Step/acestep-v15-turbo-shift3

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ACE-Step 1.5

Pushing the Boundaries of Open-Source Music Generation

Project |

Hugging Face |

ModelScope |

Space Demo |

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Tech Report

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Model Details

🚀 ACE-Step v1.5 is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware.

Key Features

  • 💰 Commercial-Ready: Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for commercial purposes.
  • 📚 Safe & Robust Training Data: The model is trained on a massive, legally compliant dataset consisting of:
  • Licensed Data: Professionally licensed music tracks.
  • Royalty-Free / No-Copyright Data: A vast collection of public domain and royalty-free music.
  • Synthetic Data: High-quality audio generated via advanced MIDI-to-Audio conversion.
  • ⚡ Extreme Speed: Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090.
  • 🖥️ Consumer Hardware Friendly: Runs locally with less than 4GB of VRAM.

Technical Capabilities

🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️

🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸

  • Developed by: [ACE-STEP]
  • Model type: [Text2Music]
  • Language(s): [50+ languages]
  • License: [MIT]

Evaluation

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🏗️ Architecture

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🦁 Model Zoo

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DiT Models

| DiT Model | Pre-Training | SFT | RL | CFG | Step | Refer audio | Text2Music | Cover | Repaint | Extract | Lego | Complete | Quality | Diversity | Fine-Tunability | Hugging Face |

|-----------|:------------:|:---:|:--:|:---:|:----:|:-----------:|:----------:|:-----:|:-------:|:-------:|:----:|:--------:|:-------:|:---------:|:---------------:|--------------|

| acestep-v15-base | ✅ | ❌ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | High | Easy | Link |

| acestep-v15-sft | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | High | Medium | Easy | Link |

| acestep-v15-turbo | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | Link |

| acestep-v15-turbo-rl | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |

LM Models

| LM Model | Pretrain from | Pre-Training | SFT | RL | CoT metas | Query rewrite | Audio Understanding | Composition Capability | Copy Melody | Hugging Face |

|----------|---------------|:------------:|:---:|:--:|:---------:|:-------------:|:-------------------:|:----------------------:|:-----------:|--------------|

| acestep-5Hz-lm-0.6B | Qwen3-0.6B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Weak | ✅ |

| acestep-5Hz-lm-1.7B | Qwen3-1.7B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Medium | ✅ |

| acestep-5Hz-lm-4B | Qwen3-4B | ✅ | ✅ | ✅ | ✅ | ✅ | Strong | Strong | Strong | ✅ |

🙏 Acknowledgements

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

📖 Citation

If you find this project useful for your research, please consider citing:

@misc{gong2026acestep,
	title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
	author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
	howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
	year={2026},
	note={GitHub repository}
}