99 lines
5.4 KiB
Markdown
99 lines
5.4 KiB
Markdown
---
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library_name: transformers
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license: mit
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pipeline_tag: text-to-audio
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tags:
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- audio
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- music
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- text2music
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---
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<h1 align="center">ACE-Step 1.5</h1>
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<h1 align="center">Pushing the Boundaries of Open-Source Music Generation</h1>
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<p align="center">
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<a href="https://ace-step.github.io/ace-step-v1.5.github.io/">Project</a> |
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<a href="https://huggingface.co/collections/ACE-Step/ace-step-15">Hugging Face</a> |
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<a href="https://modelscope.cn/models/ACE-Step/ACE-Step-v1-5">ModelScope</a> |
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<a href="https://huggingface.co/spaces/ACE-Step/Ace-Step-v1.5">Space Demo</a> |
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<a href="https://discord.gg/PeWDxrkdj7">Discord</a>
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<a href="https://arxiv.org/abs/2602.00744">Tech Report</a>
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</p>
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## Model Details
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🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware.
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### Key Features
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* **💰 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**.
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* **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of:
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* **Licensed Data:** Professionally licensed music tracks.
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* **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music.
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* **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion.
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* **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090.
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* **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
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### Technical Capabilities
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🌉 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. 🎚️
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🔮 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. 🎸
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- **Developed by:** [ACE-STEP]
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- **Model type:** [Text2Music]
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- **Language(s):** [50+ languages]
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- **License:** [MIT]
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## Evaluation
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## 🏗️ Architecture
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## 🦁 Model Zoo
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### DiT Models
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| DiT Model | Pre-Training | SFT | RL | CFG | Step | Refer audio | Text2Music | Cover | Repaint | Extract | Lego | Complete | Quality | Diversity | Fine-Tunability | Hugging Face |
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|-----------|:------------:|:---:|:--:|:---:|:----:|:-----------:|:----------:|:-----:|:-------:|:-------:|:----:|:--------:|:-------:|:---------:|:---------------:|--------------|
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| `acestep-v15-base` | ✅ | ❌ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | High | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-base) |
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| `acestep-v15-sft` | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | High | Medium | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-sft) |
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| `acestep-v15-turbo` | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | [Link](https://huggingface.co/ACE-Step/Ace-Step1.5) |
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| `acestep-v15-turbo-rl` | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |
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### LM Models
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| LM Model | Pretrain from | Pre-Training | SFT | RL | CoT metas | Query rewrite | Audio Understanding | Composition Capability | Copy Melody | Hugging Face |
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|----------|---------------|:------------:|:---:|:--:|:---------:|:-------------:|:-------------------:|:----------------------:|:-----------:|--------------|
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| `acestep-5Hz-lm-0.6B` | Qwen3-0.6B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Weak | ✅ |
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| `acestep-5Hz-lm-1.7B` | Qwen3-1.7B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Medium | ✅ |
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| `acestep-5Hz-lm-4B` | Qwen3-4B | ✅ | ✅ | ✅ | ✅ | ✅ | Strong | Strong | Strong | ✅ |
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## 🙏 Acknowledgements
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This project is co-led by ACE Studio and StepFun.
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## 📖 Citation
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If you find this project useful for your research, please consider citing:
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```BibTeX
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@misc{gong2026acestep,
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title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
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author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo},
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howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
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year={2026},
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note={GitHub repository}
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} |