ACE-Step/acestep-v15-xl-sft

ACE-Step 1.5 XL — SFT (4B DiT)

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Model Details

This is the XL (4B) SFT variant of ACE-Step 1.5 — a supervised fine-tuned model with ~4B parameters. SFT provides higher audio quality with CFG (Classifier-Free Guidance) support for fine-grained prompt adherence control.

XL Architecture

Parameter	Value
DiT Decoder hidden_size	2560
DiT Decoder layers	32
DiT Decoder attention heads	32
Encoder hidden_size	2048
Encoder layers	8
Total params	~4B
Weights size (bf16)	~18.8 GB
Inference steps	50 (with CFG)

GPU Requirements

VRAM	Support
≥12 GB	With CPU offload + INT8 quantization
≥16 GB	With CPU offload
≥20 GB	Without offload
≥24 GB	Full quality (XL + 4B LM)

All LM models (0.6B / 1.7B / 4B) are fully compatible with XL.

Key Features

• 💰 Commercial-Ready: Trained on legally compliant datasets. Generated music can be used for commercial purposes.
• 📚 Safe Training Data: Licensed music, royalty-free/public domain, and synthetic (MIDI-to-Audio) data.
• 🎯 CFG Support: Fine-tune prompt adherence with guidance scale control.
• 🔮 Highest Quality: SFT + 4B parameters = the highest quality variant.

Quick Start

# Install ACE-Step
git clone https://github.com/ace-step/ACE-Step-1.5.git
cd ACE-Step-1.5
pip install -e .

# Download this model
huggingface-cli download ACE-Step/acestep-v15-xl-sft --local-dir ./checkpoints/acestep-v15-xl-sft

# Run with Gradio UI
python acestep --config-path acestep-v15-xl-sft

Model Zoo

XL (4B) DiT Models

DiT Model	CFG	Steps	Quality	Diversity	Tasks	Hugging Face	ModelScope
acestep-v15-xl-base	✅	50	High	High	All (extract, lego, complete)	Link	Link
acestep-v15-xl-sft	✅	50	Very High	Medium	Standard	This repo	Link
acestep-v15-xl-turbo	❌	8	Very High	Medium	Standard	Link	Link

LM Models (all compatible with XL)

LM Model	Params	Audio Understanding	Composition	Hugging Face	ModelScope
acestep-5Hz-lm-0.6B	0.6B	Medium	Medium	Link	Link
acestep-5Hz-lm-1.7B	1.7B	Medium	Medium	Included in main	Included in main
acestep-5Hz-lm-4B	4B	Strong	Strong	Link	Link

Acknowledgements

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

Citation

@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}
}