#1414th of 23 in Open models
ace-step-1.5
Text-to-music model with LM planner, DiT decoder, Gradio UI and REST API
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- Stars
- 13.3k
- License
- MIT
- Last commit
- Aug 2026
- Last release
- May 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
ACE-Step 1.5 generates full songs from text prompts and lyrics using a Qwen3-based language model that plans the song and a diffusion transformer (2B or 4B XL) that renders audio. It also does cover generation, repainting, stem separation, vocal-to-accompaniment and LoRA training. It ships a Gradio UI on port 7860, a REST API on port 8001, a CLI and a Python API.
Who it is for: Musicians and developers generating music locally, with LoRA fine-tuning
Strengths
- Runs locally with under 4GB VRAM using the DiT-only turbo model; README claims under 10s per song on RTX 3090
- Covers text-to-music, cover, repaint, stem separation, and vocal-to-BGM in one model family
- LoRA training from a few songs: 8 songs in about 1 hour on a 12GB 3090, from the Gradio UI
- Several interfaces: Gradio UI, REST API, CLI, Python API, and a separate VST3 plugin
Weaknesses
- XL 4B models need at least 12GB VRAM with offload and quantization, 20GB for comfortable use
- Low-VRAM setups (6GB or less) run without the LM, losing planning and query rewriting
- Quality claims (between Suno v4.5 and v5) are the authors' own, with no numbers in the README
- Python 3.11-3.12 only; CUDA recommended, other backends (MPS, ROCm, XPU, CPU) are secondary
What it needs
- GPU optional
- Docker + Compose
- Needs uv, Python 3.11-3.12, Gradio
- Models: ACE-Step DiT 2B (base, sft, turbo), ACE-Step XL DiT 4B (base, sft, turbo), acestep-5Hz-lm 0.6B/1.7B/4B (Qwen3-based), LoRA adapters
- port 7860
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